multi layer neural network with configurable activation functions and propagation and projection methods suitable for homeokinesis controller More...

#include <controllernet.h>

Inheritance diagram for ControllerNet:

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Collaboration diagram for ControllerNet:

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Public Member Functions
	ControllerNet (const std::vector< Layer > &layers, bool useBypass=false)

virtual	~ControllerNet ()

virtual void	init (unsigned int inputDim, unsigned int outputDim, double unit_map=0.0, double rand=0.2, RandGen *randGen=0)
	initialisation of the network with the given number of input and output units. More...

virtual const matrix::Matrix	process (const matrix::Matrix &input)
	passive processing of the input. More...

virtual const matrix::Matrix	processX (const matrix::Matrix &input, const matrix::Matrix &injection, unsigned int injectInLayer)
	like process just with the opportunity to overwrite the activation of a specific layer More...

virtual void	damp (double damping)
	damps the weights and the biases by multiplying (1-damping) More...

virtual const matrix::Matrix &	response () const
	response matrix of neural network (for current activation, see process) $J_ij = \frac{\partial y_i}{\partial x_j}$ $J = G_n' W_n G_{n-1}' W_{n-1} ... G_1' W_1$ with is the weight matrix of layer n and is a diagonal matrix with as values on the diagonal. More...

virtual matrix::Matrix	responsePart (int from, int to) const
	like response, just that only a range of layers is considered The Bypass is not considered here. More...

virtual const matrix::Matrix &	responseLinear () const
	linear response matrix of neural network $R = W_n W_{n-1} ... W_1$ with is the weight matrix of layer n. More...

virtual const matrix::Matrix	backpropagation (const matrix::Matrix &error, matrix::Matrices errors=0, matrix::Matrices zetas=0) const
	backpropagation of vector error through network. More...

virtual const matrix::Matrix	backpropagationX (const matrix::Matrix &error, matrix::Matrices errors=0, matrix::Matrices zetas=0, int startWithLayer=-1) const
	like backpropagation but with special features: we can start from any layer and the bypass-discounting can be used (see disseration Georg Martius) WARNING: the errors and zetas above the `startWithLayer' are undefined More...

virtual const matrix::Matrix	backprojection (const matrix::Matrix &error, matrix::Matrices errors=0, matrix::Matrices zetas=0) const
	backprojection of vector error through network. More...

virtual const matrix::Matrix	forwardpropagation (const matrix::Matrix &error, matrix::Matrices errors=0, matrix::Matrices zetas=0) const
	forwardpropagation of vector error through network. More...

virtual const matrix::Matrix	forwardprojection (const matrix::Matrix &error, matrix::Matrices errors=0, matrix::Matrices zetas=0) const
	forwardprojection of vector error through network. More...

virtual unsigned int	getInputDim () const
	returns the number of input neurons More...

virtual unsigned int	getOutputDim () const
	returns the number of output neurons More...

virtual const matrix::Matrix &	getLayerOutput (int layer) const
	returns activation of the given layer. More...

virtual unsigned int	getLayerNum () const

virtual const Layer &	getLayer (unsigned int layer) const
	layers 0 is the first hidden layer More...

virtual Layer &	getLayer (unsigned int layer)
	layers 0 is the first hidden layer More...

virtual const matrix::Matrix &	getWeights (int to_layer) const
	weight matrix 0 connects input with the first hidden layer Negative values count from the end (-1 is the last layer) More...

virtual matrix::Matrix &	getWeights (int to_layer)
	weight matrix 0 connects input with the first hidden layer Negative values count from the end (-1 is the last layer) More...

virtual const matrix::Matrix &	getByPass () const

virtual matrix::Matrix &	getByPass ()

virtual const matrix::Matrix &	getBias (int of_layer) const
	Note: layers 0 is the first hidden layer Negative values count from the end (-1 is the last layer) More...

virtual matrix::Matrix &	getBias (int of_layer)
	Note: layers 0 is the first hidden layer Negative values count from the end (-1 is the last layer) More...

bool	store (FILE *f) const
	stores the layer binary into file stream More...

bool	restore (FILE *f)
	restores the layer binary from file stream More...

bool	write (FILE *f) const
	writes the layer ASCII into file stream (not in the storable interface) More...

