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https://github.com/mihakralj/QuanTAlib.git
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tests and cleanup
This commit is contained in:
Miha Kralj
@@ -0,0 +1,138 @@
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namespace QuanTAlib;
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/// <summary>
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/// Represents a Huber Loss calculator that combines the best properties of L2 squared loss for normal data
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/// and L1 absolute loss for outliers.
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/// </summary>
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/// <remarks>
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/// The Huberloss class calculates the Huber Loss using circular buffers
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/// to efficiently manage the actual and predicted data points within the specified period.
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/// </remarks>
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public class Huberloss : AbstractBase
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{
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private readonly CircularBuffer _actualBuffer;
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private readonly CircularBuffer _predictedBuffer;
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private readonly double _delta;
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/// <summary>
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/// Initializes a new instance of the Huberloss class with the specified period and delta.
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/// </summary>
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/// <param name="period">The period over which to calculate the Huber Loss.</param>
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/// <param name="delta">The threshold at which to switch from squared to linear loss.</param>
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/// <exception cref="ArgumentOutOfRangeException">
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/// Thrown when period is less than 1 or delta is less than or equal to 0.
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/// </exception>
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public Huberloss(int period, double delta = 1.0)
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{
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if (period < 1)
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{
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throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1.");
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}
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if (delta <= 0)
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{
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throw new ArgumentOutOfRangeException(nameof(delta), "Delta must be greater than 0.");
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}
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WarmupPeriod = period;
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_actualBuffer = new CircularBuffer(period);
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_predictedBuffer = new CircularBuffer(period);
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_delta = delta;
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Name = $"Huberloss(period={period}, delta={delta})";
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Init();
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}
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/// <summary>
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/// Initializes a new instance of the Mape class with the specified source and period.
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/// </summary>
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/// <param name="source">The source object to subscribe to for value updates.</param>
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/// <param name="period">The period over which to calculate the Mean Absolute Percentage Error.</param>
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public Huberloss(object source, int period) : this(period)
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{
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var pubEvent = source.GetType().GetEvent("Pub");
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pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
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}
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/// <summary>
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/// Initializes the Huberloss instance by clearing the buffers.
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/// </summary>
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public override void Init()
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{
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base.Init();
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_actualBuffer.Clear();
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_predictedBuffer.Clear();
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}
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/// <summary>
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/// Manages the state of the Huberloss instance based on whether new values are being processed.
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/// </summary>
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/// <param name="isNew">Indicates whether the current inputs are new values.</param>
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protected override void ManageState(bool isNew)
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{
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if (isNew)
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{
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_lastValidValue = Input.Value;
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_index++;
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}
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}
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/// <summary>
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/// Performs the Huber Loss calculation for the current period.
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/// </summary>
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/// <returns>
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/// The calculated Huber Loss value for the current period.
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/// </returns>
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/// <remarks>
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/// This method calculates the Huber Loss using the formula:
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/// L(a, p) = 0.5 * (a - p)^2 for |a - p| <= delta
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/// L(a, p) = delta * |a - p| - 0.5 * delta^2 for |a - p| > delta
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/// where a is the actual value, p is the predicted value, and delta is the threshold.
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/// </remarks>
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protected override double Calculation()
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{
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ManageState(Input.IsNew);
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double actual = Input.Value;
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_actualBuffer.Add(actual, Input.IsNew);
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double predicted = double.IsNaN(Input2.Value) ? _actualBuffer.Average() : Input2.Value;
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_predictedBuffer.Add(predicted, Input.IsNew);
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double huberLoss = 0;
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if (_actualBuffer.Count > 0)
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{
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var actualValues = _actualBuffer.GetSpan().ToArray();
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var predictedValues = _predictedBuffer.GetSpan().ToArray();
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double sumLoss = 0;
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for (int i = 0; i < _actualBuffer.Count; i++)
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{
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double error = Math.Abs(actualValues[i] - predictedValues[i]);
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if (error <= _delta)
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{
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sumLoss += 0.5 * error * error;
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}
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else
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{
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sumLoss += _delta * error - 0.5 * _delta * _delta;
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}
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}
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huberLoss = sumLoss / _actualBuffer.Count;
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}
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IsHot = _index >= WarmupPeriod;
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return huberLoss;
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}
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/// <summary>
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/// Calculates the Huber Loss for the given actual and predicted values.
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/// </summary>
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/// <param name="actual">The actual value.</param>
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/// <param name="predicted">The predicted value.</param>
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/// <returns>The calculated Huber Loss.</returns>
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public double Calc(double actual, double predicted)
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{
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Input = new TValue(DateTime.Now, actual);
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Input2 = new TValue(DateTime.Now, predicted);
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return Calculation();
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}
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}
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@@ -0,0 +1,123 @@
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namespace QuanTAlib;
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/// <summary>
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/// Represents a Mean Absolute Error calculator that measures the average absolute difference
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/// between actual values and predicted values.
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/// </summary>
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/// <remarks>
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/// The Mae class calculates the Mean Absolute Error using circular buffers
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/// to efficiently manage the actual and predicted data points within the specified period.
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/// </remarks>
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public class Mae : AbstractBase
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{
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private readonly CircularBuffer _actualBuffer;
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private readonly CircularBuffer _predictedBuffer;
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/// <summary>
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/// Initializes a new instance of the Mae class with the specified period.
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/// </summary>
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/// <param name="period">The period over which to calculate the Mean Absolute Error.</param>
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/// <exception cref="ArgumentOutOfRangeException">
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/// Thrown when period is less than 1.
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/// </exception>
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public Mae(int period)
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{
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if (period < 1)
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{
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throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1.");
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}
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WarmupPeriod = period;
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_actualBuffer = new CircularBuffer(period);
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_predictedBuffer = new CircularBuffer(period);
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Name = $"Mae(period={period})";
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Init();
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}
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/// <summary>
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/// Initializes a new instance of the Mae class with the specified source and period.
