test_multineat.py

from __future__ import absolute_import
import unittest
import MultiNEAT as NEAT
import settings


class TestMultiNeat(unittest.TestCase):
    def evaluate(self, genome):
        # this creates a neural network (phenotype) from the genome
        net = NEAT.NeuralNetwork()
        genome.BuildPhenotype(net)

        # let's input just one pattern to the net, activate it once and get the output
        # The last input is always used as bias and also when you activate the network always set
        # the last input to 1.0
        net.Input([1.0, 0.0, 1.0])
        net.Activate()
        output = net.Output()

        if settings.VERBOSE:
            s = ''
            for node in output:
                s += str(node) + '   '
            print(s)

        # the output can be used as any other Python iterable. For the purposes of the tutorial,
        # we will consider the fitness of the individual to be the neural network that outputs
        # constantly 0.0 from the first output (the second output is ignored)

        fitness = 1.0 - output[0]
        return fitness

    def test_multi_neat(self):
        params = NEAT.Parameters()
        params.PopulationSize = 100
        genome = NEAT.Genome(
            0,  # ID
            3,  # number of inputs. Note: always add one extra input, for bias
            0,  # number of hidden nodes
            2,  # number of outputs
            False,  # FS_NEAT; auto-determine an appropriate set of inputs for the evolved networks
            NEAT.ActivationFunction.UNSIGNED_SIGMOID,  # OutputActType
            NEAT.ActivationFunction.UNSIGNED_SIGMOID,  # HiddenActType
            0,  # SeedType
            params  # Parameters
        )
        seed = 42
        pop = NEAT.Population(
            genome,
            params,
            True,  # whether the population should be randomized
            1.0,  # how much the population should be randomized,
            seed
        )

        for generation in range(3):
            # retrieve a list of all genomes in the population
            genome_list = NEAT.GetGenomeList(pop)

            # apply the evaluation function to all genomes
            for genome in genome_list:
                fitness = self.evaluate(genome)
                genome.SetFitness(fitness)

            # advance to the next generation
            pop.Epoch()


if __name__ == '__main__':
    unittest.main()