mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-11-27 23:11:09 +00:00
c909da9b08
* pre-commit: Upgrade psf/black for stable style 2023 Updating https://github.com/psf/black ... updating 22.12.0 -> 23.1.0 for their `2023 stable style`. * https://github.com/psf/black/blob/main/CHANGES.md#2310 > This is the first [psf/black] release of 2023, and following our stability policy, it comes with a number of improvements to our stable style… Also, add https://github.com/tox-dev/pyproject-fmt and https://github.com/abravalheri/validate-pyproject to pre-commit. I only modified `.pre-commit-config.yaml` and all other files were modified by pre-commit.ci and psf/black. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
"""
|
|
https://en.wikipedia.org/wiki/Self-organizing_map
|
|
"""
|
|
import math
|
|
|
|
|
|
class SelfOrganizingMap:
|
|
def get_winner(self, weights: list[list[float]], sample: list[int]) -> int:
|
|
"""
|
|
Compute the winning vector by Euclidean distance
|
|
|
|
>>> SelfOrganizingMap().get_winner([[1, 2, 3], [4, 5, 6]], [1, 2, 3])
|
|
1
|
|
"""
|
|
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]]:
|
|
"""
|
|
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]]
|
|
"""
|
|
for i in range(len(weights)):
|
|
weights[j][i] += alpha * (sample[i] - weights[j][i])
|
|
return weights
|
|
|
|
|
|
# Driver code
|
|
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}")
|
|
|
|
|
|
# running the main() function
|
|
if __name__ == "__main__":
|
|
main()
|