def test_easy_forward_propagation(self):
genome = GenomeMock([(2, 4, 0, True), (1, 3, 0, True), (2, 3, 0, True),
(1, 4, 0, True)], 2, 2)
nn = NeuralNetwork()
nn.generate_network(genome)
y = nn.forward([3, 22])
self.assertEqual(len(y), 2)
self.assertEqual(y, [0.5, 0.5])
genome = GenomeMock([(2, 4, 1, True), (1, 3, 0, True), (2, 3, 0, True),
(1, 4, 0, True)], 2, 2)
nn.generate_network(genome)
y = nn.forward([3, 22])
self.assertEqual(y[0], 0.5)
self.assertAlmostEqual(y[1], 0.9926085)
genome = GenomeMock([(2, 4, 1, True), (1, 3, 0, True), (2, 3, 0, True),
(1, 4, -2, True)], 2, 2)
nn.generate_network(genome)
y = nn.forward([3, 22])
self.assertEqual(y[0], 0.5)
self.assertAlmostEqual(y[1], 0.00739157)
genome = GenomeMock([(2, 4, 1, True), (1, 3, 0, False),
(2, 3, 0, False), (1, 4, -2, True)], 2, 2)
nn.generate_network(genome)
y = nn.forward([3, 22])
self.assertEqual(y[0], 0.5)
self.assertAlmostEqual(y[1], 0.00739157)
def test_input_neurons_have_input_signal(self):
genome = GenomeMock([(2, 4, 0, True), (1, 3, 0, True), (2, 3, 0, True),
(1, 4, 0, True)], 2, 2)
nn = NeuralNetwork()
nn.generate_network(genome)
nn.forward([3, 22])
self.assertEqual(nn._input_neurons[1]._input_signals, [3])
self.assertEqual(nn._input_neurons[2]._input_signals, [22])
def test_input_length_exception(self):
genome = GenomeMock([(2, 4, 0, True), (1, 3, 0, True), (2, 3, 0, True),
(1, 4, 0, True)], 2, 2)
nn = NeuralNetwork()
nn.generate_network(genome)
with self.assertRaises(Exception) as e:
nn.forward([1])
self.assertEqual(str(e.exception), "Expected 2 inputs, got 1 instead")
def test_hard_forward_propagation(self):
genome = GenomeMock([(1, 5, 3, True), (2, 5, -2, True),
(1, 6, -1, True), (5, 6, -3.4, True),
(3, 6, 4, True), (6, 4, 5, True)], 3, 1)
nn = NeuralNetwork()
nn.generate_network(genome)
y = nn.forward([0.2, 2, -0.02])
self.assertAlmostEqual(y[0], 0.5144, places=4)
genome = GenomeMock([(1, 5, 3, True),
(2, 5, -2, True), (1, 7, -1, True),
(5, 7, -3.4, True), (3, 7, 4, True),
(7, 6, 2, True), (6, 4, 0.3, True)], 3, 1)
nn.generate_network(genome)
y = nn.forward([0.2, 2, -0.02])
self.assertAlmostEqual(y[0], 0.6778, places=4)
def test_evolve_xor(self):
print("testing advanced xor")
Group._GROUP_ID = 0
Generation._GENERATION_ID = 0
specie = Group()
c1 = ConnectionGene(1, 3, enabled=True)
c2 = ConnectionGene(2, 3, enabled=True)
c3 = ConnectionGene(2, 3, enabled=True)
c4 = ConnectionGene(2, 3, enabled=True)
c5 = ConnectionGene(2, 3, enabled=True)
c6 = ConnectionGene(2, 3, enabled=True)
c7 = ConnectionGene(2, 3, enabled=True)
c8 = ConnectionGene(2, 3, enabled=True)
c9 = ConnectionGene(2, 3, enabled=True)
c10 = ConnectionGene(2, 3, enabled=True)
c11 = ConnectionGene(2, 3, enabled=True)
c12 = ConnectionGene(2, 3, enabled=True)
c13 = ConnectionGene(2, 3, enabled=True)
c14 = ConnectionGene(2, 3, enabled=True)
c15 = ConnectionGene(2, 3, enabled=True)
c16 = ConnectionGene(2, 3, enabled=True)
c17 = ConnectionGene(2, 3, enabled=True)
c18 = ConnectionGene(2, 3, enabled=True)
c19 = ConnectionGene(2, 3, enabled=True)
c20 = ConnectionGene(2, 3, enabled=True)
c21 = ConnectionGene(2, 3, enabled=True)
c22 = ConnectionGene(2, 3, enabled=True)
c23 = ConnectionGene(2, 3, enabled=True)
c24 = ConnectionGene(2, 3, enabled=True)
c25 = ConnectionGene(2, 3, enabled=True)
c26 = ConnectionGene(2, 3, enabled=True)
c27 = ConnectionGene(2, 3, enabled=True)
c28 = ConnectionGene(2, 3, enabled=True)
c29 = ConnectionGene(2, 3, enabled=True)
c30 = ConnectionGene(2, 3, enabled=True)
c31 = ConnectionGene(2, 3, enabled=True)
c31 = ConnectionGene(2, 3, enabled=True)
c33 = ConnectionGene(2, 3, enabled=True)
for i in range(10):
specie.add_genome(
Genome([
[1, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[1, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[1, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[1, 12,
random.normalvariate(mu=0.0, sigma=1.0), True],
[2, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[2, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[2, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[2, 12,
random.normalvariate(mu=0.0, sigma=1.0), True],
[3, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[3, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[3, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[3, 12,
