Chris@87: """ Test functions for linalg module
Chris@87: """
Chris@87: from __future__ import division, absolute_import, print_function
Chris@87: 
Chris@87: 
Chris@87: from numpy.testing import *
Chris@87: import numpy as np
Chris@87: from numpy import linalg, arange, float64, array, dot, transpose
Chris@87: 
Chris@87: rlevel = 1
Chris@87: 
Chris@87: class TestRegression(TestCase):
Chris@87:     def test_eig_build(self, level = rlevel):
Chris@87:         """Ticket #652"""
Chris@87:         rva = array([1.03221168e+02 +0.j,
Chris@87:                -1.91843603e+01 +0.j,
Chris@87:                -6.04004526e-01+15.84422474j,
Chris@87:                -6.04004526e-01-15.84422474j,
Chris@87:                -1.13692929e+01 +0.j,
Chris@87:                -6.57612485e-01+10.41755503j,
Chris@87:                -6.57612485e-01-10.41755503j,
Chris@87:                1.82126812e+01 +0.j,
Chris@87:                1.06011014e+01 +0.j,
Chris@87:                7.80732773e+00 +0.j,
Chris@87:                -7.65390898e-01 +0.j,
Chris@87:                1.51971555e-15 +0.j,
Chris@87:                -1.51308713e-15 +0.j])
Chris@87:         a = arange(13*13, dtype = float64)
Chris@87:         a.shape = (13, 13)
Chris@87:         a = a%17
Chris@87:         va, ve = linalg.eig(a)
Chris@87:         va.sort()
Chris@87:         rva.sort()
Chris@87:         assert_array_almost_equal(va, rva)
Chris@87: 
Chris@87:     def test_eigh_build(self, level = rlevel):
Chris@87:         """Ticket 662."""
Chris@87:         rvals = [68.60568999, 89.57756725, 106.67185574]
Chris@87: 
Chris@87:         cov = array([[ 77.70273908,   3.51489954,  15.64602427],
Chris@87:                      [3.51489954,  88.97013878,  -1.07431931],
Chris@87:                      [15.64602427,  -1.07431931,  98.18223512]])
Chris@87: 
Chris@87:         vals, vecs = linalg.eigh(cov)
Chris@87:         assert_array_almost_equal(vals, rvals)
Chris@87: 
Chris@87:     def test_svd_build(self, level = rlevel):
Chris@87:         """Ticket 627."""
Chris@87:         a = array([[ 0., 1.], [ 1., 1.], [ 2., 1.], [ 3., 1.]])
Chris@87:         m, n = a.shape
Chris@87:         u, s, vh = linalg.svd(a)
Chris@87: 
Chris@87:         b = dot(transpose(u[:, n:]), a)
Chris@87: 
Chris@87:         assert_array_almost_equal(b, np.zeros((2, 2)))
Chris@87: 
Chris@87:     def test_norm_vector_badarg(self):
Chris@87:         """Regression for #786: Froebenius norm for vectors raises
Chris@87:         TypeError."""
Chris@87:         self.assertRaises(ValueError, linalg.norm, array([1., 2., 3.]), 'fro')
Chris@87: 
Chris@87:     def test_lapack_endian(self):
Chris@87:         # For bug #1482
Chris@87:         a = array([[5.7998084,  -2.1825367 ],
Chris@87:                    [-2.1825367,   9.85910595]], dtype='>f8')
Chris@87:         b = array(a, dtype='<f8')
Chris@87: 
Chris@87:         ap = linalg.cholesky(a)
Chris@87:         bp = linalg.cholesky(b)
Chris@87:         assert_array_equal(ap, bp)
Chris@87: 
Chris@87:     def test_large_svd_32bit(self):
Chris@87:         # See gh-4442, 64bit would require very large/slow matrices.
Chris@87:         x = np.eye(1000, 66)
Chris@87:         np.linalg.svd(x)
Chris@87: 
Chris@87:     def test_svd_no_uv(self):
Chris@87:         # gh-4733
Chris@87:         for shape in (3, 4), (4, 4), (4, 3):
Chris@87:             for t in float, complex:
Chris@87:                 a = np.ones(shape, dtype=t)
Chris@87:                 w = linalg.svd(a, compute_uv=False)
Chris@87:                 c = np.count_nonzero(np.absolute(w) > 0.5)
Chris@87:                 assert_equal(c, 1)
Chris@87:                 assert_equal(np.linalg.matrix_rank(a), 1)
Chris@87:                 assert_array_less(1, np.linalg.norm(a, ord=2))
Chris@87: 
Chris@87: 
Chris@87: if __name__ == '__main__':
Chris@87:     run_module_suite()