Cory Balaton
a060f93554
- Add a docstring. - Add type hinting. - Change algorithm from steepest ascent to simple hill climbing.
67 lines
1.9 KiB
Python
67 lines
1.9 KiB
Python
import copy
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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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from common import indexes_to_cities, plot_plan, read_data
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def hill_climbing(distances: npt.NDArray) -> Tuple[float, npt.NDArray]:
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"""A simple hill climbing algorithm.
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The algorithm starts on a random permutation and attempts to improve
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the circuit by trying to switch neighboring elements. Each iteration
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tries to switch adjacent neighbors and sees which one yields the largest
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improvement.
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Args:
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distances npt.NDArray: A matrix containing the distances between cities.
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Returns:
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Tuple[float, npt.NDArray] A tuple containing the distance of the
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solution and the solution itself.
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"""
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size: int = len(distances) # The size of the permutation array
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perm: npt.NDArray = np.arange(size) # Create an array from 0..size
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np.random.shuffle(perm) # Get random permutation
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# Get the distance of the random permutation
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current_distance: float = np.sum(
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[distances[perm[i - 1], perm[i]] for i in range(size)]
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)
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found_improvement: bool = True
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while found_improvement:
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found_improvement = False # Assume we haven't found an improvement
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tmp_distance: float = current_distance
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# Try to find an improvement
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for i in range(size):
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perm[[i - 1, i]] = perm[[i, i - 1]] # Swap i - 1 and i
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tmp_distance: float = np.sum(
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[distances[perm[i - 1], perm[i]] for i in range(size)]
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)
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if tmp_distance < current_distance:
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current_distance = tmp_distance
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found_improvement = True
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break
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perm[[i - 1, i]] = perm[[i, i - 1]] # Swap back i - 1 and i
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return (current_distance, 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(indexes_to_cities(perm, cities))
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