135 lines
3.7 KiB
C#
135 lines
3.7 KiB
C#
/// \file Exponential.cs
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/// <summary>
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/// Contains the class representing a random number generator Exponential(\f$\lambda\f$).
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/// </summary>
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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namespace BotSharp.Algorithm.HiddenMarkovModel.MathUtils.Distribution
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{
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/// <summary>
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/// Class representing a random number generator Exponential(\f$\lambda\f$).
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/// The \f$\lambda\f$ is the rate parameter or inverse scale of the Exponential distribution
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/// <ol>
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/// <li>The mean is \f$\mu = \frac{1}{\lambda}\f$</li>
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/// <li>The variance is \f$\sigma^2 = \frac{1}{\lambda^2}\f$</li>
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/// <li>The skewness is 2</li>
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/// </ol>
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/// </summary>
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public class Exponential : DistributionModel
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{
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protected double mLnlambda;
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protected double mLambda;
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/// <summary>
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/// Constructor
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/// </summary>
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/// <param name="seed">The seed for the random number generator</param>
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public Exponential(uint seed)
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: base(seed)
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{
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}
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public Exponential()
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{
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}
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/// <summary>
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/// Return the log of the PDF(x)
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/// </summary>
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/// <param name="x"></param>
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/// <returns></returns>
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public override double LogProbabilityFunction(double x)
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{
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return mLnlambda - mLambda * x;
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}
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public override double GetCDF(double x)
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{
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return 1 - System.Math.Exp(-mLambda * x);
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}
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public override double GetPDF(double x)
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{
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return mLambda * System.Math.Exp(-mLambda * x);
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}
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/// <summary>
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/// Constructor
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/// </summary>
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public Exponential(double rate)
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{
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mLambda = rate;
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mMean = 1 / mLambda;
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mLnlambda = System.Math.Log(mLambda);
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}
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public override DistributionModel Clone()
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{
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return new Exponential(mLambda);
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}
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/// <summary>
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/// Method that returns a random number generated from the Exponential distribution with mean \f$\mu = 1\f$
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/// </summary>
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/// <returns></returns>
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private double GetExponential()
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{
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return -System.Math.Log(GetUniform());
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}
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/// <summary>
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/// Method that returns a random number generated from the Exponential distribution with \f$\lambda=\frac{1}{\mu}\f$ (\f$\mu\f$ is the mean of the distribution)
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/// </summary>
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/// <returns></returns>
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public override double Next()
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{
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if (mMean <= 0.0)
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{
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string msg = string.Format("Mean must be positive. Received {0}.", mMean);
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throw new ArgumentOutOfRangeException(msg);
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}
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return mMean * GetExponential();
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}
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public override void Process(double[] values)
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{
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int count = values.Length;
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if (count == 0)
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{
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mMean = 0;
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mStdDev = 0;
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return;
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}
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mMean = values.Average();
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mLambda = 1 / mMean;
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mLnlambda = System.Math.Log(mLambda);
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}
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public override void Process(double[] values, double[] weights)
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{
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double sum = 0;
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int count = values.Length;
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for (int i = 0; i < count; ++i)
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{
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sum += (values[i] * weights[i]);
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}
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double weight_sum = 0;
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for (int i = 0; i < count; ++i)
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{
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weight_sum += weights[i];
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}
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mMean = sum / weight_sum;
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mLambda = 1 / mMean;
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mLnlambda = System.Math.Log(mLambda);
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}
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}
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}
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