[libocas] 01/60: Imported Upstream version 0.93

Christian Kastner chrisk-guest at moszumanska.debian.org
Mon Aug 25 03:34:41 UTC 2014


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chrisk-guest pushed a commit to branch master
in repository libocas.

commit 4f65fae240503ab6741188ace0f7090d78dd38e3
Author: Christian Kastner <debian at kvr.at>
Date:   Mon Jun 14 17:08:40 2010 +0200

    Imported Upstream version 0.93
---
 AUTHORS                      |    3 +
 ChangeLog                    |   36 +
 Contents.m                   |   29 +
 INSTALL                      |   46 +
 LICENSE                      |  674 ++++++++++++++
 Makefile                     |   63 ++
 README                       |   74 ++
 RELEASES                     |    3 +
 compute_errors_mex.c         |   87 ++
 data/example4_test.light     | 2000 ++++++++++++++++++++++++++++++++++++++++++
 data/example4_test.mat       |  Bin 0 -> 294784 bytes
 data/example4_train.light    |  300 +++++++
 data/example4_train.mat      |  Bin 0 -> 44608 bytes
 data/gender_images.mat       |  Bin 0 -> 2733662 bytes
 data/lena.jpg                |  Bin 0 -> 162986 bytes
 data/refernce_solution.mat   |  Bin 0 -> 5824 bytes
 data/riply_trn.light         |  250 ++++++
 data/riply_trn.mat           |  Bin 0 -> 4744 bytes
 data/riply_tst.light         | 1000 +++++++++++++++++++++
 data/riply_tst.mat           |  Bin 0 -> 18240 bytes
 html/C_sc.png                |  Bin 0 -> 1148 bytes
 html/ChangeLog               |   36 +
 html/formulas.tex            |   42 +
 html/fx_sc.png               |  Bin 0 -> 1177 bytes
 html/index.html              |  134 +++
 html/lin_rule_sc.png         |  Bin 0 -> 2974 bytes
 html/linear_svm_sc.png       |  Bin 0 -> 12965 bytes
 html/makros.tex              |  144 +++
 html/multi_lin_rule_sc.png   |  Bin 0 -> 3799 bytes
 html/multi_linear_svm_sc.png |  Bin 0 -> 11607 bytes
 html/multi_trn_set_sc.png    |  Bin 0 -> 3831 bytes
 html/multi_w_sc.png          |  Bin 0 -> 1920 bytes
 html/new.gif                 |  Bin 0 -> 144 bytes
 html/set_y_sc.png            |  Bin 0 -> 1496 bytes
 html/trn_set_sc.png          |  Bin 0 -> 6665 bytes
 html/w_sc.png                |  Bin 0 -> 679 bytes
 html/wx_sc.png               |  Bin 0 -> 1141 bytes
 html/x_sc.png                |  Bin 0 -> 436 bytes
 lbpfilter_mex.c              |   78 ++
 lbppyr.m                     |   59 ++
 lbppyr_features_mex.c        |  160 ++++
 lbppyr_mex.c                 |  134 +++
 lib_svmlight_format.c        |   97 ++
 lib_svmlight_format.h        |   13 +
 liblbp.c                     |  257 ++++++
 liblbp.h                     |   15 +
 libocas.c                    | 1490 +++++++++++++++++++++++++++++++
 libocas.h                    |  123 +++
 libocas_test.m               |  171 ++++
 libqp.h                      |   86 ++
 libqp_splx.c                 |  407 +++++++++
 linclass.c                   |  632 +++++++++++++
 msvmocas.c                   |  540 ++++++++++++
 msvmocas.m                   |   63 ++
 msvmocas_mex.c               |  364 ++++++++
 msvmocas_test.m              |   49 ++
 ocas_helper.c                | 1778 +++++++++++++++++++++++++++++++++++++
 ocas_helper.h                |  104 +++
 ocas_lbp_helper.c            |  414 +++++++++
 ocas_lbp_helper.h            |   81 ++
 sparse_mat.c                 |  123 +++
 sparse_mat.h                 |   47 +
 svmlight_linclass.m          |   17 +
 svmlight_linclass_mex.c      |  319 +++++++
 svmocas.c                    |  720 +++++++++++++++
 svmocas.m                    |   67 ++
 svmocas_lbp.m                |   71 ++
 svmocas_lbp_example.m        |  118 +++
 svmocas_lbp_mex.c            |  382 ++++++++
 svmocas_mex.c                |  489 +++++++++++
 svmocas_parseout.m           |  131 +++
 version.h                    |    3 +
 72 files changed, 14523 insertions(+)

diff --git a/AUTHORS b/AUTHORS
new file mode 100644
index 0000000..45423e6
--- /dev/null
+++ b/AUTHORS
@@ -0,0 +1,3 @@
+Vojtech Franc <xfrancv at cmp.felk.cvut.cz>
+Soeren Sonnenburg <Soeren.Sonnenburg at tu-berlin.de>
+
diff --git a/ChangeLog b/ChangeLog
new file mode 100644
index 0000000..5e762e3
--- /dev/null
+++ b/ChangeLog
@@ -0,0 +1,36 @@
+2010-06-11  Vojtech Franc
+        * Added functions which implement the COFFIN framework for training 
+        * translation invariant image classifier from virtual example.
+        * Added functions: 
+        *    lbppyr.m, lbppyr_mex.c, liblbp.h, ocas_lbp_helper.h, svmocas_lbp.m
+        *    lbpfilter_mex.c, lbppyr_features_mex.c, liblbp.c, ocas_lbp_helper.c 
+        *    svmocas_lbp_example.m, svmocas_lbp_mex.c
+2009-08-03  Vojtech Franc
+	* BUG FIX: OCAS solver was crashing on some data (reported by Alex Binder). 
+	  The problem was a bug in "qsort_data" function which did not expect a sequnce of length 1
+	  as its input. The same bug was fixed also in "parallel_qsort_data".
+	* A new functionality added to the stand alone application "svmocas" (mex interface has it already). 
+	  Each examplean have a different regularization constant which is taken from specified text file.	  
+2009-07-31  Vojtech Franc
+	* BUG FIX: Fixed memory leak problem (missing "LIBQP_FREE( x_neq )") in libqp_splx.c (reported by Alex Binder)
+2009-02-24  Vojtech Franc
+	* Implemented version of binary SVMOCAS which supports using different C
+	* for each training example. Currently, this functionality is used in
+	* the Matlab's intefrace only. 
+2008-12-10  Vojtech Franc
+	* Added stand alone implementaion of multi-class OCAS solver (msvmocas)
+	* Added stand alone implementation of linear classifier (linclass)
+	* old benchmarking and testing scripts for Matlab removed
+	* Added new (simpler) testing script for Matlab 
+	* Data moved to ./data folder
+	* Updated HTML page, README and INSTALL files
+2008-12-02  Vojtech Franc
+	* Added multi-class version of OCAS solver including interface to Matlab (msvmocas_mex.c).
+	  It supports sparse and full matrices and loading examples directly from SVM^light file.
+	  The parallel version has not been implemented yet. 
+	* Added new implementation of linear classifier (svmlight_linclass_mex.c). 
+	* Added function which computes cls. error and AUC (compute_errors_mex.c).
+2008-11-10  Vojtech Franc
+	* Added support for represenation of SVMlight data as full matrix.
+	  The representation (spare or full) which requires less memory is used. 
+	
diff --git a/Contents.m b/Contents.m
new file mode 100644
index 0000000..9644ca0
--- /dev/null
+++ b/Contents.m
@@ -0,0 +1,29 @@
+% OCAS solver for training linear SVM classifiers from large-scale data
+%
+% Copyright (C) 2008,2009,2010 
+% Vojtech Franc, xfrancv at cmp.felk.cvut.cz
+% Soeren Sonnenburg, soeren.sonnenburg at tu-berlin.de
+%
+% SVM solvers:
+%   svmocas             Train linear binary (two-class) SVM classifier using 
+%                       OCAS solver.
+%   svmocas_lbp         Train linear SVM classifier for images represented by 
+%                       LBP features. 
+%   msvmocas            Train multi-class linear SVM classifier using OCAS solver.
+%
+% Auxciliary functions:
+%   svmlight_linclass   Classify examples in SVM^light file by linear rule.
+%   svmocas_parseout    Parsing of text output of SVMOCAS solver.
+%
+% Examples
+%   libocas_test        This script tests functionality of SVMOCAS and 
+%                       MSVMOCAS solvers.
+%   svmocas_lbp_example Example on using SVMOCAS_LBP from training translation
+%                       invariant image classifiers.
+%
+% LBP features for image classification:
+%   lbpfilter           Computes LBP for each 3x3 subwindow in input image.
+%   lbppyr              Computes LBP features on scale-pyramid of input image.
+%   lbppyr_features     Computes pyramid of LBP features for each defined 
+%                       window in input images. 
+%
\ No newline at end of file
diff --git a/INSTALL b/INSTALL
new file mode 100644
index 0000000..447497f
--- /dev/null
+++ b/INSTALL
@@ -0,0 +1,46 @@
+Issue make 
+
+    make
+
+which should produce
+
+    svmocas         ... standalone application for training binary linear SVM classifiers
+    msvmocas        ... standalone application for training multi-class linear SVM classifiers
+    linclass        ... implementation of linear classification rule
+    svmocas.so      ... Linux library
+
+In addition, if mex compiler is in path then the following Matlab functions
+    
+    msvmocas.mexXXX          ... Training multi-class linear SVM classifier
+    svmocas.mexXXX           ... Training two-class linear SVM classifier
+    svmlight_linclass.mexXXX ... Linear classifier loading examples directly form SVM^light file
+    svmocas_lbp.mexXXX       ... Training two-class linear SVM classifier for grey-scale images
+    
+    lbpfilter.mexXXX         ... functions computing LBP features on grey-scale images
+    lbppyr_features.mexXXX 
+    lbppyr.mexXXX
+
+
+MATLAB
+======
+
+First, CD to the root folder of OCASLIB and then:
+
+To test LIBOCAS library type
+    libocas_test
+
+To test SVMOCAS_LBP try
+    svmocas_lbp_example
+
+To get help type 
+    help Content
+    
+
+STANDALONE APPLICATIONS
+=======================
+
+To get help type
+    ./svmocas
+    ./msvmocas
+    ./linclass
+
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..94a9ed0
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
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diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..2ed4145
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,63 @@
+MEXDETECTED := $(shell mex -h >/dev/null 2>&1 && echo yes)
+MEX := mex
+MEXFLAGS := $(shell if uname -m | grep -q x86_64 ; then echo -largeArrayDims ; fi)
+MEXSUFFIX := $(shell if uname -m | grep -q x86_64 ; then echo mexa64 ; else echo mexglx ; fi)
+CC := gcc
+#CFLAGS := -g -lm -Wall -pthread
+CFLAGS := -lm -msse -O3 -fPIC -fstrict-aliasing -fomit-frame-pointer -Wall -pthread
+#CFLAGS := -lm -msse -O3 -fPIC -fstrict-aliasing -fomit-frame-pointer -Wall
+#CFLAGS := -lm -msse -O3 -fPIC -fopenmp -fstrict-aliasing -fomit-frame-pointer -Wall
+
+
+ifeq (yes,$(MEXDETECTED))
+all: svmocas.$(MEXSUFFIX) svmlight_linclass.$(MEXSUFFIX) libocas.so svmocas msvmocas linclass msvmocas.$(MEXSUFFIX) compute_errors.$(MEXSUFFIX) lbppyr.$(MEXSUFFIX) svmocas_lbp.$(MEXSUFFIX) lbppyr_features.$(MEXSUFFIX) lbpfilter.$(MEXSUFFIX)
+
+compute_errors.$(MEXSUFFIX): compute_errors_mex.c libocas.h ocas_helper.h ocas_helper.c
+		$(MEX) $(MEXFLAGS) -DLIBOCAS_MATLAB -O -output compute_errors.$(MEXSUFFIX) compute_errors_mex.c ocas_helper.c lib_svmlight_format.c
+
+svmocas.$(MEXSUFFIX): libocas.c libocas.h libqp_splx.c libqp.h svmocas_mex.c lib_svmlight_format.c lib_svmlight_format.h ocas_helper.c ocas_helper.h
+		$(MEX) $(MEXFLAGS) -DLIBOCAS_MATLAB -O -output svmocas.$(MEXSUFFIX) svmocas_mex.c lib_svmlight_format.c ocas_helper.c  libocas.c libqp_splx.c
+		
+		
+linclass_light.$(MEXSUFFIX): linclass_light_mex.c lib_svmlight_format.c lib_svmlight_format.h ocas_helper.c ocas_helper.h
+		$(MEX) $(MEXFLAGS) -DLIBOCAS_MATLAB -O -output linclass_light.$(MEXSUFFIX) linclass_light_mex.c lib_svmlight_format.c ocas_helper.c
+
+		
+svmlight_linclass.$(MEXSUFFIX): svmlight_linclass_mex.c lib_svmlight_format.c lib_svmlight_format.h ocas_helper.c ocas_helper.h
+		$(MEX) $(MEXFLAGS) -DLIBOCAS_MATLAB -O -output svmlight_linclass.$(MEXSUFFIX) svmlight_linclass_mex.c lib_svmlight_format.c ocas_helper.c
+		
+msvmocas.$(MEXSUFFIX): libocas.c libocas.h libqp_splx.c libqp.h msvmocas_mex.c lib_svmlight_format.c lib_svmlight_format.h ocas_helper.c ocas_helper.h
+		$(MEX) $(MEXFLAGS) -DLIBOCAS_MATLAB -O -output msvmocas.$(MEXSUFFIX) msvmocas_mex.c lib_svmlight_format.c ocas_helper.c libocas.c libqp_splx.c
+		
+lbppyr.$(MEXSUFFIX): lbppyr_mex.c
+		$(MEX) $(MEXFLAGS) -O -output lbppyr.$(MEXSUFFIX) lbppyr_mex.c 	
+
+svmocas_lbp.$(MEXSUFFIX): libocas.c libocas.h libqp_splx.c libqp.h svmocas_lbp_mex.c ocas_lbp_helper.c ocas_lbp_helper.h liblbp.h liblbp.c
+		$(MEX) $(MEXFLAGS) -DLIBOCAS_MATLAB -O -output svmocas_lbp.$(MEXSUFFIX) svmocas_lbp_mex.c ocas_lbp_helper.c  libocas.c libqp_splx.c liblbp.c
+
+lbppyr_features.$(MEXSUFFIX): lbppyr_features_mex.c liblbp.c liblbp.h
+		$(MEX) -g $(MEXFLAGS) -DLIBOCAS_MATLAB -O -output lbppyr_features.$(MEXSUFFIX) lbppyr_features_mex.c liblbp.c
+
+lbpfilter.$(MEXSUFFIX): lbpfilter_mex.c
+		$(MEX) $(MEXFLAGS) -DLIBOCAS_MATLAB -O -output lbpfilter.$(MEXSUFFIX) lbpfilter_mex.c
+
+
+else 
+all: libocas.so svmocas msvmocas
+endif
+
+svmocas:	svmocas.c lib_svmlight_format.c sparse_mat.c ocas_helper.c ocas_helper.h libocas.h sparse_mat.h libocas.c
+		$(CC) $(CFLAGS) -o $@ svmocas.c lib_svmlight_format.c sparse_mat.c ocas_helper.c libocas.c libqp_splx.c
+
+msvmocas:	msvmocas.c lib_svmlight_format.c sparse_mat.c ocas_helper.c ocas_helper.h libocas.h sparse_mat.h libocas.c
+		$(CC) $(CFLAGS) -o $@ msvmocas.c lib_svmlight_format.c sparse_mat.c ocas_helper.c libocas.c libqp_splx.c
+
+linclass:	linclass.c lib_svmlight_format.c libocas.h 
+		$(CC) $(CFLAGS) -o $@ linclass.c lib_svmlight_format.c  
+
+
+libocas.so:	libocas.c libocas.h libqp_splx.c libqp.h 
+		$(CC) $(CFLAGS) -shared -o $@ libocas.c libqp_splx.c
+
+clean: 
+		rm -f *~ svmocas.$(MEXSUFFIX) svmlight_linclass.$(MEXSUFFIX) svmocas msvmocas linclass libocas.so msvmocas.$(MEXSUFFIX) lbpfilter.$(MEXSUFFIX) lbppyr_features.$(MEXSUFFIX) svmocas_lbp.$(MEXSUFFIX) lbppyr.$(MEXSUFFIX) compute_errors.$(MEXSUFFIX)
diff --git a/README b/README
new file mode 100644
index 0000000..da840c9
--- /dev/null
+++ b/README
@@ -0,0 +1,74 @@
+LIBOCAS - Library implementing Optimized Cutting Plane Algorithm (OCAS) 
+solver for training linear SVM classifiers
+
+FEATURES
+ - SVM solvers for training linear classifiers from large scale-data.
+ - Binary (two-class) and genuine multi-class SVM formulations.
+ - Optimized code written in C.
+ - A stand alone application and MEX interface for Matlab.
+ - Reads examples from SVM^light format.
+ - Optimized for both sparse and dense features.
+ - Parallelized version of the binary solver.
+ - Allows using different C for each training example (Matlab's interace to binary solver).
+ - Tools for classification.
+ - Training translation invariant image classifiers from virtual examples.
+ - Functions for computing image features based on Local Binary Patterns (LBP).
+
+
+PROBLEM FORMULATION 
+
+OCAS solver is currently implemented for training binary (two-class) and multi-class
+SVM classifiers:
+
+1. Binary case: OCAS solves the following unconstrained convex optimization task
+    
+   W^*,W0^* = argmin 0.5*(W'*W+W0^2) + C*sum max( 0, 1-y(i)*(W'*X(:,i)+W0*X0) )
+                W,W0                  i=1:nData
+
+where C is the regularization constant, X [nDim x nData] are training feature
+vectors and y [nData x 1] are their binary labels (+1/-1). The result are 
+parameters W^* [nDim x 1], W0^* [1 x 1] of the linear rule
+
+      f(X) = sign( X'*W + W0 )
+
+2. Multi-class case: OCAS solves the following unconstrained convex optimization task
+
+   W^*  =  argmin  0.5*sum_y (W(:,y)'*W(:,y)) + C*  sum     max( (y~=y(i)) + (W(:,y) - W(:,y(i)))'*X(:,i)) 
+             W                                    i=1:nData  y 
+
+where C is the regularization constant, X [nDim x nData] are training feature
+vectors and y [nData x 1] are their labels from 1 to Y. The result are 
+parameters W^* [nDim x Y] of the linear rule
+
+      f(X) = argmax X'*W(:,y)
+               y
+
+AVAILABILITY
+
+LIBOCAS can be downloaded from 
+    http://cmp.felk.cvut.cz/~xfrancv/ocas/html/index.html
+
+PLATFORMS
+
+GNU/Linux. 
+
+
+LICENSE
+
+LIBOCAS is licensed under the GPL version 3 (cf. LICENSE).
+
+
+REFERENCES
+
+V. Franc, S. Sonnenburg. Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization. 
+  The Journal of Machine Learning Research (JMLR), vol. 10, pp. 2157--2192. October 2009. 
+  http://jmlr.csail.mit.edu/papers/volume10/franc09a/franc09a.pdf
+
+V. Franc, S. Sonnenburg. OCAS optimized cutting plane algorithm for Support Vector 
+  Machines. In Proceedings of ICML. Omnipress, 2008.
+  http://cmp.felk.cvut.cz/~xfrancv/papers/Franc-OCAS-ICML08.pdf
+
+S. Sonnenburg, V. Franc.  COFFIN: A Computational Framework for Linear SVMs.  
+  In Proceedings of the 27nd International Machine Learning Conference (ICML'10). 
+  Haifa 2010.
+  http://cmp.felk.cvut.cz/~xfrancv/papers/Sonnenburg-COFFIN-ICML10.pdf
diff --git a/RELEASES b/RELEASES
new file mode 100644
index 0000000..d7508b0
--- /dev/null
+++ b/RELEASES
@@ -0,0 +1,3 @@
+Version 0.92    2009-08-03      SVN Revision 770
+Version 0.91    2009-07-14      SVN Revision 760
+Version 0.9	2008-12-10	SVN Revision 654
diff --git a/compute_errors_mex.c b/compute_errors_mex.c
new file mode 100644
index 0000000..de418d6
--- /dev/null
+++ b/compute_errors_mex.c
@@ -0,0 +1,87 @@
+/*=================================================================
+ * COMPUTE_ERRORS computes classification error and area under ROC.
+ *
+ *  Synopsis:
+ *    [error,auc] = compute_errors(score,true_labels)
+ *
+ *  Input:
+ *   scores [1 x n] scores of binary classifier; the classifier decides 
+ *      according to the sign of the score.
+ *   true_labels [1 x n] true labels; 1st class label = 1, 
+ *                                    2nd class label != 1
+ *  Output:
+ *   error [1x1] number of mis-classifications
+ *   auc [1x1] Area under ROC 
+ *
+ *=================================================================*/
+
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <mex.h>
+#include <sys/time.h>
+#include <time.h>
+#include <errno.h>
+
+#include "libocas.h"
+#include "ocas_helper.h"
+
+#if !defined(MX_API_VER) || MX_API_VER<0x07040000
+#define mwSize int
+#define mwIndex int
+#endif
+
+/*======================================================================
+  Main code.
+========================================================================*/
+
+void mexFunction( int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[] )
+{
+  int *true_labels_int;
+  uint32_t n1, n2, i;
+  double auc, error;
+
+  double *scores, *true_labels;
+
+  if( nrhs != 2 )
+    mexErrMsgTxt("Two input arguments must be passed.\n"
+                 "\n"
+                 "[error,auc] = compute_errors(score,true_labels)\n"
+                 "Inputs: score [N x 1 (double)] positive class score >= 0 otherwise negative class \n"
+                 "        true_labels [N x 1 (double)] positive class 1; negative class ~= 1\n"
+                 "Outputs: error [1x1] number of misclassifications\n"
+                 "         auc [1x1] Arrea Under ROC\n");
+
+  scores = mxGetPr(prhs[0]);
+  true_labels = mxGetPr(prhs[1]);
+
+  n1 = LIBOCAS_MAX(mxGetM(prhs[0]),mxGetN(prhs[0]));
+  n2 = LIBOCAS_MAX(mxGetM(prhs[1]),mxGetN(prhs[1]));
+
+  if(n1 != n2)
+    mexErrMsgTxt("The input vectors must be of the same size.");
+
+
+  true_labels_int = mxCalloc(n1,sizeof(int));
+  if(true_labels_int == NULL)
+    mexErrMsgTxt("Not enough memory.");
+
+  error = 0;
+  for(i = 0; i < n1; i++)
+  {
+    true_labels_int[i] = (int)true_labels[i];
+
+    if((true_labels[i] == 1 && scores[i] <= 0) || (true_labels[i] != 1 && scores[i] > 0))
+      error++;
+  }
+
+  auc = compute_auc(scores, true_labels_int, n1);
+
+  mxFree(true_labels_int);
+
+  plhs[0] = mxCreateDoubleScalar(error);
+  plhs[1] = mxCreateDoubleScalar(auc);
+
+  return;
+}
+
diff --git a/data/example4_test.light b/data/example4_test.light
new file mode 100644
index 0000000..3078b45
--- /dev/null
+++ b/data/example4_test.light
@@ -0,0 +1,2000 @@
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diff --git a/data/example4_test.mat b/data/example4_test.mat
new file mode 100644
index 0000000..1b2aeb4
Binary files /dev/null and b/data/example4_test.mat differ
diff --git a/data/example4_train.light b/data/example4_train.light
new file mode 100644
index 0000000..d7d422a
--- /dev/null
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diff --git a/data/riply_tst.mat b/data/riply_tst.mat
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diff --git a/html/C_sc.png b/html/C_sc.png
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diff --git a/html/ChangeLog b/html/ChangeLog
new file mode 100644
index 0000000..5e762e3
--- /dev/null
+++ b/html/ChangeLog
@@ -0,0 +1,36 @@
+2010-06-11  Vojtech Franc
+        * Added functions which implement the COFFIN framework for training 
+        * translation invariant image classifier from virtual example.
+        * Added functions: 
+        *    lbppyr.m, lbppyr_mex.c, liblbp.h, ocas_lbp_helper.h, svmocas_lbp.m
+        *    lbpfilter_mex.c, lbppyr_features_mex.c, liblbp.c, ocas_lbp_helper.c 
+        *    svmocas_lbp_example.m, svmocas_lbp_mex.c
+2009-08-03  Vojtech Franc
+	* BUG FIX: OCAS solver was crashing on some data (reported by Alex Binder). 
+	  The problem was a bug in "qsort_data" function which did not expect a sequnce of length 1
+	  as its input. The same bug was fixed also in "parallel_qsort_data".
+	* A new functionality added to the stand alone application "svmocas" (mex interface has it already). 
+	  Each examplean have a different regularization constant which is taken from specified text file.	  
+2009-07-31  Vojtech Franc
+	* BUG FIX: Fixed memory leak problem (missing "LIBQP_FREE( x_neq )") in libqp_splx.c (reported by Alex Binder)
+2009-02-24  Vojtech Franc
+	* Implemented version of binary SVMOCAS which supports using different C
+	* for each training example. Currently, this functionality is used in
+	* the Matlab's intefrace only. 
+2008-12-10  Vojtech Franc
+	* Added stand alone implementaion of multi-class OCAS solver (msvmocas)
+	* Added stand alone implementation of linear classifier (linclass)
+	* old benchmarking and testing scripts for Matlab removed
+	* Added new (simpler) testing script for Matlab 
+	* Data moved to ./data folder
+	* Updated HTML page, README and INSTALL files
+2008-12-02  Vojtech Franc
+	* Added multi-class version of OCAS solver including interface to Matlab (msvmocas_mex.c).
+	  It supports sparse and full matrices and loading examples directly from SVM^light file.
+	  The parallel version has not been implemented yet. 
+	* Added new implementation of linear classifier (svmlight_linclass_mex.c). 
+	* Added function which computes cls. error and AUC (compute_errors_mex.c).
+2008-11-10  Vojtech Franc
+	* Added support for represenation of SVMlight data as full matrix.
+	  The representation (spare or full) which requires less memory is used. 
+	
diff --git a/html/formulas.tex b/html/formulas.tex
new file mode 100644
index 0000000..bf2ce83
--- /dev/null
+++ b/html/formulas.tex
@@ -0,0 +1,42 @@
+\documentclass[11pt]{article}
+
+\usepackage{times,latexsym,amsmath,amssymb,amsfonts,bbm,pifont}
+
+\input{makros}
+
+\begin{document}
+
+\pagestyle{empty}
+
+\[
+   \{\#w_y^* \mid y\in \SY\} = \argmin_{\{\#w_y\in\Re^n \mid y\in \SY\}} \Bigg [
+   \frac{1}{2} \sum_{y\in \SY} \|\#w_y\|^2 + C \sum_{i=1}^m
+   \max_{y\in \SY} \Big (\leftbb y \neq y_i\rightbb + \lz
+   \#w_{y}-\#w_{y_i}, \#x_i\pz \Big ) \Bigg ]
+\]
+
+\[
+  \{(\#x_1,y_1),\ldots,(\#x_m,y_m)\}\in (\Re^n\times \SY)^m
+\]
+
+\[
+  \SY=\{1,\ldots,Y\}
+\]
+
+\[
+  \{\#w_y^* \mid y\in \SY\}
+\]
+
+\[
+  f(\#x) = \sgn\left( \lz \#w^*,\#x\pz \right )
+\]
+
+\[
+ \lz \#w^*,\#x\pz
+\]
+
+$f(\#x) = \argmax_{y\in\SY} \lz \#w^*_y,\#x\pz$
+
+
+
+\end{document}
diff --git a/html/fx_sc.png b/html/fx_sc.png
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diff --git a/html/index.html b/html/index.html
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+++ b/html/index.html
@@ -0,0 +1,134 @@
+<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN">
+<html> <head> <meta http-equiv="Content-Type" content="text/html; charset=iso-8859-2"> 
+<head>
+<title>LIBOCAS - Library implementing OCAS solver</title>
+</head>
+
+<BODY leftmargin="0" topmargin="0" marginwidth="25" marginheight="0">
+
+<div align="center">
+<H2>LIBOCAS - Library implementing OCAS solver <br>for training linear SVM
+classifiers from large-scale data</H2>
+<A href="http://cmp.felk.cvut.cz/~xfrancv/">Vojtech Franc</a>, 
+<A href="http://ida.first.fraunhofer.de/homepages/sonne/first/">Soeren
+Sonnenburg</a>
+<p>
+Last Modified: 11-Jun-2010
+
+</div>
+
+<hr>
+
+<h4>Introduction</h4>
+
+<p>
+The library implements Optimized Cutting Plane Algorithm (OCAS)
+for training linear SVM classifiers from large-scale data. 
+The computational effort of OCAS scales with O(m log m) where m is the sample size.
+In an extensive empirical evaluation OCAS significantly outperforms current
+state of the art SVM solvers, like SVM^light, SVM^perf and BMRM, achieving
+speedups of over 1,000 on some datasets over SVM^light and 20 over SVM^perf,
+while obtaining the same precise Support Vector solution. OCAS even in the early
+optimization steps shows often faster convergence than the so far in this domain
+prevailing approximative methods SGD and Pegasos. Effectively parallelizing 
+OCAS we were able to train on a dataset of size 15 million examples
+(itself about 32GB in size) in just 671 seconds --- a competing string kernel SVM
+required 97,484 seconds to train on 10 million examples sub-sampled from this
+dataset.
+
+<p>
+<img align="top" border="0" src="new.gif">The library also implements COFFIN framework for
+efficient training of translation invariant image classifiers from virtual
+examples. As an example, we used the implemented framework to train a linear SVM
+on a gender classification dataset of almost 5 million images on a plain
+notebook with just 4GB of memory.
+
+
+<h4>Features</h4>
+
+<ul>
+   <li>SVM solvers for training linear classifiers from large scale-data.</li>
+   <li>Binary (two-class) and genuine multi-class SVM formulations.</li>
+   <li>Optimized code written in C.</li>
+   <li>A stand alone application and MEX interface for Matlab.</li>
+   <li>Reads examples from SVM^light format.</li>
+   <li>Optimized for both sparse and dense features.</li>  
+   <li>Parallelized version of the binary solver.</li>
+   <li>Allows using different C for each training example (Matlab's interace to binary solver).</li>
+   <li>Tools for classification.</li>
+   <li><img align="top" border="0" src="new.gif"> Training translation invariant image classifiers from virtual examples.</li>
+   <li><img align="top" border="0" src="new.gif"> Functions for computing image features based on Local Binary Patterns (LBP).</li>
+
+</ul>
+
+<h4>Problem formulation</h4>
+
+OCAS solver is currently implemented for training binary (two-class) and multi-class
+SVM classifiers:
+
+<p><b>1. Binary classification problem:</b> OCAS solves the following unconstrained convex optimization task
+<div align="center">
+  <img align="top" vspace="2" src="linear_svm_sc.png">
+</div>
+where <img align="top" src="C_sc.png"> is the regularization constant and
+<img align="top" border="0" src="trn_set_sc.png"> are training examples. The result is 
+the parameter vector <img align="top" border="0" src="w_sc.png"> of the linear rule
+<img align="top" border="0" src="lin_rule_sc.png">
+which assigns vector <img align="top" border="0" src="x_sc.png"> a class label +1 and/or -1. 
+
+<p><b>2. Multi-class classification probem:</b> OCAS solves the following unconstrained convex optimization task
+<div align="center">
+  <img align="top" vspace="2" src="multi_linear_svm_sc.png">
+</div>
+where <img align="top" src="C_sc.png"> is the regularization constant, 
+<img align="top" border="0" src="set_y_sc.png"> is a finite set of labels and
+<img align="top" border="0" src="multi_trn_set_sc.png"> are training
+examples. The result is a set of parameter vectors <img align="top"
+border="0" src="multi_w_sc.png"> of the linear rule
+<img align="top" border="0" src="multi_lin_rule_sc.png">
+which assigns vector <img align="top" border="0" src="x_sc.png"> a class label
+from <img align="top" border="0" src="set_y_sc.png">. 
+
+<h4>Download</h4>
+
+LIBOCAS can be downloaded from here: 
+<ul>
+  <li><img align="top" border="0" src="new.gif"> Version 0.93, 2010-06-11, <a
+  href="http://cmp.felk.cvut.cz/~xfrancv/ocas/libocas_v093.zip">libocas_v093.zip</a> </li>
+  <li>Version 0.92, 2008-08-03, <a
+  href="http://cmp.felk.cvut.cz/~xfrancv/ocas/libocas_v092.zip">libocas_v092.zip</a> </li>
+  <li>Version 0.91, 2008-07-14, <a
+  href="http://cmp.felk.cvut.cz/~xfrancv/ocas/libocas_v091.zip">libocas_v091.zip</a> </li>
+  <li>Version 0.90, 2008-10-12, <a
+  href="http://cmp.felk.cvut.cz/~xfrancv/ocas/libocas_v09.zip">libocas_v09.zip</a> </li>
+</ul>
+
+<h4><a href="ChangeLog">Change Log</a></h4>
+
+<h4>Platforms</h4>
+
+GNU/Linux. 
+
+<h4>Licensing Information</h4>
+
+LIBOCAS is licensed under the GPL version 3.
+
+<h4>References</h4>
+
+<ul>
+<li>S. Sonnenburg, V. Franc.  <b>COFFIN: A Computational Framework for Linear SVMs.</b>  
+  <i> In Proceedings of ICML.</i>  Haifa, 2010.
+ [<a href="http://cmp.felk.cvut.cz/~xfrancv/papers/Sonnenburg-COFFIN-ICML10.pdf">pdf</a>]</li>
+
+<li>V. Franc, S. Sonnenburg. <b>Optimized Cutting Plane Algorithm for Large-Scale
+  Risk Minimization. </b> <i>The Journal of Machine Learning Research (JMLR)</i>,
+  vol. 10, pp. 2157--2192. October 2009. [<a href="http://jmlr.csail.mit.edu/papers/volume10/franc09a/franc09a.pdf">pdf</a>]</li>
+
+<li>V. Franc, S. Sonnenburg. <b>OCAS optimized cutting plane algorithm for Support Vector 
+ Machines</b>. <i>In Proceedings of ICML</i>. Helsinki, 2008. 
