Package org.apache.commons.math3.linear

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

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        //since the normal matrix is symmetric, we only need to compute half of it.
        final int nR = jacobian.getRowDimension();
        final int nC = jacobian.getColumnDimension();
        //allocate space for return values
        final RealMatrix normal = MatrixUtils.createRealMatrix(nC, nC);
        final RealVector jTr = new ArrayRealVector(nC);
        //for each measurement
        for (int i = 0; i < nR; ++i) {
            //compute JTr for measurement i
            for (int j = 0; j < nC; j++) {
                jTr.setEntry(j, jTr.getEntry(j) +
                        residuals.getEntry(i) * jacobian.getEntry(i, j));
            }

            // add the the contribution to the normal matrix for measurement i
            for (int k = 0; k < nC; ++k) {
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                // solve the linearized least squares problem
                RealMatrix mA = new BlockRealMatrix(a);
                DecompositionSolver solver = useLU ?
                        new LUDecomposition(mA).getSolver() :
                        new QRDecomposition(mA).getSolver();
                final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
                // update the estimated parameters
                for (int i = 0; i < nC; ++i) {
                    currentPoint[i] += dX[i];
                }
            } catch (SingularMatrixException e) {
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    /**
     * @param coefficients The coefficients for the linear equation being optimized
     * @param constantTerm The constant term of the linear equation
     */
    public LinearObjectiveFunction(double[] coefficients, double constantTerm) {
        this(new ArrayRealVector(coefficients), constantTerm);
    }
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     * Compute the value of the linear equation at the current point
     * @param point point at which linear equation must be evaluated
     * @return value of the linear equation at the current point
     */
    public double getValue(final double[] point) {
        return coefficients.dotProduct(new ArrayRealVector(point, false)) + constantTerm;
    }
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        // Compute cost.
        cost = computeCost(res);

        // Compute weighted residuals.
        final ArrayRealVector residuals = new ArrayRealVector(res);
        weightedResiduals = weightMatrixSqrt.operate(residuals).toArray();
    }
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     * @return the cost.
     * @see #computeResiduals(double[])
     * @since 3.1
     */
    protected double computeCost(double[] residuals) {
        final ArrayRealVector r = new ArrayRealVector(residuals);
        return FastMath.sqrt(r.dotProduct(getWeight().operate(r)));
    }
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     * @param relationship The type of (in)equality used in the constraint
     * @param value The value of the constraint (right hand side)
     */
    public LinearConstraint(final double[] coefficients, final Relationship relationship,
                            final double value) {
        this(new ArrayRealVector(coefficients), relationship, value);
    }
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                            final double[] rhsCoefficients, final double rhsConstant) {
        double[] sub = new double[lhsCoefficients.length];
        for (int i = 0; i < sub.length; ++i) {
            sub[i] = lhsCoefficients[i] - rhsCoefficients[i];
        }
        this.coefficients = new ArrayRealVector(sub, false);
        this.relationship = relationship;
        this.value        = rhsConstant - lhsConstant;
    }
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                // solve the linearized least squares problem
                RealMatrix mA = new BlockRealMatrix(a);
                DecompositionSolver solver = useLU ?
                        new LUDecomposition(mA).getSolver() :
                        new QRDecomposition(mA).getSolver();
                final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
                // update the estimated parameters
                for (int i = 0; i < nC; ++i) {
                    currentPoint[i] += dX[i];
                }
            } catch (SingularMatrixException e) {
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            throws NullArgumentException, NoDataException, DimensionMismatchException {

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