Package org.apache.mahout.cf.taste.model

Examples of org.apache.mahout.cf.taste.model.DataModel

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    double correlation = new EuclideanDistanceSimilarity(dataModel).userSimilarity(1, 2);
    assertCorrelationEquals(1.0, correlation);
  }

  public void testFullCorrelation1Weighted() throws Exception {
    DataModel dataModel = getDataModel(
            new long[] {1, 2},
            new Double[][] {
                    {3.0, -2.0},
                    {3.0, -2.0},
            });
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    assertNotNull(mostSimilar);
    assertEquals(0, mostSimilar.length);
  }

  private static UserBasedRecommender buildRecommender() throws TasteException {
    DataModel dataModel = getDataModel();
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(1, similarity, dataModel);
    return new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
  }
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    }
  }

  private void writeDebugRecommendations(long userID, Iterable<RecommendedItem> items, PrintWriter writer)
      throws TasteException {
    DataModel dataModel = recommender.getDataModel();
    writer.print("User:");
    writer.println(userID);
    writer.print("Recommender: ");
    writer.println(recommender);
    writer.println();
    writer.print("Top ");
    writer.print(NUM_TOP_PREFERENCES);
    writer.println(" Preferences:");
    PreferenceArray rawPrefs = dataModel.getPreferencesFromUser(userID);
    int length = rawPrefs.length();
    PreferenceArray sortedPrefs = rawPrefs.clone();
    sortedPrefs.sortByValueReversed();
    // Cap this at NUM_TOP_PREFERENCES just to be brief
    int max = Math.min(NUM_TOP_PREFERENCES, length);
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    assertEquals(0.18, firstRecommended.getValue(), EPSILON);
  }

  public void testHowMany() throws Exception {

    DataModel dataModel = getDataModel(
            new long[] {1, 2, 3, 4, 5},
            new Double[][] {
                    {0.1, 0.2},
                    {0.2, 0.3, 0.3, 0.6},
                    {0.4, 0.4, 0.5, 0.9},
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    }
  }

  public void testRescorer() throws Exception {

    DataModel dataModel = getDataModel(
            new long[] {1, 2, 3},
            new Double[][] {
                    {0.1, 0.2},
                    {0.2, 0.3, 0.3, 0.6},
                    {0.4, 0.4, 0.5, 0.9},
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    assertEquals(0, third.getItemID());
    assertEquals(0.2, third.getValue(), EPSILON);
  }

  private static ItemBasedRecommender buildRecommender() {
    DataModel dataModel = getDataModel();
    Collection<GenericItemSimilarity.ItemItemSimilarity> similarities =
        new ArrayList<GenericItemSimilarity.ItemItemSimilarity>(3);
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(0, 1, 1.0));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(0, 2, 0.5));
    similarities.add(new GenericItemSimilarity.ItemItemSimilarity(1, 2, 0.0));
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    return new GenericItemBasedRecommender(dataModel, similarity);
  }

  private static ItemBasedRecommender buildRecommender2() {

    DataModel dataModel = getDataModel(
        new long[] {1, 2, 3, 4},
        new Double[][] {
                {0.1, 0.3, 0.9, 0.8},
                {0.2, 0.3, 0.3, 0.4},
                {0.4, 0.3, 0.5, 0.1, 0.1},
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    double correlation = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED).userSimilarity(1, 2);
    assertCorrelationEquals(1.0, correlation);
  }

  public void testFullCorrelation2() throws Exception {
    DataModel dataModel = getDataModel(
            new long[] {1, 2},
            new Double[][] {
                    {3.0, 3.0},
                    {3.0, 3.0},
            });
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    double correlation = new EuclideanDistanceSimilarity(dataModel).userSimilarity(1, 2);
    assertTrue(Double.isNaN(correlation));
  }

  public void testNoCorrelation1() throws Exception {
    DataModel dataModel = getDataModel(
            new long[] {1, 2},
            new Double[][] {
                    {3.0, -2.0},
                    {-3.0, 2.0},
            });
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    double correlation = new EuclideanDistanceSimilarity(dataModel).userSimilarity(1, 2);
    assertCorrelationEquals(0.424465381883345, correlation);
  }

  public void testNoCorrelation1Weighted() throws Exception {
    DataModel dataModel = getDataModel(
            new long[] {1, 2},
            new Double[][] {
                    {3.0, -2.0},
                    {-3.0, 2.0},
            });
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  • com.manning.hip.ch9.MovieUserRecommender
  • io.prediction.examples.java.recommendations.tutorial5.MahoutAlgorithm
  • mia.recommender.ch02.EvaluatorIntro
  • mia.recommender.ch02.IREvaluatorIntro
  • mia.recommender.ch02.ItemBaseRecommender
  • mia.recommender.ch02.ItemBaseRecommenderEvaluation
  • mia.recommender.ch02.UserBaseRecommender
  • mia.recommender.ch02.UserBaseRecommenderEvaluation
  • mia.recommender.ch03.CreateGenericDataModel
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