scipy.stats.uniform_direction#

scipy.stats.uniform_direction = <scipy.stats._multivariate.uniform_direction_gen object>[source]#

A vector-valued uniform direction.

Return a random direction (unit vector). The dim keyword specifies the dimensionality of the space.

Parameters:
dimscalar

Dimension of directions.

seed{None, int, numpy.random.Generator,

Used for drawing random variates. If seed is None, the 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.

Methods

rvs(dim=None, size=1, random_state=None)

Draw random directions.

Notes

This distribution generates unit vectors uniformly distributed on the surface of a hypersphere. These can be interpreted as random directions. For example, if dim is 3, 3D vectors from the surface of \(S^2\) will be sampled.

References

[1]

Marsaglia, G. (1972). “Choosing a Point from the Surface of a Sphere”. Annals of Mathematical Statistics. 43 (2): 645-646.

Examples

>>> import numpy as np
>>> from scipy.stats import uniform_direction
>>> x = uniform_direction.rvs(3)
>>> np.linalg.norm(x)
1.

This generates one random direction, a vector on the surface of \(S^2\).

Alternatively, the object may be called (as a function) to return a frozen distribution with fixed dim parameter. Here, we create a uniform_direction with dim=3 and draw 5 observations. The samples are then arranged in an array of shape 5x3.

>>> rng = np.random.default_rng()
>>> uniform_sphere_dist = uniform_direction(3)
>>> unit_vectors = uniform_sphere_dist.rvs(5, random_state=rng)
>>> unit_vectors
array([[ 0.56688642, -0.1332634 , -0.81294566],
       [-0.427126  , -0.74779278,  0.50830044],
       [ 0.3793989 ,  0.92346629,  0.05715323],
       [ 0.36428383, -0.92449076, -0.11231259],
       [-0.27733285,  0.94410968, -0.17816678]])
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