args = arg_parser.parse_args()
with open('best_individual.json') as best_agent_weights:
weights = json.load(best_agent_weights)
if args.scenario == 'pull':
genotype_class = BeerTrackerPullGenotype
elif args.scenario == 'wall':
genotype_class = BeerTrackerWallGenotype
else:
genotype_class = BeerTrackerGenotype
nn = Rnn(num_input_nodes=genotype_class.num_input_nodes,
num_hidden_nodes=genotype_class.num_hidden_nodes,
num_output_nodes=genotype_class.num_output_nodes)
nn.set_weights(weights)
g = gfx.Gfx()
g.fps = 8
for i in range(args.num_scenarios):
seed = i + ((997 * args.generation) if args.mode == 'dynamic' else 0)
print '---', 'seed', seed, '---'
bt = BeerTracker(nn=nn, seed=seed, scenario=args.scenario)
bt.gfx = g
bt.run()
print bt.world.agent.num_small_misses, 'small miss(es)'
print bt.world.agent.num_large_misses, 'large miss(es)'
print bt.world.agent.num_partial_captures, 'partial capture(s)'
print bt.world.agent.num_small_captures, 'small capture(s)'
print bt.world.agent.num_large_captures, 'large capture(s)'
class BeerTrackerIndividual(Individual):
range_map = {
'weight': (-5.0, 5.0),
'internal_bias': (-10.0, 0.0),
'gain': (1.0, 5.0),
'time_constant': (1.0, 2.0)
}
genotype_class = None
def calculate_summed_weight(self, i, range_key):
j = i * BeerTrackerIndividual.genotype_class.bits_per_weight
weight = sum(self.genotype.
dna[j:j +
BeerTrackerIndividual.genotype_class.bits_per_weight])
weight = self.range_map[range_key][0] + \
(self.range_map[range_key][1] - self.range_map[range_key][0]) * \
float(weight) / BeerTrackerIndividual.genotype_class.bits_per_weight
return weight
def calculate_bitshifted_weight(self, i, range_key):
j = i * BeerTrackerIndividual.genotype_class.bits_per_weight
bits = self.genotype.dna[j:j + BeerTrackerIndividual.genotype_class.
bits_per_weight]
weight = 0
for bit in bits:
weight = (weight << 1) | bit
weight = self.range_map[range_key][0] + \
(self.range_map[range_key][1] - self.range_map[range_key][0]) * \
float(weight) / (2 ** BeerTrackerIndividual.genotype_class.bits_per_weight)
return weight
def calculate_phenotype(self):
self.phenotype = Rnn(
BeerTrackerIndividual.genotype_class.num_input_nodes,
BeerTrackerIndividual.genotype_class.num_hidden_nodes,
BeerTrackerIndividual.genotype_class.num_output_nodes)
weights = []
calculate_weight = self.calculate_summed_weight
for i in range(BeerTrackerIndividual.genotype_class.rnn.
edge_chunks['input_hidden']):
weight = calculate_weight(i, 'weight')
weights.append(weight)
for i in range(BeerTrackerIndividual.genotype_class.rnn.
edge_chunks['hidden_hidden']):
weight = calculate_weight(i, 'weight')
weights.append(weight)
for i in range(BeerTrackerIndividual.genotype_class.rnn.
edge_chunks['bias_hidden']):
weight = calculate_weight(i, 'internal_bias')
weights.append(weight)
for i in range(BeerTrackerIndividual.genotype_class.rnn.
edge_chunks['hidden_output']):
weight = calculate_weight(i, 'weight')
weights.append(weight)
for i in range(BeerTrackerIndividual.genotype_class.rnn.
edge_chunks['hidden_gains']):
weight = calculate_weight(i, 'gain')
weights.append(weight)
for i in range(BeerTrackerIndividual.genotype_class.rnn.
edge_chunks['hidden_time_constants']):
weight = calculate_weight(i, 'time_constant')
weights.append(weight)
self.phenotype.set_weights(weights)