Merge branch 'patch-1' of https://github.com/AHuzail/Python into patch-1

Author: AHuzail

neural_network/artificial_neural_network.py

@@ -11,8 +11,18 @@ class SimpleANN:
     - Example demonstrates solving the XOR problem.
     """
 
+<<<<<<< HEAD
     def __init__(self, input_size: int, hidden_size: int, output_size: int,
                  learning_rate: float = 0.1) -> None:
+=======
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        output_size: int,
+        learning_rate: float = 0.1,
+    ) -> None:
+>>>>>>> 79fed0c49891c60d2134ee45c6f6592c19f4cef5
         """
         Initialize the neural network with random weights and biases.
 
@@ -93,8 +103,14 @@ def feedforward(self, inputs: np.ndarray) -> np.ndarray:
         self.final_output = self.sigmoid(self.final_input)
         return self.final_output
 
+<<<<<<< HEAD
     def backpropagation(self, inputs: np.ndarray, targets: np.ndarray,
                        outputs: np.ndarray) -> None:
+=======
+    def backpropagation(
+        self, inputs: np.ndarray, targets: np.ndarray, outputs: np.ndarray
+    ) -> None:
+>>>>>>> 79fed0c49891c60d2134ee45c6f6592c19f4cef5
         """
         Perform backpropagation to adjust the weights and biases.
 
@@ -122,15 +138,25 @@ def backpropagation(self, inputs: np.ndarray, targets: np.ndarray,
             np.sum(output_gradient, axis=0, keepdims=True) * self.learning_rate
         )
 
+<<<<<<< HEAD
         self.weights_input_hidden += (
             inputs.T.dot(hidden_gradient) * self.learning_rate
         )
+=======
+        self.weights_input_hidden += inputs.T.dot(hidden_gradient) * self.learning_rate
+>>>>>>> 79fed0c49891c60d2134ee45c6f6592c19f4cef5
         self.bias_hidden += (
             np.sum(hidden_gradient, axis=0, keepdims=True) * self.learning_rate
         )
 
+<<<<<<< HEAD
     def train(self, inputs: np.ndarray, targets: np.ndarray,
               epochs: int = 10000, verbose: bool = False) -> None:
+=======
+    def train(
+        self, inputs: np.ndarray, targets: np.ndarray, epochs: int = 10000
+    ) -> None:
+>>>>>>> 79fed0c49891c60d2134ee45c6f6592c19f4cef5
         """
         Train the neural network on the given input and target data.