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📄 test_defmatrix.py import collections.abc import numpy as np from numpy import matrix, asmatrix, bmat from numpy.testing import ( assert_, assert_equal, assert_almost_equal, assert_array_equal, assert_array_almost_equal, assert_raises ) from numpy.linalg import matrix_power from numpy.matrixlib import mat class TestCtor: def test_basic(self): A = np.array([[1, 2], [3, 4]]) mA = matrix(A) assert_(np.all(mA.A == A)) B = bmat("A,A;A,A") C = bmat([[A, A], [A, A]]) D = np.array([[1, 2, 1, 2], [3, 4, 3, 4], [1, 2, 1, 2], [3, 4, 3, 4]]) assert_(np.all(B.A == D)) assert_(np.all(C.A == D)) E = np.array([[5, 6], [7, 8]]) AEresult = matrix([[1, 2, 5, 6], [3, 4, 7, 8]]) assert_(np.all(bmat([A, E]) == AEresult)) vec = np.arange(5) mvec = matrix(vec) assert_(mvec.shape == (1, 5)) def test_exceptions(self): # Check for ValueError when called with invalid string data. assert_raises(ValueError, matrix, "invalid") def test_bmat_nondefault_str(self): A = np.array([[1, 2], [3, 4]]) B = np.array([[5, 6], [7, 8]]) Aresult = np.array([[1, 2, 1, 2], [3, 4, 3, 4], [1, 2, 1, 2], [3, 4, 3, 4]]) mixresult = np.array([[1, 2, 5, 6], [3, 4, 7, 8], [5, 6, 1, 2], [7, 8, 3, 4]]) assert_(np.all(bmat("A,A;A,A") == Aresult)) assert_(np.all(bmat("A,A;A,A", ldict={'A':B}) == Aresult)) assert_raises(TypeError, bmat, "A,A;A,A", gdict={'A':B}) assert_( np.all(bmat("A,A;A,A", ldict={'A':A}, gdict={'A':B}) == Aresult)) b2 = bmat("A,B;C,D", ldict={'A':A,'B':B}, gdict={'C':B,'D':A}) assert_(np.all(b2 == mixresult)) class TestProperties: def test_sum(self): """Test whether matrix.sum(axis=1) preserves orientation. Fails in NumPy <= 0.9.6.2127. """ M = matrix([[1, 2, 0, 0], [3, 4, 0, 0], [1, 2, 1, 2], [3, 4, 3, 4]]) sum0 = matrix([8, 12, 4, 6]) sum1 = matrix([3, 7, 6, 14]).T sumall = 30 assert_array_equal(sum0, M.sum(axis=0)) assert_array_equal(sum1, M.sum(axis=1)) assert_equal(sumall, M.sum()) assert_array_equal(sum0, np.sum(M, axis=0)) assert_array_equal(sum1, np.sum(M, axis=1)) assert_equal(sumall, np.sum(M)) def test_prod(self): x = matrix([[1, 2, 3], [4, 5, 6]]) assert_equal(x.prod(), 720) assert_equal(x.prod(0), matrix([[4, 10, 18]])) assert_equal(x.prod(1), matrix([[6], [120]])) assert_equal(np.prod(x), 720) assert_equal(np.prod(x, axis=0), matrix([[4, 10, 18]])) assert_equal(np.prod(x, axis=1), matrix([[6], [120]])) y = matrix([0, 1, 3]) assert_(y.prod() == 0) def test_max(self): x = matrix([[1, 2, 3], [4, 5, 6]]) assert_equal(x.max(), 6) assert_equal(x.max(0), matrix([[4, 5, 6]])) assert_equal(x.max(1), matrix([[3], [6]])) assert_equal(np.max(x), 6) assert_equal(np.max(x, axis=0), matrix([[4, 5, 6]])) assert_equal(np.max(x, axis=1), matrix([[3], [6]])) def test_min(self): x = matrix([[1, 2, 3], [4, 5, 6]]) assert_equal(x.min(), 1) assert_equal(x.min(0), matrix([[1, 2, 3]])) assert_equal(x.min(1), matrix([[1], [4]])) assert_equal(np.min(x), 1) assert_equal(np.min(x, axis=0), matrix([[1, 2, 3]])) assert_equal(np.min(x, axis=1), matrix([[1], [4]])) def test_ptp(self): x = np.arange(4).reshape((2, 2)) assert_(x.ptp() == 3) assert_(np.all(x.ptp(0) == np.array([2, 2]))) assert_(np.all(x.ptp(1) == np.array([1, 1]))) def test_var(self): x = np.arange(9).reshape((3, 3)) mx = x.view(np.matrix) assert_equal(x.var(ddof=0), mx.var(ddof=0)) assert_equal(x.var(ddof=1), mx.var(ddof=1)) def test_basic(self): import numpy.linalg as linalg A = np.array([[1., 2.], [3., 4.]]) mA = matrix(A) assert_(np.allclose(linalg.inv(A), mA.I)) assert_(np.all(np.array(np.transpose(A) == mA.T))) assert_(np.all(np.array(np.transpose(A) == mA.H))) assert_(np.all(A == mA.A)) B = A + 2jA mB = matrix(B) assert_(np.allclose(linalg.inv(B), mB.I)) assert_(np.all(np.array(np.transpose(B) == mB.T))) assert_(np.all(np.array(np.transpose(B).conj() == mB.H))) def test_pinv(self): x = matrix(np.arange(6).reshape(2, 3)) xpinv = matrix([[-0.77777778, 0.27777778], [-0.11111111, 