Remove duplicates from the solutions
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85e6b072c6
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932a867720
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@ -27,7 +27,8 @@ def get_furthest_element(element, data):
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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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return {"point": furthest_point, "distance": furthest_row["distance"]}
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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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@ -36,10 +37,12 @@ def greedy_algorithm(n, m, 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 = get_furthest_element(element=int(last_solution), data=data)
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centroid, furthest_index = get_furthest_element(
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element=int(last_solution), data=data
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)
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solutions = solutions.append(dict(centroid), ignore_index=True)
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data = data.drop(centroid["point"], columns=["source", "destination"])
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print(solutions)
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data = data.drop(furthest_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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@ -56,7 +59,8 @@ 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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greedy_algorithm(n, m, data)
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solutions = greedy_algorithm(n, m, data)
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print(solutions)
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if __name__ == "__main__":
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