xmm Namespace Reference

Classes
class	Attribute
	Generic Attribute. More...
class	Attribute< std::vector< std::string > >
	Generic Attribute (Vector Specialization) More...
class	Attribute< std::vector< T > >
	Generic Attribute (Vector Specialization) More...
class	AttributeBase
	Base Class for Generic Attributes. More...
class	CircularBuffer
	Simple CircularBuffer Class. More...
class	ClassParameters
	Class-specific Model Parameters. More...
class	ClassParameters< GMM >
	Parameters specific to each class of a Gaussian Mixture Model. More...
class	ClassParameters< HMM >
	Parameters specific to each class of a Hidden Markov Model. More...
class	ClassParameters< KMeans >
	Parameters specific to each class of a K-Means Algorithm. More...
struct	ClassResults
	Class-specific Results of the filtering/inference process. More...
struct	ClassResults< HMM >
	Results of Hidden Markov Models for a single class. More...
class	Configuration
	Model configuration. More...
struct	Ellipse
	Structure for storing Ellipse parameters. More...
class	EventGenerator
	Generator class for a specific type of events. More...
class	GaussianDistribution
	Multivariate Gaussian Distribution. More...
class	GMM
	Gaussian Mixture Model for Continuous Recognition and Regression (Multi-class) More...
class	HierarchicalHMM
	Hierarchical Hidden Markov Model for Continuous Recognition and Regression (Multi-class) More...
class	HMM
class	JsonException
	Exception class for handling JSON parsing errors. More...
class	KMeans
	K-Means Clustering algorithm. More...
class	Matrix
	Dirty and very incomplete Matrix Class. More...
class	Model
	Probabilistic machine learning model for multiclass recognition and regression. More...
class	Phrase
	Data phrase. More...
class	PhraseEvent
	Event that can be thrown by a phrase to a training set. More...
struct	Results
	Results of the filtering/inference process (for a Model with multiple classes). More...
struct	Results< KMeans >
	Results of the clustering process. More...
class	SharedParameters
	Shared Parameters for models with multiple classes. More...
class	SingleClassGMM
	Single-Class Gaussian Mixture Model. More...
class	SingleClassHMM
	Single-Class Hidden Markov Model. More...
class	SingleClassProbabilisticModel
	Generic Template for Machine Learning Probabilistic models based on the EM algorithm. More...
class	TrainingEvent
	Event for monitoring the training process. More...
class	TrainingSet
	Base class for the definition of training sets. More...
class	Writable
	Abstract class for handling JSON + File I/O. More...

Enumerations
enum	MemoryMode { MemoryMode::OwnMemory, MemoryMode::SharedMemory }
	Type of memory management for training sets and phrases. More...
enum	Multimodality { Multimodality::Unimodal, Multimodality::Bimodal }
	Number of modalities in the data phrase. More...
enum	MultiClassRegressionEstimator { MultiClassRegressionEstimator::Likeliest = 0, MultiClassRegressionEstimator::Mixture = 1 }
	Regression estimator for multiclass models. More...
enum	MultithreadingMode { MultithreadingMode::Sequential, MultithreadingMode::Parallel, MultithreadingMode::Background }
	Multithreading mode for multiple-class training. More...

Functions
template<typename T >
void	checkLimits (T const &value, T const &limit_min, T const &limit_max)
	checks the validity of the requested value with respect to the current limits More...
template<>
void	checkLimits< bool > (bool const &value, bool const &limit_min, bool const &limit_max)
template<>
void	checkLimits< unsigned char > (unsigned char const &value, unsigned char const &limit_min, unsigned char const &limit_max)
template<>
void	checkLimits< char > (char const &value, char const &limit_min, char const &limit_max)
template<>
void	checkLimits< unsigned int > (unsigned int const &value, unsigned int const &limit_min, unsigned int const &limit_max)
template<>
void	checkLimits< int > (int const &value, int const &limit_min, int const &limit_max)
template<>
void	checkLimits< long > (long const &value, long const &limit_min, long const &limit_max)
template<>
void	checkLimits< float > (float const &value, float const &limit_min, float const &limit_max)
template<>
void	checkLimits< double > (double const &value, double const &limit_min, double const &limit_max)
template<>
void	checkLimits< std::string > (std::string const &value, std::string const &limit_min, std::string const &limit_max)
template<typename T >
Json::Value	array2json (T const *a, unsigned int n)
	Writes a C-style array to a Json Value. More...
template<typename T >
void	json2array (Json::Value const &root, T *a, unsigned int n)
	Reads a C-style array from a Json Value. More...
template<>
void	json2array (Json::Value const &root, float *a, unsigned int n)
template<>
void	json2array (Json::Value const &root, double *a, unsigned int n)
template<>
void	json2array (Json::Value const &root, bool *a, unsigned int n)
template<>
void	json2array (Json::Value const &root, std::string *a, unsigned int n)
template<typename T >
Json::Value	vector2json (std::vector< T > const &a)
	Writes a vector to a Json Value. More...
template<typename T >
void	json2vector (Json::Value const &root, std::vector< T > &a, unsigned int n)
	Reads a vector from a Json Value. More...
template<>
void	json2vector (Json::Value const &root, std::vector< float > &a, unsigned int n)
template<>
void	json2vector (Json::Value const &root, std::vector< double > &a, unsigned int n)
template<>
void	json2vector (Json::Value const &root, std::vector< bool > &a, unsigned int n)
template<>
void	json2vector (Json::Value const &root, std::vector< std::string > &a, unsigned int n)
template<typename T >
T *	reallocate (T *src, unsigned int dim_src, unsigned int dim_dst)
	Reallocate a C-like array (using c++ std::copy) More...
Ellipse	covariance2ellipse (double c_xx, double c_xy, double c_yy)
template<>
void	checkLimits< GaussianDistribution::CovarianceMode > (GaussianDistribution::CovarianceMode const &value, GaussianDistribution::CovarianceMode const &limit_min, GaussianDistribution::CovarianceMode const &limit_max)
template<typename T >
T	euclidean_distance (const T *vector1, const T *vector2, unsigned int dimension)
	Simple Euclidian distance measure. More...
template<>
void	checkLimits< HMM::TransitionMode > (HMM::TransitionMode const &value, HMM::TransitionMode const &limit_min, HMM::TransitionMode const &limit_max)
template<>
void	checkLimits< HMM::RegressionEstimator > (HMM::RegressionEstimator const &value, HMM::RegressionEstimator const &limit_min, HMM::RegressionEstimator const &limit_max)

