diff --git a/.editorconfig b/.editorconfig
new file mode 100644
index 0000000..cea7e16
--- /dev/null
+++ b/.editorconfig
@@ -0,0 +1,273 @@
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diff --git a/.github/workflows/gradle.yml b/.github/workflows/gradle.yml
new file mode 100644
index 0000000..3c48f59
--- /dev/null
+++ b/.github/workflows/gradle.yml
@@ -0,0 +1,47 @@
+name: CI
+
+on:
+ push:
+ branches: [ main ]
+ pull_request:
+ branches: [ main ]
+
+jobs:
+ build:
+ runs-on: ubuntu-latest
+ strategy:
+ matrix:
+ java: [ '11', '17', '21', '23' ]
+
+ steps:
+ - uses: actions/checkout@v2
+ - name: Setup Java
+ uses: actions/setup-java@v1
+ with:
+ java-version: ${{ matrix.java }}
+ - name: Build with Gradle
+ run: ./gradlew build
+
+ validateGradleWrapper:
+ runs-on: ubuntu-latest
+
+ steps:
+ - uses: actions/checkout@v2
+ - uses: gradle/wrapper-validation-action@v1.0.4
+
+ publishCoverage:
+ runs-on: ubuntu-latest
+
+ steps:
+ - uses: actions/checkout@v2
+ - name: Setup Java
+ uses: actions/setup-java@v1
+ with:
+ java-version: 11
+ - name: Build with Gradle
+ run: ./gradlew jacocoTestReport
+ - name: Upload coverage to Codecov
+ uses: codecov/codecov-action@v1
+ with:
+ files: ./build/reports/jacoco/test/jacocoTestReport.xml
+ name: codecov
diff --git a/.gitignore b/.gitignore
index 3291295..d055963 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
.*
+!.github/
*.class
*.o
@@ -6,6 +7,11 @@
!.gitignore
!.travis.yml
+!.editorconfig
build/
+out/
+classes/
data/
+
+src/test/datasets/
diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index 13f352e..0000000
--- a/.travis.yml
+++ /dev/null
@@ -1,5 +0,0 @@
-language: java
-jdk:
- - oraclejdk8
- - oraclejdk7
- - openjdk6
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..fb6f88b
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,11 @@
+## Contributing ##
+
+We are happy to receive pull-requests if you want to contribute new features or bugfixes to the project.
+Please use the code style settings that you can find in the [code-style directory](code-style/).
+If you use IntelliJ, please make sure it properly picks up the settings from the [EditorConfig file](.editorconfig)
+(this should be the default behavior though).
+
+If you want to thank the author for this library or want to support the maintenance work,
+we are happy to receive a donation.
+
+[](https://www.paypal.me/BenediktWaldvogel)
diff --git a/COPYRIGHT b/LICENSE
similarity index 96%
rename from COPYRIGHT
rename to LICENSE
index 94371bb..73dd579 100644
--- a/COPYRIGHT
+++ b/LICENSE
@@ -1,5 +1,5 @@
-Copyright (c) 2007-2014 The LIBLINEAR Project.
+Copyright (c) 2007-2022 The LIBLINEAR Project.
All rights reserved.
Redistribution and use in source and binary forms, with or without
diff --git a/README.md b/README.md
index eddafc0..b564d18 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,8 @@
-[](https://travis-ci.org/bwaldvogel/liblinear-java)
+[](https://github.com/bwaldvogel/liblinear-java/actions)
+[](http://maven-badges.herokuapp.com/maven-central/de.bwaldvogel/liblinear)
+[](https://codecov.io/gh/bwaldvogel/liblinear-java)
+[](https://opensource.org/licenses/BSD-3-Clause)
+[](https://www.paypal.me/BenediktWaldvogel)
This is the Java version of LIBLINEAR.
@@ -11,6 +15,20 @@ The upstream changelog can be found at
The upstream GitHub project can be found at
https://github.com/cjlin1/liblinear
+## Dependencies ##
+
+The only requirement is Java 11 or later.
+
+## Usage ##
+
+```xml
+
+ de.bwaldvogel
+ liblinear
+ 2.44
+
+```
+
Please be aware that the code would be written differently at various places, i.e.
- Java coding style,
@@ -30,7 +48,7 @@ for the following reasons:
new bugs that are caused by porting to Java.
- **Code Reviews**:
- It should be more easy to conduct code reviews since the sources can be compared to the original version.
+ It should be easier to conduct code reviews since the sources can be compared to the original version.
Below follows a slightly modified version of the original README file.
@@ -43,13 +61,23 @@ The three most important methods for programmatic usage that you might be intere
- `Linear.predict(…)`
- `Linear.predictProbability(…)`
+## Contributing ##
+
+Please read the [contributing guidelines](CONTRIBUTING.md) if you want to contribute code to the project.
+
+If you want to thank the author for this library or want to support the maintenance work,
+we are happy to receive a donation.
+
+[](https://www.paypal.me/BenediktWaldvogel)
+
-------------------------------------------------------------------------------
LIBLINEAR is a simple package for solving large-scale regularized linear
-classification and regression. It currently supports
+classification, regression and outlier detection. It currently supports
- L2-regularized logistic regression/L2-loss support vector classification/L1-loss support vector classification
- L1-regularized L2-loss support vector classification/L1-regularized logistic regression
-- L2-regularized L2-loss support vector regression/L1-loss support vector regression.
+- L2-regularized L2-loss support vector regression/L1-loss support vector regression
+- one-class support vector machine.
This document explains the usage of LIBLINEAR.
To get started, please read the **Quick Start** section first.
@@ -134,31 +162,42 @@ sparse data, use `-l 0` to keep the sparsity.
11 -- L2-regularized L2-loss support vector regression (primal)
12 -- L2-regularized L2-loss support vector regression (dual)
13 -- L2-regularized L1-loss support vector regression (dual)
+ for outlier detection
+ 21 -- one-class support vector machine (dual)
-c cost : set the parameter C (default 1)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
+ -n nu : set the parameter nu of one-class SVM (default 0.5)
-e epsilon : set tolerance of termination criterion
-s 0 and 2
|f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,
where f is the primal function and pos/neg are # of
positive/negative data (default 0.01)
-s 11
- |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001)
- -s 1, 3, 4 and 7
- Dual maximal violation <= eps; similar to libsvm (default 0.1)
+ |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.0001)
+ -s 1, 3, 4, 7, and 21
+ Dual maximal violation <= eps; similar to libsvm (default 0.1 except 0.01 for -s 21)
-s 5 and 6
|f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,
where f is the primal function (default 0.01)
- -s 12 and 13\n"
+ -s 12 and 13
|f'(alpha)|_1 <= eps |f'(alpha0)|,
where f is the dual function (default 0.1)
-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)
+ -R : not regularize the bias; must with -B 1 to have the bias; DON'T use this unless you know what it is
+ (for -s 0, 2, 5, 6, 11)
-wi weight: weights adjust the parameter C of different classes (see README for details)
-v n: n-fold cross validation mode
+ -C : find parameters (C for -s 0, 2 and C, p for -s 11)
-q : quiet mode (no outputs)
Option -v randomly splits the data into n parts and calculates cross
validation accuracy on them.
+Option -C conducts cross validation under different parameters and finds
+the best one. This option is supported only by -s 0, -s 2 (for finding
+C) and -s 11 (for finding C, p). If the solver is not specified, -s 2
+is used.
+
Formulations:
For L2-regularized logistic regression (-s 0), we solve
@@ -213,7 +252,24 @@ where
Q is a matrix with Q_ij = x_i^T x_j.
-If `bias >= 0`, w becomes `[w; w_{n+1}]` and x becomes `[x; bias]`.
+For one-class SVM dual (-s 21), we solve
+
+ min_alpha 0.5(alpha^T Q alpha)
+ s.t. 0 <= alpha_i <= 1 and \sum alpha_i = nu*l,
+
+where
+
+ Q is a matrix with Q_ij = x_i^T x_j.
+
+If `bias >= 0`, w becomes `[w; w_{n+1}]` and x becomes `[x; bias]`. For
+example, L2-regularized logistic regression (-s 0) becomes
+
+ min_w w^Tw/2 + (w_{n+1})^2/2 + C \sum log(1 + exp(-y_i [w; w_{n+1}]^T[x_i; bias]))
+
+Some may prefer not having `(w_{n+1})^2/2` (i.e., bias variable not
+regularized). For primal solvers (-s 0, 2, 5, 6, 11), we provide an
+option -R to remove `(w_{n+1})^2/2`. However, -R is generally not needed
+as for most data with/without `(w_{n+1})^2/2` give similar performances.
The primal-dual relationship implies that -s 1 and -s 2 give the same
model, -s 0 and -s 7 give the same, and -s 11 and -s 12 give the same.
@@ -262,12 +318,35 @@ Train linear SVM with L2-loss function.
Train a logistic regression model.
+ > train -s 21 -n 0.1 data_file
+
+Train a linear one-class SVM which selects roughly 10% data as outliers.
+
> train -v 5 -e 0.001 data_file
-Do five-fold cross-validation using L2-loss svm.
+Do five-fold cross-validation using L2-loss SVM.
Use a smaller stopping tolerance 0.001 than the default
0.1 if you want more accurate solutions.
+ > train -C data_file
+
+Conduct cross validation many times by L2-loss SVM
+and find the parameter C which achieves the best cross
+validation accuracy.
+
+ > train -C -s 0 -v 3 -c 0.5 -e 0.0001 data_file
+
+For parameter selection by -C, users can specify other
+solvers (currently -s 0, -s 2 and -s 11 are supported) and
+different number of CV folds. Further, users can use
+the -c option to specify the smallest C value of the
+search range. This option is useful when users want to
+rerun the parameter selection procedure from a specified
+C under a different setting, such as a stricter stopping
+tolerance -e 0.0001 in the above example. Similarly, for
+-s 11, users can use the -p option to specify the
+maximal p value of the search range.
+
> train -c 10 -w1 2 -w2 5 -w3 2 four_class_data_file
Train four classifiers:
@@ -289,6 +368,11 @@ Output probability estimates (for logistic regression only).
Library Usage
=============
+These functions and structures are declared in the header file `linear.h`.
+You can see `train.c` and `predict.c` for examples showing how to use them.
+We define LIBLINEAR_VERSION and declare `extern int liblinear_version;`
+in linear.h, so you can check the version number.
+
- Function: `model* train(const struct problem *prob,
const struct parameter *param);`
@@ -344,15 +428,17 @@ Library Usage
int solver_type;
/* these are for training only */
- double eps; /* stopping criteria */
+ double eps; /* stopping tolerance */
double C;
+ double nu; /* one-class SVM only */
int nr_weight;
int *weight_label;
double* weight;
double p;
+ double *init_sol;
};
- solver_type can be one of L2R_LR, L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L1R_L2LOSS_SVC, L1R_LR, L2R_LR_DUAL, L2R_L2LOSS_SVR, L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL.
+ solver_type can be one of L2R_LR, L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L1R_L2LOSS_SVC, L1R_LR, L2R_LR_DUAL, L2R_L2LOSS_SVR, L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL, ONECLASS_SVM.
for classification
- `L2R_LR` L2-regularized logistic regression (primal)
- `L2R_L2LOSS_SVC_DUAL` L2-regularized L2-loss support vector classification (dual)
@@ -366,9 +452,12 @@ Library Usage
- `L2R_L2LOSS_SVR` L2-regularized L2-loss support vector regression (primal)
- `L2R_L2LOSS_SVR_DUAL` L2-regularized L2-loss support vector regression (dual)
- `L2R_L1LOSS_SVR_DUAL` L2-regularized L1-loss support vector regression (dual)
+ for outlier detection
+ - `ONECLASS_SVM` one-class support vector machine (dual)
C is the cost of constraints violation.
p is the sensitiveness of loss of support vector regression.
+ nu in ONECLASS_SVM approximates the fraction of data as outliers.
eps is the stopping criterion.
nr_weight, weight_label, and weight are used to change the penalty
@@ -383,6 +472,10 @@ Library Usage
If you do not want to change penalty for any of the classes,
just set nr_weight to 0.
+ init_sol includes the initial weight vectors (supported for only some
+ solvers). See the explanation of the vector w in the model
+ structure.
+
*NOTE* To avoid wrong parameters, check_parameter() should be
called before train().
@@ -396,6 +489,7 @@ Library Usage
double *w;
int *label; /* label of each class */
double bias;
+ double rho; /* one-class SVM only */
};
param describes the parameters used to obtain the model.
@@ -403,7 +497,8 @@ Library Usage
nr_class and nr_feature are the number of classes and features,
respectively. nr_class = 2 for regression.
- The nr_feature*nr_class array w gives feature weights. We use one
+ The array w gives feature weights; its size is
+ nr_feature*nr_class but is nr_feature if nr_class = 2. We use one
against the rest for multi-class classification, so each feature
index corresponds to nr_class weight values. Weights are
organized in the following way
@@ -415,11 +510,13 @@ Library Usage
+------------------+------------------+------------+
```
+ The array label stores class labels.
+
If bias >= 0, x becomes [x; bias]. The number of features is
increased by one, so w is a (nr_feature+1)*nr_class array. The
value of bias is stored in the variable bias.
- The array label stores class labels.
+ rho is the bias term used in one-class SVM only.
- Function: `void cross_validation(const problem *prob, const parameter *param, int nr_fold, double *target);`
@@ -431,6 +528,34 @@ Library Usage
The format of prob is same as that for train().
+- Function: `void find_parameters(const struct problem *prob,
+ const struct parameter *param, int nr_fold, double start_C,
+ double start_p, double *best_C, double *best_p, double *best_score);`
+
+ This function is similar to cross_validation. However, instead of
+ conducting cross validation under specified parameters. For -s 0, 2, it
+ conducts cross validation many times under parameters C = start_C,
+ 2*start_C, 4*start_C, 8*start_C, ..., and finds the best one with
+ the highest cross validation accuracy. For -s 11, it conducts cross
+ validation many times with a two-fold loop. The outer loop considers a
+ default sequence of p = 19/20*max_p, ..., 1/20*max_p, 0 and
+ under each p value the inner loop considers a sequence of parameters
+ C = start_C, 2*start_C, 4*start_C, ..., and finds the best one with the
+ lowest mean squared error.
+
+ If start_C <= 0, then this procedure calculates a small enough C
+ for prob as the start_C. The procedure stops when the models of
+ all folds become stable or C reaches max_C.
+
+ If start_p <= 0, then this procedure calculates a maximal p for prob as
+ the start_p. Otherwise, the procedure starts with the first
+ i/20*max_p <= start_p so the outer sequence is i/20*max_p,
+ (i-1)/20*max_p, ..., 0.
+
+ The best C, the best p, and the corresponding accuracy (or MSE) are
+ assigned to *best_C, *best_p and *best_score, respectively. For
+ classification, *best_p is not used, and the returned value is -1.
+
- Function: `double predict(const model *model_, const feature_node *x);`
For a classification model, the predicted class for x is returned.
@@ -442,11 +567,11 @@ Library Usage
This function gives nr_w decision values in the array dec_values.
nr_w=1 if regression is applied or the number of classes is two. An exception is
- multi-class svm by Crammer and Singer (-s 4), where nr_w = 2 if there are two classes. For all other situations, nr_w is the
+ multi-class SVM by Crammer and Singer (-s 4), where nr_w = 2 if there are two classes. For all other situations, nr_w is the
number of classes.
We implement one-vs-the rest multi-class strategy (-s 0,1,2,3,5,6,7)
- and multi-class svm by Crammer and Singer (-s 4) for multi-class SVM.
+ and multi-class SVM by Crammer and Singer (-s 4) for multi-class SVM.
The class with the highest decision value is returned.
- Function: `double predict_probability(const struct model *model_,
@@ -480,15 +605,21 @@ Library Usage
starts from 1, while label_idx starts from 0. If feat_idx is not in the
valid range (1 to nr_feature), then a zero value will be returned. For
classification models, if label_idx is not in the valid range (0 to
- nr_class-1), then a zero value will be returned; for regression models,
- label_idx is ignored.
+ nr_class-1), then a zero value will be returned; for regression models
+ and one-class SVM models, label_idx is ignored.
- Function: `double get_decfun_bias(const struct model *model_, int label_idx);`
This function gives the bias term corresponding to the class with the
label_idx. For classification models, if label_idx is not in a valid range
(0 to nr_class-1), then a zero value will be returned; for regression
- models, label_idx is ignored.
+ models, label_idx is ignored. This function cannot be called for a one-class
+ SVM model.
+
+- Function: `double get_decfun_rho(const struct model *model_);`
+
+ This function gives rho, the bias term used in one-class SVM only. This
+ function can only be called for a one-class SVM model.
- Function: `const char *check_parameter(const struct problem *prob,
const struct parameter *param);`
@@ -508,6 +639,11 @@ Library Usage
This function returns 1 if the model is a regression model; otherwise
it returns 0.
+- Function: `int check_oneclass_model(const struct model *model);`
+
+ This function returns 1 if the model is a one-class SVM model; otherwise
+ it returns 0.
+
- Function: `int save_model(const char *model_file_name,
const struct model *model_);`
diff --git a/build.gradle b/build.gradle
index 5064835..6af5d77 100644
--- a/build.gradle
+++ b/build.gradle
@@ -1,95 +1,172 @@
-apply plugin: 'java'
-apply plugin: 'eclipse'
-apply plugin: 'nexus'
-apply plugin: 'org.standardout.versioneye'
+buildscript {
+ repositories {
+ mavenCentral()
+ }
+ dependencyLocking {
+ lockAllConfigurations()
+ }
+}
-version = '1.95'
+plugins {
+ id 'java-library'
+ id 'maven-publish'
+ id 'signing'
+ id 'jacoco'
+ id 'de.undercouch.download' version 'latest.release'
+ id 'me.champeau.gradle.jmh' version 'latest.release'
+}
-sourceCompatibility = 1.6
+group = "de.bwaldvogel"
+version = '2.44'
-compileJava.options.encoding = 'UTF-8'
+java {
+ sourceCompatibility = '11'
+ targetCompatibility = '11'
+}
-ext {
- title = 'liblinear'
- description = 'Java port of Liblinear'
- url = 'https://www.github.com/bwaldvogel/liblinear'
+tasks.withType(JavaCompile) {
+ options.encoding = 'UTF-8'
+ options.compilerArgs.addAll(['-Xlint:all', '-Werror'])
}
jar {
manifest {
- attributes 'Implementation-Title': title, 'Implementation-Version': version
+ attributes 'Implementation-Title': project.name, 'Implementation-Version': archiveVersion
}
}
-// https://github.com/bmuschko/gradle-nexus-plugin
-buildscript {
- repositories {
- jcenter()
- }
-
- dependencies {
- classpath 'org.gradle.api.plugins:gradle-nexus-plugin:0.7.1'
- classpath 'org.standardout:gradle-versioneye-plugin:1.2.0'
- }
+task sourceJar(type: Jar) {
+ archiveClassifier = "sources"
+ from sourceSets.main.allJava
}
-ext {
- powermockVersion = '1.6.4'
+task javadocJar(type: Jar, dependsOn: javadoc) {
+ archiveClassifier = "javadoc"
+ from javadoc.destinationDir
}
-modifyPom {
- project {
- groupId 'de.bwaldvogel'
- artifactId 'liblinear'
- name title
- description description
- url url
- inceptionYear '2008'
+publishing {
+ publications {
+ mavenJava(MavenPublication) {
+ groupId = project.group
+ artifactId = project.name
+ version = project.version
+ pom {
+ name = project.name
+ description = 'Java port of Liblinear'
+ url = 'https://github.com/bwaldvogel/liblinear'
+ inceptionYear = '2008'
+
+ licenses {
+ license {
+ name = 'The BSD License'
+ url = 'http://www.opensource.org/licenses/bsd-license.php'
+ distribution = 'repo'
+ }
+ }
+ developers {
+ developer {
+ id = 'bwaldvogel'
+ name = 'Benedikt Waldvogel'
+ email = 'mail@bwaldvogel.de'
+ }
+ }
+ scm {
+ url = 'git@github.com:bwaldvogel/liblinear.git'
+ connection = 'scm:git:git@github.com:bwaldvogel/liblinear.git'
+ developerConnection = 'scm:git:git@github.com:bwaldvogel/liblinear.git'
+ }
+ }
+
+ from components.java
- scm {
- url 'git@github.com:bwaldvogel/liblinear.git'
- connection 'scm:git:git@github.com:bwaldvogel/liblinear.git'
- developerConnection 'scm:git:git@github.com:bwaldvogel/liblinear.git'
- }
+ artifact sourceJar
+ artifact javadocJar
- licenses {
- license {
- name 'The BSD License'
- url 'http://www.opensource.org/licenses/bsd-license.php'
- distribution 'repo'
+ versionMapping {
+ usage('java-api') {
+ fromResolutionOf('runtimeClasspath')
+ }
+ usage('java-runtime') {
+ fromResolutionResult()
+ }
}
}
-
- developers {
- developer {
- id 'bwaldvogel'
- name 'Benedikt Waldvogel'
- email 'mail@bwaldvogel.de'
+ }
+ repositories {
+ maven {
+ url = "https://oss.sonatype.org/service/local/staging/deploy/maven2"
+ credentials {
+ username = project.hasProperty('nexusUsername') ? project.property('nexusUsername') : System.getenv('NEXUS_USERNAME')
+ password = project.hasProperty('nexusPassword') ? project.property('nexusPassword') : System.getenv('NEXUS_PASSWORD')
}
}
}
}
-nexus {
- attachSources = true
- attachTests = false
- attachJavadoc = true
- sign = true
+signing {
+ useGpgCmd()
+ sign publishing.publications.mavenJava
}
repositories {
mavenCentral()
}
-task wrapper(type: Wrapper) {
- gradleVersion = '2.10'
+jacocoTestReport {
+ reports {
+ html.required = true
+ xml.required = true
+ csv.required = false
+ }
+ dependsOn test
+}
+
+apply from: 'datasets.gradle'
+
+test.dependsOn downloadAndVerifyDatasets
+
+test {
+ useJUnitPlatform()
+
+ maxHeapSize = "256m"
+}
+
+wrapper {
+ gradleVersion = "8.12"
+ distributionType = Wrapper.DistributionType.ALL
+}
+
+jmh {
+ include = ['de\\.bwaldvogel\\.liblinear\\.LinearBenchmark.*']
+ jvmArgs = ["-Ddataset.directory=" + projectDir + "/src/test/datasets"]
+}
+
+tasks.named('jmh').configure {
+ dependsOn downloadAndVerifyRcv1
}
dependencies {
- testCompile group: 'junit', name: 'junit', version: '4.12'
- testCompile group: 'org.easytesting', name: 'fest-assert', version: '1.4'
- testCompile group: 'org.powermock', name: 'powermock-module-junit4', version: powermockVersion
- testCompile group: 'org.powermock', name: 'powermock-api-mockito', version: powermockVersion
+ testImplementation group: 'org.junit.jupiter', name: 'junit-jupiter-api', version: 'latest.release'
+ testImplementation group: 'org.junit.jupiter', name: 'junit-jupiter-params', version: 'latest.release'
+ testRuntimeOnly group: 'org.junit.jupiter', name: 'junit-jupiter-engine', version: 'latest.release'
+ testImplementation group: 'org.slf4j', name: 'slf4j-api', version: 'latest.release'
+ testImplementation group: 'org.assertj', name: 'assertj-core', version: 'latest.release'
+ testImplementation group: 'org.mockito', name: 'mockito-core', version: 'latest.release'
+ testImplementation group: 'nl.jqno.equalsverifier', name: 'equalsverifier', version: 'latest.release'
+
+ testRuntimeOnly group: 'ch.qos.logback', name: 'logback-classic', version: 'latest.release'
+
+ jmh group: 'org.apache.commons', name: 'commons-compress', version: 'latest.release'
+ jmh 'org.openjdk.jmh:jmh-generator-annprocess:latest.release' // for IntelliJ
+
+ components.all { ComponentMetadataDetails details ->
+ if (details.id.version =~ /(?i).+(-|\.)(CANDIDATE|RC|BETA|ALPHA|PR|M\d+).*/) {
+ details.status = 'milestone'
+ }
+ }
}
-// vim: set ts=4 sw=4 et:
+dependencyLocking {
+ lockAllConfigurations()
+}
diff --git a/buildscript-gradle.lockfile b/buildscript-gradle.lockfile
new file mode 100644
index 0000000..b463142
--- /dev/null
+++ b/buildscript-gradle.lockfile
@@ -0,0 +1,11 @@
+# This is a Gradle generated file for dependency locking.
+# Manual edits can break the build and are not advised.
+# This file is expected to be part of source control.
+de.undercouch.download:de.undercouch.download.gradle.plugin:5.6.0=classpath
+de.undercouch:gradle-download-task:5.6.0=classpath
+me.champeau.gradle.jmh:me.champeau.gradle.jmh.gradle.plugin:0.5.3=classpath
+me.champeau.gradle:jmh-gradle-plugin:0.5.3=classpath
+net.sf.jopt-simple:jopt-simple:4.6=classpath
+org.apache.commons:commons-math3:3.2=classpath
+org.openjdk.jmh:jmh-core:1.27=classpath
+empty=
diff --git a/eclipse/cleanup.xml b/code-style/eclipse/cleanup.xml
similarity index 100%
rename from eclipse/cleanup.xml
rename to code-style/eclipse/cleanup.xml
diff --git a/eclipse/formatter.xml b/code-style/eclipse/formatter.xml
similarity index 100%
rename from eclipse/formatter.xml
rename to code-style/eclipse/formatter.xml
diff --git a/datasets.gradle b/datasets.gradle
new file mode 100644
index 0000000..131ed88
--- /dev/null
+++ b/datasets.gradle
@@ -0,0 +1,77 @@
+task downloadDatasetSplice(type: Download) {
+ def baseUrl = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary'
+ src([
+ "${baseUrl}/splice",
+ "${baseUrl}/splice.t"
+ ])
+ dest 'src/test/datasets/splice/'
+ overwrite false
+}
+
+task verifyDatasetSpliceTrain(type: Verify, dependsOn: downloadDatasetSplice) {
+ src 'src/test/datasets/splice/splice'
+ algorithm 'SHA-256'
+ checksum 'eee33566cfe8988ec26122460cc712d66c5c5b79b966b259408e803addaa584d'
+}
+
+task verifyDatasetSpliceTest(type: Verify, dependsOn: downloadDatasetSplice) {
+ src 'src/test/datasets/splice/splice.t'
+ algorithm 'SHA-256'
+ checksum 'f40da7cfd17860f3f1a1bb7d118f8e86c306d232ca6ac6ddefbaaef266abaa03'
+}
+
+task downloadDatasetDna(type: Download) {
+ def baseUrl = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass'
+ src([
+ "${baseUrl}/dna.scale",
+ "${baseUrl}/dna.scale.t"
+ ])
+ dest 'src/test/datasets/dna.scale/'
+ overwrite false
+}
+
+task verifyDatasetDnaTrain(type: Verify, dependsOn: downloadDatasetDna) {
+ src 'src/test/datasets/dna.scale/dna.scale'
+ algorithm 'SHA-256'
+ checksum '03d39477c5e48415e9cd18e006efc8f7a3b5c3deaee8289a72511c35070e3cfe'
+}
+
+task verifyDatasetDnaTest(type: Verify, dependsOn: downloadDatasetDna) {
+ src 'src/test/datasets/dna.scale/dna.scale.t'
+ algorithm 'SHA-256'
+ checksum 'd2eeed170e4e38ae2aba6aa4cd27a8ca4ecceb01a7751e8e509bb4e1a6bcef39'
+}
+
+task downloadDatasetRcv1(type: Download) {
+ def baseUrl = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary'
+ src([
+ "${baseUrl}/rcv1_train.binary.bz2",
+ "${baseUrl}/rcv1_test.binary.bz2"
+ ])
+ dest 'src/test/datasets/rcv1/'
+ overwrite false
+}
+
+task verifyDatasetRcv1Train(type: Verify, dependsOn: downloadDatasetRcv1) {
+ src 'src/test/datasets/rcv1/rcv1_train.binary.bz2'
+ algorithm 'SHA-256'
+ checksum 'dda0c6381811484e284e70dd0641335917debce50948346416fde7fb32fad2e2'
+}
+
+task verifyDatasetRcv1Test(type: Verify, dependsOn: downloadDatasetRcv1) {
+ src 'src/test/datasets/rcv1/rcv1_test.binary.bz2'
+ algorithm 'SHA-256'
+ checksum '975ab6c4fd8ea2a88b82e7ba2659ef21bf980ac61b3d8f13d6991739ec0f7aa7'
+}
+
+task downloadAndVerifyRcv1 {
+ dependsOn 'verifyDatasetRcv1Train'
+ dependsOn 'verifyDatasetRcv1Test'
+}
+
+task downloadAndVerifyDatasets {
+ dependsOn 'verifyDatasetSpliceTrain'
+ dependsOn 'verifyDatasetSpliceTest'
+ dependsOn 'verifyDatasetDnaTrain'
+ dependsOn 'verifyDatasetDnaTest'
+}
diff --git a/deploy.sh b/deploy.sh
deleted file mode 100755
index 2a3c855..0000000
--- a/deploy.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-#!/bin/bash
-gradle clean build uploadArchives
diff --git a/gradle.lockfile b/gradle.lockfile
new file mode 100644
index 0000000..faf5ce3
--- /dev/null
+++ b/gradle.lockfile
@@ -0,0 +1,40 @@
+# This is a Gradle generated file for dependency locking.
+# Manual edits can break the build and are not advised.
+# This file is expected to be part of source control.
+ch.qos.logback:logback-classic:1.5.15=jmhRuntimeClasspath,testRuntimeClasspath
+ch.qos.logback:logback-core:1.5.15=jmhRuntimeClasspath,testRuntimeClasspath
+commons-codec:commons-codec:1.17.1=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+commons-io:commons-io:2.16.1=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+net.bytebuddy:byte-buddy-agent:1.15.4=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+net.bytebuddy:byte-buddy:1.15.11=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+net.sf.jopt-simple:jopt-simple:5.0.4=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+nl.jqno.equalsverifier:equalsverifier:3.18=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.apache.commons:commons-compress:1.27.1=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+org.apache.commons:commons-lang3:3.16.0=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+org.apache.commons:commons-math3:3.6.1=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+org.apiguardian:apiguardian-api:1.1.2=testCompileClasspath
+org.assertj:assertj-core:3.27.0=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.jacoco:org.jacoco.agent:0.8.12=jacocoAgent,jacocoAnt
+org.jacoco:org.jacoco.ant:0.8.12=jacocoAnt
+org.jacoco:org.jacoco.core:0.8.12=jacocoAnt
+org.jacoco:org.jacoco.report:0.8.12=jacocoAnt
+org.junit.jupiter:junit-jupiter-api:5.11.4=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.junit.jupiter:junit-jupiter-engine:5.11.4=jmhRuntimeClasspath,testRuntimeClasspath
+org.junit.jupiter:junit-jupiter-params:5.11.4=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.junit.platform:junit-platform-commons:1.11.4=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.junit.platform:junit-platform-engine:1.11.4=jmhRuntimeClasspath,testRuntimeClasspath
+org.junit:junit-bom:5.11.4=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.mockito:mockito-core:5.14.2=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.objenesis:objenesis:3.4=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.openjdk.jmh:jmh-core:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+org.openjdk.jmh:jmh-generator-annprocess:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+org.openjdk.jmh:jmh-generator-asm:1.25=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+org.openjdk.jmh:jmh-generator-bytecode:1.25=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+org.openjdk.jmh:jmh-generator-reflection:1.25=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+org.opentest4j:opentest4j:1.3.0=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.ow2.asm:asm-commons:9.7=jacocoAnt
+org.ow2.asm:asm-tree:9.7=jacocoAnt
+org.ow2.asm:asm:5.0.3=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+org.ow2.asm:asm:9.7=jacocoAnt
+org.slf4j:slf4j-api:2.0.16=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+empty=annotationProcessor,compileClasspath,jmhAnnotationProcessor,runtimeClasspath,signatures,testAnnotationProcessor
diff --git a/gradle.properties b/gradle.properties
deleted file mode 100644
index 950524d..0000000
--- a/gradle.properties
+++ /dev/null
@@ -1 +0,0 @@
-versioneye.projectid=54e330f0d1ec577c97000009
diff --git a/gradle/wrapper/gradle-wrapper.jar b/gradle/wrapper/gradle-wrapper.jar
index 13372ae..a4b76b9 100644
Binary files a/gradle/wrapper/gradle-wrapper.jar and b/gradle/wrapper/gradle-wrapper.jar differ
diff --git a/gradle/wrapper/gradle-wrapper.properties b/gradle/wrapper/gradle-wrapper.properties
index f549e42..e0fd020 100644
--- a/gradle/wrapper/gradle-wrapper.properties
+++ b/gradle/wrapper/gradle-wrapper.properties
@@ -1,6 +1,7 @@
-#Sat Jan 30 12:30:07 CET 2016
distributionBase=GRADLE_USER_HOME
distributionPath=wrapper/dists
+distributionUrl=https\://services.gradle.org/distributions/gradle-8.12-all.zip
+networkTimeout=10000
+validateDistributionUrl=true
zipStoreBase=GRADLE_USER_HOME
zipStorePath=wrapper/dists
-distributionUrl=https\://services.gradle.org/distributions/gradle-2.10-bin.zip
diff --git a/gradlew b/gradlew
index 9d82f78..f3b75f3 100755
--- a/gradlew
+++ b/gradlew
@@ -1,74 +1,129 @@
-#!/usr/bin/env bash
+#!/bin/sh
+
+#
+# Copyright © 2015-2021 the original authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
##############################################################################
-##
-## Gradle start up script for UN*X
-##
+#
+# Gradle start up script for POSIX generated by Gradle.
+#
+# Important for running:
+#
+# (1) You need a POSIX-compliant shell to run this script. If your /bin/sh is
+# noncompliant, but you have some other compliant shell such as ksh or
+# bash, then to run this script, type that shell name before the whole
+# command line, like:
+#
+# ksh Gradle
+#
+# Busybox and similar reduced shells will NOT work, because this script
+# requires all of these POSIX shell features:
+# * functions;
+# * expansions «$var», «${var}», «${var:-default}», «${var+SET}»,
+# «${var#prefix}», «${var%suffix}», and «$( cmd )»;
+# * compound commands having a testable exit status, especially «case»;
+# * various built-in commands including «command», «set», and «ulimit».
+#
+# Important for patching:
+#
+# (2) This script targets any POSIX shell, so it avoids extensions provided
+# by Bash, Ksh, etc; in particular arrays are avoided.
+#
+# The "traditional" practice of packing multiple parameters into a
+# space-separated string is a well documented source of bugs and security
+# problems, so this is (mostly) avoided, by progressively accumulating
+# options in "$@", and eventually passing that to Java.
+#
+# Where the inherited environment variables (DEFAULT_JVM_OPTS, JAVA_OPTS,
+# and GRADLE_OPTS) rely on word-splitting, this is performed explicitly;
+# see the in-line comments for details.
+#
+# There are tweaks for specific operating systems such as AIX, CygWin,
+# Darwin, MinGW, and NonStop.
+#
+# (3) This script is generated from the Groovy template
+# https://github.com/gradle/gradle/blob/HEAD/platforms/jvm/plugins-application/src/main/resources/org/gradle/api/internal/plugins/unixStartScript.txt
+# within the Gradle project.
+#
+# You can find Gradle at https://github.com/gradle/gradle/.
+#
##############################################################################
-# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
-DEFAULT_JVM_OPTS=""
+# Attempt to set APP_HOME
+
+# Resolve links: $0 may be a link
+app_path=$0
+
+# Need this for daisy-chained symlinks.
+while
+ APP_HOME=${app_path%"${app_path##*/}"} # leaves a trailing /; empty if no leading path
+ [ -h "$app_path" ]
+do
+ ls=$( ls -ld "$app_path" )
+ link=${ls#*' -> '}
+ case $link in #(
+ /*) app_path=$link ;; #(
+ *) app_path=$APP_HOME$link ;;
+ esac
+done
-APP_NAME="Gradle"
-APP_BASE_NAME=`basename "$0"`
+# This is normally unused
+# shellcheck disable=SC2034
+APP_BASE_NAME=${0##*/}
+# Discard cd standard output in case $CDPATH is set (https://github.com/gradle/gradle/issues/25036)
+APP_HOME=$( cd -P "${APP_HOME:-./}" > /dev/null && printf '%s\n' "$PWD" ) || exit
# Use the maximum available, or set MAX_FD != -1 to use that value.
-MAX_FD="maximum"
+MAX_FD=maximum
-warn ( ) {
+warn () {
echo "$*"
-}
+} >&2
-die ( ) {
+die () {
echo
echo "$*"
echo
exit 1
-}
+} >&2
# OS specific support (must be 'true' or 'false').
cygwin=false
msys=false
darwin=false
-case "`uname`" in
- CYGWIN* )
- cygwin=true
- ;;
- Darwin* )
- darwin=true
- ;;
- MINGW* )
- msys=true
- ;;
+nonstop=false
+case "$( uname )" in #(
+ CYGWIN* ) cygwin=true ;; #(
+ Darwin* ) darwin=true ;; #(
+ MSYS* | MINGW* ) msys=true ;; #(
+ NONSTOP* ) nonstop=true ;;
esac
-# Attempt to set APP_HOME
-# Resolve links: $0 may be a link
-PRG="$0"
-# Need this for relative symlinks.
-while [ -h "$PRG" ] ; do
- ls=`ls -ld "$PRG"`
- link=`expr "$ls" : '.*-> \(.*\)$'`
- if expr "$link" : '/.*' > /dev/null; then
- PRG="$link"
- else
- PRG=`dirname "$PRG"`"/$link"
- fi
-done
-SAVED="`pwd`"
-cd "`dirname \"$PRG\"`/" >/dev/null
-APP_HOME="`pwd -P`"
-cd "$SAVED" >/dev/null
-
CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
+
# Determine the Java command to use to start the JVM.
if [ -n "$JAVA_HOME" ] ; then
if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
# IBM's JDK on AIX uses strange locations for the executables
- JAVACMD="$JAVA_HOME/jre/sh/java"
+ JAVACMD=$JAVA_HOME/jre/sh/java
else
- JAVACMD="$JAVA_HOME/bin/java"
+ JAVACMD=$JAVA_HOME/bin/java
fi
if [ ! -x "$JAVACMD" ] ; then
die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
@@ -77,84 +132,120 @@ Please set the JAVA_HOME variable in your environment to match the
location of your Java installation."
fi
else
- JAVACMD="java"
- which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+ JAVACMD=java
+ if ! command -v java >/dev/null 2>&1
+ then
+ die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
Please set the JAVA_HOME variable in your environment to match the
location of your Java installation."
+ fi
fi
# Increase the maximum file descriptors if we can.
-if [ "$cygwin" = "false" -a "$darwin" = "false" ] ; then
- MAX_FD_LIMIT=`ulimit -H -n`
- if [ $? -eq 0 ] ; then
- if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
- MAX_FD="$MAX_FD_LIMIT"
- fi
- ulimit -n $MAX_FD
- if [ $? -ne 0 ] ; then
- warn "Could not set maximum file descriptor limit: $MAX_FD"
- fi
- else
- warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
- fi
+if ! "$cygwin" && ! "$darwin" && ! "$nonstop" ; then
+ case $MAX_FD in #(
+ max*)
+ # In POSIX sh, ulimit -H is undefined. That's why the result is checked to see if it worked.
+ # shellcheck disable=SC2039,SC3045
+ MAX_FD=$( ulimit -H -n ) ||
+ warn "Could not query maximum file descriptor limit"
+ esac
+ case $MAX_FD in #(
+ '' | soft) :;; #(
+ *)
+ # In POSIX sh, ulimit -n is undefined. That's why the result is checked to see if it worked.
+ # shellcheck disable=SC2039,SC3045
+ ulimit -n "$MAX_FD" ||
+ warn "Could not set maximum file descriptor limit to $MAX_FD"
+ esac
fi
-# For Darwin, add options to specify how the application appears in the dock
-if $darwin; then
- GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
-fi
+# Collect all arguments for the java command, stacking in reverse order:
+# * args from the command line
+# * the main class name
+# * -classpath
+# * -D...appname settings
+# * --module-path (only if needed)
+# * DEFAULT_JVM_OPTS, JAVA_OPTS, and GRADLE_OPTS environment variables.
+
+# For Cygwin or MSYS, switch paths to Windows format before running java
+if "$cygwin" || "$msys" ; then
+ APP_HOME=$( cygpath --path --mixed "$APP_HOME" )
+ CLASSPATH=$( cygpath --path --mixed "$CLASSPATH" )
+
+ JAVACMD=$( cygpath --unix "$JAVACMD" )
-# For Cygwin, switch paths to Windows format before running java
-if $cygwin ; then
- APP_HOME=`cygpath --path --mixed "$APP_HOME"`
- CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
- JAVACMD=`cygpath --unix "$JAVACMD"`
-
- # We build the pattern for arguments to be converted via cygpath
- ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
- SEP=""
- for dir in $ROOTDIRSRAW ; do
- ROOTDIRS="$ROOTDIRS$SEP$dir"
- SEP="|"
- done
- OURCYGPATTERN="(^($ROOTDIRS))"
- # Add a user-defined pattern to the cygpath arguments
- if [ "$GRADLE_CYGPATTERN" != "" ] ; then
- OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
- fi
# Now convert the arguments - kludge to limit ourselves to /bin/sh
- i=0
- for arg in "$@" ; do
- CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
- CHECK2=`echo "$arg"|egrep -c "^-"` ### Determine if an option
-
- if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then ### Added a condition
- eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
- else
- eval `echo args$i`="\"$arg\""
+ for arg do
+ if
+ case $arg in #(
+ -*) false ;; # don't mess with options #(
+ /?*) t=${arg#/} t=/${t%%/*} # looks like a POSIX filepath
+ [ -e "$t" ] ;; #(
+ *) false ;;
+ esac
+ then
+ arg=$( cygpath --path --ignore --mixed "$arg" )
fi
- i=$((i+1))
+ # Roll the args list around exactly as many times as the number of
+ # args, so each arg winds up back in the position where it started, but
+ # possibly modified.
