# randomgen.mtrand.RandomState.multinomial¶

- RandomState.multinomial(
*n*,*pvals*,*size=None*)¶ Draw samples from a multinomial distribution.

The multinomial distribution is a multivariate generalization 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**intNumber of experiments.

**pvals**sequence of floats, length pProbabilities of each of the

`p`

different outcomes. These must sum to 1 (however, the last element is always assumed to account for the remaining probability, as long as`sum(pvals[:-1]) <= 1)`

.**size**int or tuple of ints, optionalOutput 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**ndarrayThe 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.

Examples

Throw a dice 20 times:

>>> np.random.multinomial(20, [1/6.]*6, size=1) array([[4, 1, 7, 5, 2, 1]]) # random

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], # random [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 die is more likely to land on number 6:

>>> np.random.multinomial(100, [1/7.]*5 + [2/7.]) array([11, 16, 14, 17, 16, 26]) # random

The probability inputs should be normalized. As an implementation detail, the value of the last entry is ignored and assumed to take up any leftover probability mass, but this should not be relied on. A biased coin which has twice as much weight on one side as on the other should be sampled like so:

>>> np.random.multinomial(100, [1.0 / 3, 2.0 / 3]) # RIGHT array([38, 62]) # random

not like:

>>> np.random.multinomial(100, [1.0, 2.0]) # WRONG Traceback (most recent call last): ValueError: pvals < 0, pvals > 1 or pvals contains NaNs