from operator import itemgetter, attrgetter
import math
from math import copysign
from random import *
import timeit
from timeit import Timer as t
from matplotlib.pyplot import *
from numpy import *
def sigmoid (x):
return math.tanh(x)
class NN:
# ni,nh,no = n of input (i), hidden (h) and output (o) nodes
# ai,ah,ao = output value for nodes in i,h,o layers
# Ah, Ao = Astrocyte activation value/counter of partner neuronal activity (h,o layers)
# Ah_countdown = counts down duration of astrocyte action in itererations
# wi,wo = input and output weights initialized to random values
def __init__(self, NI, NH, NO):
self.ni = NI
self.nh = NH
self.no = NO
self.ai = ones(ni)
self.ah = ones(nh)
self.ao = ones(no)
self.Ah = [0]*self.nh
self.Ao = [0]*self.no
self.Ah_countdwn = [0]*self.nh
self.Ao_countdwn = [0]*self.no
self.wi = random.uniform(-2,2,(ni,nh))
self.wo = random.uniform(-2,2,(nh,no))
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 runNGA (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) ]))
# check neuron activation
if self.ah[j] > 0: self.Ah[j] +=1
else: self.Ah[j] -=1
# check if astrocyte should be activated
if self.Ah[j] >= Athresh:
self.Ah_countdwn[j] = Adur
self.Ah[j] = 0
elif self.Ah[j] <= -Athresh:
self.Ah_countdwn[j] = -Adur
self.Ah[j] = 0
# check if astrocyte is active --> perform actions
if self.Ah_countdwn[j] > 0:
for i in range(ni):
if not self.wi[i][j] > wl:
self.wi[i][j] += (wl-self.wi[i][j])*0.25
self.Ah_countdwn[j] -= 1
elif self.Ah_countdwn[j] < 0:
for i in range(ni):
if not self.wi[i][j] < -wl:
self.wi[i][j] -= (wl+self.wi[i][j])*0.5
self.Ah_countdwn[j] += 1
for k in range(self.no):
self.ao[k] = sigmoid(sum([ self.ah[j] * self.wo[j][k] for j in range(self.nh) ]))
# check neuron activation
if self.ao[k] > 0: self.Ao[k] += 1
else: self.Ao[k] -= 1
# check if astrocyte should be activated
if self.Ao[k] >= Athresh:
self.Ao_countdwn[k] = Adur
self.Ao[k] = 0
elif self.Ao[k] <= -Athresh:
self.Ao_countdwn[k] = -Adur
self.Ao[k] = 0
# check if astrocyte is active --> perform actions
if self.Ao_countdwn[k] > 0:
for j in range(nh):
if not self.wo[j][k] > wl:
self.wo[j][k] += (wl-self.wo[j][k])*0.25
self.Ao_countdwn[k] -= 1
elif self.Ao_countdwn[k] < 0:
for j in range(nh):
if not self.wo[j][k] < -wl:
self.wo[j][k] -= (wl+self.wo[j][k])*0.5
self.Ao_countdwn[k] += 1
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([ round(i,3) for i in self.runNN(inputs) ]).rjust(65), 'Target', p[1], result)
results+= eval(repr([self.runNN(inputs)]))
targets += [p[1]]
return results, targets
def sumErrors (self):
error = 0.0
for p in pat:
inputs = p[0]
targets = p[1]
self.runNN(inputs)
error += self.calcError(targets)
inverr = 1.0/error
return inverr
def calcError (self, targets):
error = 0.0
for k in range(len(targets)):
error += 0.5 * (targets[k]-self.ao[k])**2
return error
def assignWeights (self, weights, I):
io = 0
for i in range(self.ni):
for j in range(self.nh):
self.wi[i][j] = weights[I][io][i][j]
io = 1
for 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 = 0
for 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 = 1
for 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 I,'\n',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, returns a list of the weights tupled with their
