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(ArrayRealVector v) throws NullArgumentException 

Source Link

Document

Construct a vector from another vector, using a deep copy.

Usage

From source file:gamlss.distributions.PE.java

/** Calculate and set initial value of nu.
 * @param y - vector of values of response variable
 * @return vector of initial values of nu
 *//*from  w w w. j  a  v  a 2 s .  c o  m*/
private ArrayRealVector setNuInitial(final ArrayRealVector y) {
    //nu.initial = expression( nu <- rep(1.8, length(y)))
    tempV = new ArrayRealVector(y.getDimension());
    tempV.set(1.8);
    return tempV;
}

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From source file:com.joptimizer.functions.SOCPLogarithmicBarrier.java

/**
 * Calculates the initial value for the s parameter in Phase I.
 * Return s = max(||Ai.x+bi|| - (ci.x+di))
 * @see "S.Boyd and L.Vandenberghe, Convex Optimization, 11.6.2"
 *///  www . j  av a 2  s  .c om
public double calculatePhase1InitialFeasiblePoint(double[] originalNotFeasiblePoint, double tolerance) {
    double s = -Double.MAX_VALUE;
    RealVector x = new ArrayRealVector(originalNotFeasiblePoint);
    for (int i = 0; i < socpConstraintParametersList.size(); i++) {
        SOCPConstraintParameters param = socpConstraintParametersList.get(i);
        RealMatrix A = param.getA();
        RealVector b = param.getB();
        RealVector c = param.getC();
        double d = param.getD();
        s = Math.max(s,
                (A.operate(x).subtract(b).getNorm() - (c.dotProduct(x) + d)) * Math.pow(tolerance, -0.5));
    }
    return s;
}

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From source file:gamlss.distributions.SST.java

/** Calculate and set initial value of sigma.
 * @param y - vector of values of response variable
 * @return vector of initial values of sigma
 *///  w w w.  j a  v  a 2s  .co  m
private ArrayRealVector setSigmaInitial(final ArrayRealVector y) {
    //sigma.initial = expression(sigma<- rep(sd(y), length(y))),
    tempV = new ArrayRealVector(y.getDimension());
    final double out = new StandardDeviation().evaluate(y.getDataRef());
    tempV.set(out);
    return tempV;
}

---

From source file:edu.utexas.cs.tactex.subscriptionspredictors.LWRCustOldAppache.java

private ArrayRealVector createNormalizedXVector(Set<Double> xValues, double min, double max) {
    Double[] dummy1 = new Double[1]; // needed to determine the type of toArray?     
    ArrayRealVector xVector = new ArrayRealVector(xValues.toArray(dummy1));
    xVector.mapSubtractToSelf(min);/* w  w w.  ja v  a 2 s  .co m*/
    xVector.mapDivideToSelf(max - min);
    // translating [0,1]=>[0.1,0.9]
    xVector.mapMultiplyToSelf(SQUEEZE);
    xVector.mapAddToSelf(OFFSET);
    return xVector;
}

---

From source file:edu.stanford.cfuller.imageanalysistools.fitting.BisquareLinearFitTest.java

@Test
public void testwls7() {

    for (int i = 0; i < this.x.getDimension(); i++) {
        this.x.setEntry(i, i);
        this.w.setEntry(i, 1);
    }//from w w  w .  j ava2s  .com

    double[] testY = { 5, 17, 8, 2, 1 };

    this.y = new ArrayRealVector(testY);

    this.expected.setEntry(0, 1.433);
    this.expected.setEntry(1, 0);

    this.bslf.disableIntercept();

    this.result = this.bslf.wlsFit(x, y, w);

    this.success &= (this.result.subtract(this.expected).getNorm() < this.eps);

    assertTrue(success);

}

---

From source file:gamlss.distributions.JSUo.java

/** Calculate and set initial value of nu.
 * @param y - vector of values of response variable
 * @return vector of initial values of nu
 *///from  w  w w  .ja  v a  2s .  c o  m
private ArrayRealVector setNuInitial(final ArrayRealVector y) {
    //nu.initial = expression(nu <- rep(0, length(y))),
    return new ArrayRealVector(y.getDimension());
}

---

From source file:gamlss.distributions.BCPE.java

/** Calculates initial value of sigma.
 * @param y - vector of values of response variable
 * @return - a vector of  initial values of sigma
 *//*from w  w w . j  av a 2s  .  c om*/
private ArrayRealVector setSigmaInitial(final ArrayRealVector y) {
    //sigma.initial = expression(sigma<- rep(0.1, length(y)))
    tempV = new ArrayRealVector(y.getDimension());
    tempV.set(0.1);
    return tempV;
}

---

From source file:edu.stanford.cfuller.imageanalysistools.fitting.BisquareLinearFit.java

/**
* Calculates the leverages of data points for least squares fitting (assuming equal variances).
* 
* @param indVarValues The values of the independent variable used for the fitting.
* @return a RealVector containing a leverage value for each independent variable value.
*//*from   www  .  j a  v a  2 s . co m*/
protected RealVector calculateLeverages(RealVector indVarValues) {

