# torch_multinomial: Multinomial In torch: Tensors and Neural Networks with 'GPU' Acceleration

Multinomial

## Usage

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

## 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

## 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.

## Note

 1 2 3 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.

 1 2 3 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). 

## Examples

 1 2 3 4 5 6 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 documentation built on Oct. 7, 2021, 9:22 a.m.