rcsc::BPNetwork1< INPUT, HIDDEN, OUTPUT, FuncH, FuncO > Class Template Reference

Back Propagetion Neural Network. More...

#include <bpn1.h>

Public Types
typedef double	value_type
	typedef of the value type
typedef std::array< value_type, INPUT >	input_array
	typedef of the input array type that uses template parameter.
typedef std::array< value_type, OUTPUT >	output_array
	typedef of the output array type that uses template parameter.

Public Member Functions
	BPNetwork1 ()
	default constructor More...
	BPNetwork1 (const value_type &eta, const value_type &alpha)
	create with learning parameter More...
template<typename RNG >
	BPNetwork1 (const value_type &eta, const value_type &alpha, RNG &rng)
	create with random number generator More...
void	init ()
	init member variables
template<typename RNG >
void	randomize (RNG &rng)
	create unit connection randomly More...
void	propagate (const input_array &input, output_array &output) const
	simulate network. More...
value_type	train (const input_array &input, const output_array &teacher)
	update unit connection weights using teacher signal More...
bool	read (std::istream &is)
	read network structure from input stream. More...
std::ostream &	print (std::ostream &os) const
	put network structure to stream by "one" line More...

Detailed Description

template<std::size_t INPUT, std::size_t HIDDEN, std::size_t OUTPUT, typename FuncH = SigmoidFunc, typename FuncO = SigmoidFunc>
class rcsc::BPNetwork1< INPUT, HIDDEN, OUTPUT, FuncH, FuncO >

Back Propagetion Neural Network.

This class can take only one hidden layer, but unit number and activation function can be specified by template parameters.

Constructor & Destructor Documentation

BPNetwork1() [1/3]

template<std::size_t INPUT, std::size_t HIDDEN, std::size_t OUTPUT, typename FuncH = SigmoidFunc, typename FuncO = SigmoidFunc>

rcsc::BPNetwork1< INPUT, HIDDEN, OUTPUT, FuncH, FuncO >::BPNetwork1 ( )

inline

default constructor

default training parameter is set.

BPNetwork1() [2/3]

template<std::size_t INPUT, std::size_t HIDDEN, std::size_t OUTPUT, typename FuncH = SigmoidFunc, typename FuncO = SigmoidFunc>

rcsc::BPNetwork1< INPUT, HIDDEN, OUTPUT, FuncH, FuncO >::BPNetwork1 ( const value_type &  eta, const value_type &  alpha  )

inline

create with learning parameter

Parameters

eta	training parameter
alpha	training parameter

BPNetwork1() [3/3]

template<std::size_t INPUT, std::size_t HIDDEN, std::size_t OUTPUT, typename FuncH = SigmoidFunc, typename FuncO = SigmoidFunc>

template<typename RNG >

rcsc::BPNetwork1< INPUT, HIDDEN, OUTPUT, FuncH, FuncO >::BPNetwork1 ( const value_type &  eta, const value_type &  alpha, RNG &  rng  )

inline

create with random number generator

Parameters

eta	training parameter
alpha	training parameter
rng	referenct to the random number generator object

Member Function Documentation

print()

template<std::size_t INPUT, std::size_t HIDDEN, std::size_t OUTPUT, typename FuncH = SigmoidFunc, typename FuncO = SigmoidFunc>

std::ostream & rcsc::BPNetwork1< INPUT, HIDDEN, OUTPUT, FuncH, FuncO >::print ( std::ostream &  os ) const

inline

put network structure to stream by "one" line

Parameters

os	reference to the output stream

Returns

reference to the output stream

propagate()

template<std::size_t INPUT, std::size_t HIDDEN, std::size_t OUTPUT, typename FuncH = SigmoidFunc, typename FuncO = SigmoidFunc>

void rcsc::BPNetwork1< INPUT, HIDDEN, OUTPUT, FuncH, FuncO >::propagate ( const input_array &  input, output_array &  output  ) const

inline

simulate network.

Parameters

input	input data
output	reference to the data holder variable

randomize()

template<std::size_t INPUT, std::size_t HIDDEN, std::size_t OUTPUT, typename FuncH = SigmoidFunc, typename FuncO = SigmoidFunc>

template<typename RNG >

void rcsc::BPNetwork1< INPUT, HIDDEN, OUTPUT, FuncH, FuncO >::randomize ( RNG &  rng )

inline

create unit connection randomly

Parameters

rng	referenct to the random number generator object

read()

template<std::size_t INPUT, std::size_t HIDDEN, std::size_t OUTPUT, typename FuncH = SigmoidFunc, typename FuncO = SigmoidFunc>

bool rcsc::BPNetwork1< INPUT, HIDDEN, OUTPUT, FuncH, FuncO >::read ( std::istream &  is )

inline

read network structure from input stream.

Parameters

is	reference to the input stream

Returns

true if successfully ended.

train()

template<std::size_t INPUT, std::size_t HIDDEN, std::size_t OUTPUT, typename FuncH = SigmoidFunc, typename FuncO = SigmoidFunc>

value_type rcsc::BPNetwork1< INPUT, HIDDEN, OUTPUT, FuncH, FuncO >::train ( const input_array &  input, const output_array &  teacher  )

inline

update unit connection weights using teacher signal

Parameters

input	input data
teacher	teaching signal data

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

• /home/kali/rc/tools/include/rcsc/ann/bpn1.h