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/// </summary>
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/// <param name="source">The source object to subscribe to for value updates.</param>
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/// <param name="period">The period over which to calculate the Mean Absolute Error.</param>
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public Mae(object source, int period) : this(period)
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{
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var pubEvent = source.GetType().GetEvent("Pub");
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pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
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}
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/// <summary>
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/// Initializes the Mae instance by clearing the buffers.
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/// </summary>
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public override void Init()
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{
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base.Init();
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_actualBuffer.Clear();
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_predictedBuffer.Clear();
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}
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/// <summary>
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/// Manages the state of the Mae instance based on whether a new value is being processed.
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/// </summary>
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/// <param name="isNew">Indicates whether the current input is a new value.</param>
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protected override void ManageState(bool isNew)
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{
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if (isNew)
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{
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_lastValidValue = Input.Value;
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_index++;
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}
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}
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/// <summary>
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/// Performs the Mean Absolute Error calculation for the current period.
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/// </summary>
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/// <returns>
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/// The calculated Mean Absolute Error value for the current period.
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/// </returns>
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/// <remarks>
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/// This method calculates the Mean Absolute Error using the formula:
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/// MAE = sum(|actual - predicted|) / n
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/// where actual is each actual value, predicted is each predicted value, and n is the number of values.
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/// If Input2.Value is NaN, it uses the average of actual values as the predicted value.
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/// </remarks>
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protected override double Calculation()
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{
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ManageState(Input.IsNew);
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double actual = Input.Value;
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_actualBuffer.Add(actual, Input.IsNew);
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double predicted = double.IsNaN(Input2.Value) ? _actualBuffer.Average() : Input2.Value;
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_predictedBuffer.Add(predicted, Input.IsNew);
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double mae = 0;
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if (_actualBuffer.Count > 0)
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{
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var actualValues = _actualBuffer.GetSpan().ToArray();
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var predictedValues = _predictedBuffer.GetSpan().ToArray();
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double sumOfAbsoluteDifferences = 0;
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for (int i = 0; i < _actualBuffer.Count; i++)
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{
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sumOfAbsoluteDifferences += Math.Abs(actualValues[i] - predictedValues[i]);
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}
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mae = sumOfAbsoluteDifferences / _actualBuffer.Count;
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}
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IsHot = _index >= WarmupPeriod;
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return mae;
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}
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/// <summary>
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/// Calculates the Mean Absolute Error for the given actual and predicted values.
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/// </summary>
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/// <param name="actual">The actual value.</param>
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/// <param name="predicted">The predicted value.</param>
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/// <returns>The calculated Mean Absolute Error.</returns>
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public double Calc(double actual, double predicted)
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{
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Input = new TValue(DateTime.Now, actual);
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Input2 = new TValue(DateTime.Now, predicted);
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return Calculation();
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}
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}
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@@ -0,0 +1,132 @@
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namespace QuanTAlib;
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/// <summary>
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/// Represents a Mean Absolute Percentage Deviation calculator that measures the average absolute percentage difference
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/// between actual values and predicted values.
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/// </summary>
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/// <remarks>
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/// The Mapd class calculates the Mean Absolute Percentage Deviation using circular buffers
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/// to efficiently manage the actual and predicted data points within the specified period.
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/// </remarks>
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public class Mapd : AbstractBase
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{
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private readonly CircularBuffer _actualBuffer;
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private readonly CircularBuffer _predictedBuffer;
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/// <summary>
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/// Initializes a new instance of the Mapd class with the specified period.
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/// </summary>
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/// <param name="period">The period over which to calculate the Mean Absolute Percentage Deviation.</param>
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/// <exception cref="ArgumentOutOfRangeException">
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/// Thrown when period is less than 1.
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/// </exception>
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public Mapd(int period)
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{
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if (period < 1)
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{
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throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1.");
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}
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WarmupPeriod = period;
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_actualBuffer = new CircularBuffer(period);
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_predictedBuffer = new CircularBuffer(period);
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Name = $"Mapd(period={period})";
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Init();
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}
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/// <summary>
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/// Initializes a new instance of the Mapd class with the specified source and period.
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/// </summary>
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/// <param name="source">The source object to subscribe to for value updates.</param>
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/// <param name="period">The period over which to calculate the Mean Absolute Percentage Deviation.</param>
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public Mapd(object source, int period) : this(period)
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{
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var pubEvent = source.GetType().GetEvent("Pub");
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pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
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}
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/// <summary>
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/// Initializes the Mapd instance by clearing the buffers.
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/// </summary>
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public override void Init()
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{
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base.Init();
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_actualBuffer.Clear();
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_predictedBuffer.Clear();
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}
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/// <summary>
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/// Manages the state of the Mapd instance based on whether a new value is being processed.
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/// </summary>
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/// <param name="isNew">Indicates whether the current input is a new value.</param>
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protected override void ManageState(bool isNew)
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{
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if (isNew)
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{
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_lastValidValue = Input.Value;
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_index++;
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}
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}
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/// <summary>
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/// Performs the Mean Absolute Percentage Deviation calculation for the current period.
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/// </summary>
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/// <returns>
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/// The calculated Mean Absolute Percentage Deviation value for the current period.
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/// </returns>
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/// <remarks>
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/// This method calculates the Mean Absolute Percentage Deviation using the formula:
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/// MAPD = (sum(|actual - predicted| / |actual|) / n) * 100
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/// where actual is each actual value, predicted is each predicted value, and n is the number of values.
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/// If there's only one value in the buffer or if any actual value is zero, those values are excluded from the calculation.