+ #
+ # NB: a `for` loop captures its iteration list before it begins, so
+ # changing the positional parameters here affects neither the number of
+ # iterations, nor the values presented in `arg`.
+ shift # remove old arg
+ set -- "$@" "$arg" # push replacement arg
done
- case $i in
- (0) set -- ;;
- (1) set -- "$args0" ;;
- (2) set -- "$args0" "$args1" ;;
- (3) set -- "$args0" "$args1" "$args2" ;;
- (4) set -- "$args0" "$args1" "$args2" "$args3" ;;
- (5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
- (6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
- (7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
- (8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
- (9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
- esac
fi
-# Split up the JVM_OPTS And GRADLE_OPTS values into an array, following the shell quoting and substitution rules
-function splitJvmOpts() {
- JVM_OPTS=("$@")
-}
-eval splitJvmOpts $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS
-JVM_OPTS[${#JVM_OPTS[*]}]="-Dorg.gradle.appname=$APP_BASE_NAME"
-exec "$JAVACMD" "${JVM_OPTS[@]}" -classpath "$CLASSPATH" org.gradle.wrapper.GradleWrapperMain "$@"
+# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"'
+
+# Collect all arguments for the java command:
+# * DEFAULT_JVM_OPTS, JAVA_OPTS, JAVA_OPTS, and optsEnvironmentVar are not allowed to contain shell fragments,
+# and any embedded shellness will be escaped.
+# * For example: A user cannot expect ${Hostname} to be expanded, as it is an environment variable and will be
+# treated as '${Hostname}' itself on the command line.
+
+set -- \
+ "-Dorg.gradle.appname=$APP_BASE_NAME" \
+ -classpath "$CLASSPATH" \
+ org.gradle.wrapper.GradleWrapperMain \
+ "$@"
+
+# Stop when "xargs" is not available.
+if ! command -v xargs >/dev/null 2>&1
+then
+ die "xargs is not available"
+fi
+
+# Use "xargs" to parse quoted args.
+#
+# With -n1 it outputs one arg per line, with the quotes and backslashes removed.
+#
+# In Bash we could simply go:
+#
+# readarray ARGS < <( xargs -n1 <<<"$var" ) &&
+# set -- "${ARGS[@]}" "$@"
+#
+# but POSIX shell has neither arrays nor command substitution, so instead we
+# post-process each arg (as a line of input to sed) to backslash-escape any
+# character that might be a shell metacharacter, then use eval to reverse
+# that process (while maintaining the separation between arguments), and wrap
+# the whole thing up as a single "set" statement.
+#
+# This will of course break if any of these variables contains a newline or
+# an unmatched quote.
+#
+
+eval "set -- $(
+ printf '%s\n' "$DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS" |
+ xargs -n1 |
+ sed ' s~[^-[:alnum:]+,./:=@_]~\\&~g; ' |
+ tr '\n' ' '
+ )" '"$@"'
+
+exec "$JAVACMD" "$@"
diff --git a/gradlew.bat b/gradlew.bat
index aec9973..9b42019 100644
--- a/gradlew.bat
+++ b/gradlew.bat
@@ -1,4 +1,22 @@
-@if "%DEBUG%" == "" @echo off
+@rem
+@rem Copyright 2015 the original author or authors.
+@rem
+@rem Licensed under the Apache License, Version 2.0 (the "License");
+@rem you may not use this file except in compliance with the License.
+@rem You may obtain a copy of the License at
+@rem
+@rem https://www.apache.org/licenses/LICENSE-2.0
+@rem
+@rem Unless required by applicable law or agreed to in writing, software
+@rem distributed under the License is distributed on an "AS IS" BASIS,
+@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+@rem See the License for the specific language governing permissions and
+@rem limitations under the License.
+@rem
+@rem SPDX-License-Identifier: Apache-2.0
+@rem
+
+@if "%DEBUG%"=="" @echo off
@rem ##########################################################################
@rem
@rem Gradle startup script for Windows
@@ -8,26 +26,30 @@
@rem Set local scope for the variables with windows NT shell
if "%OS%"=="Windows_NT" setlocal
-@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
-set DEFAULT_JVM_OPTS=
-
set DIRNAME=%~dp0
-if "%DIRNAME%" == "" set DIRNAME=.
+if "%DIRNAME%"=="" set DIRNAME=.
+@rem This is normally unused
set APP_BASE_NAME=%~n0
set APP_HOME=%DIRNAME%
+@rem Resolve any "." and ".." in APP_HOME to make it shorter.
+for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi
+
+@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+set DEFAULT_JVM_OPTS="-Xmx64m" "-Xms64m"
+
@rem Find java.exe
if defined JAVA_HOME goto findJavaFromJavaHome
set JAVA_EXE=java.exe
%JAVA_EXE% -version >NUL 2>&1
-if "%ERRORLEVEL%" == "0" goto init
+if %ERRORLEVEL% equ 0 goto execute
-echo.
-echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
+echo. 1>&2
+echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. 1>&2
+echo. 1>&2
+echo Please set the JAVA_HOME variable in your environment to match the 1>&2
+echo location of your Java installation. 1>&2
goto fail
@@ -35,54 +57,36 @@ goto fail
set JAVA_HOME=%JAVA_HOME:"=%
set JAVA_EXE=%JAVA_HOME%/bin/java.exe
-if exist "%JAVA_EXE%" goto init
+if exist "%JAVA_EXE%" goto execute
-echo.
-echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
+echo. 1>&2
+echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME% 1>&2
+echo. 1>&2
+echo Please set the JAVA_HOME variable in your environment to match the 1>&2
+echo location of your Java installation. 1>&2
goto fail
-:init
-@rem Get command-line arguments, handling Windowz variants
-
-if not "%OS%" == "Windows_NT" goto win9xME_args
-if "%@eval[2+2]" == "4" goto 4NT_args
-
-:win9xME_args
-@rem Slurp the command line arguments.
-set CMD_LINE_ARGS=
-set _SKIP=2
-
-:win9xME_args_slurp
-if "x%~1" == "x" goto execute
-
-set CMD_LINE_ARGS=%*
-goto execute
-
-:4NT_args
-@rem Get arguments from the 4NT Shell from JP Software
-set CMD_LINE_ARGS=%$
-
:execute
@rem Setup the command line
set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
+
@rem Execute Gradle
-"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%
+"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %*
:end
@rem End local scope for the variables with windows NT shell
-if "%ERRORLEVEL%"=="0" goto mainEnd
+if %ERRORLEVEL% equ 0 goto mainEnd
:fail
rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
rem the _cmd.exe /c_ return code!
-if not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
-exit /b 1
+set EXIT_CODE=%ERRORLEVEL%
+if %EXIT_CODE% equ 0 set EXIT_CODE=1
+if not ""=="%GRADLE_EXIT_CONSOLE%" exit %EXIT_CODE%
+exit /b %EXIT_CODE%
:mainEnd
if "%OS%"=="Windows_NT" endlocal
diff --git a/release.sh b/release.sh
new file mode 100755
index 0000000..7930f38
--- /dev/null
+++ b/release.sh
@@ -0,0 +1,2 @@
+#!/bin/bash
+./gradlew --no-daemon clean build publish
diff --git a/settings.gradle b/settings.gradle
new file mode 100644
index 0000000..a6ba6e7
--- /dev/null
+++ b/settings.gradle
@@ -0,0 +1 @@
+rootProject.name = "liblinear"
diff --git a/src/jmh/java/de/bwaldvogel/liblinear/LinearBenchmark.java b/src/jmh/java/de/bwaldvogel/liblinear/LinearBenchmark.java
new file mode 100644
index 0000000..8374cfe
--- /dev/null
+++ b/src/jmh/java/de/bwaldvogel/liblinear/LinearBenchmark.java
@@ -0,0 +1,112 @@
+package de.bwaldvogel.liblinear;
+
+import java.io.IOException;
+import java.io.InputStream;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.nio.file.Paths;
+import java.util.concurrent.TimeUnit;
+
+import org.apache.commons.compress.compressors.bzip2.BZip2CompressorInputStream;
+import org.openjdk.jmh.annotations.Benchmark;
+import org.openjdk.jmh.annotations.BenchmarkMode;
+import org.openjdk.jmh.annotations.Fork;
+import org.openjdk.jmh.annotations.Measurement;
+import org.openjdk.jmh.annotations.Mode;
+import org.openjdk.jmh.annotations.OutputTimeUnit;
+import org.openjdk.jmh.annotations.Param;
+import org.openjdk.jmh.annotations.Scope;
+import org.openjdk.jmh.annotations.Setup;
+import org.openjdk.jmh.annotations.State;
+import org.openjdk.jmh.annotations.Warmup;
+import org.openjdk.jmh.runner.Runner;
+import org.openjdk.jmh.runner.RunnerException;
+import org.openjdk.jmh.runner.options.Options;
+import org.openjdk.jmh.runner.options.OptionsBuilder;
+
+@Fork(1)
+@BenchmarkMode(Mode.AverageTime)
+@OutputTimeUnit(TimeUnit.MILLISECONDS)
+@Warmup(iterations = 2, time = 5, timeUnit = TimeUnit.SECONDS)
+@Measurement(iterations = 5, time = 3, timeUnit = TimeUnit.SECONDS)
+public class LinearBenchmark {
+
+ @Benchmark
+ public void readProblem(DatasetParameters datasetParameters) throws Exception {
+ Path trainingFile = getTrainingFile(datasetParameters.dataset);
+ try (InputStream inputStream = getInputStream(trainingFile)) {
+ Train.readProblem(inputStream, -1);
+ }
+ }
+
+ @Benchmark
+ public void train(BenchmarkParameters benchmarkParameters) {
+ Linear.disableDebugOutput();
+ Linear.train(benchmarkParameters.problem, new Parameter(benchmarkParameters.solverType, 1, 1e-3));
+ }
+
+ public enum Dataset {
+ RCV1, Splice, DnaScale
+ }
+
+
+ @State(Scope.Benchmark)
+ public static class BenchmarkParameters {
+
+ @Param
+ private Dataset dataset;
+
+ @Param({"L2R_LR", "L2R_L2LOSS_SVC_DUAL"})
+ private SolverType solverType;
+
+ private Problem problem;
+
+ @Setup
+ public void loadDataset() throws Exception {
+ Path trainingFile = getTrainingFile(dataset);
+ try (InputStream inputStream = getInputStream(trainingFile)) {
+ problem = Train.readProblem(inputStream, -1);
+ }
+ }
+
+ }
+
+ @State(Scope.Benchmark)
+ public static class DatasetParameters {
+
+ @Param
+ private Dataset dataset;
+
+ }
+
+ public static void main(String[] args) throws RunnerException {
+ Options opt = new OptionsBuilder()
+ .include(".*" + LinearBenchmark.class.getSimpleName() + ".*")
+ .build();
+
+ new Runner(opt).run();
+ }
+
+ private static InputStream getInputStream(Path path) throws IOException {
+ InputStream inputStream = Files.newInputStream(path);
+ if (path.toString().endsWith(".bz2")) {
+ return new BZip2CompressorInputStream(inputStream);
+ }
+ return inputStream;
+ }
+
+ private static Path getTrainingFile(Dataset dataset) {
+ Path datasetDirectory = Paths.get(System.getProperty("dataset.directory", "src/test/datasets"));
+ switch (dataset) {
+ case RCV1:
+ return datasetDirectory.resolve("rcv1").resolve("rcv1_train.binary.bz2");
+ case Splice:
+ return datasetDirectory.resolve("splice").resolve("splice");
+ case DnaScale:
+ return datasetDirectory.resolve("dna.scale").resolve("dna.scale");
+ default:
+ throw new IllegalArgumentException("Unknown dataset: " + dataset);
+ }
+ }
+
+}
diff --git a/src/main/java/de/bwaldvogel/liblinear/ArraySorter.java b/src/main/java/de/bwaldvogel/liblinear/ArraySorter.java
index bf7c45c..5b0e3b0 100644
--- a/src/main/java/de/bwaldvogel/liblinear/ArraySorter.java
+++ b/src/main/java/de/bwaldvogel/liblinear/ArraySorter.java
@@ -8,11 +8,11 @@ final class ArraySorter {
*
* This code is borrowed from Sun's JDK 1.6.0.07
*/
- public static void reversedMergesort(double[] a) {
+ static void reversedMergesort(double[] a) {
reversedMergesort(a, 0, a.length);
}
- private static void reversedMergesort(double x[], int off, int len) {
+ private static void reversedMergesort(double[] x, int off, int len) {
// Insertion sort on smallest arrays
if (len < 7) {
for (int i = off; i < len + off; i++)
@@ -40,14 +40,17 @@ private static void reversedMergesort(double x[], int off, int len) {
int a = off, b = a, c = off + len - 1, d = c;
while (true) {
while (b <= c && x[b] >= v) {
- if (x[b] == v) swap(x, a++, b);
+ if (x[b] == v)
+ swap(x, a++, b);
b++;
}
while (c >= b && x[c] <= v) {
- if (x[c] == v) swap(x, c, d--);
+ if (x[c] == v)
+ swap(x, c, d--);
c--;
}
- if (b > c) break;
+ if (b > c)
+ break;
swap(x, b++, c--);
}
@@ -59,14 +62,16 @@ private static void reversedMergesort(double x[], int off, int len) {
vecswap(x, b, n - s, s);
// Recursively sort non-partition-elements
- if ((s = b - a) > 1) reversedMergesort(x, off, s);
- if ((s = d - c) > 1) reversedMergesort(x, n - s, s);
+ if ((s = b - a) > 1)
+ reversedMergesort(x, off, s);
+ if ((s = d - c) > 1)
+ reversedMergesort(x, n - s, s);
}
/**
* Swaps x[a] with x[b].
*/
- private static void swap(double x[], int a, int b) {
+ private static void swap(double[] x, int a, int b) {
double t = x[a];
x[a] = x[b];
x[b] = t;
@@ -75,7 +80,7 @@ private static void swap(double x[], int a, int b) {
/**
* Swaps x[a .. (a+n-1)] with x[b .. (b+n-1)].
*/
- private static void vecswap(double x[], int a, int b, int n) {
+ private static void vecswap(double[] x, int a, int b, int n) {
for (int i = 0; i < n; i++, a++, b++)
swap(x, a, b);
}
@@ -83,7 +88,7 @@ private static void vecswap(double x[], int a, int b, int n) {
/**
* Returns the index of the median of the three indexed doubles.
*/
- private static int med3(double x[], int a, int b, int c) {
+ private static int med3(double[] x, int a, int b, int c) {
return (x[a] < x[b] ? (x[b] < x[c] ? b : x[a] < x[c] ? c : a) : (x[b] > x[c] ? b : x[a] > x[c] ? c : a));
}
diff --git a/src/main/java/de/bwaldvogel/liblinear/Blas.java b/src/main/java/de/bwaldvogel/liblinear/Blas.java
new file mode 100644
index 0000000..7d90184
--- /dev/null
+++ b/src/main/java/de/bwaldvogel/liblinear/Blas.java
@@ -0,0 +1,189 @@
+package de.bwaldvogel.liblinear;
+
+class Blas {
+
+ static double dnrm2_(int n, double[] x, MutableInt incx) {
+ return dnrm2_(new MutableInt(n), x, incx);
+ }
+
+ private static double dnrm2_(MutableInt n, double[] x, MutableInt incx) {
+ final double norm;
+ double scale, absxi, ssq, temp;
+
+ /* DNRM2 returns the euclidean norm of a vector via the function
+ name, so that
+
+ DNRM2 := sqrt( x'*x )
+
+ -- This version written on 25-October-1982.
+ Modified on 14-October-1993 to inline the call to SLASSQ.
+ Sven Hammarling, Nag Ltd. */
+
+ /* Dereference inputs */
+ int nn = n.get();
+ int iincx = incx.get();
+
+ if (nn > 0 && iincx > 0) {
+ if (nn == 1) {
+ norm = Math.abs(x[0]);
+ } else {
+ scale = 0.0;
+ ssq = 1.0;
+
+ /* The following loop is equivalent to this call to the LAPACK
+ auxiliary routine: CALL SLASSQ( N, X, INCX, SCALE, SSQ ) */
+
+ for (int ix = (nn - 1) * iincx; ix >= 0; ix -= iincx) {
+ if (x[ix] != 0.0) {
+ absxi = Math.abs(x[ix]);
+ if (scale < absxi) {
+ temp = scale / absxi;
+ ssq = ssq * (temp * temp) + 1.0;
+ scale = absxi;
+ } else {
+ temp = absxi / scale;
+ ssq += temp * temp;
+ }
+ }
+ }
+ norm = scale * Math.sqrt(ssq);
+ }
+ } else
+ norm = 0.0;
+
+ return norm;
+ }
+
+ static double ddot_(int n, double[] sx, int incx, double[] sy, int incy) {
+ return ddot_(new MutableInt(n), sx, new MutableInt(incx), sy, new MutableInt(incy));
+ }
+
+ private static double ddot_(MutableInt n, double[] sx, MutableInt incx, double[] sy, MutableInt incy) {
+ int i, m, nn, iincx, iincy;
+ double stemp;
+ int ix, iy;
+
+ /* forms the dot product of two vectors.
+ uses unrolled loops for increments equal to one.
+ jack dongarra, linpack, 3/11/78.
+ modified 12/3/93, array(1) declarations changed to array(*) */
+
+ /* Dereference inputs */
+ nn = n.get();
+ iincx = incx.get();
+ iincy = incy.get();
+
+ stemp = 0.0;
+ if (nn > 0) {
+ if (iincx == 1 && iincy == 1) /* code for both increments equal to 1 */ {
+ m = nn - 4;
+ for (i = 0; i < m; i += 5)
+ stemp += sx[i] * sy[i] + sx[i + 1] * sy[i + 1] + sx[i + 2] * sy[i + 2] +
+ sx[i + 3] * sy[i + 3] + sx[i + 4] * sy[i + 4];
+
+ for (; i < nn; i++) /* clean-up loop */
+ stemp += sx[i] * sy[i];
+ } else /* code for unequal increments or equal increments not equal to 1 */ {
+ ix = 0;
+ iy = 0;
+ if (iincx < 0)
+ ix = (1 - nn) * iincx;
+ if (iincy < 0)
+ iy = (1 - nn) * iincy;
+ for (i = 0; i < nn; i++) {
+ stemp += sx[ix] * sy[iy];
+ ix += iincx;
+ iy += iincy;
+ }
+ }
+ }
+
+ return stemp;
+ }
+
+ static int daxpy_(int n, double sa, double[] sx, int incx, double[] sy, int incy) {
+ return daxpy_(new MutableInt(n), new MutableDouble(sa), sx, new MutableInt(incx), sy, new MutableInt(incy));
+ }
+
+ static int daxpy_(MutableInt n, MutableDouble sa, double[] sx, MutableInt incx, double[] sy, MutableInt incy) {
+ int i, m, ix, iy, nn, iincx, iincy;
+ double ssa;
+
+ /* constant times a vector plus a vector.
+ uses unrolled loop for increments equal to one.
+ jack dongarra, linpack, 3/11/78.
+ modified 12/3/93, array(1) declarations changed to array(*) */
+
+ /* Dereference inputs */
+ nn = n.get();
+ ssa = sa.get();
+ iincx = incx.get();
+ iincy = incy.get();
+
+ if (nn > 0 && ssa != 0.0) {
+ if (iincx == 1 && iincy == 1) /* code for both increments equal to 1 */ {
+ m = nn - 3;
+ for (i = 0; i < m; i += 4) {
+ sy[i] += ssa * sx[i];
+ sy[i + 1] += ssa * sx[i + 1];
+ sy[i + 2] += ssa * sx[i + 2];
+ sy[i + 3] += ssa * sx[i + 3];
+ }
+ for (; i < nn; ++i) /* clean-up loop */
+ sy[i] += ssa * sx[i];
+ } else /* code for unequal increments or equal increments not equal to 1 */ {
+ ix = iincx >= 0 ? 0 : (1 - nn) * iincx;
+ iy = iincy >= 0 ? 0 : (1 - nn) * iincy;
+ for (i = 0; i < nn; i++) {
+ sy[iy] += ssa * sx[ix];
+ ix += iincx;
+ iy += iincy;
+ }
+ }
+ }
+
+ return 0;
+ }
+
+ static int dscal_(int n, double sa, double[] sx, int incx) {
+ return dscal_(new MutableInt(n), new MutableDouble(sa), sx, new MutableInt(incx));
+ }
+
+ static int dscal_(MutableInt n, MutableDouble sa, double[] sx, MutableInt incx) {
+ int i, m, nincx, nn, iincx;
+ double ssa;
+
+ /* scales a vector by a constant.
+ uses unrolled loops for increment equal to 1.
+ jack dongarra, linpack, 3/11/78.
+ modified 3/93 to return if incx .le. 0.
+ modified 12/3/93, array(1) declarations changed to array(*) */
+
+ /* Dereference inputs */
+ nn = n.get();
+ iincx = incx.get();
+ ssa = sa.get();
+
+ if (nn > 0 && iincx > 0) {
+ if (iincx == 1) /* code for increment equal to 1 */ {
+ m = nn - 4;
+ for (i = 0; i < m; i += 5) {
+ sx[i] = ssa * sx[i];
+ sx[i + 1] = ssa * sx[i + 1];
+ sx[i + 2] = ssa * sx[i + 2];
+ sx[i + 3] = ssa * sx[i + 3];
+ sx[i + 4] = ssa * sx[i + 4];
+ }
+ for (; i < nn; ++i) /* clean-up loop */
+ sx[i] = ssa * sx[i];
+ } else /* code for increment not equal to 1 */ {
+ nincx = nn * iincx;
+ for (i = 0; i < nincx; i += iincx)
+ sx[i] = ssa * sx[i];
+ }
+ }
+
+ return 0;
+ }
+
+}
diff --git a/src/main/java/de/bwaldvogel/liblinear/DoubleArrayPointer.java b/src/main/java/de/bwaldvogel/liblinear/DoubleArrayPointer.java
index df9f9a2..0a20f2d 100644
--- a/src/main/java/de/bwaldvogel/liblinear/DoubleArrayPointer.java
+++ b/src/main/java/de/bwaldvogel/liblinear/DoubleArrayPointer.java
@@ -4,15 +4,15 @@
final class DoubleArrayPointer {
private final double[] _array;
- private int _offset;
+ private int _offset;
-
- public void setOffset(int offset) {
- if (offset < 0 || offset >= _array.length) throw new IllegalArgumentException("offset must be between 0 and the length of the array");
+ void setOffset(int offset) {
+ if (offset < 0 || offset >= _array.length)
+ throw new IllegalArgumentException("offset must be between 0 and the length of the array");
_offset = offset;
}
- public DoubleArrayPointer( final double[] array, final int offset ) {
+ DoubleArrayPointer(final double[] array, final int offset) {
_array = array;
setOffset(offset);
}
diff --git a/src/main/java/de/bwaldvogel/liblinear/FeatureNode.java b/src/main/java/de/bwaldvogel/liblinear/FeatureNode.java
index 68cb3e7..b5caf3f 100644
--- a/src/main/java/de/bwaldvogel/liblinear/FeatureNode.java
+++ b/src/main/java/de/bwaldvogel/liblinear/FeatureNode.java
@@ -2,11 +2,17 @@
public class FeatureNode implements Feature {
- public final int index;
- public double value;
+ public final int index;
+ public double value;
- public FeatureNode( final int index, final double value ) {
- if (index < 0) throw new IllegalArgumentException("index must be >= 0");
+ public FeatureNode(int index, double value) {
+ this(index, value, true);
+ }
+
+ FeatureNode(int index, double value, boolean validateIndex) {
+ if (validateIndex && index <= 0) {
+ throw new IllegalArgumentException("index must be > 0");
+ }
this.index = index;
this.value = value;
}
@@ -14,6 +20,7 @@ public FeatureNode( final int index, final double value ) {
/**
* @since 1.9
*/
+ @Override
public int getIndex() {
return index;
}
@@ -21,6 +28,7 @@ public int getIndex() {
/**
* @since 1.9
*/
+ @Override
public double getValue() {
return value;
}
@@ -28,6 +36,7 @@ public double getValue() {
/**
* @since 1.9
*/
+ @Override
public void setValue(double value) {
this.value = value;
}
@@ -45,12 +54,17 @@ public int hashCode() {
@Override
public boolean equals(Object obj) {
- if (this == obj) return true;
- if (obj == null) return false;
- if (getClass() != obj.getClass()) return false;
+ if (this == obj)
+ return true;
+ if (obj == null)
+ return false;
+ if (getClass() != obj.getClass())
+ return false;
FeatureNode other = (FeatureNode)obj;
- if (index != other.index) return false;
- if (Double.doubleToLongBits(value) != Double.doubleToLongBits(other.value)) return false;
+ if (index != other.index)
+ return false;
+ if (Double.doubleToLongBits(value) != Double.doubleToLongBits(other.value))
+ return false;
return true;
}
diff --git a/src/main/java/de/bwaldvogel/liblinear/Function.java b/src/main/java/de/bwaldvogel/liblinear/Function.java
index 2ad02f9..9645f2d 100644
--- a/src/main/java/de/bwaldvogel/liblinear/Function.java
+++ b/src/main/java/de/bwaldvogel/liblinear/Function.java
@@ -1,6 +1,5 @@
package de.bwaldvogel.liblinear;
-// origin: tron.h
interface Function {
double fun(double[] w);
@@ -10,4 +9,40 @@ interface Function {
void Hv(double[] s, double[] Hs);
int get_nr_variable();
+
+ void get_diag_preconditioner(double[] M);
+
+ // Note: This implementation is unused but function::linesearch_and_update
+ // from upstream newton.cpp seems to be unused as well
+ default double linesearch_and_update(double[] w, double[] s, MutableDouble f, double[] g, double alpha) {
+ double gTs = 0;
+ double eta = 0.01;
+ int n = get_nr_variable();
+ int max_num_linesearch = 20;
+ double[] w_new = new double[n];
+ double fold = f.get();
+
+ for (int i = 0; i < n; i++)
+ gTs += s[i] * g[i];
+
+ int num_linesearch = 0;
+ for (num_linesearch = 0; num_linesearch < max_num_linesearch; num_linesearch++) {
+ for (int i = 0; i < n; i++)
+ w_new[i] = w[i] + alpha * s[i];
+ f.set(fun(w_new));
+ if (f.get() - fold <= eta * alpha * gTs)
+ break;
+ else
+ alpha *= 0.5;
+ }
+
+ if (num_linesearch >= max_num_linesearch) {
+ f.set(fold);
+ return 0;
+ } else
+ System.arraycopy(w_new, 0, w, 0, n);
+
+ return alpha;
+ }
+
}
diff --git a/src/main/java/de/bwaldvogel/liblinear/Heap.java b/src/main/java/de/bwaldvogel/liblinear/Heap.java
new file mode 100644
index 0000000..9e1d3d9
--- /dev/null
+++ b/src/main/java/de/bwaldvogel/liblinear/Heap.java
@@ -0,0 +1,70 @@
+package de.bwaldvogel.liblinear;
+
+class Heap {
+
+ enum HeapType {
+ MIN, MAX,
+ }
+
+
+ private int size;
+ private final HeapType type;
+ private final Feature[] a;
+
+ Heap(int max_size, HeapType type) {
+ this.size = 0;
+ this.a = new Feature[max_size];
+ this.type = type;
+ }
+
+ private boolean cmp(Feature left, Feature right) {
+ if (this.type == HeapType.MIN) {
+ return left.getValue() > right.getValue();
+ } else {
+ return left.getValue() < right.getValue();
+ }
+ }
+
+ int size() {
+ return size;
+ }
+
+ void push(Feature node) {
+ a[size] = node;
+ size++;
+ int i = size - 1;
+ while (i > 0) {
+ int p = (i - 1) / 2;
+ if (cmp(a[p], a[i])) {
+ Linear.swap(a, i, p);
+ i = p;
+ } else {
+ break;
+ }
+ }
+ }
+
+ void pop() {
+ size--;
+ a[0] = a[size];
+ int i = 0;
+ while (i * 2 + 1 < size) {
+ int l = i * 2 + 1;
+ int r = i * 2 + 2;
+ if (r < size && cmp(a[l], a[r])) {
+ l = r;
+ }
+ if (cmp(a[i], a[l])) {
+ Linear.swap(a, i, l);
+ i = l;
+ } else {
+ break;
+ }
+ }
+ }
+
+ Feature top() {
+ return this.a[0];
+ }
+
+}
diff --git a/src/main/java/de/bwaldvogel/liblinear/IntArrayPointer.java b/src/main/java/de/bwaldvogel/liblinear/IntArrayPointer.java
index e708b80..b9b0862 100644
--- a/src/main/java/de/bwaldvogel/liblinear/IntArrayPointer.java
+++ b/src/main/java/de/bwaldvogel/liblinear/IntArrayPointer.java
@@ -4,15 +4,15 @@
final class IntArrayPointer {
private final int[] _array;
- private int _offset;
+ private int _offset;
-
- public void setOffset(int offset) {
- if (offset < 0 || offset >= _array.length) throw new IllegalArgumentException("offset must be between 0 and the length of the array");
+ void setOffset(int offset) {
+ if (offset < 0 || offset >= _array.length)
+ throw new IllegalArgumentException("offset must be between 0 and the length of the array");
_offset = offset;
}
- public IntArrayPointer( final int[] array, final int offset ) {
+ IntArrayPointer(final int[] array, final int offset) {
_array = array;
setOffset(offset);
}
diff --git a/src/main/java/de/bwaldvogel/liblinear/InvalidInputDataException.java b/src/main/java/de/bwaldvogel/liblinear/InvalidInputDataException.java
index 7a9a66c..b1676f4 100644
--- a/src/main/java/de/bwaldvogel/liblinear/InvalidInputDataException.java
+++ b/src/main/java/de/bwaldvogel/liblinear/InvalidInputDataException.java
@@ -1,57 +1,29 @@
package de.bwaldvogel.liblinear;
-import java.io.File;
-
public class InvalidInputDataException extends Exception {
private static final long serialVersionUID = 2945131732407207308L;
- private final int _line;
-
- private File _file;
+ private final int _line;
- public InvalidInputDataException( String message, File file, int line ) {
+ public InvalidInputDataException(String message, int line) {
super(message);
- _file = file;
_line = line;
}
- public InvalidInputDataException( String message, String filename, int line ) {
- this(message, new File(filename), line);
- }
-
- public InvalidInputDataException( String message, File file, int lineNr, Exception cause ) {
+ public InvalidInputDataException(String message, int lineNr, Exception cause) {
super(message, cause);
- _file = file;
_line = lineNr;
}
- public InvalidInputDataException( String message, String filename, int lineNr, Exception cause ) {
- this(message, new File(filename), lineNr, cause);
- }
-
- public File getFile() {
- return _file;
- }
-
- /**
- * This methods returns the path of the file.
- * The method name might be misleading.
- *
- * @deprecated use {@link #getFile()} instead
- */
- public String getFilename() {
- return _file.getPath();
- }
-
public int getLine() {
return _line;
}
@Override
public String toString() {
- return super.toString() + " (" + _file + ":" + _line + ")";
+ return super.toString() + " (line " + getLine() + ")";
}
}
diff --git a/src/main/java/de/bwaldvogel/liblinear/L2R_ErmFunction.java b/src/main/java/de/bwaldvogel/liblinear/L2R_ErmFunction.java
new file mode 100644
index 0000000..844a89f
--- /dev/null
+++ b/src/main/java/de/bwaldvogel/liblinear/L2R_ErmFunction.java
@@ -0,0 +1,128 @@
+package de.bwaldvogel.liblinear;
+
+// L2-regularized empirical risk minimization
+// min_w w^Tw/2 + \sum C_i \xi(w^Tx_i), where \xi() is the loss
+abstract class L2R_ErmFunction implements Function {
+
+ final double[] C;
+ final Problem prob;
+ double[] wx;
+ double[] tmp; // a working array
+ private double wTw;
+ final boolean regularize_bias;
+
+ L2R_ErmFunction(Problem prob, Parameter parameter, double[] C) {
+ int l = prob.l;
+
+ this.prob = prob;
+
+ wx = new double[l];
+ tmp = new double[l];
+ this.C = C;
+ this.regularize_bias = parameter.regularize_bias;
+ }
+
+ void Xv(double[] v, double[] Xv) {
+ int i;
+ int l = prob.l;
+ Feature[][] x = prob.x;
+
+ for (i = 0; i < l; i++)
+ Xv[i] = SparseOperator.dot(v, x[i]);
+ }
+
+ void XTv(double[] v, double[] XTv) {
+ int l = prob.l;
+ int w_size = get_nr_variable();
+ Feature[][] x = prob.x;
+
+ for (int i = 0; i < w_size; i++)
+ XTv[i] = 0;
+
+ for (int i = 0; i < l; i++) {
+ SparseOperator.axpy(v[i], x[i], XTv);
+ }
+ }
+
+ protected abstract double C_times_loss(int i, double wx_i);
+
+ @Override
+ public double fun(double[] w) {
+ int i;
+ double f = 0;
+ int l = prob.l;
+ int w_size = get_nr_variable();
+
+ wTw = 0;
+ Xv(w, wx);
+
+ for (i = 0; i < w_size; i++)
+ wTw += w[i] * w[i];
+ if (!regularize_bias)
+ wTw -= w[w_size - 1] * w[w_size - 1];
+ for (i = 0; i < l; i++)
+ f += C_times_loss(i, wx[i]);
+ f = f + 0.5 * wTw;
+
+ return (f);
+ }
+
+ @Override
+ public int get_nr_variable() {
+ return prob.n;
+ }
+
+ // On entry *f must be the function value of w
+ // On exit w is updated and *f is the new function value
+ @Override
+ public double linesearch_and_update(double[] w, double[] s, MutableDouble f, double[] g, double alpha) {
+ int i;
+ int l = prob.l;
+ double sTs = 0;
+ double wTs = 0;
+ double gTs = 0;
+ double eta = 0.01;
+ int w_size = get_nr_variable();
+ int max_num_linesearch = 20;
+ double fold = f.get();
+ Xv(s, tmp);
+
+ for (i = 0; i < w_size; i++) {
+ sTs += s[i] * s[i];
+ wTs += s[i] * w[i];
+ gTs += s[i] * g[i];
+ }
+ if (!regularize_bias) {
+ // bias not used in calculating (w + \alpha s)^T (w + \alpha s)
+ sTs -= s[w_size - 1] * s[w_size - 1];
+ wTs -= s[w_size - 1] * w[w_size - 1];
+ }
+
+ int num_linesearch = 0;
+ for (num_linesearch = 0; num_linesearch < max_num_linesearch; num_linesearch++) {
+ double loss = 0;
+ for (i = 0; i < l; i++) {
+ double inner_product = tmp[i] * alpha + wx[i];
+ loss += C_times_loss(i, inner_product);
+ }
+ f.set(loss + (alpha * alpha * sTs + wTw) / 2.0 + alpha * wTs);
+ if (f.get() - fold <= eta * alpha * gTs) {
+ for (i = 0; i < l; i++)
+ wx[i] += alpha * tmp[i];
+ break;
+ } else
+ alpha *= 0.5;
+ }
+
+ if (num_linesearch >= max_num_linesearch) {
+ f.set(fold);
+ return 0;
+ } else
+ for (i = 0; i < w_size; i++)
+ w[i] += alpha * s[i];
+
+ wTw += alpha * alpha * sTs + 2 * alpha * wTs;
+ return alpha;
+ }
+
+}
diff --git a/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvcFunction.java b/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvcFunction.java
index 0a857a0..a19303c 100644
--- a/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvcFunction.java
+++ b/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvcFunction.java
@@ -1,114 +1,96 @@
package de.bwaldvogel.liblinear;
-class L2R_L2_SvcFunction implements Function {
+class L2R_L2_SvcFunction extends L2R_ErmFunction {
- protected final Problem prob;
- protected final double[] C;
- protected final int[] I;
- protected final double[] z;
+ protected final int[] I;
+ protected int sizeI;
- protected int sizeI;
-
- public L2R_L2_SvcFunction( Problem prob, double[] C ) {
- int l = prob.l;
-
- this.prob = prob;
+ public L2R_L2_SvcFunction(Problem prob, Parameter param, double[] C) {
+ super(prob, param, C);
+ I = new int[prob.l];
+ }
- z = new double[l];
- I = new int[l];
- this.C = C;
+ @Override
+ protected double C_times_loss(int i, double wx_i) {
+ double d = 1 - prob.y[i] * wx_i;
+ if (d > 0)
+ return C[i] * d * d;
+ else
+ return 0;
}
- public double fun(double[] w) {
+ @Override
+ public void grad(double[] w, double[] g) {
int i;
- double f = 0;
double[] y = prob.y;
int l = prob.l;
int w_size = get_nr_variable();
- Xv(w, z);
-
- for (i = 0; i < w_size; i++)
- f += w[i] * w[i];
- f /= 2.0;
+ sizeI = 0;
for (i = 0; i < l; i++) {
- z[i] = y[i] * z[i];
- double d = 1 - z[i];
- if (d > 0) f += C[i] * d * d;
+ tmp[i] = wx[i] * y[i];
+ if (tmp[i] < 1) {
+ tmp[sizeI] = C[i] * y[i] * (tmp[i] - 1);
+ I[sizeI] = i;
+ sizeI++;
+ }
}
+ subXTv(tmp, g);
- return (f);
- }
-
- public int get_nr_variable() {
- return prob.n;
+ for (i = 0; i < w_size; i++)
+ g[i] = w[i] + 2 * g[i];
+ if (!regularize_bias)
+ g[w_size - 1] -= w[w_size - 1];
}
- public void grad(double[] w, double[] g) {
- double[] y = prob.y;
- int l = prob.l;
+ @Override
+ public void get_diag_preconditioner(double[] M) {
int w_size = get_nr_variable();
+ Feature[][] x = prob.x;
- sizeI = 0;
- for (int i = 0; i < l; i++) {
- if (z[i] < 1) {
- z[sizeI] = C[i] * y[i] * (z[i] - 1);
- I[sizeI] = i;
- sizeI++;
+ for (int i = 0; i < w_size; i++)
+ M[i] = 1;
+ if (!regularize_bias)
+ M[w_size - 1] = 0;
+
+ for (int i = 0; i < sizeI; i++) {
+ int idx = I[i];
+ for (Feature s : x[idx]) {
+ M[s.getIndex() - 1] += s.getValue() * s.getValue() * C[idx] * 2;
}
}
- subXTv(z, g);
-
- for (int i = 0; i < w_size; i++)
- g[i] = w[i] + 2 * g[i];
}
+ @Override
public void Hv(double[] s, double[] Hs) {
int i;
int w_size = get_nr_variable();
- double[] wa = new double[sizeI];
+ Feature[][] x = prob.x;
- subXv(s, wa);
- for (i = 0; i < sizeI; i++)
- wa[i] = C[I[i]] * wa[i];
+ for (i = 0; i < w_size; i++)
+ Hs[i] = 0;
+ for (i = 0; i < sizeI; i++) {
+ Feature[] xi = x[I[i]];
+ double xTs = SparseOperator.dot(s, xi);
+ xTs = C[I[i]] * xTs;
- subXTv(wa, Hs);
+ SparseOperator.axpy(xTs, xi, Hs);
+ }
for (i = 0; i < w_size; i++)
Hs[i] = s[i] + 2 * Hs[i];
+ if (!regularize_bias)
+ Hs[w_size - 1] -= s[w_size - 1];
}
protected void subXTv(double[] v, double[] XTv) {
int i;
int w_size = get_nr_variable();
+ Feature[][] x = prob.x;
for (i = 0; i < w_size; i++)
XTv[i] = 0;
-
- for (i = 0; i < sizeI; i++) {
- for (Feature s : prob.x[I[i]]) {
- XTv[s.getIndex() - 1] += v[i] * s.getValue();
- }
- }
- }
-
- private void subXv(double[] v, double[] Xv) {
-
- for (int i = 0; i < sizeI; i++) {
- Xv[i] = 0;
- for (Feature s : prob.x[I[i]]) {
- Xv[i] += v[s.getIndex() - 1] * s.getValue();
- }
- }
- }
-
- protected void Xv(double[] v, double[] Xv) {
-
- for (int i = 0; i < prob.l; i++) {
- Xv[i] = 0;
- for (Feature s : prob.x[i]) {
- Xv[i] += v[s.getIndex() - 1] * s.getValue();
- }
- }
+ for (i = 0; i < sizeI; i++)
+ SparseOperator.axpy(v[i], x[I[i]], XTv);
}
}
diff --git a/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvrFunction.java b/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvrFunction.java
index 88f250a..25c9ff5 100644
--- a/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvrFunction.java
+++ b/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvrFunction.java
@@ -5,63 +5,53 @@
*/
public class L2R_L2_SvrFunction extends L2R_L2_SvcFunction {
- private double p;
+ private final double p;
- public L2R_L2_SvrFunction( Problem prob, double[] C, double p ) {
- super(prob, C);
- this.p = p;
+ public L2R_L2_SvrFunction(Problem prob, Parameter param, double[] C) {
+ super(prob, param, C);
+ this.p = param.p;
}
@Override
- public double fun(double[] w) {
- double f = 0;
- double[] y = prob.y;
- int l = prob.l;
- int w_size = get_nr_variable();
- double d;
-
- Xv(w, z);
-
- for (int i = 0; i < w_size; i++)
- f += w[i] * w[i];
- f /= 2;
- for (int i = 0; i < l; i++) {
- d = z[i] - y[i];
- if (d < -p)
- f += C[i] * (d + p) * (d + p);
- else if (d > p) f += C[i] * (d - p) * (d - p);
- }
-
- return f;
+ protected double C_times_loss(int i, double wx_i) {
+ double d = wx_i - prob.y[i];
+ if (d < -p)
+ return C[i] * (d + p) * (d + p);
+ else if (d > p)
+ return C[i] * (d - p) * (d - p);
+ return 0;
}
@Override
public void grad(double[] w, double[] g) {
+ int i;
double[] y = prob.y;
int l = prob.l;
int w_size = get_nr_variable();
+ double d;
sizeI = 0;
- for (int i = 0; i < l; i++) {
- double d = z[i] - y[i];
+ for (i = 0; i < l; i++) {
+ d = wx[i] - y[i];
// generate index set I
if (d < -p) {
- z[sizeI] = C[i] * (d + p);
+ tmp[sizeI] = C[i] * (d + p);
I[sizeI] = i;
sizeI++;
} else if (d > p) {
- z[sizeI] = C[i] * (d - p);
+ tmp[sizeI] = C[i] * (d - p);
I[sizeI] = i;
sizeI++;
}
}
- subXTv(z, g);
+ subXTv(tmp, g);
- for (int i = 0; i < w_size; i++)
+ for (i = 0; i < w_size; i++)
g[i] = w[i] + 2 * g[i];
-
+ if (!regularize_bias)
+ g[w_size - 1] -= w[w_size - 1];
}
}
diff --git a/src/main/java/de/bwaldvogel/liblinear/L2R_LrFunction.java b/src/main/java/de/bwaldvogel/liblinear/L2R_LrFunction.java
index 6b58ae0..69b0f16 100644
--- a/src/main/java/de/bwaldvogel/liblinear/L2R_LrFunction.java
+++ b/src/main/java/de/bwaldvogel/liblinear/L2R_LrFunction.java
@@ -1,107 +1,83 @@
package de.bwaldvogel.liblinear;
-class L2R_LrFunction implements Function {
+class L2R_LrFunction extends L2R_ErmFunction {
- private final double[] C;
- private final double[] z;
private final double[] D;
- private final Problem prob;
- public L2R_LrFunction( Problem prob, double[] C ) {
+ L2R_LrFunction(Problem prob, Parameter param, double[] C) {
+ super(prob, param, C);
int l = prob.l;
-
- this.prob = prob;
-
- z = new double[l];
D = new double[l];
- this.C = C;
}
+ @Override
+ protected double C_times_loss(int i, double wx_i) {
+ double ywx_i = wx_i * prob.y[i];
+ if (ywx_i >= 0)
+ return C[i] * Math.log(1 + Math.exp(-ywx_i));
+ else
+ return C[i] * (-ywx_i + Math.log(1 + Math.exp(ywx_i)));
+ }
- private void Xv(double[] v, double[] Xv) {
+ @Override
+ public void grad(double[] w, double[] g) {
+ int i;
+ double[] y = prob.y;
+ int l = prob.l;
+ int w_size = get_nr_variable();
- for (int i = 0; i < prob.l; i++) {
- Xv[i] = 0;
- for (Feature s : prob.x[i]) {
- Xv[i] += v[s.getIndex() - 1] * s.getValue();
- }
+ for (i = 0; i < l; i++) {
+ tmp[i] = 1 / (1 + Math.exp(-y[i] * wx[i]));
+ D[i] = tmp[i] * (1 - tmp[i]);
+ tmp[i] = C[i] * (tmp[i] - 1) * y[i];
}
+ XTv(tmp, g);
+
+ for (i = 0; i < w_size; i++)
+ g[i] = w[i] + g[i];
+ if (!regularize_bias)
+ g[w_size - 1] -= w[w_size - 1];
}
- private void XTv(double[] v, double[] XTv) {
+ @Override
+ public void get_diag_preconditioner(double[] M) {
int l = prob.l;
int w_size = get_nr_variable();
Feature[][] x = prob.x;
for (int i = 0; i < w_size; i++)
- XTv[i] = 0;
+ M[i] = 1;
+ if (!regularize_bias)
+ M[w_size - 1] = 0;
for (int i = 0; i < l; i++) {
- for (Feature s : x[i]) {
- XTv[s.getIndex() - 1] += v[i] * s.getValue();
+ for (Feature xi : x[i]) {
+ M[xi.getIndex() - 1] += xi.getValue() * xi.getValue() * C[i] * D[i];
}
}
}
-
- public double fun(double[] w) {
+ @Override
+ public void Hv(double[] s, double[] Hs) {
int i;
- double f = 0;
- double[] y = prob.y;
int l = prob.l;
int w_size = get_nr_variable();
-
- Xv(w, z);
+ Feature[][] x = prob.x;
for (i = 0; i < w_size; i++)
- f += w[i] * w[i];
- f /= 2.0;
+ Hs[i] = 0;
for (i = 0; i < l; i++) {
- double yz = y[i] * z[i];
- if (yz >= 0)
- f += C[i] * Math.log(1 + Math.exp(-yz));
- else
- f += C[i] * (-yz + Math.log(1 + Math.exp(yz)));
- }
-
- return (f);
- }
+ Feature[] xi = x[i];
+ double xTs = SparseOperator.dot(s, xi);
- public void grad(double[] w, double[] g) {
- int i;
- double[] y = prob.y;
- int l = prob.l;
- int w_size = get_nr_variable();
+ xTs = C[i] * D[i] * xTs;
- for (i = 0; i < l; i++) {
- z[i] = 1 / (1 + Math.exp(-y[i] * z[i]));
- D[i] = z[i] * (1 - z[i]);
- z[i] = C[i] * (z[i] - 1) * y[i];
+ SparseOperator.axpy(xTs, xi, Hs);
}
- XTv(z, g);
-
- for (i = 0; i < w_size; i++)
- g[i] = w[i] + g[i];
- }
-
- public void Hv(double[] s, double[] Hs) {
- int i;
- int l = prob.l;
- int w_size = get_nr_variable();
- double[] wa = new double[l];
-
- Xv(s, wa);
- for (i = 0; i < l; i++)
- wa[i] = C[i] * D[i] * wa[i];
-
- XTv(wa, Hs);
for (i = 0; i < w_size; i++)
Hs[i] = s[i] + Hs[i];
- // delete[] wa;
- }
-
- public int get_nr_variable() {
- return prob.n;
+ if (!regularize_bias)
+ Hs[w_size - 1] -= s[w_size - 1];
}
}
diff --git a/src/main/java/de/bwaldvogel/liblinear/Linear.java b/src/main/java/de/bwaldvogel/liblinear/Linear.java
index 98ff061..0fb809b 100644
--- a/src/main/java/de/bwaldvogel/liblinear/Linear.java
+++ b/src/main/java/de/bwaldvogel/liblinear/Linear.java
@@ -1,46 +1,47 @@
package de.bwaldvogel.liblinear;
+import static de.bwaldvogel.liblinear.SolverType.*;
+
import java.io.BufferedReader;
-import java.io.BufferedWriter;
-import java.io.Closeable;
import java.io.EOFException;
import java.io.File;
-import java.io.FileInputStream;
-import java.io.FileOutputStream;
import java.io.IOException;
-import java.io.InputStreamReader;
-import java.io.OutputStreamWriter;
import java.io.PrintStream;
import java.io.Reader;
import java.io.Writer;
import java.nio.charset.Charset;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.Arrays;
import java.util.Formatter;
import java.util.Locale;
import java.util.Random;
import java.util.regex.Pattern;
+import de.bwaldvogel.liblinear.Heap.HeapType;
+
/**
*
*
- * The usage should be pretty similar to the C version of liblinear.
