content: import java.io.BufferedReader;
  import java.io.BufferedWriter;
  import java.io.FileReader;
  import java.io.FileWriter;
  import java.io.IOException;
  import java.util.HashMap;
  import java.util.Map;
  
  
  public class Model {
      Map<Integer,SparseVector> class2Vector = new HashMap<Integer, SparseVector>();
      private Model() {
          
      }
      public Model(int maxLab, int maxFeatureId) {
          for (int i = 1; i <= maxLab; i++) {
              class2Vector.put(i, new SparseVector(maxFeatureId));
          }
      }
      
      public void addModel(Model m, double coeff) {
          for (Integer i : class2Vector.keySet()) {
              class2Vector.get(i).add(m.class2Vector.get(i), coeff);
          }
      }
      
      public static Model load(String fileName) throws IOException {
          BufferedReader in = new BufferedReader(new FileReader(fileName));
          
          Model mod = new Model();
          Example ex = null;
          while ((ex = Example.readExample(in))!= null) {
              mod.class2Vector.put(ex.label, ex.sv);
          }
          in.close();
          return mod;
          
      }
      
      public void save(String fileName) throws IOException {
          BufferedWriter out = new BufferedWriter(new FileWriter(fileName));
          for (Integer i : class2Vector.keySet()) {
              Example ex = new Example(i, class2Vector.get(i));
              ex.saveExample(out);
          }
          out.close();
      }
      
      public int predictLabel(SparseVector sv) {
          int optLab = -1;
          double opt = Double.NEGATIVE_INFINITY;
          for (int lab : class2Vector.keySet()) {
              double score = class2Vector.get(lab).innerProductForNonSparse(sv);
              if (score > opt) {
                  optLab = lab;
                  opt = score;
              }
          }
          return optLab;
      }
      
  }
  