# fitness scores.
# Calculates the MSE for each instance by subtraction from target value and sums over all
# instances.
# Fitness score for each individual is a fraction of the total error of the population.
def pairPop (pop):
weights, errors = [], []
for i in range(len(pop)):
weights.append([pop[i].wi,pop[i].wo])
errors.append(pop[i].sumErrors())
fitnesses = calcFit(errors)
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)
print
del pop
return zip(weights, errors, fitnesses)
# Takes weights for a pop and ranks the weights
# Returns the weights paired and sorted in order of fitness
def rankPop (newpopW):
print 'RANKPOP START'
pop, errors, copy = [ 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 iteration
pop[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 ]
print 'END RANKPOP'
return rankedPop, eval(repr(rankedPop[0][1])), 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)
ch1.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
def NGA(newpopW):
print 'NGA START'
pop = [ NN(ni,nh,no) for i in range(pop_size) ]
#pop = [ NN(ni,nh,no) ]*pop_size
weights = []
for i in range(pop_size):
pop[i].assignWeights(newpopW, i)
pop[i].testWeights(newpopW, i)
for p in range(len(pat)):
inputs, targets = pat[p][0], pat[p][1]
for m in range(m_iters):
pop[i].runNGA(inputs)
weights.append([pop[i].wi,pop[i].wo])
del pop
print 'END NGA'
return weights
graphical_error_scale = 500
max_iterations = 100
pop_size = 100
mutation_rate = 0.1
crossover_rate = 0.8
ni, nh, no = 4,6,3 # number of input (i), hidden (h) and output (o) nodes
m_iters = 6 # number of iterations per instance
Athresh = 3 # threshold count for activation of astrocyte
Adur = 2 # duration of action of astrocyte
wl = 2.0 # weight limit
def main ():
# Generates a population of NNs with random weights and rank them based on their error
pop = [ NN(ni,nh,no) for i in range(pop_size) ]
pairedPop = pairPop(pop)
rankedPop = sorted(pairedPop, key = itemgetter(-1), reverse = True)
# Keep iterating new pops until max_iterations
iters = 0
tops, avgs = [], []
newpopW = iteratePop(rankedPop)
while iters != max_iterations:
if iters%1 == 0:
print 'Iteration'.rjust(150), iters
newpopW = NGA(newpopW)
rankedPop, toperr, avgerr = rankPop(newpopW)
newpopW = iteratePop(rankedPop)
tops.append(toperr)
avgs.append(avgerr)
iters+=1
# test a NN with the fittest weights
tester = NN (ni,nh,no)
fittestWeights = [ x[0] for x in rankedPop ]
tester.assignWeights(fittestWeights, 0)
results, targets = tester.test(testpat)
plotresults(results,targets)
def plotresults(results,targets):
for i in range(len(results)):
print results[i], targets[i]
lp = len(pat)
x = arange(0,lp/3)
title2 = 'Test after '+str(iters)+' iterations'
title(title2)
for i in range(3):
sp = '3'+'1'+str(i)
subplot(int(sp))
ylabel('Node output')
xlabel('Instances')
for x in range(lp/3):
plot( results[x+(i*(lp/3))], label='Instance'+str(x))
print results[x+(i*(lp/3))]
plot( targets[i*(lp/3)], 'kx-', markersize = 10, linewidth = 3)
legend(loc = 'upper right')
annotate(s='Target Values', xy = (110, 0),color = 'black', family = 'sans-serif', size ='small')
annotate(s='Test Values', xy = (110, 0.5),color = 'red', family = 'sans-serif', size ='small', weight = 'bold')
figure(2)
title('Top individual error evolution')
title('Population average error evolution')
plot( avgs, '-g', linewidth = 0.5)
plot( tops, '-r', linewidth = 2)
