odify genome for the official release

README.md

@@ -75,7 +75,7 @@ from pipeline import Pipeline
 from algorithm.neat import *
 
 from problem.rl_env import BraxEnv
-from utils import Act
+from tensorneat.utils import Act
 
 if __name__ == '__main__':
     pipeline = Pipeline(

examples/brax/ant.py

@@ -2,7 +2,7 @@ from pipeline import Pipeline
 from algorithm.neat import *
 
 from problem.rl_env import BraxEnv
-from utils import Act
+from tensorneat.common import Act
 
 if __name__ == "__main__":
     pipeline = Pipeline(

examples/brax/half_cheetah.py

@@ -4,7 +4,7 @@ from pipeline import Pipeline
 from algorithm.neat import *
 
 from problem.rl_env import BraxEnv
-from utils import Act
+from tensorneat.common import Act
 
 
 def sample_policy(randkey, obs):

examples/brax/reacher.py

@@ -2,7 +2,7 @@ from pipeline import Pipeline
 from algorithm.neat import *
 
 from problem.rl_env import BraxEnv
-from utils import Act
+from tensorneat.common import Act
 
 if __name__ == "__main__":
     pipeline = Pipeline(

examples/brax/walker.py

@@ -2,7 +2,7 @@ from pipeline import Pipeline
 from algorithm.neat import *
 
 from problem.rl_env import BraxEnv
-from utils import Act
+from tensorneat.common import Act
 
 import jax, jax.numpy as jnp
 

examples/func_fit/xor.py

@@ -2,7 +2,7 @@ from pipeline import Pipeline
 from algorithm.neat import *
 
 from problem.func_fit import XOR3d
-from utils import ACT_ALL, AGG_ALL, Act, Agg
+from tensorneat.common import ACT_ALL, AGG_ALL, Act, Agg
 
 if __name__ == "__main__":
     pipeline = Pipeline(

examples/func_fit/xor3d_hyperneat.py

@@ -1,7 +1,7 @@
 from pipeline import Pipeline
 from algorithm.neat import *
 from algorithm.hyperneat import *
-from utils import Act
+from tensorneat.common import Act
 
 from problem.func_fit import XOR3d
 

examples/gymnax/cartpole_hyperneat.py

@@ -3,7 +3,7 @@ import jax
 from pipeline import Pipeline
 from algorithm.neat import *
 from algorithm.hyperneat import *
-from utils import Act
+from tensorneat.common import Act
 
 from problem.rl_env import GymNaxEnv
 

examples/gymnax/mountain_car_continuous.py

@@ -2,7 +2,7 @@ from pipeline import Pipeline
 from algorithm.neat import *
 
 from problem.rl_env import GymNaxEnv
-from utils import Act
+from tensorneat.common import Act
 
 if __name__ == "__main__":
     pipeline = Pipeline(

examples/gymnax/pendulum.py

@@ -2,7 +2,7 @@ from pipeline import Pipeline
 from algorithm.neat import *
 
 from problem.rl_env import GymNaxEnv
-from utils import Act
+from tensorneat.common import Act
 
 if __name__ == "__main__":
     pipeline = Pipeline(

examples/interpret_visualize/genome_sympy.ipynb

@@ -11,7 +11,7 @@
     "from algorithm.neat.genome.advance import AdvanceInitialize\n",
     "from algorithm.neat.gene.node.default_without_response import NodeGeneWithoutResponse\n",
     "from utils.graph import topological_sort_python\n",
-    "from utils import Act, Agg\n",
+    "from tensorneat.utils import Act, Agg\n",
     "\n",
     "import numpy as np"
    ],

examples/interpret_visualize/genome_sympy.py

@@ -3,7 +3,7 @@ import jax, jax.numpy as jnp
 from algorithm.neat import *
 from algorithm.neat.genome.dense import DenseInitialize
 from utils.graph import topological_sort_python
-from utils import *
+from tensorneat.common import *
 
 if __name__ == "__main__":
     genome = DenseInitialize(

examples/interpret_visualize/visualize_genome.ipynb

@@ -19,7 +19,7 @@
     "from algorithm.neat.genome.advance import AdvanceInitialize\n",
     "from algorithm.neat.gene.node.default_without_response import NodeGeneWithoutResponse\n",
     "from utils.graph import topological_sort_python\n",
-    "from utils import Act, Agg\n",
+    "from tensorneat.utils import Act, Agg\n",
     "\n",
     "genome = AdvanceInitialize(\n",
     "    num_inputs=16,\n",

examples/jumanji/2048_test.ipynb

@@ -29,7 +29,7 @@
     "from algorithm.neat.gene.node.default_without_response import NodeGeneWithoutResponse\n",
     "\n",
     "from problem.rl_env.jumanji.jumanji_2048 import Jumanji_2048\n",
-    "from utils import Act, Agg\n",
+    "from tensorneat.utils import Act, Agg\n",
     "\n",
     "pipeline = Pipeline(\n",
     "    algorithm=NEAT(\n",

examples/jumanji/train_2048.py

@@ -4,7 +4,7 @@ from pipeline import Pipeline
 from algorithm.neat import *
 from algorithm.neat.gene.node.default_without_response import NodeGeneWithoutResponse
 from problem.rl_env.jumanji.jumanji_2048 import Jumanji_2048
-from utils import Act, Agg
+from tensorneat.common import Act, Agg
 
 
 def rot_li(li):

examples/tmp.py

@@ -0,0 +1,10 @@
+import jax, jax.numpy as jnp
+
+from tensorneat.algorithm import NEAT
+from tensorneat.algorithm.neat import DefaultGenome
+
+key = jax.random.key(0)
+genome = DefaultGenome(num_inputs=5, num_outputs=3, init_hidden_layers=(1, ))
+state = genome.setup()
+nodes, conns = genome.initialize(state, key)
+print(genome.repr(state, nodes, conns))

tensorneat/algorithm/base.py

@@ -1,4 +1,4 @@
-from utils import State, StatefulBaseClass
+from tensorneat.common import State, StatefulBaseClass
 
 
 class BaseAlgorithm(StatefulBaseClass):

tensorneat/algorithm/hyperneat/hyperneat.py

@@ -2,7 +2,7 @@ from typing import Callable
 
 import jax, jax.numpy as jnp
 
-from utils import State, Act, Agg
+from tensorneat.common import State, Act, Agg
 from .. import BaseAlgorithm, NEAT
 from ..neat.gene import BaseNodeGene, BaseConnGene
 from ..neat.genome import RecurrentGenome

tensorneat/algorithm/hyperneat/substrate/base.py

@@ -1,4 +1,4 @@
-from utils import StatefulBaseClass
+from tensorneat.common import StatefulBaseClass
 
 
 class BaseSubstrate(StatefulBaseClass):

tensorneat/algorithm/neat/__init__.py

@@ -1,4 +1,3 @@
-from .ga import *
 from .gene import *
 from .genome import *
 from .species import *

tensorneat/algorithm/neat/ga/crossover/base.py

@@ -1,6 +0,0 @@
-from utils import StatefulBaseClass
-
-
-class BaseCrossover(StatefulBaseClass):
-    def __call__(self, state, randkey, genome, nodes1, nodes2, conns1, conns2):
-        raise NotImplementedError

tensorneat/algorithm/neat/ga/mutation/base.py

@@ -1,6 +0,0 @@
-from utils import StatefulBaseClass
-
-
-class BaseMutation(StatefulBaseClass):
-    def __call__(self, state, randkey, genome, nodes, conns, new_node_key):
-        raise NotImplementedError

tensorneat/algorithm/neat/gene/base.py

@@ -1,5 +1,5 @@
 import jax, jax.numpy as jnp
-from utils import State, StatefulBaseClass, hash_array
+from tensorneat.common import State, StatefulBaseClass, hash_array
 
 
 class BaseGene(StatefulBaseClass):

tensorneat/algorithm/neat/gene/conn/default.py

@@ -2,7 +2,7 @@ import jax.numpy as jnp
 import jax.random
 import numpy as np
 import sympy as sp
-from utils import mutate_float
+from tensorneat.common import mutate_float
 from . import BaseConnGene
 
 

tensorneat/algorithm/neat/gene/node/default.py

@@ -4,7 +4,7 @@ import numpy as np
 import jax, jax.numpy as jnp
 import sympy as sp
 
