51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
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from numpy.random import choice, seed
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def get_first_random_solution(m, data):
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seed(42)
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random_indexes = choice(len(data.index), size=m, replace=False)
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return data.loc[random_indexes]
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def element_in_dataframe(solution, element):
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duplicates = solution.query(
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f"(source == {element.source} and destination == {element.destination}) or (source == {element.destination} and destination == {element.source})"
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)
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return not duplicates.empty
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def replace_worst_element(previous, data):
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solution = previous.copy()
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worst_index = solution["distance"].astype(float).idxmin()
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random_element = data.sample().squeeze()
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while element_in_dataframe(solution=solution, element=random_element):
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random_element = data.sample().squeeze()
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solution.loc[worst_index] = random_element
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return solution, worst_index
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def get_random_solution(previous, data):
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solution, worst_index = replace_worst_element(previous, data)
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previous_worst_distance = previous["distance"].loc[worst_index]
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while solution.distance.loc[worst_index] <= previous_worst_distance:
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solution, _ = replace_worst_element(previous=solution, data=data)
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return solution
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def explore_neighbourhood(element, data, max_iterations=100000):
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neighbourhood = []
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neighbourhood.append(element)
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for _ in range(max_iterations):
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previous_solution = neighbourhood[-1]
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neighbour = get_random_solution(previous=previous_solution, data=data)
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neighbourhood.append(neighbour)
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return neighbour
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def local_search(m, data):
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first_solution = get_first_random_solution(m=m, data=data)
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best_solution = explore_neighbourhood(
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element=first_solution, data=data, max_iterations=100
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)
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return best_solution
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