uo9xpb74
/
mathnet-numerics
forked from tsai/mathnet-numerics
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

Fixed a bug in the StudentT sampler. 

Reworked the unit tests for sampling methods to be more accurate.
la-knuth
Jurgen Van Gael 17 years ago
parent
ad6cbbebd8
commit
f612db93a1
4 changed files with 67 additions and 90 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 4
      src/Numerics/Distributions/Continuous/StudentT.cs
  2. 6
      src/Numerics/Version.cs
  3. 83
      src/UnitTests/DistributionTests/CommonDistributionTests.cs
  4. 64
      src/UnitTests/DistributionTests/Multivariate/NormalGammaTests.cs

4
src/Numerics/Distributions/Continuous/StudentT.cs
View File

@ -473,9 +473,9 @@ namespace MathNet.Numerics.Distributions
{
double dummy = 0.0;
var n = Normal.SampleBoxMuller(rnd, out dummy);
var g = Gamma.Sample(rnd, dof / 2.0, 1.0);
var g = Gamma.Sample(rnd, 0.5 * dof, 0.5);
return Math.Sqrt(2.0 * dof / g) * n;
return Math.Sqrt(dof / g) * n;
}
}
}

6
src/Numerics/Version.cs
View File

@ -22,9 +22,9 @@
// OTHER DEALINGS IN THE SOFTWARE.
// </copyright>
/* This file is automatically generated - do not modify it. Change Version.tt instead.
Last generated on: 4/23/2010 10:04:41 AM
Last generated on: 03/05/2010 21:09:03
*/
using System.Reflection;
[assembly: AssemblyVersion("2010.04.23.604")]
[assembly: AssemblyFileVersion("2010.04.23.604")]
[assembly: AssemblyVersion("2010.05.3.1269")]
[assembly: AssemblyFileVersion("2010.05.3.1269")]

