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713f732 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | 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)}") |