### Proposed new feature or change:
Description
========
Often you want to randomize a "direction" that doesn't have "size". This can be
useful when:
* You want to randomize the direction of an NPC walk developing in a 2D or 3D
games, where its speed is a predefined constant.
* You're developing particles simulation with all particles moving in random
directions
* etc.
This random direction should be an `n`th dimensional unit vector which is
randomize **uniformly** from the unit sphere. That means that sections of the
unit sphere with equal areas should have the same chance of getting a vector
from.
Implementation
===========
If one wants to randomize `n`th dimensional direction uniformly, one must do
the following:
1. Generate `n` components of the vector using the standard normal distribution
2. Find the norm the generated vector
3. Divide the vector by its norm
In simple Python code:
```python
import numpy as np
def sphere_uniform(n: int):
vec = np.random.normal(size=n)
norm = np.sqrt(np.sum(vec ** 2))
return vec / norm
```
This implementation suggestion is based on [this blog
post](https://towardsdatascience.com/the-best-way-to-pick-a-unit-vector-7bd0cc54f9b).
I'm not sure if there's a faster way of implementing it using C++ and Cython,
but I'm confident that someone here will know.
Why Implement It in Numpy?
===================
I believe that random unit vectors are common enough to be a part of Numpy.
Scientists, gamers and engineers use random directions all the time, and I
believe there is no reason why Numpy won't provide this ability.
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