def percentile(a,
q,
axis=None,
out=None,
interpolation='linear',
keepdims=False):
"""Computes the q-th percentile of the data along the specified axis.
Args:
a (cupy.ndarray): Array for which to compute percentiles.
q (float, tuple of floats or cupy.ndarray): Percentiles to compute
in the range between 0 and 100 inclusive.
axis (int or tuple of ints): Along which axis or axes to compute the
percentiles. The flattened array is used by default.
out (cupy.ndarray): Output array.
interpolation (str): Interpolation method when a quantile lies between
two data points. ``linear`` interpolation is used by default.
Supported interpolations are``lower``, ``higher``, ``midpoint``,
``nearest`` and ``linear``.
keepdims (bool): If ``True``, the axis is remained as an axis of
size one.
Returns:
cupy.ndarray: The percentiles of ``a``, along the axis if specified.
.. seealso:: :func:`numpy.percentile`
"""
q = cupy.asarray(q, dtype=a.dtype)
if q.ndim == 0:
q = q[None]
zerod = True
else:
zerod = False
if q.ndim > 1:
raise ValueError('Expected q to have a dimension of 1.\n'
'Actual: {0} != 1'.format(q.ndim))
if keepdims:
if axis is None:
keepdim = (1, ) * a.ndim
else:
keepdim = list(a.shape)
for ax in axis:
keepdim[ax % a.ndim] = 1
keepdim = tuple(keepdim)
# Copy a since we need it sorted but without modifying the original array
if isinstance(axis, int):
axis = axis,
if axis is None:
ap = a.flatten()
nkeep = 0
else:
# Reduce axes from a and put them last
axis = tuple(ax % a.ndim for ax in axis)
keep = set(range(a.ndim)) - set(axis)
nkeep = len(keep)
for i, s in enumerate(sorted(keep)):
a = a.swapaxes(i, s)
ap = a.reshape(a.shape[:nkeep] + (-1, )).copy()
axis = -1
ap.sort(axis=axis)
Nx = ap.shape[axis]
indices = q * 0.01 * (Nx - 1.) # percents to decimals
if interpolation == 'lower':
indices = cupy.floor(indices).astype(cupy.int32)
elif interpolation == 'higher':
indices = cupy.ceil(indices).astype(cupy.int32)
elif interpolation == 'midpoint':
indices = 0.5 * (cupy.floor(indices) + cupy.ceil(indices))
elif interpolation == 'nearest':
# TODO(hvy): Implement nearest using around
raise ValueError('\'nearest\' interpolation is not yet supported. '
'Please use any other interpolation method.')
elif interpolation == 'linear':
pass
else:
raise ValueError('Unexpected interpolation method.\n'
'Actual: \'{0}\' not in (\'linear\', \'lower\', '
'\'higher\', \'midpoint\')'.format(interpolation))
if indices.dtype == cupy.int32:
ret = cupy.rollaxis(ap, axis)
ret = ret.take(indices, axis=0, out=out)
else:
if out is None:
ret = cupy.empty(ap.shape[:-1] + q.shape, dtype=cupy.float64)
else:
ret = cupy.rollaxis(out, 0, out.ndim)
cupy.ElementwiseKernel(
'S idx, raw T a, raw int32 offset', 'U ret', '''
ptrdiff_t idx_below = floor(idx);
U weight_above = idx - idx_below;
ptrdiff_t offset_i = _ind.get()[0] * offset;
ret = a[offset_i + idx_below] * (1.0 - weight_above)
+ a[offset_i + idx_below + 1] * weight_above;
''',
'percentile_weightnening')(indices, ap,
ap.shape[-1] if ap.ndim > 1 else 0, ret)
ret = cupy.rollaxis(ret, -1) # Roll q dimension back to first axis
if zerod:
ret = ret.squeeze(0)
if keepdims:
if q.size > 1:
keepdim = (-1, ) + keepdim
ret = ret.reshape(keepdim)
return core._internal_ascontiguousarray(ret)
def _quantile_unchecked(a,
q,
axis=None,
out=None,
interpolation='linear',
keepdims=False):
if q.ndim == 0:
q = q[None]
zerod = True
else:
zerod = False
if q.ndim > 1:
raise ValueError('Expected q to have a dimension of 1.\n'
'Actual: {0} != 1'.format(q.ndim))
if keepdims:
if axis is None:
keepdim = (1, ) * a.ndim
else:
keepdim = list(a.shape)
for ax in axis:
keepdim[ax % a.ndim] = 1
keepdim = tuple(keepdim)
# Copy a since we need it sorted but without modifying the original array
if isinstance(axis, int):
axis = axis,
if axis is None:
ap = a.flatten()
nkeep = 0
else:
# Reduce axes from a and put them last
axis = tuple(ax % a.ndim for ax in axis)
keep = set(range(a.ndim)) - set(axis)
nkeep = len(keep)
for i, s in enumerate(sorted(keep)):
a = a.swapaxes(i, s)
ap = a.reshape(a.shape[:nkeep] + (-1, )).copy()
axis = -1
ap.sort(axis=axis)
Nx = ap.shape[axis]
indices = q * (Nx - 1.)
if interpolation == 'lower':
indices = cupy.floor(indices).astype(cupy.int32)
elif interpolation == 'higher':
indices = cupy.ceil(indices).astype(cupy.int32)
elif interpolation == 'midpoint':
indices = 0.5 * (cupy.floor(indices) + cupy.ceil(indices))
elif interpolation == 'nearest':
# TODO(hvy): Implement nearest using around
raise ValueError('\'nearest\' interpolation is not yet supported. '
'Please use any other interpolation method.')
elif interpolation == 'linear':
pass
else:
raise ValueError('Unexpected interpolation method.\n'
'Actual: \'{0}\' not in (\'linear\', \'lower\', '
'\'higher\', \'midpoint\')'.format(interpolation))
if indices.dtype == cupy.int32:
ret = cupy.rollaxis(ap, axis)
ret = ret.take(indices, axis=0, out=out)
else:
if out is None:
ret = cupy.empty(ap.shape[:-1] + q.shape, dtype=cupy.float64)
else:
ret = cupy.rollaxis(out, 0, out.ndim)
cupy.ElementwiseKernel(
'S idx, raw T a, raw int32 offset', 'U ret', '''
ptrdiff_t idx_below = floor(idx);
U weight_above = idx - idx_below;
ptrdiff_t offset_i = _ind.get()[0] * offset;
ret = a[offset_i + idx_below] * (1.0 - weight_above)
+ a[offset_i + idx_below + 1] * weight_above;
''',
'percentile_weightnening')(indices, ap,
ap.shape[-1] if ap.ndim > 1 else 0, ret)
ret = cupy.rollaxis(ret, -1) # Roll q dimension back to first axis
if zerod:
ret = ret.squeeze(0)
if keepdims:
if q.size > 1:
keepdim = (-1, ) + keepdim
ret = ret.reshape(keepdim)
return core._internal_ascontiguousarray(ret)