public static Quartiles FromUnsorted(IReadOnlyList <double> values)
{
QuantileEstimatorHelper.CheckArguments(values, 0);
var sortedValues = values.CopyToArray();
Array.Sort(sortedValues);
return(FromSorted(sortedValues));
}
public static Quartiles FromSorted([NotNull] IReadOnlyList <double> values)
{
QuantileEstimatorHelper.CheckArguments(values, 0);
double GetQuantile(double q) => SimpleQuantileEstimator.Instance.GetQuantileFromSorted(values, q);
double q0 = GetQuantile(0.00);
double q1 = GetQuantile(0.25);
double q2 = GetQuantile(0.50);
double q3 = GetQuantile(0.75);
double q4 = GetQuantile(1.00);
return(new Quartiles(q0, q1, q2, q3, q4));
}
/// <summary>
/// Calculates the requested quantile from the set of values
/// </summary>
/// <remarks>
/// The implementation is expected to be consistent with the one from Excel.
/// It's a quite common to export bench output into .csv for further analysis
/// And it's a good idea to have same results from all tools being used.
/// </remarks>
/// <param name="data">Sequence of the values to be calculated</param>
/// <param name="probability">Value in range [0;1]</param>
/// <returns>Quantile from the set of values</returns>
// Based on: http://stackoverflow.com/a/8137526
public double GetQuantileFromSorted(IReadOnlyList <double> data, double probability)
{
QuantileEstimatorHelper.CheckArguments(data, probability);
// DONTTOUCH: the following code was taken from http://stackoverflow.com/a/8137526 and it is proven
// to work in the same way the excel's counterpart does.
// So it's better to leave it as it is unless you do not want to reimplement it from scratch:)
double realIndex = probability * (data.Count - 1);
int index = (int)realIndex;
double frac = realIndex - index;
if (index + 1 < data.Count)
{
return(data[index] * (1 - frac) + data[index + 1] * frac);
}
return(data[index]);
}
public double GetQuantileFromSorted(IReadOnlyList <double> data, double probability)
{
QuantileEstimatorHelper.CheckArguments(data, probability);
int n = data.Count;
double a = (n + 1) * probability, b = (n + 1) * (1 - probability);
var distribution = new BetaDistribution(a, b);
double result = 0;
double betaCdfRight = 0;
for (int j = 0; j < data.Count; j++)
{
double betaCdfLeft = betaCdfRight;
betaCdfRight = distribution.Cdf((j + 1) * 1.0 / n);
result += (betaCdfRight - betaCdfLeft) * data[j];
}
return(result);
}