- * Please consider reading the README file of liblinear.
+ * The usage should be pretty similar to the C version of liblinear.
+ * Please consider reading the README file of liblinear.
*
* The port was done by Benedikt Waldvogel (mail at bwaldvogel.de)
*
- * @version 1.95
+ * @version 2.44
*/
public class Linear {
- static final Charset FILE_CHARSET = Charset.forName("ISO-8859-1");
+ static final int VERSION = 244;
- static final Locale DEFAULT_LOCALE = Locale.ENGLISH;
+ static final Charset FILE_CHARSET = StandardCharsets.ISO_8859_1;
- private static Object OUTPUT_MUTEX = new Object();
- private static PrintStream DEBUG_OUTPUT = System.out;
+ private static final Locale DEFAULT_LOCALE = Locale.ENGLISH;
- private static final long DEFAULT_RANDOM_SEED = 0L;
- static Random random = new Random(DEFAULT_RANDOM_SEED);
+ private static final Object OUTPUT_MUTEX = new Object();
+ private static PrintStream DEBUG_OUTPUT = System.out;
/**
* @param target predicted classes
@@ -59,7 +60,7 @@ public static void crossValidation(Problem prob, Parameter param, int nr_fold, d
for (i = 0; i < l; i++)
perm[i] = i;
for (i = 0; i < l; i++) {
- int j = i + random.nextInt(l - i);
+ int j = i + param.random.nextInt(l - i);
swap(perm, i, j);
}
for (i = 0; i <= nr_fold; i++)
@@ -94,6 +95,98 @@ public static void crossValidation(Problem prob, Parameter param, int nr_fold, d
}
}
+ public static ParameterSearchResult findParameters(Problem prob, Parameter param, int nr_fold, double start_C, double start_p) {
+ double best_C = Double.NaN;
+ double best_score = Double.NaN;
+ // prepare CV folds
+ int i;
+ int l = prob.l;
+ int[] perm = new int[l];
+ Problem[] subprob = new Problem[nr_fold];
+
+ if (nr_fold > l) {
+ nr_fold = l;
+ System.err.println("WARNING: # folds > # data. Will use # folds = # data instead (i.e., leave-one-out cross validation)");
+ }
+ int[] fold_start = new int[nr_fold + 1];
+ for (i = 0; i < l; i++)
+ perm[i] = i;
+ for (i = 0; i < l; i++) {
+ int j = i + param.random.nextInt(l - i);
+ swap(perm, i, j);
+ }
+ for (i = 0; i <= nr_fold; i++)
+ fold_start[i] = i * l / nr_fold;
+
+ for (i = 0; i < nr_fold; i++) {
+ int begin = fold_start[i];
+ int end = fold_start[i + 1];
+ int j, k;
+
+ assert subprob[i] == null;
+ subprob[i] = new Problem();
+ subprob[i].bias = prob.bias;
+ subprob[i].n = prob.n;
+ subprob[i].l = l - (end - begin);
+ subprob[i].x = new Feature[subprob[i].l][];
+ subprob[i].y = new double[subprob[i].l];
+
+ k = 0;
+ for (j = 0; j < begin; j++) {
+ subprob[i].x[k] = prob.x[perm[j]];
+ subprob[i].y[k] = prob.y[perm[j]];
+ ++k;
+ }
+ for (j = end; j < l; j++) {
+ subprob[i].x[k] = prob.x[perm[j]];
+ subprob[i].y[k] = prob.y[perm[j]];
+ ++k;
+ }
+ }
+
+ Parameter param_tmp = param.clone();
+ double best_p = -1;
+ if (param.getSolverType() == L2R_LR || param.getSolverType() == L2R_L2LOSS_SVC) {
+ if (start_C <= 0)
+ start_C = calc_start_C(prob, param_tmp);
+ double max_C = 1024;
+ start_C = Math.min(start_C, max_C);
+ ParameterCSearchResult best_tmp = find_parameter_C(prob, param_tmp, start_C, max_C, fold_start, perm, subprob, nr_fold);
+ best_C = best_tmp.getBestC();
+ best_score = best_tmp.getBestScore();
+ } else if (param.getSolverType() == L2R_L2LOSS_SVR) {
+ double max_p = calc_max_p(prob);
+ int num_p_steps = 20;
+ double max_C = 1048576;
+ best_score = Double.POSITIVE_INFINITY;
+ i = num_p_steps - 1;
+ if (start_p > 0)
+ i = Math.min((int)(start_p / (max_p / num_p_steps)), i);
+ for (; i >= 0; i--) {
+ param_tmp.p = i * max_p / num_p_steps;
+ double start_C_tmp;
+ if (start_C <= 0)
+ start_C_tmp = calc_start_C(prob, param_tmp);
+ else
+ start_C_tmp = start_C;
+ start_C_tmp = Math.min(start_C_tmp, max_C);
+
+ ParameterCSearchResult best_tmp = find_parameter_C(prob, param_tmp, start_C_tmp, max_C, fold_start, perm, subprob, nr_fold);
+
+ if (best_tmp.getBestScore() < best_score) {
+ best_p = param_tmp.p;
+ best_C = best_tmp.getBestC();
+ best_score = best_tmp.getBestScore();
+ }
+ }
+ } else {
+ // Note: The upstream version of liblinear does *NOT* throw an exception in this case
+ throw new IllegalArgumentException("Unsupported solver: " + param.getSolverType());
+ }
+
+ return new ParameterSearchResult(best_C, best_score, best_p);
+ }
+
/** used as complex return type */
private static class GroupClassesReturn {
@@ -102,7 +195,7 @@ private static class GroupClassesReturn {
final int nr_class;
final int[] start;
- GroupClassesReturn( int nr_class, int[] label, int[] start, int[] count ) {
+ GroupClassesReturn(int nr_class, int[] label, int[] start, int[] count) {
this.nr_class = nr_class;
this.label = label;
this.start = start;
@@ -133,8 +226,8 @@ private static GroupClassesReturn groupClasses(Problem prob, int[] perm) {
if (j == nr_class) {
if (nr_class == max_nr_class) {
max_nr_class *= 2;
- label = copyOf(label, max_nr_class);
- count = copyOf(count, max_nr_class);
+ label = Arrays.copyOf(label, max_nr_class);
+ count = Arrays.copyOf(count, max_nr_class);
}
label[nr_class] = this_label;
count[nr_class] = 1;
@@ -175,7 +268,8 @@ private static GroupClassesReturn groupClasses(Problem prob, int[] perm) {
static void info(String message) {
synchronized (OUTPUT_MUTEX) {
- if (DEBUG_OUTPUT == null) return;
+ if (DEBUG_OUTPUT == null)
+ return;
DEBUG_OUTPUT.printf(message);
DEBUG_OUTPUT.flush();
}
@@ -183,7 +277,8 @@ static void info(String message) {
static void info(String format, Object... args) {
synchronized (OUTPUT_MUTEX) {
- if (DEBUG_OUTPUT == null) return;
+ if (DEBUG_OUTPUT == null)
+ return;
DEBUG_OUTPUT.printf(format, args);
DEBUG_OUTPUT.flush();
}
@@ -195,7 +290,8 @@ static void info(String format, Object... args) {
* @throws NumberFormatException see {@link Double#parseDouble(String)}
*/
static double atof(String s) {
- if (s == null || s.length() < 1) throw new IllegalArgumentException("Can't convert empty string to integer");
+ if (s == null || s.length() < 1)
+ throw new IllegalArgumentException("Can't convert empty string to integer");
double d = Double.parseDouble(s);
if (Double.isNaN(d) || Double.isInfinite(d)) {
throw new IllegalArgumentException("NaN or Infinity in input: " + s);
@@ -209,30 +305,14 @@ static double atof(String s) {
* @throws NumberFormatException see {@link Integer#parseInt(String)}
*/
static int atoi(String s) throws NumberFormatException {
- if (s == null || s.length() < 1) throw new IllegalArgumentException("Can't convert empty string to integer");
+ if (s == null || s.length() < 1)
+ throw new IllegalArgumentException("Can't convert empty string to integer");
// Integer.parseInt doesn't accept '+' prefixed strings
- if (s.charAt(0) == '+') s = s.substring(1);
+ if (s.charAt(0) == '+')
+ s = s.substring(1);
return Integer.parseInt(s);
}
- /**
- * Java5 'backport' of Arrays.copyOf
- */
- public static double[] copyOf(double[] original, int newLength) {
- double[] copy = new double[newLength];
- System.arraycopy(original, 0, copy, 0, Math.min(original.length, newLength));
- return copy;
- }
-
- /**
- * Java5 'backport' of Arrays.copyOf
- */
- public static int[] copyOf(int[] original, int newLength) {
- int[] copy = new int[newLength];
- System.arraycopy(original, 0, copy, 0, Math.min(original.length, newLength));
- return copy;
- }
-
/**
* Loads the model from inputReader.
* It uses {@link java.util.Locale#ENGLISH} for number formatting.
@@ -253,22 +333,19 @@ public static Model loadModel(Reader inputReader) throws IOException {
reader = new BufferedReader(inputReader);
}
- String line = null;
+ String line;
while ((line = reader.readLine()) != null) {
String[] split = whitespace.split(line);
if (split[0].equals("solver_type")) {
- SolverType solver = SolverType.valueOf(split[1]);
- if (solver == null) {
- throw new RuntimeException("unknown solver type");
- }
- model.solverType = solver;
+ model.solverType = SolverType.valueOf(split[1]);
} else if (split[0].equals("nr_class")) {
model.nr_class = atoi(split[1]);
- Integer.parseInt(split[1]);
} else if (split[0].equals("nr_feature")) {
model.nr_feature = atoi(split[1]);
} else if (split[0].equals("bias")) {
model.bias = atof(split[1]);
+ } else if (split[0].equals("rho")) {
+ model.rho = atof(split[1]);
} else if (split[0].equals("w")) {
break;
} else if (split[0].equals("label")) {
@@ -282,10 +359,12 @@ public static Model loadModel(Reader inputReader) throws IOException {
}
int w_size = model.nr_feature;
- if (model.bias >= 0) w_size++;
+ if (model.bias >= 0)
+ w_size++;
int nr_w = model.nr_class;
- if (model.nr_class == 2 && model.solverType != SolverType.MCSVM_CS) nr_w = 1;
+ if (model.nr_class == 2 && model.solverType != MCSVM_CS)
+ nr_w = 1;
model.w = new double[w_size * nr_w];
int[] buffer = new int[128];
@@ -302,6 +381,11 @@ public static Model loadModel(Reader inputReader) throws IOException {
model.w[i * nr_w + j] = atof(new String(buffer, 0, b));
break;
} else {
+ if (b >= buffer.length) {
+ throw new RuntimeException("illegal weight in model file at index " + (i * nr_w + j) + ", with string content '" +
+ new String(buffer, 0, buffer.length) + "', is not terminated " +
+ "with a whitespace character, or is longer than expected (" + buffer.length + " characters max).");
+ }
buffer[b++] = ch;
}
}
@@ -313,23 +397,23 @@ public static Model loadModel(Reader inputReader) throws IOException {
/**
* Loads the model from the file with ISO-8859-1 charset.
- * It uses {@link java.util.Locale#ENGLISH} for number formatting.
+ * It uses {@link Locale#ENGLISH} for number formatting.
+ *
+ * @deprecated use {@link Linear#loadModel(Path)} instead
*/
+ @Deprecated
public static Model loadModel(File modelFile) throws IOException {
- BufferedReader inputReader = new BufferedReader(new InputStreamReader(new FileInputStream(modelFile), FILE_CHARSET));
- try {
- return loadModel(inputReader);
- }
- finally {
- inputReader.close();
- }
+ return loadModel(modelFile.toPath());
}
- static void closeQuietly(Closeable c) {
- if (c == null) return;
- try {
- c.close();
- } catch (Throwable t) {}
+ /**
+ * Loads the model from the file with ISO-8859-1 charset.
+ * It uses {@link java.util.Locale#ENGLISH} for number formatting.
+ */
+ public static Model loadModel(Path modelPath) throws IOException {
+ try (Reader inputReader = Files.newBufferedReader(modelPath, FILE_CHARSET)) {
+ return loadModel(inputReader);
+ }
}
public static double predict(Model model, Feature[] x) {
@@ -390,7 +474,7 @@ public static double predictValues(Model model, Feature[] x, double[] dec_values
double[] w = model.w;
int nr_w;
- if (model.nr_class == 2 && model.solverType != SolverType.MCSVM_CS)
+ if (model.nr_class == 2 && model.solverType != MCSVM_CS)
nr_w = 1;
else
nr_w = model.nr_class;
@@ -407,16 +491,22 @@ public static double predictValues(Model model, Feature[] x, double[] dec_values
}
}
}
+ if (model.solverType.isOneClass()) {
+ dec_values[0] -= model.rho;
+ }
if (model.nr_class == 2) {
if (model.solverType.isSupportVectorRegression())
return dec_values[0];
+ else if (model.solverType.isOneClass())
+ return (dec_values[0] > 0) ? 1 : -1;
else
return (dec_values[0] > 0) ? model.label[0] : model.label[1];
} else {
int dec_max_idx = 0;
for (int i = 1; i < model.nr_class; i++) {
- if (dec_values[i] > dec_values[dec_max_idx]) dec_max_idx = i;
+ if (dec_values[i] > dec_values[dec_max_idx])
+ dec_max_idx = i;
}
return model.label[dec_max_idx];
}
@@ -425,7 +515,8 @@ public static double predictValues(Model model, Feature[] x, double[] dec_values
static void printf(Formatter formatter, String format, Object... args) throws IOException {
formatter.format(format, args);
IOException ioException = formatter.ioException();
- if (ioException != null) throw ioException;
+ if (ioException != null)
+ throw ioException;
}
/**
@@ -437,13 +528,14 @@ static void printf(Formatter formatter, String format, Object... args) throws IO
public static void saveModel(Writer modelOutput, Model model) throws IOException {
int nr_feature = model.nr_feature;
int w_size = nr_feature;
- if (model.bias >= 0) w_size++;
+ if (model.bias >= 0)
+ w_size++;
int nr_w = model.nr_class;
- if (model.nr_class == 2 && model.solverType != SolverType.MCSVM_CS) nr_w = 1;
+ if (model.nr_class == 2 && model.solverType != MCSVM_CS)
+ nr_w = 1;
- Formatter formatter = new Formatter(modelOutput, DEFAULT_LOCALE);
- try {
+ try (Formatter formatter = new Formatter(modelOutput, DEFAULT_LOCALE)) {
printf(formatter, "solver_type %s\n", model.solverType.name());
printf(formatter, "nr_class %d\n", model.nr_class);
@@ -456,18 +548,21 @@ public static void saveModel(Writer modelOutput, Model model) throws IOException
}
printf(formatter, "nr_feature %d\n", nr_feature);
- printf(formatter, "bias %.16g\n", model.bias);
+ printf(formatter, "bias %.17g\n", model.bias);
+
+ if (model.solverType.isOneClass())
+ printf(formatter, "rho %.17g\n", model.rho);
printf(formatter, "w\n");
for (int i = 0; i < w_size; i++) {
for (int j = 0; j < nr_w; j++) {
double value = model.w[i * nr_w + j];
- /** this optimization is the reason for {@link Model#equals(double[], double[])} */
+ /* this optimization is the reason for {@link Model#equals(double[], double[])} */
if (value == 0.0) {
printf(formatter, "%d ", 0);
} else {
- printf(formatter, "%.16g ", value);
+ printf(formatter, "%.17g ", value);
}
}
printf(formatter, "\n");
@@ -475,20 +570,30 @@ public static void saveModel(Writer modelOutput, Model model) throws IOException
formatter.flush();
IOException ioException = formatter.ioException();
- if (ioException != null) throw ioException;
- }
- finally {
- formatter.close();
+ if (ioException != null)
+ throw ioException;
}
}
/**
* Writes the model to the file with ISO-8859-1 charset.
- * It uses {@link java.util.Locale#ENGLISH} for number formatting.
+ * It uses {@link Locale#ENGLISH} for number formatting.
+ *
+ * @deprecated use {@link Linear#saveModel(Path, Model)} instead
*/
+ @Deprecated
public static void saveModel(File modelFile, Model model) throws IOException {
- BufferedWriter modelOutput = new BufferedWriter(new OutputStreamWriter(new FileOutputStream(modelFile), FILE_CHARSET));
- saveModel(modelOutput, model);
+ saveModel(modelFile.toPath(), model);
+ }
+
+ /**
+ * Writes the model to the file with ISO-8859-1 charset.
+ * It uses {@link java.util.Locale#ENGLISH} for number formatting.
+ */
+ public static void saveModel(Path modelPath, Model model) throws IOException {
+ try (Writer modelWriter = Files.newBufferedWriter(modelPath, FILE_CHARSET)) {
+ saveModel(modelWriter, model);
+ }
}
/*
@@ -510,13 +615,13 @@ private static int GETI(byte[] y, int i) {
* D is a diagonal matrix
*
* In L1-SVM case:
- * upper_bound_i = Cp if y_i = 1
- * upper_bound_i = Cn if y_i = -1
- * D_ii = 0
+ * upper_bound_i = Cp if y_i = 1
+ * upper_bound_i = Cn if y_i = -1
+ * D_ii = 0
* In L2-SVM case:
- * upper_bound_i = INF
- * D_ii = 1/(2*Cp) if y_i = 1
- * D_ii = 1/(2*Cn) if y_i = -1
+ * upper_bound_i = INF
+ * D_ii = 1/(2*Cp) if y_i = 1
+ * D_ii = 1/(2*Cn) if y_i = -1
*
* Given:
* x, y, Cp, Cn
@@ -524,16 +629,19 @@ private static int GETI(byte[] y, int i) {
*
* solution will be put in w
*
+ * this function returns the number of iterations
+ *
* See Algorithm 3 of Hsieh et al., ICML 2008
*
*/
- private static void solve_l2r_l1l2_svc(Problem prob, double[] w, double eps, double Cp, double Cn, SolverType solver_type) {
+ private static int solve_l2r_l1l2_svc(Problem prob, Parameter param, double[] w, double Cp, double Cn, int max_iter) {
int l = prob.l;
int w_size = prob.n;
+ double eps = param.eps;
+ SolverType solver_type = param.solverType;
int i, s, iter = 0;
double C, d, G;
double[] QD = new double[l];
- int max_iter = 1000;
int[] index = new int[l];
double[] alpha = new double[l];
byte[] y = new byte[l];
@@ -548,7 +656,7 @@ private static void solve_l2r_l1l2_svc(Problem prob, double[] w, double eps, dou
// default solver_type: L2R_L2LOSS_SVC_DUAL
double diag[] = new double[] {0.5 / Cn, 0, 0.5 / Cp};
double upper_bound[] = new double[] {Double.POSITIVE_INFINITY, 0, Double.POSITIVE_INFINITY};
- if (solver_type == SolverType.L2R_L1LOSS_SVC_DUAL) {
+ if (solver_type == L2R_L1LOSS_SVC_DUAL) {
diag[0] = 0;
diag[2] = 0;
upper_bound[0] = Cn;
@@ -573,11 +681,10 @@ private static void solve_l2r_l1l2_svc(Problem prob, double[] w, double eps, dou
for (i = 0; i < l; i++) {
QD[i] = diag[GETI(y, i)];
- for (Feature xi : prob.x[i]) {
- double val = xi.getValue();
- QD[i] += val * val;
- w[xi.getIndex() - 1] += y[i] * alpha[i] * val;
- }
+ Feature[] xi = prob.x[i];
+ QD[i] += SparseOperator.nrm2_sq(xi);
+ SparseOperator.axpy(y[i] * alpha[i], xi, w);
+
index[i] = i;
}
@@ -586,19 +693,16 @@ private static void solve_l2r_l1l2_svc(Problem prob, double[] w, double eps, dou
PGmin_new = Double.POSITIVE_INFINITY;
for (i = 0; i < active_size; i++) {
- int j = i + random.nextInt(active_size - i);
+ int j = i + param.random.nextInt(active_size - i);
swap(index, i, j);
}
for (s = 0; s < active_size; s++) {
i = index[s];
- G = 0;
byte yi = y[i];
+ Feature[] xi = prob.x[i];
- for (Feature xi : prob.x[i]) {
- G += w[xi.getIndex() - 1] * xi.getValue();
- }
- G = G * yi - 1;
+ G = yi * SparseOperator.dot(w, xi) - 1;
C = upper_bound[GETI(y, i)];
G += alpha[i] * diag[GETI(y, i)];
@@ -633,17 +737,16 @@ private static void solve_l2r_l1l2_svc(Problem prob, double[] w, double eps, dou
double alpha_old = alpha[i];
alpha[i] = Math.min(Math.max(alpha[i] - G / QD[i], 0.0), C);
d = (alpha[i] - alpha_old) * yi;
-
- for (Feature xi : prob.x[i]) {
- w[xi.getIndex() - 1] += d * xi.getValue();
- }
+ SparseOperator.axpy(d, xi, w);
}
}
iter++;
- if (iter % 10 == 0) info(".");
+ if (iter % 10 == 0)
+ info(".");
- if (PGmax_new - PGmin_new <= eps) {
+ if (PGmax_new - PGmin_new <= eps &&
+ Math.abs(PGmax_new) <= eps && Math.abs(PGmin_new) <= eps) {
if (active_size == l)
break;
else {
@@ -656,12 +759,13 @@ private static void solve_l2r_l1l2_svc(Problem prob, double[] w, double eps, dou
}
PGmax_old = PGmax_new;
PGmin_old = PGmin_new;
- if (PGmax_old <= 0) PGmax_old = Double.POSITIVE_INFINITY;
- if (PGmin_old >= 0) PGmin_old = Double.NEGATIVE_INFINITY;
+ if (PGmax_old <= 0)
+ PGmax_old = Double.POSITIVE_INFINITY;
+ if (PGmin_old >= 0)
+ PGmin_old = Double.NEGATIVE_INFINITY;
}
info("%noptimization finished, #iter = %d%n", iter);
- if (iter >= max_iter) info("%nWARNING: reaching max number of iterations%nUsing -s 2 may be faster (also see FAQ)%n%n");
// calculate objective value
@@ -671,10 +775,13 @@ private static void solve_l2r_l1l2_svc(Problem prob, double[] w, double eps, dou
v += w[i] * w[i];
for (i = 0; i < l; i++) {
v += alpha[i] * (alpha[i] * diag[GETI(y, i)] - 2);
- if (alpha[i] > 0) ++nSV;
+ if (alpha[i] > 0)
+ ++nSV;
}
info("Objective value = %g%n", v / 2);
info("nSV = %d%n", nSV);
+
+ return iter;
}
// To support weights for instances, use GETI(i) (i)
@@ -693,11 +800,11 @@ private static int GETI_SVR(int i) {
* D is a diagonal matrix
*
* In L1-SVM case:
- * upper_bound_i = C
- * lambda_i = 0
+ * upper_bound_i = C
+ * lambda_i = 0
* In L2-SVM case:
- * upper_bound_i = INF
- * lambda_i = 1/(2*C)
+ * upper_bound_i = INF
+ * lambda_i = 1/(2*C)
*
* Given:
* x, y, p, C
@@ -705,23 +812,25 @@ private static int GETI_SVR(int i) {
*
* solution will be put in w
*
+ * this function returns the number of iterations
+ *
* See Algorithm 4 of Ho and Lin, 2012
*/
- private static void solve_l2r_l1l2_svr(Problem prob, double[] w, Parameter param) {
+ private static int solve_l2r_l1l2_svr(Problem prob, Parameter param, double[] w, int max_iter) {
+ SolverType solver_type = param.solverType;
int l = prob.l;
double C = param.C;
double p = param.p;
int w_size = prob.n;
double eps = param.eps;
int i, s, iter = 0;
- int max_iter = param.getMaxIters();
int active_size = l;
int[] index = new int[l];
double d, G, H;
double Gmax_old = Double.POSITIVE_INFINITY;
double Gmax_new, Gnorm1_new;
- double Gnorm1_init= -1.0; // Gnorm1_init is initialized at the first iteration
+ double Gnorm1_init = -1.0; // Gnorm1_init is initialized at the first iteration
double[] beta = new double[l];
double[] QD = new double[l];
double[] y = prob.y;
@@ -730,9 +839,11 @@ private static void solve_l2r_l1l2_svr(Problem prob, double[] w, Parameter param
double[] lambda = new double[] {0.5 / C};
double[] upper_bound = new double[] {Double.POSITIVE_INFINITY};
- if (param.solverType == SolverType.L2R_L1LOSS_SVR_DUAL) {
+ if (solver_type == L2R_L1LOSS_SVR_DUAL) {
lambda[0] = 0;
upper_bound[0] = C;
+ } else {
+ assert solver_type == L2R_L2LOSS_SVR_DUAL;
}
// Initial beta can be set here. Note that
@@ -743,23 +854,19 @@ private static void solve_l2r_l1l2_svr(Problem prob, double[] w, Parameter param
for (i = 0; i < w_size; i++)
w[i] = 0;
for (i = 0; i < l; i++) {
- QD[i] = 0;
- for (Feature xi : prob.x[i]) {
- double val = xi.getValue();
- QD[i] += val * val;
- w[xi.getIndex() - 1] += beta[i] * val;
- }
+ Feature[] xi = prob.x[i];
+ QD[i] = SparseOperator.nrm2_sq(xi);
+ SparseOperator.axpy(beta[i], xi, w);
index[i] = i;
}
-
while (iter < max_iter) {
Gmax_new = 0;
Gnorm1_new = 0;
for (i = 0; i < active_size; i++) {
- int j = i + random.nextInt(active_size - i);
+ int j = i + param.random.nextInt(active_size - i);
swap(index, i, j);
}
@@ -768,11 +875,8 @@ private static void solve_l2r_l1l2_svr(Problem prob, double[] w, Parameter param
G = -y[i] + lambda[GETI_SVR(i)] * beta[i];
H = QD[i] + lambda[GETI_SVR(i)];
- for (Feature xi : prob.x[i]) {
- int ind = xi.getIndex() - 1;
- double val = xi.getValue();
- G += val * w[ind];
- }
+ Feature[] xi = prob.x[i];
+ G += SparseOperator.dot(w, xi);
double Gp = G + p;
double Gn = G - p;
@@ -822,22 +926,22 @@ else if (Gn > H * beta[i])
else
d = -beta[i];
- if (Math.abs(d) < 1.0e-12) continue;
+ if (Math.abs(d) < 1.0e-12)
+ continue;
double beta_old = beta[i];
beta[i] = Math.min(Math.max(beta[i] + d, -upper_bound[GETI_SVR(i)]), upper_bound[GETI_SVR(i)]);
d = beta[i] - beta_old;
- if (d != 0) {
- for (Feature xi : prob.x[i]) {
- w[xi.getIndex() - 1] += d * xi.getValue();
- }
- }
+ if (d != 0)
+ SparseOperator.axpy(d, xi, w);
}
- if (iter == 0) Gnorm1_init = Gnorm1_new;
+ if (iter == 0)
+ Gnorm1_init = Gnorm1_new;
iter++;
- if (iter % 10 == 0) info(".");
+ if (iter % 10 == 0)
+ info(".");
if (Gnorm1_new <= eps * Gnorm1_init) {
if (active_size == l)
@@ -854,7 +958,6 @@ else if (Gn > H * beta[i])
}
info("%noptimization finished, #iter = %d%n", iter);
- if (iter >= max_iter) info("%nWARNING: reaching max number of iterations%nUsing -s 11 may be faster%n%n");
// calculate objective value
double v = 0;
@@ -864,11 +967,14 @@ else if (Gn > H * beta[i])
v = 0.5 * v;
for (i = 0; i < l; i++) {
v += p * Math.abs(beta[i]) - y[i] * beta[i] + 0.5 * lambda[GETI_SVR(i)] * beta[i] * beta[i];
- if (beta[i] != 0) nSV++;
+ if (beta[i] != 0)
+ nSV++;
}
info("Objective value = %g%n", v);
info("nSV = %d%n", nSV);
+
+ return iter;
}
/**
@@ -888,24 +994,26 @@ else if (Gn > H * beta[i])
*
* solution will be put in w
*
+ * this function returns the number of iterations
+ *
* See Algorithm 5 of Yu et al., MLJ 2010
*
*
* @since 1.7
*/
- private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, double Cp, double Cn) {
+ private static int solve_l2r_lr_dual(Problem prob, Parameter param, double[] w, double Cp, double Cn, int max_iter) {
int l = prob.l;
int w_size = prob.n;
+ double eps = param.eps;
int i, s, iter = 0;
- double xTx[] = new double[l];
- int max_iter = 1000;
- int index[] = new int[l];
- double alpha[] = new double[2 * l]; // store alpha and C - alpha
- byte y[] = new byte[l];
+ double[] xTx = new double[l];
+ int[] index = new int[l];
+ double[] alpha = new double[2 * l]; // store alpha and C - alpha
+ byte[] y = new byte[l];
int max_inner_iter = 100; // for inner Newton
double innereps = 1e-2;
double innereps_min = Math.min(1e-8, eps);
- double upper_bound[] = new double[] {Cn, 0, Cp};
+ double[] upper_bound = new double[] {Cn, 0, Cp};
for (i = 0; i < l; i++) {
if (prob.y[i] > 0) {
@@ -926,31 +1034,26 @@ private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, doub
for (i = 0; i < w_size; i++)
w[i] = 0;
for (i = 0; i < l; i++) {
- xTx[i] = 0;
- for (Feature xi : prob.x[i]) {
- double val = xi.getValue();
- xTx[i] += val * val;
- w[xi.getIndex() - 1] += y[i] * alpha[2 * i] * val;
- }
+ Feature[] xi = prob.x[i];
+ xTx[i] = SparseOperator.nrm2_sq(xi);
+ SparseOperator.axpy(y[i] * alpha[2 * i], xi, w);
index[i] = i;
}
while (iter < max_iter) {
for (i = 0; i < l; i++) {
- int j = i + random.nextInt(l - i);
+ int j = i + param.random.nextInt(l - i);
swap(index, i, j);
}
int newton_iter = 0;
double Gmax = 0;
for (s = 0; s < l; s++) {
i = index[s];
- byte yi = y[i];
+ final byte yi = y[i];
double C = upper_bound[GETI(y, i)];
double ywTx = 0, xisq = xTx[i];
- for (Feature xi : prob.x[i]) {
- ywTx += w[xi.getIndex() - 1] * xi.getValue();
- }
- ywTx *= y[i];
+ Feature[] xi = prob.x[i];
+ ywTx = yi * SparseOperator.dot(w, xi);
double a = xisq, b = ywTx;
// Decide to minimize g_1(z) or g_2(z)
@@ -964,7 +1067,8 @@ private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, doub
// g_t(z) = z*log(z) + (C-z)*log(C-z) + 0.5a(z-alpha_old)^2 + sign*b(z-alpha_old)
double alpha_old = alpha[ind1];
double z = alpha_old;
- if (C - z < 0.5 * C) z = 0.1 * z;
+ if (C - z < 0.5 * C)
+ z = 0.1 * z;
double gp = a * (z - alpha_old) + sign * b + Math.log(z / (C - z));
Gmax = Math.max(Gmax, Math.abs(gp));
@@ -972,7 +1076,8 @@ private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, doub
final double eta = 0.1; // xi in the paper
int inner_iter = 0;
while (inner_iter <= max_inner_iter) {
- if (Math.abs(gp) < innereps) break;
+ if (Math.abs(gp) < innereps)
+ break;
double gpp = a + C / (C - z) / z;
double tmpz = z - gp / gpp;
if (tmpz <= 0)
@@ -989,16 +1094,16 @@ private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, doub
{
alpha[ind1] = z;
alpha[ind2] = C - z;
- for (Feature xi : prob.x[i]) {
- w[xi.getIndex() - 1] += sign * (z - alpha_old) * yi * xi.getValue();
- }
+ SparseOperator.axpy(sign * (z - alpha_old) * yi, xi, w);
}
}
iter++;
- if (iter % 10 == 0) info(".");
+ if (iter % 10 == 0)
+ info(".");
- if (Gmax < eps) break;
+ if (Gmax < eps)
+ break;
if (newton_iter <= l / 10) {
innereps = Math.max(innereps_min, 0.1 * innereps);
@@ -1007,7 +1112,6 @@ private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, doub
}
info("%noptimization finished, #iter = %d%n", iter);
- if (iter >= max_iter) info("%nWARNING: reaching max number of iterations%nUsing -s 0 may be faster (also see FAQ)%n%n");
// calculate objective value
@@ -1017,8 +1121,10 @@ private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, doub
v *= 0.5;
for (i = 0; i < l; i++)
v += alpha[2 * i] * Math.log(alpha[2 * i]) + alpha[2 * i + 1] * Math.log(alpha[2 * i + 1]) - upper_bound[GETI(y, i)]
- * Math.log(upper_bound[GETI(y, i)]);
+ * Math.log(upper_bound[GETI(y, i)]);
info("Objective value = %g%n", v);
+
+ return iter;
}
/**
@@ -1034,16 +1140,22 @@ private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, doub
*
* solution will be put in w
*
+ * this function returns the number of iterations
+ *
* See Yuan et al. (2010) and appendix of LIBLINEAR paper, Fan et al. (2008)
+ *
+ * To not regularize the bias (i.e., regularize_bias = 0), a constant feature = 1
+ * must have been added to the original data. (see -B and -R option)
*
*
* @since 1.5
*/
- private static void solve_l1r_l2_svc(Problem prob_col, double[] w, double eps, double Cp, double Cn) {
+ private static int solve_l1r_l2_svc(Problem prob_col, Parameter param, double[] w,
+ double Cp, double Cn, double eps, int max_iter) {
int l = prob_col.l;
int w_size = prob_col.n;
+ boolean regularize_bias = param.regularize_bias;
int j, s, iter = 0;
- int max_iter = 1000;
int active_size = w_size;
int max_num_linesearch = 20;
@@ -1093,7 +1205,7 @@ private static void solve_l1r_l2_svc(Problem prob_col, double[] w, double eps, d
Gnorm1_new = 0;
for (j = 0; j < active_size; j++) {
- int i = j + random.nextInt(active_size - j);
+ int i = j + param.random.nextInt(active_size - j);
swap(index, i, j);
}
@@ -1117,50 +1229,65 @@ private static void solve_l1r_l2_svc(Problem prob_col, double[] w, double eps, d
H *= 2;
H = Math.max(H, 1e-12);
- double Gp = G + 1;
- double Gn = G - 1;
double violation = 0;
- if (w[j] == 0) {
- if (Gp < 0)
- violation = -Gp;
- else if (Gn > 0)
- violation = Gn;
- else if (Gp > Gmax_old / l && Gn < -Gmax_old / l) {
- active_size--;
- swap(index, s, active_size);
- s--;
- continue;
- }
- } else if (w[j] > 0)
- violation = Math.abs(Gp);
- else
- violation = Math.abs(Gn);
-
+ double Gp = 0, Gn = 0;
+ if (j == w_size - 1 && !regularize_bias)
+ violation = Math.abs(G);
+ else {
+ Gp = G + 1;
+ Gn = G - 1;
+ if (w[j] == 0) {
+ if (Gp < 0)
+ violation = -Gp;
+ else if (Gn > 0)
+ violation = Gn;
+ else if (Gp > Gmax_old / l && Gn < -Gmax_old / l) {
+ active_size--;
+ swap(index, s, active_size);
+ s--;
+ continue;
+ }
+ } else if (w[j] > 0)
+ violation = Math.abs(Gp);
+ else
+ violation = Math.abs(Gn);
+ }
Gmax_new = Math.max(Gmax_new, violation);
Gnorm1_new += violation;
// obtain Newton direction d
- if (Gp < H * w[j])
- d = -Gp / H;
- else if (Gn > H * w[j])
- d = -Gn / H;
- else
- d = -w[j];
+ if (j == w_size - 1 && !regularize_bias)
+ d = -G / H;
+ else {
+ if (Gp < H * w[j])
+ d = -Gp / H;
+ else if (Gn > H * w[j])
+ d = -Gn / H;
+ else
+ d = -w[j];
+ }
- if (Math.abs(d) < 1.0e-12) continue;
+ if (Math.abs(d) < 1.0e-12)
+ continue;
- double delta = Math.abs(w[j] + d) - Math.abs(w[j]) + G * d;
+ double delta;
+ if (j == w_size - 1 && !regularize_bias)
+ delta = G * d;
+ else
+ delta = Math.abs(w[j] + d) - Math.abs(w[j]) + G * d;
d_old = 0;
int num_linesearch;
for (num_linesearch = 0; num_linesearch < max_num_linesearch; num_linesearch++) {
d_diff = d_old - d;
- cond = Math.abs(w[j] + d) - Math.abs(w[j]) - sigma * delta;
+ if (j == w_size - 1 && !regularize_bias)
+ cond = -sigma * delta;
+ else
+ cond = Math.abs(w[j] + d) - Math.abs(w[j]) - sigma * delta;
appxcond = xj_sq[j] * d * d + G_loss * d + cond;
if (appxcond <= 0) {
- for (Feature x : prob_col.x[j]) {
- b[x.getIndex() - 1] += d_diff * x.getValue();
- }
+ Feature[] x = prob_col.x[j];
+ SparseOperator.axpy(d_diff, x, b);
break;
}
@@ -1209,10 +1336,10 @@ else if (Gn > H * w[j])
b[i] = 1;
for (int i = 0; i < w_size; i++) {
- if (w[i] == 0) continue;
- for (Feature x : prob_col.x[i]) {
- b[x.getIndex() - 1] -= w[i] * x.getValue();
- }
+ if (w[i] == 0)
+ continue;
+ Feature[] x = prob_col.x[i];
+ SparseOperator.axpy(-w[i], x, b);
}
}
}
@@ -1221,9 +1348,10 @@ else if (Gn > H * w[j])
Gnorm1_init = Gnorm1_new;
}
iter++;
- if (iter % 10 == 0) info(".");
+ if (iter % 10 == 0)
+ info(".");
- if (Gmax_new <= eps * Gnorm1_init) {
+ if (Gnorm1_new <= eps * Gnorm1_init) {
if (active_size == w_size)
break;
else {
@@ -1238,7 +1366,8 @@ else if (Gn > H * w[j])
}
info("%noptimization finished, #iter = %d%n", iter);
- if (iter >= max_iter) info("%nWARNING: reaching max number of iterations%n");
+ if (iter >= max_iter)
+ info("%nWARNING: reaching max number of iterations%n");
// calculate objective value
@@ -1253,11 +1382,16 @@ else if (Gn > H * w[j])
