Fix examples

Examples.sln

@@ -0,0 +1,49 @@
+
+Microsoft Visual Studio Solution File, Format Version 12.00
+# Visual Studio 2013
+VisualStudioVersion = 12.0.30501.0
+MinimumVisualStudioVersion = 10.0.40219.1
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Examples", "src\Examples\Examples.csproj", "{8239A6FF-1EF3-4DA4-A860-95C392DD6899}"
+EndProject
+Project("{F2A71F9B-5D33-465A-A702-920D77279786}") = "FSharpExamples", "src\FSharpExamples\FSharpExamples.fsproj", "{BC81EA37-8EE6-4BF9-B8A9-B30497AEF8B1}"
+EndProject
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Numerics", "src\Numerics\Numerics.csproj", "{B7CAE5F4-A23F-4438-B5BE-41226618B695}"
+EndProject
+Project("{F2A71F9B-5D33-465A-A702-920D77279786}") = "FSharp", "src\FSharp\FSharp.fsproj", "{37E8E802-A354-4114-BFC1-6E1357DA605B}"
+EndProject
+Global
+	GlobalSection(SolutionConfigurationPlatforms) = preSolution
+		Debug|Any CPU = Debug|Any CPU
+		Release|Any CPU = Release|Any CPU
+		Release-Signed|Any CPU = Release-Signed|Any CPU
+	EndGlobalSection
+	GlobalSection(ProjectConfigurationPlatforms) = postSolution
+		{8239A6FF-1EF3-4DA4-A860-95C392DD6899}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{8239A6FF-1EF3-4DA4-A860-95C392DD6899}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{8239A6FF-1EF3-4DA4-A860-95C392DD6899}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{8239A6FF-1EF3-4DA4-A860-95C392DD6899}.Release|Any CPU.Build.0 = Release|Any CPU
+		{8239A6FF-1EF3-4DA4-A860-95C392DD6899}.Release-Signed|Any CPU.ActiveCfg = Release-Signed|Any CPU
+		{8239A6FF-1EF3-4DA4-A860-95C392DD6899}.Release-Signed|Any CPU.Build.0 = Release-Signed|Any CPU
+		{BC81EA37-8EE6-4BF9-B8A9-B30497AEF8B1}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{BC81EA37-8EE6-4BF9-B8A9-B30497AEF8B1}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{BC81EA37-8EE6-4BF9-B8A9-B30497AEF8B1}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{BC81EA37-8EE6-4BF9-B8A9-B30497AEF8B1}.Release|Any CPU.Build.0 = Release|Any CPU
+		{BC81EA37-8EE6-4BF9-B8A9-B30497AEF8B1}.Release-Signed|Any CPU.ActiveCfg = Release-Signed|Any CPU
+		{BC81EA37-8EE6-4BF9-B8A9-B30497AEF8B1}.Release-Signed|Any CPU.Build.0 = Release-Signed|Any CPU
+		{B7CAE5F4-A23F-4438-B5BE-41226618B695}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{B7CAE5F4-A23F-4438-B5BE-41226618B695}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{B7CAE5F4-A23F-4438-B5BE-41226618B695}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{B7CAE5F4-A23F-4438-B5BE-41226618B695}.Release|Any CPU.Build.0 = Release|Any CPU
+		{B7CAE5F4-A23F-4438-B5BE-41226618B695}.Release-Signed|Any CPU.ActiveCfg = Release-Signed|Any CPU
+		{B7CAE5F4-A23F-4438-B5BE-41226618B695}.Release-Signed|Any CPU.Build.0 = Release-Signed|Any CPU
+		{37E8E802-A354-4114-BFC1-6E1357DA605B}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{37E8E802-A354-4114-BFC1-6E1357DA605B}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{37E8E802-A354-4114-BFC1-6E1357DA605B}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{37E8E802-A354-4114-BFC1-6E1357DA605B}.Release|Any CPU.Build.0 = Release|Any CPU
+		{37E8E802-A354-4114-BFC1-6E1357DA605B}.Release-Signed|Any CPU.ActiveCfg = Release-Signed|Any CPU
+		{37E8E802-A354-4114-BFC1-6E1357DA605B}.Release-Signed|Any CPU.Build.0 = Release-Signed|Any CPU
+	EndGlobalSection
+	GlobalSection(SolutionProperties) = preSolution
+		HideSolutionNode = FALSE
+	EndGlobalSection
+EndGlobal

RELEASENOTES.md

@@ -1,4 +1,4 @@
-### 3.0.1 - 2014-06-22
+### 3.0.1 - 2014-06-24
 * Patch release, fixing a bug in new Matrix.ToMatrixString and Vector.ToVectorString routines.
 
