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normalization of methods

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This commit is contained in:
Miha Kralj
2026-02-10 21:33:16 -08:00
parent 915d7a007b
commit 6d6259a47d
527 changed files with 10525 additions and 2123 deletions
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lib/errors/huber/Huber.Tests.cs
+2 -2
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@@ -332,7 +332,7 @@ public class HuberTests
predicted.Add(now.AddMinutes(i), 100.5); // Small constant error
}
var results = Huber.Calculate(actual, predicted, 3);
var results = Huber.Batch(actual, predicted, 3);
Assert.Equal(10, results.Count);
// Error = 0.5, Huber (small error) = 0.5 * 0.5^2 = 0.125
@@ -355,7 +355,7 @@ public class HuberTests
predicted.Add(DateTime.UtcNow, i);
}
Assert.Throws<ArgumentException>(() => Huber.Calculate(actual, predicted, 3));
Assert.Throws<ArgumentException>(() => Huber.Batch(actual, predicted, 3));
}
[Fact]
lib/errors/huber/Huber.cs
+9 -2
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@@ -67,7 +67,7 @@ public sealed class Huber : BiInputIndicatorBase
/// <summary>
/// Calculates Huber Loss for two time series.
/// </summary>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period, double delta = 1.345)
public static TSeries Batch(TSeries actual, TSeries predicted, int period, double delta = 1.345)
{
if (actual.Count != predicted.Count)
{
@@ -127,4 +127,11 @@ public sealed class Huber : BiInputIndicatorBase
// Apply rolling mean
ErrorHelpers.ApplyRollingMean(errors, output, period);
}
}
public static (TSeries Results, Huber Indicator) Calculate(TSeries actual, TSeries predicted, int period, double delta = 1.345)
{
var indicator = new Huber(period, delta);
TSeries results = Batch(actual, predicted, period, delta);
return (results, indicator);
}
}
lib/errors/logcosh/LogCosh.Tests.cs
+3 -3
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@@ -239,7 +239,7 @@ public class LogCoshTests
iterativeResults[i] = logCoshIterative.Update(actualSeries[i], predictedSeries[i]).Value;
}
var batchResults = LogCosh.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var batchResults = LogCosh.Batch(actualSeries, predictedSeries, DefaultPeriod);
Assert.Equal(count, batchResults.Count);
for (int i = 0; i < count; i++)
@@ -283,7 +283,7 @@ public class LogCoshTests
predictedArr[i] = pred;
}
var tseriesResult = LogCosh.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var tseriesResult = LogCosh.Batch(actualSeries, predictedSeries, DefaultPeriod);
LogCosh.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), DefaultPeriod);
for (int i = 0; i < 100; i++)
@@ -332,7 +332,7 @@ public class LogCoshTests
predicted.Add(DateTime.UtcNow.Ticks, 98);
Assert.Throws<ArgumentException>(() => LogCosh.Calculate(actual, predicted, DefaultPeriod));
Assert.Throws<ArgumentException>(() => LogCosh.Batch(actual, predicted, DefaultPeriod));
}
[Fact]
lib/errors/logcosh/LogCosh.cs
+9 -2
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@@ -58,7 +58,7 @@ public sealed class LogCosh : BiInputIndicatorBase
/// <summary>
/// Calculates LogCosh for entire series.
/// </summary>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
=> CalculateImpl(actual, predicted, period, Batch);
/// <summary>
@@ -97,4 +97,11 @@ public sealed class LogCosh : BiInputIndicatorBase
}
}
}
}
public static (TSeries Results, LogCosh Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new LogCosh(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
}
lib/errors/maape/Maape.Tests.cs
+3 -3
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@@ -212,7 +212,7 @@ public class MaapeTests
streamingResults[i] = maapeIterative.Update(actualArr[i], predictedArr[i]).Value;
}
var batchResults = Maape.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var batchResults = Maape.Batch(actualSeries, predictedSeries, DefaultPeriod);
Assert.Equal(count, batchResults.Count);
for (int i = 0; i < count; i++)
@@ -256,7 +256,7 @@ public class MaapeTests
predictedArr[i] = pred;
}
var tseriesResult = Maape.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var tseriesResult = Maape.Batch(actualSeries, predictedSeries, DefaultPeriod);
Maape.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), DefaultPeriod);
for (int i = 0; i < 100; i++)
@@ -305,7 +305,7 @@ public class MaapeTests
predicted.Add(DateTime.UtcNow.Ticks, 98);
Assert.Throws<ArgumentException>(() => Maape.Calculate(actual, predicted, DefaultPeriod));
Assert.Throws<ArgumentException>(() => Maape.Batch(actual, predicted, DefaultPeriod));
}
[Fact]
lib/errors/maape/Maape.cs
+9 -2
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@@ -49,7 +49,7 @@ public sealed class Maape : BiInputIndicatorBase
/// <summary>
/// Calculates Mean Arctangent Absolute Percentage Error for two time series.
/// </summary>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
{
if (actual.Count != predicted.Count)
{
@@ -116,6 +116,13 @@ public sealed class Maape : BiInputIndicatorBase
}
}
public static (TSeries Results, Maape Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Maape(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
/// <summary>
/// Computes arctangent percentage errors.