nnz++;
}
}
+ if (!regularize_bias)
+ v -= Math.abs(w[w_size - 1]);
for (j = 0; j < l; j++)
- if (b[j] > 0) v += C[GETI(y, j)] * b[j] * b[j];
+ if (b[j] > 0)
+ v += C[GETI(y, j)] * b[j] * b[j];
info("Objective value = %g%n", v);
info("#nonzeros/#features = %d/%d%n", nnz, w_size);
+
+ return iter;
}
/**
@@ -1273,17 +1407,22 @@ else if (Gn > H * w[j])
*
* solution will be put in w
*
+ * this function returns the number of iterations
+ *
* See Yuan et al. (2011) and appendix of LIBLINEAR paper, Fan et al. (2008)
+ *
+ * To not regularize the bias (i.e., regularize_bias = 0), a constant feature = 1
+ * must have been added to the original data. (see -B and -R option)
*
*
* @since 1.5
*/
- private static void solve_l1r_lr(Problem prob_col, double[] w, double eps, double Cp, double Cn) {
+ private static int solve_l1r_lr(Problem prob_col, Parameter param, double[] w, double Cp, double Cn, double eps, int max_iter) {
int l = prob_col.l;
int w_size = prob_col.n;
+ boolean regularize_bias = param.regularize_bias;
int j, s, newton_iter = 0, iter = 0;
int max_newton_iter = 100;
- int max_iter = 1000;
int max_num_linesearch = 20;
int active_size;
int QP_active_size;
@@ -1342,6 +1481,9 @@ private static void solve_l1r_lr(Problem prob_col, double[] w, double eps, doubl
}
}
}
+ if (!regularize_bias)
+ w_norm -= Math.abs(w[w_size - 1]);
+
for (j = 0; j < l; j++) {
exp_wTx[j] = Math.exp(exp_wTx[j]);
double tau_tmp = 1 / (1 + exp_wTx[j]);
@@ -1367,33 +1509,38 @@ private static void solve_l1r_lr(Problem prob_col, double[] w, double eps, doubl
}
Grad[j] = -tmp + xjneg_sum[j];
- double Gp = Grad[j] + 1;
- double Gn = Grad[j] - 1;
double violation = 0;
- if (w[j] == 0) {
- if (Gp < 0)
- violation = -Gp;
- else if (Gn > 0)
- violation = Gn;
- //outer-level shrinking
- else if (Gp > Gmax_old / l && Gn < -Gmax_old / l) {
- active_size--;
- swap(index, s, active_size);
- s--;
- continue;
- }
- } else if (w[j] > 0)
- violation = Math.abs(Gp);
- else
- violation = Math.abs(Gn);
-
+ if (j == w_size - 1 && !regularize_bias)
+ violation = Math.abs(Grad[j]);
+ else {
+ double Gp = Grad[j] + 1;
+ double Gn = Grad[j] - 1;
+ if (w[j] == 0) {
+ if (Gp < 0)
+ violation = -Gp;
+ else if (Gn > 0)
+ violation = Gn;
+ //outer-level shrinking
+ else if (Gp > Gmax_old / l && Gn < -Gmax_old / l) {
+ active_size--;
+ swap(index, s, active_size);
+ s--;
+ continue;
+ }
+ } else if (w[j] > 0)
+ violation = Math.abs(Gp);
+ else
+ violation = Math.abs(Gn);
+ }
Gmax_new = Math.max(Gmax_new, violation);
Gnorm1_new += violation;
}
- if (newton_iter == 0) Gnorm1_init = Gnorm1_new;
+ if (newton_iter == 0)
+ Gnorm1_init = Gnorm1_new;
- if (Gnorm1_new <= eps * Gnorm1_init) break;
+ if (Gnorm1_new <= eps * Gnorm1_init)
+ break;
iter = 0;
QP_Gmax_old = Double.POSITIVE_INFINITY;
@@ -1408,7 +1555,7 @@ else if (Gp > Gmax_old / l && Gn < -Gmax_old / l) {
QP_Gnorm1_new = 0;
for (j = 0; j < QP_active_size; j++) {
- int i = random.nextInt(QP_active_size - j);
+ int i = param.random.nextInt(QP_active_size - j);
swap(index, i, j);
}
@@ -1422,46 +1569,51 @@ else if (Gp > Gmax_old / l && Gn < -Gmax_old / l) {
G += x.getValue() * D[ind] * xTd[ind];
}
- double Gp = G + 1;
- double Gn = G - 1;
double violation = 0;
- if (wpd[j] == 0) {
- if (Gp < 0)
- violation = -Gp;
- else if (Gn > 0)
- violation = Gn;
- //inner-level shrinking
- else if (Gp > QP_Gmax_old / l && Gn < -QP_Gmax_old / l) {
- QP_active_size--;
- swap(index, s, QP_active_size);
- s--;
- continue;
- }
- } else if (wpd[j] > 0)
- violation = Math.abs(Gp);
- else
- violation = Math.abs(Gn);
-
+ if (j == w_size - 1 && !regularize_bias) {
+ // bias term not shrunken
+ violation = Math.abs(G);
+ z = -G / H;
+ } else {
+ double Gp = G + 1;
+ double Gn = G - 1;
+ if (wpd[j] == 0) {
+ if (Gp < 0)
+ violation = -Gp;
+ else if (Gn > 0)
+ violation = Gn;
+ //inner-level shrinking
+ else if (Gp > QP_Gmax_old / l && Gn < -QP_Gmax_old / l) {
+ QP_active_size--;
+ swap(index, s, QP_active_size);
+ s--;
+ continue;
+ }
+ } else if (wpd[j] > 0)
+ violation = Math.abs(Gp);
+ else
+ violation = Math.abs(Gn);
+
+ // obtain solution of one-variable problem
+ if (Gp < H * wpd[j])
+ z = -Gp / H;
+ else if (Gn > H * wpd[j])
+ z = -Gn / H;
+ else
+ z = -wpd[j];
+ }
QP_Gmax_new = Math.max(QP_Gmax_new, violation);
QP_Gnorm1_new += violation;
- // obtain solution of one-variable problem
- if (Gp < H * wpd[j])
- z = -Gp / H;
- else if (Gn > H * wpd[j])
- z = -Gn / H;
- else
- z = -wpd[j];
-
- if (Math.abs(z) < 1.0e-12) continue;
+ if (Math.abs(z) < 1.0e-12) {
+ continue;
+ }
z = Math.min(Math.max(z, -10.0), 10.0);
wpd[j] += z;
- for (Feature x : prob_col.x[j]) {
- int ind = x.getIndex() - 1;
- xTd[ind] += x.getValue() * z;
- }
+ Feature[] x = prob_col.x[j];
+ SparseOperator.axpy(z, x, xTd);
}
iter++;
@@ -1481,19 +1633,25 @@ else if (Gn > H * wpd[j])
QP_Gmax_old = QP_Gmax_new;
}
- if (iter >= max_iter) info("WARNING: reaching max number of inner iterations%n");
+ if (iter >= max_iter) {
+ info("WARNING: reaching max number of inner iterations%n");
+ }
delta = 0;
w_norm_new = 0;
for (j = 0; j < w_size; j++) {
delta += Grad[j] * (wpd[j] - w[j]);
- if (wpd[j] != 0) w_norm_new += Math.abs(wpd[j]);
+ if (wpd[j] != 0)
+ w_norm_new += Math.abs(wpd[j]);
}
+ if (!regularize_bias)
+ w_norm_new -= Math.abs(wpd[w_size - 1]);
delta += (w_norm_new - w_norm);
negsum_xTd = 0;
for (int i = 0; i < l; i++)
- if (y[i] == -1) negsum_xTd += C[GETI(y, i)] * xTd[i];
+ if (y[i] == -1)
+ negsum_xTd += C[GETI(y, i)] * xTd[i];
int num_linesearch;
for (num_linesearch = 0; num_linesearch < max_num_linesearch; num_linesearch++) {
@@ -1520,8 +1678,11 @@ else if (Gn > H * wpd[j])
w_norm_new = 0;
for (j = 0; j < w_size; j++) {
wpd[j] = (w[j] + wpd[j]) * 0.5;
- if (wpd[j] != 0) w_norm_new += Math.abs(wpd[j]);
+ if (wpd[j] != 0)
+ w_norm_new += Math.abs(wpd[j]);
}
+ if (!regularize_bias)
+ w_norm_new -= Math.abs(wpd[w_size - 1]);
delta *= 0.5;
negsum_xTd *= 0.5;
for (int i = 0; i < l; i++)
@@ -1535,17 +1696,18 @@ else if (Gn > H * wpd[j])
exp_wTx[i] = 0;
for (int i = 0; i < w_size; i++) {
- if (w[i] == 0) continue;
- for (Feature x : prob_col.x[i]) {
- exp_wTx[x.getIndex() - 1] += w[i] * x.getValue();
- }
+ if (w[i] == 0)
+ continue;
+ Feature[] x = prob_col.x[i];
+ SparseOperator.axpy(w[i], x, exp_wTx);
}
for (int i = 0; i < l; i++)
exp_wTx[i] = Math.exp(exp_wTx[i]);
}
- if (iter == 1) inner_eps *= 0.25;
+ if (iter == 1)
+ inner_eps *= 0.25;
newton_iter++;
Gmax_old = Gmax_new;
@@ -1555,7 +1717,9 @@ else if (Gn > H * wpd[j])
info("=========================%n");
info("optimization finished, #iter = %d%n", newton_iter);
- if (newton_iter >= max_newton_iter) info("WARNING: reaching max number of iterations%n");
+ if (newton_iter >= max_newton_iter) {
+ info("WARNING: reaching max number of iterations%n");
+ }
// calculate objective value
@@ -1566,6 +1730,8 @@ else if (Gn > H * wpd[j])
v += Math.abs(w[j]);
nnz++;
}
+ if (!regularize_bias)
+ v -= Math.abs(w[w_size - 1]);
for (j = 0; j < l; j++)
if (y[j] == 1)
v += C[GETI(y, j)] * Math.log(1 + 1 / exp_wTx[j]);
@@ -1574,6 +1740,245 @@ else if (Gn > H * wpd[j])
info("Objective value = %g%n", v);
info("#nonzeros/#features = %d/%d%n", nnz, w_size);
+
+ return newton_iter;
+ }
+
+ /*
+ * A two-level coordinate descent algorithm for
+ * a scaled one-class SVM dual problem
+ *
+ * min_\alpha 0.5(\alpha^T Q \alpha),
+ * s.t. 0 <= \alpha_i <= 1 and
+ * e^T \alpha = \nu l
+ *
+ * where Qij = xi^T xj
+ *
+ * Given:
+ * x, nu
+ * eps is the stopping tolerance
+ *
+ * solution will be put in w and rho
+ *
+ * this function returns the number of iterations
+ *
+ * See Algorithm 7 in supplementary materials of Chou et al., SDM 2020.
+ *
+ * @since 2.40
+ */
+ static int solve_oneclass_svm(Problem prob, Parameter param, double[] w, MutableDouble rho, int max_iter) {
+ int l = prob.l;
+ int w_size = prob.n;
+ double eps = param.eps;
+ double nu = param.nu;
+ int i, j, s, iter = 0;
+ double Gi, Gj;
+ double Qij, quad_coef, delta, sum;
+ double old_alpha_i;
+ double[] QD = new double[l];
+ double[] G = new double[l];
+ int[] index = new int[l];
+ double[] alpha = new double[l];
+ int max_inner_iter;
+ int active_size = l;
+
+ double negGmax; // max { -grad(f)_i | alpha_i < 1 }
+ double negGmin; // min { -grad(f)_i | alpha_i > 0 }
+
+ int[] most_violating_i = new int[l];
+ int[] most_violating_j = new int[l];
+
+ int n = (int)(nu * l); // # of alpha's at upper bound
+ for (i = 0; i < n; i++)
+ alpha[i] = 1;
+ if (n < l)
+ alpha[i] = nu * l - n;
+ for (i = n + 1; i < l; i++)
+ alpha[i] = 0;
+
+ for (i = 0; i < w_size; i++)
+ w[i] = 0;
+ for (i = 0; i < l; i++) {
+ Feature[] xi = prob.x[i];
+ QD[i] = SparseOperator.nrm2_sq(xi);
+ SparseOperator.axpy(alpha[i], xi, w);
+
+ index[i] = i;
+ }
+
+ while (iter < max_iter) {
+ negGmax = Double.NEGATIVE_INFINITY;
+ negGmin = Double.POSITIVE_INFINITY;
+
+ for (s = 0; s < active_size; s++) {
+ i = index[s];
+ Feature[] xi = prob.x[i];
+ G[i] = SparseOperator.dot(w, xi);
+ if (alpha[i] < 1)
+ negGmax = Math.max(negGmax, -G[i]);
+ if (alpha[i] > 0)
+ negGmin = Math.min(negGmin, -G[i]);
+ }
+
+ if (negGmax - negGmin < eps) {
+ if (active_size == l)
+ break;
+ else {
+ active_size = l;
+ info("*");
+ continue;
+ }
+ }
+
+ for (s = 0; s < active_size; s++) {
+ i = index[s];
+ if ((alpha[i] == 1 && -G[i] > negGmax) ||
+ (alpha[i] == 0 && -G[i] < negGmin)) {
+ active_size--;
+ Linear.swap(index, s, active_size);
+ s--;
+ }
+ }
+
+ max_inner_iter = Math.max(active_size / 10, 1);
+ Heap min_heap = new Heap(max_inner_iter, HeapType.MIN);
+ Heap max_heap = new Heap(max_inner_iter, HeapType.MAX);
+
+ for (s = 0; s < active_size; s++) {
+ i = index[s];
+ FeatureNode node = new FeatureNode(i, -G[i], false);
+
+ if (alpha[i] < 1) {
+ if (min_heap.size() < max_inner_iter)
+ min_heap.push(node);
+ else if (min_heap.top().getValue() < node.getValue()) {
+ min_heap.pop();
+ min_heap.push(node);
+ }
+ }
+
+ if (alpha[i] > 0) {
+ if (max_heap.size() < max_inner_iter)
+ max_heap.push(node);
+ else if (max_heap.top().getValue() > node.getValue()) {
+ max_heap.pop();
+ max_heap.push(node);
+ }
+ }
+ }
+ max_inner_iter = Math.min(min_heap.size(), max_heap.size());
+ while (max_heap.size() > max_inner_iter)
+ max_heap.pop();
+ while (min_heap.size() > max_inner_iter)
+ min_heap.pop();
+
+ for (s = max_inner_iter - 1; s >= 0; s--) {
+ most_violating_i[s] = min_heap.top().getIndex();
+ most_violating_j[s] = max_heap.top().getIndex();
+ min_heap.pop();
+ max_heap.pop();
+ }
+
+ for (s = 0; s < max_inner_iter; s++) {
+ i = most_violating_i[s];
+ j = most_violating_j[s];
+
+ if ((alpha[i] == 0 && alpha[j] == 0) ||
+ (alpha[i] == 1 && alpha[j] == 1))
+ continue;
+
+ Feature[] xi = prob.x[i];
+ Feature[] xj = prob.x[j];
+
+ Gi = SparseOperator.dot(w, xi);
+ Gj = SparseOperator.dot(w, xj);
+
+ int violating_pair = 0;
+ if (alpha[i] < 1 && alpha[j] > 0 && -Gj + 1e-12 < -Gi)
+ violating_pair = 1;
+ else if (alpha[i] > 0 && alpha[j] < 1 && -Gi + 1e-12 < -Gj)
+ violating_pair = 1;
+ if (violating_pair == 0)
+ continue;
+
+ Qij = SparseOperator.sparse_dot(xi, xj);
+ quad_coef = QD[i] + QD[j] - 2 * Qij;
+ if (quad_coef <= 0)
+ quad_coef = 1e-12;
+ delta = (Gi - Gj) / quad_coef;
+ old_alpha_i = alpha[i];
+ sum = alpha[i] + alpha[j];
+ alpha[i] = alpha[i] - delta;
+ alpha[j] = alpha[j] + delta;
+ if (sum > 1) {
+ if (alpha[i] > 1) {
+ alpha[i] = 1;
+ alpha[j] = sum - 1;
+ }
+ } else {
+ if (alpha[j] < 0) {
+ alpha[j] = 0;
+ alpha[i] = sum;
+ }
+ }
+ if (sum > 1) {
+ if (alpha[j] > 1) {
+ alpha[j] = 1;
+ alpha[i] = sum - 1;
+ }
+ } else {
+ if (alpha[i] < 0) {
+ alpha[i] = 0;
+ alpha[j] = sum;
+ }
+ }
+ delta = alpha[i] - old_alpha_i;
+ SparseOperator.axpy(delta, xi, w);
+ SparseOperator.axpy(-delta, xj, w);
+ }
+ iter++;
+ if (iter % 10 == 0)
+ info(".");
+ }
+ info("%noptimization finished, #iter = %d%n", iter);
+ if (iter >= max_iter)
+ info("%nWARNING: reaching max number of iterations%n%n");
+
+ // calculate object value
+ double v = 0;
+ for (i = 0; i < w_size; i++)
+ v += w[i] * w[i];
+ int nSV = 0;
+ for (i = 0; i < l; i++) {
+ if (alpha[i] > 0)
+ ++nSV;
+ }
+ info("Objective value = %f%n", v / 2);
+ info("nSV = %d%n", nSV);
+
+ // calculate rho
+ double nr_free = 0;
+ double ub = Double.POSITIVE_INFINITY, lb = Double.NEGATIVE_INFINITY, sum_free = 0;
+ for (i = 0; i < l; i++) {
+ double G_ = SparseOperator.dot(w, prob.x[i]);
+ if (alpha[i] == 1)
+ lb = Math.max(lb, G_);
+ else if (alpha[i] == 0)
+ ub = Math.min(ub, G_);
+ else {
+ ++nr_free;
+ sum_free += G_;
+ }
+ }
+
+ if (nr_free > 0)
+ rho.set(sum_free / nr_free);
+ else
+ rho.set((ub + lb) / 2);
+
+ info("rho = %f%n", rho.get());
+
+ return iter;
}
// transpose matrix X from row format to column format
@@ -1631,17 +2036,36 @@ static void swap(IntArrayPointer array, int idxA, int idxB) {
array.set(idxB, temp);
}
+ static void swap(Feature[] array, int idxA, int idxB) {
+ Feature temp = array[idxA];
+ array[idxA] = array[idxB];
+ array[idxB] = temp;
+ }
/**
* @throws IllegalArgumentException if the feature nodes of prob are not sorted in ascending order
*/
public static Model train(Problem prob, Parameter param) {
+ if (prob == null) {
+ throw new IllegalArgumentException("problem must not be null");
+ }
+ if (param == null) {
+ throw new IllegalArgumentException("parameter must not be null");
+ }
+ if (param.eps <= 0) {
+ throw new IllegalArgumentException("eps <= 0");
+ }
+ if (param.C <= 0) {
+ throw new IllegalArgumentException("C <= 0");
+ }
+ if (param.p < 0 && param.solverType == L2R_L2LOSS_SVR) {
+ throw new IllegalArgumentException("p < 0");
+ }
- if (prob == null) throw new IllegalArgumentException("problem must not be null");
- if (param == null) throw new IllegalArgumentException("parameter must not be null");
-
- if (prob.n == 0) throw new IllegalArgumentException("problem has zero features");
- if (prob.l == 0) throw new IllegalArgumentException("problem has zero instances");
+ if (prob.n == 0)
+ throw new IllegalArgumentException("problem has zero features");
+ if (prob.l == 0)
+ throw new IllegalArgumentException("problem has zero instances");
for (Feature[] nodes : prob.x) {
int indexBefore = 0;
@@ -1653,6 +2077,29 @@ public static Model train(Problem prob, Parameter param) {
}
}
+ if (prob.bias >= 0 && param.solverType.isOneClass()) {
+ throw new IllegalArgumentException("prob->bias >=0, but this is ignored in ONECLASS_SVM");
+ }
+
+ if (!param.regularize_bias) {
+ if (prob.bias != 1.0)
+ throw new IllegalArgumentException("To not regularize bias, must specify -B 1 along with -R");
+
+ if (param.solverType != L2R_LR
+ && param.solverType != L2R_L2LOSS_SVC
+ && param.solverType != L1R_L2LOSS_SVC
+ && param.solverType != L1R_LR
+ && param.solverType != L2R_L2LOSS_SVR)
+ throw new IllegalArgumentException("-R option supported only for solver L2R_LR, L2R_L2LOSS_SVC, L1R_L2LOSS_SVC, L1R_LR, and L2R_L2LOSS_SVR");
+ }
+
+ if (param.init_sol != null
+ && param.getSolverType() != L2R_LR
+ && param.getSolverType() != L2R_L2LOSS_SVC
+ && param.getSolverType() != L2R_L2LOSS_SVR) {
+ throw new IllegalArgumentException("Initial-solution specification supported only for solvers L2R_LR, L2R_L2LOSS_SVC, and L2R_L2LOSS_SVR");
+ }
+
int l = prob.l;
int n = prob.n;
int w_size = prob.n;
@@ -1668,12 +2115,25 @@ public static Model train(Problem prob, Parameter param) {
if (param.solverType.isSupportVectorRegression()) {
model.w = new double[w_size];
+ if (param.init_sol != null)
+ for (int i = 0; i < w_size; i++)
+ model.w[i] = param.init_sol[i];
+ else
+ for (int i = 0; i < w_size; i++)
+ model.w[i] = 0;
model.nr_class = 2;
model.label = null;
checkProblemSize(n, model.nr_class);
train_one(prob, param, model.w, 0, 0);
+ } else if (param.solverType.isOneClass()) {
+ model.w = new double[w_size];
+ model.nr_class = 2;
+ model.label = null;
+ MutableDouble rho = new MutableDouble();
+ solve_oneclass_svm(prob, param, model.w, rho, param.max_iters);
+ model.rho = rho.get();
} else {
int[] perm = new int[l];
@@ -1698,9 +2158,11 @@ public static Model train(Problem prob, Parameter param) {
for (int i = 0; i < param.getNumWeights(); i++) {
int j;
for (j = 0; j < nr_class; j++)
- if (param.weightLabel[i] == label[j]) break;
+ if (param.weightLabel[i] == label[j])
+ break;
- if (j == nr_class) throw new IllegalArgumentException("class label " + param.weightLabel[i] + " specified in weight is not found");
+ if (j == nr_class)
+ throw new IllegalArgumentException("class label " + param.weightLabel[i] + " specified in weight is not found");
weighted_C[j] *= param.weight[i];
}
@@ -1719,7 +2181,7 @@ public static Model train(Problem prob, Parameter param) {
sub_prob.x[k] = x[k];
// multi-class svm by Crammer and Singer
- if (param.solverType == SolverType.MCSVM_CS) {
+ if (param.solverType == MCSVM_CS) {
model.w = new double[n * nr_class];
for (int i = 0; i < nr_class; i++) {
for (int j = start[i]; j < start[i] + count[i]; j++) {
@@ -1727,7 +2189,7 @@ public static Model train(Problem prob, Parameter param) {
}
}
- SolverMCSVM_CS solver = new SolverMCSVM_CS(sub_prob, nr_class, weighted_C, param.eps);
+ SolverMCSVM_CS solver = new SolverMCSVM_CS(sub_prob, nr_class, weighted_C, param.eps, param.random);
solver.solve(model.w);
} else {
if (nr_class == 2) {
@@ -1740,6 +2202,13 @@ public static Model train(Problem prob, Parameter param) {
for (; k < sub_prob.l; k++)
sub_prob.y[k] = -1;
+ if (param.init_sol != null)
+ for (int i = 0; i < w_size; i++)
+ model.w[i] = param.init_sol[i];
+ else
+ for (int i = 0; i < w_size; i++)
+ model.w[i] = 0;
+
train_one(sub_prob, param, model.w, weighted_C[0], weighted_C[1]);
} else {
model.w = new double[w_size * nr_class];
@@ -1756,6 +2225,13 @@ public static Model train(Problem prob, Parameter param) {
for (; k < sub_prob.l; k++)
sub_prob.y[k] = -1;
+ if (param.init_sol != null)
+ for (int j = 0; j < w_size; j++)
+ w[j] = param.init_sol[j * nr_class + i];
+ else
+ for (int j = 0; j < w_size; j++)
+ w[j] = 0;
+
train_one(sub_prob, param, w, weighted_C[i], param.C);
for (int j = 0; j < n; j++)
@@ -1777,83 +2253,268 @@ private static void checkProblemSize(int n, int nr_class) {
}
private static void train_one(Problem prob, Parameter param, double[] w, double Cp, double Cn) {
- double eps = param.eps;
- int pos = 0;
- for (int i = 0; i < prob.l; i++)
- if (prob.y[i] > 0) {
- pos++;
+ SolverType solver_type = param.solverType;
+ int dual_solver_max_iter = 300;
+ int iter;
+
+ // upstream: (solver_type==L2R_L2LOSS_SVR || solver_type==L2R_L1LOSS_SVR_DUAL || solver_type==L2R_L2LOSS_SVR_DUAL)
+ boolean is_regression = solver_type.isSupportVectorRegression();
+
+ // Some solvers use Cp,Cn but not C array; extensions possible but no plan for now
+ double[] C = new double[prob.l];
+ double primal_solver_tol = param.eps;
+ if (is_regression) {
+ for (int i = 0; i < prob.l; i++)
+ C[i] = param.C;
+ } else {
+ int pos = 0;
+ for (int i = 0; i < prob.l; i++) {
+ if (prob.y[i] > 0) {
+ pos++;
+ C[i] = Cp;
+ } else
+ C[i] = Cn;
}
- int neg = prob.l - pos;
-
- double primal_solver_tol = eps * Math.max(Math.min(pos, neg), 1) / prob.l;
+ int neg = prob.l - pos;
+ primal_solver_tol = param.eps * Math.max(Math.min(pos, neg), 1) / prob.l;
+ }
- Function fun_obj = null;
- switch (param.solverType) {
+ switch (solver_type) {
case L2R_LR: {
- double[] C = new double[prob.l];
- for (int i = 0; i < prob.l; i++) {
- if (prob.y[i] > 0)
- C[i] = Cp;
- else
- C[i] = Cn;
- }
- fun_obj = new L2R_LrFunction(prob, C);
- Tron tron_obj = new Tron(fun_obj, primal_solver_tol);
- tron_obj.tron(w);
+ L2R_LrFunction fun_obj = new L2R_LrFunction(prob, param, C);
+ Newton newton_obj = new Newton(fun_obj, primal_solver_tol, param.max_iters);
+ newton_obj.newton(w);
break;
}
case L2R_L2LOSS_SVC: {
- double[] C = new double[prob.l];
- for (int i = 0; i < prob.l; i++) {
- if (prob.y[i] > 0)
- C[i] = Cp;
- else
- C[i] = Cn;
- }
- fun_obj = new L2R_L2_SvcFunction(prob, C);
- Tron tron_obj = new Tron(fun_obj, primal_solver_tol);
- tron_obj.tron(w);
+ L2R_L2_SvcFunction fun_obj = new L2R_L2_SvcFunction(prob, param, C);
+ Newton newton_obj = new Newton(fun_obj, primal_solver_tol, param.max_iters);
+ newton_obj.newton(w);
break;
}
- case L2R_L2LOSS_SVC_DUAL:
- solve_l2r_l1l2_svc(prob, w, eps, Cp, Cn, SolverType.L2R_L2LOSS_SVC_DUAL);
+ case L2R_L2LOSS_SVC_DUAL: {
+ iter = solve_l2r_l1l2_svc(prob, param, w, Cp, Cn, dual_solver_max_iter);
+ if (iter >= dual_solver_max_iter) {
+ info("%nWARNING: reaching max number of iterations%nSwitching to use -s 2%n%n");
+ // primal_solver_tol obtained from eps for dual may be too loose
+ primal_solver_tol *= 0.1;
+ L2R_L2_SvcFunction fun_obj = new L2R_L2_SvcFunction(prob, param, C);
+ Newton newton_obj = new Newton(fun_obj, primal_solver_tol, param.max_iters);
+ newton_obj.newton(w);
+ }
break;
- case L2R_L1LOSS_SVC_DUAL:
- solve_l2r_l1l2_svc(prob, w, eps, Cp, Cn, SolverType.L2R_L1LOSS_SVC_DUAL);
+ }
+ case L2R_L1LOSS_SVC_DUAL: {
+ iter = solve_l2r_l1l2_svc(prob, param, w, Cp, Cn, dual_solver_max_iter);
+ if (iter >= dual_solver_max_iter)
+ info("%nWARNING: reaching max number of iterations%nUsing -s 2 may be faster (also see FAQ)%n%n");
break;
+ }
case L1R_L2LOSS_SVC: {
Problem prob_col = transpose(prob);
- solve_l1r_l2_svc(prob_col, w, primal_solver_tol, Cp, Cn);
+ solve_l1r_l2_svc(prob_col, param, w, Cp, Cn, primal_solver_tol, param.max_iters);
break;
}
case L1R_LR: {
Problem prob_col = transpose(prob);
- solve_l1r_lr(prob_col, w, primal_solver_tol, Cp, Cn);
+ solve_l1r_lr(prob_col, param, w, Cp, Cn, primal_solver_tol, param.max_iters);
break;
}
- case L2R_LR_DUAL:
- solve_l2r_lr_dual(prob, w, eps, Cp, Cn);
+ case L2R_LR_DUAL: {
+ iter = solve_l2r_lr_dual(prob, param, w, Cp, Cn, dual_solver_max_iter);
+ if (iter >= dual_solver_max_iter) {
+ info("%nWARNING: reaching max number of iterations%nSwitching to use -s 0%n%n");
+ // primal_solver_tol obtained from eps for dual may be too loose
+ primal_solver_tol *= 0.1;
+ L2R_LrFunction fun_obj = new L2R_LrFunction(prob, param, C);
+ Newton newton_obj = new Newton(fun_obj, primal_solver_tol, param.max_iters);
+ newton_obj.newton(w);
+ }
break;
+ }
case L2R_L2LOSS_SVR: {
- double[] C = new double[prob.l];
- for (int i = 0; i < prob.l; i++)
- C[i] = param.C;
+ L2R_L2_SvrFunction fun_obj = new L2R_L2_SvrFunction(prob, param, C);
+ Newton newton_obj = new Newton(fun_obj, primal_solver_tol, param.max_iters);
+ newton_obj.newton(w);
+ break;
+
+ }
+ case L2R_L1LOSS_SVR_DUAL: {
+ iter = solve_l2r_l1l2_svr(prob, param, w, dual_solver_max_iter);
+ if (iter >= dual_solver_max_iter)
+ info("%nWARNING: reaching max number of iterations%nUsing -s 11 may be faster (also see FAQ)%n%n");
- fun_obj = new L2R_L2_SvrFunction(prob, C, param.p);
- Tron tron_obj = new Tron(fun_obj, param.eps);
- tron_obj.tron(w);
break;
}
- case L2R_L1LOSS_SVR_DUAL:
- case L2R_L2LOSS_SVR_DUAL:
- solve_l2r_l1l2_svr(prob, w, param);
+ case L2R_L2LOSS_SVR_DUAL: {
+ iter = solve_l2r_l1l2_svr(prob, param, w, dual_solver_max_iter);
+ if (iter >= dual_solver_max_iter) {
+ info("%nWARNING: reaching max number of iterations%nSwitching to use -s 11%n%n");
+ // primal_solver_tol obtained from eps for dual may be too loose
+ primal_solver_tol *= 0.001;
+ L2R_L2_SvrFunction fun_obj = new L2R_L2_SvrFunction(prob, param, C);
+ Newton newton_obj = new Newton(fun_obj, primal_solver_tol, param.max_iters);
+ newton_obj.newton(w);
+ }
break;
-
+ }
default:
throw new IllegalStateException("unknown solver type: " + param.solverType);
}
}
+ // Calculate the initial C for parameter selection
+ private static double calc_start_C(Problem prob, Parameter param) {
+ int i;
+ double xTx, max_xTx;
+ max_xTx = 0;
+ for (i = 0; i < prob.l; i++) {
+ xTx = 0;
+ for (Feature xi : prob.x[i]) {
+ double val = xi.getValue();
+ xTx += val * val;
+ }
+ if (xTx > max_xTx)
+ max_xTx = xTx;
+ }
+
+ double min_C = 1.0;
+ if (param.getSolverType() == L2R_LR)
+ min_C = 1.0 / (prob.l * max_xTx);
+ else if (param.getSolverType() == L2R_L2LOSS_SVC)
+ min_C = 1.0 / (2 * prob.l * max_xTx);
+ else if (param.getSolverType() == L2R_L2LOSS_SVR) {
+ double sum_y, loss, y_abs;
+ double delta2 = 0.1;
+ sum_y = 0;
+ loss = 0;
+ for (i = 0; i < prob.l; i++) {
+ y_abs = Math.abs(prob.y[i]);
+ sum_y += y_abs;
+ loss += Math.max(y_abs - param.p, 0.0) * Math.max(y_abs - param.p, 0.0);
+ }
+ if (loss > 0)
+ min_C = delta2 * delta2 * loss / (8 * sum_y * sum_y * max_xTx);
+ else
+ min_C = Double.POSITIVE_INFINITY;
+ }
+
+ return Math.pow(2, Math.floor(Math.log(min_C) / Math.log(2.0)));
+ }
+
+ private static double calc_max_p(Problem prob) {
+ int i;
+ double max_p = 0.0;
+ for (i = 0; i < prob.l; i++)
+ max_p = Math.max(max_p, Math.abs(prob.y[i]));
+
+ return max_p;
+ }
+
+ public static ParameterCSearchResult find_parameter_C(Problem prob, Parameter param_tmp, double start_C, double max_C, int[] fold_start, int[] perm,
+ Problem[] subprob, int nr_fold) {
+ double best_C;
+ double best_score = Double.NaN;
+ // variables for CV
+ int i;
+ double[] target = new double[prob.l];
+
+ // variables for warm start
+ double ratio = 2;
+ double[][] prev_w = new double[nr_fold][];
+ for (i = 0; i < nr_fold; i++)
+ prev_w[i] = null;
+ int num_unchanged_w = 0;
+ PrintStream default_print_string = DEBUG_OUTPUT;
+
+ if (param_tmp.getSolverType() == L2R_LR || param_tmp.getSolverType() == L2R_L2LOSS_SVC)
+ best_score = 0.0;
+ else if (param_tmp.getSolverType() == L2R_L2LOSS_SVR)
+ best_score = Double.POSITIVE_INFINITY;
+ best_C = start_C;
+
+ param_tmp.C = start_C;
+ while (param_tmp.C <= max_C) {
+ //Output disabled for running CV at a particular C
+ disableDebugOutput();
+
+ for (i = 0; i < nr_fold; i++) {
+ int j;
+ int begin = fold_start[i];
+ int end = fold_start[i + 1];
+
+ param_tmp.init_sol = prev_w[i];
+ Model submodel = train(subprob[i], param_tmp);
+
+ int total_w_size;
+ if (submodel.nr_class == 2)
+ total_w_size = subprob[i].n;
+ else
+ total_w_size = subprob[i].n * submodel.nr_class;
+
+ if (prev_w[i] == null) {
+ prev_w[i] = new double[total_w_size];
+ for (j = 0; j < total_w_size; j++)
+ prev_w[i][j] = submodel.w[j];
+ } else if (num_unchanged_w >= 0) {
+ double norm_w_diff = 0;
+ for (j = 0; j < total_w_size; j++) {
+ norm_w_diff += (submodel.w[j] - prev_w[i][j]) * (submodel.w[j] - prev_w[i][j]);
+ prev_w[i][j] = submodel.w[j];
+ }
+ norm_w_diff = Math.sqrt(norm_w_diff);
+
+ if (norm_w_diff > 1e-15)
+ num_unchanged_w = -1;
+ } else {
+ for (j = 0; j < total_w_size; j++)
+ prev_w[i][j] = submodel.w[j];
+ }
+
+ for (j = begin; j < end; j++)
+ target[perm[j]] = predict(submodel, prob.x[perm[j]]);
+ }
+ setDebugOutput(default_print_string);
+
+ if (param_tmp.getSolverType() == L2R_LR || param_tmp.getSolverType() == L2R_L2LOSS_SVC) {
+ int total_correct = 0;
+ for (i = 0; i < prob.l; i++)
+ if (target[i] == prob.y[i])
+ ++total_correct;
+ double current_rate = (double)total_correct / prob.l;
+ if (current_rate > best_score) {
+ best_C = param_tmp.C;
+ best_score = current_rate;
+ }
+
+ info("log2c=%7.2f\trate=%g%n", Math.log(param_tmp.C) / Math.log(2.0), 100.0 * current_rate);
+ } else if (param_tmp.getSolverType() == L2R_L2LOSS_SVR) {
+ double total_error = 0.0;
+ for (i = 0; i < prob.l; i++) {
+ double y = prob.y[i];
+ double v = target[i];
+ total_error += (v - y) * (v - y);
+ }
+ double current_error = total_error / prob.l;
+ if (current_error < best_score) {
+ best_C = param_tmp.C;
+ best_score = current_error;
+ }
+
+ info("log2c=%7.2f\tp=%7.2f\tMean squared error=%g%n", Math.log(param_tmp.C) / Math.log(2.0), param_tmp.p, current_error);
+ }
+
+ num_unchanged_w++;
+ if (num_unchanged_w == 5)
+ break;
+ param_tmp.C = param_tmp.C * ratio;
+ }
+
+ if (param_tmp.C > max_C)
+ info("WARNING: maximum C reached.%n");
+ return new ParameterCSearchResult(best_C, best_score);
+ }
+
public static void disableDebugOutput() {
setDebugOutput(null);
}
@@ -1868,12 +2529,16 @@ public static void setDebugOutput(PrintStream debugOutput) {
}
}
+ public static int getVersion() {
+ return VERSION;
+ }
+
/**
* resets the PRNG
*
- * this is i.a. needed for regression testing (eg. the Weka wrapper)
+ * @deprecated Use {@link Parameter#setRandom(Random)} instead
*/
+ @Deprecated
public static void resetRandom() {
- random = new Random(DEFAULT_RANDOM_SEED);
}
}
diff --git a/src/main/java/de/bwaldvogel/liblinear/Model.java b/src/main/java/de/bwaldvogel/liblinear/Model.java
index b93bb90..6eba91f 100644
--- a/src/main/java/de/bwaldvogel/liblinear/Model.java
+++ b/src/main/java/de/bwaldvogel/liblinear/Model.java
@@ -1,37 +1,42 @@
package de.bwaldvogel.liblinear;
-import static de.bwaldvogel.liblinear.Linear.copyOf;
+import static de.bwaldvogel.liblinear.SolverType.*;
import java.io.File;
import java.io.IOException;
import java.io.Reader;
import java.io.Serializable;
import java.io.Writer;
+import java.nio.file.Path;
import java.util.Arrays;
+import java.util.Objects;
/**
* Model stores the model obtained from the training procedure
*
- * use {@link Linear#loadModel(File)} and {@link Linear#saveModel(File, Model)} to load/save it
+ * use {@link Linear#loadModel(Path)} and {@link Linear#saveModel(Path, Model)} to load/save it
*/
public final class Model implements Serializable {
private static final long serialVersionUID = -6456047576741854834L;
- double bias;
+ double bias;
/** label of each class */
- int[] label;
+ int[] label;
- int nr_class;
+ int nr_class;
- int nr_feature;
+ int nr_feature;
- SolverType solverType;
+ SolverType solverType;
/** feature weight array */
- double[] w;
+ double[] w;
+
+ /** one-class SVM only */
+ double rho;
/**
* @return number of classes
@@ -48,11 +53,16 @@ public int getNrFeature() {
}
public int[] getLabels() {
- return copyOf(label, nr_class);
+ return Arrays.copyOf(label, nr_class);
+ }
+
+ public SolverType getSolverType() {
+ return solverType;
}
/**
- * The nr_feature*nr_class array w gives feature weights. We use one
+ * The array w gives feature weights; its size is
+ * nr_feature*nr_class but is nr_feature if nr_class = 2. We use one
* against the rest for multi-class classification, so each feature
* index corresponds to nr_class weight values. Weights are
* organized in the following way
@@ -71,7 +81,7 @@ public int[] getLabels() {
* @return a copy of the feature weight array as described
*/
public double[] getFeatureWeights() {
- return Linear.copyOf(w, w.length);
+ return Arrays.copyOf(w, w.length);
}
/**
@@ -92,13 +102,13 @@ private double get_w_value(int idx, int label_idx) {
if (idx < 0 || idx > nr_feature) {
return 0;
}
- if (solverType.isSupportVectorRegression()) {
+ if (solverType.isSupportVectorRegression() || solverType.isOneClass()) {
return w[idx];
} else {
if (label_idx < 0 || label_idx >= nr_class) {
return 0;
}
- if (nr_class == 2 && solverType != SolverType.MCSVM_CS) {
+ if (nr_class == 2 && solverType != MCSVM_CS) {
if (label_idx == 0) {
return w[idx];
} else {
@@ -123,7 +133,8 @@ private double get_w_value(int idx, int label_idx) {
*/
// feat_idx: starting from 1 to nr_feature
// label_idx: starting from 0 to nr_class-1 for classification models;
- // for regression models, label_idx is ignored.
