corrections

lib/errors/Rse.cs

@@ -1,30 +1,15 @@
 namespace QuanTAlib;
 
-/// <summary>
-/// Represents a Relative Squared Error calculator that measures the ratio of the sum of squared errors
-/// to the sum of squared differences between actual values and the mean of actual values.
-/// </summary>
-/// <remarks>
-/// The Rse class calculates the Relative Squared Error using circular buffers
-/// to efficiently manage the data points within the specified period.
-/// </remarks>
 public class Rse : AbstractBase
 {
     private readonly CircularBuffer _actualBuffer;
     private readonly CircularBuffer _predictedBuffer;
 
-    /// <summary>
-    /// Initializes a new instance of the Rse class with the specified period.
-    /// </summary>
-    /// <param name="period">The period over which to calculate the Relative Squared Error.</param>
-    /// <exception cref="ArgumentOutOfRangeException">
-    /// Thrown when period is less than 2.
-    /// </exception>
     public Rse(int period)
     {
-        if (period < 2)
+        if (period < 1)
         {
-            throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 2.");
+            throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1.");
         }
         WarmupPeriod = period;
         _actualBuffer = new CircularBuffer(period);
@@ -33,20 +18,12 @@ public class Rse : AbstractBase
         Init();
     }
 
-    /// <summary>
-    /// Initializes a new instance of the Mape class with the specified source and period.
-    /// </summary>
-    /// <param name="source">The source object to subscribe to for value updates.</param>
-    /// <param name="period">The period over which to calculate the Mean Absolute Percentage Error.</param>
     public Rse(object source, int period) : this(period)
     {
         var pubEvent = source.GetType().GetEvent("Pub");
         pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
     }
 
-    /// <summary>
-    /// Initializes the Rse instance by clearing the buffers.
-    /// </summary>
     public override void Init()
     {
         base.Init();
@@ -54,10 +31,6 @@ public class Rse : AbstractBase
         _predictedBuffer.Clear();
     }
 
-    /// <summary>
-    /// Manages the state of the Rse instance based on whether new values are being processed.
-    /// </summary>
-    /// <param name="isNew">Indicates whether the current inputs are new values.</param>
     protected override void ManageState(bool isNew)
     {
         if (isNew)
@@ -67,17 +40,6 @@ public class Rse : AbstractBase
         }
     }
 
-    /// <summary>
-    /// Performs the Relative Squared Error calculation for the current period.
-    /// </summary>
-    /// <returns>
-    /// The calculated Relative Squared Error value for the current period.
-    /// </returns>
-    /// <remarks>
-    /// This method calculates the Relative Squared Error using the formula:
-    /// RSE = sum((actual - predicted)^2) / sum((actual - mean(actual))^2)
-    /// where actual is each actual value, predicted is each predicted value, and mean(actual) is the average of actual values.
-    /// </remarks>
     protected override double Calculation()
     {
         ManageState(Input.IsNew);
@@ -89,44 +51,27 @@ public class Rse : AbstractBase
         _predictedBuffer.Add(predicted, Input.IsNew);
 
         double rse = 0;
-        if (_actualBuffer.Count >= 2)
+        if (_actualBuffer.Count > 0)
         {
             var actualValues = _actualBuffer.GetSpan().ToArray();
             var predictedValues = _predictedBuffer.GetSpan().ToArray();
 
-            double actualMean = actualValues.Average();
             double sumSquaredError = 0;
-            double sumSquaredDifferenceFromMean = 0;
+            double sumSquaredActual = 0;
+            double meanActual = actualValues.Average();
 
             for (int i = 0; i < _actualBuffer.Count; i++)
             {
                 double error = actualValues[i] - predictedValues[i];
                 sumSquaredError += error * error;
-
-                double differenceFromMean = actualValues[i] - actualMean;
-                sumSquaredDifferenceFromMean += differenceFromMean * differenceFromMean;
+                double deviation = actualValues[i] - meanActual;
+                sumSquaredActual += deviation * deviation;
             }
 
-            if (sumSquaredDifferenceFromMean != 0)
-            {
-                rse = sumSquaredError / sumSquaredDifferenceFromMean;
-            }
+            rse = Math.Sqrt(sumSquaredError / sumSquaredActual);
         }
 
         IsHot = _index >= WarmupPeriod;
         return rse;
     }
-
-    /// <summary>
-    /// Calculates the Relative Squared Error for the given actual and predicted values.
-    /// </summary>
-    /// <param name="actual">The actual value.</param>
-    /// <param name="predicted">The predicted value.</param>
-    /// <returns>The calculated Relative Squared Error.</returns>
-    public double Calc(double actual, double predicted)
-    {
-        Input = new TValue(DateTime.Now, actual);
-        Input2 = new TValue(DateTime.Now, predicted);
-        return Calculation();
-    }
 }