-from utils import (
+from tensorneat.common import (
     Act,
     Agg,
     act_func,

tensorneat/algorithm/neat/gene/node/default_without_response.py

@@ -3,7 +3,7 @@ from typing import Tuple
 import jax, jax.numpy as jnp
 import numpy as np
 import sympy as sp
-from utils import (
+from tensorneat.common import (
     Act,
     Agg,
     act_func,

tensorneat/algorithm/neat/gene/node/kan_node.py

@@ -1,6 +1,6 @@
 import jax.numpy as jnp
 from . import BaseNodeGene
-from utils import Agg
+from tensorneat.common import Agg
 
 
 class KANNode(BaseNodeGene):

tensorneat/algorithm/neat/gene/node/min_max_node.py

@@ -2,7 +2,7 @@ from typing import Tuple
 
 import jax, jax.numpy as jnp
 
-from utils import Act, Agg, act_func, agg_func, mutate_int, mutate_float
+from tensorneat.common import Act, Agg, act_func, agg_func, mutate_int, mutate_float
 from . import BaseNodeGene
 
 

tensorneat/algorithm/neat/gene/node/normalized.py

@@ -2,7 +2,7 @@ from typing import Tuple
 
 import jax, jax.numpy as jnp
 
-from utils import Act, Agg, act_func, agg_func, mutate_int, mutate_float
+from tensorneat.common import Act, Agg, act_func, agg_func, mutate_int, mutate_float
 from . import BaseNodeGene
 
 

tensorneat/algorithm/neat/genome/__init__.py

@@ -1,5 +1,4 @@
 from .base import BaseGenome
 from .default import DefaultGenome
 from .recurrent import RecurrentGenome
-from .hidden import HiddenInitialize
+
-from .dense import DenseInitialize

tensorneat/algorithm/neat/genome/base.py

@@ -1,8 +1,16 @@
+from typing import Callable, Sequence
+
 import numpy as np
-import jax, jax.numpy as jnp
+import jax
+from jax import vmap, numpy as jnp
 from ..gene import BaseNodeGene, BaseConnGene
-from ..ga import BaseMutation, BaseCrossover
+from .operations import BaseMutation, BaseCrossover, BaseDistance
-from utils import State, StatefulBaseClass, topological_sort_python, hash_array
+from tensorneat.common import (
+    State,
+    StatefulBaseClass,
+    hash_array,
+)
+from .utils import valid_cnt
 
 
 class BaseGenome(StatefulBaseClass):
@@ -18,120 +26,159 @@ class BaseGenome(StatefulBaseClass):
         conn_gene: BaseConnGene,
         mutation: BaseMutation,
         crossover: BaseCrossover,
+        distance: BaseDistance,
+        output_transform: Callable = None,
+        input_transform: Callable = None,
+        init_hidden_layers: Sequence[int] = (),
     ):
+        
+        # check transform functions
+        if input_transform is not None:
+            try:
+                _ = input_transform(jnp.zeros(num_inputs))
+            except Exception as e:
+                raise ValueError(f"Output transform function failed: {e}")
+
+        if output_transform is not None:
+            try:
+                _ = output_transform(jnp.zeros(num_outputs))
+            except Exception as e:
+                raise ValueError(f"Output transform function failed: {e}")
+
+        # prepare for initialization
+        all_layers = [num_inputs] + list(init_hidden_layers) + [num_outputs]
+        layer_indices = []
+        next_index = 0
+        for layer in all_layers:
+            layer_indices.append(list(range(next_index, next_index + layer)))
+            next_index += layer
+
+        all_init_nodes = []
+        all_init_conns_in_idx = []
+        all_init_conns_out_idx = []
+        for i in range(len(layer_indices) - 1):
+            in_layer = layer_indices[i]
+            out_layer = layer_indices[i + 1]
+            for in_idx in in_layer:
+                for out_idx in out_layer:
+                    all_init_conns_in_idx.append(in_idx)
+                    all_init_conns_out_idx.append(out_idx)
+            all_init_nodes.extend(in_layer)
+
+        if max_nodes < len(all_init_nodes):
+            raise ValueError(
+                f"max_nodes={max_nodes} must be greater than or equal to the number of initial nodes={len(all_init_nodes)}"
+            )
+
+        if max_conns < len(all_init_conns_in_idx):
+            raise ValueError(
+                f"max_conns={max_conns} must be greater than or equal to the number of initial connections={len(all_init_conns_in_idx)}"
+            )
+        
         self.num_inputs = num_inputs
         self.num_outputs = num_outputs
-        self.input_idx = np.arange(num_inputs)
-        self.output_idx = np.arange(num_inputs, num_inputs + num_outputs)
         self.max_nodes = max_nodes
         self.max_conns = max_conns
         self.node_gene = node_gene
         self.conn_gene = conn_gene
         self.mutation = mutation
         self.crossover = crossover
+        self.distance = distance
+        self.output_transform = output_transform
+        self.input_transform = input_transform
+
+        self.input_idx = np.array(layer_indices[0])
+        self.output_idx = np.array(layer_indices[-1])
+        self.all_init_nodes = np.array(all_init_nodes)
+        self.all_init_conns = np.c_[all_init_conns_in_idx, all_init_conns_out_idx]
 
     def setup(self, state=State()):
         state = self.node_gene.setup(state)
         state = self.conn_gene.setup(state)
-        state = self.mutation.setup(state)
+        state = self.mutation.setup(state, self)
-        state = self.crossover.setup(state)
+        state = self.crossover.setup(state, self)
+        state = self.distance.setup(state, self)
         return state
 
     def transform(self, state, nodes, conns):
         raise NotImplementedError
 
-    def restore(self, state, transformed):
-        raise NotImplementedError
-
     def forward(self, state, transformed, inputs):
         raise NotImplementedError
 
+    def sympy_func(self):
+        raise NotImplementedError
+
+    def visualize(self):
+        raise NotImplementedError
+
     def execute_mutation(self, state, randkey, nodes, conns, new_node_key):
-        return self.mutation(state, randkey, self, nodes, conns, new_node_key)
+        return self.mutation(state, randkey, nodes, conns, new_node_key)
 
     def execute_crossover(self, state, randkey, nodes1, conns1, nodes2, conns2):
-        return self.crossover(state, randkey, self, nodes1, conns1, nodes2, conns2)
+        return self.crossover(state, randkey, nodes1, conns1, nodes2, conns2)
+
+    def execute_distance(self, state, nodes1, conns1, nodes2, conns2):
+        return self.distance(state, nodes1, conns1, nodes2, conns2)
 
     def initialize(self, state, randkey):
-        """
-        Default initialization method for the genome.
-        Add an extra hidden node.
-        Make all input nodes and output nodes connected to the hidden node.
-        All attributes will be initialized randomly using gene.new_random_attrs method.
-
-        For example, a network with 2 inputs and 1 output, the structure will be:
-        nodes:
-            [
-                [0, attrs0],  # input node 0
-                [1, attrs1],  # input node 1
-                [2, attrs2],  # output node 0
-                [3, attrs3],  # hidden node
-                [NaN, NaN],  # empty node
-            ]
-        conns:
-            [
-                [0, 3, attrs0],  # input node 0 -> hidden node
-                [1, 3, attrs1],  # input node 1 -> hidden node
-                [3, 2, attrs2], # hidden node -> output node 0
-                [NaN, NaN],
-                [NaN, NaN],
-            ]
-        """
-
         k1, k2 = jax.random.split(randkey)  # k1 for nodes, k2 for conns
+
+        all_nodes_cnt = len(self.all_init_nodes)
+        all_conns_cnt = len(self.all_init_conns)
+
         # initialize nodes
-        new_node_key = (
-            max([*self.input_idx, *self.output_idx]) + 1
-        )  # the key for the hidden node
-        node_keys = jnp.concatenate(
-            [self.input_idx, self.output_idx, jnp.array([new_node_key])]
-        )  # the list of all node keys
-
-        # initialize nodes and connections with NaN
         nodes = jnp.full((self.max_nodes, self.node_gene.length), jnp.nan)
-        conns = jnp.full((self.max_conns, self.conn_gene.length), jnp.nan)
+        # create node indices
+        node_indices = self.all_init_nodes
+        # create node attrs
+        rand_keys_n = jax.random.split(k1, num=all_nodes_cnt)
+        node_attr_func = vmap(self.node_gene.new_random_attrs, in_axes=(None, 0))
+        node_attrs = node_attr_func(state, rand_keys_n)
 
