add License and pyproject.toml

src/tensorneat/genome/gene/node/default.py

@@ -0,0 +1,220 @@
+from typing import Optional, Union, Sequence, Callable
+
+import numpy as np
+import jax, jax.numpy as jnp
+import sympy as sp
+
+from tensorneat.common import (
+    Act,
+    Agg,
+    act_func,
+    agg_func,
+    mutate_int,
+    mutate_float,
+    convert_to_sympy,
+)
+
+from .base import BaseNode
+
+
+class DefaultNode(BaseNode):
+    "Default node gene, with the same behavior as in NEAT-python."
+
+    custom_attrs = ["bias", "response", "aggregation", "activation"]
+
+    def __init__(
+        self,
+        bias_init_mean: float = 0.0,
+        bias_init_std: float = 1.0,
+        bias_mutate_power: float = 0.15,
+        bias_mutate_rate: float = 0.2,
+        bias_replace_rate: float = 0.015,
+        bias_lower_bound: float = -5,
+        bias_upper_bound: float = 5,
+        response_init_mean: float = 1.0,
+        response_init_std: float = 0.0,
+        response_mutate_power: float = 0.15,
+        response_mutate_rate: float = 0.2,
+        response_replace_rate: float = 0.015,
+        response_lower_bound: float = -5,
+        response_upper_bound: float = 5,
+        aggregation_default: Optional[Callable] = None,
+        aggregation_options: Union[Callable, Sequence[Callable]] = Agg.sum,
+        aggregation_replace_rate: float = 0.1,
+        activation_default: Optional[Callable] = None,
+        activation_options: Union[Callable, Sequence[Callable]] = Act.sigmoid,
+        activation_replace_rate: float = 0.1,
+    ):
+        super().__init__()
+
+        if isinstance(aggregation_options, Callable):
+            aggregation_options = [aggregation_options]
+        if isinstance(activation_options, Callable):
+            activation_options = [activation_options]
+
+        if aggregation_default is None:
+            aggregation_default = aggregation_options[0]
+        if activation_default is None:
+            activation_default = activation_options[0]
+
+        self.bias_init_mean = bias_init_mean
+        self.bias_init_std = bias_init_std
+        self.bias_mutate_power = bias_mutate_power
+        self.bias_mutate_rate = bias_mutate_rate
+        self.bias_replace_rate = bias_replace_rate
+        self.bias_lower_bound = bias_lower_bound
+        self.bias_upper_bound = bias_upper_bound
+
+        self.response_init_mean = response_init_mean
+        self.response_init_std = response_init_std
+        self.response_mutate_power = response_mutate_power
+        self.response_mutate_rate = response_mutate_rate
+        self.response_replace_rate = response_replace_rate
+        self.reponse_lower_bound = response_lower_bound
+        self.response_upper_bound = response_upper_bound
+
+        self.aggregation_default = aggregation_options.index(aggregation_default)
+        self.aggregation_options = aggregation_options
+        self.aggregation_indices = np.arange(len(aggregation_options))
+        self.aggregation_replace_rate = aggregation_replace_rate
+
+        self.activation_default = activation_options.index(activation_default)
+        self.activation_options = activation_options
+        self.activation_indices = np.arange(len(activation_options))
+        self.activation_replace_rate = activation_replace_rate
+
+    def new_identity_attrs(self, state):
+        bias = 0
+        res = 1
+        agg = self.aggregation_default
+        act = self.activation_default
+
+        return jnp.array([bias, res, agg, act])  # activation=-1 means Act.identity
+
+    def new_random_attrs(self, state, randkey):
+        k1, k2, k3, k4 = jax.random.split(randkey, num=4)
+        bias = jax.random.normal(k1, ()) * self.bias_init_std + self.bias_init_mean
+        bias = jnp.clip(bias, self.bias_lower_bound, self.bias_upper_bound)
+        res = (
+            jax.random.normal(k2, ()) * self.response_init_std + self.response_init_mean
+        )
