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SIMD Refactor: Merge simd-dev into dev (#55)
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: aider (openrouter/anthropic/claude-sonnet-4) <aider@aider.chat> Co-authored-by: Warp <agent@warp.dev>
This commit is contained in:
Miha Kralj
co-authored by
Claude Opus 4.5
aider
Warp
Claude Opus 4.5
aider
Warp
parent
5bcdf8d614
commit
86fe32a682
@@ -0,0 +1,295 @@
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using System.Runtime.CompilerServices;
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using System.Runtime.InteropServices;
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namespace QuanTAlib;
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/// <summary>
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/// RAE: Relative Absolute Error
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/// </summary>
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/// <remarks>
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/// RAE measures the total absolute error relative to the total absolute error of
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/// a simple predictor (the mean). It provides a normalized measure that indicates
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/// how well the model performs compared to predicting the mean for all values.
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///
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/// Formula:
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/// RAE = Σ|actual - predicted| / Σ|actual - mean(actual)|
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///
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/// Key properties:
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/// - RAE < 1 means better than mean predictor
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/// - RAE = 1 means same as mean predictor
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/// - RAE > 1 means worse than mean predictor
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/// - Scale-independent ratio
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/// </remarks>
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[SkipLocalsInit]
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public sealed class Rae : AbstractBase
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{
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private readonly RingBuffer _actualBuffer;
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private readonly RingBuffer _absErrorBuffer;
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private readonly RingBuffer _absBaselineBuffer;
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[StructLayout(LayoutKind.Auto)]
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private record struct State(
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double ActualSum,
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double AbsErrorSum,
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double AbsBaselineSum,
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double LastValidActual,
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double LastValidPredicted,
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int TickCount);
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private State _state;
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private State _p_state;
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private const int ResyncInterval = 1000;
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public Rae(int period)
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{
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if (period <= 0)
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throw new ArgumentException("Period must be greater than 0", nameof(period));
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_actualBuffer = new RingBuffer(period);
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_absErrorBuffer = new RingBuffer(period);
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_absBaselineBuffer = new RingBuffer(period);
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Name = $"Rae({period})";
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WarmupPeriod = period;
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}
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public override bool IsHot => _actualBuffer.IsFull;
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public TValue Update(TValue actual, TValue predicted, bool isNew = true)
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{
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double actualVal = actual.Value;
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double predictedVal = predicted.Value;
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if (!double.IsFinite(actualVal))
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actualVal = double.IsFinite(_state.LastValidActual) ? _state.LastValidActual : 0.0;
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else
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_state.LastValidActual = actualVal;
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if (!double.IsFinite(predictedVal))
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predictedVal = double.IsFinite(_state.LastValidPredicted) ? _state.LastValidPredicted : 0.0;
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else
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_state.LastValidPredicted = predictedVal;
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if (isNew)
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{
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_p_state = _state;
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// Update actual buffer for mean calculation
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double removedActual = _actualBuffer.Count == _actualBuffer.Capacity ? _actualBuffer.Oldest : 0.0;
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_state.ActualSum = _state.ActualSum - removedActual + actualVal;
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_actualBuffer.Add(actualVal);
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// Calculate mean and baseline error
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double mean = _state.ActualSum / _actualBuffer.Count;
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double absError = Math.Abs(actualVal - predictedVal);
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double absBaseline = Math.Abs(actualVal - mean);
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// Update error buffer
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double removedError = _absErrorBuffer.Count == _absErrorBuffer.Capacity ? _absErrorBuffer.Oldest : 0.0;
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_state.AbsErrorSum = _state.AbsErrorSum - removedError + absError;
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_absErrorBuffer.Add(absError);
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// Update baseline buffer
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double removedBaseline = _absBaselineBuffer.Count == _absBaselineBuffer.Capacity ? _absBaselineBuffer.Oldest : 0.0;
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_state.AbsBaselineSum = _state.AbsBaselineSum - removedBaseline + absBaseline;
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_absBaselineBuffer.Add(absBaseline);
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_state.TickCount++;
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if (_actualBuffer.IsFull && _state.TickCount >= ResyncInterval)
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{
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_state.TickCount = 0;
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_state.ActualSum = _actualBuffer.RecalculateSum();
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_state.AbsErrorSum = _absErrorBuffer.RecalculateSum();
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_state.AbsBaselineSum = _absBaselineBuffer.RecalculateSum();
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}
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}
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else
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{
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_state = _p_state;
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// Update actual buffer
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double removedActual = _actualBuffer.Count == _actualBuffer.Capacity ? _actualBuffer.Oldest : 0.0;
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_state.ActualSum = _state.ActualSum - removedActual + actualVal;
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_actualBuffer.UpdateNewest(actualVal);
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_state.ActualSum = _actualBuffer.RecalculateSum();
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// Calculate mean and errors
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double mean = _state.ActualSum / _actualBuffer.Count;
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double absError = Math.Abs(actualVal - predictedVal);
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double absBaseline = Math.Abs(actualVal - mean);
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// Update error buffer
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_absErrorBuffer.UpdateNewest(absError);
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_state.AbsErrorSum = _absErrorBuffer.RecalculateSum();
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// Update baseline buffer
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_absBaselineBuffer.UpdateNewest(absBaseline);
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_state.AbsBaselineSum = _absBaselineBuffer.RecalculateSum();
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}
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double result = _state.AbsBaselineSum > 1e-10 ? _state.AbsErrorSum / _state.AbsBaselineSum : 1.0;
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Last = new TValue(actual.Time, result);
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PubEvent(Last, isNew);
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return Last;
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public TValue Update(double actual, double predicted, bool isNew = true)
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{
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return Update(new TValue(DateTime.UtcNow, actual), new TValue(DateTime.UtcNow, predicted), isNew);
