Untitled

import numpy as np

class Perceptron:
    def __init__(self, input_size, learning_rate=0.1):
        self.weights = np.zeros(input_size + 1)
        self.learning_rate = learning_rate

    def predict(self, inputs):
        summation = np.dot(inputs, self.weights[1:]) + self.weights[0]
        return 1 if summation > 0 else 0

    def train(self, training_inputs, labels, epochs):
        for _ in range(epochs):
            for inputs, label in zip(training_inputs, labels):
                prediction = self.predict(inputs)
                self.weights[1:] += self.learning_rate * (label - prediction) * inputs
                self.weights[0] += self.learning_rate * (label - prediction)

# Example usage:
if __name__ == "__main__":
    training_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
    labels = np.array([0, 0, 0, 1])
    perceptron = Perceptron(2)
    perceptron.train(training_inputs, labels, epochs=100)

    test_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
    for inputs in test_inputs:
        print(f"Input: {inputs}, Predicted Output: {perceptron.predict(inputs)}")