neuralNetwork< T > Class Template Reference

#include <neuralNetwork.h>

Inheritance diagram for neuralNetwork< T >:

Collaboration diagram for neuralNetwork< T >:

Public Member Functions
	neuralNetwork (const int &num_inputs, const std::vector< int > &which_inputs, const int &num_hidden_layers, const int &num_hidden_nodes, const std::vector< T > &weights, const std::vector< T > &wHiddenOutput, const std::vector< T > &inRanges, const std::vector< T > &inBases, const T &outRange, const T &outBase)
	neuralNetwork (const int &num_inputs, const std::vector< int > &which_inputs, const int &num_hidden_layer, const int &num_hidden_nodes)
	~neuralNetwork ()
T	run (const std::vector< T > &inputVector)
void	reset ()
int	getNumInputs () const
std::vector< int >	getWhichInputs () const
int	getNumHiddenLayers () const
void	setNumHiddenLayers (int num_hidden_layers)
int	getNumHiddenNodes () const
void	setEpochs (const int &epochs)
std::vector< T >	getWeights () const
std::vector< T >	getWHiddenOutput () const
std::vector< T >	getInRanges () const
std::vector< T >	getInBases () const
T	getOutRange () const
T	getOutBase () const
void	getJSONDescription (Json::Value &currentModel)
void	train (const std::vector< trainingExampleTemplate< T > > &trainingSet)
	These pertain to the training, and aren't need to run a trained model //. More...
Public Member Functions inherited from baseModel< T >
virtual	~baseModel ()

Additional Inherited Members
Protected Member Functions inherited from baseModel< T >
template<typename TT >
Json::Value	vector2json (TT vec)

Detailed Description

template<typename T>
class neuralNetwork< T >

Class for implementing a Neural Network.

This class includes both running and training, and constructors for reading trained models from JSON.

Constructor & Destructor Documentation

neuralNetwork() [1/2]

template<typename T >

neuralNetwork< T >::neuralNetwork	(	const int &	num_inputs,
		const std::vector< int > &	which_inputs,
		const int &	num_hidden_layers,
		const int &	num_hidden_nodes,
		const std::vector< T > &	_weights,
		const std::vector< T > &	w_hidden_output,
		const std::vector< T > &	in_ranges,
		const std::vector< T > &	in_bases,
		const T &	out_range,
		const T &	out_base
	)

This is the constructor for building a trained model from JSON.

This is the constructor for a model imported from JSON.

neuralNetwork() [2/2]

template<typename T >

neuralNetwork< T >::neuralNetwork	(	const int &	num_inputs,
		const std::vector< int > &	which_inputs,
		const int &	num_hidden_layers,
		const int &	num_hidden_nodes
	)

This constructor creates a neural network that needs to be trained.

Parameters

num_inputs	is the number of inputs the network will process
which_inputs	is an vector of which values in the input vector are being fed to the network. ex: {0,2,4}
num_hidden_layer	is the number of hidden layers in the network. Must be at least 1.
num_hidden_nodes	is the number of hidden nodes in each hidden layer. Often, this is the same as num_inputs

Returns

A neuralNetwork instance with randomized weights and no normalization values. These will be set or adjusted during training.

This is the constructor for a model that needs to be trained.

~neuralNetwork()

template<typename T >

neuralNetwork< T >::~neuralNetwork

(

)

destructor

This destructor is not needed.

Member Function Documentation

getInBases()

template<typename T >

std::vector< T > neuralNetwork< T >::getInBases

(

)

const

getInRanges()

template<typename T >

std::vector< T > neuralNetwork< T >::getInRanges

(

)

const

getJSONDescription()

template<typename T >

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

virtual

Implements baseModel< T >.

getNumHiddenLayers()

template<typename T >

int neuralNetwork< T >::getNumHiddenLayers

(

)

const

getNumHiddenNodes()

template<typename T >

int neuralNetwork< T >::getNumHiddenNodes

(

)

const

getNumInputs()

template<typename T >

int neuralNetwork< T >::getNumInputs ( ) const

virtual

Implements baseModel< T >.

getOutBase()

template<typename T >

T neuralNetwork< T >::getOutBase

(

)

const

getOutRange()

template<typename T >

T neuralNetwork< T >::getOutRange

(

)

const

getWeights()

template<typename T >

std::vector< T > neuralNetwork< T >::getWeights

(

)

const

getWhichInputs()

template<typename T >

std::vector< int > neuralNetwork< T >::getWhichInputs ( ) const

virtual

Implements baseModel< T >.

getWHiddenOutput()

template<typename T >

std::vector< T > neuralNetwork< T >::getWHiddenOutput

(

)

const

reset()

template<typename T >

void neuralNetwork< T >::reset ( )

virtual

Implements baseModel< T >.

run()

template<typename T >

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

virtual

Generate an output value from a single input vector.

Parameters

A	standard vector of type T that feed-forward regression will run on.

Returns

A single value, which is the result of the feed-forward operation

Implements baseModel< T >.

setEpochs()

template<typename T >

void neuralNetwork< T >::setEpochs

(

const int &

epochs

)

setNumHiddenLayers()

template<typename T >

void neuralNetwork< T >::setNumHiddenLayers

(

int

num_hidden_layers

)

train()

template<typename T >

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

virtual

These pertain to the training, and aren't need to run a trained model //.

Train a model using backpropagation.

Parameters

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

Implements baseModel< T >.

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

• src/neuralNetwork.h
• src/neuralNetwork.cpp