Package org.apache.commons.math3.distribution

Examples of org.apache.commons.math3.distribution.UniformRealDistribution.sample()

47
48
49
50
51
52
53
54
55
56
57
        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 });
View Full Code Here
40
41
42
43
44
45
46
47
48
49
50
        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
View Full Code Here
50
51
52
53
54
55
56
57
58
59
60
        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 });
View Full Code Here
81
82
83
84
85
86
87
88
89
90
91
        RandomDataImpl random = new RandomDataImpl();

        // Generate 10 distinct random values
        for (int i = 0; i < 10; i++) {
            final RealDistribution u = new UniformRealDistribution(i + 0.5, i + 0.75);
            original[i] = u.sample();
        }

        // Generate a random permutation, making sure it is not the identity
        boolean isIdentity = true;
        do {
View Full Code Here
146
147
148
149
150
151
152
153
154
155
156
  public static double rNorm(double mean, double sd) {
    RealDistribution dist = new NormalDistribution(RANDOM.getRandomGenerator(),
                                                   mean,
                                                   sd,
                                                   NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    return dist.sample();
  }
 
  /**
   * Return the normal density function value for the sample x
   *
 
View Full Code Here
47
48
49
50
51
52
53
54
55
56
57
        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 });
View Full Code Here
81
82
83
84
85
86
87
88
89
90
91
        RandomDataImpl random = new RandomDataImpl();

        // Generate 10 distinct random values
        for (int i = 0; i < 10; i++) {
            final RealDistribution u = new UniformRealDistribution(i + 0.5, i + 0.75);
            original[i] = u.sample();
        }

        // Generate a random permutation, making sure it is not the identity
        boolean isIdentity = true;
        do {
View Full Code Here
50
51
52
53
54
55
56
57
58
59
60
        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 });
View Full Code Here
47
48
49
50
51
52
53
54
55
56
57
        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 });
View Full Code Here
248
249
250
251
252
253
254
255
256
257
258
     */
    private double[] generateSample() {
        final IntegerDistribution size = new UniformIntegerDistribution(10, 100);
        final RealDistribution randomData = new UniformRealDistribution(-100, 100);
        final int sampleSize = size.sample();
        final double[] out = randomData.sample(sampleSize);
        return out;
    }

    /**
     * Generates a partition of <sample> into up to 5 sequentially selected
View Full Code Here
TOP
Copyright © 2018 www.massapi.com. All rights reserved.
All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.