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import numpy as np
class Perceptron:
 def __init__(self, learning_rate=0.01, n_iterations=1000):
 self.learning_rate = learning_rate
 self.n_iterations = n_iterations
 self.weights = None
 self.bias = None
 def fit(self, X, y):
 n_samples, n_features = X.shape
 # Initialize weights and bias
 self.weights = np.zeros(n_features)
 self.bias = 0
 # Convert labels to 0 and 1 if necessary (e.g., -1 and 1)
 # Assuming y contains 0s and 1s for binary classification
 # If your labels are -1 and 1, you might need: y_ = np.where(y > 0, 1, 0)
 for _ in range(self.n_iterations):
 for idx, x_i in enumerate(X):
 # Calculate the linear output
 linear_output = np.dot(x_i, self.weights) + self.bias
# Apply the step activation function
 # For perceptron, typically a step function (0 or 1)
 # You can also use a sigmoid for a slightly smoother approach if desired
 prediction = 1 if linear_output >= 0 else 0 
 # Update weights and bias based on the error
 update = self.learning_rate * (y[idx] - prediction)
 self.weights += update * x_i
 self.bias += update
 def predict(self, X):
 linear_output = np.dot(X, self.weights) + self.bias
 # Apply the step activation function for prediction
 return np.where(linear_output >= 0, 1, 0)
# Example Usage:
if __name__ == "__main__":
 # Sample data for a simple AND gate
 X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
 y = np.array([0, 0, 0, 1])
 perceptron = Perceptron(learning_rate=0.1, n_iterations=100)
 perceptron.fit(X, y)
 print("Weights:", perceptron.weights)
 print("Bias:", perceptron.bias)
test_X = np.array([[0, 0], [0, 1], [1, 0], [1, 1], [0.5, 0.5]])
 predictions = perceptron.predict(test_X)
 print("Predictions for test_X:", predictions)