Examples of org.apache.mahout.common.distance.DistanceMeasure.distance()

Package org.apache.mahout.common.distance

Class org.apache.mahout.common.distance.DistanceMeasure

Examples of org.apache.mahout.common.distance.DistanceMeasure.distance()

org.apache.mahout.common.distance.DistanceMeasure.distance()
Returns the distance metric applied to the arguments @param v1 a Vector defining a multidimensional point in some feature space @param v2 a Vector defining a multidimensional point in some feature space @return a scalar doubles of the distance

    c_1.setIndex(0);
    // Construct a set of weighted things which can be used for random selection.  Initial weights are
    // set to the squared distance from c_1
    for (int i = 0; i < datapoints.size(); ++i) {
      WeightedVector row = datapoints.get(i);
      double w = distanceMeasure.distance(c_1, row) * 2 * Math.log(1 + row.getWeight());
      seedSelector.set(i, w);
    }


    // From here, seeds are selected with probability proportional to:
    //

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      // Don't select this one again.
      seedSelector.delete(seedIndex);
      // Re-weight everything according to the minimum distance to a seed.
      for (int currSeedIndex : seedSelector) {
        WeightedVector curr = datapoints.get(currSeedIndex);
        double newWeight = nextSeed.getWeight() * distanceMeasure.distance(nextSeed, curr);
        if (newWeight < seedSelector.getWeight(currSeedIndex)) {
          seedSelector.set(currSeedIndex, newWeight);
        }
      }
    }

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      // centroid.
      closestClusterDistances.clear();
      for (Vector center : centroids) {
        // If a centroid has no points assigned to it, the clustering failed.
        Vector closestOtherCluster = centroids.searchFirst(center, true).getValue();
        closestClusterDistances.add(distanceMeasure.distance(center, closestOtherCluster));
      }


      // Copies the current cluster centroids to newClusters and sets their weights to 0. This is
      // so we calculate the new centroids as we go through the datapoints.
      List<Centroid> newCentroids = Lists.newArrayList();

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    }
    Writer writer = Files.newWriter(new File(affinities.toString()), Charsets.UTF_8);
    try {
      for (int i = 0; i < SAMPLE_DATA.size(); i++) {
        for (int j = 0; j < SAMPLE_DATA.size(); j++) {
          writer.write(i + "," + j + ',' + measure.distance(SAMPLE_DATA.get(i).get(), SAMPLE_DATA.get(j).get()) + '\n');
        }
      }
    } finally {
      writer.close();
    }

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      int maxValueIndex) throws IOException {
    Cluster cluster = clusterModels.get(maxValueIndex);


    DistanceMeasureCluster distanceMeasureCluster = (DistanceMeasureCluster) cluster;
    DistanceMeasure distanceMeasure = distanceMeasureCluster.getMeasure();
    double distance = distanceMeasure.distance(cluster.getCenter(), weightedPropertyVectorWritable.getVector());


    weightedPropertyVectorWritable.getProperties().put(new Text("distance"), new Text(Double.toString(distance)));
    writer.append(new IntWritable(cluster.getId()), weightedPropertyVectorWritable);
  }

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    Cluster cluster = clusterModels.get(clusterIndex);
    clusterId.set(cluster.getId());


    DistanceMeasureCluster distanceMeasureCluster = (DistanceMeasureCluster) cluster;
    DistanceMeasure distanceMeasure = distanceMeasureCluster.getMeasure();
    double distance = distanceMeasure.distance(cluster.getCenter(), vw.get());


    Map<Text, Text> props = Maps.newHashMap();
    props.put(new Text("distance"), new Text(Double.toString(distance)));
    context.write(clusterId, new WeightedPropertyVectorWritable(weight, vw.get(), props));
  }

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    }
    Writer writer = Files.newWriter(new File(affinities.toString()), Charsets.UTF_8);
    try {
      for (int i = 0; i < SAMPLE_DATA.size(); i++) {
        for (int j = 0; j < SAMPLE_DATA.size(); j++) {
          writer.write(i + "," + j + ',' + measure.distance(SAMPLE_DATA.get(i).get(), SAMPLE_DATA.get(j).get()) + '\n');
        }
      }
    } finally {
      Closeables.closeQuietly(writer);
    }

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    Writer writer = null;
    try {
      writer = Files.newWriter(new File(affinities.toString()), Charsets.UTF_8);
      for (int i = 0; i < SAMPLE_DATA.size(); i++) {
        for (int j = 0; j < SAMPLE_DATA.size(); j++) {
          writer.write(i + "," + j + ',' + measure.distance(SAMPLE_DATA.get(i).get(), SAMPLE_DATA.get(j).get()) + '\n');
        }
      }
    } finally {
      Closeables.close(writer, false);
    }

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