/// </summary>
@@ -178,4 +185,4 @@ public sealed class Maape : BiInputIndicatorBase
output[i] = absActual > Epsilon ? Math.Atan(absError / absActual) : Math.PI / 2.0;
}
}
}
}
lib/errors/mae/Mae.Tests.cs
+2 -2
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@@ -308,7 +308,7 @@ public class MaeTests
predicted.Add(now.AddMinutes(i), i * 10 + 5);
}
var results = Mae.Calculate(actual, predicted, 3);
var results = Mae.Batch(actual, predicted, 3);
Assert.Equal(10, results.Count);
// All errors are 5, so MAE should be 5
@@ -331,7 +331,7 @@ public class MaeTests
predicted.Add(DateTime.UtcNow, i);
}
Assert.Throws<ArgumentException>(() => Mae.Calculate(actual, predicted, 3));
Assert.Throws<ArgumentException>(() => Mae.Batch(actual, predicted, 3));
}
[Fact]
lib/errors/mae/Mae.cs
+9 -2
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@@ -45,7 +45,7 @@ public sealed class Mae : BiInputIndicatorBase
/// <param name="predicted">Predicted values series</param>
/// <param name="period">MAE period</param>
/// <returns>MAE series</returns>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
=> CalculateImpl(actual, predicted, period, Batch);
/// <summary>
@@ -89,4 +89,11 @@ public sealed class Mae : BiInputIndicatorBase
}
}
}
}
public static (TSeries Results, Mae Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Mae(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
}
lib/errors/mapd/Mapd.Tests.cs
+2 -2
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@@ -292,7 +292,7 @@ public class MapdTests
predicted.Add(now.AddMinutes(i), 110);
}
var results = Mapd.Calculate(actual, predicted, 3);
var results = Mapd.Batch(actual, predicted, 3);
Assert.Equal(10, results.Count);
// |100-110|/110 * 100 = 9.0909...%
@@ -315,7 +315,7 @@ public class MapdTests
predicted.Add(DateTime.UtcNow, i + 1);
}
Assert.Throws<ArgumentException>(() => Mapd.Calculate(actual, predicted, 3));
Assert.Throws<ArgumentException>(() => Mapd.Batch(actual, predicted, 3));
}
[Fact]
lib/errors/mapd/Mapd.cs
+9 -2
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@@ -48,7 +48,7 @@ public sealed class Mapd : BiInputIndicatorBase
/// <summary>
/// Calculates Mean Absolute Percentage Deviation for two time series.
/// </summary>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
{
if (actual.Count != predicted.Count)
{
@@ -104,6 +104,13 @@ public sealed class Mapd : BiInputIndicatorBase
ErrorHelpers.ApplyRollingMean(errors, output, period);
}
public static (TSeries Results, Mapd Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Mapd(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
/// <summary>
/// Computes MAPD errors (percentage errors divided by predicted).
/// </summary>
@@ -164,4 +171,4 @@ public sealed class Mapd : BiInputIndicatorBase
: 0.0;
}
}
}
}
lib/errors/mape/Mape.Tests.cs
+2 -2
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@@ -308,7 +308,7 @@ public class MapeTests
predicted.Add(now.AddMinutes(i), 110); // 10% error
}
var results = Mape.Calculate(actual, predicted, 3);
var results = Mape.Batch(actual, predicted, 3);
Assert.Equal(10, results.Count);
Assert.Equal(10.0, results.Last.Value, 10);
@@ -330,7 +330,7 @@ public class MapeTests
predicted.Add(DateTime.UtcNow, i + 1);
}
Assert.Throws<ArgumentException>(() => Mape.Calculate(actual, predicted, 3));
Assert.Throws<ArgumentException>(() => Mape.Batch(actual, predicted, 3));
}
[Fact]
lib/errors/mape/Mape.cs
+9 -2
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@@ -41,7 +41,7 @@ public sealed class Mape : BiInputIndicatorBase
/// <summary>
/// Calculates MAPE for entire series.
/// </summary>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
=> CalculateImpl(actual, predicted, period, Batch);
/// <summary>
@@ -65,4 +65,11 @@ public sealed class Mape : BiInputIndicatorBase
ErrorHelpers.ComputePercentageErrors(actual, predicted, percentErrors, Epsilon);
ErrorHelpers.ApplyRollingMean(percentErrors, output, period);
}
}
public static (TSeries Results, Mape Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Mape(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
}
lib/errors/mase/Mase.Tests.cs
+3 -3
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@@ -241,7 +241,7 @@ public class MaseTests
iterativeResults.Add(maseIterative.Update(actual[i], predicted[i]).Value);
}
var batchResults = Mase.Calculate(actual, predicted, Period);
var batchResults = Mase.Batch(actual, predicted, Period);
Assert.Equal(iterativeResults.Count, batchResults.Count);
for (int i = 0; i < iterativeResults.Count; i++)
@@ -282,7 +282,7 @@ public class MaseTests
double[] predictedArr = predictedSeries.Values.ToArray();
double[] output = new double[100];
var tseriesResult = Mase.Calculate(actualSeries, predictedSeries, Period);
var tseriesResult = Mase.Batch(actualSeries, predictedSeries, Period);
Mase.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), Period);
for (int i = 0; i < 100; i++)
@@ -303,7 +303,7 @@ public class MaseTests
}
// 1. Batch Mode (static method)
var batchSeries = Mase.Calculate(actualSeries, predictedSeries, Period);
var batchSeries = Mase.Batch(actualSeries, predictedSeries, Period);
double expected = batchSeries.Last.Value;
// 2. Span Mode
lib/errors/mase/Mase.cs
+10 -3
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@@ -162,7 +162,7 @@ public sealed class Mase : AbstractBase
public override TSeries Update(TSeries source)
{
throw new NotSupportedException("MASE requires two inputs. Use Calculate(actualSeries, predictedSeries, period).");
throw new NotSupportedException("MASE requires two inputs. Use Batch(actualSeries, predictedSeries, period).");
}
public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
@@ -179,7 +179,7 @@ public sealed class Mase : AbstractBase
Last = default;
}
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
{
if (actual.Count != predicted.Count)
{
@@ -352,4 +352,11 @@ public sealed class Mase : AbstractBase
}
}
}
}
public static (TSeries Results, Mase Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Mase(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
}
lib/errors/mdae/Mdae.Tests.cs
+3 -3
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@@ -192,7 +192,7 @@ public class MdaeTests
iterativeResults.Add(mdaeIterative.Update(actual, predicted).Value);
}
var batchResults = Mdae.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var batchResults = Mdae.Batch(actualSeries, predictedSeries, DefaultPeriod);
Assert.Equal(100, iterativeResults.Count);
Assert.Equal(iterativeResults.Count, batchResults.Count);
@@ -237,7 +237,7 @@ public class MdaeTests
predictedArr[i] = pred;
}
var tseriesResult = Mdae.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var tseriesResult = Mdae.Batch(actualSeries, predictedSeries, DefaultPeriod);
Mdae.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), DefaultPeriod);
for (int i = 0; i < 100; i++)
@@ -286,7 +286,7 @@ public class MdaeTests
predicted.Add(DateTime.UtcNow.Ticks, 98);
Assert.Throws<ArgumentException>(() => Mdae.Calculate(actual, predicted, DefaultPeriod));
Assert.Throws<ArgumentException>(() => Mdae.Batch(actual, predicted, DefaultPeriod));
}
[Fact]
lib/errors/mdae/Mdae.cs
+10 -3
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@@ -114,7 +114,7 @@ public sealed class Mdae : AbstractBase
public override TSeries Update(TSeries source)
{
throw new NotSupportedException("MdAE requires two inputs. Use Calculate(actualSeries, predictedSeries, period).");
throw new NotSupportedException("MdAE requires two inputs. Use Batch(actualSeries, predictedSeries, period).");
}
public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
@@ -161,7 +161,7 @@ public sealed class Mdae : AbstractBase
return (_sortBuffer[mid - 1] + _sortBuffer[mid]) * 0.5;
}
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
{
if (actual.Count != predicted.Count)
{
@@ -299,6 +299,13 @@ public sealed class Mdae : AbstractBase
}
}
public static (TSeries Results, Mdae Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Mdae(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
/// <summary>
/// QuickSelect for Span - finds the k-th smallest element in O(n) average time.
