Minor fixes

Author: rushter

examples/rbm.py

@@ -13,7 +13,7 @@ def print_curve(rbm):
     def moving_average(a, n=25):
         ret = np.cumsum(a, dtype=float)
         ret[n:] = ret[n:] - ret[:-n]
-        return ret[n - 1 :] / n
+        return ret[n - 1:] / n
 
     plt.plot(moving_average(rbm.errors))
     plt.show()

examples/rl_deep_q_learning.py

@@ -31,7 +31,7 @@ def mlp_model(n_actions, batch_size=64):
     # You can stop training process using Ctrl+C signal
     # Read more about this problem: https://gym.openai.com/envs/CartPole-v0
     model.train(render=False)
-except:
+except KeyboardInterrupt:
     pass
 # Render trained model
 model.play(episodes=100)

mla/base/__init__.py

@@ -1 +1,2 @@
+# coding:utf-8
 from .base import *

mla/base/base.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import numpy as np
 
 

mla/datasets/__init__.py

@@ -1 +1,2 @@
+# coding:utf-8
 from mla.datasets.base import *

mla/datasets/base.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import os
 import numpy as np
 
@@ -60,7 +61,7 @@ def load_nietzsche():
     sentences = []
     next_chars = []
     for i in range(0, len(text) - maxlen, step):
-        sentences.append(text[i : i + maxlen])
+        sentences.append(text[i: i + maxlen])
         next_chars.append(text[i + maxlen])
 
     X = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)

mla/ensemble/__init__.py

@@ -1 +1,2 @@
+# coding:utf-8
 from .random_forest import RandomForestClassifier, RandomForestRegressor

mla/ensemble/base.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import numpy as np
 from scipy import stats
 

mla/ensemble/gbm.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import numpy as np
 
 # logistic function

mla/ensemble/random_forest.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import numpy as np
 
 from mla.base import BaseEstimator

mla/ensemble/tree.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import random
 
 import numpy as np

mla/fm.py

@@ -1,3 +1,5 @@
+# coding:utf-8
+
 from mla.base import BaseEstimator
 from mla.metrics import mean_squared_error, binary_crossentropy
 import autograd.numpy as np

mla/gaussian_mixture.py

@@ -1,3 +1,5 @@
+# coding:utf-8
+
 import random
 import numpy as np
 from scipy.stats import multivariate_normal
@@ -65,7 +67,7 @@ def _initialize(self):
         self.weights = np.ones(self.K)
         if self.init == "random":
             self.means = [self.X[x] for x in random.sample(range(self.n_samples), self.K)]
-            self.covs = [np.cov(self.X.T) for _ in range(K)]
+            self.covs = [np.cov(self.X.T) for _ in range(self.K)]
 
         elif self.init == "kmeans":
             kmeans = KMeans(K=self.K, max_iters=self.max_iters // 3, init="++")

mla/kmeans.py

@@ -1,3 +1,5 @@
+# coding:utf-8
+
 import random
 import seaborn as sns
 import matplotlib.pyplot as plt

mla/knn.py

@@ -1,3 +1,5 @@
+# coding:utf-8
+
 from collections import Counter
 
 import numpy as np

mla/linear_models.py

@@ -1,3 +1,5 @@
+# coding:utf-8
+
 import logging
 
 import autograd.numpy as np

mla/metrics/__init__.py

@@ -1 +1,2 @@
+# coding:utf-8
 from .metrics import *

mla/metrics/base.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import numpy as np
 
 

mla/metrics/distance.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import numpy as np
 import math
 

mla/metrics/metrics.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import autograd.numpy as np
 
 EPS = 1e-15
@@ -83,5 +84,5 @@ def get_metric(name):
     """Return metric function by name"""
     try:
         return globals()[name]
-    except:
+    except Exception:
         raise ValueError("Invalid metric function.")

mla/naive_bayes.py

@@ -1,3 +1,5 @@
+# coding:utf-8
+
 import numpy as np
 from mla.base import BaseEstimator
 from mla.neuralnet.activations import softmax

mla/neuralnet/activations.py

@@ -47,5 +47,5 @@ def get_activation(name):
     """Return activation function by name"""
     try:
         return globals()[name]
-    except:
+    except Exception:
         raise ValueError("Invalid activation function.")

mla/neuralnet/initializations.py

@@ -71,5 +71,5 @@ def get_initializer(name):
     """Returns initialization function by the name."""
     try:
         return globals()[name]
-    except:
+    except Exception:
         raise ValueError("Invalid initialization function.")

