org.apache.commons.math3.optim.nonlinear.vector.jacobian.LevenbergMarquardtOptimizer Java Examples

The following examples show how to use org.apache.commons.math3.optim.nonlinear.vector.jacobian.LevenbergMarquardtOptimizer. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
Source File: PolynomialFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testNoError() {
    Random randomizer = new Random(64925784252l);
    for (int degree = 1; degree < 10; ++degree) {
        PolynomialFunction p = buildRandomPolynomial(degree, randomizer);

        PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());
        for (int i = 0; i <= degree; ++i) {
            fitter.addObservedPoint(1.0, i, p.value(i));
        }

        final double[] init = new double[degree + 1];
        PolynomialFunction fitted = new PolynomialFunction(fitter.fit(init));

        for (double x = -1.0; x < 1.0; x += 0.01) {
            double error = FastMath.abs(p.value(x) - fitted.value(x)) /
                           (1.0 + FastMath.abs(p.value(x)));
            Assert.assertEquals(0.0, error, 1.0e-6);
        }
    }
}
Example #2
Source File: CurveFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testMath303() {
    LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
    CurveFitter<ParametricUnivariateFunction> fitter = new CurveFitter<ParametricUnivariateFunction>(optimizer);
    fitter.addObservedPoint(2.805d, 0.6934785852953367d);
    fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
    fitter.addObservedPoint(1.655d, 0.9474675497289684);
    fitter.addObservedPoint(1.725d, 0.9013594835804194d);

    ParametricUnivariateFunction sif = new SimpleInverseFunction();

    double[] initialguess1 = new double[1];
    initialguess1[0] = 1.0d;
    Assert.assertEquals(1, fitter.fit(sif, initialguess1).length);

    double[] initialguess2 = new double[2];
    initialguess2[0] = 1.0d;
    initialguess2[1] = .5d;
    Assert.assertEquals(2, fitter.fit(sif, initialguess2).length);
}
Example #3
Source File: PolynomialFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testNoError() {
    Random randomizer = new Random(64925784252l);
    for (int degree = 1; degree < 10; ++degree) {
        PolynomialFunction p = buildRandomPolynomial(degree, randomizer);

        PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());
        for (int i = 0; i <= degree; ++i) {
            fitter.addObservedPoint(1.0, i, p.value(i));
        }

        final double[] init = new double[degree + 1];
        PolynomialFunction fitted = new PolynomialFunction(fitter.fit(init));

        for (double x = -1.0; x < 1.0; x += 0.01) {
            double error = FastMath.abs(p.value(x) - fitted.value(x)) /
                           (1.0 + FastMath.abs(p.value(x)));
            Assert.assertEquals(0.0, error, 1.0e-6);
        }
    }
}
Example #4
Source File: CurveFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testMath303() {
    LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
    CurveFitter<ParametricUnivariateFunction> fitter = new CurveFitter<ParametricUnivariateFunction>(optimizer);
    fitter.addObservedPoint(2.805d, 0.6934785852953367d);
    fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
    fitter.addObservedPoint(1.655d, 0.9474675497289684);
    fitter.addObservedPoint(1.725d, 0.9013594835804194d);

    ParametricUnivariateFunction sif = new SimpleInverseFunction();

    double[] initialguess1 = new double[1];
    initialguess1[0] = 1.0d;
    Assert.assertEquals(1, fitter.fit(sif, initialguess1).length);

    double[] initialguess2 = new double[2];
    initialguess2[0] = 1.0d;
    initialguess2[1] = .5d;
    Assert.assertEquals(2, fitter.fit(sif, initialguess2).length);
}
Example #5
Source File: HarmonicFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testInitialGuess() {
    Random randomizer = new Random(45314242l);
    final double a = 0.2;
    final double w = 3.4;
    final double p = 4.1;
    HarmonicOscillator f = new HarmonicOscillator(a, w, p);

