def de(kl, interval, pops, generation, num_inputx, num_hiddenx, num_outputx, data_learning):
global num_hidden
global num_input
global num_output
global inps
format = data_learning[-4:-1] + data_learning[-1]
if format == ".dat":
inps = loader.loadDat(data_learning)
elif format == ".csv":
inps = loader.loadCsv(data_learning)
inps = loader.stringifyVar(inps, loader.normalizeVar(loader.getVar(inps)))
num_hidden = num_hiddenx
num_input = num_inputx
num_output = num_outputx
pop = initPop(pops, kl, interval)
n = 0
best1 = fitnessRanking(pop, pops)[0]
best2 = list(best1)
while n <= generation:
best1 = fitnessRanking(pop, pops)[0]
mut = mutation(pop, best1[1], best2[1], pop[best1[0]])
off = [crossOver((mut, pop[i]), best1[1], best2[1], kl) for i in xrange(pops)]
for i in xrange(pops):
if fitness(off[i]) > fitness(pop[i]):
pop[i] = off[i]
best2 = best1
print n, (1 / best2[1]) - 0.0001
n += 1
ret = pop[fitnessRanking(pop, pops)[0][0]]
return ret
def de(kl,interval,pops,generation,num_hiddenx,data_learning): global num_hidden global inps inps = loader.loadCsv(data_learning) inps = loader.stringifyVar(inps,loader.normalizeVar(loader.getVar(inps))) num_hidden = num_hiddenx pop = initPop(pops,kl,interval) n = 0 best1 = fitnessRanking(pop,pops)[0] best2 = list(best1) while(n<=generation): best1 = fitnessRanking(pop,pops)[0] mut = mutation(pop,best1[1],best2[1],pop[best1[0]]) off = [crossOver((mut,pop[i]),best1[1],best2[1],kl) for i in xrange(pops)] for i in xrange(pops): if(fitness(off[i])>fitness(pop[i])): pop[i] = off[i] best2 = best1 print n,1/best2[1] n += 1 ret = pop[fitnessRanking(pop,pops)[0][0]] return ret
import numpy as np
import math
import ann
import loader
inps = loader.load('paru.xlsx')
inps = loader.stringifyVar(inps,loader.normalizeVar(loader.getVar(inps)))
# inps = loader.loadDat('titanic.dat')
def fitness(kr):
# return plus(kr)
# print len(kr)
return 1/(ann.epoch(inps,kr)+0.1)
# return ann.epoch(inps,kr)
def plus(kr):
return 1/((kr[0]+kr[1])+0.001)
def initKromosom(kl,interval):
kr = np.array([(np.random.uniform()*(2*interval))-interval for i in xrange(kl)])
# kr = []
# for i in range(kl):
# kr.append((np.random.uniform()*(2*interval))-interval)
return kr
def initPop(pops,kl,interval):
pop = np.array([initKromosom(kl,interval) for i in xrange(pops)])
# pop = []
# for i in range(pops):
# pop.append(initKromosom(kl,interval))
return pop
