def main ():
"""
Fresh population of NN individuals are evalutated, paired with
their fitness scores and ordered by fitness in descending order (fittest first)
"""
# Rank first random population
pop = createPop()
pairedPop = pairPop(pop)
# ordered by fitness in descending order
rankedPop = sorted(pairedPop, key=itemgetter(-1), reverse=True)
# tst.correctlyranked(rankedPop)
# Keep iterating new pops until max_iterations
iters = 0
tops, avgs = [], []
while iters != max_iterations:
if iters%1 == 0:
print 'Iteration'.rjust(150), iters
newpopW = evolveNewPop(rankedPop)
rankedPop, toperr, avgerr = rankPop(newpopW,pop)
tops.append(toperr)
avgs.append(avgerr)
iters+=1
# test a NN with the fittest weights
tester = NN ()
fittestWeights = [ x[0] for x in rankedPop ]
tester.assignWeights(fittestWeights, 0)
results, targets = tester.test(testpat)
x = np.arange(0,150)
title2 = 'Test after '+str(iters)+' iterations'
plt.title(title2)
plt.ylabel('Node output')
plt.xlabel('Instances')
plt.plot(results, 'xr', linewidth=1.5, label='Results')
plt.plot(targets, 's', color='black', linewidth=3, label='Targets')
plt.legend(loc='lower right')
plt.figure(2)
plt.subplot(121)
plt.title('Top individual error evolution')
plt.ylabel('Inverse error')
plt.xlabel('Iterations')
plt.plot( tops, '-g', linewidth=1)
plt.subplot(122)
plt.plot( avgs, '-g', linewidth=1)
plt.title('Population average error evolution')
plt.ylabel('Inverse error')
plt.xlabel('Iterations')
plt.show()
print 'max_iterations',max_iterations,'\tpop_size', \
pop_size,'pop_size*0.15', \
int(pop_size*0.15),'\tmutation_rate', \
NN.mutation_rate,'crossover_rate', \
NN.crossover_rate,'ni, nh, no', NN.ni, NN.nh, NN.no
def main():
"""
Fresh population of NN individuals are evalutated, paired with
their fitness scores and ordered by fitness in descending order (fittest first)
"""
# Rank first random population
pop = createPop()
pairedPop = pairPop(pop)
# ordered by fitness in descending order
rankedPop = sorted(pairedPop, key=itemgetter(-1), reverse=True)
# tst.correctlyranked(rankedPop)
# Keep iterating new pops until max_iterations
iters = 0
tops, avgs = [], []
while iters != max_iterations:
if iters % 1 == 0:
print 'Iteration'.rjust(150), iters
newpopW = evolveNewPop(rankedPop)
rankedPop, toperr, avgerr = rankPop(newpopW, pop)
tops.append(toperr)
avgs.append(avgerr)
iters += 1
# test a NN with the fittest weights
tester = NN()
fittestWeights = rankedPop[0][0]
tester.assignWeights(fittestWeights)
results, targets = tester.test(testpat)
title2 = 'Test after ' + str(iters) + ' iterations'
plt.title(title2)
plt.ylabel('Node output')
plt.xlabel('Instances')
plt.plot(results, 'xr', linewidth=1.5, label='Results')
plt.plot(targets, 's', color='black', linewidth=3, label='Targets')
plt.legend(loc='lower right')
plt.figure(2)
plt.subplot(121)
plt.title('Top individual error evolution')
plt.ylabel('Inverse error')
plt.xlabel('Iterations')
plt.plot(tops, '-g', linewidth=1)
plt.subplot(122)
plt.plot(avgs, '-g', linewidth=1)
plt.title('Population average error evolution')
plt.ylabel('Inverse error')
plt.xlabel('Iterations')
plt.show()
print 'max_iterations',max_iterations,'\tpop_size', \
NN.pop_size,'pop_size*0.15', \
int(NN.pop_size*0.15),'\tmutation_rate', \
NN.mutation_rate,'crossover_rate', \
NN.crossover_rate,'ni, nh, no', NN.ni, NN.nh, NN.no
def testFittestInd(rankedPop):
tester = NN ()
fittestWeights = rankedPop[0][0]
tester.assignWeights(fittestWeights)
count = 0
for i, t in tester.test_pat:
o = tester.activate(i)
print i, o, t, '++' if list(o) == list(t) else '----'
count += 1 if list(o) == list(t) else -1
print float(count) / len(tester.test_pat)
err, per = tester.sumErrors(test_data=True)
print per
return err, per
def testFittestInd(rankedPop):
tester = NN()
fittestWeights = rankedPop[0][0]
tester.assignWeights(fittestWeights)
count = 0
for i, t in tester.test_pat:
o = tester.activate(i)
print i, o, t, '++' if list(o) == list(t) else '----'
count += 1 if list(o) == list(t) else -1
print float(count) / len(tester.test_pat)
err, per = tester.sumErrors(test_data=True)
print per
return err, per
def testFittestInd(rankedPop):
tester = NN()
fittestWeights = rankedPop[0][0]
tester.assignWeights(fittestWeights)
right = 0
for i, t, c, l in tester.test_pat:
o = tester.activate(i)
print i, o, t, '++' if list(o) == list(t) else '----'
right += 1 if list(o) == list(t) else 0
print right, len(tester.test_pat)
print 100 * (float(right) / len(tester.test_pat))
err, per = tester.sumErrors(test_data=True)
print per
return err, per
def testFittestInd(rankedPop):
tester = NN ()
fittestWeights = rankedPop[0][0]
tester.assignWeights(fittestWeights)
right = 0
for i, t, c, l in tester.test_pat:
o = tester.activate(i)
print i, o, t, '++' if list(o) == list(t) else '----'
right += 1 if list(o) == list(t) else 0
print right, len(tester.test_pat)
print 100 * (float(right) / len(tester.test_pat))
err, per = tester.sumErrors(test_data=True)
print per
return err, per
def testFittestInd(rankedPop):
tester = NN ()
fittestWeights = rankedPop[0][0]
tester.assignWeights(fittestWeights)
return tester.sumErrors(test_data=True)
def testFittestInd(rankedPop):
tester = NN()
fittestWeights = rankedPop[0][0]
tester.assignWeights(fittestWeights)
return tester.sumErrors(test_data=True)
