Migrated log normal distribution to shape fitter

FastRng/Double/Distributions/LogNormal.cs

@@ -1,35 +0,0 @@
-using System;
-using System.Threading;
-using System.Threading.Tasks;
-
-namespace FastRng.Double.Distributions
-{
-    public sealed class LogNormal : IDistribution
-    {
-        private double sigma = 1.0;
-        public IRandom Random { get; set; }
-
-        public double Mu { get; set; } = 0.0;
-
-        public double Sigma
-        {
-            get => this.sigma;
-            set
-            {
-                if(value <= 0.0)
-                    throw new ArgumentOutOfRangeException(message: "Sigma must be greater than 0", null);
-                
-                this.sigma = value;
-            }
-        }
-
-        public async ValueTask<double> GetDistributedValue(CancellationToken token = default)
-        {
-            if (this.Random == null)
-                return double.NaN;
-
-            var normal = await this.Random.NextNumber(new Normal(), token); // TODO: Check all distributions. Used distributions must be static readonly! Probably, after refactoring, no distribution should depend on any other!
-            return Math.Exp(normal);
-        }
-    }
-}

FastRng/Double/Distributions/LogNormalS1M0.cs

@@ -0,0 +1,43 @@
+using System;
+using System.Threading;
+using System.Threading.Tasks;
+
+namespace FastRng.Double.Distributions
+{
+    public sealed class LogNormalS1M0 : IDistribution
+    {
+        private const double SIGMA = 1.0;
+        private const double MU = 0.0;
+        private const double CONSTANT = 1.51998658387455;
+        
+        private static readonly double FACTOR;
+        
+        private ShapeFitter fitter;
+        private IRandom random;
+
+        static LogNormalS1M0()
+        {
+            FACTOR = SIGMA * Math.Sqrt(2 * Math.PI);
+        }
+        
+        public IRandom Random
+        {
+            get => this.random;
+            set
+            {
+                this.random = value;
+                this.fitter = new ShapeFitter(LogNormalS1M0.ShapeFunction, this.random, 100);
+            }
+        }
+
+        private static double ShapeFunction(double x) => (CONSTANT / (x * FACTOR)) * Math.Exp( -(Math.Pow(Math.Log(x) - MU, 2) / (2 * Math.Pow(SIGMA, 2))));
+        
+        public async ValueTask<double> GetDistributedValue(CancellationToken token = default)
+        {
+            if (this.Random == null)
+                return double.NaN;
+            
+            return await this.fitter.NextNumber(token);
+        }
+    }
+}

FastRngTests/Double/Distributions/LogNormalS1M0.cs

@@ -8,31 +8,40 @@ using NUnit.Framework;
 namespace FastRngTests.Double.Distributions
 {
     [ExcludeFromCodeCoverage]
-    public class LogNormal
+    public class LogNormalS1M0
     {
         [Test]
         [Category(TestCategories.COVER)]
         [Category(TestCategories.NORMAL)]
         public async Task TestLogNormalDistribution01()
         {
-            const double MU = 0.1;
+            var dist = new FastRng.Double.Distributions.LogNormalS1M0();
-            const double SIGMA = 0.25;
+            var fra = new FrequencyAnalysis();
-            var mean = Math.Exp(MU + SIGMA * SIGMA * 0.5);
-            var variance = Math.Abs(Math.Exp(SIGMA * SIGMA) - 1) * Math.Exp(2 * MU + SIGMA * SIGMA);
-            
-            var dist = new FastRng.Double.Distributions.LogNormal{ Mu = MU, Sigma = SIGMA };
-            var stats = new RunningStatistics();
             var rng = new MultiThreadedRng();
             
             for (var n = 0; n < 100_000; n++)
-                stats.Push(await rng.NextNumber(dist));
+                fra.CountThis(await rng.NextNumber(dist));
             
             rng.StopProducer();
-            TestContext.WriteLine($"mean={mean} vs. {stats.Mean}");
+            var result = fra.NormalizeAndPlotEvents(TestContext.WriteLine);
-            TestContext.WriteLine($"variance={variance} vs {stats.Variance}");
+
+            Assert.That(result[0], Is.EqualTo(0.001505531).Within(0.003));
+            Assert.That(result[1], Is.EqualTo(0.014408709).Within(0.01));
+            Assert.That(result[2], Is.EqualTo(0.043222256).Within(0.014));
             