ylabel('Inverse error')
xlabel('Iterations')
show()
print 'max_iterations',max_iterations,'\tpop_size',pop_size,'pop_size*0.15',int(pop_size*0.15),'\tmutation_rate',mutation_rate,'crossover_rate',crossover_rate,'ni, nh, no',ni, nh, no
def crossover (m1, m2):
r = random.randint(0, (ni*nh)+(nh*no) ) # ni*nh+nh*no = total n of weights
output1 = [ [[0.0]*nh]*ni ,[[0.0]*no]*nh ]
output2 = [ [[0.0]*nh]*ni ,[[0.0]*no]*nh ]
for i in range(len(m1)):
for j in range(len(m1[i])):
for k in range(len(m1[i][j])):
if r >= 0:
output1[i][j][k] = m1[i][j][k]
output2[i][j][k] = m2[i][j][k]
elif r < 0:
output1[i][j][k] = m2[i][j][k]
output2[i][j][k] = m1[i][j][k]
r -=1
return output1, output2
def mutate (m):
for i in range(len(m)):
for j in range(len(m[i])):
for k in range(len(m[i][j])):
if random.random() < mutation_rate:
m[i][j][k] = random.uniform(-2.0,2.0)
pat = [
[[5.1, 3.5, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.9, 3.0, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.7, 3.2, 1.3, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.6, 3.1, 1.5, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.6, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.4, 3.9, 1.7, 0.4], [1, 0, 0], ['Iris-setosa']] ,
[[4.6, 3.4, 1.4, 0.3], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.4, 1.5, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.4, 2.9, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.9, 3.1, 1.5, 0.1], [1, 0, 0], ['Iris-setosa']] ,
[[5.4, 3.7, 1.5, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.8, 3.4, 1.6, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.8, 3.0, 1.4, 0.1], [1, 0, 0], ['Iris-setosa']] ,
[[4.3, 3.0, 1.1, 0.1], [1, 0, 0], ['Iris-setosa']] ,
[[5.8, 4.0, 1.2, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.7, 4.4, 1.5, 0.4], [1, 0, 0], ['Iris-setosa']] ,
[[5.4, 3.9, 1.3, 0.4], [1, 0, 0], ['Iris-setosa']] ,
[[5.1, 3.5, 1.4, 0.3], [1, 0, 0], ['Iris-setosa']] ,
[[5.7, 3.8, 1.7, 0.3], [1, 0, 0], ['Iris-setosa']] ,
[[5.1, 3.8, 1.5, 0.3], [1, 0, 0], ['Iris-setosa']] ,
[[5.4, 3.4, 1.7, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.1, 3.7, 1.5, 0.4], [1, 0, 0], ['Iris-setosa']] ,
[[4.6, 3.6, 1.0, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.1, 3.3, 1.7, 0.5], [1, 0, 0], ['Iris-setosa']] ,
[[4.8, 3.4, 1.9, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.0, 1.6, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.4, 1.6, 0.4], [1, 0, 0], ['Iris-setosa']] ,
[[5.2, 3.5, 1.5, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.2, 3.4, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.7, 3.2, 1.6, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.8, 3.1, 1.6, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.4, 3.4, 1.5, 0.4], [1, 0, 0], ['Iris-setosa']] ,
[[5.2, 4.1, 1.5, 0.1], [1, 0, 0], ['Iris-setosa']] ,
[[5.5, 4.2, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.9, 3.1, 1.5, 0.1], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.2, 1.2, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.5, 3.5, 1.3, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.9, 3.1, 1.5, 0.1], [1, 0, 0], ['Iris-setosa']] ,
[[4.4, 3.0, 1.3, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.1, 3.4, 1.5, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.5, 1.3, 0.3], [1, 0, 0], ['Iris-setosa']] ,
[[4.5, 2.3, 1.3, 0.3], [1, 0, 0], ['Iris-setosa']] ,
