logseries(self, p, size=None, chunks='auto', **kwargs)
This docstring was copied from numpy.random.mtrand.RandomState.logseries.
Some inconsistencies with the Dask version may exist.
Samples are drawn from a log series distribution with specified shape parameter, 0 < p
< 1.
New code should use the logseries
method of a default_rng()
instance instead; please see the :None:ref:`random-quick-start`.
The probability density for the Log Series distribution is
$$P(k) = \frac{-p^k}{k \ln(1-p)},$$where p = probability.
The log series distribution is frequently used to represent species richness and occurrence, first proposed by Fisher, Corbet, and Williams in 1943 [2]. It may also be used to model the numbers of occupants seen in cars [3].
Shape parameter for the distribution. Must be in the range (0, 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 p
is a scalar. Otherwise, np.array(p).size
samples are drawn.
Drawn samples from the parameterized logarithmic series distribution.
Draw samples from a logarithmic series distribution.
Generator.logseries
which should be used for new code.
scipy.stats.logser
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>>> a = .6 # doctest: +SKIP
... s = np.random.logseries(a, 10000) # doctest: +SKIP
... import matplotlib.pyplot as plt # doctest: +SKIP
... count, bins, ignored = plt.hist(s) # doctest: +SKIP
# plot against distribution
This example is valid syntax, but we were not able to check execution>>> def logseries(k, p): # doctest: +SKIPSee :
... return -p**k/(k*np.log(1-p))
... plt.plot(bins, logseries(bins, a)*count.max()/ # doctest: +SKIP
... logseries(bins, a).max(), 'r')
... plt.show() # doctest: +SKIP
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