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stat.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: define statistical functions of a tensor
import paddle
from paddle import _C_ops
from paddle.framework import in_dynamic_mode
from ..common_ops_import import Variable
from ..fluid.data_feeder import check_type, check_variable_and_dtype
from ..framework import LayerHelper, core
from .math import _get_reduce_axis_with_tensor
from .search import where
__all__ = []
def mean(x, axis=None, keepdim=False, name=None):
"""
Computes the mean of the input tensor's elements along ``axis``.
Args:
x (Tensor): The input Tensor with data type float32, float64.
axis (int|list|tuple, optional): The axis along which to perform mean
calculations. ``axis`` should be int, list(int) or tuple(int). If
``axis`` is a list/tuple of dimension(s), mean is calculated along
all element(s) of ``axis`` . ``axis`` or element(s) of ``axis``
should be in range [-D, D), where D is the dimensions of ``x`` . If
``axis`` or element(s) of ``axis`` is less than 0, it works the
same way as :math:`axis + D` . If ``axis`` is None, mean is
calculated over all elements of ``x``. Default is None.
keepdim (bool, optional): Whether to reserve the reduced dimension(s)
in the output Tensor. If ``keepdim`` is True, the dimensions of
the output Tensor is the same as ``x`` except in the reduced
dimensions(it is of size 1 in this case). Otherwise, the shape of
the output Tensor is squeezed in ``axis`` . Default is False.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor, results of average along ``axis`` of ``x``, with the same data
type as ``x``.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([[[1., 2., 3., 4.],
[5., 6., 7., 8.],
[9., 10., 11., 12.]],
[[13., 14., 15., 16.],
[17., 18., 19., 20.],
[21., 22., 23., 24.]]])
out1 = paddle.mean(x)
# 12.5
out2 = paddle.mean(x, axis=-1)
# [[ 2.5 6.5 10.5]
# [14.5 18.5 22.5]]
out3 = paddle.mean(x, axis=-1, keepdim=True)
# [[[ 2.5]
# [ 6.5]
# [10.5]]
# [[14.5]
# [18.5]
# [22.5]]]
out4 = paddle.mean(x, axis=[0, 2])
# [ 8.5 12.5 16.5]
"""
if in_dynamic_mode():
return _C_ops.mean(x, axis, keepdim)
else:
reduce_all, axis = _get_reduce_axis_with_tensor(axis, x)
check_variable_and_dtype(
x,
'x/input',
['uint16', 'float16', 'float32', 'float64'],
'mean/reduce_mean',
)
check_type(
axis, 'axis/dim', (int, list, tuple, Variable), 'mean/reduce_mean'
)
if isinstance(axis, (list, tuple)):
for item in axis:
check_type(
item,
'elements of axis/dim',
(int, Variable),
'mean/reduce_mean',
)
helper = LayerHelper('mean', **locals())
attrs = {'dim': axis, 'keep_dim': keepdim, 'reduce_all': reduce_all}
out = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='reduce_mean',
inputs={'X': x},
outputs={'Out': out},
attrs=attrs,
)
return out
def var(x, axis=None, unbiased=True, keepdim=False, name=None):
"""
Computes the variance of ``x`` along ``axis`` .
Args:
x (Tensor): The input Tensor with data type float16, float32, float64.
axis (int|list|tuple, optional): The axis along which to perform variance calculations. ``axis`` should be int, list(int) or tuple(int).
- If ``axis`` is a list/tuple of dimension(s), variance is calculated along all element(s) of ``axis`` . ``axis`` or element(s) of ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` .
- If ``axis`` or element(s) of ``axis`` is less than 0, it works the same way as :math:`axis + D` .
