Only insert element that are not duplicated
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5e6a6d00e9
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@ -3,14 +3,17 @@ from numpy.random import choice, seed
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def get_first_random_solution(m, data):
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def get_first_random_solution(m, data):
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seed(42)
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seed(42)
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random_indexes = choice(len(data.index), size=m)
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random_indexes = choice(len(data.index), size=m, replace=False)
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return data.iloc[random_indexes]
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return data.loc[random_indexes]
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def replace_worst_element(previous, data):
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def replace_worst_element(previous, data):
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solution = previous.copy()
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solution = previous.copy()
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worst_index = solution["distance"].astype(float).idxmin()
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worst_index = solution["distance"].astype(float).idxmin()
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solution.loc[worst_index] = data.sample(random_state=42).squeeze()
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random_element = data.sample().squeeze()
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while solution.isin(random_element.values.ravel()).any().any():
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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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return solution, worst_index
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@ -28,11 +31,6 @@ def get_random_solution(previous, data):
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return solution
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return solution
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def remove_duplicates(element, data):
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duplicate_free_df = data.query(
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"(source not in @element.source) or (destination not in @element.destination)"
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)
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return duplicate_free_df
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def explore_neighbourhood(element, data, max_iterations=100000):
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def explore_neighbourhood(element, data, max_iterations=100000):
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@ -41,7 +39,6 @@ def explore_neighbourhood(element, data, max_iterations=100000):
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for _ in range(max_iterations):
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for _ in range(max_iterations):
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previous_solution = neighbourhood[-1]
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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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neighbour = get_random_solution(previous=previous_solution, data=data)
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data = remove_duplicates(element=neighbour, data=data)
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if neighbour.equals(previous_solution):
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if neighbour.equals(previous_solution):
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break
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break
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neighbourhood.append(neighbour)
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neighbourhood.append(neighbour)
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