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편집 파일: test_matrix_linalg.py

""" 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