mindspore.ops.CumSum

class mindspore.ops.CumSum(exclusive=False, reverse=False)[source]

Computes the cumulative sum of input tensor along axis.

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

  • exclusive (bool) – By default, this op performs an inclusive cumsum, which means that the first element of the input is identical to the first element of the output. Default: False .

  • reverse (bool) – If True , perform inverse cumulative sum. Default: False .

Inputs:

  • input (Tensor) - The input Tensor with shape \((N, *)\) where \(*\) means any number of additional dimensions.

  • axis (int) - The axis to accumulate the tensor’s value. Only constant value is allowed. Must be in the range [-rank(input), rank(input)).

Outputs:

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

Raises

  • TypeError – If exclusive or reverse is not a bool.

  • TypeError – If axis is not an int.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[3, 4, 6, 10], [1, 6, 7, 9], [4, 3, 8, 7], [1, 3, 7, 9]]).astype(np.float32))
>>> cumsum = ops.CumSum()
>>> # case 1: along the axis 0
>>> y = 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 axis 1
>>> y = cumsum(x, 1)
>>> print(y)
[[ 3.  7. 13. 23.]
 [ 1.  7. 14. 23.]
 [ 4.  7. 15. 22.]
 [ 1.  4. 11. 20.]]
>>> # Next demonstrate exclusive and reverse, along axis 1
>>> # case 3: exclusive = True
>>> cumsum = ops.CumSum(exclusive=True)
>>> y = cumsum(x, 1)
>>> print(y)
[[ 0.  3.  7. 13.]
 [ 0.  1.  7. 14.]
 [ 0.  4.  7. 15.]
 [ 0.  1.  4. 11.]]
>>> # case 4: reverse = True
>>> cumsum = ops.CumSum(reverse=True)
>>> y = cumsum(x, 1)
>>> print(y)
[[23. 20. 16. 10.]
 [23. 22. 16.  9.]
 [22. 18. 15.  7.]
 [20. 19. 16.  9.]]