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search.py
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search.py
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import cupy
from cupy import _core
from cupy._core import fusion
from cupy import _util
from cupy._core import _routines_indexing as _indexing
from cupy._core import _routines_statistics as _statistics
def argmax(a, axis=None, dtype=None, out=None, keepdims=False):
"""Returns the indices of the maximum along an axis.
Args:
a (cupy.ndarray): Array to take argmax.
axis (int): Along which axis to find the maximum. ``a`` is flattened by
default.
dtype: Data type specifier.
out (cupy.ndarray): Output array.
keepdims (bool): If ``True``, the axis ``axis`` is preserved as an axis
of length one.
Returns:
cupy.ndarray: The indices of the maximum of ``a`` along an axis.
.. note::
``dtype`` and ``keepdim`` arguments are specific to CuPy. They are
not in NumPy.
.. note::
``axis`` argument accepts a tuple of ints, but this is specific to
CuPy. NumPy does not support it.
.. seealso:: :func:`numpy.argmax`
"""
# TODO(okuta): check type
return a.argmax(axis=axis, dtype=dtype, out=out, keepdims=keepdims)
def nanargmax(a, axis=None, dtype=None, out=None, keepdims=False):
"""Return the indices of the maximum values in the specified axis ignoring
NaNs. For all-NaN slice ``-1`` is returned.
Subclass cannot be passed yet, subok=True still unsupported
Args:
a (cupy.ndarray): Array to take nanargmax.
axis (int): Along which axis to find the maximum. ``a`` is flattened by
default.
Returns:
cupy.ndarray: The indices of the maximum of ``a``
along an axis ignoring NaN values.
.. note:: For performance reasons, ``cupy.nanargmax`` returns
``out of range values`` for all-NaN slice
whereas ``numpy.nanargmax`` raises ``ValueError``
.. seealso:: :func:`numpy.nanargmax`
"""
if a.dtype.kind in 'biu':
return argmax(a, axis=axis)
return _statistics._nanargmax(a, axis, dtype, out, keepdims)
def argmin(a, axis=None, dtype=None, out=None, keepdims=False):
"""Returns the indices of the minimum along an axis.
Args:
a (cupy.ndarray): Array to take argmin.
axis (int): Along which axis to find the minimum. ``a`` is flattened by
default.
dtype: Data type specifier.
out (cupy.ndarray): Output array.
keepdims (bool): If ``True``, the axis ``axis`` is preserved as an axis
of length one.
Returns:
cupy.ndarray: The indices of the minimum of ``a`` along an axis.
.. note::
``dtype`` and ``keepdim`` arguments are specific to CuPy. They are
not in NumPy.
.. note::
``axis`` argument accepts a tuple of ints, but this is specific to
CuPy. NumPy does not support it.
.. seealso:: :func:`numpy.argmin`
"""
# TODO(okuta): check type
return a.argmin(axis=axis, dtype=dtype, out=out, keepdims=keepdims)
def nanargmin(a, axis=None, dtype=None, out=None, keepdims=False):
"""Return the indices of the minimum values in the specified axis ignoring
NaNs. For all-NaN slice ``-1`` is returned.
Subclass cannot be passed yet, subok=True still unsupported
Args:
a (cupy.ndarray): Array to take nanargmin.
axis (int): Along which axis to find the minimum. ``a`` is flattened by
default.
Returns:
cupy.ndarray: The indices of the minimum of ``a``
along an axis ignoring NaN values.
.. note:: For performance reasons, ``cupy.nanargmin`` returns
``out of range values`` for all-NaN slice
whereas ``numpy.nanargmin`` raises ``ValueError``
.. seealso:: :func:`numpy.nanargmin`
"""
if a.dtype.kind in 'biu':
return argmin(a, axis=axis)
return _statistics._nanargmin(a, axis, dtype, out, keepdims)
def nonzero(a):
"""Return the indices of the elements that are non-zero.
Returns a tuple of arrays, one for each dimension of a,
containing the indices of the non-zero elements in that dimension.
Args:
a (cupy.ndarray): array
Returns:
tuple of arrays: Indices of elements that are non-zero.
.. warning::
This function may synchronize the device.
.. seealso:: :func:`numpy.nonzero`
"""
_util.check_array(a, arg_name='a')
return a.nonzero()
def flatnonzero(a):
"""Return indices that are non-zero in the flattened version of a.
This is equivalent to a.ravel().nonzero()[0].
Args:
a (cupy.ndarray): input array
Returns:
cupy.ndarray: Output array,
containing the indices of the elements of a.ravel() that are non-zero.
