remove create_func....

examples/func_fit/xor.py

@@ -0,0 +1,36 @@
+from config import *
+from pipeline import Pipeline
+from algorithm import NEAT
+from algorithm.neat.gene import NormalGene, NormalGeneConfig
+from problem.func_fit import XOR, FuncFitConfig
+
+if __name__ == '__main__':
+    config = Config(
+        basic=BasicConfig(
+            seed=42,
+            fitness_target=-1e-2,
+            pop_size=10000
+        ),
+        neat=NeatConfig(
+            max_nodes=50,
+            max_conns=100,
+            max_species=30,
+            conn_add=0.8,
+            conn_delete=0,
+            node_add=0.4,
+            node_delete=0,
+            inputs=2,
+            outputs=1
+        ),
+        gene=NormalGeneConfig(),
+        problem=FuncFitConfig(
+            error_method='rmse'
+        )
+    )
+
+    algorithm = NEAT(config, NormalGene)
+    pipeline = Pipeline(config, algorithm, XOR)
+    state = pipeline.setup()
+    pipeline.pre_compile(state)
+    state, best = pipeline.auto_run(state)
+    pipeline.show(state, best)

examples/func_fit/xor_hyperneat.py

@@ -0,0 +1,40 @@
+from config import *
+from pipeline import Pipeline
+from algorithm.neat import NormalGene, NormalGeneConfig
+from algorithm.hyperneat import HyperNEAT, NormalSubstrate, NormalSubstrateConfig
+from problem.func_fit import XOR3d, FuncFitConfig
+
+
+if __name__ == '__main__':
+    config = Config(
+        basic=BasicConfig(
+            seed=42,
+            fitness_target=0,
+            pop_size=1000
+        ),
+        neat=NeatConfig(
+            max_nodes=50,
+            max_conns=100,
+            max_species=30,
+            inputs=4,
+            outputs=1
+        ),
+        hyperneat=HyperNeatConfig(
+            inputs=3,
+            outputs=1
+        ),
+        substrate=NormalSubstrateConfig(
+            input_coors=((-1, -1), (-0.5, -1), (0.5, -1), (1, -1)),
+        ),
+        gene=NormalGeneConfig(
+            activation_default='tanh',
+            activation_options=('tanh', ),
+        ),
+        problem=FuncFitConfig()
+    )
+
+    algorithm = HyperNEAT(config, NormalGene, NormalSubstrate)
+    pipeline = Pipeline(config, algorithm, XOR3d)
+    state = pipeline.setup()
+    state, best = pipeline.auto_run(state)
+    pipeline.show(state, best)

examples/func_fit/xor_recurrent.py

@@ -0,0 +1,40 @@
+from config import *
+from pipeline import Pipeline
+from algorithm import NEAT
+from algorithm.neat.gene import RecurrentGene, RecurrentGeneConfig
+from problem.func_fit import XOR3d, FuncFitConfig
+
+
+if __name__ == '__main__':
+    config = Config(
+        basic=BasicConfig(
+            seed=42,
+            fitness_target=-1e-2,
+            generation_limit=300,
+            pop_size=1000
+        ),
+        neat=NeatConfig(
+            network_type="recurrent",
+            max_nodes=50,
+            max_conns=100,
+            max_species=30,
+            conn_add=0.5,
+            conn_delete=0.5,
+            node_add=0.4,
+            node_delete=0.4,
+            inputs=3,
+            outputs=1
+        ),
+        gene=RecurrentGeneConfig(
+            activate_times=10
+        ),
+        problem=FuncFitConfig(
+            error_method='rmse'
+        )
+    )
+
+    algorithm = NEAT(config, RecurrentGene)
+    pipeline = Pipeline(config, algorithm, XOR3d)
+    state = pipeline.setup()
+    state, best = pipeline.auto_run(state)
+    pipeline.show(state, best)