faif::ml Namespace Reference

machine learning namespace (mainly classifier algorithms) More...

Classes
struct	AddEvidenceFunctor
class	Belief
	belief is value id with probability More...
struct	BeliefConcept
	the belief concept More...
class	Classifier
	the clasiffier interface More...
class	DecisionTree
	Decision Tree Classifier. More...
struct	DecisionTreeTrainParams
	param for training decision tree More...
class	DistanceNominalValue
	Distance metrics for Nomianal Values Collection, used in K Nearest Neighbors classifier. More...
struct	FusionInternal
	typedef's for Dempster-Shafer combination rule (for fusion) and some internal (helping) methods More...
class	KNearestNeighbor
	k Nearest Neighbor classifier More...
class	NaiveBayesian
	Naive Bayesian Classifier. More...
struct	NormalizationFunctor
struct	PrintCountAttrFunctor
	class to show the classifier state, print the attribs and counters More...
struct	PrintCountersFunctor
	print the cauters for given category More...
class	RandomForest
	Random Forest Classifier. More...
struct	RandomForestParams
	random forest's parameters More...
class	SvmClassifier
class	TrainExampleCategoryCounters

Functions
template<typename Val >
std::ostream &	operator<< (std::ostream &os, const Belief< Val > &b)
template<typename Val >
std::ostream &	operator<< (std::ostream &os, const std::vector< Belief< Val > > &c)
double	calcEntropy (double freq)
	calculate x * log(x) value. If x == 0 return 0. More...
template<typename Val >
std::ostream &	operator<< (std::ostream &os, const Classifier< Val > &c)
template<typename It , typename Val >
Classifier< Val >::ExampleTest	createExample (It begin, It end, const Classifier< Val > &classifier)
	create the test example from iterator range or C-like table of values More...
template<typename It , typename Val >
Classifier< Val >::ExampleTrain	createExample (It begin, It end, const typename Classifier< Val >::AttrValue &cat, const Classifier< Val > &classifier)
	create the train example from range or C-like table of values More...
template<typename Val >
Classifier< Val >::ExampleTest	createExample (const std::vector< std::pair< std::string, typename Classifier< Val >::AttrValue > > &collection, const Classifier< Val > &classifier)
	create the test example from collection of pairs: attribute(domain) identifier and attribute value More...
template<typename Val >
Classifier< Val >::ExampleTest	createExampleStrict (const std::vector< std::pair< std::string, typename Classifier< Val >::AttrValue > > &collection, const Classifier< Val > &classifier)
	create the test example from collection of pairs: attribute(domain) identifier and attribute value. Throws exception if the string identifiers not match the required domains identifiers More...
template<typename Val >
Classifier< Val >::ExampleTrain	createExample (const std::vector< std::pair< std::string, typename Classifier< Val >::AttrValue > > &collection, const typename Classifier< Val >::AttrValue &cat, const Classifier< Val > &classifier)
	create the train example from collection of pairs: attribute(domain) identifier and attribute value More...
template<typename Belief >
Belief::Beliefs	fusion (const typename std::vector< typename Belief::Beliefs > &input)
	connect categories using the Dempster-Shafer combination rule, e.g. bel(cat) = bel1(cat) * bel2(cat) * ... * beln(cat) / SUM(bel) More...
template<typename Val >
int	checkClassifier (const typename Classifier< Val >::ExamplesTrain &test, Classifier< Val > &classifier)
	check the classifier More...
template<typename Val >
int	testRange (std::vector< const typename Classifier< Val >::ExampleTrain * > &tcollect, int start_idx, int end_idx, Classifier< Val > &classifier)
template<typename Val >
double	checkCross (const typename Classifier< Val >::ExamplesTrain &examples, int k, Classifier< Val > &classifier)

Detailed Description

machine learning namespace (mainly classifier algorithms)

Function Documentation

template<typename Val >

std::ostream& faif::ml::operator<<	(	std::ostream &	os,
		const std::vector< Belief< Val > > &	c
	)

stream operator - for debugging

double faif::ml::calcEntropy ( double  freq )

inline

calculate x * log(x) value. If x == 0 return 0.

