Package org.apache.mahout.common.distance

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

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      Map<String,Cluster> clusterMap = loadClusterMap(clusters);
      for (String key : collector.getKeys()) {
        Cluster cluster = clusterMap.get(key);
        List<KMeansInfo> values = collector.getValue(key);
        for (KMeansInfo value : values) {
          double distance = euclideanDistanceMeasure.distance(cluster.getCenter(), value.getPointTotal());
          for (Cluster c : clusters) {
            assertTrue("distance error", distance <= euclideanDistanceMeasure.distance(value.getPointTotal(),
              c.getCenter()));
          }
        }
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        Cluster cluster = clusterMap.get(key);
        List<KMeansInfo> values = collector.getValue(key);
        for (KMeansInfo value : values) {
          double distance = euclideanDistanceMeasure.distance(cluster.getCenter(), value.getPointTotal());
          for (Cluster c : clusters) {
            assertTrue("distance error", distance <= euclideanDistanceMeasure.distance(value.getPointTotal(),
              c.getCenter()));
          }
        }
      }
    }
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      // proportional distance threshold for points that should be involved in calculating the
      // centroid.
      closestClusterDistances.clear();
      for (Vector center : centroids) {
        Vector closestOtherCluster = centroids.search(center, 2).get(1).getValue();
        closestClusterDistances.add(l2.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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      Map<String, Cluster> clusterMap = loadClusterMap(clusters);
      for (Text key : mapWriter.getKeys()) {
        AbstractCluster cluster = clusterMap.get(key.toString());
        List<ClusterObservations> values = mapWriter.getValue(key);
        for (ClusterObservations value : values) {
          double distance = measure.distance(cluster.getCenter(), value.getS1());
          for (AbstractCluster c : clusters) {
            assertTrue("distance error", distance <= measure.distance(value.getS1(), c.getCenter()));
          }
        }
      }
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        AbstractCluster cluster = clusterMap.get(key.toString());
        List<ClusterObservations> values = mapWriter.getValue(key);
        for (ClusterObservations value : values) {
          double distance = measure.distance(cluster.getCenter(), value.getS1());
          for (AbstractCluster c : clusters) {
            assertTrue("distance error", distance <= measure.distance(value.getS1(), c.getCenter()));
          }
        }
      }
    }
  }
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      Map<String, Cluster> clusterMap = loadClusterMap(clusters);
      for (Text key : mapWriter.getKeys()) {
        AbstractCluster cluster = clusterMap.get(key.toString());
        List<ClusterObservations> values = mapWriter.getValue(key);
        for (ClusterObservations value : values) {
          double distance = measure.distance(cluster.getCenter(), value.getS1());
          for (AbstractCluster c : clusters) {
            assertTrue("distance error", distance <= measure.distance(value.getS1(), c.getCenter()));
          }
        }
      }
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        AbstractCluster cluster = clusterMap.get(key.toString());
        List<ClusterObservations> values = mapWriter.getValue(key);
        for (ClusterObservations value : values) {
          double distance = measure.distance(cluster.getCenter(), value.getS1());
          for (AbstractCluster c : clusters) {
            assertTrue("distance error", distance <= measure.distance(value.getS1(), c.getCenter()));
          }
        }
      }
    }
  }
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      // now verify that all points are correctly allocated
      for (String key : collector.getKeys()) {
        Cluster cluster = clusterMap.get(key);
        List<KMeansInfo> values = collector.getValue(key);
        for (KMeansInfo value : values) {
          double distance = euclideanDistanceMeasure.distance(cluster
              .getCenter(), value.getPointTotal());
          for (Cluster c : clusters) {
            assertTrue("distance error", distance <= euclideanDistanceMeasure
                .distance(value.getPointTotal(), c.getCenter()));
          }
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        List<KMeansInfo> values = collector.getValue(key);
        for (KMeansInfo value : values) {
          double distance = euclideanDistanceMeasure.distance(cluster
              .getCenter(), value.getPointTotal());
          for (Cluster c : clusters) {
            assertTrue("distance error", distance <= euclideanDistanceMeasure
                .distance(value.getPointTotal(), c.getCenter()));
          }
        }
      }
    }
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      Map<String, Cluster> clusterMap = loadClusterMap(clusters);
      for (Text key : mapWriter.getKeys()) {
        AbstractCluster cluster = clusterMap.get(key.toString());
        List<ClusterObservations> values = mapWriter.getValue(key);
        for (ClusterObservations value : values) {
          double distance = measure.distance(cluster.getCenter(), value.getS1());
          for (AbstractCluster c : clusters) {
            assertTrue("distance error", distance <= measure.distance(value.getS1(), c.getCenter()));
          }
        }
      }
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