diff --git a/Makefile b/Makefile index 539a0ea..f717f45 100644 --- a/Makefile +++ b/Makefile @@ -3,12 +3,20 @@ SHELL=/bin/bash -O expand_aliases # 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=$(DATA_PATH)/digits_test + +fashion_test: + glia_fashion.py VAE --glia=True --num_epochs=150 --use_gpu=False --lr=0.008 --lr_vae=0.01 --debug=True --seed_value=None --save=$(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. @@ -556,17 +561,17 @@ 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 +582,15 @@ 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 +632,137 @@ fashion_exp4: 'glia_fashion.py RP --glia=False --num_epochs=150 --random_projection=SP --use_gpu=True --lr=0.004 --seed_value=None --save=$(DATA_PATH)/fashion_exp4_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 # --------------------------------------------------------------------------- -# TODO: Try dropout as a test of reliability? With so few connections, -# reliability may be a problem? \ No newline at end of file +# 10/28/2019 +# Try some noise connections (on digit learning) +# e09448ddcc631f87f290c9858fb6c7bf50f330ff +# +# SUM: Noise at 0.1 and 0.2 started to decrease avg accuracy, but only slightly. +# Do another run with more noise. +digits_exp159: + # sigma: 0.1 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --noise=True --sigma=0.01 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(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=$(DATA_PATH)/digits_exp159_s2_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + +# Try some dropped connections (on digit learning) +# e09448ddcc631f87f290c9858fb6c7bf50f330ff +# SUM: +digits_exp160: + # p: 0.1 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --drop=True --p=0.01 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(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=$(DATA_PATH)/digits_exp160_p2_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + +# --------------------------------------------------------------------------- +# 10/30/2019 +# fe8d4c6618ab7e68e3edb597b69d114fa97b9dfb +# expansion of exp159 -- more noise! +# +# SUM: +digits_exp161: + # sigma: 0.1 + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --noise=True --sigma=0.3 --num_epochs=150 --use_gpu=True --lr=0.004 --vae_path=$(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=$(DATA_PATH)/digits_exp161_s8_{1}{2} --device_num={1}' ::: 0 1 2 3 ::: 1 2 3 4 5 + + +# --------------------------------------------------------------------------- +# 5/18/21 +# f5a1221 +# +# In a recent commit (f5a1221) I added loging of loss and acc, by epoch. Rerrun +# the main results to get these curves (for neuralIPS) + +# --- +# Org exp codes: +# digits: 151-152 VAE +# digits: 155-156 RP + +# Glia +digits_exp162: + parallel -j 16 -v \ + --nice 19 --delay 2 --colsep ',' \ + 'glia_digits.py VAE --glia=True --num_epochs=150 --use_gpu=True --lr=0.004 --lr_vae=0.01 --seed_value=None --save=$(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 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 cc65522..0df3214 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 @@ -181,6 +176,76 @@ 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') @@ -219,6 +284,8 @@ def train_vae(model, 100. * batch_idx / len(train_loader), loss.item() / len(data))) + return train_loss + def test_vae(model, device, @@ -240,11 +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: @@ -285,8 +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), @@ -297,6 +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 @@ -348,6 +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: @@ -363,25 +434,23 @@ 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 @@ -394,41 +463,46 @@ 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") @@ -437,7 +511,6 @@ 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 +519,11 @@ 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, @@ -456,7 +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) @@ -474,29 +555,26 @@ 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 - # 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 +589,50 @@ def run_VAE(glia=False, print(saved["vae_dict"]) print(f">>> Loaded VAE from {vae_path}") + # Config glia: if glia: - if sigma > 0: - model = PerceptronLeak( - z_features=z_features, - activation_function=activation_function, - sigma=sigma) + 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: @@ -534,68 +640,80 @@ def run_VAE(glia=False, print(model_vae) 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) + # 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") @@ -621,6 +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) @@ -642,36 +769,36 @@ 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") @@ -686,45 +813,54 @@ 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..77cb178 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 @@ -347,6 +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") @@ -363,25 +365,23 @@ 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 +394,46 @@ 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") @@ -456,6 +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) @@ -474,25 +482,23 @@ 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 +519,9 @@ 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 +540,77 @@ 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") @@ -621,6 +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) @@ -642,36 +660,36 @@ 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 +704,53 @@ 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") 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") 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() diff --git a/glia/gn.py b/glia/gn.py index 2d15dfe..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 @@ -310,3 +305,16 @@ 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 diff --git a/notebooks/digits_exp151-156.ipynb b/notebooks/digits_exp151-156.ipynb index 431711b..af06013 100644 --- a/notebooks/digits_exp151-156.ipynb +++ b/notebooks/digits_exp151-156.ipynb @@ -2,37 +2,151 @@ "cells": [ { "cell_type": "code", - "execution_count": 46, + "execution_count": 72, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + " setTimeout(function() {\n", + " var nbb_cell_id = 72;\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", + " " + ], + "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": 73, + "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 = 73;\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": "markdown", + "metadata": {}, + "source": [ + "Shared fns" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 74, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "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": {}, + "output_type": "display_data" + } + ], "source": [ "def get_data(files, *keys):\n", " \"\"\"Get data keys from saved digit exps.\"\"\"\n", @@ -40,37 +154,165 @@ " 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": 106, + "execution_count": 75, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "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": {}, + "output_type": "display_data" + } + ], "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", - "exp152_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp152_*\") \n", - "exp152 = get_data(exp152_files, \"correct\")\n", + " files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp152_*\")\n", + " exp152 = get_data(files, \"correct\")\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", + " # Gather\n", + " models = [exp151, exp152]\n", + " model_names = [\"Astrocytes\", \"Neurons\"]\n", "\n", - "exp154_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp154_*\") \n", - "exp154 = get_data(exp154_files, \"correct\")\n", + " # Sanity\n", + " assert len(model_names) == len(models)\n", "\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", + " return model_names, models" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "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": {}, + "output_type": "display_data" + } + ], + "source": [ + "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", - "exp156_files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp156_*\") \n", - "exp156 = get_data(exp156_files, \"correct\")" + " # Sanity\n", + " assert len(model_names) == len(models)\n", + "\n", + " return model_names, models" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "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": {}, + "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", + " 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" ] }, { @@ -80,43 +322,212 @@ "# Learn VAE " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load data" + ] + }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 78, "metadata": {}, "outputs": [ { "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]}))" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "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": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_names, models = load_digit_online_exps()\n", + "# Show example\n", + "i = 0\n", + "model_names[i], models[i]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Est stats and plot" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { - "height": 264, + "height": 311, "width": 257 } }, "output_type": "display_data" + }, + { + "data": { + "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": {}, + "output_type": "display_data" } ], "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, 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": 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": {}, @@ -126,41 +537,203 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 81, "metadata": {}, "outputs": [ { "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]}))" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "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": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_names, models = load_digit_pretrain_exps()\n", + "# Show example\n", + "i = 0\n", + "model_names[i], models[i]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Est stats and plot" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { - "height": 264, + "height": 311, "width": 257 } }, "output_type": "display_data" + }, + { + "data": { + "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": {}, + "output_type": "display_data" } ], "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, 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": 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": {}, @@ -170,41 +743,196 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 84, "metadata": {}, "outputs": [ { "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]}))" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "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": {}, + "output_type": "display_data" + } + ], + "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": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { - "height": 264, + "height": 311, "width": 257 } }, "output_type": "display_data" + }, + { + "data": { + "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": {}, + "output_type": "display_data" } ], "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, 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": 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, diff --git a/notebooks/digits_exp157-8.ipynb b/notebooks/digits_exp157-8.ipynb index 2649341..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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CwSBCoRACgQAUCoX0C9TXg4koivD7/VAoFAgEAgiFQggGgwls9ZXV2tqa6CZI/H4/TCYTdDod0tLSYl64nC8SiQCA1HPcF57jal+d3+v1wu12w+/3o62tDUqlEjKZrEMwCQQC8Hg88Pv9cLvd8Hq9CWx16oh+v4qOcIn+O/r5iKKIQCAgrV0RDoelz4uuvFOnTqG5uTlmPRdBEGLKCIIgvYyJ7mDAYE9EfRmDPRElhMFgwODBg5GZmYn09PSYRaXOFwwGkZOTg/T09Kt+Yba+8uLC4/HgyJEjcLvdMBqNHV66REVfvjgcDuj1eowZMybhz9Ddf0tXi+jLLo/HA1EUpSHd0ZcswFfBJLpORfQlGl15JpMJgiDA7/fD6XRCq9V22Kc++vk4nU74/X4IgsCFDXuJ2+2Wvna0Wm2HUB8VPS6KIjweD9xud282k4joojDYE6WQvtCTGiWTyWCxWBCJRKDX66FSqSCXyzsM9Y5EItIWURaLBTKZrE88x9XeIzxgwADo9Xq43W4pEEZ7haNEUYzpZdTr9RgwYECimpxSNBoNAEh71kciEXi9XmkOd3QRveiQ73A4HHMdXVkWiwVGoxEKhUIK9tEh+FHR73VOpxMKhQJGoxEWiyVRTU4p0bnzoVDogi8CozsXROfiExH1VQz2RCkm0b2pUeFwWFrQKzoMUhTFmCHfkUgEMpkMgUAAgUAAcrkc4XA44c+QCj3CRUVFyMnJgdVqRSQSkYYVy+VyKThGIhHpb6VSiZycHBQVFSW66SnBaDRi2LBh0gJsKpUKMpmsQ3CMrrrucrkwcOBAbtfVS0wmEwoLC3HixAmEQiHY7XbodLoO2915PB6EQiHodDoUFhayx76XZGdnw2AwoLm5GS6XC2lpaV2WdblciEQiMBgMyM7O7sVWEhFdHAZ7IkqI9PR0FBYWoqWlBZFIRJrr+HXRHnuFQgG9Xo/CwkKkp6cnqMWpRavVoqSkBM3NzXA6nVKP7/k9wtFjZrMZJSUl0Gq1iWz2FdUXRopEORwOmM1m5ObmSnOEuxIIBJCWlgaz2QyHw9EnnuNqH/Gi0WhQXFyMuro6OBwOBAIB2Gw2aUpLdAqLIAgwm80wGo0oLi7miIpekpWVhcLCQjQ2NqKlpQUKhaLDVAngyylJLS0t0Gq1KCwsRFZWVgJaS0TUMwz2RJQQRqMRAwYMQFNTkxQcPR5Phx5HvV4PhUKBtLQ0DBgwgD2OvUSpVGLkyJFobW1FbW0tvF6vNFIiOoRYJpNJ292VlJRg5MiRMYu3XY0SPVokyuVyoa2tDY2NjQAQ85Il6vxjgiDA5XIl/BlSYcSLXC5HYWEhhg0bhvr6eng8Hvh8PmlakVwuR1paGjQaDXQ6HQYMGIDCwsJuF6mk+ElLS8OIESNw+vRptLS0oKWlBXq9HlqtFnK5XJra4na7IYoiCgoKMGLEiG579omIEu3q/g2MiPosQRAwaNAgnD17FidPnkQoFILRaJRWIVYoFFIvpFKpRFFREQYNGsQ5jr0kOrT+pptuQlpaGurq6mC1Wjts12WxWFBcXIyhQ4ciJyfnqg/2fYVer5d6etVqNZxOJzweDwBIn49MJpN2NfD7/TCbzdDr9QlueWqQy+XIycnBtddeK22X5nA4EAqFpGCvVCqlefXRqS8M9r1Dr9dj+PDhOH36NGpra3Hu3DnY7Xb4fL6YERUGgwEZGRkoKSnB8OHD+fVDRH0afwMjooRQKBTIz8/H0KFDoVKp0NbWBpvNFjMUNRwOo1+/fkhPT0dJSQny8/O5qncvEQQBRqMReXl50Ov1KCoqQmtrK86cOYNAIABBEJCbm4vMzExkZmbCZDLBaDTyxUsvMZlMyMjIQGtrq/RZeb1eeDweKTjqdDpp0Ta1Wo2MjAzO4e4larUaRqMR2dnZMJvNaGhoQGtrK3w+n/TiRaPRIDMzEwUFBVJ5tVqd6KanBKVSiby8PIwaNQp6vR4tLS1obW2VRo9FR4llZmYiKysLw4YNQ15eHl9cElGfdlV8h6qursaSJUtw9OhRBINBDBs2DHPmzMHYsWMveO2TTz6JtWvXXrDctGnT8Nxzz0n/njlzJj755JMuy2/evJmrQxNdgF6vR2lpKZRKJc6cOQOXywWHwyH9YmU0GmEwGJCbm4uBAweyt6QXRX+xNZvNUKvVyMzMhN1uR0lJifT56PV6mEwmyGQyaLVapKWl8cVLL9FoNCgoKEBrayscDoe0iF50z225XC6thu/xeJCRkYGCggLO4e4lSqUSWq0WZrMZ4XAYmZmZaG9vh81mk/az79evH8xmM0RRhEKhgFarZXDsRdGe+Egkgvz8fDgcDrS3t0ufT3REjNFoRElJCTIyMhLdZCKibsXtJ8j777+PCRMm9PovDWvWrMH8+fMhCALKysoQiUSwe/duVFRUoLKyEjNmzOj2+htuuAGhUKjTc5FIBJs2bUIkEsGQIUNizh05cgQ6nQ633nprp9cygBBdmEqlgtFoxMCBA5GdnQ2bzQaXyyUFR4PBgH79+kGv18NoNKbE3Ny+RK/XIz09HW1tbQiFQsjJyYmZI6xSqRAOh6FUKpGens7ve71IEAQUFRXh3LlzaGhogM/nk7bkAr4c7RL9t8lkQkFBAYqKijiiohelpaXB5/PBarVCEAT0798fOTk5MV8/Pp8Pfr8fRqOR87d7mVqtRn5+PuRyOZqbm2Gz2WI+n+jLl5ycHOTm5nI0BRH1eXEL9k888QT0ej0mTpyIKVOm4Nvf/na8bt2llpYWLFiwAGlpafjb3/6G0tJSAMCBAwcwa9YsLFq0COPHj+92e5IZM2Z0Gf5feeUVRCIRTJw4ETNnzpSONzY2or29HaNHj8YLL7wQ34ciSjEajUaaT6/T6RAIBKShqoIgQK1WS/vcU++KvngBALfbDa/XGxMMRVGEwWDgi5cEUCgU6NevH0pLS6FSqXDmzBmcO3euQ499RkYGcnNzUVxcjH79+nFERS9Sq9XSvvQOhwNWqxUajQZyuRzBYBBOp1MqY7FYGBwTQKfToaCgACaTCU6nE06nU+qxT0tLk/7wsyGiZBC3YD9q1Cjs3bsXa9euxXvvvYfMzExMmTIFU6ZMkQJ3vK1cuRKBQAA//elPY+oYPnw4Kioq8PLLL2PVqlV4+OGHL/ree/fuxZIlS5CRkYGFCxfGnKupqQEADBs27PIegIgAfBkgo0NWzw/2DCKJxRcvfZder4fFYoHVapX22fZ6vVKw12q10nQJi8XCERUJoNPpoFAooNFo4PV64ff7EYlEoFKpYDAYpCksDI6Jo1aroVarYTabpc9HLpdDrVZzagQRJZW4fcf6y1/+grNnz+L999/H+vXrcezYMbz++ut44403cO2112Lq1KmYPHlyXPeu/eijjwAAt912W4dzEyZMwMsvv4ydO3dedLCPRCKorKxEJBLBk08+CbPZHHP+8OHDABjsieItOs+U+ha+eOnbtFqtNC0CgBRMgC+/phhOEisaHEOhEINjH6ZUKvl5EFFSi+t3sOzsbFRUVKCiogK1tbVYt24dNm7ciCNHjmDx4sV4/vnnUVZWhqlTp+K22267rF/gRVHE8ePHIZfLMXDgwA7ni4qKIJfLcfz4cemX0J76+9//jqNHj2LEiBG44447OpyPBnu73Y7Zs2fj0KFD8Pv9uO6663q8aB8RUbLhi5e+xe12IxQKIT09HXK5HH6/v8MaCGq1GpFIBKFQCG63u8OLauo9DI5ERHQlXbGfMCUlJXj88cfx+OOPY+/evdi2bRt27NiBqqoqVFdXQ6vV4vbbb0d5eTluvPHGi76/3W5HIBCAxWLpdDGg6KInbW1tcLvdMBgMPbpvKBTCa6+9BgD4+c9/3mmZ6FD8BQsWoLS0FDfeeCNOnjyJPXv2YM+ePfh//+//xczJ78qaNWt6tCL/1+ukS8feEiK6WoTDYfj9funnYPRYMBiUXmarVCppRIXVaoXf75cWpiQiIqKrS6+kmqFDh8JqteLcuXNobGyU9tqNzse/9tpr8etf/xrf+ta3enxPr9cLAN32HkVX6L+YYL9x40Y0Nzdj8ODBGDduXIfzVqsVzc3NUCqVWLx4MX7wgx/EXPvEE09g8eLFGDVqVIeV9M/X2NiIPXv29KhddOn8fj+cTmfM/MZosOf8RiJKRoFAAIFAIObFtkKh6DK0C4IgXcNRF0RERFefKxbsA4EAduzYgffffx87d+6E3++HKIrQarWYMmUK7rzzTrS0tODdd9/F3r17MXPmTLzyyiudzpfvTHT+YHdEUbzodv/lL38BAMyePbvT8xaLBbt27YLD4UBRUVHMuUmTJmH//v1Yvnw53nrrLVRWVnZbV35+PkaNGtWjdtXU1MDpdPaoLH3F4/HAarXC4XDA7/d3uiKxz+eDxWKBTqdLdHOJiHpEFEWIotijn4XAlz8zo9cQERHR1SeuwV4URVRXV+ODDz7Ali1b4HK5pF88brrpJkydOhUTJ06MCVDTpk3DSy+9hKVLl+Kll17qcbCP3sPv93dZJnqup70Tp0+fxueffw6dTocJEyZ0WS66NU1nbrnlFixfvhyHDh26YH3l5eUoLy/vUdvuu+8+9u5fJL/fD6vVCqvVCrVajaysrJi1FoxGI1wuF6xWK4Ave7vYc09EyUAmk0Emk0kL5l1IJBKBUqm8qPVmiIiIKHnELdj/z//8D/7xj3+gra1N6hG45pprcOedd2LKlCnIycnp8tq77roLS5cuRUNDQ4/rMxgM0Ol0sNls0p6jXxcKhWCz2aBWq6V9mC/kX//6F4Avw/mlDlWMrvrv8/ku6XqKH6fTCYfDAbVajbS0tA7nZTKZdNzhcECj0TDYE1FSEAQBgiDA4/H0qHwgEIBOp+t0TRoiIiJKfnEL9itXrgQA9OvXD5MnT8add96Jb3zjGz261m63Iz09/aLm2MtkMgwaNAgHDhzAyZMnMWjQoJjzdXV1iEQiMfvbX8iOHTsAoNve+urqaqxduxbDhg3D/fff3+F89OVEdy8y6MoLhULSnPqsrKxuyxoMBrS0tMDr9Xb6koiIqK+JjjCKhvvuphJ5PB4IggC1Ws2F84iIiK5ScUswEydOxJ133olx48ZddDAaOnQoqqqqLrrOsWPH4sCBA9iyZUuHYL9lyxYA6HQBvM6IooiDBw8CAEaOHNllOZ/Ph/Xr12Pfvn34r//6rw7P+t577wEAxowZ0+PnoPjz+/3SnPoLDT2VyWTQaDTSNQz2RJQM9Ho9AoEA7HY7AHQa7j0eDwKBAEwmE/R6fW83kYiIiHpJz1bd6YFXX30Vt956K5RKJVpbWzuc37t3L44cORKv6gB8OUddrVZj2bJlUigHgM8//xyvv/46NBoN7r33Xul4U1MTTpw4Ic2p/rovvvgCLpcL2dnZyM7O7rLOMWPGID8/H42NjXj++edj5je+++672LRpEzIzMzF9+vQ4PSVdikgkIq1+H+VyuXDq1CmcOHECp06dgsvlks7J5XLpGiKiZKBSqWA0GmEymRCJRGC1WuFyueDxeKT1QyKRCEwmE4xGI1QqVaKbTERERFdIXLsmz549i/nz52P37t3YtWtXzNz2ZcuWYefOnSgrK8NvfvObuAxVLygowLx581BZWYm7774bZWVlEEURu3fvRigUwuLFi5Geni6VnzdvHvbs2YMHH3wQDz30UMy9okPo+/fv322dgiDghRdewOzZs/HnP/8ZW7duxeDBg3H69GnU1NRAp9Phd7/7Xadzuqn3yOVyafX7c+fOob6+HqdPn0ZTUxOCwSBUKhXy8vLQv39/DBgwACqVCiqVqscrTBMR9QUajQYKhQKCIEj72ouiCKVSCZ1OB7VaDb1ez1BPRER0lYtbsG9tbcVdd92Fs2fPQqlUoqWlJSbYZ2dnQ6PRYNeuXbjvvvuwZs2auITfH//4x8jLy8Prr7+Offv2QRAEjBw5Ej/72c/w7W9/u8f3sdlsAHo2N37kyJFYu3YtXnvtNVRVVWHbtm3o168fysvLMXfu3Au+HKArT61WQ61Wo7a2FrW1tTh8+DBOnToFr9cr9eRrtVoUFhZi6NChKCkpwXXXXcfF84go6ahUKpjNZoTDYSnYy2QyCILAOfVEREQpQkrc3+cAACAASURBVCbGaVPbZ555Bm+//TbKysrw/PPPS6vDf117ezsee+wxVFdXo6KiAo899lg8qk4J0e3uRo0ahRUrViS6OUmhpqYG77zzDj7++GO0tbXB6/XGTJ1QKBTQarVIT0/HmDFjMH36dAwZMiSBLb7yWltbEQwGE92MpKdSqTr9Hne5+PnEBz+fvu1KfT5ERESpLG7jjnfu3AmtVouXX365yx/YZrMZzz//PARBwObNm+NVNVGnjh07hl27dqG2thZtbW1wOp0IBoPSH6fTiba2NtTW1mLXrl04duxYoptMRERERER00eIW7FtbW1FcXAyz2dxtOYvFguLiYjQ1NcWraqIOXC4XqqqqUFtbC6/Xi2AwiEgkEhPso//2er2ora1FVVVVzIJ61PvC4TC8Xi88Hk+HERZERERERNS5uM2xN5vN0jz1C3G5XN3uuUt0uU6ePIn9+/fDbrdDpVJBJpMhHA5Lc09FUYQoilAoFJDL5bDb7di/fz9OnjyJ6667LtHNTznBYBButztm8a/oHGEu/kVERERE1L249dgPHToUzc3N0v7xXamqqkJDQwOGDRsWr6qJOjh27BgaGhrg8/liVojW6/UxfyuVSoiiCJ/Ph4aGBg7HTwCfzwebzQabzYb29naEQiGIoohQKIT29nbpnM/nS3RTiYiIiIj6pLj12N9zzz3YsWMHfvnLX2LevHm48847odFopPOBQAAffPABnnvuOchkMtxzzz3xqpqog2AwiMLCQlgsFphMJmg0GqmnPir6b5/PB7vdDoPBwIWxelkwGITD4YDdbocgCLBYLB3KeDwe2O12AF8ueMieeyIiIiKiWHEL9uPHj8c999yDt956C8888wwWLlyI/Px86PV6eDweNDQ0SEOh77rrLkyYMCFeVVMf0tramugmAPjyRZLRaJS2gYruT3/+qvgAoNPppO3vAoFAn3mGVFg12u12w+12QxCELqfnRI9Hy11oHQ8iIiIiolQTt2APAAsWLMA3vvENLF26FPX19aivr485n5ubizlz5rC3/irXF3q9nU4nampq4HQ6YTaboVAoEIlEOvTYy+VyhMNhtLe3Iy0tDePHj+8T7U+FXulwOCzNqY/21IfDYQSDQWmOvUqlgkKhgE6ng9Vqhd/vRzgc5t7cRERERERfE9dgDwDl5eUoLy9HfX09Tp06hfb2dmi1WhQVFWHQoEHxro6oUxkZGTAYDHA6nXC73VCr1VAqlTGBMBos/X4/AMBgMCAjIyNRTU45gUAAgUAAgiBIuxP4/X5pxwK5XA6VSgW1Wg2tVgtBEKRrtFptoptPRERERNRnxD3YRw0YMAADBgy4Urcn6lZWVhYsFgtaW1ulhdgikUhMT3g0QEYX17NYLMjKykpgq1NLdGeCYDAIn88Hj8eDYDAIQRCkXQy8Xi9UKhUCgQAUCgUUCkXMqAsiIiIiIrqCwd7v9yMSicQci/aQtrS0YOvWrfj5z39+paqnFNevXz/k5+ejsbERPp8PMpkMkUgEoVBIKhOJRCCTyaBUKqHRaJCfn49+/folsNWpRSaTIRQKweFwSC9dTCYTZDKZVEar1cLn88HpdEIul0OpVMacJyIiIiKiOAf7jRs34v/+7/9QV1fXIdR3hsGerhRBEJCfn4+srCxpf/Tzt0uLBvroPun5+fkQBCFBLU49giAgHA7D4XDAbDZ3OrxeJpNBq9VCFEW0t7fDZDLxMyIiIiIiOk/cgv3u3bvx2GOP9WiYrNlsxne+8514VU3UQSgUQmZmJgYNGoTm5mZpmLfP55Pmb2s0GqhUKuh0OuTk5CAzMzOmR5+IiIiIiCgZxC3Yr1ixAqIoYvTo0fj5z38OjUaD6dOnY8qUKfjZz36G5uZmrF69Ghs2bEBubi6ee+65eFVN1KmMjAwUFxdDp9Ohvb0d7e3tABCzqrrZbIbZbEZ2djYXzutl0XnzRqMRoVAIXq8XGo0mZqi9KIrw+XwIhUIwGo1QKBRcPI+IiIiI6DxxC/b79++HIAh44YUXpK2riouLceDAARQVFaGoqAhlZWVIT0/HihUr8NZbb+EnP/lJvKonihEdXm+xWJCeno4zZ86gra1NWvtBLpdDrVYjPT0dubm5EEURer0earU60U1PGdFFC41GI8LhMDweD9rb26FWqyGTySCKIvx+PwRBQFpaGhQKBZRKJRfPIyIiIiI6T9yCfXt7OwYMGCCFegAoLS3F5s2b4Xa7odfrAQAPPvgg3n77bWzYsIHBnq4YrVYLg8EAtVoNg8GA9PR0OJ1OtLe3Sz32ZrMZaWlpEEURLpcLBoOBPcG9SCaTSXvVGwwGCIIQs92dQqGAyWSStrvz+/3SNURERERE9JW4BXu1Wt2ht7OwsBCiKOLEiRMYPnw4AMBoNKKoqAh1dXXxqpqog2hwb29vh8fjgdFoRHZ2NtLT06Uee6VSiUAgAIfDgbS0NJjN5ph97unKEgQBgiDA4/HAYDBApVIhHA4jGAxCFEUp9Ec/E6fTCZ1Ox8XziIiIiIjOI4/XjfLy8tDQ0IBgMCgd69+/PwDg6NGjMWVFUYTX641X1USdMpvNyMnJgVqthtvthsPhQDgcBgBpNXa32w21Wo2cnByYzeYEtzi1KBQKqNVqKdx3x+PxQBAEqNVqvnwhIiIiIjpP3IJ9WVkZnE4nfvvb30pzYIcNGwZRFLF+/Xrp2KlTp1BXV4fc3Nx4VU3UKZVKhczMTBQUFEhD7qPb3vn9foiiiLS0NBQUFCAzMxMqlSrRTU45er0eer0eHo8Hzc3NsFqtsFqtaGtrk/53dFeDaFkiIiIiIooVt6H4M2fOxOrVq/HnP/8Z//jHP/DPf/4TQ4cOxdChQ7F3715UVFSgtLQUGzduRDgcxje/+c14VU3UJY1Gg+zsbBgMBng8HrjdbmmOvV6vh06ng16vZ6hPEJVKBUEQIIoibDYbvF4vdDodFAqFtKCeVqtFRkYGBEHg50RERERE1Im4BfuCggL8/ve/x/z58+FyuaR5sPPnz8fs2bNRVVWF6upqiKIIk8mEhx9+OF5VE3VLpVJJC+UFAgFp/rYgCBzWnWDBYBCBQAAA0K9fP6SnpyMUCknrIFgsFkQiEQBfbo8XDAYZ7omIiIiIzhO3YA8AY8aMwYcffohDhw5Jx2688Ua8++67WLFiBRobG1FUVIT777+fQ/Gp1ykUCq5638e43W5p1wydTtfl4nnR0RaCIHAtBCIiIiKi88Qt2P/1r3/FNddcg7KyMtxwww0x50pKSlBZWRmvqojoKhAOh+H3+xEIBKRtMhUKRaejKHQ6HaxWK/x+vzSVgoiIiIiIvhS3YL9kyRK4XC7s2LEDRqMxXrcloqtUIBBAIBDo8fZ1giBI13DkBRERERHRV+K2Kr7dbkdxcTFDPRH1iCiKEEURcnnPvg3J5XLpGiIiIiIi+krcgv2QIUNQX18Pm80Wr1sS0VVMJpNBJpNJi+NdSCQSka4hIiIiIqKvxC3YP/vss0hLS8M999yD1atXo7a2Fu3t7fB6vV3+IaLUJQiCNLy+J6LD9ns6dJ+IiIiIKFXEbY79o48+CplMhvr6ejz99NMXLC+TyXD48OF4VU9ESUahUECtVkMQBHg8Huh0ui7LejweCIIAtVrNhfOIiIiIiM4Tt2BfW1t7UeU5T5Z6Wzgc5j72fYxer0cgEIDdbgeATsO9x+NBIBCAyWSCXq/v7SYSEREREfV5cQv2H374YbxuRRRXwWAQbrdb2lrt68FerVZDr9dDpVIlupkpSaVSSQtuut1uWK1WCIIAuVyOSCQiDb83mUwwGo38nIiIiIiIOhG3YJ+fnx+vWxHFjc/ng8PhgNvtlkKiXC5HOByWhncHAgEYjUZoNJpENzclaTQaKBQKCIIQ8/JFqVRCp9Px5QsRERER0QXELdgT9TXBYBAOhwN2ux2CIMBisXQo4/F4pGHgCoWC4TFBVCoVzGYzp0sQEREREV2CuAX7+fPnX1R5mUyGZ599Nl7VE3XgdrvhdrshCEKXC7NFj0fLmc3m3mwinUehUECr1Sa6GURERERESSVuwX7t2rWQyWRdLor39b2no71xDPZ0pYTDYWlYd2c99V+n0+lgtVrh9/sRDofZQ0xEREREREklbsF+1qxZXZ7z+XxobW3Fv//9bzgcDlRUVOD666+PV9VEHQQCAWlOfU9E59oHAgH2GBMRERERUVKJW7CfN2/eBcu4XC488sgj+Otf/4rp06fHq2qiDkRRhCiKkMvlPSovl8ula4iIiIiIiJJJry6eZzAYsHjxYtxyyy343e9+hxdffDEu962ursaSJUtw9OhRBINBDBs2DHPmzMHYsWN7dH0oFMINN9yAQCDQ6fns7Gzs3Lkz5pjP58Py5cuxfv16NDQ0IC0tDePHj8fDDz+MrKysy34mujwymQwymQzhcLhH5SORCJRKZcyUESIiIiIiomTQ66viZ2RkYNCgQfjkk0/icr81a9Zg/vz5EAQBZWVliEQi2L17NyoqKlBZWYkZM2Zc8B7Hjx9HIBBAYWEhRowY0eH8+QuqBYNBzJ07F1VVVcjNzcW4cePwxRdfYPXq1di+fTv+/ve/Iy8vLy7PR5dGEAQIggCPx9Oj8oFAADqdrsdD94mIiIiIiPqKhGx3Z7fb4XK5Lvs+LS0tWLBgAdLS0vC3v/0NpaWlAIADBw5g1qxZWLRoEcaPH4/s7Oxu71NTUwMAKC8vx89+9rML1rty5UpUVVVh/Pjx+N3vfieFwZdeeglLlixBZWUllixZcplPR5dDoVBArVZL4b6rVfEBSPvZq9VqLpxHRERERERJp2cTkONo+fLlaGpqwoABAy77XitXrkQgEMD9998vhXoAGD58OCoqKuD3+7Fq1aoL3ufw4cMAgGHDhl2wrCiKePPNNyGTyfDUU0/F9PA+8sgjKC4uxrZt23D69OlLeCKKJ71eD71ej0Ag0GXPvcfjQSAQkMoSERERERElm7j12D/yyCNdnhNFEYFAAHV1dTh16hRkMhmmTZt22XV+9NFHAIDbbrutw7kJEybg5Zdfxs6dO/Hwww93e59oj31Pgv3Ro0dx9uxZDBkyBAUFBTHn5HI5vvvd7+KNN97Azp078eMf/7inj0JXgEqlgtFoBPDlPvVWqxWCIEAulyMSiUir5ptMJhiNRqhUqgS3mIiIiIiI6OLFLdj/85//7HHZiRMn4ic/+cll1SeKIo4fPw65XI6BAwd2OF9UVAS5XI7jx49DFMUuF0UTRRE1NTXIzMzE1q1bsWrVKpw4cQJqtRqjR4/Ggw8+GHP/48ePAwBKSko6vV+07LFjxy7