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

anonimoper

Hi, i want to get distribution table model of naive bayes. I use eclipse but i don't get distribution table model. I get performance vector.

This is code:

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");
	}
}

this is result:

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))

i want to get this:http://www.subirimagenes.net/i/140502022203717274.png

Marco_Boeck

Hi,

you want your second result, the DistributionModel. You can then pass that to the  DistributionTableModel(DistributionModel model) constructor which is an AbstractTableModel. You then have the table model you want.

Regards,
Marco