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Diffstat (limited to '.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_defmatrix.py')
| -rw-r--r-- | .venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_defmatrix.py | 453 |
1 files changed, 453 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_defmatrix.py b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_defmatrix.py new file mode 100644 index 00000000..4cb5f3a3 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/numpy/matrixlib/tests/test_defmatrix.py @@ -0,0 +1,453 @@ +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 + 2j*A + 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((3*mA).A, (3*A))) + + mA2 = matrix(A) + mA2 *= 3 + assert_(np.allclose(mA2.A, 3*A)) + + 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, m**2) + 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): + A*object() + + +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)) |