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core-dump/Scripts/NativeLinearRegression.cs

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+using System;
+using Unity.Burst;
+using Unity.Collections;
+using Unity.Jobs;
+using Unity.Mathematics;
+
+namespace Timing
+{
+	public sealed class NativeLinearRegression : IDisposable
+	{
+		public int SampleCount { get; private set; }
+		public int MaxSampleCount { get; }
+
+		private NativeArray<double2> _samples;
+
+		public NativeLinearRegression(int maxSampleCount = 12)
+		{
+			MaxSampleCount = maxSampleCount;
+			_samples = new NativeArray<double2>(maxSampleCount, Allocator.Persistent);
+		}
+
+		public void Sample(double2 plot)
+		{
+			if (SampleCount < MaxSampleCount)
+				SampleCount++;
+			else
+				for (var i = 1; i < SampleCount; i++)
+					_samples[i - 1] = _samples[i];
+
+			_samples[SampleCount - 1] = plot;
+		}
+
+		public double Predict(in double x)
+		{
+			using var result = new NativeArray<double>(2, Allocator.TempJob);
+
+			var jobData = new LinearRegressionJob
+			{
+				samples = _samples.Slice(0, SampleCount),
+				yInterceptAndSlope = result
+			};
+
+			jobData.Schedule().Complete();
+
+			var yIntercept = result[0];
+			var slope = result[1];
+
+			return x * slope + yIntercept;
+		}
+
+		public void Clear()
+		{
+			SampleCount = 0;
+		}
+
+		public void Dispose()
+		{
+			_samples.Dispose();
+		}
+	}
+
+	[BurstCompile]
+	public struct LinearRegressionJob : IJob
+	{
+		[ReadOnly]
+		public NativeSlice<double2> samples;
+
+		[WriteOnly]
+		public NativeArray<double> yInterceptAndSlope;
+
+		public void Execute()
+		{
+			double sumOfX = 0;
+			double sumOfY = 0;
+			double sumOfXSq = 0;
+			double sumCoDeviates = 0;
+
+			var sampleCount = samples.Length;
+
+			for (var i = 0; i < sampleCount; i++)
+			{
+				var plot = samples[i];
+				sumCoDeviates += plot.x * plot.y;
+				sumOfX += plot.x;
+				sumOfY += plot.y;
+				sumOfXSq += plot.x * plot.x;
+			}
+
+			var ssX = sumOfXSq - sumOfX * sumOfX / sampleCount;
+			var sCo = sumCoDeviates - sumOfX * sumOfY / sampleCount;
+
+			var meanX = sumOfX / sampleCount;
+			var meanY = sumOfY / sampleCount;
+
+			// y-intercept
+			yInterceptAndSlope[0] = meanY - sCo / ssX * meanX;
+
+			// slope
+			yInterceptAndSlope[1] = sCo / ssX;
+		}
+	}
+}