Package org.apache.commons.math3.optim

Examples of org.apache.commons.math3.optim.PointVectorValuePair

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        Arrays.fill(w, 1);

        StatisticalReferenceDataset.LeastSquaresProblem problem
            = dataset.getLeastSquaresProblem();

        final PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(1),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 new Target(y),
                                 new Weight(w),
                                 new InitialGuess(a));

        final double[] sig = optimizer.computeSigma(optimum.getPoint(), 1e-14);

        final int dof = y.length - a.length;
        final double[] expected = dataset.getParametersStandardDeviations();
        for (int i = 0; i < sig.length; i++) {
            final double actual = FastMath.sqrt(optimizer.getChiSquare() / dof) * sig[i];
 
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            // Direct solution (using simple regression).
            final double[] regress = problem.solve();

            // Estimation of the standard deviation (diagonal elements of the
            // covariance matrix).
            final PointVectorValuePair optimum
                = optim.optimize(new MaxEval(Integer.MAX_VALUE),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 new Target(problem.target()),
                                 new Weight(problem.weight()),
                                 new InitialGuess(init));
            final double[] sigma = optim.computeSigma(optimum.getPoint(), 1e-14);

            // Accumulate statistics.
            for (int i = 0; i < numParams; i++) {
                paramsFoundByDirectSolution[i].addValue(regress[i]);
                sigmaEstimate[i].addValue(sigma[i]);
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    @Test
    public void testTrivial() {
        LinearProblem problem
            = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               problem.getModelFunction(),
                               problem.getModelFunctionJacobian(),
                               problem.getTarget(),
                               new Weight(new double[] { 1 }),
                               new InitialGuess(new double[] { 0 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(1.5, optimum.getPoint()[0], 1e-10);
        Assert.assertEquals(3.0, optimum.getValue()[0], 1e-10);
    }
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        LinearProblem problem
            = new LinearProblem(new double[][] { { 1, -1 }, { 0, 2 }, { 1, -2 } },
                                new double[] { 4, 6, 1 });

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               problem.getModelFunction(),
                               problem.getModelFunctionJacobian(),
                               problem.getTarget(),
                               new Weight(new double[] { 1, 1, 1 }),
                               new InitialGuess(new double[] { 0, 0 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(7, optimum.getPoint()[0], 1e-10);
        Assert.assertEquals(3, optimum.getPoint()[1], 1e-10);
        Assert.assertEquals(4, optimum.getValue()[0], 1e-10);
        Assert.assertEquals(6, optimum.getValue()[1], 1e-10);
        Assert.assertEquals(1, optimum.getValue()[2], 1e-10);
    }
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                { 0, 0, 0, 2, 0, 0 },
                { 0, 0, 0, 0, 2, 0 },
                { 0, 0, 0, 0, 0, 2 }
        }, new double[] { 0, 1.1, 2.2, 3.3, 4.4, 5.5 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               problem.getModelFunction(),
                               problem.getModelFunctionJacobian(),
                               problem.getTarget(),
                               new Weight(new double[] { 1, 1, 1, 1, 1, 1 }),
                               new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        for (int i = 0; i < problem.target.length; ++i) {
            Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1e-10);
        }
    }
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                {  1,  0, 0 },
                { -1,  1, 0 },
                {  0, -1, 1 }
        }, new double[] { 1, 1, 1});
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               problem.getModelFunction(),
                               problem.getModelFunctionJacobian(),
                               problem.getTarget(),
                               new Weight(new double[] { 1, 1, 1 }),
                               new InitialGuess(new double[] { 0, 0, 0 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(1, optimum.getPoint()[0], 1e-10);
        Assert.assertEquals(2, optimum.getPoint()[1], 1e-10);
        Assert.assertEquals(3, optimum.getPoint()[2], 1e-10);
    }
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                {  0,  0,   0,  0, epsilon, 1 },
                {  0,  0,   0,  0,       1, 1 }
        }, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2});