[[4.4, 3.2, 1.3, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.5, 1.6, 0.6], [1, 0, 0], ['Iris-setosa']] ,
[[5.1, 3.8, 1.9, 0.4], [1, 0, 0], ['Iris-setosa']] ,
[[4.8, 3.0, 1.4, 0.3], [1, 0, 0], ['Iris-setosa']] ,
[[5.1, 3.8, 1.6, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.6, 3.2, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.3, 3.7, 1.5, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.3, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[7.0, 3.2, 4.7, 1.4], [0, 1, 0], ['Iris-versicolor']] ,
[[6.4, 3.2, 4.5, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[6.9, 3.1, 4.9, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[5.5, 2.3, 4.0, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[6.5, 2.8, 4.6, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[5.7, 2.8, 4.5, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[6.3, 3.3, 4.7, 1.6], [0, 1, 0], ['Iris-versicolor']] ,
[[4.9, 2.4, 3.3, 1.0], [0, 1, 0], ['Iris-versicolor']] ,
[[6.6, 2.9, 4.6, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[5.2, 2.7, 3.9, 1.4], [0, 1, 0], ['Iris-versicolor']] ,
[[5.0, 2.0, 3.5, 1.0], [0, 1, 0], ['Iris-versicolor']] ,
[[5.9, 3.0, 4.2, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[6.0, 2.2, 4.0, 1.0], [0, 1, 0], ['Iris-versicolor']] ,
[[6.1, 2.9, 4.7, 1.4], [0, 1, 0], ['Iris-versicolor']] ,
[[5.6, 2.9, 3.6, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[6.7, 3.1, 4.4, 1.4], [0, 1, 0], ['Iris-versicolor']] ,
[[5.6, 3.0, 4.5, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[5.8, 2.7, 4.1, 1.0], [0, 1, 0], ['Iris-versicolor']] ,
[[6.2, 2.2, 4.5, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[5.6, 2.5, 3.9, 1.1], [0, 1, 0], ['Iris-versicolor']] ,
[[5.9, 3.2, 4.8, 1.8], [0, 1, 0], ['Iris-versicolor']] ,
[[6.1, 2.8, 4.0, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[6.3, 2.5, 4.9, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[6.1, 2.8, 4.7, 1.2], [0, 1, 0], ['Iris-versicolor']] ,
[[6.4, 2.9, 4.3, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[6.6, 3.0, 4.4, 1.4], [0, 1, 0], ['Iris-versicolor']] ,
[[6.8, 2.8, 4.8, 1.4], [0, 1, 0], ['Iris-versicolor']] ,
[[6.7, 3.0, 5.0, 1.7], [0, 1, 0], ['Iris-versicolor']] ,
[[6.0, 2.9, 4.5, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[5.7, 2.6, 3.5, 1.0], [0, 1, 0], ['Iris-versicolor']] ,
[[5.5, 2.4, 3.8, 1.1], [0, 1, 0], ['Iris-versicolor']] ,
[[5.5, 2.4, 3.7, 1.0], [0, 1, 0], ['Iris-versicolor']] ,
[[5.8, 2.7, 3.9, 1.2], [0, 1, 0], ['Iris-versicolor']] ,
[[6.0, 2.7, 5.1, 1.6], [0, 1, 0], ['Iris-versicolor']] ,
[[5.4, 3.0, 4.5, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[6.0, 3.4, 4.5, 1.6], [0, 1, 0], ['Iris-versicolor']] ,
[[6.7, 3.1, 4.7, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[6.3, 2.3, 4.4, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[5.6, 3.0, 4.1, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[5.5, 2.5, 4.0, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[5.5, 2.6, 4.4, 1.2], [0, 1, 0], ['Iris-versicolor']] ,
[[6.1, 3.0, 4.6, 1.4], [0, 1, 0], ['Iris-versicolor']] ,
[[5.8, 2.6, 4.0, 1.2], [0, 1, 0], ['Iris-versicolor']] ,
[[5.0, 2.3, 3.3, 1.0], [0, 1, 0], ['Iris-versicolor']] ,
[[5.6, 2.7, 4.2, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[5.7, 3.0, 4.2, 1.2], [0, 1, 0], ['Iris-versicolor']] ,
