# Creates a categorical distribution parameterized by either `probs`

or
`logits`

(but not both).

Source: `R/distributions-categorical.R`

`distr_categorical.Rd`

Creates a categorical distribution parameterized by either `probs`

or
`logits`

(but not both).

## Arguments

- probs
(Tensor): event probabilities

- logits
(Tensor): event log probabilities (unnormalized)

- validate_args
Additional arguments

## Note

It is equivalent to the distribution that `torch_multinomial()`

samples from.

Samples are integers from \(\{0, \ldots, K-1\}\) where `K`

is `probs$size(-1)`

.

If `probs`

is 1-dimensional with length-`K`

, each element is the relative probability
of sampling the class at that index.

If `probs`

is N-dimensional, the first N-1 dimensions are treated as a batch of
relative probability vectors.

The `probs`

argument must be non-negative, finite and have a non-zero sum,
and it will be normalized to sum to 1 along the last dimension. attr:`probs`

will return this normalized value.
The `logits`

argument will be interpreted as unnormalized log probabilities
and can therefore be any real number. It will likewise be normalized so that
the resulting probabilities sum to 1 along the last dimension. attr:`logits`

will return this normalized value.

See also: `torch_multinomial()`

## Examples

```
if (torch_is_installed()) {
m <- distr_categorical(torch_tensor(c(0.25, 0.25, 0.25, 0.25)))
m$sample() # equal probability of 1,2,3,4
}
#> torch_tensor
#> 1
#> [ CPULongType{} ]
```