Generate: Map, RandomMap; prepare to phase out SignalGenerator

src/Examples/Signals/Random.cs

@@ -25,6 +25,7 @@
 // </copyright>
 
 using System;
+using MathNet.Numerics;
 using MathNet.Numerics.Distributions;
 using MathNet.Numerics.Signals;
 
@@ -64,7 +65,7 @@ namespace Examples.SignalsExamples
         {
             // 1. Get 10 random samples of f(x) = (x * x) / 2 using continuous uniform distribution on [-10, 10]
             var uniform = new ContinuousUniform(-10, 10);
-            var result = SignalGenerator.Random(Function, uniform, 10);
+            var result = Generate.RandomMap(10, uniform, Function);
             Console.WriteLine(@" 1. Get 10 random samples of f(x) = (x * x) / 2 using continuous uniform distribution on [-10, 10]");
             for (var i = 0; i < result.Length; i++)
             {
@@ -76,8 +77,8 @@ namespace Examples.SignalsExamples
 
             // 2. Get 10 random samples of f(x) = (x * x) / 2 using Exponential(1) distribution and retrieve sample points
             var exponential = new Exponential(1);
-            double[] samplePoints;
+            double[] samplePoints = Generate.Random(10, exponential);
-            result = SignalGenerator.Random(Function, exponential, 10, out samplePoints);
+            result = Generate.Map(samplePoints, Function);
             Console.WriteLine(@"2. Get 10 random samples of f(x) = (x * x) / 2 using Exponential(1) distribution and retrieve sample points");
             Console.Write(@"Points: ");
             for (var i = 0; i < samplePoints.Length; i++)
@@ -97,7 +98,7 @@ namespace Examples.SignalsExamples
 
             // 3. Get 10 random samples of f(x, y) = (x * y) / 2 using ChiSquare(10) distribution
             var chiSquare = new ChiSquared(10);
-            result = SignalGenerator.Random(TwoDomainFunction, chiSquare, 10);
+            result = Generate.RandomMap2(10, chiSquare, TwoDomainFunction);
             Console.WriteLine(@" 3. Get 10 random samples of f(x, y) = (x * y) / 2 using ChiSquare(10) distribution");
             for (var i = 0; i < result.Length; i++)
             {

src/Numerics/Generate.cs

@@ -32,10 +32,15 @@ using System;
 using System.Collections.Generic;
 using System.Linq;
 using MathNet.Numerics.Distributions;
+using MathNet.Numerics.Properties;
 using MathNet.Numerics.Random;
 
 namespace MathNet.Numerics
 {
+#if !NOSYSNUMERICS
+    using System.Numerics;
+#endif
+
     public static class Generate
     {
         /// <summary>
@@ -301,6 +306,95 @@ namespace MathNet.Numerics
             }
         }
 
