Add genome implementation
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parent
10bc528353
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3 changed files with 154 additions and 13 deletions
145
genome.py
145
genome.py
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@ -1,7 +1,11 @@
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import math
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from typing import List
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import random
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from typing import List, Dict
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from connectiongene import ConnectionGene
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from innovationcounter import InnovationCounter
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from neatconfig import *
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from nodegene import NodeGene
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class Genome:
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@ -9,6 +13,7 @@ class Genome:
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self.fitness: float = 0
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self.adjusted_fitness: float = 0
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self.connection_gene_list: List[ConnectionGene] = []
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self.nodes: Dict[int, NodeGene] = {}
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if genome is not None:
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self.fitness: float = genome.fitness
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self.adjusted_fitness: float = genome.adjusted_fitness
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@ -16,36 +21,154 @@ class Genome:
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@staticmethod
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def cross_over(parent1: 'Genome', parent2: 'Genome') -> 'Genome':
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pass
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if parent1.fitness < parent2.fitness:
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parent1, parent2 = parent2, parent1
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gene_map1: Dict[int, ConnectionGene] = {}
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gene_map2: Dict[int, ConnectionGene] = {}
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for connection in parent1.connection_gene_list:
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gene_map1[connection.innovation] = connection
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for connection in parent2.connection_gene_list:
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gene_map2[connection.innovation] = connection
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child: Genome = Genome()
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for key in {*gene_map1, *gene_map2}:
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if key in gene_map1 and key in gene_map2:
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trait: ConnectionGene = random.choice([gene_map1, gene_map2])[key].copy()
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if (not gene_map1[key].enabled) or (not gene_map2[key].enabled) and random.random() < 0.75:
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trait.enabled = False
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elif parent1.fitness == parent2.fitness:
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trait: ConnectionGene = random.choice([gene_map1, gene_map2]).get(key, None)
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else:
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trait: ConnectionGene = gene_map1.get(key, None)
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if trait is not None:
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child.connection_gene_list.append(trait.copy())
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return child
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@staticmethod
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def is_same_species(genome1: 'Genome', genome2: 'Genome') -> bool:
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pass
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gene_map1: Dict[int, ConnectionGene] = {}
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gene_map2: Dict[int, ConnectionGene] = {}
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for connection in genome1.connection_gene_list:
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gene_map1[connection.innovation] = connection
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for connection in genome2.connection_gene_list:
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gene_map2[connection.innovation] = connection
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matching: int = 0
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disjoint: int = 0
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excess: int = 0
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weight: float = 0
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max_innovation: int = min(max([0, *gene_map1]), max([0, *gene_map2]))
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for key in {*gene_map1, *gene_map2}:
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if key in gene_map1 and key in gene_map2:
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matching += 1
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weight += abs(gene_map1[key].weight - gene_map2[key].weight)
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elif key <= max_innovation:
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disjoint += 1
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else:
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excess += 1
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n: int = matching + disjoint + excess
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if n:
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delta: float = EXCESS_COEFFICIENT * excess + DISJOINT_COEFFICIENT * disjoint
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delta /= n
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delta += WEIGHT_COEFFICIENT * weight / matching
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return delta < COMPATIBILITY_THRESHOLD
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return False
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def generate_network(self):
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pass
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self.nodes.clear()
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for i in range(INPUTS):
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self.nodes[i] = NodeGene(0)
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self.nodes[INPUTS] = NodeGene(1)
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for i in range(INPUTS + 1, INPUTS + 1 + OUTPUTS):
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self.nodes[i] = NodeGene(0)
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for connection in self.connection_gene_list:
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if connection.into not in self.nodes:
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self.nodes[connection.into] = NodeGene(0)
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if connection.out not in self.nodes:
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self.nodes[connection.out] = NodeGene(0)
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self.nodes[connection.out].incoming.append(connection)
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def evaluate_network(self, inputs: List[float]) -> List[float]:
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pass
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self.generate_network()
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for i in range(INPUTS):
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self.nodes[i].value = inputs[i]
