Example usage for org.apache.commons.math3.linear RealVector dotProduct

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear RealVector dotProduct.

Prototype

public double dotProduct(RealVector v) throws DimensionMismatchException 

Source Link

Document

Compute the dot product of this vector with v .

Usage

From source file:org.lenskit.mf.svd.DotProductKernel.java

@Override
public double apply(double bias, @Nonnull RealVector user, @Nonnull RealVector item) {
    return bias + user.dotProduct(item);
}

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From source file:org.lenskit.pf.HPFItemScorer.java

@Nonnull
@Override// ww  w. j  ava2  s.  c  om
public ResultMap scoreWithDetails(long user, @Nonnull Collection<Long> items) {
    RealVector uvec = model.getUserVector(user);
    if (uvec == null) {
        return Results.newResultMap();
    }

    List<Result> results = new ArrayList<>(items.size());
    LongIterator iter = LongIterators.asLongIterator(items.iterator());
    while (iter.hasNext()) {
        long item = iter.nextLong();
        RealVector ivec = model.getItemVector(item);
        if (ivec != null) {
            double score = uvec.dotProduct(ivec);
            if (isProbPrediction) {
                score = 1 - Math.exp(-score);
            }
            results.add(Results.create(item, score));
        }
    }
    return Results.newResultMap(results);
}

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From source file:org.rhwlab.dispim.nucleus.BHCNucleusData.java

public String getRadiusLabel(int i) {
    // find the adjusted eigenvector closest to the original unadjusted eigenvector for dimension i
    // this keeps the order of the adjusted eigenvectors the same as the original unadjusted eigenvectors
    // the eigendecomposition returns the eigenvectors sorted by eigenvalue
    // this procedure puts them back in their original order
    int adjustedI = 0;
    double maxD = 0.0;
    RealVector aV = eigenA.getEigenvector(i);
    for (int j = 0; j < A.getColumnDimension(); ++j) {
        RealVector v = adjustedEigenA.getEigenvector(j);
        double d = Math.abs(v.dotProduct(aV));
        if (d > maxD) {
            maxD = d;//  ww  w .j  av  a  2 s  .  c  o  m
            adjustedI = j;
        }
    }
    RealVector v = adjustedEigenA.getEigenvector(adjustedI);
    //        double eigenVal = adjustedEigenA.getRealEigenvalue(adjustedI);
    //        double r = 1.0/Math.sqrt(Ace3D_Frame.R*eigenVal);
    double r = this.getRadius(adjustedI);
    return String.format("%4.1f(%.2f,%.2f,%.2f)", r, v.getEntry(0), v.getEntry(1), v.getEntry(2));
}

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From source file:org.rhwlab.dispim.nucleus.Division.java

private boolean related(RealVector axis1, RealVector axis2) {
    double cos = axis1.dotProduct(axis2);
    boolean ret = cos >= cosThresh;
    if (!ret) {/*from  ww w.  j a  va 2 s . c o m*/
        System.out.printf("Cosine: %f\n", cos);

    }
    return ret;
}

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From source file:org.rhwlab.dispim.nucleus.NucleusData.java

public double prob(double[] p) {

    RealVector v = new ArrayRealVector(p);
    double d2 = Math.sqrt(this.adjustedEigenA.getDeterminant());
    double ex = -0.5 * v.dotProduct(adjustedA.operate(v));
    return d2 * Math.exp(ex);
}

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From source file:org.ssascaling.model.timeseries.learner.apache.OLSMultipleLinearRegression.java

/**
 * Returns the sum of squared residuals.
 *
 * @return residual sum of squares//from  w  w  w .j a  va  2  s  .co m
 * @since 2.2
 */
public double calculateResidualSumOfSquares() {
    final RealVector residuals = calculateResiduals();
    return residuals.dotProduct(residuals);
}

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From source file:playground.sergioo.facilitiesGenerator2012.WorkFacilitiesGeneration.java

