logistic(self, loc=0.0, scale=1.0, size=None, chunks='auto', **kwargs)
This docstring was copied from numpy.random.mtrand.RandomState.logistic.
Some inconsistencies with the Dask version may exist.
Samples are drawn from a logistic distribution with specified parameters, loc (location or mean, also median), and scale (>0).
New code should use the logistic
method of a default_rng()
instance instead; please see the :None:ref:`random-quick-start`.
The probability density for the Logistic distribution is
$$P(x) = P(x) = \frac{e^{-(x-\mu)/s}}{s(1+e^{-(x-\mu)/s})^2},$$where $\mu$ = location and $s$ = scale.
The Logistic distribution is used in Extreme Value problems where it can act as a mixture of Gumbel distributions, in Epidemiology, and by the World Chess Federation (FIDE) where it is used in the Elo ranking system, assuming the performance of each player is a logistically distributed random variable.
Parameter of the distribution. Default is 0.
Parameter of the distribution. Must be non-negative. Default is 1.
Output shape. If the given shape is, e.g., (m, n, k)
, then m * n * k
samples are drawn. If size is None
(default), a single value is returned if loc
and scale
are both scalars. Otherwise, np.broadcast(loc, scale).size
samples are drawn.
Drawn samples from the parameterized logistic distribution.
Draw samples from a logistic distribution.
Generator.logistic
which should be used for new code.
scipy.stats.logistic
probability density function, distribution or cumulative density function, etc.
Draw samples from the distribution:
This example is valid syntax, but we were not able to check execution>>> loc, scale = 10, 1 # doctest: +SKIP
... s = np.random.logistic(loc, scale, 10000) # doctest: +SKIP
... import matplotlib.pyplot as plt # doctest: +SKIP
... count, bins, ignored = plt.hist(s, bins=50) # doctest: +SKIP
# plot against distribution
This example is valid syntax, but we were not able to check execution>>> def logist(x, loc, scale): # doctest: +SKIPSee :
... return np.exp((loc-x)/scale)/(scale*(1+np.exp((loc-x)/scale))**2)
... lgst_val = logist(bins, loc, scale) # doctest: +SKIP
... plt.plot(bins, lgst_val * count.max() / lgst_val.max()) # doctest: +SKIP
... plt.show() # doctest: +SKIP
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