    RealMatrix indVarMatrix = null;

    if (this.noIntercept) {
        indVarMatrix = new Array2DRowRealMatrix(indVarValues.getDimension(), 1);
    } else {
        indVarMatrix = new Array2DRowRealMatrix(indVarValues.getDimension(), 2);
    }

    indVarMatrix.setColumnVector(0, indVarValues);

    if (!this.noIntercept) {
        indVarMatrix.setColumnVector(1, indVarValues.mapMultiply(0).mapAdd(1));
    }

    RealVector leverages = new ArrayRealVector(indVarValues.getDimension());

    QRDecomposition xQR = new QRDecomposition(indVarMatrix);

    RealMatrix xR = xQR.getR();

    int smallerDim = xR.getRowDimension() < xR.getColumnDimension() ? xR.getRowDimension()
            : xR.getColumnDimension();

    RealMatrix xRSq = xR.getSubMatrix(0, smallerDim - 1, 0, smallerDim - 1);

    QRDecomposition xRQR = new QRDecomposition(xRSq);

    RealMatrix xRInv = xRQR.getSolver().getInverse();

    RealMatrix xxRInv = indVarMatrix.multiply(xRInv);

    for (int i = 0; i < indVarValues.getDimension(); i++) {
        double sum = 0;
        for (int j = 0; j < xxRInv.getColumnDimension(); j++) {
            sum += Math.pow(xxRInv.getEntry(i, j), 2);
        }
        leverages.setEntry(i, sum);
    }

    return leverages;

}

---

From source file:de.andreasschoknecht.LS3.Query.java

/**
 * Calculate the LSSM values with respect to a document collection.
 *
 * @param Sk The matrix Sk of singular values
 * @param Vtk The matrix Vtk of the singular value decomposition
 */// w w  w  .  j  av  a 2  s. c  o m
void calculateLSSMValues(RealMatrix Sk, RealMatrix Vtk) {
    // scale Vtk with singular value matrix Sk
    RealMatrix scaledVtk = Sk.multiply(Vtk);

    // the query model as vector
    ArrayRealVector queryVector = new ArrayRealVector(pseudoDocument);

    int docsNumber = scaledVtk.getColumnDimension();
    lssmValues = new double[docsNumber];

    for (int i = 0; i < docsNumber; i++) {
        RealVector columnVector = scaledVtk.getColumnVector(i);
        lssmValues[i] = (queryVector.cosine(columnVector) + 1) / 2;
    }
}

---

From source file:gamlss.algorithm.TEST.java

private void fourParTest() {

    String fileName = "Data/distTest2.csv";
    CSVFileReader readData = new CSVFileReader(fileName);
    readData.readFile();//from   w  w  w  .j  a  v a  2s  .c om
    ArrayList<String> data = readData.storeValues;

    ArrayRealVector y = new ArrayRealVector(data.size());
    ArrayRealVector mu = new ArrayRealVector(data.size());
    ArrayRealVector sigma = new ArrayRealVector(data.size());
    ArrayRealVector nu = new ArrayRealVector(data.size());
    ArrayRealVector tau = new ArrayRealVector(data.size());

    for (int i = 0; i < data.size(); i++) {
        String[] line = data.get(i).split(",");
        y.setEntry(i, Double.parseDouble(line[0]));
        mu.setEntry(i, Double.parseDouble(line[1]));
        sigma.setEntry(i, Double.parseDouble(line[2]));
        nu.setEntry(i, Double.parseDouble(line[3]));
        tau.setEntry(i, Double.parseDouble(line[4]));
    }

    double[] outA = new double[y.getDimension()];
    String folder = "C:\\Users\\Daniil\\Desktop\\Gamlss_exp/outDistG.csv";
    ArrayRealVector out = null;

    SST dist = new SST();

