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

    }


    @Override
    public IDoubleArray propagate(double t)
    {
        IDoubleArray res = null;


        if (reversible)
        {
            IDoubleArray evalPower = Doubles.create.array(evalReal.size());
            for (int i=0; i<evalPower.size(); i++)
                evalPower.set(i, Math.pow(evalReal.get(i),t));


            IDoubleArray D = Doubles.create.diag(evalPower);
            res = Algebra.util.product(R, Algebra.util.product(D, L));
        }
        else
        {
            throw(new IllegalArgumentException("Trying to use a nonreversible rate matrix propagator. This is not implemented yet."));

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    }


    @Override
    public IDoubleArray estimate(IDataSequence data)
    {
        IDoubleArray par = Doubles.create.array(2);


        // mean
        double mean = 0;
        for (IDoubleArray a : data)
            mean += a.get(0);
        mean /= (double)data.size();


        // var
        double var = 0;
        for (IDoubleArray a : data)
        {
            double d = a.get(0) - mean;
            var += d*d;
        }
        var /= (double)(data.size()-1);


        par.set(0, mean);
        par.set(1, var);


        return par;
    }

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    }


    @Override
    public IDoubleArray estimate(IDataSequence data, IDoubleArray weights)
    {
        IDoubleArray par = Doubles.create.array(2);
        double W = 0;


        // mean
        double mean = 0;
        for (int i=0; i<data.size(); i++)
        {
            mean += weights.get(i) * data.get(i).get(0);
            W += weights.get(i);
        }
        mean /= W;


        // var
        double var = 0;
        for (int i=0; i<data.size(); i++)
        {
            double d = data.get(i).get(0) - mean;
            var += weights.get(i) * d*d;
        }
        var /= W-1;


        par.set(0, mean);
        par.set(1, var);


        return par;
    }

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    }


    @Override
    public IDoubleArray propagate(double t)
    {
        IDoubleArray res = null;


        if (reversible)
        {
            IDoubleArray evalPower = Doubles.create.array(evalReal.size());
            for (int i=0; i<evalPower.size(); i++)
                evalPower.set(i, Math.pow(evalReal.get(i),t));


            IDoubleArray D = Doubles.create.diag(evalPower);
            res = Algebra.util.product(R, Algebra.util.product(D, L));
        }
        else
        {
            if (((int)t - t) != 0)

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        // select Cluster centers from data
        int i = 0;
        for (Iterator<IDoubleArray> it = data.iterator(); it.hasNext();)
        {
            IDoubleArray current = it.next();


            // do we have this index?
            if (Arrays.binarySearch(sortedIndexes, i) >= 0)
            {
                clusterCenters.add(current.copy());
            }




            this.assignments.add(-1);

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        // make the assignments
        //for (int i = 0; i < n; i++)
        int i = 0;
        for (Iterator<IDoubleArray> it = data.iterator(); it.hasNext();)
        {
            IDoubleArray current = it.next();


            // find the closest center
            int closest = 0;
            double closestDistance = metric.distance(current, clusterCenters.get(0));

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    private void updateCenters()
    {
        // set all cluster centers to zero
        for (int i = 0; i < numberOfClusters; i++)
        {
            IDoubleArray clusterCenter = clusterCenters.get(i);
            clusterCenter.zero();
        }


        // sum up all data points per cluster they are assigned to
        double[] assignmentWeight = new double[this.numberOfClusters];


        int j = 0;
        for (Iterator<IDoubleArray> it = data.iterator(); it.hasNext();)
        //for (int j = 0; j < data.size(); j++)
        {


            IDoubleArray currentVector = it.next();


            int assignedTo = assignments.get(j);
            assignmentWeight[assignedTo] += 1;


            IDoubleArray clusterCenter = clusterCenters.get(assignedTo);
            Algebra.util.addTo(clusterCenter, currentVector);


            j++;
        }

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    }


    //@Override
    public IDoubleArray getMembership(int i)
    {
        IDoubleArray membership = Doubles.create.array(numberOfClusters);
        membership.set(i, 1.0d);


        return membership;
    }

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    {
        // test input!
        //System.out.println("Splitting state "+state+"\t to probability "+p1+"\tk1 "+k1+"\tk2 "+k2);


