Example usage for org.apache.commons.math3.linear ArrayRealVector ArrayRealVector

Introduction

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

Prototype

public ArrayRealVector(double[] v1, double[] v2) 

Source Link

Document

Construct a vector by appending one vector to another vector.

Usage

From source file:gamlss.distributions.SST.java

/** Computes the global Deviance Increament.
 * @param y - vector of response variable values
 * @return vector of global Deviance Increament values 
 *//*from  w  ww . jav  a  2  s. c  o m*/
public final ArrayRealVector globalDevianceIncreament(final ArrayRealVector y) {
    //G.dev.incr  = function(y,mu,sigma,nu,tau,...)  
    //-2*dST3(y,mu,sigma,nu,tau,log=TRUE),
    size = y.getDimension();
    double[] out = new double[size];

    double[] muArr = distributionParameters.get(DistributionSettings.MU).getDataRef();
    double[] sigmaArr = distributionParameters.get(DistributionSettings.SIGMA).getDataRef();
    double[] nuArr = distributionParameters.get(DistributionSettings.NU).getDataRef();
    double[] tauArr = distributionParameters.get(DistributionSettings.TAU).getDataRef();

    for (int i = 0; i < size; i++) {

        out[i] = (-2)
                * dSST(y.getEntry(i), muArr[i], sigmaArr[i], nuArr[i], tauArr[i], Controls.LOG_LIKELIHOOD);
    }
    return new ArrayRealVector(out, false);
}

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From source file:gamlss.distributions.BCPE.java

/** Second cross derivative of likelihood function
 *  in respect to mu and nu (d2ldmdd = d2l/dmu*dnu).
 * @param y - vector of values of response variable
 * @return  a vector of Second cross derivative
 *///  w  w w.  j av  a 2 s  .com
private ArrayRealVector d2ldmdn(final ArrayRealVector y) {

    ArrayRealVector mu = distributionParameters.get(DistributionSettings.MU);
    ArrayRealVector sigma = distributionParameters.get(DistributionSettings.SIGMA);
    ArrayRealVector nu = distributionParameters.get(DistributionSettings.NU);
    ArrayRealVector tau = distributionParameters.get(DistributionSettings.TAU);

    size = y.getDimension();
    double[] out = new double[size];
    for (int i = 0; i < size; i++) {
        //d2ldmdv = function(mu,sigma,nu,tau) 
        //(2*(tau-1)-(tau+1)*(sigma^2)*(nu^2))/(4*mu),
        out[i] = (2 * (tau.getEntry(i) - 1) - (tau.getEntry(i) + 1) * (sigma.getEntry(i) * sigma.getEntry(i))
                * (nu.getEntry(i) * nu.getEntry(i))) / (4 * mu.getEntry(i));
    }
    return new ArrayRealVector(out, false);
}

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From source file:gamlss.distributions.BCPE.java

/** Second cross derivative of likelihood function 
 * in respect to mu and tau (d2ldmdd = d2l/dmu*dtau).
 * @param y - vector of values of response variable
 * @return  a vector of Second cross derivative
 *//* w  w w.  j a  v  a  2  s.co  m*/
private ArrayRealVector d2ldmdt(final ArrayRealVector y) {

    ArrayRealVector mu = distributionParameters.get(DistributionSettings.MU);
    ArrayRealVector sigma = distributionParameters.get(DistributionSettings.SIGMA);
    ArrayRealVector nu = distributionParameters.get(DistributionSettings.NU);
    ArrayRealVector tau = distributionParameters.get(DistributionSettings.TAU);

    size = y.getDimension();
    double[] out = new double[size];
    for (int i = 0; i < size; i++) {
        //d2ldmdt = (nu/(mu*tau))*(1+tau+(3/2)
        //*(digamma(1/tau)-digamma(3/tau)))
        out[i] = (nu.getEntry(i) / (mu.getEntry(i) * tau.getEntry(i))) * (1 + tau.getEntry(i)
                + (3 / 2) * (Gamma.digamma(1 / tau.getEntry(i)) - Gamma.digamma(3 / tau.getEntry(i))));

    }
    return new ArrayRealVector(out, false);
}

---

From source file:gamlss.distributions.BCPE.java

/** Second cross derivative of likelihood function 
 * in respect to sigma and nu (d2ldmdd = d2l/dsigma*dnu).
 * @param y - vector of values of response variable
 * @return  a vector of Second cross derivative
 *//*w w  w  .  ja v  a 2s .c  o  m*/
private ArrayRealVector d2ldsdn(final ArrayRealVector y) {

    ArrayRealVector mu = distributionParameters.get(DistributionSettings.MU);
    ArrayRealVector sigma = distributionParameters.get(DistributionSettings.SIGMA);
    ArrayRealVector nu = distributionParameters.get(DistributionSettings.NU);
    ArrayRealVector tau = distributionParameters.get(DistributionSettings.TAU);

    size = y.getDimension();
    double[] out = new double[size];
    for (int i = 0; i < size; i++) {
        //2ldddv = function(sigma,nu,tau) -(sigma*nu*tau)/2
        out[i] = -(sigma.getEntry(i) * nu.getEntry(i) * tau.getEntry(i)) / 2;

