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Méthode numpy.random.RandomState.dirichlet

Signature de la méthode dirichlet

Description

dirichlet.__doc__

        dirichlet(alpha, size=None)

        Draw samples from the Dirichlet distribution.

        Draw `size` samples of dimension k from a Dirichlet distribution. A
        Dirichlet-distributed random variable can be seen as a multivariate
        generalization of a Beta distribution. The Dirichlet distribution
        is a conjugate prior of a multinomial distribution in Bayesian
        inference.

        .. note::
            New code should use the ``dirichlet`` method of a ``default_rng()``
            instance instead; please see the :ref:`random-quick-start`.

        Parameters
        ----------
        alpha : sequence of floats, length k
            Parameter of the distribution (length ``k`` for sample of
            length ``k``).
        size : int or tuple of ints, optional
            Output shape.  If the given shape is, e.g., ``(m, n)``, then
            ``m * n * k`` samples are drawn.  Default is None, in which case a
            vector of length ``k`` is returned.

        Returns
        -------
        samples : ndarray,
            The drawn samples, of shape ``(size, k)``.

        Raises
        -------
        ValueError
            If any value in ``alpha`` is less than or equal to zero

        See Also
        --------
        Generator.dirichlet: which should be used for new code.

        Notes
        -----
        The Dirichlet distribution is a distribution over vectors
        :math:`x` that fulfil the conditions :math:`x_i>0` and
        :math:`\sum_{i=1}^k x_i = 1`.

        The probability density function :math:`p` of a
        Dirichlet-distributed random vector :math:`X` is
        proportional to

        .. math:: p(x) \propto \prod_{i=1}^{k}{x^{\alpha_i-1}_i},

        where :math:`\alpha` is a vector containing the positive
        concentration parameters.

        The method uses the following property for computation: let :math:`Y`
        be a random vector which has components that follow a standard gamma
        distribution, then :math:`X = \frac{1}{\sum_{i=1}^k{Y_i}} Y`
        is Dirichlet-distributed

        References
        ----------
        .. [1] David McKay, "Information Theory, Inference and Learning
               Algorithms," chapter 23,
               http://www.inference.org.uk/mackay/itila/
        .. [2] Wikipedia, "Dirichlet distribution",
               https://en.wikipedia.org/wiki/Dirichlet_distribution

        Examples
        --------
        Taking an example cited in Wikipedia, this distribution can be used if
        one wanted to cut strings (each of initial length 1.0) into K pieces
        with different lengths, where each piece had, on average, a designated
        average length, but allowing some variation in the relative sizes of
        the pieces.

        >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()

        >>> import matplotlib.pyplot as plt
        >>> plt.barh(range(20), s[0])
        >>> plt.barh(range(20), s[1], left=s[0], color='g')
        >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')
        >>> plt.title("Lengths of Strings")