213 lines
7.9 KiB
Python
213 lines
7.9 KiB
Python
"""
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Vectorization of genome representation.
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Utilizes Tuple[nodes: Array, connections: Array] to encode the genome, where:
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1. N is a pre-set value that determines the maximum number of nodes in the network, and will increase if the genome becomes
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too large to be represented by the current value of N.
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2. nodes is an array of shape (N, 5), dtype=float, with columns corresponding to: key, bias, response, activation function
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(act), and aggregation function (agg).
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3. connections is an array of shape (2, N, N), dtype=float, with the first axis representing weight and connection enabled
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status.
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Empty nodes or connections are represented using np.nan.
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"""
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from typing import Tuple
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from functools import partial
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import numpy as np
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from numpy.typing import NDArray
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from jax import numpy as jnp
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from jax import jit
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from jax import Array
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from algorithms.neat.genome.utils import fetch_first, fetch_last
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EMPTY_NODE = np.array([np.nan, np.nan, np.nan, np.nan, np.nan])
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def create_initialize_function(config):
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pop_size = config.neat.population.pop_size
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N = config.basic.init_maximum_nodes
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num_inputs = config.basic.num_inputs
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num_outputs = config.basic.num_outputs
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default_bias = config.neat.gene.bias.init_mean
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default_response = config.neat.gene.response.init_mean
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# default_act = config.neat.gene.activation.default
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# default_agg = config.neat.gene.aggregation.default
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default_act = 0
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default_agg = 0
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default_weight = config.neat.gene.weight.init_mean
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return partial(initialize_genomes, pop_size, N, num_inputs, num_outputs, default_bias, default_response,
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default_act, default_agg, default_weight)
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def initialize_genomes(pop_size: int,
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N: int,
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num_inputs: int, num_outputs: int,
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default_bias: float = 0.0,
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default_response: float = 1.0,
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default_act: int = 0,
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default_agg: int = 0,
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default_weight: float = 1.0) \
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-> Tuple[NDArray, NDArray, NDArray, NDArray]:
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"""
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Initialize genomes with default values.
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Args:
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pop_size (int): Number of genomes to initialize.
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N (int): Maximum number of nodes in the network.
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num_inputs (int): Number of input nodes.
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num_outputs (int): Number of output nodes.
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default_bias (float, optional): Default bias value for output nodes. Defaults to 0.0.
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default_response (float, optional): Default response value for output nodes. Defaults to 1.0.
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default_act (int, optional): Default activation function index for output nodes. Defaults to 1.
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default_agg (int, optional): Default aggregation function index for output nodes. Defaults to 0.
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default_weight (float, optional): Default weight value for connections. Defaults to 0.0.
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Raises:
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AssertionError: If the sum of num_inputs, num_outputs, and 1 is greater than N.
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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 num_inputs + num_outputs + 1 <= N, f"Too small N: {N} for input_size: " \
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f"{num_inputs} and output_size: {num_outputs}!"
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pop_nodes = np.full((pop_size, N, 5), np.nan)
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pop_connections = np.full((pop_size, 2, N, N), np.nan)
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input_idx = np.arange(num_inputs)
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output_idx = np.arange(num_inputs, num_inputs + num_outputs)
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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] = default_bias
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pop_nodes[:, output_idx, 2] = default_response
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pop_nodes[:, output_idx, 3] = default_act
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pop_nodes[:, output_idx, 4] = default_agg
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for i in input_idx:
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for j in output_idx:
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pop_connections[:, 0, i, j] = default_weight
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pop_connections[:, 1, i, j] = 1
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return pop_nodes, pop_connections, input_idx, output_idx
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def expand(pop_nodes: NDArray, pop_connections: NDArray, new_N: int) -> Tuple[NDArray, NDArray]:
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"""
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Expand the genome to accommodate more nodes.
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:param pop_nodes: (pop_size, N, 5)
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:param pop_connections: (pop_size, 2, N, N)
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:param new_N:
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:return:
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"""
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pop_size, old_N = pop_nodes.shape[0], pop_nodes.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_connections = np.full((pop_size, 2, new_N, new_N), np.nan)
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new_pop_connections[:, :, :old_N, :old_N] = pop_connections
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return new_pop_nodes, new_pop_connections
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def expand_single(nodes: NDArray, connections: NDArray, new_N: int) -> Tuple[NDArray, NDArray]:
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"""
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Expand a single genome to accommodate more nodes.
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:param nodes: (N, 5)
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:param connections: (2, N, N)
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:param new_N:
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:return:
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"""
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old_N = nodes.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_connections = np.full((2, new_N, new_N), np.nan)
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new_connections[:, :old_N, :old_N] = connections
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return new_nodes, new_connections
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@jit
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def add_node(new_node_key: int, nodes: Array, connections: Array,
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bias: float = 0.0, response: float = 1.0, act: int = 0, agg: int = 0) -> Tuple[Array, Array]:
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"""
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add a new node to the genome.
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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_node_key, bias, response, act, agg]))
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return nodes, connections
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@jit
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def delete_node(node_key: int, nodes: Array, connections: Array) -> Tuple[Array, Array]:
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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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"""
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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(idx, nodes, connections)
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@jit
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def delete_node_by_idx(idx: int, nodes: Array, connections: Array) -> Tuple[Array, Array]:
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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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"""
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node_keys = nodes[:, 0]
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# move the last node to the deleted node's position
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last_real_idx = fetch_last(~jnp.isnan(node_keys))
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nodes = nodes.at[idx].set(nodes[last_real_idx])
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nodes = nodes.at[last_real_idx].set(EMPTY_NODE)
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return nodes, connections
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@jit
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def add_connection(from_node: int, to_node: int, nodes: Array, connections: Array,
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weight: float = 0.0, enabled: bool = True) -> Tuple[Array, Array]:
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"""
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add a new connection to the genome.
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"""
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node_keys = nodes[:, 0]
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from_idx = fetch_first(node_keys == from_node)
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to_idx = fetch_first(node_keys == to_node)
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return add_connection_by_idx(from_idx, to_idx, nodes, connections, weight, enabled)
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@jit
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def add_connection_by_idx(from_idx: int, to_idx: int, nodes: Array, connections: Array,
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weight: float = 0.0, enabled: bool = True) -> Tuple[Array, Array]:
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"""
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add a new connection to the genome.
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"""
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connections = connections.at[:, from_idx, to_idx].set(jnp.array([weight, enabled]))
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return nodes, connections
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@jit
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def delete_connection(from_node: int, to_node: int, nodes: Array, connections: Array) -> Tuple[Array, Array]:
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"""
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delete a connection from the genome.
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"""
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node_keys = nodes[:, 0]
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from_idx = fetch_first(node_keys == from_node)
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to_idx = fetch_first(node_keys == to_node)
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return delete_connection_by_idx(from_idx, to_idx, nodes, connections)
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@jit
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def delete_connection_by_idx(from_idx: int, to_idx: int, nodes: Array, connections: Array) -> Tuple[Array, Array]:
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"""
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delete a connection from the genome.
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"""
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connections = connections.at[:, from_idx, to_idx].set(np.nan)
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return nodes, connections
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# if __name__ == '__main__':
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# pop_nodes, pop_connections, input_keys, output_keys = initialize_genomes(100, 5, 2, 1)
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# print(pop_nodes, pop_connections)
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