MATLAB File Help: prtRvUniform
prtRvUniform
  prtRvUniform  Uniform random variable
 
    RV = prtRvUniform creates a prtRvUniform object with empty
    upperBounds and lowerBounds. The upperBounds and lowerBounds must
    be set either directly, or by calling the MLE method. upperBounds
    and lowerBounds specify the range of the uniform variable.
 
    RV = prtRvUniform(PROPERTY1, VALUE1,...) creates a prtRvUniform
    object RV with properties as specified by PROPERTY/VALUE pairs.
 
    A prtRvUniform object inherits all properties from the prtRv class.
    In addition, it has the following properties:
 
    upperBounds - 1 x nDims double vector specifying the upper bound of
                  the region with uniform density
    lowerBounds - 1 x nDims double vector specifying the lower bound of
                  the region with uniform density
    
   A prtRvUniform object inherits all methods from the prtRv class.
   The MLE  method can be used to estimate the distribution parameters
   from data.
 
   Example:
 
   dataSet = prtDataGenUnimodal;        % Load a dataset consisting of
                                        % 2 features
   dataSet = retainFeatures(dataSet,1); % Retain only the first feature
 
   RV = prtRvUniform;                   % Create a prtRvUniform object
   RV = RV.mle(dataSet);                % Compute the bounds
                                        % form the data
   RV.plotPdf                           % Plot the pdf
See also
Class Details
Superclasses prtRv
Sealed false
Construct on load false
Constructor Summary
prtRvUniform Uniform random variable 
Property Summary
dataSet The training prtDataSet, only stored if verboseStorage is true.  
dataSetSummary Structure that summarizes prtDataSet. 
isCrossValidateValid  
isSupervised  
isTrained Indicates if prtAction object has been trained. 
lowerBounds The upper bounds of the random variable 
name  
nameAbbreviation  
plotOptions  
showProgressBar  
upperBounds The lower bounds of the random variable 
userData User specified data 
verboseStorage Specifies whether or not to store the training prtDataset. 
Method Summary
  area  
  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.