Vous êtes un professionnel et vous avez besoin d'une formation ? Calcul scientifique
avec Python Voir le programme détaillé

Classe « Generator »

Méthode numpy.random.Generator.multivariate_normal

Signature de la méthode multivariate_normal

def multivariate_normal(self, mean, cov, size=None, check_valid='warn', tol=1e-08, *, method='svd') 

Description

help(Generator.multivariate_normal)

        multivariate_normal(mean, cov, size=None, check_valid='warn',
                            tol=1e-8, *, method='svd')

        Draw random samples from a multivariate normal distribution.

        The multivariate normal, multinormal or Gaussian distribution is a
        generalization of the one-dimensional normal distribution to higher
        dimensions.  Such a distribution is specified by its mean and
        covariance matrix.  These parameters are analogous to the mean
        (average or "center") and variance (the squared standard deviation,
        or "width") of the one-dimensional normal distribution.

        Parameters
        ----------
        mean : 1-D array_like, of length N
            Mean of the N-dimensional distribution.
        cov : 2-D array_like, of shape (N, N)
            Covariance matrix of the distribution. It must be symmetric and
            positive-semidefinite for proper sampling.
        size : int or tuple of ints, optional
            Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are
            generated, and packed in an `m`-by-`n`-by-`k` arrangement.  Because
            each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.
            If no shape is specified, a single (`N`-D) sample is returned.
        check_valid : { 'warn', 'raise', 'ignore' }, optional
            Behavior when the covariance matrix is not positive semidefinite.
        tol : float, optional
            Tolerance when checking the singular values in covariance matrix.
            cov is cast to double before the check.
        method : { 'svd', 'eigh', 'cholesky'}, optional
            The cov input is used to compute a factor matrix A such that
            ``A @ A.T = cov``. This argument is used to select the method
            used to compute the factor matrix A. The default method 'svd' is
            the slowest, while 'cholesky' is the fastest but less robust than
            the slowest method. The method `eigh` uses eigen decomposition to
            compute A and is faster than svd but slower than cholesky.

        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.

        Notes
        -----
        The mean is a coordinate in N-dimensional space, which represents the
        location where samples are most likely to be generated.  This is
        analogous to the peak of the bell curve for the one-dimensional or
        univariate normal distribution.

        Covariance indicates the level to which two variables vary together.
        From the multivariate normal distribution, we draw N-dimensional
        samples, :math:`X = [x_1, x_2, ... x_N]`.  The covariance matrix
        element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.
        The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its
        "spread").

        Instead of specifying the full covariance matrix, popular
        approximations include:

        - Spherical covariance (`cov` is a multiple of the identity matrix)
        - Diagonal covariance (`cov` has non-negative elements, and only on
          the diagonal)

        This geometrical property can be seen in two dimensions by plotting
        generated data-points:

        >>> mean = [0, 0]
        >>> cov = [[1, 0], [0, 100]]  # diagonal covariance

        Diagonal covariance means that points are oriented along x or y-axis:

        >>> import matplotlib.pyplot as plt
        >>> rng = np.random.default_rng()
        >>> x, y = rng.multivariate_normal(mean, cov, 5000).T
        >>> plt.plot(x, y, 'x')
        >>> plt.axis('equal')
        >>> plt.show()

        Note that the covariance matrix must be positive semidefinite (a.k.a.
        nonnegative-definite). Otherwise, the behavior of this method is
        undefined and backwards compatibility is not guaranteed.

        This function internally uses linear algebra routines, and thus results
        may not be identical (even up to precision) across architectures, OSes,
        or even builds. For example, this is likely if ``cov`` has multiple equal
        singular values and ``method`` is ``'svd'`` (default). In this case,
        ``method='cholesky'`` may be more robust.

        References
        ----------
        .. [1] Papoulis, A., "Probability, Random Variables, and Stochastic
               Processes," 3rd ed., New York: McGraw-Hill, 1991.
        .. [2] Duda, R. O., Hart, P. E., and Stork, D. G., "Pattern
               Classification," 2nd ed., New York: Wiley, 2001.

        Examples
        --------
        >>> mean = (1, 2)
        >>> cov = [[1, 0], [0, 1]]
        >>> rng = np.random.default_rng()
        >>> x = rng.multivariate_normal(mean, cov, (3, 3))
        >>> x.shape
        (3, 3, 2)

        We can use a different method other than the default to factorize cov:

        >>> y = rng.multivariate_normal(mean, cov, (3, 3), method='cholesky')
        >>> y.shape
        (3, 3, 2)

        Here we generate 800 samples from the bivariate normal distribution
        with mean [0, 0] and covariance matrix [[6, -3], [-3, 3.5]].  The
        expected variances of the first and second components of the sample
        are 6 and 3.5, respectively, and the expected correlation
        coefficient is -3/sqrt(6*3.5) ≈ -0.65465.

        >>> cov = np.array([[6, -3], [-3, 3.5]])
        >>> pts = rng.multivariate_normal([0, 0], cov, size=800)

        Check that the mean, covariance, and correlation coefficient of the
        sample are close to the expected values:

        >>> pts.mean(axis=0)
        array([ 0.0326911 , -0.01280782])  # may vary
        >>> np.cov(pts.T)
        array([[ 5.96202397, -2.85602287],
               [-2.85602287,  3.47613949]])  # may vary
        >>> np.corrcoef(pts.T)[0, 1]
        -0.6273591314603949  # may vary

        We can visualize this data with a scatter plot.  The orientation
        of the point cloud illustrates the negative correlation of the
        components of this sample.

        >>> import matplotlib.pyplot as plt
        >>> plt.plot(pts[:, 0], pts[:, 1], '.', alpha=0.5)
        >>> plt.axis('equal')
        >>> plt.grid()
        >>> plt.show()

        

Vous êtes un professionnel et vous avez besoin d'une formation ? Programmation Python
Les compléments Voir le programme détaillé