Example usage for org.apache.commons.math3.linear RealVector getDimension

Introduction

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

Prototype

public abstract int getDimension();

Source Link

Document

Returns the size of the vector.

Usage

From source file:com.cloudera.oryx.common.math.OpenMapRealVector.java

@Override
public double getDistance(RealVector v) {
    checkVectorDimensions(v.getDimension());
    if (v instanceof OpenMapRealVector) {
        return getDistance((OpenMapRealVector) v);
    } else {//  w w w. ja v  a  2 s  . c  om
        return super.getDistance(v);
    }
}

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From source file:com.cloudera.oryx.common.math.OpenMapRealVector.java

@Override
public double getL1Distance(RealVector v) {
    checkVectorDimensions(v.getDimension());
    if (v instanceof OpenMapRealVector) {
        return getL1Distance((OpenMapRealVector) v);
    } else {// w  w  w  . jav  a2  s  .  c o m
        return super.getL1Distance(v);
    }
}

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From source file:com.cloudera.oryx.common.math.OpenMapRealVector.java

@Override
public double getLInfDistance(RealVector v) {
    checkVectorDimensions(v.getDimension());
    if (v instanceof OpenMapRealVector) {
        return getLInfDistance((OpenMapRealVector) v);
    } else {/*w w w.  j  a va 2  s  .com*/
        return super.getLInfDistance(v);
    }
}

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From source file:com.cloudera.oryx.common.math.OpenMapRealVector.java

@Override
public RealVector subtract(RealVector v) {
    checkVectorDimensions(v.getDimension());
    if (v instanceof OpenMapRealVector) {
        return subtract((OpenMapRealVector) v);
    } else {/*  w  w  w  .j a  v  a  2s.com*/
        return super.subtract(v);
    }
}

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From source file:com.cloudera.oryx.common.math.OpenMapRealVector.java

@SuppressWarnings("deprecation")
@Deprecated//from w  w w .j a  va  2 s .  c  o m
@Override
public OpenMapRealVector ebeDivide(RealVector v) {
    checkVectorDimensions(v.getDimension());
    OpenMapRealVector res = new OpenMapRealVector(this);
    /*
     * MATH-803: it is not sufficient to loop through non zero entries of
     * this only. Indeed, if this[i] = 0d and v[i] = 0d, then
     * this[i] / v[i] = NaN, and not 0d.
     */
    int n = getDimension();
    for (int i = 0; i < n; i++) {
        res.setEntry(i, this.getEntry(i) / v.getEntry(i));
    }
    return res;
}

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From source file:com.cloudera.oryx.common.math.OpenMapRealVector.java

@SuppressWarnings("deprecation")
@Deprecated/*from  ww w  .ja v a2  s.  c  o  m*/
@Override
public OpenMapRealVector ebeMultiply(RealVector v) {
    checkVectorDimensions(v.getDimension());
    OpenMapRealVector res = new OpenMapRealVector(this);
    OpenIntToDoubleHashMap.Iterator iter = entries.iterator();
    while (iter.hasNext()) {
        iter.advance();
        res.setEntry(iter.key(), iter.value() * v.getEntry(iter.key()));
    }
    /*
     * MATH-803: the above loop assumes that 0d * x  = 0d for any double x,
     * which allows to consider only the non-zero entries of this. However,
     * this fails if this[i] == 0d and (v[i] = NaN or v[i] = Infinity).
     *
     * These special cases are handled below.
     */
    if (v.isNaN() || v.isInfinite()) {
        int n = getDimension();
        for (int i = 0; i < n; i++) {
            double y = v.getEntry(i);
            if (Double.isNaN(y)) {
                res.setEntry(i, Double.NaN);
            } else if (Double.isInfinite(y)) {
                double x = this.getEntry(i);
                res.setEntry(i, x * y);
            }
        }
    }
    return res;
}

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From source file:com.cloudera.oryx.common.math.OpenMapRealVector.java

@Override
public void setSubVector(int index, RealVector v) {
    checkIndex(index);//from   w w w .j  a v a 2  s  . c o m
    checkIndex(index + v.getDimension() - 1);
    for (int i = 0; i < v.getDimension(); i++) {
        setEntry(i + index, v.getEntry(i));
    }
}

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

/**
* Calculates the standardized adjusted residuals (according to the same definition used by MATLAB) of the data points for fitting.
* 
* @param indVarValues The values of the independent variable used for the fitting.
* @param depVarValues The values of the dependent variable used for the fitting.
* @param leverages the leverages of the independent variables, as compted by {@link #calculateLeverages(RealVector)}
* @param fitParams the results of a (possibly weighted) least squares fit to the data, containing one or two components: a slope and an optional y-intercept.
* @return a RealVector containing an adjusted residual value for each data point
*//*from www  .ja  v  a  2s.  co  m*/
protected RealVector calculateStandardizedAdjustedResiduals(RealVector indVarValues, RealVector depVarValues,
        RealVector leverages, RealVector fitParams) {

    RealVector predictedValues = indVarValues.mapMultiply(fitParams.getEntry(0));

