MATLAB File Help: prtPreProcHistEq
  prtPreProcHistEq   Histogram equalization pre-processing
    HISTEQ = prtPreProcHistEq creates a histogram equalization pre
    processing object. A prtPreProcHistEq object processes the input
    so that the distribution of each feature is approximately uniform
    in the range [0,1].
    prtPreProcHistEq has the following properties:
    nSamples    - The number of samples to use when learning the
                histogtram of the training data.  The default is inf
                (which uses all the data), however for large data sets
                this can be slow.
    A prtPreProcHistEq object also inherits all properties and functions from
    the prtAction class
    dataSet = prtDataGenIris;              % Load a data set
    dataSet = dataSet.retainFeatures(1:2); % Use only the first 2
                                           % Features
    histEq = prtPreProcHistEq;             % Create the
                                           % prtPreProcHistEq Object
    histEq = histEq.train(dataSet);        % Train the object
    dataSetNew =;      % Equalize the histogram
    % Plot
    subplot(2,1,1); plot(dataSet);
    title('Original Data');
    subplot(2,1,2); plot(dataSetNew);
    title('HistEq Data');
See Also
Class Details
Superclasses prtPreProc
Sealed false
Construct on load false
Constructor Summary
prtPreProcHistEq Allow for string, value pairs 
Property Summary
binEdges The bin edges 
dataSet The training prtDataSet, only stored if verboseStorage is true.  
dataSetSummary Structure that summarizes prtDataSet. 
isCrossValidateValid True 
isSupervised False 
isTrained Indicates if prtAction object has been trained. 
nSamples The number of samples to process. 
name Histogram Equalization 
nameAbbreviation HistEq 
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. 
  run Run a prtAction object on a prtDataSet object. 
  set set the object properties 
  train Train a prtAction object using training a prtDataSet object.