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Merge dev into main: v0.8.7 Kahan compensated summation

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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
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lib/errors/rse/Rse.cs
+30 -36
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@@ -19,6 +19,8 @@ namespace QuanTAlib;
/// - RSE = 1 means same as mean predictor
/// - RSE > 1 means worse than mean predictor
/// - Related to R² by: R² = 1 - RSE
///
/// Uses Kahan compensated summation to prevent floating-point drift without periodic resync.
/// </remarks>
[SkipLocalsInit]
public sealed class Rse : AbstractBase
@@ -32,14 +34,14 @@ public sealed class Rse : AbstractBase
double ActualSum,
double SqErrorSum,
double SqBaselineSum,
double ActualComp,
double SqErrorComp,
double SqBaselineComp,
double LastValidActual,
double LastValidPredicted,
int TickCount);
double LastValidPredicted);
private State _state;
private State _p_state;
private const int ResyncInterval = 1000;
public Rse(int period)
{
if (period <= 0)
@@ -90,9 +92,15 @@ public sealed class Rse : AbstractBase
{
_p_state = _state;
// Update actual buffer for mean calculation
// Update actual buffer for mean calculation — Kahan compensated
double removedActual = _actualBuffer.Count == _actualBuffer.Capacity ? _actualBuffer.Oldest : 0.0;
_state.ActualSum = _state.ActualSum - removedActual + actualVal;
{
double delta = actualVal - removedActual;
double y = delta - _state.ActualComp;
double t = _state.ActualSum + y;
_state.ActualComp = (t - _state.ActualSum) - y;
_state.ActualSum = t;
}
_actualBuffer.Add(actualVal);
// Calculate mean and baseline error
@@ -102,24 +110,27 @@ public sealed class Rse : AbstractBase
double sqError = error * error;
double sqBaseline = baselineError * baselineError;
// Update squared error buffer
// Update squared error buffer — Kahan compensated
double removedError = _sqErrorBuffer.Count == _sqErrorBuffer.Capacity ? _sqErrorBuffer.Oldest : 0.0;
_state.SqErrorSum = _state.SqErrorSum - removedError + sqError;
{
double delta = sqError - removedError;
double y = delta - _state.SqErrorComp;
double t = _state.SqErrorSum + y;
_state.SqErrorComp = (t - _state.SqErrorSum) - y;
_state.SqErrorSum = t;
}
_sqErrorBuffer.Add(sqError);
// Update squared baseline buffer
// Update squared baseline buffer — Kahan compensated
double removedBaseline = _sqBaselineBuffer.Count == _sqBaselineBuffer.Capacity ? _sqBaselineBuffer.Oldest : 0.0;
_state.SqBaselineSum = _state.SqBaselineSum - removedBaseline + sqBaseline;
_sqBaselineBuffer.Add(sqBaseline);
_state.TickCount++;
if (_actualBuffer.IsFull && _state.TickCount >= ResyncInterval)
{
_state.TickCount = 0;
_state.ActualSum = _actualBuffer.RecalculateSum();
_state.SqErrorSum = _sqErrorBuffer.RecalculateSum();
_state.SqBaselineSum = _sqBaselineBuffer.RecalculateSum();
double delta = sqBaseline - removedBaseline;
double y = delta - _state.SqBaselineComp;
double t = _state.SqBaselineSum + y;
_state.SqBaselineComp = (t - _state.SqBaselineSum) - y;
_state.SqBaselineSum = t;
}
_sqBaselineBuffer.Add(sqBaseline);
}
else
{
@@ -303,7 +314,6 @@ public sealed class Rse : AbstractBase
output[i] = sqBaselineSum > 1e-10 ? sqErrorSum / sqBaselineSum : 1.0;
}
int tickCount = 0;
for (; i < len; i++)
{
double act = actual[i];
@@ -348,22 +358,6 @@ public sealed class Rse : AbstractBase
}
output[i] = sqBaselineSum > 1e-10 ? sqErrorSum / sqBaselineSum : 1.0;
tickCount++;
if (tickCount >= ResyncInterval)
{
tickCount = 0;
double recalcActual = 0, recalcError = 0, recalcBaseline = 0;
for (int k = 0; k < period; k++)
{
recalcActual += actualBuffer[k];
recalcError += sqErrorBuffer[k];
recalcBaseline += sqBaselineBuffer[k];
}
actualSum = recalcActual;
sqErrorSum = recalcError;
sqBaselineSum = recalcBaseline;
}
}
}
@@ -373,4 +367,4 @@ public sealed class Rse : AbstractBase
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
}
}