Package org.apache.mahout.classifier

Examples of org.apache.mahout.classifier.AbstractVectorClassifier

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        URL modelUrl = new URL(url);
        boolean needUpdate = false;
        if (currentUrl == null || latestVersion != version) {
          log.warn("Loading model from " + modelUrl);

          AbstractVectorClassifier model = ModelSerializer
              .readBinary(modelUrl.openStream(),
                  OnlineLogisticRegression.class);

          modelHandler.setModel(model);
          currentUrl = url;
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    List<Model<VectorWritable>> models = new ArrayList<Model<VectorWritable>>();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new Cluster(new DenseVector(2).assign(1), 0, measure));
    models.add(new Cluster(new DenseVector(2), 1, measure));
    models.add(new Cluster(new DenseVector(2).assign(-1), 2, measure));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.107, 0.787, 0.107]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.867, 0.117, 0.016]", AbstractCluster.formatVector(pdf, null));
  }
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    List<Model<VectorWritable>> models = new ArrayList<Model<VectorWritable>>();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new MeanShiftCanopy(new DenseVector(2).assign(1), 0, measure));
    models.add(new MeanShiftCanopy(new DenseVector(2), 1, measure));
    models.add(new MeanShiftCanopy(new DenseVector(2).assign(-1), 2, measure));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    try {
      classifier.classify(new DenseVector(2));
      fail("Expected NotImplementedException");
    } catch (NotImplementedException e) {
    }
  }
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    List<Model<VectorWritable>> models = new ArrayList<Model<VectorWritable>>();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new SoftCluster(new DenseVector(2).assign(1), 0, measure));
    models.add(new SoftCluster(new DenseVector(2), 1, measure));
    models.add(new SoftCluster(new DenseVector(2).assign(-1), 2, measure));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.000, 1.000, 0.000]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.735, 0.184, 0.082]", AbstractCluster.formatVector(pdf, null));
  }
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  public void testGaussianClusterClassification() {
    List<Model<VectorWritable>> models = new ArrayList<Model<VectorWritable>>();
    models.add(new GaussianCluster(new DenseVector(2).assign(1), new DenseVector(2).assign(1), 0));
    models.add(new GaussianCluster(new DenseVector(2), new DenseVector(2).assign(1), 1));
    models.add(new GaussianCluster(new DenseVector(2).assign(-1), new DenseVector(2).assign(1), 2));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.274, 0.452, 0.274]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.806, 0.180, 0.015]", AbstractCluster.formatVector(pdf, null));
  }
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  public void testASNClusterClassification() {
    List<Model<VectorWritable>> models = new ArrayList<Model<VectorWritable>>();
    models.add(new AsymmetricSampledNormalModel(0, new DenseVector(2).assign(1), new DenseVector(2).assign(1)));
    models.add(new AsymmetricSampledNormalModel(1, new DenseVector(2), new DenseVector(2).assign(1)));
    models.add(new AsymmetricSampledNormalModel(2, new DenseVector(2).assign(-1), new DenseVector(2).assign(1)));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.212, 0.576, 0.212]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.952, 0.047, 0.000]", AbstractCluster.formatVector(pdf, null));
  }
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    List<Model<VectorWritable>> models = new ArrayList<Model<VectorWritable>>();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new DistanceMeasureCluster(new DenseVector(2).assign(1), 0, measure));
    models.add(new DistanceMeasureCluster(new DenseVector(2), 1, measure));
    models.add(new DistanceMeasureCluster(new DenseVector(2).assign(-1), 2, measure));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.107, 0.787, 0.107]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.867, 0.117, 0.016]", AbstractCluster.formatVector(pdf, null));
  }
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    List<Model<VectorWritable>> models = new ArrayList<Model<VectorWritable>>();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new Canopy(new DenseVector(2).assign(1), 0, measure));
    models.add(new Canopy(new DenseVector(2), 1, measure));
    models.add(new Canopy(new DenseVector(2).assign(-1), 2, measure));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.107, 0.787, 0.107]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.867, 0.117, 0.016]", AbstractCluster.formatVector(pdf, null));
  }
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    trainNaiveBayes.run(new String[] { "--input", inputFile.getAbsolutePath(), "--output", outputDir.getAbsolutePath(),
        "--labels", "stolen,not_stolen", "--tempDir", tempDir.getAbsolutePath() });

    NaiveBayesModel naiveBayesModel = NaiveBayesModel.materialize(new Path(outputDir.getAbsolutePath()), conf);

    AbstractVectorClassifier classifier = new StandardNaiveBayesClassifier(naiveBayesModel);

    assertEquals(2, classifier.numCategories());

    Vector prediction = classifier.classify(trainingInstance(COLOR_RED, TYPE_SUV, ORIGIN_DOMESTIC).get());

    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
  }
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        "--labels", "stolen,not_stolen", "--trainComplementary",
        "--tempDir", tempDir.getAbsolutePath() });

    NaiveBayesModel naiveBayesModel = NaiveBayesModel.materialize(new Path(outputDir.getAbsolutePath()), conf);

    AbstractVectorClassifier classifier = new ComplementaryNaiveBayesClassifier(naiveBayesModel);

    assertEquals(2, classifier.numCategories());

    Vector prediction = classifier.classify(trainingInstance(COLOR_RED, TYPE_SUV, ORIGIN_DOMESTIC).get());

    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
  }
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Related Classes of org.apache.mahout.classifier.AbstractVectorClassifier

  • mia.classifier.ch16.server.Server$ServerWatcher
  • org.apache.mahout.classifier.naivebayes.NaiveBayesTest
  • org.apache.mahout.classifier.sgd.TrainLogisticTest
  • org.apache.mahout.clustering.TestVectorModelClassifier
  • org.apache.mahout.math.DenseMatrix
  • org.apache.mahout.math.DenseVector
  • org.apache.mahout.math.Matrix
  • org.apache.mahout.math.Vector
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