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Fonction loguniform - module scipy.stats

Signature de la fonction loguniform

def loguniform(*args, **kwds) 

Description

loguniform.__doc__

A loguniform or reciprocal continuous random variable.

    As an instance of the `rv_continuous` class, `loguniform` 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(a, b, loc=0, scale=1, size=1, random_state=None)
        Random variates.
    pdf(x, a, b, loc=0, scale=1)
        Probability density function.
    logpdf(x, a, b, loc=0, scale=1)
        Log of the probability density function.
    cdf(x, a, b, loc=0, scale=1)
        Cumulative distribution function.
    logcdf(x, a, b, loc=0, scale=1)
        Log of the cumulative distribution function.
    sf(x, a, b, loc=0, scale=1)
        Survival function  (also defined as ``1 - cdf``, but `sf` is sometimes more accurate).
    logsf(x, a, b, loc=0, scale=1)
        Log of the survival function.
    ppf(q, a, b, loc=0, scale=1)
        Percent point function (inverse of ``cdf`` --- percentiles).
    isf(q, a, b, loc=0, scale=1)
        Inverse survival function (inverse of ``sf``).
    moment(n, a, b, loc=0, scale=1)
        Non-central moment of order n
    stats(a, b, loc=0, scale=1, moments='mv')
        Mean('m'), variance('v'), skew('s'), and/or kurtosis('k').
    entropy(a, b, 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=(a, b), 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(a, b, loc=0, scale=1)
        Median of the distribution.
    mean(a, b, loc=0, scale=1)
        Mean of the distribution.
    var(a, b, loc=0, scale=1)
        Variance of the distribution.
    std(a, b, loc=0, scale=1)
        Standard deviation of the distribution.
    interval(alpha, a, b, loc=0, scale=1)
        Endpoints of the range that contains fraction alpha [0, 1] of the
        distribution

    Notes
    -----
    The probability density function for this class is:

    .. math::

        f(x, a, b) = \frac{1}{x \log(b/a)}

    for :math:`a \le x \le b`, :math:`b > a > 0`. This class takes
    :math:`a` and :math:`b` as shape parameters.

    The probability density above is defined in the "standardized" form. To shift
    and/or scale the distribution use the ``loc`` and ``scale`` parameters.
    Specifically, ``loguniform.pdf(x, a, b, loc, scale)`` is identically
    equivalent to ``loguniform.pdf(y, a, b) / 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 loguniform
    >>> import matplotlib.pyplot as plt
    >>> fig, ax = plt.subplots(1, 1)
    
    Calculate the first four moments:
    
    >>> a, b = 0.01, 1.25
    >>> mean, var, skew, kurt = loguniform.stats(a, b, moments='mvsk')
    
    Display the probability density function (``pdf``):
    
    >>> x = np.linspace(loguniform.ppf(0.01, a, b),
    ...                 loguniform.ppf(0.99, a, b), 100)
    >>> ax.plot(x, loguniform.pdf(x, a, b),
    ...        'r-', lw=5, alpha=0.6, label='loguniform 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 = loguniform(a, b)
    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
    
    Check accuracy of ``cdf`` and ``ppf``:
    
    >>> vals = loguniform.ppf([0.001, 0.5, 0.999], a, b)
    >>> np.allclose([0.001, 0.5, 0.999], loguniform.cdf(vals, a, b))
    True
    
    Generate random numbers:
    
    >>> r = loguniform.rvs(a, b, size=1000)
    
    And compare the histogram:
    
    >>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
    >>> ax.legend(loc='best', frameon=False)
    >>> plt.show()
    

    This doesn't show the equal probability of ``0.01``, ``0.1`` and
    ``1``. This is best when the x-axis is log-scaled:

    >>> import numpy as np
    >>> fig, ax = plt.subplots(1, 1)
    >>> ax.hist(np.log10(r))
    >>> ax.set_ylabel("Frequency")
    >>> ax.set_xlabel("Value of random variable")
    >>> ax.xaxis.set_major_locator(plt.FixedLocator([-2, -1, 0]))
    >>> ticks = ["$10^{{ {} }}$".format(i) for i in [-2, -1, 0]]
    >>> ax.set_xticklabels(ticks)  # doctest: +SKIP
    >>> plt.show()

    This random variable will be log-uniform regardless of the base chosen for
    ``a`` and ``b``. Let's specify with base ``2`` instead:

    >>> rvs = loguniform(2**-2, 2**0).rvs(size=1000)

    Values of ``1/4``, ``1/2`` and ``1`` are equally likely with this random
    variable.  Here's the histogram:

    >>> fig, ax = plt.subplots(1, 1)
    >>> ax.hist(np.log2(rvs))
    >>> ax.set_ylabel("Frequency")
    >>> ax.set_xlabel("Value of random variable")
    >>> ax.xaxis.set_major_locator(plt.FixedLocator([-2, -1, 0]))
    >>> ticks = ["$2^{{ {} }}$".format(i) for i in [-2, -1, 0]]
    >>> ax.set_xticklabels(ticks)  # doctest: +SKIP
    >>> plt.show()