Exemple #1
0
 protected void init_spaces() {
   userspace = new float[maxuid + 1][];
   itemspace = new float[maxiid + 1][];
   float frag =
       (float) Math.sqrt((avgrating * 0.1f / factor)); // pu * qi is about 10% effect of r^ui
   for (int u = 0; u < userspace.length; u++) {
     userspace[u] = new float[factor];
     for (int f = 0; f < factor; f++) {
       userspace[u][f] = (float) (frag * (Utilities.randomDouble()));
     }
   }
   System.out.println(itemspace.length);
   for (int i = 0; i < itemspace.length; i++) {
     itemspace[i] = new float[factor];
     for (int f = 0; f < factor; f++) {
       itemspace[i][f] = (float) (frag * (Utilities.randomDouble()));
     }
   }
   userbias = new float[maxuid + 1];
   itembias = new float[maxiid + 1];
   for (int u = 0; u < userbias.length; u++) {
     userbias[u] = (float) Utilities.randomDouble(-0.5, 0.5); //
   }
   for (int i = 0; i < itembias.length; i++) {
     itembias[i] = (float) Utilities.randomDouble(-1.5, 1.5);
   }
 }
Exemple #2
0
  private void _train() throws Exception {
    System.out.println("--------------------");
    float learningSpeed = this.alpha;

    for (int loop = 0; loop < this.loops; loop++) {
      dataEntry.reOpen();
      double totalError = 0;
      int n = 0;
      long timeStart = System.currentTimeMillis();
      // core computation
      for (Vector v = dataEntry.getNextVector(); v != null; v = dataEntry.getNextVector()) {
        UserRatings ur = new UserRatings(v);
        for (RatingInfo ri = ur.getNormalNextRating(); ri != null; ri = ur.getNormalNextRating()) {
          float eui =
              ri.rating
                  - (this.avgrating
                      + userbias[ri.userId]
                      + itembias[ri.itemId]
                      + Utilities.innerProduct(userspace[ri.userId], itemspace[ri.itemId]));
          // perform gradient on user/item bias
          userbias[ri.userId] += learningSpeed * (eui - this.lambda * userbias[ri.userId]);
          itembias[ri.itemId] += learningSpeed * (eui - this.lambda * itembias[ri.itemId]);
          // perform gradient on pu/qi
          for (int f = 0; f < this.factor; f++) {
            userspace[ri.userId][f] =
                userspace[ri.userId][f]
                    + learningSpeed
                        * (eui * itemspace[ri.itemId][f] - this.lambda * userspace[ri.userId][f]);
            itemspace[ri.itemId][f] =
                itemspace[ri.itemId][f]
                    + learningSpeed
                        * (eui * userspace[ri.userId][f] - this.lambda * itemspace[ri.itemId][f]);
          }
          totalError += Math.abs(eui);
          n += 1;
        }
      }

      long timeSpent = System.currentTimeMillis() - timeStart;
      learningSpeed *= this.convergence;
      System.out.println(
          String.format(
              "loop:%d\ttime(ms):%d\tavgerror:%.6f\tnext alpha:%.5f",
              loop, timeSpent, (totalError / n), learningSpeed));
      //			System.out.print("loop " + loop + " finished~  Time spent: " + (timeSpent / 1000.0) + "
      // next speed :" + learningSpeed);
      //			System.out.println(" total training ratings = " + n);
    }
    dataEntry.close();
  }
Exemple #3
0
 protected float predict(int userId, int itemId) {
   return this.avgrating
       + userbias[userId]
       + itembias[itemId]
       + Utilities.innerProduct(userspace[userId], itemspace[itemId]);
 }