- If ``axis`` is None, variance is calculated over all elements of ``x``. Default is None.
unbiased (bool, optional): Whether to use the unbiased estimation. If ``unbiased`` is True, the divisor used in the computation is :math:`N - 1`, where :math:`N` represents the number of elements along ``axis`` , otherwise the divisor is :math:`N`. Default is True.
keep_dim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the input unless keep_dim is true. Default is False.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor, results of variance along ``axis`` of ``x``, with the same data type as ``x``.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([[1.0, 2.0, 3.0], [1.0, 4.0, 5.0]])
out1 = paddle.var(x)
# 2.66666667
out2 = paddle.var(x, axis=1)
# [1. 4.33333333]
"""
if not in_dynamic_mode():
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'var'
)
u = mean(x, axis, True, name)
out = paddle.sum(paddle.pow((x - u), 2), axis, keepdim=keepdim, name=name)
dtype = x.dtype
n = paddle.cast(paddle.numel(x), paddle.int64) / paddle.cast(
paddle.numel(out), paddle.int64
)
n = n.astype(dtype)
if unbiased:
one_const = paddle.ones([], x.dtype)
n = where(n > one_const, n - 1.0, one_const)
n.stop_gradient = True
out /= n
return out
def std(x, axis=None, unbiased=True, keepdim=False, name=None):
"""
Computes the standard-deviation of ``x`` along ``axis`` .
Args:
x (Tensor): The input Tensor with data type float16, float32, float64.
axis (int|list|tuple, optional): The axis along which to perform
standard-deviation calculations. ``axis`` should be int, list(int)
or tuple(int). If ``axis`` is a list/tuple of dimension(s),
standard-deviation is calculated along all element(s) of ``axis`` .
``axis`` or element(s) of ``axis`` should be in range [-D, D),
where D is the dimensions of ``x`` . If ``axis`` or element(s) of
``axis`` is less than 0, it works the same way as :math:`axis + D` .
If ``axis`` is None, standard-deviation is calculated over all
elements of ``x``. Default is None.
unbiased (bool, optional): Whether to use the unbiased estimation. If
``unbiased`` is True, the standard-deviation is calculated via the
unbiased estimator. If ``unbiased`` is True, the divisor used in
the computation is :math:`N - 1`, where :math:`N` represents the
number of elements along ``axis`` , otherwise the divisor is
:math:`N`. Default is True.
keepdim (bool, optional): Whether to reserve the reduced dimension(s)
in the output Tensor. If ``keepdim`` is True, the dimensions of
the output Tensor is the same as ``x`` except in the reduced
dimensions(it is of size 1 in this case). Otherwise, the shape of
the output Tensor is squeezed in ``axis`` . Default is False.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor, results of standard-deviation along ``axis`` of ``x``, with the
same data type as ``x``.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([[1.0, 2.0, 3.0], [1.0, 4.0, 5.0]])
out1 = paddle.std(x)
# 1.63299316
out2 = paddle.std(x, unbiased=False)
# 1.49071205
out3 = paddle.std(x, axis=1)
# [1. 2.081666]
"""
if not in_dynamic_mode():
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'std'
)
out = var(**locals())
return paddle.sqrt(out)
def numel(x, name=None):
"""
Returns the number of elements for a tensor, which is a 0-D int64 Tensor with shape [].
Args:
x (Tensor): The input Tensor, it's data type can be bool, float16, float32, float64, int32, int64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: The number of elements for the input Tensor, whose shape is [].
Examples:
.. code-block:: python
import paddle
x = paddle.full(shape=[4, 5, 7], fill_value=0, dtype='int32')
numel = paddle.numel(x) # 140
"""
if in_dynamic_mode():
return _C_ops.numel(x)
else:
if not isinstance(x, Variable):
raise TypeError("x must be a Tensor in numel")
helper = LayerHelper('numel', **locals())
out = helper.create_variable_for_type_inference(
dtype=core.VarDesc.VarType.INT64
)
helper.append_op(type='size', inputs={'Input': x}, outputs={'Out': out})
return out
def nanmedian(x, axis=None, keepdim=False, name=None):
r"""
Compute the median along the specified axis, while ignoring NaNs.
