diff --git a/.gitignore b/.gitignore index cc4a768c..fb251ac3 100644 --- a/.gitignore +++ b/.gitignore @@ -2,7 +2,6 @@ *.swp *.pyc *.so -*.c cover doc/_build doc/generated diff --git a/.mailmap b/.mailmap new file mode 100644 index 00000000..b529ed57 --- /dev/null +++ b/.mailmap @@ -0,0 +1,3 @@ +Andreas Mueller +Andreas Mueller +Andreas Mueller diff --git a/.travis.yml b/.travis.yml index c091873c..3673eff9 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,22 +1,43 @@ +sudo: false language: python -python: - - "2.7" +addons: + apt: + sources: + - boost-latest + packages: + - python-scipy + - python-nose + - python-pip + - cython + - cmake + - time + - libatlas-dev + - libatlas3gf-base + - liblapack-dev + - liblapack3gf + - libhdf5-serial-dev + - libboost1.55-dev + - libboost-python1.55-dev virtualenv: - system_site_packages: true -before_install: - - sudo add-apt-repository ppa:ukplc-team/testing -y - - sudo add-apt-repository ppa:cython-dev/master-ppa -y - - sudo apt-get update -qq - - sudo apt-get install python-scipy python-cvxopt python-sklearn libhdf5-serial-dev libboost1.49-dev libboost-python1.49-dev cython - - git clone https://github.com/opengm/opengm.git - - cd opengm - - cmake . -DWITH_BOOST=TRUE -DWITH_HDF5=TRUE -DBUILD_PYTHON_WRAPPER=TRUE -DBUILD_EXAMPLES=FALSE -DBUILD_TESTING=FALSE - - make -j6 --quiet - - sudo make install - - cd .. - - sudo rm -rf /dev/shm - - sudo ln -s /run/shm /dev/shm -install: - - pip install --use-mirrors -r requirements.txt - - python setup.py build_ext --inplace -script: make test + system_site_packages: true +env: + matrix: + - DISTRIB="ubuntu" PYTHON_VERSION="2.7" OPENGM="true" + ## ubuntu without opengm + - DISTRIB="ubuntu" PYTHON_VERSION="2.7" OPENGM="false" + ## This environment tests the newest supported anaconda env + - DISTRIB="conda" PYTHON_VERSION="2.7" OPENGM="false" + NUMPY_VERSION="1.13.3" SCIPY_VERSION="0.19.1" + # python3.5 only because of cvxopt? + - DISTRIB="conda3" PYTHON_VERSION="3.5" OPENGM="false" + NUMPY_VERSION="1.13" SCIPY_VERSION="1.0" + - DISTRIB="conda3" PYTHON_VERSION="3.6" OPENGM="false" + NUMPY_VERSION="1.14" SCIPY_VERSION="1.1" +install: source continuous_integration/install.sh +script: bash continuous_integration/test_script.sh +after_success: + # Ignore coveralls failures as the coveralls server is not very reliable + # but we don't want travis to report a failure in the github UI just + # because the coverage report failed to be published. + - if [[ "$COVERAGE" == "true" ]]; then coveralls || echo "failed"; fi +cache: apt diff --git a/CHANGELOG b/CHANGELOG new file mode 100644 index 00000000..0b762fd8 --- /dev/null +++ b/CHANGELOG @@ -0,0 +1,28 @@ +0.3.2 +===== +- 2 bug fixes + +0.3.1 +===== +- Added new model NodeTypeEdgeFeatureGraphCRF +- all tests are passing, CIok +- Python3 compatibility + +0.3 +=== +- Removed libdai bindings that were very experimental. + +0.2.3 +===== +- Added max-prod inference, including loopy bp, tree bp and a special case for chains. + +0.2 +=== +- Added BCFW. +- Added averaging in stochastic gradient descent. +- Added "snakes" dataset. +- Much faster interface to OpenGM. +- Speed improvements in loss-augmented inference. +- Renamed psi to joint_feature, as the joint feature function is sometimes also called phi, with psi referring to the energy. +- Removed the GLPK dependency: now cvxopt is used to solve linear programs. + diff --git a/Makefile b/Makefile index 734e106d..2814b117 100644 --- a/Makefile +++ b/Makefile @@ -1,11 +1,19 @@ +NOSETESTS ?= nosetests + all: python setup.py build_ext -i coverage: nosetests --with-coverage --cover-html --cover-package=pystruct pystruct -test: - nosetests -sv pystruct +test-code: all + $(NOSETESTS) -s -v pystruct + clean: find | grep .pyc | xargs rm + +test-doc: + $(NOSETESTS) -s -v doc/*.rst doc/modules/ + +test: test-code test-doc diff --git a/README.md b/README.md index b05b30cb..49471dea 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,9 @@ [![Build Status](https://travis-ci.org/pystruct/pystruct.png)](https://travis-ci.org/pystruct/pystruct) +[![pypi version](http://img.shields.io/pypi/v/pystruct.svg?style=flat)](https://pypi.python.org/pypi/pystruct/) +[![licence](http://img.shields.io/badge/licence-BSD-blue.svg?style=flat)](https://github.com/pystruct/pystruct/blob/master/LICENSE) +[![DOI](https://zenodo.org/badge/21369/pystruct/pystruct.svg)](https://zenodo.org/badge/latestdoi/21369/pystruct/pystruct) + + PyStruct ======== @@ -12,11 +17,19 @@ to make use of structured prediction algorithms. The design tries to stay as close as possible to the interface and conventions of [scikit-learn](http://scikit-learn.org). -Currently the project is mostly maintained by Andreas Mueller, but contributions are very welcome. -I plan a stable release soon. +You can install pystruct using + +> pip install pystruct + +Some of the functionality (namely OneSlackSSVM and NSlackSSVM) requires that cvxopt is installed. +See the [installation instructions](http://pystruct.github.io/intro.html) for more details. The full documentation and installation instructions can be found at the website: http://pystruct.github.io You can contact the authors either via the [mailing list](https://groups.google.com/forum/#!forum/pystruct) or on [github](https://github.com/pystruct/pystruct). + +Currently the project is mostly maintained by Andreas Mueller, but contributions are very welcome. + +Jean-Luc Meunier (Naver Labs Europe) contributed a new model and did some maintenance, in the course of the EU READ project. See [READ_Contribution.md](https://github.com/pystruct/pystruct/blob/master/READ_Contribution.md) diff --git a/READ_Contribution.md b/READ_Contribution.md new file mode 100644 index 00000000..c7a4f537 --- /dev/null +++ b/READ_Contribution.md @@ -0,0 +1,123 @@ +# Contribution by EU READ Project +During the course of the EU READ project, Naver Labs Europe made two contributions to the pystruct and AD3 projects: + - **supporting nodes of different nature in CRF graphs** + - **supporting hard-logic constraints when predicting** + +In practice: + * a new CRF model is proposed, __*NodeTypeEdgeFeatureGraphCRF*__ + * the __*predict*__ method accepts now an optional constraint parameter + + More details are given in next sections. + + You can contact the author at jean-luc.meunier@naverlabs.com + + +## Credit +Developed for the EU project READ. The READ project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 674943. + +## Tests +To test your install, run the test of the new CRF model: +> python pystruct/tests/test_models/test_node_type_edge_feature_graph_crf.py + +(You should see a "OK" displayed at the end of the script execution.) + +## Example +Building on the [Snakes](https://pystruct.github.io/auto_examples/plot_snakes.html#sphx-glr-auto-examples-plot-snakes-py) example, there is now a new example called "HiddenSnakes". (Code in examples/plot_hidden_short_snakes_typed.py ) + +The idea is that some picture do not contain any snake despite 10 pixels have a Snake body colour. Why? Because they do not form a valid 10-long snake, as 1 pixel has a wrong colour destroying the continuity of the snake. + +The original task remains but is more difficult: some non-blue pixels are now labelled 'background'. An additional task consists in labeling the picture as Snake or NoSnake. + +This double task is solved by the use of an additional type of node that represents the picture itself, with 7 simplistic features. There are additional edges, from each pixel to the picture node. That's all. And it improves a lot from the results of the *EdgeFeatureGraphCRF*-based model. + +In addition, we injected some more domain knowledge to illustrate the use of the hard logic constraints. In this case we enforce *at most one pixel of label L per picture, for L in [1, 10]*. This gives an extra accuracy bonus. + +## Prediction with Hard-Logic Constraints + +You can now pass a __list of logical constraints__ to the predict method, with a *constraints=* named parameter. + + Each constraint is tuple like *( operator, nodes, labels, negated )* + where: + - *operator* is one of 'XOR' 'XOROUT' 'ATMOSTONE' 'OR' 'OROUT' 'ANDOUT' 'IMPLY' + - *nodes* is the list of the index of each node involved in this constraint + - *labels* is the list of node label. If the labels are the same for all nodes, you can pass it directly as a scalar value. + - *negated* is a list of boolean indicating if the corresponding argument must be negated. Again, if all values are the same, pass a single boolean value instead of a list. + +The operators whose name ends with 'OUT' impose that the operator applied on the all-but-last arguments yields the truth value of the last one. +>For instance XOROUT(a,b,c) <=> XOR(a,b) = c + +When used jointly with the new *NodeTypeEdgeFeatureGraphCRF* model, the structure of the constraints list slightly differs. See in next section. + + +## CRF Graph with Nodes of Different Nature +Pystruct CRF graphs assumes that the nodes of the graph all have the same nature. In consequence, all nodes share the same weights and the same set of possible labels. Similarly, all edges have the same nature and share the same edge weights. +This was a limitation with regards to our needs (for a Document Understanding task). So we propose a new CRF model called *NodeTypeEdgeFeatureGraphCRF*. + +*NodeTypeEdgeFeatureGraphCRF* supports multiple node of multiple nature, which we call **node types**. Each type has its own weights and own set of possible labels. Similarly, edges have different nature depending on the type of their sources and target-nodes. In a graph with N types, there are N^2 types of edges. + +*NodeTypeEdgeFeatureGraphCRF* generalizes *EdgeFeatureGraphCRF*, so edges have features. NOTE: I think that you can mimics the absence opf feature on edges (as in *GraphCRF* model) by specifying one feature per edge, whose value is 1 for all edges. + +**This extension has an impact on:** + * the constructor + * the structure of the label weights, if not uniform + * the structure of the Xs + * the values in Ys + * the structure of the optional constraint list at prediction + +### Class Constructor +You need now to define the number of node types and the number of features per type (of node, and of edge) when instantiating *NodeTypeEdgeFeatureGraphCRF*. + + def __init__(self + , n_types #how many node type? + , l_n_states #how many labels per node type? + , l_n_features #how many features per node type? + , a_n_edge_features #how many features per edge type? (array-like) shape=(n_type, n_type) -> n_feature_per_type_pair + , inference_method="ad3" + , l_class_weight=None): #class_weight per node type or None or None + + +### Xs and Ys +In single type CRF, like *EdgeFeatureGraphCRF*, an instance *X* is represented as a tuple + + (*node_features*, *edges*, *edge_features*) representing the graph. + +* *node_feature*s is of shape (*n_node*, *n_features*) +* *edges* is an array of shape (*n_edges*, 2) +* *edge_features* is of shape (*n_edges*, *n_edge_features*) + + Labels y are given as array of shape (*n_nodes*,) + +In multiple type graphs, with *_n_types* types, an instance *X* is represented as a tuple + + (*l_node_features*, *l_edges*, *l_edge_features*) representing the graph. +* *l_node_feature*s is a list of length *n_types* containing arrays of shape (*n_typ_node*, *n_typ_features*), where *n_typ_node* is the number of nodes of that type, while *n_typ_features* is the number of features for this type of nodes. +* *l_edges* is a list of length *n_types*^2 . Each of its elements contains an array of shape (*n_typ_edge, 2) defining the edges from nodes of type *i* to nodes of type *j*, with *i* and *j* in [0, *n_types*-1], *j* being the secondary index (inner loop). The index of the nodes in each type starts at 0. +* *l_edge_features* is a list of length *n_types*^2. It contains the features of the edges for each pair of types, in same order as in previous parameter. Each item is an array of shape (*n_typ_edges*, *n_typ_edge_features*). if *n_typ_edge_features* is 0, then *n_typ_edges* should be 0 as well for all instances of graph! If you want an edge without features, set *n_typ_edge_features* to 1 and pass 1.0 as feature for all edges (of that type). + +Each *Y* remains a vector array. While the label could start at 0 for all types, we have chosen not to do so. (Essentially,to make clear that types do not blend into each other, which is clear when you show a confusion matrix). So the labels of the first type start at 0, while labels of next type starts right after the value of the last label of previous type. +*NodeTypeEdgeFeatureGraphCRF* provides 2 convenience methods: +* *flattenY*( [ [2,0,0], [3,3,4] ] ) --> [ 2,0,0, 5,5,7] (assuming type 0 has 3 labels) +* *unflattenY*(Xs, [ 2,0,0, 5,5,7] ) --> [ [2,0,0], [3,3,4] ] (you'll also need to pass the Xs) + +### Constraints on Multitype Graphs +As for the Xs and Ys, the constraint must be partitioned by type. + + The constraints must be a list of tuples like: + +Either + + ( *operator*, *l_nodes*, *l_labels*, *l_negated* ) + with operator being one 'XOR' 'ATMOSTONE' 'OR' + +Or + + ( *operator*, *l_nodes*, *l_labels*, *l_negated* , (*type*, *node*, *label*, *negated*)) + with operator being one 'XOROUT' 'OROUT' 'ANDOUT' 'IMPLY' + +- *l_nodes* is a list of nodes per type. Each item is a list of the index of the node of that type involved in this constraint +- *l_labels* is a list of labels per type. Each item is a list of the label of the involved node. If the labels are all the same for a type, you can pass it directly as a scalar value. +- *l_negate*d is a list of "negated" per type. Each item is a list of booleans indicating if the node must be negated. Again, if all values are the same for a type, pass a single boolean value instead of a list + +- the last (*type*, *nod*e, *label*, *negated*) allows to refer to the outcome of an 'OUT' operator. + + diff --git a/benchmarks/multi_label_tree.py b/benchmarks/multi_label_tree.py new file mode 100644 index 00000000..f26f94e0 --- /dev/null +++ b/benchmarks/multi_label_tree.py @@ -0,0 +1,82 @@ +""" +========================== +Multi-label classification +========================== +This example shows how to use structured support vector machines +(or structured prediction in general) to do multi-label classification. + +This method has been investigated in +Finley, Joachims 2008 +"Training Structural SVMs when Exact Inference is Intractable" + +And is an interesting test-bed for non-trivial structured prediction. +We compare independent predictions, full interactions and tree-structured +interactions with respect to run-time and accuracy. +By default, the "scene" dataset is used, but it is also possible to use the +"yeast" datasets, both of which are used in the literature. + +To compute the Chow-Liu tree for the tree structured model, you need +to install either a recent scipy or scikit-learn version. +""" +import itertools + +import numpy as np +from scipy import sparse + +from sklearn.metrics import hamming_loss +from sklearn.datasets import fetch_mldata +from sklearn.metrics import mutual_info_score +from scipy.sparse.csgraph import minimum_spanning_tree + +from pystruct.learners import OneSlackSSVM +from pystruct.models import MultiLabelClf +from pystruct.datasets import load_scene + + +def chow_liu_tree(y_): + # compute mutual information using sklearn + n_labels = y_.shape[1] + mi = np.zeros((n_labels, n_labels)) + for i in range(n_labels): + for j in range(n_labels): + mi[i, j] = mutual_info_score(y_[:, i], y_[:, j]) + mst = minimum_spanning_tree(sparse.csr_matrix(-mi)) + edges = np.vstack(mst.nonzero()).T + edges.sort(axis=1) + return edges + + +dataset = "scene" +# dataset = "yeast" + +if dataset == "yeast": + yeast = fetch_mldata("yeast") + + X = yeast.data + X = np.hstack([X, np.ones((X.shape[0], 1))]) + y = yeast.target.toarray().astype(np.int).T + + X_train, X_test = X[:1500], X[1500:] + y_train, y_test = y[:1500], y[1500:] + +else: + scene = load_scene() + X_train, X_test = scene['X_train'], scene['X_test'] + y_train, y_test = scene['y_train'], scene['y_test'] + +n_labels = y_train.shape[1] +full = np.vstack([x for x in itertools.combinations(range(n_labels), 2)]) +tree = chow_liu_tree(y_train) + +#tree_model = MultiLabelClf(edges=tree, inference_method=('ogm', {'alg': 'dyn'})) +tree_model = MultiLabelClf(edges=tree, inference_method='max-product') + +tree_ssvm = OneSlackSSVM(tree_model, inference_cache=50, C=.1, tol=0.01) + +print("fitting tree model...") +tree_ssvm.fit(X_train, y_train) + +print("Training loss tree model: %f" + % hamming_loss(y_train, np.vstack(tree_ssvm.predict(X_train)))) +print("Test loss tree model: %f" + % hamming_loss(y_test, np.vstack(tree_ssvm.predict(X_test)))) diff --git a/benchmarks/plot_letters.py b/benchmarks/plot_letters.py new file mode 100644 index 00000000..4d46609e --- /dev/null +++ b/benchmarks/plot_letters.py @@ -0,0 +1,29 @@ +""" +================================ +Sequence classifcation benchmark +================================ +This is a stripped-down version of the "plot_letters.py" example +targetted to benchmark inference and learning algorithms on chains. +""" +import numpy as np + +from pystruct.datasets import load_letters +from pystruct.models import ChainCRF +from pystruct.learners import OneSlackSSVM + +abc = "abcdefghijklmnopqrstuvwxyz" + +letters = load_letters() +X, y, folds = letters['data'], letters['labels'], letters['folds'] +# we convert the lists to object arrays, as that makes slicing much more +# convenient +X, y = np.array(X), np.array(y) +X_train, X_test = X[folds == 1], X[folds != 1] +y_train, y_test = y[folds == 1], y[folds != 1] + +# Train linear chain CRF +model = ChainCRF() +ssvm = OneSlackSSVM(model=model, C=.1, tol=0.1, verbose=3, max_iter=20) +ssvm.fit(X_train, y_train) + +print("Test score with chain CRF: %f" % ssvm.score(X_test, y_test)) diff --git a/benchmarks/random_tree_crf.py b/benchmarks/random_tree_crf.py new file mode 100644 index 00000000..036b2d00 --- /dev/null +++ b/benchmarks/random_tree_crf.py @@ -0,0 +1,44 @@ +import numpy as np +from scipy import sparse + +try: + from sklearn.model_selection import train_test_split +except ImportError: + from sklearn.cross_validation import train_test_split + +from scipy.sparse.csgraph import minimum_spanning_tree + +from pystruct.learners import SubgradientSSVM +from pystruct.models import GraphCRF + + +def make_random_trees(n_samples=50, n_nodes=100, n_states=7, n_features=10): + crf = GraphCRF(inference_method='max-product', n_states=n_states, + n_features=n_features) + weights = np.random.randn(crf.size_joint_feature) + X, y = [], [] + for i in range(n_samples): + distances = np.random.randn(n_nodes, n_nodes) + features = np.random.randn(n_nodes, n_features) + tree = minimum_spanning_tree(sparse.csr_matrix(distances)) + edges = np.c_[tree.nonzero()] + X.append((features, edges)) + y.append(crf.inference(X[-1], weights)) + + return X, y, weights + + +X, y, weights = make_random_trees(n_nodes=1000) + +X_train, X_test, y_train, y_test = train_test_split(X, y) + +#tree_model = MultiLabelClf(edges=tree, inference_method=('ogm', {'alg': 'dyn'})) +tree_model = GraphCRF(inference_method='max-product') + +tree_ssvm = SubgradientSSVM(tree_model, max_iter=4, C=1, verbose=10) + +print("fitting tree model...") +tree_ssvm.fit(X_train, y_train) + +print("Training loss tree model: %f" % tree_ssvm.score(X_train, y_train)) +print("Test loss tree model: %f" % tree_ssvm.score(X_test, y_test)) diff --git a/continuous_integration/install.sh b/continuous_integration/install.sh new file mode 100644 index 00000000..83b853b3 --- /dev/null +++ b/continuous_integration/install.sh @@ -0,0 +1,106 @@ +#!/bin/bash +# This script is meant to be called by the "install" step defined in +# .travis.yml. See http://docs.travis-ci.com/ for more details. +# The behavior of the script is controlled by environment variabled defined +# in the .travis.yml in the top level folder of the project. + +# License: 3-clause BSD + +set -e + +# Fix the compilers to workaround avoid having the Python 3.4 build +# lookup for g++44 unexpectedly. +export CC=gcc +export CXX=g++ +export PIP=pip + +# add cython repository +#sudo add-apt-repository ppa:cython-dev/master-ppa -y +#sudo apt-get update -qq + +if [[ "$OPENGM" == "true" ]]; then + git clone https://github.com/opengm/opengm.git + cd opengm + # old cmake . -DCMAKE_INSTALL_PREFIX=/home/travis/.local -DWITH_BOOST=TRUE -DWITH_HDF5=TRUE -DBUILD_PYTHON_WRAPPER=TRUE -DBUILD_EXAMPLES=FALSE -DBUILD_TESTING=FALSE + # old make -j2 --quiet + cmake . -DCMAKE_INSTALL_PREFIX=/home/travis/.local -DWITH_BOOST=TRUE -DWITH_HDF5=TRUE -DWITH_AD3=FALSE -DWITH_TRWS=FALSE -DWITH_QPBO=FALSE -DWITH_MRF=FALSE -DWITH_GCO=FALSE -DWITH_CONICBUNDLE=FALSE -DWITH_MAXFLOW=FALSE -DWITH_MAXFLOW_IBFS=FALSE -DBUILD_PYTHON_WRAPPER=TRUE -DBUILD_COMMANDLINE=FALSE -DCI=TRUE + make -j1 --quiet + make install + cd .. +fi + +if [[ "$DISTRIB" == "conda" ]]; then + # Deactivate the travis-provided virtual environment and setup a + # conda-based environment instead + deactivate + + # Use the miniconda installer for faster download / install of conda + # itself + wget https://repo.continuum.io/miniconda/Miniconda2-4.3.31-Linux-x86_64.sh \ + -O miniconda.sh + chmod +x miniconda.sh && ./miniconda.sh -b -p $HOME/miniconda2 + export PATH=$HOME/miniconda2/bin:$PATH + conda update --yes conda + + # Configure the conda environment and put it in the path using the + # provided versions + + conda create -n testenv --yes python=$PYTHON_VERSION pip nose cython\ + scikit-learn cvxopt pytest future \ + numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION + + source activate testenv + +elif [[ "$DISTRIB" == "conda3" ]]; then + # Deactivate the travis-provided virtual environment and setup a + # conda-based environment instead + deactivate + + # Use the miniconda installer for faster download / install of conda + # itself + wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \ + -O miniconda.sh + chmod +x miniconda.sh && ./miniconda.sh -b -p $HOME/miniconda3 + export PATH=$HOME/miniconda3/bin:$PATH + conda update --yes conda + + # Configure the conda environment and put it in the path using the + # provided versions + + conda create -n testenv --yes python=$PYTHON_VERSION pip nose cython\ + scikit-learn cvxopt pytest future \ + numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION + + source activate testenv + +elif [[ "$DISTRIB" == "ubuntu" ]]; then + # Use standard ubuntu packages in their default version + # except for cython :-/ + $PIP install --user cvxopt + $PIP install --user future # for AD3 +fi + +if [[ "$COVERAGE" == "true" ]]; then + $PIP install coverage coveralls +fi + +python --version +python -c "import numpy; print('numpy %s' % numpy.__version__)" +python -c "import scipy; print('scipy %s' % scipy.__version__)" +# install our favorite inference packages +# Need Transkribus/AD3 for now $PIP install pyqpbo ad3 scikit-learn +$PIP install pyqpbo scikit-learn + +# Build scikit-learn in the install.sh script to collapse the verbose +# build output in the travis output when it succeeds. +python --version +python -c "import numpy; print('numpy %s' % numpy.__version__)" +python -c "import scipy; print('scipy %s' % scipy.__version__)" +python setup.py build_ext --inplace + +#get Transkribus/AD3 +git clone https://github.com/andre-martins/AD3 +pushd AD3 +python setup.py install +popd +python -c "import ad3; print(ad3.__version__)" diff --git a/continuous_integration/test_script.sh b/continuous_integration/test_script.sh new file mode 100644 index 00000000..325c01e0 --- /dev/null +++ b/continuous_integration/test_script.sh @@ -0,0 +1,29 @@ +#!/bin/bash +# This script is meant to be called by the "script" step defined in +# .travis.yml. See http://docs.travis-ci.com/ for more details. +# The behavior of the script is controlled by environment variabled defined +# in the .travis.yml in the top level folder of the project. + +# License: 3-clause BSD + +set -e + +python --version +python -c "import numpy; print('numpy %s' % numpy.__version__)" +python -c "import scipy; print('scipy %s' % scipy.__version__)" +python -c "import sklearn; print('sklearn %s' % sklearn.__version__)" +python -c "import ad3; print(ad3.__version__)" +python -c "import pystruct; print(pystruct.__version__)" + +python -c "from pystruct.inference import get_installed; print('pystruct inference algorithms: %s' % get_installed())" + + +# Do not use "make test" or "make test-coverage" as they enable verbose mode +# which renders travis output too slow to display in a browser. +if [[ "$COVERAGE" == "true" ]]; then + nosetests -sv --with-coverage pystruct +else + nosetests -sv pystruct +fi + +make test-doc diff --git a/doc/conf.py b/doc/conf.py index 51b6e782..539a37c2 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -15,10 +15,13 @@ import sys import os import sphinx_bootstrap_theme +import pystruct +#import sphinx_gallery # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. +sys.path.insert(0, os.path.abspath('../')) sys.path.insert(0, os.path.abspath('sphinxext')) # -- General configuration ---------------------------------------------------- @@ -28,9 +31,9 @@ # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones. -extensions = ['gen_rst', 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', +extensions = ['sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.doctest', 'sphinx.ext.pngmath', - 'sphinx.ext.viewcode', 'numpy_ext.numpydoc'] + 'sphinx.ext.viewcode', 'numpy_ext.numpydoc', 'sphinx_gallery.gen_gallery'] autosummary_generate = True @@ -60,9 +63,19 @@ # built documents. # # The short X.Y version. -version = '0.1' +version = pystruct.__version__ # The full version, including alpha/beta/rc tags. -release = '0.1' +release = pystruct.__version__ + +sphinx_gallery_conf = { + 'reference_url': { + # The module you locally document uses a None + 'pystruct': None, + + # External python modules use their documentation websites + 'sklearn': 'http://scikit-learn.org/stable', + 'matplotlib': 'http://matplotlib.org', + 'numpy': 'http://docs.scipy.org/doc/numpy-1.9.1'}} # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. @@ -263,11 +276,23 @@ # A list of tuples containting pages to link to. The value should # be in the form [(name, page), ..] 'navbar_links': [ + ('Start', 'index'), + ('Installation', 'installation'), ('Introduction', 'intro'), + ('User Guide', 'user_guide'), ('Examples', 'auto_examples/index'), - ('References', 'references'), + ('API', 'references'), ], + # Render the next and previous page links in navbar. (Default: true) + 'navbar_sidebarrel': False, + + # Render the current pages TOC in the navbar. (Default: true) + 'navbar_pagenav': False, + + # Tab name for the current pages TOC. (Default: "Page") + 'navbar_pagenav_name': "Page", + # Global TOC depth for "site" navbar tab. (Default: 1) # Switching to -1 shows all levels. 'globaltoc_depth': 0, @@ -279,7 +304,7 @@ # will break. # # Values: "true" (default) or "false" - 'globaltoc_includehidden': "true", + 'globaltoc_includehidden': "false", # HTML navbar class (Default: "navbar") to attach to
element. # For black navbar, do "navbar navbar-inverse" @@ -297,7 +322,9 @@ # # Options are nothing with "" (default) or the name of a valid theme # such as "amelia" or "cosmo". - # - # Note that this is served off CDN, so won't be available offline. - #'bootswatch_theme': "united", + 'bootswatch_theme': "cerulean", + + # Choose Bootstrap version. + # Values: "3" (default) or "2" (in quotes) + 'bootstrap_version': "3", } diff --git a/doc/index.rst b/doc/index.rst index d0de1fc5..7799759c 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -10,34 +10,33 @@ random fields (M3N) or structural support vector machines. If you are new to structured learning, have a look at :ref:`intro`. +An overview of the different models can be found in :ref:`user_guide`. + The goal of PyStruct is to provide a well-documented tool for researchers as well as non-experts to make use of structured prediction algorithms. The design tries to stay as close as possible to the interface and conventions of `scikit-learn `_. -PyStruct 0.1 is out now! Install it via pip: +The current version is PyStruct 0.2.5 which you can install via pip: pip install pystruct +Detailed installation instructions can be found under :ref:`installation`. + Starting with this first stable release, PyStruct will remain stable with respect to API and will provide backward compatibility. You can contact the authors either via the `mailing list `_ or on `github `_. -Installation -============= -To install pystruct, you need cvxopt, cython and scikit-learn. - -The easiest way to install pystruct is using pip: - - pip install pystruct - -This will also install the additional inference packages ad3 and pyqpbo. - -You might also want to check out `OpenGM `_, -a library containing many many inference algorithms that can be used with -PyStruct. +Citing +====== +If you find PyStruct helpful, please cite `our paper `_: + + | Andreas C. Mueller, Sven Behnke + | PyStruct - Structured prediction in Python + | Journal of machine learning, 2014 + | `bibtex here `_ Introduction @@ -75,7 +74,7 @@ These perform inference: they run your model on data in order to make predictions. There are some options to use different solvers for inference. A linear -programming solver using GLPK is included. I have Python interfaces for several +programming solver using cvxopt is included. I have Python interfaces for several other methods on github, including LibDAI, QPBO, AD3. This is where the heavy lifting is done and in some sense these backends are @@ -92,3 +91,5 @@ solver (which should be a faster undergenerating solver, such as QPBO). auto_examples/index references.rst intro.rst + installation.rst + user_guide.rst diff --git a/doc/installation.rst b/doc/installation.rst new file mode 100644 index 00000000..e508fcb3 --- /dev/null +++ b/doc/installation.rst @@ -0,0 +1,71 @@ +.. _installation: + +Installation +============= +To install pystruct, you need cvxopt, cython and scikit-learn (which requires numpy and scipy). + +The easiest way to install pystruct is using pip:: + + pip install pystruct + +This will also install the additional inference package ad3. + +Installation instructions for the requirements are below. + +Linux (Ubuntu) +-------------- +The easiest way to get all requirements is via the package manager, that is apt on Ubuntu and Debian:: + + sudo apt-get install build-essential python-dev python-setuptools python-numpy \\ + python-scipy libatlas-dev libatlas3gf-base python-cvxopt + +To install the current versions of scikit-learn and pystruct, you can use pip:: + + pip install --user --upgrade scikit-learn pystruct + +OS X & Windows +--------------- +Follow instructions on the `scikit-learn website `_ and +then `install CVXOPT `_. +Finally, you can install pystruct simply using:: + + pip install --user --upgrade pystruct + + +Alternative: Anaconda +--------------------- +In particular for OS X and Windows, an alternative is to use the `Anaconda Python `_ distribution. +The anaconda environment comes with its own Python and a package manager named conda. +You can install cvxopt using the conda package manager:: + + conda install cvxopt + +And then install pystruct:: + + pip install --user --upgrade pystruct + +Additional inference packages +============================= +While PyStruct implements some simple inference algorithms, these are not as optimized as other available code. +Therefore it is recommended to install additional inference packages. +By default PyStruct will also install the AD3 package, which contains a high-quality solver +that can be chosen via ``inference_method='ad3'``. +Another solver that is helpful for highly connected graphs like grid-graphs is QPBO, which +can be installed via the pyqpbo package:: + + pip install --user pyqpbo + +Unfortunately QPBO might not compile with newer C compilers, so we decided to not make it a dependency. + +.. currentmodule:: pystruct.inference + +There is a very high quality collection of inference algorithms in +the `OpenGM `_ library, which +is highly recommended. The algorithms in OpenGM can be chosen by specifying +``inference_algorithm=('ogm', {'alg': ALGORITHM})`` where ALGORITHM can be a +wide variety of algorithms, including dynamic programming, TRWS, graph cuts and +many more, see :func:`inference_ogm`. + +In particular for tree-structured (not chain) models, the implementation of dynamic +programming max-product belief propagation in OpenGM is much faster than the +one in PyStruct. diff --git a/doc/intro.rst b/doc/intro.rst index c053ce55..075b5f50 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -17,9 +17,6 @@ more or less arbitrary. This means the goal is not to predict a label or a number, but a possibly much more complicated object like a sequence or a graph. -What does that mean? --------------------- - In structured prediction, we often deal with finite, but large output spaces Y. This situation could be dealt with using classification with a very large number of classes. The idea behind structured prediction is that we can do @@ -41,7 +38,7 @@ each word individually. This seems somehow better, since we could learn to get most of the word in a sentence right. On the other hand, we lose all context. So for example the expression "car door" is way more likely than "car boar", while predicted individually these could be easily confused. -For a similar example, see :ref:`plot_letters.py`. +For a similar example, see :ref:`example_plot_letters.py`. Structured prediction tries to overcome these problems by considering the output (here the sentence) as a whole and using a loss function that is @@ -72,22 +69,46 @@ How do we specify f? How do we compute y*? As I said above, the output set Y is usually a finite but very large set (all graphs, all sentences in the English language, all images of a given resolution). Finding the argmax in the above equation by exhaustive search is -therefore out of the question. So we need to restrict ourselves to f such that +therefore out of the question. We need to restrict ourselves to f such that we can do the maximization over y efficiently. The most popular tool for -building such f is using energy functions or conditional random fields (CRFs) -[which are basically the same for finding y*]. +building such f is using energy functions or conditional random fields (CRFs). There are basically three challenges in doing structured learning and prediction: -* Choosing a parametric form of f -* solving :math:`\arg\max_y f(x, y)` -* learning parameters for f to minimize a loss. +* Choosing a parametric form of f. +* Solving :math:`\arg\max_y f(x, y)`. +* Learning parameters for f to minimize a loss. + +PyStruct takes :math:`f` to be a linear function of some parameters ``w`` and a joint feature function of ``x`` and ``y``: + + +.. math:: + + f(x, y) = w^T \text{joint\_feature}(x, y) + +So that the prediction is given by + +.. math:: + + y^* = \arg \max_{y \in Y} w^T \text{joint\_feature}(x, y) + + +Here :math:`w` are parameters that are learned from data, and ``joint_feature`` is +defined by the user-specified structure of the model. +The definition of ``joint_feature`` is given by the :ref:`models`. +PyStruct assumes that ``y`` is a discrete vector, and most models in PyStruct +assume a pairwise decomposition of the energy f over entries of ``y``, that is + +.. math:: + + f(x, y) = w^T\ \text{joint\_feature}(x, y) = \sum_{i \in V} w_i^T\ \text{joint\_feature}_i(x, y_i) + \sum_{(i, j) \in E} w_{i, j}^T\ \text{joint\_feature}_{i, j}(x, y_i, y_j) + +Here V are a set of nodes corresponding to the entries of ``y``, and E are a set of edges between the nodes. +The particular form of :math:`\text{joint\_feature}_i` and :math:`\text{joint\_feature}_{i, j}` depends on the model used. See the :ref:`user_guide` for details on the models. -PyStruct takes :math:`f` to be a linear function of some parameters and a feature function :math:`Psi`. -Then the parametric form is given by the :ref:`models`. The second problem, computation of the argmax, is done via third party inference solvers. The interfaces to these are explained at :ref:`inference`. -The last part, the learning is actually the core part of PyStruct. +The last part, the learning of :math:`w` is actually the core part of PyStruct. There are several different algorithms implemented, which you can find under :ref:`learning`. There have been many publications and book on this topics. For a nice introduction (in the context of computer vision), I recommend diff --git a/doc/modules/generated/sklearn.svm.SVC.examples b/doc/modules/generated/sklearn.svm.SVC.examples new file mode 100644 index 00000000..3f3c7fe3 --- /dev/null +++ b/doc/modules/generated/sklearn.svm.SVC.examples @@ -0,0 +1,26 @@ + +Examples using ``sklearn.svm.SVC`` +---------------------------------- + + + +.. raw:: html + + +
+
+ + +.. figure:: ../../auto_examples/./images/thumb/plot_binary_svm.png + :target: ./../../auto_examples/./plot_binary_svm.html + + :ref:`example_plot_binary_svm.py` + + +.. raw:: html + + +

There are many parameters to tune and we can make 1-slack as good as the rest for the price of ... +

+
+ diff --git a/doc/references.rst b/doc/references.rst index 784d022f..0cf92dcd 100644 --- a/doc/references.rst +++ b/doc/references.rst @@ -32,6 +32,7 @@ The rest is experimental / for testing. learners.LatentSSVM learners.SubgradientLatentSSVM learners.PrimalDSStructuredSVM + learners.FrankWolfeSSVM .. _models: @@ -82,6 +83,7 @@ Conditional Random Fields models.EdgeFeatureGraphCRF models.LatentGraphCRF models.LatentNodeCRF + models.ChainCRF models.GridCRF models.DirectionalGridCRF @@ -102,7 +104,6 @@ Inference inference.inference_dispatch inference.inference_qpbo - inference.inference_dai inference.inference_lp inference.inference_ad3 inference.inference_ogm diff --git a/doc/sphinxext/gen_rst.py b/doc/sphinxext/gen_rst.py deleted file mode 100644 index fa5e70e7..00000000 --- a/doc/sphinxext/gen_rst.py +++ /dev/null @@ -1,918 +0,0 @@ -""" -Example generation for pystruct. Stolen from scikit-learn. - -Generate the rst files for the examples by iterating over the python -example files. - -Files that generate images should start with 'plot' - -""" -from time import time -import os -import shutil -import traceback -import glob -import sys -from StringIO import StringIO -import cPickle -import re -import urllib2 -import gzip -import posixpath - -try: - from PIL import Image -except: - import Image - -import matplotlib -matplotlib.use('Agg') - -import token -import tokenize -import numpy as np - - -############################################################################### -# A tee object to redict streams to multiple outputs - -class Tee(object): - - def __init__(self, file1, file2): - self.file1 = file1 - self.file2 = file2 - - def write(self, data): - self.file1.write(data) - self.file2.write(data) - - def flush(self): - self.file1.flush() - self.file2.flush() - -############################################################################### -# Documentation link resolver objects - - -def get_data(url): - """Helper function to get data over http or from a local file""" - if url.startswith('http://'): - resp = urllib2.urlopen(url) - encoding = resp.headers.dict.get('content-encoding', 'plain') - data = resp.read() - if encoding == 'plain': - pass - elif encoding == 'gzip': - data = StringIO(data) - data = gzip.GzipFile(fileobj=data).read() - else: - raise RuntimeError('unknown encoding') - else: - with open(url, 'r') as fid: - data = fid.read() - fid.close() - - return data - - -def parse_sphinx_searchindex(searchindex): - """Parse a Sphinx search index - - Parameters - ---------- - searchindex : str - The Sphinx search index (contents of searchindex.js) - - Returns - ------- - filenames : list of str - The file names parsed from the search index. - objects : dict - The objects parsed from the search index. - """ - def _select_block(str_in, start_tag, end_tag): - """Select first block delimited by start_tag and end_tag""" - start_pos = str_in.find(start_tag) - if start_pos < 0: - raise ValueError('start_tag not found') - depth = 0 - for pos in range(start_pos, len(str_in)): - if str_in[pos] == start_tag: - depth += 1 - elif str_in[pos] == end_tag: - depth -= 1 - - if depth == 0: - break - sel = str_in[start_pos + 1:pos] - return sel - - def _parse_dict_recursive(dict_str): - """Parse a dictionary from the search index""" - dict_out = dict() - pos_last = 0 - pos = dict_str.find(':') - while pos >= 0: - key = dict_str[pos_last:pos] - if dict_str[pos + 1] == '[': - # value is a list - pos_tmp = dict_str.find(']', pos + 1) - if pos_tmp < 0: - raise RuntimeError('error when parsing dict') - value = dict_str[pos + 2: pos_tmp].split(',') - # try to convert elements to int - for i in range(len(value)): - try: - value[i] = int(value[i]) - except ValueError: - pass - elif dict_str[pos + 1] == '{': - # value is another dictionary - subdict_str = _select_block(dict_str[pos:], '{', '}') - value = _parse_dict_recursive(subdict_str) - pos_tmp = pos + len(subdict_str) - else: - raise ValueError('error when parsing dict: unknown elem') - - key = key.strip('"') - if len(key) > 0: - dict_out[key] = value - - pos_last = dict_str.find(',', pos_tmp) - if pos_last < 0: - break - pos_last += 1 - pos = dict_str.find(':', pos_last) - - return dict_out - - # parse objects - query = 'objects:' - pos = searchindex.find(query) - if pos < 0: - raise ValueError('"objects:" not found in search index') - - sel = _select_block(searchindex[pos:], '{', '}') - objects = _parse_dict_recursive(sel) - - # parse filenames - query = 'filenames:' - pos = searchindex.find(query) - if pos < 0: - raise ValueError('"filenames:" not found in search index') - filenames = searchindex[pos + len(query) + 1:] - filenames = filenames[:filenames.find(']')] - filenames = [f.strip('"') for f in filenames.split(',')] - - return filenames, objects - - -class SphinxDocLinkResolver(object): - """ Resolve documentation links using searchindex.js generated by Sphinx - - Parameters - ---------- - doc_url : str - The base URL of the project website. - searchindex : str - Filename of searchindex, relative to doc_url. - extra_modules_test : list of str - List of extra module names to test. - relative : bool - Return relative links (only useful for links to documentation of this - package). - """ - - def __init__(self, doc_url, searchindex='searchindex.js', - extra_modules_test=None, relative=False): - self.doc_url = doc_url - self.relative = relative - self._link_cache = {} - - self.extra_modules_test = extra_modules_test - self._page_cache = {} - if doc_url.startswith('http://'): - if relative: - raise ValueError('Relative links are only supported for local ' - 'URLs (doc_url cannot start with "http://)"') - searchindex_url = doc_url + '/' + searchindex - else: - searchindex_url = os.path.join(doc_url, searchindex) - - # detect if we are using relative links on a Windows system - if os.name.lower() == 'nt' and not doc_url.startswith('http://'): - if not relative: - raise ValueError('You have to use relative=True for the local' - 'package on a Windows system.') - self._is_windows = True - else: - self._is_windows = False - - # download and initialize the search index - sindex = get_data(searchindex_url) - filenames, objects = parse_sphinx_searchindex(sindex) - - self._searchindex = dict(filenames=filenames, objects=objects) - - def _get_link(self, cobj): - """Get a valid link, False if not found""" - - fname_idx = None - full_name = cobj['module_short'] + '.' + cobj['name'] - if full_name in self._searchindex['objects']: - value = self._searchindex['objects'][full_name] - if isinstance(value, dict): - value = value[value.keys()[0]] - fname_idx = value[0] - elif cobj['module_short'] in self._searchindex['objects']: - value = self._searchindex['objects'][cobj['module_short']] - if cobj['name'] in value.keys(): - fname_idx = value[cobj['name']][0] - - if fname_idx is not None: - fname = self._searchindex['filenames'][fname_idx] + '.html' - - if self._is_windows: - fname = fname.replace('/', '\\') - link = os.path.join(self.doc_url, fname) - else: - link = posixpath.join(self.doc_url, fname) - - if link in self._page_cache: - html = self._page_cache[link] - else: - html = get_data(link) - self._page_cache[link] = html - - # test if cobj appears in page - comb_names = [cobj['module_short'] + '.' + cobj['name']] - if self.extra_modules_test is not None: - for mod in self.extra_modules_test: - comb_names.append(mod + '.' + cobj['name']) - url = False - for comb_name in comb_names: - if html.find(comb_name) >= 0: - url = link + '#' + comb_name - link = url - else: - link = False - - return link - - def resolve(self, cobj, this_url): - """Resolve the link to the documentation, returns None if not found - - Parameters - ---------- - cobj : dict - Dict with information about the "code object" for which we are - resolving a link. - cobi['name'] : function or class name (str) - cobj['module_short'] : shortened module name (str) - cobj['module'] : module name (str) - this_url: str - URL of the current page. Needed to construct relative URLs - (only used if relative=True in constructor). - - Returns - ------- - link : str | None - The link (URL) to the documentation. - """ - full_name = cobj['module_short'] + '.' + cobj['name'] - link = self._link_cache.get(full_name, None) - if link is None: - # we don't have it cached - link = self._get_link(cobj) - # cache it for the future - self._link_cache[full_name] = link - - if link is False or link is None: - # failed to resolve - return None - - if self.relative: - link = os.path.relpath(link, start=this_url) - if self._is_windows: - # replace '\' with '/' so it on the web - link = link.replace('\\', '/') - - # for some reason, the relative link goes one directory too high up - link = link[3:] - - return link - - -############################################################################### -rst_template = """ - -.. _%(short_fname)s: - -%(docstring)s - -**Python source code:** :download:`%(fname)s <%(fname)s>` - -.. literalinclude:: %(fname)s - :lines: %(end_row)s- - """ - -plot_rst_template = """ - -.. _%(short_fname)s: - -%(docstring)s - -%(image_list)s - -%(stdout)s - -**Python source code:** :download:`%(fname)s <%(fname)s>` - -.. literalinclude:: %(fname)s - :lines: %(end_row)s- - -**Total running time of the example:** %(time_elapsed) .2f seconds - """ - -# The following strings are used when we have several pictures: we use -# an html div tag that our CSS uses to turn the lists into horizontal -# lists. -HLIST_HEADER = """ -.. rst-class:: horizontal - -""" - -HLIST_IMAGE_TEMPLATE = """ - * - - .. image:: images/%s - :scale: 47 -""" - -SINGLE_IMAGE = """ -.. image:: images/%s - :align: center -""" - - -def extract_docstring(filename): - """ Extract a module-level docstring, if any - """ - lines = file(filename).readlines() - start_row = 0 - if lines[0].startswith('#!'): - lines.pop(0) - start_row = 1 - - docstring = '' - first_par = '' - tokens = tokenize.generate_tokens(iter(lines).next) - for tok_type, tok_content, _, (erow, _), _ in tokens: - tok_type = token.tok_name[tok_type] - if tok_type in ('NEWLINE', 'COMMENT', 'NL', 'INDENT', 'DEDENT'): - continue - elif tok_type == 'STRING': - docstring = eval(tok_content) - # If the docstring is formatted with several paragraphs, extract - # the first one: - paragraphs = '\n'.join(line.rstrip() - for line in docstring.split('\n')).split('\n\n') - if len(paragraphs) > 0: - first_par = paragraphs[0] - break - return docstring, first_par, erow + 1 + start_row - - -def generate_example_rst(app): - """ Generate the list of examples, as well as the contents of - examples. - """ - root_dir = os.path.join(app.builder.srcdir, 'auto_examples') - example_dir = os.path.abspath(app.builder.srcdir + '/../' + 'examples') - try: - plot_gallery = eval(app.builder.config.plot_gallery) - except TypeError: - plot_gallery = bool(app.builder.config.plot_gallery) - if not os.path.exists(example_dir): - os.makedirs(example_dir) - if not os.path.exists(root_dir): - os.makedirs(root_dir) - - # we create an index.rst with all examples - fhindex = file(os.path.join(root_dir, 'index.rst'), 'w') - #Note: The sidebar button has been removed from the examples page for now - # due to how it messes up the layout. Will be fixed at a later point - fhindex.write("""\ - -.. raw:: html - - - - -Examples -======== - -.. _examples-index: -""") - # Here we don't use an os.walk, but we recurse only twice: flat is - # better than nested. - generate_dir_rst('.', fhindex, example_dir, root_dir, plot_gallery) - for dir in sorted(os.listdir(example_dir)): - if os.path.isdir(os.path.join(example_dir, dir)): - generate_dir_rst(dir, fhindex, example_dir, root_dir, plot_gallery) - fhindex.flush() - -def extract_line_count(filename, target_dir): - # Extract the line count of a file - example_file = os.path.join(target_dir, filename) - lines = file(example_file).readlines() - start_row = 0 - if lines[0].startswith('#!'): - lines.pop(0) - start_row = 1 - tokens = tokenize.generate_tokens(lines.__iter__().next) - check_docstring = True - erow_docstring = 0 - for tok_type, _, _, (erow, _), _ in tokens: - tok_type = token.tok_name[tok_type] - if tok_type in ('NEWLINE', 'COMMENT', 'NL', 'INDENT', 'DEDENT'): - continue - elif ((tok_type == 'STRING') and (check_docstring == True)): - erow_docstring = erow - check_docstring = False - return erow_docstring+1+start_row, erow+1+start_row - -def line_count_sort(file_list, target_dir): - # Sort the list of examples by line-count - new_list = filter(lambda x: x.endswith('.py'), file_list) - unsorted = np.zeros(shape=(len(new_list), 2)) - unsorted = unsorted.astype(np.object) - for count, exmpl in enumerate(new_list): - docstr_lines, total_lines = extract_line_count(exmpl, target_dir) - unsorted[count][1] = total_lines - docstr_lines - unsorted[count][0] = exmpl - index = np.lexsort((unsorted[:,0].astype(np.str), - unsorted[:,1].astype(np.float))) - return np.array(unsorted[index][:,0]).tolist() - -def generate_dir_rst(dir, fhindex, example_dir, root_dir, plot_gallery): - """ Generate the rst file for an example directory. - """ - if not dir == '.': - target_dir = os.path.join(root_dir, dir) - src_dir = os.path.join(example_dir, dir) - else: - target_dir = root_dir - src_dir = example_dir - if not os.path.exists(os.path.join(src_dir, 'README.txt')): - print 80 * '_' - print ('Example directory %s does not have a README.txt file' - % src_dir) - print 'Skipping this directory' - print 80 * '_' - return - fhindex.write(""" - - -%s - - -""" % file(os.path.join(src_dir, 'README.txt')).read()) - if not os.path.exists(target_dir): - os.makedirs(target_dir) - sorted_listdir = line_count_sort(os.listdir(src_dir), - src_dir) - for fname in sorted_listdir: - if fname.endswith('py'): - generate_file_rst(fname, target_dir, src_dir, plot_gallery) - thumb = os.path.join(dir, 'images', 'thumb', fname[:-3] + '.png') - link_name = os.path.join(dir, fname).replace(os.path.sep, '_') - fhindex.write(""" - -.. raw:: html - -
- - -""") - - fhindex.write('.. figure:: %s\n' % thumb) - if link_name.startswith('._'): - link_name = link_name[2:] - if dir != '.': - fhindex.write(' :target: ./%s/%s.html\n\n' % (dir, - fname[:-3])) - else: - fhindex.write(' :target: ./%s.html\n\n' % link_name[:-3]) - fhindex.write(""" :ref:`%s` - - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - %s/%s - -""" % (link_name, dir, fname[:-3])) - fhindex.write(""" -.. raw:: html - -
- """) # clear at the end of the section - -# modules for which we embed links into example code -DOCMODULES = ['sklearn', 'matplotlib', 'numpy', 'scipy', 'pystruct'] - - -def make_thumbnail(in_fname, out_fname, width, height): - """Make a thumbnail with the same aspect ratio centered in an - image with a given width and height - """ - img = Image.open(in_fname) - width_in, height_in = img.size - scale_w = width / float(width_in) - scale_h = height / float(height_in) - - if height_in * scale_w <= height: - scale = scale_w - else: - scale = scale_h - - width_sc = int(round(scale * width_in)) - height_sc = int(round(scale * height_in)) - - # resize the image - img.thumbnail((width_sc, height_sc), Image.ANTIALIAS) - - # insert centered - thumb = Image.new('RGB', (width, height), (255, 255, 255)) - pos_insert = ((width - width_sc) / 2, (height - height_sc) / 2) - thumb.paste(img, pos_insert) - - thumb.save(out_fname) - - -def get_short_module_name(module_name, obj_name): - """ Get the shortest possible module name """ - parts = module_name.split('.') - short_name = module_name - for i in range(len(parts) - 1, 0, -1): - short_name = '.'.join(parts[:i]) - try: - exec('from %s import %s' % (short_name, obj_name)) - except ImportError: - # get the last working module name - short_name = '.'.join(parts[:(i + 1)]) - break - return short_name - - -def generate_file_rst(fname, target_dir, src_dir, plot_gallery): - """ Generate the rst file for a given example. - """ - base_image_name = os.path.splitext(fname)[0] - image_fname = '%s_%%s.png' % base_image_name - - this_template = rst_template - last_dir = os.path.split(src_dir)[-1] - # to avoid leading . in file names, and wrong names in links - if last_dir == '.' or last_dir == 'examples': - last_dir = '' - else: - last_dir += '_' - short_fname = last_dir + fname - src_file = os.path.join(src_dir, fname) - example_file = os.path.join(target_dir, fname) - shutil.copyfile(src_file, example_file) - - # The following is a list containing all the figure names - figure_list = [] - - image_dir = os.path.join(target_dir, 'images') - thumb_dir = os.path.join(image_dir, 'thumb') - if not os.path.exists(image_dir): - os.makedirs(image_dir) - if not os.path.exists(thumb_dir): - os.makedirs(thumb_dir) - image_path = os.path.join(image_dir, image_fname) - stdout_path = os.path.join(image_dir, - 'stdout_%s.txt' % base_image_name) - time_path = os.path.join(image_dir, - 'time_%s.txt' % base_image_name) - thumb_file = os.path.join(thumb_dir, fname[:-3] + '.png') - time_elapsed = 0 - if plot_gallery and fname.startswith('plot'): - # generate the plot as png image if file name - # starts with plot and if it is more recent than an - # existing image. - first_image_file = image_path % 1 - if os.path.exists(stdout_path): - stdout = open(stdout_path).read() - else: - stdout = '' - if os.path.exists(time_path): - time_elapsed = float(open(time_path).read()) - - if (not os.path.exists(first_image_file) or - os.stat(first_image_file).st_mtime <= - os.stat(src_file).st_mtime): - # We need to execute the code - print 'plotting %s' % fname - t0 = time() - import matplotlib.pyplot as plt - plt.close('all') - cwd = os.getcwd() - try: - # First CD in the original example dir, so that any file - # created by the example get created in this directory - orig_stdout = sys.stdout - os.chdir(os.path.dirname(src_file)) - my_buffer = StringIO() - my_stdout = Tee(sys.stdout, my_buffer) - sys.stdout = my_stdout - my_globals = {'pl': plt} - execfile(os.path.basename(src_file), my_globals) - time_elapsed = time() - t0 - sys.stdout = orig_stdout - my_stdout = my_buffer.getvalue() - - # get variables so we can later add links to the documentation - example_code_obj = {} - for var_name, var in my_globals.iteritems(): - if not hasattr(var, '__module__'): - continue - if not isinstance(var.__module__, basestring): - continue - if var.__module__.split('.')[0] not in DOCMODULES: - continue - - # get the type as a string with other things stripped - tstr = str(type(var)) - tstr = (tstr[tstr.find('\'') - + 1:tstr.rfind('\'')].split('.')[-1]) - # get shortened module name - module_short = get_short_module_name(var.__module__, - tstr) - cobj = {'name': tstr, 'module': var.__module__, - 'module_short': module_short, - 'obj_type': 'object'} - example_code_obj[var_name] = cobj - - # find functions so we can later add links to the documentation - funregex = re.compile('[\w.]+\(') - with open(src_file, 'rt') as fid: - for line in fid.readlines(): - if line.startswith('#'): - continue - for match in funregex.findall(line): - fun_name = match[:-1] - try: - exec('this_fun = %s' % fun_name, my_globals) - except Exception: - #print 'extracting function failed' - #print err - continue - this_fun = my_globals['this_fun'] - if not callable(this_fun): - continue - if not hasattr(this_fun, '__module__'): - continue - if not isinstance(this_fun.__module__, basestring): - continue - if (this_fun.__module__.split('.')[0] - not in DOCMODULES): - continue - - # get shortened module name - fun_name_short = fun_name.split('.')[-1] - module_short = get_short_module_name( - this_fun.__module__, fun_name_short) - cobj = {'name': fun_name_short, - 'module': this_fun.__module__, - 'module_short': module_short, - 'obj_type': 'function'} - example_code_obj[fun_name] = cobj - fid.close() - - if len(example_code_obj) > 0: - # save the dictionary, so we can later add hyperlinks - codeobj_fname = example_file[:-3] + '_codeobj.pickle' - with open(codeobj_fname, 'wb') as fid: - cPickle.dump(example_code_obj, fid, - cPickle.HIGHEST_PROTOCOL) - fid.close() - - if '__doc__' in my_globals: - # The __doc__ is often printed in the example, we - # don't with to echo it - my_stdout = my_stdout.replace( - my_globals['__doc__'], - '') - my_stdout = my_stdout.strip() - if my_stdout: - stdout = '**Script output**::\n\n %s\n\n' % ( - '\n '.join(my_stdout.split('\n'))) - open(stdout_path, 'w').write(stdout) - open(time_path, 'w').write('%f' % time_elapsed) - os.chdir(cwd) - - # In order to save every figure we have two solutions : - # * iterate from 1 to infinity and call plt.fignum_exists(n) - # (this requires the figures to be numbered - # incrementally: 1, 2, 3 and not 1, 2, 5) - # * iterate over [fig_mngr.num for fig_mngr in - # matplotlib._pylab_helpers.Gcf.get_all_fig_managers()] - for fig_num in (fig_mngr.num for fig_mngr in - matplotlib._pylab_helpers.Gcf.get_all_fig_managers()): - # Set the fig_num figure as the current figure as we can't - # save a figure that's not the current figure. - plt.figure(fig_num) - plt.savefig(image_path % fig_num) - figure_list.append(image_fname % fig_num) - except: - print 80 * '_' - print '%s is not compiling:' % fname - traceback.print_exc() - print 80 * '_' - finally: - os.chdir(cwd) - sys.stdout = orig_stdout - - print " - time elapsed : %.2g sec" % time_elapsed - else: - figure_list = [f[len(image_dir):] - for f in glob.glob(image_path % '[1-9]')] - #for f in glob.glob(image_path % '*')] - - # generate thumb file - this_template = plot_rst_template - if os.path.exists(first_image_file): - make_thumbnail(first_image_file, thumb_file, 200, 140) - - if not os.path.exists(thumb_file): - # create something to replace the thumbnail - make_thumbnail('images/no_image.png', thumb_file, 200, 140) - - docstring, short_desc, end_row = extract_docstring(example_file) - - # Depending on whether we have one or more figures, we're using a - # horizontal list or a single rst call to 'image'. - if len(figure_list) == 1: - figure_name = figure_list[0] - image_list = SINGLE_IMAGE % figure_name.lstrip('/') - else: - image_list = HLIST_HEADER - for figure_name in figure_list: - image_list += HLIST_IMAGE_TEMPLATE % figure_name.lstrip('/') - - f = open(os.path.join(target_dir, fname[:-2] + 'rst'), 'w') - f.write(this_template % locals()) - f.flush() - - -def embed_code_links(app, exception): - """Embed hyperlinks to documentation into example code""" - try: - if exception is not None: - return - print 'Embedding documentation hyperlinks in examples..' - - # Add resolvers for the packages for which we want to show links - doc_resolvers = {} - doc_resolvers['sklearn'] = SphinxDocLinkResolver(app.builder.outdir, - relative=True) - - doc_resolvers['matplotlib'] = SphinxDocLinkResolver( - 'http://matplotlib.org') - - doc_resolvers['numpy'] = SphinxDocLinkResolver( - 'http://docs.scipy.org/doc/numpy-1.6.0') - - doc_resolvers['scipy'] = SphinxDocLinkResolver( - 'http://docs.scipy.org/doc/scipy-0.11.0/reference') - - example_dir = os.path.join(app.builder.srcdir, 'auto_examples') - html_example_dir = os.path.abspath(os.path.join(app.builder.outdir, - 'auto_examples')) - - # patterns for replacement - link_pattern = '%s' - orig_pattern = '%s' - period = '.' - - for dirpath, _, filenames in os.walk(html_example_dir): - for fname in filenames: - print '\tprocessing: %s' % fname - full_fname = os.path.join(html_example_dir, dirpath, fname) - subpath = dirpath[len(html_example_dir) + 1:] - pickle_fname = os.path.join(example_dir, subpath, - fname[:-5] + '_codeobj.pickle') - - if os.path.exists(pickle_fname): - # we have a pickle file with the objects to embed links for - with open(pickle_fname, 'rb') as fid: - example_code_obj = cPickle.load(fid) - fid.close() - str_repl = {} - # generate replacement strings with the links - for name, cobj in example_code_obj.iteritems(): - this_module = cobj['module'].split('.')[0] - - if this_module not in doc_resolvers: - continue - - link = doc_resolvers[this_module].resolve(cobj, - full_fname) - if link is not None: - parts = name.split('.') - name_html = orig_pattern % parts[0] - for part in parts[1:]: - name_html += period + orig_pattern % part - str_repl[name_html] = link_pattern % (link, name_html) - # do the replacement in the html file - if len(str_repl) > 0: - with open(full_fname, 'rt') as fid: - lines_in = fid.readlines() - fid.close() - with open(full_fname, 'wt') as fid: - for line in lines_in: - for name, link in str_repl.iteritems(): - line = line.replace(name, link) - fid.write(line) - fid.close() - except urllib2.HTTPError, e: - print ("The following HTTP Error has occurred:\n") - print e.code - except urllib2.URLError, e: - print ("\n...\n" - "Warning: Embedding the documentation hyperlinks requires " - "internet access.\nPlease check your network connection.\n" - "Unable to continue embedding due to a URL Error: \n") - print e.args - print '[done]' - - -def setup(app): - app.connect('builder-inited', generate_example_rst) - app.add_config_value('plot_gallery', True, 'html') - - # embed links after build is finished - app.connect('build-finished', embed_code_links) - - # Sphinx hack: sphinx copies generated images to the build directory - # each time the docs are made. If the desired image name already - # exists, it appends a digit to prevent overwrites. The model is, - # the directory is never cleared. This means that each time you build - # the docs, the number of images in the directory grows. - # - # This question has been asked on the sphinx development list, but there - # was no response: http://osdir.com/ml/sphinx-dev/2011-02/msg00123.html - # - # The following is a hack that prevents this behavior by clearing the - # image build directory each time the docs are built. If sphinx - # changes their layout between versions, this will not work (though - # it should probably not cause a crash). Tested successfully - # on Sphinx 1.0.7 - build_image_dir = '_build/html/_images' - if os.path.exists(build_image_dir): - filelist = os.listdir(build_image_dir) - for filename in filelist: - if filename.endswith('png'): - os.remove(os.path.join(build_image_dir, filename)) diff --git a/doc/sphinxext/numpy_ext/docscrape.py b/doc/sphinxext/numpy_ext/docscrape.py index e9670c05..93097893 100644 --- a/doc/sphinxext/numpy_ext/docscrape.py +++ b/doc/sphinxext/numpy_ext/docscrape.py @@ -6,8 +6,12 @@ import textwrap import re import pydoc -from StringIO import StringIO from warnings import warn +# Try Python 2 first, otherwise load from Python 3 +try: + from StringIO import StringIO +except: + from io import StringIO class Reader(object): @@ -115,7 +119,7 @@ def __getitem__(self, key): return self._parsed_data[key] def __setitem__(self, key, val): - if not self._parsed_data.has_key(key): + if key not in self._parsed_data: warn("Unknown section %s" % key) else: self._parsed_data[key] = val @@ -435,7 +439,7 @@ def __init__(self, func, role='func', doc=None, config={}): argspec = inspect.formatargspec(*argspec) argspec = argspec.replace('*', '\*') signature = '%s%s' % (func_name, argspec) - except TypeError, e: + except TypeError as e: signature = '%s()' % func_name self['Signature'] = signature @@ -457,8 +461,8 @@ def __str__(self): 'meth': 'method'} if self._role: - if not roles.has_key(self._role): - print "Warning: invalid role %s" % self._role + if self._role not in roles: + print("Warning: invalid role %s" % self._role) out += '.. %s:: %s\n \n\n' % (roles.get(self._role, ''), func_name) diff --git a/doc/sphinxext/numpy_ext/docscrape_sphinx.py b/doc/sphinxext/numpy_ext/docscrape_sphinx.py index bcf7e707..ca28300e 100644 --- a/doc/sphinxext/numpy_ext/docscrape_sphinx.py +++ b/doc/sphinxext/numpy_ext/docscrape_sphinx.py @@ -2,10 +2,9 @@ import inspect import textwrap import pydoc -import sphinx -from docscrape import NumpyDocString -from docscrape import FunctionDoc -from docscrape import ClassDoc +from .docscrape import NumpyDocString +from .docscrape import FunctionDoc +from .docscrape import ClassDoc class SphinxDocString(NumpyDocString): @@ -156,6 +155,7 @@ def _str_references(self): out += [''] # Latex collects all references to a separate bibliography, # so we need to insert links to it + import sphinx # local import to avoid test dependency if sphinx.__version__ >= "0.6": out += ['.. only:: latex', ''] else: @@ -188,14 +188,14 @@ def __str__(self, indent=0, func_role="obj"): out += self._str_index() + [''] out += self._str_summary() out += self._str_extended_summary() - for param_list in ('Parameters', 'Returns', 'Raises'): + for param_list in ('Parameters', 'Returns', 'Raises', 'Attributes'): out += self._str_param_list(param_list) out += self._str_warnings() out += self._str_see_also(func_role) out += self._str_section('Notes') out += self._str_references() out += self._str_examples() - for param_list in ('Attributes', 'Methods'): + for param_list in ('Methods',): out += self._str_member_list(param_list) out = self._str_indent(out, indent) return '\n'.join(out) diff --git a/doc/sphinxext/numpy_ext/numpydoc.py b/doc/sphinxext/numpy_ext/numpydoc.py index 62adb56a..6ff03e0d 100644 --- a/doc/sphinxext/numpy_ext/numpydoc.py +++ b/doc/sphinxext/numpy_ext/numpydoc.py @@ -17,12 +17,14 @@ """ +from __future__ import unicode_literals + +import sys # Only needed to check Python version import os import re import pydoc -from docscrape_sphinx import get_doc_object -from docscrape_sphinx import SphinxDocString -from sphinx.util.compat import Directive +from .docscrape_sphinx import get_doc_object +from .docscrape_sphinx import SphinxDocString import inspect @@ -34,17 +36,20 @@ def mangle_docstrings(app, what, name, obj, options, lines, if what == 'module': # Strip top title - title_re = re.compile(ur'^\s*[#*=]{4,}\n[a-z0-9 -]+\n[#*=]{4,}\s*', + title_re = re.compile(r'^\s*[#*=]{4,}\n[a-z0-9 -]+\n[#*=]{4,}\s*', re.I | re.S) - lines[:] = title_re.sub(u'', u"\n".join(lines)).split(u"\n") + lines[:] = title_re.sub('', "\n".join(lines)).split("\n") else: - doc = get_doc_object(obj, what, u"\n".join(lines), config=cfg) - lines[:] = unicode(doc).split(u"\n") + doc = get_doc_object(obj, what, "\n".join(lines), config=cfg) + if sys.version_info[0] < 3: + lines[:] = unicode(doc).splitlines() + else: + lines[:] = str(doc).splitlines() if app.config.numpydoc_edit_link and hasattr(obj, '__name__') and \ obj.__name__: if hasattr(obj, '__module__'): - v = dict(full_name=u"%s.%s" % (obj.__module__, obj.__name__)) + v = dict(full_name="%s.%s" % (obj.__module__, obj.__name__)) else: v = dict(full_name=obj.__name__) lines += [u'', u'.. htmlonly::', ''] @@ -55,7 +60,7 @@ def mangle_docstrings(app, what, name, obj, options, lines, references = [] for line in lines: line = line.strip() - m = re.match(ur'^.. \[([a-z0-9_.-])\]', line, re.I) + m = re.match(r'^.. \[([a-z0-9_.-])\]', line, re.I) if m: references.append(m.group(1)) @@ -64,8 +69,8 @@ def mangle_docstrings(app, what, name, obj, options, lines, if references: for i, line in enumerate(lines): for r in references: - if re.match(ur'^\d+$', r): - new_r = u"R%d" % (reference_offset[0] + int(r)) + if re.match(r'^\d+$', r): + new_r = "R%d" % (reference_offset[0] + int(r)) else: new_r = u"%s%d" % (r, reference_offset[0]) lines[i] = lines[i].replace(u'[%s]_' % r, @@ -91,16 +96,20 @@ def mangle_signature(app, what, name, obj, doc = SphinxDocString(pydoc.getdoc(obj)) if doc['Signature']: - sig = re.sub(u"^[^(]*", u"", doc['Signature']) - return sig, u'' + sig = re.sub("^[^(]*", "", doc['Signature']) + return sig, '' def setup(app, get_doc_object_=get_doc_object): global get_doc_object get_doc_object = get_doc_object_ - app.connect('autodoc-process-docstring', mangle_docstrings) - app.connect('autodoc-process-signature', mangle_signature) + if sys.version_info[0] < 3: + app.connect(b'autodoc-process-docstring', mangle_docstrings) + app.connect(b'autodoc-process-signature', mangle_signature) + else: + app.connect('autodoc-process-docstring', mangle_docstrings) + app.connect('autodoc-process-signature', mangle_signature) app.add_config_value('numpydoc_edit_link', None, False) app.add_config_value('numpydoc_use_plots', None, False) app.add_config_value('numpydoc_show_class_members', True, True) @@ -113,9 +122,13 @@ def setup(app, get_doc_object_=get_doc_object): # Docstring-mangling domains #----------------------------------------------------------------------------- -from docutils.statemachine import ViewList -from sphinx.domains.c import CDomain -from sphinx.domains.python import PythonDomain +try: + import sphinx # lazy to avoid test dependency +except ImportError: + CDomain = PythonDomain = object +else: + from sphinx.domains.c import CDomain + from sphinx.domains.python import PythonDomain class ManglingDomainBase(object): @@ -170,6 +183,8 @@ def run(self): lines = list(self.content) mangle_docstrings(env.app, objtype, name, None, None, lines) + # local import to avoid testing dependency + from docutils.statemachine import ViewList self.content = ViewList(lines, self.content.parent) return base_directive.run(self) diff --git a/doc/user_guide.rst b/doc/user_guide.rst new file mode 100644 index 00000000..057c9727 --- /dev/null +++ b/doc/user_guide.rst @@ -0,0 +1,510 @@ + +.. currentmodule:: pystruct + +.. _user_guide: + +User Guide +============= + +This page explains how to use the most common of the implemented models. +Each model corresponds to a differents structured prediction task, or possibly +a different parametrization of the model. As such, the training data ``X`` and +training labels ``Y`` has slightly different forms for each model. + +A model is given by four functions, ``joint_feature``, ``inference``, ``loss`` +and ``loss_augmented_inference``. If you just want to use the included models, +you don't need to worry about these, and can just use the ``fit``, ``predict`` interface +of the learner. + +Details on model specification +-------------------------------- +For those interested in what happens behind the scenes, or those who might want to +adjust a model, there is a short explanation of these functions for each model below. +For all models, the ``joint_feature(x, y)`` function takes a data point and a +tentative prediction, and computes a continuous vector of a fixed length that +captures the relation between features and label. Learning (that is +``learner.fit(X, y)``) will learn a parameter vector ``w``, and predictions +will be made using + +.. math:: + + y^* = \arg \max_{y} w^T \text{joint\_feature}(x, y) + +That means the number of parameters in the model is the same as the +dimensionality of ``joint_feature``. + +The actual maximization is performed in the ``inference(x, w)`` function, which +takes a sample ``x`` and a parameter vector ``w`` and outputs a ``y^*``, +which (at least approximately) maximizes the above equation. + +The ``loss(y_true, y_pred)`` function gives a numeric loss for a ground truth +labeling ``y_true`` and a prediction ``y_pred``, and finally +``loss_augmented_inference(x, y, w)`` gives an (approximate) maximizer for + +.. math:: + + y^* = \arg \max_{y} w^T \text{joint\_feature}(x, y) + \text{loss}(y_\text{true}, y) + +A good place to understand these definitions is :ref:`multi_class_svm`. + +.. note:: + + Currently all models expect labels to be integers from 0 to n_states (or + n_classes). Starting labels at 1 or using other labels might lead to + errors and / or incorrect results. + +.. note:: + + None of the model include a bias (intercept) by default. + Therefore it is usually a good idea to add a constant feature to all + feature vectors, both for unary and pairwise features. + +.. _multi_class_svm: + +Multi-class SVM +=============== +A precursor for structured SVMs was the multi-class SVM by `Crammer and Singer +`_. +While in practice it is often faster to use an One-vs-Rest approach and an +optimize binary SVM, this is a good hello-world example for structured +predicition and using pystruct. In the case of multi-class SVMs, in contrast +to more structured models, the labels set Y is just the number of classes, so +inference can be performed by just enumerating Y. + +Lets say we want to classify the classical iris dataset. There are three classes and four features:: + + >>> import numpy as np + >>> from sklearn.datasets import load_iris + >>> iris = load_iris() + >>> iris.data.shape, iris.target.shape + ((150, 4), (150,)) + >>> np.unique(iris.target) + array([0, 1, 2]) + +We split the data into training and test set:: + + >>> from sklearn.model_selection import train_test_split + >>> X_train, X_test, y_train, y_test = train_test_split( + ... iris.data, iris.target, test_size=0.4, random_state=0) + +The Crammer-Singer model is implemented in :class:`models.MultiClassClf`. +As this is a simple multi-class classification task, we can pass in training data +as numpy arrays of shape ``(n_samples, n_features)`` and training labels as +numpy array of shape (n_samples,) with classes from 0 to 2. + +For training, we pick the learner :class:`learners.NSlackSSVM`, which works +well with few samples and requires little tuning:: + + >>> from pystruct.learners import NSlackSSVM + >>> from pystruct.models import MultiClassClf + >>> clf = NSlackSSVM(MultiClassClf()) + +The learner has the same interface as a scikit-learn estimator:: + + >>> clf.fit(X_train, y_train) + NSlackSSVM(C=1.0, batch_size=100, break_on_bad=False, check_constraints=True, + inactive_threshold=1e-05, inactive_window=50, logger=None, + max_iter=100, model=MultiClassClf(n_features=4, n_classes=3), + n_jobs=1, negativity_constraint=None, show_loss_every=0, + switch_to=None, tol=0.001, verbose=0) + + >>> clf.predict(X_test) # doctest: +NORMALIZE_WHITESPACE + array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1, 0, 0, 2, 1, 0, + 0, 2, 0, 0, 1, 1, 0, 2, 2, 0, 2, 2, 1, 0, 2, 1, 1, 2, 0, 2, 0, 0, 1, + 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 2]) + + >>> clf.score(X_test, y_test) #doctest: +ELLIPSIS + 0.96... + +Details on the implementation +--------------------------------- +For this simple model, the ``joint_feature(x, y)`` is a vector of size ``n_features * n_classes``, +which corresponds to one copy of the input features for each possibly class. +For any given pair ``(x, y)`` the features in ``x`` will be put at the position corresponding +to the class in ``y``. +Correspondingly, the weights that are learned are one vector of length ``n_features`` for each class: +``w = np.hstack([w_class_0, ..., w_class_1])``. + +For this simple model, and inference is just the argmax over the inner product with each of these ``w_class_i``:: + + >>> y_pred = np.argmax(np.dot(w.reshape(n_classes, n_features), x)) # doctest: +SKIP + +To perform max-margin learning, we also need the loss-augmented inference. PyStruct has an optimized version, +but a pure python version would look like this:: + + >>> scores = np.dot(w.reshape(n_classes, n_features), x) # doctest: +SKIP + >>> scores[np.arange(n_classes) != y] += 1 # doctest: +SKIP + >>> y_pred = np.argmax(scores) # doctest: +SKIP + +Essentialy the response (score / energy) of wrong label is down weighted by 1, the loss of doing an incorrect prediction. + +.. _multi_label_svm: + +Multi-label SVM +=============== +A multi-label classification task is one where each sample can be labeled with any number of classes. +In other words, there are n_classes many binary labels, each indicating whether a sample belongs +to a given class or not. This could be treated as n_classes many independed binary classification +problems, as the scikit-learn OneVsRest classifier does. +However, it might be beneficial to exploit correlations between labels to achieve better generalization. + +In the scene classification dataset, each sample is a picture of an outdoor scene, +representated using simple color aggregation. The labels characterize the kind of scene, which can be +"beach", "sunset", "fall foilage", "field", "mountain" or "urban". Each image can belong to multiple classes, +such as "fall foilage" and "field". Clearly some combinations are more likely than others. + +We could try to model all possible combinations, which would result in a 2 ** 6 += 64 class multi-class classification problem. This would allow us explicitly model all correlations between labels, +but it would prevent us from predicting combinations that don't appear in the training set. +Even if a combination did appear in the training set, the numer of samples in each class would be very small. +A compromise between modeling all correlations and modelling no correlations is modeling only pairwise correlations, +which is the approach implemented in :class:`models.MultiLabelClf`. + +The input to this model is similar to the :ref:`multi_class_svm`, with the training data ``X_train`` simple +a numpy array of shape ``(n_samples, n_features)`` and the training labels a binary indicator matrix +of shape ``(n_samples, n_classes)``:: + + >>> from pystruct.datasets import load_scene + >>> scene = load_scene() + >>> X_train, X_test = scene['X_train'], scene['X_test'] + >>> y_train, y_test = scene['y_train'], scene['y_test'] + >>> X_train.shape + (1211, 294) + >>> y_train.shape + (1211, 6) + +We use the :class:`learners.NSlackSSVM` learner, passing it the :class:`models.MultiLabelClf` model:: + + >>> from pystruct.learners import NSlackSSVM + >>> from pystruct.models import MultiLabelClf + >>> clf = NSlackSSVM(MultiLabelClf()) + +Training looks as before, only that ``y_train`` is now a matrix:: + + >>> # clf.fit(X_train, y_train) + >>> # clf.predict(X_test) + >>> # clf.score(X_test, y_test) + +With only 64 possible label-combinations, we can actually enumerate all states. +Unfortunately, in general, inference in a fully connected binary graph is in +gerneral NP-hard, so we might need to rely on approximate inference, like loopy +believe propagation or AD3. + +An alternative to using approximate inference for larger numbers of labels is to not create a fully connected graph, +but restrict ourself to pairwise interactions on a tree over the labels. In the above example of outdoor scenes, +some labels might be informative about others, maybe a beach picture is likely to be of a sunset, while +an urban scene might have as many sunset as non-sunset samples. The optimum tree-structure for such a problem +can easily be found using the Chow-Liu tree, which is simply the maximum weight spanning tree over the graph, where +edge-weights are given by the mutual information between labels on the training set. +You can use the Chow-Liu tree method simply by specifying ``edges="chow_liu"``. +This allows us to use efficient and exact max-product message passing for +inference:: + + >>> clf = NSlackSSVM(MultiLabelClf(edges="chow_liu")) + +Training looks as before, only that ``y_train`` is now a matrix:: + + >>> # clf.fit(X_train, y_train) + >>> # clf.predict(X_test) + >>> # clf.score(X_test, y_test) + +This model for multi-label classification with full connectivity is taken from the paper +T. Finley, T. Joachims, Training Structural SVMs when Exact Inference is Intractable. + + +Details on the implementation +--------------------------------- +The model creates a graph over ``n_classes`` binary nodes, together with edges +between each pair of classes. Each binary node has represents one class, and +therefor will get its own column in the weight-vector, similar to the +crammer-singer multi-class classification. + +In addition, there is a pairwise weight between each pair of labels. +This leads to a feature function of this form: + +The implementation of the inference for this model creates a graph with unary +potentials (given by the inner product of features and weights), and pairwise +potentials given by the pairwise weight. This graph is then passed to the +general graph-inference, which runs the selected algorithm. + + +Conditional-Random-Field-like graph models +========================================== +The following models are all pairwise models over nodes, that is they model a +labeling of a graph, using features at the nodes, and relation between +neighboring nodes. The main assumption in these models in PyStruct is that +nodes are homogeneous, that is they all have the same meaning. That means that +each node has the same number of classes, and these classes mean the same +thing. In practice that means that weights are shared across all nodes and +edges, and the model adapts via features. +This is in contrast to the :class:`models.MultiLabelClf`, which builds a binary graph +were nodes mean different things (each node represents a different class), so +they do not share weights. + +.. note:: + + I call these models Conditional Random Fields (CRFs), but this a slight abuse of notation, + as PyStruct actually implements perceptron and max-margin learning, not maximum likelihood learning. + So these models might better be called Maximum Margin Random Fields. However, in the computer vision + community, it seems most pairwise models are called CRFs, independent of the