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recursiveFractalSearch.py
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recursiveFractalSearch.py
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import math
import numpy as np
import random
import timeit
from threading import Thread
import functools
dist_ar = [] # 거리표(global)
cities_count = 0 # 도시 수(global)
dots_list = [] # 도시 리스트(global)
# Hyper Parameter
limits = 60 * 12 # 제한시간
Fractal_size = 5 # 재귀 수
# 시간제한 데코레이터
def timeout(seconds_before_timeout):
def deco(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
res = [Exception('function [%s] timeout [%s seconds] exceeded!' % (func.__name__, seconds_before_timeout))]
def newFunc():
try:
res[0] = func(*args, **kwargs)
except Exception as e:
res[0] = e
t = Thread(target=newFunc)
t.daemon = True
try:
t.start()
t.join(seconds_before_timeout)
except Exception as e:
print('error starting thread')
raise e
ret = res[0]
if isinstance(ret, BaseException):
raise ret
return ret
return wrapper
return deco
# 거리표 제작(param : 문제 경로)
def make_distArray(str):
global dist_ar
global limit_time
global cities_count
global dots_list
reader = open(str, mode='rt', encoding='utf-8')
dots_list = reader.read().split("\n") # ['x1 y1', 'x2 y2', 'x3 y3' ... 'xn yn']
cities_count = int(dots_list.pop(0))
x_list = [] # ['x1', 'x2', 'x3' ... 'xn']
y_list = [] # ['y1', 'y2', 'y3' ... 'yn']
for i in range(cities_count):
temp = dots_list[i].split(" ")
x_list.append(float(temp[0]))
y_list.append(float(temp[1]))
dist_ar = []
for n in range(cities_count):
temp = []
for m in range(cities_count):
temp.append((math.sqrt(((x_list[m] - x_list[n]) ** 2) + ((y_list[m] - y_list[n]) ** 2))))
dist_ar.append(temp)
dist_ar = np.array(dist_ar)
print(dist_ar)
# 거리표를 이용한 적합도 매칭 함수
def cal_fit(stri):
fit = 0
for steps in range(len(stri) - 1):
fit += dist_ar[stri[steps], stri[steps + 1]]
return fit
def optFunc(stri) :
route = stri
fitness = cal_fit(route)
while 1 :
breaker = True
for i in range(len(route)):
for j in range(len(route)):
new_route = optSwap(route, i, j)
new_fitness = cal_fit(new_route)
if new_fitness < fitness:
route = new_route
fitness = new_fitness
breaker = False
break
if breaker == False :
break
if breaker == True :
break
return route
def optSwap(route,head,tail):
new_route = []
new_route += route[0:head]
new_route += reversed(route[head:tail+1])
new_route += route[tail+1:len(route)]
return new_route
def randomTwo(ranges) :
randomList = []
randomList += random.sample(range(0,ranges), 2)
randomList.sort()
return randomList
def randomFour(ranges) :
randomList = []
randomList += random.sample(range(0,ranges), 4)
randomList.sort()
return randomList
def twoOptMove(nest, pointList) :
nest = nest[:]
new_nest = optSwap(nest, pointList[0], pointList[1])
return new_nest
def doublebridgeMove(nest, pointList) :
nest = nest[:]
new_nest = optSwap(nest, pointList[0], pointList[1])
new_nest = optSwap(new_nest, pointList[1], pointList[3])
return new_nest
def makeFractal(route, calls) :
global population
if not calls > Fractal_size :
calls += 1
small = twoOptMove(route, sorted(randomTwo(cities_count)))
large = doublebridgeMove(route, sorted(randomFour(cities_count)))
population.append(small)
population.append(large)
makeFractal(small, calls)
makeFractal(large, calls)
def makeArr(population) :
fits = []
for i in range(len(population)) :
fits.append(cal_fit(population[i]))
arr = np.array([population, fits])
return arr.T
@timeout(limits)
def run() :
global population
generation = 0
optSol = random.sample(range(0, cities_count), cities_count)
population.append(optSol)
calls = 0
while 1 :
makeFractal(optSol, calls)
population = makeArr(population)
population = population[np.argsort(population[:, 1])] # fitness 기준 정렬
optSol = population[0,0]
if generation % 5000 == 0 :
print(generation, "세대 최적해", population[0,1])
population = []
population.append(optSol)
generation += 1
calls = 0
population = [] # 전역변수로 선언한 것
try :
make_distArray("dots/opt_cycle200.in")
start = timeit.default_timer()
run()
stop = timeit.default_timer()
print(stop - start)
except :
stop = timeit.default_timer()
print(stop - start)