change a lot a lot a lot!!!!!!!

examples/a.py

@@ -1,11 +1,28 @@
-import numpy as np
-import jax.numpy as jnp
+import jax
+from jax import numpy as jnp
 
-a = jnp.zeros((5, 5))
-k1 = jnp.array([1, 2, 3])
-k2 = jnp.array([2, 3, 4])
-v = jnp.array([1, 1, 1])
+from config import Config
+from core import Genome
 
-a = a.at[k1, k2].set(v)
+config = Config()
+from dataclasses import asdict
+
+print(asdict(config))
+
+pop_nodes = jnp.ones((Config.basic.pop_size, Config.neat.maximum_nodes, 3))
+pop_conns = jnp.ones((Config.basic.pop_size, Config.neat.maximum_conns, 5))
+
+pop_genomes1 = jax.vmap(Genome)(pop_nodes, pop_conns)
+pop_genomes2 = Genome(pop_nodes, pop_conns)
+
+print(pop_genomes)
+print(pop_genomes[0])
+
+@jax.vmap
+def pop_cnts(genome):
+    return genome.count()
+
+cnts = pop_cnts(pop_genomes)
+
+print(cnts)
 
-print(a)

examples/b.py

@@ -0,0 +1,19 @@
+from enum import Enum
+from jax import jit
+
+class NetworkType(Enum):
+    ANN = 0
+    SNN = 1
+    LSTM = 2
+
+
+
+
+@jit
+def func(d):
+    return d[0] + 1
+
+
+d = {0: 1, 1: NetworkType.ANN.value}
+
+print(func(d))

examples/rnn_forward_test.py

@@ -1,44 +0,0 @@
-import jax
-import jax.numpy as jnp
-from jax.tree_util import register_pytree_node_class
-
-
-@register_pytree_node_class
-class Genome:
-    def __init__(self, nodes, conns):
-        self.nodes = nodes
-        self.conns = conns
-
-    def update_nodes(self, nodes):
-        return Genome(nodes, self.conns)
-
-    def update_conns(self, conns):
-        return Genome(self.nodes, conns)
-
-    def tree_flatten(self):
-        children = self.nodes, self.conns
-        aux_data = None
-        return children, aux_data
-
-    @classmethod
-    def tree_unflatten(cls, aux_data, children):
-        return cls(*children)
-
-    def __repr__(self):
-        return f"Genome ({self.nodes}, \n\t{self.conns})"
-
-    @jax.jit
-    def add_node(self, a: int):
-        nodes = self.nodes.at[0, :].set(a)
-        return self.update_nodes(nodes)
-
-
-nodes, conns = jnp.array([[1, 2, 3, 4, 5]]), jnp.array([[1, 2, 3, 4]])
-g = Genome(nodes, conns)
-print(g)
-
-g = g.add_node(1)
-print(g)
-
-g = jax.jit(g.add_node)(2)
-print(g)

examples/xor.ini

@@ -1,12 +0,0 @@
-[basic]
-activate_times = 5
-fitness_threshold = 4
-
-[population]
-pop_size = 1000
-
-[neat]
-network_type = "recurrent"
-num_inputs = 4
-num_outputs = 1
-

examples/xor.py

@@ -1,10 +1,10 @@
 import jax
 import numpy as np
 
+from config import Config, BasicConfig
 from pipeline import Pipeline
-from config import Configer
-from algorithm import NEAT
-from algorithm.neat import  RecurrentGene
+from algorithm.neat.gene import NormalGene, NormalGeneConfig
+from algorithm.neat.neat import NEAT
 
 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)
@@ -21,13 +21,11 @@ def evaluate(forward_func):
     return fitnesses
 
 
-def main():
-    config = Configer.load_config("xor.ini")
-    algorithm = NEAT(config, RecurrentGene)
-    pipeline = Pipeline(config, algorithm)
-    best = pipeline.auto_run(evaluate)
-    print(best)
-
-
 if __name__ == '__main__':
-    main()
+    config = Config(
+        basic=BasicConfig(fitness_target=4),
+        gene=NormalGeneConfig()
+    )
+    algorithm = NEAT(config, NormalGene)
+    pipeline = Pipeline(config, algorithm)
+    pipeline.auto_run(evaluate)

examples/xor_hyperneat.py

@@ -1,33 +0,0 @@
-import jax
-import numpy as np
-
-from pipeline import Pipeline
-from config import Configer
-from algorithm import NEAT, HyperNEAT
-from algorithm.neat import RecurrentGene
-from algorithm.hyperneat import BaseSubstrate
-
-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)
-    fitnesses = 4 - np.sum((outs - xor_outputs) ** 2, axis=(1, 2))
-    return fitnesses
-
-
-def main():
-    config = Configer.load_config("xor.ini")
-    algorithm = HyperNEAT(config, RecurrentGene, BaseSubstrate)
-    pipeline = Pipeline(config, algorithm)
-    pipeline.auto_run(evaluate)
-
-
-if __name__ == '__main__':
-    main()