+        /// <summary>
+        /// Generate samples by sampling a function at the provided points.
+        /// </summary>
+        public static TR[] Map<T, TR>(T[] points, Func<T, TR> map)
+        {
+            var res = new TR[points.Length];
+            for (int i = 0; i < points.Length; i++)
+            {
+                res[i] = map(points[i]);
+            }
+            return res;
+        }
+
+        /// <summary>
+        /// Generate a sample sequence by sampling a function at the provided point sequence.
+        /// </summary>
+        public static IEnumerable<TR> MapSequence<T, TR>(IEnumerable<T> points, Func<T, TR> map)
+        {
+            return points.Select(map);
+        }
+
+        /// <summary>
+        /// Generate samples by sampling a function at the provided points.
+        /// </summary>
+        public static TR[] Map2<TA, TB, TR>(TA[] pointsA, TB[] pointsB, Func<TA, TB, TR> map)
+        {
+            if (pointsA.Length != pointsB.Length)
+            {
+                throw new ArgumentException(Resources.ArgumentArraysSameLength, "pointsB");
+            }
+
+            var res = new TR[pointsA.Length];
+            for (int i = 0; i < res.Length; i++)
+            {
+                res[i] = map(pointsA[i], pointsB[i]);
+            }
+            return res;
+        }
+
+        /// <summary>
+        /// Generate a sample sequence by sampling a function at the provided point sequence.
+        /// </summary>
+        public static IEnumerable<TR> Map2Sequence<TA, TB, TR>(IEnumerable<TA> pointsA, IEnumerable<TB> pointsB, Func<TA, TB, TR> map)
+        {
+            return pointsA.Zip(pointsB, map);
+        }
+
+        /// <summary>
+        /// Generate samples by sampling a function at samples from a probability distribution.
+        /// </summary>
+        public static T[] RandomMap<T>(int length, IContinuousDistribution distribution, Func<double, T> map)
+        {
+            var res = new T[length];
+            for (int i = 0; i < res.Length; i++)
+            {
+                res[i] = map(distribution.Sample());
+            }
+            return res;
+        }
+
+        /// <summary>
+        /// Generate a sample sequence by sampling a function at samples from a probability distribution.
+        /// </summary>
+        public static IEnumerable<T> RandomMapSequence<T>(IContinuousDistribution distribution, Func<double, T> map)
+        {
+            return distribution.Samples().Select(map);
+        }
+
+        /// <summary>
+        /// Generate samples by sampling a function at sample pairs from a probability distribution.
+        /// </summary>
+        public static T[] RandomMap2<T>(int length, IContinuousDistribution distribution, Func<double, double, T> map)
+        {
+            var res = new T[length];
+            for (int i = 0; i < res.Length; i++)
+            {
+                res[i] = map(distribution.Sample(), distribution.Sample());
+            }
+            return res;
+        }
+
+        /// <summary>
+        /// Generate a sample sequence by sampling a function at sample pairs from a probability distribution.
+        /// </summary>
+        public static IEnumerable<T> RandomMap2Sequence<T>(IContinuousDistribution distribution, Func<double, double, T> map)
+        {
+            return distribution.Samples().Zip(distribution.Samples(), map);
+        }
+
         /// <summary>
         /// Create random samples.
         /// </summary>
@@ -317,6 +411,22 @@ namespace MathNet.Numerics
             return distribution.Samples();
         }
 
+        /// <summary>
+        /// Create random samples.
+        /// </summary>
+        public static Complex[] RandomComplex(int length, IContinuousDistribution distribution)
+        {
+            return RandomMap2(length, distribution, (r, i) => new Complex(r, i));
+        }
+
+        /// <summary>
+        /// Create an infinite random sample sequence.
+        /// </summary>
+        public static IEnumerable<Complex> RandomComplex(IContinuousDistribution distribution)
+        {
+            return RandomMap2Sequence(distribution, (r, i) => new Complex(r, i));
+        }
+
         /// <summary>
         /// Create samples with independent amplitudes of normal distribution and a flat spectral density.
         /// </summary>
@@ -357,26 +467,5 @@ namespace MathNet.Numerics
         {
             return Stable.Samples(MersenneTwister.Default, alpha, beta, scale, location);
         }
-
-        /// <summary>
-        /// Generate sample by sampling a function at the provided points.
-        /// </summary>
-        public static TV[] Map<TU, TV>(TU[] points, Func<TU, TV> map)
-        {
-            var res = new TV[points.Length];
-            for (int i = 0; i < points.Length; i++)
-            {
-                res[i] = map(points[i]);
-            }
-            return res;
-        }
-
-        /// <summary>
-        /// Generate a sample sequence by sampling a function at the provided point sequence.
-        /// </summary>
-        public static IEnumerable<TV> MapSequence<TU, TV>(IEnumerable<TU> points, Func<TU, TV> map)
-        {
-            return points.Select(map);
-        }
     }
 }

src/Numerics/Signals/SignalGenerator.Chebyshev.cs

@@ -56,12 +56,7 @@ namespace MathNet.Numerics.Signals
             int sampleCount)
         {
             var roots = FindRoots.ChebychevPolynomialFirstKind(sampleCount, intervalBegin, intervalEnd);
-            var samples = new T[roots.Length];
+            return Generate.Map(roots, function);
-            for (int i = 0; i < samples.Length; i++)
-            {
-                samples[i] = function(roots[i]);
-            }
-            return samples;
         }
 