private static Set<PointPerson> getPCATransformation(Collection<PointPerson> points) {
    RealMatrix pointsM = new Array2DRowRealMatrix(points.iterator().next().getDimension(), points.size());
    int k = 0;//w w  w.j  av  a  2 s  . com
    for (PointND<Double> point : points) {
        for (int f = 0; f < point.getDimension(); f++)
            pointsM.setEntry(f, k, point.getElement(f));
        k++;
    }
    RealMatrix means = new Array2DRowRealMatrix(pointsM.getRowDimension(), 1);
    for (int r = 0; r < means.getRowDimension(); r++) {
        double mean = 0;
        for (int c = 0; c < pointsM.getColumnDimension(); c++)
            mean += pointsM.getEntry(r, c) / pointsM.getColumnDimension();
        means.setEntry(r, 0, mean);
    }
    RealMatrix deviations = new Array2DRowRealMatrix(pointsM.getRowDimension(), pointsM.getColumnDimension());
    for (int r = 0; r < deviations.getRowDimension(); r++)
        for (int c = 0; c < deviations.getColumnDimension(); c++)
            deviations.setEntry(r, c, pointsM.getEntry(r, c) - means.getEntry(r, 0));
    RealMatrix covariance = deviations.multiply(deviations.transpose())
            .scalarMultiply(1 / (double) pointsM.getColumnDimension());
    EigenDecomposition eigenDecomposition = new EigenDecomposition(covariance, 0);
    RealMatrix eigenVectorsT = eigenDecomposition.getVT();
    RealVector eigenValues = new ArrayRealVector(eigenDecomposition.getD().getRowDimension());
    for (int r = 0; r < eigenDecomposition.getD().getRowDimension(); r++)
        eigenValues.setEntry(r, eigenDecomposition.getD().getEntry(r, r));
    for (int i = 0; i < eigenValues.getDimension(); i++) {
        for (int j = i + 1; j < eigenValues.getDimension(); j++)
            if (eigenValues.getEntry(i) < eigenValues.getEntry(j)) {
                double tempValue = eigenValues.getEntry(i);
                eigenValues.setEntry(i, eigenValues.getEntry(j));
                eigenValues.setEntry(j, tempValue);
                RealVector tempVector = eigenVectorsT.getRowVector(i);
                eigenVectorsT.setRowVector(i, eigenVectorsT.getRowVector(j));
                eigenVectorsT.setRowVector(j, tempVector);
            }
        eigenVectorsT.setRowVector(i,
                eigenVectorsT.getRowVector(i).mapMultiply(Math.sqrt(1 / eigenValues.getEntry(i))));
    }
    RealVector standardDeviations = new ArrayRealVector(pointsM.getRowDimension());
    for (int r = 0; r < covariance.getRowDimension(); r++)
        standardDeviations.setEntry(r, Math.sqrt(covariance.getEntry(r, r)));
    double zValue = standardDeviations.dotProduct(new ArrayRealVector(pointsM.getRowDimension(), 1));
    RealMatrix zScore = deviations.scalarMultiply(1 / zValue);
    pointsM = eigenVectorsT.multiply(zScore);
    Set<PointPerson> pointsC = new HashSet<PointPerson>();
    k = 0;
    for (PointPerson point : points) {
        PointPerson pointC = new PointPerson(point.getId(), point.getOccupation(),
                new Double[] { pointsM.getEntry(0, k), pointsM.getEntry(1, k) }, point.getPlaceType());
        pointC.setWeight(point.getWeight());
        pointsC.add(pointC);
        k++;
    }
    return pointsC;
}

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From source file:rcdemo.math.ClosedHermiteSpline.java

@Override
public double compute(double t, int deriv) {
    int n = (int) length();
    int k = (int) Math.floor(t);
    t = t - k;//from  w w  w  .j a  v a  2s.c  o  m
    k = k % n;
    if (k < 0) {
        k += n;
    }

    double t2 = t * t;
    double t3 = t2 * t;
    double d[];
    switch (deriv) {
    case 0:
        d = new double[] { 2 * t3 - 3 * t2 + 1, t3 - 2 * t2 + t, -2 * t3 + 3 * t2, t3 - t2 };
        break;
    case 1:
        d = new double[] { 6 * t2 - 6 * t, 3 * t2 - 4 * t + 1, -6 * t2 + 6 * t, 3 * t2 - 2 * t };
        break;
    case 2:
        d = new double[] { 12 * t - 6, 6 * t - 4, -12 * t + 6, 6 * t - 2 };
        break;
    default:
        throw new RuntimeException("Derivative requested to high");
    }
    RealVector h = new ArrayRealVector(d);
    RealVector c = a.getColumnVector(k);
    return h.dotProduct(c);
}

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From source file:rcdemo.track.TrackODE.java

@Override
public void computeSecondDerivatives(double t, double[] y, double[] yDot, double[] yDDot) {
    double s = y[0];
    double dsdt = yDot[0];
    RealVector x = track.getx(s);/*  www . j a v a2 s. c  om*/
    RealVector dxds = track.getDxDs(s);
    RealVector ddxdss = track.getDDxDss(s);
    RealVector u = dxds;
    RealVector v = dxds.mapMultiply(dsdt);
    RealVector F = forceModel.getForce(x, v);
    double ddsdtt = F.subtract(ddxdss.mapMultiply(dsdt * dsdt)).dotProduct(u) / dxds.dotProduct(u);

    //double E = 0.5 * v.dotProduct(v) + forceModel.getPotentialEnergy(x, v);
    //System.out.format("%4.1f: %s %s %s\n", t, s, x, E);
    //System.out.format("%7.5f: %s %s %s\n", t, s, x, E);

    yDDot[0] = ddsdtt;
}

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From source file:rcdemo.track.TrackODE.java

public double getEnergy(double s, double dsdt) {
    RealVector x = track.getx(s);// w ww. jav  a2 s . c  o m
    RealVector dxds = track.getDxDs(s);
    RealVector v = dxds.mapMultiply(dsdt);

    double E = 0.5 * v.dotProduct(v) + forceModel.getPotentialEnergy(x, v);
    return E;
}

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