enh: infer_shapes for MatrixInverse

theano/sandbox/linalg/ops.py

@@ -300,6 +300,14 @@ def tag_solve_triangular(node):
                     return [Solve('lower_triangular')(A, b)]
                 else:
                     return [Solve('upper_triangular')(A, b)]
+            if (isinstance(A.owner.op, DimShuffle)
+                and A.owner.op.new_order == (1, 0)):
+                A_T, = A.owner.inputs
+                if isinstance(A_T.owner.op, type(cholesky)):
+                    if A_T.owner.op.lower:
+                        return [Solve('upper_triangular')(A, b)]
+                    else:
+                        return [Solve('lower_triangular')(A, b)]
 
                         
 @register_canonicalize

theano/tensor/nlinalg.py

@@ -111,6 +111,9 @@ class MatrixInverse(Op):
             return [None]
         return [-matrix_dot(xi, ev, xi)]
 
+    def infer_shape(self, node, shapes):
+        return shapes
+
 matrix_inverse = MatrixInverse()
 
 

theano/tensor/slinalg.py

@@ -171,8 +171,15 @@ class Solve(Op):
 
     def perform(self, node, inputs, output_storage):
         A, b = inputs
-        #TODO: use the A_structure to go faster
-        output_storage[0][0] = scipy.linalg.solve(A, b)
+        if self.A_structure == 'lower_triangular':
+            rval = scipy.linalg.solve_triangular(
+                A, b, lower=True)
+        elif self.A_structure == 'upper_triangular':
+            rval = scipy.linalg.solve_triangular(
+                A, b, lower=False)
+        else:
+            rval = scipy.linalg.solve(A, b)
+        output_storage[0][0] = rval
         
     # computes shape of x where x = inv(A) * b
     def infer_shape(self, node, shapes):