         /// <summary>
@@ -83,12 +78,7 @@ namespace MathNet.Numerics.Signals
             int sampleCount)
         {
             var roots = FindRoots.ChebychevPolynomialSecondKind(sampleCount, intervalBegin, intervalEnd);
-            var samples = new T[roots.Length];
+            return Generate.Map(roots, function);
-            for (int i = 0; i < samples.Length; i++)
-            {
-                samples[i] = function(roots[i]);
-            }
-            return samples;
         }
     }
 }

src/Numerics/Signals/SignalGenerator.Equidistant.cs

@@ -49,6 +49,7 @@ namespace MathNet.Numerics.Signals
         /// <returns>The generated sample vector.</returns>
         /// <exception cref="ArgumentNullException" />
         /// <exception cref="ArgumentOutOfRangeException" />
+        [Obsolete]
         public static T[] EquidistantInterval<T>(
             Func<double, T> function,
             double intervalBegin,
@@ -102,6 +103,7 @@ namespace MathNet.Numerics.Signals
         /// <returns>The generated sample vector.</returns>
         /// <exception cref="ArgumentNullException" />
         /// <exception cref="ArgumentOutOfRangeException" />
+        [Obsolete]
         public static T[] EquidistantInterval<T>(
             Func<double, T> function,
             double intervalBegin,
@@ -161,6 +163,7 @@ namespace MathNet.Numerics.Signals
         /// <returns>The generated sample vector.</returns>
         /// <exception cref="ArgumentNullException" />
         /// <exception cref="ArgumentOutOfRangeException"/>
+        [Obsolete]
         public static T[] EquidistantPeriodic<T>(
             Func<double, T> function,
             double periodLength,
@@ -203,6 +206,7 @@ namespace MathNet.Numerics.Signals
         /// <returns>The generated sample vector.</returns>
         /// <exception cref="ArgumentNullException" />
         /// <exception cref="ArgumentOutOfRangeException"/>
+        [Obsolete]
         public static T[] EquidistantPeriodic<T>(
             Func<double, T> function,
             double periodLength,
@@ -246,6 +250,7 @@ namespace MathNet.Numerics.Signals
         /// <returns>The generated sample vector.</returns>
         /// <exception cref="ArgumentNullException" />
         /// <exception cref="ArgumentOutOfRangeException"/>
+        [Obsolete]
         public static T[] EquidistantStartingAt<T>(
             Func<double, T> function,
             double start,
@@ -286,6 +291,7 @@ namespace MathNet.Numerics.Signals
         /// <returns>The generated sample vector.</returns>
         /// <exception cref="ArgumentNullException" />
         /// <exception cref="ArgumentOutOfRangeException"/>
+        [Obsolete]
         public static T[] EquidistantStartingAt<T>(
             Func<double, T> function,
             double start,
@@ -326,6 +332,7 @@ namespace MathNet.Numerics.Signals
         /// <typeparam name="T">The value type of the function to sample.</typeparam>
         /// <returns>The generated sample enumerator.</returns>
         /// <exception cref="ArgumentNullException" />
+        [Obsolete]
         public static IEnumerable<T> EquidistantContinuous<T>(
             Func<double, T> function,
             double start,
@@ -354,6 +361,7 @@ namespace MathNet.Numerics.Signals
         /// <typeparam name="T">The value type of the function to sample.</typeparam>
         /// <returns>The generated samples integer-domain function.</returns>
         /// <exception cref="ArgumentNullException" />
+        [Obsolete]
         public static Func<int, T> EquidistantToFunction<T>(
             Func<double, T> function,
             double start,

