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

Continued implementation of the normal distribution. 

Signed-off-by: jvangael <jurgen.vangael@gmail.com>
pull/36/head
Jurgen Van Gael 17 years ago
parent
f3553857fa
commit
05269f5c7a
3 changed files with 267 additions and 20 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. 110
      src/Managed.UnitTests/DistributionTests/Continuous/NormalTests.cs
  2. 175
      src/Managed/Distributions/Continuous/Normal.cs
  3. 2
      src/Managed/Distributions/IDistribution.cs

110
src/Managed.UnitTests/DistributionTests/Continuous/NormalTests.cs
View File

@ -215,5 +215,115 @@ namespace MathNet.Numerics.UnitTests
var n = new Normal();
n.Mean = mean;
}
[Test]
[Row(-0.0)]
[Row(0.0)]
[Row(0.1)]
[Row(1.0)]
[Row(10.0)]
[Row(Double.PositiveInfinity)]
public void ValidateEntropy(double sdev)
{
var n = new Normal(1.0, sdev);
AssertEx.AreEqual<double>(MathNet.Numerics.Constants.LogSqrt2PiE + Math.Log(n.StdDev), n.Entropy);
}
[Test]
[Row(-0.0)]
[Row(0.0)]
[Row(0.1)]
[Row(1.0)]
[Row(10.0)]
[Row(Double.PositiveInfinity)]
public void ValidateSkewness(double sdev)
{
var n = new Normal(1.0, sdev);
AssertEx.AreEqual<double>(0.0, n.Skewness);
}
[Test]
[Row(Double.NegativeInfinity)]
[Row(-0.0)]
[Row(0.0)]
[Row(0.1)]
[Row(1.0)]
[Row(10.0)]
[Row(Double.PositiveInfinity)]
public void ValidateMode(double mean)
{
var n = new Normal(mean, 1.0);
AssertEx.AreEqual<double>(mean, n.Mode);
}
[Test]
[Row(Double.NegativeInfinity)]
[Row(-0.0)]
[Row(0.0)]
[Row(0.1)]
[Row(1.0)]
[Row(10.0)]
[Row(Double.PositiveInfinity)]
public void ValidateMedian(double mean)
{
var n = new Normal(mean, 1.0);
AssertEx.AreEqual<double>(mean, n.Median);
}
[Test]
public void ValidateMinimum()
{
var n = new Normal();
AssertEx.AreEqual<double>(System.Double.NegativeInfinity, n.Minimum);
}
[Test]
public void ValidateMaximum()
{
var n = new Normal();
AssertEx.AreEqual<double>(System.Double.PositiveInfinity, n.Maximum);
}
[Test]
[Row(0.0, 0.0)]
[Row(0.0, 0.1)]
[Row(0.0, 1.0)]
[Row(0.0, 10.0)]
[Row(10.0, 1.0)]
[Row(-5.0, 100.0)]
[Row(0.0, Double.PositiveInfinity)]
public void ValidateDensity(double mean, double sdev)
{
var n = Normal.WithMeanStdDev(mean, sdev);
for(int i = 0; i < 11; i++)
{
double x = i - 5.0;
double d = (mean - x)/sdev;
double pdf = Math.Exp(-0.5*d*d)/(sdev*Constants.Sqrt2Pi);
AssertEx.AreEqual<double>(pdf, n.Density(x));
}
}
[Test]
[Row(0.0, 0.0)]
[Row(0.0, 0.1)]
[Row(0.0, 1.0)]
[Row(0.0, 10.0)]
[Row(10.0, 1.0)]
[Row(-5.0, 100.0)]
[Row(0.0, Double.PositiveInfinity)]
public void ValidateDensityLn(double mean, double sdev)
{
var n = Normal.WithMeanStdDev(mean, sdev);
for (int i = 0; i < 11; i++)
{
double x = i - 5.0;
double d = (mean - x) / sdev;
double pdfln = -0.5 * d * d - Math.Log(sdev) - Constants.LogSqrt2Pi;
AssertEx.AreEqual<double>(pdfln, n.DensityLn(x));
}
}
test samplers
}
}

