From e09448ddcc631f87f290c9858fb6c7bf50f330ff Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Mon, 28 Oct 2019 12:21:25 -0700 Subject: [PATCH 01/22] Added glia models for: - Connection noise - Connection loss (drop) These requried API changes to run_VAE Exps in the Makefile were update to match. --- Makefile | 53 ++++++++++++++---- glia/exp/glia_digits.py | 115 ++++++++++++++++++++++++++++++++++++++-- glia/gn.py | 14 +++++ 3 files changed, 169 insertions(+), 13 deletions(-) diff --git a/Makefile b/Makefile index 539a0ea..88c7599 100644 --- a/Makefile +++ b/Makefile @@ -556,17 +556,17 @@ digits_exp157: # sigma: 0.1 parallel -j 16 -v \ --nice 19 --delay 2 --colsep ',' \ - 'glia_digits.py VAE --glia=True --sigma=0.1 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp157_s01_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + 'glia_digits.py VAE --glia=True --leak=True --sigma=0.1 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp157_s01_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 # sigma: 0.2 parallel -j 16 -v \ --nice 19 --delay 2 --colsep ',' \ - 'glia_digits.py VAE --glia=True --sigma=0.2 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp157_s02_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + 'glia_digits.py VAE --glia=True --leak=True --sigma=0.2 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp157_s02_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 parallel -j 16 -v \ --nice 19 --delay 2 --colsep ',' \ - 'glia_digits.py VAE --glia=True --sigma=0.5 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp157_s05_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + 'glia_digits.py VAE --glia=True --leak=True --sigma=0.5 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp157_s05_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 parallel -j 16 -v \ --nice 19 --delay 2 --colsep ',' \ - 'glia_digits.py VAE --glia=True --sigma=0.6 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp157_s06_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + 'glia_digits.py VAE --glia=True --leak=True --sigma=0.6 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp157_s06_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 # --------------------------------------------------------------------------- # 10-10-2019 @@ -577,15 +577,15 @@ digits_exp158: # sigma: 0.3 parallel -j 16 -v \ --nice 19 --delay 2 --colsep ',' \ - 'glia_digits.py VAE --glia=True --sigma=0.3 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp158_s03_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + 'glia_digits.py VAE --glia=True --leak=True --sigma=0.3 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp158_s03_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 # sigma: 0.35 parallel -j 16 -v \ --nice 19 --delay 2 --colsep ',' \ - 'glia_digits.py VAE --glia=True --sigma=0.35 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp158_s035_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + 'glia_digits.py VAE --glia=True --leak=True --sigma=0.35 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp158_s035_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 # sigma: 0.4 parallel -j 16 -v \ --nice 19 --delay 2 --colsep ',' \ - 'glia_digits.py VAE --glia=True --sigma=0.4 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp158_s04_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + 'glia_digits.py VAE --glia=True --leak=True --sigma=0.4 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp158_s04_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 # --------------------------------------------------------------------------- # 10-20-2019 @@ -627,5 +627,40 @@ fashion_exp4: 'glia_fashion.py RP --glia=False --num_epochs=150 --random_projection=SP --use_gpu=True --lr=0.004 --seed_value=None --save=$(DATA_PATH)/fashion_exp4_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 # --------------------------------------------------------------------------- -# TODO: Try dropout as a test of reliability? With so few connections, -# reliability may be a problem? \ No newline at end of file +# Try some noise connections (on digit learning) +# +# SUM: +digits_exp159: + # sigma: 0.1 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --noise=True --sigma=0.01 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp159_s01_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + # sigma: 0.2 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --noise=True --sigma=0.05 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp159_s05_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --noise=True --sigma=0.1 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp159_s1_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --noise=True --sigma=0.2 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp159_s2_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + +# Try some dropped connections (on digit learning) +# +# SUM: +digits_exp160: + # p: 0.1 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --drop=True --p=0.01 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp160_p01_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + # p: 0.2 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --drop=True --p=0.05 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp160_p05_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --drop=True --p=0.1 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp160_p1_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --drop=True --p=0.2 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp160_p2_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 diff --git a/glia/exp/glia_digits.py b/glia/exp/glia_digits.py index cc65522..8ec89a4 100644 --- a/glia/exp/glia_digits.py +++ b/glia/exp/glia_digits.py @@ -181,6 +181,78 @@ def forward(self, x): return F.log_softmax(x, dim=1) +class PerceptronNoise(nn.Module): + """A minst digit perceptron, with noisy connections.""" + + def __init__(self, z_features=20, activation_function='Softmax', sigma=1): + # -------------------------------------------------------------------- + # Init + super().__init__() + if z_features < 12: + raise ValueError("z_features must be >= 12.") + + self.z_features = z_features + self.sigma = sigma + + # Lookup activation function (a class) + AF = getattr(nn, activation_function) + + # -------------------------------------------------------------------- + # Def fc1: + glia1 = [] + for s in reversed(range(12, self.z_features + 2, 2)): + glia1.append(gn.Gather(s)) + glia1.append(gn.Noise(s - 2, sigma=sigma)) + glia1.append(gn.Slide(s - 2)) + glia1.append(gn.Noise(s - 2, sigma=sigma)) + + # Linear on the last output, for digit decode + if s > 12: + glia1.append(AF(dim=1)) + self.glia1 = nn.Sequential(*glia1) + + def forward(self, x): + x = self.glia1(x) + + return F.log_softmax(x, dim=1) + + +class PerceptronDrop(nn.Module): + """A minst digit perceptron, missing connections with probability p.""" + + def __init__(self, z_features=20, activation_function='Softmax', p=.05): + # -------------------------------------------------------------------- + # Init + super().__init__() + if z_features < 12: + raise ValueError("z_features must be >= 12.") + + self.z_features = z_features + self.p = p + + # Lookup activation function (a class) + AF = getattr(nn, activation_function) + + # -------------------------------------------------------------------- + # Def fc1: + glia1 = [] + for s in reversed(range(12, self.z_features + 2, 2)): + glia1.append(gn.Gather(s)) + glia1.append(nn.Dropout(p=self.p)) + glia1.append(gn.Slide(s - 2)) + glia1.append(nn.Dropout(p=self.p)) + + # Linear on the last output, for digit decode + if s > 12: + glia1.append(AF(dim=1)) + self.glia1 = nn.Sequential(*glia1) + + def forward(self, x): + x = self.glia1(x) + + return F.log_softmax(x, dim=1) + + # Reconstruction + KL divergence losses summed over all elements and batch def loss_function(recon_x, x, mu, logvar): BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784), reduction='sum') @@ -437,7 +509,6 @@ def run_VAE_only(batch_size=128, def run_VAE(glia=False, - sigma=0, batch_size=128, test_batch_size=128, num_epochs=10, @@ -446,6 +517,11 @@ def run_VAE(glia=False, lr_vae=1e-3, z_features=20, activation_function='Softmax', + sigma=0, + p=0, + leak=False, + noise=False, + drop=False, use_gpu=False, device_num=None, seed_value=1, @@ -496,7 +572,6 @@ def run_VAE(glia=False, # ------------------------------------------------------------------------ # Decision model - # Init if vae_path is None: model_vae = VAE(z_features=z_features).to(device) optimizer_vae = optim.Adam(model_vae.parameters(), lr=lr_vae) @@ -511,22 +586,53 @@ def run_VAE(glia=False, print(saved["vae_dict"]) print(f">>> Loaded VAE from {vae_path}") + # Config glia: if glia: - if sigma > 0: + if leak: + # Transmitter leak? + if noise or drop: + raise ValueError("leak, noise and drop are exclusive") + if sigma < 0: + raise ValueError("sigma must be postive") + model = PerceptronLeak( z_features=z_features, activation_function=activation_function, sigma=sigma) + elif noise: + # Connection noise? + if leak or drop: + raise ValueError("leak, noise and drop are exclusive") + if sigma < 0: + raise ValueError("sigma must be postive") + + model = PerceptronNoise( + z_features=z_features, + activation_function=activation_function, + sigma=sigma) + elif drop: + # Connection loss + if p < 0: + raise ValueError("p must be postive") + if leak or noise: + raise ValueError("leak, noise and drop are exclusive") + + model = PerceptronDrop( + z_features=z_features, + activation_function=activation_function, + p=p) else: + # The default model model = PerceptronGlia( z_features=z_features, activation_function=activation_function).to(device) + # Or do neurons.... else: model = PerceptronNet( z_features=z_features, activation_function=activation_function).to(device) - # - + # ----------------------------------------------------------------------- optimizer = optim.Adam(model.parameters(), lr=lr) if debug: @@ -534,6 +640,7 @@ def run_VAE(glia=False, print(model_vae) print(model) + # ----------------------------------------------------------------------- # Learn classes for epoch in range(1, num_epochs + 1): # Learn z? diff --git a/glia/gn.py b/glia/gn.py index 2d15dfe..8abbd57 100644 --- a/glia/gn.py +++ b/glia/gn.py @@ -310,3 +310,17 @@ def forward(self, input): x = input.float().view(-1, 1, self.in_features, 1) output = self.GaussianBlur2d(x) return output.view(*input.shape) + + +class Noise(nn.Module): + """Model Independent connection noise as Guassian noise""" + + def __init__(self, in_features, sigma=1): + + super().__init__() + self.in_features = in_features + self.sigma = sigma + + def forward(self, input): + noise = self.sigma * torch.randn(*input.size()) + return input + noise From ce1649e9337dc7c4ca30706e3a396bd4a480e4de Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Mon, 28 Oct 2019 12:23:01 -0700 Subject: [PATCH 02/22] Add hash --- Makefile | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/Makefile b/Makefile index 88c7599..5dcc727 100644 --- a/Makefile +++ b/Makefile @@ -627,9 +627,10 @@ fashion_exp4: 'glia_fashion.py RP --glia=False --num_epochs=150 --random_projection=SP --use_gpu=True --lr=0.004 --seed_value=None --save=$(DATA_PATH)/fashion_exp4_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 # --------------------------------------------------------------------------- +# 10/28/2019 # Try some noise connections (on digit learning) -# -# SUM: +# e09448ddcc631f87f290c9858fb6c7bf50f330ff +# SUM: digits_exp159: # sigma: 0.1 parallel -j 16 -v \ @@ -647,7 +648,7 @@ digits_exp159: 'glia_digits.py VAE --glia=True --noise=True --sigma=0.2 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp159_s2_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 # Try some dropped connections (on digit learning) -# +# e09448ddcc631f87f290c9858fb6c7bf50f330ff # SUM: digits_exp160: # p: 0.1 From 147443f688757b892366687cc7c81155848e4185 Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Mon, 28 Oct 2019 19:06:14 -0700 Subject: [PATCH 03/22] delete outdated test notebook --- notebooks/test_noise_loss.ipynb | 366 -------------------------------- 1 file changed, 366 deletions(-) delete mode 100644 notebooks/test_noise_loss.ipynb diff --git a/notebooks/test_noise_loss.ipynb b/notebooks/test_noise_loss.ipynb deleted file mode 100644 index 349b13b..0000000 --- a/notebooks/test_noise_loss.ipynb +++ /dev/null @@ -1,366 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import numpy as np\n", - "\n", - "from IPython.display import Image\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'retina'\n", - "\n", - "import seaborn as sns\n", - "sns.set(font_scale=2)\n", - "sns.set_style('ticks')\n", - "\n", - "matplotlib.rcParams.update({'font.size': 16})\n", - "matplotlib.rc('axes', titlesize=16)\n", - "\n", - "import torch\n", - "import torch.functional as F\n", - "import glob\n", - "from collections import defaultdict\n", - "\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.nn.functional as F\n", - "import torch.optim as optim\n", - "from torchvision import datasets, transforms\n", - "from torchvision.utils import save_image\n", - "\n", - "from glia.gn import WeightNoise\n", - "from glia.gn import WeightLoss\n", - "from glia import gn\n", - "\n", - "class GliaNet(nn.Module):\n", - " \"\"\"A simple test model\"\"\"\n", - "\n", - " def __init__(self, z_features=10, activation_function='Softmax'):\n", - " # --------------------------------------------------------------------\n", - " # Init\n", - " super().__init__()\n", - " if z_features < 2:\n", - " raise ValueError(\"z_features must be > 2.\")\n", - " self.z_features = z_features\n", - "\n", - " # Lookup activation function (a class)\n", - " AF = getattr(nn, activation_function)\n", - "# self.phi = AF(dim=1)\n", - " \n", - " # --------------------------------------------------------------------\n", - " # Def fc1:\n", - " self.fc1 = gn.Slide(self.z_features)\n", - " self.fc2 = gn.Gather(self.z_features)\n", - "\n", - " def forward(self, x, verbose=False):\n", - " x = self.fc1(x)\n", - " if verbose: print(x)\n", - " x = self.fc2(x)\n", - " if verbose: print(x)\n", - " \n", - " return x" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create some shared input" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[0.3606, 0.2939, 0.8955, 0.9413]])\n" - ] - } - ], - "source": [ - "x = torch.rand(1, 4)\n", - "net = GliaNet(4)\n", - "print(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GliaNet(\n", - " (fc1): Slide(in_features=4, out_features=4, bias=True)\n", - " (fc2): Gather(in_features=4, out_features=2, bias=True)\n", - ")\n" - ] - } - ], - "source": [ - "print(net)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OrderedDict([('fc1.weight', tensor([[-0.3687, -0.3640, -0.4445, 0.4557],\n", - " [ 0.3500, 0.3443, -0.0640, -0.3020],\n", - " [ 0.0855, 0.2554, -0.3448, 0.2174],\n", - " [-0.4958, 0.4272, 0.3583, 0.3005]])), ('fc1.bias', tensor([-0.2882, 0.0052, -0.4574, -0.0070])), ('fc2.weight', tensor([[ 0.0156, -0.4211, 0.0527, -0.4432],\n", - " [-0.2517, 0.2604, -0.1322, -0.1776]])), ('fc2.bias', tensor([-0.0076, 0.4532]))])\n" - ] - } - ], - "source": [ - "print(net.state_dict())" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[-0.0691, -0.0426]], grad_fn=)\n" - ] - } - ], - "source": [ - "print(net(x))" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OrderedDict([('fc1.weight', tensor([[ 0.3393, -0.1487, -0.3940, -0.0267],\n", - " [ 0.0843, 0.3380, -0.3070, 0.3612],\n", - " [-0.0080, -0.3376, -0.0311, 0.2769],\n", - " [-0.0065, -0.2602, 0.2513, -0.0621]])), ('fc1.bias', tensor([-0.0601, -0.3608, -0.0242, -0.2862])), ('fc2.weight', tensor([[-0.2137, -0.3110, -0.2661, 0.0418],\n", - " [ 0.2199, 0.1162, -0.0369, 0.1128]])), ('fc2.bias', tensor([0.0565, 0.4851]))])\n" - ] - } - ], - "source": [ - "clone = GliaNet(4)\n", - "clone.load_state_dict(net.state_dict())\n", - "print(clone.state_dict())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Add noise to the net`" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GliaNet(\n", - " (fc1): Slide(in_features=4, out_features=4, bias=True)\n", - " (fc2): Gather(in_features=4, out_features=2, bias=True)\n", - ")" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "noise = WeightNoise(.05)\n", - "net.apply(noise)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OrderedDict([('fc1.weight', tensor([[ 0.2928, -0.2119, -0.3444, -0.0609],\n", - " [ 0.0761, 0.3833, -0.2535, 0.3296],\n", - " [-0.0302, -0.3918, -0.0610, 0.2345],\n", - " [-0.0604, -0.2299, 0.2196, -0.0627]])), ('fc1.bias', tensor([-0.0601, -0.3608, -0.0242, -0.2862])), ('fc2.weight', tensor([[-0.2165, -0.3550, -0.3198, 0.0012],\n", - " [ 0.1935, 0.0760, -0.0800, 0.0693]])), ('fc2.bias', tensor([0.0565, 0.4851]))])\n" - ] - } - ], - "source": [ - "print(net.state_dict())" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[-0.0307, 0.0425]], grad_fn=)\n" - ] - } - ], - "source": [ - "print(net(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Drop connections" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GliaNet(\n", - " (fc1): Slide(in_features=4, out_features=4, bias=True)\n", - " (fc2): Gather(in_features=4, out_features=2, bias=True)\n", - ")" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lost = WeightLoss(.1)\n", - "net.apply(lost)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OrderedDict([('fc1.weight', tensor([[ 0.2928, -0.2119, -0.3444, -0.0609],\n", - " [ 0.0761, 0.3833, -0.0000, 0.3296],\n", - " [-0.0302, -0.3918, -0.0610, 0.2345],\n", - " [-0.0604, -0.2299, 0.2196, -0.0627]])), ('fc1.bias', tensor([-0.0601, -0.3608, -0.0242, -0.2862])), ('fc2.weight', tensor([[-0.2165, -0.3550, -0.3198, 0.0012],\n", - " [ 0.1935, 0.0760, -0.0800, 0.0693]])), ('fc2.bias', tensor([0.0565, 0.4851]))])\n" - ] - } - ], - "source": [ - "print(net.state_dict())" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[-0.0128, 0.0000]], grad_fn=)\n" - ] - } - ], - "source": [ - "print(net(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Check clone" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OrderedDict([('fc1.weight', tensor([[ 0.3393, -0.1487, -0.3940, -0.0267],\n", - " [ 0.0843, 0.3380, -0.3070, 0.3612],\n", - " [-0.0080, -0.3376, -0.0311, 0.2769],\n", - " [-0.0065, -0.2602, 0.2513, -0.0621]])), ('fc1.bias', tensor([-0.0601, -0.3608, -0.0242, -0.2862])), ('fc2.weight', tensor([[-0.2137, -0.3110, -0.2661, 0.0418],\n", - " [ 0.2199, 0.1162, -0.0369, 0.1128]])), ('fc2.bias', tensor([0.0565, 0.4851]))])\n" - ] - } - ], - "source": [ - "print(clone.state_dict())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 4a3f7cd1ec9c9f41f8f7153ee5bb441eb62fede6 Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Wed, 30 Oct 2019 12:59:57 -0700 Subject: [PATCH 04/22] add exp159 analysis notebook --- notebooks/digits_exp159.ipynb | 208 ++++++++++++++++++++++++++++++++++ 1 file changed, 208 insertions(+) create mode 100644 notebooks/digits_exp159.ipynb diff --git a/notebooks/digits_exp159.ipynb b/notebooks/digits_exp159.ipynb new file mode 100644 index 0000000..43522ee --- /dev/null +++ b/notebooks/digits_exp159.ipynb @@ -0,0 +1,208 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "\n", + "from IPython.display import Image\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "import seaborn as sns\n", + "sns.set(font_scale=2)\n", + "sns.set_style('ticks')\n", + "\n", + "matplotlib.rcParams.update({'font.size': 16})\n", + "matplotlib.rc('axes', titlesize=16)\n", + "\n", + "import torch\n", + "import glob\n", + "from collections import defaultdict" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def get_data(files, *keys):\n", + " \"\"\"Get data keys from saved digit exps.\"\"\"\n", + " data = defaultdict(list)\n", + " for f in files:\n", + " d = torch.load(f)\n", + " for k in keys:\n", + " data[k].append(d[k]) \n", + " \n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "exp155_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\") \n", + "exp155 = get_data(exp155_files, \"correct\")\n", + "\n", + "exp159_s01_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s01_*\") \n", + "exp159_s01 = get_data(exp159_s01_files, \"correct\")\n", + "\n", + "exp159_s05_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s05_*\") \n", + "exp159_s05 = get_data(exp159_s05_files, \"correct\")\n", + "\n", + "exp159_s1_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s1_*\") \n", + "exp159_s1 = get_data(exp159_s1_files, \"correct\")\n", + "\n", + "exp159_s2_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s2_*\") \n", + "exp159_s2 = get_data(exp159_s2_files, \"correct\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(list,\n", + " {'correct': [0.8229,\n", + " 0.8313,\n", + " 0.812,\n", + " 0.8166,\n", + " 0.8203,\n", + " 0.8142,\n", + " 0.1141,\n", + " 0.8227,\n", + " 0.8247,\n", + " 0.8179,\n", + " 0.815,\n", + " 0.8088,\n", + " 0.8186,\n", + " 0.8102,\n", + " 0.8182,\n", + " 0.8047,\n", + " 0.8098,\n", + " 0.8023,\n", + " 0.8177,\n", + " 0.7979]})" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp159_s01" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 397, + "width": 535 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------------------------------------\n", + "# Est stats\n", + "model_names = [\"0.0\", \"0.01\", \"0.05\", \"0.1\", \"0.2\"]\n", + "models = [exp155, exp159_s01, exp159_s05, exp159_s1, exp159_s2]\n", + "medians = [\n", + " np.median(exp155[\"correct\"]), \n", + " np.median(exp159_s01[\"correct\"]),\n", + " np.median(exp159_s05[\"correct\"]),\n", + " np.median(exp159_s1[\"correct\"]),\n", + " np.median(exp159_s2[\"correct\"]),\n", + "]\n", + "\n", + "means = [\n", + " np.mean(exp155[\"correct\"]), \n", + " np.mean(exp159_s01[\"correct\"]),\n", + " np.mean(exp159_s05[\"correct\"]),\n", + " np.mean(exp159_s1[\"correct\"]),\n", + " np.mean(exp159_s2[\"correct\"]),\n", + "]\n", + "\n", + "# -------------------------------------------------\n", + "# Visualize \n", + "fig = plt.figure(figsize=(8, 6))\n", + "grid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\n", + "\n", + "# Mean\n", + "plt.subplot(grid[0, 0])\n", + "plt.bar(model_names, means, color=\"grey\", alpha=0.2, width=0.5)\n", + "for name, model in zip(model_names, models):\n", + " plt.scatter(x=np.repeat(name, 20), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", + "plt.ylim(0, 1.1)\n", + "plt.ylabel(\"Mean\\ncorrect\")\n", + "plt.xlabel(\"Leak (std dev)\")\n", + "_ = sns.despine()\n", + "\n", + "# Median\n", + "plt.subplot(grid[1, 0])\n", + "plt.bar(model_names, medians, color=\"grey\", alpha=0.2, width=0.5)\n", + "for name, model in zip(model_names, models):\n", + " plt.scatter(x=np.repeat(name, 20), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", + "plt.ylim(0, 1.1)\n", + "plt.ylabel(\"Median\\ncorrect\")\n", + "plt.xlabel(\"Leak (std dev)\")\n", + "_ = sns.despine()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From fe8d4c6618ab7e68e3edb597b69d114fa97b9dfb Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Wed, 30 Oct 2019 13:00:09 -0700 Subject: [PATCH 05/22] Add results sum --- Makefile | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 5dcc727..b0a0727 100644 --- a/Makefile +++ b/Makefile @@ -630,7 +630,9 @@ fashion_exp4: # 10/28/2019 # Try some noise connections (on digit learning) # e09448ddcc631f87f290c9858fb6c7bf50f330ff -# SUM: +# +# SUM: Noise at 0.1 and 0.2 started to decrease avg accuracy, but only slightly. +# Do another run with more noise. digits_exp159: # sigma: 0.1 parallel -j 16 -v \ From 17585724cc09b6b18fd92099c1d523a7267e28ec Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Wed, 30 Oct 2019 13:03:19 -0700 Subject: [PATCH 06/22] Add digits_exp161 --- Makefile | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/Makefile b/Makefile index b0a0727..5e45e32 100644 --- a/Makefile +++ b/Makefile @@ -667,3 +667,31 @@ digits_exp160: parallel -j 16 -v \ --nice 19 --delay 2 --colsep ',' \ 'glia_digits.py VAE --glia=True --drop=True --p=0.2 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp160_p2_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + +# --------------------------------------------------------------------------- +# 10/30/2019 +# fe8d4c6618ab7e68e3edb597b69d114fa97b9dfb +# expansion of exp159 -- more noise! +# +# SUM: +digits_exp161: + # sigma: 0.1 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --noise=True --sigma=0.3 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp161_s3_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + # sigma: 0.2 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --noise=True --sigma=0.4 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp161_4_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --noise=True --sigma=0.5 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp161_s5_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --noise=True --sigma=0.6 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp161_s6_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --noise=True --sigma=0.7 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp161_s7_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --noise=True --sigma=0.8 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp161_s8_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 From 683602119f34827742153dce88b283511777bb02 Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Wed, 30 Oct 2019 13:04:26 -0700 Subject: [PATCH 07/22] Tweak plot vis --- notebooks/digits_exp159.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/digits_exp159.ipynb b/notebooks/digits_exp159.ipynb index 43522ee..16766a2 100644 --- a/notebooks/digits_exp159.ipynb +++ b/notebooks/digits_exp159.ipynb @@ -162,7 +162,7 @@ " plt.scatter(x=np.repeat(name, 20), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", "plt.ylim(0, 1.1)\n", "plt.ylabel(\"Mean\\ncorrect\")\n", - "plt.xlabel(\"Leak (std dev)\")\n", + "plt.xlabel(\"Wave noise (std dev)\")\n", "_ = sns.despine()\n", "\n", "# Median\n", @@ -172,7 +172,7 @@ " plt.scatter(x=np.repeat(name, 20), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", "plt.ylim(0, 1.1)\n", "plt.ylabel(\"Median\\ncorrect\")\n", - "plt.xlabel(\"Leak (std dev)\")\n", + "plt.xlabel(\"Wave noise (std dev)\")\n", "_ = sns.despine()" ] }, From af87b9e3a8d0fac5283ceeba009d4482f30bcda2 Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Fri, 15 Nov 2019 15:32:26 -0800 Subject: [PATCH 08/22] - Add exp160 analysis notebook - Update 159 notebook --- notebooks/digits_exp159.ipynb | 6 +- notebooks/digits_exp160.ipynb | 208 ++++++++++++++++++++++++++++++++++ 2 files changed, 211 insertions(+), 3 deletions(-) create mode 100644 notebooks/digits_exp160.ipynb diff --git a/notebooks/digits_exp159.ipynb b/notebooks/digits_exp159.ipynb index 16766a2..0aeee8b 100644 --- a/notebooks/digits_exp159.ipynb +++ b/notebooks/digits_exp159.ipynb @@ -109,12 +109,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -200,7 +200,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.0" } }, "nbformat": 4, diff --git a/notebooks/digits_exp160.ipynb b/notebooks/digits_exp160.ipynb new file mode 100644 index 0000000..439e5b0 --- /dev/null +++ b/notebooks/digits_exp160.ipynb @@ -0,0 +1,208 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "\n", + "from IPython.display import Image\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "import seaborn as sns\n", + "sns.set(font_scale=2)\n", + "sns.set_style('ticks')\n", + "\n", + "matplotlib.rcParams.update({'font.size': 16})\n", + "matplotlib.rc('axes', titlesize=16)\n", + "\n", + "import torch\n", + "import glob\n", + "from collections import defaultdict" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def get_data(files, *keys):\n", + " \"\"\"Get data keys from saved digit exps.\"\"\"\n", + " data = defaultdict(list)\n", + " for f in files:\n", + " d = torch.load(f)\n", + " for k in keys:\n", + " data[k].append(d[k]) \n", + " \n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "exp155_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\") \n", + "exp155 = get_data(exp155_files, \"correct\")\n", + "\n", + "exp160_p01_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p01_*\") \n", + "exp160_p01 = get_data(exp160_p01_files, \"correct\")\n", + "\n", + "exp160_p05_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p05_*\") \n", + "exp160_p05 = get_data(exp160_p05_files, \"correct\")\n", + "\n", + "exp160_p1_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p1_*\") \n", + "exp160_p1 = get_data(exp160_p1_files, \"correct\")\n", + "\n", + "exp160_p2_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p2_*\") \n", + "exp160_p2 = get_data(exp160_p2_files, \"correct\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(list,\n", + " {'correct': [0.7921,\n", + " 0.8053,\n", + " 0.8025,\n", + " 0.8055,\n", + " 0.7878,\n", + " 0.7893,\n", + " 0.8018,\n", + " 0.8093,\n", + " 0.793,\n", + " 0.785,\n", + " 0.8064,\n", + " 0.8002,\n", + " 0.8062,\n", + " 0.7942,\n", + " 0.794,\n", + " 0.8014,\n", + " 0.8013,\n", + " 0.7961,\n", + " 0.8002,\n", + " 0.799]})" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp160_p01" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OHj3K7Ows7XZ7glWLiIjsDmMLNKy19xpjbqMXAgA8D/jAuJ5/lKy1oTHmx4FPA3nWQ41rgd/sH3VjzL30dkWpA+n+tTcAg49cnP7t4PFngeeO51XIXjOY1lwqlfA8j/379+M4TvT9fD7P2toapVJvYpS66IuI7LxkMsnp06f58pe/TL1eJx6P4zgOjuNEMzTCMKTdbnP69GkqlQoHDhxQbxgREZFtiF38kpH6g/6tAzzXGPPoMT//yFhrvwp8H1Bm4wyLQbPQLPBwetvVfhfwBOAWYGbomuHHnAWeYa29b6wvRPaMarVKpVKJGs4NhxkAjuNEy00qlQrVanVClYqIXD263S4nT57k1KlTNBq9Vam+77O4uMjp06dZXFzE93t9xRuNBqdOneLkyZN0u91Jli0iIrIrjDXQsNa+Hfj7/mkCeK8x5uZx1jBK1tpPAY8CPsq5IcXgGLb5e4PHfAR4pLX2c+OpXPaaIAiinhkzMzMXvHZmZoZWq0Wj0SAIgjFVKCJydVpdXWV5eZlarcba2honTpzg7rvvjkKOkydPcvfdd3PixAnW1tao1WosLy+zuro66dJFRESm3iTaZ/848H7g39NbovG5ft+JtwNfstZ2JlDTZbPWfh14qjHmycB/ojdr48LvKHua9Jbc/L619pM7V6FcDVqtVtQzY/PMjM0cxyGVSkWPURd9EZGds7S0RLlcpl6vR7tPtVotksnkhuUmzWYz2o2qXC6ztLTEDTfcMOnyRUREptpY38kYY36g/+Uf0QszrqH35v9l/aNljPk6vb4Ttct4itBa+52jqPVSWWs/Dny8v9vJo+gtN7keKNLrn7EGrACLwO3Av1hr9fG4jES32412MxlYWlri5MmTtFotPM/jyJEj7Nu3D4BYLBY9RkREds5gxkWtVqPdbhOLxXBdlzAM6Xa70TbEnU6HtbU1kslkdL2IiIhc2Lg/mn0PWy/DGHyknALM0P2XYrDcY6L6M0xu6x8iYxGLxaLdTE6cOMGXvvQlrLU8+OCD+L6P67ocPnwYYwy33HILs7OzJJPJDQGIiIiM3mDGxdraGq7r4jgOQRBEs+nCMCQMQ+LxOGEYsra2Rrlc1uw5ERGRbZjU/5abw4eJBxEiu5nneXiex+23387x48f50pe+xNLSEo7jRDM37rzzTr7whS9w55138vjHP54nPOEJ2uVERGSHpVIprrvuOhKJBNlsNgouNu9y4jgOnU6HWq3GkSNHSKVSky5dRERk6k0i0HA23YrIFUokEpw+fZoPfehDfO5zn4s65g/30wjDkIceeoilpSXW1ta48cYbOXr06KRKFhEZqcXFxUmXsKVyuUw2m2V+fp5kMonrusRisXN+Pne7XXzfJ5VKkc1mKZfLU/maFhYWJl2CiIhIZNyBxm+M+fkmwhiTBB4BHARmgQLwhf6uKINrrgfu321NUGV6HT9+nC9+8YusrKyQTCaB3lKUwSd/g34ZtVqNL37xixw/fpzHPOYxkyxZRGSk2u32pEs4x9mzZ7n77rs5ffo0yWQyagY6vKQkCIKoOWi73ebgwYOcPXt26l7P4P8WERGRaTHWQMNau2cDDWNMBvhJ4NnAtwKb5/K/DvjU0PkbgMcbY/4U+B/W2pWxFCp70tLSErfddhunTp0Cer8cdzodEolENJ05CIJoqvOpU6e47bbbWFpaihqFiojIzojFYiSTSdLpdDQbo9PpRIEzQDwej0IO9TcSERHZHv2POQLGmF8AHqAXUjyBXnNTZ+jYynXAPPBLwL1DO8CIXLI77riDu+66i3q9ThiGxGIxPM8jHo8Ti8WIx+N4nhfN2KjX69x1113ccccdky5dRGRPKxQKZDIZEokEMzMzZDIZXNclHo9Hh+u6ZDIZZmZmSCQSZDIZCoXCpEsXERGZemqhfQWMMXngL4HvZT24CNnY5PRCgcZgh5cC8G5jzC9Za1+/M9XKXnbixAkWFxcJwxDXdXFdFzi3h8bgvnq9zuLiIidOnJhEuSIiV42ZmRkKhQLLy8t0u12y2SypVGrDDI1BsFGv10mlUhQKBWZmZiZdusi2BUFAq9WKGpF7nqedekRkLPST5jL1+2T8LfDk/l2DEGM4wNhy9xZjzCyQY2P44QCvMcbcb619z8gLlj2tXC7TaDQIgoBEIkE8Hgeg2+1u+IUZeg1EgyCg0WhQLpcnWbaIyJ6Xy+W47rrrWFpaotVq0W63SSQSG97sdbvdqF9GPp/nuuuuI5fLTapkkW1rtVpUq1UajcY5gUY6nSaXy2lHNRHZUXsq0DDGXGutvX9MT/dGemHGcCBxEngr8BHgK8AiW4caZeAHgVcAt7IebDjAm4wxH1VPDbkUR48e5eEPfzi1Wi3qQN/tdjcEGrFYLFqXfeDAAbLZrHY5ERHZYblcjuuvv57Tp09H4XOz2dzQYLPdbpNKpcjn8xSLRa6//noFGjL16vU6pVKJSqVCq9UilUoRi8Vot9tUq1U8z6PZbDI3N0cmk5l0uSKyR+26QMMYEweS9PpUzAD7gEcB/wH4j5zbjHMnangE8NOshxAAfwD8krXWH7puy8dba0Pgb4C/Mca8FHgNEO9/ex/wEuA3d6R4uWzTuH3eQC6XY//+/ayurpJIJHBdd0OzOSA6932fubk5CoUCuVxuKl+XtgUUkb0inU5z7bXXcvr0ac6cOUOtVqNer+P7fvRp9qCHRjab5cCBA1x77bWk0+lJly5yXq1Wi1KpRKlUwvM89u/fv+F3jnw+z9raGqVSCSDq5SUiMmoTCzSMMd8B/CjwbcACkOnXc76eExfjcJ4lHjvg11lvqBoCf2CtfdnlDGSt/X1jTA14C+sByU+gQGMqTdsWesMefPBBTpw4QSwWI5VKRZ+UDHS7XZrNJs1mk263y3XXXQdM32vStoAispfE43GuueYabrrpJuLxeDRFv91uR4FGMpmMpuZff/31XHPNNdEyQZFpVK1WqVQqeJ635Wwix3Gi+yuVCqlUSoGGiOyIsQcaxpgs8BfAM/t3XW6AMRHGGA/4btbDh6/R26nksllr/8QY8yPAk/p3HTXG3GitveeKipWrxvz8PIcOHeLMmTO02206nQ5ra2tRc1AA3/ejpSee53Ho0CHm5+cnWLWIyNWhWCxy0003AbCyssLMzAyNRoNOp0M8HiedTpPNZpmdneWmm26iWCxOuGKR8xv04Wq1Wuzfvx+AZrPJ2tpa1MtrZmaGVCrFzMwMZ8+e3dDnS0RklCbxU+XdwPcMnY9qVsW4gpHH05tNMuh78b+ttaP4iPstrAca0FtGo0BDtiWbzXLw4EFmZ2dptVq0Wi2CICAIguiaWCwW/ZLheR4HDx4km81OsGoRkatDMplkYWGBIAhIJpMsLi5GDZoTiQSpVIqFhQUOHz7MwsKCZqrJVBv8npFKpahWqywuLlIul6nVatGso2w2S7FYZGFhgVQqFT1GgYaIjNpYf6oYY55JL8zYzramFzLciLML3AvcDrzzigrcnkEXxcESl4+MaNzP9m8Hr+3AiMaVq0Cn0+HgwYMcOXKEcrmM7/s0m03a7XbUOyOZTJJKpXBdl2KxyMGDB+l0OpMuXUTkqjFYWhKG4TnbtuZyOQUZsisMmo6Xy2VKpRKLi4vUajVyuRyxWIwgCFheXmZ5eZlKpcLc3Bzz8/N0u91Jly4ie9C4Y9L/PPT1IMj4Ar0GmSeABvBq4CZ6b+zf0f+eCxT6938XcAvrb/xD4GXW2n/Y4doHNgcND41o3OVN52pvLpdk37593HDDDSwtLVGpVKhWq4RhGH1aMljPms/n2bdvH/v27Zt0ySIiV4V2u02lUqHRaFAsFtm/fz9BEESBRiKRwPd9Go0GlUqFeDyucEOmViwWo1arcf/991Mul8lmsxw7doxOpxP9zjE3N0e5XObkyZOsra2d09dLRGRUxhZoGGMKwLez3nsiBF5rrX3FpuseC7y8f3rUWvveLcZ6HvBH9EKOOPAXxphbrLUP7uBLGNi8vGRUHY4Km86rIxpXrgKJRIJ8Ps+RI0coFAosLy9TrVZpNpvRGu1UKkUul2N+fj4KNjT1U0Rk59VqNWq1WrSbCXDOz9/B+eA69dGQaeV5Hqurq5w6dYp8Po/rupw5c+acHhq5XA7f9zl16hQHDhxQU1AR2RHjfDfzKHrhw2Bmxb8Bv7zFdZ+gF2g4wL83xsxYa9eGL7DWvtMY8y/0lmnkgVl6PSievkO1Dzuz6fwmRjNL41v6t4OwZ2kEY8pVIpvNUigUKJVKHDt2jLm5OVZWVqIdTQY7n8zOzpLL5ahWqxQKBfXQEBHZYZ1Oh1arFW2ZfSGZTIZSqUSr1YrCaJFpEwQBrVaLer1Ot9vl5MmTVKvVLZdRzczM0Gw2o95e+iBFREZtnD9Vru/fDt6w/7G1dquGoJ8b+jpOrwnnhzZfZK29yxjzU/SajAJ8jzHmydbaj4+u5C3d278d1P504J9HMO6zN51/bQRjylXCdV1yuRyFQm+iz8GDBykUCucEGul0mkajQaFQIJfLbdgFRURERs/3fXzf3/bPW9d1o8ek0+kdrk7k0lUqFZrNJkEQcM8999But3EcB8/ziMVidDodarUaq6urJJNJDhw4QLPZjLZvFREZpXEuZtu8P+TxrS6y1p5h4+yEx5xvwP5ylE8N3fWSy65u+24DSv2vHeBFxpgrakZgjPlG4EdYD0lWgM9fyZhy9Rn0xfB9n3a7zdzcHAcOHIiOubk52u02vu+rh4aIyJiEYUgYhtvuHxCLxaLHiEyjIAhYWVmhXC7TarUIw5AgCKhUKqysrFCpVKIeMa1Wi3K5zMrKyoad10RERmWcgcbmjyYu1O/iy0Nf33KRcd/ev3WApxpjdnR+prW2C7yP9ZkmM8BfGWMuq3uXMWaOXuPTxNCY/3Ce2Ssi55XNZrnmmmu45pprCIKABx98MGpCV6lUePDBBwmCILpGy01ERHae4zg4jrPtHR663W70GJFptby8zMrKCslkkna7zdLSEmfOnImOpaUl2u02yWSSlZUVlpc3974XERmNcQYaa5vO6xe49p6hr7/xIuN+eujrLPDYSynqMv0W4Pe/doAnAx81xhy7lEH6DVBvA76B9dkZXXo7vYhcsmKxyLFjxzh27BiHDx/G87xoGujhw4ej76nZnIjIeLiuGy0j2Y7B8hQtCZRpFYYhtVqNM2fORDM12u028Xg8OtrtdjQz48yZM9RqNc06EpEdMc4eGlttS3q+nTwGfSoc4CZjjHOBGQuDmR6D79/AeZazjIq19j5jzO8Br2B915ZvA75qjHkX8HfAnVs91hhzGHgK8MPA01iflTG4/TNr7Vd2sn7Z27LZLNlsFt/3qdVqUROubDarX5BFRMYsHo/jeR6u61Kv16NdTrZSr9dxXRfP89QQVKZWq9WiWq3SaDSo1WoUi0XS6fSGZVXdbpcgCDh16hSxWIxqtUqr1Zpg1SKyV40z0FjcdH4D598dZHiGhte/9p7zXNvZdL5w6aVdlv9Kb/bIM1gPJDzgR/sHbAwq/pMx5sXAcDekwfcGPgv8/M6WLVcLfcInIjIdBiHz6uoqwJahRr1ex/d97UAlU8/3/ejfq+M4+L5PGIa02+2oEflgKUqn0yEIguh6EZFRG2egcTsb37w/Bfjkea69e9P5t3L+QOPgpvOxvIOz1obGmOcDfwY8h/XXttWiV4fe9rKbDT/mU8APWGv1015ERGQPSSaT5PO9XwNqtRqlUgnXdYnFYnS73WiZSaFQIJ/Pk0xeVlsukbEIggDf9+l2uxQKBVZWVqjX6xv6voRhSCaToVgssrq6iu/7agoqIjtibD00rLXLrDf7dICfM8acbyH/V+j1khi84f/eCwz95KExAcpXUOYlsdY2rLXPA15Eb+eTQQ3hNg+HXi+O3wWebK3dPItFRERE9oBUKsXs7Cyzs7MUi0USiQSO45BIJCgWi9H3tK2lTLvBcpLBzItOp0MYhnS73egIw5BOp0O9Xo+u3W5jXBGRSzHOpqAA/8j6MosF4EPGmOs2X2StbQBf7J86wPOMMd+w+TpjTAb4RTbO/DjfTI4dY619C3AUeDG9JqVtenVf6LgL+B3gmLX2v1prNy+dERERkT0kmUxSLBZsVsxGAAAgAElEQVSjrbOHj2KxqJkZsmsMlpisrKzgui7z8/Ps27dvw63ruqysrNBut9UQVER2zDiXnAC8CXgpMPgf+1vpNdJ8B/An1tpPDV37LuDf0QsrXODDxpgXWms/BmCM+WbgzcCNrAcaHeBzO/4qtmCtbQL/C/hfxhgPeAy9kGMWKAINerM4zgK3WWuXJlGniIiITFY8HiedTk+6DJHLkkgkohkYg68HfTMGBiFGIpEgCILoaxGRURvrTxZr7f3GmP8B/Brryy5c4AX0ZjUMBxpvpdd4c6Z/3VF6ocYa0AT29a8bbrz5PmttZadfhzHmJqBrrb13q+9ba1ucvz+IiIiIiMiuNAgu4vE4CwsLdDodGo3Ghqaf3W6XdDrNzMwMZ8+e3fA4EZFRGveSE4DfAN7Dxn4TsL5VKwD9fhK/tek6h952rwusL90Y8IFX7UjF53ojcJcx5sPGmOcYY/QTWkRERET2vHa7jeu60Xbw2WyWXC4Xbdsai8XI5XIbvu+6Lu12e8KVi8heNPZAw1rbBZ4LvAKosh5KnDPbwVr7WuDtXLjZpkOvgejPWWv/bUeLB4wx1wBPo/dn953AO4BH7vTzioiIiIhMWhAEURPbdruN7/vEYjFmZmYoFArMzMwQi8XwfZ92ux1dq11ORGQnTGKGBtba0Fr7GuAY8PPAh4GvnufaFwC/wHr4sfn4OvBMa+2fjaF0gG+n9+c2mFlyu7X2tjE9t8i2DaaA1ut1Go0GnY76zoqIiMiVSaVSFAqFaGZGPB7HcRxc1yWZTOK6Lo7jEI/Ho5kahUJBO/iIyI6YaHcea+0q8Ef940LXvcEY82bgWcC3AHPAEnAc+LC1dpxz2I4NfR0Cnx/jc4tcVLvdplar0Wq18H2fMAyjXzQ8zyObzWodq4iIiFyW+fl5ZmZmSCaTUQ+NcrlMs9mMfucoFAoUi0Xi8TjlcpmZmRnm5+cnXbqI7EG7pt1wfyvXv5x0HcDmpqOtiVQhsoVms0mlUqFWq+H7Pq7rEovFor3gXdfF933y+bw+KREREZFLls1mmZ+fJ5PJEI/HyWQyuK5LpVKh2+0Si8XI5/N4nker1SKTyTA/P082m5106SKyB+2aQGOKbF5e8riJVCGySbvdplKpsLq6iuu6zM3NnXNNvV5ndXUV6HUn10wNERERuRRhGGKModFocPr0aRzHIRaLceTIkeiadrtNt9slDEMOHjyIMYYwDC8wqojI5VGgcYmstbcbY/6ZXi8NgFuNMT9krX3PJOsSqdVq1Go1XNclk8lsec3g/sF1xWJxnCWKiIjINi0uLk66hC01m00AisUi9XqdZrMZ9cwYCMOQMAxJpVLR7xpLS0tTOTt0YWFh0iWIyBWYqkDDGHMtcCtwEJgFCsDHrbXvH7rmVuBea+3SZKoE4EeAjwLfQK8x6duNMfuAP7HWqoWzjF2n04l6Zmw1M2NYJpOhVCrRarXodDobfgERERGR6TGNW536vh/1zMhkMnQ6HVZXV8/p21UoFMhkMjSbTcrlMnNzc1P3O4dmqorsfhMPNPohxkuBZwOHt7ikC7x/6PyVwFONMX8NvNJaa3e+yo2stQ/2g5W3AM8BXOBNwK8bYz4GfIberi0loAxccshhrb1/dBXLXuf7ftQzYzsGvTR83yedTu9wdSIiIrJXxONxwjAkCAKOHj1Kq9WiVqtRrVajHhqD3U08z+PUqVOEYTh1YYaI7A0TCzSMMUngdcCLgDi9mQ6bbbXY7jp6dT8beJYx5pettb+3U3VuxRjzvqHTZWCeXv0Hgef3jysRMgVhk+weg6mdsdj2dmKOxWLRY0RERES2q9Pp4DhONDsjl8uRyWSYnZ2NAo1EIkE8HqfRaJDJZHAcR9vHi8iOmMibZmPMYeB9wCNZDzI2v7PaKuCA3rapYf/7SeC1xphj1tqX7USt5/F9bKx3+Ovz1S2yYxzHuaRfFrrdLolEAsfRP1cRERHZvkFvjGw2Gy2JSaVSeJ634ZpGo0G73SabzZJKpfQhiojsiO19nDtCxpg88I/Av6P35n/w083pH2ucJxQwxswDmaHHDYKNlxhjXryzlW9beIWHyCVzXTdaRrIdg+Up212iIiIiIgJEPTIymQy5XI5ut0u5XKZer9NoNKjX65TLZbrdbjR7w3VdfYgiIjti7IEG8GfAt7AxkLgN+AngWmttoX/dOW/urbXLwKOA97JxZocDvNoYc2TzY3aQs0OHyCWLx+N4nofrutTr9QteW6/XcV0Xz/O0nlVEREQuyeADEcdxyOfzFItFCoVC9DtFPB6nUChQLBbJ5/NRAKIPUURkJ4x1yYkx5knAs1gPIULgFy+lB4a19g7g2caYZwJvpzdjAyBLr7noL4206K1dP4bnELkk2WwW3/dZXV0F2HLr1nq9ju/7FAoFstnsuEsUERGRXW74Q5QgCMjn83Q6HdrtdrTLSTKZJB6P60MUEdlx4+6h8Sv920GYcdkNPa21f2eMeT69XhyDgOQFjCHQsNZ+faefQ+RSJZNJ8vk8ALVajVKphOu6xGIxut1utMykUCiQz+e1VZmIiIhclq0+RNkcWOhDFBEZh7EtOTHGzABPZn0pyResta+5kjGtte+nF2gMlmrsM8Z805WMuR3GmJuMMTfs9POIXKpUKsXs7Cyzs7MUi8Wo8WcikaBYLEbfS6VSky5VREREdqnBhyiFQoFut0upVGJtbY16vc7a2hqlUolut6sPUWRXCoIg2oq4VqsRBMGkS5ILGOcMjccDLuu9M/54ROO+DXjG0PkjgDtHNPb5vBF4mjHm/9J7HX9jrW3v8HOKbEsymaRYLNLpdPB9P5r+6bqupnuKiIjISKRSKeLxOK7r0mq1ot85EokEmUwGz/PIZrMKM2TXaLVaVKtVGo0GrVYr2obY8zzS6TS5XG7Dbj4yHcYZaBzu3w6Wm/zTiMb9l/7tYObHwojG3ZIx5hrgafRmt3wn8BTgcfQam4pMjXg8TjqdnnQZIiIiskfpQxTZK+r1OqVSiUqlQqvVIpVKEYvFaLfbVKtVPM+j2WwyNze3ZZ86mZxxBhr7N52fGdG4q5vOd3qR3rfTCzMGfTtut9YqzBARERGRq5I+RJHdrNVqUSqVKJVKeJ7H/v37N2wznM/no6VUsN4YV6bDOAONzXtJZoGVEYw7t+l8c8AxaseGvg6Bz+/w84mIiIiIiMgOqFarVCoVPM8jl8ud833HcaL7K5UKqVRKgcYUGVtTUM6dkTGq5p2P7t8OYrTFEY17PpVN560dfj4REREREREZsSAIop4ZMzMzF7x2ZmaGVqtFo9FQo9ApMs5Aw/ZvB70uvn9E4z5/0/lXRjTu+WxeXvK4HX4+ERERERERGbFWqxX1zBheZrIVx3FIpVLRY2Q6jC3QsNZ+EXiof+oAL7zSrU+NMU8AnsV6SHLaWvtvVzLmxVhrbwf+md5rcIBbjTE/tJPPKSIiIiIiIqPV7Xaj3Uy2IxaLRY+R6TDOGRoA72V9lxMPeK8xpng5A/XDkPew3pwzBP52RHVezI8Ad/W/doC3G2NeZIwZZ08SERERERERuUyxWCwKKbZjEH5sNwCRnTfuN+C/A7yQXkPQEHgE8GljzE9Yaz+93UGMMc8F/giYZX12hg+8erTlbs1a+6Ax5lbgLcBzABd4E/DrxpiPAZ8BvgqUgDJwyYusrLX3j65iERERERERGeZ5Hp7nUa1WyefzF1x2EoYhzWaTmZkZNQWdImMNNKy1Z40xvw68nvUgwgCfNMZ8Evg74M5ND3OMMfuBhwFPAZ4H3Mz6rIzB7eustSd3/lWAMeZ9Q6fLwHy/joP0enps7utxqULGHzaJiIiIiIhcNRKJBOl0Gs/zWFtb23KXk4G1tTU8zyOdTpNI6K3atBj734S19g3GGAP8DOuhhgN8W/9g6D6AX+gfm+8Ph+57v7X2v+1AuefzfZuef/jrC3eTERERERERkamQy+VoNpuUSiWgt5vJ8EyNMAxZW1uj1WoxNzd3wdBDxm9S0dKL6W1/+vL++XCwsdnm+zaHB38F/ORIq7sy4cUvuSAFIiIiIiKya3Q6HXzfJwxDHMfBdV3i8fikyxLZFs/zmJubA6BSqXD27FlSqVTUW6PZbEbXzM3NabnJlJlIoGGt7QKv6PebeB3wTf1vbScMGLzhPwO80lr7lh0ocTsUPIiIiIjIVavdblOr1Wi1WucEGp7nkc1mSSaTky5T5KIymQzxeJxUKkWj0aDVatHtdkkmk8zMzJBOp8nlcgozptBEF/9Ya/8P8H+MMd8D/Bi9JSdHL/CQCvBperub/IW11t/5Krd0/YSeV0RERERk4prNJpVKhVqtRqPROGeKfjqdxvd98vk8qVRqgpWKbM+gQejg33YQBCQSCf0bnnJT0c3EWvtB4IMAxpgj9EKNWaAINOjtFnK2d6md+Ka/1tqvT7oGEREREZFJaLfbVCoVlpeXCYKAeDyO7/vRlpbJZJK1tTWazSYA8XhcMzVk6rVaLarV6oYZGrFYjFarpRkaU2wqAo1h/Z1KxrJbiYiIiIiIXJparcby8jKNRoMwDPF9H8/zcByHTqdDvV7HdV3a7TbLy8u4rkuxWJx02SLnVa/XKZVKVCoVWq1W1EOj3W5TrVajmRtzc3NkMplJlytDpi7QEBERERGR6dTpdKhWq5TLZWKxWBRWDC85SafTNJtNfN+n1WoxMzNDLpdTo1CZSq1Wi1KpRKlUwvM89u/fv+Hfcz6fZ21tLdoFJR6Pa6bGFFGgISIiIiIi2+L7ftRfYNAscTPHcaL719bWqFQq7Nu3b8trRSatWq1SqVTwPI9cLheFF+12m2QyuWGr1kqlQiqVUqAxRUYSaBhjvn0U44yCtfafJ/XcxpgM8BTgScCtwAIwD6SAOr2mpvcBFvgn4MPW2upkqhURERERuTRBENBqtQiC4KKNElOpFOVyObpeZNoEQRD1zIjFYtx1112cPn2aUqkUNQWdm5vj4MGDXHvttXS7XRqNRvQ9mbxR/S18nO1tubrTQiYw68QYUwReCryEXjPTgeGtXXPAAeAm4KnAzwN1Y8yfA6+y1i6NqVwRERERkcsSBEH0Zm54Wv5WHMchkUhEjxGZNq1Wi1arxcrKCvfeey8nTpxgcXEx6gkThiH33nsvCwsLnDlzhhtuuIFcLker1VKgMSVG/bdw4Z9qe5Ax5lbg3cARNr7+kPOHPIPrssDPAs8zxrzAWvsPO1aoiIiIiMgVSiQSJBIJarUaYRheMNQIw5AgCMhms3rzJ1Op2+2ytLTEl770Jb72ta/R7XbJZrOEYUgYhsRiMbLZLKVSiXK5zNraGplMhgMHDky6dOmLjXi8cILH2Bljnklv6cggzBiux7nAsfm6OeDvjDE/POaXICIiIiKybYlEAs/zSCQS0bas3W6XVqu1YbtLgGazueF6kWkTi8W45557sNZSq9UAon/Hg6a2jUYD6O3uY63lnnvuIRYb9dtouVyaoXGZjDGPAN4OeGwMJwBOAJ8F7gTK9Ppn5OgtR3k4vf4a1/SvHYQxceCtxpi7rbW3jeEliIiIiIhcEtd1KRQKVCoV6vU6a2trxGIxgiCg2+0Si8VIJBIbvi4UCriuO+nSRc7Rbrd56KGHeOCBB9i/f38Uxg3PPBrM1nAchwceeICHHnqIdrs9qZJlk1EHGoM35w4QAB8D3gl8ENgzf+vGmBjwl/SWjAy/5g8Dv22t/cQ2xvhO4FfoNREdBCIuvVDjEdba7k7ULiIiIiJyueLxODMzM2QyGZaWlqjX6zSbTdLpNPF4nE6nQ6PRIJVKkclk2LdvHzMzM9qyVabS0tISZ86codPp4Ps+yWSSTqdDs9mMQrlUKkU8Hsf3fTqdDmfOnGFpaYlisTjp8oXRBRp1INP/evDmPE6v+eVTgVXgr+mFGx/bA2/WXwB8I+uzMkLg5dba1293AGvtR4GPGmN+EXgN68HINwHPB/6/kVYsIiIiIjICyWSSZDJJu90mlUpRLBbpdrvRG8BCoUCz2Yy2vUwmk5MuWWRLq6urUT+YZrNJpVKh1WptmJXhOA6e5+G6LmEYUqvVWF1dnXTp0jeqQGMB+D7gucD3AoNNpgdv+IvAC/vHkjHmvcBfWWv/aUTPP24/278dhBmvvJQwY5i19nXGmBTwW6z/ef0cCjREREREZAq1220cx2H//v2EYXjOjg9hGDI3Nxe9GdT0fJlWg14Zvu9TqVSo1WrRTI1YLEa326XdbuO6LtlsliAIot4aMh1GEmhYaxv0dvp4tzEmCzwDeA7wPfR6TAw49MKPnwF+xhhzuv+4d1prPzOKWnaaMWYf8K2shw//BvzOFQ77auDZ9PprADzWGLNgrV28wnFFREREREam0+nQarWIxWIcOHCAZrNJq9Wi3W5HMzSSySSe55FKpVhbW6PVatHpdLTsRKbOzMwM7Xab5eVlCoUCiUQC13U3/FvtdDp0u12azSarq6scOXKEmZmZCVYtw0bebthaWwPeAbzDGJMDnklv5sZT6fWIGHCAQ8BLgJcYY+4H3kVv5sa/jLquEXocG3cqebO19op2WbHWdo0xbwbe1L/LAR4DaBtXEREREZkag0+zXdeNjk6nQ7vdjqboJ5PJ6A2h67rRY9Lp9EVGFxmvfD4f/ftttVocOnQIx3GiECMWixGPxwnDkFOnTtFut+l0OuTz+UmXLn07un+StbZKbyeQtxtjCsCz6M3c+K5Nz+0Ax4CXAy83xtxDr9/GX1lrv7yTNV6Gg/3bQajxf0c07sf7t4Nw5IYRjSsiIiIiMhKD3gLD21bG4/Hzzr6IxWLRY0SmTbfbJZVKRctLVldXo14Zwz00fN/f0CR0sBuKTN7YNoS21q4CbwPeZoyZBX6A3syN76DXQHTAAW4CfhX4VWPMV+jN+HiXtfbucdV7Afs2nZ8e0bibx8mNaFwRERERkZEYvMHrdDrbur7b7ZJIJDZsgykyLZrNJtlslkKhAMDKygq+7xOPx3EchzAM6XQ6UQ+NwW2z2Zxw5TIQu/glo2etXbHW/qm19mn0lp38LL0ZCpujW4ferh+/CXzVGPN5Y8wvGWOOjbXgjeqbzkcVPGweZ21E44qIiIiIjMRgmYnv+9F9g20uG40GzWZzQ9gxvDxFZNoEQUAqlWJubi6aaTTYfjgIgqj3y+CYm5sjlUoRBMGkS5e+sc3QOB9r7RLwZuDNxpj99JpjPgf4tk2XOsAj+8fvGmM+R29ZyruttQ+NseTNjTq/Gbh/BON+c/92sJRFDUFFREREZKrE4/FoC8vV1VUcxzlvU9AwDHFdF8/z1BBUplIqlSIej0dbECeTySisGyw5AaJGoe12m3g8TiqVmmTZMmTigcYwa+1Zeo0x32SMOUQv3HguvUacsP5m3wFu7R+vM8Z8EvgreuHG0g6X+aX+7WA2ybOBfxzBuM/ddG5HMKaIiIiIyEhls1mq1SpnzpwhCIIo5BgsRanX63Q6HRKJBIcPHyabzU66ZJEtFQoF4vE4vu+Tz+dJJpN0Op1zemgMwoxKpUI8Ho+WqMjkTVWgMcxaewp4I/BGY8wRerM2nk0vxIBeqDG4fWL/eAMbt4ndibq+bIxZAub7z/2jxpjfs9b+6+WOaYx5BPAjrIc1i9baL4ykYBERERERETmH53lce+21rKysMDMzQz6fp9vtRqHGIMyIxWJUKhXW1ta49tpr8bwdfcspl2BqA41h1tqTwOuB1/fDjR9kfebGYKaEw/hez9uBl/WfOwH8rTHm2/ohzCXpz0T5W9Ybo4b9cxERERGRqVOr1QiCgPn5eWKx2IYlJ4NPrz3Po9vtEgQBtVqNYrE46bJlghYXp3M1falUwvM8FhYWop17kskkicT628rBzKNsNks6ncbzPM6cOUO73Z5U2ee1sLAw6RLGblcEGgPGmDTwGODRgJlgKa8HXkzvzy8ErgduN8b8tLX2H7Y7iDHm++j1DznE+uwMH/jvI69YREREROQKdTodWq0Wvu8zNzcX3ddut6NPtJPJZNQzo1Qq0Wq1ouaKcvWaxgBgMOsiCALq9TrlcplWq7VhV54wDPE8j2KxSCaTYW1tjUqlQi43XZtSJpPJSZcwEVMfaBhjZoBn0JuV8d1AeujbE9nQ2lp70hjz68Cr+zWE9EKJvzfG/Bu9GRyfA74MlK21gTEmAczS27XlscCP0msEOtwXJARea60dRZNREREREZGR8n0/2rlkYLADxFYGO6L4vk86nd7yGpFJSSaTpFIp0uk06XSabrdLtVql2+1GAV0sFiOXyzE/Pw8QNQ+V6TCVgYYxJg88E/gh4Kms98UYRGXhpnMf+AjwrnHVCPxPejuxPJ31UMMBHg787vCFxpiAc/+sN78WgPdba39tR6oVEREREblCg2aJg+n5FxOLxaLHiEybTCaD53n4vo/neRw+fJhut0uj0Yh27Umn08RiMdrtNq1WC8/zyGQyky5d+qYm0DDGFIHvpxdifBcwiL2G3/gPQoPB0oyPAO8G/tZauzrOeq21oTHmWcCfAj82VN9wzQNbRXjDP9UdelvQ/tSo6xQRERERGZXBrg+dTmdb13e7XRKJxIYp/CLTIp1OR+FEt9slm82esy1rKpWi0+lQKpWAXgii2UbTY6KBhjFmDngWvd1LvmOonvOFGG3WZ2KMPcTYzFobAP+PMeYTwK8BR/vf2k4EPXiNp4BXWWv/eAdKFBEREREZGdd1cV2Xer2+ret93yeTyWxYoiIyTfL5PMVikXq9zsrKSrQV8UC5XI5CuWKxSD6fn2C1stnYAw1jzALwA/RmYjyJ9d09LhZivBv4m0mHGFux1v6JMebP6c3UeDq91zV3gYesAMeB9wB/aa2dvg45IiIiIiKbxONxPM+LQo0LTb2v1+u4rovneWoIKlPJ931SqRQHDhzgoYceolwuEwTBhl1OBufFYpEDBw6QSqXUE2aKjCXQMMYcoNfU84eAJwKDRXcXCjE+yvpMjPI46rwS/VDircBbjTEOcCOwAMwDeWCNXpBx2lp798QKFRERERG5AtlsFt/3WV3tfc64VahRr9fxfZ9CoUA2mx13iSLbMujv4nke+XyeZDKJ7/vR/YMlVq7rRlu2qifMdNmxQMMYcw3rIcYTWA8vLhZiDGZiTH2IsRVjTNpa2wC+1j+2uuY5wCrwKWvt2jjrExERERG5EslkMpp2X6vVKJVKuK5LLBaj2+1Gu6AUCoXoTaLINHIch2azSRAE7N+/H8dxaDQaNJvNqCnoYBeUMAxptVo0m031hJkiIw00jDFH6YUYz6a3Nel2QoyPsT4TY2WU9YyLMeabgZcC/xF4C/DKizzkl4FHAA1jzDvobdVqd7ZKEREREZHRSKVSxONxXNel1WpFn2onEolo54hsNqswQ6ZaPB4nDEPq9Tr79u3DcRyy2SztdjsKNJLJZLRbz8rKCrOzs1pCNUVGEmgYY36RXojxmKG7zxdiBGycibErQwwAY0wKeB3wov5dDnDzNh56Q//aDPATwI8aY37TWvvqHSlURERERGTEkskkxWKRTqcTBRqD6fl6wye7QafTwXEcMpkMzWYz2qLV87xzrm02m2QymUva5Ud23qhmaLyG9dBiYHOIMZiJsatDjAFjTAb4IBuX04RcJNAwxszS66kxvMWrB/y2MeYGa+1P70zFIv8/e3ceJulVFvz/2/vMNLMmkxVCQhJuSEgIBAOyxoVFEdkF0fAisqiAyguKLCrwsigiiIgLi4Qfq5BXFAVE4SVsAQKEJSRwQyAhMWayzEwyM93Te/3+eKp7qp/pma7qrq6l6/u5rr66zqnnOeeu6VM93XefRZIkqfkGBgbcIFFdqVKpsGHDhoVZGVDMPqpdUlKpVJiYmGB6eprR0VE2bNjgHhodpNl7aNT+kj6fxOj6mRhH8G7gwdXHta/7jGXu2wR8FrgfsLl07zMj4ieZ+ZomxypJkiRJqjE/o2jTpk0LS09uv/12RkZG6OvrW9g3Y3h4mM2bNy9c7x4anWMtNgXto/glfQ9wV+DFwIsjYg26OkwlM89e604i4hcpltjUJiNupVh+8r6j3ZuZNwI/FxEjFPuNvJpiCcr8jJY/iYh/yczvrlH4kiRJktTzhoeHF44g3rx588KeMPN7aAwMDLB161ZGRkbYsGEDBw4cWLhHnaHZCY3aTUCPq37U1q+1Vs39+d+l8mXA4zLztnobyMxJ4AMR8THgX4GfoYh/AHgJcFGTYpUkSZIklQwMDDAyMsLw8DAzMzNs2bKF2dlZpqenF/aEGRoaYmBggPHxcYaHhxkZGXGPmA7S3+T2KjUfR6pfq4+WiIgdwM9yaEbF/wCPbiSZUat6bOuTgF3Vqj7gSdUZHJIkSZKkNTI6Osro6ChTU1OMj48zMDCwcFTr/Gk+4+PjTE1NLVyrztHMhEZfmz9a5f41jysUR67esZoGq/uLvJVDr2MYeOBq2pQkSZIkHd3Q0BBbtmxh69atzM3NsWfPHg4cOMD4+DgHDhxgz549zM3NsXXrVrZs2eJRxB2mWUtOXtWkdrrBXauf5/cK+VST2v0E8FoOzTY5g2LzUEmSJEnSGpmfiTG/n8bY2Bizs7MMDAywefNmNm3axOjoqMmMDtSUhEZm9lJCY3upfFOT2r2hVN7RpHYlSZIkSVp31uKUk/VuqlTeDqxqyUnVhlJ5ogltSpIkSZKOYmJign379jE2NsbBgwfp7y92ZpidneX2229ncnKSqakptmzZwoYN5V/b1E4mNBp3c6l8L+C6JrR79+rn+aUstzShTUmSJEnSEUxPT7Nv3z5uu+02Zmdn6e/vZ3Jykrm5Ofr7+xkcHOTAgQMcPHgQKE5GcelJ52j2KSe94DvVz/N7XTylSe0+sVT+QZPalSRJkiQtYWxsjN27d3Pw4EGmpqbYv38/s7OzQDFDY//+/UxNTXHw4EF2797N2NhYmyNWLRMajZo8TxoAACAASURBVLuSxUesPjUi7reaBiPiTOBZHEqS3JaZ31hNm5IkSZKkI5udneXAgQPcfvvtzMzM0N/fz9atW9m0aRMbN25k06ZNbN26lf7+fmZmZrj99ts5cODAQsJD7WdCo0GZWQE+yKGlIQPAv0bE3VbSXkScAPw7MFLT5j83J1pJkiRJ0lKmpqa44447mJmZYXh4mI0bN9LX17fomr6+PjZu3Mjw8DAzMzPccccdTE2Vt1VUu5jQWJk3AZPVxxXgROCbEfE7EbGxngYiYiging58GziTQ7MzJoDXNDleSZIkSVKNmZkZJicnmZmZWXazzw0bNiy6Xp3BTUFXIDNvjIhXAq+nSERUgM3AW4HXR8R/AN8AfgjsA8aBjcAW4AzgPsCjgG0cmpUx//lVmXljK1+PJEmSJPWamZkZZmZmGBwcPGxmRllfXx+Dg4ML96gzmNBYuTcA51FsCjqf1OijSGw8qfpxNPPvmEpN3bsz8w1NjlOSJEmSVDI4OMjg4CBjY2NUKpWjJjUqlQozMzOMjo4yOOiv0Z3CJScrVN1L49eBt7A4OTGfoOhb5qM2CVKhWGbynBaFL0mSJEk9bXBwkJGREQYHB5mYmDjqtRMTE4uuV2cwobEKmTmbmS8EHgp8kaUTFkt9UHPd/wMuzMw/ycy51r4CSZIkSepNw8PDbNmyhcHBwYWjWSuVyqJrKpXKwpGug4ODbNmyheHh4TZFrDJTS02QmV8EHlY9fvUJFAmOc4CTOTR7A4pkxm7g68BlwL9k5ndbHK4kSZIk9byBgQE2b97Mtm3bOHjwIHNzc+zdu3fR0pNKpcLIyAhDQ0Ns3LiRzZs3MzAw0MaoVcuERhNl5g+BP69+EBFDFBuBbgQOAHdUl6pIkiRJktpsdHSUY445hl27djE2Nsbs7OxCcqO/v5+NGzcyNTW1cN3o6Gi7Q1YNExprKDOnKWZkSJIkSZI6zNDQEMPDwwwODjI9Pc3Y2NhCuVKpcODAgYWNQIeHhxkaGmp3yKphQkOSJEmS1JOmp6eZmpoCYPv27RxzzDHMzMwszNAYHBxkbq7Y6nBqaorp6WmTGh3EhIYkSZIkqSeNjY0xNjbG6OgomzZtYnZ2lunp6YVjXIeGhhgYGGB8fHxh9sa2bdvaHbaqTGhIkiRJknrO7Owsk5OTTE1NsWPHDqDYKHSpTT83bdrEnj17mJycZHZ21o1BO4THtkqSJEmSes7U1BRTU1N1H8M6PDy8cI86gwkNSZIkSVLPqVQqVCoV+vvr+7W4v79/4R51BhMakiRJkqSe09fXR19f38Kmn8uZm5tbuEedwYSGJEmSJKnnDA8PLywjqcf88pR6l6ho7ZnQkCRJkiT1nIGBAUZGRhgeHmZ8fPyo146PjzM8PMzIyIgbgnYQExqSJEmSpJ40OjrK6OgoU1NTR0xqjI+PMzU1tXCtOofHtkqSJEmSetLQ0BBbtmwBYGxsjD179jA8PEx/fz9zc3MLy0y2bt3Kli1bGBoaanPEqmVCQ5IkSZLUszZs2MDAwADDw8NMTk4yNTVFpVJhcHCQTZs2MTIywujoqMmMDmRCQ5IkSZLU04aGhti2bRuzs7MLCY2+vj6Gh4fdM6ODmdCQJEmSJIlio9CNGze2OwzVyU1BJUmSJElS1zGhIUmSJEmSuo4JDUmSJEmS1HVMaEiSJEmSpK5jQkOSJEmSJHUdExqSJEmSJKnrmNCQJEmSJEldx4SGJEmSJEnqOiY0JEmSJElS1zGhIUmSJEmSuo4JDUmSJEmS1HVMaEiSJEmSpK5jQkOSJEmSJHWdwXYHoPaJiCHgicATgPsBxwF9wI3Aj4CPAJdk5r62BSlJkiRJ0hKcodGjIuLBwNXAB4EnA6cBo8Am4EzgUcC7gOsi4lfaFackSZIkSUsxodGDIuIJwGeAM+q4fDvwTxHxmrWNSpIkSZKk+pnQ6DERcV/g/cBwTfWVwHOBC4D7Av8LuKx068sj4n+1JEhJkiRJkpbhHho9JCIGgIuBDTXVFwPPyczpmrpvRsR7gZcBtTMz3hoRH8/M29Y6VkmSJEmSjsYZGr3lIuCcmvIXgWeVkhkAZGYlM18L/GVN9Wbg5WsboiRJkiRJyzOh0VueXyq/ODNnl7nnFcBNNeVnR8TG5oYlSZIkSVJjTGj0iIg4HTi/puq7mfnV5e7LzAngPTVVo8CjmxyeJEmSJEkNMaHROx5RKn+ygXs/VSo/fpWxSJIkSZK0KiY0esf9SuVlZ2fU+DpQqSk/cPXhSJIkSZK0ciY0esfZpfLV9d6YmQeA/6mpOjUiRpsSlSRJkiRJK2BCo3fctVS+ocH7/7tUPnXloUiSJEmStDomNHpARPQDO2uqxqqzLhpxS6m8c8mrJEmSJElqARMavWELMFBT3reCNvaXyttXHo4kSZIkSasz2O4A1BIjpfLBFbQxuUybh4mIZwDPqLP9nwb43ve+x0UXXdRQYK0yNTXV7hB6xvDwcLtD6AmO6dZwPLeG47k1HM+t45huDcd0azieW6MbxvPll19+KfCtzPz9ZrRnQqM3lEf2zAramC6Vh+q451TgYY10sn//fi6//PJGbpEkSZIkdYeGfj9cjgmN3lAplftW0MZAqTxbxz3XAZ+rs/3zq33sAa6pPywt4zxgK3AH8K02xyKtluNZ64njWeuNY1rrieN5bTXt39SERm8oz/GqZ3ZFWXmsTCx3Q2ZeDFy8gr7UJBFxKUUW9FuZeWF7o5FWx/Gs9cTxrPXGMa31xPHcPdwUtDeUNwEdXUEbdyqVx1cYiyRJkiRJq2ZCowdk5gQwVlO1LSIaXXayrVS+eXVRSZIkSZK0ciY0escNNY+HgB0N3n9CqbxrdeFIkiRJkrRyJjR6R3mjzbvVe2N1NsdpNVX7MvOmpkQlSZIkSdIKmNDoHVeUyuc0cO/pwKaa8pWrD0eSJEmSpJUzodE7vlgqP7SBe8vXXrq6UCRJkiRJWh0TGr3j88CBmvJjImJDnfc+uVT+RHNCkiRJkiRpZUxo9IjMnAT+qaZqB/Dby90XERcAj6yp+l5mXtbk8CRJkiRJaogJjd7yJmC2pvz6iDji0pOIOBH4CFB7xOufr1FskiRJkiTVbbDdAah1MvPqiPhb4AXVqhHgPyLiRcA7M3N6/tqIeDjwLuAuNU18BXhvq+JVU1xMsefJdW2NQmqOi3E8a/24GMez1peLcUxr/bgYx3NX6KtUKu2OQS0UERuBTwEPKT11K/ANYBI4Gzij9PzNwAWZef2aBylJkiRJ0jJMaPSgiBgF/hl4RJ23/Bj4hcz8wdpFJUmSJElS/Uxo9LCI+HXgZcA9j3DJbuAfgNdl5ljLApMkSZIkaRkmNERE3B24H3ACxb4ae4Arga9n5lQ7Y5MkSZIkaSkmNCRJkiRJUtfx2FZJkiRJktR1TGhIkiRJkqSuM9juACRBRAwBTwSeQLGfyXFAH3Aj8CPgI8AlmbmvRfFsB54BPBI4F9gBjFfjuRJ4P/CpzJxpRTxqL8fnQr/3pjjeegB4T2Y+o5ntqzV6ZTxHxM8D/7WK0F6bma9Yxf3qUJ32HjiSiLiEIk6A0zLzujaGow7VaeM5Ih4EPB54IHBXiu/pc8BtwDXA54APZuYPWxFPL3APDanNIuLBwLuBM5a5dC/wW5n54TWO59nAXwKbl7n0auDXMvNbaxmP2svxudDvBuDLwHnVKhMaXaiXxnNEvAh44yrCM6GxDnXae+BIIuIpwIdqqkxo6DCdNJ4j4lzgHcAFdVw+RzG+X5CZe9Yqpl7hkhOpjSLiCcBnWP4bMcB24J8i4jVrGM8bgbez/A/XAGcBX42IR69VPGovx+dCv/3AeziUzFAX6sHxfO8VBaZ1q9PeA0cSERcA72p1v+ounTSeI+KpwOXUl8yA4nfwpwFXRMQ91iKmXuIMDalNIuK+wJeADTXVVwJ/A3wTmAHOAZ5LMW2t1jMy8z1Njud51b5rfRy4GPgBMAr8NPAC4NSaa/YDD8zM7zYzHrWX43Oh3wHgH4Gnl55yhkYX6cXxHBHfoXhNANcCj2gwzD3+5XD96LT3wFHivD/wCYpp+rWcoaEFnTSeI+LngE8CQzXVeylma3yGYunLMHAmxdKYJ7F4UsG1wP0z89ZmxdRrTGhIbVD9JembHPphE4ofZJ+TmdOla/uAlwG1WeX9wN0y87YmxXMKkBz6j6FCMTXv7UtceyfgvcDjaqq/nJnl/zDUpRyfC23tBN7H0r8ImtDoEr04niNiGDjAoR+wP5qZT1ht7OpOnfYeOEqcvwb8A0VCr8yEhoDOGs8RMQJcBZxeU/1p4ClHSghHxAOBj1Ls9THPnylWwSUnUntcxOJvxF8EnlX+RgyQmZXMfC3FOut5m4GXNzGeV7M4y/1nS/1wXY3nAPBU4LKa6p+OiMc2MR61V8+Pz4h4GPBtGv+rtjpPL47ns1j818LvrCRQrRud9h5YJCI2RsQ7KBLISyUzpFqdNJ4vYnEy40rgsUeb3ZaZlwGPAWZr24mIuzUppp5jQkNqj+eXyi/OzNklrzzkFcBNNeVnR8TG1QYSEcdQ/MA8bzeLM9mHycxJ4LdL1b+32ljUMXp2fEbEiRHxDxTTRE+seWq516/O1Yvjubx/xpV1Baj1qmPeA2UR8SSK5PGzSk/5PVdH0knj+Wml8kszc3y5mzLzcooTrOb1A/5hcIVMaEgtFhGnA+fXVH03M7+63H2ZOUGxMeG8UaAZG3I+DhipKX+wzm/G3wG+UlP10Ig4vgnxqI16eXxGxGspjlR7DsXRrPO+w+E/bKsL9PB4Lm9ga0KjR3Xge2A+rvtExFcpjtQ8s+apWeAPKf7qLi3SSeM5IgaBB9VU7Qc+1UATHy+V77OaeHqZCQ2p9cpT2D/ZwL3lb5SPX2Us0Lx4BoBfXn04arNeHp8vAzbVlOeAt1BsznhdA/2qc/TqeK6doXGQIlGn3tRp74F5j+XwEyF+APxsZv5FE/vR+tJJ4/lMis0+530vM2cauH9XqXzckldpWSY0pNa7X6m8bGa5xtcpNpCb14yNOFcTz+WlshuDdj/HZ+Ey4AGZ+fv1/AVdHatXx3NtQuOqzJxroB+tL532HljKAeBVwL0z8/Nr1IfWh04azz8ATqOYpfFk4LUN3n9iqbx/lfH0LBMaUuudXSpfXe+N1Q3i/qem6tSIWPEGWhGxieKb8bxbMnN3A038oFQuvzZ1n14fn5cDv5yZD8rMrzXQlzpTz43niLgLi4+8vLL0/EBEHBcRJ1dPQ9H61jHvgSXsB94MnJ6Zr6wuC5COpmPGc2bOZuZ1mXlZZl6SmR9rsInybJOfrDSWXjfY7gCkHnTXUvmGBu//b+DkmvKpFEdGrcRdgL6a8vUriKXWaUtepW7Sy+PzApMY604vjufyhqDfiYgdwNOBJ1L8hXPhlJWIuBb4D+AdmfnNBmNS5+uk90CtDwF/mZn+VVqN6NTx3JCIOA741VJ1I/tvqIYzNKQWioh+YGdN1Vg1Y9yIW0rlnUteVZ8TSuWbG7m5+teU2h9Gjqm+RnWhXh+fJjPWlx4ez+WExsOAayn+Ev5gFh8ZC0Vi5LeBb0TE+yNiSyNxqXN14HtgQWZ+32SGGtHJ43kF3gzUnrJyM/DZNsXS9fzFQ2qtLSw+PWHfCtoo/wCwfeXhHHbvauPpA7auPBy1meNT60mvjudyQuNxFP8Wy+mjOILw8og4dQWxqfN02ntAWo11MZ4j4pkcftzr6xvcUFQ1TGhIrTVSKh9cQRuTy7TZiE6LR+3VaeOh0+JRd+m08dOqeMpHtgLsBd4E3J/iL5IbKWZmPJ3DN9UL4OPO1FgXOu09IK1G14/niHgU8Pel6q8Bb2tlHOuNe2hIrVXegG0l2djpUnlohbFA58Wj9uq08dBp8ai7dNr4WfN4qhvcnV665jPARZl5U6n+uurHeyPihcAbOfSHrrMoEiDPWkGM6hyd9h6QVqOrx3NEPBL4aKnP3cBTnJ2xOs7QkFqrUir3LXnV0Q2UyrMrjAU6Lx61V6eNh06LR92l08ZPK+K5E/B2ik0+vw9cCvzSEsmMRTLzzcDLS9XPiIhYQYzqHJ32HpBWo2vHc0Q8BfgYi/cwOgg8LjOvbUUM65kzNKTWmiqVV5IZLr9vV3PMWafFo/bqtPHQafGou3Ta+FnzeDLzZooNPlfiL4DfAO5eLQ8ATwVetcL21H6d9h6QVqMrx3NE/D7FjLfaBMx8MuOLa91/L3CGhtRa5Q2MVnL+9Z1K5fEVxgKdF4/aq9PGQ6fFo+7SaeOn0+JZJDNngXeXqh/RrPbVFh095qQGddV4joihiPgHihNNapMZB4BHZ+Z/rlXfvcaEhtRC1WP3xmqqtkVEo1PmtpXKDR39V3JbqdzQbs/V2Gs3jttXfY3qQo5PrSeO5xX5cql8apPbVwt14HtAWrFuGs8RcQzwn8BzSk/dAlyYmR7R2kQmNKTWu6Hm8RCwo8H7TyiVdzUplqXaXs4xLJ7yt5pY1Bkcn1pPHM+NKf9wf+wa9KHW6qT3gLRaHT+eI+Is4HLgwtJTPwAemJnfaHafvc6EhtR615TKd6v3xmom+rSaqn3LbfZ2NNX11rVncp/WYLa7vJv+91caizqG41PrieO5MeX15S4v6H4d8x6QmqCjx3NEXAhctkRcXwB+OjN/1Mz+VDChIbXeFaXyOQ3cezqwqaZ85erD4Zs1j0dZ/M1+OeXYmxGP2svxqfWkp8ZzRJwUEedFxMMj4mkR0egMi/JfL29p8H51nk57D0ir0bHjOSJ+meKEqa2lp94H/Hxm7mlmfzrEhIbUeuUdjR/awL3lay9dXShA58Wj9uq08dBp8ai7dNr4Wet4/o0iafKfwPuBRzbQPsADS+WvNni/Ok+nvQek1ejI8RwRvwRcAoyUnnpVZl6UmeUTWtREJjSk1vs8xQ7H8x4TERuOdHHJk0vlTzQhno8v08eSImIj8Oiaqv0UU+rU3RyfWk96bTx/s1R+XL2BRUQ/cFGp2l34u1+nvQek1ei48RwR9wY+zOI9jmaB38zMVzajDx2dCQ2pxTJzEvinmqodwG8vd19EXMDiv7Z9LzMva0JIXwZ+WFN+VEScX8d9z2PxZkzvr742dTHHp9aTHhzP/7dUfmxE3KPO2J4PnFFTvhn4SJ33qkN14HtAWrFOG88RMVqNZ2NN9TTwpMz8x9W2r/qY0JDa400U2dt5r4+II06bi4gTKX6wrN1A7s+bEUhmVoA31lT1A5dExPFHiedngNfWVE1TvCatD45PrSe9NJ4/RbGT/rwh4AMRseUI18/38ZhSXACvNgm4bnTMe0Bqgk4az68DolT33Mz8lya1rzr0VSqVdscg9aSI+GvgBTVVB4EXAe/MzOma6x4OvAu4S821XwEelJlzR2j7lcCf1lT9JDNPPUosgxRHTN2n9h7gWZn56ZrrhoFnA3/B4mz0GzLzJUdqX93H8XlYDBcCtefGvyczn7GaNtU6vTSeqwmQz7D4h/cfAM+rbb967THAHwEvBAZqnvoU8AvVBIzWgU56D9QZ76XAw2qqTsvM61bTptaPThjP1UTJj4HaJS+fopjtthLTmfmTFd7b08rHc0lqnZcA5wEPqZY3An8LvCoivgFMAmezeAowFNOAn3Kkb8QrkZkzEfEUirWJ87vc3xX4r4i4BriKYqOj84Gdpds/D7yiWbGoYzg+tZ70zHjOzM9GxO8Bf11Tffdq+zcA36J4vXcG7sfhPwt+gWK6tMmM9aVj3gNSE3TCeH4+i5MZUCxr+eES19bjJ8CpqwmoV7nkRGqTzDwI/AKHb7q2E3gU8FgO/0b8Y+ChmXn9GsTzQ+BC4NrSU2dUY3kUh/9w/Sng0bXZcK0Pjk+tJ702njPzrRQbfO4rPXUX4DHAk4AHcHgy433AozLzAFpXOu09IK1Gh4znX2pSO1olExpSG2XmWGY+kuIHz+8d5dLdFOv0zs3MHxzlutXGkxQZ7VdTZLGPeCnwLIopyf7gu045PrWe9Np4zsz3Vdt/M3D7US6dBD4JPKR6vOB4vX2ou3Tae0BajQ4Yz6c3sS2tgntoSB0kIu5OMQX4BIopx3uAK4Gvt/oM6+oRfven2OzoeGAOuAX4OnC105F7j+NT60kvjeeIGADuDZwLHEsxM+MW4H+AL2Xm/tW0r+7USe8BabUcz73LhIYkSZIkSeo6LjmRJEmSJEldx4SGJEmSJEnqOiY0JEmSJElS1zGhIUmSJEmSuo4JDUmSJEmS1HVMaEiSJEmSpK5jQkOSJEmSJHUdExqSJEmSJKnrmNCQJEmSJEldx4SGJEmSJEnqOiY0JEmSJElS1zGhIUmSJEmSuo4JDUmSJEmS1HVMaEiSJEmSpK4z2O4AJEmS1lJEHA9cBRwDjANnZub/tDcq1YqIfwCeUy2+PDNf1854JEndoa9SqbQ7BkmStA5ExIXAZ1dw6ywwDYwBu4EbgMuBTwNfyszJVcb1MeAx1eKrMvOVq2nvCH1cB9y1puo3MvPiZvezXlWTTj8ENlOMhZ/KzG+3NypJUqdzyYkkSWq3AWADxQyKuwM/B7wU+Azw44j4rYhY0azSiHgmh5IZu4C/WH24arbMvBl4Q7U4BLw3IobbGJIkqQuY0JAkSZ3sJODvgE9GxJ0auTEidgJ/WVP1qswca2Zwaqo3ATdVH58D/GEbY5EkdQH30JAkSWvpwxRLSI6mn+Kv8puBk4GfAraWrvl54D8i4mGZOVtn368HtlUfXwO8s8771AaZOR4R/wf422rVSyPiPZm53PiRJPUoExqSJGkt/V1mXtrIDRGxEXgB8FoW/6zyIOCFwBvraON84Ddqql6dmTONxKG2eBfwR8ApwCaKr/VT2hqRJKljueREkiR1lMw8mJlvAH51iadfFRHH1NHMn3Po55xrgQ80Kz6tncycYnHC6lci4n7tikeS1NlMaEiSpI6UmZcA7y1VbwJ+/Wj3RcQDKTYWnfe2BpapqP3+EThQU/7jdgUiSepsJjQkSVIn+8sl6h61zD1/WvN4Bnhf88LRWqtu3Prhmqpfjojz2hWPJKlzmdCQJEkdKzO/DdxWqr7Xka6PiLOBR9RUfaJ6JKi6y7tL5f/dligkSR3NTUElSVKnuxY4tqZ83FGufU6pXF6y0pCIuCtwf+DOFCex/DdwdWZ+czXtHqW/s4AHA8cANwJfyswf1XnvAHBf4B4U/14jwC0UR6Felpl3NDnWPuB84D7ATmCq2tfXMzNX03ZmfjEifgzcrVr1pIj43cy8fTXtSpLWFxMakiSp05X3v5he6qKI2MDi/TVmgP9aSYcR8XjgD4EHHOH57wJ/lpnvX0HblZriVZl5r4jYSrF3xBOWuP4y4IWZefkR2rsH8AfAk4AtR+h2ptrOmzPzX+qM80LgszVV78nMZ1SfeyrwGuD0I9x7NfBm4B8zc66e/pbwSeB51ccbgYuAt66wLUnSOuSSE0mS1OnKMzJuPcJ1jwJ21JS/0uishIg4JiI+AfwzR0hmVN0LeF9EXFJNpKxYRAwB/84SyYyqBwJ3WuK+kYh4E3A18EyOnMyA4o9YDwU+GhGXRcSpK4y1PyLeDnyQIyQzqs4C3gF8OSLuvJK+gP8slZ+2wnYkSeuUCQ1JktSxImIncFqp+kjLGR5TKn+qwb6OBT4P/EIDtz0R+BAw0EhfJa+hWGZyJD9m8UwJImIL8AXghUBfg/39NHB59TSYRv018OwGrr8A+Ep16U6jPksxy2ahrYg42nIjSVKPMaEhSZI62UUc/gv7p8sXVfdzeHSp+rJ6O6ne/08UMwtq3Qq8HDiPYp+IM4BfA75Uc81jKfbYWIk7Ay+qKV8F/B3wNxTJlQrwzsxcWKYSEcMUM0h+qtTWBPA24OeBUyhmttwHeAlwXenancDHI+JosyzKHs6hJSAAPwF+H7gnxZ4d5wIvpdi3o9bJwL9V465bZu4Hrqyp6gd+sZE2JEnrm3toSJKkjhQRpwAvK1VPs/hIz3lnAseX6q5qoLtnAj9bqvs88ITM3F1Tdxvwo4j4IMXJG39B4zMkam2tefxC4K9r95yobhJaXmLzJ8DPleq+VY312lL9rcC3IuKtFHtaPLfmuW3A/42In8rMJfclKTmp5vFHgGdm5oGaut3AlRHxDuAS4MKa586h2JPkNXX0U+sqiqTMvIcAFzfYhiRpnXKGhiRJ6jgRcS7Fhp7HlJ66ODOvX+KW8myFPfUe11o9HeTlpeqrgV8sJTMWZGYlM/+SwxMuK/XGzPyr8gaamXl1Zi4kNKpLcH6vdO9VwIVLJDNq2zmYmb9FMfOj1r1ZvJFqPf4N+NVSMqO2r90Us2W+VXrqRRGxucG+ykmp8tdZktTDTGhIkqS2iojBiNgREfeKiKdHxEeBbwB3L136E4rlE0u5b6ncyOyMn+PwfTp+JzPH6rj3DRSxrsZUtZ16/C6LNwidA369gc1PX8zh/zYvqS65qcdu4FmZWT55ZpHMHAd+k2LJzLxtwJPr7Gfed0vlsyJipME2JEnrlEtOJEnSWvpsRDSjnX3AkzJz7xGev1upfKSNQ5dSPl3ke5n5uXpuzMy5iHgL8P810F/ZN2pnYSyjvE/IJzKzPBPiiDJzMiLeALynpjooloR8p44m/j4zy3tkHKmvKyLicyxeevJkiuNp6/X9UnkAuCvwgwbakCStU87QkCRJne6bwIMy8+tHuaZ8isaeBtp/SKn8Lw3cC8UGnfXsQXEkX6nnoojYQbFEpNb7VtDfh4HJUt3D6rz3PctfclhftR5cXeJTr6W+jis5MUWStA6Z0JAkSZ1ojGKvhicC52dmeelB2Qml8r56OqmevHGPUvW364qwqro0pZEZIWXlWQhHcj6H/+x2eaOdZeYEh+9vcUEdt96WmT9ssLvycpw7cfhsmqNZ6utY/lpLknqUS04kSdJa+jBwwzLXzFLMGNgL/A9FcuCqOk/emDdaslmeLgAAIABJREFUKteV0KD4a385SdDoL+1Q7EtxrxXcB/XPJtlZKh882kagy7gauP9R2l5KPUtSypZaGnIadf4bZ+ZMRBwENtZUl7/WkqQeZUJDkiStpb/LzEtb0M+mUrnehMb2JerqvbfWkfb2qMftdV5XPvGl3vvq6bPc9lJuW0E/+yg2Bq3ddHRbg23cweKERvlrLUnqUS45kSRJ60GlVD7qKRw1ljox4+AK+l9JEmTeTJ3XbSmV6zmF5UjGS+UNddyzv9FOqsfQlvfrGG6wmblSufy1liT1KBMakiRpPThQKtf7V/yJJepWMgNgaAX3NKr8Glez9GJzqVxOcCyl0UQEETHI4cmSRhMx5a9H+d9BktSjTGhIkqT1YKUJjaWWijS6JAIOnz2xFsqxriTOeeWlNvXMMFnJa9y6RF2jS1c2lsomNCRJgAkNSZK0PtxaKtezySXATzh8ycc9V9B/K44S3VUqb4yI01bY1jmlcj2bizZyOsm8s5aoq3vT1YjYwuHLgspfa0lSjzKhIUmS1oNrSuW71HNT9SSVq0rV91tB//ddwT2N+hqH7x9x/6UuPJqIGAXOLlXXc+zsPSOiPFtiOeeXyjdkZjkxczRLfR1XcgqNJGkdMqEhSZLWg3JC45QG7v1MqfykiBio9+aIeAiwo4H+ViQz91Ict1rr11fQ1FM5fM+Pz9Vx3yDwSw329ZRS+dMN3l/+Ok6y/DHAkqQeYUJDkiStB+VZFuUZCEfzgVL5RBpLFPxuA9eu1iWl8i9ExH3qvTkiRoAXlaqvB66os4kXNNDXA4EHlKrfV+/9VeWv4/eqJ6dIkmRCQ5IkrQtfKJWPjYg713NjZn4D+FKp+k0Rsewsj4h4AvCk+kJsirex+GSWfuC9EbHU5ptLeSOH7xHytw0kCR4SEb+93EXVvS/eVar+LvDZOvuZV07WfL7B+yVJ65gJDUmS1PUy8waKDT5rNbKvxR+weH+KHcDnImKpTS0BiIjHA+9voI9Vy8xbgTeXqs8GPnu0DUIjYkNEvA14fumpHwFvaTCMt0bEs4/S10nAfwH3KD31R5lZ3gNkOeWvYT1LYyRJPWKw3QFIkiQ1yWeAZ9aUfwb4WD03ZuaXI+LPgJfWVJ8KfDMi/h74J4p9OoaBewO/CTy+5toJYMOKI2/MnwAPAR5cU3cf4OqIeCfwzxQbZ04AJwMPB34HKCc8DgJPzcwJGjMAvD0ingL8PcVmpWMUJ708lmIJTnnGyDsz8+ONdBIRJ7A4KTKLCQ1JUg0TGpIkab34MIsTGj/f4P2vAO4MXFRTN0zxC/rR9sn4IvCDUt9rJjNnIuKJFMma2lNONlDMwCjPwljKfuCJmfn1Brv/HPCw6uOfq34s59+B5zXYDxz+9ft0Zu5eQTuSpHXKJSeSJGm9+Axwa035XtXlD3Wp7iPxDOBPKWYD1ONyij00puvtpxky8xbgQuCdQKObZH4ZuCAz/2sFXf8SxXKSeswBfwY8PjOnVtDXI0rl8uatkqQeZ0JDkiStC5k5A3yoVN3Qhp2ZOZeZrwbOBT4CHOkX8VuAVwMPzsybG421GTJzIjOfTbEE5kPAvqNcPkuR8Hkc8KDM/P4K+zwAPJJixsV1R7hskmKJzrmZ+dLq16UhETEM/HJN1Rjw0UbbkSStb32VSqN7M0mSJHWmiLg78H2gr1r15cx84Cra2wY8EDgTGAVuBn4MfGElv6ivpYgYpFiCchpwHMUSlDso9tP4WmbubbC9CymdSpKZfTXP91H82wRwAnCAYunNZZl5tORKPX0/FviXmqq3ZWY9S2kkST3EhIYkSVpXIuLjwC9WixXgjMz8cRtD6krLJTTWuO8PA0+uFivAPTLzB63oW5LUPVxyIkmS1ps31jzuA367XYGocRFxIsXSmHkfM5khSVqKCQ1JkrSuZOZnWTyz4JkRsbFd8ahhzwGGqo8rFJu0SpJ0GBMakiRpPXp5zeMdLD6KVR0qIjYAz62puiQzv92ueCRJnc2EhiRJWncy88vAJTVVL42IoSNdr47xbODE6uNJ4GVtjEWS1OFMaEiSpPXqhRQnbwCcCjy9faFoORExArykpuoNmXlNu+KRJHU+ExqSJGldysz/Bv6kpupPqksa1JmeB5xcffwj4HVtjEWS1AVMaEiSpPXsLcD/qz4+BXhxG2PREUTEscAfV4uzwEWZOdHGkCRJXcCEhiRJWrcyc45iqcnuatUfRcRJbQxJS/s/wLb5x9U9UCRJOqq+SqXS7hgkSZIkSZIa4gwNSZIkSZLUdUxoSJIkSZKkrmNCQ5IkSZIkdR0TGpIkSZIkqeuY0JAkSZIkSV3HhIYkSZIkSeo6JjQkSZIkSVLXMaEhSZIkSZK6jgkNSZIkSZLUdUxoSJIkSZKkrmNCQ5IkSZIkdR0TGpIkSZIkqeuY0JAkSZIkSV3HhIYkSZIkSeo6JjQkSZIkSVLXMaEhSZIkSZK6jgkNSZIkSZLUdUxoSJIkSZKkrmNCQ5IkSZIkdR0TGpIkSZIkqeuY0JAkSZIkSV3HhIYkSZIkSeo6JjQkSZIkSVLXMaEhSZIkSZK6jgkNSZIkSZLUdUxoSJIkSZKkrmNCQ5IkSZIkdR0TGpIkSZIkqeuY0JAkSZIkSV3HhIYkSZIkSeo6JjQkSZIkSVLXMaEhSZIkSZK6jgkNSZIkSZLUdUxoSJIkSZKkrmNCQ5IkSZIkdR0TGpIkSZIkqeuY0JAkSZIkSV3HhIYkSZIkSeo6JjQkSZIkSVLXMaEhSZIkSZK6jgkNSZIkSZLUdUxoSJIkSZKkrmNCQ5IkSZIkdR0TGpIkSZIkqeuY0JAkSZIkSV3HhIYkSZIkSeo6JjQkSZIkSVLXMaEhSZIkSZK6jgkNSZIkSZLUdUxoSJIkSZKkrmNCQ5IkSZIkdR0TGpIkSZIkqeuY0JAkSZIkSV3HhIYkSZIkSeo6JjQkSZIkSVLXMaEhSZIkSZK6jgkNSZIkSZLUdUxoSJIkSZKkrmNCQ5IkSZIkdR0TGpIkSZIkqeuY0JAkSZIkSV3HhIYkSZIkSeo6JjQkSZIkSVLXMaEhSZIkSZK6jgkNSZIkSZLUdUxoSJIkSZKkrmNCQ5IkSZIkdR0TGpIkSZIkqeuY0JAkSZIkSV3HhIYkSZIkSeo6JjQkSZIkSVLXMaEhSZIkSZK6zmC7A5AAIuKvgPOAb2Xm77c7HkmSJElSZzOhoU5xHvCwCy644GHA77U7GEmSJElS0/U1szGXnEiSJEmSpK5jQkOSJEmSJHUdExqSJEmSJKnrmNCQJEmSJEldx01BdZiIeD7w1mrxNzLz4jaGI0mSJEnSYZyhoUUi4kzgde2OQ5IkSZKkozGhoQURcQzwMWBzu2ORJEmSJOloTGgIgIjYCXwauEe7Y5EkSZIkaTkmNERE3Bf4CnBeu2ORJEmSJKkeJjR6XEQ8D7gMuFu7Y5EkSZIkqV6ectKjIuJ84A3Az5aemgUGWh+RJEmSJEn1M6HRYyLiWIojWZ8C9JWe/hBwPfCHrY5LkiRJkqRGuOSk99wLeCqLkxl3AM/NzF8FDrYlKkmSJEmSGuAMjd42B3wA+MPMvKndwUiSJEmSVC8TGr1pFvhX4JWZeWW7g5EkSZIkqVEmNHrP94C7Zeb17Q5EkiRJkqSVMqHRYzLz5nbHIEmSJEnSarkpqCRJkiRJ6jomNCRJkiRJUtdxyYnWTEQ8A3hGnZeft3aRSJIkSZLWGxMaWkunAg9rdxCSJEmSpPXHhIbW0nXA5+q89jxg69qFIkmSJElaT0xoaM1k5sXAxfVcGxGX4mwOSZIkSVKd3BRUkiRJkiR1HRMakiRJkiSp65jQkCRJkiRJXceEhiRJkiRJ6jomNCRJkiRJUtcxoSFJkiRJkrqOCQ1JkiRJktR1TGhIkiRJkqSuY0JDkiRJkiR1HRMakiRJkiSp65jQkCRJkiRJXceEhiRJkiRJ6jqD7Q5AnSUzXwm8ss1hSJIkSZJ0VM7QkCRJkiRJXceEhiRJkiRJ6jomNCRJkiRJUtcxoSFJkiRJkrqOCQ1JkiRJktR1TGhIkiRJkqSuY0JDkiRJkiR1HRMakiRJkiSp6wy2OwCAiOgHtgIbgQGgb6VtZeb1zYpLkiRJkiR1prYlNCLibOBZwC8Cp7OKJEaNCh2SpFmJiBgC7gmcA/xnZt7a5pAkSZIkSepIbfnlPyL+CngexZKXZiQyWioiflxTfHtm/lkT2jwDuJpihgrA44B/W227kiRJkiStRy1PaETEO4BnciiRUWlS061MjJxa/VwBdjSpzd0c+npUgLs2qV1JkiRJktadliY0IuJngN+k+IW9NpHRdbM0aF4iZt6mUrtbmty+JEmSJEnrRqtnaPxpqdwHXAX8I/AtYBdwkOYnC7rBC6uf+yhe/3QbY5EkSZIkqaO1LKEREccCD6b4ZX3+l/a/AV6YmbOtimM5EfHHwM80cMtTIuJ+q+hyBDgFOIlD/zYAP1lFm5IkSZIkrWutnKHxYIpNQOeXm3w1M3+3hf3X68PAy4GhZa6bTzzcufqxGuX9RGaBz62yTUmSJEmS1q3+FvZ1YvXz/C/vb2lh33XLzATeSGv39ajdU6QCvCUzb25h/5IkSZIkdZVWJjTKm1x+vYV9N+o1wHUUSY2lPmod6ZqVfECxDOcla/KqJKlLzczMMDY2xv79+xkbG2NmZqbdIUmSJKnNWrnk5JZSeU8L+25IZk5ExNOARx7hkj/l0IyKLwP/tcKuZoEpYB/w38DXM3PXCtuSpHVncnKS/fv3s2vXLq6//nomJycZGRnhlFNO4YQTTmDz5s2MjIy0O0xJkiS1QSsTGjeWyscDe1vYf0My8yvAV5Z6LiJqT2u5LDNf1ZqoJKl3jI+Pc9VVV3HFFVdw9dVXc/311zM1NcXw8DCnnHIKZ511Fve97305++yz2bRp0/INSpIkaV1pZULjS8A4sLFafijw/Rb232zzJ7VIkppscnKSr33ta3zgAx/g29/+NrfeeivT09PMzc3R39/Pd77zHb761a9yxRVX8LSnPY0HPOABztSQJEnqMS1LaGTmWET8O/ArFImA3wHe3qr+m+w9NY+/1rYoJGmdykze/e538/nPf56xsTGmp6fp7z+07dPc3BwHDhzglltuYXJyku3bt3Puuee2MWJJkiS1WitnaECx98QvAyPAORHxB5n5Fy2OYdUy8zfaHYMkrVczMzP867/+K1/4whfYvXs3/f399PX10dfXtzBDA2BqaoqJiQm+8IUvcPrpp3PWWWcxONjq/9YkSZLULq085WT+SNSXcOhUj9dHxCsiYqiVcayFiLhLRPxWRDy8jms/GRFfjohXRcRdWxGfJHWLm2++mUsvvZRdu3YxNzcHFDMyjvR4165dXHrppdx8s6ddS5Ik9ZKW/ykrM98aERXgrygSKq8CnhcRHwYuB66hOPVjbIXtX9+sWOsREacBfw48nuL1/D3Ln3pyD+AU4ALgZRHxPuBFmdmxJ79IUqtcccUVXHPNNUxPTzM0NERfXx8DAwMMDAwsXDM/W2Nubo7p6WmuueYarrjiCk4++eQ2Ri5JkqRWamlCIyI+VlO8GTiRYqbG8cDzm9BFhRa+poh4AvBu4E4UrwOKZMXR7hkE7lwt9gEDwNOBR0TEozLzyjUKV5K6wi233MKpp57Kcccdx6ZNmxgeHl5YdjKvUqkwNzfH1NQU4+PjbNq0iVtuKZ8OLkmSpPWs1TM0fonFJ4PUPu6ji0TEo4EPcejfsELxGo6a0KCYmTH/Z8b5199Hkdy5NCIelJndfPqLpC5x6623tjuEJY2NjbF161Y2btzI5s2bF83MKJudnWX//v0MDw8zNjbWka9p586d7Q5BkiRpXeqk3dNWewRqyxIiEXEM8I8U/361SYndwL8vc/sNFEfWPhS4CIiaNrYDH4yICzJzutlxS1LZ9HTnfau55ZZb+O53v8vExAQ7duxYNqGxZ88eNmzYwLnnnttxr2doqOu3iJIkSepYLd0UtKpvjT5a6cXATg7NypgDXgmckpnPPdqNmTmdmV/MzNcBZwEvAGZqLjkX+M21CFqSusHxxx/P4OAgfX19TExMMDc3R6WyOOc9v+RkYmKCvr4+BgcHOf7449sUsSRJktqh1TM0Tmtxf00XEf3AMzmUzKgAF2XmhxptKzMrwNsi4laK5Svzbf4+xeaiktRzTjnlFLZv387ExAQzMzNMT08zMDCwcFwrFKebzM7OMjMzw8DAANu3b+eUU05pY9SSJElqtZYmNDLzJ63sb43ci0OzMyrAJStJZtTKzA9HxK8Bj6lWnRkRp2XmtasLVZK6z0knncRpp53Gnj17mJ6eZmpqir6+vkXLN6anpxdmbYyMjHDaaadx0kkntStkSZIktUE7lpx0u/Oqn+eXubyzSe2+q1S+f5PalaSusnPnTs4//3xOPvlkNm7cSH9//8LxrPMzNubm5ujv72fjxo2cfPLJnH/++W6+qa4xMzPD2NgY+/fvZ2xsjJmZmeVvkiRJh+mkTUG7xTGl8reb1O4V1c/zC8VdDC6pJw0PD3POOedw4403ctVVV7Fv3z4mJiaYmpqiUqnQ19fH8PAwGzZsYMuWLZx99tmcc845DA8Ptzt06agmJyfZv3//wsfMzAyDg4Ns3rx54WNkZKTdYUqS1DVMaDRuQ6k826R2DyzTjyT1jNNPP50HPOABVCoVbrvtNm6//faFDUL7+/vZsGED27Zt49hjj+UBD3gAp59+ertDlo5qfHycm266iV27drFv3z76+g7tZ16pVNiyZQsnnHACJ554Ips2bWpjpJIkdY91k9CIiO3A72Tma9e4q32l8p0pjmtdrfKMjGa0KUldaXR0lHPPPZehoSGuueYabrrppoVNQgcHB9mwYQMnnngiZ5xxBve85z0ZHR1td8jSEU1OTnLjjTdy7bXXMj09vTCG+/r6qFQqTExMcPvttzM2Nsbc3BynnHKKMzUkSapDWxMaEbEBuA/FJpubqvEcbV+PfmAAGKKYwXAn4FjgvhR7TgwCa53QuKb6eX5pyM/TnGUnD6t+nj85ZVcT2pSkrrVt2zbOPfdcTjrpJG655RZuvfVWpqenGRoaYufOnRx33HEce+yxJjPU8W677TZuuOEGDh48yPbt2xkcHGR8fJzZ2VkGBgbYvHkzMzMz7N27lxtuuIFNmzZx8skntztsSZI6XlsSGhFxMvA64AkUiYxmmE8ErLWvA3PV/vqA34mIt2bm1EobjIg+4Lc4dGzrHHBZE2JVD5uZmWFycnJhiv7IyAiDg+tmUpZ6xOjoKKOjo5x44okLmycODg4yOjrqnhnqCjMzM9x2223s3buXjRs3snv3bvbu3csdd9yx8P1569atbN++nZGREfbu3cttt93G8ccf7/dsSZKW0fL/KSPiwcC/A5s5dFLIarUikQFAZu6OiM8DF1arTgXeCPzuKpp9BcXpKfNHwX4tM/esoj31sPlN5w4ePHhYQmPjxo1uOqeuNDw8bAJDXWlsbIw77riDffv2cfvtt7Nr1y727NnD7OyhLbgGBgbYsWMHJ5xwAv39/dxxxx2MjY2xdevWNkYuSVLna2lCIyK2AB8BtlSrmpWImE+M3Nqk9pbzNxQJjfkZFc+LiAHgxZl5sN5GImIQeCXw0pq2KsBfNzle9Yjx8XH27NnDvn37mJycZMOGDfT39zM9Pc3+/fsZGRlhYmKCHTt2uOmcJLXAwYMH2bt3LzfffDO33nrrwkyjmZmZhYTz4OAgY2Nj7Nmzh507d7Jjxw4OHjxoQkOSpGW0eobG71JsflmbyJhPRswCExRLUOZ/sZ8CZoBhin0z5lVq7p0BXgN8Bfh/axV4yUeBy4Gf4lAi4reAR0fEO4B/Br6fmUsmbCLiTOCxwLOAMzn0eivANzLzQ2v+CrTuTE5OsmfPHvbs2cPIyAjHHXfcol30t2zZwoEDB9izp5j8MzAw4EwNSWqBm266iWuvvZb9+/dTqVQYHx9f2BB0/vOmTZvYt28fBw4cYNu2be0OWZKkrtDqhMYzWZyM2Af8MfDRzPxvgIj4FPDw6jV/k5l/UK3fAJxRfe5FwEnVawaA4zLzP1vyCoDMrETEM4AvU8w2mU9qnAL/P3v3HibZVdf7/73rsnfdu7pneqYnk0kySXCRBIQfaERAQBBUVJRLQBTkqKCi8sOj8HiOelBRROWAHhA9oEJUFBGRy0FFBQweLgqBoEJgZXJjkslcerqmu+67bvv8UVW7q2t6erqnq6uqpz+v56mneu9ae9W3JvV0ur611vfLa3u3qjHmXrqvsQoke2OvBfoV7PqfNvvXnwFeMJ5XIZebUqlEsVjE8zyy2ex5jzuOE54vFoskEgklNEREdpjjOCwuLnLfffcRj8fpdDq0Wi0ajUZYE8Z1XRqNBpFIhLNnz3LllVeuSUiLiIjI+saW0DDGXEe33kT/w3sbeKa1drj45UfoJi0c4LnAqwGstXXgS8CXjDG3Au8EntUb93JjzD9aaz+086+ky1r7VWPMdwMfAmZZm6iBbtLikay/GqVv8JozwLOstfftTMRyOWu1WmHNjAMHDmw4NpPJcObMGWq1WvjHtIiI7Azf91lcXOTcuXPhaoxKpUIQBOGWE8dxSKfT4eOLi4v4vj/p0EVERKbeRi1SR+2RAz8HwPvWSWZAd9VD39XGmKPDA6y154Dn0d1mAt2EwO/1VnGMjbX2U3Rbxn6M1a4nwdBt0PBj/Ws+CjzaWvvZ8UQulxvf98OaGRf7Vs9xHBKJRHiNiIjsnH4x0HK5zNLSEktLS5TLZer1Oo1Gg3q9ft5jy8vLFIvFSYcuIiIy9caZ0Og3VO9/2nrvBcZ9kW7b0n4y4PHrDbLWtuhuz6j3xh4GXjaSSLfAWvs1a+3TgacCfwlUWE1UbHTzgfcBT7LWPsNae2rcscvlo9PphN/09ZXLZY4fP84999zD8ePHKZfL4WORSCS8RkREds7KykqYqKhWq/i+T7vdDldoBEFAu93G932q1WqY3FhZWZl06CIiIlNvnGvNhzf1//t6g6y1dWPMPXSLZQbAY4E/v8DYB4wxf8FqbY6XAG8ZWcRbYK29Dbit1+3kMXRXpBwF8nTrZ5SBc3Q7sdwOfKGXlBHZtkgkEnYzOXv2LF/72td44IEHeOihh2g2m8Tjca644gqOHDnC1VdfTTweJx6Pr0mAiIjI6HU6Hc6dO0e1WsXzPKLRKO12m2azGRYFhW6h5n7B0HPnzinhLCIisgnjTGgMr4Nf2mCspZvQALjpIvN+iG5CwwEebYyZs9YWLi3E7bPWtoHP9W4iY+F5Hp7ncezYMY4dO8add97J8ePHqdVq4cqNZDLJVVddxY033sjDHvYwHvGIR6goqIjIDvN9P2zVOrgtsL86w3GcsI6G4zi0Wi0qlYq2BIqIiGzCOBMa57Yw9p7evQOYi4z9j4GfHeCbgb/dwnOJ7HqxWIxyucztt9/OJz/5SZaWlqjVarTb7XBMNBrlzJkz3HfffTzxiU/kmmuuUUFQEZEd5rouD3/4w5mZmcF13fD3biwWCxMarVZ3wWa/+8mhQ4dwXXeSYYuIiOwK4/w0M7wiYx8XTnLcO/DzlcaYpLW2doGx/Tn6NTcOXWJ8IrvaXXfdxWc+8xmOHTsW7smOx+Ph49VqlWq1SqFQIBqN8vVf//XccMMNE4xYRGR0FhcXJx3CuiqVCplMhrm5OWKxGNFolEgkQjQaDce02206nQ7tdptWq0Umk6FSqUzla5qfn590CCIiIqFxJjSODx0/Erj7AmPvGfjZAR7BhbdwDH+FsW/roY2WMSYOPApYoNvSdQa4o9cVpT/mKHC8t0VFZFvK5TKf+tSnOHbsGLVaLfxDudlshmP6RUDb7TbHjh3jU5/6FE972tPIZDKTCltEZKQGf+dNi1KpxN13383p06dJJBJrEhnD2u029XqdgwcPUiqVpu71DCbJRUREpsE4ExqfB6p0C2QCPBd4/wXG2t79YKeTCyU0vm7oeCL/9zfGpIAfBW4BvgEYLk7wRuBTA8e/CzzeGPPHwG/1WtGKXJL777+fL37xi6ysrBCPx3EcJ6yi7zgOQRAQBEH4zeDKygpf/OIXuf/++3nEIx4x6fBFRC5bqVSKZDJJJBIJa2gMbgfs6xcFbTQaJJNJUqnUBKIVERHZXcbW4qDX0ePTrLYtfYEx5tsuMPxeoDRw/MINpn5u775fdHSjYqM7whjzs8ADdJMUTwASrG3Rup5r6K4meTVwrzHmOTsfqVyu7rrrLh588EHq9TpBEBCLxUilUqTT6TX3/T3b9XqdBx98kLvuumvSoYuIXNaSySQzMzOk02lisRiu64a3eDy+5jgWi5FOp5mZmSGZTF58cpEp0S9mWyqVwiK4IiLjMO6KgH8JfBvdlRdR4P8YY34d+F1rbaU/yFobGGM+Dnxv79Q3GmN+0lr7+4OTGWO+GfjJ3nz9xMG67WB3gjEmB/wF8J0Dzx+wurIENk5o9OOeAd5rjHm1tfZNOxOtXM6azSZXXXUVc3NzzMzMhN8CBsHqW7F/XK/XWVlZIZPJTN1yZhGRy00ymWRhYYGlpSUajQaO44Qr6YZX0UUiEWZmZlhYWFBCQ3YF3/cplUrUajV83w87q3meRzKZJJvNqqOaiOyocSc03gX8EnA13Q/zHvBa4OeNMa+31r5+YOyf0U1o9D/0v8UY8zjgA0AN+Fbgp+jW0Oh/altkbdeTHdOrk/EB4Cm9U/0YBhMYg4mNwWtngSxrkx8O8AZjzHFr7V+PPGDZtmksztbXaDTI5XLE43Hy+TyRSHfx1XCXE+guf+63cW00GlP5ulR0TkQuF+l0miNHjrC0tESxWMT3fZrNZvh7Gro1jlzXxfM8crkcR44cIZ1OTzBqkYvrFxrvv68TiQSRSIRms0mpVMLzPOr1OnNzc9pCJSI7ZqwJDWttwxjzI8A/0l2f2ZjiAAAgAElEQVSh0U9WpIH9Q8PfTzc58ciBcT/Yu/U5A48FwNustZ2dfA0D3kw3mTGYkHgQeAfwUeArdBMs6yU1lululfl54GZWExsO8FZjzMdUU2M6TeuKhlKpxFe+8hVKpRL5fJ5oNEqn0zlvhUYkEqHdbrO8vEw2m+UpT3nK1L0mFZ0TkcvJzMwMR44c4dSpU2SzWUqlEuVyOSzUHIlEiEQiZDIZstlsmACZmZmZdOgiF+T7PoVCgUKhgOd5HDhwAMdZ/U4vl8tRLpcpFApA90sVrdQQkZ0w7hUaWGtvM8a8APhTuomM/ieue4fGBcaYHwNuo7uSY3BbSd9gsuAu4Dd3IuZhxphHAS8biuktwKuttY2Bceteb60N6CZs3m+MeSXwBroJHugmdl5Bd+WKyKbs37+fTCYT7l31PC9sD9jXbrfxfR/f9wHIZDLs3z+cRxQRkVFyXZdDhw5x1VVXcebMGfbt20e5XKZer9Nut4lGoyQSibDj1IEDBzh06BCuO9zETWR6lEolisUinueRzWbPe9xxnPB8sVgkkUgooSEiO2JsRUEHWWvfT7cV67tY7Upy7zrjPgs8Hyiyugpj8Ebv/N3AM621tZ2NPPQauv92/ZjebK195WAyY7Ostf+Lbh2QwdUmPzLCWGUPOHDgAHNzc2HRz1arRaPRoNVqhbf+cb9o6NzcHAcOHJh06CIil71Dhw5x3XXXMTc3RzabZWFh4bxbNptlbm6O6667jkOHDk06ZJELarVaYc2MfiKuXq9z9uxZTp06xdmzZ6nX60D3yxPf96nVaioUKiI7YiIJDQBr7destT8ELNDdRvL5C4z7MPD1dLdyVFjbPeQM3VUZj7XW3jeOuI0xHvDtrCZU7qbbqeSSWWv/CPgEq6s9jhhjrtvOnLK3zM7OcvjwYTKZDLFYDMdx6HQ6axIanU4Hx3GIxWJkMhkOHz7M7OzspEMXEbnspdNpjh49ynXXXUcymaRer9PpdHfIdjod6vU6yWSS6667jqNHj6p+hky1/mrPRCJBqVTinnvu4ctf/jJ33nknX/3qV7nzzjv58pe/zD333EOpVCKRSKxZISoiMkpj33IyzFq7TLf7yUZjHgBeaoz5SbrdQeaAs8A9ve0b4/R4IMXqKpH/ba0dRRGCtwNPHjh+DHDPCOaVPcB1XQ4fPsyBAweoVCr4vh9+O9IXi8XCJZ/pdJrDhw9rSbOIyJgkEgn279+P7/u4rkuj0QhraLiuy9zcHPv37yeRSEw6VJEN9eu/LC8vUygUWFxcpFKpkM1miUQitFotlpaWwkK4c3Nz7Nu3L0ziiYiM0sQTGlvR29Jx14TDONK7728R+eiI5v233n0/QXNwRPPKHtBqtZifn+f666/n1KlTVKtVms1m+C1gJBIhkUgQj8dJpVIsLCwwPz+v5Z8iImPQbDYpFou0Wi2OHDnCNddcQ61WC2toJJNJ2u02jUaDYrFINBpVgWSZWpFIhEqlwvHjx1leXiadTnPttdeu6dwzPz/P0tISDz74IOVyOeyAIiIyarsqoTElhhMND41o3qWh4/MrLIlsYP/+/Rw9epRUKsXy8jLLy8sA4R/MAPl8nnw+z8GDB1UQVERkTCqVCpVKBdd1w/aVFyqQ2B+Xz+fHGaLIpnmex8rKCidPnmTfvn3Mz8/TaDSoVqvh3xypVIr5+XmCIODkyZMcPHhQRUFFZEcoobF1w9tLRvXbebg/W2lE88oe0N9G0l/WefLkSZaWlvB9P1yh4Xke+/bt49ChQwRBQDqd1h8XIiI7rN9hqtFoMDc3t+HYVCpFoVDA9/01yWiRadJqtfB9n2azSRAEPPDAA6ysrFCpVGi1WsRiMdLpNDMzM7iuS7PZxPf98DERkVHSb5WtOz10fD2jWaXxiN59fyvL2RHMKXtEMpkkk8ngeR6ZTIZ9+/ZRKpVYXl4O/yjO5/Nks1mCIKBcLpPJZEgmk5MOXUTkstZoNGg0GpuuWdSvr9FoNPQ7WqZSsVik3W6TSCS49957OXfuHPV6fc2Wkk6nQyKRYHZ2ln379tFut8P2rSIiozSShIYx5uNDpwJr7dM2MW7U1n3eEeu3l+3Xuvgu4F9GMO8tQ8d3j2BO2SP6CYvl5WWq1Sq5XI6DBw+GRbgikQixWCzcn53NZsnn8/r2T0RkhwVBQBAEm64fEIlEwmtEplF/hcbKygqnT5+mXC7T6XTW/E3RbrepVqs0Gg1isVi4QkNEZNRGtULjKax+wHcGft5o3Kht9Lyj9DmgAMz2nvMnjDFvsNZe8ooKY8wNdFvXBr05z3GBNrYiF5LP51lYWODEiRNUKhXq9Tqe5+E4TviHRbvdxvM8FhYWtD9bRGQMHMcJfw9vRqfTCdtvi0yjWCzG4uIiDz30EM1mk3g8Tq1WC7egOI5DNBrF8zyazSYPPfQQBw4c0HYTEdkRKje8RdbaDvAhVhMoGeA9xphLKkdujJkD3k83udSf8+8m0I5Wdrl4PM78/DxXXnlluLWk377V932CICCbzXLllVcyPz+vCvoiImPgum64jWQz+ttT1FZbppXruhSLRY4fP06r1aLdbocJuP7Kon4Sr9Vqcfz4cYrFot7TIrIjRpkq3exXCZfDVw6/BvwAEKf7ep4CfMwY82Jr7dc2O4kx5puAvwCOsro6owO8ftQBy96QSCQ4ePAgmUyGarVKpVIJa2ik02lSqRTpdFrJDBGRMel/U+26LtVqNexysp5qtYrruniepy2BMrWq1SrLy8s0Gg3Onj1LMpmk0+mESY1OpxMWDa3VajQajXBLrFaHisiojSqh8asjHjfVrLX3GWN+B/h5VhMRTwS+aoz5K+CDwJ3rXWuMOQw8lW5C5Bmsrsro37/TWvuVHX8RctmKx+NhAdBGoxEu/3RdV38gi4hMQDqdptFosLKyArBuUqNfb2BmZoZ0Oj3uEEU2rVgsUq/XabVatFqtcJtUp9MJ/+aIRCK0Wi3q9ToA9XqdYrHIFVdcMeHoReRyM5KEhrV2U4mKzY7bJX4BuAF4FqsJCQ94Ue8GaxMVP2aM+SlgsLzzcN2PfwN+emfDlr0iGo2qQr6IyBSIx+PkcjkAKpUKhUIB13WJRCJ0Op1wm8nMzAy5XE6r6GSq1et1arVaWHB8ZWWFWq12XkIjmUySzWZptVrUarUwuSEiMkqqznOJrLWBMeaFwDuB57O2KOowB8itc37wmk8Bz7HWbm6TrYiIiOwaiUSCaDSK67r4vh+uoIvFYqRSKTzP05ZA2RUikQi+71OpVMhkMmGCY7CQbT+xkUqlqFQq+L6/6U4/IiJboYTGNlhra8D399rRvg7Y13toswU9HcAHfgf4H9bazZVAFxERkV2nvyWw3W5rS6DsWrFYjFarRblcplqtkslkyGazOI4TvqeDIKDZbHLmzBk6nQ6tVktdTkRkR+g3ywhYa99ujPlT4Ifpbjd5LHCxUs4W+GvgLdbaMzscooiIiEwJbQmU3SwWi9FsNmk0GsRisXCV0eAKjE6nQ6VSCetsNJtNJTREZEfoN8uIWGvrwB8Af2CM8YBvBI4As0AeqAEF4AzwOWvt2UnFKiIiIiJyKVzX5ejRo9TrdXK5HNFolE6nQyQSCVdn9I+vvfZaisUiR48eVdtWEdkRSmhcAmPM9UDHWnvveo9ba33gk+ONSkREREQuF4uLi5MOYV2Li4t4nsfCwgLJZJJmsxl2O+nrFwyNx+NhjZjFxcWp7OAzPz8/6RBEZBtGktAwxqz7wX4CAmvtdWN4njcDzzDG/DPwh8D7rbXNMTyviIiIiOwRzeb0/XlZLBZZXFzkxIkTxOPxsCYMrBYDBcLaMM1mk2g0SrFYnLrXoyK8IrvfqFZoXMNqi9JJ2mwxzktmjLkCeAYQAZ4GPBV4HPC5nX5uka1Q0TkREREZtXg8TjKZDOvAxGIxksnkeSs0+sf9x5U8EJGdMOotJzueUNjAuJIpT6KbzOgncG631iqZIVOj2WyGLdKGExpqCygiIiLbMTc3Ry6XIwgCZmZmCIKARqNBp9MJ/+aIRCK4rovjOKysrJDL5Zibm5t06CJyGRplQmOrCYX1kh+bnWPw2nGvCrl6KI7Pj/n5RS6oXq9TLBapVCo0Gg1c1yUSidBut6lWq7iuS6PRIJfLkUgkJh2uiIiI7DKZTIbZ2VkymQzRaJRoNLpuDQ3P82i322vGi4iM2qgSGj+8xfFPBH6U1cSEA9wN/BXwBeDLwDmgCMSBHN1EwmOA7wO+deC6GvBTwN9fevhbUhw69sf0vCIbajabFItFVlZWcF133W9CqtUqKysrQLdtoFZqiIiIyFa0220OHjzI/Pw8p06dCreUBEEQrtBwHId6vU6r1WJhYYGDBw/SbrcnHbqIXIZGktCw1v7JZscaY74d+EFWt2w8ALzCWvuhC1xSB0rACeDTwO8ZYx4BvBN4LJCkW5jz5dbaP7rkF7F5w9tLHjeG5xS5qEqlQqVSwXVdUqnUumP65/vj8vn8OEMUERGRXS4IAmZnZ1lYWKBSqVCr1fB9n0QiEbZtrdfrOI5DNptlYWGB2dlZgmCSO9NF5HIVGeeTGWMWgHcB/bXu/wk8doNkxrqstV8CngD8U+9UFHiLMebRo4p1g+e+HfgXuskYB7jZGPO8nX5ekY202+2wZsaFkhl9qVSKRqOB7/v6tkRERES2xHEc2u02c3Nz3HDDDVx99dXk83mi0SiO4xCNRsnn81x99dXccMMNzM3N0W63w+4nIiKjNOqioBfzG8C+3s8V4HustWcvZSJrbcMY83zgK8BBwAN+m24Hkp32g8DHgK+jm9R4lzFmP/BH1trWGJ5fZI1GoxHWzNiMfi2NRqMRVikXERERuZhoNEoQBLRaLY4ePcoVV1wRbnltt9tEo1FmZmbI5XJ4nsfJkycJgkCd1kRkR4wtoWGMyQHPZ7Vuxu9Za49vZ05r7Yox5neB3+ydeqox5oi19oHtzLuJ5z1hjLkZeDvd1+QCbwVeY4z5OPCvwFeBArAMbDnJsd1/G9lb+vtWI5HNLbqKRCLhNSIiIiKb1V9tkUqlaLfbZLNZUqkU+/fvp9PpEIlEiMViRKNRarUaqVQqXNUhIjJq41yh8S1Afy18ALx/RPP+LasJDQe4mW5djh1jjBncIrNEd9WJAywAL+zdtiNg/KtnZBfrF+Da7B8LnU6HWCym5Z8iIiKyJUEQkEgkSKfTNJtNABKJBJ7nrRlTq9VoNpuk02kSiYS+RBGRHTHOD81XDR3fO6J5H+rd939LXjmieTfy3axtHTvJNrIiuK6L67pUq9VNje/X2tjsFhURERER6H6J0i9AHgQB1WqV5eVlPM8Li4L6vo/rumSz2XC8vkQRkZ0wzqKgM0PHG1cu3LzZoeNJf0ILtnkT2bJoNIrneZtKalSrVVzXxfM87WcVERGRLel/ieI4Drlcjnw+z8zMTPg3Rb+GRj6fJ5fLhQkNfYkiIjthnCs0zg0dP4rRbA15Uu/eoZsQODOCOTdDaWaZKul0mkajwcrKCsC63U6q1SqNRoOZmRnS6fS4QxQREZFdbvBLlFarRS6Xo91u02w2CYIAx3GIx+NEo1F9iSIiO26cCY0v9e77qxBeCnx4OxMaYyLA/9+bs59guH07c27S0TE8h8iWxONxcrkcAJVKhUKhgOu6RCIROp1O2AWlX3k8Ho9POGIRERHZjdb7EmU4YaEvUURkHMaZ0Pgc3Y4fM3STD99jjHmBtfY925jz14BHs5okudta++XthXlx1tqv7fRziFyKRCJBNBrFdV1836fRaBAEAbFYjFQqhed5pNNpJTNERETkkulLFBGZFmNLaFhrG8aYdwE/zeqKij8xxnjW2j/dylzGmDjwOuDnBuYKgDePNuoLPv/1QMdaO6rCpiIjE4/HyefztNvtMKHR37+q5Z4iIiIyCvoSRS5XrVYL3/fDNsSe5xGLqQHltBr3f5nXAS8BMnQTEC7wTmPMS4DfB/7WWlu/0MXGmFngBXS3mRhWExkB3S0t/3tHo1/1ZuAZxph/Bv4QeL+1tjmm5xbZlGg0SjKZnHQYIiIicpnSlyhyOfF9n1KpRK1WOy+hkUwmyWaza9oTy3QYa0LDWnvaGPNjwLtZTUQ4wFN6t7Yx5hhwDCgCNbrdUPLADazWrujXy+hfvwzcYq1t7/RrMMZcATyDboeYpwFPBR5Hd0uNiIiIiMieoi9RZLerVqsUCgWKxSK+75NIJIhEIjSbTUqlEp7nUa/XmZubW7fwvkzO2NfOWGvfY4zZB7yld6pf/8LpxXMD8PB1Lh3sKjJ4zVngO621d+1AuOt5Et1kRj+Zcru1VskMERERERGRXcb3fQqFAoVCAc/zOHDgAI6z+tEzl8tRLpcpFArAaqcfmQ6RSTyptfb36a5uuJ9uUmBw68hgsqJ/Y+jx/vmPAI+21n5+XLEDVw/8HADjfG4REREREREZkVKpRLFYxPM8stnsmmQGgOM44XaTYrFIqVSaUKSynokkNACstbfRrYPxUuAzvdPDSQzWOd+h2+716dbaZ1prHxpLwKuKQ8f+mJ9fREREREREtqnVaoU1MzKZzIZjM5kMvu9Tq9VotVpjilAuZqLlWq21LeAdwDuMMQvAE4FH0a2VMQt4QB04B9wD3AF8wlq7PJmIgfNrZTxuIlGIiIiIiIjIJfN9P6yZMbwyY5jjOCQSifAadT6ZDlPzX8Faewr4695tallrbzfG/AvdWhoANxtjnmetneq4RUREREREZFWn0wm7mWxGJBIJr5HpMLEtJ7vcDwL9IqQO8C5jzE8YY6YmQSQiIiIiIiIXFolEwiTFZvSTH5tNgMjO0wfwS2CtPWGMuRl4O/B8wAXeCrzGGPNx4F+BrwIFui1lt7zJylp7fHQRi4iIiIiIyCDP8/A8j1KpRC6X23DbSRAE1Ot1MpmMupxMkalKaBhjrgJuBhbo1tCYAW6z1n54YMzNwL3W2rOTiRKMMR8aOFwC9tFdqbEAvLB3246AKftvIyIiIiIicjmJxWIkk0k8z6NcLpPNZi84tlwu43keyWRS9TOmyMT/S/SSGK8EbgEOrzOk39Wk75eBpxtj/gb4ZWut3fkoz/PdrLaXZejnjavJiIiIiIiIyFTIZrPU63UKhQLQ7WYyuFIjCALK5TK+7zM3N7dh0kPGb2IJDWNMHHgj8BNAlPUTAcE6566hG/ctwLONMf/NWvs7OxXnJVgv5q1QQkREREREdo12u02j0SAIAhzHwXVdotHopMMS2RTP85ibmwOgWCxy5swZEolEWFujXq+HY+bm5rTdZMpMJKFhjDkMfAh4NKsf4IcTARf6YH91b6wDxIH/aYy52lr7MzsR6waUeBARERGRPavZbFKpVKhWq1QqFdrtNtFolHQ6TSqVIp1OE4/HJx2myEWlUimi0SiJRIJSqUSpVKLVahGLxcjn82SzWbLZrJIZU2jsCQ1jTA74e+ARvVP95EQ/QVAC1l3HY4zZB6QGrqN33SuMMcestW/dkaDPd3RMzyMiIiIiMnXq9Tpnz55laWmJSqWyZon+4uIi6XSaffv2sX//fhKJxAQjFZHL2SRWaLyTbjJjMCHxOeAPgI9aax80xnRYZ+uGtXbJGPMY4BeB5/bG9BMirzfGfNBa++BOvwBr7dd2+jlERERERKZRs9lkcXGREydO0Gq1iEajeJ6H4zgEQYDv+5RKJWq1GkEQsLCwoJUaMtWq1SqFQoFisYjv+yQSCTzPo9PpsLy8TK1Wo16vMzc3RyqVuviEMjZjTWgYY54MPJvVJEQA/NxWamBYa78I3GKM+V7gXayu2EjTLS766pEGLSIiIiIioeXlZU6dOoXv++RyORKJxJoVGslkknq9TrFY5NSpUyQSCebn5ycYsciF+b5PoVCgUCjgeR4HDhxY837O5XKUy+WwaGg/gSfTITLm5/vvvft+MuOSC3paaz9Itz1qfy4H+KFRBCkiIiIiIudrt9ssLy9TKpXIZrMkk8k1H/4AHMchmUySzWYplUosLy/TbrcnFLHIxkqlEsViEc/zyGaztNttqtUq5XKZarVKu90O62cUi0VKpdKkQ5YBY0toGGMywFNY3Upyh7X2DduZ01r7YbrFRfu/RfcbY27czpwiIiIiIrK+Wq1GuVwOkxYb6Sc7yuUytVptTBGKbF6r1aJWq+H7PvF4nEKhwH333ccdd9zB7bffzh133MF9991HoVAgHo/j+z61Wo1WqzXp0KVnnFtOHg+4rNa9+MMRzXsr8KyB40cBd45o7i0xxqSApwJPBm4G5oF9QAKoAkXgPsACnwD+yVqrFJ+IiIiI7Aq+74cf/oZXZgxzHCf8EOj7PplMZkxRimxO/70ZBAF33303d999N6dOnTqva8/CwgLXX389+/fvD6+JxSbSMFSGjPO/wuHefX+LyCdGNO8Xevf9lR9j36BnjMnTrd/xCmB24KHB3/JZ4CBwPfB04KeBqjHmT4BfsdaeHVO4IiIiIiLbcrFkxlbHiUxCp9OhXq9z7Ngx7rzzTk6fPk25XF5TI+P06dOcOXOG06dPc+ONN3LTTTfR6XQmGLUMGmcNjQNDx6dHNO/K0HF6RPNuijHmZuDfgdcAc6xtQRusc2NgTBp4OfBVY8wzxxi2iIiIiMiWeZ6H53k0Gg2C4LymhGsEQUCj0QivEZk2kUiEEydO8IUvfIFjx45Rq9XIZDLhe9bzPDKZDLVajWPHjvGFL3yBEydOEImMuxSlXMg4/0tUh45HlXiYGzoeTnDsmF6nlU8AV7K68mSwleyFbsPj5oAPGmN+YFyxi4iIiIhsVTKZJJPJEARBWBej0+mEtQV83w+/ve63bc1kMhettyEyCdFolHvvvZe77rqLTqdzwfdpMpmk0+lw1113ce+99xKNRsccqVzIOLecDK/IuBF4cATzPrZ3308ULI5gzosyxjyKbttYj7XJCYD7gX+jW8tjmW4yJ0t3O8oj6dbXuKI3tp/ajgLvMMYcs9Z+bgwvQURERERkS6LRKPl8nuXlZZaXl6lUKkQiEVqtFp1Oh0gkQiwWo9Pp0Gw2yWaz5PN5fQCUqbSyssKJEycolUokk0mCIKDdblOv18P3cyKRIBqNEolEKJVKnDhxgpWVFRKJxKTDF8ab0LC9+/4H+O8D/nEE875w6PgrI5hzQ8aYCPAXdFeZDG4j+Sfg1621/3cTczyNbhvbp7KaEHHpJjUeZa3VxiwRERERmTr5fJ58Ps/S0hJLS0tA9xvsaDRKu90OV270kxn5fH6S4Ypc0MmTJykUCsRiMRqNBidPngyLhAZBgOM4OI6D53m4rkssFqNQKHDy5EkOHjw46fCFMW45sdb+O/BQ79ABftgYc+125jTGPAF4NqtJhVPW2i9tZ85N+iHgBtauyniVtfbbN5PMALDWfsxa+23Aq1lbPPRGzk/SiIiIiIhMjUQiQTqdJpfLkU6nwxUY/a4Q/fP6FlumWalUolar0Ww2KRaLLC0tUSgUwlbD5XKZQqHA0tISxWKRZrNJrVajVFKjymkx7l4z76PbCSSgu1XjfcaYb7XWLm91ol4y5K9ZTSoEwAdGGOtGXt677z/vL1tr33QpE1lr32iMSQC/xupr+Ungz0cRqIiIiIjIKFUqFVqtFocOHcJxHKrVKrVaLWxzmUwmSaVSBEFAq9WiUqlolYZMpVgsRr1eZ2VlhVQqRSwWw3XdNVuk2u122A2lWq1Sr9fVsnWKjLs86+uAcu/nAHgU8BljzDdvZRJjzAuAz9Ftg9rXAF4/iiAv8tz7gW9gdVXIl+i+ru14PfAfA8ffZIwZe/tZEREREZGNtNttfN+n0WgwMzNDLpdjfn6eQ4cOcfjwYQ4dOsT8/Dy5XI6ZmRkajQa+79Nutycdush55ubmwtUZALOzs+TzeVKpFIlEglQqRT6fZ3Z2FiBcpTE3N9yXQiZlrKkla+0ZY8xrgDexmhAwwCeNMZ8EPki3kOYgxxhzAHgY3XoT3w88nNXVEf37N1prR1Fk9GIex9pOJW+z1m7cs+oirLUdY8zbgLf2TjnANwJ/t515RURERERGqdFo0Gg0cF03PBeNRi9Y9NN13fAadTqRaROPx3FdlyAIaDab+L6P4zh0Op2whkYkEgkfD4IA13WJx+OTDl16xr5Wxlr7u8YYA/w4awtqPrF3Y+AcwM/2bsPnB5MIH7bW/tIOhLuehYE4AuCfRzTvbb37/uvaVn0REREREZFR6xdLjEQ2t9C7/2EwCLb1/Z/IjrnppptwnO5HzH43k0gkguM4BEFAp9Oh0+kwNzfHtddey4033jjhiGXQpDb//BRQBF7VOx5MbAwbPhcMPfYe4EdHGt3G9g8dnxrRvMPzZEc0r4iIiIjISPS7Pmx2C0mn0yEWi4UfGGVvWlxcnHQI6zp37hy5XI7Dhw+H9TFarVaYgOvfu64bbkHJ5XKcO3duKl/T/Pzeq1owkYRGryXpzxtjPg68kW5nD1ibrLiQ/m/D03SLcb59B0LcSHXoOAucG8G8wwmM8rqjREREREQmxHVdXNelWl39k7jdbofL8R3HIR6Ph1tQGo0GqVRqzRYV2ZuazeakQzhPs9nkzJkzHD9+nEajQalUol6vnzcukUiQzWZxXRfP82g2m1P3evbqNpiJlme11v4D8A/GmO8AXkx3y8mRDS4pAp+h293kz6y1jZ2P8jzDqbibgOMjmPem3n1/K8v0pfxEREREZE+LRqN4nofruqysrOA4Dr7v02w26XQ6RCIR4vE4nueF9QY8z7tgjQ2RScpkMsTjcXzfx/M89u/fH3bn6b+f+yuMgiDA933i8TiZTGbSoUvPVPSbsdZ+BPgIgDHmSrpJjVkgD9SAAnCmO9R2JhVnT78bSX81yS3A349g3hcMHdsRzCkiIiIiMlLpdJpSqcTp06dptVphkqO/FaVardJut4nFYhw+fJh0Oj3pkEXW1W/R2q+dMSi8hEwAACAASURBVDc3R6fTod1uhyuO+o8VCoVwrFYcTY+pSGgM6nUqGUe3kktirf2yMeYssI/uaooXGWN+x1r7n5c6pzHmUcAPstq1ZdFae8dIAhYREREREZHzNJtNkskk6XSaaDRKs9kkFosRjUbDhIbjODSbTRKJBPF4nGQyOXXbTfayqUto7BLvAn6GbgIiBnzAGPNEa+3JrU5kjDkEfADor8MLesciIiIiIlOnUqnQarXYt28fkUhkzZaTaDTKzMwMnufR6XRotVpUKhXy+fykwxY5T61WI5FIcOTIEarVKsvLy5TL5TVFbIMgwPM88vk8qVSKRCJBrVZjdnZ2gpFLnxIal+ZNdDu1xOgmII4CtxtjXmat/bvNTmKM+W7gbcAhVldnNIDfGHnEIiIiIiLb1G638X2fRqPB3NxceO5CRUELhQK+79Nut1VHQ6ZOJBLB8zySySTJZJJOp0OpVKLT6YTv50gkQjabZd++fQB4nrfptsWy80aS0DDGPGkU84yCtfZfxvAcDxpjXgO8nm4iIqCblPg/xpgv0V3B8Vngy8CytbZljInRrQtyI/BNwIvoFgLtFwHt3/9Pa+0oioyKiIiIiIxUo9Gg0WisqSHQryuwHtd1w2uSyeS4whTZlH7B2iAIiEajHD58mE6nQ61WC4uCJpNJIpEIrVYrTMx5njfp0KVnVCs0bmNzLVd3Wn8LyDj8Nt2uLN/FalLDAR4J/ObgQGNMa524+uuYBv/dPmyt/R87Eq2IiIiIyDYFQUAQBJv+hjoSiYTXiEybdDpNIpGgXq+Ty+XCWhqJRCIck0gkaLfblMtlKpUKiURChW6nyKjXyjhTcBsLa20APBv4s4HnHUxsDN7i65zrj6V3/JfAC8cUvoiIiIjIlvWLJHY6m2s82Ol0wmtEpk00GiWdTpPJZAiCgEqlwunTpymVSuHt9OnTVCoVgiAgk8mESQ+ZDqNOaAQTvI2dtbZlrX0J8GN0O7MMJys2uvXHngJ+3Fr7A9ba6thfhIiIiIjIJrmuG24j2Yz+9hS1uZRp1Gg0yGQy7N+/n2azSalUWndcqVSi2Wyyf/9+MpnMpt//svMupxUaE2Ot/SPgeuClwPuBc2wc6zLwt8APA9dYa/9wAmGLiIiIiGxJv36A67pUqxt/F1etVnFdN6xTIDJtgiAgFouFqzT6tTFisRjxeJxYrFs1wPO8cHVGLBbTFqopMup6E4NbKFrAx+lupfgIcFk367XWNoF3AO8wxjjAdcA8sA/IAWW6iY5T1tpjEwtURERERGQb0uk0jUaDlZUVAFKp1HljqtUqjUaDmZkZ1RuQqeU4DvV6nUgkwpVXXonv+5TLZer1ejhmdnY2THa0223q9bq2UE2RUSU0qkD/N1l/S0UUeHrvtgL8Dd3kxsettZvbdLcLGWOS1toacHfvtt6Y59P9N/mUtbY8zvhERERERLYjHo+Ty+UAqFQqFAoFXNclEonQ6XTCbSYzMzPkcjni8fiEIxZZX7/DSbVa5aqrrsJxHJrNJrVaLexokkwmicfjBEHA8ePHmZ2d1YqjKTKqhMY88N3AC4DvBPo9mfq1IvJ0t1f8MHDWGPM+4D3W2k+M6PknyhhzE/BK4HuAtwO/fJFL/hvwKKBmjHk33VatdmejFBEREREZjUQiQTQaxXVdfN+n0WiEy/dTqRSe55FOp5XMkKnWbrdxHIdUKkW9Xg+TF+u9b+v1OqlUCsdxaLfbE4hW1jOShEZvRcJ7gfcaY9LAs4DnA98BDDbpdegmP34c+HFjzKnedX9prf3XUcQyTsaYBPBG4Cd6pxzg4Zu49Nre2BTwI8CLjDGvtda+fkcCFREREREZsXg8Tj6fp91uhwkNx3FwXVffYMuuEARB2Ia12exWSEgkEmu2lARBQL1ep9lshm1eVUNjeoy6hgbW2grwbuDdxpgs8L10V248HRgsb+wAh4BXAK8wxhwH/oruyo0vjDquUTPGpOjWBnkCa9u2bpjQMMbM0q2pMVhvxAN+3RhzrbX2ZTsTsYiIiIjI6PWX5YvsNv0EXCqVCreeLC8v43kejuMQBAG+7+O6LtlsNhyvGhrTY9RdTtaw1paste+y1n4PcJDuaoSPAMNrdBzgauBVwOeMMXcZY17b28oxrd4JPJG1bVodut1ONpIC/plukdDha3/EGPNLOxWwiIiIiIiIdPVbCjuOQy6XI5/PMzMzE64wikajzMzMkM/nyeVyYUJDbYinx44mNAZZa1estbdaa59JN7nxMuCjwHCB0H5S4BeB/zDGfMkY84vGmIeNK9aLMcY8E7iFtcmIs3RrY2wYp7X2hLX2acAB4EXAfawmNhzgNcaYR+xc9CIiIiIiIjLYhrjVapHL5Zibm2Nubo59+/aFP+dyOVqtltoQT6GRbznZDGvtOeCPgT82xuwHnkt3W8qTWN2+Qe/nG4HXAq81xnyRbqeUv7LWfm28Ua/xs0PHnwa+z1p7drMTWGt94C+MMR8CPgh8K6vdYX4eePGIYhUREREREZF1rNeGeDhhoTbE02tsKzQuxFp71lr7NmvtU4Er6NbU+L/rDHWARwO/CdxrjPmMMeaVxpgrxhguxpg54Kmsrqh4CPiurSQzBvXatj4PONU75QDPM8Z4F75KREREREREtqvfhnhmZoZOp0OhUKBcLlOtVimXyxQKBTqdjtoQT6mJJzQGWWvPWGvfaq19MnAl8DPAZwaGOAP3NwNvAo4bY24zxry8t9pjp33TwM8B3ZarK9uZsLdi5S2svj4XePx25hQREREREZGLSyQSzM7OMjs7Sz6fJxaL4TgOsViMfD4fPpZIJCYdqgyZqoTGIGvtSWvtm621T2C1YOi/DQxxercI8C3A7wEnxhDa1QPPD/API5r373r3/e4nFysuKiIiIiIiIiPQb0M8OztLJpMhmUySyWTCJIdWZkynidTQ2Cpr7YN0V2O8yRhzJas1Nx7H2van43g9s0PHJ0c07wNDx3MjmldEREREREQ20Gw2qVQqVKtVKpUK7XabaDRKrVYjlUqRTqeV1JhCuyKh0WeMSQLfCDwWMBMKozF0PAtsa8tJz/D6pfoI5hQREREREZEN1Ot1zp49y9LSEpVKBcdZ7VOxuLhIOp1m37597N+/X9tOpszUJzSMMRngWXRXZXw7kBx4OFj3op11euj4EcD9I5j363r3/RauZ0Ywp4iIiIiIiFxAs9lkcXGREydO0Gq1wlaujuMQBAG+71MqlajVagRBwMLCglZqTJGpTGgYY3LA99Lt/vF0oN/xo58qC4aOG8BHgb8aQ3j/MRTDC4APj2De5w4d3zWCOUVEREREROQClpeXOXXqFL7vk8vlSCQSa1ZoJJNJ6vU6xWKRU6dOkUgkmJ+fn2DEMmhqEhrGmDzwfXSTGN8G9NNeg0mMfqtUh9UkxnuBD2y308gW/CfdFqsHe3F8vzHmf1lrb7/UCY0xDwNeyurrO2ut/fwoghUREREREZHztdttlpeXKZVK5PN5ksnkeWMcxyGZTBIEAcvLyywvLzM3N0c0Gp1AxDJsogkNY8wc8GzgFuBbB+K5UBKjyepKjHEmMULW2sAY827gv/ZiiwIfNMZ8i7X23q3OZ4xZoLvCo78KJQD+ZlTxioiIiIiIyPlqtRrlcjlMWmwkmUyysrJCuVymVquRyWTGFKVsZOwJDWPMPPAcuisxnkw3IQAXT2K8F3j/JJIY63gT8JOASzfWQ8Adxpj/DrzTWlu72ATGmDjwQuANwDyrr7kO/PoOxS0iIiIiIiKA7/v4vo/rumu2mazHcRxc1w2vUUJjOowloWGMOUi3RsTzgG8BIr2HNkpifIzVlRjL44hzs6y1J4wxvwK8ntXYs8BbgNcbYz4CfB44BhSBKt1ipjngeuD/A74DyLNaBLR//6vW2hPjfD0iIiIiIiJ7VRBsrtfEZsfJ+OxYQsMYcwWrSYwnsJq8uFgSo78SY6qSGOv4beDRdIuCDr6WLN3X/LyLXD9c4BS6qzt+e8RxioiIiIiIyBDP8/A8L+xgstEqjSAIaDabpNNpPM+74DgZr5EmNIwxR+gmMW4BvonNJTE+zupKjHOjjGcn9WppvIhugdBXsvra+jZes7S2U0sHeB3wKyMOU0RERERERNaRTCbJZDKcO3eOWq1GKpW64Nh+0iOTyVy03oaMz0gSGsaYn6ObxPjGgdMXSmK0WLsSY9ckMYZZa9vAfzXGvI9uQuJbBh7eaD1S/98CugmdX7HWfnJnohQREREREZFh0WiUfD4fdjrp18lotVp0Oh0ikQixWIxGo0GpVCKbzZLP59XhZIqMaoXGG1hNWvQNJzH6KzF2dRJjPb1kxJN77VefAzwJeCRwmPP/TZaA24FP012V8qUxhysiIiIiIiJAPp9nYWGBr33ta5w5c4ZOp3PemEgkQjqdZmFhgXw+P4Eo5UJGXUNjcBtFP4mx61dibJa19hjwW71bv5NJjm5B0DKwYq1VJRkREREREZEpEI/HyWazZLNZSqUSpVKJeDwePt5sNsPHs9nsmsdk8naiKGi/W0cBuBp4FfAqY8wOPNV5AmvtTeN4os2w1jbprsgQERERERGRKdNsNmk0GsTjca688kqCIKBer9Nut4lGoyQSCRzHIRqN0mg0aDabSmpMkVEnNAaLgB7o3QbP7zStfhAREREREZFNqVQqVCoV0uk0qVSKdrtNs9kMu57E43Gi0SjVapVKpYLrutp2MkV2asvJZs+P0riSJiIiIiIiIrLLtdttfN+n0WgwNzcHdAuFrlf0M5VKUSgU8H0/XL0hkzfKhIYSCiIiIiIiIrIrNBoNGo0GrutuarzruuE1at06HUaV0PjVEc0jIiIiIiIisuOCICAIAiKRyKbGRyKR8BqZDiNJaFhrldAQERERERGRXcNxHBzHod1ub2p8p9MhFovhONqcMC02l4oSERERERERuYy4rhtuI9mM/vaUzW5RkZ2nhIaIiIiIiIjsOdFoFM/zcF2XarW64dhqtYrruniep4KgU0QJDREREREREdmT0uk06XSaRqNxwaRGtVql0WiEY2V6jLptq4iIiIiIiMiuEI/HyeVyAFQqFQqFAq7rEolE6HQ64TaTmZkZcrkc8Xh8whHLICU0REREREREZM9KJBJEo1Fc18X3fRqNBkEQEIvFSKVSeJ5HOp1WMmMKKaEhIiIiIiIie1o8Hiefz9Nut8OEhuM4uK6rmhlTTAkNEREREREREbqFQpPJ5KTDkE1SUVARERERERER2XWU0BARERERERGRXUcJDRERERERERHZdZTQEBEREREREZFdRwkNEREREREREdl1lNAQERERERERkV1HCQ0RERERERER2XWU0BARERERERGRXUcJDRERERERERHZdZTQEBEREREREZFdRwkNEREREREREdl1lNAQERERERERkV1HCQ0RERERERER2XWU0BARERERERGRXSc26QBkcowxceC5wHOAbwAOAA5wArgHeC/w19ba4sSCFBEREREREVmHVmjsUcaYJwJ3Au8GbgGOAmkgBTwM+A7gj4H7jTHPn1ScIiIiIiIiIutRQmMPMsY8B/gYcP0mhs8C7zHG/PrORiUiIiIiIiKyeUpo7DHGmMcAfw64A6f/E/hx4GbgMcBLgE8PXfqLxpiXjCVIERERERERkYtQDY09xBgTBW4FEgOnbwV+zFrbHDh3hzHmz4BfAAZXZrzFGPO31tqzOx2riIiIiIiIyEa0QmNveTHwyIHjTwIvHUpmAGCtDay1rwPeOHA6C/zizoYoIiIiIiIicnFKaOwtPz10/Cprbfsi1/wScHLg+GXGmORowxIRERERERHZGiU09ghjzHXAYwdOfcla+28Xu85aWwf+ZOBUGviuEYcnIiIiIiIisiVKaOwdzxg6/vstXPsPQ8fP3mYsIiIiIiIiItuihMbe8Q1DxxddnTHgdiAYOH789sMRERERERERuXRKaOwdNw0d37nZC621ZeChgVPXGGPSI4lKRERERERE5BIoobF3XD10/MAWr39w6PiaSw9FREREREREZHuU0NgDjDERYH7gVKW36mIrzgwdz687SkRERERERGQMlNDYG3JAdOC4eAlzlIaOZy89HBEREREREZHtiU06ABkLb+i4dglz+BeZ8zzGmP8C/JdNzv/NAF/5yld48YtfvKXAxqXRaEw6hD3Ddd1Jh7An6D09Hno/j4fez+Oh9/P46D09HnpPj4fez+OxG97Pn/3sZ28Dvmit/ZlRzKeExt4w/M5uXcIczaHj+CauuQZ48laepFQq8dnPfnYrl4iIiIiIiMjusKXPhxejhMbeEAwdO5cwR3TouL2Ja+4HPrHJ+R/be44CcPfmw5KLeDQwA6wAX5xwLCLbpfezXE70fpbLjd7TcjnR+3lnjezfVAmNvWF4jddmVlcMG36v1C92gbX2VuDWS3guGRFjzG10s6BftNY+ZbLRiGyP3s9yOdH7WS43ek/L5UTv591DRUH3huEioOlLmCMzdFy9xFhEREREREREtk0JjT3AWlsHKgOn8saYrW47yQ8dn95eVCIiIiIiIiKXTgmNveOBgZ/jwNwWr18YOj61vXBERERERERELp0SGnvHcKHNazd7YW81x9GBU0Vr7cmRRCUiIiIiIiJyCZTQ2Du+MHT8yC1cex2QGjj+z+2HIyIiIiIiInLplNDYOz45dPykLVw7PPa27YUiIiIiIiIisj1KaOwd/wKUB46/xxiT2OS1twwd/91oQhIRERERERG5NEpo7BHWWh94z8CpOeDlF7vOGHMz8O0Dp75irf30iMMTERERERER2RIlNPaWNwHtgePXG2MuuPXEGHMIeC8w2OL1t3YoNhEREREREZFNi006ABkfa+2dxpjfB17RO+UBHzHG/BzwR9baZn+sMebpwB8DRwam+Ffgz8YVr4zErXRrntw/0ShERuNW9H6Wy8et6P0sl5db0XtaLh+3ovfzruAEQTDpGGSMjDFJ4B+Abxl6aBH4POADNwHXDz1+GrjZWnt8x4MUERERERERuQglNPYgY0wa+BvgGZu85F7gO621d+1cVCIiIiIiIiKbp4TGHmaMeRHwC8ANFxiyBLwN+A1rbWVsgYmIiIiIiIj8v/buO0q2qk70+PdyyQgiCIKogAg/RAQRBQURZkwYMeADAy5EMIyRp6OijgpjGkV56pgx8MSEzOjSQQeFhxhAEURFkB8oQRyVrGSEe+/741TD6d3V3XWqurtOVX0/a7FW7V3n7P3rW7ua6l/tMA8TGiIitgMeAWxGta/GdcB5wNmZ+fdhxiZJkiRJUjcmNCRJkiRJ0sjx2FZJkiRJkjRyPLZVaoGIWAN4DvBsquU/mwLLgP8Bfg98HTgxM29YonjuBRwMPAnYCdgIuKUTz3nAl4CTM/POpYhHw+X4vKvfnalOg1oOHJeZBy9k+1oakzKeI+LxwPcHCO3dmfm2Ae5XS7XtPTCbiDiRKk6ArTPzsiGGo5Zq23iOiD2BZwF7AFtS/U5fCVwD/A44HfhKZl68FPFMApecSEMWEY8BPs/Mo3JL1wMvz8wTFjmew4APAuvPc+kFwAsy85eLGY+Gy/F5V79rA2cCD+tUmdAYQZM0niPi9cDRA4RnQmMMte09MJuIOAD4aq3KhIZmaNN4joidgM8Au/Vw+Uqq8f3qzLxusWKaFC45kYYoIp4NnMr8v4gB7gV8LSLetYjxHA18mvk/XAPsAPwsIp66WPFouByfd/W7GnAcdyczNIImcDzv3FdgGlttew/MJiJ2Az671P1qtLRpPEfEgcBZ9JbMgOpv8OcDv4iI7RcjpkniDA1pSCLi4cBPgLVr1ecB/w6cC9wJPBR4GdW0tbqDM/O4BY7nlZ2+604CvgBcBKwHPBp4NbBV7ZobgT0y8zcLGY+Gy/F5V7/Lgc8BLyqecobGCJnE8RwRv6b6mQAuBZ7YMMzr/OZwfLTtPTBHnLsD36Gapl/nDA3dpU3jOSIeB3wXWKNWfT3VbI1TqZa+rAlsS7U0Zn+mTyq4FNg9M69eqJgmjQkNaQg6fySdy90fNqH6IPvSzLyjuHYZ8BagnlW+EXhgZl6zQPE8AEju/h/DKqqpeZ/ucu09gC8Cz6xVn5mZ5f8wNKIcn3e1tQlwPN3/EDShMSImcTxHxJrATdz9AfsbmfnsQWPXaGrbe2COOF8AfIoqoVcyoSGgXeM5ItYCzge2qVWfAhwwW0I4IvYAvkG118cUP1MMwCUn0nAcxPRfxD8GDi1/EQNk5qrMfDfVOusp6wNvXcB4jmJ6lvt93T5cd+K5CTgQOKNW/eiI2G8B49FwTfz4jIi9gV/R/Ftttc8kjucdmP5t4a/7CVRjo23vgWkiYp2I+AxVArlbMkOqa9N4PojpyYzzgP3mmt2WmWcATwdW1NuJiAcuUEwTx4SGNByvKspvyMwVXa+829uAP9fKh0XEOoMGEhEbU31gnnIt0zPZM2Tm7cAriurXDhqLWmNix2dEbB4Rn6KaJrp57an5fn611ySO53L/jPN6ClDjqjXvgVJE7E+VPD60eMrfuZpNm8bz84vyEZl5y3w3ZeZZVCdYTVkN8IvBPpnQkJZYRGwD7Fqr+k1m/my++zLzNqqNCaesByzEhpzPBNaqlb/S4y/jXwM/rVU9NiLuswDxaIgmeXxGxLupjlR7KdXRrFN+zcwP2xoBEzyeyw1sTWhMqBa+B6bi2iUifkZ1pOa2tadWAG+k+tZdmqZN4zkiVgf2rFXdCJzcoImTivIug8QzyUxoSEuvnML+3Qb3lr8onzVgLLBw8SwHnjF4OBqySR6fbwHWrZVXAh+m2pzxsgb9qj0mdTzXZ2jcSpWo02Rq23tgyn7MPBHiIuAfM/MDC9iPxkubxvO2VJt9TvltZt7Z4P6/FOVNu16leZnQkJbeI4ryvJnlmrOpNpCbshAbcQ4Sz1lF2Y1BR5/js3IG8KjMfF0v36CrtSZ1PNcTGudn5soG/Wi8tO090M1NwJHAzpn5w0XqQ+OhTeP5ImBrqlkazwXe3fD+zYvyjQPGM7FMaEhL7yFF+YJeb+xsEPenWtVWEdH3BloRsS7VL+MpV2XmtQ2auKgolz+bRs+kj8+zgGdk5p6Z+fMGfamdJm48R8T9mX7k5XnF88sjYtOI2KJzGorGW2veA13cCBwDbJOZ7+wsC5Dm0prxnJkrMvOyzDwjM0/MzG81bKKcbXJ5v7FMutWHHYA0gbYsylc0vP+PwBa18lZUR0b14/7Aslr5D33EUrd116s0SiZ5fO5mEmPsTOJ4LjcE/XVEbAS8CHgO1Tecd52yEhGXAv8NfCYzz20Yk9qvTe+Buq8CH8xMv5VWE20dz41ExKbA84rqJvtvqMYZGtISiojVgE1qVTd3MsZNXFWUN+l6VW82K8pXNrm5821K/cPIxp2fUSNo0senyYzxMsHjuUxo7A1cSvVN+GOYfmQsVImRVwDnRMSXImKDJnGpvVr4HrhLZl5oMkNNtHk89+EYoH7KypXAaUOKZeT5h4e0tDZg+ukJN/TRRvkB4F79hzPj3kHjWQbcs/9wNGSOT42TSR3PZULjmVT/FvNZRnUE4VkRsVUfsal92vYekAYxFuM5Ig5h5nGv7224oahqTGhIS2utonxrH23cPk+bTbQtHg1X28ZD2+LRaGnb+FmqeMojWwGuBz4E7E71jeQ6VDMzXsTMTfUCOMmZGmOhbe8BaRAjP54jYl/gk0X1z4GPLWUc48Y9NKSlVW7A1k829o6ivEafsUD74tFwtW08tC0ejZa2jZ9Fj6ezwd02xTWnAgdl5p+L+ss6/30xIg4HjubuL7p2oEqAHNpHjGqPtr0HpEGM9HiOiCcB3yj6vBY4wNkZg3GGhrS0VhXlZV2vmtvyoryiz1igffFouNo2HtoWj0ZL28bPUsRzD+DTVJt8Xgj8AHhal2TGNJl5DPDWovrgiIg+YlR7tO09IA1iZMdzRBwAfIvpexjdCjwzMy9dihjGmTM0pKX196LcT2a4fN8OcsxZ2+LRcLVtPLQtHo2Wto2fRY8nM6+k2uCzHx8AXgxs1ykvBw4EjuyzPQ1f294D0iBGcjxHxOuoZrzVEzBTyYwfL3b/k8AZGtLSKjcw6uf863sU5Vv6jAXaF4+Gq23joW3xaLS0bfy0LZ5pMnMF8Pmi+okL1b6GotVjTmpopMZzRKwREZ+iOtGknsy4CXhqZn5vsfqeNCY0pCXUOXbv5lrVhhHRdMrchkW50dF/hWuKcqPdnjux1zeOu6HzM2oEOT41ThzPfTmzKG+1wO1rCbXwPSD1bZTGc0RsDHwPeGnx1FXAPpnpEa0LyISGtPSuqD1eA9io4f2bFeW/LFAs3dqez8ZMn/I3SCxqB8enxonjuZnyw/29F6EPLa02vQekQbV+PEfEDsBZwD7FUxcBe2TmOQvd56QzoSEtvd8V5Qf2emMnE711reqG+TZ7m0tnvXX9TO6tG2a7y930L+w3FrWG41PjxPHcTLm+3OUFo6817wFpAbR6PEfEPsAZXeL6EfDozPz9QvanigkNaen9oig/tMG92wDr1srnDR4O59Yer8f0X/bzKWNfiHg0XI5PjZOJGs8Rcd+IeFhEPCEinh8RTWdYlN9eXtXwfrVP294D0iBaO54j4hlUJ0zds3jqeODxmXndQvanu5nQkJZeuaPxYxvcW177g8FCAdoXj4arbeOhbfFotLRt/Cx2PN+mSpp8D/gS8KQG7QPsUZR/1vB+tU/b3gPSIFo5niPiacCJwFrFU0dm5kGZWZ7QogVkQkNaej+k2uF4ytMjYu3ZLi48tyh/ZwHiOWmePrqKiHWAp9aqbqSaUqfR5vjUOJm08XxuUX5mr4FFxGrAQUW1u/CPvra9B6RBtG48R8TOwAlM3+NoBfCSzHznQvShuZnQkJZYZt4OfK1WtRHwivnui4jdmP5t228z84wFCOlMgyNonwAAEjhJREFU4OJaed+I2LWH+17J9M2YvtT52TTCHJ8aJxM4nv+jKO8XEdv3GNurgAfVylcCX+/xXrVUC98DUt/aNp4jYr1OPOvUqu8A9s/Mzw3avnpjQkMajg9RZW+nvDciZp02FxGbU32wrG8g928LEUhmrgKOrlWtBpwYEfeZI55/AN5dq7qD6mfSeHB8apxM0ng+mWon/SlrAF+OiA1muX6qj6cXcQEcZRJwbLTmPSAtgDaN5/cAUdS9LDO/uUDtqwfLVq1aNewYpIkUER8BXl2ruhV4PXBsZt5Ru+4JwGeB+9eu/SmwZ2aunKXtdwLvqFVdnplbzRHL6lRHTO1Svwc4NDNPqV23JnAY8AGmZ6Pfn5lvmq19jR7H54wY9gHq58Yfl5kHD9Kmls4kjedOAuRUpn94vwh4Zb39zrUbA28GDgeW1546GXhyJwGjMdCm90CP8f4A2LtWtXVmXjZImxofbRjPnUTJJUB9ycvJVLPd+nFHZl7e570TrTyeS9LSeRPwMGCvTnkd4OPAkRFxDnA78BCmTwGGahrwAbP9Iu5HZt4ZEQdQrU2c2uV+S+D7EfE74HyqjY52BTYpbv8h8LaFikWt4fjUOJmY8ZyZp0XEa4GP1Kq367R/BfBLqp/3fsAjmPlZ8EdU06VNZoyX1rwHpAXQhvH8KqYnM6Ba1nJxl2t7cTmw1SABTSqXnEhDkpm3Ak9m5qZrmwD7Avsx8xfxJcBjM/MPixDPxcA+wKXFUw/qxLIvMz9cnww8tZ4N13hwfGqcTNp4zsyPUm3weUPx1P2BpwP7A49iZjLjeGDfzLwJjZW2vQekQbRkPD9tgdrRgExoSEOUmTdn5pOoPnj+do5Lr6Vap7dTZl40x3WDxpNUGe2jqLLYs14KHEo1JdkPvmPK8alxMmnjOTOP77R/DPDXOS69HfgusFfneMFbeu1Do6Vt7wFpEC0Yz9ssYFsagHtoSC0SEdtRTQHejGrK8XXAecDZS32GdecIv92pNju6D7ASuAo4G7jA6ciTx/GpcTJJ4zkilgM7AzsB96aamXEV8CfgJ5l54yDtazS16T0gDcrxPLlMaEiSJEmSpJHjkhNJkiRJkjRyTGhIkiRJkqSRY0JDkiRJkiSNHBMakiRJkiRp5JjQkCRJkiRJI8eEhiRJkiRJGjkmNCRJkiRJ0sgxoSFJkiRJkkaOCQ1JkiRJkjRyTGhIkiRJkqSRY0JDkiRJkiSNHBMakiRJkiRp5JjQkCRJkiRJI8eEhiRJkiRJGjkmNCRJkiRJ0shZfdgBSJIkLaaIuA9wPrAxcAuwbWb+abhRqS4iPgW8tFN8a2a+Z5jxSJJGw7JVq1YNOwZJkjQGImIf4LQ+bl0B3AHcDFwLXAGcBZwC/CQzbx8wrm8BT+8Uj8zMdw7S3ix9XAZsWat6cWZ+YaH7GVedpNPFwPpUY+GRmfmr4UYlSWo7l5xIkqRhWw6sTTWDYjvgccARwKnAJRHx8ojoa1ZpRBzC3cmMvwAfGDxcLbTMvBJ4f6e4BvDFiFhziCFJkkaACQ1JktRm9wU+AXw3Iu7R5MaI2AT4YK3qyMy8eSGD04L6EPDnzuOHAm8cYiySpBHgHhqSJGkxnUC1hGQuq1F9K78+sAXwSOCexTWPB/47IvbOzBU99v1eYMPO498Bx/Z4n4YgM2+JiH8FPt6pOiIijsvM+caPJGlCmdCQJEmL6ROZ+YMmN0TEOsCrgXcz/bPKnsDhwNE9tLEr8OJa1VGZeWeTODQUnwXeDDwAWJfqtT5gqBFJklrLJSeSJKlVMvPWzHw/8LwuTx8ZERv30My/cffnnEuBLy9UfFo8mfl3pies/ldEPGJY8UiS2s2EhiRJaqXMPBH4YlG9LvDCue6LiD2oNhad8rEGy1Q0fJ8DbqqV/2VYgUiS2s2EhiRJarMPdqnbd5573lF7fCdw/MKFo8XW2bj1hFrVMyLiYcOKR5LUXiY0JElSa2Xmr4BriuodZ7s+Ih4CPLFW9Z3OkaAaLZ8vyv97KFFIklrNTUElSVLbXQrcu1bedI5rX1qUyyUrjUTElsDuwP2oTmL5I3BBZp47SLtz9LcD8BhgY+B/gJ9k5u97vHc58HBge6p/r7WAq6iOQj0jM/+2wLEuA3YFdgE2Af7e6evszMxB2s7MH0fEJcADO1X7R8RrMvOvg7QrSRovJjQkSVLblftf3NHtoohYm+n7a9wJfL+fDiPiWcAbgUfN8vxvgPdl5pf6aHtVrXh+Zu4YEfek2jvi2V2uPwM4PDPPmqW97YF/BvYHNpil2zs77RyTmd/sMc59gNNqVcdl5sGd5w4E3gVsM8u9FwDHAJ/LzJW99NfFd4FXdh6vAxwEfLTPtiRJY8glJ5Ikqe3KGRlXz3LdvsBGtfJPm85KiIiNI+I7wH8ySzKjY0fg+Ig4sZNI6VtErAH8F12SGR17APfoct9aEfEh4ALgEGZPZkD1JdZjgW9ExBkRsVWfsa4WEZ8GvsIsyYyOHYDPAGdGxP366Qv4XlF+fp/tSJLGlAkNSZLUWhGxCbB1UT3bcoanF+WTG/Z1b+CHwJMb3PYc4KvA8iZ9Fd5FtcxkNpcwfaYEEbEB8CPgcGBZw/4eDZzVOQ2mqY8AhzW4fjfgp52lO02dRjXL5q62ImKu5UaSpAljQkOSJLXZQcz8g/2U8qLOfg5PLarP6LWTzv1fo5pZUHc18FbgYVT7RDwIeAHwk9o1+1HtsdGP+wGvr5XPBz4B/DtVcmUVcGxm3rVMJSLWpJpB8siirduAjwGPBx5ANbNlF+BNwGXFtZsAJ0XEXLMsSk/g7iUgAJcDrwMeTLVnx07AEVT7dtRtAXy7E3fPMvNG4Lxa1WrAU5q0IUkab+6hIUmSWikiHgC8pai+g+lHek7ZFrhPUXd+g+4OAf6xqPsh8OzMvLZWdw3w+4j4CtXJGx+g+QyJunvWHh8OfKS+50Rnk9Byic3bgccVdb/sxHppUX818MuI+CjVnhYvqz23IfAfEfHIzOy6L0nhvrXHXwcOycybanXXAudFxGeAE4F9as89lGpPknf10E/d+VRJmSl7AV9o2IYkaUw5Q0OSJLVOROxEtaHnxsVTX8jMP3S5pZytcF2vx7V2Tgd5a1F9AfCUIplxl8xclZkfZGbCpV9HZ+b/KTfQzMwLMvOuhEZnCc5ri3vPB/bpksyot3NrZr6cauZH3c5M30i1F98GnlckM+p9XUs1W+aXxVOvj4j1G/ZVJqXK11mSNMFMaEiSpKGKiNUjYqOI2DEiXhQR3wDOAbYrLr2cavlENw8vyk1mZzyOmft0/FNm3tzDve+ninUQf++004vXMH2D0JXACxtsfvoGZv7bvKmz5KYX1wKHZmZ58sw0mXkL8BKqJTNTNgSe22M/U35TlHeIiLUatiFJGlMuOZEkSYvptIhYiHZuAPbPzOtnef6BRXm2jUO7KU8X+W1mnt7LjZm5MiI+DPzfBv2VzqnPwphHuU/IdzKznAkxq8y8PSLeDxxXqw6qJSG/7qGJT2ZmuUfGbH39IiJOZ/rSk+dSHU/bqwuL8nJgS+CiBm1IksaUMzQkSVLbnQvsmZlnz3FNeYrGdQ3a36sof7PBvVBt0NnLHhSz+WkvF0XERlRLROqO76O/E4Dbi7q9e7z3uPkvmdFX3WM6S3x61e117OfEFEnSGDKhIUmS2uhmqr0angPsmpnl0oPSZkX5hl466Zy8sX1R/aueIuzoLE1pMiOkVM5CmM2uzPzsdlbTzjLzNmbub7FbD7dek5kXN+yuXI5zD2bOpplLt9exfK0lSRPKJSeSJGkxnQBcMc81K6hmDFwP/IkqOXB+jydvTFmvKPeU0KD6tr9MEjT9ox2qfSl27OM+6H02ySZF+da5NgKdxwXA7nO03U0vS1JK3ZaGbE2P/8aZeWdE3AqsU6suX2tJ0oQyoSFJkhbTJzLzB0vQz7pFudeExr261PV6b91se3v04q89Xlee+NLrfb30WbbdzTV99HMD1cag9U1HN2zYxt+YntAoX2tJ0oRyyYkkSRoHq4rynKdw1HQ7MePWPvrvJwky5c4er9ugKPdyCstsbinKa/dwz41NO+kcQ1vu17Fmw2ZWFuXytZYkTSgTGpIkaRzcVJR7/Rb/ti51/cwAWKOPe5oqf8ZBll6sX5TLBEc3TRMRRMTqzEyWNE3ElK9H+e8gSZpQJjQkSdI46Deh0W2pSNMlETBz9sRiKGPtJ84p5VKbXmaY9PMz3rNLXdOlK+sUZRMakiTAhIYkSRoPVxflXja5BLicmUs+HtxH/0txlOhfivI6EbF1n209tCj3srlok9NJpuzQpa7nTVcjYgNmLgsqX2tJ0oQyoSFJksbB74ry/Xu5qXOSyvlF9SP66P/hfdzT1M+ZuX/E7t0unEtErAc8pKju5djZB0dEOVtiPrsW5Ssys0zMzKXb69jPKTSSpDFkQkOSJI2DMqHxgAb3nlqU94+I5b3eHBF7ARs16K8vmXk91XGrdS/so6kDmbnnx+k93Lc68LSGfR1QlE9peH/5Ot7O/McAS5ImhAkNSZI0DspZFuUMhLl8uShvTrNEwWsaXDuoE4vykyNil15vjoi1gNcX1X8AftFjE69u0NcewKOK6uN7vb+jfB1/2zk5RZIkExqSJGks/Kgo3zsi7tfLjZl5DvCTovpDETHvLI+IeDawf28hLoiPMf1kltWAL0ZEt803uzmamXuEfLxBkmCviHjFfBd19r74bFH9G+C0HvuZUiZrftjwfknSGDOhIUmSRl5mXkG1wWddk30t/pnp+1NsBJweEd02tQQgIp4FfKlBHwPLzKuBY4rqhwCnzbVBaESsHREfA15VPPV74MMNw/hoRBw2R1/3Bb4PbF889ebMLPcAmU/5GvayNEaSNCFWH3YAkiRJC+RU4JBa+R+Ab/VyY2aeGRHvA46oVW8FnBsRnwS+RrVPx5rAzsBLgGfVrr0NWLvvyJt5O7AX8Jha3S7ABRFxLPCfVBtn3gZsATwB+CegTHjcChyYmbfRzHLg0xFxAPBJqs1Kb6Y66WU/qiU45YyRYzPzpCadRMRmTE+KrMCEhiSpxoSGJEkaFycwPaHx+Ib3vw24H3BQrW5Nqj/Q59on48fARUXfiyYz74yI51Ala+qnnKxNNQOjnIXRzY3AczLz7Ibdnw7s3Xn8uM5/8/kv4JUN+4GZr98pmXltH+1IksaUS04kSdK4OBW4ulbesbP8oSedfSQOBt5BNRugF2dR7aFxR6/9LITMvArYBzgWaLpJ5pnAbpn5/T66fhrVcpJerATeBzwrM//eR19PLMrl5q2SpAlnQkOSJI2FzLwT+GpR3WjDzsxcmZlHATsBXwdm+0P8KuAo4DGZeWXTWBdCZt6WmYdRLYH5KnDDHJevoEr4PBPYMzMv7LPPm4AnUc24uGyWy26nWqKzU2Ye0XldGomINYFn1KpuBr7RtB1J0nhbtmpV072ZJEmS2ikitgMuBJZ1qs7MzD0GaG9DYA9gW2A94ErgEuBH/fyhvpgiYnWqJShbA5tSLUH5G9V+Gj/PzOsbtrcPxakkmbms9vwyqn+bADYDbqJaenNGZs6VXOml7/2Ab9aqPpaZvSylkSRNEBMakiRprETEScBTOsVVwIMy85IhhjSS5ktoLHLfJwDP7RRXAdtn5kVL0bckaXS45ESSJI2bo2uPlwGvGFYgai4iNqdaGjPlWyYzJEndmNCQJEljJTNPY/rMgkMiYp1hxaPGXgqs0Xm8imqTVkmSZjChIUmSxtFba483YvpRrGqpiFgbeFmt6sTM/NWw4pEktZsJDUmSNHYy80zgxFrVERGxxmzXqzUOAzbvPL4deMsQY5EktZwJDUmSNK4Opzp5A2Ar4EXDC0XziYi1gDfVqt6fmb8bVjySpPYzoSFJksZSZv4ReHut6u2dJQ1qp1cCW3Qe/x54zxBjkSSNABMakiRpnH0Y+H+dxw8A3jDEWDSLiLg38C+d4grgoMy8bYghSZJGgAkNSZI0tjJzJdVSk2s7VW+OiPsOMSR196/AhlOPO3ugSJI0p2WrVq0adgySJEmSJEmNOENDkiRJkiSNHBMakiRJkiRp5JjQkCRJkiRJI8eEhiRJkiRJGjkmNCRJkiRJ0sgxoSFJkiRJkkaOCQ1JkiRJkjRyTGhIkiRJkqSRY0JDkiRJkiSNHBMakiRJkiRp5JjQkCRJkiRJI8eEhiRJkiRJGjkmNCRJkiRJ0sj5/9Yku3mmx43nAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 400, + "width": 538 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------------------------------------\n", + "# Est stats\n", + "model_names = [\"0.0\", \"0.01\", \"0.05\", \"0.1\", \"0.2\"]\n", + "models = [exp155, exp160_p01, exp160_p05, exp160_p1, exp160_p2]\n", + "medians = [\n", + " np.median(exp155[\"correct\"]), \n", + " np.median(exp160_p01[\"correct\"]),\n", + " np.median(exp160_p05[\"correct\"]),\n", + " np.median(exp160_p1[\"correct\"]),\n", + " np.median(exp160_p2[\"correct\"]),\n", + "]\n", + "\n", + "means = [\n", + " np.mean(exp155[\"correct\"]), \n", + " np.mean(exp160_p01[\"correct\"]),\n", + " np.mean(exp160_p05[\"correct\"]),\n", + " np.mean(exp160_p1[\"correct\"]),\n", + " np.mean(exp160_p2[\"correct\"]),\n", + "]\n", + "\n", + "# -------------------------------------------------\n", + "# Visualize \n", + "fig = plt.figure(figsize=(8, 6))\n", + "grid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\n", + "\n", + "# Mean\n", + "plt.subplot(grid[0, 0])\n", + "plt.bar(model_names, means, color=\"grey\", alpha=0.2, width=0.5)\n", + "for name, model in zip(model_names, models):\n", + " plt.scatter(x=np.repeat(name, 20), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", + "plt.ylim(0, 1.1)\n", + "plt.ylabel(\"Mean\\ncorrect\")\n", + "plt.xlabel(\"P(drop)\")\n", + "_ = sns.despine()\n", + "\n", + "# Median\n", + "plt.subplot(grid[1, 0])\n", + "plt.bar(model_names, medians, color=\"grey\", alpha=0.2, width=0.5)\n", + "for name, model in zip(model_names, models):\n", + " plt.scatter(x=np.repeat(name, 20), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", + "plt.ylim(0, 1.1)\n", + "plt.ylabel(\"Median\\ncorrect\")\n", + "plt.xlabel(\"P(drop)\")\n", + "_ = sns.despine()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From e52b4dfbb6499ff8243c5fc8edfde96b339ba3bf Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Wed, 4 Dec 2019 12:19:05 -0800 Subject: [PATCH 09/22] Tweak Janelia figs --- notebooks/figures_janelia.ipynb | 310 ++++++++++++++++++++++++++++++++ 1 file changed, 310 insertions(+) create mode 100644 notebooks/figures_janelia.ipynb diff --git a/notebooks/figures_janelia.ipynb b/notebooks/figures_janelia.ipynb new file mode 100644 index 0000000..6b30388 --- /dev/null +++ b/notebooks/figures_janelia.ipynb @@ -0,0 +1,310 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "\n", + "from IPython.display import Image\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "import seaborn as sns\n", + "sns.set(font_scale=1.5)\n", + "sns.set_style('ticks')\n", + "\n", + "matplotlib.rcParams.update({'font.size': 16})\n", + "matplotlib.rc('axes', titlesize=16)\n", + "\n", + "import torch\n", + "import glob\n", + "from collections import defaultdict" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def get_data(files, *keys):\n", + " \"\"\"Get data keys from saved digit exps.\"\"\"\n", + " data = defaultdict(list)\n", + " for f in files:\n", + " d = torch.load(f)\n", + " for k in keys:\n", + " data[k].append(d[k]) \n", + " \n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Load digits (VAE online)\n", + "exp151_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp151_*\") \n", + "exp151 = get_data(exp151_files, \"correct\")\n", + "\n", + "exp152_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp152_*\") \n", + "exp152 = get_data(exp152_files, \"correct\")\n", + "\n", + "# Load fashion\n", + "exp1_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp1*\") \n", + "exp1 = get_data(exp1_files, \"correct\")\n", + "\n", + "exp2_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp2_*\") \n", + "exp2 = get_data(exp2_files, \"correct\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Load leak controls (digits)\n", + "exp155_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\") \n", + "exp155 = get_data(exp155_files, \"correct\")\n", + "\n", + "exp157_s01_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s01_*\") \n", + "exp157_s01 = get_data(exp157_s01_files, \"correct\")\n", + "\n", + "exp157_s02_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s02_*\") \n", + "exp157_s02 = get_data(exp157_s02_files, \"correct\")\n", + "\n", + "exp158_s03_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp158_s03_*\") \n", + "exp158_s03 = get_data(exp158_s03_files, \"correct\")\n", + "\n", + "# exp158_s035_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp158_s035_*\") \n", + "# exp158_s035 = get_data(exp158_s035_files, \"correct\")\n", + "\n", + "exp158_s04_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp158_s04_*\") \n", + "exp158_s04 = get_data(exp158_s04_files, \"correct\")\n", + "\n", + "exp157_s05_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s05_*\") \n", + "exp157_s05 = get_data(exp157_s05_files, \"correct\")\n", + "\n", + "exp157_s06_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s06_*\") \n", + "exp157_s06 = get_data(exp157_s06_files, \"correct\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Load connection noise controls (digits)\n", + "exp155_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\") \n", + "exp155 = get_data(exp155_files, \"correct\")\n", + "\n", + "exp159_s01_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s01_*\") \n", + "exp159_s01 = get_data(exp159_s01_files, \"correct\")\n", + "\n", + "exp159_s05_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s05_*\") \n", + "exp159_s05 = get_data(exp159_s05_files, \"correct\")\n", + "\n", + "exp159_s1_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s1_*\") \n", + "exp159_s1 = get_data(exp159_s1_files, \"correct\")\n", + "\n", + "exp159_s2_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s2_*\") \n", + "exp159_s2 = get_data(exp159_s2_files, \"correct\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Load connection loss controls (digits)\n", + "exp155_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\") \n", + "exp155 = get_data(exp155_files, \"correct\")\n", + "\n", + "exp160_p01_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p01_*\") \n", + "exp160_p01 = get_data(exp160_p01_files, \"correct\")\n", + "\n", + "exp160_p05_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p05_*\") \n", + "exp160_p05 = get_data(exp160_p05_files, \"correct\")\n", + "\n", + "exp160_p1_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p1_*\") \n", + "exp160_p1 = get_data(exp160_p1_files, \"correct\")\n", + "\n", + "exp160_p2_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p2_*\") \n", + "exp160_p2 = get_data(exp160_p2_files, \"correct\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Figure 1\n", + "\n", + "Overall AAN results, for digits only" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Init the figure\n", + "fig = plt.figure(figsize=(20, 1), constrained_layout=True)\n", + "grid = plt.GridSpec(nrows=26, ncols=10, wspace=-.40, hspace=0.4, figure=fig)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 274, + "width": 351 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Panel 1 - digits\n", + "plt.subplot(grid[0:18, 0])\n", + "medians = [np.median(exp151[\"correct\"]), np.median(exp152[\"correct\"])]\n", + "plt.scatter(x=np.repeat(0.25, 20), y=exp151[\"correct\"], s=20, color=\"black\", alpha=0.5, marker=\"o\")\n", + "plt.scatter(x=np.repeat(0.75, 20), y=exp152[\"correct\"], s=20, color=\"black\", alpha=0.5, marker=\"o\")\n", + "plt.scatter(x=[0.25, 0.75], y=medians, color=\"red\", alpha=1, s=300, linewidth=3, marker=\"_\")\n", + "plt.xticks(np.array([0.25, 0.75]), ('Astrocytes', 'Neurons'))\n", + "plt.ylim(0, 1)\n", + "plt.xlim(0, 1)\n", + "plt.xticks(rotation=90)\n", + "plt.ylabel(\"Correct\")\n", + "\n", + "# -------------------------------------------------\n", + "model_names = [\"0.0\",\"0.1\", \"0.2\", \"0.3\", \"0.4\", \"0.5\"]\n", + "models = [exp155, exp157_s01, exp157_s02, exp158_s03, exp158_s04, exp157_s05, exp157_s06]\n", + "medians = [\n", + " np.median(exp155[\"correct\"]), \n", + " np.median(exp157_s01[\"correct\"]),\n", + " np.median(exp157_s02[\"correct\"]),\n", + " np.median(exp158_s03[\"correct\"]),\n", + " np.median(exp158_s04[\"correct\"]),\n", + " np.median(exp157_s05[\"correct\"]),\n", + "]\n", + "plt.subplot(grid[0:5, 3:8])\n", + "plt.scatter(x=model_names, y=medians, color=\"red\", alpha=1, s=200, linewidth=3, marker=\"_\")\n", + "for name, model in zip(model_names, models):\n", + " plt.scatter(x=np.repeat(name, 20), y=model[\"correct\"], color=\"black\", alpha=0.6, s=20)\n", + "plt.ylim(0, 1.1)\n", + "plt.ylabel(\"\")\n", + "plt.xlabel(\"Diffusion (std dev)\")\n", + "_ = sns.despine()\n", + "\n", + "\n", + "# -------------------------------------------------\n", + "model_names = [\"0.0\", \"0.01\", \"0.05\", \"0.1\", \"0.2\"]\n", + "models = [exp155, exp159_s01, exp159_s05, exp159_s1, exp159_s2]\n", + "medians = [\n", + " np.median(exp155[\"correct\"]), \n", + " np.median(exp159_s01[\"correct\"]),\n", + " np.median(exp159_s05[\"correct\"]),\n", + " np.median(exp159_s1[\"correct\"]),\n", + " np.median(exp159_s2[\"correct\"]),\n", + "]\n", + "\n", + "plt.subplot(grid[10:15, 3:8])\n", + "plt.scatter(x=model_names, y=medians, color=\"red\", alpha=1, s=200, linewidth=3, marker=\"_\")\n", + "for name, model in zip(model_names, models):\n", + " plt.scatter(x=np.repeat(name, 20), y=model[\"correct\"], color=\"black\", alpha=0.6, s=20)\n", + "plt.ylim(0, 1.1)\n", + "plt.ylabel(\"\")\n", + "plt.xlabel(\"Noise (std dev)\")\n", + "_ = sns.despine()\n", + "\n", + "# -------------------------------------------------\n", + "model_names = [\"0.0\", \"0.01\", \"0.05\", \"0.1\", \"0.2\"]\n", + "models = [exp155, exp160_p01, exp160_p05, exp160_p1, exp160_p2]\n", + "medians = [\n", + " np.median(exp155[\"correct\"]), \n", + " np.median(exp160_p01[\"correct\"]),\n", + " np.median(exp160_p05[\"correct\"]),\n", + " np.median(exp160_p1[\"correct\"]),\n", + " np.median(exp160_p2[\"correct\"]),\n", + "]\n", + "plt.subplot(grid[20:25, 3:8])\n", + "plt.scatter(x=model_names, y=medians, color=\"red\", alpha=1, s=200, linewidth=3, marker=\"_\")\n", + "for name, model in zip(model_names, models):\n", + " plt.scatter(x=np.repeat(name, 20), y=model[\"correct\"], color=\"black\", alpha=0.6, s=20)\n", + "plt.ylim(0, 1.1)\n", + "plt.ylabel(\"\")\n", + "plt.xlabel(\"p(unstabel)\")\n", + "_ = sns.despine()\n", + "\n", + "plt.savefig(\"figure_janelia_digits.png\", bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From d52f0ea579814b84478093d5ab0d5d038d3993af Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Wed, 4 Dec 2019 12:19:14 -0800 Subject: [PATCH 10/22] Tweeak notebooks --- notebooks/digits_exp151-156.ipynb | 2 +- notebooks/digits_exp157-8.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/digits_exp151-156.ipynb b/notebooks/digits_exp151-156.ipynb index 431711b..d442154 100644 --- a/notebooks/digits_exp151-156.ipynb +++ b/notebooks/digits_exp151-156.ipynb @@ -229,7 +229,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.0" } }, "nbformat": 4, diff --git a/notebooks/digits_exp157-8.ipynb b/notebooks/digits_exp157-8.ipynb index 2649341..76a8000 100644 --- a/notebooks/digits_exp157-8.ipynb +++ b/notebooks/digits_exp157-8.ipynb @@ -215,7 +215,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.0" } }, "nbformat": 4, From c8da541e2a5d9544e22e15521f6d3a156a279d2f Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Tue, 11 May 2021 12:51:52 -0700 Subject: [PATCH 11/22] Tweak Figs for NIPs Add key file to build Figs --- notebooks/digits_exp151-156.ipynb | 508 +++++++++++++++++++++----- notebooks/digits_exp157-8.ipynb | 567 ++++++++++++++++++++++++------ notebooks/digits_exp159_161.ipynb | 505 ++++++++++++++++++++++++++ notebooks/digits_exp160.ipynb | 477 +++++++++++++++++++++---- notebooks/figures_neuralips.key | Bin 0 -> 4827372 bytes 5 files changed, 1802 insertions(+), 255 deletions(-) create mode 100644 notebooks/digits_exp159_161.ipynb create mode 100755 notebooks/figures_neuralips.key diff --git a/notebooks/digits_exp151-156.ipynb b/notebooks/digits_exp151-156.ipynb index d442154..156c7e8 100644 --- a/notebooks/digits_exp151-156.ipynb +++ b/notebooks/digits_exp151-156.ipynb @@ -2,37 +2,90 @@ "cells": [ { "cell_type": "code", - "execution_count": 46, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 24;\n var nbb_unformatted_code = \"import os\\nimport numpy as np\\nimport matplotlib\\nimport matplotlib.pyplot as plt\\nimport torch\\nimport glob\\nfrom collections import defaultdict\";\n var nbb_formatted_code = \"import os\\nimport numpy as np\\nimport matplotlib\\nimport matplotlib.pyplot as plt\\nimport torch\\nimport glob\\nfrom collections import defaultdict\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], "source": [ "import os\n", "import numpy as np\n", - "\n", - "from IPython.display import Image\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", - "\n", + "import torch\n", + "import glob\n", + "from collections import defaultdict" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The nb_black extension is already loaded. To reload it, use:\n %reload_ext nb_black\nThe autoreload extension is already loaded. To reload it, use:\n %reload_ext autoreload\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 25;\n var nbb_unformatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\nsns.set(font_scale=2)\\nsns.set_style('ticks')\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\nplt.rcParams[\\\"legend.title_fontsize\\\"] = \\\"14\\\"\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n var nbb_formatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\n\\nsns.set(font_scale=2)\\nsns.set_style(\\\"ticks\\\")\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\nplt.rcParams[\\\"legend.title_fontsize\\\"] = \\\"14\\\"\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "# Pretty plots\n", "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'retina'\n", + "%config InlineBackend.figure_format='retina'\n", + "%config IPCompleter.greedy=True\n", "\n", "import seaborn as sns\n", "sns.set(font_scale=2)\n", "sns.set_style('ticks')\n", "\n", - "matplotlib.rcParams.update({'font.size': 16})\n", - "matplotlib.rc('axes', titlesize=16)\n", - "\n", - "import torch\n", - "import glob\n", - "from collections import defaultdict" + "plt.rcParams[\"axes.facecolor\"] = \"white\"\n", + "plt.rcParams[\"figure.facecolor\"] = \"white\"\n", + "plt.rcParams[\"font.size\"] = \"14\"\n", + "plt.rcParams[\"legend.title_fontsize\"] = \"14\"\n", + "# Uncomment for local development\n", + "%load_ext nb_black\n", + "%load_ext autoreload\n", + "%autoreload 2" ] }, + { + "source": [ + "Shared fns" + ], + "cell_type": "markdown", + "metadata": {} + }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 26;\n var nbb_unformatted_code = \"def get_data(files, *keys):\\n \\\"\\\"\\\"Get data keys from saved digit exps.\\\"\\\"\\\"\\n data = defaultdict(list)\\n for f in files:\\n d = torch.load(f)\\n for k in keys:\\n data[k].append(d[k]) \\n \\n return data\";\n var nbb_formatted_code = \"def get_data(files, *keys):\\n \\\"\\\"\\\"Get data keys from saved digit exps.\\\"\\\"\\\"\\n data = defaultdict(list)\\n for f in files:\\n d = torch.load(f)\\n for k in keys:\\n data[k].append(d[k])\\n\\n return data\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], "source": [ "def get_data(files, *keys):\n", " \"\"\"Get data keys from saved digit exps.\"\"\"\n", @@ -47,30 +100,102 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 27;\n var nbb_unformatted_code = \"def load_digit_online_exps():\\n # Get\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp151_*\\\") \\n exp151 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp152_*\\\") \\n exp152 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp151, exp152]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n var nbb_formatted_code = \"def load_digit_online_exps():\\n # Get\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp151_*\\\")\\n exp151 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp152_*\\\")\\n exp152 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp151, exp152]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], "source": [ - "# Learn VAE online\n", - "exp151_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp151_*\") \n", - "exp151 = get_data(exp151_files, \"correct\")\n", + "def load_digit_online_exps():\n", + " # Get\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp151_*\") \n", + " exp151 = get_data(files, \"correct\")\n", + "\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp152_*\") \n", + " exp152 = get_data(files, \"correct\")\n", + "\n", + " # Gather\n", + " models = [exp151, exp152]\n", + " model_names = [\"Astrocytes\", \"Neurons\"]\n", "\n", - "exp152_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp152_*\") \n", - "exp152 = get_data(exp152_files, \"correct\")\n", + " # Sanity\n", + " assert len(model_names) == len(models)\n", "\n", - "# Pretrain VAE (fixed for all exps)\n", - "exp153_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp153_*\") \n", - "exp153 = get_data(exp153_files, \"correct\")\n", + " return model_names, models" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 28;\n var nbb_unformatted_code = \" \\ndef load_digit_pretrain_exps(): \\n # Pretrain VAE (fixed for all exps)\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp153_*\\\") \\n exp153 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp154_*\\\") \\n exp154 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp153, exp154]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n var nbb_formatted_code = \"def load_digit_pretrain_exps():\\n # Pretrain VAE (fixed for all exps)\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp153_*\\\")\\n exp153 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp154_*\\\")\\n exp154 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp153, exp154]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + " \n", + "def load_digit_pretrain_exps(): \n", + " # Pretrain VAE (fixed for all exps)\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp153_*\") \n", + " exp153 = get_data(files, \"correct\")\n", "\n", - "exp154_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp154_*\") \n", - "exp154 = get_data(exp154_files, \"correct\")\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp154_*\") \n", + " exp154 = get_data(files, \"correct\")\n", "\n", + " # Gather\n", + " models = [exp153, exp154]\n", + " model_names = [\"Astrocytes\", \"Neurons\"]\n", + "\n", + " # Sanity\n", + " assert len(model_names) == len(models)\n", + "\n", + " return model_names, models" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 29;\n var nbb_unformatted_code = \"def load_digit_rand_exps(): \\n# Sparse projection\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155_*\\\") \\n exp155 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp156_*\\\") \\n exp156 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp155, exp156]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n var nbb_formatted_code = \"def load_digit_rand_exps():\\n # Sparse projection\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155_*\\\")\\n exp155 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp156_*\\\")\\n exp156 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp155, exp156]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "def load_digit_rand_exps(): \n", "# Sparse projection\n", - "exp155_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155_*\") \n", - "exp155 = get_data(exp155_files, \"correct\")\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155_*\") \n", + " exp155 = get_data(files, \"correct\")\n", + "\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp156_*\") \n", + " exp156 = get_data(files, \"correct\")\n", "\n", - "exp156_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp156_*\") \n", - "exp156 = get_data(exp156_files, \"correct\")" + " # Gather\n", + " models = [exp155, exp156]\n", + " model_names = [\"Astrocytes\", \"Neurons\"]\n", + "\n", + " # Sanity\n", + " assert len(model_names) == len(models)\n", + "\n", + " return model_names, models" ] }, { @@ -80,40 +205,123 @@ "# Learn VAE " ] }, + { + "source": [ + "Load data" + ], + "cell_type": "markdown", + "metadata": {} + }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 30, "metadata": {}, "outputs": [ { + "output_type": "execute_result", "data": { - "image/png": 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\n", "text/plain": [ - "