src/Numerics/Signals/SignalGenerator.Random.cs

@@ -48,34 +48,13 @@ namespace MathNet.Numerics.Signals
         /// <returns>The generated sample vector.</returns>
         /// <exception cref="ArgumentNullException" />
         /// <exception cref="ArgumentOutOfRangeException" />
+        [Obsolete]
         public static T[] Random<T>(
             Func<double, T> function,
             IContinuousDistribution distribution,
             int sampleCount)
         {
-            if (ReferenceEquals(function, null))
+            return Generate.RandomMap(sampleCount, distribution, function);
-            {
-                throw new ArgumentNullException("function");
-            }
-
-            if (ReferenceEquals(distribution, null))
-            {
-                throw new ArgumentNullException("distribution");
-            }
-
-            if (sampleCount < 0)
-            {
-                throw new ArgumentOutOfRangeException("sampleCount");
-            }
-
-            var samples = new T[sampleCount];
-
-            for (int i = 0; i < samples.Length; i++)
-            {
-                samples[i] = function(distribution.Sample());
-            }
-
-            return samples;
         }
 
         /// <summary>
@@ -89,38 +68,15 @@ namespace MathNet.Numerics.Signals
         /// <returns>The generated sample vector.</returns>
         /// <exception cref="ArgumentNullException" />
         /// <exception cref="ArgumentOutOfRangeException" />
+        [Obsolete]
         public static T[] Random<T>(
             Func<double, T> function,
             IContinuousDistribution distribution,
             int sampleCount,
             out double[] samplePoints)
         {
-            if (ReferenceEquals(function, null))
+            samplePoints = Generate.Random(sampleCount, distribution);
-            {
+            return Generate.Map(samplePoints, function);
-                throw new ArgumentNullException("function");
-            }
-
-            if (ReferenceEquals(distribution, null))
-            {
-                throw new ArgumentNullException("distribution");
-            }
-
-            if (sampleCount < 0)
-            {
-                throw new ArgumentOutOfRangeException("sampleCount");
-            }
-
-            var samples = new T[sampleCount];
-            samplePoints = new double[sampleCount];
-
-            for (int i = 0; i < samples.Length; i++)
-            {
-                double current = distribution.Sample();
-                samplePoints[i] = current;
-                samples[i] = function(current);
-            }
-
-            return samples;
         }
 
         /// <summary>
@@ -133,34 +89,13 @@ namespace MathNet.Numerics.Signals
         /// <returns>The generated sample vector.</returns>
         /// <exception cref="ArgumentNullException" />
         /// <exception cref="ArgumentOutOfRangeException" />
+        [Obsolete]
         public static T[] Random<T>(
             Func<double, double, T> function,
             IContinuousDistribution distribution,
             int sampleCount)
         {
-            if (ReferenceEquals(function, null))
+            return Generate.RandomMap2(sampleCount, distribution, function);
-            {
-                throw new ArgumentNullException("function");
-            }
-
-            if (ReferenceEquals(distribution, null))
-            {
-                throw new ArgumentNullException("distribution");
-            }
-
-            if (sampleCount < 0)
-            {
-                throw new ArgumentOutOfRangeException("sampleCount");
-            }
-
-            var samples = new T[sampleCount];
-
-            for (int i = 0; i < samples.Length; i++)
-            {
-                samples[i] = function(distribution.Sample(), distribution.Sample());
-            }
-
-            return samples;
         }
     }
-}
+}

src/UnitTests/IntegralTransformsTests/FourierTest.cs

@@ -33,12 +33,9 @@ using NUnit.Framework;
 
 namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
 {
-    using Random = System.Random;
 
-#if NOSYSNUMERICS
+#if !NOSYSNUMERICS
-    using Complex = Numerics.Complex;
+    using System.Numerics;
-#else
-    using Complex = System.Numerics.Complex;
 #endif
 
     /// <summary>
@@ -52,7 +49,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         /// </summary>
         IContinuousDistribution GetUniform(int seed)
         {
-            return new ContinuousUniform(-1, 1, new Random(seed));
+            return new ContinuousUniform(-1, 1, new System.Random(seed));
         }
 
