[SOLVED] How to get distribution table model of naive bayes?

package fdf;

import com.rapidminer.RapidMiner;
import com.rapidminer.Process;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.IOContainer;
import com.rapidminer.operator.IOObject;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.Model;




import java.io.File;
import java.io.IOException;
import java.util.Iterator;

import com.rapidminer.operator.io.ExcelExampleSource; 
import com.rapidminer.operator.learner.bayes.DistributionModel;
import com.rapidminer.repository.IOObjectEntry;
import com.rapidminer.repository.ProcessEntry;
import com.rapidminer.repository.RepositoryLocation;
import com.rapidminer.tools.XMLException;

public class Prueba {

	public static void main(String args[]) throws OperatorException, IOException, XMLException {

	    RapidMiner.setExecutionMode(RapidMiner.ExecutionMode.COMMAND_LINE);
	    System.setProperty("rapidminer.home", "C:\\Users\\lDanny\\workspace\\rapidminer-5.3.013\\rapidminer");
	    RapidMiner.init();
	    
	    Process location = new Process(new File("C:\\Users\\lDanny\\.RapidMiner5\\repositories\\Local Repository\\PrimeraVez.rmp"));
	    IOContainer resultExample = location.run();
	    
	    System.out.println(resultExample.toString());
		System.out.println("am here");
	}
}

IOContainer (3 objects):
SimpleExampleSet:
32 examples,
1778 regular attributes,
special attributes = {
    label = #0: categoria (nominal/single_value)/values=[H.1.2, H.3.3]
}
(created by Set Role)
Distribution model for label attribute categoria


Class H.1.2 (0.875)
1778 distributions

Class H.3.3 (0.125)
1778 distributions

(created by Naive Bayes)

PerformanceVector [
-----accuracy: 100.00% +/- 0.00% (mikro: 100.00%)
ConfusionMatrix:
True:	H.1.2	H.3.3
H.1.2:	28	0
H.3.3:	0	4
-----precision: 100.00% (positive class: H.3.3)
ConfusionMatrix:
True:	H.1.2	H.3.3
H.1.2:	28	0
H.3.3:	0	4
-----recall: 100.00% (positive class: H.3.3)
ConfusionMatrix:
True:	H.1.2	H.3.3
H.1.2:	28	0
H.3.3:	0	4
-----AUC (optimistic): 0.900 +/- 0.200 (mikro: 0.900) (positive class: H.3.3)
-----AUC: 0.500 +/- 0.000 (mikro: 0.500) (positive class: H.3.3)
-----AUC (pessimistic): 0.900 +/- 0.200 (mikro: 0.900) (positive class: H.3.3)
]
(created by Performance (2))