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""" Test functions for linalg module
"""
from __future__ import division, absolute_import, print_function
from numpy.testing import *
import numpy as np
from numpy import linalg, arange, float64, array, dot, transpose
rlevel = 1
class TestRegression(TestCase):
def test_eig_build(self, level = rlevel):
"""Ticket #652"""
rva = array([1.03221168e+02 +0.j,
-1.91843603e+01 +0.j,
-6.04004526e-01+15.84422474j,
-6.04004526e-01-15.84422474j,
-1.13692929e+01 +0.j,
-6.57612485e-01+10.41755503j,
-6.57612485e-01-10.41755503j,
1.82126812e+01 +0.j,
1.06011014e+01 +0.j,
7.80732773e+00 +0.j,
-7.65390898e-01 +0.j,
1.51971555e-15 +0.j,
-1.51308713e-15 +0.j])
a = arange(13*13, dtype = float64)
a.shape = (13, 13)
a = a%17
va, ve = linalg.eig(a)
va.sort()
rva.sort()
assert_array_almost_equal(va, rva)
def test_eigh_build(self, level = rlevel):
"""Ticket 662."""
rvals = [68.60568999, 89.57756725, 106.67185574]
cov = array([[ 77.70273908, 3.51489954, 15.64602427],
[3.51489954, 88.97013878, -1.07431931],
[15.64602427, -1.07431931, 98.18223512]])
vals, vecs = linalg.eigh(cov)
assert_array_almost_equal(vals, rvals)
def test_svd_build(self, level = rlevel):
"""Ticket 627."""
a = array([[ 0., 1.], [ 1., 1.], [ 2., 1.], [ 3., 1.]])
m, n = a.shape
u, s, vh = linalg.svd(a)
b = dot(transpose(u[:, n:]), a)
assert_array_almost_equal(b, np.zeros((2, 2)))
def test_norm_vector_badarg(self):
"""Regression for #786: Froebenius norm for vectors raises
TypeError."""
self.assertRaises(ValueError, linalg.norm, array([1., 2., 3.]), 'fro')
def test_lapack_endian(self):
# For bug #1482
a = array([[5.7998084, -2.1825367 ],
[-2.1825367, 9.85910595]], dtype='>f8')
b = array(a, dtype='<f8')
ap = linalg.cholesky(a)
bp = linalg.cholesky(b)
assert_array_equal(ap, bp)
def test_large_svd_32bit(self):
# See gh-4442, 64bit would require very large/slow matrices.
x = np.eye(1000, 66)
np.linalg.svd(x)
def test_svd_no_uv(self):
# gh-4733
for shape in (3, 4), (4, 4), (4, 3):
for t in float, complex:
a = np.ones(shape, dtype=t)
w = linalg.svd(a, compute_uv=False)
c = np.count_nonzero(np.absolute(w) > 0.5)
assert_equal(c, 1)
assert_equal(np.linalg.matrix_rank(a), 1)
assert_array_less(1, np.linalg.norm(a, ord=2))
if __name__ == '__main__':
run_module_suite()
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