new architecture

2024-01-27 00:52:39 +08:00
parent 4efe9a53c1
commit aac41a089d
65 changed files with 1651 additions and 1783 deletions
--- a/test/test_genome.py
+++ b/test/test_genome.py
@@ -0,0 +1,113 @@
+from algorithm.neat import *
+from utils import Act, Agg
+
+import jax, jax.numpy as jnp
+
+
+def test_default():
+
+    # index, bias, response, activation, aggregation
+    nodes = jnp.array([
+        [0, 0, 1, 0, 0], # in[0]
+        [1, 0, 1, 0, 0], # in[1]
+        [2, 0.5, 1, 0, 0],  # out[0],
+        [3, 1, 1, 0, 0],  # hidden[0],
+        [4, -1, 1, 0, 0],  # hidden[1],
+    ])
+
+    # in_node, out_node, enable, weight
+    conns = jnp.array([
+        [0, 3, 1, 0.5], # in[0] -> hidden[0]
+        [1, 4, 1, 0.5], # in[1] -> hidden[1]
+        [3, 2, 1, 0.5], # hidden[0] -> out[0]
+        [4, 2, 1, 0.5], # hidden[1] -> out[0]
+    ])
+
+    genome = DefaultGenome(
+        num_inputs=2,
+        num_outputs=1,
+        node_gene=DefaultNodeGene(
+            activation_default=Act.identity,
+            activation_options=(Act.identity, ),
+            aggregation_default=Agg.sum,
+            aggregation_options=(Agg.sum, ),
+        ),
+    )
+
+    transformed = genome.transform(nodes, conns)
+    print(*transformed, sep='\n')
+
+    inputs = jnp.array([[0, 0],[0, 1], [1, 0], [1, 1]])
+    outputs = jax.jit(jax.vmap(genome.forward, in_axes=(0, None)))(inputs, transformed)
+    print(outputs)
+    assert jnp.allclose(outputs, jnp.array([[0.5], [0.75], [0.75], [1]]))
+    # expected: [[0.5], [0.75], [0.75], [1]]
+
+    print('\n-------------------------------------------------------\n')
+
+    conns = conns.at[0, 2].set(False)  # disable in[0] -> hidden[0]
+    print(conns)
+
+    transformed = genome.transform(nodes, conns)
+    print(*transformed, sep='\n')
+
+    inputs = jnp.array([[0, 0],[0, 1], [1, 0], [1, 1]])
+    outputs = jax.vmap(genome.forward, in_axes=(0, None))(inputs, transformed)
+    print(outputs)
+    assert jnp.allclose(outputs, jnp.array([[0], [0.25], [0], [0.25]]))
+    # expected: [[0.5], [0.75], [0.5], [0.75]]
+
+
+def test_recurrent():
+
+    # index, bias, response, activation, aggregation
+    nodes = jnp.array([
+        [0, 0, 1, 0, 0], # in[0]
+        [1, 0, 1, 0, 0], # in[1]
+        [2, 0.5, 1, 0, 0],  # out[0],
+        [3, 1, 1, 0, 0],  # hidden[0],
+        [4, -1, 1, 0, 0],  # hidden[1],
+    ])
+
+    # in_node, out_node, enable, weight
+    conns = jnp.array([
+        [0, 3, 1, 0.5], # in[0] -> hidden[0]
+        [1, 4, 1, 0.5], # in[1] -> hidden[1]
+        [3, 2, 1, 0.5], # hidden[0] -> out[0]
+        [4, 2, 1, 0.5], # hidden[1] -> out[0]
+    ])
+
+    genome = RecurrentGenome(
+        num_inputs=2,
+        num_outputs=1,
+        node_gene=DefaultNodeGene(
+            activation_default=Act.identity,
+            activation_options=(Act.identity, ),
+            aggregation_default=Agg.sum,
+            aggregation_options=(Agg.sum, ),
+        ),
+        activate_time=3,
+    )
+
+    transformed = genome.transform(nodes, conns)
+    print(*transformed, sep='\n')
+
+    inputs = jnp.array([[0, 0],[0, 1], [1, 0], [1, 1]])
+    outputs = jax.jit(jax.vmap(genome.forward, in_axes=(0, None)))(inputs, transformed)
+    print(outputs)
+    assert jnp.allclose(outputs, jnp.array([[0.5], [0.75], [0.75], [1]]))
+    # expected: [[0.5], [0.75], [0.75], [1]]
+
+    print('\n-------------------------------------------------------\n')
+
+    conns = conns.at[0, 2].set(False)  # disable in[0] -> hidden[0]
+    print(conns)
+
+    transformed = genome.transform(nodes, conns)
+    print(*transformed, sep='\n')
+
+    inputs = jnp.array([[0, 0],[0, 1], [1, 0], [1, 1]])
+    outputs = jax.vmap(genome.forward, in_axes=(0, None))(inputs, transformed)
+    print(outputs)
+    assert jnp.allclose(outputs, jnp.array([[0], [0.25], [0], [0.25]]))
+    # expected: [[0.5], [0.75], [0.5], [0.75]]