Three iterations of updates for perceptron weights

perceptron-update-weights-three-epochs.py

weights = initialize_weights(train_data.shape[1])
for epoch in range(3):
    sum_of_squared_errors = 0.0
    for sample in range(train_data.shape[0]):
        vector = train_data.loc[sample].values
        label = train_label.loc[sample]
        prediction = predict(vector, weights, 'step')
        error = label - prediction
        sum_of_squared_errors += error**2
        weights = weights + error * vector
    print(f'SSE: {sum_of_squared_errors}')
print(f'Weights: {weights}')