Fixing Pylint errors.

homemade/anomaly_detection/gaussian_anomaly_detection.py

@@ -62,6 +62,7 @@ def estimate_gaussian(data):
 
     @staticmethod
     def select_threshold(labels, probabilities):
+        # pylint: disable=R0914
         """Finds the best threshold (epsilon) to use for selecting outliers"""
 
         best_epsilon = 0

homemade/logistic_regression/logistic_regression.py

@@ -7,9 +7,11 @@
 
 
 class LogisticRegression:
+    # pylint: disable=R0902
     """Logistic Regression Class"""
 
     def __init__(self, data, labels, polynomial_degree=0, sinusoid_degree=0, normalize_data=False):
+        # pylint: disable=R0913
         """Logistic regression constructor.
 
         :param data: training set.

homemade/neural_network/multilayer_perceptron.py

@@ -8,6 +8,7 @@
 class MultilayerPerceptron:
     """Multilayer Perceptron Class"""
 
+    # pylint: disable=R0913
     def __init__(self, data, labels, layers, epsilon, normalize_data=False):
         """Multilayer perceptron constructor.
 
@@ -71,6 +72,7 @@ def predict(self, data):
     def gradient_descent(
             data, labels, unrolled_theta, layers, regularization_param, max_iteration, alpha
     ):
+        # pylint: disable=R0913
         """Gradient descent function.
 
         Iteratively optimizes theta model parameters.
@@ -138,6 +140,7 @@ def gradient_step(data, labels, unrolled_thetas, layers, regularization_param):
 
         return thetas_unrolled_gradients
 
+    # pylint: disable=R0914
     @staticmethod
     def cost_function(data, labels, thetas, layers, regularization_param):
         """Cost function.
@@ -213,6 +216,7 @@ def feedforward_propagation(data, thetas, layers):
         # Output layer should not contain bias units.
         return in_layer_activation[:, 1:]
 
+    # pylint: disable=R0914
     @staticmethod
     def back_propagation(data, labels, thetas, layers, regularization_param):
         """Backpropagation function"""