import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
/**
 * View a convergence checker specified for a {@link PointVectorValuePair} as one
 * specified for an {@link Evaluation}.
 *
 * @param checker the convergence checker to adapt.
 * @return a convergence checker that delegates to {@code checker}.
 */
public static ConvergenceChecker<Evaluation> evaluationChecker(final ConvergenceChecker<PointVectorValuePair> checker) {
    return new ConvergenceChecker<Evaluation>() {
        /** {@inheritDoc} */
        public boolean converged(final int iteration,
                                 final Evaluation previous,
                                 final Evaluation current) {
            return checker.converged(
                    iteration,
                    new PointVectorValuePair(
                            previous.getPoint().toArray(),
                            previous.getResiduals().toArray(),
                            false),
                    new PointVectorValuePair(
                            current.getPoint().toArray(),
                            current.getResiduals().toArray(),
                            false)
            );
        }
    };
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
/**
 * View a convergence checker specified for a {@link PointVectorValuePair} as one
 * specified for an {@link Evaluation}.
 *
 * @param checker the convergence checker to adapt.
 * @return a convergence checker that delegates to {@code checker}.
 */
public static ConvergenceChecker<Evaluation> evaluationChecker(final ConvergenceChecker<PointVectorValuePair> checker) {
    return new ConvergenceChecker<Evaluation>() {
        public boolean converged(final int iteration,
                                 final Evaluation previous,
                                 final Evaluation current) {
            return checker.converged(
                    iteration,
                    new PointVectorValuePair(
                            previous.getPoint().toArray(),
                            previous.getResiduals().toArray(),
                            false),
                    new PointVectorValuePair(
                            current.getPoint().toArray(),
                            current.getResiduals().toArray(),
                            false)
            );
        }
    };
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
/**
 * @return a comparator for sorting the optima.
 */
private Comparator<PointVectorValuePair> getPairComparator() {
    return new Comparator<PointVectorValuePair>() {
        private final RealVector target = new ArrayRealVector(optimizer.getTarget(), false);
        private final RealMatrix weight = optimizer.getWeight();

        public int compare(final PointVectorValuePair o1,
                           final PointVectorValuePair o2) {
            if (o1 == null) {
                return (o2 == null) ? 0 : 1;
            } else if (o2 == null) {
                return -1;
            }
            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));
        }
    };
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
@Override
@Test(expected=SingularMatrixException.class)
public void testNonInvertible() {
    /*
     * Overrides the method from parent class, since the default singularity
     * threshold (1e-14) does not trigger the expected exception.
     */
    LinearProblem problem = new LinearProblem(new double[][] {
            {  1, 2, -3 },
            {  2, 1,  3 },
            { -3, 0, -9 }
    }, 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.assertTrue(FastMath.sqrt(optimizer.getTargetSize()) * optimizer.getRMS() > 0.6);

    optimizer.computeCovariances(optimum.getPoint(), 1.5e-14);
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
@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);
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
@Test
public void testQRColumnsPermutation() {

    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);
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
@Test
public void testNoDependency() {
    LinearProblem problem = new LinearProblem(new double[][] {
            { 2, 0, 0, 0, 0, 0 },
            { 0, 2, 0, 0, 0, 0 },
            { 0, 0, 2, 0, 0, 0 },
            { 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);
    }
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
@Test
public void testOneSet() {

    LinearProblem problem = new LinearProblem(new double[][] {
            {  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);
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
@Test
public void testMoreEstimatedParametersUnsorted() {
    LinearProblem problem = new LinearProblem(new double[][] {
            { 1, 1,  0,  0, 0,  0 },
            { 0, 0,  1,  1, 1,  0 },
            { 0, 0,  0,  0, 1, -1 },
            { 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);
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
@Test
public void testRedundantEquations() {
    LinearProblem problem = new LinearProblem(new double[][] {
            { 1,  1 },
            { 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);
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
@Test(expected=DimensionMismatchException.class)
public void testInconsistentSizes1() {
    LinearProblem problem
        = new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } },
                            new double[] { -1, 1 });
    AbstractLeastSquaresOptimizer optimizer = createOptimizer();
    PointVectorValuePair optimum =
        optimizer.optimize(new MaxEval(100),
                           problem.getModelFunction(),
                           problem.getModelFunctionJacobian(),
                           problem.getTarget(),
                           new Weight(new double[] { 1, 1 }),
                           new InitialGuess(new double[] { 0, 0 }));
    Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
    Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
    Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);

    optimizer.optimize(new MaxEval(100),
                       problem.getModelFunction(),
                       problem.getModelFunctionJacobian(),
                       problem.getTarget(),
                       new Weight(new double[] { 1 }),
                       new InitialGuess(new double[] { 0, 0 }));
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
@Test(expected=DimensionMismatchException.class)
public void testInconsistentSizes2() {
    LinearProblem problem
        = new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } },
                            new double[] { -1, 1 });
    AbstractLeastSquaresOptimizer optimizer = createOptimizer();
    PointVectorValuePair optimum
        = optimizer.optimize(new MaxEval(100),
                             problem.getModelFunction(),
                             problem.getModelFunctionJacobian(),
                             problem.getTarget(),
                             new Weight(new double[] { 1, 1 }),
                             new InitialGuess(new double[] { 0, 0 }));
    Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
    Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
    Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);