[[5.7, 2.9, 4.2, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[6.2, 2.9, 4.3, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[5.1, 2.5, 3.0, 1.1], [0, 1, 0], ['Iris-versicolor']] ,
[[5.7, 2.8, 4.1, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[6.3, 3.3, 6.0, 2.5], [0, 0, 1], ['Iris-virginica']] ,
[[5.8, 2.7, 5.1, 1.9], [0, 0, 1], ['Iris-virginica']] ,
[[7.1, 3.0, 5.9, 2.1], [0, 0, 1], ['Iris-virginica']] ,
[[6.3, 2.9, 5.6, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.5, 3.0, 5.8, 2.2], [0, 0, 1], ['Iris-virginica']] ,
[[7.6, 3.0, 6.6, 2.1], [0, 0, 1], ['Iris-virginica']] ,
[[4.9, 2.5, 4.5, 1.7], [0, 0, 1], ['Iris-virginica']] ,
[[7.3, 2.9, 6.3, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.7, 2.5, 5.8, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[7.2, 3.6, 6.1, 2.5], [0, 0, 1], ['Iris-virginica']] ,
[[6.5, 3.2, 5.1, 2.0], [0, 0, 1], ['Iris-virginica']] ,
[[6.4, 2.7, 5.3, 1.9], [0, 0, 1], ['Iris-virginica']] ,
[[6.8, 3.0, 5.5, 2.1], [0, 0, 1], ['Iris-virginica']] ,
[[5.7, 2.5, 5.0, 2.0], [0, 0, 1], ['Iris-virginica']] ,
[[5.8, 2.8, 5.1, 2.4], [0, 0, 1], ['Iris-virginica']] ,
[[6.4, 3.2, 5.3, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[6.5, 3.0, 5.5, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[7.7, 3.8, 6.7, 2.2], [0, 0, 1], ['Iris-virginica']] ,
[[7.7, 2.6, 6.9, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[6.0, 2.2, 5.0, 1.5], [0, 0, 1], ['Iris-virginica']] ,
[[6.9, 3.2, 5.7, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[5.6, 2.8, 4.9, 2.0], [0, 0, 1], ['Iris-virginica']] ,
[[7.7, 2.8, 6.7, 2.0], [0, 0, 1], ['Iris-virginica']] ,
[[6.3, 2.7, 4.9, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.7, 3.3, 5.7, 2.1], [0, 0, 1], ['Iris-virginica']] ,
[[7.2, 3.2, 6.0, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.2, 2.8, 4.8, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.1, 3.0, 4.9, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.4, 2.8, 5.6, 2.1], [0, 0, 1], ['Iris-virginica']] ,
[[7.2, 3.0, 5.8, 1.6], [0, 0, 1], ['Iris-virginica']] ,
[[7.4, 2.8, 6.1, 1.9], [0, 0, 1], ['Iris-virginica']] ,
[[7.9, 3.8, 6.4, 2.0], [0, 0, 1], ['Iris-virginica']] ,
[[6.4, 2.8, 5.6, 2.2], [0, 0, 1], ['Iris-virginica']] ,
[[6.3, 2.8, 5.1, 1.5], [0, 0, 1], ['Iris-virginica']] ,
[[6.1, 2.6, 5.6, 1.4], [0, 0, 1], ['Iris-virginica']] ,
[[7.7, 3.0, 6.1, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[6.3, 3.4, 5.6, 2.4], [0, 0, 1], ['Iris-virginica']] ,
[[6.4, 3.1, 5.5, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.0, 3.0, 4.8, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.9, 3.1, 5.4, 2.1], [0, 0, 1], ['Iris-virginica']] ,
[[6.7, 3.1, 5.6, 2.4], [0, 0, 1], ['Iris-virginica']] ,
[[6.9, 3.1, 5.1, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[5.8, 2.7, 5.1, 1.9], [0, 0, 1], ['Iris-virginica']] ,
[[6.8, 3.2, 5.9, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[6.7, 3.3, 5.7, 2.5], [0, 0, 1], ['Iris-virginica']] ,
[[6.7, 3.0, 5.2, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[6.3, 2.5, 5.0, 1.9], [0, 0, 1], ['Iris-virginica']] ,
[[6.5, 3.0, 5.2, 2.0], [0, 0, 1], ['Iris-virginica']] ,
[[6.2, 3.4, 5.4, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[5.9, 3.0, 5.1, 1.8], [0, 0, 1], ['Iris-virginica']]
]
testpat = [
