new architecture

algorithm/neat/ga/crossover/default.py

@@ -0,0 +1,66 @@
+import jax, jax.numpy as jnp
+
+from .base import BaseCrossover
+
+class DefaultCrossover(BaseCrossover):
+    def __call__(self, randkey, genome, nodes1, nodes2, conns1, conns2):
+        """
+        use genome1 and genome2 to generate a new genome
+        notice that genome1 should have higher fitness than genome2 (genome1 is winner!)
+        """
+        randkey_1, randkey_2, key = jax.random.split(randkey, 3)
+
+        # crossover nodes
+        keys1, keys2 = nodes1[:, 0], nodes2[:, 0]
+        # make homologous genes align in nodes2 align with nodes1
+        nodes2 = self.align_array(keys1, keys2, nodes2, False)
+
+        # For not homologous genes, use the value of nodes1(winner)
+        # For homologous genes, use the crossover result between nodes1 and nodes2
+        new_nodes = jnp.where(jnp.isnan(nodes1) | jnp.isnan(nodes2), nodes1, self.crossover_gene(randkey_1, nodes1, nodes2))
+
+        # crossover connections
+        con_keys1, con_keys2 = conns1[:, :2], conns2[:, :2]
+        conns2 = self.align_array(con_keys1, con_keys2, conns2, True)
+
+        new_conns = jnp.where(jnp.isnan(conns1) | jnp.isnan(conns2), conns1, self.crossover_gene(randkey_2, conns1, conns2))
+
+        return new_nodes, new_conns
+
+    def align_array(self, seq1, seq2, ar2, is_conn: bool):
+        """
+        After I review this code, I found that it is the most difficult part of the code. Please never change it!
+        make ar2 align with ar1.
+        :param seq1:
+        :param seq2:
+        :param ar2:
+        :param is_conn:
+        :return:
+        align means to intersect part of ar2 will be at the same position as ar1,
+        non-intersect part of ar2 will be set to Nan
+        """
+        seq1, seq2 = seq1[:, jnp.newaxis], seq2[jnp.newaxis, :]
+        mask = (seq1 == seq2) & (~jnp.isnan(seq1))
+
+        if is_conn:
+            mask = jnp.all(mask, axis=2)
+
+        intersect_mask = mask.any(axis=1)
+        idx = jnp.arange(0, len(seq1))
+        idx_fixed = jnp.dot(mask, idx)
+
+        refactor_ar2 = jnp.where(intersect_mask[:, jnp.newaxis], ar2[idx_fixed], jnp.nan)
+
+        return refactor_ar2
+
+    def crossover_gene(self, rand_key, g1, g2):
+        """
+        crossover two genes
+        :param rand_key:
+        :param g1:
+        :param g2:
+        :return:
+        only gene with the same key will be crossover, thus don't need to consider change key
+        """
+        r = jax.random.uniform(rand_key, shape=g1.shape)
+        return jnp.where(r > 0.5, g1, g2)