    HarmonicFitter fitter =
        new HarmonicFitter(new LevenbergMarquardtOptimizer());
    for (double x = 0.0; x < 10.0; x += 0.1) {
        fitter.addObservedPoint(1, x,
                                f.value(x) + 0.01 * randomizer.nextGaussian());
    }

    final double[] fitted = fitter.fit(new double[] { 0.15, 3.6, 4.5 });
    Assert.assertEquals(a, fitted[0], 1.2e-3);
    Assert.assertEquals(w, fitted[1], 3.3e-3);
    Assert.assertEquals(p, MathUtils.normalizeAngle(fitted[2], p), 1.7e-2);
}
Example #6
Source File: HarmonicFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testInitialGuess() {
    Random randomizer = new Random(45314242l);
    final double a = 0.2;
    final double w = 3.4;
    final double p = 4.1;
    HarmonicOscillator f = new HarmonicOscillator(a, w, p);

    HarmonicFitter fitter =
        new HarmonicFitter(new LevenbergMarquardtOptimizer());
    for (double x = 0.0; x < 10.0; x += 0.1) {
        fitter.addObservedPoint(1, x,
                                f.value(x) + 0.01 * randomizer.nextGaussian());
    }

    final double[] fitted = fitter.fit(new double[] { 0.15, 3.6, 4.5 });
    Assert.assertEquals(a, fitted[0], 1.2e-3);
    Assert.assertEquals(w, fitted[1], 3.3e-3);
    Assert.assertEquals(p, MathUtils.normalizeAngle(fitted[2], p), 1.7e-2);
}
Example #7
Source File: CurveFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testMath304() {
    LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
    CurveFitter<ParametricUnivariateFunction> fitter = new CurveFitter<ParametricUnivariateFunction>(optimizer);
    fitter.addObservedPoint(2.805d, 0.6934785852953367d);
    fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
    fitter.addObservedPoint(1.655d, 0.9474675497289684);
    fitter.addObservedPoint(1.725d, 0.9013594835804194d);

    ParametricUnivariateFunction sif = new SimpleInverseFunction();

    double[] initialguess1 = new double[1];
    initialguess1[0] = 1.0d;
    Assert.assertEquals(1.6357215104109237, fitter.fit(sif, initialguess1)[0], 1.0e-14);

    double[] initialguess2 = new double[1];
    initialguess2[0] = 10.0d;
    Assert.assertEquals(1.6357215104109237, fitter.fit(sif, initialguess1)[0], 1.0e-14);
}
Example #8
Source File: HarmonicFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void test1PercentError() {
    Random randomizer = new Random(64925784252l);
    final double a = 0.2;
    final double w = 3.4;
    final double p = 4.1;
    HarmonicOscillator f = new HarmonicOscillator(a, w, p);

    HarmonicFitter fitter =
        new HarmonicFitter(new LevenbergMarquardtOptimizer());
    for (double x = 0.0; x < 10.0; x += 0.1) {
        fitter.addObservedPoint(1, x,
                                f.value(x) + 0.01 * randomizer.nextGaussian());
    }

    final double[] fitted = fitter.fit();
    Assert.assertEquals(a, fitted[0], 7.6e-4);
    Assert.assertEquals(w, fitted[1], 2.7e-3);
    Assert.assertEquals(p, MathUtils.normalizeAngle(fitted[2], p), 1.3e-2);
}
Example #9
Source File: HarmonicFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testNoError() {
    final double a = 0.2;
    final double w = 3.4;
    final double p = 4.1;
    HarmonicOscillator f = new HarmonicOscillator(a, w, p);

    HarmonicFitter fitter =
        new HarmonicFitter(new LevenbergMarquardtOptimizer());
    for (double x = 0.0; x < 1.3; x += 0.01) {
        fitter.addObservedPoint(1, x, f.value(x));
    }

    final double[] fitted = fitter.fit();
    Assert.assertEquals(a, fitted[0], 1.0e-13);
    Assert.assertEquals(w, fitted[1], 1.0e-13);
    Assert.assertEquals(p, MathUtils.normalizeAngle(fitted[2], p), 1e-13);