0.11111111], [ 0.55555556, -0.05555556]]) assert_almost_equal(x.I, xpinv) def test_comparisons(self): A = np.arange(100).reshape(10, 10) mA = matrix(A) mB = matrix(A) + 0.1 assert_(np.all(mB == A+0.1)) assert_(np.all(mB == matrix(A+0.1))) assert_(not np.any(mB == matrix(A-0.1))) assert_(np.all(mA < mB)) assert_(np.all(mA <= mB)) assert_(np.all(mA <= mA)) assert_(not np.any(mA < mA)) assert_(not np.any(mB < mA)) assert_(np.all(mB >= mA)) assert_(np.all(mB >= mB)) assert_(not np.any(mB > mB)) assert_(np.all(mA == mA)) assert_(not np.any(mA == mB)) assert_(np.all(mB != mA)) assert_(not np.all(abs(mA) > 0)) assert_(np.all(abs(mB > 0))) def test_asmatrix(self): A = np.arange(100).reshape(10, 10) mA = asmatrix(A) A[0, 0] = -10 assert_(A[0, 0] == mA[0, 0]) def test_noaxis(self): A = matrix([[1, 0], [0, 1]]) assert_(A.sum() == matrix(2)) assert_(A.mean() == matrix(0.5)) def test_repr(self): A = matrix([[1, 0], [0, 1]]) assert_(repr(A) == "matrix([[1, 0],\n [0, 1]])") def test_make_bool_matrix_from_str(self): A = matrix('True; True; False') B = matrix([[True], [True], [False]]) assert_array_equal(A, B) class TestCasting: def test_basic(self): A = np.arange(100).reshape(10, 10) mA = matrix(A) mB = mA.copy() O = np.ones((10, 10), np.float64) 0.1 mB = mB + O assert_(mB.dtype.type == np.float64) assert_(np.all(mA != mB)) assert_(np.all(mB == mA+0.1)) mC = mA.copy() O = np.ones((10, 10), np.complex128) mC = mC * O assert_(mC.dtype.type == np.complex128) assert_(np.all(mA != mB)) class TestAlgebra: def test_basic(self): import numpy.linalg as linalg A = np.array([[1., 2.], [3., 4.]]) mA = matrix(A) B = np.identity(2) for i in range(6): assert_(np.allclose((mA i).A, B)) B = np.dot(B, A) Ainv = linalg.inv(A) B = np.identity(2) for i in range(6): assert_(np.allclose((mA -i).A, B)) B = np.dot(B, Ainv) assert_(np.allclose((mA * mA).A, np.dot(A, A))) assert_(np.allclose((mA + mA).A, (A + A))) assert_(np.allclose((3mA).A, (3A))) mA2 = matrix(A) mA2 = 3 assert_(np.allclose(mA2.A, 3A)) def test_pow(self): """Test raising a matrix to an integer power works as expected.""" m = matrix("1. 2.; 3. 4.") m2 = m.copy() m2 = 2 mi = m.copy() mi = -1 m4 = m2.copy() m4 = 2 assert_array_almost_equal(m2, m2) assert_array_almost_equal(m4, np.dot(m2, m2)) assert_array_almost_equal(np.dot(mi, m), np.eye(2)) def test_scalar_type_pow(self): m = matrix([[1, 2], [3, 4]]) for scalar_t in [np.int8, np.uint8]: two = scalar_t(2) assert_array_almost_equal(m 2, m two) def test_notimplemented(self): '''Check that 'not implemented' operations produce a failure.''' A = matrix([[1., 2.], [3., 4.]]) # __rpow__ with assert_raises(TypeError): 1.0*A # __mul__ with something not a list, ndarray, tuple, or scalar with assert_raises(TypeError): Aobject() class TestMatrixReturn: def test_instance_methods(self): a = matrix([1.0], dtype='f8') methodargs = { 'astype': ('intc',), 'clip': (0.0, 1.0), 'compress': ([1],), 'repeat': (1,), 'reshape': (1,), 'swapaxes': (0, 0), 'dot': np.array([1.0]), } excluded_methods = [ 'argmin', 'choose', 'dump', 'dumps', 'fill', 'getfield', 'getA', 'getA1', 'item', 'nonzero', 'put', 'putmask', 'resize', 'searchsorted', 'setflags', 'setfield', 'sort', 'partition', 'argpartition', 'take', 'tofile', 'tolist', 'tostring', 'tobytes', 'all', 'any', 'sum', 'argmax', 'argmin', 'min', 'max', 'mean', 'var', 'ptp', 'prod', 'std', 'ctypes', 'itemset', ] for attrib in dir(a): if attrib.startswith('_') or attrib in excluded_methods: continue f = getattr(a, attrib) if isinstance(f, collections.abc.Callable): # reset contents of a a.astype('f8') a.fill(1.0) if attrib in methodargs: args = methodargs[attrib] else: args = () b = f(*args) assert_(type(b) is matrix, "%s" % attrib) assert_(type(a.real) is matrix) assert_(type(a.imag) is matrix) c, d = matrix([0.0]).nonzero() assert_(type(c) is np.ndarray) assert_(type(d) is np.ndarray) class TestIndexing: def test_basic(self): x = asmatrix(np.zeros((3, 