Examples of WeightedVectorWritable

Examples of org.apache.mahout.clustering.WeightedVectorWritable

      if ((distance < nearestDistance) || (nearestCluster == null)) {
        nearestCluster = cluster;
        nearestDistance = distance;
      }
    }
    writer.append(new IntWritable(nearestCluster.getId()), new WeightedVectorWritable(1, point));
  }

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Examples of org.apache.mahout.clustering.WeightedVectorWritable

                              WeightedVectorWritable point,
                              DistanceMeasure measure,
                              Map<Integer, List<VectorWritable>> representativePoints,
                              Map<Integer, WeightedVectorWritable> mostDistantPoints) {
    int key = clusterId.get();
    WeightedVectorWritable currentMDP = mostDistantPoints.get(key);


    List<VectorWritable> repPoints = representativePoints.get(key);
    double totalDistance = 0.0;
    for (VectorWritable refPoint : repPoints) {
      totalDistance += measure.distance(refPoint.get(), point.getVector());
    }
    if (currentMDP == null || currentMDP.getWeight() < totalDistance) {
      mostDistantPoints.put(key, new WeightedVectorWritable(totalDistance, point.getVector().clone()));
    }
  }

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Examples of org.apache.mahout.clustering.WeightedVectorWritable

        Writable key = ClassUtils.instantiateAs(reader.getKeyClassName(), Writable.class);
        VectorWritable vw = ClassUtils.instantiateAs(reader.getValueClassName(), VectorWritable.class);
        while (reader.next(key, vw)) {
          Canopy closest = clusterer.findClosestCanopy(vw.get(), clusters);
          writer.append(new IntWritable(closest.getId()),
              new WeightedVectorWritable(1, vw.get()));
          vw = ClassUtils.instantiateAs(reader.getValueClassName(), VectorWritable.class);
        }
      } finally {
        Closeables.closeQuietly(reader);
        Closeables.closeQuietly(writer);

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Examples of org.apache.mahout.clustering.WeightedVectorWritable

    Map<Integer, List<WeightedVectorWritable>> clusterIdToPoints = getClusterIdToPoints();
    List<WeightedVectorWritable> points = clusterIdToPoints.get(value.getId());
    if (points != null) {
      writer.write("\tWeight : [props - optional]:  Point:\n\t");
      for (Iterator<WeightedVectorWritable> iterator = points.iterator(); iterator.hasNext(); ) {
        WeightedVectorWritable point = iterator.next();
        writer.write(String.valueOf(point.getWeight()));
        if (point instanceof WeightedPropertyVectorWritable) {
          WeightedPropertyVectorWritable tmp = (WeightedPropertyVectorWritable) point;
          Map<Text, Text> map = tmp.getProperties();
          writer.write(" : [");
          for (Map.Entry<Text, Text> entry : map.entrySet()) {
            writer.write(entry.getKey().toString());
            writer.write("=");
            writer.write(entry.getValue().toString());
          }
          writer.write("]");
        }


        writer.write(": ");


        writer.write(AbstractCluster.formatVector(point.getVector(), dictionary));
        if (iterator.hasNext()) {
          writer.write("\n\t");
        }
      }
      writer.write('\n');

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Examples of org.apache.mahout.clustering.WeightedVectorWritable

      if (distance < nearestDistance || nearestCluster == null) {
        nearestCluster = cluster;
        nearestDistance = distance;
      }
    }
    writer.append(new IntWritable(nearestCluster.getId()), new WeightedVectorWritable(1, point));
  }

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Examples of org.apache.mahout.clustering.WeightedVectorWritable

      List<WeightedVectorWritable> list = pointClusterInfo.get(clusterId);
      if (list == null) {
        list = Lists.newArrayList();
        pointClusterInfo.put(clusterId, list);
      }
      list.add(new WeightedVectorWritable(clusterPdf, point));
      double totalProb = 0;
      for (int i = 0; i < clusterList.size(); i++) {
        //SoftCluster cluster = clusterList.get(i);
        double probWeight = clusterer.computeProbWeight(clusterDistanceList.get(i), clusterDistanceList);
        totalProb += probWeight;

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Examples of org.apache.mahout.clustering.WeightedVectorWritable

  public void emitPointToClosestCanopy(Vector point,
                                       Iterable<Canopy> canopies,
                                       Mapper<?,?,IntWritable,WeightedVectorWritable>.Context context)
    throws IOException, InterruptedException {
    Canopy closest = findClosestCanopy(point, canopies);
    context.write(new IntWritable(closest.getId()), new WeightedVectorWritable(1, point));
    context.setStatus("Emit Closest Canopy ID:" + closest.getIdentifier());
  }

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Examples of org.apache.mahout.clustering.WeightedVectorWritable

        clusterId = clusters.get(i).getId();
        clusterPdf = pdf;
      }
    }
    // System.out.println("cluster-" + clusterId + ": " + ClusterBase.formatVector(point, null));
    context.write(new IntWritable(clusterId), new WeightedVectorWritable(clusterPdf, point));
  }

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Examples of org.apache.mahout.clustering.WeightedVectorWritable

    throws IOException, InterruptedException {
    for (int i = 0; i < clusters.size(); i++) {
      double pdf = pi.get(i);
      if (pdf > threshold) {
        // System.out.println("cluster-" + clusterId + ": " + ClusterBase.formatVector(point, null));
        context.write(new IntWritable(i), new WeightedVectorWritable(pdf, point));
      }
    }
  }

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Examples of org.apache.mahout.clustering.WeightedVectorWritable

    throws IOException {
    for (int i = 0; i < clusters.size(); i++) {
      double pdf = pi.get(i);
      if (pdf > threshold) {
        // System.out.println("cluster-" + clusterId + ": " + ClusterBase.formatVector(point, null));
        writer.append(new IntWritable(i), new WeightedVectorWritable(pdf, point));
      }
    }
  }

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