+[<a href="http://cmp.felk.cvut.cz/~xfrancv/papers/Franc-OCAS-ICML08.pdf">pdf</a>]</li>
+
+</ul>
+
+</BODY>
+</HTML>
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diff --git a/html/makros.tex b/html/makros.tex
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+++ b/html/makros.tex
@@ -0,0 +1,144 @@
+%---------------------------------------------------------------------------
+% Title: TeX makros
+% Modifications:
+% 16-Mar-2005, VF
+% 16-May-2003, VF
+%---------------------------------------------------------------------------
+
+%\newtheorem{corrolary}[theorem]{Corrolary}
+%\newtheorem{theorem}{theorem}[section]
+
+\newcommand{\head}[1]{\par\medskip\noindent{\bf #1: }}
+\newcommand{\rem}[1]{{\it \color{red} #1}}
+
+
+\def\proof{\noindent\mbox{\sc proof: }}
+\def\eproof{\rm\hspace*{\fill}$\rule{7pt}{7pt}$\vspace{10pt}}
+
+%-- MMMakro ------------------                                                  
+\def\##1{\relax\ifmmode\mathchoice      % italic bold vector, e.g. $\#{x}$
+{\mbox{\boldmath$\displaystyle#1$}}
+{\mbox{\boldmath$\textstyle#1$}}
+{\mbox{\boldmath$\scriptstyle#1$}}
+{\mbox{\boldmath$\scriptscriptstyle#1$}}\else
+\hbox{\boldmath$\textstyle#1$}\fi}
+\def\m#1{\mathbf{#1}}                  % bold matrix, e.g. $\m{A}$
+%---------------------------------------
+
+\def\dQ{\mathrm{d}Q}
+\def\bar#1{\overline{#1}}
+
+\def\Emph#1{\emph{#1}}
+\def\Const#1{\mathrm{#1}}
+
+\def\boxit#1{\vbox{\hrule\hbox{\vrule\vbox{\medskip\noindent#1                  
+             \medskip}\vrule}\hrule}}                                           
+
+\newcommand{\enVert}[1]{\left\lVert#1\right\rVert}
+\let\norm=\enVert
+
+\renewcommand{\Re}{\mathbb{R}}
+\newcommand{\Ne}{\mathbb{N}}
+\newcommand{\Na}{\mathbb{N}}
+\newcommand{\FF}{\cal F}
+\newcommand{\FX}{\cal X}
+\newcommand{\FR}{\Re^n}
+%\newcommand{\FS}{{S}}
+%\newcommand{\dim}{\mathop dim}
+
+\newcommand{\transpose}{{\text{T}}}
+\newcommand{\w}{{\boldsymbol{w}}}
+\newcommand{\xib}{{\boldsymbol{\xi}}}
+\newcommand{\x}{{\boldsymbol{x}}}
+\newcommand{\SG}{{\cal G}}
+\newcommand{\SA}{{\cal A}}
+\newcommand{\SB}{{\cal B}}
+\newcommand{\SE}{{\cal E}}
+\newcommand{\SF}{{\cal F}}
+\newcommand{\SX}{{\cal X}}
+\newcommand{\SCX}{\overline{\cal X}}
+\newcommand{\SFX}{\tilde{\cal X}}
+\newcommand{\SIsv}{{{\cal I}_{\emptyset}}}
+\newcommand{\SY}{{\cal Y}}
+\newcommand{\SZ}{{\cal Z}}
+\newcommand{\ST}{{\cal T}}
+\newcommand{\SH}{{\cal H}}
+\newcommand{\SM}{{\cal M}}
+\newcommand{\SC}{{\cal C}}
+\newcommand{\SN}{{\cal N}}
+\newcommand{\SW}{{\cal W}}
+\newcommand{\Ssv}{{\cal S}}
+\newcommand{\Ss}{{\cal S}}
+\newcommand{\SQ}{{\cal Q}}
+\newcommand{\SR}{\Re^n}
+\newcommand{\SI}{{\cal I}}
+\newcommand{\SO}{{\cal O}}
+\newcommand{\SJ}{{\cal J}}
+\newcommand{\SL}{{\cal L}}
+\newcommand{\SD}{{\cal D}}
+\newcommand{\SV}{{\cal V}}
+\newcommand{\STheta}{\Theta}
+\newcommand{\SSV}{{\cal I_{SV}}}
+
+\newcommand{\eye}{\m{E}}
+\newcommand{\one}{{\cal \m{1}}}
+\newcommand{\nconst}{r}
+
+\def\M3N{M$^3$N}
+
+
+\def\kmse{\mathbb{\varepsilon}_{KMS}} 
+\def\mse{\mathbb{\varepsilon}_{MS}} 
+\def\qe{\mathbb{\varepsilon}_{QE}}
+\def\rerr{\mathbb{\varepsilon}_{R}}
+\def\tsterr{ \mathop{\rm TstErr}}
+\def\nsv{ nsv }
+\def\maxerr{\mathop{\rm \varepsilon_{max}}}
+%\def\keval{\mathop{\rm KerEval}}
+\def\keval{\mathop{\rm ker\_eval}}
+\def\cputime{\mathop{ \rm CPU_{time}}}
+\def\ls{\mathop{\rm \varepsilon_{LS}}}
+\def\Err{ \mathop{\rm Err}}
+\def\snr{\mathop{\rm SNR}}
+\def\dontknow{\mathop{\rm dont\_know}}
+
+\def\beq{\begin{equation}}
+\def\eeq{\end{equation}}
+\def\equ#1{(\ref{#1})}
+
+\def\veps{\varepsilon}
+\def\Veps{\Varepsilon}
+
+\def\lin{\mathop{\rm lin}}                                                
+\def\argmax{\mathop{\rm argmax}}                                                
+\def\argmin{\mathop{\rm argmin}}                                                
+\def\Span{\mathop{\rm Span}}      
+\def\Card{\mathop{\rm Card}}      
+\def\Con{\mathop{\rm co}}
+\def\diag{\mathop{\rm diag}}      
+\def\sgn{\mathop{\rm sgn}}
+
+\def\nm#1{\#\phi(#1)}
+\def\tnm#1{\tilde{\#\phi}(#1)}
+\def\tv#1{{\tilde{\##1}}}
+
+\def\lz{\langle}
+\def\pz{\rangle}
+
+\def\det#1{\mathop{\rm det} ({#1} )}
+
+\def\alphat{\#\alpha^{(t)}}
+\def\clip{\mathop{\rm clip}}
+\def\sgn{\mathop{\rm sgn}}
+
+\def\leftbb{\mathopen{\rlap{$[$}\hskip1.3pt[}}
+\def\rightbb{\mathclose{\rlap{$]$}\hskip1.3pt]}}
+\def\ass{\mathrel {:=}}
+\newcommand{\vphi}{\varphi}
+\newcommand{\boldvphi}{\#\varphi}
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "makros"
+%%% TeX-master: "disertace"
+%%% End: 
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diff --git a/lbpfilter_mex.c b/lbpfilter_mex.c
new file mode 100644
index 0000000..5681682
--- /dev/null
+++ b/lbpfilter_mex.c
@@ -0,0 +1,78 @@
+/*=================================================================
+ * LBPFILTER computes LBP for each 3x3 subwindow in input image.
+ *
+ * Synopsis:
+ *  F = lbpfilter(I)
+ * where
+ *  I [h x w (double)] is input image.
+ *  F [h x w (double)] is image of LBP responses;
+ *   the border colums and rows, for which LBP is not defined, are set 0.
+ *
+ *=================================================================*/
+
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <mex.h>
+#include <time.h>
+#include <errno.h>
+
+#define MIN(A,B) ((A) > (B) ? (B) : (A))
+#define MAX(A,B) ((A) < (B) ? (B) : (A))
+#define ABS(A) ((A) < 0 ? -(A) : (A))
+#define INDEX(ROW,COL,NUM_ROWS) ((COL)*(NUM_ROWS)+(ROW))
+
+
+/*======================================================================
+  Main code plus interface to Matlab.
+========================================================================*/
+
+void mexFunction( int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[] )
+{
+
+  double *I, *P;
+  int w,h, x,y;
+  double center;
+  unsigned char pattern;
+
+  if( nrhs != 1 )
+    mexErrMsgTxt("One input argument required.\n\n"
+                 "LBPFILTER computes LBP for each 3x3 subwindow in input image.\n"
+                 "\n"
+                 "Synopsis:\n"
+                 "  F = lbpfilter(I)\n"
+                 "where\n"
+                 "  I [h x w (double)] is input image.\n"
+                 "  F [h x w (double)] is image of LBP responses; \n"
+                 "    the border colums and rows, for which LBP is not defined, are set 0.\n"
+                 );
+
+  I = (double*)mxGetPr(prhs[0]);
+  h = mxGetM(prhs[0]);
+  w = mxGetN(prhs[0]);
+
+  plhs[0] = mxCreateDoubleMatrix(h,w,mxREAL);
+  P = (double*)mxGetPr(plhs[0]);
+
+  for(x=1; x < w-1; x++)
+  {
+    for(y=1; y< h-1; y++)
+    {
+      pattern = 0;
+      center = I[INDEX(y,x,h)];
+      if(I[INDEX(y-1,x-1,h)] < center) pattern = pattern | 0x01;
+      if(I[INDEX(y-1,x,h)] < center)   pattern = pattern | 0x02;
+      if(I[INDEX(y-1,x+1,h)] < center) pattern = pattern | 0x04;
+      if(I[INDEX(y,x-1,h)] < center)   pattern = pattern | 0x08;
+      if(I[INDEX(y,x+1,h)] < center)   pattern = pattern | 0x10;
+      if(I[INDEX(y+1,x-1,h)] < center) pattern = pattern | 0x20;
+      if(I[INDEX(y+1,x,h)] < center)   pattern = pattern | 0x40;
+      if(I[INDEX(y+1,x+1,h)] < center) pattern = pattern | 0x80;
+
+      P[INDEX(y,x,h)] = (double)pattern; 
+    }
+  }
+  
+  return;
+}
+
diff --git a/lbppyr.m b/lbppyr.m
new file mode 100644
index 0000000..cfcba4b
--- /dev/null
+++ b/lbppyr.m
@@ -0,0 +1,59 @@
+function F = lbppyr(I, P)
+% LBPPYR computes LBP features on scale-pyramid of input image.
+%
+% Synopsis:
+%  F = lbppyr(I, P)
+%
+% Description:
+%  Local Binary Pattern (LBP) is 1 Byte number which encodes grey-scale
+%  intensities of a given 3x3 window.
+% 
+%  For P = 1, LBPPYR computes LBP features for all 3x3 windows
+%  in the input image I. The LBP features are stucked to a column 
+%  vector F.
+%
+%  For P > 1, the input image I is down-scaled (P-1)-times to create 
+%  a scale-pyramid of height P. Each subsequent level of the pyramid is
+%  obtained by downscaling the original image by 2. Having the pyramid,
+%  the LBP features are computed on each leavel of the pyramid and 
+%  they are stucked to a column vector F.
+%
+%  For more details on LBP see:
+%   Ojala, et. al: Multiresolution gray-scale and rotation invariant 
+%   texture classification with local binary patterns. IEEE PAMI, 
+%   24(7):971-987,2002.
+%
+% Input:
+%  I [H x W (uint8)] Input image.
+%  lbp_pyramid [1 x 1] Height of the LBP pyramids.
+%  
+% Output:
+%  F [nDim x 1] LBP features stucked to a column vector.
+%
+% Example:
+%  I = imread('./data/lena.jpg');
+%  F = lbppyr(I,4);
+%
+% Matlab code equivalent to LBPPYR_MEX.C:
+% 
+%  [h,w] = size(I);
+%  I = uint32(I);
+%  F = [];
+%  k = 1; 
+%  while k <= P & min(w,h) >= 3
+%     k = k + 1;
+%     tmp = lbpfilter(double(I));
+%     tmp = tmp(2:end-1,2:end-1);
+%     F = [F; tmp(:)];    
+%     if mod(w,2) == 1
+%        I = I(:,1:w-1);
+%        w= w - 1;
+%     end
+%     if mod(h,2) == 1
+%        I = I(1:h-1,:);
+%        h = h - 1;
+%     end
+%     I = I(1:2:h,1:2:w)+I(1:2:h,2:2:w)+I(2:2:h,1:2:w)+I(2:2:h,2:2:w);
+%     [h,w] = size(I);
+%  end
+%
\ No newline at end of file
diff --git a/lbppyr_features_mex.c b/lbppyr_features_mex.c
new file mode 100644
index 0000000..e31ab8b
--- /dev/null
+++ b/lbppyr_features_mex.c
@@ -0,0 +1,160 @@
+/*=================================================================================
+ * LBPPYR_FEATURES computes pyramid of LBP features for each defined window in input images.
+ * 
+ * Synopsis:
+ *   [Feature,CroppedWin]= lbppyr_features(Images,imSize,Wins,winSize,height_of_pyramid,verb) 
+ * Input: 
+ *   Images [(im_H*im_W) x nImages (uint8)]
+ *   imSize [2 x 1 (double)] imSize = [im_H im_W]
+ *   Wins [4 x nExamples (uint32)]  [image_index; top_left_col; top_left_row; mirror]
+ *   winSize [2 x 1 (double)] [win_H win_W]
+ *   height_of_pyramid [1 x 1 (double)] 
+ * Output: 
+ *   Features [nDims x nExamples (int8)]
+ *   CroppedWin [(win_H*win_W) x nExamples]
+ *
+ *======================================================================================*/ 
+
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <mex.h>
+
+#define INDEX(ROW,COL,NUM_ROWS) ((COL)*(NUM_ROWS)+(ROW))
+
+
+/*======================================================================
+  Main code plus interface to Matlab.
+========================================================================*/
+
+void mexFunction( int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[] )
+{
+  uint32_t i, j;
+  double *tmp;
+  char *Features ;
+  uint8_t *CroppedWin;
+  int verb;
+
+  if(nrhs < 5 || nrhs > 6)
+     mexErrMsgTxt("Improper number of input arguments.\n\n"
+                  "LBPPYR_FEATURES computes pyramid of LBP features for each defined window in input images.\n\n"
+                  "Synopsis: \n"
+                  "  [Features,CroppedWin]= lbppyr_features(Images,imSize,Wins,winSize,height_of_pyramid,verb) \n"
+                  "\n"
+                  "  Input: \n"
+                  "    Images [(im_H*im_W) x nImages (uint8)]\n"
+                  "    imSize [2 x 1 (double)] imSize = [im_H im_W]\n"
+                  "    Wins [3 x nExamples (uint32)]  [image_idx; x1; y1;mirror] (1-based)\n"
+                  "    winSize [2 x 1 (double)] [win_H win_W]\n"
+                  "    height_of_pyramid [1 x 1 (double)]\n"
+                  "    verb [1x1] \n" 
+                  "  Output: \n"
+                  "    Features [nDims x nExamples (char)]\n"
+                  "    CroppedWin [(win_H*win_W) x nExamples]\n");
+
+  if(nrhs == 6)
+    verb = (int)mxGetScalar(prhs[5]);
+  else
+    verb = 1;
+
+  uint8_t * Images = (uint8_t*)mxGetPr(prhs[0]);
+  uint32_t nImages = mxGetN(prhs[0]);
+
+  tmp = (double*)mxGetPr(prhs[1]);
+  uint32_t im_H = (uint32_t)tmp[0];
+  uint32_t im_W = (uint32_t)tmp[1];
+
+  if(mxGetM(prhs[0]) != im_H*im_W)
+    mexErrMsgTxt("Dimension of Images does not match to im_H*im_W.");
+
+  uint32_t *Wins = (uint32_t*)mxGetPr(prhs[2]);
+
+  tmp = (double*)mxGetPr(prhs[3]);
+  uint16_t win_H = (uint16_t)tmp[0];
+  uint16_t win_W = (uint16_t)tmp[1];
+
+  uint16_t nPyramids = (uint32_t)mxGetScalar(prhs[4]);
+  uint32_t nDim =  liblbp_pyr_get_dim(win_H,win_W,nPyramids);
+
+  uint32_t nData = mxGetN(prhs[2]);
+
+  if(verb)
+  {
+    mexPrintf("Input data:\n"
+              "   # of images     : %d\n"
+              "   image height    : %d\n"
+              "   image width     : %d\n",
+              nImages, im_H, im_W);
+
+    mexPrintf("Feature represenation:\n"
+              "   base window height        : %d\n"
+              "   base window width         : %d\n"
+              "   nPyramids                 : %d\n"
+            "   # of virtual examples     : %d\n"
+              "   # of features per example : %d\n",
+              win_H, win_W, nPyramids, nData, nDim);
+  }
+
+  /* learned weight vector */
+/*  plhs[0] = mxCreateNumericMatrix(nDim,nData, mxINT64_CLASS, mxREAL);*/
+/*  Features = (int64_t*)mxGetPr(plhs[0]);*/
+  plhs[0] = mxCreateNumericMatrix(nDim,nData, mxINT8_CLASS, mxREAL);
+  Features = (char*)mxGetPr(plhs[0]);
+
+  plhs[1] = mxCreateNumericMatrix(win_H*win_W, nData, mxUINT8_CLASS, mxREAL);
+  CroppedWin = (uint8_t*)mxGetPr(plhs[1]);
+
+
+  uint32_t cnt, cnt0, mirror,x,x1,y,y1,idx;
+  uint32_t *win;
+  uint8_t *img_ptr;
+  
+  win = (uint32_t*)mxCalloc(win_H*win_W,sizeof(uint32_t));
+  if(win == NULL) 
+    mexErrMsgTxt("Not enough memory for croped_window.");
+
+  cnt=0;
+  for(i=0; i < nData; i++)
+  {
+
+    idx = Wins[INDEX(0,i,4)]-1;
+    x1  = Wins[INDEX(1,i,4)]-1;
+    y1  = Wins[INDEX(2,i,4)]-1;
+    mirror = Wins[INDEX(3,i,4)];
+
+    img_ptr = &Images[idx*im_H*im_W];
+ 
+    cnt0 = 0;
+    if(mirror==0)
+    {
+      for(x=x1; x < x1+win_W; x++)
+        for(y=y1; y < y1+win_H; y++)
+        {
+          CroppedWin[cnt++] = img_ptr[INDEX(y,x,im_H)];
+          win[cnt0++] = img_ptr[INDEX(y,x,im_H)];
+        }
+         
+    }
+    else
+    {
+      for(x=x1+win_W-1; x >= x1; x--)
+        for(y=y1; y < y1+win_H; y++)
+        {
+          CroppedWin[cnt++] = img_ptr[INDEX(y,x,im_H)];
+          win[cnt0++] = img_ptr[INDEX(y,x,im_H)];
+        }
+    }
+    
+/*    lbppyr_features(uint32_t *vec, uint32_t *win);*/
+/*    liblbp_pyr_features( &Features[nDim*i], win);*/
+/*    mexPrintf("i=%d, nDim=%d, im_H=%d, im_W=%d\n", i,nDim,im_H,im_W);*/
+    liblbp_pyr_features(&Features[nDim*i], nDim, win, win_H, win_W);
+    
+
+  }
+  
+  mxFree(win);
+
+  return;
+}
+
diff --git a/lbppyr_mex.c b/lbppyr_mex.c
new file mode 100644
index 0000000..a005149
--- /dev/null
+++ b/lbppyr_mex.c
@@ -0,0 +1,134 @@
+/*=================================================================
+ * LBPPYR computes LBP features on scale-pyramid of input image.
+ * 
+ * Synopsis:
+ *  F = lbppyr_mat(I, P)
+ * where
+ *  I [H x W (uint8)] is input image.
+ *  P [1 x 1 (double)] is height of the scale-pyramid.
+ *  F [N x 1 (uint8)] LBP features stucked to a column vector. 
+ *
+ *=================================================================*/
+
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <mex.h>
+#include <time.h>
+#include <errno.h>
+
+#define MIN(A,B) ((A) > (B) ? (B) : (A))
+#define MAX(A,B) ((A) < (B) ? (B) : (A))
+#define ABS(A) ((A) < 0 ? -(A) : (A))
+#define INDEX(ROW,COL,NUM_ROWS) ((COL)*(NUM_ROWS)+(ROW))
+
+
+/*======================================================================
+  Main code plus interface to Matlab.
+========================================================================*/
+
+void mexFunction( int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[] )
+{
+
+  uint8_t *I, K;
+  uint32_t *P;
+  uint32_t *J;
+  int w,h,x,y,i,j,N;
+  int ww,hh; 
+  uint32_t center;
+  uint32_t offset;
+  uint8_t pattern;
+
+  if( nrhs != 2 )
+    mexErrMsgTxt("Two input arguments required.\n\n"
+                 "LBPPYR computes LBP features on scale-pyramid of input image.\n"
+                 "Synopsis:\n"
+                 "  F = lbppyr(I, P)\n"
+                 "where\n"
+                 "  I [H x W (uint8)] is input image.\n"
+                 "  P [1 x 1 (double)] is height of the scale-pyramid.\n"
+                 "  F [N x 1 (uint8)] LBP features stucked to a column vector. \n");
+
+  I = (uint8_t*)mxGetPr(prhs[0]);
+  K = (uint8_t)mxGetScalar(prhs[1]);
+
+  h = mxGetM(prhs[0]);
+  w = mxGetN(prhs[0]);
+
+  /*  printf("h: %d\n", h);
+  printf("w: %d\n", w);
+  printf("K: %d\n", K);
+  */
+
+  /* count number of LBPs */
+  for(ww=w, hh=h, N=0, i=0; i < K && MIN(ww,hh) >= 3; i++)
+  {
+    N += (ww-2)*(hh-2);
+
+    if(ww % 2) ww--;
+    if(hh % 2) hh--;
+    ww = ww/2;
+    hh = hh/2;
+  }
+  K = i;
+  N = 256*N;
+  
+/*  printf("N: %d (=256*%d)\n", N,N/256);*/
+/*  printf("K: %d\n",K);*/
+  /*  printf("%d/%d = %d*%d + %d \n", b,a, a, b/a, b % a);*/
+  
+  plhs[0] = mxCreateNumericMatrix(N, 1, mxUINT32_CLASS, mxREAL);
+  P = (uint32_t*)mxGetPr(plhs[0]);
+
+  J = mxCalloc(h*w, sizeof(uint32_t));
+  if(J ==NULL)
+    mexErrMsgTxt("Not enough memory.");
+
+  for(x=0; x < w; x++)
+    for(y=0; y < h; y++)
+      J[INDEX(y,x,h)] = I[INDEX(y,x,h)];
+  
+  for(ww=w, hh=h, i=0, offset = 0; i < K; i++)
+  {
+    for(x=1; x < ww-1; x++)
+    {
+      for(y=1; y< hh-1; y++)
+      {
+        pattern = 0;
+        center = J[INDEX(y,x,h)];
+        if(J[INDEX(y-1,x-1,h)] < center) pattern = pattern | 0x01;
+        if(J[INDEX(y-1,x,h)] < center)   pattern = pattern | 0x02;
+        if(J[INDEX(y-1,x+1,h)] < center) pattern = pattern | 0x04;
+        if(J[INDEX(y,x-1,h)] < center)   pattern = pattern | 0x08;
+        if(J[INDEX(y,x+1,h)] < center)   pattern = pattern | 0x10;
+        if(J[INDEX(y+1,x-1,h)] < center) pattern = pattern | 0x20;
+        if(J[INDEX(y+1,x,h)] < center)   pattern = pattern | 0x40;
+        if(J[INDEX(y+1,x+1,h)] < center) pattern = pattern | 0x80;
+
+        /*        P[cnt++] = pattern; */
+        P[offset+pattern] = 1;
+        offset += 256;
+      }
+    }
+
+    if( i < K-1 )
+    {
+      if(ww % 2 == 1) ww--;
+      if(hh % 2 == 1) hh--;
+
+      ww = ww/2;
+      for(x=0; x < ww; x++)
+        for(j=0; j < hh; j++)
+          J[INDEX(j,x,h)] = J[INDEX(j,2*x,h)] + J[INDEX(j,2*x+1,h)];
+
+      hh = hh/2;
+      for(y=0; y < hh; y++)
+        for(j=0; j < ww; j++)
+          J[INDEX(y,j,h)] = J[INDEX(2*y,j,h)] + J[INDEX(2*y+1,j,h)];
+    }
+    
+  }
+  
+  return;
+}
+
diff --git a/lib_svmlight_format.c b/lib_svmlight_format.c
new file mode 100644
index 0000000..135d33d
--- /dev/null
+++ b/lib_svmlight_format.c
@@ -0,0 +1,97 @@
+#include "lib_svmlight_format.h"
+
+static int32_t next_occurence(char *line, int32_t start, char what)
+{
+  int32_t i;
+  for(i=start; i < LIBSLF_MAXLINELEN && line[i] != '\0'; i++)
+  {
+    if(line[i] == what)
+      return(i);
+  }
+
+  return(-1);
+}
+
+int32_t svmlight_format_parse_line(char *line, int32_t *label, uint32_t *feat_idx, double *feat_val)
+{  
+  int32_t beg, end, nnzf=0;
+
+  end = next_occurence(line,0,' ');
+  beg = end;
+  if(end == -1) 
+    return(-1);
+
+  *label = (int32_t)atol(line);
+
+  int go = 1;
+  while(go) {
+    end = next_occurence(line,beg,':');
+
+    if(end == -1)
+      return(nnzf);
+
+    feat_idx[nnzf] = (uint32_t)atol(&line[beg]);
+
+    beg = end + 1;
+
+    end = next_occurence(line,beg,' ');
+    if(end == -1) {
+      end = next_occurence(line,beg,'\n');
+      if(end == -1)
+        return(-1);
+
+      go = 0;
+    }
+
+    feat_val[nnzf] = atof(&line[beg]);    
+
+    beg = end;
+
+    nnzf++;
+  }
+
+  return(nnzf);
+}
+
+
+/* difference to svmlight_format_parse_line is that here the label is float */
+int32_t svmlight_format_parse_line_doubley(char *line, double *label, uint32_t *feat_idx, double *feat_val)
+{  
+  int32_t beg, end, nnzf=0;
+
+  end = next_occurence(line,0,' ');
+  beg = end;
+  if(end == -1) 
+    return(-1);
+
+  *label = (double)atof(line);
+
+  int go = 1;
+  while(go) {
+    end = next_occurence(line,beg,':');
+
+    if(end == -1)
+      return(nnzf);
+
+    feat_idx[nnzf] = (uint32_t)atol(&line[beg]);
+
+    beg = end + 1;
+
+    end = next_occurence(line,beg,' ');
+    if(end == -1) {
+      end = next_occurence(line,beg,'\n');
+      if(end == -1)
+        return(-1);
+
+      go = 0;
+    }
+
+    feat_val[nnzf] = atof(&line[beg]);    
+
+    beg = end;
+
+    nnzf++;
+  }
+
+  return(nnzf);
+}
diff --git a/lib_svmlight_format.h b/lib_svmlight_format.h
new file mode 100644
index 0000000..952ed69
--- /dev/null
+++ b/lib_svmlight_format.h
@@ -0,0 +1,13 @@
+#ifndef lib_svmlight_format_h
+#define lib_svmlight_format_h
+
+#include <stdlib.h>
+#include <stdint.h>
+
+#define LIBSLF_MAXLINELEN 1000000
+
+int32_t svmlight_format_parse_line(char *line, int32_t *label, uint32_t *feat_idx, double *feat_val);
+int32_t svmlight_format_parse_line_doubley(char *line, double *label, uint32_t *feat_idx, double *feat_val);
+
+
+#endif
diff --git a/liblbp.c b/liblbp.c
new file mode 100644
index 0000000..4a8125d
--- /dev/null
+++ b/liblbp.c
@@ -0,0 +1,257 @@
+#include "liblbp.h"
+
+
+/*-----------------------------------------------------------------------
+  -----------------------------------------------------------------------*/
+void liblbp_pyr_features(char *vec, uint32_t vec_nDim, uint32_t *img, uint16_t img_nRows, uint16_t img_nCols )
+{
+  uint32_t offset, ww, hh, x, y,center,j ;
+  uint8_t pattern;
+
+  offset=0;
+/*  ww=win_W;*/
+/*  hh=win_H;*/
+  ww=img_nCols;
+  hh=img_nRows;
+  while(1)
+  {
+    for(x=1; x < ww-1; x++)
+    {
+      for(y=1; y< hh-1; y++)
+      {
+        pattern = 0;
+        center = img[LIBLBP_INDEX(y,x,img_nRows)];
+        if(img[LIBLBP_INDEX(y-1,x-1,img_nRows)] < center) pattern = pattern | 0x01;
+        if(img[LIBLBP_INDEX(y-1,x,img_nRows)] < center)   pattern = pattern | 0x02;
+        if(img[LIBLBP_INDEX(y-1,x+1,img_nRows)] < center) pattern = pattern | 0x04;
+        if(img[LIBLBP_INDEX(y,x-1,img_nRows)] < center)   pattern = pattern | 0x08;
+        if(img[LIBLBP_INDEX(y,x+1,img_nRows)] < center)   pattern = pattern | 0x10;
+        if(img[LIBLBP_INDEX(y+1,x-1,img_nRows)] < center) pattern = pattern | 0x20;
+        if(img[LIBLBP_INDEX(y+1,x,img_nRows)] < center)   pattern = pattern | 0x40;
+        if(img[LIBLBP_INDEX(y+1,x+1,img_nRows)] < center) pattern = pattern | 0x80;
+
+        vec[offset+pattern]++;
+        offset += 256; 
+
+      }
+    }
+    if(vec_nDim <= offset) 
+      return;
+
+    if(ww % 2 == 1) ww--;
+    if(hh % 2 == 1) hh--;
+
+    ww = ww/2;
+    for(x=0; x < ww; x++)
+      for(j=0; j < hh; j++)
+        img[LIBLBP_INDEX(j,x,img_nRows)] = img[LIBLBP_INDEX(j,2*x,img_nRows)] + 
+          img[LIBLBP_INDEX(j,2*x+1,img_nRows)];
+
+    hh = hh/2;
+    for(y=0; y < hh; y++)
+      for(j=0; j < ww; j++)
+        img[LIBLBP_INDEX(y,j,img_nRows)] = img[LIBLBP_INDEX(2*y,j,img_nRows)] + 
+          img[LIBLBP_INDEX(2*y+1,j,img_nRows)];
+    
+  }
+
+  return;
+}
+
+
+/*-----------------------------------------------------------------------
+  -----------------------------------------------------------------------*/
+double liblbp_pyr_dotprod(double *vec, uint32_t vec_nDim, uint32_t *img, uint16_t img_nRows, uint16_t img_nCols)
+{
+  double dot_prod = 0;
+  uint32_t offset=0;
+  uint32_t ww, hh, center, x, y, j;
+  uint8_t pattern;
+  
+/*  ww=win_W;*/
+/*  hh=win_H;*/
+  ww=img_nCols;
+  hh=img_nRows;
+  while(1)
+  {
+    for(x=1; x < ww-1; x++)
+    {
+      for(y=1; y< hh-1; y++)
+      {
+        pattern = 0;
+        center = img[LIBLBP_INDEX(y,x,img_nRows)];
+        if(img[LIBLBP_INDEX(y-1,x-1,img_nRows)] < center) pattern = pattern | 0x01;
+        if(img[LIBLBP_INDEX(y-1,x,img_nRows)] < center)   pattern = pattern | 0x02;
+        if(img[LIBLBP_INDEX(y-1,x+1,img_nRows)] < center) pattern = pattern | 0x04;
+        if(img[LIBLBP_INDEX(y,x-1,img_nRows)] < center)   pattern = pattern | 0x08;
+        if(img[LIBLBP_INDEX(y,x+1,img_nRows)] < center)   pattern = pattern | 0x10;
+        if(img[LIBLBP_INDEX(y+1,x-1,img_nRows)] < center) pattern = pattern | 0x20;
+        if(img[LIBLBP_INDEX(y+1,x,img_nRows)] < center)   pattern = pattern | 0x40;
+        if(img[LIBLBP_INDEX(y+1,x+1,img_nRows)] < center) pattern = pattern | 0x80;
+
+        dot_prod += vec[offset+pattern];
+        offset += 256; 
+
+
+      }
+    }
+    if(vec_nDim <= offset) 
+      return(dot_prod);
+
+
+    if(ww % 2 == 1) ww--;
+    if(hh % 2 == 1) hh--;
+
+    ww = ww/2;
+    for(x=0; x < ww; x++)
+      for(j=0; j < hh; j++)
+        img[LIBLBP_INDEX(j,x,img_nRows)] = img[LIBLBP_INDEX(j,2*x,img_nRows)] + 
+                                          img[LIBLBP_INDEX(j,2*x+1,img_nRows)];
+
+    hh = hh/2;
+    for(y=0; y < hh; y++)
+      for(j=0; j < ww; j++)
+        img[LIBLBP_INDEX(y,j,img_nRows)] = img[LIBLBP_INDEX(2*y,j,img_nRows)] + 
+                                           img[LIBLBP_INDEX(2*y+1,j,img_nRows)];    
+  }
+ 
+  
+}
+
+
+/*-----------------------------------------------------------------------
+  -----------------------------------------------------------------------*/
+void liblbp_pyr_addvec(int64_t *vec, uint32_t vec_nDim, uint32_t *img, uint16_t img_nRows, uint16_t img_nCols)
+{
+  uint32_t offset, ww, hh, x, y, center,j ;
+  uint8_t pattern;
+
+  offset=0;
+/*  ww=win_W;*/
+/*  hh=win_H;*/
+  ww=img_nCols;
+  hh=img_nRows;
+  while(1)
+  {
+    for(x=1; x < ww-1; x++)
+    {
+      for(y=1; y< hh-1; y++)
+      {
+        pattern = 0;
+        center = img[LIBLBP_INDEX(y,x,img_nRows)];
+        if(img[LIBLBP_INDEX(y-1,x-1,img_nRows)] < center) pattern = pattern | 0x01;
+        if(img[LIBLBP_INDEX(y-1,x,img_nRows)] < center)   pattern = pattern | 0x02;
+        if(img[LIBLBP_INDEX(y-1,x+1,img_nRows)] < center) pattern = pattern | 0x04;
+        if(img[LIBLBP_INDEX(y,x-1,img_nRows)] < center)   pattern = pattern | 0x08;
+        if(img[LIBLBP_INDEX(y,x+1,img_nRows)] < center)   pattern = pattern | 0x10;
+        if(img[LIBLBP_INDEX(y+1,x-1,img_nRows)] < center) pattern = pattern | 0x20;
+        if(img[LIBLBP_INDEX(y+1,x,img_nRows)] < center)   pattern = pattern | 0x40;
+        if(img[LIBLBP_INDEX(y+1,x+1,img_nRows)] < center) pattern = pattern | 0x80;
+
+        vec[offset+pattern]++;
+        offset += 256; 
+
+      }
+    }
+    if(vec_nDim <= offset) 
+      return;
+
+    if(ww % 2 == 1) ww--;
+    if(hh % 2 == 1) hh--;
+
+    ww = ww/2;
+    for(x=0; x < ww; x++)
+      for(j=0; j < hh; j++)
+        img[LIBLBP_INDEX(j,x,img_nRows)] = img[LIBLBP_INDEX(j,2*x,img_nRows)] + 
+             img[LIBLBP_INDEX(j,2*x+1,img_nRows)];
+
+    hh = hh/2;
+    for(y=0; y < hh; y++)
+      for(j=0; j < ww; j++)
+        img[LIBLBP_INDEX(y,j,img_nRows)] = img[LIBLBP_INDEX(2*y,j,img_nRows)] + 
+          img[LIBLBP_INDEX(2*y+1,j,img_nRows)];
+    
+  }
+
+  return;
+}
+
+
+
+/*-----------------------------------------------------------------------
+  -----------------------------------------------------------------------*/
+void liblbp_pyr_subvec(int64_t *vec, uint32_t vec_nDim, uint32_t *img, uint16_t img_nRows, uint16_t img_nCols)
+{
+  uint32_t offset, ww, hh, x, y,center,j ;
+  uint8_t pattern;
+
+  offset=0;
+/*  ww=win_W;*/
+/*  hh=win_H;*/
+  ww=img_nCols;
+  hh=img_nRows;
+  while(1)
+  {
+    for(x=1; x < ww-1; x++)
+    {
+      for(y=1; y< hh-1; y++)
+      {
+        pattern = 0;
+        center = img[LIBLBP_INDEX(y,x,img_nRows)];
+        if(img[LIBLBP_INDEX(y-1,x-1,img_nRows)] < center) pattern = pattern | 0x01;
+        if(img[LIBLBP_INDEX(y-1,x,img_nRows)] < center)   pattern = pattern | 0x02;
+        if(img[LIBLBP_INDEX(y-1,x+1,img_nRows)] < center) pattern = pattern | 0x04;
+        if(img[LIBLBP_INDEX(y,x-1,img_nRows)] < center)   pattern = pattern | 0x08;
+        if(img[LIBLBP_INDEX(y,x+1,img_nRows)] < center)   pattern = pattern | 0x10;
+        if(img[LIBLBP_INDEX(y+1,x-1,img_nRows)] < center) pattern = pattern | 0x20;
+        if(img[LIBLBP_INDEX(y+1,x,img_nRows)] < center)   pattern = pattern | 0x40;
+        if(img[LIBLBP_INDEX(y+1,x+1,img_nRows)] < center) pattern = pattern | 0x80;
+
+        vec[offset+pattern]--;
+        offset += 256; 
+
+      }
+    }
+    if(vec_nDim <= offset) 
+      return;
+
+    if(ww % 2 == 1) ww--;
+    if(hh % 2 == 1) hh--;
+
+    ww = ww/2;
+    for(x=0; x < ww; x++)
+      for(j=0; j < hh; j++)
+        img[LIBLBP_INDEX(j,x,img_nRows)] = img[LIBLBP_INDEX(j,2*x,img_nRows)] + 
+          img[LIBLBP_INDEX(j,2*x+1,img_nRows)];
+
+    hh = hh/2;
+    for(y=0; y < hh; y++)
+      for(j=0; j < ww; j++)
+        img[LIBLBP_INDEX(y,j,img_nRows)] = img[LIBLBP_INDEX(2*y,j,img_nRows)] + 
+          img[LIBLBP_INDEX(2*y+1,j,img_nRows)];
+    
+  }
+
+  return;
+}
+
+
+/*-----------------------------------------------------------------------
+  -----------------------------------------------------------------------*/
+
+uint32_t liblbp_pyr_get_dim(uint16_t img_nRows, uint16_t img_nCols, uint16_t nPyramids)
+{
+  uint32_t w, h, N, i;
+
+  for(w=img_nCols, h=img_nRows, N=0, i=0; i < nPyramids && LIBLBP_MIN(w,h) >= 3; i++)
+  {
+    N += (w-2)*(h-2);
+
+    if(w % 2) w--;
+    if(h % 2) h--;
+    w = w/2;
+    h = h/2;
+  }
+  return(256*N);
+}
+
diff --git a/liblbp.h b/liblbp.h
new file mode 100644
index 0000000..d144ed2
--- /dev/null
+++ b/liblbp.h
@@ -0,0 +1,15 @@
+#ifndef _liblbp_h
+#define _liblbp_h
+
+#include <stdint.h>
+
+#define LIBLBP_INDEX(ROW,COL,NUM_ROWS) ((COL)*(NUM_ROWS)+(ROW))
+#define LIBLBP_MIN(A,B) ((A) > (B) ? (B) : (A))
+
+extern void liblbp_pyr_features(char *vec, uint32_t vec_nDim, uint32_t *img, uint16_t img_nRows, uint16_t img_nCols );
+extern double liblbp_pyr_dotprod(double *vec, uint32_t vec_nDim, uint32_t *img, uint16_t img_nRows, uint16_t img_nCols);
+extern void liblbp_pyr_addvec(int64_t *vec, uint32_t vec_nDim, uint32_t *img, uint16_t img_nRows, uint16_t img_nCols);
+extern void liblbp_pyr_subvec(int64_t *vec, uint32_t vec_nDim, uint32_t *img, uint16_t img_nRows, uint16_t img_nCols);
+extern uint32_t liblbp_pyr_get_dim(uint16_t img_nRows, uint16_t img_nCols, uint16_t nPyramids);
+
+#endif
diff --git a/libocas.c b/libocas.c
new file mode 100644
index 0000000..b1d0182
--- /dev/null
+++ b/libocas.c
@@ -0,0 +1,1490 @@
+/*-----------------------------------------------------------------------
+ * libocas.c: Implementation of the OCAS solver for training 
+ *            linear SVM classifiers.