    HarmonicOscillator ff = new HarmonicOscillator(fitted[0], fitted[1], fitted[2]);

    for (double x = -1.0; x < 1.0; x += 0.01) {
        Assert.assertTrue(FastMath.abs(f.value(x) - ff.value(x)) < 1e-13);
    }
}
Example #10
Source File: PolynomialFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testNoError() {
    Random randomizer = new Random(64925784252l);
    for (int degree = 1; degree < 10; ++degree) {
        PolynomialFunction p = buildRandomPolynomial(degree, randomizer);

        PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());
        for (int i = 0; i <= degree; ++i) {
            fitter.addObservedPoint(1.0, i, p.value(i));
        }

        final double[] init = new double[degree + 1];
        PolynomialFunction fitted = new PolynomialFunction(fitter.fit(init));

        for (double x = -1.0; x < 1.0; x += 0.01) {
            double error = FastMath.abs(p.value(x) - fitted.value(x)) /
                           (1.0 + FastMath.abs(p.value(x)));
            Assert.assertEquals(0.0, error, 1.0e-6);
        }
    }
}
Example #11
Source File: PolynomialFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testLargeSample() {
    Random randomizer = new Random(0x5551480dca5b369bl);
    double maxError = 0;
    for (int degree = 0; degree < 10; ++degree) {
        PolynomialFunction p = buildRandomPolynomial(degree, randomizer);

        PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());
        for (int i = 0; i < 40000; ++i) {
            double x = -1.0 + i / 20000.0;
            fitter.addObservedPoint(1.0, x,
                                    p.value(x) + 0.1 * randomizer.nextGaussian());
        }

        final double[] init = new double[degree + 1];
        PolynomialFunction fitted = new PolynomialFunction(fitter.fit(init));

        for (double x = -1.0; x < 1.0; x += 0.01) {
            double error = FastMath.abs(p.value(x) - fitted.value(x)) /
                          (1.0 + FastMath.abs(p.value(x)));
            maxError = FastMath.max(maxError, error);
            Assert.assertTrue(FastMath.abs(error) < 0.01);
        }
    }
    Assert.assertTrue(maxError > 0.001);
}
Example #12
Source File: PolynomialFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testFit() {
    final RealDistribution rng = new UniformRealDistribution(-100, 100);
    rng.reseedRandomGenerator(64925784252L);

    final LevenbergMarquardtOptimizer optim = new LevenbergMarquardtOptimizer();
    final PolynomialFitter fitter = new PolynomialFitter(optim);
    final double[] coeff = { 12.9, -3.4, 2.1 }; // 12.9 - 3.4 x + 2.1 x^2
    final PolynomialFunction f = new PolynomialFunction(coeff);

    // Collect data from a known polynomial.
    for (int i = 0; i < 100; i++) {
        final double x = rng.sample();
        fitter.addObservedPoint(x, f.value(x));
    }

    // Start fit from initial guesses that are far from the optimal values.
    final double[] best = fitter.fit(new double[] { -1e-20, 3e15, -5e25 });

    TestUtils.assertEquals("best != coeff", coeff, best, 1e-12);
}
Example #13
Source File: PolynomialFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testSmallError() {
    Random randomizer = new Random(53882150042l);
    double maxError = 0;
    for (int degree = 0; degree < 10; ++degree) {
        PolynomialFunction p = buildRandomPolynomial(degree, randomizer);

        PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());
        for (double x = -1.0; x < 1.0; x += 0.01) {
            fitter.addObservedPoint(1.0, x,
                                    p.value(x) + 0.1 * randomizer.nextGaussian());
        }

        final double[] init = new double[degree + 1];
        PolynomialFunction fitted = new PolynomialFunction(fitter.fit(init));

        for (double x = -1.0; x < 1.0; x += 0.01) {
            double error = FastMath.abs(p.value(x) - fitted.value(x)) /
                          (1.0 + FastMath.abs(p.value(x)));
            maxError = FastMath.max(maxError, error);
            Assert.assertTrue(FastMath.abs(error) < 0.1);
        }
    }
    Assert.assertTrue(maxError > 0.01);
}
Example #14
Source File: HarmonicFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testInitialGuess() {
    Random randomizer = new Random(45314242l);
    final double a = 0.2;
    final double w = 3.4;
    final double p = 4.1;
    HarmonicOscillator f = new HarmonicOscillator(a, w, p);

