Create artificial_neural_network.py

DIRECTORY.md

@@ -22,6 +22,7 @@
   * [Rat In Maze](backtracking/rat_in_maze.py)
   * [Sudoku](backtracking/sudoku.py)
   * [Sum Of Subsets](backtracking/sum_of_subsets.py)
+  * [Word Break](backtracking/word_break.py)
   * [Word Ladder](backtracking/word_ladder.py)
   * [Word Search](backtracking/word_search.py)
 
@@ -99,6 +100,7 @@
   * [Elgamal Key Generator](ciphers/elgamal_key_generator.py)
   * [Enigma Machine2](ciphers/enigma_machine2.py)
   * [Fractionated Morse Cipher](ciphers/fractionated_morse_cipher.py)
+  * [Gronsfeld Cipher](ciphers/gronsfeld_cipher.py)
   * [Hill Cipher](ciphers/hill_cipher.py)
   * [Mixed Keyword Cypher](ciphers/mixed_keyword_cypher.py)
   * [Mono Alphabetic Ciphers](ciphers/mono_alphabetic_ciphers.py)
@@ -211,6 +213,7 @@
     * [Lazy Segment Tree](data_structures/binary_tree/lazy_segment_tree.py)
     * [Lowest Common Ancestor](data_structures/binary_tree/lowest_common_ancestor.py)
     * [Maximum Fenwick Tree](data_structures/binary_tree/maximum_fenwick_tree.py)
+    * [Maximum Sum Bst](data_structures/binary_tree/maximum_sum_bst.py)
     * [Merge Two Binary Trees](data_structures/binary_tree/merge_two_binary_trees.py)
     * [Mirror Binary Tree](data_structures/binary_tree/mirror_binary_tree.py)
     * [Non Recursive Segment Tree](data_structures/binary_tree/non_recursive_segment_tree.py)
@@ -284,6 +287,7 @@
     * [Dijkstras Two Stack Algorithm](data_structures/stacks/dijkstras_two_stack_algorithm.py)
     * [Infix To Postfix Conversion](data_structures/stacks/infix_to_postfix_conversion.py)
     * [Infix To Prefix Conversion](data_structures/stacks/infix_to_prefix_conversion.py)
+    * [Lexicographical Numbers](data_structures/stacks/lexicographical_numbers.py)
     * [Next Greater Element](data_structures/stacks/next_greater_element.py)
     * [Postfix Evaluation](data_structures/stacks/postfix_evaluation.py)
     * [Prefix Evaluation](data_structures/stacks/prefix_evaluation.py)
@@ -820,6 +824,7 @@
     * [Softplus](neural_network/activation_functions/softplus.py)
     * [Squareplus](neural_network/activation_functions/squareplus.py)
     * [Swish](neural_network/activation_functions/swish.py)
+  * [Artificial Neural Network](neural_network/artificial_neural_network.py)
   * [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py)
   * [Convolution Neural Network](neural_network/convolution_neural_network.py)
   * [Input Data](neural_network/input_data.py)
@@ -1201,6 +1206,7 @@
   * [Binary Tree Traversal](searches/binary_tree_traversal.py)
   * [Double Linear Search](searches/double_linear_search.py)
   * [Double Linear Search Recursion](searches/double_linear_search_recursion.py)
+  * [Exponential Search](searches/exponential_search.py)
   * [Fibonacci Search](searches/fibonacci_search.py)
   * [Hill Climbing](searches/hill_climbing.py)
   * [Interpolation Search](searches/interpolation_search.py)

