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examples/integrations/learnergy/dropout_rbm.py

@@ -1,5 +1,5 @@
 import torchvision
-from learnergy.models.binary import DropoutRBM
+from learnergy.models.bernoulli import DropoutRBM
 
 from opytimizer import Opytimizer
 from opytimizer.core import Function

examples/integrations/learnergy/rbm.py

@@ -1,5 +1,5 @@
 import torchvision
-from learnergy.models.binary import RBM
+from learnergy.models.bernoulli import RBM
 
 from opytimizer import Opytimizer
 from opytimizer.core import Function

examples/integrations/nalp/lstm.py

@@ -1,8 +1,8 @@
 import tensorflow as tf
-from nalp.corpus.text import TextCorpus
-from nalp.datasets.language_modeling import LanguageModelingDataset
-from nalp.encoders.integer import IntegerEncoder
-from nalp.models.generators.lstm import LSTMGenerator
+from nalp.corpus import TextCorpus
+from nalp.datasets import LanguageModelingDataset
+from nalp.encoders import IntegerEncoder
+from nalp.models.generators import LSTMGenerator
 
 from opytimizer import Opytimizer
 from opytimizer.core import Function
@@ -12,17 +12,13 @@
 # Creates a character TextCorpus from file
 corpus = TextCorpus(from_file='examples/integrations/nalp/chapter1_harry.txt', corpus_type='char')
 
-# Creates an IntegerEncoder
+# Creating an IntegerEncoder, learning encoding and encoding tokens
 encoder = IntegerEncoder()
-
-# Learns the encoding based on the TextCorpus dictionary and reverse dictionary
 encoder.learn(corpus.vocab_index, corpus.index_vocab)
-
-# Applies the encoding on new data
 encoded_tokens = encoder.encode(corpus.tokens)
 
-# Creates Language Modeling Dataset
-dataset = LanguageModelingDataset(encoded_tokens, max_length=10, batch_size=64)
+# Creating Language Modeling Dataset
+dataset = LanguageModelingDataset(encoded_tokens, max_contiguous_pad_length=10, batch_size=64)
 
 
 def lstm(opytimizer):

examples/integrations/nalp/rnn.py

@@ -1,8 +1,8 @@
 import tensorflow as tf
-from nalp.corpus.text import TextCorpus
-from nalp.datasets.language_modeling import LanguageModelingDataset
-from nalp.encoders.integer import IntegerEncoder
-from nalp.models.generators.rnn import RNNGenerator
+from nalp.corpus import TextCorpus
+from nalp.datasets import LanguageModelingDataset
+from nalp.encoders import IntegerEncoder
+from nalp.models.generators import RNNGenerator
 
 from opytimizer import Opytimizer
 from opytimizer.core import Function
@@ -12,17 +12,13 @@
 # Creates a character TextCorpus from file
 corpus = TextCorpus(from_file='examples/integrations/nalp/chapter1_harry.txt', corpus_type='char')
 
-# Creates an IntegerEncoder
+# Creating an IntegerEncoder, learning encoding and encoding tokens
 encoder = IntegerEncoder()
-
-# Learns the encoding based on the TextCorpus dictionary and reverse dictionary
 encoder.learn(corpus.vocab_index, corpus.index_vocab)
-
-# Applies the encoding on new data
 encoded_tokens = encoder.encode(corpus.tokens)
 
-# Creates Language Modeling Dataset
-dataset = LanguageModelingDataset(encoded_tokens, max_length=10, batch_size=64)
+# Creating Language Modeling Dataset
+dataset = LanguageModelingDataset(encoded_tokens, max_contiguous_pad_length=10, batch_size=64)
 
 
 def rnn(opytimizer):

examples/integrations/opfython/supervised_opf_feature_selection.py

@@ -9,7 +9,7 @@
 from opytimizer.optimizers.boolean import BPSO
 from opytimizer.spaces import BooleanSpace
 
-# Loading digits dataset
+# Loads digits dataset
 digits = load_digits()
 
 # Gathers samples and targets
@@ -19,7 +19,7 @@
 # Adding 1 to labels, i.e., OPF should have labels from 1+
 Y += 1
 
-# Splitting data into training and testing sets
+# Splits data into training and testing sets
 X_train, X_val, Y_train, Y_val = s.split(
     X, Y, percentage=0.5, random_state=1)
 

examples/integrations/opfython/unsupervised_opf_clustering.py

@@ -8,7 +8,7 @@
 from opytimizer.optimizers.swarm import PSO
 from opytimizer.spaces import SearchSpace
 
