enable gradient

VERSION

@@ -1 +1 @@
-1.0.5
+1.0.6

gs_divergence/__init__.py

@@ -1,4 +1,4 @@
 from gs_divergence.geodesical_skew_divergence import gs_div
 from gs_divergence.symmetrized_geodesical_skew_divergence import symmetrized_gs_div
 
-__version__ = '1.0.5'
+__version__ = '1.0.6'

gs_divergence/alpha_geodesic.py

@@ -11,18 +11,18 @@ def alpha_geodesic(
     $\alpha$-geodesic between two probability distributions
     """
 
-    a += 1e-12
-    b += 1e-12
+    a_ = a + 1e-12
+    b_ = b + 1e-12
     if alpha == 1:
-        return torch.exp((1 - lmd) * torch.log(a) + lmd * torch.log(b))
+        return torch.exp((1 - lmd) * torch.log(a_) + lmd * torch.log(b_))
     elif alpha >= 1e+9:
         return torch.min(a, b)
     elif alpha <= -1e+9:
         return torch.max(a, b)
     else:
         p = (1 - alpha) / 2
-        lhs = a ** p
-        rhs = b ** p
+        lhs = a_ ** p
+        rhs = b_ ** p
         g = ((1 - lmd) * lhs + lmd * rhs) ** (1/p)
 
         if alpha > 0 and (g == 0).sum() > 0:

tests/test_alpha_geodesic.py

@@ -75,3 +75,11 @@ def test_value_inf(self):
         res = torch.min(a, b)
 
         self.assertTrue(torch.equal(g, res))
+
+    def test_grad(self):
+        a = torch.tensor([[0.1, 0.2, 0.7], [0.5, 0.5, 0.0]], requires_grad=True)
+        b = torch.tensor([[0.4, 0.4, 0.2], [0.2, 0.1, 0.7]])
+
+        g = alpha_geodesic(a, b, alpha=1, lmd=0.5)
+
+        self.assertIsNotNone(g.grad_fn)