223 lines
7.8 KiB
Python
223 lines
7.8 KiB
Python
import jax
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from jax import Array, numpy as jnp
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from .base import BaseGene
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from .activation import Activation
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from .aggregation import Aggregation
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from ..utils import unflatten_connections, I_INT
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from ..genome import topological_sort
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class NormalGene(BaseGene):
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node_attrs = ['bias', 'response', 'aggregation', 'activation']
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conn_attrs = ['weight']
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@staticmethod
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def setup(state, config):
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return state.update(
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bias_init_mean=config['bias_init_mean'],
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bias_init_std=config['bias_init_std'],
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bias_mutate_power=config['bias_mutate_power'],
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bias_mutate_rate=config['bias_mutate_rate'],
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bias_replace_rate=config['bias_replace_rate'],
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response_init_mean=config['response_init_mean'],
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response_init_std=config['response_init_std'],
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response_mutate_power=config['response_mutate_power'],
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response_mutate_rate=config['response_mutate_rate'],
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response_replace_rate=config['response_replace_rate'],
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activation_default=config['activation_default'],
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activation_options=config['activation_options'],
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activation_replace_rate=config['activation_replace_rate'],
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aggregation_default=config['aggregation_default'],
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aggregation_options=config['aggregation_options'],
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aggregation_replace_rate=config['aggregation_replace_rate'],
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weight_init_mean=config['weight_init_mean'],
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weight_init_std=config['weight_init_std'],
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weight_mutate_power=config['weight_mutate_power'],
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weight_mutate_rate=config['weight_mutate_rate'],
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weight_replace_rate=config['weight_replace_rate'],
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)
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@staticmethod
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def new_node_attrs(state):
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return jnp.array([state.bias_init_mean, state.response_init_mean,
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state.activation_default, state.aggregation_default])
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@staticmethod
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def new_conn_attrs(state):
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return jnp.array([state.weight_init_mean])
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@staticmethod
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def mutate_node(state, attrs: Array, key):
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k1, k2, k3, k4 = jax.random.split(key, num=4)
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bias = NormalGene._mutate_float(k1, attrs[0], state.bias_init_mean, state.bias_init_std,
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state.bias_mutate_power, state.bias_mutate_rate, state.bias_replace_rate)
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res = NormalGene._mutate_float(k2, attrs[1], state.response_init_mean, state.response_init_std,
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state.response_mutate_power, state.response_mutate_rate,
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state.response_replace_rate)
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act = NormalGene._mutate_int(k3, attrs[2], state.activation_options, state.activation_replace_rate)
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agg = NormalGene._mutate_int(k4, attrs[3], state.aggregation_options, state.aggregation_replace_rate)
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return jnp.array([bias, res, act, agg])
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@staticmethod
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def mutate_conn(state, attrs: Array, key):
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weight = NormalGene._mutate_float(key, attrs[0], state.weight_init_mean, state.weight_init_std,
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state.weight_mutate_power, state.weight_mutate_rate,
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state.weight_replace_rate)
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return jnp.array([weight])
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@staticmethod
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def distance_node(state, node1: Array, node2: Array):
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# bias + response + activation + aggregation
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return jnp.abs(node1[1] - node2[1]) + jnp.abs(node1[2] - node2[2]) + \
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(node1[3] != node2[3]) + (node1[4] != node2[4])
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@staticmethod
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def distance_conn(state, con1: Array, con2: Array):
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return (con1[2] != con2[2]) + jnp.abs(con1[3] - con2[3]) # enable + weight
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@staticmethod
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def forward_transform(nodes, conns):
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u_conns = unflatten_connections(nodes, conns)
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u_conns = jnp.where(jnp.isnan(u_conns[0, :]), jnp.nan, u_conns) # enable is false, then the connections is nan
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u_conns = u_conns[1:, :] # remove enable attr
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conn_exist = jnp.any(~jnp.isnan(u_conns), axis=0)
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seqs = topological_sort(nodes, conn_exist)
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return seqs, nodes, u_conns
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@staticmethod
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def create_forward(config):
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config['activation_funcs'] = [Activation.name2func[name] for name in config['activation_option_names']]
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config['aggregation_funcs'] = [Aggregation.name2func[name] for name in config['aggregation_option_names']]
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def act(idx, z):
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"""
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calculate activation function for each node
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"""
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idx = jnp.asarray(idx, dtype=jnp.int32)
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# change idx from float to int
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res = jax.lax.switch(idx, config['activation_funcs'], z)
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return res
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def agg(idx, z):
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"""
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calculate activation function for inputs of node
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"""
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idx = jnp.asarray(idx, dtype=jnp.int32)
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def all_nan():
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return 0.
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def not_all_nan():
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return jax.lax.switch(idx, config['aggregation_funcs'], z)
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return jax.lax.cond(jnp.all(jnp.isnan(z)), all_nan, not_all_nan)
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def forward(inputs, transform) -> Array:
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"""
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jax forward for single input shaped (input_num, )
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nodes, connections are a single genome
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:argument inputs: (input_num, )
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:argument cal_seqs: (N, )
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:argument nodes: (N, 5)
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:argument connections: (2, N, N)
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:return (output_num, )
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"""
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cal_seqs, nodes, cons = transform
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input_idx = config['input_idx']
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output_idx = config['output_idx']
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N = nodes.shape[0]
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ini_vals = jnp.full((N,), jnp.nan)
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ini_vals = ini_vals.at[input_idx].set(inputs)
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weights = cons[0, :]
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def cond_fun(carry):
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values, idx = carry
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return (idx < N) & (cal_seqs[idx] != I_INT)
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def body_func(carry):
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values, idx = carry
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i = cal_seqs[idx]
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def hit():
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ins = values * weights[:, i]
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z = agg(nodes[i, 4], ins) # z = agg(ins)
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z = z * nodes[i, 2] + nodes[i, 1] # z = z * response + bias
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z = act(nodes[i, 3], z) # z = act(z)
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new_values = values.at[i].set(z)
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return new_values
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def miss():
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return values
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# the val of input nodes is obtained by the task, not by calculation
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values = jax.lax.cond(jnp.isin(i, input_idx), miss, hit)
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# if jnp.isin(i, input_idx):
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# values = miss()
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# else:
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# values = hit()
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return values, idx + 1
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# carry = (ini_vals, 0)
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# while cond_fun(carry):
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# carry = body_func(carry)
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# vals, _ = carry
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vals, _ = jax.lax.while_loop(cond_fun, body_func, (ini_vals, 0))
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return vals[output_idx]
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return forward
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@staticmethod
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def _mutate_float(key, val, init_mean, init_std, mutate_power, mutate_rate, replace_rate):
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k1, k2, k3 = jax.random.split(key, num=3)
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noise = jax.random.normal(k1, ()) * mutate_power
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replace = jax.random.normal(k2, ()) * init_std + init_mean
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r = jax.random.uniform(k3, ())
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val = jnp.where(
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r < mutate_rate,
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val + noise,
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jnp.where(
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(mutate_rate < r) & (r < mutate_rate + replace_rate),
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replace,
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val
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)
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)
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return val
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@staticmethod
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def _mutate_int(key, val, options, replace_rate):
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k1, k2 = jax.random.split(key, num=2)
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r = jax.random.uniform(k1, ())
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val = jnp.where(
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r < replace_rate,
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jax.random.choice(k2, options),
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val
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)
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return val
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