Examples of Array2DRowRealMatrix

org.apache.commons.math.linear.Array2DRowRealMatrix
ath.gatech.edu/~bourbaki/math2601/Web-notes/2num.pdf"> LU decomposition to support linear system solution and inverse.
The LU decomposition is performed as needed, to support the following operations:
- solve
- isSingular
- getDeterminant
- inverse
Usage notes:
- The LU decomposition is cached and reused on subsequent calls. If data are modified via references to the underlying array obtained using getDataRef(), then the stored LU decomposition will not be discarded. In this case, you need to explicitly invoke LUDecompose() to recompute the decomposition before using any of the methods above.
- As specified in the {@link RealMatrix} interface, matrix element indexingis 0-based -- e.g., getEntry(0, 0) returns the element in the first row, first column of the matrix.
@version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $
org.apache.commons.math3.linear.Array2DRowRealMatrix
Implementation of {@link RealMatrix} using a {@code double[][]} array tostore entries. @version $Id: Array2DRowRealMatrix.java 1296537 2012-03-03 00:45:19Z sebb $

Examples of org.apache.commons.math3.linear.Array2DRowRealMatrix

            return new ArrayRealVector(value.value(params), false);
        }


        /** {@inheritDoc} */
        public RealMatrix computeJacobian(final double[] params) {
            return new Array2DRowRealMatrix(jacobian.value(params), false);
        }

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Examples of org.apache.commons.math3.linear.Array2DRowRealMatrix

            }


            // Compute new covariance matrices
            final RealMatrix[] newCovMats = new RealMatrix[k];
            for (int j = 0; j < k; j++) {
                newCovMats[j] = new Array2DRowRealMatrix(numCols, numCols);
            }
            for (int i = 0; i < n; i++) {
                for (int j = 0; j < k; j++) {
                    final RealMatrix vec
                        = new Array2DRowRealMatrix(MathArrays.ebeSubtract(data[i], newMeans[j]));
                    final RealMatrix dataCov
                        = vec.multiply(vec.transpose()).scalarMultiply(gamma[i][j]);
                    newCovMats[j] = newCovMats[j].add(dataCov);
                }
            }


            // Converting to arrays for use by fitted model

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Examples of org.apache.commons.math3.linear.Array2DRowRealMatrix

        final double[] y0   = equations.getCompleteState();
        final double[] y    = y0.clone();
        final double[] yDot = new double[y.length];
        final double[] yTmp = new double[y.length];
        final double[] predictedScaled = new double[y.length];
        Array2DRowRealMatrix nordsieckTmp = null;


        // set up two interpolators sharing the integrator arrays
        final NordsieckStepInterpolator interpolator = new NordsieckStepInterpolator();
        interpolator.reinitialize(y, forward,
                                  equations.getPrimaryMapper(), equations.getSecondaryMappers());


        // set up integration control objects
        initIntegration(equations.getTime(), y0, t);


        // compute the initial Nordsieck vector using the configured starter integrator
        start(equations.getTime(), y, t);
        interpolator.reinitialize(stepStart, stepSize, scaled, nordsieck);
        interpolator.storeTime(stepStart);


        double hNew = stepSize;
        interpolator.rescale(hNew);


        isLastStep = false;
        do {


            double error = 10;
            while (error >= 1.0) {


                stepSize = hNew;


                // predict a first estimate of the state at step end (P in the PECE sequence)
                final double stepEnd = stepStart + stepSize;
                interpolator.setInterpolatedTime(stepEnd);
                final ExpandableStatefulODE expandable = getExpandable();
                final EquationsMapper primary = expandable.getPrimaryMapper();
                primary.insertEquationData(interpolator.getInterpolatedState(), yTmp);
                int index = 0;
                for (final EquationsMapper secondary : expandable.getSecondaryMappers()) {
                    secondary.insertEquationData(interpolator.getInterpolatedSecondaryState(index), yTmp);
                    ++index;
                }


