diff options
Diffstat (limited to 'numpy/matrixlib')
| -rw-r--r-- | numpy/matrixlib/defmatrix.py | 126 | ||||
| -rw-r--r-- | numpy/matrixlib/tests/test_defmatrix.py | 10 | ||||
| -rw-r--r-- | numpy/matrixlib/tests/test_interaction.py | 369 | ||||
| -rw-r--r-- | numpy/matrixlib/tests/test_masked_matrix.py | 239 | ||||
| -rw-r--r-- | numpy/matrixlib/tests/test_matrix_linalg.py | 103 | ||||
| -rw-r--r-- | numpy/matrixlib/tests/test_multiarray.py | 8 | ||||
| -rw-r--r-- | numpy/matrixlib/tests/test_numeric.py | 8 | ||||
| -rw-r--r-- | numpy/matrixlib/tests/test_regression.py | 8 |
8 files changed, 760 insertions, 111 deletions
diff --git a/numpy/matrixlib/defmatrix.py b/numpy/matrixlib/defmatrix.py index 1f5c94921..7baa401a8 100644 --- a/numpy/matrixlib/defmatrix.py +++ b/numpy/matrixlib/defmatrix.py @@ -3,10 +3,14 @@ from __future__ import division, absolute_import, print_function __all__ = ['matrix', 'bmat', 'mat', 'asmatrix'] import sys +import warnings import ast import numpy.core.numeric as N -from numpy.core.numeric import concatenate, isscalar, binary_repr, identity, asanyarray -from numpy.core.numerictypes import issubdtype +from numpy.core.numeric import concatenate, isscalar +# While not in __all__, matrix_power used to be defined here, so we import +# it for backward compatibility. +from numpy.linalg import matrix_power + def _convert_from_string(data): for char in '[]': @@ -63,118 +67,14 @@ def asmatrix(data, dtype=None): """ return matrix(data, dtype=dtype, copy=False) -def matrix_power(M, n): - """ - Raise a square matrix to the (integer) power `n`. - - For positive integers `n`, the power is computed by repeated matrix - squarings and matrix multiplications. If ``n == 0``, the identity matrix - of the same shape as M is returned. If ``n < 0``, the inverse - is computed and then raised to the ``abs(n)``. - - Parameters - ---------- - M : ndarray or matrix object - Matrix to be "powered." Must be square, i.e. ``M.shape == (m, m)``, - with `m` a positive integer. - n : int - The exponent can be any integer or long integer, positive, - negative, or zero. - - Returns - ------- - M**n : ndarray or matrix object - The return value is the same shape and type as `M`; - if the exponent is positive or zero then the type of the - elements is the same as those of `M`. If the exponent is - negative the elements are floating-point. - - Raises - ------ - LinAlgError - If the matrix is not numerically invertible. - - See Also - -------- - matrix - Provides an equivalent function as the exponentiation operator - (``**``, not ``^``). - - Examples - -------- - >>> from numpy import linalg as LA - >>> i = np.array([[0, 1], [-1, 0]]) # matrix equiv. of the imaginary unit - >>> LA.matrix_power(i, 3) # should = -i - array([[ 0, -1], - [ 1, 0]]) - >>> LA.matrix_power(np.matrix(i), 3) # matrix arg returns matrix - matrix([[ 0, -1], - [ 1, 0]]) - >>> LA.matrix_power(i, 0) - array([[1, 0], - [0, 1]]) - >>> LA.matrix_power(i, -3) # should = 1/(-i) = i, but w/ f.p. elements - array([[ 0., 1.], - [-1., 0.]]) - - Somewhat more sophisticated example - - >>> q = np.zeros((4, 4)) - >>> q[0:2, 0:2] = -i - >>> q[2:4, 2:4] = i - >>> q # one of the three quaternion units not equal to 1 - array([[ 0., -1., 0., 0.], - [ 1., 0., 0., 0.], - [ 0., 0., 0., 1.], - [ 0., 0., -1., 0.]]) - >>> LA.matrix_power(q, 2) # = -np.eye(4) - array([[-1., 0., 0., 0.], - [ 0., -1., 0., 0.], - [ 0., 0., -1., 0.], - [ 0., 0., 0., -1.]]) - - """ - M = asanyarray(M) - if M.ndim != 2 or M.shape[0] != M.shape[1]: - raise ValueError("input must be a square array") - if not issubdtype(type(n), N.integer): - raise TypeError("exponent must be an integer") - - from numpy.linalg import inv - - if n==0: - M = M.copy() - M[:] = identity(M.shape[0]) - return