+ *  
+ * Copyright (C) 2008 Vojtech Franc, xfrancv at cmp.felk.cvut.cz
+ *                    Soeren Sonnenburg, soeren.sonnenburg at first.fraunhofer.de
+ *
+ * This program is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU General Public 
+ * License as published by the Free Software Foundation; 
+ * Version 3, 29 June 2007
+ *-------------------------------------------------------------------- */
+
+#include <stdlib.h>
+#include <string.h>
+#include <math.h>
+#include <sys/time.h>
+#include <time.h>
+#include <stdio.h>
+#include <stdint.h>
+
+#include "libocas.h"
+#include "libqp.h"
+
+#define LAMBDA 0.1      /* must be from (0,1>   1..means that OCAS becomes equivalent to CPA */
+
+static const uint32_t QPSolverMaxIter = 10000000;
+
+static double *H;
+static uint32_t BufSize;
+
+/*----------------------------------------------------------------------
+ Returns pointer at i-th column of Hessian matrix.
+  ----------------------------------------------------------------------*/
+static const double *get_col( uint32_t i)
+{
+  return( &H[ BufSize*i ] );
+} 
+
+/*----------------------------------------------------------------------
+  Returns time of the day in seconds. 
+  ----------------------------------------------------------------------*/
+static double get_time()
+{
+	struct timeval tv;
+	if (gettimeofday(&tv, NULL)==0)
+		return tv.tv_sec+((double)(tv.tv_usec))/1e6;
+	else
+		return 0.0;
+}
+
+/*----------------------------------------------------------------------
+  Linear binary Ocas-SVM solver.
+  ----------------------------------------------------------------------*/
+ocas_return_value_T svm_ocas_solver(
+            double C,
+            uint32_t nData, 
+            double TolRel,
+            double TolAbs,
+            double QPBound,
+            double MaxTime,
+            uint32_t _BufSize,
+            uint8_t Method,
+            void (*compute_W)(double*, double*, double*, uint32_t, void*),
+            double (*update_W)(double, void*),
+            int (*add_new_cut)(double*, uint32_t*, uint32_t, uint32_t, void*),
+            int (*compute_output)(double*, void* ),
+            int (*sort)(double*, double*, uint32_t),
+			void (*ocas_print)(ocas_return_value_T),
+			void* user_data) 
+{
+  ocas_return_value_T ocas={0,0,0,0,0,0,0,0,0,0,0,0,0,0};
+  double *b, *alpha, *diag_H;
+  double *output, *old_output;
+  double xi, sq_norm_W, QPSolverTolRel, dot_prod_WoldW, sq_norm_oldW;
+  double A0, B0, GradVal, t, t1, t2, *Ci, *Bi, *hpf, *hpb;
+  double start_time, ocas_start_time;
+  uint32_t cut_length;
+  uint32_t i, *new_cut;
+  uint32_t *I;
+  uint8_t S = 1;
+  libqp_state_T qp_exitflag;
+
+  ocas_start_time = get_time();
+  ocas.qp_solver_time = 0;
+  ocas.output_time = 0;
+  ocas.sort_time = 0;
+  ocas.add_time = 0;
+  ocas.w_time = 0;
+  ocas.print_time = 0;
+
+  BufSize = _BufSize;
+
+  QPSolverTolRel = TolRel*0.5;
+
+  H=NULL;
+  b=NULL;
+  alpha=NULL;
+  new_cut=NULL;
+  I=NULL;
+  diag_H=NULL;
+  output=NULL;
+  old_output=NULL;
+  hpf=NULL;
+  hpb = NULL;
+  Ci=NULL;
+  Bi=NULL;
+
+  /* Hessian matrix contains dot product of normal vectors of selected cutting planes */
+  H = (double*)LIBOCAS_CALLOC(BufSize*BufSize,sizeof(double));
+  if(H == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+  
+  /* bias of cutting planes */
+  b = (double*)LIBOCAS_CALLOC(BufSize,sizeof(double));
+  if(b == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  alpha = (double*)LIBOCAS_CALLOC(BufSize,sizeof(double));
+  if(alpha == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  /* indices of examples which define a new cut */
+  new_cut = (uint32_t*)LIBOCAS_CALLOC(nData,sizeof(uint32_t));
+  if(new_cut == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  I = (uint32_t*)LIBOCAS_CALLOC(BufSize,sizeof(uint32_t));
+  if(I == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  for(i=0; i< BufSize; i++) I[i] = 1;
+
+  diag_H = (double*)LIBOCAS_CALLOC(BufSize,sizeof(double));
+  if(diag_H == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  output = (double*)LIBOCAS_CALLOC(nData,sizeof(double));
+  if(output == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  old_output = (double*)LIBOCAS_CALLOC(nData,sizeof(double));
+  if(old_output == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  /* array of hinge points used in line-serach  */
+  hpf = (double*) LIBOCAS_CALLOC(nData, sizeof(hpf[0]));
+  if(hpf == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  hpb = (double*) LIBOCAS_CALLOC(nData, sizeof(hpb[0]));
+  if(hpb == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  /* vectors Ci, Bi are used in the line search procedure */
+  Ci = (double*)LIBOCAS_CALLOC(nData,sizeof(double));
+  if(Ci == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  Bi = (double*)LIBOCAS_CALLOC(nData,sizeof(double));
+  if(Bi == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+  
+  ocas.nCutPlanes = 0;
+  ocas.exitflag = 0;
+  ocas.nIter = 0;
+
+  /* Compute initial value of Q_P assuming that W is zero vector.*/
+  sq_norm_W = 0;
+  xi = nData;
+  ocas.Q_P = 0.5*sq_norm_W + C*xi;
+  ocas.Q_D = 0;
+
+  /* Compute the initial cutting plane */
+  cut_length = nData;
+  for(i=0; i < nData; i++)
+    new_cut[i] = i;
+
+  ocas.trn_err = nData;
+  ocas.ocas_time = get_time() - ocas_start_time;
+  /*  ocas_print("%4d: tim=%f, Q_P=%f, Q_D=%f, Q_P-Q_D=%f, Q_P-Q_D/abs(Q_P)=%f\n",
+          ocas.nIter,cur_time, ocas.Q_P,ocas.Q_D,ocas.Q_P-ocas.Q_D,(ocas.Q_P-ocas.Q_D)/LIBOCAS_ABS(ocas.Q_P));
+  */ 
+  ocas_print(ocas);
+  
+  /* main loop */
+  while( ocas.exitflag == 0 )
+  {
+    ocas.nIter++;
+
+    /* append a new cut to the buffer and update H */
+    b[ocas.nCutPlanes] = -(double)cut_length;
+
+    start_time = get_time();
+
+    if(add_new_cut( &H[LIBOCAS_INDEX(0,ocas.nCutPlanes,BufSize)], new_cut, cut_length, ocas.nCutPlanes, user_data ) != 0)
+    {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+    }
+
+    ocas.add_time += get_time() - start_time;
+
+    /* copy new added row:  H(ocas.nCutPlanes,ocas.nCutPlanes,1:ocas.nCutPlanes-1) = H(1:ocas.nCutPlanes-1:ocas.nCutPlanes)' */
+    diag_H[ocas.nCutPlanes] = H[LIBOCAS_INDEX(ocas.nCutPlanes,ocas.nCutPlanes,BufSize)];
+    for(i=0; i < ocas.nCutPlanes; i++) {
+      H[LIBOCAS_INDEX(ocas.nCutPlanes,i,BufSize)] = H[LIBOCAS_INDEX(i,ocas.nCutPlanes,BufSize)];
+    }
+
+    ocas.nCutPlanes++;    
+    
+    /* call inner QP solver */
+    start_time = get_time();
+
+    qp_exitflag = libqp_splx_solver(&get_col, diag_H, b, &C, I, &S, alpha,
+                                  ocas.nCutPlanes, QPSolverMaxIter, 0.0, QPSolverTolRel, -LIBOCAS_PLUS_INF,0);
+
+    ocas.qp_exitflag = qp_exitflag.exitflag;
+
+    ocas.qp_solver_time += get_time() - start_time;
+    ocas.Q_D = -qp_exitflag.QP;
+
+    ocas.nNZAlpha = 0;
+    for(i=0; i < ocas.nCutPlanes; i++) {
+      if( alpha[i] != 0) ocas.nNZAlpha++;
+    }
+
+    sq_norm_oldW = sq_norm_W;
+    start_time = get_time();
+    compute_W( &sq_norm_W, &dot_prod_WoldW, alpha, ocas.nCutPlanes, user_data );
+    ocas.w_time += get_time() - start_time;
+    
+    /* select a new cut */
+    switch( Method )
+    {
+      /* cutting plane algorithm implemented in SVMperf and BMRM */
+      case 0: 
+
+        start_time = get_time();
+        if( compute_output( output, user_data ) != 0)
+        {
+          ocas.exitflag=-2;
+          goto cleanup;
+        }
+        ocas.output_time += get_time()-start_time;
+
+        xi = 0;
+        cut_length = 0;
+        ocas.trn_err = 0;
+        for(i=0; i < nData; i++)
+        { 
+          if(output[i] <= 0) ocas.trn_err++;
+          
+          if(output[i] <= 1) {
+            xi += 1 - output[i];
+            new_cut[cut_length] = i; 
+            cut_length++;
+          }
+        }
+        ocas.Q_P = 0.5*sq_norm_W + C*xi;
+
+        ocas.ocas_time = get_time() - ocas_start_time;
+
+        /*        ocas_print("%4d: tim=%f, Q_P=%f, Q_D=%f, Q_P-Q_D=%f, 1-Q_D/Q_P=%f, nza=%4d, err=%.2f%%, qpf=%d\n",
+                  ocas.nIter,cur_time, ocas.Q_P,ocas.Q_D,ocas.Q_P-ocas.Q_D,(ocas.Q_P-ocas.Q_D)/LIBOCAS_ABS(ocas.Q_P), 
+                  ocas.nNZAlpha, 100*(double)ocas.trn_err/(double)nData, ocas.qp_exitflag );
+        */ 
+
+        start_time = get_time();
+        ocas_print(ocas);
+        ocas.print_time += get_time() - start_time;
+
+        break;
+
+
+      /* Ocas strategy */
+      case 1:
+
+        /* Linesearch */
+        A0 = sq_norm_W -2*dot_prod_WoldW + sq_norm_oldW;
+        B0 = dot_prod_WoldW - sq_norm_oldW;
+
+        memcpy( old_output, output, sizeof(double)*nData );
+
+        start_time = get_time();
+        if( compute_output( output, user_data ) != 0)
+        {
+          ocas.exitflag=-2;
+          goto cleanup;
+        }
+        ocas.output_time += get_time()-start_time;
+
+        uint32_t num_hp = 0;
+        GradVal = B0;
+        for(i=0; i< nData; i++) {
+
+          Ci[i] = C*(1-old_output[i]);
+          Bi[i] = C*(old_output[i] - output[i]);
+
+          double val;
+          if(Bi[i] != 0)
+            val = -Ci[i]/Bi[i];
+          else
+            val = -LIBOCAS_PLUS_INF;
+          
+          if (val>0)
+          {
+/*            hpi[num_hp] = i;*/
+            hpb[num_hp] = Bi[i];
+            hpf[num_hp] = val;
+            num_hp++;
+          }
+
+          if( (Bi[i] < 0 && val > 0) || (Bi[i] > 0 && val <= 0)) 
+            GradVal += Bi[i];
+          
+        }
+
+        t = 0;
+        if( GradVal < 0 )
+        {
+          start_time = get_time();
+/*          if( sort(hpf, hpi, num_hp) != 0)*/
+          if( sort(hpf, hpb, num_hp) != 0 )
+          {
+            ocas.exitflag=-2;
+            goto cleanup;
+          }
+          ocas.sort_time += get_time() - start_time;
+
+          double t_new, GradVal_new;
+          i = 0;
+          while( GradVal < 0 && i < num_hp )
+          {
+            t_new = hpf[i];
+            GradVal_new = GradVal + LIBOCAS_ABS(hpb[i]) + A0*(t_new-t);
+
+            if( GradVal_new >= 0 )
+            {
+              t = t + GradVal*(t-t_new)/(GradVal_new - GradVal);
+            }
+            else
+            {
+              t = t_new;
+              i++;
+            }
+
+            GradVal = GradVal_new;
+          }
+        }
+
+        /*
+        t = hpf[0] - 1;
+        i = 0;
+        GradVal = t*A0 + Bsum;
+        while( GradVal < 0 && i < num_hp && hpf[i] < LIBOCAS_PLUS_INF ) {
+          t = hpf[i];
+          Bsum = Bsum + LIBOCAS_ABS(Bi[hpi[i]]);
+          GradVal = t*A0 + Bsum;
+          i++;
+        }
+        */
+        t = LIBOCAS_MAX(t,0);          /* just sanity check; t < 0 should not ocure */
+
+        t1 = t;                /* new (best so far) W */
+        t2 = t+LAMBDA*(1.0-t);   /* new cutting plane */
+        /*        t2 = t+(1.0-t)/10.0;   */
+
+        /* update W to be the best so far solution */
+        sq_norm_W = update_W( t1, user_data );
+
+        /* select a new cut */
+        xi = 0;
+        cut_length = 0;
+        ocas.trn_err = 0;
+        for(i=0; i < nData; i++ ) {
+
+          if( (old_output[i]*(1-t2) + t2*output[i]) <= 1 ) 
+          {
+            new_cut[cut_length] = i; 
+            cut_length++;
+          }
+
+          output[i] = old_output[i]*(1-t1) + t1*output[i];
+
+          if( output[i] <= 1) xi += 1-output[i];
+          if( output[i] <= 0) ocas.trn_err++;
+
+        }
+
+        ocas.Q_P = 0.5*sq_norm_W + C*xi;
+
+        ocas.ocas_time = get_time() - ocas_start_time;
+
+        /*        ocas_print("%4d: tim=%f, Q_P=%f, Q_D=%f, Q_P-Q_D=%f, 1-Q_D/Q_P=%f, nza=%4d, err=%.2f%%, qpf=%d\n",
+                   ocas.nIter, cur_time, ocas.Q_P,ocas.Q_D,ocas.Q_P-ocas.Q_D,(ocas.Q_P-ocas.Q_D)/LIBOCAS_ABS(ocas.Q_P),
+                   ocas.nNZAlpha, 100*(double)ocas.trn_err/(double)nData, ocas.qp_exitflag );
+        */ 
+        
+        start_time = get_time();
+        ocas_print(ocas);
+        ocas.print_time += get_time() - start_time;
+
+        break;
+    }
+
+    /* Stopping conditions */
+    if( ocas.Q_P - ocas.Q_D <= TolRel*LIBOCAS_ABS(ocas.Q_P)) ocas.exitflag = 1; 
+    if( ocas.Q_P - ocas.Q_D <= TolAbs) ocas.exitflag = 2; 
+    if( ocas.Q_P <= QPBound) ocas.exitflag = 3; 
+    if( ocas.ocas_time >= MaxTime) ocas.exitflag = 4; 
+    if(ocas.nCutPlanes >= BufSize) ocas.exitflag = -1;
+         
+  } /* end of the main loop */
+
+cleanup:
+
+  LIBOCAS_FREE(H);
+  LIBOCAS_FREE(b);
+  LIBOCAS_FREE(alpha);
+  LIBOCAS_FREE(new_cut);
+  LIBOCAS_FREE(I);
+  LIBOCAS_FREE(diag_H);
+  LIBOCAS_FREE(output);
+  LIBOCAS_FREE(old_output);
+  LIBOCAS_FREE(hpf);
+/*  LIBOCAS_FREE(hpi);*/
+  LIBOCAS_FREE(hpb);
+  LIBOCAS_FREE(Ci);
+  LIBOCAS_FREE(Bi);
+
+  ocas.ocas_time = get_time() - ocas_start_time;
+
+  return(ocas);
+}
+
+
+/*----------------------------------------------------------------------
+  Binary linear Ocas-SVM solver which allows using different C for each 
+  training example.
+  ----------------------------------------------------------------------*/
+ocas_return_value_T svm_ocas_solver_difC(
+            double *C,
+            uint32_t nData, 
+            double TolRel,
+            double TolAbs,
+            double QPBound,
+            double MaxTime,
+            uint32_t _BufSize,
+            uint8_t Method,
+            void (*compute_W)(double*, double*, double*, uint32_t, void*),
+            double (*update_W)(double, void*),
+            int (*add_new_cut)(double*, uint32_t*, uint32_t, uint32_t, void*),
+            int (*compute_output)(double*, void* ),
+            int (*sort)(double*, double*, uint32_t),
+			void (*ocas_print)(ocas_return_value_T),
+			void* user_data) 
+{
+  ocas_return_value_T ocas={0,0,0,0,0,0,0,0,0,0,0,0,0,0};
+  double *b, *alpha, *diag_H;
+  double *output, *old_output;
+  double xi, sq_norm_W, QPSolverTolRel, dot_prod_WoldW, sq_norm_oldW;
+  double A0, B0, GradVal, t, t1, t2, *Ci, *Bi, *hpf, *hpb;
+  double start_time, ocas_start_time;
+  double qp_b = 1.0;
+  double new_b;
+  uint32_t cut_length;
+  uint32_t i, *new_cut;
+  uint32_t *I;
+  uint8_t S = 1;
+  libqp_state_T qp_exitflag;
+
+  ocas_start_time = get_time();
+  ocas.qp_solver_time = 0;
+  ocas.output_time = 0;
+  ocas.sort_time = 0;
+  ocas.add_time = 0;
+  ocas.w_time = 0;
+  ocas.print_time = 0;
+
+  BufSize = _BufSize;
+
+  QPSolverTolRel = TolRel*0.5;
+
+  H=NULL;
+  b=NULL;
+  alpha=NULL;
+  new_cut=NULL;
+  I=NULL;
+  diag_H=NULL;
+  output=NULL;
+  old_output=NULL;
+  hpf=NULL;
+  hpb = NULL;
+  Ci=NULL;
+  Bi=NULL;
+
+  /* Hessian matrix contains dot product of normal vectors of selected cutting planes */
+  H = (double*)LIBOCAS_CALLOC(BufSize*BufSize,sizeof(double));
+  if(H == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+  
+  /* bias of cutting planes */
+  b = (double*)LIBOCAS_CALLOC(BufSize,sizeof(double));
+  if(b == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  alpha = (double*)LIBOCAS_CALLOC(BufSize,sizeof(double));
+  if(alpha == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  /* indices of examples which define a new cut */
+  new_cut = (uint32_t*)LIBOCAS_CALLOC(nData,sizeof(uint32_t));
+  if(new_cut == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  I = (uint32_t*)LIBOCAS_CALLOC(BufSize,sizeof(uint32_t));
+  if(I == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  for(i=0; i< BufSize; i++) I[i] = 1;
+
+  diag_H = (double*)LIBOCAS_CALLOC(BufSize,sizeof(double));
+  if(diag_H == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  output = (double*)LIBOCAS_CALLOC(nData,sizeof(double));
+  if(output == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  old_output = (double*)LIBOCAS_CALLOC(nData,sizeof(double));
+  if(old_output == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  /* array of hinge points used in line-serach  */
+  hpf = (double*) LIBOCAS_CALLOC(nData, sizeof(hpf[0]));
+  if(hpf == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  hpb = (double*) LIBOCAS_CALLOC(nData, sizeof(hpb[0]));
+  if(hpb == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  /* vectors Ci, Bi are used in the line search procedure */
+  Ci = (double*)LIBOCAS_CALLOC(nData,sizeof(double));
+  if(Ci == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  Bi = (double*)LIBOCAS_CALLOC(nData,sizeof(double));
+  if(Bi == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+  
+  ocas.nCutPlanes = 0;
+  ocas.exitflag = 0;
+  ocas.nIter = 0;
+
+  /* Compute initial value of Q_P assuming that W is zero vector.*/
+  sq_norm_W = 0;
+  xi = nData;
+/*  ocas.Q_P = 0.5*sq_norm_W + C*xi;*/
+  ocas.Q_D = 0;
+
+  /* Compute the initial cutting plane */
+  cut_length = nData;
+  new_b = 0;
+  for(i=0; i < nData; i++)
+  {
+    new_cut[i] = i;
+    new_b += C[i];
+  }
+
+  ocas.Q_P = 0.5*sq_norm_W + new_b;
+
+
+  ocas.trn_err = nData;
+  ocas.ocas_time = get_time() - ocas_start_time;
+  /*  ocas_print("%4d: tim=%f, Q_P=%f, Q_D=%f, Q_P-Q_D=%f, Q_P-Q_D/abs(Q_P)=%f\n",
+          ocas.nIter,cur_time, ocas.Q_P,ocas.Q_D,ocas.Q_P-ocas.Q_D,(ocas.Q_P-ocas.Q_D)/LIBOCAS_ABS(ocas.Q_P));
+  */ 
+  ocas_print(ocas);
+  
+  /* main loop */
+  while( ocas.exitflag == 0 )
+  {
+    ocas.nIter++;
+
+    /* append a new cut to the buffer and update H */
+/*    b[ocas.nCutPlanes] = -(double)cut_length*C;*/
+    b[ocas.nCutPlanes] = -new_b;
+
+    start_time = get_time();
+
+    if(add_new_cut( &H[LIBOCAS_INDEX(0,ocas.nCutPlanes,BufSize)], new_cut, cut_length, ocas.nCutPlanes, user_data ) != 0)
+    {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+    }
+
+    ocas.add_time += get_time() - start_time;
+
+    /* copy new added row:  H(ocas.nCutPlanes,ocas.nCutPlanes,1:ocas.nCutPlanes-1) = H(1:ocas.nCutPlanes-1:ocas.nCutPlanes)' */
+    diag_H[ocas.nCutPlanes] = H[LIBOCAS_INDEX(ocas.nCutPlanes,ocas.nCutPlanes,BufSize)];
+    for(i=0; i < ocas.nCutPlanes; i++) {
+      H[LIBOCAS_INDEX(ocas.nCutPlanes,i,BufSize)] = H[LIBOCAS_INDEX(i,ocas.nCutPlanes,BufSize)];
+    }
+
+    ocas.nCutPlanes++;    
+    
+    /* call inner QP solver */
+    start_time = get_time();
+
+/*    qp_exitflag = libqp_splx_solver(&get_col, diag_H, b, &C, I, &S, alpha,*/
+/*                                  ocas.nCutPlanes, QPSolverMaxIter, 0.0, QPSolverTolRel, -LIBOCAS_PLUS_INF,0);*/
+    qp_exitflag = libqp_splx_solver(&get_col, diag_H, b, &qp_b, I, &S, alpha,
+                                  ocas.nCutPlanes, QPSolverMaxIter, 0.0, QPSolverTolRel, -LIBOCAS_PLUS_INF,0);
+
+    ocas.qp_exitflag = qp_exitflag.exitflag;
+
+    ocas.qp_solver_time += get_time() - start_time;
+    ocas.Q_D = -qp_exitflag.QP;
+
+    ocas.nNZAlpha = 0;
+    for(i=0; i < ocas.nCutPlanes; i++) {
+      if( alpha[i] != 0) ocas.nNZAlpha++;
+    }
+
+    sq_norm_oldW = sq_norm_W;
+    start_time = get_time();
+    compute_W( &sq_norm_W, &dot_prod_WoldW, alpha, ocas.nCutPlanes, user_data );
+    ocas.w_time += get_time() - start_time;
+    
+    /* select a new cut */
+    switch( Method )
+    {
+      /* cutting plane algorithm implemented in SVMperf and BMRM */
+      case 0: 
+
+        start_time = get_time();
+        if( compute_output( output, user_data ) != 0)
+        {
+          ocas.exitflag=-2;
+          goto cleanup;
+        }
+        ocas.output_time += get_time()-start_time;
+
+        xi = 0;
+        cut_length = 0;
+        ocas.trn_err = 0;
+        new_b = 0;
+        for(i=0; i < nData; i++)
+        { 
+          if(output[i] <= 0) ocas.trn_err++;
+          
+/*          if(output[i] <= 1) {*/
+/*            xi += 1 - output[i];*/
+          if(output[i] <= C[i]) {
+            xi += C[i] - output[i];
+            new_cut[cut_length] = i; 
+            cut_length++;
+            new_b += C[i];
+          }
+        }
+/*        ocas.Q_P = 0.5*sq_norm_W + C*xi;*/
+        ocas.Q_P = 0.5*sq_norm_W + xi;
+
+        ocas.ocas_time = get_time() - ocas_start_time;
+
+        /*        ocas_print("%4d: tim=%f, Q_P=%f, Q_D=%f, Q_P-Q_D=%f, 1-Q_D/Q_P=%f, nza=%4d, err=%.2f%%, qpf=%d\n",
+                  ocas.nIter,cur_time, ocas.Q_P,ocas.Q_D,ocas.Q_P-ocas.Q_D,(ocas.Q_P-ocas.Q_D)/LIBOCAS_ABS(ocas.Q_P), 
+                  ocas.nNZAlpha, 100*(double)ocas.trn_err/(double)nData, ocas.qp_exitflag );
+        */ 
+
+        start_time = get_time();
+        ocas_print(ocas);
+        ocas.print_time += get_time() - start_time;
+
+        break;
+
+
+      /* Ocas strategy */
+      case 1:
+
+        /* Linesearch */
+        A0 = sq_norm_W -2*dot_prod_WoldW + sq_norm_oldW;
+        B0 = dot_prod_WoldW - sq_norm_oldW;
+
+        memcpy( old_output, output, sizeof(double)*nData );
+
+        start_time = get_time();
+        if( compute_output( output, user_data ) != 0)
+        {
+          ocas.exitflag=-2;
+          goto cleanup;
+        }
+        ocas.output_time += get_time()-start_time;
+
+        uint32_t num_hp = 0;
+        GradVal = B0;
+        for(i=0; i< nData; i++) {
+
+/*          Ci[i] = C*(1-old_output[i]);*/
+/*          Bi[i] = C*(old_output[i] - output[i]);*/
+          Ci[i] = (C[i]-old_output[i]);
+          Bi[i] = old_output[i] - output[i];
+
+          double val;
+          if(Bi[i] != 0)
+            val = -Ci[i]/Bi[i];
+          else
+            val = -LIBOCAS_PLUS_INF;
+          
+          if (val>0)
+          {
+/*            hpi[num_hp] = i;*/
+            hpb[num_hp] = Bi[i];
+            hpf[num_hp] = val;
+            num_hp++;
+          }
+
+          if( (Bi[i] < 0 && val > 0) || (Bi[i] > 0 && val <= 0)) 
+            GradVal += Bi[i];
+          
+        }
+
+        t = 0;
+        if( GradVal < 0 )
+        {
+          start_time = get_time();
+/*          if( sort(hpf, hpi, num_hp) != 0)*/
+          if( sort(hpf, hpb, num_hp) != 0 )
+          {
+            ocas.exitflag=-2;
+            goto cleanup;
+          }
+          ocas.sort_time += get_time() - start_time;
+
+          double t_new, GradVal_new;
+          i = 0;
+          while( GradVal < 0 && i < num_hp )
+          {
+            t_new = hpf[i];
+            GradVal_new = GradVal + LIBOCAS_ABS(hpb[i]) + A0*(t_new-t);
+
+            if( GradVal_new >= 0 )
+            {
+              t = t + GradVal*(t-t_new)/(GradVal_new - GradVal);
+            }
+            else
+            {
+              t = t_new;
+              i++;
+            }
+
+            GradVal = GradVal_new;
+          }
+        }
+
+        /*
+        t = hpf[0] - 1;
+        i = 0;
+        GradVal = t*A0 + Bsum;
+        while( GradVal < 0 && i < num_hp && hpf[i] < LIBOCAS_PLUS_INF ) {
+          t = hpf[i];
+          Bsum = Bsum + LIBOCAS_ABS(Bi[hpi[i]]);
+          GradVal = t*A0 + Bsum;
+          i++;
+        }
+        */
+        t = LIBOCAS_MAX(t,0);          /* just sanity check; t < 0 should not ocure */
+
+        t1 = t;                /* new (best so far) W */
+        t2 = t+(1.0-t)*LAMBDA;   /* new cutting plane */
+        /*        t2 = t+(1.0-t)/10.0;   new cutting plane */
+
+        /* update W to be the best so far solution */
+        sq_norm_W = update_W( t1, user_data );
+
+        /* select a new cut */
+        xi = 0;
+        cut_length = 0;
+        ocas.trn_err = 0;
+        new_b = 0;
+        for(i=0; i < nData; i++ ) {
+
+/*          if( (old_output[i]*(1-t2) + t2*output[i]) <= 1 ) */
+          if( (old_output[i]*(1-t2) + t2*output[i]) <= C[i] ) 
+          {
+            new_cut[cut_length] = i; 
+            cut_length++;
+            new_b += C[i];
+          }
+
+          output[i] = old_output[i]*(1-t1) + t1*output[i];
+
+/*          if( output[i] <= 1) xi += 1-output[i];*/
+          if( output[i] <= C[i]) xi += C[i]-output[i];
+          if( output[i] <= 0) ocas.trn_err++;
+
+        }
+
+/*        ocas.Q_P = 0.5*sq_norm_W + C*xi;*/
+        ocas.Q_P = 0.5*sq_norm_W + xi;
+
+        ocas.ocas_time = get_time() - ocas_start_time;
+
+        /*        ocas_print("%4d: tim=%f, Q_P=%f, Q_D=%f, Q_P-Q_D=%f, 1-Q_D/Q_P=%f, nza=%4d, err=%.2f%%, qpf=%d\n",
+                   ocas.nIter, cur_time, ocas.Q_P,ocas.Q_D,ocas.Q_P-ocas.Q_D,(ocas.Q_P-ocas.Q_D)/LIBOCAS_ABS(ocas.Q_P),
+                   ocas.nNZAlpha, 100*(double)ocas.trn_err/(double)nData, ocas.qp_exitflag );
+        */ 
+        
+        start_time = get_time();
+        ocas_print(ocas);
+        ocas.print_time += get_time() - start_time;
+
+        break;
+    }
+
+    /* Stopping conditions */
+    if( ocas.Q_P - ocas.Q_D <= TolRel*LIBOCAS_ABS(ocas.Q_P)) ocas.exitflag = 1; 
+    if( ocas.Q_P - ocas.Q_D <= TolAbs) ocas.exitflag = 2; 
+    if( ocas.Q_P <= QPBound) ocas.exitflag = 3; 
+    if( ocas.ocas_time >= MaxTime) ocas.exitflag = 4; 
+    if(ocas.nCutPlanes >= BufSize) ocas.exitflag = -1;
+         
+  } /* end of the main loop */
+
+cleanup:
+
+  LIBOCAS_FREE(H);
+  LIBOCAS_FREE(b);
+  LIBOCAS_FREE(alpha);
+  LIBOCAS_FREE(new_cut);
+  LIBOCAS_FREE(I);
+  LIBOCAS_FREE(diag_H);
+  LIBOCAS_FREE(output);
+  LIBOCAS_FREE(old_output);
+  LIBOCAS_FREE(hpf);
+/*  LIBOCAS_FREE(hpi);*/
+  LIBOCAS_FREE(hpb);
+  LIBOCAS_FREE(Ci);
+  LIBOCAS_FREE(Bi);
+
+  ocas.ocas_time = get_time() - ocas_start_time;
+
+  return(ocas);
+}
+
+
+
+/*----------------------------------------------------------------------
+  Multiclass SVM-Ocas solver 
+  ----------------------------------------------------------------------*/
+
+/* Helper function needed by the multi-class SVM linesearch.
+
+  - This function finds a simplified representation of a piece-wise linear function 
+  by splitting the domain into intervals and fining active terms for these intevals */ 
+static void findactive(double *Theta, double *SortedA, uint32_t *nSortedA, double *A, double *B, int n,
+            int (*sort)(double*, double*, uint32_t))
+{     
+  double tmp, theta;
+  uint32_t i, j, idx, idx2 = 0, start;
+
+  sort(A,B,n);
+
+  tmp = B[0];
+  idx = 0;
+  i = 0;
+  while( i < n-1 && A[i] == A[i+1])
+  {
+    if( B[i+1] > B[idx] )
+    {
+      idx = i+1;
+      tmp = B[i+1];
+    }
+    i++;
+  }
+
+  (*nSortedA) = 1;
+  SortedA[0] = A[idx];
+
+  while(1)
+  {
+    start = idx + 1;
+    while( start < n && A[idx] == A[start])
+      start++;
+    
+    theta = LIBOCAS_PLUS_INF;
+    for(j=start; j < n; j++)
+    {
+      tmp = (B[j] - B[idx])/(A[idx]-A[j]);
+      if( tmp < theta)
+      {
+        theta = tmp;
+        idx2 = j;
+      }
+    }
+
+    if( theta < LIBOCAS_PLUS_INF)
+    {
+      Theta[(*nSortedA) - 1] = theta;
+      SortedA[(*nSortedA)] = A[idx2];
+      (*nSortedA)++;
+      idx = idx2;
+    }
+    else
+      return;
+  }
+}
+
+
+/*----------------------------------------------------------------------
+  Multiclass linear OCAS-SVM solver.
