def test_stabilise():
# params = Parameters(seed=3, input_type=InputType.PULSE, cis_count=3,
params = Parameters(seed=5, input_type=InputType.PULSE, cis_count=3,
cue_channels=2, reg_channels=4, out_channels=1)
world = World(params)
factory = Factory(world)
target = DefaultTarget(world, fitness_func2,
scoring_method=ScoringMethod.EXPONENTIAL_VEC,
strength=.2)
ntarget = NoisyTarget(world, fitness_func2,
scoring_method=ScoringMethod.EXPONENTIAL_VEC,
strength=.2, perturb_count=1, perturb_prop=.2)
select = SelectionModel(world)
pop = Population(factory, 5000)
while 1:
pop.assess(ntarget)
w, b = pop.worst_and_best()
print b
if b == 1.0:
break
pop.select(select)
pop.mutate(.002)
# Get a network!
n = pop.get_best()[0]
print '-->', target.assess(n)
print '-->', ntarget.assess(n)
# Get an attractor state
orig = n.attractors[2][0]
perturbed = orig.copy()
# Flip it out of that state
# See if it goes back in to the attractor
perturbed.flip(5)
trans, attr, rates = n.stabilise(perturbed)
assert orig != attr[0]
# Push it HARDER
perturbed.flip(6)
trans, attr, rates = n.stabilise(perturbed)
assert orig != attr[0]
def test_xor():
"""We should be able to find xor!"""
params = Parameters(seed=2, cis_count=2, cue_channels=2, reg_channels=1,
out_channels=1)
world = World(params)
factory = Factory(world)
target = DefaultTarget(world, fitness_func1)
select = SelectionModel(world)
pop = Population(factory, 10000)
while 1:
pop.assess(target)
pop.select(select)
fits = pop.fitnesses
max_fit = max(fits)
if max_fit == 1.0:
break
pop.mutate(.05)
def make_initial_population(self, replicate, factory, size):
p = Population(factory)
n = factory.create_network()
p.fill(n, size)
return p