M - elif n<0: - M = inv(M) - n *= -1 - - result = M - if n <= 3: - for _ in range(n-1): - result=N.dot(result, M) - return result - - # binary decomposition to reduce the number of Matrix - # multiplications for n > 3. - beta = binary_repr(n) - Z, q, t = M, 0, len(beta) - while beta[t-q-1] == '0': - Z = N.dot(Z, Z) - q += 1 - result = Z - for k in range(q+1, t): - Z = N.dot(Z, Z) - if beta[t-k-1] == '1': - result = N.dot(result, Z) - return result - - class matrix(N.ndarray): """ matrix(data, dtype=None, copy=True) + .. note:: It is no longer recommended to use this class, even for linear + algebra. Instead use regular arrays. The class may be removed + in the future. + Returns a matrix from an array-like object, or from a string of data. A matrix is a specialized 2-D array that retains its 2-D nature through operations. It has certain special operators, such as ``*`` @@ -210,6 +110,12 @@ class matrix(N.ndarray): """ __array_priority__ = 10.0 def __new__(subtype, data, dtype=None, copy=True): + warnings.warn('the matrix subclass is not the recommended way to ' + 'represent matrices or deal with linear algebra (see ' + 'https://docs.scipy.org/doc/numpy/user/' + 'numpy-for-matlab-users.html). ' + 'Please adjust your code to use regular ndarray.', + PendingDeprecationWarning, stacklevel=2) if isinstance(data, matrix): dtype2 = data.dtype if (dtype is None): diff --git a/numpy/matrixlib/tests/test_defmatrix.py b/numpy/matrixlib/tests/test_defmatrix.py index a02a05c09..e74e83cdb 100644 --- a/numpy/matrixlib/tests/test_defmatrix.py +++ b/numpy/matrixlib/tests/test_defmatrix.py @@ -1,5 +1,13 @@ from __future__ import division, absolute_import, print_function +# As we are testing matrices, we ignore its PendingDeprecationWarnings +try: + import pytest + pytestmark = pytest.mark.filterwarnings( + 'ignore:the matrix subclass is not:PendingDeprecationWarning') +except ImportError: + pass + try: # Accessing collections abstract classes from collections # has been deprecated since Python 3.3 @@ -13,7 +21,7 @@ from numpy.testing import ( assert_, assert_equal, assert_almost_equal, assert_array_equal, assert_array_almost_equal, assert_raises ) -from numpy.matrixlib.defmatrix import matrix_power +from numpy.linalg import matrix_power from numpy.matrixlib import mat class TestCtor(object): diff --git a/numpy/matrixlib/tests/test_interaction.py b/numpy/matrixlib/tests/test_interaction.py new file mode 100644 index 000000000..fb4d8f98c --- /dev/null +++ b/numpy/matrixlib/tests/test_interaction.py @@ -0,0 +1,369 @@ +"""Tests of interaction of matrix with other parts of numpy. + +Note that tests with MaskedArray and linalg are done in separate files. +""" +from __future__ import division, absolute_import, print_function + +# As we are testing matrices, we ignore its PendingDeprecationWarnings +try: + import pytest + pytestmark = pytest.mark.filterwarnings( + 'ignore:the matrix subclass is not:PendingDeprecationWarning') +except ImportError: + pass + +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, 3*3)) + + +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(object): + # 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. + try: + assert_equal(np.array([1, 2]), np.matrix([1, 2])) + except AssertionError as e: + msg = str(e) + msg2 = msg.replace("shapes (2L,), (1L, 2L)", "shapes (2,), (1, 2)") + msg_reference = textwrap.dedent("""\ + + Arrays are not equal + + (shapes (2,), (1, 2) mismatch) + x: array([1, 2]) + y: matrix([[1, 2]])""") + try: + assert_equal(msg, msg_reference) + except AssertionError: + assert_equal(msg2, msg_reference) + else: + raise AssertionError("Did not raise") + + +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) diff --git