84 lines
2 KiB
C++
84 lines
2 KiB
C++
#pragma once
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#include <cmath>
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namespace cgv{
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namespace math {
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///Akaike's: An Information Criterion
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///k...number of parameters
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///L...maximized value of likelihood function for estimated model
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///n/k should be > 40 otherwise use AICc (n ... number of samples)
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template<typename T>
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T AIC(int k, const T& L)
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{
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return (const T)2.0*(T)k - (T)2*(T)log((double)L);
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}
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///Akaike's: An information criterion for
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///k...number of parameters
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///n...number of samples
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///RSS...the residual sum of squares
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///n/k should be greater 40 otherwise use AICc
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template<typename T>
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T AIC_ls(int k, int n, const T&RSS)
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{
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return 2*k + (T)n*log(RSS/(T)n);
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}
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///Akaike's: An information criterion with second order correction term
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///k...number of parameters
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///n.. number of samples
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///L...maximized value of likelihood function for estimated model
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template<typename T>
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T AICc(int k, int n, const T& L)
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{
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return AIC<T>(k,L) + (T)(2*k*(k+1))/((T)(n-k-1));
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}
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///Akaike's: An information criterion
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///k...number of parameters
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///n...number of samples
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///RSS...the residual sum of squares
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template<typename T>
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T AICc_ls(int k, int n, const T&RSS)
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{
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return (T)log((T)RSS/(T)n) + (T)(n+k)/(T)(n-k-2) ;
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}
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///Akaike's: An information criterion
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///k...number of parameters
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///n...number of samples
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///RSS...the residual sum of squares from the estimated model
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template<typename T>
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T AICu_ls(int k, int n, const T&RSS)
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{
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return (T)log((T)RSS/(n-k)) + (T)(n+k)/(T)(n-k-2) ;
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}
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///Bayesian information criterion (Schwarz Information Criterion)
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///k...number of parameters
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///n...number of samples
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template<typename T>
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T BIC(int k, int n, const T& L)
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{
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return -(T)2*(T)log((T)L) + (T)k*log((T)n);
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}
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///Bayesian information criterion (Schwarz Information Criterion)
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///k...number of parameters
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///n...number of samples
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///RSS...the residual sum of squares from the estimated model
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template<typename T>
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T BIC_ls(int k, int n, const T& RSS)
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{
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return (T)n*log((T)RSS/(T)n) + (T)k*log((T)n);
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}
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}
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}
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