 ### 3.0.0 - 2014-06-21

build.fsx

@@ -533,6 +533,11 @@ Target "PublishTag" (fun _ -> publishReleaseTag "Math.NET Numerics" "" packageVe
 Target "NativePublishTag" (fun _ -> publishReleaseTag "Math.NET Numerics Native Providers" "native-" nativePackageVersion nativeReleaseNotes)
 Target "DataPublishTag" (fun _ -> publishReleaseTag "Math.NET Numerics Data Extensions" "data-" dataPackageVersion dataReleaseNotes)
 
+Target "PublishMirrors" (fun _ ->
+    let repo = "../mirror-numerics"
+    Git.CommandHelper.runSimpleGitCommand repo "remote update" |> printfn "%s"
+    Git.CommandHelper.runSimpleGitCommand repo "push mirrors" |> printfn "%s")
+
 Target "PublishDocs" (fun _ ->
     let repo = "../mathnet-websites"
     Git.Branches.pull repo "origin" "master"

src/Examples/ConsoleHelper.cs

@@ -25,20 +25,22 @@
 // </copyright>
 
 using System;
+using System.Collections.Generic;
+using System.Linq;
 using MathNet.Numerics.Statistics;
 
 namespace Examples
 {
     /// <summary>
-    /// Helper fucntions to output into Console window
+    /// Helper functions to output into Console window
     /// </summary>
     public static class ConsoleHelper
     {
         /// <summary>
-        /// Disoplay histogram from the array
+        /// Display histogram from the array
         /// </summary>
         /// <param name="data">Source array</param>
-        public static void DisplayHistogram(double[] data)
+        public static void DisplayHistogram(IEnumerable<double> data)
         {
             var blockSymbol = Convert.ToChar(9608);
 
@@ -48,7 +50,7 @@ namespace Examples
 
             var histogram = new Histogram(data, rowMaxLength);
 
-            // Find the absolute peak 
+            // Find the absolute peak
             var maxBucketCount = 0.0;
             for (var i = 0; i < histogram.BucketCount; i++)
             {
@@ -105,5 +107,14 @@ namespace Examples
 
             Console.WriteLine();
         }
+
+        /// <summary>
+        /// Display histogram from the array
+        /// </summary>
+        /// <param name="data">Source array</param>
+        public static void DisplayHistogram(IEnumerable<int> data)
+        {
+            DisplayHistogram(data.Select(x => (double)x));
+        }
     }
 }

src/Examples/ContinuousDistributions/BetaDistribution.cs

@@ -118,47 +118,25 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the Beta(5, 1) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Beta(5, 1) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            Beta.Samples(data, 5, 1);
-            {
-                data[i] = beta.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Beta(2, 5) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Beta(2, 5) distribution and display histogram");
-            beta.A = 2;
+            Beta.Samples(data, 2, 5);
-            beta.B = 5;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = beta.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the Beta distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the Beta(0.5, 0.5) distribution and display histogram");
-            beta.A = 0.5;
+            Beta.Samples(data, 0.5, 0.5);
-            beta.B = 0.5;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = beta.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 7. Generate 100000 samples of the Beta distribution and display histogram
             Console.WriteLine(@"7. Generate 100000 samples of the Beta(2, 2) distribution and display histogram");
-            beta.A = 2;
+            Beta.Samples(data, 2, 2);
-            beta.B = 2;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = beta.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/ChiDistribution.cs

@@ -118,33 +118,19 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the Chi(1) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Chi(1) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            Chi.Samples(data, 1);
-            {
-                data[i] = chi.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Chi(2) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Chi(2) distribution and display histogram");
-            chi.DegreesOfFreedom = 2;
+            Chi.Samples(data, 2);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = chi.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the Chi(5) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the Chi(5) distribution and display histogram");
-            chi.DegreesOfFreedom = 5;
+            Chi.Samples(data, 5);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = chi.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/ChiSquareDistribution.cs

@@ -121,33 +121,19 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the ChiSquare(1) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the ChiSquare(1) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            ChiSquared.Samples(data, 1);
-            {
-                data[i] = chiSquare.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the ChiSquare(4) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the ChiSquare(4) distribution and display histogram");
-            chiSquare.DegreesOfFreedom = 4;
+            ChiSquared.Samples(data, 4);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = chiSquare.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the ChiSquare(8) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the ChiSquare(8) distribution and display histogram");
-            chiSquare.DegreesOfFreedom = 8;
+            ChiSquared.Samples(data, 8);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = chiSquare.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/ContinuousUniformDistribution.cs

