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

/** Second derivative d2ldm2= (d^2l)/(dmu^2),
 *  where l - log-likelihood function.//from w w  w  .j a v  a 2s  .  co m
 * @param y - vector of values of response variable
 * @return  a vector of second derivative d2ldm2= (d^2l)/(dmu^2)
 */
private ArrayRealVector d2ldm2(final ArrayRealVector y) {

    double[] out = new double[size];
    for (int i = 0; i < size; i++) {
        //d2ldm2 <- -dldm*dldm
        out[i] = -dldm[i] * dldm[i];
    }
    muV = null;
    sigmaV = null;
    nuV = null;
    tauV = null;
    return new ArrayRealVector(out, false);
}

---

From source file:gamlss.distributions.PE.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
 *///  www . ja  va  2 s .  c om
private ArrayRealVector d2ldsdn(final ArrayRealVector y) {

    ArrayRealVector sigmaT = distributionParameters.get(DistributionSettings.SIGMA);
    ArrayRealVector nuT = distributionParameters.get(DistributionSettings.NU);

    double[] out = new double[size];
    for (int i = 0; i < size; i++) {

        //d2ldddv = function(y,mu,sigma,nu) (1/(2*sigma))
        //*((3/nu)*(digamma(1/nu)-digamma(3/nu))+2+(2/nu))
        out[i] = (1 / (2 * sigmaT.getEntry(i))) * ((3 / nuT.getEntry(i))
                * (Gamma.digamma(1 / nuT.getEntry(i)) - Gamma.digamma(3 / nuT.getEntry(i))) + 2
                + (2 / nuT.getEntry(i)));
    }
    return new ArrayRealVector(out, false);
}

---

From source file:gamlss.distributions.SST.java

/** Second derivative d2lds2= (d^2l)/(dsigma^2), 
 * where l - log-likelihood function./*from  w w w. ja va2  s .c  o  m*/
 * @param y - vector of values of response variable
 * @return  a vector of second derivative d2lds2= (d^2l)/(dsigma^2)
 */
private ArrayRealVector d2lds2(final ArrayRealVector y) {

    double[] out = new double[size];
    for (int i = 0; i < size; i++) {
        //d2ldd2 <- -dldd*dldd
        out[i] = -dlds[i] * dlds[i];
    }
    muV = null;
    sigmaV = null;
    nuV = null;
    tauV = null;
    return new ArrayRealVector(out, false);

}

---

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

/**
 * test examples in user guide/*from   w ww .j  av a2  s.c  o m*/
 */
@Test
public void testExamples() {
    // Create a real matrix with two rows and three columns
    double[][] matrixData = { { 1d, 2d, 3d }, { 2d, 5d, 3d } };
    RealMatrix m = new BigSparseRealMatrix(matrixData);
    // One more with three rows, two columns
    double[][] matrixData2 = { { 1d, 2d }, { 2d, 5d }, { 1d, 7d } };
    RealMatrix n = new BigSparseRealMatrix(matrixData2);
    // Now specialOperation m by n
    RealMatrix p = m.multiply(n);
    Assert.assertEquals(2, p.getRowDimension());
    Assert.assertEquals(2, p.getColumnDimension());
    // Invert p
    RealMatrix pInverse = new LUDecomposition(p).getSolver().getInverse();
    Assert.assertEquals(2, pInverse.getRowDimension());
    Assert.assertEquals(2, pInverse.getColumnDimension());

    // Solve example
    double[][] coefficientsData = { { 2, 3, -2 }, { -1, 7, 6 }, { 4, -3, -5 } };
    RealMatrix coefficients = new BigSparseRealMatrix(coefficientsData);
    RealVector constants = new ArrayRealVector(new double[] { 1, -2, 1 }, false);
    RealVector solution = new LUDecomposition(coefficients).getSolver().solve(constants);
    final double cst0 = constants.getEntry(0);
    final double cst1 = constants.getEntry(1);
    final double cst2 = constants.getEntry(2);
    final double sol0 = solution.getEntry(0);
    final double sol1 = solution.getEntry(1);
    final double sol2 = solution.getEntry(2);
    Assert.assertEquals(2 * sol0 + 3 * sol1 - 2 * sol2, cst0, 1E-12);
    Assert.assertEquals(-1 * sol0 + 7 * sol1 + 6 * sol2, cst1, 1E-12);
    Assert.assertEquals(4 * sol0 - 3 * sol1 - 5 * sol2, cst2, 1E-12);
}

