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bundles / numpy 2.4.3 / numpy / random / RandomState / hypergeometric

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numpy.random:RandomState.hypergeometric

Signature

def   hypergeometric ( ngood ,  nbad ,  nsample ,  size = None )

Summary

Draw samples from a Hypergeometric distribution.

Extended Summary

Samples are drawn from a hypergeometric distribution with specified parameters, ngood (ways to make a good selection), nbad (ways to make a bad selection), and nsample (number of items sampled, which is less than or equal to the sum ngood + nbad).

Parameters

ngood : int or array_like of ints

Number of ways to make a good selection. Must be nonnegative.

nbad : int or array_like of ints

Number of ways to make a bad selection. Must be nonnegative.

nsample : int or array_like of ints

Number of items sampled. Must be at least 1 and at most ngood + nbad.

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 ngood, nbad, and nsample are all scalars. Otherwise, np.broadcast(ngood, nbad, nsample).size samples are drawn.

Returns

out : ndarray or scalar

Drawn samples from the parameterized hypergeometric distribution. Each sample is the number of good items within a randomly selected subset of size nsample taken from a set of ngood good items and nbad bad items.

Notes

The probability mass function (PMF) for the Hypergeometric distribution is

where and

for P(x) the probability of x good results in the drawn sample, g = ngood, b = nbad, and n = nsample.

Consider an urn with black and white marbles in it, ngood of them are black and nbad are white. If you draw nsample balls without replacement, then the hypergeometric distribution describes the distribution of black balls in the drawn sample.

Note that this distribution is very similar to the binomial distribution, except that in this case, samples are drawn without replacement, whereas in the Binomial case samples are drawn with replacement (or the sample space is infinite). As the sample space becomes large, this distribution approaches the binomial.

Examples

Draw samples from the distribution: 
ngood, nbad, nsamp = 100, 2, 10
✗
s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)
from matplotlib.pyplot import hist
✓
hist(s)
✗
Suppose you have an urn with 15 white and 15 black marbles. If you pull 15 marbles at random, how likely is it that 12 or more of them are one color? 
s = np.random.hypergeometric(15, 15, 15, 100000)
✓
sum(s>=12)/100000. + sum(s<=3)/100000.
✗

See also

random.Generator.hypergeometric

which should be used for new code.

scipy.stats.hypergeom

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

Aliases

  • numpy.random.hypergeometric
  • numpy.random.RandomState.hypergeometric