175
src/Managed/Distributions/Continuous/Normal.cs
View File

@ -32,7 +32,8 @@ namespace MathNet.Numerics.Distributions
using System.Collections.Generic;
/// <summary>
/// Implements the univariate Normal (or Gaussian) distribution.
/// Implements the univariate Normal (or Gaussian) distribution. For details about this distribution, see
/// <a href="http://en.wikipedia.org/wiki/Normal_distribution">Wikipedia - Normal distribution</a>.
/// </summary>
public class Normal : IContinuousDistribution
{
@ -43,24 +44,28 @@ namespace MathNet.Numerics.Distributions
/// <summary>
/// Constructs a standard normal distribution. This is a normal distribution with mean 0.0
/// and standard deviation 1.0.
/// and standard deviation 1.0. The distribution will
/// be initialized with the default <seealso cref="System.Random"/> random number generator.
/// </summary>
public Normal() : this(0.0, 1.0)
{
}
/// <summary>
/// Construct a normal distribution with a particular mean and standard deviation.
/// Construct a normal distribution with a particular mean and standard deviation. The distribution will
/// be initialized with the default <seealso cref="System.Random"/> random number generator.
/// </summary>
/// <param name="mean">The mean of the normal distribution.</param>
/// <param name="stddev">The standard deviation of the normal distribution.</param>
public Normal(double mean, double stddev)
{
SetParameters(mean, stddev);
RandomSource = new Random();
}
/// <summary>
/// Constructs a normal distribution from a mean and standard deviation.
/// Constructs a normal distribution from a mean and standard deviation. The distribution will
/// be initialized with the default <seealso cref="System.Random"/> random number generator.
/// </summary>
/// <param name="mean">The mean of the normal distribution.</param>
/// <param name="stddev">The standard deviation of the normal distribution.</param>
@ -70,7 +75,8 @@ namespace MathNet.Numerics.Distributions
}
/// <summary>
/// Constructs a normal distribution from a mean and variance.
/// Constructs a normal distribution from a mean and variance. The distribution will
/// be initialized with the default <seealso cref="System.Random"/> random number generator.
/// </summary>
/// <param name="mean">The mean of the normal distribution.</param>
/// <param name="stddev">The variance of the normal distribution.</param>
@ -80,7 +86,8 @@ namespace MathNet.Numerics.Distributions
}
/// <summary>
/// Constructs a normal distribution from a mean and precision.
/// Constructs a normal distribution from a mean and precision. The distribution will
/// be initialized with the default <seealso cref="System.Random"/> random number generator.
/// </summary>
/// <param name="mean">The mean of the normal distribution.</param>
/// <param name="stddev">The precision of the normal distribution.</param>
@ -141,6 +148,9 @@ namespace MathNet.Numerics.Distributions
}
}
/// <summary>
/// The precision of the normal distribution.
/// </summary>
public double Precision
{
get { return 1.0 / (mStdDev * mStdDev); }
@ -148,38 +158,117 @@ namespace MathNet.Numerics.Distributions
}
#region IDistribution implementation
public Random RandomNumberGenerator { get; set; }
/// <summary>
/// The random number generator which is used to draw random samples.
/// </summary>
public Random RandomSource { get; set; }
/// <summary>
/// The mean of the normal distribution.
/// </summary>
public double Mean
{
get { return mMean; }
set { SetParameters(value, mStdDev); }
}
/// <summary>
/// The variance of the normal distribution.
/// </summary>
public double Variance
{
get { return mStdDev * mStdDev; }
set { SetParameters(mMean, value); }
}
/// <summary>
/// The standard deviation of the normal distribution.
/// </summary>
public double StdDev
{
get { return mStdDev; }
set { SetParameters(mMean, value); }
}
public double Entropy { get { throw new NotImplementedException(); } }
public double Skewness { get { throw new NotImplementedException(); } }
/// <summary>
/// The entropy of the normal distribution.
/// </summary>
public double Entropy { get { return Math.Log(mStdDev) + Constants.LogSqrt2PiE; } }
/// <summary>
/// The skewness of the normal distribution.
/// </summary>
public double Skewness { get { return 0.0; } }
#endregion
#region IContinuousDistribution implementation
public double Mode { get { throw new NotImplementedException(); } }
public double Median { get { throw new NotImplementedException(); } }
public double Minimum { get { throw new NotImplementedException(); } }
public double Maximum { get { throw new NotImplementedException(); } }
