def setUp(self): self.pop = createPop() rand_individual = self.pop[random.randint(0, len(self.pop))] self.ch1 = [rand_individual.wi, rand_individual.wo] rand_individual = self.pop[random.randint(0, len(self.pop))] self.ch2 = [rand_individual.wi, rand_individual.wo] self.o1, self.o2 = crossover(self.ch1, self.ch2, NN)
def runTrial():
"""
Fresh population of NN individuals are evalutated, paired with
their fitness scores and ordered by fitness in descending order (fittest first)
"""
pop = createPop()
pairedPop = pairPop(pop)
rankedPop = sorted(pairedPop, key=itemgetter(-1), reverse=True)
all_results = {
'train_mses_top' : [],
'train_mses_avg' : [],
'train_percents_avg' : [],
'train_percents_top' : [],
'test_mse' : 0,
'test_percent' : 0
}
for iters in range(max_iterations):
# raw_input('==============NEXT==============')
if not iters % 1:
print 'Iteration'.rjust(120), iters
newpopW = evolveNewPop(rankedPop)
print '\n \nEVOLVE NEW POP'
rankedPop, top_mse, avg_mse, top_per, avg_per = rankPop(newpopW, pop, verbose=NN.pop_size)
if iters > 0 and iters < 1000:
newpopW = trainPP(rankedPop, pop, incr=0.25, decr=0.5)
print '\n \nTRAIN PP'
rankedPop, top_mse, avg_mse, top_per, avg_per = rankPop(newpopW, pop, verbose=NN.pop_size)
all_results['train_mses_top'].append(top_mse)
all_results['train_mses_avg'].append(avg_mse)
all_results['train_percents_top'].append(top_per)
all_results['train_percents_avg'].append(avg_per)
test_mse, test_per = testFittestInd(rankedPop)
all_results['test_mse'] = test_mse
all_results['test_percent'] = test_per
return all_results
def runTrial(iters):
"""
Fresh population of NN individuals are evalutated, paired with
their fitness scores and ordered by fitness in descending order (fittest first)
"""
pop = createPop()
pairedPop = pairPop(pop, verbose=False)
rankedPop = sorted(pairedPop, key=itemgetter(-1), reverse=True)
all_results = {
'train_mses_top': [],
'train_mses_avg': [],
'train_percents_avg': [],
'train_percents_top': [],
'test_mse': 0,
'test_percent': 0
}
for iters in range(iters):
# raw_input('==============NEXT==============')
if not iters % 1:
print 'Iteration'.rjust(120), iters
newpopW = evolveNewPop(rankedPop)
print '\n \nEVOLVE NEW POP'
rankedPop, top_mse, avg_mse, top_per, avg_per = rankPop(newpopW,
pop,
verbose=False)
print top_per
all_results['train_mses_top'].append(top_mse)
all_results['train_mses_avg'].append(avg_mse)
all_results['train_percents_top'].append(top_per)
all_results['train_percents_avg'].append(avg_per)
test_mse, test_per = testFittestInd(rankedPop)
all_results['test_mse'] = test_mse
all_results['test_percent'] = test_per
return all_results
def setUp(self): self.pop = createPop() self.pairedPop = pairPop(self.pop) self.rankedPop = sorted(self.pairedPop, key=itemgetter(-1), reverse=True) self.rankedWeights = [x[0] for x in self.rankedPop] self.fitnessScores = [x[-1] for x in self.rankedPop] self.ch1, self.ch2 = selectTwoIndividuals(self.fitnessScores, self.rankedWeights)
def runTrial():
"""
Fresh population of NN individuals are evalutated, paired with
their fitness scores and ordered by fitness in descending order (fittest first)
"""
pop = createPop()
pairedPop = pairPop(pop)
rankedPop = sorted(pairedPop, key=itemgetter(-1), reverse=True)
all_results = {
'train_mses_top' : [],
'train_mses_avg' : [],
'train_percents_avg' : [],
'train_percents_top' : [],
'test_mse' : 0,
'test_percent' : 0
}
for iters in range(max_iterations):
if not iters % 1:
print 'Iteration'.rjust(150), iters
