InvertMotorNStep Class Reference

class for robot controller that uses the georg's matrixlib for direct matrix inversion for n channels (simple one layer networks) More...

#include <invertmotornstep.h>

Inherits InvertMotorController.

Inherited by Deprivation, ProActive, and ProActive2.

Inheritance diagram for InvertMotorNStep:

[legend]Collaboration diagram for InvertMotorNStep:

[legend]List of all members.


Public Member Functions
	InvertMotorNStep (const InvertMotorNStepConf &conf=getDefaultConf())
virtual void	init (int sensornumber, int motornumber)
	initialisation of the controller with the given sensor/ motornumber Must be called before use.
virtual	~InvertMotorNStep ()
virtual int	getSensorNumber () const
	returns the number of sensors the controller was initialised with or 0 if not initialised
virtual int	getMotorNumber () const
	returns the mumber of motors the controller was initialised with or 0 if not initialised
virtual void	step (const sensor , int number_sensors, motor , int number_motors)
	performs one step (includes learning).
virtual void	stepNoLearning (const sensor , int number_sensors, motor , int number_motors)
	performs one step without learning. Calulates motor commands from sensor inputs.
virtual bool	store (FILE *f) const
	stores the controller values to a given file.
virtual bool	restore (FILE *f)
	loads the controller values from a given file.
virtual std::list< iparamkey >	getInternalParamNames () const
	The list of the names of all internal parameters given by getInternalParams().
virtual std::list< iparamval >	getInternalParams () const
virtual std::list< ILayer >	getStructuralLayers () const
	Specifies which parameter vector forms a structural layer (in terms of a neural network) The ordering is important.
virtual std::list< IConnection >	getStructuralConnections () const
	Specifies which parameter matrix forms a connection between layers (in terms of a neural network) The orderning is not important.
virtual void	setTeachingMode (bool onOff)
virtual bool	getTeachingMode ()
virtual void	setMotorTeachingSignal (const motor *teaching, int len)
void	calcCandHUpdatesTeaching (matrix::Matrix &C_update, matrix::Matrix &H_update, int y_delay)
	calculates the Update for C and H using the teaching signal
void	getLastMotors (motor *motors, int len)
Static Public Member Functions
InvertMotorNStepConf	getDefaultConf ()
Protected Member Functions
virtual void	fillBuffersAndControl (const sensor x_, int number_sensors, motor y_, int number_motors)
	puts the sensors in the ringbuffer, generate controller values and put them in the
virtual void	calcEtaAndBufferIt (int delay)
	calculates the first shift into the motor space useing delayed motor values.
virtual void	calcXsi (int delay)
	calculates xsi for the current time step using the delayed y values
virtual void	learnController ()
	learn H,C with motors y and corresponding sensors x
virtual void	calcCandHUpdates (matrix::Matrix &C_update, matrix::Matrix &H_update, int y_delay)
	calculates the Update for C and H
virtual void	updateCandH (const matrix::Matrix &C_update, const matrix::Matrix &H_update, double squashSize)
	updates the matrix C and H
virtual void	learnModel (int delay)
	learn A, (and S) using motors y and corresponding sensors x
virtual matrix::Matrix	model (const matrix::Matrix *x_buffer, int delay, const matrix::Matrix &y)
	calculates the predicted sensor values
virtual matrix::Matrix	calculateControllerValues (const matrix::Matrix &x_smooth)
	returns controller output for given sensor values
matrix::Matrix	calcDerivatives (const matrix::Matrix *buffer, int delay)
	Calculates first and second derivative and returns both in on matrix (above).
Protected Attributes
unsigned short	number_sensors
unsigned short	number_motors
matrix::Matrix	A
	Model Matrix (motors to sensors).
matrix::Matrix	S
	additional Model Matrix (sensors derivatives to sensors)
matrix::Matrix	C
	Controller Matrix.
matrix::Matrix	H
	Controller Bias.
matrix::Matrix	B
	Model Bias.
NoiseGenerator *	BNoiseGen
	Noisegenerator for noisy bias.
matrix::Matrix	R
	C*A.
matrix::Matrix	SmallID
	small identity matrix in the dimension of R
matrix::Matrix	xsi
	current output error
double	xsi_norm
	norm of matrix
double	xsi_norm_avg
	average norm of xsi (used to define whether Modell learns)
double	pain
	if the modelling error (xsi) is too high we have a pain signal
matrix::Matrix *	x_buffer
matrix::Matrix *	y_buffer
matrix::Matrix *	eta_buffer
matrix::Matrix	zero_eta
matrix::Matrix	x_smooth
matrix::Matrix	y_teaching
	teaching motor signal
InvertMotorNStepConf	conf

Detailed Description

class for robot controller that uses the georg's matrixlib for direct matrix inversion for n channels (simple one layer networks)

