73 lines
2.2 KiB
Python
73 lines
2.2 KiB
Python
from preprocessing import parse_file
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from pandas import DataFrame
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from sys import argv
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def get_first_solution(n, data):
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distance_sum = DataFrame(columns=["point", "distance"])
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for element in range(n):
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element_df = data.query(f"source == {element} or destination == {element}")
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distance = element_df["distance"].sum()
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distance_sum = distance_sum.append(
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{"point": element, "distance": distance}, ignore_index=True
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)
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furthest_index = distance_sum["distance"].idxmax()
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furthest_row = distance_sum.iloc[furthest_index]
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furthest_row["distance"] = 0
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return furthest_row
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def get_different_element(original, row):
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if row.source == original:
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return row.destination
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return row.source
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def get_furthest_element(element, data):
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element_df = data.query(f"source == {element} or destination == {element}")
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furthest_index = element_df["distance"].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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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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solutions = solutions.append(dict(centroid), ignore_index=True)
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data = data.drop(index)
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return solutions
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# NOTE In each step, switch to the element that gives the least amount
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def local_search():
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pass
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def show_results(solutions):
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distance_sum = solutions["distance"].sum()
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print(solutions)
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print("Total distance: " + str(distance_sum))
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def usage(argv):
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print(f"Usage: python {argv[0]} <file>")
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exit(1)
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def main():
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if len(argv) != 2:
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usage(argv)
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n, m, data = parse_file(argv[1])
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solutions = greedy_algorithm(n, m, data)
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show_results(solutions)
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if __name__ == "__main__":
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main()
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