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November 26, 2021 04:28
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# Make a prediction with weights | |
def predict(row, weights): | |
activation = weights[0] | |
for i in range(len(row)-1): | |
activation += weights[i + 1] * row[i] | |
return 1.0 if activation >= 0.0 else 0.0 | |
# Estimate Perceptron weights using stochastic gradient descent | |
def train_weights(train, l_rate, n_epoch): | |
weights = [0.0 for i in range(len(train[0]))] | |
for epoch in range(n_epoch): | |
sum_error = 0.0 | |
for row in train: | |
prediction = predict(row, weights) | |
error = row[-1] - prediction | |
sum_error += error**2 | |
weights[0] = weights[0] + l_rate * error | |
for i in range(len(row)-1): | |
weights[i + 1] = weights[i + 1] + l_rate * error * row[i] | |
print('>epoch=%d, lrate=%.3f, error=%.3f' % (epoch, l_rate, sum_error)) | |
return weights | |
# Calculate weights | |
dataset = [[2.7810836,2.550537003,0], | |
[1.465489372,2.362125076,0], | |
[3.396561688,4.400293529,0], | |
[1.38807019,1.850220317,0], | |
[3.06407232,3.005305973,0], | |
[7.627531214,2.759262235,1], | |
[5.332441248,2.088626775,1], | |
[6.922596716,1.77106367,1], | |
[8.675418651,-0.242068655,1], | |
[7.673756466,3.508563011,1]] | |
l_rate = 0.1 | |
n_epoch = 5 | |
weights = train_weights(dataset, l_rate, n_epoch) | |
print(weights) |
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