--- /dev/null
+
+Contributors
+============
+
+A total of 6 people contributed to this release. People with a "+" by their
+names contributed a patch for the first time.
+
+* Charles Harris
+* Max Balandat +
+* @Mibu287 +
+* Pan Jan +
+* Sebastian Berg
+* @panpiort8 +
+
+Pull requests merged
+====================
+
+A total of 5 pull requests were merged for this release.
+
+* `#15916 <https://github.com/numpy/numpy/pull/15916>`__: BUG: Fix eigh and cholesky methods of numpy.random.multivariate_normal
+* `#15929 <https://github.com/numpy/numpy/pull/15929>`__: BUG,MAINT: Remove incorrect special case in string to number...
+* `#15930 <https://github.com/numpy/numpy/pull/15930>`__: BUG: Guarantee array is in valid state after memory error occurs...
+* `#15954 <https://github.com/numpy/numpy/pull/15954>`__: BUG: Check that `pvals` is 1D in `_generator.multinomial`.
+* `#16017 <https://github.com/numpy/numpy/pull/16017>`__: BUG: Alpha parameter must be 1D in `generator.dirichlet`
.. toctree::
:maxdepth: 3
+ 1.18.3 <release/1.18.3-notes>
1.18.2 <release/1.18.2-notes>
1.18.1 <release/1.18.1-notes>
1.18.0 <release/1.18.0-notes>
NumPy 1.18.2 Release Notes
==========================
-This small elease contains a fix for a performance regression in numpy/random
+This small release contains a fix for a performance regression in numpy/random
and several bug/maintenance updates.
The Python versions supported in this release are 3.5-3.8. Downstream
--- /dev/null
+.. currentmodule:: numpy
+
+==========================
+NumPy 1.18.3 Release Notes
+==========================
+
+This release contains various bug/regression fixes.
+
+The Python versions supported in this release are 3.5-3.8. Downstream
+developers should use Cython >= 0.29.15 for Python 3.8 support and OpenBLAS >=
+3.7 to avoid errors on the Skylake architecture.
+
+
+Highlights
+==========
+
+* Fix for the `method='eigh'` and `method='cholesky'` methods in
+ `numpy.random.multivariate_normal`. Those were producing samples from the
+ wrong distribution.
+
+
+Contributors
+============
+
+A total of 6 people contributed to this release. People with a "+" by their
+names contributed a patch for the first time.
+
+* Charles Harris
+* Max Balandat +
+* @Mibu287 +
+* Pan Jan +
+* Sebastian Berg
+* @panpiort8 +
+
+
+Pull requests merged
+====================
+
+A total of 5 pull requests were merged for this release.
+
+* `#15916 <https://github.com/numpy/numpy/pull/15916>`__: BUG: Fix eigh and cholesky methods of numpy.random.multivariate_normal
+* `#15929 <https://github.com/numpy/numpy/pull/15929>`__: BUG,MAINT: Remove incorrect special case in string to number...
+* `#15930 <https://github.com/numpy/numpy/pull/15930>`__: BUG: Guarantee array is in valid state after memory error occurs...
+* `#15954 <https://github.com/numpy/numpy/pull/15954>`__: BUG: Check that `pvals` is 1D in `_generator.multinomial`.