method of training. + +.. _chain_crf: + +ChainCRF +---------- +One of the most common use-cases for structured prediction is chain-structured +outputs, implemented in :class:`models.ChainCRF`. These occur naturaly in +sequence labeling tasks, such as Part-of-Speech tagging or named entity +recognition in natural language processing, or segmentation and phoneme +recognition in speech processing. + +As an example dataset, we will use the toy OCR dataset letters. In this +dataset, each sample is a handwritten word, segmented into letters. This +dataset has a slight oddity, in that the first letter of every word was +removed, as it was capitalized, and therefore different from all the other +letters. + +Each letter is a node in our chain, and neighboring letters are connected with +an edge. The length of the chain varies with the number of letters in the +word. As in all CRF-like models, the nodes in :class:`models.ChainCRF` all have +the same meaning and share parameters. + +The letters dataset comes with prespecified folds, we take one fold to be the +training set, and the rest to be the test set, as in `Max-Margin Markov +Networks `_:: + + >>> from pystruct.datasets import load_letters + >>> letters = load_letters() + >>> X, y, folds = letters['data'], letters['labels'], letters['folds'] + >>> X, y = np.array(X), np.array(y) + >>> X_train, X_test = X[folds == 1], X[folds != 1] + >>> y_train, y_test = y[folds == 1], y[folds != 1] + >>> len(X_train) + 704 + >>> len(X_test) + 6173 + +The training data is a array of samples, where each sample is a numpy array of +shape ``(n_nodes, n_features)``. Here n_nodes is the length of the input +sequence, that is the length of the word in our case. That means the input +array actually has dtype object. We can not store the features in a simple +array, as the input sequences can have different length:: + + >>> X_train[0].shape + (9, 128) + >>> y_train[0].shape + (9,) + >>> X_train[10].shape + (7, 128) + >>> y_train[10].shape + (7,) + +Edges don't need to be specified, as the input features are assumed to be in +the order of the nodes in the chain. + +The default inference method is max-product message passing on the chain (aka +viterbi), which is always exact and efficientl. We use the +:class:`learners.FrankWolfeSSVM`, which is a very efficient learner when +inference is fast:: + + >>> from pystruct.models import ChainCRF + >>> from pystruct.learners import FrankWolfeSSVM + >>> model = ChainCRF() + >>> ssvm = FrankWolfeSSVM(model=model, C=.1, max_iter=10) + >>> ssvm.fit(X_train, y_train) # doctest: +NORMALIZE_WHITESPACE + FrankWolfeSSVM(C=0.1, batch_mode=False, check_dual_every=10, + do_averaging=True, line_search=True, logger=None, max_iter=10, + model=ChainCRF(n_states: 26, inference_method: max-product), + n_jobs=1, random_state=None, sample_method='perm', + show_loss_every=0, tol=0.001, verbose=0) + >>> ssvm.score(X_test, y_test) # doctest: +ELLIPSIS + 0.78... + +Details on the implementation +--------------------------------- + +The unary potentials in each node are given as the inner product of the features +at this node (the input image) with the weights (which are shared over all nodes): + + +The pairwise potentials are identical over the whole chain and given simply by +the weights: + +In principle it is possible to also use feature in the pairwise potentials. +This is not implemented in the :class:`models.ChainCRF`, but can be done using +:ref:`edge_feature_graph_crf`. + +.. note:: + + While pystruct is able to work with chain CRFs, it is not explicitly built + with these in mind, and there are libraries that optimize much more for + this special case, such as `seqlearn + `_ and `CRF++ + `_. + +.. _graph_crf: + +GraphCRF +--------- +The :class:`models.GraphCRF` model is a generalization of the :ref:`chain_crf` to +arbitray graphs. While in the chain model, the direction of the edge is +usually important, for many graphs, the direction of the edge has no semantic +meaning. Therefore, by default, the pairwise interaction matrix of the +:class:`models.GraphCRF` is forced to be symmetric. + +Each training sample for the :class:`models.GraphCRF` is a tuple ``(features, +edges)``, where ``features`` is a numpy array of node-features (of shape +``(n_nodes, n_features)``), and ``edges`` is a array of edges between nodes, of +shape ``(n_edges, 2)``. Each row of the edge array are the indices of the two +nodes connected by the edge, starting from zero. + +To reproduce the :class:`models.ChainCRF` model above with :class:`models.GraphCRF`, we can simply +generate the indices of a chain:: + + >>> features, y, folds = letters['data'], letters['labels'], letters['folds'] + >>> features, y = np.array(features), np.array(y) + >>> features_train, features_test = features[folds == 1], features[folds != 1] + >>> y_train, y_test = y[folds == 1], y[folds != 1] + +For a single word made out of 9 characters:: + + >>> features_0 = features_train[0] + >>> features_0.shape + (9, 128) + >>> n_nodes = features_0.shape[0] + >>> edges_0 = np.vstack([np.arange(n_nodes - 1), np.arange(1, n_nodes)]) + >>> edges_0 + array([[0, 1, 2, 3, 4, 5, 6, 7], + [1, 2, 3, 4, 5, 6, 7, 8]]) + >>> x = (features_0, edges_0) + +For the whole training dataset:: + + >>> f_t = features_train + >>> X_train = [(features_i, np.vstack([np.arange(features_i.shape[0] - 1), np.arange(1, features_i.shape[0])])) + ... for features_i in f_t] + >>> X_train[0] # doctest: +NORMALIZE_WHITESPACE + (array([[0, 0, 0, ..., 0, 0, 0], + [0, 0, 0, ..., 0, 0, 0], + [0, 0, 0, ..., 0, 0, 0], + ..., + [0, 0, 0, ..., 0, 0, 0], + [0, 0, 0, ..., 0, 0, 0], + [0, 0, 1, ..., 0, 1, 1]], dtype=uint8), array([[0, 1, 2, 3, 4, 5, 6, 7], + [1, 2, 3, 4, 5, 6, 7, 8]])) + +Now we can fit a (directed) :class:`models.GraphCRF` on this data:: + + >>> from pystruct.models import GraphCRF + >>> from pystruct.learners import FrankWolfeSSVM + >>> model = GraphCRF(directed=True, inference_method="max-product") + >>> ssvm = FrankWolfeSSVM(model=model, C=.1, max_iter=10) + >>> ssvm.fit(X_train, y_train) # doctest: +NORMALIZE_WHITESPACE + FrankWolfeSSVM(C=0.1, batch_mode=False, check_dual_every=10, + do_averaging=True, line_search=True, logger=None, max_iter=10, + model=GraphCRF(n_states: 26, inference_method: max-product), + n_jobs=1, random_state=None, sample_method='perm', + show_loss_every=0, tol=0.001, verbose=0) + + + +Details on the implementation +--------------------------------- +The potentials are the same as in the ChainCRF model, with unary potentials given +as a shared linear function of the features, and pairwise potentials the same +for all nodes. + + +.. _edge_feature_graph_crf: + +EdgeFeatureGraphCRF +------------------- + +This model is the most general of the CRF models, and contains all other CRF +models as a special case. This model assumes again that the parameters of the +potentials are shared over all nodes and over all edges, but the pairwise +potentials are now also computed as a linear function of the features. + +Each training sample for :class:`models.EdgeFeatureGraphCRF` is a tuple +``(node_features, edges, edge_features)``, where ``node_features`` is a numpy +array of node-features (of shape ``(n_nodes, n_node_features)``), ``edges`` is +a array of edges between nodes, of shape ``(n_edges, 2)`` as in +:ref:`graph_crf`, and ``edge_features`` is a feature for each edge, given as a +numpy array of shape ``(n_edges, n_edge_features)``. + +The edge features allow the pairwise interactions to be modulated by the +context. Two features important for image segmentation, for example, are color +differences between the (super)pixels at given nodes, and whether one is above +the other. If two neighboring nodes correspond to regions of simlar color, +they are more likely to have the same label. For the vertical direction, a node +above a node representing "sky" is more likely to also represent "sky" than +"water". + +A great example of the importance of edge features is +:ref:`example_plot_snakes.py`. + + +Latent Variable Models +========================== +Latent variable models are models that involve interactions with variables that +are not observed during training. These are often modelling a "hidden cause" of +the data, which might make it easier to learn about the actual observations. + +Latent variable models are usually much harder to fit than fully observed +models, and require fitting using either :class:`learners.LatentSSVM`, or +:class:`learners.LatentSubgradientSSVM`. :class:`learners.LatentSSVM` alternates between +inferring the unobserved variables with fitting any of the other SSVM models +(such as :class:`learners.OneSlackSSVM`). Each iteration of this alternation is as +expensive as building a fully observed model, and good initialization can be +very important. This method was published in `Learning Structural SVMs with +Latent Variables +`_. + +The :class:`learners.LatentSubgradientSSVM` approach tries to reestimate the latent +variables for each batch, and corresponds to a subgradient descent on the +non-convex objective involving the maximization over hidden variables. I am +unaware of any literature on this approach. + + +LatentGraphCRF aka Hidden Dynamics CRF +---------------------------------------- +:class:`models.LatentGraphCRF` implements the "Hidden Dynamics CRF" approach. +Here, each output state is split into several hidden sub-states, which allows for +more complex interactions. + +This can be seen as a structured variant of the latent SVM approach as follows: +If there is a single node in the graph (that is doing multi-class +classification), we introduce latent subclasses for each of the target classes. +We can then learn a separate classifier for each of the subclasses, which might +be easier. An example is given in :ref:`example_plot_latent_svm_as_crf.py`, +where images of odd numbers are classified against images of even numbers. It +is much easier to learn a linear classifier that separates one digit from the +other digits, than trying to learn a linear separation between even and odd +digits. + +For more complex graphs, not only the unary potentials benefit, but also the +pairwise potentials, which are now between substates. +The original paper motivates this extension by action recognition. +A complex action like a juming jack is made up of several distinct sub-actions, +and there is a distinct order in which the sub-actions are performed. +The latent dynamic CRF can learn this order. + +See :ref:`example_plot_latent_crf.py` for an example on a 2d grid. + +How to Write Your Own Model +============================ +TODO + +Tips on Choosing a Learner +========================== +There is an extensive benchmarking in my thesis, TODO. + + +SubgradientSSVM : Good for many datapoints, fast inference. Usually worse than FrankWolfeSSVM, but takes less memory. Good for obtaining reasonable solutions fast. +NSlackSSVM : Good for very few datapoints (hundreds), slow inferece. Good for obtaining high precision solutions. +OneSlackSSVM : Good for mid-size data sets (thousands), slow inference. Good for obtaining high precision solutions. +FrankWolfeSSVM : Good for fast inference, large datasets. Good for obtaining reasonable solutions fast. + +Tips on Choosing an Inference Algorithm +======================================= +TODO diff --git a/examples/image_segmentation.py b/examples/image_segmentation.py index 2ef95f4a..1af7dfd7 100644 --- a/examples/image_segmentation.py +++ b/examples/image_segmentation.py @@ -5,7 +5,7 @@ This example demonstrates learning a superpixel CRF for semantic image segmentation. To run the experiment, please download the pre-processed data from: -http://www.ais.uni-bonn.de/~amueller/data/ +http://www.ais.uni-bonn.de/deep_learning/downloads.html The data consists of superpixels, unary potentials, and the connectivity structure of the superpixels. @@ -20,19 +20,22 @@ would need the Pascal VOC 2010 dataset. """ import numpy as np -import cPickle +try: + import cPickle as pickle +except ImportError: + import pickle from pystruct import learners import pystruct.models as crfs from pystruct.utils import SaveLogger -data_train = cPickle.load(open("data_train.pickle")) +data_train = pickle.load(open("data_train_dict.pickle")) C = 0.01 n_states = 21 -print("number of samples: %s" % len(data_train.X)) -class_weights = 1. / np.bincount(np.hstack(data_train.Y)) +print("number of samples: %s" % len(data_train['X'])) +class_weights = 1. / np.bincount(np.hstack(data_train['Y'])) class_weights *= 21. / np.sum(class_weights) print(class_weights) @@ -48,13 +51,13 @@ tol=0.0001, show_loss_every=5, logger=SaveLogger(experiment_name + ".pickle", save_every=100), inactive_threshold=1e-3, inactive_window=10, batch_size=100) -ssvm.fit(data_train.X, data_train.Y) +ssvm.fit(data_train['X'], data_train['Y']) -data_val = cPickle.load(open("data_val.pickle")) -y_pred = ssvm.predict(data_val.X) +data_val = pickle.load(open("data_val_dict.pickle")) +y_pred = ssvm.predict(data_val['X']) # we throw away void superpixels and flatten everything -y_pred, y_true = np.hstack(y_pred), np.hstack(data_val.Y) +y_pred, y_true = np.hstack(y_pred), np.hstack(data_val['Y']) y_pred = y_pred[y_true != 255] y_true = y_true[y_true != 255] diff --git a/examples/multi_class_svm.py b/examples/multi_class_svm.py index f62ffef0..c91df704 100644 --- a/examples/multi_class_svm.py +++ b/examples/multi_class_svm.py @@ -11,7 +11,11 @@ #from sklearn.datasets import fetch_mldata from sklearn.datasets import load_digits -from sklearn.cross_validation import train_test_split +try: + from sklearn.model_selection import train_test_split +except ImportError: + from sklearn.cross_validation import train_test_split + from sklearn.svm import LinearSVC from pystruct.models import MultiClassClf @@ -89,5 +93,3 @@ time_fw_batch_svm = time() - start print("Score with pystruct frankwolfe batch ssvm: %f (took %f seconds)" % (np.mean(y_pred == y_test), time_fw_batch_svm)) -#from IPython.core.debugger import Tracer -#Tracer()() diff --git a/examples/multi_label.py b/examples/multi_label.py index 4666a515..984ac7ed 100644 --- a/examples/multi_label.py +++ b/examples/multi_label.py @@ -1,11 +1,11 @@ """ -========================= -Mult-label classification -========================= +========================== +Multi-label classification +========================== This example shows how to use structured support vector machines (or structured prediction in general) to do multi-label classification. -This method hab been investigated in +This method has been investigated in Finley, Joachims 2008 "Training Structural SVMs when Exact Inference is Intractable" @@ -26,11 +26,7 @@ from sklearn.metrics import hamming_loss from sklearn.datasets import fetch_mldata from sklearn.metrics import mutual_info_score -try: - from sklearn.utils import minimum_spanning_tree -except ImportError: - raise ImportError("Please install a recent version of scikit-learn or" - "scipy to build minimum spanning trees.") +from scipy.sparse.csgraph import minimum_spanning_tree from pystruct.learners import OneSlackSSVM from pystruct.models import MultiLabelClf @@ -41,8 +37,8 @@ def chow_liu_tree(y_): # compute mutual information using sklearn n_labels = y_.shape[1] mi = np.zeros((n_labels, n_labels)) - for i in xrange(n_labels): - for j in xrange(n_labels): + for i in range(n_labels): + for j in range(n_labels): mi[i, j] = mutual_info_score(y_[:, i], y_[:, j]) mst = minimum_spanning_tree(sparse.csr_matrix(-mi)) edges = np.vstack(mst.nonzero()).T @@ -51,7 +47,7 @@ def chow_liu_tree(y_): dataset = "scene" -#dataset = "yeast" +# dataset = "yeast" if dataset == "yeast": yeast = fetch_mldata("yeast") @@ -74,7 +70,7 @@ def chow_liu_tree(y_): full_model = MultiLabelClf(edges=full, inference_method='qpbo') independent_model = MultiLabelClf(inference_method='unary') -tree_model = MultiLabelClf(edges=tree) +tree_model = MultiLabelClf(edges=tree, inference_method="max-product") full_ssvm = OneSlackSSVM(full_model, inference_cache=50, C=.1, tol=0.01) diff --git a/examples/multiclass_comparision_svm_struct.py b/examples/multiclass_comparision_svm_struct.py index 44beb584..0e3782aa 100644 --- a/examples/multiclass_comparision_svm_struct.py +++ b/examples/multiclass_comparision_svm_struct.py @@ -31,7 +31,10 @@ from sklearn.datasets import dump_svmlight_file from sklearn.datasets import fetch_mldata, load_iris, load_digits from sklearn.metrics import accuracy_score -from sklearn.cross_validation import train_test_split +try: + from sklearn.model_selection import train_test_split +except ImportError: + from sklearn.cross_validation import train_test_split import matplotlib.pyplot as plt from pystruct.models import MultiClassClf diff --git a/examples/plot_binary_svm.py b/examples/plot_binary_svm.py index c31429d3..6af9cb68 100644 --- a/examples/plot_binary_svm.py +++ b/examples/plot_binary_svm.py @@ -10,14 +10,18 @@ We don't really have a chance to beat LibSVM but that's ok ;) """ -print __doc__ +print(__doc__) from time import time import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_digits -from sklearn.cross_validation import train_test_split +try: + from sklearn.model_selection import train_test_split +except ImportError: + from sklearn.cross_validation import train_test_split + from sklearn.svm import SVC from pystruct.models import BinaryClf diff --git a/examples/plot_directional_grid.py b/examples/plot_directional_grid.py index 22d33cb2..36d41b52 100644 --- a/examples/plot_directional_grid.py +++ b/examples/plot_directional_grid.py @@ -2,6 +2,7 @@ =========================================== Learning directed interactions on a 2d grid =========================================== + Simple pairwise model with arbitrary interactions on a 4-connected grid. There are different pairwise potentials for the four directions. All the examples are basically the same, three vertical stripes. diff --git a/examples/plot_grid_crf.py b/examples/plot_grid_crf.py index 966730d9..37940db0 100644 --- a/examples/plot_grid_crf.py +++ b/examples/plot_grid_crf.py @@ -21,7 +21,7 @@ X, Y = generate_crosses_explicit(n_samples=50, noise=10) crf = GridCRF(neighborhood=4) -clf = ssvm.OneSlackSSVM(model=crf, C=100, n_jobs=-1, inference_cache=100, +clf = ssvm.OneSlackSSVM(model=crf, C=100, inference_cache=100, tol=.1) clf.fit(X, Y) Y_pred = np.array(clf.predict(X)) diff --git a/examples/plot_hidden_short_snakes_typed.py b/examples/plot_hidden_short_snakes_typed.py new file mode 100644 index 00000000..b78a936a --- /dev/null +++ b/examples/plot_hidden_short_snakes_typed.py @@ -0,0 +1,604 @@ +""" +============================================== +Conditional Interactions on the Snakes Dataset +============================================== + +This is a variant of plot_snakes.py + +Snake are hidding, so we have 2 categorisers: +- determining if a snake is in the picture, +- identifying its head to tail body (at pixel-level) + +We use the NodeTypeEdgeFeatureGraphCRF class with 2 type of nodes. + + +This example uses the snake dataset introduced in +Nowozin, Rother, Bagon, Sharp, Yao, Kohli: Decision Tree Fields ICCV 2011 + +This dataset is specifically designed to require the pairwise interaction terms +to be conditioned on the input, in other words to use non-trival edge-features. + +The task is as following: a "snake" of length ten wandered over a grid. For +each cell, it had the option to go up, down, left or right (unless it came from +there). The input consists of these decisions, while the desired output is an +annotation of the snake from 0 (head) to 9 (tail). See the plots for an +example. + +As input features we use a 3x3 window around each pixel (and pad with background +where necessary). We code the five different input colors (for up, down, left, right, +background) using a one-hot encoding. This is a rather naive approach, not using any +information about the dataset (other than that it is a 2d grid). + +The task can not be solved using the simple DirectionalGridCRF - which can only +infer head and tail (which are also possible to infer just from the unary +features). If we add edge-features that contain the features of the nodes that are +connected by the edge, the CRF can solve the task. + +From an inference point of view, this task is very hard. QPBO move-making is +not able to solve it alone, so we use the relaxed AD3 inference for learning. + +PS: This example runs a bit (5 minutes on 12 cores, 20 minutes on one core for me). +But it does work as well as Decision Tree Fields ;) + + + + JL Meunier - January 2017 + + Developed for the EU project READ. The READ project has received funding + from the European Union's Horizon 2020 research and innovation programme + under grant agreement No 674943 + + Copyright Xerox + +""" +from __future__ import (absolute_import, division, print_function) + +import sys, os, time +import random +try: + import cPickle as pickle +except: + import pickle + +import numpy as np +import matplotlib.pyplot as plt + +from sklearn.metrics import confusion_matrix, accuracy_score +from sklearn.linear_model import LogisticRegression +#from sklearn.grid_search import GridSearchCV +from sklearn.model_selection import GridSearchCV + +from pystruct.learners import OneSlackSSVM +from pystruct.datasets import load_snakes +from pystruct.models import NodeTypeEdgeFeatureGraphCRF + +from plot_snakes import one_hot_colors, neighborhood_feature, prepare_data + +from plot_hidden_snakes import augmentWithNoSnakeImages, shuffle_in_unison, shorten_snakes + + + +#============================================================================================== + +bFIXED_RANDOM_SEED = True + +NCELL=10 + +nbSWAP_Pixel_Pict_TYPES = 0 #0,1,2 are useful (this was for DEBUG) + +bMAKE_PICT_EASY = False #DEBUG: we had a feature on the picture that tells directly if a snake is present or not + +#INFERENCE="ad3+" #ad3+ is required when there are hard logic constraints +INFERENCE="ad3" #ad3 is faster than ad3+ +N_JOBS=8 + +MAXITER=750 + +sMODELFILE = None +#sMODELFILE = "model.pkl" #we save the model in a file and do not re-trian if the file exists + +#============================================================================================== + +def printConfig(): + print("== NCELL=", NCELL) + print("== FIXED_SEED=", bFIXED_RANDOM_SEED) + print("== INFERENCE =", INFERENCE) + print("== N_JOBS =", N_JOBS) + print("== SWAP=", nbSWAP_Pixel_Pict_TYPES) + print("== EASY=", bMAKE_PICT_EASY) + print("== MAX_ITER=", MAXITER) + print("== MODEL FILE=", sMODELFILE) + +if __name__ == '__main__': + printConfig() + + +def plot_snake(picture): + plt.imshow(picture, interpolation='nearest') + plt.show() + +def prepare_picture_data(X): + """ + compute picture features (on 1-hot encoded pictures) + """ + lPictFeat = list() + for a_hot_picture in X: + #count number of cells of each color + #feat = np.zeros((1,5), dtype=np.int8) + feat = np.zeros((1,7), dtype=np.int64) + + #Histogram of pixels from 0 to 4 + """ + Test accuracy: 0.500 + [[45 55] + [45 55]] + """ + for i in range(5): + ai, aj = np.where(a_hot_picture[...,i] == 1) + feat[0,i] = len(ai) + + #adding height and width of the snake + """ + Test accuracy: 0.420 Test accuracy: 0.515 Test accuracy: 0.495 + [[39 61] [[48 52] [[52 48] + [55 45]] [45 55]] [53 47]] + """ + ai, aj = np.where(a_hot_picture[...,3] != 1) + feat[0,5] = max(ai)-min(ai) #height + feat[0,6] = max(aj)-min(aj) #width + + lPictFeat.append(feat) + + return lPictFeat + +def convertToTwoType(X_train, #list of hot pictures + X_train_directions, # list of node_feat (2D array) , edges (_ x 2 array), edge_feat (2D array) for pixel nodes + Y_train, # list of 2D arrays + X_train_pict_feat, #a list of picture_node_features + Y_train_pict, #a list of integers [0,1] + nCell=10): + """ + return X,Y for NodeTypeEdgeFeatureGraphCRF + + + X and Y + ------- + Node features are given as a list of n_types arrays of shape (n_type_nodes, n_type_features): + - n_type_nodes is the number of nodes of that type + - n_type_features is the number of features for this type of node + + Edges are given as a list of n_types x n_types arrays of shape (n_type_edges, 2). + Columns are resp.: node index (in corresponding node type), node index (in corresponding node type) + + Edge features are given as a list of n_types x n_types arrays of shape (n_type_type_edge, n_type_type_edge_features) + - n_type_type_edge is the number of edges of type type_type + - n_type_type_edge_features is the number of features for edge of type type_type + + An instance ``X`` is represented as a tuple ``([node_features, ..], [edges, ..], [edge_features, ..])`` + + Labels ``Y`` are given as one array of shape (n_nodes) The meaning of a label depends upon the node type. + + """ + + lX, lY = list(), list() + + for (X, + (aPixelFeat, aPixelPixelEdges, aPixelPixelEdgeFeat), + aPixelLbl, + aPictFeat, + iPictLbl) in zip(X_train, X_train_directions, Y_train, X_train_pict_feat, Y_train_pict ): + + + aPixelPictEdges = np.zeros( (aPixelFeat.shape[0], 2), np.int64) + aPixelPictEdges[:,0] = np.arange(aPixelFeat.shape[0]) + features = neighborhood_feature(X) + aPixelPictEdgeFeat = features + + lNodeFeat = [aPixelFeat, aPictFeat] + lEdge = [aPixelPixelEdges, + aPixelPictEdges, #pixel to picture + None, #picture to pixel + None] #picture to picture + lEdgeFeat = [aPixelPixelEdgeFeat, + aPixelPictEdgeFeat, + None, + None] + + #Y is flat for each graph + y = np.zeros((aPixelLbl.size+1, ), dtype=np.int64) + y[:-1] = aPixelLbl.ravel() + y[-1] = int(iPictLbl)+nCell+1 + + x = (lNodeFeat, lEdge, lEdgeFeat) + + lX.append(x) + lY.append(y) + + return lX,lY + +def swap_node_types(l_perm, l_n_state, lX, lY, constraints=None): + """ + lX and lY have been produced for a CRF configured with l_n_state + + We permute this as indicated by the permutation (typically for the snake: l_perm=[1, 0] ) + + """ + _lX, _lY = [], [] + _constraints = None + + n_types = len(l_n_state) + a_perm = np.asarray(l_perm) #e.g. 3 for l_n_state = [2, 3, 4] + a_cumsum_n_state = np.asarray([sum(l_n_state[:i]) for i in range(len(l_n_state))]) # [0, 2, 5] + a_delta_y_by_y = np.asarray([item for i,n in enumerate(l_n_state) for item in n*(a_cumsum_n_state[i:i+1]).tolist()]) # [0, 0, 2, 2, 2, 5, 5, 5, 5] + a_typ_by_y = np.asarray([item for i,n in enumerate(l_n_state) for item in n*[i]]) # [0, 0, 1, 1, 1, 2, 2, 2, 2] + + _l_n_state = [l_n_state[i] for i in l_perm] + _a_cumsum_n_state = np.asarray([sum(_l_n_state[:i]) for i in range(len(_l_n_state))]) + + for (lNF, lE, lEF), Y in zip(lX, lY): + + _lNF = [lNF[i] for i in l_perm] + + _Y = np.zeros(Y.shape, dtype=Y.dtype) + #we need to re-arrange the Ys accordingly + l_n_nodes = [nf.shape[0] for nf in lNF] + _l_n_nodes = [nf.shape[0] for nf in _lNF] + cumsum_n_nodes = [0] + [sum( l_n_nodes[:i+1]) for i in range(len( l_n_nodes))] + _cumsum_n_nodes = [0] + [sum(_l_n_nodes[:i+1]) for i in range(len(_l_n_nodes))] + for i in range(len(lNF)): + j = l_perm[i] + _Y[_cumsum_n_nodes[j]:_cumsum_n_nodes[j+1]] = Y[cumsum_n_nodes[i]:cumsum_n_nodes[i+1]] + + _Y = _Y - a_delta_y_by_y[_Y] + _a_cumsum_n_state[a_perm[a_typ_by_y[_Y]]] + + _lE = [lE[i*n_types+j] for i in l_perm for j in l_perm] + _lEF = [lEF[i*n_types+j] for i in l_perm for j in l_perm] + + _lX.append( (_lNF, _lE, _lEF) ) + _lY.append(_Y) + + if constraints: + print("WARNING: some constraints are not properly swapped because the " + "node order has a meaning.") + _constraints = list() + for _lConstraints in constraints: + for (op, l_l_unary, l_l_state, l_lnegated) in _lConstraints: + #keep the op but permute by types + _l_l_unary = [l_l_unary [i] for i in l_perm] + _l_l_state = [l_l_state [i] for i in l_perm] + _l_lnegated = [l_lnegated[i] for i in l_perm] + _lConstraints.append( (op, _l_l_unary, _l_l_state, _l_lnegated)) + _constraints.append(_lConstraints) + + return _lX, _lY, _constraints + +def listConstraints(lX, ncell=NCELL): + """ + produce the list of constraints for this list of multi-type graphs + """ + lConstraints = list() + for _lNF, _lE, _lEF in lX: + nf_pixel, nf_pict = _lNF + nb_pixels = len(nf_pixel) + l_l_unary = [ range(nb_pixels), [0]] + l_l_states = [ 0, 0 ] #we pass a scalar for each type instead of a list since the values are the same across each type + l_l_negated = [ False, False ] #same + + lConstraint_for_X = [("ANDOUT", l_l_unary, l_l_states, l_l_negated)] #we have a list of constraints per X + + for _state in range(1, ncell+1): + lConstraint_for_X.append( ("XOROUT" , l_l_unary + , [ _state, 1 ] #exactly one cell in state _state with picture label being snake + , l_l_negated) + ) #we have a list of constraints per X + + lConstraints.append( lConstraint_for_X ) + return lConstraints + +def listConstraints_ATMOSTONE(lX, ncell=NCELL): + """ + produce the list of constraints for this list of multi-type graphs + """ + lConstraints = list() + for _lNF, _lE, _lEF in lX: + nf_pixel, nf_pict = _lNF + nb_pixels = len(nf_pixel) + + lConstraint_for_X = list() + + for _state in range(1, ncell+1): + lConstraint_for_X.append( ("ATMOSTONE" , [ range(nb_pixels), []] + , [ _state, None ] #atmost one cell in state _state whatever picture label + , [ False, None ]) + ) #we have a list of constraints per X + + lConstraints.append( lConstraint_for_X ) + return lConstraints + + +def makeItEasy(lX_pict_feat, lY_pict): + """ + add the picture label in a feature... + """ + for X,y in zip(lX_pict_feat, lY_pict): + X[0] = y + + +def appendIntVectorToCsv(fd, name, aV): + saV = np.array_str(aV, max_line_width=99999, precision=0) + saV = saV.strip()[1:-1] #removal of brackets + saV = ','.join(saV.split()) + fd.write("%s,%s\n"%(name, saV)) + fd.flush() + +def REPORT(l_Y_GT, lY_Pred, t=None, ncell=NCELL, filename=None, bHisto=False, name=""): + if t: + print("\t( predict DONE IN %.1fs)"%t) + + _flat_GT, _flat_P = (np.hstack([y.ravel() for y in l_Y_GT]), + np.hstack([y.ravel() for y in lY_Pred])) + confmat = confusion_matrix(_flat_GT, _flat_P) + print(confmat) + print("\ttrace =", confmat.trace()) + score = accuracy_score(_flat_GT, _flat_P) + print("\tAccuracy= %.3f"%score) + + #CSV out? + if filename: + histo = np.histogram(np.hstack(_flat_GT), bins=range(ncell+2)) + diag = np.diag(confmat) + with open(filename, "ab") as fdCSV: + if bHisto: appendIntVectorToCsv(fdCSV, name+"_histo,", histo[0]) + appendIntVectorToCsv(fdCSV, name+",%.3f"%score, diag) + +if __name__ == '__main__': + + if bFIXED_RANDOM_SEED: + np.random.seed(1605) + random.seed(98) + else: + np.random.seed() + random.seed() + + print("Please be patient...") + snakes = load_snakes() + + #-------------------------------------------------------------------------------------------------- + X_train, Y_train = snakes['X_train'], snakes['Y_train'] + #X_train, Y_train = X_train[:3], Y_train[:3] + print("TRAIN SET ", len(X_train), len(Y_train)) + + if NCELL <10: X_train, Y_train = shorten_snakes(X_train, Y_train, NCELL) + + nb_hidden, X_train, Y_train = augmentWithNoSnakeImages(X_train, Y_train, "train", False, nCell=NCELL) + print("TRAIN SET ",len(X_train), len(Y_train)) + Y_train_pict = np.array([1]*(len(X_train)-nb_hidden) + [0]*nb_hidden) + + X_train = [one_hot_colors(x) for x in X_train] + + X_train, Y_train, Y_train_pict = shuffle_in_unison(X_train, Y_train, Y_train_pict) + + + X_train_pict_feat = prepare_picture_data(X_train) + if bMAKE_PICT_EASY: + print("Making the train picture task easy") + makeItEasy(X_train_pict_feat, Y_train_pict) + + X_train_directions, X_train_edge_features = prepare_data(X_train) + #-------------------------------------------------------------------------------------------------- + X_test, Y_test = snakes['X_test'], snakes['Y_test'] + + if NCELL <10: X_test, Y_test = shorten_snakes(X_test, Y_test, NCELL) + + nb_hidden, X_test, Y_test = augmentWithNoSnakeImages(X_test, Y_test, "test", False, nCell=NCELL) + Y_test_pict = np.array([1]*(len(X_test)-nb_hidden) + [0]*nb_hidden) + print("TEST SET ", len(X_test), len(Y_test)) + + X_test = [one_hot_colors(x) for x in X_test] + + #useless X_test, Y_test, Y_test_pict = shuffle_in_unison(X_test, Y_test, Y_test_pict) + + X_test_pict_feat = prepare_picture_data(X_test) + if bMAKE_PICT_EASY: + print("Making the test picture task easy") + makeItEasy(X_test_pict_feat, Y_test_pict) + + X_test_directions, X_test_edge_features = prepare_data(X_test) + + #-------------------------------------------------------------------------------------------------- + print("===================================================================" + "===================================") + if True: + from pystruct.models.edge_feature_graph_crf import EdgeFeatureGraphCRF + print("ONE TYPE TRAINING AND TESTING: PIXELS") + +# inference = 'ad3+' +# inference = 'qpbo' + inference=INFERENCE + inference = "qpbo" + crf = EdgeFeatureGraphCRF(inference_method=inference) + ssvm = OneSlackSSVM(crf, inference_cache=50, C=.1, tol=0, + max_iter=MAXITER, + n_jobs=N_JOBS + #,verbose=1 + , switch_to='ad3' + ) + + Y_train_flat = [y_.ravel() for y_ in Y_train] + print( "\ttrain label histogram : ", + np.histogram(np.hstack(Y_train_flat), bins=range(NCELL+2))) + + t0 = time.time() + ssvm.fit(X_train_edge_features, Y_train_flat) + print("FIT DONE IN %.1fs"%(time.time() - t0)) + sys.stdout.flush() + + t0 = time.time() + _Y_pred = ssvm.predict( X_test_edge_features ) + REPORT(Y_test, _Y_pred, time.time() - t0) + + #-------------------------------------------------------------------------------------------------- + if True: + print("_"*50) + print("ONE TYPE TRAINING AND TESTING: PICTURES") + + print( "\ttrain label histogram : ", + np.histogram(Y_train_pict, bins=range(3))) + + lr = LogisticRegression(class_weight='balanced') + + mdl = GridSearchCV(lr , {'C':[0.1, 0.5, 1.0, 2.0] }) + + XX = np.vstack(X_train_pict_feat) + + t0 = time.time() + mdl.fit(XX, Y_train_pict) + print("FIT DONE IN %.1fs"%(time.time() - t0)) + + t0 = time.time() + _Y_pred = mdl.predict( np.vstack(X_test_pict_feat) ) + REPORT([Y_test_pict], _Y_pred, time.time() - t0) + + #-------------------------------------------------------------------------------------------------- + print("===================================================================" + "===================================") + + + # first, train on X with directions only: + #crf = NodeTypeEdgeFeatureGraphCRF(1, [11], [45], [[2]], inference_method=inference) + # first, train on X with directions only: +# l_weights = [ +# [10.0/200] + [10.0/200]*10, +# [10.0/20 , 10.0/20] +# ] +# print("WEIGHTS:", l_weights + if nbSWAP_Pixel_Pict_TYPES %2 == 0: + l_n_states = [NCELL+1, 2] # 11 states for pixel nodes, 2 states for pictures + l_n_feat = [45, 7] # 45 features for pixels, 7 for pictures + ll_n_feat = [[180, 45], # 2 feature between pixel nodes, 1 between pixel and picture + [45 , 0]] # , nothing between picture nodes (no picture_to_picture edge anyway) + else: + l_n_states = [2, NCELL+1] + l_n_feat = [7, 45] + ll_n_feat = [[0, 45], [45 , 180]] + + if not sMODELFILE or not os.path.exists(sMODELFILE): + print(" TRAINING MULTI-TYPE MODEL ") + #TRAINING + crf = NodeTypeEdgeFeatureGraphCRF(2, # How many node types? + l_n_states, # How many states per type? + l_n_feat, # How many node features per type? + ll_n_feat, # How many edge features per type x type? + inference_method=INFERENCE + # , l_class_weight = l_weights + ) + print(crf) + + ssvm = OneSlackSSVM(crf, inference_cache=50, C=.1, tol=0, + max_iter=MAXITER, + n_jobs=N_JOBS + #,verbose=1 + #, switch_to='ad3' + ) + + print("===============================================================" + "=======================================") + print("YY[0].shape", Y_train[0].shape) + XX, YY = convertToTwoType(X_train, + X_train_edge_features, # list of node_feat , edges, edge_feat for pixel nodes + Y_train, + X_train_pict_feat, #a list of picture_node_features + Y_train_pict, #a list of integers [0,1] + nCell=NCELL) + + if nbSWAP_Pixel_Pict_TYPES: + if nbSWAP_Pixel_Pict_TYPES % 2 == 0: + XX, YY = swap_node_types([1,0], [NCELL+1, 2], XX, YY) + XX, YY = swap_node_types([1,0], [2 , NCELL+1], XX, YY) + else: + XX, YY = swap_node_types([1,0], [NCELL+1, 2], XX, YY) + + + print( "\tlabel histogram : ", + np.histogram(np.hstack([y.ravel() for y in YY]), + bins=range(14))) + + + print("YY[0].shape", YY[0].shape) + crf.initialize(XX, YY)# check if the data is properly built + sys.stdout.flush() + + t0 = time.time() + ssvm.fit(XX, YY) + print("FIT DONE IN %.1fs"%(time.time() - t0)) + sys.stdout.flush() + + ssvm.alphas = None + ssvm.constraints_ = None + ssvm.inference_cache_ = None + if sMODELFILE: + print("Saving model in: ", sMODELFILE) + with open(sMODELFILE, "wb") as fd: + cPickle.dump(ssvm, fd) + else: + #REUSE PREVIOUSLY TRAINED MODEL + print(" RUSING PREVIOULSLY TRAINED MULTI-TYPE MODEL: ", sMODELFILE) + + with open(sMODELFILE, "rb") as fd: + ssvm = pickle.load(fd) + + + print("INFERENCE WITH ", INFERENCE) + XX_test, YY_test =convertToTwoType(X_test, + X_test_edge_features, # list of node_feat , edges, edge_feat for pixel nodes + Y_test, + X_test_pict_feat, #a list of picture_node_features + Y_test_pict, #a list of integers [0,1] + nCell=NCELL) + print( "\tlabel histogram (PIXELs and PICTUREs): ", + np.histogram(np.hstack([y.ravel() for y in YY_test]), + bins=range(14))) + + +# l_constraints = listConstraints(XX_test) + l_constraints = listConstraints_ATMOSTONE(XX_test) + + if nbSWAP_Pixel_Pict_TYPES %2 == 1: + XX_test, YY_test, l_constraints = swap_node_types([1,0], [NCELL+1, 2], XX_test, YY_test, l_constraints) + + print("\t- results without constraints (using %s)"%INFERENCE) + t0 = time.time() + YY_pred = ssvm.predict( XX_test ) + REPORT(YY_test, YY_pred, time.time() - t0) + + print("_"*50) + print("\t- results exploiting constraints (using ad3+)") + ssvm.model.inference_method = "ad3+" + t0 = time.time() + YY_pred = ssvm.predict( XX_test, l_constraints ) + REPORT(YY_test, YY_pred, time.time() - t0) + + + print("_"*50) + + if INFERENCE == "ad3": + ssvm.model.inference_method = "ad3+" + else: + ssvm.model.inference_method = "ad3" + print("\t- results without constraints (using %s)"%ssvm.model.inference_method) + + t0 = time.time() + YY_pred = ssvm.predict( XX_test ) + REPORT(YY_test, YY_pred, time.time() - t0) + + print("DONE") + + printConfig() + + +""" + + + + +""" \ No newline at end of file diff --git a/examples/plot_hidden_short_snakes_typed_gen.py b/examples/plot_hidden_short_snakes_typed_gen.py new file mode 100644 index 00000000..dce94d1d --- /dev/null +++ b/examples/plot_hidden_short_snakes_typed_gen.py @@ -0,0 +1,414 @@ +""" +============================================== +Conditional Interactions on the Snakes Dataset +============================================== + +This is a variant of plot_snakes.py + +Snake are hidding, so we have 2 tasks: +- determining if a snake is in the picture, +- identifying its head to tail body. + +We use the NodeTypeEdgeFeatureGraphCRF class with 2 type of nodes. + +HERE WE GENERATE THE SNAKES AT RANDOM INSTEAD OF USING THE SNAKE DATASET + + +This example uses the snake dataset introduced in +Nowozin, Rother, Bagon, Sharp, Yao, Kohli: Decision Tree Fields ICCV 2011 + +This dataset is specifically designed to require the pairwise interaction terms +to be conditioned on the input, in other words to use non-trival edge-features. + +The task is as following: a "snake" of length ten wandered over a grid. For +each cell, it had the option to go up, down, left or right (unless it came from +there). The input consists of these decisions, while the desired output is an +annotation of the snake from 0 (head) to 9 (tail). See the plots for an +example. + +As input features we use a 3x3 window around each pixel (and pad with background +where necessary). We code the five different input colors (for up, down, left, right, +background) using a one-hot encoding. This is a rather naive approach, not using any +information about the dataset (other than that it is a 2d grid). + +The task can not be solved using the simple DirectionalGridCRF - which can only +infer head and tail (which are also possible to infer just from the unary +features). If we add edge-features that contain the features of the nodes that are +connected by the edge, the CRF can solve the task. + +From an inference point of view, this task is very hard. QPBO move-making is +not able to solve it alone, so we use the relaxed AD3 inference for learning. + +PS: This example runs a bit (5 minutes on 12 cores, 20 minutes on one core for me). +But it does work as well as Decision Tree Fields ;) + + + + JL Meunier - January 2017 + + Developed for the EU project READ. The READ project has received funding + from the European Union's Horizon 2020 research and innovation programme + under grant agreement No 674943 + + Copyright Xerox + +""" + +import sys, os, time +import random, cPickle + +import numpy as np + +from sklearn.metrics import confusion_matrix, accuracy_score +from sklearn.linear_model import LogisticRegression +from sklearn.grid_search import GridSearchCV + +from pystruct.learners import OneSlackSSVM +from pystruct.datasets import load_snakes +from pystruct.models import NodeTypeEdgeFeatureGraphCRF + +from plot_snakes import one_hot_colors + +from plot_hidden_snakes import augmentWithNoSnakeImages, shuffle_in_unison, shorten_snakes + +from plot_hidden_short_snakes_typed import plot_snake, prepare_data, prepare_picture_data, convertToTwoType,listConstraints, listConstraints_ATMOSTONE, REPORT + +#============================================================================================== + +bFIXED_RANDOM_SEED = True + +NCELL=10 + +#INFERENCE="ad3+" #ad3+ is required when there are hard logic constraints +INFERENCE="ad3" #ad3 is faster than ad3+ +N_JOBS=8 +#MAXITER=750 +lNbSAMPLE=[200, 400, 600, 800] #how many sample do we generate for each experiment? +nbEXPERIMENT = 10 + +# N_JOBS=1 +# lNbSAMPLE=[20] +# nbEXPERIMENT=1 +# MAXITER=3 +#============================================================================================== + +def printConfig(): + print "== NCELL=", NCELL + print "== FIXED_SEED=", bFIXED_RANDOM_SEED + print "== INFERENCE =", INFERENCE + print "== N_JOBS =", N_JOBS + #print "== MAX_ITER=", MAXITER + print "== lNbSAMPLE=", lNbSAMPLE + print "== nbEXPERIMENT=", nbEXPERIMENT + +if __name__ == '__main__': printConfig() + + +class GenSnakeException(Exception): pass + +def genSnakes(N, dUniqueSnakelij, ncell=NCELL): + """ + Generate snakes at random. + dUniqueSnakelij contains the signature of all Snakes. We ensure unicity of each Snake. + Return N tuple (snakes, Y) + """ + ltSnakeY = [] + + ndim = 1+ ncell+1+ncell +1 #where we'll draw each snake. Border, possible straight snake, centre, possible straight snake, border + aBoard = np.zeros( (ndim, ndim) , dtype=np.int8) + im,jm = 1+ ncell, 1+ ncell #middle of board + lDirection = range(4) #assume it is N, E, S, W + lDirectionIncr = [(-1,0), (0,1), (1,0), (0,-1)] + lDirectionColor = [ [255,0,0], [255,255,0], [0,255,0], [0,255,255] ] + for _n in range(N): + while True: + aBoard[:,:] = -1 #all background + i,j = im,jm + lij = list() + ldir=list() + aSnake, Y = None, None + + try: + for _ncell in range(ncell): + random.shuffle(lDirection) #we will try each direction in turn + for dir in lDirection: + _i, _j = i+lDirectionIncr[dir][0], j+lDirectionIncr[dir][1] + if aBoard[_i,_j] == -1: break #ok, valid direction, we jump on a background pixel + if aBoard[_i,_j] != -1: raise GenSnakeException("Failed to generate a snake") #got stuck + aBoard[i,j] = dir + lij.append( (i,j) ) + ldir.append(dir) + i,j = _i,_j + try: + dUniqueSnakelij[tuple(lij)] + raise GenSnakeException("Same as in trainset") + except KeyError: + dUniqueSnakelij[tuple(lij)] = True + #ok we have a Snake, let's create the image with background borders + imin,jmin = map(min, zip(*lij)) + imax,jmax = map(max, zip(*lij)) + aSnake = np.zeros((imax-imin+3, jmax-jmin+3, 3), dtype=np.uint8) + aSnake[:,:,2] = 255 #0,0,255 + aY = np.zeros((imax-imin+3, jmax-jmin+3) , dtype=np.uint8) + for _lbl, ((_i,_j), _dir) in enumerate(zip(lij, ldir)): + aSnake[_i-imin+1, _j-jmin+1,:] = lDirectionColor[_dir] + aY [_i-imin+1, _j-jmin+1] = _lbl + 1 + + break + except GenSnakeException: pass + ltSnakeY.append( (aSnake, aY) ) +# print aSnake +# print aY +# plot_snake(aSnake) + return ltSnakeY + +def plot_many_snakes(lX, nv=10, nh=20, ncell=NCELL): + """ + Plot the one-hot-encoded snake on grids of size nv x nh + """ + N = ncell+1 #to have border + i = 0 + while i < len(lX): + j = min(i+nv*nh, len(lX)) + lImg = lX[i:j] + allimg = np.zeros(shape=(N*nv,N*nh,3), dtype=np.uint8) + ih,iw = 0,0 + for i_img, img in enumerate(lImg): + h,w,c = img.shape + assert c == 3 + allimg[ih:ih+h, iw:iw+w, :] = img + iw += N + if i_img % nh == (nh-1): + ih += N + iw = 0 + plot_snake(allimg) + i = j + +def plot_mistakes(lY_ref, lY_pred, lX_pict, ncell=NCELL): + """ + Plot snake wrongly predicted, first NoSnake pictures, then Snake pictures + """ + _ltSnake = (list(), list()) #misclassified NoSnake pictures, misclassified Snake pictures + for _y_ref, _y_pred, _x in zip(lY_ref, lY_pred, lX_pict): + assert _y_ref.shape==_y_pred.shape + assert _y_ref.size ==_x.size/3+1 + assert _y_ref[-1] in [ncell+1,ncell+2] + if _y_ref[-1] != _y_pred[-1]: + iSnake = _y_ref[-1] - ncell - 1 #0=NoSnake 1=Snake + _ltSnake[iSnake].append(_x) + + print "NoSnake pictures predicted as Snake" + plot_many_snakes(_ltSnake[0]) + print "Snake pictures predicted as NoSnake" + plot_many_snakes(_ltSnake[1]) + + + +if __name__ == '__main__': + + if bFIXED_RANDOM_SEED: + np.random.seed(1605) + random.seed(98) + else: + np.random.seed() + random.seed() + + print("Please be patient...") + snakes = load_snakes() + + #-------------------------------------------------------------------------------------------------- + #we always test against the original test set + X_test, Y_test = snakes['X_test'], snakes['Y_test'] + #plot_many_snakes(X_test) +# X_test_img = X_test + if NCELL <10: X_test, Y_test = shorten_snakes(X_test, Y_test, NCELL) + + nb_hidden, X_test, Y_test = augmentWithNoSnakeImages(X_test, Y_test, "test", False, nCell=NCELL) + Y_test_pict = np.array([1]*(len(X_test)-nb_hidden) + [0]*nb_hidden) + print "TEST SET ", len(X_test), len(Y_test) + X_test_pict = X_test + X_test = [one_hot_colors(x) for x in X_test] + X_test_pict_feat = prepare_picture_data(X_test) + X_test_directions, X_test_edge_features = prepare_data(X_test) + + #-------------------------------------------------------------------------------------------------- + for iExp in range(nbEXPERIMENT): + print "#"*75 + print "# EXPERIMENT %d / %d"%(iExp+1, nbEXPERIMENT) + print "#"*75 + + dUniqueSnakelij = dict() + + lXY = genSnakes(max(lNbSAMPLE), dUniqueSnakelij) + X_train_all, Y_train_all = zip(*lXY) + X_train_all, Y_train_all = list(X_train_all), list(Y_train_all) + print "***** GENERATED %d snakes of length %d *****"%(len(X_train_all), NCELL) + + #Also generate an additional test set + NTEST=100 + lXYTest = genSnakes( NTEST, dUniqueSnakelij ) + X_test_gen, Y_test_gen = zip(*lXYTest) + X_test_gen, Y_test_gen = list(X_test_gen), list(Y_test_gen) + print "***** GENERATED %d snakes of length %d *****"%(NTEST, NCELL) +# plot_many_snakes(X_test_img+X_test_gen) + nb_hidden, X_test_gen, Y_test_gen = augmentWithNoSnakeImages(X_test_gen, Y_test_gen, "test_gen", False, nCell=NCELL) + Y_test_gen_pict = np.array([1]*(len(X_test_gen)-nb_hidden) + [0]*nb_hidden) + print "GENERATED TEST SET ", len(X_test_gen), len(Y_test_gen) + + X_test_gen = [one_hot_colors(x) for x in X_test_gen] + X_test_gen_pict_feat = prepare_picture_data(X_test_gen) + X_test_gen_directions, X_test_gen_edge_features = prepare_data(X_test_gen) + + for nbSample in lNbSAMPLE: + print "======================================================================================================" + print "TRAINING" + X_train, Y_train = X_train_all[0:nbSample], Y_train_all[0:nbSample] + + nb_hidden, X_train, Y_train = augmentWithNoSnakeImages(X_train, Y_train, "train", False, nCell=NCELL) + print "TRAIN SET ",len(X_train), len(Y_train) + Y_train_pict = np.array([1]*(len(X_train)-nb_hidden) + [0]*nb_hidden) + + X_train = [one_hot_colors(x) for x in X_train] + X_train, Y_train, Y_train_pict = shuffle_in_unison(X_train, Y_train, Y_train_pict) + X_train_pict_feat = prepare_picture_data(X_train) + X_train_directions, X_train_edge_features = prepare_data(X_train) + + #-------------------------------------------------------------------------------------------------- + if True: + print "===========================================================================" + from pystruct.models.edge_feature_graph_crf import EdgeFeatureGraphCRF + print "ONE TYPE TRAINING AND TESTING: PIXELS" + + inference = "qpbo" + crf = EdgeFeatureGraphCRF(inference_method=inference) + ssvm = OneSlackSSVM(crf, inference_cache=50, C=.1, tol=.1, +# max_iter=MAXITER, + n_jobs=N_JOBS + #,verbose=1 + , switch_to='ad3' + ) + + Y_train_flat = [y_.ravel() for y_ in Y_train] + print "\ttrain label histogram : ", np.histogram(np.hstack(Y_train_flat), bins=range(NCELL+2)) + + t0 = time.time() + ssvm.fit(X_train_edge_features, Y_train_flat) + print "FIT DONE IN %.1fs"%(time.time() - t0) + sys.stdout.flush() + + t0 = time.time() + _Y_pred = ssvm.predict( X_test_edge_features ) + REPORT(Y_test, _Y_pred, time.time() - t0, NCELL, "gen_singletype_%d.csv"%nbSample, True, "singletype_%d"%nbSample) + _Y_pred = ssvm.predict( X_test_gen_edge_features ) + REPORT(Y_test_gen, _Y_pred, None , NCELL, "gen_singletype_gentest_%d.csv"%nbSample, True, "singletype_%d_gentest"%nbSample) + + #-------------------------------------------------------------------------------------------------- + if True: + print "_"*50 + print "ONE TYPE TRAINING AND TESTING: PICTURES" + + print "\ttrain label histogram : ", np.histogram(Y_train_pict, bins=range(3)) + + lr = LogisticRegression(class_weight='balanced') + + mdl = GridSearchCV(lr , {'C':[0.1, 0.5, 1.0, 2.0] }) + + XX = np.vstack(X_train_pict_feat) + + t0 = time.time() + mdl.fit(XX, Y_train_pict) + print "FIT DONE IN %.1fs"%(time.time() - t0) + + t0 = time.time() + _Y_pred = mdl.predict( np.vstack(X_test_pict_feat) ) + REPORT([Y_test_pict], _Y_pred, time.time() - t0, 2, "gen_picture.csv", True, "picture_logit_%d"%nbSample) + + #-------------------------------------------------------------------------------------------------- + print "======================================================================================================" + + l_n_states = [NCELL+1, 2] # 11 states for pixel nodes, 2 states for pictures + l_n_feat = [45, 7] # 45 features for pixels, 7 for pictures + ll_n_feat = [[180, 45], # 2 feature between pixel nodes, 1 between pixel and picture + [45 , 0]] # , nothing between picture nodes (no picture_to_picture edge anyway) + + print " TRAINING MULTI-TYPE MODEL " + #TRAINING + crf = NodeTypeEdgeFeatureGraphCRF(2, # How many node types? + l_n_states, # How many states per type? + l_n_feat, # How many node features per type? + ll_n_feat, # How many edge features per type x type? + inference_method=INFERENCE + ) + print crf + ssvm = OneSlackSSVM(crf, inference_cache=50, C=.1, tol=0.1, +# max_iter=MAXITER, + n_jobs=N_JOBS + ) + + print "======================================================================================================" + print "YY[0].shape", Y_train[0].shape + XX, YY = convertToTwoType(X_train, + X_train_edge_features, # list of node_feat , edges, edge_feat for pixel nodes + Y_train, + X_train_pict_feat, #a list of picture_node_features + Y_train_pict, #a list of integers [0,1] + nCell=NCELL) + + print "\tlabel histogram : ", np.histogram( np.hstack([y.ravel() for y in YY]), bins=range(14)) + + + print "YY[0].shape", YY[0].shape + crf.initialize(XX, YY)# check if the data is properly built + sys.stdout.flush() + + t0 = time.time() + ssvm.fit(XX, YY) + print "FIT DONE IN %.1fs"%(time.time() - t0) + sys.stdout.flush() + + print "_"*50 + XX_test, YY_test =convertToTwoType(X_test, + X_test_edge_features, # list of node_feat , edges, edge_feat for pixel nodes + Y_test, + X_test_pict_feat, #a list of picture_node_features + Y_test_pict, #a list of integers [0,1] + nCell=NCELL) + print "\tlabel histogram (PIXELs and PICTUREs): ", np.histogram( np.hstack([y.ravel() for y in YY_test]), bins=range(14)) + XX_test_gen, YY_test_gen =convertToTwoType(X_test_gen, + X_test_gen_edge_features, # list of node_feat , edges, edge_feat for pixel nodes + Y_test_gen, + X_test_pict_feat, #a list of picture_node_features + Y_test_pict, #a list of integers [0,1] + nCell=NCELL) + + + l_constraints = listConstraints_ATMOSTONE(XX_test , NCELL) + l_constraints_gen = listConstraints_ATMOSTONE(XX_test_gen, NCELL) + + print "_"*50 + print "\t- results without constraints (using %s)"%INFERENCE + t0 = time.time() + YY_pred = ssvm.predict( XX_test ) + REPORT(YY_test, YY_pred, time.time() - t0 , NCELL+2, "gen_multitype_%d.csv"%nbSample, True, "multitype_%d"%nbSample) + #plot_mistakes(YY_test, YY_pred, X_test_pict) + YY_pred = ssvm.predict( XX_test_gen ) + REPORT(YY_test_gen, YY_pred, None , NCELL+2, "gen_multitype_gentest_%d.csv"%nbSample, True, "multitype_%d_gentest"%nbSample) + + print "_"*50 + print "\t- results exploiting constraints (using ad3+)" + ssvm.model.inference_method = "ad3+" + t0 = time.time() + YY_pred = ssvm.predict( XX_test , l_constraints ) + REPORT(YY_test, YY_pred, time.time() - t0 , NCELL+2, "gen_multitype_constraints_%d.csv"%nbSample, True, "multitype_constraints_%d"%nbSample) + YY_pred = ssvm.predict( XX_test_gen , l_constraints_gen ) + REPORT(YY_test_gen, YY_pred, None , NCELL+2, "gen_multitype_constraints_gentest_%d.csv"%nbSample, True, "multitype_constraints_%d_gentest"%nbSample) + + + print "_"*50 + + print "One Experiment DONE" + + print "ALL EXPERIMENTS DONE" + + printConfig() + \ No newline at end of file diff --git a/examples/plot_hidden_snakes.py b/examples/plot_hidden_snakes.py new file mode 100644 index 00000000..0e533038 --- /dev/null +++ b/examples/plot_hidden_snakes.py @@ -0,0 +1,321 @@ +""" +============================================== +Conditional Interactions on the Snakes Dataset +============================================== + +This is a variant of plot_snakes.py + +Snake are hiding!! Therefore, some picture have colored pixels despite they do not contain any snake. + + JL Meunier - January 2017 + + Developed for the EU project READ. The READ project has received funding + from the European Union's Horizon 2020 research and innovation programme + under grant agreement No 674943 + + Copyright Xerox + +This example uses the snake dataset introduced in +Nowozin, Rother, Bagon, Sharp, Yao, Kohli: Decision Tree Fields ICCV 2011 + +This dataset is specifically designed to require the pairwise interaction terms +to be conditioned on the input, in other words to use non-trival edge-features. + +The task is as following: a "snake" of length ten wandered over a grid. For +each cell, it had the option to go up, down, left or right (unless it came from +there). The input consists of these decisions, while the desired output is an +annotation of the snake from 0 (head) to 9 (tail). See the plots for an +example. + +As input features we use a 3x3 window around each pixel (and pad with background +where necessary). We code the five different input colors (for up, down, left, right, +background) using a one-hot encoding. This is a rather naive approach, not using any +information about the dataset (other than that it is a 2d grid). + +The task can not be solved using the simple DirectionalGridCRF - which can only +infer head and tail (which are also possible to infer just from the unary +features). If we add edge-features that contain the features of the nodes that are +connected by the edge, the CRF can solve the task. + +From an inference point of view, this task is very hard. QPBO move-making is +not able to solve it alone, so we use the relaxed AD3 inference for learning. + +PS: This example runs a bit (5 minutes on 12 cores, 20 minutes on one core for me). +But it does work as well as Decision Tree Fields ;) +""" +from __future__ import (absolute_import, division, print_function) + +import numpy as np +import matplotlib.pyplot as plt +import random +from sklearn.preprocessing import label_binarize +from sklearn.metrics import confusion_matrix, accuracy_score +import time + +from pystruct.learners import OneSlackSSVM +from pystruct.datasets import load_snakes +from pystruct.models import EdgeFeatureGraphCRF +from pystruct.models import NodeTypeEdgeFeatureGraphCRF + +from plot_snakes import one_hot_colors, prepare_data + + +def isSnakePresent(a_hot_picture, nCell=10): + """ + Algorithmic check, to make sure that after tempering with the snake we do not have a snake! :-) + Works on the 1-hot encoded picture + """ + ai, aj = np.where(a_hot_picture[...,3] != 1) + + #let's start from each cell until we can walk thru an entire snake + #yeah, brute force, but otherwise it is tricky to check!! + bSnake = False + for i0,j0 in zip(ai,aj): + + lij = walkThruSnake(a_hot_picture, (i0, j0), nCell) + if len(lij) == nCell-1: + bSnake = True + break + return bSnake + +def walkThruSnake(a_hot_picture, tIJ, nCell=10): + """ + Walk thru the snake from I,J + Return the list of visited cells (excluding start cell) + """ + (i,j) = tIJ + lij = list() + color_index = np.where(a_hot_picture[i,j,:]==1)[0][0] + while len(lij) < nCell -1: + dj = np.array( [ 0, 0, 1, None, -1])[color_index] + di = np.array( [-1, 1, 0, None, 0])[color_index] + i += di + j += dj + color_index = np.where(a_hot_picture[i,j,:]==1)[0][0] + if color_index == 3: break #background + if (i,j) in lij: break #crossing itself, or looping + lij.append((i,j)) + return lij + +def changeOneSnakeCell(a_picture, bOneHot=True, nCell=10): #in place!! + """ + Change the color of 1 snake cells into another snake cell color + """ + if bOneHot: + ai, aj = np.where(a_picture[...,3] != 1) + else: + _p = np.copy(a_picture) + _p = one_hot_colors(_p) + ai, aj = np.where(_p[...,3] != 1) + assert len(ai) == nCell, (len(ai), nCell) + + iChange = random.randint(0,nCell-1) + + for i in range(10): + iFromCell = random.randint(0,nCell-1) + if (a_picture[ai[iChange], aj[iChange],:] != a_picture[ai[iFromCell], aj[iFromCell],:]).any(): + a_picture[ai[iChange], aj[iChange],:] = a_picture[ai[iFromCell], aj[iFromCell],:] + #so that we do not care about which color is valid... + break + + return a_picture + +def distortSnake(a_picture, bOneHot=True, nCell=10): + """ + Shuffle either the snake's cells or the pcitures' pixels. + """ + bDOCUMENT = False #to show the change on screen + + if bDOCUMENT: + pict_mem = np.copy(a_picture) + + changeOneSnakeCell(a_picture, bOneHot, nCell=nCell) + + if bDOCUMENT: + if bOneHot: + zz = a_picture + else: + zz = one_hot_colors(a_picture) + if not isSnakePresent(zz, nCell): + plot_snake(pict_mem) + plot_snake(a_picture) + +def convertToSingleTypeX(X): + """ + For NodeTypeEdgeFeatureGraphCRF X is structured differently. + But NodeTypeEdgeFeatureGraphCRF can handle graphs with a single node type. One simply needs to convert X to the new structure using this method. + """ + return [([nf], [e], [ef]) for (nf,e,ef) in X] + + +def plot_snake(picture): + plt.imshow(picture, interpolation='nearest') + plt.show() + + +def augmentWithNoSnakeImages(X,Y, name, bOneHot=True, iMult=1, nCell=10): + """ + return the number of added picture (ADDED AT THE END OF INPUT LISTS) + """ + print("ADDING PICTURE WIHOUT SNAKES!!! %d elements in %s"%(len(X), name)) + + X_NoSnake = [] + Y_NoSnake = [] + for i in range(int(iMult)): + X_NoSnake.extend([np.copy(x) for x in X]) + Y_NoSnake.extend([np.copy(y) for y in Y]) #shorten_sakes does modify Y... + + if True: + #best method for our experiment + for x in X_NoSnake: distortSnake(x, bOneHot, nCell) + else: + shorten_snakes(X_NoSnake, Y_NoSnake, nCell-1) + + newX = list() + newY = list() + for x,y in zip(X_NoSnake, Y_NoSnake): + _x = x if bOneHot else one_hot_colors(x) + if isSnakePresent(_x): + print("\t- DISCARDING a shuffled snake which is still a snake!!!!") +# if True and not bOneHot: plot_snake(x) + else: + newX.append(x) + newY.append(np.zeros(y.shape, dtype=np.int32)) + assert len(newX)==len(newY) + return len(newX), X+newX, Y+newY + +def shuffle_in_unison(*args): + lTuple = list(zip(*args)) + random.shuffle(lTuple) + return zip(*lTuple) + +def shorten_snakes(lX,lY, N): + """ + It is faster to work on shorter snakes, but easier as well for the models + """ + newlX,newlY = list(), list() + for X, Y in zip(lX,lY): + assert X.shape[:2] == Y.shape, (X.shape, Y.shape) + ai, aj = np.where(Y>N) + X[ai,aj,:] = X[0,0,:] + Y[ai,aj] = 0 + #crop + ai, aj = np.where(Y!=0) + aimin,aimax = min(ai)-1, max(ai)+2 + ajmin,ajmax = min(aj)-1, max(aj)+2 + newlY.append( Y[aimin:aimax, ajmin:ajmax] ) + newlX.append( X[aimin:aimax, ajmin:ajmax,:]) + + return newlX, newlY + +#===================================================================================================== +if __name__ == '__main__': + np.random.seed(1605) + random.seed(98) + + print("Please be patient. Learning will take 5-20 minutes.") + + #if you want to shorten all the snakes + #NCELL = 3 + NCELL = 10 + print("NCELL=", NCELL) + + + snakes = load_snakes() + + # --- TRAIN + X_train, Y_train = snakes['X_train'], snakes['Y_train'] + #X_train, Y_train = X_train[:10], Y_train[:10] #if you want to debug... + if NCELL < 10: X_train, Y_train = shorten_snakes(X_train, Y_train, NCELL) + + nbNoSnake, X_train, Y_train = augmentWithNoSnakeImages(X_train, Y_train, "train", bOneHot=False, nCell=NCELL) + X_train = [one_hot_colors(x) for x in X_train] + X_train, Y_train = shuffle_in_unison(X_train, Y_train) + X_train_directions, X_train_edge_features = prepare_data(X_train) + Y_train_flat = [y_.ravel() for y_ in Y_train] + + print("%d picture for training"%len(X_train)) + + # --- TEST + X_test, Y_test = snakes['X_test'], snakes['Y_test'] + if NCELL < 10: X_test, Y_test = shorten_snakes(X_test, Y_test, NCELL) + _, X_test, Y_test = augmentWithNoSnakeImages(X_test, Y_test, "test", bOneHot=False, nCell=NCELL) + + X_test = [one_hot_colors(x) for x in X_test] + X_test_directions, X_test_edge_features = prepare_data(X_test) + Y_test_flat = [y_.ravel() for y_ in Y_test] + + print("%d picture for test"%len(X_test)) + + # ------------------------------------------------------------------------------------- + + inference = 'qpbo' + bClassic = True #True => use the old good EdgeFeatureGraphCRF + + # now, use more informative edge features: + t0 = time.time() + if bClassic: + print("EdgeFeatureGraphCRF") + crf = EdgeFeatureGraphCRF(inference_method=inference) + ssvm = OneSlackSSVM(crf, inference_cache=50, C=.1, tol=.1, + #WHY THIS??? max_iter=100, + #why not this switch_to=ad3??? + switch_to='ad3', + #verbose=1, + n_jobs=2, + ) + ssvm.fit( X_train_edge_features , Y_train_flat) + else: + print("NodeTypeEdgeFeatureGraphCRF") + crf = NodeTypeEdgeFeatureGraphCRF(1, [11], [45], [[180]], inference_method=inference) + ssvm = OneSlackSSVM(crf, inference_cache=50, C=.1, tol=.1, + switch_to='ad3', + #JL adds a max-iter sometimes + #max_iter=100, + n_jobs=1) + ssvm.fit( convertToSingleTypeX(X_train_edge_features) , Y_train_flat) + print("Training time = %.1fs"%(time.time()-t0)) + + if bClassic: + Y_pred2 = ssvm.predict( X_test_edge_features ) + else: + Y_pred2 = ssvm.predict( convertToSingleTypeX(X_test_edge_features) ) + print("Results using input features for edges") + print("Test accuracy: %.3f" + % accuracy_score(np.hstack(Y_test_flat), np.hstack(Y_pred2))) + print(confusion_matrix(np.hstack(Y_test_flat), np.hstack(Y_pred2))) + + + + #------------------------------------------------------------------------------------------------------------------------ + #Predict under constraints + if True and not bClassic: + def buildConstraintsFromSingleTyped(X, bOne=True): + """ + We iterate over each graph, and make sure that for each, we constrain to have a single instances of classes 1 to 9 + (or atmost one) + + The constraints must be a list of tuples like ( , , , ) + where: + - operator is one of 'XOR' 'XOROUT' 'ATMOSTONE' 'OR' 'OROUT' 'ANDOUT' 'IMPLY' + - unaries is a list of the index of the unaries involved in this constraint + - states is a list of unary states, 1 per involved unary. If the states are all the same, you can pass it directly as a scalar value. + - negated is a list of boolean indicated if the unary must be negated. Again, if all values are the same, pass a single boolean value instead of a list + """ + sLogicOp = "XOR" if bOne else "ATMOSTONE" + lConstraint = [] + for ([nf], [e], [ef]) in X: + n_nodes = nf.shape[0] + lConstraintPerGraph = [ (sLogicOp, range(n_nodes), i, False) for i in range(1,NCELL+1) ] #only one + lConstraint.append( lConstraintPerGraph ) + return lConstraint + + X_3 = convertToSingleTypeX(X_test_edge_features) + lC = buildConstraintsFromSingleTyped(X_3, False) + Y_pred2 = ssvm.predict( X_3, lC ) + print("Results using also input features for edges") + print("Inference with an ATMOST constraint per snake label") + print("Test accuracy: %.3f" + % accuracy_score(np.hstack(Y_test_flat), np.hstack(Y_pred2))) + print(confusion_matrix(np.hstack(Y_test_flat), np.hstack(Y_pred2))) + diff --git a/examples/plot_latent_crf.py b/examples/plot_latent_crf.py index 7cdaceb9..4eb3343d 100644 --- a/examples/plot_latent_crf.py +++ b/examples/plot_latent_crf.py @@ -3,19 +3,22 @@ Latent Dynamics CRF =================== -Solving a 2d grid problem by introducing latent variable interactions. -The input data is the same as in plot_grid_crf, a cross pattern. -But now, the center is not given an extra state. That makes the problem -much harder to solve for a pairwise model. -We can still solve it by introducing latent dynamics. In essence we allow -an additional state with different interactions, that maps to the same -state (the cross) in the ground truth. +Solving a 2d grid problem by introducing latent variable interactions. The +input data is the same as in plot_grid_crf, a cross pattern. But now, the +center is not given an extra state. That makes the problem much harder to solve +for a pairwise model. +We can still solve it by introducing latent dynamics. In essence we allow an +additional state with different interactions, that maps to the same state (the +cross) in the ground truth. """ import numpy as np import matplotlib.pyplot as plt -from sklearn.cross_validation import train_test_split +try: + from sklearn.model_selection import train_test_split +except ImportError: + from sklearn.cross_validation import train_test_split from pystruct.models import LatentGridCRF from pystruct.learners import LatentSSVM, OneSlackSSVM @@ -31,8 +34,8 @@ tol=.1) clf = LatentSSVM(base_ssvm=base_ssvm) clf.fit(X_train, Y_train) -print("loss training set: %f" % clf.score(X_train, Y_train)) -print("loss test set: %f" % clf.score(X_test, Y_test)) +print("Score training set: %f" % clf.score(X_train, Y_train)) +print("Score test set: %f" % clf.score(X_test, Y_test)) Y_pred = clf.predict(X_test) diff --git a/examples/plot_latent_node.py b/examples/plot_latent_node.py index 7343b32a..293ee170 100644 --- a/examples/plot_latent_node.py +++ b/examples/plot_latent_node.py @@ -2,6 +2,7 @@ ================================= Latent Variable Hierarchical CRF ================================= + Solving a 2d grid toy problem by introducing an additional layer of latent variables. """ @@ -53,16 +54,16 @@ def plot_boxes(boxes, size=4, title=""): G = [make_grid_edges(x) for x in X] -asdf = zip(X_flat, G) -svm.fit(asdf, Y_flat) -plot_boxes(svm.predict(asdf), title="Non-latent SSVM predictions") -print("Training score binary grid CRF: %f" % svm.score(asdf, Y_flat)) +X_grid_edges = list(zip(X_flat, G)) +svm.fit(X_grid_edges, Y_flat) +plot_boxes(svm.predict(X_grid_edges), title="Non-latent SSVM predictions") +print("Training score binary grid CRF: %f" % svm.score(X_grid_edges, Y_flat)) # using one latent variable for each 2x2 rectangle latent_crf = LatentNodeCRF(n_labels=2, n_features=1, n_hidden_states=2, inference_method='lp') -ssvm = OneSlackSSVM(model=latent_crf, max_iter=200, C=100, verbose=1, +ssvm = OneSlackSSVM(model=latent_crf, max_iter=200, C=100, n_jobs=-1, show_loss_every=10, inference_cache=50) latent_svm = LatentSSVM(ssvm) @@ -70,8 +71,8 @@ def plot_boxes(boxes, size=4, title=""): edges = [] node_indices = np.arange(4 * 4).reshape(4, 4) for i, (x, y) in enumerate(itertools.product([0, 2], repeat=2)): - for j in xrange(x, x + 2): - for k in xrange(y, y + 2): + for j in range(x, x + 2): + for k in range(y, y + 2): edges.append([i + 4 * 4, node_indices[j, k]]) G = [np.vstack([make_grid_edges(x), edges]) for x in X] @@ -82,7 +83,7 @@ def plot_boxes(boxes, size=4, title=""): plot_boxes(H_init, title="Top: Random initial hidden states. Bottom: Ground" "truth labeling.") -X_ = zip(X_flat, G, [2 * 2 for x in X_flat]) +X_ = list(zip(X_flat, G, [2 * 2 for x in X_flat])) latent_svm.fit(X_, Y_flat, H_init) diff --git a/examples/plot_latent_svm_as_crf.py b/examples/plot_latent_svm_as_crf.py index 3f7544c4..755ee64e 100644 --- a/examples/plot_latent_svm_as_crf.py +++ b/examples/plot_latent_svm_as_crf.py @@ -15,7 +15,10 @@ import numpy as np import matplotlib.pyplot as plt -from sklearn.cross_validation import train_test_split +try: + from sklearn.model_selection import train_test_split +except ImportError: + from sklearn.cross_validation import train_test_split from sklearn.datasets import load_digits from pystruct.models import GraphCRF, LatentGraphCRF @@ -73,7 +76,7 @@ plt.suptitle("Example digits from each of\nthe ten latent classes.") n_latent_classes = 10 n_examples = 7 -for latent_class in xrange(n_latent_classes): +for latent_class in range(n_latent_classes): examples = X_test[h_pred == latent_class][:n_examples] for k, example in enumerate(examples): plt.subplot(n_latent_classes, n_examples, diff --git a/examples/plot_letters.py b/examples/plot_letters.py index 4f1fa7f9..a3b8d79f 100644 --- a/examples/plot_letters.py +++ b/examples/plot_letters.py @@ -32,11 +32,10 @@ import matplotlib.pyplot as plt from sklearn.svm import LinearSVC -#from sklearn.metrics import confusion_matrix from pystruct.datasets import load_letters from pystruct.models import ChainCRF -from pystruct.learners import OneSlackSSVM +from pystruct.learners import FrankWolfeSSVM abc = "abcdefghijklmnopqrstuvwxyz" @@ -55,7 +54,7 @@ # Train linear chain CRF model = ChainCRF() -ssvm = OneSlackSSVM(model=model, C=.1, inference_cache=50, tol=0.1, verbose=3) +ssvm = FrankWolfeSSVM(model=model, C=.1, max_iter=11) ssvm.fit(X_train, y_train) print("Test score with chain CRF: %f" % ssvm.score(X_test, y_test)) @@ -81,10 +80,11 @@ a.text(5, 14, abc[y_chain], color="#FF5555", size=25) a.set_xticks(()) a.set_yticks(()) - for ii in xrange(i + 1, max_word_len): + for ii in range(i + 1, max_word_len): axes_row[ii].set_visible(False) plt.matshow(ssvm.w[26 * 8 * 16:].reshape(26, 26)) +plt.colorbar() plt.title("Transition parameters of the chain CRF.") plt.xticks(np.arange(25), abc) plt.yticks(np.arange(25), abc) diff --git a/examples/plot_potts_model.py b/examples/plot_potts_model.py new file mode 100644 index 00000000..a13ba108 --- /dev/null +++ b/examples/plot_potts_model.py @@ -0,0 +1,48 @@ +""" +================================================= +Comparing inference times on a simple Potts model +================================================= + +Simple comparison of inference times on a Potts model (smoothing) +on a 2d grid of random noise of 5 classes. + +The plots show the label results together with energies (lower is better) +and inference time. +The results are quite representative of the algorithms in general. +AD3 is quite fast and gives good results (identical to lp), while +the general purpose lp solver is too slow for practical purposes. +QPBO is somewhat worse than the other methods, but significantly faster. +Our implementation of max-product message passing is not competative with +the high quality solutions found by AD3. +""" + +import numpy as np +import matplotlib.pyplot as plt + +from time import time + +from pystruct.inference import inference_dispatch, compute_energy +from pystruct.utils import make_grid_edges + +size = 20 +n_states = 5 + +rnd = np.random.RandomState(2) +x = rnd.normal(size=(size, size, n_states)) +pairwise = np.eye(n_states) +edges = make_grid_edges(x) +unaries = x.reshape(-1, n_states) + +fig, ax = plt.subplots(1, 5, figsize=(20, 5)) +for a, inference_method in zip(ax, ['ad3', 'qpbo', 'max-product', + ('max-product', {'max_iter': 10}), 'lp']): + start = time() + y = inference_dispatch(unaries, pairwise, edges, + inference_method=inference_method) + took = time() - start + a.matshow(y.reshape(size, size)) + energy = compute_energy(unaries, pairwise, edges, y) + a.set_title(str(inference_method) + "\n time: %.2f energy %.2f" % (took, energy)) + a.set_xticks(()) + a.set_yticks(()) +plt.show() diff --git a/examples/plot_snakes.py b/examples/plot_snakes.py index 54512960..f56f42d5 100644 --- a/examples/plot_snakes.py +++ b/examples/plot_snakes.py @@ -30,8 +30,10 @@ PS: This example runs a bit (5 minutes on 12 cores, 20 minutes on one core for me). But it does work as well as Decision Tree Fields ;) """ +from __future__ import (absolute_import, division, print_function) + import numpy as np -import matplotlib.pyplot as plt +# import matplotlib.pyplot as plt from sklearn.preprocessing import label_binarize from sklearn.metrics import confusion_matrix, accuracy_score @@ -44,7 +46,7 @@ def one_hot_colors(x): x = x / 255 - flat = np.dot(x.reshape(-1, 3), 2 ** np.arange(3)) + flat = np.dot(x.reshape(-1, 3), 2 ** np.arange(3)) one_hot = label_binarize(flat, classes=[1, 2, 3, 4, 6]) return one_hot.reshape(x.shape[0], x.shape[1], 5) @@ -84,30 +86,34 @@ def prepare_data(X): edge_features = np.zeros((edges.shape[0], features.shape[1], 4)) edge_features[:len(right), :, 0] = features[right[:, 0]] edge_features[:len(right), :, 1] = features[right[:, 1]] - edge_features[len(right):, :, 0] = features[down[:, 0]] - edge_features[len(right):, :, 1] = features[down[:, 1]] +#---ORIGINAL CODE +# edge_features[len(right):, :, 0] = features[down[:, 0]] +# edge_features[len(right):, :, 1] = features[down[:, 1]] + edge_features[len(right):, :, 2] = features[down[:, 0]] + edge_features[len(right):, :, 3] = features[down[:, 1]] +#---END OF FIX edge_features = edge_features.reshape(edges.shape[0], -1) X_directions.append((features, edges, edge_features_directions)) X_edge_features.append((features, edges, edge_features)) return X_directions, X_edge_features - -def main(): - print("Please be patient. Will take 5-20 minutes.") +if __name__ == '__main__': + print("Please be patient. Learning will take 5-20 minutes.") snakes = load_snakes() X_train, Y_train = snakes['X_train'], snakes['Y_train'] - + X_train = [one_hot_colors(x) for x in X_train] Y_train_flat = [y_.ravel() for y_ in Y_train] - + X_train_directions, X_train_edge_features = prepare_data(X_train) - + + inference = 'qpbo' # first, train on X with directions only: - crf = EdgeFeatureGraphCRF(inference_method='qpbo') - ssvm = OneSlackSSVM(crf, inference_cache=50, C=.1, tol=.1, switch_to='ad3', - n_jobs=-1) + crf = EdgeFeatureGraphCRF(inference_method=inference) + ssvm = OneSlackSSVM(crf, inference_cache=50, C=.1, tol=.1, max_iter=100, + n_jobs=1) ssvm.fit(X_train_directions, Y_train_flat) - + # Evaluate using confusion matrix. # Clearly the middel of the snake is the hardest part. X_test, Y_test = snakes['X_test'], snakes['Y_test'] @@ -119,9 +125,9 @@ def main(): print("Test accuracy: %.3f" % accuracy_score(np.hstack(Y_test_flat), np.hstack(Y_pred))) print(confusion_matrix(np.hstack(Y_test_flat), np.hstack(Y_pred))) - + # now, use more informative edge features: - crf = EdgeFeatureGraphCRF(inference_method='qpbo') + crf = EdgeFeatureGraphCRF(inference_method=inference) ssvm = OneSlackSSVM(crf, inference_cache=50, C=.1, tol=.1, switch_to='ad3', n_jobs=-1) ssvm.fit(X_train_edge_features, Y_train_flat) @@ -130,26 +136,21 @@ def main(): print("Test accuracy: %.3f" % accuracy_score(np.hstack(Y_test_flat), np.hstack(Y_pred2))) print(confusion_matrix(np.hstack(Y_test_flat), np.hstack(Y_pred2))) - - # plot stuff - fig, axes = plt.subplots(2, 2) - axes[0, 0].imshow(snakes['X_test'][0], interpolation='nearest') - axes[0, 0].set_title('Input') - y = Y_test[0].astype(np.int) - bg = 2 * (y != 0) # enhance contrast - axes[0, 1].matshow(y + bg, cmap=plt.cm.Greys) - axes[0, 1].set_title("Ground Truth") - axes[1, 0].matshow(Y_pred[0].reshape(y.shape) + bg, cmap=plt.cm.Greys) - axes[1, 0].set_title("Prediction w/o edge features") - axes[1, 1].matshow(Y_pred2[0].reshape(y.shape) + bg, cmap=plt.cm.Greys) - axes[1, 1].set_title("Prediction with edge features") - for a in axes.ravel(): - a.set_xticks(()) - a.set_yticks(()) - plt.show() - from IPython.core.debugger import Tracer - Tracer()() - - -if __name__ == "__main__": - main() + +# if True: +# # plot stuff +# fig, axes = plt.subplots(2, 2) +# axes[0, 0].imshow(snakes['X_test'][0], interpolation='nearest') +# axes[0, 0].set_title('Input') +# y = Y_test[0].astype(np.int) +# bg = 2 * (y != 0) # enhance contrast +# axes[0, 1].matshow(y + bg, cmap=plt.cm.Greys) +# axes[0, 1].set_title("Ground Truth") +# axes[1, 0].matshow(Y_pred[0].reshape(y.shape) + bg, cmap=plt.cm.Greys) +# axes[1, 0].set_title("Prediction w/o edge features") +# axes[1, 1].matshow(Y_pred2[0].reshape(y.shape) + bg, cmap=plt.cm.Greys) +# axes[1, 1].set_title("Prediction with edge features") +# for a in axes.ravel(): +# a.set_xticks(()) +# a.set_yticks(()) +# plt.show() diff --git a/examples/plot_snakes_constraints.py b/examples/plot_snakes_constraints.py new file mode 100644 index 00000000..7108e830 --- /dev/null +++ b/examples/plot_snakes_constraints.py @@ -0,0 +1,269 @@ +""" +============================================== +Conditional Interactions on the Snakes Dataset +============================================== +This example uses the snake dataset introduced in +Nowozin, Rother, Bagon, Sharp, Yao, Kohli: Decision Tree Fields ICCV 2011 + +This dataset is specifically designed to require the pairwise interaction terms +to be conditioned on the input, in other words to use non-trival edge-features. + +The task is as following: a "snake" of length ten wandered over a grid. For +each cell, it had the option to go up, down, left or right (unless it came from +there). The input consists of these decisions, while the desired output is an +annotation of the snake from 0 (head) to 9 (tail). See the plots for an +example. + +As input features we use a 3x3 window around each pixel (and pad with background +where necessary). We code the five different input colors (for up, down, left, right, +background) using a one-hot encoding. This is a rather naive approach, not using any +information about the dataset (other than that it is a 2d grid). + +The task can not be solved using the simple DirectionalGridCRF - which can only +infer head and tail (which are also possible to infer just from the unary +features). If we add edge-features that contain the features of the nodes that are +connected by the edge, the CRF can solve the task. + +From an inference point of view, this task is very hard. QPBO move-making is +not able to solve it alone, so we use the relaxed AD3 inference for learning. + +PS: This example runs a bit (5 minutes on 12 cores, 20 minutes on one core for me). +But it does work as well as Decision Tree Fields ;) + +UPDATE: we also inject domain knowledge at inference time by telling that there +is at-most or exactly one of each annotation from 1 to 10 (0 is background). + + JL Meunier - January 2017 + + Developed for the EU project READ. The READ project has received funding + from the European Union's Horizon 2020 research and innovation programme + under grant agreement No 674943 + + Copyright Xerox + +""" +import time +import numpy as np + +from sklearn.metrics import confusion_matrix, accuracy_score + +from pystruct.learners import OneSlackSSVM +from pystruct.datasets import load_snakes +from pystruct.models import EdgeFeatureGraphCRF + +from plot_snakes import one_hot_colors, prepare_data + +def REPORT(l_Y_GT, lY_Pred, t=None): + if t: print "\t( predict DONE IN %.1fs)"%t + + _flat_GT, _flat_P = (np.hstack([y.ravel() for y in l_Y_GT]), + np.hstack([y.ravel() for y in lY_Pred])) + confmat = confusion_matrix(_flat_GT, _flat_P) + print confmat + print "\ttrace =", confmat.trace() + print "\tAccuracy= %.3f"%accuracy_score(_flat_GT, _flat_P) + + +print("Please be patient. Learning will take 5-20 minutes.") +snakes = load_snakes() +X_train, Y_train = snakes['X_train'], snakes['Y_train'] +#X_train, Y_train = X_train[:5], Y_train[:5] + +X_train = [one_hot_colors(x) for x in X_train] +Y_train_flat = [y_.ravel() for y_ in Y_train] + +X_train_directions, X_train_edge_features = prepare_data(X_train) +print "%d picture for training"%len(X_train) + +# Evaluate using confusion matrix. +# Clearly the middel of the snake is the hardest part. +X_test, Y_test = snakes['X_test'], snakes['Y_test'] +X_test = [one_hot_colors(x) for x in X_test] +Y_test_flat = [y_.ravel() for y_ in Y_test] +X_test_directions, X_test_edge_features = prepare_data(X_test) +print "%d picture for test"%len(X_test) + + +print "- TRAINING ONLY WITH DIRECTIONAL EDGE FEATURES -----" +#inference = 'qpbo' +#I'm interested in AD3 inference. +inference = 'ad3' + +# first, train on X with directions only: +crf = EdgeFeatureGraphCRF(inference_method=inference) +ssvm = OneSlackSSVM(crf, inference_cache=50, C=.1, tol=.1, max_iter=100, + n_jobs=1) +t0 = time.time() +ssvm.fit(X_train_directions, Y_train_flat) +print("Model EdgeFeatureGraphCRF fitted. %.1fs"%(time.time()-t0)) + +Y_GT = np.hstack(Y_test_flat) +print("- Results using only directional features for edges. %.1fs"%(time.time()-t0)) +t0 = time.time() +Y_pred = ssvm.predict(X_test_directions) +REPORT(Y_GT, Y_pred, time.time()-t0) + +print "- Result with binarized graph" +t0 = time.time() +Y_pred = ssvm.predict(X_test_directions, [True]*len(X_test_directions)) +REPORT(Y_GT, Y_pred, time.time()-t0) + + +#Predict under constraints +def buildConstraints(X, bOne=True): + """ + We iterate over each graph, and make sure that for each, we constrain to have a single instances of classes 1 to 9 + (or atmost one) + + The constraints must be a list of tuples like ( , , , ) + where: + - operator is one of 'XOR' 'XOROUT' 'ATMOSTONE' 'OR' 'OROUT' 'ANDOUT' 'IMPLY' + - unaries is a list of the index of the unaries involved in this constraint + - states is a list of unary states, 1 per involved unary. If the states are all the same, you can pass it directly as a scalar value. + - negated is a list of boolean indicated if the unary must be negated. Again, if all values are the same, pass a single boolean value instead of a list + """ + sLogicOp = "XOR" if bOne else "ATMOSTONE" + lConstraint = [] + for (node_features, edges, edge_features) in X: + n_nodes = node_features.shape[0] + lConstraintPerGraph = [ (sLogicOp, range(n_nodes), i, False) for i in range(1,10) ] #only one + lConstraint.append( lConstraintPerGraph ) + return lConstraint + + +print "- Results of inference under constraints" +lConstraint = buildConstraints(X_test_directions) +t0 = time.time() +Y_pred = ssvm.predict(X_test_directions, lConstraint) +REPORT(Y_GT, Y_pred, time.time()-t0) + +# now, use more informative edge features: +print "- NOW TRAINING WITH BETTER EDGE FEATURES -----" +inference = 'qpbo' +crf = EdgeFeatureGraphCRF(inference_method=inference) +ssvm = OneSlackSSVM(crf, inference_cache=50, C=.1, tol=.1, switch_to='ad3', + n_jobs=1) +t0 = time.time() +ssvm.fit(X_train_edge_features, Y_train_flat) +print("Model EdgeFeatureGraphCRF fitted. %.1fs"%(time.time()-t0)) + + +print("- Results using also input features for edges. %.1fs"%(time.time()-t0)) +t0 = time.time() +Y_pred = ssvm.predict(X_test_edge_features) +REPORT(Y_GT, Y_pred, time.time()-t0) + +print "- Result with binarized graph" +t0 = time.time() +Y_pred = ssvm.predict(X_test_edge_features, [True]*len(X_test_edge_features)) +REPORT(Y_GT, Y_pred, time.time()-t0) + +#Predict under constraints +print "- Results of inference under constraints" +lConstraint = buildConstraints(X_test_edge_features) +t0 = time.time() +Y_pred = ssvm.predict(X_test_edge_features, lConstraint) +REPORT(Y_GT, Y_pred, time.time()-t0) + + +""" +Please be patient. Learning will take 5-20 minutes. +200 picture for training +100 picture for test +- TRAINING ONLY WITH DIRECTIONAL EDGE FEATURES ----- +Model EdgeFeatureGraphCRF fitted. 