knnClassification< T > Class Template Reference

#include <knnClassification.h>

Inheritance diagram for knnClassification< T >:

Collaboration diagram for knnClassification< T >:

Public Member Functions
	knnClassification (const int &num_inputs, const std::vector< size_t > &which_inputs, const std::vector< trainingExampleTemplate< T > > &trainingSet, const int k)
	~knnClassification ()
void	addNeighbour (const int &classNum, const std::vector< T > &features)
T	run (const std::vector< T > &inputVector) override
void	train (const std::vector< trainingExampleTemplate< T > > &trainingSet) override
void	train (const std::vector< trainingExampleTemplate< T > > &trainingSet, const std::size_t whichOutput) override
void	reset () override
size_t	getNumInputs () const override
std::vector< size_t >	getWhichInputs () const override
int	getK () const
void	setK (int newK)
void	getJSONDescription (Json::Value &currentModel) override
Public Member Functions inherited from baseModel< T >
virtual	~baseModel ()

Additional Inherited Members
Protected Member Functions inherited from baseModel< T >
template<typename TT , class Dummy = int>
Json::Value	vector2json (TT vec)
template<class Dummy = int>
Json::Value	vector2json (std::vector< unsigned long > vec)

Detailed Description

template<typename T>
class knnClassification< T >

Class for implementing a knn classifier

Constructor & Destructor Documentation

knnClassification()

template<typename T >

knnClassification< T >::knnClassification	(	const int &	num_inputs,
		const std::vector< size_t > &	which_inputs,
		const std::vector< trainingExampleTemplate< T > > &	trainingSet,
		const int	k
	)

Constructor that takes training examples in

Parameters

int	Number of inputs expected in the training and input vectors
vector	of input numbers to be fed into the classifer.
vector	of training examples
int	how many near neighbours to evaluate

~knnClassification()

template<typename T >

knnClassification< T >::~knnClassification

Member Function Documentation

addNeighbour()

template<typename T >

void knnClassification< T >::addNeighbour	(	const int &	classNum,
		const std::vector< T > &	features
	)

add another example to the existing training set

Parameters

class	number of example
feature	vector of example

getJSONDescription()

template<typename T >

void knnClassification< T >::getJSONDescription ( Json::Value &  currentModel )

overridevirtual

Populate a JSON value with a description of the current model

Parameters

A	JSON value to be populated

Implements baseModel< T >.

getK()

template<typename T >

int knnClassification< T >::getK

Get the number of nearest neighbours used by the kNN algorithm.

getNumInputs()

template<typename T >

size_t knnClassification< T >::getNumInputs

overridevirtual

Find out how many inputs the model expects

Returns

Integer number of intpus

Implements baseModel< T >.

getWhichInputs()

template<typename T >

std::vector< size_t > knnClassification< T >::getWhichInputs

overridevirtual

Find out which inputs in a vector will be used

Returns

Vector of ints, specifying input indices.

Implements baseModel< T >.

reset()

template<typename T >

void knnClassification< T >::reset

overridevirtual

Reset the model to its empty state.

Implements baseModel< T >.

run()

template<typename T >

T knnClassification< T >::run ( const std::vector< T > &  inputVector )

overridevirtual

Generate an output value from a single input vector.

Parameters

A	standard vector of type T to be evaluated.

Returns

A single value of type T: the nearest class as determined by k-nearest neighbor.

Implements baseModel< T >.

setK()

template<typename T >

void knnClassification< T >::setK

(

int

newK

)

Change the number of nearest neighbours used by the kNN algorithm.

Parameters

new	value for k

train() [1/2]

template<typename T >

void knnClassification< T >::train ( const std::vector< trainingExampleTemplate< T > > &  trainingSet )

overridevirtual

Fill the model with a vector of examples.

Parameters

The	training set is a vector of training examples that contain both a vector of input values and a value specifying desired output class.

Implements baseModel< T >.

train() [2/2]

template<typename T >

void knnClassification< T >::train ( const std::vector< trainingExampleTemplate< T > > &  trainingSet, const std::size_t  whichOutput  )

overridevirtual

Fill the model with a vector of examples. Use this when the model is part of a modelSet.

Parameters

The	training set is a vector of training examples that contain both a vector of input values and a value specifying desired output class.

Implements baseModel< T >.

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

• src/knnClassification.h
• src/knnClassification.cpp