xml doc rewrite

lib/errors/Rse.cs

@@ -1,10 +1,43 @@
+using System;
+using System.Linq;
 namespace QuanTAlib;
 
+/// <summary>
+/// RSE: Relative Squared Error
+/// A normalized error metric that compares the squared error of predictions to
+/// the variance of actual values. RSE provides a scale-independent measure of
+/// prediction accuracy relative to the inherent variability in the data.
+/// </summary>
+/// <remarks>
+/// The RSE calculation process:
+/// 1. Calculates sum of squared prediction errors
+/// 2. Calculates sum of squared deviations from mean (variance)
+/// 3. Divides squared error by variance and takes square root
+///
+/// Key characteristics:
+/// - Scale-independent (normalized by data variance)
+/// - Range typically between 0 and 1
+/// - Easy interpretation relative to data variance
+/// - Penalizes large errors more than small ones
+/// - Accounts for data variability
+///
+/// Formula:
+/// RSE = √(Σ(actual - predicted)² / Σ(actual - mean(actual))²)
+///
+/// Sources:
+///     https://en.wikipedia.org/wiki/Relative_squared_error
+///     https://www.sciencedirect.com/topics/engineering/relative-squared-error
+///
+/// Note: Values less than 1 indicate predictions better than using mean
+/// </remarks>
+
 public class Rse : AbstractBase
 {
     private readonly CircularBuffer _actualBuffer;
     private readonly CircularBuffer _predictedBuffer;
 
+    /// <param name="period">The number of points over which to calculate the RSE.</param>
+    /// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 1.</exception>
     public Rse(int period)
     {
         if (period < 1)
@@ -18,6 +51,8 @@ public class Rse : AbstractBase
         Init();
     }
 
+    /// <param name="source">The data source object that publishes updates.</param>
+    /// <param name="period">The number of points over which to calculate the RSE.</param>
     public Rse(object source, int period) : this(period)
     {
         var pubEvent = source.GetType().GetEvent("Pub");
@@ -47,6 +82,7 @@ public class Rse : AbstractBase
         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);