2), float)) y = np.zeros((3, 1), float) y[:, 0] = [0.8, 0.2, 0.3] x[:, 1] = y > 0.5 assert_equal(x, [[0, 1], [0, 0], [0, 0]]) class TestNewScalarIndexing: a = matrix([[1, 2], [3, 4]]) def test_dimesions(self): a = self.a x = a[0] assert_equal(x.ndim, 2) def test_array_from_matrix_list(self): a = self.a x = np.array([a, a]) assert_equal(x.shape, [2, 2, 2]) def test_array_to_list(self): a = self.a assert_equal(a.tolist(), [[1, 2], [3, 4]]) def test_fancy_indexing(self): a = self.a x = a[1, [0, 1, 0]] assert_(isinstance(x, matrix)) assert_equal(x, matrix([[3, 4, 3]])) x = a[[1, 0]] assert_(isinstance(x, matrix)) assert_equal(x, matrix([[3, 4], [1, 2]])) x = a[[[1], [0]], [[1, 0], [0, 1]]] assert_(isinstance(x, matrix)) assert_equal(x, matrix([[4, 3], [1, 2]])) def test_matrix_element(self): x = matrix([[1, 2, 3], [4, 5, 6]]) assert_equal(x[0][0], matrix([[1, 2, 3]])) assert_equal(x[0][0].shape, (1, 3)) assert_equal(x[0].shape, (1, 3)) assert_equal(x[:, 0].shape, (2, 1)) x = matrix(0) assert_equal(x[0, 0], 0) assert_equal(x[0], 0) assert_equal(x[:, 0].shape, x.shape) def test_scalar_indexing(self): x = asmatrix(np.zeros((3, 2), float)) assert_equal(x[0, 0], x[0][0]) def test_row_column_indexing(self): x = asmatrix(np.eye(2)) assert_array_equal(x[0,:], [[1, 0]]) assert_array_equal(x[1,:], [[0, 1]]) assert_array_equal(x[:, 0], [[1], [0]]) assert_array_equal(x[:, 1], [[0], [1]]) def test_boolean_indexing(self): A = np.arange(6) A.shape = (3, 2) x = asmatrix(A) assert_array_equal(x[:, np.array([True, False])], x[:, 0]) assert_array_equal(x[np.array([True, False, False]),:], x[0,:]) def test_list_indexing(self): A = np.arange(6) A.shape = (3, 2) x = asmatrix(A) assert_array_equal(x[:, [1, 0]], x[:, ::-1]) assert_array_equal(x[[2, 1, 0],:], x[::-1,:]) class TestPower: def test_returntype(self): a = np.array([[0, 1], [0, 0]]) assert_(type(matrix_power(a, 2)) is np.ndarray) a = mat(a) assert_(type(matrix_power(a, 2)) is matrix) def test_list(self): assert_array_equal(matrix_power([[0, 1], [0, 0]], 2), [[0, 0], [0, 0]]) class TestShape: a = np.array([[1], [2]]) m = matrix([[1], [2]]) def test_shape(self): assert_equal(self.a.shape, (2, 1)) assert_equal(self.m.shape, (2, 1)) def test_numpy_ravel(self): assert_equal(np.ravel(self.a).shape, (2,)) assert_equal(np.ravel(self.m).shape, (2,)) def test_member_ravel(self): assert_equal(self.a.ravel().shape, (2,)) assert_equal(self.m.ravel().shape, (1, 2)) def test_member_flatten(self): assert_equal(self.a.flatten().shape, (2,)) assert_equal(self.m.flatten().shape, (1, 2)) def test_numpy_ravel_order(self): x = np.array([[1, 2, 3], [4, 5, 6]]) assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6]) assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6]) assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6]) assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6]) x = matrix([[1, 2, 3], [4, 5, 6]]) assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6]) assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6]) assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6]) assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6]) def test_matrix_ravel_order(self): x = matrix([[1, 2, 3], [4, 5, 6]]) assert_equal(x.ravel(), [[1, 2, 3, 4, 5, 6]]) assert_equal(x.ravel(order='F'), [[1, 4, 2, 5, 3, 6]]) assert_equal(x.T.ravel(), [[1, 4, 2, 5, 3, 6]]) assert_equal(x.T.ravel(order='A'), [[1, 2, 3, 4, 5, 6]]) def test_array_memory_sharing(self): assert_(np.may_share_memory(self.a, self.a.ravel())) assert_(not np.may_share_memory(self.a, self.a.flatten())) def test_matrix_memory_sharing(self): assert_(np.may_share_memory(self.m, self.m.ravel())) assert_(not np.may_share_memory(self.m, self.m.flatten())) def test_expand_dims_matrix(self): # matrices are always 2d - so expand_dims only makes sense when the # type is changed away from matrix. a = np.arange(10).reshape((2, 5)).view(np.matrix) expanded = np.expand_dims(a, axis=1) assert_equal(expanded.ndim, 3) assert_(not isinstance(expanded, np.matrix))	14.63 KB	2026-01-20 12:58
📄 test_interaction.py """Tests of interaction of matrix with other parts of numpy. Note that tests with MaskedArray and linalg are done in separate files. """ import pytest import textwrap import warnings import numpy as np from numpy.testing import (assert_, assert_equal, assert_raises, assert_raises_regex, assert_array_equal, assert_almost_equal, assert_array_almost_equal) def test_fancy_indexing(): # The matrix class messes with the shape. While this is always # weird (getitem is not used, it does not have setitem nor knows # about fancy indexing), this tests gh-3110 # 2018-04-29: moved here from core.tests.test_index. m = np.matrix([[1, 2], [3, 4]]) assert_(isinstance(m[[0, 1, 0], :], np.matrix)) # gh-3110. Note the transpose currently because matrices do not # support dimension fixing for fancy indexing correctly. x = np.asmatrix(np.arange(50).reshape(5, 10)) assert_equal(x[:2, np.array(-1)], x[:2, -1].T) def test_polynomial_mapdomain(): # test that polynomial preserved matrix subtype. # 2018-04-29: moved here from polynomial.tests.polyutils. dom1 = [0, 4] dom2 = [1, 3] x = np.matrix([dom1, dom1]) res = np.polynomial.polyutils.mapdomain(x, dom1, dom2) assert_(isinstance(res, np.matrix)) def test_sort_matrix_none(): # 2018-04-29: moved here from core.tests.test_multiarray a = np.matrix([[2, 1, 0]]) actual = np.sort(a, axis=None) expected = np.matrix([[0, 1, 2]]) assert_equal(actual, expected) assert_(type(expected) is np.matrix) def test_partition_matrix_none(): # gh-4301 # 2018-04-29: moved here from core.tests.test_multiarray a = np.matrix([[2, 1, 0]]) actual = np.partition(a, 1, axis=None) expected = np.matrix([[0, 1, 2]]) assert_equal(actual, expected) assert_(type(expected) is np.matrix) def test_dot_scalar_and_matrix_of_objects(): # Ticket #2469 # 2018-04-29: moved here from core.tests.test_multiarray arr = np.matrix([1, 2], dtype=object) desired = np.matrix([[3, 6]], dtype=object) assert_equal(np.dot(arr, 3), desired) assert_equal(np.dot(3, arr), desired) def test_inner_scalar_and_matrix(): # 2018-04-29: moved here from core.tests.test_multiarray for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?': sca = np.array(3, dtype=dt)[()] arr = np.matrix([[1, 2], [3, 4]], dtype=dt) desired = np.matrix([[3, 6], [9, 12]], dtype=dt) assert_equal(np.inner(arr, sca), desired) assert_equal(np.inner(sca, arr), desired) def test_inner_scalar_and_matrix_of_objects(): # Ticket #4482 # 2018-04-29: moved here from core.tests.test_multiarray arr = np.matrix([1, 2], dtype=object) desired = np.matrix([[3, 6]], dtype=object) assert_equal(np.inner(arr, 3), desired) assert_equal(np.inner(3, arr), desired) def test_iter_allocate_output_subtype(): # Make sure that the subtype with priority wins # 2018-04-29: moved here from core.tests.test_nditer, given the # matrix specific shape test. # matrix vs ndarray a = np.matrix([[1, 2], [3, 4]]) b = np.arange(4).reshape(2, 2).T i = np.nditer([a, b, None], [], [['readonly'], ['readonly'], ['writeonly', 'allocate']]) assert_(type(i.operands[2]) is np.matrix) assert_(type(i.operands[2]) is not np.ndarray) assert_equal(i.operands[2].shape, (2, 2)) # matrix always wants things to be 2D b = np.arange(4).reshape(1, 2, 2) assert_raises(RuntimeError, np.nditer, [a, b, None], [], [['readonly'], ['readonly'], ['writeonly', 'allocate']]) # but if subtypes are disabled, the result can still work i = np.nditer([a, b, None], [], [['readonly'], ['readonly'], ['writeonly', 'allocate', 'no_subtype']]) assert_(type(i.operands[2]) is np.ndarray) assert_(type(i.operands[2]) is not np.matrix) assert_equal(i.operands[2].shape, (1, 2, 2)) def like_function(): # 2018-04-29: moved here from core.tests.test_numeric a = np.matrix([[1, 2], [3, 4]]) for like_function in np.zeros_like, np.ones_like, np.empty_like: b = like_function(a) assert_(type(b) is np.matrix) c = like_function(a, subok=False) assert_(type(c) is not np.matrix) def test_array_astype(): # 2018-04-29: copied here from core.tests.test_api # subok=True passes through a matrix a = np.matrix([[0, 1, 2], [3, 4, 5]], dtype='f4') b = a.astype('f4', subok=True, copy=False) assert_(a is b) # subok=True is default, and creates a subtype on a cast b = a.astype('i4', copy=False) assert_equal(a, b) assert_equal(type(b), np.matrix) # subok=False never returns a matrix b = a.astype('f4', subok=False, copy=False) assert_equal(a, b) assert_(not (a is b)) assert_(type(b) is not np.matrix) def test_stack(): # 2018-04-29: copied here from core.tests.test_shape_base # check np.matrix cannot be stacked m = np.matrix([[1, 2], [3, 4]]) assert_raises_regex(ValueError, 'shape too large to be a matrix', np.stack, [m, m]) def test_object_scalar_multiply(): # Tickets #2469 and #4482 # 2018-04-29: moved here from core.tests.test_ufunc arr = np.matrix([1, 2], dtype=object) desired = np.matrix([[3, 6]], dtype=object) assert_equal(np.multiply(arr, 3), desired) assert_equal(np.multiply(3, arr), desired) def test_nanfunctions_matrices(): # Check that it works and that type and # shape are preserved # 2018-04-29: moved here from core.tests.test_nanfunctions mat = np.matrix(np.eye(3)) for f in [np.nanmin, np.nanmax]: res = f(mat, axis=0) assert_(isinstance(res, np.matrix)) assert_(res.shape == (1, 3)) res = f(mat, axis=1) assert_(isinstance(res, np.matrix)) assert_(res.shape == (3, 1)) res = f(mat) assert_(np.isscalar(res)) # check that rows of nan are dealt with for subclasses (#4628) mat[1] = np.nan for f in [np.nanmin, np.nanmax]: with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') res = f(mat, axis=0) assert_(isinstance(res, np.matrix)) assert_(not np.any(np.isnan(res))) assert_(len(w) == 0) with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') res = f(mat, axis=1) assert_(isinstance(res, np.matrix)) assert_(np.isnan(res[1, 0]) and not np.isnan(res[0, 0]) and not np.isnan(res[2, 0])) assert_(len(w) == 1, 'no warning raised') assert_(issubclass(w[0].category, RuntimeWarning)) with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') res = f(mat) assert_(np.isscalar(res)) assert_(res != np.nan) assert_(len(w) == 0) def test_nanfunctions_matrices_general(): # Check that it works and that type and # shape are preserved # 2018-04-29: moved here from core.tests.test_nanfunctions mat = np.matrix(np.eye(3)) for f in (np.nanargmin, np.nanargmax, np.nansum, np.nanprod, np.nanmean, np.nanvar, np.nanstd): res = f(mat, axis=0) assert_(isinstance(res, np.matrix)) assert_(res.shape == (1, 3)) res = f(mat, axis=1) assert_(isinstance(res, np.matrix)) assert_(res.shape == (3, 1)) res = f(mat) assert_(np.isscalar(res)) for f in np.nancumsum, np.nancumprod: res = f(mat, axis=0) assert_(isinstance(res, np.matrix)) assert_(res.shape == (3, 3)) res = f(mat, axis=1) assert_(isinstance(res, np.matrix)) assert_(res.shape == (3, 3)) res = f(mat) assert_(isinstance(res, np.matrix)) assert_(res.shape == (1, 33)) def test_average_matrix(): # 2018-04-29: moved here from core.tests.test_function_base. y = np.matrix(np.random.rand(5, 5)) assert_array_equal(y.mean(0), np.average(y, 0)) a = np.matrix([[1, 2], [3, 4]]) w = np.matrix([[1, 2], [3, 4]]) r = np.average(a, axis=0, weights=w) assert_equal(type(r), np.matrix) assert_equal(r, [[2.5, 10.0/3]]) def test_trapz_matrix(): # Test to make sure matrices give the same answer as ndarrays # 2018-04-29: moved here from core.tests.test_function_base. x = np.linspace(0, 5) y = x x r = np.trapz(y, x) mx = np.matrix(x) my = np.matrix(y) mr = np.trapz(my, mx) assert_almost_equal(mr, r) def test_ediff1d_matrix(): # 2018-04-29: moved here from core.tests.test_arraysetops. assert(isinstance(np.ediff1d(np.matrix(1)), np.matrix)) assert(isinstance(np.ediff1d(np.matrix(1), to_begin=1), np.matrix)) def