enet efficient elastic net algorithm.
 
    DESCRIPTION
    Fast implementation of elastic net linear and logistic regression by
    Friedman et al. Without arguments, this method automatically determines 
    the lambda path and uses cross-validation to find the best lambda. The
    parameter 'family' determines whether to use linear regression
    (gaussian) or logistic regression (binomial or multinomial). The mixing
    parameter alpha controls the influence of the 11 (alpha=1) and l2 regularizer
    (alpha = 0). Negative weights indicate the class with label 1. Positive
    weights indicate the class with label 2.
 
    REFERENCE
    Regularization paths for generalized linear models via coordinate descent 
    by Friedman et al.
 
    EXAMPLE
    X = randn(100,20); Y = [ones(50,1); 2*ones(50,1)]; 
    X(1:50,1:10) = X(1:50,1:10)+1; X(51:100,11:20) = X(51:100,11:20)+1;
    m = dml.enet('family','binomial');
    m = m.train(X,Y);
    Z = m.test(X);
 
    X = rand(15,20); Y = [1 1 1 1 1 2 2 2 2 2 3 3 3 3 3]'; X(1:5,:) = X(1:5,:)+1;
    m = dml.enet('family','multinomial');
    m = m.train(X,Y);
    Z = m.test(X);
 
    DEVELOPER
    Marcel van Gerven (m.vangerven@donders.ru.nl)