Classe « Generator »

Méthode numpy.random.Generator.chisquare

Signature de la méthode chisquare

Description

chisquare.__doc__

        chisquare(df, size=None)

        Draw samples from a chi-square distribution.

        When `df` independent random variables, each with standard normal
        distributions (mean 0, variance 1), are squared and summed, the
        resulting distribution is chi-square (see Notes).  This distribution
        is often used in hypothesis testing.

        Parameters
        ----------
        df : float or array_like of floats
             Number of degrees of freedom, must be > 0.
        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.  If size is ``None`` (default),
            a single value is returned if ``df`` is a scalar.  Otherwise,
            ``np.array(df).size`` samples are drawn.

        Returns
        -------
        out : ndarray or scalar
            Drawn samples from the parameterized chi-square distribution.

        Raises
        ------
        ValueError
            When `df` <= 0 or when an inappropriate `size` (e.g. ``size=-1``)
            is given.

        Notes
        -----
        The variable obtained by summing the squares of `df` independent,
        standard normally distributed random variables:

        .. math:: Q = \sum_{i=0}^{\mathtt{df}} X^2_i

        is chi-square distributed, denoted

        .. math:: Q \sim \chi^2_k.

        The probability density function of the chi-squared distribution is

        .. math:: p(x) = \frac{(1/2)^{k/2}}{\Gamma(k/2)}
                         x^{k/2 - 1} e^{-x/2},

        where :math:`\Gamma` is the gamma function,

        .. math:: \Gamma(x) = \int_0^{-\infty} t^{x - 1} e^{-t} dt.

        References
        ----------
        .. [1] NIST "Engineering Statistics Handbook"
               https://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm

        Examples
        --------
        >>> np.random.default_rng().chisquare(2,4)
        array([ 1.89920014,  9.00867716,  3.13710533,  5.62318272]) # random