Change name to eigvalsh

theano/sandbox/linalg/__init__.py

@@ -2,5 +2,5 @@
 from kron import kron
 from ops import (cholesky, matrix_inverse, solve,
         diag, extract_diag, alloc_diag,
-        det, psd, eig, eigh, geigvalsh,
+        det, psd, eig, eigh, eigvalsh,
         trace, spectral_radius_bound)

theano/sandbox/linalg/ops.py

@@ -1099,7 +1099,7 @@ class EighGrad(Op):
         return [shapes[0]]
 
 
-class GEigvalsh(Op):
+class Eigvalsh(Op):
     """Generalized eigenvalues of a Hermetian positive definite eigensystem
     """
 
@@ -1133,14 +1133,14 @@ class GEigvalsh(Op):
     def grad(self, inputs, g_outputs):
         a, b = inputs
         gw, = g_outputs
-        return GEigvalshGrad(self.lower)(a, b, gw)
+        return EigvalshGrad(self.lower)(a, b, gw)
 
     def infer_shape(self, node, shapes):
         n = shapes[0][0]
         return [(n,)]
 
 
-class GEigvalshGrad(Op):
+class EigvalshGrad(Op):
     """Gradient of generalized eigenvalues of a Hermetian positive definite
     eigensystem
     """
@@ -1192,5 +1192,5 @@ class GEigvalshGrad(Op):
         return [shapes[0], shapes[1]]
 
 
-def geigvalsh(a, b, lower=True):
-    return GEigvalsh(lower)(a, b)
+def eigvalsh(a, b, lower=True):
+    return Eigvalsh(lower)(a, b)

theano/sandbox/linalg/tests/test_linalg.py

@@ -32,7 +32,7 @@ from theano.sandbox.linalg.ops import (cholesky,
                                        Eig,
                                        inv_as_solve,
                                        )
-from theano.sandbox.linalg import eig, eigh, geigvalsh
+from theano.sandbox.linalg import eig, eigh, eigvalsh
 from nose.plugins.skip import SkipTest
 from nose.plugins.attrib import attr
 
@@ -575,29 +575,28 @@ def test_matrix_inverse_solve():
     assert isinstance(out.owner.op, Solve)
 
 
-def test_geigvalsh():
+def test_eigvalsh():
     if not imported_scipy:
         raise SkipTest("Scipy needed for the geigvalsh op.")
     import scipy.linalg
 
     A = theano.tensor.dmatrix('a')
     B = theano.tensor.dmatrix('b')
-    f = function([A, B], geigvalsh(A, B))
+    f = function([A, B], eigvalsh(A, B))
 
     rng = numpy.random.RandomState(utt.fetch_seed())
     a = rng.randn(5, 5)
     a = a + a.T
-    b = 10 * numpy.eye(5, 5) + rng.randn(5, 5)
-
-    w = f(a, b)
-    refw = scipy.linalg.eigvalsh(a, b)
-    numpy.testing.assert_array_almost_equal(w, refw)
+    for b in [10 * numpy.eye(5, 5) + rng.randn(5, 5), None]:
+        w = f(a, b)
+        refw = scipy.linalg.eigvalsh(a, b)
+        numpy.testing.assert_array_almost_equal(w, refw)
 
 
-def test_geigvalsh_grad():
+def test_eigvalsh_grad():
     rng = numpy.random.RandomState(utt.fetch_seed())
     a = rng.randn(5, 5)
     a = a + a.T
     b = 10 * numpy.eye(5, 5) + rng.randn(5, 5)
-    tensor.verify_grad(lambda a, b: geigvalsh(a, b).dot([1, 2, 3, 4, 5]),
+    tensor.verify_grad(lambda a, b: eigvalsh(a, b).dot([1, 2, 3, 4, 5]),
                        [a, b], rng=numpy.random)