Example usage for org.apache.commons.math.linear RealVector getDimension

Introduction

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

Prototype

int getDimension();

Source Link

Document

Returns the size of the vector.

Usage

From source file:fi.smaa.libror.MaximalVectorComputation.java

/**
 * Checks whether v1 dominates v2./*www. j a v  a 2 s .  com*/
 * 
 * @param v1
 * @param v2
 * @return true, if v1 \succ v2, false otherwise
 */
public static boolean dominates(RealVector v1, RealVector v2) {
    assert (v1.getDimension() == v2.getDimension());

    boolean largerFound = false;

    for (int i = 0; i < v1.getDimension(); i++) {
        if (v1.getEntry(i) > v2.getEntry(i)) {
            largerFound = true;
        } else if (v1.getEntry(i) < v2.getEntry(i)) {
            return false;
        }
    }
    return largerFound;
}

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From source file:gda.spring.propertyeditors.RealVectorPropertyEditorTest.java

protected void assertVectorsEqual(double[] expected, RealVector actual) {
    assertEquals("Length does not match", expected.length, actual.getDimension());
    for (int i = 0; i < expected.length; i++) {
        assertEquals("Element " + i + " does not match", expected[i], actual.getData()[i]);
    }/*www  . java2 s.  co m*/
}

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From source file:fi.smaa.libror.RejectionValueFunctionSampler.java

private FullValueFunction sampleValueFunction() throws SamplingException {
    int curIter = 1;

    do {//w ww .  jav a  2  s  .c  o  m
        FullValueFunction vf = new FullValueFunction();
        for (int i = 0; i < model.getNrCriteria(); i++) {
            RealVector lvls = model.getPerfMatrix().getLevels()[i];
            PartialValueFunction pvf = new PartialValueFunction(lvls.getDimension());
            vf.addValueFunction(pvf);
            sampleRandomPartialVF(pvf);
        }

        double[] w = new double[model.getNrCriteria()];
        RandomUtil.createSumToOneRand(w);
        vf.setWeights(w);
        if (acceptance.check(vf)) {
            return vf;
        }
        misses++;
        curIter++;
    } while (curIter <= maxTries);
    throw new SamplingException("Cannot sample a VF within " + maxTries + " iterations");
}

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From source file:com.opengamma.analytics.math.linearalgebra.SVDecompositionCommonsResultTest.java

private void assertRealVectorEquals(final RealVector v1, final DoubleMatrix1D v2) {
    final int n = v1.getDimension();
    assertEquals(n, v2.getNumberOfElements());
    for (int i = 0; i < n; i++) {
        assertEquals(v1.getEntry(i), v2.getEntry(i), EPS);
    }/* ww  w  . ja va 2s  . c om*/
}

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From source file:fi.smaa.libror.UTAGMSSolver.java

private int getNrLPVariables() {
    // utility of all alts + epsilon
    int sum = 0;/*  w  ww  . ja va  2s.c o m*/
    for (RealVector vec : model.getPerfMatrix().getLevels()) {
        sum += vec.getDimension();
    }
    return sum + 1;
}

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From source file:fi.smaa.libror.UTAGMSSolver.java

private List<LinearConstraint> buildMonotonousConstraints(int critIndex) {
    List<LinearConstraint> constList = new ArrayList<LinearConstraint>();
    RealVector levels = model.getPerfMatrix().getLevels()[critIndex];
    for (int i = 0; i < levels.getDimension() - 1; i++) {
        double[] lhs = new double[getNrLPVariables()];
        double[] rhs = new double[getNrLPVariables()];
        lhs[getConstraintOffset(critIndex) + i] = 1.0;
        rhs[getConstraintOffset(critIndex) + i + 1] = 1.0;
        if (strictValueFunctions) {
            lhs[lhs.length - 1] = 1.0; // epsilon
        }/*  w w w .j  a v  a  2 s  .co  m*/
        constList.add(new LinearConstraint(lhs, 0.0, Relationship.LEQ, rhs, 0.0));
    }
    return constList;
}

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From source file:eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.java

/**
 * {@inheritDoc}//from  ww  w .j  a v  a  2  s  .  c o m
 */
@Override
public void updateNaturalFromMomentParameters() {

    /*
     * First step: means and convariances
     */
    CompoundVector globalMomentParam = (CompoundVector) this.momentParameters;
    double mean_X = globalMomentParam.getXYbaseMatrix().getEntry(0);
    RealVector mean_Y = globalMomentParam.getTheta_beta0BetaRV();

    double cov_XX = globalMomentParam.getcovbaseMatrix().getEntry(0, 0) - mean_X * mean_X;
    RealMatrix cov_YY = globalMomentParam.getcovbaseMatrix().getSubMatrix(1, nOfParents, 1, nOfParents)
            .subtract(mean_Y.outerProduct(mean_Y));
    RealVector cov_XY = globalMomentParam.getcovbaseMatrix().getSubMatrix(0, 0, 1, nOfParents).getRowVector(0)
            .subtract(mean_Y.mapMultiply(mean_X));
    //RealVector cov_YX = cov_XY; //outerProduct transposes the vector automatically

    /*
     * Second step: betas and variance
     */
    RealMatrix cov_YYInverse = new LUDecompositionImpl(cov_YY).getSolver().getInverse();
    RealVector beta = cov_YYInverse.preMultiply(cov_XY);

    this.betas = new double[beta.getDimension()];
    for (int i = 0; i < beta.getDimension(); i++) {
        this.betas[i] = beta.getEntry(i);
    }
    this.beta0 = mean_X - beta.dotProduct(mean_Y);
    this.variance = cov_XX - beta.dotProduct(cov_XY);

}

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From source file:datafu.pig.hash.lsh.metric.MetricUDF.java

/**
 * This UDF expects a query vector as the first element, a threshold (double) as the second, and a bag of vectors.
 * Vectors are represented by tuples with doubles as elements or bags of tuples representing position and value
 * in the case of sparse vectors./*  w ww.  ja  v  a  2  s . c o  m*/
 * 
 * It returns one of the tuples of the bag of vectors.  For an example of its use, please see datafu.pig.hash.lsh.CosineDistanceHash.
 * 
 * @see datafu.pig.hash.lsh.CosineDistanceHash
 */
@Override
public Tuple exec(Tuple input) throws IOException {
    Object firstElement = input.get(0);
    double distanceRange = ((Number) input.get(1)).doubleValue();
    DataBag vectorBag = (DataBag) input.get(2);
    RealVector referenceVector = null;
    if (firstElement instanceof Tuple) {
        //in which case the first element is a non-sparse tuple
        referenceVector = DataTypeUtil.INSTANCE.convert((Tuple) firstElement, dim);
    } else {
        //in which case the first element is a bag, representing a sparse tuple
        referenceVector = DataTypeUtil.INSTANCE.convert(input, dim);
    }

    for (Tuple vecTuple : vectorBag) {
        Object vectorObj = vecTuple.get(0);
        RealVector v2 = null;
        if (vectorObj instanceof Tuple) {
            v2 = DataTypeUtil.INSTANCE.convert((Tuple) vecTuple.get(0), referenceVector.getDimension());
        } else {
            v2 = DataTypeUtil.INSTANCE.convert(vecTuple, referenceVector.getDimension());
        }
        double dist = dist(referenceVector, v2);
        if (dist < distanceRange) {
            return vecTuple;
        }
    }
    return null;
}

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