#include <GMMParameterEstimator.h>

Public Member Functions
void	addDatum (Eigen::Vector3d pSample)

void	addDatum (Eigen::Quaternion< double > pSample)

void	getModel (GaussianMixtureModel &gmm)

	GMMParameterEstimator (unsigned int pNumberOfDimensions, unsigned int pNumberOfKernelsMin, unsigned int pNumberOfKernelsMax, unsigned int pNumberOfRuns, unsigned int pNumberOfSyntheticSamples, double pIntervalPosition, double pIntervalOrientation, std::string pPathOrientationPlots, unsigned int pAttemptsPerKernel)

void	learn ()

void	plotModel ()

	~GMMParameterEstimator ()

Private Member Functions
bool	runExpectationMaximization (const std::vector< std::vector< double >> data, unsigned int nc, unsigned int &nparams, double &llk, double &bic, GaussianMixtureModel &model, bool useGenericMatrices=true)

Private Attributes
unsigned int	mAttemptsPerRun

GaussianMixtureModel	mBestGMM

std::vector< std::vector< double > >	mData

double	mIntervalOrientation

double	mIntervalPosition

unsigned int	mNumberDimensions

unsigned int	mNumberKernels

unsigned int	mNumberKernelsMax

unsigned int	mNumberKernelsMin

unsigned int	mNumberOfRuns

unsigned int	mNumberOfSyntheticSamples

std::string	mPathOrientationPlots

Detailed Description

This is a learner for gaussian mixture models (GMMs). It uses an EM algorithm to learn the mixture distribution. A maximumum likelihood process is used, so different numbers of kernels up to a maximal number are tried. The GMM with the highest likelihood is taken.

The implementation is taken from the probt documentation and can be found in the following subdirectory: /doc/html/d1/d22/a00057.html'

Author: Joachim Gehrung

Version: See SVN

Definition at line 57 of file GMMParameterEstimator.h.

Constructor & Destructor Documentation

ProbabilisticSceneRecognition::GMMParameterEstimator::GMMParameterEstimator	(	unsigned int	pNumberOfDimensions,
		unsigned int	pNumberOfKernelsMin,
		unsigned int	pNumberOfKernelsMax,
		unsigned int	pNumberOfRuns,
		unsigned int	pNumberOfSyntheticSamples,
		double	pIntervalPosition,
		double	pIntervalOrientation,
		std::string	pPathOrientationPlots,
		unsigned int	pAttemptsPerKernel
	)

Constructor.

Parameters

pNumberOfDimensions	The number of dimensions of the learning samples.
pNumberOfKernelsMin	The minimal number of kernels to try during GMM learning.
pNumberOfKernelsMax	The maximal number of kernels to try during GMM learning.
pNumberOfRuns	The number of runs per kernel.
pNumberOfSyntheticSamples	The number of synthetic voised samples to gereate from a given seed sample.
pIntervalPosition	The position interval for the sample relaxiation.
pIntervalOrientation	The orientation interval for the sample relaxiation.
pPathOrientationPlots	Path to the orientation plots.
pAttemptsPerKernel	Attempts to find a valid kernel until learner gives up.

Definition at line 22 of file GMMParameterEstimator.cpp.

ProbabilisticSceneRecognition::GMMParameterEstimator::~GMMParameterEstimator ( )

Destructor.

Definition at line 36 of file GMMParameterEstimator.cpp.

Member Function Documentation

void ProbabilisticSceneRecognition::GMMParameterEstimator::addDatum ( Eigen::Vector3d pSample )

Adds a single datum to the learner.

Parameters

pSample A single sample.

Definition at line 40 of file GMMParameterEstimator.cpp.

void ProbabilisticSceneRecognition::GMMParameterEstimator::addDatum ( Eigen::Quaternion< double > pSample )

Adds a single datum to the learner.

Parameters

pSample A single sample.

Definition at line 74 of file GMMParameterEstimator.cpp.

void ProbabilisticSceneRecognition::GMMParameterEstimator::getModel ( GaussianMixtureModel & gmm )

Copies the gaussian mixture model into the given container.

