10000 ENH: Allow use of svd on empty arrays by eric-wieser · Pull Request #11424 · numpy/numpy · GitHub
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ENH: Allow use of svd on empty arrays #11424

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Merged
merged 1 commit into from
Jun 27, 2018
Merged

ENH: Allow use of svd on empty arrays #11424

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2 changes: 1 addition & 1 deletion numpy/linalg/linalg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1527,7 +1527,6 @@ def svd(a, full_matrices=True, compute_uv=True):

"""
a, wrap = _makearray(a)
_assertNoEmpty2d(a)
_assertRankAtLeast2(a)
t, result_t = _commonType(a)

Expand Down Expand Up @@ -1644,6 +1643,7 @@ def cond(x, p=None):

"""
x = asarray(x) # in case we have a matrix
_assertNoEmpty2d(x)
if p is None or p == 2 or p == -2:
s = svd(x, compute_uv=False)
with errstate(all='ignore'):
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26 changes: 13 additions & 13 deletions numpy/linalg/tests/test_linalg.py
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Expand Up @@ -644,10 +644,6 @@ class ArraySubclass(np.ndarray):
class SVDCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):

def do(self, a, b, tags):
if 'size-0' in tags:
assert_raises(LinAlgError, linalg.svd, a, 0)
return

u, s, vt = linalg.svd(a, 0)
assert_allclose(a, dot_generalized(np.asarray(u) * np.asarray(s)[..., None, :],
np.asarray(vt)),
Expand All @@ -670,15 +666,19 @@ def check(dtype):
for dtype in [single, double, csingle, cdouble]:
check(dtype)

def test_0_size(self):
# These raise errors currently
# (which does not mean that it may not make sense)
a = np.zeros((0, 0), dtype=np.complex64)
assert_raises(linalg.LinAlgError, linalg.svd, a)
a = np.zeros((0, 1), dtype=np.complex64)
assert_raises(linalg.LinAlgError, linalg.svd, a)
a = np.zeros((1, 0), dtype=np.complex64)
assert_raises(linalg.LinAlgError, linalg.svd, a)
def test_empty_identity(self):
""" Empty input should put an identity matrix in u or vh """
x = np.empty((4, 0))
u, s, vh = linalg.svd(x, compute_uv=True)
assert_equal(u.shape, (4, 4))
assert_equal(vh.shape, (0, 0))
assert_equal(u, np.eye(4))

x = np.empty((0, 4))
u, s, vh = linalg.svd(x, compute_uv=True)
assert_equal(u.shape, (0, 0))
assert_equal(vh.shape, (4, 4))
assert_equal(vh, np.eye(4))


class CondCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
Expand Down
14 changes: 11 additions & 3 deletions numpy/linalg/umath_linalg.c.src
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Expand Up @@ -2735,19 +2735,18 @@ static NPY_INLINE void
(fortran_int)dimensions[0],
(fortran_int)dimensions[1])) {
LINEARIZE_DATA_t a_in, u_out, s_out, v_out;
fortran_int min_m_n = params.M < params.N ? params.M : params.N;

init_linearize_data(&a_in, params.N, params.M, steps[1], steps[0]);
if ('N' == params.JOBZ) {
/* only the singular values are wanted */
fortran_int min_m_n = params.M < params.N? params.M : params.N;
init_linearize_data(&s_out, 1, min_m_n, 0, steps[2]);
} else {
fortran_int u_columns, v_rows;
fortran_int min_m_n = params.M < params.N? params.M : params.N;
if ('S' == params.JOBZ) {
u_columns = min_m_n;
v_rows = min_m_n;
} else {
} else { /* JOBZ == 'A' */
u_columns = params.M;
v_rows = params.N;
}
Expand All @@ -2771,6 +2770,15 @@ static NPY_INLINE void
if ('N' == params.JOBZ) {
delinearize_@REALTYPE@_matrix(args[1], params.S, &s_out);
} else {
if ('A' == params.JOBZ && min_m_n == 0) {
/* Lapack has betrayed us and left these uninitialized,
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NIT: comment should start on the next line.

* so produce an identity matrix for whichever of u
* and v is not empty.
*/
identity_@TYPE@_matrix(params.U, params.M);
identity_@TYPE@_matrix(params.VT, params.N);
}

delinearize_@TYPE@_matrix(args[1], params.U, &u_out);
delinearize_@REALTYPE@_matrix(args[2], params.S, &s_out);
delinearize_@TYPE@_matrix(args[3], params.VT, &v_out);
Expand Down
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