" + "('Astrocytes',\n", + " defaultdict(list,\n", + " {'correct': [0.8348,\n", + " 0.1135,\n", + " 0.1135,\n", + " 0.1135,\n", + " 0.8002,\n", + " 0.1135,\n", + " 0.8765,\n", + " 0.7719,\n", + " 0.8828,\n", + " 0.1135,\n", + " 0.8593,\n", + " 0.8646,\n", + " 0.8862,\n", + " 0.8506,\n", + " 0.1135,\n", + " 0.867,\n", + " 0.8815,\n", + " 0.8798,\n", + " 0.1135,\n", + " 0.8375]}))" ] }, + "metadata": {}, + "execution_count": 30 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 30;\n var nbb_unformatted_code = \"model_names, models = load_digit_online_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n var nbb_formatted_code = \"model_names, models = load_digit_online_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "model_names, models = load_digit_online_exps()\n", + "# Show example\n", + "i = 0\n", + "model_names[i], models[i]" + ] + }, + { + "source": [ + "Est stats and plot" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/png": 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\n" + }, "metadata": { "image/png": { - "height": 264, - "width": 257 + "width": 257, + "height": 285 } + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 31;\n var nbb_unformatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Online\\\", loc=\\\"left\\\")\\n_ = sns.despine()\";\n var nbb_formatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Online\\\", loc=\\\"left\\\")\\n_ = sns.despine()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " }, - "output_type": "display_data" + "metadata": {} } ], "source": [ - "# Est stats\n", - "models = [\"AAN\", \"ANN\"]\n", - "means = [np.median(exp151[\"correct\"]), np.median(exp152[\"correct\"])]\n", + "# Est\n", + "means = [np.mean(exp[\"correct\"]) for exp in models]\n", + "stds = [np.std(exp[\"correct\"]) for exp in models]\n", + "medians = [np.median(exp[\"correct\"]) for exp in models]\n", + "assert len(means) == len(models)\n", "\n", + "# Plot grid\n", "fig = plt.figure(figsize=(3, 4))\n", "grid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\n", - "plt.bar(models, means, color=\"grey\", alpha=0.2, width=0.5)\n", - "plt.scatter(x=np.repeat(0, 20), y=exp151[\"correct\"], color=\"black\", alpha=0.2)\n", - "plt.scatter(x=np.repeat(1, 20), y=exp152[\"correct\"], color=\"black\", alpha=0.2)\n", - "plt.xticks(np.array([0,1]), ('Astrocytes', 'Neurons'))\n", + "plt.subplot(grid[0, 0])\n", + "\n", + "# Mean\n", + "plt.bar(model_names, means, color=\"grey\", alpha=0.2, width=0.5)\n", + "\n", + "# Points\n", + "for name, model in zip(model_names, models):\n", + " n = len(model[\"correct\"])\n", + " plt.scatter(x=np.repeat(name, n), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", + "\n", + "# Axes\n", "plt.ylim(0, 1.1)\n", - "plt.ylabel(\"Correct\")\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.title(\"Online\", loc=\"left\")\n", "_ = sns.despine()" ] }, @@ -126,38 +334,114 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 32, "metadata": {}, "outputs": [ { + "output_type": "execute_result", "data": { - "image/png": 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\n", "text/plain": [ - "
" + "('Astrocytes',\n", + " defaultdict(list,\n", + " {'correct': [0.8357,\n", + " 0.1135,\n", + " 0.8122,\n", + " 0.8222,\n", + " 0.1135,\n", + " 0.1135,\n", + " 0.8242,\n", + " 0.8198,\n", + " 0.7776,\n", + " 0.8245,\n", + " 0.828,\n", + " 0.8182,\n", + " 0.8075,\n", + " 0.8196,\n", + " 0.8279,\n", + " 0.8216,\n", + " 0.8285,\n", + " 0.8197,\n", + " 0.8302,\n", + " 0.8183]}))" ] }, + "metadata": {}, + "execution_count": 32 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 32;\n var nbb_unformatted_code = \"model_names, models = load_digit_pretrain_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n var nbb_formatted_code = \"model_names, models = load_digit_pretrain_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "model_names, models = load_digit_pretrain_exps()\n", + "# Show example\n", + "i = 0\n", + "model_names[i], models[i]" + ] + }, + { + "source": [ + "Est stats and plot" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/png": 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\n" + }, "metadata": { "image/png": { - "height": 264, - "width": 257 + "width": 257, + "height": 311 } + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 33;\n var nbb_unformatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Latent\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n var nbb_formatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Latent\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " }, - "output_type": "display_data" + "metadata": {} } ], "source": [ - "# Est stats\n", - "models = [\"AAN\", \"ANN\"]\n", - "means = [np.median(exp153[\"correct\"]), np.median(exp154[\"correct\"])]\n", + "# Est\n", + "means = [np.mean(exp[\"correct\"]) for exp in models]\n", + "stds = [np.std(exp[\"correct\"]) for exp in models]\n", + "medians = [np.median(exp[\"correct\"]) for exp in models]\n", + "assert len(means) == len(models)\n", "\n", + "# Plot grid\n", "fig = plt.figure(figsize=(3, 4))\n", "grid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\n", - "plt.bar(models, means, color=\"grey\", alpha=0.2, width=0.5)\n", - "plt.scatter(x=np.repeat(0, 20), y=exp153[\"correct\"], color=\"black\", alpha=0.2)\n", - "plt.scatter(x=np.repeat(1, 20), y=exp154[\"correct\"], color=\"black\", alpha=0.2)\n", - "plt.xticks(np.array([0,1]), ('Astrocytes', 'Neurons'))\n", + "plt.subplot(grid[0, 0])\n", + "\n", + "# Mean\n", + "plt.bar(model_names, means, color=\"grey\", alpha=0.2, width=0.5)\n", + "\n", + "# Points\n", + "for name, model in zip(model_names, models):\n", + " n = len(model[\"correct\"])\n", + " plt.scatter(x=np.repeat(name, n), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", + "\n", + "# Axes\n", "plt.ylim(0, 1.1)\n", - "plt.ylabel(\"Correct\")\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.title(\"Latent\\nprojection\", loc=\"right\")\n", "_ = sns.despine()" ] }, @@ -170,47 +454,109 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 34, "metadata": {}, "outputs": [ { + "output_type": "execute_result", "data": { - "image/png": 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\n", "text/plain": [ - "
" + "('Astrocytes',\n", + " defaultdict(list,\n", + " {'correct': [0.65,\n", + " 0.6698,\n", + " 0.6726,\n", + " 0.687,\n", + " 0.6786,\n", + " 0.6645,\n", + " 0.7206,\n", + " 0.6784,\n", + " 0.6683,\n", + " 0.6844,\n", + " 0.6876,\n", + " 0.5402,\n", + " 0.6789,\n", + " 0.6847,\n", + " 0.6808,\n", + " 0.6459,\n", + " 0.679,\n", + " 0.6791,\n", + " 0.6695,\n", + " 0.6871]}))" ] }, + "metadata": {}, + "execution_count": 34 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 34;\n var nbb_unformatted_code = \"model_names, models = load_digit_rand_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n var nbb_formatted_code = \"model_names, models = load_digit_rand_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "model_names, models = load_digit_rand_exps()\n", + "# Show example\n", + "i = 0\n", + "model_names[i], models[i]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/png": 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\n" + }, "metadata": { "image/png": { - "height": 264, - "width": 257 + "width": 257, + "height": 311 } + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 35;\n var nbb_unformatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Random\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n var nbb_formatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Random\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " }, - "output_type": "display_data" + "metadata": {} } ], "source": [ - "# Est stats\n", - "models = [\"AAN\", \"ANN\"]\n", - "means = [np.median(exp155[\"correct\"]), np.median(exp156[\"correct\"])]\n", + "# Est\n", + "means = [np.mean(exp[\"correct\"]) for exp in models]\n", + "stds = [np.std(exp[\"correct\"]) for exp in models]\n", + "medians = [np.median(exp[\"correct\"]) for exp in models]\n", + "assert len(means) == len(models)\n", "\n", + "# Plot grid\n", "fig = plt.figure(figsize=(3, 4))\n", "grid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\n", - "plt.bar(models, means, color=\"grey\", alpha=0.2, width=0.5)\n", - "plt.scatter(x=np.repeat(0, 20), y=exp155[\"correct\"], color=\"black\", alpha=0.2)\n", - "plt.scatter(x=np.repeat(1, 20), y=exp156[\"correct\"], color=\"black\", alpha=0.2)\n", - "plt.xticks(np.array([0,1]), ('Astrocytes', 'Neurons'))\n", + "plt.subplot(grid[0, 0])\n", + "\n", + "# Mean\n", + "plt.bar(model_names, means, color=\"grey\", alpha=0.2, width=0.5)\n", + "\n", + "# Points\n", + "for name, model in zip(model_names, models):\n", + " n = len(model[\"correct\"])\n", + " plt.scatter(x=np.repeat(name, n), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", + "\n", + "# Axes\n", "plt.ylim(0, 1.1)\n", - "plt.ylabel(\"Correct\")\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.title(\"Random\\nprojection\", loc=\"right\")\n", "_ = sns.despine()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -229,9 +575,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.6.7-final" } }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/notebooks/digits_exp157-8.ipynb b/notebooks/digits_exp157-8.ipynb index 76a8000..173d0fc 100644 --- a/notebooks/digits_exp157-8.ipynb +++ b/notebooks/digits_exp157-8.ipynb @@ -2,9 +2,37 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 10;\n", + " var nbb_unformatted_code = \"import os\\nimport numpy as np\\n\\nfrom IPython.display import Image\\nimport matplotlib\\nimport matplotlib.pyplot as plt\\n\\nimport torch\\nimport glob\\nfrom collections import defaultdict\";\n", + " var nbb_formatted_code = \"import os\\nimport numpy as np\\n\\nfrom IPython.display import Image\\nimport matplotlib\\nimport matplotlib.pyplot as plt\\n\\nimport torch\\nimport glob\\nfrom collections import defaultdict\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import os\n", "import numpy as np\n", @@ -13,26 +41,115 @@ "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", + "import torch\n", + "import glob\n", + "from collections import defaultdict" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The nb_black extension is already loaded. To reload it, use:\n", + " %reload_ext nb_black\n", + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 11;\n", + " var nbb_unformatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\n\\nsns.set(font_scale=1)\\nsns.set_style(\\\"ticks\\\")\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\n\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n", + " var nbb_formatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\n\\nsns.set(font_scale=1)\\nsns.set_style(\\\"ticks\\\")\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\n\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Pretty plots\n", "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'retina'\n", + "%config InlineBackend.figure_format='retina'\n", + "%config IPCompleter.greedy=True\n", "\n", "import seaborn as sns\n", - "sns.set(font_scale=2)\n", - "sns.set_style('ticks')\n", "\n", - "matplotlib.rcParams.update({'font.size': 16})\n", - "matplotlib.rc('axes', titlesize=16)\n", + "sns.set(font_scale=1)\n", + "sns.set_style(\"ticks\")\n", "\n", - "import torch\n", - "import glob\n", - "from collections import defaultdict" + "plt.rcParams[\"axes.facecolor\"] = \"white\"\n", + "plt.rcParams[\"figure.facecolor\"] = \"white\"\n", + "plt.rcParams[\"font.size\"] = \"14\"\n", + "\n", + "# Uncomment for local development\n", + "%load_ext nb_black\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Shared fns" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 12;\n", + " var nbb_unformatted_code = \"def get_data(files, *keys):\\n \\\"\\\"\\\"Get data keys from saved digit exps.\\\"\\\"\\\"\\n data = defaultdict(list)\\n for f in files:\\n d = torch.load(f)\\n for k in keys:\\n data[k].append(d[k])\\n\\n return data\";\n", + " var nbb_formatted_code = \"def get_data(files, *keys):\\n \\\"\\\"\\\"Get data keys from saved digit exps.\\\"\\\"\\\"\\n data = defaultdict(list)\\n for f in files:\\n d = torch.load(f)\\n for k in keys:\\n data[k].append(d[k])\\n\\n return data\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def get_data(files, *keys):\n", " \"\"\"Get data keys from saved digit exps.\"\"\"\n", @@ -40,154 +157,396 @@ " for f in files:\n", " d = torch.load(f)\n", " for k in keys:\n", - " data[k].append(d[k]) \n", - " \n", + " data[k].append(d[k])\n", + "\n", " return data" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load and gather data" + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 13;\n", + " var nbb_unformatted_code = \"def load_leak_exps():\\n # Load\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\\\")\\n exp155 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s01_*\\\")\\n exp157_s01 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s02_*\\\")\\n exp157_s02 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp158_s03_*\\\")\\n exp158_s03 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp158_s035_*\\\")\\n exp158_s035 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp158_s04_*\\\")\\n exp158_s04 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s05_*\\\")\\n exp157_s05 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s06_*\\\")\\n exp157_s06 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n model_names = [\\\"0.0\\\", \\\"0.1\\\", \\\"0.2\\\", \\\"0.3\\\", \\\"0.35\\\", \\\"0.4\\\", \\\"0.5\\\", \\\"0.6\\\"]\\n models = [\\n exp155,\\n exp157_s01,\\n exp157_s02,\\n exp158_s03,\\n exp158_s035,\\n exp158_s04,\\n exp157_s05,\\n exp157_s06,\\n ]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n", + " var nbb_formatted_code = \"def load_leak_exps():\\n # Load\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\\\")\\n exp155 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s01_*\\\")\\n exp157_s01 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s02_*\\\")\\n exp157_s02 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp158_s03_*\\\")\\n exp158_s03 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp158_s035_*\\\")\\n exp158_s035 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp158_s04_*\\\")\\n exp158_s04 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s05_*\\\")\\n exp157_s05 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s06_*\\\")\\n exp157_s06 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n model_names = [\\\"0.0\\\", \\\"0.1\\\", \\\"0.2\\\", \\\"0.3\\\", \\\"0.35\\\", \\\"0.4\\\", \\\"0.5\\\", \\\"0.6\\\"]\\n models = [\\n exp155,\\n exp157_s01,\\n exp157_s02,\\n exp158_s03,\\n exp158_s035,\\n exp158_s04,\\n exp157_s05,\\n exp157_s06,\\n ]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "exp155_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\") \n", - "exp155 = get_data(exp155_files, \"correct\")\n", + "def load_leak_exps():\n", + " # Load\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\")\n", + " exp155 = get_data(files, \"correct\")\n", + "\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s01_*\")\n", + " exp157_s01 = get_data(files, \"correct\")\n", "\n", - "exp157_s01_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s01_*\") \n", - "exp157_s01 = get_data(exp157_s01_files, \"correct\")\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s02_*\")\n", + " exp157_s02 = get_data(files, \"correct\")\n", "\n", - "exp157_s02_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s02_*\") \n", - "exp157_s02 = get_data(exp157_s02_files, \"correct\")\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp158_s03_*\")\n", + " exp158_s03 = get_data(files, \"correct\")\n", "\n", - "exp158_s03_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp158_s03_*\") \n", - "exp158_s03 = get_data(exp158_s03_files, \"correct\")\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp158_s035_*\")\n", + " exp158_s035 = get_data(files, \"correct\")\n", "\n", - "exp158_s035_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp158_s035_*\") \n", - "exp158_s035 = get_data(exp158_s035_files, \"correct\")\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp158_s04_*\")\n", + " exp158_s04 = get_data(files, \"correct\")\n", "\n", - "exp158_s04_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp158_s04_*\") \n", - "exp158_s04 = get_data(exp158_s04_files, \"correct\")\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s05_*\")\n", + " exp157_s05 = get_data(files, \"correct\")\n", "\n", - "exp157_s05_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s05_*\") \n", - "exp157_s05 = get_data(exp157_s05_files, \"correct\")\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s06_*\")\n", + " exp157_s06 = get_data(files, \"correct\")\n", "\n", - "exp157_s06_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp157_s06_*\") \n", - "exp157_s06 = get_data(exp157_s06_files, \"correct\")\n" + " # Gather\n", + " model_names = [\"0.0\", \"0.1\", \"0.2\", \"0.3\", \"0.35\", \"0.4\", \"0.5\", \"0.6\"]\n", + " models = [\n", + " exp155,\n", + " exp157_s01,\n", + " exp157_s02,\n", + " exp158_s03,\n", + " exp158_s035,\n", + " exp158_s04,\n", + " exp157_s05,\n", + " exp157_s06,\n", + " ]\n", + "\n", + " # Sanity\n", + " assert len(model_names) == len(models)\n", + "\n", + " return model_names, models" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 14;\n", + " var nbb_unformatted_code = \"model_names, models = load_leak_exps()\";\n", + " var nbb_formatted_code = \"model_names, models = load_leak_exps()\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_names, models = load_leak_exps()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Show example" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "defaultdict(list,\n", - " {'correct': [0.65,\n", - " 0.6698,\n", - " 0.6726,\n", - " 0.687,\n", - " 0.6786,\n", - " 0.6645,\n", - " 0.7206,\n", - " 0.6784,\n", - " 0.6683,\n", - " 0.6844,\n", - " 0.6876,\n", - " 0.5402,\n", - " 0.6789,\n", - " 0.6847,\n", - " 0.6808,\n", - " 0.6459,\n", - " 0.679,\n", - " 0.6791,\n", - " 0.6695,\n", - " 0.6871]})" + "('0.0',\n", + " defaultdict(list,\n", + " {'correct': [0.65,\n", + " 0.6698,\n", + " 0.6726,\n", + " 0.687,\n", + " 0.6786,\n", + " 0.6645,\n", + " 0.7206,\n", + " 0.6784,\n", + " 0.6683,\n", + " 0.6844,\n", + " 0.6876,\n", + " 0.5402,\n", + " 0.6789,\n", + " 0.6847,\n", + " 0.6808,\n", + " 0.6459,\n", + " 0.679,\n", + " 0.6791,\n", + " 0.6695,\n", + " 0.6871]}))" ] }, - "execution_count": 6, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 15;\n", + " var nbb_unformatted_code = \"i = 0\\nmodel_names[i], models[i]\";\n", + " var nbb_formatted_code = \"i = 0\\nmodel_names[i], models[i]\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "model_names[i], models[i]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Est stats" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 16;\n", + " var nbb_unformatted_code = \"means = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\";\n", + " var nbb_formatted_code = \"means = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "exp155" + "means = [np.mean(exp[\"correct\"]) for exp in models]\n", + "stds = [np.std(exp[\"correct\"]) for exp in models]\n", + "medians = [np.median(exp[\"correct\"]) for exp in models]\n", + "assert len(means) == len(models)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot means" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": { "image/png": { - "height": 397, - "width": 535 - }, - "needs_background": "light" + "height": 185, + "width": 501 + } + }, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 17;\n", + " var nbb_unformatted_code = \"fig = plt.figure(figsize=(8, 7))\\ngrid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\\n\\n# Mean\\nplt.subplot(grid[0, 0])\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.8)\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.xlabel(\\\"Leak\\\")\\n_ = sns.despine()\";\n", + " var nbb_formatted_code = \"fig = plt.figure(figsize=(8, 7))\\ngrid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\\n\\n# Mean\\nplt.subplot(grid[0, 0])\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.8)\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.xlabel(\\\"Leak\\\")\\n_ = sns.despine()\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "# -------------------------------------------------\n", - "# Est stats\n", - "model_names = [\"0.0\",\"0.1\", \"0.2\", \"0.3\", \"0.35\", \"0.4\", \"0.5\", \"0.6\"]\n", - "models = [exp155, exp157_s01, exp157_s02, exp158_s03, exp158_s035, exp158_s04, exp157_s05, exp157_s06]\n", - "medians = [\n", - " np.median(exp155[\"correct\"]), \n", - " np.median(exp157_s01[\"correct\"]),\n", - " np.median(exp157_s02[\"correct\"]),\n", - " np.median(exp158_s03[\"correct\"]),\n", - " np.median(exp158_s035[\"correct\"]),\n", - " np.median(exp158_s04[\"correct\"]),\n", - " np.median(exp157_s05[\"correct\"]),\n", - " np.median(exp157_s06[\"correct\"]),\n", - "]\n", - "\n", - "means = [\n", - " np.mean(exp155[\"correct\"]), \n", - " np.mean(exp157_s01[\"correct\"]),\n", - " np.mean(exp157_s02[\"correct\"]),\n", - " np.mean(exp158_s03[\"correct\"]),\n", - " np.mean(exp158_s035[\"correct\"]),\n", - " np.mean(exp158_s04[\"correct\"]),\n", - " np.mean(exp157_s05[\"correct\"]),\n", - " np.mean(exp157_s06[\"correct\"]),\n", - "]\n", - "\n", - "# -------------------------------------------------\n", - "# Visualize \n", - "fig = plt.figure(figsize=(8, 6))\n", + "fig = plt.figure(figsize=(8, 7))\n", "grid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\n", "\n", "# Mean\n", "plt.subplot(grid[0, 0])\n", - "plt.bar(model_names, means, color=\"grey\", alpha=0.2, width=0.5)\n", + "plt.bar(model_names, means, color=\"grey\", alpha=0.2, width=0.8)\n", "for name, model in zip(model_names, models):\n", - " plt.scatter(x=np.repeat(name, 20), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", + " n = len(model[\"correct\"])\n", + " plt.scatter(x=np.repeat(name, n), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", "plt.ylim(0, 1.1)\n", - "plt.ylabel(\"Mean\\ncorrect\")\n", - "plt.xlabel(\"Leak (std dev)\")\n", - "_ = sns.despine()\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.xlabel(\"Leak\")\n", + "_ = sns.despine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot median" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 165, + "width": 507 + } + }, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 18;\n", + " var nbb_unformatted_code = \"fig = plt.figure(figsize=(8, 6))\\ngrid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\\n\\n# Mean\\nplt.subplot(grid[0, 0])\\nplt.bar(model_names, medians, color=\\\"grey\\\", alpha=0.2, width=0.8)\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.xlabel(\\\"Leak\\\")\\n_ = sns.despine()\";\n", + " var nbb_formatted_code = \"fig = plt.figure(figsize=(8, 6))\\ngrid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\\n\\n# Mean\\nplt.subplot(grid[0, 0])\\nplt.bar(model_names, medians, color=\\\"grey\\\", alpha=0.2, width=0.8)\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.xlabel(\\\"Leak\\\")\\n_ = sns.despine()\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8, 6))\n", + "grid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\n", "\n", - "# Median\n", - "plt.subplot(grid[1, 0])\n", - "plt.bar(model_names, medians, color=\"grey\", alpha=0.2, width=0.5)\n", + "# Mean\n", + "plt.subplot(grid[0, 0])\n", + "plt.bar(model_names, medians, color=\"grey\", alpha=0.2, width=0.8)\n", "for name, model in zip(model_names, models):\n", - " plt.scatter(x=np.repeat(name, 20), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", + " n = len(model[\"correct\"])\n", + " plt.scatter(x=np.repeat(name, n), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", "plt.ylim(0, 1.1)\n", - "plt.ylabel(\"Median\\ncorrect\")\n", - "plt.xlabel(\"Leak (std dev)\")\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.xlabel(\"Leak\")\n", "_ = sns.despine()" ] }, @@ -215,7 +574,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.6.7" } }, "nbformat": 4, diff --git a/notebooks/digits_exp159_161.ipynb b/notebooks/digits_exp159_161.ipynb new file mode 100644 index 0000000..436de86 --- /dev/null +++ b/notebooks/digits_exp159_161.ipynb @@ -0,0 +1,505 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 9;\n", + " var nbb_unformatted_code = \"import os\\nimport numpy as np\\n\\nfrom IPython.display import Image\\nimport matplotlib\\nimport matplotlib.pyplot as plt\\n\\nimport torch\\nimport glob\\nfrom collections import defaultdict\";\n", + " var nbb_formatted_code = \"import os\\nimport numpy as np\\n\\nfrom IPython.display import Image\\nimport matplotlib\\nimport matplotlib.pyplot as plt\\n\\nimport torch\\nimport glob\\nfrom collections import defaultdict\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "import numpy as np\n", + "\n", + "from IPython.display import Image\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import torch\n", + "import glob\n", + "from collections import defaultdict" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The nb_black extension is already loaded. To reload it, use:\n", + " %reload_ext nb_black\n", + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 10;\n", + " var nbb_unformatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\n\\nsns.set(font_scale=1)\\nsns.set_style(\\\"ticks\\\")\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\n\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n", + " var nbb_formatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\n\\nsns.set(font_scale=1)\\nsns.set_style(\\\"ticks\\\")\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\n\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Pretty plots\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format='retina'\n", + "%config IPCompleter.greedy=True\n", + "\n", + "import seaborn as sns\n", + "\n", + "sns.set(font_scale=1)\n", + "sns.set_style(\"ticks\")\n", + "\n", + "plt.rcParams[\"axes.facecolor\"] = \"white\"\n", + "plt.rcParams[\"figure.facecolor\"] = \"white\"\n", + "plt.rcParams[\"font.size\"] = \"14\"\n", + "\n", + "# Uncomment for local development\n", + "%load_ext nb_black\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 11;\n", + " var nbb_unformatted_code = \"def get_data(files, *keys):\\n \\\"\\\"\\\"Get data keys from saved digit exps.\\\"\\\"\\\"\\n data = defaultdict(list)\\n for f in files:\\n d = torch.load(f)\\n for k in keys:\\n data[k].append(d[k])\\n\\n return data\";\n", + " var nbb_formatted_code = \"def get_data(files, *keys):\\n \\\"\\\"\\\"Get data keys from saved digit exps.\\\"\\\"\\\"\\n data = defaultdict(list)\\n for f in files:\\n d = torch.load(f)\\n for k in keys:\\n data[k].append(d[k])\\n\\n return data\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def get_data(files, *keys):\n", + " \"\"\"Get data keys from saved digit exps.\"\"\"\n", + " data = defaultdict(list)\n", + " for f in files:\n", + " d = torch.load(f)\n", + " for k in keys:\n", + " data[k].append(d[k])\n", + "\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 12;\n", + " var nbb_unformatted_code = \"files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\\\")\\nexp155 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s01_*\\\")\\nexp159_s01 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s05_*\\\")\\nexp159_s05 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s1_*\\\")\\nexp159_s1 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s2_*\\\")\\nexp159_s2 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_s3_*\\\")\\nexp161_s3 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_4_*\\\")\\nexp161_s4 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_s5_*\\\")\\nexp161_s5 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_s6_*\\\")\\nexp161_s6 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_s7_*\\\")\\nexp161_s7 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_s8_*\\\")\\nexp161_s8 = get_data(files, \\\"correct\\\")\";\n", + " var nbb_formatted_code = \"files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\\\")\\nexp155 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s01_*\\\")\\nexp159_s01 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s05_*\\\")\\nexp159_s05 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s1_*\\\")\\nexp159_s1 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s2_*\\\")\\nexp159_s2 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_s3_*\\\")\\nexp161_s3 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_4_*\\\")\\nexp161_s4 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_s5_*\\\")\\nexp161_s5 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_s6_*\\\")\\nexp161_s6 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_s7_*\\\")\\nexp161_s7 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_s8_*\\\")\\nexp161_s8 = get_data(files, \\\"correct\\\")\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\")\n", + "exp155 = get_data(files, \"correct\")\n", + "\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s01_*\")\n", + "exp159_s01 = get_data(files, \"correct\")\n", + "\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s05_*\")\n", + "exp159_s05 = get_data(files, \"correct\")\n", + "\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s1_*\")\n", + "exp159_s1 = get_data(files, \"correct\")\n", + "\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp159_s2_*\")\n", + "exp159_s2 = get_data(files, \"correct\")\n", + "\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_s3_*\")\n", + "exp161_s3 = get_data(files, \"correct\")\n", + "\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_4_*\")\n", + "exp161_s4 = get_data(files, \"correct\")\n", + "\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_s5_*\")\n", + "exp161_s5 = get_data(files, \"correct\")\n", + "\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_s6_*\")\n", + "exp161_s6 = get_data(files, \"correct\")\n", + "\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_s7_*\")\n", + "exp161_s7 = get_data(files, \"correct\")\n", + "\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp161_s8_*\")\n", + "exp161_s8 = get_data(files, \"correct\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 13;\n", + " var nbb_unformatted_code = \"model_names = [\\n \\\"0.00\\\",\\n \\\"0.01\\\",\\n \\\"0.05\\\",\\n \\\"0.10\\\",\\n \\\"0.20\\\",\\n \\\"0.30\\\",\\n \\\"0.40\\\",\\n \\\"0.50\\\",\\n \\\"0.60\\\",\\n \\\"0.70\\\",\\n \\\"0.80\\\",\\n]\\nmodels = [\\n exp155,\\n exp159_s01,\\n exp159_s05,\\n exp159_s1,\\n exp159_s2,\\n exp161_s3,\\n exp161_s4,\\n exp161_s5,\\n exp161_s6,\\n exp161_s7,\\n exp161_s8,\\n]\\n\\n# Sanity check\\nassert len(model_names) == len(models)\";\n", + " var nbb_formatted_code = \"model_names = [\\n \\\"0.00\\\",\\n \\\"0.01\\\",\\n \\\"0.05\\\",\\n \\\"0.10\\\",\\n \\\"0.20\\\",\\n \\\"0.30\\\",\\n \\\"0.40\\\",\\n \\\"0.50\\\",\\n \\\"0.60\\\",\\n \\\"0.70\\\",\\n \\\"0.80\\\",\\n]\\nmodels = [\\n exp155,\\n exp159_s01,\\n exp159_s05,\\n exp159_s1,\\n exp159_s2,\\n exp161_s3,\\n exp161_s4,\\n exp161_s5,\\n exp161_s6,\\n exp161_s7,\\n exp161_s8,\\n]\\n\\n# Sanity check\\nassert len(model_names) == len(models)\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_names = [\n", + " \"0.00\",\n", + " \"0.01\",\n", + " \"0.05\",\n", + " \"0.10\",\n", + " \"0.20\",\n", + " \"0.30\",\n", + " \"0.40\",\n", + " \"0.50\",\n", + " \"0.60\",\n", + " \"0.70\",\n", + " \"0.80\",\n", + "]\n", + "models = [\n", + " exp155,\n", + " exp159_s01,\n", + " exp159_s05,\n", + " exp159_s1,\n", + " exp159_s2,\n", + " exp161_s3,\n", + " exp161_s4,\n", + " exp161_s5,\n", + " exp161_s6,\n", + " exp161_s7,\n", + " exp161_s8,\n", + "]\n", + "\n", + "# Sanity check\n", + "assert len(model_names) == len(models)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 14;\n", + " var nbb_unformatted_code = \"# -------------------------------------------------\\n# Est stats\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\n\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\n\\nassert len(means) == len(models)\";\n", + " var nbb_formatted_code = \"# -------------------------------------------------\\n# Est stats\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\n\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\n\\nassert len(means) == len(models)\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# -------------------------------------------------\n", + "# Est stats\n", + "medians = [np.median(exp[\"correct\"]) for exp in models]\n", + "\n", + "means = [np.mean(exp[\"correct\"]) for exp in models]\n", + "stds = [np.std(exp[\"correct\"]) for exp in models]\n", + "\n", + "assert len(means) == len(models)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Means" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 165, + "width": 618 + } + }, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 15;\n", + " var nbb_unformatted_code = \"fig = plt.figure(figsize=(10, 6))\\ngrid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\\n\\n# Mean\\nplt.subplot(grid[0, 0])\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.8)\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.xlabel(\\\"Noise\\\")\\n_ = sns.despine()\";\n", + " var nbb_formatted_code = \"fig = plt.figure(figsize=(10, 6))\\ngrid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\\n\\n# Mean\\nplt.subplot(grid[0, 0])\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.8)\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.xlabel(\\\"Noise\\\")\\n_ = sns.despine()\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 6))\n", + "grid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\n", + "\n", + "# Mean\n", + "plt.subplot(grid[0, 0])\n", + "plt.bar(model_names, means, color=\"grey\", alpha=0.2, width=0.8)\n", + "for name, model in zip(model_names, models):\n", + " n = len(model[\"correct\"])\n", + " plt.scatter(x=np.repeat(name, n), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", + "plt.ylim(0, 1.1)\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.xlabel(\"Noise\")\n", + "_ = sns.despine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Median" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 165, + "width": 618 + } + }, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 16;\n", + " var nbb_unformatted_code = \"fig = plt.figure(figsize=(10, 6))\\ngrid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\\n\\nplt.subplot(grid[1, 0])\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.8)\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.xlabel(\\\"Noise\\\")\\n_ = sns.despine()\";\n", + " var nbb_formatted_code = \"fig = plt.figure(figsize=(10, 6))\\ngrid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\\n\\nplt.subplot(grid[1, 0])\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.8)\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.xlabel(\\\"Noise\\\")\\n_ = sns.despine()\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 6))\n", + "grid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\n", + "\n", + "plt.subplot(grid[1, 0])\n", + "plt.bar(model_names, means, color=\"grey\", alpha=0.2, width=0.8)\n", + "for name, model in zip(model_names, models):\n", + " n = len(model[\"correct\"])\n", + " plt.scatter(x=np.repeat(name, n), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", + "plt.ylim(0, 1.1)\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.xlabel(\"Noise\")\n", + "_ = sns.despine()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/digits_exp160.ipynb b/notebooks/digits_exp160.ipynb index 439e5b0..5a20d96 100644 --- a/notebooks/digits_exp160.ipynb +++ b/notebooks/digits_exp160.ipynb @@ -2,9 +2,37 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 10;\n", + " var nbb_unformatted_code = \"import os\\nimport numpy as np\\n\\nfrom IPython.display import Image\\nimport matplotlib\\nimport matplotlib.pyplot as plt\\n\\nimport torch\\nimport glob\\nfrom collections import defaultdict\";\n", + " var nbb_formatted_code = \"import os\\nimport numpy as np\\n\\nfrom IPython.display import Image\\nimport matplotlib\\nimport matplotlib.pyplot as plt\\n\\nimport torch\\nimport glob\\nfrom collections import defaultdict\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import os\n", "import numpy as np\n", @@ -13,26 +41,108 @@ "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", + "import torch\n", + "import glob\n", + "from collections import defaultdict" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The nb_black extension is already loaded. To reload it, use:\n", + " %reload_ext nb_black\n", + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 11;\n", + " var nbb_unformatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\n\\nsns.set(font_scale=1)\\nsns.set_style(\\\"ticks\\\")\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\n\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n", + " var nbb_formatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\n\\nsns.set(font_scale=1)\\nsns.set_style(\\\"ticks\\\")\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\n\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Pretty plots\n", "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'retina'\n", + "%config InlineBackend.figure_format='retina'\n", + "%config IPCompleter.greedy=True\n", "\n", "import seaborn as sns\n", - "sns.set(font_scale=2)\n", - "sns.set_style('ticks')\n", "\n", - "matplotlib.rcParams.update({'font.size': 16})\n", - "matplotlib.rc('axes', titlesize=16)\n", + "sns.set(font_scale=1)\n", + "sns.set_style(\"ticks\")\n", "\n", - "import torch\n", - "import glob\n", - "from collections import defaultdict" + "plt.rcParams[\"axes.facecolor\"] = \"white\"\n", + "plt.rcParams[\"figure.facecolor\"] = \"white\"\n", + "plt.rcParams[\"font.size\"] = \"14\"\n", + "\n", + "# Uncomment for local development\n", + "%load_ext nb_black\n", + "%load_ext autoreload\n", + "%autoreload 2" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 12;\n", + " var nbb_unformatted_code = \"def get_data(files, *keys):\\n \\\"\\\"\\\"Get data keys from saved digit exps.\\\"\\\"\\\"\\n data = defaultdict(list)\\n for f in files:\\n d = torch.load(f)\\n for k in keys:\\n data[k].append(d[k])\\n\\n return data\";\n", + " var nbb_formatted_code = \"def get_data(files, *keys):\\n \\\"\\\"\\\"Get data keys from saved digit exps.\\\"\\\"\\\"\\n data = defaultdict(list)\\n for f in files:\\n d = torch.load(f)\\n for k in keys:\\n data[k].append(d[k])\\n\\n return data\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def get_data(files, *keys):\n", " \"\"\"Get data keys from saved digit exps.\"\"\"\n", @@ -40,36 +150,71 @@ " for f in files:\n", " d = torch.load(f)\n", " for k in keys:\n", - " data[k].append(d[k]) \n", - " \n", + " data[k].append(d[k])\n", + "\n", " return data" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 13;\n", + " var nbb_unformatted_code = \"files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\\\")\\nexp155 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p01_*\\\")\\nexp160_p01 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p05_*\\\")\\nexp160_p05 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p1_*\\\")\\nexp160_p1 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p2_*\\\")\\nexp160_p2 = get_data(files, \\\"correct\\\")\";\n", + " var nbb_formatted_code = \"files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\\\")\\nexp155 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p01_*\\\")\\nexp160_p01 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p05_*\\\")\\nexp160_p05 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p1_*\\\")\\nexp160_p1 = get_data(files, \\\"correct\\\")\\n\\nfiles = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p2_*\\\")\\nexp160_p2 = get_data(files, \\\"correct\\\")\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "exp155_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\") \n", - "exp155 = get_data(exp155_files, \"correct\")\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155*\")\n", + "exp155 = get_data(files, \"correct\")\n", "\n", - "exp160_p01_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p01_*\") \n", - "exp160_p01 = get_data(exp160_p01_files, \"correct\")\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p01_*\")\n", + "exp160_p01 = get_data(files, \"correct\")\n", "\n", - "exp160_p05_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p05_*\") \n", - "exp160_p05 = get_data(exp160_p05_files, \"correct\")\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p05_*\")\n", + "exp160_p05 = get_data(files, \"correct\")\n", "\n", - "exp160_p1_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p1_*\") \n", - "exp160_p1 = get_data(exp160_p1_files, \"correct\")\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p1_*\")\n", + "exp160_p1 = get_data(files, \"correct\")\n", "\n", - "exp160_p2_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p2_*\") \n", - "exp160_p2 = get_data(exp160_p2_files, \"correct\")" + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p2_*\")\n", + "exp160_p2 = get_data(files, \"correct\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Show example" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -98,81 +243,273 @@ " 0.799]})" ] }, - "execution_count": 8, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 14;\n", + " var nbb_unformatted_code = \"exp160_p01\";\n", + " var nbb_formatted_code = \"exp160_p01\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "exp160_p01" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Gather data" + ] + }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 15;\n", + " var nbb_unformatted_code = \"model_names = [\\\"0.00\\\", \\\"0.01\\\", \\\"0.05\\\", \\\"0.10\\\", \\\"0.20\\\"]\\nmodels = [exp155, exp160_p01, exp160_p05, exp160_p1, exp160_p2]\";\n", + " var nbb_formatted_code = \"model_names = [\\\"0.00\\\", \\\"0.01\\\", \\\"0.05\\\", \\\"0.10\\\", \\\"0.20\\\"]\\nmodels = [exp155, exp160_p01, exp160_p05, exp160_p1, exp160_p2]\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_names = [\"0.00\", \"0.01\", \"0.05\", \"0.10\", \"0.20\"]\n", + "models = [exp155, exp160_p01, exp160_p05, exp160_p1, exp160_p2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Est stats" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 16;\n", + " var nbb_unformatted_code = \"means = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\";\n", + " var nbb_formatted_code = \"means = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], "text/plain": [ - "
" + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "means = [np.mean(exp[\"correct\"]) for exp in models]\n", + "stds = [np.std(exp[\"correct\"]) for exp in models]\n", + "medians = [np.median(exp[\"correct\"]) for exp in models]\n", + "assert len(means) == len(models)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot means" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" ] }, "metadata": { "image/png": { - "height": 400, - "width": 538 - }, - "needs_background": "light" + "height": 165, + "width": 395 + } }, "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 17;\n", + " var nbb_unformatted_code = \"fig = plt.figure(figsize=(6, 6))\\ngrid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\\n\\n# Mean\\nplt.subplot(grid[0, 0])\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.8)\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.xlabel(\\\"Drop probability\\\")\\n_ = sns.despine()\";\n", + " var nbb_formatted_code = \"fig = plt.figure(figsize=(6, 6))\\ngrid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\\n\\n# Mean\\nplt.subplot(grid[0, 0])\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.8)\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.xlabel(\\\"Drop probability\\\")\\n_ = sns.despine()\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# -------------------------------------------------\n", - "# Est stats\n", - "model_names = [\"0.0\", \"0.01\", \"0.05\", \"0.1\", \"0.2\"]\n", - "models = [exp155, exp160_p01, exp160_p05, exp160_p1, exp160_p2]\n", - "medians = [\n", - " np.median(exp155[\"correct\"]), \n", - " np.median(exp160_p01[\"correct\"]),\n", - " np.median(exp160_p05[\"correct\"]),\n", - " np.median(exp160_p1[\"correct\"]),\n", - " np.median(exp160_p2[\"correct\"]),\n", - "]\n", - "\n", - "means = [\n", - " np.mean(exp155[\"correct\"]), \n", - " np.mean(exp160_p01[\"correct\"]),\n", - " np.mean(exp160_p05[\"correct\"]),\n", - " np.mean(exp160_p1[\"correct\"]),\n", - " np.mean(exp160_p2[\"correct\"]),\n", - "]\n", - "\n", - "# -------------------------------------------------\n", - "# Visualize \n", - "fig = plt.figure(figsize=(8, 6))\n", + "fig = plt.figure(figsize=(6, 6))\n", "grid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\n", "\n", "# Mean\n", "plt.subplot(grid[0, 0])\n", - "plt.bar(model_names, means, color=\"grey\", alpha=0.2, width=0.5)\n", + "plt.bar(model_names, means, color=\"grey\", alpha=0.2, width=0.8)\n", "for name, model in zip(model_names, models):\n", - " plt.scatter(x=np.repeat(name, 20), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", + " n = len(model[\"correct\"])\n", + " plt.scatter(x=np.repeat(name, n), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", "plt.ylim(0, 1.1)\n", - "plt.ylabel(\"Mean\\ncorrect\")\n", - "plt.xlabel(\"P(drop)\")\n", - "_ = sns.despine()\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.xlabel(\"Drop probability\")\n", + "_ = sns.despine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot medians" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 165, + "width": 395 + } + }, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 18;\n", + " var nbb_unformatted_code = \"fig = plt.figure(figsize=(6, 6))\\ngrid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\\n\\n# Mean\\nplt.subplot(grid[0, 0])\\nplt.bar(model_names, medians, color=\\\"grey\\\", alpha=0.2, width=0.8)\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.xlabel(\\\"Drop probability\\\")\\n_ = sns.despine()\";\n", + " var nbb_formatted_code = \"fig = plt.figure(figsize=(6, 6))\\ngrid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\\n\\n# Mean\\nplt.subplot(grid[0, 0])\\nplt.bar(model_names, medians, color=\\\"grey\\\", alpha=0.2, width=0.8)\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.xlabel(\\\"Drop probability\\\")\\n_ = sns.despine()\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(6, 6))\n", + "grid = plt.GridSpec(2, 1, wspace=0.3, hspace=0.8)\n", "\n", - "# Median\n", - "plt.subplot(grid[1, 0])\n", - "plt.bar(model_names, medians, color=\"grey\", alpha=0.2, width=0.5)\n", + "# Mean\n", + "plt.subplot(grid[0, 0])\n", + "plt.bar(model_names, medians, color=\"grey\", alpha=0.2, width=0.8)\n", "for name, model in zip(model_names, models):\n", - " plt.scatter(x=np.repeat(name, 20), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", + " n = len(model[\"correct\"])\n", + " plt.scatter(x=np.repeat(name, n), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", "plt.ylim(0, 1.1)\n", - "plt.ylabel(\"Median\\ncorrect\")\n", - "plt.xlabel(\"P(drop)\")\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.xlabel(\"Drop probability\")\n", "_ = sns.despine()" ] }, @@ -200,7 +537,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.6.7" } }, "nbformat": 4, diff --git a/notebooks/figures_neuralips.key b/notebooks/figures_neuralips.key new file mode 100755 index 0000000000000000000000000000000000000000..ee7f689a4df591fd714a6e7acb91fcab56dcb70a GIT binary patch literal 4827372 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+++++++++++++++++++++++--- notebooks/figures_neuralips.key | Bin 4827372 -> 9675130 bytes notebooks/xor_exp3-4.ipynb | 13 +- 5 files changed, 987 insertions(+), 178 deletions(-) diff --git a/notebooks/digits_exp151-156.ipynb b/notebooks/digits_exp151-156.ipynb index 156c7e8..af06013 100644 --- a/notebooks/digits_exp151-156.ipynb +++ b/notebooks/digits_exp151-156.ipynb @@ -2,16 +2,35 @@ "cells": [ { "cell_type": "code", - "execution_count": 24, + "execution_count": 72, "metadata": {}, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": "", - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 24;\n var nbb_unformatted_code = \"import os\\nimport numpy as np\\nimport matplotlib\\nimport matplotlib.pyplot as plt\\nimport torch\\nimport glob\\nfrom collections import defaultdict\";\n var nbb_formatted_code = \"import os\\nimport numpy as np\\nimport matplotlib\\nimport matplotlib.pyplot as plt\\nimport torch\\nimport glob\\nfrom collections 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nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -26,23 +45,45 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 73, "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "The nb_black extension is already loaded. To reload it, use:\n %reload_ext nb_black\nThe autoreload extension is already loaded. To reload it, use:\n %reload_ext autoreload\n" + "The nb_black extension is already loaded. To reload it, use:\n", + " %reload_ext nb_black\n", + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" ] }, { - "output_type": "display_data", "data": { - "text/plain": "", - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 25;\n var nbb_unformatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\nsns.set(font_scale=2)\\nsns.set_style('ticks')\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\nplt.rcParams[\\\"legend.title_fontsize\\\"] = \\\"14\\\"\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n var nbb_formatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\n\\nsns.set(font_scale=2)\\nsns.set_style(\\\"ticks\\\")\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\nplt.rcParams[\\\"legend.title_fontsize\\\"] = \\\"14\\\"\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; 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i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -52,8 +93,9 @@ "%config IPCompleter.greedy=True\n", "\n", "import seaborn as sns\n", + "\n", "sns.set(font_scale=2)\n", - "sns.set_style('ticks')\n", + "sns.set_style(\"ticks\")\n", "\n", "plt.rcParams[\"axes.facecolor\"] = \"white\"\n", "plt.rcParams[\"figure.facecolor\"] = \"white\"\n", @@ -66,24 +108,43 @@ ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Shared fns" - ], - "cell_type": "markdown", - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 74, "metadata": {}, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": "", - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 26;\n var nbb_unformatted_code = \"def get_data(files, *keys):\\n \\\"\\\"\\\"Get data keys from saved digit exps.\\\"\\\"\\\"\\n data = defaultdict(list)\\n for f in files:\\n d = torch.load(f)\\n for k in keys:\\n data[k].append(d[k]) \\n \\n return data\";\n var nbb_formatted_code = \"def get_data(files, *keys):\\n \\\"\\\"\\\"Get data keys from saved digit exps.\\\"\\\"\\\"\\n data = defaultdict(list)\\n for f in files:\\n d = torch.load(f)\\n for k in keys:\\n data[k].append(d[k])\\n\\n return data\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 74;\n", + " var nbb_unformatted_code = \"def get_data(files, *keys):\\n \\\"\\\"\\\"Get data keys from saved digit exps.\\\"\\\"\\\"\\n data = defaultdict(list)\\n for f in files:\\n d = torch.load(f)\\n for k in keys:\\n data[k].append(d[k])\\n\\n return data\";\n", + " var nbb_formatted_code = \"def get_data(files, *keys):\\n \\\"\\\"\\\"Get data keys from saved digit exps.\\\"\\\"\\\"\\n data = defaultdict(list)\\n for f in files:\\n d = torch.load(f)\\n for k in keys:\\n data[k].append(d[k])\\n\\n return data\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -93,32 +154,51 @@ " for f in files:\n", " d = torch.load(f)\n", " for k in keys:\n", - " data[k].append(d[k]) \n", - " \n", + " data[k].append(d[k])\n", + "\n", " return data" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 75, "metadata": {}, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": "", - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 27;\n var nbb_unformatted_code = \"def load_digit_online_exps():\\n # Get\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp151_*\\\") \\n exp151 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp152_*\\\") \\n exp152 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp151, exp152]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n var nbb_formatted_code = \"def load_digit_online_exps():\\n # Get\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp151_*\\\")\\n exp151 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp152_*\\\")\\n exp152 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp151, exp152]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 75;\n", + " var nbb_unformatted_code = \"def load_digit_online_exps():\\n # Get\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp151_*\\\")\\n exp151 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp152_*\\\")\\n exp152 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp151, exp152]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n", + " var nbb_formatted_code = \"def load_digit_online_exps():\\n # Get\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp151_*\\\")\\n exp151 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp152_*\\\")\\n exp152 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp151, exp152]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ "def load_digit_online_exps():\n", " # Get\n", - " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp151_*\") \n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp151_*\")\n", " exp151 = get_data(files, \"correct\")\n", "\n", - " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp152_*\") \n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp152_*\")\n", " exp152 = get_data(files, \"correct\")\n", "\n", " # Gather\n", @@ -133,26 +213,44 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 76, "metadata": {}, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": "", - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 28;\n var nbb_unformatted_code = \" \\ndef load_digit_pretrain_exps(): \\n # Pretrain VAE (fixed for all exps)\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp153_*\\\") \\n exp153 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp154_*\\\") \\n exp154 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp153, exp154]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n var nbb_formatted_code = \"def load_digit_pretrain_exps():\\n # Pretrain VAE (fixed for all exps)\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp153_*\\\")\\n exp153 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp154_*\\\")\\n exp154 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp153, exp154]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 76;\n", + " var nbb_unformatted_code = \"def load_digit_pretrain_exps():\\n # Pretrain VAE (fixed for all exps)\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp153_*\\\")\\n exp153 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp154_*\\\")\\n exp154 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp153, exp154]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n", + " var nbb_formatted_code = \"def load_digit_pretrain_exps():\\n # Pretrain VAE (fixed for all exps)\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp153_*\\\")\\n exp153 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp154_*\\\")\\n exp154 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp153, exp154]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ - " \n", - "def load_digit_pretrain_exps(): \n", + "def load_digit_pretrain_exps():\n", " # Pretrain VAE (fixed for all exps)\n", - " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp153_*\") \n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp153_*\")\n", " exp153 = get_data(files, \"correct\")\n", "\n", - " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp154_*\") \n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp154_*\")\n", " exp154 = get_data(files, \"correct\")\n", "\n", " # Gather\n", @@ -167,25 +265,44 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 77, "metadata": {}, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": "", - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 29;\n var nbb_unformatted_code = \"def load_digit_rand_exps(): \\n# Sparse projection\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155_*\\\") \\n exp155 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp156_*\\\") \\n exp156 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp155, exp156]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n var nbb_formatted_code = \"def load_digit_rand_exps():\\n # Sparse projection\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155_*\\\")\\n exp155 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp156_*\\\")\\n exp156 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp155, exp156]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 77;\n", + " var nbb_unformatted_code = \"def load_digit_rand_exps():\\n # Sparse projection\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155_*\\\")\\n exp155 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp156_*\\\")\\n exp156 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp155, exp156]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n", + " var nbb_formatted_code = \"def load_digit_rand_exps():\\n # Sparse projection\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155_*\\\")\\n exp155 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/digits_exp156_*\\\")\\n exp156 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp155, exp156]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "def load_digit_rand_exps(): \n", - "# Sparse projection\n", - " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155_*\") \n", + "def load_digit_rand_exps():\n", + " # Sparse projection\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp155_*\")\n", " exp155 = get_data(files, \"correct\")\n", "\n", - " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp156_*\") \n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp156_*\")\n", " exp156 = get_data(files, \"correct\")\n", "\n", " # Gather\n", @@ -206,19 +323,18 @@ ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Load data" - ], - "cell_type": "markdown", - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 78, "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "('Astrocytes',\n", @@ -245,16 +361,36 @@ " 0.8375]}))" ] }, + "execution_count": 78, "metadata": {}, - "execution_count": 30 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { - "text/plain": "", - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 30;\n var nbb_unformatted_code = \"model_names, models = load_digit_online_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n var nbb_formatted_code = \"model_names, models = load_digit_online_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 78;\n", + " var nbb_unformatted_code = \"model_names, models = load_digit_online_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n", + " var nbb_formatted_code = \"model_names, models = load_digit_online_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -265,37 +401,58 @@ ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Est stats and plot" - ], - "cell_type": "markdown", - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 79, "metadata": {}, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": "

", - "image/png": 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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "image/png": { - "width": 257, - "height": 285 + "height": 311, + "width": 257 } - } + }, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": "", - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 31;\n var nbb_unformatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Online\\\", loc=\\\"left\\\")\\n_ = sns.despine()\";\n var nbb_formatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Online\\\", loc=\\\"left\\\")\\n_ = sns.despine()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 79;\n", + " var nbb_unformatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, medians, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Latent\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n", + " var nbb_formatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, medians, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Latent\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -311,7 +468,7 @@ "plt.subplot(grid[0, 0])\n", "\n", "# Mean\n", - "plt.bar(model_names, means, color=\"grey\", alpha=0.2, width=0.5)\n", + "plt.bar(model_names, medians, color=\"grey\", alpha=0.2, width=0.5)\n", "\n", "# Points\n", "for name, model in zip(model_names, models):\n", @@ -321,10 +478,56 @@ "# Axes\n", "plt.ylim(0, 1.1)\n", "plt.ylabel(\"Accuracy\")\n", - "plt.title(\"Online\", loc=\"left\")\n", + "plt.title(\"Latent\\nprojection\", loc=\"right\")\n", "_ = sns.despine()" ] }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.83615, 0.9116]\n", + "Astrocyte percent diff: 0.09023500568079892\n" + ] + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 80;\n", + " var nbb_unformatted_code = \"print(medians)\\nprint(f\\\"Astrocyte percent diff: {(medians[1] - medians[0])/ medians[0]}\\\")\";\n", + " var nbb_formatted_code = \"print(medians)\\nprint(f\\\"Astrocyte percent diff: {(medians[1] - medians[0])/ medians[0]}\\\")\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(medians)\n", + "print(f\"Astrocyte percent diff: {(medians[1] - medians[0])/ medians[0]}\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -334,11 +537,10 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 81, "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "('Astrocytes',\n", @@ -365,16 +567,36 @@ " 0.8183]}))" ] }, + "execution_count": 81, "metadata": {}, - "execution_count": 32 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { - "text/plain": "", - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 32;\n var nbb_unformatted_code = \"model_names, models = load_digit_pretrain_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n var nbb_formatted_code = \"model_names, models = load_digit_pretrain_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 81;\n", + " var nbb_unformatted_code = \"model_names, models = load_digit_pretrain_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n", + " var nbb_formatted_code = \"model_names, models = load_digit_pretrain_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -385,37 +607,58 @@ ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Est stats and plot" - ], - "cell_type": "markdown", - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 82, "metadata": {}, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": "
", - "image/png": 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\n" 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "image/png": { - "width": 257, - "height": 311 + "height": 311, + "width": 257 } - } + }, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": "", - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 33;\n var nbb_unformatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Latent\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n var nbb_formatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Latent\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 82;\n", + " var nbb_unformatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, medians, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Latent\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n", + " var nbb_formatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, medians, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Latent\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -431,7 +674,7 @@ "plt.subplot(grid[0, 0])\n", "\n", "# Mean\n", - "plt.bar(model_names, means, color=\"grey\", alpha=0.2, width=0.5)\n", + "plt.bar(model_names, medians, color=\"grey\", alpha=0.2, width=0.5)\n", "\n", "# Points\n", "for name, model in zip(model_names, models):\n", @@ -445,6 +688,52 @@ "_ = sns.despine()" ] }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.81975, 0.94465]\n", + "Astrocyte percent diff: 0.15236352546508083\n" + ] + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 83;\n", + " var nbb_unformatted_code = \"print(medians)\\nprint(f\\\"Astrocyte percent diff: {(medians[1] - medians[0])/ medians[0]}\\\")\";\n", + " var nbb_formatted_code = \"print(medians)\\nprint(f\\\"Astrocyte percent diff: {(medians[1] - medians[0])/ medians[0]}\\\")\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(medians)\n", + "print(f\"Astrocyte percent diff: {(medians[1] - medians[0])/ medians[0]}\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -454,11 +743,10 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 84, "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "('Astrocytes',\n", @@ -485,16 +773,36 @@ " 0.6871]}))" ] }, + "execution_count": 84, "metadata": {}, - "execution_count": 34 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { - "text/plain": "", - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 34;\n var nbb_unformatted_code = \"model_names, models = load_digit_rand_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n var nbb_formatted_code = \"model_names, models = load_digit_rand_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 84;\n", + " var nbb_unformatted_code = \"model_names, models = load_digit_rand_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n", + " var nbb_formatted_code = \"model_names, models = load_digit_rand_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -506,29 +814,50 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 85, "metadata": {}, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "image/png": { - "width": 257, - "height": 311 + "height": 311, + "width": 257 } - } + }, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "text/plain": "", - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 35;\n var nbb_unformatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Random\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n var nbb_formatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, means, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Random\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 85;\n", + " var nbb_unformatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, medians, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Random\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n", + " var nbb_formatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, medians, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Random\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -544,7 +873,7 @@ "plt.subplot(grid[0, 0])\n", "\n", "# Mean\n", - "plt.bar(model_names, means, color=\"grey\", alpha=0.2, width=0.5)\n", + "plt.bar(model_names, medians, color=\"grey\", alpha=0.2, width=0.5)\n", "\n", "# Points\n", "for name, model in zip(model_names, models):\n", @@ -557,6 +886,59 @@ "plt.title(\"Random\\nprojection\", loc=\"right\")\n", "_ = sns.despine()" ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.67875, 0.7697]\n", + "Astrocyte percent diff: 0.13399631675874782\n" + ] + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 86;\n", + " var nbb_unformatted_code = \"print(medians)\\nprint(f\\\"Astrocyte percent diff: {(medians[1] - medians[0])/ medians[0]}\\\")\";\n", + " var nbb_formatted_code = \"print(medians)\\nprint(f\\\"Astrocyte percent diff: {(medians[1] - medians[0])/ medians[0]}\\\")\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(medians)\n", + "print(f\"Astrocyte percent diff: {(medians[1] - medians[0])/ medians[0]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -575,9 +957,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7-final" + "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/notebooks/digits_exp159_161.ipynb b/notebooks/digits_exp159_161.ipynb index 436de86..c734494 100644 --- a/notebooks/digits_exp159_161.ipynb +++ b/notebooks/digits_exp159_161.ipynb @@ -393,7 +393,7 @@ "\n", "# Mean\n", "plt.subplot(grid[0, 0])\n", - "plt.bar(model_names, means, color=\"grey\", alpha=0.2, width=0.8)\n", + "plt.bar(model_names, medians, color=\"grey\", alpha=0.2, width=0.8)\n", "for name, model in zip(model_names, models):\n", " n = len(model[\"correct\"])\n", " plt.scatter(x=np.repeat(name, n), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", diff --git a/notebooks/fashion_exp1-4.ipynb b/notebooks/fashion_exp1-4.ipynb index 9eabefe..ac1cb9f 100644 --- a/notebooks/fashion_exp1-4.ipynb +++ b/notebooks/fashion_exp1-4.ipynb @@ -2,37 +2,106 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "import os\n", "import numpy as np\n", - "\n", - "from IPython.display import Image\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", - "\n", + "import torch\n", + "import glob\n", + "from collections import defaultdict" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 23;\n", + " var nbb_unformatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\n\\nsns.set(font_scale=2)\\nsns.set_style(\\\"ticks\\\")\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\nplt.rcParams[\\\"legend.title_fontsize\\\"] = \\\"14\\\"\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n", + " var nbb_formatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\n\\nsns.set(font_scale=2)\\nsns.set_style(\\\"ticks\\\")\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\nplt.rcParams[\\\"legend.title_fontsize\\\"] = \\\"14\\\"\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Pretty plots\n", "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'retina'\n", + "%config InlineBackend.figure_format='retina'\n", + "%config IPCompleter.greedy=True\n", "\n", "import seaborn as sns\n", - "sns.set(font_scale=2)\n", - "sns.set_style('ticks')\n", "\n", - "matplotlib.rcParams.update({'font.size': 16})\n", - "matplotlib.rc('axes', titlesize=16)\n", + "sns.set(font_scale=2)\n", + "sns.set_style(\"ticks\")\n", "\n", - "import torch\n", - "import glob\n", - "from collections import defaultdict" + "plt.rcParams[\"axes.facecolor\"] = \"white\"\n", + "plt.rcParams[\"figure.facecolor\"] = \"white\"\n", + "plt.rcParams[\"font.size\"] = \"14\"\n", + "plt.rcParams[\"legend.title_fontsize\"] = \"14\"\n", + "# Uncomment for local development\n", + "%load_ext nb_black\n", + "%load_ext autoreload\n", + "%autoreload 2" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 24;\n", + " var nbb_unformatted_code = \"def get_data(files, *keys):\\n \\\"\\\"\\\"Get data keys from saved digit exps.\\\"\\\"\\\"\\n data = defaultdict(list)\\n for f in files:\\n d = torch.load(f)\\n for k in keys:\\n data[k].append(d[k]) \\n \\n return data\";\n", + " var nbb_formatted_code = \"def get_data(files, *keys):\\n \\\"\\\"\\\"Get data keys from saved digit exps.\\\"\\\"\\\"\\n data = defaultdict(list)\\n for f in files:\\n d = torch.load(f)\\n for k in keys:\\n data[k].append(d[k])\\n\\n return data\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def get_data(files, *keys):\n", " \"\"\"Get data keys from saved digit exps.\"\"\"\n", @@ -40,30 +109,79 @@ " for f in files:\n", " d = torch.load(f)\n", " for k in keys:\n", - " data[k].append(d[k]) \n", - " \n", + " data[k].append(d[k])\n", + "\n", " return data" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 25;\n", + " var nbb_unformatted_code = \"def load_fashion_online_exps():\\n # Get\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp1*\\\") \\n exp1 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp2_*\\\") \\n exp2 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp1, exp2]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n \\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\\n\\ndef load_fashion_rand_exps():\\n # Sparse projection\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp3_*\\\") \\n exp3 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp4_*\\\") \\n exp4 = get_data(files, \\\"correct\\\")\\n \\n # Gather\\n models = [exp3, exp4]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n \\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n", + " var nbb_formatted_code = \"def load_fashion_online_exps():\\n # Get\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp1*\\\")\\n exp1 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp2_*\\\")\\n exp2 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp1, exp2]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\\n\\n\\ndef load_fashion_rand_exps():\\n # Sparse projection\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp3_*\\\")\\n exp3 = get_data(files, \\\"correct\\\")\\n\\n files = glob.glob(\\\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp4_*\\\")\\n exp4 = get_data(files, \\\"correct\\\")\\n\\n # Gather\\n models = [exp3, exp4]\\n model_names = [\\\"Astrocytes\\\", \\\"Neurons\\\"]\\n\\n # Sanity\\n assert len(model_names) == len(models)\\n\\n return model_names, models\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Learn VAE online\n", - "exp1_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp1*\") \n", - "exp1 = get_data(exp1_files, \"correct\")\n", + "def load_fashion_online_exps():\n", + " # Get\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp1*\")\n", + " exp1 = get_data(files, \"correct\")\n", + "\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp2_*\")\n", + " exp2 = get_data(files, \"correct\")\n", + "\n", + " # Gather\n", + " models = [exp1, exp2]\n", + " model_names = [\"Astrocytes\", \"Neurons\"]\n", + "\n", + " # Sanity\n", + " assert len(model_names) == len(models)\n", + "\n", + " return model_names, models\n", "\n", - "exp2_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp2_*\") \n", - "exp2 = get_data(exp2_files, \"correct\")\n", "\n", - "# Sparse projection\n", - "exp3_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp3_*\") \n", - "exp3 = get_data(exp3_files, \"correct\")\n", + "def load_fashion_rand_exps():\n", + " # Sparse projection\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp3_*\")\n", + " exp3 = get_data(files, \"correct\")\n", "\n", - "exp4_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp4_*\") \n", - "exp4 = get_data(exp4_files, \"correct\")" + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/fashion_exp4_*\")\n", + " exp4 = get_data(files, \"correct\")\n", + "\n", + " # Gather\n", + " models = [exp3, exp4]\n", + " model_names = [\"Astrocytes\", \"Neurons\"]\n", + "\n", + " # Sanity\n", + " assert len(model_names) == len(models)\n", + "\n", + " return model_names, models" ] }, { @@ -75,42 +193,196 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "text/plain": [ + "('Astrocytes',\n", + " defaultdict(list,\n", + " {'correct': [0.1135,\n", + " 0.1135,\n", + " 0.7609,\n", + " 0.8736,\n", + " 0.8481,\n", + " 0.8631,\n", + " 0.8121,\n", + " 0.8173,\n", + " 0.1135,\n", + " 0.8828,\n", + " 0.7738,\n", + " 0.867,\n", + " 0.8121,\n", + " 0.8399,\n", + " 0.88,\n", + " 0.8454,\n", + " 0.1135,\n", + " 0.8401,\n", + " 0.1135,\n", + " 0.8103]}))" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 26;\n", + " var nbb_unformatted_code = \"model_names, models = load_fashion_online_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n", + " var nbb_formatted_code = \"model_names, models = load_fashion_online_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_names, models = load_fashion_online_exps()\n", + "# Show example\n", + "i = 0\n", + "model_names[i], models[i]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { - "height": 261, + "height": 311, "width": 257 - }, - "needs_background": "light" + } }, "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 27;\n", + " var nbb_unformatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, medians, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Latent\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n", + " var nbb_formatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, medians, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Latent\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# Est stats\n", - "models = [\"AAN\", \"ANN\"]\n", - "means = [np.median(exp1[\"correct\"]), np.median(exp2[\"correct\"])]\n", + "# Est\n", + "means = [np.mean(exp[\"correct\"]) for exp in models]\n", + "stds = [np.std(exp[\"correct\"]) for exp in models]\n", + "medians = [np.median(exp[\"correct\"]) for exp in models]\n", + "assert len(means) == len(models)\n", "\n", + "# Plot grid\n", "fig = plt.figure(figsize=(3, 4))\n", "grid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\n", - "plt.bar(models, means, color=\"grey\", alpha=0.2, width=0.5)\n", - "plt.scatter(x=np.repeat(0, 20), y=exp1[\"correct\"], color=\"black\", alpha=0.2)\n", - "plt.scatter(x=np.repeat(1, 20), y=exp2[\"correct\"], color=\"black\", alpha=0.2)\n", - "plt.xticks(np.array([0,1]), ('Astrocytes', 'Neurons'))\n", + "plt.subplot(grid[0, 0])\n", + "\n", + "# Mean\n", + "plt.bar(model_names, medians, color=\"grey\", alpha=0.2, width=0.5)\n", + "\n", + "# Points\n", + "for name, model in zip(model_names, models):\n", + " n = len(model[\"correct\"])\n", + " plt.scatter(x=np.repeat(name, n), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", + "\n", + "# Axes\n", "plt.ylim(0, 