random.normalvariate(mu=0.0, sigma=1.0), True],
[4, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[4, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[4, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[4, 12,
random.normalvariate(mu=0.0, sigma=1.0), True],
[5, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[5, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[5, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[5, 12,
random.normalvariate(mu=0.0, sigma=1.0), True],
[6, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[6, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[6, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[6, 12,
random.normalvariate(mu=0.0, sigma=1.0), True],
[7, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[7, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[7, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[7, 12,
random.normalvariate(mu=0.0, sigma=1.0), True],
[8, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[8, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[8, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[8, 12,
random.normalvariate(mu=0.0, sigma=1.0), True]
], 8, 4))
mutation_coefficients = {
'add_connection': 0.5,
'split_connection': 0.2,
'change_weight': 0.8,
'new_connection_abs_max_weight': 1.0,
'max_weight_mutation': 0.25
}
compatibility_coefficients = {
'excess_factor': 1.0,
'disjoint_factor': 1.0,
'weight_difference_factor': 0.5
}
log = Logger()
gen = Generation([specie],
mutation_coefficients=mutation_coefficients,
compatibility_coefficients=compatibility_coefficients,
logger=log)
i = 1
while i < 8:
print(i)
gen = gen.create_new_generation()
i += 1
best_nn = NeuralNetwork(Generation.best_genome)
print(best_nn.forward([0, 1, 1, 0, 0, 1, 1, 0]))
print(str(best_nn._genome.fitness) + "/" + str(256 * 4))
def test_simple_xor(self):
print("testing simple xor")
Group._GROUP_ID = 0
Generation._GENERATION_ID = 0
specie = Group()
for i in range(16):
for j in range(17, 21):
ConnectionGene(i + 1, j, enabled=True)
connection_list = []
z = 0
for i in range(16):
for j in range(17, 21):
connection_list.append([
i + 1, j,
random.normalvariate(mu=0.0, sigma=1.0), True, z
])
z += 1
print(connection_list)
for i in range(10):
specie.add_genome(
Genome(
[[1, 3,
random.normalvariate(mu=0.0, sigma=1.0), True, 0],
[2, 3,
random.normalvariate(mu=0.0, sigma=1.0), True, 1]], 2,
1))
mutation_coefficients = {
'add_connection': 0.5,
'split_connection': 0.2,
'change_weight': 0.8,
'new_connection_abs_max_weight': 1.0,
'max_weight_mutation': 0.5
}
compatibility_coefficients = {
'excess_factor': 1.0,
'disjoint_factor': 1.0,
'weight_difference_factor': 2.0
}
log = Logger()
gen = Generation([specie],
mutation_coefficients=mutation_coefficients,
compatibility_coefficients=compatibility_coefficients,
logger=log)
i = 1
while i < 150:
print(i)
gen = gen.create_new_generation()
i += 1
best_nn = NeuralNetwork(Generation.best_genome)
a = (best_nn.forward([0.0, 0.0]))
b = (best_nn.forward([0.0, 1.0]))
c = (best_nn.forward([1.0, 0.0]))
d = (best_nn.forward([1.0, 1.0]))
print(a)
print(b)
print(c)
print(d)
print(4.0 - (a[0] - 0)**2 - (b[0] - 1)**2 - (c[0] - 1)**2 -
(d[0] - 0)**2)
print(best_nn._genome.fitness)
groups_count = []
for generation_log in gen.logger.log.values():
groups_count.append(len(generation_log.groups_log))
plt.plot(list(gen.logger.log.keys()), groups_count)
plt.xlabel("Generation")
plt.ylabel("Number of groups")
plt.title("Groups amount change over evolution")
plt.savefig("plot of gen count")
plt.clf()
last_gen_groups_fitness = []
for fit in gen.logger.log[gen.id -
1].groups_fitness_scores_log.values():
last_gen_groups_fitness.append(fit[0][2])
plt.plot(list(gen.logger.log[gen.id - 1].groups_log.keys()),
last_gen_groups_fitness, 'ro')
plt.xlabel("Group")
plt.ylabel("Group adjusted fitness")
plt.title("Adjusted fitness of groups in last generation")
plt.savefig("plot of last gen fitness")
plt.clf()
plt.plot(list(Generation.best_fitnesses.keys()),
list(Generation.best_fitnesses.values()))
plt.xlabel("Generation")
plt.ylabel("Fitness score")
plt.title("Fitness score progression")
plt.savefig("plot of fitness")