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Implement furthest element computation

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coolneng 2021-04-11 22:07:57 +02:00
parent 27f3baca07
commit 85e6b072c6
Signed by: coolneng
GPG Key ID: 9893DA236405AF57
1 changed files with 21 additions and 12 deletions
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33
src/processing.py
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@ -1,16 +1,8 @@
from preprocessing import parse_file
from pandas import DataFrame, Series
from pandas import DataFrame
from sys import argv
def get_furthest_element(element, data):
element_df = data.query(f"source == {element} or destination == {element}")
furthest_index = element_df["distance"].idxmax()
furthest_row = data.iloc[furthest_index]
print(furthest_row)
return furthest_row
def get_first_solution(n, data):
distance_sum = DataFrame(columns=["point", "distance"])
for element in range(n):
@ -24,16 +16,33 @@ def get_first_solution(n, data):
return furthest_row
def get_different_element(original, row):
if row.source == original:
return row.destination
return row.source
def get_furthest_element(element, data):
element_df = data.query(f"source == {element} or destination == {element}")
furthest_index = element_df["distance"].idxmax()
furthest_row = data.iloc[furthest_index]
furthest_point = get_different_element(original=element, row=furthest_row)
return {"point": furthest_point, "distance": furthest_row["distance"]}
def greedy_algorithm(n, m, data):
solutions = DataFrame(columns=["point", "distance"])
first_solution = get_first_solution(n, data)
solutions = solutions.append(first_solution, ignore_index=True)
for _ in range(m):
centroid = solutions.apply(get_furthest_element, 1, data)
solutions = solutions.append(centroid)
last_solution = solutions["point"].tail(n=1)
centroid = get_furthest_element(element=int(last_solution), data=data)
solutions = solutions.append(dict(centroid), ignore_index=True)
data = data.drop(centroid["point"], columns=["source", "destination"])
print(solutions)
# NOTE In each step, switch the element that gives the least amount
# NOTE In each step, switch to the element that gives the least amount
def local_search():
pass