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.TF.java

/** Second derivative d2ldn2= (d^2l)/(dnu^2), 
 * where l - log-likelihood function.//  w  ww  .ja  va 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 <- trigamma(v3)-trigamma(v2)+(2*(nu+5))/(nu*(nu+1)*(nu+3))
        out[i] = Gamma.trigamma(v3[i]) - Gamma.trigamma(v2[i]) + (2.0 * (nuV.getEntry(i) + 5))
                / (nuV.getEntry(i) * (nuV.getEntry(i) + 1) * (nuV.getEntry(i) + 3));

        //d2ldv2 <- d2ldv2/4
        out[i] = out[i] / 4.0;

        //d2ldv2 <- ifelse(d2ldv2 < -1e-15, d2ldv2,-1e-15)
        if (out[i] > -1e-15) {
            out[i] = -1e-15;
        }
    }
    return new ArrayRealVector(out, false);
}

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From source file:gamlss.distributions.ST1.java

/** First derivative dldn = dl/dnu, where l - log-likelihood function.
 * @param y - vector of values of response variable
 * @return  a vector of First derivative dldn = dl/dnu
 *//*from   w  w  w . ja v  a  2  s.  c om*/
public final ArrayRealVector dldn(final ArrayRealVector y) {

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

        //dwdv <- w/nu
        //dldv <- (dt(w,df=tau)/pt(w,df=tau))*dwdv
        tdDist.setDegreesOfFreedom(tauV.getEntry(i));
        dldn[i] = (tdDist.density(w[i]) / tdDist.cumulativeProbability(w[i])) * w[i] / nuV.getEntry(i);
    }
    z = null;
    w = null;
    lam = null;
    return new ArrayRealVector(dldn, false);
}

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From source file:gamlss.distributions.JSUo.java

/** Second derivative d2lds2= (d^2l)/(dsigma^2), 
 * where l - log-likelihood function.//  ww  w . java 2s  . c om
 * @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];
        if (out[i] > -1e-15) {
            out[i] = -1e-15;
        }
    }
    muV = null;
    sigmaV = null;
    nuV = null;
    tauV = null;
    return new ArrayRealVector(out, false);

}

---

From source file:gamlss.utilities.MatrixFunctions.java

/**
* Calculate sqrt of the the vector entries.
* @param v - vector/*from  w w  w  .j a v  a  2  s  .c o m*/
* @return sqrt(vector)
*/
public static ArrayRealVector sqrtVec(final ArrayRealVector v) {
    int size = v.getDimension();
    double[] tempArr = new double[size];
    for (int i = 0; i < size; i++) {
        tempArr[i] = FastMath.sqrt(v.getEntry(i));
    }
    return new ArrayRealVector(tempArr, false);
}

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From source file:com.bolatu.gezkoncsvlogger.GyroOrientation.RotationKalmanFilter.java

/**
 * Correct the current state estimate with an actual measurement.
 *
 * @param z//ww  w .  ja  v a  2 s . co  m
 *            the measurement vector
 * @throws NullArgumentException
 *             if the measurement vector is {@code null}
 * @throws DimensionMismatchException
 *             if the dimension of the measurement vector does not fit
 * @throws SingularMatrixException
 *             if the covariance matrix could not be inverted
 */
public void correct(final double[] z)
        throws NullArgumentException, DimensionMismatchException, SingularMatrixException {
    correct(new ArrayRealVector(z, false));
}

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From source file:gamlss.distributions.PE.java

/** Second derivative d2lds2= (d^2l)/(dsigma^2), 
 * where l - log-likelihood function./*from w ww .j a  v a  2 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 <- -nu/(sigma^2)
        out[i] = -nuV.getEntry(i) / (sigmaV.getEntry(i) * sigmaV.getEntry(i));
    }
    muV = null;
    sigmaV = null;
    nuV = null;
    return new ArrayRealVector(out, false);
}

---

From source file:gamlss.distributions.TF2.java

/** Second derivative d2ldn2= (d^2l)/(dnu^2), 
 * where l - log-likelihood function.//from   w  w  w  . j a  va  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) {

