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 wasi
.- 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)
, thenm * 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 entryout[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])