[[5.1, 3.5, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.9, 3.0, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.7, 3.2, 1.3, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.6, 3.1, 1.5, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.6, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.4, 3.9, 1.7, 0.4], [1, 0, 0], ['Iris-setosa']] ,
[[4.6, 3.4, 1.4, 0.3], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.4, 1.5, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.4, 2.9, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.9, 3.1, 1.5, 0.1], [1, 0, 0], ['Iris-setosa']] ,
[[5.4, 3.7, 1.5, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.8, 3.4, 1.6, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.8, 3.0, 1.4, 0.1], [1, 0, 0], ['Iris-setosa']] ,
[[4.3, 3.0, 1.1, 0.1], [1, 0, 0], ['Iris-setosa']] ,
[[5.8, 4.0, 1.2, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.7, 4.4, 1.5, 0.4], [1, 0, 0], ['Iris-setosa']] ,
[[5.4, 3.9, 1.3, 0.4], [1, 0, 0], ['Iris-setosa']] ,
[[5.1, 3.5, 1.4, 0.3], [1, 0, 0], ['Iris-setosa']] ,
[[5.7, 3.8, 1.7, 0.3], [1, 0, 0], ['Iris-setosa']] ,
[[5.1, 3.8, 1.5, 0.3], [1, 0, 0], ['Iris-setosa']] ,
[[5.4, 3.4, 1.7, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.1, 3.7, 1.5, 0.4], [1, 0, 0], ['Iris-setosa']] ,
[[4.6, 3.6, 1.0, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.1, 3.3, 1.7, 0.5], [1, 0, 0], ['Iris-setosa']] ,
[[4.8, 3.4, 1.9, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.0, 1.6, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.4, 1.6, 0.4], [1, 0, 0], ['Iris-setosa']] ,
[[5.2, 3.5, 1.5, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.2, 3.4, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.7, 3.2, 1.6, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.8, 3.1, 1.6, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.4, 3.4, 1.5, 0.4], [1, 0, 0], ['Iris-setosa']] ,
[[5.2, 4.1, 1.5, 0.1], [1, 0, 0], ['Iris-setosa']] ,
[[5.5, 4.2, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.9, 3.1, 1.5, 0.1], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.2, 1.2, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.5, 3.5, 1.3, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.9, 3.1, 1.5, 0.1], [1, 0, 0], ['Iris-setosa']] ,
[[4.4, 3.0, 1.3, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.1, 3.4, 1.5, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.5, 1.3, 0.3], [1, 0, 0], ['Iris-setosa']] ,
[[4.5, 2.3, 1.3, 0.3], [1, 0, 0], ['Iris-setosa']] ,
[[4.4, 3.2, 1.3, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.5, 1.6, 0.6], [1, 0, 0], ['Iris-setosa']] ,
[[5.1, 3.8, 1.9, 0.4], [1, 0, 0], ['Iris-setosa']] ,
[[4.8, 3.0, 1.4, 0.3], [1, 0, 0], ['Iris-setosa']] ,
[[5.1, 3.8, 1.6, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[4.6, 3.2, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.3, 3.7, 1.5, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[5.0, 3.3, 1.4, 0.2], [1, 0, 0], ['Iris-setosa']] ,
[[7.0, 3.2, 4.7, 1.4], [0, 1, 0], ['Iris-versicolor']] ,
[[6.4, 3.2, 4.5, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[6.9, 3.1, 4.9, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[5.5, 2.3, 4.0, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[6.5, 2.8, 4.6, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[5.7, 2.8, 4.5, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[6.3, 3.3, 4.7, 1.6], [0, 1, 0], ['Iris-versicolor']] ,
[[4.9, 2.4, 3.3, 1.0], [0, 1, 0], ['Iris-versicolor']] ,