    //d2ldv2 <- d2ldv2 + (ds1dv^2)*TF()$d2ldd2(sigma1, nu)
    tf.setDistributionParameter(DistributionSettings.MU, muV);
    tf.setDistributionParameter(DistributionSettings.SIGMA, sigma1);
    tf.setDistributionParameter(DistributionSettings.NU, nuV);

    tf.firstDerivative(DistributionSettings.SIGMA, y);
    tempV = tf.secondDerivative(DistributionSettings.SIGMA, y);

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

        //v2 <- nu/2
        //v3 <-(nu+1)/2
        //d2ldv2 <- trigamma(v3)-trigamma(v2)+(2*(nu+5))/(nu*(nu+1)*(nu+3))
        //d2ldv2 <- d2ldv2/4
        out[i] = (Gamma.trigamma((nuV.getEntry(i) + 1) / 2.0) - Gamma.trigamma(nuV.getEntry(i) / 2.0)
                + (2.0 * (nuV.getEntry(i) + 5))
                        / (nuV.getEntry(i) * (nuV.getEntry(i) + 1) * (nuV.getEntry(i) + 3)))
                / 4.0;
        out[i] = out[i] + (ds1dv[i] * ds1dv[i]) * tempV.getEntry(i);
    }
    sigma1 = null;
    ds1dv = null;
    muV = null;
    sigmaV = null;
    nuV = null;
    return new ArrayRealVector(out, false);
}

---

From source file:gamlss.distributions.ST1.java

/** First derivative dldtau = dl/dtau, where l - log-likelihood function.
 * @param y - vector of values of response variable
 * @return  a vector of First derivative dldtau = dl/dtau
 *///  www.ja  v  a 2  s .co  m
public final ArrayRealVector dldt(final ArrayRealVector y) {

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

        //j <- (pt(w,df=tau+0.001,log.p=TRUE)
        //-pt(w,df=tau-0.001,log.p=TRUE))/0.002
        tdDist.setDegreesOfFreedom(tauV.getEntry(i) + 0.001);
        final double part1 = FastMath.log(tdDist.cumulativeProbability(w[i]));

        tdDist.setDegreesOfFreedom(tauV.getEntry(i) - 0.001);
        final double part2 = FastMath.log(tdDist.cumulativeProbability(w[i]));

        final double j = (part1 - part2) / 0.002;

        //dldt <- j + (digamma((tau+1)/2)-digamma(tau/2)
        //-(1/tau)-log(1+(z^2)/tau)+lam*(z^2)/tau)/2
        dldt[i] = j + (Gamma.digamma((tauV.getEntry(i) + 1) / 2) - Gamma.digamma(tauV.getEntry(i) / 2)
                - (1 / tauV.getEntry(i)) - FastMath.log(1 + (z[i] * z[i]) / tauV.getEntry(i))
                + lam[i] * (z[i] * z[i]) / tauV.getEntry(i)) / 2.0;
    }
    z = null;
    w = null;
    lam = null;
    return new ArrayRealVector(dldt, false);
}

---

From source file:gamlss.utilities.MatrixFunctions.java

/**
* Calculate log of the the vector entries.
* @param v - vector/*  w  ww.  ja  v a 2 s  .  c  o  m*/
* @return log(vector)
*/
public static ArrayRealVector logVec(final ArrayRealVector v) {
    int size = v.getDimension();
    double[] tempArr = new double[size];
    for (int i = 0; i < size; i++) {
        tempArr[i] = FastMath.log(v.getEntry(i));
    }
    return new ArrayRealVector(tempArr, false);
}

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From source file:gamlss.distributions.GT.java

/** Second derivative d2ldm2= (d^2l)/(dmu^2),
 *  where l - log-likelihood function./*from w  w w .j a va 2 s.  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];
        //d2ldm2 <- ifelse(d2ldm2 < -1e-15, d2ldm2,-1e-15)
        if (out[i] > -1e-15) {
            out[i] = -1e-15;
        }
    }
    muV = null;
    sigmaV = null;
    nuV = null;
    tauV = null;
    return new ArrayRealVector(out, false);
}

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