def test_genome_crossover(self):
a = NEAT.Genome(1, 3, 0, 1, False,
NEAT.ActivationFunction.UNSIGNED_SIGMOID,
NEAT.ActivationFunction.UNSIGNED_SIGMOID, 0,
self._params, 0)
self._innov_db.Init_with_genome(a)
a.Mutate(False, NEAT.SearchMode.COMPLEXIFYING, self._innov_db,
self._params, self._rng)
b = NEAT.Genome(a)
b.SetID(2)
for i in range(10):
b.Mutate(False, NEAT.SearchMode.COMPLEXIFYING, self._innov_db,
self._params, self._rng)
# b.PrintAllTraits()
c = a.Mate(b, True, True, self._rng, self._params)
c.SetID(3)
self.assertEqual(c.GetID(), 3)
output_a = self.activate_network(a)
output_b = self.activate_network(b)
output_c = self.activate_network(c)
self.assertIsNotNone(output_c)
self.assertNotAlmostEqual(output_a, output_c)
self.assertNotAlmostEqual(output_b, output_c)
for i in range(10):
c.Mutate(False, NEAT.SearchMode.COMPLEXIFYING, self._innov_db,
self._params, self._rng)
output_c = self.activate_network(c)
self.assertIsNotNone(output_c)
self.assertNotAlmostEqual(output_a, output_c)
self.assertNotAlmostEqual(output_b, output_c)
def test_serialize_deserialize(self):
str_genome = self.g.Serialize()
cloned = multineat.Genome()
cloned.Deserialize(str_genome)
self.assertEqual(self.g.GetID(), cloned.GetID())
self.assertEqual(self.g.NumInputs(), cloned.NumInputs())
self.assertEqual(self.g.NumOutputs(), cloned.NumOutputs())
self.assertEqual(self.g.GetFitness(), cloned.GetFitness())
self.assertEqual(self.g.GetAdjFitness(), cloned.GetAdjFitness())
self.assertEqual(self.g.GetDepth(), cloned.GetDepth())
self.assertEqual(self.g.IsEvaluated(), cloned.IsEvaluated())
for i, n_gene in enumerate(self.g.NeuronGenes):
self.assertEqual(n_gene.A, cloned.NeuronGenes[i].A)
self.assertEqual(n_gene.B, cloned.NeuronGenes[i].B)
self.assertEqual(n_gene.TimeConstant, cloned.NeuronGenes[i].TimeConstant)
self.assertEqual(n_gene.Bias, cloned.NeuronGenes[i].Bias)
self.assertEqual(n_gene.ActFunction, cloned.NeuronGenes[i].ActFunction)
self.assertEqual(n_gene.Type, cloned.NeuronGenes[i].Type)
for i, l_gene in enumerate(self.g.LinkGenes):
self.assertEqual(l_gene.FromNeuronID, cloned.LinkGenes[i].FromNeuronID)
self.assertEqual(l_gene.ToNeuronID, cloned.LinkGenes[i].ToNeuronID)
self.assertEqual(l_gene.InnovationID, cloned.LinkGenes[i].InnovationID)
self.assertEqual(l_gene.Weight, cloned.LinkGenes[i].Weight)
def test_genome_clone(self):
a = NEAT.Genome(1, 3, 0, 1, False,
NEAT.ActivationFunction.UNSIGNED_SIGMOID,
NEAT.ActivationFunction.UNSIGNED_SIGMOID, 0,
self._params, 0)
self._innov_db.Init_with_genome(a)
a.Mutate(False, NEAT.SearchMode.COMPLEXIFYING, self._innov_db,
self._params, self._rng)
b = NEAT.Genome(a)
self.assertEqual(a.NumNeurons(), b.NumNeurons())
self.assertEqual(a.NumLinks(), b.NumLinks())
self.assertEqual(a.NumInputs(), b.NumInputs())
self.assertEqual(a.NumOutputs(), b.NumOutputs())
self.assertAlmostEqual(self.activate_network(a),
self.activate_network(b))
def test_genome(self):
a = NEAT.Genome(1, 3, 0, 1, False,
NEAT.ActivationFunction.UNSIGNED_SIGMOID,
NEAT.ActivationFunction.UNSIGNED_SIGMOID, 0,
self._params, 0)
# a.PrintAllTraits()
self.assertEqual(a.GetID(), 1)
self.assertIsNotNone(self.activate_network(a))
a.SetID(2)
self.assertEqual(a.GetID(), 2)
def setUp(self):
self._params = self.parameters()
self._net = multineat.NeuralNetwork()
self._rng = multineat.RNG()
self._rng.Seed(0)
self._innov_db = multineat.InnovationDatabase()
self.g = multineat.Genome(42, 3, 7, 1, False,
multineat.ActivationFunction.UNSIGNED_SIGMOID,
multineat.ActivationFunction.UNSIGNED_SIGMOID,
0, self._params, 0)
for i in range(100):
self.g.Mutate(False, multineat.SearchMode.COMPLEXIFYING, self._innov_db, self._params, self._rng)
def test_genome_mutate(self):
a = NEAT.Genome(1, 3, 0, 1, False,
NEAT.ActivationFunction.UNSIGNED_SIGMOID,
NEAT.ActivationFunction.UNSIGNED_SIGMOID, 0,
self._params, 0)
self._innov_db.Init_with_genome(a)
output_1 = self.activate_network(a)
for i in range(10):
a.Mutate(False, NEAT.SearchMode.COMPLEXIFYING, self._innov_db,
self._params, self._rng)
output_2 = self.activate_network(a)
# a.PrintAllTraits()
self.assertEqual(a.GetID(), 1)
self.assertIsNotNone(output_1)
self.assertNotAlmostEqual(output_1, output_2)
def getbest(i, seed=0):
g = NEAT.Genome(0, 3, 0, 1, False, NEAT.ActivationFunction.UNSIGNED_SIGMOID,
NEAT.ActivationFunction.UNSIGNED_SIGMOID, 0, params, 0)
pop = NEAT.Population(g, params, True, 1.0, seed)
pop.RNG.Seed(seed)
generations = 0
for generation in range(1000):
genome_list = NEAT.GetGenomeList(pop)
fitness_list = EvaluateGenomeList_Serial(genome_list, evaluate, display=False)
NEAT.ZipFitness(genome_list, fitness_list)
pop.Epoch()
generations = generation
best = max(fitness_list)
if best > 15.0:
break
return generations