C# Distributions.ChiSquared类代码示例

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本文整理汇总了C#中MathNet.Numerics.Distributions.ChiSquared类的典型用法代码示例。如果您正苦于以下问题：C# ChiSquared类的具体用法？C# ChiSquared怎么用？C# ChiSquared使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。

ChiSquared类属于MathNet.Numerics.Distributions命名空间，在下文中一共展示了ChiSquared类的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的C#代码示例。

示例1: ValidateMaximum

 public void ValidateMaximum()
 {
     var n = new ChiSquared(1.0);
     Assert.AreEqual(Double.PositiveInfinity, n.Maximum);
 }

开发者ID:ActivePHOENiX，项目名称:mathnet-numerics，代码行数:5，代码来源:ChiSquareTests.cs

示例2: ValidateDensityLn

 public void ValidateDensityLn(double dof, double x, double expected)
 {
     var chiSquared = new ChiSquared(dof);
     Assert.That(chiSquared.DensityLn(x), Is.EqualTo(expected).Within(13));
     Assert.That(ChiSquared.PDFLn(dof, x), Is.EqualTo(expected).Within(13));
 }

开发者ID:ActivePHOENiX，项目名称:mathnet-numerics，代码行数:6，代码来源:ChiSquareTests.cs

示例3: ValidateMedian

 public void ValidateMedian(double dof)
 {
     var n = new ChiSquared(dof);
     Assert.AreEqual(dof - (2.0 / 3.0), n.Median);
 }

开发者ID:ActivePHOENiX，项目名称:mathnet-numerics，代码行数:5，代码来源:ChiSquareTests.cs

示例4: ValidateMinimum

 public void ValidateMinimum()
 {
     var n = new ChiSquared(1.0);
     Assert.AreEqual(0.0, n.Minimum);
 }

开发者ID:ActivePHOENiX，项目名称:mathnet-numerics，代码行数:5，代码来源:ChiSquareTests.cs

示例5: ValidateToString

 public void ValidateToString()
 {
     var n = new ChiSquared(1.0);
     Assert.AreEqual("ChiSquared(k = 1)", n.ToString());
 }

开发者ID:ActivePHOENiX，项目名称:mathnet-numerics，代码行数:5，代码来源:ChiSquareTests.cs

示例6: SetDofFailsWithNonPositiveDoF

 public void SetDofFailsWithNonPositiveDoF(double dof)
 {
     var n = new ChiSquared(1.0);
     Assert.Throws<ArgumentOutOfRangeException>(() => n.DegreesOfFreedom = dof);
 }

开发者ID:nakamoton，项目名称:mathnet-numerics，代码行数:5，代码来源:ChiSquareTests.cs

示例7: Run

        /// <summary>
        /// Run example
        /// </summary>
        public void Run()
        {
            // 1. Get 10 random samples of f(x) = (x * x) / 2 using continuous uniform distribution on [-10, 10]
            var uniform = new ContinuousUniform(-10, 10);
            var result = Generate.RandomMap(10, uniform, Function);
            Console.WriteLine(@" 1. Get 10 random samples of f(x) = (x * x) / 2 using continuous uniform distribution on [-10, 10]");
            for (var i = 0; i < result.Length; i++)
            {
                Console.Write(result[i].ToString("N") + @" ");
            }

            Console.WriteLine();
            Console.WriteLine();

            // 2. Get 10 random samples of f(x) = (x * x) / 2 using Exponential(1) distribution and retrieve sample points
            var exponential = new Exponential(1);
            double[] samplePoints = Generate.Random(10, exponential);
            result = Generate.Map(samplePoints, Function);
            Console.WriteLine(@"2. Get 10 random samples of f(x) = (x * x) / 2 using Exponential(1) distribution and retrieve sample points");
            Console.Write(@"Points: ");
            for (var i = 0; i < samplePoints.Length; i++)
            {
                Console.Write(samplePoints[i].ToString("N") + @" ");
            }

            Console.WriteLine();
            Console.Write(@"Values: ");
            for (var i = 0; i < result.Length; i++)
            {
                Console.Write(result[i].ToString("N") + @" ");
            }

            Console.WriteLine();
            Console.WriteLine();

            // 3. Get 10 random samples of f(x, y) = (x * y) / 2 using ChiSquare(10) distribution
            var chiSquare = new ChiSquared(10);
            result = Generate.RandomMap2(10, chiSquare, TwoDomainFunction);
            Console.WriteLine(@" 3. Get 10 random samples of f(x, y) = (x * y) / 2 using ChiSquare(10) distribution");
            for (var i = 0; i < result.Length; i++)
            {
                Console.Write(result[i].ToString("N") + @" ");
            }

            Console.WriteLine();
        }

开发者ID:Jungwon，项目名称:mathnet-numerics，代码行数:49，代码来源:Random.cs

示例8: ValidateInverseCumulativeDistribution

 public void ValidateInverseCumulativeDistribution(double dof, double x, double expected)
 {
     var chiSquared = new ChiSquared(dof);
     Assert.That(chiSquared.InverseCumulativeDistribution(x), Is.EqualTo(expected).Within(1e-14));
     Assert.That(ChiSquared.InvCDF(dof, x), Is.EqualTo(expected).Within(1e-14));
 }

