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Code reformat for PEP style on examples
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@jr-garcia
jr-garcia committed Aug 2, 2016
1 parent 489581b commit 9e9a6f4
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Showing 3 changed files with 163 additions and 172 deletions.
189 changes: 93 additions & 96 deletions examples/TestESHyperNEAT_xor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,96 +13,95 @@

from concurrent.futures import ProcessPoolExecutor, as_completed


params = NEAT.Parameters()
params.PopulationSize = 200;

params.DynamicCompatibility = True;
params.CompatTreshold = 2.0;
params.YoungAgeTreshold = 15;
params.SpeciesMaxStagnation = 100;
params.OldAgeTreshold = 35;
params.MinSpecies = 5;
params.MaxSpecies = 10;
params.RouletteWheelSelection = False;

params.MutateRemLinkProb = 0.02;
params.RecurrentProb = 0;
params.OverallMutationRate = 0.15;
params.MutateAddLinkProb = 0.08;
params.MutateAddNeuronProb = 0.01;
params.MutateWeightsProb = 0.90;
params.MaxWeight = 8.0;
params.WeightMutationMaxPower = 0.2;
params.WeightReplacementMaxPower = 1.0;

params.MutateActivationAProb = 0.0;
params.ActivationAMutationMaxPower = 0.5;
params.MinActivationA = 0.05;
params.MaxActivationA = 6.0;

params.MutateNeuronActivationTypeProb = 0.03;

params.ActivationFunction_SignedSigmoid_Prob = 0.0;
params.ActivationFunction_UnsignedSigmoid_Prob = 0.0;
params.ActivationFunction_Tanh_Prob = 1.0;
params.ActivationFunction_TanhCubic_Prob = 0.0;
params.ActivationFunction_SignedStep_Prob = 1.0;
params.ActivationFunction_UnsignedStep_Prob = 0.0;
params.ActivationFunction_SignedGauss_Prob = 1.0;
params.ActivationFunction_UnsignedGauss_Prob = 0.0;
params.ActivationFunction_Abs_Prob = 0.0;
params.ActivationFunction_SignedSine_Prob = 1.0;
params.ActivationFunction_UnsignedSine_Prob = 0.0;
params.ActivationFunction_Linear_Prob = 1.0;

params.DivisionThreshold = 0.5;
params.VarianceThreshold = 0.03;
params.BandThreshold = 0.3;
params.InitialDepth = 2;
params.MaxDepth = 3;
params.IterationLevel = 1;
params.Leo = False;
params.GeometrySeed = False;
params.LeoSeed = False;
params.LeoThreshold = 0.3;
params.CPPN_Bias = -1.0;
params.Qtree_X = 0.0;
params.Qtree_Y = 0.0;
params.Width = 1.;
params.Height = 1.;
params.Elitism = 0.1;
params.PopulationSize = 200

params.DynamicCompatibility = True
params.CompatTreshold = 2.0
params.YoungAgeTreshold = 15
params.SpeciesMaxStagnation = 100
params.OldAgeTreshold = 35
params.MinSpecies = 5
params.MaxSpecies = 10
params.RouletteWheelSelection = False

params.MutateRemLinkProb = 0.02
params.RecurrentProb = 0
params.OverallMutationRate = 0.15
params.MutateAddLinkProb = 0.08
params.MutateAddNeuronProb = 0.01
params.MutateWeightsProb = 0.90
params.MaxWeight = 8.0
params.WeightMutationMaxPower = 0.2
params.WeightReplacementMaxPower = 1.0

params.MutateActivationAProb = 0.0
params.ActivationAMutationMaxPower = 0.5
params.MinActivationA = 0.05
params.MaxActivationA = 6.0

params.MutateNeuronActivationTypeProb = 0.03

params.ActivationFunction_SignedSigmoid_Prob = 0.0
params.ActivationFunction_UnsignedSigmoid_Prob = 0.0
params.ActivationFunction_Tanh_Prob = 1.0
params.ActivationFunction_TanhCubic_Prob = 0.0
params.ActivationFunction_SignedStep_Prob = 1.0
params.ActivationFunction_UnsignedStep_Prob = 0.0
params.ActivationFunction_SignedGauss_Prob = 1.0
params.ActivationFunction_UnsignedGauss_Prob = 0.0
params.ActivationFunction_Abs_Prob = 0.0
params.ActivationFunction_SignedSine_Prob = 1.0
params.ActivationFunction_UnsignedSine_Prob = 0.0
params.ActivationFunction_Linear_Prob = 1.0

params.DivisionThreshold = 0.5
params.VarianceThreshold = 0.03
params.BandThreshold = 0.3
params.InitialDepth = 2
params.MaxDepth = 3
params.IterationLevel = 1
params.Leo = False
params.GeometrySeed = False
params.LeoSeed = False
params.LeoThreshold = 0.3
params.CPPN_Bias = -1.0
params.Qtree_X = 0.0
params.Qtree_Y = 0.0
params.Width = 1.
params.Height = 1.
params.Elitism = 0.1

rng = NEAT.RNG()
rng.TimeSeed()

substrate = NEAT.Substrate([(-1., -1., 0.0), (1., -1., 0.0), (0., -1., 0.0)],
[],
[(0., 1., 0.0)])

