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

  public double[] solveFToD(float[] b) {
    ArrayRealVector bVec = new ArrayRealVector(b.length);
    for (int i = 0; i < b.length; i++) {
      bVec.setEntry(i, b[i]);
    }
    RealVector vec = solver.solve(bVec);
    double[] result = new double[b.length];
    for (int i = 0; i < result.length; i++) {
      result[i] = vec.getEntry(i);
    }
    return result;
  }

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        // semi-definite matrix, we can use the cholesky decomposition
        DecompositionSolver solver = new CholeskyDecomposition(s).getSolver();
        RealMatrix invertedS = solver.getInverse();


        // Inn = z(k) - H * xHat(k)-
        RealVector innovation = z.subtract(measurementMatrix.operate(stateEstimation));


        // calculate gain matrix
        // K(k) = P(k)- * H' * (H * P(k)- * H' + R)^-1
        // K(k) = P(k)- * H' * S^-1
        RealMatrix kalmanGain = errorCovariance.multiply(measurementMatrixT).multiply(invertedS);

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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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    /**
     * {@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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                    }
                    hs.setEntry(i, hs.getEntry(i) + modelSecondDerivativesValues.getEntry(ih) * s.getEntry(j));
                    ih++;
                }
            }
            final RealVector tmp = interpolationPoints.operate(s).ebeMultiply(modelSecondDerivativesParameters);
            for (int k = 0; k < npt; k++) {
                if (modelSecondDerivativesParameters.getEntry(k) != ZERO) {
                    for (int i = 0; i < n; i++) {
                        hs.setEntry(i, hs.getEntry(i) + tmp.getEntry(k) * interpolationPoints.getEntry(k, i));
                    }
                }
            }
            if (crvmin != ZERO) {
                state = 50; break;

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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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     * @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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