115.9s +- Results using only directional features for edges. 115.9s + ( predict DONE IN 0.7s) +[[2750 0 0 0 0 0 0 0 0 0 0] + [ 0 100 0 0 0 0 0 0 0 0 0] + [ 0 0 59 0 22 4 6 1 7 1 0] + [ 0 3 1 29 5 31 8 18 1 3 1] + [ 0 1 13 2 30 11 25 1 13 3 1] + [ 0 1 1 9 4 46 11 15 3 9 1] + [ 0 1 7 2 24 10 21 7 23 2 3] + [ 0 0 1 6 7 35 10 17 3 21 0] + [ 0 0 7 2 14 10 16 4 25 0 22] + [ 0 0 0 3 7 14 4 12 2 58 0] + [ 0 0 5 3 11 3 7 0 5 0 66]] + trace = 3201 + Accuracy= 0.854 +- Result with binarized graph + ( predict DONE IN 0.7s) +[[2750 0 0 0 0 0 0 0 0 0 0] + [ 0 100 0 0 0 0 0 0 0 0 0] + [ 0 0 59 0 22 4 6 1 7 1 0] + [ 0 3 1 29 5 31 8 18 1 3 1] + [ 0 1 13 2 30 11 25 1 13 3 1] + [ 0 1 1 9 4 46 11 15 3 9 1] + [ 0 1 7 2 24 10 21 7 23 2 3] + [ 0 0 1 6 7 35 10 17 3 21 0] + [ 0 0 7 2 14 10 16 4 25 0 22] + [ 0 0 0 3 7 14 4 12 2 58 0] + [ 0 0 5 3 11 3 7 0 5 0 66]] + trace = 3201 + Accuracy= 0.854 +- Results of inference under constraints + ( predict DONE IN 0.7s) +[[2750 0 0 0 0 0 0 0 0 0 0] + [ 0 100 0 0 0 0 0 0 0 0 0] + [ 0 0 59 0 22 4 6 1 7 1 0] + [ 0 3 1 29 5 31 8 18 1 3 1] + [ 0 1 13 2 30 11 25 1 13 3 1] + [ 0 1 1 9 4 46 11 15 3 9 1] + [ 0 1 7 2 24 10 21 7 23 2 3] + [ 0 0 1 6 7 35 10 17 3 21 0] + [ 0 0 7 2 14 10 16 4 25 0 22] + [ 0 0 0 3 7 14 4 12 2 58 0] + [ 0 0 5 3 11 3 7 0 5 0 66]] + trace = 3201 + Accuracy= 0.854 +- NOW TRAINING WITH BETTER EDGE FEATURES ----- +Model EdgeFeatureGraphCRF fitted. 679.6s +- Results using also input features for edges. 679.6s + ( predict DONE IN 0.9s) +[[2749 0 0 0 0 0 0 0 1 0 0] + [ 0 100 0 0 0 0 0 0 0 0 0] + [ 0 0 100 0 0 0 0 0 0 0 0] + [ 0 0 0 100 0 0 0 0 0 0 0] + [ 0 0 0 0 98 0 1 0 1 0 0] + [ 0 0 0 2 0 98 0 0 0 0 0] + [ 0 0 0 0 2 0 98 0 0 0 0] + [ 0 1 0 0 0 2 0 97 0 0 0] + [ 0 0 1 0 0 0 1 0 98 0 0] + [ 0 0 0 1 0 0 0 0 0 99 0] + [ 0 0 0 0 1 0 0 0 0 0 99]] + trace = 3736 + Accuracy= 0.996 +- Result with binarized graph + ( predict DONE IN 0.9s) +[[2749 0 0 0 0 0 0 0 1 0 0] + [ 0 100 0 0 0 0 0 0 0 0 0] + [ 0 0 100 0 0 0 0 0 0 0 0] + [ 0 0 0 100 0 0 0 0 0 0 0] + [ 0 0 0 0 98 0 1 0 1 0 0] + [ 0 0 0 2 0 98 0 0 0 0 0] + [ 0 0 0 0 2 0 98 0 0 0 0] + [ 0 1 0 0 0 2 0 97 0 0 0] + [ 0 0 1 0 0 0 1 0 98 0 0] + [ 0 0 0 1 0 0 0 0 0 99 0] + [ 0 0 0 0 1 0 0 0 0 0 99]] + trace = 3736 + Accuracy= 0.996 +- Results of inference under constraints + ( predict DONE IN 0.9s) +[[2749 0 0 0 0 0 0 0 1 0 0] + [ 0 100 0 0 0 0 0 0 0 0 0] + [ 0 0 100 0 0 0 0 0 0 0 0] + [ 0 0 0 100 0 0 0 0 0 0 0] + [ 0 0 0 0 98 0 1 0 1 0 0] + [ 0 0 0 2 0 98 0 0 0 0 0] + [ 0 0 0 0 2 0 98 0 0 0 0] + [ 0 1 0 0 0 2 0 97 0 0 0] + [ 0 0 1 0 0 0 1 0 98 0 0] + [ 0 0 0 1 0 0 0 0 0 99 0] + [ 0 0 0 0 1 0 0 0 0 0 99]] + trace = 3736 + Accuracy= 0.996 + + +""" \ No newline at end of file diff --git a/examples/plot_snakes_typed.py b/examples/plot_snakes_typed.py new file mode 100644 index 00000000..92d37189 --- /dev/null +++ b/examples/plot_snakes_typed.py @@ -0,0 +1,161 @@ +""" +============================================== +Conditional Interactions on the Snakes Dataset +============================================== + +This is a variant of plot_snakes.py where we use the NodeTypeEdgeFeatureGraphCRF +class instead of EdgeFeatureGraphCRF, despite there is only 1 type of nodes. +So this should give exact same results as plot_snakes.py + + +This example uses the snake dataset introduced in +Nowozin, Rother, Bagon, Sharp, Yao, Kohli: Decision Tree Fields ICCV 2011 + +This dataset is specifically designed to require the pairwise interaction terms +to be conditioned on the input, in other words to use non-trival edge-features. + +The task is as following: a "snake" of length ten wandered over a grid. For +each cell, it had the option to go up, down, left or right (unless it came from +there). The input consists of these decisions, while the desired output is an +annotation of the snake from 0 (head) to 9 (tail). See the plots for an +example. + +As input features we use a 3x3 window around each pixel (and pad with background +where necessary). We code the five different input colors (for up, down, left, right, +background) using a one-hot encoding. This is a rather naive approach, not using any +information about the dataset (other than that it is a 2d grid). + +The task can not be solved using the simple DirectionalGridCRF - which can only +infer head and tail (which are also possible to infer just from the unary +features). If we add edge-features that contain the features of the nodes that are +connected by the edge, the CRF can solve the task. + +From an inference point of view, this task is very hard. QPBO move-making is +not able to solve it alone, so we use the relaxed AD3 inference for learning. + +PS: This example runs a bit (5 minutes on 12 cores, 20 minutes on one core for me). +But it does work as well as Decision Tree Fields ;) + + JL Meunier - January 2017 + + Developed for the EU project READ. The READ project has received funding + from the European Union's Horizon 2020 research and innovation programme + under grant agreement No 674943 + + Copyright Xerox + +""" +from __future__ import (absolute_import, division, print_function) + +import numpy as np +# import matplotlib.pyplot as plt + +from sklearn.preprocessing import label_binarize +from sklearn.metrics import confusion_matrix, accuracy_score + +from pystruct.learners import OneSlackSSVM +from pystruct.datasets import load_snakes +from pystruct.utils import make_grid_edges, edge_list_to_features +#from pystruct.models import EdgeFeatureGraphCRF +from pystruct.models import NodeTypeEdgeFeatureGraphCRF + +from plot_snakes import one_hot_colors, neighborhood_feature, prepare_data + +def convertToSingleTypeX(X): + """ + For NodeTypeEdgeFeatureGraphCRF X is structured differently. + But NodeTypeEdgeFeatureGraphCRF can handle graph with a single node type. One needs to convert X to the new structure using this method. + """ + return [([nf], [e], [ef]) for (nf,e,ef) in X] + +if __name__ == '__main__': + print("Please be patient. Learning will take 5-20 minutes.") + snakes = load_snakes() + X_train, Y_train = snakes['X_train'], snakes['Y_train'] + + X_train = [one_hot_colors(x) for x in X_train] + Y_train_flat = [y_.ravel() for y_ in Y_train] + + + X_train_directions, X_train_edge_features = prepare_data(X_train) + + inference = 'ad3+' + # first, train on X with directions only: + crf = NodeTypeEdgeFeatureGraphCRF(1, [11], [45], [[2]], inference_method=inference) + ssvm = OneSlackSSVM(crf, inference_cache=50, C=.1, tol=.1, max_iter=100, + n_jobs=1) + ssvm.fit(convertToSingleTypeX(X_train_directions), Y_train_flat) + + # Evaluate using confusion matrix. + # Clearly the middel of the snake is the hardest part. + X_test, Y_test = snakes['X_test'], snakes['Y_test'] + X_test = [one_hot_colors(x) for x in X_test] + Y_test_flat = [y_.ravel() for y_ in Y_test] + X_test_directions, X_test_edge_features = prepare_data(X_test) + Y_pred = ssvm.predict( convertToSingleTypeX(X_test_directions) ) + print("Results using only directional features for edges") + print("Test accuracy: %.3f" + % accuracy_score(np.hstack(Y_test_flat), np.hstack(Y_pred))) + print(confusion_matrix(np.hstack(Y_test_flat), np.hstack(Y_pred))) + + # now, use more informative edge features: + crf = NodeTypeEdgeFeatureGraphCRF(1, [11], [45], [[180]], inference_method=inference) + ssvm = OneSlackSSVM(crf, inference_cache=50, C=.1, tol=.1, + # switch_to='ad3', + #verbose=1, + n_jobs=8) + ssvm.fit( convertToSingleTypeX(X_train_edge_features), Y_train_flat) + Y_pred2 = ssvm.predict( convertToSingleTypeX(X_test_edge_features) ) + print("Results using also input features for edges") + print("Test accuracy: %.3f" + % accuracy_score(np.hstack(Y_test_flat), np.hstack(Y_pred2))) + print(confusion_matrix(np.hstack(Y_test_flat), np.hstack(Y_pred2))) + +# if False: +# # plot stuff +# fig, axes = plt.subplots(2, 2) +# axes[0, 0].imshow(snakes['X_test'][0], interpolation='nearest') +# axes[0, 0].set_title('Input') +# y = Y_test[0].astype(np.int) +# bg = 2 * (y != 0) # enhance contrast +# axes[0, 1].matshow(y + bg, cmap=plt.cm.Greys) +# axes[0, 1].set_title("Ground Truth") +# axes[1, 0].matshow(Y_pred[0].reshape(y.shape) + bg, cmap=plt.cm.Greys) +# axes[1, 0].set_title("Prediction w/o edge features") +# axes[1, 1].matshow(Y_pred2[0].reshape(y.shape) + bg, cmap=plt.cm.Greys) +# axes[1, 1].set_title("Prediction with edge features") +# for a in axes.ravel(): +# a.set_xticks(()) +# a.set_yticks(()) +# plt.show() + +""" +Please be patient. Learning will take 5-20 minutes. +Results using only directional features for edges +Test accuracy: 0.847 +[[2750 0 0 0 0 0 0 0 0 0 0] + [ 0 99 0 0 1 0 0 0 0 0 0] + [ 0 2 68 3 9 4 6 4 3 1 0] + [ 0 4 11 45 8 14 5 6 0 6 1] + [ 0 1 22 18 31 2 14 4 3 5 0] + [ 0 3 7 38 12 22 5 4 2 7 0] + [ 0 2 19 16 26 8 16 2 9 2 0] + [ 0 6 14 26 10 15 5 12 2 10 0] + [ 0 0 12 15 16 4 16 2 18 4 13] + [ 0 2 5 18 6 8 5 3 2 50 1] + [ 0 1 11 4 13 1 2 0 2 2 64]] +Results using also input features for edges +Test accuracy: 0.998 +[[2749 0 0 0 0 0 0 0 1 0 0] + [ 0 100 0 0 0 0 0 0 0 0 0] + [ 0 0 100 0 0 0 0 0 0 0 0] + [ 0 0 0 99 0 0 0 0 0 1 0] + [ 0 0 0 0 99 0 1 0 0 0 0] + [ 0 0 0 1 0 98 0 1 0 0 0] + [ 0 0 0 0 1 0 99 0 0 0 0] + [ 0 0 0 0 0 1 0 99 0 0 0] + [ 0 0 0 0 0 0 0 0 100 0 0] + [ 0 0 0 0 0 0 0 1 0 99 0] + [ 0 0 0 0 0 0 0 0 0 0 100]] + +""" \ No newline at end of file diff --git a/examples/plot_svm_objectives.py b/examples/plot_ssvm_objective_curves.py similarity index 89% rename from examples/plot_svm_objectives.py rename to examples/plot_ssvm_objective_curves.py index 1ec2dfa2..09f32e31 100644 --- a/examples/plot_svm_objectives.py +++ b/examples/plot_ssvm_objective_curves.py @@ -1,7 +1,7 @@ """ -==================== -SVM objective values -==================== +================================== +SSVM Convergence Curves +================================== Showing the relation between cutting plane and primal objectives, as well as the different algorithms. We use exact inference here, so the plots are easier to interpret. @@ -20,7 +20,7 @@ X, Y = generate_crosses_explicit(n_samples=50, noise=10, size=6, n_crosses=1) n_labels = len(np.unique(Y)) -crf = GridCRF(n_states=n_labels, inference_method="dai") +crf = GridCRF(n_states=n_labels, inference_method=("ad3", {'branch_and_bound': True})) n_slack_svm = NSlackSSVM(crf, check_constraints=False, max_iter=50, batch_size=1, tol=0.001) @@ -28,7 +28,7 @@ max_iter=100, tol=0.001, inference_cache=50) subgradient_svm = SubgradientSSVM(crf, learning_rate=0.001, max_iter=20, decay_exponent=0, momentum=0) -bcfw_svm = FrankWolfeSSVM(crf, max_iter=50, verbose=2, check_dual_every=4) +bcfw_svm = FrankWolfeSSVM(crf, max_iter=50, check_dual_every=4) #n-slack cutting plane ssvm n_slack_svm.fit(X, Y) @@ -63,7 +63,7 @@ label="Block-Coordinate Frank-Wolfe Dual") plt.plot(bcfw_svm.timestamps_[1:], bcfw_svm.primal_objective_curve_, label="Block-Coordinate Frank-Wolfe Primal") -plt.legend() +plt.legend(loc="best") plt.yscale('log') plt.xlabel("training time") plt.show() diff --git a/examples/svm_as_crf.py b/examples/svm_as_crf.py index 9784da37..7a35225d 100644 --- a/examples/svm_as_crf.py +++ b/examples/svm_as_crf.py @@ -2,6 +2,7 @@ =========== SVM as CRF =========== + A CRF with one node is the same as a multiclass SVM. Evaluation on iris dataset (really easy). """ @@ -10,7 +11,10 @@ import numpy as np from sklearn.datasets import load_iris -from sklearn.cross_validation import train_test_split +try: + from sklearn.model_selection import train_test_split +except ImportError: + from sklearn.cross_validation import train_test_split from pystruct.models import GraphCRF from pystruct.learners import NSlackSSVM diff --git a/pystruct/__init__.py b/pystruct/__init__.py index 1bfc769c..f9aa3e11 100644 --- a/pystruct/__init__.py +++ b/pystruct/__init__.py @@ -1 +1 @@ -__version__ = "0.2-dev" +__version__ = "0.3.2" diff --git a/pystruct/datasets/dataset_loaders.py b/pystruct/datasets/dataset_loaders.py index 70967dcc..099ff4c1 100644 --- a/pystruct/datasets/dataset_loaders.py +++ b/pystruct/datasets/dataset_loaders.py @@ -1,10 +1,24 @@ -import cPickle -from os.path import dirname -from os.path import join +from os.path import dirname, join +import sys + +try: + import cPickle as pickle +except ImportError: + import pickle import numpy as np +def _safe_unpickle(file_name): + with open(file_name, "rb") as data_file: + if sys.version_info >= (3, 0): + # python3 unpickling of python2 unicode + data = pickle.load(data_file, encoding="latin1") + else: + data = pickle.load(data_file) + return data + + def load_letters(): """Load the OCR letters dataset. @@ -12,10 +26,16 @@ def load_letters(): Each example consists of a word, segmented into letters. The first letter of each word is ommited from the data, as it was a capital letter (in contrast to all other letters). + + + References + ---------- + http://papers.nips.cc/paper/2397-max-margin-markov-networks.pdf + http://groups.csail.mit.edu/sls/archives/root/publications/1995/Kassel%20Thesis.pdf + http://www.seas.upenn.edu/~taskar/ocr/ """ module_path = dirname(__file__) - data_file = open(join(module_path, 'letters.pickle')) - data = cPickle.load(data_file) + data = _safe_unpickle(join(module_path, 'letters.pickle')) # we add an easy to use image representation: data['images'] = [np.hstack([l.reshape(16, 8) for l in word]) for word in data['data']] @@ -23,12 +43,34 @@ def load_letters(): def load_scene(): + """Load the scene multi-label dataset. + + This is a benchmark multilabel dataset. + n_classes = 6 + n_fetures = 294 + n_samples_test = 1196 + n_samples_train = 1211 + + References + ---------- + Matthew R. Boutell, Jiebo Luo, Xipeng Shen, and Christopher M. Brown. + Learning multi-label scene classification. + """ module_path = dirname(__file__) - data_file = open(join(module_path, 'scene.pickle')) - return cPickle.load(data_file) + return _safe_unpickle(join(module_path, 'scene.pickle')) def load_snakes(): + """Load the synthetic snake datasets. + + Taken from: + Nowozin, S., Rother, C., Bagon, S., Sharp, T., Yao, B., & Kohli, P. + Decision Tree Fields, ICCV 2011 + + This is a 2d grid labeling task where conditinal pairwise interactions are + important. + See the reference for an explanation. + """ + module_path = dirname(__file__) - data_file = open(join(module_path, 'snakes.pickle')) - return cPickle.load(data_file) + return _safe_unpickle(join(module_path, 'snakes.pickle')) diff --git a/pystruct/datasets/letters.py b/pystruct/datasets/letters.py deleted file mode 100644 index 5d282e70..00000000 --- a/pystruct/datasets/letters.py +++ /dev/null @@ -1,6 +0,0 @@ -import cPickle -from os.path import dirname -from os.path import join - -import numpy as np - diff --git a/pystruct/datasets/synthetic_grids.py b/pystruct/datasets/synthetic_grids.py index 1fdfe926..2bc40087 100644 --- a/pystruct/datasets/synthetic_grids.py +++ b/pystruct/datasets/synthetic_grids.py @@ -47,13 +47,13 @@ def make_simple_2x2(seed=0, n_flips=4, n_samples=20): np.random.seed(seed) X = [] Y = [] - for i in xrange(n_samples): + for i in range(n_samples): y = np.zeros((4, 4), dtype=np.int) j, k = 2 * np.random.randint(2, size=2) y[j: j + 2, k: k + 2] = 1 Y.append(y) x = y.copy() - for flip in xrange(n_flips): + for flip in range(n_flips): a, b = np.random.randint(4, size=2) x[a, b] = np.random.randint(2) x[x == 0] = -1 @@ -64,9 +64,9 @@ def make_simple_2x2(seed=0, n_flips=4, n_samples=20): def generate_easy(n_samples=5, noise=5, box_size=3, total_size=8, seed=0): np.random.seed(seed) Y = np.zeros((n_samples, total_size, total_size), dtype=np.int) - for i in xrange(n_samples): + for i in range(n_samples): t_old, l_old = -10, -10 - for j in xrange(2): + for j in range(2): #while True: #t, l = np.random.randint(1, size - 3, size=2) #if (t, l) in [(4, 4)]: @@ -94,9 +94,9 @@ def generate_bars(n_samples=5, noise=5, bars_size=3, total_size=8, random_seed=0, separate_labels=True): np.random.seed(random_seed) Y = np.zeros((n_samples, total_size, total_size), dtype=np.int) - for i in xrange(n_samples): + for i in range(n_samples): t_old, l_old = -10, -10 - for j in xrange(2): + for j in range(2): #while True: #t, l = np.random.randint(1, size - 3, size=2) #if (t, l) in [(4, 4)]: @@ -129,7 +129,7 @@ def generate_square_with_hole(n_samples=5, noise=5, total_size=8): box_size = 3 np.random.seed(0) Y = np.zeros((n_samples, total_size, total_size), dtype=np.int) - for i in xrange(n_samples): + for i in range(n_samples): t_old, l_old = -10, -10 t, l = np.random.randint(1, total_size - box_size, size=2) Y[i, t:t + box_size, l:l + box_size] = 1 @@ -149,9 +149,9 @@ def generate_crosses(n_samples=5, noise=30, total_size=10, n_crosses=2, seed=0): np.random.seed(seed) Y = np.zeros((n_samples, total_size, total_size), dtype=np.int) - for i in xrange(n_samples): + for i in range(n_samples): t_old, l_old = -3, -3 - for j in xrange(n_crosses): + for j in range(n_crosses): while True: t, l = np.random.randint(1, total_size - 3, size=2) if np.abs(t - t_old) > 2 or np.abs(l - l_old) > 2: @@ -176,9 +176,9 @@ def generate_xs(n_samples=5, noise=30): np.random.seed(0) size = 8 Y = np.zeros((n_samples, size, size), dtype=np.int) - for i in xrange(n_samples): + for i in range(n_samples): t_old, l_old = -3, -3 - for j in xrange(1): + for j in range(1): while True: t, l = np.random.randint(1, size - 3, size=2) if np.abs(t - t_old) > 2 and np.abs(l - l_old): @@ -248,9 +248,9 @@ def generate_easy_explicit(n_samples=5, noise=5): size = 9 np.random.seed(0) Y = np.zeros((n_samples, size, size), dtype=np.int) - for i in xrange(n_samples): + for i in range(n_samples): t_old, l_old = -4, -4 - for j in xrange(1): + for j in range(1): #while True: #t, l = np.random.randint(1, size - 3, size=2) #if (t, l) in [(4, 4)]: @@ -282,9 +282,9 @@ def generate_easy_explicit(n_samples=5, noise=5): def generate_crosses_explicit(n_samples=5, noise=30, size=9, n_crosses=2): np.random.seed(0) Y = np.zeros((n_samples, size, size), dtype=np.int) - for i in xrange(n_samples): + for i in range(n_samples): t_old, l_old = -3, -3 - for j in xrange(n_crosses): + for j in range(n_crosses): while True: t, l = np.random.randint(1, size - 1, size=2) if np.abs(t - t_old) > 2 or np.abs(l - l_old) > 2: @@ -304,8 +304,6 @@ def generate_crosses_explicit(n_samples=5, noise=30, size=9, n_crosses=2): ix, iy, iz = np.ogrid[:X.shape[0], :X.shape[1], :X.shape[2]] X[ix, iy, iz, Y_flips] = 1 X[ix, iy, iz, 2 * Y_flips] = 1 - #Y = (Y != 0).astype(np.int) - #X = X[:, :, :, :2] return X, Y @@ -314,8 +312,8 @@ def generate_crosses_latent(n_samples=5, noise=30): np.random.seed(0) size = 8 Y = np.zeros((n_samples, size, size), dtype=np.int) - for i in xrange(n_samples): - for j in xrange(2): + for i in range(n_samples): + for j in range(2): t, l = np.random.randint(size - 2, size=2) Y[i, t + 1, l:l + 3] = 2 Y[i, t:t + 3, l + 1] = 2 diff --git a/pystruct/inference/__init__.py b/pystruct/inference/__init__.py index 7028288d..041432ee 100644 --- a/pystruct/inference/__init__.py +++ b/pystruct/inference/__init__.py @@ -1,8 +1,10 @@ -from .inference_methods import (inference_qpbo, inference_dai, inference_lp, +from .inference_methods import (inference_qpbo, inference_lp, inference_ad3, inference_ogm, inference_dispatch, get_installed, - compute_energy) + inference_ad3plus, InferenceException) +from .common import compute_energy -__all__ = ["inference_qpbo", "inference_dai", "inference_lp", "inference_ad3", +__all__ = ["inference_qpbo", "inference_lp", "inference_ad3", "inference_dispatch", "get_installed", "compute_energy", - "inference_ogm"] + "inference_ogm", + "inference_ad3plus", "InferenceException"] \ No newline at end of file diff --git a/pystruct/inference/_viterbi.c b/pystruct/inference/_viterbi.c new file mode 100644 index 00000000..bc43405e --- /dev/null +++ b/pystruct/inference/_viterbi.c @@ -0,0 +1,6281 @@ +/* Generated by Cython 0.21.1 */ + +#define PY_SSIZE_T_CLEAN +#ifndef CYTHON_USE_PYLONG_INTERNALS +#ifdef PYLONG_BITS_IN_DIGIT +#define CYTHON_USE_PYLONG_INTERNALS 0 +#else +#include "pyconfig.h" +#ifdef PYLONG_BITS_IN_DIGIT +#define CYTHON_USE_PYLONG_INTERNALS 1 +#else +#define CYTHON_USE_PYLONG_INTERNALS 0 +#endif +#endif +#endif +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000) + #error Cython requires Python 2.6+ or Python 3.2+. +#else +#define CYTHON_ABI "0_21_1" +#include +#ifndef offsetof +#define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION +#define CYTHON_COMPILING_IN_PYPY 1 +#define CYTHON_COMPILING_IN_CPYTHON 0 +#else +#define CYTHON_COMPILING_IN_PYPY 0 +#define CYTHON_COMPILING_IN_CPYTHON 1 +#endif +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 +#define Py_OptimizeFlag 0 +#endif +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyType_Type +#endif +#if PY_MAJOR_VERSION >= 3 + #define Py_TPFLAGS_CHECKTYPES 0 + #define Py_TPFLAGS_HAVE_INDEX 0 + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#if PY_VERSION_HEX < 0x030400a1 && !defined(Py_TPFLAGS_HAVE_FINALIZE) + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ? \ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) +#else + #define CYTHON_PEP393_ENABLED 0 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) + #define __Pyx_PyFrozenSet_Size(s) PyObject_Size(s) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ? \ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) + #define __Pyx_PyFrozenSet_Size(s) PySet_Size(s) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? 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PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#ifndef CYTHON_INLINE + #if defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + /* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and + a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is + a quiet NaN. */ + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#ifdef __cplusplus +template +void __Pyx_call_destructor(T* x) { + x->~T(); +} +#endif + + +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#if defined(WIN32) || defined(MS_WINDOWS) +#define _USE_MATH_DEFINES +#endif +#include +#define __PYX_HAVE__pystruct__inference___viterbi +#define __PYX_HAVE_API__pystruct__inference___viterbi +#include "string.h" +#include "stdio.h" +#include "stdlib.h" +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#ifdef PYREX_WITHOUT_ASSERTIONS +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) ( \ + (sizeof(type) < sizeof(Py_ssize_t)) || \ + (sizeof(type) > sizeof(Py_ssize_t) && \ + likely(v < (type)PY_SSIZE_T_MAX || \ + v == (type)PY_SSIZE_T_MAX) && \ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN || \ + v == (type)PY_SSIZE_T_MIN))) || \ + (sizeof(type) == sizeof(Py_ssize_t) && \ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX || \ + v == (type)PY_SSIZE_T_MAX))) ) +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromUString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromUString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromUString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromUString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromUString(s) __Pyx_PyUnicode_FromString((const char*)s) +#if PY_MAJOR_VERSION < 3 +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) +{ + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#else +#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen +#endif +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None) +#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_COMPILING_IN_CPYTHON +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ + +static PyObject *__pyx_m; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif defined(_Complex_I) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj +#endif + + +static const char *__pyx_f[] = { + "pystruct/inference/_viterbi.pyx", + "__init__.pxd", + "type.pxd", +}; +#define IS_UNSIGNED(type) (((type) -1) > 0) +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":723 + * # in Cython to enable them only on the right systems. + * + * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + */ +typedef npy_int8 __pyx_t_5numpy_int8_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":724 + * + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t + */ +typedef npy_int16 __pyx_t_5numpy_int16_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":725 + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< + * ctypedef npy_int64 int64_t + * #ctypedef npy_int96 int96_t + */ +typedef npy_int32 __pyx_t_5numpy_int32_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726 + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< + * #ctypedef npy_int96 int96_t + * #ctypedef npy_int128 int128_t + */ +typedef npy_int64 __pyx_t_5numpy_int64_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":730 + * #ctypedef npy_int128 int128_t + * + * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + */ +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":731 + * + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t + */ +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":732 + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< + * ctypedef npy_uint64 uint64_t + * #ctypedef npy_uint96 uint96_t + */ +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":733 + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< + * #ctypedef npy_uint96 uint96_t + * #ctypedef npy_uint128 uint128_t + */ +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":737 + * #ctypedef npy_uint128 uint128_t + * + * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< + * ctypedef npy_float64 float64_t + * #ctypedef npy_float80 float80_t + */ +typedef npy_float32 __pyx_t_5numpy_float32_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":738 + * + * ctypedef npy_float32 float32_t + * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< + * #ctypedef npy_float80 float80_t + * #ctypedef npy_float128 float128_t + */ +typedef npy_float64 __pyx_t_5numpy_float64_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":747 + * # The int types are mapped a bit surprising -- + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t + */ +typedef npy_long __pyx_t_5numpy_int_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":748 + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong longlong_t + * + */ +typedef npy_longlong __pyx_t_5numpy_long_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":749 + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_ulong uint_t + */ +typedef npy_longlong __pyx_t_5numpy_longlong_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":751 + * ctypedef npy_longlong longlong_t + * + * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t + */ +typedef npy_ulong __pyx_t_5numpy_uint_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":752 + * + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulonglong_t + * + */ +typedef npy_ulonglong __pyx_t_5numpy_ulong_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":753 + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_intp intp_t + */ +typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":755 + * ctypedef npy_ulonglong ulonglong_t + * + * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< + * ctypedef npy_uintp uintp_t + * + */ +typedef npy_intp __pyx_t_5numpy_intp_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":756 + * + * ctypedef npy_intp intp_t + * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< + * + * ctypedef npy_double float_t + */ +typedef npy_uintp __pyx_t_5numpy_uintp_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":758 + * ctypedef npy_uintp uintp_t + * + * ctypedef npy_double float_t # <<<<<<<<<<<<<< + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t + */ +typedef npy_double __pyx_t_5numpy_float_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":759 + * + * ctypedef npy_double float_t + * ctypedef npy_double double_t # <<<<<<<<<<<<<< + * ctypedef npy_longdouble longdouble_t + * + */ +typedef npy_double __pyx_t_5numpy_double_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":760 + * ctypedef npy_double float_t + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cfloat cfloat_t + */ +typedef npy_longdouble __pyx_t_5numpy_longdouble_t; +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< float > __pyx_t_float_complex; + #else + typedef float _Complex __pyx_t_float_complex; + #endif +#else + typedef struct { float real, imag; } __pyx_t_float_complex; +#endif + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< double > __pyx_t_double_complex; + #else + typedef double _Complex __pyx_t_double_complex; + #endif +#else + typedef struct { double real, imag; } __pyx_t_double_complex; +#endif + + +/*--- Type declarations ---*/ + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":762 + * ctypedef npy_longdouble longdouble_t + * + * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< + * ctypedef npy_cdouble cdouble_t + * ctypedef npy_clongdouble clongdouble_t + */ +typedef npy_cfloat __pyx_t_5numpy_cfloat_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":763 + * + * ctypedef npy_cfloat cfloat_t + * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< + * ctypedef npy_clongdouble clongdouble_t + * + */ +typedef npy_cdouble __pyx_t_5numpy_cdouble_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":764 + * ctypedef npy_cfloat cfloat_t + * ctypedef npy_cdouble cdouble_t + * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cdouble complex_t + */ +typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; + +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":766 + * ctypedef npy_clongdouble clongdouble_t + * + * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< + * + * cdef inline object PyArray_MultiIterNew1(a): + */ +typedef npy_cdouble __pyx_t_5numpy_complex_t; +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, int); + void (*DECREF)(void*, PyObject*, int); + void (*GOTREF)(void*, PyObject*, int); + void (*GIVEREF)(void*, PyObject*, int); + void* (*SetupContext)(const char*, int, const char*); + void (*FinishContext)(void**); + } __Pyx_RefNannyAPIStruct; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); + #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; +#ifdef WITH_THREAD + #define __Pyx_RefNannySetupContext(name, acquire_gil) \ + if (acquire_gil) { \ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure(); 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}} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) +#else + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) +#endif +#define __Pyx_XDECREF_SET(r, v) do { \ + PyObject *tmp = (PyObject *) r; \ + r = v; __Pyx_XDECREF(tmp); \ + } while (0) +#define __Pyx_DECREF_SET(r, v) do { \ + PyObject *tmp = (PyObject *) r; \ + r = v; __Pyx_DECREF(tmp); \ + } while (0) +#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) +#define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \ + const char* function_name); + +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact); + +static CYTHON_INLINE int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, + __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); + +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +static void __Pyx_RaiseBufferFallbackError(void); + +#define __Pyx_BufPtrStrided2d(type, buf, i0, s0, i1, s1) (type)((char*)buf + i0 * s0 + i1 * s1) +#define __Pyx_BufPtrStrided3d(type, buf, i0, s0, i1, s1, i2, s2) (type)((char*)buf + i0 * s0 + i1 * s1 + i2 * s2) +#define __Pyx_BufPtrCContig2d(type, buf, i0, s0, i1, s1) ((type)((char*)buf + i0 * s0) + i1) +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); +#else +#define __Pyx_PyObject_CallNoArg(func) __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL) +#endif + +#define __Pyx_BufPtrCContig1d(type, buf, i0, s0) ((type)buf + i0) +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); + +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +typedef struct { + int code_line; + PyCodeObject* code_object; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0}; +static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; + +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value); + +static CYTHON_INLINE Py_intptr_t __Pyx_PyInt_As_Py_intptr_t(PyObject *); + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #define __Pyx_CREAL(z) ((z).real()) + #define __Pyx_CIMAG(z) ((z).imag()) + #else + #define __Pyx_CREAL(z) (__real__(z)) + #define __Pyx_CIMAG(z) (__imag__(z)) + #endif +#else + #define __Pyx_CREAL(z) ((z).real) + #define __Pyx_CIMAG(z) ((z).imag) +#endif +#if (defined(_WIN32) || defined(__clang__)) && defined(__cplusplus) && CYTHON_CCOMPLEX + #define __Pyx_SET_CREAL(z,x) ((z).real(x)) + #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) +#else + #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) + #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) +#endif + +static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); + +#if CYTHON_CCOMPLEX + #define __Pyx_c_eqf(a, b) ((a)==(b)) + #define __Pyx_c_sumf(a, b) ((a)+(b)) + #define __Pyx_c_difff(a, b) ((a)-(b)) + #define __Pyx_c_prodf(a, b) ((a)*(b)) + #define __Pyx_c_quotf(a, b) ((a)/(b)) + #define __Pyx_c_negf(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zerof(z) ((z)==(float)0) + #define __Pyx_c_conjf(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_absf(z) (::std::abs(z)) + #define __Pyx_c_powf(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zerof(z) ((z)==0) + #define __Pyx_c_conjf(z) (conjf(z)) + #if 1 + #define __Pyx_c_absf(z) (cabsf(z)) + #define __Pyx_c_powf(a, b) (cpowf(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); + #if 1 + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex); + #endif +#endif + +static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); + +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq(a, b) ((a)==(b)) + #define __Pyx_c_sum(a, b) ((a)+(b)) + #define __Pyx_c_diff(a, b) ((a)-(b)) + #define __Pyx_c_prod(a, b) ((a)*(b)) + #define __Pyx_c_quot(a, b) ((a)/(b)) + #define __Pyx_c_neg(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero(z) ((z)==(double)0) + #define __Pyx_c_conj(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs(z) (::std::abs(z)) + #define __Pyx_c_pow(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero(z) ((z)==0) + #define __Pyx_c_conj(z) (conj(z)) + #if 1 + #define __Pyx_c_abs(z) (cabs(z)) + #define __Pyx_c_pow(a, b) (cpow(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex); + #if 1 + static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex, __pyx_t_double_complex); + #endif +#endif + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +static int __Pyx_check_binary_version(void); + +#if !defined(__Pyx_PyIdentifier_FromString) +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) +#else + #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) +#endif +#endif + +static PyObject *__Pyx_ImportModule(const char *name); + +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + + +/* Module declarations from 'cython' */ + +/* Module declarations from 'cpython.buffer' */ + +/* Module declarations from 'cpython.ref' */ + +/* Module declarations from 'libc.string' */ + +/* Module declarations from 'libc.stdio' */ + +/* Module declarations from 'cpython.object' */ + +/* Module declarations from '__builtin__' */ + +/* Module declarations from 'cpython.type' */ +static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; + +/* Module declarations from 'libc.stdlib' */ + +/* Module declarations from 'numpy' */ + +/* Module declarations from 'numpy' */ +static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; +static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; +static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; +static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; +static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ + +/* Module declarations from 'pystruct.inference._viterbi' */ +static __pyx_t_5numpy_float64_t __pyx_v_8pystruct_9inference_8_viterbi_NEGINF; +static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float64_t = { "float64_t", NULL, sizeof(__pyx_t_5numpy_float64_t), { 0 }, 0, 'R', 0, 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_nn_npy_intp = { "npy_intp", NULL, sizeof(npy_intp), { 0 }, 0, IS_UNSIGNED(npy_intp) ? 'U' : 'I', IS_UNSIGNED(npy_intp), 0 }; +#define __Pyx_MODULE_NAME "pystruct.inference._viterbi" +int __pyx_module_is_main_pystruct__inference___viterbi = 0; + +/* Implementation of 'pystruct.inference._viterbi' */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_RuntimeError; +static PyObject *__pyx_pf_8pystruct_9inference_8_viterbi_viterbi(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_score, PyArrayObject *__pyx_v_trans_score); /* proto */ +static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */ +static char __pyx_k_B[] = "B"; +static char __pyx_k_H[] = "H"; +static char __pyx_k_I[] = "I"; +static char __pyx_k_L[] = "L"; +static char __pyx_k_O[] = "O"; +static char __pyx_k_Q[] = "Q"; +static char __pyx_k_b[] = "b"; +static char __pyx_k_d[] = "d"; +static char __pyx_k_f[] = "f"; +static char __pyx_k_g[] = "g"; +static char __pyx_k_h[] = "h"; +static char __pyx_k_i[] = "i"; +static char __pyx_k_j[] = "j"; +static char __pyx_k_k[] = "k"; +static char __pyx_k_l[] = "l"; +static char __pyx_k_q[] = "q"; +static char __pyx_k_Zd[] = "Zd"; +static char __pyx_k_Zf[] = "Zf"; +static char __pyx_k_Zg[] = "Zg"; +static char __pyx_k_np[] = "np"; +static char __pyx_k_inf[] = "inf"; +static char __pyx_k_intp[] = "intp"; +static char __pyx_k_main[] = "__main__"; +static char __pyx_k_path[] = "path"; +static char __pyx_k_test[] = "__test__"; +static char __pyx_k_backp[] = "backp"; +static char __pyx_k_dtype[] = "dtype"; +static char __pyx_k_empty[] = "empty"; +static char __pyx_k_numpy[] = "numpy"; +static char __pyx_k_range[] = "range"; +static char __pyx_k_score[] = "score"; +static char __pyx_k_argmax[] = "argmax"; +static char __pyx_k_import[] = "__import__"; +static char __pyx_k_maxind[] = "maxind"; +static char __pyx_k_maxval[] = "maxval"; 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if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":976 + * arr.base = baseptr + * + * cdef inline object get_array_base(ndarray arr): # <<<<<<<<<<<<<< + * if arr.base is NULL: + * return None + */ + + /*--- Wrapped vars code ---*/ + + goto __pyx_L0; + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_1); + __Pyx_XDECREF(__pyx_t_2); + if (__pyx_m) { + if (__pyx_d) { + __Pyx_AddTraceback("init pystruct.inference._viterbi", __pyx_clineno, __pyx_lineno, __pyx_filename); + } + Py_DECREF(__pyx_m); __pyx_m = 0; + } else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_ImportError, "init pystruct.inference._viterbi"); + } + __pyx_L0:; 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"" : "s", num_found); +} + +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); +} +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (none_allowed && obj == Py_None) return 1; + else if (exact) { + if (likely(Py_TYPE(obj) == type)) return 1; + #if PY_MAJOR_VERSION == 2 + else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(PyObject_TypeCheck(obj, type))) return 1; + } + __Pyx_RaiseArgumentTypeInvalid(name, obj, type); + return 0; +} + +static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { + unsigned int n = 1; + return *(unsigned char*)(&n) != 0; +} +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t < '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparseable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static CYTHON_INLINE PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number; + int ndim = ctx->head->field->type->ndim; +; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if (ctx->enc_type == *ts && got_Z == ctx->is_complex && + ctx->enc_packmode == ctx->new_packmode) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} +static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { + buf->buf = NULL; + buf->obj = NULL; + buf->strides = __Pyx_zeros; + buf->shape = __Pyx_zeros; + buf->suboffsets = __Pyx_minusones; +} +static CYTHON_INLINE int __Pyx_GetBufferAndValidate( + Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, + int nd, int cast, __Pyx_BufFmt_StackElem* stack) +{ + if (obj == Py_None || obj == NULL) { + __Pyx_ZeroBuffer(buf); + return 0; + } + buf->buf = NULL; + if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; + if (buf->ndim != nd) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + nd, buf->ndim); + goto fail; + } + if (!cast) { + __Pyx_BufFmt_Context ctx; + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; + } + if ((unsigned)buf->itemsize != dtype->size) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", + buf->itemsize, (buf->itemsize > 1) ? "s" : "", + dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; + return 0; +fail:; + __Pyx_ZeroBuffer(buf); + return -1; +} +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { + if (info->buf == NULL) return; + if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; + __Pyx_ReleaseBuffer(info); +} + +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { + PyObject *result; +#if CYTHON_COMPILING_IN_CPYTHON + result = PyDict_GetItem(__pyx_d, name); + if (likely(result)) { + Py_INCREF(result); + } else { +#else + result = PyObject_GetItem(__pyx_d, name); + if (!result) { + PyErr_Clear(); +#endif + result = __Pyx_GetBuiltinName(name); + } + return result; +} + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = func->ob_type->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(PyObject_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +static void __Pyx_RaiseBufferFallbackError(void) { + PyErr_SetString(PyExc_ValueError, + "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); +} + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = PyCFunction_GET_FUNCTION(func); + self = PyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +#if CYTHON_COMPILING_IN_CPYTHON +static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_New(1); + if (unlikely(!args)) return NULL; + Py_INCREF(arg); + PyTuple_SET_ITEM(args, 0, arg); + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { +#ifdef __Pyx_CyFunction_USED + if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { +#else + if (likely(PyCFunction_Check(func))) { +#endif + if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { + return __Pyx_PyObject_CallMethO(func, arg); + } + } + return __Pyx__PyObject_CallOneArg(func, arg); +} +#else +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject* args = PyTuple_Pack(1, arg); + return (likely(args)) ? __Pyx_PyObject_Call(func, args, NULL) : NULL; +} +#endif + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { +#ifdef __Pyx_CyFunction_USED + if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { +#else + if (likely(PyCFunction_Check(func))) { +#endif + if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { + return __Pyx_PyObject_CallMethO(func, NULL); + } + } + return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL); +} +#endif + +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { +#if CYTHON_COMPILING_IN_CPYTHON + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyThreadState *tstate = PyThreadState_GET(); + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_Restore(type, value, tb); +#endif +} +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { +#if CYTHON_COMPILING_IN_CPYTHON + PyThreadState *tstate = PyThreadState_GET(); + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(type, value, tb); +#endif +} + +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + if (PyObject_IsSubclass(instance_class, type)) { + type = instance_class; + } else { + instance_class = NULL; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } +#if PY_VERSION_HEX >= 0x03030000 + if (cause) { +#else + if (cause && cause != Py_None) { +#endif + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(tmp_type, tmp_value, tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = PyThreadState_GET(); + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = (start + end) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + py_code = __pyx_find_code_object(c_line ? c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? c_line : py_line, py_code); + } + py_frame = PyFrame_New( + PyThreadState_GET(), /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + py_frame->f_lineno = py_line; + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); + PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } + Py_DECREF(obj); + view->obj = NULL; +} +#endif + + + static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.')) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(1); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + #endif + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_VERSION_HEX < 0x03030000 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value) { + const Py_intptr_t neg_one = (Py_intptr_t) -1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(Py_intptr_t) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(Py_intptr_t) <= sizeof(unsigned long long)) { + return PyLong_FromUnsignedLongLong((unsigned long long) value); + } + } else { + if (sizeof(Py_intptr_t) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(Py_intptr_t) <= sizeof(long long)) { + return PyLong_FromLongLong((long long) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(Py_intptr_t), + little, !is_unsigned); + } +} + +#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value) \ + { \ + func_type value = func_value; \ + if (sizeof(target_type) < sizeof(func_type)) { \ + if (unlikely(value != (func_type) (target_type) value)) { \ + func_type zero = 0; \ + if (is_unsigned && unlikely(value < zero)) \ + goto raise_neg_overflow; \ + else \ + goto raise_overflow; \ + } \ + } \ + return (target_type) value; \ + } + +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #endif +#endif + +static CYTHON_INLINE Py_intptr_t __Pyx_PyInt_As_Py_intptr_t(PyObject *x) { + const Py_intptr_t neg_one = (Py_intptr_t) -1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(Py_intptr_t) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(Py_intptr_t, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (Py_intptr_t) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: __PYX_VERIFY_RETURN_INT(Py_intptr_t, digit, ((PyLongObject*)x)->ob_digit[0]); + } + #endif +#endif + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } + if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, PyLong_AsUnsignedLong(x)) + } else if (sizeof(Py_intptr_t) <= sizeof(unsigned long long)) { + __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long long, PyLong_AsUnsignedLongLong(x)) + } + } else { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: __PYX_VERIFY_RETURN_INT(Py_intptr_t, digit, +(((PyLongObject*)x)->ob_digit[0])); + case -1: __PYX_VERIFY_RETURN_INT(Py_intptr_t, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]); + } + #endif +#endif + if (sizeof(Py_intptr_t) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT(Py_intptr_t, long, PyLong_AsLong(x)) + } else if (sizeof(Py_intptr_t) <= sizeof(long long)) { + __PYX_VERIFY_RETURN_INT(Py_intptr_t, long long, PyLong_AsLongLong(x)) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + Py_intptr_t val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (Py_intptr_t) -1; + } + } else { + Py_intptr_t val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (Py_intptr_t) -1; + val = __Pyx_PyInt_As_Py_intptr_t(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to Py_intptr_t"); + return (Py_intptr_t) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to Py_intptr_t"); + return (Py_intptr_t) -1; +} + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) -1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(long) <= sizeof(unsigned long long)) { + return PyLong_FromUnsignedLongLong((unsigned long long) value); + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(long long)) { + return PyLong_FromLongLong((long long) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrtf(z.real*z.real + z.imag*z.imag); + #else + return hypotf(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + float denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(a, a); + case 3: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(z, a); + case 4: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } + r = a.real; + theta = 0; + } else { + r = __Pyx_c_absf(a); + theta = atan2f(a.imag, a.real); + } + lnr = logf(r); + z_r = expf(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cosf(z_theta); + z.imag = z_r * sinf(z_theta); + return z; + } + #endif +#endif + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrt(z.real*z.real + z.imag*z.imag); + #else + return hypot(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + double denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(a, a); + case 3: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(z, a); + case 4: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } + r = a.real; + theta = 0; + } else { + r = __Pyx_c_abs(a); + theta = atan2(a.imag, a.real); + } + lnr = log(r); + z_r = exp(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cos(z_theta); + z.imag = z_r * sin(z_theta); + return z; + } + #endif +#endif + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) -1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(int) <= sizeof(unsigned long long)) { + return PyLong_FromUnsignedLongLong((unsigned long long) value); + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(long long)) { + return PyLong_FromLongLong((long long) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) -1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, ((PyLongObject*)x)->ob_digit[0]); + } + #endif +#endif + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong(x)) + } else if (sizeof(int) <= sizeof(unsigned long long)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong(x)) + } + } else { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +(((PyLongObject*)x)->ob_digit[0])); + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]); + } + #endif +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong(x)) + } else if (sizeof(int) <= sizeof(long long)) { + __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong(x)) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) -1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, ((PyLongObject*)x)->ob_digit[0]); + } + #endif +#endif + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong(x)) + } else if (sizeof(long) <= sizeof(unsigned long long)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong(x)) + } + } else { +#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + switch (Py_SIZE(x)) { + case 0: return 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +(((PyLongObject*)x)->ob_digit[0])); + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, -(sdigit) ((PyLongObject*)x)->ob_digit[0]); + } + #endif +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong(x)) + } else if (sizeof(long) <= sizeof(long long)) { + __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong(x)) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +#ifndef __PYX_HAVE_RT_ImportModule +#define __PYX_HAVE_RT_ImportModule +static PyObject *__Pyx_ImportModule(const char *name) { + PyObject *py_name = 0; + PyObject *py_module = 0; + py_name = __Pyx_PyIdentifier_FromString(name); + if (!py_name) + goto bad; + py_module = PyImport_Import(py_name); + Py_DECREF(py_name); + return py_module; +bad: + Py_XDECREF(py_name); + return 0; +} +#endif + +#ifndef __PYX_HAVE_RT_ImportType +#define __PYX_HAVE_RT_ImportType +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, + size_t size, int strict) +{ + PyObject *py_module = 0; + PyObject *result = 0; + PyObject *py_name = 0; + char warning[200]; + Py_ssize_t basicsize; +#ifdef Py_LIMITED_API + PyObject *py_basicsize; +#endif + py_module = __Pyx_ImportModule(module_name); + if (!py_module) + goto bad; + py_name = __Pyx_PyIdentifier_FromString(class_name); + if (!py_name) + goto bad; + result = PyObject_GetAttr(py_module, py_name); + Py_DECREF(py_name); + py_name = 0; + Py_DECREF(py_module); + py_module = 0; + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%.200s.%.200s is not a type object", + module_name, class_name); + goto bad; + } +#ifndef Py_LIMITED_API + basicsize = ((PyTypeObject *)result)->tp_basicsize; +#else + py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); + if (!py_basicsize) + goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; +#endif + if (!strict && (size_t)basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility", + module_name, class_name); + if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; + } + else if ((size_t)basicsize != size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s has the wrong size, try recompiling", + module_name, class_name); + goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(py_module); + Py_XDECREF(result); + return NULL; +} +#endif + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { +#if PY_VERSION_HEX < 0x03030000 + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +#else + if (__Pyx_PyUnicode_READY(o) == -1) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (PyUnicode_IS_ASCII(o)) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +#endif + } else +#endif +#if !CYTHON_COMPILING_IN_PYPY + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { + PyNumberMethods *m; + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (PyInt_Check(x) || PyLong_Check(x)) +#else + if (PyLong_Check(x)) +#endif + return Py_INCREF(x), x; + m = Py_TYPE(x)->tp_as_number; +#if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = PyNumber_Long(x); + } +#else + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Long(x); + } +#endif + if (res) { +#if PY_MAJOR_VERSION < 3 + if (!PyInt_Check(res) && !PyLong_Check(res)) { +#else + if (!PyLong_Check(res)) { +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + name, name, Py_TYPE(res)->tp_name); + Py_DECREF(res); + return NULL; + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) + return PyInt_AS_LONG(b); +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 + #if CYTHON_USE_PYLONG_INTERNALS + switch (Py_SIZE(b)) { + case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0]; + case 0: return 0; + case 1: return ((PyLongObject*)b)->ob_digit[0]; + } + #endif + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/pystruct/inference/_viterbi.pyx b/pystruct/inference/_viterbi.pyx new file mode 100644 index 00000000..47863650 --- /dev/null +++ b/pystruct/inference/_viterbi.pyx @@ -0,0 +1,64 @@ +# Copyright Lars Buitinck 2013. + +"""Fast inferce for chains using dynamic programming.""" + +cimport cython +cimport numpy as np +import numpy as np + +np.import_array() + +cdef np.float64_t NEGINF = -np.inf + + +@cython.boundscheck(False) +@cython.wraparound(False) +def viterbi(np.ndarray[ndim=2, dtype=np.float64_t] score, + np.ndarray[ndim=3, dtype=np.float64_t] trans_score): + """First-order Viterbi algorithm. + + Parameters + ---------- + score : array, shape = (n_samples, n_states) + Scores per sample/class combination; in a linear model, X * w.T. + May be overwritten. + trans_score : array, shape = (n_samples, n_states, n_states), optional + Scores per sample/transition combination. + + References + ---------- + L. R. Rabiner (1989). A tutorial on hidden Markov models and selected + applications in speech recognition. Proc. IEEE 77(2):257-286. + """ + + cdef np.ndarray[ndim=2, dtype=np.npy_intp, mode='c'] backp + cdef np.ndarray[ndim=1, dtype=np.npy_intp, mode='c'] path + cdef np.float64_t candidate, maxval + cdef np.npy_intp i, j, k, n_samples, n_states + + n_samples, n_states = score.shape[0], score.shape[1] + + backp = np.empty((n_samples, n_states), dtype=np.intp) + + # Forward recursion. score is reused as the DP table. + for i in range(1, n_samples): + for k in range(n_states): + maxind = 0 + maxval = NEGINF + for j in range(n_states): + candidate = score[i - 1, j] + score[i, k] + trans_score[i - 1, j, k] + if candidate > maxval: + maxind = j + maxval = candidate + + score[i, k] = maxval + backp[i, k] = maxind + + # Path backtracking + path = np.empty(n_samples, dtype=np.intp) + path[n_samples - 1] = score[n_samples - 1, :].argmax() + + for i in range(n_samples - 2, -1, -1): + path[i] = backp[i + 1, path[i + 1]] + + return path diff --git a/pystruct/inference/common.py b/pystruct/inference/common.py new file mode 100644 index 00000000..a43c2a43 --- /dev/null +++ b/pystruct/inference/common.py @@ -0,0 +1,51 @@ +import numpy as np + + +def _validate_params(unary_potentials, pairwise_params, edges): + n_states = unary_potentials.shape[-1] + if pairwise_params.shape == (n_states, n_states): + # only one matrix given + pairwise_potentials = np.repeat(pairwise_params[np.newaxis, :, :], + edges.shape[0], axis=0) + else: + if pairwise_params.shape != (edges.shape[0], n_states, n_states): + raise ValueError("Expected pairwise_params either to " + "be of shape n_states x n_states " + "or n_edges x n_states x n_states, but" + " got shape %s" % repr(pairwise_params.shape)) + pairwise_potentials = pairwise_params + return n_states, pairwise_potentials + + +def compute_energy(unary_potentials, pairwise_potentials, edges, labels): + """Compute energy of labels for given energy function. + + Convenience function with same interface as inference functions to easily + compare solutions. + + Parameters + ---------- + unary_potentials : nd-array + Unary potentials of energy function. + + pairwise_potentials : nd-array + Pairwise potentials of energy function. + + edges : nd-array + Edges of energy function. + + labels : nd-array + Variable assignment to evaluate. + + Returns + ------- + energy : float + Energy of assignment. + """ + + n_states, pairwise_potentials = \ + _validate_params(unary_potentials, pairwise_potentials, edges) + energy = np.sum(unary_potentials[np.arange(len(labels)), labels]) + for edge, pw in zip(edges, pairwise_potentials): + energy += pw[labels[edge[0]], labels[edge[1]]] + return energy diff --git a/pystruct/inference/inference_methods.py b/pystruct/inference/inference_methods.py index c1763c03..1b85793c 100644 --- a/pystruct/inference/inference_methods.py +++ b/pystruct/inference/inference_methods.py @@ -1,11 +1,12 @@ import numpy as np from .linear_programming import lp_general_graph - +from .maxprod import inference_max_product +from .common import _validate_params def get_installed(method_filter=None): if method_filter is None: - method_filter = ['ad3', 'qpbo', 'dai', 'ogm', 'lp'] + method_filter = ["max-product", 'ad3', 'ad3+', 'qpbo', 'ogm', 'lp'] installed = [] unary = np.zeros((1, 1)) @@ -13,73 +14,63 @@ def get_installed(method_filter=None): edges = np.empty((0, 2), dtype=np.int) for method in method_filter: try: - inference_dispatch(unary, pw, edges, inference_method=method) + if method != 'ad3+': + inference_dispatch(unary, pw, edges, inference_method=method) + else: + inference_dispatch(unary, np.zeros((0,1,1)) + , np.zeros((0,2), dtype=np.int) + , inference_method=method) installed.append(method) except ImportError: pass return installed - -def compute_energy(unary_potentials, pairwise_potentials, edges, labels): - """Compute energy of labels for given energy function. - - Convenience function with same interface as inference functions to easily - compare solutions. - - Parameters - ---------- - unary_potentials : nd-array - Unary potentials of energy function. - - pairwise_potentials : nd-array - Pairwise potentials of energy function. - - edges : nd-array - Edges of energy function. - - labels : nd-array - Variable assignment to evaluate. - - Returns - ------- - energy : float - Energy of assignment. +class InferenceException(Exception): """ - - n_states, pairwise_potentials = \ - _validate_params(unary_potentials, pairwise_potentials, edges) - energy = np.sum(unary_potentials[np.arange(len(labels)), labels]) - for edge, pw in zip(edges, pairwise_potentials): - energy += pw[labels[edge[0]], labels[edge[1]]] - return energy - + When inference status is fractional or unsolved, this exception can be + raised. + (If relaxed is not True and if an inference exception is requested by the + calling code) + The exception message is the solver status. + """ + pass def inference_dispatch(unary_potentials, pairwise_potentials, edges, inference_method, return_energy=False, **kwargs): - """Wrapper function to dispatch between inference method by string. + """ + Computes the maximizing assignment of a pairwise discrete energy function. + + Wrapper function to dispatch between inference method by string. Parameters ---------- - unary_potentials : nd-array + unary_potentials : nd-array, shape (n_nodes, n_states) Unary potentials of energy function. - pairwise_potentials : nd-array + pairwise_potentials : nd-array, shape (n_states, n_states) + or (n_states, n_states, n_edges). Pairwise potentials of energy function. + If the first case, edge potentials are assumed to be the same for all + edges. + In the second case, the sequence needs to correspond to the edges. - edges : nd-array - Edges of energy function. + edges : nd-array, shape (n_edges, 2) + Graph edges for pairwise potentials, given as pair of node indices. As + pairwise potentials are not assumed to be symmetric, the direction of + the edge matters. inference_method : string Possible choices currently are: * 'qpbo' for QPBO alpha-expansion (fast but approximate). - * 'dai' for libDAI wrappers (default to junction tree). - * 'lp' for build-in lp relaxation via GLPK (slow). + * 'lp' for build-in lp relaxation via cvxopt (slow). * 'ad3' for AD^3 subgradient based dual solution of LP. * 'ogm' for OpenGM wrappers. + * 'max-product' for max-product message passing. * 'unary' for using unary potentials only. It is also possible to pass a tuple (string, dict) where the dict - contains additional keyword arguments. + contains additional keyword arguments, like + ``('ad3', {'branch_and_bound': True})``. relaxed : bool (default=False) Whether to return a relaxed solution (when appropriate) @@ -103,62 +94,55 @@ def inference_dispatch(unary_potentials, pairwise_potentials, edges, inference_method = inference_method[0] # append additional_kwargs, but take care not to modify the dicts we # got - kwargs = dict(kwargs.items() + additional_kwargs.items()) + kwargs = kwargs.copy() + kwargs.update(additional_kwargs) if inference_method == "qpbo": return inference_qpbo(unary_potentials, pairwise_potentials, edges, **kwargs) - elif inference_method == "dai": - return inference_dai(unary_potentials, pairwise_potentials, edges, - return_energy=return_energy, **kwargs) elif inference_method == "lp": return inference_lp(unary_potentials, pairwise_potentials, edges, return_energy=return_energy, **kwargs) elif inference_method == "ad3": return inference_ad3(unary_potentials, pairwise_potentials, edges, return_energy=return_energy, **kwargs) + elif inference_method == "ad3+": + return inference_ad3plus(unary_potentials, pairwise_potentials, edges, + return_energy=return_energy, **kwargs) elif inference_method == "ogm": return inference_ogm(unary_potentials, pairwise_potentials, edges, return_energy=return_energy, **kwargs) elif inference_method == "unary": return inference_unaries(unary_potentials, pairwise_potentials, edges, **kwargs) + elif inference_method == "max-product": + return inference_max_product(unary_potentials, pairwise_potentials, + edges, **kwargs) else: - raise ValueError("inference_method must be 'lp', 'ad3', 'qpbo', 'ogm'" - " or 'dai', got %s" % inference_method) - - -def _validate_params(unary_potentials, pairwise_params, edges): - n_states = unary_potentials.shape[-1] - if pairwise_params.shape == (n_states, n_states): - # only one matrix given - pairwise_potentials = np.repeat(pairwise_params[np.newaxis, :, :], - edges.shape[0], axis=0) - else: - if pairwise_params.shape != (edges.shape[0], n_states, n_states): - raise ValueError("Expected pairwise_params either to " - "be of shape n_states x n_states " - "or n_edges x n_states x n_states, but" - " got shape %s. n_states=%d, n_edge=%d." - % (repr(pairwise_params.shape), n_states, - edges.shape[0])) - pairwise_potentials = pairwise_params - return n_states, pairwise_potentials + raise ValueError("inference_method must be 'max-product', 'lp', 'ad3'," + " 'qpbo' or 'ogm', got %s" % inference_method) def inference_ogm(unary_potentials, pairwise_potentials, edges, - return_energy=False, alg='dd', init=None, **kwargs): + return_energy=False, alg='dd', init=None, + reserveNumFactorsPerVariable=2, **kwargs): """Inference with OpenGM backend. Parameters ---------- - unary_potentials : nd-array + unary_potentials : nd-array, shape (n_nodes, n_states) Unary potentials of energy function. - pairwise_potentials : nd-array + pairwise_potentials : nd-array, shape (n_states, n_states) + or (n_states, n_states, n_edges). Pairwise potentials of energy function. + If the first case, edge potentials are assumed to be the same for all + edges. + In the second case, the sequence needs to correspond to the edges. - edges : nd-array - Edges of energy function. + edges : nd-array, shape (n_edges, 2) + Graph edges for pairwise potentials, given as pair of node indices. As + pairwise potentials are not assumed to be symmetric, the direction of + the edge matters. alg : string Possible choices currently are: @@ -177,6 +161,12 @@ def inference_ogm(unary_potentials, pairwise_potentials, edges, init : nd-array Initial solution for starting inference (ignored by some algorithms). + reserveNumFactorsPerVariable : + reserve a certain number of factors for each variable can speed up + the building of a graphical model. + ( For a 2d grid with second order factors one should set this to 5 + 4 2-factors and 1 unary factor for most pixels ) + Returns ------- labels : nd-array @@ -187,12 +177,30 @@ def inference_ogm(unary_potentials, pairwise_potentials, edges, n_states, pairwise_potentials = \ _validate_params(unary_potentials, pairwise_potentials, edges) n_nodes = len(unary_potentials) - gm = opengm.gm([n_states] * n_nodes) - for i, un in enumerate(unary_potentials): - gm.addFactor(gm.addFunction(-un.astype(np.float32)), i) - for pw, edge in zip(pairwise_potentials, edges): - gm.addFactor(gm.addFunction(-pw.astype(np.float32)), - edge.astype(np.uint64)) + + gm = opengm.gm(np.ones(n_nodes, dtype=opengm.label_type) * n_states) + + nFactors = int(n_nodes + edges.shape[0]) + gm.reserveFactors(nFactors) + gm.reserveFunctions(nFactors, 'explicit') + + # all unaries as one numpy array + # (opengm's value_type == float64 but all types are accepted) + unaries = np.require(unary_potentials, dtype=opengm.value_type) * -1.0 + # add all unart functions at once + fidUnaries = gm.addFunctions(unaries) + visUnaries = np.arange(n_nodes, dtype=opengm.label_type) + # add all unary factors at once + gm.addFactors(fidUnaries, visUnaries) + + # add all pariwise functions at once + # - first axis of secondOrderFunctions iterates over the function) + + secondOrderFunctions = -np.require(pairwise_potentials, + dtype=opengm.value_type) + fidSecondOrder = gm.addFunctions(secondOrderFunctions) + gm.addFactors(fidSecondOrder, edges.astype(np.uint64)) + if alg == 'bp': inference = opengm.inference.BeliefPropagation(gm) elif alg == 'dd': @@ -238,14 +246,20 @@ def inference_qpbo(unary_potentials, pairwise_potentials, edges, **kwargs): Parameters ---------- - unary_potentials : nd-array + unary_potentials : nd-array, shape (n_nodes, n_states) Unary potentials of energy function. - pairwise_potentials : nd-array + pairwise_potentials : nd-array, shape (n_states, n_states) + or (n_states, n_states, n_edges). Pairwise potentials of energy function. + If the first case, edge potentials are assumed to be the same for all + edges. + In the second case, the sequence needs to correspond to the edges. - edges : nd-array - Edges of energy function. + edges : nd-array, shape (n_edges, 2) + Graph edges for pairwise potentials, given as pair of node indices. As + pairwise potentials are not assumed to be symmetric, the direction of + the edge matters. Returns ------- @@ -267,114 +281,180 @@ def inference_qpbo(unary_potentials, pairwise_potentials, edges, **kwargs): return y.reshape(shape_org) -def inference_dai(unary_potentials, pairwise_potentials, edges, - return_energy=False, alg='jt', **kwargs): - """Inference with LibDAI backend. +def inference_lp(unary_potentials, pairwise_potentials, edges, relaxed=False, + return_energy=False, **kwargs): + """Inference with build-in LP solver using cvxopt backend. Parameters ---------- - unary_potentials : nd-array + unary_potentials : nd-array, shape (n_nodes, n_states) Unary potentials of energy function. - pairwise_potentials : nd-array + pairwise_potentials : nd-array, shape (n_states, n_states) + or (n_states, n_states, n_edges). Pairwise potentials of energy function. + If the first case, edge potentials are assumed to be the same for all + edges. + In the second case, the sequence needs to correspond to the edges. - edges : nd-array - Edges of energy function. + edges : nd-array, shape (n_edges, 2) + Graph edges for pairwise potentials, given as pair of node indices. As + pairwise potentials are not assumed to be symmetric, the direction of + the edge matters. - alg : string, (default='jt') - Inference algorithm to use. - Defaults to Junction Tree. THIS WILL BLOW UP for loopy graphs. + relaxed : bool (default=False) + Whether to return the relaxed solution (``True``) or round to the next + integer solution (``False``). + + return_energy : bool (default=False) + Additionally return the energy of the returned solution (according to + the solver). If relaxed=False, this is the energy of the relaxed, not + the rounded solution. Returns ------- labels : nd-array Approximate (usually) MAP variable assignment. + If relaxed=False, this is a tuple of unary and edge 'marginals'. """ - from daimrf import mrf shape_org = unary_potentials.shape[:-1] n_states, pairwise_potentials = \ _validate_params(unary_potentials, pairwise_potentials, edges) - n_states = unary_potentials.shape[-1] - log_unaries = unary_potentials.reshape(-1, n_states) - max_entry = max(np.max(log_unaries), 1) - unaries = np.exp(log_unaries / max_entry) - - y = mrf(unaries, edges.astype(np.int64), - np.exp(pairwise_potentials / max_entry), alg=alg) - y = y.reshape(shape_org) + unaries = unary_potentials.reshape(-1, n_states) + res = lp_general_graph(-unaries, edges, -pairwise_potentials) + unary_marginals, pairwise_marginals, energy = res + if relaxed: + unary_marginals = unary_marginals.reshape(unary_potentials.shape) + y = (unary_marginals, pairwise_marginals) + else: + y = np.argmax(unary_marginals, axis=-1) + y = y.reshape(shape_org) if return_energy: - return y, compute_energy(unary_potentials, pairwise_potentials, edges, - y) + return y, energy return y -def inference_lp(unary_potentials, pairwise_potentials, edges, relaxed=False, - return_energy=False, **kwargs): - """Inference with build-in LP solver using GLPK backend. +def inference_ad3(unary_potentials, pairwise_potentials, edges, relaxed=False, + verbose=0, return_energy=False, branch_and_bound=False, + inference_exception=None): + """Inference with AD3 dual decomposition subgradient solver. Parameters ---------- - unary_potentials : nd-array + unary_potentials : nd-array, shape (n_nodes, n_states) Unary potentials of energy function. - pairwise_potentials : nd-array + pairwise_potentials : nd-array, shape (n_states, n_states) + or (n_states, n_states, n_edges). Pairwise potentials of energy function. + If the first case, edge potentials are assumed to be the same for all + edges. + In the second case, the sequence needs to correspond to the edges. - edges : nd-array - Edges of energy function. + edges : nd-array, shape (n_edges, 2) + Graph edges for pairwise potentials, given as pair of node indices. As + pairwise potentials are not assumed to be symmetric, the direction of + the edge matters. relaxed : bool (default=False) Whether to return the relaxed solution (``True``) or round to the next integer solution (``False``). + verbose : int (default=0) + Degree of verbosity for solver. + return_energy : bool (default=False) Additionally return the energy of the returned solution (according to the solver). If relaxed=False, this is the energy of the relaxed, not the rounded solution. + branch_and_bound : bool (default=False) + Whether to attempt to produce an integral solution using + branch-and-bound. + Returns ------- labels : nd-array Approximate (usually) MAP variable assignment. If relaxed=False, this is a tuple of unary and edge 'marginals'. + + Code updated on Feb 2017 to deal with multiple node types, by JL Meunier + , for the EU READ project (grant agreement No 674943) + """ - shape_org = unary_potentials.shape[:-1] - n_states, pairwise_potentials = \ - _validate_params(unary_potentials, pairwise_potentials, edges) + import ad3 + bMultiType = isinstance(unary_potentials, list) + if bMultiType: + res = ad3.general_graph(unary_potentials, edges, pairwise_potentials + , verbose=verbose + , n_iterations=4000, exact=branch_and_bound) + else: + #usual code + n_states, pairwise_potentials = \ + _validate_params(unary_potentials, pairwise_potentials, edges) + unaries = unary_potentials.reshape(-1, n_states) + res = ad3.general_graph(unaries, edges, pairwise_potentials + , verbose=verbose, n_iterations=4000 + , exact=branch_and_bound) + + unary_marginals, pairwise_marginals, energy, solver_status = res + if verbose: + print(solver_status) - unaries = unary_potentials.reshape(-1, n_states) - res = lp_general_graph(-unaries, edges, -pairwise_potentials) - unary_marginals, pairwise_marginals, energy = res - #n_fractional = np.sum(unary_marginals.max(axis=-1) < .99) - #if n_fractional: - #print("fractional solutions found: %d" % n_fractional) - if relaxed: - unary_marginals = unary_marginals.reshape(unary_potentials.shape) - y = (unary_marginals, pairwise_marginals) + if solver_status in ["fractional", "unsolved"] and relaxed: + if bMultiType: + y = (unary_marginals, pairwise_marginals) #those two are lists + else: + #usual code + unary_marginals = unary_marginals.reshape(unary_potentials.shape) + y = (unary_marginals, pairwise_marginals) + #print solver_status, pairwise_marginals else: - y = np.argmax(unary_marginals, axis=-1) - y = y.reshape(shape_org) + if bMultiType: + #we now get a list of unary marginals + if inference_exception and solver_status in ["fractional" + , "unsolved"]: + raise InferenceException(solver_status) + ly = list() + _cum_n_states = 0 + for unary_marg in unary_marginals: + ly.append( _cum_n_states + np.argmax(unary_marg, axis=-1) ) + # number of states for that type + _cum_n_states += unary_marg.shape[1] + y = np.hstack(ly) + else: + #usual code + y = np.argmax(unary_marginals, axis=-1) + if return_energy: - return y, energy + return y, -energy return y -def inference_ad3(unary_potentials, pairwise_potentials, edges, relaxed=False, - verbose=0, return_energy=False, branch_and_bound=False): - """Inference with AD3 dual decomposition subgradient solver. +def inference_ad3plus(l_unary_potentials, l_pairwise_potentials, l_edges + , relaxed=False + , verbose=0, return_energy=False, branch_and_bound=False + , constraints=None, inference_exception=None): + """ + Inference with AD3 dual decomposition subgradient solver. Parameters ---------- - unary_potentials : nd-array + unary_potentials : nd-array, shape (n_nodes, n_states) Unary potentials of energy function. - pairwise_potentials : nd-array + pairwise_potentials : nd-array, shape (n_states, n_states) + or (n_states, n_states, n_edges). Pairwise potentials of energy function. + If the first case, edge potentials are assumed to be the same for all + edges. + In the second case, the sequence needs to correspond to the edges. - edges : nd-array - Edges of energy function. + edges : nd-array, shape (n_edges, 2) + Graph edges for pairwise potentials, given as pair of node indices. As + pairwise potentials are not assumed to be symmetric, the direction of + the edge matters. relaxed : bool (default=False) Whether to return the relaxed solution (``True``) or round to the next @@ -392,35 +472,76 @@ def inference_ad3(unary_potentials, pairwise_potentials, edges, relaxed=False, Whether to attempt to produce an integral solution using branch-and-bound. + constraints : list of logical constraints or None (default:=None) + A logical constraint is tuple like + ( , , , ) + where: + - operator is one of: + 'XOR' 'XOROUT' 'ATMOSTONE' 'OR' 'OROUT' 'ANDOUT' 'IMPLY' + - unaries is a list of the index of each unary involved in this + constraint + - states is a list of unary states (class), 1 per involved unary. If the + states are all the same, you can pass it directly as a scalar value. + - negated is a list of boolean indicating if the unary must be negated. + Again, if all values are the same, pass a single boolean value instead + of a list + + NOTE: this hard logic constraint mechanism has been developed for the + EU project READ, by JL Meunier (Xerox), in November 2016. + The READ project has received funding from the European Union's Horizon + 2020 research and innovation programme under grant agreement No 674943. + Returns ------- labels : nd-array Approximate (usually) MAP variable assignment. If relaxed=False, this is a tuple of unary and edge 'marginals'. + """ import ad3 - n_states, pairwise_potentials = \ - _validate_params(unary_potentials, pairwise_potentials, edges) - - unaries = unary_potentials.reshape(-1, n_states) - res = ad3.general_graph(unaries, edges, pairwise_potentials, verbose=1, - n_iterations=4000, exact=branch_and_bound) - unary_marginals, pairwise_marginals, energy, solver_status = res +# n_states, pairwise_potentials = \ +# _validate_params(unary_potentials, pairwise_potentials, edges) +# unaries = unary_potentials.reshape(-1, n_states) + bMultiType = isinstance(l_unary_potentials, list) + + res = ad3.general_constrained_graph(l_unary_potentials, l_edges + , l_pairwise_potentials, constraints + , verbose=verbose + , n_iterations=4000 + , exact=branch_and_bound) + + l_unary_marginals, l_pairwise_marginals, energy, solver_status = res if verbose: - print solver_status[0], + print(solver_status) - if solver_status in ["fractional", "unsolved"] and relaxed: - unary_marginals = unary_marginals.reshape(unary_potentials.shape) - y = (unary_marginals, pairwise_marginals) + if relaxed and solver_status in ["fractional", "unsolved"]: + y = (l_unary_marginals, l_pairwise_marginals) else: - y = np.argmax(unary_marginals, axis=-1) + if inference_exception and solver_status in ["fractional", "unsolved"]: + raise InferenceException(solver_status) + if bMultiType: + #we now get a list of unary marginals + ly = list() + _cum_n_states = 0 + for unary_marg in l_unary_marginals: + ly.append( _cum_n_states + np.argmax(unary_marg, axis=-1) ) + #number of states for that type + _cum_n_states += unary_marg.shape[1] + y = np.hstack(ly) + # when we will simplify y: + #y = [_cum_n_statesnp.argmax(unary_marg, axis=-1) for unary_marg + # in l_unary_marginals] + else: + y = np.argmax(l_unary_marginals, axis=-1) + if return_energy: return y, -energy return y -def inference_unaries(unary_potentials, pairwise_potentials, edges, verbose=0, - **kwargs): + +def inference_unaries(unary_potentials, pairwise_potentials, edges, verbose=0 + , **kwargs): """Inference that only uses unary potentials. This methods can be used as a sanity check, as acceleration if no @@ -428,14 +549,17 @@ def inference_unaries(unary_potentials, pairwise_potentials, edges, verbose=0, Parameters ---------- - unary_potentials : nd-array + unary_potentials : nd-array, shape (n_nodes, n_states) Unary potentials of energy function. - pairwise_potentials : nd-array + pairwise_potentials : nd-array, shape (n_states, n_states) + or (n_states, n_states, n_edges). Pairwise potentials of energy function. + These will be ignored. - edges : nd-array - Edges of energy function. + edges : nd-array, shape (n_edges, 2) + Graph edges for pairwise potentials, given as pair of node indices. + These will be ignored. verbose : int (default=0) Degree of verbosity for solver. diff --git a/pystruct/inference/linear_programming.py b/pystruct/inference/linear_programming.py index 4944ad64..abd24b1b 100644 --- a/pystruct/inference/linear_programming.py +++ b/pystruct/inference/linear_programming.py @@ -13,7 +13,7 @@ def lp_general_graph(unaries, edges, edge_weights): raise ValueError("Number of edge weights different from number of" "edges") - n_nodes, n_states = unaries.shape + n_nodes, n_states = map(int, unaries.shape) n_edges = len(edges) # variables: n_nodes * n_states for nodes, @@ -21,8 +21,8 @@ def lp_general_graph(unaries, edges, edge_weights): n_variables = n_nodes * n_states + n_edges * n_states ** 2 # constraints: one per node, - # and n_nodes * n_states for pairwise - n_constraints = n_nodes + 2 * n_edges * n_states + # and n_nodes * n_states for pairwise minus one redundant per edge + n_constraints = n_nodes + n_edges * (2 * n_states - 1) # offset to get to the edge variables in columns edges_offset = n_nodes * n_states @@ -30,24 +30,23 @@ def lp_general_graph(unaries, edges, edge_weights): data, I, J = [], [], [] # summation constraints - for i in xrange(n_nodes): - for j in xrange(n_states): + for i in range(n_nodes): + for j in range(n_states): data.append(1) I.append(i) J.append(i * n_states + j) #constraints[i, i * n_states + j] = 1 - + # we row_idx tracks constraints = rows in constraint matrix + row_idx = n_nodes # edge marginalization constraint - for i in xrange(2 * n_edges * n_states): - row_idx = i + n_nodes - #print("i: %d" % i) + for i in range(2 * n_edges * n_states): edge = i // (2 * n_states) - #print("edge: %d" % edge) state = (i % n_states) - #print("state: %d" % state) vertex_in_edge = i % (2 * n_states) // n_states vertex = edges[edge][vertex_in_edge] - #print("vertex: %d" % vertex) + if vertex_in_edge == 1 and state == n_states - 1: + # the last summation constraint is redundant. + continue # for one vertex iterate over all states of the other vertex #[row_idx, int(vertex) * n_states + state] = -1 data.append(-1) @@ -56,18 +55,19 @@ def lp_general_graph(unaries, edges, edge_weights): edge_var_index = edges_offset + edge * n_states ** 2 if vertex_in_edge == 0: # first vertex in edge - for j in xrange(n_states): + for j in range(n_states): data.append(1) I.append(row_idx) J.append(edge_var_index + state * n_states + j) #[row_idx, edge_var_index + state * n_states + j] = 1 else: # second vertex in edge - for j in xrange(n_states): + for j in range(n_states): data.append(1) I.append(row_idx) J.append(edge_var_index + j * n_states + state) #[row_idx, edge_var_index + j * n_states + state] = 1 + row_idx += 1 coef = np.ravel(unaries) # pairwise: @@ -80,14 +80,16 @@ def lp_general_graph(unaries, edges, edge_weights): h = cvxopt.matrix(np.zeros(n_variables)) # for positivity inequalities # unary and pairwise summation constratints A = cvxopt.spmatrix(data, I, J) - b_ = np.zeros(n_constraints) # zeros for pairwise summation constraints - b_[:n_nodes] = 1 # ones for unary cummation constraints + assert(n_constraints == A.size[0]) + b_ = np.zeros(A.size[0]) # zeros for pairwise summation constraints + b_[:n_nodes] = 1 # ones for unary summation constraints b = cvxopt.matrix(b_) - # silence glpk - cvxopt.solvers.options['LPX_K_MSGLEV'] = False - - result = cvxopt.solvers.lp(c, G, h, A, b, solver='glpk') + # don't be verbose. + show_progress_backup = cvxopt.solvers.options.get('show_progress', False) + cvxopt.solvers.options['show_progress'] = False + result = cvxopt.solvers.lp(c, G, h, A, b) + cvxopt.solvers.options['show_progress'] = show_progress_backup x = np.array(result['x']) unary_variables = x[:n_nodes * n_states].reshape(n_nodes, n_states) diff --git a/pystruct/inference/maxprod.py b/pystruct/inference/maxprod.py new file mode 100644 index 00000000..b08900e5 --- /dev/null +++ b/pystruct/inference/maxprod.py @@ -0,0 +1,164 @@ +import numpy as np +from scipy import sparse + +from .common import _validate_params +from ..utils.graph_functions import is_forest + + +def edges_to_graph(edges, n_vertices=None): + if n_vertices is None: + n_vertices = np.max(edges) + 1 + graph = sparse.coo_matrix((np.ones(len(edges)), edges.T), + shape=(n_vertices, n_vertices)).tocsr() + return graph + + +def is_chain(edges, n_vertices): + """Check if edges specify a chain and are in order.""" + return (np.all(edges[:, 0] == np.arange(0, n_vertices - 1)) + and np.all(edges[:, 1] == np.arange(1, n_vertices))) + + +def inference_max_product(unary_potentials, pairwise_potentials, edges, + max_iter=30, damping=0.5, tol=1e-5, relaxed=None): + """Max-product inference. + + In case the edges specify a tree, dynamic programming is used + producing a result in only a single pass. + + Parameters + ---------- + unary_potentials : nd-array + Unary potentials of energy function. + + pairwise_potentials : nd-array + Pairwise potentials of energy function. + + edges : nd-array + Edges of energy function. + + max_iter : int (default=10) + Maximum number of iterations. Ignored if graph is a tree. + + damping : float (default=.5) + Daming of messages in loopy message passing. + Ignored if graph is a tree. + + tol : float (default=1e-5) + Stopping tollerance for loopy message passing. + """ + from ._viterbi import viterbi + n_states, pairwise_potentials = \ + _validate_params(unary_potentials, pairwise_potentials, edges) + if is_chain(edges=edges, n_vertices=len(unary_potentials)): + y = viterbi(unary_potentials.astype(np.float).copy(), + # sad second copy b/c numpy 1.6 + np.array(pairwise_potentials, dtype=np.float)) + elif is_forest(edges=edges, n_vertices=len(unary_potentials)): + y = tree_max_product(unary_potentials, pairwise_potentials, edges) + else: + y = iterative_max_product(unary_potentials, pairwise_potentials, edges, + max_iter=max_iter, damping=damping) + return y + + +def tree_max_product(unary_potentials, pairwise_potentials, edges): + n_vertices, n_states = unary_potentials.shape + parents = -np.ones(n_vertices, dtype=np.int) + visited = np.zeros(n_vertices, dtype=np.bool) + neighbors = [[] for i in range(n_vertices)] + pairwise_weights = [[] for i in range(n_vertices)] + for pw, edge in zip(pairwise_potentials, edges): + neighbors[edge[0]].append(edge[1]) + pairwise_weights[edge[0]].append(pw) + neighbors[edge[1]].append(edge[0]) + pairwise_weights[edge[1]].append(pw.T) + + messages_forward = np.zeros((n_vertices, n_states)) + messages_backward = np.zeros((n_vertices, n_states)) + pw_forward = np.zeros((n_vertices, n_states, n_states)) + # build a breadth first search of the tree + traversal = [] + lonely = 0 + while lonely < n_vertices: + for i in range(lonely, n_vertices): + if not visited[i]: + queue = [i] + lonely = i + 1 + visited[i] = True + break + lonely = n_vertices + + while queue: + node = queue.pop(0) + traversal.append(node) + for pw, neighbor in zip(pairwise_weights[node], neighbors[node]): + if not visited[neighbor]: + parents[neighbor] = node + queue.append(neighbor) + visited[neighbor] = True + pw_forward[neighbor] = pw + + elif not parents[node] == neighbor: + raise ValueError("Graph not a tree") + # messages from leaves to root + for node in traversal[::-1]: + parent = parents[node] + if parent != -1: + message = np.max(messages_backward[node] + unary_potentials[node] + + pw_forward[node], axis=1) + message -= message.max() + messages_backward[parent] += message + # messages from root back to leaves + for node in traversal: + parent = parents[node] + if parent != -1: + message = messages_forward[parent] + unary_potentials[parent] + pw_forward[node].T + # leaves to root messages from other children + message += messages_backward[parent] - np.max(messages_backward[node] + + unary_potentials[node] + + pw_forward[node], axis=1) + message = message.max(axis=1) + message -= message.max() + messages_forward[node] += message + + return np.argmax(unary_potentials + messages_forward + messages_backward, axis=1) + + +def iterative_max_product(unary_potentials, pairwise_potentials, edges, + max_iter=10, damping=.5, tol=1e-5): + n_edges = len(edges) + n_vertices, n_states = unary_potentials.shape + messages = np.zeros((n_edges, 2, n_states)) + all_incoming = np.zeros((n_vertices, n_states)) + for i in range(max_iter): + diff = 0 + for e, (edge, pairwise) in enumerate(zip(edges, pairwise_potentials)): + # update message from edge[0] to edge[1] + update = (all_incoming[edge[0]] + pairwise.T + + unary_potentials[edge[0]] + - messages[e, 1]) + old_message = messages[e, 0].copy() + new_message = np.max(update, axis=1) + new_message -= np.max(new_message) + new_message = damping * old_message + (1 - damping) * new_message + messages[e, 0] = new_message + update = new_message - old_message + all_incoming[edge[1]] += update + diff += np.abs(update).sum() + + # update message from edge[1] to edge[0] + update = (all_incoming[edge[1]] + pairwise + + unary_potentials[edge[1]] + - messages[e, 0]) + old_message = messages[e, 1].copy() + new_message = np.max(update, axis=1) + new_message -= np.max(messages[e, 1]) + new_message = damping * old_message + (1 - damping) * new_message + messages[e, 1] = new_message + update = new_message - old_message + all_incoming[edge[0]] += update + diff += np.abs(update).sum() + if diff < tol: + break + return np.argmax(all_incoming + unary_potentials, axis=1) diff --git a/pystruct/learners/downhill_simplex_ssvm.py b/pystruct/learners/downhill_simplex_ssvm.py index fedf0194..b0f90e70 100644 --- a/pystruct/learners/downhill_simplex_ssvm.py +++ b/pystruct/learners/downhill_simplex_ssvm.py @@ -16,13 +16,13 @@ def fit(self, X, Y): def func(w): objective = 0 for x, y in zip(X, Y): - y_hat, delta_psi, slack, loss = find_constraint(self.model, + y_hat, delta_joint_feature, slack, loss = find_constraint(self.model, x, y, w) objective += slack objective /= float(len(X)) objective += np.sum(w ** 2) / float(self.C) / 2. return objective - w = 1e-5 * np.ones(self.model.size_psi) + w = 1e-5 * np.ones(self.model.size_joint_feature) res = fmin(func, x0=w + 1, full_output=1) res2 = fmin(func, x0=w, full_output=1) self.w = res[0] if res[1] < res2[1] else res2[0] diff --git a/pystruct/learners/frankwolfe_ssvm.py b/pystruct/learners/frankwolfe_ssvm.py index 154c1ec5..a74375ed 100644 --- a/pystruct/learners/frankwolfe_ssvm.py +++ b/pystruct/learners/frankwolfe_ssvm.py @@ -8,6 +8,7 @@ # Implements structured SVM as described in Joachims et. al. # Cutting-Plane Training of Structural SVMs +import warnings from time import time import numpy as np from sklearn.utils import check_random_state @@ -21,9 +22,11 @@ class FrankWolfeSSVM(BaseSSVM): This implementation is somewhat experimental. Use with care. - This implementation follows the paper: - Lacoste-Julien, Jaggi, Schmidt, Pletscher JMLR 2013 - Block-Coordinage Frank-Wolfe Optimization for Structural SVMs + References + ---------- + * Lacoste-Julien, Jaggi, Schmidt, Pletscher: + Block-Coordinate Frank-Wolfe Optimization for Structural SVMs,xi + JMLR 2013 With batch_mode=False, this implements the online (block-coordinate) version of the algorithm (BCFW) @@ -85,7 +88,7 @@ class FrankWolfeSSVM(BaseSSVM): Attributes ---------- - w : nd-array, shape=(model.size_psi,) + w : nd-array, shape=(model.size_joint_feature,) The learned weights of the SVM. ``loss_curve_`` : list of float @@ -106,8 +109,8 @@ def __init__(self, model, max_iter=1000, C=1.0, verbose=0, n_jobs=1, do_averaging=True, sample_method='perm', random_state=None): if n_jobs != 1: - raise ValueError("FrankWolfeSSVM does not support multiprocessing" - " yet. Ignoring n_jobs != 1.") + warnings.warn("FrankWolfeSSVM does not support multiprocessing" + " yet. Ignoring n_jobs != 1.") if sample_method not in ['perm', 'rnd', 'seq']: raise ValueError("sample_method can only be perm, rnd, or seq") @@ -123,23 +126,21 @@ def __init__(self, model, max_iter=1000, C=1.0, verbose=0, n_jobs=1, self.sample_method = sample_method self.random_state = random_state - def _calc_dual_gap(self, X, Y, l): + def _calc_dual_gap(self, X, Y): n_samples = len(X) - psi_gt = self.model.batch_psi(X, Y, Y) # FIXME don't calculate this again + # FIXME don't calculate this again + joint_feature_gt = self.model.batch_joint_feature(X, Y, Y) Y_hat = self.model.batch_loss_augmented_inference(X, Y, self.w, relaxed=True) - dpsi = psi_gt - self.model.batch_psi(X, Y_hat) + djoint_feature = joint_feature_gt - self.model.batch_joint_feature(X, Y_hat) ls = np.sum(self.model.batch_loss(Y, Y_hat)) - ws = dpsi * self.C - l = l * n_samples * self.C + ws = djoint_feature * self.C + l_rescaled = self.l * n_samples * self.C - dual_val = -0.5 * np.sum(self.w ** 2) + l + dual_val = -0.5 * np.sum(self.w ** 2) + l_rescaled w_diff = self.w - ws - dual_gap = w_diff.T.dot(self.w) - l + ls * self.C + dual_gap = w_diff.T.dot(self.w) - l_rescaled + ls * self.C primal_val = dual_val + dual_gap - self.primal_objective_curve_.append(primal_val) - self.objective_curve_.append(dual_val) - self.timestamps_.append(time() - self.timestamps_[0]) return dual_val, dual_gap, primal_val def _frank_wolfe_batch(self, X, Y): @@ -152,14 +153,14 @@ def _frank_wolfe_batch(self, X, Y): """ l = 0.0 n_samples = float(len(X)) - psi_gt = self.model.batch_psi(X, Y, Y) + joint_feature_gt = self.model.batch_joint_feature(X, Y, Y) - for k in xrange(self.max_iter): + for iteration in range(self.max_iter): Y_hat = self.model.batch_loss_augmented_inference(X, Y, self.w, relaxed=True) - dpsi = psi_gt - self.model.batch_psi(X, Y_hat) + djoint_feature = joint_feature_gt - self.model.batch_joint_feature(X, Y_hat) ls = np.mean(self.model.batch_loss(Y, Y_hat)) - ws = dpsi * self.C + ws = djoint_feature * self.C w_diff = self.w - ws dual_gap = 1.0 / (self.C * n_samples) * w_diff.T.dot(self.w) - l + ls @@ -170,7 +171,7 @@ def _frank_wolfe_batch(self, X, Y): gamma = dual_gap / (np.sum(w_diff ** 2) / (self.C * n_samples) + eps) gamma = max(0.0, min(1.0, gamma)) else: - gamma = 2.0 / (k + 2.0) + gamma = 2.0 / (iteration + 2.0) dual_val = -0.5 * np.sum(self.w ** 2) + l * (n_samples * self.C) dual_gap_display = dual_gap * n_samples * self.C @@ -180,13 +181,16 @@ def _frank_wolfe_batch(self, X, Y): self.objective_curve_.append(dual_val) self.timestamps_.append(time() - self.timestamps_[0]) if self.verbose > 0: - print("k = %d, dual: %f, dual_gap: %f, primal: %f, gamma: %f" - % (k, dual_val, dual_gap_display, primal_val, gamma)) + print("iteration %d, dual: %f, dual_gap: %f, primal: %f, gamma: %f" + % (iteration, dual_val, dual_gap_display, primal_val, gamma)) # update w and l self.w = (1.0 - gamma) * self.w + gamma * ws l = (1.0 - gamma) * l + gamma * ls + if self.logger is not None: + self.logger(self, iteration) + if dual_gap < self.tol: return @@ -197,16 +201,15 @@ def _frank_wolfe_bc(self, X, Y): """ n_samples = len(X) w = self.w.copy() - w_mat = np.zeros((n_samples, self.model.size_psi)) + w_mat = np.zeros((n_samples, self.model.size_joint_feature)) l_mat = np.zeros(n_samples) - l_avg = 0.0 l = 0.0 k = 0 rng = check_random_state(self.random_state) - for p in xrange(self.max_iter): + for iteration in range(self.max_iter): if self.verbose > 0: - print("Iteration %d" % p) + print("Iteration %d" % iteration) perm = np.arange(n_samples) if self.sample_method == 'perm': @@ -217,16 +220,17 @@ def _frank_wolfe_bc(self, X, Y): for j in range(n_samples): i = perm[j] x, y = X[i], Y[i] - y_hat, delta_psi, slack, loss = find_constraint(self.model, x, y, w) + y_hat, delta_joint_feature, slack, loss = find_constraint(self.model, x, y, w) # ws and ls - ws = delta_psi * self.C + ws = delta_joint_feature * self.C ls = loss / n_samples # line search if self.line_search: eps = 1e-15 w_diff = w_mat[i] - ws - gamma = (w_diff.T.dot(w) - (self.C * n_samples)*(l_mat[i] - ls)) / (np.sum(w_diff ** 2) + eps) + gamma = (w_diff.T.dot(w) + - (self.C * n_samples) * (l_mat[i] - ls)) / (np.sum(w_diff ** 2) + eps) gamma = max(0.0, min(1.0, gamma)) else: gamma = 2.0 * n_samples / (k + 2.0 * n_samples) @@ -240,20 +244,28 @@ def _frank_wolfe_bc(self, X, Y): l += l_mat[i] if self.do_averaging: - rho = 2.0 / (k + 2.0) - self.w = (1.0 - rho) * self.w + rho * w - l_avg = (1.0 - rho) * l_avg + rho * l + rho = 2. / (k + 2.) + self.w = (1. - rho) * self.w + rho * w + self.l = (1. - rho) * self.l + rho * l else: self.w = w + self.l = l k += 1 - if (self.check_dual_every != 0) and (p % self.check_dual_every == 0): - dual_val, dual_gap, primal_val = self._calc_dual_gap(X, Y, l) + if (self.check_dual_every != 0) and (iteration % self.check_dual_every == 0): + dual_val, dual_gap, primal_val = self._calc_dual_gap(X, Y) + self.primal_objective_curve_.append(primal_val) + self.objective_curve_.append(dual_val) + self.timestamps_.append(time() - self.timestamps_[0]) if self.verbose > 0: print("dual: %f, dual_gap: %f, primal: %f" % (dual_val, dual_gap, primal_val)) - if dual_gap < self.tol: - return + + if self.logger is not None: + self.logger(self, iteration) + + if dual_gap < self.tol: + return def fit(self, X, Y, constraints=None, initialize=True): """Learn parameters using (block-coordinate) Frank-Wolfe learning. @@ -278,7 +290,8 @@ def fit(self, X, Y, constraints=None, initialize=True): self.model.initialize(X, Y) self.objective_curve_, self.primal_objective_curve_ = [], [] self.timestamps_ = [time()] - self.w = getattr(self, "w", np.zeros(self.model.size_psi)) + self.w = getattr(self, "w", np.zeros(self.model.size_joint_feature)) + self.l = getattr(self, "l", 0) try: if self.batch_mode: self._frank_wolfe_batch(X, Y) @@ -286,4 +299,11 @@ def fit(self, X, Y, constraints=None, initialize=True): self._frank_wolfe_bc(X, Y) except KeyboardInterrupt: pass + if self.verbose: + print("Calculating final objective.") + self.timestamps_.append(time() - self.timestamps_[0]) + self.primal_objective_curve_.append(self._objective(X, Y)) + self.objective_curve_.append(self.objective_curve_[-1]) + if self.logger is not None: + self.logger(self, 'final') return self diff --git a/pystruct/learners/latent_structured_svm.py b/pystruct/learners/latent_structured_svm.py index f2785b46..a5c4fa36 100644 --- a/pystruct/learners/latent_structured_svm.py +++ b/pystruct/learners/latent_structured_svm.py @@ -29,6 +29,9 @@ class LatentSSVM(BaseSSVM): If the base_ssvm is a 1-slack SSVM, the inference cache will be reused. Both methods drastically speed up learning. + If base_ssvm is an 1-slack SSVM, this corresponds to the approach of + Yu and Joachims, Learning Structural SVMs with Latent Variables. + Parameters ---------- base_ssvm : object @@ -42,7 +45,7 @@ class LatentSSVM(BaseSSVM): Attributes ---------- - w : nd-array, shape=(model.size_psi,) + w : nd-array, shape=(model.size_joint_feature,) The learned weights of the SVM. """ @@ -76,7 +79,7 @@ def fit(self, X, Y, H_init=None, initialize=True): """ self.model.initialize(X, Y) - w = np.zeros(self.model.size_psi) + w = np.zeros(self.model.size_joint_feature) constraints = None ws = [] if H_init is None: @@ -84,8 +87,9 @@ def fit(self, X, Y, H_init=None, initialize=True): self.H_init_ = H_init H = H_init - for iteration in xrange(self.latent_iter): - print("LATENT SVM ITERATION %d" % iteration) + for iteration in range(self.latent_iter): + if self.verbose: + print("LATENT SVM ITERATION %d" % iteration) # find latent variables for ground truth: if iteration == 0: pass @@ -93,21 +97,23 @@ def fit(self, X, Y, H_init=None, initialize=True): H_new = [self.model.latent(x, y, w) for x, y in zip(X, Y)] changes = [np.any(h_new != h) for h_new, h in zip(H_new, H)] if not np.any(changes): - print("no changes in latent variables of ground truth." - " stopping.") + if self.verbose > 0: + print("no changes in latent variables of ground truth." + " stopping.") break - print("changes in H: %d" % np.sum(changes)) + if self.verbose: + print("changes in H: %d" % np.sum(changes)) # update constraints: if isinstance(self.base_ssvm, NSlackSSVM): - constraints = [[] for i in xrange(len(X))] + constraints = [[] for i in range(len(X))] for sample, h, i in zip(self.base_ssvm.constraints_, H_new, np.arange(len(X))): for constraint in sample: const = find_constraint(self.model, X[i], h, w, constraint[0]) - y_hat, dpsi, _, loss = const - constraints[i].append([y_hat, dpsi, loss]) + y_hat, djoint_feature, _, loss = const + constraints[i].append([y_hat, djoint_feature, loss]) H = H_new if iteration > 0: self.base_ssvm.fit(X, H, constraints=constraints, @@ -182,3 +188,11 @@ def n_jobs(self): @n_jobs.setter def n_jobs(self, n_jobs_): self.base_ssvm.n_jobs = n_jobs_ + + @property + def verbose(self): + return self.base_ssvm.verbose + + @verbose.setter + def verbose(self, verbose_): + self.base_ssvm.verbose = verbose_ diff --git a/pystruct/learners/n_slack_ssvm.py b/pystruct/learners/n_slack_ssvm.py index c5d35276..675ca702 100644 --- a/pystruct/learners/n_slack_ssvm.py +++ b/pystruct/learners/n_slack_ssvm.py @@ -12,7 +12,7 @@ import cvxopt import cvxopt.solvers -from sklearn.externals.joblib import Parallel, delayed +from joblib import Parallel, delayed from sklearn.utils import gen_even_slices from .ssvm import BaseSSVM @@ -91,7 +91,7 @@ class NSlackSSVM(BaseSSVM): Attributes ---------- - w : nd-array, shape=(model.size_psi,) + w : nd-array, shape=(model.size_joint_feature,) The learned weights of the SVM. old_solution : dict @@ -108,6 +108,15 @@ class NSlackSSVM(BaseSSVM): ``timestamps_`` : list of int Total training time stored before each iteration. + + References + ---------- + * Tsochantaridis, Ioannis and Joachims, Thorsten and Hofmann, Thomas and + Altun, Yasemin and Singer, Yoram: Large margin methods for structured + and interdependent output variables, JMLR 2006 + + * Joachims, Thorsten and Finley, Thomas and Yu, Chun-Nam John: + Cutting-plane training of structural SVMs, JMLR 2009 """ def __init__(self, model, max_iter=100, C=1.0, check_constraints=True, @@ -131,12 +140,12 @@ def __init__(self, model, max_iter=100, C=1.0, check_constraints=True, def _solve_n_slack_qp(self, constraints, n_samples): C = self.C - psis = [c[1] for sample in constraints for c in sample] + joint_features = [c[1] for sample in constraints for c in sample] losses = [c[2] for sample in constraints for c in sample] - psi_matrix = np.vstack(psis).astype(np.float) - n_constraints = len(psis) - P = cvxopt.matrix(np.dot(psi_matrix, psi_matrix.T)) + joint_feature_matrix = np.vstack(joint_features).astype(np.float) + n_constraints = len(joint_features) + P = cvxopt.matrix(np.dot(joint_feature_matrix, joint_feature_matrix.T)) # q contains loss from margin-rescaling q = cvxopt.matrix(-np.array(losses, dtype=np.float)) # constraints are a bit tricky. first, all alpha must be >zero @@ -152,24 +161,27 @@ def _solve_n_slack_qp(self, constraints, n_samples): if self.negativity_constraint is None: #empty constraints zero_constr = np.zeros(0) - psis_constr = np.zeros((0, n_constraints)) + joint_features_constr = np.zeros((0, n_constraints)) else: - psis_constr = psi_matrix.T[self.negativity_constraint] + joint_features_constr = joint_feature_matrix.T[self.negativity_constraint] zero_constr = np.zeros(len(self.negativity_constraint)) # put together G = cvxopt.sparse(cvxopt.matrix(np.vstack((-idy, blocks, - psis_constr)))) + joint_features_constr)))) tmp2 = np.ones(n_samples) * C h = cvxopt.matrix(np.hstack((tmp1, tmp2, zero_constr))) # solve QP model cvxopt.solvers.options['feastol'] = 1e-5 - solution = cvxopt.solvers.qp(P, q, G, h) + try: + solution = cvxopt.solvers.qp(P, q, G, h) + except ValueError: + solution = {'status': 'error'} if solution['status'] != "optimal": print("regularizing QP!") - P = cvxopt.matrix(np.dot(psi_matrix, psi_matrix.T) - + 1e-8 * np.eye(psi_matrix.shape[0])) + P = cvxopt.matrix(np.dot(joint_feature_matrix, joint_feature_matrix.T) + + 1e-8 * np.eye(joint_feature_matrix.shape[0])) solution = cvxopt.solvers.qp(P, q, G, h) if solution['status'] != "optimal": raise ValueError("QP solver failed. Try regularizing your QP.") @@ -188,7 +200,7 @@ def _solve_n_slack_qp(self, constraints, n_samples): # calculate per example box constraint: print("Box constraints at C: %d" % np.sum(1 - box / C < 1e-3)) print("dual objective: %f" % -solution['primal objective']) - self.w = np.dot(a, psi_matrix) + self.w = np.dot(a, joint_feature_matrix) return -solution['primal objective'] def _check_bad_constraint(self, y_hat, slack, old_constraints): @@ -241,8 +253,8 @@ def fit(self, X, Y, constraints=None, warm_start=None, initialize=True): contraints : iterable Known constraints for warm-starts. List of same length as X. Each entry is itself a list of constraints for a given instance x . - Each constraint is of the form [y_hat, delta_psi, loss], where - y_hat is a labeling, ``delta_psi = psi(x, y) - psi(x, y_hat)`` + Each constraint is of the form [y_hat, delta_joint_feature, loss], where + y_hat is a labeling, ``delta_joint_feature = joint_feature(x, y) - joint_feature(x, y_hat)`` and loss is the loss for predicting y_hat instead of the true label y. @@ -250,17 +262,18 @@ def fit(self, X, Y, constraints=None, warm_start=None, initialize=True): Whether to initialize the model for the data. Leave this true except if you really know what you are doing. """ - print("Training n-slack dual structural SVM") + if self.verbose: + print("Training n-slack dual structural SVM") cvxopt.solvers.options['show_progress'] = self.verbose > 3 if initialize: self.model.initialize(X, Y) - self.w = np.zeros(self.model.size_psi) + self.w = np.zeros(self.model.size_joint_feature) n_samples = len(X) stopping_criterion = False if constraints is None: # fresh start - constraints = [[] for i in xrange(n_samples)] - self.last_active = [[] for i in xrange(n_samples)] + constraints = [[] for i in range(n_samples)] + self.last_active = [[] for i in range(n_samples)] self.objective_curve_ = [] self.primal_objective_curve_ = [] self.timestamps_ = [time()] @@ -270,7 +283,7 @@ def fit(self, X, Y, constraints=None, warm_start=None, initialize=True): try: # catch ctrl+c to stop training # we have to update at least once after going through the dataset - for iteration in xrange(self.max_iter): + for iteration in range(self.max_iter): # main loop self.timestamps_.append(time() - self.timestamps_[0]) if self.verbose > 0: @@ -303,7 +316,7 @@ def fit(self, X, Y, constraints=None, warm_start=None, initialize=True): for i, x, y, constraint in zip(indices_b, X_b, Y_b, candidate_constraints): # loop over samples in batch - y_hat, delta_psi, slack, loss = constraint + y_hat, delta_joint_feature, slack, loss = constraint slack_sum += slack if self.verbose > 3: @@ -311,14 +324,14 @@ def fit(self, X, Y, constraints=None, warm_start=None, initialize=True): if not loss > 0: # can have y != y_hat but loss = 0 in latent svm. - # we need this here as dpsi is then != 0 + # we need this here as djoint_feature is then != 0 continue if self._check_bad_constraint(y_hat, slack, constraints[i]): continue - constraints[i].append([y_hat, delta_psi, loss]) + constraints[i].append([y_hat, delta_joint_feature, loss]) new_constraints_batch += 1 # after processing the slice, solve the qp @@ -341,19 +354,22 @@ def fit(self, X, Y, constraints=None, warm_start=None, initialize=True): (new_constraints, objective, primal_objective)) if new_constraints == 0: - print("no additional constraints") + if self.verbose: + print("no additional constraints") stopping_criterion = True if (iteration > 1 and self.objective_curve_[-1] - self.objective_curve_[-2] < self.tol): - print("objective converged.") + if self.verbose: + print("objective converged.") stopping_criterion = True if stopping_criterion: if (self.switch_to is not None and self.model.inference_method != self.switch_to): - print("Switching to %s inference" % - str(self.switch_to)) + if self.verbose: + print("Switching to %s inference" % + str(self.switch_to)) self.model.inference_method_ = \ self.model.inference_method self.model.inference_method = self.switch_to @@ -369,12 +385,18 @@ def fit(self, X, Y, constraints=None, warm_start=None, initialize=True): self.logger(self, iteration) except KeyboardInterrupt: pass - if self.logger is not None: - self.logger(self, 'final') self.constraints_ = constraints if self.verbose and self.n_jobs == 1: print("calls to inference: %d" % self.model.inference_calls) + + if verbose: + print("Computing final objective.") + self.timestamps_.append(time() - self.timestamps_[0]) + self.primal_objective_curve_.append(self._objective(X, Y)) + self.objective_curve_.append(objective) + if self.logger is not None: + self.logger(self, 'final') return self def prune_constraints(self, constraints, a): diff --git a/pystruct/learners/one_slack_ssvm.py b/pystruct/learners/one_slack_ssvm.py index 473ef8d9..4944b28f 100644 --- a/pystruct/learners/one_slack_ssvm.py +++ b/pystruct/learners/one_slack_ssvm.py @@ -11,7 +11,7 @@ import cvxopt import cvxopt.solvers -from sklearn.externals.joblib import Parallel, delayed +from joblib import Parallel, delayed from .ssvm import BaseSSVM from ..utils import loss_augmented_inference @@ -49,12 +49,12 @@ class OneSlackSSVM(BaseSSVM): verbose : int Verbosity. - negativity_constraint: list of ints + negativity_constraint : list of ints Indices of parmeters that are constraint to be negative. This is useful for learning submodular CRFs (inference is formulated as maximization in SSVMs, flipping some signs). - break_on_bad: bool default=False + break_on_bad : bool default=False Whether to break (start debug mode) when inference was approximate. n_jobs : int, default=1 @@ -80,10 +80,11 @@ class OneSlackSSVM(BaseSSVM): exhausted. Using inference_cache > 0 is only advisable if computation time is dominated by inference. - cache_tol : float, default=None + cache_tol : float, None or 'auto' default='auto' Tolerance when to reject a constraint from cache (and do inference). If None, ``tol`` will be used. Higher values might lead to faster - learning. + learning. 'auto' uses a heuristic to determine the cache tolerance + based on the duality gap, as described in [3]. inactive_threshold : float, default=1e-5 Threshold for dual variable of a constraint to be considered inactive. @@ -103,7 +104,7 @@ class OneSlackSSVM(BaseSSVM): Attributes ---------- - w : nd-array, shape=(model.size_psi,) + w : nd-array, shape=(model.size_joint_feature,) The learned weights of the SVM. old_solution : dict @@ -121,6 +122,17 @@ class OneSlackSSVM(BaseSSVM): ``timestamps_`` : list of int Total training time stored before each iteration. + References + ---------- + [1] Thorsten Joachims, and Thomas Finley and Chun-Nam John Yu: + Cutting-plane training of structural SVMs, JMLR 2009 + + [2] Andreas Mueller: Methods for Learning Structured Prediction in + Semantic Segmentation of Natural Images, PhD Thesis. 2014 + + [3] Andreas Mueller and Sven Behnke: Learning a Loopy Model For Semantic + Segmentation Exactly, VISAPP 2014 + """ def __init__(self, model, max_iter=10000, C=1.0, check_constraints=False, @@ -146,12 +158,12 @@ def __init__(self, model, max_iter=10000, C=1.0, check_constraints=False, def _solve_1_slack_qp(self, constraints, n_samples): C = np.float(self.C) * n_samples # this is how libsvm/svmstruct do it - psis = [c[0] for c in constraints] + joint_features = [c[0] for c in constraints] losses = [c[1] for c in constraints] - psi_matrix = np.vstack(psis) - n_constraints = len(psis) - P = cvxopt.matrix(np.dot(psi_matrix, psi_matrix.T)) + joint_feature_matrix = np.vstack(joint_features) + n_constraints = len(joint_features) + P = cvxopt.matrix(np.dot(joint_feature_matrix, joint_feature_matrix.T)) # q contains loss from margin-rescaling q = cvxopt.matrix(-np.array(losses, dtype=np.float)) # constraints: all alpha must be >zero @@ -159,15 +171,15 @@ def _solve_1_slack_qp(self, constraints, n_samples): tmp1 = np.zeros(n_constraints) # positivity constraints: if self.negativity_constraint is None: - #empty constraints + # empty constraints zero_constr = np.zeros(0) - psis_constr = np.zeros((0, n_constraints)) + joint_features_constr = np.zeros((0, n_constraints)) else: - psis_constr = psi_matrix.T[self.negativity_constraint] + joint_features_constr = joint_feature_matrix.T[self.negativity_constraint] zero_constr = np.zeros(len(self.negativity_constraint)) # put together - G = cvxopt.sparse(cvxopt.matrix(np.vstack((-idy, psis_constr)))) + G = cvxopt.sparse(cvxopt.matrix(np.vstack((-idy, joint_features_constr)))) h = cvxopt.matrix(np.hstack((tmp1, zero_constr))) # equality constraint: sum of all alpha must be = C @@ -176,23 +188,14 @@ def _solve_1_slack_qp(self, constraints, n_samples): # solve QP model cvxopt.solvers.options['feastol'] = 1e-5 - #if hasattr(self, 'old_solution'): - #s = self.old_solution['s'] - ## put s slightly inside the cone.. - #s = cvxopt.matrix(np.vstack([s, [[1e-10]]])) - #z = self.old_solution['z'] - #z = cvxopt.matrix(np.vstack([z, [[1e-10]]])) - #initvals = {'x': self.old_solution['x'], 'y': - #self.old_solution['y'], 'z': z, - #'s': s} - #else: - #initvals = {} - #solution = cvxopt.solvers.qp(P, q, G, h, A, b, initvals=initvals) - solution = cvxopt.solvers.qp(P, q, G, h, A, b) + try: + solution = cvxopt.solvers.qp(P, q, G, h, A, b) + except ValueError: + solution = {'status': 'error'} if solution['status'] != "optimal": print("regularizing QP!") - P = cvxopt.matrix(np.dot(psi_matrix, psi_matrix.T) - + 1e-8 * np.eye(psi_matrix.shape[0])) + P = cvxopt.matrix(np.dot(joint_feature_matrix, joint_feature_matrix.T) + + 1e-8 * np.eye(joint_feature_matrix.shape[0])) solution = cvxopt.solvers.qp(P, q, G, h, A, b) if solution['status'] != "optimal": raise ValueError("QP solver failed. Try regularizing your QP.") @@ -207,7 +210,7 @@ def _solve_1_slack_qp(self, constraints, n_samples): if self.verbose > 1: print("%d support vectors out of %d points" % (np.sum(sv), n_constraints)) - self.w = np.dot(a, psi_matrix) + self.w = np.dot(a, joint_feature_matrix) # we needed to flip the sign to make the dual into a minimization # model return -solution['primal objective'] @@ -235,7 +238,7 @@ def prune_constraints(self, constraints, a): del constraints[idx] del self.alphas[idx] - def _check_bad_constraint(self, violation, dpsi_mean, loss, + def _check_bad_constraint(self, violation, djoint_feature_mean, loss, old_constraints, break_on_bad, tol=None): violation_difference = violation - self.last_slack_ if self.verbose > 1: @@ -250,8 +253,8 @@ def _check_bad_constraint(self, violation, dpsi_mean, loss, if self.verbose: print("new constraint too weak.") return True - equals = [True for dpsi_, loss_ in old_constraints - if (np.all(dpsi_ == dpsi_mean) and loss == loss_)] + equals = [True for djoint_feature_, loss_ in old_constraints + if (np.all(djoint_feature_ == djoint_feature_mean) and loss == loss_)] if np.any(equals): return True @@ -266,13 +269,43 @@ def _check_bad_constraint(self, violation, dpsi_mean, loss, # significantly larger, don't add constraint. # if smaller, complain about approximate inference. if violation - violation_tmp < -1e-5: - print("bad inference: %f" % (violation_tmp - violation)) + if self.verbose: + print("bad inference: %f" % (violation_tmp - violation)) if break_on_bad: raise ValueError("Bad inference: new violation is" " weaker than previous constraint.") return True return False + @classmethod + def constraint_equal(cls, y_1, y_2): + """ + This now more complex. y_1 and/or y_2 (I think) can be: array, pair of + arrays, pair of list of arrays (multitype) + We need to compare those! + """ + if isinstance(y_1, tuple): + # y_1 is relaxed Y + # y_1 and y_2 might be lists of ndarray (multitype) instead of + # ndarray (single type) + u_m_1, pw_m_1 = y_1 + if isinstance(y_2, tuple): # we then compare two relaxed Ys + u_m_2, pw_m_2 = y_2 + # now, do we multitype or single type relaxed marginals?? + if isinstance(u_m_1, list): + return all(np.all(_um1 == _um2) for _um1, _um2 + in zip( u_m_1, u_m_2)) \ + and all(np.all(_pw1 == _pw2) for _pw1, _pw2 + in zip(pw_m_1, pw_m_2)) + else: + return np.all(u_m_1 == u_m_2) and np.all(pw_m_1, pw_m_2) + else: + # NOTE original code was possibly comparing array and scalar + # return np.all(y_1[0] == y_2[0]) and np.all(y_1[1] == y_2[1]) + return False + # might compare array and tuple... :-/ Was like that, I keep + return np.all(y_1 == y_2) + def _update_cache(self, X, Y, Y_hat): """Updated cached constraints.""" if self.inference_cache == 0: @@ -281,13 +314,8 @@ def _update_cache(self, X, Y, Y_hat): or self.inference_cache_ is None): self.inference_cache_ = [[] for y in Y_hat] - def constraint_equal(y_1, y_2): - if isinstance(y_1, tuple): - return np.all(y_1[0] == y_2[1]) and np.all(y_1[1] == y_2[1]) - return np.all(y_1 == y_2) - for sample, x, y, y_hat in zip(self.inference_cache_, X, Y, Y_hat): - already_there = [constraint_equal(y_hat, cache[2]) + already_there = [self.constraint_equal(y_hat, cache[2]) for cache in sample] if np.any(already_there): continue @@ -297,10 +325,10 @@ def constraint_equal(y_1, y_2): # this makes it a little less efficient in the caching case. # the idea is that if we cache, inference is way more expensive # and this doesn't matter much. - sample.append((self.model.psi(x, y_hat), + sample.append((self.model.joint_feature(x, y_hat), self.model.loss(y, y_hat), y_hat)) - def _constraint_from_cache(self, X, Y, psi_gt, constraints): + def _constraint_from_cache(self, X, Y, joint_feature_gt, constraints): if (not getattr(self, 'inference_cache_', False) or self.inference_cache_ is False): if self.verbose > 10: @@ -310,34 +338,35 @@ def _constraint_from_cache(self, X, Y, psi_gt, constraints): if (self.cache_tol == 'auto' and gap < self.cache_tol_): # do inference if gap has become to small if self.verbose > 1: - print("Last gap too small (%f < %f), not loading constraint from cache." + print("Last gap too small (%f < %f), not loading constraint" + " from cache." % (gap, self.cache_tol_)) raise NoConstraint Y_hat = [] - psi_acc = np.zeros(self.model.size_psi) + joint_feature_acc = np.zeros(self.model.size_joint_feature) loss_mean = 0 for cached in self.inference_cache_: - # cached has entries of form (psi, loss, y_hat) - violations = [np.dot(psi, self.w) + loss - for psi, loss, _ in cached] - psi, loss, y_hat = cached[np.argmax(violations)] + # cached has entries of form (joint_feature, loss, y_hat) + violations = [np.dot(joint_feature, self.w) + loss + for joint_feature, loss, _ in cached] + joint_feature, loss, y_hat = cached[np.argmax(violations)] Y_hat.append(y_hat) - psi_acc += psi + joint_feature_acc += joint_feature loss_mean += loss - dpsi = (psi_gt - psi_acc) / len(X) + djoint_feature = (joint_feature_gt - joint_feature_acc) / len(X) loss_mean = loss_mean / len(X) - violation = loss_mean - np.dot(self.w, dpsi) - if self._check_bad_constraint(violation, dpsi, loss_mean, constraints, + violation = loss_mean - np.dot(self.w, djoint_feature) + if self._check_bad_constraint(violation, djoint_feature, loss_mean, constraints, break_on_bad=False): if self.verbose > 1: print("No constraint from cache.") raise NoConstraint - return Y_hat, dpsi, loss_mean + return Y_hat, djoint_feature, loss_mean - def _find_new_constraint(self, X, Y, psi_gt, constraints, check=True): + def _find_new_constraint(self, X, Y, joint_feature_gt, constraints, check=True): if self.n_jobs != 1: # do inference in parallel verbose = max(0, self.verbose - 3) @@ -348,21 +377,23 @@ def _find_new_constraint(self, X, Y, psi_gt, constraints, check=True): else: Y_hat = self.model.batch_loss_augmented_inference( X, Y, self.w, relaxed=True) - # compute the mean over psis and losses + # compute the mean over joint_features and losses if getattr(self.model, 'rescale_C', False): - dpsi = (psi_gt - self.model.batch_psi(X, Y_hat, Y)) / len(X) + djoint_feature = (joint_feature_gt + - self.model.batch_joint_feature(X, Y_hat, Y)) / len(X) else: - dpsi = (psi_gt - self.model.batch_psi(X, Y_hat)) / len(X) + djoint_feature = (joint_feature_gt + - self.model.batch_joint_feature(X, Y_hat)) / len(X) loss_mean = np.mean(self.model.batch_loss(Y, Y_hat)) - violation = loss_mean - np.dot(self.w, dpsi) + violation = loss_mean - np.dot(self.w, djoint_feature) if check and self._check_bad_constraint( - violation, dpsi, loss_mean, constraints, + violation, djoint_feature, loss_mean, constraints, break_on_bad=self.break_on_bad): raise NoConstraint - return Y_hat, dpsi, loss_mean + return Y_hat, djoint_feature, loss_mean def fit(self, X, Y, constraints=None, warm_start=False, initialize=True): """Learn parameters using cutting plane method. @@ -399,22 +430,22 @@ def fit(self, X, Y, constraints=None, warm_start=False, initialize=True): self.cache_tol_ = self.cache_tol if not warm_start: - self.w = np.zeros(self.model.size_psi) + self.w = np.zeros(self.model.size_joint_feature) constraints = [] self.objective_curve_, self.primal_objective_curve_ = [], [] self.cached_constraint_ = [] self.alphas = [] # dual solutions # append constraint given by ground truth to make our life easier - constraints.append((np.zeros(self.model.size_psi), 0)) + constraints.append((np.zeros(self.model.size_joint_feature), 0)) self.alphas.append([self.C]) self.inference_cache_ = None self.timestamps_ = [time()] elif warm_start == "soft": - self.w = np.zeros(self.model.size_psi) + self.w = np.zeros(self.model.size_joint_feature) constraints = [] self.alphas = [] # dual solutions # append constraint given by ground truth to make our life easier - constraints.append((np.zeros(self.model.size_psi), 0)) + constraints.append((np.zeros(self.model.size_joint_feature), 0)) self.alphas.append([self.C]) else: @@ -422,16 +453,16 @@ def fit(self, X, Y, constraints=None, warm_start=False, initialize=True): self.last_slack_ = -1 - # get the psi of the ground truth + # get the joint_feature of the ground truth if getattr(self.model, 'rescale_C', False): - psi_gt = self.model.batch_psi(X, Y, Y) + joint_feature_gt = self.model.batch_joint_feature(X, Y, Y) else: - psi_gt = self.model.batch_psi(X, Y) + joint_feature_gt = self.model.batch_joint_feature(X, Y) try: # catch ctrl+c to stop training - for iteration in xrange(self.max_iter): + for iteration in range(self.max_iter): # main loop cached_constraint = False if self.verbose > 0: @@ -439,13 +470,13 @@ def fit(self, X, Y, constraints=None, warm_start=False, initialize=True): if self.verbose > 2: print(self) try: - Y_hat, dpsi, loss_mean = self._constraint_from_cache( - X, Y, psi_gt, constraints) + Y_hat, djoint_feature, loss_mean = self._constraint_from_cache( + X, Y, joint_feature_gt, constraints) cached_constraint = True except NoConstraint: try: - Y_hat, dpsi, loss_mean = self._find_new_constraint( - X, Y, psi_gt, constraints) + Y_hat, djoint_feature, loss_mean = self._find_new_constraint( + X, Y, joint_feature_gt, constraints) self._update_cache(X, Y, Y_hat) except NoConstraint: if self.verbose: @@ -465,12 +496,12 @@ def fit(self, X, Y, constraints=None, warm_start=False, initialize=True): self.timestamps_.append(time() - self.timestamps_[0]) self._compute_training_loss(X, Y, iteration) - constraints.append((dpsi, loss_mean)) + constraints.append((djoint_feature, loss_mean)) # compute primal objective - last_slack = -np.dot(self.w, dpsi) + loss_mean + last_slack = -np.dot(self.w, djoint_feature) + loss_mean primal_objective = (self.C * len(X) - * np.max(last_slack, 0) + * max(last_slack, 0) + np.sum(self.w ** 2) / 2) self.primal_objective_curve_.append(primal_objective) self.cached_constraint_.append(cached_constraint) @@ -482,8 +513,8 @@ def fit(self, X, Y, constraints=None, warm_start=False, initialize=True): if self.cache_tol == "auto" and not cached_constraint: self.cache_tol_ = (primal_objective - objective) / 4 - self.last_slack_ = np.max([(-np.dot(self.w, dpsi) + loss_mean) - for dpsi, loss_mean in constraints]) + self.last_slack_ = np.max([(-np.dot(self.w, djoint_feature) + loss_mean) + for djoint_feature, loss_mean in constraints]) self.last_slack_ = max(self.last_slack_, 0) if self.verbose > 0: @@ -502,23 +533,17 @@ def fit(self, X, Y, constraints=None, warm_start=False, initialize=True): print(self.w) except KeyboardInterrupt: pass - if self.logger is not None: - self.logger(self, 'final') if self.verbose and self.n_jobs == 1: print("calls to inference: %d" % self.model.inference_calls) # compute final objective: - Y_hat, dpsi, loss_mean = self._find_new_constraint( - X, Y, psi_gt, constraints, check=False) - last_slack = -np.dot(self.w, dpsi) + loss_mean - primal_objective = (self.C * len(X) - * np.max(last_slack, 0) - + np.sum(self.w ** 2) / 2) - # we recompute the primal using inference + self.timestamps_.append(time() - self.timestamps_[0]) + primal_objective = self._objective(X, Y) self.primal_objective_curve_.append(primal_objective) - # add dummy points to objective_curve and timestamps self.objective_curve_.append(objective) self.cached_constraint_.append(False) - self.timestamps_.append(time() - self.timestamps_[0]) + + if self.logger is not None: + self.logger(self, 'final') if self.verbose > 0: print("final primal objective: %f gap: %f" diff --git a/pystruct/learners/ssvm.py b/pystruct/learners/ssvm.py index 93e48ef5..35a1d56f 100644 --- a/pystruct/learners/ssvm.py +++ b/pystruct/learners/ssvm.py @@ -1,9 +1,9 @@ import numpy as np -from sklearn.externals.joblib import Parallel, delayed +from joblib import Parallel, delayed from sklearn.base import BaseEstimator -from ..utils import inference +from ..utils import inference, objective_primal class BaseSSVM(BaseEstimator): @@ -18,7 +18,7 @@ def __init__(self, model, max_iter=100, C=1.0, verbose=0, self.n_jobs = n_jobs self.logger = logger - def predict(self, X): + def predict(self, X, constraints=None): """Predict output on examples in X. Parameters @@ -26,6 +26,8 @@ def predict(self, X): X : iterable Traing instances. Contains the structured input objects. + constraints : None or a list of hard logic constraints + Returns ------- Y_pred : list @@ -34,12 +36,24 @@ def predict(self, X): """ verbose = max(0, self.verbose - 3) if self.n_jobs != 1: - prediction = Parallel(n_jobs=self.n_jobs, verbose=verbose)( - delayed(inference)(self.model, x, self.w) for x in X) + if constraints: + prediction = Parallel(n_jobs=self.n_jobs, verbose=verbose)( + delayed(inference)(self.model, x, self.w, constraints=c) + for x, c in zip(X, constraints)) + else: + prediction = Parallel(n_jobs=self.n_jobs, verbose=verbose)( + delayed(inference)(self.model, x, self.w) for x in X) return prediction else: if hasattr(self.model, 'batch_inference'): - return self.model.batch_inference(X, self.w) + if constraints: + return self.model.batch_inference(X, self.w, + constraints=constraints) + else: + return self.model.batch_inference(X, self.w) + if constraints: + return [self.model.inference(x, self.w, constraints=c) + for x, c in zip(X, constraints)] return [self.model.inference(x, self.w) for x in X] def score(self, X, Y): @@ -79,3 +93,11 @@ def _compute_training_loss(self, X, Y, iteration): if self.verbose > 0: print("current loss: %f" % (display_loss)) self.loss_curve_.append(display_loss) + + def _objective(self, X, Y): + if type(self).__name__ == 'OneSlackSSVM': + variant = 'one_slack' + else: + variant = 'n_slack' + return objective_primal(self.model, self.w, X, Y, self.C, + variant=variant, n_jobs=self.n_jobs) diff --git a/pystruct/learners/structured_perceptron.py b/pystruct/learners/structured_perceptron.py index fe7b36e6..e130fc58 100644 --- a/pystruct/learners/structured_perceptron.py +++ b/pystruct/learners/structured_perceptron.py @@ -1,5 +1,5 @@ import numpy as np -from sklearn.externals.joblib import Parallel, delayed +from joblib import Parallel, delayed from .ssvm import BaseSSVM @@ -56,7 +56,7 @@ class StructuredPerceptron(BaseSSVM): Attributes ---------- - w : nd-array, shape=(model.size_psi,) + w : nd-array, shape=(model.size_joint_feature,) The learned weights of the SVM. ``loss_curve_`` : list of float @@ -98,8 +98,8 @@ def fit(self, X, Y, initialize=True): """ if initialize: self.model.initialize(X, Y) - size_psi = self.model.size_psi - self.w = np.zeros(size_psi) + size_joint_feature = self.model.size_joint_feature + self.w = np.zeros(size_joint_feature) if self.average is not False: if self.average is True: self.average = 0 @@ -108,12 +108,12 @@ def fit(self, X, Y, initialize=True): "implemented at the moment is `-1`. Try " "`max_iter - k` but be aware of the " "possibility of early stopping.") - w_bar = np.zeros(size_psi) + w_bar = np.zeros(size_joint_feature) n_obs = 0 self.loss_curve_ = [] max_losses = np.sum([self.model.max_loss(y) for y in Y]) try: - for iteration in xrange(self.max_iter): + for iteration in range(self.max_iter): if self.average == -1: # By resetting at every iteration we effectively get # averaging over the last one. @@ -132,8 +132,8 @@ def fit(self, X, Y, initialize=True): current_loss = self.model.loss(y, y_hat) losses += current_loss if current_loss: - self.w += effective_lr * (self.model.psi(x, y) - - self.model.psi(x, y_hat)) + self.w += effective_lr * (self.model.joint_feature(x, y) - + self.model.joint_feature(x, y_hat)) if self.average is not False and iteration >= self.average: n_obs += 1 w_bar = ((1 - 1. / n_obs) * w_bar + @@ -145,8 +145,8 @@ def fit(self, X, Y, initialize=True): current_loss = self.model.loss(y, y_hat) losses += current_loss if current_loss: - self.w += effective_lr * (self.model.psi(x, y) - - self.model.psi(x, y_hat)) + self.w += effective_lr * (self.model.joint_feature(x, y) - + self.model.joint_feature(x, y_hat)) if (self.average is not False and iteration >= self.average): n_obs += 1 @@ -158,7 +158,8 @@ def fit(self, X, Y, initialize=True): str(self.w))) print("effective learning rate: %f" % effective_lr) if self.loss_curve_[-1] == 0: - print("Loss zero. Stopping.") + if self.verbose: + print("Loss zero. Stopping.") break except KeyboardInterrupt: diff --git a/pystruct/learners/subgradient_latent_ssvm.py b/pystruct/learners/subgradient_latent_ssvm.py index 1fd162f3..1522e76d 100644 --- a/pystruct/learners/subgradient_latent_ssvm.py +++ b/pystruct/learners/subgradient_latent_ssvm.py @@ -6,7 +6,7 @@ from time import time import numpy as np -from sklearn.externals.joblib import Parallel, delayed, cpu_count +from joblib import Parallel, delayed, cpu_count from sklearn.utils import gen_even_slices from .subgradient_ssvm import SubgradientSSVM @@ -40,16 +40,13 @@ class SubgradientLatentSSVM(SubgradientSSVM): verbose : int, default=0 Verbosity. - learning_rate : float, default=0.001 - Learning rate used in subgradient descent. + learning_rate : float or 'auto', default='auto' + Learning rate used in subgradient descent. If 'auto', the pegasos + schedule is used, which starts with ``learning_rate = n_samples * C``. - momentum : float, default=0.9 + momentum : float, default=0.0 Momentum used in subgradient descent. - adagrad : bool (default=False) - Whether to use adagrad gradient scaling. - Ignores if True, momentum is ignored. - n_jobs : int, default=1 Number of parallel jobs for inference. -1 means as many as cpus. @@ -58,23 +55,33 @@ class SubgradientLatentSSVM(SubgradientSSVM): purposes). Zero means never, otherwise it will be computed very show_loss_every'th epoch. - decay_exponent : float, default=0 + decay_exponent : float, default=1 Exponent for decaying learning rate. Effective learning rate is - ``learning_rate / (t0 + t)** decay_exponent``. Zero means no decay. - Ignored if adagrad=True. + ``learning_rate / (decay_t0 + t)** decay_exponent``. Zero means no decay. decay_t0 : float, default=10 Offset for decaying learning rate. Effective learning rate is - ``learning_rate / (t0 + t)** decay_exponent``. Zero means no decay. - Ignored if adagrad=True. + ``learning_rate / (decay_t0 + t)** decay_exponent``. break_on_no_constraints : bool, default=True Break when there are no new constraints found. + averaging : string, default=None + Whether and how to average weights. Possible options are 'linear', 'squared' and None. + The string reflects the weighting of the averaging: + + - linear: ``w_avg ~ w_1 + 2 * w_2 + ... + t * w_t`` + + - squared: ``w_avg ~ w_1 + 4 * w_2 + ... + t**2 * w_t`` + + Uniform averaging is not implemented as it is worse than linear + weighted averaging or no averaging. + + Attributes ---------- - w : nd-array, shape=(model.size_psi,) + w : nd-array, shape=(model.size_joint_feature,) The learned weights of the SVM. ``loss_curve_`` : list of float @@ -87,16 +94,16 @@ class SubgradientLatentSSVM(SubgradientSSVM): Total training time stored before each iteration. """ - def __init__(self, model, max_iter=100, C=1.0, verbose=0, momentum=0.9, - learning_rate=0.001, adagrad=False, n_jobs=1, - show_loss_every=0, decay_exponent=0, decay_t0=10, - break_on_no_constraints=True, logger=None): + def __init__(self, model, max_iter=100, C=1.0, verbose=0, momentum=0., + learning_rate='auto', n_jobs=1, + show_loss_every=0, decay_exponent=1, decay_t0=10, + break_on_no_constraints=True, logger=None, averaging=None): SubgradientSSVM.__init__( self, model, max_iter, C, verbose=verbose, n_jobs=n_jobs, show_loss_every=show_loss_every, decay_exponent=decay_exponent, - momentum=momentum, learning_rate=learning_rate, adagrad=adagrad, + momentum=momentum, learning_rate=learning_rate, break_on_no_constraints=break_on_no_constraints, logger=logger, - decay_t0=decay_t0) + decay_t0=decay_t0, averaging=averaging) def fit(self, X, Y, H_init=None, warm_start=False, initialize=True): """Learn parameters using subgradient descent. @@ -125,19 +132,24 @@ def fit(self, X, Y, H_init=None, warm_start=False, initialize=True): print("Training latent subgradient structural SVM") if initialize: self.model.initialize(X, Y) - self.grad_old = np.zeros(self.model.size_psi) + self.grad_old = np.zeros(self.model.size_joint_feature) if not warm_start: self.w = getattr(self, "w", np.random.normal( - 0, 1, size=self.model.size_psi)) + 0, 1, size=self.model.size_joint_feature)) self.timestamps_ = [time()] self.objective_curve_ = [] + if self.learning_rate == "auto": + self.learning_rate_ = self.C * len(X) + else: + self.learning_rate_ = self.learning_rate else: # hackety hack self.timestamps_[0] = time() - self.timestamps_[-1] + w = self.w.copy() n_samples = len(X) try: # catch ctrl+c to stop training - for iteration in xrange(self.max_iter): + for iteration in range(self.max_iter): self.timestamps_.append(time() - self.timestamps_[0]) positive_slacks = 0 objective = 0. @@ -146,17 +158,18 @@ def fit(self, X, Y, H_init=None, warm_start=False, initialize=True): if self.n_jobs == 1: # online learning for x, y in zip(X, Y): - h = self.model.latent(x, y, self.w) + h = self.model.latent(x, y, w) h_hat = self.model.loss_augmented_inference( - x, h, self.w, relaxed=True) - delta_psi = (self.model.psi(x, h) - - self.model.psi(x, h_hat)) - slack = (-np.dot(delta_psi, self.w) + x, h, w, relaxed=True) + delta_joint_feature = ( + self.model.joint_feature(x, h) + - self.model.joint_feature(x, h_hat)) + slack = (-np.dot(delta_joint_feature, w) + self.model.loss(h, h_hat)) objective += np.maximum(slack, 0) if slack > 0: positive_slacks += 1 - self._solve_subgradient(delta_psi, n_samples) + w = self._solve_subgradient(delta_joint_feature, n_samples, w) else: #generate batches of size n_jobs #to speed up inference @@ -174,23 +187,22 @@ def fit(self, X, Y, H_init=None, warm_start=False, initialize=True): candidate_constraints = Parallel( n_jobs=self.n_jobs, verbose=verbose)(delayed(find_constraint_latent)( - self.model, x, y, self.w) + self.model, x, y, w) for x, y in zip(X_b, Y_b)) - dpsi = np.zeros(self.model.size_psi) + djoint_feature = np.zeros(self.model.size_joint_feature) for x, y, constraint in zip(X_b, Y_b, candidate_constraints): - y_hat, delta_psi, slack, loss = constraint + y_hat, delta_joint_feature, slack, loss = constraint objective += slack - dpsi += delta_psi + djoint_feature += delta_joint_feature if slack > 0: positive_slacks += 1 - dpsi /= float(len(X_b)) - self._solve_subgradient(dpsi, n_samples) + djoint_feature /= float(len(X_b)) + w = self._solve_subgradient(djoint_feature, n_samples, w) # some statistics objective *= self.C objective += np.sum(self.w ** 2) / 2. - #objective /= float(n_samples) if positive_slacks == 0: print("No additional constraints") @@ -213,9 +225,15 @@ def fit(self, X, Y, H_init=None, warm_start=False, initialize=True): except KeyboardInterrupt: pass - print("final objective: %f" % self.objective_curve_[-1]) - if self.verbose and self.n_jobs == 1: - print("calls to inference: %d" % self.model.inference_calls) + self.timestamps_.append(time() - self.timestamps_[0]) + self.objective_curve_.append(self._objective(X, Y)) + if self.logger is not None: + self.logger(self, 'final') + if self.verbose: + if self.objective_curve_: + print("final objective: %f" % self.objective_curve_[-1]) + if self.verbose and self.n_jobs == 1: + print("calls to inference: %d" % self.model.inference_calls) return self def predict(self, X): @@ -252,3 +270,15 @@ def score(self, X, Y): for y, y_pred in zip(Y, self.predict(X))] max_losses = [self.model.max_loss(y) for y in Y] return 1. - np.sum(losses) / float(np.sum(max_losses)) + + def _objective(self, X, Y): + constraints = Parallel( + n_jobs=self.n_jobs, + verbose=max(self.verbose - 1, 0))(delayed(find_constraint_latent)( + self.model, x, y, self.w) + for x, y in zip(X, Y)) + slacks = list(zip(*constraints))[2] + slacks = np.maximum(slacks, 0) + + objective = np.sum(slacks) * self.C + np.sum(self.w ** 2) / 2. + return objective diff --git a/pystruct/learners/subgradient_ssvm.py b/pystruct/learners/subgradient_ssvm.py index 568f069c..ed4ce466 100644 --- a/pystruct/learners/subgradient_ssvm.py +++ b/pystruct/learners/subgradient_ssvm.py @@ -1,8 +1,8 @@ from time import time import numpy as np -from sklearn.externals.joblib import Parallel, delayed, cpu_count -from sklearn.utils import gen_even_slices +from joblib import Parallel, delayed, cpu_count +from sklearn.utils import gen_even_slices, shuffle from .ssvm import BaseSSVM from ..utils import find_constraint @@ -30,51 +30,64 @@ class SubgradientSSVM(BaseSSVM): updates. C : float, default=1. - Regularization parameter + Regularization parameter. verbose : int, default=0 Verbosity. - learning_rate : float, default=0.001 - Learning rate used in subgradient descent. + learning_rate : float or 'auto', default='auto' + Learning rate used in subgradient descent. If 'auto', the pegasos + schedule is used, which starts with ``learning_rate = n_samples * C``. - momentum : float, default=0.9 + momentum : float, default=0.0 Momentum used in subgradient descent. - adagrad : bool (default=False) - Whether to use adagrad gradient scaling. - Ignores if True, momentum is ignored. - n_jobs : int, default=1 Number of parallel jobs for inference. -1 means as many as cpus. batch_size : int, default=None - Ignored if n_jobs > 1. If n_jobs=1, inference will be done in batches - of size batch_size. + Ignored if n_jobs > 1. If n_jobs=1, inference will be done in mini + batches of size batch_size. If n_jobs=-1, batch learning will be + performed, that is the whole dataset will be used to compute each + subgradient. show_loss_every : int, default=0 Controlls how often the hamming loss is computed (for monitoring purposes). Zero means never, otherwise it will be computed very show_loss_every'th epoch. - decay_exponent : float, default=0 + decay_exponent : float, default=1 Exponent for decaying learning rate. Effective learning rate is - ``learning_rate / (t0 + t)** decay_exponent``. Zero means no decay. - Ignored if adagrad=True. + ``learning_rate / (decay_t0 + t)** decay_exponent``. Zero means no + decay. decay_t0 : float, default=10 Offset for decaying learning rate. Effective learning rate is - ``learning_rate / (t0 + t)** decay_exponent``. Zero means no decay. - Ignored if adagrad=True. + ``learning_rate / (decay_t0 + t)** decay_exponent``. break_on_no_constraints : bool, default=True Break when there are no new constraints found. logger : logger object. + averaging : string, default=None + Whether and how to average weights. Possible options are 'linear', + 'squared' and None. + The string reflects the weighting of the averaging: + + - ``linear: w_avg ~ w_1 + 2 * w_2 + ... + t * w_t`` + + - ``squared: w_avg ~ w_1 + 4 * w_2 + ... + t**2 * w_t`` + + Uniform averaging is not implemented as it is worse than linear + weighted averaging or no averaging. + + shuffle : bool, default=False + Whether to shuffle the dataset in each iteration. + Attributes ---------- - w : nd-array, shape=(model.size_psi,) + w : nd-array, shape=(model.size_joint_feature,) The learned weights of the SVM. ``loss_curve_`` : list of float @@ -85,47 +98,58 @@ class SubgradientSSVM(BaseSSVM): ``timestamps_`` : list of int Total training time stored before each iteration. + + References + ---------- + * Nathan Ratliff, J. Andrew Bagnell and Martin Zinkevich: + (Online) Subgradient Methods for Structured Prediction, AISTATS 2007 + + * Shalev-Shwartz, Shai and Singer, Yoram and Srebro, Nathan and Cotter, + Andrew: Pegasos: Primal estimated sub-gradient solver for svm, + Mathematical Programming 2011 """ - def __init__(self, model, max_iter=100, C=1.0, verbose=0, momentum=0.9, - learning_rate=0.001, adagrad=False, n_jobs=1, - show_loss_every=0, decay_exponent=0, + def __init__(self, model, max_iter=100, C=1.0, verbose=0, momentum=0.0, + learning_rate='auto', n_jobs=1, + show_loss_every=0, decay_exponent=1, break_on_no_constraints=True, logger=None, batch_size=None, - decay_t0=10): + decay_t0=10, averaging=None, shuffle=False): BaseSSVM.__init__(self, model, max_iter, C, verbose=verbose, n_jobs=n_jobs, show_loss_every=show_loss_every, logger=logger) + self.averaging = averaging self.break_on_no_constraints = break_on_no_constraints self.momentum = momentum self.learning_rate = learning_rate self.t = 0 - self.adagrad = adagrad self.decay_exponent = decay_exponent self.decay_t0 = decay_t0 self.batch_size = batch_size + self.shuffle = shuffle - def _solve_subgradient(self, dpsi, n_samples): + def _solve_subgradient(self, djoint_feature, n_samples, w): """Do a single subgradient step.""" + grad = (djoint_feature - w / (self.C * n_samples)) - grad = (dpsi - self.w / (self.C * n_samples)) - - if self.adagrad: - self.grad_old += grad ** 2 - self.w += self.learning_rate * grad / (1. + np.sqrt(self.grad_old)) - print("grad old %f" % np.mean(self.grad_old)) - print("effective lr %f" % (self.learning_rate / - np.mean(1. + np.sqrt(self.grad_old)))) + self.grad_old = ((1 - self.momentum) * grad + + self.momentum * self.grad_old) + if self.decay_exponent == 0: + effective_lr = self.learning_rate_ else: - self.grad_old = ((1 - self.momentum) * grad - + self.momentum * self.grad_old) - if self.decay_exponent == 0: - effective_lr = self.learning_rate - else: - effective_lr = (self.learning_rate - / (self.t + self.decay_t0) - ** self.decay_exponent) - self.w += effective_lr * self.grad_old - + effective_lr = (self.learning_rate_ + / (self.t + self.decay_t0) + ** self.decay_exponent) + w += effective_lr * self.grad_old + + if self.averaging == 'linear': + rho = 2. / (self.t + 2.) + self.w = (1. - rho) * self.w + rho * w + elif self.averaging == 'squared': + rho = 6. * (self.t + 1) / ((self.t + 2) * (2 * self.t + 3)) + self.w = (1. - rho) * self.w + rho * w + else: + self.w = w self.t += 1. + return w def fit(self, X, Y, constraints=None, warm_start=False, initialize=True): """Learn parameters using subgradient descent. @@ -152,29 +176,36 @@ def fit(self, X, Y, constraints=None, warm_start=False, initialize=True): """ if initialize: self.model.initialize(X, Y) - print("Training primal subgradient structural SVM") - self.grad_old = np.zeros(self.model.size_psi) + if self.verbose: + print("Training primal subgradient structural SVM") + self.grad_old = np.zeros(self.model.size_joint_feature) + self.w = getattr(self, "w", np.zeros(self.model.size_joint_feature)) + w = self.w.copy() if not warm_start: - self.w = getattr(self, "w", np.zeros(self.model.size_psi)) self.objective_curve_ = [] self.timestamps_ = [time()] + if self.learning_rate == "auto": + self.learning_rate_ = self.C * len(X) + else: + self.learning_rate_ = self.learning_rate else: self.timestamps_ = (np.array(self.timestamps_) - time()).tolist() try: # catch ctrl+c to stop training - for iteration in xrange(self.max_iter): + for iteration in range(self.max_iter): + if self.shuffle: + X, Y = shuffle(X, Y) if self.n_jobs == 1: - objective, positive_slacks = self._sequential_learning(X, - Y) + objective, positive_slacks, w = self._sequential_learning(X, Y, w) else: - objective, positive_slacks = self._parallel_learning(X, Y) + objective, positive_slacks, w = self._parallel_learning(X, Y, w) # some statistics - objective *= self.C - objective += np.sum(self.w ** 2) / 2. + objective = objective * self.C + np.sum(w ** 2) / 2. if positive_slacks == 0: - print("No additional constraints") + if self.verbose: + print("No additional constraints") if self.break_on_no_constraints: break if self.verbose > 0: @@ -184,7 +215,7 @@ def fit(self, X, Y, constraints=None, warm_start=False, initialize=True): "objective: %f" % (positive_slacks, objective)) self.timestamps_.append(time() - self.timestamps_[0]) - self.objective_curve_.append(objective) + self.objective_curve_.append(self._objective(X, Y)) if self.verbose > 2: print(self.w) @@ -195,13 +226,23 @@ def fit(self, X, Y, constraints=None, warm_start=False, initialize=True): except KeyboardInterrupt: pass - if self.objective_curve_: - print("final objective: %f" % self.objective_curve_[-1]) - if self.verbose and self.n_jobs == 1: - print("calls to inference: %d" % self.model.inference_calls) + + if self.verbose: + print("Computing final objective") + + self.timestamps_.append(time() - self.timestamps_[0]) + self.objective_curve_.append(self._objective(X, Y)) + if self.logger is not None: + self.logger(self, 'final') + if self.verbose: + if self.objective_curve_: + print("final objective: %f" % self.objective_curve_[-1]) + if self.verbose and self.n_jobs == 1: + print("calls to inference: %d" % self.model.inference_calls) + return self - def _parallel_learning(self, X, Y): + def _parallel_learning(self, X, Y, w): n_samples = len(X) objective, positive_slacks = 0, 0 verbose = max(0, self.verbose - 3) @@ -213,7 +254,7 @@ def _parallel_learning(self, X, Y): if self.n_jobs == -1: n_jobs = cpu_count() else: - n_jobs = self.j_jobs + n_jobs = self.n_jobs n_batches = int(np.ceil(float(len(X)) / n_jobs)) slices = gen_even_slices(n_samples, n_batches) @@ -223,46 +264,49 @@ def _parallel_learning(self, X, Y): candidate_constraints = Parallel( n_jobs=self.n_jobs, verbose=verbose)(delayed(find_constraint)( - self.model, x, y, self.w) + self.model, x, y, w) for x, y in zip(X_b, Y_b)) - dpsi = np.zeros(self.model.size_psi) + djoint_feature = np.zeros(self.model.size_joint_feature) for x, y, constraint in zip(X_b, Y_b, candidate_constraints): - y_hat, delta_psi, slack, loss = constraint + y_hat, delta_joint_feature, slack, loss = constraint if slack > 0: objective += slack - dpsi += delta_psi + djoint_feature += delta_joint_feature positive_slacks += 1 - self._solve_subgradient(dpsi, n_samples) - return objective, positive_slacks + w = self._solve_subgradient(djoint_feature, n_samples, w) + return objective, positive_slacks, w - def _sequential_learning(self, X, Y): + def _sequential_learning(self, X, Y, w): n_samples = len(X) objective, positive_slacks = 0, 0 if self.batch_size in [None, 1]: # online learning for x, y in zip(X, Y): - y_hat, delta_psi, slack, loss = \ - find_constraint(self.model, x, y, self.w) + y_hat, delta_joint_feature, slack, loss = \ + find_constraint(self.model, x, y, w) objective += slack if slack > 0: positive_slacks += 1 - self._solve_subgradient(delta_psi, n_samples) + self._solve_subgradient(delta_joint_feature, n_samples, w) else: # mini batch learning - n_batches = int(np.ceil(float(len(X)) / self.batch_size)) - slices = gen_even_slices(n_samples, n_batches) + if self.batch_size == -1: + slices = [slice(0, len(X))] + else: + n_batches = int(np.ceil(float(len(X)) / self.batch_size)) + slices = gen_even_slices(n_samples, n_batches) for batch in slices: X_b = X[batch] Y_b = Y[batch] Y_hat = self.model.batch_loss_augmented_inference( - X_b, Y_b, self.w, relaxed=True) - delta_psi = (self.model.batch_psi(X_b, Y_b) - - self.model.batch_psi(X_b, Y_hat)) + X_b, Y_b, w, relaxed=True) + delta_joint_feature = (self.model.batch_joint_feature(X_b, Y_b) + - self.model.batch_joint_feature(X_b, Y_hat)) loss = np.sum(self.model.batch_loss(Y_b, Y_hat)) - violation = loss - np.dot(self.w, delta_psi) + violation = np.maximum(0, loss - np.dot(w, delta_joint_feature)) objective += violation positive_slacks += self.batch_size - self._solve_subgradient(delta_psi / len(X_b), n_samples) - return objective, positive_slacks + self._solve_subgradient(delta_joint_feature / len(X_b), n_samples, w) + return objective, positive_slacks, w diff --git a/pystruct/learners/svm.py b/pystruct/learners/svm.py index b4f870a2..31d73f93 100644 --- a/pystruct/learners/svm.py +++ b/pystruct/learners/svm.py @@ -35,7 +35,7 @@ def fit(self, X, y): self.a = a[sv] self.sv = X[sv] self.sv_y = y[sv] - print "%d support vectors out of %d points" % (len(self.a), n_samples) + print("%d support vectors out of %d points" % (len(self.a), n_samples)) # Intercept self.b = 0 diff --git a/pystruct/models/__init__.py b/pystruct/models/__init__.py index d1632d4f..8b02ab64 100644 --- a/pystruct/models/__init__.py +++ b/pystruct/models/__init__.py @@ -9,9 +9,12 @@ from .unstructured_svm import BinaryClf, MultiClassClf from .multilabel_svm import MultiLabelClf from .edge_feature_graph_crf import EdgeFeatureGraphCRF +from .typed_crf import TypedCRF +from .node_type_edge_feature_graph_crf import NodeTypeEdgeFeatureGraphCRF __all__ = ["StructuredModel", "CRF", "GridCRF", "GraphCRF", "DirectionalGridCRF", "BinaryClf", "LatentGridCRF", "LatentDirectionalGridCRF", "MultiClassClf", "LatentGraphCRF", "MultiLabelClf", "ChainCRF", "LatentNodeCRF", "EdgeFeatureGraphCRF", - "EdgeFeatureLatentNodeCRF"] + "EdgeFeatureLatentNodeCRF", + "TypedCRF", "NodeTypeEdgeFeatureGraphCRF"] diff --git a/pystruct/models/base.py b/pystruct/models/base.py index 61d293e8..8e3fa44a 100644 --- a/pystruct/models/base.py +++ b/pystruct/models/base.py @@ -5,52 +5,55 @@ class StructuredModel(object): """Interface definition for Structured Learners. This class defines what is necessary to use the structured svm. - You have to implement at least psi and inference. + You have to implement at least joint_feature and inference. """ def __repr__(self): - return ("%s, size_psi: %d" - % (type(self).__name__, self.size_psi)) + return ("%s, size_joint_feature: %d" + % (type(self).__name__, self.size_joint_feature)) def __init__(self): """Initialize the model. - Needs to set self.size_psi, the dimensionalty of the joint features for - an instance with labeling (x, y). + Needs to set self.size_joint_feature, the dimensionality of the joint + features for an instance with labeling (x, y). """ - self.size_psi = None + self.size_joint_feature = None def _check_size_w(self, w): - if w.shape != (self.size_psi,): + if w.shape != (self.size_joint_feature,): raise ValueError("Got w of wrong shape. Expected %s, got %s" % - (self.size_psi, w.shape)) + (self.size_joint_feature, w.shape)) def initialize(self, X, Y): # set any data-specific parameters in the model pass - def psi(self, x, y): + def joint_feature(self, x, y): raise NotImplementedError() - def batch_psi(self, X, Y, Y_true=None): - psi_ = np.zeros(self.size_psi) + def batch_joint_feature(self, X, Y, Y_true=None): + joint_feature_ = np.zeros(self.size_joint_feature) if getattr(self, 'rescale_C', False): for x, y, y_true in zip(X, Y, Y_true): - psi_ += self.psi(x, y, y_true) + joint_feature_ += self.joint_feature(x, y, y_true) else: for x, y in zip(X, Y): - psi_ += self.psi(x, y) - return psi_ + joint_feature_ += self.joint_feature(x, y) + return joint_feature_ - def _loss_augmented_dpsi(self, x, y, y_hat, w): + def _loss_augmented_djoint_feature(self, x, y, y_hat, w): # debugging only! x_loss_augmented = self.loss_augment(x, y, w) - return (self.psi(x_loss_augmented, y) - - self.psi(x_loss_augmented, y_hat)) + return (self.joint_feature(x_loss_augmented, y) + - self.joint_feature(x_loss_augmented, y_hat)) - def inference(self, x, w, relaxed=None): + def inference(self, x, w, relaxed=None, constraints=None): raise NotImplementedError() - def batch_inference(self, X, w, relaxed=None): + def batch_inference(self, X, w, relaxed=None, constraints=None): # default implementation of batch inference + if constraints: + return [self.inference(x, w, relaxed=relaxed, constraints=c) + for x, c in zip(X, constraints)] return [self.inference(x, w, relaxed=relaxed) for x in X] @@ -95,7 +98,7 @@ def batch_loss_augmented_inference(self, X, Y, w, relaxed=None): for x, y in zip(X, Y)] def _set_class_weight(self): - if not hasattr(self, 'size_psi'): + if not hasattr(self, 'size_joint_feature'): # we are not initialized yet return diff --git a/pystruct/models/chain_crf.py b/pystruct/models/chain_crf.py index d98bb516..bf77df80 100644 --- a/pystruct/models/chain_crf.py +++ b/pystruct/models/chain_crf.py @@ -26,19 +26,15 @@ class ChainCRF(GraphCRF): Parameters ---------- - n_states : int, default=2 + n_states : int, default=None Number of states for all variables. + Inferred from data if not provided. inference_method : string or None, default=None Function to call do do inference and loss-augmented inference. - Possible values are: - - - 'qpbo' for QPBO + alpha expansion. - - 'dai' for LibDAI bindings (which has another parameter). - - 'lp' for Linear Programming relaxation using GLPK. - - 'ad3' for AD3 dual decomposition. - - If None, ad3 is used if installed, otherwise lp. + Defaults to "max-product" for max-product belief propagation. + As chains can be solved exactly and efficiently, other settings + are not recommended. class_weight : None, or array-like Class weights. If an array-like is passed, it must have length @@ -51,6 +47,8 @@ class ChainCRF(GraphCRF): """ def __init__(self, n_states=None, n_features=None, inference_method=None, class_weight=None, directed=True): + if inference_method is None: + inference_method = "max-product" GraphCRF.__init__(self, n_states=n_states, n_features=n_features, inference_method=inference_method, class_weight=class_weight, directed=directed) @@ -76,5 +74,5 @@ def initialize(self, X, Y): raise ValueError("Expected %d states, got %d" % (self.n_states, n_states)) - self._set_size_psi() + self._set_size_joint_feature() self._set_class_weight() diff --git a/pystruct/models/crf.py b/pystruct/models/crf.py index 41f3f987..ba466829 100644 --- a/pystruct/models/crf.py +++ b/pystruct/models/crf.py @@ -12,12 +12,12 @@ def __init__(self, n_states=None, n_features=None, inference_method=None, self.n_states = n_states if inference_method is None: # get first in list that is installed - inference_method = get_installed(['ad3', 'lp'])[0] + inference_method = get_installed(['ad3', 'max-product', 'lp'])[0] self.inference_method = inference_method self.inference_calls = 0 self.n_features = n_features self.class_weight = class_weight - self._set_size_psi() + self._set_size_joint_feature() self._set_class_weight() def initialize(self, X, Y): @@ -37,11 +37,11 @@ def initialize(self, X, Y): raise ValueError("Expected %d states, got %d" % (self.n_states, n_states)) - self._set_size_psi() + self._set_size_joint_feature() self._set_class_weight() def __repr__(self): - return ("%s(n_states: %d, inference_method: %s)" + return ("%s(n_states: %s, inference_method: %s)" % (type(self).__name__, self.n_states, self.inference_method)) @@ -52,12 +52,19 @@ def _check_size_x(self, x): " got %s instead." % (self.n_features, features.shape[1])) + def loss_augment_unaries(self, unary_potentials, y): + """ + we define it as a method so that subclasses can specialize it. + """ + loss_augment_unaries(unary_potentials, np.asarray(y), + self.class_weight) + def loss_augmented_inference(self, x, y, w, relaxed=False, return_energy=False): """Loss-augmented Inference for x relative to y using parameters w. Finds (approximately) - armin_y_hat np.dot(w, psi(x, y_hat)) + loss(y, y_hat) + armin_y_hat np.dot(w, joint_feature(x, y_hat)) + loss(y, y_hat) using self.inference_method. @@ -73,7 +80,7 @@ def loss_augmented_inference(self, x, y, w, relaxed=False, Ground truth labeling relative to which the loss will be measured. - w : ndarray, shape=(size_psi,) + w : ndarray, shape=(size_joint_feature,) Parameters for the CRF energy function. relaxed : bool, default=False @@ -103,17 +110,19 @@ def loss_augmented_inference(self, x, y, w, relaxed=False, unary_potentials = self._get_unary_potentials(x, w) pairwise_potentials = self._get_pairwise_potentials(x, w) edges = self._get_edges(x) - loss_augment_unaries(unary_potentials, np.asarray(y), self.class_weight) + + self.loss_augment_unaries(unary_potentials, y) return inference_dispatch(unary_potentials, pairwise_potentials, edges, self.inference_method, relaxed=relaxed, return_energy=return_energy) - def inference(self, x, w, relaxed=False, return_energy=False): + def inference(self, x, w, relaxed=False, return_energy=False, + constraints=None): """Inference for x using parameters w. Finds (approximately) - armin_y np.dot(w, psi(x, y)) + armin_y np.dot(w, joint_feature(x, y)) using self.inference_method. @@ -125,7 +134,7 @@ def inference(self, x, w, relaxed=False, return_energy=False): unaries are an nd-array of shape (n_nodes, n_states), edges are an nd-array of shape (n_edges, 2) - w : ndarray, shape=(size_psi,) + w : ndarray, shape=(size_joint_feature,) Parameters for the CRF energy function. relaxed : bool, default=False @@ -137,6 +146,9 @@ def inference(self, x, w, relaxed=False, return_energy=False): return_energy : bool, default=False Whether to return the energy of the solution (x, y) that was found. + constraints : None or list, default=False + hard logic constraints, if any + Returns ------- y_pred : ndarray or tuple @@ -156,6 +168,16 @@ def inference(self, x, w, relaxed=False, return_energy=False): pairwise_potentials = self._get_pairwise_potentials(x, w) edges = self._get_edges(x) - return inference_dispatch(unary_potentials, pairwise_potentials, edges, - self.inference_method, relaxed=relaxed, - return_energy=return_energy) + if constraints: + return inference_dispatch(unary_potentials, pairwise_potentials, + edges, + self.inference_method, + relaxed=relaxed, + return_energy=return_energy, + constraints=constraints) + else: + return inference_dispatch(unary_potentials, pairwise_potentials, + edges, + self.inference_method, + relaxed=relaxed, + return_energy=return_energy) diff --git a/pystruct/models/edge_feature_graph_crf.py b/pystruct/models/edge_feature_graph_crf.py index 7d949d8c..72cd6d49 100644 --- a/pystruct/models/edge_feature_graph_crf.py +++ b/pystruct/models/edge_feature_graph_crf.py @@ -21,27 +21,27 @@ class EdgeFeatureGraphCRF(GraphCRF): edge_features)`` where edges is an array of shape (n_edges, 2), representing the graph. - Labels ``y`` are given as array of shape (n_features) + Labels ``y`` are given as array of shape (n_nodes) Parameters ---------- - n_states : int, default=2 - Number of states for all variables. + n_states : int, default=None + Number of states for all variables. Inferred from data if not provided. n_features : int, default=None - Number of features per node. None means n_states. + Number of features per node. Inferred from data if not provided. - n_edge_features : int, default=1 - Number of features per edge. + n_edge_features : int, default=None + Number of features per edge. Inferred from data if not provided. inference_method : string, default="ad3" Function to call do do inference and loss-augmented inference. Possible values are: - - 'qpbo' for QPBO + alpha expansion. - - 'dai' for LibDAI bindings (which has another parameter). - - 'lp' for Linear Programming relaxation using GLPK. + - 'max-product' for max-product belief propagation. + - 'lp' for Linear Programming relaxation using cvxopt. - 'ad3' for AD3 dual decomposition. + - 'qpbo' for QPBO + alpha expansion. class_weight : None, or array-like Class weights. If an array-like is passed, it must have length @@ -71,11 +71,11 @@ def __init__(self, n_states=None, n_features=None, n_edge_features=None, GraphCRF.