.. warning::
This function may synchronize the device.
.. seealso:: :func:`numpy.flatnonzero`
"""
_util.check_array(a, arg_name='a')
return a.ravel().nonzero()[0]
_where_ufunc = _core.create_ufunc(
'cupy_where',
('???->?', '?bb->b', '?BB->B', '?hh->h', '?HH->H', '?ii->i', '?II->I',
'?ll->l', '?LL->L', '?qq->q', '?QQ->Q', '?ee->e', '?ff->f',
# On CUDA 6.5 these combinations don't work correctly (on CUDA >=7.0, it
# works).
# See issue #551.
'?hd->d', '?Hd->d',
'?dd->d', '?FF->F', '?DD->D'),
'out0 = in0 ? in1 : in2')
def where(condition, x=None, y=None):
"""Return elements, either from x or y, depending on condition.
If only condition is given, return ``condition.nonzero()``.
Args:
condition (cupy.ndarray): When True, take x, otherwise take y.
x (cupy.ndarray): Values from which to choose on ``True``.
y (cupy.ndarray): Values from which to choose on ``False``.
Returns:
cupy.ndarray: Each element of output contains elements of ``x`` when
``condition`` is ``True``, otherwise elements of ``y``. If only
``condition`` is given, return the tuple ``condition.nonzero()``,
the indices where ``condition`` is True.
.. warning::
This function may synchronize the device if both ``x`` and ``y`` are
omitted.
.. seealso:: :func:`numpy.where`
"""
missing = (x is None, y is None).count(True)
if missing == 1:
raise ValueError('Must provide both \'x\' and \'y\' or neither.')
if missing == 2:
return nonzero(condition) # may synchronize
if fusion._is_fusing():
return fusion._call_ufunc(_where_ufunc, condition, x, y)
return _where_ufunc(condition.astype('?'), x, y)
def argwhere(a):
"""Return the indices of the elements that are non-zero.
Returns a (N, ndim) dimantional array containing the
indices of the non-zero elements. Where `N` is number of
non-zero elements and `ndim` is dimension of the given array.
Args:
a (cupy.ndarray): array
Returns:
cupy.ndarray: Indices of elements that are non-zero.
.. seealso:: :func:`numpy.argwhere`
"""
_util.check_array(a, arg_name='a')
return _indexing._ndarray_argwhere(a)
# This is to allow using the same kernels for all dtypes, ints & floats
# as nan is a special case
_preamble = '''
template<typename T>
__device__ bool _isnan(T val) {
return val != val;
}
'''
_hip_preamble = r'''
#ifdef __HIP_DEVICE_COMPILE__
#define no_thread_divergence(do_work, to_return) \
if (!is_done) { \
do_work; \
is_done = true; \
}
#else
#define no_thread_divergence(do_work, to_return) \
do_work; \
if (to_return) { return; }
#endif
'''
_searchsorted_code = '''
#ifdef __HIP_DEVICE_COMPILE__
bool is_done = false;
#endif
// Array is assumed to be monotonically
// increasing unless a check is requested with the
// `assume_increasing = False` parameter.
// `digitize` allows increasing and decreasing arrays.
bool inc = true;
if (!assume_increasing && n_bins >= 2) {
// In the case all the bins are nan the array is considered
// to be decreasing in numpy
inc = (bins[0] <= bins[n_bins-1])
|| (!_isnan<T>(bins[0]) && _isnan<T>(bins[n_bins-1]));
}
if (_isnan<S>(x)) {
long long pos = (inc ? n_bins : 0);
if (!side_is_right) {
if (inc) {
while (pos > 0 && _isnan<T>(bins[pos-1])) {
--pos;
}
} else {
while (pos < n_bins && _isnan<T>(bins[pos])) {
++pos;
}
}
}
no_thread_divergence( y = pos , true )
}
bool greater = false;
if (side_is_right) {
greater = inc && x >= bins[n_bins-1];
} else {
greater = (inc ? x > bins[n_bins-1] : x <= bins[n_bins-1]);
}
if (greater) {
no_thread_divergence( y = n_bins , true )
}
long long left = 0;
// In the case the bins is all NaNs, digitize
// needs to place all the valid values to the right
if (!inc) {
while (_isnan<T>(bins[left]) && left < n_bins) {
++left;
}
if (left == n_bins) {
no_thread_divergence( y = n_bins , true )
}
if (side_is_right
&& !_isnan<T>(bins[n_bins-1]) && !_isnan<S>(x)
&& bins[n_bins-1] > x) {
no_thread_divergence( y = n_bins , true )
}
}
long long right = n_bins-1;
while (left < right) {
long long m = left + (right - left) / 2;
