Exemple #1
0
def cross_gene_sequences(gene_a, gene_b):
    atomised_a = gene_a.strip().split()
    atomised_b = gene_b.strip().split()

    new_action = random.choice([atomised_a.pop(0), atomised_b.pop(0)])

    func_a = parse_func(atomised_a)
    func_b = parse_func(atomised_b)

    node_a = random.choice(func_a.as_list())
    node_b = random.choice(func_b.as_list())

    if node_a.parent is None:
        return new_action + " " + str(func_b)
    elif node_a.parent.left == node_a:
        node_a.parent.left = node_b
    else:
        node_a.parent.right = node_b

    root = node_a.parent

    while root.parent is not None:
        root = root.parent

    return new_action + " " + str(root)
Exemple #2
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def cross_gene_sequences(gene_a, gene_b):
    atomised_a = gene_a.strip().split()
    atomised_b = gene_b.strip().split()

    new_action = random.choice([atomised_a.pop(0), atomised_b.pop(0)])

    func_a = parse_func(atomised_a)
    func_b = parse_func(atomised_b)

    node_a = random.choice(func_a.as_list())
    node_b = random.choice(func_b.as_list())

    if node_a.parent is None:
        return new_action + " " + str(func_b)
    elif node_a.parent.left == node_a:
        node_a.parent.left = node_b
    else:
        node_a.parent.right = node_b

    root = node_a.parent

    while root.parent is not None:
        root = root.parent

    return new_action + " " + str(root)
Exemple #3
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    def __init__(self, meta, sequence):

        # Set up first the metagenome
        meta_genes = [float(g) for g in meta.strip().split()]

        self.colors = [
            meta_genes[i:i + 3] + [1.0]
            for i in range(*Genome.META_INDICES["colors"], 3)
        ]

        self.fuzziness = Genome.meta_item(meta_genes, "fuzziness")
        self.const_bounds = Genome.meta_item(meta_genes, "const_bounds")
        self.fun_gen_depth = Genome.meta_item(meta_genes, "fun_gen_depth")
        self.incr_range = Genome.meta_item(meta_genes, "incr_range")
        self.mult_range = Genome.meta_item(meta_genes, "mult_range")

        self.mute_rates = {}
        self.mute_rates["mute"] = Genome.meta_item(meta_genes, "mute")
        self.mute_rates["genome"] = Genome.meta_item(meta_genes, "genome")
        self.mute_rates["gene_action"] = Genome.meta_item(
            meta_genes, "gene_action")
        self.mute_rates["struct_mod"] = Genome.meta_item(
            meta_genes, "struct_mod")
        self.mute_rates["leaf_type"] = Genome.meta_item(
            meta_genes, "leaf_type")

        self.mute_rates["genome_rel"] = dict(
            zip(list(GenomeMutes), Genome.meta_item(meta_genes, "genome_rel")))
        self.mute_rates["const_rel"] = dict(
            zip(list(ConstMutes), Genome.meta_item(meta_genes, "const_rel")))
        self.mute_rates["leaf_rel"] = dict(
            zip(list(RefType), Genome.meta_item(meta_genes, "leaf_rel")))
        self.mute_rates["enum_rel"] = dict(
            zip(list(EnumMutes), Genome.meta_item(meta_genes, "enum_rel")))
        self.mute_rates["struct_rel"] = dict(
            zip(list(StructMutes), Genome.meta_item(meta_genes, "struct_rel")))

        # Now handle the behavioural genes
        gene_sequences = [s.strip().split() for s in sequence.split("|")]
        self.genes = [Gene(None, None) for _ in range(len(gene_sequences))]

        for i in range(len(self.genes)):
            action = goomba.Action(int(gene_sequences[i].pop(0)))
            func = parse_func(gene_sequences[i])
            self.fuzzify(func)
            self.genes[i].action = action
            self.genes[i].function = func

        self.link()
Exemple #4
0
    def __init__(self, meta, sequence):

        # Set up first the metagenome
        meta_genes = [float(g) for g in meta.strip().split()]

        self.colors = [meta_genes[i:i+3] + [1.0] for i in range(*Genome.META_INDICES["colors"], 3)]

        self.fuzziness = Genome.meta_item(meta_genes, "fuzziness")
        self.const_bounds = Genome.meta_item(meta_genes, "const_bounds")
        self.fun_gen_depth = Genome.meta_item(meta_genes, "fun_gen_depth")
        self.incr_range = Genome.meta_item(meta_genes, "incr_range")
        self.mult_range = Genome.meta_item(meta_genes, "mult_range")

        self.mute_rates = {}
        self.mute_rates["mute"] = Genome.meta_item(meta_genes, "mute")
        self.mute_rates["genome"] = Genome.meta_item(meta_genes, "genome")
        self.mute_rates["gene_action"] = Genome.meta_item(meta_genes, "gene_action")
        self.mute_rates["struct_mod"] = Genome.meta_item(meta_genes, "struct_mod")
        self.mute_rates["leaf_type"] = Genome.meta_item(meta_genes, "leaf_type")

        self.mute_rates["genome_rel"] = dict(zip(list(GenomeMutes),
                                                 Genome.meta_item(meta_genes, "genome_rel")))
        self.mute_rates["const_rel"] = dict(zip(list(ConstMutes),
                                                Genome.meta_item(meta_genes, "const_rel")))
        self.mute_rates["leaf_rel"] = dict(zip(list(RefType),
                                               Genome.meta_item(meta_genes, "leaf_rel")))
        self.mute_rates["enum_rel"] = dict(zip(list(EnumMutes),
                                               Genome.meta_item(meta_genes, "enum_rel")))
        self.mute_rates["struct_rel"] = dict(zip(list(StructMutes),
                                                 Genome.meta_item(meta_genes, "struct_rel")))

        # Now handle the behavioural genes
        gene_sequences = [s.strip().split() for s in sequence.split("|")]
        self.genes = [Gene(None, None) for _ in range(len(gene_sequences))]

        for i in range(len(self.genes)):
            action = goomba.Action(int(gene_sequences[i].pop(0)))
            func = parse_func(gene_sequences[i])
            self.fuzzify(func)
            self.genes[i].action = action
            self.genes[i].function = func

        self.link()