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}
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public override TValue Update(TValue input, bool isNew = true)
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{
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throw new NotSupportedException("RAE requires two inputs. Use Update(actual, predicted).");
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}
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public override TSeries Update(TSeries source)
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{
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throw new NotSupportedException("RAE requires two inputs. Use Calculate(actualSeries, predictedSeries, period).");
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}
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public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
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{
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throw new NotSupportedException("RAE requires two inputs.");
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}
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public override void Reset()
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{
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_actualBuffer.Clear();
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_absErrorBuffer.Clear();
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_absBaselineBuffer.Clear();
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_state = default;
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_p_state = default;
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Last = default;
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}
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public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
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{
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if (actual.Count != predicted.Count)
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throw new ArgumentException("Actual and predicted series must have the same length", nameof(predicted));
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int len = actual.Count;
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var t = new List<long>(len);
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var v = new List<double>(len);
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CollectionsMarshal.SetCount(t, len);
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CollectionsMarshal.SetCount(v, len);
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var tSpan = CollectionsMarshal.AsSpan(t);
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var vSpan = CollectionsMarshal.AsSpan(v);
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Batch(actual.Values, predicted.Values, vSpan, period);
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actual.Times.CopyTo(tSpan);
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return new TSeries(t, v);
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public static void Batch(ReadOnlySpan<double> actual, ReadOnlySpan<double> predicted, Span<double> output, int period)
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{
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if (actual.Length != predicted.Length || actual.Length != output.Length)
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throw new ArgumentException("All spans must have the same length", nameof(output));
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if (period <= 0)
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throw new ArgumentException("Period must be greater than 0", nameof(period));
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int len = actual.Length;
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if (len == 0) return;
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const int StackAllocThreshold = 256;
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Span<double> actualBuffer = period <= StackAllocThreshold
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? stackalloc double[period]
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: new double[period];
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Span<double> absErrorBuffer = period <= StackAllocThreshold
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? stackalloc double[period]
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: new double[period];
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Span<double> absBaselineBuffer = period <= StackAllocThreshold
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? stackalloc double[period]
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: new double[period];
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double actualSum = 0;
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double absErrorSum = 0;
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double absBaselineSum = 0;
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double lastValidActual = 0;
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double lastValidPredicted = 0;
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for (int k = 0; k < len; k++)
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{
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if (double.IsFinite(actual[k])) { lastValidActual = actual[k]; break; }
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}
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for (int k = 0; k < len; k++)
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{
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if (double.IsFinite(predicted[k])) { lastValidPredicted = predicted[k]; break; }
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}
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int bufferIndex = 0;
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int i = 0;
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int warmupEnd = Math.Min(period, len);
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for (; i < warmupEnd; i++)
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{
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double act = actual[i];
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double pred = predicted[i];
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if (double.IsFinite(act)) lastValidActual = act; else act = lastValidActual;
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if (double.IsFinite(pred)) lastValidPredicted = pred; else pred = lastValidPredicted;
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actualSum += act;
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actualBuffer[i] = act;
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double mean = actualSum / (i + 1);
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double absError = Math.Abs(act - pred);
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double absBaseline = Math.Abs(act - mean);
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absErrorSum += absError;
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absBaselineSum += absBaseline;
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absErrorBuffer[i] = absError;
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absBaselineBuffer[i] = absBaseline;
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output[i] = absBaselineSum > 1e-10 ? absErrorSum / absBaselineSum : 1.0;
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}
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int tickCount = 0;
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for (; i < len; i++)
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{
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double act = actual[i];
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double pred = predicted[i];
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if (double.IsFinite(act)) lastValidActual = act; else act = lastValidActual;
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if (double.IsFinite(pred)) lastValidPredicted = pred; else pred = lastValidPredicted;
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actualSum = actualSum - actualBuffer[bufferIndex] + act;
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actualBuffer[bufferIndex] = act;
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double mean = actualSum / period;
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double absError = Math.Abs(act - pred);
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double absBaseline = Math.Abs(act - mean);
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absErrorSum = absErrorSum - absErrorBuffer[bufferIndex] + absError;
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absBaselineSum = absBaselineSum - absBaselineBuffer[bufferIndex] + absBaseline;
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absErrorBuffer[bufferIndex] = absError;
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absBaselineBuffer[bufferIndex] = absBaseline;
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bufferIndex++;
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if (bufferIndex >= period) bufferIndex = 0;
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output[i] = absBaselineSum > 1e-10 ? absErrorSum / absBaselineSum : 1.0;
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tickCount++;
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if (tickCount >= ResyncInterval)
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{
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tickCount = 0;
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double recalcActual = 0, recalcError = 0, recalcBaseline = 0;
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for (int k = 0; k < period; k++)
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{
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recalcActual += actualBuffer[k];
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recalcError += absErrorBuffer[k];
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recalcBaseline += absBaselineBuffer[k];
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}
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actualSum = recalcActual;
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absErrorSum = recalcError;
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absBaselineSum = recalcBaseline;
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}
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}
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}
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}␍
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