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41 lines
1.2 KiB
41 lines
1.2 KiB
import numpy as np
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class Perceptron(object):
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"""Implements a perceptron network"""
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def __init__(self, input_size, lr=1, epochs=100):
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self.W = np.zeros(input_size+1)
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# add one for bias
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self.epochs = epochs
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self.lr = lr
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#activation function
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def activation_fn(self, x):
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#return (x >= 0).astype(np.float32)
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return 1 if x >= 0 else 0
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#we need a prediction function to run an input through the perceptron and return an output.
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def predict(self, x):
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z = self.W.T.dot(x)
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a = self.activation_fn(z)
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return a
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def fit(self, X, d):
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for _ in range(self.epochs):
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for i in range(d.shape[0]):
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x = np.insert(X[i], 0, 1)
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y = self.predict(x)
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e = d[i] - y
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self.W = self.W + self.lr * e * x
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#the easiset set of data that we can provide is the AND gate. Given is set of inputs and outputs.
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if __name__ == '__main__':
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X = np.array([
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[0, 0],
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[0, 1],
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[1, 0],
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[1, 1]
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])
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d = np.array([0, 0, 0, 1])
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perceptron = Perceptron(input_size=2)
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perceptron.fit(X, d)
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print(perceptron.W)
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