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

Introduction

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

Prototype

public RealVector mapAdd(double d) 

Source Link

Document

Add a value to each entry.

Usage

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  w ww .  j  a v a  2 s  .  com
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);

}

---