MATLAB File Help: prtClassMatlabNnet
  prtClassMatlabNnet  Support vector machine classifier using the MATLAB neural network toolbox (requires NNET toolbox)
    CLASSIFIER = prtClassMatlabNnet returns a neural network classifier
    using the MATLAB NNET toolbox (additonal product, not included)
   A prtClassMatlabNnet object inherits all properties from the
   abstract class prtClass. In addition is has the following
   properties; complete documentation for these properties can be found
   in the help for the newpr.m function in the MATLAB NNET toolbox.
    Si, TFi, BTF, BLF, PF, IPF, OPF, DDF
  % Example usage:
    TestDataSet = prtDataGenBimodal;       % Create some test and
    TrainingDataSet = prtDataGenBimodal;   % training data
    classifier = prtClassMatlabNnet;           % Create a classifier
    classifier = classifier.train(TrainingDataSet);    % Train
    classified = run(classifier, TestDataSet);         % Test
    [pf,pd] = prtScoreRoc(classified,TestDataSet);
    h = plot(pf,pd,'linewidth',3);
    title('ROC'); xlabel('Pf'); ylabel('Pd');
Class Details
Superclasses prtClass
Sealed false
Construct on load false
Constructor Summary
prtClassMatlabNnet Support vector machine classifier using the MATLAB neural network toolbox (requires NNET toolbox) 
Property Summary
BLF See help for newpr 
BTF See help for newpr 
DDF See help for newpr 
IPF See help for newpr 
OPF See help for newpr 
PF See help for newpr 
Si Number of layers in hidden element 
TFi See help for newpr 
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. 
name MATLAB Neural Network 
nameAbbreviation MLNN 
nnet The base neural network 
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.