Java Code Examples for org.apache.commons.math3.distribution.RealDistribution#reseedRandomGenerator()

The following examples show how to use org.apache.commons.math3.distribution.RealDistribution#reseedRandomGenerator() . 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: SyntheticOptions.java    From beam with Apache License 2.0 6 votes vote down vote up 
public static Sampler fromRealDistribution(final RealDistribution dist) {
  return new Sampler() {
    private static final long serialVersionUID = 0L;

    @Override
    public double sample(long seed) {
      dist.reseedRandomGenerator(seed);
      return dist.sample();
    }

    @Override
    public Object getDistribution() {
      return dist;
    }
  };
}
Example 2
Source File: SimpleCurveFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testPolynomialFit() {
    final Random randomizer = new Random(53882150042L);
    final RealDistribution rng = new UniformRealDistribution(-100, 100);
    rng.reseedRandomGenerator(64925784252L);

    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.
    final WeightedObservedPoints obs = new WeightedObservedPoints();
    for (int i = 0; i < 100; i++) {
        final double x = rng.sample();
        obs.add(x, f.value(x) + 0.1 * randomizer.nextGaussian());
    }

    final ParametricUnivariateFunction function = new PolynomialFunction.Parametric(); 
    // Start fit from initial guesses that are far from the optimal values.
    final SimpleCurveFitter fitter
        = SimpleCurveFitter.create(function,
                                   new double[] { -1e20, 3e15, -5e25 });
    final double[] best = fitter.fit(obs.toList());

    TestUtils.assertEquals("best != coeff", coeff, best, 2e-2);
}
Example 3
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 4
Source File: PolynomialCurveFitterTest.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 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.
    final WeightedObservedPoints obs = new WeightedObservedPoints();
    for (int i = 0; i < 100; i++) {
        final double x = rng.sample();
        obs.add(x, f.value(x));
    }

    // Start fit from initial guesses that are far from the optimal values.
    final PolynomialCurveFitter fitter
        = PolynomialCurveFitter.create(0).withStartPoint(new double[] { -1e-20, 3e15, -5e25 });
    final double[] best = fitter.fit(obs.toList());

    TestUtils.assertEquals("best != coeff", coeff, best, 1e-12);
}
Example 5
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 6
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 7
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 8
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 9
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 10
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 11
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 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: SimpleCurveFitterTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
public void testPolynomialFit() {
    final Random randomizer = new Random(53882150042L);
    final RealDistribution rng = new UniformRealDistribution(-100, 100);
    rng.reseedRandomGenerator(64925784252L);

    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.
    final WeightedObservedPoints obs = new WeightedObservedPoints();
    for (int i = 0; i < 100; i++) {
        final double x = rng.sample();
        obs.add(x, f.value(x) + 0.1 * randomizer.nextGaussian());
    }

    final ParametricUnivariateFunction function = new PolynomialFunction.Parametric(); 
    // Start fit from initial guesses that are far from the optimal values.
    final SimpleCurveFitter fitter
        = SimpleCurveFitter.create(function,
                                   new double[] { -1e20, 3e15, -5e25 });
    final double[] best = fitter.fit(obs.toList());

    TestUtils.assertEquals("best != coeff", coeff, best, 2e-2);
}
Example 14
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 15
Source File: PolynomialCurveFitterTest.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 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.
    final WeightedObservedPoints obs = new WeightedObservedPoints();
    for (int i = 0; i < 100; i++) {
        final double x = rng.sample();
        obs.add(x, f.value(x));
    }

    // Start fit from initial guesses that are far from the optimal values.
    final PolynomialCurveFitter fitter
        = PolynomialCurveFitter.create(0).withStartPoint(new double[] { -1e-20, 3e15, -5e25 });
    final double[] best = fitter.fit(obs.toList());

    TestUtils.assertEquals("best != coeff", coeff, best, 1e-12);
}
Example 16
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);
}