+* `#16017 <https://github.com/numpy/numpy/pull/16017>`__: BUG: Alpha parameter must be 1D in `generator.dirichlet`
* #oskip = 1*18,(PyArray_DESCR(aop)->elsize)*3,1*2,
* 1*18,(PyArray_DESCR(aop)->elsize)*3,1*2,
* 1*18,(PyArray_DESCR(aop)->elsize)*3,1*2#
- * #convert = 1*18, 0*3, 1*2,
- * 1*18, 0*3, 1*2,
- * 0*23#
- * #convstr = (Int*9, Long*2, Float*4, Complex*3, Tuple*3, Long*2)*3#
*/
-#if @convert@
-
-#define IS_@from@
-
static void
@from@_to_@to@(void *input, void *output, npy_intp n,
void *vaip, void *aop)
int oskip = @oskip@;
for (i = 0; i < n; i++, ip+=skip, op+=oskip) {
- PyObject *new;
PyObject *temp = PyArray_Scalar(ip, PyArray_DESCR(aip), (PyObject *)aip);
if (temp == NULL) {
return;
}
-
-#if defined(NPY_PY3K) && defined(IS_STRING)
- /* Work around some Python 3K */
- new = PyUnicode_FromEncodedObject(temp, "ascii", "strict");
- Py_DECREF(temp);
- temp = new;
- if (temp == NULL) {
- return;
- }
-#endif
- /* convert from Python object to needed one */
- {
- PyObject *args;
-
- /* call out to the Python builtin given by convstr */
- args = Py_BuildValue("(N)", temp);
-#if defined(NPY_PY3K)
-#define PyInt_Type PyLong_Type
-#endif
- new = Py@convstr@_Type.tp_new(&Py@convstr@_Type, args, NULL);
-#if defined(NPY_PY3K)
-#undef PyInt_Type
-#endif
- Py_DECREF(args);
- temp = new;
- if (temp == NULL) {
- return;
- }
- }
-
if (@to@_setitem(temp, op, aop)) {
Py_DECREF(temp);
return;
}
}
-#undef IS_@from@
-
-#else
-
-static void
-@from@_to_@to@(void *input, void *output, npy_intp n,
- void *vaip, void *aop)
-{
- @fromtyp@ *ip = input;
- @totyp@ *op = output;
- PyArrayObject *aip = vaip;
-
- npy_intp i;
- int skip = PyArray_DESCR(aip)->elsize;
- int oskip = @oskip@;
-
- for (i = 0; i < n; i++, ip+=skip, op+=oskip) {
- PyObject *temp = PyArray_Scalar(ip, PyArray_DESCR(aip), (PyObject *)aip);
- if (temp == NULL) {
- return;
- }
- if (@to@_setitem(temp, op, aop)) {
- Py_DECREF(temp);
- return;
- }
- Py_DECREF(temp);
- }
-}
-
-#endif
/**end repeat**/
case NPY_HALF:
case NPY_FLOAT:
case NPY_DOUBLE:
- case NPY_LONGDOUBLE:
size = 32;
break;
+ case NPY_LONGDOUBLE:
+ size = 48;
+ break;
case NPY_CFLOAT:
case NPY_CDOUBLE:
+ size = 2 * 32;
+ break;
case NPY_CLONGDOUBLE:
- size = 64;
+ size = 2 * 48;
break;
case NPY_OBJECT:
size = 64;
return -1;
}
- /* Free old dimensions and strides */
- npy_free_cache_dim_array(self);
nd = PyArray_NDIM(ret);
- ((PyArrayObject_fields *)self)->nd = nd;
if (nd > 0) {
/* create new dimensions and strides */
- ((PyArrayObject_fields *)self)->dimensions = npy_alloc_cache_dim(2 * nd);
- if (PyArray_DIMS(self) == NULL) {
+ npy_intp *_dimensions = npy_alloc_cache_dim(2 * nd);
+ if (_dimensions == NULL) {
Py_DECREF(ret);
- PyErr_SetString(PyExc_MemoryError,"");
+ PyErr_NoMemory();
return -1;
}
- ((PyArrayObject_fields *)self)->strides = PyArray_DIMS(self) + nd;
+ /* Free old dimensions and strides */
+ npy_free_cache_dim_array(self);
+ ((PyArrayObject_fields *)self)->nd = nd;
+ ((PyArrayObject_fields *)self)->dimensions = _dimensions;
+ ((PyArrayObject_fields *)self)->strides = _dimensions + nd;
+
if (nd) {
memcpy(PyArray_DIMS(self), PyArray_DIMS(ret), nd*sizeof(npy_intp));
