/
meanvar.py
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/
meanvar.py
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import functools
import numpy
import cupy
from cupy.core import _routines_statistics as _statistics
# TODO(okuta): Implement median
def average(a, axis=None, weights=None, returned=False):
"""Returns the weighted average along an axis.
Args:
a (cupy.ndarray): Array to compute average.
axis (int): Along which axis to compute average. The flattened array
is used by default.
weights (cupy.ndarray): Array of weights where each element
corresponds to the value in ``a``. If ``None``, all the values
in ``a`` have a weight equal to one.
returned (bool): If ``True``, a tuple of the average and the sum
of weights is returned, otherwise only the average is returned.
Returns:
cupy.ndarray or tuple of cupy.ndarray: The average of the input array
along the axis and the sum of weights.
.. warning::
This function may synchronize the device if ``weight`` is given.
.. seealso:: :func:`numpy.average`
"""
# TODO(niboshi): Avoid synchronization.
a = cupy.asarray(a)
if weights is None:
avg = a.mean(axis)
scl = avg.dtype.type(a.size / avg.size)
else:
wgt = cupy.asarray(weights)
if issubclass(a.dtype.type, (numpy.integer, numpy.bool_)):
result_dtype = functools.reduce(numpy.promote_types,
(a.dtype, wgt.dtype, 'f8'))
else:
result_dtype = numpy.promote_types(a.dtype, wgt.dtype)
# Sanity checks
if a.shape != wgt.shape:
if axis is None:
raise TypeError(
'Axis must be specified when shapes of a and weights '
'differ.')
if wgt.ndim != 1:
raise TypeError(
'1D weights expected when shapes of a and weights differ.')
if wgt.shape[0] != a.shape[axis]:
raise ValueError(
'Length of weights not compatible with specified axis.')
# setup wgt to broadcast along axis
wgt = cupy.broadcast_to(wgt, (a.ndim - 1) * (1,) + wgt.shape)
wgt = wgt.swapaxes(-1, axis)
scl = wgt.sum(axis=axis, dtype=result_dtype)
if cupy.any(scl == 0.0): # synchronize!
raise ZeroDivisionError(
'Weights sum to zero, can\'t be normalized')
avg = cupy.multiply(a, wgt, dtype=result_dtype).sum(axis) / scl
if returned:
if scl.shape != avg.shape:
scl = cupy.broadcast_to(cupy.array(scl), avg.shape).copy()
return avg, scl
else:
return avg
def mean(a, axis=None, dtype=None, out=None, keepdims=False):
"""Returns the arithmetic mean along an axis.
Args:
a (cupy.ndarray): Array to compute mean.
axis (int): Along which axis to compute mean. The flattened array is
used by default.
dtype: Data type specifier.
out (cupy.ndarray): Output array.
keepdims (bool): If ``True``, the axis is remained as an axis of
size one.
Returns:
cupy.ndarray: The mean of the input array along the axis.
.. seealso:: :func:`numpy.mean`
"""
# TODO(okuta): check type
return a.mean(axis=axis, dtype=dtype, out=out, keepdims=keepdims)
def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
"""Returns the variance along an axis.
Args:
a (cupy.ndarray): Array to compute variance.
axis (int): Along which axis to compute variance. The flattened array
is used by default.
dtype: Data type specifier.
out (cupy.ndarray): Output array.
keepdims (bool): If ``True``, the axis is remained as an axis of
size one.
Returns:
cupy.ndarray: The variance of the input array along the axis.
.. seealso:: :func:`numpy.var`
"""
# TODO(okuta): check type
return a.var(axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims)
def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
"""Returns the standard deviation along an axis.
Args:
a (cupy.ndarray): Array to compute standard deviation.
axis (int): Along which axis to compute standard deviation. The
flattened array is used by default.
dtype: Data type specifier.
out (cupy.ndarray): Output array.
keepdims (bool): If ``True``, the axis is remained as an axis of
size one.
Returns:
cupy.ndarray: The standard deviation of the input array along the axis.
.. seealso:: :func:`numpy.std`
"""
# TODO(okuta): check type
return a.std(axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims)
def nanmean(a, axis=None, dtype=None, out=None, keepdims=False):
"""Returns the arithmetic mean along an axis ignoring NaN values.
Args:
a (cupy.ndarray): Array to compute mean.
axis (int): Along which axis to compute mean. The flattened array is
used by default.
dtype: Data type specifier.
out (cupy.ndarray): Output array.
keepdims (bool): If ``True``, the axis is remained as an axis of
size one.
Returns:
cupy.ndarray: The mean of the input array along the axis ignoring NaNs.
.. seealso:: :func:`numpy.nanmean`
"""
if a.dtype.kind in 'biu':
return a.mean(axis=axis, dtype=dtype, out=out, keepdims=keepdims)
# TODO(okuta): check type
return _statistics._nanmean(
a, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
"""Returns the variance along an axis ignoring NaN values.
Args:
a (cupy.ndarray): Array to compute variance.
axis (int): Along which axis to compute variance. The flattened array
is used by default.
dtype: Data type specifier.
out (cupy.ndarray): Output array.
keepdims (bool): If ``True``, the axis is remained as an axis of
size one.
Returns:
cupy.ndarray: The variance of the input array along the axis.
.. seealso:: :func:`numpy.nanvar`
"""
if a.dtype.kind in 'biu':
return a.var(axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims)
# TODO(okuta): check type
return _statistics._nanvar(
a, axis=axis, dtype=dtype, out=out, ddof=ddof, keepdims=keepdims)
def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
"""Returns the standard deviation along an axis ignoring NaN values.
Args:
a (cupy.ndarray): Array to compute standard deviation.
axis (int): Along which axis to compute standard deviation. The
flattened array is used by default.
dtype: Data type specifier.
out (cupy.ndarray): Output array.
keepdims (bool): If ``True``, the axis is remained as an axis of
size one.
Returns:
cupy.ndarray: The standard deviation of the input array along the axis.
.. seealso:: :func:`numpy.nanstd`
"""
if a.dtype.kind in 'biu':
return a.std(axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims)
# TODO(okuta): check type
return _statistics._nanstd(
a, axis=axis, dtype=dtype, out=out, ddof=ddof, keepdims=keepdims)