+  ----------------------------------------------------------------------*/
+ocas_return_value_T msvm_ocas_solver(
+            double C,
+            double *data_y,
+            uint32_t nY,
+            uint32_t nData, 
+            double TolRel,
+            double TolAbs,
+            double QPBound,
+            double MaxTime,
+            uint32_t _BufSize,
+            uint8_t Method,
+            void (*compute_W)(double*, double*, double*, uint32_t, void*),
+            double (*update_W)(double, void*),
+            int (*add_new_cut)(double*, uint32_t*, uint32_t, void*),
+            int (*compute_output)(double*, void* ),
+            int (*sort)(double*, double*, uint32_t),
+			void (*ocas_print)(ocas_return_value_T),
+			void* user_data) 
+{
+  ocas_return_value_T ocas={0,0,0,0,0,0,0,0,0,0,0,0,0,0};
+  double *b, *alpha, *diag_H;
+  double *output, *old_output;
+  double xi, sq_norm_W, QPSolverTolRel, QPSolverTolAbs, dot_prod_WoldW, sq_norm_oldW;
+  double A0, B0, t, t1, t2, R, tmp, element_b, x;
+  double *A, *B, *theta, *Theta, *sortedA, *Add;
+  double start_time, ocas_start_time, grad_sum, grad, min_x = 0, old_x, old_grad;
+  uint32_t i, y, y2, ypred = 0, *new_cut, cnt1, cnt2, j, nSortedA, idx;
+  uint32_t *I;
+  uint8_t S = 1;
+  libqp_state_T qp_exitflag;
+
+  ocas_start_time = get_time();
+  ocas.qp_solver_time = 0;
+  ocas.output_time = 0;
+  ocas.sort_time = 0;
+  ocas.add_time = 0;
+  ocas.w_time = 0;
+  ocas.print_time = 0;
+
+  BufSize = _BufSize;
+
+  QPSolverTolRel = TolRel*0.5;
+  QPSolverTolAbs = TolAbs*0.5;
+
+  H=NULL;
+  b=NULL;
+  alpha=NULL;
+  new_cut=NULL;
+  I=NULL;
+  diag_H=NULL;
+  output=NULL;
+  old_output=NULL;
+  A = NULL;
+  B = NULL;
+  theta = NULL;
+  Theta = NULL;
+  sortedA = NULL;
+  Add = NULL;
+
+  /* Hessian matrix contains dot product of normal vectors of selected cutting planes */
+  H = (double*)LIBOCAS_CALLOC(BufSize*BufSize,sizeof(double));
+  if(H == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+  
+  /* bias of cutting planes */
+  b = (double*)LIBOCAS_CALLOC(BufSize,sizeof(double));
+  if(b == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  alpha = (double*)LIBOCAS_CALLOC(BufSize,sizeof(double));
+  if(alpha == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  /* indices of examples which define a new cut */
+  new_cut = (uint32_t*)LIBOCAS_CALLOC(nData,sizeof(uint32_t));
+  if(new_cut == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  I = (uint32_t*)LIBOCAS_CALLOC(BufSize,sizeof(uint32_t));
+  if(I == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  for(i=0; i< BufSize; i++) 
+    I[i] = 1;
+
+  diag_H = (double*)LIBOCAS_CALLOC(BufSize,sizeof(double));
+  if(diag_H == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  output = (double*)LIBOCAS_CALLOC(nData*nY,sizeof(double));
+  if(output == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  old_output = (double*)LIBOCAS_CALLOC(nData*nY,sizeof(double));
+  if(old_output == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  /* auxciliary variables used in the linesearch */
+  A = (double*)LIBOCAS_CALLOC(nData*nY,sizeof(double));
+  if(A == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  B = (double*)LIBOCAS_CALLOC(nData*nY,sizeof(double));
+  if(B == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  theta = (double*)LIBOCAS_CALLOC(nY,sizeof(double));
+  if(theta == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  sortedA = (double*)LIBOCAS_CALLOC(nY,sizeof(double));
+  if(sortedA == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  Theta = (double*)LIBOCAS_CALLOC(nData*nY,sizeof(double));
+  if(Theta == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  Add = (double*)LIBOCAS_CALLOC(nData*nY,sizeof(double));
+  if(Add == NULL)
+  {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+  }
+
+  /* Set initial values*/
+  ocas.nCutPlanes = 0;
+  ocas.exitflag = 0;
+  ocas.nIter = 0;
+  ocas.Q_D = 0;
+  ocas.trn_err = nData;
+  R = (double)nData;
+  sq_norm_W = 0;
+  element_b = (double)nData;
+  ocas.Q_P = 0.5*sq_norm_W + C*R;
+
+  /* initial cutting plane */
+  for(i=0; i < nData; i++)
+  {
+    y2 = (uint32_t)data_y[i]-1;
+
+    if(y2 > 0)
+      new_cut[i] = 0;
+    else
+      new_cut[i] = 1;
+      
+  }
+
+  ocas.ocas_time = get_time() - ocas_start_time;
+
+  start_time = get_time();
+  ocas_print(ocas);
+  ocas.print_time += get_time() - start_time;
+  
+  /* main loop of the OCAS */
+  while( ocas.exitflag == 0 )
+  {
+    ocas.nIter++;
+
+    /* append a new cut to the buffer and update H */
+    b[ocas.nCutPlanes] = -(double)element_b;
+
+    start_time = get_time();
+
+    if(add_new_cut( &H[LIBOCAS_INDEX(0,ocas.nCutPlanes,BufSize)], new_cut, ocas.nCutPlanes, user_data ) != 0)
+    {
+	  ocas.exitflag=-2;
+	  goto cleanup;
+    }
+
+    ocas.add_time += get_time() - start_time;
+
+    /* copy newly appended row: H(ocas.nCutPlanes,ocas.nCutPlanes,1:ocas.nCutPlanes-1) = H(1:ocas.nCutPlanes-1:ocas.nCutPlanes)' */
+    diag_H[ocas.nCutPlanes] = H[LIBOCAS_INDEX(ocas.nCutPlanes,ocas.nCutPlanes,BufSize)];
+    for(i=0; i < ocas.nCutPlanes; i++) 
+    {
+      H[LIBOCAS_INDEX(ocas.nCutPlanes,i,BufSize)] = H[LIBOCAS_INDEX(i,ocas.nCutPlanes,BufSize)];
+    }
+
+    ocas.nCutPlanes++;    
+    
+    /* call inner QP solver */
+    start_time = get_time();
+
+    qp_exitflag = libqp_splx_solver(&get_col, diag_H, b, &C, I, &S, alpha,
+                                  ocas.nCutPlanes, QPSolverMaxIter, QPSolverTolAbs, QPSolverTolRel, -LIBOCAS_PLUS_INF,0);
+
+    ocas.qp_exitflag = qp_exitflag.exitflag;
+
+    ocas.qp_solver_time += get_time() - start_time;
+    ocas.Q_D = -qp_exitflag.QP;
+
+    ocas.nNZAlpha = 0;
+    for(i=0; i < ocas.nCutPlanes; i++) 
+      if( alpha[i] != 0) ocas.nNZAlpha++;
+
+    sq_norm_oldW = sq_norm_W;
+    start_time = get_time();
+    compute_W( &sq_norm_W, &dot_prod_WoldW, alpha, ocas.nCutPlanes, user_data );
+    ocas.w_time += get_time() - start_time;
+    
+    /* select a new cut */
+    switch( Method )
+    {
+      /* cutting plane algorithm implemented in SVMperf and BMRM */
+      case 0: 
+
+        start_time = get_time();
+        if( compute_output( output, user_data ) != 0)
+        {
+          ocas.exitflag=-2;
+          goto cleanup;
+        }
+        ocas.output_time += get_time()-start_time;
+
+        /* the following loop computes: */
+        element_b = 0.0;    /*  element_b = R(old_W) - g'*old_W */ 
+        R = 0;              /*  R(W) = sum_i max_y ( [[y != y_i]] + (w_y- w_y_i)'*x_i )    */
+        ocas.trn_err = 0;   /*  trn_err = sum_i [[y != y_i ]]                              */
+                            /* new_cut[i] = argmax_i ( [[y != y_i]] + (w_y- w_y_i)'*x_i )  */
+        for(i=0; i < nData; i++)
+        {
+          y2 = (uint32_t)data_y[i]-1;
+
+          for(xi=-LIBOCAS_PLUS_INF, y=0; y < nY; y++)
+          {
+            if(y2 != y && xi < output[LIBOCAS_INDEX(y,i,nY)])
+            {
+              xi = output[LIBOCAS_INDEX(y,i,nY)];
+              ypred = y;
+            }
+          }
+
+          if(xi >= output[LIBOCAS_INDEX(y2,i,nY)]) 
+            ocas.trn_err ++;
+
+          xi = LIBOCAS_MAX(0,xi+1-output[LIBOCAS_INDEX(y2,i,nY)]);
+          R += xi;
+          if(xi > 0)
+          {
+            element_b++;
+            new_cut[i] = ypred;
+          }
+          else
+            new_cut[i] = y2;
+        }
+
+        ocas.Q_P = 0.5*sq_norm_W + C*R;
+
+        ocas.ocas_time = get_time() - ocas_start_time;
+
+        start_time = get_time();
+        ocas_print(ocas);
+        ocas.print_time += get_time() - start_time;
+
+        break;
+
+      /* The OCAS solver */
+      case 1:
+        memcpy( old_output, output, sizeof(double)*nData*nY );
+
+        start_time = get_time();
+        if( compute_output( output, user_data ) != 0)
+        {
+          ocas.exitflag=-2;
+          goto cleanup;
+        }
+        ocas.output_time += get_time()-start_time;
+
+        A0 = sq_norm_W - 2*dot_prod_WoldW + sq_norm_oldW;
+        B0 = dot_prod_WoldW - sq_norm_oldW;
+        
+        for(i=0; i < nData; i++)
+        {
+          y2 = (uint32_t)data_y[i]-1;
+
+          for(y=0; y < nY; y++)
+          {
+            A[LIBOCAS_INDEX(y,i,nY)] = C*(output[LIBOCAS_INDEX(y,i,nY)] - old_output[LIBOCAS_INDEX(y,i,nY)]
+                                       + old_output[LIBOCAS_INDEX(y2,i,nY)] - output[LIBOCAS_INDEX(y2,i,nY)]);
+            B[LIBOCAS_INDEX(y,i,nY)] = C*(old_output[LIBOCAS_INDEX(y,i,nY)] - old_output[LIBOCAS_INDEX(y2,i,nY)]
+                                       + (double)(y != y2));
+          }
+        }
+
+        /* linesearch */
+/*      new_x = msvm_linesearch_mex(A0,B0,AA*C,BB*C);*/
+        
+        grad_sum = B0;
+        cnt1 = 0;
+        cnt2 = 0;
+        for(i=0; i < nData; i++)
+        {
+          findactive(theta,sortedA,&nSortedA,&A[i*nY],&B[i*nY],nY,sort);
+        
+          idx = 0;
+          while( idx < nSortedA-1 && theta[idx] < 0 )
+            idx++;
+    
+          grad_sum += sortedA[idx];
+    
+          for(j=idx; j < nSortedA-1; j++)
+          {
+            Theta[cnt1] = theta[j];
+            cnt1++;
+          }
+
+          for(j=idx+1; j < nSortedA; j++)
+          {
+            Add[cnt2] = -sortedA[j-1]+sortedA[j];
+            cnt2++;
+          }
+        }
+
+        start_time = get_time();
+        sort(Theta,Add,cnt1);
+        ocas.sort_time += get_time() - start_time;
+
+        grad = grad_sum;
+        if(grad >= 0)
+        {
+          min_x = 0;
+        }
+        else
+        {
+          old_x = 0;
+          old_grad = grad;
+
+          for(i=0; i < cnt1; i++)
+          {   
+            x = Theta[i];
+    
+            grad = x*A0 + grad_sum;
+    
+            if(grad >=0)
+            {
+        
+              min_x = (grad*old_x - old_grad*x)/(grad - old_grad);
+                        
+              break;
+            }
+            else
+            {
+              grad_sum = grad_sum + Add[i];
+        
+              grad = x*A0 + grad_sum;
+              if( grad >= 0)
+              {
+                min_x = x;
+                break;
+              }
+            }
+            
+            old_grad = grad;
+            old_x = x;
+          }
+        }
+        /* end of the linesearch which outputs min_x */
+
+        t = min_x;
+        t1 = t;                /* new (best so far) W */
+        t2 = t+(1.0-t)*LAMBDA;   /* new cutting plane */
+        /*        t2 = t+(1.0-t)/10.0;    */
+
+        /* update W to be the best so far solution */
+        sq_norm_W = update_W( t1, user_data );
+        
+        /* the following code  computes a new cutting plane: */
+        element_b = 0.0;    /*  element_b = R(old_W) - g'*old_W */ 
+                            /* new_cut[i] = argmax_i ( [[y != y_i]] + (w_y- w_y_i)'*x_i )  */
+        for(i=0; i < nData; i++)
+        {
+          y2 = (uint32_t)data_y[i]-1;
+
+          for(xi=-LIBOCAS_PLUS_INF, y=0; y < nY; y++)
+          {
+            tmp = old_output[LIBOCAS_INDEX(y,i,nY)]*(1-t2) + t2*output[LIBOCAS_INDEX(y,i,nY)];
+            if(y2 != y && xi < tmp)
+            {
+              xi = tmp;
+              ypred = y;
+            }
+          }
+
+          tmp = old_output[LIBOCAS_INDEX(y2,i,nY)]*(1-t2) + t2*output[LIBOCAS_INDEX(y2,i,nY)];
+          xi = LIBOCAS_MAX(0,xi+1-tmp);
+          if(xi > 0)
+          {
+            element_b++;
+            new_cut[i] = ypred;
+          }
+          else
+            new_cut[i] = y2;
+        }
+
+        /* compute Risk, class. error and update outputs to correspond to the new W */
+        ocas.trn_err = 0;   /*  trn_err = sum_i [[y != y_i ]]                       */
+        R = 0;
+        for(i=0; i < nData; i++)
+        {
+          y2 = (uint32_t)data_y[i]-1;
+          
+          for(tmp=-LIBOCAS_PLUS_INF, y=0; y < nY; y++)
+          {
+            output[LIBOCAS_INDEX(y,i,nY)] = old_output[LIBOCAS_INDEX(y,i,nY)]*(1-t1) + t1*output[LIBOCAS_INDEX(y,i,nY)];
+            
+            if(y2 != y && tmp < output[LIBOCAS_INDEX(y,i,nY)])
+            {
+              ypred = y;
+              tmp = output[LIBOCAS_INDEX(y,i,nY)];
+            }
+          }
+
+          R += LIBOCAS_MAX(0,1+tmp - output[LIBOCAS_INDEX(y2,i,nY)]);
+          if( tmp >= output[LIBOCAS_INDEX(y2,i,nY)])
+            ocas.trn_err ++;
+        }
+
+        ocas.Q_P = 0.5*sq_norm_W + C*R;
+
+
+        /* get time and print status */
+        ocas.ocas_time = get_time() - ocas_start_time;
+
+        start_time = get_time();
+        ocas_print(ocas);
+        ocas.print_time += get_time() - start_time;
+
+        break;
+
+    }
+
+    /* Stopping conditions */
+    if( ocas.Q_P - ocas.Q_D <= TolRel*LIBOCAS_ABS(ocas.Q_P)) ocas.exitflag = 1; 
+    if( ocas.Q_P - ocas.Q_D <= TolAbs) ocas.exitflag = 2; 
+    if( ocas.Q_P <= QPBound) ocas.exitflag = 3; 
+    if( ocas.ocas_time >= MaxTime) ocas.exitflag = 4; 
+    if(ocas.nCutPlanes >= BufSize) ocas.exitflag = -1;
+         
+  } /* end of the main loop */
+
+cleanup:
+
+  LIBOCAS_FREE(H);
+  LIBOCAS_FREE(b);
+  LIBOCAS_FREE(alpha);
+  LIBOCAS_FREE(new_cut);
+  LIBOCAS_FREE(I);
+  LIBOCAS_FREE(diag_H);
+  LIBOCAS_FREE(output);
+  LIBOCAS_FREE(old_output);
+  LIBOCAS_FREE(A);
+  LIBOCAS_FREE(B);
+  LIBOCAS_FREE(theta);
+  LIBOCAS_FREE(Theta);
+  LIBOCAS_FREE(sortedA);
+  LIBOCAS_FREE(Add);
+
+  ocas.ocas_time = get_time() - ocas_start_time;
+
+  return(ocas);
+}
+
+
+
diff --git a/libocas.h b/libocas.h
new file mode 100644
index 0000000..f1b45ed
--- /dev/null
+++ b/libocas.h
@@ -0,0 +1,123 @@
+/*-----------------------------------------------------------------------
+ * libocas.h: Implementation of the OCAS solver for training 
+ *            linear SVM classifiers.
+ *  
+ * Copyright (C) 2008, 2009 Vojtech Franc, xfrancv at cmp.felk.cvut.cz
+ *                          Soeren Sonnenburg, soeren.sonnenburg at first.fraunhofer.de
+ *
+ * This program is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU General Public 
+ * License as published by the Free Software Foundation; 
+ *-------------------------------------------------------------------- */
+
+
+#ifndef libocas_h
+#define libocas_h
+
+#include <stdint.h>
+#include "libqp.h"
+
+#ifdef LIBOCAS_MATLAB
+
+#include "mex.h"
+#define LIBQP_MATLAB
+#define LIBOCAS_PLUS_INF mxGetInf()
+#define LIBOCAS_CALLOC(x,y) mxCalloc(x,y)
+#define LIBOCAS_FREE(x) mxFree(x)
+
+#else
+
+#define LIBOCAS_PLUS_INF (-log(0.0))
+#define LIBOCAS_CALLOC(x,y) calloc(x,y)
+#define LIBOCAS_FREE(x) free(x)
+
+#endif
+
+#define LIBOCAS_INDEX(ROW,COL,NUM_ROWS) ((COL)*(NUM_ROWS)+(ROW))
+#define LIBOCAS_MIN(A,B) ((A) > (B) ? (B) : (A))
+#define LIBOCAS_MAX(A,B) ((A) < (B) ? (B) : (A))
+#define LIBOCAS_ABS(A) ((A) < 0 ? -(A) : (A))
+
+
+
+typedef struct {
+  uint32_t nIter;        /* number of iterations */
+  uint32_t nCutPlanes;   /* number of cutitng buffered planes */
+  uint32_t nNZAlpha;     /* number of non-zero Lagrangeans (effective number of CPs) */
+  uint32_t trn_err;      /* number of training errors */
+  double Q_P;            /* primal objective value */
+  double Q_D;            /* dual objective value */
+  double output_time;    /* time spent in computing outputs */
+  double sort_time;      /* time spent in sorting */
+  double add_time;       /* time spent in adding examples to compute cutting planes */
+  double w_time;         /* time spent in computing parameter vector  */
+  double qp_solver_time; /* time spent in inner QP solver  */
+  double ocas_time;      /* total time spent in svm_ocas_solver */
+  double print_time;     /* time spent in ocas_print function */
+  int8_t qp_exitflag;    /* exitflag from the last call of the inner QP solver */
+  int8_t exitflag;       /*  1 .. ocas.Q_P - ocas.Q_D <= TolRel*ABS(ocas.Q_P) 
+                             2 .. ocas.Q_P - ocas.Q_D <= TolAbs 
+                             3 .. ocas.Q_P <= QPBound
+                             4 .. optimization time >= MaxTime 
+                            -1 .. ocas.nCutPlanes >= BufSize 
+                            -2 .. not enough memory for the solver */
+} ocas_return_value_T;
+
+/* binary linear SVM solver */
+ocas_return_value_T svm_ocas_solver(
+         double C,            /* regularizarion constant */
+         uint32_t nData,      /* number of exmaples */
+         double TolRel,       /* halts if 1-Q_P/Q_D <= TolRel */
+         double TolAbs,       /* halts if Q_P-Q_D <= TolRel */
+         double QPBound,      /* halts if QP <= QPBound */
+         double MaxTime,      /* maximal time in seconds spent in optmization */
+         uint32_t BufSize,    /* maximal number of buffered cutting planes  */
+         uint8_t Method,      /* 0..standard CP (SVM-Perf,BMRM), 1..OCAS */
+         void (*compute_W)(double*, double*, double*, uint32_t, void*),
+         double (*update_W)(double, void*),
+         int (*add_new_cut)(double*, uint32_t*, uint32_t, uint32_t, void*),
+         int (*compute_output)( double*, void* ),
+         int (*sort)(double*, double*, uint32_t),
+         void (*ocas_print)(ocas_return_value_T),
+         void* user_data);
+
+/* binary linear SVM solver which allows using different C for each example*/
+ocas_return_value_T svm_ocas_solver_difC(
+         double *C,           /* regularizarion constants for each example */
+         uint32_t nData,      /* number of exmaples */
+         double TolRel,       /* halts if 1-Q_P/Q_D <= TolRel */
+         double TolAbs,       /* halts if Q_P-Q_D <= TolRel */
+         double QPBound,      /* halts if QP <= QPBound */
+         double MaxTime,      /* maximal time in seconds spent in optmization */
+         uint32_t BufSize,    /* maximal number of buffered cutting planes  */
+         uint8_t Method,      /* 0..standard CP (SVM-Perf,BMRM), 1..OCAS */
+         void (*compute_W)(double*, double*, double*, uint32_t, void*),
+         double (*update_W)(double, void*),
+         int (*add_new_cut)(double*, uint32_t*, uint32_t, uint32_t, void*),
+         int (*compute_output)( double*, void* ),
+         int (*sort)(double*, double*, uint32_t),
+         void (*ocas_print)(ocas_return_value_T),
+         void* user_data);
+
+/* multi-class (Singer-Crammer formulation) linear SVM solver */
+ocas_return_value_T msvm_ocas_solver(
+            double C,
+            double *data_y,
+            uint32_t nY,
+            uint32_t nData, 
+            double TolRel,
+            double TolAbs,
+            double QPBound,
+            double MaxTime,
+            uint32_t _BufSize,
+            uint8_t Method,
+            void (*compute_W)(double*, double*, double*, uint32_t, void*),
+            double (*update_W)(double, void*),
+            int (*add_new_cut)(double*, uint32_t*, uint32_t, void*),
+            int (*compute_output)(double*, void* ),
+            int (*sort)(double*, double*, uint32_t),
+			void (*ocas_print)(ocas_return_value_T),
+			void* user_data);
+
+#endif /* libocas_h */
+
diff --git a/libocas_test.m b/libocas_test.m
new file mode 100644
index 0000000..4e0847e
--- /dev/null
+++ b/libocas_test.m
@@ -0,0 +1,171 @@
+% This script tests functionality of SVMOCAS and MSVMOCAS solvers.
+%
+% It runs the solvers on example data and compares results to solutions
+% stored in reference files. 
+%  
+
+% two-class problem 
+BinaryTrnFile = './data/riply_trn.light';
+
+% multi-class problem
+MulticlassTrnFile = './data/example4_train.light';
+
+% file to store/load reference solution
+ReferenceFile = './data/refernce_solution';
+
+% if 1 save results to reference files else compares the results to the
+% reference solutions
+CREATE_REFERNCE_FILES = 0;
+
+% Solver options
+opt.C = 1;
+opt.Method = 1;
+opt.TolRel = 0.01;
+opt.TolAbs = 0;
+opt.QPBound = 0;
+opt.BufSize = 2000;
+opt.MaxTime = inf;
+opt.X0 = 1;
+opt.verb = 0;
+
+fprintf('Training binary SVM classifier by SVMOCAS...');
+[bin.W,bin.W0,bin.stat] = svmocas(BinaryTrnFile,opt.X0,opt.C,opt.Method,opt.TolRel,...
+                             opt.TolAbs,opt.QPBound,opt.BufSize,inf,opt.MaxTime,opt.verb);
+fprintf('done.\n');
+
+fprintf('Training multi-class SVM classifier by MSVMOCAS...');
+[multi.W,multi.stat] = msvmocas(MulticlassTrnFile,opt.C,opt.Method,opt.TolRel,...
+                             opt.TolAbs,opt.QPBound,opt.BufSize,inf,opt.MaxTime,opt.verb);
+fprintf('done.\n');
+
+
+if CREATE_REFERNCE_FILES == 1,    
+    fprintf('Saving reference solutions to %s\n', ReferenceFile);
+    save(ReferenceFile,'bin','multi');    
+else
+    ref = load(ReferenceFile);    
+        
+    test(1).dif = sum(abs(bin.W - ref.bin.W)+abs(bin.W0-ref.bin.W0));
+    test(1).name = 'sum(|W-ref.W| + |W0-ref.W0])';
+    test(2).dif = abs(bin.stat.Q_P - ref.bin.stat.Q_P);
+    test(2).name = 'PrimalVal - ref.PrimalVal   ';
+    test(3).dif = abs(bin.stat.Q_D - ref.bin.stat.Q_D);
+    test(3).name = 'DualVal - ref.DualVal       ';
+    
+    fprintf('\nSVMOCAS (solver for binary classifiation problems):\n');
+    for i=1:length(test)
+        fprintf('   %s = %.20f ... ',test(i).name,test(i).dif);
+        if test(i).dif == 0
+            fprintf('SOLUTIONS EQUAL - OK\n');
+        else
+            fprintf('SOLUTION DIFFERS\n');
+        end
+    end        
+
+    test(1).dif = sum(sum(abs(multi.W - ref.multi.W)));
+    test(1).name = 'sum(|W-ref.W|)           ';
+    test(2).dif = abs(multi.stat.Q_P - ref.multi.stat.Q_P);
+    test(2).name = 'PrimalVal - ref.PrimalVal';
+    test(3).dif = abs(multi.stat.Q_D - ref.multi.stat.Q_D);
+    test(3).name = 'DualVal - ref.DualVal    ';
+    
+    fprintf('\nMSVMOCAS: (solver for multi-class problems\n');
+    for i=1:length(test)
+        fprintf('   %s = %.20f ... ',test(i).name, test(i).dif);
+        if test(i).dif == 0
+            fprintf('SOLUTIONS EQUAL - OK\n');
+        else
+            fprintf('SOLUTION DIFFERS\n');
+        end
+    end        
+    
+    
+end
+
+
+break;
+
+for i=1:length(DataSets)  
+   fprintf('\nDataset: %s\n', DataSets{i});
+   
+   [exp{i}.W,exp{i}.W0,exp{i}.stat] = svmocas(DataSets{i},opt.X0,opt.C,opt.Method,...
+                opt.TolRel,opt.TolAbs,opt.QPBound,opt.BufSize,inf,opt.MaxTime);  
+end
+
+fprintf('\n\nRESULTS SUMMARY\n================================\n\n');
+for i=1:length(DataSets)
+   fprintf('\nDataset: %s\n--------------------------------\n', DataSets{i});
+   
+   % remove suffix
+   sol_fname = DataSets{i};
+   idx = findstr(sol_fname,'.');
+   sol_fname = sol_fname(1:idx(end)-1);
+         
+   sol_fname = [sol_fname '_ocas_C' num2str(opt.C) '_solution.mat'];
+   if SAVE_AS_REFERENCE,
+      if exist(sol_fname)
+         fprintf('Solution file %s already exists.\n', sol_fname);
+         error('Erase the file or set SAVE_AS_REFERENCE = 0 and run the test again.');
+      else
+          fprintf('Saving solution to %s ...', sol_fname);
+          ref_sol = exp{i};
+          ref_opt = opt;
+          save(sol_fname,'ref_sol','ref_opt');
+          fprintf('done.\n');      
+          ref_sol = [];
+      end
+   else
+      if exist(sol_fname)
+          load(sol_fname,'ref_sol','ref_opt');          
+
+          fprintf('\nReference solution\n');
+          fprintf('file: %s\n', sol_fname);
+          fprintf(['settings: C: %f, Method: %d, TolRel: %f, TolAbs: %f, ' ...
+                   'QPBound: %f, BufSize: %d, MaxTime: %f, X0: %f \n'], ...
+                  ref_opt.C, ref_opt.Method, ref_opt.TolRel, ref_opt.TolAbs, ...
+                  ref_opt.QPBound, ref_opt.BufSize, ref_opt.MaxTime, ref_opt.X0);
+          fprintf(['solution: QP: %.10f, QD: %.10f, nIter: %d, nCutPlanes: %d, '...
+                'exitflag: %d, ocas_time: %f, total_time: %f\n'],...
+                  ref_sol.stat.Q_P, ref_sol.stat.Q_D, ref_sol.stat.nIter, ref_sol.stat.nCutPlanes, ...
+                  ref_sol.stat.exitflag, ref_sol.stat.ocas_time, ref_sol.stat.total_time);
+      end       
+   end
+   
+   fprintf('\nCurrent solution \n');
+   fprintf(['settings: C: %f, Method: %d, TolRel: %f, TolAbs: %f, ' ...
+                   'QPBound: %f, BufSize: %d, MaxTime: %f, X0: %f \n'], ...
+                  opt.C, opt.Method, opt.TolRel, opt.TolAbs, ...
+                  opt.QPBound, opt.BufSize, opt.MaxTime, opt.X0);
+   fprintf(['solution: QP: %.10f, QD: %.10f, nIter: %d, nCutPlanes: %d, '...
+            'exitflag: %d, ocas_time: %f, total_time: %f\n'],...
+           exp{i}.stat.Q_P, exp{i}.stat.Q_D, exp{i}.stat.nIter, exp{i}.stat.nCutPlanes, ...
+           exp{i}.stat.exitflag, exp{i}.stat.ocas_time, exp{i}.stat.total_time);
+   
+   if ~isempty(ref_sol)
+       opt_dif = any([opt.Method~=ref_opt.Method opt.C~=ref_opt.C opt.TolRel ~= ref_opt.TolRel ...
+                      opt.TolAbs~=ref_opt.TolAbs opt.QPBound~=ref_opt.QPBound ...
+                      opt.BufSize~=ref_opt.BufSize opt.MaxTime~=ref_opt.MaxTime opt.X0~=ref_opt.X0]);
+       sol_dif = max(max(abs(exp{i}.W-ref_sol.W)),abs(exp{i}.W0-ref_sol.W0));
+       fprintf('\nSolution difference: max(abs(ref.W-W)) = %f. Solutions are ', sol_dif); 
+       if ~sol_dif 
+           fprintf('EQUAL.\n'); 
+       else
+           fprintf('DIFFERENT.\n');
+       end
+       fprintf('Current and refrence settings are ');
+       if ~opt_dif 
+           fprintf('EQUAL.\n'); 
+       else
+           fprintf('DIFFERENT.\n');
+       end
+       fprintf('Comparison result: ');
+       if sol_dif && ~opt_dif ,
+           fprintf('THIS IS NOT GOOD ... BUT DON''T PANIC.\n');
+       elseif ~sol_dif && ~opt_dif
+           fprintf('OK.\n');
+       elseif opt_dif
+           fprintf('UNDECIDED since options are different.\n');
+       end
+       
+   end
+end
diff --git a/libqp.h b/libqp.h
new file mode 100644
index 0000000..e3c0bf2
--- /dev/null
+++ b/libqp.h
@@ -0,0 +1,86 @@
+/*-----------------------------------------------------------------------
+ * libqp.h: Library for Quadratic Programming optimization.
+ *
+ * The library provides two solvers:
+ *   1. Solver for QP task with simplex constraints.
+ *      See function ./lib/libqp_splx.c for definition of the QP task. 
+ *
+ *   2. Solver for QP task with box constraints and a single linear 
+ *      equality constraint. 
+ *      See function ./lib/libqp_gsmo.c for definiton of the QP task. 
+ *  
+ * Copyright (C) 2006-2008 Vojtech Franc, xfrancv at cmp.felk.cvut.cz
+ * Center for Machine Perception, CTU FEL Prague
+ *
+ * This program is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU General Public 
+ * License as published by the Free Software Foundation; 
+ * Version 3, 29 June 2007
+ *-------------------------------------------------------------------- */
+
+#ifndef libqp_h
+#define libqp_h
+
+#include <stdint.h>
+#include <math.h>
+
+#ifdef LIBQP_MATLAB
+#include "mex.h"
+#define LIBQP_PLUS_INF mxGetInf()
+#define LIBQP_CALLOC(x,y) mxCalloc(x,y)
+#define LIBQP_FREE(x) mxFree(x)
+#else
+#define LIBQP_PLUS_INF (-log(0.0))
+#define LIBQP_CALLOC(x,y) calloc(x,y)
+#define LIBQP_FREE(x) free(x)
+#endif
+
+#define LIBQP_INDEX(ROW,COL,NUM_ROWS) ((COL)*(NUM_ROWS)+(ROW))
+#define LIBQP_MIN(A,B) ((A) > (B) ? (B) : (A))
+#define LIBQP_MAX(A,B) ((A) < (B) ? (B) : (A))
+#define LIBQP_ABS(A) ((A) < 0 ? -(A) : (A))
+
+/* QP solver return value */
+typedef struct {
+  uint32_t nIter;       /* number of iterations */ 
+  double QP;            /* primal objective value */ 
+  double QD;            /* dual objective value */  
+  int8_t exitflag;      /* -1 ... not enough memory 
+                            0 ... nIter >= MaxIter 
+                            1 ... QP - QD <= TolRel*ABS(QP)
+                            2 ... QP - QD <= TolAbs
+                            3 ... QP <= QP_TH
+                            4 ... eps-KKT conditions satisfied */
+} libqp_state_T; 
+
+/* QP solver for tasks with simplex constraints */
+libqp_state_T libqp_splx_solver(const double* (*get_col)(uint32_t),
+                  double *diag_H,
+                  double *f,
+                  double *b,
+                  uint32_t *I,
+                  uint8_t *S,
+                  double *x,
+                  uint32_t n,
+                  uint32_t MaxIter,
+                  double TolAbs,
+                  double TolRel,
+                  double QP_TH,
+                  void (*print_state)(libqp_state_T state));
+
+/* Generalized SMO algorithm */
+libqp_state_T libqp_gsmo_solver(const double* (*get_col)(uint32_t),
+            double *diag_H,
+            double *f,
+            double *a,
+            double b,
+            double *LB,
+            double *UB,
+            double *x,
+            uint32_t n,
+            uint32_t MaxIter,
+            double TolKKT,
+            void (*print_state)(libqp_state_T state));
+
+
+#endif /* libqp_h */
diff --git a/libqp_splx.c b/libqp_splx.c
new file mode 100644
index 0000000..37a36eb
--- /dev/null
+++ b/libqp_splx.c
@@ -0,0 +1,407 @@
+/*-----------------------------------------------------------------------
+ * libqp_splx.c: solver for Quadratic Programming task with 
+ * simplex constraints.
+ *
+ * DESCRIPTION
+ *  The library provides function which solves the following instance of
+ *  a convex Quadratic Programmin task:
+ *  
+ *   min QP(x):= 0.5*x'*H*x + f'*x  
+ *    x
+ *
+ * subject to:   
+ *   sum_{i in I_k} x[i] == b[k]  for all k such that S[k] == 0 
+ *   sum_{i in I_k} x[i] <= b[k]  for all k such that S[k] == 1
+ *                             x(i) >= 0 for all i=1:n
+ *   
+ *  where I_k = { i | I[i] == k}, k={1,...,m}.
+ *
+ * A precision of the found solution is controled by the input argumens
+ * MaxIter, TolAbs, QP_TH and MaxIter which define the stopping conditions:
+ * 
+ *  nIter >= MaxIter     ->  exitflag = 0   Number of iterations
+ *  QP-QD <= TolAbs      ->  exitflag = 1   Abs. tolerance (duality gap)
+ *  QP-QD <= QP*TolRel   ->  exitflag = 2   Relative tolerance
+ *  QP <= QP_TH          ->  exitflag = 3   Threshold on objective value
+ *
+ * where QP and QD are primal respectively dual objective values.
+ *
+ * INPUT ARGUMENTS
+ *  get_col   function which returns pointer to the i-th column of H.
+ *  diag_H [double n x 1] vector containing values on the diagonal of H.
+ *  f [double n x 1] vector.
+ *  b [double n x 1] vector of positive numbers.
+ *  I [uint16_T n x 1] vector containing numbers 1...m. 
+ *  S [uint8_T n x 1] vector containing numbers 0 and 1.
