def main() -> None:
    # Training Examples ( m, n )
    training_samples = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

    # weight initialization ( n, C )
    weights = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

    # training
    self_organizing_map = SelfOrganizingMap()
    epochs = 3
    alpha = 0.5

    for _ in range(epochs):
        for j in range(len(training_samples)):
            # training sample
            sample = training_samples[j]

            # Compute the winning vector
            winner = self_organizing_map.get_winner(weights, sample)

            # Update the winning vector
            weights = self_organizing_map.update(weights, sample, winner, alpha)

    # classify test sample
    sample = [0, 0, 0, 1]
    winner = self_organizing_map.get_winner(weights, sample)

    # results
    print(f"Clusters that the test sample belongs to : {winner}")
    print(f"Weights that have been trained : {weights}")