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], eigvalsh(A, B))
rng = numpy.random.RandomState(utt.fetch_seed())
a = rng.randn(5, 5)
a = a + a.T
for b in [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)
# We need to test None separatly, as otherwise DebugMode will
# complain, as this isn't a valid ndarray.
b = None
B = theano.tensor.NoneConst
f = function([A], eigvalsh(A, B))
w = f(a)
refw = scipy.linalg.eigvalsh(a, b)
numpy.testing.assert_array_almost_equal(w, refw)
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], eigvalsh(A, B))
rng = numpy.random.RandomState(utt.fetch_seed())
a = rng.randn(5, 5)
a = a + a.T
for b in [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)
# We need to test None separatly, as otherwise DebugMode will
# complain, as this isn't a valid ndarray.
b = None
B = theano.tensor.NoneConst
f = function([A], eigvalsh(A, B))
w = f(a)
refw = scipy.linalg.eigvalsh(a, b)
numpy.testing.assert_array_almost_equal(w, refw)
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: eigvalsh(a, b).dot([1, 2, 3, 4, 5]),
[a, b], rng=numpy.random)
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: eigvalsh(a, b).dot([1, 2, 3, 4, 5]),
[a, b], rng=numpy.random)
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], eigvalsh(A, B))
rng = numpy.random.RandomState(utt.fetch_seed())
a = rng.randn(5, 5)
a = a + a.T
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)