+ *  x [double n x 1] solution vector; must be feasible.
+ *  n [uint32_t 1 x 1] dimension of H.
+ *  MaxIter [uint32_t 1 x 1] max number of iterations.
+ *  TolAbs [double 1 x 1] Absolute tolerance.
+ *  TolRel [double 1 x 1] Relative tolerance.
+ *  QP_TH  [double 1 x 1] Threshold on the primal value.
+ *  print_state  print function; if == NULL it is not called.
+ *
+ * RETURN VALUE
+ *  structure [libqp_state_T] 
+ *  .QP [1 x 1] Primal objective value.
+ *  .QD [1 x 1] Dual objective value.
+ *  .nIter [1 x 1] Number of iterations.
+ *  .exitflag [1 x 1] Indicates which stopping condition was used:
+ *    -1  ... Not enough memory.
+ *     0  ... Maximal number of iteations reached: nIter >= MaxIter.
+ *     1  ... Relarive tolerance reached: QP-QD <= abs(QP)*TolRel
+ *     2  ... Absolute tolerance reached: QP-QD <= TolAbs
+ *     3  ... Objective value reached threshold: QP <= QP_TH.
+ *
+ * REFERENCE
+ *  The algorithm is described in:
+ *  V. Franc, V. Hlavac. A Novel Algorithm for Learning Support Vector Machines
+ *   with Structured Output Spaces. Research Report K333 22/06, CTU-CMP-2006-04. 
+ *   May, 2006. ftp://cmp.felk.cvut.cz/pub/cmp/articles/franc/Franc-TR-2006-04.ps
+ *
+ * Copyright (C) 2006-2008 Vojtech Franc, xfrancv at cmp.felk.cvut.cz
+ * Center for Machine Perception, CTU FEL Prague
+ *
+ * This program is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU General Public 
+ * License as published by the Free Software Foundation; 
+ * Version 3, 29 June 2007
+ *-------------------------------------------------------------------- */
+
+#include <math.h>
+#include <stdlib.h>
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <limits.h>
+
+#include "libqp.h"
+
+libqp_state_T libqp_splx_solver(const double* (*get_col)(uint32_t),
+                  double *diag_H,
+                  double *f,
+                  double *b,
+                  uint32_t *I,
+                  uint8_t *S,
+                  double *x,
+                  uint32_t n,
+                  uint32_t MaxIter,
+                  double TolAbs,
+                  double TolRel,
+                  double QP_TH,
+				  void (*print_state)(libqp_state_T state))
+{
+  double *d;
+  double *col_u, *col_v;
+  double *x_neq;
+  double tmp;
+  double improv;
+  double tmp_num;
+  double tmp_den=0;
+  double tau=0;
+  double delta;
+  uint32_t *inx;
+  uint32_t *nk;
+  uint32_t m;
+  uint32_t u=0;
+  uint32_t v=0;
+  uint32_t k;
+  uint32_t i, j;
+  libqp_state_T state;
+
+  
+  /* ------------------------------------------------------------ 
+    Initialization                                               
+  ------------------------------------------------------------ */
+  state.nIter = 0;
+  state.QP = LIBQP_PLUS_INF;
+  state.QD = -LIBQP_PLUS_INF;
+  state.exitflag = 100;
+
+  inx=NULL;
+  nk=NULL;
+  d=NULL;
+  x_neq = NULL;
+
+  /* count number of constraints */
+  for( i=0, m=0; i < n; i++ ) 
+    m = LIBQP_MAX(m,I[i]);
+
+  /* auxciliary variables for tranforming equalities to inequalities */
+  x_neq = (double*) LIBQP_CALLOC(m, sizeof(double));
+  if( x_neq == NULL )
+  {
+	  state.exitflag=-1;
+	  goto cleanup;
+  }
+
+  /* inx is translation table between variable index i and its contraint */
+  inx = (uint32_t*) LIBQP_CALLOC(m*n, sizeof(uint32_t));
+  if( inx == NULL )
+  {
+	  state.exitflag=-1;
+	  goto cleanup;
+  }
+
+  /* nk is the number of variables coupled by i-th linear constraint */
+  nk = (uint32_t*) LIBQP_CALLOC(m, sizeof(uint32_t));
+  if( nk == NULL )
+  {
+	  state.exitflag=-1;
+	  goto cleanup;
+  }
+
+  /* setup auxciliary variables */
+  for( i=0; i < m; i++ ) 
+    x_neq[i] = b[i];
+
+
+  /* create inx and nk */
+  for( i=0; i < n; i++ ) {
+     k = I[i]-1;
+     inx[LIBQP_INDEX(nk[k],k,n)] = i;
+     nk[k]++;     
+
+     if(S[k] != 0) 
+       x_neq[k] -= x[i];
+  }
+    
+  /* d = H*x + f is gradient*/
+  d = (double*) LIBQP_CALLOC(n, sizeof(double));
+  if( d == NULL )
+  {
+	  state.exitflag=-1;
+	  goto cleanup;
+  }
+ 
+  /* compute gradient */
+  for( i=0; i < n; i++ ) 
+  {
+    d[i] += f[i];
+    if( x[i] > 0 ) {
+      col_u = (double*)get_col(i);      
+      for( j=0; j < n; j++ ) {
+          d[j] += col_u[j]*x[i];
+      }
+    }
+  }
+  
+  /* compute state.QP = 0.5*x'*(f+d);
+             state.QD = 0.5*x'*(f-d); */
+  for( i=0, state.QP = 0, state.QD=0; i < n; i++) 
+  {
+    state.QP += x[i]*(f[i]+d[i]);
+    state.QD += x[i]*(f[i]-d[i]);
+  }
+  state.QP = 0.5*state.QP;
+  state.QD = 0.5*state.QD;
+  
+  for( i=0; i < m; i++ ) 
+  {
+    for( j=0, tmp = LIBQP_PLUS_INF; j < nk[i]; j++ ) 
+      tmp = LIBQP_MIN(tmp, d[inx[LIBQP_INDEX(j,i,n)]]);
+
+    if(S[i] == 0) 
+      state.QD += b[i]*tmp;
+    else
+      state.QD += b[i]*LIBQP_MIN(tmp,0);
+  }
+  
+  /* print initial state */
+  if( print_state != NULL) 
+    print_state( state );
+
+  /* ------------------------------------------------------------ 
+    Main optimization loop 
+  ------------------------------------------------------------ */
+  while( state.exitflag == 100 ) 
+  {
+    state.nIter ++;
+
+    /* go over blocks of variables coupled by lin. constraint */
+    for( k=0; k < m; k++ ) 
+    {       
+        
+      /* compute u = argmin_{i in I_k} d[i] 
+             delta =  sum_{i in I_k} x[i]*d[i] - b*min_{i in I_k} */
+      for( j=0, tmp = LIBQP_PLUS_INF, delta = 0; j < nk[k]; j++ ) 
+      {
+        i = inx[LIBQP_INDEX(j,k,n)];
+        delta += x[i]*d[i];
+        if( tmp > d[i] ) {
+          tmp = d[i];
+          u = i;
+        }
+      }
+
+      if(S[k] != 0 && d[u] > 0) 
+        u = -1;
+      else
+        delta -= b[k]*d[u];
+            
+      /* if satisfied then k-th block of variables needs update */
+      if( delta > TolAbs/m && delta > TolRel*LIBQP_ABS(state.QP)/m) 
+      {         
+        /* for fixed u select v = argmax_{i in I_k} Improvement(i) */
+        if( u != -1 ) 
+        {
+          col_u = (double*)get_col(u);
+          improv = -LIBQP_PLUS_INF;
+          for( j=0; j < nk[k]; j++ ) 
+          {
+            i = inx[LIBQP_INDEX(j,k,n)];
+           
+            if(x[i] > 0 && i != u) 
+            {
+              tmp_num = x[i]*(d[i] - d[u]); 
+              tmp_den = x[i]*x[i]*(diag_H[u] - 2*col_u[i] + diag_H[i]);
+              if( tmp_den > 0 ) 
+              {
+                if( tmp_num < tmp_den ) 
+                  tmp = tmp_num*tmp_num / tmp_den;
+                else 
+                  tmp = tmp_num - 0.5 * tmp_den;
+                 
+                if( tmp > improv ) 
+                { 
+                  improv = tmp;
+                  tau = LIBQP_MIN(1,tmp_num/tmp_den);
+                  v = i;
+                } 
+              }
+            }
+          }
+
+          /* check if virtual variable can be for updated */
+          if(x_neq[k] > 0 && S[k] != 0) 
+          {
+            tmp_num = -x_neq[k]*d[u]; 
+            tmp_den = x_neq[k]*x_neq[k]*diag_H[u];
+            if( tmp_den > 0 ) 
+            {
+              if( tmp_num < tmp_den ) 
+                tmp = tmp_num*tmp_num / tmp_den;
+              else 
+                tmp = tmp_num - 0.5 * tmp_den;
+                 
+              if( tmp > improv ) 
+              { 
+                improv = tmp;
+                tau = LIBQP_MIN(1,tmp_num/tmp_den);
+                v = -1;
+              } 
+            }
+          }
+
+          /* minimize objective w.r.t variable u and v */
+          if(v != -1)
+          {
+            tmp = x[v]*tau;
+            x[u] += tmp;
+            x[v] -= tmp;
+
+            /* update d = H*x + f */
+            col_v = (double*)get_col(v);
+            for(i = 0; i < n; i++ )              
+              d[i] += tmp*(col_u[i]-col_v[i]);
+          }
+          else
+          {
+            tmp = x_neq[k]*tau;
+            x[u] += tmp;
+            x_neq[k] -= tmp;
+
+            /* update d = H*x + f */
+            for(i = 0; i < n; i++ )              
+              d[i] += tmp*col_u[i];
+          }
+        }
+        else
+        {
+          improv = -LIBQP_PLUS_INF;
+          for( j=0; j < nk[k]; j++ ) 
+          {
+            i = inx[LIBQP_INDEX(j,k,n)];
+           
+            if(x[i] > 0) 
+            {
+              tmp_num = x[i]*d[i]; 
+              tmp_den = x[i]*x[i]*diag_H[i];
+              if( tmp_den > 0 ) 
+              {
+                if( tmp_num < tmp_den ) 
+                  tmp = tmp_num*tmp_num / tmp_den;
+                else 
+                  tmp = tmp_num - 0.5 * tmp_den;
+                 
+                if( tmp > improv ) 
+                { 
+                  improv = tmp;
+                  tau = LIBQP_MIN(1,tmp_num/tmp_den);
+                  v = i;
+                } 
+              }
+            }
+          }
+
+          tmp = x[v]*tau;
+          x_neq[k] += tmp;
+          x[v] -= tmp;
+
+          /* update d = H*x + f */
+          col_v = (double*)get_col(v);
+          for(i = 0; i < n; i++ )              
+            d[i] -= tmp*col_v[i];
+        }
+
+        /* update objective value */
+        state.QP = state.QP - improv;
+      }
+    }
+    
+    /* Compute primal and dual objectives */
+    for( i=0, state.QP = 0, state.QD=0; i < n; i++) 
+    {
+       state.QP += x[i]*(f[i]+d[i]);
+       state.QD += x[i]*(f[i]-d[i]);
+    }
+    state.QP = 0.5*state.QP;
+    state.QD = 0.5*state.QD;
+
+    for( k=0; k < m; k++ ) 
+    { 
+      for( j=0,tmp = LIBQP_PLUS_INF; j < nk[k]; j++ ) {
+        i = inx[LIBQP_INDEX(j,k,n)];
+        tmp = LIBQP_MIN(tmp, d[i]);
+      }
+      
+      if(S[k] == 0) 
+        state.QD += b[k]*tmp;
+      else
+        state.QD += b[k]*LIBQP_MIN(tmp,0);
+    }
+
+    /* print state */
+    if( print_state != NULL) 
+      print_state( state );
+
+    /* check stopping conditions */
+    if(state.QP-state.QD <= LIBQP_ABS(state.QP)*TolRel ) state.exitflag = 1;
+    else if( state.QP-state.QD <= TolAbs ) state.exitflag = 2;
+    else if( state.QP <= QP_TH ) state.exitflag = 3;
+    else if( state.nIter >= MaxIter) state.exitflag = 0;
+  }
+
+  /*----------------------------------------------------------
+    Clean up
+  ---------------------------------------------------------- */
+cleanup:
+  LIBQP_FREE( d );
+  LIBQP_FREE( inx );
+  LIBQP_FREE( nk );
+  LIBQP_FREE( x_neq );
+  
+  return( state ); 
+}
+
+
diff --git a/linclass.c b/linclass.c
new file mode 100644
index 0000000..7cb99d5
--- /dev/null
+++ b/linclass.c
@@ -0,0 +1,632 @@
+/*-----------------------------------------------------------------------
+ * linclass.c: Implementation of linear classification rule classifying 
+ *  examples from the SVM^light format.
+ *   
+ * Copyright (C) 2008, 2009 Vojtech Franc, xfrancv at cmp.felk.cvut.cz
+ *                    Soeren Sonnenburg, soeren.sonnenburg at first.fraunhofer.de
+ *
+ * This program is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU General Public 
+ * License as published by the Free Software Foundation; 
+ *-------------------------------------------------------------------- */
+
+
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <math.h>
+#include <stdlib.h>
+#include <stdint.h>
+
+#include "lib_svmlight_format.h"
+#include "libocas.h"
+#include "version.h"
+
+#define MODELFILE_MAXLINELEN 1000000
+
+void print_usage(void)
+{
+  printf("LINCLASS: Predict labels by linear classication rule\n" 
+         "          " OCAS_VERSION "\n"
+         "\n"
+         "   usage: linclass [options] example_file model_file\n"
+         "\n"
+         "Arguments:\n"
+         "         example_file    text file with testing examples stored in SVM^light format\n"
+         "         model_file      text file which contains either binary (two-class) linear\n"
+         "                         rule f(x)=w'*x+w0 or multi-class rule f(x)=W'*x which are\n"
+         "                         produced by svmocas and msvmocas, respectively\n"
+         "Options:\n"
+         "         -e              prints classification error computed from predicted\n"
+         "                         labels and labels contained in the example_file.\n"
+         "         -h              this help\n"
+         "         -o output_file  save predictions to output_file rather than to stdout.\n"
+         "         -t [0,1]        output type: 0 .. predicted labels (default)\n"
+         "                                      1 .. discriminant values\n"
+         "         -v [0,1]        verbosity level (default 0).\n"
+         "\n"
+         "Examples\n"
+         "  Train SVM classifier from riply_trn.light with regularization constant C = 10,\n"
+         "  bias switched on, verbosity switched off and model saved to svmocas.model\n"
+         "    ./svmocas -c 10 -b 1 -v 0 riply_trn.light svmocas.model \n"
+         "\n"
+         "  Compute testing error of the classifier stored in svmocas.model using testing\n"
+         "  examples from riply_tst.light and save predicted labels to riply_tst.pred\n"
+         "    ./linclass -e -o riply_tst.pred riply_tst.light svmocas.model\n"
+         "\n"
+         );
+}
+
+int main(int argc, char *argv[])
+{
+  uint32_t i, j;
+  int len;
+  int recognized;
+  int exitflag = 0;
+  int verb;
+  int binary_problem;
+  int output_type;
+  int print_error;
+  char *line;
+
+  char *model_fname;
+  char *input_fname;
+  char *output_fname;
+  FILE *fid, *fout;
+  
+  double *feat_val;
+  uint32_t *feat_idx;
+
+  int go = 1;
+  char *endptr, *begptr;
+  int nLines = 0;
+  int nCols = 0, tmp_nCols;
+  double val;
+  double *W;
+  double W0;
+  uint32_t nY, nDim;
+
+  long line_cnt = 0;
+  int label, pred_label=0;
+  long max_dim = 0;
+  long nnzf;
+  double dfce, max_dfce;
+  long nErrors = 0;
+  long *nClassErrors, *nClass;
+
+  /* init */
+  fid = NULL;
+  fout = NULL;
+  feat_val = NULL;
+  feat_idx = NULL;
+  W = NULL;
+  W0 = 0;
+  output_fname = NULL;
+  nClassErrors = NULL;
+  nClass = NULL;
+
+  /* default setting of input arguments*/
+  verb = 0;
+  print_error = 0;
+  output_type = 0;  
+
+  /* Allocate memory */
+  line = calloc(MODELFILE_MAXLINELEN, sizeof(char));
+  if( line == NULL )
+  {
+    fprintf(stderr,"Not enough memmory to allocate line buffer.\n");
+    goto clean_up;
+  }
+
+  feat_idx = calloc(LIBSLF_MAXLINELEN, sizeof(uint32_t));
+  if( feat_idx == NULL )
+  {
+    fprintf(stderr,"Not enough memmory to allocate feat_idx.\n");
+    goto clean_up;
+  }
+
+  feat_val = calloc(LIBSLF_MAXLINELEN, sizeof(double));
+  if( feat_val == NULL )
+  {
+    fprintf(stderr,"Not enough memmory to allocate feat_val.\n");
+    goto clean_up;
+  }
+  
+
+  /*-----------------------------------------------------------
+    Process input arguments 
+  ------------------------------------------------------------*/
+  if(argc ==1 || strcmp(argv[1], "-h") == 0)  
+  {
+    print_usage();
+    goto clean_up;
+  }
+
+  if(argc < 2)
+  {
+    fprintf(stderr,"Not enough input arguments.\n\n");
+    goto clean_up;
+  }
+
+
+  for (i = 1; i < argc-2; i++)  
+  {
+    recognized = 0;
+    if (strcmp(argv[i], "-h") == 0)  
+    {
+      print_usage();
+      goto clean_up;
+    }
+
+    if (strcmp(argv[i], "-e") == 0)  
+    {
+      print_error = 1;
+      recognized = 1;
+      continue;
+    }
+
+    if (strcmp(argv[i], "-v") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -v\n"); 
+        goto clean_up;  
+      }
+      verb = atoi(argv[i+1]); 
+      if(verb < 0 || verb > 1)
+      {
+        fprintf(stderr,"A value after the argument -v must be either 0 or 1.\n"); 
+        goto clean_up;  
+      }
+        
+      i++;
+      recognized = 1;
+      continue;
+    }
+
+    if (strcmp(argv[i], "-t") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -t\n"); 
+        goto clean_up;  
+      }
+      output_type = atoi(argv[i+1]); 
+      if(output_type != 0 && output_type != 1)
+      {
+        fprintf(stderr,"A value after the argument -t must be either 0 or 1.\n"); 
+        goto clean_up;  
+      }
+        
+      i++;
+      recognized = 1;
+      continue;
+    }
+
+    if (strcmp(argv[i], "-o") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a string after argument -o\n"); 
+        goto clean_up;  
+      }
+
+      len = strlen(argv[i+1]);
+      output_fname = calloc(len+1,sizeof(char));
+      strcpy(output_fname, argv[i+1]);
+
+      i++;
+      recognized = 1;
+      continue;
+    }
+
+    if(recognized == 0)
+    {
+      fprintf(stderr,"Unknown input argument: %s\n", argv[i]);
+      goto clean_up;  
+    }
+        
+  }
+
+  len = strlen(argv[argc-2]);
+  input_fname = calloc(len+1,sizeof(char));
+  strcpy(input_fname, argv[argc-2]);
+
+  len = strlen(argv[argc-1]);
+  model_fname = calloc(len+1,sizeof(char));
+  strcpy(model_fname, argv[argc-1]);
+
+  if(verb)
+  {
+    printf("Verbosity: %d\n", verb);
+    printf("Output type: %d\n", output_type);
+    printf("Print error: %d\n", print_error);
+    printf("Example file: %s\n", input_fname);
+    printf("Model file: %s\n", model_fname);
+    if( output_fname != NULL)
+      printf("Output file: %s\n", output_fname);
+    else
+      printf("Output file: stdout\n");
+  }
+
+
+  /*----------------------------------------------------------------
+    Load classification rule which is either 
+     vector [nDim x 1] + bias [1x1]
+    or 
+     matrix [nDim x nY]
+  -------------------------------------------------------------------*/
+  
+  /* load W from model file */
+  fid = fopen(model_fname, "r");
+  if(fid == NULL) {
+    fprintf(stderr,"Cannot open model file.\n");
+    perror("fopen error ");
+    goto clean_up;
+  }
+  
+  if(verb)
+  {
+    printf("Analysing model file... ");
+    fflush(stdout);
+  }
+
+  /* read the first line */
+  if(fgets(line,LIBSLF_MAXLINELEN, fid) == NULL ) 
+  {
+    fprintf(stderr,"Empty example file.\n");
+    goto clean_up;
+  }
+  else
+  {
+    nLines = 1;
+    begptr = line;
+    while(1)
+    {
+      val = strtod(begptr, &endptr);
+
+      if(val == 0 && begptr == endptr)
+        break;
+
+      nCols++;
+      begptr = endptr;
+    }
+  }
+
+  go = 1;
+  while(go) 
+  {
+    begptr = line;
+
+    tmp_nCols = 0;
+    while(1)
+    {
+      val = strtod(begptr, &endptr);
+
+      if(val == 0 && begptr == endptr)
+        break;
+
+      tmp_nCols++;
+      begptr = endptr;
+    }
+    if( tmp_nCols != nCols)
+    {
+      fprintf(stderr,"Error: Model file contains lines with different number of colums.\n");
+      goto clean_up;
+    }
+
+    if(fgets(line,LIBSLF_MAXLINELEN, fid) == NULL ) 
+    {
+      go = 0;
+    }
+    else
+      nLines++;
+  }
+  
+  if(verb)
+    printf("done.\n"
+           "Number of lines: %d\n"
+           "Number of columns: %d\n",
+           nLines,nCols);
+
+  if(nCols == 1)
+  {
+    nY = 2;
+    nDim = nLines-1;
+    binary_problem = 1;
+
+    /* learned weight vector */
+    W = (double*)calloc(nDim,sizeof(double));
+    if(W == NULL)
+    {
+      fprintf(stderr,"Not enough memory for vector W.\n");
+      goto clean_up;
+    }
+
+    if(verb)
+    {
+      printf("Model file contains binary classification rule.\n");
+      printf("Reading model file...");
+    }
+
+    fseek(fid,0,SEEK_SET);
+    for(i=0; i <= nDim; i++)
+    {
+      if(fgets(line,LIBSLF_MAXLINELEN, fid) == NULL ) 
+      {
+        fprintf(stderr,"Model file corrupted.\n");
+        goto clean_up;
+      }
+
+      begptr = line;
+      val = strtod(begptr, &endptr);
+
+      if(val == 0 && begptr == endptr)
+      {
+        fprintf(stderr,"Model file corrupted.\n");
+        goto clean_up;
+      }
+
+      if(i < nDim)
+        W[i] = val;
+      else
+        W0 = val;
+    }
+
+    if(verb)
+      printf("done.\n");
+
+  }
+  else
+  {
+    nY = nCols;
+    nDim = nLines;
+    binary_problem = 0;
+
+    /* learned weight vector */
+    W = (double*)calloc(nDim*nY,sizeof(double));
+    if(W == NULL)
+    {
+      fprintf(stderr,"Not enough memory for matrix W.\n");
+      goto clean_up;
+    }
+
+    if(verb)
+    {
+      printf("Model file contains multi-class classification rule.\n");
+      printf("Reading model file...");
+    }
+
+    fseek(fid,0,SEEK_SET);
+    for(i=0; i < nDim; i++)
+    {
+      if(fgets(line,LIBSLF_MAXLINELEN, fid) == NULL ) 
+      {
+        fprintf(stderr,"Model file corrupted.\n");
+        goto clean_up;
+      }
+
+      begptr = line;
+      for(j=0; j < nY; j++)
+      {
+        val = strtod(begptr, &endptr);
+
+        if(val == 0 && begptr == endptr)
+        {
+          fprintf(stderr,"Model file corrupted.\n");
+          goto clean_up;
+        }
+        begptr = endptr;
+        
+        
+        W[LIBOCAS_INDEX(i,j,nDim)] = val;
+      }
+    }
+    if(verb)
+      printf("done.\n");
+  }
+  
+  fclose(fid);
+
+/*  printf("W0=%f, W = [ ", W0);*/
+/*  for(i=0; i < nDim; i++)*/
+/*    printf("%f ", W[i]);*/
+/*  printf("]\n");*/
+/*  printf("W = [\n");*/
+/*  for(j=0; j < nDim; j++)*/
+/*  {*/
+/*    for(i=0; i < nY; i++)*/
+/*      printf("%f ", W[LIBOCAS_INDEX(j,i,nDim)]);*/
+/*    printf("\n");*/
+/*  }*/
+
+/*  load_time = get_time() - load_time;*/
+
+  /*-----------------------------------------------------
+    Read examples and classify them.
+    -----------------------------------------------------*/
+
+  fid = fopen(input_fname, "r");
+  if(fid == NULL) {
+    fprintf(stderr,"Cannot open input file.\n");
+    perror("fopen error ");
+    goto clean_up;
+  }
+
+  if(output_fname == NULL)
+    fout = stdout;
+  else
+  {
+    fout = fopen(output_fname, "w+");
+    if(fid == NULL) {
+      fprintf(stderr,"Cannot open output file.\n");
+      perror("fopen error ");
+      fclose(fid);
+      goto clean_up;
+    }
+  }
+
+  if(verb)
+  { 
+    if(output_fname != NULL)
+      printf("Classifying...");
+    else
+      printf("Outputs:\n");
+  }
+
+  nClassErrors = (long*)calloc(nY,sizeof(long));
+  if(nClassErrors == NULL)
+  {
+    fprintf(stderr,"Not enough memory for vector nClassError.\n");
+    goto clean_up;
+  }
+
+  nClass = (long*)calloc(nY,sizeof(long));
+  if(nClass == NULL)
+  {
+    fprintf(stderr,"Not enough memory for vector nClass.\n");
+    goto clean_up;
+  }
+  
+
+  go = 1;
+  while(go) { 
+    
+    if(fgets(line,LIBSLF_MAXLINELEN, fid) == NULL ) 
+    {
+      go = 0;
+    }
+    else
+    {
+      line_cnt ++;
+      nnzf = svmlight_format_parse_line(line, &label, feat_idx, feat_val);
+      
+      if(nnzf == -1) 
+      {
+         fprintf(stderr,"Parsing error on line %ld .\n", line_cnt);
+         fprintf(stderr,"Probably defective input file.\n");
+         goto clean_up;
+      }
+
+      max_dim = LIBOCAS_MAX(max_dim,feat_idx[nnzf-1]);
+
+      if(binary_problem == 1)
+      {
+        dfce = W0;
+        for(i=0; i < nnzf; i++)
+        {
+          if(feat_idx[i]-1 < nDim)
+            dfce += feat_val[i]*W[feat_idx[i]-1];
+        }
+
+        if(label == +1)
+          nClass[0]++;
+        else
+          nClass[1]++;
+
+        if(dfce >=0 && label == -1)
+        {
+          nClassErrors[1]++;
+          nErrors++;
+        }
+        else if (dfce < 0 && label== +1)
+        {
+          nClassErrors[0]++;
+          nErrors++;
+        }
+
+        if(output_type == 0)
+        {
+          if(dfce >=0 )
+            fprintf(fout,"+1\n");
+          else
+            fprintf(fout,"-1\n");
+            
+        }
+        else
+          fprintf(fout,"%.20f\n", dfce);
+      }
+      else
+      {
+        max_dfce = -LIBOCAS_PLUS_INF;
+        for(j=0; j < nY; j++)
+        {
+          dfce = 0;
+          for(i=0; i < nnzf; i++)
+          {
+            if(feat_idx[i]-1 < nDim)
+              dfce += feat_val[i]*W[LIBOCAS_INDEX(feat_idx[i]-1,j,nDim)];
+          }
+          if(output_type==1)
+            fprintf(fout,"%.20f ", dfce);
+
+          if(max_dfce < dfce)
+          {
+            max_dfce = dfce;
+            pred_label = j+1;
+          }
+        }
+        if(output_type==0)
+            fprintf(fout,"%d", pred_label);
+
+        fprintf(fout,"\n");
+
+        nClass[label-1]++;
+
+        if(label != pred_label)
+        {
+          nErrors++;
+          nClassErrors[label-1]++;
+        }
+
+      }
+    }
+  }  
+
+  if(verb)
+  {
+    if(output_fname != NULL)
+      printf("done.\n");
+
+    printf("Number of examples: %ld\n"
+           "Maximal dimensionality: %ld\n", line_cnt, max_dim);
+  }
+  if(print_error)
+  {
+    printf("Classification error: %f%%(%ld/%ld)\n", 100.0*(double)nErrors/(double)line_cnt,nErrors,line_cnt);
+    printf("Per-class errors: ");
+    if(binary_problem)
+    {
+      printf("+1: %f%%(%ld/%ld) -1: %f%%(%ld/%ld)\n", 
+             100.0*(double)nClassErrors[0]/(double)nClass[0], nClassErrors[0],nClass[0],
+             100.0*(double)nClassErrors[1]/(double)nClass[1], nClassErrors[1],nClass[1]);
+    }
+    else 
+    {
+      for(i=0; i < nY; i++)
+        printf("%d: %f%%(%ld/%ld) ", i+1, 100.0*(double)nClassErrors[i]/(double)nClass[i], 
+               nClassErrors[i],nClass[i]);
+      printf("\n");
+    } 
+  } 
+
+
+  fclose(fid);
+  fclose(fout);
+
+  exitflag = 1;
+  
+clean_up:
+
+  free(W);
+  free(line);
+  free(feat_val);
+  free(feat_idx);
+  free(nClassErrors);
+  free(nClass);
+
+  return(exitflag);
+}
+
+
+
diff --git a/msvmocas.c b/msvmocas.c
new file mode 100644
index 0000000..59c1d4a
--- /dev/null
+++ b/msvmocas.c
@@ -0,0 +1,540 @@
+/*-----------------------------------------------------------------------
+ * msvmocas.c: Standalone application implementing the OCAS folver for 
+ *   training multi-class linear SVM classifiers.
+ *   
+ * Copyright (C) 2008,2009 Vojtech Franc, xfrancv at cmp.felk.cvut.cz
+ *                    Soeren Sonnenburg, soeren.sonnenburg at first.fraunhofer.de
+ *
+ * This program is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU General Public 
+ * License as published by the Free Software Foundation; 
+ *-------------------------------------------------------------------- */
+
+
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <math.h>
+#include <stdlib.h>
+
+#include "lib_svmlight_format.h"
+#include "libocas.h"
+#include "sparse_mat.h"
+#include "ocas_helper.h"
+
+#include "version.h"
+
+void print_usage(void)
+{
+  printf("MSVMOCAS: Optimized Cutting Plane Algorithm for Support Vector Machines\n" 
+         "          Training linear multi-class SVM classifier from examples\n"
+         "          " OCAS_VERSION "\n"
+         "\n"
+         "   usage: msvmocas [options] example_file model_file\n"
+         "\n"
+         "Arguments:\n"
+         "         example_file  file with training examples stored in SVM^light format\n"
+         "         model_file    text file to store learned linear rule f(x)=W'*x; the model file\n"
+         "                       contains M columns and D lines (M is number of classes, D is data\n"
+         "                       dimension) corresponding to the elements of the matrix W [D x M].\n"
+         "General options:\n"
+         "         -h            this help\n"
+         "         -v [0,1]      verbosity level (default 1).\n"
+         "Learning options:\n"
+         "         -c float      regularization constant C (default 1).\n"
+         "         -n int        use only first n examples for training. By default n equals to\n"
+         "                       the number of examples in the example_file.\n"
+         "Optimization options:\n" 
+         "         -m [0,1]      solver to be used: 0 ... standard cutting plane (BMRM, SVM^perf)\n"
+         "                                          1 ... OCAS (default 1).\n"
+         "         -s int        cache size for cutting planes (default 2000).\n"
+         "Stopping conditions:\n"
+         "         -a float      absolute tolerance TolAbs: halt if QP-QD <= TolAbs (default 0).\n"
+         "         -r float      relative tolerance TolRel: halt if QP-QD <= abs(QP)*TolRel (default 0.01).\n"
+         "         -q float      desired objective value QPValue: halt if QP <= QPValue (default 0).\n"         
+         "         -t float      halts if the solver time (loading time is not counted) exceeds\n"
+         "                       the given time in seconds (default inf).\n\n"
+         "Example:\n"
+         "  Train multi-class SVM classifier from example file ./data/example4_train.light with\n"
+         "  regularization constant set to C = 10, verbosity switched off and save model to\n"
+         "  file ./data/msvmocas.model\n"
+         "    ./msvmocas -c 10 -v 0 ./data/example4_train.light ./data/msvmocas.model\n"
+         "\n"  
+         "  Compute testing error of the classifier stored in ./data/msvmocas.model using testing\n"
+         "  examples from ./data/example4_test.light and save predicted labels to ./data/example4_test.pred\n"
+         "    ./linclass -e -o ./data/exaple4_test.pred ./data/example4_test.light ./data/msvmocas.model\n"
+         "\n"
+         );
+}
+
+
+int main(int argc, char *argv[])
+{
+  double C, TolRel, TolAbs, QPBound, MaxTime;
+  uint32_t i, j, BufSize;
+  uint16_t Method;
+  ocas_return_value_T ocas;
+  int len;
+  int recognized;
+  int exitflag = 1;
+  int verb;
+
+  /* timing variables */
+  double load_time;
+  double total_time;
+
+  char *model_fname;
+  char *input_fname;
+  FILE *fid;
+
+  /* start time measuring */
+  total_time = get_time();
+
+  /* init */
+  data_X = NULL;
+  data_y = NULL;
+  W = NULL;
+  oldW = NULL;
+  sparse_A.nz_dims = NULL;
+  sparse_A.index = NULL;
+  sparse_A.value = NULL;
+  new_a = NULL;
+  full_A = NULL;
+
+  /* default setting of input arguments*/
+  C = 1.0;
+  Method = 1;
+  TolRel = 0.01;
+  TolAbs = 0.0;
+  QPBound = 0.0;
+  BufSize = 2000;
+  MaxTime = (double)LIBOCAS_PLUS_INF;
+  nData = -1;
+  verb = 1;
+
+  /*-----------------------------------------------------------
+    Process input arguments 
+  ------------------------------------------------------------*/
+  if(argc ==1 || strcmp(argv[1], "-h") == 0)  
+  {
+    print_usage();
+    goto clean_up;
+  }
+
+  if(argc < 3)
+  {
+    fprintf(stderr,"Not enough input arguments.\n\n");
+    goto clean_up;
+  }
+
+
+  for (i = 1; i < argc-2; i++)  
+  {
+    recognized = 0;
+    if (strcmp(argv[i], "-h") == 0)  
+    {
+      print_usage();
+      goto clean_up;
+    }
+
+    if (strcmp(argv[i], "-c") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -c\n"); 
+        goto clean_up;  
+      }
+      C = atof(argv[i+1]); 
+      if(C <=0)
+      { 
+        fprintf(stderr,"Parameter C must be geater than zero.\n"); 
+        goto clean_up; 
+      } 
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-n") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -n\n"); 
+        goto clean_up;  
+      }
+      nData = atol(argv[i+1]); 
+      if(nData <=0)
+      { 
+        fprintf(stderr,"A value after the argument -n must be greater than zero.\n"); 
+        goto clean_up; 
+      } 
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-s") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -s\n"); 
+        goto clean_up;  
+      }
+      BufSize = atol(argv[i+1]); 
+      if(nData <=0)
+      { 
+        fprintf(stderr,"A value after the argument -s must be greater than zero.\n"); 
+        goto clean_up; 
+      } 
+      i++;
+      recognized = 1;
+    }
+
+
+    if (strcmp(argv[i], "-m") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -m\n"); 
+        goto clean_up;  
+      }
+      Method = atoi(argv[i+1]); 
+      if(Method != 0 && Method != 1)
+      { 
+        fprintf(stderr,"A value after the argument -m must be 0 or 1.\n"); 
+        goto clean_up; 
+      } 
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-v") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -v\n"); 
+        goto clean_up;  
+      }
+      verb = atoi(argv[i+1]); 
+      if(verb < 0 || verb > 1)
+      {
+        fprintf(stderr,"A value after the argument -v must be either 0 or 1.\n"); 
+        goto clean_up;  
+      }
+        
+
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-a") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -a\n"); 
+        goto clean_up;  
+      }
+      TolAbs = atof(argv[i+1]); 
+      if(TolAbs < 0)
+      { 
+        fprintf(stderr,"A value after the argument -a must be a positive scalar.\n"); 
+        goto clean_up; 
+      } 
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-r") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -r\n"); 
+        goto clean_up;  
+      }
+      TolRel = atof(argv[i+1]); 
+      if(TolRel < 0)
+      { 
+        fprintf(stderr,"A value after the argument -r must be a positive scalar.\n"); 
+        goto clean_up; 
+      } 
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-q") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -q\n"); 
+        goto clean_up;  
+      }
+      QPBound = atof(argv[i+1]); 
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-t") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -t\n"); 
+        goto clean_up;  
+      }
+      MaxTime = atof(argv[i+1]); 
+      if(MaxTime <=0)
+      {
+        fprintf(stderr,"A value after the argument -t must be a positive scalar.\n"); 
+        goto clean_up; 
+      } 
+      i++;
+      recognized = 1;
+    }
+
+    if(recognized == 0)
+    {
+      fprintf(stderr,"Unknown input argument: %s\n", argv[i]);
+      goto clean_up;  
+    }
+        
+  }
+
+  len = strlen(argv[argc-2]);
+  input_fname = calloc(len+1,sizeof(char));
+  strcpy(input_fname, argv[argc-2]);
+
+  len = strlen(argv[argc-1]);
+  model_fname = calloc(len+1,sizeof(char));
+  strcpy(model_fname, argv[argc-1]);
+
+  if(verb)
+    printf("Input file: %s\n", input_fname);
+
+  /*----------------------------------------------------------------
+    Load input examples
+  -------------------------------------------------------------------*/
+  load_time = get_time();
+  if( load_svmlight_file(input_fname,verb) == -1 || data_X == NULL || data_y == NULL)
+    goto clean_up;
+
+  load_time = get_time() - load_time;
+  
+  /* get examples' dimension */
+  nDim = mxGetM(data_X);
+
+  /* if not given set number of training examples to be used */
+  if(nData == -1)
+    nData = mxGetN(data_X);
+  else if(nData <= 0 || nData > mxGetN(data_X))
+  {
+    fprintf(stderr,"Number of examples in the input file is %d.\n", mxGetN(data_X));
+    fprintf(stderr,"A value after argument -n must be less or equal to the number of examples.\n");
+    goto clean_up;
+  }
+
+  /* get number of classes */
+  for(i=0, nY = 0; i < nData; i++)
+    nY = LIBOCAS_MAX(nY, (uint32_t)data_y[i]);
+
+
+  /*----------------------------------------------------------------
+    Print setting
+  -------------------------------------------------------------------*/
+  if(verb)
+  {
+    printf("Input file statistics:\n"
+           "   # of examples  : %d\n"
+           "   # of classes   : %d\n"
+           "   dimensionality : %d\n",
+           mxGetN(data_X), nY, nDim);
+    if( mxIsSparse(data_X) )
+      printf("   density        : %.2f%% (sparse matrix representation used)\n",
+             100.0*(double)mxGetNZMAX(data_X)/((double)nDim*(double)(mxGetN(data_X))));
+    else
+      printf("   density        : 100%% (full matrix representation used)\n");
+    
+    printf("Setting:\n"
+           "   C              : %f\n"
+           "   # of examples  : %d\n"
+           "   solver         : %d\n"
+           "   cache size     : %d\n"
+           "   TolAbs         : %f\n"
+           "   TolRel         : %f\n"
+           "   QPValue        : %f\n"
+           "   MaxTime        : %f [s]\n"
+           "   Verbosity      : %d\n",
+           C, nData, Method,BufSize,TolAbs,TolRel, QPBound, MaxTime,verb);
+  }
+
+
+  /*----------------------------------------------------------------
+    Allocate memory for working variables and cutting plane cache
+  -------------------------------------------------------------------*/
+
+  /* learned weight vector */
+  W = (double*)mxCalloc(nDim*nY,sizeof(double));
+  if(W == NULL)
+  {
+    fprintf(stderr,"Not enough memory for matrix W.\n");
+    goto clean_up;
+  }
+    
+  oldW = (double*)mxCalloc(nDim*nY,sizeof(double));
+  if(oldW == NULL) 
+  {
+    fprintf(stderr,"Not enough memory for matrix oldW.");
+    goto clean_up;
+  }
+
+  /* allocate buffer for computing cutting plane */
+  new_a = (double*)mxCalloc(nDim*nY,sizeof(double));
+  if(new_a == NULL) 
+  {
+    fprintf(stderr,"Not enough memory for auxciliary cutting plane buffer new_a.");  
+    goto clean_up;
+  }
+
+  if(mxIsSparse(data_X))
+  {
+
+    /* init cutting plane buffer */
+    sparse_A.nz_dims = mxCalloc(BufSize,sizeof(uint32_t));
+    sparse_A.index = mxCalloc(BufSize,sizeof(sparse_A.index[0]));
+    sparse_A.value = mxCalloc(BufSize,sizeof(sparse_A.value[0]));
+    if(sparse_A.nz_dims == NULL || sparse_A.index == NULL || sparse_A.value == NULL) 
+    {
+        fprintf(stderr,"Not enough memory for cutting plane buffer sparse_A.");  
+        goto clean_up;
+    }
+
+    /*----------------------------------------------------------------
+      Run OCAS run ...
