tick.prox.
ProxElasticNet
(strength: float, ratio: float, range: tuple = None, positive=False)[source]¶Proximal operator of the ElasticNet regularization.
strength : float
Level of ElasticNet regularization
range : tuple
of two int
, default=`None`
Range on which the prox is applied. If
None
then the prox is applied on the whole vector
ratio : float
, default=0
The ElasticNet mixing parameter, with 0 <= ratio <= 1. For ratio = 0 this is ridge (L2) regularization For ratio = 1 this is lasso (L1) regularization For 0 < ratio < 1, the regularization is a linear combination of L1 and L2.
positive : bool
, default=`False`
If True, apply the penalization together with a projection onto the set of vectors with non-negative entries
dtype : {'float64', 'float32'}
Type of the arrays used.
__init__
(strength: float, ratio: float, range: tuple = None, positive=False)[source]¶Initialize self. See help(type(self)) for accurate signature.
call
(coeffs, step=1.0, out=None)¶Apply proximal operator on a vector. It computes:
coeffs : numpy.ndarray
, shape=(n_coeffs,)
Input vector on which is applied the proximal operator
step : float
or np.array
, default=1.
The amount of penalization is multiplied by this amount
If
float
, the amount of penalization is multiplied by this amountIf
np.array
, then each coordinate of coeffs (within the given range), receives an amount of penalization multiplied by t (available only for separable prox)
out : numpy.ndarray
, shape=(n_params,), default=None
If not
None
, the output is stored in the givenout
. Otherwise, a new vector is created.
output : numpy.ndarray
, shape=(n_coeffs,)
Same object as out
Notes
step
must have the same size as coeffs
whenever range is
None
, or a size matching the one given by the range
otherwise