Estoy tratando de recuperar clases de WEKA usando MATLAB y WEKA API. Todo se ve bien, pero las clases son siempre 0. ¿Alguna idea?

Mi conjunto de datos tiene 241 atributos, aplicando WEKA a este conjunto de datos obtengo resultados correctos.

Se crean los objetos del primer tren y prueba que el clasificador se construye y se realiza classifyInstance. Pero esto da un resultado incorrecto

    train = [xtrain ytrain];
    test =  [xtest];

    save ('train.txt','train','-ASCII');    
    save ('test.txt','test','-ASCII');

%## paths
WEKA_HOME = 'C:\Program Files\Weka-3-7';
javaaddpath([WEKA_HOME '\weka.jar']);

fName = 'train.txt';

%## read file

loader = weka.core.converters.MatlabLoader();

loader.setFile( java.io.File(fName) );
train = loader.getDataSet();
train.setClassIndex( train.numAttributes()-1 );

% setting class as nominal

v(1) = java.lang.String('-R');
v(2) = java.lang.String('242');
options = cat(1,v(1:end));

filter = weka.filters.unsupervised.attribute.NumericToNominal();
filter.setOptions(options); 
filter.setInputFormat(train);   
train = filter.useFilter(train, filter);

fName = 'test.txt';

%## read file

loader = weka.core.converters.MatlabLoader();

loader.setFile( java.io.File(fName) );
test = loader.getDataSet();

%## dataset
relationName = char(test.relationName);
numAttr = test.numAttributes;
numInst = test.numInstances;

%## classification
classifier = weka.classifiers.trees.J48();
classifier.buildClassifier( train );
fprintf('Classifier: %s %s\n%s', ...
    char(classifier.getClass().getName()), ...
    char(weka.core.Utils.joinOptions(classifier.getOptions())), ...
    char(classifier.toString()) )

classes =[];

for i=1:numInst

     classes(i) = classifier.classifyInstance(test.instance(i-1));


end

Aquí hay un nuevo código pero aún no funciona - clases = 0. La salida de Weka para el mismo algoritmo y conjunto de datos está bien

=== Precisión detallada por clase ===

               TP Rate   FP Rate   Precision   Recall  F-Measure   ROC Area  Class
                 0.99      0.015      0.985     0.99      0.988      0.991    0
                 0.985     0.01       0.99      0.985     0.988      0.991    1
Weighted Avg.    0.988     0.012      0.988     0.988     0.988      0.991

=== Matriz de confusión ===

    a    b   <-- classified as
 1012   10 |    a = 0

   15 1003 |    b = 1

    ytest1 = ones(size(xtest,1),1); 

    train = [xtrain ytrain];
    test =  [xtest ytest1];

    save ('train.txt','train','-ASCII');    
    save ('test.txt','test','-ASCII');

%## paths
WEKA_HOME = 'C:\Program Files\Weka-3-7';
javaaddpath([WEKA_HOME '\weka.jar']);

fName = 'train.txt';

%## read file

loader = weka.core.converters.MatlabLoader();

loader.setFile( java.io.File(fName) );
train = loader.getDataSet();
train.setClassIndex( train.numAttributes()-1 );

v(1) = java.lang.String('-R');
v(2) = java.lang.String('242');
options = cat(1,v(1:end));

filter = weka.filters.unsupervised.attribute.NumericToNominal();
filter.setOptions(options); 
filter.setInputFormat(train);   
train = filter.useFilter(train, filter);

fName = 'test.txt';

%## read file

loader = weka.core.converters.MatlabLoader();

loader.setFile( java.io.File(fName) );
test = loader.getDataSet();

filter = weka.filters.unsupervised.attribute.NumericToNominal();
filter.setOptions( weka.core.Utils.splitOptions('-R last') );
filter.setInputFormat(test);   
test = filter.useFilter(test, filter);


%## dataset
relationName = char(test.relationName);
numAttr = test.numAttributes;
numInst = test.numInstances;

%## classification
classifier = weka.classifiers.trees.J48();

classifier.buildClassifier( train );
fprintf('Classifier: %s %s\n%s', ...
    char(classifier.getClass().getName()), ...
    char(weka.core.Utils.joinOptions(classifier.getOptions())), ...
    char(classifier.toString()) )

classes = zeros(numInst,1);
for i=1:numInst   
     classes(i) = classifier.classifyInstance(test.instance(i-1));     
end

aquí hay un fragmento de código para la distribución de clases en Java

// output predictions
    System.out.println("# - actual - predicted - error - distribution");
    for (int i = 0; i < test.numInstances(); i++) {
      double pred = cls.classifyInstance(test.instance(i));
      double[] dist = cls.distributionForInstance(test.instance(i));
      System.out.print((i+1));
      System.out.print(" - ");
      System.out.print(test.instance(i).toString(test.classIndex()));
      System.out.print(" - ");
      System.out.print(test.classAttribute().value((int) pred));
      System.out.print(" - ");
      if (pred != test.instance(i).classValue())
    System.out.print("yes");
      else
    System.out.print("no");
      System.out.print(" - ");
      System.out.print(Utils.arrayToString(dist));
      System.out.println();

Lo convertí a código MATLAB como este

classes = zeros(numInst,1);
for i=1:numInst
     pred = classifier.classifyInstance(test.instance(i-1));  
     classes(i) = str2num(char(test.classAttribute().value(( pred))));
end

but las clases se emiten incorrectamente.

En su respuesta no muestra que pred contiene clases y probabilidades predProb. ¡Solo imprímelo!