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/// </remarks>
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protected override double Calculation()
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{
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ManageState(Input.IsNew);
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double actual = Input.Value;
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_actualBuffer.Add(actual, Input.IsNew);
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double predicted = double.IsNaN(Input2.Value) ? _actualBuffer.Average() : Input2.Value;
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_predictedBuffer.Add(predicted, Input.IsNew);
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double mapd = 0;
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if (_actualBuffer.Count > 0)
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{
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var actualValues = _actualBuffer.GetSpan().ToArray();
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var predictedValues = _predictedBuffer.GetSpan().ToArray();
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double sumOfAbsolutePercentageDeviations = 0;
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int validCount = 0;
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for (int i = 0; i < _actualBuffer.Count; i++)
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{
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if (actualValues[i] != 0)
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{
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sumOfAbsolutePercentageDeviations += Math.Abs((actualValues[i] - predictedValues[i]) / actualValues[i]);
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validCount++;
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}
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}
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if (validCount > 0)
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{
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mapd = (sumOfAbsolutePercentageDeviations / validCount) * 100;
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}
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}
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IsHot = _index >= WarmupPeriod;
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return mapd;
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}
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/// <summary>
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/// Calculates the Mean Absolute Percentage Deviation for the given actual and predicted values.
|
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/// </summary>
|
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/// <param name="actual">The actual value.</param>
|
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/// <param name="predicted">The predicted value.</param>
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/// <returns>The calculated Mean Absolute Percentage Deviation.</returns>
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public double Calc(double actual, double predicted)
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{
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Input = new TValue(DateTime.Now, actual);
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Input2 = new TValue(DateTime.Now, predicted);
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return Calculation();
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}
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}
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@@ -0,0 +1,132 @@
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namespace QuanTAlib;
|
||||
|
||||
/// <summary>
|
||||
/// Represents a Mean Absolute Percentage Error calculator that measures the average absolute percentage difference
|
||||
/// between actual values and predicted values.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// The Mape class calculates the Mean Absolute Percentage Error using a circular buffer
|
||||
/// to efficiently manage the data points within the specified period.
|
||||
/// </remarks>
|
||||
public class Mape : AbstractBase
|
||||
{
|
||||
private readonly CircularBuffer _actualBuffer;
|
||||
private readonly CircularBuffer _predictedBuffer;
|
||||
|
||||
/// <summary>
|
||||
/// Initializes a new instance of the Mape class with the specified period.
|
||||
/// </summary>
|
||||
/// <param name="period">The period over which to calculate the Mean Absolute Percentage Error.</param>
|
||||
/// <exception cref="ArgumentOutOfRangeException">
|
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/// Thrown when period is less than 1.
|
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/// </exception>
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public Mape(int period)
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||||
{
|
||||
if (period < 1)
|
||||
{
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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 = $"Mape(period={period})";
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||||
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 Mape(object source, int period) : this(period)
|
||||
{
|
||||
var pubEvent = source.GetType().GetEvent("Pub");
|
||||
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Initializes the Mape instance by clearing the buffers.
|
||||
/// </summary>
|
||||
public override void Init()
|
||||
{
|
||||
base.Init();
|
||||
_actualBuffer.Clear();
|
||||
_predictedBuffer.Clear();
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Manages the state of the Mape 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 Percentage Error calculation for the current period.
|
||||
/// </summary>
|
||||
/// <returns>
|
||||
/// The calculated Mean Absolute Percentage Error value for the current period.
|
||||
/// </returns>
|
||||
/// <remarks>
|
||||
/// This method calculates the Mean Absolute Percentage Error using the formula:
|
||||
/// MAPE = (sum(|actual - predicted| / |actual|) / n) * 100
|
||||
/// where actual is each actual value, predicted is each predicted value, and n is the number of values.
|
||||
/// If any actual value is zero, it is excluded from the calculation to avoid division by 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 mape = 0;
|
||||
if (_actualBuffer.Count > 0)
|
||||
{
|
||||
var actualValues = _actualBuffer.GetSpan().ToArray();
|
||||
var predictedValues = _predictedBuffer.GetSpan().ToArray();
|
||||
|
||||
double sumAbsolutePercentageError = 0;
|
||||
int validCount = 0;
|
||||
|
||||
for (int i = 0; i < _actualBuffer.Count; i++)
|
||||
{
|
||||
if (actualValues[i] != 0)
|
||||
{
|
||||
sumAbsolutePercentageError += Math.Abs((actualValues[i] - predictedValues[i]) / actualValues[i]);
|
||||
validCount++;
|
||||
}
|
||||
}
|
||||
|
||||
if (validCount > 0)
|
||||
{
|
||||
mape = (sumAbsolutePercentageError / validCount) * 100;
|
||||
}
|
||||
}
|
||||
|
||||
IsHot = _index >= WarmupPeriod;
|
||||
return mape;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Calculates the Mean Absolute Percentage 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 Absolute Percentage Error.</returns>
|
||||
public double Calc(double actual, double predicted)
|
||||
{
|
||||
Input = new TValue(DateTime.Now, actual);
|
||||
Input2 = new TValue(DateTime.Now, predicted);
|
||||
return Calculation();
|
||||
}
|
||||
}
|
||||
@@ -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();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,135 @@
|
||||
namespace QuanTAlib;
|
||||
|
||||
/// <summary>
|
||||
/// Represents a Mean Directional Accuracy calculator that measures the average accuracy
|
||||
/// of predicted directional changes compared to actual directional changes.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// The Mda class calculates the Mean Directional Accuracy using a circular buffer
|
||||
/// to efficiently manage the data points within the specified period.
|
||||
/// Mean Directional Accuracy is useful in financial analysis for evaluating the performance
|
||||
/// of forecasting models in predicting the direction of price movements.