1.1)\n", - "plt.ylabel(\"Correct\")\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.title(\"Latent\\nprojection\", loc=\"right\")\n", "_ = sns.despine()" ] }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.8147, 0.91235]\n", + "Astrocyte percent diff: 0.11986007119184978\n" + ] + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 28;\n", + " var nbb_unformatted_code = \"print(medians)\\nprint(f\\\"Astrocyte percent diff: {(medians[1] - medians[0])/ medians[0]}\\\")\";\n", + " var nbb_formatted_code = \"print(medians)\\nprint(f\\\"Astrocyte percent diff: {(medians[1] - medians[0])/ medians[0]}\\\")\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(medians)\n", + "print(f\"Astrocyte percent diff: {(medians[1] - medians[0])/ medians[0]}\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -120,42 +392,196 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "text/plain": [ + "('Astrocytes',\n", + " defaultdict(list,\n", + " {'correct': [0.7112,\n", + " 0.7133,\n", + " 0.6708,\n", + " 0.6727,\n", + " 0.7077,\n", + " 0.6692,\n", + " 0.7074,\n", + " 0.7029,\n", + " 0.6926,\n", + " 0.6447,\n", + " 0.7003,\n", + " 0.7057,\n", + " 0.6996,\n", + " 0.6488,\n", + " 0.7073,\n", + " 0.696,\n", + " 0.6679,\n", + " 0.6942,\n", + " 0.6988,\n", + " 0.5925]}))" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 29;\n", + " var nbb_unformatted_code = \"model_names, models = load_fashion_rand_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n", + " var nbb_formatted_code = \"model_names, models = load_fashion_rand_exps()\\n# Show example\\ni = 0\\nmodel_names[i], models[i]\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_names, models = load_fashion_rand_exps()\n", + "# Show example\n", + "i = 0\n", + "model_names[i], models[i]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" ] }, "metadata": { "image/png": { - "height": 261, + "height": 311, "width": 257 - }, - "needs_background": "light" + } }, "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 30;\n", + " var nbb_unformatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, medians, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Random\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n", + " var nbb_formatted_code = \"# Est\\nmeans = [np.mean(exp[\\\"correct\\\"]) for exp in models]\\nstds = [np.std(exp[\\\"correct\\\"]) for exp in models]\\nmedians = [np.median(exp[\\\"correct\\\"]) for exp in models]\\nassert len(means) == len(models)\\n\\n# Plot grid\\nfig = plt.figure(figsize=(3, 4))\\ngrid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\\nplt.subplot(grid[0, 0])\\n\\n# Mean\\nplt.bar(model_names, medians, color=\\\"grey\\\", alpha=0.2, width=0.5)\\n\\n# Points\\nfor name, model in zip(model_names, models):\\n n = len(model[\\\"correct\\\"])\\n plt.scatter(x=np.repeat(name, n), y=model[\\\"correct\\\"], color=\\\"black\\\", alpha=0.1)\\n\\n# Axes\\nplt.ylim(0, 1.1)\\nplt.ylabel(\\\"Accuracy\\\")\\nplt.title(\\\"Random\\\\nprojection\\\", loc=\\\"right\\\")\\n_ = sns.despine()\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# Est stats\n", - "models = [\"AAN\", \"ANN\"]\n", - "means = [np.median(exp3[\"correct\"]), np.median(exp4[\"correct\"])]\n", + "# Est\n", + "means = [np.mean(exp[\"correct\"]) for exp in models]\n", + "stds = [np.std(exp[\"correct\"]) for exp in models]\n", + "medians = [np.median(exp[\"correct\"]) for exp in models]\n", + "assert len(means) == len(models)\n", "\n", + "# Plot grid\n", "fig = plt.figure(figsize=(3, 4))\n", "grid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\n", - "plt.bar(models, means, color=\"grey\", alpha=0.2, width=0.5)\n", - "plt.scatter(x=np.repeat(0, 20), y=exp3[\"correct\"], color=\"black\", alpha=0.2)\n", - "plt.scatter(x=np.repeat(1, 20), y=exp4[\"correct\"], color=\"black\", alpha=0.2)\n", - "plt.xticks(np.array([0,1]), ('Astrocytes', 'Neurons'))\n", + "plt.subplot(grid[0, 0])\n", + "\n", + "# Mean\n", + "plt.bar(model_names, medians, color=\"grey\", alpha=0.2, width=0.5)\n", + "\n", + "# Points\n", + "for name, model in zip(model_names, models):\n", + " n = len(model[\"correct\"])\n", + " plt.scatter(x=np.repeat(name, n), y=model[\"correct\"], color=\"black\", alpha=0.1)\n", + "\n", + "# Axes\n", "plt.ylim(0, 1.1)\n", - "plt.ylabel(\"Correct\")\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.title(\"Random\\nprojection\", loc=\"right\")\n", "_ = sns.despine()" ] }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.6974, 0.7726999999999999]\n", + "Astrocyte percent diff: 0.10797246917120723\n" + ] + }, + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 31;\n", + " var nbb_unformatted_code = \"print(medians)\\nprint(f\\\"Astrocyte percent diff: {(medians[1] - medians[0])/ medians[0]}\\\")\";\n", + " var nbb_formatted_code = \"print(medians)\\nprint(f\\\"Astrocyte percent diff: {(medians[1] - medians[0])/ medians[0]}\\\")\";\n", + " var nbb_cells = Jupyter.notebook.get_cells();\n", + " for (var i = 0; i < nbb_cells.length; ++i) {\n", + " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", + " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", + " nbb_cells[i].set_text(nbb_formatted_code);\n", + " }\n", + " break;\n", + " }\n", + " }\n", + " }, 500);\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(medians)\n", + "print(f\"Astrocyte percent diff: {(medians[1] - medians[0])/ medians[0]}\")" + ] + }, { "cell_type": "code", "execution_count": null, diff 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" ] }, "metadata": { "image/png": { - "height": 264, + "height": 261, "width": 257 } }, @@ -89,7 +89,8 @@ "source": [ "# Est stats\n", "models = [\"AAN\", \"ANN\"]\n", - "means = [np.median(exp3[\"correct\"]), np.median(exp3[\"correct\"])]\n", + "means = [np.mean(exp3[\"correct\"]), np.mean(exp3[\"correct\"])]\n", + "medians = [np.median(exp3[\"correct\"]), np.median(exp3[\"correct\"])]\n", "\n", "fig = plt.figure(figsize=(3, 4))\n", "grid = plt.GridSpec(1, 1, wspace=0.3, hspace=0.8)\n", From 528db4fc2f7f2622a94cb6b68648a736b6f276c1 Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Mon, 17 May 2021 15:37:40 -0700 Subject: [PATCH 13/22] fmt w/ black --- glia/gn.py | 14 ++++---------- 1 file changed, 4 insertions(+), 10 deletions(-) diff --git a/glia/gn.py b/glia/gn.py index 8abbd57..3b4f22f 100644 --- a/glia/gn.py +++ b/glia/gn.py @@ -75,7 +75,6 @@ def __call__(self, m): class Base(Module): """Base Glia class. DO NOT USE DIRECTLY.q""" - def __init__(self, in_features, out_features, bias=True): super().__init__() self.in_features = in_features @@ -107,7 +106,6 @@ class Slide(Base): bias : bool Add a bias (leave as False). """ - def __init__(self, in_features, bias=True): # Init out out_features = in_features @@ -172,7 +170,6 @@ class Spread(Base): bias : bool Add a bias (leave as False). """ - def __init__(self, in_features, bias=True): # Init out out_features = in_features + 2 @@ -230,7 +227,6 @@ class Gather(Base): bias : bool Add a bias (leave as False). """ - def __init__(self, in_features, bias=True): # Init out out_features = max(in_features - 2, 1) @@ -290,17 +286,16 @@ def forward(self, input): # TODO figure out how to make GaussianBlur play well with z class Leak(nn.Module): """Model transmitter leak with Guassian blur""" - def __init__(self, in_features, sigma=1, kernel_size=3): super().__init__() self.in_features = in_features self.sigma = sigma self.kernel_size = kernel_size - self.GaussianBlur2d = GaussianBlur2d( - sigma=(self.sigma, self.sigma), - kernel_size=(self.kernel_size, self.kernel_size), - border_type='constant') + self.GaussianBlur2d = GaussianBlur2d(sigma=(self.sigma, self.sigma), + kernel_size=(self.kernel_size, + self.kernel_size), + border_type='constant') def forward(self, input): # The only grad-compatible fn I can find in the ecosystem @@ -314,7 +309,6 @@ def forward(self, input): class Noise(nn.Module): """Model Independent connection noise as Guassian noise""" - def __init__(self, in_features, sigma=1): super().__init__() From e600593d2be10d23c306e20de73c2b27b4e9e55f Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Mon, 17 May 2021 15:37:51 -0700 Subject: [PATCH 14/22] add test recipes --- Makefile | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/Makefile b/Makefile index 5e45e32..b3d8b58 100644 --- a/Makefile +++ b/Makefile @@ -1,14 +1,22 @@ SHELL=/bin/bash -O expand_aliases # DATA_PATH=/Users/type/Code/glia_playing_atari/data/ -# DATA_PATH=/Users/qualia/Code/glia_playing_atari/data -DATA_PATH=/home/stitch/Code/glia_playing_atari/data/ +DATA_PATH=/Users/qualia/Code/glia_playing_atari/data +# DATA_PATH=/home/stitch/Code/glia_playing_atari/data/ + +# ---------------------------------------------------------------------------- +# Tests - should always run fine +digits_test: + glia_digits.py VAE --glia=True --num_epochs=150 --use_gpu=False --lr=0.004 --lr_vae=0.01 --debug=True --seed_value=None --save=$(DATA_PATH)/digits_test + +fashion_test: + glia_fashion.py VAE --glia=True --num_epochs=3 --use_gpu=False --lr=0.008 --lr_vae=0.01 --debug=True --seed_value=None --save=$(DATA_PATH)/fashion_test # ---------------------------------------------------------------------------- xor_exp1: glia_xor.py --glia=False --debug=True xor_exp2: - glia_xor.py --glia=True --debug=True --lr=0.001 + glia_xor.py --glia=True --debug=True # ---------------------------------------------------------------------------- # low N epoch. SOA doesn't matter @@ -180,9 +188,6 @@ tune_digits_exp10: # --------------------------------------------------------------------------- # 5-6-2019 # 7dd363c757700feb81647b4aa213b574401d9e66 -digits_test: - glia_digits.py VAE --glia=True --epochs=10 --progress=True --use_gpu=False - # Glia comp is not analogoues to point source intiation followed by a circular # Ca traveling wave, than eventually gets summarized/decoded to digits. @@ -695,3 +700,5 @@ digits_exp161: parallel -j 16 -v \ --nice 19 --delay 2 --colsep ',' \ 'glia_digits.py VAE --glia=True --noise=True --sigma=0.8 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp161_s8_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + + From ae783986df7b1a4476345a9999d0f29d70a236d2 Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Mon, 17 May 2021 15:39:03 -0700 Subject: [PATCH 15/22] Add logs of loss/correct for digits and fashion --- glia/exp/glia_digits.py | 438 ++++++++++++++++++++------------------- glia/exp/glia_fashion.py | 417 +++++++++++++++++++------------------ 2 files changed, 443 insertions(+), 412 deletions(-) diff --git a/glia/exp/glia_digits.py b/glia/exp/glia_digits.py index 8ec89a4..ffa38ec 100644 --- a/glia/exp/glia_digits.py +++ b/glia/exp/glia_digits.py @@ -20,19 +20,19 @@ from glia import gn from random import shuffle +from copy import deepcopy class GP(nn.Module): """A Gaussian Random Projection""" - def __init__(self, n_features=784, n_components=20, random_state=None): super().__init__() self.n_features = n_features self.n_components = n_components self.decode = torch.Tensor( - gaussian_random_matrix( - self.n_components, self.n_features, - random_state=random_state)).float() + gaussian_random_matrix(self.n_components, + self.n_features, + random_state=random_state)).float() def forward(self, x): z = torch.einsum("bi,ji->bj", x.reshape(x.shape[0], 784), self.decode) @@ -41,15 +41,14 @@ def forward(self, x): class SP(nn.Module): """A Sparse Random Projection""" - def __init__(self, n_features=784, n_components=20, random_state=None): super().__init__() self.n_features = n_features self.n_components = n_components self.decode = torch.Tensor( - sparse_random_matrix( - self.n_components, self.n_features, - random_state=random_state).todense()).float() + sparse_random_matrix(self.n_components, + self.n_features, + random_state=random_state).todense()).float() def forward(self, x): z = torch.einsum("bi,ji->bj", x.reshape(x.shape[0], 784), self.decode) @@ -58,7 +57,6 @@ def forward(self, x): class VAE(nn.Module): """A MINST-shaped VAE.""" - def __init__(self, z_features=20): super().__init__() self.z_features = z_features @@ -92,7 +90,6 @@ class PerceptronNet(nn.Module): Note: assumes input is from a VAE. """ - def __init__(self, z_features=20, activation_function='Softmax'): super().__init__() self.z_features = z_features @@ -114,7 +111,6 @@ class PerceptronGlia(nn.Module): Note: assumes input is from a VAE. """ - def __init__(self, z_features=20, activation_function='Softmax'): # -------------------------------------------------------------------- # Init @@ -147,7 +143,6 @@ def forward(self, x): class PerceptronLeak(nn.Module): """A minst digit perceptron, with blured connections. """ - def __init__(self, z_features=20, activation_function='Softmax', sigma=1): # -------------------------------------------------------------------- # Init @@ -183,7 +178,6 @@ def forward(self, x): class PerceptronNoise(nn.Module): """A minst digit perceptron, with noisy connections.""" - def __init__(self, z_features=20, activation_function='Softmax', sigma=1): # -------------------------------------------------------------------- # Init @@ -219,7 +213,6 @@ def forward(self, x): class PerceptronDrop(nn.Module): """A minst digit perceptron, missing connections with probability p.""" - def __init__(self, z_features=20, activation_function='Softmax', p=.05): # -------------------------------------------------------------------- # Init @@ -291,6 +284,8 @@ def train_vae(model, 100. * batch_idx / len(train_loader), loss.item() / len(data))) + return train_loss + def test_vae(model, device, @@ -312,11 +307,10 @@ def test_vae(model, comparison = torch.cat( [data[:n], recon_batch.view(batch_size, 1, 28, 28)[:n]]) - save_image( - comparison.cpu(), - os.path.join(data_path, - "reconstruction_{}.png".format(epoch)), - nrow=n) + save_image(comparison.cpu(), + os.path.join(data_path, + "reconstruction_{}.png".format(epoch)), + nrow=n) test_loss /= len(test_loader.dataset) if progress or debug: @@ -357,8 +351,8 @@ def train(model, if (batch_idx % log_interval == 0) and debug: print(">>> Train example target[:5]: {}".format( target[:5].tolist())) - print(">>> Train example output[:5]: {}".format( - pred[:5, 0].tolist())) + print(">>> Train example output[:5]: {}".format(pred[:5, + 0].tolist())) print( '>>> Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.10f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), @@ -369,6 +363,8 @@ def train(model, loss, correct, len(train_loader.dataset), 100. * correct / len(train_loader.dataset))) + return loss + def test(model, model_vae, device, test_loader, progress=False, debug=False): # Test @@ -435,25 +431,23 @@ def run_VAE_only(batch_size=128, # ------------------------------------------------------------------------ # Get and pre-process data kwargs = {'num_workers': 1, 'pin_memory': True} if use_gpu else {} - train_loader = torch.utils.data.DataLoader( - datasets.MNIST( - data_path, - train=True, - download=True, - transform=transforms.ToTensor(), - ), - batch_size=batch_size, - shuffle=True, - **kwargs) - test_loader = torch.utils.data.DataLoader( - datasets.MNIST( - data_path, - train=False, - transform=transforms.ToTensor(), - ), - batch_size=test_batch_size, - shuffle=True, - **kwargs) + train_loader = torch.utils.data.DataLoader(datasets.MNIST( + data_path, + train=True, + download=True, + transform=transforms.ToTensor(), + ), + batch_size=batch_size, + shuffle=True, + **kwargs) + test_loader = torch.utils.data.DataLoader(datasets.MNIST( + data_path, + train=False, + transform=transforms.ToTensor(), + ), + batch_size=test_batch_size, + shuffle=True, + **kwargs) # ------------------------------------------------------------------------ # Decision model @@ -466,41 +460,46 @@ def run_VAE_only(batch_size=128, print(model_vae) # Learn classes + train_loss = [] + test_loss = [] for epoch in range(1, num_epochs + 1): # Learn z? - train_vae( - model_vae, - device, - train_loader, - optimizer_vae, - epoch, - log_interval=log_interval, - debug=debug, - progress=progress) - - test_loss = test_vae( - model_vae, - device, - test_loader, - epoch, - test_batch_size, - debug=debug, - progress=progress, - data_path=data_path) + loss = train_vae(model_vae, + device, + train_loader, + optimizer_vae, + epoch, + log_interval=log_interval, + debug=debug, + progress=progress) + # Log + train_loss.append(deepcopy(float(loss))) + + # Test + loss = test_vae(model_vae, + device, + test_loader, + epoch, + test_batch_size, + debug=debug, + progress=progress, + data_path=data_path) + # Log + test_loss.append(deepcopy(float(loss))) print(">>> Training complete") - print(">>> VAE loss: {:.5f}".format(test_loss)) - - state = dict( - vae_dict=model_vae.cpu().state_dict(), - batch_size=batch_size, - test_batch_size=test_batch_size, - num_epochs=num_epochs, - lr_vae=lr_vae, - use_gpu=use_gpu, - device_num=device_num, - test_loss=test_loss, - seed_value=seed_value) + print(">>> VAE loss: {:.5f}".format(test_loss[-1])) + + state = dict(vae_dict=model_vae.cpu().state_dict(), + batch_size=batch_size, + test_batch_size=test_batch_size, + num_epochs=num_epochs, + lr_vae=lr_vae, + use_gpu=use_gpu, + device_num=device_num, + train_loss=train_loss, + test_loss=test_loss, + seed_value=seed_value) if save is not None: torch.save(state, save + ".pytorch") @@ -550,25 +549,23 @@ def run_VAE(glia=False, # ------------------------------------------------------------------------ # Get and pre-process data kwargs = {'num_workers': 1, 'pin_memory': True} if use_gpu else {} - train_loader = torch.utils.data.DataLoader( - datasets.MNIST( - data_path, - train=True, - download=True, - transform=transforms.ToTensor(), - ), - batch_size=batch_size, - shuffle=True, - **kwargs) - test_loader = torch.utils.data.DataLoader( - datasets.MNIST( - data_path, - train=False, - transform=transforms.ToTensor(), - ), - batch_size=test_batch_size, - shuffle=True, - **kwargs) + train_loader = torch.utils.data.DataLoader(datasets.MNIST( + data_path, + train=True, + download=True, + transform=transforms.ToTensor(), + ), + batch_size=batch_size, + shuffle=True, + **kwargs) + test_loader = torch.utils.data.DataLoader(datasets.MNIST( + data_path, + train=False, + transform=transforms.ToTensor(), + ), + batch_size=test_batch_size, + shuffle=True, + **kwargs) # ------------------------------------------------------------------------ # Decision model @@ -595,10 +592,9 @@ def run_VAE(glia=False, if sigma < 0: raise ValueError("sigma must be postive") - model = PerceptronLeak( - z_features=z_features, - activation_function=activation_function, - sigma=sigma) + model = PerceptronLeak(z_features=z_features, + activation_function=activation_function, + sigma=sigma) elif noise: # Connection noise? if leak or drop: @@ -606,10 +602,9 @@ def run_VAE(glia=False, if sigma < 0: raise ValueError("sigma must be postive") - model = PerceptronNoise( - z_features=z_features, - activation_function=activation_function, - sigma=sigma) + model = PerceptronNoise(z_features=z_features, + activation_function=activation_function, + sigma=sigma) elif drop: # Connection loss if p < 0: @@ -617,10 +612,9 @@ def run_VAE(glia=False, if leak or noise: raise ValueError("leak, noise and drop are exclusive") - model = PerceptronDrop( - z_features=z_features, - activation_function=activation_function, - p=p) + model = PerceptronDrop(z_features=z_features, + activation_function=activation_function, + p=p) else: # The default model model = PerceptronGlia( @@ -642,67 +636,78 @@ def run_VAE(glia=False, # ----------------------------------------------------------------------- # Learn classes + vae_loss = [] # log losses + train_loss = [] + test_loss = [] + test_correct = [] for epoch in range(1, num_epochs + 1): # Learn z? if vae_path is None: - train_vae( - model_vae, - device, - train_loader, - optimizer_vae, - epoch, - log_interval=log_interval, - debug=debug, - progress=progress) - - test_loss = test_vae( - model_vae, - device, - test_loader, - epoch, - test_batch_size, - debug=debug, - progress=progress, - data_path=data_path) + train_vae(model_vae, + device, + train_loader, + optimizer_vae, + epoch, + log_interval=log_interval, + debug=debug, + progress=progress) + # VAE learn + loss = test_vae(model_vae, + device, + test_loader, + epoch, + test_batch_size, + debug=debug, + progress=progress, + data_path=data_path) + # Log + vae_loss.append(deepcopy(float(loss))) # Glia learn - train( - model, - model_vae, - device, - train_loader, - optimizer, - epoch, - log_interval=log_interval, - progress=progress, - debug=debug) - - test_loss, correct = test( - model, - model_vae, - device, - test_loader, - debug=debug, - progress=progress) + loss = train(model, + model_vae, + device, + train_loader, + optimizer, + epoch, + log_interval=log_interval, + progress=progress, + debug=debug) + # Log + train_loss.append(deepcopy(float(loss))) + + # Glia test + loss, correct = test(model, + model_vae, + device, + test_loader, + debug=debug, + progress=progress) + # Log + test_loss.append(deepcopy(float(loss))) + test_correct.append(deepcopy(float(correct))) print(">>> Training complete") - print(">>> Loss: {:.5f}, Correct: {:.2f}".format(test_loss, 100 * correct)) - - state = dict( - model_dict=model.cpu().state_dict(), - vae_dict=model_vae.cpu().state_dict(), - glia=glia, - batch_size=batch_size, - test_batch_size=test_batch_size, - num_epochs=num_epochs, - lr=lr, - vae_path=vae_path, - lr_vae=lr_vae, - use_gpu=use_gpu, - device_num=device_num, - test_loss=test_loss, - correct=correct, - seed_value=seed_value) + print(">>> Loss: {:.5f}, Correct: {:.2f}".format(test_loss[-1], + 100 * correct)) + + state = dict(model_dict=model.cpu().state_dict(), + vae_dict=model_vae.cpu().state_dict(), + glia=glia, + batch_size=batch_size, + test_batch_size=test_batch_size, + num_epochs=num_epochs, + lr=lr, + vae_path=vae_path, + lr_vae=lr_vae, + use_gpu=use_gpu, + device_num=device_num, + vae_loss=vae_loss, + train_loss=train_loss, + test_loss=test_loss, + test_correct=test_correct, + correct=correct, + seed_value=seed_value) if save is not None: torch.save(state, save + ".pytorch") @@ -749,36 +754,36 @@ def run_RP(glia=False, # ------------------------------------------------------------------------ # Get and pre-process data kwargs = {'num_workers': 1, 'pin_memory': True} if use_gpu else {} - train_loader = torch.utils.data.DataLoader( - datasets.MNIST( - data_path, - train=True, - download=True, - transform=transforms.ToTensor(), - ), - batch_size=batch_size, - shuffle=True, - **kwargs) - test_loader = torch.utils.data.DataLoader( - datasets.MNIST( - data_path, - train=False, - transform=transforms.ToTensor(), - ), - batch_size=test_batch_size, - shuffle=True, - **kwargs) + train_loader = torch.utils.data.DataLoader(datasets.MNIST( + data_path, + train=True, + download=True, + transform=transforms.ToTensor(), + ), + batch_size=batch_size, + shuffle=True, + **kwargs) + test_loader = torch.utils.data.DataLoader(datasets.MNIST( + data_path, + train=False, + transform=transforms.ToTensor(), + ), + batch_size=test_batch_size, + shuffle=True, + **kwargs) # ------------------------------------------------------------------------ # Decision model # Init if random_projection == 'SP': # Perceptrons assume 20; might not be ideal - model_rp = SP( - n_features=784, n_components=z_features, random_state=prng) + model_rp = SP(n_features=784, + n_components=z_features, + random_state=prng) elif random_projection == 'GP': - model_rp = GP( - n_features=784, n_components=z_features, random_state=prng) + model_rp = GP(n_features=784, + n_components=z_features, + random_state=prng) else: raise ValueError("random_projection must be GP or SP") @@ -793,45 +798,54 @@ def run_RP(glia=False, optimizer = optim.Adam(model.parameters(), lr=lr) # Learn classes + train_loss = [] + test_loss = [] + test_correct = [] for epoch in range(1, num_epochs + 1): # Glia learn - train( - model, - model_rp, - device, - train_loader, - optimizer, - epoch, - log_interval=log_interval, - progress=progress, - debug=debug) - - test_loss, correct = test( - model, - model_rp, - device, - test_loader, - debug=debug, - progress=progress) + loss = train(model, + model_rp, + device, + train_loader, + optimizer, + epoch, + log_interval=log_interval, + progress=progress, + debug=debug) + + # Log + train_loss.append(deepcopy(float(loss))) + + # Test + loss, correct = test(model, + model_rp, + device, + test_loader, + debug=debug, + progress=progress) + # Log + test_loss.append(deepcopy(float(loss))) + test_correct.append(deepcopy(float(correct))) print(">>> Training complete") print(">>> Loss: {:.5f}, Digit correct: {:.2f}".format( - test_loss, 100 * correct)) - - state = dict( - model_dict=model.cpu().state_dict(), - model_rp=random_projection, - glia=glia, - batch_size=batch_size, - test_batch_size=test_batch_size, - num_epochs=num_epochs, - lr=lr, - use_gpu=use_gpu, - device_num=device_num, - test_loss=test_loss, - correct=correct, - seed=seed_value) + test_loss[-1], 100 * correct)) + + state = dict(model_dict=model.cpu().state_dict(), + model_rp=random_projection, + glia=glia, + batch_size=batch_size, + test_batch_size=test_batch_size, + num_epochs=num_epochs, + lr=lr, + use_gpu=use_gpu, + device_num=device_num, + train_loss=train_loss, + test_loss=test_loss, + test_correct=test_correct, + correct=correct, + seed=seed_value) if save is not None: torch.save(state, save + ".pytorch") diff --git a/glia/exp/glia_fashion.py b/glia/exp/glia_fashion.py index 7ebf2be..66a5a9d 100644 --- a/glia/exp/glia_fashion.py +++ b/glia/exp/glia_fashion.py @@ -20,19 +20,19 @@ from glia import gn from random import shuffle +from copy import deepcopy class GP(nn.Module): """A Gaussian Random Projection""" - def __init__(self, n_features=784, n_components=20, random_state=None): super().__init__() self.n_features = n_features self.n_components = n_components self.decode = torch.Tensor( - gaussian_random_matrix( - self.n_components, self.n_features, - random_state=random_state)).float() + gaussian_random_matrix(self.n_components, + self.n_features, + random_state=random_state)).float() def forward(self, x): z = torch.einsum("bi,ji->bj", x.reshape(x.shape[0], 784), self.decode) @@ -41,15 +41,14 @@ def forward(self, x): class SP(nn.Module): """A Sparse Random Projection""" - def __init__(self, n_features=784, n_components=20, random_state=None): super().__init__() self.n_features = n_features self.n_components = n_components self.decode = torch.Tensor( - sparse_random_matrix( - self.n_components, self.n_features, - random_state=random_state).todense()).float() + sparse_random_matrix(self.n_components, + self.n_features, + random_state=random_state).todense()).float() def forward(self, x): z = torch.einsum("bi,ji->bj", x.reshape(x.shape[0], 784), self.decode) @@ -58,7 +57,6 @@ def forward(self, x): class VAE(nn.Module): """A MINST-shaped VAE.""" - def __init__(self, z_features=20): super().__init__() self.z_features = z_features @@ -92,7 +90,6 @@ class PerceptronNet(nn.Module): Note: assumes input is from a VAE. """ - def __init__(self, z_features=20, activation_function='Softmax'): super().__init__() self.z_features = z_features @@ -114,7 +111,6 @@ class PerceptronGlia(nn.Module): Note: assumes input is from a VAE. """ - def __init__(self, z_features=20, activation_function='Softmax'): # -------------------------------------------------------------------- # Init @@ -147,7 +143,6 @@ def forward(self, x): class PerceptronLeak(nn.Module): """A minst digit perceptron, with blured connections. """ - def __init__(self, z_features=20, activation_function='Softmax', sigma=1): # -------------------------------------------------------------------- # Init @@ -219,6 +214,8 @@ def train_vae(model, 100. * batch_idx / len(train_loader), loss.item() / len(data))) + return train_loss + def test_vae(model, device, @@ -240,15 +237,15 @@ def test_vae(model, comparison = torch.cat( [data[:n], recon_batch.view(batch_size, 1, 28, 28)[:n]]) - save_image( - comparison.cpu(), - os.path.join(data_path, - "reconstruction_{}.png".format(epoch)), - nrow=n) + save_image(comparison.cpu(), + os.path.join(data_path, + "reconstruction_{}.png".format(epoch)), + nrow=n) test_loss /= len(test_loader.dataset) if progress or debug: print('>>> VAE test loss: {:.4f}'.format(test_loss)) + return test_loss @@ -285,8 +282,8 @@ def train(model, if (batch_idx % log_interval == 0) and debug: print(">>> Train example target[:5]: {}".format( target[:5].tolist())) - print(">>> Train example output[:5]: {}".format( - pred[:5, 0].tolist())) + print(">>> Train example output[:5]: {}".format(pred[:5, + 0].tolist())) print( '>>> Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.10f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), @@ -297,6 +294,8 @@ def train(model, loss, correct, len(train_loader.dataset), 100. * correct / len(train_loader.dataset))) + return loss + def test(model, model_vae, device, test_loader, progress=False, debug=False): # Test @@ -363,25 +362,23 @@ def run_VAE_only(batch_size=128, # ------------------------------------------------------------------------ # Get and pre-process data kwargs = {'num_workers': 1, 'pin_memory': True} if use_gpu else {} - train_loader = torch.utils.data.DataLoader( - datasets.FashionMNIST( - data_path, - train=True, - download=True, - transform=transforms.ToTensor(), - ), - batch_size=batch_size, - shuffle=True, - **kwargs) - test_loader = torch.utils.data.DataLoader( - datasets.FashionMNIST( - data_path, - train=False, - transform=transforms.ToTensor(), - ), - batch_size=test_batch_size, - shuffle=True, - **kwargs) + train_loader = torch.utils.data.DataLoader(datasets.FashionMNIST( + data_path, + train=True, + download=True, + transform=transforms.ToTensor(), + ), + batch_size=batch_size, + shuffle=True, + **kwargs) + test_loader = torch.utils.data.DataLoader(datasets.FashionMNIST( + data_path, + train=False, + transform=transforms.ToTensor(), + ), + batch_size=test_batch_size, + shuffle=True, + **kwargs) # ------------------------------------------------------------------------ # Decision model @@ -394,41 +391,46 @@ def run_VAE_only(batch_size=128, print(model_vae) # Learn classes + train_loss = [] + test_loss = [] for epoch in range(1, num_epochs + 1): # Learn z? - train_vae( - model_vae, - device, - train_loader, - optimizer_vae, - epoch, - log_interval=log_interval, - debug=debug, - progress=progress) - - test_loss = test_vae( - model_vae, - device, - test_loader, - epoch, - test_batch_size, - debug=debug, - progress=progress, - data_path=data_path) + loss = train_vae(model_vae, + device, + train_loader, + optimizer_vae, + epoch, + log_interval=log_interval, + debug=debug, + progress=progress) + # Log + train_loss.append(deepcopy(float(loss))) + + test_loss = test_vae(model_vae, + device, + test_loader, + epoch, + test_batch_size, + debug=debug, + progress=progress, + data_path=data_path) + + # Log + test_loss.append(deepcopy(float(loss))) print(">>> Training complete") print(">>> VAE loss: {:.5f}".format(test_loss)) - state = dict( - vae_dict=model_vae.cpu().state_dict(), - batch_size=batch_size, - test_batch_size=test_batch_size, - num_epochs=num_epochs, - lr_vae=lr_vae, - use_gpu=use_gpu, - device_num=device_num, - test_loss=test_loss, - seed_value=seed_value) + state = dict(vae_dict=model_vae.cpu().state_dict(), + batch_size=batch_size, + test_batch_size=test_batch_size, + num_epochs=num_epochs, + lr_vae=lr_vae, + use_gpu=use_gpu, + device_num=device_num, + train_loss=train_loss, + test_loss=test_loss, + seed_value=seed_value) if save is not None: torch.save(state, save + ".pytorch") @@ -474,25 +476,23 @@ def run_VAE(glia=False, # ------------------------------------------------------------------------ # Get and pre-process data kwargs = {'num_workers': 1, 'pin_memory': True} if use_gpu else {} - train_loader = torch.utils.data.DataLoader( - datasets.FashionMNIST( - data_path, - train=True, - download=True, - transform=transforms.ToTensor(), - ), - batch_size=batch_size, - shuffle=True, - **kwargs) - test_loader = torch.utils.data.DataLoader( - datasets.FashionMNIST( - data_path, - train=False, - transform=transforms.ToTensor(), - ), - batch_size=test_batch_size, - shuffle=True, - **kwargs) + train_loader = torch.utils.data.DataLoader(datasets.FashionMNIST( + data_path, + train=True, + download=True, + transform=transforms.ToTensor(), + ), + batch_size=batch_size, + shuffle=True, + **kwargs) + test_loader = torch.utils.data.DataLoader(datasets.FashionMNIST( + data_path, + train=False, + transform=transforms.ToTensor(), + ), + batch_size=test_batch_size, + shuffle=True, + **kwargs) # ------------------------------------------------------------------------ # Decision model @@ -513,10 +513,9 @@ def run_VAE(glia=False, if glia: if sigma > 0: - model = PerceptronLeak( - z_features=z_features, - activation_function=activation_function, - sigma=sigma) + model = PerceptronLeak(z_features=z_features, + activation_function=activation_function, + sigma=sigma) else: model = PerceptronGlia( z_features=z_features, @@ -535,67 +534,77 @@ def run_VAE(glia=False, print(model) # Learn classes + vae_loss = [] # log losses + train_loss = [] + test_loss = [] + test_correct = [] for epoch in range(1, num_epochs + 1): # Learn z? if vae_path is None: - train_vae( - model_vae, - device, - train_loader, - optimizer_vae, - epoch, - log_interval=log_interval, - debug=debug, - progress=progress) - - test_loss = test_vae( - model_vae, - device, - test_loader, - epoch, - test_batch_size, - debug=debug, - progress=progress, - data_path=data_path) + train_vae(model_vae, + device, + train_loader, + optimizer_vae, + epoch, + log_interval=log_interval, + debug=debug, + progress=progress) + + loss = test_vae(model_vae, + device, + test_loader, + epoch, + test_batch_size, + debug=debug, + progress=progress, + data_path=data_path) + # Log + vae_loss.append(deepcopy(float(loss))) # Glia learn - train( - model, - model_vae, - device, - train_loader, - optimizer, - epoch, - log_interval=log_interval, - progress=progress, - debug=debug) - - test_loss, correct = test( - model, - model_vae, - device, - test_loader, - debug=debug, - progress=progress) + loss = train(model, + model_vae, + device, + train_loader, + optimizer, + epoch, + log_interval=log_interval, + progress=progress, + debug=debug) + # Log + train_loss.append(deepcopy(float(loss))) + + loss, correct = test(model, + model_vae, + device, + test_loader, + debug=debug, + progress=progress) + # Log + test_loss.append(deepcopy(float(loss))) + test_correct.append(deepcopy(float(correct))) print(">>> Training complete") - print(">>> Loss: {:.5f}, Correct: {:.2f}".format(test_loss, 100 * correct)) - - state = dict( - model_dict=model.cpu().state_dict(), - vae_dict=model_vae.cpu().state_dict(), - glia=glia, - batch_size=batch_size, - test_batch_size=test_batch_size, - num_epochs=num_epochs, - lr=lr, - vae_path=vae_path, - lr_vae=lr_vae, - use_gpu=use_gpu, - device_num=device_num, - test_loss=test_loss, - correct=correct, - seed_value=seed_value) + print(">>> Loss: {:.5f}, Correct: {:.2f}".format(test_loss[-1], + 100 * correct)) + + state = dict(model_dict=model.cpu().state_dict(), + vae_dict=model_vae.cpu().state_dict(), + glia=glia, + batch_size=batch_size, + test_batch_size=test_batch_size, + num_epochs=num_epochs, + lr=lr, + vae_path=vae_path, + lr_vae=lr_vae, + use_gpu=use_gpu, + device_num=device_num, + vae_loss=vae_loss, + train_loss=train_loss, + test_loss=test_loss, + test_correct=test_correct, + correct=correct, + seed_value=seed_value) if save is not None: torch.save(state, save + ".pytorch") @@ -642,36 +651,36 @@ def run_RP(glia=False, # ------------------------------------------------------------------------ # Get and pre-process data kwargs = {'num_workers': 1, 'pin_memory': True} if use_gpu else {} - train_loader = torch.utils.data.DataLoader( - datasets.FashionMNIST( - data_path, - train=True, - download=True, - transform=transforms.ToTensor(), - ), - batch_size=batch_size, - shuffle=True, - **kwargs) - test_loader = torch.utils.data.DataLoader( - datasets.FashionMNIST( - data_path, - train=False, - transform=transforms.ToTensor(), - ), - batch_size=test_batch_size, - shuffle=True, - **kwargs) + train_loader = torch.utils.data.DataLoader(datasets.FashionMNIST( + data_path, + train=True, + download=True, + transform=transforms.ToTensor(), + ), + batch_size=batch_size, + shuffle=True, + **kwargs) + test_loader = torch.utils.data.DataLoader(datasets.FashionMNIST( + data_path, + train=False, + transform=transforms.ToTensor(), + ), + batch_size=test_batch_size, + shuffle=True, + **kwargs) # ------------------------------------------------------------------------ # Decision model # Init if random_projection == 'SP': # Perceptrons assume 20; might not be ideal - model_rp = SP( - n_features=784, n_components=z_features, random_state=prng) + model_rp = SP(n_features=784, + n_components=z_features, + random_state=prng) elif random_projection == 'GP': - model_rp = GP( - n_features=784, n_components=z_features, random_state=prng) + model_rp = GP(n_features=784, + n_components=z_features, + random_state=prng) else: raise ValueError("random_projection must be GP or SP") @@ -686,45 +695,53 @@ def run_RP(glia=False, optimizer = optim.Adam(model.parameters(), lr=lr) # Learn classes + train_loss = [] + test_loss = [] + test_correct = [] for epoch in range(1, num_epochs + 1): # Glia learn - train( - model, - model_rp, - device, - train_loader, - optimizer, - epoch, - log_interval=log_interval, - progress=progress, - debug=debug) - - test_loss, correct = test( - model, - model_rp, - device, - test_loader, - debug=debug, - progress=progress) + loss = train(model, + model_rp, + device, + train_loader, + optimizer, + epoch, + log_interval=log_interval, + progress=progress, + debug=debug) + # Log + train_loss.append(deepcopy(float(loss))) + + loss, correct = test(model, + model_rp, + device, + test_loader, + debug=debug, + progress=progress) + + # Log + test_loss.append(deepcopy(float(loss))) + test_correct.append(deepcopy(float(correct))) print(">>> Training complete") print(">>> Loss: {:.5f}, Digit correct: {:.2f}".format( - test_loss, 100 * correct)) - - state = dict( - model_dict=model.cpu().state_dict(), - model_rp=random_projection, - glia=glia, - batch_size=batch_size, - test_batch_size=test_batch_size, - num_epochs=num_epochs, - lr=lr, - use_gpu=use_gpu, - device_num=device_num, - test_loss=test_loss, - correct=correct, - seed=seed_value) + test_loss[-1], 100 * correct)) + + state = dict(model_dict=model.cpu().state_dict(), + model_rp=random_projection, + glia=glia, + batch_size=batch_size, + test_batch_size=test_batch_size, + num_epochs=num_epochs, + lr=lr, + use_gpu=use_gpu, + device_num=device_num, + train_loss=train_loss, + test_loss=test_loss, + test_correct=test_correct, + correct=correct, + seed=seed_value) if save is not None: torch.save(state, save + ".pytorch") From f5a1221254e43ea232d21ed24f40b1bd4283b246 Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Mon, 17 May 2021 15:39:10 -0700 Subject: [PATCH 16/22] add notebooks for test recipes. the tests are very basic. --- notebooks/test_digits.ipynb | 240 +++++++++++++++++++++++++++++++++++ notebooks/test_fashion.ipynb | 240 +++++++++++++++++++++++++++++++++++ 2 files changed, 480 insertions(+) create mode 100644 notebooks/test_digits.ipynb create mode 100644 notebooks/test_fashion.ipynb diff --git a/notebooks/test_digits.ipynb b/notebooks/test_digits.ipynb new file mode 100644 index 0000000..47c035b --- /dev/null +++ b/notebooks/test_digits.ipynb @@ -0,0 +1,240 @@ +{ + "cells": [ + { + "source": [ + "# Test digits\n", + "Things are working and seem sane." