add KNN

tensorneat/algorithm/hyperneat/hyperneat.py

@@ -16,8 +16,8 @@ class HyperNEAT(BaseAlgorithm):
         neat: NEAT,
         below_threshold: float = 0.3,
         max_weight: float = 5.0,
-        activation=Act.sigmoid,
         aggregation=Agg.sum,
+        activation=Act.sigmoid,
         activate_time: int = 10,
         output_transform: Callable = Act.sigmoid,
     ):
@@ -34,7 +34,7 @@ class HyperNEAT(BaseAlgorithm):
             num_outputs=substrate.num_outputs,
             max_nodes=substrate.nodes_cnt,
             max_conns=substrate.conns_cnt,
-            node_gene=HyperNodeGene(activation, aggregation),
+            node_gene=HyperNodeGene(aggregation, activation),
             conn_gene=HyperNEATConnGene(),
             activate_time=activate_time,
             output_transform=output_transform,
@@ -57,7 +57,6 @@ class HyperNEAT(BaseAlgorithm):
         query_res = jax.vmap(self.neat.forward, in_axes=(None, 0, None))(
             state, self.substrate.query_coors, transformed
         )
-
         # mute the connection with weight below threshold
         query_res = jnp.where(
             (-self.below_threshold < query_res) & (query_res < self.below_threshold),
@@ -77,12 +76,15 @@ class HyperNEAT(BaseAlgorithm):
         h_nodes, h_conns = self.substrate.make_nodes(
             query_res
         ), self.substrate.make_conn(query_res)
+
         return self.hyper_genome.transform(state, h_nodes, h_conns)
 
     def forward(self, state, inputs, transformed):
         # add bias
         inputs_with_bias = jnp.concatenate([inputs, jnp.array([1])])
-        return self.hyper_genome.forward(state, inputs_with_bias, transformed)
+
+        res = self.hyper_genome.forward(state, inputs_with_bias, transformed)
+        return res
 
     @property
     def num_inputs(self):
@@ -106,12 +108,12 @@ class HyperNEAT(BaseAlgorithm):
 class HyperNodeGene(BaseNodeGene):
     def __init__(
         self,
-        activation=Act.sigmoid,
         aggregation=Agg.sum,
+        activation=Act.sigmoid,
     ):
         super().__init__()
-        self.activation = activation
         self.aggregation = aggregation
+        self.activation = activation
 
     def forward(self, state, attrs, inputs, is_output_node=False):
         return jax.lax.cond(

tensorneat/algorithm/hyperneat/substrate/default.py

@@ -14,7 +14,7 @@ class DefaultSubstrate(BaseSubstrate):
         return self.nodes
 
     def make_conn(self, query_res):
-        return self.conns.at[:, 3:].set(query_res)  # change weight
+        return self.conns.at[:, 2:].set(query_res)  # change weight
 
     @property
     def query_coors(self):

tensorneat/algorithm/hyperneat/substrate/full.py

@@ -56,10 +56,9 @@ def analysis_substrate(input_coors, output_coors, hidden_coors):
 
     nodes = np.concatenate((input_idx, output_idx, hidden_idx))[..., np.newaxis]
     conns = np.zeros(
-        (correspond_keys.shape[0], 4), dtype=np.float32
+        (correspond_keys.shape[0], 3), dtype=np.float32
-    )  # input_idx, output_idx, enabled, weight
+    )  # input_idx, output_idx, weight
-    conns[:, 0:2] = correspond_keys
+    conns[:, :2] = correspond_keys
-    conns[:, 2] = 1  # enabled is True
 
     return query_coors, nodes, conns
 

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

@@ -0,0 +1,27 @@
+import jax.numpy as jnp
+from . import BaseNodeGene
+from utils import Agg
+
+
+class KANNode(BaseNodeGene):
+    "Node gene for KAN, with only a sum aggregation."
+
+    custom_attrs = []
+
+    def __init__(self):
+        super().__init__()
+
+    def new_identity_attrs(self, state):
+        return jnp.array([])
+
+    def new_random_attrs(self, state, randkey):
+        return jnp.array([])
+
+    def mutate(self, state, randkey, attrs):
+        return jnp.array([])
+
+    def distance(self, state, attrs1, attrs2):
+        return 0
+
+    def forward(self, state, attrs, inputs, is_output_node=False):
+        return Agg.sum(inputs)

