- torch.multinomial(input, num_samples, replacement=False, *, generator=None, out=None) LongTensor ¶
Returns a tensor where each row contains
num_samplesindices sampled from the multinomial probability distribution located in the corresponding row of tensor
The rows of
inputdo 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).
inputis a vector,
outis a vector of size
inputis a matrix with m rows,
outis an matrix of shape .
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_samplesmust be lower than number of non-zero elements in
input(or the min number of non-zero elements in each row of
inputif it is a matrix).
- Keyword Arguments:
>>> 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, 4) # ERROR! RuntimeError: invalid argument 2: invalid multinomial distribution (with replacement=False, not enough non-negative category to sample) at ../aten/src/TH/generic/THTensorRandom.cpp:320 >>> torch.multinomial(weights, 4, replacement=True) tensor([ 2, 1, 1, 1])