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scipy.special.

# stirling2

scipy.special.stirling2(N, K, *, exact=False)

  Generate Stirling number(s) of the second kind.

  Stirling numbers of the second kind count the number of ways to partition a set with N elements into K non-empty subsets.

  The values this function returns are calculated using a dynamic program which avoids redundant computation across the subproblems in the solution. For array-like input, this implementation also avoids redundant computation across the different Stirling number calculations.

  The numbers are sometimes denoted

  \[{N \brace{K}}\]

  see [1] for details. This is often expressed-verbally-as “N subset K”.

  Parameters:
    Nint, ndarray

      Number of things.

    Kint, ndarray

      Number of non-empty subsets taken.

    exactbool, optional

      Uses dynamic programming (DP) with floating point numbers for smaller arrays and uses a second order approximation due to Temme for larger entries of N and K that allows trading speed for accuracy. See [2] for a description. Temme approximation is used for values `n>50`. The max error from the DP has max relative error `4.5*10^-16` for `n<=50` and the max error from the Temme approximation has max relative error `5*10^-5` for `51 <= n < 70` and `9*10^-6` for `70 <= n < 101`. Note that these max relative errors will decrease further as n increases.

  Returns:
    valint, float, ndarray

      The number of partitions.

  See also

  `comb`

    The number of combinations of N things taken k at a time.

  Notes

  - If N < 0, or K < 0, then 0 is returned.

  - If K > N, then 0 is returned.

  The output type will always be int or ndarray of object. The input must contain either numpy or python integers otherwise a TypeError is raised.

  References

  [1]

  R. L. Graham, D. E. Knuth and O. Patashnik, “Concrete Mathematics: A Foundation for Computer Science,” Addison-Wesley Publishing Company, Boston, 1989. Chapter 6, page 258.

  [2]

  Temme, Nico M. “Asymptotic estimates of Stirling numbers.” Studies in Applied Mathematics 89.3 (1993): 233-243.

  Examples

      >>> import numpy as np
      >>> from scipy.special import stirling2
      >>> k = np.array([3, -1, 3])
      >>> n = np.array([10, 10, 9])
      >>> stirling2(n, k)
      array([9330.0, 0.0, 3025.0])