newpopW = evolveNewPop(rankedPop)
rankedPop, top_mse, avg_mse, top_per, avg_per = rankPop(newpopW, pop)
all_results['train_mses_top'].append(top_mse)
all_results['train_mses_avg'].append(avg_mse)
all_results['train_percents_top'].append(top_per)
all_results['train_percents_avg'].append(avg_per)
test_mse, test_per = testFittestInd(rankedPop)
all_results['test_mse'] = test_mse
all_results['test_percent'] = test_per
return all_results
def setUp(self):
self.pop = createPop()
rand_individual = self.pop[random.randint(0, len(self.pop))]
self.ch1 = [rand_individual.wi, rand_individual.wo]
rand_individual = self.pop[random.randint(0, len(self.pop))]
self.ch2 = [rand_individual.wi, rand_individual.wo]
self.o1, self.o2 = crossover(self.ch1, self.ch2, NN)
def setUp(self): self.pop = createPop() paired = pairPop(self.pop, verbose=False) ranked = sorted(paired, key=itemgetter(-1), reverse=True) self.newpopW = evolveNewPop(ranked) for i in range(len(self.pop)): self.pop[i].assignWeights(self.newpopW[i])
def setUp(self): self.pop = createPop() self.pairedPop = pairPop(self.pop) self.rankedPop = sorted(self.pairedPop, key=itemgetter(-1), reverse=True) self.rankedWeights = [x[0] for x in self.rankedPop] self.fitnessScores = [x[-1] for x in self.rankedPop] self.newpopW = evolveNewPop(self.rankedPop)
def setUp(self): self.pop = createPop() self.pairedPop = pairPop(self.pop) self.rankedPop = sorted(self.pairedPop, key=itemgetter(-1), reverse=True) self.rankedWeights = [x[0] for x in self.rankedPop] self.fitnessScores = [x[-1] for x in self.rankedPop] self.newpopW = evolveNewPop(self.rankedPop)
def setUp(self): self.pop = createPop() paired = pairPop(self.pop, verbose=False) ranked = sorted(paired, key=itemgetter(-1), reverse=True) self.newpopW = evolveNewPop(ranked) for i in range(len(self.pop)): self.pop[i].assignWeights(self.newpopW[i])
def setUp(self): self.pop = createPop() self.pairedPop = pairPop(self.pop) self.rankedPop = sorted(self.pairedPop, key=itemgetter(-1), reverse=True) self.rankedWeights = [x[0] for x in self.rankedPop] self.fitnessScores = [x[-1] for x in self.rankedPop] self.ch1, self.ch2 = selectTwoIndividuals(self.fitnessScores, self.rankedWeights)
def setUp(self): self.pop = createPop() self.pairedPop = pairPop(self.pop) self.rankedPop = sorted(self.pairedPop, key=itemgetter(-1), reverse=True) self.rankedWeights = [x[0] for x in self.rankedPop] self.fitnessScores = [x[-1] for x in self.rankedPop] self.copy = array(self.fitnessScores).copy() self.index = roulette(self.fitnessScores)
def setUp(self): self.pop = createPop() self.pairedPop = pairPop(self.pop) self.rankedPop = sorted(self.pairedPop, key=itemgetter(-1), reverse=True) self.rankedWeights = [x[0] for x in self.rankedPop] self.fitnessScores = [x[-1] for x in self.rankedPop] self.copy = array(self.fitnessScores).copy() self.index = roulette(self.fitnessScores)
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 setUp(self):
self.pop = createPop()
def setUp(self):
self.pop = createPop()
self.pairedPop = pairPop(self.pop)
def setUp(self): self.pop = createPop()
def setUp(self): self.pop = createPop() pairedPop = pairPop(self.pop) rankedPop = sorted(pairedPop, key=itemgetter(-1), reverse=True) self.newpopW = evolveNewPop(rankedPop)
def setUp(self): self.pop = createPop() self.inv_err, self.perc_err = self.pop[0].sumErrors()
def setUp(self): self.pop = createPop() self.pairedPop = pairPop(self.pop)
def setUp(self):
self.pop = createPop()
self.inv_err, self.perc_err = self.pop[0].sumErrors()
