complete normal neat algorithm

examples/xor.ini

@@ -0,0 +1,5 @@
+[basic]
+forward_way = "common"
+
+[population]
+fitness_threshold = 4

examples/xor.py

@@ -0,0 +1,31 @@
+import jax
+import numpy as np
+
+from algorithm import Configer, NEAT
+from algorithm.neat import NormalGene, Pipeline
+
+xor_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
+xor_outputs = np.array([[0], [1], [1], [0]], dtype=np.float32)
+
+
+def evaluate(forward_func):
+    """
+    :param forward_func: (4: batch, 2: input size) -> (pop_size, 4: batch, 1: output size)
+    :return:
+    """
+    outs = forward_func(xor_inputs)
+    outs = jax.device_get(outs)
+    # print(outs)
+    fitnesses = 4 - np.sum((outs - xor_outputs) ** 2, axis=(1, 2))
+    return fitnesses
+
+
+def main():
+    config = Configer.load_config("xor.ini")
+    algorithm = NEAT(config, NormalGene)
+    pipeline = Pipeline(config, algorithm)
+    pipeline.auto_run(evaluate)
+
+
+if __name__ == '__main__':
+    main()

examples/xor_test.py

@@ -1,17 +1,32 @@
 import jax
+import numpy as np
 
 from algorithm.config import Configer
-from algorithm.neat import NEAT
+from algorithm.neat import NEAT, NormalGene, Pipeline
+from algorithm.neat.genome import create_mutate
+
+xor_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
+
+def single_genome(func, nodes, conns):
+    t = NormalGene.forward_transform(nodes, conns)
+    out1 = func(xor_inputs[0], t)
+    out2 = func(xor_inputs[1], t)
+    out3 = func(xor_inputs[2], t)
+    out4 = func(xor_inputs[3], t)
+    print(out1, out2, out3, out4)
 
 if __name__ == '__main__':
     config = Configer.load_config()
-    neat = NEAT(config)
+    neat = NEAT(config, NormalGene)
     randkey = jax.random.PRNGKey(42)
     state = neat.setup(randkey)
-    state = neat.mutate(state)
-    print(state)
-    pop_nodes, pop_conns = state.pop_nodes, state.pop_conns
-    print(neat.distance(state, pop_nodes[0], pop_conns[0], pop_nodes[1], pop_conns[1]))
-    print(neat.crossover(state, pop_nodes[0], pop_conns[0], pop_nodes[1], pop_conns[1]))
+    forward_func = NormalGene.create_forward(config)
+    mutate_func =  create_mutate(config, NormalGene)
+
+
+    nodes, conns = state.pop_nodes[0], state.pop_conns[0]
+    single_genome(forward_func, nodes, conns)
+    nodes, conns = mutate_func(state, randkey, nodes, conns, 10000)
+    single_genome(forward_func, nodes, conns)