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



(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


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.


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} ]