/// Uses insertion sort for small arrays and Lomuto partition for larger arrays.
@@ -378,4 +385,4 @@ public sealed class Mdae : AbstractBase
return span[left];
}
}
}
lib/errors/mdape/Mdape.Tests.cs
+3 -3
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@@ -194,7 +194,7 @@ public class MdapeTests
iterativeResults.Add(mdapeIterative.Update(actualSeries[i], predictedSeries[i]));
}
var batchResults = Mdape.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var batchResults = Mdape.Batch(actualSeries, predictedSeries, DefaultPeriod);
Assert.Equal(iterativeResults.Count, batchResults.Count);
for (int i = 0; i < batchResults.Count; i++)
@@ -238,7 +238,7 @@ public class MdapeTests
predictedArr[i] = pred;
}
var tseriesResult = Mdape.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var tseriesResult = Mdape.Batch(actualSeries, predictedSeries, DefaultPeriod);
Mdape.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), DefaultPeriod);
for (int i = 0; i < tseriesResult.Count; i++)
@@ -287,7 +287,7 @@ public class MdapeTests
predicted.Add(DateTime.UtcNow.Ticks, 98);
Assert.Throws<ArgumentException>(() => Mdape.Calculate(actual, predicted, DefaultPeriod));
Assert.Throws<ArgumentException>(() => Mdape.Batch(actual, predicted, DefaultPeriod));
}
[Fact]
lib/errors/mdape/Mdape.cs
+10 -3
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@@ -68,7 +68,7 @@ public sealed class Mdape : AbstractBase
public override TSeries Update(TSeries source)
{
throw new NotSupportedException("MdAPE requires two inputs. Use Calculate(actualSeries, predictedSeries, period).");
throw new NotSupportedException("MdAPE requires two inputs. Use Batch(actualSeries, predictedSeries, period).");
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
@@ -164,7 +164,7 @@ public sealed class Mdape : AbstractBase
return (_sortBuffer[mid - 1] + _sortBuffer[mid]) * 0.5;
}
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
{
if (actual.Count != predicted.Count)
{
@@ -263,6 +263,13 @@ public sealed class Mdape : AbstractBase
}
}
public static (TSeries Results, Mdape Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Mdape(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
/// <summary>
/// Dual-heap based sliding window median calculator.
/// Maintains O(log n) insert/remove and O(1) median query.
@@ -405,4 +412,4 @@ public sealed class Mdape : AbstractBase
}
}
}
}
}
lib/errors/me/Me.Tests.cs
+2 -2
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@@ -334,7 +334,7 @@ public class MeTests
predicted.Add(now.AddMinutes(i), i * 10 + 5);
}
var results = Me.Calculate(actual, predicted, 3);
var results = Me.Batch(actual, predicted, 3);
Assert.Equal(10, results.Count);
// All errors are -5, so ME should be -5
@@ -357,7 +357,7 @@ public class MeTests
predicted.Add(DateTime.UtcNow, i);
}
Assert.Throws<ArgumentException>(() => Me.Calculate(actual, predicted, 3));
Assert.Throws<ArgumentException>(() => Me.Batch(actual, predicted, 3));
}
[Fact]
lib/errors/me/Me.cs
+9 -2
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@@ -40,7 +40,7 @@ public sealed class Me : BiInputIndicatorBase
/// <summary>
/// Calculates ME for entire series.
/// </summary>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
=> CalculateImpl(actual, predicted, period, Batch);
/// <summary>
@@ -78,4 +78,11 @@ public sealed class Me : BiInputIndicatorBase
}
}
}
}
public static (TSeries Results, Me Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Me(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
}
lib/errors/mpe/Mpe.Tests.cs
+3 -3
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@@ -236,7 +236,7 @@ public class MpeTests
iterativeResults.Add(mpeIterative.Update(actualSeries[i], predictedSeries[i]).Value);
}
var batchResults = Mpe.Calculate(actualSeries, predictedSeries, 10);
var batchResults = Mpe.Batch(actualSeries, predictedSeries, 10);
Assert.Equal(iterativeResults.Count, batchResults.Count);
for (int i = 0; i < iterativeResults.Count; i++)
@@ -280,7 +280,7 @@ public class MpeTests
predictedSeries.Add(bar.Time, bar.Close * 0.95);
}
var tseriesResult = Mpe.Calculate(actualSeries, predictedSeries, 10);
var tseriesResult = Mpe.Batch(actualSeries, predictedSeries, 10);
Mpe.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), 10);
for (int i = 0; i < 100; i++)
@@ -314,7 +314,7 @@ public class MpeTests
actual.Add(DateTime.UtcNow.Ticks + 1, 100);
predicted.Add(DateTime.UtcNow.Ticks, 90);
Assert.Throws<ArgumentException>(() => Mpe.Calculate(actual, predicted, 5));
Assert.Throws<ArgumentException>(() => Mpe.Batch(actual, predicted, 5));
}
[Fact]
lib/errors/mpe/Mpe.cs
+9 -2
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@@ -52,7 +52,7 @@ public sealed class Mpe : BiInputIndicatorBase
/// <summary>
/// Calculates MPE for entire series.
