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74 lines
2.0 KiB
Python
74 lines
2.0 KiB
Python
"""
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https://en.wikipedia.org/wiki/Self-organizing_map
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"""
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import math
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class SelfOrganizingMap:
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def get_winner(self, weights: list[list[float]], sample: list[int]) -> int:
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"""
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Compute the winning vector by Euclidean distance
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>>> SelfOrganizingMap().get_winner([[1, 2, 3], [4, 5, 6]], [1, 2, 3])
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1
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"""
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d0 = 0.0
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d1 = 0.0
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for i in range(len(sample)):
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d0 += math.pow((sample[i] - weights[0][i]), 2)
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d1 += math.pow((sample[i] - weights[1][i]), 2)
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return 0 if d0 > d1 else 1
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return 0
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def update(
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self, weights: list[list[int | float]], sample: list[int], j: int, alpha: float
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) -> list[list[int | float]]:
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"""
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Update the winning vector.
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>>> SelfOrganizingMap().update([[1, 2, 3], [4, 5, 6]], [1, 2, 3], 1, 0.1)
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[[1, 2, 3], [3.7, 4.7, 6]]
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"""
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for i in range(len(weights)):
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weights[j][i] += alpha * (sample[i] - weights[j][i])
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return weights
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# Driver code
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def main() -> None:
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# Training Examples ( m, n )
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training_samples = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
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# weight initialization ( n, C )
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weights = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
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# training
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self_organizing_map = SelfOrganizingMap()
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epochs = 3
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alpha = 0.5
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for _ in range(epochs):
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for j in range(len(training_samples)):
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# training sample
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sample = training_samples[j]
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# Compute the winning vector
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winner = self_organizing_map.get_winner(weights, sample)
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# Update the winning vector
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weights = self_organizing_map.update(weights, sample, winner, alpha)
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# classify test sample
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sample = [0, 0, 0, 1]
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winner = self_organizing_map.get_winner(weights, sample)
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# results
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print(f"Clusters that the test sample belongs to : {winner}")
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print(f"Weights that have been trained : {weights}")
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# running the main() function
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if __name__ == "__main__":
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main()
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