tensorplay.functional.logcumsumexp

tensorplay.functional.logcumsumexp(input, dim, *, out=None) → Tensor[source]

Returns the logarithm of the cumulative summation of the exponentiation of elements of input in the dimension dim.

For summation index $j$ given by dim and other indices $i$, the result is

$$\text{logcumsumexp}(x)_{ij} = \log \sum\limits_{k=0}^{j} \exp(x_{ik})$$

Parameters:

• input (Tensor) – the input tensor.

• dim (int) – the dimension to do the operation over

Keyword Arguments:

out (Tensor, optional) – the output tensor.

Example:

>>> a = tensorplay.randn(10)
>>> tensorplay.logcumsumexp(a, dim=0)
tensor([-0.42296738, -0.04462666,  0.86278635,  0.94622083,  1.05277811,
         1.39202815,  1.83525007,  1.84492621,  2.06084887,  2.06844475]))