bool look_right = true;
if (side_is_right) {
look_right = (inc ? bins[m] <= x : bins[m] > x);
} else {
look_right = (inc ? bins[m] < x : bins[m] >= x);
}
if (look_right) {
left = m + 1;
} else {
right = m;
}
}
no_thread_divergence( y = right , false )
'''
_searchsorted_kernel = _core.ElementwiseKernel(
'S x, raw T bins, int64 n_bins, bool side_is_right, '
'bool assume_increasing',
'int64 y',
_searchsorted_code,
name='cupy_searchsorted_kernel', preamble=_preamble+_hip_preamble)
_hip_preamble = r'''
#ifdef __HIP_DEVICE_COMPILE__
#define no_thread_divergence(do_work, to_return) \
if (!is_done) { \
do_work; \
is_done = true; \
}
#else
#define no_thread_divergence(do_work, to_return) \
do_work; \
if (to_return) { \
out = (y == n_bins ? false : bins[y] == x); \
if (invert) out = !out; \
return; \
}
#endif
'''
_exists_kernel = _core.ElementwiseKernel(
'S x, raw T bins, int64 n_bins, bool invert',
'bool out',
'''
const bool assume_increasing = true;
const bool side_is_right = false;
long long y;
'''
+ _searchsorted_code + '''
out = (y == n_bins ? false : bins[y] == x);
if (invert) out = !out;
''', name='cupy_exists_kernel', preamble=_preamble+_hip_preamble)
_exists_and_searchsorted_kernel = _core.ElementwiseKernel(
'S x, raw T bins, int64 n_bins, bool invert',
'bool out, int64 y',
'''
const bool assume_increasing = true;
const bool side_is_right = false;
'''
+ _searchsorted_code + '''
out = (y == n_bins ? false : bins[y] == x);
if (invert) out = !out;
''', name='cupy_exists_and_searchsorted_kernel',
preamble=_preamble+_hip_preamble)
def searchsorted(a, v, side='left', sorter=None):
"""Finds indices where elements should be inserted to maintain order.
Find the indices into a sorted array ``a`` such that,
if the corresponding elements in ``v`` were inserted before the indices,
the order of ``a`` would be preserved.
Args:
a (cupy.ndarray): Input array. If ``sorter`` is ``None``, then
it must be sorted in ascending order,
otherwise ``sorter`` must be an array of indices that sort it.
v (cupy.ndarray): Values to insert into ``a``.
side : {'left', 'right'}
If ``left``, return the index of the first suitable location found
If ``right``, return the last such index.
If there is no suitable index, return either 0 or length of ``a``.
sorter : 1-D array_like
Optional array of integer indices that sort array ``a`` into
ascending order. They are typically the result of
:func:`~cupy.argsort`.
Returns:
cupy.ndarray: Array of insertion points with the same shape as ``v``.
.. note:: When a is not in ascending order, behavior is undefined.
.. seealso:: :func:`numpy.searchsorted`
"""
return _searchsorted(a, v, side, sorter, True)
def _searchsorted(a, v, side, sorter, assume_increasing):
"""`assume_increasing` is used in the kernel to
skip monotonically increasing or decreasing verification
inside the cuda kernel.
"""
if not isinstance(a, cupy.ndarray):
raise NotImplementedError('Only int or ndarray are supported for a')
if not isinstance(v, cupy.ndarray):
raise NotImplementedError('Only int or ndarray are supported for v')
if a.ndim > 1:
raise ValueError('object too deep for desired array')
if a.ndim < 1:
raise ValueError('object of too small depth for desired array')
if a.size == 0:
return cupy.zeros(v.shape, dtype=cupy.int64)
a_iscomplex = a.dtype.kind == 'c'
v_iscomplex = v.dtype.kind == 'c'
if a_iscomplex and not v_iscomplex:
v = v.astype(a.dtype)
elif v_iscomplex and not a_iscomplex:
a = a.astype(v.dtype)
# Numpy does not check if the array is monotonic inside searchsorted
# which leads to undefined behavior in such cases.
if sorter is not None:
if sorter.dtype.kind not in ('i', 'u'):
raise TypeError('sorter must be of integer type')
if sorter.size != a.size:
raise ValueError('sorter.size must equal a.size')
a = a.take(sorter)
y = cupy.zeros(v.shape, dtype=cupy.int64)
_searchsorted_kernel(v, a, a.size, side == 'right', assume_increasing, y)
return y
# TODO(okuta): Implement extract