-        # set keys for input nodes, output nodes and hidden node
+        nodes = nodes.at[:all_nodes_cnt, 0].set(node_indices)  # set node indices
-        nodes = nodes.at[node_keys, 0].set(node_keys)
+        nodes = nodes.at[:all_nodes_cnt, 1:].set(node_attrs)  # set node attrs
-
-        # generate random attributes for nodes
-        node_keys = jax.random.split(k1, len(node_keys))
-        random_node_attrs = jax.vmap(
-            self.node_gene.new_random_attrs, in_axes=(None, 0)
-        )(state, node_keys)
-        nodes = nodes.at[: len(node_keys), 1:].set(random_node_attrs)
 
         # initialize conns
-        # input-hidden connections
+        conns = jnp.full((self.max_conns, self.conn_gene.length), jnp.nan)
-        input_conns = jnp.c_[
+        # create input and output indices
-            self.input_idx, jnp.full_like(self.input_idx, new_node_key)
+        conn_indices = self.all_init_conns
-        ]
+        # create conn attrs
-        conns = conns.at[self.input_idx, :2].set(input_conns)  # in-keys, out-keys
+        rand_keys_c = jax.random.split(k2, num=all_conns_cnt)
+        conns_attr_func = jax.vmap(
+            self.conn_gene.new_random_attrs,
+            in_axes=(
+                None,
+                0,
+            ),
+        )
+        conns_attrs = conns_attr_func(state, rand_keys_c)
 
-        # output-hidden connections
+        conns = conns.at[:all_conns_cnt, :2].set(conn_indices)  # set conn indices
-        output_conns = jnp.c_[
+        conns = conns.at[:all_conns_cnt, 2:].set(conns_attrs)  # set conn attrs
-            jnp.full_like(self.output_idx, new_node_key), self.output_idx
-        ]
-        conns = conns.at[self.output_idx, :2].set(output_conns)  # in-keys, out-keys
-
-        conn_keys = jax.random.split(k2, num=len(self.input_idx) + len(self.output_idx))
-        # generate random attributes for conns
-        random_conn_attrs = jax.vmap(
-            self.conn_gene.new_random_attrs, in_axes=(None, 0)
-        )(state, conn_keys)
-        conns = conns.at[: len(conn_keys), 2:].set(random_conn_attrs)
 
         return nodes, conns
 
-    def update_by_batch(self, state, batch_input, transformed):
+    def network_dict(self, state, nodes, conns):
-        """
+        return {
-        Update the genome by a batch of data.
+            "nodes": self._get_node_dict(state, nodes),
-        """
+            "conns": self._get_conn_dict(state, conns),
-        raise NotImplementedError
+        }
+
+    def get_input_idx(self):
+        return self.input_idx.tolist()
+
+    def get_output_idx(self):
+        return self.output_idx.tolist()
+
+    def hash(self, nodes, conns):
+        nodes_hashs = vmap(hash_array)(nodes)
+        conns_hashs = vmap(hash_array)(conns)
+        return hash_array(jnp.concatenate([nodes_hashs, conns_hashs]))
 
     def repr(self, state, nodes, conns, precision=2):
         nodes, conns = jax.device_get([nodes, conns])
-        nodes_cnt, conns_cnt = self.valid_cnt(nodes), self.valid_cnt(conns)
+        nodes_cnt, conns_cnt = valid_cnt(nodes), valid_cnt(conns)
         s = f"{self.__class__.__name__}(nodes={nodes_cnt}, conns={conns_cnt}):\n"
         s += f"\tNodes:\n"
         for node in nodes:
@@ -152,11 +199,7 @@ class BaseGenome(StatefulBaseClass):
             s += f"\t\t{self.conn_gene.repr(state, conn, precision=precision)}\n"
         return s
 
-    @classmethod
+    def _get_conn_dict(self, state, conns):
-    def valid_cnt(cls, arr):
-        return jnp.sum(~jnp.isnan(arr[:, 0]))
-
-    def get_conn_dict(self, state, conns):
         conns = jax.device_get(conns)
         conn_dict = {}
         for conn in conns:
@@ -167,7 +210,7 @@ class BaseGenome(StatefulBaseClass):
             conn_dict[(in_idx, out_idx)] = cd
         return conn_dict
 
-    def get_node_dict(self, state, nodes):
+    def _get_node_dict(self, state, nodes):
         nodes = jax.device_get(nodes)
         node_dict = {}
         for node in nodes:
@@ -177,92 +220,3 @@ class BaseGenome(StatefulBaseClass):
             idx = nd["idx"]
             node_dict[idx] = nd
         return node_dict
-
-    def network_dict(self, state, nodes, conns):
-        return {
-            "nodes": self.get_node_dict(state, nodes),
-            "conns": self.get_conn_dict(state, conns),
-        }
-
-    def get_input_idx(self):
-        return self.input_idx.tolist()
-
-    def get_output_idx(self):
-        return self.output_idx.tolist()
-
-    def sympy_func(self, state, network, sympy_output_transform=None):
-        raise NotImplementedError
-
-    def visualize(
-        self,
-        network,
-        rotate=0,
-        reverse_node_order=False,
-        size=(300, 300, 300),
-        color=("blue", "blue", "blue"),
-        save_path="network.svg",
-        save_dpi=800,
-        **kwargs,
-    ):
-        import networkx as nx
-        from matplotlib import pyplot as plt
-
-        nodes_list = list(network["nodes"])
-        conns_list = list(network["conns"])
-        input_idx = self.get_input_idx()
-        output_idx = self.get_output_idx()
-        topo_order, topo_layers = topological_sort_python(nodes_list, conns_list)
-        node2layer = {
-            node: layer for layer, nodes in enumerate(topo_layers) for node in nodes
-        }
-        if reverse_node_order:
-            topo_order = topo_order[::-1]
-
-        G = nx.DiGraph()
-
-        if not isinstance(size, tuple):
-            size = (size, size, size)
-        if not isinstance(color, tuple):
-            color = (color, color, color)
-
-        for node in topo_order:
-            if node in input_idx:
-                G.add_node(node, subset=node2layer[node], size=size[0], color=color[0])
-            elif node in output_idx:
-                G.add_node(node, subset=node2layer[node], size=size[2], color=color[2])
-            else:
-                G.add_node(node, subset=node2layer[node], size=size[1], color=color[1])
-
-        for conn in conns_list:
-            G.add_edge(conn[0], conn[1])
-        pos = nx.multipartite_layout(G)
-
-        def rotate_layout(pos, angle):
-            angle_rad = np.deg2rad(angle)
-            cos_angle, sin_angle = np.cos(angle_rad), np.sin(angle_rad)
-            rotated_pos = {}
-            for node, (x, y) in pos.items():
-                rotated_pos[node] = (
-                    cos_angle * x - sin_angle * y,
-                    sin_angle * x + cos_angle * y,
-                )
-            return rotated_pos
-
-        rotated_pos = rotate_layout(pos, rotate)
-
-        node_sizes = [n["size"] for n in G.nodes.values()]
-        node_colors = [n["color"] for n in G.nodes.values()]
-
-        nx.draw(
-            G,
-            pos=rotated_pos,
-            node_size=node_sizes,
-            node_color=node_colors,
-            **kwargs,
-        )
-        plt.savefig(save_path, dpi=save_dpi)
-
-    def hash(self, nodes, conns):
-        nodes_hashs = jax.vmap(hash_array)(nodes)
-        conns_hashs = jax.vmap(hash_array)(conns)
-        return hash_array(jnp.concatenate([nodes_hashs, conns_hashs]))

tensorneat/algorithm/neat/genome/default.py

@@ -1,25 +1,23 @@
 import warnings
-from typing import Callable
 
-import jax, jax.numpy as jnp
+import jax
+from jax import vmap, numpy as jnp
 import numpy as np
 import sympy as sp
-from utils import (
+
-    unflatten_conns,
+from . import BaseGenome
+from ..gene import DefaultNodeGene, DefaultConnGene
+from .operations import DefaultMutation, DefaultCrossover, DefaultDistance
+from .utils import unflatten_conns, extract_node_attrs, extract_conn_attrs
+
+from tensorneat.common import (
     topological_sort,
     topological_sort_python,
     I_INF,
-    extract_node_attrs,
-    extract_conn_attrs,
-    set_node_attrs,
-    set_conn_attrs,
     attach_with_inf,
     SYMPY_FUNCS_MODULE_NP,
     SYMPY_FUNCS_MODULE_JNP,
 )
-from . import BaseGenome
-from ..gene import BaseNodeGene, BaseConnGene, DefaultNodeGene, DefaultConnGene
-from ..ga import BaseMutation, BaseCrossover, DefaultMutation, DefaultCrossover
 
