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Class optimization

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Miha
2024-10-27 16:11:08 -07:00
parent b2fcdda785
commit 6c67a0cf31
77 changed files with 2634 additions and 1455 deletions
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lib/statistics/Zscore.cs
+40 -14
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@@ -1,5 +1,4 @@
using System;
using System.Linq;
using System.Runtime.CompilerServices;
namespace QuanTAlib;
/// <summary>
@@ -43,22 +42,26 @@ namespace QuanTAlib;
/// Note: Assumes approximately normal distribution
/// </remarks>
public class Zscore : AbstractBase
[SkipLocalsInit]
public sealed class Zscore : AbstractBase
{
private readonly int Period;
private readonly CircularBuffer _buffer;
private const double Epsilon = 1e-10;
private const int MinimumPoints = 2;
/// <param name="period">The number of points to consider for Z-score calculation.</param>
/// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 2.</exception>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public Zscore(int period)
{
if (period < 2)
if (period < MinimumPoints)
{
throw new ArgumentOutOfRangeException(nameof(period),
"Period must be greater than or equal to 2 for Z-score calculation.");
}
Period = period;
WarmupPeriod = 2;
WarmupPeriod = MinimumPoints;
_buffer = new CircularBuffer(period);
Name = $"ZScore(period={period})";
Init();
@@ -66,18 +69,21 @@ public class Zscore : AbstractBase
/// <param name="source">The data source object that publishes updates.</param>
/// <param name="period">The number of points to consider for Z-score calculation.</param>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public Zscore(object source, int period) : this(period)
{
var pubEvent = source.GetType().GetEvent("Pub");
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public override void Init()
{
base.Init();
_buffer.Clear();
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
protected override void ManageState(bool isNew)
{
if (isNew)
@@ -87,23 +93,43 @@ public class Zscore : AbstractBase
}
}
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
private static double CalculateMean(ReadOnlySpan<double> values)
{
double sum = 0;
for (int i = 0; i < values.Length; i++)
{
sum += values[i];
}
return sum / values.Length;
}
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
private static double CalculateStandardDeviation(ReadOnlySpan<double> values, double mean)
{
double sumSquaredDeviations = 0;
for (int i = 0; i < values.Length; i++)
{
double deviation = values[i] - mean;
sumSquaredDeviations += deviation * deviation;
}
return Math.Sqrt(sumSquaredDeviations / (values.Length - 1));
}
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
protected override double Calculation()
{
ManageState(Input.IsNew);
_buffer.Add(Input.Value, Input.IsNew);
double zScore = 0;
if (_buffer.Count >= 2) // Need at least 2 points for standard deviation
if (_buffer.Count >= MinimumPoints) // Need at least 2 points for standard deviation
{
var values = _buffer.GetSpan().ToArray();
double mean = values.Average();
double n = values.Length;
ReadOnlySpan<double> values = _buffer.GetSpan();
double mean = CalculateMean(values);
double standardDeviation = CalculateStandardDeviation(values, mean);
// Calculate sample standard deviation
double sumSquaredDeviations = values.Sum(x => Math.Pow(x - mean, 2));
double standardDeviation = Math.Sqrt(sumSquaredDeviations / (n - 1));
if (standardDeviation != 0) // Avoid division by zero
if (standardDeviation > Epsilon) // Avoid division by zero
{
zScore = (Input.Value - mean) / standardDeviation;
}