#include <iostream>
#include <string>
#include <vector>
#include <fstream>
#include <cstdio>
#include <cstdlib>
#include <cmath>
#include <cassert>
#include <ctime>
#include "./TimeGaussMIX_MDL.hh"
Go to the source code of this file.
Functions | |
void | usage (std::string argv0) |
Usage function. | |
int | main (int argc, char *argv[]) |
Main procedure for Gaussian mixture modeling. More... |
This is the main program for infering by MDL using a data 2-part coding, a Gaussian mixture model of unknown complexity (paramters and number of Gaussians).
Modeling procedures can be chained. For clustering consecutive TL feature spaces, the initialization is done using the Gaussian parameters infered for the previous modeling. Furthermore, the algorithm checks that there is a sufficient number of Gaussians (fixed by the user) after an initialisation, and adds new Gaussian if necessary.
This is the main program which incorporates the users input/output parameters and launch the optimisation procedure by intializing and using methods of the object Temporal_MDL_classifier.
This algorithm, which makes the hypothesis that the Gaussian in the mixture are sufficiently separatyed to not interfer, may be extended to the general case.
However, to extend the modeling to the general case, the whole program seriously REQUIRES TO BE RE-CODED (This program was my first try in C++ so the developpment was chaotic!!)
Definition in file Clustering.cpp.
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Main procedure for Gaussian mixture modeling. The main procedure begins by a creating a Temporal_MDL_classifier object. After initializing this object, its method "Cluster_optimiser" is used to search the model which induces the minimum description length of the data. This method produces also the output classfile.
Definition at line 71 of file Clustering.cpp. |