# Std_mean

Source:`R/gen-namespace-docs.R`

, `R/gen-namespace-examples.R`

, `R/gen-namespace.R`

`torch_std_mean.Rd`

Std_mean

## Arguments

- self
(Tensor) the input tensor.

- dim
(int or tuple of ints) the dimension or dimensions to reduce.

- unbiased
(bool) whether to use the unbiased estimation or not

- keepdim
(bool) whether the output tensor has

`dim`

retained or not.

## std_mean(input, unbiased=TRUE) -> (Tensor, Tensor)

Returns the standard-deviation and mean of all elements in the `input`

tensor.

If `unbiased`

is `FALSE`

, then the standard-deviation will be calculated
via the biased estimator. Otherwise, Bessel's correction will be used.

## std_mean(input, dim, unbiased=TRUE, keepdim=False) -> (Tensor, Tensor)

Returns the standard-deviation and mean of each row of the `input`

tensor in the
dimension `dim`

. If `dim`

is a list of dimensions,
reduce over all of them.

If `keepdim`

is `TRUE`

, the output tensor is of the same size
as `input`

except in the dimension(s) `dim`

where it is of size 1.
Otherwise, `dim`

is squeezed (see `torch_squeeze`

), resulting in the
output tensor having 1 (or `len(dim)`

) fewer dimension(s).

If `unbiased`

is `FALSE`

, then the standard-deviation will be calculated
via the biased estimator. Otherwise, Bessel's correction will be used.

## Examples

```
if (torch_is_installed()) {
a = torch_randn(c(1, 3))
a
torch_std_mean(a)
a = torch_randn(c(4, 4))
a
torch_std_mean(a, 1)
}
#> [[1]]
#> torch_tensor
#> 0.3698
#> 0.3130
#> 0.6728
#> 1.1034
#> [ CPUFloatType{4} ]
#>
#> [[2]]
#> torch_tensor
#> -0.1066
#> 0.0862
#> 0.1781
#> 0.1767
#> [ CPUFloatType{4} ]
#>
```