Add basic structure

connectiongene.py

@@ -0,0 +1,10 @@
+class ConnectionGene:
+    def __init__(self, into: int, out: int, innovation: int, weight: float, enabled: bool):
+        self.into: int = into
+        self.out: int = out
+        self.innovation: int = innovation
+        self.weight: float = weight
+        self.enabled: bool = enabled
+
+    def copy(self) -> 'ConnectionGene':
+        return ConnectionGene(self.into, self.out, self.innovation, self.weight, self.enabled)

genome.py

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+import math
+from typing import List
+
+from connectiongene import ConnectionGene
+
+
+class Genome:
+    def __init__(self, genome: 'Genome' = None):
+        self.fitness: float = 0
+        self.adjusted_fitness: float = 0
+        self.connection_gene_list: List[ConnectionGene] = []
+        if genome is not None:
+            self.fitness: float = genome.fitness
+            self.adjusted_fitness: float = genome.adjusted_fitness
+            self.connection_gene_list: List[ConnectionGene] = genome.connection_gene_list
+
+    @staticmethod
+    def cross_over(parent1: 'Genome', parent2: 'Genome') -> 'Genome':
+        pass
+
+    @staticmethod
+    def is_same_species(genome1: 'Genome', genome2: 'Genome') -> 'Genome':
+        pass
+
+    def generate_network(self):
+        pass
+
+    def evaluate_network(self, inputs: List[float]) -> List[float]:
+        pass
+
+    @staticmethod
+    def sigmoid(x: float) -> float:
+        return 1 / (1 + math.exp(-x))
+
+    def mutate(self):
+        pass
+
+    def mutate_weight(self):
+        pass
+
+    def mutate_add_connection(self):
+        pass
+
+    def mutate_add_node(self):
+        pass
+
+    def mutate_enable(self):
+        pass
+
+    def mutate_disable(self):
+        pass

innovationcounter.py

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+class InnovationCounter:
+    innovation: int = 0
+
+    @staticmethod
+    def new_innovation():
+        InnovationCounter.innovation += 1
+        return InnovationCounter.innovation

neatconfig.py

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+POPULATION: int = 300

nodegene.py

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+from typing import List
+
+from connectiongene import ConnectionGene
+
+
+class NodeGene:
+    def __init__(self):
+        self.value: float = 0
+        self.incoming: List[ConnectionGene] = []

pool.py

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+from typing import List, Callable
+
+from genome import Genome
+from neatconfig import *
+from species import Species
+
+
+class Pool:
+    def __init__(self):
+        self.species: List[Species] = []
+        for _ in range(POPULATION):
+            self.add_to_species(Genome())
+
+    def add_to_species(self, genome: Genome):
+        pass
+
+    def evaluate_fitness(self, d: Callable[[Genome], float]):
+        pass
+
+    def get_top_genome(self) -> Genome:
+        pass
+
+    def calculate_global_adjusted_fitness(self) -> float:
+        pass
+
+    def remove_weak_gnomes_from_species(self):
+        pass
+
+    def calculate_gnome_adjusted_fitness(self) -> float:
+        pass
+
+    def breed_new_generation(self):
+        pass

species.py

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+from typing import List
+
+from genome import Genome
+
+
+class Species:
+    def __init__(self, genome: Genome = None):
+        self.genomes: List[Genome] = []
+        if genome is not None:
+            self.genomes.append(genome)
+
+    def calculate_genome_adjusted_fitness(self):
+        pass
+
+    def get_total_adjusted_fitness(self) -> float:
+        pass
+
+    def remove_weak_genomes(self):
+        pass
+
+    def get_top_genome(self) -> Genome:
+        pass
+
+    def breed_child(self) -> Genome:
+        pass