182 lines
6.3 KiB
Python
182 lines
6.3 KiB
Python
"""
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Vectorization of genome representation.
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Utilizes Tuple[nodes: Array(N, 5), connections: Array(C, 4)] to encode the genome, where:
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nodes: [key, bias, response, act, agg]
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connections: [in_key, out_key, weight, enable]
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N: Maximum number of nodes in the network.
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C: Maximum number of connections in the network.
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"""
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from typing import Tuple, Dict
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import numpy as np
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from numpy.typing import NDArray
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from jax import jit, numpy as jnp
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from .utils import fetch_first
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def initialize_genomes(N: int, C: int, config: Dict) -> Tuple[NDArray, NDArray]:
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"""
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Initialize genomes with default values.
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Args:
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N (int): Maximum number of nodes in the network.
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C (int): Maximum number of connections in the network.
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config (Dict): Configuration dictionary.
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Returns:
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Tuple[NDArray, NDArray, NDArray, NDArray]: pop_nodes, pop_connections, input_idx, and output_idx arrays.
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"""
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# Reserve one row for potential mutation adding an extra node
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assert config['num_inputs'] + config['num_outputs'] + 1 <= N, \
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f"Too small N: {N} for input_size: {config['num_inputs']} and output_size: {config['num_inputs']}!"
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assert config['num_inputs'] * config['num_outputs'] + 1 <= C, \
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f"Too small C: {C} for input_size: {config['num_inputs']} and output_size: {config['num_outputs']}!"
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pop_nodes = np.full((config['pop_size'], N, 5), np.nan)
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pop_cons = np.full((config['pop_size'], C, 4), np.nan)
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input_idx = config['input_idx']
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output_idx = config['output_idx']
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pop_nodes[:, input_idx, 0] = input_idx
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pop_nodes[:, output_idx, 0] = output_idx
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# pop_nodes[:, output_idx, 1] = config['bias_init_mean']
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pop_nodes[:, output_idx, 1] = np.random.normal(loc=config['bias_init_mean'], scale=config['bias_init_std'],
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size=(config['pop_size'], 1))
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pop_nodes[:, output_idx, 2] = np.random.normal(loc=config['response_init_mean'], scale=config['response_init_std'],
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size=(config['pop_size'], 1))
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pop_nodes[:, output_idx, 3] = np.random.choice(config['activation_options'], size=(config['pop_size'], 1))
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pop_nodes[:, output_idx, 4] = np.random.choice(config['aggregation_options'], size=(config['pop_size'], 1))
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grid_a, grid_b = np.meshgrid(input_idx, output_idx)
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grid_a, grid_b = grid_a.flatten(), grid_b.flatten()
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p = config['num_inputs'] * config['num_outputs']
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pop_cons[:, :p, 0] = grid_a
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pop_cons[:, :p, 1] = grid_b
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pop_cons[:, :p, 2] = np.random.normal(loc=config['weight_init_mean'], scale=config['weight_init_std'],
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size=(config['pop_size'], p))
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pop_cons[:, :p, 3] = 1
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return pop_nodes, pop_cons
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def expand_single(nodes: NDArray, cons: NDArray, new_N: int, new_C: int) -> Tuple[NDArray, NDArray]:
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"""
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Expand a single genome to accommodate more nodes or connections.
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:param nodes: (N, 5)
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:param cons: (C, 4)
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:param new_N:
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:param new_C:
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:return: (new_N, 5), (new_C, 4)
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"""
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old_N, old_C = nodes.shape[0], cons.shape[0]
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new_nodes = np.full((new_N, 5), np.nan)
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new_nodes[:old_N, :] = nodes
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new_cons = np.full((new_C, 4), np.nan)
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new_cons[:old_C, :] = cons
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return new_nodes, new_cons
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def expand(pop_nodes: NDArray, pop_cons: NDArray, new_N: int, new_C: int) -> Tuple[NDArray, NDArray]:
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"""
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Expand the population to accommodate more nodes or connections.
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:param pop_nodes: (pop_size, N, 5)
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:param pop_cons: (pop_size, C, 4)
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:param new_N:
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:param new_C:
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:return: (pop_size, new_N, 5), (pop_size, new_C, 4)
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"""
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pop_size, old_N, old_C = pop_nodes.shape[0], pop_nodes.shape[1], pop_cons.shape[1]
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new_pop_nodes = np.full((pop_size, new_N, 5), np.nan)
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new_pop_nodes[:, :old_N, :] = pop_nodes
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new_pop_cons = np.full((pop_size, new_C, 4), np.nan)
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new_pop_cons[:, :old_C, :] = pop_cons
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return new_pop_nodes, new_pop_cons
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@jit
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def count(nodes: NDArray, cons: NDArray) -> Tuple[NDArray, NDArray]:
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"""
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Count how many nodes and connections are in the genome.
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"""
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node_cnt = jnp.sum(~jnp.isnan(nodes[:, 0]))
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cons_cnt = jnp.sum(~jnp.isnan(cons[:, 0]))
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return node_cnt, cons_cnt
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@jit
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def add_node(nodes: NDArray, cons: NDArray, new_key: int,
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bias: float = 0.0, response: float = 1.0, act: int = 0, agg: int = 0) -> Tuple[NDArray, NDArray]:
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"""
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Add a new node to the genome.
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The new node will place at the first NaN row.
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"""
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exist_keys = nodes[:, 0]
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idx = fetch_first(jnp.isnan(exist_keys))
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nodes = nodes.at[idx].set(jnp.array([new_key, bias, response, act, agg]))
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return nodes, cons
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@jit
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def delete_node(nodes: NDArray, cons: NDArray, node_key: int) -> Tuple[NDArray, NDArray]:
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"""
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Delete a node from the genome. Only delete the node, regardless of connections.
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Delete the node by its key.
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"""
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node_keys = nodes[:, 0]
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idx = fetch_first(node_keys == node_key)
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return delete_node_by_idx(nodes, cons, idx)
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@jit
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def delete_node_by_idx(nodes: NDArray, cons: NDArray, idx: int) -> Tuple[NDArray, NDArray]:
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"""
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Delete a node from the genome. Only delete the node, regardless of connections.
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Delete the node by its idx.
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"""
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nodes = nodes.at[idx].set(np.nan)
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return nodes, cons
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@jit
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def add_connection(nodes: NDArray, cons: NDArray, i_key: int, o_key: int,
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weight: float = 1.0, enabled: bool = True) -> Tuple[NDArray, NDArray]:
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"""
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Add a new connection to the genome.
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The new connection will place at the first NaN row.
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"""
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con_keys = cons[:, 0]
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idx = fetch_first(jnp.isnan(con_keys))
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cons = cons.at[idx].set(jnp.array([i_key, o_key, weight, enabled]))
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return nodes, cons
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@jit
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def delete_connection(nodes: NDArray, cons: NDArray, i_key: int, o_key: int) -> Tuple[NDArray, NDArray]:
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"""
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Delete a connection from the genome.
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Delete the connection by its input and output node keys.
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"""
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idx = fetch_first((cons[:, 0] == i_key) & (cons[:, 1] == o_key))
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return delete_connection_by_idx(nodes, cons, idx)
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@jit
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def delete_connection_by_idx(nodes: NDArray, cons: NDArray, idx: int) -> Tuple[NDArray, NDArray]:
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"""
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Delete a connection from the genome.
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Delete the connection by its idx.
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"""
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cons = cons.at[idx].set(np.nan)
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return nodes, cons
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