Fix bug from gammaln and psi function

theano/scalar/basic_scipy.py

@@ -81,7 +81,7 @@ class GammaLn(UnaryScalarOp):
     """
     @staticmethod
     def st_impl(x):
-        return special.gammaln(x)
+        return scipy.special.gammaln(x)
 
     def impl(self, x):
         return GammaLn.st_impl(x)
@@ -97,7 +97,7 @@ class GammaLn(UnaryScalarOp):
         if node.inputs[0].type in float_types:
             return """%(z)s =
                 lgamma(%(x)s);""" % locals()
-        raise NotImplementedError('only floatingpoint is implemented')
+        raise NotImplementedError('only floating point is implemented')
 
     def __eq__(self, other):
         return type(self) == type(other)
@@ -113,12 +113,14 @@ class Psi(UnaryScalarOp):
     """
     @staticmethod
     def st_impl(x):
-        return special.psi(x)
+        return scipy.special.psi(x)
 
     def impl(self, x):
         return Psi.st_impl(x)
 
-    #def grad()  no gradient now 
+    def grad(self, inputs, outputs_gradients):
+        raise NotImplementedError()
+        return [None]
 
     def c_support_code(self):
         return (
@@ -167,7 +169,7 @@ double _psi(double x){
         if node.inputs[0].type in float_types:
             return """%(z)s =
                 _psi(%(x)s);""" % locals()
-        raise NotImplementedError('only floatingpoint is implemented')
+        raise NotImplementedError('only floating point is implemented')
 
     def __eq__(self, other):
         return type(self) == type(other)

theano/tensor/tests/test_basic.py

@@ -1282,37 +1282,45 @@ ErfcInplaceTester = makeBroadcastTester(
     inplace=True,
     skip=skip_scipy)
 
+_good_broadcast_unary_gammaln = dict(
+    normal=(rand_ranged(-1 + 1e-2, 10, (2, 3)),),
+    empty=(numpy.asarray([]),),)
+_grad_broadcast_unary_gammaln = dict(
+    normal=(rand_ranged(1e-8, 10, (2, 3)),),)
+
 GammaLnTester = makeBroadcastTester(
     op=tensor.gammaln,
     expected=expected_gammaln,
-    good=_good_broadcast_unary_normal_no_int_no_complex,
-    grad=_grad_broadcast_unary_normal,
+    good=_good_broadcast_unary_gammaln,
+    grad=_grad_broadcast_unary_gammaln,
     eps=2e-10,
     mode=mode_no_scipy,
     skip=skip_scipy)
 GammaLnInplaceTester = makeBroadcastTester(
     op=inplace.gammaln_inplace,
-    expected=expected_erfc,
-    good=_good_broadcast_unary_normal_no_int_no_complex,
-    grad=_grad_broadcast_unary_normal,
+    expected=expected_gammaln,
+    good=_good_broadcast_unary_gammaln,
+    grad=_grad_broadcast_unary_gammaln,
     eps=2e-10,
     mode=mode_no_scipy,
     inplace=True,
     skip=skip_scipy)
 
+_good_broadcast_unary_psi = dict(
+    normal=(rand_ranged(1, 10, (2, 3)),),
+    empty=(numpy.asarray([]),),)
+
 PsiTester = makeBroadcastTester(
     op=tensor.psi,
     expected=expected_psi,
-    good=_good_broadcast_unary_normal_no_int_no_complex,
-    grad=_grad_broadcast_unary_normal,
+    good=_good_broadcast_unary_psi,
     eps=2e-10,
     mode=mode_no_scipy,
     skip=skip_scipy)
 PsiInplaceTester = makeBroadcastTester(
     op=inplace.psi_inplace,
     expected=expected_psi,
-    good=_good_broadcast_unary_normal_no_int_no_complex,
-    grad=_grad_broadcast_unary_normal,
+    good=_good_broadcast_unary_psi,
     eps=2e-10,
     mode=mode_no_scipy,
     inplace=True,