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https://github.com/mihakralj/QuanTAlib.git
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88 lines
3.1 KiB
C#
88 lines
3.1 KiB
C#
using System.Runtime.CompilerServices;
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namespace QuanTAlib;
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/// <summary>
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/// MSE: Mean Squared Error
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/// </summary>
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/// <remarks>
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/// MSE measures the average of the squares of the errors between actual and
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/// predicted values. It penalizes larger errors more heavily than MAE.
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///
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/// Formula:
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/// MSE = (1/n) * Σ(actual - predicted)²
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///
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/// Uses a RingBuffer for O(1) streaming updates with running sum.
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///
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/// Key properties:
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/// - Always non-negative (MSE ≥ 0)
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/// - Units are squared (e.g., if data is in dollars, MSE is in dollars²)
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/// - Heavily penalizes outliers due to squaring
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/// - MSE = 0 indicates perfect prediction
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/// </remarks>
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[SkipLocalsInit]
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public sealed class Mse : BiInputIndicatorBase
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{
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/// <summary>
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/// Creates MSE with specified period.
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/// </summary>
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/// <param name="period">Number of values to average (must be > 0)</param>
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public Mse(int period) : base(period, $"Mse({period})") { }
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/// <summary>
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/// Computes squared error: (actual - predicted)²
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/// </summary>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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protected override double ComputeError(double actual, double predicted)
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{
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double diff = actual - predicted;
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return diff * diff;
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}
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/// <summary>
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/// Calculates MSE for the entire series pair.
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/// </summary>
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/// <param name="actual">Actual values series</param>
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/// <param name="predicted">Predicted values series</param>
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/// <param name="period">MSE period</param>
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/// <returns>MSE series</returns>
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public static TSeries Batch(TSeries actual, TSeries predicted, int period)
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=> CalculateImpl(actual, predicted, period, Batch);
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/// <summary>
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/// Calculates MSE in-place using pre-allocated spans.
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/// Uses SIMD acceleration when available.
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/// </summary>
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/// <param name="actual">Actual values</param>
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/// <param name="predicted">Predicted values</param>
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/// <param name="output">Output span (must be same length as inputs)</param>
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/// <param name="period">MSE period (must be > 0)</param>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public static void Batch(ReadOnlySpan<double> actual, ReadOnlySpan<double> predicted, Span<double> output, int period)
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{
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ValidateBatchInputs(actual, predicted, output, period);
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if (actual.Length == 0)
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{
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return;
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}
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// Allocate temporary buffer for squared errors
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const int StackAllocThreshold = 256;
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Span<double> sqErrors = actual.Length <= StackAllocThreshold
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? stackalloc double[actual.Length]
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: new double[actual.Length];
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// Compute squared errors using shared SIMD helper
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ErrorHelpers.ComputeSquaredErrors(actual, predicted, sqErrors);
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// Apply rolling mean using shared helper
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ErrorHelpers.ApplyRollingMean(sqErrors, output, period);
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}
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public static (TSeries Results, Mse Indicator) Calculate(TSeries actual, TSeries predicted, int period)
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{
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var indicator = new Mse(period);
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TSeries results = Batch(actual, predicted, period);
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return (results, indicator);
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}
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} |