C# (CSharp) Accord.Statistics.Distributions.Univariate KolmogorovSmirnovDistribution.ComplementaryDistributionFunctionの例

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.

The Complementary Cumulative Distribution Function (CCDF) is the complement of the Cumulative Distribution Function, or 1 minus the CDF.

public ComplementaryDistributionFunction ( double x ) : double
x	double
return	double

コード例 #1

ファイル: KolmogorovSmirnovDistributionTest.cs プロジェクト: BiYiTuan/framework

        public void ConstructorTest()
        {
            var ks = new KolmogorovSmirnovDistribution(samples: 42);

            double mean = ks.Mean;     // 0.13404812830261556
            double median = ks.Median;   // 0.12393613519421857
            double var = ks.Variance; // 0.019154717445778062

            double cdf = ks.DistributionFunction(x: 0.27); // 0.99659863602996079

            double ccdf = ks.ComplementaryDistributionFunction(x: 0.27); // 0.0034013639700392062
            double icdf = ks.InverseDistributionFunction(p: cdf); // 0.26999997446092017

            double chf = ks.CumulativeHazardFunction(x: 0.27); // 5.6835787601476619

            string str = ks.ToString(); // "KS(x; n = 42)"

            Assert.AreEqual(0.13404812830261556, mean);
            Assert.AreEqual(0.12393613519421857, median, 1e-6);
            Assert.AreEqual(0.019154717445778062, var);
            Assert.AreEqual(5.6835787601476619, chf);
            Assert.AreEqual(0.99659863602996079, cdf);
            Assert.AreEqual(0.0034013639700392062, ccdf);
            Assert.AreEqual(0.27, icdf, 1e-6);
            Assert.AreEqual("KS(x; n = 42)", str);
        }

コード例 #2

ファイル: KolmogorovSmirnovDistributionTest.cs プロジェクト: CanerPatir/framework

        public void ConstructorTest()
        {
            var ks = new KolmogorovSmirnovDistribution(samples: 42);

            double mean = ks.Mean;     // 0.13404812830261556
            double median = ks.Median;   // 0.12393613519421857
            double var = ks.Variance; // 0.019154717445778062

            try { double mode = ks.Mode; Assert.Fail(); }
            catch { }

            double cdf = ks.DistributionFunction(x: 0.27); // 0.99659863602996079

            double ccdf = ks.ComplementaryDistributionFunction(x: 0.27); // 0.0034013639700392062
            double icdf = ks.InverseDistributionFunction(p: cdf); // 0.26999997446092017

            double chf = ks.CumulativeHazardFunction(x: 0.27); // 5.6835787601476619

            string str = ks.ToString(); // "KS(x; n = 42)"

            Assert.AreEqual(0.13404812830261556, mean);
            Assert.AreEqual(0.12393613519421857, median, 1e-6);
            Assert.AreEqual(0.019154717445778062, var);
            Assert.AreEqual(5.6835787601476619, chf);
            Assert.AreEqual(0.99659863602996079, cdf);
            Assert.AreEqual(0.0034013639700392062, ccdf);
            Assert.AreEqual(0.27, icdf, 1e-6);
            Assert.AreEqual("KS(x; n = 42)", str);

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

            Assert.AreEqual(0.076658557134805502, range1.Min);
            Assert.AreEqual(0.20517007413251875, range1.Max);
            Assert.AreEqual(0.064613698945741663, range2.Min);
            Assert.AreEqual(0.24612731591151149, range2.Max);
            Assert.AreEqual(0.064613698945741649, range3.Min);
            Assert.AreEqual(0.24612731591151149, range3.Max);
        }

コード例 #3

ファイル: KolmogorovSmirnovDistributionTest.cs プロジェクト: CanerPatir/framework

        public void NaN_Test()
        {
            var ks = new KolmogorovSmirnovDistribution(samples: 42);

            double nan = double.NaN;

            try
            {
                ks.DistributionFunction(x: nan);
                Assert.Fail();
            }
            catch (ArgumentOutOfRangeException) { }

            try
            {
                ks.ComplementaryDistributionFunction(x: nan);
                Assert.Fail();
            }
            catch (ArgumentOutOfRangeException) { }

            try
            {
                ks.InverseDistributionFunction(p: nan);
                Assert.Fail();
            }
            catch (ArgumentOutOfRangeException) { }
            try
            {
                ks.CumulativeHazardFunction(x: nan);
                Assert.Fail();
            }
            catch (ArgumentOutOfRangeException) { }

        }