phenotype.py

import jax
import jax.numpy as jnp
from jax import random
from jax.example_libraries.stax import Dense, Relu, serial

# Define a function to translate the genome into phenotype
def translate_genome(genome):
    # Perform some calculations using the genome to generate the phenotype
    phenotype = jnp.sum(genome) # Example calculation
    
    return phenotype

# Define a function to evaluate the fitness of an individual
def evaluate_fitness(genome):
    phenotype = translate_genome(genome)
    fitness = 1 / (1 + jnp.abs(phenotype - 10)) # Example fitness function
    return fitness


def genome_to_network_params(genome):
    # Extract the node and connection genes from the genome
    node_genes = genome.nodes
    connection_genes = genome.connections
    
    # Determine the number of input, output, and hidden nodes
    num_inputs = sum(1 for node_gene in node_genes if node_gene.node_type == 'input')
    num_outputs = sum(1 for node_gene in node_genes if node_gene.node_type == 'output')
    num_hidden = len(node_genes) - num_inputs - num_outputs
    
    # Initialize the weights
    if num_hidden > 0:
        input_weights_shape = (num_inputs, num_hidden)
        hidden_weights_shape = (num_hidden, num_hidden)
        output_weights_shape = (num_hidden, num_outputs)
    
        input_weights = jnp.zeros(input_weights_shape)
        hidden_weights = jnp.zeros(hidden_weights_shape)
        output_weights = jnp.zeros(output_weights_shape)
        
        params = {'input_weights': input_weights, 'hidden_weights': hidden_weights, 'output_weights': output_weights}

    else:
        weights_shape = (num_inputs, num_outputs)
        weights = jnp.zeros(weights_shape)
        params = {'weights': weights}
    
    # Set the weights based on the connection genes
    for connection_gene in connection_genes:
        if connection_gene.enabled:
            if node_genes[connection_gene.out_node].node_type == 'hidden':
                out_node_index = next((i for i, node_gene in enumerate(node_genes) if node_gene.node_id == connection_gene.out_node), None) - num_inputs
                in_node_index = next((i for i, node_gene in enumerate(node_genes) if node_gene.node_id == connection_gene.in_node), None)
                params['hidden_weights'][in_node_index, out_node_index] = connection_gene.weight
    
    return params

def activation(x):
        return jnp.tanh(x)

def forward(params, x):
     pass