multinomial¶

paddle. multinomial ( x, num_samples=1, replacement=False, name=None ) [source]

Returns a Tensor filled with random values sampled from a Multinomical distribution. The input x is a tensor with probabilities for generating the random number. Each element in x should be larger or equal to 0, but not all 0. replacement indicates whether it is a replaceable sample. If replacement is True, a category can be sampled more than once.

Parameters

x (Tensor) – A tensor with probabilities for generating the random number. The data type should be float32, float64.
num_samples (int, optional) – Number of samples, default is 1.
replacement (bool, optional) – Whether it is a replaceable sample, default is False.
name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name.

Returns

A Tensor filled with sampled category index after num_samples times samples.

Return type

Tensor

Examples

           >>> import paddle
>>> paddle.seed(100) # on CPU device

>>> x = paddle.rand([2,4])
>>> print(x)
>>> 
Tensor(shape=[2, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0.55355281, 0.20714243, 0.01162981, 0.51577556],
 [0.36369765, 0.26091650, 0.18905126, 0.56219709]])
>>> 

>>> paddle.seed(200) # on CPU device
>>> out1 = paddle.multinomial(x, num_samples=5, replacement=True)
>>> print(out1)
>>> 
Tensor(shape=[2, 5], dtype=int64, place=Place(cpu), stop_gradient=True,
[[3, 3, 0, 0, 0],
 [3, 3, 3, 1, 0]])
>>> 

>>> # out2 = paddle.multinomial(x, num_samples=5)
>>> # InvalidArgumentError: When replacement is False, number of samples
>>> #  should be less than non-zero categories

>>> paddle.seed(300) # on CPU device
>>> out3 = paddle.multinomial(x, num_samples=3)
>>> print(out3)
>>> 
Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
[[3, 0, 1],
 [3, 1, 0]])
>>> 

          