Package org.apache.commons.math3.distribution

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

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    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);
    }
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    /** {@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;
 
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    /** {@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 *
 
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     */
    protected RealDistribution getKernel(SummaryStatistics bStats) {
        if (bStats.getN() == 1) {
            return new ConstantRealDistribution(bStats.getMean());
        } else {
            return new NormalDistribution(randomData.getRandomGenerator(),
                bStats.getMean(), bStats.getStandardDeviation(),
                NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        }
    }
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        // - 0.5 is a continuity correction
        final double z = (Wmin - ES - 0.5) / FastMath.sqrt(VarS);

        // No try-catch or advertised exception because args are valid
        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final NormalDistribution standardNormal = new NormalDistribution(null, 0, 1);

        return 2*standardNormal.cumulativeProbability(z);
    }
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        final double z = (Umin - EU) / FastMath.sqrt(VarU);

        // No try-catch or advertised exception because args are valid
        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final NormalDistribution standardNormal = new NormalDistribution(null, 0, 1);

        return 2 * standardNormal.cumulativeProbability(z);
    }
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                                            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);
    }
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            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);
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        }

        // Fill values array with random data from N(mu, sigma)
        // and fill valuesList with values from values array with
        // values[i] repeated weights[i] times, each i
        final RealDistribution valueDist = new NormalDistribution(mu, sigma);
        List<Double> valuesList = new ArrayList<Double>();
        for (int i = 0; i < len; i++) {
            double value = valueDist.sample();
            values[i] = value;
            for (int j = 0; j < intWeights[i]; j++) {
                valuesList.add(new Double(value));
            }
        }
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        final ProcessModel pm = new DefaultProcessModel(A, B, Q, initialState, initialErrorCovariance);
        final MeasurementModel mm = new DefaultMeasurementModel(H, R);
        final KalmanFilter filter = new KalmanFilter(pm, mm);

        final RandomGenerator rng = new Well19937c(1000);
        final NormalDistribution dist = new NormalDistribution(rng, 0, measurementNoise);

        for (int i = 0; i < iterations; i++) {
            // get the "real" cannonball position
            double x = cannonball.getX();
            double y = cannonball.getY();
           
            // apply measurement noise to current cannonball position
            double nx = x + dist.sample();
            double ny = y + dist.sample();

            cannonball.step();
           
            filter.predict(controlVector);
            // correct the filter with our measurements
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