Class NewGLMNET

JSAT/src/jsat/classifiers/linear/NewGLMNET.java:59–978

NewGLMNET is a batch method for solving Elastic Net regularized Logistic Regression problems of the form 0.5 (1-α) ||w|| 2 + α ||w|| 1 + C ∑ N i=1 ℓ (w T x i + b, y i ). For α

 * @author Edward Raff
 */
public class NewGLMNET implements WarmClassifier, Parameterized, SingleWeightVectorModel
{

    private static final long serialVersionUID = 4133368677783573518L;
    //TODO make these other fields configurable as well
    private static final double DEFAULT_BETA = 0.5;
    private static final double DEFAULT_V = 1e-12;
    private static final double DEFAULT_GAMMA = 0;
    private static final double DEFAULT_SIGMA = 0.01;
    /**
     * The default tolerance for training is {@value #DEFAULT_EPS}. 
     */
    public static final double DEFAULT_EPS = 1e-2;
    /**
     * The default number of outer iterations of the training algorithm is 
     * {@value #DEFAULT_MAX_OUTER_ITER} . 
     */
    public static final int DEFAULT_MAX_OUTER_ITER = 100;
    
    /**
     * Weight vector
     */
    private Vec w;
    /**
     * Bias term
     */
    private double b;
    private double beta = DEFAULT_BETA;
    private double v = DEFAULT_V;
    private double gamma = DEFAULT_GAMMA;
    private double sigma = DEFAULT_SIGMA;
    private double C;
    private double alpha;
    private int maxOuterIters = DEFAULT_MAX_OUTER_ITER;
    private double e_out = DEFAULT_EPS;
    private boolean useBias = true;
    /**
     * The maximum allowed line-search steps
     */
    private int maxLineSearchSteps = 20;

    /**
     * Creates a new L<sub>1</sub> regularized Logistic Regression solver with 
     * C = 1. 
     */
    public NewGLMNET()
    {
        this(1);
    }
    
    /**
     * Creates a new L<sub>1</sub> regularized Logistic Regression solver
     * @param C the regularization term
     */
    public NewGLMNET(double C)
    {
        this(C, 1);
    }