def evaluate_network(self, evaluation_function):
print "Evaluating the networks..."
# Reset statistics.
self.evaluation = []
self.max_fitness = []
self.node_count = []
self.link_count = []
# Load population.
population = neat.loadFromPopulation(self.filename)
# Go through each generation, build the best network, and evaluate the completion results.
for generation in xrange(population.getGenerationCount()):
# First individual is always the most fit.
ind = population.getIndividual(0, generation)
self.max_fitness.append(ind.getFitness())
self.node_count.append(ind.getNodesCount())
self.link_count.append(ind.getLinksCount())
# Build network.
net = ind.spawnFastPhenotypeStack()
# Evaluate the network.
self.evaluation.append(evaluation_function(net))
def evaluate_network( self, evaluation_function ):
print "Evaluating the networks..."
# Reset statistics.
self.evaluation = []
self.max_fitness = []
self.node_count = []
self.link_count = []
# Load population.
population = neat.loadFromPopulation( self.filename )
# Go through each generation, build the best network, and evaluate the completion results.
for generation in xrange(population.getGenerationCount()):
# First individual is always the most fit.
ind = population.getIndividual( 0, generation )
self.max_fitness.append( ind.getFitness() )
self.node_count.append( ind.getNodesCount() )
self.link_count.append( ind.getLinksCount() )
# Build network.
net = ind.spawnFastPhenotypeStack()
# Evaluate the network.
self.evaluation.append( evaluation_function(net) )
import PyHyperNEAT as neat
import sys
file = sys.argv[1]
pop = neat.loadFromPopulation( file )
generations = pop.getGenerationCount()
individuals = pop.getIndividualCount(generations-1)
print "Loaded population from '%s'..." % file
print " - Generations: %d" % generations
print " - Ind/gen: %d" % individuals
print "Determining maximum fitness..."
speciesIDs = set()
max_fit = -1
max_fit_id = -1
max_fit_species = -1
for i in xrange(individuals):
ind = pop.getIndividual( i, generations-1 )
fitness = ind.getFitness()
speciesID = ind.getSpeciesID()
if speciesID not in speciesIDs:
print " - Found new species ID: %d" % speciesID
speciesIDs.add( speciesID )
speciesIDs.add(speciesID)
print " - Evaluating individual %d (fitness %f; species %d)..." % (i, fitness, speciesID)
if fitness > max_fit:
print " - Found new fitness leader! %f -> %f" % (max_fit, fitness)
max_fit = fitness
max_fit_id = i