Radius-incorporated-MKL-algorithm/binarymkl/pegasos

(A slightly modified version of original PEGASOS algorithm to enforce positive weights of SVM,
Modified by: Abhishek Kumar)

A README file for the code implementimng the PEGASOS algorithm for 
sparse vectors.
For technical details see:
"Pegasos: Primal Estimated sub-GrAdient SOlver for SVM".
Shai Shalev-Shwartz, Yoram Singer, and Nathan Srebro.
ICML, 2007.

This code is distributed under GNU General Public License (see license.txt
for details).
Copyright (c) 2007 Shai Shalev-Shwartz. All Rights Reserved.

Installation:
============

  Run the commands:
    > tar xvfz pegasos.tgz
    > make
    
  To test the installation, run the commands:
   > tar xvfz data.tgz
   > pegasos  -testFile example/test.dat example/train.dat

Usage:
=====

   pegasos [options] <data-file>

DESCRIPTION:
  The software solves the SVM optimization problem. That is, 
  given a matrix X of examples (each row is an exmaple) and a column
  vector y of labels in {+1,-1}, the software finds the vector w that
  minimizes (using Matlab notation):
     lambda/2 w'*w + 1/m * sum(max(0,1- y' * (X*w)))
  The instances and labels file should be in SVM-Light format.  
  See: http://svmlight.joachims.org/

OPTIONS:
 -lambda regularization parameter (default = 0.001)
 -iter number of iterations (default = 100/lambda)
 -k size of block for stochastic gradient (default = 1)
 -modelFile name of model file (default = noModelFile)
 -testFile name of test data file (default = noTestFile)


List of files:
=============

README - this file
license.txt - GNU public license file
Makefile
main.cc - main file
pegasos_optimize.{h,cc} - main optimization files
WeightVector.{h,cc} - implements a data structure for w
simple_sparse_vec_hash.{h,cc} - implements a data structure 
                                for instances
test_objective.cc - a procedure for calculating the objective value
cmd_line.{h,cc}, cmd_option.h - implements the command line 
                 (written by Joseph Keshet)

data.tgz  - small example data