Public Member Functions inherited from Configurable
	Configurable ()

	Configurable (const std::string &name, const std::string &revision)
	intialise with name and revision (use "$ID$") More...

virtual	~Configurable ()

virtual void	notifyOnChange (const paramkey &key)
	Is called when a parameter was changes via setParam(). More...

virtual void	addParameter (const paramkey &key, paramval *val, paramval minBound, paramval maxBound, const paramdescr &descr=paramdescr())
	This is the new style for adding configurable parameters. More...

virtual void	addParameter (const paramkey &key, paramval *val, const paramdescr &descr=paramdescr())
	See addParameter(const paramkey& key, paramval* val, paramval minBound, paramval maxBound, const paramdescr& descr) More...

virtual void	addParameter (const paramkey &key, parambool *val, const paramdescr &descr=paramdescr())
	See addParameter(const paramkey& key, paramval* val) but for bool values. More...

virtual void	addParameter (const paramkey &key, paramint *val, paramint minBound, paramint maxBound, const paramdescr &descr=paramdescr())
	See addParameter(const paramkey& key, paramval* val) but for int values. More...

virtual void	addParameter (const paramkey &key, paramint *val, const paramdescr &descr=paramdescr())

virtual void	addParameterDef (const paramkey &key, paramval *val, paramval def, paramval minBound, paramval maxBound, const paramdescr &descr=paramdescr())
	This function is only provided for convenience. More...

virtual void	addParameterDef (const paramkey &key, paramval *val, paramval def, const paramdescr &descr=paramdescr())

virtual void	addParameterDef (const paramkey &key, parambool *val, parambool def, const paramdescr &descr=paramdescr())
	See addParameterDef(const paramkey&, paramval*, paramval) More...

virtual void	addParameterDef (const paramkey &key, paramint *val, paramint def, paramint minBound, paramint maxBound, const paramdescr &descr=paramdescr())
	See addParameterDef(const paramkey&, paramval*, paramval) More...

virtual void	addParameterDef (const paramkey &key, paramint *val, paramint def, const paramdescr &descr=paramdescr())

virtual void	setParamDescr (const paramkey &key, const paramdescr &descr, bool traverseChildren=true)
	sets a description for the given parameter More...

int	getId () const
	return the id of the configurable objects, which is created by random on initialisation More...

virtual paramkey	getName () const
	return the name of the object More...

virtual paramkey	getRevision () const
	returns the revision of the object More...

virtual void	setName (const paramkey &name, bool callSetNameOfInspectable=true)
	Sets the name of the configurable. More...

virtual void	setRevision (const paramkey &revision)
	sets the revision Hint: { return "$ID$"; } More...

virtual paramval	getParam (const paramkey &key, bool traverseChildren=true) const
	returns the value of the requested parameter or 0 (+ error message to stderr) if unknown. More...

virtual bool	hasParam (const paramkey &key, bool traverseChildren=true) const
	Returns if the requested parameter is part of the configurable or their children. More...

virtual bool	setParam (const paramkey &key, paramval val, bool traverseChildren=true)
	sets the value of the given parameter or does nothing if unknown. More...

virtual void	setParamBounds (const paramkey &key, paramval minBound, paramval maxBound, bool traverseChildren=true)
	Sets the bounds (minBound and maxBound) of the given parameter. More...

virtual void	setParamBounds (const paramkey &key, paramint minBound, paramint maxBound, bool traverseChildren=true)

virtual void	setParamBounds (const paramkey &key, paramvalBounds bounds, bool traverseChildren=true)

virtual void	setParamBounds (const paramkey &key, paramintBounds bounds, bool traverseChildren=true)

virtual paramlist	getParamList () const
	The list of all parameters with there value as allocated lists. More...

virtual std::list< paramkey >	getAllParamNames (bool traverseChildren=true)
	returns all names that are configureable More...

virtual parammap	getParamValMap () const

virtual paramintmap	getParamIntMap () const

virtual paramboolmap	getParamBoolMap () const

virtual paramdescr	getParamDescr (const paramkey &key, bool traverseChildren=true) const
	returns the description for the given parameter More...