开发者ID:ActivePHOENiX，项目名称:mathnet-numerics，代码行数:6，代码来源:ChiSquareTests.cs

示例9: ValidateDensity

 public void ValidateDensity(double dof, double x)
 {
     var n = new ChiSquared(dof);
     double expected = (Math.Pow(x, (dof / 2.0) - 1.0) * Math.Exp(-x / 2.0)) / (Math.Pow(2.0, dof / 2.0) * SpecialFunctions.Gamma(dof / 2.0));
     Assert.AreEqual(expected, n.Density(x));
     Assert.AreEqual(expected, ChiSquared.PDF(dof, x));
 }

开发者ID:smoothdeveloper，项目名称:mathnet-numerics，代码行数:7，代码来源:ChiSquareTests.cs

示例10: ValidateDensityLn

 public void ValidateDensityLn(double dof, double x)
 {
     var n = new ChiSquared(dof);
     double expected = (-x / 2.0) + (((dof / 2.0) - 1.0) * Math.Log(x)) - ((dof / 2.0) * Math.Log(2)) - SpecialFunctions.GammaLn(dof / 2.0);
     Assert.AreEqual(expected, n.DensityLn(x));
     Assert.AreEqual(expected, ChiSquared.PDFLn(dof, x));
 }

开发者ID:smoothdeveloper，项目名称:mathnet-numerics，代码行数:7，代码来源:ChiSquareTests.cs

示例11: ValidateCumulativeDistribution

 public void ValidateCumulativeDistribution(double dof, double x)
 {
     var n = new ChiSquared(dof);
     double expected = SpecialFunctions.GammaLowerIncomplete(dof / 2.0, x / 2.0) / SpecialFunctions.Gamma(dof / 2.0);
     Assert.AreEqual(expected, n.CumulativeDistribution(x));
     Assert.AreEqual(expected, ChiSquared.CDF(dof, x));
 }

开发者ID:smoothdeveloper，项目名称:mathnet-numerics，代码行数:7，代码来源:ChiSquareTests.cs

示例12: SetDofFailsWithNonPositiveDoF

 public void SetDofFailsWithNonPositiveDoF(double dof)
 {
     var n = new ChiSquared(1.0);
     Assert.That(() => n.DegreesOfFreedom = dof, Throws.ArgumentException);
 }

开发者ID:smoothdeveloper，项目名称:mathnet-numerics，代码行数:5，代码来源:ChiSquareTests.cs

示例13: Run

        /// <summary>
        /// Run example
        /// </summary>
        /// <seealso cref="http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient">Pearson product-moment correlation coefficient</seealso>
        public void Run()
        {
            // 1. Initialize the new instance of the ChiSquare distribution class with parameter dof = 5.
            var chiSquare = new ChiSquared(5);
            Console.WriteLine(@"1. Initialize the new instance of the ChiSquare distribution class with parameter DegreesOfFreedom = {0}", chiSquare.DegreesOfFreedom);
            Console.WriteLine(@"{0} distributuion properties:", chiSquare);
            Console.WriteLine(@"{0} - Largest element", chiSquare.Maximum.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Smallest element", chiSquare.Minimum.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Mean", chiSquare.Mean.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Median", chiSquare.Median.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Mode", chiSquare.Mode.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Variance", chiSquare.Variance.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Standard deviation", chiSquare.StdDev.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Skewness", chiSquare.Skewness.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine();

            // 2. Generate 1000 samples of the ChiSquare(5) distribution
            Console.WriteLine(@"2. Generate 1000 samples of the ChiSquare(5) distribution");
            var data = new double[1000];
            for (var i = 0; i < data.Length; i++)
            {
                data[i] = chiSquare.Sample();
            }

            // 3. Get basic statistics on set of generated data using extention methods
            Console.WriteLine(@"3. Get basic statistics on set of generated data using extention methods");
            Console.WriteLine(@"{0} - Largest element", data.Maximum().ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Smallest element", data.Minimum().ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Mean", data.Mean().ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Median", data.Median().ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Biased population variance", data.PopulationVariance().ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Variance", data.Variance().ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Standard deviation", data.StandardDeviation().ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Biased sample standard deviation", data.PopulationStandardDeviation().ToString(" #0.00000;-#0.00000"));
            Console.WriteLine();

            // 4. Compute the basic statistics of data set using DescriptiveStatistics class
            Console.WriteLine(@"4. Compute the basic statistics of data set using DescriptiveStatistics class");
            var descriptiveStatistics = new DescriptiveStatistics(data);
            Console.WriteLine(@"{0} - Kurtosis", descriptiveStatistics.Kurtosis.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Largest element", descriptiveStatistics.Maximum.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Smallest element", descriptiveStatistics.Minimum.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Mean", descriptiveStatistics.Mean.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Variance", descriptiveStatistics.Variance.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Standard deviation", descriptiveStatistics.StandardDeviation.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine(@"{0} - Skewness", descriptiveStatistics.Skewness.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine();

