import numpy as np

# Activation function: step function
def step_function(x):
    return 1 if x >= 0 else 0

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

    def predict(self, x):
        x = np.insert(x, 0, 1)  # Add bias input
        weighted_sum = np.dot(self.weights, x)
        return step_function(weighted_sum)

    def train(self, X, y, epochs=10):
        for _ in range(epochs):
            for xi, target in zip(X, y):
                xi = np.insert(xi, 0, 1)  # Add bias input
                prediction = self.predict(xi[1:])
                error = target - prediction
                self.weights += self.lr * error * xi

# Example: AND logic gate
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([0, 0, 0, 1])

model = Perceptron(input_size=2)
model.train(X, y)

# Test
for x in X:
    print(f"Input: {x}, Output: {model.predict(x)}")