[[6.6, 2.9, 4.6, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[5.2, 2.7, 3.9, 1.4], [0, 1, 0], ['Iris-versicolor']] ,
[[5.0, 2.0, 3.5, 1.0], [0, 1, 0], ['Iris-versicolor']] ,
[[5.9, 3.0, 4.2, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[6.0, 2.2, 4.0, 1.0], [0, 1, 0], ['Iris-versicolor']] ,
[[6.1, 2.9, 4.7, 1.4], [0, 1, 0], ['Iris-versicolor']] ,
[[5.6, 2.9, 3.6, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[6.7, 3.1, 4.4, 1.4], [0, 1, 0], ['Iris-versicolor']] ,
[[5.6, 3.0, 4.5, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[5.8, 2.7, 4.1, 1.0], [0, 1, 0], ['Iris-versicolor']] ,
[[6.2, 2.2, 4.5, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[5.6, 2.5, 3.9, 1.1], [0, 1, 0], ['Iris-versicolor']] ,
[[5.9, 3.2, 4.8, 1.8], [0, 1, 0], ['Iris-versicolor']] ,
[[6.1, 2.8, 4.0, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[6.3, 2.5, 4.9, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[6.1, 2.8, 4.7, 1.2], [0, 1, 0], ['Iris-versicolor']] ,
[[6.4, 2.9, 4.3, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[6.6, 3.0, 4.4, 1.4], [0, 1, 0], ['Iris-versicolor']] ,
[[6.8, 2.8, 4.8, 1.4], [0, 1, 0], ['Iris-versicolor']] ,
[[6.7, 3.0, 5.0, 1.7], [0, 1, 0], ['Iris-versicolor']] ,
[[6.0, 2.9, 4.5, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[5.7, 2.6, 3.5, 1.0], [0, 1, 0], ['Iris-versicolor']] ,
[[5.5, 2.4, 3.8, 1.1], [0, 1, 0], ['Iris-versicolor']] ,
[[5.5, 2.4, 3.7, 1.0], [0, 1, 0], ['Iris-versicolor']] ,
[[5.8, 2.7, 3.9, 1.2], [0, 1, 0], ['Iris-versicolor']] ,
[[6.0, 2.7, 5.1, 1.6], [0, 1, 0], ['Iris-versicolor']] ,
[[5.4, 3.0, 4.5, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[6.0, 3.4, 4.5, 1.6], [0, 1, 0], ['Iris-versicolor']] ,
[[6.7, 3.1, 4.7, 1.5], [0, 1, 0], ['Iris-versicolor']] ,
[[6.3, 2.3, 4.4, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[5.6, 3.0, 4.1, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[5.5, 2.5, 4.0, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[5.5, 2.6, 4.4, 1.2], [0, 1, 0], ['Iris-versicolor']] ,
[[6.1, 3.0, 4.6, 1.4], [0, 1, 0], ['Iris-versicolor']] ,
[[5.8, 2.6, 4.0, 1.2], [0, 1, 0], ['Iris-versicolor']] ,
[[5.0, 2.3, 3.3, 1.0], [0, 1, 0], ['Iris-versicolor']] ,
[[5.6, 2.7, 4.2, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[5.7, 3.0, 4.2, 1.2], [0, 1, 0], ['Iris-versicolor']] ,
[[5.7, 2.9, 4.2, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[6.2, 2.9, 4.3, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[5.1, 2.5, 3.0, 1.1], [0, 1, 0], ['Iris-versicolor']] ,
[[5.7, 2.8, 4.1, 1.3], [0, 1, 0], ['Iris-versicolor']] ,
[[6.3, 3.3, 6.0, 2.5], [0, 0, 1], ['Iris-virginica']] ,
[[5.8, 2.7, 5.1, 1.9], [0, 0, 1], ['Iris-virginica']] ,
[[7.1, 3.0, 5.9, 2.1], [0, 0, 1], ['Iris-virginica']] ,
[[6.3, 2.9, 5.6, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.5, 3.0, 5.8, 2.2], [0, 0, 1], ['Iris-virginica']] ,
[[7.6, 3.0, 6.6, 2.1], [0, 0, 1], ['Iris-virginica']] ,
[[4.9, 2.5, 4.5, 1.7], [0, 0, 1], ['Iris-virginica']] ,
[[7.3, 2.9, 6.3, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.7, 2.5, 5.8, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[7.2, 3.6, 6.1, 2.5], [0, 0, 1], ['Iris-virginica']] ,
[[6.5, 3.2, 5.1, 2.0], [0, 0, 1], ['Iris-virginica']] ,
[[6.4, 2.7, 5.3, 1.9], [0, 0, 1], ['Iris-virginica']] ,
[[6.8, 3.0, 5.5, 2.1], [0, 0, 1], ['Iris-virginica']] ,