                // evaluate a first estimate of the derivative (first E in the PECE sequence)
                computeDerivatives(stepEnd, yTmp, yDot);


                // update Nordsieck vector
                for (int j = 0; j < y0.length; ++j) {
                    predictedScaled[j] = stepSize * yDot[j];
                }
                nordsieckTmp = updateHighOrderDerivativesPhase1(nordsieck);
                updateHighOrderDerivativesPhase2(scaled, predictedScaled, nordsieckTmp);


                // apply correction (C in the PECE sequence)
                error = nordsieckTmp.walkInOptimizedOrder(new Corrector(y, predictedScaled, yTmp));


                if (error >= 1.0) {
                    // reject the step and attempt to reduce error by stepsize control
                    final double factor = computeStepGrowShrinkFactor(error);
                    hNew = filterStep(stepSize * factor, forward, false);

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Examples of org.apache.commons.math3.linear.Array2DRowRealMatrix


        /*
         * Here the rounding part comes into play: use
         * RealMatrix instead of FieldMatrix<BigFraction>
         */
        final RealMatrix H = new Array2DRowRealMatrix(m, m);


        for (int i = 0; i < m; ++i) {
            for (int j = 0; j < m; ++j) {
                H.setEntry(i, j, HBigFraction.getEntry(i, j).doubleValue());
            }
        }


        final RealMatrix Hpower = H.power(n);


        double pFrac = Hpower.getEntry(k - 1, k - 1);


        for (int i = 1; i <= n; ++i) {
            pFrac *= (double) i / (double) n;

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Examples of org.apache.commons.math3.linear.Array2DRowRealMatrix

            }
        }


        this.means = MathArrays.copyOf(means);


        covarianceMatrix = new Array2DRowRealMatrix(covariances);


        // Covariance matrix eigen decomposition.
        final EigenDecomposition covMatDec = new EigenDecomposition(covarianceMatrix);


        // Compute and store the inverse.
        covarianceMatrixInverse = covMatDec.getSolver().getInverse();
        // Compute and store the determinant.
        covarianceMatrixDeterminant = covMatDec.getDeterminant();


        // Eigenvalues of the covariance matrix.
        final double[] covMatEigenvalues = covMatDec.getRealEigenvalues();


        for (int i = 0; i < covMatEigenvalues.length; i++) {
            if (covMatEigenvalues[i] < 0) {
                throw new NonPositiveDefiniteMatrixException(covMatEigenvalues[i], i, 0);
            }
        }


        // Matrix where each column is an eigenvector of the covariance matrix.
        final Array2DRowRealMatrix covMatEigenvectors = new Array2DRowRealMatrix(dim, dim);
        for (int v = 0; v < dim; v++) {
            final double[] evec = covMatDec.getEigenvector(v).toArray();
            covMatEigenvectors.setColumn(v, evec);
        }


        final RealMatrix tmpMatrix = covMatEigenvectors.transpose();


        // Scale each eigenvector by the square root of its eigenvalue.
        for (int row = 0; row < dim; row++) {
            final double factor = FastMath.sqrt(covMatEigenvalues[row]);
            for (int col = 0; col < dim; col++) {
                tmpMatrix.multiplyEntry(row, col, factor);
            }
        }


        samplingMatrix = covMatEigenvectors.multiply(tmpMatrix);
    }

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Examples of org.apache.commons.math3.linear.Array2DRowRealMatrix

                               final double[][] processNoise,
                               final double[] initialStateEstimate,
                               final double[][] initialErrorCovariance)
            throws NullArgumentException, NoDataException, DimensionMismatchException {


        this(new Array2DRowRealMatrix(stateTransition),
                new Array2DRowRealMatrix(control),
                new Array2DRowRealMatrix(processNoise),
                new ArrayRealVector(initialStateEstimate),
                new Array2DRowRealMatrix(initialErrorCovariance));
    }

