public void EmpiricalDistributionConstructorTest3()
{
double[] samples = { 5, 5, 1, 4, 1, 2, 2, 3, 3, 3, 4, 3, 3, 3, 4, 3, 2, 3 };
EmpiricalDistribution distribution = new EmpiricalDistribution(samples);
double mean = distribution.Mean; // 3
double median = distribution.Median; // 2.9999993064186787
double var = distribution.Variance; // 1.2941176470588236
double mode = distribution.Mode; // 3
double chf = distribution.CumulativeHazardFunction(x: 4.2); // 2.1972245773362191
double cdf = distribution.DistributionFunction(x: 4.2); // 0.88888888888888884
double pdf = distribution.ProbabilityDensityFunction(x: 4.2); // 0.181456280142802
double lpdf = distribution.LogProbabilityDensityFunction(x: 4.2); // -1.7067405350495708
double hf = distribution.HazardFunction(x: 4.2); // 1.6331065212852196
double ccdf = distribution.ComplementaryDistributionFunction(x: 4.2); //0.11111111111111116
double icdf = distribution.InverseDistributionFunction(p: cdf); // 4.1999999999999993
double smoothing = distribution.Smoothing; // 0.67595864392399474
string str = distribution.ToString(); // Fn(x; S)
Assert.AreEqual(samples, distribution.Samples);
Assert.AreEqual(0.67595864392399474, smoothing);
Assert.AreEqual(3.0, mode);
Assert.AreEqual(3.0, mean);
Assert.AreEqual(2.9999993064186787, median);
Assert.AreEqual(1.2941176470588236, var);
Assert.AreEqual(2.1972245773362191, chf);
Assert.AreEqual(0.88888888888888884, cdf);
Assert.AreEqual(0.18145628014280227, pdf, 1e-15);
Assert.AreEqual(-1.7067405350495708, lpdf);
Assert.AreEqual(1.6331065212852196, hf, 1e-15);
Assert.AreEqual(0.11111111111111116, ccdf);
Assert.AreEqual(4.1999999999999993, icdf);
Assert.AreEqual("Fn(x; S)", str);
var range1 = distribution.GetRange(0.95);
var range2 = distribution.GetRange(0.99);
var range3 = distribution.GetRange(0.01);
Assert.AreEqual(0.99999947547912593, range1.Min);
Assert.AreEqual(5.0000002464240794, range1.Max);
Assert.AreEqual(0.99999913215637204, range2.Min);
Assert.AreEqual(5.0000004605903117, range2.Max);
Assert.AreEqual(0.99999913215637204, range3.Min);
Assert.AreEqual(5.0000004605903117, range3.Max);
}
private List <RecordMongo> typicalDay(List <List <RecordMongo> > possDayValues, string vcode)
{
List <double> longTermValues = new List <double>();
//list of all candidate days cdfs
List <EmpiricalDistribution> dayCDFS = new List <EmpiricalDistribution>();
foreach (List <RecordMongo> day in possDayValues)
{
List <double> dayValues = new List <double>();
foreach (RecordMongo rm in day)
{
if (rm.value != -999.9)
{
//only actual values in the cdfs
longTermValues.Add(rm.value);
dayValues.Add(rm.value);
}
}
dayCDFS.Add(new EmpiricalDistribution(dayValues.ToArray()));
}
//longterm cdf all days found
EmpiricalDistribution longterm = new EmpiricalDistribution(longTermValues.ToArray());
List <double> finkelSch = new List <double>();
var range = longterm.GetRange(0.9);
double inc = (range.Max - range.Min) / 20;
foreach (EmpiricalDistribution candDay in dayCDFS)
{
double sample = range.Min;
double fs = 0;
while (sample <= range.Max)
{
fs += Math.Abs(candDay.DistributionFunction(sample) - longterm.DistributionFunction(sample));
sample += inc;
}
//24 is the n values per day
finkelSch.Add(fs / 24);
}
int minindex = finkelSch.IndexOf(finkelSch.Min());
List <RecordMongo> selectedday = possDayValues[minindex];
return(selectedday);
}