substrate.m_allow_input_hidden_links = False;
substrate.m_allow_input_output_links = False;
substrate.m_allow_hidden_hidden_links = False;
substrate.m_allow_hidden_output_links = False;
substrate.m_allow_output_hidden_links = False;
substrate.m_allow_output_output_links = False;
substrate.m_allow_looped_hidden_links = False;
substrate.m_allow_looped_output_links = False;
substrate.m_allow_input_hidden_links = False
substrate.m_allow_input_output_links = False
substrate.m_allow_hidden_hidden_links = False
substrate.m_allow_hidden_output_links = False
substrate.m_allow_output_hidden_links = False
substrate.m_allow_output_output_links = False
substrate.m_allow_looped_hidden_links = False
substrate.m_allow_looped_output_links = False

substrate.m_allow_input_hidden_links = True;
substrate.m_allow_input_output_links = False;
substrate.m_allow_hidden_output_links = True;
substrate.m_allow_hidden_hidden_links = False;
substrate.m_allow_input_hidden_links = True
substrate.m_allow_input_output_links = False
substrate.m_allow_hidden_output_links = True
substrate.m_allow_hidden_hidden_links = False

substrate.m_hidden_nodes_activation = NEAT.ActivationFunction.SIGNED_SIGMOID;
substrate.m_output_nodes_activation = NEAT.ActivationFunction.UNSIGNED_SIGMOID;
substrate.m_hidden_nodes_activation = NEAT.ActivationFunction.SIGNED_SIGMOID
substrate.m_output_nodes_activation = NEAT.ActivationFunction.UNSIGNED_SIGMOID

substrate.m_with_distance = False;
substrate.m_with_distance = False

substrate.m_max_weight_and_bias = 8.0;
substrate.m_max_weight_and_bias = 8.0

def evaluate_xor(genome):

def evaluate_xor(genome):
net = NEAT.NeuralNetwork()

try:
Expand All @@ -114,45 +113,44 @@ def evaluate_xor(genome):

net.Flush()

net.Input([1,0,1])
net.Input([1, 0, 1])
[net.Activate() for _ in range(depth)]
o = net.Output()
error += abs(o[0] - 1)
if o[0] > 0.75:
correct +=1.
correct += 1.

net.Flush()
net.Input([0,1,1])
net.Input([0, 1, 1])
[net.Activate() for _ in range(depth)]
o = net.Output()
error += abs(o[0] - 1)
if o[0] > 0.75:
correct +=1.
correct += 1.

net.Flush()
net.Input([1,1,1])
net.Input([1, 1, 1])
[net.Activate() for _ in range(depth)]
o = net.Output()
error += abs(o[0] - 0)
if o[0] < 0.25:
correct +=1.
correct += 1.

net.Flush()
net.Input([0,0,1])
net.Input([0, 0, 1])
[net.Activate() for _ in range(depth)]
o = net.Output()
error += abs(o[0] - 0)
if o[0] < 0.25:
correct +=1.
correct += 1.

return (4 - error)**2
return (4 - error) ** 2

except Exception as ex:
print('Exception:', ex)
return 0.0



def getbest(run):
g = NEAT.Genome(0,
substrate.GetMinCPPNInputs(),
Expand All @@ -166,41 +164,41 @@ def getbest(run):

pop = NEAT.Population(g, params, True, 1.0, run)
for generation in range(1000):
#Evaluate genomes
# Evaluate genomes
genome_list = NEAT.GetGenomeList(pop)

fitnesses = EvaluateGenomeList_Serial(genome_list, evaluate_xor, display=False)
[genome.SetFitness(fitness) for genome, fitness in zip(genome_list, fitnesses)]

print('Gen: %d Best: %3.5f' % (generation, max(fitnesses)))

# Print best fitness
#print("---------------------------")
#print("Generation: ", generation)
#print("max ", max([x.GetLeader().GetFitness() for x in pop.Species]))
# print("---------------------------")
# print("Generation: ", generation)
# print("max ", max([x.GetLeader().GetFitness() for x in pop.Species]))


# Visualize best network's Genome

net = NEAT.NeuralNetwork()
pop.Species[0].GetLeader().BuildPhenotype(net)
img = np.zeros((500, 500, 3), dtype=np.uint8)
img += 10
NEAT.DrawPhenotype(img, (0, 0, 500, 500), net )
NEAT.DrawPhenotype(img, (0, 0, 500, 500), net)
cv2.imshow("CPPN", img)
# Visualize best network's Pheotype
net = NEAT.NeuralNetwork()
pop.Species[0].GetLeader().BuildESHyperNEATPhenotype(net, substrate, params)
img = np.zeros((500, 500, 3), dtype=np.uint8)
img += 10

NEAT.DrawPhenotype(img, (0, 0, 500, 500), net, substrate=True )
NEAT.DrawPhenotype(img, (0, 0, 500, 500), net, substrate=True)
cv2.imshow("NN", img)
cv2.waitKey(1)

if max(fitnesses) > 15.0:
break

# Epoch
generations = generation
pop.Epoch()
Expand All @@ -217,4 +215,3 @@ def getbest(run):

print('All:', gens)
print('Average:', avg_gens)

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