 
 class DefaultGenome(BaseGenome):
@@ -31,15 +29,18 @@ class DefaultGenome(BaseGenome):
         self,
         num_inputs: int,
         num_outputs: int,
-        max_nodes=5,
+        max_nodes=50,
-        max_conns=4,
+        max_conns=100,
-        node_gene: BaseNodeGene = DefaultNodeGene(),
+        node_gene=DefaultNodeGene(),
-        conn_gene: BaseConnGene = DefaultConnGene(),
+        conn_gene=DefaultConnGene(),
-        mutation: BaseMutation = DefaultMutation(),
+        mutation=DefaultMutation(),
-        crossover: BaseCrossover = DefaultCrossover(),
+        crossover=DefaultCrossover(),
-        output_transform: Callable = None,
+        distance=DefaultDistance(),
-        input_transform: Callable = None,
+        output_transform=None,
+        input_transform=None,
+        init_hidden_layers=(),
     ):
+
         super().__init__(
             num_inputs,
             num_outputs,
@@ -49,22 +50,12 @@ class DefaultGenome(BaseGenome):
             conn_gene,
             mutation,
             crossover,
+            distance,
+            output_transform,
+            input_transform,
+            init_hidden_layers,
         )
 
-        if input_transform is not None:
-            try:
-                _ = input_transform(np.zeros(num_inputs))
-            except Exception as e:
-                raise ValueError(f"Output transform function failed: {e}")
-        self.input_transform = input_transform
-
-        if output_transform is not None:
-            try:
-                _ = output_transform(np.zeros(num_outputs))
-            except Exception as e:
-                raise ValueError(f"Output transform function failed: {e}")
-        self.output_transform = output_transform
-
     def transform(self, state, nodes, conns):
         u_conns = unflatten_conns(nodes, conns)
         conn_exist = u_conns != I_INF
@@ -73,10 +64,6 @@ class DefaultGenome(BaseGenome):
 
         return seqs, nodes, conns, u_conns
 
-    def restore(self, state, transformed):
-        seqs, nodes, conns, u_conns = transformed
-        return nodes, conns
-
     def forward(self, state, transformed, inputs):
 
         if self.input_transform is not None:
@@ -86,8 +73,8 @@ class DefaultGenome(BaseGenome):
 
         ini_vals = jnp.full((self.max_nodes,), jnp.nan)
         ini_vals = ini_vals.at[self.input_idx].set(inputs)
-        nodes_attrs = jax.vmap(extract_node_attrs)(nodes)
+        nodes_attrs = vmap(extract_node_attrs)(nodes)
-        conns_attrs = jax.vmap(extract_conn_attrs)(conns)
+        conns_attrs = vmap(extract_conn_attrs)(conns)
 
         def cond_fun(carry):
             values, idx = carry
@@ -105,7 +92,7 @@ class DefaultGenome(BaseGenome):
             def otherwise():
                 conn_indices = u_conns[:, i]
                 hit_attrs = attach_with_inf(conns_attrs, conn_indices)
-                ins = jax.vmap(self.conn_gene.forward, in_axes=(None, 0, 0))(
+                ins = vmap(self.conn_gene.forward, in_axes=(None, 0, 0))(
                     state, hit_attrs, values
                 )
 
@@ -130,85 +117,14 @@ class DefaultGenome(BaseGenome):
         else:
             return self.output_transform(vals[self.output_idx])
 
-    def update_by_batch(self, state, batch_input, transformed):
+    def network_dict(self, state, nodes, conns):
-
+        network = super().network_dict(state, nodes, conns)
-        if self.input_transform is not None:
+        topo_order, topo_layers = topological_sort_python(
-            batch_input = jax.vmap(self.input_transform)(batch_input)
+            set(network["nodes"]), set(network["conns"])
-
-        cal_seqs, nodes, conns, u_conns = transformed
-
-        batch_size = batch_input.shape[0]
-        batch_ini_vals = jnp.full((batch_size, self.max_nodes), jnp.nan)
-        batch_ini_vals = batch_ini_vals.at[:, self.input_idx].set(batch_input)
-        nodes_attrs = jax.vmap(extract_node_attrs)(nodes)
-        conns_attrs = jax.vmap(extract_conn_attrs)(conns)
-
-        def cond_fun(carry):
-            batch_values, nodes_attrs_, conns_attrs_, idx = carry
-            return (idx < self.max_nodes) & (cal_seqs[idx] != I_INF)
-
-        def body_func(carry):
-            batch_values, nodes_attrs_, conns_attrs_, idx = carry
-            i = cal_seqs[idx]
-
-            def input_node():
-                batch, new_attrs = self.node_gene.update_input_transform(
-                    state, nodes_attrs_[i], batch_values[:, i]
-                )
-                return (
-                    batch_values.at[:, i].set(batch),
-                    nodes_attrs_.at[i].set(new_attrs),
-                    conns_attrs_,
-                )
-
-            def otherwise():
-
-                conn_indices = u_conns[:, i]
-                hit_attrs = attach_with_inf(conns_attrs, conn_indices)
-                batch_ins, new_conn_attrs = jax.vmap(
-                    self.conn_gene.update_by_batch,
-                    in_axes=(None, 0, 1),
-                    out_axes=(1, 0),
-                )(state, hit_attrs, batch_values)
-
-                batch_z, new_node_attrs = self.node_gene.update_by_batch(
-                    state,
-                    nodes_attrs_[i],
-                    batch_ins,
-                    is_output_node=jnp.isin(i, self.output_idx),
-                )
-
-                return (
-                    batch_values.at[:, i].set(batch_z),
-                    nodes_attrs_.at[i].set(new_node_attrs),
-                    conns_attrs_.at[conn_indices].set(new_conn_attrs),
-                )
-
-            # the val of input nodes is obtained by the task, not by calculation
-            (batch_values, nodes_attrs_, conns_attrs_) = jax.lax.cond(
-                jnp.isin(i, self.input_idx),
-                input_node,
-                otherwise,
-            )
-
-            return batch_values, nodes_attrs_, conns_attrs_, idx + 1
-
-        batch_vals, nodes_attrs, conns_attrs, _ = jax.lax.while_loop(
-            cond_fun, body_func, (batch_ini_vals, nodes_attrs, conns_attrs, 0)
         )
-
+        network["topo_order"] = topo_order
-        nodes = jax.vmap(set_node_attrs)(nodes, nodes_attrs)
+        network["topo_layers"] = topo_layers
-        conns = jax.vmap(set_conn_attrs)(conns, conns_attrs)
+        return network
-
-        new_transformed = (cal_seqs, nodes, conns, u_conns)
-
-        if self.output_transform is None:
-            return batch_vals[:, self.output_idx], new_transformed
-        else:
-            return (
-                jax.vmap(self.output_transform)(batch_vals[:, self.output_idx]),
-                new_transformed,
-            )
 
     def sympy_func(
         self,
@@ -241,7 +157,8 @@ class DefaultGenome(BaseGenome):
 
         input_idx = self.get_input_idx()
         output_idx = self.get_output_idx()
-        order, _ = topological_sort_python(set(network["nodes"]), set(network["conns"]))
+        order = network["topo_order"]
+
         hidden_idx = [
             i for i in network["nodes"] if i not in input_idx and i not in output_idx
         ]
@@ -260,8 +177,12 @@ class DefaultGenome(BaseGenome):
         for i in order:
 
             if i in input_idx:
-                nodes_exprs[symbols[-i - 1]] = symbols[-i - 1]  # origin equal to its symbol
+                nodes_exprs[symbols[-i - 1]] = symbols[
-                nodes_exprs[symbols[i]] = sympy_input_transform[i - min(input_idx)](symbols[-i - 1])  # normed i
+                    -i - 1
+                ]  # origin equal to its symbol
+                nodes_exprs[symbols[i]] = sympy_input_transform[i - min(input_idx)](
+                    symbols[-i - 1]
+                )  # normed i
 
