MATLAB File Help: prtRvUniformImproper
  prtRvUniformImproper  Improper uniform random variable
    RV = prtRvUniformImproper creates a prtRvUniformImproper object
    with unknown dimensionality nDimensions. nDimensions can be set
    manually or using the MLE method. A prtRvUniformImproper
    models an improper pdf that always yields a value of 1 no matter
    the input. prtRvUniformImproper is sometimes useful for creating 
    one class classifiers. See the examples below for more information
    The draw method of prtRvUniformImproper draws values uniformly
    distributed from realmin to realmax in each dimension.
    RV = prtRvUniformImproper(PROPERTY1, VALUE1,...) creates a
    prtRvMultinomial object RV with properties as specified by
    A prtRvUniformImproper object inherits all properties from the
    prtRv class. In addition, it has the following properties:
    nDimensions - dimensionality of the data modeled by this RV.
   A prtRvUniformImproper object inherits all methods from the prtRv
   class. The MLE  method can be used to set the parameters from data.
   % In this example we show that the PDF of a prtRvUniformImproper is
   % always 1
   dataSet = prtDataGenUnimodal;        % Load a dataset consisting of
                                        % 2 features
   dataSet = retainFeatures(dataSet,1); % Retain only the first feature
                                        % only for the example.
   RV = prtRvUniformImproper;           % Create a prtRvUniform object
   RV = RV.mle(dataSet);                % Compute the bounds
   RV.plotPdf([-10 10]);                % We must manually specify
                                        % plot limits since
                                        % prtRvUniformImproper does not
                                        % have actual plot limits
   % In this example we show how to build a one class MAP classifier
   dataSet = prtDataGenUnimodal;        % Load a dataset consisting of
                                        % 2 features
   % Create and train a GLRT classifier that uses a 
   % prtRvUniformImproper to model class 0 and a prtRvMvn to model
   % class 1
   glrtClass = train(prtClassGlrt('rvH0',prtRvUniformImproper,'rvH1',prtRvMvn),dataSet);
   plot(glrtClass) % Contours only show the log-likelihood of class 1
See also
Class Details
Superclasses prtRv
Sealed false
Construct on load false
Constructor Summary
prtRvUniformImproper Improper uniform random variable 
Property Summary
dataSet The training prtDataSet, only stored if verboseStorage is true.  
dataSetSummary Structure that summarizes prtDataSet. 
isTrained Indicates if prtAction object has been trained. 
userData User specified data 
verboseStorage Specifies whether or not to store the training prtDataset. 
Method Summary
  cdf Output the cdf of the random variable evaluated at the points specified 
  crossValidate Cross validate prtAction using prtDataSet and cross validation keys. 
  draw Draw random samples from the distribution described by the prtRv object 
  get get the object properties 
  kfolds Perform K-folds cross-validation of prtAction 
  logPdf Output the log pdf of the random variable evaluated at the points specified 
  mle Compute the maximum likelihood estimate  
  optimize Optimize action parameter by exhaustive function maximization. 
  pdf Output the pdf of the random variable evaluated at the points specified 
  plotCdf Plot the cdf of the prtRv 
  plotLogPdf Plot the pdf of the prtRv 
  plotPdf Plot the pdf of the prtRv 
  run Run a prtAction object on a prtDataSet object. 
  set set the object properties 
  train Train a prtAction object using training a prtDataSet object.