        // compute correlation matrix
        IDoubleArray pi_old = msm.stationaryDistribution(T_old);
        IDoubleArray Pi_old = doublesNew.diag(pi_old);
        IDoubleArray C_old = alg.product(Pi_old, T_old);
        // useful stuff
        int n = T_old.rows();
        double pi_state_old = pi_old.get(state);


        //System.out.println("C_old = \n"+C_old);
        //System.out.println();
        //System.out.println("pi_old = \n"+pi_old);


        // fill unaffected states
        IDoubleArray C_split = doublesNew.matrix(n+1,n+1);
        IDoubleArray pi_split = doublesNew.array(n+1);
        for (int i=0; i<n; i++)
            for (int j=0; j<n; j++)
                if (i != state && j != state)
                {
                    C_split.set(i,j, C_old.get(i,j));
                    pi_split.set(i, pi_old.get(i));
                }


        // compute stationary probabilities of the new states
        double pi_split_1 = p1 * pi_old.get(state);
        double pi_split_2 = pi_old.get(state) - pi_split_1;
        pi_split.set(state, pi_split_1);
        pi_split.set(n, pi_split_2);


        //System.out.println("pi_split_1 = "+pi_split_1);
        //System.out.println("pi_split_2 = "+pi_split_2);


        // compute diagonal elements
        double d1 = pi_split_1 * Math.exp(-k1);
        double d2 = pi_split_2 * Math.exp(-k2);
        C_split.set(state,state,d1);
        C_split.set(n,n,d2);
        //System.out.println("d1 = "+d1);
        //System.out.println("d2 = "+d2);


        // set transition elements in the splitting block
        double split_block_trans = 0.5 * (C_old.get(state,state) - d1 - d2);
        //System.out.println("initial st: "+split_block_trans);


        if (split_block_trans < 0 || split_block_trans > Math.min(pi_split_1 - d1, pi_split_2 - d2))
        {
            split_block_trans = Math.min(pi_split_1 - d1, pi_split_2 - d2) / (double)(10*n+1);
            //System.out.println("correcting to: "+split_block_trans);
        }
        C_split.set(state, n, split_block_trans);
        C_split.set(n, state, split_block_trans);


        //System.out.println("trans_el = "+split_block_trans);


        // set remaining elements
        double sum_old = pi_state_old - C_old.get(state,state);
        double rest1 = pi_split_1 - d1 - split_block_trans;
        double rest2 = pi_split_2 - d2 - split_block_trans;
        //System.out.println("sum_old = "+sum_old);
        //System.out.println("rest1 = "+rest1);
        //System.out.println("rest2 = "+rest2);
        for (int i=0; i<n; i++)
        {
            if (i != state)
            {
                C_split.set(state, i, rest1 * C_old.get(state,i)/sum_old);
                C_split.set(i, state, C_split.get(state,i));
                C_split.set(n, i, rest2 * C_old.get(state,i)/sum_old);
                C_split.set(i, n, C_split.get(n,i));
            }
        }


        //System.out.println("C_split = \n"+C_split);


        // normalize
        IDoubleArray T_split = C_split.copy();
        alg.normalizeRows(T_split, 1);


        //System.out.println("T_split = \n"+T_split);


        IDoubleArray pi_res = msm.stationaryDistribution(T_split);
        double err = alg.distance(pi_res, pi_split);
        //System.out.println("pi error = "+err);


        double p1_res = pi_res.get(state) / (pi_res.get(state)+pi_res.get(n));
        double k1_res = -Math.log(T_split.get(state,state));
        double k2_res = -Math.log(T_split.get(n,n));
        //System.out.println("Have obtained parameters: p1 "+p1_res+"\tk1 "+k1_res+"\tk2 "+k2_res);
        //System.out.println("Compared to reference:    p1 "+p1+"\tk1 "+k1+"\tk2 "+k2);

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        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;
    }

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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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