tests and cleanup

lib/errors/Mase.cs

@@ -0,0 +1,136 @@
+namespace QuanTAlib;
+
+/// <summary>
+/// Represents a Mean Absolute Scaled Error calculator that measures the ratio of the mean absolute error
+/// of the forecast values to the mean absolute error of the naive forecast.
+/// </summary>
+/// <remarks>
+/// The Mase class calculates the Mean Absolute Scaled Error using circular buffers
+/// to efficiently manage the data points within the specified period.
+/// </remarks>
+public class Mase : AbstractBase
+{
+    private readonly CircularBuffer _actualBuffer;
+    private readonly CircularBuffer _forecastBuffer;
+    private readonly int _period;
+
+    /// <summary>
+    /// Initializes a new instance of the Mase class with the specified period.
+    /// </summary>
+    /// <param name="period">The period over which to calculate the Mean Absolute Scaled Error.</param>
+    /// <exception cref="ArgumentOutOfRangeException">
+    /// Thrown when period is less than 3.
+    /// </exception>
+    public Mase(int period)
+    {
+        if (period < 3)
+        {
+            throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 3.");
+        }
+        _period = period;
+        WarmupPeriod = period;
+        _actualBuffer = new CircularBuffer(period);
+        _forecastBuffer = new CircularBuffer(period);
+        Name = $"Mase(period={period})";
+        Init();
+    }
+
+    /// <summary>
+    /// Initializes a new instance of the Mase 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 Scaled Error.</param>
+    public Mase(object source, int period) : this(period)
+    {
+        var pubEvent = source.GetType().GetEvent("Pub");
+        pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
+    }
+
+    /// <summary>
+    /// Initializes the Mase instance by clearing the buffers.
+    /// </summary>
+    public override void Init()
+    {
+        base.Init();
+        _actualBuffer.Clear();
+        _forecastBuffer.Clear();
+    }
+
+    /// <summary>
+    /// Manages the state of the Mase 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 Absolute Scaled Error calculation for the current period.
+    /// </summary>
+    /// <returns>
+    /// The calculated Mean Absolute Scaled Error value for the current period.
+    /// </returns>
+    /// <remarks>
+    /// This method calculates the Mean Absolute Scaled Error using the formula:
+    /// MASE = mean(|actual - forecast|) / mean(|actual[t] - actual[t-1]|)
+    /// where actual is each actual value and forecast is each forecast value.
+    /// If there are fewer than 3 values in the buffers, the method returns 0.
+    /// </remarks>
+    protected override double Calculation()
+    {
+        ManageState(Input.IsNew);
+
+        double actual = Input.Value;
+        _actualBuffer.Add(actual, Input.IsNew);
+
+        double forecast = double.IsNaN(Input2.Value) ? _actualBuffer.Average() : Input2.Value;
+        _forecastBuffer.Add(forecast, Input.IsNew);
+
+        double mase = 0;
+        if (_actualBuffer.Count >= 3)
+        {
+            var actualValues = _actualBuffer.GetSpan().ToArray();
+            var forecastValues = _forecastBuffer.GetSpan().ToArray();
+
+            double sumAbsoluteError = 0;
+            double sumAbsoluteNaiveError = 0;
+
+            int count = Math.Min(_actualBuffer.Count, _period);
+
+            for (int i = 1; i < count; i++)
+            {
+                sumAbsoluteError += Math.Abs(actualValues[i] - forecastValues[i]);
+                sumAbsoluteNaiveError += Math.Abs(actualValues[i] - actualValues[i - 1]);
+            }
+
+            double meanAbsoluteError = sumAbsoluteError / (count - 1);
+            double meanAbsoluteNaiveError = sumAbsoluteNaiveError / (count - 1);
+
+            if (meanAbsoluteNaiveError != 0)
+            {
+                mase = meanAbsoluteError / meanAbsoluteNaiveError;
+            }
+        }
+
+        IsHot = _index >= WarmupPeriod;
+        return mase;
+    }
+
+    /// <summary>
+    /// Calculates the Mean Absolute Scaled Error for the given actual and forecast values.
+    /// </summary>
+    /// <param name="actual">The actual value.</param>
+    /// <param name="forecast">The forecast value.</param>
+    /// <returns>The calculated Mean Absolute Scaled Error.</returns>
+    public double Calc(double actual, double forecast)
+    {
+        Input = new TValue(DateTime.Now, actual);
+        Input2 = new TValue(DateTime.Now, forecast);
+        return Calculation();
+    }
+}