             else:
                 in_conns = [c for c in network["conns"] if c[1] == i]
@@ -325,3 +246,73 @@ class DefaultGenome(BaseGenome):
             output_exprs,
             forward_func,
         )
+
+    def visualize(
+        self,
+        network,
+        rotate=0,
+        reverse_node_order=False,
+        size=(300, 300, 300),
+        color=("blue", "blue", "blue"),
+        save_path="network.svg",
+        save_dpi=800,
+        **kwargs,
+    ):
+        import networkx as nx
+        from matplotlib import pyplot as plt
+
+        nodes_list = list(network["nodes"])
+        conns_list = list(network["conns"])
+        input_idx = self.get_input_idx()
+        output_idx = self.get_output_idx()
+
+        topo_order, topo_layers = network["topo_order"], network["topo_layers"]
+        node2layer = {
+            node: layer for layer, nodes in enumerate(topo_layers) for node in nodes
+        }
+        if reverse_node_order:
+            topo_order = topo_order[::-1]
+
+        G = nx.DiGraph()
+
+        if not isinstance(size, tuple):
+            size = (size, size, size)
+        if not isinstance(color, tuple):
+            color = (color, color, color)
+
+        for node in topo_order:
+            if node in input_idx:
+                G.add_node(node, subset=node2layer[node], size=size[0], color=color[0])
+            elif node in output_idx:
+                G.add_node(node, subset=node2layer[node], size=size[2], color=color[2])
+            else:
+                G.add_node(node, subset=node2layer[node], size=size[1], color=color[1])
+
+        for conn in conns_list:
+            G.add_edge(conn[0], conn[1])
+        pos = nx.multipartite_layout(G)
+
+        def rotate_layout(pos, angle):
+            angle_rad = np.deg2rad(angle)
+            cos_angle, sin_angle = np.cos(angle_rad), np.sin(angle_rad)
+            rotated_pos = {}
+            for node, (x, y) in pos.items():
+                rotated_pos[node] = (
+                    cos_angle * x - sin_angle * y,
+                    sin_angle * x + cos_angle * y,
+                )
+            return rotated_pos
+
+        rotated_pos = rotate_layout(pos, rotate)
+
+        node_sizes = [n["size"] for n in G.nodes.values()]
+        node_colors = [n["color"] for n in G.nodes.values()]
+
+        nx.draw(
+            G,
+            pos=rotated_pos,
+            node_size=node_sizes,
+            node_color=node_colors,
+            **kwargs,
+        )
+        plt.savefig(save_path, dpi=save_dpi)

tensorneat/algorithm/neat/genome/dense.py

@@ -1,56 +0,0 @@
-import jax, jax.numpy as jnp
-from .default import DefaultGenome
-
-
-class DenseInitialize(DefaultGenome):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.max_nodes >= self.num_inputs + self.num_outputs
-        assert self.max_conns >= self.num_inputs * self.num_outputs
-
-    def initialize(self, state, randkey):
-
-        k1, k2 = jax.random.split(randkey, num=2)
-
-        input_idx, output_idx = self.input_idx, self.output_idx
-        input_size = len(input_idx)
-        output_size = len(output_idx)
-
-        nodes = jnp.full(
-            (self.max_nodes, self.node_gene.length), jnp.nan, dtype=jnp.float32
-        )
-
-        nodes = nodes.at[input_idx, 0].set(input_idx)
-        nodes = nodes.at[output_idx, 0].set(output_idx)
-
-        total_idx = input_size + output_size
-        rand_keys_n = jax.random.split(k1, num=total_idx)
-
-        node_attr_func = jax.vmap(self.node_gene.new_random_attrs, in_axes=(None, 0))
-        node_attrs = node_attr_func(state, rand_keys_n)
-        nodes = nodes.at[:total_idx, 1:].set(node_attrs)
-
-        conns = jnp.full(
-            (self.max_conns, self.conn_gene.length), jnp.nan, dtype=jnp.float32
-        )
-
-        input_to_output_ids, output_ids = jnp.meshgrid(
-            input_idx, output_idx, indexing="ij"
-        )
-        total_conns = input_size * output_size
-        conns = conns.at[:total_conns, :2].set(
-            jnp.column_stack([input_to_output_ids.flatten(), output_ids.flatten()])
-        )
-
-        rand_keys_c = jax.random.split(k2, num=total_conns)
-        conns_attr_func = jax.vmap(
-            self.conn_gene.new_random_attrs,
-            in_axes=(
-                None,
-                0,
-            ),
-        )
-        conns_attrs = conns_attr_func(state, rand_keys_c)
-        conns = conns.at[:total_conns, 2:].set(conns_attrs)
-
-        return nodes, conns

tensorneat/algorithm/neat/genome/hidden.py

@@ -1,70 +0,0 @@
-import jax, jax.numpy as jnp
-from .default import DefaultGenome
-
-
-class HiddenInitialize(DefaultGenome):
-    def __init__(self, hidden_cnt=8, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.hidden_cnt = hidden_cnt
-
-    def initialize(self, state, randkey):
-
-        k1, k2 = jax.random.split(randkey, num=2)
-
-        input_idx, output_idx = self.input_idx, self.output_idx
-        input_size = len(input_idx)
-        output_size = len(output_idx)
-
-        hidden_idx = jnp.arange(
-            input_size + output_size, input_size + output_size + self.hidden_cnt
-        )
-        nodes = jnp.full(
-            (self.max_nodes, self.node_gene.length), jnp.nan, dtype=jnp.float32
-        )
-
-        nodes = nodes.at[input_idx, 0].set(input_idx)
-        nodes = nodes.at[output_idx, 0].set(output_idx)
-        nodes = nodes.at[hidden_idx, 0].set(hidden_idx)
-
-        total_idx = input_size + output_size + self.hidden_cnt
-        rand_keys_n = jax.random.split(k1, num=total_idx)
-
-        node_attr_func = jax.vmap(self.node_gene.new_random_attrs, in_axes=(None, 0))
-        node_attrs = node_attr_func(state, rand_keys_n)
-        nodes = nodes.at[:total_idx, 1:].set(node_attrs)
-
-        conns = jnp.full(
-            (self.max_conns, self.conn_gene.length), jnp.nan, dtype=jnp.float32
-        )
-
-        input_to_hidden_ids, hidden_ids = jnp.meshgrid(
-            input_idx, hidden_idx, indexing="ij"
-        )
-        total_input_to_hidden_conns = input_size * self.hidden_cnt
-        conns = conns.at[:total_input_to_hidden_conns, :2].set(
-            jnp.column_stack([input_to_hidden_ids.flatten(), hidden_ids.flatten()])
-        )
-
-        hidden_to_output_ids, output_ids = jnp.meshgrid(
-            hidden_idx, output_idx, indexing="ij"
-        )
-        total_hidden_to_output_conns = self.hidden_cnt * output_size
-        conns = conns.at[
-            total_input_to_hidden_conns : total_input_to_hidden_conns
-            + total_hidden_to_output_conns,
-            :2,
-        ].set(jnp.column_stack([hidden_to_output_ids.flatten(), output_ids.flatten()]))
-
-        total_conns = total_input_to_hidden_conns + total_hidden_to_output_conns
-        rand_keys_c = jax.random.split(k2, num=total_conns)
-        conns_attr_func = jax.vmap(
-            self.conn_gene.new_random_attrs,
-            in_axes=(
-                None,
-                0,
-            ),
-        )
-        conns_attrs = conns_attr_func(state, rand_keys_c)
-        conns = conns.at[:total_conns, 2:].set(conns_attrs)
-
-        return nodes, conns

tensorneat/algorithm/neat/genome/operations/__init__.py

@@ -1,2 +1,3 @@
 from .crossover import BaseCrossover, DefaultCrossover
 from .mutation import BaseMutation, DefaultMutation
+from .distance import BaseDistance, DefaultDistance

tensorneat/algorithm/neat/genome/operations/crossover/base.py

@@ -0,0 +1,12 @@
+from tensorneat.common import StatefulBaseClass, State
+
+
+class BaseCrossover(StatefulBaseClass):
+
+    def setup(self, state=State(), genome = None):
+        assert genome is not None, "genome should not be None"
+        self.genome = genome
+        return state
+
+    def __call__(self, state, randkey, nodes1, nodes2, conns1, conns2):
+        raise NotImplementedError

tensorneat/algorithm/neat/genome/operations/crossover/default.py

@@ -1,7 +1,8 @@
-import jax, jax.numpy as jnp
+import jax
+from jax import vmap, numpy as jnp
 
 from .base import BaseCrossover
-from utils.tools import (
+from ...utils import (
     extract_node_attrs,
     extract_conn_attrs,
     set_node_attrs,
@@ -10,14 +11,14 @@ from utils.tools import (
 
 
 class DefaultCrossover(BaseCrossover):
-    def __call__(self, state, randkey, genome, nodes1, conns1, nodes2, conns2):
+    def __call__(self, state, randkey, nodes1, conns1, nodes2, conns2):
         """
         use genome1 and genome2 to generate a new genome
         notice that genome1 should have higher fitness than genome2 (genome1 is winner!)
         """
         randkey1, randkey2 = jax.random.split(randkey, 2)
-        randkeys1 = jax.random.split(randkey1, genome.max_nodes)
+        randkeys1 = jax.random.split(randkey1, self.genome.max_nodes)
-        randkeys2 = jax.random.split(randkey2, genome.max_conns)
+        randkeys2 = jax.random.split(randkey2, self.genome.max_conns)
 