|
||||
/// </remarks>
|
||||
public class Mda : AbstractBase
|
||||
{
|
||||
private readonly CircularBuffer _actualBuffer;
|
||||
private readonly CircularBuffer _forecastBuffer;
|
||||
|
||||
/// <summary>
|
||||
/// Initializes a new instance of the Mda class with the specified period.
|
||||
/// </summary>
|
||||
/// <param name="period">The period over which to calculate the Mean Directional Accuracy.</param>
|
||||
/// <exception cref="ArgumentOutOfRangeException">
|
||||
/// Thrown when period is less than 2.
|
||||
/// </exception>
|
||||
public Mda(int period)
|
||||
{
|
||||
if (period < 2)
|
||||
{
|
||||
throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 2.");
|
||||
}
|
||||
WarmupPeriod = 1;
|
||||
_actualBuffer = new CircularBuffer(period);
|
||||
_forecastBuffer = new CircularBuffer(period);
|
||||
Name = $"Mda(period={period})";
|
||||
Init();
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Initializes a new instance of the Mda 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 Directional Accuracy.</param>
|
||||
public Mda(object source, int period) : this(period)
|
||||
{
|
||||
var pubEvent = source.GetType().GetEvent("Pub");
|
||||
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Initializes the Mda instance by clearing the buffers.
|
||||
/// </summary>
|
||||
public override void Init()
|
||||
{
|
||||
base.Init();
|
||||
_actualBuffer.Clear();
|
||||
_forecastBuffer.Clear();
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Manages the state of the Mda 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 Directional Accuracy calculation for the current period.
|
||||
/// </summary>
|
||||
/// <returns>
|
||||
/// The calculated Mean Directional Accuracy value for the current period.
|
||||
/// </returns>
|
||||
/// <remarks>
|
||||
/// This method calculates the Mean Directional Accuracy using the formula:
|
||||
/// MDA = (number of correct directional predictions / total number of predictions) * 100
|
||||
/// A correct directional prediction is when the sign of the actual change matches
|
||||
/// the sign of the predicted change.
|
||||
/// The result is expressed as a percentage, where 100% indicates perfect directional accuracy
|
||||
/// and 50% indicates performance no better than random guessing.
|
||||
/// </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 mda = 0;
|
||||
if (_actualBuffer.Count > 1)
|
||||
{
|
||||
var actualValues = _actualBuffer.GetSpan().ToArray();
|
||||
var forecastValues = _forecastBuffer.GetSpan().ToArray();
|
||||
|
||||
int correctPredictions = 0;
|
||||
int totalPredictions = actualValues.Length - 1;
|
||||
|
||||
for (int i = 1; i < actualValues.Length; i++)
|
||||
{
|
||||
double actualChange = actualValues[i] - actualValues[i - 1];
|
||||
double forecastChange = forecastValues[i] - actualValues[i - 1];
|
||||
|
||||
if ((actualChange >= 0 && forecastChange >= 0) || (actualChange < 0 && forecastChange < 0))
|
||||
{
|
||||
correctPredictions++;
|
||||
}
|
||||
}
|
||||
|
||||
mda = (double)correctPredictions / totalPredictions * 100;
|
||||
}
|
||||
|
||||
IsHot = _actualBuffer.Count > 1; // MDA calc is valid from bar 2
|
||||
return mda;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Calculates the Mean Directional Accuracy 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 Directional Accuracy.</returns>
|
||||
public double Calc(double actual, double forecast)
|
||||
{
|
||||
Input = new TValue(DateTime.Now, actual);
|
||||
Input2 = new TValue(DateTime.Now, forecast);
|
||||
return Calculation();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,122 @@
|
||||
namespace QuanTAlib;
|
||||
|
||||
/// <summary>
|
||||
/// Represents a Mean Error calculator that measures the average difference
|
||||
/// between actual values and predicted values.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// The Me class calculates the Mean Error using a circular buffer
|
||||
/// to efficiently manage the data points within the specified period.
|
||||
/// </remarks>
|
||||
public class Me : AbstractBase
|
||||
{
|
||||
private readonly CircularBuffer _actualBuffer;
|
||||
private readonly CircularBuffer _predictedBuffer;
|
||||
|
||||
/// <summary>
|
||||
/// Initializes a new instance of the Me class with the specified period.
|
||||
/// </summary>
|
||||
/// <param name="period">The period over which to calculate the Mean Error.</param>
|
||||
/// <exception cref="ArgumentOutOfRangeException">
|
||||
/// Thrown when period is less than 1.
|
||||
/// </exception>
|
||||
public Me(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 = $"Me(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 Me(object source, int period) : this(period)
|
||||
{
|
||||
var pubEvent = source.GetType().GetEvent("Pub");
|
||||
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Initializes the Me instance by clearing the buffers.
|
||||
/// </summary>
|
||||
public override void Init()
|
||||
{
|
||||
base.Init();
|
||||
_actualBuffer.Clear();
|
||||
_predictedBuffer.Clear();
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Manages the state of the Me 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 Error calculation for the current period.
|
||||
/// </summary>
|
||||
/// <returns>
|
||||
/// The calculated Mean Error value for the current period.
|
||||
/// </returns>
|
||||
/// <remarks>
|
||||
/// This method calculates the Mean Error using the formula:
|
||||
/// ME = sum(actual - predicted) / n
|
||||
/// where actual is each actual value, predicted is each predicted value, and n is the number of values.
