mars.tensor.random.logseries(p, size=None, chunk_size=None, gpu=None, dtype=None)[source]#

Draw samples from a logarithmic series distribution.

Samples are drawn from a log series distribution with specified shape parameter, 0 < p < 1.

  • p (float or array_like of floats) – Shape parameter for the distribution. Must be in the range (0, 1).

  • size (int or tuple of ints, optional) – 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.

  • chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension

  • gpu (bool, optional) – Allocate the tensor on GPU if True, False as default

  • dtype (data-type, optional) – Data-type of the returned tensor.


out – Drawn samples from the parameterized logarithmic series distribution.

Return type

Tensor or scalar

See also


probability density function, distribution or cumulative density function, etc.


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].



Buzas, Martin A.; Culver, Stephen J., Understanding regional species diversity through the log series distribution of occurrences: BIODIVERSITY RESEARCH Diversity & Distributions, Volume 5, Number 5, September 1999 , pp. 187-195(9).


Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The relation between the number of species and the number of individuals in a random sample of an animal population. Journal of Animal Ecology, 12:42-58.


D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small Data Sets, CRC Press, 1994.


Wikipedia, “Logarithmic distribution”,


Draw samples from the distribution:

>>> import mars.tensor as mt
>>> import matplotlib.pyplot as plt
>>> a = .6
>>> s = mt.random.logseries(a, 10000)
>>> count, bins, ignored = plt.hist(s.execute())

# plot against distribution

>>> def logseries(k, p):
...     return -p**k/(k*mt.log(1-p))
>>> plt.plot(bins, (logseries(bins, a)*count.max()/
...          logseries(bins, a).max()).execute(), 'r')