C# Normal.Samples方法代码示例

        public static IEnumerable<float> TestData()
        {
            var g = new Normal(0.0, 1.0);
            var randy = new MersenneTwister();

            g.RandomSource = randy;
            var dbls = new double[100000];
            g.Samples(dbls);
            return dbls.Select(d => (float) d);
        }

        public void NormalityTestAlgorithmTest()
        {
            int sampleSize = 100;
            int numberOfMonteCarloTests = 5;
            double result = 0;
            NormalityTest.NormalityTestFunc normalityTestFunc = NormalityTest.JaqueBeraTest;

            // normal distribution
            Console.WriteLine("normal var");
            for (int i = 0; i < numberOfMonteCarloTests; i++)
            {
                var distribution = new Normal(50, 1);
                distribution.RandomSource = new Random(DateTime.Now.Millisecond * i);
                var normalSamples = distribution.Samples().Take(sampleSize);

                double[] reallIsNormalSamples = normalSamples.ToArray();

                result = normalityTestFunc(reallIsNormalSamples);
                Console.WriteLine("reallIsNormalSamples_" + i + ": " + result);
            }

            Console.WriteLine();

            // random distribution.
            Console.WriteLine("rand var");
            Random rnd = new Random();
            double[] randomArray = new double[sampleSize];
            for (int i = 0; i < numberOfMonteCarloTests; i++)
            {
                for (int j = 0; j < sampleSize; j++)
                {
                    randomArray[j] = rnd.Next(1, 100);
                }

                result = normalityTestFunc(randomArray);
                Console.WriteLine("randomArray_" + i + ": " + result);
            }

            Console.WriteLine();

            // Uniform distribution
            Console.WriteLine("rand var");
            var uniformSamples = new Normal(100, 0).Samples().Take(sampleSize);

            double[] uniformSamplesRandomVar = uniformSamples.ToArray();

            result = normalityTestFunc(uniformSamplesRandomVar);
            Console.WriteLine("uniformSamples: " + result);
        }

        public void CanEstimateParameters(double mean, double stddev)
        {
            var original = new Normal(mean, stddev, new Random(100));
            var estimated = Normal.Estimate(original.Samples().Take(10000));

            AssertHelpers.AlmostEqualRelative(mean, estimated.Mean, 1);
            AssertHelpers.AlmostEqualRelative(stddev, estimated.StdDev, 1);
        }

 public void CanSampleSequence()
 {
     var n = new Normal();
     var ied = n.Samples();
     GC.KeepAlive(ied.Take(5).ToArray());
 }

        public void StabilityMeanVariance()
        {
            // Test around 10^9, potential stability issues
            var gaussian = new Normal(1e+9, 2, new MersenneTwister(100));

            AssertHelpers.AlmostEqualRelative(1e+9, Statistics.Mean(gaussian.Samples().Take(10000)), 10);
            AssertHelpers.AlmostEqualRelative(4d, Statistics.Variance(gaussian.Samples().Take(10000)), 0);
            AssertHelpers.AlmostEqualRelative(2d, Statistics.StandardDeviation(gaussian.Samples().Take(10000)), 1);
            AssertHelpers.AlmostEqualRelative(1e+9, Statistics.RootMeanSquare(gaussian.Samples().Take(10000)), 10);

            AssertHelpers.AlmostEqualRelative(1e+9, ArrayStatistics.Mean(gaussian.Samples().Take(10000).ToArray()), 10);
            AssertHelpers.AlmostEqualRelative(4d, ArrayStatistics.Variance(gaussian.Samples().Take(10000).ToArray()), 0);
            AssertHelpers.AlmostEqualRelative(2d, ArrayStatistics.StandardDeviation(gaussian.Samples().Take(10000).ToArray()), 1);
            AssertHelpers.AlmostEqualRelative(1e+9, ArrayStatistics.RootMeanSquare(gaussian.Samples().Take(10000).ToArray()), 10);

            AssertHelpers.AlmostEqualRelative(1e+9, StreamingStatistics.Mean(gaussian.Samples().Take(10000)), 10);
            AssertHelpers.AlmostEqualRelative(4d, StreamingStatistics.Variance(gaussian.Samples().Take(10000)), 0);
            AssertHelpers.AlmostEqualRelative(2d, StreamingStatistics.StandardDeviation(gaussian.Samples().Take(10000)), 1);
            AssertHelpers.AlmostEqualRelative(1e+9, StreamingStatistics.RootMeanSquare(gaussian.Samples().Take(10000)), 10);

            AssertHelpers.AlmostEqualRelative(1e+9, new RunningStatistics(gaussian.Samples().Take(10000)).Mean, 10);
            AssertHelpers.AlmostEqualRelative(4d, new RunningStatistics(gaussian.Samples().Take(10000)).Variance, 0);
            AssertHelpers.AlmostEqualRelative(2d, new RunningStatistics(gaussian.Samples().Take(10000)).StandardDeviation, 1);
        }

        private static double[] GenerateNoisePath(int simulationSteps, int mean, double noiseVariance)
        {
            var standardNormal = new Normal(mean, noiseVariance);

            return standardNormal.Samples().Take(simulationSteps).ToArray();
        }

 public void CanSampleSequence()
 {
     var n = new Normal();
     var ied = n.Samples();
     var e = ied.Take(5).ToArray();
 }

 public static double[] Randn(int n, double mu = 0.0, double sigma = 1.0)
 {
     Normal d = new Normal(mu, sigma);
     double[] ret = d.Samples().Take(n).ToArray();
     return ret;
 }