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# numpy.random.logseries

`numpy.random.``logseries`(p, size=None)

  Draw samples from a Logarithmic Series distribution.

  Samples are drawn from a Log Series distribution with specified parameter, p (probability, 0 < p < 1).

  Parameters : | loc : float scale : float > 0. size : {tuple, int} Output shape. If the given shape is, e.g., `(m, n, k)`, then `m * n * k` samples are drawn.
  Returns : | samples : {ndarray, scalar} where the values are all integers in [0, n].

  See also

  `scipy.stats.distributions.logser`
    probability density function, distribution or cumulative density function, etc.

  Notes

  The probability density for the Log Series distribution is

  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].

  References

  [R88] | 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).

  [R89] | 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.

  [R90] | D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small Data Sets, CRC Press, 1994.

  [R91] | Wikipedia, “Logarithmic-distribution”, http://en.wikipedia.org/wiki/Logarithmic-distribution

  Examples

  Draw samples from the distribution:

      >>> a = .6
      >>> s = np.random.logseries(a, 10000)
      >>> count, bins, ignored = plt.hist(s)

  # plot against distribution

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

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