xml doc rewrite

lib/errors/Rmsle.cs

@@ -1,10 +1,44 @@
+using System;
 namespace QuanTAlib;
 
+/// <summary>
+/// RMSLE: Root Mean Square Logarithmic Error
+/// A variation of RMSE that operates on log-transformed values. RMSLE is particularly
+/// useful for data with exponential growth or when relative errors in larger values
+/// should be treated similarly to relative errors in smaller values.
+/// </summary>
+/// <remarks>
+/// The RMSLE calculation process:
+/// 1. Adds 1 to both actual and predicted values (to handle zeros)
+/// 2. Takes natural log of both values
+/// 3. Calculates squared difference of logs
+/// 4. Averages the squared differences
+/// 5. Takes the square root
+///
+/// Key characteristics:
+/// - Scale-independent due to log transformation
+/// - Penalizes underestimates more than overestimates
+/// - Handles exponential trends well
+/// - More sensitive to relative differences
+/// - Can handle zero values (adds 1 before log)
+///
+/// Formula:
+/// RMSLE = √((1/n) * Σ(log(actual + 1) - log(predicted + 1))²)
+///
+/// Sources:
+///     https://www.kaggle.com/wiki/RootMeanSquaredLogarithmicError
+///     https://medium.com/analytics-vidhya/root-mean-square-log-error-rmse-vs-rmlse-935c6cc1802a
+///
+/// Note: Square root of MSLE, useful for data with exponential growth
+/// </remarks>
+
 public class Rmsle : AbstractBase
 {
     private readonly CircularBuffer _actualBuffer;
     private readonly CircularBuffer _predictedBuffer;
 
+    /// <param name="period">The number of points over which to calculate the RMSLE.</param>
+    /// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 1.</exception>
     public Rmsle(int period)
     {
         if (period < 1)
@@ -18,6 +52,8 @@ public class Rmsle : 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 RMSLE.</param>
     public Rmsle(object source, int period) : this(period)
     {
         var pubEvent = source.GetType().GetEvent("Pub");
@@ -47,6 +83,7 @@ public class Rmsle : 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);