@@ -121,23 +121,13 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the ContinuousUniform(0, 1) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the ContinuousUniform(0, 1) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            ContinuousUniform.Samples(data, 0.0, 1.0);
-            {
-                data[i] = continuousUniform.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the ContinuousUniform(2, 10) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the ContinuousUniform(2, 10) distribution and display histogram");
-            continuousUniform.UpperBound = 10;
+            ContinuousUniform.Samples(data, 2, 10);
-            continuousUniform.LowerBound = 2;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = continuousUniform.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/ErlangDistribution.cs

@@ -118,34 +118,19 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the Erlang(1, 2.0) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Erlang(1, 2.0) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            Erlang.Samples(data, 1, 2.0);
-            {
-                data[i] = erlang.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Erlang(3, 2.0) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Erlang(3, 2.0) distribution and display histogram");
-            erlang.Shape = 3;
+            Erlang.Samples(data, 3, 2.0);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = erlang.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the Erlang(9, 0.5) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the Erlang(9, 0.5) distribution and display histogram");
-            erlang.Shape = 9;
+            Erlang.Samples(data, 9, 0.5);
-            erlang.Scale = 0.5;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = erlang.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/ExponentialDistribution.cs

@@ -121,33 +121,19 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the Exponential(1) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Exponential(1) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            Exponential.Samples(data, 1.0);
-            {
-                data[i] = exponential.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Exponential(9) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Exponential(9) distribution and display histogram");
-            exponential.Rate = 9;
+            Exponential.Samples(data, 9.0);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = exponential.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the Exponential(0.01) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the Exponential(0.01) distribution and display histogram");
-            exponential.Rate = 0.01;
+            Exponential.Samples(data, 0.01);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = exponential.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/FisherSnedecorDistribution.cs

@@ -115,35 +115,19 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the FisherSnedecor(50, 20) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the FisherSnedecor(50, 20) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            FisherSnedecor.Samples(data, 50, 20);
-            {
-                data[i] = fisherSnedecor.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the FisherSnedecor(20, 10) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the FisherSnedecor(20, 10) distribution and display histogram");
-            fisherSnedecor.DegreesOfFreedom1 = 20;
+            FisherSnedecor.Samples(data, 20, 10);
-            fisherSnedecor.DegreesOfFreedom2 = 10;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = fisherSnedecor.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the FisherSnedecor(100, 100) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the FisherSnedecor(100, 100) distribution and display histogram");
-            fisherSnedecor.DegreesOfFreedom1 = 100;
+            FisherSnedecor.Samples(data, 100, 100);
-            fisherSnedecor.DegreesOfFreedom2 = 100;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = fisherSnedecor.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/GammaDistribution.cs

@@ -118,23 +118,13 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the Gamma(1, 2) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Gamma(1, 2) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            Gamma.Samples(data, 1, 2);
-            {
-                data[i] = gamma.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Gamma(8) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Gamma(5, 1) distribution and display histogram");
-            gamma.Shape = 5;
+            Gamma.Samples(data, 5, 1);
-            gamma.Scale = 1;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = gamma.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/InverseGammaDistribution.cs

@@ -118,33 +118,19 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the InverseGamma(4, 0.5) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the InverseGamma(4, 0.5) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            InverseGamma.Samples(data, 4, 0.5);
-            {
-                data[i] = inverseGamma.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the InverseGamma(8, 0.5) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the InverseGamma(8, 0.5) distribution and display histogram");
-            inverseGamma.Shape = 8;
+            InverseGamma.Samples(data, 8, 0.5);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = inverseGamma.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the InverseGamma(2, 1) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the InverseGamma(8, 2) distribution and display histogram");
-            inverseGamma.Scale = 2;
+            InverseGamma.Samples(data, 8, 2);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = inverseGamma.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/LaplaceDistribution.cs

@@ -121,34 +121,19 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the Laplace(0, 1) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Laplace(0, 1) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            Laplace.Samples(data, 0.0, 1.0);
-            {
-                data[i] = laplace.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Laplace(0, 4) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Laplace(0, 4) distribution and display histogram");
-            data = new double[100000];
+            Laplace.Samples(data, 0.0, 4.0);
-            laplace.Scale = 4;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = laplace.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the Laplace(-10, 4) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the Laplace(-10 4) distribution and display histogram");
-            laplace.Location = -10;
+            Laplace.Samples(data, -10.0, 4.0);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = laplace.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/LogNormalDistribution.cs