public double Density(double x) { throw new NotImplementedException(); }
public double DensityLn(double x) { throw new NotImplementedException(); }
/// <summary>
/// The mode of the normal distribution.
/// </summary>
public double Mode { get { return mMean; } }
/// <summary>
/// The median of the normal distribution.
/// </summary>
public double Median { get { return mMean; } }
/// <summary>
/// The minimum of the normal distribution.
/// </summary>
public double Minimum { get { return System.Double.NegativeInfinity; } }
/// <summary>
/// The maximum of the normal distribution.
/// </summary>
public double Maximum { get { return System.Double.PositiveInfinity; } }
/// <summary>
/// Computes the density of the normal distribution.
/// </summary>
/// <param name="x">The location at which to compute the density.</param>
public double Density(double x)
{
double d = (x - mMean) / mStdDev;
return Math.Exp(-0.5*d*d) / (Constants.Sqrt2Pi*mStdDev);
}
/// <summary>
/// Computes the log density of the normal distribution.
/// </summary>
/// <param name="x">The location at which to compute the log density.</param>
public double DensityLn(double x)
{
double d = (x - mMean) / mStdDev;
return -0.5 * d * d - Math.Log(mStdDev) - Constants.LogSqrt2Pi;
}
public double CumulativeDistribution(double x) { throw new NotImplementedException(); }
public double Sample() { throw new NotImplementedException(); }
public IEnumerable<double> Samples() { throw new NotImplementedException(); }
/// <summary>
/// Generates a sample from the normal distribution using the <i>Box-Muller</i> algorithm.
/// </summary>
public double Sample()
{
double r2;
return mMean + mStdDev * SampleBoxMuller(rng, r2);
}
/// <summary>
/// Generates a sequence of samples from the normal distribution using the <i>Box-Muller</i> algorithm.
/// </summary>
public IEnumerable<double> Samples()
{
double r2;
while (true)
{
double r1 = SampleBoxMuller(rng, r2);
yield return mean + stddev * r1;
yield return mean + stddev * r2;
}
}
#endregion
public double InverseCumulativeDistribution(double p)
@ -187,7 +276,55 @@ namespace MathNet.Numerics.Distributions
throw new NotImplementedException();
}
public static double Sample(System.Random rng, double mean, double stddev) { throw new NotImplementedException(); }
public static IEnumerable<double> Samples(System.Random rng, double mean, double stddev) { throw new NotImplementedException(); }
/// <summary>
/// Generates a sample from the normal distribution using the <i>Box-Muller</i> algorithm.
/// </summary>
/// <param name="rng">The random number generator to use.</param>
/// <param name="mean">The mean of the normal distribution from which to generate samples.</param>
/// <param name="stddev">The standard deviation of the normal distribution from which to generate samples.</param>
public static double Sample(System.Random rng, double mean, double stddev)
{
double r2;
return mean + stddev * SampleBoxMuller(rng, r2);
}
/// <summary>
/// Generates a sequence of samples from the normal distribution using the <i>Box-Muller</i> algorithm.
/// </summary>
/// <param name="rng">The random number generator to use.</param>
/// <param name="mean">The mean of the normal distribution from which to generate samples.</param>
/// <param name="stddev">The standard deviation of the normal distribution from which to generate samples.</param>
public static IEnumerable<double> Samples(System.Random rng, double mean, double stddev)
{
double r2;
while(true)
{
double r1 = SampleBoxMuller(rng, r2);
yield return mean + stddev * r1;
yield return mean + stddev * r2;
}
}
/// <summary>
/// Samples a pair of standard normal distributed random variables using the <i>Box-Muller</i> algorithm.
/// </summary>
/// <param name="rnd">The random number generator to use.</param>
/// <param name="r2">The second random number.</param>
internal static double SampleBoxMuller(System.Random rnd, out double r2)
{
double v1 = 2.0 * rnd.NextDouble() - 1.0;
double v2 = 2.0 * rnd.NextDouble() - 1.0;
double r = v1 * v1 + v2 * v2;
while (r >= 1.0 || r == 0.0)
{
v1 = 2.0 * rnd.NextDouble() - 1.0;
v2 = 2.0 * rnd.NextDouble() - 1.0;
r = v1 * v1 + v2 * v2;
}
double fac = System.Math.Sqrt(-2.0 * System.Math.Log(r) / r);
r2 = v2 * fac;
return v1 * fac;
}
}
}

2
src/Managed/Distributions/IDistribution.cs
View File

@ -38,7 +38,7 @@ namespace MathNet.Numerics.Distributions
/// <summary>
/// Gets or sets the random number generator which is used to generate random samples from the distribution.
/// </summary>
Random RandomNumberGenerator { get; set; }
Random RandomSource { get; set; }
/// <summary>
/// The mean of the distribution.

Write Preview
Loading…
Cancel
Save