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/genome/gene/conn/__init__.py

@@ -1,2 +1,3 @@
 from .base import BaseConn
 from .default import DefaultConn
+from .origin import OriginConn

src/tensorneat/genome/gene/conn/origin.py

@@ -0,0 +1,60 @@
+import jax, jax.numpy as jnp
+from .default import DefaultConn
+
+
+class OriginConn(DefaultConn):
+    """
+    Implementation of connections in origin NEAT Paper.
+    Details at https://github.com/EMI-Group/tensorneat/issues/11.
+    """
+
+    # add historical_marker into fixed_attrs
+    fixed_attrs = ["input_index", "output_index", "historical_marker"]
+    custom_attrs = ["weight"]
+
+    def __init__(
+        self,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+
+    def crossover(self, state, randkey, attrs1, attrs2):
+        # random pick one of attrs, without attrs exchange
+        return jnp.where(
+            # origin code, generate multiple random numbers, without attrs exchange
+            # jax.random.normal(randkey, attrs1.shape) > 0,
+            jax.random.normal(randkey)
+            > 0,  # generate one random number, without attrs exchange
+            attrs1,
+            attrs2,
+        )
+
+    def get_historical_marker(self, state, gene_array):
+        return gene_array[2]
+    
+    def repr(self, state, conn, precision=2, idx_width=3, func_width=8):
+        in_idx, out_idx, historical_marker, weight = conn
+
+        in_idx = int(in_idx)
+        out_idx = int(out_idx)
+        historical_marker = int(historical_marker)
+        weight = round(float(weight), precision)
+
+        return "{}(in: {:<{idx_width}}, out: {:<{idx_width}}, historical_marker: {:<{idx_width}}, weight: {:<{float_width}})".format(
+            self.__class__.__name__,
+            in_idx,
+            out_idx,
+            historical_marker,
+            weight,
+            idx_width=idx_width,
+            float_width=precision + 3,
+        )
+
+    def to_dict(self, state, conn):
+        return {
+            "in": int(conn[0]),
+            "out": int(conn[1]),
+            "historical_marker": int(conn[2]),
+            "weight": jnp.float32(conn[3]),
+        }

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

@@ -1,3 +1,4 @@
 from .base import BaseNode
 from .default import DefaultNode
 from .bias import BiasNode
+from .origin import OriginNode

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

@@ -0,0 +1,27 @@
+import jax, jax.numpy as jnp
+from .default import DefaultNode
+
+
+class OriginNode(DefaultNode):
+    """
+    Implementation of nodes in origin NEAT Paper.
+    Details at https://github.com/EMI-Group/tensorneat/issues/11.
+    """
+
+    def __init__(
+        self,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+
+    def crossover(self, state, randkey, attrs1, attrs2):
+        # random pick one of attrs, without attrs exchange
+        return jnp.where(
+            # origin code, generate multiple random numbers, without attrs exchange
+            # jax.random.normal(randkey, attrs1.shape) > 0,
+            jax.random.normal(randkey)
+            > 0,  # generate one random number, without attrs exchange
+            attrs1,
+            attrs2,
+        )