£Á°èZ¨Ä…–K§‚«“ô4“ÒÙ´dîfUÙÃÅ WKbyʦ•ꎅȮFÒ¿ÊÎóCozá¬S@6{Í:›œêZÌ:Š•_%:¢¾¾~;‘Ã~芩ÊǍí`ÔÑ©ú뙵'5I¿fš×WO%ø9¾«¾DK|€ùÍD”Ýs]nHÕ¶êםӼ㞪éUWŸÈË%DÒÕ¬ï‘]/Åcx ‰ï2ß]ä6G[]S£Ôϯrs{úëóµmÒï#UQxo·õÞCe]"±/aÙ&Eã4ú9Jé_ÞåëdãöKë)AÞ ¯¹ægƒÛowЍø^d™ý½ßB7áyMä9ÜÖUã !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! """ Test functions for linalg module using the matrix class.""" import numpy as np from numpy.linalg.tests.test_linalg import ( LinalgCase, apply_tag, TestQR as _TestQR, LinalgTestCase, _TestNorm2D, _TestNormDoubleBase, _TestNormSingleBase, _TestNormInt64Base, SolveCases, InvCases, EigvalsCases, EigCases, SVDCases, CondCases, PinvCases, DetCases, LstsqCases) CASES = [] # square test cases CASES += apply_tag('square', [ LinalgCase("0x0_matrix", np.empty((0, 0), dtype=np.double).view(np.matrix), np.empty((0, 1), dtype=np.double).view(np.matrix), tags={'size-0'}), LinalgCase("matrix_b_only", np.array([[1., 2.], [3., 4.]]), np.matrix([2., 1.]).T), LinalgCase("matrix_a_and_b", np.matrix([[1., 2.], [3., 4.]]), np.matrix([2., 1.]).T), ]) # hermitian test-cases CASES += apply_tag('hermitian', [ LinalgCase("hmatrix_a_and_b", np.matrix([[1., 2.], [2., 1.]]), None), ]) # No need to make generalized or strided cases for matrices. class MatrixTestCase(LinalgTestCase): TEST_CASES = CASES class TestSolveMatrix(SolveCases, MatrixTestCase): pass class TestInvMatrix(InvCases, MatrixTestCase): pass class TestEigvalsMatrix(EigvalsCases, MatrixTestCase): pass class TestEigMatrix(EigCases, MatrixTestCase): pass class TestSVDMatrix(SVDCases, MatrixTestCase): pass class TestCondMatrix(CondCases, MatrixTestCase): pass class TestPinvMatrix(PinvCases, MatrixTestCase): pass class TestDetMatrix(DetCases, MatrixTestCase): pass class TestLstsqMatrix(LstsqCases, MatrixTestCase): pass class _TestNorm2DMatrix(_TestNorm2D): array = np.matrix class TestNormDoubleMatrix(_TestNorm2DMatrix, _TestNormDoubleBase): pass class TestNormSingleMatrix(_TestNorm2DMatrix, _TestNormSingleBase): pass class TestNormInt64Matrix(_TestNorm2DMatrix, _TestNormInt64Base): pass class TestQRMatrix(_TestQR): array = np.matrix