         /// <summary>
@@ -97,8 +94,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         [Test]
         public void Radix2ThrowsWhenNotPowerOfTwo()
         {
-            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), GetUniform(1), 0x7F);
+            var samples = Generate.RandomComplex(0x7F, GetUniform(1));
-
             var dft = new DiscreteFourierTransform();
 
             Assert.Throws(typeof (ArgumentException), () => dft.Radix2Forward(samples, FourierOptions.Default));

src/UnitTests/IntegralTransformsTests/HartleyTest.cs

@@ -33,12 +33,9 @@ using NUnit.Framework;
 
 namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
 {
-    using Random = System.Random;
 
-#if NOSYSNUMERICS
+#if !NOSYSNUMERICS
-    using Complex = Numerics.Complex;
+    using System.Numerics;
-#else
-    using Complex = System.Numerics.Complex;
 #endif
 
     /// <summary>
@@ -52,7 +49,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         /// </summary>
         IContinuousDistribution GetUniform(int seed)
         {
-            return new ContinuousUniform(-1, 1, new Random(seed));
+            return new ContinuousUniform(-1, 1, new System.Random(seed));
         }
 
         /// <summary>
@@ -85,7 +82,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         public void NaiveMatchesDft(HartleyOptions hartleyOptions, FourierOptions fourierOptions)
         {
             var dht = new DiscreteHartleyTransform();
-            var samples = SignalGenerator.Random(x => x, GetUniform(1), 0x80);
+            var samples = Generate.Random(0x80, GetUniform(1));
 
             VerifyMatchesDft(
                 samples,

src/UnitTests/IntegralTransformsTests/InverseTransformTest.cs

@@ -24,6 +24,7 @@
 // OTHER DEALINGS IN THE SOFTWARE.
 // </copyright>
 
+using System;
 using MathNet.Numerics.Distributions;
 using MathNet.Numerics.IntegralTransforms;
 using MathNet.Numerics.IntegralTransforms.Algorithms;
@@ -32,12 +33,9 @@ using NUnit.Framework;
 
 namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
 {
-    using Random = System.Random;
 
-#if NOSYSNUMERICS
+#if !NOSYSNUMERICS
-    using Complex = Numerics.Complex;
+    using System.Numerics;
-#else
-    using Complex = System.Numerics.Complex;
 #endif
 
     /// <summary>
@@ -51,7 +49,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         /// </summary>
         IContinuousDistribution GetUniform(int seed)
         {
-            return new ContinuousUniform(-1, 1, new Random(seed));
+            return new ContinuousUniform(-1, 1, new System.Random(seed));
         }
 
         /// <summary>
@@ -64,7 +62,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         {
             var dft = new DiscreteFourierTransform();
 
-            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), GetUniform(1), 0x80);
+            var samples = Generate.RandomComplex(0x80, GetUniform(1));
             var work = new Complex[samples.Length];
             samples.CopyTo(work, 0);
 
@@ -85,7 +83,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         {
             var dft = new DiscreteFourierTransform();
 
-            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), GetUniform(1), 0x8000);
+            var samples = Generate.RandomComplex(0x8000, GetUniform(1));
             var work = new Complex[samples.Length];
             samples.CopyTo(work, 0);
 
@@ -106,7 +104,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         {
             var dft = new DiscreteFourierTransform();
 
-            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), GetUniform(1), 0x7FFF);
+            var samples = Generate.RandomComplex(0x7FFF, GetUniform(1));
             var work = new Complex[samples.Length];
             samples.CopyTo(work, 0);
 
@@ -127,7 +125,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         {
             var dht = new DiscreteHartleyTransform();
 
-            var samples = SignalGenerator.Random(x => x, GetUniform(1), 0x80);
+            var samples = Generate.Random(0x80, GetUniform(1));
             var work = new double[samples.Length];
             samples.CopyTo(work, 0);
 
