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  R
     */
    @Override
    public IDoubleArray getR() {
        final IDoubleArray X = Doubles.create.array(n, n);


        for (int i = 0; i < n; i++) {


            for (int j = 0; j < n; j++) {


                if (i < j) {
                    X.set(i, j, qrMatrix.get(i, j));
                } else if (i == j) {
                    X.set(i, j, Rdiag[i]);
                } else {
                    X.set(i, j, 0.0d);
                }
            }
        }


        return X;

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     *
     * @return  Q
     */
    @Override
    public IDoubleArray getQ() {
        final IDoubleArray X = Doubles.create.array(m, n);


        for (int k = n - 1; k >= 0; k--) {


            for (int i = 0; i < m; i++) {
                X.set(i, k, 0.0d);
            }


            X.set(k, k, 1.0d);


            for (int j = k; j < n; j++) {


                if (qrMatrix.get(k, k) != 0.0d) {
                    double s = 0.0;


                    for (int i = k; i < m; i++) {
                        s += qrMatrix.get(i, k) * X.get(i, j);
                    }


                    s = -s / qrMatrix.get(k, k);


                    for (int i = k; i < m; i++) {
                        final double a = s * qrMatrix.get(i, k);
                        X.set(i, j, X.get(i, j) + a);
                    }
                }
            }
        } // end for

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    {
        if (data.dimension() == count.length)
        {
            for (int i=0; i<data.size(); i++)
            {
                IDoubleArray arr = data.get(i);
                double w = weights.get(i);
                for (int j=0; j<count.length; j++)
                {
                    count[j] += w*arr.get(j);
                }
            }
        }
        else
        {
            if (data.dimension() == 1)
            {
                for (int i=0; i<data.size(); i++)
                {
                    IDoubleArray arr = data.get(i);
                    count[(int)arr.get(0)] += weights.get(i);
                }
            }
            else
                throw new IllegalArgumentException("incompatible dimension of observation");
        }

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        {
            throw new RuntimeException("No first element .... aborting.");
        }




        IDoubleArray current;


        while(it.hasNext())
        {
            current = it.next();

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        if (!this.isFullRank()) {
            throw new RuntimeException("Matrix is rank deficient.");
        }


        // Copy right hand side
        final IDoubleArray X = B.copy();


        // Compute Y = transpose(Q)*B
        for (int k = 0; k < n; k++) {


            for (int j = 0; j < b_cols; j++) {
                double s = 0.0;


                for (int i = k; i < m; i++) {
                    s += qrMatrix.get(i, k) * X.get(i, j);
                }


                s = -s / qrMatrix.get(k, k);


                for (int i = k; i < m; i++) {
                    final double a = s * qrMatrix.get(i, k);
                    X.set(i, j, X.get(i, j) + a);
                }
            }
        }


        // Solve R*X = Y;
        for (int k = n - 1; k >= 0; k--) {


            for (int j = 0; j < b_cols; j++) {
                X.set(k, j, X.get(k, j) / Rdiag[k]);
            }


            for (int i = 0; i < k; i++) {


                for (int j = 0; j < b_cols; j++) {
                    final double a = X.get(k, j) * qrMatrix.get(i, k);
                    X.set(i, j, X.get(i, j) - a);
                }
            }
        }


        return X.viewBlock(0, 0, n, b_cols);
    }

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     */
    @Override
    final public void computeTransform()
    {
        // PCA
        IDoubleArray Cov = moments.getCov();
        IEigenvalueDecomposition evd = alg.evd(Cov);
        IDoubleArray evalPCA = evd.getEvalNorm();
        IDoubleArray evecPCA = evd.getRightEigenvectorMatrix().viewReal();
        
        // normalize principal components
        IDoubleArray S = doublesNew.array(evalPCA.size());
        for (int i=0; i<S.size(); i++)
            S.set(i, 1.0*Math.sqrt(evalPCA.get(i)));
        // normalize weights by dividing by the standard deviation of the pcs 
        IDoubleArray evecPCAscaled = alg.product(evecPCA, doublesNew.diag(S));


        // time-lagged covariance matrix
        this.CovTauSym = moments.getCovLagged();
        // symmetrize
        CovTauSym = alg.addWeightedToNew(0.5, CovTauSym, 0.5, alg.transposeToNew(CovTauSym)); // symmetrize


        // TICA weights
        IDoubleArray pcCovTau = alg.product(alg.product(alg.transposeToNew(evecPCAscaled), CovTauSym), evecPCAscaled);


        IEigenvalueDecomposition evd2 = alg.evd(pcCovTau);
        this.evalTICA = evd2.getEvalNorm();
        this.evecTICA = alg.product(evecPCAscaled, evd2.getRightEigenvectorMatrix().viewReal());        
    }

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     * @param x
     */
    @Override
    public IDoubleArray transform(IDoubleArray x)
    {
        IDoubleArray out = doublesNew.array(dimOut);
        transform(x, out);
        return out;
    }

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        {
            in = doublesNew.array(in.getArray());
        }


        // subtract mean
        IDoubleArray x = alg.subtract(in, moments.getMean());
        
        // make a row
        if (x.rows() > 1)
            x = alg.transposeToNew(x);
        
        IDoubleArray y = alg.product(x, evecTICA);
        int d = Math.min(in.size(),out.size());
        for (int i=0; i<d; i++)
            out.set(i, y.get(i));
    }

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    }


    @Override
    public IDoubleArray assignFuzzy(IDoubleArray p)
    {
        IDoubleArray res = Doubles.create.array(centers.size());
        int s = assign(p);
        if (s != -1)
            res.set(s, 1);
        return(res);
    }

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        // Using the function: 1/4 x^4 - 1/2 x^2 + 1/2 y^2
        IEnergyModel pot = potNew.multivariateFromExpression(new String[]{"x","y"},
                "1/4 x^4 - 1/2 x^2 + 1/2 y^2", // function expression
                "x^3-x", "y"); // derivatives
        // integrator
        IDoubleArray masses = doublesNew.arrayFrom(1.0, 1.0);
        double dt = 0.1, gamma = 1, kT = 0.2;
        IIntegratorThermostatted langevin = dynNew.langevinLeapFrog(pot, masses, 0.1, gamma, kT);
        // run
        IDoubleArray x0 = doublesNew.arrayFrom(0,0);
        int nsteps = 100000, nsave = 10;
        IDataSequence seq = dyn.run(x0, langevin, nsteps, nsave);
        
        // TICA
        int lag = 1;
        TICA tica = new TICA(lag);
        tica.addData(seq);
        tica.computeTransform();
        
        System.out.println("mean: \t"+doubles.toString(tica.getMeanVector(), "\t"));
        System.out.println("cov: \t"+doubles.toString(tica.getCovarianceMatrix(), "\t", "\n"));
        System.out.println("covTau: \t"+doubles.toString(tica.getCovarianceMatrixLagged(), "\t", "\n"));
        System.out.println();
        System.out.println("eval: \t"+doubles.toString(tica.getEigenvalues(), "\t"));
        System.out.println("evec1: \t"+doubles.toString(tica.getEigenvector(0), "\t"));
        System.out.println("evec2: \t"+doubles.toString(tica.getEigenvector(1), "\t"));
        
        tica.setDimension(1);
        IDoubleArray y1 = tica.transform(doublesNew.arrayFrom(2,2));
        System.out.println("y1 = \t"+doubles.toString(y1, "\t"));


        IDoubleArray y2 = tica.transform(doublesNew.arrayFrom(4,4));
        System.out.println("y2 = \t"+doubles.toString(y2, "\t"));
    }

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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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