@@ -121,34 +121,19 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the LogNormal(0, 1) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the LogNormal(0, 1) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            LogNormal.Samples(data, 0.0, 1.0);
-            {
-                data[i] = logNormal.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the LogNormal(0, 0.5) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the LogNormal(0, 0.5) distribution and display histogram");
-            logNormal.Sigma = 0.5;
+            LogNormal.Samples(data, 0.0, 0.5);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = logNormal.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the LogNormal(5, 0.25) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the LogNormal(5, 0.25) distribution and display histogram");
-            logNormal.Mu = 5;
+            LogNormal.Samples(data, 5.0, 0.25);
-            logNormal.Sigma = 0.25;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = logNormal.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/NormalDistribution.cs

@@ -121,23 +121,13 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the Normal(0, 1) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Normal(0, 1) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            Normal.Samples(data, 0.0, 1.0);
-            {
-                data[i] = normal.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Normal(-10, 0.2) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Normal(-10, 0.01) distribution and display histogram");
-            normal.Mean = -10;
+            Normal.Samples(data, -10.0, 0.01);
-            normal.StdDev = 0.01;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = normal.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/ParetoDistribution.cs

@@ -121,34 +121,19 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the Pareto(1, 3) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Pareto(1, 3) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            Pareto.Samples(data, 1.0, 3.0);
-            {
-                data[i] = pareto.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Pareto(1, 1) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Pareto(1, 1) distribution and display histogram");
-            pareto.Shape = 1;
+            Pareto.Samples(data, 1.0, 1.0);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = pareto.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the Pareto(10, 5) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the Pareto(10, 50) distribution and display histogram");
-            pareto.Shape = 50;
+            Pareto.Samples(data, 10.0, 50.0);
-            pareto.Scale = 10;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = pareto.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/RayleighDistribution.cs

@@ -121,33 +121,19 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the Rayleigh(1) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Rayleigh(1) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            Rayleigh.Samples(data, 1.0);
-            {
-                data[i] = rayleigh.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Rayleigh(4) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Rayleigh(4) distribution and display histogram");
-            rayleigh.Scale = 4;
+            Rayleigh.Samples(data, 4.0);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = rayleigh.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the Rayleigh(0.5) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the Rayleigh(0.5) distribution and display histogram");
-            rayleigh.Scale = 0.5;
+            Rayleigh.Samples(data, 0.5);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = rayleigh.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/StableDistribution.cs

@@ -118,36 +118,19 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the Stable(1) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Stable(2, 0, 1, 0) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            Stable.Samples(data, 2, 0, 1, 0);
-            {
-                data[i] = stable.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Stable(1, 0, 1, 0) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Stable(1, 0, 1, 0) distribution and display histogram");
-            stable.Alpha = 1;
+            Stable.Samples(data, 1, 0, 1, 0);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = stable.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the Stable(1.5, 1, 1, 5) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the Stable(1.5, 1, 1, 5) distribution and display histogram");
-            stable.Alpha = 1.5;
+            Stable.Samples(data, 1.5, 1, 1, 5);
-            stable.Beta = 1;
-            stable.Location = 5;
-            stable.Scale = 5;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = stable.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/StudentTDistribution.cs

@@ -117,32 +117,18 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the StudentT(0, 1, 1) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the StudentT(0, 1, 1) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            StudentT.Samples(data, 0, 1, 1);
-            {
-                data[i] = studentT.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
 
             // 5. Generate 100000 samples of the StudentT(0, 1, 5) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the StudentT(0, 1, 5) distribution and display histogram");
-            studentT.DegreesOfFreedom = 5;
+            StudentT.Samples(data, 0, 1, 5);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = studentT.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the StudentT(0, 1, 10) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the StudentT(0, 1, 10) distribution and display histogram");
-            studentT.DegreesOfFreedom = 10;
+            StudentT.Samples(data, 0, 1, 10);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = studentT.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/TriangularDistribution.cs

@@ -63,7 +63,7 @@ namespace Examples.ContinuousDistributionsExamples
         /// <a href="https://en.wikipedia.org/wiki/Triangular_distribution">Triangular distribution</a>
         public void Run()
         {
-            // 1. Initialize 
+            // 1. Initialize
             var triangular = new Triangular(0, 1, 0.3);
             Console.WriteLine(@"1. Initialize the new instance of the Triangular distribution class with parameters Lower = {0}, Upper = {1}, Mode = {2}", triangular.LowerBound, triangular.UpperBound, triangular.Mode);
             Console.WriteLine();
@@ -119,26 +119,15 @@ namespace Examples.ContinuousDistributionsExamples
             Console.WriteLine();
 
