Fix codacy error "parameters differ from overriden 'fit' method"

Author: ahn1340

examples/example_extending_classification.py

@@ -35,7 +35,7 @@ def __init__(self,
         self.alpha = alpha
         self.random_state = random_state
 
-    def fit(self, X, Y):
+    def fit(self, X, y):
         self.num_nodes_per_layer = int(self.num_nodes_per_layer)
         self.hidden_layer_depth = int(self.hidden_layer_depth)
         self.alpha = float(self.alpha)
@@ -49,7 +49,7 @@ def fit(self, X, Y):
                                        activation=self.activation,
                                        random_state=self.random_state,
                                        )
-        self.estimator.fit(X, Y)
+        self.estimator.fit(X, y)
         return self
 
     def predict(self, X):

examples/example_extending_preprocessor.py

@@ -32,7 +32,7 @@ def __init__(self, shrinkage, solver, n_components, tol, random_state=None):
         self.random_state = random_state
         self.preprocessor = None
 
-    def fit(self, X, Y=None):
+    def fit(self, X, y=None):
         self.shrinkage = float(self.shrinkage)
         self.n_components = int(self.n_components)
         self.tol = float(self.tol)
@@ -45,7 +45,7 @@ def fit(self, X, Y=None):
                 n_components=self.n_components,
                 tol=self.tol,
             )
-        self.preprocessor.fit(X, Y)
+        self.preprocessor.fit(X, y)
         return self
 
     def transform(self, X):

examples/example_extending_regression.py

@@ -29,7 +29,7 @@ def __init__(self, alpha, kernel, gamma, degree, random_state=None):
         self.random_state = random_state
         self.estimator = None
 
-    def fit(self, X, Y):
+    def fit(self, X, y):
         self.alpha = float(self.alpha)
         self.gamma = float(self.gamma)
         self.degree = int(self.degree)
@@ -40,7 +40,7 @@ def fit(self, X, Y):
                                                           gamma=self.gamma,
                                                           degree=self.degree,
                                                           )
-        self.estimator.fit(X, Y)
+        self.estimator.fit(X, y)
         return self
 
     def predict(self, X):