Class LogisticRegressionL1L2GL

source code

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                   properties.Function --+            
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                   properties.Gradient --+   |        
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properties.LipschitzContinuousGradient --+   |        
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                properties.Eigenvalues --+   |        
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           properties.ProximalOperator --+   |        
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               properties.NesterovFunction --+        
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               properties.ProximalOperator --+        
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                   properties.Continuation --+        
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                   properties.DualFunction --+        
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                 properties.StronglyConvex --+        
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                       properties.StepSize --+        
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                        LinearRegressionL1L2TV --+    
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                            LinearRegressionL1L2GL --+
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                                                    LogisticRegressionL1L2GL

Combination (sum) of RidgeLogisticRegression, L1 and TotalVariation.

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

y : Numpy array. 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.

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

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

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