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HuberData Class Reference
[Huber]

#include <HuberData.h>

Inheritance diagram for HuberData:

Data List of all members.

Public Member Functions

 HuberData (int nobservations_in, int npredictors_in, double cutoff, double *X=0, double *y=0)
 ~HuberData ()
virtual void XtMult (double beta, SimpleVector &y, double alpha, SimpleVector &x)
virtual void XtTransMult (double beta, SimpleVector &y, double alpha, SimpleVector &x)
virtual double datanorm ()
virtual void datarandom ()
virtual void print ()

Static Public Member Functions

HuberDatatextInput (char filename[], double cutoff, int &iErr)

Public Attributes

int m
int n
DenseGenMatrixHandle Xt
SimpleVectorHandle Y
double cutoff
int nobservations
int npredictors

Detailed Description

Data class for Huber.


Constructor & Destructor Documentation

HuberData::HuberData int  nobservations_in,
int  npredictors_in,
double  cutoff,
double *  X = 0,
double *  y = 0
 

make data object using data structures already allocated

HuberData::~HuberData  ) 
 

destructor


Member Function Documentation

virtual double HuberData::datanorm  )  [virtual]
 

compute the norm of the problem data

Implements Data.

virtual void HuberData::print  )  [inline, virtual]
 

print the problem data

Implements Data.

HuberData* HuberData::textInput char  filename[],
double  cutoff,
int &  iErr
[static]
 

reads problem data from a text file.

Parameters:
filename name of input file. First entry of file is the integer "nobservations" representing number of observations. Second entry "npredictors" represents number of predictr variables. Subsequent entries represent successive rows of the predictor matrix X, with each row followed by the value of the corresponding target variable Y.

virtual void HuberData::XtMult double  beta,
SimpleVector y,
double  alpha,
SimpleVector x
[virtual]
 

performs operation y <- beta*y + alpha*Xt*x

virtual void HuberData::XtTransMult double  beta,
SimpleVector y,
double  alpha,
SimpleVector x
[virtual]
 

performs operation y <- beta*y + alpha*Xt^T*x; that is, y <- beta*y + alpha*X*x


Member Data Documentation

double HuberData::cutoff
 

cutoff parameter: value at which the loss function turns from a least-squares function into an absolute-value function

int HuberData::m
 

dimensions of the equivalent LCP reformulation of the Huber regression problem

int HuberData::n
 

dimensions of the equivalent LCP reformulation of the Huber regression problem

int HuberData::nobservations
 

number of observations

int HuberData::npredictors
 

number of predictors

DenseGenMatrixHandle HuberData::Xt
 

store the transpose of the matrix X as a dense matrix. Dimensions of Xt are npredictors x nobservations

SimpleVectorHandle HuberData::Y
 

right-hand side (targets)


The documentation for this class was generated from the following file:
Generated on Wed Mar 22 13:58:34 2006 for OOQP by doxygen 1.3.5