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using System;
using System.Collections.Generic;
using System.Linq;
namespace IStation.Numerics.Statistics.Mcmc
{
///
/// Provides utilities to analysis the convergence of a set of samples from
/// a .
///
public static class MCMCDiagnostics
{
///
/// Computes the auto correlations of a series evaluated by a function f.
///
/// The series for computing the auto correlation.
/// The lag in the series
/// The function used to evaluate the series.
/// The auto correlation.
/// Throws if lag is zero or if lag is
/// greater than or equal to the length of Series.
public static double ACF(IEnumerable series, int lag, Func f)
{
if (lag < 0)
{
throw new ArgumentOutOfRangeException(nameof(lag), "Lag must be positive");
}
int length = series.Count();
if (lag >= length)
{
throw new ArgumentOutOfRangeException(nameof(lag), "Lag must be smaller than the sample size");
}
var transformedSeries = series.Select(f);
var enumerable = transformedSeries as double[] ?? transformedSeries.ToArray();
var firstSeries = enumerable.Take(length-lag);
var secondSeries = enumerable.Skip(lag);
return Correlation.Pearson(firstSeries, secondSeries);
}
///
/// Computes the effective size of the sample when evaluated by a function f.
///
/// The samples.
/// The function use for evaluating the series.
/// The effective size when auto correlation is taken into account.
public static double EffectiveSize(IEnumerable series, Func f)
{
int length = series.Count();
double rho = ACF(series, 1, f);
return ((1 - rho) / (1 + rho)) * length;
}
}
}