xml doc rewrite

lib/errors/Smape.cs

@@ -1,10 +1,42 @@
+using System;
 namespace QuanTAlib;
 
+/// <summary>
+/// SMAPE: Symmetric Mean Absolute Percentage Error
+/// A variation of MAPE that treats positive and negative errors symmetrically.
+/// SMAPE uses the average of actual and predicted values in the denominator,
+/// making it more robust than MAPE for values close to zero.
+/// </summary>
+/// <remarks>
+/// The SMAPE calculation process:
+/// 1. Calculates absolute difference between actual and predicted
+/// 2. Divides by sum of absolute actual and predicted values
+/// 3. Averages these ratios and multiplies by 200%
+///
+/// Key characteristics:
+/// - Symmetric treatment of errors
+/// - Range is 0% to 200%
+/// - More robust than MAPE near zero
+/// - Scale-independent
+/// - Handles both positive and negative values
+///
+/// Formula:
+/// SMAPE = (200/n) * Σ|actual - predicted| / (|actual| + |predicted|)
+///
+/// Sources:
+///     https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error
+///     https://www.sciencedirect.com/science/article/abs/pii/0169207085900059
+///
+/// Note: More stable than MAPE when actual values are close to zero
+/// </remarks>
+
 public class Smape : AbstractBase
 {
     private readonly CircularBuffer _actualBuffer;
     private readonly CircularBuffer _predictedBuffer;
 
+    /// <param name="period">The number of points over which to calculate the SMAPE.</param>
+    /// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 1.</exception>
     public Smape(int period)
     {
         if (period < 1)
@@ -18,6 +50,8 @@ public class Smape : 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 SMAPE.</param>
     public Smape(object source, int period) : this(period)
     {
         var pubEvent = source.GetType().GetEvent("Pub");
@@ -47,6 +81,7 @@ public class Smape : 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);