r+Sg+NBoNFAoFBEGQ9rUXRRFKpRI6nQ5qtRp6vZ6hnoiIiIiIklbcgv2DDz7Y7XmFQgGj0YiRI0diyJAhl12f3W5HIBCAxWLpdMEzpVKJfv36oa2tDW63GwaDodP7nD59Gi6XCy6XC08//TRGjhyJm266CTU1NdiwYQO2b9+OZcuW4Zvf/CYAoLW1FQCQmZnZ6f2ix9va2i77GSk+VCoVzGYzwuGwFOxlMhkEQeCceiIiIiIiSnq9Fuzjzev1AgC0Wm2XZTQaDQB0G+yj8+uzs7OxdOlS6aVDKBTCiy++iD/96U/4xS9+gX/9619Qq9XSXO2u6o3W2ZPV2NesWYO1a9desBzw1XQBunQKhaLb/16IiIiIiIiSUdxXxff5fNi6dSsmTZoUc3zVqlXw+/2YOnWqNO/5csjlF173TxTFC5a5/fbbsX37dsjl8pjV85VKJZ544gns3r0bhw4dwpYtWzB58mSp3u6G9ve07sbGRuzZs+eC5YiIiIiIiIi6EtdgX11djV/84hdwOBz45je/GROUN23ahN27d2PJkiV48cUX8e1vf/uy6opuX+b3+7ssEz3XXS+tTCZDbm5up+fkcjnGjRuHQ4cO4eDBg5g8ebJUr8/nu+Q6o/Lz8zFq1KgLlgO+7LF3Op09KktERERERESpI27B/sCBA5gzZw5CoRBKS0sRDAZjzk+aNAk+nw/79+/H3Llz8e6773a66F1PGQwG6HQ62Gw2hEIhKJWxjxIKhWCz2aBWqy9rhEBGRgaAr4J8VlYWAODcuXOdlr/QHPyvKy8vR3l5eY/acd9997F3n4iIiIiIiDqI2z72y5YtQygUwqxZs7B+/foOW8HdddddePvtt1FRUQGv14ulS5deVn0ymQyDBg1COBzGyZMnO5yvq6tDJBKJ2d++M3/961/x6KOPorq6utPzDQ0NAICcnBwAkO4XXR3/fCdOnIgpR0RERERERHQlxS3Y79u3DxaLBY8//ni35R599FGYTKYug/TFGDt2LABgy5YtHc5Fj40bN67be5w+fRqbNm3qdBE7v98vbeP3ne98BwBwzTXXID8/H4cPH8aZM2diykciEWzduhUymUxqGxEREREREdGVFLdg73Q6kZeXd8Htw5RKJfr374/29vbLrrO8vBxqtRrLli3DwYMHpeOff/45Xn/9dWg0Gtx7773S8aamJpw4cQJWq1U6Nn36dCgUCrz//vtSiAeAYDCIhQsXorGxETfffDOuu+466dzdd9+NcDiMX/3qVzGr37/yyis4efIkJkyYgMLCwst+PiIiIiIiIqILidsc+6ysLJw+fRrhcLjbcB+JRNDY2Aiz2XzZdRYUFGDevHmorKzE3XffjbKyMoiiiN27dyMUCmHx4sVIT0+Xys+bNw979uzBgw8+iIceeggAMGjQIDz55JN49tln8fDDD+Mb3/gG8vLy8J///AfNzc0YOHAgnnvuuZh677//fmzfvh1VVVWYOHEiRo4cibq6Ohw7dgx5eXl46qmnLvvZiIiIiIiIiHoibj32ZWVlcDgceO2117ot9+abb8Jms/V4NfgL+fGPf4wlS5ZgxIgR2LdvHw4ePIiRI0fizTffxJ133tmje/zkJz/Bm2++iTFjxqC+vh7btm2DRqPBAw88gHfeeSfm5QAACIKAN954A3PnzoVWq8W2bdvgdrsxY8YMrFq1Slpgj4iIiIiIiOhKk4k92XC9B44dO4bp06cjGAxi0qRJKC8vR0lJCXQ6HbxeL44fP45169Zh/fr1kMvlWL16NYYMGRKPqlNCdFX8UaNGYcWKFYluTpdaW1s77IhAF0+lUvVoZ4WLxc8nPvj59G38fPq2K/X5EBERpbK4DcUvLS1FZWUlnn76aWzYsAEbN27sUEYURSiVSixcuJChnoiIiIiIiCgO4jYUHwCmTp2KdevW4Uc/+hEyMzMhiqL0x2w244477sA777yDadOmxbNaIiIiIiIiopQVtx77qOLiYixcuBAAEAgEYLPZoNVqYTQa410VERERERERUcqLa489APh8PmkYviAIyM7OhtFoxKpVq7BixQo4HI54V0lERERERESUsuIa7KurqzFu3Dg89thjOHv2bMy5TZs24dlnn8X3vvc97Nq1K57VEhEREREREaWsuAX7AwcOYM6cObDb7SgpKemwcvCkSZMwYsQIWK1WzJ07F1988UW8qiYiIiIiIiJKWXEL9suWLUMoFMKsWbOwfv16FBQUxJy/66678Pbbb6OiogJerxdLly6NV9VEREREREREKStuwX7fvn2wWCx4/PHHuy336KOPwmQyobq6Ol5VExEREREREaWsuAV7p9OJvLw8KBSKbssplUr0798f7e3t8aqaiIiIiIiIKGXFLdhnZWXh9OnTCIfD3ZaLRCJobGyE2WyOV9VEREREREREKStuwb6srAwOhwOvvfZat+XefPNN2Gw2jBo1Kl5VExEREREREaUsZbxuNHPmTLz//vv4wx/+gLq6OpSXl6OkpAQ6nQ5erxfHjx/HunXrsH79eiiVSlRUVMSraiIiIiIiIqKUFbdgX1paisrKSjz99NPYsGEDNm7c2KGMKIpQKpVYuHAhhgwZEq+qiYiIiIiIiFJW3IbiA8DUqVOxbt06/OhHP0JmZiZEUZT+mM1m3HHHHXjnnXcwbdq0eFZLRERERERElLLi1mMfVVxcjIULFwIAAoEAbDYbtFotjEYjAMBqteJPf/oT3n33XWzYsCHe1RMRERERERGllLgH+68TBAHZ2dkAgI8++girV6/Gtm3bEAqFrmS1RERERERERCnjigb7M2fO4N1338WaNWtw5swZAF/Os5fJZLjpppuuZNVEREREREREKSHuwT4UCuHDDz/E6tWrsWvXLkQiEYiiCADIzMzEtGnT8KMf/Qj9+/ePd9VEREREREREKSduwf6LL77A6tWrsW7dOthsNgCQAr3BYMDixYsxfvx4KBSKeFVJRERERERElPIuK9j7fD5s2rQJq1evxmeffQbgq6H2119/PX7wgx9g4cKFMBgMuPXWW+PSYCIiIiIiIiL6yiUF+4MHD2L16tXYsGED3G631DM/ePBgTJ48GZMnT0ZeXh4ASCvkExEREREREVH8XVKwnz59OmQyGURRxJAhQzBhwgR873vfw8CBA+PdPiIiIiIiIiLqxmUNxR8zZgy+973vYcyYMcjJyYlXm4iIiIiIiIiohy4p2E+bNg2bN2/Gxx9/jKqqKgBfDsP//ve/j0mTJqGgoCCujSQiIiIiIiKizskv5aLf/OY3qKqqwv/+7/9i9OjRkMvlqKmpwUsvvYQJEyZgxowZWLFiBVpbW+PdXiIiIiIiIiL6mkseiq/RaDBlyhRMmTIFra2tWL9+PdavX4+jR4/iP//5Dw4cOIDnnnsOABAMBuFyuWAwGOLWcCIiIiIiIiK6xB7782VmZmL27NlYt24d3nvvPcycORPp6ekIh8MAAKvViu985zv4xS9+gR07diASicSjWiIiIiIiIqKUF5dg/3WDBw/G/PnzsXPnTvzxj3/E5MmToVar4ff78Y9//AMPPPAAbr75ZixevDjeVRMRERERERGlnLgHe+nGcjluvvlmvPjii/j444+xaNEifOtb3wIAnDt3Dn/+85+vVNVEREREREREKeOytrvrKYPBgB/+8If44Q9/iKamJrz33nt4//33e6NqIiIiIiIioqvaFeux70peXh7mzp2LTZs29XbVRERERERERFedXg/2RERERERERBQ/DPZERERERERESYzBnoiIiIiIiCiJMdgTERERERERJTEGeyIiIiIiIqIk1ivb3V1p1dXVWLJkCY4ePYpgMIhhw4Zhzpw5GDt2bI/vsX//fixbtgyfffYZHA4HzGYzbrrpJsydOxfXXHNNh/IzZ87EJ5980uX9Nm/ejAEDBlzS8xARERERERH1VNIH+zVr1mD+/PkQBAFlZWWIRCLYvXs3KioqUFlZiRkzZlzwHuvXr8eTTz6JcDiMoUOH4oYbbsDx48fxwQcf4MMPP8SyZctw4403xlxz5MgR6HQ63HrrrZ3eU6/Xx+X5iIiIiIiIiLqT1MG+paUFCxYsQFpaGv72t7+htLQUAHDgwAHMmjULixYtwvjx45Gdnd3lPaxWK5555hmIoog//OEPuO222wAAoijij3/8I37729/il7/8JTZv3gyVSgUAaGxsRHt7O0aPHo0XXnjhyj8oERERERERUReSeo79ypUrEQgEcP/990uhHgCGDx+OiooK+P1+rFq1qtt7bN68GW63G1OmTJFCPQDIZDL89Kc/xdChQ9HU1ITPPvtMOldTUwMAGDZsWJyfiIiIiIiIiOjiJHWw/+ijjwAgJpBHTZgwAQCwc+fObu8RiUQwdOhQlJWVdXo+Ok++paVFOnb48GEADPZERERERESUeEk7FF8URRw/fhxyuRwDBw7scL6oqAhyuRzHjx+HKIqQyWSd3ufee+/Fvffe2+m5SCSCQ4cOAQBycnKk49Fgb7fbMXv2bBw6dAh+vx/XXXfdRS/aR0RERERERHQ5krbH3m63IxAIwGw2QxCEDueVSiX69esHr9cLt9t9SXWsWbMGp06dQlZWFq6//nrpeHQo/oIFC9DS0oIbb7wRBQUF2LNnDyoqKrB8+fJLeygiIiIiIiKii5S0PfZerxcAoNVquyyj0WgAAG63GwaD4aLuf/DgQSxatAgA8Nhjj0Gp/PL/KqvViubmZiiVSixevBg/+MEPpGs2btyIJ554AosXL8aoUaMwZMiQbutYs2YN1q5d26P2RF8mEBEREREREX1d0gZ7ufzCgw1EUbykex84cAD//d//DY/Hg7vvvhtTp06VzlksFuzatQsOhwNFRUUx102aNAn79+/H8uXL8dZbb6GysrLbehobG7Fnz55LaiMRERERERERkMTBXqfTAQD8fn+XZaLnuuvVP9+OHTvw6KOPwuPxoLy8HAsWLOhQxmKxwGKxdHr9LbfcguXLl0tz87uTn5+PUaNG9ahdNTU1cDqdPSpLREREREREqSNpg73BYIBOp4PNZkMoFJKGykeFQiHYbDao1WoYjcYe3fPtt99GZWUlwuEwZs2ahXnz5nW56F5XMjMzAQA+n++CZcvLy1FeXt6j+953333s3SciIiL6/+3deVhT554H8G8IJCQkbLKUJYgbglZcqrhXbe3mtbXDOKNWrbZW63X0djp9rNrN1l6n1/F27qj16gxqW8WOjha9altr61KtdmRGGHFBEARlDSAIIUBCkjN/eJN7KWvlZCPfz/P4tM85b978Xn5PCL/znvO+RETUitsunieRSNC/f3+YzWYUFha2Ol9QUACLxdJif/uOfPLJJ1i7di0EQcCaNWuwevXqNov6CxcuYOXKlfjss8/a7Ke4uBhAy1X0iYiIiIiIiOzFbQt7ALZt5b7//vtW56zHJk2a1Gk/e/bswZYtW+Dj44OPP/4YCxcubLdtU1MTjhw5gt27d8NkMrU6f/jwYQDAhAkTujIEIiIiIiIiom5x68I+OTkZcrkcKSkpuHr1qu34lStXsGPHDvj6+rbYo760tBT5+fmorq62HcvJycGGDRsAABs2bMC0adM6fM8JEyYgKioKJSUlvL6myAAAIABJREFU2LhxI8xms+3cl19+iW+++QahoaGYOXOmWMMkIiIiIiIiapfbPmMPANHR0Vi1ahXWrVuH2bNnY8yYMRAEARcvXoTJZMKGDRvQq1cvW/tVq1YhPT0dy5cvx4oVKwAA27dvR3NzM/z9/XH69GmcPn26zfeaPXs2Ro4cCZlMht///vdYtGgRPvvsM5w6dQrx8fEoKipCdnY2lEoltmzZArVa7ZCfAREREREREXk2ty7sAWDu3LmIjIzEjh07cOnSJchkMowYMQK//vWvMXbs2E5fb12Qrq6uDkePHm233bhx4zBy5EgAwIgRI3Do0CFs27YN58+fx+nTpxEUFITk5GQsW7YMGo1GnMERERERERERdcLtC3vg/hZzU6ZM6bTdnj17Wh07f/78A71nbGys7RZ+IiIiIiIiImdx62fsiYiIiIiIiDwdC3siIiIiIiIiN8bCnoiIiIiIiMiNsbAnIiIiIiIicmMs7ImIiIiIiIjcGAt7IiIiIiIiIjfGwp6IiIiIiIjIjbGwJyIiIiIiInJjLOyJiIiIiIiI3BgLeyIiIiIiIiI3xsKeiIiIiIiIyI2xsCciIiIiIiJyYyzsiYiIiIiIiNyYt7MDICKy0ul0qKyshNFohEwmQ2hoKNRqtbPDoj8zGo3Q6/UwmUzw9vaGn58fZDKZs8OiP7t37x5KS0thMBggl8sRGRmJwMBAZ4dFf3bjxg1cvHgR9fX1UKlUGD16NOLj450dFv3Z1atXceHCBeh0OqjVaowbNw4PP/yws8OiPzt27Bj+9Kc/oaamBkFBQZgxYwamT5/u7LDoz5gf18DCnoicrqKiAvn5+dBqtaitrYXZbIZUKkVAQADCw8PRr18/hIWFOTtMj6XX61FVVQWdToeGhgZbfpRKJdRqNUJCQuDn5+fsMD1WcXExsrOzUVZWhpqaGlt+goKCEBERgYSEBERHRzs7TI916tQpHDp0CDdv3kRlZaUtP6GhoRgwYAD+5m/+Bo899pizw/RYx48fx8GDB3Hz5k1UVVXZ8rN3714MGDAAM2fOxNNPP+3sMD3Wxo0bsXv3bhQVFaGxsREWiwVeXl44cOAANBoNXnzxRaxcudLZYXos5se1sLAnIqe6ffs2srKyUFRUhPr6eqjVavj4+MBgMECr1aK0tBRVVVVITExE7969nR2ux7HOAldVVaGxsRFKpRJSqRQGgwE1NTVQKBTQ6/WcHXaS3NxcpKen486dO9DpdPD394e3tzcMBgPKy8tRXFwMrVaLpKQkxMXFOTtcj7Nv3z6kpqYiLy/PNlPv5eUFi8UCrVaLwsJCFBQUoKKiArNnz3Z2uB7n888/x+7du3Hr1i3o9XqoVCpIpVIYjUZkZ2fjzp07KCgogFarxYIFC5wdrsdZsWIF9u7di9raWlvBKJFIYDKZUFtbC51Oh48++gh37tzBli1bnB2ux2F+XA8LeyJymoqKCmRlZSEvLw9qtRqDBw+GVCq1nTebzSgrK0NeXh4AQKFQcObegfR6PUpLS1FaWgqlUomYmBhIJBLbeUEQUFNTg9LSUgCAj48PZ+4dqLi4GOnp6cjNzYVarUZiYiK8vf/ytW4ymVBcXIzc3FwAgFKp5My9A506dQqpqanIysqCSqXCoEGDWn1+iouLkZWVhdTUVISFhXHm3oGOHz+O3bt3Izs7G35+fhg2bFiLR4uMRiMKCgqQnZ2N3bt3Izw8nDP3DrRx40bs3bsXNTU1tjuQFAqF7XxjYyPq6upQU1ODvXv3IiYmhjPDDsT8uCYunkdETpOfn4+ioiKo1WpER0e3KOoBQCqVIjo6Gmq1GkVFRcjPz3dSpJ6pqqoKVVVVUCqVCA4OblGUAIBEIkFwcDCUSqWtLTmOdUZRrVYjNja2RVEPAN7e3oiNjYVarcadO3eQnZ3tpEg906FDh5CbmwulUgmNRgOLxdLivMVigUajgVKpRG5uLg4dOuSkSD3TwYMHcevWLfj5+WHgwIGt1guRyWQYOHAg/Pz8cOvWLRw8eNBJkXqm3bt3o7a2FlKpFOHh4S2KRuD+hf7w8HBIpVLU1tZi9+7dTorUMzE/romFPRE5hU6ng1arRX19PSIiIjpsGxERgfr6emi1Wuh0OgdF6NmMRiN0Oh0aGxsRFBTUYdugoCA0NjZCp9PBaDQ6KELPdu/ePZSVlUGn03U6Cx8dHQ2dToeysjLcu3fPQRF6ths3biA3Nxd1dXUIDw8HAMjlcvj4+Nj+yeVyAEB4eDjq6uqQm5uLGzduODNsj3H16lXcvHkTer0effr06bBtnz59oNfrcfPmTVy9etVBEXq2Y8eOoaioCBaLBf7+/h229ff3h8ViQVFREY4dO+agCD0b8+O6eCs+ETlFZWUlamtroVarW83U/5xUKoVarUZtbS0qKyu5Ur4D6PV6NDQ0QKlUtpqp/zmJRAKlUomGhgbo9XqulO8ApaWlqKmpsT1T3xFvb2/4+/vbHpvgWgj2d/PmTQQGBiIpKQkajabDHJlMJgQEBEChUODmzZtcKd8BcnNzERYWhoCAgE4LewDw9fWFXC5Hbm4uV8p3gMzMTMTHx8NsNqNXr16dtr979y6kUikyMzO5ErsDMD+ui4U9ETmF0WiE2WyGj49Pl9r7+PjAbDZzRthBTCaTbXXorpBKpTCbzTCZTHaOjADAYDDAbDZ3WtRbeXt7w2w2w2Aw2Dky56msrHR2CDa1tbW27SBVKlWn7QMCAuDj42O7eOlsoaGhovfpCuOy0ul08PPzg1KphK+vb6ftg4KCIJFIbFuyOltPz4/BYEBwcDAEQUBAQECn7SUSCSQSCQwGg0uMg/lpyRPy4ypY2BORU8hkMtvq6l3R3NwMpVLJ2WAH8fb2/kX5MZvNkMvlXS40qXvkcvkvyo/JZIJMJrPd/t1TNTc3OzsEAPcL+9zcXDQ1NXUpR7du3YKvry8eeeQRp4+hqxdbH4Szx2ZVX1+PvLw8NDU1obGxsdP21vyMGjXK6WPwhPxUV1fj2rVrv3hGODY21uljYH5a85T8uAI+Y09EThEaGoqAgADodDqYzeYO25rNZuh0OgQEBPToK62uxDqb1dDQAEEQOmwrCILttn2uiu8YkZGRCAoKQl1dXad3SZhMJtTV1SEoKAiRkZEOitCzxcfHIyAgAE1NTZ3+Idvc3IympiYEBATwNnwHSUxMRGBgIBobG7uUn8bGRgQGBiIxMdFBEXq2SZMmQS6Xw2KxdHphzGAwwGKxQC6XY9KkSQ6K0LMxP66LhT0ROYVarUZ4eDhUKhXKyso6bFtWVgaVSoXw8HA+X+8gMpkMarUaCoUCNTU1Hba17mevVqt5R4WDBAYGIiIiAmq1GsXFxR22LS4uhlqtRkREBJ+vd5DY2FhERETA19cXWq22w7ZarRa+vr6IiIhAbGysYwL0cP369YNGo4FCobBt19me0tJSKBQKaDQa9OvXz0EReraJEyciPDwcXl5eqK+v77BtfX09vLy8EB4ejokTJzooQs/G/LguFvZE5DTWP650Oh2Ki4tbzdybzWYUFxdDp9PxjyonCAkJQUhICBoaGlBdXd1q5l4QBFRXV6OhocHWlhwnISEBMTEx0Ol0KCwsbDVzbzKZUFhYCJ1Oh5iYGCQkJDgpUs8jk8kwYcIEhIWFoaGhAcXFxa1mhpubm1FcXIyGhgaEhYVhwoQJvDDmQFOnTkVUVBQaGxtx+/btNvNz+/ZtNDY2IioqClOnTnVSpJ5p+vTpUKlUsFgsuHv3bquZYYPBgLt378JisUClUnFRNgdjflwTH4YkIqcJCwuz3dpYVFSEa9euQa1Ww8fHB83NzdDpdFCpVOjfvz8SExMRFhbm5Ig9i5+fn+3W7aqqKty5cwdKpdK2UF5DQwMUCgUiIyMRGRnJ2/AdLDo6GklJSQCAO3fuICsry7ZKvvX2e7Vajbi4OCQlJXW6LR6JRyqVIikpCVqtFufOnUNFRQXy8vLa/PxER0dj4sSJSEpK6vJildR9Y8eOxd27d/HVV1+hpKQEubm5UCgUtvw0NjZCoVCgT58++NWvfoWxY8c6O2SPMn/+fJSVleH48eOor69HXV0dvLz+Mh9psVjg5eUFf39/PP3005g/f74To/U8zI9rYmFPRE7Vu3dvKBQKhISEQKvVora2FmazGUqlEhEREQgPD0e/fv1Y1DtJYGAgfHx84OfnB51Oh4aGBttCeUFBQVCr1QgJCWFR7yRxcXFQKpXIzs5GWVkZampqYDabIZPJEBUVhYiICCQkJLCodwI/Pz889dRTUKlUyMjIsP1+EwQBUqkUoaGhCA8Px4gRIzB+/Hh+hpxg+vTp6NWrF77//nsUFRXh3r17tt1aHnroIWg0GkydOpVFvZO8+eabiIiIwLFjx6DVam3Pa3t5eUGpVCI8PBzTp09n0egkzI/rYWFPRE4XFhaGsLAw21ZCRqMRMpkMoaGhfKbeBfj5+cHPzw9GoxF6vR4mkwne3t627bzIuaKjoxEdHY179+6htLQUBoMBcrkckZGRfKbeiXx8fODv74/x48dj2LBhKCwsRGFhIZqamuDr64vY2FjExsbCz88P/v7+PX61Zlc1duxYjB07Fvn5+cjKyoJer4efnx8SExP5+JcLmD9/PubPn49z587hhx9+QF1dHfz9/TFp0iQ+s+0CmB/XwsKeiFyGWq1mIe/CZDIZC3kXFhgYyELexfj6+kIqlUImk0GpVKJ///4QBAESicS2/aCfnx+LehfQr18/FvIubOLEiSwUXRjz4xpY2BMRERHZiY+PDwIDA2E2m2E0GlsU9nymnoiIxMLCnoiIiMjOpFIpFAqFs8MgIqIeitvdEREREREREbkxFvZEREREREREboyFPREREREREZEbY2FPRERERERE5MZY2BMRERERERG5sR6xKv6FCxewfft25OTkoLm5GYMHD8aSJUt+0X6KWq0WW7duxfnz51FZWYmIiAg899xzWLx4cZv7Njc1NeHzzz/HkSNHUFxcDLVajcmTJ+M3v/kNwsLCxBweERERERERUbvcfsY+LS0NL730EjIzM5GYmIjhw4cjMzMTr7zyCvbv39+lPsrLy/H3f//32L9/P/z9/TF58mTo9Xps3rwZixYtQnNzc4v2zc3NWLZsGf71X/8Ver0ekyZNQmBgIA4cOIDk5GSUlpbaY6hERERERERErbh1YV9RUYG1a9dCrVbjyy+/REpKCnbu3IkvvvgCKpUK69evh1ar7bSf999/H+Xl5Xjttddw6NAhbN68GSdOnMC4ceOQnp6OPXv2tGifmpqK8+fPY/LkyThx4gQ2b96MY8eOYenSpaisrMS6devsNWQiIiIiIiKiFty6sE9NTYXRaMTChQsRFxdnO56YmIhXXnkFBoOh01n7W7du4cyZM4iJicHSpUttx5VKJdavXw+pVIrU1FTbcUEQ8Omnn0IikeDdd99tcZv+a6+9hj59+uD06dMoKioScaREREREREREbXPrwv7cuXMAgKlTp7Y698QTTwAAzp4922EfP/74IwRBwJQpU+Dl1fLHERkZiUGDBqGkpAR5eXkAgJycHGi1WsTHxyM6OrpFey8vLzz22GNdel8iIiIiIiIiMbhtYS8IAvLy8uDl5YW+ffu2Oh8bGwsvLy/k5eVBEIR2+7EW7AMGDGjzvLXv3NzcB2pPREREREREZE9uW9jX1tbCaDQiMDCwzVXrvb29ERQUhMbGRuj1+nb7qaioAIB2V7IPDQ0FAFRVVQEAKisrWxxvr/3du3e7OBIiIiIiIiKiB+e22901NjYCABQKRbttfH19AQB6vR4qlarDfqxt2+ujoaGhxX/be9+ft+9IWloaDh061Gk7AMjMzAQAZGdnY/78+V16jTMYjUZnh9BjtHXBqruYH/EwP66N+XFtzI9rY35cG/Pj2pgf12aP/NhDfHw83n777V/0Grct7H/+PHxbOroF/+f9SCSSDvuw/veXtu9ISUkJ0tPTO23313Q63S9+DREREREREfVcblvYK5VKAIDBYGi3jfVcR7P61n6ampq61Mcvbd+RqKgoJCUlddoOAK5evQqLxYKAgAD07t27S6+h1rKzs6HT6aBWq5GQkODscKgNzJFrY35cG/Pj2pgf18b8uDbmx7UxP+KKj4//xa9x28JepVJBqVSipqYGJpMJ3t4th2IymVBTUwO5XA5/f/92+7E+W299hv7nrM/UW9t1tX17z+D/teTkZCQnJ3fajsQzf/58pKenIyEhAXv27HF2ONQG5si1MT+ujflxbcyPa2N+XBvz49qYH+dz28XzJBIJ+vfvD7PZjMLCwlbnCwoKYLFYWuxv3xbr6vbW1e5/Lj8/HwBs/Vj/29X2RERERERERPbktoU9AEycOBEA8P3337c6Zz02adKkLvVx6tQpWCyWFudKS0uRnZ2NqKgo9O/fHwDQr18/REVF4fr16ygrK2vR3mKx4NSpU5BIJLZ+iYiIiIiIiOzJrQv75ORkyOVypKSk4OrVq7bjV65cwY4dO+Dr64sXXnjBdry0tBT5+fmorq62HdNoNJg4cSIKCgqwadMm2/GGhga88847MJvNeOmll1q87+zZs2E2m/H222+3WP1+06ZNKCwsxBNPPIGYmBh7DJmIiIiIiIioBbd9xh4AoqOjsWrVKqxbtw6zZ8/GmDFjIAgCLl68CJPJhA0bNqBXr1629qtWrUJ6ejqWL1+OFStW2I6vXbsWc+bMwfbt23Hq1Cn06dMHGRkZqKysxKOPPoo5c+a0eN+FCxfizJkzOH/+PJ588kmMGDECBQUFyM3NRWRkJN59912H/QyIiIiIiIjIs7n1jD0AzJ07F9u3b8fQoUNx6dIlXL16FSNGjMCnn36KGTNmdKkPjUaDAwcOIDk5GdXV1Thz5gwCAgLwxhtv4JNPPmm1MJ9MJsPOnTuxbNkyKBQKnD59Gnq9HrNmzcL+/fttC+wRERERERER2Ztbz9hbTZkyBVOmTOm0XUcrNEZEROCjjz7q8nsqFAq89tpreO2117r8GiIiIiIiIiKxuf2MPREREREREZEnY2FPRERERERE5MZY2BMRERERERG5Men777//vrODIHKkhIQEJCUlISEhwdmhUDuYI9fG/Lg25se1MT+ujflxbcyPa2N+nEsiCILg7CCIiIiIiIiI6MHwVnwiIiIiIiIiN8bCnoiIiIiIiMiNsbAnIiIiIiIicmMs7ImIiIiIiIjcGAt7IiIiIiIiIjfm7ewAiMRw4cIFbN++HTk5OWhubsbgwYOxZMkSTJw4sct9aLVabN26FefPn0dlZSUiIiLw3HPPYfHixZDJZHaMvucTIz9/rbq6GtOmTcPjjz+O9evXixyt5xEjP//3f/+HlJQUZGZmoq6uDoGBgRg9ejSWLVuGfv362TH6nk+M/GRmZmL79u3IzMxEU1MTNBoNpk2bhkWLFsHX19eO0fcczsjDggUL8N///d/t9nfixAn07t37gcbjKcT+/gGA9957D/v378dHH32E5ORkEaP1PN3Nj8lkwvDhw2E0Gts8Hx4ejrNnz4oZskcR4/PT3NyM1NRUHD58GIWFhZBKpRgyZAiWLFmC8ePH2zF6z8Pt7sjtpaWlYc2aNZDJZBgzZgwsFgsuXryI5uZmrFu3DrNmzeq0j/LycsyaNQvl5eUYNGgQNBoNMjIyUFlZiaSkJOzatQs+Pj4OGE3PI0Z+/lpjYyMWL16M//mf/8HMmTNZ2HeTGPk5cuQIVq9eDbPZjEGDBiEyMhJ5eXkoLCyEQqFASkoKRo0a5YDR9Dxi5OfHH3/EkiVLYDabMXToUISEhODKlSuoqKhAQkICUlNToVKpHDAa9+WsPIwePRpGoxGPP/54m32uXr0aISEhooyxJxL7+wcAzp49i8WLFwMAC/tuEiM/N27cwIwZMxATE4OhQ4e2Oh8YGIh33nnHHuH3eGLkx2g0YsmSJfjpp58QGBiIRx55BFVVVbh8+TIkEgk++eQTTJ061QGj8RACkRvTarXCww8/LDzyyCNCTk6O7fjly5eFESNGCEOGDBHKy8s77efVV18V4uLihK1bt9qO6fV6YeHChUJcXJywc+dOu8Tf04mVH6vS0lLh7/7u74S4uDghLi5OeOutt+wRtscQIz93794Vhg8fLsTHxwvfffed7bjFYhG2b98uxMXFCZMnTxaMRqPdxtFTiZGfpqYmISkpSRg4cKBw4sQJ2/HGxkbb770//OEPdhtDT+CsPBQXFwtxcXHCwoULxR2QhxD7+0cQBKGmpkYYP3687Tvoyy+/FDtsjyFWftLS0oS4uDjhj3/8oz3D9Thi5WfTpk1CXFycMG/ePEGn09mOf/3118LAgQOFpKQkobm52S5j8ER8xp7cWmpqKoxGIxYuXIi4uDjb8cTERLzyyiswGAzYv39/h33cunULZ86cQUxMDJYuXWo7rlQqsX79ekilUqSmptptDD2ZGPkB7t9ql5qaiueffx6XL1+GRqOxZ9geQ4z8nDhxAnq9Hs8991yLq+4SiQSvvvoqBg0ahNLSUmRmZtptHD2VGPn54YcfcO/ePTz22GN44oknbMd9fX2xbNkyAMC5c+fsM4Aewll5yM7OBgAMHjxYrKF4FLG+f/7aBx98gHv37rU5M0y/jFj5uX79OgB+TsQmRn6amprw2Wefwd/fH5s2bWpxR9IzzzyDxx9/HCqVCnl5eXYbh6dhYU9uzfqHUFu38Vj/eOrs2aoff/wRgiBgypQp8PJq+ZGIjIzEoEGDUFJSwl88D0CM/ADApUuX8OGHH8JkMuGDDz6w/SFM3SNGfiwWCwYNGoQxY8a0ed76/G9FRUV3QvVIYuTnySefxJkzZ/Duu++2OqfX6wEA3t5cbqcjzsoDC5buEev7x+rYsWP4+uuvsXz5cgwYMECcID2YWPnhBTD7EOvva71ejxkzZiA4OLjV+a1bt+LkyZOIj48XIWICuHgeuTFBEJCXlwcvLy/07du31fnY2Fh4eXkhLy8PgiBAIpG02Y+1YG/vi7pv3764cuUKcnNz0b9/f/EG0MOJlR8AUCgUWLhwIV599VUEBwcjLS3NnqF7BLHy88ILL+CFF15o85zFYsG1a9cAAA899JB4wXsAMT8/ERERrY5VVlbi97//PQDgueeeEy/wHsaZebAW9rW1tVi0aBGuXbsGg8GAhx9+uNuLv/V0YuYNuL+47rp16zB06FAsXrwY7733nr1C9whi5UcQBGRnZyM0NBSnTp3C/v37kZ+fD7lcjnHjxmH58uVt9k8dEys/1u//IUOGwGg04sSJE8jIyIDZbMaIESMwbdo0rl8lMs7Yk9uqra2F0WhEYGBgm6vWe3t7IygoCI2NjbYZkbZYZxLDwsLaPB8aGgoAqKqqEiFqzyFWfoD7t36tWbOmzSu+9GDEzE970tLScOfOHYSFhWHYsGHdDdmj2Cs/n376KebPn48pU6YgJycHS5cuxdy5c8UMvUdxZh6sM5Fr165FRUUFRo0ahejoaKSnp+OVV17B559/3r3B9WBi5+3tt9+GwWDA7373O0ilUnuE7FHEyk9RURHq6+tRWVmJ9957D3K5HKNHj4ZcLsdXX32FmTNn4tKlS/YcSo8kVn7u3LkD4P6Fgr/927/FG2+8gb1792Lfvn148803MXPmTN7NJzIW9uS2GhsbAdyfzW2Pdfugjn7xWPtpb8sn6/GGhoYHitNTiZUfsg975+fq1au2HQveeOMN3u79C9krP8ePH0d6ejqam5shkUhQXl6O6urq7gXbgzkrD9XV1SgvL4e3tzc+/vhjHD16FFu2bMHRo0fxhz/8Ad7e3tiwYYOt+KeWxMzbF198gXPnzuH111/n7K9IxMqP9a6W8PBwpKWlYe/evdi+fTtOnjyJl19+GXq9Hq+//joMBoOI0fd8YuVHp9MBANavX4+mpibs3LkTly5dwuHDh5GUlIQbN27gN7/5DQRu0CYaFvbktn7+PHxbuvLLwtpPR7d6dbUv+gux8kP2Yc/8ZGVlYdGiRWhoaMDs2bPx/PPPP1A/nsxe+dm8eTOysrJw+PBhjB49GocPH8aCBQtgMpkeJMwez1l5CA4Oxk8//YSvvvoK06dPb/HaadOmYe7cuTCbzfjP//zPX/zenkCsvN25cwcbN27EqFGjsGDBAjFCI4iXn6eeegpnzpzBgQMHkJCQYDvu7e2NlStXYvDgwdBqtfj++++7Fa+nESs/1gsqBoMBu3btwoQJE6BSqZCQkICUlBRERkYiMzMTFy5c6HbMdB8Le3JbSqUSADq8Ems919FVR2s/TU1ND9wHtSZWfsg+7JWfH374AQsWLMC9e/eQnJyMtWvXdi9QD2Wv/ISHh0MulyMhIQHbtm3DwIEDkZubi+PHj3cv4B7KmXkIDg5GbGxsm6+fMmUKgL88w0otiZE3s9mMN998E8D9/eo7ew6fuk6sz5VEIkFERATCw8NbnfPy8sKkSZMA3L+DjLpOrPxYz02cOLHVbka+vr62dUXS09O7FS/9Be+NJLelUqmgVCpRU1MDk8nU6lZfk8mEmpoayOVy+Pv7t9uP9dn69p6hr6ysbNGOukas/JB92CM/+/btw7p162A2m/HSSy9h1apV/GP4ATni8+Pj44NnnnkGOTk5uH79equZYXLdPFjXfmnvgrSnEyNv3333HTIzM6HRaLBp06YW5y5fvgwA+K//+i9cuHABTz75JJ588kn7DKYHctTfByEhIQD4OfmlxMqPdV2kqKioNs9bj9fU1IgUOXHGntyWRCJB//79YTabUVhY2Op8QUEBLBZLi/0322JdDb+97ezy8/MBoNN+qCWx8kP2IXZ+PvnkE6xduxaCIGDNmjVYvXo1i/puECs/3377LVatWoWffvqpzfPWhZF4K37bnJWHCxcuYOXKlfjss8/abF9cXAyAu020R4y8WdfVKSoqwtGjR1v8sy4KlpkgtGHmAAALBklEQVSZiaNHjyInJ8cu4+ipxPpc7d27F//4j//Y7q3c/Jw8GLHyYz3f3gJ51omzoKCg7gVMNizsya1Zt/tp6/kp6zHrrVid9XHq1ClYLJYW50pLS5GdnY2oqChudfcAxMgP2Y9Y+dmzZw+2bNkCHx8ffPzxx1i4cKGocXoqMfJTUFCAw4cPY9++fW2et+5VzD2g2+eMPDQ1NeHIkSPYvXt3mxddDh8+DACYMGFCF0fhebqbt+TkZOTk5LT5b+bMmQDu36Kfk5ODFStW2GEEPZsYn6uioiJ88803OHToUKtzBoMB3377LQBg/Pjx3Q3X44iRn0cffRQAcP78edtCen/txx9/BACMHDmyW7HSX7CwJ7eWnJwMuVyOlJSUFs9QXblyBTt27ICvr2+LPbZLS0uRn5/fYvVhjUaDiRMnoqCgoMXtdg0NDXjnnXdstxXTLydGfsh+xMhPTk4ONmzYAADYsGEDpk2b5rgB9HBi5GfGjBnw9fXF8ePHcfToUdtxs9mMzZs346effkJUVBSefvppxwzKDTkjDxMmTEBUVBRKSkqwceNGmM1m22u+/PJLfPPNNwgNDbUVmNQav39cmxj5mTlzJqRSKY4ePWor4gGgubkZH374IUpKSvDoo4/i4YcfdsygehAx8tO3b19MnjwZdXV1WL16dYtHInbs2IGMjAz069cP48aNc8ygPIBE4LLU5Ob27t2LdevWwcfHB2PGjIEgCLh48SJMJhM2bNiAGTNm2NrOnz8f6enpWL58eYsr7EVFRZgzZw4qKysRFxeHPn36ICMjA5WVlXj00Uexbds2btf1gMTIz8+lpaVhzZo1mDlzpm1LNXow3c3P66+/jq+//hr+/v4dXr2fPXs2r8o/ADE+P4cPH8Zbb70Fs9mMwYMH46GHHsKNGzdQUlKC4OBg7Ny5E4MGDXLG8NyGM/KQkZFh210iJiYG8fHxKCoqQnZ2NpRKJXbt2oXhw4c79Ofgbuzx/QPc39f+4MGD+Oijj5CcnGzvYfRYYuRn9+7d+Od//mcIgoAhQ4YgMjISly9fRnl5Ofr27YvU1FT06tXLGcNze2Lkp6KiAi+++CIKCgoQEhKCYcOG4fbt27h58yb8/f3x6aef8sKLiFipkNubO3cuIiMjsWPHDly6dAkymQwjRozAr3/9a4wdO7ZLfWg0Ghw4cACbN2/G2bNncfv2bWg0Grz44otYsGABi/puECM/ZD/dzY91Ndu6uroWM5E/N27cOBb2D0CMz8/zzz+P3r174z/+4z+QkZGB3NxchIWFYd68eViyZEmbK0pTS87Iw4gRI3Do0CFs27YN58+fx+nTpxEUFITk5GQsW7as1SrT1Bq/f1ybGPl58cUXMWDAAOzYsQNZWVnIyclBZGQkli5diiVLlsDPz8/Oo+i5xMhPWFgYDh48iJSUFBw/fhxnz55FQEAAZsyYgX/4h39A79697TwKz8IZeyIiIiIiIiI3xmfsiYiIiIiIiNwYC3siIiIiIiIiN8bCnoiIiIiIiMiNsbAnIiIiIiIicmMs7ImIiIiIiIjcGAt7IiIiIiIiIjfGwp6IiIiIiIjIjbGwJyIiIiIiInJjLOyJiIiIiIiI3BgLeyIiIiIiIiI3xsKeiIiIiIiIyI2xsCciIqIHcvHiRQwcOBADBw50diitWGMbPXq0s0MhIiKyOxb2RERERERERG6MhT0RERERERGRG2NhT0REREREROTGWNgTERERERERuTFvZwdAREREnsVoNOKLL77A0aNHcevWLQiCgD59+mD69OmYN28e5HJ5q9cYDAYcPHgQJ06cQG5uLnQ6HXx9fdGnTx8888wzmDdvHmQyWZfe/7e//S327NkDtVqNXbt2ITExUewhEhERORQLeyIiInKYe/fuYfHixcjKyoKXlxc0Gg18fX2Rk5OD69ev46uvvsLOnTsRFBRke41Op8OCBQtw7do1SKVSxMTEICIiAiUlJcjKykJWVhYuXLiAHTt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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()" ] }, diff --git a/notebooks/digits_exp159.ipynb b/notebooks/digits_exp159.ipynb new file mode 100644 index 0000000..0aeee8b --- /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": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "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(\"Wave noise (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(\"Wave noise (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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/digits_exp159_161.ipynb b/notebooks/digits_exp159_161.ipynb new file mode 100644 index 0000000..c734494 --- /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\\\"] = 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"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, 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", + "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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GbWYB/UAgwFBtmHbu3Im2tjakUikoigLgX11WTeZ/FwoFqKqKtrY27Ny5k6EaEREREdEoUrJQ7fnnn8ell1464kdXVq1ahXvvvReyLGP27NnQdR0bN27EwoULsXjxYlx77bVHff7HPvYxaJo24DVd1/Hyyy9D13VMmzat6NoHH3wAl8uFz3zmMwM+l7tr6GR1eA2zkaYoCrxeL1wul1U/zTyeKIoivF4vPB4PXC4XvF4vFEUpy3oPb54wmgWDQUyZMgWdnZ3IZrMoFArQdb2oYL55/FYURfh8PkyZMmVMBIrl1NHRgWg0Ck3TIEmSFWDabDbr3+auQTPQjEaj6OjoKPfSiYiIiIhoCEoWqn3zm9+E2+3GvHnzcMUVV+D8888v1a2PqLu7Gw888AC8Xi9++9vfWp/wb9myBTfffDMeeughzJkzB6FQ6Ij3uPbaa48YvD366KPQdR3z5s3DjTfeaD3e0dGBvr4+XHDBBfjBD35Q2hdFNIZJkoRgMAhBENDb24t4PI7q6mpr147NZoPP50N1dTUCgcCYCbfKxePx4Oyzz0ZHRwcOHDgAVVWRy+VgGEbROEVRrG6rZ599NjweT5lWPDak02lks1nkcjk4nU7YbDZrd6DJ3CVYKBSQy+WQzWaRTqfLuGoiIiIiIhqqkoVqs2bNwubNm7F69Wr88Y9/RDAYxBVXXIErrrjihB1nWbFiBXK5HP7jP/6jaI4ZM2Zg4cKFeOSRR7By5UrccccdQ7735s2bsXTpUgQCATz44INF11pbWwEAZ5xxxvBeANEpyOFwIBQKwePxIJ1OI5VKWcc/3W43XC4X3G43A7UScDqdmDFjBjo7O6EoCsLhMNLpNPL5vBVkSpIEl8uFQCCAadOmYcaMGXA6neVe+qgmy3LRbsCB/l82v/a5XA7AhzsGZVkesTUSEREREdHwlSxU+/Wvf42uri48//zzeO6557Bjxw4sW7YMTz75JE4//XTMnz8fl19+eUmPFf3tb38DAFxyySX9rl166aV45JFHsG7duiGHarquY/HixdB1Hd/61rdQUVFRdH3btm0AGKoRHS9JklBRUQGv12vtnBIEAbIsw2azlXt5Y4bb7cbUqVORy+Uwffp0dHV1obu7G/l83goyJUlCTU0NQqEQamtrMXXqVB5fHyafz2cFaeZutIH+vy4UClb5AUmS4PP5RnSdREREREQ0PCVtVBAKhbBw4UIsXLgQO3fuxLPPPouXXnoJH3zwAZYsWYKHH34Ys2fPxvz583HJJZcMazeEYRhoa2uDKIqYNGlSv+sTJkyAKIpoa2vrVyD6WP7whz9g+/btOPvss/GFL3yh33UzVIvFYliwYAG2bt0KVVVx5plnDrpBAhHBak4w1pW7QUQgEIDNZkMgEMD48eORSCSsxgRmHTtFUVBZWQkAZWsOAYyNBhH19fX45Cc/iYMHD0KSJNjtdoiiWFRTzaxvp2ka8vk86urqUF9fX+6ljznhcBjt7e3IZrNwOByYOHEiAoFAuZdFRERERGPECev+2dzcjG984xv4xje+gc2bN+O1117D66+/jvXr12PDhg1wOp247LLL0NLSgvPOO2/I94/FYsjlcqiqqhrwyIzdbkdlZSV6e3uRSqUGXSNI0zT8/Oc/BwB89atfHXCMefzzgQcewJQpU3Deeedhz5492LRpEzZt2oT/9//+X1ENtiNZtWrVoDqPHj4nnRhmZ0SzYL6iKLDbx0RzXDpJlKtBhKIocDqdiEQiEEURTqcTdru9aKeaKIowDANOp7NszSGA0jeIKFeYqSgKxo0bZwVoZkMCURStMeZ/m9cbGxuhKErZ1jwWwszD7dq1C5s3b8bu3bvR1dWFXC5ndR2eNGkSPv7xj+O0004r9zKJiIiIaJQbkdRg+vTpiEQiCIfD6OjoQCaTQTqdtuqvnX766fjOd76Dj3/844O+ZyaTAYCj7nIxO5EOJVR76aWXcOjQIUydOhUXX3xxv+uRSASHDh2C3W7HkiVL8PnPf77oud/85jexZMkSzJo1q1/H0I/q6OjApk2bBrUuOjFUVUUikUAmk+kXqjmdTqsDJdFo9dHmEKlUqqhLs2EYcLvdY7Y5RDkCQrvdjlwuh4MHDyKTyUDXdRQKhaIGEWZjDjPorKmpgd1uZ7fbEnj33Xfxpz/9Ca2trejq6oIsy1bA+cEHH+CDDz5Ae3s7PvvZz+Kcc84p93KJiIiIaBQ7YaFaLpfD66+/jueffx7r1q2DqqrWTogrrrgCV155Jbq7u/HMM89g8+bNuPHGG/Hoo48OWB9tIId/4n8kH+1wNxi//vWvAQALFiwY8HpVVRXeeOMNxONxTJgwoeja5z73Obz77rt46qmn8Lvf/Q6LFy8+6lwNDQ2YNWvWoNbV2tqKRCIxqLE0OOl0GpFIBPF4HKqqwuFwQBRF5PN5JBIJKIqCbDaLqqoquFyuci+X6LixOcTICgQCOP3003Hw4EFrV/VHu66aNQRlWYbf78fpp5/OY4klsGvXLrz00kv429/+hkKhAFmWi+ra2e12HDx4EN3d3dB1HV6vlzvWiIiIiOi4lTRUMwwDGzZswAsvvIBXX30VyWQShmFAFEV84hOfwPz58zFv3ryigOKqq67Cj3/8Yzz++OP48Y9/POhQzbyHqqpHHGNeG2zNpv379+O9996Dy+XCpZdeesRxVVVVqKqqGvDa3Llz8dRTT2Hr1q3HnK+lpQUtLS2DWtsNN9zAXW0lpKoqIpEIIpEIFEVBTU1NUd09n8+HZDKJSCQC4MPaX9yxRqMZm0OMHKfTiebmZnR2dmLfvn1QVRXJZBL5fN7aMSVJklXLrqmpCc3NzadEfcET7Y033sCmTZuQSCQgyzISiUS/Y7dOpxPpdBqbNm1CU1MTQzUiIiIiOm4lC9X+67/+C3/605/Q29trfRp/2mmn4corr8QVV1yB2traIz73mmuuweOPP44DBw4Mej6PxwOXy4VoNGoV3D6cpmmIRqNQFGXQHdX+8pe/APgwGDveX27MujTZbPa4nk8jI5FIIB6PQ1EUeL3eftcFQbAej8fjcDgcDNVoTDhVmkOU24QJE6z3p3Q6jUAggHQ6bR0xd7lcsNlscLlcmDFjRr+dzzR04XAY77zzDnbv3g1FUZDL5aDrOvL5vPV1lyTJOoq7e/duvPPOO/jsZz/LXYJEREREdFxKFqqtWLECAFBZWYnLL78cV155Jc4666xBPTcWi6G6unpINdUEQcDkyZOxZcsW7NmzB5MnTy663t7eDl3XMWXKlEHf8/XXXweAo+5S27BhA1avXo0zzjgDN910U7/rZjB4tBCRykvTNKuGWk1NzVHHejwedHd3I5PJDBjeEhENpLKyEtOnT4cgCNi3bx/C4XBRgC8IAgKBAJqamjBt2jSr8yodv7a2NuzYscOqj5nP55HNZqFpmjXGbrfD4XBAkiTk83ns2LEDbW1tDNWIiIiI6LiULCGYN28errzySlx88cVDDh6mT5+O9evXD3nOiy66CFu2bMGrr77aL1R79dVXAWDAZgMDMQwD77//PgBg5syZRxyXzWbx3HPP4a233sK///u/93utf/zjHwEAF1544aBfB40sVVWtGmqHH/kciCAIcDgc1nMYqhHRYNXW1kJRFAQCAYTDYUSjUau2V2VlJQKBABobGxmolUhXVxe6u7vR19cHu90OTdOQz+dht9shiiJ0XUcmk0E2m7Wud3d3o6urq9xLJyIiIqJRqmQJwWOPPWb9u6enxzoGadq8eTM8Hg+mTp1aqinR0tKCZcuW4YknnsCFF16IM888EwDw3nvvYdmyZXA4HPjiF79oje/s7EQmk0FlZWW/mmi7d+9GMplEKBRCKBQ64pwXXnghGhoa0NHRgYcffhh33323VY/omWeewcsvv4xgMIirr766ZK+TSkvXdesokMmsn5bP5yFJEqqqqqyOseYvY7qul2vJRDRKVVZWorKyEplMpqhcQWVlJY/hllgymUQ0GkU6nYYkSZAkCQ6Ho6hmoN1uR6FQQCaTQT6fRzQaRTKZLOOqiYiIiGg0K+m2m66uLtx7773YuHEj3njjjaJaZk888QTWrVuH2bNn47//+79LcjyysbER99xzDxYvXozrrrsOs2fPhmEY2LhxIzRNw5IlS1BdXW2Nv+eee7Bp0yYsWrQIt99+e9G9zGOb48aNO+qcsizjBz/4ARYsWIBf/epXWLNmDaZOnYr9+/ejtbUVLpcLP/nJTwas00UnB1EUrS6f4XAYe/fuxf79+9HZ2WmFavX19Rg3bhzGjx9v/XI2mI6zREQDcTqdDNFOMJfLhWw2C1VVIUmS1YTj8O/dNpsNhUIB+Xweqqoim82yuzMRERERHbeShWo9PT245ppr0NXVBbvdju7u7qJQLRQKweFw4I033sANN9yAVatWlSR4+tKXvoT6+nosW7YMb731FmRZxsyZM3Hrrbfi/PPPH/R9otEogMHVQps5cyZWr16Nn//851i/fj1ee+01VFZWoqWlBbfddtsxgzkqL0VRoCgKdu7ciZ07d2Lbtm3Yt28fMpmMtYPN6XSiqakJ06dPR3NzM84880w2KiAiOolVVVVh+vTpqKurg8fjgdvtPuLYVCqFZDI54M51Gr5sNot4PG7tzPT5fHA4HOVeFhEREVHJlSxU+9nPfoauri7Mnj0bDz/8cL/jn4sXL8Z//ud/4q677sKGDRvwi1/8AnfddVdJ5p47dy7mzp17zHHLly8/4rX58+dj/vz5g55zwoQJWLJkyaDH08nDbrcjmUxi8+bN+Pvf/47e3l5kMhkUCgVrjM1mQ3d3N9rb23HhhRdiwoQJrKdGRDQIPT09ZZm3r68P1dXVsNlskGXZ2mV8eO1MwzCQz+dhs9ngcDhQUVGBvr6+sq35oz8rjXbxeBwHDx5EOBxGJBKxQrWqqioEAgHU1dUNuiM7ERER0WhQspRg3bp1cDqdeOSRR1BRUTHgmIqKCjz88MOYO3cuXnnllZKFakRDtWPHDrzxxhvYuXMnDMNAoVCAJEnW9XQ6jXQ6jUgkApvNhhkzZmDatGllXDER0eiRz+dHfM5oNIp9+/ahp6cHNpsNhmFA1/WimmqFQgGiKEIQBBQKBQSDQUSj0bKs9/D3nLEgHA7jgw8+wJ49exAOhyFJEgzDgCAI2LVrFwKBACZMmICpU6ey2yoRERGNGSU9/tnc3HzEQM1UVVWFiRMnYteuXaWammhIkskk1q9fj507dyKTyVi/cB3+S5XZmKBQKGDnzp1Yv349PvOZz1jNC4iI6OTi9Xrh9/uRTCYhiiI0TUMul0OhULDCHUEQIEkS7HY7dF2H3+9nDdQSiMfj2LZtG/75z38il8tB13Wk02nr6y5JErq6uhCNRqHrOs455xzuWCMiIqIxoWShWkVFhVWX7FiSySQLA1PZ7NmzB++++y5isZh1NOjwX7oMw4BhGFaB61gshnfffRd79uyxOswSEdHJpbGxEbW1tQiHw9ZuNPP7+UdDNVmWoes6amtr0djYWO6lj3p79+7F1q1b0dXVBeDDXYPxeByFQgE2mw0+nw+VlZUAgK1bt6KyshJnnXVWOZdMREREVBIlC9WmT5+OdevW4dVXX8Ull1xyxHHr16/HgQMHcMEFF5RqaqIh2bFjBw4cOIBsNgu73Q673Q5FUYo6xJm71MzucAcOHMCOHTsYqhERnaRCoRCamprQ1taGTCZjdf80wzXzb7MDqNmQJhQKlXvpo1o2m8WePXuwY8cORKNRhMNhxGKxfs1//H4/AoEAUqkUGhsb0dzczOYFRERENOqJxx4yONdffz0Mw8Ddd9+NlStXIpvNFl3P5XJYtWoVvv71r0MQBFx//fWlmppoSDo7OxGLxaDrOiRJgqIokGUZoihaf2RZhqIokCQJuq4jFouhs7Oz3EsnIqIjkGUZkyZNQiAQsJoVeL1eeDyeor/NsC0QCGDSpEmQZbncSx/VIpEI2tra0N7ejtbWVuzevdt6n43H49b75+7du9Ha2or29na0tbUhEomUe+ljjqZpSKVSSCQSSKVS0DSt3EsiIiIa80q2U23OnDm4/vrr8bvf/Q7f/e538eCDD6KhoQFutxvpdBoHDhywjthdc801uCeOFLIAACAASURBVPTSS0s1NdGQFAoFaJoGXddht9utbnCHHwE1j36adXc0TSvqDkpERCeXQqGA6upqTJgwAcCHpSZyuVy/RgUOh8Mqml9dXW0dUaTjE4/HsX37duzatQupVAoAIAhC0RFcXdeRzWaRzWaRTqdRW1uLeDyO+vr6Mq9+bFBVFYlEAplMBqqqWjsEFUWB0+mE1+uFoijlXiYREdGYVLJQDQAeeOABnHXWWXj88cexd+9e7N27t+h6XV0dbrnlFu5So7JqamrCOeecg0QiAb/fb3WJMwzDGmPW3ikUCgiFQvB6vWhqairjqomI6GjM44aTJ09GKBTCnj170Nvbi2w2a31g4nA4rODN6/VC13VkMhk2oRmGZDKJnTt3oru7G5Ikwel0wmazFQWV5vtpJpNBKpXCzp07kUwmy7jqscPsVB6Px6GqKhwOB0RRRD6fRyKRgKIoyGazqKqqYj1jIiKiE6CkoRoAtLS0oKWlBXv37sW+ffvQ19cHp9OJCRMmYPLkyaWejkapnp6ess3t8XhQV1dndYAzjwINVFMtl8uhqqoKgUAAHo+nLOsOBoMjPicR0WijqipUVUV1dTXGjRuH+vp6hMNh9PT0QNM02O12BINBBAIBVFRUIJ1OW89hqHb8YrEYwuEwstksXC4XJEnqN8YsraBpGhKJhFV3jYZHVVVEIhFEIhEoioKamhoIgmBd9/l8SCaT1lFbm83GHWtEREQlVvJQzTR+/HiMHz/+RN2exoB8Pl+WeSVJQk9PD7Zv3w4AsNvtEEWx6BeBfD5vHfsEgNNPPx2SJI34mgf65YSIiI7MMAw4nU44nU4EAgGMHz/eOuLpdDqt76vmUUUant7eXqRSKei6bh31BNCv66r5mK7rSKVS6O3tLeeyx4REIoF4PA5FUeD1evtdFwTBejwej8PhcDBUIyIiKrETFqqZNR0OZ3ZT7O7uxpo1a/DVr371RE1PdESVlZVoaGhAR0cHstksBEEoCtCAD3eqCYIAu90Oh8OBhoYGVFZWlnHVRER0NIqiQFEUZDIZK8yRJGnADyfMWpput5shwzClUikYhgFRFFEoFKxGVR/d/Q18+HOgKIowDIOh5jBpmmbVUKupqTnqWI/Hg+7ubmQyGWvXJhEREZVGSd9VX3rpJfzP//wP2tvb+wVqA2GoRuUgyzIaGhpQU1ODVCoFVVX7das1wzRFUeB2u9HQ0MAOcUREJzGn0wmPx4NoNIpMJnPU+lFm8ObxeOB0OkdwlWOP2UXb/IDK3LH20QYR5m41QRCs7tp0/Myjyw6Ho+jI50DMeoLmcxiqERERlU7J3lU3btyIu+66q6jY+5FUVFTgk5/8ZKmmJhoSTdMQDAYxefJkHDp0COl0Gvl8Htls1uqY5XA4IEkSXC4XamtrEQwG2ZqeiOgkZrPZUFFRgb6+PiQSCStIODxwMAwD2WwWiUQCXq8XFRUV7Pw5TLW1tdZuP8MwYLfbUSgUij5cNbtqm++jiqKgtra2LOsdK8wA8/AdgZqmIZfLWY/LsmwFaKIoWs8hIiKi0ilZqLZ8+XIYhoELLrgAX/3qV+FwOHD11VfjiiuuwK233opDhw7h6aefxosvvoi6ujp8//vfL9XUREMWCAQwceJEuFwu9PX1oa+vDwCsujvAh+FvRUUFQqEQAoFAOZdLRESDUFFRgdraWnR0dCCVSiGbzVq7qAzDgKqqKBQKVqhTUVFR7iWPeo2NjQgEAti7d6+1Q82so2YewzWPh+bzeYiiiEAggMbGxnIvfVQzmz/k83moqopUKoVEIoFUKmX9LON2u+H1euF2u6HrOiRJKgrhiIiIaPhKFqq9++67kGUZP/jBD1BVVQUAmDhxIrZs2YIJEyZgwoQJmD17Nqqrq7F8+XL87ne/w5e//OVSTU80aOaRzqqqKlRXV+PgwYPo7e216gCKoghFUVBdXY26ujoYhsG6O0REo4AkSQgGgxAEwSqgr6qqdd0wDHi9XlRXVyMQCPAIYgm4XC6MGzcOO3bsQDabtQIdu91uBWqaplm717xeL8aNG3fU47l0bGYNwe7ubhiGgXA4jHg83q+Wnc/nQyAQgCAIGDduHH+WISIiKrGShWp9fX0YP368FagBwJQpU/DKK68glUrB7XYDABYtWoTf//73ePHFFxmqUVmYdXcURYHH40F1dTUSiQT6+vqsXwYqKirg9XphGAaSySTr7hARjRIOhwOhUAgejwfpdLrfzh2XywW3281ArUREUcS4ceNQX1+PSCRiBWhmHTWzC6gkSXA6naiqqsK4ceO4Y2qYzM7lfX19OHjwICRJgt1u77czMxaLIRwOo66uDuPHj2c9NSIiohIr2Tur+YnZ4ZqammAYBnbt2oUZM2YAAHw+HyZMmID29vZSTU00JIfX3Umn0/D5fAiFQqiurrZ2qtntduRyOcTjcdbdISIaZSRJsj4cyeVy1jFEWZb5vbzEJEnCtGnTAACxWAzRaBTpdNpqWCAIAkRRhMvlQmVlJfx+P6ZNm8ZQswRUVUU8HkcikUAgEIDP57N2BNpsNiiKYtUYdLvdRbs2iYiIqDRKFqrV19fjwIEDyOfz1g9K48aNAwBs377dCtWAD49fZDKZUk1NNGRHq7tTKBSQTqdZd4eIaJSz2WynzC7jnp6esswbi8UgSZJVfzSZTFrvq4c3/3G73fB4PLDZbJAkCbFYrGy71YLBYFnmLSVN05BIJKDrOkKhELLZrHUE1wwzHQ4HKisrEQqFoOs6EokENE3jbjUiIqISKtm76uzZs7FixQr86Ec/wt133w1BEHDGGWfAMAw899xzuPrqqyEIAvbt24f29nbU19eXamqiIWPdHSIiGmvy+fyIz2kYBnK5HBKJBBwOR9FOwMM7r9psNui6jnQ6jcrKShiGUZb1jpX381QqhVgsZh317OzsRG9vb7/un5lMBhMnToQgCIjFYkilUvD7/eVePhER0ZhRslDtxhtvxNNPP41f/epX+NOf/oQ///nPmD59OqZPn47Nmzdj4cKFmDJlCl566SUUCgWce+65pZqa6Liw7g4REdHw2O12+P1+VFRUQNM0OBwO+P1+qKpq7Zgyy4NomoaKigr4/X7ulhqmTCaDRCKBSCSCvr4+JJNJCIIAt9ttfd01TUMymUR7ezsqKirg8/mQyWQYqhEREZVQyX6iaWxsxE9/+lPce++9SCaTkGUZAHDvvfdiwYIFWL9+PTZs2ADDMOD3+3HHHXeUamqi48a6O0RERMdPlmUEAgH09fVZ3T/NcMdkNitwOp1wOBwIBALWz4l0/MLhMPbu3Yt8Pg+HwwGfz9evll02m0U4HEYsFoPH4yn3komIiMackn5MeOGFF+Kvf/0rtm7daj123nnn4ZlnnsHy5cvR0dGBCRMm4KabbkJdXV0ppyYallOp7g4REVGp2Gw2eDwehEIhxONxZLNZpNPpojFmyGYGP2ZtNTp+kiQhGo2is7MToVAINpsN6XQakiRBEATouo5sNgtJkiCKIjo7O1FfX8/d90RERCVWslDtN7/5DU477TTMnj0bH/vYx4quNTc3Y/HixaWaioiIiIhOEm63G9XV1TAMAy6XC1VVVchkMlZJBfNDK5vNhurq6qJdbHR8NE1DJpNBJpOBpmkQBAEul8vaqSaKIiRJgqZp/cYSERFR6ZQsVFu6dCmSySRef/11+Hy+Ut2WiIiIiE5ikiRZP/ulUilkMpminwV1XYfT6YTb7YbP5+NuqRJIpVLI5/NwuVxQVRWpVAqiKPY7/qnrOlRVhcvlQj6fRyqVKvfSiYiIxpSShWqxWAyTJ09moEZERER0ijE7f8qyDJfL1a9OqaIobP5TQrlcDrIsw+FwQBAERKNRK7w0w7RMJgNRFOFwOOBwOCDLMnK5XLmXTkRENKaULFSbNm0a2traEI1GUVlZWarbEhEREdEoYDb/KRQKbP5zgtntdgiCYB37NIM0QRAAAKIoWo87HA4kEgkIgsCuq0RERCVWsnfW733ve1iwYAGuv/56LFiwAOeccw6CwaDVRn0gLAxPRERENLaw+c+J5/f7oSgK8vk83G43ampqkM1mkc1moet60Q61VCqFSCQCRVHg9/vLvXQiIqIxpWSh2te+9jUIgoC9e/fi/vvvP+Z4QRCwbdu2Uk1PRERERHRKkGUZfr8fTqcTqqrC5/NZuwTNHYI2mw35fB6qqsLpdMLv90OW5XIvnYiIaEwpWai2c+fOIY03DKNUUxMNG4+qEBER0WgSCoVQW1uLbDaLSCRi1bUzFQoFZLNZ2Gw21NbWIhQKlXG1REREY1PJQrW//vWvpboV0YgxO2GpqsqiykRERDQqiKKI6upqTJ48GV1dXejp6UFHRwdEUbTG6LqO6upqBINBhEIhVFdXF10nIiKi4StZqNbQ0FCqWxGNiGw2i3g8jlQqZXXREkURhUIB6XTa6pLl8/ngcDjKvVwiIiIiAB+Gah6PB5WVlchkMsjlcpAkCaqqWjXVFEWBy+WCz+dDZWUlPB4PQzUiIqISYwsgOiXl83nE43HEYjHIsoyqqqp+Y9LpNGKxGIAPiy5zxxoRERGdDBRFgcPhsOql1dXVIZfLIZ/PW6GaJEmQZRl2ux2qqsLhcBy1gRgRERENXclCtXvvvXdI4wVBwPe+971STU80JKlUCqlUCrIsw+VyDTjGfNwcV1FRMZJLJCIiIhqQ3W6HpmnWjjSHw4FEIoFcLgfgw9rFkiTB4/HAMAyIoghN02C38/N0IiKiUirZO+vq1ashCMIRGxAIgmD926xbxVCNyqFQKFg11AbaoXY4l8uFSCQCVVVRKBTYvICIiIjKTtM02Gw2GIYBVVWhaRoEQYDf77fGFAoFJJNJ2O122O122Gw2BmsngKZp/Y7d8mtMRHTqKNl3/JtvvvmI17LZLHp6evCPf/wD8XgcCxcuxDnnnFOqqYmGJJfLWTXUBsOsrZbL5eB0Ok/w6oiIiIiOTlVVqKoKWZaRTqcBfLh7TVEU60Puw8M2WZat5zDwKQ1VVZFIJJDJZPqFak6nE16vl8dtiYhOASV7V73nnnuOOSaZTOLOO+/Eb37zG1x99dWlmppoSAzDsI5CDIYoitZziIiIiMpN13Ukk0nouo5gMAhBEJBOp5FKpaxwx+v1wuVyWQGbOZ6GL51OIxKJIB6PW/XqRFFEPp9HIpGAoijIZrOoqqo6YpkRIiIaG0b0oyqPx4MlS5Zg7ty5+MlPfoIf/vCHJbnvhg0bsHTpUmzfvh35fB5nnHEGbrnlFlx00UWDer6mafjYxz5m1aH4qFAohHXr1hU9ls1m8dRTT+G5557DgQMH4PV6MWfOHNxxxx2oqakZ9muiE0cQBAiCgEKhMKjxuq7DbrcXHWEmIiIiKhdd162dZ1VVVchkMgD+VW7l8L9dLpcV/jBUGz5VVRGJRBCJRKAoCmpqaop+RvT5fEgmk4hEIgA+bHbFHWtERGPXiO//DgQCmDx5Mt58882S3G/VqlW49957IcsyZs+eDV3XsXHjRixcuBCLFy/Gtddee8x7tLW1IZfLoampCWeffXa/6x8tUJ/P53Hbbbdh/fr1qKurw8UXX4zdu3fj6aefxtq1a/GHP/wB9fX1JXl9VHqyLBcdlziWXC4Hl8s16OOiRERERCMhm80iGo0inU4jk8lYu/A1TUNfXx9UVYXL5UI2my3zSseORCKBeDwORVHg9XqhaRpyuZy1Q1CWZXi9XgBAPB5n11UiojGuLEUVYrEYksnksO/T3d2NBx54AF6vF7/97W8xZcoUAMCWLVtw880346GHHsKcOXMQCoWOep/W1lYAQEtLC2699dZjzrtixQqsX78ec+bMwU9+8hMrbPnxj3+MpUuXYvHixVi6dOkwXx2dKOYnhmawdrRt+el0GrIsQ1EUNikgIiKik4IoihAEAfl8Hvv374eiKBBFsSjcsdlsSCaT6O3thcPhgCAIgy59QQPTNM2qoeb3+xGJRJDJZPqFak6nEy6Xywo72SCCiGjsGvF31qeeegqdnZ0YP378sO+1YsUK5HI53HTTTVagBgAzZszAwoULoaoqVq5cecz7bNu2DQBwxhlnHHOsYRj45S9/CUEQcN999xXtXrrzzjsxceJEvPbaa9i/f/9xvCIaKW63G263G7lc7og71tLpNHK5nDWWiIiI6GRghmqZTAbJZBKxWAzxeByapsEwDGiahng8bn2QnclkGKqVgHnkFgAikQjC4TCi0WjR1z0ajSIcDlvHPw9/DhERjT0l+8jkzjvvPOI1wzCQy+XQ3t6Offv2QRAEXHXVVcOe829/+xsA4JJLLul37dJLL8UjjzyCdevW4Y477jjqfcydaoMJ1bZv346uri5MmzYNjY2NRddEUcSnP/1pPPnkk1i3bh2+9KUvDfal0AiTJAk+nw8AkEqlEIlEIMsyRFGErutWd1C/3w+fzwdJksq8YiIiIqIP2Ww2GIaBVCoFRVGOWidWURSkUikYhsFd98Ok6zqy2SxisRg0TYMsy6iuri6qqebxeJBKpdDX1we73Q5JkljLjohoDCtZqPbnP/950GPnzZuHL3/5y8OazzAMtLW1QRRFTJo0qd/1CRMmQBRFtLW1wTCMIxaZNwwDra2tCAaDWLNmDVauXIldu3ZBURRccMEFWLRoUdH929raAADNzc0D3s8cu2PHjmG9PjrxHA4HbDab1WY+l8vBMAzY7Xa4XC4oigK3281AjYiIiE4qhUIBmUwGTqcTDofD2n2vaRp0XYfNZoPD4YAsy0ilUhBFEZlMZtBNmmhgoiginU4jmUzC5/PB4/H0GyMIAjweD5LJJOLxOFwuF3cIEhGNYSUL1RYtWnTU6zabDT6fDzNnzsS0adOGPV8sFkMul0NVVdWABeTtdjsqKyvR29uLVCo14JseAOzfvx/JZBLJZBL3338/Zs6ciU984hNobW3Fiy++iLVr1+KJJ57AueeeCwDo6ekBAASDwQHvZz7e29s77NdIJ54kSaioqEChULBCNUEQIMsyP80lIiKik1Iul4MgCJAkCU6nE5Ikwe12o1AoFNVUy+VycDqdKBQKEATBKmtBx8fcIZhIJFBbW3vUsS6XCx0dHQgGg/yZkohoDBuxUK3UzNbhTqfziGMcDgcAHDVUM+uphUIhPP7441bgp2kafvjDH+J///d/8fWvfx1/+ctfoCiKVX/rSPOacw6ms+SqVauwevXqY44D/nVElU4Mm8121P+XiIiIiE4W+XwegiCguroaTqcTqVQKiUQCsixDEARomoZ0Om19eKgoitXYgI6fGU56PB6k0+kj/n4BwLouCAJ3CBIRjWElb0OTzWaxZs0afO5znyt6fOXKlVBVFfPnz7dqWQ3HYLZRG4ZxzDGXXXYZ1q5dC1EUi7qE2u12fPOb38TGjRuxdetWvPrqq7j88suteY92nHSwc3d0dGDTpk3HHEdEREREZJIkCZIkQRAEKzTLZrPI5/PW8U/zaKjT6UQ2m7WeQ8dP13W4XC74fD7kcjkkk0k4HA7r6y6KIiRJQjabRS6Xg8/ng8vlYk01IqIxrKSh2oYNG/D1r38d8Xgc5557blFI9fLLL2Pjxo1YunQpfvjDH+L8888f1lwulwsAjtpNx7x2tB1IgiCgrq5uwGuiKOLiiy/G1q1b8f777+Pyyy+35s1ms8c9p6mhoQGzZs065jjgw51qiURiUGOJiIiIaOySZdk69pnP54tKWZjhjlnKIplMWsdEByqZQoMniiIcDge8Xi+y2Sx6enrQ19dndf8UBAF2ux0VFRUIBoNwOBxwOBysqXYCaJoGVVWt/98VRYHdXvL9IkREx1Sy7zxbtmzBLbfcAk3TMGXKlH7byz/3uc8hm83i3XffxW233YZnnnlmwAYDg+XxeOByuaw21h/9Jmq2tFYUZVg74wKBAIB/hWg1NTUAgHA4POD4Y9VcO1xLSwtaWloGtY4bbriBu9qIiIiICIqioLq6GpFIxPpA1+12F32oaxgGkskkVFWF3W5HdXU1FEUp15LHBEVRoCgKVFWFqqqIx+MDhmqiKMLn80EQBOs5VBqqqiKRSCCTyfQL1ZxOJ7xeL7/eRDSiSvaxyRNPPAFN03DzzTfjueeeQ2NjY9H1a665Br///e+xcOFCZDIZPP7448OaTxAETJ48GYVCAXv27Ol3vb29HbquY8qUKUe9z29+8xt87Wtfw4YNGwa8fuDAAQCwipGa9zO7gH7Url27isYREREREZWS3W6H3+9HMBiEJEnQdR29vb1IJBJIJpNIJBLo7e2FruuQJAnBYBB+v587eYbJDMyi0Sh2794NwzBQWVmJ2tpa1NXVoba2FpWVlTAMA7t370Y0GoUoivy6l0g6nUZPTw+6u7sRDoeRz+dhGAby+TzC4TC6u7vR09MzqNrWRESlUrJQ7a233kJVVRW+8Y1vHHXc1772Nfj9/iOGWENx0UUXAQBeffXVftfMxy6++OKj3mP//v14+eWXB2wYoKoq/vznPwMAPvnJTwIATjvtNDQ0NGDbtm04ePBg0Xhd17FmzRoIgmCtjYiIiIio1LxeL0KhENxuNxwOByoqKmC324uOIDocDrjdboRCIXi93nIveUwwd6glEgmIoohgMIhAIGD9CQaDEEURiUQC8Xj8qKVqaPBUVUUkEkEkEoHNZkNNTQ38fj+8Xi/8fj9qampgs9msMfy6E9FIKVmolkgkUF9ff8yW0Xa7HePGjUNfX9+w52xpaYGiKHjiiSfw/vvvW4+/9957WLZsGRwOB774xS9aj3d2dmLXrl2IRCLWY1dffTVsNhuef/55K0ADPuyq9OCDD6KjowOf+tSncOaZZ1rXrrvuOhQKBXz7298u+iTk0UcfxZ49e3DppZeiqalp2K+PiIjGhkKhgEwmg3Q6jUwmw05wRDRs5hHQpqYm1NTUwO12o6qqCoFAAFVVVXC73aipqUFTUxOPfpaIpmlIJBIoFApobGyEw+FANBpFMplEKpVCMplENBqFw+FAY2MjCoUCEokENE0r99JHPTOkVBQFXq+3X9M4QRCso59m6ElENBJKthe5pqYG+/fvR6FQOGqwpus6Ojo6UFFRMew5Gxsbcc8992Dx4sW47rrrMHv2bBiGgY0bN0LTNCxZsgTV1dXW+HvuuQebNm3CokWLcPvttwMAJk+ejG9961v43ve+hzvuuANnnXUW6uvr8c9//hOHDh3CpEmT8P3vf79o3ptuuglr167F+vXrMW/ePMycORPt7e3YsWMH6uvrcd999w37tRER0eiXz+eRSqWgqipyuZxVc0eWZSiKArfbzW58RKOYWUu3nMzOkjabraimsfm9Rdd1JJNJJJPJsqzPNJh6wye7VCqFWCwGp9OJuro6ZDKZo3ZdPXjwIGKxGFKpFPx+f7mXP2ppmmbVUDPrW2ua1q8xh91uh8fjQXd3NzKZzIB1t4mISq1k32Vmz56NVatW4ec//zkWLVp0xHG//OUvEY1Gcfnll5dk3i996Uuor6/HsmXL8NZbb0GWZcycORO33nrroDuMfvnLX0ZzczOWLVuGLVu2YPv27aivr8dXvvIV3HLLLXC73UXjZVnGk08+iV/84hd44YUX8NprryEYDOLaa6/FokWLrG/2RER06spms4jH40ilUsjlcpBlGaIoolAoIJ1OQ5Zl5HI5+Hw+OByOci+XiI7TR5tzlYPD4YAkSf3Ce5vNZtWcKqex8uGBGaKZzQcURTli11Xgw92E2WwWmUyGodowmI0hHA4HcrkcUqkUMplMv6+70+m0jkObz2GoRkQnWsm+y9x44414/vnn8bOf/Qzt7e1oaWlBc3MzXC4XMpkM2tra8Oyzz+K5556D3W7HwoULSzU15s6di7lz5x5z3PLly4947fzzzx90CAcATqcTd955J+68885BP4eIiE4N+Xwe8XgcsVgMsiyjqqqq35h0Oo1YLAbgwx0mY+WXTiIqD3OXFJ14hx89PNrX/aNHFOn46LoOXdehqqp1zNYM2QRBgKZpSCaTVohpt9ths9msXZxERCdSyUK1KVOmYPHixbj//vvx4osv4qWXXuo3xjAM2O12PPjgg5g2bVqppiYiIjqppFIppFIpyLIMl8s14BjzcXNcKcoiEBHRiWMe7QyHw9aOwCMxDAPZbBaBQIBh5zCJooh8Po9oNIpCoQBZlhEIBIq+/h6PB6lUCn19fdYHVaJYsvLhRERHVNLvNPPnz8ezzz6Lf/u3f0MwGIRhGNafiooKfOELX8D//d//4aqrrirltERERCeNQqFg1VA7UqBmcrlcyOVyUFWVzQuIiE5ybrcbfr8fhmEcs0ZdMpmEYRjw+/39SsnQ0CiKAk3TEIlEIEkSPB7PgI0KPB4PJElCJBKBpmlszkFEI6Lkh8wnTpyIBx98EACQy+UQjUbhdDrx/9m78yip6jN//O+699a9tVd1Nw00iyxKyRL3iHxNMOKSxUSjRKMx8agHxYQxMTPOBDWJZHA8CRNN4hbJYDQenSSGERS3BOMSFRcyihIioggM0Ht3dXdV3apbt+7y+4Pf/aSLbqDpLrqo5v06h0Os+nTVrYfK7arnPp/nicVi5X4qIiKiw45pmqKH2kB4vdVM02Q1AxHRYUxRFIwaNQqdnZ3o7u6Gz+dDOBwuSfC4risqpmpqajBq1Cj29SIiGsHKXhNrGIbY+qmqKsaMGYNYLIbHHnsMjzzyCNLpdLmfkoiI6LDhVWgPdNuJJEniZ4iI6PA2atQoTJw4EcFgEJlMBh0dHchkMshmsyX/HQwGMXHiRIwaNarSh1z1vIEDtbW1KBaLogqwN696sFgsora2FoqioFAoVOiIiehIUtbLJq+//jr++Z//Gel0GqeccgrGjBkj7nvuuefw1ltv4f7778edd955UEMBiIiIqoXP54PP5xvwdk7HcaAoChtaExFVAU3TMH78eEiShJaWFqTTaeTzX7RujgAAIABJREFUeXG/1/Zm7NixaGho4BbEMnAcB36/HzU1NWIoQWdnJzRNg8/ng+u6KBQKoj+poijw+/0cVEBEw6JsSbWNGzdi4cKFsCwLyWSyz+ju8847D4Zh4N1338WiRYvw+OOPY+rUqeV6eiIiosOCqqpQVRW5XG5A673eawPdLkpERJUVCoUwYcIExONxZDIZZDIZWJYFRVEQjUbFHybUykOSJEiSBE3TEI/HEQgEkM/nYZomHMeBLMsIhUIIBoMIh8MwDEP8DBHRoVa2pNqKFStgWRauvvpqLF68uM/9X/3qV/HVr34Vd9xxBx544AH86le/wrJly8r19ERERIcFWZahaZpIrO1vWEEul4OqqtA0DbIsD+NREh06tm3DNE0xHVFVVb6/acTRNA2apiGRSKBQKMBxHJH4YQ+18vJinclkEIvFxOACL6kmSRJUVYWiKHBdF93d3YhEIkxqEtGwKNsZ/+2330ZtbS3+9V//db/rvvvd72LlypV4/fXXy/XUREREh5VwOAzTNNHT0wMA/SbWcrkcTNPkZDgaMYrFInRdF9NveyfVNE1DOByG3++v9GGOWExmVoZlWdB1XVSqybLMpFqZKYqCYDAITdOQzWYRjUahKEq/cc5ms9A0DcFgkP8ORDQsynamyWQyOPbYYw/4y1tRFEycOBFbtmwp11MTEREdVvx+v5h6res6UqkUVFWFJElwHEdMB43H44jFYkw0UNUzDAPpdBq6rov3tyRJsG1bVGSapolYLIZAIFDpwx1RmMysjHQ6jebmZnR3dyOTycC2bciyjGg0ikQigYaGBvF7gIYuGo3CMAykUikAQCQS6TN1NZvNolAooLa2FtFotFKHSkRHmLIl1UaPHo1du3aJXyj74jgOGhsbkUgkyvXUREREh51AIABZlqGqasmXXUVREAqF+GWXRoxisYh0Oo2enh6oqora2to+a3K5nKjclGWZ7/syYTKzMjo6OrB9+3Y0NzdD13VEo1HIsoxisYj29naEw2Gk02lMmTKF0z/LRNM0cW5Jp9Noa2tDIBAQF6sMwxBramtrufWTiIZN2ZJqc+bMwapVq3D//ffj+uuv3+e6hx56CF1dXfjiF79YrqcmIiI6LPn9fiQSCW7LohFN13Xoug5VVffZQ9C73VvHi6tDx2RmZaTTaWzfvh07duxAJBLBtGnTShriO46D9vZ27NixA8Ce4TWsWCuPUCgEWZbFoAKvl53f70ckEkEwGOSACCIadmVLql155ZV46qmncN9992H79u2YP38+pk2bhlAohHw+j61bt+LJJ5/EmjVroCgKrrnmmnI9NRER0WFNlmUEg8FKHwZR2dm2LSox+0vq9BYKhZBKpVAoFA64s4EOjMnMymhubkZzczMikQjGjBnT535JksTtzc3NiMViTKqVkTe0wLIsDoggosNC2c48yWQSS5cuxa233opnnnkGzz77bJ813raX2267DTNmzCjXUxMRERFRBZimKbYdDoS3HdE0TSaah4DJzMowDAPd3d3QdR3Tpk3b79r6+np89NFH6O7uhmEY3H5bZvsaVEBENNykAy8ZuAsvvBBPPvkkLrnkEtTX18N1XfEnkUjg/PPPx//8z//goosuKufTEhEREVEFeJ/zem9/2x9JksTP0OANJZlJg5dOp5HJZBCNRsV73rZt5PN56LqOfD4P27YB7HmvR6NRZDIZpNPpSh42EREdQmVP70+ZMgW33XYbgD2/8Lu6uhAMBkXZcyqVwoMPPojHH38czzzzTLmfnoiIiIiGic/ng8/nE4mEA3EcB4qilEzto4PHZGZlWJYlqv1M00Qul4NhGCgWi2Ibot/vRyAQEP2/bNuGZVmVPnQiIjpEDmnNrKqqoqfAq6++ipUrV+Kll17iLxYiIiKiEUBVVaiqilwuN6D1pmkiFAoNuMKK+tdfMtO2bRSLRTEQxe/3i62eTGaWh6IokGUZ2WwWAMTUVU3T4PP5YFmW6F9XKBSQy+UQj8e5TZGIaAQ7pGf45uZmPP7441i1ahWam5sBQPyiP+200w7lUxMRERHRISbLMjRNE4m1fTXMB/ZMolRVFZqmsa/XEPVOZpqmCcMwkMvlxPZDbzhKKBRCIBBgMrNMYrEYVFVFc3Mz6urqoGkaamtrS5KV4XAYuVwO3d3d6OzsRH19PQcVEBGNYGVPqlmWhRdeeAErV67EG2+8AcdxRKl5fX09LrroIlxyySWYOHFiuZ+aiIiIiIZZOByGaZro6ekBgH4Ta17yJx6PIxwOD/chjjheMtNxHOzevVsk1fbmJdVisRiTmWUQCASgqip8Ph+y2Wy/QyJ8Ph/C4TA6Ozvh8/mgqiqHFBARjWBlS6pt27YNK1euxJNPPomuri4AEMm0SCSCZcuW4cwzz+QvcyIiIqIRxO/3i0ocXdeRSqWgqiokSYLjOKKhfjweRywWg9/vr/ARjwx+vx+GYWD37t3w+XwIBoNiG6LruigUCujs7ITrupg6dSrjXgaWZSEWiyEWi0HXdbS3t6Ourq6kt53jOOjs7IRt22KtZVncAkojgmVZKBQKooegpml8b9MRb0j/DzAMA8899xxWrlyJDRs2APjH9s4TTzwRX/rSl3DbbbchEong7LPPLssBExEREdHhJRAIQJZl0UvKNE24rgtFURAKhaBpGsLhMBM7ZeRt91QUpd9+aT6fD5qmwbIs5PN55HI5VgkOUaFQgKqqOProo9HV1YX29nZ8+OGHfZJq8XgcEydORE1Njfj/BBMPVM0KhQIymQzy+XyfpFowGEQ0GoWmaZU+TKKKGNTZfdOmTVi5ciWeeeYZ6LouKtKmT5+OL37xi/jiF7+IcePGAYCYBEpEREREI5ff70cikYBpmtB1XVTnhMNh9vIqM9u20d3dDcMwMHbsWEiSBNM0YVkWHMeBLMuIRqNQVRWO40DXdXR3d6O2tpa7RobAcRw4joO6ujqoqoru7m4Ui0WkUinRy662thZ+vx9jxoxBNBoVP0NUrXK5HFKpFNLpNAqFAgKBACRJQrFYRCaTgaZpMAwDtbW1++2rSTRSDSqpdvHFF4vS8hkzZuDcc8/F5z//eUydOrXcx0dEREREVaBYLELX9ZJKNZ/PJ6YjslKtfPL5PLLZLHw+H2KxGHw+HxzHQbFYFBUkfr8fkiTBdV3kcjlks1nk83lEIpFKH37VkiQJkiShtbVVJNRc10U4HBbvd9d1USwW0draikKhgDFjxpRUshFVk0KhgFQqhVQqBU3TMHr06JKq2Fgshmw2i1QqBeAf/R6JjiRDqkP+9Kc/jc9//vP49Kc/jbFjx5brmIiIiIioihiGgXQ6DV3XRQ81SZJg27aY+mmaJmKxGJu2l0GhUBBbEb0vuN5WrL15zfK9n2FSbfA0TUOhUMC2bdvQ0dEBy7IgSZL4N/D5fJAkCe3t7ejq6sKoUaOQSCSYZKCqlclkkE6noWkaotFon/t9Pp+4PZ1OIxAI8P1OR5xBJdUuuugirF27Fq+99hrWrVsHYM/Wzy984Qs477zzMGHChLIeJBEREREdnorFItLpNHp6eqCqar8TEXO5nJgOKssyK9bKxGvBUq51tH+KoiCTyaCpqQnd3d1i21soFBJJZC+x4FVsTps2jf3UDgE2zD/0vH6MhUIBo0eP3u/aSCSCtrY25PN5DuagI86g3u0//vGPsWTJEqxduxZPPvkk3nzzTWzevBkffPABfv7zn+P444/Hl770JXz+858v9/ESERER0WFE13Xoug5VVffZT8e73VuXSCSG8xBHHE3ToGka8vm82Ha4L952xHA4zAqSITIMA93d3ejo6EChUIAsyxg3blxJn7pEIoG2tjbRV9DrfccKzfJgw/zh41W3BgKB/Z5jgD0Va14ymYM5yo9J5MPboP8lAoEALrjgAlxwwQVob2/HmjVrsGbNGmzZsgXvvfceNm7ciJ/85CcA9lzBzGazLDcnIiIiGkFs2xYVOf1VqPUWCoWQSqVQKBREU3canGAwiEgkgq6uLuTz+f02B/cSb5FIBMFgcBiPcuRJp9Nobm6GLMuor6+HpmnIZDJiG67rumKbs6Zp0HUdzc3NonqNhoYN84eXN2Sjd09AwzCQzWZFNVokEhHvbUmSOJijzJhErg5lSW/W19djwYIFWLBgAT744AOsXr0azzzzDDo6OgAAqVQKn/rUp3DWWWfhwgsvxNy5c9mwk4iIiKjKmaYpeqgNhNdbzTRNJniGQJZlJBIJdHd3I5PJwOfzwe/3w7Zt8aVLlmWRbIhGo0gkEkxkDlGxWISmaRg3bhwmT54M0zRRLBZh27aoGPS2N6uqih07dkDTNBSLxUofetVjw/zh5w3m8Lb4t7e3o7u7G7qui/NMOBxGIpFAfX09XNcVA1Jo6JhErh5lrxmcPn06br75ZixevBivvfYannzySbzwwgswDAN//OMf8cc//hF1dXU4//zzsXjx4nI/PRERERENE9d14brugL9EedMo2eNr6BKJBMaOHYsdO3agtbVVTKL0vux6ibZwOIyxY8eOqC237e3tFXnetrY2FAoF+P1+kUBwXReWZYm4K4pScn+hUEBbW9uAE8/lVl9fX5HnLTc2zB9+3jbzXbt2Qdd1dHR0IJfLIRKJQJIkWJaFVCqFzs5O9PT0IBwO45hjjmHcy4BJ5OpyyDbiSpKEM844A2eccQay2Sz+9Kc/4YknnsD//u//oqOjA7/5zW+YVCMiIiKqYj6fDz6fD7ZtD2i94zhQFOWA/XnowPx+P6LRKAKBAFpbW5FKpUr+HWRZRm1tLerq6hCNRkfccIhKVH95SYSOjg44jgNd15HL5VAoFESlmqZpCIVCCIfD2L17N0aNGgXLsipyvCPl35wN8ytDURQUi0W0traipaUFo0aNwpQpU0ouotTX16OzsxNbt27F2LFjMWnSJMa8DJhEri7D8o6PRCL4yle+gq985StoamrCE088gaeeemo4npqIiIiIDhFVVaGqKnK53IDWm6aJUChUsaqdkcTbApTNZiFJEoLBICzLEskdRVEgSRKy2SwymQzC4fCISbJUSigUQiQSQUtLC/7v//4PruuWbMuybRupVEpU9Xi97Lg1a2j6a5hvWRZM0xQVgqqqioQ9G+aXj67ryOfzYhDN3hdEfD6fOKfn83noul6hIx05mESuPsMe9XHjxmHRokVYtGjRcD81EREREZWRt+XES6ztL3mQy+Wgqio0TWNvrzLo7u7Grl27RDWD3++H4zgiqeb1Q0qn09i1axcCgcCI2QpYKeFwGLFYDLlcDrquIxKJoL6+vuT9HA6H0d7ejmw2K9aHw+EKHnX1690wv1AoiETP3km1YDCIcDjMhvllYhiGuGAyadIk5PN5sR3RG8xRKBSgqiomTZqEtrY25HI5TrsdIk5drT6MOhERERENWjgchmma6OnpAYB+E2u5XA6maSIejzPBUAa2baO9vR0tLS0A/tFQHIBIMniJBdu20dLSgkQigdraWiY0h8CrzPSGbASDQXR1dUFVVRFvbwiH67oIBoPiZ2jweieIverM3kkHy7KQzWZF43ZJkhCLxdgwf4i8eNfX16Ourk4kzIrFIhzHgSzLCAaDCAQCCIVCcF1XbFtkUm3w+pu6uj9MIlcek2pERERENGh+vx+xWAzAnq1CqVSqT5JBVVXE43HEYjFuQSyDfD6Pzs5O6LqOcDgMy7LE1h8vydB7K5Cu6+js7EQ+n0ckEqnw0Vcv0zShKApisRgikQgsy4KmacjlciLeXny9iilFUQ5qQi715fWKamtrE9WxNTU1JcmdmpoaGIaBrq4u2LaNWCzGHlNDZFkWbNuGLMsiOWzbdp8KQS9RL8sybNuGZVkVPvLq1nvq6kA4jsOpqxXGpBoRERERDUkgEBBfvAqFAkzThOu6UBQFoVAImqaxp1cZ5XI5dHd3i21Wfr8fkUikZKuQtzXLsiwYhoHu7m4xuY8GR9d1uK6LiRMnIpvNoqWlBZ2dneL97vP5kMvlUFdXh7q6OkQiEbiuC13XmVQbAkVRRDIH2JO8aW1tha7rIrkTDocRDoeRz+cB7Knm5Fa4oVEUBbIslyR3vOq0/ti2Db/fz7gPkTd1NZPJIBaL7XcLqOu6MAwDkUiESeQK4jueiIiIiIbM7/cjkUiIL79ekqF3JQOVR7FYhK7r4ktsf1utvF47wJ4vu7quV2QC5UjiVe54E2/z+TyKxSJc14XrugD2/Nvk8/mSdazcGRqvCtBxHHR3d2P37t0oFAp9egh6k1cTiQQURWHj9iGKxWKIRqNob28/4HZEx3HEVlGvcpkGR1EUBINBaJqGbDaLaDS6z8Ec3rbnYDDI93oFMfJEREREVDb7q2Sg8pAkCa7rwjRNUZ3g9U/zvnTJsiwSDV6Sk9uDhsbbytnY2Ih8Po9gMIhoNCqSaj6fT2y/7ejogK7rmDp1Kr/sDlGhUIBhGACArq4uUR3Yu/rP+++6ujokEgkYhsHG7UMUCASQSCTE8I0xY8bsc217ezvC4TASiQT7qZVBNBqFYRhobm5GV1eX2A7qnd+9wTSyLKOhoQHRaLTSh3xEGxFnmddffx3Lly/Hli1bUCwWMWvWLCxcuBBz584d8GO8++67WLFiBTZs2IB0Oo1EIoHTTjsNixYtwtFHH91n/ZVXXok333xzn4+3du1aTJo0aVCvh4iIiIhoXxRFEX3rstms+MJl27ZI7siyLL549a5soMELh8PI5XJoampCNBpFTU2NmITo8bbddnV1oampCWPHjuVwjiFyHAednZ1ob29HoVBAMBiEJEklFbGBQACapqFQKKC9vR21tbU46qijKn3oVa+hoQHpdBo7duwAANTW1sKyLHFeURQFqVQK2WwWkydPRkNDQ2UPeITQNE0kJ9va2qDrOqLRqOhbl8lkEA6H0dDQIN77VDlV/5t11apVuPnmm6GqKubMmQPHcfDWW2/hmmuuwdKlS3HppZce8DHWrFmDm266CbZtY+bMmTjppJOwdetWPP3003jhhRewYsUKnHrqqSU/88EHHyAUCuHss8/u9zH5y5OIiIiIDgWvgqS1tRWdnZ2QZVkk0YB/JHZs24Zt25AkiRUkZVIoFFAsFqEoCgKBgEgw9N6GGAgEoCgKisUiCoVCpQ+56jmOg46ODjQ2Npb0ZexdueNVrdm2jcbGRtTV1XEaYhnEYjFMmTIFhUIBO3bswIcffghZlsX73bZt1NXVYfLkyZgyZQq3fpaJV53p8/kwevRoyLJcsv1zzJgxYou5V5XJxFrlVHVSra2tDUuWLEE0GsVvf/tbJJNJAMDGjRtx9dVX4/bbb8eZZ56531LVVCqFH/3oR3BdF/fddx/OOeccAHs+jPzXf/0Xfvazn+F73/se1q5dK07ijY2N6O7uxumnn4477rjj0L9QIiIiIqL/n6IoiMfjCAaD6OrqEoMi9lYsFmEYBmpqahCPx1mpNkQ9PT0A9iQaLMtCS0sLAoFAn6SaYRhwXVckGHp6elBfX1/JQ69qlmWhu7sb7e3tiMViME0T+XxefDfzkm7BYBCqqiKdTqO7u5u97MokFAohEolAkiTYto1cLieSO5qmQZIkRCIRhEKhSh/qiJHJZJBOpxGNRvfbU81bx2q1yqrqxgqPPvooTNPEVVddJRJqAHD88cfjmmuuQaFQwGOPPbbfx1i7di10XccFF1wgEmrAnuau1113HWbOnImmpiZs2LBB3Ld582YAwKxZs8r8ioiIiIiI9k9VVYRCIYRCIdTU1CAQCMB1XRSLRfHHdV0EAgHU1NSItZxAOTSGYUCSJBFzXdfR1NSEnp4eZLNZ9PT0oKmpCbqui9h7STYavHw+j+7ubui6LraAapoGx3FEtVrvrZ+6rqO7u1tMAqXBKxQKaGxsRFtbG+LxOKZNm4YZM2aIP9OmTUM8HkdbWxsaGxtZmVkGlmUhn8+jUCiIac3eJG0veeldIIlEIigUCsjn80wiV1BVX6569dVXAaAkGeY599xz8Ytf/AKvvPIKvvOd7+zzMRzHwcyZMzFnzpx+7580aRLef/99tLW1idvef/99AEyqEREREVFl+P1+xGIxJBIJmKaJbDbbp6daJBKBqqpwHKdk2xwNjlepA+yp3vH6G3n97GRZRiKRgCzL0DRN3M4BEUPjvb8LhQIsyxJbbvfuZSdJkpjQms1mYZpmBY96ZOjo6MCuXbuQz+dRU1MDWZbFdFtvKI1t2+jq6sKuXbsQCoUwfvz4Sh92VSsUCigUCggEAiXv8f54/QS9n2E1cmVUbdRd18XWrVshSRKmTp3a5/7JkydDkiRs3bpVfLjoz+WXX47LL7+83/scx8Hf//53AMDYsWPF7V5SraenBwsWLMDf//53FAoFfOITnzjoAQlERERERAfDNE3Isoz6+noUCgU4joN4PA7DMMT2IO8LmbdFy+vJw8msg+dtgUun0zjqqKMwatQoFAoF5HI5kWQIhULQNA35fB47d+5EbW2tqDahwXEcRyTWEomEqMrZe/qnN8DDS6ixp9rQeFNsvS3mHR0dSKfTyOfz4jwTDAYRi8WgaRq6urrQ0dGBMWPGMLkzBI7jiPgOhCRJ4meoMqr23d7T0wPTNFFbW9tvKbuiKKipqUFnZyd0XR/UL7NVq1Zh586dGD16NE488URxu7f9c8mSJUgmkzj11FOxY8cOrF+/HuvXr8ctt9yCK6+8cvAvjoiIiIhoH1zXhaIoiMViosdRoVBANBotWaNpmqioUhQFrutW8KirXzAYRCgUguM40HUdtm2Lqilgz5fhfD4vetk5joNQKMREZhl41VGWZSEQCMC27ZLpn8FgUCSObdvm1s8y0HUdPT09yGQyyGazSKVS0HUdqqrC5/PBdV10dnYiHA6jtrYWruuip6cHuq4jHo9X+vCrliRJYqLzQHiVyKyIrZyqTap5J8r9/ZLyJhwNJqm2adMm3H777QCAG2+8UWTbU6kUWlpaoCgKli1bhi996UviZ5599ln827/9G5YtW4bZs2djxowZ+32OVatWYfXq1QM6Hi+RR0RERERHNp/PB5/PB7/fL7Z4elMpvQoHv98PTdMQDAZRKBTEz9Dg2baNRCKBRCKB9vZ2qKqKQCAgLvC7rgvDMGAYBkzTFGu9LaM0OMFgEBMnTkQ+n0c0GhXx3nv7J7CnYi2TyWDixIlMZg5RPp9HZ2cnOjs7RT9B73zSO5lpGAZ2796NQCCAeDyOfD7PpNoQaJoGTdOQyWQQi8X2e972zjmRSISDCiqoapNqA8nEDvZq3MaNG3Httdcil8vhsssuw4UXXijuq62txRtvvIF0Oo3JkyeX/Nx5552Hd999Fw8//DB+97vfYenSpft9nsbGRqxfv35Qx0hERERERyZVVaGqKnK5HCKRCPx+v+jt5X3Z9fv9YiJoJpPhoIIycF0XkUgEtbW1osdX761ajuPAMAwUi0X4/X6x9XOkVAi2t7dX5HlTqRRUVUUkEoEsy5Akqd/thZZllfQS9H6uEkbKtNdUKoWOjg74/X74fD50dHT0mXbrvce97bk0NIqiIBgMQtM0ZLPZkgrkvWWzWXHxhFtuK6dqI++N7N3fhBHvvoO5SvGXv/wF3/3ud5HL5TB//nwsWbKkz5ra2lrU1tb2+/Pz5s3Dww8/LHqx7c/48eMxe/bsAR3X5s2bkclkBrSWiIiIiEYurxG+l1jztnh6SbTecrkcVFUVfdVo8Hw+HxzHQSQSwdFHH410Oo1MJiO2JXpNw+vr60Wfqb0b6le7gW5JKyfTNNHe3o6mpiYEAoGSCbceL5Hs9/thGAbC4TBM06zI8Y6UoSA+nw+5XA7Nzc2ora0VSeS9KwQNw4CmaUilUhg3btyIer9XSjQahWEYSKVSAPb0c9w77l5iv7a2dr+JNzr0qjap5o2T7erqgmVZfTKzlmWhq6sLmqYhFosN6DF///vfY+nSpbBtG1dffTUWL1580CcF76rEQEZnz58/H/Pnzx/Q415xxRWsaiMiIiIiABBJg56eHgD/uODcWy6Xg2maiMfjCIfDw32II44sy3BdF5ZlYcKECSgUCtB1HZlMRlSrRaNRhMNhaJqG5uZmuK7LZOYQeUnkQCAgEla2bYu+Xt7fXlINAJPIZWDbNnRdRzabheu6CAaDcF23T6Wa11tN13XRa5CGRtM0UcSTTqexc+dO2LYtzjOyLCMej4tiH279rKyqTar5fD4cc8wx2LhxI3bs2IFjjjmm5P7t27fDcRwkk8kBPd69996Le+65B5Ik4eabb8ZVV13V77rXX38dq1evxqxZs/pds3v3bgCl00KJiIiIiMrJ7/eLC8e6routbt42RNM0oaoq4vE4YrHYiKmeqSQvkRMKhWDbtkig1dTUlPSykyQJ+XweoVAIPp+PSYYh8mKuqips24amaYhGo316CJqmKaaCerGnwbMsC4VCAZZlIZ1OwzRN+Hy+PlOGvWo113XFehq6UCgEXdeRTqexe/duNDc3i63lDQ0N8Pl8qK+v7/eCCg2vqk2qAcDcuXOxceNG/PnPf+6TVPvzn/8MAPjMZz5zwMd55JFHcM8998Dv9+M///M/cd555+1zrWEYWLNmDd5++2184xvf6FMh98QTTwAAPv3pTx/syyEiIiIiGrBAIABZlsWgAm8aoqIoCIVC0DQN4XCYCbUycV0XgUAA4XBYbCsMBAIlVSKu64oJoOFwWCQdaPACgQCCwaAYQlcsFvu0xTEMA8FgUFRk9l5Pg2OaJhzHgW3bcF0X6XQahmGUfP/1prEGg0GxPdo0zQoe9cjR0dGBTZs24YMPPkBzc7P495AkCR0dHejo6EA+n8dxxx2HUaNGVfpwj2hVPXd1/vz50DQNK1aswKZNm8Ttf/vb3/DAAw8gEAjg8ssvF7c3NTXh448/FnuTAWDLli1YtmwZAGDZsmX7TagBe5Jl48ePR2NjI37605/9u+RDAAAgAElEQVSWXHl6/PHH8dxzz6G+vh4XX3xxuV4mEREREVG//H4/EokERo0a1edPIpFgQq2MfD6fqIKKRqNwHAfd3d3I5XLI5/PI5XLo7u6G4ziIRqOiuooVU0PjVaqFQiExgMObuur3+8UUVlmW4ff7xVrGfei8Lc5efzovaeYl8R3HQbFYhGma0HV9v/3OaeDS6TTeeecdrFu3Dtu2bUM+n4eiKFBVFYqiIJ/PY9u2bVi3bh3eeecdpNPpSh/yEa2qK9UmTJiAxYsXY+nSpbjsssswZ84cuK6Lt956C5ZlYdmyZairqxPrFy9ejPXr1+P666/Ht7/9bQDA8uXLUSwWEYvF8NJLL+Gll17q97kuu+wyfPKTn4SqqrjjjjuwYMEC/OY3v8GLL76I6dOnY9euXdi8eTNCoRDuueceNgskIiIiomEjy/JBDeeig9d76mo0GhX/O5/Pw7ZtMXkyFAohEAggm82Kn6Gh8eLoVWJKktSnx5TjOCVraWgCgQDq6uowdepUkVD2qmG9nmp7315XV8cKwTL46KOP8M4776C5uVn0xPQS9K7riiRmc3Mz3nnnHdTV1eGUU06p9GEfsao6qQYAX//61zFu3Dg88MADePvtt6GqKk4++WR861vfwv/7f//vgD/vNf9Pp9N46qmn9rnu9NNPxyc/+UkAwMknn4zVq1fj/vvvx7p16/DSSy+hpqYG8+fPx6JFizBx4sTyvDgiIiIiIjos9J66ms/n91sJlc/nOXW1TCRJgiRJYiuzaZrIZDKwLEsk1RRFEYlOr7egJFX1piwAQHt7e8Weu6urC8Ce972iKNA0rd/EvZfM9HqpecMCK8EbGljNDMPA5s2bsW3bNiQSCTGwwBsCIcuyGIZiWRa2bduGzZs3Y9asWUxoVkjVJ9UAYN68eZg3b94B1z3yyCN9blu3bt2gnnPy5Mli2ygREREREY184XAYmUwGra2tsCxLJNr8fj9c10U2m0VPTw8URcH48eM5dbUMHMeBLMuQZRmSJIl+dl6vOu/vYrEotoH2rlyrdt7rHW6ZTAY7duzAtm3bRJLMsqySZLJXOQjs2SqqKAoymUzJbrHhMlK2ure1tWH37t3i/dzU1IR0Ot0nqRaLxRCJRFAsFrF79260tbXhqKOOqvThH5FGRFKNiIiIiIiIRi7TNEuatfdut+Pd7k2opKHzBhR4lWqWZcF13ZLpnl4VZu/7Oe12aDo6OtDV1YVCoYDdu3ejo6MD3d3dkCRJbLt1HEf00iwUCujq6kJHRweTahXCpBoREREREdEA6LoOy7JQV1cHSZJQKBREA3dZlhGPx6FpGhzHgWVZ0HUdiUSi0odd1bw4e4mzmpoaABB9vHw+H+LxOAAgl8uJRvojYftnJdm2LfqmWZYFSZLg9/tLthjatg1JkkQFm8/nY1JtiEzTRE9PDzo6OsR72XEcMYnVq9hsb29HOp2GqqqIxWKculpBTKoREREREREdgG3bIrnj9TmybRvFYlEkd7zplACQSqVQKBTEF2EaHG/CpGVZYhuid1vvnmqSJEFV1ZJEJw2e3++Hoijw+Xwivt6f3hVTvf8oijJitmFWSiwWg67raGxsFOeNYrFYcg6xbRt+v19sCR0zZgxisVilDvmIx6QaERERERHRAXjbD3tPlvT6d/XHm4pomiYnsw6Btw3R5/NBkiR0dnYil8uJhI/rukilUgiFQqipqRG3sWJqaAKBgEimRSIR+P1+GIYBx3FKksiBQADFYhHd3d1QFIXN8ocoHA5D13V0dXUhEAggGAxCUZSS84xXEZjP52EYBnRdZ//GCmJSjYiIiIiI6ABc14XrugPeVuhV9HiN9GlwLMsS/bp6enoAAMFgsKR3mlcd1dPTA1VVxc/Q4Pl8PkSjUYRCIfFe1jStpELQG9AhSRJCoRCi0Sh72g2R4zgYN24cZs2aJYYSeMnN3mu87eW2bWPcuHGszKwgJtWIiIiIiIgO4GB7Rnnb4ZhkGLpisQjDMGDbNmKxmEju9K6YKhQKSKfTYmsoDY0kSYjH44jH4ygWi2JbrTft09sO7ff7oWmaWDsSetm1t7dX7LkbGxuhaRpGjRolqjMlSeozddWLu5fsbGxsRCQSqcgx19fXV+R5DxdMqhERERERER2AqqpQVRW5XG5A603TRCgUKtkuSgdPURTRu66mpgbFYhHpdFpUAXrJzlAohHg8jq6uLti2LZI/NDiyLCMSiSAej6O7u1tUC7quW9LLzpsOGo/HEYlERkz/wEolZpuamvDRRx+htbUVmqahUCiIQREeL2Hv3a/rOpqamjB16tRhP1720GNSjYiIiIiI6IBkWYamaSKxFgqF9rk2l8tBVVVomjZikgyV5vVJy+fzyOfzJb29vEoeb5siDZ2maQgGg5BlGaFQSPRUAyDiDuzpveYN6AgGg2KYBA1O716B3lRP0zRLKmRlWRZJ/ra2NjFMgiqDkSciIiIiIhqAcDgM0zRFb6/+Emu5XA6maSIej7N5eBlYlgVZliFJEtrb26Eoikj2eGzbhmmayOVyCAQCkGWZPdWGSFVV+P1++Hw+RCIROI6DSCTSp0LQ+7dxHAd+v5+VmUPk9VDzkmS1tbUwDAOFQkFUCGqahkAggGw2K9byXFM5TKoRERERERENgN/vRywWAwDouo5UKgVVVUVSwZsOGo/HEYvFuDXqEPASOb0regAwiVZmXmWUl1DzEsiFQkEk1byqtFwuJ7Yncurq0ESjUdTU1KC1tRX5fF4klXvzegwWi0VIkoSamhpEo9EKHTExqUZERERERDRAXiWUqqooFAowTROu60JRFIRCIWiahnA4zIRamSiKIvp4jRkzBpZliT5TvcViMSiKgq6uLvHvQYNnWRb8fr9I1njx7l0R5SXQ4vE4gD1JZyY3hyYcDqOhoQG7d+9GPp8X73VVVSHLsqjK9PrZRSIRNDQ0sFKtgnimISIiIiIiOgh+vx+JREJ8wfUqd7wvvlRe3tbCWCwGSZJEMtPbDuf1r3McB9lsFo7jVPqQq57XHL+urg6yLCOdTvcZ0iFJEkKhEGKxGGzbhiRJTKoNUSAQQH19PWKxGAqFAiRJQiAQEPd7CX0voRmLxVBfX1+yhoYXk2pEVDH8IEpERETVzGvOToeOV6XjJRm8iZSO44ikmiRJKBaLyOfziMViUFWVyZ0h8np1+Xw+1NbWIhQKwTAMMSRCkiQEg0EEAgEEAgF0dHSU9AKjwfH6o9XU1Ij+df0N5ggGg/D5fKipqRF92KgyGHkiGnbFYhG6rpdsmfCSatwyQURERES9eYk0b/qnaZqiib5lWSgWi7BtG5qmIRQKIRKJVPqQq56iKNA0DYqiwLIsRKNRBAIBaJoG27ZFQtnv9yOfz5esp6FRVRXRaBTxeBydnZ1QFEVMAPUq1cLhMOrq6uA4DodDVBjf8UQ0rAzDQDqdhq7ropmvJEmwbVuMnzdNE7FYjGXMhwCrA4mIiKiaaJqGSCQiJnv29PSIBu7e5xnXdREOhxGPx2EYBiKRiGiiT4PjVQd6n9uz2Sx8Ph9s2xaVar0/UyqKIqoEafAMw4DP50N9fb0YROD3+/tUZiYSCUQiEZFcNgyDVbMVwqQaEQ2bYrGIdDqNnp4eqKqK2traPmtyuZwYUy/LMivWyoTVgUREROXHi1WHXjAYRCQSQVdXFwKBAEKhEPL5PAzDEEmGQCCAYDAIx3Fg2zYikQgTDEMkyzKi0ShCoRDa2tpgGAYMw0AoFBLTbr1EZyAQwOjRoxGNRvn+HyLLskSSslgsIhqNora2FsViUZxn/H6/+G9viy63O1cOk2pENGx0XYeu61BVVYzl3pt3u7cukUgM5yGOSKwOJCIiKi9erBo+siwjkUigu7sbuVwOsVgMtbW1JRVTsiyLi7fRaBSJRILJnTLw+/3w+/1ia20ikSipmIrFYsjn87BtW6ylofGm3QJATU0NAIjP7x6vMhPY8zmf024ri5EnomFh27b44NlfhVpvoVAIqVQKhUJB9A6gwWF1IBERUXnxYtXwSyQSGDt2LBobG6HrOmRZhqZpYjtiLpcTiZ+xY8fyomyZFItF+Hw+jB49Gq7rwjCMkuSN4zioq6uDz+eDz+dDsVis4NGODIFAALIswzAMNDQ0oFgsip6BvZPIXhJz+/btkGWZ55oKYlKNiIaFaZp9rrLsj/eB1DRNlu8PAasDiYiIyocXqyrD7/ejvr4ePp8PnZ2dokrQ47ouotEo6urqMGrUKMa8DLwL4pIkYcyYMTAMA4VCAcViUSR3/H4/NE1DIBBANpvlBfEy8foI5vN5UR1oWZaIu6IokCQJ3d3d7B94GGBSjYiGhTcSWpKkAa2XJEn8DA0OqwOJiIjKixerKicQCGDMmDFiaIGu6+IzSzgcRigU4rbbMup9Qdz7Y9t2n95e3mdGXhAvD9d1kUgkkMlkkMlkAADxeLzP9s/u7m6RdEskEvzOVEFMqhHRsPDKwm3bHtB6x3FE400aHFYHEhERlQ8vVlWe3+9HIpFANBrlgIhDrL8L4rIs7zPOvCBeHj6fD+FwGGPGjEEwGERXVxcaGxv7HRAxevRoxGIxhMNhfmeqICbViGhYeFe4crncgNabpolQKMSx3EPA6kAiIqLy4cWqw4csy4zpIcYL4pXhfWfSNA3jx49HOBxGNptFPp+H4zjw+/0YO3YsIpEIEolESTUhVQaTakQ0LLyGsl5ibV9bJgCIJr+apvGq4xDwwxAREVH58GIVHUl4Qbwyen9nkiQJEyZMgGmaYhiHLMsizvzOdHhgUo2Ihk04HIZpmqJ5b3+JtVwuB9M0EY/HxahoGhx+GCIiIiofXqyiIwkviFdOf9+Z9v58zu9Mhw8m1Yho2Pj9fsRiMQB7mvemUilxFcZxHFG+HI/HEYvF2Gh2iPhhiIiIqHx4sYqONLwgXhn8zlRdmFQjomEVCAQgyzJUVRXNfl3XhaIoCIVC0DSNk5vKiB+GiIiIyoMXq+hIw+RO5fA7U/VgUo2Ihp03ucm2bU5uOsT4YYiIiKh8eLGKjjRM7lQOvzNVBybViKhiOLlpePDDEBERUXnwYhUdiZjcqSx+Zzq8MalGRHQE4IchIiKi8uDFKjpSMblD1BeTakRERxB+GCIiIho6XqwiIiKASTUiIiIiIqJB4cUqIqIjm1TpAyAiIiIiIiIiIqo2TKoREREREREREREdJCbViIiIiIiIiIiIDhKTakRERERERERERAeJSTUiIiIiIiIiIqKDNCKmf77++utYvnw5tmzZgmKxiFmzZmHhwoWYO3fugB+jtbUV9913H9atW4f29nY0NDTgggsuwLXXXgtVVfusNwwDDz/8MNasWYPdu3cjGo3izDPPxHe+8x2MHj26nC+PiIiIiIiIiIgOM1VfqbZq1SpcffXV2LBhA44//nicdNJJ2LBhA6655ho89thjA3qMlpYWfPWrX8Vjjz2GWCyGM888E7qu4+6778aCBQtQLBZL1heLRSxatAg/+9nPoOs6PvOZzyCRSGDlypWYP38+mpqaDsVLJSIiIiIiIiKiw0RVJ9Xa2tqwZMkSRKNRPP7441ixYgV+/etf47e//S0ikQhuv/12tLa2HvBxfvSjH6GlpQU33HADVq9ejbvvvhtr167F6aefjvXr1+ORRx4pWf/oo49i3bp1OPPMM7F27VrcfffdePrpp/HNb34T7e3tWLp06aF6yUREREREREREdBio6qTao48+CtM0cdVVVyGZTIrbjz/+eFxzzTUoFAoHrFbbtm0bXn75ZRx11FH45je/KW4PhUK4/fbbIcsyHn30UXG767p46KGH4PP58MMf/rBka+gNN9yAKVOm4KWXXsKuXbvK+EqJiIiIiIiIiOhwUtVJtVdffRUAcM455/S579xzzwUAvPLKK/t9jNdeew2u62LevHmQpNJwjBs3DjNnzkRjYyO2bt0KANiyZQtaW1sxffp0TJgwoWS9JEk466yzBvS8RERERERERERUvao2qea6LrZu3QpJkjB16tQ+90+ePBmSJGHr1q1wXXefj+Mly6ZNm9bv/d5jf/jhh4NaT0REREREREREI0/VJtV6enpgmiYSiUS/0zkVRUFNTQ3y+Tx0Xd/n47S1tQHAPid21tfXAwA6OjoAAO3t7SW372t9Z2fnAF8JERERERERERFVG6XSBzBY+XweABAMBve5JhAIAAB0XUckEtnv43hr9/UYuVyu5O99Pe/e6/dn1apVWL169QHXAcCGDRsAAJs3b8YVV1wxoJ85nJmmWelDqBr9JY0Hi3EfOMa9Mhj3ymDcK4NxrwzGvTIY98pg3CujXHFnzA8O414Z5TzPVNr06dPx/e9//6B+pmqTanv3P+vP/rZ97v04Pp9vv4/h/X2w6/ensbER69evP+C63jKZzEH/DBERERERERERlVfVJtVCoRAAoFAo7HONd9/+qtm8xzEMY0CPcbDr92f8+PGYPXv2AdcBwKZNm+A4DuLxOCZNmjSgn6GDs3nzZmQyGUSjUcyYMaPSh3PEYNyHH2NeGYx7ZTDulcG4VwbjXhmMe2Uw7pXBuFcG4z48pk+fftA/U7VJtUgkglAohK6uLliWBUUpfSmWZaGrqwuapiEWi+3zcbxeal7PtL15PdS8dQNdv6+ea73Nnz8f8+fPP+A6Gh5XXHEF1q9fjxkzZuCRRx6p9OEcMRj34ceYVwbjXhmMe2Uw7pXBuFcG414ZjHtlMO6Vwbgfvqp2UIHP58MxxxwD27axY8eOPvdv374djuMgmUzu93G8KZ7eVM+9ffzxxwAgHsf7e6DriYiIiIiIiIho5KnapBoAzJ07FwDw5z//uc993m2f+cxnBvQYL774IhzHKbmvqakJmzdvxvjx43HMMccAAI4++miMHz8e77//Ppqbm0vWO46DF198ET6fTzwuERERERERERGNPFWdVJs/fz40TcOKFSuwadMmcfvf/vY3PPDAAwgEArj88svF7U1NTfj444+RSqXEbRMnTsTcuXOxfft23HXXXeL2XC6HH/zgB7BtG1dffXXJ81522WWwbRvf//73S6Z83nXXXdixYwfOPfdcHHXUUYfiJRMRERERERER0WGganuqAcCECROwePFiLF26FJdddhnmzJkD13Xx1ltvwbIsLFu2DHV1dWL94sWLsX79elx//fX49re/LW5fsmQJvva1r2H58uV48cUXMWXKFLzzzjtob2/HGWecga997Wslz3vVVVfh5Zdfxrp16/DZz34WJ598MrZv344PP/wQ48aNww9/+MNhiwEREREREREREQ2/qq5UA4Cvf/3rWL58OU444QS8/fbb2LRpE04++WQ89NBD+PKXvzygx5g4cSJWrlyJ+fPnI5VK4eWXX0Y8HseNN96Ie++9t88QBFVV8etf/xqLFi1CMBjESy+9BF3Xcemll+Kxxx4TwwyIiIiIiIiIiGhkqupKNc+8efMwb968A67b35SMhoYG/PjHPx7wcwaDQdxwww244YYbBvwzREREREREREQ0MlR9pRoREREREREREdFwY1KNiIiIiIiIiIjoIDGpRkREREREREREdJDkH/3oRz+q9EEQHS5mzJiB2bNnY8aMGZU+lCMK4z78GPPKYNwrg3GvDMa9Mhj3ymDcK4NxrwzGvTIY98OTz3Vdt9IHQUREREREREREVE24/ZOIiIiIiIiIiOggMalGRERERERERER0kJhUIyIiIiIiIiIiOkhMqhERERERERERER0kJtWIiIiIiIiIiIgOklLpAyAqh9dffx3Lly/Hli1bUCwWMWvWLCxcuBBz584d8GO0trbivvvuw7p169De3o6GhgZccMEFuPbaa6Gqap/1hmHg4Ycfxpo1a7B7925Eo1GceeaZ+M53voPRo0eX8+UdtioR995c18WVV16J5uZmPP/880N9OVWjEnG/8sor8eabb+7z8dauXYtJkyYN6vVUi3LEvbdUKoXzzjsPZ599Nm6//fZ+1/A8U564v/vuu1ixYgU2bNiAdDqNRCKB0047DYsWLcLRRx/dZz3jXp64b9iwAcuXL8eGDRtgGAYmTpyI8847DwsWLEAgEOiznnEv/3kGAG699VY89thj+PGPf4z58+f3uZ9xH3rcLcvCSSedBNM0+71/zJgxeOWVV0puY9zL834vFot49NFH8cQTT2DHjh2QZRnHHXccFi5ciE996lN91h/pcR9KzG+66SasXr36gOsuuugi/OQnPxH/faTHHCjPe/3jjz/GvffeizfffBPpdBp1dXU444wz8E//9E9oaGjos55xHz4+13XdSh8E0VCsWrUKN998M1RVxZw5c+A4Dt566y0Ui0UsXboUl1566QEfo6WlBZdeeilaWlowc+ZMTJw4Ee+88w7a29sxe/ZsPPjgg/D7/WJ9sVjEddddh3Xr1qGhoQHHH388tm3bho8++gj19fX4wx/+gHHjxh3Kl11xlYj73pYtW4YHH3wQRx111BGTVKtU3E877TSYpomzzz6738e86aabMGrUqLK8xsNROeLeWz6fx7XXXou//vWvuPjii/tNqvE8U564r1mzBjfddBNs28bMmTMxbtw4bN26FTt27EAwGMSKFStw6qmnivWMe3ni/tprr2HhwoWwbRsnnHACRo0ahb/97W9oa2vDjBkz8OijjyISiYj1jHv5zzMA8Morr+Daa68FgH6Taox7eeL+wQcf4Mtf/jKOOuoonHDCCX3uTyQS+MEPfiD+m3EvT9xN08TChQvxxhtvIJFI4JRTTkFHRwfee+89+Hw+3HvvvTjnnHPE+iM97kON+WOPPYa//vWv/d7nOA6ee+45OI6DW265BVdeeSUAxhwoz3t948aNuPLKK5HL5ZBMJjFp0iRs2bIFO3fuRE1NDX73u99hypQpYj3jPsxcoirW2trqfuITn3BPOeUUd8uWLeL29957zz355JPd4447zm1paTng41x33XVuMpl077vvPnGbruvuVVdd5SaTSffXv/51yfoHH3zQTSaT7sKFC91CoSBu/9nPfuYmk0n3uuuuK8OrO3xVKu4ewzDcW265xU0mk24ymXTPOeecob+oKlCpuO/evdtNJpPuVVddVb4XU0XKFXdPU1OTe8kll4j37y233NLvOp5nhh73zs5O96STTnKnT5/uPv/88+J2x3Hc5cuXu8lk0j3zzDNd0zTFfYz70ONuGIY7e/Zs99hjj3XXrl0rbs/n8+L88/Of/7zkZxj38p5nXNd1u7q63E996lPiXPP444/3WcO4lyfuq1atcpPJpPvLX/5yQM/LuJcn7nfddZebTCbdb3zjG24mkxG3P/vss+6xxx7rzp492y0Wi+L2Iznuh+Ic09svfvELN5lMutdff33J7UdyzF23fHG/6KKL3GQy6T700EPiNsuy3FtvvbXfOB7pcR9uTKpRVbvzzjvdZDLp3nPPPX3u++Uvf+kmk0n3rrvu2u9jfPzxx+6xxx7rnnPOOa5t2yX3NTY2ujNmzHDnzZsnbnMcx507d6577LHHurt27SpZb9u2+7nPfc5NJpPuzp07h/DKDm+ViLvnL3/5i/uFL3zBTSaT7tlnn31EJdUqFffnn3/eTSaT7k9/+tOhv4gqVI64u67rFotF95FHHnFnz55d8v7tL6nG80x54v673/3OTSaT7ve+971+77/wwgvdZDLpvvXWW67rMu6uW564/+lPf3KTyaT7rW99q8997733nptMJt358+eL2xj38p1nevvud7/rzpo1SyTx906qMe7li/t//Md/uMlk0v3LX/5ywLWMe3nins/n3ZNOOsn95Cc/6XZ2dva5f9GiRe5ZZ53lbt682XVdxv1QnGM8f/3rX93p06e7p59+utvV1SVuP9Jj7rrliXtXV5ebTCbd2bNn97mvs7PTTSaT7sknnyxuY9yHHwcVUFV79dVXAaCktNtz7rnnAkCfHhZ7e+211+C6LubNmwdJKv2/xLhx4zBz5kw0NjZi69atAIAtW7agtbUV06dPx4QJE0rWS5KEs846a0DPW80qEXfPtddei+3bt+OKK67Ar371q6G8jKpTqbi///77AIBZs2YN6firVTniDgBvv/02brvtNliWhX//93/HokWL9rmW55nyxN1xHMycORNz5szp936vD2BbWxsAxh0oT9w/+9nP4uWXX8YPf/jDPvfpug4AUJR/tPVl3Mt3nvE8/fTTePbZZ3H99ddj2rRp/a5h3MsX982bNwMY2O9Jxr18n2d0XceXv/xl1NbW9rn/vvvuwwsvvIDp06cDYNzLfY7xOI6DpUuXwnEc3HTTTUgkEuK+Iz3mQHniLssygD2/PzOZTMl9XV1dAIB4PC5uY9yHH5NqVLVc18XWrVshSRKmTp3a5/7JkydDkiRs3boV7n5aB3rJg3196PQe+8MPPxzU+pGmUnH3fO5zn8MTTzyBH/zgB9A0bbAvo+pUMu5eUq2npwcLFizAnDlzcNJJJ+GKK64QHxZGqnLFHQCCwSCuuuoqPP/887jsssv2u5bnmfLE/fLLL8fq1atx0UUX9bnPcRz8/e9/BwCMHTsWAONezvd7Q0NDn8bJ7e3tuOOOOwAAF1xwgbidcS9f3IE9g2iWLl2KE044QfRT6w/jXp64u66LzZs3o76+Hi+++CIuvvhinHTSSZgzZw7+5V/+Bdu2bStZz7iXJ+7e+fu4446DaZp4+umnsXTpUixZsgRPPvkkisViyfojOe7lPsf09oc//AFbtmzBCSecgPPPP7/kviM55kD54h6NRnHiiSeiWCzi29/+Nj788EMYhoGNGzfixhtvBAAsWLBArD/S414JTKpR1erp6YFpmkgkEv1OK1QUBTU1Ncjn8+LKeH+8CoV9TUGpr68HAHR0dADY86Wg9+37Wt/Z2TnAV1JdKhV3z913341jjz12sIdftSoZd+8K/JIlS9DW1oZTTz0VE3bYHYIAABT/SURBVCZMwPr163HNNdfg4YcfHvTrOtyVK+4AcPzxx+Pmm2/u94r63nieKV/c92XVqlXYuXMnRo8ejRNPPBEA436o4v7QQw/hiiuuwLx587BlyxZ885vfxNe//nVxP+Ne3rh///vfR6FQwE9+8hNR4dAfxr08cd+1axey2Sza29tx6623QtM0nHbaadA0Dc888wwuvvhivP3222I9416euO/cuRPAnsTFV77yFdx444347//+b/z+97/H9773PVx88cXiMw9wZMf9UJ3bLcvC/fffDwD4p3/6pz73H8kxB8ob95/+9KeYOnUq3njjDZx//vk44YQTcMkll2Dnzp248847+Tu1wphUo6qVz+cB7Kn+2JdAIAAA+z1ReY/jrd3XY+RyuZK/9/W8e68faSoV9yNdpeKeSqXQ0tICRVFw55134qmnnsI999yDp556Cj//+c+hKAqWLVsmEm8jTbnifrB4njm0cd+0aZOYuHrjjTeKrYiM+6GJ+x//+EesX78exWIRPp8PLS0tSKVS4n7GvXxx/+3/1969B0V53WEcfwBXKBqMBEVFmCBGxEu81KC1iqlEjQQqoaahcbylNm2TxmhrEmNsdbzUodhQL4lpvSRDQmtqRZKNtxGtjEYs0yDRyM0SUC4xmqolgqIs9A9n37iyoBsWN7rfzwyj856z5/A+s77gb8973r/+VQcOHNDcuXPtroy4Hrk7J3frau7AwEClp6crLS1Nb775pvbu3aunn35aNTU1mjt3rurq6iSRu7Nyt94Gt3z5cl2+fFkbN27Uxx9/rIyMDEVGRqqwsFCzZ882VgC5c+5tdW3fsWOHTp8+rb59+2rMmDFN2t05c8m5uXft2lWPP/64TCaTwsPDNXbsWPXs2VM1NTXatGmTKioqjL7unrsrUFTDHevG/aDsuZUlzNZxPDw8WhzD+qej/e82rsrd3bkqd39/f2VnZ2v79u2KjY216RsTE6MpU6bIYrHob3/7203nvhM5K/dvOq+7/vtoy9yPHj2qn/70p6qtrVViYqLi4+ObzEvuzfsm57569Wo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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 new file mode 100644 index 0000000..5a20d96 --- /dev/null +++ b/notebooks/digits_exp160.ipynb @@ -0,0 +1,545 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "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", + "\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": 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 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": 12, + "metadata": {}, + "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", + " 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": 13, + "metadata": {}, + "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": [ + "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_exp160_p01_*\")\n", + "exp160_p01 = get_data(files, \"correct\")\n", + "\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p05_*\")\n", + "exp160_p05 = get_data(files, \"correct\")\n", + "\n", + "files = glob.glob(\"/Users/qualia/Code/glia_playing_atari/data/digits_exp160_p1_*\")\n", + "exp160_p1 = get_data(files, \"correct\")\n", + "\n", + "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": 14, + "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": 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": 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": { + "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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zIT8/H5IkIZlMYvjw4aiqquLPAiIiE52Rn6ijR49GMBhES0sL6urqkEwmkUgkjPyMs88+Gz/96U/xL//yL10+ZzKZBIDjrn5kK1fF4/EuBxdvv/02GhoaMHLkyA4Di2AwiIaGBthsNsyfPx9XX311znsfeOABzJ8/H+PGjWtXYeqr6urqsGXLli7Ni05NNrEzmUy2Cy6cTicTO/sBURR7dTBBXROPxxEOhxGJRBAKhdDQ0IBgMIhMJmOMsVqtCAQCKC0thcViQTgcRjweh8/n68GZExH1Ld0WXKTTabz//vt46623sH79esiyDF3X4XQ6MW3aNFxzzTVoamrCG2+8ga1bt+LWW2/Fs88+22H+REeObYrVmVNZlPntb38LAJgxY0aHx7MrJZFIBJWVlTnHpkyZgm3btmHx4sV4/fXXMXfu3ONeq7y8HOPGjevSvLIrF9R1iUQCwWAQkUgEsizD4XDAYrFAURREo1FIkoRUKoVAIMDETqJeLplMoq2tDY2NjWhubjZWq1RVNR4q2Gw2xONxBINBFBUVIRAIIJlMMrggIjKRqcGFruvYuHEj/vSnP+Hdd99FLBaDruuwWCy4+OKLMX36dFx55ZU5H+SuvfZaPP3003jhhRfw9NNPdzm4yJ5DluVOx2SPdTW349ChQ/jnP/8Jl8uFyZMndzouEAggEAh0eOyyyy7D4sWLsWPHjhNer6amBjU1NV2a2y233MJVjpMgyzKCwSCCwSAkSUJxcXFO3k1eXh5isRiCwSAAGH0PiKj3qq+vx4EDBxCNRqHrOhKJhJHMnf3d5XIhEokgFou1K9ZBRESnz7Tg4n/+53/w5z//Ga2trcaKwbBhw3DNNddg2rRpKC0t7fS9119/PV544QUcPny4y9fzeDxwuVxoa2sz9lIfK5u0J0kS8vLyunTOv/71rwCOBginmmye3QudSqVO6f1kjmg0ikgkAkmS4PV62x0XBMF4PRKJwOFwMLgg6sUEQUBzczMOHDgAu90OTdOgqirS6bTxb4Qoikin07BYLGhpacHAgQNPqtgHERGdmGnBxauvvgoAyM/Px9SpU3HNNdfg3HPP7dJ7w+EwCgoKTirnQhAEDB8+HNu3b8fnn3+O4cOH5xw/cOAANE07qR4b77//PgAcd9Vi48aNWLFiBaqqqnDbbbe1O54NkI4XTFH3UlXVyLEoLi4+7liPx4OmpiYkk8kOg1Qi6h1kWUZzczPa2tqMVYp4PG50ZbdYLBAEAW632zje3Nx83NVvIiI6eaZ9krryyitxzTXXYOLEiSf9AW306NHYsGHDSV9zwoQJ2L59O9599912wcW7774LAB0mZXdE13V8+umnAICxY8d2Oi6VSmHlypX46KOP8J//+Z/t7vWPf/wjAGD8+PFdvg8ylyzLRo7FiZ5KCoIAh8NhvIfBBVHvlE3kjsViRkd2VVVhtVqNMZlMBqlUyujUHgqFEIlEenDWRER9z4mzorvoueeew+WXXw6bzYbm5uZ2x7du3Yrdu3ebdTkAR3MWJEnCwoULjcAAAP75z3/ipZdegsPhwM0332y8fuTIEezfv9/YZ3+szz77DLFYDCUlJSgpKen0muPHj0d5eTnq6urwxBNP5FQieeONN/DOO++gqKgI1113nUl3SSdL0zTjSWVWLBbDwYMHsX//fhw8eBCxWMw4ZrFYjPcQUe8UDofR2tqKWCyGRCIBWZaRyWSMlQtd15HJZCDLMhKJBGKxGFpbWxEOh3t66kREfYqpj2kbGxsxe/ZsbN68GZs2bcrJdVi4cCHWr1+P6upq/PznPzdl29DAgQPx4IMPYu7cubjxxhtRXV0NXdexefNmqKqK+fPno6CgwBj/4IMPYsuWLbjrrrtw991355wru51p0KBBx72mKIp48sknMWPGDPzmN7/BmjVrMHLkSBw6dAi7du2Cy+XCL3/5yw73+dOZYbFYjKpQLS0t+OKLL3Do0CEcOXLEaKRWVlaGQYMGYfDgwbDb7bDb7V2qQEZEX0+apqGtrQ2JRAKSJMFqtSKTyUBRlJxGqlar1Uj2bmtr40MFIiKTmRZcNDc34/rrr0djYyNsNhuamppygouSkhI4HA5s2rQJt9xyC5YvX27KB/DvfOc7KCsrw0svvYSPPvoIoihi7Nix+P73v49vfOMbXT5PW1sbgK7lSowdOxYrVqzAr3/9a2zYsAFr165Ffn4+ampqMGvWrBMGKNS9JEmCJEnYt28f9u3bh507d+LgwYNIJpPGiobT6URFRQVGjx6Ns846C+eccw4Tuol6MVmWjfKzx26JzK5aCIJg5F0IggBVVRGPx5lzQURkMtOCi+effx6NjY2orq7GE0880a6D7Ny5c/Hf//3fuP/++7Fx40a8+OKLuP/++0259mWXXYbLLrvshOOO19l6+vTpmD59epevWVlZifnz53d5PJ05NpsNsVgMW7duxd/+9je0trYimUy2a6bV1NSEAwcOYPz48aisrGS+BVEvJooiRo4cCZ/PB1EUje9nm81mBBeqqgKAUUVqwIABbLBIRGQy0z5NrV+/Hk6nE88880yntcP9fj+eeOIJXHbZZVi9erVpwQXRV+3duxebNm3Cvn37jL3WdrvdOJ5IJIwme1arFWPGjDlhR3Wi/qSj3Lmvs3g8Do/Hg0AgAJvNBqvVCovF0i6hW9M0ZDIZqKoKj8eDeDzeq+71qw/uiIi+bkzdFnXWWWedsClRIBDAkCFDsH//frMuTZQjFothw4YN2LdvH5LJpPHhQlEUY0w2gTuTyWDfvn3YsGEDLr/8cng8np6aNtHXzrHfM1930WgUtbW1aGxshMPhyAkqvipbNaqkpATRaLTX3OexD0iIiL6uTMtg9fv9Rt7CicRisZwu3URm+vzzz7Ft2zaEw2Fjj3U2kDj29+we7HA4jG3btuHzzz/v6akT0SlyuVxwOp2wWCxwOBwQRRFWq7XdL1EU4XA4jNwr/ltERGQu04KL0aNHo6Ghwegv0ZkNGzbg8OHDqKqqMuvSRDn27t2Lw4cPI5VKQdd12Gw2uFwuuN3unN+ze7FTqRQOHz6MvXv39vTUiegUOZ1O+Hw+uN1uoxt39pfdbs/5f5vNBrfbDZ/PB6fT2dNTp26QTdiPRqNGoj8RnRmmBRc33XQTdF3Hj3/8YyxduhSpVCrneDqdxvLly/HDH/4QgiDgpptuMuvSRDmOHDmCcDgMTdNgt9shSRJEUTRK1FosFoiiCEmSYLfboWkawuEwjhw50tNTJ6JT5HQ6UVpaCp/PZ6xYfjWosNvtxoqlz+dDaWkpg4s+RpZltLS0oL6+vt2vlpYWVgcjOgNMy7mYNGkSbrrpJrz++uv42c9+hnnz5qG8vBxutxuJRAKHDx82Ghpdf/31mDx5slmXJsqRTdbUNM1YnVAUxfj7JwiCkexps9mgaRpUVc2pJkVEvYvb7cagQYPQ2tqKSCQCWZahKEpO/xpN04wHC3l5eRg0aBDcbncPzprMlC3Skf36Z7e/KYqCaDQKSZKQSqUQCAS4HY6oG5lae3POnDk499xz8cILL+CLL77AF198kXN8wIABuOOOO7hqQd2qoqIC559/PqLRKHw+n9E0S9d1Y0y21n0mk0FJSQm8Xi8qKip6cNZEdDp8Ph8GDRqEhoYGeL1eRKNRxGIxo3hDdtXS4/HA6/UawYjP5+vpqZMJZFlGMBhEMBiEJEkoLi42ep0AQF5eHmKxGILBIICj5cjZ24ioe5he2L+mpgY1NTX44osvcPDgQYRCITidTlRWVmL48OFmX47OkN5UqtHj8WDAgAGw2+3G3uvsSkVWNrE7nU4jEAigsLAQHo+nV90nS1ISfUkURQwYMAAVFRVoampCQUEBYrEYUqkUMpkMrFYrHA6HURGuuLiYfS76kGg0ikgkAkmSOmzQKwiC8XokEoHD4WBwQdRNuq1r2ODBgzF48ODuOj31gN5UrrG5uRl79uwBcLSJlsViySnjqCiKsR0KAM4++2zY7fZedY9ElGvAgAGIRqNQVRWKosDj8bQLLrK5GMOGDcOAAQN6espkAlVVkUwmIcsyiouLAQCpVAqxWAyqqsJms8Hj8RjBZVNTE5LJpHGMiMzVbd9VsixD07Sc1zKZDGRZRlNTE9asWYMf/OAH3XV56sfy8/NRXl6Ouro6pFIpoxTtsdVCsqVobTYbHA4HysvLkZ+f34OzJqLT5Xa7MWTIEKiqii+++ALBYND4d0jTNGO//eDBgzFkyBDmW/QRsiwbORbRaBTNzc0IhUKIx+PGlji32w2/34+ioiI4HA7jPQwuiMxn6nfV22+/jf/93//FgQMH2gUWHWFwQd1BFEWUl5ejuLgY8Xgcsiy3q16WDSokSYLb7UZ5eTm3RxD1AQ6HA4WFhZBlGaIoIp1OGx8wRVE0tkE6HI6eniqZJJtXEwqFEAwG0dzcjHg8Dq/XC4vFAlVV0draaiT7BwIBFBQUdOlzChGdPNOCi82bN+P+++/PSZrtjN/vxze/+U2zLk2UQ1VVFBUVYfjw4WhoaEAikYCiKEilUsaHDIfDAbvdDpfLhdLSUhQVFbEOOlEvpygKIpEIVFXFoEGDUFlZiWQyaWyLcjqdRq5VJBKB1WrlFsM+wGKxIB6PG3mebrcbQ4cOzcmzKyoqQmtrKw4fPoxYLGZUkiIi85kWXCxZsgS6ruOSSy7BD37wAzgcDlx33XWYNm0avv/976OhoQHLli3DqlWrMGDAADz++ONmXZqoncLCQgwZMgQulwuhUAihUAgAjA8ZwNEg1+/3o6SkBIWFhT05XSIyQTweRzwehyiKRqnRzpJ2s+P8fv+ZnCJ1A0mSEA6HUV9fj4KCAhQVFSGdTiORSBg/810uF4qKiqDrOurr61FSUsKEbqJuYlpwsW3bNoiiiCeffBKBQAAAMGTIEGzfvh2VlZWorKxEdXU1CgoKsGTJErz++uv47ne/a9bliQzZrU7Zpe/6+nq0trYaeUAWiwWSJKGgoAADBgyArutwu938h4aoF8vm9GUrwB2Py+VCMBiELMs5Dxyod1JV1ehrous6Dh06hHA4bHTmPrYjuyiKUBQFsiwzoZuom5j2XRUKhTB48OCcH+ojRozA6tWrEY/HjcS5u+66C7///e+xatUqBhfULZxOJzweDyRJgsfjQUFBAaLRKEKhkPFBwu/3w+v1Qtd1xGIxeDweduol6sXS6TTS6XSXc6ey+RjpdJrf+71cJBJBJpOBw+HAZ599hra2NqRSqXblxx0OB/Lz81FQUIBMJmOUpCUic5kWXEiS1O7Jb0VFBXRdx/79+zFmzBgARxvZVFZW4sCBA2ZdmihHNngIhUJIJBLIy8tDSUmJkcCX7cyd3Xft9Xrh9/v59JKoF8s2yuzqPnqLxdKuuSb1TtmVi3A4jMbGRqN54rE/0zOZDBKJBNLpNGw2m7FyQUTmMy2bqaysDIcPH87pEzBo0CAAMPoNZOm6jmQyadalidrx+/0oLS2FJEmIx+PGky0AxhOreDwOSZJQWlrKfddEvZwgCEbZ6a7IlqM+tosz9U42mw3Nzc04cuQIFEWB3W43Sg8nk0mjmEe2l9GRI0fQ3NzMLVFE3cS04KK6uhrRaBS/+MUvjCdBVVVV0HUdK1euNF47ePAgDhw4wOZF1K3sdjuKioowcOBAY/tTtiStLMvQdR1erxcDBw5EUVERK8YQ9XKiKBpbnboiu4WKJah7P1EUEYlEcPDgQaiqikwmYwSN2c8egiAgk8lAVVUcPHgQkUiEX3uibmJa2H7rrbdi2bJl+M1vfoM///nP+Mtf/oLRo0dj9OjR2Lp1K2bOnIkRI0bg7bffRiaTwYUXXmjWpYk65HA4UFJSAo/Hg0QigXg8buRcuN1uuFwuuN1uBhZEfYDVaoUkSRBFEYlEwqgW1ZFEIgFRFCFJErdD9gGJRAKhUAjpdBotLS1wOp3GyhRwdJUqm/CdTCaRTqeNbbNctSYyn2nBxcCBA/GrX/0Ks2fPRiwWM54IzJ49GzNmzMCGDRuwceNG6LoOn8+He+65x6xLE3XKbrcbydvpdBq6rkMQBIiiyA8VRH2M2+1GOp1GOBwGgA4DjOy+e5/Pxw7dfUQkEkEqlYKqqlBVFZqmwWazQdM049E/U8sAACAASURBVGd+tpletqFqKpVCJBJBWVlZD8+eqO8xdcPh+PHj8d5772HHjh3GaxdddBHeeOMNLFmyBHV1daisrMRtt93GbVF0RmUbaBFR32W325GXlwfgaB+LYDAIURRhsVigaZqxFcrn8yEvL4+rln1ENrciW6wjHA4jmUy2Cy6cTie8Xi9UVTVyMYjIfKYFF7/73e8wbNgwVFdX44ILLsg5dtZZZ2Hu3LlmXYqIiKhDDocDVqsVoigafS90XYfNZoPL5TL64DCw6DssFgtkWUY8HofH4zGCjWOT9bNBhsvlQjwehyzL7NBN1E1MCy4WLFiAWCyG999/33hyREREdKZlt0NmMhluh+wHbDYbVFVFLBZDIpGAx+OB1+uFIAjG117XdSiKgqamJmiaxgZ6RN3ItO+scDiM4cOHM7AgIqKvBW6H7B9sNhsURTF6WGRXqb7aRC/bsVtVVSiKwuCCqJuY9p01atQo1NbWoq2tDfn5+WadloiIiKhTx+ZV+Hw+6LqOcDgMq9VqrFpkKwX6fD60tbUZrxOR+UzbcPjYY4/B6/XipptuwrJly7Bv3z6EQiEkk8lOfxERERGdjng8DkEQjMqAFosFmUwGyWQSiUQCyWQSmUwGFosFXq8Xfr8fgiAgHo/39NSJ+iTTVi7uu+8+CIKAL774Ao888sgJxwuCgJ07d5p1eSIiIuqHNE2D1+uFx+Npt9VJ0zRje5QkSbDZbEZORle7uRPRyTEtuNi3b99JjedyJJ1JTOwkIuqbshXAsn1L7HY73G53TvCQLUd87HFJknpkvkR9nWnBxXvvvWfWqYhMoyiKUXbwq8EFS1ISEfV+xcXFCAQC0HXd+F2WZWQyGeNnfraDuyAICAaDCAQCKC4u7umpE/VJpgUX5eXlZp2KyBTZDqzxeNxonpXdi5tIJCCKItLpNPLy8uBwOHp6ukREdAp8Ph+KioqQl5dnVItSFAWZTMYYk22ip6oq8vLyUFRUBJ/P14OzJuq7WIeN+iRFURCJRBAOhyGKIgKBQLsxiUQC4XAYwNGSlVzBICLqfXRdR1VVFZLJJBoaGmCz2VBeXp6z/VoQBCiKAlVVUVpaiqqqKm7PJuompgUXs2fPPqnxgiDgscceM+vyRDni8Tji8ThEUYTL5epwTPb17Di/338mp0hE9LXU3Nzc01M4KalUCjabDYWFhUilUkilUtB1HaIoGiVnsz0wPB4PCgsLYbPZ0NLS0qtWrYuKinp6CkRdYlpwsWLFiuPWjRYEwfjv7B5IBhfUHTKZjJFj0dGKxbFcLheCwaCxP5dJ3kRER1d/e4t0Oo1QKARVVeHxeCDLMkKhEBRFMT5vZLu2ezweqKqKUCiEQCDQa37mc2WdehPTgovbb7+902OpVArNzc34+9//jkgkgpkzZ+L8888369JEOdLptJFj0RXZ3It0Os1uvkREvYzVaoWu61BVFUOGDEFZWZmxLfbY5nl5eXmQJAn19fXQdb3XBBZEvY1pwcWDDz54wjGxWAz33nsvfve73+G6664z69JEOXRdh67rRm3zE7FYLMZ7iIiod8lkMhAEAS6XC5lMBl6vFy6XC4WFhUafC5vNBqvVimQyCZfLBUEQchK+icg8ZzSh2+PxYP78+bjsssvwy1/+Ek899ZQp5924cSMWLFiAPXv2QFEUVFVV4Y477sCECRO69H5VVXHBBRcgnU53eLykpATr16/PeS2VSmHx4sVYuXIlDh8+DK/Xi0mTJuGee+5hebseJgjCSf3DoWkabDZbztY9IiLqHXRdh8PhgNvtNrZzORyOnD4Wuq4jmUxCURS43W44HA4+UCLqJme8WlRhYSGGDx+ODz/80JTzLV++HLNnz4Yoiqiuroamadi8eTNmzpyJuXPn4oYbbjjhOWpra5FOp1FRUYHzzjuv3fGvJvoqioJZs2Zhw4YNGDBgACZOnIjPPvsMy5Ytw7p16/CHP/wBZWVlptwfnTxRFCGKIhKJRJfGp9NpuFyuLm+jIiKir49s7yKXywVd15FIJBAKhYy+Ftm+F6Iowuv1GuP5QImoe/RIKdpwOIxYLHba52lqasKcOXPg9Xrx2muvYcSIEQCA7du34/bbb8ejjz6KSZMmoaSk5Ljn2bVrFwCgpqYG3//+90943VdffRUbNmzApEmT8Mtf/tL4UPr0009jwYIFmDt3LhYsWHCad0enKtssKRtgdFYtCoDR70KSJO6/JSLqhY59oOT1eiGKImRZhqIo0DTNyLmQJAkOhwOxWMx4DxGZr2ub0k20ePFiHDlyBIMHDz7tc7366qtIp9O47bbbjMACAMaMGYOZM2dClmUsXbr0hOfZuXMnAKCqquqEY3Vdx6JFiyAIAh5++OGcH0733nsvhgwZgrVr1+LQoUOncEdkFrfbDbfbjXQ63ekKRiKRQDqdNsYSEVHvc+wDpWyTvEAggEAggIKCAuO/8/LyoKoqHygRdTPTVi7uvffeTo9la0wfOHAABw8ehCAIuPbaa0/7mh988AEA4Iorrmh3bPLkyXjmmWewfv163HPPPcc9T3bloivBxZ49e9DY2IhRo0Zh4MCBOccsFgu+9a1v4eWXX8b69evxne98p6u3Qiaz2+3Iy8sDcLSPRTAYNDp0a5pmVJPKVhBhmT8iot4r+zAp2xjV5XK1Cx6yD5R8Ph8fKBF1I9OCi7/85S9dHnvllVfiu9/97mldT9d11NbWwmKxYOjQoe2OV1ZWwmKxoLa21qhz3dl5du3ahaKiIqxZswZLly7F/v37IUkSLrnkEtx1110556+trQUAnHXWWR2eLzt27969p3V/dPocDgesVquxRJ5Op6HrOmw2G1wuFyRJgtvtZmBBRNTL8YES0deHacHFXXfdddzjVqsVeXl5GDt2LEaNGnXa1wuHw0aTtI72TdpsNuTn56O1tRXxeBwej6fD8xw6dAixWAyxWAyPPPIIxo4di4svvhi7du3CqlWrsG7dOixcuBAXXnghgC87l3bWKTP7emtr62nfI52+bOOkTCZjBBfZZD4uiRMR9R18oETA0QqgsiwbZYglSYLN1iMpxv3WGQsuzJZMJgHguE3PHA4HABw3uMjmW5SUlOCFF14wAh9VVfHUU0/hlVdewQ9/+EP89a9/hSRJxv79zq6bvWZXKhUtX74cK1asOOE44MutW3RqrFYrG+QREfVxfKDUf8myjGg0imQy2S64cDqd8Hq9OeWJqfuYHsqlUimsWbMGU6ZMyXl96dKlkGUZ06dPN5YuT0dXGqR1pYb1t7/9baxbtw4WiyWnqpTNZsMDDzyAzZs3Y8eOHXj33XcxdepU47rH22bV1WvX1dVhy5YtJxxHREREXccHSv1LIpFAMBhEJBKBLMtwOBywWCxQFAXRaBSSJCGVSiEQCBy3giSZw9TgYuPGjfjhD3+ISCSCCy+8MOfD+jvvvIPNmzdjwYIFeOqpp/CNb3zjtK6V/cshy3KnY7LHjvcDRhAEDBgwoMNjFosFEydOxI4dO/Dpp59i6tSpxnVTqdQpXzOrvLwc48aNO+E44OjKRTQa7dJYIiIiov5AlmUEg0EEg0FIkoTi4uKcB8B5eXmIxWIIBoMAvqwuRt3HtOBi+/btuOOOO6CqKkaMGGF0ycyaMmUKUqkUtm3bhlmzZuGNN97oMBG7qzweD1wuF9ra2qCqarv9dKqqoq2tDZIkndZKSWFhIYAvg4ls9+2WlpYOx58oJ+NYNTU1qKmp6dI8brnlFq5yEBERER0jGo0iEolAkiR4vd52xwVBMF6PRCLtureT+Uzrc7Fw4UKoqorbb78dK1eubFem9frrr8fvf/97zJw5E8lkEi+88MJpXU8QBAwfPhyZTAaff/55u+MHDhyApmk5/S868rvf/Q733XcfNm7c2OHxw4cPAwBKS0sBwDhftmrUV+3fvz9nHBERERGZT1VVI8eis9zaLI/HA1mWkUwmoarqGZph/2RacPHRRx8hEAjgRz/60XHH3XffffD5fJ1+mD8ZEyZMAAC8++677Y5lX5s4ceJxz3Ho0CG88847HSZWy7JslNj95je/CQAYNmwYysvLsXPnTtTX1+eM1zQNa9asgSAIxtyIiIiIyHyyLBs5Fp3lwmYJggCHw2G8h7qPacFFNBpFWVnZCasx2Gw2DBo0CKFQ6LSvWVNTA0mSsHDhQnz66afG6//85z/x0ksvweFw4OabbzZeP3LkCPbv32/suwOA6667DlarFW+99VZOrw5FUTBv3jzU1dXh0ksvxTnnnGMcu/HGG5HJZPCTn/wkpyrUs88+i88//xyTJ09GRUXFad8fEREREXVM0zSjKlRXZPueaJrWzTPr30zLuSguLsahQ4eQyWSOG2Bomoa6ujr4/f7TvubAgQPx4IMPYu7cubjxxhtRXV0NXdexefNmqKqK+fPno6CgwBj/4IMPYsuWLbjrrrtw9913AwCGDx+Ohx56CI899hjuuecenHvuuSgrK8M//vEPNDQ0YOjQoXj88cdzrnvbbbdh3bp12LBhA6688kqMHTsWBw4cwN69e1FWVoaHH374tO+NiIiIiDpnsViMqlBdoWka7HZ7l4MROjWm/elWV1cjEong17/+9XHHLVq0CG1tbV2uknQi3/nOd7BgwQKcd955+Oijj/Dpp59i7NixWLRoEa655pouneO73/0uFi1ahPHjx+OLL77A2rVr4XA4cOedd+L//u//cgIUABBFES+//DJmzZoFp9OJtWvXIh6P44YbbsDSpUuNpG8iIiIi6h6SJBllZk/UAkDXdaRSKeM91H0EvSsNGbpg7969uO6666AoCqZMmYKamhqcddZZcLlcSCaTqK2txZtvvomVK1fCYrFg2bJlpnTq7i+y1aLGjRuHJUuWnPHrNzc3d/nJAHU/u93epYpkZuDX/uvlTH3t+XX/+uHXvv86kz/ze5uWlhY0NTXBarV2WC0qKxqNIpPJoLi42KgESt3DtG1RI0aMwNy5c/HII49g1apVePvtt9uN0XUdNpsN8+bNY2BBRERERKfF6/UilUoZ+bQejycnuVvXdcRiMciyjEAgcNwAhMxhahO96dOn47zzzsMrr7yC999/H01NTcax/Px8jB8/HjNmzMDIkSPNvCwRERGRIZPJIJ1OQ9d1CIIAURRPWHCGeidJkhAIBAAc7WPR1NRkdOjWNM3YChUIBBAIBLgl6gwwNbgAgCFDhmDevHkAgHQ6jba2NjidztNqZEdERER0IoqiIB6PI5FIIB6PG0Vm3G43XC4X3G437HZ7T0+TTOZyuWC1WuFwOBCNRhGNRo0Gy36/H16vF16vl4HFGWJ6cJFKpbBmzRpMmTIFoiiipKQEALB06VKk02lcc801DDSIiIjIVKlUCi0tLWhtbUU8Hs/ZGtPc3Ay3242CggIUFhbC4XD04EyJ+jZTa3Ft3LgREydOxP3334/GxsacY++88w4ee+wxXHXVVdi0aZOZlyUiIqJ+TFEUNDc34/Dhw4hGoxAEAZIkweFwQJIkCIKAaDSKw4cPM2G9D0okEmhubkZTUxNCoRDsdruxShUKhdDU1ITm5uac3mTUfUxbudi+fTvuuOMOqKqKESNGtPvGnTJlClKpFLZt24ZZs2bhjTfewNChQ826PBEREfVToVAIDQ0NkGUZeXl57To2O51OpFIpRCIRNDQ0wOFwsPpSHyHLMoLBIILBICRJQnFxcc7XPi8vD7FYzEj4tlqt3B7VzUxbuVi4cCFUVcXtt9+OlStXYuDAgTnHr7/+evz+97/HzJkzkUwm8cILL5h1aSIiIuqnMpkMQqEQotEovF4vnE5nzodLABAEAU6nE16vF9FoFKFQCJlMpodmTGaKRqOIRCKQJAlerxeZTAaJRAKxWAyJRAKZTMbIt4hEIohGoz095T7PtJWLjz76CIFAAD/60Y+OO+6+++7DsmXLsHHjRrMuTURERP1UMplELBYzAojjcTqdCIfDiMViSCaT8Hg8Z2iW1B1UVUUymYQsy/D5fAgGg2htbUVTUxMURYHdbkdxcTEKCgrgcrmQSCSQTCaNZG/qHqb9yUajUZx99tknLPVms9kwaNAg7Nmzx6xLExERUT8lyzJkWYbdbm+3YvFVgiDAbrcb72Fw0btlv466rqO2tha1tbVoaGhoVymstLQUw4cPR2FhofEeBhfdx7Q/2eLiYhw6dMj4YnZG0zTU1dXB7/ebdWkiIiLq504UWJzsOPr6y/ax2LdvH3bu3InGxkbEYrGcnIrGxkY0NTWhsbERo0ePRlVVFTRN68FZ932m5VxUV1cjEong17/+9XHHLVq0CG1tbRg3bpxZlyYiIqJ+SpIkSJJkNM07Hl3XkU6njfdQ72axWFBXV4ePP/4Y+/btM7a6Zb++kiTB4/EgmUxi3759+Pjjj1FXVweLxdRiqfQVpv3p3nrrrbDb7Xj++edx//33Y8OGDWhqakIsFkNzczM2bdqEhx56CE899RRsNhtmzpxp1qWJiIion3I6nfB4PNB1HclkEsDRJ9qyLBv78bNPqpPJJHRdh8fjOWF+Bn39Wa1WfPbZZ9i7dy80Tev0a+p0OqFpGvbu3YvPPvuM3dq7mWnbokaMGIG5c+fikUcewapVq/D222+3G6PrOmw2G+bNm4dRo0aZdWkiIiLqp6xWK/x+P0KhEEKhEOLxOCwWC1RVhaZpsFgssNls0DQNiqLA6/XC7/fzA2YfEA6HUVdXh2g0CqfTCV3XkclkkEqljK+9w+GA1WqFxWJBNBpFXV0dwuEwGyl2I1OzWaZPn47zzjsPr7zyCt5//300NTUZx/Lz8zF+/HjMmDEDI0eONPOyRERE1I/5/X74/X60traitbUVwNGn1VarFZlMxljRyAYWzPvsG+rr6xEMBmGz2ZBOp1FfX28keOu6DkEQjIaKoijCZrMhGAyivr4eJSUlPT39Psv0VPkhQ4Zg3rx5AIB0Oo22tjY4nU7k5eUBAILBIF555RW88cYbWLVqldmXJyIion7I4XDA7XZDEARkMhkjcTtbMchqtcLlcvGJdR8SjUaRTCahKAoikQji8TjS6TTsdjssFouxWiWKItxutxFostdF9+rWOlyiKBqR4QcffIBly5Zh7dq1UFW1Oy9LRERE/Ug8HoeqqhgwYAAEQTD6GWQrWDqdTrhcLui6DlVVEY/HuXrRB9hsNqRSKYTDYbhcLthsNoiimLPlLZPJGFWlEokEUqkUy9B2s279062vr8cbb7yB5cuXo76+HgCMZaqLL764Oy9NRERE/UAmk4Esy0in0wgEAgAAt9sNRVGMzxx2u934wBkMBiHL8glL59PXXyAQMFYtXC4X8vPzjZWrbM6F1WqFrusIBoOIRCJQFMX4e0Ldw/TgQlVVvPfee1i2bBk2bdoETdOM0nBFRUW49tpr8R//8R8YNGiQ2ZcmIiKifiadTiOdTkMUReM1q9XaaeAgiqLxHlaM6t3sdjtEUYSu61AUBbIsQxAE47OnIAiwWCzGcV3XIYoi7HZ7T0+9TzMtuPjss8+wbNkyvPnmm2hrawMAI6jweDyYP38+Jk2axKcEREREZJps8m5XexdkP2yeqCcGff2lUil4vV7k5eVBlmW0trYalaEEQYCu69A0DZlMBqqqIi8vD16vF6lUqqen3qedVnCRSqXwzjvvYNmyZfjkk08AfLnt6fzzz8fVV1+NefPmwePx4PLLLzdlwkRERERZ2YpAmUymS+M1TYPNZmOn7j5AVVX4fD6UlJQgFAohFoshnU4bfyeyQaQoivB4PPD7/fD5fMz97WanFFx8+umnWLZsGVatWoV4PG5E/yNHjsTUqVMxdepUlJWVAYBROYqIiIjIbKIoQhRFJBIJ47VMJtNpzkU6nYbL5crZRkW9k8PhgCRJsNvt8Hq9yGQyHa5IOZ1OeL1e2O12SJLEimHd7JSCi+uuu86ICEeNGoXJkyfjqquuwtChQ82eHxEREVGnrFar0ccgHA5DEATIsgxFUYyk3uyHyuxTbEmSuE27D/D5fBBFEclkEg6HAwMGDDDKzx77tc+WpU0mkxBFET6fr6en3qed1rao8ePH46qrrsL48eNRWlpq1pyIiIiIusztdiMajaKxsRGqqhoBR3a7VCKRQCaTgc1mQ3l5Odxud09PmUzgcrlQXl6Os88+G6IoIhAIGDkW2VWrbA5GMBhEOp1GeXk5XC5XT0+9Tzul4OLaa6/F6tWr8be//Q0bNmwAcHRL1L/+679iypQpGDhwoKmTJCIiIqLu1dzc3NNTOCmRSMQIKrIJ/Xa7PaePRTbAzMvLg6ZpEEURTU1NvSapu6ioqKencNJOKbj4+c9/jjlz5mD16tV488038eGHH2LXrl3YvXs3nn76aYwZMwZXX301rrrqKrPnS0RERJQj20SvoKAAFoslZ1uU1WqFz+eDJEnQNI1N9E5AUZSenkKXtbW1IRKJIJPJIBqNIhQKGeVos3RdhyRJ8Pv9cLlciEQiaGtr6xVliHtrydxT3hblcDgwbdo0TJs2Dc3NzVi5ciVWrlyJPXv24B//+Ae2b9+Oxx9/HMDRv6ixWAwej8e0iRMRERF11ETveAndbKLXd1gsFkiSBKfTCafTCU3TEI1G2/W58Hq9KCgoAABIktTlssV0akzpc1FUVIQZM2ZgxowZ2L17N1asWIFVq1ahpaUFwNFv5G9+85v41re+henTp2PChAn8whIREdFpYxO9/iubmK/rOqxWK8rLy43E7WxCt9PphMVigaqqRkApSVJPT71PM/0T/siRIzF79mysX78eL774IqZOnQpJkiDLMv785z/jzjvvxKWXXor58+ebfWkiIiLqZ9hEr/9yu91wOBxIpVKwWq1wu93w+/3Iz89HQUEB8vPz4ff74Xa7YbVakUql4HA4mNDfzUzr0P1VFosFl156KS699FLEYjH85S9/wR//+Eds3boVLS0t+M1vfoMHH3ywuy5PRERE/QCb6PVf2YDC4/FA13XE43Ekk8mcQDMcDsPpdELXdXg8HiPQoO5zRvYmeTwe/Pu//zuWLFmC9957D/fccw8qKyvPxKWJiIioD8s20Uun010an91CxSZ6vV86nYbH40FhYSEURUE0Gu1wXDQahaIoKCwshMfj6fLfFTo1ZzzxoaysDLNmzcI777xzpi9NREREfcyxTfSO7dLdkUQiwSZ6fYiu67DZbMbqRTaXwmaz5ZSklSTJWLWw2WzcEtfNum1bFBEREdGZ4Ha7kU6nEQ6HAaDDJmmJRALpdBo+n4977vsIQRCQSqVgsVgwcOBAyLKMWCyW08MiPz/fCDwymQxSqRS3xHUzBhdERETUq9ntduTl5QE42vMiGAxCFEVYLBZommZshfL5fMjLy+u1/QMoV7ZSVCKRQEVFBQRBgKIoSCaTRmUop9MJu90OXddx8OBB5Ofnc9WqmzG4ICIiol7P4XDAarVCFEWj70V224zL5YIkSXC73Qws+pBMJgNBEOByuZBKpYxAoqOvcSqVgsvlOqnkfzo1DC6IiIioT7Db7fD7/chkMkZwIQgCRFHk0+o+SNd1o7RstrO4w+Fo16E7lUpBURSjdC1zLroXgwsiIiLqU7LbYahvywaOLpfL2B4VCoUgSRIEQYCu65BlGaIowuv1GuOZc9G9+kRwsXHjRixYsAB79uyBoiioqqrCHXfcgQkTJnT5HNu2bcPChQvxySefIBKJwO/34+KLL8asWbMwbNiwduNvvfVWfPjhh52eb/Xq1Rg8ePAp3Q8RERERHV+2pHAikYDX6zW2xCmKAk3TYLVa4fP5IEkSHA4HYrEYyxCfAb0+uFi+fDlmz54NURRRXV0NTdOwefNmzJw5E3PnzsUNN9xwwnOsXLkSDz30EDKZDEaPHo0LLrgAtbW1+NOf/oT33nsPCxcuxEUXXZTznt27d8PlcuHyyy/v8JysREFERETUfY4tQ6yqKvLy8pDJZKAoirElzm63w2q1sgzxGdSrg4umpibMmTMHXq8Xr732GkaMGAEA2L59O26//XY8+uijmDRpEkpKSjo9RzAYxM9+9jPouo7nn38eV1xxBYCje/RefPFF/OIXv8CPf/xjrF692kgQqqurQygUwiWXXIInn3yy+2+UiIiIiNrpqAzxV4MHliE+s854Ez0zvfrqq0in07jtttuMwAIAxowZg5kzZ0KWZSxduvS451i9ejXi8TimTZtmBBbA0X183/ve9zB69GgcOXIEn3zyiXFs165dAICqqiqT74iIiIiIuipbhtjn80HTNASDQcRiMSQSCcRiMQSDQWiaxjLEZ1CvDi4++OADAMgJCrImT54MAFi/fv1xz6FpGkaPHo3q6uoOj2fzJpqamozXdu7cCYDBBREREVFPczgcyM/PR35+Pvx+P2w2GwRBgM1mg9/vN445HI6enmq/0Gu3Rem6jtraWlgsFgwdOrTd8crKSlgsFtTW1hr77jpy88034+abb+7wmKZp2LFjBwCgtLTUeD0bXITDYcyYMQM7duyALMs455xzTjqRnIiIiIhOT7YMcTqdRjweh6qqsNlscLvdTOA+w3rtykU4HEY6nYbf7+/wL43NZkN+fj6SySTi8fgpXWP58uU4ePAgiouLcf755xuvZ7dFzZkzB01NTbjoooswcOBAbNmyBTNnzsTixYtP7aaIiIiI6KQpioJQKISWlha0tLSgtbXV+O9QKGT0waDu12tXLpLJJAAct451dvkrHo/D4/Gc1Pk//fRTPProowCA+++/Hzbb0T+qYDCIhoYG2Gw2zJ8/H1dffbXxnrfffhsPPPAA5s+fj3HjxmHUqFHHvcby5cuxYsWKLs0nG9AQERER0ZdSqZQRUMTj8ZzdKs3NzXC73SgoKEBhYSG3Rp0BvTa4sFhOvOhyqh0Yt2/fjv/6r/9CIpHAjTfeiOnTpxvHAoEANm3ahEgkgsrKypz3TZkyBdu2bcPixYvx+uuvTYrghQAAIABJREFUY+7cuce9Tl1dHbZs2XJKcyQiIiLq7xRFQXNzM+rq6qCqqlGe9tgmetFoFMlkErquo7S0lEnd3azXBhculwsAIMtyp2Oyx06mS+f777+P++67D4lEAjU1NZgzZ067MYFAAIFAoMP3X3bZZVi8eLGRq3E85eXlGDduXJfmtWvXLkSj0S6NJSIiIuoPQqEQGhoaIMsy8vLy4HA4clYunE4nUqkUIpEIGhoa4HA4UFRU1IMz7vt6bXDh8XjgcrnQ1tZmJO0cS1VVtLW1QZIk5OXldemc/7+9e4+P+c73OP6aXCYSCUHTkhuKJEW1Uo1oKRbtHtUi1YqqFUvdtuqc7e4DxUOrx+5Jre1x2zoltEurLkUptmhC3VZ2lW5dNjYkighR99xmMjPnj56ZIzKJRH8xCe/n49FHmu/3+/v+ft/5MOYzv+/3+/v000+ZPn06NpuNYcOGMWHChCo/It75B7aoqOiWbRMSEkhISKhUv0OGDNFdDhEREZH/Y7PZuHz5MteuXSM4ONjtl8kmkwl/f38cDgeXL1/m8uXLNGzYUA/Sq0a1dkG3yWSiZcuW2Gw2srOzy9RnZWVht9tLPf+iIvPmzWPatGk4HA4mTZrExIkT3SYWe/bs4be//S0ffvih235Onz4NlN5dSkRERESMVVhYyPXr110JREX8/f0xmUxcv37dtW5XqketTS4A15av27ZtK1PnLOvatest+1m6dClz587F19eXWbNmkZSUVG7boqIi1q9fz5///GdKSkrK1K9btw6Azp07V2YIIiIiInIbiouLKS4uxmw233Kmiclkwmw2u46R6lOrk4uEhAT8/PxYuHAhhw4dcpV/9913LFq0iDp16pR6hkVOTg7Hjx/n4sWLrrKMjAySk5MBSE5Opnfv3hWes3PnzoSFhXHmzBlmzpyJzWZz1X322Wds3ryZkJAQBgwYYNQwRURERKQcld3A53Y3+pGqqbVrLgDCw8OZMGEC06dPJzExkfj4eBwOB/v27aOkpITk5GQaNWrkaj9hwgTS09N57bXXGDduHAALFizAarVSr1490tLSSEtLc3uuxMREOnTogNls5g9/+APDhw/nww8/JDU1lZiYGE6dOsXRo0cJCAhg7ty5BAUF3ZHXQERERORe5Ofnh5+fn2snqIruXjgcDqxWK3Xr1sXPz+8OXuW9p1YnFwCDBw8mNDSURYsWsX//fsxmM7GxsYwZM4ZOnTrd8njnIumrV6+yYcOGcts98cQTdOjQAYDY2FjWrl3L+++/z+7du0lLS6NBgwYkJCQwduxYIiIijBmciIiIiLjl7+9PYGAgly5dorCw0LWTqDvOBCQwMLBKu4hK1dX65AJ+3P61e/fut2y3dOnSMmW7d+++rXM2a9bMNZ1KRERERO4sb29vgoODXTtGOddVlJSUYLfb8fLywsfHB4vFwrVr1wgKCiI4OFg7RVWzuyK5EBEREZF7T3BwMI0bN+bkyZOcP38eu91epo2Xlxd169alcePGBAcHe+Aq7y1KLkRERESkVvL19SUoKIigoCCuXbvGtWvXSj2B22q1uuqDgoL0dO47QMmFiIiIiNRKVqsVi8WCr68v4eHhOBwOioqKsNlseHt7u57Y7e3tjcViwWq1KsGoZkouRERERKRWys/PJz8/n7p16xIQEIDNZsNqtbp2j/L19cXb25uCggLy8/Mxm82aGlXNlFyIiIiISK1js9koLi7GYrHQsGFD4MdF3u4WbAcEBHDx4kWKi4tddzWketTqh+iJiIiIyL3JYrFgsVgwm82Vam82m13HSPVRciEiIiIitY7D4cDhcODlVbmPs15eXq5jpPoouRARERGRWsdkMmEymdxuP+uO3W53HSPVR8mFiIiIiNQ6ZrPZNdWpMpxTqCo7jUpuj5ILEREREal1vL298fPzw2w2U1BQUGHbgoICzGYzfn5+WsxdzZRciIiIiEitVLduXerWrYvFYik3wSgoKMBisbjaSvXSVrQiIiIiUiv5+vpSr1494MdnXly8eBGz2YyXlxd2u901Fap+/frUq1dPD9C7A5RciIiIiEitVadOHby9vTGbza7nXjgcDnx8fAgICMDPz4+6desqsbhDlFyIiIiISK3m6+tLcHAwNpvNlVyYTCbMZrPWWNxhSi5ERERE5K7g7e2Nv7+/py/jnqYF3SIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYgglFyIiIiIiYggfT1+AEfbs2cOCBQvIyMjAarXSpk0bRo4cSZcuXSrdx7lz55g/fz67d+8mLy+PJk2a8Pzzz/Pqq69iNpvLtC8qKuKjjz5i/fr1nD59mqCgILp168brr7/O/fffb+TwRERERERqhVp/52LNmjUMGzaMAwcO0K5dO9q3b8+BAwcYMWIEK1asqFQfubm5vPTSS6xYsYJ69erRrVs38vPzmTNnDsOHD8dqtZZqb7VaGTt2LH/84x/Jz8+na9euBAcHs2rVKhISEsjJyamOoYqIiIiI1Gi1Ork4f/4806ZNIygoiM8++4yFCxeSkpLCJ598QmBgIDNmzODcuXO37Oett94iNzeX8ePHs3btWubMmcOWLVt44oknSE9PZ+nSpaXaL1u2jN27d9OtWze2bNnCnDlz+OKLLxg9ejR5eXlMnz69uoYsIiIiIlJj1erkYtmyZVgsFpKSkoiKinKVt2vXjhEjRlBcXHzLuxcnTpxg+/btREZGMnr0aFd5QEAAM2bMwNvbm2XLlrnKHQ4HS5YswWQyMXXq1FJTpsaPH0/z5s1JS0vj1KlTBo5URERERKTmq9XJxc6dOwHo2bNnmbpevXoB8PXXX1fYx65du3A4HHTv3h0vr9IvR2hoKK1bt+bMmTNkZmYCkJGRwblz54iJiSE8PLxUey8vL372s59V6rwiIiIiInebWptcOBwOMjMz8fLy4sEHHyxT36xZM7y8vMjMzMThcJTbjzNpaNWqldt6Z9/Hjh27rfYiIiIiIveKWptcXLlyBYvFQnBwsNvdnHx8fGjQoAGFhYXk5+eX28/58+cByt3hKSQkBIALFy4AkJeXV6q8vPY//PBDJUciIiIiInJ3qLVb0RYWFgLg7+9fbps6deoAkJ+fT2BgYIX9ONuW10dBQUGpn+Wd9+b2FVmzZg1r1669ZTuAAwcOAHD06FGGDBlSqWOMZLFY7vg5pWLukurqoNjXPHci9op7zaTY37sU+3vTnfq3vjwxMTFMnjy5SsfU2uTi5vUR7lQ0HermfkwmU4V9OH9WtX1Fzpw5Q3p6+i3b3ejatWtVPkZERERE5E6otclFQEAAAMXFxeW2cdZVdHfD2U9RUVGl+qhq+4qEhYURFxd3y3YAhw4dwm63U79+fZo2bVqpY6S0o0ePcu3aNYKCgnjooYc8fTlyByn29y7F/t6l2N+7FHvjxMTEVPmYWptcBAYGEhAQwKVLlygpKcHHp/RQSkpKuHTpEn5+ftSrV6/cfpxrLZxrKm7mXGPhbFfZ9uWtybhRQkICCQkJt2wnxhgyZAjp6ek89NBDZZ5dInc3xf7epdjfuxT7e5di71m1dkG3yWSiZcuW2Gw2srOzy9RnZWVht9tLPf/CHeeuT85doG52/PhxAFc/zp+VbS8iIiIicq+otckFQJcuXQDYtm1bmTpnWdeuXSvVR2pqKna7vVRdTk4OR48eJSwsjJYtWwLQokULwsLCOHLkCGfPni3V3m63k5qaislkcvUrIiIiInKvqNXJRUJCAn5+fixcuJBDhw65yr/77jsWLVpEnTp1ePnll13lOTk5HD9+nIsXL7rKIiIi6NKlC1lZWcyePdtVXlBQwJQpU7DZbAwbNqzUeRMTE7HZbEyePLnUrlCzZ88mOzubXr16ERkZWR1DFhERERGpsWrtmguA8PBwJkyYwPTp00lMTCQ+Ph6Hw8G+ffsoKSkhOTmZRo0audpPmDCB9PR0XnvtNcaNG+cqnzZtGoMGDWLBggWkpqbSvHlzvvnmG/Ly8njqqacYNGhQqfMmJSWxfft2du/ezdNPP01sbCxZWVkcO3aM0NBQpk6desdeAxERERGRmqJW37kAGDx4MAsWLOCRRx5h//79HDp0iNjYWJYsWULfvn0r1UdERASrVq0iISGBixcvsn37durXr88bb7zBvHnzyiwWN5vNpKSkMHbsWPz9/UlLSyM/P5+BAweyYsWKch/IJyIiIiJyN6vVdy6cunfvTvfu3W/ZrqIdA5o0acLvf//7Sp/T39+f8ePHM378+EofIyIiIiJyN6v1dy5ERERERKRmUHIhIiIiIiKGUHIhIiIiIiKGuCvWXIhURv/+/YmLiyMsLMzTlyJ3mGJ/71Ls712K/b1Lsfcsk8PhcHj6IkREREREpPbTtCgRERERETGEkgsRERERETGEkgsRERERETGEkgsRERERETGEkgsRERERETGEtqKVWmPPnj0sWLCAjIwMrFYrbdq0YeTIkXTp0qXSfZw7d4758+eze/du8vLyaNKkCc8//zyvvvoqZrO5TPuioiI++ugj1q9fz+nTpwkKCqJbt268/vrr3H///UYOTyrgidjfyOFwMHToUM6ePcvWrVt/6nCkCjwR+6FDh/LXv/613P62bNlC06ZNb2s8UjlGxP1GFy9epHfv3vTo0YMZM2a4baP3+5rBiNgfPHiQhQsXcuDAAa5evUpwcDAdO3Zk7NixtGjRokx7xd5Y2opWaoU1a9YwadIkzGYz8fHx2O129u3bh9VqZfr06QwcOPCWfeTm5jJw4EByc3Np3bo1ERERfPPNN+Tl5REXF8fixYvx9fV1tbdarYwaNYrdu3fTpEkT2rVrx4kTJ/jXv/5FSEgIK1euJDQ0tDqHLXgm9jdLTk5m8eLFREZGKrm4gzwV+44dO2KxWOjRo4fbPidOnMh9991nyBilLCPifqPCwkJeffVV/va3vzFgwAC3yYXe72sGI2K/fv16Jk6ciM1mo3Xr1oSGhpKZmUl2djb+/v4sXLiQxx9/3NVesa8GDpEa7ty5c462bds6HnvsMUdGRoar/Ntvv3XExsY6Hn74YUdubu4t+xk1apQjKirKMX/+fFdZfn6+IykpyREVFeVISUkp1X7x4sWOqKgox8iRIx3FxcWu8j/+8Y+OqKgox6hRowwYnVTEU7F3Kioqcrz55puOqKgoR1RUlKNnz54/fVBSKZ6K/enTpx1RUVGOpKQk4wYjlWZU3J1ycnIcL774ouvv8Jtvvum2nd7vPc+I2P/www+O9u3bO2JiYhxbt251ldvtdseCBQscUVFRjm7dujksFourTrE3ntZcSI23bNkyLBYLSUlJREVFucrbtWvHiBEjKC4uZsWKFRX2ceLECbZv305kZCSjR492lQcEBDBjxgy8vb1ZtmyZq9zhcLBkyRJMJhNTp04tNXVi/PjxNG/enLS0NE6dOmXgSOVmnoi909dff03//v1ZvXo1ERERxg1KKsVTsT969CgAbdq0MXA0UllGxB2gpKSEZcuW0a9fP7799tsK/w7r/b5mMCL2W7ZsIT8/n+eff56ePXu6yk0mE6NGjaJ169bk5ORw4MABQLGvLkoupMbbuXMnQKk3CqdevXoBP34QrMiuXbtwOBx0794dL6/Sf+xDQ0Np3bo1Z86cITMzE4CMjAzOnTtHTEwM4eHhpdp7eXnxs5/9rFLnlZ/GE7F3evXVV8nKymLIkCH8z//8z08ZhtwGT8X+yJEjgJILTzEi7gD79+/nnXfeoaSkhLfffpuxY8eW21bv9zWDEbG32+20bt2a+Ph4t/XOtVLnz58HFPvqouRCajSHw0FmZiZeXl48+OCDZeqbNWuGl5cXmZmZOCpYPuT88NCqVSu39c6+jx07dlvtxXieir3TM888w7p165gyZQp+fn63Owy5DZ6MvTO5uHLlCsOHDyc+Pp727dszZMgQ14cfqR5GxR3A39+fpKQktm7dSmJiYoVt9X7veUbF/uWXX2bt2rX079+/TJ3dbufw4cMANG7cGFDsq4uSC6nRrly5gsViITg42O2uLj4+PjRo0IDCwkLy8/PL7cf5LUV5uz6EhIQAcOHCBQDy8vJKlZfX/ocffqjkSKSqPBV7pzlz5hAdHX27ly8/gSdj75wWNW3aNM6fP8/jjz9OeHg46enpjBgxgo8++ui2xyUVMyru8ONUmkmTJtGwYcNbnlfv955nZOzLs2bNGr7//nvuv/9+Hn30UUCxry5KLqRGKywsBH78Fqo8derUAajwDcfZj7NteX0UFBSU+lneeW9uL8bzVOzF8zwV+4sXL5Kbm4uPjw+zZs1iw4YNzJ07lw0bNvDee+/h4+NDcnKyKwERYxkV96rS+73nVXfsDx065Nol7I033sDH58cnMSj21UPJhdRoN8+TdudWt8dv7MdkMlXYh/NnVduL8TwVe/E8T8W+YcOG7N27l40bN9KnT59SbXv37s3gwYOx2WwsX778lueWqjMq7rd7Xr1HeE51xv4f//gHw4cPp6CggMTERPr161fmvIq9sZRcSI0WEBAAQHFxcbltnHUVfePh7KeoqKhSfVS1vRjPU7EXz/Nk7Bs2bEizZs3ctu/evTuAa962GMuouN/uefUe4TnVFfsdO3YwdOhQLl++TEJCAtOmTXN7XsXeWEoupEYLDAwkICCAS5cuUVJSUqa+pKSES5cu4efnR7169crtxznn+uZ59U7OeZfOdpVtX948TfnpPBV78byaGnvn3/fyPojIT2NU3KtK7/eeVx2x//TTTxkzZgwFBQUMGzaM3/3ud2XukCj21UPJhdRoJpOJli1bYrPZyM7OLlOflZWF3W4vtSe2O86dIG7ebtTp+PHjAK5+nD8r216M56nYi+d5KvZ79uzht7/9LR9++KHb9qdPnwb+f6cZMZZRca8qvd97ntGxnzdvHtOmTcPhcDBp0iQmTpzoduqTYl89lFxIjdelSxcAtm3bVqbOWda1a9dK9ZGamordbi9Vl5OTw9GjRwkLC6Nly5YAtGjRgrCwMI4cOcLZs2dLtbfb7aSmpmIymVz9SvXwROylZvBE7IuKili/fj1//vOf3X57um7dOgA6d+5cxdFIZRkR96rS+33NYFTsly5dyty5c/H19WXWrFkkJSWV21axrx5KLqTGS0hIwM/Pj4ULF3Lo0CFX+XfffceiRYuoU6cOL7/8sqs8JyeH48ePc/HiRVdZREQEXbp0ISsri9mzZ7vKCwoKmDJlCjabjWHDhpU6b2JiIjabjcmTJ5faKWL27NlkZ2fTq1cvIiMjq2PI8n88FXvxPE/EvnPnzoSFhXHmzBlmzpyJzWZz1X322Wds3ryZkJAQBgwYUF3DvucZEffbofd7zzMi9hkZGSQnJwOQnJxM7969b3lexd54JoeWwEst8PHHHzN9+nR8fX2Jj4/H4XCwb98+SkpKSE5Opm/fvq62Q4YMIT09nddee41x48a5yk+dOsWgQYPIy8sjKiqK5s2b880335CXl8dTTz3F+++/79qeDsBisZCUlMT+/fsJCQkhNjaWrKwsjh07RmhoKCtWrNA8/TvAE7G/2enTp+nRoweRkZFs3bq1Wscr/88Tsf/mm29cO8tERkYSExPDqVOnOHr0KAEBASxevJj27dvf0dfhXmNE3G+2Zs0aJk2axIABA1xbkt5I7/c1w0+N/X/8x3+wadMm6tWrV+FdjsTERDp06AAo9tWh/H9NRWqQwYMHExoayqJFi9i/fz9ms5nY2FjGjBlDp06dKtVHREQEq1atYs6cOXz99decPHmSiIgIfvGLXzB06NAyHy7NZjMpKSl88MEHfPHFF6SlpRESEsLAgQN57bXX9GZzh3gi9lIzeCL2sbGxrF27lvfff5/du3eTlpZGgwYNSEhIYOzYsURERFTHUOUGRsS9qvR+XzP81Ninp6cDcPXqVTZs2FBuuyeeeMKVXCj2xtOdCxERERERMYTWXIiIiIiIiCGUXIiIiIiIiCGUXIiIiIiIiCGUXIiIiIiIiCGUXIiIiIiIiCGUXIiIiIiIiCGUXIiIiIiIiCGUXIiIiIiIiCGUXIiIiIiIiCGUXIiIiIiIiCGUXIiIiIiIiCGUXIiI1ECnT58mOjra7X9t27YlLi6OhIQE3nvvPc6ePevpy70r3Pia5+fnV+u51qxZQ3R0NAkJCVU6znl9x44dc5UNGTKE6Oholi1b5irbt28f0dHRdOzYsUwfdrudrKys2794EZEKKLkQEanh2rZtS2xsrOu/mJgYAgMDOXLkCAsWLODZZ5/lL3/5i6cvU2qB7777jgEDBrBy5UpPX4qI3KV8PH0BIiJSsdmzZxMeHl6mPDc3l3fffZeNGzfym9/8hvr169OpUycPXKHcKZs2bQIgIiKiwnbt2rVj06ZN+PiU/mf+448/5vDhw27vaIiIGEHJhYhILdW4cWNmzZpFSUkJX375JZMnT+bLL7/E19fX05cm1aRFixaVaufv71/ptiIiRtK0KBGRWsxkMjFt2jT8/Pw4c+YMX3zxhacvSURE7mFKLkREarlGjRrRvXt3ANLS0lzlzgXKffr0ITMzk4EDB/Lwww/TuXPnUot/r169yty5c3nuued45JFHaN++PQkJCSxatIiioqIy53MuID548CBpaWm89NJLPProo3Tu3Jlx48Zx6NChKl3/7fQ3d+5coqOjSUlJ4ZNPPuGpp56iXbt29OnTh5MnT7raHT58mDfeeIMuXbrQtm1b4uPjGT16NHv27KnwmiwWC3PmzKFHjx48/PDD9OrVi3fffZcrV664bX/9+nU++OADEhMT6dixI23atCEuLo5XXnmFVatWYbfbyz3X+fPnmThxIp06deKRRx4hISGBZcuWUVJSUqatuwXd7ty8oNv5Z2Ht2rUALF68mOjoaCZOnEhqairR0dHExcVhsVjc9rdu3Tqio6NJSkqq8LwiIkouRETuAu3btwfg73//e5m6a9euMXz4cI4dO0bLli25du2aa8pMdnY2ffv2Zd68eRw/fpxmzZoRHh7OkSNHmDlzJi+99BJ5eXluz/n5558zZswYMjIyePDBBykpKWHLli0kJiayefPmKo/hdvrbsmULb7/9Nt7e3oSFhVFQUOBaj/Dxxx/z4osv8sUXX1BcXExMTAw+Pj6kpaUxbNgw3n333XKv5Ve/+hXz58/HarXSqlUrzp49S0pKCv379y+zO1dOTg79+vVj1qxZHD58mPvuu49WrVphs9n429/+xpQpU3jrrbfcnufy5cu89NJLrF27luDgYMLCwjh8+DDvvPMOo0ePxmq1Vvl1dMfPz4/Y2FgaNWoEwAMPPEBsbCzNmjXjqaeeomHDhly5coVdu3a5PX79+vUA9O3b15DrEZG7l5ILEZG7QFhYGAA//PBDmQ+kubm51KlTh23btrF27Vp27NhBfHw8VquV0aNHk5OTQ1xcHKmpqXz++eds2LCBzZs3Ex0dTUZGBv/+7//u9pyffPIJTz75JNu3b2fNmjXs3LmTpKQkrFYrb775JufPn6/SGG6nv4MHDzJ06FBSU1PZvHkzq1evxsvLi3379vHOO+9gt9v59a9/zZ49e1i9ejU7d+4kOTkZs9lMSkoKq1atcnstBw8e5O2332bHjh2sWbOGrVu30rp1a86cOcOUKVNKtf3973/PqVOnePzxx9mxYwcbN25k3bp17N27l1/+8pcArFy50m2SdubMGYqKili+fDmbN29m06ZNfPzxx9SvX5+dO3eyZMmSKr2G5QkJCWH58uU89dRTADz77LMsX76c0aNH4+Pjw7PPPgvgdlpdXl4ef/3rX/H396dXr16GXI+I3L2UXIiI3AXq1q3r+n93U3d++ctfur61Dg4OxmQysXHjRrKysrjvvvv405/+ROPGjV3tmzdvzgcffEBAQAB///vf2bFjR5k+mzRpwty5c2nQoAEAvr6+TJo0ibi4OAoKCkpNvaqM2+nP19eX8ePHYzKZAGjYsCEAf/rTn3A4HAwcOJBRo0a5dk0ymUz069ePN954A4A5c+Zgs9nK9Dt06FASExNd/TZp0oQ5c+bg4+PDrl27yMjIAKC4uJhvv/0Wk8nE22+/7To/gNls5je/+Q1msxmHw8GJEyfcjvu//uu/iI2Ndf3eoUMHJk+eDMBHH33kdnqU0fr16wdAamoqBQUFpeo2btyIzWajR48eBAYGVvu1iEjtpuRCROQucOPdCucH4hs9+uijZcqcCcNzzz1HUFBQmfrGjRu7vqnevn17mfoXXniBgICAMuUDBgwASq//qIzb6S8qKqpUYgWQn5/vmh728ssvuz3XwIEDMZvNnD9/nsOHD5epd3dcREQEcXFxAOzcuRP4cbrR119/zcGDB93uzlRcXEz9+vUB3K5fadKkCd26dStT/m//9m/4+/tz4cIFjhw54nYMRmrbti2tWrWisLCQr776qlSdpkSJSFUouRARuQtcv37d9f/uEoWQkJAyZdnZ2QA89NBD5fbbunXrUm1v1LZtW7fHREVFAfD999+X2687t9Ofu3GdOnWKkpISfH19adWqlds+/f39efDBB4GyY6tbt265z5Fo2bIlQJknXNepU4fTp0+zYcMG5s2bx8SJE3nhhReIi4tzTYdyt6g7JibG7XnMZjNNmzZ1e67q8vzzzwM/3qlwOnHihGsdyZNPPnlHrkNEajc950JE5C7gnHITHh6O2WwuU+/n51emLD8/H6DMN/83ctY5296oXr16FR5TVFSE1Wqt9HPlP2/wAAAGr0lEQVQ3bqe/isbl7++Pl1f536E575LcPDZ3d09urrvxLkROTg4zZszgq6++wuFwuMpDQkJ45pln2LlzZ7m7TFX02rs7V3Xq27cv7733Hrt27eLy5csEBwfz+eefA9CnTx+8vb3vyHWISO2m5EJE5C5w4MABwP30p/I4P7zeeNfjZteuXSvV9kblfeh19hcYGFilB/oZ1Z/zA3thYSF2u73cBMPZ781jKywsLLdvZyLivDtUWFhIUlISJ0+eJDw8nEGDBtG2bVtatGjhuqvSuXPncvu7eX1DReeqbg888ADx8fHs2bOHrVu38uKLL7qeCK4pUSJSWZoWJSJSy509e5a9e/cCP87Vr6zmzZsDcPTo0XLbONcjREZGlqnLzMx0e8w///lP4P+nEFWWUf1FRETg4+OD1Wot93kQBQUFrulGzulHTtevX+fChQsVXotzutW2bds4efIkwcHBrF69mhEjRhAfH+9KLIqLi7l8+XK51+puuhn8mGg5r6+8qV3VwZlEbNu2jczMTL7//ntatWrlmh4nInIrSi5ERGo555arLVu2dD1MrzK6du0K/Lj9qPMOxY1yc3Ndi3vdffv++eefl5oGBOBwOFi9ejUAPXv2rPS1GNlf3bp16dChAwDLly9322blypVYrVaCg4Np06ZNmfp169aVKTt27Bj79+/Hy8vLtaXrmTNnAAgNDXXtcnXzmJyL7d3tSnXixAn+8Y9/lCn/7LPPsFgshIeHVzlJq4hzsf/Nr7PT008/TUBAAHv37nU9W8S5FkNEpDKUXIiI1FLZ2dm8/vrrfPXVV/j6+vLOO+9UaV587969ad68ORcuXGDs2LHk5ua66rKyshg5ciSFhYW0b9+eHj16lDn+8OHDvPXWWxQXFwM/PtX6P//zP9m/fz8hISEMGjSoSuMxsr+xY8fi5eXFihUr+OCDD1zbuTocDtatW8esWbMAeP31191OtZozZw7btm1z/Z6VlcW4ceOw2+307dvXteC7WbNmAGRkZJCamupqb7VaWbVqFb/73e9cZc5x3ezXv/41x48fd/2+fft2/vCHPwA/PszP3e5ft8s5ZSwnJ8dtfUBAAE8//TTFxcUsXrwYLy8vnnvuOcPOLyJ3P625EBGp4caPH19qkXZxcTHnz5937UIUGBjIzJkzSz0roTLMZjPz589nxIgRpKen06NHD1q2bIndbudf//oXDoeD6Oho3nvvPbdJS6tWrfj000/ZtGkTTZs25fvvv+fKlSvUr1+f//7v/67yMxGM7K9jx45MnjyZGTNmMGvWLFJSUoiMjOTs2bOu123o0KEMHjy4zLGNGjWiadOm/OpXvyIiIoLAwEAyMjKw2+20b9++1EP0evbsSZs2bTh8+DBjxowhIiKCevXqcerUKa5evUpwcDCRkZFkZGS4fQjgY489RlZWFn369KFVq1YUFxe7pkq98sorJCQkVOk1vJXo6GgAvvzyS5577jni4uKYOnVqqTb9+vVj3bp1FBQUEB8fT5MmTQy9BhG5uym5EBGp4Q4dOlTqdx8fH4KCgnjsscfo0qULAwcOLPXwtqpo0aIF69atY8mSJWzbto3s7Gx8fX15+OGH6dOnD4mJiW53ZAJITEwkODiYRYsWcezYMRo1asTPf/5zRo4cSXh4eJWvxej+XnnlFdq1a8eHH35Ieno6R48epWHDhjzzzDMMGjSITp06uT3Ox8eHlJQUZs+ezaZNmzh37hzNmzenX79+JCUllUr0fHx8WLp0KSkpKWzZsoXTp09z4cIFQkNDGTBgAMOGDSM1NZVp06aRmprKsGHDSp0rNDSU5ORkZs6cyd69e7FarbRv355f/OIX9O7du8pjvpX+/fuTmZnJhg0byM7OdrtDV8eOHXnggQc4d+6cFnKLSJWZHOVNvBQREXFjyJAhpKenM3XqVF555ZUa15/8NAUFBTz55JM4HA527dqlp3KLSJVozYWIiIi4bN68mYKCAn7+858rsRCRKtO0KBERkXvcyZMn8fb25sSJE7z77rsAuoskIrdFyYWIiMg9buXKlSxatMj1+wsvvEDbtm09eEUiUlspuRAREbnHtWnThsDAQHx8fOjTpw8TJkzw9CWJSC2lBd0iIiIiImIILegWERERERFDKLkQERERERFDKLkQERERERFDKLkQERERERFDKLkQERERERFDKLkQERERERFDKLkQERERERFDKLkQERERERFDKLkQERERERFDKLkQERERERFDKLkQERERERFDKLkQERERERFDKLkQERERERFD/C/5GKMuYHzJrgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "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": [ + "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=0.8)\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(\"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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+ "text/plain": [ + "
" + ] + }, + "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", + "# 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", + " 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(\"Drop probability\")\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/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 --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 +} diff --git a/notebooks/test_digits.ipynb b/notebooks/test_digits.ipynb new file mode 100644 index 0000000..e03a1c3 --- /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": "
