54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
from typing import Tuple
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import numpy as np
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import numpy.typing as npt
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import copy
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from common import plot_plan, read_data
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original_perm = None
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def hill_climbing(distances: npt.NDArray) -> Tuple[float, npt.NDArray]:
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size = len(distances)
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perm = np.arange(size)
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np.random.shuffle(perm)
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print(perm)
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global original_perm
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original_perm = copy.deepcopy(perm)
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current_route: float = np.sum([distances[perm[i - 1], perm[i]] for i in range(size)])
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found_improvement = True
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while found_improvement:
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found_improvement = False
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tmp_improvement: float = current_route
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improvement_index: int = -1
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for i in range(size):
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perm[i - 1], perm[i] = perm[i], perm[i - 1]
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tmp_route: float = np.sum([distances[perm[i - 1], perm[i]] for i in range(size)])
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if tmp_route < tmp_improvement:
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tmp_improvement = tmp_route
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improvement_index = i
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found_improvement = True
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perm[i - 1], perm[i] = perm[i], perm[i - 1]
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if found_improvement:
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current_route = tmp_improvement
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perm[improvement_index - 1], perm[improvement_index] = (
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perm[improvement_index],
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perm[improvement_index - 1],
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)
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print(perm)
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return (current_route, perm)
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if __name__ == "__main__":
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cities, data = read_data("./european_cities.csv")
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distance, perm = hill_climbing(data[:10,:10])
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plot_plan(list(map(lambda i: cities[i], list(perm))))
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plot_plan(list(map(lambda i: cities[i], list(original_perm))))
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