OrdinaryKriging Infiniy

[Tenma]

In OrdinaryKriging.train(), when lup.det() is infinity that the LUPDecomposition will get a wrong X. However, I can get a right X when using singular value decomposition. So, I think it should be uesd SingularValueDecomposition instead of LUPDecomposition while lup.det() is infinity. Currently, just when lup.det() is NaN or very close to zero will use SingularValueDecomposition.

[EdwardRaff]

HI Michael, do you have an example of this problem you could share by chance?

Either way, it sounds like adding an infinity check to this line fixed it for you - is that correct?

[Tenma]

Sorry, I could not provide the test data for you. The problem happen when I apply OrdinaryKriging for soil nutrient interplating, and I use the 3857 projected coordinate system, and the position near (12900000, 2850000).

I guess it might overflow while calculating lup.det(), and I use near 500 sample points to interplate soil nutrient.

Currently, I fix this problem by appending a condition:

if(Double.isInfinite(lup.det()) || Double.isNaN(lup.det()) || Math.abs(lup.det()) < 1e-5) {
    SingularValueDecomposition svd = new SingularValueDecomposition(V);
    X = svd.solve(Y);
}

And I also shift all the sample points to a original point by minus original point vector such as (12900000, 2850000), it is an operator to avoid overflowing. I got a better result in RMSE or MAE in test set than the old code in OrdinaryKriging. However, I could not prove it theologically.

public class FixOrdinaryKriging implements Regressor, Parameterized {
    private Vec firstOriginalVec = null;
    @Override
    public double regress(DataPoint data)
    {
        Vec x = data.getNumericalValues();
        if(this.firstOriginalVec != null){
            x = x.subtract(firstOriginalVec);
        }

       /* same as OrdinaryKriging */
        int npt = X.length()-1;
        double[] distVals = new double[npt+1];
        for (int i = 0; i < npt; i++)
            distVals[i] = vari.val(x.pNormDist(2, dataSet.getDataPoint(i).getNumericalValues()));
        distVals[npt] = 1.0;

        return X.dot(toDenseVec(distVals));
    }

    @Override
    public void train(RegressionDataSet dataSet, ExecutorService threadPool)
    {
        dataSet = dataSet.shallowClone();
        if(this.firstOriginalVec == null){
            this.firstOriginalVec = dataSet.getDataPoints().get(0).getNumericalValues().clone();
        }

        if(this.firstOriginalVec != null){
            for(DataPoint dataPoint : dataSet.getDataPoints()){
                dataPoint.getNumericalValues().mutableSubtract(this.firstOriginalVec);
            }
        }

        /* same as OrdinaryKriging */
        /* same as OrdinaryKriging */

        if(Double.isInfinite(lup.det()) || Double.isNaN(lup.det()) || Math.abs(lup.det()) < 1e-5)
        {
            SingularValueDecomposition svd = new SingularValueDecomposition(V);
            X = svd.solve(Y);
        }
    }
}

Sorry with my terrible English.