public void Run()
{
var random = new RandomDistribution(42);
var data = new List <double>();
data.AddRange(random.Gaussian(200, mean: 20, stdDev: 1));
data.AddRange(random.Gaussian(200, mean: 22, stdDev: 1));
const double binSize = 0.5;
Console.WriteLine("*** Simple Histogram ***");
Console.WriteLine(SimpleHistogramBuilder.Instance.Build(data, binSize).ToString());
Console.WriteLine("*** Adaptive Histogram ***");
Console.WriteLine(AdaptiveHistogramBuilder.Instance.Build(data, binSize).ToString());
}
public void Run()
{
var random = new RandomDistribution(42);
var data = new List <double>();
const int n = 20;
for (int i = 0; i < n; i++)
{
data.AddRange(random.Gaussian(100, mean: 20 * i, stdDev: 5));
}
var rqqIndexes = RqqPeltChangePointDetector.Instance.GetChangePointIndexes(data.ToArray());
var edIndexes = EdPeltChangePointDetector.Instance.GetChangePointIndexes(data.ToArray());
Console.WriteLine("RqqPelt EdPelt");
for (int i = 0, j = 0, k = 1; k < n; k++)
{
string rqqIndex = "-", edIndex = "-";
if (i < rqqIndexes.Length && rqqIndexes[i] < k * 100 + 10)
{
rqqIndex = rqqIndexes[i++].ToString();
}
if (j < edIndexes.Length && edIndexes[j] < k * 100 + 10)
{
edIndex = edIndexes[j++].ToString();
}
Console.WriteLine($"{rqqIndex.PadLeft(7)} {edIndex.PadLeft(6)}");
}
}
public void BimodalProgression(int count, int meanFactor, int stdDev, int error)
{
var random = new RandomDistribution(42);
var shuffler = new Shuffler(42);
var data = new List <double>();
for (int i = 0; i < count; i++)
{
data.AddRange(random.Gaussian(30, 0, stdDev));
data.AddRange(random.Gaussian(70, (i + 1) * meanFactor, stdDev));
shuffler.Shuffle(data, i * 100, 100);
}
var indexes = detector.GetChangePointIndexes(data.ToArray(), 20);
Check100(count, error, indexes);
}
public void GaussianMeanProgression(int count, int meanFactor, int stdDev, int error)
{
var random = new RandomDistribution(42);
var data = new List <double>();
for (int i = 0; i < count; i++)
{
data.AddRange(random.Gaussian(100, mean: meanFactor * i, stdDev: stdDev));
}
var indexes = detector.GetChangePointIndexes(data.ToArray(), 20);
Check100(count, error, indexes);
}
public void GaussianStdDevProgression(int error, [NotNull] string stdDevValuesString)
{
var random = new RandomDistribution(42);
var stdDevValues = stdDevValuesString.Split(';').Select(double.Parse).ToArray();
var data = new List <double>();
foreach (double stdDev in stdDevValues)
{
data.AddRange(random.Gaussian(100, mean: 0, stdDev: stdDev));
}
var indexes = detector.GetChangePointIndexes(data.ToArray(), 20);
Check100(stdDevValues.Length, error, indexes);
}
public void Run()
{
var random = new RandomDistribution(42);
var data = new List <double>();
data.AddRange(random.Gaussian(200, mean: 20));
data.AddRange(random.Gaussian(200, mean: 22));
List <double>
unimodal = new List <double>(),
bimodal = new List <double>(),
trimodal = new List <double>();
unimodal.AddRange(random.Gaussian(200, mean: 20));
bimodal.AddRange(random.Gaussian(200, mean: 20));
bimodal.AddRange(random.Gaussian(200, mean: 30));
trimodal.AddRange(random.Gaussian(200, mean: 20));
trimodal.AddRange(random.Gaussian(200, mean: 30));
trimodal.AddRange(random.Gaussian(200, mean: 40));
Console.WriteLine("Unimodal : " + MValueCalculator.Calculate(unimodal));
Console.WriteLine("Bimodal : " + MValueCalculator.Calculate(bimodal));
Console.WriteLine("Trimodal : " + MValueCalculator.Calculate(trimodal));
}