If the valid count of elements is a even number,
the average value of both elements in the middle is calculated as the median.
Args:
x (Tensor): The input Tensor, it's data type can be int32, int64, float16, float32, float64.
axis (None|int|list|tuple, optional):
The axis along which to perform median calculations ``axis`` should be int or list of int.
``axis`` should be in range [-D, D), where D is the dimensions of ``x`` .
If ``axis`` is less than 0, it works the same way as :math:`axis + D`.
If ``axis`` is None, median is calculated over all elements of ``x``. Default is None.
keepdim (bool, optional): Whether to reserve the reduced dimension(s)
in the output Tensor. If ``keepdim`` is True, the dimensions of
the output Tensor is the same as ``x`` except in the reduced
dimensions(it is of size 1 in this case). Otherwise, the shape of
the output Tensor is squeezed in ``axis`` . Default is False.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor, results of median along ``axis`` of ``x``. The output dtype is the same as `x`.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([[float('nan'), 2. , 3. ], [0. , 1. , 2. ]])
y1 = x.nanmedian()
# y1 is 2.
y2 = x.nanmedian(0)
# y2 is [0., 1.5, 2.5]
y3 = x.nanmedian(0, keepdim=True)
# y3 is [[0., 1.5, 2.5]]
y4 = x.nanmedian((0, 1))
# y4 is 2.
"""
if not isinstance(x, Variable):
raise TypeError("In median, the input x should be a Tensor.")
if isinstance(axis, (list, tuple)) and len(axis) == 0:
raise ValueError("Axis list should not be empty.")
if axis is None:
axis = []
elif isinstance(axis, tuple):
axis = list(axis)
elif isinstance(axis, int):
axis = [axis]
if in_dynamic_mode():
return _C_ops.nanmedian(x, axis, keepdim)
else:
check_variable_and_dtype(
x,
'X',
['int32', 'int64', 'float16', 'float32', 'float64'],
'nanmedian',
)
helper = LayerHelper('nanmedian', **locals())
attrs = {'axis': axis, 'keepdim': keepdim}
out = helper.create_variable_for_type_inference(x.dtype)
medians = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='nanmedian',
inputs={'X': x},
outputs={'Out': out, 'MedianIndex': medians},
attrs=attrs,
)
return out
def median(x, axis=None, keepdim=False, name=None):
"""
Compute the median along the specified axis.
Args:
x (Tensor): The input Tensor, it's data type can be bool, float16, float32, float64, int32, int64.
axis (int, optional): The axis along which to perform median calculations ``axis`` should be int.
``axis`` should be in range [-D, D), where D is the dimensions of ``x`` .
If ``axis`` is less than 0, it works the same way as :math:`axis + D`.
If ``axis`` is None, median is calculated over all elements of ``x``. Default is None.
keepdim (bool, optional): Whether to reserve the reduced dimension(s)
in the output Tensor. If ``keepdim`` is True, the dimensions of
the output Tensor is the same as ``x`` except in the reduced
dimensions(it is of size 1 in this case). Otherwise, the shape of
the output Tensor is squeezed in ``axis`` . Default is False.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor, results of median along ``axis`` of ``x``. If data type of ``x`` is float64, data type of results will be float64, otherwise data type will be float32.