|
||||
/// </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 me = 0;
|
||||
if (_actualBuffer.Count > 0)
|
||||
{
|
||||
var actualValues = _actualBuffer.GetSpan().ToArray();
|
||||
var predictedValues = _predictedBuffer.GetSpan().ToArray();
|
||||
|
||||
double sumError = 0;
|
||||
for (int i = 0; i < _actualBuffer.Count; i++)
|
||||
{
|
||||
sumError += actualValues[i] - predictedValues[i];
|
||||
}
|
||||
|
||||
me = sumError / _actualBuffer.Count;
|
||||
}
|
||||
|
||||
IsHot = _index >= WarmupPeriod;
|
||||
return me;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Calculates the Mean 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 Error.</returns>
|
||||
public double Calc(double actual, double predicted)
|
||||
{
|
||||
Input = new TValue(DateTime.Now, actual);
|
||||
Input2 = new TValue(DateTime.Now, predicted);
|
||||
return Calculation();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,132 @@
|
||||
namespace QuanTAlib;
|
||||
|
||||
/// <summary>
|
||||
/// Represents a Mean Percentage Error calculator that measures the average percentage difference
|
||||
/// between actual values and predicted values.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// The Mpe class calculates the Mean Percentage Error using a circular buffer
|
||||
/// to efficiently manage the data points within the specified period.
|
||||
/// </remarks>
|
||||
public class Mpe : AbstractBase
|
||||
{
|
||||
private readonly CircularBuffer _actualBuffer;
|
||||
private readonly CircularBuffer _predictedBuffer;
|
||||
|
||||
/// <summary>
|
||||
/// Initializes a new instance of the Mpe class with the specified period.
|
||||
/// </summary>
|
||||
/// <param name="period">The period over which to calculate the Mean Percentage Error.</param>
|
||||
/// <exception cref="ArgumentOutOfRangeException">
|
||||
/// Thrown when period is less than 1.
|
||||
/// </exception>
|
||||
public Mpe(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 = $"Mpe(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 Mpe(object source, int period) : this(period)
|
||||
{
|
||||
var pubEvent = source.GetType().GetEvent("Pub");
|
||||
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Initializes the Mpe instance by clearing the buffers.
|
||||
/// </summary>
|
||||
public override void Init()
|
||||
{
|
||||
base.Init();
|
||||
_actualBuffer.Clear();
|
||||
_predictedBuffer.Clear();
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Manages the state of the Mpe 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 Percentage Error calculation for the current period.
|
||||
/// </summary>
|
||||
/// <returns>
|
||||
/// The calculated Mean Percentage Error value for the current period.
|
||||
/// </returns>
|
||||
/// <remarks>
|
||||
/// This method calculates the Mean Percentage Error using the formula:
|
||||
/// MPE = (sum((actual - predicted) / actual) / n) * 100
|
||||
/// where actual is each actual value, predicted is each predicted value, and n is the number of values.
|
||||
/// If any actual value is zero, it is excluded from the calculation to avoid division by 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 mpe = 0;
|
||||
if (_actualBuffer.Count > 0)
|
||||
{
|
||||
var actualValues = _actualBuffer.GetSpan().ToArray();
|
||||
var predictedValues = _predictedBuffer.GetSpan().ToArray();
|
||||
|
||||
double sumPercentageError = 0;
|
||||
int validCount = 0;
|
||||
|
||||
for (int i = 0; i < _actualBuffer.Count; i++)
|
||||
{
|
||||
if (actualValues[i] != 0)
|
||||
{
|
||||
sumPercentageError += (actualValues[i] - predictedValues[i]) / actualValues[i];
|
||||
validCount++;
|
||||
}
|
||||
}
|
||||
|
||||
if (validCount > 0)
|
||||
{
|
||||
mpe = (sumPercentageError / validCount) * 100;
|
||||
}
|
||||
}
|
||||
|
||||
IsHot = _index >= WarmupPeriod;
|
||||
return mpe;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Calculates the Mean Percentage 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 Percentage Error.</returns>
|
||||
public double Calc(double actual, double predicted)
|
||||
{
|
||||
Input = new TValue(DateTime.Now, actual);
|
||||
Input2 = new TValue(DateTime.Now, predicted);
|
||||
return Calculation();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,124 @@
|
||||
namespace QuanTAlib;
|
||||
|
||||
/// <summary>
|
||||
/// Represents a Mean Squared Error calculator that measures the average of the squares
|
||||
/// of the differences between actual values and predicted values.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// The Mse class calculates the Mean Squared Error using a circular buffer
|
||||
/// to efficiently manage the data points within the specified period.
|
||||
/// </remarks>
|
||||
public class Mse : AbstractBase
|
||||
{
|
||||
private readonly CircularBuffer _actualBuffer;
|
||||
private readonly CircularBuffer _predictedBuffer;
|
||||
|
||||
/// <summary>
|
||||
/// Initializes a new instance of the Mse class with the specified period.
|
||||
/// </summary>
|
||||
/// <param name="period">The period over which to calculate the Mean Squared Error.</param>
|
||||
/// <exception cref="ArgumentOutOfRangeException">
|
||||
/// Thrown when period is less than 1.
|
||||
/// </exception>
|
||||
public Mse(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 = $"Mse(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 Mse(object source, int period) : this(period)
|
||||
{
|
||||
var pubEvent = source.GetType().GetEvent("Pub");
|
||||
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Initializes the Mse instance by clearing the buffers.
|
||||
/// </summary>
|
||||
public override void Init()
|
||||
{
|
||||
base.Init();
|
||||
_actualBuffer.Clear();
|
||||
_predictedBuffer.Clear();
|
||||
}
|
||||
|
||||
|
||||
/// <summary>
|
||||
/// Manages the state of the Mse 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 Error calculation for the current period.
|
||||
/// </summary>
|
||||
/// <returns>
|
||||
/// The calculated Mean Squared Error value for the current period.
