def create(genome, config):
        """ Receives a genome and returns its phenotype (a FeedForwardNetwork). """

        # Gather expressed connections.
        connections = [cg.key for cg in itervalues(genome.connections) if cg.enabled]

        layers = feed_forward_layers(config.genome_config.input_keys, config.genome_config.output_keys, connections)
        node_evals = []
        for layer in layers:
            for node in layer:
                inputs = []
                node_expr = [] # currently unused
                for conn_key in connections:
                    inode, onode = conn_key
                    if onode == node:
                        cg = genome.connections[conn_key]
                        inputs.append((inode, cg.weight))
                        node_expr.append("v[{}] * {:.7e}".format(inode, cg.weight))


                ng = genome.nodes[node]
                aggregation_function = config.genome_config.aggregation_function_defs.get(ng.aggregation)
                activation_function = config.genome_config.activation_defs.get(ng.activation)
                node_evals.append((node, activation_function, aggregation_function, ng.bias, ng.response, inputs))

        return FeedForwardNetwork(config.genome_config.input_keys, config.genome_config.output_keys, node_evals)
def create ( genome , config ) : connections = [ cg . key for cg in itervalues ( genome . connections ) if cg . enabled ] layers = feed_forward_layers ( config . genome_config . input_keys , config . genome_config . output_keys , connections ) node_evals = [ ] for layer in layers : for node in layer : inputs = [ ] node_expr = [ ] for conn_key in connections : inode , onode = conn_key if onode == node : cg = genome . connections [ conn_key ] inputs . append ( ( inode , cg . weight ) ) node_expr . append ( "v[{}] * {:.7e}" . format ( inode , cg . weight ) ) ng = genome . nodes [ node ] aggregation_function = config . genome_config . aggregation_function_defs . get ( ng . aggregation ) activation_function = config . genome_config . activation_defs . get ( ng . activation ) node_evals . append ( ( node , activation_function , aggregation_function , ng . bias , ng . response , inputs ) ) return FeedForwardNetwork ( config . genome_config . input_keys , config . genome_config . output_keys , node_evals )
Receives a genome and returns its phenotype (a FeedForwardNetwork).