| | """ Test functions for linalg module using the matrix class."""
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| | import numpy as np
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| |
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| | from numpy.linalg.tests.test_linalg import (
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| | LinalgCase, apply_tag, TestQR as _TestQR, LinalgTestCase,
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| | _TestNorm2D, _TestNormDoubleBase, _TestNormSingleBase, _TestNormInt64Base,
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| | SolveCases, InvCases, EigvalsCases, EigCases, SVDCases, CondCases,
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| | PinvCases, DetCases, LstsqCases)
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| |
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| |
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| | CASES = []
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| |
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| |
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| | CASES += apply_tag('square', [
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| | LinalgCase("0x0_matrix",
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| | np.empty((0, 0), dtype=np.double).view(np.matrix),
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| | np.empty((0, 1), dtype=np.double).view(np.matrix),
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| | tags={'size-0'}),
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| | LinalgCase("matrix_b_only",
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| | np.array([[1., 2.], [3., 4.]]),
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| | np.matrix([2., 1.]).T),
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| | LinalgCase("matrix_a_and_b",
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| | np.matrix([[1., 2.], [3., 4.]]),
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| | np.matrix([2., 1.]).T),
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| | ])
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| |
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| |
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| | CASES += apply_tag('hermitian', [
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| | LinalgCase("hmatrix_a_and_b",
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| | np.matrix([[1., 2.], [2., 1.]]),
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| | None),
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| | ])
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| |
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| |
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| |
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| | class MatrixTestCase(LinalgTestCase):
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| | TEST_CASES = CASES
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| |
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| |
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| | class TestSolveMatrix(SolveCases, MatrixTestCase):
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| | pass
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| |
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| |
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| | class TestInvMatrix(InvCases, MatrixTestCase):
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| | pass
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| |
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| |
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| | class TestEigvalsMatrix(EigvalsCases, MatrixTestCase):
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| | pass
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| |
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| |
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| | class TestEigMatrix(EigCases, MatrixTestCase):
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| | pass
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| |
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| |
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| | class TestSVDMatrix(SVDCases, MatrixTestCase):
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| | pass
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| |
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| |
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| | class TestCondMatrix(CondCases, MatrixTestCase):
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| | pass
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| |
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| |
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| | class TestPinvMatrix(PinvCases, MatrixTestCase):
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| | pass
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| |
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| |
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| | class TestDetMatrix(DetCases, MatrixTestCase):
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| | pass
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| |
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| |
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| | class TestLstsqMatrix(LstsqCases, MatrixTestCase):
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| | pass
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| |
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| |
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| | class _TestNorm2DMatrix(_TestNorm2D):
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| | array = np.matrix
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| |
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| |
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| | class TestNormDoubleMatrix(_TestNorm2DMatrix, _TestNormDoubleBase):
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| | pass
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| |
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| |
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| | class TestNormSingleMatrix(_TestNorm2DMatrix, _TestNormSingleBase):
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| | pass
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| |
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| |
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| | class TestNormInt64Matrix(_TestNorm2DMatrix, _TestNormInt64Base):
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| | pass
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| |
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| |
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| | class TestQRMatrix(_TestQR):
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| | array = np.matrix
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| |
|