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# derived from copyright 1999-2021 Alibaba Group Holding Ltd.
#
# 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,
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import numpy as np
from ... import opcodes as OperandDef
from ..datasource import tensor as astensor
from .core import TensorReduction, TensorReductionMixin
class TensorSum(TensorReduction, TensorReductionMixin):
_op_type_ = OperandDef.SUM
_func_name = "sum"
def __init__(self, axis=None, keepdims=None, combine_size=None, stage=None, **kw):
stage = self._rewrite_stage(stage)
super().__init__(
_axis=axis,
_keepdims=keepdims,
_combine_size=combine_size,
stage=stage,
**kw
)
[docs]def sum(a, axis=None, dtype=None, out=None, keepdims=None, combine_size=None):
"""
Sum of tensor elements over a given axis.
Parameters
----------
a : array_like
Elements to sum.
axis : None or int or tuple of ints, optional
Axis or axes along which a sum is performed. The default,
axis=None, will sum all of the elements of the input tensor. If
axis is negative it counts from the last to the first axis.
If axis is a tuple of ints, a sum is performed on all of the axes
specified in the tuple instead of a single axis or all the axes as
before.
dtype : dtype, optional
The type of the returned tensor and of the accumulator in which the
elements are summed. The dtype of `a` is used by default unless `a`
has an integer dtype of less precision than the default platform
integer. In that case, if `a` is signed then the platform integer
is used while if `a` is unsigned then an unsigned integer of the
same precision as the platform integer is used.
out : Tensor, optional
Alternative output tensor in which to place the result. It must have
the same shape as the expected output, but the type of the output
values will be cast if necessary.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input tensor.
If the default value is passed, then `keepdims` will not be
passed through to the `sum` method of sub-classes of
`Tensor`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
combine_size: int, optional
The number of chunks to combine.
Returns
-------
sum_along_axis : Tensor
An array with the same shape as `a`, with the specified
axis removed. If `a` is a 0-d tensor, or if `axis` is None, a scalar
is returned. If an output array is specified, a reference to
`out` is returned.
See Also
--------
Tensor.sum : Equivalent method.
cumsum : Cumulative sum of tensor elements.
trapz : Integration of tensor values using the composite trapezoidal rule.
mean, average
Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow.
The sum of an empty array is the neutral element 0:
>>> import mars.tensor as mt
>>> mt.sum([]).execute()
0.0
Examples
--------
>>> mt.sum([0.5, 1.5]).execute()
2.0
>>> mt.sum([0.5, 0.7, 0.2, 1.5], dtype=mt.int32).execute()
1
>>> mt.sum([[0, 1], [0, 5]]).execute()
6
>>> mt.sum([[0, 1], [0, 5]], axis=0).execute()
array([0, 6])
>>> mt.sum([[0, 1], [0, 5]], axis=1).execute()
array([1, 5])
If the accumulator is too small, overflow occurs:
>>> mt.ones(128, dtype=mt.int8).sum(dtype=mt.int8).execute()
-128
"""
a = astensor(a)
if dtype is None:
if a.dtype == object:
dtype = a.dtype
else:
dtype = np.empty((1,), dtype=a.dtype).sum().dtype
else:
dtype = np.dtype(dtype)
op = TensorSum(axis=axis, dtype=dtype, keepdims=keepdims, combine_size=combine_size)
return op(a, out=out)