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Examples of org.apache.commons.math3.linear.Array2DRowRealMatrix

    public DefaultProcessModel(final double[][] stateTransition,
                               final double[][] control,
                               final double[][] processNoise)
            throws NullArgumentException, NoDataException, DimensionMismatchException {


        this(new Array2DRowRealMatrix(stateTransition),
                new Array2DRowRealMatrix(control),
                new Array2DRowRealMatrix(processNoise), null, null);
    }

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Examples of org.apache.commons.math3.linear.Array2DRowRealMatrix

        MathUtils.checkNotNull(transitionMatrix);
        transitionMatrixT = transitionMatrix.transpose();


        // create an empty matrix if no control matrix was given
        if (processModel.getControlMatrix() == null) {
            controlMatrix = new Array2DRowRealMatrix();
        } else {
            controlMatrix = processModel.getControlMatrix();
        }


        measurementMatrix = measurementModel.getMeasurementMatrix();

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Examples of org.apache.commons.math3.linear.Array2DRowRealMatrix

     * @throws DimensionMismatchException
     *             if any of the input matrices is non-rectangular
     */
    public DefaultMeasurementModel(final double[][] measMatrix, final double[][] measNoise)
            throws NullArgumentException, NoDataException, DimensionMismatchException {
        this(new Array2DRowRealMatrix(measMatrix), new Array2DRowRealMatrix(measNoise));
    }

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Examples of org.apache.commons.math3.linear.Array2DRowRealMatrix


        // create a matrix of the correct size
        int width = numDecisionVariables + numSlackVariables +
        numArtificialVariables + getNumObjectiveFunctions() + 1; // + 1 is for RHS
        int height = constraints.size() + getNumObjectiveFunctions();
        Array2DRowRealMatrix matrix = new Array2DRowRealMatrix(height, width);


        // initialize the objective function rows
        if (getNumObjectiveFunctions() == 2) {
            matrix.setEntry(0, 0, -1);
        }
        int zIndex = (getNumObjectiveFunctions() == 1) ? 0 : 1;
        matrix.setEntry(zIndex, zIndex, maximize ? 1 : -1);
        RealVector objectiveCoefficients =
            maximize ? f.getCoefficients().mapMultiply(-1) : f.getCoefficients();
        copyArray(objectiveCoefficients.toArray(), matrix.getDataRef()[zIndex]);
        matrix.setEntry(zIndex, width - 1,
            maximize ? f.getConstantTerm() : -1 * f.getConstantTerm());


        if (!restrictToNonNegative) {
            matrix.setEntry(zIndex, getSlackVariableOffset() - 1,
                getInvertedCoefficientSum(objectiveCoefficients));
        }


        // initialize the constraint rows
        int slackVar = 0;
        int artificialVar = 0;
        for (int i = 0; i < constraints.size(); i++) {
            LinearConstraint constraint = constraints.get(i);
            int row = getNumObjectiveFunctions() + i;


            // decision variable coefficients
            copyArray(constraint.getCoefficients().toArray(), matrix.getDataRef()[row]);


            // x-
            if (!restrictToNonNegative) {
                matrix.setEntry(row, getSlackVariableOffset() - 1,
                    getInvertedCoefficientSum(constraint.getCoefficients()));
            }


            // RHS
            matrix.setEntry(row, width - 1, constraint.getValue());


            // slack variables
            if (constraint.getRelationship() == Relationship.LEQ) {
                matrix.setEntry(row, getSlackVariableOffset() + slackVar++, 1);  // slack
            } else if (constraint.getRelationship() == Relationship.GEQ) {
                matrix.setEntry(row, getSlackVariableOffset() + slackVar++, -1); // excess
            }


            // artificial variables
            if ((constraint.getRelationship() == Relationship.EQ) ||
                    (constraint.getRelationship() == Relationship.GEQ)) {
                matrix.setEntry(0, getArtificialVariableOffset() + artificialVar, 1);
                matrix.setEntry(row, getArtificialVariableOffset() + artificialVar++, 1);
                matrix.setRowVector(0, matrix.getRowVector(0).subtract(matrix.getRowVector(row)));
            }
        }


        return matrix;
    }

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