MATLAB File Help: prtRvVq
  prtRvVq  Vector quantization random variable
    RV = prtRvVq creates a prtRvVq object with empty means and
    probabilities. The means and probabilties must be set either
    directly, or by calling the MLE method.
    Vector quanitization uses k-means to discretize the data space
    using nCategories means. The means are the discrete points in space and
    have probabilies representing their prominence in the data. The
    pdf is calculated by mapping to the nearest entry of the means and
    giving the data point the corresponding entry in probabilities.
    RV = prtRvVq(PROPERTY1, VALUE1,...) creates a prtRvVq object RV
    with properties as specified by PROPERTY/VALUE pairs.
    A prtRvVq object inherits all properties from the prtRv class. In
    addition, it has the following properties:
    nCategories   - The number of categories
    means         - The means of each catergory that are used to 
                    approximate the density
    probabilities - The probabilities of each category
   A prtRvVq object inherits all methods from the prtRv class. The MLE
   method can be used to estimate the distribution parameters from
   dataSet = prtDataGenUnimodal;        % Load a dataset consisting of
                                        % 2 features
   dataSet = retainFeatures(dataSet,1); % Retain only the first feature
   RV = prtRvVq;                        % Create a prtRvVq object
   RV = RV.mle(dataSet);                % Compute the VQ parameters
                                        % form the data
   RV.plotPdf                           % Plot the pdf
See also
Class Details
Superclasses prtRv
Sealed false
Construct on load false
Constructor Summary
prtRvVq Vector quantization 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. 
means The means 
nCategories The number of categories 
probabilities The probabilities 
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.