test_apply_along_axis_matrix(): # this test is particularly malicious because matrix # refuses to become 1d # 2018-04-29: moved here from core.tests.test_shape_base. def double(row): return row * 2 m = np.matrix([[0, 1], [2, 3]]) expected = np.matrix([[0, 2], [4, 6]]) result = np.apply_along_axis(double, 0, m) assert_(isinstance(result, np.matrix)) assert_array_equal(result, expected) result = np.apply_along_axis(double, 1, m) assert_(isinstance(result, np.matrix)) assert_array_equal(result, expected) def test_kron_matrix(): # 2018-04-29: moved here from core.tests.test_shape_base. a = np.ones([2, 2]) m = np.asmatrix(a) assert_equal(type(np.kron(a, a)), np.ndarray) assert_equal(type(np.kron(m, m)), np.matrix) assert_equal(type(np.kron(a, m)), np.matrix) assert_equal(type(np.kron(m, a)), np.matrix) class TestConcatenatorMatrix: # 2018-04-29: moved here from core.tests.test_index_tricks. def test_matrix(self): a = [1, 2] b = [3, 4] ab_r = np.r_['r', a, b] ab_c = np.r_['c', a, b] assert_equal(type(ab_r), np.matrix) assert_equal(type(ab_c), np.matrix) assert_equal(np.array(ab_r), [[1, 2, 3, 4]]) assert_equal(np.array(ab_c), [[1], [2], [3], [4]]) assert_raises(ValueError, lambda: np.r_['rc', a, b]) def test_matrix_scalar(self): r = np.r_['r', [1, 2], 3] assert_equal(type(r), np.matrix) assert_equal(np.array(r), [[1, 2, 3]]) def test_matrix_builder(self): a = np.array([1]) b = np.array([2]) c = np.array([3]) d = np.array([4]) actual = np.r_['a, b; c, d'] expected = np.bmat([[a, b], [c, d]]) assert_equal(actual, expected) assert_equal(type(actual), type(expected)) def test_array_equal_error_message_matrix(): # 2018-04-29: moved here from testing.tests.test_utils. with pytest.raises(AssertionError) as exc_info: assert_equal(np.array([1, 2]), np.matrix([1, 2])) msg = str(exc_info.value) msg_reference = textwrap.dedent("""\ Arrays are not equal (shapes (2,), (1, 2) mismatch) x: array([1, 2]) y: matrix([[1, 2]])""") assert_equal(msg, msg_reference) def test_array_almost_equal_matrix(): # Matrix slicing keeps things 2-D, while array does not necessarily. # See gh-8452. # 2018-04-29: moved here from testing.tests.test_utils. m1 = np.matrix([[1., 2.]]) m2 = np.matrix([[1., np.nan]]) m3 = np.matrix([[1., -np.inf]]) m4 = np.matrix([[np.nan, np.inf]]) m5 = np.matrix([[1., 2.], [np.nan, np.inf]]) for assert_func in assert_array_almost_equal, assert_almost_equal: for m in m1, m2, m3, m4, m5: assert_func(m, m) a = np.array(m) assert_func(a, m) assert_func(m, a)	11.6 KB	2026-01-20 12:58
📄 test_masked_matrix.py import numpy as np from numpy.testing import assert_warns from numpy.ma.testutils import (assert_, assert_equal, assert_raises, assert_array_equal) from numpy.ma.core import (masked_array, masked_values, masked, allequal, MaskType, getmask, MaskedArray, nomask, log, add, hypot, divide) from numpy.ma.extras import mr_ from numpy.compat import pickle class MMatrix(MaskedArray, np.matrix,): def __new__(cls, data, mask=nomask): mat = np.matrix(data) _data = MaskedArray.__new__(cls, data=mat, mask=mask) return _data def __array_finalize__(self, obj): np.matrix.__array_finalize__(self, obj) MaskedArray.__array_finalize__(self, obj) return @property def _series(self): _view = self.view(MaskedArray) _view._sharedmask = False return _view class TestMaskedMatrix: def test_matrix_indexing(self): # Tests conversions and indexing x1 = np.matrix([[1, 2, 3], [4, 3, 2]]) x2 = masked_array(x1, mask=[[1, 0, 0], [0, 1, 0]]) x3 = masked_array(x1, mask=[[0, 1, 0], [1, 0, 0]]) x4 = masked_array(x1) # test conversion to strings str(x2) # raises? repr(x2) # raises? # tests of indexing assert_(type(x2[1, 0]) is type(x1[1, 0])) assert_(x1[1, 0] == x2[1, 0]) assert_(x2[1, 1] is masked) assert_equal(x1[0, 2], x2[0, 2]) assert_equal(x1[0, 1:], x2[0, 1:]) assert_equal(x1[:, 2], x2[:, 2]) assert_equal(x1[:], x2[:]) assert_equal(x1[1:], x3[1:]) x1[0, 2] = 9 x2[0, 2] = 9 