Hi,
I am trying to use the java bindings for libsvm in my application. Here is a simple test for the lib:
import java.io.*;
import java.util.*;
import libsvm.*;
public class Test{
public static void main(String[] args) throws Exception{
// Preparing the SVM param
svm_parameter param=new svm_parameter();
param.svm_type=svm_parameter.C_SVC;
param.kernel_type=svm_parameter.RBF;
param.gamma=0.5;
param.nu=0.5;
param.cache_size=20000;
param.C=1;
param.eps=0.001;
param.p=0.1;
HashMap<Integer, HashMap<Integer, Double>> featuresTraining=new HashMap<Integer, HashMap<Integer, Double>>();
HashMap<Integer, Integer> labelTraining=new HashMap<Integer, Integer>();
HashMap<Integer, HashMap<Integer, Double>> featuresTesting=new HashMap<Integer, HashMap<Integer, Double>>();
HashSet<Integer> features=new HashSet<Integer>();
//Read in training data
BufferedReader reader=null;
try{
reader=new BufferedReader(new FileReader("a1a.train"));
String line=null;
int lineNum=0;
while((line=reader.readLine())!=null){
featuresTraining.put(lineNum, new HashMap<Integer,Double>());
String[] tokens=line.split("\\s+");
int label=Integer.parseInt(tokens[0]);
labelTraining.put(lineNum, label);
for(int i=1;i<tokens.length;i++){
String[] fields=tokens[i].split(":");
int featureId=Integer.parseInt(fields[0]);
double featureValue=Double.parseDouble(fields[1]);
features.add(featureId);
featuresTraining.get(lineNum).put(featureId, featureValue);
}
lineNum++;
}
reader.close();
}catch (Exception e){
}
//Read in test data
try{
reader=new BufferedReader(new FileReader("a1a.t"));
String line=null;
int lineNum=0;
while((line=reader.readLine())!=null){
featuresTesting.put(lineNum, new HashMap<Integer,Double>());
String[] tokens=line.split("\\s+");
for(int i=1; i<tokens.length;i++){
String[] fields=tokens[i].split(":");
int featureId=Integer.parseInt(fields[0]);
double featureValue=Double.parseDouble(fields[1]);
featuresTesting.get(lineNum).put(featureId, featureValue);
}
lineNum++;
}
reader.close();
}catch (Exception e){
}
//Train the SVM model
svm_problem prob=new svm_problem();
int numTrainingInstances=featuresTraining.keySet().size();
prob.l=numTrainingInstances;
prob.y=new double[prob.l];
prob.x=new svm_node[prob.l][];
for(int i=0;i<numTrainingInstances;i++){
HashMap<Integer,Double> tmp=featuresTraining.get(i);
prob.x[i]=new svm_node[tmp.keySet().size()];
int indx=0;
for(Integer id:tmp.keySet()){
svm_node node=new svm_node();
node.index=id;
node.value=tmp.get(id);
prob.x[i][indx]=node;
indx++;
}
prob.y[i]=labelTraining.get(i);
}
svm_model model=svm.svm_train(prob,param);
for(Integer testInstance:featuresTesting.keySet()){
HashMap<Integer, Double> tmp=new HashMap<Integer, Double>();
int numFeatures=tmp.keySet().size();
svm_node[] x=new svm_node[numFeatures];
int featureIndx=0;
for(Integer feature:tmp.keySet()){
x[featureIndx]=new svm_node();
x[featureIndx].index=feature;
x[featureIndx].value=tmp.get(feature);
featureIndx++;
}
double d=svm.svm_predict(model, x);
System.out.println(testInstance+"\t"+d);
}
}
}
It is a trivial example but the results are always returning class 0 which is wrong. Can someone explain what am I missing?
Thanks
asked
Oct 12 '12 at 16:28
Machine_Learner
6●4●4●6