add sympy support; which can transfer your network into sympy expression;

add visualize in genome;
add related tests.

tensorneat/examples/interpret_visualize/genome_sympy.py

@@ -0,0 +1,30 @@
+import jax, jax.numpy as jnp
+
+from algorithm.neat import *
+from algorithm.neat.genome.advance import AdvanceInitialize
+from utils.graph import topological_sort_python
+
+if __name__ == '__main__':
+    genome = AdvanceInitialize(
+        num_inputs=17,
+        num_outputs=6,
+        hidden_cnt=8,
+        max_nodes=50,
+        max_conns=500,
+    )
+
+    state = genome.setup()
+
+    randkey = jax.random.PRNGKey(42)
+    nodes, conns = genome.initialize(state, randkey)
+
+    network = genome.network_dict(state, nodes, conns)
+    print(set(network["nodes"]), set(network["conns"]))
+    order, _ = topological_sort_python(set(network["nodes"]), set(network["conns"]))
+    print(order)
+
+    input_idx, output_idx = genome.get_input_idx(), genome.get_output_idx()
+    print(input_idx, output_idx)
+
+    print(genome.repr(state, nodes, conns))
+    print(network)