mla/neuralnet/layers/__init__.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 from .basic import *
 from .convnet import *
 from .normalization import *

mla/neuralnet/layers/basic.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import autograd.numpy as np
 from autograd import elementwise_grad
 

mla/neuralnet/layers/convnet.py

@@ -145,7 +145,7 @@ def image_to_column(images, filter_shape, stride, padding):
         y_bound = y + stride[0] * out_height
         for x in range(f_width):
             x_bound = x + stride[1] * out_width
-            col[:, :, y, x, :, :] = images[:, :, y : y_bound : stride[0], x : x_bound : stride[1]]
+            col[:, :, y, x, :, :] = images[:, :, y: y_bound: stride[0], x: x_bound: stride[1]]
 
     col = col.transpose(0, 4, 5, 1, 2, 3).reshape(n_images * out_height * out_width, -1)
     return col
@@ -177,9 +177,9 @@ def column_to_image(columns, images_shape, filter_shape, stride, padding):
         y_bound = y + stride[0] * out_height
         for x in range(f_width):
             x_bound = x + stride[1] * out_width
-            img[:, :, y : y_bound : stride[0], x : x_bound : stride[1]] += columns[:, :, y, x, :, :]
+            img[:, :, y: y_bound: stride[0], x: x_bound: stride[1]] += columns[:, :, y, x, :, :]
 
-    return img[:, :, padding[0] : height + padding[0], padding[1] : width + padding[1]]
+    return img[:, :, padding[0]: height + padding[0], padding[1]: width + padding[1]]
 
 
 def convoltuion_shape(img_height, img_width, filter_shape, stride, padding):

mla/neuralnet/layers/normalization.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 from mla.neuralnet.layers import Layer, PhaseMixin, ParamMixin
 from mla.neuralnet.parameters import Parameters
 import numpy as np

mla/neuralnet/layers/recurrent/__init__.py

@@ -1,2 +1,3 @@
+# coding:utf-8
 from .rnn import *
 from .lstm import *

mla/neuralnet/layers/recurrent/lstm.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import autograd.numpy as np
 from autograd import elementwise_grad
 from six.moves import range

mla/neuralnet/layers/recurrent/rnn.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import autograd.numpy as np
 from autograd import elementwise_grad
 from six.moves import range

mla/neuralnet/loss.py

@@ -5,8 +5,7 @@
 
 def get_loss(name):
     """Returns loss function by the name."""
-
     try:
         return globals()[name]
-    except:
+    except KeyError:
         raise ValueError("Invalid metric function.")

mla/neuralnet/nnet.py

@@ -13,8 +13,9 @@
 
 """
 Architecture inspired from:
-https://github.com/fchollet/keras
-https://github.com/andersbll/deeppy
+
+    https://github.com/fchollet/keras
+    https://github.com/andersbll/deeppy
 """
 
 

mla/neuralnet/optimizers.py

@@ -9,7 +9,8 @@
 
 """
 References:
-Gradient descent optimization algorithms  http://sebastianruder.com/optimizing-gradient-descent/index.html
+
+    Gradient descent optimization algorithms  http://sebastianruder.com/optimizing-gradient-descent/index.html
 """
 
 

mla/neuralnet/parameters.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import numpy as np
 
 from mla.neuralnet.initializations import get_initializer

mla/pca.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 from scipy.linalg import svd
 import numpy as np
 import logging
@@ -47,8 +48,8 @@ def _decompose(self, X):
 
         s_squared = s ** 2
         variance_ratio = s_squared / (s_squared).sum()
-        logging.info("Explained variance ratio: %s" % (variance_ratio[0 : self.n_components]))
-        self.components = Vh[0 : self.n_components]
+        logging.info("Explained variance ratio: %s" % (variance_ratio[0: self.n_components]))
+        self.components = Vh[0: self.n_components]
 
     def transform(self, X):
         X = X.copy()

mla/rbm.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import logging
 
 from mla.base import BaseEstimator
@@ -52,7 +53,7 @@ def _init_weights(self):
         self.errors = []
 
     def _train(self):
-        """Use CD-1 training procedure, basically an exact inference for `positive_associations`, 
+        """Use CD-1 training procedure, basically an exact inference for `positive_associations`,
         followed by a "non burn-in" block Gibbs Sampling for the `negative_associations`."""
 
         for i in range(self.max_epochs):

mla/rl/__init__.py

@@ -1 +1 @@
-
+# coding:utf-8

mla/rl/dqn.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import logging
 import random
 

mla/svm/__init__.py

@@ -1 +1 @@
-
+# coding:utf-8

mla/svm/kernerls.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import numpy as np
 import scipy.spatial.distance as dist
 

mla/tsne.py

@@ -1,3 +1,4 @@
+# coding:utf-8
 import logging
 
 import numpy as np

setup.cfg

@@ -2,4 +2,7 @@
 universal=1
 
 [metadata]
-description-file=README.md
+description-file=README.md
+
+[flake8]
+max-line-length = 120