|
||||
/// </returns>
|
||||
/// <remarks>
|
||||
/// This method calculates the Mean Squared Error using the formula:
|
||||
/// MSE = sum((actual - predicted)^2) / n
|
||||
/// where actual is each actual value, predicted is each predicted value, and n is the number of values.
|
||||
/// </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 mse = 0;
|
||||
if (_actualBuffer.Count > 0)
|
||||
{
|
||||
var actualValues = _actualBuffer.GetSpan().ToArray();
|
||||
var predictedValues = _predictedBuffer.GetSpan().ToArray();
|
||||
|
||||
double sumSquaredError = 0;
|
||||
for (int i = 0; i < _actualBuffer.Count; i++)
|
||||
{
|
||||
double error = actualValues[i] - predictedValues[i];
|
||||
sumSquaredError += error * error;
|
||||
}
|
||||
|
||||
mse = sumSquaredError / _actualBuffer.Count;
|
||||
}
|
||||
|
||||
IsHot = _index >= WarmupPeriod;
|
||||
return mse;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Calculates the Mean 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 Mean Squared Error.</returns>
|
||||
public double Calc(double actual, double predicted)
|
||||
{
|
||||
Input = new TValue(DateTime.Now, actual);
|
||||
_lastValidValue = predicted;
|
||||
return Calculation();
|
||||
}
|
||||
}
|
||||
@@ -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();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,129 @@
|
||||
namespace QuanTAlib;
|
||||
|
||||
/// <summary>
|
||||
/// Represents a Relative Absolute Error calculator that measures the ratio of the sum of absolute errors
|
||||
/// to the sum of absolute differences between actual values and the mean of actual values.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// The Rae class calculates the Relative Absolute Error using circular buffers
|
||||
/// to efficiently manage the data points within the specified period.
|
||||
/// </remarks>
|
||||
public class Rae : AbstractBase
|
||||
{
|
||||
private readonly CircularBuffer _actualBuffer;
|
||||
private readonly CircularBuffer _predictedBuffer;
|
||||
|
||||
/// <summary>
|
||||
/// Initializes a new instance of the Rae class with the specified period.
|
||||
/// </summary>
|
||||
/// <param name="period">The period over which to calculate the Relative Absolute Error.</param>
|
||||
/// <exception cref="ArgumentOutOfRangeException">
|
||||
/// Thrown when period is less than 2.
|
||||
/// </exception>
|
||||
public Rae(int period)
|
||||
{
|
||||
if (period < 2)
|
||||
{
|
||||
throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 2.");
|
||||
}
|
||||
WarmupPeriod = period;
|
||||
_actualBuffer = new CircularBuffer(period);
|
||||
_predictedBuffer = new CircularBuffer(period);
|
||||
Name = $"Rae(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 Rae(object source, int period) : this(period)
|
||||
{
|
||||
var pubEvent = source.GetType().GetEvent("Pub");
|
||||
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Initializes the Rae instance by clearing the buffers.
|
||||
/// </summary>
|
||||
public override void Init()
|
||||
{
|
||||
base.Init();
|
||||
_actualBuffer.Clear();
|
||||
_predictedBuffer.Clear();
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Manages the state of the Rae 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 Relative Absolute Error calculation for the current period.
|
||||
/// </summary>
|
||||
/// <returns>
|
||||
/// The calculated Relative Absolute Error value for the current period.
|
||||
/// </returns>
|
||||
/// <remarks>
|
||||
/// This method calculates the Relative Absolute Error using the formula:
|
||||
/// RAE = sum(|actual - predicted|) / sum(|actual - mean(actual)|)
|
||||
/// 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);
|
||||
|
||||
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 rae = 0;
|
||||
if (_actualBuffer.Count >= 2)
|
||||
{
|
||||
var actualValues = _actualBuffer.GetSpan().ToArray();
|
||||
var predictedValues = _predictedBuffer.GetSpan().ToArray();
|
||||
|
||||
double actualMean = actualValues.Average();
|
||||
double sumAbsoluteError = 0;
|
||||
double sumAbsoluteDifferenceFromMean = 0;
|
||||
|
||||
for (int i = 0; i < _actualBuffer.Count; i++)
|
||||
{
|
||||
sumAbsoluteError += Math.Abs(actualValues[i] - predictedValues[i]);
|
||||
sumAbsoluteDifferenceFromMean += Math.Abs(actualValues[i] - actualMean);
|
||||
}
|
||||
|
||||
if (sumAbsoluteDifferenceFromMean != 0)
|
||||
{
|
||||
rae = sumAbsoluteError / sumAbsoluteDifferenceFromMean;
|
||||
}
|
||||
}
|
||||
|
||||
IsHot = _index >= WarmupPeriod;
|
||||
return rae;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Calculates the Relative Absolute 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 Absolute Error.</returns>
|
||||
public double Calc(double actual, double predicted)
|
||||
{
|
||||
Input = new TValue(DateTime.Now, actual);
|
||||
Input2 = new TValue(DateTime.Now, predicted);
|
||||
return Calculation();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,122 @@
|
||||
namespace QuanTAlib;
|
||||
|
||||
/// <summary>
|
||||
/// Represents a Root Mean Squared Error calculator that measures the square root of the average
|
||||
/// of the squares of the differences between actual values and predicted values.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// The Rmse class calculates the Root Mean Squared Error using a circular buffer
|
||||
/// to efficiently manage the data points within the specified period.
|
||||
/// </remarks>
|
||||
public class Rmse : AbstractBase
|
||||
{
|
||||
private readonly CircularBuffer _actualBuffer;
|
||||
private readonly CircularBuffer _predictedBuffer;
|
||||
|
||||
/// <summary>
|
||||
/// Initializes a new instance of the Rmse class with the specified period.