    HarmonicFitter fitter =
        new HarmonicFitter(new LevenbergMarquardtOptimizer());
    for (double x = 0.0; x < 10.0; x += 0.1) {
        fitter.addObservedPoint(1, x,
                                f.value(x) + 0.01 * randomizer.nextGaussian());
    }

    final double[] fitted = fitter.fit(new double[] { 0.15, 3.6, 4.5 });
    Assert.assertEquals(a, fitted[0], 1.2e-3);
    Assert.assertEquals(w, fitted[1], 3.3e-3);
    Assert.assertEquals(p, MathUtils.normalizeAngle(fitted[2], p), 1.7e-2);
}
Example #15
Source File: PolynomialFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testSmallError() {
    Random randomizer = new Random(53882150042l);
    double maxError = 0;
    for (int degree = 0; degree < 10; ++degree) {
        PolynomialFunction p = buildRandomPolynomial(degree, randomizer);

        PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());
        for (double x = -1.0; x < 1.0; x += 0.01) {
            fitter.addObservedPoint(1.0, x,
                                    p.value(x) + 0.1 * randomizer.nextGaussian());
        }

        final double[] init = new double[degree + 1];
        PolynomialFunction fitted = new PolynomialFunction(fitter.fit(init));

        for (double x = -1.0; x < 1.0; x += 0.01) {
            double error = FastMath.abs(p.value(x) - fitted.value(x)) /
                          (1.0 + FastMath.abs(p.value(x)));
            maxError = FastMath.max(maxError, error);
            Assert.assertTrue(FastMath.abs(error) < 0.1);
        }
    }
    Assert.assertTrue(maxError > 0.01);
}
Example #16
Source File: PolynomialFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testLargeSample() {
    Random randomizer = new Random(0x5551480dca5b369bl);
    double maxError = 0;
    for (int degree = 0; degree < 10; ++degree) {
        PolynomialFunction p = buildRandomPolynomial(degree, randomizer);

        PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());
        for (int i = 0; i < 40000; ++i) {
            double x = -1.0 + i / 20000.0;
            fitter.addObservedPoint(1.0, x,
                                    p.value(x) + 0.1 * randomizer.nextGaussian());
        }

        final double[] init = new double[degree + 1];
        PolynomialFunction fitted = new PolynomialFunction(fitter.fit(init));

        for (double x = -1.0; x < 1.0; x += 0.01) {
            double error = FastMath.abs(p.value(x) - fitted.value(x)) /
                          (1.0 + FastMath.abs(p.value(x)));
            maxError = FastMath.max(maxError, error);
            Assert.assertTrue(FastMath.abs(error) < 0.01);
        }
    }
    Assert.assertTrue(maxError > 0.001);
}
Example #17
Source File: CurveFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testMath303() {
    LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
    CurveFitter<ParametricUnivariateFunction> fitter = new CurveFitter<ParametricUnivariateFunction>(optimizer);
    fitter.addObservedPoint(2.805d, 0.6934785852953367d);
    fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
    fitter.addObservedPoint(1.655d, 0.9474675497289684);
    fitter.addObservedPoint(1.725d, 0.9013594835804194d);

    ParametricUnivariateFunction sif = new SimpleInverseFunction();

    double[] initialguess1 = new double[1];
    initialguess1[0] = 1.0d;
    Assert.assertEquals(1, fitter.fit(sif, initialguess1).length);

    double[] initialguess2 = new double[2];
    initialguess2[0] = 1.0d;
    initialguess2[1] = .5d;
    Assert.assertEquals(2, fitter.fit(sif, initialguess2).length);
}
Example #18
Source File: CurveFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testMath304() {
    LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
    CurveFitter<ParametricUnivariateFunction> fitter = new CurveFitter<ParametricUnivariateFunction>(optimizer);
    fitter.addObservedPoint(2.805d, 0.6934785852953367d);
    fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
    fitter.addObservedPoint(1.655d, 0.9474675497289684);
    fitter.addObservedPoint(1.725d, 0.9013594835804194d);

