https en wikipedia orgwikiselforganizingmap compute the winning vector by euclidean distance selforganizingmap getwinner1 2 3 4 5 6 1 2 3 1 update the winning vector selforganizingmap update1 2 3 4 5 6 1 2 3 1 0 1 1 2 3 3 7 4 7 6 driver code training examples m n weight initialization n c training training sample compute the winning vector update the winning vector classify test sample results running the main function compute the winning vector by euclidean distance selforganizingmap get_winner 1 2 3 4 5 6 1 2 3 1 update the winning vector selforganizingmap update 1 2 3 4 5 6 1 2 3 1 0 1 1 2 3 3 7 4 7 6 driver code training examples m n weight initialization n c training training sample compute the winning vector update the winning vector classify test sample results running the main function
import math


class SelfOrganizingMap:
    def get_winner(self, weights: list[list[float]], sample: list[int]) -> int:
        
        d0 = 0.0
        d1 = 0.0
        for i in range(len(sample)):
            d0 += math.pow((sample[i] - weights[0][i]), 2)
            d1 += math.pow((sample[i] - weights[1][i]), 2)
            return 0 if d0 > d1 else 1
        return 0

    def update(
        self, weights: list[list[int | float]], sample: list[int], j: int, alpha: float
    ) -> list[list[int | float]]:
        
        for i in range(len(weights)):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
        return weights



def main() -> None:
    
    training_samples = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

    
    weights = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

    
    self_organizing_map = SelfOrganizingMap()
    epochs = 3
    alpha = 0.5

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

            
            winner = self_organizing_map.get_winner(weights, sample)

            
            weights = self_organizing_map.update(weights, sample, winner, alpha)

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

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



if __name__ == "__main__":
    main()