         # crossover nodes
         keys1, keys2 = nodes1[:, 0], nodes2[:, 0]
@@ -26,33 +27,33 @@ class DefaultCrossover(BaseCrossover):
 
         # For not homologous genes, use the value of nodes1(winner)
         # For homologous genes, use the crossover result between nodes1 and nodes2
-        node_attrs1 = jax.vmap(extract_node_attrs)(nodes1)
+        node_attrs1 = vmap(extract_node_attrs)(nodes1)
-        node_attrs2 = jax.vmap(extract_node_attrs)(nodes2)
+        node_attrs2 = vmap(extract_node_attrs)(nodes2)
 
         new_node_attrs = jnp.where(
             jnp.isnan(node_attrs1) | jnp.isnan(node_attrs2),  # one of them is nan
             node_attrs1,  # not homologous genes or both nan, use the value of nodes1(winner)
-            jax.vmap(genome.node_gene.crossover, in_axes=(None, 0, 0, 0))(
+            vmap(self.genome.node_gene.crossover, in_axes=(None, 0, 0, 0))(
                 state, randkeys1, node_attrs1, node_attrs2
             ),  # homologous or both nan
         )
-        new_nodes = jax.vmap(set_node_attrs)(nodes1, new_node_attrs)
+        new_nodes = vmap(set_node_attrs)(nodes1, new_node_attrs)
 
         # crossover connections
         con_keys1, con_keys2 = conns1[:, :2], conns2[:, :2]
         conns2 = self.align_array(con_keys1, con_keys2, conns2, is_conn=True)
 
-        conns_attrs1 = jax.vmap(extract_conn_attrs)(conns1)
+        conns_attrs1 = vmap(extract_conn_attrs)(conns1)
-        conns_attrs2 = jax.vmap(extract_conn_attrs)(conns2)
+        conns_attrs2 = vmap(extract_conn_attrs)(conns2)
 
         new_conn_attrs = jnp.where(
             jnp.isnan(conns_attrs1) | jnp.isnan(conns_attrs2),
             conns_attrs1,  # not homologous genes or both nan, use the value of conns1(winner)
-            jax.vmap(genome.conn_gene.crossover, in_axes=(None, 0, 0, 0))(
+            vmap(self.genome.conn_gene.crossover, in_axes=(None, 0, 0, 0))(
                 state, randkeys2, conns_attrs1, conns_attrs2
             ),  # homologous or both nan
         )
-        new_conns = jax.vmap(set_conn_attrs)(conns1, new_conn_attrs)
+        new_conns = vmap(set_conn_attrs)(conns1, new_conn_attrs)
 
         return new_nodes, new_conns
 

tensorneat/algorithm/neat/genome/operations/distance/__init__.py

@@ -0,0 +1,2 @@
+from .base import BaseDistance
+from .default import DefaultDistance

tensorneat/algorithm/neat/genome/operations/distance/base.py

@@ -0,0 +1,15 @@
+from tensorneat.common import StatefulBaseClass, State
+
+
+class BaseDistance(StatefulBaseClass):
+
+    def setup(self, state=State(), genome = None):
+        assert genome is not None, "genome should not be None"
+        self.genome = genome
+        return state
+
+    def __call__(self, state, nodes1, nodes2, conns1, conns2):
+        """
+        The distance between two genomes
+        """
+        raise NotImplementedError

tensorneat/algorithm/neat/genome/operations/distance/default.py

@@ -0,0 +1,105 @@
+from jax import vmap, numpy as jnp
+
+from .base import BaseDistance
+from ...utils import extract_node_attrs, extract_conn_attrs
+
+
+class DefaultDistance(BaseDistance):
+    def __init__(
+        self,
+        compatibility_disjoint: float = 1.0,
+        compatibility_weight: float = 0.4,
+    ):
+        self.compatibility_disjoint = compatibility_disjoint
+        self.compatibility_weight = compatibility_weight
+
+    def __call__(self, state, nodes1, nodes2, conns1, conns2):
+        """
+        The distance between two genomes
+        """
+        d = self.node_distance(state, nodes1, nodes2) + self.conn_distance(
+            state, conns1, conns2
+        )
+        return d
+
+    def node_distance(self, state, nodes1, nodes2):
+        """
+        The distance of the nodes part for two genomes
+        """
+        node_cnt1 = jnp.sum(~jnp.isnan(nodes1[:, 0]))
+        node_cnt2 = jnp.sum(~jnp.isnan(nodes2[:, 0]))
+        max_cnt = jnp.maximum(node_cnt1, node_cnt2)
+
+        # align homologous nodes
+        # this process is similar to np.intersect1d.
+        nodes = jnp.concatenate((nodes1, nodes2), axis=0)
+        keys = nodes[:, 0]
+        sorted_indices = jnp.argsort(keys, axis=0)
+        nodes = nodes[sorted_indices]
+        nodes = jnp.concatenate(
+            [nodes, jnp.full((1, nodes.shape[1]), jnp.nan)], axis=0
+        )  # add a nan row to the end
+        fr, sr = nodes[:-1], nodes[1:]  # first row, second row
+
+        # flag location of homologous nodes
+        intersect_mask = (fr[:, 0] == sr[:, 0]) & ~jnp.isnan(nodes[:-1, 0])
+
+        # calculate the count of non_homologous of two genomes
+        non_homologous_cnt = node_cnt1 + node_cnt2 - 2 * jnp.sum(intersect_mask)
+
+        # calculate the distance of homologous nodes
+        fr_attrs = vmap(extract_node_attrs)(fr)
+        sr_attrs = vmap(extract_node_attrs)(sr)
+        hnd = vmap(self.genome.node_gene.distance, in_axes=(None, 0, 0))(
+            state, fr_attrs, sr_attrs
+        )  # homologous node distance
+        hnd = jnp.where(jnp.isnan(hnd), 0, hnd)
+        homologous_distance = jnp.sum(hnd * intersect_mask)
+
+        val = (
+            non_homologous_cnt * self.compatibility_disjoint
+            + homologous_distance * self.compatibility_weight
+        )
+
+        val = jnp.where(max_cnt == 0, 0, val / max_cnt)  # normalize
+
+        return val
+
+    def conn_distance(self, state, conns1, conns2):
+        """
+        The distance of the conns part for two genomes
+        """
+        con_cnt1 = jnp.sum(~jnp.isnan(conns1[:, 0]))
+        con_cnt2 = jnp.sum(~jnp.isnan(conns2[:, 0]))
+        max_cnt = jnp.maximum(con_cnt1, con_cnt2)
+
+        cons = jnp.concatenate((conns1, conns2), axis=0)
+        keys = cons[:, :2]
+        sorted_indices = jnp.lexsort(keys.T[::-1])
+        cons = cons[sorted_indices]
+        cons = jnp.concatenate(
+            [cons, jnp.full((1, cons.shape[1]), jnp.nan)], axis=0
+        )  # add a nan row to the end
+        fr, sr = cons[:-1], cons[1:]  # first row, second row
+
+        # both genome has such connection
+        intersect_mask = jnp.all(fr[:, :2] == sr[:, :2], axis=1) & ~jnp.isnan(fr[:, 0])
+
+        non_homologous_cnt = con_cnt1 + con_cnt2 - 2 * jnp.sum(intersect_mask)
+
+        fr_attrs = vmap(extract_conn_attrs)(fr)
+        sr_attrs = vmap(extract_conn_attrs)(sr)
+        hcd = vmap(self.genome.conn_gene.distance, in_axes=(None, 0, 0))(
+            state, fr_attrs, sr_attrs
+        )  # homologous connection distance
+        hcd = jnp.where(jnp.isnan(hcd), 0, hcd)
+        homologous_distance = jnp.sum(hcd * intersect_mask)
+
+        val = (
+            non_homologous_cnt * self.compatibility_disjoint
+            + homologous_distance * self.compatibility_weight
+        )
+
+        val = jnp.where(max_cnt == 0, 0, val / max_cnt)  # normalize
+
+        return val