a/numpy/matrixlib/tests/test_masked_matrix.py b/numpy/matrixlib/tests/test_masked_matrix.py new file mode 100644 index 000000000..adc2e5419 --- /dev/null +++ b/numpy/matrixlib/tests/test_masked_matrix.py @@ -0,0 +1,239 @@ +from __future__ import division, absolute_import, print_function + +# As we are testing matrices, we ignore its PendingDeprecationWarnings +try: + import pytest + pytestmark = pytest.mark.filterwarnings( + 'ignore:the matrix subclass is not:PendingDeprecationWarning') +except ImportError: + pass + +import pickle + +import numpy as np +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_ + + +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 + + def _get_series(self): + _view = self.view(MaskedArray) + _view._sharedmask = False + return _view + _series = property(fget=_get_series) + + +class TestMaskedMatrix(object): + 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) + a_pickled = pickle.loads(a.dumps()) + 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(object): + # Test suite for masked subclasses of ndarray. + + def setup(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)) + 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(object): + # 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)) diff --git a/numpy/matrixlib/tests/test_matrix_linalg.py b/numpy/matrixlib/tests/test_matrix_linalg.py new file mode 100644 index 000000000..85c7693b4 --- /dev/null +++ b/numpy/matrixlib/tests/test_matrix_linalg.py @@ -0,0 +1,103 @@ +""" Test functions for linalg module using the matrix class.""" +from __future__ import division, absolute_import, print_function + +# As we are testing matrices, we ignore its PendingDeprecationWarnings +try: + import pytest + pytestmark = pytest.mark.filterwarnings( + 'ignore:the matrix subclass is not:PendingDeprecationWarning') +except ImportError: + pass + +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 diff --git a/numpy/matrixlib/tests/test_multiarray.py b/numpy/matrixlib/tests/test_multiarray.py index 6d84bd477..2f04b49d6 100644 --- a/numpy/matrixlib/tests/test_multiarray.py +++ b/numpy/matrixlib/tests/test_multiarray.py @@ -1,5 +1,13 @@ from __future__ import division, absolute_import, print_function +# As we are testing matrices, we ignore its PendingDeprecationWarnings +try: + import pytest + pytestmark = pytest.mark.filterwarnings( + 'ignore:the matrix subclass is not:PendingDeprecationWarning') +except ImportError: + pass + import numpy as np from numpy.testing import assert_, assert_equal, assert_array_equal diff --git a/numpy/matrixlib/tests/test_numeric.py b/numpy/matrixlib/tests/test_numeric.py index 95e1c8001..cfdada126 100644 --- a/numpy/matrixlib/tests/test_numeric.py +++ b/numpy/matrixlib/tests/test_numeric.py @@ -1,5 +1,13 @@ from __future__ import division, absolute_import, print_function +# As we are testing matrices, we ignore its PendingDeprecationWarnings +try: + import pytest + pytestmark = pytest.mark.filterwarnings( + 'ignore:the matrix subclass is not:PendingDeprecationWarning') +except ImportError: + pass + import numpy as np from numpy.testing import assert_equal diff --git a/numpy/matrixlib/tests/test_regression.py b/numpy/matrixlib/tests/test_regression.py index 70e147279..439704ccf 100644 --- a/numpy/matrixlib/tests/test_regression.py +++ b/numpy/matrixlib/tests/test_regression.py @@ -1,5 +1,13 @@ from __future__ import division, absolute_import, print_function +# As we are testing matrices, we ignore its PendingDeprecationWarnings +try: + import pytest + pytestmark = pytest.mark.filterwarnings( + 'ignore:the matrix subclass is not:PendingDeprecationWarning') +except ImportError: + pass + import numpy as np from numpy.testing import assert_, assert_equal, assert_raises |