    }
    return new ArrayRealVector(out, false);
}

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From source file:gamlss.distributions.BCPE.java

/** Second cross derivative of likelihood function
 *  in respect to sigma and tau (d2ldmdd = d2l/dsigma*dtau).
 * @param y - vector of values of response variable
 * @return  a vector of Second cross derivative
 *//*from www . j  a va2s  .com*/
private ArrayRealVector d2ldsdt(final ArrayRealVector y) {

    ArrayRealVector mu = distributionParameters.get(DistributionSettings.MU);
    ArrayRealVector sigma = distributionParameters.get(DistributionSettings.SIGMA);
    ArrayRealVector nu = distributionParameters.get(DistributionSettings.NU);
    ArrayRealVector tau = distributionParameters.get(DistributionSettings.TAU);

    size = y.getDimension();
    double[] out = new double[size];
    for (int i = 0; i < size; i++) {
        //d2ldddt = (1/(sigma*tau))*(1+tau+(3/2)
        //*(digamma(1/tau)-digamma(3/tau)))
        out[i] = (1 / (sigma.getEntry(i) * tau.getEntry(i))) * (1 + tau.getEntry(i)
                + (3 / 2) * (Gamma.digamma(1 / tau.getEntry(i)) - Gamma.digamma(3 / tau.getEntry(i))));

    }
    return new ArrayRealVector(out, false);
}

---

From source file:gamlss.distributions.BCPE.java

/** Second cross derivative of likelihood function 
 * in respect to nu and tau (d2ldmdd = d2l/dnu*dtau).
 * @param y - vector of values of response variable
 * @return  a vector of Second cross derivative
 */// www .  j  a  v  a2  s .co m
private ArrayRealVector d2ldndt(final ArrayRealVector y) {

    ArrayRealVector mu = distributionParameters.get(DistributionSettings.MU);
    ArrayRealVector sigma = distributionParameters.get(DistributionSettings.SIGMA);
    ArrayRealVector nu = distributionParameters.get(DistributionSettings.NU);
    ArrayRealVector tau = distributionParameters.get(DistributionSettings.TAU);

    size = y.getDimension();
    double[] out = new double[size];
    for (int i = 0; i < size; i++) {
        //d2ldvdt  <- (((sigma^2)*nu)/(2*tau))
        //*(1+(tau/3)+0.5*(digamma(1/tau)-digamma(3/tau)))
        out[i] = (((sigma.getEntry(i) * sigma.getEntry(i)) * nu.getEntry(i)) / (2 * tau.getEntry(i)))
                * (1 + (tau.getEntry(i) / 3)
                        + 0.5 * (Gamma.digamma(1 / tau.getEntry(i)) - Gamma.digamma(3 / tau.getEntry(i))));
    }
    return new ArrayRealVector(out, false);
}

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From source file:gamlss.distributions.BCPE.java

/** Computes the global Deviance Increament.
 * @param y - vector of response variable values
 * @return vector of global Deviance Increament values 
 *//*from   w ww. j av  a 2 s .c o  m*/
public final ArrayRealVector globalDevianceIncreament(final ArrayRealVector y) {
    // G.dev.incr  = function(y,mu,sigma,nu,tau,...)
    //-2*dBCPE(y,mu,sigma,nu,tau,log=TRUE),
    size = y.getDimension();
    double[] out = new double[size];

    double[] muArr = distributionParameters.get(DistributionSettings.MU).getDataRef();
    double[] sigmaArr = distributionParameters.get(DistributionSettings.SIGMA).getDataRef();
    double[] nuArr = distributionParameters.get(DistributionSettings.NU).getDataRef();
    double[] tauArr = distributionParameters.get(DistributionSettings.TAU).getDataRef();

    for (int i = 0; i < size; i++) {

        out[i] = (-2)
                * dBCPE(y.getEntry(i), muArr[i], sigmaArr[i], nuArr[i], tauArr[i], Controls.LOG_LIKELIHOOD);
    }
    return new ArrayRealVector(out, false);
}

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From source file:lirmm.inria.fr.math.BigSparseRealMatrixTest.java

private RealVector columnToVector(double[][] column) {
    double[] data = new double[column.length];
    for (int i = 0; i < data.length; ++i) {
        data[i] = column[i][0];//  w w w . j av  a  2 s.com
    }
    return new ArrayRealVector(data, false);
}

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From source file:org.knowrob.vis.model.util.algorithm.ACCUM.java

