def pairPop (pop, verbose=True):
"""Takes a population of NNs and calculates the error and returns a
list containing the weights of each individual with its associated
errors and fitnesses"""
weights, errors = [], []
for i in range(len(pop)):
weights.append([pop[i].wi.copy(), pop[i].wo.copy()])
errors.append(pop[i].sumErrors())
fitnesses = calcFit(errors)
if verbose:
for i in range(int(NN.pop_size * 0.15)):
print str(i).zfill(2), '1/sum(MSEs)', str(errors[i]).rjust(15), \
str(int(errors[i] * NN.graphical_error_scale) * '-').rjust(20), \
'fitness'.rjust(12), str(fitnesses[i]).rjust(17), \
str(int(fitnesses[i] * 1000) * '-').rjust(20)
return zip(weights, errors, fitnesses)
def pairPop(pop, verbose=True):
"""Takes a population of NNs and calculates the error and returns a
list containing the weights of each individual with its associated
errors and fitnesses"""
weights, errors = [], []
for i in range(len(pop)):
weights.append([deepcopy(pop[i].wi), deepcopy(pop[i].wo)])
errors.append(pop[i].sumErrors())
fitnesses = calcFit(errors)
if verbose:
for i in range(int(NN.pop_size * 0.15)):
print str(i).zfill(2), '1/sum(MSEs)', str(errors[i]).rjust(15), \
str(int(errors[i] * NN.graphical_error_scale) * '-').rjust(20), \
'fitness'.rjust(12), str(fitnesses[i]).rjust(17), \
str(int(fitnesses[i] * 1000) * '-').rjust(20)
return zip(weights, errors, fitnesses)
def pairPop(pop, verbose=NN.eliteN):
"""Takes a population of NNs and calculates the error and returns a
list containing the weights of each individual with its associated
errors and fitnesses"""
weights, inv_errs, perc_accs = [], [], []
for i in range(len(pop)):
weights.append([pop[i].wi.copy(), pop[i].wo.copy()])
inv_err, perc_acc = pop[i].sumErrors()
inv_errs.append(inv_err)
perc_accs.append(perc_acc)
fitnesses = calcFit(perc_accs)
if verbose:
for i in range(verbose):
print str(i).zfill(2), 'perc acc', str(perc_accs[i]).rjust(15), \
str(int(perc_accs[i] * NN.graphical_error_scale) * '-').rjust(20), \
'fitness'.rjust(12), str(fitnesses[i]).rjust(17), \
str(int(fitnesses[i] * 1000) * '-').rjust(20)
print 'lowest fittness', min(fitnesses)
return zip(weights, inv_errs, perc_accs, fitnesses)
def pairPop(pop, verbose=NN.eliteN):
"""Takes a population of NNs and calculates the error and returns a
list containing the weights of each individual with its associated
errors and fitnesses"""
weights, inv_errs, perc_accs = [], [], []
for i in range(len(pop)):
weights.append([pop[i].wi.copy(), pop[i].wo.copy()])
inv_err, perc_acc = pop[i].sumErrors()
inv_errs.append(inv_err)
perc_accs.append(perc_acc)
fitnesses = calcFit(perc_accs)
if verbose:
for i in range(verbose):
print str(i).zfill(2), 'perc acc', str(perc_accs[i]).rjust(15), \
str(int(perc_accs[i] * NN.graphical_error_scale) * '-').rjust(20), \
'fitness'.rjust(12), str(fitnesses[i]).rjust(17), \
str(int(fitnesses[i] * 1000) * '-').rjust(20)
print 'lowest fittness', min(fitnesses)
return zip(weights, inv_errs, perc_accs, fitnesses)