Example usage for org.apache.commons.math.util MathUtils distance

List of usage examples for org.apache.commons.math.util MathUtils distance

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Introduction

In this page you can find the example usage for org.apache.commons.math.util MathUtils distance.

Prototype

public static double distance(int[] p1, int[] p2)

Source Link

Document

Calculates the L2 (Euclidean) distance between two points.

Usage

From source file:br.upe.ecomp.doss.algorithm.apso.APSO.java

private double calculateMeanDistanceToOtherPaticles(int index) {
    double euclideanDistance = 0;
    for (int i = 0; i < getParticles().length; i++) {
        if (index != i) {
            euclideanDistance += MathUtils.distance(getParticles()[index].getCurrentPosition(),
                    getParticles()[i].getCurrentPosition());
        }//from  w  w  w  . j  ava 2 s .  c o  m
    }
    return euclideanDistance / (getParticles().length - 1);
}

From source file:acromusashi.stream.ml.clustering.kmeans.KmeansCalculator.java

/**
 * ???????/*w  w  w .  j a va 2s.  c  om*/
 * 
 * @param basePoints ??
 * @param newPoints ?
 * @param convergenceThres ??
 * @return ?????true?????????false
 */
public static boolean isConvergenced(List<KmeansPoint> basePoints, List<KmeansPoint> newPoints,
        double convergenceThres) {
    boolean result = true;

    for (int pointIndex = 0; pointIndex < basePoints.size(); pointIndex++) {
        double distance = MathUtils.distance(basePoints.get(pointIndex).getDataPoint(),
                newPoints.get(pointIndex).getDataPoint());
        if (distance > convergenceThres) {
            result = false;
            break;
        }
    }

    return result;
}

From source file:br.upe.ecomp.doss.algorithm.volitivepso.VolitivePSO.java

private void performCollectiveVolitiveMovement() {

    RandomData random = new RandomDataImpl();
    double[] barycenter = calculateBarycenter();

    double actualSwarmWeight = getSwarmWeigth();

    double euclidianDistance;
    double[] position;
    double currentPosition;

    WeightedPSOParticle[] particles = (WeightedPSOParticle[]) getParticles();
    for (WeightedPSOParticle particle : particles) {

        euclidianDistance = MathUtils.distance(particle.getCurrentPosition(), barycenter);
        position = new double[getDimensions()];

        for (int i = 0; i < getDimensions(); i++) {
            currentPosition = particle.getCurrentPosition()[i];

            if (actualSwarmWeight > previousSwarmWeight) {

                position[i] = currentPosition - stepVol[i] * random.nextUniform(0.0, 1.0)
                        * ((currentPosition - barycenter[i]) / euclidianDistance);
            } else {
                position[i] = currentPosition + stepVol[i] * random.nextUniform(0.0, 1.0)
                        * ((currentPosition - barycenter[i]) / euclidianDistance);
            }//from  w  ww.  ja v  a 2s  .  co m
            if (position[i] > getProblem().getUpperBound(i) || position[i] < getProblem().getLowerBound(i)) {
                position[i] = currentPosition;
            }
        }

        // if (getProblem().compareFitness(fish.getCurrentFitness(),
        // getProblem().getFitness(position))) {
        particle.updateCurrentPosition(position, getProblem().getFitness(position));

        if (getProblem().isFitnessBetterThan(particle.getBestFitness(), particle.getCurrentFitness())) {
            particle.updateBestPosition(particle.getCurrentPosition(), particle.getCurrentFitness());
        }
        // }
    }

    previousSwarmWeight = actualSwarmWeight;
}

From source file:br.upe.ecomp.doss.algorithm.volitiveapso.VolitiveAPSO.java

private void performCollectiveVolitiveMovement() {
    RandomData random = new RandomDataImpl();
    double[] barycenter = calculateBarycenter();

    double actualSwarmWeight = getSwarmWeigth();

    double euclidianDistance;
    double[] position;
    double currentPosition;

    WeightedAPSOParticle[] particles = (WeightedAPSOParticle[]) getParticles();
    for (WeightedAPSOParticle particle : particles) {

        euclidianDistance = MathUtils.distance(particle.getCurrentPosition(), barycenter);
        position = new double[getDimensions()];

        for (int i = 0; i < getDimensions(); i++) {
            currentPosition = particle.getCurrentPosition()[i];

            if (actualSwarmWeight > previousSwarmWeight) {

                position[i] = currentPosition - stepVol[i] * random.nextUniform(0.0, 1.0)
                        * ((currentPosition - barycenter[i]) / euclidianDistance);
            } else {
                position[i] = currentPosition + stepVol[i] * random.nextUniform(0.0, 1.0)
                        * ((currentPosition - barycenter[i]) / euclidianDistance);
            }//from  w  w w . j a  v a2s  .  c  o m
            if (position[i] > getProblem().getUpperBound(i) || position[i] < getProblem().getLowerBound(i)) {
                position[i] = currentPosition;
            }
        }

