def test_init_1(self):
print("\n\ntest_init_1.1")
generation = Generation()
genome = Genome(generation=generation, input_nodes=2, output_nodes=2)
for con in genome.connections():
print(con)
for node in genome.nodes():
print(node)
print('-' * 40)
for node in generation.nodes():
print(node)
print('-' * 60)
viz.construct(genome, 'first')
print("\n\ntest_init_1.2")
genome_1 = Genome(generation, 2, 2)
for con in genome_1.connections():
print(con)
for node in genome_1.nodes():
print(node)
print('-' * 40)
for node in generation.nodes():
print(node)
viz.construct(genome_1, 'second')
print('-' * 20 + 'Mutations')
for mutation in generation.mutations():
print(mutation)
def test_evaluate(self):
generation = Generation()
generation.step(solve_task,
tmp_reward,
first=True,
input_neurons=2,
output_neurons=1,
input=[0, 1])
for i, org in enumerate(generation.organisms()):
viz.construct(org.genome(), f'Organism {i}')
print(org.score())
def test_add_connection(self):
print('\n\ntest_add_connection\nBEFORE 0')
generation = Generation()
genome = Genome(generation, 2, 1)
for con in genome.connections():
print(con)
viz.construct(genome, 'before_0')
genome._add_node()
genome._add_connection()
genome._add_connection()
genome._add_connection()
print('\nAFTER 0\n')
for con in genome.connections():
print(con)
viz.construct(genome, 'after_0')
def test_init_2(self):
generation = Generation()
genome = Genome(generation=generation, input_nodes=1, output_nodes=1)
genome._add_node()
new_generation = Generation(_copy=True, prev_generation=generation)
genome_copied = Genome(generation=new_generation,
_copy=True,
genome_to_copy=genome)
for node in generation.nodes():
print(node)
print('-' * 20)
viz.construct(genome, 'first_1')
genome_copied._add_node()
for node in new_generation.nodes():
print(node)
viz.construct(genome_copied, 'first_copied_1')
def test_add_node(self):
print('-' * 20 + 'test_add_node\nBEFORE 0')
generation = Generation()
genome = Genome(generation, 2, 1)
for con in genome.connections():
print(con)
for node in genome.nodes():
print(node)
viz.construct(genome, 'before_0')
genome._add_node()
genome._add_node()
genome._add_node()
for node in generation.nodes():
print(node)
print('\nAFTER 0 \n')
for con in genome.connections():
print(con)
print('-' * 40)
for node in genome.nodes():
print(node)
viz.construct(genome, 'after_0')
print('\n\ntest_add_node\nBEFORE 1')
genome_1 = Genome(generation, 1, 1)
viz.construct(genome_1, 'before_1')
for con in genome_1.connections():
print(con)
for node in genome_1.nodes():
print(node)
genome_1._add_node()
genome_1._add_node()
genome_1._add_node()
genome_1._add_node()
print('\nAFTER 0 \n')
for con in genome_1.connections():
print(con)
for node in genome_1.nodes():
print(node)
viz.construct(genome_1, 'after_1')
def start_simulation(self, X_train, y_train, score):
sys.setrecursionlimit(2500)
new_gen = Generation()
print('Simulation started...')
i = 0
while i < number_of_generations:
prev_gen = new_gen
avg_fitness = prev_gen.step(reward_function=self._reward,
solve_task=self._solve_task,
X_train=X_train,
y_train=y_train)
org = prev_gen.ready(score)
if org is not None:
print('Done!')
print(org)
construct(org.genome(), 'The Best', view=True)
break
print(prev_gen.info())
prev_gen.visualize_species()
new_gen = prev_gen.reproduce(avg_fitness)
i += 1
def test_predict(self):
generation = Generation()
genome = Genome(generation=generation, input_nodes=2, output_nodes=2)
nn = NeuralNetwork(genome=genome)
construct(nn.genome(), 'Before')
nn.add_node()
construct(nn.genome(), '1 Node Added')
nn.add_node()
construct(nn.genome(), '2 Nodes Added')
nn.add_connection()
construct(nn.genome(), 'Connection Added')
print("CONNECTIONS")
for con in nn.genome().connections():
print(con)
print("CONNECTIONS")
for pred in nn.predict([2, 3]):
print(pred)
def test_excesses_and_disjoints(self):
generation = Generation()
genome_1 = Genome(generation, 1, 2)
genome_2 = genome_1.copy_genome()
viz.construct(genome_1, 'First Before')
genome_1._add_node()
genome_1._add_connection()
for con in genome_1.connections():
print(con)
viz.construct(genome_1, 'First')
genome_2._add_node()
viz.construct(genome_2, 'Second')
E, D = _excesses_disjoints(genome_1, genome_2)
print('-' * 20)
for con in genome_2.connections():
print(con)
print(f'Excesses: {E}')
print(f'Disjoints: {D}')
def test_offspring(self):
generation = Generation()
genome_1 = Genome(generation, 2, 2)
genome_2 = Genome(generation, 2, 2)
# for i in range(2):
# genome_1._add_connection()
# genome_2._add_connection()
# for i in range(1):
# genome_1._add_node()
# genome_2._add_node()
viz.construct(genome_1, 'Genome 1')
viz.construct(genome_2, 'Genome 2')
for con in genome_1.connections():
print(con)
print('-' * 40)
for con in genome_2.connections():
print(con)
genome = produce_offspring(generation, genome_1, genome_2)
print('-' * 40)
for node in genome.nodes():
print(node)
print('\n' + '-' * 40)
for con in genome.connections():
print(con)
viz.construct(genome, 'offspring')
E, D = _excesses_disjoints(genome_1, genome_2)
print('-' * 20)
print(f'Excesses: {E}')
print(f'Disjoints: {D}')
def visualize_species(self):
for s in self._species:
if not s.empty():
rep = random.choice(s.representatives())
construct(rep.genome(), str(s), view=False)