/usr/lib/python2.7/dist-packages/numpy/linalg/tests/test_regression.py is in python-numpy 1:1.12.1-3.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | """ Test functions for linalg module
"""
from __future__ import division, absolute_import, print_function
import warnings
import numpy as np
from numpy import linalg, arange, float64, array, dot, transpose
from numpy.testing import (
TestCase, run_module_suite, assert_equal, assert_array_equal,
assert_array_almost_equal, assert_array_less
)
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))
def test_norm_object_array(self):
# gh-7575
testvector = np.array([np.array([0, 1]), 0, 0], dtype=object)
norm = linalg.norm(testvector)
assert_array_equal(norm, [0, 1])
self.assertEqual(norm.dtype, np.dtype('float64'))
norm = linalg.norm(testvector, ord=1)
assert_array_equal(norm, [0, 1])
self.assertNotEqual(norm.dtype, np.dtype('float64'))
norm = linalg.norm(testvector, ord=2)
assert_array_equal(norm, [0, 1])
self.assertEqual(norm.dtype, np.dtype('float64'))
self.assertRaises(ValueError, linalg.norm, testvector, ord='fro')
self.assertRaises(ValueError, linalg.norm, testvector, ord='nuc')
self.assertRaises(ValueError, linalg.norm, testvector, ord=np.inf)
self.assertRaises(ValueError, linalg.norm, testvector, ord=-np.inf)
with warnings.catch_warnings():
warnings.simplefilter("error", DeprecationWarning)
self.assertRaises((AttributeError, DeprecationWarning),
linalg.norm, testvector, ord=0)
self.assertRaises(ValueError, linalg.norm, testvector, ord=-1)
self.assertRaises(ValueError, linalg.norm, testvector, ord=-2)
testmatrix = np.array([[np.array([0, 1]), 0, 0],
[0, 0, 0]], dtype=object)
norm = linalg.norm(testmatrix)
assert_array_equal(norm, [0, 1])
self.assertEqual(norm.dtype, np.dtype('float64'))
norm = linalg.norm(testmatrix, ord='fro')
assert_array_equal(norm, [0, 1])
self.assertEqual(norm.dtype, np.dtype('float64'))
self.assertRaises(TypeError, linalg.norm, testmatrix, ord='nuc')
self.assertRaises(ValueError, linalg.norm, testmatrix, ord=np.inf)
self.assertRaises(ValueError, linalg.norm, testmatrix, ord=-np.inf)
self.assertRaises(ValueError, linalg.norm, testmatrix, ord=0)
self.assertRaises(ValueError, linalg.norm, testmatrix, ord=1)
self.assertRaises(ValueError, linalg.norm, testmatrix, ord=-1)
self.assertRaises(TypeError, linalg.norm, testmatrix, ord=2)
self.assertRaises(TypeError, linalg.norm, testmatrix, ord=-2)
self.assertRaises(ValueError, linalg.norm, testmatrix, ord=3)
if __name__ == '__main__':
run_module_suite()
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