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

Introduction

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

Prototype

public double getNorm() 

Source Link

Document

Returns the L₂ norm of the vector.

Usage

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

@Nonnull
static RealVector unitL2norm(@Nonnull final RealVector x) {
    double x0 = x.getEntry(0);
    double sign = MathUtils.sign(x0);
    x.setEntry(0, x0 + sign * x.getNorm());
    return x.unitVector();
}

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

/**
 * Find the first singular vector/value of a matrix A based on the Power method.
 *
 * @see http//from  w  w  w .  j  a  v  a 2  s.c om
 *      ://www.cs.yale.edu/homes/el327/datamining2013aFiles/07_singular_value_decomposition.pdf
 * @param A target matrix
 * @param x0 initial vector
 * @param nIter number of iterations for the Power method
 * @param u 1st left singular vector
 * @param v 1st right singular vector
 * @return 1st singular value
 */
public static double power1(@Nonnull final RealMatrix A, @Nonnull final double[] x0, final int nIter,
        @Nonnull final double[] u, @Nonnull final double[] v) {
    Preconditions.checkArgument(A.getColumnDimension() == x0.length,
            "Column size of A and length of x should be same");
    Preconditions.checkArgument(A.getRowDimension() == u.length,
            "Row size of A and length of u should be same");
    Preconditions.checkArgument(x0.length == v.length, "Length of x and u should be same");
    Preconditions.checkArgument(nIter >= 1, "Invalid number of iterations: " + nIter);

    RealMatrix AtA = A.transpose().multiply(A);

    RealVector x = new ArrayRealVector(x0);
    for (int i = 0; i < nIter; i++) {
        x = AtA.operate(x);
    }

    double xNorm = x.getNorm();
    for (int i = 0, n = v.length; i < n; i++) {
        v[i] = x.getEntry(i) / xNorm;
    }

    RealVector Av = new ArrayRealVector(A.operate(v));
    double s = Av.getNorm();

    for (int i = 0, n = u.length; i < n; i++) {
        u[i] = Av.getEntry(i) / s;
    }

    return s;
}

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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   w  w  w.jav a  2 s . co  m*/
 * @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;
    }
}

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From source file:com.mothsoft.alexis.engine.textual.DocumentFeatures.java

public double cosine(final RealVector v1, final RealVector v2) {
    final double norm = v1.getNorm();
    final double norm2 = v2.getNorm();
    final double divisor = norm * norm2;

    if (divisor == 0.0) {
        return 0;
    } else {//from  w  w  w .  j  a v  a 2s .co  m
        return v1.dotProduct(v2) / (divisor);
    }
}

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

@Override
public void run() {
    final RealMatrix cov_reg = MatrixUtils.createRealIdentityMatrix(X.getRowDimension())
            .scalarMultiply(shrinkage);/*from  w  ww .j a  v a2 s  . co  m*/
    for (int k = 0; k < K; ++k) {
        System.out.println("k = " + k);
        System.out.println("\tCovariance");
        final RealMatrix cov = Xi_.multiply(Xi_.transpose()).add(cov_reg);
        //         System.out.println( cov );
        System.out.println("\tM");
        final RealMatrix M = cov; // XL.multiply( Si_ ).multiply( XLt ).add( cov.scalarMultiply( eta ) );
        // TODO: You only need the largest eigenvalue, so the full
        // decomposition is a ton of unnecessary work.
        System.out.println("\tM[" + M.getRowDimension() + "x" + M.getColumnDimension() + "]");
        final EigenDecomposition evd = new EigenDecomposition(M);
        final RealVector w = evd.getEigenvector(0);
        w.mapMultiplyToSelf(1.0 / w.getNorm());
        //         if( Math.abs( w.getNorm() - 1.0 ) > 1e-2 ) {
        //            System.out.println( "! Non-unit eigenvector: ||w|| = " + w.getNorm() );
        //            System.out.println( Math.abs( w.getNorm() - 1.0 ) );
        //            assert( false );
        //         }
        W.add(w);
        final RealMatrix w_wt = w.outerProduct(w);
        final RealMatrix S_tilde = XLt.multiply(w_wt).multiply(XL);
        T(S_tilde, Si_);
        Si_ = Si_.subtract(S_tilde.scalarMultiply(alpha));
        Xi_ = Xi_.subtract(w_wt.multiply(Xi_));
    }
}

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From source file:com.opengamma.strata.math.impl.matrix.CommonsMatrixAlgebra.java

