cr.sparse.dict.gaussian_mtx

cr.sparse.dict.gaussian_mtx(key, N, D, normalize_atoms=True)[source]

A dictionary/sensing matrix where entries are drawn independently from normal distribution.

Parameters

  • key – a PRNG key used as the random key.

  • N (int) – Number of rows of the sensing matrix

  • D (int) – Number of columns of the sensing matrix

  • normalize_atoms (bool) – Whether the columns of sensing matrix are normalized (default True)

Returns

A Gaussian sensing matrix of shape (N, D)

Return type

(jax.numpy.ndarray)

Example

>>> from jax import random
>>> import cr.sparse as crs
>>> import cr.sparse.dict
>>> m, n = 8, 16
>>> Phi = cr.sparse.dict.gaussian_mtx(random.PRNGKey(0), m, n)
>>> print(Phi.shape)
(8, 16)
>>> print(crs.norms_l2_cw(Phi))
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
>>> print(cr.sparse.dict.coherence(Phi))
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
>>> print(cr.sparse.dict.babel(Phi))
[0.85866616 1.59791754 2.13943785 2.61184779 2.9912899  3.38281051
3.74641682 4.08225813 4.29701559 4.49942648 4.68680188 4.83106192
4.95656728 5.05541184 5.10697535]