/// </summary>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
=> CalculateImpl(actual, predicted, period, Batch);
/// <summary>
@@ -78,6 +78,13 @@ public sealed class Mpe : BiInputIndicatorBase
ErrorHelpers.ApplyRollingMean(errors, output, period);
}
public static (TSeries Results, Mpe Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Mpe(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private static void ComputeSignedPercentageErrors(ReadOnlySpan<double> actual, ReadOnlySpan<double> predicted, Span<double> output)
{
@@ -139,4 +146,4 @@ public sealed class Mpe : BiInputIndicatorBase
output[i] = 100.0 * (act - pred) / divisor;
}
}
}
}
lib/errors/mrae/Mrae.Tests.cs
+2 -2
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@@ -284,7 +284,7 @@ public class MraeTests
predicted.Add(now.AddMinutes(i), i * 110); // 10% error
}
var results = Mrae.Calculate(actual, predicted, 3);
var results = Mrae.Batch(actual, predicted, 3);
Assert.Equal(10, results.Count);
Assert.Equal(0.1, results.Last.Value, 10);
@@ -306,7 +306,7 @@ public class MraeTests
predicted.Add(DateTime.UtcNow, i * 10);
}
Assert.Throws<ArgumentException>(() => Mrae.Calculate(actual, predicted, 3));
Assert.Throws<ArgumentException>(() => Mrae.Batch(actual, predicted, 3));
}
[Fact]
lib/errors/mrae/Mrae.cs
+9 -2
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@@ -49,7 +49,7 @@ public sealed class Mrae : BiInputIndicatorBase
/// <summary>
/// Calculates Mean Relative Absolute Error for two time series.
/// </summary>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
{
if (actual.Count != predicted.Count)
{
@@ -104,6 +104,13 @@ public sealed class Mrae : BiInputIndicatorBase
ErrorHelpers.ApplyRollingMean(errors, output, period);
}
public static (TSeries Results, Mrae Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Mrae(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
/// <summary>
/// Computes relative errors (0-1 scale, not percentage).
/// </summary>
@@ -163,4 +170,4 @@ public sealed class Mrae : BiInputIndicatorBase
: 0.0;
}
}
}
}
lib/errors/mse/Mse.Tests.cs
+1 -1
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@@ -287,7 +287,7 @@ public class MseTests
predicted.Add(now.AddMinutes(i), i * 10 + 5);
}
var results = Mse.Calculate(actual, predicted, 3);
var results = Mse.Batch(actual, predicted, 3);
Assert.Equal(10, results.Count);
// All errors are 5², so MSE should be 25
lib/errors/mse/Mse.cs
+9 -2
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@@ -46,7 +46,7 @@ public sealed class Mse : BiInputIndicatorBase
/// <param name="predicted">Predicted values series</param>
/// <param name="period">MSE period</param>
/// <returns>MSE series</returns>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
=> CalculateImpl(actual, predicted, period, Batch);
/// <summary>
@@ -78,4 +78,11 @@ public sealed class Mse : BiInputIndicatorBase
// Apply rolling mean using shared helper
ErrorHelpers.ApplyRollingMean(sqErrors, output, period);
}
}
public static (TSeries Results, Mse Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Mse(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
}
lib/errors/msle/Msle.Tests.cs
+3 -3
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@@ -241,7 +241,7 @@ public class MsleTests
iterativeResults.Add(msleIterative.Update(actualSeries[i], predictedSeries[i]).Value);
}
var batchResults = Msle.Calculate(actualSeries, predictedSeries, 10);
var batchResults = Msle.Batch(actualSeries, predictedSeries, 10);
Assert.Equal(iterativeResults.Count, batchResults.Count);
for (int i = 0; i < iterativeResults.Count; i++)
@@ -285,7 +285,7 @@ public class MsleTests
predictedSeries.Add(bar.Time, bar.Close * 0.95);
}
var tseriesResult = Msle.Calculate(actualSeries, predictedSeries, 10);
var tseriesResult = Msle.Batch(actualSeries, predictedSeries, 10);
Msle.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), 10);
for (int i = 0; i < 100; i++)
@@ -319,7 +319,7 @@ public class MsleTests
actual.Add(DateTime.UtcNow.Ticks + 1, 100);
predicted.Add(DateTime.UtcNow.Ticks, 90);
Assert.Throws<ArgumentException>(() => Msle.Calculate(actual, predicted, 5));
Assert.Throws<ArgumentException>(() => Msle.Batch(actual, predicted, 5));
}
[Fact]
lib/errors/msle/Msle.cs
+9 -2
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@@ -46,7 +46,7 @@ public sealed class Msle : BiInputIndicatorBase
/// <summary>
/// Calculates MSLE for entire series.
/// </summary>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
=> CalculateImpl(actual, predicted, period, Batch);
/// <summary>
@@ -72,6 +72,13 @@ public sealed class Msle : BiInputIndicatorBase
ErrorHelpers.ApplyRollingMean(errors, output, period);
}
public static (TSeries Results, Msle Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Msle(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private static void ComputeLogSquaredErrors(ReadOnlySpan<double> actual, ReadOnlySpan<double> predicted, Span<double> output)
{
@@ -127,4 +134,4 @@ public sealed class Msle : BiInputIndicatorBase
output[i] = logError * logError;
}
}
}
}
lib/errors/pseudohuber/PseudoHuber.Tests.cs
+3 -3
View File
@@ -342,7 +342,7 @@ public class PseudoHuberTests
}
// Calculate batch
var batchResults = PseudoHuber.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var batchResults = PseudoHuber.Batch(actualSeries, predictedSeries, DefaultPeriod);
// Compare
Assert.Equal(iterativeResults.Count, batchResults.Count);
@@ -389,7 +389,7 @@ public class PseudoHuberTests
predictedSeries.Add(bar.Time, predictedData[i]);
}
var tseriesResult = PseudoHuber.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var tseriesResult = PseudoHuber.Batch(actualSeries, predictedSeries, DefaultPeriod);
PseudoHuber.Batch(actualData.AsSpan(), predictedData.AsSpan(), output.AsSpan(), DefaultPeriod);
for (int i = 0; i < 100; i++)
@@ -445,7 +445,7 @@ public class PseudoHuberTests
actual.Add(DateTime.UtcNow, 101);
predicted.Add(DateTime.UtcNow, 99);
Assert.Throws<ArgumentException>(() => PseudoHuber.Calculate(actual, predicted, 5));
Assert.Throws<ArgumentException>(() => PseudoHuber.Batch(actual, predicted, 5));
}
#endregion
lib/errors/pseudohuber/PseudoHuber.cs
+9 -2
View File
@@ -64,7 +64,7 @@ public sealed class PseudoHuber : BiInputIndicatorBase
/// <summary>
/// Calculates Pseudo-Huber Loss for two time series.
