Java Code Examples for org.apache.commons.math3.ml.distance.EuclideanDistance#compute()

@Override
@SuppressWarnings({"unchecked"})
public Object doWork(Object first, Object second) throws IOException{
  if(null == first){
    throw new IOException(String.format(Locale.ROOT,"Invalid expression %s - null found for the first value",toExpression(constructingFactory)));
  }
  if(null == second){
    throw new IOException(String.format(Locale.ROOT,"Invalid expression %s - null found for the second value",toExpression(constructingFactory)));
  }
  if(!(first instanceof List<?>)){
    throw new IOException(String.format(Locale.ROOT,"Invalid expression %s - found type %s for the first value, expecting a list of numbers",toExpression(constructingFactory), first.getClass().getSimpleName()));
  }
  if(!(second instanceof List<?>)){
    throw new IOException(String.format(Locale.ROOT,"Invalid expression %s - found type %s for the second value, expecting a list of numbers",toExpression(constructingFactory), first.getClass().getSimpleName()));
  }

  EuclideanDistance distance = new EuclideanDistance();
  return distance.compute(
    ((List)first).stream().mapToDouble(value -> ((BigDecimal)value).doubleValue()).toArray(),
    ((List)second).stream().mapToDouble(value -> ((BigDecimal)value).doubleValue()).toArray()
  );
}
 

private void iterate(){
	ArrayList<ClusterNode> temp = new ArrayList<ClusterNode>();
	EuclideanDistance ed = new EuclideanDistance();
	for (int i = 0; i < clusters.size();i++){
		double min = Double.POSITIVE_INFINITY;
		int best = -1;
		for (int a = 0; a < clusters.size();a++){
			if (i != a){
				double dis = ed.compute(clusters.get(i).getKey(), clusters.get(a).getKey());
				if (dis < min){
					min = dis;
					best = a;
				}
			}
		}
		
	}
}
 

private void initializeNeighborhood() {
  EuclideanDistance euclideanDistance = new EuclideanDistance();
  double[] x = new double[numberOfWeightVectors];
  int[] idx = new int[numberOfWeightVectors];

  for (int i = 0; i < numberOfWeightVectors; i++) {
    // calculate the distances based on weight vectors
    for (int j = 0; j < numberOfWeightVectors; j++) {
      x[j] = euclideanDistance.compute(weightVector[i], weightVector[j]);
      idx[j] = j;
    }

    // find 'niche' nearest neighboring subproblems
    minFastSort(x, idx, numberOfWeightVectors, neighborhoodSize);

    System.arraycopy(idx, 0, neighborhood[i], 0, neighborhoodSize);
  }
}
 

@Override
public void tune(DataSequence observedSeries,
                 DataSequence expectedSeries) throws Exception {
    // Compute the time-series of errors.
    HashMap<String, ArrayList<Float>> allErrors = aes.initAnomalyErrors(observedSeries, expectedSeries);
    List<IdentifiedDoublePoint> points = new ArrayList<IdentifiedDoublePoint>();
    EuclideanDistance ed = new EuclideanDistance();
    int n = observedSeries.size();
    
    for (int i = 0; i < n; i++) {
        double[] d = new double[(aes.getIndexToError().keySet()).size()];
       
        for (int e = 0; e < (aes.getIndexToError().keySet()).size(); e++) {
             d[e] = allErrors.get(aes.getIndexToError().get(e)).get(i);
        }
        points.add(new IdentifiedDoublePoint(d, i));
    }
    
    double sum = 0.0;
    double count = 0.0;
    for (int i = 0; i < n; i++) {
        for (int j = 0; j < n; j++) {
            sum += ed.compute(points.get(i).getPoint(), points.get(j).getPoint());
            count++;
        }
    }
    eps = ((double) this.sDAutoSensitivity) * (sum / count);   
    minPoints = ((int) Math.ceil(((double) this.amntAutoSensitivity) * ((double) n)));     
    dbscan = new DBSCANClusterer<IdentifiedDoublePoint>(eps, minPoints);
}