--------------------------------------------
--- MATLAB/OCTAVE interface of LIBLINEAR ---
--------------------------------------------

Table of Contents
=================

- Introduction
- Installation
- Usage
- Returned Model Structure
- Other Utilities
- Examples
- Additional Information


Introduction
============

This tool provides a simple interface to LIBLINEAR, a library for
large-scale regularized linear classification and regression
(http://www.csie.ntu.edu.tw/~cjlin/liblinear).  It is very easy to use
as the usage and the way of specifying parameters are the same as that
of LIBLINEAR.

Installation
============

We recommend using make.m on both MATLAB and OCTAVE. Just type 'make'
to build 'libsvmread.mex', 'libsvmwrite.mex', 'train.mex', and
'predict.mex'.

On MATLAB or Octave:

        >> make

If make.m does not work on MATLAB, try 'mex -setup' to choose a
suitable compiler for mex. Make sure your compiler is accessible and
workable. Then type 'make' to start the installation.

Example:

		>> mex -setup

		Options files control which compiler to use, the compiler and link
   	    command options, and the runtime libraries to link against.

    	Using the 'mex -setup' command selects an options file that is
    	placed in ~/.matlab/R2012a and used by default for 'mex'. An
    	options file in the current working directory or specified on
    	the command line overrides the default options file in
    	~/.matlab/R2012a.
 
		To override the default options file, use the 'mex -f' command
  	  	(see 'mex -help' for more information).

		The options files available for mex are:
		
		  1: /nfs/linux/matlab2012a/bin/mexopts.sh : 
		      Template Options file for building gcc MEX-files
		 
		
		  0: Exit with no changes
		
		  
		Enter the number of the compiler (0-1):
		1
		
		Overwrite ~/.matlab/R2012a/mexopts.sh ([y]/n)? 
		y
		
		/nfs/linux/matlab2012a/bin/mexopts.sh is being copied to 
		~/.matlab/R2012a/mexopts.sh


>> make

On Unix systems, if neither make.m nor 'mex -setup' works, please use
Makefile and type 'make' in a command window. Note that we assume
your MATLAB is installed in '/usr/local/matlab'. If not, please change
MATLABDIR in Makefile.

Example:
        linux> make

To use octave, type 'make octave':

Example:
        linux> make octave

For a list of supported/compatible compilers for MATLAB, please check
the following page:

http://www.mathworks.com/support/compilers/current_release/

Usage
=====

matlab> model = train(training_label_vector, training_instance_matrix [,'liblinear_options', 'col']);

        -training_label_vector:
            An m by 1 vector of training labels. (type must be double)
        -training_instance_matrix:
            An m by n matrix of m training instances with n features.
            It must be a sparse matrix. (type must be double)
        -liblinear_options:
            A string of training options in the same format as that of LIBLINEAR.
        -col:
            if 'col' is set, each column of training_instance_matrix is a data instance. Otherwise each row is a data instance.

matlab> [predicted_label, accuracy, decision_values/prob_estimates] = predict(testing_label_vector, testing_instance_matrix, model [, 'liblinear_options', 'col']);

        -testing_label_vector:
            An m by 1 vector of prediction labels. If labels of test
            data are unknown, simply use any random values. (type must be double)
        -testing_instance_matrix:
            An m by n matrix of m testing instances with n features.
            It must be a sparse matrix. (type must be double)
        -model:
            The output of train.
        -liblinear_options:
            A string of testing options in the same format as that of LIBLINEAR.
        -col:
            if 'col' is set, each column of testing_instance_matrix is a data instance. Otherwise each row is a data instance.

Returned Model Structure
========================

The 'train' function returns a model which can be used for future
prediction.  It is a structure and is organized as [Parameters, nr_class,
nr_feature, bias, Label, w]:

        -Parameters: Parameters
        -nr_class: number of classes; = 2 for regression
        -nr_feature: number of features in training data (without including the bias term)
        -bias: If >= 0, we assume one additional feature is added to the end
            of each data instance.
        -Label: label of each class; empty for regression
        -w: a nr_w-by-n matrix for the weights, where n is nr_feature
            or nr_feature+1 depending on the existence of the bias term.
            nr_w is 1 if nr_class=2 and -s is not 4 (i.e., not
            multi-class svm by Crammer and Singer). It is
            nr_class otherwise.

If the '-v' option is specified, cross validation is conducted and the
returned model is just a scalar: cross-validation accuracy for 
classification and mean-squared error for regression.

Result of Prediction
====================

The function 'predict' has three outputs. The first one,
predicted_label, is a vector of predicted labels. The second output,
accuracy, is a vector including accuracy (for classification), mean
squared error, and squared correlation coefficient (for regression).
The third is a matrix containing decision values or probability
estimates (if '-b 1' is specified). If k is the number of classes
and k' is the number of classifiers (k'=1 if k=2, otherwise k'=k), for decision values,
each row includes results of k' binary linear classifiers. For probabilities,
each row contains k values indicating the probability that the testing instance is in
each class. Note that the order of classes here is the same as 'Label'
field in the model structure.

Other Utilities
===============

A matlab function libsvmread reads files in LIBSVM format: 

[label_vector, instance_matrix] = libsvmread('data.txt'); 

Two outputs are labels and instances, which can then be used as inputs
of svmtrain or svmpredict. 

A matlab function libsvmwrite writes Matlab matrix to a file in LIBSVM format:

libsvmwrite('data.txt', label_vector, instance_matrix]

The instance_matrix must be a sparse matrix. (type must be double)

These codes are prepared by Rong-En Fan and Kai-Wei Chang from National
Taiwan University.

Examples
========

Train and test on the provided data heart_scale:

matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
matlab> model = train(heart_scale_label, heart_scale_inst, '-c 1');
matlab> [predict_label, accuracy, dec_values] = predict(heart_scale_label, heart_scale_inst, model); % test the training data

Note that for testing, you can put anything in the testing_label_vector.

For probability estimates, you need '-b 1' only in the testing phase:

matlab> [predict_label, accuracy, prob_estimates] = predict(heart_scale_label, heart_scale_inst, model, '-b 1');

Additional Information
======================

Please cite LIBLINEAR as follows

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin.
LIBLINEAR: A Library for Large Linear Classification, Journal of
Machine Learning Research 9(2008), 1871-1874.Software available at
http://www.csie.ntu.edu.tw/~cjlin/liblinear

For any question, please contact Chih-Jen Lin <cjlin@csie.ntu.edu.tw>.

