gavindiaz/QuanTAlib
1
0
Fork 0
mirror of https://github.com/mihakralj/QuanTAlib.git synced 2026-08-19 11:08:05 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge dev into main: v0.8.7 Kahan compensated summation

Browse Source
This commit is contained in:
Miha Kralj
2026-03-13 22:01:52 -07:00
parent 86a1db286c 67ad6f0cba
commit 9697facfb4
79 changed files with 2923 additions and 2495 deletions
Download Patch File Download Diff File Expand all files Collapse all files
lib/errors/wrmse/Wrmse.cs
+20 -14
View File
@@ -14,7 +14,7 @@ namespace QuanTAlib;
/// Formula:
/// WRMSE = √(Σ(w_i * (actual_i - predicted_i)²) / Σ(w_i))
///
/// Uses dual RingBuffers for O(1) streaming updates with running sums.
/// Uses dual RingBuffers for O(1) streaming updates with Kahan compensated running sums.
///
/// Key properties:
/// - Always non-negative (WRMSE ≥ 0)
@@ -22,6 +22,8 @@ namespace QuanTAlib;
/// - Weights allow emphasizing important observations
/// - Reduces to RMSE when all weights are equal
/// - WRMSE = 0 indicates perfect prediction
///
/// Kahan compensated summation prevents floating-point drift without periodic resync.
/// </remarks>
[SkipLocalsInit]
public sealed class Wrmse : AbstractBase
@@ -33,14 +35,14 @@ public sealed class Wrmse : AbstractBase
private record struct State(
double WeightedErrorSum,
double WeightSum,
double WeightedErrorComp,
double WeightComp,
double LastValidActual,
double LastValidPredicted,
double LastValidWeight,
int TickCount);
double LastValidWeight);
private State _state;
private State _p_state;
private const int ResyncInterval = 1000;
private const double DefaultWeight = 1.0;
/// <summary>
@@ -124,21 +126,25 @@ public sealed class Wrmse : AbstractBase
double removedWeightedError = _weightedErrorBuffer.Count == _weightedErrorBuffer.Capacity
? _weightedErrorBuffer.Oldest : 0.0;
_state.WeightedErrorSum = _state.WeightedErrorSum - removedWeightedError + weightedError;
{
double delta = weightedError - removedWeightedError;
double y = delta - _state.WeightedErrorComp;
double t = _state.WeightedErrorSum + y;
_state.WeightedErrorComp = (t - _state.WeightedErrorSum) - y;
_state.WeightedErrorSum = t;
}
_weightedErrorBuffer.Add(weightedError);
double removedWeight = _weightBuffer.Count == _weightBuffer.Capacity
? _weightBuffer.Oldest : 0.0;
_state.WeightSum = _state.WeightSum - removedWeight + weight;
_weightBuffer.Add(weight);
_state.TickCount++;
if (_weightedErrorBuffer.IsFull && _state.TickCount >= ResyncInterval)
{
_state.TickCount = 0;
_state.WeightedErrorSum = _weightedErrorBuffer.RecalculateSum();
_state.WeightSum = _weightBuffer.RecalculateSum();
double delta = weight - removedWeight;
double y = delta - _state.WeightComp;
double t = _state.WeightSum + y;
_state.WeightComp = (t - _state.WeightSum) - y;
_state.WeightSum = t;
}
_weightBuffer.Add(weight);
}
else
{
@@ -332,4 +338,4 @@ public sealed class Wrmse : AbstractBase
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
}
}