Remove selected solution from dataset properly
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04c92add44
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@ -29,20 +29,22 @@ def get_furthest_element(element, data):
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furthest_index = element_df["distance"].astype(float).idxmax()
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furthest_row = data.iloc[furthest_index]
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furthest_point = get_different_element(original=element, row=furthest_row)
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furthest_element = {"point": furthest_point, "distance": furthest_row["distance"]}
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return furthest_element, furthest_index
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return {"point": furthest_point, "distance": furthest_row["distance"]}
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def remove_solution_dataset(data, solution):
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return data.query(f"source != {solution} and destination != {solution}")
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# FIXME Remove duplicated elements properly
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def greedy_algorithm(n, m, data):
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solutions = DataFrame(columns=["point", "distance"])
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first_solution = get_first_solution(n, data)
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solutions = solutions.append(first_solution, ignore_index=True)
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for _ in range(m):
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last_solution = solutions["point"].tail(n=1)
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centroid, index = get_furthest_element(element=int(last_solution), data=data)
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last_solution = int(solutions["point"].tail(n=1))
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centroid = get_furthest_element(element=last_solution, data=data)
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solutions = solutions.append(dict(centroid), ignore_index=True)
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data = data.drop(index)
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data = remove_solution_dataset(data=data, solution=last_solution)
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return solutions
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@ -72,9 +74,11 @@ def execute_algorithm(choice, n, m, data):
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def show_results(solutions):
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distance_sum = solutions["distance"].sum()
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duplicates = solutions.duplicated()
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print(solutions)
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print("Total distance: " + str(distance_sum))
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print(solutions.duplicated())
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if solutions[duplicates].empty:
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print("No duplicates found")
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def usage(argv):
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