+      -------------------------------------------------------------------*/
+    if(verb)
+    {
+      printf("Starting optimization:\n");
+
+      ocas = msvm_ocas_solver( C, data_y, nY, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method,
+                               &msvm_sparse_compute_W, &msvm_full_update_W, &msvm_sparse_add_new_cut,
+                               &msvm_sparse_compute_output, &qsort_data, &ocas_print, 0);
+    }
+    else
+    {
+      ocas = msvm_ocas_solver( C, data_y, nY, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method,
+                               &msvm_sparse_compute_W, &msvm_full_update_W, &msvm_sparse_add_new_cut,
+                               &msvm_sparse_compute_output, &qsort_data, &ocas_print_null, 0);
+    }
+  }
+  else
+  {
+    /* init cutting plane buffer */
+    full_A = mxCalloc(BufSize*nDim*nY,sizeof(double));
+    if( full_A == NULL )
+    {
+      fprintf(stderr,"Not enough memory for cutting plane buffer full_A.");  
+      goto clean_up;
+    }
+
+    if(verb)
+    {
+      printf("Starting optimization:\n");
+    
+      ocas = msvm_ocas_solver( C, data_y, nY, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method,
+                               &msvm_full_compute_W, &msvm_full_update_W, &msvm_full_add_new_cut,
+                               &msvm_full_compute_output, &qsort_data, &ocas_print, 0); 
+    }
+    else
+    {
+      ocas = msvm_ocas_solver( C, data_y, nY, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method,
+                               &msvm_full_compute_W, &msvm_full_update_W, &msvm_full_add_new_cut,
+                               &msvm_full_compute_output, &qsort_data, &ocas_print_null, 0); 
+
+    }
+
+  }
+
+
+  total_time=get_time()-total_time;
+
+  if(verb)
+  {
+    printf("Stopping condition: ");
+    switch( ocas.exitflag )
+    {
+       case 1: printf("1-Q_D/Q_P <= TolRel(=%f) satisfied.\n", TolRel); break;
+       case 2: printf("Q_P-Q_D <= TolAbs(=%f) satisfied.\n", TolAbs); break;
+       case 3: printf("Q_P <= QPBound(=%f) satisfied.\n", QPBound); break;
+       case 4: printf("Optimization time (=%f) >= MaxTime(=%f).\n", ocas.ocas_time, MaxTime); break;
+       case -1: printf("Has not converged!\n" ); break;
+       case -2: printf("Not enough memory for the solver.\n" ); break;
+    }
+
+    printf("Timing statistics:\n"
+           "   load_time      : %f[s]\n"
+           "   qp_solver_time : %f[s]\n"
+           "   sort_time      : %f[s]\n"
+           "   output_time    : %f[s]\n"
+           "   add_time       : %f[s]\n"
+           "   w_time         : %f[s]\n"
+           "   print_time     : %f[s]\n"
+           "   ocas_time      : %f[s]\n"
+           "   total_time     : %f[s]\n",
+           load_time, ocas.qp_solver_time, ocas.sort_time, ocas.output_time, 
+           ocas.add_time, ocas.w_time, ocas.print_time, ocas.ocas_time, total_time);
+
+    printf("Training error: %.4f%%\n", 100*(double)ocas.trn_err/(double)nData);
+  }
+
+  /* save W to the model file */
+  fid = fopen(model_fname, "w+");
+  if(fid == NULL) {
+    perror("fopen error ");
+    mexPrintf("Cannot open model file.");
+    goto clean_up;
+  }
+
+  for(i=0; i < nDim; i++) 
+  {
+    for(j=0; j < nY; j++)
+    {
+      fprintf(fid, "%.20f ", W[LIBOCAS_INDEX(i,j,nDim)]);
+    }
+    fprintf(fid, "\n");
+  }
+
+  fclose(fid);
+
+  if(verb)
+    printf("Result saved to model file: %s\n", model_fname);
+
+  exitflag = 0;
+
+clean_up:
+
+  mxDestroyArray(data_X);
+  mxFree(data_y);
+  mxFree(W);
+  mxFree(oldW);
+  mxFree(new_a);
+  mxFree(full_A);
+
+  mxFree(sparse_A.nz_dims);
+  if( sparse_A.index !=NULL) 
+  {
+    for(i=0; i < BufSize; i++)
+      if(sparse_A.index[i] != NULL)
+        mxFree(sparse_A.index[i]);
+
+    mxFree(sparse_A.index);
+  }
+  if( sparse_A.value != NULL)
+  {
+    for(i=0; i < BufSize; i++)
+      if(sparse_A.value[i] != NULL)
+        mxFree(sparse_A.value[i]);
+    
+    mxFree(sparse_A.value);
+  }
+
+  return(exitflag);
+}
+
+
+
diff --git a/msvmocas.m b/msvmocas.m
new file mode 100644
index 0000000..6a48dd0
--- /dev/null
+++ b/msvmocas.m
@@ -0,0 +1,63 @@
+% MSVMOCAS Train multi-class linear SVM classifier using OCAS solver.
+%
+% Synopsis:
+%  [W,stat] = msvmocas(X,y,C,Method,TolRel,TolAbs,QPBound,BufSize,nExamples,MaxTime,verb)
+%  [W,stat] = msvmocas(svmlight_data_file,C,Method,TolRel,TolAbs,QPBound,BufSize,nExamples,MaxTime,verb)
+%
+% Desription:
+%  This function trains multi-class linear SVM classifier by solving
+%
+%      W^* = argmin 0.5*sum_y (W(:,y)'*W(:,y)) + C*  sum     max( (y~=y(i)) + (W(:,y) - W(:,y(i))'*X(:,i))
+%              W                                   i=1:nData   y
+%
+%  The function accepts examples either in Matlab matrix X (both sparse and dense) and 
+%  a dense vector y or as path to a file in SVM^light format.
+%
+% Reference:
+%  V. Franc, S. Sonnenburg. To be published. 2009
+%
+% Input:
+%   data_file [string] Training examples stored in SVM^light format.
+%
+%   X [nDim x nExamples] training inputs (sparse or dense matrix).
+%   y [nExamples x 1] labels; intgers 1,2,...nY
+%   C [1x1] regularization constant
+%   Method [1x1] 0..cutting plane; 1..OCAS  (default 1)
+%   TolRel [1x1] halts if Q_P-Q_D <= abs(Q_P)*TolRel  (default 0.01)
+%   TolAbs [1x1] halts if Q_P-Q_D <= TolAbs  (default 0)
+%   QPValue [1x1] halts if Q_P <= QPBpound  (default 0)
+%   BufSize [1x1] Initial size of active constrains buffer (default 2000)
+%   nExamples [1x1] Number of training examplesused for training; must be >0 and <= size(X,2).
+%     If nExamples = inf then nExamples is set to size(X,2).
+%   MaxTime [1x1] halts if time used by solver (data loading time is not counted) exceeds
+%    MaxTime given in seconds. Use MaxTime=inf (default) to switch off this stopping condition. 
+%   verb [1x1] if non-zero then prints some info; (default 1)
+%
+% Output:
+%   W [nDim x nY] Paramater vectors of decision rule; [dummy,ypred] = max(W'*x)
+%   stat [struct] Optimizer statistics (field names are self-explaining).
+%
+% Example:
+%  C = 1; 
+%
+%  % case 1: loading data directly from file in SVM^light format
+%  [W,stat] = msvmocas('example4_train.light',C);
+%
+%  % case 2: using data loaded in Matlab
+%  load('example4_train','X','y');
+%  [W,stat] = msvmocas(X,y,C);
+%
+%  % classification
+%  load('example4_test.mat','X','y');
+%  [dummy,ypred] = max(W'*X);
+%  sum(ypred(:) ~= y(:))/length(y)
+% 
+
+%
+% Copyright (C) 2008 Vojtech Franc, xfrancv at cmp.felk.cvutr.cz
+%                    Soeren Sonnenburg, soeren.sonnenburg at first.fraunhofer.de
+%
+% This program is free software; you can redistribute it and/or
+% modify it under the terms of the GNU General Public 
+% License as published by the Free Software Foundation; 
+% Version 3, 29 June 2007
diff --git a/msvmocas_mex.c b/msvmocas_mex.c
new file mode 100644
index 0000000..330972e
--- /dev/null
+++ b/msvmocas_mex.c
@@ -0,0 +1,364 @@
+/*=================================================================================
+ * svmocas_mex.c: Matlab MEX interface for OCAS solver training the linear SVM classifiers.
+ * 
+ * Synopsis:
+ *  [W,stat] = msvmocas(X,y,C,Method,TolRel,TolAbs,QPBound,BufSize,nData,MaxTime,verb)
+ *  [W,stat] = msvmocas(svmlight_data_file,C,Method,TolRel,TolAbs,QPBound,BufSize,nData,MaxTime,verb)
+ *
+ * Synopsis:
+ *  [W,stat] = msvmocas(X,y,C,Method,TolRel,TolAbs,QPBound,BufSize,nData,MaxTime)
+ * Copyright (C) 2008 Vojtech Franc, xfrancv at cmp.felk.cvut.cz
+ *
+ * This program is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU General Public 
+ * License as published by the Free Software Foundation; 
+ *======================================================================================*/ 
+
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <mex.h>
+
+/*#define LIBOCAS_MATLAB*/
+
+#include "libocas.h"
+#include "ocas_helper.h"
+
+#define DEFAULT_METHOD 1
+#define DEFAULT_TOLREL 0.01
+#define DEFAULT_TOLABS 0.0
+#define DEFAULT_QPVALUE 0.0
+#define DEFAULT_BUFSIZE 2000
+#define DEFAULT_MAXTIME mxGetInf()
+#define DEFAULT_VERB 1
+
+
+
+/*======================================================================
+  Main code plus interface to Matlab.
+========================================================================*/
+
+void mexFunction( int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[] )
+{
+  double C, TolRel, TolAbs, MaxTime, trn_err, QPBound;
+  double *ptr;
+  uint32_t i, j, BufSize;
+  uint16_t Method;
+  int verb;
+  ocas_return_value_T ocas;
+
+  /* timing variables */
+  double init_time;
+  double total_time;
+
+  total_time = get_time();
+  init_time = total_time;
+
+  if(nrhs < 1)
+    mexErrMsgTxt("Improper number of input arguments.");
+
+  /* get input arguments */ 
+  if(mxIsChar(prhs[0]) == false) 
+  {
+
+    if(nrhs < 3 || nrhs > 11)
+      mexErrMsgTxt("Improper number of input arguments.");
+
+    /*  [W,stat] = msvmocas(X,y,C,Method,TolRel,TolAbs,QPBound,BufSize,nData,MaxTime)*/
+
+    data_X = (mxArray*)prhs[0];
+    if (!(mxIsDouble(data_X)))
+      mexErrMsgTxt("Input argument X must be of type double.");
+
+    if (mxGetNumberOfDimensions(data_X) != 2)
+      mexErrMsgTxt("Input argument X must be two dimensional.");
+
+    data_y = (double*)mxGetPr(prhs[1]);
+
+    if(LIBOCAS_MAX(mxGetM(prhs[1]),mxGetN(prhs[1])) != mxGetN(prhs[0]))
+      mexErrMsgTxt("Length of vector y must equal to the number of columns of matrix X.");
+
+    C = (double)mxGetScalar(prhs[2]);
+
+    if(nrhs >= 4)
+      Method = (uint32_t)mxGetScalar(prhs[3]);
+    else
+      Method = DEFAULT_METHOD;
+
+    if(nrhs >= 5)
+      TolRel = (double)mxGetScalar(prhs[4]);
+    else
+      TolRel = DEFAULT_TOLREL;
+
+    if(nrhs >= 6)    
+      TolAbs = (double)mxGetScalar(prhs[5]);
+    else
+      TolAbs = DEFAULT_TOLABS;
+
+    if(nrhs >= 7)
+      QPBound = (double)mxGetScalar(prhs[6]);
+    else
+      QPBound = DEFAULT_QPVALUE;
+    
+    if(nrhs >= 8)
+      BufSize = (uint32_t)mxGetScalar(prhs[7]);
+    else
+      BufSize = DEFAULT_BUFSIZE;
+
+    if(nrhs >= 9 && mxIsInf(mxGetScalar(prhs[8])) == false)
+      nData = (uint32_t)mxGetScalar(prhs[8]);
+    else
+      nData = mxGetN(data_X);
+      
+    if(nData < 1 || nData > mxGetN(prhs[0])) 
+      mexErrMsgTxt("Improper value of argument nData.");
+
+    if(nrhs >= 10)
+      MaxTime = (double)mxGetScalar(prhs[9]);
+    else
+      MaxTime = DEFAULT_MAXTIME;
+
+    if(nrhs >= 11)
+      verb = (int)mxGetScalar(prhs[10]);
+    else
+      verb = DEFAULT_VERB;
+
+
+  }
+  else
+  {
+    /*  [W,stat] = msvmocas(svmlight_data_file,C,Method,TolRel,TolAbs,QPBound,BufSize,nData,MaxTime)*/
+    char *fname;
+    int fname_len;
+
+    if(nrhs < 2 || nrhs > 10)
+      mexErrMsgTxt("Improper number of input arguments.");
+
+    if(!mxIsChar(prhs[0]))
+      mexErrMsgTxt("First input argument must be of type string.");
+
+    fname_len = mxGetNumberOfElements(prhs[0]) + 1;   
+    fname = mxCalloc(fname_len, sizeof(char));    
+
+    if (mxGetString(prhs[0], fname, fname_len) != 0)     
+      mexErrMsgTxt("Could not convert first input argument to string.");
+
+    if(nrhs >= 10)
+      verb = (int)mxGetScalar(prhs[9]);
+    else
+      verb = DEFAULT_VERB;
+
+    /* load data */
+    if( load_svmlight_file(fname,verb) == -1 || data_X == NULL || data_y == NULL)
+      mexErrMsgTxt("Cannot load input file.");
+
+    C = (double)mxGetScalar(prhs[1]);
+
+    if(nrhs >= 3)
+      Method = (uint32_t)mxGetScalar(prhs[2]);
+    else
+      Method = DEFAULT_METHOD;
+
+    if(nrhs >= 4)
+      TolRel = (double)mxGetScalar(prhs[3]);
+    else
+      TolRel = DEFAULT_TOLREL;
+
+    if(nrhs >= 5)    
+      TolAbs = (double)mxGetScalar(prhs[4]);
+    else
+      TolAbs = DEFAULT_TOLABS;
+
+    if(nrhs >= 6)
+      QPBound = (double)mxGetScalar(prhs[5]);
+    else
+      QPBound = DEFAULT_QPVALUE;
+    
+    if(nrhs >= 7)
+      BufSize = (uint32_t)mxGetScalar(prhs[6]);
+    else
+      BufSize = DEFAULT_BUFSIZE;
+
+    if(nrhs >= 8 && mxIsInf(mxGetScalar(prhs[7])) == false)
+      nData = (uint32_t)mxGetScalar(prhs[7]);
+    else
+      nData = mxGetN(data_X);
+
+    if(nData < 1 || nData > mxGetN(data_X)) 
+      mexErrMsgTxt("Improper value of argument nData.");
+
+    if(nrhs >= 9)
+      MaxTime = (double)mxGetScalar(prhs[8]);
+    else
+      MaxTime = DEFAULT_MAXTIME;
+
+
+  }
+
+/*  nDim = mxGetM(prhs[0]);*/
+  nDim = mxGetM(data_X);
+  for(i=0, nY = 0; i < nData; i++)
+    nY = LIBOCAS_MAX(nY, (uint32_t)data_y[i]);
+
+
+  /*----------------------------------------------------------------
+    Print setting
+  -------------------------------------------------------------------*/
+  if(verb)
+  {
+    mexPrintf("Input data statistics:\n"
+              "   # of examples  : %d\n"
+              "   # of classes   : %d\n"
+              "   dimensionality : %d\n",
+              nData, nY, nDim);
+    
+    if( mxIsSparse(data_X)== true ) 
+      mexPrintf("   density        : %.2f%%\n",
+                100.0*(double)mxGetNzmax(data_X)/((double)nDim*(double)(mxGetN(data_X))));
+    else
+      mexPrintf("    density       : 100%% (full)\n");
+
+    mexPrintf("Setting:\n"
+         "   C              : %f\n"
+         "   # of examples  : %d\n"
+         "   solver         : %d\n"
+         "   cache size     : %d\n"
+         "   TolAbs         : %f\n"
+         "   TolRel         : %f\n"
+         "   QPValue        : %f\n"
+         "   MaxTime        : %f [s]\n",
+         C, nData, Method,BufSize,TolAbs,TolRel, QPBound, MaxTime);
+  }
+  
+  /* learned weight vector */
+  plhs[0] = (mxArray*)mxCreateDoubleMatrix(nDim,nY,mxREAL);
+  W = (double*)mxGetPr(plhs[0]);
+  if(W == NULL) mexErrMsgTxt("Not enough memory for vector W.");
+
+  oldW = (double*)mxCalloc(nY*nDim,sizeof(double));
+  if(oldW == NULL) mexErrMsgTxt("Not enough memory for vector oldW.");
+
+  /* allocate buffer for computing cutting plane */
+  new_a = (double*)mxCalloc(nY*nDim,sizeof(double));
+  if(new_a == NULL) 
+    mexErrMsgTxt("Not enough memory for auxciliary cutting plane buffer new_a.");  
+
+
+  if( mxIsSparse(data_X)== true ) 
+  {
+    /* init cutting plane buffer */
+    sparse_A.nz_dims = mxCalloc(BufSize,sizeof(uint32_t));
+    sparse_A.index = mxCalloc(BufSize,sizeof(sparse_A.index[0]));
+    sparse_A.value = mxCalloc(BufSize,sizeof(sparse_A.value[0]));
+    if(sparse_A.nz_dims == NULL || sparse_A.index == NULL || sparse_A.value == NULL) 
+      mexErrMsgTxt("Not enough memory for cutting plane buffer sparse_A.");  
+
+    if(verb)
+      mexPrintf("Starting optimization:\n");
+
+    init_time=get_time()-init_time;
+
+    if(verb)
+      ocas = msvm_ocas_solver( C, data_y, nY, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method,
+                             &msvm_sparse_compute_W, &msvm_full_update_W, &msvm_sparse_add_new_cut,
+                             &msvm_sparse_compute_output, &qsort_data, &ocas_print, 0);
+    else
+      ocas = msvm_ocas_solver( C, data_y, nY, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method,
+                             &msvm_sparse_compute_W, &msvm_full_update_W, &msvm_sparse_add_new_cut,
+                             &msvm_sparse_compute_output, &qsort_data, &ocas_print_null, 0);
+  }
+  else
+  {
+    /* init cutting plane buffer */
+    full_A = mxCalloc(BufSize*nDim*nY,sizeof(double));
+    if( full_A == NULL )
+      mexErrMsgTxt("Not enough memory for cutting plane buffer full_A.");    
+
+    if(verb)
+      mexPrintf("Starting optimization:\n");
+
+    init_time=get_time()-init_time;
+
+    if(verb)
+      ocas = msvm_ocas_solver( C, data_y, nY, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method,
+                             &msvm_full_compute_W, &msvm_full_update_W, &msvm_full_add_new_cut,
+                             &msvm_full_compute_output, &qsort_data, &ocas_print, 0); 
+    else
+      ocas = msvm_ocas_solver( C, data_y, nY, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method,
+                             &msvm_full_compute_W, &msvm_full_update_W, &msvm_full_add_new_cut,
+                             &msvm_full_compute_output, &qsort_data, &ocas_print_null, 0); 
+
+  }
+ 
+  if(verb)
+  {
+    mexPrintf("Stopping condition: ");
+    switch( ocas.exitflag )
+    {
+       case 1: mexPrintf("1-Q_D/Q_P <= TolRel(=%f) satisfied.\n", TolRel); break;
+       case 2: mexPrintf("Q_P-Q_D <= TolAbs(=%f) satisfied.\n", TolAbs); break;
+       case 3: mexPrintf("Q_P <= QPBound(=%f) satisfied.\n", QPBound); break;
+       case 4: mexPrintf("Optimization time (=%f) >= MaxTime(=%f).\n", ocas.ocas_time, MaxTime); break;
+       case -1: mexPrintf("Has not converged!\n" ); break;
+       case -2: mexPrintf("Not enough memory for the solver.\n" ); break;
+    }
+  }
+
+  total_time=get_time()-total_time;
+  if(verb)
+  {
+    mexPrintf("Timing statistics:\n"
+			"   init_time      : %f[s]\n"
+			"   qp_solver_time : %f[s]\n"
+			"   sort_time      : %f[s]\n"
+			"   output_time    : %f[s]\n"
+			"   add_time       : %f[s]\n"
+			"   w_time         : %f[s]\n"
+			"   print_time     : %f[s]\n"
+			"   ocas_time      : %f[s]\n"
+			"   total_time     : %f[s]\n",
+			init_time, ocas.qp_solver_time, ocas.sort_time, ocas.output_time, 
+            ocas.add_time, ocas.w_time, ocas.print_time, ocas.ocas_time, total_time);
+
+    mexPrintf("Training error: %.4f%%\n", 100*(double)ocas.trn_err/(double)nData);
+  }
+
+  
+  /* return ocas optimizer statistics */
+  /* typedef struct {
+     uint32_t nIter;    
+     uint32_t nCutPlanes;
+     double trn_err;      
+     double Q_P;          
+     double Q_D;
+     double output_time;
+     double sort_time;
+     double solver_time;
+     int8_t exitflag;       
+     } ocas_return_value_T; */
+
+  const char *field_names[] = {"nTrnErrors","Q_P","Q_D","nIter","nCutPlanes","exitflag",
+                               "init_time","output_time","sort_time","qp_solver_time","add_time",
+                               "w_time","ocas_time","total_time"}; 
+  mwSize dims[2] = {1,1};  
+
+  plhs[1] = mxCreateStructArray(2, dims, (sizeof(field_names)/sizeof(*field_names)), field_names);
+  
+  mxSetField(plhs[1],0,"nIter",mxCreateDoubleScalar((double)ocas.nIter));
+  mxSetField(plhs[1],0,"nCutPlanes",mxCreateDoubleScalar((double)ocas.nCutPlanes));
+  mxSetField(plhs[1],0,"nTrnErrors",mxCreateDoubleScalar(ocas.trn_err)); 
+  mxSetField(plhs[1],0,"Q_P",mxCreateDoubleScalar(ocas.Q_P)); 
+  mxSetField(plhs[1],0,"Q_D",mxCreateDoubleScalar(ocas.Q_D)); 
+  mxSetField(plhs[1],0,"init_time",mxCreateDoubleScalar(init_time)); 
+  mxSetField(plhs[1],0,"output_time",mxCreateDoubleScalar(ocas.output_time)); 
+  mxSetField(plhs[1],0,"sort_time",mxCreateDoubleScalar(ocas.sort_time)); 
+  mxSetField(plhs[1],0,"qp_solver_time",mxCreateDoubleScalar(ocas.qp_solver_time)); 
+  mxSetField(plhs[1],0,"add_time",mxCreateDoubleScalar(ocas.add_time)); 
+  mxSetField(plhs[1],0,"w_time",mxCreateDoubleScalar(ocas.w_time)); 
+  mxSetField(plhs[1],0,"ocas_time",mxCreateDoubleScalar(ocas.ocas_time)); 
+  mxSetField(plhs[1],0,"total_time",mxCreateDoubleScalar(total_time)); 
+  mxSetField(plhs[1],0,"exitflag",mxCreateDoubleScalar((double)ocas.exitflag)); 
+
+  return;
+}
+
diff --git a/msvmocas_test.m b/msvmocas_test.m
new file mode 100644
index 0000000..55d8e5c
--- /dev/null
+++ b/msvmocas_test.m
@@ -0,0 +1,49 @@
+trn_file = 'example4_train.light';
+tst_file = 'example4_test.light';
+
+C = 1;
+TolRel = 0.01;
+TolAbs = 0.00;
+QPBound = 0;
+BufSize = 2000;
+nData = inf;
+MaxTime = inf;
+verb = 0;
+
+Method = 0; % Cutting Plane Algorithm
+fprintf('Training SVM by Cutting Plane Algorithm...');
+[cp_W,cp_stat] = msvmocas(trn_file,C,Method,TolRel,TolAbs,QPBound,BufSize,nData,MaxTime,verb);
+fprintf('done\n');
+
+fprintf('Evaluating classifier on testing data...');
+[score,true_labels] = svmlight_linclass(tst_file,cp_W,[]);
+[dummy,pred_labels] = max(score);
+cp_tst_err = sum(pred_labels(:) ~= true_labels(:))/length(true_labels);
+fprintf('done\n');
+
+
+fprintf('Training time: %f[s], #trn_errors: %d, nIter: %d\n',...
+        cp_stat.total_time, cp_stat.nTrnErrors, cp_stat.nIter);
+fprintf('Objval  primal: %f, dual: %f, gap: %f\n', ...
+        cp_stat.Q_P, cp_stat.Q_D,cp_stat.Q_P-cp_stat.Q_D);
+fprintf('Testing error: %f %%\n',cp_tst_err*100);
+   
+
+Method = 1; % OCAS 
+fprintf('\nTraining SVM by OCAS...');
+[ocas_W,ocas_stat] = msvmocas(trn_file,C,Method,TolRel,TolAbs,QPBound,BufSize,nData,MaxTime,verb);
+fprintf('done\n');
+
+fprintf('Evaluating classifier on testing data...');
+[score,true_labels] = svmlight_linclass(tst_file,ocas_W,[]);
+[dummy,pred_labels] = max(score);
+ocas_tst_err = sum(pred_labels(:) ~= true_labels(:))/length(true_labels);
+fprintf('done\n');
+
+
+fprintf('Training time: %f[s], #trn_errors: %d, nIter: %d\n',...
+        ocas_stat.total_time, ocas_stat.nTrnErrors, ocas_stat.nIter);
+fprintf('Objval  primal: %f, dual: %f, gap: %f\n', ...
+        ocas_stat.Q_P, ocas_stat.Q_D,ocas_stat.Q_P-ocas_stat.Q_D);
+fprintf('Testing error: %f %%\n',ocas_tst_err*100);
+
diff --git a/ocas_helper.c b/ocas_helper.c
new file mode 100644
index 0000000..7372086
--- /dev/null
+++ b/ocas_helper.c
@@ -0,0 +1,1778 @@
+/*-----------------------------------------------------------------------
+ * ocas_helper.c: Implementation of helper functions for the OCAS solver.
+ *
+ * It supports both sparse and dense matrices and loading data from
+ * the SVM^light format.
+ *-------------------------------------------------------------------- */
+
+#define _FILE_OFFSET_BITS  64
+
+#include <pthread.h>
+
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <sys/time.h>
+#include <stdlib.h>
+#include <time.h>
+
+#include "lib_svmlight_format.h"
+#include "libocas.h"
+#include "ocas_helper.h"
+
+mxArray *data_X;
+uint32_t nDim, nData, nY;
+double *data_y;
+cutting_plane_buf_T sparse_A;
+double *full_A;
+double *W;
+double *oldW;
+double *new_a;
+
+double *A0;
+double W0;
+double oldW0;
+double X0;
+
+/* parallelization via threads */
+struct thread_params_output
+{
+	double* output;
+	uint32_t start;
+	uint32_t end;
+};
+
+struct thread_qsort
+{
+	double* output;
+/*	uint32_t* index;*/
+	double* data;
+	uint32_t size;
+};
+
+struct thread_params_add
+{
+  double *new_a;
+  uint32_t *new_cut;
+  uint32_t start;
+  uint32_t end;
+};
+
+
+typedef enum 
+{ 
+  FALSE = 0,
+  TRUE = 1
+} 
+boolean;
+
+
+static int qsort_threads;
+static pthread_t* threads = NULL;
+static uint32_t* thread_slices = NULL;
+static int num_threads;
+static const int sort_limit=4096;
+static struct thread_params_output* params_output;
+static struct thread_params_add* params_add;
+
+/* use multi-threads only if minimal number of examples to add is higher than the constant*/
+static const uint32_t MinimalParallelCutLenght = 100;  
+
+/***********************************************************************
+   Multiclass SVM helper functions.                                   