[[5.7, 2.5, 5.0, 2.0], [0, 0, 1], ['Iris-virginica']] ,
[[5.8, 2.8, 5.1, 2.4], [0, 0, 1], ['Iris-virginica']] ,
[[6.4, 3.2, 5.3, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[6.5, 3.0, 5.5, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[7.7, 3.8, 6.7, 2.2], [0, 0, 1], ['Iris-virginica']] ,
[[7.7, 2.6, 6.9, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[6.0, 2.2, 5.0, 1.5], [0, 0, 1], ['Iris-virginica']] ,
[[6.9, 3.2, 5.7, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[5.6, 2.8, 4.9, 2.0], [0, 0, 1], ['Iris-virginica']] ,
[[7.7, 2.8, 6.7, 2.0], [0, 0, 1], ['Iris-virginica']] ,
[[6.3, 2.7, 4.9, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.7, 3.3, 5.7, 2.1], [0, 0, 1], ['Iris-virginica']] ,
[[7.2, 3.2, 6.0, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.2, 2.8, 4.8, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.1, 3.0, 4.9, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.4, 2.8, 5.6, 2.1], [0, 0, 1], ['Iris-virginica']] ,
[[7.2, 3.0, 5.8, 1.6], [0, 0, 1], ['Iris-virginica']] ,
[[7.4, 2.8, 6.1, 1.9], [0, 0, 1], ['Iris-virginica']] ,
[[7.9, 3.8, 6.4, 2.0], [0, 0, 1], ['Iris-virginica']] ,
[[6.4, 2.8, 5.6, 2.2], [0, 0, 1], ['Iris-virginica']] ,
[[6.3, 2.8, 5.1, 1.5], [0, 0, 1], ['Iris-virginica']] ,
[[6.1, 2.6, 5.6, 1.4], [0, 0, 1], ['Iris-virginica']] ,
[[7.7, 3.0, 6.1, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[6.3, 3.4, 5.6, 2.4], [0, 0, 1], ['Iris-virginica']] ,
[[6.4, 3.1, 5.5, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.0, 3.0, 4.8, 1.8], [0, 0, 1], ['Iris-virginica']] ,
[[6.9, 3.1, 5.4, 2.1], [0, 0, 1], ['Iris-virginica']] ,
[[6.7, 3.1, 5.6, 2.4], [0, 0, 1], ['Iris-virginica']] ,
[[6.9, 3.1, 5.1, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[5.8, 2.7, 5.1, 1.9], [0, 0, 1], ['Iris-virginica']] ,
[[6.8, 3.2, 5.9, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[6.7, 3.3, 5.7, 2.5], [0, 0, 1], ['Iris-virginica']] ,
[[6.7, 3.0, 5.2, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[6.3, 2.5, 5.0, 1.9], [0, 0, 1], ['Iris-virginica']] ,
[[6.5, 3.0, 5.2, 2.0], [0, 0, 1], ['Iris-virginica']] ,
[[6.2, 3.4, 5.4, 2.3], [0, 0, 1], ['Iris-virginica']] ,
[[5.9, 3.0, 5.1, 1.8], [0, 0, 1], ['Iris-virginica']]
]
if __name__ == "__main__":
main()
Diff to Previous Revision
--- revision 4 2012-09-07 16:40:43
+++ revision 5 2012-10-02 16:18:36
@@ -21,10 +21,10 @@
self.nh = NH
self.no = NO
self.ai = ones(ni)
- self.ah = ones(nh) # a.cumsum( acis = 1) cumsum of rows
- self.ao = ones(no) # a.min( axis = 0) min in cols
- self.Ah = [0]*self.nh # self.Ah[j].sum() to sum
- self.Ao = [0]*self.no # for element in a.flat # gives all elements # a.ravel()/a.shape =
+ self.ah = ones(nh)
+ self.ao = ones(no)
+ self.Ah = [0]*self.nh
+ self.Ao = [0]*self.no
self.Ah_countdwn = [0]*self.nh
self.Ao_countdwn = [0]*self.no
self.wi = random.uniform(-2,2,(ni,nh))
@@ -210,7 +210,6 @@
index2 = roulette(fitnessScores)
while index1 == index2: # ensures different chromos are used for breeeding
index2 = roulette(fitnessScores)
- #index1, index2 = 3,4
ch1.extend(eval(repr(rankedWeights[index1])))
ch2.extend(eval(repr(rankedWeights[index2])))
if random.random() < crossover_rate:
@@ -329,8 +328,6 @@
return output1, output2
def mutate (m):
- # could include a constant to control
- # how much the weight is mutated by
for i in range(len(m)):
for j in range(len(m[i])):
for k in range(len(m[i][j])):
