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NotesParametersReturns
multivariate_hypergeom_frozen.rvs(self, size=1, random_state=None)

Notes

See class definition for a detailed description of parameters.

Also note that NumPy's multivariate_hypergeometric sampler is not used as it doesn't support broadcasting.

Parameters

size : integer or iterable of integers, optional

Number of samples to draw. Default is None, in which case a single variate is returned as an array with shape m.shape.

seed : {None, int, np.random.RandomState, np.random.Generator}, optional

Used for drawing random variates. If seed is None, the ~np.random.RandomState singleton is used. If seed is an int, a new RandomState instance is used, seeded with seed. If seed is already a RandomState or Generator instance, then that object is used. Default is None.

Returns

rvs : array_like

Random variates of shape size or m.shape (if size=None).

Draw random samples from a multivariate hypergeometric distribution.

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


GitHub : /scipy/stats/_multivariate.py#5068
type: <class 'function'>
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