---

From source file:gamlss.distributions.TF2.java

/** Computes the global Deviance Increament.
 * @param y - vector of response variable values
 * @return vector of global Deviance Increament values 
 *//*  w w  w .j av  a2 s  . co m*/
public final ArrayRealVector globalDevianceIncreament(final ArrayRealVector y) {
    //G.dev.incr  = function(y,mu,sigma,nu,tau,...)  
    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();

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

        out[i] = (-2) * dTF2(y.getEntry(i), muArr[i], sigmaArr[i], nuArr[i], Controls.LOG_LIKELIHOOD);
    }
    return new ArrayRealVector(out, false);
}

---

From source file:gamlss.distributions.GT.java

/** Second cross derivative of likelihood function in 
 * respect to mu and sigma (d2ldmdd = d2l/dmu*dsigma).
 * @param y - vector of values of response variable
 * @return  a vector of Second cross derivative
 *//* w  w w.  ja  v a 2  s .com*/
private ArrayRealVector d2ldmds(final ArrayRealVector y) {
    double[] out = new double[size];
    for (int i = 0; i < size; i++) {
        // d2ldmdd <- -(dldm*dldd)
        out[i] = -dldm[i] * dlds[i];
    }
    return new ArrayRealVector(out, false);
}

---

From source file:gamlss.distributions.BCPE.java

/** Second derivative d2ldm2= (d^2l)/(dmu^2), 
 * where l - log-likelihood function./*from   www  .j a v a2s  . c  o  m*/
 * @param y - vector of values of response variable
 * @return  a vector of second derivative d2ldm2= (d^2l)/(dmu^2)
*/
private ArrayRealVector d2ldm2(final ArrayRealVector y) {

    double[] out = new double[size];
    for (int i = 0; i < size; i++) {

        //d2ldm2 <- if (any(tau<1.05)) -dldm*dldm else d2ldm2
        if (tauV.getEntry(i) < 1.05) {
            out[i] = -dldm[i] * dldm[i];
        } else {

            //d2ldm2 <- -((tau*tau)*gamma(2-(1/tau))*gamma(3/tau))
            ///(mu^2*sigma^2*(gamma(1/tau))^2)
            //d2ldm2 <- d2ldm2-(tau*nu^2)/mu^2
            out[i] = -((tauV.getEntry(i) * tauV.getEntry(i))
                    * FastMath.exp(Gamma.logGamma(2 - (1 / tauV.getEntry(i))))
                    * FastMath.exp(Gamma.logGamma(3 / tauV.getEntry(i))))
                    / (muV.getEntry(i) * muV.getEntry(i) * sigmaV.getEntry(i) * sigmaV.getEntry(i)
                            * (FastMath.exp(Gamma.logGamma(1 / tauV.getEntry(i))))
                            * (FastMath.exp(Gamma.logGamma(1 / tauV.getEntry(i)))));
            out[i] = out[i] - (tauV.getEntry(i) * nuV.getEntry(i) * nuV.getEntry(i))
                    / (muV.getEntry(i) * muV.getEntry(i));
        }
    }
    muV = null;
    sigmaV = null;
    nuV = null;
    tauV = null;
    return new ArrayRealVector(out, false);
}

---

From source file:gamlss.distributions.PE.java

/** Computes the global Deviance Increament.
 * @param y - vector of response variable values
 * @return vector of global Deviance Increament values 
 *//*  ww w. java  2 s.co 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();

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

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

---

From source file:gamlss.distributions.SST.java

/** Second derivative d2ldn2= (d^2l)/(dnu^2), 
 * where l - log-likelihood function./*  w w  w .  jav a 2 s  .c o  m*/
 * @param y - vector of values of response variable
 * @return  a vector of second derivative d2ldn2= (d^2l)/(dnu^2)
 */
private ArrayRealVector d2ldn2(final ArrayRealVector y) {

    double[] out = new double[size];
    for (int i = 0; i < size; i++) {
        //d2ldv2 <- -dldv*dldv
        out[i] = -dldn[i] * dldn[i];
    }
    muV = null;
    sigmaV = null;
    nuV = null;
    tauV = null;
    return new ArrayRealVector(out, false);
}

---

From source file:gamlss.distributions.GT.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  .  c  o m*/
private ArrayRealVector d2ldmdn(final ArrayRealVector y) {
    double[] out = new double[size];
    for (int i = 0; i < size; i++) {
        //d2ldmdv <- -(dldm*dldv)
        out[i] = -dldm[i] * dldn[i];
    }
    return new ArrayRealVector(out, false);
}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• 15
• 16
• 17
• 18
• 19
• 20
• »