|
||||
/// </summary>
|
||||
/// <param name="period">The period over which to calculate the Root Mean Squared Error.</param>
|
||||
/// <exception cref="ArgumentOutOfRangeException">
|
||||
/// Thrown when period is less than 1.
|
||||
/// </exception>
|
||||
public Rmse(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 = $"Rmse(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 Rmse(object source, int period) : this(period)
|
||||
{
|
||||
var pubEvent = source.GetType().GetEvent("Pub");
|
||||
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
|
||||
}
|
||||
/// <summary>
|
||||
/// Initializes the Rmse instance by clearing the buffers.
|
||||
/// </summary>
|
||||
public override void Init()
|
||||
{
|
||||
base.Init();
|
||||
_actualBuffer.Clear();
|
||||
_predictedBuffer.Clear();
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Manages the state of the Rmse 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 Root Mean Squared Error calculation for the current period.
|
||||
/// </summary>
|
||||
/// <returns>
|
||||
/// The calculated Root Mean Squared Error value for the current period.
|
||||
/// </returns>
|
||||
/// <remarks>
|
||||
/// This method calculates the Root Mean Squared Error using the formula:
|
||||
/// RMSE = sqrt(sum((actual - predicted)^2) / n)
|
||||
/// where actual is each actual value, predicted is each predicted value, and n is the number of values.
|
||||
/// </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 rmse = 0;
|
||||
if (_actualBuffer.Count > 0)
|
||||
{
|
||||
var actualValues = _actualBuffer.GetSpan().ToArray();
|
||||
var predictedValues = _predictedBuffer.GetSpan().ToArray();
|
||||
|
||||
double sumSquaredError = 0;
|
||||
for (int i = 0; i < _actualBuffer.Count; i++)
|
||||
{
|
||||
double error = actualValues[i] - predictedValues[i];
|
||||
sumSquaredError += error * error;
|
||||
}
|
||||
|
||||
rmse = Math.Sqrt(sumSquaredError / _actualBuffer.Count);
|
||||
}
|
||||
|
||||
IsHot = _index >= WarmupPeriod;
|
||||
return rmse;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Calculates the Root Mean 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 Root Mean Squared Error.</returns>
|
||||
public double Calc(double actual, double predicted)
|
||||
{
|
||||
Input = new TValue(DateTime.Now, actual);
|
||||
Input2 = new TValue(DateTime.Now, predicted);
|
||||
return Calculation();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,126 @@
|
||||
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)
|
||||
{
|
||||
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 = $"Rmsle(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 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();
|
||||
_actualBuffer.Clear();
|
||||
_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)
|
||||
{
|
||||
_lastValidValue = Input.Value;
|
||||
_index++;
|
||||
}
|
||||
}
|
||||
|
||||
/// <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);
|
||||
|
||||
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 rmsle = 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;
|
||||
}
|
||||
|
||||
rmsle = Math.Sqrt(sumSquaredLogError / _actualBuffer.Count);
|
||||
}
|
||||
|
||||
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();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,132 @@
|
||||
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)
|
||||
{
|
||||
throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 2.");
|
||||
}
|
||||
WarmupPeriod = period;
|
||||
_actualBuffer = new CircularBuffer(period);
|
||||
_predictedBuffer = new CircularBuffer(period);
|
||||
Name = $"Rse(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 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();
|
||||
_actualBuffer.Clear();
|
||||
_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)
|
||||
{
|
||||
_lastValidValue = Input.Value;
|
||||
_index++;
|
||||
}
|
||||
}
|
||||
|
||||
/// <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);
|
||||
|
||||
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 rse = 0;
|
||||
if (_actualBuffer.Count >= 2)
|
||||
{
|
||||
var actualValues = _actualBuffer.GetSpan().ToArray();
|
||||
var predictedValues = _predictedBuffer.GetSpan().ToArray();
|
||||
|
||||
double actualMean = actualValues.Average();
|
||||
double sumSquaredError = 0;
|
||||
double sumSquaredDifferenceFromMean = 0;
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
if (sumSquaredDifferenceFromMean != 0)
|
||||
{
|
||||
rse = sumSquaredError / sumSquaredDifferenceFromMean;
|
||||
}
|
||||
}
|
||||
|
||||
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();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,132 @@
|
||||
namespace QuanTAlib;
|
||||
|
||||
/// <summary>
|
||||
/// Represents a Coefficient of Determination (R-squared) calculator that measures the proportion of
|
||||
/// the variance in the dependent variable that is predictable from the independent variable(s).
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// The Rsquared class calculates the Coefficient of Determination using circular buffers
|
||||
/// to efficiently manage the actual and predicted data points within the specified period.
|
||||
/// </remarks>
|
||||
public class Rsquared : AbstractBase
|
||||
{
|
||||
private readonly CircularBuffer _actualBuffer;
|
||||
private readonly CircularBuffer _predictedBuffer;
|
||||
|
||||
/// <summary>
|
||||
/// Initializes a new instance of the Rsquared class with the specified period.
|
||||
/// </summary>
|
||||
/// <param name="period">The period over which to calculate the Coefficient of Determination.</param>
|
||||
/// <exception cref="ArgumentOutOfRangeException">
|
||||
/// Thrown when period is less than 2.
|
||||
/// </exception>
|
||||
public Rsquared(int period)
|
||||
{
|
||||
if (period < 2)
|
||||
{
|
||||
throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 2.");
|
||||
}
|
||||
WarmupPeriod = period;
|
||||
_actualBuffer = new CircularBuffer(period);
|
||||
_predictedBuffer = new CircularBuffer(period);
|
||||
Name = $"Rsquared(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 Rsquared(object source, int period) : this(period)
|
||||
{
|
||||
var pubEvent = source.GetType().GetEvent("Pub");
|
||||
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Initializes the Rsquared instance by clearing the buffers.
