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

        return parser;
    }


    private static IDoubleArray cumulate(IDoubleArray data)
    {
        IDoubleArray res = doublesNew.array(data.size() + 1);
        res.set(0, 0);
        for (int i = 1; i < res.size(); i++)
        {
            res.set(i, res.get(i - 1) + data.get(i - 1));
        }
        return res;
    }

View Full Code Here

        // check transition matrix structure and determine components
        calculateComponents();


        for (int c=0; c<components.size(); c++)
        {
            IDoubleArray Tsub = T.view(components.get(c).getArray(),components.get(c).getArray()).copy();
            for (int i=0; i<Tsub.rows(); i++)
            {
                IDoubleArray row = Tsub.viewRow(i);
                Algebra.util.scale(1.0/Doubles.util.sum(row), row);
            }


            //System.out.println(c+" "+componentIsClosed(components[c]));


            if (!componentIsClosed(components.get(c)))
                continue; // no probability in this set.


            IDoubleArray pisub = calculateSub(Tsub);


            /*if (c == 0)
            {
            System.out.println("Submatrix: "+Tsub.length);
            for (int j=0; j<Tsub.length; j++)
                System.out.println(j+" "+DoubleArrays.sum(Tsub[j]));


            Graph g = Graph.fromWeightMatrix(Tsub);
            int[][] C = g.strongComponents();
            System.out.println("Number of components: "+C.length);


            System.out.println("PI: ");
            DoubleArrays.print(pisub,"\n");


            System.out.println("T sub:");
            AlgebraPrimitive.writeMatrixSparse(Tsub,System.out);


                System.exit(0);
            }
             *
             */


            this.componentsPi.set(c, pisub);


            for (int i=0; i<pisub.size(); i++)
            {
                int s = components.get(c).get(i);
                pi.set(s, pisub.get(i));
                //this.componentsPi.set(c).set(s, pisub.get(i));
                //this.componentsPi.get(c).set(s, pisub.get(i));
                //System.out.println("trying to get from component "+c+" with size "+components.get(c));
                //System.out.println("trying to get from pi comp with size "+componentsPi.get(c));

View Full Code Here

        {
            throw (new IllegalArgumentException("Number of time points does not match number of data points in correlate."));
        }


        // cumulate data
        IDoubleArray cumdata = cumulate(data);


        Window w1 = new Window(time, data, windowSize);
        w1.init(windowSize/2.0);


        Window w2 = new Window(time, data, windowSize);
        w2.init(windowSize/2.0 + tau);


        // estimate
        double sum = 0;
        double count = 0;


        while(!w2.endOfData())
        {
            /*
                if (tau == 0)
                {
                    System.out.println("t = "+w1.t+"\t times ["+w1.tl+","+w1.tr+"] -> ["+w2.tl+","+w2.tr+"]");
                    System.out.println("  w1=("+w1.l+", "+w1.r+") w2="+w2.l+", "+w2.r);
                }*/


            // update convolution sums
            if ((!w1.isEmpty()) && (!w2.isEmpty()))
            {
                double x1 = (cumdata.get(w1.r) - cumdata.get(w1.l))/((double)(w1.r-w1.l));
                double x2 = (cumdata.get(w2.r) - cumdata.get(w2.l))/((double)(w2.r-w2.l));
                sum += x1*x2;
                count += 1.0;


                /*if (tau == 0)
                {

View Full Code Here

        if (!success)
        {
            return (false);
        }


        IDoubleArray gradient = model.getGradient();


        for (int i = 0; i < x.size(); i++)
        {
            // calculate acceleration from forces
            a.set(i, -gradient.get(i) / masses.get(i));
            // update velocities to full step
            v.set(i, v.get(i) + 0.5 * dt * a.get(i));
        }


        return (true);

View Full Code Here

        return (pi);
    }


    private IDoubleArray calculateSub(IDoubleArray Tsub)
    {
        IDoubleArray pisub = null;
        if (!reversible)
        {
            pisub = calculateSubGeneral(Tsub);
        }
        else

View Full Code Here

     * @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;
    }

View Full Code Here

    private IDoubleArray calculateSubGeneral(IDoubleArray Tsub)
    {
        IEigenvalueDecomposition evd = Algebra.util.evd(Tsub, true, false);
        evd.sortNormDescending();


        IDoubleArray l1 = evd.getLeftEigenvector(0).copy();


        Algebra.util.scale(1.0 / Doubles.util.sum(l1), l1);


        return(l1);
    }

View Full Code Here

        Iterator<IDoubleArray> it = data.iterator();
        if (!it.hasNext())
        {
            throw (new RuntimeException("Trying to cluster an empty data set"));
        }
        IDoubleArray v_0 = it.next().copy();
        clusters.add(v_0);
        assignedClusters.add(0);
        minDistances.add(0);
        //clusterCenterIndexes[0]=0;


        // consider second data point
        /*
         * if (!it.hasNext()) throw(new RuntimeException("Trying to cluster a
         * data set with one data point")); IVector v_i = it.next();
         * assignedClusters.add(0); minDistances.add(distanceMetric.measure(v_i,
         * v_0));
         */


        // first iteration: go over data, calculate distance to first center and assign first center
        System.out.println("KCenter: iteration 1/" + numberOfClusters + ".");
        IDoubleArray v_i = null;
        double maxMinDistance = 0;
        int count = 0;
        while (it.hasNext())
        {
            IDoubleArray p_j = it.next().copy();


            // calculate distance to cluster 0
            double d = distanceMetric.distance(p_j, v_0);
            minDistances.add(d);


            // is this the largest distance so far? Then memorize as next center candidate
            if (d >= maxMinDistance)
            {
                count++;
                maxMinDistance = d;
                v_i = p_j;
            }


            // assign every data point p_j to cluster 0
            assignedClusters.add(0);
        }




        // for all other clusters, add the most distance point as center and the reassign
        // for k clusters do k-2 further passes to the data
        for (int i = 1; i < numberOfClusters; i++)
        {
            System.out.println("KCenter: iteration " + (i + 1) + "/" + numberOfClusters + ".");


            // add most distance point as a new center
            clusters.add(v_i);


            int j = 0;
            maxMinDistance = 0;
            IDoubleArray v_next = null;
            for (it = data.iterator(); it.hasNext(); j++) //for (int j = 0; j < dataPoints.size(); j++)
            {
                IDoubleArray p_j = it.next().copy();


                // get minimal distance of p_j to cluster centers
                double currentDistance = minDistances.get(j);


                // calculate new distance of p_j to the possible new cluster center

View Full Code Here

        return(l1);
    }


    private IDoubleArray calculateSubReversible(IDoubleArray Tsubrev)
    {
        IDoubleArray pisub = Doubles.create.array(Tsubrev.rows());
        pisub.set(0,1);


        boolean[] done = new boolean[Tsubrev.rows()];
        done[0] = true;


        LinkedList<Integer> todo = new LinkedList<Integer>();

View Full Code Here

        double minimalDistance = Double.MAX_VALUE;
        int indexOfNearestCluster = -1;


        for (int i = 0; i < numberOfClusters; i++)
        {
            IDoubleArray v_i = clusters.get(i);


            // calculate distance
            double currentDistance = distanceMetric.distance(p, v_i);


            if (currentDistance < minimalDistance)

View Full Code Here

0 1 2 3 4 5 6 7 8 9

TOP

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

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.