-# Loading digits dataset
+# Loads digits dataset
 digits = load_digits()
 
 # Gathers samples and targets
@@ -18,7 +18,7 @@
 # Adding 1 to labels, i.e., OPF should have labels from 1+
 Y += 1
 
-# Splitting data into training and testing sets
+# Splits data into training and testing sets
 X_train, X_test, Y_train, Y_test = s.split(
     X, Y, percentage=0.5, random_state=1)
 

examples/integrations/pytorch/enhanced_neural_network.py

@@ -10,18 +10,18 @@
 from opytimizer.optimizers.swarm import PSO
 from opytimizer.spaces import SearchSpace
 
-# Loading digits dataset
+# Loads digits dataset
 digits = load_digits()
 
 # Gathers samples and targets
 X = digits.data
 Y = digits.target
 
-# Splitting the data
+# Splits the data
 X_train, X_val, Y_train, Y_val = train_test_split(
     X, Y, test_size=0.5, random_state=42)
 
-# Converting from numpy array to torch tensors
+# Converts from numpy array to torch tensors
 X_train = torch.from_numpy(X_train).float()
 X_val = torch.from_numpy(X_val).float()
 Y_train = torch.from_numpy(Y_train).long()
@@ -32,7 +32,7 @@ def fit(model, loss, opt, x, y):
     x = Variable(x, requires_grad=False)
     y = Variable(y, requires_grad=False)
 
-    # Resetting the gradient
+    # Resets the gradient
     opt.zero_grad()
 
     # Performs the foward pass
@@ -55,7 +55,7 @@ def predict(model, x_val):
     # Performs backward pass with this variable
     output = model.forward(x)
 
-    # Getting the index of the prediction
+    # Gets the index of the prediction
     y_val = output.data.numpy().argmax(axis=1)
 
     return y_val

examples/integrations/pytorch/linear_regression.py

@@ -21,7 +21,7 @@ def fit(model, loss, opt, x, y):
     x = Variable(x, requires_grad=False)
     y = Variable(y, requires_grad=False)
 
-    # Resetting the gradient
+    # Resets the gradient
     opt.zero_grad()
 
     # Performs the foward pass

examples/integrations/pytorch/logistic_regression.py

@@ -10,18 +10,18 @@
 from opytimizer.optimizers.swarm import PSO
 from opytimizer.spaces import SearchSpace
 
-# Loading digits dataset
+# Loads digits dataset
 digits = load_digits()
 
 # Gathers samples and targets
 X = digits.data
 Y = digits.target
 
-# Splitting the data
+# Splits the data
 X_train, X_val, Y_train, Y_val = train_test_split(
     X, Y, test_size=0.5, random_state=42)
 
-# Converting from numpy array to torch tensors
+# Converts from numpy array to torch tensors
 X_train = torch.from_numpy(X_train).float()
 X_val = torch.from_numpy(X_val).float()
 Y_train = torch.from_numpy(Y_train).long()
@@ -32,7 +32,7 @@ def fit(model, loss, opt, x, y):
     x = Variable(x, requires_grad=False)
     y = Variable(y, requires_grad=False)
 
-    # Resetting the gradient
+    # Resets the gradient
     opt.zero_grad()
 
     # Performs the foward pass
@@ -55,7 +55,7 @@ def predict(model, x_val):
     # Performs backward pass with this variable
     output = model.forward(x)
 
-    # Getting the index of the prediction
+    # Gets the index of the prediction
     y_val = output.data.numpy().argmax(axis=1)
 
     return y_val

examples/integrations/pytorch/lstm.py

@@ -10,26 +10,26 @@
 from opytimizer.optimizers.swarm import PSO
 from opytimizer.spaces import SearchSpace
 
-# Loading digits dataset
+# Loads digits dataset
 digits = load_digits()
 
 # Gathers samples and targets
 X = digits.data
 Y = digits.target
 
-# Splitting the data
+# Splits the data
 X_train, X_val, Y_train, Y_val = train_test_split(
     X, Y, test_size=0.5, random_state=42)
 
