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

     * @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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        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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        return res;
    }


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

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        while (lagtimes.get(lagtimes.size() - 1) < maxtime)
        {
            lagtimes.append((int) ((lagtimes.get(lagtimes.size() - 1) + 1) * lagtmult));
        }
        //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, 1.0);
            //averageWidths.set(i, Math.max(1.0, windowfraction * lagtimes.get(i)));
            //System.out.println(lagtimes.get(i)+"\t"+averageWidths.get(i));
        }
        //System.out.println();
        //System.exit(0);
        EventBinningCorrelator correlator = new EventBinningCorrelator(lagtimes, averageWidths, samplingtime);




        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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 */
public class ArrayElementProduct //implements IMatrixSum
{
    public IDoubleArray multiplyToNewDense(final IDoubleArray a, final IDoubleArray b)
    {
        IDoubleArray target = a.copy();
        multiplyDense(a,b,target);
        return target;
    }

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        return target;
    }


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

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 */
public class DiscretePotentialMetropolisMarkovChain extends MarkovChain
{
    public DiscretePotentialMetropolisMarkovChain(IGriddedFunction f, double kT)
    {
        IDoubleArray _T = Doubles.create.array(f.size(), f.size());


        for (int i=0; i<f.size(); i++)
        {
            IDoubleArray xi = f.get(i);
            double Ei = f.f(xi);
            IIntArray neighbors = f.getNeighborIndexes(i);


            double psum = 0;


            for (int j=0; j<neighbors.size(); j++)
            {
                int n = neighbors.get(j);
                IDoubleArray xj = f.get(neighbors.get(j));
                double Ej = f.f(xj);
                double pjump = (1.0 / neighbors.size()) * Math.min(1, Math.exp(-(Ej-Ei) / kT));
                psum += pjump;
                _T.set(i, n, pjump);
            }

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    @Override
    public boolean step()
    {
        model.setCoordinates(x);
        model.calculate();
        IDoubleArray grad = model.getGradient();


        //System.out.println("step, d="+DoubleArrays.norm(x.getAll()));


        for (int i=0; i<x.size(); i++)
        {
            double gm = (gamma*masses.get(i));
            double r = random.nextGaussian();
            //System.out.println(" grad "+i+" "+grad.get(i));
            double dx = -dt * grad.get(i) / gm  + Math.sqrt(2*dt*kT/gm)*r;
            //System.out.println(" dx "+i+" "+dx);
            x.set(i, x.get(i) + dx);
        }


        return(true);

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 */
public class ArrayTranspose //implements IMatrixTranspose
{
    public IDoubleArray transposeToNew(final IDoubleArray a)
    {
        IDoubleArray res = a.create(a.columns(),a.rows());
        transpose(a,res);
        return res;
    }

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     * @return The upper triangular decomposition matrix R, or {@code null} if
     * the algorithm fails.
     */
    private IDoubleArray computeDecomposition(IDoubleArray input) {
        int n = input.rows();
        IDoubleArray result = Doubles.create.array(n, n);
        result.zero();


        for (int i = 0; i < n; i++) {
            for (int j = 0; j <= i; j++) {
                double sum = 0.0;
                for (int k = 0; k < j; k++) {
                    sum += result.get(i, k) * result.get(j, k);
                }
                if (i == j) {
                    result.set(i, i, Math.sqrt(input.get(i, i) - sum));
                } else {
                    result.set(i, j, 1 / result.get(j, j) *
                            (input.get(i, j) - sum));
                }
            }
            if (result.get(i, i) <= 0) {
                result = null;
                break;
            }
        }

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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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