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               problem.getModelFunction(),
                               problem.getModelFunctionJacobian(),
                               problem.getTarget(),
                               new Weight(new double[] { 1, 1, 1, 1, 1, 1 }),
                               new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(3, optimum.getPoint()[0], 1e-10);
        Assert.assertEquals(4, optimum.getPoint()[1], 1e-10);
        Assert.assertEquals(-1, optimum.getPoint()[2], 1e-10);
        Assert.assertEquals(-2, optimum.getPoint()[3], 1e-10);
        Assert.assertEquals(1 + epsilon, optimum.getPoint()[4], 1e-10);
        Assert.assertEquals(1 - epsilon, optimum.getPoint()[5], 1e-10);
    }
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                {  7, 5,  6,  5 },
                {  8, 6, 10,  9 },
                {  7, 5,  9, 10 }
        }, new double[] { 32, 23, 33, 31 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum1 =
            optimizer.optimize(new MaxEval(100),
                               problem1.getModelFunction(),
                               problem1.getModelFunctionJacobian(),
                               problem1.getTarget(),
                               new Weight(new double[] { 1, 1, 1, 1 }),
                               new InitialGuess(new double[] { 0, 1, 2, 3 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(1, optimum1.getPoint()[0], 1e-10);
        Assert.assertEquals(1, optimum1.getPoint()[1], 1e-10);
        Assert.assertEquals(1, optimum1.getPoint()[2], 1e-10);
        Assert.assertEquals(1, optimum1.getPoint()[3], 1e-10);

        LinearProblem problem2 = new LinearProblem(new double[][] {
                { 10.00, 7.00, 8.10, 7.20 },
                {  7.08, 5.04, 6.00, 5.00 },
                {  8.00, 5.98, 9.89, 9.00 },
                {  6.99, 4.99, 9.00, 9.98 }
        }, new double[] { 32, 23, 33, 31 });
        PointVectorValuePair optimum2 =
            optimizer.optimize(new MaxEval(100),
                               problem2.getModelFunction(),
                               problem2.getModelFunctionJacobian(),
                               problem2.getTarget(),
                               new Weight(new double[] { 1, 1, 1, 1 }),
                               new InitialGuess(new double[] { 0, 1, 2, 3 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(-81, optimum2.getPoint()[0], 1e-8);
        Assert.assertEquals(137, optimum2.getPoint()[1], 1e-8);
        Assert.assertEquals(-34, optimum2.getPoint()[2], 1e-8);
        Assert.assertEquals( 22, optimum2.getPoint()[3], 1e-8);
    }
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                { 0, 0, -1,  1, 0,  1 },
                { 0, 0,  0, -1, 1,  0 }
       }, new double[] { 3, 12, -1, 7, 1 });

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               problem.getModelFunction(),
                               problem.getModelFunctionJacobian(),
                               problem.getTarget(),
                               new Weight(new double[] { 1, 1, 1, 1, 1 }),
                               new InitialGuess(new double[] { 2, 2, 2, 2, 2, 2 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(3, optimum.getPointRef()[2], 1e-10);
        Assert.assertEquals(4, optimum.getPointRef()[3], 1e-10);
        Assert.assertEquals(5, optimum.getPointRef()[4], 1e-10);
        Assert.assertEquals(6, optimum.getPointRef()[5], 1e-10);
    }
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                { 1, -1 },
                { 1,  3 }
        }, new double[] { 3, 1, 5 });

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               problem.getModelFunction(),
                               problem.getModelFunctionJacobian(),
                               problem.getTarget(),
                               new Weight(new double[] { 1, 1, 1 }),
                               new InitialGuess(new double[] { 1, 1 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(2, optimum.getPointRef()[0], 1e-10);
        Assert.assertEquals(1, optimum.getPointRef()[1], 1e-10);
    }
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Related Classes of org.apache.commons.math3.optim.PointVectorValuePair

  • 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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