prtClassLogisticDiscriminant  Logistic Discriminant classifier
 
     CLASSIFIER = prtClassLogisticDiscriminant returns a LogisticDiscriminant classifier
 
     CLASSIFIER = prtClassLogisticDiscriminant(PROPERTY1, VALUE1, ...) constructs a
     prtClassLogisticDiscriminant object CLASSIFIER with properties as specified by
     PROPERTY/VALUE pairs.
 
     A prtClassLogisticDiscriminant object inherits all properties from the abstract class
     prtClass. In addition is has the following properties:
 
    wTolerance       - The convergance tolerance of the weights
    irlsStepSize     - Step size used in training. Can be set to a
                       double, or 'hessian'. If 'hessian', IRLS is 
                       solved using the Hessian to estimate steps.
    maxIter          - maximum IRLS iterations
    nIterations      - number of iterations used, set during training
    wInitTechnique   - Technique to initialize weights, can be set to
                       'FLD', 'randn', and 'manual'
    manualInitialW   - The values the weights are initialized to if 
                       wInitTechnique is set to 'manual'
    wTolerance       - Convergence tolerance on weight vector 
    handleNonPosDefR - What to do when R is non-positive definte, can
                       be set to 'regularize' or 'exit'. When set to 
                       regularize, the classifier will attempt to
                       regularize the matrix. When set to exit the 
                       classifier will exit.
 
    w                - The regression weights, estimated during training
                       w(1) corresponds to the DC bias and w(2:end)
                       corresponds to the weights for the features
 
     For more information on LogisticDiscriminant classifiers, refer to the
     following URL:
   
     http://en.wikipedia.org/wiki/Logistic_regression
 
     A prtClassLogisticDiscriminant object inherits the TRAIN, RUN, 
     CROSSVALIDATE and KFOLDS methods from prtAction. It also inherits 
     the PLOT method from prtClass.
 
     Example:
 
      TestDataSet = prtDataGenUnimodal;           % Create some test and
      TrainingDataSet = prtDataGenUnimodal;       % training data
      classifier = prtClassLogisticDiscriminant;  % Create a classifier
      classifier = classifier.train(TrainingDataSet);       % Train
      classified = run(classifier, TestDataSet);            % Test
      subplot(2,1,1);
      classifier.plot;
      subplot(2,1,2);
      [pf,pd] = prtScoreRoc(classified,TestDataSet);
      h = plot(pf,pd,'linewidth',3);
      title('ROC'); xlabel('Pf'); ylabel('Pd');