tensorneat/algorithm/neat/genome/operations/mutation/base.py

@@ -0,0 +1,12 @@
+from tensorneat.common import StatefulBaseClass, State
+
+
+class BaseMutation(StatefulBaseClass):
+
+    def setup(self, state=State(), genome = None):
+        assert genome is not None, "genome should not be None"
+        self.genome = genome
+        return state
+
+    def __call__(self, state, randkey, genome, nodes, conns, new_node_key):
+        raise NotImplementedError

tensorneat/algorithm/neat/genome/operations/mutation/default.py

@@ -1,11 +1,14 @@
-import jax, jax.numpy as jnp
+import jax
+from jax import vmap, numpy as jnp
 from . import BaseMutation
-from utils import (
+from tensorneat.common import (
     fetch_first,
     fetch_random,
     I_INF,
-    unflatten_conns,
     check_cycles,
+)
+from ...utils import (
+    unflatten_conns,
     add_node,
     add_conn,
     delete_node_by_pos,
@@ -225,17 +228,17 @@ class DefaultMutation(BaseMutation):
         nodes_randkeys = jax.random.split(k1, num=genome.max_nodes)
         conns_randkeys = jax.random.split(k2, num=genome.max_conns)
 
-        node_attrs = jax.vmap(extract_node_attrs)(nodes)
+        node_attrs = vmap(extract_node_attrs)(nodes)
-        new_node_attrs = jax.vmap(genome.node_gene.mutate, in_axes=(None, 0, 0))(
+        new_node_attrs = vmap(genome.node_gene.mutate, in_axes=(None, 0, 0))(
             state, nodes_randkeys, node_attrs
         )
-        new_nodes = jax.vmap(set_node_attrs)(nodes, new_node_attrs)
+        new_nodes = vmap(set_node_attrs)(nodes, new_node_attrs)
 
-        conn_attrs = jax.vmap(extract_conn_attrs)(conns)
+        conn_attrs = vmap(extract_conn_attrs)(conns)
-        new_conn_attrs = jax.vmap(genome.conn_gene.mutate, in_axes=(None, 0, 0))(
+        new_conn_attrs = vmap(genome.conn_gene.mutate, in_axes=(None, 0, 0))(
             state, conns_randkeys, conn_attrs
         )
-        new_conns = jax.vmap(set_conn_attrs)(conns, new_conn_attrs)
+        new_conns = vmap(set_conn_attrs)(conns, new_conn_attrs)
 
         # nan nodes not changed
         new_nodes = jnp.where(jnp.isnan(nodes), jnp.nan, new_nodes)

tensorneat/algorithm/neat/genome/recurrent.py

@@ -1,11 +1,11 @@
 from typing import Callable
 
 import jax, jax.numpy as jnp
-from utils import unflatten_conns
+from .utils import unflatten_conns
 
 from . import BaseGenome
-from ..gene import BaseNodeGene, BaseConnGene, DefaultNodeGene, DefaultConnGene
+from ..gene import DefaultNodeGene, DefaultConnGene
-from ..ga import BaseMutation, BaseCrossover, DefaultMutation, DefaultCrossover
+from .operations import DefaultMutation, DefaultCrossover
 
 
 class RecurrentGenome(BaseGenome):
@@ -17,13 +17,13 @@ class RecurrentGenome(BaseGenome):
         self,
         num_inputs: int,
         num_outputs: int,
-        max_nodes: int,
+        max_nodes = 50,
-        max_conns: int,
+        max_conns = 100,
-        node_gene: BaseNodeGene = DefaultNodeGene(),
+        node_gene=DefaultNodeGene(),
-        conn_gene: BaseConnGene = DefaultConnGene(),
+        conn_gene=DefaultConnGene(),
-        mutation: BaseMutation = DefaultMutation(),
+        mutation=DefaultMutation(),
-        crossover: BaseCrossover = DefaultCrossover(),
+        crossover=DefaultCrossover(),
-        activate_time: int = 10,
+        activate_time=10,
         output_transform: Callable = None,
     ):
         super().__init__(

tensorneat/algorithm/neat/genome/utils.py

@@ -0,0 +1,109 @@
+import jax
+from jax import vmap, numpy as jnp
+
+from tensorneat.common import fetch_first, I_INF
+
+
+def unflatten_conns(nodes, conns):
+    """
+    transform the (C, CL) connections to (N, N), which contains the idx of the connection in conns
+    connection length, N means the number of nodes, C means the number of connections
+    returns the unflatten connection indices with shape (N, N)
+    """
+    N = nodes.shape[0]  # max_nodes
+    C = conns.shape[0]  # max_conns
+    node_keys = nodes[:, 0]
+    i_keys, o_keys = conns[:, 0], conns[:, 1]
+
+    def key_to_indices(key, keys):
+        return fetch_first(key == keys)
+
+    i_idxs = vmap(key_to_indices, in_axes=(0, None))(i_keys, node_keys)
+    o_idxs = vmap(key_to_indices, in_axes=(0, None))(o_keys, node_keys)
+
+    # Is interesting that jax use clip when attach data in array
+    # however, it will do nothing when setting values in an array
+    # put the index of connections in the unflatten array
+    unflatten = (
+        jnp.full((N, N), I_INF, dtype=jnp.int32)
+        .at[i_idxs, o_idxs]
+        .set(jnp.arange(C, dtype=jnp.int32))
+    )
+
+    return unflatten
+
+
+def valid_cnt(nodes_or_conns):
+    return jnp.sum(~jnp.isnan(nodes_or_conns[:, 0]))
+
+
+def extract_node_attrs(node):
+    """
+    node: Array(NL, )
+    extract the attributes of a node
+    """
+    return node[1:]  # 0 is for idx
+
+
+def set_node_attrs(node, attrs):
+    """
+    node: Array(NL, )
+    attrs: Array(NL-1, )
+    set the attributes of a node
+    """
+    return node.at[1:].set(attrs)  # 0 is for idx
+
+
+def extract_conn_attrs(conn):
+    """
+    conn: Array(CL, )
+    extract the attributes of a connection
+    """
+    return conn[2:]  # 0, 1 is for in-idx and out-idx
+
+
+def set_conn_attrs(conn, attrs):
+    """
+    conn: Array(CL, )
+    attrs: Array(CL-2, )
+    set the attributes of a connection
+    """
+    return conn.at[2:].set(attrs)  # 0, 1 is for in-idx and out-idx
+
+
+def add_node(nodes, new_key: int, attrs):
+    """
+    Add a new node to the genome.
+    The new node will place at the first NaN row.
+    """
+    exist_keys = nodes[:, 0]
+    pos = fetch_first(jnp.isnan(exist_keys))
+    new_nodes = nodes.at[pos, 0].set(new_key)
+    return new_nodes.at[pos, 1:].set(attrs)
+
+
+def delete_node_by_pos(nodes, pos):
+    """
+    Delete a node from the genome.
+    Delete the node by its pos in nodes.
+    """
+    return nodes.at[pos].set(jnp.nan)
+
+
+def add_conn(conns, i_key, o_key, attrs):
+    """
+    Add a new connection to the genome.
+    The new connection will place at the first NaN row.
+    """
+    con_keys = conns[:, 0]
+    pos = fetch_first(jnp.isnan(con_keys))
+    new_conns = conns.at[pos, 0:2].set(jnp.array([i_key, o_key]))
+    return new_conns.at[pos, 2:].set(attrs)
+
+
+def delete_conn_by_pos(conns, pos):
+    """
+    Delete a connection from the genome.
+    Delete the connection by its idx.
+    """
+    return conns.at[pos].set(jnp.nan)

tensorneat/algorithm/neat/neat.py

@@ -1,5 +1,5 @@
 import jax, jax.numpy as jnp
-from utils import State
+from tensorneat.common import State
 from .. import BaseAlgorithm
 from .species import *
 

tensorneat/algorithm/neat/species/base.py

@@ -1,4 +1,4 @@
-from utils import State, StatefulBaseClass
+from tensorneat.common import State, StatefulBaseClass
 from ..genome import BaseGenome
 
 

tensorneat/algorithm/neat/species/default.py

@@ -1,9 +1,11 @@
 import jax, jax.numpy as jnp
-from utils import (
+from tensorneat.common import (
     State,
     rank_elements,
     argmin_with_mask,
     fetch_first,
+)
+from ..genome.utils import (
     extract_conn_attrs,
     extract_node_attrs,
 )
@@ -635,7 +637,9 @@ class DefaultSpecies(BaseSpecies):
 
