- mars.tensor.sum(a, axis=None, dtype=None, out=None, keepdims=None, combine_size=None)#
Sum of tensor elements over a given axis.
a (array_like) – Elements to sum.
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.
sum_along_axis – 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.
- Return type
Cumulative sum of tensor elements.
Integration of tensor values using the composite trapezoidal rule.
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
>>> 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