add KNN

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)