Returns a tensor where each row contains num_samples indices sampled from the multinomial (a stricter definition would be multivariate, refer to torch.distributions.multinomial.Multinomial for more details) probability distribution located in the corresponding row of tensor input.
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 × num_samples ) (m \times \text{num\_samples}) (m×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.
Note
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).
generator (torch.Generator, optional) – a pseudorandom number generator for sampling
out (Tensor, optional) – the output tensor.
Example:
>>> weights = torch.tensor([0, 10, 3, 0], dtype=torch.float) # create a tensor of weights >>> torch.multinomial(weights, 2) tensor([1, 2]) >>> torch.multinomial(weights, 5) # ERROR! RuntimeError: cannot sample n_sample > prob_dist.size(-1) samples without replacement >>> torch.multinomial(weights, 4, replacement=True) tensor([ 2, 1, 1, 1])
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