virtual paramvalBounds	getParamvalBounds (const paramkey &key, bool traverseChildren=true) const

virtual paramintBounds	getParamintBounds (const paramkey &key, bool traverseChildren=true) const

virtual bool	hasParamDescr (const paramkey &key, bool traverseChildren=true) const

virtual bool	hasParamvalBounds (const paramkey &key, bool traverseChildren=true) const

virtual bool	hasParamintBounds (const paramkey &key, bool traverseChildren=true) const

virtual bool	storeCfg (const char *filenamestem, const std::list< std::string > &comments=std::list< std::string >())
	stores the key values paires into the file : filenamestem.cfg including the comments given in the list More...

virtual bool	restoreCfg (const char *filenamestem)
	restores the key values paires from the file : filenamestem.cfg More...

void	print (FILE f, const char prefix, int columns=90, bool traverseChildren=true) const
	prints the keys, values and descriptions to the file. Each line is prefixed More...

bool	parse (FILE f, const char prefix=0, bool traverseChildren=true)
	parses the configuration from the given file More...

virtual void	addConfigurable (Configurable *conf)
	Adds a configurable as a child object. More...

virtual void	removeConfigurable (Configurable *conf)
	Removes a configurable as a child object. More...

virtual const configurableList &	getConfigurables () const
	Returns the list containing all configurable children. More...

virtual void	configurableChanged ()
	Indicates that the configurable itself or the configurable children attached to this configurable have changed. More...

Public Member Functions inherited from BackCaller
	BackCaller ()

virtual	~BackCaller ()

virtual void	addCallbackable (Callbackable *callbackableInstance, CallbackableType type=BackCaller::DEFAULT_CALLBACKABLE_TYPE)
	Adds a Callbackable instance to this caller instance. More...

virtual void	removeCallbackable (Callbackable *callbackableInstance, CallbackableType type=BackCaller::DEFAULT_CALLBACKABLE_TYPE)
	Removes a Callbackable instance from this caller instance. More...

virtual void	removeAllCallbackables (CallbackableType type)
	Removes all Callbackable instances from this caller instance. More...

virtual void	callBack (CallbackableType type=BackCaller::DEFAULT_CALLBACKABLE_TYPE)
	Calls all registered callbackable classes of the determined type. More...

virtual void	callBackQMP (CallbackableType type=BackCaller::DEFAULT_CALLBACKABLE_TYPE)
	Calls all registered callbackable classes of the determined type. More...

Protected Member Functions
virtual void	calcResponseIntern ()

Protected Member Functions inherited from Configurable
void	copyParameters (const Configurable &, bool traverseChildren=true)
	copies the internal params of the given configurable More...

void	printdescr (FILE f, const char prefix, const paramkey &key, int columns, int indent) const

Protected Attributes
std::vector< Layer >	layers

std::vector< matrix::Matrix >	weights

std::vector< matrix::Matrix >	bias

bool	useBypass

matrix::Matrix	bypassWeights

matrix::Matrix	input

matrix::Matrices	y

matrix::Matrices	z

matrix::Matrices	gp

matrix::Matrix	L

matrix::Matrix	R

double	lambda

bool	initialised

Additional Inherited Members
Public Types inherited from Configurable
typedef std::string	paramkey

typedef std::string	paramdescr

typedef double	paramval

typedef std::list< std::pair < paramkey, paramval > >	paramlist

typedef std::map< paramkey, paramval * >	parammap

typedef bool	parambool

typedef std::list< std::pair < paramkey, parambool > >	paramboollist

typedef std::map< paramkey, parambool * >	paramboolmap

typedef int	paramint

typedef std::list< std::pair < paramkey, paramint > >	paramintlist

typedef std::map< paramkey, paramint * >	paramintmap

typedef std::map< paramkey, paramdescr >	paramdescrmap

typedef std::pair< paramval, paramval >	paramvalBounds

typedef std::map< paramkey, paramvalBounds >	paramvalBoundsMap

typedef std::pair< paramint, paramint >	paramintBounds

typedef std::map< paramkey, paramintBounds >	paramintBoundsMap

typedef std::pair< paramkey, paramval * >	paramvalpair

typedef std::pair< paramkey, parambool * >	paramboolpair

typedef std::pair< paramkey, paramint * >	paramintpair

typedef std::vector < Configurable * >	configurableList

Public Types inherited from BackCaller
typedef unsigned long	CallbackableType

Static Public Attributes inherited from Configurable
static const CallbackableType	CALLBACK_CONFIGURABLE_CHANGED = 11