    RealVector denom = leverages.mapMultiply(-1.0).mapAddToSelf(1 + this.CLOSE_TO_ZERO)
            .mapToSelf(new org.apache.commons.math3.analysis.function.Sqrt());

    if (!this.noIntercept) {
        predictedValues = predictedValues.mapAdd(fitParams.getEntry(1));
    }

    double stddev = 0;
    double mean = 0;

    for (int i = 0; i < depVarValues.getDimension(); i++) {
        mean += depVarValues.getEntry(i);
    }

    mean /= depVarValues.getDimension();

    stddev = depVarValues.mapSubtract(mean).getNorm()
            * (depVarValues.getDimension() * 1.0 / (depVarValues.getDimension() - 1));

    RealVector residuals = depVarValues.subtract(predictedValues).ebeDivide(denom);

    RealVector absDev = residuals.map(new org.apache.commons.math3.analysis.function.Abs());

    int smallerDim = 2;

    if (this.noIntercept) {
        smallerDim = 1;
    }

    double[] resArray = residuals.map(new org.apache.commons.math3.analysis.function.Abs()).toArray();

    java.util.Arrays.sort(resArray);

    RealVector partialRes = new ArrayRealVector(absDev.getDimension() - smallerDim + 1, 0.0);

    for (int i = smallerDim - 1; i < resArray.length; i++) {
        partialRes.setEntry(i - smallerDim + 1, resArray[i]);
    }

    double resMAD = 0;

    if (partialRes.getDimension() % 2 == 0) {
        resMAD = LocalBackgroundEstimationFilter.quickFindKth(partialRes.getDimension() / 2, partialRes)
                + LocalBackgroundEstimationFilter.quickFindKth(partialRes.getDimension() / 2 - 1, partialRes);
        resMAD /= 2.0;
    } else {
        resMAD = LocalBackgroundEstimationFilter.quickFindKth((partialRes.getDimension() - 1) / 2, partialRes);
    }

    resMAD /= 0.6745;

    if (resMAD < stddev * CLOSE_TO_ZERO) {
        resMAD = stddev * CLOSE_TO_ZERO;
    }

    return residuals.mapDivide(DEFAULT_TUNING_CONST * resMAD);

}

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From source file:com.cloudera.oryx.common.math.OpenMapRealVector.java

@Override
public OpenMapRealVector append(RealVector v) {
    if (v instanceof OpenMapRealVector) {
        return append((OpenMapRealVector) v);
    }//from  ww w.j av a 2  s.  com
    OpenMapRealVector res = new OpenMapRealVector(this, v.getDimension());
    for (int i = 0; i < v.getDimension(); i++) {
        res.setEntry(i + virtualSize, v.getEntry(i));
    }
    return res;
}

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From source file:com.joptimizer.optimizers.LPPrimalDualMethodTest.java

/**
 * Problem in the form//ww  w .  ja v a 2s  .  c  om
 * min(c.x) s.t.
 * G.x < h
 * The objective function of the presolved problem has a 0-gradient.
 */
public void testCGh4() throws Exception {
    log.debug("testCGh4");

    String problemId = "4";

    //the original problem: ok until precision 1.E-7
    double[] c = Utils.loadDoubleArrayFromFile("lp" + File.separator + "c" + problemId + ".txt");
    double[][] G = Utils.loadDoubleMatrixFromFile("lp" + File.separator + "G" + problemId + ".csv",
            ",".charAt(0));
    double[] h = Utils.loadDoubleArrayFromFile("lp" + File.separator + "h" + problemId + ".txt");
    ;
    double[] expectedSol = Utils.loadDoubleArrayFromFile("lp" + File.separator + "sol" + problemId + ".txt");
    double expectedValue = Utils
            .loadDoubleArrayFromFile("lp" + File.separator + "value" + problemId + ".txt")[0];

    //double norm = MatrixUtils.createRealMatrix(A).operate(MatrixUtils.createRealVector(expectedSol)).subtract(MatrixUtils.createRealVector(b)).getNorm();
    //assertTrue(norm < 1.e-10);

    LPOptimizationRequest or = new LPOptimizationRequest();
    or.setC(c);
    or.setG(G);
    or.setH(h);
    or.setCheckKKTSolutionAccuracy(true);
    or.setToleranceKKT(1.E-7);
    or.setToleranceFeas(1.E-6);
    or.setTolerance(1.E-5);
    or.setDumpProblem(true);
    or.setRescalingDisabled(true);//this fails with false

    //optimization
    //LPPrimalDualMethodOLD opt = new LPPrimalDualMethodOLD();
    LPPrimalDualMethod opt = new LPPrimalDualMethod();

    opt.setLPOptimizationRequest(or);
    int returnCode = opt.optimize();

    if (returnCode == OptimizationResponse.FAILED) {
        fail();
    }

    LPOptimizationResponse response = opt.getLPOptimizationResponse();
    double[] sol = response.getSolution();
    RealVector cVector = new ArrayRealVector(c);
    RealVector solVector = new ArrayRealVector(sol);
    double value = cVector.dotProduct(solVector);
    log.debug("sol   : " + ArrayUtils.toString(sol));
    log.debug("value : " + value);

    //check constraints
    RealVector x = MatrixUtils.createRealVector(sol);
    RealMatrix GMatrix = MatrixUtils.createRealMatrix(G);
    RealVector hvector = MatrixUtils.createRealVector(h);
    RealVector Gxh = GMatrix.operate(x).subtract(hvector);
    for (int i = 0; i < Gxh.getDimension(); i++) {
        assertTrue(Gxh.getEntry(i) <= 0);//not strictly because some constraint has been treated as a bound
    }
    //check value
    assertEquals(expectedValue, value, or.getTolerance());

}

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