+ **********************************************************************/
+
+/*----------------------------------------------------------------------
+  sq_norm_W = sparse_compute_W( alpha, nSel ) does the following:
+
+  oldW = W;
+  W = sparse_A(:,1:nSel)'*alpha;
+  sq_norm_W = W'*W;
+  dp_WoldW = W'*oldW';
+
+  ----------------------------------------------------------------------*/
+void msvm_sparse_compute_W( double *sq_norm_W, 
+                       double *dp_WoldW, 
+                       double *alpha, 
+                       uint32_t nSel, 
+                       void* user_data )
+{
+  uint32_t i,j, nz_dims;
+
+  memcpy(oldW, W, sizeof(double)*nY*nDim ); 
+  memset(W, 0, sizeof(double)*nY*nDim );
+
+  for(i=0; i < nSel; i++) {
+    nz_dims = sparse_A.nz_dims[i];
+    if(nz_dims > 0 && alpha[i] > 0) {
+      for(j=0; j < nz_dims; j++) {
+        W[sparse_A.index[i][j]] += alpha[i]*sparse_A.value[i][j];
+      }
+    }
+  }
+
+  *sq_norm_W = 0;
+  *dp_WoldW = 0;
+  for(j=0; j < nY*nDim; j++)
+  {
+    *sq_norm_W += W[j]*W[j];
+    *dp_WoldW += W[j]*oldW[j];
+  }
+  
+  return;
+}
+
+
+/*----------------------------------------------------------------------------------
+  sq_norm_W = sparse_update_W( t ) does the following:
+
+  W = oldW*(1-t) + t*W;
+  sq_norm_W = W'*W;
+
+  ---------------------------------------------------------------------------------*/
+double msvm_full_update_W( double t, void* user_data )
+{
+  uint32_t j;
+  double sq_norm_W;         
+
+  sq_norm_W = 0;
+
+  for(j=0; j < nY*nDim; j++) {
+    W[j] = oldW[j]*(1-t) + t*W[j];
+    sq_norm_W += W[j]*W[j];
+  }          
+
+  return( sq_norm_W );
+}
+
+
+/*----------------------------------------------------------------------------------
+  sparse_add_new_cut( new_col_H, new_cut, cut_length, nSel ) does the following:
+
+    new_a = zeros(nDim,nY);
+    for i=1:nData
+       if new_cut(i) ~= data_y(i)
+          new_a(:,data_y(i)) = new_a(:,data_y(i)) + X(;,i);
+          new_a(:,new_cut(i)) = new_a(:,new_cut(i)) - X(;,i);
+       end
+    end
+
+    new_col_H = [sparse_A(:,1:nSel)'*new_a ; new_a'*new_a];
+    sparse_A(:,nSel+1) = new_a;
+
+    Warning: data_y is 1-based while new_cut is 0-based
+
+  ---------------------------------------------------------------------------------*/
+int msvm_sparse_add_new_cut( double *new_col_H, 
+                         uint32_t *new_cut, 
+                         uint32_t nSel, 
+                         void* user_data )
+{
+/*  double *new_a, */
+  double sq_norm_a;
+  uint32_t i, j, nz_dims, ptr, y;
+
+  memset(new_a, 0, sizeof(double)*nY*nDim);
+  
+  for(i=0; i < nData; i++)
+  {
+    y = (uint32_t)(data_y[i]-1);
+    if(new_cut[i] != y)
+    {
+      add_sparse_col(&new_a[nDim*y], data_X, i);
+      subtract_sparse_col(&new_a[nDim*(uint32_t)new_cut[i]], data_X, i);
+    }
+  }
+ 
+  /* compute new_a'*new_a and count number of non-zero dimensions */
+  nz_dims = 0; 
+  sq_norm_a = 0;
+  for(j=0; j < nY*nDim; j++ ) {
+    if(new_a[j] != 0) {
+      nz_dims++;
+      sq_norm_a += new_a[j]*new_a[j];
+    }
+  }
+
+  /* sparsify new_a and insert it to the last column  of sparse_A */
+  sparse_A.nz_dims[nSel] = nz_dims;
+  if(nz_dims > 0) {
+    sparse_A.index[nSel] = NULL;
+    sparse_A.value[nSel] = NULL;
+    sparse_A.index[nSel] = mxCalloc(nz_dims,sizeof(uint32_t));
+    sparse_A.value[nSel] = mxCalloc(nz_dims,sizeof(double));
+    if(sparse_A.index[nSel]==NULL || sparse_A.value[nSel]==NULL)
+    {
+/*      mexErrMsgTxt("Not enough memory for vector sparse_A.index[nSel], sparse_A.value[nSel].");*/
+      mxFree(sparse_A.index[nSel]);
+      mxFree(sparse_A.value[nSel]);
+      return(-1);
+    }
+
+    ptr = 0;
+    for(j=0; j < nY*nDim; j++ ) {
+      if(new_a[j] != 0) {
+        sparse_A.index[nSel][ptr] = j;
+        sparse_A.value[nSel][ptr++] = new_a[j];
+      }
+    }
+  }
+   
+  new_col_H[nSel] = sq_norm_a;
+  for(i=0; i < nSel; i++) {
+    double tmp = 0;
+
+    for(j=0; j < sparse_A.nz_dims[i]; j++) {
+      tmp += new_a[sparse_A.index[i][j]]*sparse_A.value[i][j];
+    }
+      
+    new_col_H[i] = tmp;
+  }
+
+  return 0;
+}
+
+
+/*----------------------------------------------------------------------
+  sparse_compute_output( output ) does the follwing:
+
+  output = W'*data_X;
+  ----------------------------------------------------------------------*/
+int msvm_sparse_compute_output( double *output, void* user_data )
+{
+  uint32_t i,y;
+
+  for(i=0; i < nData; i++) 
+  {
+    for(y=0; y < nY; y++)
+    {
+      output[LIBOCAS_INDEX(y,i,nY)] = dp_sparse_col(&W[y*nDim], data_X, i);
+    }
+  }
+  
+  return 0;
+}
+
+
+/*-----------------------------------------------------------
+  Functions working with full data.
+  -------------------------------------------------------------*/
+
+/*----------------------------------------------------------------------------------
+  full_add_new_cut( new_col_H, new_cut, cut_length, nSel ) does the following:
+
+    new_a = sum(data_X(:,find(new_cut ~=0 )),2);
+    new_col_H = [full_A(:,1:nSel)'*new_a ; new_a'*new_a];
+    full_A(:,nSel+1) = new_a;
+
+  ---------------------------------------------------------------------------------*/
+int msvm_full_add_new_cut( double *new_col_H, uint32_t *new_cut, uint32_t nSel, void* user_data)
+{
+  double sq_norm_a, *ptr;
+  uint32_t i, j, y, y2;
+
+  ptr = mxGetPr(data_X);
+
+  memset(new_a, 0, sizeof(double)*nDim*nY);
+
+  for(i=0; i < nData; i++)
+  {
+    y = (uint32_t)(data_y[i]-1);
+    y2 = (uint32_t)new_cut[i];
+    if(y2 != y)
+    {
+      for(j=0; j < nDim; j++ ) 
+      {
+        new_a[LIBOCAS_INDEX(j,y,nDim)] += ptr[LIBOCAS_INDEX(j,i,nDim)];
+        new_a[LIBOCAS_INDEX(j,y2,nDim)] -= ptr[LIBOCAS_INDEX(j,i,nDim)];
+      }
+    }
+  }
+
+  /* compute new_a'*new_a and insert new_a to the last column of full_A */
+  sq_norm_a = 0;
+  for(j=0; j < nDim*nY; j++ ) {
+    sq_norm_a += new_a[j]*new_a[j];
+    full_A[LIBOCAS_INDEX(j,nSel,nDim*nY)] = new_a[j];
+  }
+
+  new_col_H[nSel] = sq_norm_a;
+  for(i=0; i < nSel; i++) {
+    double tmp = 0;
+
+    for(j=0; j < nDim*nY; j++ ) {
+      tmp += new_a[j]*full_A[LIBOCAS_INDEX(j,i,nDim*nY)];
+    }
+    new_col_H[i] = tmp;
+  }
+
+  return 0;
+}
+
+
+/*----------------------------------------------------------------------
+  full_compute_output( output ) does the follwing:
+
+  output = data_X'*W;
+  ----------------------------------------------------------------------*/
+int msvm_full_compute_output( double *output, void* user_data )
+{
+  uint32_t i, j, y;
+  double *ptr, tmp;
+
+  ptr = mxGetPr( data_X );
+
+  for(i=0; i < nData; i++) 
+  { 
+    for(y=0; y < nY; y++)
+    {
+      tmp = 0;
+
+      for(j=0; j < nDim; j++ ) 
+      {
+        tmp += W[LIBOCAS_INDEX(j,y,nDim)]*ptr[LIBOCAS_INDEX(j,i,nDim)];
+      }
+      
+      output[LIBOCAS_INDEX(y,i,nY)] = tmp;
+    }
+  }
+  
+  return 0;
+}
+
+
+
+/*----------------------------------------------------------------------
+  sq_norm_W = full_compute_W( alpha, nSel ) does the following:
+
+  oldW = W;
+  W = full_A(:,1:nSel)'*alpha;
+  sq_norm_W = W'*W;
+  dp_WoldW = W'*oldW';
+
+  ----------------------------------------------------------------------*/
+void msvm_full_compute_W( double *sq_norm_W, double *dp_WoldW, double *alpha, uint32_t nSel, void* user_data )
+{
+  uint32_t i,j;
+
+  memcpy(oldW, W, sizeof(double)*nDim*nY ); 
+  memset(W, 0, sizeof(double)*nDim*nY);
+
+  for(i=0; i < nSel; i++) {
+    if( alpha[i] > 0 ) {
+      for(j=0; j< nDim*nY; j++ ) {
+        W[j] += alpha[i]*full_A[LIBOCAS_INDEX(j,i,nDim*nY)];
+      }
+
+    }
+  }
+
+  *sq_norm_W = 0;
+  *dp_WoldW = 0;
+  for(j=0; j < nDim*nY; j++) {
+    *sq_norm_W += W[j]*W[j];
+    *dp_WoldW += W[j]*oldW[j];
+  }
+
+  return;
+}
+
+
+
+/***********************************************************************
+  Generic functions.
+ **********************************************************************/
+
+
+/*-----------------------------------------------------------------------
+  Print statistics.
+  -----------------------------------------------------------------------*/
+void ocas_print(ocas_return_value_T value)
+{
+  mexPrintf("%4d: tim=%f, Q_P=%f, Q_D=%f, Q_P-Q_D=%f, 1-Q_D/Q_P=%f, nza=%4d, err=%.2f%%, qpf=%d\n",
+            value.nIter,value.ocas_time, value.Q_P,value.Q_D,value.Q_P-value.Q_D,(value.Q_P-value.Q_D)/LIBOCAS_ABS(value.Q_P), 
+            value.nNZAlpha, 100*(double)value.trn_err/(double)nData, value.qp_exitflag );
+}
+
+void ocas_print_null(ocas_return_value_T value)
+{
+  return;
+}
+
+
+/*-----------------------------------------------------------------------
+  Get absolute time in seconds.
+  -----------------------------------------------------------------------*/
+double get_time()
+{
+	struct timeval tv;
+	if (gettimeofday(&tv, NULL)==0)
+		return tv.tv_sec+((double)(tv.tv_usec))/1e6;
+	else
+		return 0.0;
+}
+
+
+/*=========================================================================
+ *
+ * Ocas helper functions implemented for input data represented as 
+ * Matlab sparse matrix.
+ *
+ *=========================================================================*/
+
+/*----------------------------------------------------------------------
+  in-place computes sparse_mat(:,col)= alpha * sparse_mat(:,col)
+  where alpha is a scalar and sparse_mat is Matlab sparse matrix.
+  ----------------------------------------------------------------------*/
+void mul_sparse_col(double alpha, mxArray *sparse_mat, uint32_t col)
+{
+	uint32_t nItems, ptr, i;
+	INDEX_TYPE_T *Ir, *Jc;
+	double *Pr;
+
+	Ir = mxGetIr(sparse_mat);
+	Jc = mxGetJc(sparse_mat);
+	Pr = mxGetPr(sparse_mat);
+
+	nItems = Jc[col+1] - Jc[col];
+	ptr = Jc[col];
+
+	for(i=0; i < nItems; i++)
+		Pr[ptr++]*=alpha;
+}
+
+
+/*----------------------------------------------------------------------
+ It computes full_vec = full_vec + sparse_mat(:,col)
+ where full_vec is a double array and sparse_mat is Matlab 
+ sparse matrix.
+  ----------------------------------------------------------------------*/
+void add_sparse_col(double *full_vec, mxArray *sparse_mat, uint32_t col)
+{
+  uint32_t nItems, ptr, i, row;
+  INDEX_TYPE_T *Ir, *Jc;
+  double *Pr, val;
+    
+  Ir = mxGetIr(sparse_mat);
+  Jc = mxGetJc(sparse_mat);
+  Pr = mxGetPr(sparse_mat);
+
+  nItems = Jc[col+1] - Jc[col];
+  ptr = Jc[col];
+
+  for(i=0; i < nItems; i++) {
+    val = Pr[ptr];
+    row = Ir[ptr++];
+
+    full_vec[row] += val;
+  }
+}
+
+/*----------------------------------------------------------------------
+ It computes full_vec = full_vec - sparse_mat(:,col)
+ where full_vec is a double array and sparse_mat is Matlab 
+ sparse matrix.
+  ----------------------------------------------------------------------*/
+void subtract_sparse_col(double *full_vec, mxArray *sparse_mat, uint32_t col)
+{
+  uint32_t nItems, ptr, i, row;
+  INDEX_TYPE_T *Ir, *Jc;
+  double *Pr, val;
+    
+  Ir = mxGetIr(sparse_mat);
+  Jc = mxGetJc(sparse_mat);
+  Pr = mxGetPr(sparse_mat);
+
+  nItems = Jc[col+1] - Jc[col];
+  ptr = Jc[col];
+
+  for(i=0; i < nItems; i++) {
+    val = Pr[ptr];
+    row = Ir[ptr++];
+
+    full_vec[row] -= val;
+  }
+}
+
+/*----------------------------------------------------------------------
+ It computes dp = full_vec'*sparse_mat(:,col)
+ where full_vec is a double array and sparse_mat is Matlab 
+ sparse matrix.
+  ----------------------------------------------------------------------*/
+double dp_sparse_col(double *full_vec, mxArray *sparse_mat, uint32_t col)
+{
+  uint32_t nItems, ptr, i, row;
+  INDEX_TYPE_T *Ir, *Jc;
+  double *Pr, val, dp;
+
+  Ir = mxGetIr(sparse_mat);
+  Jc = mxGetJc(sparse_mat);
+  Pr = mxGetPr(sparse_mat);
+
+  dp = 0;
+  nItems = Jc[col+1] - Jc[col];
+  ptr = Jc[col];
+
+  for(i=0; i < nItems; i++) {
+    val = Pr[ptr];
+    row = Ir[ptr++];
+
+    dp += full_vec[row]*val;
+  }
+
+  return(dp);  
+}
+
+
+/*----------------------------------------------------------------------------------
+  sq_norm_W = sparse_update_W( t ) does the following:
+
+  W = oldW*(1-t) + t*W;
+  sq_norm_W = W'*W;
+
+  ---------------------------------------------------------------------------------*/
+double sparse_update_W( double t, void* user_data )
+{
+  uint32_t j;
+  double sq_norm_W;         
+
+  W0 = oldW0*(1-t) + t*W0;
+  sq_norm_W = W0*W0;
+
+  for(j=0; j <nDim; j++) {
+    W[j] = oldW[j]*(1-t) + t*W[j];
+    sq_norm_W += W[j]*W[j];
+  }          
+
+  return( sq_norm_W );
+}
+
+/*-------------------------------------------------------------------------
+  sq_norm_W = full_update_W( t ) does the following:
+
+  W = oldW*(1-t) + t*W;
+  sq_norm_W = W'*W;
+---------------------------------------------------------------------------*/
+double full_update_W( double t, void* user_data )
+{
+  uint32_t j;
+  double sq_norm_W;         
+
+  W0 = oldW0*(1-t) + t*W0;
+  sq_norm_W = W0*W0;
+
+  for(j=0; j <nDim; j++) {
+    W[j] = oldW[j]*(1-t) + t*W[j];
+    sq_norm_W += W[j]*W[j];
+  }          
+
+  return( sq_norm_W );
+}
+
+
+/*----------------------------------------------------------------------------------
+  sparse_add_new_cut( new_col_H, new_cut, cut_length, nSel ) does the following:
+
+    new_a = sum(data_X(:,find(new_cut ~=0 )),2);
+    new_col_H = [sparse_A(:,1:nSel)'*new_a ; new_a'*new_a];
+    sparse_A(:,nSel+1) = new_a;
+
+  ---------------------------------------------------------------------------------*/
+int sparse_add_new_cut( double *new_col_H, 
+                         uint32_t *new_cut, 
+                         uint32_t cut_length, 
+                         uint32_t nSel, 
+                         void* user_data )
+{
+/*  double *new_a, */
+  double sq_norm_a;
+  uint32_t i, j, nz_dims, ptr;
+
+  memset(new_a, 0, sizeof(double)*nDim);
+  
+  for(i=0; i < cut_length; i++) {
+    add_sparse_col(new_a, data_X, new_cut[i]);
+
+    A0[nSel] += X0*data_y[new_cut[i]];    
+  }
+ 
+  /* compute new_a'*new_a and count number of non-zero dimensions */
+  nz_dims = 0; 
+  sq_norm_a = A0[nSel]*A0[nSel];
+  for(j=0; j < nDim; j++ ) {
+    if(new_a[j] != 0) {
+      nz_dims++;
+      sq_norm_a += new_a[j]*new_a[j];
+    }
+  }
+
+  /* sparsify new_a and insert it to the last column  of sparse_A */
+  sparse_A.nz_dims[nSel] = nz_dims;
+  if(nz_dims > 0) {
+    sparse_A.index[nSel] = NULL;
+    sparse_A.value[nSel] = NULL;
+    sparse_A.index[nSel] = mxCalloc(nz_dims,sizeof(uint32_t));
+    sparse_A.value[nSel] = mxCalloc(nz_dims,sizeof(double));
+    if(sparse_A.index[nSel]==NULL || sparse_A.value[nSel]==NULL)
+    {
+/*      mexErrMsgTxt("Not enough memory for vector sparse_A.index[nSel], sparse_A.value[nSel].");*/
+      mxFree(sparse_A.index[nSel]);
+      mxFree(sparse_A.value[nSel]);
+      return(-1);
+    }
+
+    ptr = 0;
+    for(j=0; j < nDim; j++ ) {
+      if(new_a[j] != 0) {
+        sparse_A.index[nSel][ptr] = j;
+        sparse_A.value[nSel][ptr++] = new_a[j];
+      }
+    }
+  }
+   
+  new_col_H[nSel] = sq_norm_a;
+  for(i=0; i < nSel; i++) {
+    double tmp = A0[nSel]*A0[i];
+
+    for(j=0; j < sparse_A.nz_dims[i]; j++) {
+      tmp += new_a[sparse_A.index[i][j]]*sparse_A.value[i][j];
+    }
+      
+    new_col_H[i] = tmp;
+  }
+
+/*  mxFree( new_a );*/
+
+  return 0;
+}
+
+
+/*----------------------------------------------------------------------------------
+  full_add_new_cut( new_col_H, new_cut, cut_length, nSel ) does the following:
+
+    new_a = sum(data_X(:,find(new_cut ~=0 )),2);
+    new_col_H = [full_A(:,1:nSel)'*new_a ; new_a'*new_a];
+    full_A(:,nSel+1) = new_a;
+
+  ---------------------------------------------------------------------------------*/
+int full_add_new_cut( double *new_col_H, 
+                       uint32_t *new_cut, 
+                       uint32_t cut_length, 
+                       uint32_t nSel,
+                       void* user_data)
+{
+/*  double *new_a, */
+  double sq_norm_a, *ptr;
+  uint32_t i, j;
+
+  ptr = mxGetPr(data_X);
+
+  memset(new_a, 0, sizeof(double)*nDim);
+
+
+  for(i=0; i < cut_length; i++) {
+    for(j=0; j < nDim; j++ ) {
+      new_a[j] += ptr[LIBOCAS_INDEX(j,new_cut[i],nDim)];
+    }
+
+    A0[nSel] += X0*data_y[new_cut[i]];    
+  }
+
+  /* compute new_a'*new_a and insert new_a to the last column of full_A */
+  sq_norm_a = A0[nSel]*A0[nSel];
+  for(j=0; j < nDim; j++ ) {
+    sq_norm_a += new_a[j]*new_a[j];
+    full_A[LIBOCAS_INDEX(j,nSel,nDim)] = new_a[j];
+  }
+
+  new_col_H[nSel] = sq_norm_a;
+  for(i=0; i < nSel; i++) {
+    double tmp = A0[nSel]*A0[i];
+
+    for(j=0; j < nDim; j++ ) {
+      tmp += new_a[j]*full_A[LIBOCAS_INDEX(j,i,nDim)];
+    }
+    new_col_H[i] = tmp;
+  }
+
+/*  mxFree( new_a );*/
+
+  return 0;
+}
+
+
+/*----------------------------------------------------------------------
+  sparse_compute_output( output ) does the follwing:
+
+  output = data_X'*W;
+  ----------------------------------------------------------------------*/
+int sparse_compute_output( double *output, void* user_data )
+{
+  uint32_t i;
+
+  for(i=0; i < nData; i++) { 
+    output[i] = data_y[i]*X0*W0 + dp_sparse_col(W, data_X, i);
+  }
+  
+  return 0;
+}
+
+/*----------------------------------------------------------------------
+  full_compute_output( output ) does the follwing:
+
+  output = data_X'*W;
+  ----------------------------------------------------------------------*/
+int full_compute_output( double *output, void* user_data )
+{
+  uint32_t i, j;
+  double *ptr, tmp;
+
+  ptr = mxGetPr( data_X );
+
+  for(i=0; i < nData; i++) { 
+    tmp = data_y[i]*X0*W0;
+
+    for(j=0; j < nDim; j++ ) {
+      tmp += W[j]*ptr[LIBOCAS_INDEX(j,i,nDim)];
+    }
+    output[i] = tmp;
+  }
+  
+  return 0;
+}
+
+
+
+/*----------------------------------------------------------------------
+  sq_norm_W = sparse_compute_W( alpha, nSel ) does the following:
+
+  oldW = W;
+  W = sparse_A(:,1:nSel)'*alpha;
+  sq_norm_W = W'*W;
+  dp_WoldW = W'*oldW';
+
+  ----------------------------------------------------------------------*/
+void sparse_compute_W( double *sq_norm_W, 
+                       double *dp_WoldW, 
+                       double *alpha, 
+                       uint32_t nSel, 
+                       void* user_data )
+{
+  uint32_t i,j, nz_dims;
+
+  memcpy(oldW, W, sizeof(double)*nDim ); 
+  memset(W, 0, sizeof(double)*nDim);
+
+  oldW0 = W0;
+  W0 = 0;
+
+  for(i=0; i < nSel; i++) {
+    nz_dims = sparse_A.nz_dims[i];
+    if(nz_dims > 0 && alpha[i] > 0) {
+      for(j=0; j < nz_dims; j++) {
+        W[sparse_A.index[i][j]] += alpha[i]*sparse_A.value[i][j];
+      }
+    }
+    W0 += A0[i]*alpha[i];
+  }
+
+  *sq_norm_W = W0*W0;
+  *dp_WoldW = W0*oldW0;
+  for(j=0; j < nDim; j++) {
+    *sq_norm_W += W[j]*W[j];
+    *dp_WoldW += W[j]*oldW[j];
+  }
+  
+  return;
+}
+
+
+/*----------------------------------------------------------------------
+  sq_norm_W = full_compute_W( alpha, nSel ) does the following:
+
+  oldW = W;
+  W = full_A(:,1:nSel)'*alpha;
+  sq_norm_W = W'*W;
+  dp_WoldW = W'*oldW';
+
+  ----------------------------------------------------------------------*/
+void full_compute_W( double *sq_norm_W, double *dp_WoldW, double *alpha, uint32_t nSel, void* user_data )
+{
+  uint32_t i,j;
+
+  memcpy(oldW, W, sizeof(double)*nDim ); 
+  memset(W, 0, sizeof(double)*nDim);
+
+  oldW0 = W0;
+  W0 = 0;
+
+  for(i=0; i < nSel; i++) {
+    if( alpha[i] > 0 ) {
+      for(j=0; j< nDim; j++ ) {
+        W[j] += alpha[i]*full_A[LIBOCAS_INDEX(j,i,nDim)];
+      }
+
+      W0 += A0[i]*alpha[i];
+    }
+  }
+
+  *sq_norm_W = W0*W0;
+  *dp_WoldW = W0*oldW0;
+  for(j=0; j < nDim; j++) {
+    *sq_norm_W += W[j]*W[j];
+    *dp_WoldW += W[j]*oldW[j];
+  }
+
+  return;
+}
+
+
+/* ==========================================================================
+ Multi-thread version of the OCAS helper functions.
+
+ int parallel_sparse_compute_output( double *output, void* user_data )
+
+ void destroy_parallel_ocas(void)
+ int init_parallel_ocas(int number_of_threads)
+
+============================================================================*/
+
+
+/*----------------------------------------------------------------------
+  parallel_sparse_compute_output( output, user_data ) does the following
+
+  output = data_X'*W + W0*X0*y[i];
+  ----------------------------------------------------------------------*/
+
+static void* parallel_compute_output_helper(void* p)
+{
+	struct thread_params_output* params = (struct thread_params_output*) p;
+	double* output=params->output;
+	uint32_t start=params->start;
+	uint32_t end=params->end;
+	uint32_t i;
+
+    for(i=start; i < end; i++) 
+      output[i] = data_y[i]*X0*W0 + dp_sparse_col(W, data_X, i);
+
+    return(NULL);
+}
+
+int parallel_sparse_compute_output( double *output, void* user_data )
+{
+  /*  one-thraed code looks like:
+
+  uint32_t i;
+
+  for(i=0; i < nData; i++) { 
+    output[i] = data_y[i]*X0*W0 + dp_sparse_col(W, data_X, i);
+  }
+  */
+
+/*  struct thread_params_output params;*/
+
+  int nthreads=num_threads-1;
+  int end=0;
+  int t;
+  
+  if (nData < num_threads)
+  {
+    nthreads=nData-1;
+  }
+
+  for (t=0; t<nthreads; t++)
+  {
+    params_output[t].output = output;
+    if (t==0)
+      params_output[t].start = 0;
+    else
+      params_output[t].start = thread_slices[t-1];
+    params_output[t].end = thread_slices[t];
+    
+    if (pthread_create(&threads[t], NULL, parallel_compute_output_helper, (void*)&params_output[t]) != 0)
+    {
+      mexPrintf("\nError: Thread creation failed.\n");
+      return(-1);
+    }
+
+    end=params_output[t].end;
+  }
+
+  params_output[t].output = output;
+  params_output[t].start = end;
+  params_output[t].end = nData;
+
+  parallel_compute_output_helper(&params_output[t]);
+
+  for (t=0; t<nthreads; t++)
+  {
+    if (pthread_join(threads[t], NULL) != 0)
+    {
+      mexPrintf("\nError: pthread_join failed.\n");
+      return(-1);
+    }
+  }
+
+  return 0;
+}
+
+/* Ihis function initialize parallel processing . It must be
+ called prior to paralel_XXXX  OCAS heleper functions. */
+int init_parallel_ocas(int number_of_threads)
+{
+  num_threads = number_of_threads;
+
+  thread_slices = (uint32_t*)mxCalloc(num_threads,sizeof(uint32_t));
+  if(thread_slices == NULL) 
+  {
+    mexPrintf("Not enough memory for vector num_threads.");
+    goto clean_up;
+
+  }
+
+  threads = (pthread_t*)mxCalloc(num_threads,sizeof(pthread_t));
+  if(threads== NULL) 
+  {
+    mexPrintf("Not enough memory for threads structure.");
+    goto clean_up;
+  }
+
+  params_output = (struct thread_params_output*)mxCalloc(num_threads,sizeof(struct thread_params_output));
+  if(params_output== NULL) 
+  {
+    mexPrintf("Not enough memory for params structure.");
+    goto clean_up;
+  }
+
+  params_add = (struct thread_params_add*)mxCalloc(num_threads,sizeof(struct thread_params_add));
+  if(params_add== NULL) 
+  {
+    mexPrintf("Not enough memory for params structure.");
+    goto clean_up;
+  }
+
+  /* The following code finds splits of the data such that each
+  data slice contains approximately the same number of nonzero elements. */ 
+  uint32_t i;
+  int nnz_split = 0;
+  INDEX_TYPE_T* Jc = mxGetJc(data_X);
+
+  for(i=0; i < nData; i++)
+    nnz_split += Jc[i+1] - Jc[i];
+
+  nnz_split/=num_threads;
+  uint64_t accum_nnz = 0;
+  int thr = 0;
+
+  mexPrintf("Data slices: ");
+  for(i=0; i < nData; i++)
+  {
+    INDEX_TYPE_T nItems = Jc[i+1] - Jc[i];
+
+    if (accum_nnz < nnz_split*(thr+1))
+      accum_nnz+=nItems;
+    else
+    {
+      thread_slices[thr]=i;
+      mexPrintf("%d ", thread_slices[thr]);
+      accum_nnz+=nItems;
+      thr++;
+    }
+  }
+  mexPrintf("\n");
+  
+  return(0);
+
+clean_up:    
+
+  mxFree(threads);
+  mxFree(thread_slices);
+
+  return(-1);
+}
+
+/* release memory allocated for parallelized functions */
+void destroy_parallel_ocas(void)
+{
+  mxFree(params_add);
+  mxFree(params_output);
+  mxFree(threads);
+  mxFree(thread_slices);
+}
+
+/*----------------------------------------------------------------------------------
+  Parallel version of 
+  sparse_add_new_cut( new_col_H, new_cut, cut_length, nSel ) does the following:
+
+    new_a = sum(data_X(:,find(new_cut ~=0 )),2);
+    new_col_H = [sparse_A(:,1:nSel)'*new_a ; new_a'*new_a];
+    sparse_A(:,nSel+1) = new_a;
+
+  ---------------------------------------------------------------------------------*/
+
+static void* parallel_sparse_add_helper(void* p)
+{
+	struct thread_params_add* params = (struct thread_params_add*) p;
+	double* local_new_a=params->new_a;
+    uint32_t* new_cut=params->new_cut;
+	uint32_t start=params->start;
+	uint32_t end=params->end;
+	uint32_t i;
+
+    for(i=start; i <= end; i++) 
+      add_sparse_col(local_new_a, data_X, new_cut[i]);
+
+    return(NULL);
+}
+
+int parallel_sparse_add_new_cut( double *new_col_H, 
+                         uint32_t *new_cut, 
+                         uint32_t cut_length, 
+                         uint32_t nSel, 
+                         void* user_data )
+{
+/*  double *new_a, */
+  double sq_norm_a;
+  uint32_t i, j, nz_dims, ptr;
+
+  /* temporary vector */
+/*  new_a = (double*)mxCalloc(nDim,sizeof(double));*/
+/*  if(new_a == NULL) */
+/*    return(-1); */
+  memset(new_a, 0, sizeof(double)*nDim*num_threads);
+  
+  if((cut_length < MinimalParallelCutLenght) && (cut_length >= num_threads))
+  {
+
+    for(i=0; i < cut_length; i++) {
+      add_sparse_col(new_a, data_X, new_cut[i]);
+
+      A0[nSel] += X0*data_y[new_cut[i]];    
+    }
+  }
+  else
+  {
+    
+/*    struct thread_params_add params;*/
+/*    params.new_cut = new_cut;*/
+
+    uint32_t chunk = cut_length/num_threads;
+    uint32_t start = 0;
+    uint32_t end = chunk-1;
+    int t;
+    for(t = 0; t < num_threads-1; t++)
+    {
+      params_add[t].start = start;
+      params_add[t].end = end;
+      params_add[t].new_a = &new_a[nDim*(t+1)];
+      params_add[t].new_cut = new_cut;
+
+      start = end+1;
+      end = end+chunk;
+
+      if (pthread_create(&threads[t], NULL, parallel_sparse_add_helper, (void*)&params_add[t]) != 0)
+      {
+        mexPrintf("\nError: Thread creation failed.\n");
+        return(-1);
+      }
+       
+    }
+
+    params_add[t].start = start;
+    params_add[t].end = cut_length-1;
+    params_add[t].new_a = new_a;
+    params_add[t].new_cut = new_cut;
+
+    parallel_sparse_add_helper((void*)&params_add[t]);
+
+    for (t=0; t<num_threads-1; t++)
+    {
+      if (pthread_join(threads[t], NULL) != 0)
+      {
+        return(-1);
+      }
+
+      double* a = &new_a[nDim*(t+1)];
+
+      for (i=0; i<nDim; i++)
+        new_a[i]+=a[i];
+    }
+
+    for(i=0; i < cut_length; i++) 
+      A0[nSel] += X0*data_y[new_cut[i]];    
+  }
+ 
+  /* compute new_a'*new_a and count number of non-zero dimensions */
+  nz_dims = 0; 
+  sq_norm_a = A0[nSel]*A0[nSel];
+  for(j=0; j < nDim; j++ ) {
+    if(new_a[j] != 0) {
+      nz_dims++;
+      sq_norm_a += new_a[j]*new_a[j];
+    }
+  }
+
+  /* sparsify new_a and insert it to the last column  of sparse_A */
+  sparse_A.nz_dims[nSel] = nz_dims;
+  if(nz_dims > 0) {
+    sparse_A.index[nSel] = NULL;
+    sparse_A.value[nSel] = NULL;
+    sparse_A.index[nSel] = mxCalloc(nz_dims,sizeof(uint32_t));
+    sparse_A.value[nSel] = mxCalloc(nz_dims,sizeof(double));
+    if(sparse_A.index[nSel]==NULL || sparse_A.value[nSel]==NULL)
+    {
+/*      mexErrMsgTxt("Not enough memory for vector sparse_A.index[nSel], sparse_A.value[nSel].");*/
+      mxFree(sparse_A.index[nSel]);
+      mxFree(sparse_A.value[nSel]);
+      return(-1);
+    }
+
+    ptr = 0;
+    for(j=0; j < nDim; j++ ) {
+      if(new_a[j] != 0) {
+        sparse_A.index[nSel][ptr] = j;
+        sparse_A.value[nSel][ptr++] = new_a[j];
+      }
+    }
+  }
+   
+  new_col_H[nSel] = sq_norm_a;
+  for(i=0; i < nSel; i++) {
+    double tmp = A0[nSel]*A0[i];
+
+    for(j=0; j < sparse_A.nz_dims[i]; j++) {
+      tmp += new_a[sparse_A.index[i][j]]*sparse_A.value[i][j];
+    }
+      
+    new_col_H[i] = tmp;
+  }
+
+/*  mxFree( new_a );*/
+
+  return 0;
+}
+
+
+/*=========================================================================
+ *
+ * Ocas helper functions implemented for input data represented as
+ * an array of doubles.
+ *
+ *=========================================================================*/
+
+
+
+/*=======================================================================
+ OCAS helper functions for sorting numbers.
+=======================================================================*/
+static void swapf(double* a, double* b)
+{
+	double dummy=*b;
+	*b=*a;
+	*a=dummy;
+}
+
+/*static void swapi(uint32_t* a, uint32_t* b)*/
+/*{*/
+/*	int dummy=*b;*/
+/*	*b=*a;*/
+/*	*a=dummy;*/
+/*}*/
+
+/* sort arrays value and data according to value in ascending order */
+int qsort_data(double* value, double* data, uint32_t size)
+{
+    if(size == 1)
+      return 0;
+
+	if (size==2)
+	{
+		if (value[0] > value[1])
+		{
+			swapf(&value[0], &value[1]);
+/*			swapi(&data[0], &data[1]);*/
+			swapf(&data[0], &data[1]);
+		}
+		return 0;
+	}
+	double split=value[size/2];
+
+	uint32_t left=0;
+	uint32_t right=size-1;
+
+	while (left<=right)
+	{
+		while (value[left] < split)
+			left++;
+		while (value[right] > split)
+			right--;
+
+		if (left<=right)
+		{
+			swapf(&value[left], &value[right]);
+/*			swapi(&data[left], &data[right]);*/
+			swapf(&data[left], &data[right]);
+			left++;
+			right--;
+		}
+	}
+
+	if (right+1> 1)
+		qsort_data(value,data,right+1);
+
+	if (size-left> 1)
+		qsort_data(&value[left],&data[left], size-left);
+
+
+    return 0;
+}
+
+/*-------------------------------------------------------------------------
+ Parallel version of qsort_data.
+  -------------------------------------------------------------------------*/
+void* parallel_qsort_helper(void* p)
+{
+	struct thread_qsort* ps=(struct thread_qsort*) p;
+	double* output=ps->output;
+/*	uint32_t* data=ps->data;*/
+	double* data=ps->data;
+	uint32_t size=ps->size;
+
+    if(size == 1)
+      return 0;
+
+
+	if (size==2)
+	{
+		if (output[0] > output [1])
+		{
+			swapf(&output[0], &output[1]);
+/*			swapi(&data[0], &data[1]);*/
+			swapf(&data[0], &data[1]);
+		}
+		return(NULL);
+	}
+	/*double split=output[(((uint64_t) size)*rand())/(((uint64_t)RAND_MAX)+1)];*/
+	double split=output[size/2];
+
+	uint32_t left=0;
+	uint32_t right=size-1;
+
+	while (left<=right)
+	{
+		while (output[left] < split)
+			left++;
+		while (output[right] > split)
+			right--;
+
+		if (left<=right)
+		{
+			swapf(&output[left], &output[right]);
+/*			swapi(&index[left], &index[right]);*/
+			swapf(&data[left], &data[right]);
+			left++;
+			right--;
+		}
+	}
+	boolean lthread_start=FALSE;
+	boolean rthread_start=FALSE;
+	pthread_t lthread;
+	pthread_t rthread;
+	struct thread_qsort t1;
+	struct thread_qsort t2;
+
+	if (right+1> 1 && (right+1< sort_limit || qsort_threads >= num_threads-1))
+		qsort_data(output,data,right+1);
+	else if (right+1> 1)
+	{
+		qsort_threads++;
+		lthread_start=TRUE;
+		t1.output=output;
+		t1.data=data;
+		t1.size=right+1;
+		if (pthread_create(&lthread, NULL, parallel_qsort_helper, (void*) &t1) != 0)
+		{
+			lthread_start=FALSE;
+			qsort_threads--;
+			qsort_data(output,data,right+1);
+		}
+	}
+
+
+	if (size-left> 1 && (size-left< sort_limit || qsort_threads >= num_threads-1))
+		qsort_data(&output[left],&data[left], size-left);
+	else if (size-left> 1)
+	{
+		qsort_threads++;
+		rthread_start=TRUE;
+		t2.output=&output[left];
+		t2.data=&data[left];
+		t2.size=size-left;
+		if (pthread_create(&rthread, NULL, parallel_qsort_helper, (void*)&t2) != 0)
+		{
+			rthread_start=FALSE;
+			qsort_threads--;
+			qsort_data(&output[left],&data[left], size-left);
+		}
+	}
+
+	if (lthread_start)
+	{
+		pthread_join(lthread, NULL);
+		qsort_threads--;
+	}
+
+	if (rthread_start)
+	{
+		pthread_join(rthread, NULL);
+		qsort_threads--;
+	}
+
+    return(NULL);
+}
+
+
+int parallel_qsort_data(double* value, double* data, uint32_t size)
+{
+  struct thread_qsort qthr;
+
+  qsort_threads=0;
+
+  qthr.output=value;
+  qthr.data=data;
+  qthr.size=size;
+  parallel_qsort_helper((void*)&qthr);
+
+  return(0);
+}
+
+/* =================================================================================
+ Other auxiliary functions.