-            Assert.That(stats.Mean, Is.EqualTo(mean).Within(0.1), "Mean is out of range");
+            Assert.That(result[21], Is.EqualTo(0.876212056).Within(0.099));
-            Assert.That(stats.Variance, Is.EqualTo(variance).Within(0.1), "Variance is out of range");
+            Assert.That(result[22], Is.EqualTo(0.895582226).Within(0.099));
+            Assert.That(result[23], Is.EqualTo(0.912837250).Within(0.099));
+            
+            Assert.That(result[50], Is.EqualTo(0.948062005).Within(0.099));
+            
+            Assert.That(result[75], Is.EqualTo(0.768584762).Within(0.089));
+            Assert.That(result[85], Is.EqualTo(0.697303612).Within(0.089));
+            Assert.That(result[90], Is.EqualTo(0.663570581).Within(0.089));
+            
+            Assert.That(result[97], Is.EqualTo(0.618792767).Within(0.089));
+            Assert.That(result[98], Is.EqualTo(0.612636410).Within(0.089));
+            Assert.That(result[99], Is.EqualTo(0.606540679).Within(0.089));
         }
         
         [Test]
@@ -41,9 +50,10 @@ namespace FastRngTests.Double.Distributions
         public async Task TestLogNormalGeneratorWithRange01()
         {
             var rng = new MultiThreadedRng();
+            var dist = new FastRng.Double.Distributions.LogNormalS1M0();
             var samples = new double[1_000];
             for (var n = 0; n < samples.Length; n++)
-                samples[n] = await rng.NextNumber(-1.0, 1.0, new FastRng.Double.Distributions.LogNormal());
+                samples[n] = await rng.NextNumber(-1.0, 1.0, dist);
             
             rng.StopProducer();
             Assert.That(samples.Min(), Is.GreaterThanOrEqualTo(-1.0), "Min out of range");
@@ -56,9 +66,10 @@ namespace FastRngTests.Double.Distributions
         public async Task TestLogNormalGeneratorWithRange02()
         {
             var rng = new MultiThreadedRng();
+            var dist = new FastRng.Double.Distributions.LogNormalS1M0();
             var samples = new double[1_000];
             for (var n = 0; n < samples.Length; n++)
-                samples[n] = await rng.NextNumber(0.0, 1.0, new FastRng.Double.Distributions.LogNormal());
+                samples[n] = await rng.NextNumber(0.0, 1.0, dist);
             
             rng.StopProducer();
             Assert.That(samples.Min(), Is.GreaterThanOrEqualTo(0.0), "Min is out of range");
@@ -71,7 +82,7 @@ namespace FastRngTests.Double.Distributions
         public async Task TestLogNormalGeneratorWithRange03()
         {
             var rng = new MultiThreadedRng();
-            var dist = new FastRng.Double.Distributions.LogNormal { Random = rng }; // Test default parameters
+            var dist = new FastRng.Double.Distributions.LogNormalS1M0 { Random = rng }; // Test default parameters
             
             var samples = new double[1_000];
             for (var n = 0; n < samples.Length; n++)
@@ -82,29 +93,12 @@ namespace FastRngTests.Double.Distributions
             Assert.That(samples.Max(), Is.LessThanOrEqualTo(1.0), "Max is out of range");
         }
 
-        [Test]
-        [Category(TestCategories.COVER)]
-        [Category(TestCategories.NORMAL)]
-        public void ParameterTest01()
-        {
-            var dist = new FastRng.Double.Distributions.LogNormal();
-            
-            Assert.DoesNotThrow(() => dist.Mu = -45);
-            Assert.DoesNotThrow(() => dist.Mu = 15);
-            Assert.DoesNotThrow(() => dist.Mu = 0);
-            
-            Assert.Throws<ArgumentOutOfRangeException>(() => dist.Sigma = 0);
-            Assert.Throws<ArgumentOutOfRangeException>(() => dist.Sigma = -78);
-            Assert.DoesNotThrow(() => dist.Sigma = 0.0001);
-            Assert.DoesNotThrow(() => dist.Sigma = 4);
-        }
-
         [Test]
         [Category(TestCategories.COVER)]
         [Category(TestCategories.NORMAL)]
         public async Task NoRandomNumberGenerator01()
         {
-            var dist = new FastRng.Double.Distributions.LogNormal();
+            var dist = new FastRng.Double.Distributions.LogNormalS1M0();
             Assert.DoesNotThrowAsync(async () => await dist.GetDistributedValue());
             Assert.That(await dist.GetDistributedValue(), Is.NaN);
         }