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""" Test functions for linalg module
"""
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')
if __name__ == '__main__':
run_module_suite()
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