Classe « Generator »

Méthode numpy.random.Generator.multinomial

Signature de la méthode multinomial

Description

multinomial.__doc__

        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 : int or array-like of ints
            Number of experiments.
        pvals : sequence of floats, length p
            Probabilities 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, 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 : ndarray
            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.

        Examples
        --------
        Throw a dice 20 times:

        >>> rng = np.random.default_rng()
        >>> rng.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:

        >>> rng.multinomial(20, [1/6.]*6, size=2)
        array([[3, 4, 3, 3, 4, 3],
               [2, 4, 3, 4, 0, 7]])  # random

        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.

        Now, do one experiment throwing the dice 10 time, and 10 times again,
        and another throwing the dice 20 times, and 20 times again:

        >>> rng.multinomial([[10], [20]], [1/6.]*6, size=(2, 2))
        array([[[2, 4, 0, 1, 2, 1],
                [1, 3, 0, 3, 1, 2]],
               [[1, 4, 4, 4, 4, 3],
                [3, 3, 2, 5, 5, 2]]])  # random

        The first array shows the outcomes of throwing the dice 10 times, and
        the second shows the outcomes from throwing the dice 20 times.

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

        >>> rng.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:

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

        not like:

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