Examples of EuclideanDistanceMeasure

org.apache.mahout.common.distance.EuclideanDistanceMeasure
This class implements a Euclidean distance metric by summing the square root of the squared differences between each coordinate.
If you don't care about the true distance and only need the values for comparison, then the base class, {@link SquaredEuclideanDistanceMeasure}, will be faster since it doesn't do the actual square root of the squared differences.
org.apache.mahout.utils.EuclideanDistanceMeasure
This class implements a Euclidian distance metric by summing the square root of the squared differences between each coordinate

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

   * Story: User can exercise the reference implementation to verify that the test datapoints are clustered in
   * a reasonable manner.
   */
  @Test
  public void testReferenceImplementation() {
    MeanShiftCanopyClusterer clusterer = new MeanShiftCanopyClusterer(new EuclideanDistanceMeasure(), 4.0, 1.0, 0.5);
    List<MeanShiftCanopy> canopies = new ArrayList<MeanShiftCanopy>();
    // add all points to the canopies
    int nextCanopyId = 0;
    for (Vector aRaw : raw) {
      clusterer.mergeCanopy(new MeanShiftCanopy(aRaw, nextCanopyId++, euclideanDistanceMeasure), canopies);

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Examples of org.apache.mahout.utils.EuclideanDistanceMeasure

        + canopy.toString()));
  }


  public boolean shiftToMean() {
    Vector centroid = computeCentroid();
    converged = new EuclideanDistanceMeasure().distance(centroid, center) < convergenceDelta;
    center = centroid;
    numPoints = 1;
    pointTotal = centroid.copy();
    return converged;
  }

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