@Override
public double getNorm2(Matrix m) {
    ArgChecker.notNull(m, "m");
    if (m instanceof DoubleArray) {
        RealVector temp = CommonsMathWrapper.wrap((DoubleArray) m);
        return temp.getNorm();
    } else if (m instanceof DoubleMatrix) {
        RealMatrix temp = CommonsMathWrapper.wrap((DoubleMatrix) m);
        SingularValueDecomposition svd = new SingularValueDecomposition(temp);
        return svd.getNorm();
    }//from  w  w  w. j a  va  2  s .c  o  m
    throw new IllegalArgumentException("Can only find norm2 of DoubleMatrix; have " + m.getClass());
}

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From source file:ir.project.TFIDFBookVector.java

public double cosineSimilarity(TFIDFBookVector other) {
    RealVector otherVector = other.getVector();

    // get dot product
    double dotProduct = this.vector.dotProduct(otherVector);

    // get Euclidian norms
    double norm1 = this.vector.getNorm();
    double norm2 = otherVector.getNorm();

    // calculate cosine similarity
    double sim = dotProduct / (norm1 * norm2);

    return sim;//from  w w  w.jav a2  s.  c  om

}

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From source file:com.cloudera.oryx.kmeans.computation.cluster.KSketchIndex.java

public void add(RealVector vec, int centerId) {
    points.get(centerId).add(vec);//from  w  w  w  .  j  a  v  a 2 s  .c om
    double length = vec.getNorm();
    lengthSquared.get(centerId).add(length * length);
    pointsPerFold[centerId]++;
    updated = true;
}

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From source file:com.joptimizer.util.MPSParserNetlibTest.java

/**
 * Tests the parsing of a netlib problem.
 */// w  ww. j  a  v a  2  s. c  o m
public void xxxtestSingleNetlib() throws Exception {
    log.debug("testSingleNetlib");
    //String problemName = "afiro";
    //String problemName = "afiroPresolved";
    //String problemName = "adlittle";
    //String problemName = "kb2";
    //String problemName = "sc50a";
    //String problemName = "sc50b";
    //String problemName = "blend";
    //String problemName = "scorpion";
    //String problemName = "recipe";
    //String problemName = "recipePresolved";
    //String problemName = "sctap1";
    //String problemName = "fit1d";
    //String problemName = "israel";
    //String problemName = "grow15";
    //String problemName = "etamacro";
    //String problemName = "pilot";
    //String problemName = "pilot4";
    //String problemName = "osa-14";
    //String problemName = "brandyPresolved";
    String problemName = "maros";

    File f = Utils.getClasspathResourceAsFile("lp" + File.separator + "netlib" + File.separator + problemName
            + File.separator + problemName + ".mps");
    MPSParser mpsParser = new MPSParser();
    mpsParser.parse(f);

    Properties expectedSolProps = null;
    try {
        //this is the solution of the mps problem given by Mathematica
        expectedSolProps = load(Utils.getClasspathResourceAsFile(
                "lp" + File.separator + "netlib" + File.separator + problemName + File.separator + "sol.txt"));
    } catch (Exception e) {
    }

    log.debug("name: " + mpsParser.getName());
    log.debug("n   : " + mpsParser.getN());
    log.debug("meq : " + mpsParser.getMeq());
    log.debug("mieq: " + mpsParser.getMieq());
    log.debug("meq+mieq: " + (mpsParser.getMeq() + mpsParser.getMieq()));
    List<String> variablesNames = mpsParser.getVariablesNames();
    log.debug("x: " + ArrayUtils.toString(variablesNames));
    //      log.debug("c: " + ArrayUtils.toString(p.getC()));
    //      log.debug("G: " + ArrayUtils.toString(p.getG()));
    //      log.debug("h: " + ArrayUtils.toString(p.getH()));
    //      log.debug("A: " + ArrayUtils.toString(p.getA()));
    //      log.debug("b: " + ArrayUtils.toString(p.getB()));
    //      log.debug("lb:" + ArrayUtils.toString(p.getLb()));
    //      log.debug("ub:" + ArrayUtils.toString(p.getUb()));

    //check consistency: if the problem was correctly parsed, the expectedSol must be its solution
    double delta = 1.e-7;
    if (expectedSolProps != null) {
        //key = variable name
        //value = sol value
        assertEquals(expectedSolProps.size(), variablesNames.size());
        RealVector expectedSol = new ArrayRealVector(variablesNames.size());
        for (int i = 0; i < variablesNames.size(); i++) {
            expectedSol.setEntry(i, Double.parseDouble(expectedSolProps.getProperty(variablesNames.get(i))));
        }
        log.debug("expectedSol: " + ArrayUtils.toString(expectedSol.toArray()));