Referenced by faif::ml::TrainExampleCategoryCounters< Val >::entropy(), and faif::ml::DecisionTree< Val >::prune().

template<typename Val >

std::ostream& faif::ml::operator<<	(	std::ostream &	os,
		const Classifier< Val > &	c
	)

ostream operator

template<typename It , typename Val >

Classifier<Val>::ExampleTest faif::ml::createExample	(	It	begin,
		It	end,
		const Classifier< Val > &	classifier
	)

create the test example from iterator range or C-like table of values

References faif::ml::Classifier< Val >::getAttrDomains().

template<typename It , typename Val >

Classifier<Val>::ExampleTrain faif::ml::createExample	(	It	begin,
		It	end,
		const typename Classifier< Val >::AttrValue &	cat,
		const Classifier< Val > &	classifier
	)

create the train example from range or C-like table of values

References faif::ml::Classifier< Val >::getAttrDomains(), and faif::ml::Classifier< Val >::getCategoryDomain().

template<typename Val >

Classifier<Val>::ExampleTest faif::ml::createExample	(	const std::vector< std::pair< std::string, typename Classifier< Val >::AttrValue > > &	collection,
		const Classifier< Val > &	classifier
	)

create the test example from collection of pairs: attribute(domain) identifier and attribute value

References faif::ml::Classifier< Val >::getAttrDomains().

template<typename Val >

Classifier<Val>::ExampleTest faif::ml::createExampleStrict	(	const std::vector< std::pair< std::string, typename Classifier< Val >::AttrValue > > &	collection,
		const Classifier< Val > &	classifier
	)

create the test example from collection of pairs: attribute(domain) identifier and attribute value. Throws exception if the string identifiers not match the required domains identifiers

References faif::ml::Classifier< Val >::getAttrDomains().

template<typename Val >

Classifier<Val>::ExampleTrain faif::ml::createExample	(	const std::vector< std::pair< std::string, typename Classifier< Val >::AttrValue > > &	collection,
		const typename Classifier< Val >::AttrValue &	cat,
		const Classifier< Val > &	classifier
	)

create the train example from collection of pairs: attribute(domain) identifier and attribute value

References faif::ml::Classifier< Val >::getAttrDomains(), and faif::ml::Classifier< Val >::getCategoryDomain().

template<typename Belief >

Belief::Beliefs faif::ml::fusion

(

const typename std::vector< typename Belief::Beliefs > &

input

)

connect categories using the Dempster-Shafer combination rule, e.g. bel(cat) = bel1(cat) * bel2(cat) * ... * beln(cat) / SUM(bel)

template<typename Val >

int faif::ml::checkClassifier	(	const typename Classifier< Val >::ExamplesTrain &	test,
		Classifier< Val > &	classifier
	)

check the classifier

Returns

the number of test examples correctly classified

Parameters

test	examples
classifier

Referenced by testRange().

template<typename Val >

int faif::ml::testRange	(	std::vector< const typename Classifier< Val >::ExampleTrain * > &	tcollect,
		int	start_idx,
		int	end_idx,
		Classifier< Val > &	classifier
	)

f. pomocnicza, zwraca liczbe prawidlowo zaklasyfikowanych przykladow z tcollect. Przyklady testujace to <start_idx, end_idx). Reszta - to przyklady trenujace.

References checkClassifier(), faif::ml::Classifier< Val >::reset(), and faif::ml::Classifier< Val >::train().

template<typename Val >

double faif::ml::checkCross	(	const typename Classifier< Val >::ExamplesTrain &	examples,
		int	k,
		Classifier< Val > &	classifier
	)

check the classifier, return the probability of proper classification result. The example set is divided on k sections (randomly), one section is the testing set, the rest k-1 sections are the training set. This test is repeated k times.

Parameters

examples	training examples (the part is randomly choosen as testing)
k	num sections for cross-validation
classifier	classifier

Returns

the probability of proper classification