            // Generate 1000 samples of the ChiSquare(2.5) distribution
            var chiSquareB = new ChiSquared(2);
            var dataB = new double[1000];
            for (var i = 0; i < data.Length; i++)
            {
                dataB[i] = chiSquareB.Sample();
            }

            // 5. Correlation coefficient between 1000 samples of ChiSquare(5) and ChiSquare(2.5)
            Console.WriteLine(@"5. Correlation coefficient between 1000 samples of ChiSquare(5) and ChiSquare(2.5) is {0}", Correlation.Pearson(data, dataB).ToString("N04"));
            Console.WriteLine(@"6. Ranked correlation coefficient between 1000 samples of ChiSquare(5) and ChiSquare(2.5) is {0}", Correlation.Spearman(data, dataB).ToString("N04"));
            Console.WriteLine();

            // 6. Correlation coefficient between 1000 samples of f(x) = x * 2 and f(x) = x * x
            data = Generate.LinearSpacedMap(1000, 0, 100, x => x * 2);
            dataB = Generate.LinearSpacedMap(1000, 0, 100, x => x * x);
            Console.WriteLine(@"7. Correlation coefficient between 1000 samples of f(x) = x * 2 and f(x) = x * x is {0}", Correlation.Pearson(data, dataB).ToString("N04"));
            Console.WriteLine(@"8. Ranked correlation coefficient between 1000 samples of f(x) = x * 2 and f(x) = x * x is {0}", Correlation.Spearman(data, dataB).ToString("N04"));
            Console.WriteLine();
        }

开发者ID:Jungwon，项目名称:mathnet-numerics，代码行数:72，代码来源:Statistics.cs

示例14: CanSample

 public void CanSample()
 {
     var n = new ChiSquared(1.0);
     n.Sample();
 }

开发者ID:ActivePHOENiX，项目名称:mathnet-numerics，代码行数:5，代码来源:ChiSquareTests.cs

示例15: ValidateVariance

 public void ValidateVariance(double dof)
 {
     var n = new ChiSquared(dof);
     Assert.AreEqual(2 * dof, n.Variance);
 }

开发者ID:ActivePHOENiX，项目名称:mathnet-numerics，代码行数:5，代码来源:ChiSquareTests.cs

示例16: CanSampleSequence

 public void CanSampleSequence()
 {
     var n = new ChiSquared(1.0);
     var ied = n.Samples();
     GC.KeepAlive(ied.Take(5).ToArray());
 }

开发者ID:ActivePHOENiX，项目名称:mathnet-numerics，代码行数:6，代码来源:ChiSquareTests.cs

示例17: ValidateStdDev

 public void ValidateStdDev(double dof)
 {
     var n = new ChiSquared(dof);
     Assert.AreEqual(Math.Sqrt(n.Variance), n.StdDev);
 }

开发者ID:ActivePHOENiX，项目名称:mathnet-numerics，代码行数:5，代码来源:ChiSquareTests.cs

示例18: CanCreateChiSquare

 public void CanCreateChiSquare(double dof)
 {
     var n = new ChiSquared(dof);
     Assert.AreEqual(dof, n.DegreesOfFreedom);
 }

开发者ID:ActivePHOENiX，项目名称:mathnet-numerics，代码行数:5，代码来源:ChiSquareTests.cs

示例19: ValidateMode

 public void ValidateMode(double dof)
 {
     var n = new ChiSquared(dof);
     Assert.AreEqual(dof - 2, n.Mode);
 }

开发者ID:ActivePHOENiX，项目名称:mathnet-numerics，代码行数:5，代码来源:ChiSquareTests.cs

示例20: ValidateMean

 public void ValidateMean(double dof)
 {
     var n = new ChiSquared(dof);
     Assert.AreEqual(dof, n.Mean);
 }

开发者ID:ActivePHOENiX，项目名称:mathnet-numerics，代码行数:5，代码来源:ChiSquareTests.cs

注：本文中的MathNet.Numerics.Distributions.ChiSquared类示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。

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C# Distributions.ChiSquared类代码示例

示例1: ValidateMaximum

示例2: ValidateDensityLn

示例3: ValidateMedian

示例4: ValidateMinimum

示例5: ValidateToString

示例6: SetDofFailsWithNonPositiveDoF

示例7: Run

示例8: ValidateInverseCumulativeDistribution

示例9: ValidateDensity

示例10: ValidateDensityLn

示例11: ValidateCumulativeDistribution

示例12: SetDofFailsWithNonPositiveDoF

示例13: Run

示例14: CanSample

示例15: ValidateVariance

示例16: CanSampleSequence

示例17: ValidateStdDev

示例18: CanCreateChiSquare

示例19: ValidateMode

示例20: ValidateMean

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