public void MaritzJarrettConfidenceIntervalWeighedTest1()
{
var random = new Random(42);
var distribution = new BetaDistribution(2, 10);
double[] data = distribution.Random(random).Next(10);
var sample1 = new Sample(data);
var sample2 = new Sample(data, new ConstantSequence(1).GenerateArray(data.Length));
var sample3 = new Sample(
data.Concat(new[] { 10.0, 10.0, 10.0 }).ToArray(),
new ConstantSequence(1).GenerateArray(data.Length).Concat(new[] { 0.0, 0.0, 0.0 }).ToArray()
);
var estimator = HarrellDavisQuantileEstimator.Instance;
var probability = Probability.Half;
var level = ConfidenceLevel.L90;
var ci1 = estimator.GetQuantileConfidenceIntervalEstimator(sample1, probability).GetConfidenceInterval(level);
var ci2 = estimator.GetQuantileConfidenceIntervalEstimator(sample2, probability).GetConfidenceInterval(level);
var ci3 = estimator.GetQuantileConfidenceIntervalEstimator(sample3, probability).GetConfidenceInterval(level);
Output.WriteLine("CI1: " + ci1.ToString("N9"));
Output.WriteLine("CI2: " + ci2.ToString("N9"));
Output.WriteLine("CI3: " + ci3.ToString("N9"));
var comparer = new AbsoluteEqualityComparer(1e-9);
Assert.True(ci1.Equals(ci2, comparer));
Assert.True(ci1.Equals(ci3, comparer));
}
public void HarrellDavisQuantileEstimatorWeightedTest1()
{
var random = new Random(42);
var distribution = new BetaDistribution(2, 10);
double[] data1 = distribution.Random(random).Next(10);
double[] data2 = data1.Select(x => x + 100).ToArray();
double[] data = data1.Concat(data2).ToArray();
var samples = new List <Sample> {
new Sample(data1)
};
for (int i = 1; i <= 40; i++)
{
samples.Add(new Sample(data, ExponentialDecaySequence.CreateFromHalfLife(i).GenerateArray(data.Length)));
}
samples.Add(new Sample(data));
var medians = samples.Select(s => HarrellDavisQuantileEstimator.Instance.GetMedian(s)).ToList();
for (int i = 0; i < medians.Count; i++)
{
Output.WriteLine($"Median[{i}] = {medians[i]:N9}");
}
for (int i = 0; i < medians.Count - 1; i++)
{
Assert.True(medians[i] < medians[i + 1]);
}
}
public void MaritzJarrettConfidenceIntervalTest(double confidenceLevel, double minSuccessRate, double maxSuccessRate)
{
var random = new Random(42);
var distribution = new BetaDistribution(2, 10);
var generator = new LimitedRandomGenerator(distribution.Random(random).Next(1000), random);
var estimator = HarrellDavisQuantileEstimator.Instance;
double median = distribution.Median;
const int iterations = 1000;
for (int n = 5; n <= 10; n++)
{
int successCount = 0;
for (int i = 0; i < iterations; i++)
{
var sample = generator.Next(n).ToSample();
var confidenceInterval = estimator
.GetQuantileConfidenceIntervalEstimator(sample, 0.5)
.GetConfidenceInterval(confidenceLevel);
if (confidenceInterval.Contains(median))
{
successCount++;
}
}
double successRate = successCount * 1.0 / iterations;
Output.WriteLine($"n = {n}, successRate = {successRate:N4}");
Assert.True(minSuccessRate <= successRate && successRate <= maxSuccessRate);
}
}
public void BetaGenerateTest()
{
Accord.Math.Tools.SetupGenerator(0);
int n = 100000;
double[] samples = BetaDistribution
.Random(alpha: 2, beta: 3, samples: n);
Assert.AreEqual(n, samples.Length);
var actual = BetaDistribution.Estimate(samples);
Assert.AreEqual(2, actual.Alpha, 1e-2);
Assert.AreEqual(3, actual.Beta, 1e-2);
}
public void BetaGenerateTest3()
{
Accord.Math.Tools.SetupGenerator(0);
int n = 1000000;
double[] samples = BetaDistribution
.Random(alpha: 0.4, beta: 0.2, samples: n);
Assert.AreEqual(n, samples.Length);
var actual = BetaDistribution.Estimate(samples,
new BetaOptions {
Method = BetaEstimationMethod.MaximumLikelihood
});
Assert.AreEqual(0.4, actual.Alpha, 0.005);
Assert.AreEqual(0.2, actual.Beta, 0.003);
}