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{{{ http://code.activestate.com/r... (r1)

from operator import itemgetter, attrgetter
import math
import random
import string
import timeit
from timeit import Timer as t
import matplotlib.pyplot as plt
import numpy as np

def sigmoid (x):
return math.tanh(x)

def makeMatrix ( I, J, fill=0.0):
m = []
for i in range(I):

m.append([fill]*J)

return m

def randomizeMatrix ( matrix, a, b):
for i in range ( len (matrix) ):

for j in range ( len (matrix[0]) ):  matrix[i][j] = random.uniform(a,b)

class NN:
def __init__(self, NI, NH, NO):

self.ni = NIself.nh = NHself.no = NOself.ai = [1.0]*self.niself.ah = [1.0]*self.nhself.ao = [1.0]*self.noself.wi = [ [0.0]*self.nh for i in range(self.ni) ]self.wo = [ [0.0]*self.no for j in range(self.nh) ]randomizeMatrix ( self.wi, -0.2, 0.2 )randomizeMatrix ( self.wo, -2.0, 2.0 )

def runNN (self, inputs):

if len(inputs) != self.ni:  print 'incorrect number of inputs'for i in range(self.ni):  self.ai[i] = inputs[i]for j in range(self.nh):  self.ah[j] = sigmoid(sum([ self.ai[i]*self.wi[i][j] for i in range(self.ni) ]))for k in range(self.no):  self.ao[k] = sigmoid(sum([ self.ah[j]*self.wo[j][k] for j in range(self.nh) ]))return self.ao

def weights(self):

print 'Input weights:'for i in range(self.ni):  print self.wi[i]printprint 'Output weights:'for j in range(self.nh):  print self.wo[j]print ''

def test(self, patterns):

results, targets = [], []for p in patterns:  inputs = p[0]  rounded = [ round(i) for i in self.runNN(inputs) ]  if rounded == p[1]: result = '+++++'  else: result = '-----'  print '%s %s %s %s %s %s %s' %( 'Inputs:', p[0], '-->', str(self.runNN(inputs)).rjust(65), 'Target', p[1], result)  results+= self.runNN(inputs)  targets += p[1]return results, targets

def sumErrors (self):

error = 0.0for p in pat:  inputs = p[0]  targets = p[1]  self.runNN(inputs)  error += self.calcError(targets)inverr = 1.0/errorreturn inverr

def calcError (self, targets):

error = 0.0for k in range(len(targets)):  error += 0.5 * (targets[k]-self.ao[k])**2return error

def assignWeights (self, weights, I):

io = 0for i in range(self.ni):  for j in range(self.nh):    self.wi[i][j] = weights[I][io][i][j]io = 1for j in range(self.nh):  for k in range(self.no):    self.wo[j][k] = weights[I][io][j][k]

def testWeights (self, weights, I):

same = []io = 0for i in range(self.ni):  for j in range(self.nh):    if self.wi[i][j] != weights[I][io][i][j]:      same.append(('I',i,j, round(self.wi[i][j],2),round(weights[I][io][i][j],2),round(self.wi[i][j] - weights[I][io][i][j],2)))io = 1for j in range(self.nh):  for k in range(self.no):    if self.wo[j][k] !=  weights[I][io][j][k]:      same.append((('O',j,k), round(self.wo[j][k],2),round(weights[I][io][j][k],2),round(self.wo[j][k] - weights[I][io][j][k],2)))if same != []:  print same

def roulette (fitnessScores):
cumalativeFitness = 0.0
r = random.random()
for i in range(len(fitnessScores)):

cumalativeFitness += fitnessScores[i]if cumalativeFitness > r:  return i    

def calcFit (numbers): # each fitness is a fraction of the total error
total, fitnesses = sum(numbers), []
for i in range(len(numbers)):

fitnesses.append(numbers[i]/total)

return fitnesses

takes a population of NN objects

def pairPop (pop):
weights, errors = [], []
for i in range(len(pop)): # for each individual

weights.append([pop[i].wi,pop[i].wo])   # append input & output weights of individual to list of all pop weightserrors.append(pop[i].sumErrors())       # append 1/sum(MSEs) of individual to list of pop errors

fitnesses = calcFit(errors) # fitnesses are a fraction of the total error
for i in range(int(pop_size*0.15)):

print str(i).zfill(2), '1/sum(MSEs)', str(errors[i]).rjust(15), str(int(errors[i]*graphical_error_scale)*'-').rjust(20), 'fitness'.rjust(12), str(fitnesses[i]).rjust(17), str(int(fitnesses[i]*1000)*'-').rjust(20)

del pop
return zip(weights, errors,fitnesses) # weights become item[0] and fitnesses[1] in this way fitness is paired with its weight in a tuple

def rankPop (newpopW,pop):
errors, copy = [], [] # a fresh pop of NN's are assigned to a list of len pop_size
#pop = [NN(ni,nh,no)]*pop_size # this does not work as they are all copies of eachother
pop = [NN(ni,nh,no) for i in range(pop_size) ]
for i in range(pop_size): copy.append(newpopW[i])
for i in range(pop_size):

pop[i].assignWeights(newpopW, i)                                    # each individual is assigned the weights generated from previous iterationpop[i].testWeights(newpopW, i)

for i in range(pop_size):

pop[i].testWeights(newpopW, i)

pairedPop = pairPop(pop) # the fitness of these weights is calculated and tupled with the weights
rankedPop = sorted(pairedPop, key = itemgetter(-1), reverse = True) # weights are sorted in descending order of fitness (fittest first)
errors = [ eval(repr(x[1])) for x in rankedPop ]
return rankedPop, eval(repr(rankedPop0)), float(sum(errors))/float(len(errors))

def iteratePop (rankedPop):
rankedWeights = [ item[0] for item in rankedPop]
fitnessScores = [ item[-1] for item in rankedPop]
newpopW = [ eval(repr(x)) for x in rankedWeights[:int(pop_size*0.15)] ]
while len(newpopW) <= pop_size: # Breed two randomly selected but different chromos until pop_size reached

ch1, ch2 = [], []index1 = roulette(fitnessScores)                                   index2 = roulette(fitnessScores)while index1 == index2:                                             # ensures different chromos are used for breeeding  index2 = roulette(fitnessScores)#index1, index2 = 3,4ch1.extend(eval(repr(rankedWeights[index1])))ch2.extend(eval(repr(rankedWeights[index2])))if random.random() < crossover_rate:  ch1, ch2 = crossover(ch1, ch2)mutate(ch1)mutate(ch2)newpopW.append(ch1)newpopW.append(ch2)

return newpopW