Examples:
.. code-block:: python
import paddle
x = paddle.arange(12).reshape([3, 4])
# Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
# [[0 , 1 , 2 , 3 ],
# [4 , 5 , 6 , 7 ],
# [8 , 9 , 10, 11]])
y1 = paddle.median(x)
# Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
# 5.50000000)
y2 = paddle.median(x, axis=0)
# Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
# [4., 5., 6., 7.])
y3 = paddle.median(x, axis=1)
# Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
# [1.50000000, 5.50000000, 9.50000000])
y4 = paddle.median(x, axis=0, keepdim=True)
# Tensor(shape=[1, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[4., 5., 6., 7.]])
"""
if not isinstance(x, Variable):
raise TypeError("In median, the input x should be a Tensor.")
if x.size == 0:
raise ValueError("In median, the size of input x should not be 0.")
is_flatten = False
dims = len(x.shape)
if dims == 0:
assert axis in [
-1,
0,
None,
], 'when input 0-D, axis can only be [-1, 0] or default None'
is_flatten = True
if axis is None:
is_flatten = True
if is_flatten:
x = paddle.flatten(x)
axis = 0
else:
if not isinstance(axis, int) or not (axis < dims and axis >= -dims):
raise ValueError(
"In median, axis should be none or an integer in range [-rank(x), rank(x))."
)
if axis < 0:
axis += dims
sz = x.shape[axis]
kth = sz >> 1
tensor_topk, idx = paddle.topk(x, kth + 1, axis=axis, largest=False)
dtype = 'float64' if x.dtype == core.VarDesc.VarType.FP64 else 'float32'
if sz & 1 == 0:
out_tensor = paddle.slice(
tensor_topk, axes=[axis], starts=[kth - 1], ends=[kth]
) + paddle.slice(tensor_topk, axes=[axis], starts=[kth], ends=[kth + 1])
out_tensor = paddle.cast(out_tensor, dtype=dtype) / 2
else:
out_tensor = paddle.cast(
paddle.slice(
tensor_topk, axes=[axis], starts=[kth], ends=[kth + 1]
),
dtype=dtype,
)
out_tensor = out_tensor + paddle.sum(
paddle.cast(paddle.isnan(x), dtype=dtype) * x, axis=axis, keepdim=True
)
if is_flatten:
if keepdim:
out_tensor = out_tensor.reshape([1] * dims)
else:
out_tensor = out_tensor.reshape([])
else:
if not keepdim:
out_tensor = out_tensor.squeeze(axis)
return out_tensor
def _compute_quantile(x, q, axis=None, keepdim=False, ignore_nan=False):
"""
Compute the quantile of the input along the specified axis.
Args:
x (Tensor): The input Tensor, it's data type can be float32, float64, int32, int64.
q (int|float|list): The q for calculate quantile, which should be in range [0, 1]. If q is a list,
each q will be calculated and the first dimension of output is same to the number of ``q`` .
axis (int|list, optional): The axis along which to calculate quantile. ``axis`` should be int or list of int.
``axis`` should be in range [-D, D), where D is the dimensions of ``x`` .
If ``axis`` is less than 0, it works the same way as :math:`axis + D`.
If ``axis`` is a list, quantile is calculated over all elements of given axises.
If ``axis`` is None, quantile is calculated over all elements of ``x``. Default is None.
keepdim (bool, optional): Whether to reserve the reduced dimension(s)
in the output Tensor. If ``keepdim`` is True, the dimensions of
the output Tensor is the same as ``x`` except in the reduced
dimensions(it is of size 1 in this case). Otherwise, the shape of
the output Tensor is squeezed in ``axis`` . Default is False.
ignore_nan: (bool, optional): Whether to ignore NaN of input Tensor.
If ``ignore_nan`` is True, it will calculate nanquantile.
Otherwise it will calculate quantile. Default is False.
Returns:
Tensor, results of quantile along ``axis`` of ``x``.
In order to obtain higher precision, data type of results will be float64.
"""
# Validate x
if not isinstance(x, Variable):
raise TypeError("input x should be a Tensor.")
# Validate q
if isinstance(q, (int, float)):
q = [q]
elif isinstance(q, (list, tuple)):
if len(q) <= 0:
raise ValueError("q should not be empty")
else:
raise TypeError("Type of q should be int, float, list or tuple.")