+ // for regression and one-class SVM models, label_idx is
+ // ignored.
public double getDecfunCoef(int featIdx, int labelIdx) {
if (featIdx > nr_feature) {
return 0;
@@ -140,6 +151,9 @@ public double getDecfunCoef(int featIdx, int labelIdx) {
* @since 1.95
*/
public double getDecfunBias(int labelIdx) {
+ if (solverType.isOneClass()) {
+ throw new IllegalArgumentException("Can not be called for a one-class SVM model");
+ }
int biasIdx = nr_feature;
if (bias <= 0) {
return 0;
@@ -148,6 +162,19 @@ public double getDecfunBias(int labelIdx) {
}
}
+ /**
+ * This function gives rho, the bias term used in one-class SVM only.
+ *
+ * This function can only be called for a one-class SVM model.
+ *
+ * @since 2.40
+ */
+ public double getDecfunRho() {
+ if (!solverType.isOneClass()) {
+ throw new IllegalArgumentException("Can be called only for a one-class SVM model");
+ }
+ return rho;
+ }
@Override
public String toString() {
@@ -161,34 +188,28 @@ public String toString() {
@Override
public int hashCode() {
- final int prime = 31;
- int result = 1;
- long temp;
- temp = Double.doubleToLongBits(bias);
- result = prime * result + (int)(temp ^ (temp >>> 32));
- result = prime * result + Arrays.hashCode(label);
- result = prime * result + nr_class;
- result = prime * result + nr_feature;
- result = prime * result + ((solverType == null) ? 0 : solverType.hashCode());
- result = prime * result + Arrays.hashCode(w);
+ int result = Objects.hash(getBias(), nr_class, nr_feature, getSolverType(), rho);
+ result = 31 * result + Arrays.hashCode(label);
+ result = 31 * result + arrayHashCode(w);
return result;
}
@Override
- public boolean equals(Object obj) {
- if (this == obj) return true;
- if (obj == null) return false;
- if (getClass() != obj.getClass()) return false;
- Model other = (Model)obj;
- if (Double.doubleToLongBits(bias) != Double.doubleToLongBits(other.bias)) return false;
- if (!Arrays.equals(label, other.label)) return false;
- if (nr_class != other.nr_class) return false;
- if (nr_feature != other.nr_feature) return false;
- if (solverType == null) {
- if (other.solverType != null) return false;
- } else if (!solverType.equals(other.solverType)) return false;
- if (!equals(w, other.w)) return false;
- return true;
+ public boolean equals(Object o) {
+ if (this == o) {
+ return true;
+ }
+ if (o == null || getClass() != o.getClass()) {
+ return false;
+ }
+ Model model = (Model)o;
+ return Double.compare(model.getBias(), getBias()) == 0
+ && nr_class == model.nr_class
+ && nr_feature == model.nr_feature
+ && Double.compare(model.rho, rho) == 0
+ && Arrays.equals(label, model.label)
+ && getSolverType() == model.getSolverType()
+ && arrayEquals(w, model.w);
}
/**
@@ -196,24 +217,53 @@ public boolean equals(Object obj) {
*
* @see Linear#saveModel(java.io.Writer, Model)
*/
- protected static boolean equals(double[] a, double[] a2) {
- if (a == a2) return true;
- if (a == null || a2 == null) return false;
+ private static boolean arrayEquals(double[] a, double[] a2) {
+ if (a == a2)
+ return true;
+ if (a == null || a2 == null)
+ return false;
int length = a.length;
- if (a2.length != length) return false;
+ if (a2.length != length)
+ return false;
for (int i = 0; i < length; i++)
- if (a[i] != a2[i]) return false;
+ if (a[i] != a2[i])
+ return false;
return true;
}
/**
- * see {@link Linear#saveModel(java.io.File, Model)}
+ * see {@link Arrays#hashCode(double[])} but treat 0.0 and -0.0 the same
+ */
+ private static int arrayHashCode(double[] w) {
+ if (w == null)
+ return 0;
+
+ int result = 1;
+ for (double element : w) {
+ if (element == -0.0) {
+ element = 0.0;
+ }
+ result = 31 * result + Double.hashCode(element);
+ }
+ return result;
+ }
+
+ /**
+ * @deprecated use {@link Model#save(Path)} instead
*/
- public void save(File file) throws IOException {
- Linear.saveModel(file, this);
+ @Deprecated
+ public void save(File modelFile) throws IOException {
+ save(modelFile.toPath());
+ }
+
+ /**
+ * see {@link Linear#saveModel(Path, Model)}
+ */
+ public void save(Path modelPath) throws IOException {
+ Linear.saveModel(modelPath, this);
}
/**
@@ -224,10 +274,18 @@ public void save(Writer writer) throws IOException {
}
/**
- * see {@link Linear#loadModel(File)}
+ * @deprecated use {@link Model#load(Path)} instead
+ */
+ @Deprecated
+ public static Model load(File modelFile) throws IOException {
+ return load(modelFile.toPath());
+ }
+
+ /**
+ * see {@link Linear#loadModel(Path)}
*/
- public static Model load(File file) throws IOException {
- return Linear.loadModel(file);
+ public static Model load(Path modelPath) throws IOException {
+ return Linear.loadModel(modelPath);
}
/**
diff --git a/src/main/java/de/bwaldvogel/liblinear/MutableDouble.java b/src/main/java/de/bwaldvogel/liblinear/MutableDouble.java
new file mode 100644
index 0000000..7dbfe35
--- /dev/null
+++ b/src/main/java/de/bwaldvogel/liblinear/MutableDouble.java
@@ -0,0 +1,27 @@
+package de.bwaldvogel.liblinear;
+
+class MutableDouble {
+
+ private boolean initialized;
+ private double value;
+
+ public MutableDouble() {
+ }
+
+ public MutableDouble(double value) {
+ set(value);
+ }
+
+ void set(double value) {
+ this.value = value;
+ this.initialized = true;
+ }
+
+ double get() {
+ if (!initialized) {
+ throw new IllegalStateException("Value not yet initialized");
+ }
+ return value;
+ }
+
+}
diff --git a/src/main/java/de/bwaldvogel/liblinear/MutableInt.java b/src/main/java/de/bwaldvogel/liblinear/MutableInt.java
new file mode 100644
index 0000000..b6e3f4e
--- /dev/null
+++ b/src/main/java/de/bwaldvogel/liblinear/MutableInt.java
@@ -0,0 +1,24 @@
+package de.bwaldvogel.liblinear;
+
+class MutableInt {
+
+ private boolean initialized;
+ private int value;
+
+ public MutableInt(int value) {
+ set(value);
+ }
+
+ void set(int value) {
+ this.value = value;
+ this.initialized = true;
+ }
+
+ int get() {
+ if (!initialized) {
+ throw new IllegalStateException("Value not yet initialized");
+ }
+ return value;
+ }
+
+}
diff --git a/src/main/java/de/bwaldvogel/liblinear/Newton.java b/src/main/java/de/bwaldvogel/liblinear/Newton.java
new file mode 100644
index 0000000..8e0a63e
--- /dev/null
+++ b/src/main/java/de/bwaldvogel/liblinear/Newton.java
@@ -0,0 +1,161 @@
+package de.bwaldvogel.liblinear;
+
+import static de.bwaldvogel.liblinear.Linear.*;
+
+
+class Newton {
+
+ private final Function fun_obj;
+ private final double eps;
+ private final int max_iter;
+ private final double eps_cg;
+
+ Newton(Function fun_obj, double eps, int max_iter) {
+ this(fun_obj, eps, max_iter, 0.5);
+ }
+
+ Newton(Function fun_obj, double eps, int max_iter, double eps_cg) {
+ this.fun_obj = fun_obj;
+ this.eps = eps;
+ this.max_iter = max_iter;
+ this.eps_cg = eps_cg;
+ }
+
+ void newton(double[] w) {
+ int n = fun_obj.get_nr_variable();
+ int i, cg_iter;
+ double step_size;
+ double f, fold, actred;
+ double init_step_size = 1;
+ boolean search = true;
+ int iter = 1;
+ MutableInt inc = new MutableInt(1);
+ double[] s = new double[n];
+ double[] r = new double[n];
+ double[] g = new double[n];
+
+ final double alpha_pcg = 0.01;
+ double[] M = new double[n];
+
+ // calculate gradient norm at w=0 for stopping condition.
+ double[] w0 = new double[n];
+ for (i = 0; i < n; i++)
+ w0[i] = 0;
+ fun_obj.fun(w0);
+ fun_obj.grad(w0, g);
+
+ double gnorm0 = Blas.dnrm2_(n, g, inc);
+
+ f = fun_obj.fun(w);
+ fun_obj.grad(w, g);
+ double gnorm = Blas.dnrm2_(n, g, inc);
+ info("init f %5.3e |g| %5.3e%n", f, gnorm);
+
+ if (gnorm <= eps * gnorm0)
+ search = false;
+
+ while (iter <= max_iter && search) {
+ fun_obj.get_diag_preconditioner(M);
+ for (i = 0; i < n; i++)
+ M[i] = (1 - alpha_pcg) + alpha_pcg * M[i];
+ cg_iter = pcg(g, M, s, r);
+
+ fold = f;
+ MutableDouble fReference = new MutableDouble(f);
+ step_size = fun_obj.linesearch_and_update(w, s, fReference, g, init_step_size);
+ f = fReference.get();
+
+ if (step_size == 0) {
+ info("WARNING: line search fails%n");
+ break;
+ }
+
+ fun_obj.grad(w, g);
+ gnorm = Blas.dnrm2_(n, g, inc);
+
+ info("iter %2d f %5.3e |g| %5.3e CG %3d step_size %4.2e%n", iter, f, gnorm, cg_iter, step_size);
+
+ if (gnorm <= eps * gnorm0)
+ break;
+ if (f < -1.0e+32) {
+ info("WARNING: f < -1.0e+32%n");
+ break;
+ }
+ actred = fold - f;
+ if (Math.abs(actred) <= 1.0e-12 * Math.abs(f)) {
+ info("WARNING: actred too small%n");
+ break;
+ }
+
+ iter++;
+ }
+
+ if (iter >= max_iter)
+ info("%nWARNING: reaching max number of Newton iterations%n");
+ }
+
+ private int pcg(double[] g, double[] M, double[] s, double[] r) {
+ int i, inc = 1;
+ int n = fun_obj.get_nr_variable();
+ double one = 1;
+ double[] d = new double[n];
+ double[] Hd = new double[n];
+ double zTr, znewTrnew, alpha, beta, cgtol, dHd;
+ double[] z = new double[n];
+ double Q = 0, newQ, Qdiff;
+
+ for (i = 0; i < n; i++) {
+ s[i] = 0;
+ r[i] = -g[i];
+ z[i] = r[i] / M[i];
+ d[i] = z[i];
+ }
+
+ zTr = Blas.ddot_(n, z, inc, r, inc);
+ double gMinv_norm = Math.sqrt(zTr);
+ cgtol = Math.min(eps_cg, Math.sqrt(gMinv_norm));
+ int cg_iter = 0;
+ int max_cg_iter = Math.max(n, 5);
+
+ while (cg_iter < max_cg_iter) {
+ cg_iter++;
+
+ fun_obj.Hv(d, Hd);
+ dHd = Blas.ddot_(n, d, inc, Hd, inc);
+ // avoid 0/0 in getting alpha
+ if (dHd <= 1.0e-16)
+ break;
+
+ alpha = zTr / dHd;
+ Blas.daxpy_(n, alpha, d, inc, s, inc);
+ alpha = -alpha;
+ Blas.daxpy_(n, alpha, Hd, inc, r, inc);
+
+ // Using quadratic approximation as CG stopping criterion
+ newQ = -0.5 * (Blas.ddot_(n, s, inc, r, inc) - Blas.ddot_(n, s, inc, g, inc));
+ Qdiff = newQ - Q;
+ if (newQ <= 0 && Qdiff <= 0) {
+ if (cg_iter * Qdiff >= cgtol * newQ)
+ break;
+ } else {
+ info("WARNING: quadratic approximation > 0 or increasing in CG%n");
+ break;
+ }
+ Q = newQ;
+
+ for (i = 0; i < n; i++)
+ z[i] = r[i] / M[i];
+ znewTrnew = Blas.ddot_(n, z, inc, r, inc);
+ beta = znewTrnew / zTr;
+ Blas.dscal_(n, beta, d, inc);
+ Blas.daxpy_(n, one, z, inc, d, inc);
+ zTr = znewTrnew;
+ }
+
+ if (cg_iter == max_cg_iter)
+ info("WARNING: reaching maximal number of CG steps%n");
+
+ return (cg_iter);
+ }
+
+}
diff --git a/src/main/java/de/bwaldvogel/liblinear/Parameter.java b/src/main/java/de/bwaldvogel/liblinear/Parameter.java
index 5d0c65b..6608592 100644
--- a/src/main/java/de/bwaldvogel/liblinear/Parameter.java
+++ b/src/main/java/de/bwaldvogel/liblinear/Parameter.java
@@ -1,46 +1,65 @@
package de.bwaldvogel.liblinear;
-import static de.bwaldvogel.liblinear.Linear.copyOf;
+import java.io.ByteArrayInputStream;
+import java.io.ByteArrayOutputStream;
+import java.io.IOException;
+import java.io.ObjectInputStream;
+import java.io.ObjectOutputStream;
+import java.util.Arrays;
+import java.util.Random;
-public final class Parameter {
+public final class Parameter implements Cloneable {
- double C;
+ private static final long DEFAULT_RANDOM_SEED = 0L;
- /** stopping criteria */
- double eps;
+ double C;
+
+ /** stopping tolerance */
+ double eps;
int max_iters = 1000; // maximal iterations
SolverType solverType;
- double[] weight = null;
+ double[] weight = null;
+
+ int[] weightLabel = null;
+
+ double p = 0.1;
+
+ double nu = 0.5;
+
+ /**
+ * Initial-solution specification (only supported for {@link SolverType#L2R_LR} and {@link SolverType#L2R_L2LOSS_SVC})
+ */
+ double[] init_sol = null;
- int[] weightLabel = null;
+ boolean regularize_bias = true;
- double p = 0.1;
+ Random random = new Random(DEFAULT_RANDOM_SEED);
- public Parameter( SolverType solver, double C, double eps ) {
+ public Parameter(SolverType solver, double C, double eps) {
setSolverType(solver);
setC(C);
setEps(eps);
}
- public Parameter( SolverType solver, double C, int max_iters, double eps ) {
+ public Parameter(SolverType solver, double C, int max_iters, double eps) {
setSolverType(solver);
setC(C);
setEps(eps);
setMaxIters(max_iters);
}
- public Parameter( SolverType solverType, double C, double eps, double p ) {
+ public Parameter(SolverType solverType, double C, double eps, double p) {
setSolverType(solverType);
setC(C);
setEps(eps);
setP(p);
}
- public Parameter( SolverType solverType, double C, double eps, int max_iters, double p ) {
+ public Parameter(SolverType solverType, double C, double eps, int max_iters, double p) {
setSolverType(solverType);
setC(C);
setEps(eps);
@@ -61,25 +80,32 @@ public Parameter( SolverType solverType, double C, double eps, int max_iters, do
* just set nr_weight to 0.
*/
public void setWeights(double[] weights, int[] weightLabels) {
- if (weights == null) throw new IllegalArgumentException("'weight' must not be null");
+ if (weights == null)
+ throw new IllegalArgumentException("'weight' must not be null");
if (weightLabels == null || weightLabels.length != weights.length)
throw new IllegalArgumentException("'weightLabels' must have same length as 'weight'");
- this.weightLabel = copyOf(weightLabels, weightLabels.length);
- this.weight = copyOf(weights, weights.length);
+ this.weightLabel = Arrays.copyOf(weightLabels, weightLabels.length);
+ this.weight = Arrays.copyOf(weights, weights.length);
}
/**
* @see #setWeights(double[], int[])
*/
public double[] getWeights() {
- return copyOf(weight, weight.length);
+ if (weight == null) {
+ return null;
+ }
+ return Arrays.copyOf(weight, weight.length);
}
/**
* @see #setWeights(double[], int[])
*/
public int[] getWeightLabels() {
- return copyOf(weightLabel, weightLabel.length);
+ if (weightLabel == null) {
+ return null;
+ }
+ return Arrays.copyOf(weightLabel, weightLabel.length);
}
/**
@@ -87,7 +113,8 @@ public int[] getWeightLabels() {
* @see #setWeights(double[], int[])
*/
public int getNumWeights() {
- if (weight == null) return 0;
+ if (weight == null)
+ return 0;
return weight.length;
}
@@ -95,7 +122,8 @@ public int getNumWeights() {
* C is the cost of constraints violation. (we usually use 1 to 1000)
*/
public void setC(double C) {
- if (C <= 0) throw new IllegalArgumentException("C must not be <= 0");
+ if (C <= 0)
+ throw new IllegalArgumentException("C must not be <= 0");
this.C = C;
}
@@ -107,7 +135,8 @@ public double getC() {
* eps is the stopping criterion. (we usually use 0.01).
*/
public void setEps(double eps) {
- if (eps <= 0) throw new IllegalArgumentException("eps must not be <= 0");
+ if (eps <= 0)
+ throw new IllegalArgumentException("eps must not be <= 0");
this.eps = eps;
}
@@ -116,7 +145,8 @@ public double getEps() {
}
public void setMaxIters(int iters) {
- if (iters <= 0) throw new IllegalArgumentException("max iters not be <= 0");
+ if (iters <= 0)
+ throw new IllegalArgumentException("max iters not be <= 0");
this.max_iters = iters;
}
@@ -125,7 +155,8 @@ public int getMaxIters() {
}
public void setSolverType(SolverType solverType) {
- if (solverType == null) throw new IllegalArgumentException("solver type must not be null");
+ if (solverType == null)
+ throw new IllegalArgumentException("solver type must not be null");
this.solverType = solverType;
}
@@ -133,16 +164,88 @@ public SolverType getSolverType() {
return solverType;
}
-
/**
* set the epsilon in loss function of epsilon-SVR (default 0.1)
*/
public void setP(double p) {
- if (p < 0) throw new IllegalArgumentException("p must not be less than 0");
+ if (p < 0)
+ throw new IllegalArgumentException("p must not be less than 0");
this.p = p;
}
public double getP() {
return p;
}
+
+ /*
+ * Sets the initial-solution specification.
+ * Only supported for {@link SolverType#L2R_LR} and {@link SolverType#L2R_L2LOSS_SVC}.
+ */
+ public void setInitSol(double[] init_sol) {
+ if (init_sol == null)
+ this.init_sol = null;
+ else {
+ this.init_sol = Arrays.copyOf(init_sol, init_sol.length);
+ }
+ }
+
+ public double[] getInitSol() {
+ if (init_sol == null) {
+ return null;
+ }
+ return Arrays.copyOf(init_sol, init_sol.length);
+ }
+
+ public void setNu(double nu) {
+ if (nu <= 0)
+ throw new IllegalArgumentException("nu must not be <=0");
+ if (nu >= 1)
+ throw new IllegalArgumentException("nu must not be >=1");
+ this.nu = nu;
+ }
+
+ public double getNu() {
+ return nu;
+ }
+
+ public void setRegularizeBias(boolean regularizeBias) {
+ this.regularize_bias = regularizeBias;
+ }
+
+ public boolean isRegularizeBias() {
+ return regularize_bias;
+ }
+
+ public void setRandom(Random random) {
+ this.random = random;
+ }
+
+ @Override
+ public Parameter clone() {
+ Parameter clone = new Parameter(solverType, C, eps, max_iters, p);
+ clone.weight = weight == null ? null : weight.clone();
+ clone.weightLabel = weightLabel == null ? null : weightLabel.clone();
+ clone.init_sol = init_sol;
+ clone.p = p;
+ clone.nu = nu;
+ clone.regularize_bias = regularize_bias;
+ clone.random = deepClone(random);
+ return clone;
+ }
+
+ private static Random deepClone(Random random) {
+ try {
+ try (ByteArrayOutputStream baos = new ByteArrayOutputStream();
+ ObjectOutputStream out = new ObjectOutputStream(baos);) {
+ out.writeObject(random);
+ try (ByteArrayInputStream bis = new ByteArrayInputStream(baos.toByteArray());
+ ObjectInputStream in = new ObjectInputStream(bis)) {
+ return (Random)in.readObject();
+ }
+ }
+ } catch (IOException | ClassNotFoundException e) {
+ throw new RuntimeException("Failed to clone " + random, e);
+ }
+ }
+
}
diff --git a/src/main/java/de/bwaldvogel/liblinear/ParameterCSearchResult.java b/src/main/java/de/bwaldvogel/liblinear/ParameterCSearchResult.java
new file mode 100644
index 0000000..07b3c13
--- /dev/null
+++ b/src/main/java/de/bwaldvogel/liblinear/ParameterCSearchResult.java
@@ -0,0 +1,21 @@
+package de.bwaldvogel.liblinear;
+
+public class ParameterCSearchResult {
+
+ private final double bestC;
+ private final double bestScore;
+
+ public ParameterCSearchResult(double bestC, double bestScore) {
+ this.bestC = bestC;
+ this.bestScore = bestScore;
+ }
+
+ public double getBestC() {
+ return bestC;
+ }
+
+ public double getBestScore() {
+ return bestScore;
+ }
+
+}
diff --git a/src/main/java/de/bwaldvogel/liblinear/ParameterSearchResult.java b/src/main/java/de/bwaldvogel/liblinear/ParameterSearchResult.java
new file mode 100644
index 0000000..f88f96f
--- /dev/null
+++ b/src/main/java/de/bwaldvogel/liblinear/ParameterSearchResult.java
@@ -0,0 +1,26 @@
+package de.bwaldvogel.liblinear;
+
+public class ParameterSearchResult {
+
+ private final double bestC;
+ private final double bestScore;
+ private final double bestP;
+
+ public ParameterSearchResult(double bestC, double bestScore, double bestP) {
+ this.bestC = bestC;
+ this.bestScore = bestScore;
+ this.bestP = bestP;
+ }
+
+ public double getBestC() {
+ return bestC;
+ }
+
+ public double getBestScore() {
+ return bestScore;
+ }
+
+ public double getBestP() {
+ return bestP;
+ }
+}
diff --git a/src/main/java/de/bwaldvogel/liblinear/Predict.java b/src/main/java/de/bwaldvogel/liblinear/Predict.java
index ce81e90..7d87e9b 100644
--- a/src/main/java/de/bwaldvogel/liblinear/Predict.java
+++ b/src/main/java/de/bwaldvogel/liblinear/Predict.java
@@ -1,20 +1,16 @@
package de.bwaldvogel.liblinear;
-import static de.bwaldvogel.liblinear.Linear.atof;
-import static de.bwaldvogel.liblinear.Linear.atoi;
-import static de.bwaldvogel.liblinear.Linear.closeQuietly;
-import static de.bwaldvogel.liblinear.Linear.printf;
-import static de.bwaldvogel.liblinear.Linear.info;
+import static de.bwaldvogel.liblinear.Linear.*;
import java.io.BufferedReader;
import java.io.BufferedWriter;
-import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.io.OutputStreamWriter;
import java.io.Writer;
+import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.Formatter;
import java.util.List;
@@ -25,14 +21,12 @@
public class Predict {
- private static boolean flag_predict_probability = false;
-
- private static final Pattern COLON = Pattern.compile(":");
+ private static final Pattern COLON = Pattern.compile(":");
/**
* Note: The streams are NOT closed
*/
- static void doPredict(BufferedReader reader, Writer writer, Model model) throws IOException {
+ static void doPredict(BufferedReader reader, Writer writer, Model model, boolean flag_predict_probability) throws IOException {
int correct = 0;
int total = 0;
double error = 0;
@@ -66,7 +60,7 @@ static void doPredict(BufferedReader reader, Writer writer, Model model) throws
String line = null;
while ((line = reader.readLine()) != null) {
- List x = new ArrayList();
+ List x = new ArrayList<>();
StringTokenizer st = new StringTokenizer(line, " \t\n");
double target_label;
try {
@@ -115,7 +109,7 @@ static void doPredict(BufferedReader reader, Writer writer, Model model) throws
printf(out, "\n");
} else {
predict_label = Linear.predict(model, nodes);
- printf(out, "%g\n", predict_label);
+ printf(out, "%.17g\n", predict_label);
}
if (predict_label == target_label) {
@@ -150,11 +144,14 @@ private static void exit_with_help() {
}
public static void main(String[] argv) throws IOException {
+ // Note: This flag is _static_ in predict.c but it causes a thread-safety issue as reported in https://github.com/bwaldvogel/liblinear-java/issues/38
+ boolean flag_predict_probability = false;
int i;
// parse options
for (i = 0; i < argv.length; i++) {
- if (argv[i].charAt(0) != '-') break;
+ if (argv[i].charAt(0) != '-')
+ break;
++i;
switch (argv[i - 1].charAt(1)) {
case 'b':
@@ -180,18 +177,12 @@ public static void main(String[] argv) throws IOException {
exit_with_help();
}
- BufferedReader reader = null;
- Writer writer = null;
- try {
- reader = new BufferedReader(new InputStreamReader(new FileInputStream(argv[i]), Linear.FILE_CHARSET));
- writer = new BufferedWriter(new OutputStreamWriter(new FileOutputStream(argv[i + 2]), Linear.FILE_CHARSET));
-
- Model model = Linear.loadModel(new File(argv[i + 1]));
- doPredict(reader, writer, model);
- }
- finally {
- closeQuietly(reader);
- closeQuietly(writer);
+ try (FileInputStream in = new FileInputStream(argv[i]);
+ BufferedReader reader = new BufferedReader(new InputStreamReader(in, Linear.FILE_CHARSET));
+ FileOutputStream out = new FileOutputStream(argv[i + 2]);
+ Writer writer = new BufferedWriter(new OutputStreamWriter(out, Linear.FILE_CHARSET))) {
+ Model model = Linear.loadModel(Paths.get(argv[i + 1]));
+ doPredict(reader, writer, model, flag_predict_probability);
}
}
}
diff --git a/src/main/java/de/bwaldvogel/liblinear/Problem.java b/src/main/java/de/bwaldvogel/liblinear/Problem.java
index f9cad56..983d766 100644
--- a/src/main/java/de/bwaldvogel/liblinear/Problem.java
+++ b/src/main/java/de/bwaldvogel/liblinear/Problem.java
@@ -2,6 +2,9 @@
import java.io.File;
import java.io.IOException;
+import java.io.InputStream;
+import java.nio.charset.Charset;
+import java.nio.file.Path;
/**
@@ -34,13 +37,13 @@
public class Problem {
/** the number of training data */
- public int l;
+ public int l;
/** the number of features (including the bias feature if bias >= 0) */
- public int n;
+ public int n;
/** an array containing the target values */
- public double[] y;
+ public double[] y;
/** array of sparse feature nodes */
public Feature[][] x;
@@ -49,12 +52,49 @@ public class Problem {
* If bias >= 0, we assume that one additional feature is added
* to the end of each data instance
*/
- public double bias;
+ public double bias = -1;
/**
- * see {@link Train#readProblem(File, double)}
+ * @deprecated use {@link Problem#readFromFile(Path, double)} instead
*/
+ @Deprecated
public static Problem readFromFile(File file, double bias) throws IOException, InvalidInputDataException {
- return Train.readProblem(file, bias);
+ return readFromFile(file.toPath(), bias);
+ }
+
+ /**
+ * see {@link Train#readProblem(Path, double)}
+ */
+ public static Problem readFromFile(Path path, double bias) throws IOException, InvalidInputDataException {
+ return Train.readProblem(path, bias);
+ }
+
+ /**
+ * @deprecated use {@link Problem#readFromFile(Path, Charset, double)} instead
+ */
+ @Deprecated
+ public static Problem readFromFile(File file, Charset charset, double bias) throws IOException, InvalidInputDataException {
+ return readFromFile(file.toPath(), charset, bias);
+ }
+
+ /**
+ * see {@link Train#readProblem(Path, Charset, double)}
+ */
+ public static Problem readFromFile(Path path, Charset charset, double bias) throws IOException, InvalidInputDataException {
+ return Train.readProblem(path, charset, bias);
+ }
+
+ /**
+ * see {@link Train#readProblem(InputStream, double)}
+ */
+ public static Problem readFromStream(InputStream inputStream, double bias) throws IOException, InvalidInputDataException {
+ return Train.readProblem(inputStream, bias);
+ }
+
+ /**
+ * see {@link Train#readProblem(InputStream, Charset, double)}
+ */
+ public static Problem readFromStream(InputStream inputStream, Charset charset, double bias) throws IOException, InvalidInputDataException {
+ return Train.readProblem(inputStream, charset, bias);
}
}
diff --git a/src/main/java/de/bwaldvogel/liblinear/SolverMCSVM_CS.java b/src/main/java/de/bwaldvogel/liblinear/SolverMCSVM_CS.java
index 106598d..2c7e22f 100644
--- a/src/main/java/de/bwaldvogel/liblinear/SolverMCSVM_CS.java
+++ b/src/main/java/de/bwaldvogel/liblinear/SolverMCSVM_CS.java
@@ -1,8 +1,9 @@
package de.bwaldvogel.liblinear;
-import static de.bwaldvogel.liblinear.Linear.copyOf;
-import static de.bwaldvogel.liblinear.Linear.info;
-import static de.bwaldvogel.liblinear.Linear.swap;
+import static de.bwaldvogel.liblinear.Linear.*;
+
+import java.util.Arrays;
+import java.util.Random;
/**
@@ -36,26 +37,19 @@ class SolverMCSVM_CS {
private final double[] G;
private final int max_iter;
private final int w_size, l;
- private final int nr_class;
- private final Problem prob;
-
- public SolverMCSVM_CS( Problem prob, int nr_class, double[] C ) {
- this(prob, nr_class, C, 0.1);
- }
-
- public SolverMCSVM_CS( Problem prob, int nr_class, double[] C, double eps ) {
- this(prob, nr_class, C, eps, 100000);
- }
-
+ private final int nr_class;
+ private final Problem prob;
+ private final Random random;
- public SolverMCSVM_CS( Problem prob, int nr_class, double[] weighted_C, double eps, int max_iter ) {
+ public SolverMCSVM_CS(Problem prob, int nr_class, double[] C, double eps, Random random) {
this.w_size = prob.n;
this.l = prob.l;
this.nr_class = nr_class;
this.eps = eps;
- this.max_iter = max_iter;
+ this.random = random;
+ this.max_iter = 100000;
this.prob = prob;
- this.C = weighted_C;
+ this.C = C;
this.B = new double[nr_class];
this.G = new double[nr_class];
}
@@ -66,8 +60,10 @@ private int GETI(int i) {
private boolean be_shrunk(int i, int m, int yi, double alpha_i, double minG) {
double bound = 0;
- if (m == yi) bound = C[GETI(i)];
- if (alpha_i == bound && G[m] < minG) return true;
+ if (m == yi)
+ bound = C[GETI(i)];
+ if (alpha_i == bound && G[m] < minG)
+ return true;
return false;
}
@@ -122,7 +118,7 @@ public void solve(double[] w) {
for (i = 0; i < active_size; i++) {
// int j = i+rand()%(active_size-i);
- int j = i + Linear.random.nextInt(active_size - i);
+ int j = i + random.nextInt(active_size - i);
swap(index, i, j);
}
for (s = 0; s < active_size; s++) {
@@ -138,7 +134,8 @@ public void solve(double[] w) {
if (Ai > 0) {
for (m = 0; m < active_size_i[i]; m++)
G[m] = 1;
- if (y_index[i] < active_size_i[i]) G[y_index[i]] = 0;
+ if (y_index[i] < active_size_i[i])
+ G[y_index[i]] = 0;
for (Feature xi : prob.x[i]) {
// double *w_i = &w[(xi.index-1)*nr_class];
@@ -152,8 +149,10 @@ public void solve(double[] w) {
double minG = Double.POSITIVE_INFINITY;
double maxG = Double.NEGATIVE_INFINITY;
for (m = 0; m < active_size_i[i]; m++) {
- if (alpha_i.get(alpha_index_i.get(m)) < 0 && G[m] < minG) minG = G[m];
- if (G[m] > maxG) maxG = G[m];
+ if (alpha_i.get(alpha_index_i.get(m)) < 0 && G[m] < minG)
+ minG = G[m];
+ if (G[m] > maxG)
+ maxG = G[m];
}
if (y_index[i] < active_size_i[i]) {
if (alpha_i.get((int)prob.y[i]) < C[GETI(i)] && G[y_index[i]] < minG) {
@@ -170,7 +169,8 @@ public void solve(double[] w) {
swap(G, m, active_size_i[i]);
if (y_index[i] == active_size_i[i])
y_index[i] = m;
- else if (y_index[i] == m) y_index[i] = active_size_i[i];
+ else if (y_index[i] == m)
+ y_index[i] = active_size_i[i];
break;
}
active_size_i[i]--;
@@ -237,7 +237,8 @@ public void solve(double[] w) {
}
info("%noptimization finished, #iter = %d%n", iter);
- if (iter >= max_iter) info("%nWARNING: reaching max number of iterations%n");
+ if (iter >= max_iter)
+ info("%nWARNING: reaching max number of iterations%n");
// calculate objective value
double v = 0;
@@ -247,7 +248,8 @@ public void solve(double[] w) {
v = 0.5 * v;
for (i = 0; i < l * nr_class; i++) {
v += alpha[i];
- if (Math.abs(alpha[i]) > 0) nSV++;
+ if (Math.abs(alpha[i]) > 0)
+ nSV++;
}
for (i = 0; i < l; i++)
v -= alpha[i * nr_class + (int)prob.y[i]];
@@ -260,10 +262,11 @@ private void solve_sub_problem(double A_i, int yi, double C_yi, int active_i, do
int r;
assert active_i <= B.length; // no padding
- double[] D = copyOf(B, active_i);
+ double[] D = Arrays.copyOf(B, active_i);
// clone(D, B, active_i);
- if (yi < active_i) D[yi] += A_i * C_yi;
+ if (yi < active_i)
+ D[yi] += A_i * C_yi;
// qsort(D, active_i, sizeof(double), compare_double);
ArraySorter.reversedMergesort(D);
diff --git a/src/main/java/de/bwaldvogel/liblinear/SolverType.java b/src/main/java/de/bwaldvogel/liblinear/SolverType.java
index 533e091..9914f00 100644
--- a/src/main/java/de/bwaldvogel/liblinear/SolverType.java
+++ b/src/main/java/de/bwaldvogel/liblinear/SolverType.java
@@ -81,23 +81,32 @@ public enum SolverType {
*/
L2R_L1LOSS_SVR_DUAL(13, false, true),
+ /**
+ * one-class support vector machine (dual)
+ *
+ * @since 2.40
+ */
+ ONECLASS_SVM(21, false, false),
+
;
private final boolean logisticRegressionSolver;
private final boolean supportVectorRegression;
private final int id;
- private SolverType( int id, boolean logisticRegressionSolver, boolean supportVectorRegression ) {
+ SolverType(int id, boolean logisticRegressionSolver, boolean supportVectorRegression) {
this.id = id;
this.logisticRegressionSolver = logisticRegressionSolver;
this.supportVectorRegression = supportVectorRegression;
}
- private static Map SOLVERS_BY_ID = new HashMap();
+ private static final Map SOLVERS_BY_ID = new HashMap<>();
+
static {
for (SolverType solverType : SolverType.values()) {
SolverType old = SOLVERS_BY_ID.put(Integer.valueOf(solverType.getId()), solverType);
- if (old != null) throw new Error("duplicate solver type ID: " + solverType.getId());
+ if (old != null)
+ throw new Error("duplicate solver type ID: " + solverType.getId());
}
}
@@ -126,4 +135,12 @@ public boolean isLogisticRegressionSolver() {
public boolean isSupportVectorRegression() {
return supportVectorRegression;
}
+
+ /**
+ * @since 2.40
+ */
+ public boolean isOneClass() {
+ return equals(ONECLASS_SVM);
+ }
+
}
diff --git a/src/main/java/de/bwaldvogel/liblinear/SparseOperator.java b/src/main/java/de/bwaldvogel/liblinear/SparseOperator.java
new file mode 100644
index 0000000..a09c691
--- /dev/null
+++ b/src/main/java/de/bwaldvogel/liblinear/SparseOperator.java
@@ -0,0 +1,52 @@
+package de.bwaldvogel.liblinear;
+
+class SparseOperator {
+
+ static double nrm2_sq(Feature[] x) {
+ double ret = 0;
+ for (Feature feature : x) {
+ ret += feature.getValue() * feature.getValue();
+ }
+ return (ret);
+ }
+
+ static double dot(double[] s, Feature[] x) {
+ double ret = 0;
+ for (Feature feature : x) {
+ ret += s[feature.getIndex() - 1] * feature.getValue();
+ }
+ return (ret);
+ }
+
+ static double sparse_dot(Feature[] x1, Feature[] x2) {
+ double ret = 0;
+
+ int pos1 = 0;
+ int pos2 = 0;
+
+ while (pos1 < x1.length && pos2 < x2.length) {
+ int index1 = x1[pos1].getIndex();
+ int index2 = x2[pos2].getIndex();
+ if (index1 == index2) {
+ ret += x1[pos1].getValue() * x2[pos2].getValue();
+ pos1++;
+ pos2++;
+ } else {
+ if (index1 > index2) {
+ pos2++;
+ } else {
+ pos1++;
+ }
+ }
+ }
+
+ return (ret);
+ }
+
+ static void axpy(double a, Feature[] x, double[] y) {
+ for (Feature feature : x) {
+ y[feature.getIndex() - 1] += a * feature.getValue();
+ }
+ }
+
+}
diff --git a/src/main/java/de/bwaldvogel/liblinear/Train.java b/src/main/java/de/bwaldvogel/liblinear/Train.java
index 5fd7060..8489d1e 100644
--- a/src/main/java/de/bwaldvogel/liblinear/Train.java
+++ b/src/main/java/de/bwaldvogel/liblinear/Train.java
@@ -1,12 +1,17 @@
package de.bwaldvogel.liblinear;
-import static de.bwaldvogel.liblinear.Linear.atof;
-import static de.bwaldvogel.liblinear.Linear.atoi;
+import static de.bwaldvogel.liblinear.Linear.*;
+import static de.bwaldvogel.liblinear.SolverType.*;
import java.io.BufferedReader;
import java.io.File;
-import java.io.FileReader;
import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
+import java.nio.charset.Charset;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.List;
import java.util.NoSuchElementException;
@@ -20,6 +25,10 @@ public static void main(String[] args) throws IOException, InvalidInputDataExcep
}
private double bias = 1;
+ private boolean find_parameters = false;
+ private boolean C_specified = false;
+ private boolean P_specified = false;
+ private boolean solver_specified = false;
private boolean cross_validation = false;
private String inputFilename;
private String modelFilename;
@@ -27,6 +36,25 @@ public static void main(String[] args) throws IOException, InvalidInputDataExcep
private Parameter param = null;
private Problem prob = null;
+ private void do_find_parameters() {
+ double start_C, start_p;
+ if (C_specified)
+ start_C = param.C;
+ else
+ start_C = -1.0;
+ if (P_specified)
+ start_p = param.p;
+ else
+ start_p = -1.0;
+
+ System.out.printf("Doing parameter search with %d-fold cross validation.%n", nr_fold);
+ ParameterSearchResult result = Linear.findParameters(prob, param, nr_fold, start_C, start_p);
+ if (param.getSolverType() == L2R_LR || param.getSolverType() == L2R_L2LOSS_SVC)
+ System.out.printf("Best C = %g CV accuracy = %g%%\n", result.getBestC(), 100.0 * result.getBestScore());
+ else if (param.getSolverType() == L2R_L2LOSS_SVR)
+ System.out.printf("Best C = %g Best p = %g CV MSE = %g\n", result.getBestC(), result.getBestP(), result.getBestScore());
+ }
+
private void do_cross_validation() {
double total_error = 0;
@@ -56,7 +84,8 @@ private void do_cross_validation() {
} else {
int total_correct = 0;
for (int i = 0; i < prob.l; i++)
- if (target[i] == prob.y[i]) ++total_correct;
+ if (target[i] == prob.y[i])
+ ++total_correct;
System.out.printf("correct: %d%n", total_correct);
System.out.printf("Cross Validation Accuracy = %g%%%n", 100.0 * total_correct / prob.l);
@@ -68,75 +97,91 @@ private void exit_with_help() {
+ "options:%n"
+ "-s type : set type of solver (default 1)%n"
+ " for multi-class classification%n"
- + " 0 -- L2-regularized logistic regression (primal)%n"
- + " 1 -- L2-regularized L2-loss support vector classification (dual)%n"
- + " 2 -- L2-regularized L2-loss support vector classification (primal)%n"
- + " 3 -- L2-regularized L1-loss support vector classification (dual)%n"
- + " 4 -- support vector classification by Crammer and Singer%n"
- + " 5 -- L1-regularized L2-loss support vector classification%n"
- + " 6 -- L1-regularized logistic regression%n"
- + " 7 -- L2-regularized logistic regression (dual)%n"
+ + " 0 -- L2-regularized logistic regression (primal)%n"
+ + " 1 -- L2-regularized L2-loss support vector classification (dual)%n"
+ + " 2 -- L2-regularized L2-loss support vector classification (primal)%n"
+ + " 3 -- L2-regularized L1-loss support vector classification (dual)%n"
+ + " 4 -- support vector classification by Crammer and Singer%n"
+ + " 5 -- L1-regularized L2-loss support vector classification%n"
+ + " 6 -- L1-regularized logistic regression%n"
+ + " 7 -- L2-regularized logistic regression (dual)%n"
+ " for regression%n"
- + " 11 -- L2-regularized L2-loss support vector regression (primal)%n"
- + " 12 -- L2-regularized L2-loss support vector regression (dual)%n"
- + " 13 -- L2-regularized L1-loss support vector regression (dual)%n"
+ + " 11 -- L2-regularized L2-loss support vector regression (primal)%n"
+ + " 12 -- L2-regularized L2-loss support vector regression (dual)%n"
+ + " 13 -- L2-regularized L1-loss support vector regression (dual)%n"
+ + " for outlier detection%n"
+ + " 21 -- one-class support vector machine (dual)%n"
+ "-c cost : set the parameter C (default 1)%n"
+ "-p epsilon : set the epsilon in loss function of SVR (default 0.1)%n"
+ + "-n nu : set the parameter nu of one-class SVM (default 0.5)%n"