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\n" + }, + "metadata": { + "image/png": { + "width": 402, + "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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\n" + }, + "metadata": { + "image/png": { + "width": 402, + "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(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": 7, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "image/png": { + "width": 433, + "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(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..cdbe2c5 --- /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": "
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\n" + }, + "metadata": { + "image/png": { + "width": 421, + "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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+ }, + "metadata": { + "image/png": { + "width": 421, + "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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jx0aDU9StW1csWbJEGvSicuXKRud+9NFHyv6GDRuKs2fPikuXLinbbG1txYMHDzJ9nbLrxYsX4tixY6J79+5S/tXfkaVLlyrHqmtIf/31V+k706BBA7Fq1Sqxb98+MWnSJGV7pUqVlKD1yZMnRs3o9AXg1NRUpSkkALF7926pxtXT01OkpKQoeVf/lqiDAVN38GfPnm2111AI4z576u+7+sZMes0Uc0Ld7E4fWKs/V23btjVZ8A8PDxfFixdXjqtWrZpYvHixePLkiVi5cqXUXA6AePPNN4UQxoM23L17V6pZ0ddCJiUlSTdG/P39TebjwoULYs6cOWLUqFGiffv2Rp8P9aIOeNWBdfHixUXNmjXTPa9cuXJG24oXL26yJnTr1q3C1dVVNGrUKMPv3vbt25W0atasmWFwlZqaKsLDw5XPb2RkpBRkLViwQDx+/FgK/o8ePWr0W6yufdIzVctbo0YNcfbsWamJqv57bCgmJkaqNXR0dBSff/651MQ7NTVVXL58WYSEhBS4gYfMxUCJLIaBUsGWmJiY7TbG+mZCVatWFePGjcv0rtunn36q/Ph+8sknyvaTJ0+KESNGmGxuNHz4cHHz5k3pjuiWLVukUaz+7//+L1v5F0KI5cuXK+l4e3tLzyEsLExqDgRAjB07Vrx48SLDNGNiYsQ777wjatWqZbLZ1YQJEzLNl7pGpG/fvsp2nU4nqlatalSINaz5MGzi2LNnT3H58mVx/Phxo0Kw4VK6dGmpQNChQwejY/r06WNy//jx45XHbm5uUkFWLSkpSSp4ffrpp5m+JkKkBSHqQpmp/lKDBw82+bwmT56cbrq9evVSjmvSpIny2NbWVoSEhIgTJ04Y9Y+zs7MTe/bskdI5dOiQUXOV1157TQkMUlJSpMKP4fvm7e2t7FP3Q1Bvnz59ulmvld7NmzfFt99+qzRxMvTgwQMxYcIE0bp1a5Md7N9++22xYMECZb127dpCq9VKfR2qV68utFqtOHHihFTYNrUYDo39448/SoUy9WdvwoQJyr7u3btLN0h+++03KR31QBD63wStVmuyP40lauYOHDggZs6cafT7aTiIwdtvvy3tX7VqlbKvTZs2Zl/vwYMHYsCAAWL48OEZNhdr2LCh0Wt0//596b01VSOqbhaY2VKtWjWpNkIdjC5YsEAKbo4fP64cd+vWLeHj4yMCAgKyNET206dPxfHjx8WPP/6oNCMG0m6a6Wtf1P0kx4wZI+Lj48X7778v5bthw4bi999/F6mpqeL06dPi1VdflW4CODk5STVLv//+u9SXrnPnzukGBgEBAcpxX3zxhdnPTe+7775TzndxcZE+zy1atFD+m4YOHapsr1ixolFNuWFzWf2ivolVr169dH+bhUj7n9m8ebNYsGBBgR+FMLsYKJHFMFAquBISEpRqdn3bcnMlJSVJfQgA08Pyqqk7s5vqyB4VFSUWLlwotaEGIBUsGzduLLRarTSCUE46Z6uDBsOaGyHSCubqvjH6gnR6fXzi4uIyHFGoYcOGZvXduXbtmnJOqVKllGYwN27cMJmu+s7inTt3pD/HvXv3GqX/999/iwEDBoj27dsLHx8f4efnJ929f/XVV4UQaTVT6qBB/Z7ExsaKhIQEqQAWHh4ujUYWHBxs8vmpB/UoVapUlv6M1XPSGA7qoO5MDUBq/pZeDdSNGzekwtL169elQlfTpk1N1lABaU2bgoODxY0bN6SmVkBaADt79myjGwjqQExd06DVaqXrqPtr/fbbb8r2KlWqZFjIUTt79qySZsmSJY2+d+fPn5dqgQyX6dOnC51OJ+Li4qQ73kFBQdIcOV999ZWSpqkR0fSLYS2QEGmFsg8//FB4eHhIw0cLkVa7kN73yLCD/4YNG5T9vr6+Qgj5e6SuUa1SpYp0bmpqqpg6dar45JNPMh2NMyUlRXzyySdKWsWLFxeTJk0SMTEx4u+//5YCxS5duhj1pYmMjJS+S+YOKmA4ImBAQIDR6GRXr16VPpvqZpJvvfWWss9Us091AJLeiHSlSpUSH3/8sdGgHuobTuramooVK1p8AJL79+9LteWzZ88Wx48fl36n1MHrkSNHxPjx48WOHTtM5uXSpUvSb1+5cuVEaGio2Lhxo/Q+6ZeZM2capREbGyvd6DN8X8yRmJgoNZlTL+oRL58+fSqNrKce7l6n00n/n+PGjZMCJP1ieIOHjDFQIothoFRwqe8Iu7m5mV2F/vTpU+kOt37JaNjdZ8+eKQUVGxsbo7bRarGxseKNN94w+Yehv8seHx8v9eNRj7ZkrqdPn0p/Ivfu3Us3P+oaLH2B4ddff5UKwc+fPxedO3c2ynPJkiVFrVq1RM+ePcX169fNyptOp5NqJvRB6Jo1a0y+Lh9//LFy7qJFi5TtPj4+Zr8ehn2Ndu7cKTXBGD58uNQxfOPGjdLQ3vXr1xdCCOku7nvvvWd0nZs3b0qFioULF5qdRyHkJjbqQR1iY2Ol9vWDBw8WkZGRynqxYsVMzsH08ccfGxUgL168aLKQVLZsWbF3716pMFKyZEmjWpTSpUun2xdE/dlWj3ymDoLd3Nykz1ZiYqJUAA0MDMz0dbp27ZpREyN1E9Lg4GCjWjIgrcZo2LBhRpMfq4ODpk2bSoFHWFiYdOypU6fE1KlTxbBhw0SnTp1EtWrVRJ06ddLtt5ERdQ2J4e+Amvr1c3V1FTqdTqxbt076LqhreNU1V/Pnz1e265uUmRIREWHyOw5AuLu7S00Rvby80v2d69Gjh3Kceh6pjJhqpqzRaMRrr70m/v33XyGE3OSvf//+0vmhoaHSeer37NGjR8rNAo1GI27duiW+//57Ua1aNQGk1SDNnz8/3X6Vhk2Y9cvYsWPNem5Zpa59cXBwkF6bYcOGZTm9ixcvSu9dhQoVpM+3OjCztbU1mlxX/Tlr1KhRtp/Xtm3bjF7DSpUqZTgps6enpxIAnjx5UtlepkwZkZSUJDZs2CC9Nz169Mh2/ooSBkpkMQyUCq7hw4dLP8iZ1QgJIcT169fT7ezp4OCQ7h+punOvvsNvZn755RepqU2zZs2kwqN6+Fn1HW1z6UcGAiBat26d4bE6nU4sW7bMqEDs5eUlVq9eLaKjo6XCD5A2n0t0dHS2B5tQd3TXNyVSd7BXB6vq11R9Z3jFihVZuubIkSOVc8uXLy/9wf73339i2rRpyvrAgQOlDtr62p2//vpL+pNX38XV6XTilVdekQrc5taOqNMwHNRh7969UmDp7Oys9HnSd5wGYFS7Fh8fLzXNUt9pNbyD7+bmpjR5unXrlnQXWr0MHTo03aBbCLmflX44dCHkeVYMRxYUQkj9fEzVCKjdvXs3wxGrBg8eLNUaODs7iw0bNmQ4d9ft27dN9nOz9uSpc+bMka7XsmVLk98pnU4nXF1dleNu3Lghxo0bJ/1GqPuO6YMtnU4n6tevLwURpoblP336tFGzV/WdfcPthsGjmrrWzZzfQ61Wm+FgBPrfgxo1aijrGzZsMEpH3URO3TxM3dT35ZdfVranpKSI69evm/UdNRXI/fnnn5melx3JyckmA2iNRiOuXLmSrTSPHTsm3XzTL/Xq1RP379+Xnl+NGjVEdHS0cq76N00/UEl26HQ6o5uQ6dVgqWt49S0K1E2fR44cqRy/YsUKYWdnJ8qWLWs0OBCZxkCJLIaBkvXpdDoRFhYmNm/eLL766qscj8AmRNofr7u7u/SDnFnfmZCQEKOCwaxZs6RRuQz7H+ip70ab2x9FiLS7oD4+PqJ+/fpGhRf1nEQNGjQwO0099YAJc+bMMeucf//912TzCMOmKpboLK6u8dN3wFY3aztw4IASyNjY2Ijo6GgRGRkpjapl7hxVek+ePDHZ0VnflOnixYvKNkdHR6mGSf+5TElJSbePwpYtW6RCjXp+nKxQBxummsWph/ZWBzyGw1urR9mqVauWFNQ9e/ZMGWXKxcXF6PN37do16fvQtm1bs57P3r17lXPatWunbP/888/TzacQaTVx6tfuzJkz4unTpyIxMVFotVoRHx8vIiIixJUrV6T+Xw4ODmLHjh1GTUj1S6VKlUz29TLF1Nw46kFXrOHu3btSwJ5R4VvdHHDDhg3SHF179uyRfof0fdZMdX7v3LmzFIydPHlSKkRrNBoxbdo0kZqaKn799VdRpUoVZV/JkiXF6dOnM3xOUVFR0m+G+jtiirq2rFy5cuLMmTOid+/e6QZN+qaxhtQDqFSoUEGpqVDP2bVs2bIM85Iew4DW2dnZ5BDelnLkyBGj5x0QEJCjNIOCgqSaZC8vL6WZYVhYmPRb4+vrK2bOnCm++OILqWVCRgGyOf7991/l857RPHnq/nve3t5Cq9VKN28Ma4QjIiJM1qiTaQyUyGIYKJkvNDRUzJw5M93+LRcuXBD9+/cXnTt3VpaOHTsKNzc3oz+EQ4cO5Sgv6nlH9EvdunXTPf78+fNS4dfe3l6ZxFPd0btVq1Ymz1cX0jIbNtpccXFxUhOurNxJjI+Pl87NyrxC0dHRYvz48elOiPrll19m49kYe/LkiRQInT59WgoOUlNTpVGkdu/eLdauXWuyEJ4Vv/76q9Fz0vc10ul0JmsUNRqNNGS4eljbQYMGiePHj4vbt29Lf+TvvPNOtl+biIgIk/0o3NzcpPb8QshDbrds2VLZbjhZqqkRyK5fvy6+++67dAs/t2/fFlOnThXbtm0zu+bw/v370vuoP09dyN+4caPJcw1rLTNb7OzslMAiNjbWaAQ4Ly+vLE1CrB61T5//zPr0WMLnn38ubGxsxNixYzN8ndUjML777rtSDXBkZKR086Fbt25CCCHVOqkXfY3T7du3pZtKzs7ORr9hCQkJYtasWcLf39+smnkhhHjttdek382M+vKom2R17dpV2X7mzBnx+uuvGzUTNWx2p5ecnCz1SduyZYt48OCB9Dtj2P/IXOqmfQDEkCFDspVOVhj2kz1//nyO09y4caNwc3MTHTp0MHotDCcMN1yy+5tr6McffxTe3t7pjqwpRNo8XeoATd0cz83Nzai5HmUNAyWyGAZK5klNTVVGUHNxcZHm+RAibajdjIYzNVxM9f3Iii+//NJkuqZGxjpz5ozUfrtUqVJSYSAyMlIqtBoO1/3s2TOjmg9L0c/CDkB4eHiI5s2bi0aNGokOHTpkODmnumajYcOG2bq2fthv9d3kiRMn5nheJzX1ABjqgnTv3r2FEPKwvxMnTpTuMs+bNy9b1zSc88mwyaOp+VCaNGkipbFnz54MP7/u7u4Z9lMzh3pQByCtOZmpQSGioqJMfv7Unf8dHBxynB9z6XQ66ft0+/ZtodPppMJ4eiPUGc6zlNGi0WiUmxl6SUlJYsyYMcLOzk707Nkzw0kr08t7q1atlGu89dZb2X4dskqr1Wb63VJPQaBumlSzZk0hhDxZc+nSpUViYqJ0I0o9WmTt2rXFkydPpGDa1dU12027DN26dUsaqCajmjn177W6P6Le3bt3xcSJE0WZMmWEs7Nzhn3B1PNKde/eXZpouXPnzjl6TuomjFu2bMlRWuaIiIhQmkNmp29SdqiHPjdcrF27akjdFFsdNBmOtEhZx0CJLIaBknkMmwkYDnygHgXM1OLs7CwVmj09PXOUnxYtWpgsUBgWrk+ePCntT+9PWD3YwUcffSTtCwwMlAoilqQu7BoutWrVSve8119/XTkuJ8OLC5F2l3bPnj3iwIEDFg2ShJCbY6mX7777Tgghz93RoEEDqeB18+bNbF/39u3bok6dOqJ06dLi2LFj0r5//vnHKD+GhbfExMR0+/AAaXPu5FRoaKjw8PAQdevWzXRwA3WN5q5du6RmdekVPq1J3dRp165d4sGDB8q6k5NTurULOp1OTJgwQXh6eopKlSqJMmXKKDcpSpYsKdzc3MRLL70kWrVqleHdaHNGXkzPkSNHhKOjo3B3d1cmsswvHj9+bPLz9tprrwkh0l4/ddPS2bNnK4+rVq0qHj9+LP3eqZu62tnZZTr/WVapv9+VK1cW8fHxJo9T3xD66aef0k1Pq9Vm+t7evn1basqongJBPYJaduzevVuUL19e9OnTJ8t9D7MrMjJSHD58ONeul5iYKObMmSPef/99MWnSJDFt2jQxb0U7v+EAACAASURBVN48sXfvXov//mfm33//Nfl5NzUfFGUNAyWyGAZK5lGPrKVf9AXQe/fuSaMxTZ06VQQHBytLaGio0Gq14sWLF1JzMcMmMzqdTvz+++9i7dq16Q6qIETaCEf6NGxtbaURhDp16qQcZ9hfxcXFJd3Zy//880/prqv6z1rdwdScOYSyIj4+3miuI/Viqk12YmKi1NbcEs01rEU9MIJ6OXv2rBAi7T0ytT8nIy/ppXcH39RcTqaGe7927ZqYPHmyePXVV0Xjxo2VzuhDhw61WIHC3HTUfVM+/vhj8d5770mF4Yy+L9agvv7MmTOlmpAOHTpkOb3cLqDFx8dnOp9YXjGcXgCAmD9/vrJfPWeWuvns559/LoSQR1RTLz///LPF8xobGyv1c5s6darJ49T9IvXf/ZxQvwb6xcbGxqilQ3bk9mexqFMP3gOk1dbnVtBYmDFQIothoJQ5nU4njUakX5o0aSJSUlKkCTIbN26c4TDd6iZRa9askfap20/r57swNUeHeoJHb29vER4ertxhtLW1VTqPvv3228pxZcuWzTCg0Gq10ihbv//+u7LPsB+NpT1//lwcPXpUHD16VJw+fVpqwmiqUKFuFpbZDOp5LTEx0WjS2jJlykifEVOjP2VnFMCsUA+QYG5zSp1OJ54/f27VfKVHHYhUqFBBuqNuOHdPblixYoVy/cGDB4vp06cr6+PGjcv1/BQmhkP5AxBHjx5V9qtHblQv+tHAkpKSpFEVgexNIGoudb9Ce3t7oxtgsbGx0o2tnNQG6qlr+fWLtUcvJOswfC/ffffdvM5SoWADIso1Fy5cQFhYGACgVKlSKFmyJADg/PnzGDlyJH777Tfl2MWLF8PW1jbdtLp376483rdvn7Rv+fLlyuO4uDjMnz8fHh4eeP311xEZGans2717t/K4Z8+eKF++PFq3bg0A0Gq12Lt3L06dOoXVq1crx61ZswaNGzdON182NjYYNWqUsr5ixQokJibi2bNnOH/+PADA1tYWHTp0SDeN7CpZsiTat2+P9u3bo2XLlqhfv76yLzQ01Oj4bdu2KY/9/f2h0WgsnidLKVGiBDp16iRt8/b2lj4j3t7eRuf179/fqvkKCAhQHrdp0wbOzs6ZnqPRaJTPfm7r2LEjbGzS/vrCw8MhhAAA+Pr64tVXX831/DRs2FB5HBISgnPnzinrzZo1y/X8FCb63zI9W1tb6TU13K/fVqdOHQBA8eLFsWDBAmVfQEAApk+fbqXcAiNGjEDTpk0BAImJiZg0aZK0/+LFi8rjunXrokSJEjm+Zs+ePVG1alVp28CBA3OcLuW+Xr16KZ9dABg8eHAe5qbwYKBElIu2b9+uPO7Tpw+++OILZf3XX39VHr/22mtGhWJD6kApODgYOp0OABAWFobDhw8bHa/VavHbb7/Bx8cHMTExSElJkQKsXr16AQD69u0r5fedd95RCpO9e/eW9qdn5MiRStBx8OBBuLq6onv37ko6zZs3R+nSpTNNJ6dq166tPL5+/brR/j179iiP/f39rZ6fnPLx8ZHWO3fuLK0bBko1a9aUCuLW0L59e3z99dfo3r07Fi9ebNVrWULp0qXRvHlzaZu9vT2WLFmSJ4FygwYNlMdXr17F6dOnlXV9oZmyp1WrVtJ6o0aNpAC9ZcuWRucMHz5cWu/duzcOHDiAdevWYcOGDUqQbQ02NjZYtGiRsr5x40ZcvnxZWf/vv/+Ux40aNbLINW1tbTF69GgpDwXht5CM2djY4Ndff0WXLl0wbdo0q9yMLJLyuEaLChE2vcucumnU5s2bRWJiolHTDgcHB3H37t1M09JqtVKbdn2fIXVzEl9fX7F7927RpUsX6Rovv/yyNIdLtWrVlGZn6vlx1Iu9vb24deuW2c81o7k9TM0NYw3qZk1vvPGGtC8iIkJ6zTNq5phfhISESK+jYRNIww7sWZmnqiiZOHGi9DrNmDEjT/NTrVo1o+9I8eLFOaxvDsXHx0vDZasn9dVT//4WL148y6P/WUPfvn2VPKmnGHjnnXeU7ZaYn03vwYMHolSpUgKA8Pf3t1i6RIUBa5SIcsmNGzcQEhICIK0Zla+vL0qUKIHvv/9eOm7y5MlGTSFMsbGxQbdu3ZT1/fv3QwiBX375Rdk2YsQI9OzZE8HBwVi7dq2y/dChQxg0aJCy3rNnT+VuupeXF2rWrGl0vSlTpqBGjRpmPltg7dq1GD9+PDw9PY32GdaMWIv62oZN79TNWOrXr59hM8f8on79+koNUd26dY1qi9zd3aUaCt4ZNs3X11d5XKdOHXz66ad5mBuYrPVr2LAh7Ozs8iA3hYejo6P0fTCsYTLc1qdPH7i6uuZK3jIydOhQ5fHmzZuVx9aoUQKASpUq4ejRo1i5cqX0P0FEbHpHlGvUze569OgBJycnAGmFtrfffhtA2p/2J598Ynaahv2Ujh07hps3bwIAnJ2dpWZyo0aNwuzZs5X12NhY5bG+2R2Q1n/EsHmdp6dnlguT7u7umD9/Pq5du4abN29iyZIleO211zBjxgx06dIlS2lll7mBkrowlZ9pNBrs3bsXq1atwr59+0w2A1q0aBGaNWuGSZMmoU2bNnmQy/yvc+fOmD59Ovr3749du3ZZpK9HTpgq9LJ/kmUMGzYMAFCmTBn07t3baP+QIUMApDVBy8pvrzX17NlTaSJ4+fJlXL58GUIIqwVK+vTefvttlClTxqLpEhV4eV2lRYUHm95lrG3btkqziR9//FHap9PpxI0bN7I8zO79+/elZiPqSedMTQCp0+mMhie3t7cXCQkJ0nHBwcHSMRlN2Jqf6XQ6aaQ4/Sh+Qsgj+ennIiLKC7/99ptR07tly5bldbYKBZ1OJ86ePSsiIyPTPebKlSs5mmvMGgYMGCCNXHnr1i1l3c3NLV+P0ElUmLBGiSgXPHz4ECdOnACQdueyT58+0n6NRgMPDw/Y29tnKd3KlSvDy8sLAJCcnIz169cr+ww7Jeuv8+2330pNO7p37w4HBwfpuE6dOqFjx44AgA8++ECquSpINBqNNKCDulapINYoUeFkqukdB3KwDI1Gg6ZNm6Js2bLpHlO3bl2TzY3z0oABA5THW7ZsMapNys8jdBIVJsXyOgNEhY0QAtOnT0dgYCCaNGkCX19f3Lp1S9nv7e0NNzc3i13Px8dHGhkJAGrVqoV27dqZPN7GxgY//PADqlevjkuXLmHevHlGxxQrVgzBwcEIDw83q79Ufubp6YkLFy4ASAuU2rZtCyEEAyXKNzw9PWFnZ4eUlBQAad9RSzetooKlV69esLe3R2JiIi5evIhNmzYp+/jZIMo9DJSIsiEoKAg3btzAmDFjjOaD2bFjB7766isAwNmzZ/HDDz9I+y09r0337t2xcOFCaduwYcMyvONoZ2eHr7/+OsN07ezsCnyQBJjup3Tv3j2lj5arqysqVqyYJ3kjAtK+a/Xq1VNqDerWrWtUy0tFi5OTE3r27KnM9aaeYy+jeeyIyLLY9I4oi9auXYs+ffpg/PjxePXVV5X5iwDg+fPnGD9+fIbn+/n5WTQ/3t7eRqNjvfHGGxa9RkFmai4lw9okNmOhvKZufseBHAiQm9+J/z8HHcAaJaLcxECJKAtOnjyJd999V1nfu3evNHP7nDlzcOfOHQBA2bJlMWXKFLRo0ULZP2TIEFSpUsWieXJ0dJSa2Xl7e6N69eoWvUZBZqpGic3uKL9RTx7ctWvXPMwJ5Re9e/c2GpHRxsYG9evXz6McERU9bHpHZKaHDx/C398fycnJ0vbPPvsMnTp1gpubG+bMmaNsnzVrFkaPHo2vv/4aERERePz4MerUqWOVvA0cOBCHDx8GACmQIzlQun79OnQ6HQMlyneGDx+O2NhY2NjYsEaYAAClSpWCr68vdu7cqWyrU6dOlgf9IaLsY6BEZIakpCT4+/vj0aNHANL6tbz00ks4f/48UlNTMWjQIHh6eiIpKQkA0LJlS4waNUo5393dHe7u7lbLn76vlJOTEwICAqx2nYLIzc0Nrq6uiIqKwvPnz/Hw4UNl4l/A9IhjRLmtWLFimTbbpaInICBACpTY7I4odzFQIspEVFQUPvroI5w6dQpA2vDemzZtQo0aNdC0aVPExsYiLCwMYWFhyjlLliwxORmotdja2mLkyJG5dr2Cpnbt2sr7d+XKFVy5ckXZx2YsRJRf9enTB8WLF1daMnAgB6LcxT5KRCZERkZi5cqV8PHxQfny5bFu3Tpl37x589C1a1fUrFkTq1atMjr3zTffRKtWrXIzu5QJdfO7vXv3KjV/lStXhouLS15li4goQ6VLl0avXr2U9TZt2uRhboiKHtYoERk4d+4cOnXqhPj4eKN9w4YNw4cffqisDxo0CMHBwVi9ejUAoEyZMpg1a1au5ZXMow6U9MPtAuyfRET536JFi2Bvb4+6devi5ZdfzuvsEBUpDJSIDCxfvtwoSGrdujWGDh2KsWPHGg0lvXDhQoSHh+P06dNYuXIlypUrl5vZJTOoAyX9qIQAAyUiyv+qVq2KDRs25HU2iIokBkpEKkII7N69W1n/8ssv8eabb2Y48aqDgwMCAwNzI3uUTeq5lNQ4kAMRERGlh32UqMjR6XTYtWsXzp07Z7Tv/PnzePjwIQDAxcUFU6ZMyTBIooIhvUCJNUpERESUHgZKVOTMnz8fffv2RevWrXH27Flpn7o26ZVXXkGxYqx0LQycnJxQqVIlaZtGo0G9evXyKEdERESU3zFQoiLnr7/+AgCkpKRg4cKF0r6goCDlsXqkISr41P2UAMDDwwMODg55lBsiIiLK7xgoUZGjnzQWADZt2oSoqCgAQEREBE6fPg0AsLGxga+vb57kj6zDMFBiszsiIiLKCAMlKnL0fZAAICkpCb/88guAtPl1hBAAgHbt2sHV1TVP8kfWYdhPiQM5EBERUUYYKFGRkpKSgsjISGnbihUrjEa76927d25njayMNUpERESUFQyUqEh5/PixUmukd+3aNQQHB+PPP/9UtrF/UuHDQImIiIiygoESFSnqZndqY8eORWxsLACgWrVqqF+/fm5mi3JBzZo1lcEbnJyc0h0ynIiIiAhgoERFjHogh+rVqyuPb968qTzu1asXNBpNbmaLckHx4sWxdOlStGzZEsuXL4ednV1eZ4mIiIjyMU4SQ0WKukapW7duCAkJwalTp6Rj2D+p8BoxYgRGjBiR19kgIiKiAoA1SlSkqAOlihUrYsyYMdL+kiVL4uWXX87lXBERERFRfsNAiYoUddO7SpUqYdCgQXB2dla2devWDSVLlsyLrBERERFRPsJAiYoUdY1SpUqV4ODggJEjRyrb/P398yJbRERERJTPsI8SFSmGTe8AYMaMGdBoNHB2dsYbb7yRV1kjIiIionyEgRIVKYZN7wDA0dER8+fPz6ssEREREVE+xKZ3VGSkpKQgIiICAKDRaFC+fPk8zhERERER5VcMlKjIePz4sfLY3d0dxYqxQpWIiIiITGOgREWG4UAORERERETpYaBERYapgRyIiIiIiExhoERFhqmBHIiIiIiITGGgREUGm94RERERkbkYKFGRwaZ3RERERGQuBkpUZLDpHRERERGZi4ESFRlsekdERERE5mKgREUGm94RERERkbkYKFGRkJKSgsjISACARqNB+fLl8zhHRERERJSfMVCiIiE8PFx5XL58eRQrViwPc0NERERE+R0DJSoS1AM5sNkdEREREWWGgRIVCRzIgYiIiIiygoESFQkMlIiIiIgoKxgoUZHApndERERElBUMlKhIYI0SEREREWUFAyUqEjiHEhERERFlBQMlKhLUTe9Yo0REREREmWGgREUCm94RERERUVYwUKJCLzk5GZGRkQAAjUYDd3f3PM4REREREeV3DJSo0Hv8+LHyuHz58ihWrFge5oaIiIiICgIGSlTosdkdEREREWUVAyUq9DiHEhERERFlFQMlKvRYo0REREREWcVAiQo9BkpERERElFUMlKjQY9M7IiIiIsoqBkpU6LFGiYiIiIiyioESFXr6OZQAcA4lIiIiIjILAyUq9J4+fao8dnNzy8OcEBEREVFBwUCJCj0GSkRERESUVQyUqFBLTk5GXFwcAMDGxgbOzs55nCMiIiIiKggYKFGhFhUVpTx2dXWFjQ0/8kRERESUOZYaqVBjszsiIiIiyg4GSlSoqWuUGCgRERERkbkYKFGhpq5RcnV1zcOcEBEREVFBwkCJCjU2vSMiIiKi7GCgRIUaAyUiIiIiyg4GSlSoMVAiIiIiouxgoESFGgMlIiIiIsoOBkpUqDFQIiIiIqLsYKBEhRoDJSIiIiLKDgZKVKgxUCIiIiKi7GCgRIUaAyUiIiIiyg4GSlRoCSEYKBERERFRtjBQokIrLi4OqampAAAHBwfY29vncY6IiIiIqKAoltcZKGhSUlKwb98+7Nu3DxcvXkRUVBSEEChfvjyqVq0KX19f+Pr6wsnJKdfydOnSJezbtw8nT55EeHg4nj59Cnt7e5QtWxZNmjSBj48POnfuDI1Gk2t5yg9Ym0RERERE2cVAKQvOnDmDyZMn486dO0b7bt++jdu3b+PIkSOYO3cuvvrqK/Ts2dOq+bl37x5mzpyJv/76y2hfSkoK4uLiEBYWhu3bt8PT0xPffvst6tata9U85SfqQMnV1TUPc0JEREREBQ2b3plp3759GDFihMkgyVBMTAzGjx+PBQsWWC0/Z86cgZ+fn8kgyZTQ0FAMHDgQJ06csFqe8puoqCjlMWuUiIiIiCgrWKNkhkuXLuHTTz9FSkqKss3T0xNDhw5FvXr1UKxYMYSGhmLjxo04d+6ccsyKFStQvXp19O/f36L5OXv2LN566y08f/5c2Va6dGkMHToUzZo1Q4UKFfD06VMcPnwYGzduVI5LSkrCe++9h927d6NixYoWzVN+xKZ3RERERJRdVg+UEhIS4OjoaO3LWI1Wq8Vnn32GpKQkZZu/vz+mT58OOzs7ZZuXlxf69euHFStWYOHChcr2r7/+Gt7e3hZr+hUfH4+PP/5YCpK8vb0xb948lC5dWtlWu3ZttGnTBoMHD8bo0aOVmrCEhATMmjULixcvtkh+8jMGSkRERESUXVZvetehQwdMmDABx44dgxDC2pezuJ07dyI0NFRZb968OWbMmCEFSXoajQbvvPMORo0apWxLSEjAihUrLJaf1atX49GjR8p627ZtsXTpUilIUnvppZewZMkS2NraKtsOHDiAx48fWyxP+RUDJSIiIiLKLqsHSi9evEBQUBBGjx6t1Hxcv37d2pe1mF9//VVanzRpkhR0mPLRRx+hXLlyyvrmzZuRmJiY47w8f/5cyk/p0qUxd+5ck0GbmqenJ7p166asa7VaHDlyJMf5ye8YKBERERFRduXaYA5CCERERGDt2rXo27cv/P39sW7dOqnDfX5z9+5dXLp0SVn39PRE48aNMz2vRIkSUr+k58+f49ChQznOz59//on4+HhlfcSIEXB3dzfrXB8fH9jb26NChQqoV6+eRQK3/I6BEhERERFlV64M5iCEUObw0Te/u3z5Mq5cuYI5c+agY8eO6N+/Pzp37pxp7UhuOnr0qLTesWNHs8/t0KEDVq1apazv378fvr6+OcpPcHCw8tjGxgYBAQFmn9u7d2/07t07R9cvaBgoEREREVF2WT1Q2rZtG3bu3Ik9e/YgMjISAKSgKTU1FYcOHcKhQ4dQunRp9OzZE35+fmbV3FjbxYsXpfWs5KlBgwbQaDRKYKgeDS+7/vnnH+Vx/fr1za5NKqoYKBERERFRdlm96Z2Xlxc+//xzHD58GGvXrkW/fv1QsmRJJYBQB00xMTHYuHEjXnvtNfj6+mLFihV4+PChtbOYrhs3bkjrtWrVMvtcR0dHKZB58OCBNFJdVt29exfR0dHKeoMGDbKdVlHBQImIiIiIsivX5lGysbFB+/bt0b59e0ybNg0HDhxAYGAgjh8/jtTUVABy0HT79m0sWrQIixcvRosWLdC/f3/06NEDDg4OuZVloyCtQoUKWTq/QoUK0uhyDx48QO3atbOVl7CwMGm9evXqyuPk5GQcOnQIu3fvRmhoKMLDw2FjYwN3d3e0aNECr7zyCtq1a5et6xZkDJSIiIiIKLvyZMJZe3t7pc9MVFQUgoKCsGvXLoSEhACQAyYhBP755x/8888/mD59Orp37w4/Pz+rF/x1Op000ISDg0OW54MynDspJwNX3L9/X1rX11b98ccfmDlzpsnhvuPj43Hr1i1s2rQJrVu3xowZM/DSSy9lOw8FSUpKCmJjYwGkfZ7KlCmTxzkiIiIiooIkTwIlNVdXVwwbNgzDhg3DnTt3sHPnTgQFBeHu3bsA5KDpxYsX2LVrF3bt2gV3d3f07dsX/fr1y1KTOHPFx8dDq9Uq69mZNNfwHH3BPTvUtSP6tBcuXIjly5ebdf6pU6cQEBCAZcuWoXnz5mZfd9u2bdi+fbtZx165csXsdK1NHZS6uLjAxibXBngkIiIiokIgzwMltWrVquGDDz7ABx98gAsXLmD37t3Yv3+/NMGqvm/T48ePsWbNGqxZswZeXl7w9/dHr169LFZzkJycLK3b29tnOY3ixYtnmGZWGAZZmzdvxv79+5X1Dh06oE+fPqhVqxbs7Oxw//597N+/H4GBgUrAFx0djXfeeQdbt25F1apVzbrugwcPcPr06WznO6+w2R0RERER5US+CpTUGjdujMaNG2Py5Mn477//8Pfff+PYsWMICQlRCv76oOnSpUu4fPky5syZg+7du2PQoEFo1apVjq6fkpIirWc2yawphkOdG6aZFUlJSdK6PkhycHDAnDlz4OPjI+2vU6cOunbtijfeeANjx45FREQEACAmJgaffPIJNm3aZNZ1K1eubPZreeXKFcTFxZl1rLWpa5QYKBERERFRVuXbQEmtWrVqqFmzJu7fv4+wsDBER0dDo9EozfKAtKApOTkZe/bswZ49e1CvXj2899576Nq1a7auqU47u9RN94DsBVt6+gEv1GxsbLB48eIM53eqX78+1q5di4CAAGWS2QsXLuDgwYPo3Llzptf19/eHv7+/WXl844038k3tE2uUiIiIiCgn8m2gFBsbi3379mHv3r04deqUFHQYBkiG24UQuHz5Mt5//314e3tj9uzZWW6SZ4naIMNAqUSJEllOQ69YMeO3qn///mZNguvp6YkRI0ZgxYoVyrZt27aZFSgVVAyUiIiIiCgn8lWglJSUhODgYOzatQtHjx5ValFMzbkEAFWqVIGfnx9atWqFgwcPYs+ePcrob/rJXg8fPowhQ4Zgw4YNcHZ2NjsvTk5O0vqLFy+y/HwM503KTj8nPVNB1pAhQ8w+f+DAgVKgdPLkSeh0ukI7yAEDJSIiIiLKiTwPlLRaLY4ePYqgoCAEBwcrAYk6ONIHPUIIODo6wtfXF/3790eLFi2UdFq1aoWJEyfi6NGj+OGHH3DixAnlvFu3buGbb77B3Llzzc5XiRIl4ODgoAQ7cXFxEEJkqUme4QAMZcuWNftcQ4Y1Yo6OjvDy8jL7/MqVK6N8+fJKIBkbG4unT5+iXLly2c5TfsZAiYiIiIhyIs8CpX///RdBQUH4448/EB0dDcA4ONJvs7GxQbt27eDn54fu3bunWzOj0WjQsWNHdOzYEXPnzsUPP/ygBEt79uzB+PHjUbFiRbPzWKFCBdy6dQtAWtO76OhouLi4mH3+kydPpPWcBEr6eZPUaWW1H1W5cuWk+ZaioqIYKBERERERmZCrgdK1a9cQFBSE3bt3K0N+p9fHCABq1qwJPz8/9OvXD+XLl8/StSZMmIADBw7g3r17ANJqrs6dO5elQKlatWpKoAQA9+7dMztQEkJIk8Q6OTkZBTtZYThRbHaaAhr2c1K/9oUNAyUiIiIiygmrB0oPHjxAUFAQgoKCcOPGDQDpB0dCCDg7O6NXr17w8/NDo0aNsn1djUaDdu3aYePGjcq2hw8fZikNLy8vHDx4UFkPDQ01O093796VghlPT88sXdtQvXr1lNoxIK22KiUlxWjQiYw8e/ZMWrfUnFP5EQMlIiIiIsoJqwdKXbt2lQr4gPGodcWKFUPHjh3Rv39/dO7cOUuF/4zoB2/QX8/BwSFL5zdv3lxaP3PmDAYMGGDWuf/884+0ntN5nZydnVG7dm2EhoYCAHQ6HU6fPo327dubdX5cXJxSuwYApUqVQoUKFXKUp/yMgRIRERER5USuNb0zNaR3vXr14Ofnhz59+sDV1dXi19TXoOgHYahbt26Wzm/ZsqU0oMPBgweRlJRk1jDff/zxh7Tu7e2dpWub0rVrVyVQAoDAwECzA6Xg4GDodDplvW3btjnOT37GQImIiIiIciLXxobWN61zc3PDyJEjsXPnTmzfvh3Dhw+3SpAEpA3A0KVLFwwdOhQTJ05E48aNs3R+8eLF0bNnT2U9Ojoav/32W6bn/ffffzh69Kiy7uHhgWbNmmXp2qYMGDBAGs47MDAQFy5cyPS8pKQkrFy5UtrWr1+/HOcnvxJCSIGStT5fRERERFR45UqNkp2dHbp06YL+/fujQ4cOsLW1zY3LYtasWTlOY+TIkdi+fbsyeex3332H+vXro2XLliaPj4iIwIcffig1NXzrrbdynA8gbd6o/v37Y+vWrQDSmt9NmDABa9euRdWqVU2eo9PpMHXqVGlQitq1a6NLly4WyVN+FB8fr0wQbG9vn+Uml0REREREVq9R+vLLL3H06FEsXLgQ3t7euRYkWUqtWrXw+uuvK+vJyckYPXo0NmzYoBTG9Y4dO4aBAwdKg0Y0adIkw9qb77//HnXq1FGWzAKYCRMmSCP33blzBwEBAdi6dSuSk5OlY+/cuYO33noLO3fuVLbZ2triyy+/LLQTzQJsdkdEREREOWf1GqXBgwfn6PyYmBhlUIa88umnn+LKlSs4c+YMQK1DIgAAIABJREFUACAxMRHTpk3D999/j/r166N48eK4ceMG7ty5I51XtmxZLFiwwKJBiYuLC9auXYtRo0YhPDwcQFpfrMmTJ2Pu3Lnw9PSEi4sL7t+/j8uXLxsNAT5p0qR0a8MKCwZKRERERJRTeTLh7NmzZ/HXX3/By8tL6gNkynvvvYfbt2+jffv2CAgIQIsWLXIpl/9jb2+P1atXY9y4cVLfo6ioKBw5csTkOVWrVsXq1atRqVIli+fHw8MDv/32Gz766COpj1J0dDROnz5t8hx7e3tMmTIFAQEBFs9PfhMVFaU8ZqBERERERNmRq+2vzp8/jwEDBmDIkCFYu3YtTpw4kek59+7dw5MnTxAYGIg33ngDw4YNM6q5yQ0ODg5Yu3Yt5s6dCw8Pj3SPK1OmDMaOHYvAwEDUqFHDavmpVKkSfv/9d8yZMwdeXl7pHmdvbw8/Pz/s2rWrSARJAGuUiIiIiCjncq1G6ZdffsHcuXOh1WqV5mDqAQZMSU5ORkREhDQP0+nTp+Hn54dly5blyRDX/fr1Q79+/RAWFoaLFy/iyZMnSE5OhrOzMzw9PdGgQQMUL17c7PTGjRuHcePGZSsvGo0Gfn5+8PPzw4MHDxASEoInT54gLi4OTk5OqFmzJpo0aQJHR8dspV9QMVAiIiIiopzKlUBp69atmDlzprKuD3wyC5QePHhgcqLaFy9eYMyYMfj555/RtGlT62Q6EzVq1LBqjVFWVa5cGZUrV87rbOQLDJSIiIiIKKes3vTu4cOH+OabbwCkBTr6IKlWrVoYPnx4hufWqFEDhw4dwrfffov27dsrQZNGo0FycjI+/fRTJCQkWPspUAHDQImIiIiIcsrqgdLq1avx/PlzJUCyt7fH7NmzERQUhLFjx2Z6foUKFdCnTx+sXbsWK1asgJOTk7Lv4cOHWL9+vTWzTwUQAyUiIiIiyimrBkqpqakIDAxUgqRixYph5cqV8PPzy1Z6L7/8MpYuXQobGxslTQZKZIiBEhERERHllFUDpcuXLytN4zQaDfz9/dGqVascpdmqVSv06tVLaYYXERGBGzdu5DivVHgwUCIiIiKinLJqoHT9+nUAUIKavn37WiRdw3QuXbpkkXSpcIiPj1cely5dOg9zQkREREQFlVUDpZiYGGk9o/mHssLT0xPA/0bBe/bsmUXSpcIhNTVVeWxnZ5eHOSEiIiKigsrqfZTUtFqtRdI1nKcoJSXFIulS4aD+3BUrlmtThRERERFRIWLVQKlUqVLS+sOHDy2SbkREhLTu6upqkXSpcGCgREREREQ5ZdVAST8hq76J3JEjRyyS7vHjxwH8r++Tu7u7RdKlwoGBEhERERHllFUDpQYNGih9RIQQ2LBhg9TRPjuSk5Oxfv16JfgqVqwYmjVrluO8UuHBQImIiIiIcsqqgZKTkxPat28PIQQ0Gg2ioqIwefJkpSYoO2bMmIF79+4BSKupat68ORwdHS2VZSoEGCgRERERUU5ZNVACgOHDhyuPhRDYv38/3nnnHTx58iRL6cTGxmLChAnYvHmzMtksAIwePdqi+aWCj4ESEREREeWU1UuRbdu2RceOHXHkyBElwDl8+DB8fHzQs2dPdO/eHQ0aNDA5MWhUVBSuXLmC4OBg7N69G7GxsUrtlEajQbt27dChQwdrPwUqYBgoEREREVFO5UopcsaMGRg4cCAiIiKUYOn58+fYunUrtm7dCgBwcHCAk5MT7O3tkZSUhPj4eCQkJChp6GuQ9OdXq1YN8+bNy43sUwHDQImIiIiIcsrqTe8AoHz58vjhhx9QqVIlqUZICKEsCQkJePz4Me7cuYPw8HDEx8dL+9Xn1KxZE6tWrYKLi0tuZJ8KGAZKRERERJRTuRIoAYCHhwd27NiBAQMGwMbGRgp+zFmEELCxsUFAQAC2bt2KatWq5VbWqQDR6XRS7aONTa59xImIiIioEMnV2+2lSpXCjBkz8Pbbb2PLli3Yv38/wsLCMj2vatWq6NatG4YOHYrKlSvnQk6poGJtEhERERFZQp6UJF966SV8/PHH+Pjjj/H06VOEhobi/v37iIuLQ2JiIhwcHODs7AxXV9d0B3ogMoWBEhERERFZQp6XJN3c3NC2bdu8zgYVEgyUiIiIiMgS2IGDChUGSkRERERkCQyUqFBhoEREREREllCgAyWdTofAwMC8zgblIwyUiIiIiMgS8qQkGRERgaioKCQmJiI1NVUZztkUnU4HnU6HlJQUJCcnIyEhAc+ePcPly5dx/PhxPH36FH379s3F3FN+xkCJiIiIiCwh10qSz58/x5o1axAYGIgHDx5YJE39XExEegyUiIiIiMgScqUkefPmTYwePRrh4eEZ1h5lBQMkMoWBEhERERFZgtVLksnJyXj33Xfx6NEjAJYLcPQBFwvDpMZAiYiIiIgsweolyY0bN+LOnTtSgJSdWiX9+UIIFCtWDH5+fmjYsCF8fHwsllcq+BgoEREREZElWL0kuWnTJqMgZ+jQoejWrRuqVKmC4sWL47333sO5c+eg0WgwfPhwjBkzBikpKYiLi8Pdu3dx7NgxbN26FS9evAAAaLVaVKpUCYMGDbJ29qmAYaBERERERJZg1eHBHz9+jBs3bgD438ALCxYswGeffYYWLVqgQoUKcHV1xcsvv6ycc+TIEbi4uMDd3R0eHh7o3LkzpkyZgh07dqBRo0ZKWkuXLsXZs2etmX0qgBgoEREREZElWDVQunLlivJYo9GgY8eO6N69u9FxTZs2BZAWAN26dQsRERFGx1SrVg0//fQTatSoAY1GA61Wi6lTp0Kn01nvCVCBw0CJiIiIiCzBqoGSfgAHfZ+kPn36mDyufv360Gg0ShO99GqKHBwcsGjRItjY2ECj0eDWrVuccJYkDJSIiIiIyBKsGijFxcVJ615eXiaPc3R0RMWKFZWA6tKlS+mm6enpCR8fH+XYTZs2WSi3VBgwUCIiIiIiS7BqoGTYLM7V1TXdYz08PJTHoaGhGaarb74nhMB///2HhISEHOSSChMGSkRERERkCVYNlEqVKiWtZ1RwrVq1KoC04CcsLCzDdOvXr6881mq1OHfuXA5ySYUJAyUiIiIisgSrBkouLi7SekxMTLrHvvTSS8rjBw8eIDk5Od1j9TVT+j5Njx8/zkk2qRBhoERERERElmDVQMnd3V1av3nzZrrH6muUgLQme7du3Ur3WHVhGACioqKymUMqbBgoEREREZElWDVQatiwIezs7JSan7/++ivdY6tXrw7gf7VE58+fT/fY+/fvS+s2NlZ9GlSAMFAiIiIiIkuwaoRRokQJNGrUCEIICCGwfft2XL161eSx1atXR4kSJZT1ffv2pZvuoUOHAPxv2HFnZ2fLZZoKNAZKRERERGQJVq+K8fHxAZBWU5ScnIzhw4ebnPvI1tYWzZs3V4KqEydO4MCBA0bH3b59Gz///LNS8wQAtWvXtt4ToAKFgRIRERERWYLVA6UBAwbAzc0NQFqwFBMTg0mTJqFbt25GcyDpJ6TVaDQQQmD8+PFYtGgRLl26hJs3b2L9+vV4/fXXER8fr5zj6OiY7vxMVPQwUCIiIiIiS7B6oOTo6IipU6cqzeT0QdCDBw+M5kvq1asXqlSpohyXkpKCFStWYMCAAejduzdmzJiBqKgoJQ2NRgN/f3/Y2dlZ+2lQAcFAiYiIiIgsIVdGQfD19cXkyZOVQRf0zebUI90BQPHixTF16lRlvz4gUi/qJnflypXDu+++mxtPgQoIBkpEREREZAm5NlzcsGHDsGHDBjRr1kypXTIMlADA29sbX375JWxtbZXASL0AaYM4uLi4YNmyZShTpkxuPQUqABgoEREREZEl5GpJsnHjxli/fj1u3ryJ4OBg1KtXz+RxgwYNgpeXF7777jucOnVKCayAtMJvjx498Omnn6JixYq5lXUqIBgoEREREZEl5ElJ0sPDAx4eHhke07BhQ/z00094/Pgxrl+/jpiYGLi4uKBRo0ZwcnLKpZxSQcNAiYiIiIgsweolyZCQEDx//hytW7fO1vnly5dH+fLlLZwrKqwYKBERERGRJVi9JDlz5kycP38eVatWRUBAAAYMGAAXFxdrX5aKKAZKRERERGQJVh3M4d69ezh37hwA4O7du1iwYAGioqKseUkq4hgoEREREZElWDVQ+vfff5XHGo0GzZs3z7RvElFOMFAiIiIiIkuwaqAUEREhrdepU8ealyNioEREREREFmHVQMlwjiOdTmfNyxExUCIiIiIii7BqoNS8eXPlsRAC//zzjzUvR8RAiYiIiIgswqqBkoeHB7p37w4hBDQaDW7cuIF169ZZ85JUxDFQIiIiIiJLsGqgBACzZs1Cs2bNIISAEAIzZ878f+3de3hNV+L/8c9JiAhxK0kEI4oogqKl01LVYoy6RKpMDYpp3EpVmV5oO3T8poMOplqjVdVBi7orSrSDKdO6tm5tXUqIMC5DhEgkkvP7wzen2TuRnCT7nJxz+n49T5/J2tlr7RVz1rP35+y119Zbb72lK1euuPrQ+AUiKAEAAMAKLr+SLF++vBYsWKCZM2dq/vz5yszM1Lx587RgwQI1btxY9957r+6++25VrFhRwcHBRbq4vf/++13Qc3gjghIAAACs4PIryW7dujl+DgoK0rVr12S325Wenq7vvvtO3333XbHat9ls+v7774vbTfgIghIAAACs4PIryWPHjslmsxm2ZZftdrurD49fGIISAAAArOD2K8mcockcoAqLoAUzghIAAACs4JYrSQIN3IWgBAAAACu4/Eryxx9/dPUhAAeCEgAAAKzg8uXBAXciKAEAAMAKBCX4FIISAAAArEBQgk8hKAEAAMAKBCX4FIISAAAArEBQgk8hKAEAAMAKLr+SXL16tasPoejoaJcfA96BoAQAAAAruPxK8uWXXy72i2ULQlBCNoISAAAArOC2K0mrXzprs9lkt9tdHsLgXQhKAAAAsILbriStDDR2u93y4AXfQFACAACAFVx+JRkeHl6keunp6UpOTlZ6erpjW3bYqlevnp566ilL+gffkjMo+fv7l2BPAAAA4M1cHpT+9a9/Fav+xYsXdeDAAS1fvlxbtmyRzWbTTz/9pEOHDukvf/kLU+9gwB0lAAAAWMHjlwevVq2aHnvsMf3jH//Qu+++q4CAAEm3V9ObOnVqCfcOnoagBAAAACt4fFDK6bHHHtMbb7zheEbpo48+0t69e0u6W/AgBCUAAABYwauCkiT16NFDzZs3d5RnzZpVgr2BpyEoAQAAwApeF5QkqU+fPpJur363c+dOXbx4sYR7BE9BUAIAAIAVvDIoNWrUyFDet29fCfUEnoagBAAAACt4ZVCqVq2apJ+XCz979mxJdgcehKAEAAAAK3hlULpw4YKhnJGRUUI9gachKAEAAMAKXhmU9uzZI+n2M0qSdNddd5Vkd+BBCEoAAACwgtcFpZSUFM2bN8/wotmaNWuWYI/gKbKyshzhWZL8/Lzu4w0AAAAP4VVXkufOndOgQYMMzyQFBwerZcuWJdgreArz3aScYRoAAAAoDJfPTVq9enWR6tntdt26dUtpaWm6cuWKDh8+rB07digzM1M2m012u102m02/+c1vmGIFSUy7AwAAgHVcfjX58ssvW/bNfva0quz2ypYtq1GjRlnSNrwfQQkAAABWcdvVZM5nR4rCZrM5ApLdbpe/v7/+3//7fwoJCbGie/ABBCUAAABYxW1Xk8W9q5QzaNWuXVuvvvqq2rZtW9xuwYcQlAAAAGAVl19NhoeHF7muzWaTv7+/AgMDVaVKFdWvX1/t2rXTr3/9a/n7+1vYS/gCghIAAACs4vKryX/961+uPgQgiaAEAAAA63jV8uBAfghKAAAAsApBCT6DoAQAAACrlGhQSktLK3CfDRs26KuvvlJKSoobegRvRlACAACAVdwelI4dO6bXXntNbdq00fvvv1/g/nPnztWQIUPUpk0bvfrqqzpx4oQbeglvRFACAACAVdwWlG7evKlJkyape/fuWr58uS5duuRU6ElISJDdbldqaqpWrFih6Ohovffee27oMbwNQQkAAABWccvVZGpqqp555hnt27fP8T4km81WYFC6evWqrl+/bnjRbHp6umbOnKmEhARNnjzZ5X2H9yAoAQAAwCpuuaP0yiuvaO/evbLb7bLZbLLZbLLb7Tp9+nS+9dLS0tS6dWsFBQXlqrtixQrNnj3bHd2HlyAoAQAAwCouD0rbtm3Txo0bDSGncuXKGjt2rDZt2pRv3dDQUP3zn//U119/rWnTpqlmzZqOwGS32zV79mwdPXrU1X8CvARBCQAAAFZxeVCaP3++ody8eXOtW7dOsbGxCg0NdaqNgIAAdevWTatXr1br1q0dYSkzM1Nz5851RbfhhQhKAAAAsIpLg1JSUpK++eYbxx2gkJAQvf/++6pSpUqR2itXrpzefvttVa1aVdLtZ5Y2bdqk9PR0K7sNL0VQAgAAgFVcGpT279/v+Nlms2nw4MEKDg4uVpsVK1ZU//79HYtCZGRkaN++fcVqE76BoAQAAACruDQonT17VpIcoaZNmzaWtNuuXTtJcqyGV9CiEPhlICgBAADAKi4NSsnJyYZySEiIJe2GhYUZyklJSZa0C+9GUAIAAIBVXBqUSpcubShfvXrVknZv3rxpKJcpU8aSduHdCEoAAACwikuD0l133WUoHzt2zJJ24+PjJf08pc98HPwyEZQAAABgFZcGpQYNGkj6+VmiDRs2WNJuXFycoRwREWFJu/BuBCUAAABYxeVBKedS3hs2bNDBgweL1WZ8fLyWLVvmCF+VK1dWVFRUsfsK70dQAgAAgFVcGpRsNpu6du1qeEHsiBEjlJCQUKT2Ll68qKFDhyo9Pd3RZseOHS3uNbwVQQkAAABWcWlQkqRBgwYpICBA0u3gdPHiRUVHR+vjjz9WWlqaU21kZGRo9erV6tGjh06dOuW4m1SmTBmNGDHCZX2HdyEoAQAAwCouv5oMDQ3VyJEjNX36dNlsNtlsNqWkpGjy5MmaPn262rZtq8aNGysiIkLly5dXYGCg0tLSlJKSolOnTun777/X9u3blZyc7LiLlP2/zz77rEJDQ139J8BLEJQAAABgFbdcTcbGxurHH3/Uhg0bHGHJbrcrJSVFmzZt0qZNm/Ktn726XfadJEmKiYlRbGysS/sN70JQAgAAgFXccjVps9k0bdo0Va1aVQsWLHCEpWzZQSi/+tn7+fn5adiwYRo1apRL+wzvQ1ACAACAVVz+jFI2f39/jR8/XosWLVLLli1lt9sd/2UHp7z+k+TY74EHHtCCBQs0evRo+fm5revwEgQlAAAAWMXtV5P33XefFi1apPj4eMXFxWnPnj06evSozp8/b7izZLPZVKlSJUVFRal58+bq0KGDIiMj3d1deBGCEgAAAKxSYleTERERGjJkiIYMGSLp9sp2169f182bNxUUFKTg4GDD9DygIAQlAAAAWMVjriZLly6typUrl3Q34MUISgAAALBKiT7o48x7lDZs2KCvvvpKKSkpbugRvBlBCQAAAFZxe1A6duyYXnvtNbVp00bvv/9+gfvPnTtXQ4YMUZs2bfTqq6/qxIkTbuglvBFBCQAAAFZxW1C6efOmJk2apO7du2v58uW6dOmSU6EnISFBdrtdqampWrFihaKjo/Xee++5ocfwNgQlAAAAWMUtV5Opqal65plntG/fPsPLYwsKSlevXtX169cNy4Snp6dr5syZSkhI0OTJk13ed3gPghIAAACs4pY7Sq+88or27t1reGeS3W7X6dOn862Xlpam1q1bKygoKFfdFStWaPbs2e7oPrwEQQkAAABWcXlQ2rZtmzZu3GgIOZUrV9bYsWO1adOmfOuGhobqn//8p77++mtNmzZNNWvWdAQmu92u2bNn6+jRo67+E+AlCEoAAACwisuD0vz58w3l5s2ba926dYqNjVVoaKhTbQQEBKhbt25avXq1Wrdu7QhLmZmZmjt3riu6DS9EUAIAAIBVXBqUkpKS9M033zjuAIWEhOj9999XlSpVitReuXLl9Pbbb6tq1aqSbj+ztGnTJqWnp1vZbXgpghIAAACs4tKgtH//fsfPNptNgwcPVnBwcLHarFixovr37+9YFCIjI0P79u0rVpvwDQQlAAAAWMWlQens2bOS5Ag1bdq0saTddu3aSZJjNbyCFoXALwNBCQAAAFZxaVBKTk42lENCQixpNywszFBOSkqypF14N4ISAAAArOLSoFS6dGlD+erVq5a0e/PmTUO5TJkylrQL70ZQAgAAgFVcGpTuuusuQ/nYsWOWtBsfHy/p5yl95uPgl4mgBAAAAKu4NCg1aNBA0s/PEm3YsMGSduPi4gzliIgIS9qFdyMoAQAAwCouD0o5l/LesGGDDh48WKw24+PjtWzZMkf4qly5sqKioordV3g/ghIAAACs4tKgZLPZ1LVrV8MLYkeMGKGEhIQitXfx4kUNHTpU6enpjjY7duxoca/hrQhKAAAAsIpLg5IkDRo0SAEBAZJuB6eLFy8qOjpaH3/8sdLS0pxqIyMjQ6tXr1aPHj106tQpx92kMmXKaMSIES7rO7wLQQkAAABWcfnVZGhoqEaOHKnp06fLZrPJZrMpJSVFkydP1vTp09W2bVs1btxYERERKl++vAIDA5WWlqaUlBSdOnVK33//vbZv367k5GTHXaTs/3322WcVGhrq6j8BXoKgBAAAAKu45WoyNjZWP/74ozZs2OAIS3a7XSkpKdq0aZM2bdqUb/3s1e2y7yRJUkxMjGJjY13ab3gXghIAAACs4parSZvNpmnTpqlq1apasGCBIyxlyw5C+dXP3s/Pz0/Dhg3TqFGjXNrnO8nIyFBcXJzi4uJ06NAhXb58WXa7XaGhoapVq5Y6d+6szp07q3z58iXSv2xZWVkaMGCAdu/eLUlq1aqVFi5cWKJ9cjWCEgAAAKzitqtJf39/jR8/Xp06ddLMmTO1Z88ex+9yhiYzu93uCFIPPPCARo4cqfvuu8/l/c3Lnj17NH78eJ06dSrX7+Lj4xUfH6+vvvpKU6dO1cSJE9WlS5cS6OVtc+bMcYSkXwqCEgAAAKzi9qvJ++67T4sWLVJ8fLzi4uK0Z88eHT16VOfPnzfcWbLZbKpUqZKioqLUvHlzdejQQZGRke7urkNcXJxeeOEFZWRkFLjv1atXNWbMGB05ckRjxoxxQ++M9u/fr3fffdftxy1pBCUAAABYpcSuJiMiIjRkyBANGTJE0u0pbdevX9fNmzcVFBSk4ODgfO80udPhw4c1btw4Q0iKjIxUv3791LBhQ5UqVUpHjx7VkiVL9O233zr2mTNnjiIiItSzZ0+39fX69esaO3asITT8UhCUAAAAYBWXLw/urNKlS6ty5coKCwtThQoVCgxJFy9e1OzZs/XYY4+5tF+ZmZl6+eWXdfPmTce2mJgYrVy5Un369FHTpk3VqFEjRUdHa/HixXr++ecN9f/85z/r8uXLLu1jTpMmTSrye6q8HUEJAAAAVvG6q8nt27dr6dKl2rJlizIzM11+vDVr1ujo0aOOcsuWLTV58mT5+/vn2tdms2n48OFKTk7Whx9+KElKSUnRnDlzNH78eJf3dd26dVq7dq3Lj+OpCEoAAACwisfcUcrP//73P73//vvq0KGDYmNj9cUXX+jWrVsFrpZnhUWLFhnKL730Up4hKafnn39e1apVc5SXLVvm9Mt1i+rMmTOaOHGio9y+fXuXHs8TEZQAAABgFY8OSv/5z3/03HPPqV27dpoxY4bOnDnjWAXPHc8vnT59WocPH3aUIyMj1axZswLrlSlTxvBc0o0bN7R161ZXdFHS7emB48aN07Vr1yRJ9erV0x//+EeXHc9TEZQAAABgFY8LSpcvX9bcuXPVqVMn/eEPf9DmzZsdd4+y37/krkUetm/fbii3bdvW6bpt2rQxlDdv3mxJn/Ly7rvvOhaRKF26tN566y2VKVPGZcfzVAQlAAAAWMVjria//vprffrpp3lOq8vr5bRly5ZVhw4d1KNHD5f16dChQ4ayM3eTskVFRclmszn6m3M1PCvt3btXc+bMcZSff/55NWzYUGfOnHHJ8TwZQQkAAABWKdGryStXrmjlypX69NNPdfr0aUk/ByHzXSO73S4/Pz/9+te/Vvfu3dWpUycFBQW5tH/Hjx83lOvVq+d03XLlyikkJETnz5+XJCUmJurGjRuW9vnatWv64x//6FjUonXr1ho8eLBl7XsbghIAAACsUiJXk7t27dLSpUu1efNmZWRkFHj3KDIyUj169FDXrl0VGhrqtn6ePXvWUA4LCytU/bCwMEdQkm6Hpfr161vSN0n605/+pMTERElShQoVNGXKFPn5edxsSrfIysoyfI5+qf8OAAAAsIbbglJSUpJWrVqlTz/9VPHx8ZLyvnuU/SxS1apV1bVrV/Xo0UP33HOPu7rpkJWVZXj/UVBQkMqVK1eoNqpUqWIoW/k+pVWrVmn9+vWO8qRJk1S9enXL2vc25rtJnvKyYgAAAHgnlwelPXv2aMmSJYqLiyvw7lHO8r///e8Svdi9fv264T1NhQ1JedVJTk4udr+k26vx/fnPf3aUe/TooS5duljStrdi2h0AAACs5JIryuTkZK1atUpLly7VyZMnJd357pEk1a5dW0FBQfrhhx8cvyvpOwLp6emGcmBgYKHbCAgIyLfNorh165bGjh2rlJQUSVKNGjX0+uuvF7vdO1m5cqVWrVrl1L45//9zN4ISAAAArGTpFeXevXu1dOlSxcXF6ebNm3nePcreVr58ef32t79Vz5491aJFC02ZMqVEL7TNMjIyDOWCXjKbl9KlS+fbZlG8/fbbOnDggKNPU6dOVfny5Yvd7p0kJiZq165dLmvfKgQlAAAAWKnYV5TXrl3T6tWrtXTpUv3000+S7nz3yM/PTw8SKeKeAAAgAElEQVQ++KCio6PVqVMnj37XjxV3tHJO3ZOKFrZy2rlzp+bOnesox8bG6r777itWmwWpUaOGWrVq5dS+P/zwg+Olt+5GUAIAAICVinxF+d1332np0qX6/PPPDXePcr4QNntbRESEevbsqejoaLeuWlccVtwNMgel4gTDq1ev6sUXX1RWVpYkqUmTJho1alSR23NWTEyMYmJinNq3f//+JXb3iaAEAAAAKxX5ivJ3v/ud4YWq5nBUsWJFx9S6wryo1VOYp7OlpqYWuo0bN24YykV5zinba6+9pv/+97+Sbq/AN23aNAJBDgQlAAAAWKnYV5TZYclutys4OFjt27dXly5d1KZNG6++YC1TpoyCgoIcYefatWu5VuYriHmVu6pVqxapL8uWLdOmTZsc5Zdffll16tQpUlu+iqAEAAAAKxX7itJutyswMFD9+vXTkCFDVKFCBSv65RHCwsJ04sQJSben3iUlJaly5cpO17906ZKhXNSgtHbtWkP59ddfL/RKd7t27VKDBg0M244cOVKk/ngighIAAACs5FfcBmw2m27evKl58+bpwQcfVO/evTVr1iz9+OOPVvSvRNWuXdtQTkhIcLqu3W7XmTNnHOXy5csrJCTEsr7BiKAEAAAAKxU5KJUqVcox5U66HQxu3bqlAwcOaPbs2erZs6c6duyo6dOnO1bD8zaNGjUylI8ePep03dOnTxuea4qMjLSsX8iNoAQAAAArFfmK8t///rfWrl2rVatWOaZwmRd0SEhI0Ny5czV37lxFRUWpZ8+eevzxx1WxYkULuu56LVu2NJT37NmjXr16OVV39+7dhrKzS2zn5a233lJaWlqh6pw/f179+/d3lJs2baq33nqryH3wdAQlAAAAWKnIV5RVqlTRwIEDNXDgQH3//fdasWKF1q9fr6SkJEm5Q9PBgwd16NAh/fWvf9Ujjzyi6OhotWvXrtjvFnKl+++/37Cgw5YtW3Tz5k2nlvneuHGjodyuXbsi96MoS6qb/10DAwNzTSX0JQQlAAAAWKnYzyhJt6eovfbaa/rqq680c+ZMtWvXTn5+fo5V4rL/s9vtSk9P1+bNm/Xss8+qbdu2evPNN/XDDz9Y0Q3LBQQEqEuXLo5yUlKSFi9eXGC9AwcOaPv27Y5y3bp11aJFC5f0EbcRlAAAAGAlS4JSttKlS6tz58567733tHXrVo0dO1Z169Z1PMtkfhnt5cuXtWDBAsXExGjx4sWFWnrbXQYNGmS4O/O3v/0t17S6nC5cuKDRo0c77qRJUmxsrEv7CIISAAAArGVpUMqpWrVqio2N1bp16/Tpp5+qT58+Cg4OzhWassvmZ3DWr1+f64WtJaFevXrq27evo5yenq5nnnlGn3zyiTIyMgz77tixQ71799bZs2cd2+6991716NHjju3PmjVLDRo0cPz36KOPWv9H/AIQlAAAAGAlt1xRNm3aVE2bNtWECRMUFxenVatW6euvv1ZWVlauu0jZ5XHjxqlMmTJq166dunTpovbt2ysgIMAd3c1l3Lhx+uGHH7Rnzx5JUlpamiZNmqRZs2apcePGCggI0PHjx3Xq1ClDvapVq2rGjBny83NZHsX/ISgBAADASm69ogwICFDXrl3VtWtXnT9/XqtWrdKqVascASNnaMq+yxQXF6e4uDgFBQXp0UcfVZcuXdS2bVu3XgwHBgZq7ty5GjVqlOHZo8uXL+urr77Ks06tWrU0d+5chYeHu6ubv2gEJQAAAFipxG51hIaGatiwYdq0aZM++eQTPfHEEwoKCrrj1LyUlBStW7dOI0aM0EMPPaQJEyZox44dysrKckt/g4KCNG/ePE2dOlV169a9436VKlXSsGHDtHbtWtWpU8ctfQNBCQAAANay2XOuOlDC0tLStHHjRq1atUq7du1yBKZsObuavb1KlSrasWOH2/t68uRJHTp0SJcuXVJ6eroqVqyoyMhIRUVFldgUwZLWv39/7dq1S61atdLChQvdeuyVK1fqiSeekCRFR0dr1apVbj0+AAAAfItHffUeGBio6OhoRUdH6+zZs1q5cqXWrFmjhIQESbmn5km3p7+VhDp16nDHyINwRwkAAABW8thVBsLDwzVy5Eht3rxZCxYsUI8ePRQYGGiYmgdkIygBAADASh4blHJq1aqVpkyZoh07dmjy5Mm677775EEzBuEBCEoAAACwklddUQYFBalXr17q1auXEhIStGLFCq1du7akuwUPQFACAACAlbzijlJeatWqpeeff15ffvllSXcFHoCgBAAAACt5bVDKxrNKkAhKAAAAsJbXByVAIigBAADAWgQl+ASCEgAAAKxEUIJPICgBAADASgQl+ASCEgAAAKxEUIJPICgBAADASgQl+ASCEgAAAKxEUIJPICgBAADASgQl+ASCEgAAAKxEUIJPICgBAADASgQl+ASCEgAAAKxEUIJPICgBAADASgQl+ASCEgAAAKxEUIJPICgBAADASgQl+ASCEgAAAKxEUIJPICgBAADASgQl+ASCEgAAAKxEUIJPICgBAADASgQl+ASCEgAAAKxEUIJPICgBAADASgQl+ASCEgAAAKxEUIJPICgBAADASgQl+ASCEgAAAKxEUIJPICgBAADASgQl+ASCEgAAAKxEUIJPICgBAADASgQl+ASCEgAAAKxEUIJPICgBAADASgQl+ASCEgAAAKxEUIJPICgBAADASgQl+ITMzEzHzwQlAAAAFBdBCT6BO0oAAACwEkEJPoGgBAAAACsRlOATCEoAAACwEkEJPoGgBAAAACsRlOATCEoAAACwEkEJPoGgBAAAACsRlOATCEoAAACwEkEJXi8rK0tZWVmOsp8fH2sAAAAUD1eU8Hrml83abLYS7A0AAAB8AUEJXo9pdwAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PUISgAAALAaQQlej6AEAAAAqxGU4PWysrIcPxOUAAAAYAWCErxep06dJEktW7ZUSEhICfcGAAAAvoCgBK/34YcfaseOHdq+fbtsNltJdwcAAAA+gHlK8Hr+/v568MEHS7obAAAA8CHcUQIAAAAAE4ISAAAAAJgQlAAAAADAhKAEAAAAACYEJQAAAAAwISgBAAAAgAlBCQAAAABMCEoAAAAAYEJQAgAAAAATghIAAAAAmBCUAAAAAMCEoAQAAAAAJgQlAAAAADAhKAEAAACACUEJAAAAAEwISgAAAABgYrPb7faS7gR8w8MPP6zz588rODhYDRs2LOnuAAAAwMfdc889mjBhgkvaLuWSVvGLdOPGDUnStWvXtGvXrhLuDQAAAFB0BCVYpmbNmjpz5oyCgoJUu3Zttx77hx9+0LVr17ibBTiB8QI4j/ECOK8kxss999zjsraZegef0L9/f+3atUutWrXSwoULS7o7gEdjvADOY7wAzvO18cJiDgAAAABgQlACAAAAABOCEgAAAACYEJQAAAAAwISgBAAAAAAmBCUAAAAAMCEoAQAAAIAJQQkAAAAATAhKAAAAAGBSqqQ7AFihZ8+eatWqlWrUqFHSXQE8HuMFcB7jBXCer40Xm91ut5d0JwAAAADAkzD1DgAAAABMCEoAAAAAYEJQAgAAAAATghIAAAAAmBCUAAAAAMCEoAQAAAAAJrxHCV4pIyNDcXFxiouL06FDh3T58mXZ7XaFhoaqVq1a6ty5szp37qzy5cuXdFcBy8yYMUNz5swpcv358+frwQcfLHC/U6dOafny5dq1a5fi4+OVkpKiSpUqKSQkRC1atFB0dLSioqKK3A/AFRYtWqQ///nPkqQ333xTMTExRWrH1Z9/zl/wBMUZLzdv3lSLFi1069atIh27RYsWWrx4sVP7lvR44T1K8Dp79uzR+PHjderUqXz3q1ixoiZOnKguXbq4qWeAaw0bNkxbtmwpcv2CglJaWpreeustffzxx8rKysq3rccee0yTJ09WlSpVitwfwCrx8fGKiYlRSkqKpKIFJXd8/jl/wRMUd7wcOnRITzzxRJGP72xQ8oTxwtQ7eJW4uDgNHDiwwEEjSVevXtWYMWM0Y8YMN/QMcL0ff/zRZW2npKQoNjZWCxcuLPAiUZK+/PJL9ejRQ4mJiS7rE+CMK1euaPjw4Y6LvqJwx+ef8xc8gRXjxZXnomyeMl6YegevcfjwYY0bN04ZGRmObZGRkerXr58aNmyoUqVK6ejRo1qyZIm+/fZbxz5z5sxRRESEevbsWRLdBiyRlJSkc+fOOcqPP/64Ro8eXag2QkND7/i78ePHa9euXY5y6dKl1atXL3Xo0EGhoaH63//+p23btmnp0qWOE+yFCxc0bNgwLVmyROXKlSvkXwQU3+XLlzV48GCdOHGiWO24+vPP+QuewKrxYg5KS5YsKdTd1cDAwHx/70njhal38AqZmZmKjo7W0aNHHdtiYmL0xhtvqHTp0oZ97Xa75syZo5kzZzq2lStXTl988QXThOC1vvnmGz399NOO8oQJEzRgwABL2t6wYYPGjBnjKFeoUEHz5s1T06ZNc+176tQpDR06VCdPnnRsGzp0qF544QVL+gI46/Dhwxo9erQSEhJy/a4wU4lc/fnn/AVPYNV4kaR+/fpp9+7dkqRKlSpp586dlvXT08YLU+/gFdasWWMYNC1bttTkyZNzDRpJstlsGj58uAYPHuzYlpKSUqyH4IGSZv4Gr0GDBpa0m5WVpenTpxu2TZ8+Pc+LREmqXbu2PvjgA1WqVMmxbcGCBTp//rwl/QGc8fHHH+t3v/tdnhd9heGOzz/nL5Q0q8ZLtiNHjjh+joyMtKTNbJ42XghK8AqLFi0ylF966SX5+/vnW+f5559XtWrVHOVly5YpLS3NJf0DXC3niUmy7uS0bds2w8mzbdu2atu2bb51atasqWeffdZRTk1N1bJlyyzpD5CfQ4cO6emnn9Ybb7yh9PR0x/aCzgd34o7PP+cvlBSrx4skJSYmKjk52VG2Oih52nghKMHjnT59WocPH3aUIyMj1axZswLrlSlTxjBP9caNG9q6dasrugi43A8//OD4uVq1aqpcubIl7W7YsMFQfvLJJ52qFxMTY/iGb+PGjZb0B8jL5cuX9cILL6hXr1765ptvDL97/PHHNWjQoCK16+rPP+cvlARXjRfJeC6SrA1KnjheCErweNu3bzeUC/q2L6c2bdoYyps3b7akT4A73bp1S8ePH3eUrZp2J0n/+c9/HD/7+/vroYcecqpe+fLl1bx5c0f52LFjio+Pt6xfQE7Hjh3T+vXrlfOx6uDgYL3xxhuaPn16gQ+H34mrP/+cv1ASXDVeJNdNA5c8c7wQlODxDh06ZCg78+1CtqioKNlsNkc55+oogLf46aefcq3+Y4Vz587p0qVLjnLdunUL9dK+Jk2aGMqML7iDn5+funfvrg0bNqhPnz5Fbscdn3/OXyhpVo2XbDmngdtsNtWvX7/YbWbzxPHC8uDweDm/SZekevXqOV23XLlyCgkJcTxom5iYqBs3bigoKMjSPgKuZJ7qYP4GLz09XVeuXFGpUqVUuXJl+fk59x3YsWPHDOW6desWql916tTJtz3ASv7+/nrsscc0cuRIS77Fdsfnn/MXSorV4yVbzvNRjRo1DEvj2+12JScnKyUlRZUqVSr0Z9UTxwtBCR7v7NmzhnJYWFih6oeFhRlWJEpMTLT0GxDA1cwLOTRo0EDx8fFavny5tm3bpuPHjztekunn56eGDRvq4Ycf1u9//3vDA65m5rFVvXr1QvXL/F6mM2fOFKo+4Ky6devqiy++UHh4uGVtuuPzz/kLJcEV40WSrl+/bvicN2jQQOnp6friiy+0du1a7du3T1evXnX8vlKlSvr1r3+trl27qkOHDgW274njhaAEj5aVlaXLly87ykFBQYV+saV5Lf2c7QHewDwnfN68eVq/fr0jHOWUlZWlw4cP6/Dhw/roo480ZMgQDR8+3DAlIVvOaUeSVLVq1UL166677jKUr1y5Uqj6gLMK+9l0hqs//5y/UFJcMV6k21/a5XzuKSkpSV26dLnjsuNJSUn6/PPP9fnnn6tJkyaaMmXKHe/ceup44RkleLTr168rMzPTUS7soMmrTs5lLQFvYA5Kn332WZ4hySw1NVV///vfNWLECMPSsNlyfvMnqVDPZ0i5x5a5PcCTufrzz/kLvsZ8Ltq7d6/T72Y6ePCgevfunWvBhmyeOl64owSPZr64K8pKLQEBAfm2CXiy8+fP5/mtWO3atdWnTx+1a9dO1atXl91u19mzZ7V9+3YtWLBA586dc+z7r3/9SxMmTNC0adMMbZjHQpkyZQrVN8YWvJmrP/+cv+BrzEFJkkqVKqXOnTurZ8+eql+/vipXrqzk5GQdPHhQa9as0caNGx13oa5fv65Ro0bpk08+UcOGDQ3teOp4ISjBo+Vc6Usq2kvSzG9zNrcJeLK8TkxPP/20xo0bl+ukEBkZqcjISPXt21cTJkzQunXrHL9bu3at2rRpox49eji2mcdCqVKFOyWY979161ah6gMlydWff85f8DXm81HNmjU1ffr0XKvTVa1aVe3bt1f79u31zTffaPTo0UpKSpJ0+x1HY8aM0fr16w1jwlPHC1Pv4NHyeq6isHLeypWK90ZqwN0qVKigmJgYtWrVSjVq1NCgQYM0fvz4XCEpp8DAQE2bNk0PP/ywYfvbb79tuJgzj6+cc8+dYZ7+x9iCN3H155/zF3zNgw8+qA4dOqhhw4aqVauW5s+fX+AS3g888IDeffddwxcLJ0+e1MqVKw37eep44Y4SPJoV3w6YB05hp1cAJal58+aGF1s6y8/PT6+//ro6d+7sCEdnzpzRvn371KpVK0m5x1dh7wiZ988vvAGextWff85f8DVjxowpUr377rtP0dHRWr58uWPb2rVr9eSTTzrKnjpeuKMEj2Z+uDY1NbXQbdy4ccNQLs4bqQFvUqtWLT3wwAOGbTt27HD8bB5f5rFSEPP+ZcuWLWQPgZLj6s8/5y/gZzlDkXT7hbA5x4SnjheCEjxamTJlDC8Lu3btWqGnR5hXPXHVspmAJzLfjcr5DozKlSsbflfYFYLM+5uXSwY8mas//5y/gJ9FRUUZpt9lZGQY3nnkqeOFoASPl/OFYxkZGY4HAp1V3HdlAN7MfPGWc/yYX7BpHisFMe+f38ttAU/jjs8/5y/gtlKlSqlSpUqGbeZ3j3nieCEowePVrl3bUHZ2zX7p9sO5Ob9BL1++vEJCQizrG+DpzHO2c05F+NWvfmX4XWHGliSdPn3aUL777rsL2Tug5Ljj88/5C/iZ+bk+83RVTxwvBCV4vEaNGhnKR48edbru6dOnDfNcIyMjLesX4GqZmZlKSEjQgQMHtG3bNn322WeFbuPixYuGcs47TJGRkYYHaAsztvLan/EFb+KOzz/nL/iKq1ev6tixY9q5c6c2btyoI0eOFKp+enp6rqlxVapUMZQ9cbwQlODxWrZsaSjv2bPH6bq7d+82lLNX+wK8QWpqqjp27Kgnn3xSQ4YM0bhx4wxzup3x7bffGspNmzZ1/BwQEKCoqChH+dy5c4Zv5Aqyd+9ex8+lS5fWvffeW6i+ASXJHZ9/zl/wFf/4xz/UtWtXDRgwQKNHj9bChQsLVX///v2GJfWrV6+e646PJ44XghI83v333294wG/Lli26efOmU3U3btxoKLdr187SvgGuVL58edWqVcuw7YsvvnC6fkJCguFE4+fnpwcffNCwzyOPPGIom8fMnezfv19nz551lFu1amUYp4A3cPXnn/MXfEXDhg0N5a1btxZqSX3ze5MeeuihXPt44nghKMHjBQQEqEuXLo5yUlKSFi9eXGC9AwcOaPv27Y5y3bp11aJFC5f0EXCVTp06GcofffSR0tPTnao7depUwzNKjz76qMLDww37dO/eXX5+P58KFixYoJSUlALbnj17tqHcq1cvp/oEeBJXf/45f8FXPPLII4b3El28eFGrVq1yqu7BgwdzTR3v379/rv08cbwQlOAVBg0aZHjD8t/+9rdct1lzunDhgkaPHm1YWjI2NtalfQRcoU+fPobnKE6fPq1JkyYVuGzq9OnTFRcX5yiXLl1ao0aNyrVfeHi44cR0/vx5jR07NtciEDnNmTNHW7dudZQjIiLUsWNHZ/4cwKO44/PP+Qu+oGLFiurWrZth29SpUwt8Vik+Pl7PPfec4QWynTt31j333JPn/p42XghK8Ar16tVT3759HeX09HQ988wz+uSTT3K9vXnHjh3q3bu3YVrEvffeqx49eritv4BVfvWrX2no0KGGbcuXL9eQIUN04sSJXPv/9NNPGjZsmN577z3D9ueee+6OJ6YxY8aoXLlyjvKWLVs0cOBAxcfHG/ZLSkrSxIkTNWPGDMP28ePH53qrOuAtXP355/wFXzF27FjD+8eSk5PVt29fLV26NNdnOT09XUuWLFGfPn0Mn+eQkBBNnDjxjsfwtPFisxf2bU5ACUlLS9Mf/vCHXA/3ValSRY0bN1ZAQICOHz+uU6dOGX5ftWpVLVu2LNeUI8BbZGZmauzYsfr8888N2202m+rXr6+IiAhlZmbq1KlTOn78eK76AwcO1CuvvJLvMb744guNHj3aMOfcZrOpcePGqlGjhq5cuaL9+/fnmi8+dOhQvfDCC8X464DimzVrlt555x1H+c0331RMTIzT9V39+ef8BU9SnPHy7bffavDgwbpx44Zhe3BwsJo2baoKFSroypUrOnjwYK5prNWqVdM///lP1a1bN99jeNJ4ISjBq9y4cUOjRo0yzEXNT61atTR37lzVqVPHxT0DXCsjI0PTp0/XRx99ZFg5KD+BgYEaOXKk09MQNm/erBdffDHXCTAvNptNQ4cO1ZgxY5xqG3Cl4gYlyfWff85f8BTFHS+HDh3S2LFjc911zU+zZs00bdq0XO9KuhNPGS9MvYNXCQoK0rx58zR16tR8v5GoVKmShg0bprVr13KSgU8oXbq0XnrpJS1dulSdO3fOd6pPxYoVFRMTow0bNhRqrnbHjh21ceNG9ejRI9eLAHNq1aqVFi5cSEiCT3H155/zF3xFVFSUVq9erZdffjnXyqw52Ww2RUVF6S9/+YuWLl3qdEiSPGe8cEcJXu3kyZM6dOiQLl26pPT0dFWsWFGRkZGKiopSQEBASXcPcJnU1FTt379fp06d0tWrV+Xv76+qVauqRo0auvfee1WqVKlitZ+Wlqbdu3fr7NmzunLligIDAxUWFqZ7771XYWFhFv0VgGdyx+ef8xd8xenTp3Xw4EFdunRJqampqlixoqpVq6YmTZooNDTUkmOU1HghKAEAAACACVPvAAAAAMCEoAQAAAAAJgQlAAAAADAhKAEAAACACUEJAAAAAEwISgAAAABgQlACAAAAABOCEgAAAACYEJQAAAAAwISgBAAAAAAmBCUAAAAAMCEoAQAAAIAJQQkAAAAATAhKAAAAAGBSqqQ7AADAzp07NWDAAJceo2fPnvrrX//q0mP8UsyaNUvvvPOOo9yqVSstXLiwBHsEANbjjhIAAAAAmBCUAAAAAMCEqXcAAI/029/+VtWrV7esvSZNmljWFgDA9xGUAAAe6amnnlLr1q1LuhsAgF8opt4BAAAAgAlBCQAAAABMCEoAAAAAYEJQAgAAAAATFnMAACAf58+f1969e3X27FndunVLFStWVP369dWkSROVKVPGkmOkpqbqu+++U2Jioi5fvqxSpUqpSpUqqlmzppo1a6bSpUtbchzp9t9z6NAhJSYmKiUlRWXLllWVKlXUuHFj3X333bLZbJYcJzExUQcOHNDFixd148YNBQcH6+6771bz5s0VGBhoyTEAwJUISgCAX7RHH31UiYmJjvKRI0ckSadOndKbb76prVu3ym6356oXHBys7t27a8SIEapatWqRjv2f//xH8+bN086dO5WRkZHnPuXKlVPbtm01fPhw3XPPPUU6TkZGhtauXavFixfr4MGDd9wvPDxcvXv31tNPP62goKAiHSsuLk5z5szR4cOH8/x9YGCgHn/8cY0aNcrS5d8BwGpMvQMAwGTbtm2Kjo7Wli1b8gxJknTt2jV9/PHH6tSpkzZt2lSo9i9cuKDBgwdr0KBB2r59+x1DkiSlpKRo48aNio6O1iuvvKK0tLRCHeu7775Tt27dNH78+HxDkiSdPXtWM2fOVLdu3bR///5CHef69euKjY3VqFGj7hiSJCktLU0rVqxQly5d9OWXXxbqGHoAEBAAAAsXSURBVADgTgQlAABy2LNnj0aOHKkbN244tX9KSoqee+45LVu2zKn9jxw5ou7du2vHjh2F6pfdbtfKlSv1+9//XhcuXHCqTlxcnPr166eTJ08W6lhnzpzRwIEDtWfPHqf2v3r1qgYMGKB///vfTh/jxo0bGj16dKEDGQC4C1PvAADIYcyYMUpPT5cklSpVSr1791bXrl0VERGh1NRUHThwQJ988ol2795tqPf666/r7rvvVsuWLe/Y9rlz5xQbG6srV64YtlerVk1PPfWU2rZtq/DwcGVmZiohIUFffvmllixZYghthw4d0vDhw7V48WIFBATc8VgHDhzQCy+8kOtuVYMGDdS7d2+1atVK1apVU3p6ug4ePKhFixbp66+/dux348YNjRkzRuvWrVPFihXz/TfLnq4oSTabTQ899JB69+6tevXq6a677tLFixe1Y8cOffDBB7p48aJj34yMDE2aNEkrV67Mt30AKAk2+53mFAAA4CY7d+7UgAEDDNsWLFig1q1bu/zY5meUsoWEhOi9995To0aNcv3Obrdr/vz5mjJlimF7nTp1tH79evn7++d5rL59+2rv3r2Gbd26ddPEiRNVvnz5POucP39eo0eP1rfffpurrT/96U951snMzFTXrl114sQJw/bRo0dr6NChd+zf3Llz9dZbbxm2DR48WC+99JJh26xZs/TOO+/kql+hQgXNmDFDbdq0ybP9//3vfxowYICOHz9u2L5mzZoiP38FAK7CHSUAgEcyB6fiePfdd9WhQwen969QoYIWLFigOnXq5Pl7m82mwYMHKz09XTNmzHBsP3nypD777DNFR0fnqrNt27ZcIalHjx6aMmVKvivNhYaGav78+Xr66acN09SWLFmigQMHqnbt2rnqrF69OldIGjt2rIYMGXLH40hSbGysTpw4YbjD8+mnn2rUqFEFLu7g7++vDz/8UE2aNLnjPnfddZf+8pe/qHfv3obtu3btIigB8Dg8owQAgMmLL754x5CUU2xsrBo3bmzYdqdnld577z1DOTw8XH/605+cWo67bNmymjZtmmE58qysLH3wwQd57m/uQ1RUlJ555pkCjyPdvuuUczny69ev55pmmJe+ffvmG5KyNWvWTPXq1TNsM4c6APAEBCUAAHIIDw/XE0884dS+/v7+6t+/v2Hb3r17Dc/hSFJycnKuqXP9+/dXuXLlnO5X7dq19fjjjxu2xcXF5VqV78KFC3key8/PuVN+WFiYWrdurerVq+uhhx5S//79ValSpQLrPfnkk061LylXoDL/ewGAJ2DqHQDAI/32t7+17D07eU1Pu5Pu3bs7HSokqVOnTpowYYIyMzMl3X5+affu3erSpYtjn127dikrK8tQr1u3bk4fI1t0dLRhWlxSUpKOHj2qBg0aOLaZV6rz8/Mr1LRDSfrggw8K9eLZoKAg1a9f3+n9q1SpYiinpqY6XRcA3IWgBADwSE899ZRbFnMwa968eaH2L1eunCIiIvTTTz85th05csQQlMzvLwoPD1e1atUK3bcmTZrI39/fEcqk26vb5QxKR48eNdSpXbv2HReKuJPChCTp9t9TmHBZtmxZQzm/90gBQElh6h0AADnkDB3OioiIMJTPnDljKF++fNlQNj+j46ygoKBcd9nMS42bj23umysEBwe7/BgA4G4EJQAAcqhcuXKh65iDQnJysqGclJRkKFeoUKHwHbtDXXNQunbtmqFcmOegiirn4g8A4CsISgAA/B9/f38FBgYWup65jnkq2fXr1w1l89Sz4hwr++W42dLS0iw7FgD8khGUAAD4P5mZmYbnf5yVkpJiKJvDifkdRMVZvMB8LHNwMh/LHJwAAM4hKAEAkIM5iDjDfMfIPH3PPF3u6tWrhe/Y/zFP6zMv1FCxYsV8+wYAcA5BCQCAHBISEgpd5/jx44ayeQEF8wp3OVfIK4zk5GT997//NWyrWbOmoWwOaadPny70cVJSUpSYmFiku2sA4CsISgAA5PD9998Xav+rV6/mWmnO/ELVpk2bGspnz57VhQsXCt23AwcO5HrBbJ06dfI9dnx8fKGn+m3evFmPPvqomjVrpo4dO+rFF18sdF8BwNsRlAAAyGHz5s2F2v/zzz83hJeyZcuqRYsWhn3MZUn67LPPCt23NWvWGMrBwcFq2LBhvsfKzMzUli1bCnWc7JfWZmRk6PTp07kWjACAXwKCEgAAOWzfvl0nT550at+MjAwtWrTIsK1Tp04qU6aMYVuVKlXUqlUrw7ZFixYV6nmokydPatOmTYZtjzzyiPz9/Q3bwsLC1LhxY8O2xYsXO32clJQUxcXFGbY99NBDTtcHAF9BUAIAIIfMzEyNHz8+1xLfeXnnnXd07Ngxw7Z+/frlue+gQYMM5bNnz+qNN97INZUuL6mpqXrxxRd18+ZNw/b+/fvnuf+AAQMM5V27dmnp0qUFHke6/TflXGyiXLly6tKli1N1AcCXEJQAADDZt2+fRo4ceccV4zIzMzVr1izNmTPHsP03v/lNrueRsrVv3z7XtLjVq1frxRdfzPfO0vnz5/WHP/xBBw4cMGzv2rWrmjVrlmedLl26qF69eoZtkyZN0rJly+54HEn66KOP9OGHHxq2Pf300255aS0AeJpSJd0BAADysnjxYm3dutXSNrt06ZJrsYM72bp1qzp37qxBgwapbdu2CgkJ0dWrV7V3714tWrRIhw8fNuxfrVo1TZw48Y7t2Ww2zZgxQ9HR0bpy5Ypj+9q1a/X111+rb9++evjhh1W9enVlZmbq9OnT+vLLL7V06dJcQSoiIkKvv/76HY8VEBCgGTNm6Mknn3S8RykzM1Ovvvqq1qxZo169eql58+aqVKmSkpOTdeDAAS1evFi7d+82tFOvXj0NHz7cqX8vAPA1BCUAgEf6/PPPLW+zfv36BQal+++/3xEYLl68qKlTp2rq1Kn51qlcubLmzZunKlWq5LtfWFiY3n//fQ0fPlyXLl1ybL948aL+/ve/6+9//3uBf8Pdd9+tuXPn5npfkllkZKRmzpypMWPGGFa92717d65AlJfw8HDNnTtXAQEBBe4LAL6IqXcAAOQQExOj1157TaVLl3Zq/2bNmmnZsmVq0KCBU/s3bdpUy5YtU8uWLYvUt6VLl+Z6d9KdtG/fXgsXLlTt2rULdZxWrVpp8eLFCg8PL3QfAcBXcEcJAACTfv36qWXLlpoyZYq++eabPBdcaNSokQYOHKhu3brJz69w3zuGh4frk08+0ZYtWzR//nzt27fvjotHBAUF6bHHHtPgwYPVqFGjQv8tTZo00fr16/Xpp59qyZIlOnr06B33bdSokQYNGqSuXbsW+m8CAF9jszuz3A4AAD7q0UcfVWJioqP85ptvKiYmxlFOTEzU/v37de7cOWVlZSksLEzNmjXTr371K8v6cP36dX377bc6f/68Ll++LEmqVKmS6tWrp6ioKEunv/33v//VgQMHdOnSJSUnJysoKEjVqlVTixYtFBoaatlxAMDbcUcJAIB81KhRQzVq1HDpMcqXL6+2bdu69BjZwsLCFBYW5pZjAYA34746AAAAAJgQlAAAAADAhKAEAAAAACYEJQAAAAAwISgBAAAAgAlBCQAAAABMCEoAAAAAYEJQAgAAAAATm91ut5d0JwAAAADAk3BHCQAAAABMCEoAAAAAYEJQAgAAAAATghIAAAAAmBCUAAAAAMCEoAQAAAAAJgQlAAAAADAhKAEAAACACUEJAAAAAEwISgAAAABgQlACAAAAABOCEgAAAACYEJQAAAAAwOT/AwfN8+3OuGZXAAAAAElFTkSuQmCC\n" + }, + "metadata": { + "image/png": { + "width": 421, + "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 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 -} diff --git a/notebooks/xor_exp3-4.ipynb b/notebooks/xor_exp3-4.ipynb index 2c19b91..d814286 100644 --- a/notebooks/xor_exp3-4.ipynb +++ b/notebooks/xor_exp3-4.