scipy.stats.logistic¶

scipy.stats.
logistic
= <scipy.stats._continuous_distns.logistic_gen object>[source]¶ A logistic (or Sechsquared) continuous random variable.
As an instance of the
rv_continuous
class,logistic
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 density function for
logistic
is:\[f(x) = \frac{\exp(x)} {(1+exp(x))^2}\]logistic
is a special case ofgenlogistic
withc == 1
.The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the
loc
andscale
parameters. Specifically,logistic.pdf(x, loc, scale)
is identically equivalent tologistic.pdf(y) / scale
withy = (x  loc) / scale
.Examples
>>> from scipy.stats import logistic >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(1, 1)
Calculate a few first moments:
>>> mean, var, skew, kurt = logistic.stats(moments='mvsk')
Display the probability density function (
pdf
):>>> x = np.linspace(logistic.ppf(0.01), ... logistic.ppf(0.99), 100) >>> ax.plot(x, logistic.pdf(x), ... 'r', lw=5, alpha=0.6, label='logistic 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 = logistic() >>> ax.plot(x, rv.pdf(x), 'k', lw=2, label='frozen pdf')
Check accuracy of
cdf
andppf
:>>> vals = logistic.ppf([0.001, 0.5, 0.999]) >>> np.allclose([0.001, 0.5, 0.999], logistic.cdf(vals)) True
Generate random numbers:
>>> r = logistic.rvs(size=1000)
And compare the histogram:
>>> ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2) >>> ax.legend(loc='best', frameon=False) >>> plt.show()
Methods
rvs(loc=0, scale=1, size=1, random_state=None)
Random variates. pdf(x, loc=0, scale=1)
Probability density function. logpdf(x, loc=0, scale=1)
Log of the probability density function. cdf(x, loc=0, scale=1)
Cumulative distribution function. logcdf(x, loc=0, scale=1)
Log of the cumulative distribution function. sf(x, loc=0, scale=1)
Survival function (also defined as 1  cdf
, but sf is sometimes more accurate).logsf(x, loc=0, scale=1)
Log of the survival function. ppf(q, loc=0, scale=1)
Percent point function (inverse of cdf
— percentiles).isf(q, loc=0, scale=1)
Inverse survival function (inverse of sf
).moment(n, loc=0, scale=1)
Noncentral moment of order n stats(loc=0, scale=1, moments='mv')
Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(loc=0, scale=1)
(Differential) entropy of the RV. fit(data, loc=0, scale=1)
Parameter estimates for generic data. expect(func, args=(), 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(loc=0, scale=1)
Median of the distribution. mean(loc=0, scale=1)
Mean of the distribution. var(loc=0, scale=1)
Variance of the distribution. std(loc=0, scale=1)
Standard deviation of the distribution. interval(alpha, loc=0, scale=1)
Endpoints of the range that contains alpha percent of the distribution