classificationTemplate< T > Class Template Reference

#include <classification.h>

Inheritance diagram for classificationTemplate< T >:

Collaboration diagram for classificationTemplate< T >:

Public Types
enum	classificationTypes { knn, svm }

Public Member Functions
	classificationTemplate ()
	classificationTemplate (classificationTypes classificationType)
	classificationTemplate (const std::vector< trainingExampleTemplate< T > > &trainingSet)
	classificationTemplate (const int &numInputs, const int &numOutputs)
	~classificationTemplate ()
bool	train (const std::vector< trainingExampleTemplate< T > > &trainingSet)
std::vector< int >	getK ()
void	setK (const int whichModel, const int newK)
Public Member Functions inherited from modelSet< T >
	modelSet ()
virtual	~modelSet ()
bool	reset ()
std::vector< T >	run (const std::vector< T > &inputVector)
std::string	getJSON ()
void	writeJSON (const std::string &filepath)
bool	putJSON (const std::string &jsonMessage)
bool	readJSON (const std::string &filepath)

Additional Inherited Members
Protected Attributes inherited from modelSet< T >
std::vector< baseModel< T > * >	myModelSet
int	numInputs
std::vector< std::string >	inputNames
int	numOutputs
bool	created

Detailed Description

template<typename T>
class classificationTemplate< T >

Class for implementing a set of classification models.

This doesn't do anything modelSet can't do. But, it's simpler and more like wekinator.

Member Enumeration Documentation

classificationTypes

template<typename T >

enum classificationTemplate::classificationTypes

Enumerator
knn
svm

Constructor & Destructor Documentation

classificationTemplate() [1/4]

template<typename T >

classificationTemplate< T >::classificationTemplate

(

)

with no arguments, just make an empty vector

classificationTemplate() [2/4]

template<typename T >

classificationTemplate< T >::classificationTemplate

(

classificationTypes

classificationType

)

speciify classification type

classificationTemplate() [3/4]

template<typename T >

classificationTemplate< T >::classificationTemplate

(

const std::vector< trainingExampleTemplate< T > > &

trainingSet

)

create based on training set inputs and outputs

classificationTemplate() [4/4]

template<typename T >

classificationTemplate< T >::classificationTemplate	(	const int &	numInputs,
		const int &	numOutputs
	)

create with proper models, but not trained

~classificationTemplate()

template<typename T >

classificationTemplate< T >::~classificationTemplate ( )

inline

destructor

Member Function Documentation

getK()

template<typename T >

std::vector< int > classificationTemplate< T >::getK

(

)

Check the K values for each model. This feature is temporary, and will be replaced by a different design.

setK()

template<typename T >

void classificationTemplate< T >::setK	(	const int	whichModel,
		const int	newK
	)

Get the K values for each model. This feature is temporary, and will be replaced by a different design.

train()

template<typename T >

bool classificationTemplate< T >::train ( const std::vector< trainingExampleTemplate< T > > &  trainingSet )

virtual

Train on a specified set, causes creation if not created

Reimplemented from modelSet< T >.

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

• src/classification.h
• src/classification.cpp