             // 10000 samples with starting parameters
-            Console.WriteLine(@"4. Generate 100000 samples of the Triangular({0}, {1}, {2}) distribution and display histogram", triangular.LowerBound, triangular.UpperBound, triangular.Mode);
+            Console.WriteLine(@"4. Generate 100000 samples of the Triangular(0, 1, 0.3) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            Triangular.Samples(data, 0.0, 1.0, 0.3);
-            {
-                data[i] = triangular.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 10000 with different parameters
-            triangular.UpperBound = 10;
+            Console.WriteLine(@"4. Generate 100000 samples of the Triangular(2, 10, 8) distribution and display histogram");
-            triangular.Mode = 8;
+            Triangular.Samples(data, 2.0, 10.0, 8.0);
-            triangular.LowerBound = 2;
-            Console.WriteLine(@"4. Generate 100000 samples of the Triangular({0}, {1}, {2}) distribution and display histogram", triangular.LowerBound, triangular.UpperBound, triangular.Mode);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = triangular.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/ContinuousDistributions/WeibullDistribution.cs

@@ -121,33 +121,19 @@ namespace Examples.ContinuousDistributionsExamples
             // 4. Generate 100000 samples of the Weibull(0.5, 1) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Weibull(0.5, 1) distribution and display histogram");
             var data = new double[100000];
-            for (var i = 0; i < data.Length; i++)
+            Weibull.Samples(data, 0.5, 1.0);
-            {
-                data[i] = weibull.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Weibull(1.5, 1) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Weibull(1.5, 1) distribution and display histogram");
-            weibull.Shape = 1.5;
+            Weibull.Samples(data, 1.5, 1.0);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = weibull.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the Weibull(5, 1) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the Weibull(5, 1) distribution and display histogram");
-            weibull.Shape = 5;
+            Weibull.Samples(data, 5.0, 1.0);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = weibull.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/DiscreteDistributions/BernoulliDistribution.cs

@@ -117,34 +117,20 @@ namespace Examples.DiscreteDistributionsExamples
 
             // 4. Generate 100000 samples of the Bernoulli(0.2) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Bernoulli(0.2) distribution and display histogram");
-            var data = new double[100000];
+            var data = new int[100000];
-            for (var i = 0; i < data.Length; i++)
+            Bernoulli.Samples(data, 0.2);
-            {
-                data[i] = bernoulli.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Bernoulli(4) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Bernoulli(0.9) distribution and display histogram");
-            bernoulli.P = 0.9;
+            Bernoulli.Samples(data, 0.9);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = bernoulli.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the Bernoulli(8) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the Bernoulli(0.5) distribution and display histogram");
-            bernoulli.P = 0.5;
+            Bernoulli.Samples(data, 0.5);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = bernoulli.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/DiscreteDistributions/BinomialDistribution.cs

@@ -120,35 +120,20 @@ namespace Examples.DiscreteDistributionsExamples
 
             // 4. Generate 100000 samples of the Binomial(0.2, 20) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Binomial(0.2, 20) distribution and display histogram");
-            var data = new double[100000];
+            var data = new int[100000];
-            for (var i = 0; i < data.Length; i++)
+            Binomial.Samples(data, 0.2, 20);
-            {
-                data[i] = binomial.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Binomial(0.7, 20) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Binomial(0.7, 20) distribution and display histogram");
-            binomial.P = 0.7;
+            Binomial.Samples(data, 0.7, 20);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = binomial.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the Binomial(0.5, 40) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the Binomial(0.5, 40) distribution and display histogram");
-            binomial.P = 0.5;
+            Binomial.Samples(data, 0.5, 40);
-            binomial.N = 40;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = binomial.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/DiscreteDistributions/CategoricalDistribution.cs

@@ -111,24 +111,15 @@ namespace Examples.DiscreteDistributionsExamples
             Console.WriteLine();
 
