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:edu.oregonstate.eecs.mcplan.ml.InformationTheoreticMetricLearner.java

/**
 * @param args/* w ww .jav  a  2 s  .c  o  m*/
 */
public static void main(final String[] args) {
    final RandomGenerator rng = new MersenneTwister(42);
    final int d = 2;
    final double u = 5.0;
    final double ell = 7.0;
    final double gamma = 1.0;
    final ArrayList<RealVector> X = new ArrayList<RealVector>();
    final RealMatrix A0 = MatrixUtils.createRealIdentityMatrix(d);

    for (final int w : new int[] { 0, 5 }) {
        for (final int h : new int[] { 0, 50 }) {
            for (int x = -1; x <= 1; ++x) {
                for (int y = -1; y <= 1; ++y) {
                    X.add(new ArrayRealVector(new double[] { x + w, y + h }));
                }
            }
        }
    }

    final ArrayList<int[]> S = new ArrayList<int[]>();
    S.add(new int[] { 4, 12 }); // Must link diagonally
    S.add(new int[] { 21, 31 });
    final ArrayList<double[]> Sd = new ArrayList<double[]>();
    for (final int[] s : S) {
        final double[] a = X.get(s[0]).subtract(X.get(s[1])).toArray();
        Sd.add(a);
    }

    final ArrayList<int[]> D = new ArrayList<int[]>();
    D.add(new int[] { 5, 23 });
    D.add(new int[] { 13, 32 }); // Cannot link vertically
    final ArrayList<double[]> Dd = new ArrayList<double[]>();
    for (final int[] dd : D) {
        final double[] a = X.get(dd[0]).subtract(X.get(dd[1])).toArray();
        Dd.add(a);
    }

    final InformationTheoreticMetricLearner itml = new InformationTheoreticMetricLearner(Sd, Dd, u, ell, A0,
            gamma, rng);
    itml.run();

    final RealMatrix A = itml.A();

    System.out.println(A0.toString());

    for (final int[] c : S) {
        final RealVector diff = X.get(c[0]).subtract(X.get(c[1]));
        System.out.println(diff.dotProduct(A0.operate(diff)));
    }
    for (final int[] c : D) {
        final RealVector diff = X.get(c[0]).subtract(X.get(c[1]));
        System.out.println(diff.dotProduct(A0.operate(diff)));
    }

    System.out.println(A.toString());

    for (final int[] c : S) {
        final RealVector diff = X.get(c[0]).subtract(X.get(c[1]));
        System.out.println(diff.dotProduct(A.operate(diff)));
    }
    for (final int[] c : D) {
        final RealVector diff = X.get(c[0]).subtract(X.get(c[1]));
        System.out.println(diff.dotProduct(A.operate(diff)));
    }

    //      int i = 0;
    //      for( final int w : new int[] { 0, 5 } ) {
    //         for( final int h : new int[] { 0, 5 } ) {
    //            for( int x = -1; x <= 1; ++x ) {
    //               for( int y = -1; y <= 1; ++y ) {
    //                  System.out.println( itml.A().operate( X.get( i++ ) ) );
    //               }
    //            }
    //         }
    //      }
}

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From source file:edu.oregonstate.eecs.mcplan.ml.KulisLowRankKernelLearner.java

/**
 * @param args/*  ww w  .  j av  a 2s .c o  m*/
 */
public static void main(final String[] args) {
    final RandomGenerator rng = new MersenneTwister(42);
    final int d = 2;
    final double u = 5.0;
    final double ell = 7.0;
    final double gamma = 1.0;
    final ArrayList<RealVector> X = new ArrayList<RealVector>();
    final RealMatrix A0 = MatrixUtils.createRealIdentityMatrix(d);

    for (final int w : new int[] { 0, 5 }) {
        for (final int h : new int[] { 0, 5 }) {
            for (int x = -1; x <= 1; ++x) {
                for (int y = -1; y <= 1; ++y) {
                    X.add(new ArrayRealVector(new double[] { x + w, y + h }));
                }
            }
        }
    }

    final ArrayList<int[]> S = new ArrayList<int[]>();
    S.add(new int[] { 4, 31 }); // Must link diagonally
    final ArrayList<int[]> D = new ArrayList<int[]>();
    D.add(new int[] { 4, 13 });
    D.add(new int[] { 22, 31 });
    D.add(new int[] { 13, 22 }); // Cannot link vertically

    final KulisLowRankKernelLearner itml = new KulisLowRankKernelLearner(X, S, D, u, ell, A0, gamma, rng);
    itml.run();

    final RealMatrix A = itml.A();

    System.out.println(A0.toString());

    for (final int[] c : S) {
        final RealVector diff = X.get(c[0]).subtract(X.get(c[1]));
        System.out.println(diff.dotProduct(A0.operate(diff)));
    }
    for (final int[] c : D) {
        final RealVector diff = X.get(c[0]).subtract(X.get(c[1]));
        System.out.println(diff.dotProduct(A0.operate(diff)));
    }

