mxnet.np.random.multinomial¶

multinomial(n, pvals, size=None, **kwargs)

Draw samples from a multinomial distribution. The multinomial distribution is a multivariate generalisation of the binomial distribution. Take an experiment with one of p possible outcomes. An example of such an experiment is throwing a dice, where the outcome can be 1 through 6. Each sample drawn from the distribution represents n such experiments. Its values, X_i = [X_0, X_1, ..., X_p], represent the number of times the outcome was i.

Parameters
• n (int) – Number of experiments.

• pvals (sequence of floats, length p) – Probabilities of each of the p different outcomes. These should sum to 1.

• size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.

Returns

out – The drawn samples, of shape size, if that was provided. If not, the shape is (N,). In other words, each entry out[i,j,...,:] is an N-dimensional value drawn from the distribution.

Return type

ndarray

Examples

Throw a dice 1000 times, and 1000 times again:

>>> np.random.multinomial(1000, [1/6.]*6, size=2)
array([[164, 161, 179, 158, 150, 188],
[178, 162, 177, 143, 163, 177]])


A loaded die is more likely to land on number 6:

>>> np.random.multinomial(100, [1/7.]*5 + [2/7.])
array([19, 14, 12, 11, 21, 23])
>>> np.random.multinomial(100, [1.0 / 3, 2.0 / 3])
array([32, 68])