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
import numpy as np
import math
import ann
import loader
import operator
from itertools import imap,starmap,izip
# inps = loader.load('paru.xlsx')
# inps = loader.stringifyVar(inps,loader.normalizeVar(loader.getVar(inps)))
inps = loader.loadDat('titanic.dat')
def fitness(kr):
return 1/(ann.mse(inps,kr)+0.1)
def initKromosom(kl,interval):
return [(np.random.uniform()*(2*interval))-interval for i in xrange(kl)]
def initPop(pops,kl,interval):
return np.array([initKromosom(kl,interval) for i in xrange(pops)])
def fitnessRanking(pop,pops):
return sorted([(i,fitness(pop[i])) for i in xrange(pops)], key=lambda x: x[1], reverse=True)
def selectParent(pop):
rang = range(len(pop))
par = []
for i in xrange(2):
x = int(math.floor(np.random.uniform()*len(rang)))
par.append(rang[x])
del rang[x]
return par
import loader import jst import de import sys import numpy as np num_input = int(sys.argv[1]) num_hidden = int(sys.argv[2]) num_output = int(sys.argv[3]) weight_interval = int(sys.argv[4]) pop_size = int(sys.argv[5]) gen_limit = int(sys.argv[6]) data_learning = sys.argv[7] format = data_learning[-4:-1]+data_learning[-1] save_weights_to = sys.argv[8] 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))) weights = de.de((num_output*num_hidden)+(num_hidden*num_input),weight_interval,pop_size,gen_limit,num_input,num_hidden,num_output,data_learning) np.savetxt(save_weights_to, np.array(weights), delimiter=',')
import sys
import numpy as np
num_input = 3
num_hidden = int(sys.argv[1])
num_output = 2
weight_interval = int(sys.argv[2])
pop_size = int(sys.argv[3])
gen_limit = int(sys.argv[4])
data_learning = sys.argv[5]
data_testing = sys.argv[6]
# print sys.argv[1]
inps = loader.loadDat(data_testing)
# inps = loader.stringifyVar(inps,loader.normalizeVar(loader.getVar(inps)))
start = time.time()
weights = de.de((num_output*num_hidden)+(num_hidden*num_input),weight_interval,pop_size,gen_limit,num_hidden,data_learning)
np.savetxt('weights.csv', np.array(weights), delimiter=',')
weights = jst.decodeWeights(weights)
end = time.time()
print end - start
match = 0
for i in range(len(inps)):
import jst import de import time import sys import numpy as np num_input = int(sys.argv[1]) num_hidden = int(sys.argv[2]) num_output = int(sys.argv[3]) file_data_testing = (sys.argv[4]) file_weights = (sys.argv[5]) format = file_data_testing[-4:-1]+file_data_testing[-1] if (format == '.dat'): inps = loader.loadDat(file_data_testing) elif (format == '.csv'): inps = loader.loadCsv(file_data_testing) inps = loader.stringifyVar(inps,loader.normalizeVar(loader.getVar(inps))) weights = jst.decodeWeights(loader.loadCsv(file_weights),num_input,num_hidden,num_output) match = 0 tp1 = 0 pp1 = 0 tn1 = 0 pn1 = 0 for i in range(len(inps)): if(jst.correct(inps[i],weights)): match += 1 if(jst.truePositive1(inps[i],weights)):