+ "-e epsilon : set tolerance of termination criterion%n"
- + " -s 0 and 2%n" + " |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,%n"
- + " where f is the primal function and pos/neg are # of%n"
- + " positive/negative data (default 0.01)%n" + " -s 11%n"
- + " |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001)%n"
- + " -s 1, 3, 4 and 7%n" + " Dual maximal violation <= eps; similar to libsvm (default 0.1)%n"
- + " -s 5 and 6%n"
- + " |f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,%n"
- + " where f is the primal function (default 0.01)%n"
- + " -s 12 and 13\n"
- + " |f'(alpha)|_1 <= eps |f'(alpha0)|,\n"
- + " where f is the dual function (default 0.1)\n"
+ + " -s 0 and 2%n"
+ + " |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,%n"
+ + " where f is the primal function and pos/neg are # of%n"
+ + " positive/negative data (default 0.01)%n"
+ + " -s 11%n"
+ + " |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.0001)%n"
+ + " -s 1, 3, 4, 7, and 21%n"
+ + " Dual maximal violation <= eps; similar to libsvm (default 0.1 except 0.01 for -s 21)%n"
+ + " -s 5 and 6%n"
+ + " |f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,%n"
+ + " where f is the primal function (default 0.01)%n"
+ + " -s 12 and 13%n"
+ + " |f'(alpha)|_1 <= eps |f'(alpha0)|,%n"
+ + " where f is the dual function (default 0.1)%n"
+ "-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)%n"
+ + "-R : not regularize the bias; must with -B 1 to have the bias; DON'T use this unless you know what it is%n"
+ + " (for -s 0, 2, 5, 6, 11)%n"
+ "-wi weight: weights adjust the parameter C of different classes (see README for details)%n"
+ "-v n: n-fold cross validation mode%n"
+ + "-C : find parameters (C for -s 0, 2 and C, p for -s 11)%n"
+ "-q : quiet mode (no outputs)%n");
System.exit(1);
}
-
- Problem getProblem() {
+ public Problem getProblem() {
return prob;
}
- double getBias() {
+ public double getBias() {
return bias;
}
- Parameter getParameter() {
+ public Parameter getParameter() {
return param;
}
- void parse_command_line(String argv[]) {
+ public void parse_command_line(String argv[]) {
int i;
// eps: see setting below
- param = new Parameter(SolverType.L2R_L2LOSS_SVC_DUAL, 1, Double.POSITIVE_INFINITY, 0.1);
+ param = new Parameter(L2R_L2LOSS_SVC_DUAL, 1, Double.POSITIVE_INFINITY, 0.1);
// default values
bias = -1;
cross_validation = false;
// parse options
for (i = 0; i < argv.length; i++) {
- if (argv[i].charAt(0) != '-') break;
- if (++i >= argv.length) exit_with_help();
+ if (argv[i].charAt(0) != '-')
+ break;
+ if (++i >= argv.length)
+ exit_with_help();
switch (argv[i - 1].charAt(1)) {
case 's':
param.solverType = SolverType.getById(atoi(argv[i]));
+ solver_specified = true;
break;
case 'c':
param.setC(atof(argv[i]));
+ C_specified = true;
break;
case 'p':
+ P_specified = true;
param.setP(atof(argv[i]));
break;
+ case 'n':
+ param.nu = atof(argv[i]);
+ break;
case 'e':
param.setEps(atof(argv[i]));
break;
@@ -161,6 +206,14 @@ void parse_command_line(String argv[]) {
i--;
Linear.disableDebugOutput();
break;
+ case 'C':
+ find_parameters = true;
+ i--;
+ break;
+ case 'R':
+ param.regularize_bias = false;
+ i--;
+ break;
default:
System.err.println("unknown option");
exit_with_help();
@@ -169,7 +222,8 @@ void parse_command_line(String argv[]) {
// determine filenames
- if (i >= argv.length) exit_with_help();
+ if (i >= argv.length)
+ exit_with_help();
inputFilename = argv[i];
@@ -181,6 +235,19 @@ void parse_command_line(String argv[]) {
modelFilename = argv[i].substring(p) + ".model";
}
+ // default solver for parameter selection is L2R_L2LOSS_SVC
+ if (find_parameters) {
+ if (!cross_validation)
+ nr_fold = 5;
+ if (!solver_specified) {
+ System.err.printf("Solver not specified. Using -s 2%n");
+ param.setSolverType(L2R_L2LOSS_SVC);
+ } else if (param.getSolverType() != L2R_LR && param.getSolverType() != L2R_L2LOSS_SVC && param.getSolverType() != L2R_L2LOSS_SVR) {
+ System.err.printf("Warm-start parameter search only available for -s 0, -s 2 and -s 11%n");
+ exit_with_help();
+ }
+ }
+
if (param.eps == Double.POSITIVE_INFINITY) {
switch (param.solverType) {
case L2R_LR:
@@ -188,7 +255,7 @@ void parse_command_line(String argv[]) {
param.setEps(0.01);
break;
case L2R_L2LOSS_SVR:
- param.setEps(0.001);
+ param.setEps(0.0001);
break;
case L2R_L2LOSS_SVC_DUAL:
case L2R_L1LOSS_SVC_DUAL:
@@ -204,6 +271,9 @@ void parse_command_line(String argv[]) {
case L2R_L2LOSS_SVR_DUAL:
param.setEps(0.1);
break;
+ case ONECLASS_SVM:
+ param.setEps(0.01);
+ break;
default:
throw new IllegalStateException("unknown solver type: " + param.solverType);
}
@@ -215,82 +285,123 @@ void parse_command_line(String argv[]) {
* @param file the SVM file
* @throws IOException obviously in case of any I/O exception ;)
* @throws InvalidInputDataException if the input file is not correctly formatted
+ * @deprecated use {@link Train#readProblem(Path, double)} instead
*/
+ @Deprecated
public static Problem readProblem(File file, double bias) throws IOException, InvalidInputDataException {
- BufferedReader fp = new BufferedReader(new FileReader(file));
- List vy = new ArrayList();
- List vx = new ArrayList();
+ return readProblem(file.toPath(), bias);
+ }
+
+ /**
+ * reads a problem from LibSVM format
+ * @throws IOException obviously in case of any I/O exception ;)
+ * @throws InvalidInputDataException if the input file is not correctly formatted
+ */
+ public static Problem readProblem(Path path, double bias) throws IOException, InvalidInputDataException {
+ try (InputStream inputStream = Files.newInputStream(path)) {
+ return readProblem(inputStream, bias);
+ }
+ }
+
+ /**
+ * @deprecated use {@link Train#readProblem(Path, Charset, double)} instead
+ */
+ @Deprecated
+ public static Problem readProblem(File file, Charset charset, double bias) throws IOException, InvalidInputDataException {
+ return readProblem(file.toPath(), charset, bias);
+ }
+
+ public static Problem readProblem(Path path, Charset charset, double bias) throws IOException, InvalidInputDataException {
+ try (InputStream inputStream = Files.newInputStream(path)) {
+ return readProblem(inputStream, charset, bias);
+ }
+ }
+
+ public static Problem readProblem(InputStream inputStream, double bias) throws IOException, InvalidInputDataException {
+ return readProblem(inputStream, Charset.defaultCharset(), bias);
+ }
+
+ public static Problem readProblem(InputStream inputStream, Charset charset, double bias) throws IOException, InvalidInputDataException {
+ BufferedReader fp = new BufferedReader(new InputStreamReader(inputStream, charset));
+ List vy = new ArrayList<>();
+ List vx = new ArrayList<>();
int max_index = 0;
int lineNr = 0;
- try {
- while (true) {
- String line = fp.readLine();
- if (line == null) break;
- lineNr++;
+ while (true) {
+ String line = fp.readLine();
+ if (line == null)
+ break;
+ lineNr++;
+
+ StringTokenizer st = new StringTokenizer(line, " \t\n\r\f:");
+ String token;
+ try {
+ token = st.nextToken();
+ } catch (NoSuchElementException e) {
+ throw new InvalidInputDataException("empty line", lineNr, e);
+ }
- StringTokenizer st = new StringTokenizer(line, " \t\n\r\f:");
- String token;
- try {
- token = st.nextToken();
- } catch (NoSuchElementException e) {
- throw new InvalidInputDataException("empty line", file, lineNr, e);
- }
+ try {
+ vy.add(atof(token));
+ } catch (NumberFormatException e) {
+ throw new InvalidInputDataException("invalid label: " + token, lineNr, e);
+ }
+ int m = st.countTokens() / 2;
+ Feature[] x;
+ if (bias >= 0) {
+ x = new Feature[m + 1];
+ } else {
+ x = new Feature[m];
+ }
+ int indexBefore = 0;
+ for (int j = 0; j < m; j++) {
+
+ token = st.nextToken();
+ int index;
try {
- vy.add(atof(token));
+ index = atoi(token);
} catch (NumberFormatException e) {
- throw new InvalidInputDataException("invalid label: " + token, file, lineNr, e);
+ throw new InvalidInputDataException("invalid index: " + token, lineNr, e);
}
- int m = st.countTokens() / 2;
- Feature[] x;
- if (bias >= 0) {
- x = new Feature[m + 1];
- } else {
- x = new Feature[m];
- }
- int indexBefore = 0;
- for (int j = 0; j < m; j++) {
-
- token = st.nextToken();
- int index;
- try {
- index = atoi(token);
- } catch (NumberFormatException e) {
- throw new InvalidInputDataException("invalid index: " + token, file, lineNr, e);
- }
+ // assert that indices are valid and sorted
+ if (index <= 0)
+ throw new InvalidInputDataException("invalid index: " + index, lineNr);
+ if (index <= indexBefore)
+ throw new InvalidInputDataException("indices must be sorted in ascending order", lineNr);
+ indexBefore = index;
- // assert that indices are valid and sorted
- if (index < 0) throw new InvalidInputDataException("invalid index: " + index, file, lineNr);
- if (index <= indexBefore) throw new InvalidInputDataException("indices must be sorted in ascending order", file, lineNr);
- indexBefore = index;
-
- token = st.nextToken();
- try {
- double value = atof(token);
- x[j] = new FeatureNode(index, value);
- } catch (NumberFormatException e) {
- throw new InvalidInputDataException("invalid value: " + token, file, lineNr);
- }
- }
- if (m > 0) {
- max_index = Math.max(max_index, x[m - 1].getIndex());
+ token = st.nextToken();
+ try {
+ double value = atof(token);
+ x[j] = new FeatureNode(index, value);
+ } catch (NumberFormatException e) {
+ throw new InvalidInputDataException("invalid value: " + token, lineNr);
}
-
- vx.add(x);
+ }
+ if (m > 0) {
+ max_index = Math.max(max_index, x[m - 1].getIndex());
}
- return constructProblem(vy, vx, max_index, bias);
- }
- finally {
- fp.close();
+ vx.add(x);
}
+
+ return constructProblem(vy, vx, max_index, bias);
+ }
+
+ public void readProblem(Path path) throws IOException, InvalidInputDataException {
+ prob = Train.readProblem(path, bias);
+ }
+
+ public void readProblem(String filename) throws IOException, InvalidInputDataException {
+ readProblem(filename, bias);
}
- void readProblem(String filename) throws IOException, InvalidInputDataException {
- prob = Train.readProblem(new File(filename), bias);
+ public void readProblem(String filename, double bias) throws IOException, InvalidInputDataException {
+ prob = Train.readProblem(Paths.get(filename), bias);
}
private static int[] addToArray(int[] array, int newElement) {
@@ -341,11 +452,21 @@ private static Problem constructProblem(List vy, List vx, int
private void run(String[] args) throws IOException, InvalidInputDataException {
parse_command_line(args);
readProblem(inputFilename);
- if (cross_validation)
+ if (find_parameters) {
+ do_find_parameters();
+ } else if (cross_validation)
do_cross_validation();
else {
Model model = Linear.train(prob, param);
- Linear.saveModel(new File(modelFilename), model);
+ Linear.saveModel(Paths.get(modelFilename), model);
}
}
+
+ boolean isFindParameters() {
+ return find_parameters;
+ }
+
+ int getNumFolds() {
+ return nr_fold;
+ }
}
diff --git a/src/main/java/de/bwaldvogel/liblinear/Tron.java b/src/main/java/de/bwaldvogel/liblinear/Tron.java
deleted file mode 100644
index 23adf07..0000000
--- a/src/main/java/de/bwaldvogel/liblinear/Tron.java
+++ /dev/null
@@ -1,260 +0,0 @@
-package de.bwaldvogel.liblinear;
-
-import static de.bwaldvogel.liblinear.Linear.info;
-
-/**
- * Trust Region Newton Method optimization
- */
-class Tron {
-
- private final Function fun_obj;
-
- private final double eps;
-
- private final int max_iter;
-
- public Tron( final Function fun_obj ) {
- this(fun_obj, 0.1);
- }
-
- public Tron( final Function fun_obj, double eps ) {
- this(fun_obj, eps, 1000);
- }
-
- public Tron( final Function fun_obj, double eps, int max_iter ) {
- this.fun_obj = fun_obj;
- this.eps = eps;
- this.max_iter = max_iter;
- }
-
- void tron(double[] w) {
- // Parameters for updating the iterates.
- double eta0 = 1e-4, eta1 = 0.25, eta2 = 0.75;
-
- // Parameters for updating the trust region size delta.
- double sigma1 = 0.25, sigma2 = 0.5, sigma3 = 4;
-
- int n = fun_obj.get_nr_variable();
- int i, cg_iter;
- double delta, snorm, one = 1.0;
- double alpha, f, fnew, prered, actred, gs;
- int search = 1, iter = 1;
- double[] s = new double[n];
- double[] r = new double[n];
- double[] w_new = new double[n];
- double[] g = new double[n];
-
- for (i = 0; i < n; i++)
- w[i] = 0;
-
- f = fun_obj.fun(w);
- fun_obj.grad(w, g);
- delta = euclideanNorm(g);
- double gnorm1 = delta;
- double gnorm = gnorm1;
-
- if (gnorm <= eps * gnorm1) search = 0;
-
- iter = 1;
-
- while (iter <= max_iter && search != 0) {
- cg_iter = trcg(delta, g, s, r);
-
- System.arraycopy(w, 0, w_new, 0, n);
- daxpy(one, s, w_new);
-
- gs = dot(g, s);
- prered = -0.5 * (gs - dot(s, r));
- fnew = fun_obj.fun(w_new);
-
- // Compute the actual reduction.
- actred = f - fnew;
-
- // On the first iteration, adjust the initial step bound.
- snorm = euclideanNorm(s);
- if (iter == 1) delta = Math.min(delta, snorm);
-
- // Compute prediction alpha*snorm of the step.
- if (fnew - f - gs <= 0)
- alpha = sigma3;
- else
- alpha = Math.max(sigma1, -0.5 * (gs / (fnew - f - gs)));
-
- // Update the trust region bound according to the ratio of actual to
- // predicted reduction.
- if (actred < eta0 * prered)
- delta = Math.min(Math.max(alpha, sigma1) * snorm, sigma2 * delta);
- else if (actred < eta1 * prered)
- delta = Math.max(sigma1 * delta, Math.min(alpha * snorm, sigma2 * delta));
- else if (actred < eta2 * prered)
- delta = Math.max(sigma1 * delta, Math.min(alpha * snorm, sigma3 * delta));
- else
- delta = Math.max(delta, Math.min(alpha * snorm, sigma3 * delta));
-
- info("iter %2d act %5.3e pre %5.3e delta %5.3e f %5.3e |g| %5.3e CG %3d%n", iter, actred, prered, delta, f, gnorm, cg_iter);
-
- if (actred > eta0 * prered) {
- iter++;
- System.arraycopy(w_new, 0, w, 0, n);
- f = fnew;
- fun_obj.grad(w, g);
-
- gnorm = euclideanNorm(g);
- if (gnorm <= eps * gnorm1) break;
- }
- if (f < -1.0e+32) {
- info("WARNING: f < -1.0e+32%n");
- break;
- }
- if (Math.abs(actred) <= 0 && prered <= 0) {
- info("WARNING: actred and prered <= 0%n");
- break;
- }
- if (Math.abs(actred) <= 1.0e-12 * Math.abs(f) && Math.abs(prered) <= 1.0e-12 * Math.abs(f)) {
- info("WARNING: actred and prered too small%n");
- break;
- }
- }
- }
-
- private int trcg(double delta, double[] g, double[] s, double[] r) {
- int n = fun_obj.get_nr_variable();
- double one = 1;
- double[] d = new double[n];
- double[] Hd = new double[n];
- double rTr, rnewTrnew, cgtol;
-
- for (int i = 0; i < n; i++) {
- s[i] = 0;
- r[i] = -g[i];
- d[i] = r[i];
- }
- cgtol = 0.1 * euclideanNorm(g);
-
- int cg_iter = 0;
- rTr = dot(r, r);
-
- while (true) {
- if (euclideanNorm(r) <= cgtol) break;
- cg_iter++;
- fun_obj.Hv(d, Hd);
-
- double alpha = rTr / dot(d, Hd);
- daxpy(alpha, d, s);
- if (euclideanNorm(s) > delta) {
- info("cg reaches trust region boundary%n");
- alpha = -alpha;
- daxpy(alpha, d, s);
-
- double std = dot(s, d);
- double sts = dot(s, s);
- double dtd = dot(d, d);
- double dsq = delta * delta;
- double rad = Math.sqrt(std * std + dtd * (dsq - sts));
- if (std >= 0)
- alpha = (dsq - sts) / (std + rad);
- else
- alpha = (rad - std) / dtd;
- daxpy(alpha, d, s);
- alpha = -alpha;
- daxpy(alpha, Hd, r);
- break;
- }
- alpha = -alpha;
- daxpy(alpha, Hd, r);
- rnewTrnew = dot(r, r);
- double beta = rnewTrnew / rTr;
- scale(beta, d);
- daxpy(one, r, d);
- rTr = rnewTrnew;
- }
-
- return (cg_iter);
- }
-
- /**
- * constant times a vector plus a vector
- *
- *
- * vector2 += constant * vector1
- *
- *
- * @since 1.8
- */
- private static void daxpy(double constant, double vector1[], double vector2[]) {
- if (constant == 0) return;
-
- assert vector1.length == vector2.length;
- for (int i = 0; i < vector1.length; i++) {
- vector2[i] += constant * vector1[i];
- }
- }
-
- /**
- * returns the dot product of two vectors
- *
- * @since 1.8
- */
- private static double dot(double vector1[], double vector2[]) {
-
- double product = 0;
- assert vector1.length == vector2.length;
- for (int i = 0; i < vector1.length; i++) {
- product += vector1[i] * vector2[i];
- }
- return product;
-
- }
-
- /**
- * returns the euclidean norm of a vector
- *
- * @since 1.8
- */
- private static double euclideanNorm(double vector[]) {
-
- int n = vector.length;
-
- if (n < 1) {
- return 0;
- }
-
- if (n == 1) {
- return Math.abs(vector[0]);
- }
-
- // this algorithm is (often) more accurate than just summing up the squares and taking the square-root afterwards
-
- double scale = 0; // scaling factor that is factored out
- double sum = 1; // basic sum of squares from which scale has been factored out
- for (int i = 0; i < n; i++) {
- if (vector[i] != 0) {
- double abs = Math.abs(vector[i]);
- // try to get the best scaling factor
- if (scale < abs) {
- double t = scale / abs;
- sum = 1 + sum * (t * t);
- scale = abs;
- } else {
- double t = abs / scale;
- sum += t * t;
- }
- }
- }
-
- return scale * Math.sqrt(sum);
- }
-
- /**
- * scales a vector by a constant
- *
- * @since 1.8
- */
- private static void scale(double constant, double vector[]) {
- if (constant == 1.0) return;
- for (int i = 0; i < vector.length; i++) {
- vector[i] *= constant;
- }
-
- }
-}
diff --git a/src/test/java/de/bwaldvogel/liblinear/ArrayPointerTest.java b/src/test/java/de/bwaldvogel/liblinear/ArrayPointerTest.java
index d7a7166..4a897ba 100644
--- a/src/test/java/de/bwaldvogel/liblinear/ArrayPointerTest.java
+++ b/src/test/java/de/bwaldvogel/liblinear/ArrayPointerTest.java
@@ -1,60 +1,55 @@
package de.bwaldvogel.liblinear;
-import static org.fest.assertions.Assertions.assertThat;
-import static org.fest.assertions.Fail.fail;
+import static org.assertj.core.api.Assertions.*;
-import org.junit.Test;
+import org.junit.jupiter.api.Test;
-public class ArrayPointerTest {
+class ArrayPointerTest {
@Test
- public void testGetIntArrayPointer() {
+ void testGetIntArrayPointer() {
int[] foo = new int[] {1, 2, 3, 4, 6};
IntArrayPointer pFoo = new IntArrayPointer(foo, 2);
assertThat(pFoo.get(0)).isEqualTo(3);
assertThat(pFoo.get(1)).isEqualTo(4);
assertThat(pFoo.get(2)).isEqualTo(6);
- try {
- pFoo.get(3);
- fail("ArrayIndexOutOfBoundsException expected");
- } catch (ArrayIndexOutOfBoundsException e) {}
+
+ assertThatExceptionOfType(ArrayIndexOutOfBoundsException.class)
+ .isThrownBy(() -> pFoo.get(3));
}
@Test
- public void testSetIntArrayPointer() {
+ void testSetIntArrayPointer() {
int[] foo = new int[] {1, 2, 3, 4, 6};
IntArrayPointer pFoo = new IntArrayPointer(foo, 2);
pFoo.set(2, 5);
assertThat(foo).isEqualTo(new int[] {1, 2, 3, 4, 5});
- try {
- pFoo.set(3, 0);
- fail("ArrayIndexOutOfBoundsException expected");
- } catch (ArrayIndexOutOfBoundsException e) {}
+
+ assertThatExceptionOfType(ArrayIndexOutOfBoundsException.class)
+ .isThrownBy(() -> pFoo.set(3, 0));
}
@Test
- public void testGetDoubleArrayPointer() {
+ void testGetDoubleArrayPointer() {
double[] foo = new double[] {1, 2, 3, 4, 6};
DoubleArrayPointer pFoo = new DoubleArrayPointer(foo, 2);
assertThat(pFoo.get(0)).isEqualTo(3);
assertThat(pFoo.get(1)).isEqualTo(4);
assertThat(pFoo.get(2)).isEqualTo(6);
- try {
- pFoo.get(3);
- fail("ArrayIndexOutOfBoundsException expected");
- } catch (ArrayIndexOutOfBoundsException e) {}
+
+ assertThatExceptionOfType(ArrayIndexOutOfBoundsException.class)
+ .isThrownBy(() -> pFoo.get(3));
}
@Test
- public void testSetDoubleArrayPointer() {
+ void testSetDoubleArrayPointer() {
double[] foo = new double[] {1, 2, 3, 4, 6};
DoubleArrayPointer pFoo = new DoubleArrayPointer(foo, 2);
pFoo.set(2, 5);
assertThat(foo).isEqualTo(new double[] {1, 2, 3, 4, 5});
- try {
- pFoo.set(3, 0);
- fail("ArrayIndexOutOfBoundsException expected");
- } catch (ArrayIndexOutOfBoundsException e) {}
+
+ assertThatExceptionOfType(ArrayIndexOutOfBoundsException.class)
+ .isThrownBy(() -> pFoo.set(3, 0));
}
}
diff --git a/src/test/java/de/bwaldvogel/liblinear/ArraySorterTest.java b/src/test/java/de/bwaldvogel/liblinear/ArraySorterTest.java
index 51a0b9e..3fdf9cf 100644
--- a/src/test/java/de/bwaldvogel/liblinear/ArraySorterTest.java
+++ b/src/test/java/de/bwaldvogel/liblinear/ArraySorterTest.java
@@ -1,22 +1,23 @@
package de.bwaldvogel.liblinear;
-import static de.bwaldvogel.liblinear.Linear.swap;
-import static org.fest.assertions.Assertions.assertThat;
+import static de.bwaldvogel.liblinear.Linear.*;
+import static org.assertj.core.api.Assertions.*;
import java.util.Random;
-import org.junit.Test;
+import org.junit.jupiter.api.Test;
-public class ArraySorterTest {
+class ArraySorterTest {
- private Random random = new Random();
+ private final Random random = new Random();
private void assertDescendingOrder(double[] array) {
double before = array[0];
for (double d : array) {
// accept that case
- if (d == 0.0 && before == -0.0) continue;
+ if (d == 0.0 && before == -0.0)
+ continue;
assertThat(d).isLessThanOrEqualTo(before);
before = d;
@@ -32,8 +33,7 @@ private void shuffleArray(double[] array) {
}
@Test
- public void testReversedMergesort() {
-
+ void testReversedMergesort() {
for (int k = 1; k <= 16 * 8096; k *= 2) {
// create random array
double[] array = new double[k];
@@ -47,7 +47,7 @@ public void testReversedMergesort() {
}
@Test
- public void testReversedMergesortWithMeanValues() {
+ void testReversedMergesortWithMeanValues() {
double[] array = new double[] {1.0, -0.0, -1.1, 2.0, 3.0, 0.0, 4.0, -0.0, 0.0};
shuffleArray(array);
ArraySorter.reversedMergesort(array);
diff --git a/src/test/java/de/bwaldvogel/liblinear/FeatureNodeTest.java b/src/test/java/de/bwaldvogel/liblinear/FeatureNodeTest.java
index 6af22b0..4a27fd7 100644
--- a/src/test/java/de/bwaldvogel/liblinear/FeatureNodeTest.java
+++ b/src/test/java/de/bwaldvogel/liblinear/FeatureNodeTest.java
@@ -1,25 +1,31 @@
package de.bwaldvogel.liblinear;
-import static org.fest.assertions.Assertions.assertThat;
+import static org.assertj.core.api.Assertions.*;
-import org.junit.Test;
+import org.junit.jupiter.api.Test;
+import nl.jqno.equalsverifier.EqualsVerifier;
+import nl.jqno.equalsverifier.Warning;
-public class FeatureNodeTest {
+
+class FeatureNodeTest {
@Test
- public void testConstructorIndexZero() {
- // since 1.5 there's no more exception here
- new FeatureNode(0, 0);
+ void testConstructorIndexZero() {
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> new FeatureNode(0, 0))
+ .withMessage("index must be > 0");
}
- @Test(expected = IllegalArgumentException.class)
- public void testConstructorIndexNegative() {
- new FeatureNode(-1, 0);
+ @Test
+ void testConstructorIndexNegative() {
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> new FeatureNode(-1, 0))
+ .withMessage("index must be > 0");
}
@Test
- public void testConstructorHappy() {
+ void testConstructorHappy() {
Feature fn = new FeatureNode(25, 27.39);
assertThat(fn.getIndex()).isEqualTo(25);
assertThat(fn.getValue()).isEqualTo(27.39);
@@ -28,4 +34,33 @@ public void testConstructorHappy() {
assertThat(fn.getIndex()).isEqualTo(1);
assertThat(fn.getValue()).isEqualTo(-0.22222);
}
+
+ @Test
+ void testEqualsAndHashCodeContract() throws Exception {
+ EqualsVerifier.forClass(FeatureNode.class)
+ .usingGetClass()
+ .suppress(Warning.NONFINAL_FIELDS)
+ .verify();
+ }
+
+ @Test
+ void testEqualsAndHashCodeNaNValue() throws Exception {
+ FeatureNode a = new FeatureNode(1, Double.NaN);
+ FeatureNode b = new FeatureNode(1, Double.NaN);
+ assertThat(a).isEqualTo(b);
+ assertThat(a).hasSameHashCodeAs(b);
+ }
+
+ @Test
+ void testEqualsWithPositiveAndNegativeZeroValue() throws Exception {
+ FeatureNode a = new FeatureNode(1, -0.0);
+ FeatureNode b = new FeatureNode(1, +0.0);
+ assertThat(a).isNotEqualTo(b);
+ }
+
+ @Test
+ void testToString() throws Exception {
+ assertThat(new FeatureNode(1, 2.0)).hasToString("FeatureNode(idx=1, value=2.0)");
+ }
+
}
diff --git a/src/test/java/de/bwaldvogel/liblinear/LinearTest.java b/src/test/java/de/bwaldvogel/liblinear/LinearTest.java
index b6af1d7..b08bc89 100644
--- a/src/test/java/de/bwaldvogel/liblinear/LinearTest.java
+++ b/src/test/java/de/bwaldvogel/liblinear/LinearTest.java
@@ -1,39 +1,45 @@
package de.bwaldvogel.liblinear;
-import static org.fest.assertions.Assertions.assertThat;
-import static org.fest.assertions.Fail.fail;
-import static org.mockito.Mockito.doThrow;
-import static org.mockito.Mockito.times;
-import static org.mockito.Mockito.verify;
+import static de.bwaldvogel.liblinear.SolverType.*;
+import static de.bwaldvogel.liblinear.TestUtils.*;
+import static org.assertj.core.api.Assertions.*;
+import static org.mockito.Mockito.*;
-import java.io.File;
import java.io.IOException;
import java.io.Writer;
+import java.nio.file.Path;
+import java.nio.file.Paths;
import java.util.ArrayList;
+import java.util.Arrays;
import java.util.Collections;
+import java.util.EnumSet;
import java.util.List;
import java.util.Random;
import java.util.Set;
import java.util.TreeSet;
-import org.fest.assertions.Delta;
-import org.junit.BeforeClass;
-import org.junit.Test;
-import org.powermock.api.mockito.PowerMockito;
+import org.assertj.core.data.Offset;
+import org.junit.jupiter.api.BeforeEach;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
-public class LinearTest {
+class LinearTest {
- private static Random random = new Random(12345);
+ private static final Random random = new Random(12345);
- @BeforeClass
- public static void disableDebugOutput() {
+ @BeforeEach
+ public void reset() throws Exception {
Linear.disableDebugOutput();
}
- public static Model createRandomModel() {
+ static Model createRandomModel() {
+ return createRandomModel(L2R_LR);
+ }
+
+ static Model createRandomModel(SolverType solverType) {
Model model = new Model();
- model.solverType = SolverType.L2R_LR;
+ model.solverType = solverType;
model.bias = 2;
model.label = new int[] {1, Integer.MAX_VALUE, 2};
model.w = new double[model.label.length * 300];
@@ -48,10 +54,13 @@ public static Model createRandomModel() {
model.nr_feature = model.w.length / model.label.length - 1;
model.nr_class = model.label.length;
+ if (solverType.isOneClass()) {
+ model.rho = random.nextDouble();
+ }
return model;
}
- public static Problem createRandomProblem(int numClasses) {
+ private static Problem createRandomProblem(int numClasses) {
Problem prob = new Problem();
prob.bias = -1;
prob.l = random.nextInt(100) + 1;
@@ -63,12 +72,12 @@ public static Problem createRandomProblem(int numClasses) {
prob.y[i] = random.nextInt(numClasses);
- Set randomNumbers = new TreeSet();
+ Set randomNumbers = new TreeSet<>();
int num = random.nextInt(prob.n) + 1;
for (int j = 0; j < num; j++) {
randomNumbers.add(random.nextInt(prob.n) + 1);
}
- List randomIndices = new ArrayList(randomNumbers);
+ List randomIndices = new ArrayList<>(randomNumbers);
Collections.sort(randomIndices);
prob.x[i] = new FeatureNode[randomIndices.size()];
@@ -83,7 +92,7 @@ public static Problem createRandomProblem(int numClasses) {
* create a very simple problem and check if the clearly separated examples are recognized as such
*/
@Test
- public void testTrainPredict() {
+ void testTrainPredict() {
Problem prob = new Problem();
prob.bias = -1;
prob.l = 4;
@@ -111,11 +120,17 @@ public void testTrainPredict() {
prob.y[3] = 0;
for (SolverType solver : SolverType.values()) {
+ if (solver.isOneClass()) {
+ continue;
+ }
for (double C = 0.1; C <= 100.; C *= 1.2) {
-
// compared the behavior with the C version
- if (C < 0.2) if (solver == SolverType.L1R_L2LOSS_SVC) continue;
- if (C < 0.7) if (solver == SolverType.L1R_LR) continue;
+ if (C < 0.2)
+ if (solver == L1R_L2LOSS_SVC)
+ continue;
+ if (C < 0.7)
+ if (solver == L1R_LR)
+ continue;
if (solver.isSupportVectorRegression()) {
continue;
@@ -125,7 +140,7 @@ public void testTrainPredict() {
Model model = Linear.train(prob, param);
double[] featureWeights = model.getFeatureWeights();
- if (solver == SolverType.MCSVM_CS) {
+ if (solver == MCSVM_CS) {
assertThat(featureWeights.length).isEqualTo(8);
} else {
assertThat(featureWeights.length).isEqualTo(4);
@@ -144,7 +159,7 @@ public void testTrainPredict() {
for (double estimate : estimates) {
estimationSum += estimate;
}
- assertThat(estimationSum).isEqualTo(1.0, Delta.delta(0.001));
+ assertThat(estimationSum).isEqualTo(1.0, offset(0.001));
}
i++;
}
@@ -153,13 +168,12 @@ public void testTrainPredict() {
}
@Test
- public void testCrossValidation() throws Exception {
-
+ void testCrossValidation() throws Exception {
int numClasses = random.nextInt(10) + 1;
Problem prob = createRandomProblem(numClasses);
- Parameter param = new Parameter(SolverType.L2R_LR, 10, 0.01);
+ Parameter param = new Parameter(L2R_LR, 10, 0.01);
int nr_fold = 10;
double[] target = new double[prob.l];
Linear.crossValidation(prob, param, nr_fold, target);
@@ -170,15 +184,11 @@ public void testCrossValidation() throws Exception {
}
@Test
- public void testLoadSaveModel() throws Exception {
-
- Model model = null;
+ void testLoadSaveModel(@TempDir Path tempDir) throws Exception {
for (SolverType solverType : SolverType.values()) {
- model = createRandomModel();
- model.solverType = solverType;
+ Model model = createRandomModel(solverType);
- File tempFile = File.createTempFile("liblinear", "modeltest");
- tempFile.deleteOnExit();
+ Path tempFile = tempDir.resolve("modeltest-" + solverType);
Linear.saveModel(tempFile, model);
Model loadedModel = Linear.loadModel(tempFile);
@@ -187,7 +197,73 @@ public void testLoadSaveModel() throws Exception {
}
@Test
- public void testTrainUnsortedProblem() {
+ void testLoadEmptyModel(@TempDir Path tempDir) throws Exception {
+ Path modelPath = tempDir.resolve("empty-model");
+
+ List lines = Arrays.asList("solver_type L2R_LR",
+ "nr_class 2",
+ "label 1 2",
+ "nr_feature 0",
+ "bias -1.0",
+ "w");
+ writeToFile(modelPath, lines);
+
+ Model model = Model.load(modelPath);
+ assertThat(model.getSolverType()).isEqualTo(L2R_LR);
+ assertThat(model.getLabels()).containsExactly(1, 2);
+ assertThat(model.getNrClass()).isEqualTo(2);
+ assertThat(model.getNrFeature()).isEqualTo(0);
+ assertThat(model.getFeatureWeights()).isEmpty();
+ assertThat(model.getBias()).isEqualTo(-1.0);
+ }
+
+ @Test
+ void testLoadSimpleModel(@TempDir Path tempDir) throws Exception {
+ Path modelPath = tempDir.resolve("simple-model");
+
+ List lines = Arrays.asList("solver_type L2R_L2LOSS_SVR",
+ "nr_class 2",
+ "label 1 2",
+ "nr_feature 6",
+ "bias -1.0",
+ "w",
+ "0.1 0.2 0.3 ",
+ "0.4 0.5 0.6 ");
+ writeToFile(modelPath, lines);
+
+ Model model = Model.load(modelPath);
+ assertThat(model.getSolverType()).isEqualTo(L2R_L2LOSS_SVR);
+ assertThat(model.getLabels()).containsExactly(1, 2);
+ assertThat(model.getNrClass()).isEqualTo(2);
+ assertThat(model.getNrFeature()).isEqualTo(6);
+ assertThat(model.getFeatureWeights()).containsExactly(0.1, 0.2, 0.3, 0.4, 0.5, 0.6);
+ assertThat(model.getBias()).isEqualTo(-1.0, offset(0.001));
+ }
+
+ @Test
+ void testLoadIllegalModel(@TempDir Path tempDir) throws Exception {
+ Path file = tempDir.resolve("illegal-model");
+
+ List lines = Arrays.asList("solver_type L2R_L2LOSS_SVR",
+ "nr_class 2",
+ "label 1 2",
+ "nr_feature 10",
+ "bias -1.0",
+ "w",
+ "0.1 0.2 0.3 ",
+ "0.4 0.5 " + repeat("0", 1024));
+ writeToFile(file, lines);
+
+ String x = repeat("0", 128);
+
+ assertThatExceptionOfType(RuntimeException.class)
+ .isThrownBy(() -> Model.load(file))
+ .withMessage("illegal weight in model file at index 5, with string content '" + x
+ + "', is not terminated with a whitespace character, or is longer than expected (128 characters max).");
+ }
+
+ @Test
+ void testTrainUnsortedProblem() {
Problem prob = new Problem();
prob.bias = -1;
prob.l = 1;
@@ -201,17 +277,15 @@ public void testTrainUnsortedProblem() {
prob.y = new double[4];
prob.y[0] = 0;
- Parameter param = new Parameter(SolverType.L2R_LR, 10, 0.1);
- try {
- Linear.train(prob, param);
- fail("IllegalArgumentException expected");
- } catch (IllegalArgumentException e) {
- assertThat(e.getMessage()).contains("nodes").contains("sorted").contains("ascending").contains("order");
- }
+ Parameter param = new Parameter(L2R_LR, 10, 0.1);
+
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> Linear.train(prob, param))
+ .withMessageContainingAll("nodes", "sorted", "ascending", "order");
}
@Test
- public void testTrainTooLargeProblem() {
+ void testTrainTooLargeProblem() {
Problem prob = new Problem();
prob.l = 1000;
prob.n = 20000000;
@@ -223,19 +297,91 @@ public void testTrainTooLargeProblem() {
}
for (SolverType solverType : SolverType.values()) {
- if (solverType.isSupportVectorRegression()) continue;
+ if (solverType.isSupportVectorRegression() || solverType.isOneClass()) {
+ continue;
+ }
Parameter param = new Parameter(solverType, 10, 0.1);
- try {
+
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> Linear.train(prob, param))
+ .withMessageContainingAll("number of classes", "too large");
+ }
+ }
+
+ @Test
+ void testTrain_IllegalParameters_BiasWithOneClassSvm() {
+ Problem prob = createRandomProblem(2);
+ prob.bias = 1;
+
+ Parameter param = new Parameter(ONECLASS_SVM, 10, 0.1);
+
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> Linear.train(prob, param))
+ .withMessage("prob->bias >=0, but this is ignored in ONECLASS_SVM");
+ }
+
+ @Test
+ void testTrain_IllegalParameters_RegularizeBias() {
+ Problem prob = createRandomProblem(2);
+ prob.bias = -1;
+
+ Parameter param = new Parameter(L2R_L1LOSS_SVR_DUAL, 10, 0.1);
+ param.setRegularizeBias(false);
+
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> Linear.train(prob, param))
+ .withMessage("To not regularize bias, must specify -B 1 along with -R");
+
+ prob.bias = 1;
+
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> Linear.train(prob, param))
+ .withMessage("-R option supported only for solver L2R_LR, L2R_L2LOSS_SVC, L1R_L2LOSS_SVC, L1R_LR, and L2R_L2LOSS_SVR");
+
+ param.setSolverType(L1R_LR);
+
+ Model model = Linear.train(prob, param);
+ assertThat(model.bias).isEqualTo(1.0);
+ }
+
+ @Test
+ void testTrain_IllegalParameters_P() throws Exception {
+ Problem prob = createRandomProblem(2);
+ Parameter param = new Parameter(L2R_L2LOSS_SVR, 10, 0.1);
+ param.p = -1;
+
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> Linear.train(prob, param))
+ .withMessage("p < 0");
+
+ // It must not fail for solvers other than L2R_L2LOSS_SVR
+ for (SolverType solverType : values()) {
+ if (solverType != L2R_L2LOSS_SVR) {
+ param.solverType = solverType;
Linear.train(prob, param);
- fail("IllegalArgumentException expected");
- } catch (IllegalArgumentException e) {
- assertThat(e.getMessage()).contains("number of classes").contains("too large");
}
}
}
@Test
- public void testPredictProbabilityWrongSolver() throws Exception {
+ void testTrain_IllegalParameters_InitialSol() {
+ Problem prob = createRandomProblem(2);
+
+ Parameter param = new Parameter(L2R_L1LOSS_SVR_DUAL, 10, 0.1);
+ param.setInitSol(new double[prob.n]);
+
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> Linear.train(prob, param))
+ .withMessage("Initial-solution specification supported only for solvers L2R_LR, L2R_L2LOSS_SVC, and L2R_L2LOSS_SVR");
+
+ param.setSolverType(L2R_LR);
+
+ Model model = Linear.train(prob, param);
+ assertThat(model).isNotNull();
+ }
+
+ @Test
+ void testPredictProbabilityWrongSolver() throws Exception {
Problem prob = new Problem();
prob.l = 1;
prob.n = 1;
@@ -246,31 +392,18 @@ public void testPredictProbabilityWrongSolver() throws Exception {
prob.y[i] = i;
}
- SolverType solverType = SolverType.L2R_L1LOSS_SVC_DUAL;
- Parameter param = new Parameter(solverType, 10, 0.1);
+ Parameter param = new Parameter(L2R_L1LOSS_SVC_DUAL, 10, 0.1);
Model model = Linear.train(prob, param);
- try {
- Linear.predictProbability(model, prob.x[0], new double[1]);
- fail("IllegalArgumentException expected");
- } catch (IllegalArgumentException e) {
- assertThat(e.getMessage()).isEqualTo("probability output is only supported for logistic regression." //
- + " This is currently only supported by the following solvers:" //
- + " L2R_LR, L1R_LR, L2R_LR_DUAL");
- }
- }
-
- @Test
- public void testRealloc() {
- int[] f = new int[] {1, 2, 3};
- f = Linear.copyOf(f, 5);
- f[3] = 4;
- f[4] = 5;
- assertThat(f).isEqualTo(new int[] {1, 2, 3, 4, 5});
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> Linear.predictProbability(model, prob.x[0], new double[1]))
+ .withMessage("probability output is only supported for logistic regression."
