cr.sparse.data.sparse_spikes

cr.sparse.data.sparse_spikes(key, N, K, S=1)[source]

Generates a set of sparse model vectors with Rademacher distributed non-zero entries.

  • Each vector is K-sparse.

  • The non-zero basis indexes are randomly selected and shared among all vectors.

  • Non-zero values are Rademacher distributed spikes (-1, 1).

Parameters

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

  • N (int) – Dimension of the model space

  • K (int) – Number of non-zero entries in the sparse model vectors

  • S (int) – Number of sparse model vectors (default 1)

Returns

A tuple consisting of (i) a matrix of sparse model vectors (ii) an index set of locations of non-zero entries

Return type

(jax.numpy.ndarray, jax.numpy.ndarray)

Example

>>> key = random.PRNGKey(3)
>>> X, Omega = sparse_spikes(key, 6, 2, 3)
>>> print(X.shape)
(6, 3)
>>> print(Omega)
[2 5]
>>> print(X)
[[ 0.  0.  0.]
[ 0.  0.  0.]
[-1.  1.  1.]
[ 0.  0.  0.]
[ 0.  0.  0.]
[ 1. -1.  1.]]