Static Public Attributes inherited from BackCaller
static const CallbackableType	DEFAULT_CALLBACKABLE_TYPE = 0
	This is the default Callbackable type. More...

Detailed Description

multi layer neural network with configurable activation functions and propagation and projection methods suitable for homeokinesis controller

Constructor & Destructor Documentation

ControllerNet	(	const std::vector< Layer > &	layers,
		bool	useBypass = `false`
	)

Parameters

layers	Layer description (the input layer is not specified (always linear))
useBypass	if true, then a connection from input to output layer is included

virtual ~ControllerNet ( )

inlinevirtual

Member Function Documentation

const Matrix backprojection	(	const matrix::Matrix &	error,
		matrix::Matrices *	errors = `0`,
		matrix::Matrices *	zetas = `0`
	)		const

virtual

backprojection of vector error through network.

The storage for the intermediate values (errors, zetas) do not need to be given. The errors(layerwise) are at the output of the neurons (index 0 is at the input level, output of layer 0 has index 1 and so on) The zetas(layerwise) are the values inside the neurons that arise when backprojecting the error signal. (zeta[0] is at layer 0)

Returns: errors[0] (result of backprojecting)

const Matrix backpropagation	(	const matrix::Matrix &	error,
		matrix::Matrices *	errors = `0`,
		matrix::Matrices *	zetas = `0`
	)		const

virtual

backpropagation of vector error through network.

The storage for the intermediate values (errors, zetas) do not need to be given. The errors(layerwise) are at the output of the neurons (index 0 is at the input level, output of layer 0 has index 1 and so on) The zetas(layerwise) are the values inside the neurons that arise when backpropagating the error signal. (zeta[0] is at layer 0)

Returns: errors[0] (result of backpropagation)

const Matrix backpropagationX	(	const matrix::Matrix &	error,
		matrix::Matrices *	errors = `0`,
		matrix::Matrices *	zetas = `0`,
		int	startWithLayer = `-1`
	)		const

virtual

like backpropagation but with special features: we can start from any layer and the bypass-discounting can be used (see disseration Georg Martius) WARNING: the errors and zetas above the `startWithLayer' are undefined

Parameters

startWithLayer the error is clamped at this layer and the processing starts there (-1: output layer)

See Also: backpropagation

void calcResponseIntern ( )

protectedvirtual

void damp ( double damping )

virtual

damps the weights and the biases by multiplying (1-damping)

const Matrix forwardprojection	(	const matrix::Matrix &	error,
		matrix::Matrices *	errors = `0`,
		matrix::Matrices *	zetas = `0`
	)		const

virtual

forwardprojection of vector error through network.

The storage for the intermediate values (errors, zetas) do not need to be given. The errors(layerwise) are at the output of the neurons (index 0 is at the input level = error, output of layer 0 has index 1 and so on) The zetas(layerwise) are the values inside the neurons that arise when forwardprojecting the error signal. (zeta[0] is at layer 0)

Returns: errors[layernum] (result of forwardprojection)

const Matrix forwardpropagation	(	const matrix::Matrix &	error,
		matrix::Matrices *	errors = `0`,
		matrix::Matrices *	zetas = `0`
	)		const

virtual

forwardpropagation of vector error through network.

