gavindiaz/QuanTAlib
1
0
Fork 0
mirror of https://github.com/mihakralj/QuanTAlib.git synced 2026-08-17 18:18:04 +00:00
Code Issues Packages Projects Releases Wiki Activity

corrections

Browse Source
This commit is contained in:
Miha Kralj
2024-10-13 17:18:31 -07:00
parent 2236f5f483
commit bbefc72d73
65 changed files with 669 additions and 1087 deletions
Download Patch File Download Diff File Expand all files Collapse all files
lib/errors/Smape.cs
+3 -57
View File
@@ -1,25 +1,10 @@
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)
@@ -33,20 +18,12 @@ public class Smape : AbstractBase
Init();
}
/// <summary>
/// Initializes a new instance of the Mape class with the specified source and period.
/// </summary>
/// <param name="source">The source object to subscribe to for value updates.</param>
/// <param name="period">The period over which to calculate the Mean Absolute Percentage Error.</param>
public 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();
@@ -54,10 +31,6 @@ public class Smape : AbstractBase
_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)
@@ -67,17 +40,6 @@ public class Smape : AbstractBase
}
}
/// <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);
@@ -94,7 +56,7 @@ public class Smape : AbstractBase
var actualValues = _actualBuffer.GetSpan().ToArray();
var predictedValues = _predictedBuffer.GetSpan().ToArray();
double sumSymmetricPercentageError = 0;
double sumSymmetricAbsolutePercentageError = 0;
int validCount = 0;
for (int i = 0; i < _actualBuffer.Count; i++)
@@ -102,31 +64,15 @@ public class Smape : AbstractBase
double denominator = Math.Abs(actualValues[i]) + Math.Abs(predictedValues[i]);
if (denominator != 0)
{
sumSymmetricPercentageError += 2 * Math.Abs(actualValues[i] - predictedValues[i]) / denominator;
sumSymmetricAbsolutePercentageError += Math.Abs(actualValues[i] - predictedValues[i]) / denominator;
validCount++;
}
}
if (validCount > 0)
{
smape = (100.0 / validCount) * sumSymmetricPercentageError;
}
smape = validCount > 0 ? (200 * sumSymmetricAbsolutePercentageError / validCount) : 0;
}
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();
}
}