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
) 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([[1.0, 2.0], [3.0, 4.0]]), np.matrix([2.0, 1.0]).T ), LinalgCase( "matrix_a_and_b", np.matrix([[1.0, 2.0], [3.0, 4.0]]), np.matrix([2.0, 1.0]).T, ), ], ) # hermitian test-cases CASES += apply_tag( "hermitian", [ LinalgCase("hmatrix_a_and_b", np.matrix([[1.0, 2.0], [2.0, 1.0]]), None),
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.