Examples of stallone.api.doubles.IDoubleArray

stallone.api.doubles.IDoubleArray
This is a joint interface for all primitive double containers with a fixed order of elements, e.g. arrays, tables, lists. In principle real-valued tensors can also be handled by this object, although we currently provide no method to directly access more than two indexes in order to keep the interface small. Single-index based access should work on higher-order tensors as well. All IDoubleArray's have a sequence in which individual elements can be iterated. The implementing object defines this sequence. Index-based methods such as get(i), set(i, x), and the iterator operate based on this sequence. This interface is the standard way of exchanging real-valued double containers, in order to make all Classes operating with such objects compatible. This option was chosen in order to prefer flexibility and ease-to-use over strict type specificity Methods operating with double containers may return more specific objects, but should accept IDoubleArray (or IComplexArray, see below) as parameters If you want to operate with complex numbers, check out IComplexArray @author noe


    private IDataList select(IIntArray indexes)
    {
        IDataList res = dataNew.list();
        Iterator<IDoubleArray> dataIterator = data.iterator();
        IDoubleArray x;
        int k = 0;
        for (int i=0; i<datasize; i++)
        {
            if (! dataIterator.hasNext())
                throw(new RuntimeException("Data set is shorter than specified"));

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     * @param data
     * @return the autocorrelations of the data on the tau grid specified
     */
    public IDoubleArray correlate(IDoubleArray time, IDoubleArray data)
    {
        IDoubleArray res = doublesNew.array(lagtimes.size());


        for (int i = 0; i < res.size(); i++)
        {
            res.set(i, correlate(time, data, lagtimes.get(i), averageWidths.get(i)));
        }


        return res;
    }

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     * @param time
     * @param data
     */
    public void add(IDoubleArray time, IDoubleArray data)
    {
        IDoubleArray c = correlate(time, data);
        for (int i = 0; i < c.size(); i++)
        {
            if (!Double.isNaN(c.get(i)))
            {
                correlation.set(i, correlation.get(i) + data.size() * c.get(i));
                weights.set(i, weights.get(i) + data.size());
            }
        }


        sum += doubles.sum(data);

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    {
    }


    private void initX()
    {
        IDoubleArray CCT = Algebra.util.add(C, Algebra.util.transposeToNew(C));
        this.X = Doubles.create.symmetric(CCT);


        Algebra.util.scale(1.0 / Algebra.util.sum(this.X), this.X);


        this.itX = this.X.nonzeroIterator();

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        sumOfWeights += time.size();
    }


    public IDoubleArray getCorrelation()
    {
        IDoubleArray res = doublesNew.array(correlation.size());
        for (int i=0; i<res.size(); i++)
            res.set(i, correlation.get(i)/weights.get(i));
        return res;
    }

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    {
        // output likelihood list
        //for (int i=0; i<this.logliks.size(); i++)
        //    System.out.println("# "+i+"\t"+this.logliks.get(i));


        IDoubleArray T = X.create(X.rows(), X.columns());
        for (IDoubleIterator it = X.nonzeroIterator(); it.hasNext(); it.advance())
        {
            int i = it.row();
            int j = it.column();
            T.set(i, j, X.get(i,j) / Xrow[i]);
            //System.out.println(i+","+j+"\t"+X.get(i,j)+"\t"+Xrow[i]+"\t"+T.get(i,j));
            //if (i != j)
            //{
            //    T.set(j, i, X.get(j,i) / Xrow[j]);
            //}

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        return res;
    }


    public IDoubleArray getCorrelationMeanFree()
    {
        IDoubleArray res = doublesNew.array(correlation.size());
        double shift =(sum/sumOfWeights)*(sum/sumOfWeights);
        for (int i=0; i<res.size(); i++)
            res.set(i, (correlation.get(i)/weights.get(i)) - shift);
        return res;
    }

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public class ArrayDifference //implements IMatrixSum
{


    public IDoubleArray subtractToNewDense(final IDoubleArray a, final IDoubleArray b)
    {
        IDoubleArray target = a.copy();
        subtractDense(a, b, target);
        return target;
    }

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        return target;
    }


    public IDoubleArray subtractToNewSparse(final IDoubleArray a, final IDoubleArray b)
    {
        IDoubleArray target = a.copy();
        subtractSparse(a, b, target);
        return target;
    }

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        while (lagtimes.get(lagtimes.size() - 1) < maxtime)
        {
            lagtimes.append((lagtimes.get(lagtimes.size() - 1) + 1) * 1.1);
        }
        //IDoubleArray lagtimes = doublesNew.arrayFrom(1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000, 100000, 200000, 500000, 1000000);
        IDoubleArray averageWidths = doublesNew.array(lagtimes.size());
        for (int i = 0; i < averageWidths.size(); i++)
        {
            averageWidths.set(i, Math.max(1.0, 0.1 * lagtimes.get(i)));
        }
        EventCorrelator correlator = new EventCorrelator(lagtimes, averageWidths);




        for (int i = 0; i < inputFiles.size(); i++)
        {
            IDoubleArray time = data.readColumn(inputFiles.get(i), timecol);
            IDoubleArray don = data.readColumn(inputFiles.get(i), col1);
            IDoubleArray acc = data.readColumn(inputFiles.get(i), col2);
            IDoubleArray E = doublesNew.array(don.size());
            for (int j = 0; j < E.size(); j++)
            {
                E.set(j, acc.get(j) / (don.get(j) + acc.get(j)));
            }


            correlator.add(time, E);
        }


        IDoubleArray corr = null;
        if (subtractMean)
            corr=correlator.getCorrelationMeanFree();
        else
            corr=correlator.getCorrelation();




        //IDoubleArray corr = correlator.correlate(time, E);


        for (int i = 0; i < lagtimes.size(); i++)
        {
            System.out.println(lagtimes.get(i) + "\t" + corr.get(i));
        }
    }

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Related Classes of stallone.api.doubles.IDoubleArray

stallone.algebra.ArrayDifference

stallone.algebra.ArrayElementDivide

stallone.algebra.ArrayElementProduct

stallone.algebra.ArraySum

stallone.algebra.EigenvalueDecomposition

stallone.algebra.RealLUDecomposition

stallone.algebra.RealQRDecomposition

stallone.api.algebra.AlgebraUtilities

stallone.api.algebra.EigensolverTest

stallone.api.cluster.ClusterUtilities

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