mindspore.mint.cumsum

mindspore.mint.cumsum(input, dim, dtype=None)[source]

Computes the cumulative sum of input Tensor along dim.

\[y_i = x_1 + x_2 + x_3 + ... + x_i\]

Parameters

input (Tensor) – The input Tensor.
dim (int) – Dim along which the cumulative sum is computed.
dtype (mindspore.dtype, optional) – The desired dtype of returned Tensor. If specified, the input Tensor will be cast to dtype before the computation. This is useful for preventing overflows. If not specified, stay the same as original Tensor. Default: None .

Returns

Tensor, the shape of the output Tensor is consistent with the input Tensor's.

Raises

TypeError – If input is not a Tensor.
ValueError – If the dim is out of range.

Supported Platforms:: Ascend

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore import mint
>>> x = Tensor(np.array([[3, 4, 6, 10], [1, 6, 7, 9], [4, 3, 8, 7], [1, 3, 7, 9]]).astype(np.float32))
>>> # case 1: along the dim 0
>>> y = mint.cumsum(x, 0)
>>> print(y)
[[ 3.  4.  6. 10.]
[ 4. 10. 13. 19.]
[ 8. 13. 21. 26.]
[ 9. 16. 28. 35.]]
>>> # case 2: along the dim 1
>>> y = mint.cumsum(x, 1)
>>> print(y)
[[ 3.  7. 13. 23.]
[ 1.  7. 14. 23.]
[ 4.  7. 15. 22.]
[ 1.  4. 11. 20.]]