    ParametricUnivariateFunction sif = new SimpleInverseFunction();

    double[] initialguess1 = new double[1];
    initialguess1[0] = 1.0d;
    Assert.assertEquals(1.6357215104109237, fitter.fit(sif, initialguess1)[0], 1.0e-14);

    double[] initialguess2 = new double[1];
    initialguess2[0] = 10.0d;
    Assert.assertEquals(1.6357215104109237, fitter.fit(sif, initialguess1)[0], 1.0e-14);
}
Example #19
Source File: PolynomialFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testFit() {
    final RealDistribution rng = new UniformRealDistribution(-100, 100);
    rng.reseedRandomGenerator(64925784252L);

    final LevenbergMarquardtOptimizer optim = new LevenbergMarquardtOptimizer();
    final PolynomialFitter fitter = new PolynomialFitter(optim);
    final double[] coeff = { 12.9, -3.4, 2.1 }; // 12.9 - 3.4 x + 2.1 x^2
    final PolynomialFunction f = new PolynomialFunction(coeff);

    // Collect data from a known polynomial.
    for (int i = 0; i < 100; i++) {
        final double x = rng.sample();
        fitter.addObservedPoint(x, f.value(x));
    }

    // Start fit from initial guesses that are far from the optimal values.
    final double[] best = fitter.fit(new double[] { -1e-20, 3e15, -5e25 });

    TestUtils.assertEquals("best != coeff", coeff, best, 1e-12);
}
Example #20
Source File: HarmonicFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void test1PercentError() {
    Random randomizer = new Random(64925784252l);
    final double a = 0.2;
    final double w = 3.4;
    final double p = 4.1;
    HarmonicOscillator f = new HarmonicOscillator(a, w, p);

    HarmonicFitter fitter =
        new HarmonicFitter(new LevenbergMarquardtOptimizer());
    for (double x = 0.0; x < 10.0; x += 0.1) {
        fitter.addObservedPoint(1, x,
                                f.value(x) + 0.01 * randomizer.nextGaussian());
    }

    final double[] fitted = fitter.fit();
    Assert.assertEquals(a, fitted[0], 7.6e-4);
    Assert.assertEquals(w, fitted[1], 2.7e-3);
    Assert.assertEquals(p, MathUtils.normalizeAngle(fitted[2], p), 1.3e-2);
}
Example #21
Source File: PolynomialFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testSmallError() {
    Random randomizer = new Random(53882150042l);
    double maxError = 0;
    for (int degree = 0; degree < 10; ++degree) {
        PolynomialFunction p = buildRandomPolynomial(degree, randomizer);

        PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());
        for (double x = -1.0; x < 1.0; x += 0.01) {
            fitter.addObservedPoint(1.0, x,
                                    p.value(x) + 0.1 * randomizer.nextGaussian());
        }

        final double[] init = new double[degree + 1];
        PolynomialFunction fitted = new PolynomialFunction(fitter.fit(init));

        for (double x = -1.0; x < 1.0; x += 0.01) {
            double error = FastMath.abs(p.value(x) - fitted.value(x)) /
                          (1.0 + FastMath.abs(p.value(x)));
            maxError = FastMath.max(maxError, error);
            Assert.assertTrue(FastMath.abs(error) < 0.1);
        }
    }
    Assert.assertTrue(maxError > 0.01);
}
Example #22
Source File: HarmonicFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testInitialGuess() {
    Random randomizer = new Random(45314242l);
    final double a = 0.2;
    final double w = 3.4;
    final double p = 4.1;
    HarmonicOscillator f = new HarmonicOscillator(a, w, p);