/// </summary>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period, double delta = 1.0)
public static TSeries Batch(TSeries actual, TSeries predicted, int period, double delta = 1.0)
{
if (actual.Count != predicted.Count)
{
@@ -124,4 +124,11 @@ public sealed class PseudoHuber : BiInputIndicatorBase
// Apply rolling mean
ErrorHelpers.ApplyRollingMean(errors, output, period);
}
}
public static (TSeries Results, PseudoHuber Indicator) Calculate(TSeries actual, TSeries predicted, int period, double delta = 1.0)
{
var indicator = new PseudoHuber(period, delta);
TSeries results = Batch(actual, predicted, period, delta);
return (results, indicator);
}
}
lib/errors/quantile/QuantileLoss.Tests.cs
+3 -3
View File
@@ -237,7 +237,7 @@ public class QuantileLossTests
var iterativeResults = actualSeries.Zip(predictedSeries, (actual, predicted) => quantileLossIterative.Update(actual.Value, predicted.Value).Value).ToList();
var batchResults = QuantileLoss.Calculate(actualSeries, predictedSeries, DefaultPeriod, 0.75);
var batchResults = QuantileLoss.Batch(actualSeries, predictedSeries, DefaultPeriod, 0.75);
Assert.Equal(iterativeResults.Count, batchResults.Count);
int count = iterativeResults.Count;
@@ -288,7 +288,7 @@ public class QuantileLossTests
predictedArr[i] = pred;
}
var tseriesResult = QuantileLoss.Calculate(actualSeries, predictedSeries, DefaultPeriod, 0.75);
var tseriesResult = QuantileLoss.Batch(actualSeries, predictedSeries, DefaultPeriod, 0.75);
QuantileLoss.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), DefaultPeriod, 0.75);
for (int i = 0; i < 100; i++)
@@ -337,7 +337,7 @@ public class QuantileLossTests
predicted.Add(DateTime.UtcNow.Ticks, 98);
Assert.Throws<ArgumentException>(() => QuantileLoss.Calculate(actual, predicted, DefaultPeriod));
Assert.Throws<ArgumentException>(() => QuantileLoss.Batch(actual, predicted, DefaultPeriod));
}
[Fact]
lib/errors/quantile/QuantileLoss.cs
+9 -2
View File
@@ -60,7 +60,7 @@ public sealed class QuantileLoss : BiInputIndicatorBase
return diff >= 0 ? Quantile * diff : (Quantile - 1.0) * diff;
}
public static TSeries Calculate(TSeries actual, TSeries predicted, int period, double quantile = 0.5)
public static TSeries Batch(TSeries actual, TSeries predicted, int period, double quantile = 0.5)
{
if (actual.Count != predicted.Count)
{
@@ -220,4 +220,11 @@ public sealed class QuantileLoss : BiInputIndicatorBase
}
}
}
}
public static (TSeries Results, QuantileLoss Indicator) Calculate(TSeries actual, TSeries predicted, int period, double quantile = 0.5)
{
var indicator = new QuantileLoss(period, quantile);
TSeries results = Batch(actual, predicted, period, quantile);
return (results, indicator);
}
}
lib/errors/rae/Rae.Tests.cs
+3 -3
View File
@@ -230,7 +230,7 @@ public class RaeTests
iterativeResults.Add(raeIterative.Update(actual[i], predicted[i]).Value);
}
var batchResults = Rae.Calculate(actual, predicted, Period);
var batchResults = Rae.Batch(actual, predicted, Period);
Assert.Equal(iterativeResults.Count, batchResults.Count);
for (int i = 0; i < iterativeResults.Count; i++)
@@ -271,7 +271,7 @@ public class RaeTests
double[] predictedArr = predictedSeries.Values.ToArray();
double[] output = new double[100];
var tseriesResult = Rae.Calculate(actualSeries, predictedSeries, Period);
var tseriesResult = Rae.Batch(actualSeries, predictedSeries, Period);
Rae.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), Period);
for (int i = 0; i < 100; i++)
@@ -292,7 +292,7 @@ public class RaeTests
}
// 1. Batch Mode (static method)
var batchSeries = Rae.Calculate(actualSeries, predictedSeries, Period);
var batchSeries = Rae.Batch(actualSeries, predictedSeries, Period);
double expected = batchSeries.Last.Value;
// 2. Span Mode
lib/errors/rae/Rae.cs
+9 -2
View File
@@ -159,7 +159,7 @@ public sealed class Rae : AbstractBase
public override TSeries Update(TSeries source)
{
throw new NotSupportedException("RAE requires two inputs. Use Calculate(actualSeries, predictedSeries, period).");
throw new NotSupportedException("RAE requires two inputs. Use Batch(actualSeries, predictedSeries, period).");
}
public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
@@ -177,7 +177,7 @@ public sealed class Rae : AbstractBase
Last = default;
}
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
{
if (actual.Count != predicted.Count)
{
@@ -355,4 +355,11 @@ public sealed class Rae : AbstractBase
}
}
}
public static (TSeries Results, Rae Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Rae(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
}
lib/errors/rmse/Rmse.Tests.cs
+1 -1
View File
@@ -241,7 +241,7 @@ public class RmseTests
predicted.Add(now.AddMinutes(i), i * 10 + 5);
}
var results = Rmse.Calculate(actual, predicted, 3);
var results = Rmse.Batch(actual, predicted, 3);
Assert.Equal(10, results.Count);
// All errors are 5, MSE = 25, RMSE = 5
lib/errors/rmse/Rmse.cs
+9 -2
View File
@@ -45,7 +45,7 @@ public sealed class Rmse : BiInputIndicatorBase
/// <summary>
/// Calculates RMSE for entire series.
