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])

# 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}")