Multinomial
Source:R/gen-namespace-docs.R
, R/gen-namespace-examples.R
, R/wrapers.R
torch_multinomial.Rd
Multinomial
Arguments
- self
(Tensor) the input tensor containing probabilities
- num_samples
(int) number of samples to draw
- replacement
(bool, optional) whether to draw with replacement or not
- generator
(
torch.Generator
, optional) a pseudorandom number generator for sampling
Note
`input` do not need to sum to one (in which case we use
The rows of -negative, finite and have
the values as weights), but must be non-zero sum. a non
Indices are ordered from left to right according to when each was sampled (first samples are placed in first column).
If input
is a vector, out
is a vector of size num_samples
.
If input
is a matrix with m
rows, out
is an matrix of shape
\((m \times \mbox{num\_samples})\).
If replacement is TRUE
, samples are drawn with replacement.
If not, they are drawn without replacement, which means that when a sample index is drawn for a row, it cannot be drawn again for that row.
`num_samples` must be lower than
When drawn without replacement, -zero elements in `input` (or the min number of non-zero
number of nonin each row of `input` if it is a matrix). elements
multinomial(input, num_samples, replacement=False, *, generator=NULL, out=NULL) -> LongTensor
Returns a tensor where each row contains num_samples
indices sampled
from the multinomial probability distribution located in the corresponding row
of tensor input
.
Examples
if (torch_is_installed()) {
weights = torch_tensor(c(0, 10, 3, 0), dtype=torch_float()) # create a tensor of weights
torch_multinomial(weights, 2)
torch_multinomial(weights, 4, replacement=TRUE)
}
#> torch_tensor
#> 2
#> 3
#> 2
#> 3
#> [ CPULongType{4} ]