Parameters

gmm	The container to copy the gaussian mixture model into.

Definition at line 208 of file GMMParameterEstimator.cpp.

void ProbabilisticSceneRecognition::GMMParameterEstimator::learn ( )

Learns a gaussian mixture model from the given data.

Definition at line 121 of file GMMParameterEstimator.cpp.

void ProbabilisticSceneRecognition::GMMParameterEstimator::plotModel ( )

Plots the model using ProBT.

Definition at line 214 of file GMMParameterEstimator.cpp.

bool ProbabilisticSceneRecognition::GMMParameterEstimator::runExpectationMaximization	(	const std::vector< std::vector< double >>	data,
		unsigned int	nc,
		unsigned int &	nparams,
		double &	llk,
		double &	bic,
		GaussianMixtureModel &	model,
		bool	useGenericMatrices = `true`
	)

private

Runs the EM algorithm for a given number of kernels. Uses OpenCV.

Parameters

data	working data.
nc	Number of kernels to do the EM algorithm for.
nparams	Number of parameters as determined by the EM algorithm.
llk	Log likelihood of the model evaluated under the learning data.
bic	Bayesian Information Criterion score of the model evaluated under the learning data.
model	Gaussian mixture model in form of a joind distribution.
useGenericMatrices	Whether to use generic matrices as covariance matrices (true) or more stable but less precise diagonal matrices.

Returns: whether EM training was successfull.

Definition at line 287 of file GMMParameterEstimator.cpp.

Member Data Documentation

unsigned int ProbabilisticSceneRecognition::GMMParameterEstimator::mAttemptsPerRun

private

Number of attempts to find a valid kernel until learner gives up.

Definition at line 168 of file GMMParameterEstimator.h.

GaussianMixtureModel ProbabilisticSceneRecognition::GMMParameterEstimator::mBestGMM

private

The learned GMM with the best BIC score from OpenCV.

Definition at line 178 of file GMMParameterEstimator.h.

std::vector<std::vector<double> > ProbabilisticSceneRecognition::GMMParameterEstimator::mData

private

The data to learn from.

Definition at line 173 of file GMMParameterEstimator.h.

double ProbabilisticSceneRecognition::GMMParameterEstimator::mIntervalOrientation

private

Definition at line 158 of file GMMParameterEstimator.h.

double ProbabilisticSceneRecognition::GMMParameterEstimator::mIntervalPosition

private

The intervals (position and orientation) for the sample relaxiation.

Definition at line 158 of file GMMParameterEstimator.h.

unsigned int ProbabilisticSceneRecognition::GMMParameterEstimator::mNumberDimensions

private

The number of dimensions of the learning samples.

Definition at line 137 of file GMMParameterEstimator.h.

unsigned int ProbabilisticSceneRecognition::GMMParameterEstimator::mNumberKernels

private

Definition at line 143 of file GMMParameterEstimator.h.

unsigned int ProbabilisticSceneRecognition::GMMParameterEstimator::mNumberKernelsMax

private

Definition at line 143 of file GMMParameterEstimator.h.

unsigned int ProbabilisticSceneRecognition::GMMParameterEstimator::mNumberKernelsMin

private

The minimal, maximal and the full number of kernels. We do EM learning of GMMs for kernels [min,max] and take the GMM with the highest likelihood.

Definition at line 143 of file GMMParameterEstimator.h.

unsigned int ProbabilisticSceneRecognition::GMMParameterEstimator::mNumberOfRuns

private

The number of runs per kernel, used for picking the best model.

Definition at line 148 of file GMMParameterEstimator.h.

unsigned int ProbabilisticSceneRecognition::GMMParameterEstimator::mNumberOfSyntheticSamples

private

For every sample, n noised samples are generated to make orientation and position learning more tolerant.

Definition at line 153 of file GMMParameterEstimator.h.

std::string ProbabilisticSceneRecognition::GMMParameterEstimator::mPathOrientationPlots

private

Path to the orientation plots.

Definition at line 163 of file GMMParameterEstimator.h.

The documentation for this class was generated from the following files:

Public Member Functions

Private Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation