Add flake8-builtins to pre-commit and fix errors (#7105)

Ignore `A003`

Co-authored-by: Christian Clauss <cclauss@me.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Dhruv Manilawala <dhruvmanila@gmail.com>

neural_network/2_hidden_layers_neural_network.py

@@ -182,7 +182,7 @@ class TwoHiddenLayerNeuralNetwork:
                 loss = numpy.mean(numpy.square(output - self.feedforward()))
                 print(f"Iteration {iteration} Loss: {loss}")
 
-    def predict(self, input: numpy.ndarray) -> int:
+    def predict(self, input_arr: numpy.ndarray) -> int:
         """
         Predict's the output for the given input values using
         the trained neural network.
@@ -201,7 +201,7 @@ class TwoHiddenLayerNeuralNetwork:
         """
 
         # Input values for which the predictions are to be made.
-        self.array = input
+        self.array = input_arr
 
         self.layer_between_input_and_first_hidden_layer = sigmoid(
             numpy.dot(self.array, self.input_layer_and_first_hidden_layer_weights)
@@ -264,7 +264,7 @@ def example() -> int:
     True
     """
     # Input values.
-    input = numpy.array(
+    test_input = numpy.array(
         (
             [0, 0, 0],
             [0, 0, 1],
@@ -282,7 +282,9 @@ def example() -> int:
     output = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]), dtype=numpy.float64)
 
     # Calling neural network class.
-    neural_network = TwoHiddenLayerNeuralNetwork(input_array=input, output_array=output)
+    neural_network = TwoHiddenLayerNeuralNetwork(
+        input_array=test_input, output_array=output
+    )
 
     # Calling training function.
     # Set give_loss to True if you want to see loss in every iteration.

neural_network/convolution_neural_network.py

@@ -140,24 +140,24 @@ class CNN:
         focus_list = np.asarray(focus1_list)
         return focus_list, data_featuremap
 
-    def pooling(self, featuremaps, size_pooling, type="average_pool"):
+    def pooling(self, featuremaps, size_pooling, pooling_type="average_pool"):
         # pooling process
         size_map = len(featuremaps[0])
         size_pooled = int(size_map / size_pooling)
         featuremap_pooled = []
         for i_map in range(len(featuremaps)):
-            map = featuremaps[i_map]
+            feature_map = featuremaps[i_map]
             map_pooled = []
             for i_focus in range(0, size_map, size_pooling):
                 for j_focus in range(0, size_map, size_pooling):
-                    focus = map[
+                    focus = feature_map[
                         i_focus : i_focus + size_pooling,
                         j_focus : j_focus + size_pooling,
                     ]
-                    if type == "average_pool":
+                    if pooling_type == "average_pool":
                         # average pooling
                         map_pooled.append(np.average(focus))
-                    elif type == "max_pooling":
+                    elif pooling_type == "max_pooling":
                         # max pooling
                         map_pooled.append(np.max(focus))
             map_pooled = np.asmatrix(map_pooled).reshape(size_pooled, size_pooled)

neural_network/perceptron.py

@@ -182,7 +182,7 @@ samples = [
     [0.2012, 0.2611, 5.4631],
 ]
 
-exit = [
+target = [
     -1,
     -1,
     -1,
@@ -222,7 +222,7 @@ if __name__ == "__main__":
     doctest.testmod()
 
     network = Perceptron(
-        sample=samples, target=exit, learning_rate=0.01, epoch_number=1000, bias=-1
+        sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
     )
     network.training()
     print("Finished training perceptron")