    HarmonicFitter fitter =
        new HarmonicFitter(new LevenbergMarquardtOptimizer());
    for (double x = 0.0; x < 10.0; x += 0.1) {
        fitter.addObservedPoint(1, x,
                                f.value(x) + 0.01 * randomizer.nextGaussian());
    }

    final double[] fitted = fitter.fit(new double[] { 0.15, 3.6, 4.5 });
    Assert.assertEquals(a, fitted[0], 1.2e-3);
    Assert.assertEquals(w, fitted[1], 3.3e-3);
    Assert.assertEquals(p, MathUtils.normalizeAngle(fitted[2], p), 1.7e-2);
}
Example #23
Source File: CurveFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testMath303() {
    LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
    CurveFitter<ParametricUnivariateFunction> fitter = new CurveFitter<ParametricUnivariateFunction>(optimizer);
    fitter.addObservedPoint(2.805d, 0.6934785852953367d);
    fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
    fitter.addObservedPoint(1.655d, 0.9474675497289684);
    fitter.addObservedPoint(1.725d, 0.9013594835804194d);

    ParametricUnivariateFunction sif = new SimpleInverseFunction();

    double[] initialguess1 = new double[1];
    initialguess1[0] = 1.0d;
    Assert.assertEquals(1, fitter.fit(sif, initialguess1).length);

    double[] initialguess2 = new double[2];
    initialguess2[0] = 1.0d;
    initialguess2[1] = .5d;
    Assert.assertEquals(2, fitter.fit(sif, initialguess2).length);
}
Example #24
Source File: PolynomialFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testLargeSample() {
    Random randomizer = new Random(0x5551480dca5b369bl);
    double maxError = 0;
    for (int degree = 0; degree < 10; ++degree) {
        PolynomialFunction p = buildRandomPolynomial(degree, randomizer);

        PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());
        for (int i = 0; i < 40000; ++i) {
            double x = -1.0 + i / 20000.0;
            fitter.addObservedPoint(1.0, x,
                                    p.value(x) + 0.1 * randomizer.nextGaussian());
        }

        final double[] init = new double[degree + 1];
        PolynomialFunction fitted = new PolynomialFunction(fitter.fit(init));

        for (double x = -1.0; x < 1.0; x += 0.01) {
            double error = FastMath.abs(p.value(x) - fitted.value(x)) /
                          (1.0 + FastMath.abs(p.value(x)));
            maxError = FastMath.max(maxError, error);
            Assert.assertTrue(FastMath.abs(error) < 0.01);
        }
    }
    Assert.assertTrue(maxError > 0.001);
}
Example #25
Source File: HarmonicFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void test1PercentError() {
    Random randomizer = new Random(64925784252l);
    final double a = 0.2;
    final double w = 3.4;
    final double p = 4.1;
    HarmonicOscillator f = new HarmonicOscillator(a, w, p);

    HarmonicFitter fitter =
        new HarmonicFitter(new LevenbergMarquardtOptimizer());
    for (double x = 0.0; x < 10.0; x += 0.1) {
        fitter.addObservedPoint(1, x,
                                f.value(x) + 0.01 * randomizer.nextGaussian());
    }

    final double[] fitted = fitter.fit();
    Assert.assertEquals(a, fitted[0], 7.6e-4);
    Assert.assertEquals(w, fitted[1], 2.7e-3);
    Assert.assertEquals(p, MathUtils.normalizeAngle(fitted[2], p), 1.3e-2);
}
Example #26
Source File: HarmonicFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testNoError() {
    final double a = 0.2;
    final double w = 3.4;
    final double p = 4.1;
    HarmonicOscillator f = new HarmonicOscillator(a, w, p);

    HarmonicFitter fitter =
        new HarmonicFitter(new LevenbergMarquardtOptimizer());
    for (double x = 0.0; x < 1.3; x += 0.01) {
        fitter.addObservedPoint(1, x, f.value(x));
    }

    final double[] fitted = fitter.fit();
    Assert.assertEquals(a, fitted[0], 1.0e-13);
    Assert.assertEquals(w, fitted[1], 1.0e-13);
    Assert.assertEquals(p, MathUtils.normalizeAngle(fitted[2], p), 1e-13);

