scipy.stats.dlaplace¶
- scipy.stats.dlaplace = <scipy.stats._discrete_distns.dlaplace_gen object at 0x450c078c>[source]¶
A Laplacian discrete random variable.
As an instance of the rv_discrete class, dlaplace 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.
Notes
The probability mass function for dlaplace is:
dlaplace.pmf(k) = tanh(a/2) * exp(-a*abs(k))
for a > 0.
dlaplace takes a as shape parameter.
The probability mass function above is defined in the “standardized” form. To shift distribution use the loc parameter. Specifically, dlaplace.pmf(k, a, loc) is identically equivalent to dlaplace.pmf(k - loc, a).
Examples
>>> from scipy.stats import dlaplace >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(1, 1)
Calculate a few first moments:
>>> a = 0.8 >>> mean, var, skew, kurt = dlaplace.stats(a, moments='mvsk')
Display the probability mass function (pmf):
>>> x = np.arange(dlaplace.ppf(0.01, a), ... dlaplace.ppf(0.99, a)) >>> ax.plot(x, dlaplace.pmf(x, a), 'bo', ms=8, label='dlaplace pmf') >>> ax.vlines(x, 0, dlaplace.pmf(x, a), colors='b', lw=5, alpha=0.5)
Alternatively, the distribution object can be called (as a function) to fix the shape and location. This returns a “frozen” RV object holding the given parameters fixed.
Freeze the distribution and display the frozen pmf:
>>> rv = dlaplace(a) >>> ax.vlines(x, 0, rv.pmf(x), colors='k', linestyles='-', lw=1, ... label='frozen pmf') >>> ax.legend(loc='best', frameon=False) >>> plt.show()
Check accuracy of cdf and ppf:
>>> prob = dlaplace.cdf(x, a) >>> np.allclose(x, dlaplace.ppf(prob, a)) True
Generate random numbers:
>>> r = dlaplace.rvs(a, size=1000)
Methods
rvs(a, loc=0, size=1, random_state=None) Random variates. pmf(x, a, loc=0) Probability mass function. logpmf(x, a, loc=0) Log of the probability mass function. cdf(x, a, loc=0) Cumulative density function. logcdf(x, a, loc=0) Log of the cumulative density function. sf(x, a, loc=0) Survival function (1 - cdf — sometimes more accurate). logsf(x, a, loc=0) Log of the survival function. ppf(q, a, loc=0) Percent point function (inverse of cdf — percentiles). isf(q, a, loc=0) Inverse survival function (inverse of sf). stats(a, loc=0, moments='mv') Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(a, loc=0) (Differential) entropy of the RV. expect(func, a, loc=0, lb=None, ub=None, conditional=False) Expected value of a function (of one argument) with respect to the distribution. median(a, loc=0) Median of the distribution. mean(a, loc=0) Mean of the distribution. var(a, loc=0) Variance of the distribution. std(a, loc=0) Standard deviation of the distribution. interval(alpha, a, loc=0) Endpoints of the range that contains alpha percent of the distribution