I am using weka algorithms thru matlab. I am creating a set of training and testing points in matlab then create a set of instances and use a classifier in weka to create a model.

javaaddpath('C:\Program Files\MATLAB\R2013a\java\jar\weka.jar');

import weka.classifiers.functions.*;
import weka.core.Attribute;
import weka.core.FastVector;
import weka.core.Instances;
import weka.core.DenseInstance;
import weka.classifiers.functions.SMOreg;
import weka.classifiers.Evaluation;

Attribute1=javaObject('weka.core.Attribute','M');
Attribute2=javaObject('weka.core.Attribute','F');
Attribute3=javaObject('weka.core.Attribute','w');
ClassAttribute=javaObject('weka.core.Attribute','Y');

fvWekaAttribute
=javaObject('weka.core.FastVector');
fvWekaAttribute
.addElement(Attribute1);
fvWekaAttribute
.addElement(Attribute2);
fvWekaAttribute
.addElement(Attribute3);
fvWekaAttribute
.addElement(ClassAttribute);

%create training points
isTrainingSet
=javaObject('weka.core.Instances','Rel',fvWekaAttribute,10);
isTrainingSet
.setClassIndex(3)

iExample
=javaObject('weka.core.DenseInstance',4);
iExample
.setValue(fvWekaAttribute.elementAt(0),1.0);
iExample
.setValue(fvWekaAttribute.elementAt(1),0.5);
iExample
.setValue(fvWekaAttribute.elementAt(2),1.0);
iExample
.setValue(fvWekaAttribute.elementAt(3),1.0);
isTrainingSet
.add(iExample);

iExample2
=javaObject('weka.core.DenseInstance',4);
iExample2
.setValue(fvWekaAttribute.elementAt(0),0);
iExample2
.setValue(fvWekaAttribute.elementAt(1),0.5);
iExample2
.setValue(fvWekaAttribute.elementAt(2),0);
iExample2
.setValue(fvWekaAttribute.elementAt(3),0);
isTrainingSet
.add(iExample2);

iExample3
=javaObject('weka.core.DenseInstance',4);
iExample3
.setValue(fvWekaAttribute.elementAt(0),4);
iExample3
.setValue(fvWekaAttribute.elementAt(1),4);
iExample3
.setValue(fvWekaAttribute.elementAt(2),4);
iExample3
.setValue(fvWekaAttribute.elementAt(3),5);
isTrainingSet
.add(iExample3);

%create testing points points
isTestingSet
=javaObject('weka.core.Instances','Rel',fvWekaAttribute,10);
isTestingSet
.setClassIndex(3)

iExample
=javaObject('weka.core.DenseInstance',4);
iExample
.setValue(fvWekaAttribute.elementAt(0),1.0);
iExample
.setValue(fvWekaAttribute.elementAt(1),0.5);
iExample
.setValue(fvWekaAttribute.elementAt(2),1.0);
iExample
.setValue(fvWekaAttribute.elementAt(3),1.0);
isTestingSet
.add(iExample);

iExample2
=javaObject('weka.core.DenseInstance',4);
iExample2
.setValue(fvWekaAttribute.elementAt(0),0);
iExample2
.setValue(fvWekaAttribute.elementAt(1),0.5);
iExample2
.setValue(fvWekaAttribute.elementAt(2),0);
iExample2
.setValue(fvWekaAttribute.elementAt(3),0);
isTestingSet
.add(iExample2);

iExample3
=javaObject('weka.core.DenseInstance',4);
iExample3
.setValue(fvWekaAttribute.elementAt(0),4);
iExample3
.setValue(fvWekaAttribute.elementAt(1),4);
iExample3
.setValue(fvWekaAttribute.elementAt(2),4);
iExample3
.setValue(fvWekaAttribute.elementAt(3),5);
isTestingSet
.add(iExample3);

cModel
= javaObject('weka.classifiers.functions.SMOreg');
cModel
.buildClassifier(isTrainingSet)

weka
.classifiers.Evaluation.evaluateModel(cModel, isTestingSet);`

Now I get an error, 'No method 'evaluateModelOnce' with matching signature found for class 'weka.classifiers.Evaluation'. .
I have zero experience with java, I believe the error is saying that the arguments of the evaluateModel is wrong? if so, How do I evaluate weka model from matlab?