parsimony.functions.combinedfunctions.LogisticRegressionL1L2TV

Class LogisticRegressionL1L2TV

Combination (sum) of RidgeLogisticRegression, L1 and TotalVariation.

Instance Methods

[hide private]

__init__(self, X, y, l1, l2, tv, A=None, mu=0.0, weights=None, penalty_start=0, mean=True)
Parameters ---------- X : Numpy array (n-by-p).

source code

gap(self, beta, beta_hat=None, eps=5e-08, max_iter=10000)
Compute the duality gap for the logistic function.

source code

Inherited from LinearRegressionL1L2TV: A, Aa, L, M, betahat, eps_max, eps_opt, estimate_mu, f, fmu, get_mu, grad, mu_max, mu_opt, parameter, phi, project, prox, reset, set_mu, set_params, step

Inherited from properties.Function: get_params

Inherited from properties.NesterovFunction: alpha, lA, lambda_max

Inherited from properties.Gradient: approx_grad

Inherited from properties.LipschitzContinuousGradient: approx_L

Inherited from properties.Eigenvalues: lambda_min

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Properties

[hide private]

Inherited from object: __class__

init(self, X, y, l1, l2, tv, A=None, mu=0.0, weights=None, penalty_start=0, mean=True)
(Constructor)

source code


Parameters
----------
X : Numpy array (n-by-p). The X matrix for the logistic regression.

y : Numpy array (n-by-1). The y vector for the logistic regression.

l1 : Non-negative float. The Lagrange multiplier, or regularisation
        constant, for the L1 penalty.

l2 : Non-negative float. The Lagrange multiplier, or regularisation
        constant, for the ridge (L2) penalty.

tv : Non-negative float. The Lagrange multiplier, or regularisation
        constant, of the smoothed TV function.

A : Numpy array (usually sparse). The linear operator for the Nesterov
        formulation for TV. May not be None!

mu : Non-negative float. The regularisation constant for the smoothing
        of the TV function.

weights: List with n elements. The sample's weights.

penalty_start : Non-negative integer. The number of columns, variables
        etc., to except from penalisation. Equivalently, the first
        index to be penalised. Default is 0, all columns are included.

mean : Boolean. Whether to compute the squared loss or the mean
        squared loss. Default is True, the mean squared loss.

Overrides: object.__init__

gap(self, beta, beta_hat=None, eps=5e-08, max_iter=10000)

source code

Compute the duality gap for the logistic function.

From the interface "DualFunction".

Overrides: properties.DualFunction.gap

Class LogisticRegressionL1L2TV

__init__(self, X, y, l1, l2, tv, A=None, mu=0.0, weights=None, penalty_start=0, mean=True) (Constructor)

gap(self, beta, beta_hat=None, eps=5e-08, max_iter=10000)

init(self, X, y, l1, l2, tv, A=None, mu=0.0, weights=None, penalty_start=0, mean=True)
(Constructor)