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Fonction vonmises_line - module scipy.stats

Signature de la fonction vonmises_line

def vonmises_line(*args, **kwds) 

Description

vonmises_line.__doc__

A Von Mises continuous random variable.

    As an instance of the `rv_continuous` class, `vonmises_line` object inherits from it
    a collection of generic methods (see below for the full list),
    and completes them with details specific for this particular distribution.
    
    Methods
    -------
    rvs(kappa, loc=0, scale=1, size=1, random_state=None)
        Random variates.
    pdf(x, kappa, loc=0, scale=1)
        Probability density function.
    logpdf(x, kappa, loc=0, scale=1)
        Log of the probability density function.
    cdf(x, kappa, loc=0, scale=1)
        Cumulative distribution function.
    logcdf(x, kappa, loc=0, scale=1)
        Log of the cumulative distribution function.
    sf(x, kappa, loc=0, scale=1)
        Survival function  (also defined as ``1 - cdf``, but `sf` is sometimes more accurate).
    logsf(x, kappa, loc=0, scale=1)
        Log of the survival function.
    ppf(q, kappa, loc=0, scale=1)
        Percent point function (inverse of ``cdf`` --- percentiles).
    isf(q, kappa, loc=0, scale=1)
        Inverse survival function (inverse of ``sf``).
    moment(n, kappa, loc=0, scale=1)
        Non-central moment of order n
    stats(kappa, loc=0, scale=1, moments='mv')
        Mean('m'), variance('v'), skew('s'), and/or kurtosis('k').
    entropy(kappa, loc=0, scale=1)
        (Differential) entropy of the RV.
    fit(data)
        Parameter estimates for generic data.
        See `scipy.stats.rv_continuous.fit <https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.fit.html#scipy.stats.rv_continuous.fit>`__ for detailed documentation of the
        keyword arguments.
    expect(func, args=(kappa,), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds)
        Expected value of a function (of one argument) with respect to the distribution.
    median(kappa, loc=0, scale=1)
        Median of the distribution.
    mean(kappa, loc=0, scale=1)
        Mean of the distribution.
    var(kappa, loc=0, scale=1)
        Variance of the distribution.
    std(kappa, loc=0, scale=1)
        Standard deviation of the distribution.
    interval(alpha, kappa, loc=0, scale=1)
        Endpoints of the range that contains fraction alpha [0, 1] of the
        distribution

    Notes
    -----
    The probability density function for `vonmises` and `vonmises_line` is:

    .. math::

        f(x, \kappa) = \frac{ \exp(\kappa \cos(x)) }{ 2 \pi I_0(\kappa) }

    for :math:`-\pi \le x \le \pi`, :math:`\kappa > 0`. :math:`I_0` is the
    modified Bessel function of order zero (`scipy.special.i0`).

    `vonmises` is a circular distribution which does not restrict the
    distribution to a fixed interval. Currently, there is no circular
    distribution framework in scipy. The ``cdf`` is implemented such that
    ``cdf(x + 2*np.pi) == cdf(x) + 1``.

    `vonmises_line` is the same distribution, defined on :math:`[-\pi, \pi]`
    on the real line. This is a regular (i.e. non-circular) distribution.

    `vonmises` and `vonmises_line` take ``kappa`` as a shape parameter.

    The probability density above is defined in the "standardized" form. To shift
    and/or scale the distribution use the ``loc`` and ``scale`` parameters.
    Specifically, ``vonmises_line.pdf(x, kappa, loc, scale)`` is identically
    equivalent to ``vonmises_line.pdf(y, kappa) / scale`` with
    ``y = (x - loc) / scale``. Note that shifting the location of a distribution
    does not make it a "noncentral" distribution; noncentral generalizations of
    some distributions are available in separate classes.

    Examples
    --------
    >>> from scipy.stats import vonmises_line
    >>> import matplotlib.pyplot as plt
    >>> fig, ax = plt.subplots(1, 1)
    
    Calculate the first four moments:
    
    >>> kappa = 3.99
    >>> mean, var, skew, kurt = vonmises_line.stats(kappa, moments='mvsk')
    
    Display the probability density function (``pdf``):
    
    >>> x = np.linspace(vonmises_line.ppf(0.01, kappa),
    ...                 vonmises_line.ppf(0.99, kappa), 100)
    >>> ax.plot(x, vonmises_line.pdf(x, kappa),
    ...        'r-', lw=5, alpha=0.6, label='vonmises_line pdf')
    
    Alternatively, the distribution object can be called (as a function)
    to fix the shape, location and scale parameters. This returns a "frozen"
    RV object holding the given parameters fixed.
    
    Freeze the distribution and display the frozen ``pdf``:
    
    >>> rv = vonmises_line(kappa)
    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
    
    Check accuracy of ``cdf`` and ``ppf``:
    
    >>> vals = vonmises_line.ppf([0.001, 0.5, 0.999], kappa)
    >>> np.allclose([0.001, 0.5, 0.999], vonmises_line.cdf(vals, kappa))
    True
    
    Generate random numbers:
    
    >>> r = vonmises_line.rvs(kappa, size=1000)
    
    And compare the histogram:
    
    >>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
    >>> ax.legend(loc='best', frameon=False)
    >>> plt.show()