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. Example: testDataSet = prtDataGenUniModal; % Create some test and trainingDataSet = prtDataGenUniModal; % training data classifier = prtClassGlrt; % Create a classifier classifier = classifier.train(trainingDataSet); % Train classifier.plot; classified = classifier.run(testDataSet); 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');
Superclasses | prtClass |
Sealed | false |
Construct on load | false |
prtClassGlrt | Generalized likelihood ratio test classifier |
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 |
showProgressBar | |
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. |
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. |