MATLAB File Help: prtClassGlrt
  prtClassGlrt  Generalized likelihood ratio test classifier
     CLASSIFIER = prtClassGlrt returns a Glrt classifier
     CLASSIFIER = prtClassGlrt(PROPERTY1, VALUE1, ...) constructs a
     prtClassGlrt object CLASSIFIER with properties as specified by
     PROPERTY/VALUE pairs.
     A prtClassGlrt object inherits all properties from the abstract class
     prtClass. In addition is has the following properties:
     rvH0 - A prtRvMvn object representing hypothesis 0
     rvH1 - A prtRvMvn object representing hypothesis 1
     A prtClassGlrt object inherits the TRAIN, RUN, CROSSVALIDATE and
     KFOLDS methods from prtAction. It also inherits the PLOT method
     from prtClass.
      testDataSet = prtDataGenUniModal;       % Create some test and
      trainingDataSet = prtDataGenUniModal;   % training data
      classifier = prtClassGlrt;              % Create a classifier
      classifier = classifier.train(trainingDataSet);    % Train
      classified =;
      [pf,pd] = prtScoreRoc(classified,testDataSet);
      h = plot(pf,pd,'linewidth',3);
      title('ROC'); xlabel('Pf'); ylabel('Pd');
See also
Class Details
Superclasses prtClass
Sealed false
Construct on load false
Constructor Summary
prtClassGlrt Generalized likelihood ratio test classifier 
Property Summary
dataSet The training prtDataSet, only stored if verboseStorage is true.  
dataSetSummary Structure that summarizes prtDataSet. 
internalDecider Optional prtDecider object for making decisions 
isCrossValidateValid True 
isNativeMary False 
isSupervised True 
isTrained Indicates if prtAction object has been trained. 
name Generalized likelihood ratio test 
nameAbbreviation GLRT 
rvH0 Mean and variance of H0 
rvH1 Mean and variance of H1 
twoClassParadigm Whether the classifier retures one output (binary) or two outputs (m-ary) when there are only two unique class labels 
userData User specified data 
verboseStorage Specifies whether or not to store the training prtDataset. 
Method Summary
  crossValidate Cross validate prtAction using prtDataSet and cross validation keys. 
  get get the object properties 
  kfolds Perform K-folds cross-validation of prtAction 
  optimize Optimize action parameter by exhaustive function maximization. 
  plot Plot the output confidence of a prtClass object 
  run Run a prtAction object on a prtDataSet object. 
  set set the object properties 
  train Train a prtAction object using training a prtDataSet object.