         # find next node key
         all_nodes_keys = state.pop_nodes[:, :, 0]
-        max_node_key = jnp.max(all_nodes_keys, where=~jnp.isnan(all_nodes_keys), initial=0)
+        max_node_key = jnp.max(
+            all_nodes_keys, where=~jnp.isnan(all_nodes_keys), initial=0
+        )
         next_node_key = max_node_key + 1
         new_node_keys = jnp.arange(self.pop_size) + next_node_key
 

tensorneat/common/__init__.py

@@ -1,4 +1,4 @@
-from utils.aggregation.agg_jnp import Agg, agg_func, AGG_ALL
+from tensorneat.common.aggregation.agg_jnp import Agg, agg_func, AGG_ALL
 from .tools import *
 from .graph import *
 from .state import State

tensorneat/common/tools.py

@@ -6,36 +6,6 @@ from jax import numpy as jnp, Array, jit, vmap
 
 I_INF = np.iinfo(jnp.int32).max  # infinite int
 
-
-def unflatten_conns(nodes, conns):
-    """
-    transform the (C, CL) connections to (N, N), which contains the idx of the connection in conns
-    connection length, N means the number of nodes, C means the number of connections
-    returns the unflatten connection indices with shape (N, N)
-    """
-    N = nodes.shape[0]  # max_nodes
-    C = conns.shape[0]  # max_conns
-    node_keys = nodes[:, 0]
-    i_keys, o_keys = conns[:, 0], conns[:, 1]
-
-    def key_to_indices(key, keys):
-        return fetch_first(key == keys)
-
-    i_idxs = vmap(key_to_indices, in_axes=(0, None))(i_keys, node_keys)
-    o_idxs = vmap(key_to_indices, in_axes=(0, None))(o_keys, node_keys)
-
-    # Is interesting that jax use clip when attach data in array
-    # however, it will do nothing when setting values in an array
-    # put the index of connections in the unflatten array
-    unflatten = (
-        jnp.full((N, N), I_INF, dtype=jnp.int32)
-        .at[i_idxs, o_idxs]
-        .set(jnp.arange(C, dtype=jnp.int32))
-    )
-
-    return unflatten
-
-
 # TODO: strange implementation
 def attach_with_inf(arr, idx):
     expand_size = arr.ndim - idx.ndim
@@ -45,40 +15,6 @@ def attach_with_inf(arr, idx):
     return jnp.where(expand_idx == I_INF, jnp.nan, arr[idx])
 
 
-def extract_node_attrs(node):
-    """
-    node: Array(NL, )
-    extract the attributes of a node
-    """
-    return node[1:]  # 0 is for idx
-
-
-def set_node_attrs(node, attrs):
-    """
-    node: Array(NL, )
-    attrs: Array(NL-1, )
-    set the attributes of a node
-    """
-    return node.at[1:].set(attrs)  # 0 is for idx
-
-
-def extract_conn_attrs(conn):
-    """
-    conn: Array(CL, )
-    extract the attributes of a connection
-    """
-    return conn[2:]  # 0, 1 is for in-idx and out-idx
-
-
-def set_conn_attrs(conn, attrs):
-    """
-    conn: Array(CL, )
-    attrs: Array(CL-2, )
-    set the attributes of a connection
-    """
-    return conn.at[2:].set(attrs)  # 0, 1 is for in-idx and out-idx
-
-
 @jit
 def fetch_first(mask, default=I_INF) -> Array:
     """
@@ -164,44 +100,6 @@ def argmin_with_mask(arr, mask):
     return min_idx
 
 
-def add_node(nodes, new_key: int, attrs):
-    """
-    Add a new node to the genome.
-    The new node will place at the first NaN row.
-    """
-    exist_keys = nodes[:, 0]
-    pos = fetch_first(jnp.isnan(exist_keys))
-    new_nodes = nodes.at[pos, 0].set(new_key)
-    return new_nodes.at[pos, 1:].set(attrs)
-
-
-def delete_node_by_pos(nodes, pos):
-    """
-    Delete a node from the genome.
-    Delete the node by its pos in nodes.
-    """
-    return nodes.at[pos].set(jnp.nan)
-
-
-def add_conn(conns, i_key, o_key, attrs):
-    """
-    Add a new connection to the genome.
-    The new connection will place at the first NaN row.
-    """
-    con_keys = conns[:, 0]
-    pos = fetch_first(jnp.isnan(con_keys))
-    new_conns = conns.at[pos, 0:2].set(jnp.array([i_key, o_key]))
-    return new_conns.at[pos, 2:].set(attrs)
-
-
-def delete_conn_by_pos(conns, pos):
-    """
-    Delete a connection from the genome.
-    Delete the connection by its idx.
-    """
-    return conns.at[pos].set(jnp.nan)
-
-
 def hash_array(arr: Array):
     arr = jax.lax.bitcast_convert_type(arr, jnp.uint32)
 

tensorneat/pipeline.py

@@ -9,7 +9,7 @@ from algorithm import BaseAlgorithm
 from problem import BaseProblem
 from problem.rl_env import RLEnv
 from problem.func_fit import FuncFit
-from utils import State, StatefulBaseClass
+from tensorneat.common import State, StatefulBaseClass
 
 
 class Pipeline(StatefulBaseClass):

tensorneat/problem/base.py

@@ -1,6 +1,6 @@
 from typing import Callable
 
-from utils import State, StatefulBaseClass
+from tensorneat.common import State, StatefulBaseClass
 
 
 class BaseProblem(StatefulBaseClass):

tensorneat/problem/func_fit/func_fit.py

@@ -1,7 +1,7 @@
 import jax
 import jax.numpy as jnp
 
-from utils import State
+from tensorneat.common import State
 from .. import BaseProblem
 
 

tensorneat/problem/rl_env/jumanji/jumanji_2048.py

@@ -1,7 +1,7 @@
 import jax, jax.numpy as jnp
 import jumanji
 
-from utils import State
+from tensorneat.common import State
 from ..rl_jit import RLEnv
 
 

tensorneat/problem/rl_env/rl_jit.py

@@ -4,7 +4,7 @@ import jax
 import jax.numpy as jnp
 import numpy as np
 
-from utils import State
+from tensorneat.common import State
 from .. import BaseProblem
 
 

tensorneat/test/crossover_mutation.py

@@ -1,5 +1,5 @@
 import jax, jax.numpy as jnp
-from utils import Act
+from tensorneat.common import Act
 from algorithm.neat import *
 import numpy as np
 

tensorneat/test/nan_fitness.py

@@ -1,5 +1,5 @@
 import jax, jax.numpy as jnp
-from utils import Act
+from tensorneat.common import Act
 from algorithm.neat import *
 import numpy as np
 

tensorneat/test/test_kan.ipynb

@@ -27,7 +27,7 @@
     "from algorithm.neat.gene.node.kan_node import KANNode\n",
     "from algorithm.neat.gene.conn.bspline import BSplineConn\n",
     "from problem.func_fit import XOR3d\n",
-    "from utils import Act\n",
+    "from tensorneat.utils import Act\n",
     "\n",
     "import jax, jax.numpy as jnp\n",
     "\n",

tensorneat/test/test_nan_fitness.py

@@ -1,5 +1,5 @@
 import jax, jax.numpy as jnp
-from utils import Act
+from tensorneat.common import Act
 from algorithm.neat import *
 import numpy as np
 

tensorneat/test/test_record_episode.ipynb

@@ -14,7 +14,7 @@
    "outputs": [],
    "source": [
     "import jax, jax.numpy as jnp\n",
-    "from utils import State\n",
+    "from tensorneat.utils import State\n",
     "from problem.rl_env import BraxEnv\n",
     "\n",
     "\n",

tensorneat/test/test_update_by_batch.ipynb

@@ -145,7 +145,7 @@
    "source": [
     "from algorithm.neat.gene.node.normalized import NormalizedNode\n",
     "from algorithm.neat.gene.conn import DefaultConnGene\n",
-    "from utils import Act\n",
+    "from tensorneat.utils import Act\n",
     "\n",
     "genome = DefaultGenome(num_inputs=3, num_outputs=2, max_nodes=10, max_conns=10,\n",
     "                       node_gene=NormalizedNode(activation_default=Act.identity, activation_options=(Act.identity,)),\n",