This commit is related to issue: https://github.com/EMI-Group/tensorneat/issues/11

1. Add origin_node and origin_conn.
2. Change the behavior of crossover and mutation. Now, TensorNEAT will use all fix_attrs(include historical marker if it has one) as identifier for gene in crossover and distance calculation.
3. Other slightly change.
4. Add two related examples: xor_origin and hopper_origin
5. Add related test file.

src/tensorneat/algorithm/hyperneat/substrate/default.py

@@ -2,7 +2,7 @@ from jax import vmap
 import numpy as np
 
 from .base import BaseSubstrate
-from tensorneat.genome.utils import set_conn_attrs
+from tensorneat.genome.utils import set_gene_attrs
 
 
 class DefaultSubstrate(BaseSubstrate):
@@ -21,7 +21,8 @@ class DefaultSubstrate(BaseSubstrate):
 
     def make_conns(self, query_res):
         # change weight of conns
-        return vmap(set_conn_attrs)(self.conns, query_res)
+        # the last column is the weight
+        return self.conns.at[:, -1].set(query_res)
 
     @property
     def query_coors(self):

src/tensorneat/algorithm/neat/neat.py

@@ -116,6 +116,25 @@ class NEAT(BaseAlgorithm):
         next_node_key = max_node_key + 1
         new_node_keys = jnp.arange(self.pop_size) + next_node_key
 
+        # find next conn historical markers for mutation if needed
+        if "historical_marker" in self.genome.conn_gene.fixed_attrs:
+            all_conns_markers = vmap(
+                self.genome.conn_gene.get_historical_marker, in_axes=(None, 0)
+            )(state, state.pop_conns)
+
+            max_conn_markers = jnp.max(
+                all_conns_markers, where=~jnp.isnan(all_conns_markers), initial=0
+            )
+            next_conn_markers = max_conn_markers + 1
+            new_conn_markers = (
+                jnp.arange(self.pop_size * 3).reshape(self.pop_size, 3)
+                + next_conn_markers
+            )
+        else:
+            # no need to generate new conn historical markers
+            # use 0
+            new_conn_markers = jnp.full((self.pop_size, 3), 0)
+
         # prepare random keys
         k1, k2, randkey = jax.random.split(state.randkey, 3)
         crossover_randkeys = jax.random.split(k1, self.pop_size)
@@ -133,9 +152,9 @@ class NEAT(BaseAlgorithm):
 
         # batch mutation
         m_n_nodes, m_n_conns = vmap(
-            self.genome.execute_mutation, in_axes=(None, 0, 0, 0, 0)
+            self.genome.execute_mutation, in_axes=(None, 0, 0, 0, 0, 0)
         )(
-            state, mutate_randkeys, n_nodes, n_conns, new_node_keys
+            state, mutate_randkeys, n_nodes, n_conns, new_node_keys, new_conn_markers
         )  # mutated_new_nodes, mutated_new_conns
 
         # elitism don't mutate