# Validate axis
dims = len(x.shape)
out_shape = list(x.shape)
if axis is None:
x = paddle.flatten(x)
axis = 0
out_shape = [1] * dims
else:
if isinstance(axis, list):
axis_src, axis_dst = [], []
for axis_single in axis:
if not isinstance(axis_single, int) or not (
axis_single < dims and axis_single >= -dims
):
raise ValueError(
"Axis should be None, int, or a list, element should in range [-rank(x), rank(x))."
)
if axis_single < 0:
axis_single = axis_single + dims
axis_src.append(axis_single)
out_shape[axis_single] = 1
axis_dst = list(range(-len(axis), 0))
x = paddle.moveaxis(x, axis_src, axis_dst)
if len(axis_dst) == 0:
x = paddle.flatten(x)
axis = 0
else:
x = paddle.flatten(x, axis_dst[0], axis_dst[-1])
axis = axis_dst[0]
else:
if not isinstance(axis, int) or not (axis < dims and axis >= -dims):
raise ValueError(
"Axis should be None, int, or a list, element should in range [-rank(x), rank(x))."
)
if axis < 0:
axis += dims
out_shape[axis] = 1
mask = x.isnan()
valid_counts = mask.logical_not().sum(
axis=axis, keepdim=True, dtype='float64'
)
indices = []
for q_num in q:
if q_num < 0 or q_num > 1:
raise ValueError("q should be in range [0, 1]")
if in_dynamic_mode():
q_num = paddle.to_tensor(q_num, dtype='float64')
if ignore_nan:
indices.append(q_num * (valid_counts - 1))
else:
# TODO: Use paddle.index_fill instead of where
index = q_num * (valid_counts - 1)
last_index = x.shape[axis] - 1
nums = paddle.full_like(index, fill_value=last_index)
index = paddle.where(mask.any(axis=axis, keepdim=True), nums, index)
indices.append(index)
sorted_tensor = paddle.sort(x, axis)
outputs = []
# TODO(chenjianye): replace the for-loop to directly take elements.
for index in indices:
indices_below = paddle.floor(index).astype(paddle.int32)
indices_upper = paddle.ceil(index).astype(paddle.int32)
tensor_upper = paddle.take_along_axis(
sorted_tensor, indices_upper, axis=axis
)
tensor_below = paddle.take_along_axis(
sorted_tensor, indices_below, axis=axis
)
weights = index - indices_below.astype('float64')
out = paddle.lerp(
tensor_below.astype('float64'),
tensor_upper.astype('float64'),
weights,
)
if not keepdim:
out = paddle.squeeze(out, axis=axis)
else:
out = out.reshape(out_shape)
outputs.append(out)
if len(q) > 1:
outputs = paddle.stack(outputs, 0)
else:
outputs = outputs[0]
return outputs
def quantile(x, q, axis=None, keepdim=False):
"""
Compute the quantile of the input along the specified axis.
If any values in a reduced row are NaN, then the quantiles for that reduction will be NaN.
Args:
x (Tensor): The input Tensor, it's data type can be float32, float64, int32, int64.
q (int|float|list): The q for calculate quantile, which should be in range [0, 1]. If q is a list,
each q will be calculated and the first dimension of output is same to the number of ``q`` .
axis (int|list, optional): The axis along which to calculate quantile. ``axis`` should be int or list of int.
``axis`` should be in range [-D, D), where D is the dimensions of ``x`` .
If ``axis`` is less than 0, it works the same way as :math:`axis + D`.
If ``axis`` is a list, quantile is calculated over all elements of given axises.
If ``axis`` is None, quantile is calculated over all elements of ``x``. Default is None.
keepdim (bool, optional): Whether to reserve the reduced dimension(s)
in the output Tensor. If ``keepdim`` is True, the dimensions of
the output Tensor is the same as ``x`` except in the reduced
dimensions(it is of size 1 in this case). Otherwise, the shape of
the output Tensor is squeezed in ``axis`` . Default is False.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor, results of quantile along ``axis`` of ``x``.