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for i in sorted(self.nodes) + [*range(INPUTS + 1, INPUTS + 1 + OUTPUTS)]:
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if i < INPUTS + OUTPUTS + 1:
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continue
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self.nodes[i].value = Genome.sigmoid(
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sum(
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self.nodes[connection.into].value
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for connection in self.nodes[i].incoming
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)
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)
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return [self.nodes[i].value for i in range(INPUTS + 1, INPUTS + 1 + OUTPUTS)]
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@staticmethod
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def sigmoid(x: float) -> float:
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return 1 / (1 + math.exp(-x))
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def mutate(self):
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pass
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if random.random() < WEIGHT_MUTATION_CHANCE:
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self.mutate_weight()
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if random.random() < CONNECTION_MUTATION_CHANCE:
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self.mutate_add_connection()
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if random.random() < NODE_MUTATION_CHANCE:
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self.mutate_add_node()
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if random.random() < ENABLE_MUTATION_CHANCE:
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self.mutate_enable()
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if random.random() < DISABLE_MUTATION_CHANCE:
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self.mutate_disable()
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def mutate_weight(self):
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pass
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for connection in self.connection_gene_list:
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if random.random() < WEIGHT_CHANCE:
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if random.random() < PERTURB_CHANCE:
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connection.weight += (2 * random.random() - 1) * STEPS
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else:
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connection.weight = 4 * random.random() - 2
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def mutate_add_connection(self):
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pass
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random1: int = random.choice(lambda i: i < INPUTS + 1 or i >= INPUTS + 1 + OUTPUTS, self.nodes)
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random2: int = random.choice(lambda i: i >= INPUTS + 1, self.nodes)
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if random1 >= random2:
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return
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if any(connection.into == random1 and connection.out == random2 for connection in self.connection_gene_list):
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return
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self.connection_gene_list.append(
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ConnectionGene(random1, random2, InnovationCounter.new_innovation(), 4 * random.random() - 2, True)
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)
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def mutate_add_node(self):
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pass
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enabled: List[ConnectionGene] = filter(lambda con: con.enabled, self.connection_gene_list)
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if not enabled:
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return
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self.generate_network()
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connection: ConnectionGene = random.choice(enabled)
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connection.enabled = False
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next_node: int = max(self.nodes) + 1
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self.connection_gene_list.append(
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ConnectionGene(connection.into, next_node, InnovationCounter.new_innovation(), 1, True)
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)
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self.connection_gene_list.append(
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ConnectionGene(next_node, connection.out, InnovationCounter.new_innovation(), connection.weight, True)
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)
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def mutate_enable(self):
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pass
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if self.connection_gene_list:
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connection: ConnectionGene = random.choice(self.connection_gene_list)
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connection.enabled = True
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def mutate_disable(self):
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pass
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if self.connection_gene_list:
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connection: ConnectionGene = random.choice(self.connection_gene_list)
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connection.enabled = False
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@ -1,2 +1,20 @@
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INPUTS: int = 2
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OUTPUTS: int = 1
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HIDDEN: int = 1000000
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POPULATION: int = 300
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COMPATIBILITY_THRESHOLD: float = 1
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EXCESS_COEFFICIENT: float = 2
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DISJOINT_COEFFICIENT: float = 2
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WEIGHT_COEFFICIENT: float = 0.4
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STEPS: float = 0.1
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PERTURB_CHANCE: float = 0.9
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WEIGHT_CHANCE: float = 0.3
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WEIGHT_MUTATION_CHANCE: float = 0.9
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NODE_MUTATION_CHANCE: float = 0.03
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CONNECTION_MUTATION_CHANCE: float = 0.05
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DISABLE_MUTATION_CHANCE: float = 0.1
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ENABLE_MUTATION_CHANCE: float = 0.2
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CROSSOVER_CHANCE: float = 0.75
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@ -4,6 +4,6 @@ from connectiongene import ConnectionGene
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class NodeGene:
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def __init__(self):
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self.value: float = 0
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def __init__(self, value: float):
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self.value: float = value
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self.incoming: List[ConnectionGene] = []
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