CSIM: Algorithm Class Reference

Algorithm Class Reference

#include <algorithm.h>

Inheritance diagram for Algorithm:

List of all members.

Detailed Description

The current algorithm interface is designed for offline-training only, i.e. all learning must occur externally. The algorithm object only "learns" through imported parameter vectors.

Public Member Functions

Algorithm (unsigned int in_rows=1, double lower_bound=0, double upper_bound=1)
virtual int advance (void)
Dummy implementation.
virtual int addIncoming (Advancable *Incoming)
This method will be called if object Incoming wants to send information to this object.
virtual int addOutgoing (Advancable *Outgoing)
This method will be called if this object wants to send information to object Outgoing.
virtual void setParameter (string name, double value)
virtual double getParameter (string name)
virtual list< string > validParameters (void)
virtual int apply (const double *S, double *X)=0
virtual void reset ()
virtual int importRepresentation (const double *rep, int rep_length)=0
virtual double * exportRepresentation (int *rep_length)=0
int getInputRows ()
void getRange (double *a, double *b)
void setRange (double a, double b)
virtual string getFormatDescription ()=0
virtual int updateInternal ()

Public Attributes

double range_low
Lower bound of algorithms range.
double range_high
Upper bound of algorithms range.

Protected Attributes

map< string, double * > params
A map storing pointers to the parameters.
int nInputRows
Number of rows for input vectors.
double dInputRows
Dummy for parameter setting.

Constructor & Destructor Documentation

Algorithm::Algorithm ( unsigned int in_rows = 1,

double lower_bound = 0,

double upper_bound = 1

)

Constructs a new learning algorithm.
Parameters:

in_rows Number of rows in each input vector.

lower_bound Lower bound of the algorithms range.

upper_bound Upper bound of the algorithms range.

Member Function Documentation

virtual int Algorithm::apply ( const double * S,

double * X

) [pure virtual]

Applies the currently learned function to the filtered and preprocessed input vector.
Parameters:

S State of the liquid (= filtered and preprocessed response of the neural microcircuit).

X Target pointer where to save the result.

Returns:
-1 if an error occured, 1 for success.

Implemented in linear_classification, and linear_regression.

virtual double* Algorithm::exportRepresentation ( int * rep_length ) [pure virtual]

Exports the representation of this algorithm for use in external objects.
Parameters:

rep_length Length of the representation vector.

Returns:
A list of parameters that represent the algorithm.

Warning:
Do not forget to free the memory reserved for the representation!

Implemented in linear_classification, and linear_regression.

virtual string Algorithm::getFormatDescription ( ) [pure virtual]

Returns a textual description of the representation format for import/export - Representation.
Implemented in linear_classification, and linear_regression.

int Algorithm::getInputRows ( ) [inline]

Returns the number of input rows.

double Algorithm::getParameter ( string name ) [virtual]

Returns the current value of a parameter.
Parameters:

name Name of the parameter.

Returns:
The value of the parameter.

void Algorithm::getRange ( double * a,

double * b

)

Returns the range of the algorithm's target values [a, b].
Parameters:

a Address of lower bound for algorithm's target values.

b Address of upper bound for algorithm's target values.

virtual int Algorithm::importRepresentation ( const double * rep,

int rep_length

) [pure virtual]

Imports the data from an externally (e.g. Matlab) trained algorithm.
Parameters:

rep Representation of the algorithm as a double vector.

rep_length Length of the representation vector.

Returns:
-1 if an error occured, 1 for success.

Implemented in linear_classification, and linear_regression.

virtual void Algorithm::reset ( void ) [inline, virtual]

Resets the information stored within the algorithm.
Implements Advancable.
Reimplemented in linear_classification, and linear_regression.

void Algorithm::setParameter ( string name,

double value

) [virtual]

Sets a parameter of the algorithm.
Parameters:

name Name of the parameter.

value Value to set for the parameter.

void Algorithm::setRange ( double a,

double b

)

Sets the range of the algorithm's target values [a, b].
Parameters:

a Lower bound for algorithm's target values.

b Upper bound for algorithm's target values.

int Algorithm::updateInternal ( ) [virtual]

This function is called after parameters are updated.
Reimplemented from csimClass.

list< string > Algorithm::validParameters ( void ) [virtual]

Returns the names of the valid parameters.
Returns:
A string list indicating the valid parameter names.

last modified 07/10/2006