Package org.apache.commons.math3.analysis

Examples of org.apache.commons.math3.analysis.MultivariateVectorFunction

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        final RealMatrix factors =
            new Array2DRowRealMatrix(new double[][] {
                    { 1, 0 },
                    { 0, 1 }
                }, false);
        LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorFunction() {
                public double[] value(double[] variables) {
                    return factors.operate(variables);
                }
            }, new double[] { 2.0, -3.0 });
        SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-6);
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        final RealMatrix factors =
            new Array2DRowRealMatrix(new double[][] {
                    { 1, 0 },
                    { 0, 1 }
                }, false);
        LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorFunction() {
                public double[] value(double[] variables) {
                    return factors.operate(variables);
                }
            }, new double[] { 2, -3 }, new double[] { 10, 0.1 });
        SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-6);
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        final RealMatrix factors =
            new Array2DRowRealMatrix(new double[][] {
                    { 1, 0 },
                    { 0, 1 }
                }, false);
        LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorFunction() {
                public double[] value(double[] variables) {
                    return factors.operate(variables);
                }
            }, new double[] { 2, -3 }, new Array2DRowRealMatrix(new double [][] {
                    { 1, 1.2 }, { 1.2, 2 }
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                    }
                });
        }

        public ObjectiveFunctionGradient getObjectiveFunctionGradient() {
            return new ObjectiveFunctionGradient(new MultivariateVectorFunction() {
                    public double[] value(double[] point) {
                        double[] r = factors.operate(point);
                        for (int i = 0; i < r.length; ++i) {
                            r[i] -= target[i];
                        }
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            = new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
        optimizer.optimize(new MaxEval(100),
                           new Target(new double[] { 0 }),
                           new Weight(new double[] { 1 }),
                           new InitialGuess(new double[] { 0 }),
                           new ModelFunction(new MultivariateVectorFunction() {
                                   public double[] value(double[] point) {
                                       throw new TestException();
                                   }
                               }));
    }
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        return w;
    }

    public ModelFunction getModelFunction() {
        return new ModelFunction(new MultivariateVectorFunction() {
                public double[] value(double[] params) {
                    final Model line = new Model(params[0], params[1]);

                    final double[] model = new double[points.size()];
                    for (int i = 0; i < points.size(); i++) {
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        this.problem = new LeastSquaresProblem();
    }

    class LeastSquaresProblem {
        public ModelFunction getModelFunction() {
            return new ModelFunction(new MultivariateVectorFunction() {
                    public double[] value(final double[] a) {
                        final int n = getNumObservations();
                        final double[] yhat = new double[n];
                        for (int i = 0; i < n; i++) {
                            yhat[i] = getModelValue(getX(i), a);
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                }
            });
    }

    public ObjectiveFunctionGradient getObjectiveFunctionGradient() {
        return new ObjectiveFunctionGradient(new MultivariateVectorFunction() {
                public double[] value(double[] params) {
                    Vector2D center = new Vector2D(params[0], params[1]);
                    double radius = getRadius(center);
                    // gradient of the sum of squared residuals
                    double dJdX = 0;
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                }
            }
        }

        public ModelFunction getModelFunction() {
            return new ModelFunction(new MultivariateVectorFunction() {
                    public double[] value(double[] point) {
                        return computeValue(point);
                    }
                });
        }
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            this.x.add(x);
            this.y.add(y);
        }

        public ModelFunction getModelFunction() {
            return new ModelFunction(new MultivariateVectorFunction() {
                    public double[] value(double[] variables) {
                        double[] values = new double[x.size()];
                        for (int i = 0; i < values.length; ++i) {
                            values[i] = (variables[0] * x.get(i) + variables[1]) * x.get(i) + variables[2];
                        }
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Related Classes of org.apache.commons.math3.analysis.MultivariateVectorFunction

  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
  • org.apache.commons.math3.analysis.function.HarmonicOscillator
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.MultivariateFunction
  • org.apache.commons.math3.analysis.QuinticFunction
  • org.apache.commons.math3.analysis.solvers.BrentSolver
  • org.apache.commons.math3.analysis.UnivariateFunction
  • org.apache.commons.math3.complex.Complex
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