    HarmonicOscillator ff = new HarmonicOscillator(fitted[0], fitted[1], fitted[2]);

    for (double x = -1.0; x < 1.0; x += 0.01) {
        Assert.assertTrue(FastMath.abs(f.value(x) - ff.value(x)) < 1e-13);
    }
}
Example #27
Source File: PolynomialFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testFit() {
    final RealDistribution rng = new UniformRealDistribution(-100, 100);
    rng.reseedRandomGenerator(64925784252L);

    final LevenbergMarquardtOptimizer optim = new LevenbergMarquardtOptimizer();
    final PolynomialFitter fitter = new PolynomialFitter(optim);
    final double[] coeff = { 12.9, -3.4, 2.1 }; // 12.9 - 3.4 x + 2.1 x^2
    final PolynomialFunction f = new PolynomialFunction(coeff);

    // Collect data from a known polynomial.
    for (int i = 0; i < 100; i++) {
        final double x = rng.sample();
        fitter.addObservedPoint(x, f.value(x));
    }

    // Start fit from initial guesses that are far from the optimal values.
    final double[] best = fitter.fit(new double[] { -1e-20, 3e15, -5e25 });

    TestUtils.assertEquals("best != coeff", coeff, best, 1e-12);
}
Example #28
Source File: PolynomialFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testSmallError() {
    Random randomizer = new Random(53882150042l);
    double maxError = 0;
    for (int degree = 0; degree < 10; ++degree) {
        PolynomialFunction p = buildRandomPolynomial(degree, randomizer);

        PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());
        for (double x = -1.0; x < 1.0; x += 0.01) {
            fitter.addObservedPoint(1.0, x,
                                    p.value(x) + 0.1 * randomizer.nextGaussian());
        }

        final double[] init = new double[degree + 1];
        PolynomialFunction fitted = new PolynomialFunction(fitter.fit(init));

        for (double x = -1.0; x < 1.0; x += 0.01) {
            double error = FastMath.abs(p.value(x) - fitted.value(x)) /
                          (1.0 + FastMath.abs(p.value(x)));
            maxError = FastMath.max(maxError, error);
            Assert.assertTrue(FastMath.abs(error) < 0.1);
        }
    }
    Assert.assertTrue(maxError > 0.01);
}
Example #29
Source File: HarmonicFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testNoError() {
    final double a = 0.2;
    final double w = 3.4;
    final double p = 4.1;
    HarmonicOscillator f = new HarmonicOscillator(a, w, p);

    HarmonicFitter fitter =
        new HarmonicFitter(new LevenbergMarquardtOptimizer());
    for (double x = 0.0; x < 1.3; x += 0.01) {
        fitter.addObservedPoint(1, x, f.value(x));
    }

    final double[] fitted = fitter.fit();
    Assert.assertEquals(a, fitted[0], 1.0e-13);
    Assert.assertEquals(w, fitted[1], 1.0e-13);
    Assert.assertEquals(p, MathUtils.normalizeAngle(fitted[2], p), 1e-13);

    HarmonicOscillator ff = new HarmonicOscillator(fitted[0], fitted[1], fitted[2]);

    for (double x = -1.0; x < 1.0; x += 0.01) {
        Assert.assertTrue(FastMath.abs(f.value(x) - ff.value(x)) < 1e-13);
    }
}
Example #30
Source File: PolynomialFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testLargeSample() {
    Random randomizer = new Random(0x5551480dca5b369bl);
    double maxError = 0;
    for (int degree = 0; degree < 10; ++degree) {
        PolynomialFunction p = buildRandomPolynomial(degree, randomizer);

        PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());
        for (int i = 0; i < 40000; ++i) {
            double x = -1.0 + i / 20000.0;
            fitter.addObservedPoint(1.0, x,
                                    p.value(x) + 0.1 * randomizer.nextGaussian());
        }

        final double[] init = new double[degree + 1];
        PolynomialFunction fitted = new PolynomialFunction(fitter.fit(init));

        for (double x = -1.0; x < 1.0; x += 0.01) {
            double error = FastMath.abs(p.value(x) - fitted.value(x)) /
                          (1.0 + FastMath.abs(p.value(x)));
            maxError = FastMath.max(maxError, error);
            Assert.assertTrue(FastMath.abs(error) < 0.01);
        }
    }
    Assert.assertTrue(maxError > 0.001);
}