private double ComputeCentroidQuantile(Centroid centroid)
{
double sum = 0;
foreach (Centroid c in centroids.Values)
{
if (c.Mean > centroid.Mean)
{
break;
}
sum += c.Count;
}
double denominator = count;
return((centroid.Count / 2 + sum) / denominator);
}
/// <summary>
/// Add a new value to the T-Digest. Note that this method is NOT thread safe.
/// </summary>
/// <param name="value">The value to add</param>
/// <param name="weight">The relative weight associated with this value. Default is 1 for all values.</param>
public void Add(double value, double weight = 1)
{
if (weight <= 0)
{
throw new ArgumentOutOfRangeException(nameof(weight), "must be greater than 0");
}
var first = count == 0;
count += weight;
if (first)
{
oldAvg = value;
newAvg = value;
Min = value;
Max = value;
}
else
{
newAvg = oldAvg + (value - oldAvg) / count;
oldAvg = newAvg;
Max = value > Max ? value : Max;
Min = value < Min ? value : Min;
}
if (centroids.Count == 0)
{
centroids.Add(value, new Centroid(value, weight));
return;
}
var closest = GetClosestCentroids(value);
var candidates = closest
.Select(c => new
{
Threshold = GetThreshold(ComputeCentroidQuantile(c)),
Centroid = c
})
.Where(c => c.Centroid.Count + weight < c.Threshold)
.ToList();
while (candidates.Count > 0 & weight > 0)
{
var cData = candidates[_rand.Next() % candidates.Count];
var deltaW = Math.Min(cData.Threshold - cData.Centroid.Count, weight);
double oldMean;
if (cData.Centroid.Update(deltaW, value, out oldMean))
{
ReInsertCentroid(oldMean, cData.Centroid);
}
weight -= deltaW;
candidates.Remove(cData);
}
if (weight > 0)
{
var toAdd = new Centroid(value, weight);
if (centroids.FindOrAdd(value, ref toAdd))
{
double oldMean;
if (toAdd.Update(weight, toAdd.Mean, out oldMean))
{
ReInsertCentroid(oldMean, toAdd);
}
}
}
if (centroids.Count > (CompressionConstant / Accuracy))
{
Compress();
}
}
private void ReInsertCentroid(double oldMean, Centroid c)
{
centroids.Remove(oldMean);
centroids.Add(c.Mean, c);
}
/// <summary>
/// Estimates the specified quantile
/// </summary>
/// <param name="quantile">The quantile to estimate. Must be between 0 and 1.</param>
/// <returns>The value for the estimated quantile</returns>
public double Quantile(double quantile)
{
if (quantile < 0 || quantile > 1)
{
throw new ArgumentOutOfRangeException(nameof(quantile), "must be between 0 and 1");
}
if (centroids.Count == 0)
{
throw new InvalidOperationException(
"Cannot call Quantile() method until first Adding values to the digest");
}
if (centroids.Count == 1)
{
return(centroids.First().Value.Mean);
}
double index = quantile * count;
if (index < 1)
{
return(Min);
}
if (index > Count - 1)
{
return(Max);
}
Centroid currentNode = centroids.First().Value;
Centroid lastNode = centroids.Last().Value;
double currentWeight = currentNode.Count;
if (Math.Abs(currentWeight - 2) < Tolerance && index <= 2)
{
// first node is a double weight with one sample at min, sou we can infer location of other sample
return(2 * currentNode.Mean - Min);
}
if (Math.Abs(centroids.Last().Value.Count - 2) < Tolerance && index > Count - 2)
{
// likewise for last centroid
return(2 * lastNode.Mean - Max);
}
double weightSoFar = currentWeight / 2.0;
if (index < weightSoFar)
{
return(WeightedAvg(Min, weightSoFar - index, currentNode.Mean, index - 1));
}
foreach (Centroid nextNode in centroids.Values.Skip(1))
{
double nextWeight = nextNode.Count;
double dw = (currentWeight + nextWeight) / 2.0;
if (index < weightSoFar + dw)
{
double leftExclusion = 0;
double rightExclusion = 0;
if (Math.Abs(currentWeight - 1) < Tolerance)
{
if (index < weightSoFar + 0.5)
{
return(currentNode.Mean);
}
leftExclusion = 0.5;
}
if (Math.Abs(nextWeight - 1) < Tolerance)
{
if (index >= weightSoFar + dw - 0.5)
{
return(nextNode.Mean);
}
rightExclusion = 0.5;
}
// centroids i and i+1 bracket our current point
// we interpolate, but the weights are diminished if singletons are present
double weight1 = index - weightSoFar - leftExclusion;
double weight2 = weightSoFar + dw - index - rightExclusion;
return(WeightedAvg(currentNode.Mean, weight2, nextNode.Mean, weight1));
}
weightSoFar += dw;
currentNode = nextNode;
currentWeight = nextWeight;
}
double w1 = index - weightSoFar;
double w2 = Count - 1 - index;
return(WeightedAvg(currentNode.Mean, w2, Max, w1));
}