    dist.setDistributionParameter(DistributionSettings.MU, mu);
    dist.setDistributionParameter(DistributionSettings.SIGMA, sigma);
    dist.setDistributionParameter(DistributionSettings.NU, nu);
    dist.setDistributionParameter(DistributionSettings.TAU, tau);
    //0   
    out = dist.firstDerivative(DistributionSettings.MU, y);
    MatrixFunctions.vectorWriteCSV(folder, out, false);
    //1
    out = dist.secondDerivative(DistributionSettings.MU, y);
    MatrixFunctions.vectorWriteCSV(folder, out, true);
    //2
    out = dist.firstDerivative(DistributionSettings.SIGMA, y);
    MatrixFunctions.vectorWriteCSV(folder, out, true);
    //3
    out = dist.secondDerivative(DistributionSettings.SIGMA, y);
    MatrixFunctions.vectorWriteCSV(folder, out, true);
    //4
    out = dist.secondCrossDerivative(DistributionSettings.MU, DistributionSettings.SIGMA, y);
    MatrixFunctions.vectorWriteCSV(folder, out, true);
    //5
    for (int i = 0; i < y.getDimension(); i++) {
        outA[i] = dist.dSST(y.getEntry(i), mu.getEntry(i), sigma.getEntry(i), nu.getEntry(i), tau.getEntry(i),
                false);
    }
    MatrixFunctions.vectorWriteCSV(folder, new ArrayRealVector(outA, false), true);
    //6
    for (int i = 0; i < y.getDimension(); i++) {
        outA[i] = dist.dSST(y.getEntry(i), mu.getEntry(i), sigma.getEntry(i), nu.getEntry(i), tau.getEntry(i),
                true);
    }
    MatrixFunctions.vectorWriteCSV(folder, new ArrayRealVector(outA, false), true);
    //7
    for (int i = 0; i < y.getDimension(); i++) {
        outA[i] = dist.pSST(y.getEntry(i), mu.getEntry(i), sigma.getEntry(i), nu.getEntry(i), tau.getEntry(i),
                true, false);
        //System.out.println(outA[i]);
    }
    MatrixFunctions.vectorWriteCSV(folder, new ArrayRealVector(outA, false), true);
    //8
    for (int i = 0; i < y.getDimension(); i++) {
        outA[i] = dist.pSST(y.getEntry(i), mu.getEntry(i), sigma.getEntry(i), nu.getEntry(i), tau.getEntry(i),
                false, true);
    }
    MatrixFunctions.vectorWriteCSV(folder, new ArrayRealVector(outA, false), true);
    //9
    for (int i = 0; i < y.getDimension(); i++) {
        outA[i] = dist.pSST(y.getEntry(i), mu.getEntry(i), sigma.getEntry(i), nu.getEntry(i), tau.getEntry(i),
                true, false);
    }
    MatrixFunctions.vectorWriteCSV(folder, new ArrayRealVector(outA, false), true);
    //10
    for (int i = 0; i < y.getDimension(); i++) {
        outA[i] = dist.pSST(y.getEntry(i), mu.getEntry(i), sigma.getEntry(i), nu.getEntry(i), tau.getEntry(i),
                false, false);
    }
    MatrixFunctions.vectorWriteCSV(folder, new ArrayRealVector(outA, false), true);
    //11
    for (int i = 0; i < y.getDimension(); i++) {
        //         outA[i] = dist.qSST(y.getEntry(i), mu.getEntry(i), sigma.getEntry(i),nu.getEntry(i),tau.getEntry(i), true, true);
    }
    //      MatrixFunctions.vectorWriteCSV(folder, new ArrayRealVector(outA, false), true);
    //12
    for (int i = 0; i < y.getDimension(); i++) {
        //         outA[i] = dist.qSST(y.getEntry(i), mu.getEntry(i), sigma.getEntry(i),nu.getEntry(i),tau.getEntry(i), false, true);
    }
    //      MatrixFunctions.vectorWriteCSV(folder, new ArrayRealVector(outA, false), true);
    //13
    for (int i = 0; i < y.getDimension(); i++) {
        outA[i] = dist.qSST(y.getEntry(i), mu.getEntry(i), sigma.getEntry(i), nu.getEntry(i), tau.getEntry(i),
                true, false);
    }
    MatrixFunctions.vectorWriteCSV(folder, new ArrayRealVector(outA, false), true);
    //14
    for (int i = 0; i < y.getDimension(); i++) {
        outA[i] = dist.qSST(y.getEntry(i), mu.getEntry(i), sigma.getEntry(i), nu.getEntry(i), tau.getEntry(i),
                false, false);
    }
    MatrixFunctions.vectorWriteCSV(folder, new ArrayRealVector(outA, false), true);
    //15
    out = dist.firstDerivative(DistributionSettings.NU, y);
    MatrixFunctions.vectorWriteCSV(folder, out, true);
    //16
    out = dist.secondDerivative(DistributionSettings.NU, y);
    MatrixFunctions.vectorWriteCSV(folder, out, true);
    //17
    out = dist.secondCrossDerivative(DistributionSettings.MU, DistributionSettings.NU, y);
    MatrixFunctions.vectorWriteCSV(folder, out, true);
    //18
    out = dist.secondCrossDerivative(DistributionSettings.SIGMA, DistributionSettings.NU, y);
    MatrixFunctions.vectorWriteCSV(folder, out, true);
    //19
    out = dist.firstDerivative(DistributionSettings.TAU, y);
    MatrixFunctions.vectorWriteCSV(folder, out, true);
    //20
    out = dist.secondDerivative(DistributionSettings.TAU, y);
    MatrixFunctions.vectorWriteCSV(folder, out, true);
    //21
    out = dist.secondCrossDerivative(DistributionSettings.MU, DistributionSettings.TAU, y);
    MatrixFunctions.vectorWriteCSV(folder, out, true);
    //22
    out = dist.secondCrossDerivative(DistributionSettings.SIGMA, DistributionSettings.TAU, y);
    MatrixFunctions.vectorWriteCSV(folder, out, true);
    //23
    out = dist.secondCrossDerivative(DistributionSettings.NU, DistributionSettings.TAU, y);
    MatrixFunctions.vectorWriteCSV(folder, out, true);

    System.out.println("Done !!!");

}

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