/// </summary>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
=> CalculateImpl(actual, predicted, period, Batch);
/// <summary>
@@ -70,4 +70,11 @@ public sealed class Rmse : BiInputIndicatorBase
ErrorHelpers.ComputeSquaredErrors(actual, predicted, sqErrors);
ErrorHelpers.ApplyRollingMeanSqrt(sqErrors, output, period);
}
}
public static (TSeries Results, Rmse Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Rmse(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
}
lib/errors/rmsle/Rmsle.Tests.cs
+3 -3
View File
@@ -222,7 +222,7 @@ public class RmsleTests
iterativeResults.Add(rmsleIterative.Update(actualSeries[i], predictedSeries[i]).Value);
}
var batchResults = Rmsle.Calculate(actualSeries, predictedSeries, 10);
var batchResults = Rmsle.Batch(actualSeries, predictedSeries, 10);
Assert.Equal(iterativeResults.Count, batchResults.Count);
for (int i = 0; i < iterativeResults.Count; i++)
@@ -266,7 +266,7 @@ public class RmsleTests
predictedSeries.Add(bar.Time, bar.Close * 0.95);
}
var tseriesResult = Rmsle.Calculate(actualSeries, predictedSeries, 10);
var tseriesResult = Rmsle.Batch(actualSeries, predictedSeries, 10);
Rmsle.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), 10);
for (int i = 0; i < 100; i++)
@@ -300,7 +300,7 @@ public class RmsleTests
actual.Add(DateTime.UtcNow.Ticks + 1, 100);
predicted.Add(DateTime.UtcNow.Ticks, 90);
Assert.Throws<ArgumentException>(() => Rmsle.Calculate(actual, predicted, 5));
Assert.Throws<ArgumentException>(() => Rmsle.Batch(actual, predicted, 5));
}
[Fact]
lib/errors/rmsle/Rmsle.cs
+9 -2
View File
@@ -49,7 +49,7 @@ public sealed class Rmsle : BiInputIndicatorBase
/// <summary>
/// Calculates RMSLE for entire series.
/// </summary>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
=> CalculateImpl(actual, predicted, period, Batch);
/// <summary>
@@ -75,6 +75,13 @@ public sealed class Rmsle : BiInputIndicatorBase
ErrorHelpers.ApplyRollingMeanSqrt(errors, output, period);
}
public static (TSeries Results, Rmsle Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Rmsle(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private static void ComputeLogSquaredErrors(ReadOnlySpan<double> actual, ReadOnlySpan<double> predicted, Span<double> output)
{
@@ -130,4 +137,4 @@ public sealed class Rmsle : BiInputIndicatorBase
output[i] = logError * logError;
}
}
}
}
lib/errors/rse/Rse.Tests.cs
+3 -3
View File
@@ -253,7 +253,7 @@ public class RseTests
iterativeResults.Add(rseIterative.Update(actual[i], predicted[i]).Value);
}
var batchResults = Rse.Calculate(actual, predicted, Period);
var batchResults = Rse.Batch(actual, predicted, Period);
Assert.Equal(iterativeResults.Count, batchResults.Count);
for (int i = 0; i < iterativeResults.Count; i++)
@@ -294,7 +294,7 @@ public class RseTests
double[] predictedArr = predictedSeries.Values.ToArray();
double[] output = new double[100];
var tseriesResult = Rse.Calculate(actualSeries, predictedSeries, Period);
var tseriesResult = Rse.Batch(actualSeries, predictedSeries, Period);
Rse.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), Period);
for (int i = 0; i < 100; i++)
@@ -315,7 +315,7 @@ public class RseTests
}
// 1. Batch Mode (static method)
var batchSeries = Rse.Calculate(actualSeries, predictedSeries, Period);
var batchSeries = Rse.Batch(actualSeries, predictedSeries, Period);
double expected = batchSeries.Last.Value;
// 2. Span Mode
lib/errors/rse/Rse.cs
+10 -3
View File
@@ -166,7 +166,7 @@ public sealed class Rse : AbstractBase
public override TSeries Update(TSeries source)
{
throw new NotSupportedException("RSE requires two inputs. Use Calculate(actualSeries, predictedSeries, period).");
throw new NotSupportedException("RSE requires two inputs. Use Batch(actualSeries, predictedSeries, period).");
}
public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
@@ -184,7 +184,7 @@ public sealed class Rse : AbstractBase
Last = default;
}
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
{
if (actual.Count != predicted.Count)
{
@@ -366,4 +366,11 @@ public sealed class Rse : AbstractBase
}
}
}
}
public static (TSeries Results, Rse Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Rse(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
}
lib/errors/rsquared/Rsquared.Tests.cs
+3 -3
View File
@@ -290,7 +290,7 @@ public class RsquaredTests
iterativeResults.Add(r2Iterative.Update(actual[i], predicted[i]).Value);
}
var batchResults = Rsquared.Calculate(actual, predicted, Period);
var batchResults = Rsquared.Batch(actual, predicted, Period);
Assert.Equal(iterativeResults.Count, batchResults.Count);
for (int i = 0; i < iterativeResults.Count; i++)
@@ -331,7 +331,7 @@ public class RsquaredTests
double[] predictedArr = predictedSeries.Values.ToArray();
double[] output = new double[100];
var tseriesResult = Rsquared.Calculate(actualSeries, predictedSeries, Period);
var tseriesResult = Rsquared.Batch(actualSeries, predictedSeries, Period);
Rsquared.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), Period);
for (int i = 0; i < 100; i++)
@@ -352,7 +352,7 @@ public class RsquaredTests
}
// 1. Batch Mode (static method)
var batchSeries = Rsquared.Calculate(actualSeries, predictedSeries, Period);
var batchSeries = Rsquared.Batch(actualSeries, predictedSeries, Period);
double expected = batchSeries.Last.Value;
// 2. Span Mode
lib/errors/rsquared/Rsquared.cs
+10 -3
View File
@@ -160,7 +160,7 @@ public sealed class Rsquared : AbstractBase
public override TSeries Update(TSeries source)
{