    System.out.println(A.toString());

    for (final int[] c : S) {
        final RealVector diff = X.get(c[0]).subtract(X.get(c[1]));
        System.out.println(diff.dotProduct(A.operate(diff)));
    }
    for (final int[] c : D) {
        final RealVector diff = X.get(c[0]).subtract(X.get(c[1]));
        System.out.println(diff.dotProduct(A.operate(diff)));
    }

    //      int i = 0;
    //      for( final int w : new int[] { 0, 5 } ) {
    //         for( final int h : new int[] { 0, 5 } ) {
    //            for( int x = -1; x <= 1; ++x ) {
    //               for( int y = -1; y <= 1; ++y ) {
    //                  System.out.println( itml.A().operate( X.get( i++ ) ) );
    //               }
    //            }
    //         }
    //      }
}

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From source file:edu.oregonstate.eecs.mcplan.ml.HilbertSpace.java

public static double inner_prod(final RealVector x, final RealVector y) {
    return x.dotProduct(y);
}

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From source file:hivemall.utils.math.StatsUtils.java

/**
 * pdf(x, x_hat) = exp(-0.5 * (x-x_hat) * inv() * (x-x_hat)T) / ( 2^0.5d * det()^0.5)
 * /*from   w  w w .  java2s  .c om*/
 * @return value of probabilistic density function
 * @link https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Density_function
 */
public static double pdf(@Nonnull final RealVector x, @Nonnull final RealVector x_hat,
        @Nonnull final RealMatrix sigma) {
    final int dim = x.getDimension();
    Preconditions.checkArgument(x_hat.getDimension() == dim,
            "|x| != |x_hat|, |x|=" + dim + ", |x_hat|=" + x_hat.getDimension());
    Preconditions.checkArgument(sigma.getRowDimension() == dim,
            "|x| != |sigma|, |x|=" + dim + ", |sigma|=" + sigma.getRowDimension());
    Preconditions.checkArgument(sigma.isSquare(), "Sigma is not square matrix");

    LUDecomposition LU = new LUDecomposition(sigma);
    final double detSigma = LU.getDeterminant();
    double denominator = Math.pow(2.d * Math.PI, 0.5d * dim) * Math.pow(detSigma, 0.5d);
    if (denominator == 0.d) { // avoid divide by zero
        return 0.d;
    }

    final RealMatrix invSigma;
    DecompositionSolver solver = LU.getSolver();
    if (solver.isNonSingular() == false) {
        SingularValueDecomposition svd = new SingularValueDecomposition(sigma);
        invSigma = svd.getSolver().getInverse(); // least square solution
    } else {
        invSigma = solver.getInverse();
    }
    //EigenDecomposition eigen = new EigenDecomposition(sigma);
    //double detSigma = eigen.getDeterminant();
    //RealMatrix invSigma = eigen.getSolver().getInverse();

    RealVector diff = x.subtract(x_hat);
    RealVector premultiplied = invSigma.preMultiply(diff);
    double sum = premultiplied.dotProduct(diff);
    double numerator = Math.exp(-0.5d * sum);

    return numerator / denominator;
}

---

From source file:com.github.thorbenlindhauer.factor.CanonicalGaussianFactor.java

public static CanonicalGaussianFactor fromMomentForm(Scope scope, RealVector meanVector,
        RealMatrix covarianceMatrix) {//from w ww .  j  a va 2s  .co m

    // TODO: perform cardinality checks etc.

    MathUtil mathUtil = new MathUtil(covarianceMatrix);
    RealMatrix precisionMatrix = mathUtil.invert();
    RealVector scaledMeanVector = precisionMatrix.operate(meanVector);
    int dimension = meanVector.getDimension();
    double normalizationConstant = -(0.5d * scaledMeanVector.dotProduct(meanVector)) - (Math
            .log(Math.pow(2.0d * Math.PI, (double) dimension / 2.0d) * Math.sqrt(mathUtil.determinant())));

    return new CanonicalGaussianFactor(scope, precisionMatrix, scaledMeanVector, normalizationConstant);
}

---

From source file:hivemall.utils.math.MatrixUtils.java

/**
 * Lanczos tridiagonalization for a symmetric matrix C to make s * s tridiagonal matrix T.
 *
 * @see http://www.cas.mcmaster.ca/~qiao/publications/spie05.pdf
 * @param C target symmetric matrix//from  ww  w. j a  va  2 s  .  com
 * @param a initial vector
 * @param T result is stored here
 */
public static void lanczosTridiagonalization(@Nonnull final RealMatrix C, @Nonnull final double[] a,
        @Nonnull final RealMatrix T) {
    Preconditions.checkArgument(Arrays.deepEquals(C.getData(), C.transpose().getData()),
            "Target matrix C must be a symmetric matrix");
    Preconditions.checkArgument(C.getColumnDimension() == a.length,
            "Column size of A and length of a should be same");
    Preconditions.checkArgument(T.getRowDimension() == T.getColumnDimension(), "T must be a square matrix");

    int s = T.getRowDimension();