@@ -144,7 +142,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         [Test]
         public void FourierDefaultTransformIsReversible()
         {
-            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), GetUniform(1), 0x7FFF);
+            var samples = Generate.RandomComplex(0x7FFF, GetUniform(1));
             var work = new Complex[samples.Length];
             samples.CopyTo(work, 0);
 

src/UnitTests/IntegralTransformsTests/MatchingNaiveTransformTest.cs

@@ -33,12 +33,9 @@ using NUnit.Framework;
 
 namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
 {
-    using Random = System.Random;
 
-#if NOSYSNUMERICS
+#if !NOSYSNUMERICS
-    using Complex = Numerics.Complex;
+    using System.Numerics;
-#else
-    using Complex = System.Numerics.Complex;
 #endif
 
     /// <summary>
@@ -52,7 +49,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         /// </summary>
         IContinuousDistribution GetUniform(int seed)
         {
-            return new ContinuousUniform(-1, 1, new Random(seed));
+            return new ContinuousUniform(-1, 1, new System.Random(seed));
         }
 
         /// <summary>
@@ -108,7 +105,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         public void FourierRadix2MatchesNaiveOnRandom(FourierOptions options)
         {
             var dft = new DiscreteFourierTransform();
-            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), GetUniform(1), 0x80);
+            var samples = Generate.RandomComplex(0x80, GetUniform(1));
 
             VerifyMatchesNaiveComplex(
                 samples,
@@ -158,7 +155,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         public void FourierBluesteinMatchesNaiveOnRandomPowerOfTwo(FourierOptions options)
         {
             var dft = new DiscreteFourierTransform();
-            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), GetUniform(1), 0x80);
+            var samples = Generate.RandomComplex(0x80, GetUniform(1));
 
             VerifyMatchesNaiveComplex(
                 samples,
@@ -183,7 +180,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         public void FourierBluesteinMatchesNaiveOnRandomNonPowerOfTwo(FourierOptions options)
         {
             var dft = new DiscreteFourierTransform();
-            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), GetUniform(1), 0x7F);
+            var samples = Generate.RandomComplex(0x7F, GetUniform(1));
 
             VerifyMatchesNaiveComplex(
                 samples,

src/UnitTests/IntegralTransformsTests/ParsevalTheoremTest.cs

@@ -28,18 +28,14 @@ using System.Linq;
 using MathNet.Numerics.Distributions;
 using MathNet.Numerics.IntegralTransforms;
 using MathNet.Numerics.IntegralTransforms.Algorithms;
-using MathNet.Numerics.Signals;
 using MathNet.Numerics.Statistics;
 using NUnit.Framework;
 
 namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
 {
-    using Random = System.Random;
 
-#if NOSYSNUMERICS
+#if !NOSYSNUMERICS
-    using Complex = Numerics.Complex;
+    using System.Numerics;
-#else
-    using Complex = System.Numerics.Complex;
 #endif
 
     /// <summary>
@@ -53,7 +49,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         /// </summary>
         IContinuousDistribution GetUniform(int seed)
         {
-            return new ContinuousUniform(-1, 1, new Random(seed));
+            return new ContinuousUniform(-1, 1, new System.Random(seed));
         }
 
         /// <summary>
@@ -64,7 +60,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         [TestCase(0x7FF)]
         public void FourierDefaultTransformSatisfiesParsevalsTheorem(int count)
         {
-            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), GetUniform(1), count);
+            var samples = Generate.RandomComplex(count, GetUniform(1));
 
             var timeSpaceEnergy = (from s in samples select s.MagnitudeSquared()).Mean();
 
@@ -87,7 +83,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         [TestCase(0x1F)]
         public void HartleyDefaultNaiveSatisfiesParsevalsTheorem(int count)
         {
-            var samples = SignalGenerator.Random(x => x, GetUniform(1), count);
+            var samples = Generate.Random(count, GetUniform(1));
 
             var timeSpaceEnergy = (from s in samples select s*s).Mean();