public void Calculate_FileWithDuplicates_ReturnsCorrectKendall()
{
// Create lists to feed into test
IEnumerable <decimal> listA = new List <decimal>();
IEnumerable <decimal> listB = new List <decimal>();
for (int i = 1; i <= 10; i++)
{
listA = listA.Append(i);
listB = listB.Append(11 - i);
}
// Add some duplicated numbers to the list
listA = listA.Append(2);
listB = listB.Append(5);
listA = listA.Append(7);
listB = listB.Append(3);
listA = listA.Append(1);
listB = listB.Append(11);
listA = listA.Append(11);
listB = listB.Append(6);
// Calculate using CorrelationCalculator
Correlations.Calculate("A", listA.ToList(), "B", listB.ToList());
// Use Extreme.Statistics Kendall method to test result against
double kendallActual = Stats.KendallTau(listA.ToList(), listB.ToList());
// Check answer
Assert.AreEqual(Math.Round(kendallActual, numberOfDpAccuracy), Math.Round((double)Correlations.kendall, numberOfDpAccuracy));
}
public void Calculate_RandomFile_ReturnsCorrectKendall()
{
// Create random lists to feed into test
IEnumerable <decimal> listA = CreateRandomList(numberOfDataPoints);
IEnumerable <decimal> listB = CreateRandomList(numberOfDataPoints);
// Use Extreme.Statistics Kendall method to test result against
double kendallActual = Stats.KendallTau(listA.ToList(), listB.ToList());
// Calculate using CorrelationCalculator
Correlations.Calculate("A", listA.ToList(), "B", listB.ToList());
// Check answer
Assert.AreEqual(Math.Round(kendallActual, numberOfDpAccuracy), Math.Round((double)Correlations.kendall, numberOfDpAccuracy));
}
public void Calculate_PerfectlyCorrelatedFile_ReturnsCorrectSpearman()
{
// Create lists to feed into test
IEnumerable <decimal> listA = new List <decimal>();
IEnumerable <decimal> listB = new List <decimal>();
for (int i = 1; i <= 10; i++)
{
listA = listA.Append(i);
listB = listB.Append(i);
}
// Calculate using CorrelationCalculator
Correlations.Calculate("A", listA.ToList(), "B", listB.ToList());
// Check answer
Assert.AreEqual(1, Math.Round((double)Correlations.spearman, numberOfDpAccuracy));
}
public void Calculate_RandomFile_ReturnsCorrectSpearman()
{
// Create random lists to feed into test
IEnumerable <decimal> listA = CreateRandomList(numberOfDataPoints);
IEnumerable <decimal> listB = CreateRandomList(numberOfDataPoints);
double[] arrayA = listA.Select(dec => (double)dec).ToArray();
double[] arrayB = listB.Select(dec => (double)dec).ToArray();
// Use Extreme.Statistics RankCorrelation method to test result against
double spearmanActual = Stats.RankCorrelation(arrayA, arrayB);
// Calculate using CorrelationCalculator
Correlations.Calculate("A", listA.ToList(), "B", listB.ToList());
// Check answer
Assert.AreEqual(Math.Round(spearmanActual, numberOfDpAccuracy), Math.Round((double)Correlations.spearman, numberOfDpAccuracy));
}