+ + " This is currently only supported by the following solvers:"
+ + " L2R_LR, L1R_LR, L2R_LR_DUAL");
}
@Test
- public void testAtoi() {
+ void testAtoi() {
assertThat(Linear.atoi("+25")).isEqualTo(25);
assertThat(Linear.atoi("-345345")).isEqualTo(-345345);
assertThat(Linear.atoi("+0")).isEqualTo(0);
@@ -279,49 +412,44 @@ public void testAtoi() {
assertThat(Linear.atoi("-2147483648")).isEqualTo(Integer.MIN_VALUE);
}
- @Test(expected = NumberFormatException.class)
- public void testAtoiInvalidData() {
- Linear.atoi("+");
- }
+ @Test
+ void testAtoiInvalidData() {
+ assertThatExceptionOfType(NumberFormatException.class)
+ .isThrownBy(() -> Linear.atoi("+"));
- @Test(expected = NumberFormatException.class)
- public void testAtoiInvalidData2() {
- Linear.atoi("abc");
- }
+ assertThatExceptionOfType(NumberFormatException.class)
+ .isThrownBy(() -> Linear.atoi("abc"));
- @Test(expected = NumberFormatException.class)
- public void testAtoiInvalidData3() {
- Linear.atoi(" ");
+ assertThatExceptionOfType(NumberFormatException.class)
+ .isThrownBy(() -> Linear.atoi(" "));
}
@Test
- public void testAtof() {
+ void testAtof() {
assertThat(Linear.atof("+25")).isEqualTo(25);
assertThat(Linear.atof("-25.12345678")).isEqualTo(-25.12345678);
assertThat(Linear.atof("0.345345299")).isEqualTo(0.345345299);
}
- @Test(expected = NumberFormatException.class)
- public void testAtofInvalidData() {
- Linear.atof("0.5t");
+ @Test
+ void testAtofInvalidData() {
+ assertThatExceptionOfType(NumberFormatException.class)
+ .isThrownBy(() -> Linear.atof("0.5t"));
}
@Test
- public void testSaveModelWithIOException() throws Exception {
+ void testSaveModelWithIOException() throws Exception {
Model model = createRandomModel();
- Writer out = PowerMockito.mock(Writer.class);
+ Writer out = mock(Writer.class);
IOException ioException = new IOException("some reason");
doThrow(ioException).when(out).flush();
- try {
- Linear.saveModel(out, model);
- fail("IOException expected");
- } catch (IOException e) {
- assertThat(e).isEqualTo(ioException);
- }
+ assertThatExceptionOfType(IOException.class)
+ .isThrownBy(() -> Linear.saveModel(out, model))
+ .withMessage("some reason");
verify(out).flush();
verify(out, times(1)).close();
@@ -350,7 +478,7 @@ public void testSaveModelWithIOException() throws Exception {
*
*/
@Test
- public void testTranspose() throws Exception {
+ void testTranspose() throws Exception {
Problem prob = new Problem();
prob.bias = -1;
prob.l = 4;
@@ -429,7 +557,7 @@ public void testTranspose() throws Exception {
*
*/
@Test
- public void testTranspose2() throws Exception {
+ void testTranspose2() throws Exception {
Problem prob = new Problem();
prob.bias = -1;
prob.l = 5;
@@ -543,8 +671,7 @@ public void testTranspose2() throws Exception {
*
*/
@Test
- public void testTranspose3() throws Exception {
-
+ void testTranspose3() throws Exception {
Problem prob = new Problem();
prob.l = 3;
prob.n = 4;
@@ -568,11 +695,7 @@ public void testTranspose3() throws Exception {
prob.x[3][0] = new FeatureNode(3, 2);
Problem transposed = Linear.transpose(prob);
- assertThat(transposed.x).hasSize(4);
- assertThat(transposed.x[0]).hasSize(2);
- assertThat(transposed.x[1]).hasSize(2);
- assertThat(transposed.x[2]).hasSize(2);
- assertThat(transposed.x[3]).hasSize(2);
+ assertThat(transposed.x).hasDimensions(4, 2);
assertThat(transposed.x[0][0]).isEqualTo(new FeatureNode(1, 2));
assertThat(transposed.x[0][1]).isEqualTo(new FeatureNode(2, 9));
@@ -586,4 +709,83 @@ public void testTranspose3() throws Exception {
assertThat(transposed.x[3][0]).isEqualTo(new FeatureNode(1, 3));
assertThat(transposed.x[3][1]).isEqualTo(new FeatureNode(2, 3));
}
+
+ @Test
+ void testFindBestParametersOnIrisDataSet() throws Exception {
+ Problem problem = Train.readProblem(Paths.get("src/test/resources/iris.scale"), -1);
+ Parameter param = new Parameter(L2R_L2LOSS_SVC, 1, 0.001, 0.1);
+ ParameterSearchResult result = Linear.findParameters(problem, param, 5, -1, -1);
+ assertThat(result.getBestC()).isEqualTo(4);
+ assertThat(result.getBestScore()).isEqualTo(0.88);
+ assertThat(result.getBestP()).isEqualTo(-1);
+ }
+
+ @Test
+ void testFindBestParameterC_IllegalSolver() throws Exception {
+ Problem problem = Train.readProblem(Paths.get("src/test/resources/iris.scale"), -1);
+
+ EnumSet supportedSolvers = EnumSet.of(L2R_LR, L2R_L2LOSS_SVC, L2R_L2LOSS_SVR);
+ for (SolverType illegalSolver : EnumSet.complementOf(supportedSolvers)) {
+ Parameter param = new Parameter(illegalSolver, 1, 0.001, 0.1);
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> Linear.findParameters(problem, param, 5, -1, -1))
+ .withMessage("Unsupported solver: " + illegalSolver);
+ }
+ }
+
+ @Test
+ void testFindBestParametersOnSpliceDataSet() throws Exception {
+ Problem problem = Train.readProblem(Paths.get("src/test/datasets/splice/splice"), -1);
+ Parameter param = new Parameter(L2R_L2LOSS_SVC, 1, 0.001, 0.1);
+ ParameterSearchResult result = Linear.findParameters(problem, param, 5, -1, -1);
+ assertThat(result.getBestC()).isEqualTo(0.001953125);
+ assertThat(result.getBestScore()).isEqualTo(0.81);
+ assertThat(result.getBestP()).isEqualTo(-1);
+ }
+
+ @Test
+ void testFindBestParametersOnSpliceDataSet_L2R_LR() throws Exception {
+ Problem problem = Train.readProblem(Paths.get("src/test/datasets/splice/splice"), -1);
+ Parameter param = new Parameter(L2R_LR, 1, 0.001, 0.1);
+ ParameterSearchResult result = Linear.findParameters(problem, param, 5, -1, -1);
+ assertThat(result.getBestC()).isEqualTo(0.015625);
+ assertThat(result.getBestScore()).isEqualTo(0.812);
+ assertThat(result.getBestP()).isEqualTo(-1);
+ }
+
+ @Test
+ void testFindBestParametersOnSpliceDataSet_L2R_L2LOSS_SVR() throws Exception {
+ Problem problem = Train.readProblem(Paths.get("src/test/datasets/splice/splice"), -1);
+ Parameter param = new Parameter(L2R_L2LOSS_SVR, 1, 0.001, 0.1);
+ ParameterSearchResult result = Linear.findParameters(problem, param, 5, -1, -1);
+ assertThat(result.getBestC()).isEqualTo(0.00390625);
+ assertThat(result.getBestScore()).isEqualTo(0.5699399182191544, Offset.offset(0.0000001));
+ assertThat(result.getBestP()).isEqualTo(0.0);
+ }
+
+ @Test
+ void testFindBestParametersOnDnaScaleDataSet() throws Exception {
+ Problem problem = Train.readProblem(Paths.get("src/test/datasets/dna.scale/dna.scale"), -1);
+ Parameter param = new Parameter(L2R_L2LOSS_SVC, 1, 0.001, 0.1);
+ ParameterSearchResult result = Linear.findParameters(problem, param, 5, -1, -1);
+ assertThat(result.getBestC()).isEqualTo(0.015625);
+ assertThat(result.getBestScore()).isEqualTo(0.9475);
+ assertThat(result.getBestP()).isEqualTo(-1);
+ }
+
+ @Test
+ void testFindBestParametersOnDnaScaleDataSet_L2R_L2LOSS_SVR() throws Exception {
+ Problem problem = Train.readProblem(Paths.get("src/test/datasets/dna.scale/dna.scale"), -1);
+ Parameter param = new Parameter(L2R_L2LOSS_SVR, 1, 0.0001, 0.1);
+ ParameterSearchResult result = Linear.findParameters(problem, param, 5, -1, -1);
+ assertThat(result.getBestC()).isEqualTo(0.015625);
+ assertThat(result.getBestScore()).isEqualTo(0.29743037982927906, Offset.offset(0.0000001));
+ assertThat(result.getBestP()).isEqualTo(0.15);
+ }
+
+ @Test
+ void testGetVersion() throws Exception {
+ assertThat(Linear.getVersion()).isEqualTo(244);
+ }
+
}
diff --git a/src/test/java/de/bwaldvogel/liblinear/ModelTest.java b/src/test/java/de/bwaldvogel/liblinear/ModelTest.java
new file mode 100644
index 0000000..d9ebd4b
--- /dev/null
+++ b/src/test/java/de/bwaldvogel/liblinear/ModelTest.java
@@ -0,0 +1,41 @@
+package de.bwaldvogel.liblinear;
+
+import static org.assertj.core.api.Assertions.*;
+
+import org.junit.jupiter.api.Test;
+
+import nl.jqno.equalsverifier.EqualsVerifier;
+import nl.jqno.equalsverifier.Warning;
+
+
+class ModelTest {
+
+ @Test
+ void testEqualsAndHashCodeContract() throws Exception {
+ EqualsVerifier.forClass(Model.class)
+ .suppress(Warning.NONFINAL_FIELDS)
+ .verify();
+ }
+
+ @Test
+ void testEqualsAndHashCode_EmptyModel() throws Exception {
+ Model model1 = new Model();
+ Model model2 = new Model();
+
+ assertThat(model1).hasSameHashCodeAs(model2);
+ assertThat(model1).isEqualTo(model2);
+ }
+
+ @Test
+ void testEqualsAndHashCode_NegativeZeroesInWeightArray() throws Exception {
+ Model model1 = new Model();
+ Model model2 = new Model();
+
+ model1.w = new double[] {1.0, 2.0, -0.0, -0.00};
+ model2.w = new double[] {1.0, 2.0, +0.0, -0.000};
+
+ assertThat(model1).hasSameHashCodeAs(model2);
+ assertThat(model1).isEqualTo(model2);
+ }
+
+}
diff --git a/src/test/java/de/bwaldvogel/liblinear/ParameterTest.java b/src/test/java/de/bwaldvogel/liblinear/ParameterTest.java
index b343fa7..70f222b 100644
--- a/src/test/java/de/bwaldvogel/liblinear/ParameterTest.java
+++ b/src/test/java/de/bwaldvogel/liblinear/ParameterTest.java
@@ -1,131 +1,202 @@
package de.bwaldvogel.liblinear;
-import static org.fest.assertions.Assertions.assertThat;
-import static org.junit.Assert.fail;
+import static de.bwaldvogel.liblinear.SolverType.*;
+import static org.assertj.core.api.Assertions.*;
-import org.junit.Before;
-import org.junit.Test;
+import java.util.Random;
+import org.junit.jupiter.api.BeforeEach;
+import org.junit.jupiter.api.Test;
-public class ParameterTest {
- private Parameter _param;
+class ParameterTest {
- @Before
+ private Parameter param;
+
+ @BeforeEach
public void setUp() {
- _param = new Parameter(SolverType.L2R_L1LOSS_SVC_DUAL, 100, 1e-3);
+ param = new Parameter(L2R_L1LOSS_SVC_DUAL, 100, 1e-3);
}
@Test
- public void testDefaults() {
- Parameter parameters = new Parameter(SolverType.L1R_LR, 1, 0.1);
+ void testDefaults() {
+ Parameter parameters = new Parameter(L1R_LR, 1, 0.1);
assertThat(parameters.getP()).isEqualTo(0.1);
assertThat(parameters.getMaxIters()).isEqualTo(1000);
}
@Test
- public void testSetWeights() {
-
- assertThat(_param.weight).isNull();
- assertThat(_param.getNumWeights()).isEqualTo(0);
+ void testSetWeights() {
+ assertThat(param.weight).isNull();
+ assertThat(param.getNumWeights()).isEqualTo(0);
double[] weights = new double[] {0, 1, 2, 3, 4, 5};
int[] weightLabels = new int[] {1, 1, 1, 1, 2, 3};
- _param.setWeights(weights, weightLabels);
+ param.setWeights(weights, weightLabels);
- assertThat(_param.getNumWeights()).isEqualTo(6);
+ assertThat(param.getNumWeights()).isEqualTo(6);
// assert parameter uses a copy
weights[0]++;
- assertThat(_param.getWeights()[0]).isEqualTo(0);
+ assertThat(param.getWeights()[0]).isEqualTo(0);
weightLabels[0]++;
- assertThat(_param.getWeightLabels()[0]).isEqualTo(1);
-
- weights = new double[] {0, 1, 2, 3, 4, 5};
- weightLabels = new int[] {1};
- try {
- _param.setWeights(weights, weightLabels);
- fail("IllegalArgumentException expected");
- } catch (IllegalArgumentException e) {
- assertThat(e.getMessage()).contains("same").contains("length");
- }
+ assertThat(param.getWeightLabels()[0]).isEqualTo(1);
+ }
+
+ @Test
+ void testSetWeights_IllegalArgument() {
+ double[] weights = new double[] {0, 1, 2, 3, 4, 5};
+ int[] weightLabels = new int[] {1};
+
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> param.setWeights(weights, weightLabels))
+ .withMessageContaining("same", "length");
}
@Test
- public void testGetWeights() {
+ void testGetWeights() {
double[] weights = new double[] {0, 1, 2, 3, 4, 5};
int[] weightLabels = new int[] {1, 1, 1, 1, 2, 3};
- _param.setWeights(weights, weightLabels);
+ param.setWeights(weights, weightLabels);
- assertThat(_param.getWeights()).isEqualTo(weights);
- _param.getWeights()[0]++; // shouldn't change the parameter as we should get a copy
- assertThat(_param.getWeights()).isEqualTo(weights);
+ assertThat(param.getWeights()).isEqualTo(weights);
+ param.getWeights()[0]++; // shouldn't change the parameter as we should get a copy
+ assertThat(param.getWeights()).isEqualTo(weights);
- assertThat(_param.getWeightLabels()).isEqualTo(weightLabels);
- _param.getWeightLabels()[0]++; // shouldn't change the parameter as we should get a copy
- assertThat(_param.getWeightLabels()[0]).isEqualTo(1);
+ assertThat(param.getWeightLabels()).isEqualTo(weightLabels);
+ param.getWeightLabels()[0]++; // shouldn't change the parameter as we should get a copy
+ assertThat(param.getWeightLabels()[0]).isEqualTo(1);
}
@Test
- public void testSetC() {
- _param.setC(0.0001);
- assertThat(_param.getC()).isEqualTo(0.0001);
- _param.setC(1);
- _param.setC(100);
- assertThat(_param.getC()).isEqualTo(100);
- _param.setC(Double.MAX_VALUE);
-
- try {
- _param.setC(-1);
- fail("IllegalArgumentException expected");
- } catch (IllegalArgumentException e) {
- assertThat(e.getMessage()).contains("must").contains("not").contains("<= 0");
- }
+ void testSetC() {
+ param.setC(0.0001);
+ assertThat(param.getC()).isEqualTo(0.0001);
+ param.setC(1);
+ param.setC(100);
+ assertThat(param.getC()).isEqualTo(100);
+ param.setC(Double.MAX_VALUE);
+
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> param.setC(-1))
+ .withMessageContainingAll("must", "not", "<= 0");
+
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> param.setC(0))
+ .withMessageContainingAll("must", "not", "<= 0");
+ }
- try {
- _param.setC(0);
- fail("IllegalArgumentException expected");
- } catch (IllegalArgumentException e) {
- assertThat(e.getMessage()).contains("must").contains("not").contains("<= 0");
- }
+ @Test
+ void testSetEps() {
+ param.setEps(0.0001);
+ assertThat(param.getEps()).isEqualTo(0.0001);
+ param.setEps(1);
+ param.setEps(100);
+ assertThat(param.getEps()).isEqualTo(100);
+ param.setEps(Double.MAX_VALUE);
+
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> param.setEps(-1))
+ .withMessageContainingAll("must", "not", "<= 0");
+
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> param.setEps(0))
+ .withMessageContainingAll("must", "not", "<= 0");
}
@Test
- public void testSetEps() {
- _param.setEps(0.0001);
- assertThat(_param.getEps()).isEqualTo(0.0001);
- _param.setEps(1);
- _param.setEps(100);
- assertThat(_param.getEps()).isEqualTo(100);
- _param.setEps(Double.MAX_VALUE);
-
- try {
- _param.setEps(-1);
- fail("IllegalArgumentException expected");
- } catch (IllegalArgumentException e) {
- assertThat(e.getMessage()).contains("must").contains("not").contains("<= 0");
+ void testSetSolverType() {
+ for (SolverType type : SolverType.values()) {
+ param.setSolverType(type);
+ assertThat(param.getSolverType()).isEqualTo(type);
}
- try {
- _param.setEps(0);
- fail("IllegalArgumentException expected");
- } catch (IllegalArgumentException e) {
- assertThat(e.getMessage()).contains("must").contains("not").contains("<= 0");
- }
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> param.setSolverType(null))
+ .withMessageContainingAll("must", "not", "null");
}
@Test
- public void testSetSolverType() {
- for (SolverType type : SolverType.values()) {
- _param.setSolverType(type);
- assertThat(_param.getSolverType()).isEqualTo(type);
- }
- try {
- _param.setSolverType(null);
- fail("IllegalArgumentException expected");
- } catch (IllegalArgumentException e) {
- assertThat(e.getMessage()).contains("must").contains("not").contains("null");
- }
+ void testSetInitSol() {
+ assertThat(param.init_sol).isNull();
+
+ double[] init_sol = new double[] {0, 1, 2, 3, 4, 5};
+ param.setInitSol(init_sol);
+
+ // assert parameter uses a copy
+ init_sol[0]++;
+ assertThat(param.getInitSol()[0]).isEqualTo(0);
+ }
+
+ @Test
+ void testSetP() throws Exception {
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> param.setP(-1))
+ .withMessage("p must not be less than 0");
+ }
+
+ @Test
+ void testSetNu() throws Exception {
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> param.setNu(-0.1))
+ .withMessage("nu must not be <=0");
+
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> param.setNu(0.0))
+ .withMessage("nu must not be <=0");
+
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> param.setNu(1.0))
+ .withMessage("nu must not be >=1");
+
+ assertThatExceptionOfType(IllegalArgumentException.class)
+ .isThrownBy(() -> param.setNu(1.5))
+ .withMessage("nu must not be >=1");
+ }
+
+ @Test
+ void testGetInitSol() {
+ assertThat(param.getInitSol()).isNull();
+
+ double[] init_sol = new double[] {0, 1, 2, 3, 4, 5};
+ param.setInitSol(init_sol);
+
+ assertThat(param.getInitSol()).isNotNull();
+ assertThat(param.getInitSol()).isEqualTo(init_sol);
+ param.getInitSol()[0]++; // shouldn't change the parameter as we should get a copy
+ assertThat(param.getInitSol()).isEqualTo(init_sol);
+ }
+
+ @Test
+ void testClone_Simple() throws Exception {
+ Parameter parameter = new Parameter(L1R_LR, 123.456, 0.123);
+ Parameter clone = parameter.clone();
+ assertThat(clone.getSolverType()).isEqualTo(L1R_LR);
+ assertThat(clone.getC()).isEqualTo(123.456);
+ assertThat(clone.getEps()).isEqualTo(0.123);
+ assertThat(clone.getWeights()).isNull();
+ assertThat(clone.getWeightLabels()).isNull();
+ assertThat(clone.getNumWeights()).isEqualTo(0);
+ }
+
+ @Test
+ void testClone_Full() throws Exception {
+ Parameter parameter = new Parameter(L1R_LR, 123.456, 0.123, 9000, 1.2);
+ parameter.setWeights(new double[] {1, 2}, new int[] {3, 4});
+ Random random = new Random(123);
+ parameter.setRandom(random);
+ Parameter clone = parameter.clone();
+ assertThat(clone.getSolverType()).isEqualTo(L1R_LR);
+ assertThat(clone.getC()).isEqualTo(123.456);
+ assertThat(clone.getEps()).isEqualTo(0.123);
+ assertThat(clone.getMaxIters()).isEqualTo(9000);
+ assertThat(clone.getP()).isEqualTo(1.2);
+ assertThat(clone.getWeights()).containsExactly(1, 2);
+ assertThat(clone.getWeightLabels()).containsExactly(3, 4);
+ assertThat(clone.getNumWeights()).isEqualTo(2);
+
+ assertThat(clone.random).isNotSameAs(random);
+ assertThat(random.nextInt()).isEqualTo(clone.random.nextInt());
}
}
diff --git a/src/test/java/de/bwaldvogel/liblinear/PredictTest.java b/src/test/java/de/bwaldvogel/liblinear/PredictTest.java
index 034e957..da4d31f 100644
--- a/src/test/java/de/bwaldvogel/liblinear/PredictTest.java
+++ b/src/test/java/de/bwaldvogel/liblinear/PredictTest.java
@@ -1,54 +1,88 @@
package de.bwaldvogel.liblinear;
-import static org.fest.assertions.Assertions.assertThat;
-import static org.mockito.Mockito.mock;
+import static org.assertj.core.api.Assertions.*;
import java.io.BufferedReader;
+import java.io.ByteArrayOutputStream;
import java.io.PrintStream;
import java.io.StringReader;
import java.io.StringWriter;
import java.io.Writer;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.List;
-import org.junit.Before;
-import org.junit.Test;
+import org.junit.jupiter.api.AfterEach;
+import org.junit.jupiter.api.BeforeEach;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
-public class PredictTest {
+class PredictTest {
- private Model testModel = LinearTest.createRandomModel();
- private StringBuilder sb = new StringBuilder();
- private Writer writer = new StringWriter();
+ private final Model testModel = LinearTest.createRandomModel();
+ private final StringBuilder sb = new StringBuilder();
+ private final Writer writer = new StringWriter();
- @Before
+ private final ByteArrayOutputStream byteArrayOutputStream = new ByteArrayOutputStream();
+
+ @BeforeEach
public void setUp() {
- System.setOut(mock(PrintStream.class)); // dev/null
+ Linear.setDebugOutput(new PrintStream(byteArrayOutputStream));
assertThat(testModel.getNrClass()).isGreaterThanOrEqualTo(2);
assertThat(testModel.getNrFeature()).isGreaterThanOrEqualTo(10);
}
- private void testWithLines(StringBuilder sb) throws Exception {
- BufferedReader reader = new BufferedReader(new StringReader(sb.toString()));
+ @AfterEach
+ public void tearDown() {
+ Linear.enableDebugOutput();
+ }
- Predict.doPredict(reader, writer, testModel);
+ private void testWithLines(StringBuilder sb) throws Exception {
+ try (StringReader stringReader = new StringReader(sb.toString());
+ BufferedReader reader = new BufferedReader(stringReader)) {
+ Predict.doPredict(reader, writer, testModel, false);
+ }
}
- @Test(expected = RuntimeException.class)
- public void testDoPredictCorruptLine() throws Exception {
+ @Test
+ void testDoPredictCorruptLine() throws Exception {
sb.append(testModel.label[0]).append(" abc").append("\n");
- testWithLines(sb);
+
+ assertThatExceptionOfType(RuntimeException.class)
+ .isThrownBy(() -> testWithLines(sb))
+ .withMessage("Wrong input format at line 1");
}
- @Test(expected = RuntimeException.class)
- public void testDoPredictCorruptLine2() throws Exception {
+ @Test
+ void testDoPredictCorruptLine2() throws Exception {
sb.append(testModel.label[0]).append(" 1:").append("\n");
- testWithLines(sb);
+
+ assertThatExceptionOfType(RuntimeException.class)
+ .isThrownBy(() -> testWithLines(sb))
+ .withMessage("Can't convert empty string to integer");
}
@Test
- public void testDoPredict() throws Exception {
+ void testDoPredict() throws Exception {
sb.append(testModel.label[0]).append(" 1:0.32393").append("\n");
sb.append(testModel.label[1]).append(" 2:-71.555 9:88223").append("\n");
testWithLines(sb);
assertThat(writer.toString()).isNotEmpty();
}
+
+ @Test
+ void testTrainAndPredict(@TempDir Path tempDir) throws Exception {
+ String modelFile = tempDir.resolve("model").toString();
+ Train.main(new String[] {"-s", "0", "src/test/datasets/dna.scale/dna.scale", modelFile});
+
+ Path predictionsFile = tempDir.resolve("predictions");
+ Predict.main(new String[] {"-b", "1", "src/test/datasets/dna.scale/dna.scale.t", modelFile, predictionsFile.toString()});
+ List predictions = Files.readAllLines(predictionsFile, Linear.FILE_CHARSET);
+ assertThat(predictions).hasSize(1187);
+ assertThat(predictions.get(0)).isEqualTo("labels 3 1 2");
+
+ String loggedString = byteArrayOutputStream.toString();
+ assertThat(loggedString).containsPattern("Accuracy = 95[.,]0253% \\(1127/1186\\)");
+ }
}
diff --git a/src/test/java/de/bwaldvogel/liblinear/RegressionTest.java b/src/test/java/de/bwaldvogel/liblinear/RegressionTest.java
new file mode 100644
index 0000000..f1e6b30
--- /dev/null
+++ b/src/test/java/de/bwaldvogel/liblinear/RegressionTest.java
@@ -0,0 +1,378 @@
+package de.bwaldvogel.liblinear;
+
+import static de.bwaldvogel.liblinear.SolverType.*;
+import static org.assertj.core.api.Assertions.*;
+
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.nio.file.Paths;
+import java.nio.file.StandardOpenOption;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.Collections;
+import java.util.EnumSet;
+import java.util.List;
+import java.util.stream.Collectors;
+import java.util.stream.Stream;
+
+import org.assertj.core.data.Offset;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+import org.junit.jupiter.params.ParameterizedTest;
+import org.junit.jupiter.params.provider.MethodSource;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+
+class RegressionTest {
+
+ private static final Logger log = LoggerFactory.getLogger(RegressionTest.class);
+
+ protected static final List SOLVERS = Stream.of(SolverType.values())
+ .filter(solver -> !solver.isOneClass())
+ .collect(Collectors.toList());
+
+ private static Collection data() {
+ List params = new ArrayList<>();
+ for (String dataset : new String[] {"splice", "dna.scale"}) {
+ for (SolverType solverType : SOLVERS) {
+ for (int bias : new int[] {-1, 1}) {
+ for (boolean regularizeBias : new boolean[] {false, true}) {
+ if (!regularizeBias && bias != 1) {
+ continue;
+ }
+ if (!regularizeBias) {
+ // -R option supported only for solver L2R_LR, L2R_L2LOSS_SVC, L1R_L2LOSS_SVC, L1R_LR, and L2R_L2LOSS_SVR
+ if (!EnumSet.of(L2R_LR, L2R_L2LOSS_SVC, L1R_L2LOSS_SVC, L1R_LR, L2R_L2LOSS_SVR).contains(solverType)) {
+ continue;
+ }
+ }
+ params.add(new TestParams(dataset, solverType, bias, regularizeBias, getExpectedAccuracy(dataset, solverType, bias, regularizeBias)));
+ }
+ }
+ }
+ }
+ return params;
+ }
+
+ private static Double getExpectedAccuracy(String dataset, SolverType solverType, int bias, boolean regularizeBias) {
+ if (solverType.isSupportVectorRegression() || solverType.isOneClass()) {
+ return null;
+ }
+ switch (dataset) {
+ case "splice":
+ switch (solverType) {
+ case L2R_LR:
+ switch (bias) {
+ case -1:
+ return 0.84184;
+ case 1:
+ return regularizeBias ? 0.84552 : 0.84414;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ case L2R_L2LOSS_SVC_DUAL:
+ switch (bias) {
+ case -1:
+ return 0.84368;
+ case 1:
+ return 0.85241;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ case L2R_L2LOSS_SVC:
+ switch (bias) {
+ case -1:
+ return 0.84276;
+ case 1:
+ return regularizeBias ? 0.85057 : 0.85149;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ case L2R_L1LOSS_SVC_DUAL:
+ switch (bias) {
+ case -1:
+ return 0.83494;
+ case 1:
+ return 0.83402;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ case MCSVM_CS:
+ switch (bias) {
+ case -1:
+ return 0.8377;
+ case 1:
+ return 0.83862;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ case L1R_L2LOSS_SVC:
+ switch (bias) {
+ case -1:
+ return 0.8478;
+ case 1:
+ return regularizeBias ? 0.8478 : 0.84782;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ case L1R_LR:
+ switch (bias) {
+ case -1:
+ return 0.8473;
+ case 1:
+ return regularizeBias ? 0.84782 : 0.84598;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ case L2R_LR_DUAL:
+ switch (bias) {
+ case -1:
+ return 0.8423;
+ case 1:
+ return 0.8492;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ default:
+ throw new IllegalArgumentException("Unexpected solverType: " + solverType);
+ }
+ case "dna.scale":
+ switch (solverType) {
+ case L2R_LR:
+ switch (bias) {
+ case -1:
+ return 0.94941;
+ case 1:
+ return regularizeBias ? 0.950253 : 0.951096;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ case L2R_L2LOSS_SVC_DUAL:
+ switch (bias) {
+ case -1:
+ case 1:
+ return 0.9452;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ case L2R_L2LOSS_SVC:
+ switch (bias) {
+ case -1:
+ return 0.94941;
+ case 1:
+ return 0.95194;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ case L2R_L1LOSS_SVC_DUAL:
+ switch (bias) {
+ case -1:
+ return 0.94688;
+ case 1:
+ return 0.94604;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ case MCSVM_CS:
+ switch (bias) {
+ case -1:
+ return 0.9292;
+ case 1:
+ return 0.92749;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ case L1R_L2LOSS_SVC:
+ switch (bias) {
+ case -1:
+ return 0.9553;
+ case 1:
+ return regularizeBias ? 0.956998 : 0.95363;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ case L1R_LR:
+ switch (bias) {
+ case -1:
+ return 0.9536;
+ case 1:
+ return regularizeBias ? 0.95194 : 0.95278;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ case L2R_LR_DUAL:
+ switch (bias) {
+ case -1:
+ return 0.9486;
+ case 1:
+ return 0.94941;
+ default:
+ throw new IllegalArgumentException("Unexpected bias: " + bias);
+ }
+ default:
+ throw new IllegalArgumentException("Unexpected solverType: " + solverType);
+ }
+ default:
+ throw new IllegalArgumentException("Unknown expectation: " + dataset + ", " + solverType + ", " + bias);
+ }
+ }
+
+ private static class TestParams {
+
+ private final String dataset;
+ private final SolverType solverType;
+ private final int bias;
+ private final boolean regularizeBias;
+ private final Double expectedAccuracy;
+
+ private TestParams(String dataset, SolverType solverType, int bias, boolean regularizeBias, Double expectedAccuracy) {
+ this.dataset = dataset;
+ this.solverType = solverType;
+ this.bias = bias;
+ this.regularizeBias = regularizeBias;
+ this.expectedAccuracy = expectedAccuracy;
+ }
+
+ @Override
+ public String toString() {
+ return "dataset: " + dataset + ", solver: " + solverType + ", bias: " + bias + (!regularizeBias ? " (not regularized)" : "");
+ }
+ }
+
+ @ParameterizedTest
+ @MethodSource("data")
+ void regressionTest(TestParams params) throws Exception {
+ log.info("Running regression test for '{}'", params);
+ Path trainingFile = Paths.get("src/test/datasets", params.dataset, params.dataset);
+ Problem problem = Train.readProblem(trainingFile, params.bias);
+ Parameter parameter = new Parameter(params.solverType, 1, 0.1);
+ parameter.setRegularizeBias(params.regularizeBias);
+ Model model = Linear.train(problem, parameter);
+ Path testFile = Paths.get("src/test/datasets", params.dataset, params.dataset + ".t");
+ Problem testProblem = Train.readProblem(testFile, params.bias);
+
+ String filename = "predictions_" + params.solverType.name();
+ if (!params.regularizeBias) {
+ filename += "_notRegularizedBias";
+ } else {
+ filename += "_bias_" + params.bias;
+ }
+ Path expectedFile = Paths.get("src/test/resources/regression", params.dataset, filename);
+ final List expectedPredictions;
+ if (!Files.exists(expectedFile)) {
+ expectedPredictions = Collections.emptyList();
+ log.warn("Recording predictions to {}", expectedFile);
+ } else {
+ expectedPredictions = Files.readAllLines(expectedFile, StandardCharsets.UTF_8);
+ assertThat(expectedPredictions).hasSize(testProblem.l);
+ assertThat(testProblem.x.length).isEqualTo(expectedPredictions.size());
+ }
+
+ int correctPredictions = 0;
+
+ for (int i = 0; i < testProblem.l; i++) {
+ Feature[] x = testProblem.x[i];
+ double[] predictedValues = new double[model.getNrClass()];
+ final double prediction;
+ if (params.solverType.isLogisticRegressionSolver()) {
+ prediction = Linear.predictProbability(model, x, predictedValues);
+ } else {
+ prediction = Linear.predictValues(model, x, predictedValues);
+ }
+
+ if (params.expectedAccuracy != null) {
+ int expectation = (int)testProblem.y[i];
+ int actual = (int)prediction;
+ if (actual == expectation) {
+ correctPredictions++;
+ }
+ }
+
+ if (expectedPredictions.isEmpty()) {
+ final String line;
+ if (model.getNrClass() == 2) {
+ line = predictedValues[0] + "\n";
+ } else {
+ line = Arrays.stream(predictedValues)
+ .mapToObj(Double::toString)
+ .collect(Collectors.joining(" ")) + " \n";
+ }
+ Files.createDirectories(expectedFile.getParent());
+ Files.write(expectedFile, line.getBytes(StandardCharsets.UTF_8), StandardOpenOption.APPEND, StandardOpenOption.CREATE);
+ continue;
+ }
+
+ List expectedValues = parseExpectedValues(expectedPredictions, i);
+
+ Offset allowedOffset = Offset.offset(1e-9);
+ if (model.getNrClass() == 2) {
+ assertThat(expectedValues).hasSize(1);
+ assertThat(predictedValues[0]).isEqualTo(expectedValues.get(0), allowedOffset);
+ } else {
+ assertThat(expectedValues).hasSameSizeAs(predictedValues);
+ for (int n = 0; n < predictedValues.length; n++) {
+ assertThat(predictedValues[n]).isEqualTo(expectedValues.get(n), allowedOffset);
+ }
+ }
+ }
+
+ if (params.expectedAccuracy != null) {
+ double accuracy = correctPredictions / (double)testProblem.l;
+ assertThat(accuracy).isEqualTo(params.expectedAccuracy.doubleValue(), Offset.offset(1e-4));
+ }
+ }
+
+ @Test
+ void testOneClass(@TempDir Path tempDir) throws Exception {
+ Path trainingFile = Paths.get("src/test/datasets/splice/splice");
+
+ Path spliceClass1 = tempDir.resolve("splice-class-1");
+ Path spliceClass2 = tempDir.resolve("splice-class-2");
+
+ for (String line : Files.readAllLines(trainingFile, StandardCharsets.ISO_8859_1)) {
+ final Path targetFile;
+ if (line.startsWith("+1")) {
+ targetFile = spliceClass1;
+ } else {
+ targetFile = spliceClass2;
+ }
+ Files.write(targetFile, Arrays.asList(line), StandardCharsets.UTF_8, StandardOpenOption.APPEND, StandardOpenOption.CREATE);
+ }
+
+ Problem problem1 = Train.readProblem(spliceClass1, StandardCharsets.UTF_8, -1);
+ Parameter param = new Parameter(ONECLASS_SVM, 1, 0.01);
+ param.setNu(0.1);
+ Model model = Linear.train(problem1, param);
+
+ Model expectedModel = Model.load(Paths.get("src/test/resources/regression/splice/one_class_model"));
+ assertThat(expectedModel).isEqualTo(model);
+
+ Problem problem2 = Train.readProblem(spliceClass2, StandardCharsets.UTF_8, -1);
+
+ // expected values determined with C-version of liblinear (v2.41)
+ assertThat(calculatePredictionAccuracy(model, problem1)).isEqualTo(0.897485, Offset.strictOffset(1e-6));
+ assertThat(calculatePredictionAccuracy(model, problem2)).isEqualTo(0.0703934, Offset.strictOffset(1e-6));
+ }
+
+ private static double calculatePredictionAccuracy(Model model, Problem problem) {
+ int correct = 0;
+ for (Feature[] x : problem.x) {
+ double prediction = Linear.predict(model, x);
+ if (prediction == problem.y[0]) {
+ correct++;
+ }
+ }
+ return (double)correct / problem.l;
+ }
+
+ private List parseExpectedValues(List expectedPredictions, int i) {
+ return Stream.of(expectedPredictions.get(i)
+ .split(" "))
+ .map(Double::parseDouble)
+ .collect(Collectors.toList());
+ }
+
+}
diff --git a/src/test/java/de/bwaldvogel/liblinear/SolverTypeTest.java b/src/test/java/de/bwaldvogel/liblinear/SolverTypeTest.java
index 910cb24..d941aee 100644
--- a/src/test/java/de/bwaldvogel/liblinear/SolverTypeTest.java
+++ b/src/test/java/de/bwaldvogel/liblinear/SolverTypeTest.java
@@ -1,21 +1,22 @@
package de.bwaldvogel.liblinear;
-import static org.junit.Assert.*;
+import static de.bwaldvogel.liblinear.SolverType.*;
+import static org.assertj.core.api.Assertions.*;
-import org.junit.Test;
+import org.junit.jupiter.api.Test;
-public class SolverTypeTest {
+class SolverTypeTest {
@Test
- public void testIsSupportVectorRegression() throws Exception {
+ void testIsSupportVectorRegression() throws Exception {
for (SolverType type : SolverType.values()) {
boolean regressionSolver = type.isSupportVectorRegression();
// from check_regression_model() in linear.cpp
- if (type == SolverType.L2R_L2LOSS_SVR || type == SolverType.L2R_L1LOSS_SVR_DUAL || type == SolverType.L2R_L2LOSS_SVR_DUAL) {
- assertTrue(type + " is a regression solver", regressionSolver);
+ if (type == L2R_L2LOSS_SVR || type == L2R_L1LOSS_SVR_DUAL || type == L2R_L2LOSS_SVR_DUAL) {
+ assertThat(regressionSolver).withFailMessage(type + " is a regression solver").isTrue();
} else {
- assertFalse(type + " is not a regression solver", regressionSolver);
+ assertThat(regressionSolver).withFailMessage(type + " is not a regression solver").isFalse();
}
}
}
diff --git a/src/test/java/de/bwaldvogel/liblinear/SparseOperatorTest.java b/src/test/java/de/bwaldvogel/liblinear/SparseOperatorTest.java
new file mode 100644
index 0000000..ef3b59c
--- /dev/null
+++ b/src/test/java/de/bwaldvogel/liblinear/SparseOperatorTest.java
@@ -0,0 +1,68 @@
+package de.bwaldvogel.liblinear;
+
+import static org.assertj.core.api.Assertions.*;
+
+import org.junit.jupiter.api.Test;
+
+
+class SparseOperatorTest {
+
+ @Test
+ void testNrm2Sq() throws Exception {
