public void TestThreePSelecter()
{
double tolarance = .5;
int iFrequency = 5;
int aveKpi = 20;
int freq = 500;
int popSize = 10_000;
int numSamples = popSize / freq;
//int numIsamples = (iFrequency > 0) ? numSamples / iFrequency : 0; // same as other implementation but less consistant with how i test other methods
int numIsamples = (iFrequency > 0) ? popSize / (iFrequency * freq) : 0;
int popVol = aveKpi * popSize;
int kz = popVol / numSamples;
var selecter = new ThreePSelecter(kz, iFrequency);
int sampleCounter = 0;
int iSampleCounter = 0;
for (int i = 0; i < popSize; i++)
{
var result = selecter.Sample(aveKpi);
if (result == SampleResult.M)
{
sampleCounter++;
}
else if (result == SampleResult.I)
{
iSampleCounter++;
}
}
// taking the double sided binomial probability with the number of samples recieved against our frequency
// will tell us the liklyhood of recieving the number of samples we recieved
// check that probability against our tolarance
var likelihood_twoSided = 2 * BinomialProbability(popSize, sampleCounter, 1 / (double)freq);
likelihood_twoSided.Should().BeLessThan(tolarance);
if (iFrequency > 1)
{
// because insurance trees should be generated for x number of
// regular samples. We should know exactly how many insurance samples
// to expect give or take one
var actualExpectediSamples = sampleCounter / (double)(iFrequency);
var iSampleDiff = Math.Abs(iSampleCounter - numIsamples);
iSampleDiff.Should().BeLessOrEqualTo(1);
}
}
public SampleResult Sample(int kpi, out int rand)
{
return(ThreePSelecter.Sample(kpi, out rand));
}