throw new NotSupportedException("R² requires two inputs. Use Calculate(actualSeries, predictedSeries, period).");
throw new NotSupportedException("R² requires two inputs. Use Batch(actualSeries, predictedSeries, period).");
}
public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
@@ -178,7 +178,7 @@ public sealed class Rsquared : AbstractBase
Last = default;
}
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
{
if (actual.Count != predicted.Count)
{
@@ -360,4 +360,11 @@ public sealed class Rsquared : AbstractBase
}
}
}
}
public static (TSeries Results, Rsquared Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Rsquared(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
}
lib/errors/smape/Smape.Tests.cs
+3 -3
View File
@@ -217,7 +217,7 @@ public class SmapeTests
iterativeResults.Add(smapeIterative.Update(actualSeries[i], predictedSeries[i]).Value);
}
var batchResults = Smape.Calculate(actualSeries, predictedSeries, 10);
var batchResults = Smape.Batch(actualSeries, predictedSeries, 10);
Assert.Equal(iterativeResults.Count, batchResults.Count);
for (int i = 0; i < iterativeResults.Count; i++)
@@ -261,7 +261,7 @@ public class SmapeTests
predictedSeries.Add(bar.Time, bar.Close * 0.95);
}
var tseriesResult = Smape.Calculate(actualSeries, predictedSeries, 10);
var tseriesResult = Smape.Batch(actualSeries, predictedSeries, 10);
Smape.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), 10);
for (int i = 0; i < 100; i++)
@@ -295,7 +295,7 @@ public class SmapeTests
actual.Add(DateTime.UtcNow.Ticks + 1, 100);
predicted.Add(DateTime.UtcNow.Ticks, 90);
Assert.Throws<ArgumentException>(() => Smape.Calculate(actual, predicted, 5));
Assert.Throws<ArgumentException>(() => Smape.Batch(actual, predicted, 5));
}
[Fact]
lib/errors/smape/Smape.cs
+9 -2
View File
@@ -43,7 +43,7 @@ public sealed class Smape : BiInputIndicatorBase
/// <summary>
/// Calculates SMAPE for entire series.
/// </summary>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
=> CalculateImpl(actual, predicted, period, Batch);
/// <summary>
@@ -70,6 +70,13 @@ public sealed class Smape : BiInputIndicatorBase
ErrorHelpers.ApplyRollingMean(symErrors, output, period);
}
public static (TSeries Results, Smape Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Smape(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private static void ComputeSmapeErrors(ReadOnlySpan<double> actual, ReadOnlySpan<double> predicted, Span<double> output)
{
@@ -122,4 +129,4 @@ public sealed class Smape : BiInputIndicatorBase
output[i] = sumAbs > Epsilon ? 200.0 * absDiff / sumAbs : 0.0;
}
}
}
}
lib/errors/theilu/TheilU.Tests.cs
+3 -3
View File
@@ -192,7 +192,7 @@ public class TheilUTests
iterativeResults.Add(theilUIterative.Update(new TValue(bar.Time, bar.Close), new TValue(bar.Time, predicted)).Value);
}
var batchResults = TheilU.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var batchResults = TheilU.Batch(actualSeries, predictedSeries, DefaultPeriod);
Assert.Equal(iterativeResults.Count, batchResults.Count);
for (int i = 0; i < iterativeResults.Count; i++)
@@ -236,7 +236,7 @@ public class TheilUTests
predictedArr[i] = pred;
}
var tseriesResult = TheilU.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var tseriesResult = TheilU.Batch(actualSeries, predictedSeries, DefaultPeriod);
TheilU.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), DefaultPeriod);
for (int i = 0; i < 100; i++)
@@ -285,7 +285,7 @@ public class TheilUTests
predicted.Add(DateTime.UtcNow.Ticks, 98);
Assert.Throws<ArgumentException>(() => TheilU.Calculate(actual, predicted, DefaultPeriod));
Assert.Throws<ArgumentException>(() => TheilU.Batch(actual, predicted, DefaultPeriod));
}
[Fact]
lib/errors/theilu/TheilU.cs
+10 -3
View File
@@ -75,7 +75,7 @@ public sealed class TheilU : AbstractBase
public override TSeries Update(TSeries source)
{
throw new NotSupportedException("TheilU requires two inputs. Use Calculate(actualSeries, predictedSeries, period).");
throw new NotSupportedException("TheilU requires two inputs. Use Batch(actualSeries, predictedSeries, period).");
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
@@ -170,7 +170,7 @@ public sealed class TheilU : AbstractBase
Last = default;
}
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
{
if (actual.Count != predicted.Count)
{
@@ -352,4 +352,11 @@ public sealed class TheilU : AbstractBase
}
}
}
}
public static (TSeries Results, TheilU Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new TheilU(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
}
lib/errors/tukey/TukeyBiweight.Tests.cs
+3 -3
View File
@@ -235,7 +235,7 @@ public class TukeyBiweightTests
iterativeResults.Add(tukeyIterative.Update(actual, predicted).Value);
}
var batchResults = TukeyBiweight.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var batchResults = TukeyBiweight.Batch(actualSeries, predictedSeries, DefaultPeriod);
Assert.Equal(iterativeResults.Count, batchResults.Count);
for (int i = 0; i < iterativeResults.Count; i++)
@@ -282,7 +282,7 @@ public class TukeyBiweightTests
predictedArr[i] = pred;
}
var tseriesResult = TukeyBiweight.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var tseriesResult = TukeyBiweight.Batch(actualSeries, predictedSeries, DefaultPeriod);
TukeyBiweight.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), DefaultPeriod);
for (int i = 0; i < 100; i++)
@@ -331,7 +331,7 @@ public class TukeyBiweightTests
predicted.Add(DateTime.UtcNow.Ticks, 98);
Assert.Throws<ArgumentException>(() => TukeyBiweight.Calculate(actual, predicted, DefaultPeriod));
