Multinomial

torch_multinomial(self, num_samples, replacement = FALSE, generator = NULL)

## Arguments

self (Tensor) the input tensor containing probabilities (int) number of samples to draw (bool, optional) whether to draw with replacement or not (torch.Generator, optional) a pseudorandom number generator for sampling

## Note

The rows of input do not need to sum to one (in which case we use
the values as weights), but must be non-negative, finite and have
a non-zero sum.


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.

When drawn without replacement, num_samples must be lower than
number of non-zero elements in input (or the min number of non-zero
elements in each row of input if it is a matrix).


## 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
#>  1
#>  1
#>  1
#>  1
#> [ CPULongType{4} ]