__init__(self, n_states, n_features, inference_method, class_weight=class_weight) - def _set_size_psi(self): - if not None in [self.n_states, self.n_features, self.n_edge_features]: - self.size_psi = (self.n_states * self.n_features - + self.n_edge_features - * self.n_states ** 2) + def _set_size_joint_feature(self): + if None not in [self.n_states, self.n_features, self.n_edge_features]: + self.size_joint_feature = (self.n_states * self.n_features + + self.n_edge_features + * self.n_states ** 2) if self.n_edge_features is not None: if np.any(np.hstack([self.symmetric_edge_features, @@ -127,7 +127,7 @@ def _get_pairwise_potentials(self, x, w): x : tuple Instance Representation. - w : ndarray, shape=(size_psi,) + w : ndarray, shape=(size_joint_feature,) Weight vector for CRF instance. Returns @@ -143,11 +143,11 @@ def _get_pairwise_potentials(self, x, w): return np.dot(edge_features, pairwise).reshape( edge_features.shape[0], self.n_states, self.n_states) - def psi(self, x, y): + def joint_feature(self, x, y): """Feature vector associated with instance (x, y). - Feature representation psi, such that the energy of the configuration - (x, y) and a weight vector w is given by np.dot(w, psi(x, y)). + Feature representation joint_feature, such that the energy of the configuration + (x, y) and a weight vector w is given by np.dot(w, joint_feature(x, y)). Parameters ---------- @@ -162,7 +162,7 @@ def psi(self, x, y): Returns ------- - p : ndarray, shape (size_psi,) + p : ndarray, shape (size_joint_feature,) Feature vector associated with state (x, y). """ @@ -202,5 +202,5 @@ def psi(self, x, y): unaries_acc = np.dot(unary_marginals.T, features) - psi_vector = np.hstack([unaries_acc.ravel(), pw.ravel()]) - return psi_vector + joint_feature_vector = np.hstack([unaries_acc.ravel(), pw.ravel()]) + return joint_feature_vector diff --git a/pystruct/models/graph_crf.py b/pystruct/models/graph_crf.py index 40f77048..4f3fa156 100644 --- a/pystruct/models/graph_crf.py +++ b/pystruct/models/graph_crf.py @@ -7,8 +7,9 @@ class GraphCRF(CRF): """Pairwise CRF on a general graph. - Pairwise potentials are symmetric and the same for all edges. - This leads to n_classes parameters for unary potentials. + Pairwise potentials the same for all edges, are symmetric by default + (``directed=False``). This leads to n_classes parameters for unary + potentials. If ``directed=True``, there are ``n_classes * n_classes`` parameters for pairwise potentials, if ``directed=False``, there are only @@ -21,16 +22,16 @@ class GraphCRF(CRF): Labels, Y, are given as an iterable of n_examples. Each label, y, in Y is given by a numpy array of shape (n_nodes,). - + There are n_states * n_features parameters for unary potentials. For edge potential parameters, there are n_state * - n_states permutations, i.e. - - state_1 state_2 state 3 - state_1 1 2 3 - state_2 4 5 6 - state_3 7 8 9 - + n_states permutations, i.e. :: + + state_1 state_2 state 3 + state_1 1 2 3 + state_2 4 5 6 + state_3 7 8 9 + The fitted parameters of this model will be returned as an array with the first n_states * n_features elements representing the unary potentials parameters, followed by the edge potential @@ -39,35 +40,38 @@ class GraphCRF(CRF): Say we have two state, A and B, and two features 1 and 2. The unary potential parameters will be returned as [A1, A2, B1, B2]. - If ``directed=True`` the edge potential parameters will return + If ``directed=True`` the edge potential parameters will return n_states * n_states parameters. The rows are senders and the columns are recievers, i.e. the edge potential state_2 -> state_1 is [2,1]; 4 in the above matrix. - - The above edge potential parameters example would be returned as + + The above edge potential parameters example would be returned as [1, 2, 3, 4, 5, 6, 7, 8, 9] (see numpy.ravel). - If edges are undirected, the edge potential parameter matrix is + If edges are undirected, the edge potential parameter matrix is assumed to be symmetric and only the lower triangle is returned, i.e. [1, 4, 5, 7, 8, 9]. - + Parameters ---------- - n_states : int, default=2 - Number of states for all variables. + n_states : int, default=None + Number of states for all variables. Inferred from data if not provided. n_features : int, default=None - Number of features per node. None means n_states. + Number of features per node. Inferred from data if not provided. inference_method : string or None, default=None Function to call do do inference and loss-augmented inference. Possible values are: - - 'qpbo' for QPBO + alpha expansion. - - 'dai' for LibDAI bindings (which has another parameter). - - 'lp' for Linear Programming relaxation using GLPK. + - 'max-product' for max-product belief propagation. + Recommended for chains an trees. Loopy belief propagation in + case of a general graph. + - 'lp' for Linear Programming relaxation using cvxopt. - 'ad3' for AD3 dual decomposition. + - 'qpbo' for QPBO + alpha expansion. + - 'ogm' for OpenGM inference algorithms. If None, ad3 is used if installed, otherwise lp. @@ -88,15 +92,16 @@ def __init__(self, n_states=None, n_features=None, inference_method=None, class_weight=class_weight) # n_states unary parameters, upper triangular for pairwise - def _set_size_psi(self): - # try to set the size of psi if possible + def _set_size_joint_feature(self): + # try to set the size of joint_feature if possible if self.n_features is not None and self.n_states is not None: if self.directed: - self.size_psi = (self.n_states * self.n_features + - self.n_states ** 2) + self.size_joint_feature = int(self.n_states * self.n_features + + self.n_states ** 2) else: - self.size_psi = (self.n_states * self.n_features - + self.n_states * (self.n_states + 1) / 2) + self.size_joint_feature = int( + self.n_states * self.n_features + + self.n_states * (self.n_states + 1) / 2) def _get_edges(self, x): return x[1] @@ -112,7 +117,7 @@ def _get_pairwise_potentials(self, x, w): x : tuple Instance Representation. - w : ndarray, shape=(size_psi,) + w : ndarray, shape=(size_joint_feature,) Weight vector for CRF instance. Returns @@ -135,7 +140,7 @@ def _get_unary_potentials(self, x, w): x : tuple Instance Representation. - w : ndarray, shape=(size_psi,) + w : ndarray, shape=(size_joint_feature,) Weight vector for CRF instance. Returns @@ -145,17 +150,17 @@ def _get_unary_potentials(self, x, w): """ self._check_size_w(w) self._check_size_x(x) - features, edges = self._get_features(x), self._get_edges(x) + features = self._get_features(x) unary_params = w[:self.n_states * self.n_features].reshape( self.n_states, self.n_features) return np.dot(features, unary_params.T) - def psi(self, x, y): + def joint_feature(self, x, y): """Feature vector associated with instance (x, y). - Feature representation psi, such that the energy of the configuration - (x, y) and a weight vector w is given by np.dot(w, psi(x, y)). + Feature representation joint_feature, such that the energy of the configuration + (x, y) and a weight vector w is given by np.dot(w, joint_feature(x, y)). Parameters ---------- @@ -170,7 +175,7 @@ def psi(self, x, y): Returns ------- - p : ndarray, shape (size_psi,) + p : ndarray, shape (size_joint_feature,) Feature vector associated with state (x, y). """ @@ -203,5 +208,5 @@ def psi(self, x, y): else: pw = compress_sym(pw) - psi_vector = np.hstack([unaries_acc.ravel(), pw]) - return psi_vector + joint_feature_vector = np.hstack([unaries_acc.ravel(), pw]) + return joint_feature_vector diff --git a/pystruct/models/grid_crf.py b/pystruct/models/grid_crf.py index 73ac4387..de0f3e14 100644 --- a/pystruct/models/grid_crf.py +++ b/pystruct/models/grid_crf.py @@ -11,7 +11,7 @@ class GridCRF(GraphCRF): This leads to n_classes parameters for unary potentials and n_classes * (n_classes + 1) / 2 parameters for edge potentials. - Unary evidence ``x`` is given as array of shape (width, height, n_states), + Unary evidence ``x`` is given as array of shape (width, height, n_features), labels ``y`` are given as array of shape (width, height). Grid sizes do not need to be constant over the dataset. @@ -24,10 +24,13 @@ class GridCRF(GraphCRF): Function to call do do inference and loss-augmented inference. Possible values are: - - 'qpbo' for QPBO + alpha expansion. - - 'dai' for LibDAI bindings (which has another parameter). - - 'lp' for Linear Programming relaxation using GLPK. + - 'max-product' for max-product belief propagation. + Recommended for chains an trees. Loopy belief propagation in + case of a general graph. + - 'lp' for Linear Programming relaxation using cvxopt. - 'ad3' for AD3 dual decomposition. + - 'qpbo' for QPBO + alpha expansion. + - 'ogm' for OpenGM inference algorithms. neighborhood : int, default=4 Neighborhood defining connection for each variable in the grid. @@ -87,7 +90,7 @@ class DirectionalGridCRF(GridCRF, EdgeFeatureGraphCRF): (horizontal and vertical) or 4 for a 8 connected neighborhood (additionally two diagonals). - Unary evidence ``x`` is given as array of shape (width, height, n_states), + Unary evidence ``x`` is given as array of shape (width, height, n_features), labels ``y`` are given as array of shape (width, height). Grid sizes do not need to be constant over the dataset. @@ -100,10 +103,13 @@ class DirectionalGridCRF(GridCRF, EdgeFeatureGraphCRF): Function to call do do inference and loss-augmented inference. Possible values are: - - 'qpbo' for QPBO + alpha expansion. - - 'dai' for LibDAI bindings (which has another parameter). - - 'lp' for Linear Programming relaxation using GLPK. + - 'max-product' for max-product belief propagation. + Recommended for chains an trees. Loopy belief propagation in + case of a general graph. + - 'lp' for Linear Programming relaxation using cvxopt. - 'ad3' for AD3 dual decomposition. + - 'qpbo' for QPBO + alpha expansion. + - 'ogm' for OpenGM inference algorithms. neighborhood : int, default=4 Neighborhood defining connection for each variable in the grid. @@ -117,10 +123,11 @@ def __init__(self, n_states=None, n_features=None, inference_method=None, n_edge_features, inference_method=inference_method) - def _set_size_psi(self): + def _set_size_joint_feature(self): if self.n_features is not None and self.n_states is not None: - self.size_psi = (self.n_states * self.n_features - + self.n_edge_features * self.n_states ** 2) + self.size_joint_feature = ( + self.n_states * self.n_features + + self.n_edge_features * self.n_states ** 2) def _check_size_x(self, x): GridCRF._check_size_x(self, x) @@ -133,15 +140,15 @@ def _get_edges(self, x, flat=True): return make_grid_edges(x, neighborhood=self.neighborhood, return_lists=not flat) - def psi(self, x, y): + def joint_feature(self, x, y): """Feature vector associated with instance (x, y). - Feature representation psi, such that the energy of the configuration - (x, y) and a weight vector w is given by np.dot(w, psi(x, y)). + Feature representation joint_feature, such that the energy of the configuration + (x, y) and a weight vector w is given by np.dot(w, joint_feature(x, y)). Parameters ---------- - x : ndarray, shape (width, height, n_states) + x : ndarray, shape (width, height, n_features) Unary evidence / input. y : ndarray or tuple @@ -155,11 +162,11 @@ def psi(self, x, y): Returns ------- - p : ndarray, shape (size_psi,) + p : ndarray, shape (size_joint_feature,) Feature vector associated with state (x, y). """ - return EdgeFeatureGraphCRF.psi(self, x, y) + return EdgeFeatureGraphCRF.joint_feature(self, x, y) def _get_edge_features(self, x): return edge_list_to_features(self._get_edges(x, flat=False)) diff --git a/pystruct/models/latent_graph_crf.py b/pystruct/models/latent_graph_crf.py index 06cb9189..c77e5b69 100644 --- a/pystruct/models/latent_graph_crf.py +++ b/pystruct/models/latent_graph_crf.py @@ -52,8 +52,9 @@ def kmeans_init(X, Y, all_edges, n_labels, n_states_per_label, km.fit(f) for feats_sample, y, h in zip(all_feats, Y, H): indicator_sample = y.ravel() == label - pred = km.predict(feats_sample[indicator_sample]).astype(np.int) - h.ravel()[indicator_sample] = pred + label_indices[label] + if np.any(indicator_sample): + pred = km.predict(feats_sample[indicator_sample]).astype(np.int) + h.ravel()[indicator_sample] = pred + label_indices[label] return H @@ -83,11 +84,13 @@ class LatentGraphCRF(GraphCRF): Function to call to do inference and loss-augmented inference. Possible values are: - - 'qpbo' for QPBO + alpha expansion. - - 'dai' for LibDAI bindings (which has another parameter). - - 'lp' for Linear Programming relaxation using GLPK. + - 'max-product' for max-product belief propagation. + Recommended for chains an trees. Loopy belief propagation in + case of a general graph. + - 'lp' for Linear Programming relaxation using cvxopt. - 'ad3' for AD3 dual decomposition. - + - 'qpbo' for QPBO + alpha expansion. + - 'ogm' for OpenGM inference algorithms. """ def __init__(self, n_labels=None, n_features=None, n_states_per_label=2, inference_method=None): @@ -96,14 +99,14 @@ def __init__(self, n_labels=None, n_features=None, n_states_per_label=2, GraphCRF.__init__(self, n_states=None, n_features=n_features, inference_method=inference_method) - def _set_size_psi(self): + def _set_size_joint_feature(self): if None in [self.n_features, self.n_labels]: return if isinstance(self.n_states_per_label, numbers.Integral): # same for all labels n_states_per_label = np.array([ - self.n_states_per_label for i in xrange(self.n_labels)]) + self.n_states_per_label for i in range(self.n_labels)]) else: n_states_per_label = np.array(self.n_states_per_label) if len(n_states_per_label) != self.n_labels: @@ -111,15 +114,15 @@ def _set_size_psi(self): "or array-like of length n_labels. Got %s" % str(n_states_per_label)) self.n_states_per_label = n_states_per_label - self.n_states = np.sum(n_states_per_label) + self.n_states = int(np.sum(n_states_per_label)) # compute mapping from latent states to labels ranges = np.cumsum(n_states_per_label) states_map = np.zeros(self.n_states, dtype=np.int) - for l in xrange(1, self.n_labels): + for l in range(1, self.n_labels): states_map[ranges[l - 1]: ranges[l]] = l self._states_map = states_map - GraphCRF._set_size_psi(self) + GraphCRF._set_size_joint_feature(self) def initialize(self, X, Y): n_features = X[0][0].shape[1] @@ -135,7 +138,7 @@ def initialize(self, X, Y): elif self.n_labels != n_labels: raise ValueError("Expected %d states, got %d" % (self.n_labels, n_labels)) - self._set_size_psi() + self._set_size_joint_feature() self._set_class_weight() def label_from_latent(self, h): @@ -179,8 +182,6 @@ def latent(self, x, y, w): self.inference_method, relaxed=False) if (self.label_from_latent(h) != y).any(): print("inconsistent h and y") - from IPython.core.debugger import Tracer - Tracer()() h = np.hstack([0, np.cumsum(self.n_states_per_label)])[y] return h @@ -194,7 +195,7 @@ def continuous_loss(self, y, y_hat): # continuous version of the loss # y_hat is the result of linear programming y_hat_org = np.zeros((y_hat.shape[0], self.n_labels)) - for s in xrange(self.n_states): + for s in range(self.n_states): y_hat_org[:, self._states_map[s]] += y_hat[:, s] y_org = self.label_from_latent(y) return GraphCRF.continuous_loss(self, y_org, y_hat_org) diff --git a/pystruct/models/latent_grid_crf.py b/pystruct/models/latent_grid_crf.py index 8f85a525..ed312b1c 100644 --- a/pystruct/models/latent_grid_crf.py +++ b/pystruct/models/latent_grid_crf.py @@ -16,8 +16,8 @@ def __init__(self, n_labels=None, n_features=None, n_states_per_label=2, n_features=self.n_features, neighborhood=neighborhood, inference_method=inference_method) - def _set_size_psi(self): - LatentGraphCRF._set_size_psi(self) + def _set_size_joint_feature(self): + LatentGraphCRF._set_size_joint_feature(self) def initialize(self, X, Y): LatentGraphCRF.initialize(self, X, Y) @@ -68,9 +68,9 @@ def __init__(self, n_labels=None, n_features=None, n_states_per_label=2, LatentGridCRF.__init__(self, n_labels, n_features, n_states_per_label, inference_method=inference_method) - def _set_size_psi(self): - LatentGridCRF._set_size_psi(self) - DirectionalGridCRF._set_size_psi(self) + def _set_size_joint_feature(self): + LatentGridCRF._set_size_joint_feature(self) + DirectionalGridCRF._set_size_joint_feature(self) def initialize(self, X, Y): LatentGridCRF.initialize(self, X, Y) diff --git a/pystruct/models/latent_node_crf.py b/pystruct/models/latent_node_crf.py index 8eb41a45..14cbd84a 100644 --- a/pystruct/models/latent_node_crf.py +++ b/pystruct/models/latent_node_crf.py @@ -23,7 +23,13 @@ def kmeans_init(X, Y, n_labels, n_hidden_states, latent_node_features=False): # iterate over samples for x, y in zip(X, Y): # first, get neighbor counts from nodes - features, edges, n_hidden = x + if len(x) == 3: + features, edges, n_hidden = x + elif len(x) == 4: + # edge features are discarded + features, edges, _, n_hidden = x + else: + raise ValueError("Something is fishy!") n_visible = features.shape[0] if latent_node_features: n_visible -= n_hidden @@ -82,10 +88,13 @@ class LatentNodeCRF(GraphCRF): Function to call to do inference and loss-augmented inference. Possible values are: - - 'qpbo' for QPBO + alpha expansion. - - 'dai' for LibDAI bindings (which has another parameter). - - 'lp' for Linear Programming relaxation using GLPK. + - 'max-product' for max-product belief propagation. + Recommended for chains an trees. Loopy belief propagation in + case of a general graph. + - 'lp' for Linear Programming relaxation using cvxopt. - 'ad3' for AD3 dual decomposition. + - 'qpbo' for QPBO + alpha expansion. + - 'ogm' for OpenGM inference algorithms. class_weight : None, or array-like Class weights. If an array-like is passed, it must have length @@ -110,7 +119,7 @@ def __init__(self, n_labels=None, n_features=None, n_hidden_states=2, inference_method=inference_method, class_weight=class_weight) - def _set_size_psi(self): + def _set_size_joint_feature(self): if None in [self.n_states, self.n_features]: return @@ -119,8 +128,8 @@ def _set_size_psi(self): else: n_input_states = self.n_labels self.n_input_states = n_input_states - self.size_psi = (n_input_states * self.n_features - + self.n_states * (self.n_states + 1) / 2) + self.size_joint_feature = int(n_input_states * self.n_features + + self.n_states * (self.n_states + 1) / 2) def initialize(self, X, Y): n_features = X[0][0].shape[1] @@ -137,7 +146,7 @@ def initialize(self, X, Y): raise ValueError("Expected %d labels, got %d" % (self.n_labels, n_labels)) self.n_states = self.n_hidden_states + n_labels - self._set_size_psi() + self._set_size_joint_feature() self._set_class_weight() def _get_pairwise_potentials(self, x, w): @@ -148,7 +157,7 @@ def _get_pairwise_potentials(self, x, w): x : tuple Instance Representation. - w : ndarray, shape=(size_psi,) + w : ndarray, shape=(size_joint_feature,) Weight vector for CRF instance. Returns @@ -168,7 +177,7 @@ def _get_unary_potentials(self, x, w): x : tuple Instance Representation. - w : ndarray, shape=(size_psi,) + w : ndarray, shape=(size_joint_feature,) Weight vector for CRF instance. Returns @@ -178,7 +187,7 @@ def _get_unary_potentials(self, x, w): """ self._check_size_w(w) self._check_size_x(x) - features, edges = self._get_features(x), self._get_edges(x) + features = self._get_features(x) unary_params = w[:self.n_input_states * self.n_features].reshape( self.n_input_states, self.n_features) @@ -255,11 +264,12 @@ def continuous_loss(self, y, y_hat): y_hat_org = y_hat[:y_org.size, :self.n_labels] return GraphCRF.continuous_loss(self, y_org, y_hat_org) - def psi(self, x, y): + def joint_feature(self, x, y): """Feature vector associated with instance (x, y). - Feature representation psi, such that the energy of the configuration - (x, y) and a weight vector w is given by np.dot(w, psi(x, y)). + Feature representation joint_feature, such that the energy of the + configuration (x, y) and a weight vector w is given by + np.dot(w, joint_feature(x, y)). Parameters ---------- @@ -274,7 +284,7 @@ def psi(self, x, y): Returns ------- - p : ndarray, shape (size_psi,) + p : ndarray, shape (size_joint_feature,) Feature vector associated with state (x, y). """ @@ -302,8 +312,9 @@ def psi(self, x, y): unaries_acc = np.dot(unary_marginals[:n_visible, :self.n_input_states].T, features) - psi_vector = np.hstack([unaries_acc.ravel(), compress_sym(pw)]) - return psi_vector + joint_feature_vector = np.hstack([unaries_acc.ravel(), + compress_sym(pw)]) + return joint_feature_vector def init_latent(self, X, Y): # treat all edges the same @@ -318,7 +329,7 @@ def max_loss(self, h): return np.sum(self.class_weight[y]) -class EdgeFeatureLatentNodeCRF(GraphCRF): +class EdgeFeatureLatentNodeCRF(LatentNodeCRF): """CRF with latent variables and edge features. Yeah that's totally not a mess. @@ -326,7 +337,6 @@ class EdgeFeatureLatentNodeCRF(GraphCRF): Input x is tuple (features, edges, edge_features, n_hidden) First features.shape[0] nodes are observed, then n_hidden unobserved nodes. - Currently unobserved nodes don't have features. Parameters ---------- @@ -342,14 +352,17 @@ class EdgeFeatureLatentNodeCRF(GraphCRF): n_features : int, default=None Number of features per node. None means n_states. - inference_method : string, default="lp" + inference_method : string, default="None" Function to call to do inference and loss-augmented inference. Possible values are: - - 'qpbo' for QPBO + alpha expansion. - - 'dai' for LibDAI bindings (which has another parameter). - - 'lp' for Linear Programming relaxation using GLPK. + - 'max-product' for max-product belief propagation. + Recommended for chains an trees. Loopy belief propagation in + case of a general graph. + - 'lp' for Linear Programming relaxation using cvxopt. - 'ad3' for AD3 dual decomposition. + - 'qpbo' for QPBO + alpha expansion. + - 'ogm' for OpenGM inference algorithms. class_weight : None, or array-like Class weights. If an array-like is passed, it must have length @@ -368,7 +381,7 @@ class EdgeFeatureLatentNodeCRF(GraphCRF): """ def __init__(self, n_labels=2, n_features=None, n_edge_features=1, - n_hidden_states=2, inference_method='lp', class_weight=None, + n_hidden_states=2, inference_method=None, class_weight=None, latent_node_features=False, symmetric_edge_features=None, antisymmetric_edge_features=None): @@ -380,19 +393,14 @@ def __init__(self, n_labels=2, n_features=None, n_edge_features=1, n_states = n_hidden_states + n_labels self.n_edge_features = n_edge_features - GraphCRF.__init__(self, n_states, n_features, - inference_method=inference_method, - class_weight=class_weight) - if latent_node_features: n_input_states = n_states else: n_input_states = n_labels self.n_input_states = n_input_states - self.size_psi = (n_input_states * self.n_features - + self.n_edge_features - * n_states ** 2) + self.size_joint_feature = ( + n_input_states * n_features + n_edge_features * n_states ** 2) self.latent_node_features = latent_node_features if symmetric_edge_features is None: @@ -409,6 +417,23 @@ def __init__(self, n_labels=2, n_features=None, n_edge_features=1, self.symmetric_edge_features = symmetric_edge_features self.antisymmetric_edge_features = antisymmetric_edge_features + GraphCRF.__init__(self, n_states, n_features, + inference_method=inference_method, + class_weight=class_weight) + + def _set_size_joint_feature(self): + if None in [self.n_states, self.n_features]: + return + + if self.latent_node_features: + n_input_states = self.n_states + else: + n_input_states = self.n_labels + self.n_input_states = n_input_states + self.size_joint_feature = ( + n_input_states * self.n_features + self.n_edge_features * + self.n_states ** 2) + def _check_size_x(self, x): GraphCRF._check_size_x(self, x) @@ -428,7 +453,7 @@ def _get_pairwise_potentials(self, x, w): x : tuple Instance Representation. - w : ndarray, shape=(size_psi,) + w : ndarray, shape=(size_joint_feature,) Weight vector for CRF instance. Returns @@ -452,7 +477,7 @@ def _get_unary_potentials(self, x, w): x : tuple Instance Representation. - w : ndarray, shape=(size_psi,) + w : ndarray, shape=(size_joint_feature,) Weight vector for CRF instance. Returns @@ -462,13 +487,13 @@ def _get_unary_potentials(self, x, w): """ self._check_size_w(w) self._check_size_x(x) - features, edges = self._get_features(x), self._get_edges(x) + features = self._get_features(x) unary_params = w[:self.n_input_states * self.n_features].reshape( self.n_input_states, self.n_features) if self.latent_node_features: unaries = np.dot(features, unary_params.T) - n_hidden = x[2] + n_hidden = x[-1] n_visible = features.shape[0] - n_hidden else: # we only have features for visible nodes @@ -515,8 +540,6 @@ def latent(self, x, y, w): self.inference_method, relaxed=False) if (h[:len(y)] != y).any(): print("inconsistent h and y") - from IPython.core.debugger import Tracer - Tracer()() h[:len(y)] = y return h @@ -541,11 +564,12 @@ def continuous_loss(self, y, y_hat): y_hat_org = y_hat[:y_org.size, :self.n_labels] return GraphCRF.continuous_loss(self, y_org, y_hat_org) - def psi(self, x, y): + def joint_feature(self, x, y): """Feature vector associated with instance (x, y). - Feature representation psi, such that the energy of the configuration - (x, y) and a weight vector w is given by np.dot(w, psi(x, y)). + Feature representation joint_feature, such that the energy of the + configuration (x, y) and a weight vector w is given by + np.dot(w, joint_feature(x, y)). Parameters ---------- @@ -560,7 +584,7 @@ def psi(self, x, y): Returns ------- - p : ndarray, shape (size_psi,) + p : ndarray, shape (size_joint_feature,) Feature vector associated with state (x, y). """ @@ -576,12 +600,12 @@ def psi(self, x, y): n_nodes = y.size gx = np.ogrid[:n_nodes] - #make one hot encoding + # make one hot encoding unary_marginals = np.zeros((n_nodes, self.n_states), dtype=np.int) gx = np.ogrid[:n_nodes] unary_marginals[gx, y] = 1 - ## pairwise + # pairwise pw = [np.outer(unary_marginals[edge[0]].T, unary_marginals[edge[1]]).ravel() for edge in edges] @@ -600,8 +624,8 @@ def psi(self, x, y): unaries_acc = np.dot(unary_marginals[:n_visible, :self.n_input_states].T, features) - psi_vector = np.hstack([unaries_acc.ravel(), pw.ravel()]) - return psi_vector + joint_feature_vector = np.hstack([unaries_acc.ravel(), pw.ravel()]) + return joint_feature_vector def init_latent(self, X, Y): # treat all edges the same diff --git a/pystruct/models/multilabel_svm.py b/pystruct/models/multilabel_svm.py index 092214e2..ed305a8e 100644 --- a/pystruct/models/multilabel_svm.py +++ b/pystruct/models/multilabel_svm.py @@ -11,7 +11,7 @@ class MultiLabelClf(CRF): binary indicator per class. This class supports different models via the "edges" parameter. - Giving no eges yields independent classifiers for each class. Giving + Giving no edges yields independent classifiers for each class. Giving "full" yields a fully connected graph over the labels, while "tree" yields the best tree-shaped graph (using the Chow-Liu algorithm). It is also possible to specify a custom connectivity structure. @@ -19,10 +19,10 @@ class MultiLabelClf(CRF): Parameters ---------- n_labels : int (default=None) - Number of labels. + Number of labels. Inferred from data if not provided. n_features : int (default=None) - Number of input features. + Number of input features. Inferred from data if not provided. edges : array-like, string or None Either None, which yields independent models, 'tree', @@ -40,13 +40,13 @@ def __init__(self, n_labels=None, n_features=None, edges=None, self.edges = edges CRF.__init__(self, 2, n_features, inference_method) - def _set_size_psi(self): - # try to set the size of psi if possible + def _set_size_joint_feature(self): + # try to set the size of joint_feature if possible if self.n_features is not None and self.n_states is not None: if self.edges is None: self.edges = np.zeros(shape=(0, 2), dtype=np.int) - self.size_psi = (self.n_features * self.n_labels - + 4 * self.edges.shape[0]) + self.size_joint_feature = (self.n_features * self.n_labels + 4 * + self.edges.shape[0]) def initialize(self, X, Y): n_features = X.shape[1] @@ -63,7 +63,7 @@ def initialize(self, X, Y): raise ValueError("Expected %d labels, got %d" % (self.n_labels, n_labels)) - self._set_size_psi() + self._set_size_joint_feature() self._set_class_weight() def _get_edges(self, x): @@ -80,15 +80,15 @@ def _get_pairwise_potentials(self, x, w): self.edges.shape[0], self.n_states, self.n_states) return pairwise_params - def psi(self, x, y): + def joint_feature(self, x, y): if isinstance(y, tuple): - #from IPython.core.debugger import Tracer - #Tracer()() + # continuous pairwise marginals y_cont, pairwise_marginals = y y_signs = 2 * y_cont[:, 1] - 1 unary_marginals = np.repeat(x[np.newaxis, :], len(y_signs), axis=0) unary_marginals *= y_signs[:, np.newaxis] else: + # discrete y y_signs = 2 * y - 1 unary_marginals = np.repeat(x[np.newaxis, :], len(y_signs), axis=0) unary_marginals *= y_signs[:, np.newaxis] diff --git a/pystruct/models/node_type_edge_feature_graph_crf.py b/pystruct/models/node_type_edge_feature_graph_crf.py new file mode 100644 index 00000000..de1e7709 --- /dev/null +++ b/pystruct/models/node_type_edge_feature_graph_crf.py @@ -0,0 +1,440 @@ +# -*- coding: utf-8 -*- + +""" + Pairwise CRF with features/strength associated to each edge and different + types of nodes + + JL. Meunier + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + + Developed for the EU project READ. The READ project has received funding + from the European Union's Horizon 2020 research and innovation programme + under grant agreement No 674943. + +""" +import numpy as np +import random + +from ..inference import inference_dispatch +from .utils import loss_augment_unaries + +from .typed_crf import TypedCRF, InconsistentLabel + + +class NodeTypeEdgeFeatureGraphCRF(TypedCRF): + """ + Pairwise CRF with features/strength associated to each edge and different + types of nodes + + Pairwise potentials are asymmetric and shared over all edges of same type. + They are weighted by an edge-specific features, though. + This allows for contrast sensitive potentials or directional potentials + (using a {-1, +1} encoding of the direction for example). + + More complicated interactions are also possible, of course. + + + Parameters + ---------- + n_types : number of node types + + l_n_states : list of int, default=None + Number of states per type of variables. + + l_n_features : list of int, default=None + Number of features per type of node. + + a_n_edge_features: an array of shape (n_types, n_types) giving the number + of features per pair of types + + NOTE: there should always be at least 1 feature for any pairs of types + which has some edge in the graph. + To mimic GraphCRF, pass 1 and make a constant feature of 1.0 for all + those edges. + + class_weight : None, or list of array-like (ndim=1) + Class weights. If a list of array-like is passed, the Ith one must have + length equal to l_n_states[i] + None means equal class weights (across node types) + + X and Y + ------- + Node features are given as a list of n_types arrays of shape + (n_type_nodes, n_type_features): + - n_type_nodes is the number of nodes of that type + - n_type_features is the number of features for this type of node + + Edges are given as a list of n_types x n_types arrays of shape + (n_type_edges, 2). + Columns are resp.: node index (in corresponding node type), node index + (in corresponding node type) + + Edge features are given as a list of n_types x n_types arrays of shape + (n_type_type_edge, n_type_type_edge_features) + - n_type_type_edge is the number of edges of type type_type + - n_type_type_edge_features is the number of features for edge of type + type_type + + An instance ``X`` is represented as a tuple ``([node_features, ..] + , [edges, ..], [edge_features, ..])`` + + Labels ``Y`` are given as one array of shape (n_nodes) + Labels are numbered from 0 so that each label across types is encoded + by a unique integer. + + Look at flattenY and unflattentY if you want to pass/obtain list of + labels per type, with first label of each type being encoded by 0 + + """ + + def __init__(self, + n_types, # how many node type? + l_n_states, # how many labels per node type? + l_n_features, # how many features per node type? + a_n_edge_features, # how many features per edge type? + inference_method="ad3", + l_class_weight=None): # class_weight per node type or None + # or None + + # how many features per node type X node type? + # (MUST be symmetric!) + self.a_n_edge_features = np.array(a_n_edge_features) + if self.a_n_edge_features.shape != (n_types, n_types): + raise ValueError("Expected a feature number matrix for edges of " + "shape (%d, %d), got " + "%s." % (n_types, n_types, + self.a_n_edge_features.shape)) + self.a_n_edge_features = self.a_n_edge_features.reshape(n_types, + n_types) + if not (self.a_n_edge_features == self.a_n_edge_features.T).all(): + raise ValueError("Expected a symmetric array of edge feature " + "numbers") + + # number of (edge) features per edge type + self.l_n_edge_features = self.a_n_edge_features.ravel() + # total number of (edge) features + self._n_edge_features = self.a_n_edge_features.sum(axis=None) + + TypedCRF.__init__(self, n_types, l_n_states, l_n_features, + inference_method=inference_method, + l_class_weight=l_class_weight) + + self._get_pairwise_potentials_initialize() + + def _set_size_joint_feature(self): + """ + We have: + - 1 weight per node feature per label per node type + - 1 weight per edge feature per label of node1 type, per label of node2 + type + + NOTE: for now, a typ1, typ2 type of edge with 0 features is simply + ignored. While it could get a state x state matrix of weights + """ + if self.l_n_features: + self.size_unaries = sum(n_states * n_features for n_states, + n_features in zip(self.l_n_states, + self.l_n_features)) + + # detailed non-optimized computation to make things clear + self.size_pairwise = 0 + for typ1, typ2 in self._iter_type_pairs(): + self.size_pairwise += self.a_n_edge_features[typ1, typ2]\ + * self.l_n_states[typ1]\ + * self.l_n_states[typ2] + + self.size_joint_feature = self.size_unaries + self.size_pairwise + + def __repr__(self): + return ("%s(n_states: %s, inference_method: %s, n_features: %s, " + "n_edge_features: %s)" + % (type(self).__name__, self.l_n_states, self.inference_method, + self.l_n_features, self.a_n_edge_features)) + + def _check_size_x(self, x): + l_edges = self._get_edges(x) + if len(l_edges) != self.n_types**2: + raise ValueError("Expected %d edge arrays " + "or None" % (self.n_types**2)) + l_edge_features = self._get_edge_features(x) + if len(l_edge_features) != self.n_types**2: + raise ValueError("Expected %d edge feature arrays " + "or None" % (self.n_types**2)) + + TypedCRF._check_size_x(self, x) + + # check that we have in total 1 feature vector per edge + for edges, edge_features in zip(l_edges, l_edge_features): + if edges is None or edge_features is None: + if edges is None and edge_features is None: + continue + if edges is None: + raise ValueError("Empty edge array but non empty " + "edge-feature array, for same type of " + "edge") + else: + raise ValueError("Empty edge-feature array but non empty " + "edge array, for same type of edge") + if edge_features.ndim != 2: + raise ValueError("Expected a 2 dimensions edge feature arrays") + if len(edges) != len(edge_features): + raise ValueError("Edge and edge feature matrices must have " + "same size in 1st dimension") + + # check edge feature size + for typ1, typ2 in self._iter_type_pairs(): + edge_features = l_edge_features[typ1*self.n_types+typ2] + if edge_features is None: + continue + if edge_features.shape[1] != self.a_n_edge_features[typ1, typ2]: + raise ValueError("Types %d x %d: bad number of edge features. " + "expected %d " + "got %d" % (typ1, typ2, + self.a_n_edge_features[typ1, + typ2], + edge_features.shape[1])) + return True + + def _get_edge_features(self, x): + # we replace None by empty array with proper shape + return [np.empty((0, _n_feat)) + if _ef is None + else _ef + for _ef, _n_feat in zip(x[2], self.l_n_edge_features)] + + def _get_pairwise_potentials_initialize(self): + """ + Putting in cache the params required to build the pairwise potentials + given x and w + """ + self._cache_pairwise_potentials = list() + + i_w, n_states1, i_states1 = 0, 0, 0 + + for typ1 in range(self.n_types): + n_states1 = self.l_n_states[typ1] + i_states1_stop = i_states1 + n_states1 + n_states2, i_states2 = 0, 0 + for typ2 in range(self.n_types): + n_features = self.a_n_edge_features[typ1, typ2] + n_states2 = self.l_n_states[typ2] + i_w_stop = i_w + n_features * n_states1 * n_states2 + i_states2_stop = i_states2 + n_states2 + + self._cache_pairwise_potentials.append((n_features, + n_states1, n_states2, + i_states1, + i_states1_stop, + i_states2, + i_states2_stop, + i_w, i_w_stop)) + + i_w, i_states2 = i_w_stop, i_states2_stop + i_states1 = i_states1_stop + + def _get_pairwise_potentials(self, x, w): + """Computes pairwise potentials for x and w. + + Parameters + ---------- + x : tuple + Instance Representation. + + w : ndarray, shape=(size_joint_feature,) + Weight vector for CRF instance. + + Returns + ------- + pairwise: list of pairwise weights of shape: + (n_edges, n_states_typA, n_states_typB) + + """ + self._check_size_w(w) + + l_edge_features = self._get_edge_features(x) + wpw = w[self.size_unaries:] + + l_pairwise_potentials = [] + + i_w = 0 + for (typ1, typ2), edge_features in zip(self._iter_type_pairs(), + l_edge_features): + n_edges, n_features = edge_features.shape + n_states1 = self.l_n_states[typ1] + n_states2 = self.l_n_states[typ2] + n_w = n_features * n_states1 * n_states2 + if n_w: + # n_states1*n_states2 x nb_feat + pw_typ_typ = wpw[i_w:i_w + n_w].reshape(n_features, -1) + l_pairwise_potentials.append(np.dot(edge_features, + pw_typ_typ + ).reshape(n_edges, + n_states1, + n_states2)) + else: + # first reshaping above complains: "ValueError: total size of + # new array must be unchanged" + l_pairwise_potentials.append(np.array([])) + i_w += n_w + + return l_pairwise_potentials + + def joint_feature(self, x, y): + """Feature vector associated with instance (x, y). + + Feature representation joint_feature, such that the energy of the + configuration + (x, y) and a weight vector w is given by np.dot(w,joint_feature(x, y)). + + Parameters + ---------- + x : tuple + Input representation. + + y : list of ndarrays or some tuple (internal use!) + Either y is a list of a integral ndarrays, giving a complete + labeling for x. + Or it is the result of a linear programming relaxation. In this + case, ``y=(unary_marginals, pariwise_marginals)``. + + Returns + ------- + p : ndarray, shape (size_joint_feature,) + Feature vector associated with state (x, y). + + """ + self._check_size_x(x) # call initialize once! + l_node_features = self._get_node_features(x) + l_edges, l_edge_features = (self._get_edges(x), + self._get_edge_features(x)) + l_n_nodes = [len(nf) for nf in self._get_node_features(x)] + l_n_edges = [len(ef) for ef in self._get_edges(x)] + + if isinstance(y, tuple): + # y is result of relaxation, tuple of unary and pairwise marginals + unary_marginals, pw = y + + if isinstance(unary_marginals, list): + # ad3+ returns a list of unaries, nothing to do here!! :) + l_unary_marginals = unary_marginals + else: + # in case we use someother method (not supported for now + # actually) + l_unary_marginals = [] + i, j = 0, 0 + # iteration by type + for (_n_nodes, _n_states) in zip(l_n_nodes, self.l_n_states): + _n_binaries = _n_nodes * _n_states + _unary_marginals = unary_marginals[i:i+_n_nodes, + j:j+_n_states] + i += _n_nodes + j += _n_states + l_unary_marginals.append(_unary_marginals) + + if isinstance(pw, list): + # ad3+ returns a list of pairwise + l_pw = pw + else: + # until we do better in ad3+ inference, but we cannot I think + # without touching the learners... + l_pw = [] + i_start = 0 + for _n_edges, (typ1, typ2) in zip(l_n_edges, + self._iter_type_pairs()): + n = self.l_n_states[typ1] * self.l_n_states[typ2] + i_stop = i_start + _n_edges + i_state_start = self.a_startindex_by_typ_typ[typ1, typ2] + _edge_marginals = pw[i_start:i_stop, + i_state_start:i_state_start+n] + i_start = i_stop + l_pw.append(_edge_marginals) + else: + self._check_size_xy(x, y) + # make one hot encoding per type + l_unary_marginals = [] + i_start = 0 + for (_n_nodes, + _n_states, + typ_start_index) in zip(l_n_nodes, + self.l_n_states, + self._l_type_startindex): + i_stop = i_start + _n_nodes + _unary_marginals = np.zeros((_n_nodes, _n_states), + dtype=np.int) + gx = np.ogrid[:_n_nodes] + _unary_marginals[gx, y[i_start:i_stop]-typ_start_index] = 1 + l_unary_marginals.append(_unary_marginals) + i_start = i_stop + + # pairwise + # same thing, but the type of an edge is a pair of node types + l_pw = [] + node_offset_by_typ = np.cumsum([0]+[0 if n is None + else n.shape[0] for n in x[0]]) + for _n_edges, (typ1, typ2), edges in zip(l_n_edges, + self._iter_type_pairs(), + l_edges): + _n_states_typ1 = self.l_n_states[typ1] + _n_states_typ2 = self.l_n_states[typ2] + _pw = np.zeros((_n_edges, _n_states_typ1 * _n_states_typ2)) + if _n_edges: + y1 = y[node_offset_by_typ[typ1] + edges[:, 0]]\ + - self._l_type_startindex[typ1] + y2 = y[node_offset_by_typ[typ2] + edges[:, 1]]\ + - self._l_type_startindex[typ2] + assert (0 <= y1).all() and (y1 <= + self.l_n_states[typ1]).all() + assert (0 <= y2).all() and (y2 <= + self.l_n_states[typ2]).all() + # set the 1s where they should + class_pair_ind = (y2 + _n_states_typ2 * y1) + _pw[np.arange(_n_edges), class_pair_ind] = 1 + l_pw.append(_pw) + + # UNARY + l_unary_acc_ravelled = [np.dot(unary_marginals.T, features).ravel() + for (unary_marginals, features) + in zip(l_unary_marginals, l_node_features)] + unaries_acc_ravelled = np.hstack(l_unary_acc_ravelled) + + # PW + l_pw_ravelled = [np.dot(ef.T, pw).ravel() for (ef, pw) + in zip(l_edge_features, l_pw)] + pairwise_acc_ravelled = np.hstack(l_pw_ravelled) + + joint_feature_vector = np.hstack([unaries_acc_ravelled, + pairwise_acc_ravelled]) + + return joint_feature_vector + + def loss_augment_unaries(self, l_unary_potentials, y): + """ + we do it type-wise + """ + i_start = 0 + a_y = np.asarray(y) + + for typ, (unary_potentials, class_weight) in enumerate( + zip(l_unary_potentials, self.l_class_weight)): + n_y = unary_potentials.shape[0] + # label 0 must correspond to 1st weight + y_typ = a_y[i_start:i_start+n_y] - self._l_type_startindex[typ] + loss_augment_unaries(unary_potentials, y_typ, class_weight) + i_start += n_y diff --git a/pystruct/models/typed_crf.py b/pystruct/models/typed_crf.py new file mode 100644 index 00000000..1c0d9407 --- /dev/null +++ b/pystruct/models/typed_crf.py @@ -0,0 +1,342 @@ +# -*- coding: utf-8 -*- + +""" + CRF with different types of nodes + + NOTE: this is an abstract class. Do not use directly. + + JL. Meunier + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + + Developed for the EU project READ. The READ project has received funding + from the European Union's Horizon 2020 research and innovation programme + under grant agreement No 674943. + +""" +import numpy as np + +from .crf import CRF +from ..inference import get_installed + + +class InconsistentLabel(Exception): + pass + + +class TypedCRF(CRF): + """Abstract base class""" + def __init__(self, + n_types, # how many node type? + l_n_states, # how many labels per node type? + l_n_features, # how many features per node type? + inference_method="ad3", + l_class_weight=None): # class_weight per node type or None + # or None + + if inference_method is None: + # get first in list that is installed + inference_method = get_installed(['ad3+', 'ad3'])[0] + self.setInferenceMethod(inference_method) + + self.inference_calls = 0 + # if inference cannot be done, raises an exception + self.inference_exception = False + + if len(l_n_states) != n_types: + raise ValueError("Expected 1 number of states per node type.") + if l_n_features is not None and len(l_n_features) != n_types: + raise ValueError("Expected 1 number pf features per node type.") + self.n_types = n_types + self.l_n_states = l_n_states + self._n_states = sum(l_n_states) # total number of states + self.l_n_features = l_n_features + self._n_features = sum(self.l_n_features) # total number of node feat. + + # number of typextype states, or number of states per type of edge + self.l_n_edge_states = [n1 * n2 + for n1 in self.l_n_states + for n2 in self.l_n_states] + + # class weights: + # either we get class weights for all types of nodes + # , or for none of them! + if l_class_weight: + if len(l_class_weight) != self.n_types: + raise ValueError("Expected 1 class weight list per node type.") + for i, n_states in enumerate(self.l_n_states): + if len(l_class_weight[i]) != n_states: + raise ValueError("Expected 1 class weight per state" + " per node type. Wrong for type %d" % i) + + # class weights are computed by type and simply concatenated + self.l_class_weight = [np.asarray(class_weight) + for class_weight in l_class_weight] + else: + self.l_class_weight = [np.ones(n) for n in self.l_n_states] + self.class_weight = np.hstack(self.l_class_weight) + + self._set_size_joint_feature() + + # internal stuff + # when putting node states in a single sequence, index of 1st state + # for type i + self._l_type_startindex = [sum(self.l_n_states[:i]) + for i in range(self.n_types+1)] + + # when putting edge states in a single sequence, index of 1st state of + # an edge of type (typ1, typ2) + self.a_startindex_by_typ_typ = np.zeros((self.n_types, self.n_types), + dtype=np.uint32) + i_state_start = 0 + for typ1, typ1_n_states in enumerate(self.l_n_states): + for typ2, typ2_n_states in enumerate(self.l_n_states): + self.a_startindex_by_typ_typ[typ1, typ2] = i_state_start + i_state_start += typ1_n_states*typ2_n_states + + # -------------- CONVENIENCE -------------------------- + def setInferenceMethod(self, inference_method): + if inference_method in ["ad3", "ad3+"]: + self.inference_method = inference_method + else: + raise Exception("You must use ad3 or ad3+ as inference method") + + def flattenY(self, lY_by_typ): + """ + It is more convenient to have the Ys grouped by type, as the Xs are, + and to have the first label of each type encoded as 0. + + This method does the job. It returns a flat Y array, with unique code + per class label, which can be passed to 'fit' + """ + lY = list() + for n_start_state, Y_typ in zip(self._l_type_startindex, lY_by_typ): + lY.append(np.asarray(Y_typ) + n_start_state) + return np.hstack(lY) + + def unflattenY(self, X, flatY): + """ + predict returns a flat array of Y (same structure as for 'fit') + This method structures the Y as a list of Y_per_type, where the first + label of any type is 0 + """ + lY = list() + i_start_node = 0 + (l_node_features, l_edges, l_edge_features) = X + for n_start_state, nf in zip(self._l_type_startindex, l_node_features): + n_nodes = nf.shape[0] + Y = flatY[i_start_node: i_start_node+n_nodes] - n_start_state + lY.append(Y) + i_start_node += n_nodes + if flatY.shape != (i_start_node,): + raise ValueError("The total number of label does not match the" + " total number of nodes:" + " %d != %d" % (flatY.shape[0], i_start_node)) + return lY + + def initialize(self, X, Y=None): + """ + It is optional to call it. Does data checking only! + """ + if isinstance(X, list): + map(self._check_size_x, X) + if not (Y is None): + map(self._check_size_xy, X, Y) + else: + self._check_size_x(X) + self._check_size_xy(X, Y) + + def setInferenceException(self, bRaiseExceptionWhenInferenceNotSuccessful): + """ + set exception on or off when inference canoot be done. + """ + self.inference_exception = bRaiseExceptionWhenInferenceNotSuccessful + return self.inference_exception + + # -------------- INTERNAL STUFF -------------------------- + def _set_size_joint_feature(self): + """ + We have: + - 1 weight per node feature per label per node type + """ + self.size_unaries = sum(n_states * n_features for n_states, n_features + in zip(self.l_n_states, self.l_n_features) + ) + self.size_joint_feature = self.size_unaries + + def __repr__(self): + return ("%s(n_states: %s, inference_method: %s)" + % (type(self).__name__, self.l_n_states, + self.inference_method)) + + def _check_size_x(self, x): + # node_features are [ i_in_typ -> features ] + l_node_features = self._get_node_features(x) + if len(l_node_features) != self.n_types: + raise ValueError("Expected one node feature array per node type.") + + for typ, typ_features in enumerate(l_node_features): + if typ_features.shape[1] != self.l_n_features[typ]: + raise ValueError("Expected %d features for type" + " %d" % (self.l_n_features[typ], typ)) + + # edges + l_edges = self._get_edges(x) + for edges in l_edges: + if edges is None: + continue + if edges.ndim != 2: + raise ValueError("Expected a 2 dimensions edge arrays") + if edges.shape[1] != 2: + raise ValueError("Expected 2 columns in edge arrays") + + for typ1, typ2 in self._iter_type_pairs(): + edges = self._get_edges_by_type(x, typ1, typ2) + + if edges is None or len(edges) == 0: + continue + # edges should point to valid node indices + nodes1, nodes2 = edges[:, 0], edges[:, 1] + if min(nodes1) < 0 or min(nodes2) < 0: + raise ValueError("At least one edge points to negative and" + " therefore invalid node index:" + " type %d to type %d" % (typ1, typ2)) + if max(nodes1) >= l_node_features[typ1].shape[0]: + raise ValueError("At least one edge starts from a non-existing" + " node index:" + " type %d to type %d" % (typ1, typ2)) + if max(nodes2) >= l_node_features[typ2].shape[0]: + raise ValueError("At least one edge points to a non-existing" + " node index:" + " type %d to type %d" % (typ1, typ2)) + return True + + def _check_size_xy(self, X, Y): + if Y is None: + return + + # make sure Y has the proper length and acceptable labels + l_node_features = self._get_node_features(X) + + nb_nodes = sum(nf.shape[0] for nf in l_node_features) + if Y.shape[0] != nb_nodes: + raise ValueError("Expected 1 label for each of the %d nodes. Got" + " %d labels." % (nb_nodes, Y.shape[0])) + + i_start = 0 + for typ, nf, n_states in zip(range(self.n_types), + l_node_features, + self.l_n_states): + nb_nodes = nf.shape[0] + if nb_nodes == 0: + continue + Y_typ = Y[i_start:i_start+nb_nodes] + if np.min(Y_typ) < 0: + raise ValueError("Got a negative label for type %d" % typ) + if np.min(Y_typ) < self._l_type_startindex[typ]: + raise InconsistentLabel("labels of type %d start at %d" + "" % (typ, + self._l_type_startindex[typ])) + if np.max(Y_typ) >= self._l_type_startindex[typ+1]: + raise InconsistentLabel("labels of type %d end at %d" + "" % (typ, + self._l_type_startindex[typ+1]-1) + ) + i_start = i_start + nb_nodes + return True + + def _get_node_features(self, x): + # we replace None by empty array with proper shape + return [np.empty((0, _n_feat)) if node_features is None + else node_features + for (node_features, _n_feat) in zip(x[0], self.l_n_features)] + + def _get_edges(self, x): + return [np.empty((0, 2)) if edges is None or len(edges) == 0 + else edges for edges in x[1]] + + def _get_edges_by_type(self, x, typ1, typ2): + return x[1][typ1 * self.n_types+typ2] + + def _iter_type_pairs(self): + for typ1 in range(self.n_types): + for typ2 in range(self.n_types): + yield (typ1, typ2) + return + + def _get_unary_potentials(self, x, w): + """Computes unary potentials for x and w. + + Parameters + ---------- + x : tuple + Instance Representation. + + w : ndarray, shape=(size_joint_feature,) + Weight vector for CRF instance. + + Returns + ------- + unaries : list of ndarray, shape=( n_nodes_typ, n_states_typ ) + Unary weights. + """ + self._check_size_w(w) + l_node_features = self._get_node_features(x) + + l_unary_potentials = [] + + i_w = 0 + for (features, n_states, n_features) in zip(l_node_features, + self.l_n_states, + self.l_n_features): + n_w = n_states*n_features + l_unary_potentials.append( + np.dot(features, + w[i_w:i_w+n_w].reshape(n_states, + n_features).T + ) + ) + i_w += n_w + assert i_w == self.size_unaries + + # nodes x features . features x states --> nodes x states + return l_unary_potentials + + def continuous_loss(self, y, l_y_hat): + # continuous version of the loss + # y is the result of linear programming + # BUT, in multitype mode, y_hat is a list of unaries + l_result = list() + cum_n_node = 0 + cum_n_state = 0 + for y_hat in l_y_hat: + n_node, n_state = y_hat.shape + # all entries minus correct ones + # select the correct range of labels and make the labels start at 0 + y_type = y[cum_n_node:cum_n_node+n_node] - cum_n_state + gx = np.indices(y_type.shape) + result = 1 - y_hat[gx, y_type] + l_result.append(result) + cum_n_node += n_node + cum_n_state += n_state + result = np.hstack(l_result) + + if hasattr(self, 'class_weight'): + return np.sum(self.class_weight[y] * result) + return np.sum(result) diff --git a/pystruct/models/unstructured_svm.py b/pystruct/models/unstructured_svm.py index b2e109e7..d3c1451c 100644 --- a/pystruct/models/unstructured_svm.py +++ b/pystruct/models/unstructured_svm.py @@ -1,7 +1,7 @@ import numpy as np from .base import StructuredModel -from .utils import crammer_singer_psi +from .utils import crammer_singer_joint_feature class BinaryClf(StructuredModel): @@ -22,29 +22,30 @@ class BinaryClf(StructuredModel): ---------- n_features : int or None, default=None Number of features of inputs x. + If None, it is inferred from data. """ def __init__(self, n_features=None): - self.size_psi = n_features + self.size_joint_feature = n_features self.n_states = 2 self.inference_calls = 0 def initialize(self, X, Y): n_features = X.shape[1] - if self.size_psi is None: - self.size_psi = n_features - elif self.size_psi != n_features: + if self.size_joint_feature is None: + self.size_joint_feature = n_features + elif self.size_joint_feature != n_features: raise ValueError("Expected %d features, got %d" - % (self.size_psi, n_features)) + % (self.size_joint_feature, n_features)) def __repr__(self): return ("%s, n_features: %d" - % (type(self).__name__, self.size_psi)) + % (type(self).__name__, self.size_joint_feature)) - def psi(self, x, y): + def joint_feature(self, x, y): """Compute joint feature vector of x and y. - Feature representation psi, such that the energy of the configuration - (x, y) and a weight vector w is given by np.dot(w, psi(x, y)). + Feature representation joint_feature, such that the energy of the configuration + (x, y) and a weight vector w is given by np.dot(w, joint_feature(x, y)). Parameters ---------- @@ -56,20 +57,20 @@ def psi(self, x, y): Returns ------- - p : ndarray, shape (size_psi,) + p : ndarray, shape (size_joint_feature,) Feature vector associated with state (x, y). """ if y not in [-1, 1]: raise ValueError("y has to be either -1 or +1, got %s" % repr(y)) return y * x / 2. - def batch_psi(self, X, Y): + def batch_joint_feature(self, X, Y): return np.sum(X * np.array(Y)[:, np.newaxis] / 2., axis=0) def inference(self, x, w, relaxed=None): """Inference for x using parameters w. - Finds armin_y np.dot(w, psi(x, y)), i.e. best possible prediction. + Finds armin_y np.dot(w, joint_feature(x, y)), i.e. best possible prediction. For a binary SVM, this is just sign(np.dot(w, x) + b)) @@ -78,7 +79,7 @@ def inference(self, x, w, relaxed=None): x : ndarray, shape (n_features,) Input sample features. - w : ndarray, shape=(size_psi,) + w : ndarray, shape=(size_joint_feature,) Parameters of the SVM. relaxed : ignored @@ -98,7 +99,7 @@ def loss_augmented_inference(self, x, y, w, relaxed=None): """Loss-augmented inference for x and y using parameters w. Minimizes over y_hat: - np.dot(psi(x, y_hat), w) + loss(y, y_hat) + np.dot(joint_feature(x, y_hat), w) + loss(y, y_hat) which is just sign(np.dot(x, w) + b - y) @@ -111,7 +112,7 @@ def loss_augmented_inference(self, x, y, w, relaxed=None): Ground truth labeling relative to which the loss will be measured. - w : ndarray, shape (size_psi,) + w : ndarray, shape (size_joint_feature,) Weights that will be used for inference. Returns @@ -147,9 +148,11 @@ class MultiClassClf(StructuredModel): ---------- n_features : int Number of features of inputs x. + If None, it is inferred from data. - n_classes : int, default=2 + n_classes : int, default=None Number of classes in dataset. + If None, it is inferred from data. class_weight : None, or array-like Class weights. If an array-like is passed, it must have length @@ -167,12 +170,12 @@ def __init__(self, n_features=None, n_classes=None, class_weight=None, self.rescale_C = rescale_C self.class_weight = class_weight self.inference_calls = 0 - self._set_size_psi() + self._set_size_joint_feature() self._set_class_weight() - def _set_size_psi(self): - if not None in [self.n_states, self.n_features]: - self.size_psi = self.n_states * self.n_features + def _set_size_joint_feature(self): + if None not in [self.n_states, self.n_features]: + self.size_joint_feature = self.n_states * self.n_features def initialize(self, X, Y): n_features = X.shape[1] @@ -188,18 +191,18 @@ def initialize(self, X, Y): elif self.n_states != n_classes: raise ValueError("Expected %d classes, got %d" % (self.n_states, n_classes)) - self._set_size_psi() + self._set_size_joint_feature() self._set_class_weight() def __repr__(self): return ("%s(n_features=%d, n_classes=%d)" % (type(self).__name__, self.n_features, self.n_states)) - def psi(self, x, y, y_true=None): + def joint_feature(self, x, y, y_true=None): """Compute joint feature vector of x and y. - Feature representation psi, such that the energy of the configuration - (x, y) and a weight vector w is given by np.dot(w, psi(x, y)). + Feature representation joint_feature, such that the energy of the configuration + (x, y) and a weight vector w is given by np.dot(w, joint_feature(x, y)). Parameters ---------- @@ -215,7 +218,7 @@ def psi(self, x, y, y_true=None): Returns ------- - p : ndarray, shape (size_psi,) + p : ndarray, shape (size_joint_feature,) Feature vector associated with state (x, y). """ # put feature vector in the place of the weights corresponding to y @@ -224,18 +227,18 @@ def psi(self, x, y, y_true=None): if self.rescale_C: if y_true is None: raise ValueError("rescale_C is true, but no y_true was passed" - " to psi.") + " to joint_feature.") result *= self.class_weight[y_true] return result.ravel() - def batch_psi(self, X, Y, Y_true=None): + def batch_joint_feature(self, X, Y, Y_true=None): result = np.zeros((self.n_states, self.n_features)) if self.rescale_C: if Y_true is None: raise ValueError("rescale_C is true, but no y_true was passed" - " to psi.") - for l in xrange(self.n_states): + " to joint_feature.") + for l in range(self.n_states): mask = Y == l class_weight = self.class_weight[Y_true[mask]][:, np.newaxis] result[l, :] = np.sum(X[mask, :] * class_weight, axis=0) @@ -244,13 +247,13 @@ def batch_psi(self, X, Y, Y_true=None): # implementation assert(X.shape[0] == Y.shape[0]) assert(X.shape[1] == self.n_features) - crammer_singer_psi(X, Y, result) + crammer_singer_joint_feature(X, Y, result) return result.ravel() def inference(self, x, w, relaxed=None, return_energy=False): """Inference for x using parameters w. - Finds armin_y np.dot(w, psi(x, y)), i.e. best possible prediction. + Finds armin_y np.dot(w, joint_feature(x, y)), i.e. best possible prediction. For an unstructured multi-class model (this model), this can easily done by enumerating all possible y. @@ -260,7 +263,7 @@ def inference(self, x, w, relaxed=None, return_energy=False): x : ndarray, shape (n_features,) Input sample features. - w : ndarray, shape=(size_psi,) + w : ndarray, shape=(size_joint_feature,) Parameters of the SVM. relaxed : ignored @@ -281,7 +284,7 @@ def loss_augmented_inference(self, x, y, w, relaxed=None, """Loss-augmented inference for x and y using parameters w. Minimizes over y_hat: - np.dot(psi(x, y_hat), w) + loss(y, y_hat) + np.dot(joint_feature(x, y_hat), w) + loss(y, y_hat) Parameters ---------- @@ -292,7 +295,7 @@ def loss_augmented_inference(self, x, y, w, relaxed=None, Ground truth labeling relative to which the loss will be measured. - w : ndarray, shape (size_psi,) + w : ndarray, shape (size_joint_feature,) Weights that will be used for inference. Returns diff --git a/pystruct/plot_learning.py b/pystruct/plot_learning.py index e8e3f478..4f69a0a3 100755 --- a/pystruct/plot_learning.py +++ b/pystruct/plot_learning.py @@ -4,7 +4,6 @@ """ import sys -import matplotlib.pyplot as plt import numpy as np from pystruct.utils import SaveLogger @@ -40,6 +39,7 @@ def plot_learning(ssvm, time=True): So if you warm-started a model, please don't count on proper alignment of time, cache hits and objective. """ + import matplotlib.pyplot as plt print(ssvm) if hasattr(ssvm, 'base_ssvm'): ssvm = ssvm.base_ssvm diff --git a/pystruct/tests/test_datasets.py b/pystruct/tests/test_datasets.py new file mode 100644 index 00000000..b6f054c5 --- /dev/null +++ b/pystruct/tests/test_datasets.py @@ -0,0 +1,8 @@ +from pystruct.datasets import load_scene, load_letters, load_snakes + + +def test_dataset_loading(): + # test that we can read the datasets. + load_scene() + load_letters() + load_snakes() diff --git a/pystruct/tests/test_inference/test_exact_inference.py b/pystruct/tests/test_inference/test_exact_inference.py index 2e75141d..aa45c167 100644 --- a/pystruct/tests/test_inference/test_exact_inference.py +++ b/pystruct/tests/test_inference/test_exact_inference.py @@ -8,17 +8,19 @@ def test_chain(): # test LP, AD3, AD3-BB and JT on a chain. # they should all be exact rnd = np.random.RandomState(0) - algorithms = get_installed([('ad3', {'branch_and_bound':False}), - ('ad3', {'branch_and_bound':True}), - ('ogm', {'alg':'dyn'}), - ('ogm', {'alg':'dd'}), - ('ogm', {'alg':'trw'}), - ('dai', {'alg':'jt'})]) - for i in xrange(10): - forward = np.c_[np.arange(9), np.arange(1, 10)] - backward = np.c_[np.arange(1, 10), np.arange(9)] - unary_potentials = rnd.normal(size=(10, 3)) - pairwise_potentials = rnd.normal(size=(3, 3)) + algorithms = get_installed([('ad3', {'branch_and_bound': False}), + ('ad3', {'branch_and_bound': True}), + ('ogm', {'alg': 'dyn'}), + ('ogm', {'alg': 'dd'}), + ('ogm', {'alg': 'trw'})]) + n_states = 3 + n_nodes = 10 + + for i in range(10): + forward = np.c_[np.arange(n_nodes - 1), np.arange(1, n_nodes)] + backward = np.c_[np.arange(1, n_nodes), np.arange(n_nodes - 1)] + unary_potentials = rnd.normal(size=(n_nodes, n_states)) + pairwise_potentials = rnd.normal(size=(n_states, n_states)) # test that reversing edges is same as transposing pairwise potentials y_forward = inference_dispatch(unary_potentials, pairwise_potentials, forward, 'lp') @@ -32,7 +34,6 @@ def test_chain(): if chain is backward and alg[0] == 'ogm': # ogm needs sorted indices continue - print(alg) y = inference_dispatch(unary_potentials, pairwise_potentials, chain, alg) assert_array_equal(y, y_lp) diff --git a/pystruct/tests/test_inference/test_maxprod.py b/pystruct/tests/test_inference/test_maxprod.py new file mode 100644 index 00000000..fa8a6893 --- /dev/null +++ b/pystruct/tests/test_inference/test_maxprod.py @@ -0,0 +1,155 @@ +import numpy as np +from nose.tools import assert_true, assert_false +from nose import SkipTest + +from numpy.testing import assert_array_equal +from scipy import sparse + +from pystruct.inference.maxprod import (is_forest, inference_max_product, + iterative_max_product, is_chain) +from pystruct.inference import inference_ad3 +from pystruct.datasets import generate_blocks, generate_blocks_multinomial +from pystruct.models import GridCRF + + +def test_is_chain(): + chain = np.c_[np.arange(9), np.arange(1, 10)] + assert_true(is_chain(chain, len(chain) + 1)) + assert_false(is_chain(chain, len(chain))) + # generate circle + circle = np.vstack([chain, [9, 0]]) + assert_false(is_chain(circle, len(circle))) + # reversed order not accepted + assert_false(is_chain(chain[::-1], len(chain) + 1)) + + +def test_is_forest(): + # generate chain + chain = np.c_[np.arange(1, 10), np.arange(9)] + assert_true(is_forest(chain, len(chain) + 1)) + assert_true(is_forest(chain)) + # generate circle + circle = np.vstack([chain, [9, 0]]) + assert_false(is_forest(circle)) + assert_false(is_forest(circle, len(chain) + 1)) + + # union of two disjoint chains + two_chains = np.vstack([chain, chain + 10]) + assert_true(is_forest(two_chains, 20)) + + # union of chain and circle + disco_graph = np.vstack([chain, circle + 10]) + assert_false(is_forest(disco_graph)) + + # generate random fully connected graph + graph = np.random.uniform(size=(10, 10)) + edges = np.c_[graph.nonzero()] + assert_false(is_forest(edges)) + + try: + from scipy.sparse.csgraph import minimum_spanning_tree + tree = minimum_spanning_tree(sparse.csr_matrix(graph)) + tree_edges = np.c_[tree.nonzero()] + assert_true(is_forest(tree_edges, 10)) + assert_true(is_forest(tree_edges)) + except ImportError: + pass + + +def test_tree_max_product_chain(): + rnd = np.random.RandomState(0) + forward = np.c_[np.arange(9), np.arange(1, 10)] + backward = np.c_[np.arange(1, 10), np.arange(9)] + for i in range(10): + unary_potentials = rnd.normal(size=(10, 3)) + pairwise_potentials = rnd.normal(size=(9, 3, 3)) + for chain in [forward, backward]: + result_ad3 = inference_ad3(unary_potentials, pairwise_potentials, + chain, branch_and_bound=True) + result_mp = inference_max_product(unary_potentials, + pairwise_potentials, chain) + assert_array_equal(result_ad3, result_mp) + + +def test_tree_max_product_tree(): + try: + from scipy.sparse.csgraph import minimum_spanning_tree + except: + raise SkipTest("Not testing trees, scipy version >= 0.11 required") + + rnd = np.random.RandomState(0) + for i in range(100): + # generate random tree using mst + graph = rnd.uniform(size=(10, 10)) + tree = minimum_spanning_tree(sparse.csr_matrix(graph)) + tree_edges = np.c_[tree.nonzero()] + + unary_potentials = rnd.normal(size=(10, 3)) + pairwise_potentials = rnd.normal(size=(9, 3, 3)) + result_ad3 = inference_ad3(unary_potentials, pairwise_potentials, + tree_edges, branch_and_bound=True) + result_mp = inference_max_product(unary_potentials, + pairwise_potentials, tree_edges) + assert_array_equal(result_ad3, result_mp) + + +def test_iterative_max_product_chain(): + rnd = np.random.RandomState(0) + chain = np.c_[np.arange(9), np.arange(1, 10)] + for i in range(10): + unary_potentials = rnd.normal(size=(10, 3)) + pairwise_potentials = rnd.normal(size=(9, 3, 3)) + result_ad3 = inference_ad3(unary_potentials, pairwise_potentials, + chain, branch_and_bound=True) + result_mp = iterative_max_product(unary_potentials, + pairwise_potentials, chain) + assert_array_equal(result_ad3, result_mp) + + +def test_iterative_max_product_tree(): + try: + from scipy.sparse.csgraph import minimum_spanning_tree + except: + raise SkipTest("Not testing trees, scipy version >= 0.11 required") + rnd = np.random.RandomState(0) + for i in range(100): + # generate random tree using mst + graph = rnd.uniform(size=(10, 10)) + tree = minimum_spanning_tree(sparse.csr_matrix(graph)) + tree_edges = np.c_[tree.nonzero()] + + unary_potentials = rnd.normal(size=(10, 3)) + pairwise_potentials = rnd.normal(size=(9, 3, 3)) + result_ad3 = inference_ad3(unary_potentials, pairwise_potentials, + tree_edges, branch_and_bound=True) + result_mp = iterative_max_product(unary_potentials, + pairwise_potentials, tree_edges) + assert_array_equal(result_ad3, result_mp) + + +def test_max_product_binary_blocks(): + X, Y = generate_blocks(n_samples=1) + x, y = X[0], Y[0] + w = np.array([1, 0, # unary + 0, 1, + 0, # pairwise + -4, 0]) + crf = GridCRF(inference_method='max-product') + crf.initialize(X, Y) + y_hat = crf.inference(x, w) + assert_array_equal(y, y_hat) + + +def test_max_product_multinomial_crf(): + X, Y = generate_blocks_multinomial(n_samples=1) + x, y = X[0], Y[0] + w = np.array([1., 0., 0., # unary + 0., 1., 0., + 0., 0., 1., + .4, # pairwise + -.3, .3, + -.5, -.1, .3]) + crf = GridCRF(inference_method='max-product') + crf.initialize(X, Y) + y_hat = crf.inference(x, w) + assert_array_equal(y, y_hat) diff --git a/pystruct/tests/test_learners/test_binary_svm.py b/pystruct/tests/test_learners/test_binary_svm.py index 12ef1b35..4ab8c06c 100644 --- a/pystruct/tests/test_learners/test_binary_svm.py +++ b/pystruct/tests/test_learners/test_binary_svm.py @@ -22,13 +22,13 @@ def test_model_1d(): Y_pred = np.hstack([pbl.inference(x, w) for x in X]) assert_array_equal(Y, Y_pred) - # check that sign of psi and inference agree + # check that sign of joint_feature and inference agree for x, y in zip(X, Y): - assert_true(np.dot(w, pbl.psi(x, y)) > np.dot(w, pbl.psi(x, -y))) + assert_true(np.dot(w, pbl.joint_feature(x, y)) > np.dot(w, pbl.joint_feature(x, -y))) - # check that sign of psi and the sign of y correspond + # check that sign of joint_feature and the sign of y correspond for x, y in zip(X, Y): - assert_true(np.dot(w, pbl.psi(x, y)) == -np.dot(w, pbl.psi(x, -y))) + assert_true(np.dot(w, pbl.joint_feature(x, y)) == -np.dot(w, pbl.joint_feature(x, -y))) def test_simple_1d_dataset_cutting_plane(): @@ -97,13 +97,13 @@ def test_blobs_batch(): pbl = BinaryClf(n_features=2) - # test psi - psi_mean = pbl.batch_psi(X, Y) - psi_mean2 = np.sum([pbl.psi(x, y) for x, y in zip(X, Y)], axis=0) - assert_array_equal(psi_mean, psi_mean2) + # test joint_feature + joint_feature_mean = pbl.batch_joint_feature(X, Y) + joint_feature_mean2 = np.sum([pbl.joint_feature(x, y) for x, y in zip(X, Y)], axis=0) + assert_array_equal(joint_feature_mean, joint_feature_mean2) # test inference - w = np.random.uniform(-1, 1, size=pbl.size_psi) + w = np.random.uniform(-1, 1, size=pbl.size_joint_feature) Y_hat = pbl.batch_inference(X, w) for i, (x, y_hat) in enumerate(zip(X, Y_hat)): assert_array_equal(Y_hat[i], pbl.inference(x, w)) diff --git a/pystruct/tests/test_learners/test_crammer_singer_svm.py b/pystruct/tests/test_learners/test_crammer_singer_svm.py index 67da6e3d..77d50a31 100644 --- a/pystruct/tests/test_learners/test_crammer_singer_svm.py +++ b/pystruct/tests/test_learners/test_crammer_singer_svm.py @@ -3,6 +3,7 @@ from numpy.testing import (assert_array_equal, assert_array_almost_equal, assert_almost_equal, assert_equal) from nose.tools import assert_greater +from nose import SkipTest from sklearn.datasets import make_blobs from sklearn.metrics import f1_score @@ -20,18 +21,18 @@ def test_crammer_singer_model(): # test inference energy rng = np.random.RandomState(0) - w = rng.uniform(size=pbl.size_psi) + w = rng.uniform(size=pbl.size_joint_feature) x = X[0] y, energy = pbl.inference(x, w, return_energy=True) - assert_almost_equal(energy, np.dot(w, pbl.psi(x, y))) + assert_almost_equal(energy, np.dot(w, pbl.joint_feature(x, y))) # test inference result: - energies = [np.dot(w, pbl.psi(x, y_hat)) for y_hat in xrange(3)] + energies = [np.dot(w, pbl.joint_feature(x, y_hat)) for y_hat in range(3)] assert_equal(np.argmax(energies), y) # test loss_augmented inference energy y, energy = pbl.loss_augmented_inference(x, Y[0], w, return_energy=True) - assert_almost_equal(energy, np.dot(w, pbl.psi(x, y)) + pbl.loss(Y[0], y)) + assert_almost_equal(energy, np.dot(w, pbl.joint_feature(x, y)) + pbl.loss(Y[0], y)) # test batch versions Y_batch = pbl.batch_inference(X, w) @@ -55,19 +56,19 @@ def test_crammer_singer_model_class_weight(): pbl = MultiClassClf(n_features=3, n_classes=3, class_weight=[1, 2, 1]) rng = np.random.RandomState(0) - w = rng.uniform(size=pbl.size_psi) + w = rng.uniform(size=pbl.size_joint_feature) # test inference energy x = X[0] y, energy = pbl.inference(x, w, return_energy=True) - assert_almost_equal(energy, np.dot(w, pbl.psi(x, y))) + assert_almost_equal(energy, np.dot(w, pbl.joint_feature(x, y))) # test inference_result: - energies = [np.dot(w, pbl.psi(x, y_hat)) for y_hat in xrange(3)] + energies = [np.dot(w, pbl.joint_feature(x, y_hat)) for y_hat in range(3)] assert_equal(np.argmax(energies), y) # test loss_augmented inference energy y, energy = pbl.loss_augmented_inference(x, Y[0], w, return_energy=True) - assert_almost_equal(energy, np.dot(w, pbl.psi(x, y)) + pbl.loss(Y[0], y)) + assert_almost_equal(energy, np.dot(w, pbl.joint_feature(x, y)) + pbl.loss(Y[0], y)) # test batch versions Y_batch = pbl.batch_inference(X, w) @@ -180,8 +181,8 @@ def test_class_weights(): svm_class_weight = OneSlackSSVM(pbl_class_weight, C=10) svm_class_weight.fit(X, Y) - assert_greater(f1_score(Y, svm_class_weight.predict(X)), - f1_score(Y, svm.predict(X))) + assert_greater(f1_score(Y, svm_class_weight.predict(X), average='macro'), + f1_score(Y, svm.predict(X), average='macro')) def test_class_weights_rescale_C(): @@ -191,7 +192,6 @@ def test_class_weights_rescale_C(): X, Y = make_blobs(n_samples=210, centers=3, random_state=1, cluster_std=3, shuffle=False) X = np.hstack([X, np.ones((X.shape[0], 1))]) - X, Y = X[:170], Y[:170] weights = 1. / np.bincount(Y) weights *= len(weights) / np.sum(weights) @@ -202,11 +202,12 @@ def test_class_weights_rescale_C(): try: linearsvm = LinearSVC(multi_class='crammer_singer', - fit_intercept=False, class_weight='auto', C=10) + fit_intercept=False, class_weight='balanced', + C=10) linearsvm.fit(X, Y) assert_array_almost_equal(svm_class_weight.w, linearsvm.coef_.ravel(), - 3) + 2) #3 --> fail :-/ except TypeError: # travis has a really old sklearn version that doesn't support # class_weight in LinearSVC diff --git a/pystruct/tests/test_learners/test_edge_feature_graph_learning.py b/pystruct/tests/test_learners/test_edge_feature_graph_learning.py index 23b988be..da2b1209 100644 --- a/pystruct/tests/test_learners/test_edge_feature_graph_learning.py +++ b/pystruct/tests/test_learners/test_edge_feature_graph_learning.py @@ -1,5 +1,5 @@ import numpy as np -from numpy.testing import assert_array_equal +from numpy.testing import assert_array_equal, assert_array_almost_equal from pystruct.models import EdgeFeatureGraphCRF from pystruct.learners import NSlackSSVM @@ -22,7 +22,7 @@ def test_multinomial_blocks_directional_simple(): G = [make_grid_edges(x, return_lists=True) for x in X_] edge_features = [edge_list_to_features(edge_list) for edge_list in G] edges = [np.vstack(g) for g in G] - X = zip([x.reshape(-1, 3) for x in X_], edges, edge_features) + X = list(zip([x.reshape(-1, 3) for x in X_], edges, edge_features)) Y = [y.ravel() for y in Y_] crf = EdgeFeatureGraphCRF(n_states=3, n_edge_features=2) @@ -39,7 +39,7 @@ def test_multinomial_blocks_directional_anti_symmetric(): G = [make_grid_edges(x, return_lists=True) for x in X_] edge_features = [edge_list_to_features(edge_list) for edge_list in G] edges = [np.vstack(g) for g in G] - X = zip([x.reshape(-1, 3) for x in X_], edges, edge_features) + X = list(zip([x.reshape(-1, 3) for x in X_], edges, edge_features)) Y = [y.ravel() for y in Y_] crf = EdgeFeatureGraphCRF(symmetric_edge_features=[0], @@ -51,5 +51,5 @@ def test_multinomial_blocks_directional_anti_symmetric(): pairwise_params = clf.w[-9 * 2:].reshape(2, 3, 3) sym = pairwise_params[0] antisym = pairwise_params[1] - assert_array_equal(sym, sym.T) - assert_array_equal(antisym, -antisym.T) + assert_array_almost_equal(sym, sym.T) + assert_array_almost_equal(antisym, -antisym.T) diff --git a/pystruct/tests/test_learners/test_frankwolfe_svm.py b/pystruct/tests/test_learners/test_frankwolfe_svm.py index 26153eae..e866fb1b 100644 --- a/pystruct/tests/test_learners/test_frankwolfe_svm.py +++ b/pystruct/tests/test_learners/test_frankwolfe_svm.py @@ -1,19 +1,74 @@ +from tempfile import mkstemp +import numpy as np from numpy.testing import assert_array_equal +from nose.tools import assert_less -from pystruct.models import GridCRF +from sklearn.datasets import load_iris +try: + from sklearn.model_selection import train_test_split +except ImportError: + from sklearn.cross_validation import train_test_split +from pystruct.models import GridCRF, GraphCRF from pystruct.datasets import generate_blocks_multinomial - from pystruct.learners import FrankWolfeSSVM +from pystruct.utils import SaveLogger def test_multinomial_blocks_frankwolfe(): - X, Y = generate_blocks_multinomial(n_samples=50, noise=0.5, - seed=0) + X, Y = generate_blocks_multinomial(n_samples=10, noise=0.5, seed=0) crf = GridCRF(inference_method='qpbo') - clf = FrankWolfeSSVM(model=crf, C=1, line_search=True, - batch_mode=False, check_dual_every=500) + clf = FrankWolfeSSVM(model=crf, C=1, max_iter=50) clf.fit(X, Y) Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred) + + +def test_multinomial_blocks_frankwolfe_batch(): + X, Y = generate_blocks_multinomial(n_samples=10, noise=0.3, seed=0) + crf = GridCRF(inference_method='qpbo') + clf = FrankWolfeSSVM(model=crf, C=1, max_iter=500, batch_mode=True) + clf.fit(X, Y) + Y_pred = clf.predict(X) + assert_array_equal(Y, Y_pred) + + +def test_svm_as_crf_pickling_bcfw(): + + iris = load_iris() + X, y = iris.data, iris.target + + X_ = [(np.atleast_2d(x), np.empty((0, 2), dtype=np.int)) for x in X] + Y = y.reshape(-1, 1) + + X_train, X_test, y_train, y_test = train_test_split(X_, Y, random_state=1) + _, file_name = mkstemp() + + pbl = GraphCRF(n_features=4, n_states=3, inference_method='unary') + logger = SaveLogger(file_name) + svm = FrankWolfeSSVM(pbl, C=10, logger=logger, max_iter=50) + svm.fit(X_train, y_train) + + assert_less(.97, svm.score(X_test, y_test)) + assert_less(.97, logger.load().score(X_test, y_test)) + + +def test_svm_as_crf_pickling_batch(): + + iris = load_iris() + X, y = iris.data, iris.target + + X_ = [(np.atleast_2d(x), np.empty((0, 2), dtype=np.int)) for x in X] + Y = y.reshape(-1, 1) + + X_train, X_test, y_train, y_test = train_test_split(X_, Y, random_state=1) + _, file_name = mkstemp() + + pbl = GraphCRF(n_features=4, n_states=3, inference_method='unary') + logger = SaveLogger(file_name) + svm = FrankWolfeSSVM(pbl, C=10, logger=logger, max_iter=50, batch_mode=False) + svm.fit(X_train, y_train) + + assert_less(.97, svm.score(X_test, y_test)) + assert_less(.97, logger.load().score(X_test, y_test)) diff --git a/pystruct/tests/test_learners/test_graph_svm.py b/pystruct/tests/test_learners/test_graph_svm.py index e9f09d66..2e6d7c99 100644 --- a/pystruct/tests/test_learners/test_graph_svm.py +++ b/pystruct/tests/test_learners/test_graph_svm.py @@ -15,8 +15,7 @@ def test_binary_blocks_cutting_plane(): #testing cutting plane ssvm on easy binary dataset # generate graphs explicitly for each example - for inference_method in get_installed(["dai", "lp", "qpbo", "ad3", 'ogm']): - print("testing %s" % inference_method) + for inference_method in get_installed(["lp", "qpbo", "ad3", 'ogm']): X, Y = generate_blocks(n_samples=3) crf = GraphCRF(inference_method=inference_method) clf = NSlackSSVM(model=crf, max_iter=20, C=100, check_constraints=True, @@ -35,7 +34,7 @@ def test_binary_blocks_cutting_plane(): X_ = [x.reshape(-1, n_states) for x in X_] Y = [y.ravel() for y in [y1, y2, y3]] - X = zip(X_, G) + X = list(zip(X_, G)) clf.fit(X, Y) Y_pred = clf.predict(X) @@ -87,5 +86,6 @@ def test_standard_svm_blobs_2d_class_weight(): break_on_bad=False) svm_class_weight.fit(X_graphs, Y[:, np.newaxis]) - assert_greater(f1_score(Y, np.hstack(svm_class_weight.predict(X_graphs))), - f1_score(Y, np.hstack(svm.predict(X_graphs)))) + assert_greater(f1_score(Y, np.hstack(svm_class_weight.predict(X_graphs)), + average='macro'), + f1_score(Y, np.hstack(svm.predict(X_graphs)), average='macro')) diff --git a/pystruct/tests/test_learners/test_latent_node_crf_learning.py b/pystruct/tests/test_learners/test_latent_node_crf_learning.py index 0a5652f0..8da3b3c4 100644 --- a/pystruct/tests/test_learners/test_latent_node_crf_learning.py +++ b/pystruct/tests/test_learners/test_latent_node_crf_learning.py @@ -3,7 +3,7 @@ import numpy as np from numpy.testing import assert_array_equal, assert_array_almost_equal from nose.tools import assert_equal, assert_true -from pystruct.models import GraphCRF, LatentNodeCRF +from pystruct.models import GraphCRF, LatentNodeCRF, EdgeFeatureLatentNodeCRF from pystruct.learners import (NSlackSSVM, LatentSSVM, SubgradientLatentSSVM, OneSlackSSVM, SubgradientSSVM) @@ -15,14 +15,14 @@ def make_edges_2x2(): edges = [] node_indices = np.arange(4 * 4).reshape(4, 4) for i, (x, y) in enumerate(itertools.product([0, 2], repeat=2)): - for j in xrange(x, x + 2): - for k in xrange(y, y + 2): + for j in range(x, x + 2): + for k in range(y, y + 2): edges.append([i + 4 * 4, node_indices[j, k]]) return edges def test_binary_blocks_cutting_plane_latent_node(): - #testing cutting plane ssvm on easy binary dataset + # testing cutting plane ssvm on easy binary dataset # we use the LatentNodeCRF without latent nodes and check that it does the # same as GraphCRF X, Y = generate_blocks(n_samples=3) @@ -43,7 +43,7 @@ def test_binary_blocks_cutting_plane_latent_node(): X_ = [x.reshape(-1, n_states) for x in X_] Y = [y.ravel() for y in [y1, y2, y3]] - X = zip(X_, G) + X = list(zip(X_, G)) clf.fit(X, Y) Y_pred = clf.predict(X) @@ -55,7 +55,7 @@ def test_binary_blocks_cutting_plane_latent_node(): check_constraints=True, break_on_bad=False, n_jobs=1), latent_iter=3) - X_latent = zip(X_, G, np.zeros(len(X_))) + X_latent = list(zip(X_, G, np.zeros(len(X_), dtype=np.int))) latent_svm.fit(X_latent, Y, H_init=Y) Y_pred = latent_svm.predict(X_latent) for y, y_pred in zip(Y, Y_pred): @@ -74,8 +74,7 @@ def test_latent_node_boxes_standard_latent(): latent_crf = LatentNodeCRF(n_labels=2, n_hidden_states=2, n_features=1) one_slack = OneSlackSSVM(latent_crf) n_slack = NSlackSSVM(latent_crf) - subgradient = SubgradientSSVM(latent_crf, max_iter=100, learning_rate=0.01, - momentum=0) + subgradient = SubgradientSSVM(latent_crf, max_iter=100) for base_svm in [one_slack, n_slack, subgradient]: base_svm.C = 10 latent_svm = LatentSSVM(base_svm, @@ -92,7 +91,7 @@ def test_latent_node_boxes_standard_latent(): X_flat = [x.reshape(-1, 1) for x in X] Y_flat = [y.ravel() for y in Y] - X_ = zip(X_flat, G, [2 * 2 for x in X_flat]) + X_ = list(zip(X_flat, G, [2 * 2 for x in X_flat])) latent_svm.fit(X_[:20], Y_flat[:20]) assert_array_equal(latent_svm.predict(X_[:20]), Y_flat[:20]) @@ -107,8 +106,7 @@ def test_latent_node_boxes_latent_subgradient(): X, Y = make_simple_2x2(seed=1) latent_crf = LatentNodeCRF(n_labels=2, n_hidden_states=2, n_features=1) - latent_svm = SubgradientLatentSSVM(model=latent_crf, max_iter=250, C=10, - learning_rate=0.1, momentum=0) + latent_svm = SubgradientLatentSSVM(model=latent_crf, max_iter=50, C=10) G = [make_grid_edges(x) for x in X] @@ -119,7 +117,7 @@ def test_latent_node_boxes_latent_subgradient(): X_flat = [x.reshape(-1, 1) for x in X] Y_flat = [y.ravel() for y in Y] - X_ = zip(X_flat, G, [4 * 4 for x in X_flat]) + X_ = list(zip(X_flat, G, [4 * 4 for x in X_flat])) latent_svm.fit(X_, Y_flat) assert_equal(latent_svm.score(X_, Y_flat), 1) @@ -135,8 +133,7 @@ def test_latent_node_boxes_standard_latent_features(): latent_node_features=True) one_slack = OneSlackSSVM(latent_crf) n_slack = NSlackSSVM(latent_crf) - subgradient = SubgradientSSVM(latent_crf, max_iter=100, learning_rate=0.01, - momentum=0) + subgradient = SubgradientSSVM(latent_crf, max_iter=100) for base_svm in [one_slack, n_slack, subgradient]: base_svm.C = 10 latent_svm = LatentSSVM(base_svm, @@ -156,7 +153,7 @@ def test_latent_node_boxes_standard_latent_features(): for x, y in zip(X_flat, Y)] Y_flat = [y.ravel() for y in Y] - X_ = zip(X_flat, G, [2 * 2 for x in X_flat]) + X_ = list(zip(X_flat, G, [2 * 2 for x in X_flat])) latent_svm.fit(X_[:10], Y_flat[:10]) assert_array_equal(latent_svm.predict(X_[:10]), Y_flat[:10]) @@ -164,3 +161,37 @@ def test_latent_node_boxes_standard_latent_features(): # we actually become prefect ^^ assert_true(.98 < latent_svm.score(X_[10:], Y_flat[10:]) <= 1) + + +def test_latent_node_boxes_edge_features(): + # learn the "easy" 2x2 boxes dataset. + # smoketest using a single constant edge feature + + X, Y = make_simple_2x2(seed=1, n_samples=40) + latent_crf = EdgeFeatureLatentNodeCRF(n_labels=2, n_hidden_states=2, n_features=1) + base_svm = OneSlackSSVM(latent_crf) + base_svm.C = 10 + latent_svm = LatentSSVM(base_svm, + latent_iter=10) + + G = [make_grid_edges(x) for x in X] + + # make edges for hidden states: + edges = make_edges_2x2() + + G = [np.vstack([make_grid_edges(x), edges]) for