示例#1
0
def generate_genome_with_hidden_units(n_input, n_output, n_hidden=3):
    # nodes
    node_genes = {}
    for i in range(n_output + n_hidden):
        node_genes = add_node(node_genes, key=i)

    # connections
    # input to hidden
    connection_genes = {}
    input_hidden_tuples = list(
        product(list(range(-1, -n_input - 1, -1)),
                list(range(n_output, n_output + n_hidden))))
    for tuple_ in input_hidden_tuples:
        connection_genes = add_connection(connection_genes, key=tuple_)

    # hidden to output
    hidden_output_tuples = list(
        product(list(range(n_output, n_output + n_hidden)),
                list(range(0, n_output))))
    for tuple_ in hidden_output_tuples:
        connection_genes = add_connection(connection_genes, key=tuple_)

    # initialize genome
    genome = Genome(key=1)
    genome.node_genes = node_genes
    genome.connection_genes = connection_genes
    return genome
示例#2
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def generate_genome_given_graph(graph, connection_weights):
    genome = Genome(key='foo')

    unique_node_keys = []
    input_nodes = []
    for connection in graph:
        for node_key in connection:
            if node_key not in unique_node_keys:
                unique_node_keys.append(node_key)

            if node_key < 0:
                input_nodes.append(node_key)
    input_nodes = set(input_nodes)

    unique_node_keys = list(
        set(unique_node_keys + genome.get_output_nodes_keys()) - input_nodes)
    nodes = {}
    for node_key in unique_node_keys:
        node = NodeGene(key=node_key).random_initialization()
        node.set_mean(0)
        node.set_std(STD)
        nodes[node_key] = node

    connections = {}
    for connection_key, weight in zip(graph, connection_weights):
        connection = ConnectionGene(key=connection_key)
        connection.set_mean(weight)
        connection.set_std(STD)
        connections[connection_key] = connection

    genome.connection_genes = connections
    genome.node_genes = nodes
    return genome
示例#3
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def generate_genome_without_hidden_units():
    # output nodes
    node_genes = {}
    node_genes = add_node(node_genes, key=0)

    connection_genes = {}
    connection_genes = add_connection(connection_genes, key=(-1, 0))
    connection_genes = add_connection(connection_genes, key=(-2, 0))

    genome = Genome(key=1)
    genome.node_genes = node_genes
    genome.connection_genes = connection_genes
    return genome