memcpy(PyArray_STRIDES(self), PyArray_STRIDES(ret), nd*sizeof(npy_intp));
}
}
else {
+ /* Free old dimensions and strides */
+ npy_free_cache_dim_array(self);
+ ((PyArrayObject_fields *)self)->nd = 0;
((PyArrayObject_fields *)self)->dimensions = NULL;
((PyArrayObject_fields *)self)->strides = NULL;
}
+
Py_DECREF(ret);
PyArray_UpdateFlags(self, NPY_ARRAY_C_CONTIGUOUS | NPY_ARRAY_F_CONTIGUOUS);
return 0;
assert_equal(np.longdouble(repr(o)), o, "repr was %s" % repr(o))
-def test_unicode():
- np.longdouble(u"1.2")
+@pytest.mark.skipif(string_to_longdouble_inaccurate, reason="Need strtold_l")
+def test_repr_roundtrip_bytes():
+ o = 1 + LD_INFO.eps
+ assert_equal(np.longdouble(repr(o).encode("ascii")), o)
-def test_string():
- np.longdouble("1.2")
+@pytest.mark.skipif(string_to_longdouble_inaccurate, reason="Need strtold_l")
+@pytest.mark.parametrize("strtype", (np.str_, np.bytes_, str, bytes))
+def test_array_and_stringlike_roundtrip(strtype):
+ """
+ Test that string representations of long-double roundtrip both
+ for array casting and scalar coercion, see also gh-15608.
+ """
+ o = 1 + LD_INFO.eps
+ if strtype in (np.bytes_, bytes):
+ o_str = strtype(repr(o).encode("ascii"))
+ else:
+ o_str = strtype(repr(o))
-def test_bytes():
- np.longdouble(b"1.2")
+ # Test that `o` is correctly coerced from the string-like
+ assert o == np.longdouble(o_str)
+ # Test that arrays also roundtrip correctly:
+ o_strarr = np.asarray([o] * 3, dtype=strtype)
+ assert (o == o_strarr.astype(np.longdouble)).all()
-@pytest.mark.skipif(string_to_longdouble_inaccurate, reason="Need strtold_l")
-def test_repr_roundtrip_bytes():
- o = 1 + LD_INFO.eps
- assert_equal(np.longdouble(repr(o).encode("ascii")), o)
+ # And array coercion and casting to string give the same as scalar repr:
+ assert (o_strarr == o_str).all()
+ assert (np.asarray([o] * 3).astype(strtype) == o_str).all()
def test_bogus_string():
# approximately zero or when the covariance is not positive-semidefinite
_factor = u * np.sqrt(abs(s))
else:
- _factor = np.sqrt(s)[:, None] * vh
+ _factor = u * np.sqrt(s)
- x = np.dot(x, _factor)
- x += mean
+ x = mean + x @ _factor.T
x.shape = tuple(final_shape)
return x
d = len(pvals)
on = <np.ndarray>np.PyArray_FROM_OTF(n, np.NPY_INT64, np.NPY_ALIGNED)
- parr = <np.ndarray>np.PyArray_FROM_OTF(
- pvals, np.NPY_DOUBLE, np.NPY_ALIGNED | np.NPY_ARRAY_C_CONTIGUOUS)
+ parr = <np.ndarray>np.PyArray_FROMANY(
+ pvals, np.NPY_DOUBLE, 1, 1, np.NPY_ARRAY_ALIGNED | np.NPY_ARRAY_C_CONTIGUOUS)
pix = <double*>np.PyArray_DATA(parr)
check_array_constraint(parr, 'pvals', CONS_BOUNDED_0_1)
if kahan_sum(pix, d-1) > (1.0 + 1e-12):
Parameters
----------
- alpha : array
- Parameter of the distribution (k dimension for sample of
- dimension k).
+ alpha : sequence of floats, length k
+ Parameter of the distribution (length ``k`` for sample of
+ length ``k``).
size : int or tuple of ints, optional
- Output shape. If the given shape is, e.g., ``(m, n, k)``, then
+ Output shape. If the given shape is, e.g., ``(m, n)``, then
``m * n * k`` samples are drawn. Default is None, in which case a
- single value is returned.
