corrections

lib/errors/Rmsle.cs

@@ -1,25 +1,10 @@
 namespace QuanTAlib;
 
-/// <summary>
-/// Represents a Root Mean Squared Logarithmic Error calculator that measures the square root of the average
-/// of the squares of the differences between the logarithms of actual values and predicted values.
-/// </summary>
-/// <remarks>
-/// The Rmsle class calculates the Root Mean Squared Logarithmic Error using a circular buffer
-/// to efficiently manage the data points within the specified period.
-/// </remarks>
 public class Rmsle : AbstractBase
 {
     private readonly CircularBuffer _actualBuffer;
     private readonly CircularBuffer _predictedBuffer;
 
-    /// <summary>
-    /// Initializes a new instance of the Rmsle class with the specified period.
-    /// </summary>
-    /// <param name="period">The period over which to calculate the Root Mean Squared Logarithmic Error.</param>
-    /// <exception cref="ArgumentOutOfRangeException">
-    /// Thrown when period is less than 1.
-    /// </exception>
     public Rmsle(int period)
     {
         if (period < 1)
@@ -33,20 +18,12 @@ public class Rmsle : 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 Rmsle(object source, int period) : this(period)
     {
         var pubEvent = source.GetType().GetEvent("Pub");
         pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
     }
 
-    /// <summary>
-    /// Initializes the Rmsle instance by clearing the buffers.
-    /// </summary>
     public override void Init()
     {
         base.Init();
@@ -54,10 +31,6 @@ public class Rmsle : AbstractBase
         _predictedBuffer.Clear();
     }
 
-    /// <summary>
-    /// Manages the state of the Rmsle 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)
@@ -67,18 +40,6 @@ public class Rmsle : AbstractBase
         }
     }
 
-    /// <summary>
-    /// Performs the Root Mean Squared Logarithmic Error calculation for the current period.
-    /// </summary>
-    /// <returns>
-    /// The calculated Root Mean Squared Logarithmic Error value for the current period.
-    /// </returns>
-    /// <remarks>
-    /// This method calculates the Root Mean Squared Logarithmic Error using the formula:
-    /// RMSLE = sqrt(sum((log(actual + 1) - log(predicted + 1))^2) / n)
-    /// where actual is each actual value, predicted is each predicted value, and n is the number of values.
-    /// We add 1 to both actual and predicted values to avoid taking the log of zero.
-    /// </remarks>
     protected override double Calculation()
     {
         ManageState(Input.IsNew);
@@ -100,8 +61,8 @@ public class Rmsle : AbstractBase
             {
                 double logActual = Math.Log(actualValues[i] + 1);
                 double logPredicted = Math.Log(predictedValues[i] + 1);
-                double logError = logActual - logPredicted;
-                sumSquaredLogError += logError * logError;
+                double error = logActual - logPredicted;
+                sumSquaredLogError += error * error;
             }
 
             rmsle = Math.Sqrt(sumSquaredLogError / _actualBuffer.Count);
@@ -110,17 +71,4 @@ public class Rmsle : AbstractBase
         IsHot = _index >= WarmupPeriod;
         return rmsle;
     }
-
-    /// <summary>
-    /// Calculates the Root Mean Squared Logarithmic 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 Root Mean Squared Logarithmic Error.</returns>
-    public double Calc(double actual, double predicted)
-    {
-        Input = new TValue(DateTime.Now, actual);
-        Input2 = new TValue(DateTime.Now, predicted);
-        return Calculation();
-    }
 }