             // 4. Generate 100000 samples of the Categorical(new []{ 0.1, 0.2, 0.25, 0.45 }) distribution and display histogram
-            Console.WriteLine(@"4. Generate 100000 samples of the Categorical(0.2, 20) distribution and display histogram");
+            Console.WriteLine(@"4. Generate 100000 samples of the Categorical(0.1, 0.2, 0.25, 0.45) distribution and display histogram");
-            var data = new double[100000];
+            var data = new int[100000];
-            for (var i = 0; i < data.Length; i++)
+            Categorical.Samples(data, new[] { 0.1, 0.2, 0.25, 0.45 });
-            {
-                data[i] = binomial.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Categorical(new []{ 0.6, 0.2, 0.1, 0.1 }) distribution and display histogram
-            Console.WriteLine(@"5. Generate 100000 samples of the Categorical(0.7, 20) distribution and display histogram");
+            Console.WriteLine(@"5. Generate 100000 samples of the Categorical(0.6, 0.2, 0.1, 0.1) distribution and display histogram");
-            binomial.P = new[] { 0.6, 0.2, 0.1, 0.1 };
+            Categorical.Samples(data, new[] { 0.6, 0.2, 0.1, 0.1 });
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = binomial.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/DiscreteDistributions/ConwayMaxwellPoissonDistribution.cs

@@ -63,40 +63,40 @@ namespace Examples.DiscreteDistributionsExamples
         public void Run()
         {
             // 1. Initialize the new instance of the ConwayMaxwellPoisson distribution class with parameters Lambda = 2, Nu = 1
-            var binomial = new ConwayMaxwellPoisson(2, 1);
+            var conwayMaxwellPoisson = new ConwayMaxwellPoisson(2, 1);
-            Console.WriteLine(@"1. Initialize the new instance of the ConwayMaxwellPoisson distribution class with parameters Lambda = {0}, Nu = {1}", binomial.Lambda, binomial.Nu);
+            Console.WriteLine(@"1. Initialize the new instance of the ConwayMaxwellPoisson distribution class with parameters Lambda = {0}, Nu = {1}", conwayMaxwellPoisson.Lambda, conwayMaxwellPoisson.Nu);
             Console.WriteLine();
 
             // 2. Distributuion properties:
-            Console.WriteLine(@"2. {0} distributuion properties:", binomial);
+            Console.WriteLine(@"2. {0} distributuion properties:", conwayMaxwellPoisson);
 
             // Cumulative distribution function
-            Console.WriteLine(@"{0} - Сumulative distribution at location '3'", binomial.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Сumulative distribution at location '3'", conwayMaxwellPoisson.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
 
             // Probability density
-            Console.WriteLine(@"{0} - Probability mass at location '3'", binomial.Probability(3).ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Probability mass at location '3'", conwayMaxwellPoisson.Probability(3).ToString(" #0.00000;-#0.00000"));
 
             // Log probability density
-            Console.WriteLine(@"{0} - Log probability mass at location '3'", binomial.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Log probability mass at location '3'", conwayMaxwellPoisson.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
 
             // Smallest element in the domain
-            Console.WriteLine(@"{0} - Smallest element in the domain", binomial.Minimum.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Smallest element in the domain", conwayMaxwellPoisson.Minimum.ToString(" #0.00000;-#0.00000"));
 
             // Mean
-            Console.WriteLine(@"{0} - Mean", binomial.Mean.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Mean", conwayMaxwellPoisson.Mean.ToString(" #0.00000;-#0.00000"));
             
             // Variance
-            Console.WriteLine(@"{0} - Variance", binomial.Variance.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Variance", conwayMaxwellPoisson.Variance.ToString(" #0.00000;-#0.00000"));
 
             // Standard deviation
-            Console.WriteLine(@"{0} - Standard deviation", binomial.StdDev.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Standard deviation", conwayMaxwellPoisson.StdDev.ToString(" #0.00000;-#0.00000"));
             Console.WriteLine();
 
             // 3. Generate 10 samples of the ConwayMaxwellPoisson distribution
             Console.WriteLine(@"3. Generate 10 samples of the ConwayMaxwellPoisson distribution");
             for (var i = 0; i < 10; i++)
             {
-                Console.Write(binomial.Sample().ToString("N05") + @" ");
+                Console.Write(conwayMaxwellPoisson.Sample().ToString("N05") + @" ");
             }
 
             Console.WriteLine();
@@ -104,35 +104,20 @@ namespace Examples.DiscreteDistributionsExamples
 