+ vector of length ``k`` is returned.
Returns
-------
samples : ndarray,
- The drawn samples, of shape (size, alpha.ndim).
+ The drawn samples, of shape ``(size, k)``.
Raises
-------
ValueError
- If any value in alpha is less than or equal to zero
+ If any value in ``alpha`` is less than or equal to zero
Notes
-----
cdef double acc, invacc
k = len(alpha)
- alpha_arr = <np.ndarray>np.PyArray_FROM_OTF(
- alpha, np.NPY_DOUBLE, np.NPY_ALIGNED | np.NPY_ARRAY_C_CONTIGUOUS)
+ alpha_arr = <np.ndarray>np.PyArray_FROMANY(
+ alpha, np.NPY_DOUBLE, 1, 1,
+ np.NPY_ARRAY_ALIGNED | np.NPY_ARRAY_C_CONTIGUOUS)
if np.any(np.less_equal(alpha_arr, 0)):
raise ValueError('alpha <= 0')
alpha_data = <double*>np.PyArray_DATA(alpha_arr)
cdef long ni
d = len(pvals)
- parr = <np.ndarray>np.PyArray_FROM_OTF(
- pvals, np.NPY_DOUBLE, np.NPY_ALIGNED | np.NPY_ARRAY_C_CONTIGUOUS)
+ parr = <np.ndarray>np.PyArray_FROMANY(
+ pvals, np.NPY_DOUBLE, 1, 1, np.NPY_ARRAY_ALIGNED | np.NPY_ARRAY_C_CONTIGUOUS)
pix = <double*>np.PyArray_DATA(parr)
check_array_constraint(parr, 'pvals', CONS_BOUNDED_0_1)
if kahan_sum(pix, d-1) > (1.0 + 1e-12):
Parameters
----------
- alpha : array
- Parameter of the distribution (k dimension for sample of
- dimension k).
+ alpha : sequence of floats, length k
+ Parameter of the distribution (length ``k`` for sample of
+ length ``k``).
size : int or tuple of ints, optional
- Output shape. If the given shape is, e.g., ``(m, n, k)``, then
+ Output shape. If the given shape is, e.g., ``(m, n)``, then
``m * n * k`` samples are drawn. Default is None, in which case a
- single value is returned.
+ vector of length ``k`` is returned.
Returns
-------
samples : ndarray,
- The drawn samples, of shape (size, alpha.ndim).
+ The drawn samples, of shape ``(size, k)``.
Raises
-------
ValueError
- If any value in alpha is less than or equal to zero
+ If any value in ``alpha`` is less than or equal to zero
See Also
--------
cdef double acc, invacc
k = len(alpha)
- alpha_arr = <np.ndarray>np.PyArray_FROM_OTF(
- alpha, np.NPY_DOUBLE, np.NPY_ALIGNED | np.NPY_ARRAY_C_CONTIGUOUS)
+ alpha_arr = <np.ndarray>np.PyArray_FROMANY(
+ alpha, np.NPY_DOUBLE, 1, 1,
+ np.NPY_ARRAY_ALIGNED | np.NPY_ARRAY_C_CONTIGUOUS)
if np.any(np.less_equal(alpha_arr, 0)):
raise ValueError('alpha <= 0')
alpha_data = <double*>np.PyArray_DATA(alpha_arr)
contig = random.multinomial(100, pvals=np.ascontiguousarray(pvals))
assert_array_equal(non_contig, contig)
+ def test_multidimensional_pvals(self):
+ assert_raises(ValueError, random.multinomial, 10, [[0, 1]])
+ assert_raises(ValueError, random.multinomial, 10, [[0], [1]])
+ assert_raises(ValueError, random.multinomial, 10, [[[0], [1]], [[1], [0]]])
+ assert_raises(ValueError, random.multinomial, 10, np.array([[0, 1], [1, 0]]))
+
class TestMultivariateHypergeometric(object):
alpha = np.array([5.4e-01, -1.0e-16])
assert_raises(ValueError, random.dirichlet, alpha)
+ # gh-15876
+ assert_raises(ValueError, random.dirichlet, [[5, 1]])
+ assert_raises(ValueError, random.dirichlet, [[5], [1]])