    optimizer.optimize(new MaxEval(100),
                       problem.getModelFunction(),
                       problem.getModelFunctionJacobian(),
                       new Target(new double[] { 1 }),
                       new Weight(new double[] { 1 }),
                       new InitialGuess(new double[] { 0, 0 }));
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
@Test
public void testCircleFittingBadInit() {
    CircleVectorial circle = new CircleVectorial();
    double[][] points = circlePoints;
    double[] target = new double[points.length];
    Arrays.fill(target, 0);
    double[] weights = new double[points.length];
    Arrays.fill(weights, 2);
    for (int i = 0; i < points.length; ++i) {
        circle.addPoint(points[i][0], points[i][1]);
    }
    AbstractLeastSquaresOptimizer optimizer = createOptimizer();
    PointVectorValuePair optimum
        = optimizer.optimize(new MaxEval(100),
                             circle.getModelFunction(),
                             circle.getModelFunctionJacobian(),
                             new Target(target),
                             new Weight(weights),
                             new InitialGuess(new double[] { -12, -12 }));
    Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
    Assert.assertTrue(optimizer.getEvaluations() < 25);
    Assert.assertEquals( 0.043, optimizer.getRMS(), 1e-3);
    Assert.assertEquals( 0.292235,  circle.getRadius(center), 1e-6);
    Assert.assertEquals(-0.151738,  center.getX(),            1e-6);
    Assert.assertEquals( 0.2075001, center.getY(),            1e-6);
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
@Test
public void testCircleFittingGoodInit() {
    CircleVectorial circle = new CircleVectorial();
    double[][] points = circlePoints;
    double[] target = new double[points.length];
    Arrays.fill(target, 0);
    double[] weights = new double[points.length];
    Arrays.fill(weights, 2);
    for (int i = 0; i < points.length; ++i) {
        circle.addPoint(points[i][0], points[i][1]);
    }
    AbstractLeastSquaresOptimizer optimizer = createOptimizer();
    PointVectorValuePair optimum =
        optimizer.optimize(new MaxEval(100),
                           circle.getModelFunction(),
                           circle.getModelFunctionJacobian(),
                           new Target(target),
                           new Weight(weights),
                           new InitialGuess(new double[] { 0, 0 }));
    Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1e-6);
    Assert.assertEquals(0.2074999736353867,  optimum.getPointRef()[1], 1e-6);
    Assert.assertEquals(0.04268731682389561, optimizer.getRMS(),       1e-8);
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
public void doTestStRD(final StatisticalReferenceDataset dataset,
                       final double errParams,
                       final double errParamsSd) {
    final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
    final double[] w = new double[dataset.getNumObservations()];
    Arrays.fill(w, 1);

    final double[][] data = dataset.getData();
    final double[] initial = dataset.getStartingPoint(0);
    final StatisticalReferenceDataset.LeastSquaresProblem problem = dataset.getLeastSquaresProblem();
    final PointVectorValuePair optimum
        = optimizer.optimize(new MaxEval(100),
                             problem.getModelFunction(),
                             problem.getModelFunctionJacobian(),
                             new Target(data[1]),
                             new Weight(w),
                             new InitialGuess(initial));

    final double[] actual = optimum.getPoint();
    for (int i = 0; i < actual.length; i++) {
        double expected = dataset.getParameter(i);
        double delta = FastMath.abs(errParams * expected);
        Assert.assertEquals(dataset.getName() + ", param #" + i,
                            expected, actual[i], delta);
    }
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
private void minpackTest(MinpackFunction function, boolean exceptionExpected) {
    LevenbergMarquardtOptimizer optimizer
        = new LevenbergMarquardtOptimizer(FastMath.sqrt(2.22044604926e-16),
                                          FastMath.sqrt(2.22044604926e-16),
                                          2.22044604926e-16);
    try {
        PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(400 * (function.getN() + 1)),
                                 function.getModelFunction(),
                                 function.getModelFunctionJacobian(),
                                 new Target(function.getTarget()),
                                 new Weight(function.getWeight()),
                                 new InitialGuess(function.getStartPoint()));
        Assert.assertFalse(exceptionExpected);
        function.checkTheoreticalMinCost(optimizer.getRMS());
        function.checkTheoreticalMinParams(optimum);
    } catch (TooManyEvaluationsException e) {
        Assert.assertTrue(exceptionExpected);
    }
}
 

import org.apache.commons.math3.optim.PointVectorValuePair; //导入依赖的package包/类
@Test
public void testTrivial() {
    LinearProblem problem
        = new LinearProblem(new double[][] { { 2 } },
                            new double[] { 3 });
    T optimizer = createOptimizer()
        .withMaxEvaluations(100)
        .withMaxIterations(getMaxIterations())
        .withModelAndJacobian(problem.getModelFunction(),
                              problem.getModelFunctionJacobian())
        .withTarget(problem.getTarget())
        .withWeight(new DiagonalMatrix(new double[] { 1 }))
        .withStartPoint(new double[] { 0 });

    PointVectorValuePair optimum = optimizer.optimize();

    Assert.assertEquals(0, optimizer.computeRMS(optimum.getPoint()), 1e-10);
    Assert.assertEquals(1.5, optimum.getPoint()[0], 1e-10);
    Assert.assertEquals(3.0, optimum.getValue()[0], 1e-10);
}