76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
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from numpy.random import choice, seed, randint
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from pandas import DataFrame
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def get_row_distance(source, destination, data):
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row = data.query(
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"""(source == @source and destination == @destination) or \
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(source == @destination and destination == @source)"""
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)
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return row["distance"].values[0]
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def compute_distance(element, solution, data):
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accumulator = 0
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distinct_elements = solution.query(f"point != {element}")
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for _, item in distinct_elements.iterrows():
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accumulator += get_row_distance(
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source=element,
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destination=item.point,
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data=data,
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)
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return accumulator
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def get_first_random_solution(n, m, data):
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solution = DataFrame(columns=["point", "distance"])
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seed(42)
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solution["point"] = choice(n, size=m, replace=False)
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solution["distance"] = solution["point"].apply(
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func=compute_distance, solution=solution, data=data
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)
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return solution
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def element_in_dataframe(solution, element):
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duplicates = solution.query(f"point == {element}")
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return not duplicates.empty
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def replace_worst_element(previous, n, 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 = randint(n)
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while element_in_dataframe(solution=solution, element=random_element):
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random_element = randint(n)
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solution["point"].loc[worst_index] = random_element
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solution["distance"].loc[worst_index] = compute_distance(
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element=solution["point"].loc[worst_index], solution=solution, data=data
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)
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return solution
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def get_random_solution(previous, n, data):
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solution = replace_worst_element(previous, n, data)
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while solution["distance"].sum() <= previous["distance"].sum():
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solution = replace_worst_element(previous=solution, n=n, data=data)
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return solution
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def explore_neighbourhood(element, n, 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, n=n, data=data)
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neighbourhood.append(neighbour)
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return neighbour
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def local_search(n, m, data):
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first_solution = get_first_random_solution(n, m, data)
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best_solution = explore_neighbourhood(
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element=first_solution, n=n, data=data, max_iterations=100
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)
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return best_solution
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