Package org.apache.commons.math3.distribution

Examples of org.apache.commons.math3.distribution.NormalDistribution

763
764
765
766
767
768
769
770
771
772
773
            randomData.nextGaussian(0, 0);
            Assert.fail("zero sigma -- MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        double[] quartiles = TestUtils.getDistributionQuartiles(new NormalDistribution(0,1));
        long[] counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextGaussian(0, 1);
            TestUtils.updateCounts(value, counts, quartiles);
View Full Code Here
174
175
176
177
178
179
180
181
182
183
184
185
186
187
            JComponent comp = null;

            comp = createComponent("Normal", -5, 5,
                                   new String[] { "μ=0,σ\u00B2=0.2", "μ=0,σ\u00B2=1", "μ=0,σ\u00B2=5", "μ=-2,σ\u00B2=0.5" },
                                   new NormalDistribution(0, FastMath.sqrt(0.2)),
                                   new NormalDistribution(),
                                   new NormalDistribution(0, FastMath.sqrt(5)),
                                   new NormalDistribution(-2, FastMath.sqrt(0.5)));
            container.add(comp, c);

            c.gridx++;
            comp = createComponent("Beta", 0, 1,
                                   new String[] { "α=β=0.5", "α=5,β=1", "α=1,β=3", "α=2,β=2", "α=2,β=5" },
View Full Code Here
62
63
64
65
66
67
68
69
70
71
72
    public static List<Vector2D> makeCircles(int samples, boolean shuffle, double noise, double factor, final RandomGenerator random) {
        if (factor < 0 || factor > 1) {
            throw new IllegalArgumentException();
        }
       
        NormalDistribution dist = new NormalDistribution(random, 0.0, noise, 1e-9);

        List<Vector2D> points = new ArrayList<Vector2D>();
        double range = 2.0 * FastMath.PI;
        double step = range / (samples / 2.0 + 1);
        for (double angle = 0; angle < range; angle += step) {
View Full Code Here
83
84
85
86
87
88
89
90
91
92
93
        return points;
    }

    public static List<Vector2D> makeMoons(int samples, boolean shuffle, double noise, RandomGenerator random) {
        NormalDistribution dist = new NormalDistribution(random, 0.0, noise, 1e-9);

        int nSamplesOut = samples / 2;
        int nSamplesIn = samples - nSamplesOut;
       
        List<Vector2D> points = new ArrayList<Vector2D>();
View Full Code Here
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
    }

    public static List<Vector2D> makeBlobs(int samples, int centers, double clusterStd,
                                           double min, double max, boolean shuffle, RandomGenerator random) {

        NormalDistribution dist = new NormalDistribution(random, 0.0, clusterStd, 1e-9);

        double range = max - min;
        Vector2D[] centerPoints = new Vector2D[centers];
        for (int i = 0; i < centers; i++) {
            double x = random.nextDouble() * range + min;
            double y = random.nextDouble() * range + min;
            centerPoints[i] = new Vector2D(x, y);
        }
       
        int[] nSamplesPerCenter = new int[centers];
        int count = samples / centers;
        Arrays.fill(nSamplesPerCenter, count);
       
        for (int i = 0; i < samples % centers; i++) {
            nSamplesPerCenter[i]++;
        }
       
        List<Vector2D> points = new ArrayList<Vector2D>();
        for (int i = 0; i < centers; i++) {
            for (int j = 0; j < nSamplesPerCenter[i]; j++) {
                Vector2D point = new Vector2D(dist.sample(), dist.sample());
                points.add(point.add(centerPoints[i]));
            }
        }
       
        if (shuffle) {
View Full Code Here
52
53
54
55
56
57
58
59
60
61
62
63
                                      double xSigma,
                                      double ySigma,
                                      long seed) {
        final RandomGenerator rng = new Well44497b(seed);
        this.radius = radius;
        cX = new NormalDistribution(rng, x, xSigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        cY = new NormalDistribution(rng, y, ySigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        tP = new UniformRealDistribution(rng, 0, MathUtils.TWO_PI,
                                         UniformRealDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    }
View Full Code Here
61
62
63
64
65
66
67
68
69
70
                                            double hi,
                                            long seed) {
        final RandomGenerator rng = new Well44497b(seed);
        slope = a;
        intercept = b;
        error = new NormalDistribution(rng, 0, sigma,
                                       NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        x = new UniformRealDistribution(rng, lo, hi,
                                        UniformRealDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    }
View Full Code Here
34
35
36
37
38
39
40
41
42
43
    public ConfidenceInterval createInterval(int numberOfTrials, int numberOfSuccesses,
                                             double confidenceLevel) {
        IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
        final double mean = (double) numberOfSuccesses / (double) numberOfTrials;
        final double alpha = (1.0 - confidenceLevel) / 2;
        final NormalDistribution normalDistribution = new NormalDistribution();
        final double difference = normalDistribution.inverseCumulativeProbability(1 - alpha) *
                                  FastMath.sqrt(1.0 / numberOfTrials * mean * (1 - mean));
        return new ConfidenceInterval(mean - difference, mean + difference, confidenceLevel);
    }
View Full Code Here
32
33
34
35
36
37
38
39
40
41
42
43
    /** {@inheritDoc} */
    public ConfidenceInterval createInterval(int numberOfTrials, int numberOfSuccesses, double confidenceLevel) {
        IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
        final double alpha = (1.0 - confidenceLevel) / 2;
        final NormalDistribution normalDistribution = new NormalDistribution();
        final double z = normalDistribution.inverseCumulativeProbability(1 - alpha);
        final double zSquared = FastMath.pow(z, 2);
        final double mean = (double) numberOfSuccesses / (double) numberOfTrials;

        final double factor = 1.0 / (1 + (1.0 / numberOfTrials) * zSquared);
        final double modifiedSuccessRatio = mean + (1.0 / (2 * numberOfTrials)) * zSquared;
 
View Full Code Here
32
33
34
35
36
37
38
39
40
41
42
43
    /** {@inheritDoc} */
    public ConfidenceInterval createInterval(int numberOfTrials, int numberOfSuccesses, double confidenceLevel) {
        IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
        final double alpha = (1.0 - confidenceLevel) / 2;
        final NormalDistribution normalDistribution = new NormalDistribution();
        final double z = normalDistribution.inverseCumulativeProbability(1 - alpha);
        final double zSquared = FastMath.pow(z, 2);
        final double modifiedNumberOfTrials = numberOfTrials + zSquared;
        final double modifiedSuccessesRatio = (1.0 / modifiedNumberOfTrials) * (numberOfSuccesses + 0.5 * zSquared);
        final double difference = z *
                                  FastMath.sqrt(1.0 / modifiedNumberOfTrials * modifiedSuccessesRatio *
 
View Full Code Here
TOP

Related Classes of org.apache.commons.math3.distribution.NormalDistribution

  • org.apache.cassandra.stress.generatedata.DataGenHexFromDistribution
  • org.apache.cassandra.stress.settings.OptionDistribution$GaussianFactory
  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • 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
    • Copyright © 2018 www.massapicom. 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.