Assert.Throws<ArgumentException>(() => TukeyBiweight.Batch(actual, predicted, DefaultPeriod));
}
[Fact]
lib/errors/tukey/TukeyBiweight.cs
+9 -2
View File
@@ -66,7 +66,7 @@ public sealed class TukeyBiweight : BiInputIndicatorBase
return _cSquaredOver6 * (1.0 - cubed);
}
public static TSeries Calculate(TSeries actual, TSeries predicted, int period, double c = DefaultC)
public static TSeries Batch(TSeries actual, TSeries predicted, int period, double c = DefaultC)
{
if (actual.Count != predicted.Count)
{
@@ -129,4 +129,11 @@ public sealed class TukeyBiweight : BiInputIndicatorBase
ArrayPool<double>.Shared.Return(rented, clearArray: false);
}
}
}
public static (TSeries Results, TukeyBiweight Indicator) Calculate(TSeries actual, TSeries predicted, int period, double c = DefaultC)
{
var indicator = new TukeyBiweight(period, c);
TSeries results = Batch(actual, predicted, period, c);
return (results, indicator);
}
}
lib/errors/wmape/Wmape.Tests.cs
+3 -3
View File
@@ -186,7 +186,7 @@ public class WmapeTests
predictedSeries.Add(bar.Time, bar.Close * (1 + (i % 2 == 0 ? 0.02 : -0.02)));
}
var batchResults = Wmape.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var batchResults = Wmape.Batch(actualSeries, predictedSeries, DefaultPeriod);
var iterativeResults = new List<double>();
for (int i = 0; i < actualSeries.Count; i++)
@@ -236,7 +236,7 @@ public class WmapeTests
predictedArr[i] = pred;
}
var tseriesResult = Wmape.Calculate(actualSeries, predictedSeries, DefaultPeriod);
var tseriesResult = Wmape.Batch(actualSeries, predictedSeries, DefaultPeriod);
Wmape.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), DefaultPeriod);
for (int i = 0; i < 100; i++)
@@ -285,7 +285,7 @@ public class WmapeTests
predicted.Add(DateTime.UtcNow.Ticks, 98);
Assert.Throws<ArgumentException>(() => Wmape.Calculate(actual, predicted, DefaultPeriod));
Assert.Throws<ArgumentException>(() => Wmape.Batch(actual, predicted, DefaultPeriod));
}
[Fact]
lib/errors/wmape/Wmape.cs
+10 -3
View File
@@ -138,7 +138,7 @@ public sealed class Wmape : AbstractBase
public override TSeries Update(TSeries source)
{
throw new NotSupportedException("WMAPE requires two inputs. Use Calculate(actualSeries, predictedSeries, period).");
throw new NotSupportedException("WMAPE requires two inputs. Use Batch(actualSeries, predictedSeries, period).");
}
public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
@@ -155,7 +155,7 @@ public sealed class Wmape : AbstractBase
Last = default;
}
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
{
if (actual.Count != predicted.Count)
{
@@ -345,4 +345,11 @@ public sealed class Wmape : AbstractBase
}
}
}
}
public static (TSeries Results, Wmape Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Wmape(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
}
lib/errors/wrmse/Wrmse.Tests.cs
+4 -4
View File
@@ -320,7 +320,7 @@ public class WrmseTests
predicted.Add(now.AddMinutes(i), i * 10 + 5);
}
var results = Wrmse.Calculate(actual, predicted, 3);
var results = Wrmse.Batch(actual, predicted, 3);
Assert.Equal(10, results.Count);
// All errors are 5, MSE = 25, RMSE = 5
@@ -342,7 +342,7 @@ public class WrmseTests
weights.Add(now.AddMinutes(i), 2.0); // Weight = 2
}
var results = Wrmse.Calculate(actual, predicted, weights, 3);
var results = Wrmse.Batch(actual, predicted, weights, 3);
Assert.Equal(10, results.Count);
// Weighted error = 2 * 100 = 200 per point, sum weights = 6 (period=3)
@@ -366,7 +366,7 @@ public class WrmseTests
}
}
Assert.Throws<ArgumentException>(() => Wrmse.Calculate(actual, predicted, 3));
Assert.Throws<ArgumentException>(() => Wrmse.Batch(actual, predicted, 3));
}
[Fact]
@@ -387,7 +387,7 @@ public class WrmseTests
}
}
Assert.Throws<ArgumentException>(() => Wrmse.Calculate(actual, predicted, weights, 3));
Assert.Throws<ArgumentException>(() => Wrmse.Batch(actual, predicted, weights, 3));
}
[Fact]
lib/errors/wrmse/Wrmse.cs
+11 -4
View File
@@ -197,7 +197,7 @@ public sealed class Wrmse : AbstractBase
/// <inheritdoc/>
public override TSeries Update(TSeries source)
{
throw new NotSupportedException("WRMSE requires two inputs. Use Calculate(actualSeries, predictedSeries, period) or Calculate(actualSeries, predictedSeries, weightsSeries, period).");
throw new NotSupportedException("WRMSE requires two inputs. Use Batch(actualSeries, predictedSeries, period) or Batch(actualSeries, predictedSeries, weightsSeries, period).");
}
/// <inheritdoc/>
@@ -221,7 +221,7 @@ public sealed class Wrmse : AbstractBase
/// <summary>
/// Calculates WRMSE for entire series with uniform weights.
/// </summary>
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, int period)
{
if (actual.Count != predicted.Count)
{
@@ -246,7 +246,7 @@ public sealed class Wrmse : AbstractBase
/// <summary>
/// Calculates WRMSE for entire series with custom weights.
/// </summary>
public static TSeries Calculate(TSeries actual, TSeries predicted, TSeries weights, int period)
public static TSeries Batch(TSeries actual, TSeries predicted, TSeries weights, int period)
{
if (actual.Count != predicted.Count || actual.Count != weights.Count)
{
@@ -332,4 +332,11 @@ public sealed class Wrmse : AbstractBase
ErrorHelpers.ComputeWeightedErrors(actual, predicted, weights, weightedErrors);
ErrorHelpers.ApplyRollingWeightedMeanSqrt(weightedErrors, weights, output, period);
}
}
public static (TSeries Results, Wrmse Indicator) Calculate(TSeries actual, TSeries predicted, int period)
{
var indicator = new Wrmse(period);
TSeries results = Batch(actual, predicted, period);
return (results, indicator);
}
}