    // initialize T with zeros
    T.setSubMatrix(new double[s][s], 0, 0);

    RealVector a0 = new ArrayRealVector(a.length);
    RealVector r = new ArrayRealVector(a);

    double beta0 = 1.d;

    for (int i = 0; i < s; i++) {
        RealVector a1 = r.mapDivide(beta0);
        RealVector Ca1 = C.operate(a1);

        double alpha1 = a1.dotProduct(Ca1);

        r = Ca1.add(a1.mapMultiply(-1.d * alpha1)).add(a0.mapMultiply(-1.d * beta0));

        double beta1 = r.getNorm();

        T.setEntry(i, i, alpha1);
        if (i - 1 >= 0) {
            T.setEntry(i, i - 1, beta0);
        }
        if (i + 1 < s) {
            T.setEntry(i, i + 1, beta1);
        }

        a0 = a1.copy();
        beta0 = beta1;
    }
}

---

From source file:edu.oregonstate.eecs.mcplan.ml.InnerProductKernel.java

@Override
public double apply(final RealVector x, final RealVector y) {
    return x.dotProduct(y);
}

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From source file:edu.oregonstate.eecs.mcplan.ml.RadialBasisFunctionKernel.java

@Override
public double apply(final RealVector x, final RealVector y) {
    final RealVector diff = x.subtract(y);
    final double sq_norm2 = diff.dotProduct(diff);
    return Math.exp(gamma_ * sq_norm2);
}

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From source file:com.cloudera.oryx.kmeans.computation.covariance.DistanceData.java

public double mahalanobisDistance(RealVector v) {
    RealVector d = v.subtract(means);
    return d.dotProduct(covInv.operate(d));
}

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From source file:edu.washington.gs.skyline.model.quantification.LinearModel.java

public List<LinearFitResult> fit(double[] observations) {
    if (observations.length != designMatrix.getRowDimension()) {
        throw new IllegalArgumentException("Wrong number of rows");
    }//w  w  w . j  a  v  a 2  s . c o m
    RealVector coefficients = qrDecomposition.getSolver().solve(new ArrayRealVector(observations));
    RealVector fittedValues = new ArrayRealVector(observations.length);
    RealVector residuals = new ArrayRealVector(observations.length);
    for (int iRow = 0; iRow < observations.length; iRow++) {
        RealVector designRow = designMatrix.getRowVector(iRow);
        fittedValues.setEntry(iRow, designRow.dotProduct(coefficients));
        residuals.setEntry(iRow, observations[iRow] - fittedValues.getEntry(iRow));
    }
    double rss = residuals.dotProduct(residuals);
    int degreesOfFreedom = observations.length - qrDecomposition.getR().getColumnDimension();
    double resVar = rss / degreesOfFreedom;
    double sigma = Math.sqrt(resVar);
    RealMatrix covarianceUnscaled = matrixCrossproductInverse;
    RealMatrix scaledCovariance = covarianceUnscaled.scalarMultiply(sigma * sigma);
    List<LinearFitResult> results = new ArrayList<>();
    for (int iContrastRow = 0; iContrastRow < contrastValues.getRowDimension(); iContrastRow++) {
        RealVector contrastRow = contrastValues.getRowVector(iContrastRow);
        double standardError = 0;
        for (int iRow = 0; iRow < independentColumnIndices.length; iRow++) {
            for (int iCol = 0; iCol < independentColumnIndices.length; iCol++) {
                standardError = contrastRow.getEntry(independentColumnIndices[iRow])
                        * scaledCovariance.getEntry(iRow, iCol)
                        * contrastRow.getEntry(independentColumnIndices[iCol]);
            }
        }
        standardError = Math.sqrt(standardError);
        double foldChange = coefficients.dotProduct(contrastRow);
        LinearFitResult linearFitResult = new LinearFitResult(foldChange);
        double tValue = foldChange / standardError;
        linearFitResult.setTValue(tValue);
        linearFitResult.setStandardError(standardError);
        linearFitResult.setDegreesOfFreedom(degreesOfFreedom);
        if (0 == degreesOfFreedom) {
            linearFitResult.setPValue(1.0);
        } else {
            TDistribution tDistribution = new TDistribution(degreesOfFreedom);
            double pValue = (1 - tDistribution.cumulativeProbability(Math.abs(tValue))) * 2;
            linearFitResult.setPValue(pValue);
        }
        results.add(linearFitResult);
    }
    return results;
}

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