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import torch\n", + "import glob\n", + "from collections import defaultdict\n", + "\n", + "from glia.util import load_checkpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 2;\n var nbb_unformatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\n\\nsns.set(font_scale=2)\\nsns.set_style(\\\"ticks\\\")\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\nplt.rcParams[\\\"legend.title_fontsize\\\"] = \\\"14\\\"\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n var nbb_formatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\n\\nsns.set(font_scale=2)\\nsns.set_style(\\\"ticks\\\")\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\nplt.rcParams[\\\"legend.title_fontsize\\\"] = \\\"14\\\"\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "# Pretty plots\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format='retina'\n", + "%config IPCompleter.greedy=True\n", + "\n", + "import seaborn as sns\n", + "\n", + "sns.set(font_scale=2)\n", + "sns.set_style(\"ticks\")\n", + "\n", + "plt.rcParams[\"axes.facecolor\"] = \"white\"\n", + "plt.rcParams[\"figure.facecolor\"] = \"white\"\n", + "plt.rcParams[\"font.size\"] = \"14\"\n", + "plt.rcParams[\"legend.title_fontsize\"] = \"14\"\n", + "# Uncomment for local development\n", + "%load_ext nb_black\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 3;\n var nbb_unformatted_code = \"digits_test = torch.load(\\n \\\"/Users/qualia/Code/glia_playing_atari/data/digits_test.pytorch\\\")\";\n var nbb_formatted_code = \"digits_test = torch.load(\\n \\\"/Users/qualia/Code/glia_playing_atari/data/digits_test.pytorch\\\"\\n)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "digits_test = torch.load(\n", + " \"/Users/qualia/Code/glia_playing_atari/data/digits_test.pytorch\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "dict_keys(['model_dict', 'vae_dict', 'glia', 'batch_size', 'test_batch_size', 'num_epochs', 'lr', 'vae_path', 'lr_vae', 'use_gpu', 'device_num', 'vae_loss', 'train_loss', 'test_loss', 'test_correct', 'correct', 'seed_value'])" + ] + }, + "metadata": {}, + "execution_count": 4 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 4;\n var nbb_unformatted_code = \"digits_test.keys()\";\n var nbb_formatted_code = \"digits_test.keys()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "digits_test.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "image/png": { + "width": 440, + "height": 287 + } + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 5;\n var nbb_unformatted_code = \"plt.plot(digits_test[\\\"train_loss\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Train error\\\")\\nsns.despine()\";\n var nbb_formatted_code = \"plt.plot(digits_test[\\\"train_loss\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Train error\\\")\\nsns.despine()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "plt.plot(digits_test[\"train_loss\"], color=\"black\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Train error\")\n", + "sns.despine()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/png": 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HAChNPvzwQ+3Zs0c33nij3njjDVeXAwAAANghhAJuZs+ePXbnaQIAAAAlCa/jAgAAAACchhAKAAAAAHAaQigAAAAAwGkIoQAAAAAApyGEAgAAAACchhAKAAAAAHAaQigAAAAAwGkIoQAAAAAApyGEAgAAAACchhAKAAAAAHAaQigAAAAAwGkIoQAAAAAApyGEAgAAAACchhAKAAAAAHAaQigAAAAAwGkIoQAAAAAApyGEAgAAAACchhAKoMiSkpLUqVMn1axZUxEREbJarUpJSXF1WQAAACiBCKEAimzNmjX67bffdOLECU2bNk1ms1lVqlSR2WzWtGnTlJSU5OoSAQAAUEIQQgEUWefOndWxY0e7vgsXLshqtSoiIkJBQUHq1auXvv/+e/3zzz8uqhIAAAAlASEUQJFVqlRJ69ev159//qk33nhDTZs2tft5enq6Fi9erKFDh6pmzZrq0qWLRo8erUOHDrmmYAAAALiMyWaz2VxdBIDiExERoZiYGLVv315Tp051WR179uyR1WqVxWLRpk2bch3Xpk0bhYeHy2w2q2nTpjKZTE6sEgAAAM5GCAXcTEkJoVc7cuSI5syZI4vForVr1yozMzPHcU2aNJHZbJbZbFbbtm0JpAAAAG6IEAq4mZIYQq8WFxenX3/9VRaLRcuWLVNaWlqO4+rUqWM8Ie3cubM8PT2dXCkAAAAcgRAKuJmSHkKvdubMGS1YsEAWi0ULFizI9ViXoKAg9evXT2azWXfccYd8fX2dXCkAAACKCxsTAXCZgIAADRw4UFFRUUpISNCcOXP00EMPqWLFinbj4uPjNWnSJN19990KCgrSAw88oF9++UXJyckuqhwAAACFRQgFUCKULVtWoaGh+umnn3Ty5EktXbpUw4YNU/Xq1e3GnTt3TpGRkRowYICCgoKMaxITE11UOQAAAAqC13EBN1OaXsfNj8zMTP3++++yWCyyWCw6ePBgjuM8PT11++23y2w2KywsTDVq1HBypQAAAMgPQijgZtwthF7NZrNp27ZtRiDdsWNHrmM7duwos9ms8PBwNWzY0IlVAgAAIC+EUMDNuHMIzervv/82ziL9448/ch3XsmVLY6fd5s2bc/QLAACACxFCATdzPYXQqx07dkxz5syR1WrV6tWrlZGRkeO4Ro0aGU9I27dvLw8PPo0HAABwJkIo4Gau1xB6tYSEBM2dO1cWi0VLlixRampqjuNq1qxpPCG97bbb5OXl5eRKAQAArj+EUMDNEELtnTt3TgsWLJDVatX8+fNzPdalUqVKCg0NVXh4uHr06KEyZco4uVIAAIDrA++hAXBr5cuX13333aeZM2cqPj5ec+fO1SOPPKJKlSrZjUtMTNSPP/6ofv36KSgoSPfdd59mzZqlc+fOuahyAAAA98STUMDN8CQ0f9LT07V27VpZLBZZrVbFxsbmOM7Hx0c9evSQ2WxWv379VKVKFSdXCgAA4F4IoYCbIYQWXGZmpjZu3Cir1arZs2dr//79OY7z8PBQSEiIcRZp7dq1nVwpAABA6UcIBdwMIbRobDabdu7caZxFunXr1lzHtm/fXmazWWazWY0bN3ZilQAAAKUXIRRwM4TQ4nXgwAHjLNINGzbkOq558+bG0S8tW7bkLFIAAIBcEEIBN0MIdZzjx48rOjpaFotFK1euzPUs0gYNGhhPSG+99VbOIgUAALgKIRRwM4RQ50hMTNS8efNksVi0ePFiXbx4Mcdx1atXV1hYmMxms7p27Spvb28nVwoAAFCyEEIBN0MIdb7k5GQtWrRIVqtV8+bN09mzZ3McFxgYqH79+ik8PFx33nmn/Pz8nFwpAACA6/GOGAAUkb+/v+655x5Nnz5dcXFxWrBggR577DEFBQXZjUtKStKUKVMUHh6uoKAg3XPPPZoxY4bOnDnjosoBAACcjyehgJvhSWjJkZGRofXr1xs77R49ejTHcd7e3urevbvCw8MVGhqqqlWrOrlSAAAA5yGEAm6GEFoy2Ww2/fnnn0Yg3bt3b47jPDw81LlzZ2On3bp16zq5UgAAAMcihAJuhhBaOuzevdsIpJs3b851XNu2bY2ddm+88UYnVggAAOAYhFDAzRBCS59Dhw5pzpw5slgsWrdunXL7bblp06bGE9I2bdpwFikAACiVCKGAmyGElm4nT540ziJdsWKF0tLSchxXt25d4wlpp06d5Onp6eRKAQAACocQCrgZQqj7SEpK0vz582WxWLRw4UJduHAhx3FVq1ZVaGiozGazunXrJh8fHydXCgAAkH8c0QIAJVRgYKAefPBBzZ49WwkJCbJarYqIiFBAQIDduLi4OE2YMEF33XWXqlatqkGDBslisej8+fMuqhwAACB3hFAAKAX8/PwUFhamKVOmKC4uTosXL9aTTz6patWq2Y07c+aMpk+frv79+ysoKEjh4eGaOnWqTp8+7aLKAQAA7PE6LuBmeB33+pKRkaHff//d2Gn30KFDOY7z8vJSt27dFB4errCwMFWvXt25hQIAAPw/QijgZgih1y+bzaYtW7bIYrHIarVq586dOY4zmUzq1KmTsdNugwYNnFwpAAC4nhFCATdDCMUVe/fuldVqlcVi0caNG3Md16pVK2On3ZtuuomjXwAAgEMRQgE3QwhFTo4ePWqcRbpmzRplZmbmOC44ONh4QtquXTsCKQAAKHaEUMDNEEJxLfHx8fr1119ltVq1dOlSpaam5jiudu3aCg8Pl9lsVufOneXl5eXkSgEAgDsihAJuhhCKgjh79qwWLFggi8WiBQsW5HqsS5UqVdSvXz+ZzWZ1795dvr6+Tq4UAAC4C45oAYDrWIUKFXT//ffr559/Vnx8vKKjozV48GBVrFjRblxCQoJ++OEH9enTR0FBQRo4cKCioqKUnJzsosoBAEBpxZNQwM3wJBTFIS0tTWvWrDF22j1x4kSO43x9fXXnnXfKbDarb9++qly5spMrBQAApQ0hFHAzhFAUt8zMTMXExMhisWj27Nk6cOBAjuM8PT3VtWtXmc1mhYWFqWbNmk6uFAAAlAaEUMDNEELhSDabTdu3b5fFYpHFYtH27dtzHXvrrbcaO+02atTIiVUCAICSjBAKuBlCKJxp//79xlmkv//+e67jbr75ZuMs0ptvvpmjXwAAuI4RQgE3QwiFq8TGxio6OloWi0WrVq1SRkZGjuMaNmxoPCHt0KGDPDzYIw8AgOsJIRRwM4RQlASnTp3S3LlzZbFYtGTJEl26dCnHcTVq1DDOIr3tttvk7e3t5EoBAICzEUIBN0MIRUlz7tw5LVq0SBaLRfPmzcv1WJdKlSqpX79+Cg8PV48ePVS2bFknVwoAAJyBd6AAAA5Vvnx5DRgwQJGRkYqPj9e8efP06KOPZjvOJTExUZMnT1ZoaKiCgoJ07733aubMmTp79qyLKgcAAI7Ak1DAzfAkFKVFenq61q1bZ+y0Gxsbm+M4Hx8fde/eXWazWf369VNQUJCTKwUAAMWJEAq4GUIoSqPMzExt2rRJVqtVs2fP1t9//53jOA8PD912223GWaR16tRxcqUAAKCoCKGAmyGEorSz2WzatWuXLBaLrFar/vrrr1zHtmvXzjj6JTg42IlVAgCAwiKEAm6GEAp3c/DgQeMs0g0bNii3P7aaNWtm7LTbqlUrziIFAKCEIoQCboYQCnf2zz//GGeRrlixQunp6TmOq1+/vvGEtGPHjpxFCgBACUIIBdwMIRTXi9OnT2vevHmyWCxatGiRLl68mOO4atWqKSwsTGazWV27dpWPj4+TKwUAAFfjn4YBAKVSxYoVFRERIavVqoSEBM2ePVsPPvigKlSoYDfu5MmT+v7779WzZ09Vq1ZNDz30kObMmaOUlBQXVQ4AwPWNEAoAKPXKlSsns9msadOmKT4+XgsXLtTjjz+uqlWr2o1LSkrS1KlTFR4eripVqqh///6aPn26kpKSXFQ5AADXH17HBdwMr+MC/5ORkaENGzYYZ5EeOXIkx3He3t664447FB4ertDQUFWrVs3JlQIAcP0ghAJuhhAK5Mxms+mvv/4yAunu3btzHGcymdS5c2eZzWaFh4erXr16Tq4UAAD3RggF3AwhFMif3bt3y2q1ymq1atOmTbmOa9OmjbHTbtOmTZ1YIQAA7okQCrgZQihQcIcPH9acOXNksVi0du3aXM8ivfHGG42zSNu2bctZpAAAFAIhFHAzhFCgaE6ePKlff/1VVqtVy5YtU1paWo7j6tatawTSf/3rX/L09HRypQAAlE6EUMDNEEKB4nPmzBnNnz9fFotFCxcuzPVYl6CgIIWGhspsNqtbt27y9fV1cqUAAJQeHNECAEAuAgIC9MADD+iXX35RfHy85syZo4ceekiBgYF24+Lj4zVx4kT17t1bVatW1YMPPqjZs2fr/PnzLqocAICSixAKAEA++Pn5KTQ0VD/99JPi4uK0ZMkSDRs2TNWrV7cbd/bsWc2YMUP33HOPqlSporCwME2ZMkWnT592UeUAAJQsvI4LuBlexwWcKzMzU7///rtx9MvBgwdzHOfl5aXbb79d4eHhCgsLU40aNZxcKQAAJQMhFHAzhFDAdWw2m7Zu3Sqr1SqLxaIdO3bkOM5kMqljx47GWaQ33HCDkysFAMB1CKGAmyGEAiXHvn37jEAaExOT67iWLVsaZ5E2a9aMo18AAG6NEAq4GUIoUDIdO3bMOIt09erVyszMzHFc48aNjSek7dq1k4cH2zcAANwLIRRwM4RQoORLSEjQr7/+KovFoqVLlyo1NTXHcbVq1TLOIu3SpYu8vLycXCkAAMWPEFoAaWlpWrJkiZYsWaIdO3YoMTFRNptN1apVU506ddSrVy/16tVL/v7+TqnnzJkzslqtWrt2rfbu3aszZ86obNmyqlatmoKDg9W3b1917ty5SH9p2bRpk6Kjo7VlyxadOHFCFy9eVFBQkKpXr66uXbuqb9++qlmzZoleQ0727Nkjs9msjIwMhYeHa+TIkYW+l6vWkBtCKFC6nD17VgsXLpTFYtH8+fNzPdalcuXK6tevn8xms7p3764yZco4uVIAAIoHITSfNm3apNdff12HDx/Oc1xAQIDeffdd9e7d26H1/Pzzzxo5cuQ1z6Br1KiRPv/8czVt2rRA9z9x4oTeeOMNrV+/Ps9xnp6eevzxx/Xvf/+7wCHL0WvIzaVLl3Tfffdp9+7dklSkEOqqNeSFEAqUXhcvXtSyZctksVgUHR2txMTEHMf5+/vr7rvvltls1l133aXy5cs7uVIAAAqPEJoPS5Ys0QsvvKC0tLR8XzN06FANHz7cIfV88skn+uGHH/I93tvbW2PGjFHXrl3zNf7AgQMaPHiwTp48me85OnTooIkTJ8rHxydf4x29htxkZmbqhRde0MKFC42+woZQV63hWgihgHtIT0/XmjVrZLFYZLVadfz48RzH+fr6qkePHjKbzerbt6+qVKni5EoBACgYQug17Ny5UwMHDtSlS5eMvuDgYA0aNEhNmzaVl5eX9u3bp5kzZ+qvv/6yu3bkyJEKDw8v1nqmT5+u999/366va9euCg8PV/369XXhwgVt2bJFU6dOVWxsrDGmXLlymjlzpoKDg/O8/7lz59S/f3+7J76VKlXSgw8+qFtvvVUVKlTQsWPHNH/+fM2fP19X/7+P2WzWxx9/7PI15CYjI0Ovv/665syZY9dfmBDqqjXkByEUcD+ZmZnauHGjcRbp/v37cxzn6empkJAQ4yzS2rVrO7lSAACujRCah4yMDIWFhWnfvn1Gn9ls1vvvvy9vb2+7sTabTePGjdOXX35p9JUrV07Lli1TpUqViqWe48ePq1evXkYgNplMeu+993TfffdlG3v+/Hm9/PLLWrZsmdHXunVrzZw5M8853n33XUVGRhrtZs2aacKECapcuXK2sWvXrtWzzz6rlJQUo2/SpEnq3LmzS9eQk8TERL300ktat25dtp8VNIS6ag35RQgF3JvNZtOOHTuMo1+2bt2a69gOHToYO+02btzYiVUCAJA79n3PQ3R0tF0Abdu2rUaMGJEtgEqXg8iwYcP06KOPGn3nz5/XuHHjiq2er7/+2u6J7BNPPJFj8JEuB+DRo0erdevWRt9ff/1lF4ayOnLkiMuLh7QAACAASURBVH7++WejHRgYqHHjxuUYQCWpS5cuGjVqlF3fqFGjlNe/azh6DTmJiYlRv379cgygheGKNQDAFSaTSTfffLPefvttbdmyRfv379dnn32mjh07Zhv7xx9/6JVXXlFwcLBuvvlmvfPOO9q6dWuev08DAOBohNA8TJs2za79yiuvyNPTM89rnn/+eQUFBRntqKgoXbx4sci1nD59WvPnzzfagYGBGjZsWJ7X+Pj46N1337XrmzJlSq7jIyMjlZGRYbSHDBmiqlWr5jlHt27d1KNHD6O9a9cubdq0KcexzljD1eLi4vT2229r8ODBio+PN/qv9b9hXpy9BgC4loYNG+rFF1/Uhg0bFBsbq++++07du3fP9nvdjh079P7776tVq1Zq1KiRXnrpJf3222+5nlcKAICjEEJzceTIEe3cudNoBwcHq2XLlte8ztfX1+470JSUFK1atarI9SxbtszuHLk+ffqobNmy17zuxhtvVKtWrYz2pk2blJCQkOPYqzfr8fDwUP/+/fNVW9angFff52rOWMMVo0eP1p133qlZs2bZBesmTZpoxIgR15wzN85cAwAUVM2aNTVs2DAtXbpUcXFxmjx5skJDQ7Md53LgwAF9/vnn6tSpk2rXrq2nnnpKy5YtK9AGfAAAFBYhNBdZX93s0qVLvq/N+k3k0qVLi1xP1qNSbrvttkLVk5GRoRUrVmQb89///lcnTpww2s2aNcv1NdysOnToYLcrbm7rdfQarjZu3DhduHDBaHt4eOihhx7SrFmzVKtWrXzPm5Uz1wAARVGpUiU9/PDDmjNnjuLj4xUVFaWBAwdmO87lxIkTGjt2rHr06KFq1app8ODBio6Otvs9FACA4kQIzcWOHTvs2vl5CnpF8+bNZTKZjHbWXXOLo54WLVrk+9qsY3OqZ/v27XbtgqzXx8dHTZo0MdpxcXF2O8Je4eg15KZ169aaNWuW3njjjXw9tcyLq9YAAEXh7++ve+65RzNmzFB8fLwWLFigxx57LNtxLqdPn9ZPP/2ksLAwBQUFacCAAYqMjNSZM2dcVDkAwB0RQnORdfv7Ro0a5fvacuXK2X1LGRsba7eDbEFduHBBx44dM9qVK1dWxYoV8319/fr17dp///13tjFZ19uwYcMC1Zh1jqz3c8YasmrRooXGjh2rmTNnFigs5sYVawCA4ubr66u77rpLEyZM0IkTJ7Rq1So9++yz2Y5zOX/+vH755Rc98MADCgoKUu/evTVx4kTFxcW5qHIAgLsghOYi66Hg1atXL9D1Wcfn9GQwv06cOGG3k2GNGjWKVMvVQeqKrOutWbNmsc7hjDVcLSoqSlFRUerWrVuB5smLs9cAAI7m5eWlkJAQffXVVzpy5Ig2btyo1157LdtZxmlpaVq4cKEef/xx1ahRQ127djWuAQCgoAihOcjMzFRiYqLR9vPzU7ly5Qp0j6xng159v4LKuoFN1tenrsXX19eu/qSkpGy7IV69e6ykfH8Pmtv4rOt1xhquVhxPPrNy9hoAwJlMJpNuueUWffTRR9qzZ4927typESNGqE2bNnbjMjMztXr1aj3//POqV6+e2rVrp48//lh79+51UeUAgNLGy9UFlETJycl2O6oWNIDmdM3Zs2cLXU/Wa/39/QtVz/nz5yVdPuj83LlzCggIKLY5sq436/dDzliDo7lyDRaLRVarNV9z7N69u8B1AcDVTCaTbrrpJt1000164403dOjQIVmtVlksFq1fv97urZBNmzZp06ZNev3113XTTTcpPDxcZrNZrVu3ttsfAQCAKwihObj6CA5J2ba2z4+rd4vN6Z5FqcfX17fY6ynqmgt6f0eswdFcuYbY2FjFxMQUeD4AKA7169fX8OHDNXz4cJ08eVLR0dGyWCxavny50tPTjXG7du3Srl279OGHH6pevXoym80ym83q2LFjkc5oBgC4F0JoDrKek1aYPzi9vb3zvGdBZA0qXl4F/58t6zVX/6VBKvqar3V/Z6zB0Vy5hlq1aql9+/b5Grt7926dO3euwLUBQH5Uq1ZNTzzxhJ544gklJSVp3rx5slqtWrhwod2xLocPH9bo0aM1evRoVa1aVWFhYTKbzbr99tuz/YMcAOD6QgjNQXG8PnT167xS4YLsFVnrufo1qPzK+u2hh4f958BFneNa63XGGhzNlWu48jQhPyIiInhqCsApAgMDNWjQIA0aNEgpKSlavHixLBaL5s6da/dZRlxcnMaPH6/x48crICBAffv2ldlsVs+ePeXn5+fCFQAAXIGNiXJQHE8xs4aywry6mVs9hXkCeK16ijqHo++fnzkczR3WAACO4ufnp/DwcE2dOlVxcXFatGiRnnzySbsjy6TLewZMmzZNZrNZVapUkdls1rRp05SUlOSiygEAzkYIzUHWDWeufr0ov7KeC1qY70pzq6cwZ45e2QznirJly+Y5R0HXfK31OmMNjuYOawAAZ/Dx8VHPnj01btw4HT9+XGvXrtXw4cNVr149u3EXLlyQ1WpVRESEgoKC1LNnT33//ff6559/XFQ5AMAZCKE58PX1tXs96Ny5cwV+9TLrTqoFPc7jahUrVszz3tdis9nswo+/v3+2J3BZ58i6u+21XGu9zliDo7nDGgDA2Tw9PdW5c2d98cUXOnjwoDZv3qw333xTN910k9249PR0LVmyREOHDlXNmjXVpUsXjR49WocOHXJN4QAAhyGE5qJ69erGf6elpRX4NaGinil5tRo1auR572s5ffq03SvFOdVy9XoLM8e11uuMNTiaO6wBAFzJZDKpdevW+uCDD7Rz507t2bNHH3/8sdq1a2c3zmazad26dXrhhRfUoEEDtWnTRiNGjNCuXbsK9T0+AKBkIYTmIusrQ0ePHs33tTabTceOHTPa/v7+2b6JKYgqVarYncN57NixAv0hnLX2G264IduYoqxXko4cOZLnHM5Yg6O5wxoAoCRp0qSJXn31VcXExOjw4cP66quvFBISkm3Ttr/++ktvvfWWmjVrpqZNm+q1117Txo0bCaQAUEoRQnOR9TWhffv25fvaI0eO2H1TGRwcXKz1pKSk2IXca8lae071FGW9Wcd7eXmpYcOGec7hiDU4gzusAQBKorp16+rZZ5/VqlWr9M8//2jixInq3bt3tk3h9u7dq5EjR6p9+/aqV6+ennvuOa1evTrbxm8AgJKLEJqLtm3b2rU3bdqU72s3btxo187v+Y4FqSfrHAWpp0OHDtnGtGjRwu4P+oKs9+jRozp58qTdvXL61tHRa3AGd1gDAJR0QUFBGjJkiObPn6/4+HjNmDFD99xzj93bKNLlP3++/vprde3aVTVq1NBjjz2mBQsW6NKlSy6qHACQH4TQXLRr185uc6KVK1fm+w+1RYsW2bVDQkKKXE/We2SdIzcXL17U6tWrjXa5cuV0yy23ZBvn5+dnF5ZjY2O1bdu2fM2xcOHCPGvNrb+41+AM7rAGAChNAgICNHDgQEVFRSk+Pl7R0dF6+OGHs20WFx8fr0mTJunuu+9WUFCQHnjgAUVFRSk5OdlFlQMAckMIzYWPj4969+5ttJOSkhQZGXnN67Zt26Z169YZ7YYNG6pNmzZFrqd169aqX7++0V67dq127NhxzeumT59ut6lS37595ePjk+PY0NBQu/bYsWOvef/k5GRNmTLFaHt6eio8PDzHsc5Yg6O5wxoAoLQqW7as+vXrp8mTJ+vkyZNaunSpnnrqqWwbx507d06RkZG69957VaVKFYWGhuqnn35SYmKiiyoHAFyNEJqHRx55RJ6enkZ71KhReb5+GRcXp+eee85uo4THH3+8WGoxmUx69NFHjXZmZqaeffbZPHdo/f333zV69Gij7e3trUceeSTX8XfddZdq1apltFesWKHx48fnOj49PV3Dhw9XfHy80RcaGqpq1aq5bA2O5g5rAAB34O3tre7du+vbb7/VsWPHtGHDBr344ovZNn27dOmSfv31Vw0ePFhVq1ZV9+7d9d133+n48eMuqhwA4PAQ+v333+vZZ5/V2rVrHT1VsWvUqJEeeOABo52amqrHHntMM2bMsDtqQ5LWr1+ve++91+4PtVatWmV7uni1b775Rk2aNDF+devWLc96+vfvb7cxTmxsrO69915t2LDBblxqaqqmT5+uJ5980q7Ohx9+2O4pXlY+Pj569dVX7fpGjRql999/P9u5oQcPHtTgwYO1Zs0aoy8gIEDPP/+8S9fgDO6wBgBwJx4eHurYsaM+++wz7d+/X1u3btU777yjm2++2W5cRkaGli9frqefflq1atVSp06d9Pnnn+u///2viyoHgOuTyebA/c3T09PVpUsX4zXEGjVq6JtvvlGzZs0cNWWxu3jxooYMGZJto55KlSqpWbNm8vHx0f79+3X48GG7n1epUkVRUVGqWbNmrvf+5ptvNGbMGKNdq1YtrVixIs96Dh06pEGDBtk9fZQuH7HSqFEjpaamaufOndleOWrXrp1+/PHHbLsM5uSTTz7RDz/8YNfn6+urli1bqmLFioqNjdXOnTvtnvh6eXnpu+++y9f3r85YQ17++OMPPfTQQ0Y7PDxcI0eOLNA9XL2GvERERCgmJkbt27fX1KlTHTYPAJQGf//9t6xWq6xWq37//fdcx7Vs2VIjRoxQnz59nFgdAFyfHPokdPPmzTp9+rSky2dnpqSk5Hh0R0lWpkwZTZgwQZ07d7brT0xM1Nq1a7V8+fJsAbROnTqaNm1angG0sOrXr68pU6aodu3adv2HDx/W8uXLtXbt2mzBp3Pnzvr+++/zHXxeeeUVPfnkkzKZTEbfpUuXFBMTo8WLF2vHjh12AdTPz884262krMHR3GENAHA9aNy4sV5++WX99ttvOnbsmMaMGaNu3brZfW4jSVu3blVoaKimTZvmokoB4Prh0BC6d+9e479NJpM6deqkMmXKOHJKh/Dz89OkSZP06aef5hmiAwMDNXToUP36669q0KCBw+q54YYbNH/+fD399NOqUqVKruMaNGigESNGaOLEidm2tb+WF154QbNmzVKHDh2yHRp+hbe3t/r27au5c+eqe/fuJW4NjuYOawCA60mtWrX09NNPa/ny5Tp58qR+/PFH9e3b1zhWLDMzUw899JB+/PFHF1cKAO7Noa/jTpgwQaNGjTKeqA0ZMkQvvviio6ZzmoMHD2rHjh1KSEhQamqqAgICFBwcrObNmzt9x9PMzExt3bpVBw8eVEJCgjw8PFS5cmU1b95cjRo1snuaWVinTp3Sn3/+qbi4OJ07d07+/v6qX7++WrVqpfLly5eKNThaSVoDr+MCQMGcPHlSPXr00Pbt242+8ePHF9vmggAAe16OvHmdOnXs2idOnHDkdE7ToEEDhz7pLAgPDw+1bt1arVu3dtgclStX1p133umw+ztjDY7mDmsAgOtVtWrVtGLFCvXo0UNbtmyRJD3xxBNKS0vTU0895eLqAMD9OPR13JCQEAUEBEi6/E3oypUrOaMLAACUOFWqVNHy5cvVtm1bo+/pp5/WV1995cKqAMA9OTSEli1bVi+//LJsNptMJpMuXLigZ599VsnJyY6cFgAAoMAqVaqkZcuWqUOHDkbf888/r88//9yFVQGA+3H4OaH9+/fXyJEj5evrK5vNpj///FO9evXS+PHjtXv3bmVmZjq6BAAAgHwJDAzUkiVL9K9//cvoe+mll/TRRx+5sCoAcC8O3ZhIkjZu3Cjp8mY+X3zxhc6cOWM8GZUuH4FSt25dBQQEqEKFCtm2TL8Wk8mkL7/8stjrBkorNiYCgKJLTk7W3XffrTVr1hh97733nt5++20XVgUA7sGhGxNJl/9CnHVnUJPJZJwzeeHCBe3du7dQu4deHWYBAACKi7+/vxYsWKB+/fppxYoVkqR33nlHaWlpev/99/n7BwAUgcNfx70i6wNXk8lk9wsAAKAkKVeunObNm2e3Q/yIESP06quvZvt7DQAg/5wSQq/8Rm2z2Yr1FwAAgCOVLVtW0dHR6t27t9H36aef6j//+Q9/FwGAQnL467gPPvigo6cAAABwmDJlyshisei+++5TdHS0JGn06NFKS0vT119/zRtdAFBADg+hb731lqOnAAAAcChfX1/9/PPPGjhwoCwWiyRpzJgxSktL03fffScPD6d94QQApR6/YwIAAOSDj4+PZs6cqfvuu8/o+/777/X4448rIyPDhZUBQOlCCAUAAMgnb29vTZs2ze5zox9++EGPPPIIQRQA8okQCgAAUABeXl766aefNHjwYKNv6tSpioiIUHp6uusKA4BSwuHfhF5LfHy8tm/frsTERCUlJenSpUsqW7as/P39Vbt2bTVo0EA1atRwdZkAAAAGT09PTZo0Sd7e3powYYIkKTIyUunp6Zo+fbq8vb1dXCEAlFwuCaGJiYn66aeftHjxYh0+fPia42vWrKkePXpo4MCBqlevnhMqBAAAyJuHh4fGjRsnLy8vjR07VpIUFRWl9PR0zZw5Uz4+Pi6uEABKJqe/jjtmzBjdcccdGj9+vA4dOpSv80BjY2P1008/6e6779bHH3+sS5cuObtsAACAbDw8PPTtt9/queeeM/qsVqv69+/P31cAIBdOC6Hnzp3TkCFD9O233+rChQuy2WwymUz5/mWz2ZSenq4pU6Zo0KBBSkxMdFbpAAAAuTKZTBo9erRefPFFo2/evHkKCwvThQsXXFgZAJRMTgmh6enpevrpp7V+/Xq78Hn1004/Pz9Vr15d9evXV1BQkMqUKWP386uv2b59u5588kn+hREAAJQIJpNJn376qV577TWjb9GiRerXr59SUlJcWBkAlDxO+Sb0k08+UUxMjEwmkyTJZrPJw8NDXbt2VVhYmFq1aqVq1aplu+7o0aPaunWroqOj7QKszWbTjh07NGrUKL3++uvOWAIAAECeTCaTPvzwQ/n4+Oi9996TJC1btkx333235s6dK39/fxdXCAAlg8OfhB48eFAzZsywC6ANGzbUL7/8orFjx6pnz545BlBJqlOnjvr06aMJEyZo1qxZatCggV0QnT59ug4cOODoJQAAAOSLyWTSu+++qw8++MDoW7Vqle666y6dO3fOhZUBQMnh8BA6btw4u8ObmzRposjISN10000Fus/NN9+sWbNmqUmTJkZfZmamJk+eXFylAgAAFIs333xTI0eONNrr1q1Tz549debMGRdWBQAlg0NDaGZmplauXGk8ufTx8dGYMWNUoUKFQt2vfPnyGjNmjHx8fIx7Ll682C7kAgAAlASvvPKKvvjiC6P922+/qUePHjp9+rQLqwIA13NoCN22bZvOnj0r6fLrKQMGDFCdOnWKdM86depowIABstlskqSzZ89q165dRa4VAACguA0fPlxff/210d64caO6d++uU6dOubAqAHAth4bQo0ePSpIRGHv06FEs973zzjvt2n///Xex3BcAAKC4/fvf/9bYsWON9ubNm3XHHXcoPj7ehVUBgOs4NIRm/Ve++vXrF8t969WrJ0nGZke81gIAAEqyoUOHauLEicbfXbZu3arbb79dJ0+edHFlAOB8Dg2haWlpdm0fH59iuW/W+2RmZhbLfQEAABxlyJAhmjx5sjw8Lv/1a+fOneratatOnDjh4soAwLkcGkIDAwPt2sX1r31Z71OpUqViuS8AAIAjPfTQQ5o6daoRRPfs2aOQkBAdO3bMxZUBgPM4NITWqFFD0v9em12/fn2x3HfdunWS/vetaVBQULHcFwAAwNEeeOABzZw5U56enpIu720REhKiI0eOuLgyAHAOh4bQ1q1bG7/B2mw2RUZGKjU1tUj3TE1NVWRkpBFsPT091aZNmyLXCgAA4CwDBgxQVFSUvL29JUkHDhxQSEiIDh486OLKAMDxHBpCy5Urp7Zt2xpPLGNjY/Xpp58W6Z6ffvqpYmNjJV1+wtqyZUv5+/sXuVYAAABnCg8P1+zZs429Lg4dOqSQkBDt37/fxZUBgGM5NIRK0sMPPyzpcmC02WyaPn26PvnkE6WnpxfoPunp6Ro5cqSmTZtm3EuSBg0aVOw1AwAAOEPfvn01Z84c+fr6Srp8vF1ISIj27dvn4soAwHEcHkLvuOMOtWrVStL/gujkyZNlNpsVHR2tS5cu5Xn9pUuXNGfOHJnNZv30009Gv8lkUtOmTXXXXXc5tH4AAABHuuuuuzR37lyVKVNGknT8+HGFhIRo9+7dLq4MABzDZLvySNGBjh49qrCwMKWkpEj634ZCJpNJXl5eCg4OVuPGjVW+fHn5+fkpJSVFZ8+e1f79+7Vv3z6lp6fbXWOz2RQQEKBZs2YV29mjgLuIiIhQTEyM2rdvr6lTp7q6HABAPq1cuVJ9+vQx/r4UFBSkFStWqHnz5i6uDACKl5czJqlTp44mTJigYcOG6ezZs8amQjabTWlpadq5c6d27dqV7bqr8/HV1/j5+emrr74igAIAALdx++23a9GiRerdu7eSk5MVHx+vrl27atmyZcZbZQDgDhz+Ou4Vbdq00YwZM3TDDTfYPdW88stms2X7ldPPGzdurF9++UW33nqrs0oHAABwii5dumjx4sUqX768JOnUqVPq1q2b/vzzTxdXBgDFx2khVJIaNmyoefPmaeTIkWrQoIFd4LziyhNPSXY/b9y4sT766CNZLBbdcMMNziwbAADAaTp16qRly5YpICBAknT69GndcccdiomJcXFlAFA8nPJNaG6OHj2q33//Xdu3b9fp06eVlJSk5ORk+fn5qUKFCgoKCtLNN9+stm3bEjyBfOKbUABwD5s3b1b37t11+vRpSVL58uW1aNEiderUycWVAUDROPyb0OTkZJUtW1aenp7ZflanTh3VqVNHAwYMcHQZAAAApUqbNm20cuVK3XHHHTp16pTOnTunnj17asGCBerSpYurywOAQnP467gffvihQkJCNGrUKB0+fNjR0wEAALiNli1batWqVapataqky/+436tXL61cudLFlQFA4Tk0hCYnJ2v+/Pk6deqUJk6cqF69evFhPQAAQAE0b95cq1atUvXq1SVJKSkp6t27t5YuXeriygCgcBwaQmNiYpSamirp8iZDdevWVdu2bR05JQAAgNtp2rSpVq1apZo1a0qSLl68qL59+2rhwoUurgwACs6hIfTQoUPGf5tMJrVp08aR0wEAALitJk2aaPXq1apTp44k6dKlSwoLC9PcuXNdXBkAFIxDQ6iHh/3tK1as6MjpAAAA3FqjRo20evVq1a9fX5KUmpoqs9ksq9Xq2sIAoAAcGkKDg4Pt2n///bcjpwMAAHB7DRo00OrVq43j69LT0zVgwABFRUW5uDIAyB+HhtCOHTuqXr16ki5/E7p+/Xrt2rXLkVMCAAC4vbp162rNmjVq3LixJCkjI0P333+/ZsyY4eLKAODaHBpCTSaTPvvsM/n5+clkMikzM1NPPPGEtm3b5shpAQAA3F6tWrW0evVq3XjjjZKkzMxMRUREaMqUKS6uDADy5vBzQlu0aKFZs2apYcOGkqSEhATdd999GjJkiCZPnqwtW7bo7Nmzji4DAADA7dSoUUOrVq1Ss2bNJF0OooMHD9akSZNcXBkA5M7L0ROMGTNGktStWzclJCTozJkzstls2rBhgzZs2GCMM5lM8vf3l6enZ4HubzKZ7O4DAABwPalWrZpWrlyp7t27a9u2bbLZbHrssceUlpamoUOHuro8AMjGKSHUZDLZ9ZlMJtlsNrs+m81WqCeiWe8NAABwvQkKCtKKFSt05513avPmzZKkYcOGKT09Xc8884yLqwMAew5/HfeKrKHTZDIV+RcAAAAuq1y5spYtW6Z27doZff/+9781evRoF1YFANk5JYReCaA2m61YfwEAAOB/KlasqKVLl6pjx45G3wsvvKBPPvnEhVUBgD2Hv4771VdfOXoKAAAA/L+AgAAtXrxYvXv31rp16yRJr776qtLS0vTmm2+6uDoAcEII7dmzp6OnAAAAwFXKly+vRYsWqU+fPlq1apUk6a233lJaWpreffddPmsC4FIOfx03OTlZGRkZjp4GAAAAVylXrpzmz5+v7t27G33vv/++3njjDT5rAuBSDg+hH374oUJCQjRq1CgdPnzY0dMBAADg//n5+Wnu3Lnq1auX0ffxxx/r5ZdfJogCcBmHhtDk5GTNnz9fp06d0sSJE9WrVy/9+eefjpwSAAAAVylTpozmzJmjPn36GH2ff/65hg8fThAF4BIODaExMTFKTU2VdHln3Lp166pt27aOnBIAAABZ+Pr6avbs2QoPDzf6vvrqKz399NPKzMx0YWUArkcODaGHDh0y/ttkMqlNmzaOnA4AAAC58PHx0axZszRgwACjb+zYsXryyScJogCcyqEh1MPD/vYVK1Z05HQAAADIg7e3t2bMmKGBAwcafRMnTtSQIUPYSBKA0zg0hAYHB9u1//77b0dOBwAAgGvw8vLS1KlTFRERYfRNnjxZDz/8sNLT011YGYDrhUNDaMeOHVWvXj1Jl78JXb9+vXbt2uXIKQEAAHANnp6e+vHHH/Xoo48afdOnT9egQYOUlpbmwsoAXA8cGkJNJpM+++wz+fn5yWQyKTMzU0888YS2bdvmyGkBAABwDZ6enpowYYKefPJJo2/WrFkaOHCgsbEkADiCw88JbdGihWbNmqWGDRtKkhISEnTfffdpyJAhmjx5srZs2aKzZ886ugwAAABk4eHhobFjx+qZZ54x+mbPnq17771Xly5dcmFlANyZl6MnGDNmjCSpW7duSkhI0JkzZ2Sz2bRhwwZt2LDBGGcymeTv7y9PT88C3d9kMtndBwAAAPlnMpn09ddfy9vbW6NHj5YkRUdHy2w2a/bs2SpTpoyLKwTgbpwSQk0mk12fyWTKdjiyzWYr1BPRrPcGAABAwZhMJo0aNUre3t769NNPJUkLFixQaGio5syZo7Jly7q4QgDu41Ix7AAAIABJREFUxOGv416RNXSaTKYi/wIAAEDxMJlMGjlypN58802jb8mSJerTp4/Onz/vwsoAuBunhNArAdRmsxXrLwAAABQfk8mkDz74QO+9957Rt2LFCvXu3VvJyckurAyAO3H467hfffWVo6cAAABAMXr77bfl7e2t119/XZK0Zs0a9erVSwsWLFCFChVcXB2A0s7hIbRnz56OngIAAADF7LXXXpO3t7deeuklSdL69et15513atGiRQoMDHRxdQBKM6d9EwoAAIDS5cUXX9SXX35ptP/44w/16NFDiYmJLqwKQGlHCAUAAECunnvuOX377bdGe9OmTbrjjjuUkJDgwqoAlGaEUAAAAOTpqaee0vjx443TCbZs2aJu3bopLi7OxZUBKI0c/k3otZw/f147duxQQkKCzpw5o+TkZLVo0UK33nqrMea///2vateuLV9fXxdWCgAAcP16/PHH5e3trUcffVQ2m03bt2/X7bffruXLl6t69equLg9AKeKSEHr27FlFRkZq0aJF2rdvnzIzM+1+/sgjj9iF0M8++0wxMTEKCwvT0KFDVbVqVWeXDAAAcN0bPHiwvLy89PDDDyszM1O7du1S165dtWLFCtWsWdPV5QEoJZz6Oq7NZtOYMWMUEhKiL7/8Urt371ZGRsY1z/6MjY1VSkqKIiMj1bNnT82ZM8eZZQMAAOD/DRo0SNOnT5enp6ckae/evQoJCdHRo0ddXBmA0sJpITQpKUkPP/ywvv32W124cEE2m00mk8nuV25iY2NlMplks9l04cIFvfbaa3YfyAMAAMB57r//fs2aNUteXpdfqtu/f79CQkJ06NAh1xYGoFRwSgi9dOmShg4dqpiYGLvwmdfTzyuSkpKUkpIiSXbXjRkzRtHR0c4oHwAAAFn0799fv/zyi7y9vSVJBw8eVEhIiA4cOODiygCUdE4Joe+88462bNliFyLr1q2rl156SVarVVu2bDHCaVaBgYGaNGmS2rVrZwTWK/f46KOPOKcKAADARUJDQ2W1WuXj4/N/7N15WFTV/wfw9wDDLiDumjuihrmkohbJpogLoSiaW4nkVrmXqVmpmVv6tdRSc01SUXNBRVGRRXPJcBd3URFEEVdcgIGZ3x/8uHGHbYaZ4QK+X8/D83CO5577ObnEZ84GAIiPj4erqyuuX78ucWREVJoZPAk9f/48QkJCRDOfI0aMwN69exEYGIimTZsWeert+++/j/Xr1+OHH34Qln0A2QccrV+/3tBDICIiIqICdO/eHbt27YK5uTkAICEhAa6urrhy5YrEkRFRaWXwJHTZsmVC8imTyTB8+HCMHz9e2MyuDX9/f/z4449CXyqVCtu3bzdA1ERERESkqS5dumDPnj2wsLAAACQlJcHNzQ2xsbESR0ZEpZFBk9D09HQcO3ZMWGbbsGFDjB07Vqc+P/zwQ7z//vvC0tyHDx9yEzwRERGRxDw9PbFv3z5YWVkBAB48eAB3d3ecP39e4siIqLQxaBJ6+vRppKenA8jex9mvX79izYCq6927t6h86dIlnfskIiIiIt24uroiLCwM1tbWALInC9zd3XHmzBmJIyOi0sSgSej9+/cBQJi1bN++vV76bd68OQAIM6yPHj3SS79EREREpBsXFxccPHgQNjY2AIDHjx/Dw8MD//77r8SREVFpYdAkNCUlRVSuUqWKXvq1tbUVlXOucCEiIiIi6bVv3x6HDh2CnZ0dgOwr9zp16oQTJ05IHBkRlQYGTUJzjuvOkbM0V1fPnz8XlXOWfBARERFR6dCmTRtERETA3t4eQPbPb15eXjh69KjEkRGR1AyahFauXFlUvnXrll76zTnyO2eZb8WKFfXSLxERERHpT6tWrRAZGSn8TJiamoouXbogOjpa4siISEoGTULr1KkD4L+9m5GRkXrpd9++faJyvXr19NIvEREREelX8+bNERUVhWrVqgEAXr58ia5du+LQoUMSR0ZEUjFoEtqsWTNhCYZKpcKWLVuQnJysU5+XL1/G3r17hcS2YsWKePvtt3WOlYiIiIgMw8nJCVFRUahRowYA4PXr1+jRowf2798vcWREJAWDJqEymQydOnWCSqWCTCbD69evMXbsWKSlpRWrv5SUFHzxxRdQKpVCn25ubvoNmoiIiIj0rkmTJoiOjsZbb70FAEhLS8OHH36I0NBQiSMjopJm0CQUAEaNGgUzMzOhfPbsWQwcOBA3b97Uqp/jx4+jd+/eSExMFGZBjY2NMWLECL3GS0RERESG0ahRI0RHRwtbtjIyMtCrVy+EhIRIHBkRlSSDJ6E1atTAsGHDhJlLlUqF2NhY+Pr6YvTo0di5cycuXLiQ57nMzEzExcVh48aNGDBgAIYOHYoHDx4IfchkMnz00UeoW7euoYdARERERHrSoEEDHD58GPXr1wcAKBQK9OnTB9u2bZM4MiIqKTJVzhGzBjZhwgTRXs6cRDK3nDpTU1MoFArkDi13e5VKBWdnZ6xduxbGxsYlET5RmTF48GCcPHkSzs7OCAoKkjocIiKifN29exceHh64ceMGgOwVbhs2bEC/fv0kjoyIDM3gM6E55s6dCz8/PyGxzJ1Q5nzllNPT04V9n/m1f//997F06VImoERERERlVO3atREVFYXGjRsDALKysjBgwAD8+eefEkdGRIZWYkmoqakpZs+ejZ9++glVq1YVEkyZTFbkV05bCwsLjBs3DqtWrYKNjU1JhU5EREREBlCrVi1ERUUJNx0olUp8/PHHWLdunbSBEZFBmZT0C318fNC1a1fs2bMHu3fvxpkzZ/Dq1asC2xsbG6NJkybo0qUL+vfvjwoVKpRgtERERERkSNWrV0dkZCQ6deqECxcuQKVSISAgAAqFAsOGDZM6PCIygBJPQgHAxMQEPXv2RM+ePaFUKnH16lUkJSXh+fPneP78OczNzWFra4tKlSrh7bffhqWlpRRhEhEREVEJqFq1KiIiItC5c2ecPXsWADB8+HAoFAp89tlnEkdHRPomSRKam5GREZo2bYqmTZtKHQoRERERSaRy5co4dOgQvLy8cOrUKQDA559/DoVCgbFjx0ocHRHpU4ntCSUiIiIiKoy9vT3Cw8PRrl07oW7cuHFYsGCBhFERkb4xCSUiIiKiUsPOzg4HDhzA+++/L9R99dVXmDNnjoRREZE+MQklIiIiolLFxsYGYWFh6Nixo1A3depUzJw5U8KoiEhfmIQSERERUaljbW2NvXv3wsPDQ6j7/vvv8e233wr3yBNR2cQklIiIiIhKJSsrK+zZswdeXl5C3axZszB58mQmokRlGJNQIiIiIiq1LCwsEBISgm7dugl18+fPx8SJE5mIEpVRTEKJiIiIqFQzNzfH9u3b4evrK9QtWrQIY8aMYSJKVAYxCSUiIiKiUs/MzAxbtmyBn5+fULd06VKMGjUKSqVSwsiISFtMQomIiIioTDA1NUVwcDD69esn1K1YsQLDhg1DVlaWhJERkTaYhBIRERFRmSGXy/Hnn39i4MCBQt2aNWsQEBDARJSojGASSkRERERliomJCf744w8MGTJEqAsKCsLgwYORmZkpXWBEpBEmoURERERU5hgbG2P16tUYNmyYULdp0yYMGDAACoVCwsiIqCgmUgdQligUChw4cAAHDhzAxYsX8fjxY6hUKlSrVg21a9eGt7c3vL29YW1tXSLxPHv2DDt27MCRI0dw9epVPHv2DBYWFqhWrRocHR3h4+MDFxcXmJgU/7c5JiYGISEhOHv2LJKSkpCWloYqVaqgevXqcHNzg4+PD2rWrPlGj+HWrVvYuXMn/v33X8THx+Pp06cwNTWFvb09nJyc4OLiAh8fH5ibmxf7HURERJSXkZERli9fDhMTEyxbtgwAsHXrVmRmZiI4OBimpqYSR0hE+ZGpeK61RmJiYjB16lTcuXOn0Ha2traYPn266C4rQ9iyZQvmzp2Lly9fFtrOwcEBCxYsQNOmTbXqPykpCd988w2OHj1aaDtjY2MMGzYMo0eP1jpRLOtjePHiBWbOnIndu3cXeSpfpUqVMGXKFPj4+Gjcf3ENHjwYJ0+ehLOzM4KCggz+PiIiIqmpVCqMHz8ev/zyi1Dn4+ODrVu3wszMTMLIiCg/Bk9CN2zYIHz/9ttvo1WrVjr3+fjxY/z++++4du0arl27hl9++QWtW7fWud+CHDhwABMmTNBqacfIkSMxfvx4g8Qzb948rFmzRuP2crkcS5cuhZubm0bt4+LiMGTIEDx48EDjd7Rr1w6rVq3S+BPHsj6GlJQUDBw4ELdv39a4fwAYNmwYvvzyS62e0RaTUCIiehOpVCpMmjQJCxYsEOq8vb2xfft2WFhYSBgZEakzeBLapEkTyGQyAEBAQAAmTZqkc5/Jycno2LGj0O+sWbPQu3dvnfvNT2xsLPr374/09HShztHREYMGDULTpk1hYmKCa9euITg4GGfOnBE9O3fuXPTq1Uuv8WzYsAEzZ84U1bm5uaFXr16oV68eXr9+jbNnzyIoKAiJiYlCGysrKwQHB8PR0bHQ/lNTU9G7d2/RjK+9vT0GDhyI9u3bw8bGBgkJCQgNDUVoaKjogmg/Pz/MmTOn3I8hMzMTffv2RWxsrFAnk8ng7e2N7t27o27dulAoFIiNjcXGjRtx+fJl0fPfffed6EQ/fWMSSkREbyqVSoVvvvlG9P/yTp06ISQkBJaWlhJGRkS5lUgSCmT/kK6vJPT58+dwdnYWktCJEyfi008/1blfdVlZWejZsyeuXbsm1Pn5+WHmzJmQy+WitiqVCsuXL8fPP/8s1FlZWSE8PBz29vZ6iefevXvw9vYWEmKZTIYZM2aI7srK8fLlS0yaNAnh4eFCXatWrRAcHFzoO6ZPn45NmzYJZScnJ6xcuRKVKlXK0/bIkSMYM2YMXr16JdStXr0aLi4u5XoMQUFBmDVrllA2NTXF4sWL4e7unqdtVlYWZs+ejT///FOos7S0RFhYGKpVq1boOIqLSSgREb3JVCoVpk+fLvrA283NDbt37y6xczuIqHAlcjpuTrKoL7lnoAAY7CjukJAQUQLaunVrzJo1K08CCmSPcdSoURg6dKhQ9/LlSyxfvlxv8SxevFg0Izt8+PB8kzcgOwFetGiRaPnzmTNnRAmduvj4eGzZskUo29nZYfny5fkmbwDwwQcfYOHChaK6hQsXorDPNcrDGHInuAAwefLkfBNQIHu/6bRp09ChQweh7tWrV9i6dWuB/RMREVHx5XzA/cMPPwh1UVFR6Nq1K1JTUyWMjIhy6HQ67unTp7X6y5yQkIDo6Ohivy8jIwOJiYlYvXo1ZDIZVCoVZDIZbG1ti91nYXLPXgHA119/DWNj40KfGTduHHbv3o2HDx8CyD6hbcKECTqfjPrkyROEhoYKZTs7O4waNarQZ0xNTTF9+nT4+voKdevXr0enTp3ybb9p0ybRJc+BgYGoWrVqoe/w8PBA586dcfDgQQDApUuXEBMTg7Zt25bLMSQnJ+PmzZtC2dbWtsAkOodMJsPw4cNx/Phxoe7w4cP44osvCn2OiIiIim/atGmQy+WYPHkyAODvv/9Gly5dsG/fPoP97EhEmtEpCb1+/TqmT59eZLucWaWDBw8KP+jrIif5zKHtqamaiI+PF824Ojo6okWLFkU+Z2Zmhl69euH3338HkD3rFRUVBW9vb53iCQ8PR0ZGhlDu0aOHRpvsmzRpgpYtW+Ls2bMAsk/5TUlJQeXKlfO03bdvn/C9kZGRxvts+/XrJ/p93bdvX74JXHkYw/3790VlR0dHjU7Ubd68uaice68rERERGcbXX38NU1NTTJgwAQBw/PhxeHl5ISwsDBUrVpQ4OqI3l07Lcfv27YtmzZpBpVIV+JVbYe20+cpJQGUyGRwcHNCyZUtdhpGvv//+W1T+4IMPNH5WfT+hPhJv9WtGOnbsWKx4srKyEBERkafNzZs3kZSUJJSdnJwKXMKqrl27dqITZQsab3kYg/qfaU2XgqvPoOdekkxERESGM378eCxevFgonzx5Ep06dcKjR48kjIrozaZTEiqTyTB9+nQYGxtDJpPl+6XeXh9fOcmojY0NFi1apNN/gIJcvHhRVNZkFjRHs2bNRGNXPzVXH/Goz6wVRr1tfvFcuHBBVNZmvKampmjcuLFQTk5OznemrzyMoXbt2qLytWvXRLO7BVE/IbdWrVoax0ZERES6GT16NJYtWyaUT58+DU9PT2H7FBGVLJ0PJnJycsKgQYNgbm6e7xfwX/JpbGxcYLuivqysrGBnZ4e33noLbdu2xdChQxEaGgoHBwed/yPk58aNG6KyNu+xsrIS7UNMTEwUnb6qrdevXyMhIUEoV6pUSaslJPXq1ROVr1+/nqeN+ngbNmyoVYzq71DvrzyMAci+6iX3zPvLly81OmRo7dq1orI2s8BERESku5EjR2LVqlXCRMG5c+fg7u6u1Z3iRKQfOu0JzTFlyhRMmTIl31/LuaIFAD7++GO9XNFSEu7duycqV69eXavnq1evLvpHLTExEY0aNSpWLElJSaJloDVq1NA6ltxyJ4M51Mdbs2ZNvb6jPIwhx5gxY0SnIM+bNw9169Yt8FqXJUuW4MCBA0K5QoUK+Pjjj7WKjYiIiHQXGBgIuVyOgIAAKJVKxMbGws3NDREREVr/bEJExVciV7SUNUqlEo8fPxbKlpaWsLKy0qoP9btBc/enrZSUFFE5vwN5CmNmZiaK/+nTp1AqlaI26stRNN1LWVB79fGWhzHkeP/99zF58mThk9T09HQMGzYMEydORHh4OK5du4YrV65g586dGDBgAJYuXSoax//+9z9UqVJFq9iIiIhIPz7++GMEBQXByCj7x+ArV67A1dW1wA+fiUj/9DITWpTC7lwsjV68eCG65kPbBDS/Z54/f17seNSfLc5Fy1ZWVnj58iWA7N+P1NRU0fHkur5DfbzPnj0TlcvDGHILCAhAkyZNMHv2bFy7dg1KpRJ79uzBnj17CnzmnXfewYwZM+Dk5KRVXACwfft27NixQ6O26vtPiYiISGzAgAGQy+Xo378/srKycP36dbi6uiIyMhJ16tSROjyics/gSWjuuxE1uY6jNFA/aKY4d3zmPmk1vz51icfMzEzv8eg6Zm37L4tjUPfOO++gb9++WLZsWZEn7DVp0gTff/99sRJQIHs598mTJ4v1LBEREeXl7+8PExMT9OvXDwqFAnFxcUIiqn5OBBHpl8GT0LJ4B5NCoRCV1a/X0IRcLi+0T22oJ0Oa3EupTv0Z9atFdB1zUf2XhzHktnPnTsyZMwdPnz7VqO8rV66gT58+8PLywowZM/Is1y5KrVq14OzsrFHby5cvIzU1Vav+iYiI3kS9evXCtm3b0KdPH2RkZOD27dvo2LEjIiMjtT7gkIg0VyLLcXWRlpaGJ0+ewNraGhUqVCiRd6pfLVMcuZfzAsVLZHOox1Oc5c3q+ydz9kHo6x1Fjbc8jCHH8uXL81wN5ODggCFDhqB9+/aoVq0a0tLSEBcXhwMHDmDTpk3C6cgHDhxAbGws/vzzT60OTvLz84Ofn59GbQcPHsxZUyIiIg35+Phg586d6NWrF9LT03H37l0hEXV0dJQ6PKJySbKDiYraI7lz5074+fnh3XffhYeHB5ydneHl5YVff/1V2BdoKPqYxVRPaIqz/LSgeAqboStuPLq+w9D9l8Q7NPk9O378eJ4EdPDgwQgJCYG/vz9q164NU1NT2NjYoGXLlpg0aRL27NmDt99+W2ifmJiIUaNG6TQ7TkRERPrTtWtX7N69W9jKc+/ePbi6uvKcBSIDKdEk9MqVK5g4cSI6dOiAOXPm5NtGpVJh+vTpmDJlCi5fvgylUgmVSgWVSoX4+HgsXboU3bt3x/nz5w0Wp/qBNq9fv9a6D/V7QYuzr7SgeIpz56h64q6+P1fXMRc13vIwBgBYsGCBqOzj44Np06YVury4Vq1aWL16NWrVqiXUXblyBZs3b9YqPiIiIjKczp07Y+/evbC0tAQA3L9/H66urrh48aLEkRGVPyWShCqVSsycORN+fn7Yu3cvnjx5gvj4+Hzbrlq1CsHBwULiKZPJRF8qlQr3799HQEAArly5YpB4zczMhH+AACA1NVXrpZ3qM73aXkmSm/q+Wm1P2lWpVKIEztraOs8sn/o7CjsZNj9Fjbc8jCEuLk70PyILCwtMnTpVo77t7e3x5ZdfiuqYhBIREZUu7u7uCAsLEz7YfvjwIdzc3HDu3DmJIyMqX0okCf3pp5+wceNGYVYTAO7evZun3aNHj7Bs2bI8SWfur5z6ly9fYuLEiXmWUOpL9erVhe8VCoXGB9Dk0PVezNzUL09W77soT548ES39zC+W3OMtzjuKGm95GIP6/4Def/99rQ4Y6ty5s+gamGvXrul0fywRERHp3wcffID9+/cLZ5E8evQI7u7uOHXqlMSREZUfBk9Cr1y5gj/++EOUWALZf6HVT0zdtm2bsCQyJ1l1c3PDqlWrsHbtWvTt21d0+ExcXBy2b99ukLjr1q0rKueXNBdEpVKJLjy2trZG1apVix1L5cqVRclLQkKCVjOz6rE3aNAgTxtdxgsgz8y2+jvKwxgePnwoKjs4OGjVv1wuz3PS3r1797Tqg4iIiAzvvffeQ3h4uHAf+ZMnT+Dp6cmD/4j0xOBJ6B9//CGcaqpSqWBubo7Jkyfj+PHjee5lDA0NFZJMmUyGNm3aYPny5XBxcUGHDh0wc+ZMzJ07V5gRValUCA4ONkjcuQ+SAbJnrTQVHx8v2o+oj5PVcsfz6tUrUZJbFPXY84tHl/GqtzcxMcn3WPOyPoaiTuPVhPo+U0PN5BMREZFunJ2dcejQIWG7z7Nnz9CpUyccO3ZM4siIyj6DJ6FHjhwREka5XI5169ZhyJAhsLGxEbW7e/curl69CuC/WdBPP/00T38ffvghPDw8hDaXLl3SetmlJlq3bi0qx8TEaPzsv//+Kyprer+jNvGov0ObeNq1a5enTfPmzUWny2oz3rt37+LBgweivvI7Wbasj0F9ea62M60AkJycLCpXqlRJ6z6IiIioZLRu3RqRkZHC/69TU1PRpUsXHDlyROLIiMo2gyah165dExJEmUyGvn37okWLFvm2jYqKEpWtra3h4uKSb1tfX19R2RCnlrVt21Z0OFFkZCTS09M1ejYsLExUdnV11Tke9T7U31GQtLQ0REdHC2UrKyu0adMmTztLS0tRspyYmKjxCcT79u0rNNaC6svaGJycnETlw4cPIy0tTaP+geyk9fbt20LZzs4uz15ZIiIiKl1atGiBqKgoYWvVixcv4O3tjcjISIkjIyq7DJqExsXFAfhvZrNLly4Ftj18+LDwvUwmQ4cOHWBsbJxv25xllznLIbVZ1qkpU1NTdOvWTSg/ffoUmzZtKvK58+fP4++//xbKDRs2xLvvvqtzPK1atUK9evWE8pEjRzRKvjds2CA6VMnHxyfPMugc6sn9smXLiuz/xYsXWL9+vVA2NjZGr1698m1b1sfQqFEjUfzPnz8XPVeUJUuWiMpubm4F/hknIiKi0qNZs2aIiooSDkF89eoVunfvjoMHD0ocGVHZZNAkVP1E2fr16+fbLiMjA//++6+wbBfI3hBekJxN4jlevHihY6T5CwgIECUJCxcuLHQJaXJyMsaOHSs6cGfYsGF6iUUmk2Ho0KFCWalUYsyYMYUuRT5x4gQWLVoklOVyOQICAgps37VrV9FdlhEREfj9998LbJ+ZmYnx48eLDuzx9fVFtWrVyu0YRo4cKSovXrxY9KFDQdatW4eQkBChbGxsnO9ycyIiIiqdmjZtiqioKNSsWRNA9n3kPj4+eVZTEVHRDJqEqt/TaGFhkW+7U6dO5VnW2KFDhwL7VT/MxVCzSQ4ODhgwYIBQzsjIwKeffoqNGzeKrgsBgKNHj6Jv376i005btmyZZ2YutyVLlqBx48bCl4eHR6Hx9O7dW3T4TmJiIvr27Ztng3xGRgY2bNiAESNGiOL85JNPRDN56kxNTTF58mRR3cKFCzFz5sw8v5e3bt3CkCFDRDPYtra2GDduXLkeg6+vL9q3by+UFQoFhg0bhvnz5+P+/ft52t+9exeTJk3CnDlzRPVDhw5Fo0aNCnwPERERlT6NGzdGdHQ0ateuDQBIT09Hz549sXv3bokjIypbZCpt7snQ0rp16zB37tzsF8lkiI6Ozveqkvnz52PNmjXCTGjNmjURERFRYL9Xr16Fr6+vsBz3u+++Q//+/Q0yhrS0NAQGBuY55Mbe3h5OTk4wNTXFjRs3cOfOHdGvV65cGVu3bhU+LcvPkiVLsHTpUqFcq1atQscNALdv38agQYPyXBdSt25dODg4ICMjA7GxsXnun2zbti3Wrl0rOrinIPPmzcOaNWtEdWZmZmjRogUqVqyIxMRExMbGimZ8TUxM8Ntvv2m0/7Wsj+H58+cYOHBgntN3jYyM0LhxY9SsWRMymQzx8fG4fv16nqtovL29sWjRojyn7erL4MGDcfLkSTg7OyMoKMgg7yAiInqT3bp1Cx4eHsJZDyYmJtiyZUuBW5KISMzEkJ3b29uLynfv3s03CT18+LCQgMpksgIPJMqRc1lwTvuc9fmGYG5ujpUrV2L06NGiZZePHz8u8GS02rVrY+XKlYUmoMVVr149rF+/HsOGDRPthb1z506eRDiHi4sLFi9erFHyBgBff/015HI5fv/9dyGBSk9PL/BuLEtLS/z0008aH8BU1sdgY2ODTZs2YerUqdi/f79Qr1QqcfnyZVy+fDnf54yMjDBq1Ch88cUXBktAiYiIyPDq16+P6OhouLu7Iy4uDpmZmfD398emTZvg7+8vdXhEpZ5BfxJu2rQpgP8OEMrvXqWbN2/ixo0bojp3d/cC+1QqldiyZYvojsac9xiKpaUlVq9ejfnz5+d7/2UOOzs7jBw5Ert27Spw/6ujf+iTAAAgAElEQVQ+NGjQAKGhofj888/zXBuSW/369TFr1iysWrUKVlZWWr1jwoQJ2Lx5M9q1a1dgwiSXy+Hj44Pdu3ejU6dOb9QYrK2tsXjxYqxZswbvvfceTEwK/jzHwsICvr6+2LVrF8aMGcMElIiIqByoU6cODh8+LGyvycrKwkcffYSNGzdKHBlR6WfQ5bgqlQouLi54/PgxVCoVbG1tsWPHDtEM4ahRoxAZGSnMhFpbW+PYsWMFnn66aNEirFixQmhft25d0WxUSbh16xYuXryIlJQUZGRkwNbWFo6OjmjWrFmBcRuKUqnEuXPncOvWLaSkpMDIyAiVKlVCs2bN4ODgIErWi+vRo0c4deoUkpOTkZqaCmtra9SrVw8tW7ZEhQoVOAZkH451+vRpJCUl4dmzZzA2NoadnR3q1auX5/5SQ+NyXCIiopKTlJQEDw8PXLlyBUD2yqe1a9fi448/ljgyotLLoEkoAPz4448ICgoSksYqVapg+PDhqFSpEkJCQhAdHS1aitunTx/88MMPefqJj4/HL7/8gr179wL4bynumDFjMGrUKEMOgahMYRJKRERUsh48eABPT0/ExsYCyF4FuGrVKtGtAET0H4PuCQWATz/9FH/99RfS0tIgk8nw8OFDzJ49O9+2pqamea6tSElJQUBAAG7evAmVSiUknwBQtWpVfspERERERJKqVq0aIiMj0alTJ5w/fx4qlQqBgYFQKBQYMWKE1OERlToG35xWrVo1zJ49W0gcc2Y9cyZgcy+1HDNmDOrWrSt63s7ODrdu3YJSqRQSUJVKBXNzc8yfP1/rfYJERERERPpWpUoVRERE4N133xXqRo4cKboJgYiylcgJKV27dsWSJUtgZ2cnuq4iJxk1MjLCpEmTEBgYmOdZExMT1KlTBzKZTLSkd/ny5WjXrl1JhE9EREREVKRKlSohPDwcbdu2FepGjx6NRYsWSRgVUelj8OW4OTw9PeHs7IyQkBAcO3YMycnJqFixIt5++23069ev0OtMGjRogLi4OFSsWBF9+/ZFYGAgbGxsSip0IiIiIiKNVKxYEQcPHkTXrl1x/PhxANkn9mdkZODrr7+WODqi0sHgBxPpQ0xMDIyMjNC8efNCr8IgIh5MREREVBqkpqaiW7duonvmf/jhB0ybNk3CqIhKhzJxYWGbNm3w7rvvMgElIiIiojKhQoUKCAsLg5ubm1D37bff4vvvv0cZmAMiMqgykYQSEREREZU1VlZWCA0NRadOnYS6mTNn4ptvvmEiSm80JqFERERERAZiaWmJXbt2wdvbW6ibM2cOJk2axESU3liSrW99/fo1jh07hlOnTuHChQt4+PAhnj9/jtTUVAwZMgQTJ04U2i5duhRGRkbw8/ND9erVpQqZiIiIiEhrFhYW2LlzJ/r06YM9e/YAABYsWACFQoFFixaJriwkehOUeBL6+PFjrFu3Dps3b8bz58+F+tz3hmZlZYmeOX78OE6fPo3ly5djwIABGDduHMzNzUs0biIiIiKi4jIzM8O2bdvw0UcfYceOHQCAX375BQqFAkuWLIGRERco0pujRP+0Hz9+HB9++CFWrlyJZ8+eCfeEAij0E6DExESoVCpkZGTgjz/+gL+/PxITE0sqbCIiIiIinZmammLz5s3w9/cX6n777TeMHDkSSqVSwsiISlaJJaHbt2/Hp59+ipSUFKhUKshkMiHxLGw9fGZmJpKTk4X2KpUK169fR0BAAB49elRS4RMRERER6Uwul2Pjxo3o37+/ULdy5UoEBgbmWQ1IVF6VSBJ67NgxfPfdd8jKyhIlkxUqVICHhweGDx9eYCKalpaG5s2b55kxvXv3Lr755puSCJ+IiIiISG9MTEwQFBSEwYMHC3Xr1q3DJ598gszMTAkjIyoZBk9C09PTMWXKFGRmZgrJp729PebOnYtjx47ht99+w4QJEwDkvyTX2toawcHBWL9+PerXry/MoqpUKkRHRyM6OtrQQyAiIiIi0itjY2OsXbsWQ4cOFeo2bNiAQYMGQaFQSBgZkeEZPAldt24dHjx4ICSOb731FrZt24aePXvCxETzc5GcnZ3x119/oW3btkIiCmT/ZSUiIiIiKmuMjY2xcuVKjBgxQqjbvHkz+vfvj4yMDAkjIzIsgyeh27dvFxJQExMTLFu2rNjXrFhaWuKXX36BnZ0dgOy9pEePHsXLly/1GTIRERERUYkwMjLCsmXL8MUXXwh127ZtQ9++fZGeni5hZESGY9Ak9Pbt27hz5w6A7KW2PXv2RKNGjXTq097eHgMGDBD2iCqVSly4cEHnWImIiIiIpCCTybB48WKMHz9eqAsJCYGfnx/S0tIkjIzIMAyahF65cgXAf6ffent766VfNzc3AOJDioiIiIiIyiqZTIaFCxdi0qRJQt3evXvh6+uL169fSxgZkf4ZNAlVv0KlYcOGeun3rbfeEpWfPXuml36JiIiIiKQik8kwd+5cTJs2Tag7cOAAevTowe1nVK4YNAlV/8tiaWlpyNcREREREZVpMpkMP/zwA2bMmCHURUREoFu3bnjx4oWEkRHpj0GT0IoVK4rKKSkpeun3wYMHhb6HiIiIiKgs++677zB79myhfPjwYXh7e+P58+cSRkWkHwZNQitXriwqnz59Wi/9Hjt2DMB/e03V30NEREREVNZNmTIFP/30k1A+evQovLy88PTpUwmjItKdQZPQFi1awMjISLiiZcuWLTr3qVAosGnTJuFQIiMjI7Rs2VLnfomIiIiISpsvv/wSP//8s1D+559/0LlzZzx+/FjCqIh0Y9Ak1N7eHs2bNxfKFy9exObNm3Xq8+eff0ZCQgKA7DXzTk5OsLW11alPIiIiIqLSauzYsfj111+FckxMDDw9PfW21Y2opBk0CQWA/v37Q6VSCbOhP/74I/bu3Vusvn799VesXr1a6AsA/P399RkuEREREVGp89lnn2HFihVC+ezZs/Dw8EBycrKEUREVj8GTUF9fXzRt2hRA9sxlRkYGJk6ciIkTJ+Lq1asa9XH8+HEMGjQIS5cuFepkMhnq1KmD3r17GyRuIiIiIqLSZPjw4VizZo2wLe3ChQtwd3fH/fv3JY6MSDsmJfGSBQsWYMCAAXj+/Lkwi7l3717s3bsXNWrUQKNGjQBAmDG9dOkSfvnlF9y5cwf//POPsOY994yqmZkZ5s2bByMjg+fRRERERESlQkBAAORyOT755BMolUpcunQJbm5uiIiIQM2aNaUOj0gjMlXOulYDO3nyJEaMGIG0tDQA/51sC0C0vDannCO/dsbGxpg/fz66d+9eApETlS2DBw/GyZMn4ezsjKCgIKnDISIiIgMIDg7GoEGDkJWVBQBwcHBAREQEateuLXFkREUrsWlEZ2dn/PXXX3BwcBBmNHO+AIjKKpVK+FKvr1y5MtauXcsElIiIiIjeWB999BE2b94ME5PshY03btyAq6srbt++LW1gRBoo0bWsDRs2xLZt2/Dtt9+iTp06omRTfcYzJznN+TUbGxuMHDkSu3fvhrOzc0mGTURERERU6vTu3Rt//fUX5HI5AODWrVtwdXVFXFycxJERFa7EluOqU6lUOHv2LGJiYnDmzBncv38fz549Q2pqKszMzGBnZwd7e3u88847aNOmDdq3bw9zc3MpQiUqU7gcl4iI6M0SGhoKPz8/ZGRkAADeeustRERECOeuEJU2JXIwUX5kMhlatWqFVq1aSRUCEREREVGZ1717d+zatQs9e/ZEWloaEhIS4OrqioiICDRp0kTq8Ijy4NGyRERERERlXJcuXbBnzx5YWFgAAJKSkuDm5obY2FiJIyPKq9hJ6M6dO4Uv/uEmIiIiIpKWp6cn9u3bBysrKwDAgwcP4O7ujvPnz0scGZFYsZfjTp48WTg8KCAgAE5OTnoLioiIiIiItOfq6oqwsDB07doVL168wMOHD+Hu7o7w8HBug6NSQ6fluBKdaURERERERAVwcXHBwYMHYWNjAwB4/PgxPDw8EBMTI3FkRNl0SkJzZkKJiIiIiKj0aN++PQ4dOgQ7OzsAwNOnT+Hp6YkTJ05IHBkRDyYiIiIiIiqX2rRpg4iICNjb2wMAnj9/Di8vLxw9elTiyOhNxySUiIiIiKicatWqFSIjI1G5cmUAQGpqKrp06YLo6GiJI6M3GZNQIiIiIqJyrHnz5oiKikK1atUAAC9fvkTXrl1x6NAhiSOjNxWTUCIiIiKics7JyQlRUVGoUaMGAOD169fo0aMH9u/fL3Fk9CZiEkpERERE9AZo0qQJoqOj8dZbbwEA0tLS8OGHHyI0NFTiyOhNwySUiIiIiOgN0ahRI0RHR6NOnToAgIyMDPTq1QshISESR0ZvEiahRERERERvkAYNGiA6Ohr169cHACgUCvTp0wfbtm2TODJ6UzAJJSIiIiJ6w9SrVw/R0dFwcHAAAGRmZqJfv37YvHmzxJHRm8BE1w5UKhXWrl2LtWvX6iMerclkMly6dEmSdxMRERERlVW1a9dGVFQUPD09cfXqVWRlZWHAgAFQKBQYNGiQ1OFROaaXmVCVSiXpFxERERERaa9WrVqIiorC22+/DQBQKpX4+OOPsW7dOmkDo3JNL0moTCaT5IuIiIiIiHRTvXp1REZG4p133gGQPcEUEBCAlStXShwZlVdleiaUiIiIiIh0V7VqVURERKBly5ZC3fDhw/Hbb79JGBWVVzrvCZXJZGjZsiXef/99fcRDREREREQSqFy5Mg4dOgQvLy+cOnUKAPD5559DoVBg7NixEkdH5YnOSSgAtGrVCl988YU+uiIiIiIiIonY29sjPDwc3t7e+OeffwAA48aNg0KhwJdffilxdFRe8IoWIiIiIiIS2NnZ4cCBA6KVjl999RXmzJkjYVRUnjAJJSIiIiIiERsbG4SFhaFjx45C3dSpUzFz5kwJo6LygkkoERERERHlYW1tjb1798LDw0Oo+/777/Htt9/ykFDSCZNQIiIiIiLKl5WVFfbs2QMvLy+hbtasWZgyZQoTUSo2JqFERERERFQgCwsLhISEoFu3bkLdvHnzMHHiRCaiVCxMQomIiIiIqFDm5ubYvn07fH19hbpFixZhzJgxTERJa0xCiYiIiIioSGZmZtiyZQv8/PyEuqVLl2LUqFFQKpUSRkZlDZNQIiIiIiLSiKmpKYKDg9GvXz+hbsWKFRg+fDgTUdIYk1AiIiIiItKYXC7Hn3/+iYEDBwp1q1evRkBAALKysiSMjMoKk+I+WLNmTeF7Ozs7vQRDRERERESln4mJCf744w/I5XKsW7cOALB+/XooFAqsX78eJibFTjPoDVDsPx0RERH6jIOIiIiIiMoQY2NjrF69GiYmJli1ahUAYNOmTcjMzMSGDRsgl8sljpBKKy7HJSIiIiKiYjEyMsKKFSswatQooW7r1q3o168fMjIyJIyMSjMmoUREREREVGxGRkb49ddfMXbsWKFux44d6NOnD9LT0yWMjEorJqFERERERKQTmUyGRYsW4csvvxTqdu/ejZ49e+L169cSRkalEZNQIiIiIiLSmUwmw/z58zFlyhShLiwsDB9++CFevXolYWRU2jAJJSIiIiIivZDJZPjxxx/x3XffCXXh4eHo0aMHXr58KWFkVJowCSUiIiIiIr2RyWSYMWMGfvjhB6EuMjISXbt2RWpqqoSRUWnBJJSIiIiIiPRu2rRpmDt3rlA+cuQIunTpgmfPnkkYFZUGTEKJiIiIiMggvv76a/zvf/8TysePH4eXlxeePHkiYVQkNSahRERERERkMOPHj8fixYuF8smTJ9GpUyc8evRIwqhISkxCiYiIiIjIoEaPHo1ly5YJ5dOnT8PT0xMPHz6UMCqSCpNQIiIiIiIyuJEjR2LVqlWQyWQAgHPnzsHDwwMPHjyQODIqaUxCiYiIiIioRAQGBmLdunUwMspOQy5evAg3NzckJSVJHBmVJCahRERERERUYj7++GMEBQUJieiVK1fg6uqKhIQEiSOjksIklIiIiIiIStSAAQMQHBwMY2NjAMD169fh6uqK+Ph4iSOjksAklIiIiIiISpy/vz+2bt0KuVwOAIiLi4Orqytu374tbWBkcExCiYiIiIhIEr169cK2bdtgamoKALh9+zY6duyImzdvShwZGRKTUCIiIiIikoyPjw927twJMzMzAMDdu3fRsWNHXLt2TeLIyFCYhBIRERERkaS6du2K3bt3w9zcHABw7949uLq64vLlyxJHRobAJJSIiIiIiCTXuXNn7N27F5aWlgCA+/fvw83NDRcvXpQ4MtI3JqFERERERFQquLu7IywsDNbW1gCA5ORkuLm54dy5cxJHRvrEJJSIiIiIiEqNDz74APv370eFChUAAI8ePYK7uztOnTolcWSkL0xCiYiIiIioVHnvvfcQHh4OW1tbAMCTJ0/g6emJkydPShwZ6QOTUCIiIiIiKnWcnZ1x6NAhVKxYEQDw7NkzdO7cGcePH5c4MtIVk1AiIiIiIiqVWrdujcjISFSqVAkA8Pz5c3h5eeHIkSMSR0a6YBJKRERERESlVosWLRAVFYWqVasCAF68eAFvb29ERkZKHBkVF5NQIiIiIiIq1Zo1a4aoqChUr14dAPDq1St0794dBw8elDgyKg4moUREREREVOo1bdoUUVFRqFmzJgDg9evX8PHxwb59+ySOjLTFJJSIiIiIiMqExo0bIzo6GrVr1wYApKeno2fPnti9e7fEkZE2mIQSEREREVGZ4eDggOjoaNSrVw8AkJGRgd69e2PHjh3SBkYaYxJKRERERERlSv369REdHY0GDRoAABQKBfz9/bF161aJIyNNMAklIiIiIqIyp06dOjh8+DAaNWoEAMjKysJHH32EjRs3ShwZFYVJKBERERERlUm1atVCdHQ0mjRpAgBQKpUYPHgw1q9fL3FkVBgmoUREREREVGbVqFEDUVFRcHJyApCdiA4ZMgRr1qyRODIqCJNQIiIiIiIq06pVq4bIyEg0b94cAKBSqRAYGIgVK1ZIHBnlh0koERERERGVeVWqVEFERARatWol1I0cORJLly6VMCrKD5NQIiIiIiIqFypVqoRDhw6hbdu2Qt3o0aOxaNEiCaMidUxCiYiIiIio3KhYsSIOHjyIDh06CHUTJkzA/PnzJYyKcmMSSkRERERE5YqtrS32798PFxcXoe7rr7/GrFmzJIyKcjAJJSIiIiKicqdChQoICwuDm5ubUPftt9/i+++/h0qlki4wYhJKRERERETlk5WVFUJDQ9GpUyehbubMmfjmm2+YiEqISSgREREREZVblpaW2LVrF7y9vYW6OXPmYNKkSUxEJcIklIiIiIiIyjULCwvs3LkTPXr0EOoWLFiA8ePHMxGVAJNQIiIiIiIq98zMzLBt2zb06tVLqPvll1/wxRdfQKlUShjZm4dJKBERERERvRFMTU2xefNm+Pv7C3W//fYbRo4cyUS0BDEJJSIiIiKiN4ZcLsfGjRvRv39/oW7lypUIDAxEVlaWhJG9OZiEEhERERHRG8XExARBQUEYPHiwULdu3ToMGTIEmZmZEkb2ZjCROoCyRKFQ4MCBAzhw4AAuXryIx48fQ6VSoVq1aqhduza8vb3h7e0Na2vrEonn2bNn2LFjB44cOYKrV6/i2bNnsLCwQLVq1eDo6AgfHx+4uLjAxKT4v80xMTEICQnB2bNnkZSUhLS0NFSpUgXVq1eHm5sbfHx8ULNmzTd6DAAQGxuLAwcO4MSJE7h//z4ePXoEc3NzVK5cGS1btoSXlxfc3d0hk8l0eg8RERER6YexsTHWrl0LuVyONWvWAAD+/PNPZGZmYv369ZDL5RJHWH7JVDwOSiMxMTGYOnUq7ty5U2g7W1tbTJ8+Hd26dTNoPFu2bMHcuXPx8uXLQts5ODhgwYIFaNq0qVb9JyUl4ZtvvsHRo0cLbWdsbIxhw4Zh9OjRWieK5WEMd+/exezZsxEREVFkW0dHR/z0009o0qSJVu/Q1uDBg3Hy5Ek4OzsjKCjIoO8iIiIiKuuUSiU+++wzrFixQqjr3bs3Nm7cCFNTUwkjK7+YhGrgwIEDmDBhAhQKhcbPjBw5EuPHjzdIPPPmzRM+rdGEXC7H0qVL4ebmplH7uLg4DBkyBA8ePND4He3atcOqVas0/otaHsYQExODESNG4MWLFxq/w8zMDCtWrECHDh00fkZbTEKJiIiItKNSqTBmzBgsXbpUqPP19cXmzZthZmYmYWTlE5PQIsTGxqJ///5IT08X6hwdHTFo0CA0bdoUJiYmuHbtGoKDg3HmzBnRs3PnzhUdAa0PGzZswMyZM0V1bm5u6NWrF+rVq4fXr1/j7NmzCAoKQmJiotDGysoKwcHBcHR0LLT/1NRU9O7dWzTja29vj4EDB6J9+/awsbFBQkICQkNDERoaKrpXyc/PD3PmzHkjxnD69GkEBgbi1atXQp2NjQ0GDRqEd999F9WrV8ejR48QHR2N4OBgUTsrKyuEhoaiRo0aRb6nOJiEEhEREWlPpVJh4sSJWLRokVDXrVs3bNu2Debm5hJGVv4wCS1EVlYWevbsiWvXrgl1fn5+mDlzZp414iqVCsuXL8fPP/8s1FlZWSE8PBz29vZ6iefevXvw9vYWEmKZTIYZM2agX79+edq+fPkSkyZNQnh4uFDXqlUrBAcHF/qO6dOnY9OmTULZyckJK1euRKVKlfK0PXLkCMaMGSNKsFavXg0XF5dyPYYXL16gR48eSEpKEupcXV2xYMEC2NjY5GkfHx+PTz/9VJQUd+nSBYsXLy50HMXFJJSIiIioeFQqFSZPnoz58+cLdV5eXti5cycsLCwkjKx84em4hQgJCREloK1bt8asWbPy3aQsk8kwatQoDB06VKh7+fIlli9frrd4Fi9eLJqRHT58eL7JG5CdAC9atAitWrUS6s6cOSNK6NTFx8djy5YtQtnOzg7Lly/PN3kDgA8++AALFy4U1S1cuBCFfa5RHsawcuVKUQLaoUMH/Prrr/kmoABQp04dLF26FMbGxkJdeHi4VkuFiYiIiMjwZDIZ5s6di2nTpgl1Bw4cQI8ePYo8x4Q0xyS0EH/++aeo/PXXX4sSifyMGzcOVapUEcpbt25FWlqazrE8efIEoaGhQtnOzg6jRo0q9BlTU1NMnz5dVLd+/foC22/atEl0N1JgYCCqVq1a6Ds8PDzQuXNnoXzp0iXExMTk27Y8jOHVq1eiPxc2NjaYP39+kaenOTo6olOnTkI5KysLR44cKfQZIiIiIip5MpkMP/zwA2bMmCHURUREoFu3blqdBUIFYxJagPj4eMTGxgplR0dHtGjRosjnzMzMRPtAX716haioKJ3jCQ8PR0ZGhlDu0aOHRksCmjRpgpYtWwrlmJgYpKSk5Nt23759wvdGRkbo3bu3RrGpz2Tm7ie38jCG/fv3i/7xGTJkSJFJbg4vLy+Ym5ujevXqaNq0qV4+nCAiIiIiw/juu+/w448/CuXDhw/D29sbz58/lzCq8oFJaAH+/vtvUfmDDz7Q+Fn1/YQHDx7UOR71a0Y6duxYrHiysrLyvU7k5s2boiWmTk5OBS5hVdeuXTvRibIFjbc8jOHQoUPC90ZGRvD399eofyA76T537hyio6Oxc+dODBo0SONniYiIiKjkTZ06FT/99JNQPnr0KLp06YKnT59KGFXZxyS0ABcvXhSVNZkFzdGsWTPIZDKhrH5qrj7iad68ucbPqrfNL54LFy6IytqM19TUFI0bNxbKycnJolNtc5SHMfz777/C905OThrPghIRERFR2fTll1+KDh89ceIEOnfujMePH0sYVdnGJLQAN27cEJUdHBw0ftbKykqUnCQmJopOX9XW69evkZCQIJQrVaqEihUravx8vXr1ROXr16/naaM+3oYNG2oVo/o71PsrD2OIj48XferVrFkzrfonIiIiorJp7Nix+PXXX4VyTEwMPD09C9wiRoUzkTqA0urevXuicvXq1bV6vnr16qLTTxMTE9GoUaNixZKUlCQ6rVXb+yXVY8+dDOZQH2/NmjX1+o7yMIZbt26JyrmT1oyMDERFRSE0NBTXrl3D/fv3YWRkhKpVq6JNmzbo2rUr3nvvPa3iISIiIqLS47PPPoOJiQlGjBgBADh79iw8PDwQHh7O1XFaYhKaD6VSKZpet7S0hJWVlVZ9qN8Nqst0vfonLJUrV9bqeTMzM1hZWQnHSj99+hRKpRJGRv9NhD98+FD0jKZ7KQtqrz7e8jAG9aQ05x+bsLAwzJ49O98rV168eIG4uDhs2bIF7dq1w6xZs1CnTh2t4iIiIiKi0mH48OGQy+UIDAyESqXChQsX4O7ujkOHDmk9afUmYxKajxcvXoiu+dA2Ac3vGV1O0VJ/1trauljx5CRwKpUKqampsLW11ds71Mf77NkzUbk8jOHRo0d52v/8889YtmyZRv3/888/8Pf3x2+//YbWrVtrFdv27duxY8cOjdpevnxZq76JiIiISHMBAQGQy+X45JNPoFQqcenSJbi5uSEiIkLrlXhvKiah+ch9jQgAmJuba91H7pNW8+tTl3jMzMz0Ho+uY9a2/7I4BvUkd+vWraJTdF1cXODj4wMHBwfI5XIkJCTg4MGD2LVrl/ChxtOnTzFq1Chs27YNtWvX1ji2xMREnDx5UqvxEBEREZFhDBo0CCYmJhg0aBCysrJw9epVuLq6IiIiQquf8d5UTELzoVAoRGVjY2Ot+5DL5YX2qQ31ZMjERPvfNvVnMjMzRWVdx1xU/+VhDOnp6aJyTgJqaWmJefPmwcvLS/TrjRs3hqenJwYPHoyRI0ciOTkZQPYM68SJE7FlyxaNY6tVqxacnZ01anv58mWkpqZq3DcRERERae+jjz6CXC7HRx99hMzMTNy4cUNIRNUPvCQxJqH5yH29SnHlXs4LFC+RzaEeT+4DfjSlVCpF5dx7KfXxjr+59rMAACAASURBVKLGWx7GoJ6U5sSwePHiQu+RdXJywurVq+Hv74+0tDQAwLlz5xAZGQl3d3eNYvPz84Ofn59GbQcPHsxZUyIiIqIS0Lt3b/z111/w9/eHQqHArVu34OrqisjISDRo0EDq8EotXtGSD33MYqonNMVZflpQPPklQ7rGo+s7DN1/SbyjqP7zm73t1atXoQloDkdHRwwZMkRUt337dq3iIyIiIqLSx9fXFzt27BC2dsXHx8PV1TXfKwUpG5PQfKgfaPP69Wut+1C/F7Q4+0oLiqc4d47mHOiTw8LCotB3aDvmosZbHsaQ3wcJAwcO1Lj/vn37isonTpzIM7tLRERERGVP9+7dsWvXLuHnx4SEBLi6uuLKlSsSR1Y6MQnNh5mZGSwtLYVyamqq1ks71Q+x0fZKktwqVqxYaN9FUalUogTO2to6T0Kl/g71k2GLUtR4y8MY7OzsRGUrKyu8/fbbGvdfq1YtVKtWTfQ+9RN3iYiIiKhs6tKlC/bs2SNMlCQlJcHNzQ2xsbESR1b6MAktQO57fhQKBZ4+farV87rei5lbjRo1Cu27KE+ePBEtKc4vFvV7jbR9R1HjLQ9jUL+EuHLlylrvH65SpYqorMv9sURERERUunh6emLfvn3C1X8PHjyAu7s7zp8/L3FkpQuT0ALUrVtXVL57967Gz6pUKiQkJAhla2vrPAmMNipXriy6wzIhIUGrmVn12PPbJK3LeIHste+FvaM8jKFOnTqicnGWaavvKy3OAU1EREREVHq5uroiLCxM2Cr28OFDeHh44MyZMxJHVnowCS2A+jLLa9euafxsfHy8KEFxdHTUazyvXr0SJblFUY89v3h0Ga96exMTEzRs2LDQd5TFMTRt2lQ085mSkqL1oVVPnjwRldWX+BIRERFR2efi4oKDBw/CxsYGAPDo0SN4eHggJiZG4shKByahBWjdurWorM0fmH///VdU1vR+R23iUX+HNvG0a9cuT5vmzZuLTpfVZrx3797FgwcPRH3ld4hPWR+Dra0tGjVqJJSVSqVWV6GkpqaKZmcrVKiQZwkxEREREZUP7du3x6FDh4RJh6dPn8LT0xMnTpyQODLpMQktQNu2bUWHE0VGRiI9PV2jZ8PCwkRlV1dXneNR70P9HQVJS0tDdHS0ULayskKbNm3ytLO0tBQly4mJiRqvXd+3b1+hsRZUXxbH4OnpKSrv2rVLo/4B4NChQ6LTcDt06KDxs0RERERU9rRp0wYRERGwt7cHkH0wpZeXF44ePSpxZNJiEloAU1NTdOvWTSg/ffoUmzZtKvK58+fP4++//xbKDRs2xLvvvqtzPK1atUK9evWE8pEjR3Dx4sUin9uwYYPoUCUfHx/hDiN1vr6+ovKyZcuK7P/FixdYv369UDY2NkavXr3ybVsextCnTx8YGf3312bXrl04d+5cke9IT0/HihUrCo2ViIiIiMqfVq1aITIyUjj0MjU1FV26dBFNsrxpmIQWIiAgAMbGxkJ54cKFhS4hTU5OxtixY0WHzQwbNkwvschkMgwdOlQoK5VKjBkzptATYE+cOIFFixYJZblcjoCAgALbd+3aFbVq1RLKERER+P333wtsn5mZifHjx+Phw4dCna+vr+gakvI2hrfeekuUoCqVSnz11VeFHoKkVCrx7bffIi4uTqhr1KgRPDw8CnyGiIiIiMqP5s2bIyoqSvgZ8+XLl+jatSsOHTokcWTSYBJaCAcHBwwYMEAoZ2Rk4NNPP8XGjRvzHEhz9OhR9O3bF/fu3RPqWrZsWehs15IlS9C4cWPhq6ikpHfv3qLDdxITE9G3b18cO3ZM1C4jIwMbNmzAiBEjRHF+8sknoplIdaamppg8ebKobuHChZg5c2aeOzdv3bqFIUOG4PDhw0Kdra0txo0bV+7H8NVXX4munLlz5w78/f2xbds2ZGRkiNreuXMHw4YNQ0hIiFBnbGyM77//XjSjSkRERETlm5OTE6KiooSfI1+/fo0ePXpg//79EkdW8mQq3hFRqLS0NAQGBuY55Mbe3h5OTk4wNTXFjRs3cOfOHdGvV65cGVu3bkXNmjUL7HvJkiVYunSpUK5VqxYiIiIKjef27dsYNGiQaOYOyL6exMHBARkZGYiNjc1z/2Tbtm2xdu1a0cE9BZk3bx7WrFkjqjMzM0OLFi1QsWJFJCYmIjY2VjTja2Jigt9++02j/a/lYQw3b97E0KFDcf/+fVG9nZ0dHB0dUbFiRSQkJODSpUt5rmGZOnUqPvnkkyLfUVyDBw/GyZMn4ezsjKCgIIO9h4iIiIi0d/36dXh4eAg3RZiammL79u3o3r27xJGVHCahGnj16hVGjx4t2utZmNq1a2PlypWoX79+oe2Kk4QCQFxcHIYNG6bxFScuLi5YvHix6J7Oovzvf//D77//rtE9lpaWlvjpp5/QqVMnjfsvD2O4d+8exo0bp9GeUAAwNzfHtGnT4O/vr/E7ioNJKBEREVHpFhcXB3d3d+Geerlcjq1bt74xZ4ZwPaAGLC0tsXr1asyfPz/f+y9z2NnZYeTIkdi1a1eRCaguGjRogNDQUHz++efCBuf81K9fH7NmzcKqVau0St4AYMKECdi8eTPatWtX4LJRuVwOHx8f7N69W6vkrbyMoWbNmti8eTPmzZuX547S3MzNzdGzZ0/s3r3b4AkoEREREZV+DRo0QHR0tJAzKBQK9OnTB9u2bZM4spLBmdBiuHXrFi5evIiUlBRkZGTA1tYWjo6OaNasWYGnthqKUqnEuXPncOvWLaSkpMDIyAiVKlVCs2bN4ODgAJlMpvM7Hj16hFOnTiE5ORmpqamwtrZGvXr10LJlS1SoUIFj+H+JiYm4cOECUlJShHc0aNAALVu21DqB1gVnQon+r707j46iSv8//glZgITNBEgMMAQIq0DAKMw5LKPAMBgBASUoBjAoKgIiLjDiBsiIOMxERD0oCMouzDCC4BJAcBRUVo0wyiJ72A1LSIiJSf/+4Ev/Up2F7nR3daXzfp3jOd5L3bpP4J4n/XRV3QIAoHw4duyYunXrpgMHDki6unfI4sWLNWjQIB9H5l0UoYCfoQgFAAAoP9LT09W9e3ft3btXklSpUiV98MEHSkpK8nFk3sPtuAAAAADgI/Xq1dOmTZvsj3cVFBRo6NChev/9930bmBdRhAIAAACAD0VFRWnjxo1q06aNJMlmsyk5OVlz5szxcWTeQREKAAAAAD5Wt25dffHFF2rXrp297+GHH9bbb7/tw6i8gyIUAAAAACygdu3a2rBhg+Lj4+19o0aN0syZM30YledRhAIAAACARYSHh2v9+vXq2LGjve+JJ57QjBkzfBiVZ1GEAgAAAICF1KpVS6mpqerUqZO975lnntG0adN8GJXnUIQCAAAAgMXUqFFDn332mbp27WrvmzhxoqZMmeLDqDyDIhQAAAAALKhatWr65JNP1K1bN3vfSy+9pBdeeEE2m82HkbmHIhQAAAAALCosLExr1qxRz5497X1Tp07Vs88+W24LUYpQAAAAALCwqlWratWqVUpISLD3TZ8+XU899VS5LEQpQgEAAADA4qpUqaKVK1eqb9++9r6UlBSNHTu23BWiFKEAAAAAUA5UrlxZK1as0IABA+x9s2bN0pgxY3wYlesoQgEAAACgnAgJCdGyZcs0aNAge99bb72lPXv2+DAq11CEAgAAAEA5EhwcrEWLFmnIkCGSpNDQUEVFRfk4KudRhAIAAABAORMUFKQFCxZo586dOnz4sCIiInwdktOCfB0AAAAAAKBs2rdv7+sQXMaVUAAAAACAaShCAQAAAACmoQgFAAAAAJiGIhQAAAAAYBqKUAAAAACAaShCAQAAAACmoQgFAAAAAJiGIhQAAAAAYBqKUAAAAACAaShCAQAAAACmoQgFAAAAAJiGIhQAAAAAYBqKUAAAAACAaShCAQAAAACmoQgFAAAAAJiGIhQAAAAAYJoAm81m83UQADyna9euOn36tKpXr66WLVv6OhwAAAD4sRYtWui5555zaUyQl2IB4CPZ2dmSpMzMTG3dutXH0QAAAABGFKGAn6lfv76OHz+u0NBQNWzY0LR5f/rpJ2VmZnIFFl7B+oI3sb7gbawxeJOv11eLFi1cHsPtuAA8YsiQIdq6das6dOighQsX+joc+BnWF7yJ9QVvY43Bm8rj+mJjIgAAAACAaShCAQAAAACmoQgFAAAAAJiGIhQAAAAAYBqKUAAAAACAaShCAQAAAACmoQgFAAAAAJiGIhQAAAAAYBqKUAAAAACAaYJ8HQAA/9C/f3916NBB9erV83Uo8EOsL3gT6wvexhqDN5XH9RVgs9lsvg4CAAAAAFAxcDsuAAAAAMA0FKEAAAAAANNQhAIAAAAATEMRCgAAAAAwDUUoAAAAAMA0FKEAAAAAANPwnlCggsnLy1NqaqpSU1O1e/duZWRkyGazKTIyUg0aNFCvXr3Uq1cvVatWzZR4Ll68qP/85z/66quvtHfvXl28eFFVq1ZVZGSkmjVrpj59+qhz584KCiJdlRdWWWP33Xefdu7cWebxe/bsYd2VI4sWLdLLL78sSZo2bZoGDBhgyrzksIrB7PVF/vI/x44d0+rVq7Vz504dPHhQFy5cUF5enmrVqqWoqCjFx8ere/fu6tChgynx+Dp38Z5QoALZvn27Jk6cqCNHjpR6XM2aNTVp0iQlJCR4NZ7ly5fr1VdfVVZWVqnHxcbGasaMGWrZsqVX44H7rLLGbDab4uPjr7u2SsOHuPLj8OHDGjBggP3f26wilBxWMZi9vshf/iUjI0N/+9vf9Mknn6igoOC6x8fFxenll19W8+bNvRaTFXIXt+MCFURqaqoeeOCB6xYH0tVvx8aNG6eUlBSvxTN9+nS98MILTv2SPXDggAYOHKhNmzZ5LR64z0pr7Pjx4259gEP5cf78eY0cOdL0f29yWMXgi/VF/vIf+/fvV9++fbVmzRqnClBJ+uGHH5SYmKjPP//cKzFZJXfxFQlQAezZs0dPP/208vLy7H3NmjVTUlKSWrZsqaCgIO3bt0/Lli3Trl277MfMnj1bMTEx6t+/v0fjWbx4sebNm2fou+2229S/f3/FxMToypUr+v7777Vw4UKlp6dLunqL55NPPqlly5apWbNmHo0H7rPaGvvpp58M7WeffVa33367S+fgKoL1ZWRkaPjw4Tp48KCp85LDKgZfrS/yl384c+aMHnzwQZ09e9beFxQUpN69e6tbt25q0KCBgoKCdPLkSW3evFn/+te/7IVhTk6Onn76aUVEROiWW27xWExWyl3cjgv4ufz8fPXr10/79u2z9w0YMEBTpkxRcHCw4VibzabZs2fr9ddft/eFhYVp/fr1Cg8P90g8J06cUK9evfTbb79JkgICAjR58mQNGjSoyLFZWVkaP3681q9fb+9r3769li1b5pFY4BlWW2OS9MYbb+itt96yt1etWqUWLVp47PzwvT179mjs2LE6duxYkT/z5u2S5LCKwVfrSyJ/+YunnnpKa9assbfr1aunt99+u8R/y9OnT2v06NFKS0uz98XExGjNmjVFfpeWhdVyF7fjAn5u1apVhuIgPj5eU6dOLTahBQQEaOTIkRo+fLi9LysrS7Nnz/ZYPG+88YY9AUrSww8/XGwClK4WJykpKWrfvr29b9euXYakCN+z2hqTpJ9//tn+/8HBwWrcuLFHzw/fWrx4se69995iCwRvI4f5P1+uL4n85Q8OHz5sKEBDQ0M1Z86cUr9MiIyM1Jw5cxQdHW04z8cff+yRmKyWuyhCAT+3aNEiQ3vChAkKDAwsdcwTTzyhOnXq2NsrVqxQTk6O27GcP39ea9eutbdr1aqlkSNHljomJCREkyZNMvQtWLDA7VjgOVZaY9cU/hDXqFEjhYSEeOzc8J3du3dr2LBhmjJlinJzc+3911tvnkIO82++Xl/XkL/Kv8IFqCTde++9atKkyXXH1apVS6NHjzb0rVu3zu14rJi7KEIBP3b06FHt2bPH3m7WrJni4uKuO65y5cqGZ/Sys7M98lD6+vXrDb/Ye/furapVq153XIsWLdSuXTt7e/v27Tp37pzb8cB9VltjkpSZmWl/luVaTCjfMjIy9OSTT+qee+7Rt99+a/izO++8U8nJyabEQQ7zT1ZZXxL5y19s3brV0L7jjjucHnvbbbcZ2o7PCJeFFXMXRSjgx77++mtDu0uXLk6P7dy5s6HtiW/iNm/ebGh37dq1TPHk5+friy++cDseuM9qa0wyXkWQ5NVt7mGO/fv3a+3atSq8jUX16tU1ZcoU/fOf/1SVKlVMiYMc5p+ssr4k8pe/OHDggKHtypcJ4eHhqlTp/5dov/76q9vxWDF3sXUW4Md2795taDtzheqa1q1bKyAgwP5LufCOpp6Kp23btk6PdTx2165dSkxMdDsmuMdqa0wq+iGOKwn+pVKlSurdu7eeeeYZ1a1b19S5yWH+z5frSyJ/+YuPPvpIp0+f1pkzZ3Tu3DmXvsj49ddfDa9zCQsLczseK+YuilDAjzl+ExcbG+v02LCwMNWtW1enT5+WJKWnpys7O1uhoaFliuXKlSs6fvy4vR0REaEbbrjB6fExMTGG9v79+8sUBzzLSmvsGsdblxw/xF2+fFmXLl1SWFiYatas6dZcME9gYKC6d++u0aNH++TqEDnMv/l6fV1D/vIPdevWLfOXGI53GBXeqKgsrJq7KEIBP3bixAlDOyoqyqXxUVFR9gJBulokNG3atEyxnDx50nCr04033uhyLIUVTqjwHSutsWsKX0moXr26oqOjtW3bNq1cuVLffvutIebKlSsrPj5ePXr00D333KPKlSu7NTe8o0mTJlq/fr3bH8bcQQ7zX1ZYX9eQv+C4+Y8rj7kUx6q5iyIU8FMFBQXKyMiwt0NDQ12+pcPxvY2Fz+cqxwfZa9eu7dL4ypUrKywszP4i5wsXLqigoMDw3ATMZbU1Jl19XqXw1dnq1atryJAhRTaJuOa3337Tli1btGXLFr3zzjt68cUX1aNHD7digOe5mi+8gRzmv6ywviTyF6Tly5cbNvuTXNvUqDhWzV1kPsBPXb58Wfn5+fZ2WZ4pcBxz6dKlMsfjOLZatWpuxWOz2ZSZmVnmeOA+q60xSTp06JDhPWgnTpwo8QOco2svCvf0O0vhH8hh8DbyV8X2888/a9q0aYa+Hj16lPpuUWdYNXdxJRTwU4W34pZUpt39HN9N5nhOd+Ipy21DnowH7rPaGpNK3sr+1ltvVWJiotq1a6eoqChlZ2frl19+0fr167Vs2TJlZ2dLuvrLNSUlRRERERo4cKBbscC/kMPgbeSviis9PV2PPvqo/d9SkmrUqKHnnnvO7XNbNXdRhAJ+Ki8vz9Auy8u2g4ODSz2nKxwTVlCQ6+nHcczvv/9e5njgPqutManozpJVqlTRSy+9pAEDBhj6Q0JCFB8fr/j4eCUlJemxxx4zjJ08ebL++Mc/qkGDBm7FA/9BDoO3kb8qpvT0dA0ZMkQnT5609wUEBGjatGkeeU7ZqrmL23EBPxUQEOD2OQrfaimVrci4xjGewg/JO6vwluWSeJbKx6y2xiSpadOmuvPOO9WuXTvVrVtXM2bMKPIBzlG9evU0f/58w2YNeXl5euONN9yKBf6FHAZvI39VPPv27dPgwYOVnp5u6J8wYYLHnu+1au7iSijgpzxxhcmxQHBn5z3HeMryLZon44H7rLbGJKlfv37q16+fy+PCw8M1btw4jR8/3t6XmpqqqVOnss4giRwG7yN/VSxbt27VqFGjijyz+fjjjys5Odlj81g1d/EVHOCnHB88v3LlisvnKPxsglS2Z/5Kisfx3M64tjPbNVWrVi1zPHCf1daYuxISEgzvKM3JydH27dt9Fg+shRwGKyN/lS8rVqzQ8OHDixSgzzzzjEaNGuXRuayauyhCAT9VuXJlwy+kzMxMl2/BcEyO7mxj7/hiZFd3QbXZbIYkWK1aNb7h9TGrrTF3BQcHq3Xr1oY+x1ukUHGRw2Bl5K/yIT8/X6+88oqef/55w91DQUFBeuWVV/TQQw95fE6r5i6KUMCPFX7BcF5eni5cuODSeHffLVWY48uRHc99PefPnzckbKu8162is9Ia8wTH+c+fP++jSGA15DBYHfnL2jIzM/Xwww/rgw8+MPSHhobq7bff1t133+2Vea2auyhCAT/WsGFDQ/vYsWNOj7XZbDp+/Li9Xa1aNdWtW7fMsdSuXdvwnqnjx4+7dNXMMfbGjRuXORZ4jpXWmCc4Pvfiy9uDYS3kMFgd+cu6Tp06pcGDB+vrr7829EdGRmrJkiX605/+5LW5rZq7KEIBP9aqVStDe9++fU6PPXr0qOEZv2bNmnk0nuzsbEMBcj2OsXsiHrjPSmssJydHhw4d0o4dO7R+/Xpt2bLF5XOcPXvW0I6IiHArJvgXchi8hfzlv44cOaJ77723SA5o1aqVVqxYoZYtW3o9BivmLopQwI/Fx8cb2q5sUrBt2zZDu0OHDh6Px3EOV+Lp2LGj2/HAfVZaY1u2bFGvXr00ePBgjRo1SpMmTXJpfG5urvbs2WPoi4uLcysm+BdyGLyF/OWfjh07pqSkJMM7QCXptttu0+LFixUZGWlKHFbMXRShgB+79dZbDRvHbNy4Ub/99ptTYz/77DND2xO3ijiew3GOkuTk5OjLL7+0t8PCwnTLLbe4HQ/cZ6U15nhV9siRI9q/f7/T4z/55BND7H/4wx942TsMyGHwFvKX/7l8+bIeeughnTlzxtCfmJiot99+2/C709usmLsoQgE/FhISooSEBHv7woULWrp06XXHpaWlGZ5baNKkiW6++Wa342nfvr1iYmLs7a+++kq7d+++7rjFixcbNrzp06ePQkJC3I4H7rPSGouKilLbtm0Nfe+++65TYzMzMzVr1ixDX1JSklvxwP+Qw+At5C//M2nSJB0+fNjQ9+CDD+rll19WYGCgqbFYMXdRhAJ+Ljk52ZDs/vGPf5R6G8aZM2c0duxYw0PrI0aM8EgsAQEBGj58uL1dUFCgxx9/vNSd2r799lulpKTY28HBwR59iTPcZ6U15vjBa/Xq1froo49KHZOVlaXHH3/c8IxM/fr1NXDgQI/EBP9BDoM3kb/8x7p16/Txxx8b+vr376/x48f7JB4r5i6KUMDPxcbGavDgwfZ2bm6uHnroIS1ZssSw5bYkbd68WYmJiTpx4oS9r127drrrrrtKPP+sWbPUvHlz+3/dunUrNZ67777bcNtRenq6EhMTi2zCkJubq8WLF+uRRx4xxDls2DDDt3nwPSutsbvuuqvIs6V//etfNX369CKvK7DZbNq8ebMGDRpkWH+BgYGaPn26qbdKwXfIYfAm8lfFY7PZ9Oabbxr66tSpo+TkZB05cqRM//3+++9F5invuSvIY2cCYFlPP/20fvrpJ/umMTk5OZo8ebJmzZqlm266SSEhITpw4ICOHDliGFe7dm2lpKSoUiXPfV8VFBSklJQUJSUl2XfyS09PV3Jysho2bKjY2Fj7BgsZGRmGsbfeequeeOIJj8UCz7HSGps5c6buv/9+HTx4UNLVDwTz5s3TwoUL1aZNG0VGRiorK0s///xzkWd1goODNWPGDJ7XQ4nIYfAm8lf5t2XLFv3888+GvrNnz6pv375lPueGDRtUv359t+KyWu6iCAUqgCpVqmjOnDkaM2aM4Tm8jIwMffXVV8WOadCggebMmaPo6GiPxxMTE6MFCxZoxIgRhluIrn3jV5zOnTvrjTfeUHBwsMfjgfustMbCw8O1aNEiTZgwwTB3Xl6edu7cWeK4yMhITZ06VV27dvVoPPA/5DB4C/mr/Nu4caOvQyiRlXIXt+MCFURoaKjee+89vfbaa2rSpEmJx9WqVUuPPvqoVq9erUaNGnktnsaNG2vt2rUaNWqUateuXeJxjRo10tSpUzV37lzDy5ZhPVZaYxEREZo7d65mzpyp9u3bl3ps/fr19dhjj+nTTz/lAxycRg6Dt5C/yrdjx475OoRSWSV3BdgK7wwBoMI4dOiQdu/erXPnzik3N1c1a9ZUs2bN1Lp1a9N3bSwoKNAPP/ygQ4cO6dy5c6pUqZIiIiLUunVrxcbGKiAgwNR44BlWWmMZGRnatWuXTp06pczMTIWGhqp27dpq0qSJmjdvbmos8D/kMHgT+Qve4svcRREKAAAAADANt+MCAAAAAExDEQoAAAAAMA1FKAAAAADANBShAAAAAADTUIQCAAAAAExDEQoAAAAAMA1FKAAAAADANBShAAAAAADTUIQCAAAAAExDEQoAAAAAMA1FKAAAAADANBShAAAAAADTUIQCAAAAAExDEQoAAAAAME2QrwMAAAC+991332no0KFenaN///569dVXvTpHRTFr1iy9+eab9naHDh20cOFCH0YEAM7jSigAAAAAwDQUoQAAAAAA03A7LgAAKNYdd9yhG2+80WPna9OmjcfOBQAovyhCAQBAse677z517NjR12EAAPwMt+MCAAAAAExDEQoAAAAAMA1FKAAAAADANBShAAAAAADTsDERAACwtNOnT2vHjh06ceKEfv/9d9WsWVNNmzZVmzZtVLlyZY/MceXKFX3//fdKT09XRkaGgoKCFB4ervr16ysuLk7BwcEemUe6+vPs3r1b6enpysrKUtWqVRUeHq6bbrpJjRs3VkBAgEfmSU9PV1pams6ePavs7GxVr15djRs3Vvv27VWlShWPzAEAZUERCgAAfKpbt25KT0+3t/fu3StJOnLkiKZNm6ZNmzbJZrMVGVe9enX17dtXjz32mGrXrl2mubds2aL33ntP3333nfLy8oo9JiwsTF26dNHIkSPVokWLMs2Tl5en1atXa+nSpfrxxx9LPC46OlqJiYkaNmyYQkNDyzRXamqqZs+erT179hT751WqVNGdd96pMWPGePQVPADgrt1F7gAAC8ZJREFULG7HBQAAlvPll1+qX79+2rhxY7EFqCRlZmZq8eLF6tmzpz7//HOXzn/mzBkNHz5cycnJ+vrrr0ssQCUpKytLn332mfr166dnn31WOTk5Ls31/fffq0+fPpo4cWKpBagknThxQq+//rr69OmjH374waV5Ll++rBEjRmjMmDElFqCSlJOTo3//+99KSEjQhg0bXJoDADyBIhQAAFjK9u3bNXr0aGVnZzt1fFZWlh5//HGtWLHCqeP37t2rvn37avPmzS7FZbPZtHLlSt1///06c+aMU2NSU1OVlJSkQ4cOuTTX8ePH9cADD2j79u1OHX/x4kUNHTpU//3vf52eIzs7W2PHjnW52AUAd3E7LgAAsJRx48YpNzdXkhQUFKTExET17t1bMTExunLlitLS0rRkyRJt27bNMO7FF19U48aNFR8fX+K5T548qREjRuj8+fOG/jp16ui+++5Tly5dFB0drfz8fB07dkwbNmzQsmXLDAXx7t27NXLkSC1dulQhISElzpWWlqYnn3yyyFXW5s2bKzExUR06dFCdOnWUm5urH3/8UYsWLdI333xjPy47O1vjxo3TmjVrVLNmzVL/zq7dwixJAQEB6tSpkxITExUbG6uIiAidPXtWmzdv1ty5c3X27Fn7sXl5eZo8ebJWrlxZ6vkBwJMCbCXd4wIAACqM7777TkOHDjX0LViwQB07dvT63I7PhF5Tt25dvfPOO2rVqlWRP7PZbJo/f76mT59u6G/UqJHWrl2rwMDAYucaPHiwduzYYejr06ePJk2apGrVqhU75vTp0xo7dqx27dpV5FwvvfRSsWPy8/PVu3dvHTx40NA/duxYPfLIIyXGN2fOHM2YMcPQN3z4cE2YMMHQN2vWLL355ptFxteoUUMpKSnq3Llzsef/9ddfNXToUB04cMDQv2rVqjI/7woAruJKKAAAKJZjUeqOt956Sz169HD6+Bo1amjBggVq1KhRsX8eEBCg4cOHKzc3VykpKfb+Q4cO6eOPP1a/fv2KjPnyyy+LFKB33XWXpk+fXuqOtJGRkZo/f76GDRtmuHV12bJleuCBB9SwYcMiYz766KMiBehTTz2lhx9+uMR5JGnEiBE6ePCg4crk8uXLNWbMmOtuVBQYGKh58+apTZs2JR4TERGhV155RYmJiYb+rVu3UoQCMA3PhAIAAMsZP358iQVoYSNGjNBNN91k6Cvp2dB33nnH0I6OjtZLL73k1CtRqlatqr///e+GV8IUFBRo7ty5xR7vGEPr1q310EMPXXce6erV0sKvhLl8+XKRW4+LM3jw4FIL0Gvi4uIUGxtr6HMsmAHAmyhCAQCApURHR+vuu+926tjAwEANGTLE0Ldjxw7Dc4+SdOnSpSK30w4ZMkRhYWFOx9WwYUPdeeedhr7U1NQiu/eeOXOm2LkqVXLuY1dUVJQ6duyoG2+8UZ06ddKQIUNUq1at644bOHCgU+eXVKRYdfz7AgBv4nZcAABQrDvuuMNj75Es7pbVkvTt29fpgk2Sevbsqeeee075+fmSrj4vum3bNiUkJNiP2bp1qwoKCgzj+vTp4/Qc1/Tr189wq+yFCxe0b98+NW/e3N7nuKNtpUqVXLoVWZLmzp3r1BXaa0JDQ9W0aVOnjw8PDze0r1y54vRYAHAXRSgAACjWfffdZ8rGRI7at2/v0vFhYWGKiYnRL7/8Yu/bu3evoQh1fD9ndHS06tSp43Jsbdq0UWBgoL3gla7uglu4CN23b59hTMOGDUvc9KgkrhSg0tWfx5XCvWrVqoZ2ae9JBQBP43ZcAABgKYULOmfFxMQY2sePHze0MzIyDG3HZyKdFRoaWuTqsOPrXhzndozNG6pXr+71OQDAUyhCAQCApdxwww0uj3Eswi5dumRoX7hwwdCuUaOG64GVMNaxCM3MzDS0XXnutKwKb2QEAFZHEQoAACwjMDBQVapUcXmc4xjH20svX75saDvejurOXLm5uYZ2Tk6Ox+YCAH9EEQoAACwjPz/f8Lyls7Kysgxtx8LP8R2b7mzE4ziXY1HqOJdjUQoAFR1FKAAAsBTHIs8Zjlc6HW/pdbyF9uLFi64H9n8cb/V13HSoZs2apcYGABUdRSgAALCUY8eOuTzmwIEDhrbjZkCOO+EW3knXFZcuXdKpU6cMffXr1ze0HQvgo0ePujxPVlaW0tPTy3RVGACsjiIUAABYyv/+9z+Xjr948WKRHWnbtGljaLdt29bQPnHihM6cOeNybGlpabLZbIa+Ro0alTr34cOHXb79d926derWrZvi4uL05z//WePHj3c5VgCwKopQAABgKevWrXPp+E8//dRQGFatWlU333yz4RjHtiR9/PHHLse2atUqQ7t69epq2bJlqXPl5+dr48aNLs2zfft2SVc3WDp69GiRzY8AoDyjCAUAAJby9ddf69ChQ04dm5eXp0WLFhn6evbsqcqVKxv6wsPD1aFDB0PfokWLXHr+9NChQ/r8888NfbfddpsCAwMNfVFRUbrpppsMfUuXLnV6nqysLKWmphr6OnXq5PR4ALA6ilAAAGAp+fn5mjhxYpHXrBTnzTff1P79+w19SUlJxR6bnJxsaJ84cUJTpkwpcnttca5cuaLx48frt99+M/QPGTKk2OOHDh1qaG/dulUffvjhdeeRrv5MhTdOCgsLU0JCglNjAaA8oAgFAACWs3PnTo0ePbrEnWXz8/M1a9YszZ4929D/l7/8pcjzn9fcfvvtRW6V/eijjzR+/PhSr4iePn1aDz74oNLS0gz9vXv3VlxcXLFjEhISFBsba+ibPHmyVqxYUeI8kvT+++9r3rx5hr5hw4YpLCys1HEAUJ4E+ToAAABgTUuXLtWmTZs8es6EhIQiG/eUZNOmTerVq5eSk5PVpUsX1a1bVxcvXtSOHTu0aNEi7dmzx3B8nTp1NGnSpBLPFxAQoJSUFPXr10/nz5+3969evVrffPONBg8erK5du+rGG29Ufn6+jh49qg0bNujDDz8sUqTGxMToxRdfLHGukJAQpaSkaODAgfb3hObn5+v555/XqlWrdM8996h9+/aqVauWLl26pLS0NC1dulTbtm0znCc2NlYjR4506u8LAMoLilAAAFCsTz/91OPnbNq06XWL0FtvvdVejJ09e1avvfaaXnvttVLH3HDDDXrvvfcUHh5e6nFRUVF69913NXLkSJ07d87ef/bsWc2cOVMzZ8687s/QuHFjzZkzp8j7QB01a9ZMr7/+usaNG2fYHXfbtm1Fis3iREdHa86cOQoJCbnusQBQnnA7LgAAsJQBAwbohRdeUHBwsFPHx8XFacWKFWrevLlTx7dt21YrVqxQfHx8mWL78MMPi7wbtCS33367Fi5cqIYNG7o0T4cOHbR06VJFR0e7HCMAWB1XQgEAgOUkJSUpPj5e06dP17ffflvs5kGtWrXSAw88oD59+qhSJde+V4+OjtaSJUu0ceNGzZ8/Xzt37ixxI6TQ0FB1795dw4cPV6tWrVz+Wdq0aaO1a9dq+fLlWrZsmfbt21fisa1atVJycrJ69+7t8s8EAOVFgM2ZLeEAAAC8pFu3bkpPT7e3p02bpgEDBtjb6enp+uGHH3Ty5EkVFBQoKipKcXFx+sMf/uCxGC5fvqxdu3bp9OnTysjIkCTVqlVLsbGxat26tUdviT116pTS0tJ07tw5Xbp0SaGhoapTp45uvvlmRUZGemweALAqroQCAABLq1evnurVq+fVOapVq6YuXbp4dY5roqKiFBUVZcpcAGBF3OcBAAAAADANRSgAAAAAwDQUoQAAAAAA01CEAgAAAABMQxEKAAAAADANRSgAAAAAwDQUoQAAAAAA01CEAgAAAABME2Cz2Wy+DgIAAAAAUDFwJRQAAAAAYBqKUAAAAACAaShCAQAAAACmoQgFAAAAAJiGIhQAAAAAYBqKUAAAAACAaShCAQAAAACmoQgFAAAAAJiGIhQAAAAAYBqKUAAAAACAaShCAQAAAACmoQgFAAAAAJiGIhQAAAAAYJr/B/b5Lqaq8wSvAAAAAElFTkSuQmCC\n" + }, + "metadata": { + "image/png": { + "width": 464, + "height": 306 + } + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 6;\n var nbb_unformatted_code = \"plt.plot(digits_test[\\\"test_loss\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Test error\\\")\\nsns.despine()\";\n var nbb_formatted_code = \"plt.plot(digits_test[\\\"test_loss\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Test error\\\")\\nsns.despine()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "plt.plot(digits_test[\"test_loss\"], color=\"black\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Test error\")\n", + "sns.despine()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "image/png": { + "width": 440, + "height": 287 + } + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 8;\n var nbb_unformatted_code = \"plt.plot(digits_test[\\\"test_correct\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Accuracy\\\")\\nsns.despine()\";\n var nbb_formatted_code = \"plt.plot(digits_test[\\\"test_correct\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Accuracy\\\")\\nsns.despine()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "plt.plot(digits_test[\"test_correct\"], color=\"black\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Accuracy\")\n", + "sns.despine()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "name": "python367jvsc74a57bd05c0fa7a4f8f1487a2aac67eb43e7b2e553808a81f9be50af9e1ab194481cfe22", + "display_name": "Python 3.6.7 64-bit" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/notebooks/test_fashion.ipynb b/notebooks/test_fashion.ipynb new file mode 100644 index 0000000..c365bb9 --- /dev/null +++ b/notebooks/test_fashion.ipynb @@ -0,0 +1,240 @@ +{ + "cells": [ + { + "source": [ + "# Test fashion\n", + "Things are working and seem sane." + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import torch\n", + "import glob\n", + "from collections import defaultdict\n", + "\n", + "from glia.util import load_checkpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 2;\n var nbb_unformatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\n\\nsns.set(font_scale=2)\\nsns.set_style(\\\"ticks\\\")\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\nplt.rcParams[\\\"legend.title_fontsize\\\"] = \\\"14\\\"\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n var nbb_formatted_code = \"# Pretty plots\\n%matplotlib inline\\n%config InlineBackend.figure_format='retina'\\n%config IPCompleter.greedy=True\\n\\nimport seaborn as sns\\n\\nsns.set(font_scale=2)\\nsns.set_style(\\\"ticks\\\")\\n\\nplt.rcParams[\\\"axes.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"figure.facecolor\\\"] = \\\"white\\\"\\nplt.rcParams[\\\"font.size\\\"] = \\\"14\\\"\\nplt.rcParams[\\\"legend.title_fontsize\\\"] = \\\"14\\\"\\n# Uncomment for local development\\n%load_ext nb_black\\n%load_ext autoreload\\n%autoreload 2\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "# Pretty plots\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format='retina'\n", + "%config IPCompleter.greedy=True\n", + "\n", + "import seaborn as sns\n", + "\n", + "sns.set(font_scale=2)\n", + "sns.set_style(\"ticks\")\n", + "\n", + "plt.rcParams[\"axes.facecolor\"] = \"white\"\n", + "plt.rcParams[\"figure.facecolor\"] = \"white\"\n", + "plt.rcParams[\"font.size\"] = \"14\"\n", + "plt.rcParams[\"legend.title_fontsize\"] = \"14\"\n", + "# Uncomment for local development\n", + "%load_ext nb_black\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 3;\n var nbb_unformatted_code = \"fashion_test = torch.load(\\n \\\"/Users/qualia/Code/glia_playing_atari/data/fashion_test.pytorch\\\")\";\n var nbb_formatted_code = \"fashion_test = torch.load(\\n \\\"/Users/qualia/Code/glia_playing_atari/data/fashion_test.pytorch\\\"\\n)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "fashion_test = torch.load(\n", + " \"/Users/qualia/Code/glia_playing_atari/data/fashion_test.pytorch\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "dict_keys(['model_dict', 'vae_dict', 'glia', 'batch_size', 'test_batch_size', 'num_epochs', 'lr', 'vae_path', 'lr_vae', 'use_gpu', 'device_num', 'vae_loss', 'train_loss', 'test_loss', 'test_correct', 'correct', 'seed_value'])" + ] + }, + "metadata": {}, + "execution_count": 4 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 4;\n var nbb_unformatted_code = \"fashion_test.keys()\";\n var nbb_formatted_code = \"fashion_test.keys()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "fashion_test.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "image/png": { + "width": 415, + "height": 287 + } + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 5;\n var nbb_unformatted_code = \"plt.plot(fashion_test[\\\"train_loss\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Train error\\\")\\nsns.despine()\";\n var nbb_formatted_code = \"plt.plot(fashion_test[\\\"train_loss\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Train error\\\")\\nsns.despine()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "plt.plot(fashion_test[\"train_loss\"], color=\"black\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Train error\")\n", + "sns.despine()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "image/png": { + "width": 415, + "height": 287 + } + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 6;\n var nbb_unformatted_code = \"plt.plot(fashion_test[\\\"test_loss\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Test error\\\")\\nsns.despine()\";\n var nbb_formatted_code = \"plt.plot(fashion_test[\\\"test_loss\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Test error\\\")\\nsns.despine()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "plt.plot(fashion_test[\"test_loss\"], color=\"black\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Test error\")\n", + "sns.despine()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "image/png": { + "width": 428, + "height": 287 + } + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": "", + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 7;\n var nbb_unformatted_code = \"plt.plot(fashion_test[\\\"test_correct\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Accuracy\\\")\\nsns.despine()\";\n var nbb_formatted_code = \"plt.plot(fashion_test[\\\"test_correct\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Accuracy\\\")\\nsns.despine()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + }, + "metadata": {} + } + ], + "source": [ + "plt.plot(fashion_test[\"test_correct\"], color=\"black\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Accuracy\")\n", + "sns.despine()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "name": "python367jvsc74a57bd05c0fa7a4f8f1487a2aac67eb43e7b2e553808a81f9be50af9e1ab194481cfe22", + "display_name": "Python 3.6.7 64-bit" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file From d8269877b0e5fe5c8c679f837bd937e39e64ebe1 Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Mon, 17 May 2021 19:12:59 -0700 Subject: [PATCH 17/22] Update tests w/ 150 epochs --- Makefile | 2 +- notebooks/test_digits.ipynb | 18 +++++++++--------- notebooks/test_fashion.ipynb | 12 ++++++------ 3 files changed, 16 insertions(+), 16 deletions(-) diff --git a/Makefile b/Makefile index b3d8b58..85c7332 100644 --- a/Makefile +++ b/Makefile @@ -9,7 +9,7 @@ digits_test: glia_digits.py VAE --glia=True --num_epochs=150 --use_gpu=False --lr=0.004 --lr_vae=0.01 --debug=True --seed_value=None --save=$(DATA_PATH)/digits_test fashion_test: - glia_fashion.py VAE --glia=True --num_epochs=3 --use_gpu=False --lr=0.008 --lr_vae=0.01 --debug=True --seed_value=None --save=$(DATA_PATH)/fashion_test + glia_fashion.py VAE --glia=True --num_epochs=150 --use_gpu=False --lr=0.008 --lr_vae=0.01 --debug=True --seed_value=None --save=$(DATA_PATH)/fashion_test # ---------------------------------------------------------------------------- xor_exp1: diff --git a/notebooks/test_digits.ipynb b/notebooks/test_digits.ipynb index 47c035b..e03a1c3 100644 --- a/notebooks/test_digits.ipynb +++ b/notebooks/test_digits.ipynb @@ -116,11 +116,11 @@ "output_type": "display_data", "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" 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\n" + "image/png": 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\n" }, "metadata": { "image/png": { - "width": 464, - "height": 306 + "width": 402, + "height": 287 } } }, @@ -177,18 +177,18 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": { "image/png": { - "width": 440, + "width": 433, "height": 287 } } @@ -197,7 +197,7 @@ "output_type": "display_data", "data": { "text/plain": "", - "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 8;\n var nbb_unformatted_code = \"plt.plot(digits_test[\\\"test_correct\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Accuracy\\\")\\nsns.despine()\";\n var nbb_formatted_code = \"plt.plot(digits_test[\\\"test_correct\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Accuracy\\\")\\nsns.despine()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 7;\n var nbb_unformatted_code = \"plt.plot(digits_test[\\\"test_correct\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Accuracy\\\")\\nsns.despine()\";\n var nbb_formatted_code = \"plt.plot(digits_test[\\\"test_correct\\\"], color=\\\"black\\\")\\nplt.xlabel(\\\"Epoch\\\")\\nplt.ylabel(\\\"Accuracy\\\")\\nsns.despine()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n " }, "metadata": {} } diff --git a/notebooks/test_fashion.ipynb b/notebooks/test_fashion.ipynb index c365bb9..cdbe2c5 100644 --- a/notebooks/test_fashion.ipynb +++ b/notebooks/test_fashion.ipynb @@ -116,11 +116,11 @@ "output_type": "display_data", "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" 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\n" 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\n" + "image/png": 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\n" }, "metadata": { "image/png": { - "width": 428, + "width": 421, "height": 287 } } From 944c3576457a1c33caafd3b35642551bcd39e757 Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Tue, 18 May 2021 10:18:51 -0700 Subject: [PATCH 18/22] Add exp recipes - digits_exp162-165 and fashion_exp5-8 --- Makefile | 64 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 64 insertions(+) diff --git a/Makefile b/Makefile index 85c7332..326b79b 100644 --- a/Makefile +++ b/Makefile @@ -702,3 +702,67 @@ digits_exp161: 'glia_digits.py VAE --glia=True --noise=True --sigma=0.8 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(DATA_PATH)/digits_exp144_VAE_only.pytorch --seed_value=None --save=$(DATA_PATH)/digits_exp161_s8_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 +# --------------------------------------------------------------------------- +# 5/18/21 +# f5a1221 +# +# In a recent commit (f5a1221) I added loging of loss and acc, by epoch. Rerrun +# the main results to get these curves (for neuralIPS) + +# --- +# Org exp codes: +# digits: 151-152 VAE +# digits: 155-156 RP + +# Glia +digits_exp162: + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --num_epochs=150 --use_gpu=True --lr=0.004 --lr_vae=0.01 --seed_value=None --save=$(DATA_PATH)/digits_exp162_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + +# Neurons +digits_exp163: + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=False --num_epochs=150 --use_gpu=True --lr=0.004 --lr_vae=0.01 --seed_value=None --save=$(DATA_PATH)/digits_exp163_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + +# Random projection +# Glia +digits_exp164: + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py RP --glia=True --num_epochs=150 --random_projection=SP --use_gpu=True --lr=0.004 --seed_value=None --save=$(DATA_PATH)/digits_exp164_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + +# Neurons +digits_exp165: + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py RP --glia=False --num_epochs=150 --random_projection=SP --use_gpu=True --lr=0.004 --seed_value=None --save=$(DATA_PATH)/digits_exp165_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + + +# --- +# Org exp codes: +# fashion: 1-2 VAE +# fashion: 3-4 RP +fashion_exp5: + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_fashion.py VAE --glia=True --num_epochs=150 --use_gpu=True --lr=0.004 --lr_vae=0.01 --seed_value=None --save=$(DATA_PATH)/fashion_exp5{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + +# Neurons +fashion_exp6: + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_fashion.py VAE --glia=False --num_epochs=150 --use_gpu=True --lr=0.004 --lr_vae=0.01 --seed_value=None --save=$(DATA_PATH)/fashion_exp6_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + +# Glia +fashion_exp7: + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_fashion.py RP --glia=True --num_epochs=150 --random_projection=SP --use_gpu=True --lr=0.004 --seed_value=None --save=$(DATA_PATH)/fashion_exp7_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + +# Neurons +fashion_exp8: + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_fashion.py RP --glia=False --num_epochs=150 --random_projection=SP --use_gpu=True --lr=0.004 --seed_value=None --save=$(DATA_PATH)/fashion_exp8_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 From fb227b23af2281ab5eeeef41df7283f9e38f2c4d Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Tue, 18 May 2021 10:20:18 -0700 Subject: [PATCH 19/22] Change data_path to churchlands --- Makefile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 326b79b..f717f45 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,7 @@ SHELL=/bin/bash -O expand_aliases # DATA_PATH=/Users/type/Code/glia_playing_atari/data/ -DATA_PATH=/Users/qualia/Code/glia_playing_atari/data -# DATA_PATH=/home/stitch/Code/glia_playing_atari/data/ +# DATA_PATH=/Users/qualia/Code/glia_playing_atari/data +DATA_PATH=/home/stitch/Code/glia_playing_atari/data/ # ---------------------------------------------------------------------------- # Tests - should always run fine From 38459fdf7cc24d4702d4ac2509136eec4c65fa22 Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Thu, 20 May 2021 13:05:12 -0700 Subject: [PATCH 20/22] Workaround for dataloader mult error --- glia/exp/glia_atari.py | 3 +++ glia/exp/glia_digits.py | 15 +++++++++++++++ glia/exp/glia_fashion.py | 9 +++++++++ glia/exp/glia_xor.py | 18 ++++++++++-------- 4 files changed, 37 insertions(+), 8 deletions(-) diff --git a/glia/exp/glia_atari.py b/glia/exp/glia_atari.py index ec88b11..05a1279 100644 --- a/glia/exp/glia_atari.py +++ b/glia/exp/glia_atari.py @@ -90,6 +90,9 @@ def test(model): def main(env_id, epochs=10, episode_life=True, glia=False): + # Workaround for DataLoader + torch.multiprocessing.set_start_method('spawn') + # Init gym env = create_atari(env_id, episode_life=episode_life) diff --git a/glia/exp/glia_digits.py b/glia/exp/glia_digits.py index ffa38ec..0df3214 100644 --- a/glia/exp/glia_digits.py +++ b/glia/exp/glia_digits.py @@ -416,6 +416,9 @@ def run_VAE_only(batch_size=128, # ------------------------------------------------------------------------ # Training settings + # Workaround for DataLoader + torch.multiprocessing.set_start_method('spawn') + # Set torch.manual_seed(seed_value) device = torch.device("cuda" if use_gpu else "cpu") if use_gpu: @@ -531,7 +534,10 @@ def run_VAE(glia=False, data_path=None): """Glia learn to see (digits)""" # ------------------------------------------------------------------------ + # Workaround for DataLoader + torch.multiprocessing.set_start_method('spawn') # Training settings + # Set if seed_value is not None: torch.manual_seed(seed_value) @@ -733,6 +739,15 @@ def run_RP(glia=False, data_path=None): """Glia learn to see (digits)""" # ------------------------------------------------------------------------ + # Workaround for DataLoader + torch.multiprocessing.set_start_method('spawn') + + # Training settings + + # Set + if seed_value is not None: + torch.manual_seed(seed_value) + # Training settings prng = np.random.RandomState(seed_value) diff --git a/glia/exp/glia_fashion.py b/glia/exp/glia_fashion.py index 66a5a9d..77cb178 100644 --- a/glia/exp/glia_fashion.py +++ b/glia/exp/glia_fashion.py @@ -346,6 +346,9 @@ def run_VAE_only(batch_size=128, """Train (only) a VAE.""" # ------------------------------------------------------------------------ + # Workaround for DataLoader + torch.multiprocessing.set_start_method('spawn') + # Training settings torch.manual_seed(seed_value) device = torch.device("cuda" if use_gpu else "cpu") @@ -458,6 +461,9 @@ def run_VAE(glia=False, data_path=None): """Glia learn to see (clothes)""" # ------------------------------------------------------------------------ + # Workaround for DataLoader + torch.multiprocessing.set_start_method('spawn') + # Training settings if seed_value is not None: torch.manual_seed(seed_value) @@ -630,6 +636,9 @@ def run_RP(glia=False, data_path=None): """Glia learn to see (clothes)""" # ------------------------------------------------------------------------ + # Workaround for DataLoader + torch.multiprocessing.set_start_method('spawn') + # Training settings prng = np.random.RandomState(seed_value) diff --git a/glia/exp/glia_xor.py b/glia/exp/glia_xor.py index d4a8d9c..01f655f 100644 --- a/glia/exp/glia_xor.py +++ b/glia/exp/glia_xor.py @@ -98,6 +98,9 @@ def main(glia=True, """Glia learns logic""" # ------------------------------------------------------------------------ + # Workaround for DataLoader + torch.multiprocessing.set_start_method('spawn') + # Training settings prng = np.random.RandomState(seed_value) if seed_value is not None: @@ -174,14 +177,13 @@ def main(glia=True, print(">>> Loss: {:.5f}, XOR correct: {:.2f}".format(loss, 100 * correct)) # - - state = dict( - model_dict=m.state_dict(), - glia=glia, - num_epochs=num_epochs, - lr=lr, - test_loss=loss, - correct=correct, - seed=seed_value) + state = dict(model_dict=m.state_dict(), + glia=glia, + num_epochs=num_epochs, + lr=lr, + test_loss=loss, + correct=correct, + seed=seed_value) if save is not None: torch.save(state, save + ".pytorch") From 021fdfb5c2155b9e67eada15b2ee68c3bbf47f64 Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Thu, 20 May 2021 13:05:22 -0700 Subject: [PATCH 21/22] fmt --- glia/exp/tune_digits.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/glia/exp/tune_digits.py b/glia/exp/tune_digits.py index b6b7b9a..ae4c1e2 100644 --- a/glia/exp/tune_digits.py +++ b/glia/exp/tune_digits.py @@ -115,16 +115,16 @@ def train(name=None, exp_func=None, config=None): # Best trial config best_config = best["config"] best_config.update(get_best_result(trials, 'correct')) - save_checkpoint( - best_config, filename=os.path.join(path, name + "_best.pkl")) + save_checkpoint(best_config, + filename=os.path.join(path, name + "_best.pkl")) # Sort and save the configs of all trials sorted_configs = {} for i, trial in enumerate(get_sorted_trials(trials, 'correct')): sorted_configs[i] = trial["config"] sorted_configs[i].update({"correct": trial["correct"]}) - save_checkpoint( - sorted_configs, filename=os.path.join(path, name + "_sorted.pkl")) + save_checkpoint(sorted_configs, + filename=os.path.join(path, name + "_sorted.pkl")) # kill ray ray.shutdown() From e0b69e69af2c4dd655cc4c39c03c20e216949616 Mon Sep 17 00:00:00 2001 From: Erik Peterson Date: Thu, 20 May 2021 13:05:35 -0700 Subject: [PATCH 22/22] Moved fig.key to the paper draft --- notebooks/figures_neuralips.key | Bin 9675130 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100755 notebooks/figures_neuralips.key diff --git 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"execution_count": 4, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -67,19 +67,19 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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