Implemented UPLO parameter for eigh.

theano/sandbox/linalg/ops.py

@@ -933,6 +933,15 @@ class Eigh(Eig):
     
     """
     _numop = staticmethod(numpy.linalg.eigh)
+    def __init__(self, UPLO='L'):
+        self.UPLO = UPLO
+
+    def __str__(self):
+        return 'Eigh{%s}' % self.UPLO
+
+    def props(self):
+        return self.UPLO,
+
     def make_node(self, x):
         x = as_tensor_variable(x)
         w = theano.tensor.vector(dtype='float64')
@@ -941,7 +950,7 @@ class Eigh(Eig):
 
     def perform(self, node, (x,), (w, v)):
         try:
-            w[0], v[0] = self._numop(x)
+            w[0], v[0] = self._numop(x, self.UPLO)
         except numpy.linalg.LinAlgError:
             logger.debug('Failed to find %s of %s' % (node.inputs[0], 
                                                       self._numop.__name__))
@@ -971,14 +980,18 @@ class Eigh(Eig):
         # Replace gradients wrt disconnected variables with
         # zeros. This is a work-around for issue #1063.
         gw, gv = _zero_disconnected([w, v], g_outputs)
-        return [EighGrad()(x, w, v, gw, gv)]
+        return [EighGrad(self.UPLO)(x, w, v, gw, gv)]
 
-eigh = Eigh()
+def eigh(a, UPLO='L'):
+     return Eigh(UPLO)(a)
 
 class EighGrad(Op):
     """Gradient of an eigensystem of a Hermitian matrix.
 
     """
+    def __init__(self, UPLO='L'):
+        self.UPLO = UPLO
+
     def props(self):
         return ()
 
@@ -989,7 +1002,7 @@ class EighGrad(Op):
         return (type(self) == type(other) and self.props() == other.props())
 
     def __str__(self):
-        return 'EigGrad'
+        return 'EighGrad{%s}' % self.UPLO
     
 
     def make_node(self, x, w, v, gw, gv):
@@ -1030,8 +1043,11 @@ class EighGrad(Op):
 
         G = lambda n: sum(v[:,m]*V.T[n].dot(v[:,m])/(w[n]-w[m])
                           for m in xrange(N) if m != n)
+        tri = numpy.tri(N)
+        if self.UPLO == 'U':
+            tri = tri.T
         outputs[0][0] = sum(outer(v[:,n], v[:,n]*W[n] + G(n))
-                            for n in xrange(N))
+                            for n in xrange(N))#*tri
 
     def infer_shape(self, node, shapes):
         return [shapes[0]]

theano/sandbox/linalg/tests/test_linalg.py

@@ -499,7 +499,16 @@ class test_Eig(utt.InferShapeTester):
         assert_array_almost_equal(numpy.dot(x,v), w * v)
 
 class test_Eigh(test_Eig):
-    op = eigh
+    op = staticmethod(eigh)
+    def test_uplo(self):
+        S = self.S
+        a = theano.tensor.matrix()
+        wu, vu = [out.eval({a: S}) for out in self.op(a, 'U')]
+        wl, vl = [out.eval({a: S}) for out in self.op(a, 'L')]
+        assert_array_almost_equal(wu, wl)
+        assert_array_almost_equal(vu*numpy.sign(vu[0,:]),
+                                  vl*numpy.sign(vl[0,:]))
+            
     def test_grad(self):
         S = self.S
         utt.verify_grad(lambda x: self.op(x + x.T)[0], [S], rng=self.rng)