MATLAB File Help: prtClassKmeansPrototypes
  prtClassKmeansPrototypes  K-means prototypes classifier
     CLASSIFIER = prtClassKmeansPrototypes returns a K-means prototypes
     CLASSIFIER = prtClassKmeansPrototypes(PROPERTY1, VALUE1, ...)
     constructs a prtClassKmeansPrototypes object CLASSIFIER with properties as
     specified by PROPERTY/VALUE pairs.
     A prtClassKmeansPrototypes object inherits all properties from the
     abstract class prtClass. In addition is has the following
     nClustersPerHypothesis -  The number of clusters per hypothesis
     clusterCenters         -  The cluster centers (set during
     For information on the  K-means prototype classifier
     algorithm, please refer to:
     Hastie, Tibshirani, Friedman, The Elements of Statistical Learning
     A prtClassKmeansPrototypes object inherits the TRAIN, RUN,
     CROSSVALIDATE and KFOLDS methods from prtAction. It also inherits
     the PLOT method from prtClass.
      TestDataSet = prtDataGenMary;      % Create some test and 
      TrainingDataSet = prtDataGenMary;  % training data
      classifier = prtClassKmeansPrototypes; % Create a classifier
      classifier = classifier.train(TrainingDataSet);    % Train
      classified = run(classifier, TestDataSet);         % Test
      [~, classes] = max(classified.getX,[],2);          % Select the
                                                         % classes
      percentCorr = prtScorePercentCorrect(classes,TestDataSet.getTargets);
See also
Class Details
Superclasses prtClass
Sealed false
Construct on load false
Constructor Summary
prtClassKmeansPrototypes Allow for string, value pairs 
Property Summary
clusterCenters The cluster centers 
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 True 
isSupervised True 
isTrained Indicates if prtAction object has been trained. 
nClustersPerHypothesis Number of clusters per hypothesis 
name K-Means Prototypes 
nameAbbreviation K-MeansProto 
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. 
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. 
  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.