             // 4. Generate 100000 samples of the ConwayMaxwellPoisson(4, 1) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the ConwayMaxwellPoisson(4, 1) distribution and display histogram");
-            var data = new double[100000];
+            var data = new int[100000];
-            for (var i = 0; i < data.Length; i++)
+            ConwayMaxwellPoisson.Samples(data, 4, 1);
-            {
-                data[i] = binomial.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the ConwayMaxwellPoisson(2, 1) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the ConwayMaxwellPoisson(2, 1) distribution and display histogram");
-            binomial.Lambda = 2;
+            ConwayMaxwellPoisson.Samples(data, 2, 1);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = binomial.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the ConwayMaxwellPoisson(5, 2) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the ConwayMaxwellPoisson(5, 2) distribution and display histogram");
-            binomial.Lambda = 5;
+            ConwayMaxwellPoisson.Samples(data, 5, 2);
-            binomial.Nu = 2;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = binomial.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/DiscreteDistributions/DiscreteUniformDistribution.cs

@@ -120,36 +120,20 @@ namespace Examples.DiscreteDistributionsExamples
 
             // 4. Generate 100000 samples of the DiscreteUniform(2, 10) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the DiscreteUniform(2, 10) distribution and display histogram");
-            var data = new double[100000];
+            var data = new int[100000];
-            for (var i = 0; i < data.Length; i++)
+            DiscreteUniform.Samples(data, 2, 10);
-            {
-                data[i] = discreteUniform.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the DiscreteUniform(-10, 10) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the DiscreteUniform(-10, 10) distribution and display histogram");
-            discreteUniform.LowerBound = -10;
+            DiscreteUniform.Samples(data, -10, 10);
-            discreteUniform.UpperBound = 10;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = discreteUniform.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the DiscreteUniform(0, 40) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the DiscreteUniform(0, 40) distribution and display histogram");
-            discreteUniform.LowerBound = 0;
+            DiscreteUniform.Samples(data, 0, 40);
-            discreteUniform.UpperBound = 40;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = discreteUniform.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/DiscreteDistributions/GeometricDistribution.cs

@@ -119,35 +119,21 @@ namespace Examples.DiscreteDistributionsExamples
             Console.WriteLine();
 
             // 4. Generate 100000 samples of the Geometric(0.2, 20) distribution and display histogram
-            Console.WriteLine(@"4. Generate 100000 samples of the Geometric(0.2, 20) distribution and display histogram");
+            Console.WriteLine(@"4. Generate 100000 samples of the Geometric(0.2) distribution and display histogram");
-            var data = new double[100000];
+            var data = new int[100000];
-            for (var i = 0; i < data.Length; i++)
+            Geometric.Samples(data, 0.2);
-            {
-                data[i] = geometric.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Geometric(0.5) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Geometric(0.5) distribution and display histogram");
-            geometric.P = 0.5;
+            Geometric.Samples(data, 0.5);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = geometric.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the Geometric(0.8) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the Geometric(0.8) distribution and display histogram");
-            geometric.P = 0.8;
+            Geometric.Samples(data, 0.8);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = geometric.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/DiscreteDistributions/HypergeometricDistribution.cs

@@ -114,25 +114,14 @@ namespace Examples.DiscreteDistributionsExamples
 
             // 4. Generate 100000 samples of the Hypergeometric(30, 15, 10) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Hypergeometric(30, 15, 10) distribution and display histogram");
-            var data = new double[100000];
+            var data = new int[100000];
-            for (var i = 0; i < data.Length; i++)
+            Hypergeometric.Samples(data, 30, 15, 10);
-            {
-                data[i] = hypergeometric.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Hypergeometric(52, 13, 5) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Hypergeometric(52, 13, 5) distribution and display histogram");
-            hypergeometric.Population = 52;
+            Hypergeometric.Samples(data, 52, 13, 5);
-            hypergeometric.Success = 13;
-            hypergeometric.Draws = 5;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = hypergeometric.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/DiscreteDistributions/NegativeBinomialDistribution.cs

@@ -113,36 +113,21 @@ namespace Examples.DiscreteDistributionsExamples
             Console.WriteLine();
 