83
src/UnitTests/DistributionTests/CommonDistributionTests.cs
View File

@ -44,11 +44,13 @@ namespace MathNet.Numerics.UnitTests.DistributionTests
public class CommonDistributionTests
{
// The number of samples we want.
public static int NumberOfTestSamples = 10000000;
// The accuracy of the histograms.
public static double SampleAccuracy = 0.01;
// The number of buckets to use to test against the cdf.
public static int NumberOfTestSamples = 3500000;
// The number of buckets in the histogram for the sampling function tests.
public static int NumberOfBuckets = 100;
// The error we want to tolerate for sampling functions.
public static double Error = 0.01;
// The error probability we want to tolerate for sampling functions.
public static double ErrorProbability = 0.001;
// The list of discrete distributions which we test.
private List<IDiscreteDistribution> discreteDistributions;
// The list of continuous distributions which we test.
@ -75,7 +77,7 @@ namespace MathNet.Numerics.UnitTests.DistributionTests
[Test]
[MultipleAsserts]
public void ValidateThatUnivariateDistributionsHaveRandomSource(int i)
public void ValidateThatUnivariateDistributionsHaveRandomSource()
{
foreach(var dd in discreteDistributions)
{
@ -90,7 +92,7 @@ namespace MathNet.Numerics.UnitTests.DistributionTests
[Test]
[MultipleAsserts]
public void CanSetRandomSource(int i)
public void CanSetRandomSource()
{
foreach(var dd in discreteDistributions)
{
@ -105,7 +107,7 @@ namespace MathNet.Numerics.UnitTests.DistributionTests
[Test]
[MultipleAsserts]
public void FailSetRandomSourceWithNullReference(int i)
public void FailSetRandomSourceWithNullReference()
{
foreach(var dd in discreteDistributions)
{
@ -127,18 +129,15 @@ namespace MathNet.Numerics.UnitTests.DistributionTests
{
Random rnd = new MersenneTwister(1);
// The test samples from the distributions, builds a histogram and checks
// whether the histogram follows the CDF.
foreach (var dd in discreteDistributions)
{
dd.RandomSource = rnd;
double[] samples = new double[NumberOfTestSamples];
for (int i = 0; i < NumberOfTestSamples; i++)
{
samples[i] = (double) dd.Sample();
samples[i] = (double)dd.Sample();
}
VapnikChervonenkisTest(Error, ErrorProbability, samples, dd);
}
foreach (var cd in continuousDistributions)
@ -149,16 +148,7 @@ namespace MathNet.Numerics.UnitTests.DistributionTests
{
samples[i] = cd.Sample();
}
var histogram = new Histogram(samples, NumberOfBuckets);
for (int i = 0; i < NumberOfBuckets; i++)
{
var bucket = histogram[i];
double empiricalProbability = bucket.Count / (double)NumberOfTestSamples;
double realProbability = cd.CumulativeDistribution(bucket.UpperBound)
- cd.CumulativeDistribution(bucket.LowerBound);
Assert.LessThan(Math.Abs(empiricalProbability - realProbability), SampleAccuracy, cd.ToString());
}
VapnikChervonenkisTest(Error, ErrorProbability, samples, cd);
}
}
@ -171,38 +161,41 @@ namespace MathNet.Numerics.UnitTests.DistributionTests
{
Random rnd = new MersenneTwister(1);
// The test samples from the distributions, builds a histogram and checks
// whether the histogram follows the CDF.
foreach (var dd in discreteDistributions)
{
dd.RandomSource = rnd;
var samples = dd.Samples().Take(NumberOfTestSamples).Select(x => (double)x);
var histogram = new Histogram(samples, NumberOfBuckets);
for (int i = 0; i < NumberOfBuckets; i++)
{
var bucket = histogram[i];
double empiricalProbability = bucket.Count / (double)NumberOfTestSamples;
double realProbability = dd.CumulativeDistribution(bucket.UpperBound)
- dd.CumulativeDistribution(bucket.LowerBound);
Assert.LessThan(Math.Abs(empiricalProbability - realProbability), SampleAccuracy, dd.ToString());
}
VapnikChervonenkisTest(Error, ErrorProbability, dd.Samples().Select(x => (double) x).Take(NumberOfTestSamples), dd);
}
foreach (var cd in continuousDistributions)
{
cd.RandomSource = rnd;
var samples = cd.Samples().Take(NumberOfTestSamples);
VapnikChervonenkisTest(Error, ErrorProbability, cd.Samples().Take(NumberOfTestSamples), cd);
}
}
var histogram = new Histogram(samples, NumberOfBuckets);
for (int i = 0; i < NumberOfBuckets; i++)
{
var bucket = histogram[i];
double empiricalProbability = bucket.Count / (double)NumberOfTestSamples;
double realProbability = cd.CumulativeDistribution(bucket.UpperBound)
- cd.CumulativeDistribution(bucket.LowerBound);
Assert.LessThan(Math.Abs(empiricalProbability - realProbability), SampleAccuracy, cd.ToString());
}
/// <summary>
/// <para>Using VC-dimension, we can bound the probability of making an error when estimating empirical probability
/// distributions. We are using Theorem 2.41 in "All Of Nonparametric Statistics".
/// http://books.google.com/books?id=MRFlzQfRg7UC&lpg=PP1&dq=all%20of%20nonparametric%20statistics&pg=PA22#v=onepage&q=%22shatter%20coe%EF%AC%83cients%20do%20not%22&f=false .</para>
/// <para>Note that for intervals on the real line the VC-dimension is 2.</para>
/// </summary>
/// <param name="epsilon">The error we are willing to tolerate.</param>
/// <param name="delta">The error probability we are willing to tolerate.</param>
/// <param name="s">The samples to use for testing.</param>
/// <param name="dist">The distribution we are testing.</param>
public static void VapnikChervonenkisTest(double epsilon, double delta, IEnumerable<double> s, IDistribution dist)
{
double N = (double) s.Count();
Assert.GreaterThan(N, Math.Ceiling(32.0 * Math.Log(16.0 / delta) / epsilon / epsilon));
var histogram = new Histogram(s, NumberOfBuckets);
for (int i = 0; i < NumberOfBuckets; i++)
{
double p = dist.CumulativeDistribution(histogram[i].UpperBound) - dist.CumulativeDistribution(histogram[i].LowerBound);
double pe = histogram[i].Count / N;
Assert.LessThan(Math.Abs(p - pe), epsilon, dist.ToString());
}
}
}