+        res = jnp.clip(res, self.reponse_lower_bound, self.response_upper_bound)
+        agg = jax.random.choice(k3, self.aggregation_indices)
+        act = jax.random.choice(k4, self.activation_indices)
+
+        return jnp.array([bias, res, agg, act])
+
+    def mutate(self, state, randkey, attrs):
+        k1, k2, k3, k4 = jax.random.split(randkey, num=4)
+        bias, res, agg, act = attrs
+        bias = mutate_float(
+            k1,
+            bias,
+            self.bias_init_mean,
+            self.bias_init_std,
+            self.bias_mutate_power,
+            self.bias_mutate_rate,
+            self.bias_replace_rate,
+        )
+        bias = jnp.clip(bias, self.bias_lower_bound, self.bias_upper_bound)
+        res = mutate_float(
+            k2,
+            res,
+            self.response_init_mean,
+            self.response_init_std,
+            self.response_mutate_power,
+            self.response_mutate_rate,
+            self.response_replace_rate,
+        )
+        res = jnp.clip(res, self.reponse_lower_bound, self.response_upper_bound)
+        agg = mutate_int(
+            k4, agg, self.aggregation_indices, self.aggregation_replace_rate
+        )
+
+        act = mutate_int(k3, act, self.activation_indices, self.activation_replace_rate)
+
+        return jnp.array([bias, res, agg, act])
+
+    def distance(self, state, attrs1, attrs2):
+        bias1, res1, agg1, act1 = attrs1
+        bias2, res2, agg2, act2 = attrs2
+        return (
+            jnp.abs(bias1 - bias2)  # bias
+            + jnp.abs(res1 - res2)  # response
+            + (agg1 != agg2)  # aggregation
+            + (act1 != act2)  # activation
+        )
+
+    def forward(self, state, attrs, inputs, is_output_node=False):
+        bias, res, agg, act = attrs
+
+        z = agg_func(agg, inputs, self.aggregation_options)
+        z = bias + res * z
+
+        # the last output node should not be activated
+        z = jax.lax.cond(
+            is_output_node, lambda: z, lambda: act_func(act, z, self.activation_options)
+        )
+
+        return z
+
+    def repr(self, state, node, precision=2, idx_width=3, func_width=8):
+        idx, bias, res, agg, act = node
+
+        idx = int(idx)
+        bias = round(float(bias), precision)
+        res = round(float(res), precision)
+        agg = int(agg)
+        act = int(act)
+
+        if act == -1:
+            act_func = Act.identity
+        else:
+            act_func = self.activation_options[act]
+        return "{}(idx={:<{idx_width}}, bias={:<{float_width}}, response={:<{float_width}}, aggregation={:<{func_width}}, activation={:<{func_width}})".format(
+            self.__class__.__name__,
+            idx,
+            bias,
+            res,
+            self.aggregation_options[agg].__name__,
+            act_func.__name__,
+            idx_width=idx_width,
+            float_width=precision + 3,
+            func_width=func_width,
+        )
+
+    def to_dict(self, state, node):
+        idx, bias, res, agg, act = node
+
+        idx = int(idx)
+        bias = jnp.float32(bias)
+        res = jnp.float32(res)
+        agg = int(agg)
+        act = int(act)
+
+        if act == -1:
+            act_func = Act.identity
+        else:
+            act_func = self.activation_options[act]
+        return {
+            "idx": idx,
+            "bias": bias,
+            "res": res,
+            "agg": self.aggregation_options[int(agg)].__name__,
+            "act": act_func.__name__,
+        }
+
+    def sympy_func(self, state, node_dict, inputs, is_output_node=False):
+        nd = node_dict
+        bias = sp.symbols(f"n_{nd['idx']}_b")
+        res = sp.symbols(f"n_{nd['idx']}_r")
+
+        z = convert_to_sympy(nd["agg"])(inputs)
+        z = bias + res * z
+
+        if is_output_node:
+            pass
+        else:
+            z = convert_to_sympy(nd["act"])(z)
+
+        return z, {bias: nd["bias"], res: nd["res"]}