+ assert_raises(ValueError, random.dirichlet, [[[5], [1]], [[1], [5]]])
+ assert_raises(ValueError, random.dirichlet, np.array([[5, 1], [1, 5]]))
+
def test_dirichlet_alpha_non_contiguous(self):
a = np.array([51.72840233779265162, -1.0, 39.74494232180943953])
alpha = a[::2]
assert_raises(ValueError, random.multivariate_normal, mean, cov,
check_valid='raise', method='eigh')
+ # check degenerate samples from singular covariance matrix
+ cov = [[1, 1], [1, 1]]
+ if method in ('svd', 'eigh'):
+ samples = random.multivariate_normal(mean, cov, size=(3, 2),
+ method=method)
+ assert_array_almost_equal(samples[..., 0], samples[..., 1],
+ decimal=6)
+ else:
+ assert_raises(LinAlgError, random.multivariate_normal, mean, cov,
+ method='cholesky')
+
cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)
with suppress_warnings() as sup:
random.multivariate_normal(mean, cov, method=method)
assert_raises(ValueError, random.multivariate_normal,
mu, np.eye(3))
+ @pytest.mark.parametrize("method", ["svd", "eigh", "cholesky"])
+ def test_multivariate_normal_basic_stats(self, method):
+ random = Generator(MT19937(self.seed))
+ n_s = 1000
+ mean = np.array([1, 2])
+ cov = np.array([[2, 1], [1, 2]])
+ s = random.multivariate_normal(mean, cov, size=(n_s,), method=method)
+ s_center = s - mean
+ cov_emp = (s_center.T @ s_center) / (n_s - 1)
+ # these are pretty loose and are only designed to detect major errors
+ assert np.all(np.abs(s_center.mean(-2)) < 0.1)
+ assert np.all(np.abs(cov_emp - cov) < 0.2)
+
def test_negative_binomial(self):
random = Generator(MT19937(self.seed))
actual = random.negative_binomial(n=100, p=.12345, size=(3, 2))
assert_raises(TypeError, np.random.multinomial, 1, p,
float(1))
+ def test_multidimensional_pvals(self):
+ assert_raises(ValueError, np.random.multinomial, 10, [[0, 1]])
+ assert_raises(ValueError, np.random.multinomial, 10, [[0], [1]])
+ assert_raises(ValueError, np.random.multinomial, 10, [[[0], [1]], [[1], [0]]])
+ assert_raises(ValueError, np.random.multinomial, 10, np.array([[0, 1], [1, 0]]))
+
class TestSetState(object):
def setup(self):
alpha = np.array([5.4e-01, -1.0e-16])
assert_raises(ValueError, np.random.mtrand.dirichlet, alpha)
+ # gh-15876
+ assert_raises(ValueError, random.dirichlet, [[5, 1]])
+ assert_raises(ValueError, random.dirichlet, [[5], [1]])
+ assert_raises(ValueError, random.dirichlet, [[[5], [1]], [[1], [5]]])
+ assert_raises(ValueError, random.dirichlet, np.array([[5, 1], [1, 5]]))
+
def test_exponential(self):
np.random.seed(self.seed)
actual = np.random.exponential(1.1234, size=(3, 2))
#-----------------------------------
# Path to the release notes
-RELEASE_NOTES = 'doc/source/release/1.18.2-notes.rst'
+RELEASE_NOTES = 'doc/source/release/1.18.3-notes.rst'
#-------------------------------------------------------
MAJOR = 1
MINOR = 18
-MICRO = 2
+MICRO = 3
ISRELEASED = True
VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO)
projects / platform / upstream / python3-numpy.git / commitdiff