cmrit
/
cmrithackathon-master
/.venv
/lib
/python3.11
/site-packages
/numpy
/linalg
/tests
/test_regression.py
| """ Test functions for linalg module | |
| """ | |
| import warnings | |
| import pytest | |
| import numpy as np | |
| from numpy import linalg, arange, float64, array, dot, transpose | |
| from numpy.testing import ( | |
| assert_, assert_raises, assert_equal, assert_array_equal, | |
| assert_array_almost_equal, assert_array_less | |
| ) | |
| class TestRegression: | |
| def test_eig_build(self): | |
| # 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): | |
| # 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): | |
| # 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: Frobenius norm for vectors raises | |
| # ValueError. | |
| assert_raises(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)) | |
| w_svdvals = linalg.svdvals(a) | |
| assert_array_almost_equal(w, w_svdvals) | |
| 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]) | |
| assert_(norm.dtype == np.dtype('float64')) | |
| norm = linalg.norm(testvector, ord=1) | |
| assert_array_equal(norm, [0, 1]) | |
| assert_(norm.dtype != np.dtype('float64')) | |
| norm = linalg.norm(testvector, ord=2) | |
| assert_array_equal(norm, [0, 1]) | |
| assert_(norm.dtype == np.dtype('float64')) | |
| assert_raises(ValueError, linalg.norm, testvector, ord='fro') | |
| assert_raises(ValueError, linalg.norm, testvector, ord='nuc') | |
| assert_raises(ValueError, linalg.norm, testvector, ord=np.inf) | |
| assert_raises(ValueError, linalg.norm, testvector, ord=-np.inf) | |
| assert_raises(ValueError, linalg.norm, testvector, ord=0) | |
| assert_raises(ValueError, linalg.norm, testvector, ord=-1) | |
| assert_raises(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]) | |
| assert_(norm.dtype == np.dtype('float64')) | |
| norm = linalg.norm(testmatrix, ord='fro') | |
| assert_array_equal(norm, [0, 1]) | |
| assert_(norm.dtype == np.dtype('float64')) | |
| assert_raises(TypeError, linalg.norm, testmatrix, ord='nuc') | |
| assert_raises(ValueError, linalg.norm, testmatrix, ord=np.inf) | |
| assert_raises(ValueError, linalg.norm, testmatrix, ord=-np.inf) | |
| assert_raises(ValueError, linalg.norm, testmatrix, ord=0) | |
| assert_raises(ValueError, linalg.norm, testmatrix, ord=1) | |
| assert_raises(ValueError, linalg.norm, testmatrix, ord=-1) | |
| assert_raises(TypeError, linalg.norm, testmatrix, ord=2) | |
| assert_raises(TypeError, linalg.norm, testmatrix, ord=-2) | |
| assert_raises(ValueError, linalg.norm, testmatrix, ord=3) | |
| def test_lstsq_complex_larger_rhs(self): | |
| # gh-9891 | |
| size = 20 | |
| n_rhs = 70 | |
| G = np.random.randn(size, size) + 1j * np.random.randn(size, size) | |
| u = np.random.randn(size, n_rhs) + 1j * np.random.randn(size, n_rhs) | |
| b = G.dot(u) | |
| # This should work without segmentation fault. | |
| u_lstsq, res, rank, sv = linalg.lstsq(G, b, rcond=None) | |
| # check results just in case | |
| assert_array_almost_equal(u_lstsq, u) | |
| def test_cholesky_empty_array(self, upper): | |
| # gh-25840 - upper=True hung before. | |
| res = np.linalg.cholesky(np.zeros((0, 0)), upper=upper) | |
| assert res.size == 0 | |
| def test_matrix_rank_rtol_argument(self, rtol): | |
| # gh-25877 | |
| x = np.zeros((4, 3, 2)) | |
| res = np.linalg.matrix_rank(x, rtol=rtol) | |
| assert res.shape == (4,) | |
| def test_openblas_threading(self): | |
| # gh-27036 | |
| # Test whether matrix multiplication involving a large matrix always | |
| # gives the same (correct) answer | |
| x = np.arange(500000, dtype=np.float64) | |
| src = np.vstack((x, -10*x)).T | |
| matrix = np.array([[0, 1], [1, 0]]) | |
| expected = np.vstack((-10*x, x)).T # src @ matrix | |
| for i in range(200): | |
| result = src @ matrix | |
| mismatches = (~np.isclose(result, expected)).sum() | |
| if mismatches != 0: | |
| assert False, ("unexpected result from matmul, " | |
| "probably due to OpenBLAS threading issues") | |