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Fix pseudorandom solution generation

This commit is contained in:
coolneng 2021-04-12 15:22:54 +02:00
parent d04d0becfe
commit b63b5b08b6
Signed by: coolneng
GPG Key ID: 9893DA236405AF57
1 changed files with 5 additions and 4 deletions
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9
src/processing.py
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@ -1,5 +1,5 @@
from preprocessing import parse_file from preprocessing import parse_file
from pandas import DataFrame from pandas import DataFrame, Series
from sys import argv from sys import argv
from random import seed, randint from random import seed, randint
from time import time from time import time
@ -30,7 +30,7 @@ def get_furthest_element(element, data):
furthest_index = element_df["distance"].astype(float).idxmax() furthest_index = element_df["distance"].astype(float).idxmax()
furthest_row = data.iloc[furthest_index] furthest_row = data.iloc[furthest_index]
furthest_point = get_different_element(original=element, row=furthest_row) furthest_point = get_different_element(original=element, row=furthest_row)
return {"point": furthest_point, "distance": furthest_row["distance"]} return Series(data={"point": furthest_point, "distance": furthest_row["distance"]})
def remove_solution_dataset(data, solution): def remove_solution_dataset(data, solution):
@ -44,14 +44,15 @@ def greedy_algorithm(n, m, data):
for _ in range(m): for _ in range(m):
last_solution = int(solutions["point"].tail(n=1)) last_solution = int(solutions["point"].tail(n=1))
centroid = get_furthest_element(element=last_solution, data=data) centroid = get_furthest_element(element=last_solution, data=data)
solutions = solutions.append(dict(centroid), ignore_index=True) solutions = solutions.append(centroid, ignore_index=True)
data = remove_solution_dataset(data=data, solution=last_solution) data = remove_solution_dataset(data=data, solution=last_solution)
return solutions return solutions
def get_pseudorandom_solution(n, data): def get_pseudorandom_solution(n, data):
seed(42) seed(42)
return data.iloc[randint(a=0, b=n)] solution = data.iloc[randint(a=0, b=n)]
return Series(data={"point": solution["destination"], "distance": 0})
def local_search(n, m, data): def local_search(n, m, data):