+ assertThat(SparseOperator.nrm2_sq(features())).isZero();
+ assertThat(SparseOperator.nrm2_sq(features(1.0))).isEqualTo(1.0);
+ assertThat(SparseOperator.nrm2_sq(features(1.0, 2.0))).isEqualTo(1 + 4);
+ assertThat(SparseOperator.nrm2_sq(features(1.0, 2.0, 3.0))).isEqualTo(5 + 9);
+ }
+
+ @Test
+ void testDot() throws Exception {
+ assertThat(SparseOperator.dot(new double[0], features())).isEqualTo(0.0);
+ assertThat(SparseOperator.dot(new double[] { 1.0 }, features(1.0))).isEqualTo(1.0);
+ assertThat(SparseOperator.dot(new double[] { 1.0, 2.0 }, features(1.0, 2.0))).isEqualTo(1 + 2 * 2);
+ assertThat(SparseOperator.dot(new double[] { 3.0, 2.0, 1.0 }, features(1.0, 2.0, 3.0))).isEqualTo(3 * 1 + 2 * 2 + 1 * 3);
+ }
+
+ @Test
+ public void testSparseDot() throws Exception {
+ Feature[] features1 = new FeatureNode[] {
+ new FeatureNode(1, 2.0),
+ new FeatureNode(2, 3.0),
+ new FeatureNode(3, 0.5),
+ new FeatureNode(5, 2.0)
+ };
+
+ Feature[] features2 = new FeatureNode[] {
+ new FeatureNode(1, 2.0),
+ new FeatureNode(3, 1.0),
+ new FeatureNode(4, 1.0)
+ };
+
+ assertThat(SparseOperator.sparse_dot(features1, features2)).isEqualTo(2.0 * 2.0 + 0.5 * 1.0);
+ }
+
+ @Test
+ void testAxpy() throws Exception {
+ assertThat(axpy(1.0, features())).isEmpty();
+ assertThat(axpy(1.5, features(2.0))).containsExactly(1.5 * 2.0);
+
+ double[] y = new double[] { 1.0, 2.0 };
+ SparseOperator.axpy(1.5, features(2.0, 3.0), y);
+ assertThat(y).containsExactly(1.0 + 1.5 * 2.0, 2.0 + 1.5 * 3.0);
+ }
+
+ private static double[] axpy(double a, Feature[] features) {
+ double[] result = new double[features.length];
+ SparseOperator.axpy(a, features, result);
+ return result;
+ }
+
+ private static Feature[] features(double... values) {
+ Feature[] features = new Feature[values.length];
+ for (int i = 0; i < values.length; i++) {
+ features[i] = new FeatureNode(i + 1, values[i]);
+ }
+ return features;
+ }
+
+}
diff --git a/src/test/java/de/bwaldvogel/liblinear/TestUtils.java b/src/test/java/de/bwaldvogel/liblinear/TestUtils.java
new file mode 100644
index 0000000..e4c2a15
--- /dev/null
+++ b/src/test/java/de/bwaldvogel/liblinear/TestUtils.java
@@ -0,0 +1,32 @@
+package de.bwaldvogel.liblinear;
+
+import java.io.BufferedWriter;
+import java.io.IOException;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.List;
+
+
+final class TestUtils {
+
+ private TestUtils() {
+ }
+
+ static void writeToFile(Path file, List lines) throws IOException {
+ try (BufferedWriter bufferedWriter = Files.newBufferedWriter(file, StandardCharsets.UTF_8)) {
+ for (String line : lines) {
+ bufferedWriter.append(line).append("\n");
+ }
+ bufferedWriter.flush();
+ }
+ }
+
+ static String repeat(String stringToRepeat, int numTimes) {
+ StringBuilder longString = new StringBuilder();
+ for (int i = 0; i < numTimes; i++) {
+ longString.append(stringToRepeat);
+ }
+ return longString.toString();
+ }
+}
diff --git a/src/test/java/de/bwaldvogel/liblinear/TrainTest.java b/src/test/java/de/bwaldvogel/liblinear/TrainTest.java
index d064b74..5157daa 100644
--- a/src/test/java/de/bwaldvogel/liblinear/TrainTest.java
+++ b/src/test/java/de/bwaldvogel/liblinear/TrainTest.java
@@ -1,24 +1,62 @@
package de.bwaldvogel.liblinear;
-import static org.fest.assertions.Assertions.assertThat;
+import static de.bwaldvogel.liblinear.SolverType.*;
+import static de.bwaldvogel.liblinear.TestUtils.*;
+import static org.assertj.core.api.Assertions.*;
-import java.io.BufferedWriter;
-import java.io.File;
-import java.io.FileWriter;
-import java.util.ArrayList;
-import java.util.Collection;
+import java.io.ByteArrayInputStream;
+import java.nio.charset.Charset;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Path;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.List;
-import org.junit.Test;
+import org.junit.jupiter.api.BeforeEach;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
-public class TrainTest {
+class TrainTest {
+
+ @BeforeEach
+ public void reset() throws Exception {
+ Linear.disableDebugOutput();
+ }
+
+ @Test
+ void testDoCrossValidationOnIrisDataSet() throws Exception {
+ for (SolverType solver : SolverType.values()) {
+ if (solver.isOneClass()) {
+ continue;
+ }
+ Train.main(new String[] {"-v", "5", "-s", "" + solver.getId(), "src/test/resources/iris.scale"});
+ }
+ }
+
+ @Test
+ void testFindBestCOnIrisDataSet() throws Exception {
+ Train.main(new String[] {"-C", "src/test/resources/iris.scale"});
+ }
@Test
- public void testParseCommandLine() {
+ void testFindBestCOnIrisDataSet_L2R_L2LOSS_SVR_DUAL() throws Exception {
+ Train.main(new String[] {"-s", "11", "-C", "src/test/resources/iris.scale"});
+ }
+
+ @Test
+ void testFindBestCOnSpliceDataSet_L2R_L2LOSS_SVR_DUAL() throws Exception {
+ Train.main(new String[] {"-s", "11", "-C", "src/test/datasets/splice/splice"});
+ }
+
+ @Test
+ void testParseCommandLine() {
Train train = new Train();
for (SolverType solver : SolverType.values()) {
train.parse_command_line(new String[] {"-B", "5.3", "-s", "" + solver.getId(), "-p", "0.01", "model-filename"});
+ assertThat(train.isFindParameters()).isFalse();
+ assertThat(train.getNumFolds()).isEqualTo(0);
Parameter param = train.getParameter();
assertThat(param.solverType).isEqualTo(solver);
// check default eps
@@ -28,7 +66,9 @@ public void testParseCommandLine() {
} else if (solver.getId() == 7) {
assertThat(param.eps).isEqualTo(0.1);
} else if (solver.getId() == 11) {
- assertThat(param.eps).isEqualTo(0.001);
+ assertThat(param.eps).isEqualTo(0.0001);}
+ else if (solver.getId() == 21) {
+ assertThat(param.eps).isEqualTo(0.01);
} else {
assertThat(param.eps).isEqualTo(0.1);
}
@@ -38,9 +78,36 @@ public void testParseCommandLine() {
}
}
+ @Test
+ void testParseCommandLine_FindC_NoSolverSpecified() {
+ Train train = new Train();
+
+ train.parse_command_line(new String[] {"-C", "model-filename"});
+ assertThat(train.isFindParameters()).isTrue();
+ assertThat(train.getNumFolds()).isEqualTo(5);
+ Parameter param = train.getParameter();
+ assertThat(param.solverType).isEqualTo(L2R_L2LOSS_SVC);
+ // check default eps
+ assertThat(param.eps).isEqualTo(0.01);
+ assertThat(param.p).isEqualTo(0.1);
+ }
+
+ @Test
+ void testParseCommandLine_FindC_SolverAndNumFoldsSpecified() {
+ Train train = new Train();
+
+ train.parse_command_line(new String[] {"-s", "0", "-v", "10", "-C", "model-filename"});
+ assertThat(train.isFindParameters()).isTrue();
+ assertThat(train.getNumFolds()).isEqualTo(10);
+ Parameter param = train.getParameter();
+ assertThat(param.solverType).isEqualTo(L2R_LR);
+ assertThat(param.eps).isEqualTo(0.01);
+ assertThat(param.p).isEqualTo(0.1);
+ }
+
@Test
// https://github.com/bwaldvogel/liblinear-java/issues/4
- public void testParseWeights() throws Exception {
+ void testParseWeights() throws Exception {
Train train = new Train();
train.parse_command_line(new String[] {"-v", "10", "-c", "10", "-w1", "1.234", "model-filename"});
Parameter parameter = train.getParameter();
@@ -54,28 +121,32 @@ public void testParseWeights() throws Exception {
}
@Test
- public void testReadProblem() throws Exception {
+ void testParseCommandLine_regularizeBias() throws Exception {
+ Train train = new Train();
+ train.parse_command_line(new String[] {"-R", "model-filename"});
+ Parameter parameter = train.getParameter();
+ assertThat(parameter.regularize_bias).isFalse();
- File file = File.createTempFile("svm", "test");
- file.deleteOnExit();
+ train.parse_command_line(new String[] {"model-filename"});
+ parameter = train.getParameter();
+ assertThat(parameter.regularize_bias).isTrue();
+ }
- Collection lines = new ArrayList();
- lines.add("1 1:1 3:1 4:1 6:1");
- lines.add("2 2:1 3:1 5:1 7:1");
- lines.add("1 3:1 5:1");
- lines.add("1 1:1 4:1 7:1");
- lines.add("2 4:1 5:1 7:1");
- BufferedWriter writer = new BufferedWriter(new FileWriter(file));
- try {
- for (String line : lines)
- writer.append(line).append("\n");
- }
- finally {
- writer.close();
- }
+ @Test
+ void testReadProblem(@TempDir Path tempDir) throws Exception {
+ Path problemPath = tempDir.resolve("problem");
+
+ List lines = Arrays.asList(
+ "1 1:1 3:1 4:1 6:1",
+ "2 2:1 3:1 5:1 7:1",
+ "1 3:1 5:1",
+ "1 1:1 4:1 7:1",
+ "2 4:1 5:1 7:1");
+
+ writeToFile(problemPath, lines);
Train train = new Train();
- train.readProblem(file.getAbsolutePath());
+ train.readProblem(problemPath);
Problem prob = train.getProblem();
assertThat(prob.bias).isEqualTo(1);
@@ -83,43 +154,43 @@ public void testReadProblem() throws Exception {
assertThat(prob.y).isEqualTo(new double[] {1, 2, 1, 1, 2});
assertThat(prob.n).isEqualTo(8);
assertThat(prob.l).isEqualTo(prob.y.length);
- assertThat(prob.x).hasSize(prob.y.length);
+ assertThat(prob.x.length).isEqualTo(prob.y.length);
- for (Feature[] nodes : prob.x) {
+ validate(prob);
+ }
- assertThat(nodes.length).isLessThanOrEqualTo(prob.n);
- for (Feature node : nodes) {
- // bias term
- if (prob.bias >= 0 && nodes[nodes.length - 1] == node) {
- assertThat(node.getIndex()).isEqualTo(prob.n);
- assertThat(node.getValue()).isEqualTo(prob.bias);
- } else {
- assertThat(node.getIndex()).isLessThan(prob.n);
- }
- }
- }
+ @Test
+ void testReadProblemFromStream() throws Exception {
+ String data = "1 1:1 3:1 4:1 6:1\n"
+ + "2 2:1 3:1 5:1 7:1\n"
+ + "1 3:1 5:1\n"
+ + "1 1:1 4:1 7:1\n"
+ + "2 4:1 5:1 7:1\n";
+
+ Charset charset = StandardCharsets.UTF_8;
+ Problem prob = Train.readProblem(new ByteArrayInputStream(data.getBytes(charset)), charset, 1);
+ assertThat(prob.bias).isEqualTo(1);
+ assertThat(prob.y).hasSize(5);
+ assertThat(prob.y).isEqualTo(new double[] {1, 2, 1, 1, 2});
+ assertThat(prob.n).isEqualTo(8);
+ assertThat(prob.l).isEqualTo(prob.y.length);
+ assertThat(prob.x.length).isEqualTo(prob.y.length);
+
+ validate(prob);
}
/**
* unit-test for Issue #1 (http://github.com/bwaldvogel/liblinear-java/issues#issue/1)
*/
@Test
- public void testReadProblemEmptyLine() throws Exception {
+ void testReadProblemEmptyLine(@TempDir Path tempDir) throws Exception {
+ Path file = tempDir.resolve("problem");
- File file = File.createTempFile("svm", "test");
- file.deleteOnExit();
+ List lines = Arrays.asList(
+ "1 1:1 3:1 4:1 6:1",
+ "2 ");
- Collection lines = new ArrayList();
- lines.add("1 1:1 3:1 4:1 6:1");
- lines.add("2 ");
- BufferedWriter writer = new BufferedWriter(new FileWriter(file));
- try {
- for (String line : lines)
- writer.append(line).append("\n");
- }
- finally {
- writer.close();
- }
+ writeToFile(file, lines);
Problem prob = Train.readProblem(file, -1.0);
assertThat(prob.bias).isEqualTo(-1);
@@ -127,86 +198,93 @@ public void testReadProblemEmptyLine() throws Exception {
assertThat(prob.y).isEqualTo(new double[] {1, 2});
assertThat(prob.n).isEqualTo(6);
assertThat(prob.l).isEqualTo(prob.y.length);
- assertThat(prob.x).hasSize(prob.y.length);
+ assertThat(prob.x.length).isEqualTo(prob.y.length);
assertThat(prob.x[0]).hasSize(4);
assertThat(prob.x[1]).hasSize(0);
}
- @Test(expected = InvalidInputDataException.class)
- public void testReadUnsortedProblem() throws Exception {
- File file = File.createTempFile("svm", "test");
- file.deleteOnExit();
+ @Test
+ void testReadUnsortedProblem(@TempDir Path tempDir) throws Exception {
+ Path file = tempDir.resolve("problem");
- Collection lines = new ArrayList();
- lines.add("1 1:1 3:1 4:1 6:1");
- lines.add("2 2:1 3:1 5:1 7:1");
- lines.add("1 3:1 5:1 4:1"); // here's the mistake: not correctly sorted
+ List lines = Arrays.asList(
+ "1 1:1 3:1 4:1 6:1",
+ "2 2:1 3:1 5:1 7:1",
+ "1 3:1 5:1 4:1"); // here's the mistake: not correctly sorted
- BufferedWriter writer = new BufferedWriter(new FileWriter(file));
- try {
- for (String line : lines)
- writer.append(line).append("\n");
- }
- finally {
- writer.close();
- }
+ writeToFile(file, lines);
Train train = new Train();
- train.readProblem(file.getAbsolutePath());
+
+ assertThatExceptionOfType(InvalidInputDataException.class)
+ .isThrownBy(() -> train.readProblem(file))
+ .withMessage("indices must be sorted in ascending order");
}
+ @Test
+ void testReadProblemWithInvalidIndex(@TempDir Path tempDir) throws Exception {
+ Path file = tempDir.resolve("problem");
- @Test(expected = InvalidInputDataException.class)
- public void testReadProblemWithInvalidIndex() throws Exception {
- File file = File.createTempFile("svm", "test");
- file.deleteOnExit();
+ List lines = Arrays.asList(
+ "1 1:1 3:1 4:1 6:1",
+ "2 2:1 3:1 5:1 -4:1");
- Collection lines = new ArrayList();
- lines.add("1 1:1 3:1 4:1 6:1");
- lines.add("2 2:1 3:1 5:1 -4:1");
+ writeToFile(file, lines);
- BufferedWriter writer = new BufferedWriter(new FileWriter(file));
- try {
- for (String line : lines)
- writer.append(line).append("\n");
- }
- finally {
- writer.close();
- }
+ Train train = new Train();
+
+ assertThatExceptionOfType(InvalidInputDataException.class)
+ .isThrownBy(() -> train.readProblem(file))
+ .withMessage("invalid index: -4");
+ }
+
+ @Test
+ void testReadProblemWithZeroIndex(@TempDir Path tempDir) throws Exception {
+ Path file = tempDir.resolve("problem");
+
+ List lines = Collections.singletonList("1 0:1 1:1");
+
+ writeToFile(file, lines);
Train train = new Train();
- try {
- train.readProblem(file.getAbsolutePath());
- } catch (InvalidInputDataException e) {
- throw e;
- }
+
+ assertThatExceptionOfType(InvalidInputDataException.class)
+ .isThrownBy(() -> train.readProblem(file))
+ .withMessage("invalid index: 0");
}
- @Test(expected = InvalidInputDataException.class)
- public void testReadWrongProblem() throws Exception {
- File file = File.createTempFile("svm", "test");
- file.deleteOnExit();
+ @Test
+ void testReadWrongProblem(@TempDir Path tempDir) throws Exception {
+ Path file = tempDir.resolve("problem");
- Collection lines = new ArrayList();
- lines.add("1 1:1 3:1 4:1 6:1");
- lines.add("2 2:1 3:1 5:1 7:1");
- lines.add("1 3:1 5:a"); // here's the mistake: incomplete line
+ List lines = Arrays.asList(
+ "1 1:1 3:1 4:1 6:1",
+ "2 2:1 3:1 5:1 7:1",
+ "1 3:1 5:a"); // here's the mistake: incomplete line
- BufferedWriter writer = new BufferedWriter(new FileWriter(file));
- try {
- for (String line : lines)
- writer.append(line).append("\n");
- }
- finally {
- writer.close();
- }
+ writeToFile(file, lines);
Train train = new Train();
- try {
- train.readProblem(file.getAbsolutePath());
- } catch (InvalidInputDataException e) {
- throw e;
+
+ assertThatExceptionOfType(InvalidInputDataException.class)
+ .isThrownBy(() -> train.readProblem(file))
+ .withMessage("invalid value: a");
+ }
+
+ private void validate(Problem prob) {
+ for (Feature[] nodes : prob.x) {
+ assertThat(nodes.length).isLessThanOrEqualTo(prob.n);
+ for (Feature node : nodes) {
+ // bias term
+ if (prob.bias >= 0 && nodes[nodes.length - 1] == node) {
+ assertThat(node.getIndex()).isEqualTo(prob.n);
+ assertThat(node.getValue()).isEqualTo(prob.bias);
+ } else {
+ assertThat(node.getIndex()).isLessThan(prob.n);
+ }
+ }
}
}
+
}
diff --git a/src/test/resources/iris.scale b/src/test/resources/iris.scale
new file mode 100644
index 0000000..bfd8450
--- /dev/null
+++ b/src/test/resources/iris.scale
@@ -0,0 +1,150 @@
+1 1:-0.555556 2:0.25 3:-0.864407 4:-0.916667
+1 1:-0.666667 2:-0.166667 3:-0.864407 4:-0.916667
+1 1:-0.777778 3:-0.898305 4:-0.916667
+1 1:-0.833333 2:-0.0833334 3:-0.830508 4:-0.916667
+1 1:-0.611111 2:0.333333 3:-0.864407 4:-0.916667
+1 1:-0.388889 2:0.583333 3:-0.762712 4:-0.75
+1 1:-0.833333 2:0.166667 3:-0.864407 4:-0.833333
+1 1:-0.611111 2:0.166667 3:-0.830508 4:-0.916667
+1 1:-0.944444 2:-0.25 3:-0.864407 4:-0.916667
+1 1:-0.666667 2:-0.0833334 3:-0.830508 4:-1
+1 1:-0.388889 2:0.416667 3:-0.830508 4:-0.916667
+1 1:-0.722222 2:0.166667 3:-0.79661 4:-0.916667
+1 1:-0.722222 2:-0.166667 3:-0.864407 4:-1
+1 1:-1 2:-0.166667 3:-0.966102 4:-1
+1 1:-0.166667 2:0.666667 3:-0.932203 4:-0.916667
+1 1:-0.222222 2:1 3:-0.830508 4:-0.75
+1 1:-0.388889 2:0.583333 3:-0.898305 4:-0.75
+1 1:-0.555556 2:0.25 3:-0.864407 4:-0.833333
+1 1:-0.222222 2:0.5 3:-0.762712 4:-0.833333
+1 1:-0.555556 2:0.5 3:-0.830508 4:-0.833333
+1 1:-0.388889 2:0.166667 3:-0.762712 4:-0.916667
+1 1:-0.555556 2:0.416667 3:-0.830508 4:-0.75
+1 1:-0.833333 2:0.333333 3:-1 4:-0.916667
+1 1:-0.555556 2:0.0833333 3:-0.762712 4:-0.666667
+1 1:-0.722222 2:0.166667 3:-0.694915 4:-0.916667
+1 1:-0.611111 2:-0.166667 3:-0.79661 4:-0.916667
+1 1:-0.611111 2:0.166667 3:-0.79661 4:-0.75
+1 1:-0.5 2:0.25 3:-0.830508 4:-0.916667
+1 1:-0.5 2:0.166667 3:-0.864407 4:-0.916667
+1 1:-0.777778 3:-0.79661 4:-0.916667
+1 1:-0.722222 2:-0.0833334 3:-0.79661 4:-0.916667
+1 1:-0.388889 2:0.166667 3:-0.830508 4:-0.75
+1 1:-0.5 2:0.75 3:-0.830508 4:-1
+1 1:-0.333333 2:0.833333 3:-0.864407 4:-0.916667
+1 1:-0.666667 2:-0.0833334 3:-0.830508 4:-1
+1 1:-0.611111 3:-0.932203 4:-0.916667
+1 1:-0.333333 2:0.25 3:-0.898305 4:-0.916667
+1 1:-0.666667 2:-0.0833334 3:-0.830508 4:-1
+1 1:-0.944444 2:-0.166667 3:-0.898305 4:-0.916667
+1 1:-0.555556 2:0.166667 3:-0.830508 4:-0.916667
+1 1:-0.611111 2:0.25 3:-0.898305 4:-0.833333
+1 1:-0.888889 2:-0.75 3:-0.898305 4:-0.833333
+1 1:-0.944444 3:-0.898305 4:-0.916667
+1 1:-0.611111 2:0.25 3:-0.79661 4:-0.583333
+1 1:-0.555556 2:0.5 3:-0.694915 4:-0.75
+1 1:-0.722222 2:-0.166667 3:-0.864407 4:-0.833333
+1 1:-0.555556 2:0.5 3:-0.79661 4:-0.916667
+1 1:-0.833333 3:-0.864407 4:-0.916667
+1 1:-0.444444 2:0.416667 3:-0.830508 4:-0.916667
+1 1:-0.611111 2:0.0833333 3:-0.864407 4:-0.916667
+2 1:0.5 3:0.254237 4:0.0833333
+2 1:0.166667 3:0.186441 4:0.166667
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diff --git a/src/test/resources/logback-test.xml b/src/test/resources/logback-test.xml
new file mode 100644
index 0000000..821e0a7
--- /dev/null
+++ b/src/test/resources/logback-test.xml
@@ -0,0 +1,15 @@
+
+
+
+
+ %d{HH:mm:ss.SSS} %-5level [%-7.7thread] [%-25.25logger{25}] - %msg%n
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/src/test/resources/regression/dna.scale/predictions_L1R_L2LOSS_SVC_bias_-1 b/src/test/resources/regression/dna.scale/predictions_L1R_L2LOSS_SVC_bias_-1
new file mode 100644
index 0000000..fae73c0
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L1R_L2LOSS_SVC_bias_-1
@@ -0,0 +1,1186 @@
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diff --git a/src/test/resources/regression/dna.scale/predictions_L1R_L2LOSS_SVC_bias_1 b/src/test/resources/regression/dna.scale/predictions_L1R_L2LOSS_SVC_bias_1
new file mode 100644
index 0000000..f31a040
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L1R_L2LOSS_SVC_bias_1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L1R_L2LOSS_SVC_notRegularizedBias b/src/test/resources/regression/dna.scale/predictions_L1R_L2LOSS_SVC_notRegularizedBias
new file mode 100644
index 0000000..86b2703
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L1R_L2LOSS_SVC_notRegularizedBias
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diff --git a/src/test/resources/regression/dna.scale/predictions_L1R_LR_bias_-1 b/src/test/resources/regression/dna.scale/predictions_L1R_LR_bias_-1
new file mode 100644
index 0000000..bd9e692
--- /dev/null
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diff --git a/src/test/resources/regression/dna.scale/predictions_L1R_LR_bias_1 b/src/test/resources/regression/dna.scale/predictions_L1R_LR_bias_1
new file mode 100644
index 0000000..992a08d
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L1R_LR_bias_1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L1R_LR_notRegularizedBias b/src/test/resources/regression/dna.scale/predictions_L1R_LR_notRegularizedBias
new file mode 100644
index 0000000..0383e54
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L1R_LR_notRegularizedBias
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_L1LOSS_SVC_DUAL_bias_-1 b/src/test/resources/regression/dna.scale/predictions_L2R_L1LOSS_SVC_DUAL_bias_-1
new file mode 100644
index 0000000..6b02b61
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_L1LOSS_SVC_DUAL_bias_-1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_L1LOSS_SVC_DUAL_bias_1 b/src/test/resources/regression/dna.scale/predictions_L2R_L1LOSS_SVC_DUAL_bias_1
new file mode 100644
index 0000000..67597f0
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_L1LOSS_SVC_DUAL_bias_1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_L1LOSS_SVR_DUAL_bias_-1 b/src/test/resources/regression/dna.scale/predictions_L2R_L1LOSS_SVR_DUAL_bias_-1
new file mode 100644
index 0000000..7504463
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_L1LOSS_SVR_DUAL_bias_-1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_L1LOSS_SVR_DUAL_bias_1 b/src/test/resources/regression/dna.scale/predictions_L2R_L1LOSS_SVR_DUAL_bias_1
new file mode 100644
index 0000000..11beaa1
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_L1LOSS_SVR_DUAL_bias_1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVC_DUAL_bias_-1 b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVC_DUAL_bias_-1
new file mode 100644
index 0000000..596c59a
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVC_DUAL_bias_-1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVC_DUAL_bias_1 b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVC_DUAL_bias_1
new file mode 100644
index 0000000..682c95f
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVC_DUAL_bias_1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVC_bias_-1 b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVC_bias_-1
new file mode 100644
index 0000000..92456cc
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVC_bias_-1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVC_bias_1 b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVC_bias_1
new file mode 100644
index 0000000..21beb73
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVC_bias_1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVC_notRegularizedBias b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVC_notRegularizedBias
new file mode 100644
index 0000000..cc9a8bc
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVC_notRegularizedBias
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVR_DUAL_bias_-1 b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVR_DUAL_bias_-1
new file mode 100644
index 0000000..6da017f
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVR_DUAL_bias_-1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVR_DUAL_bias_1 b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVR_DUAL_bias_1
new file mode 100644
index 0000000..ff3776b
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVR_DUAL_bias_1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVR_bias_-1 b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVR_bias_-1
new file mode 100644
index 0000000..a412b2d
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVR_bias_-1
@@ -0,0 +1,1186 @@
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVR_bias_1 b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVR_bias_1
new file mode 100644
index 0000000..d8c4698
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVR_bias_1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVR_notRegularizedBias b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVR_notRegularizedBias
new file mode 100644
index 0000000..e7f1790
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_L2LOSS_SVR_notRegularizedBias
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_LR_DUAL_bias_-1 b/src/test/resources/regression/dna.scale/predictions_L2R_LR_DUAL_bias_-1
new file mode 100644
index 0000000..deb2806
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_LR_DUAL_bias_-1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_LR_DUAL_bias_1 b/src/test/resources/regression/dna.scale/predictions_L2R_LR_DUAL_bias_1
new file mode 100644
index 0000000..37e2146
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_LR_DUAL_bias_1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_LR_bias_-1 b/src/test/resources/regression/dna.scale/predictions_L2R_LR_bias_-1
new file mode 100644
index 0000000..3518848
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_LR_bias_-1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_LR_bias_1 b/src/test/resources/regression/dna.scale/predictions_L2R_LR_bias_1
new file mode 100644
index 0000000..90aade1
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_LR_bias_1
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diff --git a/src/test/resources/regression/dna.scale/predictions_L2R_LR_notRegularizedBias b/src/test/resources/regression/dna.scale/predictions_L2R_LR_notRegularizedBias
new file mode 100644
index 0000000..b158234
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_L2R_LR_notRegularizedBias
@@ -0,0 +1,1186 @@
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diff --git a/src/test/resources/regression/dna.scale/predictions_MCSVM_CS_bias_-1 b/src/test/resources/regression/dna.scale/predictions_MCSVM_CS_bias_-1
new file mode 100644
index 0000000..57db9d0
--- /dev/null
+++ b/src/test/resources/regression/dna.scale/predictions_MCSVM_CS_bias_-1
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diff --git a/src/test/resources/regression/dna.scale/predictions_MCSVM_CS_bias_1 b/src/test/resources/regression/dna.scale/predictions_MCSVM_CS_bias_1
new file mode 100644
index 0000000..1b2beee
--- /dev/null
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index 0000000..d4f4785
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diff --git a/src/test/resources/regression/splice/predictions_L1R_L2LOSS_SVC_bias_-1 b/src/test/resources/regression/splice/predictions_L1R_L2LOSS_SVC_bias_-1
new file mode 100644
index 0000000..47340d1
--- /dev/null
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diff --git a/src/test/resources/regression/splice/predictions_L1R_L2LOSS_SVC_bias_1 b/src/test/resources/regression/splice/predictions_L1R_L2LOSS_SVC_bias_1
new file mode 100644
index 0000000..6eb7413
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L1R_L2LOSS_SVC_bias_1
@@ -0,0 +1,2175 @@
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diff --git a/src/test/resources/regression/splice/predictions_L1R_L2LOSS_SVC_notRegularizedBias b/src/test/resources/regression/splice/predictions_L1R_L2LOSS_SVC_notRegularizedBias
new file mode 100644
index 0000000..aab8374
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L1R_L2LOSS_SVC_notRegularizedBias
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diff --git a/src/test/resources/regression/splice/predictions_L1R_LR_bias_-1 b/src/test/resources/regression/splice/predictions_L1R_LR_bias_-1
new file mode 100644
index 0000000..8dcadf7
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L1R_LR_bias_-1
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diff --git a/src/test/resources/regression/splice/predictions_L1R_LR_bias_1 b/src/test/resources/regression/splice/predictions_L1R_LR_bias_1
new file mode 100644
index 0000000..c51c8d4
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L1R_LR_bias_1
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diff --git a/src/test/resources/regression/splice/predictions_L1R_LR_notRegularizedBias b/src/test/resources/regression/splice/predictions_L1R_LR_notRegularizedBias
new file mode 100644
index 0000000..93753f5
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L1R_LR_notRegularizedBias
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diff --git a/src/test/resources/regression/splice/predictions_L2R_L1LOSS_SVC_DUAL_bias_-1 b/src/test/resources/regression/splice/predictions_L2R_L1LOSS_SVC_DUAL_bias_-1
new file mode 100644
index 0000000..9379766
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_L1LOSS_SVC_DUAL_bias_-1
@@ -0,0 +1,2175 @@
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diff --git a/src/test/resources/regression/splice/predictions_L2R_L1LOSS_SVC_DUAL_bias_1 b/src/test/resources/regression/splice/predictions_L2R_L1LOSS_SVC_DUAL_bias_1
new file mode 100644
index 0000000..7e7937f
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_L1LOSS_SVC_DUAL_bias_1
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diff --git a/src/test/resources/regression/splice/predictions_L2R_L1LOSS_SVR_DUAL_bias_-1 b/src/test/resources/regression/splice/predictions_L2R_L1LOSS_SVR_DUAL_bias_-1
new file mode 100644
index 0000000..13f5b74
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_L1LOSS_SVR_DUAL_bias_-1
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diff --git a/src/test/resources/regression/splice/predictions_L2R_L1LOSS_SVR_DUAL_bias_1 b/src/test/resources/regression/splice/predictions_L2R_L1LOSS_SVR_DUAL_bias_1
new file mode 100644
index 0000000..4d359a0
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_L1LOSS_SVR_DUAL_bias_1
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diff --git a/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVC_DUAL_bias_-1 b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVC_DUAL_bias_-1
new file mode 100644
index 0000000..5f0cd86
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVC_DUAL_bias_-1
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diff --git a/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVC_DUAL_bias_1 b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVC_DUAL_bias_1
new file mode 100644
index 0000000..83b0bef
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVC_DUAL_bias_1
@@ -0,0 +1,2175 @@
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diff --git a/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVC_bias_-1 b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVC_bias_-1
new file mode 100644
index 0000000..0d8e0e6
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVC_bias_-1
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diff --git a/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVC_bias_1 b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVC_bias_1
new file mode 100644
index 0000000..795491b
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVC_bias_1
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diff --git a/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVC_notRegularizedBias b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVC_notRegularizedBias
new file mode 100644
index 0000000..1297d44
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVC_notRegularizedBias
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diff --git a/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVR_DUAL_bias_-1 b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVR_DUAL_bias_-1
new file mode 100644
index 0000000..eeb0254
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVR_DUAL_bias_-1
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diff --git a/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVR_DUAL_bias_1 b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVR_DUAL_bias_1
new file mode 100644
index 0000000..04bee28
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVR_DUAL_bias_1
@@ -0,0 +1,2175 @@
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diff --git a/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVR_bias_-1 b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVR_bias_-1
new file mode 100644
index 0000000..db9815d
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVR_bias_-1
@@ -0,0 +1,2175 @@
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diff --git a/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVR_bias_1 b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVR_bias_1
new file mode 100644
index 0000000..490e8bd
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVR_bias_1
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diff --git a/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVR_notRegularizedBias b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVR_notRegularizedBias
new file mode 100644
index 0000000..7c37b41
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_L2LOSS_SVR_notRegularizedBias
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diff --git a/src/test/resources/regression/splice/predictions_L2R_LR_DUAL_bias_-1 b/src/test/resources/regression/splice/predictions_L2R_LR_DUAL_bias_-1
new file mode 100644
index 0000000..fa64009
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_LR_DUAL_bias_-1
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diff --git a/src/test/resources/regression/splice/predictions_L2R_LR_DUAL_bias_1 b/src/test/resources/regression/splice/predictions_L2R_LR_DUAL_bias_1
new file mode 100644
index 0000000..9662c00
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_LR_DUAL_bias_1
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diff --git a/src/test/resources/regression/splice/predictions_L2R_LR_bias_-1 b/src/test/resources/regression/splice/predictions_L2R_LR_bias_-1
new file mode 100644
index 0000000..df99dde
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_LR_bias_-1
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diff --git a/src/test/resources/regression/splice/predictions_L2R_LR_bias_1 b/src/test/resources/regression/splice/predictions_L2R_LR_bias_1
new file mode 100644
index 0000000..fc54341
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_LR_bias_1
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diff --git a/src/test/resources/regression/splice/predictions_L2R_LR_notRegularizedBias b/src/test/resources/regression/splice/predictions_L2R_LR_notRegularizedBias
new file mode 100644
index 0000000..e0370d9
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_L2R_LR_notRegularizedBias
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diff --git a/src/test/resources/regression/splice/predictions_MCSVM_CS_bias_-1 b/src/test/resources/regression/splice/predictions_MCSVM_CS_bias_-1
new file mode 100644
index 0000000..63746b2
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_MCSVM_CS_bias_-1
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diff --git a/src/test/resources/regression/splice/predictions_MCSVM_CS_bias_1 b/src/test/resources/regression/splice/predictions_MCSVM_CS_bias_1
new file mode 100644
index 0000000..4397c84
--- /dev/null
+++ b/src/test/resources/regression/splice/predictions_MCSVM_CS_bias_1
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+0.7154961585673967
+1.1923783182034315
+0.32108837708130045
+-0.4965761952065457
+-0.995149441695165
+-0.38619934862889216
+1.8942778123508073
+-0.7493082910992124
diff --git a/updateDependencies.sh b/updateDependencies.sh
new file mode 100755
index 0000000..c7f494d
--- /dev/null
+++ b/updateDependencies.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+./gradlew dependencies --update-locks '*:*'