CSIM: linear_regression Class Reference

linear_regression Class Reference

#include <linear_regression.h>

Inheritance diagram for linear_regression:

Algorithm Advancable csimClass List of all members.

Detailed Description

Implementation of a linear regression.

Public Member Functions

Private Attributes

Friends


Constructor & Destructor Documentation

linear_regression::linear_regression unsigned int  in_rows = 1  ) 
 

Constructs a new linear regression algorithm.

Parameters:
in_rows Number of rows in each input vector.

linear_regression::~linear_regression void   ) 
 

Frees the memory.


Member Function Documentation

int linear_regression::apply const double *  S,
double *  X
[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.

Implements Algorithm.

double * linear_regression::exportRepresentation int *  rep_length  )  [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!

Implements Algorithm.

string linear_regression::getFormatDescription  )  [virtual]
 

Returns a textual description of the representation format for import/export - Representation.

Implements Algorithm.

int linear_regression::importRepresentation const double *  rep,
int  rep_length
[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.

Implements Algorithm.

void linear_regression::reset  )  [virtual]
 

Resets the information stored within the algorithm.

Reimplemented from Algorithm.


 
(C) 2003, Thomas Natschläger last modified 07/10/2006