def check_add_connection(genome_type, feed_forward):
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'test_configuration'))
config.input_nodes = 3
config.output_nodes = 4
config.hidden_nodes = 5
config.feedforward = feed_forward
N = config.input_nodes + config.hidden_nodes + config.output_nodes
connections = {}
for a in range(100):
g = genome_type.create_unconnected(a, config)
g.add_hidden_nodes(config.hidden_nodes)
for b in range(1000):
g.mutate_add_connection()
for c in g.conn_genes.values():
connections[c.key] = connections.get(c.key, 0) + 1
# TODO: The connections should be returned to the caller and checked
# against the constraints/assumptions particular to the network type.
for i in range(N):
values = []
for j in range(N):
values.append(connections.get((i, j), 0))
print("{0:2d}: {1}".format(i, " ".join("{0:3d}".format(x) for x in values)))
def test_config_options():
# sample fitness function
def eval_fitness(population):
for individual in population:
individual.fitness = 1.0
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'test_configuration'))
for hn in (0, 1, 2):
config.hidden_nodes = hn
for fc in (0, 1):
config.fully_connected = fc
for act in activation_functions.functions.keys():
config.allowed_activation = [act]
for ff in (0, 1):
config.feedforward = ff
pop = Population(config)
pop.run(eval_fitness, 250)
def test_config_options():
# sample fitness function
def eval_fitness(population):
for individual in population:
individual.fitness = 1.0
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'test_configuration'))
for hn in (0, 1, 2):
config.hidden_nodes = hn
for fc in (0, 1):
config.fully_connected = fc
for act in activation_functions.functions.keys():
config.allowed_activation = [act]
for ff in (0, 1):
config.feedforward = ff
pop = Population(config)
pop.run(eval_fitness, 250)
return pop.statistics, out
# return winner.fitness, out
def evolve_cppn(config, pattern, generations):
return evolve(config, express_cppn, pattern, generations)
def evolve_recurrent_cppn(config, pattern, max_steps, generations):
def eval_func(net, w, h):
return express_recurrent_cppn(net, w, h, max_steps)
return evolve(config, eval_func, pattern, generations)
if __name__ == '__main__':
import numpy as np
from neat.config import Config # Python neat library
from neat import nn
import patterns
target = patterns.checkerboard(16, 16, 4)
config = Config('config.txt')
config.pop_size = 50
config.hidden_nodes = 0
config.initial_connection = 'fully_connected'
config.input_nodes = 7
evolve_recurrent_cppn(config, target, 16, 50)