        // if (getProblem().compareFitness(fish.getCurrentFitness(),
        // getProblem().getFitness(position))) {
        particle.updateCurrentPosition(position, getProblem().getFitness(position));

        if (getProblem().isFitnessBetterThan(particle.getBestFitness(), particle.getCurrentFitness())) {
            particle.updateBestPosition(particle.getCurrentPosition(), particle.getCurrentFitness());
        }
        // }
    }

    previousSwarmWeight = actualSwarmWeight;
}

From source file:acromusashi.stream.ml.clustering.kmeans.KmeansCalculator.java

/**
 * ????????????/*from   w w  w . jav a  2s .c  o  m*/
 * 
 * @param targetPoint ?
 * @param centroids ?
 * @return Kmeans?
 */
public static KmeansResult nearestCentroid(double[] targetPoint, double[][] centroids) {
    int nearestCentroidIndex = 0;
    Double minDistance = Double.MAX_VALUE;
    double[] currentCentroid = null;
    Double currentDistance;
    for (int index = 0; index < centroids.length; index++) {
        currentCentroid = centroids[index];
        if (currentCentroid != null) {
            currentDistance = MathUtils.distance(targetPoint, currentCentroid);
            if (currentDistance < minDistance) {
                minDistance = currentDistance;
                nearestCentroidIndex = index;
            }
        }
    }

    currentCentroid = centroids[nearestCentroidIndex];

    KmeansResult result = new KmeansResult();
    result.setDataPoint(targetPoint);
    result.setCentroidIndex(nearestCentroidIndex);
    result.setCentroid(currentCentroid);
    result.setDistance(minDistance);

    return result;
}

From source file:br.upe.ecomp.doss.algorithm.fss.FSS.java

private void performCollectiveVolitiveMovement() {
    RandomData random = new RandomDataImpl();
    double[] barycenter = calculateBarycenter();

    double actualFishSchoolWeight = getFishSchoolWeigth();

    double euclidianDistance;
    double[] position;
    double currentPosition;
    for (Fish fish : fishSchool) {

        euclidianDistance = MathUtils.distance(fish.getCurrentPosition(), barycenter);
        position = new double[dimensions];

        for (int i = 0; i < dimensions; i++) {
            currentPosition = fish.getCurrentPosition()[i];

            if (actualFishSchoolWeight > previousFishSchoolWeight) {

                position[i] = currentPosition - stepVol[i] * random.nextUniform(0.0, 1.0)
                        * ((currentPosition - barycenter[i]) / euclidianDistance);
                // * ((currentPosition - barycenter[i]) / euclidianDistance);
            } else {
                position[i] = currentPosition + stepVol[i] * random.nextUniform(0.0, 1.0)
                        * ((currentPosition - barycenter[i]) / euclidianDistance);
                // * ((currentPosition - barycenter[i]) / euclidianDistance);
            }/*  w  w w .  j  a  v  a 2  s.  c om*/
            // * ((currentPosition - barycenter[i]) / euclidianDistance);
            if (position[i] > getProblem().getUpperBound(i) || position[i] < getProblem().getLowerBound(i)) {
                position[i] = currentPosition;
            }
        }

        // if (getProblem().compareFitness(fish.getCurrentFitness(),
        // getProblem().getFitness(position))) {
        fish.updateCurrentPosition(position, getProblem().getFitness(position));

        if (getProblem().isFitnessBetterThan(fish.getBestFitness(), fish.getCurrentFitness())) {
            fish.updateBestPosition(fish.getCurrentPosition(), fish.getCurrentFitness());
        }
        // }
    }

    previousFishSchoolWeight = actualFishSchoolWeight;
}

From source file:acromusashi.stream.ml.clustering.kmeans.KmeansCalculator.java

/**
 * ????????????/*w  w w  . j a v  a 2s .  c  om*/
 * 
 * @param targetPoint ?
 * @param centroids 
 * @return Kmeans?
 */
public static KmeansResult nearestCentroid(KmeansPoint targetPoint, List<KmeansPoint> centroids) {
    int nearestCentroidIndex = 0;
    Double minDistance = Double.MAX_VALUE;
    KmeansPoint currentCentroid = null;
    Double currentDistance;
    for (int index = 0; index < centroids.size(); index++) {
        currentCentroid = centroids.get(index);
        if (currentCentroid != null && currentCentroid.getDataPoint() != null) {
            currentDistance = MathUtils.distance(targetPoint.getDataPoint(), currentCentroid.getDataPoint());
            if (currentDistance < minDistance) {
                minDistance = currentDistance;
                nearestCentroidIndex = index;
            }
        }
    }

    currentCentroid = centroids.get(nearestCentroidIndex);

