numpy.random.multinomial¶
-
numpy.random.
multinomial
(n, pvals, size=None)¶ 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 (however, the last element is always assumed to account for the remaining probability, as long assum(pvals[:-1]) <= 1)
.size : tuple of ints
Given a size of
(M, N, K)
, thenM*N*K
samples are drawn, and the output shape becomes(M, N, K, p)
, since each sample has shape(p,)
.Examples
Throw a dice 20 times:
>>> np.random.multinomial(20, [1/6.]*6, size=1) array([[4, 1, 7, 5, 2, 1]])
It landed 4 times on 1, once on 2, etc.
Now, throw the dice 20 times, and 20 times again:
>>> np.random.multinomial(20, [1/6.]*6, size=2) array([[3, 4, 3, 3, 4, 3], [2, 4, 3, 4, 0, 7]])
For the first run, we threw 3 times 1, 4 times 2, etc. For the second, we threw 2 times 1, 4 times 2, etc.
A loaded dice is more likely to land on number 6:
>>> np.random.multinomial(100, [1/7.]*5) array([13, 16, 13, 16, 42])