machine learning - LibSvm add features using the JAVA api -

i have text , want train adding feature using java api. looking @ examples main class build training set svm_problem. appear svm_node represents feature (the index feature , value weight of feature).

what have done have map (just simplify problem) keeps association between feature , index. each of weight> example create new node :

  svm_node currentnode = new svm_node();   int index = feature.getindexinmap();   double value = feature.getweight();   currentnode.index = index;   currentnode.value = value; 

is intuition correct? svm_problem.y refers to? refer index of label? svm_problem.l length of 2 vectors?

your intuition close, svm_node pattern not feature. variable svm_problem.y array contains labels of each pattern , svm_problem.l size of training set.

also, beware svm_parameter.nr_weight weight of each label (useful if have unbalanced training set) if not going use must set value zero.

let me show simple example in c++:

#include "svm.h" #include <iostream>  using namespace std;  int main() {     svm_parameter params;       params.svm_type = c_svc;     params.kernel_type = rbf;     params.c = 1;     params.gamma = 1;     params.nr_weight = 0;     params.p= 0.0001;      svm_problem problem;     problem.l = 4;     problem.y = new double[4]{1,-1,-1,1};     problem.x = new svm_node*[4];      {     problem.x[0] = new svm_node[3];     problem.x[0][0].index = 1;     problem.x[0][0].value = 0;     problem.x[0][1].index = 2;     problem.x[0][1].value = 0;     problem.x[0][2].index = -1;      }      {     problem.x[1] = new svm_node[3];     problem.x[1][0].index = 1;     problem.x[1][0].value = 1;     problem.x[1][1].index = 2;     problem.x[1][1].value = 0;     problem.x[1][2].index = -1;     }      {     problem.x[2] = new svm_node[3];     problem.x[2][0].index = 1;     problem.x[2][0].value = 0;     problem.x[2][1].index = 2;     problem.x[2][1].value = 1;     problem.x[2][2].index = -1;     }     {     problem.x[3] = new svm_node[3];     problem.x[3][0].index = 1;     problem.x[3][0].value = 1;     problem.x[3][1].index = 2;     problem.x[3][1].value = 1;     problem.x[3][2].index = -1;      }      for(int i=0; i<4; i++)     {         cout << problem.y[i] << endl;     }      svm_model * model = svm_train(&problem, &params);     svm_save_model("mymodel.svm", model);      for(int i=0; i<4; i++)     {         double d = svm_predict(model, problem.x[i]);          cout << "prediction " << d << endl;     }     /* should free memory @ point.         example large enough */  }