    KmeansResult result = new KmeansResult();
    result.setDataPoint(targetPoint.getDataPoint());
    result.setCentroidIndex(nearestCentroidIndex);
    result.setCentroid(currentCentroid.getDataPoint());
    result.setDistance(minDistance);

    return result;
}

From source file:acromusashi.stream.ml.clustering.kmeans.KmeansCalculator.java

/**
 * ??????????/*from w  w w.  j a  v a2  s  .co  m*/
 * 
 * @param targetPoint ?
 * @param dataSet 
 * @return ??????
 */
public static KmeansResult classify(KmeansPoint targetPoint, KmeansDataSet dataSet) {
    // KMean?
    int nearestCentroidIndex = 0;
    Double minDistance = Double.MAX_VALUE;
    double[] currentCentroid = null;
    Double currentDistance;
    for (int index = 0; index < dataSet.getCentroids().length; index++) {
        currentCentroid = dataSet.getCentroids()[index];
        if (currentCentroid != null) {
            currentDistance = MathUtils.distance(targetPoint.getDataPoint(), currentCentroid);
            if (currentDistance < minDistance) {
                minDistance = currentDistance;
                nearestCentroidIndex = index;
            }
        }
    }

    currentCentroid = dataSet.getCentroids()[nearestCentroidIndex];

    KmeansResult result = new KmeansResult();
    result.setDataPoint(targetPoint.getDataPoint());
    result.setCentroidIndex(nearestCentroidIndex);
    result.setCentroid(currentCentroid);
    result.setDistance(minDistance);

    return result;
}

From source file:acromusashi.stream.ml.anomaly.lof.LofCalculator.java

/**
 * ?????DataId???<br>/*from w  w  w.jav  a 2s .c o m*/
 * ????????????????
 * <ol>
 * <li>??K????</li>
 * <li>????K????</li>
 * </ol>
 * 
 * @param addedPoint 
 * @param dataSet 
 * @param deleteId ?Id
 * @return ?????DataId?
 */
protected static Set<String> generateUpdateTargets(LofPoint addedPoint, LofDataSet dataSet, String deleteId) {
    Set<String> updateTargets = new HashSet<>();
    // ??????????????K??K??????????
    // ????????????????????
    // 1.??K????
    // 2.??????K????
    updateTargets.add(addedPoint.getDataId());
    Collection<LofPoint> pointList = dataSet.getDataMap().values();
    for (LofPoint targetPoint : pointList) {
        boolean isDeteted = false;
        boolean kDistUpdate = false;

        // ???
        if (StringUtils.equals(addedPoint.getDataId(), targetPoint.getDataId()) == true) {
            continue;
        }

        // 1.??K?????
        // K?2030?????????List??contains?????????
        if (deleteId != null && targetPoint.getkDistanceNeighbor().contains(deleteId) == true) {
            isDeteted = true;
        }

        // 2.??????K?????
        if (MathUtils.distance(addedPoint.getDataPoint(), targetPoint.getDataPoint()) < targetPoint
                .getkDistance()) {
            kDistUpdate = true;
        }

        if (isDeteted || kDistUpdate) {
            updateTargets.add(targetPoint.getDataId());
        }
    }

    return updateTargets;
}

From source file:acromusashi.stream.ml.clustering.kmeans.KmeansCalculator.java

/**
 * ????????//  www.j ava2  s .  c  om
 * 
 * @param basePoints ??
 * @param centroids ??
 * @return ?????
 */
public static double[] computeDxs(List<KmeansPoint> basePoints, List<KmeansPoint> centroids) {
    double[] dxs = new double[basePoints.size()];

    double sum = 0.0d;
    double[] nearestCentroid;
    for (int pointIndex = 0; pointIndex < basePoints.size(); pointIndex++) {
        // ??????(dx)????????
        KmeansPoint targetPoint = basePoints.get(pointIndex);
        KmeansResult kmeanResult = KmeansCalculator.nearestCentroid(targetPoint, centroids);
        nearestCentroid = kmeanResult.getCentroid();
        double dx = MathUtils.distance(targetPoint.getDataPoint(), nearestCentroid);
        double probabilityDist = Math.pow(dx, 2);
        sum += probabilityDist;
        dxs[pointIndex] = sum;
    }

    return dxs;
}