| distr_categorical {torch} | R Documentation |
Creates a categorical distribution parameterized by either probs or
logits (but not both).
Description
Creates a categorical distribution parameterized by either probs or
logits (but not both).
Usage
distr_categorical(probs = NULL, logits = NULL, validate_args = NULL)
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
}