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "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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CgYGBLtkvAABWUuJBoEWLFpL+upa/YsUKl+zX8XkDzZs3d8l+AQCwkhIPAvXq1VPjxo0l/TW6v7hhYP369fr555+NcHH99dfr+uuvL3atAABYjVsmFHrggQdMEwC9+uqrWrNmTZH2tXnzZo0dO9bYl81mU//+/V1cMQAA1uCWIPDQQw/p2muvlXT1EkF6erpefPFFPf/884qMjHRqH3v27NGkSZP0xBNP6NKlS8bywMBADRs2rCTKBgCgwvN0x0H8/Pw0ZcoUjRo1StJfDx1au3at1q5dqxo1aqhFixYKDg5WQECA/Pz8lJqaqqSkJJ08eVL79u0zHliUvWfB09NTYWFh8vHxccePAQBAheOWICBJd911l/7xj3/ojTfeMD1OWLp6S+GmTZu0adOmXLfNWk+S6ZLAq6++qk6dOrmlfgAAKiK3XBrIMmTIEM2dO1dVq1Y1TuZZX5KMRxNn/5JkWs9utyswMFALFy7UgAED3Fk+AAAVjluDgCR17dpVERERevrppxUUFJTvSV8yh4PAwECNHj1a3377rTp27Oju0gEAqHDcdmkgu4CAAI0ePVrPPfecdu/erR07dujgwYOKjo5WYmKiUlNT5e/vr2uuuUY1atRQy5YtFRISojZt2sjTs1RKBgCgQirVs6rNZlPr1q3VunXr0iwDAADLcvulAQAAUHYQBAAAsLAydcE9OTlZCQkJSkxMVPXq1VWrVq3SLgkAgAqtVIPA/v379cMPP+jXX3/Vnj17lJqaanxv+PDhGj9+vNEeP368kpKS9Pe//12dO3cujXIBAKhwSiUI7N69W++88462bt1qLHOcNMjRsWPHFBUVpXXr1qlt27YKCwtT/fr13VIvAAAVldvHCCxYsECDBg3S1q1bc50/IC8xMTGSrgaGyMhIPfDAA9qxY4dbagYAoKJyaxB48803NXPmTF25ckWSckwalJfU1FTFxsaatklISNCoUaN04MCBEq4aAICKy21B4LPPPtMnn3xiemiQJLVv316jR4/W7Nmzje/lZvDgwfL39zdNTZyUlKQJEyYoMzPTXT8GAAAViluCwIULFzRjxgzT8wJat26tVatWadGiRXr66afVrVu3PLf39fXVP//5T61du1Zdu3Y19R4cOHBAX375pTt+DAAAKhy3BIH3339fKSkpRrt9+/b69NNP1bhx40Ltp2bNmpo7d64GDBhg6llYunSpq0sGAMAS3BIEvv/+e+OkHRAQoHfeeUc+Pj5F3t/kyZMVHBxstPfu3WuMIQAAAM4r8SAQFRWl8+fPS7o60O/hhx9WzZo1i7VPLy8vDRo0yHSJYM+ePcXaJwAAVlTiQeDEiROS/pon4O6773bJfrMeQ5w1uPD06dMu2S8AAFZS4kHg3Llzpna9evVcst+goCBTOzEx0SX7BQDASko8CKSnp5vanp6umczw8uXLpraXl5dL9gsAgJWUeBAIDAw0tf/44w+X7Dc6OlrSX5ccqlev7pL9AgBgJSUeBLK68LOu5W/bts0l+92wYYOpXbt2bZfsFwAAKynxIHDLLbfI29tb0tVP7+Hh4cYUw0WVkJCgJUuWGOHC29tbISEhxa4VAACrKfEg4Ovrqw4dOhhd+KdPn9b06dOLvD+73a4JEyYoISFB0tWehnbt2hVrXgIAAKzKLRMKjRgxQpKMSYUWLVqk6dOnF/oZAUlJSXruuee0bt060/MKhg4d6vKaAQCwArcEgVtvvVV33323aVrgjz/+WL1799aKFSt08eLFfLc/c+aMPvzwQ4WGhuqnn36SJGNfbdu2VefOnd3xYwAAUOG45l4+J7z55pt66KGHdOLECSMMHDlyRC+//LIkqUaNGpL+OsFv3LhRhw4d0okTJ3Tq1Cnje9JfPQs1a9bUjBkz3PUjAABQ4bjtMcRVq1bV/PnzVb9+fdOjhO12u+x2uy5cuGCsa7fbdfjwYW3atEknT5401sm+TbVq1fT+++9ztwAAAMXgtiAgSfXr19fKlSvVs2fPHCd3x68s2ZdlbRMSEqKvv/5aN998szvLBwCgwnFrEJCkypUra+bMmVq2bJlCQ0NVqVIl4wRf0Nctt9yiuXPn6rPPPssxxTAAACg8t40RcNSqVSu98847unTpknbt2qWdO3fqjz/+UHx8vBITE+Xj46Nq1aqpRo0auvnmm9WuXbscsxQCAIDicUsQSE1Nla+vb67f8/PzU8eOHY2nCQIAAPdxy6WBf//737rnnnv0/vvv6+zZs+44JAAAcEKJB4Hk5GStWrVKp0+f1uzZs9WlSxf99ttvJX1YAADghBIPApGRkUpNTZV09bbAOnXq8FwAAADKiBIPAkePHjX+nfVcgOy3BwIAgNLj9tsHs2YQBAAApa/Eg0DDhg1N7SNHjpT0IQEAgJNKPAh06tRJderUkXR1jMDGjRt1+PDhkj4sAABwQokHAQ8PD02fPl2+vr6y2WzKyMjQiBEjdOjQoZI+NAAAKIBbxgi0a9dO4eHhqlevnqSrjxXu27evnnvuOS1fvlwHDx7UlStX3FEKAADIxi0zC3788ceSpD59+ig8PFzx8fHKyMhQRESEIiIijPX8/Px0zTXXqFKlSoXav81mM+0HAAA4xy1BYNq0aTluGcx6mmB2KSkpSklJKfT+uR0RAICicetDh7IeO5zFFSdwxzABAACc57YgkHXC5sQNAEDZ4ZYgEBYW5o7DAACAQnJLEOjXr587DgMAAArJLbcPZj10CAAAlC1u6RH497//rc2bN+vBBx9U//79FRQU5I7Dulx6errWrl2rtWvXKioqSrGxsbLb7QoKClL9+vUVGhqq0NBQBQQElGgdDz/8cLEe5bx37155erp1nCgAoIwq8R6B5ORkrVq1SqdPn9bs2bPVpUuXYp3ESktkZKR69uypF154Qd9//72io6OVkpKiS5cu6fjx49q4caNefvlldenSRWvWrCmxOux2uw4cOFBi+wcAWEuJB4HIyEjj0oDdbledOnUUEhJS0od1qbVr12rYsGE6ceJEgevGx8dr7NixmjVrVonUEh0dreTk5BLZNwDAekq8f/jo0aPGv202m9q1a1euJgDau3evXnrpJaWnpxvLmjRposGDB6t58+by9PTUwYMH9fnnn2vnzp3GOvPmzVODBg1cPlBy3759pvakSZN09913F2ofXBYAAGRx+xmhRo0a7j5kkWVkZGjixIm6fPmysax///6aMmWKvLy8jGUtWrRQnz59NG/ePL3zzjvG8tdff1133nmnS3/m/fv3m9q33XabgoODXbZ/AIC1lPilgYYNG5raR44cKelDuszXX3+tgwcPGu22bdtq6tSpphCQxWazadSoUXrssceMZcnJyZo3b55La8oeBLy8vHTjjTe6dP8AAGsp8SDQqVMn1alTR9LVMQIbN27U4cOHS/qwLhEeHm5qT5gwocAHIo0ZM0a1atUy2suXL3fp7ZPZg8ANN9wgb29vl+0bAGA9JR4EPDw8NH36dPn6+spmsykjI0MjRozQoUOHSvrQxXLy5Ent3bvXaDdp0kStW7cucDsfHx/TuICUlBStX7/eJTUlJiYqJibGVBMAAMXhlgmF2rVrp/DwcNWrV0+SdObMGfXt21fPPfecli9froMHD+rKlSvuKMVpmzZtMrXvuOMOp7ft1KmTqf3jjz+6pCbH8QFNmzZ1yX4BANbllsGCH3/8sSSpT58+Cg8PV3x8vDIyMhQREaGIiAhjPT8/P11zzTUFdr87stlspv24QlRUlKntTG9AlpYtW5oes5z9boLicAwC9AgAAIrLLUFg2rRpOW4ZzH6izJKSkqKUlJRC778kbkd0HMfQqFEjp7etXLmyrr32Wp09e1aSFBMTo5SUFPn7+xerJsdbBx2DQFJSkhISElS5cmVVrVq1WMcCAFiDW28ftNvtppO2K07gJfVY49OnT5vatWvXLtT2tWvXNoKAdDUMNG7cuFg1Ze8RqFKliurUqaMdO3ZoxYoV2rZtm6lmHx8ftW3bVl27dtWDDz4oHx+fYh0bAFAxuS0IZJ2wS+rE7UqZmZmKjY012v7+/qpcuXKh9uE4d0D2/RVFRkaGqZeiSpUqGjJkiLZv357r+pcvX9aWLVu0ZcsWffDBB5o8ebK6du1arBoAABWPW4JAWFiYOw7jMklJScrIyDDahQ0BuW2TkJBQrJqOHTtmmtjo9OnTOXot8nL27Fk9++yzGjNmjJ566qlCHXfFihVauXKlU+s6XroAAJR9bgkCrp5mt6SlpaWZ2r6+voXeh+P9/Y77LKy8TrLt27fXwIED1aZNG9WuXVspKSk6cuSIIiIi9PnnnxtjLux2u2bNmqWaNWtqwIABTh83JiYmz14HAED5x6Tzucj+XAFJhb6LQVKO2Qcd91lYjncM+Pr66tVXX1X//v1Ny729vdW2bVu1bdtWgwcP1tNPP23a9l//+pduu+021a9f36nj1q1bVx06dHBq3X379ikxMdGpdQEAZQNBIBeuGMSY/dKCVLQwkV3jxo3Vs2dPxcTE6PTp05o8ebLuvffefLepW7euPv74Y/Xv319nzpyRdDWQzJ49W2+99ZZTx+3fv3+OsJGX/MYsAADKJoJALlzxad4xCBR31H7fvn3Vt2/fQm9Xo0YNjR07VuPHjzeWrV27VlOnTuVOAgBA6QeBxMRE/frrr9q9e7diY2MVFxeny5cvy8/PTwEBAapXr55uuOEGtWvXTtWrV3dLTQEBAab2pUuXCr0Px/kQijLOwFV69Oih1157zagpNTVVkZGR+tvf/lZqNQEAyoZSCwLbt2/XBx98oG3btikzM7PA9W02m0JCQjRkyBCFhoaWaG0+Pj7y9/c3TpyJiYk55kAoiONdAoGBgS6tsTC8vLzUsmVLU7d99mcWAACsyy3PGsguISFBTz31lIYOHaotW7YoIyNDdrvd+Mou+/LMzEz9+uuvGjt2rAYPHuz0rXNFlX0CofT0dMXFxRVq+/Pnz5vapRkEcjv+xYsXS6kSAEBZ4tYgcPToUfXv318bNmwwTvA2m834kpQjFOT2/cjISA0cODDHSHpXCg4ONrVPnTrl9LZ2u13R0dFGOyAgQNdee63LaisKxzELpXmpAgBQdrjt0kBcXJyeeuop4wSZ/cQuXf3E2rBhQ11zzTXy9fVVcnKyEhISdOjQIcXHx+fY5vz583ryySe1fPnyEjnJtmjRQuvWrTPaBw8eVKtWrZza9uTJk6ZxBcV9OFBqaqrOnDmj2NhYXbx4Uf7+/rr99tsLtY9z586Z2jVr1ixWTQCAisFtQWDixIk6efKk6WQeGBiohx9+WH379lXdunXz3Pb48eP66quvtGzZMsXGxhoPLPrzzz/12muv6b333nN5vW3btjW1IyMj9eCDDzq17Y4dO0xtZ+/Dz8uWLVs0atQoox0cHKy1a9c6vX1aWpr27t1rWlaYpykCACout1wa2LFjh9avX2+cwO12u7p27arVq1frmWeeyTcESFKDBg00ZswYrVmzRvfcc49xScFut2vdunWKjIx0ec3t27c3PS1w3bp1pil+8/P999+b2nfeeWexamnRooWpfeLECR06dMjp7desWWOq/frrr3d6QiEAQMXmliAwf/584982m01du3bV7NmzC/2o3GrVqmnOnDnq2rWraRT/J5984spyJV2doa9Hjx5GOy4uTkuWLClwu927d2vTpk1Gu2HDhrrllluKVUvt2rVzXJb48MMPndo2MTFRc+bMMS0bPHhwseoBAFQcJR4ELl++rF9++cX4BF+9enWFhYXJw6Noh/bw8FBYWJhxjdtut2vjxo1Fute/IMOHDzfNCDhz5swc3f7Z/fnnn3r++edNdz+MGDHCJbU4nrxXrVqlr776Kt9tkpOTNXr0aNPAxXr16hXqWQMAgIqtxIPAb7/9ZnRL22w2DRo0SFWqVCnWPqtUqaKHH37YOOGmpaUpKiqq2LU6atSokQYNGmS009LS9MQTT+izzz7LMdvg5s2bNXDgQNNtjW3atFGfPn3y3P+cOXPUtGlT46tLly55rtunT58cYw0mTpyoadOm5bgV0G63a/PmzXrooYe0ZcsWY3mlSpU0bdo00yUPAIC1lfhgwT/++EOSjK784l4vz3LnnSsn4DoAACAASURBVHdq7ty5xuWBo0ePqn379i7Zd3YvvfSS9u3bZ4xDSE1N1b/+9S/NmTNHN910k7y9vXX48GGdOHHCtF1gYKBmzZpV5J6P3Lz77rt65JFHdPToUUlXf6cLFy7UokWLdPPNNysoKEjJycnav3+//vzzT9O2Xl5emjFjhtq1a+eyegAA5V+JB4ELFy6Y2gUNDHSW436ybjF0NV9fX82fP1/PPfec6dp/bGysNm7cmOs29evX1/z581WnTh2X1lKjRg2Fh4drwoQJpmOnp6frt99+y3O7oKAgTZ06VZ07d3ZpPQCA8q/ELw04fiJ2nNimqBz34/igIFfy9/fXRx99pOnTp6thw4Z5rletWjU99dRTWrVqlW644YYSqaVmzZpasGCB3n33XYWEhOS7br169fT000/ru+++IwQAAHJV4j0Cjg8Kio6OVq1atYq93+wD4HI7Tkno06eP+vTpo2PHjikqKkrnz59XWlqaqlatqiZNmqhly5by9vZ2en/PPfecnnvuuSLVEhoaqtDQUMXGxmrnzp36448/lJiYKH9/f2NypqZNmxZp3wAA6yjxIHD99ddL+mtWwPXr1xf4SdYZWbP+ZY09cHU3fH5uuOGGEvvEX1g1atTQPffcU9plAADKqRK/NNCqVStjXnu73a4lS5YoNja2WPuMjY3VkiVLjHDh6+urNm3aFLtWAACspsSDgJeXlzp37mx8ck9MTNT48eN15cqVIu0vPT1d48aNU2JioqSrPQ233XZbobrkAQDAVW6ZWfCJJ54w/p11j/vIkSN19uzZQu3n7NmzevLJJ7V582ZjgiJJGjlypEvrBQDAKtwSBFq1aqWePXuanhGwdetW9ezZU2+88YZ2796dY4KeLOnp6dq9e7emTp2qnj17auvWrZL+Ghtwzz33uGTMAQAAVuS2pw/+61//0u+//66YmBgjDCQlJSk8PFzh4eHy9PRUcHCwqlSpIn9/f6WkpCghIUEnT540LiNk9QBkbR8cHKywsDB3/QgAAFQ4bgsCAQEBWrx4sZ544gkdOnTI9Dhi6eon/8OHDxvLs38vS/ZtgoOD9eGHHxZ7umIAAKzMLZcGsgQFBWnJkiXq1auX8aneZrOZvrJz/F7WI4x79uypFStWGLcmAgCAonFrEJCu9gzMmDFDq1atUu/evVW1alXjBJ/fV+XKldWvXz99/fXXmjlzpipXruzu0gEAqHDcdmnAUaNGjfTWW29Jkvbv3689e/YoNjZWcXFxSkpKkr+/v6655hrVqlVLrVq1UtOmTXP0GAAAgOIptSCQXbNmzdSsWbPSLgMAAMtx+6UBAABQdpR4EEhISCjSdsnJyXrxxRe1du1apaamurgqAAAglVAQiI+P15w5c9SlSxfNnDmzSPuIjIzU6tWr9fzzz+v222/XzJkzdfHiRRdXCgCAtbk8CHzyySfq1q2b3nvvPZ0+fVrbt28v0n6yzyCYkpKiBQsWqEuXLvrss89cWS4AAJbmsiCQlJSkp556StOmTVN8fLwxGdDx48eL9LTBrVu35phD4NKlS3r99df17LPP6tKlS64qHQAAy3JJELh06ZKGDRumDRs2mCYJyrJjx45C7S81NVUXLlww5hCQZAoEP/30k5555hmlpaW5onwAACzLJUFg7NixioqKkmSeBtjf3199+/ZVo0aNCrU/X19fbdq0SV9//bWGDx+ua665JsdzBrZu3apJkya5onwAACyr2EHgyy+/1Pr1600BoFKlSho5cqTWrVunsLAwNWzYsEj7btq0qSZMmKD169dr+PDh8vC4Wm5WGFizZo3Wrl1b3B8BAADLKlYQSE5O1ltvvWUKAXXq1NHSpUv1wgsvqGrVqi4p0s/PTxMmTND8+fPl6+sr6a8wMHXqVOPphAAAoHCKFQS+/fZbxcXFSboaAgIDA/Xf//5XN910k0uKc3T77bdr3rx5Rs+AJJ07d07ff/99iRwPAICKrlhB4Msvv5QkY4Dgq6++qvr167uksLzceuutevLJJ41jStLSpUtL9JgAAFRURQ4CKSkpioqKMk7GzZo107333uuywvIzYsQI47KD3W7X77//zh0EAAAUQZGDwN69e5WZmSnp6vX6Pn36uKyogvj5+alPnz7GnQTp6enGXQsAAMB5RQ4CMTExkmScjNu0aeOaipzUsWNHU/vUqVNuPT4AABVBkYNAfHy8qX3dddcVu5jCuPHGGyX9NW+BYz0AAKBgRQ4CjlP8Vq5cudjFFEa1atVM7ZSUFLceHwCAiqDIQcDxxJ+UlFTsYgrDce6ArPkFAACA84ocBBwnC/rzzz+LXUxhOB4vICDArccHAKAiKHIQyJo2OOsa/Z49e1xTkZOyjpc1WDEoKMitxwcAoCIochBo3LixPD09jfb//d//uaQgZ0VERJjaTZo0cevxAQCoCIocBLy9vXXrrbcajwretGmTcUthSTt16pQ2b95s9EbUrFmTHgEAAIqgWFMMd+/eXdLVywOZmZl68803XVJUQWbOnKmMjAxjmuGuXbu65bgAAFQ0xQoCvXv3Nm7js9vtioiIKPF5/1esWKHvv//e6A2QpF69epXoMQEAqKiKFQT8/Pz06KOPGp/M7Xa7Xn/9dX311Veuqs9k9erVmjx5snEsm82mdu3aqV27diVyPAAAKrpiBQFJeuKJJ9SgQQNJVy8RXLlyRZMmTdKUKVOUmJhY3N1LujpHweTJk/XSSy+Z5g+oVKmSJkyY4JJjAABgRcUOAt7e3nrzzTfl5eUlScan9SVLlqhbt256++23dezYsSLt+8CBA5o+fbruvvtuLV++3NTzYLPZNG7cOLVs2bK4PwIAAJblWfAqBWvTpo2mTZumF1980XSyvnjxoubPn6/58+erbt26at++vZo3b67rr79eQUFB8vf3V6VKlXT58mUlJyfrjz/+UHR0tPbu3audO3fqzJkzkv6aKyD7uIBhw4Zp6NChrigfAADLckkQkKT77rtPNptNkyZNUmpqqnHSzjqJR0dHKyYmxunxA1nbSTLtq1KlShozZoxGjBjhqtIBALCsYl8ayC40NFRLlixR06ZNTZ/is76y5hxw5iu37Ro1aqQlS5YQAgAAcBGX9QhkadasmVasWKElS5boo48+0unTpyX9FQgKIytMNG7cWCNHjlTPnj3l4eHS7AIAgKW5PAhIkoeHhx555BE9/PDD+vnnn/Xdd99py5YtunjxolPb22w2NW7cWH/729/Uq1cv3XTTTSVRJgAAllciQSCLh4eHunbtqq5du8put+vo0aM6cuSIjh8/rri4OKWkpOjKlSvy9fVVQECA6tSpo+DgYDVr1izH0w0BAIDrlWgQyM5ms6lhw4bGUwsBAEDp44I7AAAWRhAAAMDCCAIAAFgYQQAAAAsjCAAAYGEEAQAALIwgAACAhREEAACwMIIAAAAWRhAAAMDCCAIAAFgYQQAAAAsjCAAAYGEEAQAALIwgAACAhREEAACwMIIAAAAWRhAAAMDCCAIAAFgYQQAAAAsjCAAAYGEEAQAALIwgAACAhREEAACwMIIAAAAWRhAAAMDCCAIAAFgYQQAAAAsjCAAAYGEEAQAALIwgAACAhREEAACwMIIAAAAWRhAAAMDCCAIAAFgYQQAAAAsjCAAAYGEEAQAALIwgAACAhREEAACwMIIAAAAWRhAAAMDCCAIAAFgYQQAAAAsjCAAAYGEEAQAALIwgAACAhREEAACwMIIAAAAWRhAAAMDCCAIAAFgYQQAAAAsjCAAAYGEEAQAALIwgAACAhREEAACwMIIAAAAWRhAAAMDCCAIAAFgYQQAAAAsjCAAAYGEEAQAALIwgAACAhREEAACwMIIAAAAWRhAAAMDCCAIAAFgYQQAAAAsjCAAAYGEEAQAALIwgAACAhREEAACwMIIAAAAWRhAAAMDCCAIAAFgYQQAAAAsjCAAAYGEEAQAALIwgAACAhREEAACwMIIAAAAWRhAAAMDCCAIAAFgYQQAAAAsjCAAAYGEEAQAALIwgAACAhREEAACwMIIAAAAWRhAAAMDCCAIAAFgYQQAAAAsjCAAAYGEEAQAALIwgAACAhREEAACwMIIAAAAWRhAAAMDCCAIAAFgYQQAAAAsjCAAAYGEEAQAALIwgAACAhREEAACwMIIAAAAWRhAAAMDCCAIAAFgYQQAAAAsjCAAAYGEEAQAALIwgAACAhREEAACwMIIAAAAWRhAAAMDCCAIAAFgYQQAAAAsjCAAAYGEEAQAALIwgAACAhREEAACwMIIAAAAW5lnaBZQn6enpWrt2rdauXauoqCjFxsbKbrcrKChI9evXV2hoqEJDQxUQEOCWeuLj47Vy5Upt3LhRBw4cUHx8vPz8/BQUFKQmTZqod+/e6tSpkzw9+d8MAMgdZwgnRUZG6h//+IdOnDiR43vHjx/X8ePHtXHjRk2fPl2vvfaaevToUaL1LFu2TG+++aaSk5NNy9PS0hQfH6+DBw/q22+/VaNGjTRjxgw1b968ROsBAJRPXBpwwtq1azVs2LBcQ4Cj+Ph4jR07VrNmzSqxeqZNm6ZXXnklRwjIzeHDhzVgwACtX7++xOoBAJRf9AgUYO/evXrppZeUnp5uLGvSpIkGDx6s5s2by9PTUwcPHtTnn3+unTt3GuvMmzdPDRo0UL9+/Vxaz+LFi7Vw4ULTsrvuukv9+vVTgwYNdOnSJe3atUuLFi1STEyMpKuXNF544QV9/vnnatKkiUvrAQCUbza73W4v7SLKqoyMDPXt21cHDx40lvXv319TpkyRl5eXaV273a558+bpnXfeMZZVrlxZERERqlGjhkvqOX36tEJDQ3X58mVJks1m07/+9S899NBDOdZNTk7W+PHjFRERYSwLCQnR559/7pJacjNkyBBt375dHTp00KJFiwq9/blz50yBC6XLy8tLtWrVKu0ykAteK2VLeX+tcGkgH19//bUpBLRt21ZTp07NEQKkqyflUaNG6bHHHjOWJScna968eS6rZ/bs2UYIkKSRI0fmGgKkqyFk1qxZCgkJMZbt3LnTFAwAACAI5CM8PNzUnjBhgipVqpTvNmPGjDElw+XLlys1NbXYtVy8eFGrV6822tWqVdOoUaPy3cbb21uvvfaaadmnn35a7FoAABUHQSAPJ0+e1N69e412kyZN1Lp16wK38/HxMY0LSElJcclAvYiICKWlpRntXr16yc/Pr8DtmjVrpjZt2hjtyMhInT9/vtj1AAAqBoJAHjZt2mRq33HHHU5v26lTJ1P7xx9/LHY9mzdvNrU7d+5cpHoyMjL0888/F7seAEDFQBDIQ1RUlKntTG9AlpYtW8pmsxnt7HcTuKqeVq1aOb2t47quqAcAUDEQBPJw+PBhU7tRo0ZOb1u5cmVde+21RjsmJkYpKSlFruXSpUuKjo422jVr1lT16tWd3r5Bgwam9qFDh4pcCwCgYiEI5OH06dOmdu3atQu1veP6Wff0F8WZM2eU/S7P6667rli1ZA8VAABrIwjkIjMzU7GxsUbb399flStXLtQ+HOcOyL6/wnIc3BcYGFio7X18fEz1x8XFKTMzs8j1AAAqDoJALpKSkpSRkWG0CxsCctsmISGhyPU4bluUhxplr8dutysxMbHI9QAAKg6mGM5F9tv0JMnX17fQ+/D29s53n8Wpx8fHx231rFixQitXrnRq3axBiPv27dOQIUMKV2AhaoL7OP7doGzgtVL2FPe10qxZM7388ssuqqZwCAK5cJy6s6BJhHLjOPtgcaYDdXzRF+Wxwo7bXLlyxantYmJitH379kIdKzExsdDbAABKB0EgF9lv/Suq7JcWpKKFiSyO9RTl8RCOYwI8PJy7KlS3bl116NDBqXWjoqKUmZmpqlWrKjg4uNA1VhT79u1TYmKiqlSpwuOfgXzwWvlLs2bNSu3YBIFcuOLTvGMQKEp3fl71OPtp3hX19O/fX/379y/08aws6+FLzZs3L9LDlwCr4LVSNjBYMBeOg/EuXbpU6H04zhtQlHEGedVTlDkJkpOTTW1npicGAFR8BIFc+Pj4yN/f32gnJiYWujvecaR/YW/5y85x8qDC3oFgt9tNQSAgIKBYPRQAgIqDIJCH7JPwpKenKy4urlDbF/fe/+wcJxAq7EODLl68aLq8UZxaAAAVC0EgD46D3U6dOuX0tna73TR7X0BAgGnK4cIKDAw0zQMQHR1dqB4Kx9pvvPHGItcCAKhYCAJ5aNGihal98OBBp7c9efKkaVxBkyZNXFpPSkpKoaYJdqzdFfUAACoGgkAe2rZta2pHRkY6ve2OHTtMbWdvvytMPY7HKEw9t956a7HrAQBUDASBPLRv3940YHDdunW6fPmyU9t+//33pvadd95Z7Hoc9+F4jLykpqZqw4YNRrty5cpq165dsesBAFQMBIE8eHt7q0ePHkY7Li5OS5YsKXC73bt3a9OmTUa7YcOGuuWWW4pdT0hIiOlxwhs3blRUVFSB2y1evNg00LF3795MGwsAMBAE8jF8+HDTjIAzZ87Mt0v+zz//1PPPP28ayDdixAiX1GKz2fTYY48Z7czMTI0ePTrfOwi2bdumWbNmGW0vLy8NHz7cJfUAACoGgkA+GjVqpEGDBhnttLQ0PfHEE/rss89yzDa4efNmDRw4UKdPnzaWtWnTRn369Mlz/3PmzFHTpk2Nry5duuRbzwMPPGAaNBgTE6OBAwdqy5YtpvXS0tK0ePFiPfnkk6Y6hw4daupVQMno16+fnn32WfXr16+0SwHKNF4rZYPNXpSJ6y0kNTVVjz/+eI7BgjVq1NBNN90kb29vHT58WCdOnDB9PzAwUMuXL1edOnXy3PecOXM0d+5co123bl39/PPP+dZz/PhxDR48WOfOnTMtDw4OVqNGjZSWlqa9e/cqNjbW9P327dvr448/zjFdMQDA2ugRKICvr6/mz5+vTp06mZbHxsZq48aN+umnn3KEgPr16ys8PDzfEFBUDRo00Keffqp69eqZlp84cUI//fSTNm7cmCMEdOrUSR988AEhAACQA0HACf7+/vroo480ffp0NWzYMM/1qlWrpqeeekqrVq3SDTfcUGL13HjjjVq9erWeeeaZfGcJvOGGGzR16lQtWLDANCERAABZuDRQBMeOHVNUVJTOnz+vtLQ0Va1aVU2aNFHLli3dPiI/MzNTv//+u44dO6bz58/Lw8NDNWvWVMuWLdWoUSOXPFIZAFBxEQQAALAwLg0AAGBhBAEAACyMIGAhhw8fNs1b0LRpUz3++OOlXVa+/vzzT7399tulXYYlZWRkaPHixdq7d29pl1KmTZw40fSauuOOOxQfH1+sfQ4ZMsS0z127drmoWiAngoCFLF++PMeyzZs357j9sSxIT0/XwoULFRoaqm+//ba0y7GcX3/9VQ888ICmTJmipKSk0i6nXPnzzz81ZcqU0i4DcBpBwCLS0tL09ddf51hut9udeoaCu/Xp00fTpk1TcnJyaZdiOR9++KEGDRqkffv2lXYp5da3337r9IPBgNJGELCIiIgIXbx40Wg3adLE+PeKFSuUmppaGmXl6ciRI6VdgmUdPXq0tEuoEF577bV8nwUClBUEAYv44osvjH/Xr19fDz/8sNGOj4+n+x1wsYsXL+qf//xnaZcBFIggYAExMTHaunWr0e7QoYO6d+9uerLiZ599VhqlARXaunXr9OWXX5Z2GUC+CAIW8MUXXygzM9No33HHHapZs6Y6duxoLNu7d69+//330igPqFD8/f1N7X//+9+mp5ICZY1naReAkpWZmamVK1cabT8/P911112SpPvvv1+bNm0yvrd48WK1bt26yMdKTk7W7t27dezYMSUkJMjHx0fVq1fXddddpzZt2sjHx6fI+y6q3bt3a9euXUpPT1ejRo3UsWNHp6aBPnv2rHbu3Knz588rKSlJVatWVa1atXTLLbeoRo0axa4rLS1NUVFROnz4sC5evKhKlSqpevXqatasmZo2bSpPz/L/0jxx4oT27t2rCxcu6NKlS6pZs6bq1aunW265pVgPwLp8+bLxu4uPj1elSpVUrVo1BQUFKSQkpNSfqzFq1CjNmzfPGOialJSkSZMm6ZNPPnHblN8HDhzQoUOHdP78eaWnp6tWrVoKDg5W69at5eFRPj7/nTp1Slu3blVcXJzq1aun2267zanXXlJSkn799VedOXNG8fHx8vPzU2BgoG666SYFBwe7rL7Lly8rMjJSp0+fVmxsrAICAlSnTh116NChyH+DpfUeWv7fbZCvjRs36syZM0b7zjvvlJ+fnySpe/fuev3115WYmChJ+u677zRx4sRCn+h2796tDz/8UOvXr1d6enqu6/j4+KhDhw4aOnSo7rjjjlzX6dKli2JiYnIsj4mJUdOmTY12hw4dtGjRIqOd/XHOlSpV0v/+9z8lJibqpZde0vr16037ynow1PDhw3McJz09Xd98840++eQTHThwINcaPTw81KpVK40cOVL33HNPruvk59SpU1qwYIHWrFmjhISEXNcJDAzUQw89pCeeeCLHp8uvvvpKEyZMMNotW7YsdNfzs88+qx9//NFof/PNN/rhhx9Mj8TO7tFHHzW1f/rppxxPv8ySnp6uJUuWaPHixTp+/Hiu61SpUkWhoaEaPXq0rr32WqfrPnr0qObNm6cff/xRKSkpua7j6empNm3aaNCgQerRo0epPGujbt26mjRpkml8wLZt2xQeHq4hQ4aU2HFTUlK0cOFCffHFF6bXfHY1atRQ37599fTTT6tKlSoF7nPIkCHavn270c7v/3120dHRpteH42s2yy+//GL6+1q8eLHatWunmTNnauHChbpy5YrxPS8vL91///169dVXcz0hbt26VQsWLNDWrVuVkZGRa13BwcEaNGiQBg0aVOAHAsefYfr06erTp49iY2M1a9YsrVmzJtdba729vXXPPffohRde0PXXX5/vMbK46j20qMpHNESRZR8kKF3tBcji6+urnj17Gu20tLQc6xdk/vz5GjhwoH788cc8/4Clq+l548aNeuKJJ/Tiiy8qLS2tUMcpDLvdrjFjxuQIAZIUFxengwcP5lh+9OhRDRw4UJMmTcozBEhXe1h27dqlp59+WsOGDTPdiVGQTz75RD179tTnn3+eZwiQpPPnz+s///mPevfunaPWbt26mcJBVFRUnifc3CQmJmrDhg1Gu0WLFqY7SIrj0KFD6tWrl9544418a0pMTNTy5cvVvXt3ffPNN07te9WqVbr//vv19ddf5xkCJOnKlSuKjIzUCy+8oOHDh+f7ey5JAwYMMHressyYMUPHjh0rkePt2LFD3bp105w5c/IMAdLVx6cvXLhQ3bp1M40bKkvef/99ffjhh6YQIF0Nmb/88kuOE3hiYqJefPFFDRs2TJs2bcozBEhXe6nCwsJ033336X//+1+ha/vll1/Uq1cvLVu2LM/5NdLS0vTdd9+pZ8+eub4HOSoL76EEgQrswoULWrdundEODAzUnXfeaVrnwQcfNLU///xz03iC/HzxxReaMWOGsj+3KiAgQO3bt1f37t0VGhqqkJCQHN3A3377rd58883C/jhOCw8PN13ycNSnTx9Te//+/Ro4cGCON4YqVaqoY8eOCg0NVYcOHeTr62v6/tatW/XQQw/l2ovhKCwsTGFhYbp8+bJpecOGDXX33XerW7duatCggel70dHRGjJkiGnCJ39/f917772m9VavXl3g8bN8//33pjeQvn37Or1tfiIjIzVo0KAcAaB69erq1KmTunfvrtatW5sGqKakpGjcuHEKDw/Pd99bt27V+PHjTW+Svr6+CgkJUbdu3XTfffepffv2Rk9X9u3GjRtX/B+uiF5//XVVq1bNaKempmrixIn5nqiK4ocfftBjjz2mc+fOmZYHBQXpzjvv1L333qsWLVqYekdiY2M1YsQIU89QWbBv3748e6akq6/d7D9HUlKSBg8enOOuJy8vL4WEhKh79+7q1KmTatasafp+dHS0HnnkkXzfJxzt379fo0aN0oULFyRd7X1q3bq1unfvro4dO5r+X0tXA8Ho0aN18uTJPPdZVt5DuTRQga1cudL05tm3b98c155vvvlmNW3a1PgUHBMTo//7v//L8WnGUVJSksLCwoy2l5eXJk2apAEDBuRI7LGxsZo2bZq++uorY9nSpUv16KOPmk5+ixYtMj4FdOvWzVgeFBRk6lZ0PCFnl5mZqXfffddod+jQQS1btlRcXJwiIyN15coV3Xrrrcb3z549qxEjRhiXRySpZs2aeumll9SrVy/Tz5KSkqIlS5Zo7ty5xqfSEydOaPTo0VqyZEmeXY1fffWVPvnkE9Oyzp07a+LEiWrYsKFp+ZYtW/SPf/zD+FQXFxencePGaenSpcYbYJ8+fUyTQ61evVrPPPNMnr+T7LK/YXp6eqpXr16SrnYBZ/UWzZgxQ2vXrjXWe+utt0xjR2rXrm3a57lz5zR69GjTp+9atWpp4sSJuu+++0wn//Pnz+udd94xZrm02+1644031KRJE3Xo0CFHvXa7XZMnTzbeKG02m0aNGqURI0bkuGySnJys9957TwsWLDCWrV+/Xtu2bdNtt93m1O/Hla699lq9+uqrGjt2rLFs165dmj9/vp566imXHOPQoUMaP368Kdw1aNBAL7/8su644w7TSfPUqVMKCwvTTz/9JOnqJ+xx48ZpxYoVuvHGG11ST3HNmTPHeA9o2LCh8f9t79692rVrlynEX7lyRc8884z2799vLPPy8tLjjz+uxx9/XNdcc42xPCMjQz///LPCwsKM4J6SkqKxY8dqxYoVql+/foG1LVy4UNLVv8GhQ4fqySefNF1GTUtL02effaa33nrL+BkuX76sOXPm6K233sqxv5J4Dy0qegQqMMdu/gceeCDX9Rx7BRYvXlzgvteuXWvqGhs7dqweeeSRXE+GNWrUUFhY3oRZ6AAAGFZJREFUmO6++25j2ZUrV3Kk+Lp16yo4ODjHgB5PT09jeXBwsIKCgvKsy263KzExUd7e3vrggw+0aNEiTZgwQWFhYfrhhx/08ccfm94cZ86cqT///NNo16tXT8uWLVP//v1z/Cz+/v56/PHH9d///tf0JhMVFaX//Oc/udaTlJSUI7kPGjRIH374YY4QIEm33367wsPDTZ8ufv/9d1MXY8eOHU2/gyNHjpjeDPNy9uxZ0/XerLtHpKtjJ7J+v44DnYKCgky/f8cw+corrxifkqSrv8OlS5eqV69ephAgXe2Vmjp1qiZNmmQsy8zM1IQJE3Kd1Gr79u2mT1SDBg3S888/nyMESFLlypU1btw40xwZknKdUdNdevTooR49epiWzZ0716n/XwWx2+168cUXTb+3li1baunSpercuXOO8RH169fXe++9ZxqncOnSJY0fP77YtbhK1jMannnmGX377beaPHmyJk+erKVLl+q7774zvTd89dVX2rZtm9H28fHR+++/r7Fjx5pen9LVsUP33nuvvvjiCzVr1sxYnpCQUOheoxkzZmjSpEk5xlJ5e3tr2LBheuWVV0zLf/rpp1y78UviPbSoCAIVVGRkpOl6ZNu2bfNM/ffff7/pj2/Tpk06depUvvt3vHZdUA+Ch4eHRo0aZVq2c+fOfLcpjrFjx+aoycPDw5Sejx49muMT8rvvvlvgYKhWrVrp9ddfNy0LDw839Spk+fLLL03jCG666Sb985//zHcQW7169TRmzBjTshUrVph+jt69e5u+78wbwurVq02XfVxxWeDAgQOmy0+enp6aPXu26tatm+92w4YNM3ojJOn06dO5jhdw/DtzvLSVm2eeecb0+y3JvzNnvPrqq6pVq5bRTk9Pz/EpvijWrVtnGs9SpUoVzZ07N0cXtaOXX35Zt9xyi9Hes2ePtmzZUqxaXOnuu+/W6NGjc9zdkP39Kz09Xe+9957p+y+99FKBg+hq1KihuXPnmoLkzp07TYEiP7169TL93eZm4MCBuu6664x2cnJyrsGvLL2HEgQqKMcHDOXVGyBd/TSY/bpzZmZmgRMMOQ5qcWbgTatWrTRjxgwtWbJEmzZt0kcffVTgNkXh7e2tv//97wWu9/XXX5uu1/bu3VstW7Z06hihoaFq27at0U5KStKqVatyrOd4cnv22WdzfErOTb9+/Yxr3r6+voqLizN93/EkvmbNmgL3mT0sVK1aVV26dClwm4I49h717t1bN910k1PbPv300/nuS8r5d+bM8w9q1aql2bNna9GiRdqwYYO+++47p+opKdWqVdPUqVNNyw4cOJDvtXBnOP6+hg4dajoB5SXr8kp++ypNQ4cOLXCdHTt2mMbm1KtXz+k7MurXr5/jGM4+b8Wxtyk3Hh4eatOmjWlZ9l7HLGXpPZQgUAElJSXphx9+MNqVK1fWfffdl+82jpcHVqxYkWNgW3aO19SmTp1qGo2eG5vNpt69e+uWW24xfUJytZtuuinXrmNHv/zyi6ndr1+/Qh3H8XfmuL/ExETTI3yrVq2qzp07O7VvX19f/fe//1VERIR27tyZ49arxo0bq0WLFkY7JiYm308HR44cMdVy3333OTWfQkEcP0k5DsTMT8OGDdW4cWOjvW/fPsXGxprWcbz96r333tPKlStNg6ty061bN3Xo0EG1a9culVsIHd111105/l4WLFhQ5McLp6enKzIy0rSsMD08nTp1UkBAgNHetm2b04OES1LW7Z8Fcfy769u3b6H+Pzv+v8h+ySwvXl5eatWqlVP7dxxHc+nSpRzrlKX3UIJABfTNN9+Y/vB69OhR4ImxY8eOpu7cuLi4fEejh4aGmq4Vx8XFaeTIkerRo4emTZumrVu3lugtgvm5+eabC1wnLS1Ne/bsMdrOvgFl165dO1P7t99+M7X/97//md5cmzdvXqiJglq3bq369evnOQGM40k3v/9fjj0TrrgscOHCBdMdDZUqVTL1kjjD8f+V44mxU6dOpq7uy5cva+LEibrnnns0ZcoUrV+/Pt/bCcuSSZMmmV5jGRkZmjBhQq4niYLs27fPNDbguuuuc2rAWxYPDw9Tz01SUpIOHTpU6DpcrVGjRjnu/sjNr7/+amo7vhYLUq9ePdPJOjY2tsBbO+vWret0eHYc0JzbnSJl6T2UIFABFeayQBabzab+/fubluV3eaB27doaNmxYjuVHjhzRwoULNWzYMHXo0EEjR45UeHi4oqOjnSveBZxJyhcvXjTdp3z99dcXetau66+/3vSCv3DhgunE7zjOIrfBgcXRu3dv0xvJd999l+etadlDQoMGDRQSElLs42cPAdLV669nzpzRiRMnnP7K/qlUyvnUSV9fXz3//PM5jh0TE6PFixfrySefVIcOHfToo49qwYIFZfqplQEBAQoLCzN9cj1+/LhmzJhR6H05/u4DAwML9Xs/ceJEjrEEZeF3FxgY6NR6jrdKZu9Zcpbj/Bm5dd9n58wETFkcL//l1ttSlt5DuX2wgtm/f7+pC1iSU9fLc7Nnzx7t3r07z+6wF198UQkJCVq2bFmu37906ZI2bNigDRs26PXXX1fz5s3Vq1cv9e/f3yXT9ObFccRwbhwnAqpatWqRj5X1ySwzM1Px8fGqXr26JOW4rl+YNxJn1KxZU3/729+M7sTz58/rl19+0e23325ab9euXaaR94Xpvs9P1gjvLOfO/b/2zj0oqvKN418g1sAVTEFwTS6GFmJQTYCJ+ZtBEMW8oqitBmomDYxTA06KhVkqCnnJG2WNppXxR1CKIiVjKmqWhpl4RSHYQCFdbC3luvz+cPbMvufs5Zxll+vzmXHG88o5ezjued/nfS7f52+m7NMSDAkAvfrqq1Cr1dixY4dBQ0cnNPPLL78gMzMTPj4+mDhxImbOnCkqZt6ehIaGYt68edi7dy839vXXXyMiIoLp/WEO/rO/ePGiTZ59eyP2PeS/W2LeeT78c/jX5CPGUyGVzjKHkkegm2HsC2UpppJo7O3t8eGHH2LXrl0IDQ01G6O7cuUKMjMzERUVhezsbKvepz5i3Hc6HXgdlr7kfBegviuPn2NhSv/AUvgufkPVA/phATs7O6sZAoaqJNoKf4HTkZSUhOzsbISHh5sNr/z555/Yvn07oqKisG3bNrP5BO1NcnIyfH19uePW1lakpqYaVaozhC2efWcwBMS63vXf38cee8yifBf++2gqJ8pWdJY5lAyBbkRDQ4PV6kp1HDp0yKylHBYWhr179+L48eNIS0vDmDFjTC6sGo0GK1euFIjstCf8WnlL4rSA0KDQn1z4n2GoTr6tjB07lvE0HDlyhDFGWlpamKz54OBgs6V9YrGFYWMqJhoYGIisrCycPHkSa9euxbhx40zuBHViLmvWrLH6fbaFxx9/HBkZGYz7uLq6WtJ92uLZW3MhtLXxpZ/z1NzcbFEs3VqbAWvQ0XMohQa6EQUFBcyOavTo0UhLS5N8nfnz53OlOQ0NDcjJycHChQvNnufh4QGlUgmlUonGxkZcuHABJ0+eRFFRkSBcATwS5pgwYYJJgSBbwV9AjO1ETaHVapldlIODA7P48z9Dyo5PLL169UJUVBQnHqXRaFBUVMQ1Szl9+jQj9mMtSWFA+PtFREQYFVayJk888QRiYmIQExODlpYWXLp0CadOnUJRURHOnz8viMd++eWXmDx5suiM7/YgMDAQixYtwieffMKN5ebmIiIiQlQzK/6zj4uLQ2pqqtXvk4/YBd7WSW4uLi7Mu6fRaETnF+jgv/PWDt1ZQkfNoeQR6EbwlQSnTZvGKMKJ/cN3HWdnZ0u28GUyGYKDgzkJz8LCQiiVSuZnmpqamBbJ7Ymbmxuj311ZWSl5x15WVsbUAnt6ejJua/7LKbXhTGlpKU6dOoWKigqTEyt/cS8oKOD+rq8l7+TkhKioKEn3YAp+iZQ5ESpb4ODggMDAQLz55pvYt28fTpw4gcTEREH4gJ9A2xlISkqCv78/M5aWliYooTRERz17sX0SzHkR24pCoWCODTUSMwe/uZiYrortSXvOoWQIdBMqKipw9uxZ7tjZ2dmiNrmAsJ6+srISJ06cYMZaW1u5vgT8+nlDDB48GGlpaQJBDimd86yJTCZjxIOam5sl13TzS5j4WciBgYFMzI9fTmiOPXv2YMGCBRg3bhyCgoKMPucXX3yRcff/9NNPnOGg//8WEREhyNJvCz4+PkzCUmlpqaRujMCjkIy5Z1JTU4Off/5ZVCc3d3d3LFmyRKDM2FHfM1M4Ojpi/fr1jEF6584dvP/++2bPDQoKYspKi4uLJTczevDggVkDn29QiQ2h2dow4Ve98DUVzFFeXs54ylxcXATGha3pTHMoGQLdhG+//ZZ5qcPDwy2OeXl5eQnqwfWTBisrK/H8888jPDwcixYtwqZNm0RfW18rG7DMJW8tgoODmWOplrW+7C8AQdZ3nz59GOOgrq5OtJRpa2uroDOaMcU+Ozs7pr30/fv3cebMGVy9epVpSSs2LCBFmEX/GWq1WtFthXUkJCQgMDAQkZGRiI+PZ1rjPnz4ECEhIRgzZgyn4S7WM8VXTezI75kpnn76aSxZsoQZ++GHHxiNC0PI5XJGUOrevXuiDCUdWq0WU6dORVBQEMaPH4+FCxcalMHlG4537twRdX39TYkt4L+733//vSQjOycnhzkOCQkxqtdhCzrbHEqGQDegublZsIjxteilwvcKHD9+nKtjHTx4MJOs9Pvvv4uuQebX/xpLXNNPpLJV4lFsbCzz8h88eBAlJSWizs3Pz2c8CI6OjgY1yPkKZllZWaJ+n8LCQmYR5yvB8eEv8kVFRczC4OHhISgrNAZ/QjR1v/zS1J07d4pybQOP2gSfOXMGTU1NqKysxJkzZxglQScnJ8ZdW1tbi1OnTom6Nr8mvL13e1JYuHChYIcrZufNf/abN28WnfCXk5ODiooKNDQ0oLy8HMXFxYJmX4AwvCXm+VdVVUlqjW0JYWFhjIBSVVWVQH3TGCqVSiCpLFVVtK20xxwqBTIEugHHjh1jvhx9+/ZFWFhYm645YcIExqOg1Wq5chX+DlTXKtZcgtDDhw+xZ88eZsyY5K7+Z9siyQ549DLqx8ybm5vx1ltvmRXu+OOPP7By5UpmbNasWYKe5wAwffp0pjb6119/NasxX1tbK9Cmnzt3rslzfHx8mFbBJ0+eZAyBSZMmid7x8D1Jpp7/qFGjGE+FriWxufK2W7duCbreTZgwQTCp8fNVVq9ebXYH1NLSgp07dzJjYpoVdRQODg5Yt26dZA/elClTMGDAAO74+vXropoZXblyhWl/CwBKpdLg5/M9g7m5uYyByufff//FsmXLLK7CEYu9vT0WLFjAjG3cuFHgReOjVquRlJTEqFH6+/sLdtm2pj3mUCmQIdAN4CcJjh8/nok7WoJcLmcaEek+R/dFjY+PZ0p4zp07h9dee82oS/Pq1auYP38+k9QTEBBgtFuY/qKqy4S3BWlpacxkqlKpEBsbi++++07QFOThw4fYvXs34uLimIxlb29vQUxah1wuxwcffMCMbdu2DUuXLkVNTY3g54uKijB79mzcvn2bG4uIiBC1kOkvmmVlZYzHQkq1AN+gMdfQKCMjg1lEzp49i5iYGBw5ckQQt25paUF+fj5mzpzJ7NpdXFyQnJwsuHZMTAzz/1NeXo45c+YwIQR9VCoVEhMTmW56Hh4eVq2WsAU+Pj5ISUmRdI5MJkNGRgZj4BUUFGD27NkGQ1CNjY3Izs7G3LlzmdK5QYMGYfHixQY/4+WXX2Y8Uf/88w/mz58vyKdpbW3FsWPHEBsby+n2S5HTtoTZs2dj9OjR3HF9fT0SEhKwefNmgSaCVqtFYWEhZsyYwYRAevXqhdWrV4tqBGZtbD2HSoHKB7s4NTU1gkS+toYFdEybNo3pqFdXV4f8/HxMnToVCoUCqampePfdd7l/P3/+PGbMmIFBgwZh6NCh6N27Nx48eIDy8nJBQotcLkdGRobReLSfnx8jo5qUlISRI0fCyckJvXv3tlpteL9+/bB161a88cYb3E7z7t27WLZsGdauXYsRI0bAxcUFarUaFy9eFOx0PD098emnn5osPRo/fjwWLVqEzz77jBs7cOAADh48iOHDh2PQoEFoamrC1atXUV1dzZw7bNgwgXfAGBMnTkR6ejpnwOhc+gEBAZIkWPk/e+jQIVRUVMDb2xv379/H8uXLmZawfn5+yMjIQHJyMmcoVlRUICkpCX379kVAQABcXV2h0Whw+fJlQehAJpNhw4YNBrO2dbK8ixcv5iShb968ifj4eLi7u+OZZ57h1B1VKhVKS0uZUIYuIa8ja8TFolQqUVhYaNTIMcRLL72E1NRUrFmzhvu9L126hLi4OO759OnTB3V1dSgpKRF4anSti419f+VyORISEhgZ5PLycsyaNQtDhw6Fl5cX6uvrcePGDcawjY6OhkajMbtDbwv29vbIzMxEXFwctzg2NTUhKysLn3/+OZ599lm4u7tzbYD5+Q26753YjqPWxtZzqBTIEOji5ObmMrsuhUIhufGLMUaOHAmFQsEsTvv27eN2VzNnzkRjYyOz+ACP4nX6LUL5PPnkk9iyZQv8/PyM/kx8fDyOHj3KTW719fWcq1smk2HVqlVW23E899xzyM7ORmJiIsrKyrhxjUZjsk/7qFGjsG7dOlE1vCkpKXB3d0dmZib3rLRaLUpKSozmJYSGhmLTpk2cZLE5+vbti//9738oLCxkxqXuhseMGYOnnnqKiVnq3+fEiRMZQwB41O1v9+7dePvtt5md/r1790zGlQcMGICNGzcKkr/0GT16NDZv3ox33nmH2cn+/fffgnipPv369UNmZqYk6d6OxM7ODunp6Zg0aZIk5cB58+bB09MTqampzE7Y3PPx9fXF1q1bzRqJr7/+Om7fvo2vvvqKGS8tLTXYqOiVV15Benq6oNWxLejXrx+++eYbLF26FEePHuXGm5qaBE3A9PHy8sL69evxwgsv2PweTWHLOVQKFBrowrS2tgqyX6Ojo63WdtXe3p6JYwHAhQsXGGELpVKJ/fv3Y/LkyWZ3XcOGDUNKSgry8vLM9qwPCQnBRx99ZFA5rrGx0eoNUoYMGYK8vDysWrXK5Mtlb2+P4OBgbN++Hbt375Yk5BEXF4fDhw9jypQpJrtB+vr6YvXq1fjiiy8M5h2Ygr/oG0tiNIVMJsPOnTuZnAN9DGWXA4/KGH/88UckJycL2gfzUSgUSEpKwuHDh00aAToiIyNx+PBhzJkzx6wevZeXFxISEpCfn8+4jrsCAwcOxIoVKySfFxkZicLCQixevNjsd3LIkCFYvnw5Dhw4IMpTZGdnh/feew+7du0y2aHT398fH3/8MTZs2GCVFtdikcvlyMrKwq5duxASEmLSze/r64sVK1YgLy+vw40AHbaaQ6Vg19rZhLiJLktDQwOuXbuG0tJSaDQa1NfXw9XVFW5ubvD395fUJlX/mqdPn0ZVVRU0Gg2cnZ2hUCgQGhpqcaMgMVRXV+PChQu4e/cu7t+/DycnJwwePBhBQUGSFcwM0djYiOLiYvz1119Qq9VwcHBA//79ERgYKNhtS71uSEgIF8IYO3YsduzYYfH1Ll++jEuXLkGtVsPe3h5ubm4YMWKEqAWksrISJSUlUKvV3DPs378/hg8fjiFDhlhssDY1NeHGjRu4du0a7t27hwcPHsDFxQVubm7w8/Oz2i6pK3P9+nVcu3YNdXV1+O+//+Ds7IwBAwZgxIgRFr2H+ty6dQvFxcWora2FVquFh4cH/P39rd5d01I0Gg1+++031NbWoq6uDo6OjvDw8EBAQADT46EzYos5VAxkCBBEN+LmzZuIjo7mjrdv346IiIgOvCOCIDo7FBogiG6EvqBP//79O3XZHEEQnQMyBAiim6DVarF//37uePr06W0uIyUIovtDhgBBdBMKCgq4Cg8HBweBJjlBEIQhyBAgiG7AuXPnmGY10dHRVpEeJQii+0PJggTRBZk1axZcXV3h5OQElUrFlHQ6Ozvj0KFDnVpfnyCIzgMJChFEF6RXr144fvy4YNzOzg5r1qwhI4AgCNFQaIAguiCGOsUNHDgQW7ZsYcoHCYIgzEGhAYLogqjVapw9exYqlQoymQze3t4ICwuzeaMXgiC6H2QIEARBEEQPhkIDBEEQBNGDIUOAIAiCIHowZAgQBEEQRA+GDAGCIAiC6MGQIUAQBEEQPRgyBAiCIAiiB0OGAEEQBEH0YMgQIAiCIIgeDBkCBEEQBNGDIUOAIAiCIHowZAgQBEEQRA+GDAGCIAiC6MH8H0cB2MxzDCadAAAAAElFTkSuQmCC\n", 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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",