public static CorrelationCalculator Create(CorrelationType type)
{
switch (type)
{
case CorrelationType.Pearson:
return(new PearsonCorrelationCalculator());
case CorrelationType.Kendall:
return(new KendallCorrelationCalculator());
default:
return(new PearsonCorrelationCalculator());
}
}
public Correlation Correlation(ISeries <double> input, int period, CorrelationType corrType, int periodPctChange)
{
if (cacheCorrelation != null)
{
for (int idx = 0; idx < cacheCorrelation.Length; idx++)
{
if (cacheCorrelation[idx] != null && cacheCorrelation[idx].Period == period && cacheCorrelation[idx].CorrType == corrType && cacheCorrelation[idx].PeriodPctChange == periodPctChange && cacheCorrelation[idx].EqualsInput(input))
{
return(cacheCorrelation[idx]);
}
}
}
return(CacheIndicator <Correlation>(new Correlation()
{
Period = period, CorrType = corrType, PeriodPctChange = periodPctChange
}, input, ref cacheCorrelation));
}
public static double Correlation(this IList <double> datasetX, IList <double> datasetY, CorrelationType correlationType)
{
if (datasetX.Count != datasetY.Count)
{
throw new ArgumentException("Datasets should have the same dimensions");
}
switch (correlationType)
{
case CorrelationType.Pearson:
return(CalculatePearsonCorrelation(datasetX, datasetY));
case CorrelationType.Spearman:
return(CalculateSpearmanRankCorrelation(datasetX, datasetY));
default:
throw new ArgumentException("The specified correlation is not supported");
}
}
public static Dictionary <TId, List <CorrelationType> > DetermineCorrelationSignificances(Model model, Dictionary <TId, List <double> > correlationCoefficients)
{
if (model == null)
{
throw new ArgumentNullException("model");
}
if (model.Criteria == null ||
model.Experiments == null)
{
throw new InvalidOperationException();
}
if (correlationCoefficients == null)
{
throw new ArgumentNullException("correlationCoefficients");
}
int activeExperimentsCount = model.Experiments.CountActiveExperiments();
if (activeExperimentsCount < 3)
{
throw new InvalidOperationException();
}
Dictionary <TId, List <CorrelationType> > correlationSignificances = new Dictionary <TId, List <CorrelationType> >(model.Criteria.Count);
Dictionary <double, double> significanceRow = FindSignificanceRow(activeExperimentsCount - 2);
foreach (KeyValuePair <TId, List <double> > correlationRow in correlationCoefficients)
{
correlationSignificances[correlationRow.Key] = new List <CorrelationType>(model.Criteria.Count);
for (int i = 0; i < correlationRow.Value.Count; i++)
{
double actualCoefficient = correlationRow.Value[i];
double pValue = 0.0;
foreach (KeyValuePair <double, double> significance in significanceRow)
{
if (significance.Value >= actualCoefficient)
{
pValue = significance.Key;
break;
}
}
CorrelationType correlationType = CorrelationType.Correlated;
if (pValue >= 0.1)
{
correlationType = CorrelationType.ResultsNotSignificant;
}
else if (pValue < 0.1 && pValue > 0.001)
{
correlationType = CorrelationType.Correlated;
}
else if (pValue <= 0.001)
{
correlationType = CorrelationType.SignificantlyRelated;
}
correlationSignificances[correlationRow.Key].Add(correlationType);
}
}
return(correlationSignificances);
}
public Indicators.Correlation Correlation(ISeries <double> input, int period, CorrelationType corrType, int periodPctChange)
{
return(indicator.Correlation(input, period, corrType, periodPctChange));
}
public Correlation Correlation(int period, CorrelationType corrType, int periodPctChange)
{
return(Correlation(Input, period, corrType, periodPctChange));
}
