Examples of Canopy

com.aamend.hadoop.clustering.cluster.Canopy
Author: antoine.amend@gmail.com Date: 21/03/14
org.apache.mahout.clustering.canopy.Canopy
This class models a canopy as a center point, the number of points that are contained within it according to the application of some distance metric, and a point total which is the sum of all the points and is used to compute the centroid when needed.

Examples of com.aamend.hadoop.clustering.cluster.Canopy

            double dist = measure.distance(canopy.getCenter(), point);
            if (dist < t1) {
                KEY.set(Arrays.toString(canopy.getCenter()));
                Cluster newCluster;
                if (dist < t2) {
                    newCluster = new Canopy(canopy.getId(), point, value.get().getNum());
                    LOGGER.debug("Adding (T2) {} to Cluster {}", Arrays.toString(point),
                            Arrays.toString(canopy.getCenter()));
                } else {
                    newCluster = new Canopy(canopy.getId(), point, 0L);
                    LOGGER.debug("Adding (T1) {} to Cluster {}", Arrays.toString(point),
                            Arrays.toString(canopy.getCenter()));
                }


                context.write(KEY, new CanopyWritable(newCluster));
            }


            stronglyBound = stronglyBound || dist < t2;
        }
        if (!stronglyBound) {
            nextCanopyId++;
            Cluster canopy = new Canopy(nextCanopyId, point, value.get().getNum());
            canopies.add(canopy);
            LOGGER.debug("Creating a new Cluster {}", canopy.asFormattedString());
            KEY.set(Arrays.toString(canopy.getCenter()));
            context.write(KEY, value);
        }


    }

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Examples of org.apache.mahout.clustering.canopy.Canopy

  }


  public void testCanopyAsFormatString() {
    double[] d = { 1.1, 2.2, 3.3 };
    Vector m = new DenseVector(d);
    Printable cluster = new Canopy(m, 123);
    String formatString = cluster.asFormatString(null);
    System.out.println(formatString);
    assertEquals("format", "C123: [1.100, 2.200, 3.300]", formatString);
  }

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Examples of org.apache.mahout.clustering.canopy.Canopy


  public void testCanopyAsFormatStringSparse() {
    double[] d = { 1.1, 0.0, 3.3 };
    Vector m = new SequentialAccessSparseVector(3);
    m.assign(d);
    Printable cluster = new Canopy(m, 123);
    String formatString = cluster.asFormatString(null);
    System.out.println(formatString);
    assertEquals("format", "C123: [0:1.100, 2:3.300]", formatString);
  }

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Examples of org.apache.mahout.clustering.canopy.Canopy

  }


  public void testCanopyAsFormatStringWithBindings() {
    double[] d = { 1.1, 2.2, 3.3 };
    Vector m = new DenseVector(d);
    Printable cluster = new Canopy(m, 123);
    String[] bindings = { "fee", null, null };
    String formatString = cluster.asFormatString(bindings);
    System.out.println(formatString);
    assertEquals("format", "C123: [fee:1.100, 1:2.200, 2:3.300]", formatString);
  }

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Examples of org.apache.mahout.clustering.canopy.Canopy


  public void testCanopyAsFormatStringSparseWithBindings() {
    double[] d = { 1.1, 0.0, 3.3 };
    Vector m = new SequentialAccessSparseVector(3);
    m.assign(d);
    Printable cluster = new Canopy(m, 123);
    String formatString = cluster.asFormatString(null);
    System.out.println(formatString);
    assertEquals("format", "C123: [0:1.100, 2:3.300]", formatString);
  }

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Examples of org.apache.mahout.clustering.canopy.Canopy


  @Test
  public void testCanopyAsFormatString() {
    double[] d = { 1.1, 2.2, 3.3 };
    Vector m = new DenseVector(d);
    Cluster cluster = new Canopy(m, 123, measure);
    String formatString = cluster.asFormatString(null);
    System.out.println(formatString);
    assertEquals("format", "C-123{n=0 c=[1.100, 2.200, 3.300] r=[]}", formatString);
  }

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Examples of org.apache.mahout.clustering.canopy.Canopy

  @Test
  public void testCanopyAsFormatStringSparse() {
    double[] d = { 1.1, 0.0, 3.3 };
    Vector m = new SequentialAccessSparseVector(3);
    m.assign(d);
    Cluster cluster = new Canopy(m, 123, measure);
    String formatString = cluster.asFormatString(null);
    System.out.println(formatString);
    assertEquals("format", "C-123{n=0 c=[0:1.100, 2:3.300] r=[]}", formatString);
  }

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Examples of org.apache.mahout.clustering.canopy.Canopy


  @Test
  public void testCanopyAsFormatStringWithBindings() {
    double[] d = { 1.1, 2.2, 3.3 };
    Vector m = new DenseVector(d);
    Cluster cluster = new Canopy(m, 123, measure);
    String[] bindings = { "fee", null, null };
    String formatString = cluster.asFormatString(bindings);
    System.out.println(formatString);
    assertEquals("format", "C-123{n=0 c=[fee:1.100, 1:2.200, 2:3.300] r=[]}", formatString);
  }

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Examples of org.apache.mahout.clustering.canopy.Canopy

  @Test
  public void testCanopyAsFormatStringSparseWithBindings() {
    double[] d = { 1.1, 0.0, 3.3 };
    Vector m = new SequentialAccessSparseVector(3);
    m.assign(d);
    Cluster cluster = new Canopy(m, 123, measure);
    String formatString = cluster.asFormatString(null);
    System.out.println(formatString);
    assertEquals("format", "C-123{n=0 c=[0:1.100, 2:3.300] r=[]}", formatString);
  }

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Examples of org.apache.mahout.clustering.canopy.Canopy


  @Test
  public void testCanopyClassification() {
    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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