C# (CSharp) Accord.Statistics.Distributions.Univariate PowerLognormalDistribution.ComplementaryDistributionFunction示例

ComplementaryDistributionFunction() public method

Gets the complementary cumulative distribution function (ccdf) for this distribution evaluated at point x. This function is also known as the Survival function.

public ComplementaryDistributionFunction ( double x ) : double
x	double	A single point in the distribution range.
return	double

示例#1

文件： PowerLognormalDistributionTest.cs 项目： KommuSoft/accord_framework

        public void ConstructorTest1()
        {
            var plog = new PowerLognormalDistribution(power: 4.2, shape: 1.2);

            double cdf = plog.DistributionFunction(x: 1.4); // 0.98092157745191766
            double pdf = plog.ProbabilityDensityFunction(x: 1.4); // 0.046958580233533977
            double lpdf = plog.LogProbabilityDensityFunction(x: 1.4); // -3.0584893374471496

            double ccdf = plog.ComplementaryDistributionFunction(x: 1.4); // 0.019078422548082351
            double icdf = plog.InverseDistributionFunction(p: cdf); // 1.4

            double hf = plog.HazardFunction(x: 1.4); // 10.337649063164642
            double chf = plog.CumulativeHazardFunction(x: 1.4); // 3.9591972920568446

            string str = plog.ToString(CultureInfo.InvariantCulture); // PLD(x; p = 4.2, σ = 1.2)

            Assert.AreEqual(3.9591972920568446, chf);
            Assert.AreEqual(0.98092157745191766, cdf);
            Assert.AreEqual(0.046958580233533977, pdf);
            Assert.AreEqual(-3.0584893374471496, lpdf);
            Assert.AreEqual(10.337649063164642, hf);
            Assert.AreEqual(0.019078422548082351, ccdf);
            Assert.AreEqual(1.4000000000000001, icdf);
            Assert.AreEqual("PLD(x; p = 4.2, σ = 1.2)", str);
        }

示例#2

文件： PowerLognormalDistributionTest.cs 项目： accord-net/framework

        public void ConstructorTest1()
        {
            var plog = new PowerLognormalDistribution(power: 4.2, shape: 1.2);

            try { double mean = plog.Mean; Assert.Fail(); }
            catch { }
            try { double variance = plog.Variance; Assert.Fail(); }
            catch { }
            try { double mode = plog.Mode; Assert.Fail(); }
            catch { }
            try { double median = plog.Median; Assert.Fail(); }
            catch { }

            double cdf = plog.DistributionFunction(x: 1.4); // 0.98092157745191766
            double pdf = plog.ProbabilityDensityFunction(x: 1.4); // 0.046958580233533977
            double lpdf = plog.LogProbabilityDensityFunction(x: 1.4); // -3.0584893374471496

            double ccdf = plog.ComplementaryDistributionFunction(x: 1.4); // 0.019078422548082351
            double icdf = plog.InverseDistributionFunction(p: cdf); // 1.4

            double hf = plog.HazardFunction(x: 1.4); // 10.337649063164642
            double chf = plog.CumulativeHazardFunction(x: 1.4); // 3.9591972920568446

            string str = plog.ToString(CultureInfo.InvariantCulture); // PLD(x; p = 4.2, σ = 1.2)

            Assert.AreEqual(3.9591972920568446, chf);
            Assert.AreEqual(0.98092157745191766, cdf);
            Assert.AreEqual(0.046958580233533977, pdf);
            Assert.AreEqual(-3.0584893374471496, lpdf);
            Assert.AreEqual(10.337649063164642, hf);
            Assert.AreEqual(0.019078422548082351, ccdf);
            Assert.AreEqual(1.4000000000000001, icdf);
            Assert.AreEqual("PLD(x; p = 4.2, σ = 1.2)", str);

            var range1 = plog.GetRange(0.95);
            var range2 = plog.GetRange(0.99);
            var range3 = plog.GetRange(0.01);

            Assert.AreEqual(0.066986543067356463, range1.Min);
            Assert.AreEqual(1.0304177429659382, range1.Max);
            Assert.AreEqual(0.033851065908457677, range2.Min);
            Assert.AreEqual(1.672824432596103, range2.Max);
            Assert.AreEqual(0.033851065908457677, range3.Min);
            Assert.AreEqual(1.672824432596103, range3.Max);
        }