/**
This file is part of JkernelMachines.
JkernelMachines is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
JkernelMachines is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with JkernelMachines. If not, see <http://www.gnu.org/licenses/>.
Copyright David Picard - 2012
*/
package fr.lip6.jkernelmachines.example;
import java.io.IOException;
import java.util.List;
import fr.lip6.jkernelmachines.classifier.LaSVM;
import fr.lip6.jkernelmachines.evaluation.ApEvaluator;
import fr.lip6.jkernelmachines.io.LibSVMImporter;
import fr.lip6.jkernelmachines.kernel.typed.DoubleGaussL2;
import fr.lip6.jkernelmachines.type.TrainingSample;
/**
* <p>This class is a very simple introduction to the use of jkernelmachines.</p>
*
* <p>It reads data from a file in libsvm format, then trains a SVM classifier using
* LaSVM algorithm with a Gaussian kernel. Finally, output values are computed on some
* samples and error rate is computed.</p>
*
* @author picard
*
*/
public class SVMExample {
/**
* @param args
*/
public static void main(String[] args) {
//checking arguments
if(args.length < 1) {
System.out.println("Usage: SVMExample trainfile testfile");
return;
}
//parsing samples from a file using libsvm format
List<TrainingSample<double[]>> train = null;
List<TrainingSample<double[]>> test = null;
try {
train = LibSVMImporter.importFromFile(args[0]);
}
catch (IOException e) {
System.out.println("Error parsing file "+args[0]);
return;
}
try {
test = LibSVMImporter.importFromFile(args[1]);
}
catch (IOException e) {
System.out.println("Error parsing file "+args[1]);
return;
}
//setting kernel
DoubleGaussL2 kernel = new DoubleGaussL2();
kernel.setGamma(2.0);
//setting SVM parameters
LaSVM<double[]> svm = new LaSVM<double[]>(kernel);
svm.setC(10); //C hyperparameter
//evaluation on testing set using evaluator
ApEvaluator<double[]> ape = new ApEvaluator<double[]>();
ape.setClassifier(svm);
ape.setTrainingSet(train);
ape.setTestingSet(test);
ape.evaluate(); // training and evaluating at the same time
// printing average precision obtained
System.out.println("Average Precision: "+ape.getScore());
}
}