tick.simulation.
weights_sparse_gauss
(n_weights: int = 100, nnz: int = 10, std: float = 1.0, dtype='float64') → numpy.ndarray[source]¶Sparse and gaussian model weights generator Instance of weights for a model, given by a sparse vector, where non-zero coordinates (chosen at random) are centered Gaussian with given standard-deviation
n_weights : int
, default=100
Number of weights
nnz : int
, default=10
Number of non-zero weights
std : float
, default=1.
Standard deviation of the Gaussian non-zero entries
dtype : {'float64', 'float32'}
, default=’float64’
Type of the arrays used.
output : numpy.ndarray
, shape=(n_weights,)
The weights vector