In order to obtain higher precision, data type of results will be float64.
Examples:
.. code-block:: python
import paddle
y = paddle.arange(0, 8 ,dtype="float32").reshape([4, 2])
# Tensor(shape=[4, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[0., 1.],
# [2., 3.],
# [4., 5.],
# [6., 7.]])
y1 = paddle.quantile(y, q=0.5, axis=[0, 1])
# Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=True,
# 3.50000000)
y2 = paddle.quantile(y, q=0.5, axis=1)
# Tensor(shape=[4], dtype=float64, place=Place(cpu), stop_gradient=True,
# [0.50000000, 2.50000000, 4.50000000, 6.50000000])
y3 = paddle.quantile(y, q=[0.3, 0.5], axis=0)
# Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
# [[1.80000000, 2.80000000],
# [3. , 4. ]])
y[0,0] = float("nan")
y4 = paddle.quantile(y, q=0.8, axis=1, keepdim=True)
# Tensor(shape=[4, 1], dtype=float64, place=Place(cpu), stop_gradient=True,
# [[nan ],
# [2.80000000],
# [4.80000000],
# [6.80000000]])
"""
return _compute_quantile(x, q, axis=axis, keepdim=keepdim, ignore_nan=False)
def nanquantile(x, q, axis=None, keepdim=False):
"""
Compute the quantile of the input as if NaN values in input did not exist.
If all values in a reduced row are NaN, then the quantiles for that reduction will be NaN.
Args:
x (Tensor): The input Tensor, it's data type can be float32, float64, int32, int64.
q (int|float|list): The q for calculate quantile, which should be in range [0, 1]. If q is a list,
each q will be calculated and the first dimension of output is same to the number of ``q`` .
axis (int|list, optional): The axis along which to calculate quantile. ``axis`` should be int or list of int.
``axis`` should be in range [-D, D), where D is the dimensions of ``x`` .
If ``axis`` is less than 0, it works the same way as :math:`axis + D`.
If ``axis`` is a list, quantile is calculated over all elements of given axises.
If ``axis`` is None, quantile is calculated over all elements of ``x``. Default is None.
keepdim (bool, optional): Whether to reserve the reduced dimension(s)
in the output Tensor. If ``keepdim`` is True, the dimensions of
the output Tensor is the same as ``x`` except in the reduced
dimensions(it is of size 1 in this case). Otherwise, the shape of
the output Tensor is squeezed in ``axis`` . Default is False.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor, results of quantile along ``axis`` of ``x``.
In order to obtain higher precision, data type of results will be float64.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor(
[[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]],
dtype="float32")
x[0,0] = float("nan")
y1 = paddle.nanquantile(x, q=0.5, axis=[0, 1])
# Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=True,
# 5.)
y2 = paddle.nanquantile(x, q=0.5, axis=1)
# Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True,
# [2.50000000, 7. ])
y3 = paddle.nanquantile(x, q=[0.3, 0.5], axis=0)
# Tensor(shape=[2, 5], dtype=float64, place=Place(cpu), stop_gradient=True,
# [[5. , 2.50000000, 3.50000000, 4.50000000, 5.50000000],
# [5. , 3.50000000, 4.50000000, 5.50000000, 6.50000000]])
y4 = paddle.nanquantile(x, q=0.8, axis=1, keepdim=True)
# Tensor(shape=[2, 1], dtype=float64, place=Place(cpu), stop_gradient=True,
# [[3.40000000],
# [8.20000000]])
nan = paddle.full(shape=[2, 3], fill_value=float("nan"))
y5 = paddle.nanquantile(nan, q=0.8, axis=1, keepdim=True)
# Tensor(shape=[2, 1], dtype=float64, place=Place(cpu), stop_gradient=True,
# [[nan],
# [nan]])
"""
return _compute_quantile(x, q, axis=axis, keepdim=keepdim, ignore_nan=True)