Variables
const std::vector< float >	null_vector_float

Enumeration Type Documentation

enum xmm::MultiClassRegressionEstimator

strong

Regression estimator for multiclass models.

Enumerator
Likeliest	the output is estimated as the output values of the likeliest class
Mixture	the output is estimated as a weight sum of the output values of each class

enum xmm::MultithreadingMode

strong

Multithreading mode for multiple-class training.

Enumerator
Sequential	No multithreading: all classes are trained sequentially.
Parallel	Multithreading: all classes are trained in parallel in different threads. the train function returns after all classes have finished training.
Background	Multithreading in Background: all classes are trained in parallel in different threads. the train function returns after the training has started. Warning when the train function return, models are still training in background.

Function Documentation

template<>

void xmm::checkLimits< bool >	(	bool const &	value,
		bool const &	limit_min,
		bool const &	limit_max
	)

template<>

void xmm::checkLimits< char >	(	char const &	value,
		char const &	limit_min,
		char const &	limit_max
	)

template<>

void xmm::checkLimits< double >	(	double const &	value,
		double const &	limit_min,
		double const &	limit_max
	)

template<>

void xmm::checkLimits< float >	(	float const &	value,
		float const &	limit_min,
		float const &	limit_max
	)

template<>

void xmm::checkLimits< GaussianDistribution::CovarianceMode >	(	GaussianDistribution::CovarianceMode const &	value,
		GaussianDistribution::CovarianceMode const &	limit_min,
		GaussianDistribution::CovarianceMode const &	limit_max
	)

template<>

void xmm::checkLimits< HMM::RegressionEstimator >	(	HMM::RegressionEstimator const &	value,
		HMM::RegressionEstimator const &	limit_min,
		HMM::RegressionEstimator const &	limit_max
	)

template<>

void xmm::checkLimits< HMM::TransitionMode >	(	HMM::TransitionMode const &	value,
		HMM::TransitionMode const &	limit_min,
		HMM::TransitionMode const &	limit_max
	)

template<>

void xmm::checkLimits< int >	(	int const &	value,
		int const &	limit_min,
		int const &	limit_max
	)

template<>

void xmm::checkLimits< long >	(	long const &	value,
		long const &	limit_min,
		long const &	limit_max
	)

template<>

void xmm::checkLimits< std::string >	(	std::string const &	value,
		std::string const &	limit_min,
		std::string const &	limit_max
	)

template<>

void xmm::checkLimits< unsigned char >	(	unsigned char const &	value,
		unsigned char const &	limit_min,
		unsigned char const &	limit_max
	)

template<>

void xmm::checkLimits< unsigned int >	(	unsigned int const &	value,
		unsigned int const &	limit_min,
		unsigned int const &	limit_max
	)

xmm::Ellipse xmm::covariance2ellipse	(	double	c_xx,
		double	c_xy,
		double	c_yy
	)

template<typename T >

T xmm::euclidean_distance	(	const T *	vector1,
		const T *	vector2,
		unsigned int	dimension
	)

Simple Euclidian distance measure.

Parameters

vector1	first data point
vector2	first data point
dimension	dimension of the data space

Returns

euclidian distance between the 2 points

template<>

void xmm::json2array	(	Json::Value const &	root,
		float *	a,
		unsigned int	n
	)

template<>

void xmm::json2array	(	Json::Value const &	root,
		double *	a,
		unsigned int	n
	)

template<>

void xmm::json2array	(	Json::Value const &	root,
		bool *	a,
		unsigned int	n
	)

template<>

void xmm::json2array	(	Json::Value const &	root,
		std::string *	a,
		unsigned int	n
	)

template<>

void xmm::json2vector	(	Json::Value const &	root,
		std::vector< float > &	a,
		unsigned int	n
	)

template<>

void xmm::json2vector	(	Json::Value const &	root,
		std::vector< double > &	a,
		unsigned int	n
	)

template<>

void xmm::json2vector	(	Json::Value const &	root,
		std::vector< bool > &	a,
		unsigned int	n
	)

template<>

void xmm::json2vector	(	Json::Value const &	root,
		std::vector< std::string > &	a,
		unsigned int	n
	)

template<typename T >

T* xmm::reallocate	(	T *	src,
		unsigned int	dim_src,
		unsigned int	dim_dst
	)

Reallocate a C-like array (using c++ std::copy)

Parameters

src	source array
dim_src	initial dimension
dim_dst	target dimension

Returns

resized array (content is conserved)

Variable Documentation

const std::vector<float> xmm::null_vector_float