assert_equal(x1, x2) x1[0, 1:] = 99 x2[0, 1:] = 99 assert_equal(x1, x2) x2[0, 1] = masked assert_equal(x1, x2) x2[0, 1:] = masked assert_equal(x1, x2) x2[0, :] = x1[0, :] x2[0, 1] = masked assert_(allequal(getmask(x2), np.array([[0, 1, 0], [0, 1, 0]]))) x3[1, :] = masked_array([1, 2, 3], [1, 1, 0]) assert_(allequal(getmask(x3)[1], masked_array([1, 1, 0]))) assert_(allequal(getmask(x3[1]), masked_array([1, 1, 0]))) x4[1, :] = masked_array([1, 2, 3], [1, 1, 0]) assert_(allequal(getmask(x4[1]), masked_array([1, 1, 0]))) assert_(allequal(x4[1], masked_array([1, 2, 3]))) x1 = np.matrix(np.arange(5) * 1.0) x2 = masked_values(x1, 3.0) assert_equal(x1, x2) assert_(allequal(masked_array([0, 0, 0, 1, 0], dtype=MaskType), x2.mask)) assert_equal(3.0, x2.fill_value) def test_pickling_subbaseclass(self): # Test pickling w/ a subclass of ndarray a = masked_array(np.matrix(list(range(10))), mask=[1, 0, 1, 0, 0] * 2) for proto in range(2, pickle.HIGHEST_PROTOCOL + 1): a_pickled = pickle.loads(pickle.dumps(a, protocol=proto)) assert_equal(a_pickled._mask, a._mask) assert_equal(a_pickled, a) assert_(isinstance(a_pickled._data, np.matrix)) def test_count_mean_with_matrix(self): m = masked_array(np.matrix([[1, 2], [3, 4]]), mask=np.zeros((2, 2))) assert_equal(m.count(axis=0).shape, (1, 2)) assert_equal(m.count(axis=1).shape, (2, 1)) # Make sure broadcasting inside mean and var work assert_equal(m.mean(axis=0), [[2., 3.]]) assert_equal(m.mean(axis=1), [[1.5], [3.5]]) def test_flat(self): # Test that flat can return items even for matrices [#4585, #4615] # test simple access test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1]) assert_equal(test.flat[1], 2) assert_equal(test.flat[2], masked) assert_(np.all(test.flat[0:2] == test[0, 0:2])) # Test flat on masked_matrices test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1]) test.flat = masked_array([3, 2, 1], mask=[1, 0, 0]) control = masked_array(np.matrix([[3, 2, 1]]), mask=[1, 0, 0]) assert_equal(test, control) # Test setting test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1]) testflat = test.flat testflat[:] = testflat[[2, 1, 0]] assert_equal(test, control) testflat[0] = 9 # test that matrices keep the correct shape (#4615) a = masked_array(np.matrix(np.eye(2)), mask=0) b = a.flat b01 = b[:2] assert_equal(b01.data, np.array([[1., 0.]])) assert_equal(b01.mask, np.array([[False, False]])) def test_allany_onmatrices(self): x = np.array([[0.13, 0.26, 0.90], [0.28, 0.33, 0.63], [0.31, 0.87, 0.70]]) X = np.matrix(x) m = np.array([[True, False, False], [False, False, False], [True, True, False]], dtype=np.bool_) mX = masked_array(X, mask=m) mXbig = (mX > 0.5) mXsmall = (mX < 0.5) assert_(not mXbig.all()) assert_(mXbig.any()) assert_equal(mXbig.all(0), np.matrix([False, False, True])) assert_equal(mXbig.all(1), np.matrix([False, False, True]).T) assert_equal(mXbig.any(0), np.matrix([False, False, True])) assert_equal(mXbig.any(1), np.matrix([True, True, True]).T) assert_(not mXsmall.all()) assert_(mXsmall.any()) assert_equal(mXsmall.all(0), np.matrix([True, True, False])) assert_equal(mXsmall.all(1), np.matrix([False, False, False]).T) assert_equal(mXsmall.any(0), np.matrix([True, True, False])) assert_equal(mXsmall.any(1), np.matrix([True, True, False]).T) def test_compressed(self): a = masked_array(np.matrix([1, 2, 3, 4]), mask=[0, 0, 0, 0]) b = a.compressed() assert_equal(b, a) assert_(isinstance(b, np.matrix)) a[0, 0] = masked b = a.compressed() assert_equal(b, [[2, 3, 4]]) def test_ravel(self): a = masked_array(np.matrix([1, 2, 3, 4, 5]), mask=[[0, 1, 0, 0, 0]]) aravel = a.ravel() assert_equal(aravel.shape, (1, 5)) assert_equal(aravel._mask.shape, a.shape) def test_view(self): # Test view w/ flexible dtype iterator = list(zip(np.arange(10), np.random.rand(10))) data = np.array(iterator) a = masked_array(iterator, dtype=[('a', float), ('b', float)]) a.mask[0] = (1, 0) test = a.view((float, 2), np.matrix) assert_equal(test, data) assert_(isinstance(test, np.matrix)) assert_(not isinstance(test, MaskedArray)) class TestSubclassing: # Test suite for masked subclasses of ndarray. def setup_method(self): x = np.arange(5, dtype='float') mx = MMatrix(x, mask=[0, 1, 0, 0, 0]) self.data = (x, mx) def test_maskedarray_subclassing(self): # Tests subclassing MaskedArray (x, mx) = self.data assert_(isinstance(mx._data, np.matrix)) def test_masked_unary_operations(self): # Tests masked_unary_operation (x, mx) = self.data with np.errstate(divide='ignore'): assert_(isinstance(log(mx), MMatrix)) assert_equal(log(x), np.log(x)) def test_masked_binary_operations(self): # Tests masked_binary_operation (x, mx) = self.data # Result should be a MMatrix assert_(isinstance(add(mx, mx), MMatrix)) assert_(isinstance(add(mx, x), MMatrix)) # Result should work assert_equal(add(mx, x), mx+x) assert_(isinstance(add(mx, mx)._data, np.matrix)) with assert_warns(DeprecationWarning): assert_(isinstance(add.outer(mx, mx), MMatrix)) assert_(isinstance(hypot(mx, mx), MMatrix)) assert_(isinstance(hypot(mx, x), MMatrix)) def test_masked_binary_operations2(self): # Tests domained_masked_binary_operation (x, mx) = self.data xmx = masked_array(mx.data.__array__(), mask=mx.mask) assert_(isinstance(divide(mx, mx), MMatrix)) assert_(isinstance(divide(mx, x), MMatrix)) assert_equal(divide(mx, mx), divide(xmx, xmx)) class TestConcatenator: # Tests for mr_, the equivalent of r_ for masked arrays. def test_matrix_builder(self): assert_raises(np.ma.MAError, lambda: mr_['1, 2; 3, 4']) def test_matrix(self): # Test consistency with unmasked version. If we ever deprecate # matrix, this test should either still pass, or both actual and # expected should fail to be build. actual = mr_['r', 1, 2, 3] expected = np.ma.array(np.r_['r', 1, 2, 3]) assert_array_equal(actual, expected) # outer type is masked array, inner type is matrix assert_equal(type(actual), type(expected)) assert_equal(type(actual.data), type(expected.data))	8.72 KB	2026-01-20 12:58
📄 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	2.01 KB	2026-01-20 12:58
📄 test_multiarray.py import numpy as np from numpy.testing import assert_, assert_equal, assert_array_equal class TestView: def test_type(self): x = np.array([1, 2, 3]) assert_(isinstance(x.view(np.matrix), np.matrix)) def test_keywords(self): x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)]) # We must be specific about the endianness here: y = x.view(dtype='<i2', type=np.matrix) assert_array_equal(y, [[513]]) assert_(isinstance(y, np.matrix)) assert_equal(y.dtype, np.dtype('<i2'))	554 B	2026-01-20 12:58
📄 test_numeric.py import numpy as np from numpy.testing import assert_equal class TestDot: def test_matscalar(self): b1 = np.matrix(np.ones((3, 3), dtype=complex)) assert_equal(b1*1.0, b1) def test_diagonal(): b1 = np.matrix([[1,2],[3,4]]) diag_b1 = np.matrix([[1, 4]]) array_b1 = np.array([1, 4]) assert_equal(b1.diagonal(), diag_b1) assert_equal(np.diagonal(b1), array_b1) assert_equal(np.diag(b1), array_b1)	441 B	2026-01-20 12:58
📄 test_regression.py import numpy as np from numpy.testing import assert_, assert_equal, assert_raises class TestRegression: def test_kron_matrix(self): # Ticket #71 x = np.matrix('[1 0; 1 0]') assert_equal(type(np.kron(x, x)), type(x)) def test_matrix_properties(self): # Ticket #125 a = np.matrix([1.0], dtype=float) assert_(type(a.real) is np.matrix) assert_(type(a.imag) is np.matrix) c, d = np.matrix([0.0]).nonzero() assert_(type(c) is np.ndarray) assert_(type(d) is np.ndarray) def test_matrix_multiply_by_1d_vector(self): # Ticket #473 def mul(): np.mat(np.eye(2))*np.ones(2) assert_raises(ValueError, mul) def test_matrix_std_argmax(self): # Ticket #83 x = np.asmatrix(np.random.uniform(0, 1, (3, 3))) assert_equal(x.std().shape, ()) assert_equal(x.argmax().shape, ())	927 B	2026-01-20 12:58

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