The storage for the intermediate values (errors, zetas) do not need to be given. The errors(layerwise) are at the output of the neurons (index 0 is at the input level = error, output of layer 0 has index 1 and so on) The zetas(layerwise) are the values inside the neurons that arise when forwardpropagate the error signal. (zeta[0] is at layer 0)

Returns: errors[layernum] (result of forwardpropagation)

virtual const matrix::Matrix& getBias ( int of_layer ) const

inlinevirtual

Note: layers 0 is the first hidden layer Negative values count from the end (-1 is the last layer)

virtual matrix::Matrix& getBias ( int of_layer )

inlinevirtual

Note: layers 0 is the first hidden layer Negative values count from the end (-1 is the last layer)

virtual const matrix::Matrix& getByPass ( ) const

inlinevirtual

virtual matrix::Matrix& getByPass ( )

inlinevirtual

virtual unsigned int getInputDim ( ) const

inlinevirtual

returns the number of input neurons

virtual const Layer& getLayer ( unsigned int layer ) const

inlinevirtual

layers 0 is the first hidden layer

virtual Layer& getLayer ( unsigned int layer )

inlinevirtual

layers 0 is the first hidden layer

virtual unsigned int getLayerNum ( ) const

inlinevirtual

virtual const matrix::Matrix& getLayerOutput ( int layer ) const

inlinevirtual

returns activation of the given layer.

Layer 0 is the first hidden layer. Negative values count from the end (-1 is the last layer)

virtual unsigned int getOutputDim ( ) const

inlinevirtual

returns the number of output neurons

virtual const matrix::Matrix& getWeights ( int to_layer ) const

inlinevirtual

weight matrix 0 connects input with the first hidden layer Negative values count from the end (-1 is the last layer)

virtual matrix::Matrix& getWeights ( int to_layer )

inlinevirtual

weight matrix 0 connects input with the first hidden layer Negative values count from the end (-1 is the last layer)

void init	(	unsigned int	inputDim,
		unsigned int	outputDim,
		double	unit_map = `0.0`,
		double	rand = `0.2`,
		RandGen *	randGen = `0`
	)

virtual

initialisation of the network with the given number of input and output units.

The dimensionality of the ouputlayer is automatically adjusted.

Parameters

unit_map	defines the approximate response of the network after initialisation (if unit_map=1 the weights are unit matrices).
randGen	pointer to random generator, if 0 an new one is used

const Matrix process ( const matrix::Matrix & input )

virtual

passive processing of the input.

This has to be done before calling reponse, and the back/forward propagation/projection functions. The activations and the response matrix are stored internally.

const Matrix processX	(	const matrix::Matrix &	input,
		const matrix::Matrix &	injection,
		unsigned int	injectInLayer
	)

virtual

like process just with the opportunity to overwrite the activation of a specific layer

Parameters

injections	the input that is clamped at layer injectInLayer
injectInLayer	the injection is clamped at this layer

const Matrix & response ( ) const

virtual

response matrix of neural network (for current activation, see process)

$J_ij = \frac{\partial y_i}{\partial x_j}$

$J = G_n' W_n G_{n-1}' W_{n-1} ... G_1' W_1$

with $W_n$ is the weight matrix of layer n and $ G'$ is a diagonal matrix with $ G'_ii = g'_i $ as values on the diagonal.

const Matrix & responseLinear ( ) const

virtual

linear response matrix of neural network

$R = W_n W_{n-1} ... W_1$

with $W_n$ is the weight matrix of layer n.

Matrix responsePart	(	int	from,
		int	to
	)		const

virtual

like response, just that only a range of layers is considered The Bypass is not considered here.

Parameters

from	index of layer to start: -1 at input, 0 first hidden layer ...
to	index of layer to stop: -1: last layer, 0 first hidden layer ...

bool restore ( FILE * f )

restores the layer binary from file stream

bool store ( FILE * f ) const

stores the layer binary into file stream

bool write ( FILE * f ) const

writes the layer ASCII into file stream (not in the storable interface)

Member Data Documentation

std::vector<matrix::Matrix> bias

protected

matrix::Matrix bypassWeights

protected

matrix::Matrices gp

protected

bool initialised

protected

matrix::Matrix input

protected

matrix::Matrix L

protected

double lambda

protected

std::vector<Layer> layers

protected

matrix::Matrix R

protected

bool useBypass

protected

std::vector<matrix::Matrix> weights

protected

matrix::Matrices y

protected

matrix::Matrices z

protected

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

Public Member Functions

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation