Package org.apache.mahout.clustering

Examples of org.apache.mahout.clustering.ClusterObservations

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  @Test
  public void testClusterObservationsSerialization() throws Exception {
    double[] data = { 1.1, 2.2, 3.3 };
    Vector vector = new DenseVector(data);
    ClusterObservations reference = new ClusterObservations(1, 2.0, vector, vector);
    DataOutputBuffer out = new DataOutputBuffer();
    reference.write(out);
    ClusterObservations info = new ClusterObservations();
    DataInputBuffer in = new DataInputBuffer();
    in.reset(out.getData(), out.getLength());
    info.readFields(in);
    assertEquals("probability", reference.getS0(), info.getS0(), EPSILON);
    assertEquals("point total", reference.getS1(), info.getS1());
    assertEquals("combiner", reference.getCombinerState(), info.getCombinerState());
  }
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      if ((distance < nearestDistance) || (nearestCluster == null)) {
        nearestCluster = cluster;
        nearestDistance = distance;
      }
    }
    context.write(new Text(nearestCluster.getIdentifier()), new ClusterObservations(1, point, point.times(point)));
  }
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      if (distance < nearestDistance || nearestCluster == null) {
        nearestCluster = cluster;
        nearestDistance = distance;
      }
    }
    context.write(new Text(nearestCluster.getIdentifier()), new ClusterObservations(1, point, point.times(point)));
  }
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  @Test
  public void testClusterObservationsSerialization() throws Exception {
    double[] data = { 1.1, 2.2, 3.3 };
    Vector vector = new DenseVector(data);
    ClusterObservations reference = new ClusterObservations(1, 2.0, vector, vector);
    DataOutputBuffer out = new DataOutputBuffer();
    reference.write(out);
    ClusterObservations info = new ClusterObservations();
    DataInputBuffer in = new DataInputBuffer();
    in.reset(out.getData(), out.getLength());
    info.readFields(in);
    assertEquals("probability", reference.getS0(), info.getS0(), EPSILON);
    assertEquals("point total", reference.getS1(), info.getS1());
    assertEquals("combiner", reference.getCombinerState(), info.getCombinerState());
  }
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    }

    for (int i = 0; i < clusters.size(); i++) {
      SoftCluster cluster = clusters.get(i);
      Text key = new Text(cluster.getIdentifier());
      ClusterObservations value =
          new ClusterObservations(computeProbWeight(clusterDistanceList.get(i), clusterDistanceList),
                                  point,
                                  point.times(point));
      context.write(key, value);
    }
  }
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      int count = 0;
      Vector total = new DenseVector(2);
      for (Text key : combinerWriter.getKeys()) {
        List<ClusterObservations> values = combinerWriter.getValue(key);
        assertEquals("too many values", 1, values.size());
        ClusterObservations info = values.get(0);

        count += (int) info.getS0();
        total = total.plus(info.getS1());
      }
      assertEquals("total points", 9, count);
      assertEquals("point total[0]", 27, (int) total.get(0));
      assertEquals("point total[1]", 27, (int) total.get(1));
    }
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    }

    for (int i = 0; i < clusters.size(); i++) {
      SoftCluster cluster = clusters.get(i);
      Text key = new Text(cluster.getIdentifier());
      ClusterObservations value =
          new ClusterObservations(computeProbWeight(clusterDistanceList.get(i), clusterDistanceList),
                                  point,
                                  point.times(point));
      context.write(key, value);
    }
  }
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      int count = 0;
      Vector total = new DenseVector(2);
      for (Text key : combinerWriter.getKeys()) {
        List<ClusterObservations> values = combinerWriter.getValue(key);
        assertEquals("too many values", 1, values.size());
        ClusterObservations info = values.get(0);

        count += info.getS0();
        total = total.plus(info.getS1());
      }
      assertEquals("total points", 9, count);
      assertEquals("point total[0]", 27, (int) total.get(0));
      assertEquals("point total[1]", 27, (int) total.get(1));
    }
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      int count = 0;
      Vector total = new DenseVector(2);
      for (Text key : combinerWriter.getKeys()) {
        List<ClusterObservations> values = combinerWriter.getValue(key);
        assertEquals("too many values", 1, values.size());
        ClusterObservations info = values.get(0);

        count += info.getS0();
        total = total.plus(info.getS1());
      }
      assertEquals("total points", 9, count);
      assertEquals("point total[0]", 27, (int) total.get(0));
      assertEquals("point total[1]", 27, (int) total.get(1));
    }
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  @Test
  public void testClusterObservationsSerialization() throws Exception {
    double[] data = { 1.1, 2.2, 3.3 };
    Vector vector = new DenseVector(data);
    ClusterObservations reference = new ClusterObservations(1, 2.0, vector, vector);
    DataOutputBuffer out = new DataOutputBuffer();
    reference.write(out);
    ClusterObservations info = new ClusterObservations();
    DataInputBuffer in = new DataInputBuffer();
    in.reset(out.getData(), out.getLength());
    info.readFields(in);
    assertEquals("probability", reference.getS0(), info.getS0(), EPSILON);
    assertEquals("point total", reference.getS1(), info.getS1());
    assertEquals("combiner", reference.getCombinerState(), info.getCombinerState());
  }
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Related Classes of org.apache.mahout.clustering.ClusterObservations

  • org.apache.mahout.clustering.fuzzykmeans.FuzzyKMeansClusterer
  • org.apache.mahout.clustering.fuzzykmeans.TestFuzzyKmeansClustering
  • org.apache.mahout.clustering.kmeans.KMeansClusterer
  • org.apache.mahout.clustering.kmeans.TestKmeansClustering
  • org.apache.mahout.math.VectorWritable
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