np_main.cpp File Reference

#include <iostream>
#include <fstream>
#include <chrono>
#include <iomanip>
#include <unordered_map>
#include <unistd.h>
#include <experimental/filesystem>
#include <np_mcmc.h>
#include <np_data.h>
#include <np_results.h>
#include <membertrix.h>
#include <statistics/scalarnoise_multivariatenormal.h>
#include <statistics/multivariatenormal.h>
#include <statistics/dirichlet.h>
#include <statistics/normalinvwishart.h>
#include <statistics/normalinvgamma.h>
#include <np_neal_algorithm2.h>
#include <np_neal_algorithm8.h>
#include <np_jain_neal_algorithm.h>
#include <np_triadic_algorithm.h>
#include <pretty_print.hpp>

Include dependency graph for np_main.cpp:

Enumerations
enum	algorithm_t { algorithm2, algorithm8, jain_neal_split, triadic }

Functions
void	disp_help (std::string appname)
int	main (int argc, char *argv[])

Enumeration Type Documentation

algorithm_t

enum algorithm_t

Enumerator
algorithm2
algorithm8
jain_neal_split
triadic

Function Documentation

disp_help()

void disp_help

(

std::string

appname

)

main()

int main	(	int	argc,
		char *	argv[]
	)

With the dataset twogaussians it is important to realize that this did not originate from a Dirichlet process. Even if alpha is chosen to be very low (say 0.0001), there will be multiple clusters generated, not just two. The purity will be quite high (very few misclassifications) but the specificity is low (identification of multiple clusters where there is only one).