add feedforward substrate and hyperneat and related example;

see https://github.com/EMI-Group/tensorneat/issues/9;
fix bugs in genome visualize (add plt.close())

examples/func_fit/xor_hyperneat_feedforward.py

@@ -0,0 +1,63 @@
+import jax.numpy as jnp
+
+from tensorneat.pipeline import Pipeline
+from tensorneat.algorithm.neat import NEAT
+from tensorneat.algorithm.hyperneat import HyperNEATFeedForward, MLPSubstrate
+from tensorneat.genome import DefaultGenome
+from tensorneat.common import ACT
+
+from tensorneat.problem.func_fit import XOR3d
+
+if __name__ == "__main__":
+    pipeline = Pipeline(
+        algorithm=HyperNEATFeedForward(
+            substrate=MLPSubstrate(
+                layers=[4, 5, 5, 5, 1], coor_range=(-5.0, 5.0, -5.0, 5.0)
+            ),
+            neat=NEAT(
+                pop_size=10000,
+                species_size=20,
+                survival_threshold=0.01,
+                genome=DefaultGenome(
+                    num_inputs=4,  # size of query coors
+                    num_outputs=1,
+                    init_hidden_layers=(),
+                    output_transform=ACT.tanh,
+                ),
+            ),
+            activation=ACT.tanh,
+            output_transform=ACT.sigmoid,
+        ),
+        problem=XOR3d(),
+        generation_limit=1000,
+        fitness_target=-1e-5,
+    )
+
+    # initialize state
+    state = pipeline.setup()
+    # print(state)
+    # run until terminate
+    state, best = pipeline.auto_run(state)
+    # show result
+    pipeline.show(state, best)
+
+    # visualize cppn
+    cppn_genome = pipeline.algorithm.neat.genome
+    cppn_network = cppn_genome.network_dict(state, *best)
+    cppn_genome.visualize(cppn_network, save_path="./imgs/cppn_network.svg")
+
+    # visualize hyperneat genome
+    hyperneat_genome = pipeline.algorithm.hyper_genome
+    # use cppn to calculate the weights in hyperneat genome
+    # return seqs, nodes, conns, u_conns
+    _, hyperneat_nodes, hyperneat_conns, _ = pipeline.algorithm.transform(state, best)
+    # mutate the connection with weight 0 (to visualize the network rather the substrate)
+    hyperneat_conns = jnp.where(
+        hyperneat_conns[:, 2][:, None] == 0, jnp.nan, hyperneat_conns
+    )
+    hyperneat_network = hyperneat_genome.network_dict(
+        state, hyperneat_nodes, hyperneat_conns
+    )
+    hyperneat_genome.visualize(
+        hyperneat_network, save_path="./imgs/hyperneat_network.svg"
+    )