chavalitros
/
MH-P1
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Implement furthest element computation

This commit is contained in:
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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

33
src/processing.py
View File

@ -1,16 +1,8 @@
from preprocessing import parse_file from preprocessing import parse_file
from pandas import DataFrame, Series from pandas import DataFrame
from sys import argv 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): def get_first_solution(n, data):
distance_sum = DataFrame(columns=["point", "distance"]) distance_sum = DataFrame(columns=["point", "distance"])
for element in range(n): for element in range(n):
@ -24,16 +16,33 @@ def get_first_solution(n, data):
return furthest_row 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): def greedy_algorithm(n, m, data):
solutions = DataFrame(columns=["point", "distance"]) solutions = DataFrame(columns=["point", "distance"])
first_solution = get_first_solution(n, data) first_solution = get_first_solution(n, data)
solutions = solutions.append(first_solution, ignore_index=True) solutions = solutions.append(first_solution, ignore_index=True)
for _ in range(m): for _ in range(m):
centroid = solutions.apply(get_furthest_element, 1, data) last_solution = solutions["point"].tail(n=1)
solutions = solutions.append(centroid) 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(): def local_search():
pass pass