/**
 * Calculates curvature for the vertices of a triangle. Based on
 * the trimesh2 (2.12) (Szymon Rusinkiewicz Princeton University)
 * curvature calculation algorithm.//  w  w  w. j av a 2s  . co  m
 * 
 * @see <a href="https://github.com/fcole/qrtsc/tree/master/trimesh2">trimesh2</a>
 * 
 * @param curvatures
 *            vertex curvature mapping
 * @param tri
 *            triangle to calculate curvature for
 */
private static void calculateCurvatureForTriangle(HashMap<Vertex, Curvature> curvatures, Triangle tri) {
    // Edges
    Edge e[] = tri.getEdges();

    // N-T-B coordinate system per face
    Vector3f t = new Vector3f(e[0].getEdgeValue());
    t.normalize();
    Vector3f n = new Vector3f();
    n.cross(e[0].getEdgeValue(), e[1].getEdgeValue());
    //n.normalize();
    Vector3f b = new Vector3f();
    b.cross(n, t);
    b.normalize();

    // Estimate curvature based on variation of normals
    // along edges
    float m[] = { 0.0f, 0.0f, 0.0f };
    double w[][] = { { 0.0d, 0.0d, 0.0d }, { 0.0d, 0.0d, 0.0d }, { 0.0d, 0.0d, 0.0d } };
    for (int j = 0; j < 3; j++) {

        float u = e[j].getEdgeValue().dot(t);
        float v = e[j].getEdgeValue().dot(b);
        w[0][0] += u * u;
        w[0][1] += u * v;
        // w[1][1] += v*v + u*u;
        // w[1][2] += u*v;
        w[2][2] += v * v;
        Vector3f dn = new Vector3f(tri.getPosition()[(j + 2) % 3].getNormalVector());
        Vector3f dnn = new Vector3f(tri.getPosition()[(j + 1) % 3].getNormalVector());
        dn.sub(dnn);
        float dnu = dn.dot(t);
        float dnv = dn.dot(b);
        m[0] += dnu * u;
        m[1] += dnu * v + dnv * u;
        m[2] += dnv * v;
    }
    w[1][1] = w[0][0] + w[2][2];
    w[1][2] = w[0][1];

    RealMatrix coefficients = new Array2DRowRealMatrix(w, false);
    DecompositionSolver solver = new LUDecomposition(coefficients).getSolver();
    if (!solver.isNonSingular()) {
        return;
    }

    RealVector constants = new ArrayRealVector(new double[] { m[0], m[1], m[2] }, false);
    RealVector solution = solver.solve(constants);

    m[0] = (float) solution.getEntry(0);
    m[1] = (float) solution.getEntry(1);
    m[2] = (float) solution.getEntry(2);

    // flag for ill-solutioned system (NaN = no valid info on the curvatures => then consider 
    // the curvatures to be the ones of a simple plane)
    boolean okSolution = true;
    if (isNaN(m[0]) || isNaN(m[1]) || isNaN(m[2])) {
        okSolution = false;
    }

    // Push it back out to the vertices
    for (int j = 0; j < tri.getPosition().length; j++) {
        Vertex vj = tri.getPosition()[j];
        float c1, c12, c2;
        float[] ret;

        // guard against ill-conditioned systems (NaN curvatures are viewed as a simple planar
        // corresponding curves      
        if (okSolution) {
            ret = proj_curv(t, b, m[0], m[1], m[2], curvatures.get(vj).getPrincipleDirectionMax(),
                    curvatures.get(vj).getPrincipleDirectionMin());
            if (isNaN(ret[0]) || isNaN(ret[1]) || isNaN(ret[2])) {
                c1 = 0.0f;
                c12 = 0.0f;
                c2 = 0.0f;
            } else {
                c1 = ret[0];
                c12 = ret[1];
                c2 = ret[2];
            }
        } else {
            c1 = 0.0f;
            c12 = 0.0f;
            c2 = 0.0f;
        }

        Curvature c = curvatures.get(vj);

        double wt = 0.0d;
        if (vj.getPointarea() != 0.0f) {
            if (j == 0)
                wt = tri.getCornerarea().x / vj.getPointarea();
            else if (j == 1)
                wt = tri.getCornerarea().y / vj.getPointarea();
            else
                wt = tri.getCornerarea().z / vj.getPointarea();
        }

        synchronized (c) {
            c.setCurvatureMax((float) (c.getCurvatureMax() + wt * c1));
            c.setCurvatureMinMax((float) (c.getCurvatureMinMax() + wt * c12));
            c.setCurvatureMin((float) (c.getCurvatureMin() + wt * c2));
        }
    }
}

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From source file:org.lambda3.indra.core.annoy.AnnoyVectorSpace.java

@Override
protected Map<String, RealVector> collectVectors(Iterable<? extends String> terms) {
    Map<String, RealVector> vectors = new HashMap<>();

    for (String term : terms) {
        double[] v = getVector(term);
        if (v != null) {
            RealVector vector = new ArrayRealVector(v, false);
            vectors.put(term, vector);/*  ww w.java  2s  . c om*/
        }
    }

    return vectors;
}

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