Files
QuanTAlib/lib/errors/Rmse.cs
T
2024-10-27 16:11:08 -07:00

121 lines
4.0 KiB
C#

using System.Runtime.CompilerServices;
namespace QuanTAlib;
/// <summary>
/// RMSE: Root Mean Square Error
/// A widely used error metric that measures the square root of the average squared
/// differences between predicted and actual values. RMSE provides error measurements
/// in the same units as the original data.
/// </summary>
/// <remarks>
/// The RMSE calculation process:
/// 1. Calculates error (actual - predicted) for each point
/// 2. Squares each error value
/// 3. Averages the squared errors
/// 4. Takes the square root of the average
///
/// Key characteristics:
/// - Same units as input data (unlike MSE)
/// - Penalizes large errors more than small ones
/// - Always non-negative
/// - More interpretable than MSE
/// - Commonly used in regression problems
///
/// Formula:
/// RMSE = √((1/n) * Σ(actual - predicted)²)
///
/// Sources:
/// https://en.wikipedia.org/wiki/Root-mean-square_deviation
/// https://www.statisticshowto.com/probability-and-statistics/regression-analysis/rmse-root-mean-square-error/
///
/// Note: Square root of MSE, making it more interpretable in original units
/// </remarks>
[SkipLocalsInit]
public sealed class Rmse : AbstractBase
{
private readonly CircularBuffer _actualBuffer;
private readonly CircularBuffer _predictedBuffer;
/// <param name="period">The number of points over which to calculate the RMSE.</param>
/// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 1.</exception>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public Rmse(int period)
{
if (period < 1)
{
throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1.");
}
WarmupPeriod = period;
_actualBuffer = new CircularBuffer(period);
_predictedBuffer = new CircularBuffer(period);
Name = $"Rmse(period={period})";
Init();
}
/// <param name="source">The data source object that publishes updates.</param>
/// <param name="period">The number of points over which to calculate the RMSE.</param>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public Rmse(object source, int period) : this(period)
{
var pubEvent = source.GetType().GetEvent("Pub");
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public override void Init()
{
base.Init();
_actualBuffer.Clear();
_predictedBuffer.Clear();
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
protected override void ManageState(bool isNew)
{
if (isNew)
{
_lastValidValue = Input.Value;
_index++;
}
}
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
private static double CalculateSquaredError(double actual, double predicted)
{
double error = actual - predicted;
return error * error;
}
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
protected override double Calculation()
{
ManageState(Input.IsNew);
double actual = Input.Value;
_actualBuffer.Add(actual, Input.IsNew);
// If no predicted value provided, use mean of actual values
double predicted = double.IsNaN(Input2.Value) ? _actualBuffer.Average() : Input2.Value;
_predictedBuffer.Add(predicted, Input.IsNew);
double rmse = 0;
if (_actualBuffer.Count > 0)
{
ReadOnlySpan<double> actualValues = _actualBuffer.GetSpan();
ReadOnlySpan<double> predictedValues = _predictedBuffer.GetSpan();
double sumSquaredError = 0;
for (int i = 0; i < actualValues.Length; i++)
{
sumSquaredError += CalculateSquaredError(actualValues[i], predictedValues[i]);
}
rmse = Math.Sqrt(sumSquaredError / actualValues.Length);
}
IsHot = _index >= WarmupPeriod;
return rmse;
}
}