neural_network/artificial_neural_network.py

@@ -0,0 +1,180 @@
+import numpy as np
+
+
+class SimpleANN:
+    """
+    Simple Artificial Neural Network (ANN)
+
+    - Feedforward Neural Network with 1 hidden layer and Sigmoid activation.
+    - Uses Gradient Descent for backpropagation and Mean Squared Error (MSE)
+      as the loss function.
+    - Example demonstrates solving the XOR problem.
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        output_size: int,
+        learning_rate: float = 0.1,
+    ) -> None:
+        """
+        Initialize the neural network with random weights and biases.
+
+        Args:
+            input_size (int): Number of input features.
+            hidden_size (int): Number of neurons in the hidden layer.
+            output_size (int): Number of neurons in the output layer.
+            learning_rate (float): Learning rate for gradient descent.
+
+        Example:
+        >>> ann = SimpleANN(2, 2, 1)
+        >>> isinstance(ann, SimpleANN)
+        True
+        """
+        rng = np.random.default_rng()
+        self.weights_input_hidden = rng.standard_normal((input_size, hidden_size))
+        self.weights_hidden_output = rng.standard_normal((hidden_size, output_size))
+        self.bias_hidden = np.zeros((1, hidden_size))
+        self.bias_output = np.zeros((1, output_size))
+        self.learning_rate = learning_rate
+
+    def sigmoid(self, value: np.ndarray) -> np.ndarray:
+        """
+        Sigmoid activation function.
+
+        Args:
+            value (ndarray): Input value for activation.
+
+        Returns:
+            ndarray: Activated output using sigmoid function.
+
+        Example:
+        >>> ann = SimpleANN(2, 2, 1)
+        >>> ann.sigmoid(np.array([0]))
+        array([0.5])
+        """
+        return 1 / (1 + np.exp(-value))
+
+    def sigmoid_derivative(self, sigmoid_output: np.ndarray) -> np.ndarray:
+        """
+        Derivative of the sigmoid function.
+
+        Args:
+            sigmoid_output (ndarray): Output after applying the sigmoid function.
+
+        Returns:
+            ndarray: Derivative of the sigmoid function.
+
+        Example:
+        >>> ann = SimpleANN(2, 2, 1)
+        >>> output = ann.sigmoid(np.array([0]))  # Use input 0 for testing
+        >>> ann.sigmoid_derivative(output)
+        array([0.25])
+        """
+        return sigmoid_output * (1 - sigmoid_output)
+
+    def feedforward(self, inputs: np.ndarray) -> np.ndarray:
+        """
+        Perform forward propagation through the network.
+
+        Args:
+            inputs (ndarray): Input features for the network.
+
+        Returns:
+            ndarray: Output from the network after feedforward pass.
+
+        Example:
+        >>> ann = SimpleANN(2, 2, 1)
+        >>> inputs = np.array([[0, 0], [1, 1]])
+        >>> ann.feedforward(inputs).shape
+        (2, 1)
+        """
+        self.hidden_input = np.dot(inputs, self.weights_input_hidden) + self.bias_hidden
+        self.hidden_output = self.sigmoid(self.hidden_input)
+        self.final_input = (
+            np.dot(self.hidden_output, self.weights_hidden_output) + self.bias_output
+        )
+        self.final_output = self.sigmoid(self.final_input)
+        return self.final_output
+
+    def backpropagation(
+        self, inputs: np.ndarray, targets: np.ndarray, outputs: np.ndarray
+    ) -> None:
+        """
+        Perform backpropagation to adjust the weights and biases.
+
+        Args:
+            inputs (ndarray): Input features.
+            targets (ndarray): True output labels.
+            outputs (ndarray): Output predicted by the network.
+
+        Example:
+        >>> ann = SimpleANN(2, 2, 1)
+        >>> inputs = np.array([[0, 0], [1, 1]])
+        >>> outputs = ann.feedforward(inputs)
+        >>> targets = np.array([[0], [1]])
+        >>> ann.backpropagation(inputs, targets, outputs)
+        """
+        error = targets - outputs
+        output_gradient = error * self.sigmoid_derivative(outputs)
+        hidden_error = output_gradient.dot(self.weights_hidden_output.T)
+        hidden_gradient = hidden_error * self.sigmoid_derivative(self.hidden_output)
+
+        self.weights_hidden_output += (
+            self.hidden_output.T.dot(output_gradient) * self.learning_rate
+        )
+        self.bias_output += (
+            np.sum(output_gradient, axis=0, keepdims=True) * self.learning_rate
+        )
+
+        self.weights_input_hidden += inputs.T.dot(hidden_gradient) * self.learning_rate
+        self.bias_hidden += (
+            np.sum(hidden_gradient, axis=0, keepdims=True) * self.learning_rate
+        )
+
+    def train(
+        self, inputs: np.ndarray, targets: np.ndarray, epochs: int = 10000
+    ) -> None:
+        """
+        Train the neural network on the given input and target data.
+
+        Args:
+            inputs (ndarray): Input features for training.
+            targets (ndarray): True labels for training.
+            epochs (int): Number of training iterations.
+            verbose (bool): Whether to print loss every 1000 epochs.
+
+        Example:
+        >>> ann = SimpleANN(2, 2, 1)
+        >>> inputs = np.array([[0, 0], [1, 1]])
+        >>> targets = np.array([[0], [1]])
+        >>> ann.train(inputs, targets, epochs=1, verbose=False)
+        """
+        for epoch in range(epochs):
+            outputs = self.feedforward(inputs)
+            self.backpropagation(inputs, targets, outputs)
+            if verbose and epoch % 1000 == 0:
+                loss = np.mean(np.square(targets - outputs))
+                print(f"Epoch {epoch}, Loss: {loss}")
+
+    def predict(self, inputs: np.ndarray) -> np.ndarray:
+        """
+        Predict the output for new input data.
+
+        Args:
+            inputs (ndarray): Input data for prediction.
+
+        Returns:
+            ndarray: Predicted output from the network.
+
+        Example:
+        >>> ann = SimpleANN(2, 2, 1)
+        >>> inputs = np.array([[0, 0], [1, 1]])
+        >>> ann.predict(inputs).shape
+        (2, 1)
+        """
+        return self.feedforward(inputs)
+
+
+if __name__ == "__main__":