Package org.apache.mahout.math.als

Examples of org.apache.mahout.math.als.AlternateLeastSquaresSolver


  }

  @Override
  public Factorization factorize() throws TasteException {
    log.info("starting to compute the factorization...");
    final AlternateLeastSquaresSolver solver = new AlternateLeastSquaresSolver();
    final Features features = new Features(this);

    for (int iteration = 0; iteration < numIterations; iteration++) {
      log.info("iteration {}", iteration);

      /* fix M - compute U */
      ExecutorService queue = createQueue();
      LongPrimitiveIterator userIDsIterator = dataModel.getUserIDs();
      try {
        while (userIDsIterator.hasNext()) {
          final long userID = userIDsIterator.nextLong();
          final LongPrimitiveIterator itemIDsFromUser = dataModel.getItemIDsFromUser(userID).iterator();
          final PreferenceArray userPrefs = dataModel.getPreferencesFromUser(userID);
          queue.execute(new Runnable() {
            @Override
            public void run() {
              List<Vector> featureVectors = new ArrayList<Vector>();
              while (itemIDsFromUser.hasNext()) {
                long itemID = itemIDsFromUser.nextLong();
                featureVectors.add(features.getItemFeatureColumn(itemIndex(itemID)));
              }
              Vector userFeatures = solver.solve(featureVectors, ratingVector(userPrefs), lambda, numFeatures);
              features.setFeatureColumnInU(userIndex(userID), userFeatures);
            }
          });
        }
      } finally {
        queue.shutdown();
        try {
          queue.awaitTermination(dataModel.getNumUsers(), TimeUnit.SECONDS);
        } catch (InterruptedException e) {
          throw new IllegalStateException("Error when computing user features", e);
        }
      }

      /* fix U - compute M */
      queue = createQueue();
      LongPrimitiveIterator itemIDsIterator = dataModel.getItemIDs();
      try {
        while (itemIDsIterator.hasNext()) {
          final long itemID = itemIDsIterator.nextLong();
          final PreferenceArray itemPrefs = dataModel.getPreferencesForItem(itemID);
          queue.execute(new Runnable() {
            @Override
            public void run() {
              List<Vector> featureVectors = new ArrayList<Vector>();
              for (Preference pref : itemPrefs) {
                long userID = pref.getUserID();
                featureVectors.add(features.getUserFeatureColumn(userIndex(userID)));
              }
              Vector itemFeatures = solver.solve(featureVectors, ratingVector(itemPrefs), lambda, numFeatures);
              features.setFeatureColumnInM(itemIndex(itemID), itemFeatures);
            }
          });
        }
      } finally {
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      lambda = Double.parseDouble(ctx.getConfiguration().get(LAMBDA));
      numFeatures = ctx.getConfiguration().getInt(NUM_FEATURES, -1);
      if (numFeatures < 1) {
        throw new IllegalStateException("numFeatures was not set correctly!");
      }
      solver = new AlternateLeastSquaresSolver();
    }
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  @Test
  public void solvingReducer() throws Exception {

    AlternateLeastSquaresSolver solver = new AlternateLeastSquaresSolver();

    int numFeatures = 2;
    double lambda = 0.01;
    Vector ratings = new DenseVector(new double[] { 2, 1 });
    Vector col1 = new DenseVector(new double[] { 1, 2 });
    Vector col2 = new DenseVector(new double[] { 3, 4 });

    Vector result = solver.solve(Arrays.asList(col1, col2), ratings, lambda, numFeatures);
    Vector.Element[] elems = new Vector.Element[result.size()];
    for (int n = 0; n < result.size(); n++) {
      elems[n] = result.getElement(n);
    }
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