tensorneat/examples/func_fit/xor3d_hyperneat.py

@@ -39,9 +39,9 @@ if __name__ == "__main__":
                         ),
                         output_transform=Act.tanh,  # the activation function for output node in NEAT
                     ),
-                    pop_size=10000,
+                    pop_size=1000,
                     species_size=10,
-                    compatibility_threshold=3.5,
+                    compatibility_threshold=2,
                     survival_threshold=0.03,
                 ),
             ),

tensorneat/examples/func_fit/xor_kan.py

@@ -0,0 +1,48 @@
+from pipeline import Pipeline
+from algorithm.neat import *
+from algorithm.neat.gene.node.kan_node import KANNode
+from algorithm.neat.gene.conn.bspline import BSplineConn
+
+from problem.func_fit import XOR3d
+from utils import Act
+
+if __name__ == "__main__":
+    pipeline = Pipeline(
+        algorithm=NEAT(
+            species=DefaultSpecies(
+                genome=DefaultGenome(
+                    num_inputs=3,
+                    num_outputs=1,
+                    max_nodes=50,
+                    max_conns=100,
+                    node_gene=KANNode(),
+                    conn_gene=BSplineConn(),
+                    output_transform=Act.sigmoid,  # the activation function for output node
+                    mutation=DefaultMutation(
+                        node_add=0.1,
+                        conn_add=0.1,
+                        node_delete=0.05,
+                        conn_delete=0.05,
+                    ),
+                ),
+                pop_size=1000,
+                species_size=20,
+                compatibility_threshold=1.5,
+                survival_threshold=0.01,  # magic
+            ),
+        ),
+        # problem=XOR3d(return_data=True),
+        problem=XOR3d(),
+        generation_limit=10000,
+        fitness_target=-1e-8,
+        # update_batch_size=8,
+        # pre_update=True,
+    )
+
+    # initialize state
+    state = pipeline.setup()
+    # print(state)
+    # run until terminate
+    state, best = pipeline.auto_run(state)
+    # show result
+    pipeline.show(state, best)

tensorneat/test/test_compile_time.ipynb

@@ -0,0 +1,112 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2024-06-02T08:29:04.093990Z",
+     "start_time": "2024-06-02T08:29:04.085992900Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import jax\n",
+    "from jax import vmap, jit, numpy as jnp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "outputs": [],
+   "source": [
+    "def func(x, y):\n",
+    "    return x + y\n",
+    "\n",
+    "def loop2():\n",
+    "    s = 0\n",
+    "    for i in range(1000):\n",
+    "        x = jnp.full((10000, 1), i)\n",
+    "        y = jnp.full((10000, 1), i + 1)\n",
+    "        s = (vmap(func)(x, y)).sum()\n",
+    "    return s\n",
+    "\n",
+    "def loop3():\n",
+    "    s = 0\n",
+    "    vmap_func = vmap(func)\n",
+    "    for i in range(1000):\n",
+    "        x = jnp.full((10000, 1), i)\n",
+    "        y = jnp.full((10000, 1), i + 1)\n",
+    "        s = (vmap_func(x, y)).sum()\n",
+    "    return s"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-06-02T08:31:13.023886300Z",
+     "start_time": "2024-06-02T08:31:13.003026800Z"
+    }
+   },
+   "id": "39f803029127aaa8"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Array(19990000, dtype=int32)"
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compile_loop = jit(loop3).lower().compile()\n",
+    "compile_loop()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-06-02T08:31:14.526380100Z",
+     "start_time": "2024-06-02T08:31:13.870916800Z"
+    }
+   },
+   "id": "ab9f83d0a313f51d"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "c1bd963e51aa5fd4"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}