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

add visualize in genome;
add related tests.

tensorneat/utils/aggregation/agg_sympy.py

@@ -0,0 +1,69 @@
+import sympy as sp
+
+
+class SympySum(sp.Function):
+    @classmethod
+    def eval(cls, z):
+        return sp.Add(*z)
+
+
+class SympyProduct(sp.Function):
+    @classmethod
+    def eval(cls, z):
+        return sp.Mul(*z)
+
+
+class SympyMax(sp.Function):
+    @classmethod
+    def eval(cls, z):
+        return sp.Max(*z)
+
+
+class SympyMin(sp.Function):
+    @classmethod
+    def eval(cls, z):
+        return sp.Min(*z)
+
+
+class SympyMaxabs(sp.Function):
+    @classmethod
+    def eval(cls, z):
+        return sp.Max(*z, key=sp.Abs)
+
+
+class SympyMean(sp.Function):
+    @classmethod
+    def eval(cls, z):
+        return sp.Add(*z) / len(z)
+
+
+class SympyMedian(sp.Function):
+    @classmethod
+    def eval(cls, args):
+
+        if all(arg.is_number for arg in args):
+            sorted_args = sorted(args)
+            n = len(sorted_args)
+            if n % 2 == 1:
+                return sorted_args[n // 2]
+            else:
+                return (sorted_args[n // 2 - 1] + sorted_args[n // 2]) / 2
+
+        return None
+
+    @staticmethod
+    def numerical_eval(args):
+        sorted_args = sorted(args)
+        n = len(sorted_args)
+        if n % 2 == 1:
+            return sorted_args[n // 2]
+        else:
+            return (sorted_args[n // 2 - 1] + sorted_args[n // 2]) / 2
+
+    def _sympystr(self, printer):
+        return f"median({', '.join(map(str, self.args))})"
+
+    def _latex(self, printer):
+        return (
+            r"\mathrm{median}\left(" + ", ".join(map(sp.latex, self.args)) + r"\right)"
+        )