        //check objective function value
        Map<String, LPNetlibProblem> problemsMap = LPNetlibProblem.loadAllProblems();
        LPNetlibProblem problem = problemsMap.get(problemName);
        RealVector c = new ArrayRealVector(mpsParser.getC().toArray());
        double value = c.dotProduct(expectedSol);
        log.debug("optimalValue: " + problem.optimalValue);
        log.debug("value       : " + value);
        assertEquals(problem.optimalValue, value, delta);

        //check G.x < h
        if (mpsParser.getG() != null) {
            RealMatrix G = new Array2DRowRealMatrix(mpsParser.getG().toArray());
            RealVector h = new ArrayRealVector(mpsParser.getH().toArray());
            RealVector Gxh = G.operate(expectedSol).subtract(h);
            double maxGxh = -Double.MAX_VALUE;
            for (int i = 0; i < Gxh.getDimension(); i++) {
                //log.debug(i);
                maxGxh = Math.max(maxGxh, Gxh.getEntry(i));
                assertTrue(Gxh.getEntry(i) <= 0);
            }
            log.debug("max(G.x - h): " + maxGxh);
        }

        //check A.x = b
        if (mpsParser.getA() != null) {
            RealMatrix A = new Array2DRowRealMatrix(mpsParser.getA().toArray());
            RealVector b = new ArrayRealVector(mpsParser.getB().toArray());
            RealVector Axb = A.operate(expectedSol).subtract(b);
            double norm = Axb.getNorm();
            log.debug("||A.x -b||: " + norm);
            assertEquals(0., norm, delta * mpsParser.getN());//some more tolerance
        }

        //check upper and lower bounds
        for (int i = 0; i < mpsParser.getLb().size(); i++) {
            double di = Double.isNaN(mpsParser.getLb().getQuick(i)) ? -Double.MAX_VALUE
                    : mpsParser.getLb().getQuick(i);
            assertTrue(di <= expectedSol.getEntry(i));
        }
        for (int i = 0; i < mpsParser.getUb().size(); i++) {
            double di = Double.isNaN(mpsParser.getUb().getQuick(i)) ? Double.MAX_VALUE
                    : mpsParser.getUb().getQuick(i);
            assertTrue(di >= expectedSol.getEntry(i));
        }
    }

    Utils.writeDoubleArrayToFile(mpsParser.getC().toArray(), "target" + File.separator + "c.txt");
    Utils.writeDoubleMatrixToFile(mpsParser.getG().toArray(), "target" + File.separator + "G.csv");
    Utils.writeDoubleArrayToFile(mpsParser.getH().toArray(), "target" + File.separator + "h.txt");
    Utils.writeDoubleMatrixToFile(mpsParser.getA().toArray(), "target" + File.separator + "A.csv");
    Utils.writeDoubleArrayToFile(mpsParser.getB().toArray(), "target" + File.separator + "b.txt");
    Utils.writeDoubleArrayToFile(mpsParser.getLb().toArray(), "target" + File.separator + "lb.txt");
    Utils.writeDoubleArrayToFile(mpsParser.getUb().toArray(), "target" + File.separator + "ub.txt");
}

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

private boolean mstep() {
    final double[][] mu_prime = new double[k][d];
    final int[] nh = new int[k];
    for (int i = 0; i < N; ++i) {
        final int h = assignments_[i];
        nh[h] += 1;/*from  w  w w  .  ja  v a2 s .  c o  m*/
        final RealVector xi = X_.get(i);
        //         mu_prime[h] = mu_prime[h].add( xi.subtract( mu_prime[h] ).mapDivideToSelf( i + 1 ) );
        for (int j = 0; j < d; ++j) {
            // Incremental mean calculation
            mu_prime[h][j] += (xi.getEntry(j) - mu_prime[h][j]) / nh[h];
        }
    }

    double Jprime = 0.0;
    for (int i = 0; i < N; ++i) {
        final int h = assignments_[i];
        Jprime += J(i, h);
    }

    double delta = 0.0;
    for (int h = 0; h < k; ++h) {
        final RealVector v = new ArrayRealVector(mu_prime[h], false); // No copy
        //         System.out.println( mu_[h] );
        //         System.out.println( v );
        final RealVector diff = mu_[h].subtract(v);
        delta += diff.getNorm();
        //         delta += mu_[h].getDistance( v );
        //         delta += distance( mu_[h], v );
        mu_[h] = v;
    }
    //      System.out.println( "\tdelta = " + delta );
    final double deltaJ = J_ - Jprime;
    //      System.out.println( "\tdeltaJ = " + deltaJ );
    J_ = Jprime;
    return deltaJ > convergence_tolerance_;
}

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