             // 4. Generate 100000 samples of the NegativeBinomial(0.2, 20) distribution and display histogram
-            Console.WriteLine(@"4. Generate 100000 samples of the NegativeBinomial(0.2, 20) distribution and display histogram");
+            Console.WriteLine(@"4. Generate 100000 samples of the NegativeBinomial(20, 0.2) distribution and display histogram");
-            var data = new double[100000];
+            var data = new int[100000];
-            for (var i = 0; i < data.Length; i++)
+            NegativeBinomial.Samples(data, 20, 0.2);
-            {
-                data[i] = negativeBinomial.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the NegativeBinomial(0.7, 20) distribution and display histogram
-            Console.WriteLine(@"5. Generate 100000 samples of the NegativeBinomial(0.7, 20) distribution and display histogram");
+            Console.WriteLine(@"5. Generate 100000 samples of the NegativeBinomial(20, 0.7) distribution and display histogram");
-            negativeBinomial.P = 0.7;
+            NegativeBinomial.Samples(data, 20, 0.7);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = negativeBinomial.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the NegativeBinomial(0.5, 1) distribution and display histogram
-            Console.WriteLine(@"6. Generate 100000 samples of the NegativeBinomial(0.5, 1) distribution and display histogram");
+            Console.WriteLine(@"6. Generate 100000 samples of the NegativeBinomial(1, 0.5) distribution and display histogram");
-            negativeBinomial.P = 0.5;
+            NegativeBinomial.Samples(data, 1, 0.5);
-            negativeBinomial.R = 1;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = negativeBinomial.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/DiscreteDistributions/PoissonDistribution.cs

@@ -120,34 +120,20 @@ namespace Examples.DiscreteDistributionsExamples
 
             // 4. Generate 100000 samples of the Poisson(1) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Poisson(1) distribution and display histogram");
-            var data = new double[100000];
+            var data = new int[100000];
-            for (var i = 0; i < data.Length; i++)
+            Poisson.Samples(data, 1);
-            {
-                data[i] = poisson.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Poisson(4) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Poisson(4) distribution and display histogram");
-            poisson.Lambda = 4;
+            Poisson.Samples(data, 4);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = poisson.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the Poisson(10) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the Poisson(10) distribution and display histogram");
-            poisson.Lambda = 10;
+            Poisson.Samples(data, 10);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = poisson.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/DiscreteDistributions/ZipfDistribution.cs

@@ -117,35 +117,20 @@ namespace Examples.DiscreteDistributionsExamples
 
             // 4. Generate 100000 samples of the Zipf(5, 10) distribution and display histogram
             Console.WriteLine(@"4. Generate 100000 samples of the Zipf(5, 10) distribution and display histogram");
-            var data = new double[100000];
+            var data = new int[100000];
-            for (var i = 0; i < data.Length; i++)
+            Zipf.Samples(data, 5, 10);
-            {
-                data[i] = zipf.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 5. Generate 100000 samples of the Zipf(2, 10) distribution and display histogram
             Console.WriteLine(@"5. Generate 100000 samples of the Zipf(2, 10) distribution and display histogram");
-            zipf.S = 2;
+            Zipf.Samples(data, 2, 10);
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = zipf.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
             Console.WriteLine();
 
             // 6. Generate 100000 samples of the Zipf(5, 20) distribution and display histogram
             Console.WriteLine(@"6. Generate 100000 samples of the Zipf(1, 20) distribution and display histogram");
-            zipf.S = 1;
+            Zipf.Samples(data, 1, 20);
-            zipf.N = 20;
-            for (var i = 0; i < data.Length; i++)
-            {
-                data[i] = zipf.Sample();
-            }
-
             ConsoleHelper.DisplayHistogram(data);
         }
     }

src/Examples/Examples.csproj

@@ -172,15 +172,15 @@
       <Install>true</Install>
     </BootstrapperPackage>
   </ItemGroup>
+  <ItemGroup>
+    <Folder Include="Properties\" />
+  </ItemGroup>
   <ItemGroup>
     <ProjectReference Include="..\Numerics\Numerics.csproj">
-      <Project>{B7CAE5F4-A23F-4438-B5BE-41226618B695}</Project>
+      <Project>{b7cae5f4-a23f-4438-b5be-41226618b695}</Project>
       <Name>Numerics</Name>
     </ProjectReference>
   </ItemGroup>
-  <ItemGroup>
-    <Folder Include="Properties\" />
-  </ItemGroup>
   <Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
   <!-- To modify your build process, add your task inside one of the targets below and uncomment it. 
        Other similar extension points exist, see Microsoft.Common.targets.

src/FSharpExamples/FSharpExamples.fsproj

@@ -70,6 +70,8 @@
       <RequiredTargetFramework>3.5</RequiredTargetFramework>
     </Reference>
     <Reference Include="System.Numerics" />
+  </ItemGroup>
+  <ItemGroup>
     <ProjectReference Include="..\FSharp\FSharp.fsproj">
       <Name>FSharp</Name>
       <Project>{37e8e802-a354-4114-bfc1-6e1357da605b}</Project>