|
||||
/// </summary>
|
||||
public override void Init()
|
||||
{
|
||||
base.Init();
|
||||
_actualBuffer.Clear();
|
||||
_predictedBuffer.Clear();
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Manages the state of the Rsquared 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 Coefficient of Determination calculation for the current period.
|
||||
/// </summary>
|
||||
/// <returns>
|
||||
/// The calculated Coefficient of Determination value for the current period.
|
||||
/// </returns>
|
||||
/// <remarks>
|
||||
/// This method calculates the Coefficient of Determination using the formula:
|
||||
/// R^2 = 1 - (SSres / SStot)
|
||||
/// where SSres is the sum of squared residuals and SStot is the total sum of squares.
|
||||
/// </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 rsquared = 0;
|
||||
if (_actualBuffer.Count >= 2)
|
||||
{
|
||||
var actualValues = _actualBuffer.GetSpan().ToArray();
|
||||
var predictedValues = _predictedBuffer.GetSpan().ToArray();
|
||||
|
||||
double actualMean = actualValues.Average();
|
||||
double ssRes = 0;
|
||||
double ssTot = 0;
|
||||
|
||||
for (int i = 0; i < _actualBuffer.Count; i++)
|
||||
{
|
||||
double residual = actualValues[i] - predictedValues[i];
|
||||
ssRes += residual * residual;
|
||||
|
||||
double deviation = actualValues[i] - actualMean;
|
||||
ssTot += deviation * deviation;
|
||||
}
|
||||
|
||||
if (ssTot != 0)
|
||||
{
|
||||
rsquared = 1 - (ssRes / ssTot);
|
||||
}
|
||||
}
|
||||
|
||||
IsHot = _index >= WarmupPeriod;
|
||||
return rsquared;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Calculates the Coefficient of Determination 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 Coefficient of Determination.</returns>
|
||||
public double Calc(double actual, double predicted)
|
||||
{
|
||||
Input = new TValue(DateTime.Now, actual);
|
||||
Input2 = new TValue(DateTime.Now, predicted);
|
||||
return Calculation();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,132 @@
|
||||
namespace QuanTAlib;
|
||||
|
||||
/// <summary>
|
||||
/// Represents a Symmetric Mean Absolute Percentage Error calculator that measures the percentage difference
|
||||
/// between actual and predicted values, using a symmetric formula to handle both positive and negative errors equally.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// The Smape class calculates the Symmetric Mean Absolute Percentage Error using circular buffers
|
||||
/// to efficiently manage the data points within the specified period.
|
||||
/// </remarks>
|
||||
public class Smape : AbstractBase
|
||||
{
|
||||
private readonly CircularBuffer _actualBuffer;
|
||||
private readonly CircularBuffer _predictedBuffer;
|
||||
|
||||
/// <summary>
|
||||
/// Initializes a new instance of the Smape class with the specified period.
|
||||
/// </summary>
|
||||
/// <param name="period">The period over which to calculate the Symmetric Mean Absolute Percentage Error.</param>
|
||||
/// <exception cref="ArgumentOutOfRangeException">
|
||||
/// Thrown when period is less than 1.
|
||||
/// </exception>
|
||||
public Smape(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 = $"Smape(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 Smape(object source, int period) : this(period)
|
||||
{
|
||||
var pubEvent = source.GetType().GetEvent("Pub");
|
||||
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Initializes the Smape instance by clearing the buffers.
|
||||
/// </summary>
|
||||
public override void Init()
|
||||
{
|
||||
base.Init();
|
||||
_actualBuffer.Clear();
|
||||
_predictedBuffer.Clear();
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Manages the state of the Smape 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 Symmetric Mean Absolute Percentage Error calculation for the current period.
|
||||
/// </summary>
|
||||
/// <returns>
|
||||
/// The calculated Symmetric Mean Absolute Percentage Error value for the current period.
|
||||
/// </returns>
|
||||
/// <remarks>
|
||||
/// This method calculates the Symmetric Mean Absolute Percentage Error using the formula:
|
||||
/// SMAPE = (100% / n) * sum(2 * |actual - predicted| / (|actual| + |predicted|))
|
||||
/// where actual is each actual value, predicted is each predicted value, and n is the number of values.
|
||||
/// </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 smape = 0;
|
||||
if (_actualBuffer.Count > 0)
|
||||
{
|
||||
var actualValues = _actualBuffer.GetSpan().ToArray();
|
||||
var predictedValues = _predictedBuffer.GetSpan().ToArray();
|
||||
|
||||
double sumSymmetricPercentageError = 0;
|
||||
int validCount = 0;
|
||||
|
||||
for (int i = 0; i < _actualBuffer.Count; i++)
|
||||
{
|
||||
double denominator = Math.Abs(actualValues[i]) + Math.Abs(predictedValues[i]);
|
||||
if (denominator != 0)
|
||||
{
|
||||
sumSymmetricPercentageError += 2 * Math.Abs(actualValues[i] - predictedValues[i]) / denominator;
|
||||
validCount++;
|
||||
}
|
||||
}
|
||||
|
||||
if (validCount > 0)
|
||||
{
|
||||
smape = (100.0 / validCount) * sumSymmetricPercentageError;
|
||||
}
|
||||
}
|
||||
|
||||
IsHot = _index >= WarmupPeriod;
|
||||
return smape;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Calculates the Symmetric Mean Absolute Percentage 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 Symmetric Mean Absolute Percentage Error.</returns>
|
||||
public double Calc(double actual, double predicted)
|
||||
{
|
||||
Input = new TValue(DateTime.Now, actual);
|
||||
Input2 = new TValue(DateTime.Now, predicted);
|
||||
return Calculation();
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user