tests and cleanup

lib/errors/Msle.cs

@@ -0,0 +1,126 @@
+namespace QuanTAlib;
+
+/// <summary>
+/// Represents a Mean Squared Logarithmic Error calculator that measures the average of the squares
+/// of the differences between the logarithms of actual values and predicted values.
+/// </summary>
+/// <remarks>
+/// The Msle class calculates the Mean Squared Logarithmic Error using a circular buffer
+/// to efficiently manage the data points within the specified period.
+/// </remarks>
+public class Msle : AbstractBase
+{
+    private readonly CircularBuffer _actualBuffer;
+    private readonly CircularBuffer _predictedBuffer;
+
+    /// <summary>
+    /// Initializes a new instance of the Msle class with the specified period.
+    /// </summary>
+    /// <param name="period">The period over which to calculate the Mean Squared Logarithmic Error.</param>
+    /// <exception cref="ArgumentOutOfRangeException">
+    /// Thrown when period is less than 1.
+    /// </exception>
+    public Msle(int period)
+    {
+        if (period < 1)
+        {
+            throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1.");
+        }
+        WarmupPeriod = period;
+        _actualBuffer = new CircularBuffer(period);
+        _predictedBuffer = new CircularBuffer(period);
+        Name = $"Msle(period={period})";
+        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 Msle(object source, int period) : this(period)
+    {
+        var pubEvent = source.GetType().GetEvent("Pub");
+        pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
+    }
+
+    /// <summary>
+    /// Initializes the Msle instance by clearing the buffers.
+    /// </summary>
+    public override void Init()
+    {
+        base.Init();
+        _actualBuffer.Clear();
+        _predictedBuffer.Clear();
+    }
+
+    /// <summary>
+    /// Manages the state of the Msle 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)
+        {
+            _lastValidValue = Input.Value;
+            _index++;
+        }
+    }
+
+    /// <summary>
+    /// Performs the Mean Squared Logarithmic Error calculation for the current period.
+    /// </summary>
+    /// <returns>
+    /// The calculated Mean Squared Logarithmic Error value for the current period.
+    /// </returns>
+    /// <remarks>
+    /// This method calculates the Mean Squared Logarithmic Error using the formula:
+    /// MSLE = 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);
+
+        double actual = Input.Value;
+        _actualBuffer.Add(actual, Input.IsNew);
+
+        double predicted = double.IsNaN(Input2.Value) ? _actualBuffer.Average() : Input2.Value;
+        _predictedBuffer.Add(predicted, Input.IsNew);
+
+        double msle = 0;
+        if (_actualBuffer.Count > 0)
+        {
+            var actualValues = _actualBuffer.GetSpan().ToArray();
+            var predictedValues = _predictedBuffer.GetSpan().ToArray();
+
+            double sumSquaredLogError = 0;
+            for (int i = 0; i < _actualBuffer.Count; i++)
+            {
+                double logActual = Math.Log(actualValues[i] + 1);
+                double logPredicted = Math.Log(predictedValues[i] + 1);
+                double logError = logActual - logPredicted;
+                sumSquaredLogError += logError * logError;
+            }
+
+            msle = sumSquaredLogError / _actualBuffer.Count;
+        }
+
+        IsHot = _index >= WarmupPeriod;
+        return msle;
+    }
+
+    /// <summary>
+    /// Calculates the 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 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();
+    }
+}