def __init__(self, num_inputs, weights=None):
'''ctor: if weights are not explicitly given, clamped random values are
used.'''
if weights is not None:
# The extra weight is for the bias
self.weights = weights[:] + [clamped_rand()]
self.num_inputs = len(weights) + 1
else:
self.weights = [clamped_rand() for i in xrange(num_inputs + 1)]
self.num_inputs = num_inputs + 1
def __init__(self, num_inputs, weights=None):
'''ctor: if weights are not explicitly given, clamped random values are
used.'''
if weights is not None:
# The extra weight is for the bias
self.weights = weights[:] + [clamped_rand()]
self.num_inputs = len(weights) + 1
else:
self.weights = [clamped_rand() for i in xrange(num_inputs + 1)]
self.num_inputs = num_inputs + 1
def mutate(self, genome):
'''
mutates a chromosome, based on the mutation_rate by perturbing
its weights by an amount not greater than max_perturbation.
**This potentially modifies the argument passed and returns whether
this mod took place.**
'''
mutated = False
for i in xrange(len(genome.weights)):
if random() < settings.MUTATION_RATE:
genome.weights[i] += (clamped_rand() * settings.MAX_PERTURBATION)
mutated = True
return mutated
def __init__(self, popsize, mutrate, crossrate, numweights):
'''
Constructor
'''
self._population_size = popsize
self._mutation_rate = mutrate
self._crossover_rate = crossrate
self._chromo_length = numweights
self._total_fitness = 0.0
self._generation_count = 0
self._fittest_genome = 0
self._best_fitness = 0.0
self._worst_fitness = INFINITY
self._average_fitness = 0.0
self._population = []
for i in xrange(popsize):
weights = [clamped_rand() for j in xrange(numweights)]
self._population.append(Genome(weights, 0.0))