-# Reshaping the data
+# Reshapes the data
 X_train = X_train.reshape(-1, 8, 8)
 X_val = X_val.reshape(-1, 8, 8)
 
-# Converting to sequence shape
+# Converts to sequence shape
 X_train = np.swapaxes(X_train, 0, 1)
 X_val = np.swapaxes(X_val, 0, 1)
 
-# Converting from numpy array to torch tensors
+# Converts from numpy array to torch tensors
 X_train = torch.from_numpy(X_train).float()
 X_val = torch.from_numpy(X_val).float()
 Y_train = torch.from_numpy(Y_train).long()
@@ -72,7 +72,7 @@ def fit(model, loss, opt, x, y):
     x = Variable(x, requires_grad=False)
     y = Variable(y, requires_grad=False)
 
-    # Resetting the gradient
+    # Resets the gradient
     opt.zero_grad()
 
     # Performs the foward pass
@@ -95,7 +95,7 @@ def predict(model, x_val):
     # Performs backward pass with this variable
     output = model.forward(x)
 
-    # Getting the index of the prediction
+    # Gets the index of the prediction
     y_val = output.data.numpy().argmax(axis=1)
 
     return y_val

examples/integrations/pytorch/neural_network.py

@@ -10,18 +10,18 @@
 from opytimizer.optimizers.swarm import PSO
 from opytimizer.spaces import SearchSpace
 
-# Loading digits dataset
+# Loads digits dataset
 digits = load_digits()
 
 # Gathers samples and targets
 X = digits.data
 Y = digits.target
 
-# Splitting the data
+# Splits the data
 X_train, X_val, Y_train, Y_val = train_test_split(
     X, Y, test_size=0.5, random_state=42)
 
-# Converting from numpy array to torch tensors
+# Converts from numpy array to torch tensors
 X_train = torch.from_numpy(X_train).float()
 X_val = torch.from_numpy(X_val).float()
 Y_train = torch.from_numpy(Y_train).long()
@@ -32,7 +32,7 @@ def fit(model, loss, opt, x, y):
     x = Variable(x, requires_grad=False)
     y = Variable(y, requires_grad=False)
 
-    # Resetting the gradient
+    # Resets the gradient
     opt.zero_grad()
 
     # Performs the foward pass
@@ -55,7 +55,7 @@ def predict(model, x_val):
     # Performs backward pass with this variable
     output = model.forward(x)
 
-    # Getting the index of the prediction
+    # Gets the index of the prediction
     y_val = output.data.numpy().argmax(axis=1)
 
     return y_val

examples/integrations/sklearn/k_means_clustering.py

@@ -8,7 +8,7 @@
 from opytimizer.optimizers.swarm import PSO
 from opytimizer.spaces import SearchSpace
 
-# Loading digits dataset
+# Loads digits dataset
 digits = load_digits()
 
 # Gathers samples and targets

examples/integrations/sklearn/svm.py

@@ -8,7 +8,7 @@
 from opytimizer.optimizers.swarm import PSO
 from opytimizer.spaces import SearchSpace
 
-# Loading digits dataset
+# Loads digits dataset
 digits = load_digits()
 
 # Gathers samples and targets

examples/integrations/tensorflow/cnn.py

@@ -8,7 +8,7 @@
 from opytimizer.optimizers.swarm import PSO
 from opytimizer.spaces import SearchSpace
 
-# Loading CIFAR-10 data
+# Loads CIFAR-10 data
 (X_train, Y_train), (X_val, Y_val) = datasets.cifar10.load_data()
 
 # Normalizes inputs between 0 and 1

opytimizer/optimizers/swarm/__init__.py

@@ -22,7 +22,7 @@
 from opytimizer.optimizers.swarm.mfo import MFO
 from opytimizer.optimizers.swarm.mrfo import MRFO
 from opytimizer.optimizers.swarm.pio import PIO
-from opytimizer.optimizers.swarm.pso import PSO, AIWPSO, RPSO, SAVPSO, VPSO
+from opytimizer.optimizers.swarm.pso import AIWPSO, PSO, RPSO, SAVPSO, VPSO
 from opytimizer.optimizers.swarm.sbo import SBO
 from opytimizer.optimizers.swarm.sca import SCA
 from opytimizer.optimizers.swarm.sfo import SFO