64
src/UnitTests/DistributionTests/Multivariate/NormalGammaTests.cs
View File

@ -224,27 +224,19 @@ namespace MathNet.Numerics.UnitTests.DistributionTests
var meanMarginal = cd.MeanMarginal();
var precMarginal = cd.PrecisionMarginal();
// Check the mean distribution.
var histogram = new Histogram(means, CommonDistributionTests.NumberOfBuckets);
for (int i = 0; i < CommonDistributionTests.NumberOfBuckets; i++)
{
var bucket = histogram[i];
double empiricalProbability = bucket.Count / (double)CommonDistributionTests.NumberOfTestSamples;
double realProbability = meanMarginal.CumulativeDistribution(bucket.UpperBound)
- meanMarginal.CumulativeDistribution(bucket.LowerBound);
Assert.LessThan(Math.Abs(empiricalProbability - realProbability), CommonDistributionTests.SampleAccuracy, cd.ToString());
}
// Check the precision distribution.
histogram = new Histogram(precs, CommonDistributionTests.NumberOfBuckets);
for (int i = 0; i < CommonDistributionTests.NumberOfBuckets; i++)
{
var bucket = histogram[i];
double empiricalProbability = bucket.Count / (double)CommonDistributionTests.NumberOfTestSamples;
double realProbability = precMarginal.CumulativeDistribution(bucket.UpperBound)
- precMarginal.CumulativeDistribution(bucket.LowerBound);
Assert.LessThan(Math.Abs(empiricalProbability - realProbability), CommonDistributionTests.SampleAccuracy, cd.ToString());
}
CommonDistributionTests.VapnikChervonenkisTest(
CommonDistributionTests.Error,
CommonDistributionTests.ErrorProbability,
precs,
precMarginal);
// Check the mean distribution.
CommonDistributionTests.VapnikChervonenkisTest(
CommonDistributionTests.Error,
CommonDistributionTests.ErrorProbability,
means,
meanMarginal);
}
/// <summary>
@ -265,27 +257,19 @@ namespace MathNet.Numerics.UnitTests.DistributionTests
var meanMarginal = cd.MeanMarginal();
var precMarginal = cd.PrecisionMarginal();
// Check the mean distribution.
var histogram = new Histogram(means, CommonDistributionTests.NumberOfBuckets);
for (int i = 0; i < CommonDistributionTests.NumberOfBuckets; i++)
{
var bucket = histogram[i];
double empiricalProbability = bucket.Count / (double)CommonDistributionTests.NumberOfTestSamples;
double realProbability = meanMarginal.CumulativeDistribution(bucket.UpperBound)
- meanMarginal.CumulativeDistribution(bucket.LowerBound);
Assert.LessThan(Math.Abs(empiricalProbability - realProbability), CommonDistributionTests.SampleAccuracy, cd.ToString());
}
// Check the precision distribution.
histogram = new Histogram(precs, CommonDistributionTests.NumberOfBuckets);
for (int i = 0; i < CommonDistributionTests.NumberOfBuckets; i++)
{
var bucket = histogram[i];
double empiricalProbability = bucket.Count / (double)CommonDistributionTests.NumberOfTestSamples;
double realProbability = precMarginal.CumulativeDistribution(bucket.UpperBound)
- precMarginal.CumulativeDistribution(bucket.LowerBound);
Assert.LessThan(Math.Abs(empiricalProbability - realProbability), CommonDistributionTests.SampleAccuracy, cd.ToString());
}
CommonDistributionTests.VapnikChervonenkisTest(
CommonDistributionTests.Error,
CommonDistributionTests.ErrorProbability,
precs,
precMarginal);
// Check the mean distribution.
CommonDistributionTests.VapnikChervonenkisTest(
CommonDistributionTests.Error,
CommonDistributionTests.ErrorProbability,
means,
meanMarginal);
}
}
}
Write Preview
Loading…
Cancel
Save