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

org.apache.commons.math3.linear.RealVector
Class defining a real-valued vector with basic algebraic operations.
vector element indexing is 0-based -- e.g., {@code getEntry(0)}returns the first element of the vector.

The {@code code map} and {@code mapToSelf} methods operateon vectors element-wise, i.e. they perform the same operation (adding a scalar, applying a function ...) on each element in turn. The {@code map}versions create a new vector to hold the result and do not change the instance. The {@code mapToSelf} version uses the instance itself to store theresults, so the instance is changed by this method. In all cases, the result vector is returned by the methods, allowing the fluent API style, like this:
```
 RealVector result = v.mapAddToSelf(3.4).mapToSelf(new Tan()).mapToSelf(new Power(2.3)); 
```
@since 2.1

    for (SiteWithPolynomial site : sites) {
      
      List<SiteWithPolynomial> nearestSites =
          nearestSiteMap.get(site);
      
      RealVector vector = new ArrayRealVector(SITES_FOR_APPROX);
      RealMatrix matrix = new Array2DRowRealMatrix(
          SITES_FOR_APPROX, DefaultPolynomial.NUM_COEFFS);
      
      for (int row = 0; row < SITES_FOR_APPROX; row++) {
        SiteWithPolynomial nearSite = nearestSites.get(row);
        DefaultPolynomial.populateMatrix(matrix, row, nearSite.pos.x, nearSite.pos.z);
        vector.setEntry(row, nearSite.pos.y);
      }
      
      QRDecomposition qr = new QRDecomposition(matrix);
      RealVector solution = qr.getSolver().solve(vector);
        
      double[] coeffs = solution.toArray();
      
      for (double coeff : coeffs) {
        if (coeff > 10e3) {
          continue calculatePolynomials;
        }

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                return Double.compare(weightedResidual(o1),
                                      weightedResidual(o2));
            }


            private double weightedResidual(final PointVectorValuePair pv) {
                final RealVector v = new ArrayRealVector(pv.getValueRef(), false);
                final RealVector r = target.subtract(v);
                return r.dotProduct(weight.operate(r));
            }
        };
    }

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            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));

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     *
     * @return residual sum of squares
     * @since 2.2
     */
    public double calculateResidualSumOfSquares() {
        final RealVector residuals = calculateResiduals();
        // No advertised DME, args are valid
        return residuals.dotProduct(residuals);
    }

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     * @param point Interpolation point.
     * @return the interpolated value.
     * @throws DimensionMismatchException if point dimension does not math sample
     */
    public double value(double[] point) throws DimensionMismatchException {
        final RealVector p = new ArrayRealVector(point);


        // Reset.
        for (MicrosphereSurfaceElement md : microsphere) {
            md.reset();
        }


        // Compute contribution of each sample points to the microsphere elements illumination
        for (Map.Entry<RealVector, Double> sd : samples.entrySet()) {


            // Vector between interpolation point and current sample point.
            final RealVector diff = sd.getKey().subtract(p);
            final double diffNorm = diff.getNorm();


            if (FastMath.abs(diffNorm) < FastMath.ulp(1d)) {
                // No need to interpolate, as the interpolation point is
                // actually (very close to) one of the sampled points.
                return sd.getValue();

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     * @return error variance
     * @since 2.2
     */
    @Override
    protected double calculateErrorVariance() {
        RealVector residuals = calculateResiduals();
        double t = residuals.dotProduct(getOmegaInverse().operate(residuals));
        return t / (getX().getRowDimension() - getX().getColumnDimension());


    }

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    /**
     * {@inheritDoc}
     */
    public double[] estimateRegressionParameters() {
        RealVector b = calculateBeta();
        return b.toArray();
    }

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    /**
     * {@inheritDoc}
     */
    public double[] estimateResiduals() {
        RealVector b = calculateBeta();
        RealVector e = yVector.subtract(xMatrix.operate(b));
        return e.toArray();
    }

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     *
     * @return error variance estimate
     * @since 2.2
     */
    protected double calculateErrorVariance() {
        RealVector residuals = calculateResiduals();
        return residuals.dotProduct(residuals) /
               (xMatrix.getRowDimension() - xMatrix.getColumnDimension());
    }

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     * </pre>
     *
     * @return The residuals [n,1] matrix
     */
    protected RealVector calculateResiduals() {
        RealVector b = calculateBeta();
        return yVector.subtract(xMatrix.operate(b));
    }

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