Implements standart parameters: eps, rho, mu, stepnumber4avg, stepnumber4delay

Constructor & Destructor Documentation

InvertMotorNStep ( const InvertMotorNStepConf & conf = getDefaultConf() )

~InvertMotorNStep ( ) [virtual]

Member Function Documentation

void calcCandHUpdates ( matrix::Matrix & C_update,

matrix::Matrix & H_update,

int y_delay

) [protected, virtual]

calculates the Update for C and H

void calcCandHUpdatesTeaching ( matrix::Matrix & C_update,

matrix::Matrix & H_update,

int y_delay

)

calculates the Update for C and H using the teaching signal

Matrix calcDerivatives ( const matrix::Matrix * buffer,

int delay

) [protected]

Calculates first and second derivative and returns both in on matrix (above).
We use simple discrete approximations:
$f'(x) = (f(x) - f(x-1)) / 2$

$f''(x) = f(x) - 2f(x-1) + f(x-2)$
where we have to go into the past because we do not have f(x+1). The scaling can be neglegted.

void calcEtaAndBufferIt ( int delay ) [protected, virtual]

calculates the first shift into the motor space useing delayed motor values.

Matrix calculateControllerValues ( const matrix::Matrix & x_smooth ) [protected, virtual]

returns controller output for given sensor values

Parameters:

x_smooth smoothed sensors Matrix(number_channels,1)

void calcXsi ( int delay ) [protected, virtual]

calculates xsi for the current time step using the delayed y values

Reimplemented in ProActive, and ProActive2.

void fillBuffersAndControl ( const sensor * x_,

int number_sensors,

motor * y_,

int number_motors

) [protected, virtual]

puts the sensors in the ringbuffer, generate controller values and put them in the

InvertMotorNStepConf getDefaultConf ( ) [inline, static]

Reimplemented in ProActive, and ProActive2.

list< Inspectable::iparamkey > getInternalParamNames ( ) const [virtual]

The list of the names of all internal parameters given by getInternalParams().
The naming convention is "v[i]" for vectors and "A[i][j]" for matrices, where i, j start at 0.
Returns:
: list of keys

Implements Inspectable.

Reimplemented in ProActive, and ProActive2.

list< Inspectable::iparamval > getInternalParams ( ) const [virtual]

Returns:
: list of values

Implements Inspectable.

Reimplemented in ProActive, and ProActive2.

void getLastMotors ( motor * motors,

int len

)

virtual int getMotorNumber ( ) const [inline, virtual]

returns the mumber of motors the controller was initialised with or 0 if not initialised

Reimplemented from AbstractController.

virtual int getSensorNumber ( ) const [inline, virtual]

returns the number of sensors the controller was initialised with or 0 if not initialised

Reimplemented from AbstractController.

list< Inspectable::IConnection > getStructuralConnections ( ) const [virtual]

Specifies which parameter matrix forms a connection between layers (in terms of a neural network) The orderning is not important.

Returns:
: list of layer names with dimension

Reimplemented from Inspectable.

list< Inspectable::ILayer > getStructuralLayers ( ) const [virtual]

Specifies which parameter vector forms a structural layer (in terms of a neural network) The ordering is important.
The first entry is the input layer and so on.
Returns:
: list of layer names with dimension

Reimplemented from Inspectable.

bool getTeachingMode ( ) [virtual]

void init ( int sensornumber,

int motornumber

) [virtual]

initialisation of the controller with the given sensor/ motornumber Must be called before use.

Reimplemented from AbstractController.

Reimplemented in ProActive, and ProActive2.

void learnController ( ) [protected, virtual]

learn H,C with motors y and corresponding sensors x

Reimplemented in Deprivation.

void learnModel ( int delay ) [protected, virtual]

learn A, (and S) using motors y and corresponding sensors x

Matrix model ( const matrix::Matrix * x_buffer,

int delay,

const matrix::Matrix & y

) [protected, virtual]

calculates the predicted sensor values

bool restore ( FILE * f ) [virtual]

loads the controller values from a given file.

Implements Storeable.

void setMotorTeachingSignal ( const motor * teaching,

int len

) [virtual]

void setTeachingMode ( bool onOff ) [virtual]

void step ( const sensor * ,

int number_sensors,

motor * ,

int number_motors

) [virtual]

performs one step (includes learning).
Calulates motor commands from sensor inputs.
Reimplemented from AbstractController.

Reimplemented in ProActive, and ProActive2.

void stepNoLearning ( const sensor * ,

int number_sensors,

motor * ,

int number_motors

) [virtual]

performs one step without learning. Calulates motor commands from sensor inputs.

Reimplemented from AbstractController.

Reimplemented in ProActive, and ProActive2.

bool store ( FILE * f ) const [virtual]

stores the controller values to a given file.

Implements Storeable.

void updateCandH ( const matrix::Matrix & C_update,

const matrix::Matrix & H_update,

double squashSize

) [protected, virtual]

updates the matrix C and H

Reimplemented in ProActive.