x in X] + + # reshape / flatten x and y + X_flat = [x.reshape(-1, 1) for x in X] + Y_flat = [y.ravel() for y in Y] + + #X_ = zip(X_flat, G, [2 * 2 for x in X_flat]) + # add edge features + X_ = [(x, g, np.ones((len(g), 1)), 4) for x, g in zip(X_flat, G)] + latent_svm.fit(X_[:20], Y_flat[:20]) + + assert_array_equal(latent_svm.predict(X_[:20]), Y_flat[:20]) + assert_equal(latent_svm.score(X_[:20], Y_flat[:20]), 1) + + # test that score is not always 1 + assert_true(.98 < latent_svm.score(X_[20:], Y_flat[20:]) < 1) diff --git a/pystruct/tests/test_learners/test_latent_svm.py b/pystruct/tests/test_learners/test_latent_svm.py index b170c7ed..5825c283 100644 --- a/pystruct/tests/test_learners/test_latent_svm.py +++ b/pystruct/tests/test_learners/test_latent_svm.py @@ -1,3 +1,5 @@ +import sys +import os import numpy as np from numpy.testing import assert_array_equal from nose.tools import assert_equal, assert_true @@ -118,7 +120,19 @@ def test_switch_to_ad3(): C=10. ** 3) clf = LatentSSVM(base_ssvm) + # evil hackery to get rid of ad3 output + try: + devnull = open('/dev/null', 'w') + oldstdout_fno = os.dup(sys.stdout.fileno()) + os.dup2(devnull.fileno(), 1) + replaced_stdout = True + except: + replaced_stdout = False + clf.fit(X, Y, H_init=H_init) + + if replaced_stdout: + os.dup2(oldstdout_fno, 1) assert_equal(clf.model.inference_method[0], 'ad3') Y_pred = clf.predict(X) diff --git a/pystruct/tests/test_learners/test_n_slack_ssvm.py b/pystruct/tests/test_learners/test_n_slack_ssvm.py index 0909ba86..270beab4 100644 --- a/pystruct/tests/test_learners/test_n_slack_ssvm.py +++ b/pystruct/tests/test_learners/test_n_slack_ssvm.py @@ -2,10 +2,14 @@ from tempfile import mkstemp from sklearn.datasets import load_iris +try: + from sklearn.model_selection import train_test_split +except ImportError: + from sklearn.cross_validation import train_test_split from pystruct.models import GraphCRF from pystruct.learners import NSlackSSVM -from pystruct.utils import SaveLogger, train_test_split +from pystruct.utils import SaveLogger from pystruct.datasets import (generate_blocks_multinomial, generate_blocks, generate_checker, generate_checker_multinomial) from pystruct.models import GridCRF, DirectionalGridCRF @@ -29,7 +33,7 @@ def test_n_slack_svm_as_crf_pickling(): X_train, X_test, y_train, y_test = train_test_split(X_, Y, random_state=1) _, file_name = mkstemp() - pbl = GraphCRF(n_features=4, n_states=3, inference_method='lp') + pbl = GraphCRF(n_features=4, n_states=3, inference_method=inference_method) logger = SaveLogger(file_name) svm = NSlackSSVM(pbl, C=100, n_jobs=1, logger=logger) svm.fit(X_train, y_train) @@ -43,8 +47,8 @@ def test_multinomial_blocks_cutting_plane(): X, Y = generate_blocks_multinomial(n_samples=40, noise=0.5, seed=0) n_labels = len(np.unique(Y)) crf = GridCRF(n_states=n_labels, inference_method=inference_method) - clf = NSlackSSVM(model=crf, max_iter=100, C=100, verbose=0, - check_constraints=False, batch_size=1) + clf = NSlackSSVM(model=crf, max_iter=100, C=100, check_constraints=False, + batch_size=1) clf.fit(X, Y) Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred) @@ -57,8 +61,8 @@ def test_multinomial_blocks_directional(): n_labels = len(np.unique(Y)) crf = DirectionalGridCRF(n_states=n_labels, inference_method=inference_method) - clf = NSlackSSVM(model=crf, max_iter=100, C=100, verbose=0, - check_constraints=True, batch_size=1) + clf = NSlackSSVM(model=crf, max_iter=100, C=100, check_constraints=True, + batch_size=1) clf.fit(X, Y) Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred) @@ -92,7 +96,6 @@ def test_switch_to_ad3(): # as it might use the relaxation, that is pretty much guraranteed assert_greater(ssvm_with_switch.objective_curve_[-1], ssvm.objective_curve_[-1]) - print(ssvm_with_switch.objective_curve_[-1], ssvm.objective_curve_[-1]) # test that convergence also results in switch ssvm_with_switch = NSlackSSVM(crf, max_iter=10000, switch_to=('ad3'), diff --git a/pystruct/tests/test_learners/test_one_slack_ssvm.py b/pystruct/tests/test_learners/test_one_slack_ssvm.py index 43eb8f16..ca9185a9 100644 --- a/pystruct/tests/test_learners/test_one_slack_ssvm.py +++ b/pystruct/tests/test_learners/test_one_slack_ssvm.py @@ -4,12 +4,16 @@ from nose.tools import assert_true, assert_equal, assert_less, assert_greater from sklearn.datasets import load_digits, load_iris +try: + from sklearn.model_selection import train_test_split +except ImportError: + from sklearn.cross_validation import train_test_split from pystruct.models import GridCRF, GraphCRF, BinaryClf from pystruct.learners import OneSlackSSVM from pystruct.datasets import (generate_blocks_multinomial, generate_blocks, generate_checker) -from pystruct.utils import make_grid_edges, SaveLogger, train_test_split +from pystruct.utils import make_grid_edges, SaveLogger from pystruct.inference import get_installed # we always try to get the fastest installed inference method @@ -19,7 +23,6 @@ def test_multinomial_blocks_one_slack(): #testing cutting plane ssvm on easy multinomial dataset X, Y = generate_blocks_multinomial(n_samples=10, noise=0.5, seed=0) - print(np.argmax(X[0], axis=-1)) n_labels = len(np.unique(Y)) crf = GridCRF(n_states=n_labels, inference_method=inference_method) clf = OneSlackSSVM(model=crf, max_iter=150, C=1, @@ -81,7 +84,6 @@ def test_constraint_removal(): def test_binary_blocks_one_slack_graph(): #testing cutting plane ssvm on easy binary dataset # generate graphs explicitly for each example - print("testing %s" % inference_method) X, Y = generate_blocks(n_samples=3) crf = GraphCRF(inference_method=inference_method) clf = OneSlackSSVM(model=crf, max_iter=100, C=1, @@ -101,7 +103,7 @@ def test_binary_blocks_one_slack_graph(): X_ = [x.reshape(-1, n_states) for x in X_] Y = [y.ravel() for y in [y1, y2, y3]] - X = zip(X_, G) + X = list(zip(X_, G)) clf.fit(X, Y) Y_pred = clf.predict(X) @@ -110,11 +112,12 @@ def test_binary_blocks_one_slack_graph(): def test_one_slack_constraint_caching(): - #testing cutting plane ssvm on easy multinomial dataset + # testing cutting plane ssvm on easy multinomial dataset X, Y = generate_blocks_multinomial(n_samples=10, noise=0.5, seed=0, size_x=9) n_labels = len(np.unique(Y)) - crf = GridCRF(n_states=n_labels, inference_method='lp') + exact_inference = get_installed([('ad3', {'branch_and_bound': True}), "lp"])[0] + crf = GridCRF(n_states=n_labels, inference_method=exact_inference) clf = OneSlackSSVM(model=crf, max_iter=150, C=1, check_constraints=True, break_on_bad=True, inference_cache=50, inactive_window=0) @@ -122,13 +125,18 @@ def test_one_slack_constraint_caching(): Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred) assert_equal(len(clf.inference_cache_), len(X)) - # there should be 21 constraints, which are less than the 94 iterations + # there should be 13 constraints, which are less than the 94 iterations # that are done - assert_equal(len(clf.inference_cache_[0]), 21) # check that we didn't change the behavior of how we construct the cache constraints_per_sample = [len(cache) for cache in clf.inference_cache_] - assert_equal(np.max(constraints_per_sample), 21) - assert_equal(np.min(constraints_per_sample), 21) + if exact_inference == "lp": + assert_equal(len(clf.inference_cache_[0]), 18) + assert_equal(np.max(constraints_per_sample), 18) + assert_equal(np.min(constraints_per_sample), 18) + else: + assert_equal(len(clf.inference_cache_[0]), 13) + assert_equal(np.max(constraints_per_sample), 20) + assert_equal(np.min(constraints_per_sample), 11) def test_one_slack_attractive_potentials(): @@ -188,4 +196,3 @@ def test_switch_to_ad3(): # as it might use the relaxation, that is pretty much guraranteed assert_greater(ssvm_with_switch.objective_curve_[-1], ssvm.objective_curve_[-1]) - print(ssvm_with_switch.objective_curve_[-1], ssvm.objective_curve_[-1]) diff --git a/pystruct/tests/test_learners/test_perceptron.py b/pystruct/tests/test_learners/test_perceptron.py index 54007fdc..b5624edf 100644 --- a/pystruct/tests/test_learners/test_perceptron.py +++ b/pystruct/tests/test_learners/test_perceptron.py @@ -97,10 +97,10 @@ def test_overflow_averaged(): def test_averaged(): # Under a lot of noise, averaging helps. This fails with less noise. - X, Y = generate_blocks_multinomial(n_samples=15, noise=2, seed=0) + X, Y = generate_blocks_multinomial(n_samples=15, noise=3, seed=0) X_train, Y_train = X[:10], Y[:10] X_test, Y_test = X[10:], Y[10:] - crf = GridCRF(n_states=X.shape[-1], inference_method='lp') + crf = GridCRF() clf = StructuredPerceptron(model=crf, max_iter=3) clf.fit(X_train, Y_train) no_avg_test = clf.score(X_test, Y_test) diff --git a/pystruct/tests/test_learners/test_primal_dual.py b/pystruct/tests/test_learners/test_primal_dual.py index 0ab0fbbd..f69f3207 100644 --- a/pystruct/tests/test_learners/test_primal_dual.py +++ b/pystruct/tests/test_learners/test_primal_dual.py @@ -3,9 +3,6 @@ #from structured_svm import objective_primal, PrimalDSStructuredSVM #from toy_datasets import binary -#from IPython.core.debugger import Tracer -#tracer = Tracer() - #def test_primal_dual_binary(): #for C in [1, 100, 100000]: diff --git a/pystruct/tests/test_learners/test_subgradient_latent_svm.py b/pystruct/tests/test_learners/test_subgradient_latent_svm.py index 14bed6d8..1f37c076 100644 --- a/pystruct/tests/test_learners/test_subgradient_latent_svm.py +++ b/pystruct/tests/test_learners/test_subgradient_latent_svm.py @@ -30,7 +30,6 @@ def test_with_crosses(): #show_loss_every=0, momentum=0.0, #decay_exponent=1, decay_t0=10) #clf.fit(X, Y) - #print(clf.predict_latent(X)) #Y_pred = clf.predict(X) #assert_array_equal(np.array(Y_pred), Y) @@ -46,7 +45,7 @@ def test_objective(): clfl = SubgradientLatentSSVM(model=crfl, max_iter=20, C=10., learning_rate=0.001, momentum=0.98) crfl.initialize(X, Y) - clfl.w = np.zeros(crfl.size_psi) # this disables random init + clfl.w = np.zeros(crfl.size_joint_feature) # this disables random init clfl.fit(X, Y) crf = GridCRF(n_states=n_labels, inference_method=inference_method) diff --git a/pystruct/tests/test_learners/test_subgradient_svm.py b/pystruct/tests/test_learners/test_subgradient_svm.py index ec87ad90..248e2f16 100644 --- a/pystruct/tests/test_learners/test_subgradient_svm.py +++ b/pystruct/tests/test_learners/test_subgradient_svm.py @@ -5,13 +5,17 @@ from nose.tools import assert_less from sklearn.datasets import load_iris +try: + from sklearn.model_selection import train_test_split +except ImportError: + from sklearn.cross_validation import train_test_split from pystruct.models import GridCRF, GraphCRF from pystruct.learners import SubgradientSSVM from pystruct.inference import get_installed from pystruct.datasets import (generate_blocks_multinomial, generate_checker_multinomial, generate_blocks) -from pystruct.utils import SaveLogger, train_test_split +from pystruct.utils import SaveLogger inference_method = get_installed(["qpbo", "ad3", "lp"])[0] @@ -19,22 +23,20 @@ def test_multinomial_blocks_subgradient(): #testing cutting plane ssvm on easy multinomial dataset - X, Y = generate_blocks_multinomial(n_samples=10, noise=0.3, seed=1) + X, Y = generate_blocks_multinomial(n_samples=10, noise=0.6, seed=1) n_labels = len(np.unique(Y)) crf = GridCRF(n_states=n_labels, inference_method=inference_method) - clf = SubgradientSSVM(model=crf, max_iter=50, C=10, momentum=.98, - learning_rate=0.001) + clf = SubgradientSSVM(model=crf, max_iter=50) clf.fit(X, Y) Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred) def test_multinomial_checker_subgradient(): - X, Y = generate_checker_multinomial(n_samples=10, noise=0.0) + X, Y = generate_checker_multinomial(n_samples=10, noise=0.4) n_labels = len(np.unique(Y)) crf = GridCRF(n_states=n_labels, inference_method=inference_method) - clf = SubgradientSSVM(model=crf, max_iter=50, C=10, - momentum=.98, learning_rate=0.01) + clf = SubgradientSSVM(model=crf, max_iter=50) clf.fit(X, Y) Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred) @@ -57,8 +59,7 @@ def test_binary_blocks(): #testing subgradient ssvm on easy binary dataset X, Y = generate_blocks(n_samples=5) crf = GridCRF(inference_method=inference_method) - clf = SubgradientSSVM(model=crf, C=100, learning_rate=1, decay_exponent=1, - momentum=0, decay_t0=10) + clf = SubgradientSSVM(model=crf) clf.fit(X, Y) Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred) @@ -77,9 +78,25 @@ def test_subgradient_svm_as_crf_pickling(): pbl = GraphCRF(n_features=4, n_states=3, inference_method='unary') logger = SaveLogger(file_name) - svm = SubgradientSSVM(pbl, C=10, n_jobs=1, logger=logger, - max_iter=50, momentum=0, learning_rate=0.01) + svm = SubgradientSSVM(pbl, logger=logger, max_iter=100) svm.fit(X_train, y_train) assert_less(.97, svm.score(X_test, y_test)) assert_less(.97, logger.load().score(X_test, y_test)) + + +def test_multinomial_blocks_subgradient_batch(): + #testing cutting plane ssvm on easy multinomial dataset + X, Y = generate_blocks_multinomial(n_samples=10, noise=0.6, seed=1) + n_labels = len(np.unique(Y)) + crf = GridCRF(n_states=n_labels, inference_method=inference_method) + clf = SubgradientSSVM(model=crf, max_iter=100, batch_size=-1) + clf.fit(X, Y) + Y_pred = clf.predict(X) + assert_array_equal(Y, Y_pred) + + clf2 = SubgradientSSVM(model=crf, max_iter=100, batch_size=len(X)) + clf2.fit(X, Y) + Y_pred2 = clf2.predict(X) + assert_array_equal(Y, Y_pred2) + diff --git a/pystruct/tests/test_libraries.py b/pystruct/tests/test_libraries.py index eb2a615b..1591c4c8 100644 --- a/pystruct/tests/test_libraries.py +++ b/pystruct/tests/test_libraries.py @@ -1,9 +1,20 @@ from pystruct.inference import get_installed -def test_pyqpbo() : + +def test_pyqpbo(): import pyqpbo - assert 'qpbo' in get_installed() + pyqpbo + assert 'qpbo' in get_installed(['qpbo']) + + +def test_ad3(): + import ad3 + ad3 + assert 'ad3' in get_installed(['ad3']) -def test_ad3() : +def test_ad3plus(): import ad3 - assert 'ad3' in get_installed() + ad3 + assert 'ad3+' in get_installed(['ad3+']) + + diff --git a/pystruct/tests/test_models/test_directional_crf.py b/pystruct/tests/test_models/test_directional_crf.py index 66751142..398567e0 100644 --- a/pystruct/tests/test_models/test_directional_crf.py +++ b/pystruct/tests/test_models/test_directional_crf.py @@ -61,28 +61,28 @@ def test_inference(): assert_array_equal(y, y_pred) -def test_psi_discrete(): +def test_joint_feature_discrete(): X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1) x, y = X[0], Y[0] for inference_method in get_installed(["lp", "ad3", "qpbo"]): crf = DirectionalGridCRF(inference_method=inference_method) crf.initialize(X, Y) - psi_y = crf.psi(x, y) - assert_equal(psi_y.shape, (crf.size_psi,)) + joint_feature_y = crf.joint_feature(x, y) + assert_equal(joint_feature_y.shape, (crf.size_joint_feature,)) # first horizontal, then vertical # we trust the unaries ;) - pw_psi_horz, pw_psi_vert = psi_y[crf.n_states * + pw_joint_feature_horz, pw_joint_feature_vert = joint_feature_y[crf.n_states * crf.n_features:].reshape( 2, crf.n_states, crf.n_states) xx, yy = np.indices(y.shape) - assert_array_equal(pw_psi_vert, np.diag([9 * 4, 9 * 4, 9 * 4])) - vert_psi = np.diag([10 * 3, 10 * 3, 10 * 3]) - vert_psi[0, 1] = 10 - vert_psi[1, 2] = 10 - assert_array_equal(pw_psi_horz, vert_psi) + assert_array_equal(pw_joint_feature_vert, np.diag([9 * 4, 9 * 4, 9 * 4])) + vert_joint_feature = np.diag([10 * 3, 10 * 3, 10 * 3]) + vert_joint_feature[0, 1] = 10 + vert_joint_feature[1, 2] = 10 + assert_array_equal(pw_joint_feature_horz, vert_joint_feature) -def test_psi_continuous(): +def test_joint_feature_continuous(): # FIXME # first make perfect prediction, including pairwise part X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1) @@ -106,12 +106,12 @@ def test_psi_continuous(): w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()]) y_pred = crf.inference(x, w, relaxed=True) - # compute psi for prediction - psi_y = crf.psi(x, y_pred) - assert_equal(psi_y.shape, (crf.size_psi,)) + # compute joint_feature for prediction + joint_feature_y = crf.joint_feature(x, y_pred) + assert_equal(joint_feature_y.shape, (crf.size_joint_feature,)) # first horizontal, then vertical # we trust the unaries ;) - #pw_psi_horz, pw_psi_vert = psi_y[crf.n_states * + #pw_joint_feature_horz, pw_joint_feature_vert = joint_feature_y[crf.n_states * #crf.n_features:].reshape(2, #crf.n_states, #crf.n_states) @@ -133,15 +133,15 @@ def test_energy(): res, energy = crf.inference(x, w, relaxed=True, return_energy=True) found_fractional = np.any(np.max(res[0], axis=-1) != 1) - psi = crf.psi(x, res) - energy_svm = np.dot(psi, w) + joint_feature = crf.joint_feature(x, res) + energy_svm = np.dot(joint_feature, w) assert_almost_equal(energy, -energy_svm) if not found_fractional: # exact discrete labels, test non-relaxed version res, energy = crf.inference(x, w, relaxed=False, return_energy=True) - psi = crf.psi(x, res) - energy_svm = np.dot(psi, w) + joint_feature = crf.joint_feature(x, res) + energy_svm = np.dot(joint_feature, w) assert_almost_equal(energy, -energy_svm) diff --git a/pystruct/tests/test_models/test_edge_feature_graph_crf.py b/pystruct/tests/test_models/test_edge_feature_graph_crf.py index b26adeab..f247236c 100644 --- a/pystruct/tests/test_models/test_edge_feature_graph_crf.py +++ b/pystruct/tests/test_models/test_edge_feature_graph_crf.py @@ -96,7 +96,7 @@ def test_inference(): assert_array_equal(y, y_pred) -def test_psi_discrete(): +def test_joint_feature_discrete(): X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1) x, y = X[0], Y[0] edge_list = make_grid_edges(x, 4, return_lists=True) @@ -107,22 +107,22 @@ def test_psi_discrete(): for inference_method in get_installed(["lp", "ad3", "qpbo"]): crf = EdgeFeatureGraphCRF(inference_method=inference_method) crf.initialize([x], [y_flat]) - psi_y = crf.psi(x, y_flat) - assert_equal(psi_y.shape, (crf.size_psi,)) + joint_feature_y = crf.joint_feature(x, y_flat) + assert_equal(joint_feature_y.shape, (crf.size_joint_feature,)) # first horizontal, then vertical # we trust the unaries ;) - pw_psi_horz, pw_psi_vert = psi_y[crf.n_states * + pw_joint_feature_horz, pw_joint_feature_vert = joint_feature_y[crf.n_states * crf.n_features:].reshape( 2, crf.n_states, crf.n_states) xx, yy = np.indices(y.shape) - assert_array_equal(pw_psi_vert, np.diag([9 * 4, 9 * 4, 9 * 4])) - vert_psi = np.diag([10 * 3, 10 * 3, 10 * 3]) - vert_psi[0, 1] = 10 - vert_psi[1, 2] = 10 - assert_array_equal(pw_psi_horz, vert_psi) + assert_array_equal(pw_joint_feature_vert, np.diag([9 * 4, 9 * 4, 9 * 4])) + vert_joint_feature = np.diag([10 * 3, 10 * 3, 10 * 3]) + vert_joint_feature[0, 1] = 10 + vert_joint_feature[1, 2] = 10 + assert_array_equal(pw_joint_feature_horz, vert_joint_feature) -def test_psi_continuous(): +def test_joint_feature_continuous(): # FIXME # first make perfect prediction, including pairwise part X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1) @@ -151,13 +151,13 @@ def test_psi_continuous(): crf.initialize([x], [y]) y_pred = crf.inference(x, w, relaxed=True) - # compute psi for prediction - psi_y = crf.psi(x, y_pred) - assert_equal(psi_y.shape, (crf.size_psi,)) + # compute joint_feature for prediction + joint_feature_y = crf.joint_feature(x, y_pred) + assert_equal(joint_feature_y.shape, (crf.size_joint_feature,)) # FIXME # first horizontal, then vertical # we trust the unaries ;) - #pw_psi_horz, pw_psi_vert = psi_y[crf.n_states * + #pw_joint_feature_horz, pw_joint_feature_vert = joint_feature_y[crf.n_states * #crf.n_features:].reshape(2, #crf.n_states, #crf.n_states) @@ -185,8 +185,8 @@ def test_energy_continuous(): res, energy = crf.inference(x, w, relaxed=True, return_energy=True) found_fractional = np.any(np.max(res[0], axis=-1) != 1) - psi = crf.psi(x, res) - energy_svm = np.dot(psi, w) + joint_feature = crf.joint_feature(x, res) + energy_svm = np.dot(joint_feature, w) assert_almost_equal(energy, -energy_svm) @@ -196,7 +196,7 @@ def test_energy_discrete(): crf = EdgeFeatureGraphCRF(n_states=3, inference_method=inference_method, n_edge_features=2, n_features=3) - for i in xrange(10): + for i in range(10): x = np.random.normal(size=(7, 8, 3)) edge_list = make_grid_edges(x, 4, return_lists=True) edges = np.vstack(edge_list) @@ -212,7 +212,7 @@ def test_energy_discrete(): crf._get_pairwise_potentials(x, w), edges, y_hat) - psi = crf.psi(x, y_hat) - energy_svm = np.dot(psi, w) + joint_feature = crf.joint_feature(x, y_hat) + energy_svm = np.dot(joint_feature, w) assert_almost_equal(energy, energy_svm) diff --git a/pystruct/tests/test_models/test_graph_crf.py b/pystruct/tests/test_models/test_graph_crf.py index 57a84768..d82a9642 100644 --- a/pystruct/tests/test_models/test_graph_crf.py +++ b/pystruct/tests/test_models/test_graph_crf.py @@ -52,27 +52,23 @@ def test_initialize(): def test_graph_crf_inference(): # create two samples with different graphs # two states only, pairwise smoothing - for inference_method in get_installed(['qpbo', 'lp', 'ad3', 'dai', 'ogm']): + for inference_method in get_installed(['qpbo', 'lp', 'ad3', 'ogm']): crf = GraphCRF(n_states=2, n_features=2, inference_method=inference_method) assert_array_equal(crf.inference((x_1, g_1), w), y_1) assert_array_equal(crf.inference((x_2, g_2), w), y_2) - print crf._get_pairwise_potentials((x_1, g_1), w) - def test_directed_graph_crf_inference(): # create two samples with different graphs # two states only, pairwise smoothing # same as above, only with full symmetric matrix - for inference_method in get_installed(['qpbo', 'lp', 'ad3', 'dai', 'ogm']): + for inference_method in get_installed(['qpbo', 'lp', 'ad3', 'ogm']): crf = GraphCRF(n_states=2, n_features=2, inference_method=inference_method, directed=True) assert_array_equal(crf.inference((x_1, g_1), w_sym), y_1) assert_array_equal(crf.inference((x_2, g_2), w_sym), y_2) - print crf._get_pairwise_potentials((x_1, g_1), w_sym) - def test_graph_crf_continuous_inference(): for inference_method in get_installed(['lp', 'ad3']): @@ -99,24 +95,24 @@ def test_graph_crf_energy_lp_integral(): # integral solution assert_array_almost_equal(np.max(inf_res[0], axis=-1), 1) y = np.argmax(inf_res[0], axis=-1) - # energy and psi check out - assert_almost_equal(energy_lp, -np.dot(w, crf.psi((x_1, g_1), y))) + # energy and joint_feature check out + assert_almost_equal(energy_lp, -np.dot(w, crf.joint_feature((x_1, g_1), y)), 4) def test_graph_crf_energy_lp_relaxed(): crf = GraphCRF(n_states=2, n_features=2) - for i in xrange(10): + for i in range(10): w_ = np.random.uniform(size=w.shape) inf_res, energy_lp = crf.inference((x_1, g_1), w_, relaxed=True, return_energy=True) assert_almost_equal(energy_lp, - -np.dot(w_, crf.psi((x_1, g_1), inf_res))) + -np.dot(w_, crf.joint_feature((x_1, g_1), inf_res))) # now with fractional solution x = np.array([[0, 0], [0, 0], [0, 0]]) inf_res, energy_lp = crf.inference((x, g_1), w, relaxed=True, return_energy=True) - assert_almost_equal(energy_lp, -np.dot(w, crf.psi((x, g_1), inf_res))) + assert_almost_equal(energy_lp, -np.dot(w, crf.joint_feature((x, g_1), inf_res))) def test_graph_crf_loss_augment(): @@ -126,7 +122,7 @@ def test_graph_crf_loss_augment(): crf.initialize([x], [y]) y_hat, energy = crf.loss_augmented_inference(x, y, w, return_energy=True) # check that y_hat fulfills energy + loss condition - assert_almost_equal(np.dot(w, crf.psi(x, y_hat)) + crf.loss(y, y_hat), + assert_almost_equal(np.dot(w, crf.joint_feature(x, y_hat)) + crf.loss(y, y_hat), -energy) diff --git a/pystruct/tests/test_models/test_grid_crf.py b/pystruct/tests/test_models/test_grid_crf.py index d6f08d53..6705554d 100644 --- a/pystruct/tests/test_models/test_grid_crf.py +++ b/pystruct/tests/test_models/test_grid_crf.py @@ -21,7 +21,7 @@ def test_continuous_y(): crf = GridCRF(inference_method=inference_method) crf.initialize(X, Y) - psi = crf.psi(x, y) + joint_feature = crf.joint_feature(x, y) y_cont = np.zeros_like(x) gx, gy = np.indices(x.shape[:-1]) y_cont[gx, gy, y] = 1 @@ -33,15 +33,15 @@ def test_continuous_y(): y_cont[:, :-1, :].reshape(-1, 2)) pw = vert + horz - psi_cont = crf.psi(x, (y_cont, pw)) - assert_array_almost_equal(psi, psi_cont) + joint_feature_cont = crf.joint_feature(x, (y_cont, pw)) + assert_array_almost_equal(joint_feature, joint_feature_cont) const = find_constraint(crf, x, y, w, relaxed=False) const_cont = find_constraint(crf, x, y, w, relaxed=True) - # dpsi and loss are equal: - assert_array_almost_equal(const[1], const_cont[1]) - assert_almost_equal(const[2], const_cont[2]) + # djoint_feature and loss are equal: + assert_array_almost_equal(const[1], const_cont[1], 4) + assert_almost_equal(const[2], const_cont[2], 4) # returned y_hat is one-hot version of other if isinstance(const_cont[0], tuple): @@ -49,7 +49,7 @@ def test_continuous_y(): # test loss: assert_almost_equal(crf.loss(y, const[0]), - crf.continuous_loss(y, const_cont[0][0])) + crf.continuous_loss(y, const_cont[0][0]), 4) def test_energy_lp(): @@ -61,11 +61,11 @@ def test_energy_lp(): inference_method=inference_method) while not found_fractional: x = np.random.normal(size=(2, 2, 4)) - w = np.random.uniform(size=crf.size_psi) + w = np.random.uniform(size=crf.size_joint_feature) inf_res, energy_lp = crf.inference(x, w, relaxed=True, return_energy=True) assert_almost_equal(energy_lp, - -np.dot(w, crf.psi(x, inf_res))) + -np.dot(w, crf.joint_feature(x, inf_res))) found_fractional = np.any(np.max(inf_res[0], axis=-1) != 1) @@ -81,7 +81,7 @@ def test_loss_augmentation(): y_hat, energy = crf.loss_augmented_inference(x, y, w, return_energy=True) assert_almost_equal(energy + crf.loss(y, y_hat), - -np.dot(w, crf.psi(x, y_hat))) + -np.dot(w, crf.joint_feature(x, y_hat))) def test_binary_blocks_crf(): @@ -91,7 +91,7 @@ def test_binary_blocks_crf(): 0, 1, 0, # pairwise -4, 0]) - for inference_method in get_installed(['dai', 'qpbo', 'lp', 'ad3', 'ogm']): + for inference_method in get_installed(['qpbo', 'lp', 'ad3', 'ogm']): crf = GridCRF(inference_method=inference_method) crf.initialize(X, Y) y_hat = crf.inference(x, w) @@ -121,6 +121,8 @@ def test_blocks_multinomial_crf(): -.3, .3, -.5, -.1, .3]) for inference_method in get_installed(): + #NOTE: ad3+ fails because it requires a different data structure + if inference_method == 'ad3+': continue crf = GridCRF(inference_method=inference_method) crf.initialize(X, Y) y_hat = crf.inference(x, w) @@ -133,7 +135,8 @@ def test_binary_grid_unaries(): X, Y = ds(n_samples=1) x, y = X[0], Y[0] for inference_method in get_installed(): - # dai is to expensive + #NOTE: ad3+ fails because it requires a different data structure + if inference_method == 'ad3+': continue crf = GridCRF(inference_method=inference_method) crf.initialize(X, Y) w_unaries_only = np.zeros(7) @@ -145,10 +148,7 @@ def test_binary_grid_unaries(): assert_array_equal(inf_unaries, np.argmax(x, axis=2), "Wrong unary inference for %s" % inference_method) - try: - assert(np.mean(inf_unaries == y) > 0.5) - except: - print(ds) + assert(np.mean(inf_unaries == y) > 0.5) # check that the right thing happens on noise-free data X, Y = ds(n_samples=1, noise=0) @@ -163,11 +163,11 @@ def test_multinomial_grid_unaries(): # on multinomial datasets for ds in multinomial: X, Y = ds(n_samples=1, size_x=9) - x, y = X[0], Y[0] + x = X[0] n_labels = len(np.unique(Y)) crf = GridCRF(n_states=n_labels) crf.initialize(X, Y) - w_unaries_only = np.zeros(crf.size_psi) + w_unaries_only = np.zeros(crf.size_joint_feature) w_unaries_only[:n_labels ** 2] = np.eye(n_labels).ravel() # test that inference with unaries only is the # same as argmax @@ -184,7 +184,7 @@ def test_binary_crf_exhaustive(): # tests qpbo inference against brute force # on random data / weights np.random.seed(0) - for i in xrange(10): + for i in range(10): x = np.random.uniform(-1, 1, size=(3, 2)) x = np.dstack([-x, np.zeros_like(x)]).copy() crf = GridCRF(n_features=2, n_states=2) @@ -202,7 +202,7 @@ def test_binary_crf_exhaustive_loss_augmented(): for inference_method in get_installed(['qpbo', 'lp']): crf = GridCRF(n_states=2, n_features=2, inference_method=inference_method) - for i in xrange(10): + for i in range(10): # generate data and weights y = np.random.randint(2, size=(3, 2)) x = np.random.uniform(-1, 1, size=(3, 2)) diff --git a/pystruct/tests/test_models/test_latent_crf.py b/pystruct/tests/test_models/test_latent_crf.py index 449146b1..842a7c17 100644 --- a/pystruct/tests/test_models/test_latent_crf.py +++ b/pystruct/tests/test_models/test_latent_crf.py @@ -33,7 +33,7 @@ def test_k_means_initialization(): n_labels=n_labels) H = np.vstack(H) assert_array_equal(np.unique(H), np.arange(6)) - assert_array_equal(Y, H / 3) + assert_array_equal(Y, H // 3) # for dataset with more than two states X, Y = generate_blocks_multinomial(n_samples=10) @@ -53,7 +53,7 @@ def test_k_means_initialization(): n_labels=n_labels) H = np.vstack(H) assert_array_equal(np.unique(H), np.arange(6)) - assert_array_equal(Y, H / 2) + assert_array_equal(Y, H // 2) def test_k_means_initialization_grid_crf(): @@ -87,30 +87,30 @@ def test_k_means_initialization_directional_grid_crf(): def test_blocks_crf_unaries(): X, Y = generate_blocks(n_samples=1) - x, y = X[0], Y[0] + x, _ = X[0], Y[0] unary_weights = np.repeat(np.eye(2), 2, axis=0) pairwise_weights = np.array([0, - 0, 0, - 0, 0, 0, - 0, 0, 0, 0]) + 0, 0, + 0, 0, 0, + 0, 0, 0, 0]) w = np.hstack([unary_weights.ravel(), pairwise_weights]) crf = LatentGridCRF(n_states_per_label=2, n_labels=2, n_features=2) h_hat = crf.inference(x, w) - assert_array_equal(h_hat / 2, np.argmax(x, axis=-1)) + assert_array_equal(h_hat // 2, np.argmax(x, axis=-1)) def test_blocks_crf(): X, Y = generate_blocks(n_samples=1) x, y = X[0], Y[0] pairwise_weights = np.array([0, - 0, 0, - -4, -4, 0, - -4, -4, 0, 0]) + 0, 0, + -4, -4, 0, + -4, -4, 0, 0]) unary_weights = np.repeat(np.eye(2), 2, axis=0) w = np.hstack([unary_weights.ravel(), pairwise_weights]) crf = LatentGridCRF(n_states_per_label=2, n_labels=2, n_features=2) h_hat = crf.inference(x, w) - assert_array_equal(y, h_hat / 2) + assert_array_equal(y, h_hat // 2) h = crf.latent(x, y, w) assert_equal(crf.loss(h, h_hat), 0) @@ -122,9 +122,9 @@ def test_blocks_crf_directional(): X, Y = generate_blocks(n_samples=1) x, y = X[0], Y[0] pairwise_weights = np.array([0, - 0, 0, - -4, -4, 0, - -4, -4, 0, 0]) + 0, 0, + -4, -4, 0, + -4, -4, 0, 0]) unary_weights = np.repeat(np.eye(2), 2, axis=0) w = np.hstack([unary_weights.ravel(), pairwise_weights]) pw_directional = np.array([0, 0, -4, -4, @@ -136,10 +136,9 @@ def test_blocks_crf_directional(): -4, -4, 0, 0, -4, -4, 0, 0]) w_directional = np.hstack([unary_weights.ravel(), pw_directional]) - crf = LatentGridCRF(n_states_per_label=2, inference_method='lp') + crf = LatentGridCRF(n_states_per_label=2) crf.initialize(X, Y) - directional_crf = LatentDirectionalGridCRF(n_states_per_label=2, - inference_method='lp') + directional_crf = LatentDirectionalGridCRF(n_states_per_label=2) directional_crf.initialize(X, Y) h_hat = crf.inference(x, w) h_hat_d = directional_crf.inference(x, w_directional) @@ -153,72 +152,71 @@ def test_blocks_crf_directional(): h_hat_d = directional_crf.loss_augmented_inference(x, y, w_directional) assert_array_equal(h_hat, h_hat_d) - psi = crf.psi(x, h_hat) - psi_d = directional_crf.psi(x, h_hat) - assert_array_equal(np.dot(psi, w), np.dot(psi_d, w_directional)) + joint_feature = crf.joint_feature(x, h_hat) + joint_feature_d = directional_crf.joint_feature(x, h_hat) + assert_array_equal(np.dot(joint_feature, w), np.dot(joint_feature_d, w_directional)) def test_latent_consistency_zero_pw_graph(): crf = LatentGraphCRF(n_labels=2, n_features=2, n_states_per_label=2) - for i in xrange(10): + for i in range(10): w = np.zeros(18) w[:8] = np.random.normal(size=8) y = np.random.randint(2, size=(5)) x = np.random.normal(size=(5, 2)) h = crf.latent((x, np.zeros((0, 2), dtype=np.int)), y, w) - assert_array_equal(h / 2, y) + assert_array_equal(h // 2, y) def test_latent_consistency_graph(): crf = LatentGraphCRF(n_labels=2, n_features=2, n_states_per_label=2) - for i in xrange(10): + for i in range(10): w = np.random.normal(size=18) y = np.random.randint(2, size=(4)) x = np.random.normal(size=(4, 2)) e = np.array([[0, 1], [1, 2], [2, 0]], dtype=np.int) h = crf.latent((x, e), y, w) - assert_array_equal(h / 2, y) + assert_array_equal(h // 2, y) def test_loss_augmented_inference_energy_graph(): - crf = LatentGraphCRF(n_labels=2, n_features=2, n_states_per_label=2, - inference_method='lp') - for i in xrange(10): + crf = LatentGraphCRF(n_labels=2, n_features=2, n_states_per_label=2) + for i in range(10): w = np.random.normal(size=18) - y = np.random.randint(2, size=(2)) - x = np.random.normal(size=(2, 2)) + y = np.random.randint(2, size=(3)) + x = np.random.normal(size=(3, 2)) e = np.array([[0, 1], [1, 2], [2, 0]], dtype=np.int) h_hat, energy = crf.loss_augmented_inference((x, e), y * 2, w, relaxed=True, return_energy=True) - assert_almost_equal(-energy, np.dot(w, crf.psi((x, e), h_hat)) + assert_almost_equal(-energy, np.dot(w, crf.joint_feature((x, e), h_hat)) + crf.loss(y * 2, h_hat)) def test_latent_consistency_zero_pw_grid(): crf = LatentGridCRF(n_labels=2, n_features=2, n_states_per_label=2) - for i in xrange(10): + for i in range(10): w = np.zeros(18) w[:8] = np.random.normal(size=8) y = np.random.randint(2, size=(5, 5)) x = np.random.normal(size=(5, 5, 2)) h = crf.latent(x, y, w) - assert_array_equal(h / 2, y) + assert_array_equal(h // 2, y) def test_latent_consistency_grid(): crf = LatentGridCRF(n_labels=2, n_features=2, n_states_per_label=2) - for i in xrange(10): + for i in range(10): w = np.random.normal(size=18) y = np.random.randint(2, size=(4, 4)) x = np.random.normal(size=(4, 4, 2)) h = crf.latent(x, y, w) - assert_array_equal(h / 2, y) + assert_array_equal(h // 2, y) def test_loss_augmented_inference_exhaustive_grid(): crf = LatentGridCRF(n_labels=2, n_features=2, n_states_per_label=2) - for i in xrange(10): + for i in range(10): w = np.random.normal(size=18) y = np.random.randint(2, size=(2, 2)) x = np.random.normal(size=(2, 2, 2)) @@ -238,7 +236,7 @@ def test_continuous_y(): crf = LatentGridCRF(n_labels=2, n_features=2, n_states_per_label=1, inference_method=inference_method) - psi = crf.psi(x, y) + joint_feature = crf.joint_feature(x, y) y_cont = np.zeros_like(x) gx, gy = np.indices(x.shape[:-1]) y_cont[gx, gy, y] = 1 @@ -250,15 +248,15 @@ def test_continuous_y(): y_cont[:, :-1, :].reshape(-1, 2)) pw = vert + horz - psi_cont = crf.psi(x, (y_cont, pw)) - assert_array_almost_equal(psi, psi_cont) + joint_feature_cont = crf.joint_feature(x, (y_cont, pw)) + assert_array_almost_equal(joint_feature, joint_feature_cont, 4) const = find_constraint(crf, x, y, w, relaxed=False) const_cont = find_constraint(crf, x, y, w, relaxed=True) - # dpsi and loss are equal: - assert_array_almost_equal(const[1], const_cont[1]) - assert_almost_equal(const[2], const_cont[2]) + # djoint_feature and loss are equal: + assert_array_almost_equal(const[1], const_cont[1], 4) + assert_almost_equal(const[2], const_cont[2], 4) if isinstance(const_cont[0], tuple): # returned y_hat is one-hot version of other @@ -266,4 +264,4 @@ def test_continuous_y(): # test loss: assert_almost_equal(crf.loss(y, const[0]), - crf.continuous_loss(y, const_cont[0][0])) + crf.continuous_loss(y, const_cont[0][0]), 4) diff --git a/pystruct/tests/test_models/test_latent_node_crf.py b/pystruct/tests/test_models/test_latent_node_crf.py index d27bcc13..5fe4044e 100644 --- a/pystruct/tests/test_models/test_latent_node_crf.py +++ b/pystruct/tests/test_models/test_latent_node_crf.py @@ -37,12 +37,12 @@ def test_initialize(): def test_inference_trivial(): # size 6 chain graph # first three and last three have a latent variable - features = np.array([-1, 1, -1, 1, -1, 1]) + features = np.array([-1, 1, -1, 1, -1, 1]) unary_parameters = np.array([-1, 1]) pairwise_parameters = np.array([+0, - +0, 0, - +3, 0, 0, - +0, 3, 0, 0]) + +0, 0, + +3, 0, 0, + +0, 3, 0, 0]) w = np.hstack([unary_parameters, pairwise_parameters]) crf = LatentNodeCRF(n_labels=2, n_features=1, n_hidden_states=2) # edges for latent states. Latent states named 6, 7 @@ -66,19 +66,19 @@ def test_inference_trivial(): y_unaries = np.argmax(crf._get_unary_potentials(x, w), axis=1)[:6] assert_array_equal(y_unaries, features > 0) - # test psi - energy_psi = np.dot(w, crf.psi(x, h)) - assert_almost_equal(energy_psi, -energy_lp) + # test joint_feature + energy_joint_feature = np.dot(w, crf.joint_feature(x, h)) + assert_almost_equal(energy_joint_feature, -energy_lp) # test loss h_unaries = crf.latent(x, y_unaries, w) assert_equal(crf.loss(h, h_unaries), 2) - # continuous inference and psi: + # continuous inference and joint_feature: h_continuous, energy_lp = crf.inference(x, w, return_energy=True, relaxed=True) - energy_psi = np.dot(w, crf.psi(x, h)) - assert_almost_equal(energy_psi, -energy_lp) + energy_joint_feature = np.dot(w, crf.joint_feature(x, h)) + assert_almost_equal(energy_joint_feature, -energy_lp) # test continuous loss assert_equal(crf.loss(h, h_continuous), 0) @@ -86,21 +86,18 @@ def test_inference_trivial(): #test loss-augmented inference energy h_hat, energy_lp = crf.loss_augmented_inference(x, h, w, return_energy=True) - assert_almost_equal(-energy_lp, np.dot(w, crf.psi(x, h_hat)) + + assert_almost_equal(-energy_lp, np.dot(w, crf.joint_feature(x, h_hat)) + crf.loss(h_hat, y)) - #print(h_hat) - #print(h) - #print(crf.loss(h_hat, h)) def test_inference_chain(): # same with pairwise edges: - features = np.array([-1, 1, -1, 1, -1, 1]) + features = np.array([-1, 1, -1, 1, -1, 1]) unary_parameters = np.array([-1, 1]) pairwise_parameters = np.array([+1, - +0, 1, - +3, 0, 0, - +0, 3, 0, 0]) + +0, 1, + +3, 0, 0, + +0, 3, 0, 0]) w = np.hstack([unary_parameters, pairwise_parameters]) crf = LatentNodeCRF(n_labels=2, n_features=1, n_hidden_states=2) edges = np.vstack([np.arange(5), np.arange(1, 6)]).T @@ -117,28 +114,28 @@ def test_inference_chain(): x = (features.reshape(-1, 1), all_edges, 2) h, energy_lp = crf.inference(x, w, return_energy=True) y = np.argmax(crf._get_unary_potentials(x, w), axis=1)[:6] - energy_psi = np.dot(w, crf.psi(x, h)) + energy_joint_feature = np.dot(w, crf.joint_feature(x, h)) - assert_almost_equal(energy_psi, -energy_lp) + assert_almost_equal(energy_joint_feature, -energy_lp) assert_array_equal(y, features > 0) assert_array_equal(h, [0, 0, 0, 1, 1, 1, 2, 3]) - # continuous inference and psi: + # continuous inference and joint_feature: h, energy_lp = crf.inference(x, w, return_energy=True, relaxed=True) - energy_psi = np.dot(w, crf.psi(x, h)) - assert_almost_equal(energy_psi, -energy_lp) + energy_joint_feature = np.dot(w, crf.joint_feature(x, h)) + assert_almost_equal(energy_joint_feature, -energy_lp) def test_inference_trivial_features(): # size 6 chain graph # first three and last three have a latent variable # last two features are for latent variables - features = np.array([-1, 1, -1, 1, -1, 1, 0, 0]) + features = np.array([-1, 1, -1, 1, -1, 1, 0, 0], dtype=np.float) unary_parameters = np.array([-1, 1, 0, 0]) pairwise_parameters = np.array([+0, - +0, 0, - +3, 0, 0, - +0, 3, 0, 0]) + +0, 0, + +3, 0, 0, + +0, 3, 0, 0]) w = np.hstack([unary_parameters, pairwise_parameters]) crf = LatentNodeCRF(n_labels=2, n_features=1, n_hidden_states=2, latent_node_features=True) @@ -163,19 +160,19 @@ def test_inference_trivial_features(): y_unaries = np.argmax(crf._get_unary_potentials(x, w), axis=1)[:6] assert_array_equal(y_unaries, features[:6] > 0) - # test psi - energy_psi = np.dot(w, crf.psi(x, h)) - assert_almost_equal(energy_psi, -energy_lp) + # test joint_feature + energy_joint_feature = np.dot(w, crf.joint_feature(x, h)) + assert_almost_equal(energy_joint_feature, -energy_lp) # test loss h_unaries = crf.latent(x, y_unaries, w) assert_equal(crf.loss(h, h_unaries), 2) - # continuous inference and psi: + # continuous inference and joint_feature: h_continuous, energy_lp = crf.inference(x, w, return_energy=True, relaxed=True) - energy_psi = np.dot(w, crf.psi(x, h)) - assert_almost_equal(energy_psi, -energy_lp) + energy_joint_feature = np.dot(w, crf.joint_feature(x, h)) + assert_almost_equal(energy_joint_feature, -energy_lp) # test continuous loss assert_equal(crf.loss(h, h_continuous), 0) @@ -183,7 +180,7 @@ def test_inference_trivial_features(): #test loss-augmented inference energy h_hat, energy_lp = crf.loss_augmented_inference(x, h, w, return_energy=True) - assert_almost_equal(-energy_lp, np.dot(w, crf.psi(x, h_hat)) + + assert_almost_equal(-energy_lp, np.dot(w, crf.joint_feature(x, h_hat)) + crf.loss(h_hat, y)) @@ -192,7 +189,7 @@ def test_edge_feature_latent_node_crf_no_latent(): # Test inference with different weights in different directions - X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1, size_x=10) + X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1, size_x=8) x, y = X[0], Y[0] n_states = x.shape[-1] @@ -234,11 +231,12 @@ def test_edge_feature_latent_node_crf_no_latent(): n_edge_features=2, n_hidden_states=5) w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()]) y_pred = crf.inference(x, w, relaxed=True) - assert_array_almost_equal(res[0], y_pred[0].reshape(-1, n_states + 5)) - assert_array_almost_equal(res[1], y_pred[1]) + assert_array_almost_equal(res[0], y_pred[0].reshape(-1, n_states + 5), + 4) + assert_array_almost_equal(res[1], y_pred[1], 4) assert_array_equal(y, np.argmax(y_pred[0], axis=-1)) - for inference_method in get_installed(["lp", "ad3", "qpbo"]): + for inference_method in get_installed(["qpbo", "ad3", "lp"])[:2]: # again, this time discrete predictions only crf = EdgeFeatureLatentNodeCRF(n_labels=3, inference_method=inference_method, diff --git a/pystruct/tests/test_models/test_multilabel_problem.py b/pystruct/tests/test_models/test_multilabel_problem.py index 705c6a6b..03d49937 100644 --- a/pystruct/tests/test_models/test_multilabel_problem.py +++ b/pystruct/tests/test_models/test_multilabel_problem.py @@ -5,7 +5,7 @@ from nose.tools import assert_almost_equal, assert_equal, assert_raises from pystruct.models import MultiLabelClf -from pystruct.inference.inference_methods import compute_energy +from pystruct.inference import compute_energy def test_initialization(): @@ -17,7 +17,7 @@ def test_initialization(): assert_equal(model.n_states, 2) assert_equal(model.n_labels, 3) assert_equal(model.n_features, 5) - assert_equal(model.size_psi, 5 * 3) + assert_equal(model.size_joint_feature, 5 * 3) # setting and then initializing is no-op model = MultiLabelClf(n_features=5, n_labels=3) @@ -33,7 +33,7 @@ def test_multilabel_independent(): n_features = 5 n_labels = 4 model = MultiLabelClf(n_labels=n_labels, n_features=n_features, - edges=edges) + edges=edges) rnd = np.random.RandomState(0) x = rnd.normal(size=5) @@ -43,17 +43,17 @@ def test_multilabel_independent(): y_ = np.dot(w.reshape(n_labels, n_features), x) > 0 assert_array_equal(y, y_) - # test psi / energy - psi = model.psi(x, y) + # test joint_feature / energy + joint_feature = model.joint_feature(x, y) energy = compute_energy(model._get_unary_potentials(x, w), model._get_pairwise_potentials(x, w), edges, y) - assert_almost_equal(energy, np.dot(psi, w)) + assert_almost_equal(energy, np.dot(joint_feature, w)) # for continuous y y_continuous = np.zeros((n_labels, 2)) y_continuous[np.arange(n_labels), y] = 1 assert_array_almost_equal( - psi, model.psi(x, (y_continuous, np.zeros((0, n_labels, n_labels))))) + joint_feature, model.joint_feature(x, (y_continuous, np.zeros((0, n_labels, n_labels))))) def test_multilabel_fully(): @@ -62,18 +62,18 @@ def test_multilabel_fully(): n_labels = 4 edges = np.vstack([x for x in itertools.combinations(range(n_labels), 2)]) model = MultiLabelClf(n_labels=n_labels, n_features=n_features, - edges=edges) + edges=edges) rnd = np.random.RandomState(0) x = rnd.normal(size=n_features) w = rnd.normal(size=n_features * n_labels + 4 * len(edges)) y = model.inference(x, w) - # test psi / energy - psi = model.psi(x, y) + # test joint_feature / energy + joint_feature = model.joint_feature(x, y) energy = compute_energy(model._get_unary_potentials(x, w), model._get_pairwise_potentials(x, w), edges, y) - assert_almost_equal(energy, np.dot(psi, w)) + assert_almost_equal(energy, np.dot(joint_feature, w)) # for continuous y #y_cont = model.inference(x, w, relaxed=True) @@ -89,4 +89,4 @@ def test_multilabel_fully(): y_continuous[np.arange(n_labels), y] = 1 assert_array_almost_equal( - psi, model.psi(x, (y_continuous, pairwise_marginals))) + joint_feature, model.joint_feature(x, (y_continuous, pairwise_marginals))) diff --git a/pystruct/tests/test_models/test_node_type_edge_feature_graph_crf.py b/pystruct/tests/test_models/test_node_type_edge_feature_graph_crf.py new file mode 100644 index 00000000..7919817a --- /dev/null +++ b/pystruct/tests/test_models/test_node_type_edge_feature_graph_crf.py @@ -0,0 +1,872 @@ +import pytest +import numpy as np +from numpy.testing import (assert_array_equal, assert_array_almost_equal, + assert_almost_equal, assert_equal) +from nose.tools import assert_raises + +from pystruct.models import NodeTypeEdgeFeatureGraphCRF, EdgeFeatureGraphCRF + +from pystruct.inference.linear_programming import lp_general_graph +from pystruct.inference import compute_energy, get_installed +from pystruct.utils import make_grid_edges, edge_list_to_features +from pystruct.datasets import generate_blocks_multinomial + + + +def test_checks(): + g = NodeTypeEdgeFeatureGraphCRF( + 1 #how many node type? + , [4] #how many labels per node type? + , [3] #how many features per node type? + , np.array([[3]]) #how many features per node type X node type? + ) + + g = NodeTypeEdgeFeatureGraphCRF( + 2 #how many node type? + , [2, 3 ] #how many labels per node type? + , [4, 5] #how many features per node type? + , np.array([[1, 2], [2,4]]) #how many features per node type X node type? + ) + + with pytest.raises(ValueError): + g = NodeTypeEdgeFeatureGraphCRF( + 3 #how many node type? + , [2, 3 ] #how many labels per node type? + , [4, 5] #how many features per node type? + , np.array([[1, 2], [2,4]]) #how many features per node type X node type? + ) + + with pytest.raises(ValueError): + g = NodeTypeEdgeFeatureGraphCRF( + 2 #how many node type? + , [2, 3 ] #how many labels per node type? + , [4, 5, 3] #how many features per node type? + , np.array([[1, 2], [2,4]]) #how many features per node type X node type? + ) + + with pytest.raises(ValueError): + g = NodeTypeEdgeFeatureGraphCRF( + 3 #how many node type? + , [2, 3 ] #how many labels per node type? + , [4, 5] #how many features per node type? + , np.array([[1, 2], [2,4]]) #how many features per node type X node type? + ) + + with pytest.raises(ValueError): + g = NodeTypeEdgeFeatureGraphCRF( + 2 #how many node type? + , [2, 3 ] #how many labels per node type? + , [4, 5] #how many features per node type? + , np.array([[1, 2, 3], [2,3,4]]) #how many features per node type X node type? + ) + + with pytest.raises(ValueError): + g = NodeTypeEdgeFeatureGraphCRF( + 2 #how many node type? + , [2, 3 ] #how many labels per node type? + , [4, 5] #how many features per node type? + , np.array([[1, 2], [99,4]]) #how many features per node type X node type? + ) + +def debug_joint_feature(): + # ------------------------------------------------------------------------------------------- + #print "---MORE COMPLEX GRAPH :) ---------------------------------------------------------------------" + g = NodeTypeEdgeFeatureGraphCRF( + 2 #how many node type? + , [2, 3] #how many possible labels per node type? + , [3, 4] #how many features per node type? + , np.array([ [1, 2] + , [2, 3]]) #how many features per node type X node type? + ) + + l_node_f = [ np.array([ [1,1,1], [2,2,2] ]) + , np.array([ [.11, .12, .13, .14], [.21, .22, .23, .24], [.31, .32, .33, .34]]) + ] + l_edges = [ np.array([[0, 1]]) #type 0 node 0 to type 0 node 0 + , np.array([[0, 1]]) + , None + , None + ] + l_edge_f = [ np.array([[.111]]) + , np.array([[.221, .222]]) + , None + , None + ] + + x = (l_node_f, l_edges, l_edge_f) + #print "- - - - - - - - - - - - - - - - - - - - - - - - - - - " + y = np.hstack([ np.array([0, 1]), + np.array([0, 1, 2]) + ]) + #print y + g.initialize(x, y) + jf = g.joint_feature(x,y) + #print "joint_feature = \n", `jf` + #print + assert_array_equal(jf, jf) + assert_array_almost_equal(jf + , np.array( + [ 1. , 1., 1. , 2., 2., 2. + , 0.11 , 0.12 , 0.13 , 0.14 , 0.21 , 0.22 , 0.23 , 0.24 , 0.31 , 0.32 , 0.33 , 0.34 + + , 0. , 0.111, 0. , 0. , 0. , 0.221, + 0. , 0. , 0. , 0. , 0. , 0.222, 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. + ])) + + +def get_simple_graph_structure(): + g = NodeTypeEdgeFeatureGraphCRF( + 1 #how many node type? + , [4] #how many labels per node type? + , [3] #how many features per node type? + , np.array([[3]]) #how many features per node type X node type? + ) + return g + +def get_simple_graph(): + node_f = [ np.array([[1,1,1], + [2,2,2]]) + ] + edges = [ np.array([[0,1]]) + ] #an edge from 0 to 1 + edge_f = [ np.array([[3,3,3]]) + ] + return (node_f, edges, edge_f) + +def get_simple_graph2(): + node_f = [ np.array([ [1,1,1] + , [2,2,2]]) ] + edges = [ np.array( [[0,1], #an edge from 0 to 1 + [0,0] #an edge from 0 to 0 + ]) ] + edge_f = [ np.array([ + [3,3,3], + [4,4,4] + ]) ] + return (node_f, edges, edge_f) + +def test_flatten_unflattenY(): + + g, (node_f, edges, edge_f) = get_simple_graph_structure(), get_simple_graph() + y = np.array([1,2]) + l_nf = [ np.zeros((2,3)) ] #list of node feature , per type + X = (l_nf, None, None) #we give no edge + y_ref = [ np.array([1,2]) ] + assert all( [ (y_typ1 == y_typ2).all() for y_typ1, y_typ2 in zip(g.unflattenY(X, y), y_ref) ]) + + assert (y == g.flattenY(g.unflattenY(X, y))).all() + + #============================================ + g, x, y = more_complex_graph() + + Y = [ np.array([0, 0]) + , np.array([0, 0, 0]) #we start again at zero on 2nd type + ] + + y = np.hstack([ np.array([0, 0]) + , 2+np.array([0, 0, 0]) + ]) + l_nf = [ np.zeros( (2,3) ), np.zeros( (3, 4) )] #2 node with 3 features, 3 node with 4 features + X = (l_nf, None, None) #we give no edge + assert (g.flattenY(Y) == y).all() + #print g.unflattenY(X, y) + assert all( [ (y_typ1 == y_typ2).all() for y_typ1, y_typ2 in zip(g.unflattenY(X, y), Y) ]) + + l_nf = [ np.zeros( (1,3) ), np.zeros( (3, 4) )] #2 node with 3 features, 3 node with 4 features + X = (l_nf, None, None) #we give no edge + assert_raises(ValueError, g.unflattenY, X, y) + +def test_joint_feature(): + + #print "---SIMPLE---------------------------------------------------------------------" + g, (node_f, edges, edge_f) = get_simple_graph_structure(), get_simple_graph() + + x = (node_f, edges, edge_f) + #print "- - - - - - - - - - - - - - - - - - - - - - - - - - - " + y = np.array([1,2]) + +# y = np.array([1,0]) + #print y + g.initialize(x, y) + jf = g.joint_feature(x,y) + #print "joint_feature = \n", `jf` + #print + assert_array_equal(g.joint_feature(x,y) + , np.array([ 0., 0., 0., 1., 1., 1., 2., 2., 2., 0., 0., 0. + , 0., + 0., 0., 0., 0., 0., 3., 0., 0., 0., 0., 0., 0., 0., + 0., 0., 0., 0., 0., 0., 0., 0., 3., 0., 0., 0., 0., + 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 3., 0., + 0., 0., 0., 0., 0., 0., 0., 0.]) + ) + + #print "- - - - - - - - - - - - - - - - - - - - - - - - - - - " + y = np.array([0,0]) + #print y + g.initialize(x, y) + jf = g.joint_feature(x,y) + #print "joint_feature = \n", `jf` + #print + assert_array_equal(g.joint_feature(x,y) + , np.array([ 3., 3., 3., 0., 0., 0., 0., 0., 0., 0., 0., 0., 3., + 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., + 0., 0., 3., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., + 0., 0., 0., 0., 0., 3., 0., 0., 0., 0., 0., 0., 0., + 0., 0., 0., 0., 0., 0., 0., 0.]) + ) + + #print "- - - - - - - - - - - - - - - - - - - - - - - - - - - " + y = np.array([0,1]) + node_f = [ np.array([[1.1,1.2,1.3], [2.1,2.2,2.3]]) ] + edge_f = [ np.array([[3.1,3.2,3.3]]) ] + x = (node_f, edges, edge_f) + g.initialize(x, y) + jf = g.joint_feature(x,y) + #print "joint_feature = \n", `jf` + + assert_array_equal(g.joint_feature(x,y) + , np.array([ 1.1, 1.2, 1.3, 2.1, 2.2, 2.3, 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 3.1, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 3.2, 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 3.3, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. ]) + ) + #print "---SIMPLE + 2nd EDGE--------------------------------------------------------" + node_f, edges, edge_f = get_simple_graph2() + + x = (node_f, edges, edge_f) + #print "- - - - - - - - - - - - - - - - - - - - - - - - - - - " + y = np.array([1,2]) + #print y + g.initialize(x, y) + jf = g.joint_feature(x,y) + #print "joint_feature = \n", `jf` + #print + assert_array_equal(jf + , np.array([ 0., 0., 0., 1., 1., 1., 2., 2., 2., 0., 0., 0., 0., + 0., 0., 0., 0., 4., 3., 0., 0., 0., 0., 0., 0., 0., + 0., 0., 0., 0., 0., 0., 0., 4., 3., 0., 0., 0., 0., + 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 4., 3., 0., + 0., 0., 0., 0., 0., 0., 0., 0.]) + ) + #print "- - - - - - - - - - - - - - - - - - - - - - - - - - - " + y = np.array([0,0]) + #print y + g.initialize(x, y) + #print "joint_feature = \n", `g.joint_feature(x,y)` + #print + assert_array_equal(g.joint_feature(x,y) + , np.array([ 3., 3., 3., 0., 0., 0., 0., 0., 0., 0., 0., 0., 7., + 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., + 0., 0., 7., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., + 0., 0., 0., 0., 0., 7., 0., 0., 0., 0., 0., 0., 0., + 0., 0., 0., 0., 0., 0., 0., 0.]) + ) + +def more_complex_graph(): + g = NodeTypeEdgeFeatureGraphCRF( + 2 #how many node type? + , [2, 3] #how many labels per node type? + , [3, 4] #how many features per node type? + , np.array([ [1, 2] + , [2, 3]]) #how many features per node type X node type? + ) + +# nodes = np.array( [[0,0], [0,1], [1, 0], [1, 1], [1, 2]] ) + node_f = [ np.array([ [1,1,1], [2,2,2] ]) + , np.array([ [.11, .12, .13, .14], [.21, .22, .23, .24], [.31, .32, .33, .34]]) + ] + edges = [ np.array( [ [0,1] #an edge from 0 to 1 + ]) + , np.array( [ + [0,0] #an edge from typ0:0 to typ1:0 + ]) + , None + , None + ] + edge_f = [ np.array([[.111]]) + , np.array([[.221, .222]]) + , None + , None + ] + + x = (node_f, edges, edge_f) + y = np.hstack([ np.array([0, 0]) + , 2+np.array([0, 0, 0]) + ]) + return g, x, y + +def test_joint_feature2(): + + # ------------------------------------------------------------------------------------------- + #print "---MORE COMPLEX GRAPH :) ---------------------------------------------------------------------" + g, x, y = more_complex_graph() + #print y + + + g.initialize(x, y) + jf = g.joint_feature(x,y) + #print "joint_feature = \n", `jf` + #print + assert_array_equal(jf, jf) + assert_array_almost_equal(jf + , np.array([ 3. , 3. , 3. , 0. , 0. , 0. , 0.63 , 0.66 , + 0.69 , 0.72 , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0.111, 0. , 0. , 0. , 0.221, 0. , + 0. , 0. , 0. , 0. , 0.222, 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ])) + + #print "---MORE COMPLEX GRAPH :) -- BIS -------------------------------------------------------------------" + g = NodeTypeEdgeFeatureGraphCRF( + 2 #how many node type? + , [2, 3] #how many labels per node type? + , [3, 4] #how many features per node type? + , np.array([ [1, 2] + , [2, 3]]) #how many features per node type X node type? + ) + + node_f = [ np.array([ [1,1,1], [2,2,2] ]) + , np.array([ [.11, .12, .13, .14], [.21, .22, .23, .24], [.31, .32, .33, .34]]) + ] + edges = [ np.array( [ [0,1]] ), #an edge from 0 to 1 + np.array( [ [0,2]] ) #an edge from 0 to 2 + , None, None + ] + edge_f = [ np.array([[.111]]) + , np.array([[.221, .222]]) + , None + , None + ] + + x = ( node_f, edges, edge_f) + #print "- - - - - - - - - - - - - - - - - - - - - - - - - - - " + y = np.hstack([np.array([0, 1]), + 2+np.array([0, 1, 2])]) + #print y + g.initialize(x, y) + jf = g.joint_feature(x,y) + #print "joint_feature = \n", `jf` + #print + assert_array_equal(jf, jf) + assert_array_almost_equal(jf + , np.array([ 1. , 1. , 1. , 2. , 2. , 2. , 0.11 , 0.12 , + 0.13 , 0.14 , 0.21 , 0.22 , 0.23 , 0.24 , 0.31 , 0.32 , + 0.33 , 0.34 , 0. , 0.111, 0. , 0. , 0. , 0. , + 0.221, 0. , 0. , 0. , 0. , 0. , 0.222, 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ])) + + #print "MORE COMPLEX GRAPH :) -- BIS OK" + #print "--- REORDERED MORE COMPLEX GRAPH :) ---------------------------------------------------------------------" + node_f = [ np.array([ [2,2,2], [1,1,1] ]) + , np.array([ [.31, .32, .33, .34], [.11, .12, .13, .14], [.21, .22, .23, .24]]) + ] + edges = [ np.array( [ [1, 0]] ), + np.array( [ [1,0]] ) #an edge from 0 to 2 + , None, None + ] + edge_f = [ np.array([[.111]]) + , np.array([[.221, .222]]) + , None + , None + ] + + x = ( node_f, edges, edge_f) + #print "- - - - - - - - - - - - - - - - - - - - - - - - - - - " + y = np.hstack([np.array([1, 0]), + 2+np.array([2, 0, 1])]) + #print y + g.initialize(x, y) + jf = g.joint_feature(x,y) + #print "joint_feature = \n", `jf` + #print + assert_array_equal(jf, jf) + assert_array_almost_equal(jf + , np.array([ 1. , 1. , 1. , 2. , 2. , 2. , 0.11 , 0.12 , + 0.13 , 0.14 , 0.21 , 0.22 , 0.23 , 0.24 , 0.31 , 0.32 , + 0.33 , 0.34 , 0. , 0.111, 0. , 0. , 0. , 0. , + 0.221, 0. , 0. , 0. , 0. , 0. , 0.222, 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , + 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ])) + +def test_joint_feature3(): + + # ------------------------------------------------------------------------------------------- + #print "---MORE COMPLEX GRAPH AGAIN :) ---------------------------------------------------------------------" + g = NodeTypeEdgeFeatureGraphCRF( + 2 #how many node type? + , [2, 3] #how many labels per node type? + , [3, 4] #how many features per node type? + , np.array([ [0, 2] + , [2, 3]]) #how many features per node type X node type? + ) + +# nodes = np.array( [[0,0], [0,1], [1, 0], [1, 1], [1, 2]] ) + node_f = [ np.array([ [1,1,1], [2,2,2] ]) + , np.array([ [.11, .12, .13, .14], [.21, .22, .23, .24], [.31, .32, .33, .34]]) + ] + edges = [ None + , np.array( [ + [0,1] #an edge from typ0:0 to typ1:1 + ]) + , None + , np.array( [ + [0,1], #an edge from typ0:0 to typ1:1 + [1,2] #an edge from typ1:1 to typ1:2 + ]) + ] + edge_f = [ None + , np.array([[.221, .222]]) + , None + , np.array([[.01, .02, .03 ], + [.001, .002, .003]]) + ] + + x = (node_f, edges, edge_f) + #print "- - - - - - - - - - - - - - - - - - - - - - - - - - - " + y = np.hstack([ np.array([0, 0]) + , 2+np.array([0, 0, 0]) + ]) + #print y + g.initialize(x, y) + #print g.size_unaries + #print g.size_pairwise + jf = g.joint_feature(x,y) + #print "joint_feature = \n", `jf` + #print + assert_array_equal(jf, jf) + assert_array_almost_equal(jf + , np.array([ 3. , 3. , 3. , 0. , 0. , 0. , + 0.63 , 0.66 , 0.69 , 0.72 , 0. , 0., 0., 0. , 0., 0., 0. , 0., + #edges 0 to 0 2x2 states + #typ0 typ0 EMPTY + #typ0 typ1 + .221, 0., 0., 0., 0., 0., + .222, 0., 0., 0., 0., 0., + #typ1 typ0 + 0., 0., 0., 0., 0., 0., + 0., 0., 0., 0., 0., 0., + #typ1 typ1 + 0.011, 0., 0., 0., 0., 0., 0., 0., 0., + 0.022, 0., 0., 0., 0., 0., 0., 0., 0., + 0.033, 0., 0., 0., 0., 0., 0., 0., 0. + ]) + ) + + #print "- - - - - - - - - - - - - - - - - - - - - - - - - - - " + y = np.hstack([ np.array([0, 1]) + , 2+np.array([1, 1, 0]) + ]) + #print y + g.initialize(x, y) + jf = g.joint_feature(x,y) + #print "joint_feature = \n", `jf` + #print + assert_array_equal(jf, jf) + assert_array_almost_equal(jf + , np.array([ 1. , 1. , 1. , 2. , 2. , 2. , + .31, .32, .33, .34 , .32, .34, .36, .38 , 0., 0., 0. , 0., + #edges 0 to 0 2x2 states + #typ0 typ0 EMPTY + #typ0 typ1 + 0., .221, 0., 0., 0., 0., + 0., .222, 0., 0., 0., 0., + #typ1 typ0 + 0., 0., 0., 0., 0., 0., + 0., 0., 0., 0., 0., 0., + #typ1 typ1 + 0., 0., 0., 0.001, 0.01, 0., 0., 0., 0., + 0., 0., 0., 0.002, 0.02, 0., 0., 0., 0., + 0., 0., 0., 0.003, 0.03, 0., 0., 0., 0. + ]) + ) + + w = np.array([ 1,1,1, 2,2,2, 10,10,10,10, 20,20,20,20, 30,30,30,30 ] + +[1.0]*51, dtype=np.float64 + ) + #print `w` + ret_u = g._get_unary_potentials(x, w) + #print `ret_u` + assert len(ret_u) == 2 + assert_array_almost_equal(ret_u[0], np.array([ #n_nodes x n_states + [3, 6], + [6, 12]])) + + assert_array_almost_equal(ret_u[1], np.array([ #n_nodes x n_states + [5, 10, 15], + [9, 18, 27], + [13, 26, 39]])) + + assert len(w) == g.size_joint_feature + ret_pw = g._get_pairwise_potentials(x, w) + # for _pw in ret_pw: + # print "_pw ", `_pw` + pw00, pw01, pw10, pw11 = ret_pw + assert len(pw00) == 0 + assert_array_almost_equal(pw01,np.array([ #n_edges, n_states, n_states + [[0.443, 0.443, 0.443], + [0.443, 0.443, 0.443]] + ])) + assert len(pw10) == 0 + + assert_array_almost_equal(pw11,np.array([ #n_edges, n_states, n_states + [[0.06 , 0.06 , 0.06], + [0.06 , 0.06 , 0.06], + [0.06 , 0.06 , 0.06]] + , + [[0.006, 0.006, 0.006], + [0.006, 0.006, 0.006], + [0.006, 0.006, 0.006]] + ])) + + + +def test_unary_potentials(): + #print "---SIMPLE---------------------------------------------------------------------" + #g, (node_f, edges, edge_f) = get_simple_graph_structure(), get_simple_graph() + + g = NodeTypeEdgeFeatureGraphCRF( + 1 #how many node type? + , [4] #how many labels per node type? + , [3] #how many features per node type? + , np.array([[3]]) #how many features per node type X node type? + ) + node_f = [ np.array([[1,1,1], + [2,2,2]]) + ] + edges = [ np.array([[0,1]]) + ] #an edge from 0 to 1 + edge_f = [ np.array([[3,3,3]]) + ] + x = (node_f, edges, edge_f) + #print "- - - - - - - - - - - - - - - - - - - - - - - - - - - " + y = np.hstack([ np.array([1,2])]) +# y = np.array([1,0]) + #print y + g.initialize(x, y) + + gref = EdgeFeatureGraphCRF(4,3,3) + xref = (node_f[0], edges[0], edge_f[0]) + wref = np.arange(gref.size_joint_feature) + potref = gref._get_unary_potentials(xref, wref) + #print `potref` + + w = np.arange(g.size_joint_feature) + pot = g._get_unary_potentials(x, w) + #print `pot` + assert_array_equal(pot, [potref]) + + pwpotref = gref._get_pairwise_potentials(xref, wref) + #print `pwpotref` + pwpot = g._get_pairwise_potentials(x, w) + #print `pwpot` + assert_array_equal(pwpot, [pwpotref]) + +# def test_inference_util(): +# g = NodeTypeEdgeFeatureGraphCRF( +# 3 #how many node type? +# , [2, 3, 1] #how many labels per node type? +# , [3, 4, 1] #how many features per node type? +# , np.array([ [1, 2, 2] +# , [2, 3, 2] +# , [2, 2, 1]]) #how many features per node type X node type? +# ) +# node_f = [ np.array([ [2,2,2], [1,1,1] ]) +# , np.array([ [.31, .32, .33, .34], [.11, .12, .13, .14], [.21, .22, .23, .24]]) +# , np.array([ [77], [88], [99]]) +# ] +# edges = [ np.array( [ [1, 0]] ), +# np.array( [ [1,0]] ) #an edge from 0 to 2 +# , None +# +# , None +# , None +# , None +# +# , np.array( [[1,1]] ) +# , None +# , None ] +# +# x = ( node_f, edges, None) +# +# reindexed_exdges = g._index_all_edges(x) +# #print `reindexed_exdges` +# assert_array_equal(reindexed_exdges, +# np.array( [[1,0], +# [1,2], +# [6,1]])) +# + +# def report_model_config(crf): +# print crf.n_states +# print crf.n_features +# print crf.n_edge_features + +def inference_data(): + """ + Testing with a single type of nodes. Must do as well as EdgeFeatureGraphCRF + """ + # Test inference with different weights in different directions + + X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1) + x, y = X[0], Y[0] + n_states = x.shape[-1] + + edge_list = make_grid_edges(x, 4, return_lists=True) + edges = np.vstack(edge_list) + + pw_horz = -1 * np.eye(n_states) + xx, yy = np.indices(pw_horz.shape) + # linear ordering constraint horizontally + pw_horz[xx > yy] = 1 + + # high cost for unequal labels vertically + pw_vert = -1 * np.eye(n_states) + pw_vert[xx != yy] = 1 + pw_vert *= 10 + + # generate edge weights + edge_weights_horizontal = np.repeat(pw_horz[np.newaxis, :, :], + edge_list[0].shape[0], axis=0) + edge_weights_vertical = np.repeat(pw_vert[np.newaxis, :, :], + edge_list[1].shape[0], axis=0) + edge_weights = np.vstack([edge_weights_horizontal, edge_weights_vertical]) + + # do inference + res = lp_general_graph(-x.reshape(-1, n_states), edges, edge_weights) + + edge_features = edge_list_to_features(edge_list) + x = ([x.reshape(-1, n_states)], [edges], [edge_features]) + y = y.ravel() + return x, y, pw_horz, pw_vert, res, n_states + +def test_inference_ad3plus(): + + x, y, pw_horz, pw_vert, res, n_states = inference_data() + # same inference through CRF inferface + crf = NodeTypeEdgeFeatureGraphCRF(1, [3], [3], [[2]] + , inference_method="ad3+") + crf.initialize(x, y) + #crf.initialize([x], [y]) + w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()]) + y_pred = crf.inference(x, w, relaxed=True) + if isinstance(y_pred, tuple): + # ad3 produces an integer result if it found the exact solution + #np.set_printoptions(precision=2, threshold=9999) + assert_array_almost_equal(res[0], y_pred[0][0].reshape(-1, n_states), 5) + assert_array_almost_equal(res[1], y_pred[1][0], 5) + assert_array_equal(y, np.argmax(y_pred[0][0], axis=-1), 5) + + # again, this time discrete predictions only + crf = NodeTypeEdgeFeatureGraphCRF(1, [3], [3], [[2]] + , inference_method="ad3+") + #crf.initialize([x], [y]) + w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()]) + crf.initialize(x) + y_pred = crf.inference(x, w, relaxed=False) + assert_array_equal(y, y_pred) + +def test_inference_ad3(): + + x, y, pw_horz, pw_vert, res, n_states = inference_data() + # same inference through CRF inferface + crf = NodeTypeEdgeFeatureGraphCRF(1, [3], [3], [[2]] + , inference_method="ad3") + crf.initialize(x, y) + #crf.initialize([x], [y]) + w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()]) + y_pred = crf.inference(x, w, relaxed=True) + if isinstance(y_pred, tuple): + # ad3 produces an integer result if it found the exact solution + #np.set_printoptions(precision=2, threshold=9999) + assert_array_almost_equal(res[0], y_pred[0][0].reshape(-1, n_states), 5) + assert_array_almost_equal(res[1], y_pred[1][0], 5) + assert_array_equal(y, np.argmax(y_pred[0][0], axis=-1), 5) + + # again, this time discrete predictions only + crf = NodeTypeEdgeFeatureGraphCRF(1, [3], [3], [[2]] + , inference_method="ad3") + #crf.initialize([x], [y]) + w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()]) + crf.initialize(x) + y_pred = crf.inference(x, w, relaxed=False) + assert_array_equal(y, y_pred) + +def test_joint_feature_discrete(): + """ + Testing with a single type of nodes. Must de aw well as EdgeFeatureGraphCRF + """ + X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1) + x, y = X[0], Y[0] + edge_list = make_grid_edges(x, 4, return_lists=True) + edges = np.vstack(edge_list) + edge_features = edge_list_to_features(edge_list) + x = ([x.reshape(-1, 3)], [edges], [edge_features]) + y_flat = y.ravel() + #for inference_method in get_installed(["lp", "ad3", "qpbo"]): + if True: + crf = NodeTypeEdgeFeatureGraphCRF(1, [3], [3], [[2]]) + joint_feature_y = crf.joint_feature(x, y_flat) + assert_equal(joint_feature_y.shape, (crf.size_joint_feature,)) + # first horizontal, then vertical + # we trust the unaries ;) + n_states = crf.l_n_states[0] + n_features = crf.l_n_features[0] + pw_joint_feature_horz, pw_joint_feature_vert = joint_feature_y[n_states * + n_features:].reshape( + 2, n_states, n_states) + assert_array_equal(pw_joint_feature_vert, np.diag([9 * 4, 9 * 4, 9 * 4])) + vert_joint_feature = np.diag([10 * 3, 10 * 3, 10 * 3]) + vert_joint_feature[0, 1] = 10 + vert_joint_feature[1, 2] = 10 + assert_array_equal(pw_joint_feature_horz, vert_joint_feature) + +def test_joint_feature_continuous(): + """ + Testing with a single type of nodes. Must de aw well as EdgeFeatureGraphCRF + """ + # FIXME + # first make perfect prediction, including pairwise part + X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1) + x, y = X[0], Y[0] + n_states = x.shape[-1] + edge_list = make_grid_edges(x, 4, return_lists=True) + edges = np.vstack(edge_list) + edge_features = edge_list_to_features(edge_list) + #x = (x.reshape(-1, 3), edges, edge_features) + x = ([x.reshape(-1, 3)], [edges], [edge_features]) + y = y.ravel() + + pw_horz = -1 * np.eye(n_states) + xx, yy = np.indices(pw_horz.shape) + # linear ordering constraint horizontally + pw_horz[xx > yy] = 1 + + # high cost for unequal labels vertically + pw_vert = -1 * np.eye(n_states) + pw_vert[xx != yy] = 1 + pw_vert *= 10 + + # create crf, assemble weight, make prediction +# for inference_method in get_installed(["lp", "ad3"]): +# crf = EdgeFeatureGraphCRF(inference_method=inference_method) + if True: + crf = NodeTypeEdgeFeatureGraphCRF(1, [3], [3], [[2]]) + + w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()]) + #crf.initialize([x], [y]) + #report_model_config(crf) + crf.initialize(x, y) + + y_pred = crf.inference(x, w, relaxed=True) + + # compute joint_feature for prediction + joint_feature_y = crf.joint_feature(x, y_pred) + assert_equal(joint_feature_y.shape, (crf.size_joint_feature,)) + # FIXME + # first horizontal, then vertical + # we trust the unaries ;) + #pw_joint_feature_horz, pw_joint_feature_vert = joint_feature_y[crf.n_states * + #crf.n_features:].reshape(2, + #crf.n_states, + #crf.n_states) + +def test_energy_continuous(): + # make sure that energy as computed by ssvm is the same as by lp + np.random.seed(0) + #for inference_method in get_installed(["lp", "ad3"]): + if True: + found_fractional = False + crf = NodeTypeEdgeFeatureGraphCRF(1, [3], [3], [[2]]) + + while not found_fractional: + x = np.random.normal(size=(7, 8, 3)) + edge_list = make_grid_edges(x, 4, return_lists=True) + edges = np.vstack(edge_list) + edge_features = edge_list_to_features(edge_list) + x = ([x.reshape(-1, 3)], [edges], [edge_features]) + + unary_params = np.random.normal(size=(3, 3)) + pw1 = np.random.normal(size=(3, 3)) + pw2 = np.random.normal(size=(3, 3)) + w = np.hstack([unary_params.ravel(), pw1.ravel(), pw2.ravel()]) + crf.initialize(x) + res, energy = crf.inference(x, w, relaxed=True, return_energy=True) + found_fractional = np.any(np.max(res[0], axis=-1) != 1) + joint_feature = crf.joint_feature(x, res) + energy_svm = np.dot(joint_feature, w) + + assert_almost_equal(energy, -energy_svm) + +def test_energy_discrete(): +# for inference_method in get_installed(["qpbo", "ad3"]): +# crf = EdgeFeatureGraphCRF(n_states=3, +# inference_method=inference_method, +# n_edge_features=2, n_features=3) + crf = NodeTypeEdgeFeatureGraphCRF(1, [3], [3], [[2]]) + + for i in range(10): + x = np.random.normal(size=(7, 8, 3)) + edge_list = make_grid_edges(x, 4, return_lists=True) + edges = np.vstack(edge_list) + edge_features = edge_list_to_features(edge_list) + x = ([x.reshape(-1, 3)], [edges], [edge_features]) + + unary_params = np.random.normal(size=(3, 3)) + pw1 = np.random.normal(size=(3, 3)) + pw2 = np.random.normal(size=(3, 3)) + w = np.hstack([unary_params.ravel(), pw1.ravel(), pw2.ravel()]) + crf.initialize(x) + y_hat = crf.inference(x, w, relaxed=False) + #flat_edges = crf._index_all_edges(x) + energy = compute_energy(crf._get_unary_potentials(x, w)[0], + crf._get_pairwise_potentials(x, w)[0], edges, #CAUTION: pass the flatened edges!! + y_hat) + + joint_feature = crf.joint_feature(x, y_hat) + energy_svm = np.dot(joint_feature, w) + + assert_almost_equal(energy, energy_svm) + + +if __name__ == "__main__": + np.set_printoptions(precision=3, linewidth=9999) + + if 0: + debug_joint_feature() + if 1: + test_flatten_unflattenY() + + if 1: + test_joint_feature() + if 1: + test_joint_feature2() + if 1: + test_joint_feature3() + + if 1: test_unary_potentials() +# if 1: test_inference_util() + if 1: test_inference_ad3() + if 1: test_inference_ad3plus() + if 1: test_joint_feature_discrete() + if 1: test_joint_feature_continuous() + if 1: test_energy_continuous() + if 1: test_energy_discrete() + + #print "OK" diff --git a/pystruct/tests/test_utils/test_utils_inference.py b/pystruct/tests/test_utils/test_utils_inference.py index adb2b4ec..b3ed1b92 100644 --- a/pystruct/tests/test_utils/test_utils_inference.py +++ b/pystruct/tests/test_utils/test_utils_inference.py @@ -1,8 +1,17 @@ import numpy as np from numpy.testing import assert_array_equal -from nose.tools import assert_equal +from nose.tools import assert_equal, assert_almost_equal from pystruct.utils import compress_sym, expand_sym +from pystruct.datasets import generate_blocks_multinomial +from pystruct.models import GridCRF +from pystruct.learners import (NSlackSSVM, OneSlackSSVM, SubgradientSSVM, + FrankWolfeSSVM) +from pystruct.inference import get_installed +from pystruct.utils import objective_primal + +# we always try to get the fastest installed inference method +inference_method = get_installed(["qpbo", "ad3", "lp"])[0] def test_symmetric_tools_symmetric(): @@ -34,3 +43,41 @@ def test_symmetric_tools_upper(): uncompressed = expand_sym(compressed) assert_array_equal(x_, uncompressed) + + +def test_ssvm_objectives(): + # test that the algorithms provide consistent objective curves. + # this is not that strong a test now but at least makes sure that + # the objective function is called. + X, Y = generate_blocks_multinomial(n_samples=10, noise=1.5, seed=0) + n_labels = len(np.unique(Y)) + crf = GridCRF(n_states=n_labels, inference_method=inference_method) + # once for n-slack + clf = NSlackSSVM(model=crf, max_iter=5, C=1, tol=.1) + clf.fit(X, Y) + primal_objective = objective_primal(clf.model, clf.w, X, Y, clf.C) + assert_almost_equal(clf.primal_objective_curve_[-1], primal_objective) + + # once for one-slack + clf = OneSlackSSVM(model=crf, max_iter=5, C=1, tol=.1) + clf.fit(X, Y) + primal_objective = objective_primal(clf.model, clf.w, X, Y, clf.C, + variant='one_slack') + assert_almost_equal(clf.primal_objective_curve_[-1], primal_objective) + + # now subgradient. Should also work in batch-mode. + clf = SubgradientSSVM(model=crf, max_iter=5, C=1, batch_size=-1) + clf.fit(X, Y) + primal_objective = objective_primal(clf.model, clf.w, X, Y, clf.C) + assert_almost_equal(clf.objective_curve_[-1], primal_objective) + + # frank wolfe + clf = FrankWolfeSSVM(model=crf, max_iter=5, C=1, batch_mode=True) + clf.fit(X, Y) + primal_objective = objective_primal(clf.model, clf.w, X, Y, clf.C) + assert_almost_equal(clf.primal_objective_curve_[-1], primal_objective) + # block-coordinate Frank-Wolfe + clf = FrankWolfeSSVM(model=crf, max_iter=5, C=1, batch_mode=False) + clf.fit(X, Y) + primal_objective = objective_primal(clf.model, clf.w, X, Y, clf.C) + assert_almost_equal(clf.primal_objective_curve_[-1], primal_objective) diff --git a/pystruct/tests/test_utils/test_utils_logging.py b/pystruct/tests/test_utils/test_utils_logging.py new file mode 100644 index 00000000..adb7c88b --- /dev/null +++ b/pystruct/tests/test_utils/test_utils_logging.py @@ -0,0 +1,48 @@ +import numpy as np +from tempfile import mkstemp + +from sklearn.datasets import load_iris +try: + from sklearn.model_selection import train_test_split +except ImportError: + from sklearn.cross_validation import train_test_split + +from pystruct.models import GraphCRF +from pystruct.learners import NSlackSSVM +from pystruct.utils import SaveLogger +from pystruct.inference import get_installed + +from nose.tools import assert_less, assert_almost_equal + +# we always try to get the fastest installed inference method +inference_method = get_installed(["qpbo", "ad3", "max-product", "lp"])[0] + + +def test_logging(): + iris = load_iris() + X, y = iris.data, iris.target + + X_ = [(np.atleast_2d(x), np.empty((0, 2), dtype=np.int)) for x in X] + Y = y.reshape(-1, 1) + + X_train, X_test, y_train, y_test = train_test_split(X_, Y, random_state=1) + _, file_name = mkstemp() + + pbl = GraphCRF(n_features=4, n_states=3, inference_method=inference_method) + logger = SaveLogger(file_name) + svm = NSlackSSVM(pbl, C=100, n_jobs=1, logger=logger) + svm.fit(X_train, y_train) + + score_current = svm.score(X_test, y_test) + score_auto_saved = logger.load().score(X_test, y_test) + + alt_file_name = file_name + "alt" + logger.save(svm, alt_file_name) + logger.file_name = alt_file_name + logger.load() + score_manual_saved = logger.load().score(X_test, y_test) + + assert_less(.97, score_current) + assert_less(.97, score_auto_saved) + assert_less(.97, score_manual_saved) + assert_almost_equal(score_auto_saved, score_manual_saved) diff --git a/pystruct/utils/__init__.py b/pystruct/utils/__init__.py index 83656ad0..abd196aa 100644 --- a/pystruct/utils/__init__.py +++ b/pystruct/utils/__init__.py @@ -1,4 +1,3 @@ -from .backports import train_test_split from .inference import (unwrap_pairwise, find_constraint, find_constraint_latent, inference, loss_augmented_inference, objective_primal, @@ -8,7 +7,7 @@ from .plotting import plot_grid from .graph import make_grid_edges, edge_list_to_features -__all__ = ["train_test_split", "unwrap_pairwise", +__all__ = ["unwrap_pairwise", "make_grid_edges", "find_constraint", "find_constraint_latent", "inference", "loss_augmented_inference", "objective_primal", "exhaustive_loss_augmented_inference", diff --git a/pystruct/utils/backports.py b/pystruct/utils/backports.py deleted file mode 100644 index b0e8b390..00000000 --- a/pystruct/utils/backports.py +++ /dev/null @@ -1,110 +0,0 @@ -from sklearn.utils import check_arrays -from sklearn.cross_validation import ShuffleSplit - - -# port from scikit-learn for old scikit-learn versions - -def train_test_split(*arrays, **options): - """Split arrays or matrices into random train and test subsets - - Quick utility that wraps calls to ``check_arrays`` and - ``next(iter(ShuffleSplit(n_samples)))`` and application to input - data into a single call for splitting (and optionally subsampling) - data in a oneliner. - - Parameters - ---------- - *arrays : sequence of arrays or scipy.sparse matrices with same shape[0] - Python lists or tuples occurring in arrays are converted to 1D numpy - arrays. - - test_size : float, int, or None (default is None) - If float, should be between 0.0 and 1.0 and represent the - proportion of the dataset to include in the test split. If - int, represents the absolute number of test samples. If None, - the value is automatically set to the complement of the train size. - If train size is also None, test size is set to 0.25. - - train_size : float, int, or None (default is None) - If float, should be between 0.0 and 1.0 and represent the - proportion of the dataset to include in the train split. If - int, represents the absolute number of train samples. If None, - the value is automatically set to the complement of the test size. - - random_state : int or RandomState - Pseudo-random number generator state used for random sampling. - - dtype : a numpy dtype instance, None by default - Enforce a specific dtype. - - Returns - ------- - splitting : list of arrays, length=2 * len(arrays) - List containing train-test split of input array. - - Examples - -------- - >>> import numpy as np - >>> from sklearn.cross_validation import train_test_split - >>> a, b = np.arange(10).reshape((5, 2)), range(5) - >>> a - array([[0, 1], - [2, 3], - [4, 5], - [6, 7], - [8, 9]]) - >>> list(b) - [0, 1, 2, 3, 4] - - >>> a_train, a_test, b_train, b_test = train_test_split( - ... a, b, test_size=0.33, random_state=42) - ... - >>> a_train - array([[4, 5], - [0, 1], - [6, 7]]) - >>> b_train - array([2, 0, 3]) - >>> a_test - array([[2, 3], - [8, 9]]) - >>> b_test - array([1, 4]) - - """ - n_arrays = len(arrays) - if n_arrays == 0: - raise ValueError("At least one array required as input") - - test_size = options.pop('test_size', None) - train_size = options.pop('train_size', None) - random_state = options.pop('random_state', None) - options['sparse_format'] = 'csr' - - if test_size is None and train_size is None: - test_size = 0.25 - - arrays = check_arrays(*arrays, **options) - n_samples = arrays[0].shape[0] - try: - cv = ShuffleSplit(n_samples, test_size=test_size, - train_size=train_size, - random_state=random_state, - indices=True) - except TypeError: - if test_size > 1: - test_size /= n_samples - if train_size is not None: - train_size /= n_samples - cv = ShuffleSplit(n_samples, test_fraction=test_size, - train_fraction=train_size, - random_state=random_state, - indices=True) - train, test = next(iter(cv)) - splitted = [] - for a in arrays: - splitted.append(a[train]) - splitted.append(a[test]) - return splitted - -train_test_split.__test__ = False # to avoid a pb with nosetests diff --git a/pystruct/utils/graph_functions.py b/pystruct/utils/graph_functions.py new file mode 100644 index 00000000..c7004c25 --- /dev/null +++ b/pystruct/utils/graph_functions.py @@ -0,0 +1,33 @@ +import numpy as np + + +def is_forest(edges, n_vertices=None): + if n_vertices is not None and len(edges) > n_vertices - 1: + return False + n_vertices = np.max(edges) + 1 + parents = -np.ones(n_vertices) + visited = np.zeros(n_vertices, dtype=np.bool) + neighbors = [[] for i in range(n_vertices)] + for edge in edges: + neighbors[edge[0]].append(edge[1]) + neighbors[edge[1]].append(edge[0]) + lonely = 0 + while lonely < n_vertices: + for i in range(lonely, n_vertices): + if not visited[i]: + queue = [i] + lonely = i + 1 + visited[i] = True + break + lonely = n_vertices + + while queue: + node = queue.pop() + for neighbor in neighbors[node]: + if not visited[neighbor]: + parents[neighbor] = node + queue.append(neighbor) + visited[neighbor] = True + elif not parents[node] == neighbor: + return False + return True diff --git a/pystruct/utils/inference.py b/pystruct/utils/inference.py index 746a4132..9265d99f 100644 --- a/pystruct/utils/inference.py +++ b/pystruct/utils/inference.py @@ -1,4 +1,5 @@ import itertools +from joblib import Parallel, delayed import numpy as np @@ -53,48 +54,57 @@ def compress_sym(sym_expanded, make_symmetric=True): def find_constraint(model, x, y, w, y_hat=None, relaxed=True, compute_difference=True): """Find most violated constraint, or, given y_hat, - find slack and dpsi for this constraing. + find slack and djoint_feature for this constraing. As for finding the most violated constraint, it is enough to compute - psi(x, y_hat), not dpsi, we can optionally skip computing psi(x, y) - using compute_differences=False + joint_feature(x, y_hat), not djoint_feature, we can optionally skip + computing joint_feature(x, y) using compute_differences=False """ if y_hat is None: y_hat = model.loss_augmented_inference(x, y, w, relaxed=relaxed) - psi = model.psi - if compute_difference: - delta_psi = psi(x, y) - psi(x, y_hat) + joint_feature = model.joint_feature + if getattr(model, 'rescale_C', False): + delta_joint_feature = -joint_feature(x, y_hat, y) else: - delta_psi = -psi(x, y_hat) + delta_joint_feature = -joint_feature(x, y_hat) + if compute_difference: + if getattr(model, 'rescale_C', False): + delta_joint_feature += joint_feature(x, y, y) + else: + delta_joint_feature += joint_feature(x, y) + if isinstance(y_hat, tuple): # continuous label loss = model.continuous_loss(y, y_hat[0]) else: loss = model.loss(y, y_hat) - slack = max(loss - np.dot(w, delta_psi), 0) - return y_hat, delta_psi, slack, loss + slack = max(loss - np.dot(w, delta_joint_feature), 0) + return y_hat, delta_joint_feature, slack, loss def find_constraint_latent(model, x, y, w, relaxed=True): """Find most violated constraint. As for finding the most violated constraint, it is enough to compute - psi(x, y_hat), not dpsi, we can optionally skip computing psi(x, y) - using compute_differences=False + joint_feature(x, y_hat), not djoint_feature, we can optionally skip + computing joint_feature(x, y) using compute_differences=False """ h = model.latent(x, y, w) h_hat = model.loss_augmented_inference(x, h, w, relaxed=relaxed) - psi = model.psi - delta_psi = psi(x, h) - psi(x, h_hat) + joint_feature = model.joint_feature + delta_joint_feature = joint_feature(x, h) - joint_feature(x, h_hat) loss = model.loss(y, h_hat) - slack = max(loss - np.dot(w, delta_psi), 0) - return h_hat, delta_psi, slack, loss + slack = max(loss - np.dot(w, delta_joint_feature), 0) + return h_hat, delta_joint_feature, slack, loss -def inference(model, x, w): - return model.inference(x, w) +def inference(model, x, w, constraints=None): + if constraints: + return model.inference(x, w, constraints=constraints) + else: + return model.inference(x, w) def loss_augmented_inference(model, x, y, w, relaxed=True): @@ -102,16 +112,18 @@ def loss_augmented_inference(model, x, y, w, relaxed=True): # easy debugging -def objective_primal(model, w, X, Y, C): +def objective_primal(model, w, X, Y, C, variant='n_slack', n_jobs=1): objective = 0 - psi = model.psi - for x, y in zip(X, Y): - y_hat = model.loss_augmented_inference(x, y, w) - loss = model.loss(y, y_hat) - delta_psi = psi(x, y) - psi(x, y_hat) - objective += loss - np.dot(w, delta_psi) - objective /= float(len(X)) - objective += np.sum(w ** 2) / float(C) / 2. + constraints = Parallel( + n_jobs=n_jobs)(delayed(find_constraint)( + model, x, y, w) + for x, y in zip(X, Y)) + slacks = list(zip(*constraints))[2] + + if variant == 'n_slack': + slacks = np.maximum(slacks, 0) + + objective = max(np.sum(slacks), 0) * C + np.sum(w ** 2) / 2. return objective @@ -122,8 +134,8 @@ def exhaustive_loss_augmented_inference(model, x, y, w): for y_hat in itertools.product(range(model.n_states), repeat=size): y_hat = np.array(y_hat).reshape(y.shape) #print("trying %s" % repr(y_hat)) - psi = model.psi(x, y_hat) - energy = -model.loss(y, y_hat) - np.dot(w, psi) + joint_feature = model.joint_feature(x, y_hat) + energy = -model.loss(y, y_hat) - np.dot(w, joint_feature) if energy < best_energy: best_energy = energy best_y = y_hat @@ -142,8 +154,8 @@ def exhaustive_inference(model, x, w): for y_hat in itertools.product(range(model.n_states), repeat=size): y_hat = np.array(y_hat).reshape(feats.shape[:-1]) #print("trying %s" % repr(y_hat)) - psi = model.psi(x, y_hat) - energy = -np.dot(w, psi) + joint_feature = model.joint_feature(x, y_hat) + energy = -np.dot(w, joint_feature) if energy < best_energy: best_energy = energy best_y = y_hat diff --git a/pystruct/utils/logging.py b/pystruct/utils/logging.py index fa3acda5..2ab5a956 100644 --- a/pystruct/utils/logging.py +++ b/pystruct/utils/logging.py @@ -1,4 +1,7 @@ -import cPickle +try: + import cPickle as pickle +except ImportError: + import pickle class SaveLogger(object): @@ -47,18 +50,22 @@ def __call__(self, learner, iteration=0): file_name = file_name % iteration if self.verbose > 0: print("saving %s to file %s" % (learner, file_name)) - with open(file_name, "wb") as f: - if hasattr(learner, 'inference_cache_'): - # don't store the large inference cache! - learner.inference_cache_, tmp = (None, - learner.inference_cache_) - cPickle.dump(learner, f, -1) - learner.inference_cache_ = tmp - else: - cPickle.dump(learner, f, -1) + self.save(learner, file_name) + + def save(self, learner, file_name): + """Save the model to location specified in file_name.""" + with open(file_name, "wb") as f: + if hasattr(learner, 'inference_cache_'): + # don't store the large inference cache! + learner.inference_cache_, tmp = (None, + learner.inference_cache_) + pickle.dump(learner, f, -1) + learner.inference_cache_ = tmp + else: + pickle.dump(learner, f, -1) def load(self): """Load the model stoed in file_name and return it.""" with open(self.file_name, "rb") as f: - learner = cPickle.load(f) + learner = pickle.load(f) return learner diff --git a/requirements.txt b/requirements.txt index 9e6eb92e..b3ac4911 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,8 +1,7 @@ numpy +scipy cvxopt -matplotlib -PIL -cython -scikit-learn -pyqpbo -ad3 +future +Cython>=0.19.1 +scikit-learn>=0.11 +ad3>=2.2.2 diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 00000000..bb8cc6b1 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,16 @@ +[nosetests] +# nosetests skips test files with the executable bit by default +# which can silently hide failing tests. +# There are no executable scripts within the scikit-learn project +# so let's turn the --exe flag on to avoid skipping tests by +# mistake. +exe = 1 +cover-html = 1 +cover-html-dir = coverage +cover-package = pystruct + +detailed-errors = 1 +with-doctest = 1 +doctest-tests = 1 +doctest-extension = rst +doctest-fixtures = _fixture diff --git a/setup.py b/setup.py index 70c46a21..0d1d7b4d 100644 --- a/setup.py +++ b/setup.py @@ -1,15 +1,17 @@ from setuptools import setup from setuptools.extension import Extension -from Cython.Distutils import build_ext +import numpy as np import os if os.path.exists('MANIFEST'): os.remove('MANIFEST') +include_dirs = [np.get_include()] + setup(name="pystruct", - version="0.2-dev", - install_requires=["ad3", "pyqpbo"], + version="0.3.2", + install_requires=["ad3", "numpy", "cvxopt", "future", "Cython", "scikit-learn"], packages=['pystruct', 'pystruct.learners', 'pystruct.inference', 'pystruct.models', 'pystruct.utils', 'pystruct.datasets', 'pystruct.tests', 'pystruct.tests.test_learners', @@ -21,8 +23,12 @@ author_email="t3kcit@gmail.com", url="http://pystruct.github.io", license="BSD 2-clause", - cmdclass={'build_ext': build_ext}, - ext_modules=[Extension("pystruct.models.utils", ["src/utils.pyx"])], + use_2to3=True, + ext_modules=[Extension("pystruct.models.utils", ["src/utils.c"], + include_dirs=include_dirs), + Extension("pystruct.inference._viterbi", + ["pystruct/inference/_viterbi.c"], + include_dirs=include_dirs)], classifiers=['Intended Audience :: Science/Research', 'Intended Audience :: Developers', 'License :: OSI Approved', @@ -33,9 +39,9 @@ 'Operating System :: Unix', 'Operating System :: MacOS', 'Programming Language :: Python :: 2', - 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.3', + 'Programming Language :: Python :: 3.6', + 'Programming Language :: Python :: 3.8' ], ) diff --git a/src/utils.c b/src/utils.c new file mode 100644 index 00000000..5e514253 --- /dev/null +++ b/src/utils.c @@ -0,0 +1,26217 @@ +/* Generated by Cython 0.27.3 */ + +#define PY_SSIZE_T_CLEAN +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) + #error Cython requires Python 2.6+ or Python 3.3+. +#else +#define CYTHON_ABI "0_27_3" +#define CYTHON_FUTURE_DIVISION 0 +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#define __PYX_COMMA , +#ifndef HAVE_LONG_LONG + #if PY_VERSION_HEX >= 0x02070000 + #define HAVE_LONG_LONG + #endif +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 +#elif defined(PYSTON_VERSION) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #elif !defined(CYTHON_USE_PYTYPE_LOOKUP) + #define CYTHON_USE_PYTYPE_LOOKUP 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #elif !defined(CYTHON_USE_PYLONG_INTERNALS) + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif + #ifndef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT (0 && PY_VERSION_HEX >= 0x03050000) + #endif + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1) + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #undef SHIFT + #undef BASE + #undef MASK +#endif +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define Py_OptimizeFlag 0 +#endif +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyType_Type +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#if PY_VERSION_HEX < 0x030700A0 || !defined(METH_FASTCALL) + #ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + #endif + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject **args, Py_ssize_t nargs); + typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject **args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast + #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords +#endif +#if CYTHON_FAST_PYCCALL +#define __Pyx_PyFastCFunction_Check(func)\ + ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS))))) +#else +#define __Pyx_PyFastCFunction_Check(func) 0 +#endif +#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#elif PY_VERSION_HEX >= 0x03060000 + #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() +#elif PY_VERSION_HEX >= 0x03000000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#else + #define __Pyx_PyThreadState_Current _PyThreadState_Current +#endif +#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) +#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) +#else +#define __Pyx_PyDict_NewPresized(n) PyDict_New() +#endif +#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_PYSTON + #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#ifndef __has_attribute + #define __has_attribute(x) 0 +#endif +#ifndef __has_cpp_attribute + #define __has_cpp_attribute(x) 0 +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef __Pyx_PyAsyncMethodsStruct + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) +#ifdef _MSC_VER + #ifndef _MSC_STDINT_H_ + #if _MSC_VER < 1300 + typedef unsigned char uint8_t; + typedef unsigned int uint32_t; + #else + typedef unsigned __int8 uint8_t; + typedef unsigned __int32 uint32_t; + #endif + #endif +#else + #include +#endif +#ifndef CYTHON_FALLTHROUGH + #if defined(__cplusplus) && __cplusplus >= 201103L + #if __has_cpp_attribute(fallthrough) + #define CYTHON_FALLTHROUGH [[fallthrough]] + #elif __has_cpp_attribute(clang::fallthrough) + #define CYTHON_FALLTHROUGH [[clang::fallthrough]] + #elif __has_cpp_attribute(gnu::fallthrough) + #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_attribute(fallthrough) + #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) + #else + #define CYTHON_FALLTHROUGH + #endif + #endif + #if defined(__clang__ ) && defined(__apple_build_version__) + #if __apple_build_version__ < 7000000 + #undef CYTHON_FALLTHROUGH + #define CYTHON_FALLTHROUGH + #endif + #endif +#endif + +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #elif defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif + +#if defined(WIN32) || defined(MS_WINDOWS) + #define _USE_MATH_DEFINES +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + + +#define __PYX_ERR(f_index, lineno, Ln_error) \ +{ \ + __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ +} + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__utils +#define __PYX_HAVE_API__utils +#include "pythread.h" +#include +#include +#include +#include "pystate.h" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) + #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False)) +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +#define __Pyx_PySequence_Tuple(obj)\ + (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ +static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } + +static PyObject *__pyx_m = NULL; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_cython_runtime; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static PyObject *__pyx_empty_unicode; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + + +static const char *__pyx_f[] = { + "utils.pyx", + "stringsource", +}; +/* MemviewSliceStruct.proto */ +struct __pyx_memoryview_obj; +typedef struct { + struct __pyx_memoryview_obj *memview; + char *data; + Py_ssize_t shape[8]; + Py_ssize_t strides[8]; + Py_ssize_t suboffsets[8]; +} __Pyx_memviewslice; +#define __Pyx_MemoryView_Len(m) (m.shape[0]) + +/* Atomics.proto */ +#include +#ifndef CYTHON_ATOMICS + #define CYTHON_ATOMICS 1 +#endif +#define __pyx_atomic_int_type int +#if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||\ + (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\ + !defined(__i386__) + #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1) + #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using GNU atomics" + #endif +#elif CYTHON_ATOMICS && defined(_MSC_VER) && 0 + #include + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type LONG + #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value) + #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value) + #ifdef __PYX_DEBUG_ATOMICS + #pragma message ("Using MSVC atomics") + #endif +#elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0 + #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value) + #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using Intel atomics" + #endif +#else + #undef CYTHON_ATOMICS + #define CYTHON_ATOMICS 0 + #ifdef __PYX_DEBUG_ATOMICS + #warning "Not using atomics" + #endif +#endif +typedef volatile __pyx_atomic_int_type __pyx_atomic_int; +#if CYTHON_ATOMICS + #define __pyx_add_acquisition_count(memview)\ + __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) +#else + #define __pyx_add_acquisition_count(memview)\ + __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) +#endif + +/* ForceInitThreads.proto */ +#ifndef __PYX_FORCE_INIT_THREADS + #define __PYX_FORCE_INIT_THREADS 0 +#endif + +/* NoFastGil.proto */ +#define __Pyx_PyGILState_Ensure PyGILState_Ensure +#define __Pyx_PyGILState_Release PyGILState_Release +#define __Pyx_FastGIL_Remember() +#define __Pyx_FastGIL_Forget() +#define __Pyx_FastGilFuncInit() + +/* BufferFormatStructs.proto */ +#define IS_UNSIGNED(type) (((type) -1) > 0) +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/*--- Type declarations ---*/ +struct __pyx_array_obj; +struct __pyx_MemviewEnum_obj; +struct __pyx_memoryview_obj; +struct __pyx_memoryviewslice_obj; + +/* "View.MemoryView":103 + * + * @cname("__pyx_array") + * cdef class array: # <<<<<<<<<<<<<< + * + * cdef: + */ +struct __pyx_array_obj { + PyObject_HEAD + struct __pyx_vtabstruct_array *__pyx_vtab; + char *data; + Py_ssize_t len; + char *format; + int ndim; + Py_ssize_t *_shape; + Py_ssize_t *_strides; + Py_ssize_t itemsize; + PyObject *mode; + PyObject *_format; + void (*callback_free_data)(void *); + int free_data; + int dtype_is_object; +}; + + +/* "View.MemoryView":277 + * + * @cname('__pyx_MemviewEnum') + * cdef class Enum(object): # <<<<<<<<<<<<<< + * cdef object name + * def __init__(self, name): + */ +struct __pyx_MemviewEnum_obj { + PyObject_HEAD + PyObject *name; +}; + + +/* "View.MemoryView":328 + * + * @cname('__pyx_memoryview') + * cdef class memoryview(object): # <<<<<<<<<<<<<< + * + * cdef object obj + */ +struct __pyx_memoryview_obj { + PyObject_HEAD + struct __pyx_vtabstruct_memoryview *__pyx_vtab; + PyObject *obj; + PyObject *_size; + PyObject *_array_interface; + PyThread_type_lock lock; + __pyx_atomic_int acquisition_count[2]; + __pyx_atomic_int *acquisition_count_aligned_p; + Py_buffer view; + int flags; + int dtype_is_object; + __Pyx_TypeInfo *typeinfo; +}; + + +/* "View.MemoryView":953 + * + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< + * "Internal class for passing memoryview slices to Python" + * + */ +struct __pyx_memoryviewslice_obj { + struct __pyx_memoryview_obj __pyx_base; + __Pyx_memviewslice from_slice; + PyObject *from_object; + PyObject *(*to_object_func)(char *); + int (*to_dtype_func)(char *, PyObject *); +}; + + + +/* "View.MemoryView":103 + * + * @cname("__pyx_array") + * cdef class array: # <<<<<<<<<<<<<< + * + * cdef: + */ + +struct __pyx_vtabstruct_array { + PyObject *(*get_memview)(struct __pyx_array_obj *); +}; +static struct __pyx_vtabstruct_array *__pyx_vtabptr_array; + + +/* "View.MemoryView":328 + * + * @cname('__pyx_memoryview') + * cdef class memoryview(object): # <<<<<<<<<<<<<< + * + * cdef object obj + */ + +struct __pyx_vtabstruct_memoryview { + char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *); + PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *); +}; +static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview; + + +/* "View.MemoryView":953 + * + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< + * "Internal class for passing memoryview slices to Python" + * + */ + +struct __pyx_vtabstruct__memoryviewslice { + struct __pyx_vtabstruct_memoryview __pyx_base; +}; +static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice; + +/* --- Runtime support code (head) --- */ +/* Refnanny.proto */ +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, int); + void (*DECREF)(void*, PyObject*, int); + void (*GOTREF)(void*, PyObject*, int); + void (*GIVEREF)(void*, PyObject*, int); + void* (*SetupContext)(const char*, int, const char*); + void (*FinishContext)(void**); + } __Pyx_RefNannyAPIStruct; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); + #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; +#ifdef WITH_THREAD + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + if (acquire_gil) {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ + PyGILState_Release(__pyx_gilstate_save);\ + } else {\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ + } +#else + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) +#endif + #define __Pyx_RefNannyFinishContext()\ + __Pyx_RefNanny->FinishContext(&__pyx_refnanny) + #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_XINCREF(r) do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0) + #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) + #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) +#else + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) +#endif +#define __Pyx_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_XDECREF(tmp);\ + } while (0) +#define __Pyx_DECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_DECREF(tmp);\ + } while (0) +#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) +#define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +/* PyDictContains.proto */ +static CYTHON_INLINE int __Pyx_PyDict_ContainsTF(PyObject* item, PyObject* dict, int eq) { + int result = PyDict_Contains(dict, item); + return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); +} + +/* DictGetItem.proto */ +#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY +static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { + PyObject *value; + value = PyDict_GetItemWithError(d, key); + if (unlikely(!value)) { + if (!PyErr_Occurred()) { + PyObject* args = PyTuple_Pack(1, key); + if (likely(args)) + PyErr_SetObject(PyExc_KeyError, args); + Py_XDECREF(args); + } + return NULL; + } + Py_INCREF(value); + return value; +} +#else + #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) +#endif + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; +#define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#define __Pyx_PyErr_Occurred() PyErr_Occurred() +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) +#else +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#endif +#else +#define __Pyx_PyErr_Clear() PyErr_Clear() +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* UnicodeAsUCS4.proto */ +static CYTHON_INLINE Py_UCS4 __Pyx_PyUnicode_AsPy_UCS4(PyObject*); + +/* object_ord.proto */ +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyObject_Ord(c)\ + (likely(PyUnicode_Check(c)) ? (long)__Pyx_PyUnicode_AsPy_UCS4(c) : __Pyx__PyObject_Ord(c)) +#else +#define __Pyx_PyObject_Ord(c) __Pyx__PyObject_Ord(c) +#endif +static long __Pyx__PyObject_Ord(PyObject* c); + +/* IncludeStringH.proto */ +#include + +/* BytesEquals.proto */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); + +/* UnicodeEquals.proto */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); + +/* StrEquals.proto */ +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals +#else +#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals +#endif + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* IterFinish.proto */ +static CYTHON_INLINE int __Pyx_IterFinish(void); + +/* UnpackItemEndCheck.proto */ +static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); + +/* SetItemInt.proto */ +#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\ + __Pyx_SetItemInt_Generic(o, to_py_func(i), v))) +static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v); +static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, + int is_list, int wraparound, int boundscheck); + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs); +#else +#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) +#endif +#endif + +/* PyObjectCallMethO.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +/* PyObjectCallNoArg.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); +#else +#define __Pyx_PyObject_CallNoArg(func) __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL) +#endif + +/* PyCFunctionFastCall.proto */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); +#else +#define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) +#endif + +/* PyObjectCallOneArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +/* PyObjectCallMethod0.proto */ +static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name); + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* UnpackTupleError.proto */ +static void __Pyx_UnpackTupleError(PyObject *, Py_ssize_t index); + +/* UnpackTuple2.proto */ +#define __Pyx_unpack_tuple2(tuple, value1, value2, is_tuple, has_known_size, decref_tuple)\ + (likely(is_tuple || PyTuple_Check(tuple)) ?\ + (likely(has_known_size || PyTuple_GET_SIZE(tuple) == 2) ?\ + __Pyx_unpack_tuple2_exact(tuple, value1, value2, decref_tuple) :\ + (__Pyx_UnpackTupleError(tuple, 2), -1)) :\ + __Pyx_unpack_tuple2_generic(tuple, value1, value2, has_known_size, decref_tuple)) +static CYTHON_INLINE int __Pyx_unpack_tuple2_exact( + PyObject* tuple, PyObject** value1, PyObject** value2, int decref_tuple); +static int __Pyx_unpack_tuple2_generic( + PyObject* tuple, PyObject** value1, PyObject** value2, int has_known_size, int decref_tuple); + +/* dict_iter.proto */ +static CYTHON_INLINE PyObject* __Pyx_dict_iterator(PyObject* dict, int is_dict, PyObject* method_name, + Py_ssize_t* p_orig_length, int* p_is_dict); +static CYTHON_INLINE int __Pyx_dict_iter_next(PyObject* dict_or_iter, Py_ssize_t orig_length, Py_ssize_t* ppos, + PyObject** pkey, PyObject** pvalue, PyObject** pitem, int is_dict); + +/* GetItemInt.proto */ +#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ + __Pyx_GetItemInt_Generic(o, to_py_func(i)))) +#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, + int is_list, int wraparound, int boundscheck); + +/* ListAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + Py_SIZE(list) = len+1; + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_PyList_Append(L,x) PyList_Append(L,x) +#endif + +/* MemviewSliceInit.proto */ +#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d +#define __Pyx_MEMVIEW_DIRECT 1 +#define __Pyx_MEMVIEW_PTR 2 +#define __Pyx_MEMVIEW_FULL 4 +#define __Pyx_MEMVIEW_CONTIG 8 +#define __Pyx_MEMVIEW_STRIDED 16 +#define __Pyx_MEMVIEW_FOLLOW 32 +#define __Pyx_IS_C_CONTIG 1 +#define __Pyx_IS_F_CONTIG 2 +static int __Pyx_init_memviewslice( + struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference); +static CYTHON_INLINE int __pyx_add_acquisition_count_locked( + __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); +static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( + __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); +#define __pyx_get_slice_count_pointer(memview) (memview->acquisition_count_aligned_p) +#define __pyx_get_slice_count(memview) (*__pyx_get_slice_count_pointer(memview)) +#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) +#define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__) +static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); +static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int); + +/* ArgTypeTest.proto */ +#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ + ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\ + __Pyx__ArgTypeTest(obj, type, name, exact)) +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); + +/* None.proto */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); + +/* UnaryNegOverflows.proto */ +#define UNARY_NEG_WOULD_OVERFLOW(x)\ + (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) + +static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ +/* GetAttr.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); + +/* decode_c_string_utf16.proto */ +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = 0; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = -1; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = 1; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} + +/* decode_c_string.proto */ +static CYTHON_INLINE PyObject* __Pyx_decode_c_string( + const char* cstring, Py_ssize_t start, Py_ssize_t stop, + const char* encoding, const char* errors, + PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)); + +/* PyErrExceptionMatches.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); +#else +#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) +#endif + +/* GetAttr3.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); + +/* GetModuleGlobalName.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* SwapException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +/* ListCompAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len)) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + Py_SIZE(list) = len+1; + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) +#endif + +/* PyIntBinop.proto */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace); +#else +#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace)\ + (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) +#endif + +/* ListExtend.proto */ +static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) { +#if CYTHON_COMPILING_IN_CPYTHON + PyObject* none = _PyList_Extend((PyListObject*)L, v); + if (unlikely(!none)) + return -1; + Py_DECREF(none); + return 0; +#else + return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v); +#endif +} + +/* None.proto */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); + +/* None.proto */ +static CYTHON_INLINE long __Pyx_div_long(long, long); + +/* WriteUnraisableException.proto */ +static void __Pyx_WriteUnraisable(const char *name, int clineno, + int lineno, const char *filename, + int full_traceback, int nogil); + +/* ImportFrom.proto */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); + +/* HasAttr.proto */ +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); + +/* SetVTable.proto */ +static int __Pyx_SetVtable(PyObject *dict, void *vtable); + +/* SetupReduce.proto */ +static int __Pyx_setup_reduce(PyObject* type_obj); + +/* FetchCommonType.proto */ +static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type); + +/* CythonFunction.proto */ +#define __Pyx_CyFunction_USED 1 +#include +#define __Pyx_CYFUNCTION_STATICMETHOD 0x01 +#define __Pyx_CYFUNCTION_CLASSMETHOD 0x02 +#define __Pyx_CYFUNCTION_CCLASS 0x04 +#define __Pyx_CyFunction_GetClosure(f)\ + (((__pyx_CyFunctionObject *) (f))->func_closure) +#define __Pyx_CyFunction_GetClassObj(f)\ + (((__pyx_CyFunctionObject *) (f))->func_classobj) +#define __Pyx_CyFunction_Defaults(type, f)\ + ((type *)(((__pyx_CyFunctionObject *) (f))->defaults)) +#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\ + ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g) +typedef struct { + PyCFunctionObject func; +#if PY_VERSION_HEX < 0x030500A0 + PyObject *func_weakreflist; +#endif + PyObject *func_dict; + PyObject *func_name; + PyObject *func_qualname; + PyObject *func_doc; + PyObject *func_globals; + PyObject *func_code; + PyObject *func_closure; + PyObject *func_classobj; + void *defaults; + int defaults_pyobjects; + int flags; + PyObject *defaults_tuple; + PyObject *defaults_kwdict; + PyObject *(*defaults_getter)(PyObject *); + PyObject *func_annotations; +} __pyx_CyFunctionObject; +static PyTypeObject *__pyx_CyFunctionType = 0; +#define __Pyx_CyFunction_NewEx(ml, flags, qualname, self, module, globals, code)\ + __Pyx_CyFunction_New(__pyx_CyFunctionType, ml, flags, qualname, self, module, globals, code) +static PyObject *__Pyx_CyFunction_New(PyTypeObject *, PyMethodDef *ml, + int flags, PyObject* qualname, + PyObject *self, + PyObject *module, PyObject *globals, + PyObject* code); +static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m, + size_t size, + int pyobjects); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m, + PyObject *tuple); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m, + PyObject *dict); +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m, + PyObject *dict); +static int __pyx_CyFunction_init(void); + +/* FusedFunction.proto */ +typedef struct { + __pyx_CyFunctionObject func; + PyObject *__signatures__; + PyObject *type; + PyObject *self; +} __pyx_FusedFunctionObject; +#define __pyx_FusedFunction_NewEx(ml, flags, qualname, self, module, globals, code)\ + __pyx_FusedFunction_New(__pyx_FusedFunctionType, ml, flags, qualname, self, module, globals, code) +static PyObject *__pyx_FusedFunction_New(PyTypeObject *type, + PyMethodDef *ml, int flags, + PyObject *qualname, PyObject *self, + PyObject *module, PyObject *globals, + PyObject *code); +static int __pyx_FusedFunction_clear(__pyx_FusedFunctionObject *self); +static PyTypeObject *__pyx_FusedFunctionType = NULL; +static int __pyx_FusedFunction_init(void); +#define __Pyx_FusedFunction_USED + +/* CLineInTraceback.proto */ +#ifdef CYTHON_CLINE_IN_TRACEBACK +#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) +#else +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); +#endif + +/* CodeObjectCache.proto */ +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +/* BufferStructDeclare.proto */ +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +/* MemviewSliceIsContig.proto */ +static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); + +/* OverlappingSlices.proto */ +static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize); + +/* Capsule.proto */ +static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig); + +/* IsLittleEndian.proto */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); + +/* BufferFormatCheck.proto */ +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type); + +/* TypeInfoCompare.proto */ +static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); + +/* MemviewSliceValidateAndInit.proto */ +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_short(PyObject *); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_int(PyObject *); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_PY_LONG_LONG(PyObject *); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_unsigned_char(PyObject *); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_char(PyObject *); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_unsigned_int(PyObject *); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_double(PyObject *); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +/* MemviewSliceCopyTemplate.proto */ +static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object); + +/* BytesContains.proto */ +static CYTHON_INLINE int __Pyx_BytesContains(PyObject* bytes, char character); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* ImportNumPyArray.proto */ +static PyObject *__pyx_numpy_ndarray = NULL; +static PyObject* __Pyx_ImportNumPyArrayTypeIfAvailable(void); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); + +/* FastTypeChecks.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); +#else +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) +#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) +#endif + +/* CheckBinaryVersion.proto */ +static int __Pyx_check_binary_version(void); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ +static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ +static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ + +/* Module declarations from 'cython.view' */ + +/* Module declarations from 'cython' */ + +/* Module declarations from 'utils' */ +static PyTypeObject *__pyx_array_type = 0; +static PyTypeObject *__pyx_MemviewEnum_type = 0; +static PyTypeObject *__pyx_memoryview_type = 0; +static PyTypeObject *__pyx_memoryviewslice_type = 0; +static PyObject *generic = 0; +static PyObject *strided = 0; +static PyObject *indirect = 0; +static PyObject *contiguous = 0; +static PyObject *indirect_contiguous = 0; +static int __pyx_memoryview_thread_locks_used; +static PyThread_type_lock __pyx_memoryview_thread_locks[8]; +static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ +static void *__pyx_align_pointer(void *, size_t); /*proto*/ +static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ +static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ +static PyObject *_unellipsify(PyObject *, int); /*proto*/ +static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ +static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ +static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ +static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ +static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ +static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ +static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ +static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ +static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ +static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/ +static int __pyx_memoryview_err(PyObject *, char *); /*proto*/ +static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ +static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ +static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ +static __Pyx_TypeInfo __Pyx_TypeInfo_short = { "short", NULL, sizeof(short), { 0 }, 0, IS_UNSIGNED(short) ? 'U' : 'I', IS_UNSIGNED(short), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, IS_UNSIGNED(int) ? 'U' : 'I', IS_UNSIGNED(int), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_long = { "long", NULL, sizeof(long), { 0 }, 0, IS_UNSIGNED(long) ? 'U' : 'I', IS_UNSIGNED(long), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_PY_LONG_LONG = { "long long", NULL, sizeof(PY_LONG_LONG), { 0 }, 0, IS_UNSIGNED(PY_LONG_LONG) ? 'U' : 'I', IS_UNSIGNED(PY_LONG_LONG), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_unsigned_char = { "unsigned char", NULL, sizeof(unsigned char), { 0 }, 0, IS_UNSIGNED(unsigned char) ? 'U' : 'I', IS_UNSIGNED(unsigned char), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_char = { "char", NULL, sizeof(char), { 0 }, 0, 'H', IS_UNSIGNED(char), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_unsigned_int = { "unsigned int", NULL, sizeof(unsigned int), { 0 }, 0, IS_UNSIGNED(unsigned int) ? 'U' : 'I', IS_UNSIGNED(unsigned int), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 }; +#define __Pyx_MODULE_NAME "utils" +extern int __pyx_module_is_main_utils; +int __pyx_module_is_main_utils = 0; + +/* Implementation of 'utils' */ +static PyObject *__pyx_builtin_TypeError; +static PyObject *__pyx_builtin_zip; +static PyObject *__pyx_builtin_reversed; +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_MemoryError; +static PyObject *__pyx_builtin_enumerate; +static PyObject *__pyx_builtin_Ellipsis; +static PyObject *__pyx_builtin_id; +static PyObject *__pyx_builtin_IndexError; +static const char __pyx_k_[] = "<"; +static const char __pyx_k_O[] = "O"; +static const char __pyx_k_X[] = "X"; +static const char __pyx_k_Y[] = "Y"; +static const char __pyx_k_c[] = "c"; +static const char __pyx_k_i[] = "i"; +static const char __pyx_k_j[] = "j"; +static const char __pyx_k_s[] = "s"; +static const char __pyx_k_y[] = "y"; +static const char __pyx_k__2[] = ">"; +static const char __pyx_k__3[] = "()"; +static const char __pyx_k__5[] = "|"; +static const char __pyx_k_id[] = "id"; +static const char __pyx_k_int[] = "int"; +static const char __pyx_k_new[] = "__new__"; +static const char __pyx_k_obj[] = "obj"; +static const char __pyx_k_out[] = "out"; +static const char __pyx_k_zip[] = "zip"; +static const char __pyx_k_args[] = "args"; +static const char __pyx_k_base[] = "base"; +static const char __pyx_k_char[] = "char"; +static const char __pyx_k_dict[] = "__dict__"; +static const char __pyx_k_kind[] = "kind"; +static const char __pyx_k_long[] = "long"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_mode[] = "mode"; +static const char __pyx_k_name[] = "name"; +static const char __pyx_k_ndim[] = "ndim"; +static const char __pyx_k_pack[] = "pack"; +static const char __pyx_k_size[] = "size"; +static const char __pyx_k_step[] = "step"; +static const char __pyx_k_stop[] = "stop"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_ASCII[] = "ASCII"; +static const char __pyx_k_class[] = "__class__"; +static const char __pyx_k_dtype[] = "dtype"; +static const char __pyx_k_error[] = "error"; +static const char __pyx_k_flags[] = "flags"; +static const char __pyx_k_numpy[] = "numpy"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_shape[] = "shape"; +static const char __pyx_k_short[] = "short"; +static const char __pyx_k_split[] = "split"; +static const char __pyx_k_start[] = "start"; +static const char __pyx_k_strip[] = "strip"; +static const char __pyx_k_utils[] = "utils"; +static const char __pyx_k_encode[] = "encode"; +static const char __pyx_k_format[] = "format"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_kwargs[] = "kwargs"; +static const char __pyx_k_name_2[] = "__name__"; +static const char __pyx_k_pickle[] = "pickle"; +static const char __pyx_k_reduce[] = "__reduce__"; +static const char __pyx_k_struct[] = "struct"; +static const char __pyx_k_unpack[] = "unpack"; +static const char __pyx_k_update[] = "update"; +static const char __pyx_k_fortran[] = "fortran"; +static const char __pyx_k_memview[] = "memview"; +static const char __pyx_k_strides[] = "strides"; +static const char __pyx_k_Ellipsis[] = "Ellipsis"; +static const char __pyx_k_defaults[] = "defaults"; +static const char __pyx_k_getstate[] = "__getstate__"; +static const char __pyx_k_itemsize[] = "itemsize"; +static const char __pyx_k_n_states[] = "n_states"; +static const char __pyx_k_pyx_type[] = "__pyx_type"; +static const char __pyx_k_reversed[] = "reversed"; +static const char __pyx_k_setstate[] = "__setstate__"; +static const char __pyx_k_TypeError[] = "TypeError"; +static const char __pyx_k_byteorder[] = "byteorder"; +static const char __pyx_k_enumerate[] = "enumerate"; +static const char __pyx_k_long_long[] = "long long"; +static const char __pyx_k_pyx_state[] = "__pyx_state"; +static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; +static const char __pyx_k_utils_pyx[] = "utils.pyx"; +static const char __pyx_k_IndexError[] = "IndexError"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_pyx_result[] = "__pyx_result"; +static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; +static const char __pyx_k_signatures[] = "signatures"; +static const char __pyx_k_MemoryError[] = "MemoryError"; +static const char __pyx_k_PickleError[] = "PickleError"; +static const char __pyx_k_class_weight[] = "class_weight"; +static const char __pyx_k_f_contiguous[] = "f_contiguous"; +static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; +static const char __pyx_k_stringsource[] = "stringsource"; +static const char __pyx_k_unsigned_int[] = "unsigned int"; +static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer"; +static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; +static const char __pyx_k_unsigned_char[] = "unsigned char"; +static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; +static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; +static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; +static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; +static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; +static const char __pyx_k_unary_potentials[] = "unary_potentials"; +static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; +static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; +static const char __pyx_k_strided_and_direct[] = ""; +static const char __pyx_k_loss_augment_unaries[] = "loss_augment_unaries"; +static const char __pyx_k_strided_and_indirect[] = ""; +static const char __pyx_k_contiguous_and_direct[] = ""; +static const char __pyx_k_MemoryView_of_r_object[] = ""; +static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; +static const char __pyx_k_contiguous_and_indirect[] = ""; +static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'"; +static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d."; +static const char __pyx_k_No_matching_signature_found[] = "No matching signature found"; +static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; +static const char __pyx_k_crammer_singer_joint_feature[] = "crammer_singer_joint_feature"; +static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; +static const char __pyx_k_strided_and_direct_or_indirect[] = ""; +static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; +static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; +static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; +static const char __pyx_k_Expected_at_least_d_argument_s_g[] = "Expected at least %d argument%s, got %d"; +static const char __pyx_k_Function_call_with_ambiguous_arg[] = "Function call with ambiguous argument types"; +static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))"; +static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; +static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s"; +static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)"; +static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; +static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)"; +static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; +static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; +static PyObject *__pyx_kp_s_; +static PyObject *__pyx_n_s_ASCII; +static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; +static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; +static PyObject *__pyx_kp_s_Cannot_index_with_type_s; +static PyObject *__pyx_n_s_Ellipsis; +static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; +static PyObject *__pyx_kp_s_Expected_at_least_d_argument_s_g; +static PyObject *__pyx_kp_s_Function_call_with_ambiguous_arg; +static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0; +static PyObject *__pyx_n_s_IndexError; +static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; +static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr; +static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d; +static PyObject *__pyx_n_s_MemoryError; +static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; +static PyObject *__pyx_kp_s_MemoryView_of_r_object; +static PyObject *__pyx_kp_s_No_matching_signature_found; +static PyObject *__pyx_n_b_O; +static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a; +static PyObject *__pyx_n_s_PickleError; +static PyObject *__pyx_n_s_TypeError; +static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; +static PyObject *__pyx_n_s_ValueError; +static PyObject *__pyx_n_s_View_MemoryView; +static PyObject *__pyx_n_s_X; +static PyObject *__pyx_n_s_Y; +static PyObject *__pyx_kp_s__2; +static PyObject *__pyx_kp_s__3; +static PyObject *__pyx_kp_s__5; +static PyObject *__pyx_n_s_allocate_buffer; +static PyObject *__pyx_n_s_args; +static PyObject *__pyx_n_s_base; +static PyObject *__pyx_n_s_byteorder; +static PyObject *__pyx_n_s_c; +static PyObject *__pyx_n_u_c; +static PyObject *__pyx_n_s_char; +static PyObject *__pyx_n_s_class; +static PyObject *__pyx_n_s_class_weight; +static PyObject *__pyx_n_s_cline_in_traceback; +static PyObject *__pyx_kp_s_contiguous_and_direct; +static PyObject *__pyx_kp_s_contiguous_and_indirect; +static PyObject *__pyx_n_s_crammer_singer_joint_feature; +static PyObject *__pyx_n_s_defaults; +static PyObject *__pyx_n_s_dict; +static PyObject *__pyx_n_s_dtype; +static PyObject *__pyx_n_s_dtype_is_object; +static PyObject *__pyx_n_s_encode; +static PyObject *__pyx_n_s_enumerate; +static PyObject *__pyx_n_s_error; +static PyObject *__pyx_n_s_f_contiguous; +static PyObject *__pyx_n_s_flags; +static PyObject *__pyx_n_s_format; +static PyObject *__pyx_n_s_fortran; +static PyObject *__pyx_n_u_fortran; +static PyObject *__pyx_n_s_getstate; +static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi; +static PyObject *__pyx_n_s_i; +static PyObject *__pyx_n_s_id; +static PyObject *__pyx_n_s_import; +static PyObject *__pyx_n_s_int; +static PyObject *__pyx_n_s_itemsize; +static PyObject *__pyx_kp_s_itemsize_0_for_cython_array; +static PyObject *__pyx_n_s_j; +static PyObject *__pyx_n_s_kind; +static PyObject *__pyx_n_s_kwargs; +static PyObject *__pyx_n_s_long; +static PyObject *__pyx_kp_s_long_long; +static PyObject *__pyx_n_s_loss_augment_unaries; +static PyObject *__pyx_n_s_main; +static PyObject *__pyx_n_s_memview; +static PyObject *__pyx_n_s_mode; +static PyObject *__pyx_n_s_n_states; +static PyObject *__pyx_n_s_name; +static PyObject *__pyx_n_s_name_2; +static PyObject *__pyx_n_s_ndim; +static PyObject *__pyx_n_s_new; +static PyObject *__pyx_kp_s_no_default___reduce___due_to_non; +static PyObject *__pyx_n_s_numpy; +static PyObject *__pyx_n_s_obj; +static PyObject *__pyx_n_s_out; +static PyObject *__pyx_n_s_pack; +static PyObject *__pyx_n_s_pickle; +static PyObject *__pyx_n_s_pyx_PickleError; +static PyObject *__pyx_n_s_pyx_checksum; +static PyObject *__pyx_n_s_pyx_getbuffer; +static PyObject *__pyx_n_s_pyx_result; +static PyObject *__pyx_n_s_pyx_state; +static PyObject *__pyx_n_s_pyx_type; +static PyObject *__pyx_n_s_pyx_unpickle_Enum; +static PyObject *__pyx_n_s_pyx_vtable; +static PyObject *__pyx_n_s_range; +static PyObject *__pyx_n_s_reduce; +static PyObject *__pyx_n_s_reduce_cython; +static PyObject *__pyx_n_s_reduce_ex; +static PyObject *__pyx_n_s_reversed; +static PyObject *__pyx_n_s_s; +static PyObject *__pyx_n_s_setstate; +static PyObject *__pyx_n_s_setstate_cython; +static PyObject *__pyx_n_s_shape; +static PyObject *__pyx_n_s_short; +static PyObject *__pyx_n_s_signatures; +static PyObject *__pyx_n_s_size; +static PyObject *__pyx_n_s_split; +static PyObject *__pyx_n_s_start; +static PyObject *__pyx_n_s_step; +static PyObject *__pyx_n_s_stop; +static PyObject *__pyx_kp_s_strided_and_direct; +static PyObject *__pyx_kp_s_strided_and_direct_or_indirect; +static PyObject *__pyx_kp_s_strided_and_indirect; +static PyObject *__pyx_n_s_strides; +static PyObject *__pyx_kp_s_stringsource; +static PyObject *__pyx_n_s_strip; +static PyObject *__pyx_n_s_struct; +static PyObject *__pyx_n_s_test; +static PyObject *__pyx_kp_s_unable_to_allocate_array_data; +static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; +static PyObject *__pyx_n_s_unary_potentials; +static PyObject *__pyx_n_s_unpack; +static PyObject *__pyx_kp_s_unsigned_char; +static PyObject *__pyx_kp_s_unsigned_int; +static PyObject *__pyx_n_s_update; +static PyObject *__pyx_n_s_utils; +static PyObject *__pyx_kp_s_utils_pyx; +static PyObject *__pyx_n_s_y; +static PyObject *__pyx_n_s_zip; +static PyObject *__pyx_pf_5utils_crammer_singer_joint_feature(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_signatures, PyObject *__pyx_v_args, PyObject *__pyx_v_kwargs, CYTHON_UNUSED PyObject *__pyx_v_defaults); /* proto */ +static PyObject *__pyx_pf_5utils_4crammer_singer_joint_feature(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_X, __Pyx_memviewslice __pyx_v_Y, __Pyx_memviewslice __pyx_v_out); /* proto */ +static PyObject *__pyx_pf_5utils_6crammer_singer_joint_feature(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_X, __Pyx_memviewslice __pyx_v_Y, __Pyx_memviewslice __pyx_v_out); /* proto */ +static PyObject *__pyx_pf_5utils_8crammer_singer_joint_feature(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_X, __Pyx_memviewslice __pyx_v_Y, __Pyx_memviewslice __pyx_v_out); /* proto */ +static PyObject *__pyx_pf_5utils_10crammer_singer_joint_feature(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_X, __Pyx_memviewslice __pyx_v_Y, __Pyx_memviewslice __pyx_v_out); /* proto */ +static PyObject *__pyx_pf_5utils_12crammer_singer_joint_feature(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_X, __Pyx_memviewslice __pyx_v_Y, __Pyx_memviewslice __pyx_v_out); /* proto */ +static PyObject *__pyx_pf_5utils_14crammer_singer_joint_feature(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_X, __Pyx_memviewslice __pyx_v_Y, __Pyx_memviewslice __pyx_v_out); /* proto */ +static PyObject *__pyx_pf_5utils_16crammer_singer_joint_feature(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_X, __Pyx_memviewslice __pyx_v_Y, __Pyx_memviewslice __pyx_v_out); /* proto */ +static PyObject *__pyx_pf_5utils_2loss_augment_unaries(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_signatures, PyObject *__pyx_v_args, PyObject *__pyx_v_kwargs, CYTHON_UNUSED PyObject *__pyx_v_defaults); /* proto */ +static PyObject *__pyx_pf_5utils_20loss_augment_unaries(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_unary_potentials, __Pyx_memviewslice __pyx_v_y, __Pyx_memviewslice __pyx_v_class_weight); /* proto */ +static PyObject *__pyx_pf_5utils_22loss_augment_unaries(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_unary_potentials, __Pyx_memviewslice __pyx_v_y, __Pyx_memviewslice __pyx_v_class_weight); /* proto */ +static PyObject *__pyx_pf_5utils_24loss_augment_unaries(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_unary_potentials, __Pyx_memviewslice __pyx_v_y, __Pyx_memviewslice __pyx_v_class_weight); /* proto */ +static PyObject *__pyx_pf_5utils_26loss_augment_unaries(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_unary_potentials, __Pyx_memviewslice __pyx_v_y, __Pyx_memviewslice __pyx_v_class_weight); /* proto */ +static PyObject *__pyx_pf_5utils_28loss_augment_unaries(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_unary_potentials, __Pyx_memviewslice __pyx_v_y, __Pyx_memviewslice __pyx_v_class_weight); /* proto */ +static PyObject *__pyx_pf_5utils_30loss_augment_unaries(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_unary_potentials, __Pyx_memviewslice __pyx_v_y, __Pyx_memviewslice __pyx_v_class_weight); /* proto */ +static PyObject *__pyx_pf_5utils_32loss_augment_unaries(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_unary_potentials, __Pyx_memviewslice __pyx_v_y, __Pyx_memviewslice __pyx_v_class_weight); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ +static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ +static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ +static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static PyObject 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_memoryviewslice): + * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< + * memviewsliceobj.to_object_func, + * memviewsliceobj.to_dtype_func, + */ + __Pyx_XDECREF(((PyObject *)__pyx_r)); + + /* "View.MemoryView":766 + * if isinstance(memview, _memoryviewslice): + * return memoryview_fromslice(dst, new_ndim, + * memviewsliceobj.to_object_func, # <<<<<<<<<<<<<< + * memviewsliceobj.to_dtype_func, + * memview.dtype_is_object) + */ + if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(1, 766, __pyx_L1_error) } + + /* "View.MemoryView":767 + * return memoryview_fromslice(dst, new_ndim, + * memviewsliceobj.to_object_func, + * memviewsliceobj.to_dtype_func, # <<<<<<<<<<<<<< + * memview.dtype_is_object) + * else: + */ + if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(1, 767, __pyx_L1_error) } + + /* "View.MemoryView":765 + * + * if isinstance(memview, _memoryviewslice): + * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< + * memviewsliceobj.to_object_func, + * memviewsliceobj.to_dtype_func, + */ + __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 765, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_3); + if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(1, 765, __pyx_L1_error) + __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); + __pyx_t_3 = 0; + goto __pyx_L0; + + /* "View.MemoryView":764 + * new_ndim += 1 + * + * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< + * return memoryview_fromslice(dst, new_ndim, + * memviewsliceobj.to_object_func, + */ + } + + /* "View.MemoryView":770 + * memview.dtype_is_object) + * else: + * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< + * memview.dtype_is_object) + * + */ + /*else*/ { + __Pyx_XDECREF(((PyObject *)__pyx_r)); + + /* "View.MemoryView":771 + * else: + * return memoryview_fromslice(dst, new_ndim, NULL, NULL, + * memview.dtype_is_object) # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 770, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_3); + + /* "View.MemoryView":770 + * memview.dtype_is_object) + * else: + * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< + * memview.dtype_is_object) + * + */ + if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(1, 770, __pyx_L1_error) + __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); + __pyx_t_3 = 0; + goto __pyx_L0; + } + + /* "View.MemoryView":698 + * + * @cname('__pyx_memview_slice') + * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< + * cdef int new_ndim = 0, suboffset_dim = -1, dim + * cdef bint negative_step + */ + + /* function exit code */ + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_3); + __Pyx_XDECREF(__pyx_t_9); + __Pyx_AddTraceback("View.MemoryView.memview_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = 0; + __pyx_L0:; + __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj); + __Pyx_XDECREF(__pyx_v_index); + __Pyx_XGIVEREF((PyObject *)__pyx_r); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +/* "View.MemoryView":795 + * + * @cname('__pyx_memoryview_slice_memviewslice') + * cdef int slice_memviewslice( # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, + */ + +static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) { + Py_ssize_t __pyx_v_new_shape; + int __pyx_v_negative_step; + int __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + + /* "View.MemoryView":815 + * cdef bint negative_step + * + * if not is_slice: # <<<<<<<<<<<<<< + * + * if start < 0: + */ + __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":817 + * if not is_slice: + * + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if not 0 <= start < shape: + */ + __pyx_t_1 = ((__pyx_v_start < 0) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":818 + * + * if start < 0: + * start += shape # <<<<<<<<<<<<<< + * if not 0 <= start < shape: + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) + */ + __pyx_v_start = (__pyx_v_start + __pyx_v_shape); + + /* "View.MemoryView":817 + * if not is_slice: + * + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if not 0 <= start < shape: + */ + } + + /* "View.MemoryView":819 + * if start < 0: + * start += shape + * if not 0 <= start < shape: # <<<<<<<<<<<<<< + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) + * else: + */ + __pyx_t_1 = (0 <= __pyx_v_start); + if (__pyx_t_1) { + __pyx_t_1 = (__pyx_v_start < __pyx_v_shape); + } + __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":820 + * start += shape + * if not 0 <= start < shape: + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< + * else: + * + */ + __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 820, __pyx_L1_error) + + /* "View.MemoryView":819 + * if start < 0: + * start += shape + * if not 0 <= start < shape: # <<<<<<<<<<<<<< + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) + * else: + */ + } + + /* "View.MemoryView":815 + * cdef bint negative_step + * + * if not is_slice: # <<<<<<<<<<<<<< + * + * if start < 0: + */ + goto __pyx_L3; + } + + /* "View.MemoryView":823 + * else: + * + * negative_step = have_step != 0 and step < 0 # <<<<<<<<<<<<<< + * + * if have_step and step == 0: + */ + /*else*/ { + __pyx_t_1 = ((__pyx_v_have_step != 0) != 0); + if (__pyx_t_1) { + } else { + __pyx_t_2 = __pyx_t_1; + goto __pyx_L6_bool_binop_done; + } + __pyx_t_1 = ((__pyx_v_step < 0) != 0); + __pyx_t_2 = __pyx_t_1; + __pyx_L6_bool_binop_done:; + __pyx_v_negative_step = __pyx_t_2; + + /* "View.MemoryView":825 + * negative_step = have_step != 0 and step < 0 + * + * if have_step and step == 0: # <<<<<<<<<<<<<< + * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) + * + */ + __pyx_t_1 = (__pyx_v_have_step != 0); + if (__pyx_t_1) { + } else { + __pyx_t_2 = __pyx_t_1; + goto __pyx_L9_bool_binop_done; + } + __pyx_t_1 = ((__pyx_v_step == 0) != 0); + __pyx_t_2 = __pyx_t_1; + __pyx_L9_bool_binop_done:; + if (__pyx_t_2) { + + /* "View.MemoryView":826 + * + * if have_step and step == 0: + * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 826, __pyx_L1_error) + + /* "View.MemoryView":825 + * negative_step = have_step != 0 and step < 0 + * + * if have_step and step == 0: # <<<<<<<<<<<<<< + * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) + * + */ + } + + /* "View.MemoryView":829 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape + */ + __pyx_t_2 = (__pyx_v_have_start != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":830 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: + */ + __pyx_t_2 = ((__pyx_v_start < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":831 + * if have_start: + * if start < 0: + * start += shape # <<<<<<<<<<<<<< + * if start < 0: + * start = 0 + */ + __pyx_v_start = (__pyx_v_start + __pyx_v_shape); + + /* "View.MemoryView":832 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: + */ + __pyx_t_2 = ((__pyx_v_start < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":833 + * start += shape + * if start < 0: + * start = 0 # <<<<<<<<<<<<<< + * elif start >= shape: + * if negative_step: + */ + __pyx_v_start = 0; + + /* "View.MemoryView":832 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: + */ + } + + /* "View.MemoryView":830 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: + */ + goto __pyx_L12; + } + + /* "View.MemoryView":834 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 + */ + __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":835 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + __pyx_t_2 = (__pyx_v_negative_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":836 + * elif start >= shape: + * if negative_step: + * start = shape - 1 # <<<<<<<<<<<<<< + * else: + * start = shape + */ + __pyx_v_start = (__pyx_v_shape - 1); + + /* "View.MemoryView":835 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + goto __pyx_L14; + } + + /* "View.MemoryView":838 + * start = shape - 1 + * else: + * start = shape # <<<<<<<<<<<<<< + * else: + * if negative_step: + */ + /*else*/ { + __pyx_v_start = __pyx_v_shape; + } + __pyx_L14:; + + /* "View.MemoryView":834 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 + */ + } + __pyx_L12:; + + /* "View.MemoryView":829 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape + */ + goto __pyx_L11; + } + + /* "View.MemoryView":840 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + /*else*/ { + __pyx_t_2 = (__pyx_v_negative_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":841 + * else: + * if negative_step: + * start = shape - 1 # <<<<<<<<<<<<<< + * else: + * start = 0 + */ + __pyx_v_start = (__pyx_v_shape - 1); + + /* "View.MemoryView":840 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + goto __pyx_L15; + } + + /* "View.MemoryView":843 + * start = shape - 1 + * else: + * start = 0 # <<<<<<<<<<<<<< + * + * if have_stop: + */ + /*else*/ { + __pyx_v_start = 0; + } + __pyx_L15:; + } + __pyx_L11:; + + /* "View.MemoryView":845 + * start = 0 + * + * if have_stop: # <<<<<<<<<<<<<< + * if stop < 0: + * stop += shape + */ + __pyx_t_2 = (__pyx_v_have_stop != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":846 + * + * if have_stop: + * if stop < 0: # <<<<<<<<<<<<<< + * stop += shape + * if stop < 0: + */ + __pyx_t_2 = ((__pyx_v_stop < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":847 + * if have_stop: + * if stop < 0: + * stop += shape # <<<<<<<<<<<<<< + * if stop < 0: + * stop = 0 + */ + __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); + + /* "View.MemoryView":848 + * if stop < 0: + * stop += shape + * if stop < 0: # <<<<<<<<<<<<<< + * stop = 0 + * elif stop > shape: + */ + __pyx_t_2 = ((__pyx_v_stop < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":849 + * stop += shape + * if stop < 0: + * stop = 0 # <<<<<<<<<<<<<< + * elif stop > shape: + * stop = shape + */ + __pyx_v_stop = 0; + + /* "View.MemoryView":848 + * if stop < 0: + * stop += shape + * if stop < 0: # <<<<<<<<<<<<<< + * stop = 0 + * elif stop > shape: + */ + } + + /* "View.MemoryView":846 + * + * if have_stop: + * if stop < 0: # <<<<<<<<<<<<<< + * stop += shape + * if stop < 0: + */ + goto __pyx_L17; + } + + /* "View.MemoryView":850 + * if stop < 0: + * stop = 0 + * elif stop > shape: # <<<<<<<<<<<<<< + * stop = shape + * else: + */ + __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":851 + * stop = 0 + * elif stop > shape: + * stop = shape # <<<<<<<<<<<<<< + * else: + * if negative_step: + */ + __pyx_v_stop = __pyx_v_shape; + + /* "View.MemoryView":850 + * if stop < 0: + * stop = 0 + * elif stop > shape: # <<<<<<<<<<<<<< + * stop = shape + * else: + */ + } + __pyx_L17:; + + /* "View.MemoryView":845 + * start = 0 + * + * if have_stop: # <<<<<<<<<<<<<< + * if stop < 0: + * stop += shape + */ + goto __pyx_L16; + } + + /* "View.MemoryView":853 + * stop = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * stop = -1 + * else: + */ + /*else*/ { + __pyx_t_2 = (__pyx_v_negative_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":854 + * else: + * if negative_step: + * stop = -1 # <<<<<<<<<<<<<< + * else: + * stop = shape + */ + __pyx_v_stop = -1L; + + /* "View.MemoryView":853 + * stop = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * stop = -1 + * else: + */ + goto __pyx_L19; + } + + /* "View.MemoryView":856 + * stop = -1 + * else: + * stop = shape # <<<<<<<<<<<<<< + * + * if not have_step: + */ + /*else*/ { + __pyx_v_stop = __pyx_v_shape; + } + __pyx_L19:; + } + __pyx_L16:; + + /* "View.MemoryView":858 + * stop = shape + * + * if not have_step: # <<<<<<<<<<<<<< + * step = 1 + * + */ + __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":859 + * + * if not have_step: + * step = 1 # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_step = 1; + + /* "View.MemoryView":858 + * stop = shape + * + * if not have_step: # <<<<<<<<<<<<<< + * step = 1 + * + */ + } + + /* "View.MemoryView":863 + * + * with cython.cdivision(True): + * new_shape = (stop - start) // step # <<<<<<<<<<<<<< + * + * if (stop - start) - step * new_shape: + */ + __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); + + /* "View.MemoryView":865 + * new_shape = (stop - start) // step + * + * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< + * new_shape += 1 + * + */ + __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":866 + * + * if (stop - start) - step * new_shape: + * new_shape += 1 # <<<<<<<<<<<<<< + * + * if new_shape < 0: + */ + __pyx_v_new_shape = (__pyx_v_new_shape + 1); + + /* "View.MemoryView":865 + * new_shape = (stop - start) // step + * + * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< + * new_shape += 1 + * + */ + } + + /* "View.MemoryView":868 + * new_shape += 1 + * + * if new_shape < 0: # <<<<<<<<<<<<<< + * new_shape = 0 + * + */ + __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":869 + * + * if new_shape < 0: + * new_shape = 0 # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_new_shape = 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<<<<<<<<<<<<<< + * else: + * to_object_func = NULL + */ + __pyx_t_4 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_dtype_func; + __pyx_v_to_dtype_func = __pyx_t_4; + + /* "View.MemoryView":1079 + * cdef int (*to_dtype_func)(char *, object) except 0 + * + * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< + * to_object_func = (<_memoryviewslice> memview).to_object_func + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1083 + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func + * else: + * to_object_func = NULL # <<<<<<<<<<<<<< + * to_dtype_func = NULL + * + */ + /*else*/ { + __pyx_v_to_object_func = NULL; + + /* "View.MemoryView":1084 + * else: + * to_object_func = NULL + * to_dtype_func = NULL # <<<<<<<<<<<<<< + * + * return memoryview_fromslice(memviewslice[0], memview.view.ndim, + */ + __pyx_v_to_dtype_func = NULL; + } + __pyx_L3:; + + /* "View.MemoryView":1086 + * to_dtype_func = NULL + * + * return memoryview_fromslice(memviewslice[0], memview.view.ndim, # <<<<<<<<<<<<<< + * to_object_func, to_dtype_func, + * memview.dtype_is_object) + */ + __Pyx_XDECREF(__pyx_r); + + /* "View.MemoryView":1088 + * return memoryview_fromslice(memviewslice[0], memview.view.ndim, + * to_object_func, to_dtype_func, + * memview.dtype_is_object) # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 1086, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_5); + __pyx_r = __pyx_t_5; + __pyx_t_5 = 0; + goto __pyx_L0; + + /* "View.MemoryView":1072 + * + * @cname('__pyx_memoryview_copy_object_from_slice') + * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< + * """ + * Create a new memoryview object from a given memoryview object and slice. + */ + + /* function exit code */ + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_5); + __Pyx_AddTraceback("View.MemoryView.memoryview_copy_from_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = 0; + __pyx_L0:; + __Pyx_XGIVEREF(__pyx_r); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +/* "View.MemoryView":1094 + * + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< + * if arg < 0: + * return -arg + */ + +static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { + Py_ssize_t __pyx_r; + int __pyx_t_1; + + /* "View.MemoryView":1095 + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: + * if arg < 0: # <<<<<<<<<<<<<< + * return -arg + * else: + */ + __pyx_t_1 = ((__pyx_v_arg < 0) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":1096 + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: + * if arg < 0: + * return -arg # <<<<<<<<<<<<<< + * else: + * return arg + */ + __pyx_r = (-__pyx_v_arg); + goto __pyx_L0; + + /* "View.MemoryView":1095 + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: + * if arg < 0: # <<<<<<<<<<<<<< + * return -arg + * else: + */ + } + + /* "View.MemoryView":1098 + * return -arg + * else: + * return arg # <<<<<<<<<<<<<< + * + * @cname('__pyx_get_best_slice_order') + */ + /*else*/ { + __pyx_r = __pyx_v_arg; + goto __pyx_L0; + } + + /* "View.MemoryView":1094 + * + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< + * if arg < 0: + * return -arg + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1101 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + +static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) { + int __pyx_v_i; + Py_ssize_t __pyx_v_c_stride; + Py_ssize_t __pyx_v_f_stride; + char __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + + /* "View.MemoryView":1106 + * """ + * cdef int i + * cdef Py_ssize_t c_stride = 0 # <<<<<<<<<<<<<< + * cdef Py_ssize_t f_stride = 0 + * + */ + __pyx_v_c_stride = 0; + + /* "View.MemoryView":1107 + * cdef int i + * cdef Py_ssize_t c_stride = 0 + * cdef Py_ssize_t f_stride = 0 # <<<<<<<<<<<<<< + * + * for i in range(ndim - 1, -1, -1): + */ + __pyx_v_f_stride = 0; + + /* "View.MemoryView":1109 + * cdef Py_ssize_t f_stride = 0 + * + * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] + */ + for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1L; __pyx_t_1-=1) { + __pyx_v_i = __pyx_t_1; + + /* "View.MemoryView":1110 + * + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * c_stride = mslice.strides[i] + * break + */ + __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1111 + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * + */ + __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1112 + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * for i in range(ndim): + */ + goto __pyx_L4_break; + + /* "View.MemoryView":1110 + * + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * c_stride = mslice.strides[i] + * break + */ + } + } + __pyx_L4_break:; + + /* "View.MemoryView":1114 + * break + * + * for i in range(ndim): # <<<<<<<<<<<<<< + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] + */ + __pyx_t_1 = __pyx_v_ndim; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_1; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; + + /* "View.MemoryView":1115 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break + */ + __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1116 + * for i in range(ndim): + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * + */ + __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1117 + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + */ + goto __pyx_L7_break; + + /* "View.MemoryView":1115 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break + */ + } + } + __pyx_L7_break:; + + /* "View.MemoryView":1119 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1120 + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + * return 'C' # <<<<<<<<<<<<<< + * else: + * return 'F' + */ + __pyx_r = 'C'; + goto __pyx_L0; + + /* "View.MemoryView":1119 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + } + + /* "View.MemoryView":1122 + * return 'C' + * else: + * return 'F' # <<<<<<<<<<<<<< + * + * @cython.cdivision(True) + */ + /*else*/ { + __pyx_r = 'F'; + goto __pyx_L0; + } + + /* "View.MemoryView":1101 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1125 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + +static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + CYTHON_UNUSED Py_ssize_t __pyx_v_i; + CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; + Py_ssize_t __pyx_v_dst_extent; + Py_ssize_t __pyx_v_src_stride; + Py_ssize_t __pyx_v_dst_stride; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + Py_ssize_t __pyx_t_4; + Py_ssize_t __pyx_t_5; + + /* "View.MemoryView":1132 + * + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + */ + __pyx_v_src_extent = (__pyx_v_src_shape[0]); + + /* "View.MemoryView":1133 + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] + */ + __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); + + /* "View.MemoryView":1134 + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + */ + __pyx_v_src_stride = (__pyx_v_src_strides[0]); + + /* "View.MemoryView":1135 + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< + * + * if ndim == 1: + */ + __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); + + /* "View.MemoryView":1137 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":1138 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + + /* "View.MemoryView":1139 + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + */ + __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); + if (__pyx_t_2) { + __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); + } + __pyx_t_3 = (__pyx_t_2 != 0); + __pyx_t_1 = __pyx_t_3; + __pyx_L5_bool_binop_done:; + + /* "View.MemoryView":1138 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + if (__pyx_t_1) { + + /* "View.MemoryView":1140 + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)); + + /* "View.MemoryView":1138 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + goto __pyx_L4; + } + + /* "View.MemoryView":1142 + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + */ + /*else*/ { + __pyx_t_4 = __pyx_v_dst_extent; + for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { + __pyx_v_i = __pyx_t_5; + + /* "View.MemoryView":1143 + * else: + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< + * src_data += src_stride + * dst_data += dst_stride + */ + memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize); + + /* "View.MemoryView":1144 + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * else: + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1145 + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L4:; + + /* "View.MemoryView":1137 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1147 + * dst_data += dst_stride + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * _copy_strided_to_strided(src_data, src_strides + 1, + * dst_data, dst_strides + 1, + */ + /*else*/ { + __pyx_t_4 = __pyx_v_dst_extent; + for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { + __pyx_v_i = __pyx_t_5; + + /* "View.MemoryView":1148 + * else: + * for i in range(dst_extent): + * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< + * dst_data, dst_strides + 1, + * src_shape + 1, dst_shape + 1, + */ + _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); + + /* "View.MemoryView":1152 + * src_shape + 1, dst_shape + 1, + * ndim - 1, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1153 + * ndim - 1, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L3:; + + /* "View.MemoryView":1125 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + + /* function exit code */ +} + +/* "View.MemoryView":1155 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) nogil: + */ + +static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + + /* 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copy_data_to_temp(&src, &tmp, order, ndim) + */ + __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); + + /* "View.MemoryView":1290 + * if slices_overlap(&src, &dst, ndim, itemsize): + * + * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< + * order = get_best_order(&dst, ndim) + * + */ + } + + /* "View.MemoryView":1293 + * order = get_best_order(&dst, ndim) + * + * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< + * src = tmp + * + */ + __pyx_t_6 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_6 == ((void *)NULL))) __PYX_ERR(1, 1293, __pyx_L1_error) + __pyx_v_tmpdata = __pyx_t_6; + + /* "View.MemoryView":1294 + * + * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) + * src = tmp # <<<<<<<<<<<<<< + * + * if not broadcasting: + */ + __pyx_v_src = __pyx_v_tmp; + + /* "View.MemoryView":1288 + * _err_dim(ValueError, "Dimension %d is not direct", i) + * + * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< + * + * if not slice_is_contig(src, order, ndim): + */ + } + + /* "View.MemoryView":1296 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1299 + * + * + * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + */ + __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1300 + * + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<< + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); + + /* "View.MemoryView":1299 + * + * + * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + */ + goto __pyx_L12; + } + + /* "View.MemoryView":1301 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1302 + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< + * + * if direct_copy: + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); + + /* "View.MemoryView":1301 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + } + __pyx_L12:; + + /* "View.MemoryView":1304 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + */ + __pyx_t_2 = (__pyx_v_direct_copy != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1306 + * if direct_copy: + * + * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, True) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1307 + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< + * refcount_copying(&dst, dtype_is_object, ndim, True) + * free(tmpdata) + */ + memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim)); + + /* "View.MemoryView":1308 + * refcount_copying(&dst, dtype_is_object, ndim, False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< + * free(tmpdata) + * return 0 + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1309 + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, True) + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * + */ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1310 + * refcount_copying(&dst, dtype_is_object, ndim, True) + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * if order == 'F' == get_best_order(&dst, ndim): + */ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1304 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + */ + } + + /* "View.MemoryView":1296 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * + */ + } + + /* "View.MemoryView":1312 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_2 = (__pyx_v_order == 'F'); + if (__pyx_t_2) { + __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); + } + __pyx_t_7 = (__pyx_t_2 != 0); + if (__pyx_t_7) { + + /* "View.MemoryView":1315 + * + * + * transpose_memslice(&src) # <<<<<<<<<<<<<< + * transpose_memslice(&dst) + * + */ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1315, __pyx_L1_error) + + /* "View.MemoryView":1316 + * + * transpose_memslice(&src) + * transpose_memslice(&dst) # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + */ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1316, __pyx_L1_error) + + /* 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(__pyx_v_ndim - 1); __pyx_t_1 > -1L; __pyx_t_1-=1) { + __pyx_v_i = __pyx_t_1; + + /* "View.MemoryView":1333 + * + * for i in range(ndim - 1, -1, -1): + * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<< + * mslice.strides[i + offset] = mslice.strides[i] + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + */ + (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]); + + /* "View.MemoryView":1334 + * for i in range(ndim - 1, -1, -1): + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<< + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + * + */ + (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1335 + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] # <<<<<<<<<<<<<< + * + * for i in 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0}, + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_array_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_array_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_array[] = { + {(char *)"memview", __pyx_getprop___pyx_array_memview, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; + +static PySequenceMethods __pyx_tp_as_sequence_array = { + __pyx_array___len__, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + __pyx_sq_item_array, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + 0, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyMappingMethods __pyx_tp_as_mapping_array = { + __pyx_array___len__, /*mp_length*/ + __pyx_array___getitem__, /*mp_subscript*/ + __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_array = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_array_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; + +static PyTypeObject __pyx_type___pyx_array = { + PyVarObject_HEAD_INIT(0, 0) + "utils.array", /*tp_name*/ + sizeof(struct __pyx_array_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_array, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + 0, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_array, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_array, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + __pyx_tp_getattro_array, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_array, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ + 0, /*tp_doc*/ + 0, /*tp_traverse*/ + 0, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_array, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_array, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_array, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; + +static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { + struct __pyx_MemviewEnum_obj *p; + PyObject *o; + if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + p = ((struct __pyx_MemviewEnum_obj *)o); + p->name = Py_None; Py_INCREF(Py_None); + return o; +} + +static void __pyx_tp_dealloc_Enum(PyObject *o) { + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + PyObject_GC_UnTrack(o); + Py_CLEAR(p->name); + (*Py_TYPE(o)->tp_free)(o); +} + +static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + if (p->name) { + e = (*v)(p->name, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_Enum(PyObject *o) { + PyObject* tmp; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + tmp = ((PyObject*)p->name); + p->name = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + return 0; +} + +static PyMethodDef __pyx_methods_Enum[] = { + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static PyTypeObject __pyx_type___pyx_MemviewEnum = { + PyVarObject_HEAD_INIT(0, 0) + "utils.Enum", /*tp_name*/ + sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_Enum, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_MemviewEnum___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_Enum, /*tp_traverse*/ + __pyx_tp_clear_Enum, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_Enum, /*tp_methods*/ + 0, /*tp_members*/ + 0, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + __pyx_MemviewEnum___init__, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_Enum, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; +static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; + +static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryview_obj *p; + PyObject *o; + if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryview_obj *)o); + p->__pyx_vtab = __pyx_vtabptr_memoryview; + p->obj = Py_None; Py_INCREF(Py_None); + p->_size = Py_None; Py_INCREF(Py_None); + p->_array_interface = Py_None; Py_INCREF(Py_None); + p->view.obj = NULL; + if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; + return o; + bad: + Py_DECREF(o); o = 0; + return NULL; +} + +static void __pyx_tp_dealloc_memoryview(PyObject *o) { + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + ++Py_REFCNT(o); + __pyx_memoryview___dealloc__(o); + --Py_REFCNT(o); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->obj); + Py_CLEAR(p->_size); + Py_CLEAR(p->_array_interface); + (*Py_TYPE(o)->tp_free)(o); +} + +static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + if (p->obj) { + e = (*v)(p->obj, a); if (e) return e; + } + if (p->_size) { + e = (*v)(p->_size, a); if (e) return e; + } + if (p->_array_interface) { + e = (*v)(p->_array_interface, a); if (e) return e; + } + if (p->view.obj) { + e = (*v)(p->view.obj, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_memoryview(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + tmp = ((PyObject*)p->obj); + p->obj = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_size); + p->_size = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_array_interface); + p->_array_interface = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + Py_CLEAR(p->view.obj); + return 0; +} +static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { + PyObject *r; + PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; + r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); + Py_DECREF(x); + return r; +} + +static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { + if (v) { + return __pyx_memoryview___setitem__(o, i, v); + } + else { + PyErr_Format(PyExc_NotImplementedError, + "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); + return -1; + } +} + +static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); +} + +static PyMethodDef __pyx_methods_memoryview[] = { + {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0}, + {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0}, + {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0}, + {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0}, + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_memoryview[] = { + {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, + {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, + {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, + {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, + {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, + {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, + {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, + {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, + {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; + +static PySequenceMethods __pyx_tp_as_sequence_memoryview = { + __pyx_memoryview___len__, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + __pyx_sq_item_memoryview, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + 0, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyMappingMethods __pyx_tp_as_mapping_memoryview = { + __pyx_memoryview___len__, /*mp_length*/ + __pyx_memoryview___getitem__, /*mp_subscript*/ + __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; + +static PyTypeObject __pyx_type___pyx_memoryview = { + PyVarObject_HEAD_INIT(0, 0) + "utils.memoryview", /*tp_name*/ + sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_memoryview___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + __pyx_memoryview___str__, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_memoryview, /*tp_traverse*/ + __pyx_tp_clear_memoryview, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_memoryview, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_memoryview, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_memoryview, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; +static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; + +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryviewslice_obj *p; + PyObject *o = __pyx_tp_new_memoryview(t, a, k); + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryviewslice_obj *)o); + p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; + p->from_object = Py_None; Py_INCREF(Py_None); + p->from_slice.memview = NULL; + return o; +} + +static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + ++Py_REFCNT(o); + __pyx_memoryviewslice___dealloc__(o); + --Py_REFCNT(o); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->from_object); + PyObject_GC_Track(o); + __pyx_tp_dealloc_memoryview(o); +} + +static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; + if (p->from_object) { + e = (*v)(p->from_object, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear__memoryviewslice(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + __pyx_tp_clear_memoryview(o); + tmp = ((PyObject*)p->from_object); + p->from_object = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + __PYX_XDEC_MEMVIEW(&p->from_slice, 1); + return 0; +} + +static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o); +} + +static PyMethodDef __pyx_methods__memoryviewslice[] = { + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = { + {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; + +static PyTypeObject __pyx_type___pyx_memoryviewslice = { + PyVarObject_HEAD_INIT(0, 0) + "utils._memoryviewslice", /*tp_name*/ + sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + #if CYTHON_COMPILING_IN_PYPY + __pyx_memoryview___repr__, /*tp_repr*/ + #else + 0, /*tp_repr*/ + #endif + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + #if CYTHON_COMPILING_IN_PYPY + __pyx_memoryview___str__, /*tp_str*/ + #else + 0, /*tp_str*/ + #endif + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + "Internal class for passing memoryview slices to Python", /*tp_doc*/ + __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/ + __pyx_tp_clear__memoryviewslice, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods__memoryviewslice, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets__memoryviewslice, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new__memoryviewslice, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; + +static PyMethodDef __pyx_methods[] = { + {0, 0, 0, 0} +}; + +#if PY_MAJOR_VERSION >= 3 +#if CYTHON_PEP489_MULTI_PHASE_INIT +static PyObject* __pyx_pymod_create(PyObject *spec, PyModuleDef *def); /*proto*/ +static int __pyx_pymod_exec_utils(PyObject* module); /*proto*/ +static PyModuleDef_Slot __pyx_moduledef_slots[] = { + {Py_mod_create, (void*)__pyx_pymod_create}, + {Py_mod_exec, 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const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +/* RaiseDoubleKeywords */ +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +/* ParseKeywords */ +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; 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+ if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyErrFetchRestore */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +} +#endif + +/* RaiseException */ +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + __Pyx_PyThreadState_declare + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_PyThreadState_assign + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } + if (cause) { + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +/* UnicodeAsUCS4 */ +static CYTHON_INLINE Py_UCS4 __Pyx_PyUnicode_AsPy_UCS4(PyObject* x) { + Py_ssize_t length; + #if CYTHON_PEP393_ENABLED + length = PyUnicode_GET_LENGTH(x); + if (likely(length == 1)) { + return PyUnicode_READ_CHAR(x, 0); + } + #else + length = PyUnicode_GET_SIZE(x); + if (likely(length == 1)) { + return PyUnicode_AS_UNICODE(x)[0]; + } + #if Py_UNICODE_SIZE == 2 + else if (PyUnicode_GET_SIZE(x) == 2) { + Py_UCS4 high_val = PyUnicode_AS_UNICODE(x)[0]; + if (high_val >= 0xD800 && high_val <= 0xDBFF) { + Py_UCS4 low_val = PyUnicode_AS_UNICODE(x)[1]; + if (low_val >= 0xDC00 && low_val <= 0xDFFF) { + return 0x10000 + (((high_val & ((1<<10)-1)) << 10) | (low_val & ((1<<10)-1))); + } + } + } + #endif + #endif + PyErr_Format(PyExc_ValueError, + "only single character unicode strings can be converted to Py_UCS4, " + "got length %" CYTHON_FORMAT_SSIZE_T "d", length); + return (Py_UCS4)-1; +} + +/* object_ord */ +static long __Pyx__PyObject_Ord(PyObject* c) { + Py_ssize_t size; + if (PyBytes_Check(c)) { + size = PyBytes_GET_SIZE(c); + if (likely(size == 1)) { + return (unsigned char) PyBytes_AS_STRING(c)[0]; + } +#if PY_MAJOR_VERSION < 3 + } else if (PyUnicode_Check(c)) { + return (long)__Pyx_PyUnicode_AsPy_UCS4(c); +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + } else if (PyByteArray_Check(c)) { + size = PyByteArray_GET_SIZE(c); + if (likely(size == 1)) { + return (unsigned char) PyByteArray_AS_STRING(c)[0]; + } +#endif + } else { + PyErr_Format(PyExc_TypeError, + "ord() expected string of length 1, but %.200s found", c->ob_type->tp_name); + return (long)(Py_UCS4)-1; + } + PyErr_Format(PyExc_TypeError, + "ord() expected a character, but string of length %zd found", size); + return (long)(Py_UCS4)-1; +} + +/* BytesEquals */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else + if (s1 == s2) { + return (equals == Py_EQ); + } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { + const char *ps1, *ps2; + Py_ssize_t length = PyBytes_GET_SIZE(s1); + if (length != PyBytes_GET_SIZE(s2)) + return (equals == Py_NE); + ps1 = PyBytes_AS_STRING(s1); + ps2 = PyBytes_AS_STRING(s2); + if (ps1[0] != ps2[0]) { + return (equals == Py_NE); + } else if (length == 1) { + return (equals == Py_EQ); + } else { + int result; +#if CYTHON_USE_UNICODE_INTERNALS + Py_hash_t hash1, hash2; + hash1 = ((PyBytesObject*)s1)->ob_shash; + hash2 = ((PyBytesObject*)s2)->ob_shash; + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + return (equals == Py_NE); + } +#endif + result = memcmp(ps1, ps2, (size_t)length); + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { + return (equals == Py_NE); + } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { + return (equals == Py_NE); + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +#endif +} + +/* UnicodeEquals */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else +#if PY_MAJOR_VERSION < 3 + PyObject* owned_ref = NULL; +#endif + int s1_is_unicode, s2_is_unicode; + if (s1 == s2) { + goto return_eq; + } + s1_is_unicode = PyUnicode_CheckExact(s1); + s2_is_unicode = PyUnicode_CheckExact(s2); +#if PY_MAJOR_VERSION < 3 + if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { + owned_ref = PyUnicode_FromObject(s2); + if (unlikely(!owned_ref)) + return -1; + s2 = owned_ref; + s2_is_unicode = 1; + } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { + owned_ref = PyUnicode_FromObject(s1); + if (unlikely(!owned_ref)) + return -1; + s1 = owned_ref; + s1_is_unicode = 1; + } else if (((!s2_is_unicode) & (!s1_is_unicode))) { + return __Pyx_PyBytes_Equals(s1, s2, equals); + } +#endif + if (s1_is_unicode & s2_is_unicode) { + Py_ssize_t length; + int kind; + void *data1, *data2; + if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) + return -1; + length = __Pyx_PyUnicode_GET_LENGTH(s1); + if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { + goto return_ne; + } +#if CYTHON_USE_UNICODE_INTERNALS + { + Py_hash_t hash1, hash2; + #if CYTHON_PEP393_ENABLED + hash1 = ((PyASCIIObject*)s1)->hash; + hash2 = ((PyASCIIObject*)s2)->hash; + #else + hash1 = ((PyUnicodeObject*)s1)->hash; + hash2 = ((PyUnicodeObject*)s2)->hash; + #endif + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + goto return_ne; + } + } +#endif + kind = __Pyx_PyUnicode_KIND(s1); + if (kind != __Pyx_PyUnicode_KIND(s2)) { + goto return_ne; + } + data1 = __Pyx_PyUnicode_DATA(s1); + data2 = __Pyx_PyUnicode_DATA(s2); + if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { + goto return_ne; + } else if (length == 1) { + goto return_eq; + } else { + int result = memcmp(data1, data2, (size_t)(length * kind)); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & s2_is_unicode) { + goto return_ne; + } else if ((s2 == Py_None) & s1_is_unicode) { + goto return_ne; + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +return_eq: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ); +return_ne: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_NE); +#endif +} + +/* RaiseTooManyValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* IterFinish */ +static CYTHON_INLINE int __Pyx_IterFinish(void) { +#if CYTHON_FAST_THREAD_STATE + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* exc_type = tstate->curexc_type; + if (unlikely(exc_type)) { + if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) { + PyObject *exc_value, *exc_tb; + exc_value = tstate->curexc_value; + exc_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; + Py_DECREF(exc_type); + Py_XDECREF(exc_value); + Py_XDECREF(exc_tb); + return 0; + } else { + return -1; + } + } + return 0; +#else + if (unlikely(PyErr_Occurred())) { + if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) { + PyErr_Clear(); + return 0; + } else { + return -1; + } + } + return 0; +#endif +} + +/* UnpackItemEndCheck */ +static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) { + if (unlikely(retval)) { + Py_DECREF(retval); + __Pyx_RaiseTooManyValuesError(expected); + return -1; + } else { + return __Pyx_IterFinish(); + } + return 0; +} + +/* SetItemInt */ +static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) { + int r; + if (!j) return -1; + r = PyObject_SetItem(o, j, v); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list, + CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o)); + if ((!boundscheck) || likely((n >= 0) & (n < PyList_GET_SIZE(o)))) { + PyObject* old = PyList_GET_ITEM(o, n); + Py_INCREF(v); + PyList_SET_ITEM(o, n, v); + Py_DECREF(old); + return 1; + } + } else { + PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; + if (likely(m && m->sq_ass_item)) { + if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { + Py_ssize_t l = m->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return -1; + PyErr_Clear(); + } + } + return m->sq_ass_item(o, i, v); + } + } +#else +#if CYTHON_COMPILING_IN_PYPY + if (is_list || (PySequence_Check(o) && !PyDict_Check(o))) { +#else + if (is_list || PySequence_Check(o)) { +#endif + return PySequence_SetItem(o, i, v); + } +#endif + return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v); +} + +/* PyFunctionFastCall */ + #if CYTHON_FAST_PYCALL +#include "frameobject.h" +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = f->f_localsplus; + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { + return NULL; + } + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif +#endif + +/* PyObjectCallMethO */ + #if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = PyCFunction_GET_FUNCTION(func); + self = PyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallNoArg */ + #if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, NULL, 0); + } +#endif +#ifdef __Pyx_CyFunction_USED + if (likely(PyCFunction_Check(func) || __Pyx_TypeCheck(func, __pyx_CyFunctionType))) { +#else + if (likely(PyCFunction_Check(func))) { +#endif + if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { + return __Pyx_PyObject_CallMethO(func, NULL); + } + } + return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL); +} +#endif + +/* PyCFunctionFastCall */ + #if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { + PyCFunctionObject *func = (PyCFunctionObject*)func_obj; + PyCFunction meth = PyCFunction_GET_FUNCTION(func); + PyObject *self = PyCFunction_GET_SELF(func); + int flags = PyCFunction_GET_FLAGS(func); + assert(PyCFunction_Check(func)); + assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS))); + assert(nargs >= 0); + assert(nargs == 0 || args != NULL); + /* _PyCFunction_FastCallDict() must not be called with an exception set, + because it may clear it (directly or indirectly) and so the + caller loses its exception */ + assert(!PyErr_Occurred()); + if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { + return (*((__Pyx_PyCFunctionFastWithKeywords)meth)) (self, args, nargs, NULL); + } else { + return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs); + } +} +#endif + +/* PyObjectCallOneArg */ + #if CYTHON_COMPILING_IN_CPYTHON +static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_New(1); + if (unlikely(!args)) return NULL; + Py_INCREF(arg); + PyTuple_SET_ITEM(args, 0, arg); + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, &arg, 1); + } +#endif + if (likely(PyCFunction_Check(func))) { + if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { + return __Pyx_PyObject_CallMethO(func, arg); +#if CYTHON_FAST_PYCCALL + } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { + return __Pyx_PyCFunction_FastCall(func, &arg, 1); +#endif + } + } + return __Pyx__PyObject_CallOneArg(func, arg); +} +#else +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_Pack(1, arg); + if (unlikely(!args)) return NULL; + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +#endif + +/* PyObjectCallMethod0 */ + static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) { + PyObject *method, *result = NULL; + method = __Pyx_PyObject_GetAttrStr(obj, method_name); + if (unlikely(!method)) goto bad; +#if CYTHON_UNPACK_METHODS + if (likely(PyMethod_Check(method))) { + PyObject *self = PyMethod_GET_SELF(method); + if (likely(self)) { + PyObject *function = PyMethod_GET_FUNCTION(method); + result = __Pyx_PyObject_CallOneArg(function, self); + Py_DECREF(method); + return result; + } + } +#endif + result = __Pyx_PyObject_CallNoArg(method); + Py_DECREF(method); +bad: + return result; +} + +/* RaiseNoneIterError */ + static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* UnpackTupleError */ + static void __Pyx_UnpackTupleError(PyObject *t, Py_ssize_t index) { + if (t == Py_None) { + __Pyx_RaiseNoneNotIterableError(); + } else if (PyTuple_GET_SIZE(t) < index) { + __Pyx_RaiseNeedMoreValuesError(PyTuple_GET_SIZE(t)); + } else { + __Pyx_RaiseTooManyValuesError(index); + } +} + +/* UnpackTuple2 */ + static CYTHON_INLINE int __Pyx_unpack_tuple2_exact( + PyObject* tuple, PyObject** pvalue1, PyObject** pvalue2, int decref_tuple) { + PyObject *value1 = NULL, *value2 = NULL; +#if CYTHON_COMPILING_IN_PYPY + value1 = PySequence_ITEM(tuple, 0); if (unlikely(!value1)) goto bad; + value2 = PySequence_ITEM(tuple, 1); if (unlikely(!value2)) goto bad; +#else + value1 = PyTuple_GET_ITEM(tuple, 0); Py_INCREF(value1); + value2 = PyTuple_GET_ITEM(tuple, 1); Py_INCREF(value2); +#endif + if (decref_tuple) { + Py_DECREF(tuple); + } + *pvalue1 = value1; + *pvalue2 = value2; + return 0; +#if CYTHON_COMPILING_IN_PYPY +bad: + Py_XDECREF(value1); + Py_XDECREF(value2); + if (decref_tuple) { Py_XDECREF(tuple); } + return -1; +#endif +} +static int __Pyx_unpack_tuple2_generic(PyObject* tuple, PyObject** pvalue1, PyObject** pvalue2, + int has_known_size, int decref_tuple) { + Py_ssize_t index; + PyObject *value1 = NULL, *value2 = NULL, *iter = NULL; + iternextfunc iternext; + iter = PyObject_GetIter(tuple); + if (unlikely(!iter)) goto bad; + if (decref_tuple) { Py_DECREF(tuple); tuple = NULL; } + iternext = Py_TYPE(iter)->tp_iternext; + value1 = iternext(iter); if (unlikely(!value1)) { index = 0; goto unpacking_failed; } + value2 = iternext(iter); if (unlikely(!value2)) { index = 1; goto unpacking_failed; } + if (!has_known_size && unlikely(__Pyx_IternextUnpackEndCheck(iternext(iter), 2))) goto bad; + Py_DECREF(iter); + *pvalue1 = value1; + *pvalue2 = value2; + return 0; +unpacking_failed: + if (!has_known_size && __Pyx_IterFinish() == 0) + __Pyx_RaiseNeedMoreValuesError(index); +bad: + Py_XDECREF(iter); + Py_XDECREF(value1); + Py_XDECREF(value2); + if (decref_tuple) { Py_XDECREF(tuple); } + return -1; +} + +/* dict_iter */ + static CYTHON_INLINE PyObject* __Pyx_dict_iterator(PyObject* iterable, int is_dict, PyObject* method_name, + Py_ssize_t* p_orig_length, int* p_source_is_dict) { + is_dict = is_dict || likely(PyDict_CheckExact(iterable)); + *p_source_is_dict = is_dict; + if (is_dict) { +#if !CYTHON_COMPILING_IN_PYPY + *p_orig_length = PyDict_Size(iterable); + Py_INCREF(iterable); + return iterable; +#elif PY_MAJOR_VERSION >= 3 + static PyObject *py_items = NULL, *py_keys = NULL, *py_values = NULL; + PyObject **pp = NULL; + if (method_name) { + const char *name = PyUnicode_AsUTF8(method_name); + if (strcmp(name, "iteritems") == 0) pp = &py_items; + else if (strcmp(name, "iterkeys") == 0) pp = &py_keys; + else if (strcmp(name, "itervalues") == 0) pp = &py_values; + if (pp) { + if (!*pp) { + *pp = PyUnicode_FromString(name + 4); + if (!*pp) + return NULL; + } + method_name = *pp; + } + } +#endif + } + *p_orig_length = 0; + if (method_name) { + PyObject* iter; + iterable = __Pyx_PyObject_CallMethod0(iterable, method_name); + if (!iterable) + return NULL; +#if !CYTHON_COMPILING_IN_PYPY + if (PyTuple_CheckExact(iterable) || PyList_CheckExact(iterable)) + return iterable; +#endif + iter = PyObject_GetIter(iterable); + Py_DECREF(iterable); + return iter; + } + return PyObject_GetIter(iterable); +} +static CYTHON_INLINE int __Pyx_dict_iter_next( + PyObject* iter_obj, CYTHON_NCP_UNUSED Py_ssize_t orig_length, CYTHON_NCP_UNUSED Py_ssize_t* ppos, + PyObject** pkey, PyObject** pvalue, PyObject** pitem, int source_is_dict) { + PyObject* next_item; +#if !CYTHON_COMPILING_IN_PYPY + if (source_is_dict) { + PyObject *key, *value; + if (unlikely(orig_length != PyDict_Size(iter_obj))) { + PyErr_SetString(PyExc_RuntimeError, "dictionary changed size during iteration"); + return -1; + } + if (unlikely(!PyDict_Next(iter_obj, ppos, &key, &value))) { + return 0; + } + if (pitem) { + PyObject* tuple = PyTuple_New(2); + if (unlikely(!tuple)) { + return -1; + } + Py_INCREF(key); + Py_INCREF(value); + PyTuple_SET_ITEM(tuple, 0, key); + PyTuple_SET_ITEM(tuple, 1, value); + *pitem = tuple; + } else { + if (pkey) { + Py_INCREF(key); + *pkey = key; + } + if (pvalue) { + Py_INCREF(value); + *pvalue = value; + } + } + return 1; + } else if (PyTuple_CheckExact(iter_obj)) { + Py_ssize_t pos = *ppos; + if (unlikely(pos >= PyTuple_GET_SIZE(iter_obj))) return 0; + *ppos = pos + 1; + next_item = PyTuple_GET_ITEM(iter_obj, pos); + Py_INCREF(next_item); + } else if (PyList_CheckExact(iter_obj)) { + Py_ssize_t pos = *ppos; + if (unlikely(pos >= PyList_GET_SIZE(iter_obj))) return 0; + *ppos = pos + 1; + next_item = PyList_GET_ITEM(iter_obj, pos); + Py_INCREF(next_item); + } else +#endif + { + next_item = PyIter_Next(iter_obj); + if (unlikely(!next_item)) { + return __Pyx_IterFinish(); + } + } + if (pitem) { + *pitem = next_item; + } else if (pkey && pvalue) { + if (__Pyx_unpack_tuple2(next_item, pkey, pvalue, source_is_dict, source_is_dict, 1)) + return -1; + } else if (pkey) { + *pkey = next_item; + } else { + *pvalue = next_item; + } + return 1; +} + +/* GetItemInt */ + static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { + PyObject *r; + if (!j) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyList_GET_SIZE(o); + } + if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyTuple_GET_SIZE(o); + } + if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); + if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) { + PyObject *r = PyList_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } + else if (PyTuple_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); + if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } else { + PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; + if (likely(m && m->sq_item)) { + if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { + Py_ssize_t l = m->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return NULL; + PyErr_Clear(); + } + } + return m->sq_item(o, i); + } + } +#else + if (is_list || PySequence_Check(o)) { + return PySequence_GetItem(o, i); + } +#endif + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +} + +/* MemviewSliceInit */ + static int +__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference) +{ + __Pyx_RefNannyDeclarations + int i, retval=-1; + Py_buffer *buf = &memview->view; + __Pyx_RefNannySetupContext("init_memviewslice", 0); + if (!buf) { + PyErr_SetString(PyExc_ValueError, + "buf is NULL."); + goto fail; + } else if (memviewslice->memview || memviewslice->data) { + PyErr_SetString(PyExc_ValueError, + "memviewslice is already initialized!"); + goto fail; + } + if (buf->strides) { + for (i = 0; i < ndim; i++) { + memviewslice->strides[i] = buf->strides[i]; + } + } else { + Py_ssize_t stride = buf->itemsize; + for (i = ndim - 1; i >= 0; i--) { + memviewslice->strides[i] = stride; + stride *= buf->shape[i]; + } + } + for (i = 0; i < ndim; i++) { + memviewslice->shape[i] = buf->shape[i]; + if (buf->suboffsets) { + memviewslice->suboffsets[i] = buf->suboffsets[i]; + } else { + memviewslice->suboffsets[i] = -1; + } + } + memviewslice->memview = memview; + memviewslice->data = (char *)buf->buf; + if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { + Py_INCREF(memview); + } + retval = 0; + goto no_fail; +fail: + memviewslice->memview = 0; + memviewslice->data = 0; + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} +#ifndef Py_NO_RETURN +#define Py_NO_RETURN +#endif +static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { + va_list vargs; + char msg[200]; +#ifdef HAVE_STDARG_PROTOTYPES + va_start(vargs, fmt); +#else + va_start(vargs); +#endif + vsnprintf(msg, 200, fmt, vargs); + va_end(vargs); + Py_FatalError(msg); +} +static CYTHON_INLINE int +__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)++; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE int +__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)--; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE void +__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) +{ + int first_time; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (!memview || (PyObject *) memview == Py_None) + return; + if (__pyx_get_slice_count(memview) < 0) + __pyx_fatalerror("Acquisition count is %d (line %d)", + __pyx_get_slice_count(memview), lineno); + first_time = __pyx_add_acquisition_count(memview) == 0; + if (first_time) { + if (have_gil) { + Py_INCREF((PyObject *) memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_INCREF((PyObject *) memview); + PyGILState_Release(_gilstate); + } + } +} +static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, + int have_gil, int lineno) { + int last_time; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (!memview ) { + return; + } else if ((PyObject *) memview == Py_None) { + memslice->memview = NULL; + return; + } + if (__pyx_get_slice_count(memview) <= 0) + __pyx_fatalerror("Acquisition count is %d (line %d)", + __pyx_get_slice_count(memview), lineno); + last_time = __pyx_sub_acquisition_count(memview) == 1; + memslice->data = NULL; + if (last_time) { + if (have_gil) { + Py_CLEAR(memslice->memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_CLEAR(memslice->memview); + PyGILState_Release(_gilstate); + } + } else { + memslice->memview = NULL; + } +} + +/* ArgTypeTest */ + static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + else if (exact) { + #if PY_MAJOR_VERSION == 2 + if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(__Pyx_TypeCheck(obj, type))) return 1; + } + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); + return 0; +} + +/* None */ + static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { + Py_ssize_t q = a / b; + Py_ssize_t r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* GetAttr */ + static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { +#if CYTHON_USE_TYPE_SLOTS +#if PY_MAJOR_VERSION >= 3 + if (likely(PyUnicode_Check(n))) +#else + if (likely(PyString_Check(n))) +#endif + return __Pyx_PyObject_GetAttrStr(o, n); +#endif + return PyObject_GetAttr(o, n); +} + +/* decode_c_string */ + static CYTHON_INLINE PyObject* __Pyx_decode_c_string( + const char* cstring, Py_ssize_t start, Py_ssize_t stop, + const char* encoding, const char* errors, + PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { + Py_ssize_t length; + if (unlikely((start < 0) | (stop < 0))) { + size_t slen = strlen(cstring); + if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) { + PyErr_SetString(PyExc_OverflowError, + "c-string too long to convert to Python"); + return NULL; + } + length = (Py_ssize_t) slen; + if (start < 0) { + start += length; + if (start < 0) + start = 0; + } + if (stop < 0) + stop += length; + } + length = stop - start; + if (unlikely(length <= 0)) + return PyUnicode_FromUnicode(NULL, 0); + cstring += start; + if (decode_func) { + return decode_func(cstring, length, errors); + } else { + return PyUnicode_Decode(cstring, length, encoding, errors); + } +} + +/* PyErrExceptionMatches */ + #if CYTHON_FAST_THREAD_STATE +static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; icurexc_type; + if (exc_type == err) return 1; + if (unlikely(!exc_type)) return 0; + if (unlikely(PyTuple_Check(err))) + return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); + return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); +} +#endif + +/* GetAttr3 */ + static PyObject *__Pyx_GetAttr3Default(PyObject *d) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + return NULL; + __Pyx_PyErr_Clear(); + Py_INCREF(d); + return d; +} +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { + PyObject *r = __Pyx_GetAttr(o, n); + return (likely(r)) ? r : __Pyx_GetAttr3Default(d); +} + +/* GetModuleGlobalName */ + static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { + PyObject *result; +#if !CYTHON_AVOID_BORROWED_REFS + result = PyDict_GetItem(__pyx_d, name); + if (likely(result)) { + Py_INCREF(result); + } else { +#else + result = PyObject_GetItem(__pyx_d, name); + if (!result) { + PyErr_Clear(); +#endif + result = __Pyx_GetBuiltinName(name); + } + return result; +} + +/* ExtTypeTest */ + static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(__Pyx_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +/* SaveResetException */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + #if PY_VERSION_HEX >= 0x030700A2 + *type = tstate->exc_state.exc_type; + *value = tstate->exc_state.exc_value; + *tb = tstate->exc_state.exc_traceback; + #else + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + #endif + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if PY_VERSION_HEX >= 0x030700A2 + tmp_type = tstate->exc_state.exc_type; + tmp_value = tstate->exc_state.exc_value; + tmp_tb = tstate->exc_state.exc_traceback; + tstate->exc_state.exc_type = type; + tstate->exc_state.exc_value = value; + tstate->exc_state.exc_traceback = tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +#endif + +/* GetException */ + #if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { +#endif + PyObject *local_type, *local_value, *local_tb; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + #if PY_MAJOR_VERSION >= 3 + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } + #endif + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + #if PY_VERSION_HEX >= 0x030700A2 + tmp_type = tstate->exc_state.exc_type; + tmp_value = tstate->exc_state.exc_value; + tmp_tb = tstate->exc_state.exc_traceback; + tstate->exc_state.exc_type = local_type; + tstate->exc_state.exc_value = local_value; + tstate->exc_state.exc_traceback = local_tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +} + +/* SwapException */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if PY_VERSION_HEX >= 0x030700A2 + tmp_type = tstate->exc_state.exc_type; + tmp_value = tstate->exc_state.exc_value; + tmp_tb = tstate->exc_state.exc_traceback; + tstate->exc_state.exc_type = *type; + tstate->exc_state.exc_value = *value; + tstate->exc_state.exc_traceback = *tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = *type; + tstate->exc_value = *value; + tstate->exc_traceback = *tb; + #endif + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); + PyErr_SetExcInfo(*type, *value, *tb); + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#endif + +/* Import */ + static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_MAJOR_VERSION < 3 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.')) { + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_MAJOR_VERSION < 3 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +/* PyIntBinop */ + #if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) { + #if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(op1))) { + const long b = intval; + long x; + long a = PyInt_AS_LONG(op1); + x = (long)((unsigned long)a + b); + if (likely((x^a) >= 0 || (x^b) >= 0)) + return PyInt_FromLong(x); + return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + #endif + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + const long b = intval; + long a, x; +#ifdef HAVE_LONG_LONG + const PY_LONG_LONG llb = intval; + PY_LONG_LONG lla, llx; +#endif + const digit* digits = ((PyLongObject*)op1)->ob_digit; + const Py_ssize_t size = Py_SIZE(op1); + if (likely(__Pyx_sst_abs(size) <= 1)) { + a = likely(size) ? digits[0] : 0; + if (size == -1) a = -a; + } else { + switch (size) { + case -2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case 2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case -3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case 3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case -4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case 4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + default: return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + } + x = a + b; + return PyLong_FromLong(x); +#ifdef HAVE_LONG_LONG + long_long: + llx = lla + llb; + return PyLong_FromLongLong(llx); +#endif + + + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; + double a = PyFloat_AS_DOUBLE(op1); + double result; + PyFPE_START_PROTECT("add", return NULL) + result = ((double)a) + (double)b; + PyFPE_END_PROTECT(result) + return PyFloat_FromDouble(result); + } + return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); +} +#endif + +/* None */ + static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { + PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); +} + +/* None */ + static CYTHON_INLINE long __Pyx_div_long(long a, long b) { + long q = a / b; + long r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* WriteUnraisableException */ + static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno, + CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename, + int full_traceback, CYTHON_UNUSED int nogil) { + PyObject *old_exc, *old_val, *old_tb; + PyObject *ctx; + __Pyx_PyThreadState_declare +#ifdef WITH_THREAD + PyGILState_STATE state; + if (nogil) + state = PyGILState_Ensure(); +#ifdef _MSC_VER + else state = (PyGILState_STATE)-1; +#endif +#endif + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&old_exc, &old_val, &old_tb); + if (full_traceback) { + Py_XINCREF(old_exc); + Py_XINCREF(old_val); + Py_XINCREF(old_tb); + __Pyx_ErrRestore(old_exc, old_val, old_tb); + PyErr_PrintEx(1); + } + #if PY_MAJOR_VERSION < 3 + ctx = PyString_FromString(name); + #else + ctx = PyUnicode_FromString(name); + #endif + __Pyx_ErrRestore(old_exc, old_val, old_tb); + if (!ctx) { + PyErr_WriteUnraisable(Py_None); + } else { + PyErr_WriteUnraisable(ctx); + Py_DECREF(ctx); + } +#ifdef WITH_THREAD + if (nogil) + PyGILState_Release(state); +#endif +} + +/* ImportFrom */ + static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { + PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); + if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { + PyErr_Format(PyExc_ImportError, + #if PY_MAJOR_VERSION < 3 + "cannot import name %.230s", PyString_AS_STRING(name)); + #else + "cannot import name %S", name); + #endif + } + return value; +} + +/* HasAttr */ + static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { + PyObject *r; + if (unlikely(!__Pyx_PyBaseString_Check(n))) { + PyErr_SetString(PyExc_TypeError, + "hasattr(): attribute name must be string"); + return -1; + } + r = __Pyx_GetAttr(o, n); + if (unlikely(!r)) { + PyErr_Clear(); + return 0; + } else { + Py_DECREF(r); + return 1; + } +} + +/* SetVTable */ + static int __Pyx_SetVtable(PyObject *dict, void *vtable) { +#if PY_VERSION_HEX >= 0x02070000 + PyObject *ob = PyCapsule_New(vtable, 0, 0); +#else + PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); +#endif + if (!ob) + goto bad; + if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) + goto bad; + Py_DECREF(ob); + return 0; +bad: + Py_XDECREF(ob); + return -1; +} + +/* SetupReduce */ + static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { + int ret; + PyObject *name_attr; + name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2); + if (likely(name_attr)) { + ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); + } else { + ret = -1; + } + if (unlikely(ret < 0)) { + PyErr_Clear(); + ret = 0; + } + Py_XDECREF(name_attr); + return ret; +} +static int __Pyx_setup_reduce(PyObject* type_obj) { + int ret = 0; + PyObject *object_reduce = NULL; + PyObject *object_reduce_ex = NULL; + PyObject *reduce = NULL; + PyObject *reduce_ex = NULL; + PyObject *reduce_cython = NULL; + PyObject *setstate = NULL; + PyObject *setstate_cython = NULL; +#if CYTHON_USE_PYTYPE_LOOKUP + if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto GOOD; +#else + if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto GOOD; +#endif +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD; +#else + object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD; +#endif + reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto BAD; + if (reduce_ex == object_reduce_ex) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD; +#else + object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD; +#endif + reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto BAD; + if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { + reduce_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_cython); if (unlikely(!reduce_cython)) goto BAD; + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto BAD; + setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); + if (!setstate) PyErr_Clear(); + if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { + setstate_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate_cython); if (unlikely(!setstate_cython)) goto BAD; + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto BAD; + } + PyType_Modified((PyTypeObject*)type_obj); + } + } + goto GOOD; +BAD: + if (!PyErr_Occurred()) + PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name); + ret = -1; +GOOD: +#if !CYTHON_USE_PYTYPE_LOOKUP + Py_XDECREF(object_reduce); + Py_XDECREF(object_reduce_ex); +#endif + Py_XDECREF(reduce); + Py_XDECREF(reduce_ex); + Py_XDECREF(reduce_cython); + Py_XDECREF(setstate); + Py_XDECREF(setstate_cython); + return ret; +} + +/* FetchCommonType */ + static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { + PyObject* fake_module; + PyTypeObject* cached_type = NULL; + fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI); + if (!fake_module) return NULL; + Py_INCREF(fake_module); + cached_type = (PyTypeObject*) PyObject_GetAttrString(fake_module, type->tp_name); + if (cached_type) { + if (!PyType_Check((PyObject*)cached_type)) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s is not a type object", + type->tp_name); + goto bad; + } + if (cached_type->tp_basicsize != type->tp_basicsize) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s has the wrong size, try recompiling", + type->tp_name); + goto bad; + } + } else { + if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; + PyErr_Clear(); + if (PyType_Ready(type) < 0) goto bad; + if (PyObject_SetAttrString(fake_module, type->tp_name, (PyObject*) type) < 0) + goto bad; + Py_INCREF(type); + cached_type = type; + } +done: + Py_DECREF(fake_module); + return cached_type; +bad: + Py_XDECREF(cached_type); + cached_type = NULL; + goto done; +} + +/* CythonFunction */ + static PyObject * +__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *closure) +{ + if (unlikely(op->func_doc == NULL)) { + if (op->func.m_ml->ml_doc) { +#if PY_MAJOR_VERSION >= 3 + op->func_doc = PyUnicode_FromString(op->func.m_ml->ml_doc); +#else + op->func_doc = PyString_FromString(op->func.m_ml->ml_doc); +#endif + if (unlikely(op->func_doc == NULL)) + return NULL; + } else { + Py_INCREF(Py_None); + return Py_None; + } + } + Py_INCREF(op->func_doc); + return op->func_doc; +} +static int +__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value) +{ + PyObject *tmp = op->func_doc; + if (value == NULL) { + value = Py_None; + } + Py_INCREF(value); + op->func_doc = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op) +{ + if (unlikely(op->func_name == NULL)) { +#if PY_MAJOR_VERSION >= 3 + op->func_name = PyUnicode_InternFromString(op->func.m_ml->ml_name); +#else + op->func_name = PyString_InternFromString(op->func.m_ml->ml_name); +#endif + if (unlikely(op->func_name == NULL)) + return NULL; + } + Py_INCREF(op->func_name); + return op->func_name; +} +static int +__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value) +{ + PyObject *tmp; +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) { +#else + if (unlikely(value == NULL || !PyString_Check(value))) { +#endif + PyErr_SetString(PyExc_TypeError, + "__name__ must be set to a string object"); + return -1; + } + tmp = op->func_name; + Py_INCREF(value); + op->func_name = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op) +{ + Py_INCREF(op->func_qualname); + return op->func_qualname; +} +static int +__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value) +{ + PyObject *tmp; +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) { +#else + if (unlikely(value == NULL || !PyString_Check(value))) { +#endif + PyErr_SetString(PyExc_TypeError, + "__qualname__ must be set to a string object"); + return -1; + } + tmp = op->func_qualname; + Py_INCREF(value); + op->func_qualname = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_self(__pyx_CyFunctionObject *m, CYTHON_UNUSED void *closure) +{ + PyObject *self; + self = m->func_closure; + if (self == NULL) + self = Py_None; + Py_INCREF(self); + return self; +} +static PyObject * +__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op) +{ + if (unlikely(op->func_dict == NULL)) { + op->func_dict = PyDict_New(); + if (unlikely(op->func_dict == NULL)) + return NULL; + } + Py_INCREF(op->func_dict); + return op->func_dict; +} +static int +__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value) +{ + PyObject *tmp; + if (unlikely(value == NULL)) { + PyErr_SetString(PyExc_TypeError, + "function's dictionary may not be deleted"); + return -1; + } + if (unlikely(!PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "setting function's dictionary to a non-dict"); + return -1; + } + tmp = op->func_dict; + Py_INCREF(value); + op->func_dict = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op) +{ + Py_INCREF(op->func_globals); + return op->func_globals; +} +static PyObject * +__Pyx_CyFunction_get_closure(CYTHON_UNUSED __pyx_CyFunctionObject *op) +{ + Py_INCREF(Py_None); + return Py_None; +} +static PyObject * +__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op) +{ + PyObject* result = (op->func_code) ? op->func_code : Py_None; + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) { + int result = 0; + PyObject *res = op->defaults_getter((PyObject *) op); + if (unlikely(!res)) + return -1; + #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + op->defaults_tuple = PyTuple_GET_ITEM(res, 0); + Py_INCREF(op->defaults_tuple); + op->defaults_kwdict = PyTuple_GET_ITEM(res, 1); + Py_INCREF(op->defaults_kwdict); + #else + op->defaults_tuple = PySequence_ITEM(res, 0); + if (unlikely(!op->defaults_tuple)) result = -1; + else { + op->defaults_kwdict = PySequence_ITEM(res, 1); + if (unlikely(!op->defaults_kwdict)) result = -1; + } + #endif + Py_DECREF(res); + return result; +} +static int +__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value) { + PyObject* tmp; + if (!value) { + value = Py_None; + } else if (value != Py_None && !PyTuple_Check(value)) { + PyErr_SetString(PyExc_TypeError, + "__defaults__ must be set to a tuple object"); + return -1; + } + Py_INCREF(value); + tmp = op->defaults_tuple; + op->defaults_tuple = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op) { + PyObject* result = op->defaults_tuple; + if (unlikely(!result)) { + if (op->defaults_getter) { + if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL; + result = op->defaults_tuple; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value) { + PyObject* tmp; + if (!value) { + value = Py_None; + } else if (value != Py_None && !PyDict_Check(value)) { + PyErr_SetString(PyExc_TypeError, + "__kwdefaults__ must be set to a dict object"); + return -1; + } + Py_INCREF(value); + tmp = op->defaults_kwdict; + op->defaults_kwdict = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op) { + PyObject* result = op->defaults_kwdict; + if (unlikely(!result)) { + if (op->defaults_getter) { + if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL; + result = op->defaults_kwdict; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value) { + PyObject* tmp; + if (!value || value == Py_None) { + value = NULL; + } else if (!PyDict_Check(value)) { + PyErr_SetString(PyExc_TypeError, + "__annotations__ must be set to a dict object"); + return -1; + } + Py_XINCREF(value); + tmp = op->func_annotations; + op->func_annotations = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op) { + PyObject* result = op->func_annotations; + if (unlikely(!result)) { + result = PyDict_New(); + if (unlikely(!result)) return NULL; + op->func_annotations = result; + } + Py_INCREF(result); + return result; +} +static PyGetSetDef __pyx_CyFunction_getsets[] = { + {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {(char *) "__doc__", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0}, + {(char *) "__self__", (getter)__Pyx_CyFunction_get_self, 0, 0, 0}, + {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0}, + {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0}, + {0, 0, 0, 0, 0} +}; +static PyMemberDef __pyx_CyFunction_members[] = { + {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), PY_WRITE_RESTRICTED, 0}, + {0, 0, 0, 0, 0} +}; +static PyObject * +__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, CYTHON_UNUSED PyObject *args) +{ +#if PY_MAJOR_VERSION >= 3 + return PyUnicode_FromString(m->func.m_ml->ml_name); +#else + return PyString_FromString(m->func.m_ml->ml_name); +#endif +} +static PyMethodDef __pyx_CyFunction_methods[] = { + {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0}, + {0, 0, 0, 0} +}; +#if PY_VERSION_HEX < 0x030500A0 +#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist) +#else +#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func.m_weakreflist) +#endif +static PyObject *__Pyx_CyFunction_New(PyTypeObject *type, PyMethodDef *ml, int flags, PyObject* qualname, + PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { + __pyx_CyFunctionObject *op = PyObject_GC_New(__pyx_CyFunctionObject, type); + if (op == NULL) + return NULL; + op->flags = flags; + __Pyx_CyFunction_weakreflist(op) = NULL; + op->func.m_ml = ml; + op->func.m_self = (PyObject *) op; + Py_XINCREF(closure); + op->func_closure = closure; + Py_XINCREF(module); + op->func.m_module = module; + op->func_dict = NULL; + op->func_name = NULL; + Py_INCREF(qualname); + op->func_qualname = qualname; + op->func_doc = NULL; + op->func_classobj = NULL; + op->func_globals = globals; + Py_INCREF(op->func_globals); + Py_XINCREF(code); + op->func_code = code; + op->defaults_pyobjects = 0; + op->defaults = NULL; + op->defaults_tuple = NULL; + op->defaults_kwdict = NULL; + op->defaults_getter = NULL; + op->func_annotations = NULL; + PyObject_GC_Track(op); + return (PyObject *) op; +} +static int +__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m) +{ + Py_CLEAR(m->func_closure); + Py_CLEAR(m->func.m_module); + Py_CLEAR(m->func_dict); + Py_CLEAR(m->func_name); + Py_CLEAR(m->func_qualname); + Py_CLEAR(m->func_doc); + Py_CLEAR(m->func_globals); + Py_CLEAR(m->func_code); + Py_CLEAR(m->func_classobj); + Py_CLEAR(m->defaults_tuple); + Py_CLEAR(m->defaults_kwdict); + Py_CLEAR(m->func_annotations); + if (m->defaults) { + PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); + int i; + for (i = 0; i < m->defaults_pyobjects; i++) + Py_XDECREF(pydefaults[i]); + PyObject_Free(m->defaults); + m->defaults = NULL; + } + return 0; +} +static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m) +{ + if (__Pyx_CyFunction_weakreflist(m) != NULL) + PyObject_ClearWeakRefs((PyObject *) m); + __Pyx_CyFunction_clear(m); + PyObject_GC_Del(m); +} +static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m) +{ + PyObject_GC_UnTrack(m); + __Pyx__CyFunction_dealloc(m); +} +static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg) +{ + Py_VISIT(m->func_closure); + Py_VISIT(m->func.m_module); + Py_VISIT(m->func_dict); + Py_VISIT(m->func_name); + Py_VISIT(m->func_qualname); + Py_VISIT(m->func_doc); + Py_VISIT(m->func_globals); + Py_VISIT(m->func_code); + Py_VISIT(m->func_classobj); + Py_VISIT(m->defaults_tuple); + Py_VISIT(m->defaults_kwdict); + if (m->defaults) { + PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); + int i; + for (i = 0; i < m->defaults_pyobjects; i++) + Py_VISIT(pydefaults[i]); + } + return 0; +} +static PyObject *__Pyx_CyFunction_descr_get(PyObject *func, PyObject *obj, PyObject *type) +{ + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + if (m->flags & __Pyx_CYFUNCTION_STATICMETHOD) { + Py_INCREF(func); + return func; + } + if (m->flags & __Pyx_CYFUNCTION_CLASSMETHOD) { + if (type == NULL) + type = (PyObject *)(Py_TYPE(obj)); + return __Pyx_PyMethod_New(func, type, (PyObject *)(Py_TYPE(type))); + } + if (obj == Py_None) + obj = NULL; + return __Pyx_PyMethod_New(func, obj, type); +} +static PyObject* +__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) +{ +#if PY_MAJOR_VERSION >= 3 + return PyUnicode_FromFormat("", + op->func_qualname, (void *)op); +#else + return PyString_FromFormat("", + PyString_AsString(op->func_qualname), (void *)op); +#endif +} +static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) { + PyCFunctionObject* f = (PyCFunctionObject*)func; + PyCFunction meth = f->m_ml->ml_meth; + Py_ssize_t size; + switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) { + case METH_VARARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) + return (*meth)(self, arg); + break; + case METH_VARARGS | METH_KEYWORDS: + return (*(PyCFunctionWithKeywords)meth)(self, arg, kw); + case METH_NOARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { + size = PyTuple_GET_SIZE(arg); + if (likely(size == 0)) + return (*meth)(self, NULL); + PyErr_Format(PyExc_TypeError, + "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", + f->m_ml->ml_name, size); + return NULL; + } + break; + case METH_O: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { + size = PyTuple_GET_SIZE(arg); + if (likely(size == 1)) { + PyObject *result, *arg0; + #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + arg0 = PyTuple_GET_ITEM(arg, 0); + #else + arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL; + #endif + result = (*meth)(self, arg0); + #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_DECREF(arg0); + #endif + return result; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", + f->m_ml->ml_name, size); + return NULL; + } + break; + default: + PyErr_SetString(PyExc_SystemError, "Bad call flags in " + "__Pyx_CyFunction_Call. METH_OLDARGS is no " + "longer supported!"); + return NULL; + } + PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments", + f->m_ml->ml_name); + return NULL; +} +static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) { + return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw); +} +static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) { + PyObject *result; + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func; + if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { + Py_ssize_t argc; + PyObject *new_args; + PyObject *self; + argc = PyTuple_GET_SIZE(args); + new_args = PyTuple_GetSlice(args, 1, argc); + if (unlikely(!new_args)) + return NULL; + self = PyTuple_GetItem(args, 0); + if (unlikely(!self)) { + Py_DECREF(new_args); + return NULL; + } + result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw); + Py_DECREF(new_args); + } else { + result = __Pyx_CyFunction_Call(func, args, kw); + } + return result; +} +static PyTypeObject __pyx_CyFunctionType_type = { + PyVarObject_HEAD_INIT(0, 0) + "cython_function_or_method", + sizeof(__pyx_CyFunctionObject), + 0, + (destructor) __Pyx_CyFunction_dealloc, + 0, + 0, + 0, +#if PY_MAJOR_VERSION < 3 + 0, +#else + 0, +#endif + (reprfunc) __Pyx_CyFunction_repr, + 0, + 0, + 0, + 0, + __Pyx_CyFunction_CallAsMethod, + 0, + 0, + 0, + 0, + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC, + 0, + (traverseproc) __Pyx_CyFunction_traverse, + (inquiry) __Pyx_CyFunction_clear, + 0, +#if PY_VERSION_HEX < 0x030500A0 + offsetof(__pyx_CyFunctionObject, func_weakreflist), +#else + offsetof(PyCFunctionObject, m_weakreflist), +#endif + 0, + 0, + __pyx_CyFunction_methods, + __pyx_CyFunction_members, + __pyx_CyFunction_getsets, + 0, + 0, + __Pyx_CyFunction_descr_get, + 0, + offsetof(__pyx_CyFunctionObject, func_dict), + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, +#if PY_VERSION_HEX >= 0x030400a1 + 0, +#endif +}; +static int __pyx_CyFunction_init(void) { + __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type); + if (unlikely(__pyx_CyFunctionType == NULL)) { + return -1; + } + return 0; +} +static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults = PyObject_Malloc(size); + if (unlikely(!m->defaults)) + return PyErr_NoMemory(); + memset(m->defaults, 0, size); + m->defaults_pyobjects = pyobjects; + return m->defaults; +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_tuple = tuple; + Py_INCREF(tuple); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_kwdict = dict; + Py_INCREF(dict); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->func_annotations = dict; + Py_INCREF(dict); +} + +/* FusedFunction */ + static PyObject * +__pyx_FusedFunction_New(PyTypeObject *type, PyMethodDef *ml, int flags, + PyObject *qualname, PyObject *self, + PyObject *module, PyObject *globals, + PyObject *code) +{ + __pyx_FusedFunctionObject *fusedfunc = + (__pyx_FusedFunctionObject *) __Pyx_CyFunction_New(type, ml, flags, qualname, + self, module, globals, code); + if (!fusedfunc) + return NULL; + fusedfunc->__signatures__ = NULL; + fusedfunc->type = NULL; + fusedfunc->self = NULL; + return (PyObject *) fusedfunc; +} +static void +__pyx_FusedFunction_dealloc(__pyx_FusedFunctionObject *self) +{ + PyObject_GC_UnTrack(self); + Py_CLEAR(self->self); + Py_CLEAR(self->type); + Py_CLEAR(self->__signatures__); + __Pyx__CyFunction_dealloc((__pyx_CyFunctionObject *) self); +} +static int +__pyx_FusedFunction_traverse(__pyx_FusedFunctionObject *self, + visitproc visit, + void *arg) +{ + Py_VISIT(self->self); + Py_VISIT(self->type); + Py_VISIT(self->__signatures__); + return __Pyx_CyFunction_traverse((__pyx_CyFunctionObject *) self, visit, arg); +} +static int +__pyx_FusedFunction_clear(__pyx_FusedFunctionObject *self) +{ + Py_CLEAR(self->self); + Py_CLEAR(self->type); + Py_CLEAR(self->__signatures__); + return __Pyx_CyFunction_clear((__pyx_CyFunctionObject *) self); +} +static PyObject * +__pyx_FusedFunction_descr_get(PyObject *self, PyObject *obj, PyObject *type) +{ + __pyx_FusedFunctionObject *func, *meth; + func = (__pyx_FusedFunctionObject *) self; + if (func->self || func->func.flags & __Pyx_CYFUNCTION_STATICMETHOD) { + Py_INCREF(self); + return self; + } + if (obj == Py_None) + obj = NULL; + meth = (__pyx_FusedFunctionObject *) __pyx_FusedFunction_NewEx( + ((PyCFunctionObject *) func)->m_ml, + ((__pyx_CyFunctionObject *) func)->flags, + ((__pyx_CyFunctionObject *) func)->func_qualname, + ((__pyx_CyFunctionObject *) func)->func_closure, + ((PyCFunctionObject *) func)->m_module, + ((__pyx_CyFunctionObject *) func)->func_globals, + ((__pyx_CyFunctionObject *) func)->func_code); + if (!meth) + return NULL; + Py_XINCREF(func->func.func_classobj); + meth->func.func_classobj = func->func.func_classobj; + Py_XINCREF(func->__signatures__); + meth->__signatures__ = func->__signatures__; + Py_XINCREF(type); + meth->type = type; + Py_XINCREF(func->func.defaults_tuple); + meth->func.defaults_tuple = func->func.defaults_tuple; + if (func->func.flags & __Pyx_CYFUNCTION_CLASSMETHOD) + obj = type; + Py_XINCREF(obj); + meth->self = obj; + return (PyObject *) meth; +} +static PyObject * +_obj_to_str(PyObject *obj) +{ + if (PyType_Check(obj)) + return PyObject_GetAttr(obj, __pyx_n_s_name_2); + else + return PyObject_Str(obj); +} +static PyObject * +__pyx_FusedFunction_getitem(__pyx_FusedFunctionObject *self, PyObject *idx) +{ + PyObject *signature = NULL; + PyObject *unbound_result_func; + PyObject *result_func = NULL; + if (self->__signatures__ == NULL) { + PyErr_SetString(PyExc_TypeError, "Function is not fused"); + return NULL; + } + if (PyTuple_Check(idx)) { + PyObject *list = PyList_New(0); + Py_ssize_t n = PyTuple_GET_SIZE(idx); + PyObject *string = NULL; + PyObject *sep = NULL; + int i; + if (!list) + return NULL; + for (i = 0; i < n; i++) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + PyObject *item = PyTuple_GET_ITEM(idx, i); +#else + PyObject *item = PySequence_ITEM(idx, i); +#endif + string = _obj_to_str(item); +#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_DECREF(item); +#endif + if (!string || PyList_Append(list, string) < 0) + goto __pyx_err; + Py_DECREF(string); + } + sep = PyUnicode_FromString("|"); + if (sep) + signature = PyUnicode_Join(sep, list); +__pyx_err: +; + Py_DECREF(list); + Py_XDECREF(sep); + } else { + signature = _obj_to_str(idx); + } + if (!signature) + return NULL; + unbound_result_func = PyObject_GetItem(self->__signatures__, signature); + if (unbound_result_func) { + if (self->self || self->type) { + __pyx_FusedFunctionObject *unbound = (__pyx_FusedFunctionObject *) unbound_result_func; + Py_CLEAR(unbound->func.func_classobj); + Py_XINCREF(self->func.func_classobj); + unbound->func.func_classobj = self->func.func_classobj; + result_func = __pyx_FusedFunction_descr_get(unbound_result_func, + self->self, self->type); + } else { + result_func = unbound_result_func; + Py_INCREF(result_func); + } + } + Py_DECREF(signature); + Py_XDECREF(unbound_result_func); + return result_func; +} +static PyObject * +__pyx_FusedFunction_callfunction(PyObject *func, PyObject *args, PyObject *kw) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func; + int static_specialized = (cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD && + !((__pyx_FusedFunctionObject *) func)->__signatures__); + if (cyfunc->flags & __Pyx_CYFUNCTION_CCLASS && !static_specialized) { + return __Pyx_CyFunction_CallAsMethod(func, args, kw); + } else { + return __Pyx_CyFunction_Call(func, args, kw); + } +} +static PyObject * +__pyx_FusedFunction_call(PyObject *func, PyObject *args, PyObject *kw) +{ + __pyx_FusedFunctionObject *binding_func = (__pyx_FusedFunctionObject *) func; + Py_ssize_t argc = PyTuple_GET_SIZE(args); + PyObject *new_args = NULL; + __pyx_FusedFunctionObject *new_func = NULL; + PyObject *result = NULL; + PyObject *self = NULL; + int is_staticmethod = binding_func->func.flags & __Pyx_CYFUNCTION_STATICMETHOD; + int is_classmethod = binding_func->func.flags & __Pyx_CYFUNCTION_CLASSMETHOD; + if (binding_func->self) { + Py_ssize_t i; + new_args = PyTuple_New(argc + 1); + if (!new_args) + return NULL; + self = binding_func->self; +#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_INCREF(self); +#endif + Py_INCREF(self); + PyTuple_SET_ITEM(new_args, 0, self); + for (i = 0; i < argc; i++) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + PyObject *item = PyTuple_GET_ITEM(args, i); + Py_INCREF(item); +#else + PyObject *item = PySequence_ITEM(args, i); if (unlikely(!item)) goto bad; +#endif + PyTuple_SET_ITEM(new_args, i + 1, item); + } + args = new_args; + } else if (binding_func->type) { + if (argc < 1) { + PyErr_SetString(PyExc_TypeError, "Need at least one argument, 0 given."); + return NULL; + } +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + self = PyTuple_GET_ITEM(args, 0); +#else + self = PySequence_ITEM(args, 0); if (unlikely(!self)) return NULL; +#endif + } + if (self && !is_classmethod && !is_staticmethod) { + int is_instance = PyObject_IsInstance(self, binding_func->type); + if (unlikely(!is_instance)) { + PyErr_Format(PyExc_TypeError, + "First argument should be of type %.200s, got %.200s.", + ((PyTypeObject *) binding_func->type)->tp_name, + self->ob_type->tp_name); + goto bad; + } else if (unlikely(is_instance == -1)) { + goto bad; + } + } +#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_XDECREF(self); + self = NULL; +#endif + if (binding_func->__signatures__) { + PyObject *tup; + if (is_staticmethod && binding_func->func.flags & __Pyx_CYFUNCTION_CCLASS) { + tup = PyTuple_Pack(3, args, + kw == NULL ? Py_None : kw, + binding_func->func.defaults_tuple); + if (unlikely(!tup)) goto bad; + new_func = (__pyx_FusedFunctionObject *) __Pyx_CyFunction_CallMethod( + func, binding_func->__signatures__, tup, NULL); + } else { + tup = PyTuple_Pack(4, binding_func->__signatures__, args, + kw == NULL ? Py_None : kw, + binding_func->func.defaults_tuple); + if (unlikely(!tup)) goto bad; + new_func = (__pyx_FusedFunctionObject *) __pyx_FusedFunction_callfunction(func, tup, NULL); + } + Py_DECREF(tup); + if (unlikely(!new_func)) + goto bad; + Py_XINCREF(binding_func->func.func_classobj); + Py_CLEAR(new_func->func.func_classobj); + new_func->func.func_classobj = binding_func->func.func_classobj; + func = (PyObject *) new_func; + } + result = __pyx_FusedFunction_callfunction(func, args, kw); +bad: +#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_XDECREF(self); +#endif + Py_XDECREF(new_args); + Py_XDECREF((PyObject *) new_func); + return result; +} +static PyMemberDef __pyx_FusedFunction_members[] = { + {(char *) "__signatures__", + T_OBJECT, + offsetof(__pyx_FusedFunctionObject, __signatures__), + READONLY, + 0}, + {0, 0, 0, 0, 0}, +}; +static PyMappingMethods __pyx_FusedFunction_mapping_methods = { + 0, + (binaryfunc) __pyx_FusedFunction_getitem, + 0, +}; +static PyTypeObject __pyx_FusedFunctionType_type = { + PyVarObject_HEAD_INIT(0, 0) + "fused_cython_function", + sizeof(__pyx_FusedFunctionObject), + 0, + (destructor) __pyx_FusedFunction_dealloc, + 0, + 0, + 0, +#if PY_MAJOR_VERSION < 3 + 0, +#else + 0, +#endif + 0, + 0, + 0, + &__pyx_FusedFunction_mapping_methods, + 0, + (ternaryfunc) __pyx_FusedFunction_call, + 0, + 0, + 0, + 0, + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE, + 0, + (traverseproc) __pyx_FusedFunction_traverse, + (inquiry) __pyx_FusedFunction_clear, + 0, + 0, + 0, + 0, + 0, + __pyx_FusedFunction_members, + __pyx_CyFunction_getsets, + &__pyx_CyFunctionType_type, + 0, + __pyx_FusedFunction_descr_get, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, +#if PY_VERSION_HEX >= 0x030400a1 + 0, +#endif +}; +static int __pyx_FusedFunction_init(void) { + __pyx_FusedFunctionType = __Pyx_FetchCommonType(&__pyx_FusedFunctionType_type); + if (__pyx_FusedFunctionType == NULL) { + return -1; + } + return 0; +} + +/* CLineInTraceback */ + #ifndef CYTHON_CLINE_IN_TRACEBACK +static int __Pyx_CLineForTraceback(CYTHON_UNUSED PyThreadState *tstate, int c_line) { + PyObject *use_cline; + PyObject *ptype, *pvalue, *ptraceback; +#if CYTHON_COMPILING_IN_CPYTHON + PyObject **cython_runtime_dict; +#endif + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); +#if CYTHON_COMPILING_IN_CPYTHON + cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); + if (likely(cython_runtime_dict)) { + use_cline = PyDict_GetItem(*cython_runtime_dict, __pyx_n_s_cline_in_traceback); + } else +#endif + { + PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); + if (use_cline_obj) { + use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; + Py_DECREF(use_cline_obj); + } else { + PyErr_Clear(); + use_cline = NULL; + } + } + if (!use_cline) { + c_line = 0; + PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); + } + else if (PyObject_Not(use_cline) != 0) { + c_line = 0; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + return c_line; +} +#endif + +/* CodeObjectCache */ + static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +/* AddTraceback */ + #include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + if (c_line) { + c_line = __Pyx_CLineForTraceback(tstate, c_line); + } + py_code = __pyx_find_code_object(c_line ? -c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); + } + py_frame = PyFrame_New( + tstate, /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); + PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if ((0)) {} + view->obj = NULL; + Py_DECREF(obj); +} +#endif + + + /* MemviewSliceIsContig */ + static int +__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) +{ + int i, index, step, start; + Py_ssize_t itemsize = mvs.memview->view.itemsize; + if (order == 'F') { + step = 1; + start = 0; + } else { + step = -1; + start = ndim - 1; + } + for (i = 0; i < ndim; i++) { + index = start + step * i; + if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) + return 0; + itemsize *= mvs.shape[index]; + } + return 1; +} + +/* OverlappingSlices */ + static void +__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, + void **out_start, void **out_end, + int ndim, size_t itemsize) +{ + char *start, *end; + int i; + start = end = slice->data; + for (i = 0; i < ndim; i++) { + Py_ssize_t stride = slice->strides[i]; + Py_ssize_t extent = slice->shape[i]; + if (extent == 0) { + *out_start = *out_end = start; + return; + } else { + if (stride > 0) + end += stride * (extent - 1); + else + start += stride * (extent - 1); + } + } + *out_start = start; + *out_end = end + itemsize; +} +static int +__pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize) +{ + void *start1, *end1, *start2, *end2; + __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); + __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); + return (start1 < end2) && (start2 < end1); +} + +/* Capsule */ + static CYTHON_INLINE PyObject * +__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) +{ + PyObject *cobj; +#if PY_VERSION_HEX >= 0x02070000 + cobj = PyCapsule_New(p, sig, NULL); +#else + cobj = PyCObject_FromVoidPtr(p, NULL); +#endif + return cobj; +} + +/* IsLittleEndian */ + static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) +{ + union { + uint32_t u32; + uint8_t u8[4]; + } S; + S.u32 = 0x01020304; + return S.u8[0] == 4; +} + +/* BufferFormatCheck */ + static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t < '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparseable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number; + int ndim = ctx->head->field->type->ndim; +; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if (ctx->enc_type == *ts && got_Z == ctx->is_complex && + ctx->enc_packmode == ctx->new_packmode) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} + +/* TypeInfoCompare */ + static int +__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) +{ + int i; + if (!a || !b) + return 0; + if (a == b) + return 1; + if (a->size != b->size || a->typegroup != b->typegroup || + a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { + if (a->typegroup == 'H' || b->typegroup == 'H') { + return a->size == b->size; + } else { + return 0; + } + } + if (a->ndim) { + for (i = 0; i < a->ndim; i++) + if (a->arraysize[i] != b->arraysize[i]) + return 0; + } + if (a->typegroup == 'S') { + if (a->flags != b->flags) + return 0; + if (a->fields || b->fields) { + if (!(a->fields && b->fields)) + return 0; + for (i = 0; a->fields[i].type && b->fields[i].type; i++) { + __Pyx_StructField *field_a = a->fields + i; + __Pyx_StructField *field_b = b->fields + i; + if (field_a->offset != field_b->offset || + !__pyx_typeinfo_cmp(field_a->type, field_b->type)) + return 0; + } + return !a->fields[i].type && !b->fields[i].type; + } + } + return 1; +} + +/* MemviewSliceValidateAndInit */ + static int +__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) +{ + if (buf->shape[dim] <= 1) + return 1; + if (buf->strides) { + if (spec & __Pyx_MEMVIEW_CONTIG) { + if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { + if (buf->strides[dim] != sizeof(void *)) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly contiguous " + "in dimension %d.", dim); + goto fail; + } + } else if (buf->strides[dim] != buf->itemsize) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_FOLLOW) { + Py_ssize_t stride = buf->strides[dim]; + if (stride < 0) + stride = -stride; + if (stride < buf->itemsize) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + } else { + if (spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not contiguous in " + "dimension %d", dim); + goto fail; + } else if (spec & (__Pyx_MEMVIEW_PTR)) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not indirect in " + "dimension %d", dim); + goto fail; + } else if (buf->suboffsets) { + PyErr_SetString(PyExc_ValueError, + "Buffer exposes suboffsets but no strides"); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) +{ + if (spec & __Pyx_MEMVIEW_DIRECT) { + if (buf->suboffsets && buf->suboffsets[dim] >= 0) { + PyErr_Format(PyExc_ValueError, + "Buffer not compatible with direct access " + "in dimension %d.", dim); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_PTR) { + if (!buf->suboffsets || (buf->suboffsets && buf->suboffsets[dim] < 0)) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly accessible " + "in dimension %d.", dim); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) +{ + int i; + if (c_or_f_flag & __Pyx_IS_F_CONTIG) { + Py_ssize_t stride = 1; + for (i = 0; i < ndim; i++) { + if (stride * buf->itemsize != buf->strides[i] && + buf->shape[i] > 1) + { + PyErr_SetString(PyExc_ValueError, + "Buffer not fortran contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { + Py_ssize_t stride = 1; + for (i = ndim - 1; i >- 1; i--) { + if (stride * buf->itemsize != buf->strides[i] && + buf->shape[i] > 1) { + PyErr_SetString(PyExc_ValueError, + "Buffer not C contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } + return 1; +fail: + return 0; +} +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj) +{ + struct __pyx_memoryview_obj *memview, *new_memview; + __Pyx_RefNannyDeclarations + Py_buffer *buf; + int i, spec = 0, retval = -1; + __Pyx_BufFmt_Context ctx; + int from_memoryview = __pyx_memoryview_check(original_obj); + __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); + if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) + original_obj)->typeinfo)) { + memview = (struct __pyx_memoryview_obj *) original_obj; + new_memview = NULL; + } else { + memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + original_obj, buf_flags, 0, dtype); + new_memview = memview; + if (unlikely(!memview)) + goto fail; + } + buf = &memview->view; + if (buf->ndim != ndim) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + ndim, buf->ndim); + goto fail; + } + if (new_memview) { + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; + } + if ((unsigned) buf->itemsize != dtype->size) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " + "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", + buf->itemsize, + (buf->itemsize > 1) ? "s" : "", + dtype->name, + dtype->size, + (dtype->size > 1) ? "s" : ""); + goto fail; + } + for (i = 0; i < ndim; i++) { + spec = axes_specs[i]; + if (!__pyx_check_strides(buf, i, ndim, spec)) + goto fail; + if (!__pyx_check_suboffsets(buf, i, ndim, spec)) + goto fail; + } + if (buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, + new_memview != NULL) == -1)) { + goto fail; + } + retval = 0; + goto no_fail; +fail: + Py_XDECREF(new_memview); + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_short(PyObject *obj) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS, 1, + &__Pyx_TypeInfo_short, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_int(PyObject *obj) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS, 1, + &__Pyx_TypeInfo_int, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *obj) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS, 1, + &__Pyx_TypeInfo_long, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_PY_LONG_LONG(PyObject *obj) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS, 1, + &__Pyx_TypeInfo_PY_LONG_LONG, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_unsigned_char(PyObject *obj) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS, 1, + &__Pyx_TypeInfo_unsigned_char, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_char(PyObject *obj) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS, 1, + &__Pyx_TypeInfo_char, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_unsigned_int(PyObject *obj) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS, 1, + &__Pyx_TypeInfo_unsigned_int, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_double(PyObject *obj) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS, 2, + &__Pyx_TypeInfo_double, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *obj) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS, 1, + &__Pyx_TypeInfo_double, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* MemviewSliceCopyTemplate */ + static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object) +{ + __Pyx_RefNannyDeclarations + int i; + __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; + struct __pyx_memoryview_obj *from_memview = from_mvs->memview; + Py_buffer *buf = &from_memview->view; + PyObject *shape_tuple = NULL; + PyObject *temp_int = NULL; + struct __pyx_array_obj *array_obj = NULL; + struct __pyx_memoryview_obj *memview_obj = NULL; + __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); + for (i = 0; i < ndim; i++) { + if (from_mvs->suboffsets[i] >= 0) { + PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " + "indirect dimensions (axis %d)", i); + goto fail; + } + } + shape_tuple = PyTuple_New(ndim); + if (unlikely(!shape_tuple)) { + goto fail; + } + __Pyx_GOTREF(shape_tuple); + for(i = 0; i < ndim; i++) { + temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); + if(unlikely(!temp_int)) { + goto fail; + } else { + PyTuple_SET_ITEM(shape_tuple, i, temp_int); + temp_int = NULL; + } + } + array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); + if (unlikely(!array_obj)) { + goto fail; + } + __Pyx_GOTREF(array_obj); + memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + (PyObject *) array_obj, contig_flag, + dtype_is_object, + from_mvs->memview->typeinfo); + if (unlikely(!memview_obj)) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) + goto fail; + if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, + dtype_is_object) < 0)) + goto fail; + goto no_fail; +fail: + __Pyx_XDECREF(new_mvs.memview); + new_mvs.memview = NULL; + new_mvs.data = NULL; +no_fail: + __Pyx_XDECREF(shape_tuple); + __Pyx_XDECREF(temp_int); + __Pyx_XDECREF(array_obj); + __Pyx_RefNannyFinishContext(); + return new_mvs; +} + +/* BytesContains */ + static CYTHON_INLINE int __Pyx_BytesContains(PyObject* bytes, char character) { + const Py_ssize_t length = PyBytes_GET_SIZE(bytes); + char* char_start = PyBytes_AS_STRING(bytes); + return memchr(char_start, (unsigned char)character, (size_t)length) != NULL; +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* ImportNumPyArray */ + static PyObject* __Pyx__ImportNumPyArray(void) { + PyObject *numpy_module, *ndarray_object = NULL; + numpy_module = __Pyx_Import(__pyx_n_s_numpy, NULL, 0); + if (likely(numpy_module)) { + ndarray_object = PyObject_GetAttrString(numpy_module, "ndarray"); + Py_DECREF(numpy_module); + } + if (unlikely(!ndarray_object)) { + PyErr_Clear(); + } + if (unlikely(!ndarray_object || !PyObject_TypeCheck(ndarray_object, &PyType_Type))) { + Py_XDECREF(ndarray_object); + Py_INCREF(Py_None); + ndarray_object = Py_None; + } + return ndarray_object; +} +static CYTHON_INLINE PyObject* __Pyx_ImportNumPyArrayTypeIfAvailable(void) { + if (unlikely(!__pyx_numpy_ndarray)) { + __pyx_numpy_ndarray = __Pyx__ImportNumPyArray(); + } + Py_INCREF(__pyx_numpy_ndarray); + return __pyx_numpy_ndarray; +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) + case -2: + if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { + const char neg_one = (char) -1, const_zero = (char) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(char) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (char) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (char) 0; + case 1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0]) + case 2: + if (8 * sizeof(char) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) { + return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(char) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) { + return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(char) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) { + return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (char) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(char) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (char) 0; + case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(char, digit, +digits[0]) + case -2: + if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(char) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(char) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(char) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { + return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + } +#endif + if (sizeof(char) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + char val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (char) -1; + } + } else { + char val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (char) -1; + val = __Pyx_PyInt_As_char(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to char"); + return (char) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to char"); + return (char) -1; +} + +/* FastTypeChecks */ + #if CYTHON_COMPILING_IN_CPYTHON +static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { + while (a) { + a = a->tp_base; + if (a == b) + return 1; + } + return b == &PyBaseObject_Type; +} +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (a == b) return 1; + mro = a->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(a, b); +} +#if PY_MAJOR_VERSION == 2 +static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { + PyObject *exception, *value, *tb; + int res; + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&exception, &value, &tb); + res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + if (!res) { + res = PyObject_IsSubclass(err, exc_type2); + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + } + __Pyx_ErrRestore(exception, value, tb); + return res; +} +#else +static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { + int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; + if (!res) { + res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); + } + return res; +} +#endif +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) { + if (likely(err == exc_type)) return 1; + if (likely(PyExceptionClass_Check(err))) { + return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type); + } + return PyErr_GivenExceptionMatches(err, exc_type); +} +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) { + if (likely(err == exc_type1 || err == exc_type2)) return 1; + if (likely(PyExceptionClass_Check(err))) { + return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2); + } + return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2)); +} +#endif + +/* CheckBinaryVersion */ + static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +/* InitStrings */ + static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + if (PyObject_Hash(*t->p) == -1) + PyErr_Clear(); + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#if !CYTHON_PEP393_ENABLED +static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +} +#else +static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (likely(PyUnicode_IS_ASCII(o))) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +} +#endif +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { + return __Pyx_PyUnicode_AsStringAndSize(o, length); + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { +#if PY_MAJOR_VERSION >= 3 + if (PyLong_Check(result)) { + if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, + "__int__ returned non-int (type %.200s). " + "The ability to return an instance of a strict subclass of int " + "is deprecated, and may be removed in a future version of Python.", + Py_TYPE(result)->tp_name)) { + Py_DECREF(result); + return NULL; + } + return result; + } +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + type_name, type_name, Py_TYPE(result)->tp_name); + Py_DECREF(result); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x) || PyLong_Check(x))) +#else + if (likely(PyLong_Check(x))) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = m->nb_int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = m->nb_long(x); + } + #else + if (likely(m && m->nb_int)) { + name = "int"; + res = m->nb_int(x); + } + #endif +#else + if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { + res = PyNumber_Int(x); + } +#endif + if (likely(res)) { +#if PY_MAJOR_VERSION < 3 + if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { +#else + if (unlikely(!PyLong_CheckExact(res))) { +#endif + return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(x); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)b)->ob_digit; + const Py_ssize_t size = Py_SIZE(b); + if (likely(__Pyx_sst_abs(size) <= 1)) { + ival = likely(size) ? digits[0] : 0; + if (size == -1) ival = -ival; + return ival; + } else { + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/src/utils.pyx b/src/utils.pyx index 0ed36dcf..d071f8ab 100644 --- a/src/utils.pyx +++ b/src/utils.pyx @@ -7,15 +7,17 @@ ctypedef fused some_int: cython.int cython.long cython.longlong + cython.uchar + cython.char + cython.uint -def crammer_singer_psi(double[:,:] X, some_int[:] Y, double[:, :] out): +def crammer_singer_joint_feature(double[:,:] X, some_int[:] Y, double[:, :] out): cdef int y, i - for i in xrange(X.shape[0]): + for i in range(X.shape[0]): y = Y[i] - for j in xrange(X.shape[1]): + for j in range(X.shape[1]): out[y, j] += X[i, j] -# untested! def loss_augment_unaries(double[:,:] unary_potentials, some_int[:] y, double[:] class_weight): cdef int i cdef int n_states = unary_potentials.shape[1]