+   =================================================================================*/
+
+/* ---------------------------------------------------------------------------------
+This function loads regularization constants from a text file. Each line contains
+a single constant. 
+  ---------------------------------------------------------------------------------*/
+int load_regconsts(char *fname, double **vec_C, uint32_t *len_vec_C, int verb)
+{
+  double C;
+  char *line = NULL;
+  int exitflag = 0;
+  FILE *fid;
+
+  if(verb) mexPrintf("Input file: %s\n", fname);
+
+  fid = fopen(fname, "r");
+  if(fid == NULL) {
+    perror("fopen error ");
+    mexPrintf("Cannot open input file.\n");
+    exitflag = -1;
+    goto clean_up;
+  }
+
+  line = mxCalloc(LIBSLF_MAXLINELEN, sizeof(char));
+  if( line == NULL )
+  {
+    mexPrintf("Not enough memmory to allocate line buffer.\n");
+    exitflag = -1;
+    goto clean_up;
+  }
+
+
+  if(verb) mexPrintf("Counting regularization constants...");
+  int go = 1;
+  long line_cnt = 0;
+  while(go) {
+    
+    if(fgets(line,LIBSLF_MAXLINELEN, fid) == NULL ) 
+    {
+      go = 0;
+      if(verb)
+      {
+        if( (line_cnt % 1000) != 0) 
+          mexPrintf(" %ld", line_cnt);
+        mexPrintf(" EOF.\n");
+      }
+
+    }
+    else
+    {
+      line_cnt ++;
+
+      C = atof(line);
+      
+      if(verb)
+      {
+        if( (line_cnt % 1000) == 0) {
+          mexPrintf(" %ld", line_cnt);
+          fflush(NULL);
+        }
+      }
+    }
+  }
+  
+  *vec_C = (double*)mxCalloc(line_cnt, sizeof(double));
+  if( vec_C == NULL )
+  {
+    mexPrintf("Not enough memmory to allocate vec_C.\n");
+    exitflag = -1;
+    goto clean_up;
+  }
+
+  fseek(fid, 0, SEEK_SET);
+
+  if(verb) mexPrintf("Reading regularization constants...");
+  go = 1;
+  line_cnt = 0;
+  while(go) 
+  {
+    
+    if(fgets(line,LIBSLF_MAXLINELEN, fid) == NULL ) 
+    {
+      go = 0;
+      if(verb)
+      {
+        if( (line_cnt % 1000) != 0) 
+          mexPrintf(" %ld", line_cnt);
+        mexPrintf(" EOF.\n");
+      }
+
+    }
+    else
+    {
+      (*vec_C)[line_cnt] = atof(line);
+      line_cnt ++;
+      
+      if(verb)
+      {
+        if( (line_cnt % 1000) == 0) {
+          mexPrintf(" %ld", line_cnt);
+          fflush(NULL);
+        }
+      }
+    }
+  }
+  
+  fclose(fid);
+  *len_vec_C = line_cnt;
+
+clean_up:
+  mxFree(line);
+
+  return(exitflag); 
+}
+
+
+
+/* --------------------------------------------------------------------------------- 
+This function loads SVMlight data file to sparse matlab matrix data_X and 
+dense vector data_y which both are assumed to global variables.
+  ---------------------------------------------------------------------------------*/
+int load_svmlight_file(char *fname, int verb)
+{
+  char *line = NULL;
+  FILE *fid;
+  double *feat_val = NULL;
+  double sparse_memory_requirement, full_memory_requirement;
+  uint32_t *feat_idx = NULL;
+  long nnzf;
+  int max_dim = 0;
+  long j;
+  uint64_t nnz = 0;
+  mwSize *irs = NULL, *jcs = NULL;
+  int exitflag = 0;
+  double *sr = NULL;
+  
+/*  mexPrintf("Input file: %s\n", fname);*/
+
+  fid = fopen(fname, "r");
+  if(fid == NULL) {
+    perror("fopen error ");
+    mexPrintf("Cannot open input file.\n");
+    exitflag = -1;
+    goto clean_up;
+  }
+
+  line = mxCalloc(LIBSLF_MAXLINELEN, sizeof(char));
+  if( line == NULL )
+  {
+    mexPrintf("Not enough memmory to allocate line buffer.\n");
+    exitflag = -1;
+    goto clean_up;
+  }
+
+  feat_idx = mxCalloc(LIBSLF_MAXLINELEN, sizeof(uint32_t));
+  if( feat_idx == NULL )
+  {
+    mexPrintf("Not enough memmory to allocate feat_idx.\n");
+    exitflag = -1;
+    goto clean_up;
+  }
+
+  feat_val = mxCalloc(LIBSLF_MAXLINELEN, sizeof(double));
+  if( feat_val == NULL )
+  {
+    mexPrintf("Not enough memmory to allocate feat_val.\n");
+    exitflag = -1;
+    goto clean_up;
+  }
+
+  if(verb) mexPrintf("Analysing input data...");
+  int label;
+  int go = 1;
+  long line_cnt = 0;
+
+  while(go) {
+    
+    if(fgets(line,LIBSLF_MAXLINELEN, fid) == NULL ) 
+    {
+      go = 0;
+      if(verb)
+      {
+        if( (line_cnt % 1000) != 0) 
+          mexPrintf(" %ld", line_cnt);
+        mexPrintf(" EOF.\n");
+      }
+
+    }
+    else
+    {
+      line_cnt ++;
+      nnzf = svmlight_format_parse_line(line, &label, feat_idx, feat_val);
+      
+      if(nnzf == -1) 
+      {
+         mexPrintf("Parsing error on line %ld .\n", line_cnt);
+         mexPrintf("Probably defective input file.\n");
+         exitflag = -1;
+         goto clean_up;
+      }
+
+      max_dim = LIBOCAS_MAX(max_dim,feat_idx[nnzf-1]);
+      nnz += nnzf;
+      
+      if(verb)
+      {
+        if( (line_cnt % 1000) == 0) {
+          mexPrintf(" %ld", line_cnt);
+          fflush(NULL);
+        }
+      }
+    }
+  }
+
+  fclose(fid);  
+  if(verb)
+  {
+    mexPrintf("Data statistics:\n");
+    mexPrintf("# of examples: %ld\n", line_cnt);
+    mexPrintf("dimensionality: %d\n", max_dim);
+    mexPrintf("nnz: %ld, density: %f%%\n", (long)nnz, 100*(double)nnz/((double)max_dim*(double)line_cnt));
+  }
+
+  sparse_memory_requirement = ((double)nnz*((double)sizeof(double)+(double)sizeof(mwSize)))/(1024.0*1024.0);
+  full_memory_requirement = sizeof(double)*(double)max_dim*(double)line_cnt/(1024.0*1024.0);
+
+  if(verb)
+  {
+    mexPrintf("Memory requirements for sparse matrix: %.3f MB\n", sparse_memory_requirement);
+    mexPrintf("Memory requirements for full matrix: %.3f MB\n", full_memory_requirement);
+  }
+
+  if( full_memory_requirement < sparse_memory_requirement)
+  {
+    if(verb)
+      mexPrintf("Full matrix represenation used.\n");
+
+    data_X = mxCreateDoubleMatrix(max_dim, line_cnt, mxREAL);
+
+    if( data_X == NULL)
+    {
+      mexPrintf("Not enough memory to allocate data_X .\n");
+      exitflag = -1;
+      goto clean_up;
+    }
+
+  }
+  else
+  {
+    if(verb)
+      mexPrintf("Sparse matrix represenation used.\n");
+
+    data_X = mxCreateSparse(max_dim, line_cnt, nnz, mxREAL);
+    if( data_X == NULL)
+    {
+      mexPrintf("Not enough memory to allocate data_X .\n");
+      exitflag = -1;
+      goto clean_up;
+    }
+
+    sr  = mxGetPr(data_X);
+    irs = (mwSize*)mxGetIr(data_X);
+    jcs = (mwSize*)mxGetJc(data_X);
+
+  }
+
+
+/*  mexPrintf("Required memory: %.3f MB\n", */
+/*    ((double)nnz*((double)sizeof(double)+(double)sizeof(mwSize)))/(1024.0*1024.0));*/
+
+  /*---------------------------------------------*/
+
+  data_y = mxCalloc(line_cnt, sizeof(double));
+  if(data_y == NULL)
+  {
+    mexPrintf("Not enough memory to allocate data_y.\n");
+    exitflag = -1;
+    goto clean_up;
+  }
+
+  fid = fopen(fname, "r");
+  if(fid == NULL) {
+    perror("fopen error ");
+    mexPrintf("Cannot open input file.\n");
+    exitflag = -1;
+    goto clean_up;
+  }
+
+  if(verb)
+    mexPrintf("Reading examples...");
+  
+  go = 1;
+  line_cnt = 0;
+  long k=0;
+  while(go) {
+    if(fgets(line,LIBSLF_MAXLINELEN, fid) == NULL ) 
+    {
+      go = 0;
+      if(verb)
+      {
+        if( (line_cnt % 1000) != 0) 
+          mexPrintf(" %ld", line_cnt);
+        mexPrintf(" EOF.\n");
+      }
+    }
+    else
+    {
+      line_cnt ++;
+      nnzf = svmlight_format_parse_line(line, &label, feat_idx, feat_val);
+      
+      if(nnzf == -1) 
+      {
+         mexPrintf("Parsing error on line %ld .\n", line_cnt);
+         mexPrintf("Defective input file.\n");
+         exitflag = -1;
+         goto clean_up;
+      }
+
+      data_y[line_cnt-1] = (double)label;
+
+      if( mxIsSparse( data_X) )
+      {
+        jcs[line_cnt-1] = k;
+
+        for(j = 0; j < nnzf; j++) {
+          sr[k] = feat_val[j];
+          irs[k] = feat_idx[j]-1;
+          k++;
+        }
+      }
+      else
+      {
+        double *ptr = mxGetPr(data_X);
+        for(j=0; j < nnzf; j++ ) {
+          ptr[LIBOCAS_INDEX(feat_idx[j]-1,line_cnt-1,max_dim)] = feat_val[j];
+        }
+
+      }
+      
+      if(verb)
+      {
+        if( (line_cnt % 1000) == 0) {
+          mexPrintf(" %ld", line_cnt);
+          fflush(NULL);
+        }
+      }
+    }
+  }
+
+  fclose(fid);  
+
+  if( mxIsSparse( data_X) )
+    jcs[line_cnt] = k;
+
+/*  mexPrintf("\n");*/
+
+  if(verb)
+    mexPrintf("Leaving svmlight reading function.\n");
+
+  exitflag = 0;
+
+clean_up:
+
+  mxFree(line);
+  mxFree(feat_val);
+  mxFree(feat_idx);
+
+  return(exitflag);
+}
+
+
+/*----------------------------------------------------------------------
+ Compute area under ROC (1st class label[i]==1; 2nd class label[i] != 1).
+  ----------------------------------------------------------------------*/
+double compute_auc(double *score, int *label, uint32_t nData)
+{
+  double *sorted_score = NULL;
+  double *sorted_lab = NULL;
+  uint32_t i;
+  uint32_t neg, pos;
+  double auc = -1;
+
+  sorted_score = mxCalloc(nData, sizeof(double));
+  if( sorted_score == NULL ) {
+      mexPrintf("Not enough memmory to allocate sorted_score when computing AUC.");
+      goto clean_up;
+  }
+
+  sorted_lab = mxCalloc(nData, sizeof(double));
+  if( sorted_lab == NULL )
+  {
+      mexPrintf("Not enough memmory to allocate sorted_lab when computing AUC.");
+      goto clean_up;
+  }
+
+  for(i=0; i < nData; i++)
+    if(label[i] == 1) sorted_lab[i] = 1.0; else sorted_lab[i] = 0.0;
+
+
+  memcpy(sorted_score,score,sizeof(double)*nData);
+
+  qsort_data(sorted_score, sorted_lab, nData);
+
+  pos = 0;
+  neg = 0;
+  auc = 0;
+
+  for(i = 0; i < nData; i++)
+  {
+    if(sorted_lab[i] ==1.0 )
+    {
+      pos ++;
+    }
+    else
+    {
+      neg ++;
+      auc += (double)pos;
+    }
+  }
+  auc = 1 - auc/((double)neg*(double)pos);
+
+clean_up:
+  mxFree(sorted_score);
+  mxFree(sorted_lab);
+
+  return(auc);
+}
+
diff --git a/ocas_helper.h b/ocas_helper.h
new file mode 100644
index 0000000..f6aece6
--- /dev/null
+++ b/ocas_helper.h
@@ -0,0 +1,104 @@
+/*-----------------------------------------------------------------------
+ * ocas_helper.h: Implementation of helper functions for the OCAS solver.
+ *
+ * It supports both sparse and dense matrices and loading data from
+ * the SVM^light format.
+ *-------------------------------------------------------------------- */
+
+#ifndef _ocas_helper_h
+#define _ocas_helper_h
+
+#include <stdint.h>
+
+#ifdef LIBOCAS_MATLAB
+
+#include <mex.h>
+
+#if !defined(MX_API_VER) || MX_API_VER<0x07040000
+#define mwSize int
+#define INDEX_TYPE_T int
+#define mwIndex int
+#else
+#define INDEX_TYPE_T mwSize
+#endif
+
+#else
+
+#define mwSize int
+#define mwIndex int
+
+#include "sparse_mat.h"
+
+#endif
+
+typedef struct {
+  double **value;
+  uint32_t **index;
+  uint32_t *nz_dims;    
+} cutting_plane_buf_T;
+
+
+extern mxArray *data_X;
+extern uint32_t nDim, nData, nY;
+extern double *data_y;
+extern cutting_plane_buf_T sparse_A;
+extern double *full_A;
+extern double *W;
+extern double *oldW;
+extern double *new_a;
+
+extern double *A0;
+extern double W0;
+extern double oldW0;
+extern double X0;
+
+double get_time(void);
+void ocas_print(ocas_return_value_T value);
+void ocas_print_null(ocas_return_value_T value);
+
+void mul_sparse_col(double alpha, mxArray *sparse_mat, uint32_t col);
+void add_sparse_col(double *full_vec, mxArray *sparse_mat, uint32_t col);
+void subtract_sparse_col(double *full_vec, mxArray *sparse_mat, uint32_t col);
+double dp_sparse_col(double *full_vec, mxArray *sparse_mat, uint32_t col);
+double sparse_update_W( double t, void* user_data );
+int sparse_add_new_cut( double *new_col_H, 
+                         uint32_t *new_cut, 
+                         uint32_t cut_length, 
+                         uint32_t nSel, 
+                         void* user_data );
+int sparse_compute_output( double *output, void* user_data );
+void sparse_compute_W( double *sq_norm_W, double *dp_WoldW, double *alpha, uint32_t nSel, void* user_data );
+
+void msvm_sparse_compute_W( double *sq_norm_W, double *dp_WoldW, double *alpha, uint32_t nSel, void* user_data );
+int msvm_sparse_add_new_cut( double *new_col_H, uint32_t *new_cut, uint32_t nSel, void* user_data );
+int msvm_sparse_compute_output( double *output, void* user_data );
+
+double msvm_full_update_W( double t, void* user_data );
+int msvm_full_add_new_cut( double *new_col_H, uint32_t *new_cut, uint32_t nSel, void* user_data);
+int msvm_full_compute_output( double *output, void* user_data );
+void msvm_full_compute_W( double *sq_norm_W, double *dp_WoldW, double *alpha, uint32_t nSel, void* user_data );
+
+
+double full_update_W( double t, void* user_data );
+int full_compute_output( double *output, void* user_data );
+void full_compute_W( double *sq_norm_W, double *dp_WoldW, double *alpha, uint32_t nSel, void* user_data );
+int full_add_new_cut( double *new_col_H, 
+                       uint32_t *new_cut, 
+                       uint32_t cut_length, 
+                       uint32_t nSel,
+                       void* user_data);
+
+int load_svmlight_file(char *fname, int verb);
+double compute_auc(double *score, int *label, uint32_t nData);
+int load_regconsts(char *fname, double **vec_C, uint32_t *len_vec_C, int verb);
+
+int qsort_data(double* value, double* data, uint32_t size);
+
+void destroy_parallel_ocas(void);
+int init_parallel_ocas(int number_of_threads);
+int parallel_sparse_compute_output( double *output, void* user_data );
+int parallel_qsort_data(double* value, double* data, uint32_t size);
+int parallel_sparse_add_new_cut( double *new_col_H,uint32_t *new_cut, uint32_t cut_length,uint32_t nSel,void* user_data );
+
+
+#endif
diff --git a/ocas_lbp_helper.c b/ocas_lbp_helper.c
new file mode 100644
index 0000000..aa6da0a
--- /dev/null
+++ b/ocas_lbp_helper.c
@@ -0,0 +1,414 @@
+/*-----------------------------------------------------------------------
+ * ocas_helper.c: Implementation of helper functions for the OCAS solver.
+ *
+ * It supports both sparse and dense matrices and loading data from
+ * the SVM^light format.
+ *-------------------------------------------------------------------- */
+
+#define _FILE_OFFSET_BITS  64
+
+#include <pthread.h>
+
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <sys/time.h>
+#include <stdlib.h>
+#include <time.h>
+
+#include "libocas.h"
+#include "ocas_lbp_helper.h"
+#include "liblbp.h"
+
+uint8_t *Images;
+uint32_t nImages;
+uint32_t win_H;
+uint32_t win_W;
+uint32_t im_H;
+uint32_t im_W;
+uint32_t nPyramids;
+uint32_t *croped_window;
+uint32_t *Wins;
+uint32_t win_H, win_W;
+
+uint32_t nDim, nData;
+double *data_y;
+/*double *full_A;*/
+int64_t *full_A;
+double *W;
+double *oldW;
+/*double *new_a;*/
+/*int32_t *new_a;*/
+int64_t *new_a;
+
+double *A0;
+double W0;
+double oldW0;
+double X0;
+
+
+/*-----------------------------------------------------------------------
+  Print statistics.
+  -----------------------------------------------------------------------*/
+void ocas_print(ocas_return_value_T value)
+{
+  mexPrintf("%4d: tim=%f, Q_P=%f, Q_D=%f, Q_P-Q_D=%f, 1-Q_D/Q_P=%f, nza=%4d, err=%.2f%%, qpf=%d\n",
+            value.nIter,value.ocas_time, value.Q_P,value.Q_D,value.Q_P-value.Q_D,(value.Q_P-value.Q_D)/LIBOCAS_ABS(value.Q_P), 
+            value.nNZAlpha, 100*(double)value.trn_err/(double)nData, value.qp_exitflag );
+}
+
+void ocas_print_null(ocas_return_value_T value)
+{
+  return;
+}
+
+
+/*-----------------------------------------------------------------------
+  Get absolute time in seconds.
+  -----------------------------------------------------------------------*/
+double get_time()
+{
+	struct timeval tv;
+	if (gettimeofday(&tv, NULL)==0)
+		return tv.tv_sec+((double)(tv.tv_usec))/1e6;
+	else
+		return 0.0;
+}
+
+
+/*-------------------------------------------------------------------------
+  sq_norm_W = full_update_W( t ) does the following:
+
+  W = oldW*(1-t) + t*W;
+  sq_norm_W = W'*W;
+---------------------------------------------------------------------------*/
+double full_update_W( double t, void* user_data )
+{
+  uint32_t j;
+  double sq_norm_W;         
+
+/*  mexPrintf("double full_update_W()\n");*/
+
+  W0 = oldW0*(1-t) + t*W0;
+  sq_norm_W = W0*W0;
+
+  for(j=0; j <nDim; j++) {
+    W[j] = oldW[j]*(1-t) + t*W[j];
+    sq_norm_W += W[j]*W[j];
+  }          
+
+  return( sq_norm_W );
+}
+
+
+/*----------------------------------------------------------------------------------
+  full_add_new_cut( new_col_H, new_cut, cut_length, nSel ) does the following:
+
+    new_a = sum(data_X(:,find(new_cut ~=0 )),2);
+    new_col_H = [full_A(:,1:nSel)'*new_a ; new_a'*new_a];
+    full_A(:,nSel+1) = new_a;
+
+  ---------------------------------------------------------------------------------*/
+int full_add_new_cut( double *new_col_H, 
+                       uint32_t *new_cut, 
+                       uint32_t cut_length, 
+                       uint32_t nSel,
+                       void* user_data)
+{
+/*  double *new_a, */
+  double sq_norm_a, *ptr;
+  uint32_t i, j;
+  uint32_t x1, y1, idx, x, y, cnt, mirror;
+  uint8_t *img_ptr;
+
+  /*  ptr = mxGetPr(data_X);*/
+
+/*  memset(new_a, 0, sizeof(double)*nDim);*/
+  memset(new_a, 0, sizeof(new_a[0])*nDim);
+
+  for(i=0; i < cut_length; i++)
+  {
+
+    idx = Wins[LIBOCAS_INDEX(0,new_cut[i],4)]-1;
+    x1  = Wins[LIBOCAS_INDEX(1,new_cut[i],4)]-1;
+    y1  = Wins[LIBOCAS_INDEX(2,new_cut[i],4)]-1;
+    mirror = Wins[LIBOCAS_INDEX(3,new_cut[i],4)];
+
+    img_ptr = &Images[idx*im_H*im_W];
+    
+    cnt=0;
+    if(mirror==0)
+    {
+      for(x=x1; x < x1+win_W; x++)
+        for(y=y1; y < y1+win_H; y++)
+          croped_window[cnt++] = img_ptr[LIBOCAS_INDEX(y,x,im_H)];
+    }
+    else
+    {
+      for(x=x1+win_W-1; x >= x1; x--)
+        for(y=y1; y < y1+win_H; y++)
+          croped_window[cnt++] = img_ptr[LIBOCAS_INDEX(y,x,im_H)];
+    }
+    
+    if(data_y[new_cut[i]] == +1) {
+/*      lbppyr_addvec(new_a,croped_window);*/
+      liblbp_pyr_addvec(new_a,nDim,croped_window,win_H,win_W);
+    }
+    else
+    {
+/*      lbppyr_subvec(new_a,croped_window);*/
+      liblbp_pyr_subvec(new_a,nDim,croped_window,win_H,win_W);
+    }
+      
+    A0[nSel] += X0*data_y[new_cut[i]];
+  }
+
+  /*
+  for(i=0; i < cut_length; i++) {
+    for(j=0; j < nDim; j++ ) {
+      new_a[j] += ptr[LIBOCAS_INDEX(j,new_cut[i],nDim)];
+    }
+
+    A0[nSel] += X0*data_y[new_cut[i]];    
+  }
+  */
+
+  /* compute new_a'*new_a and insert new_a to the last column of full_A */
+  sq_norm_a = A0[nSel]*A0[nSel];
+  for(j=0; j < nDim; j++ ) 
+  {
+    sq_norm_a += (double)(new_a[j]*new_a[j]);
+    full_A[LIBOCAS_INDEX(j,nSel,nDim)] = new_a[j];
+  }
+
+  new_col_H[nSel] = sq_norm_a;
+  for(i=0; i < nSel; i++) {
+    double tmp = A0[nSel]*A0[i];
+
+    for(j=0; j < nDim; j++ ) {
+      tmp += (double)(new_a[j]*full_A[LIBOCAS_INDEX(j,i,nDim)]);
+    }
+    new_col_H[i] = tmp;
+  }
+
+/*  mxFree( new_a );*/
+
+  return 0;
+}
+
+/*----------------------------------------------------------------------
+  full_compute_output( output ) does the follwing:
+
+  output = data_X'*W;
+  ----------------------------------------------------------------------*/
+int full_compute_output( double *output, void* user_data )
+{
+  uint32_t i, j, x1, y1, idx, cnt, x,y, mirror;
+  double tmp;
+  uint8_t *img_ptr;
+
+/*  mexPrintf("full_compute_output()\n");*/
+
+  /*  ptr = mxGetPr( data_X );*/
+
+  for(i=0; i < nData; i++) 
+  { 
+    tmp = data_y[i]*X0*W0;
+
+    idx = Wins[LIBOCAS_INDEX(0,i,4)]-1;
+    x1  = Wins[LIBOCAS_INDEX(1,i,4)]-1;
+    y1  = Wins[LIBOCAS_INDEX(2,i,4)]-1;
+    mirror  = Wins[LIBOCAS_INDEX(3,i,4)];
+
+    img_ptr = &Images[idx*im_H*im_W];
+    
+    cnt=0;
+    if(mirror==0)
+    {
+      for(x=x1; x < x1+win_W; x++)
+        for(y=y1; y < y1+win_H; y++)
+          croped_window[cnt++] = img_ptr[LIBOCAS_INDEX(y,x,im_H)];
+    }
+    else
+    {
+      for(x=x1+win_W-1; x >= x1; x--)
+        for(y=y1; y < y1+win_H; y++)
+          croped_window[cnt++] = img_ptr[LIBOCAS_INDEX(y,x,im_H)];
+    }
+    
+/*    output[i] = data_y[i]*(X0*W0 + lbppyr_dotprod(W,croped_window));*/
+    output[i] = data_y[i]*(X0*W0 + liblbp_pyr_dotprod(W,nDim,croped_window,win_H,win_W));
+
+    /*
+    for(j=0; j < nDim; j++ ) {
+      tmp += W[j]*ptr[LIBOCAS_INDEX(j,i,nDim)];
+    }
+    output[i] = tmp;
+    */
+  }
+  
+  return 0;
+}
+
+
+
+
+/*----------------------------------------------------------------------
+  sq_norm_W = full_compute_W( alpha, nSel ) does the following:
+
+  oldW = W;
+  W = full_A(:,1:nSel)'*alpha;
+  sq_norm_W = W'*W;
+  dp_WoldW = W'*oldW';
+
+  ----------------------------------------------------------------------*/
+void full_compute_W( double *sq_norm_W, double *dp_WoldW, double *alpha, uint32_t nSel, void* user_data )
+{
+  uint32_t i,j;
+
+  memcpy(oldW, W, sizeof(double)*nDim ); 
+  memset(W, 0, sizeof(double)*nDim);
+
+  oldW0 = W0;
+  W0 = 0;
+
+  for(i=0; i < nSel; i++) {
+    if( alpha[i] > 0 ) {
+      for(j=0; j< nDim; j++ ) {
+        W[j] += alpha[i]*(double)(full_A[LIBOCAS_INDEX(j,i,nDim)]);
+      }
+
+      W0 += A0[i]*alpha[i];
+    }
+  }
+
+  *sq_norm_W = W0*W0;
+  *dp_WoldW = W0*oldW0;
+  for(j=0; j < nDim; j++) {
+    *sq_norm_W += W[j]*W[j];
+    *dp_WoldW += W[j]*oldW[j];
+  }
+
+  return;
+}
+
+static void swapf(double* a, double* b)
+{
+	double dummy=*b;
+	*b=*a;
+	*a=dummy;
+}
+
+/*static void swapi(uint32_t* a, uint32_t* b)*/
+/*{*/
+/*	int dummy=*b;*/
+/*	*b=*a;*/
+/*	*a=dummy;*/
+/*}*/
+
+/* sort arrays value and data according to value in ascending order */
+int qsort_data(double* value, double* data, uint32_t size)
+{
+    if(size == 1)
+      return 0;
+
+	if (size==2)
+	{
+		if (value[0] > value[1])
+		{
+			swapf(&value[0], &value[1]);
+/*			swapi(&data[0], &data[1]);*/
+			swapf(&data[0], &data[1]);
+		}
+		return 0;
+	}
+	double split=value[size/2];
+
+	uint32_t left=0;
+	uint32_t right=size-1;
+
+	while (left<=right)
+	{
+		while (value[left] < split)
+			left++;
+		while (value[right] > split)
+			right--;
+
+		if (left<=right)
+		{
+			swapf(&value[left], &value[right]);
+/*			swapi(&data[left], &data[right]);*/
+			swapf(&data[left], &data[right]);
+			left++;
+			right--;
+		}
+	}
+
+	if (right+1> 1)
+		qsort_data(value,data,right+1);
+
+	if (size-left> 1)
+		qsort_data(&value[left],&data[left], size-left);
+
+
+    return 0;
+}
+
+
+/*----------------------------------------------------------------------
+ Compute area under ROC (1st class label[i]==1; 2nd class label[i] != 1).
+  ----------------------------------------------------------------------*/
+double compute_auc(double *score, int *label, uint32_t nData)
+{
+  double *sorted_score = NULL;
+  double *sorted_lab = NULL;
+  uint32_t i;
+  uint32_t neg, pos;
+  double auc = -1;
+
+  sorted_score = mxCalloc(nData, sizeof(double));
+  if( sorted_score == NULL ) {
+      mexPrintf("Not enough memmory to allocate sorted_score when computing AUC.");
+      goto clean_up;
+  }
+
+  sorted_lab = mxCalloc(nData, sizeof(double));
+  if( sorted_lab == NULL )
+  {
+      mexPrintf("Not enough memmory to allocate sorted_lab when computing AUC.");
+      goto clean_up;
+  }
+
+  for(i=0; i < nData; i++)
+    if(label[i] == 1) sorted_lab[i] = 1.0; else sorted_lab[i] = 0.0;
+
+
+  memcpy(sorted_score,score,sizeof(double)*nData);
+
+  qsort_data(sorted_score, sorted_lab, nData);
+
+  pos = 0;
+  neg = 0;
+  auc = 0;
+
+  for(i = 0; i < nData; i++)
+  {
+    if(sorted_lab[i] ==1.0 )
+    {
+      pos ++;
+    }
+    else
+    {
+      neg ++;
+      auc += (double)pos;
+    }
+  }
+  auc = 1 - auc/((double)neg*(double)pos);
+
+clean_up:
+  mxFree(sorted_score);
+  mxFree(sorted_lab);
+
+  return(auc);
+}
+
diff --git a/ocas_lbp_helper.h b/ocas_lbp_helper.h
new file mode 100644
index 0000000..679112d
--- /dev/null
+++ b/ocas_lbp_helper.h
@@ -0,0 +1,81 @@
+/*-----------------------------------------------------------------------
+ * ocas_helper.h: Implementation of helper functions for the OCAS solver.
+ *
+ * It supports both sparse and dense matrices and loading data from
+ * the SVM^light format.
+ *-------------------------------------------------------------------- */
+
+#ifndef _ocas_helper_h
+#define _ocas_helper_h
+
+#include <stdint.h>
+
+#ifdef LIBOCAS_MATLAB
+
+#include <mex.h>
+
+#if !defined(MX_API_VER) || MX_API_VER<0x07040000
+#define mwSize int
+#define INDEX_TYPE_T int
+#define mwIndex int
+#else
+#define INDEX_TYPE_T mwSize
+#endif
+
+#else
+
+#define mwSize int
+#define mwIndex int
+
+#include "sparse_mat.h"
+
+#endif
+
+extern uint8_t *Images;
+extern uint32_t nImages;
+extern uint32_t win_H;
+extern uint32_t win_W;
+extern uint32_t im_H;
+extern uint32_t im_W;
+extern uint32_t nPyramids;
+extern uint32_t *croped_window;
+extern uint32_t *Wins;
+
+extern uint32_t nDim, nData;
+extern double *data_y;
+/*extern double *full_A;*/
+/*extern int32_t *full_A;*/
+extern int64_t *full_A;
+extern double *W;
+extern double *oldW;
+/*extern double *new_a;*/
+/*extern int32_t *new_a;*/
+extern int64_t *new_a;
+
+extern double *A0;
+extern double W0;
+extern double oldW0;
+extern double X0;
+
+uint32_t lbppyr_get_dim(uint32_t win_H, uint32_t win_W, uint32_t nPyramids);
+
+double get_time(void);
+void ocas_print(ocas_return_value_T value);
+void ocas_print_null(ocas_return_value_T value);
+
+double full_update_W( double t, void* user_data );
+int full_compute_output( double *output, void* user_data );
+void full_compute_W( double *sq_norm_W, double *dp_WoldW, double *alpha, uint32_t nSel, void* user_data );
+int full_add_new_cut( double *new_col_H, 
+                       uint32_t *new_cut, 
+                       uint32_t cut_length, 
+                       uint32_t nSel,
+                       void* user_data);
+
+double compute_auc(double *score, int *label, uint32_t nData);
+
+int qsort_data(double* value, double* data, uint32_t size);
+
+void lbppyr_features(char *vec, uint32_t *win);
+
+#endif
diff --git a/sparse_mat.c b/sparse_mat.c
new file mode 100644
index 0000000..7bf3769
--- /dev/null
+++ b/sparse_mat.c
@@ -0,0 +1,123 @@
+/*-----------------------------------------------------------
+ * sparse_mat.c: Reimplementation of Matlab functions for 
+ *  dealing with sparse matrices and full matrices (support for 
+ *  full matrices added later).
+ *
+  -----------------------------------------------------------*/
+
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <math.h>
+#include <stdlib.h>
+
+#include "sparse_mat.h"
+
+
+INDEX_TYPE_T mxGetM(const mxArray *array_ptr)
+{
+  return( array_ptr->m );
+}
+
+INDEX_TYPE_T mxGetN(const mxArray *array_ptr)
+{
+  return( array_ptr->n);
+}
+
+void mxDestroyArray(mxArray *array_ptr)
+{
+  if( array_ptr != NULL)
+  {
+    free(array_ptr->pr);
+    free(array_ptr->ir);
+    free(array_ptr->jc);
+    free(array_ptr);
+  }
+}
+
+INDEX_TYPE_T mxGetNZMAX(const mxArray *array_ptr)
+{
+  return(array_ptr->nzmax);
+}
+
+mxArray *mxCreateDoubleMatrix(INDEX_TYPE_T m, INDEX_TYPE_T n, mxComplexity ComplexFlag)
+{
+  mxArray *array = NULL;
+
+  array = (mxArray*)calloc(1,sizeof(mxArray));
+  if(array == NULL)
+    return(NULL);
+
+  array->pr = NULL;
+  array->ir = NULL;
+  array->jc = NULL;
+  array->m = m;
+  array->n = n;
+  array->nzmax = m*n;
+  array->sparse = 0;
+
+  array->pr = (double*)calloc(m*n,sizeof(double));
+  if(array->pr == NULL)
+    return(NULL);
+
+  return(array);
+}
+
+mxArray *mxCreateSparse(INDEX_TYPE_T m, INDEX_TYPE_T n, NNZ_TYPE_T nzmax, mxComplexity ComplexFlag)
+{
+  mxArray *array = NULL;
+
+  array = (mxArray*)calloc(1,sizeof(mxArray));
+  if(array == NULL)
+    return(NULL);
+
+  array->pr = NULL;
+  array->ir = NULL;
+  array->jc = NULL;
+  array->m = m;
+  array->n = n;
+  array->nzmax = nzmax;
+  array->sparse = 1;
+
+  array->pr = (double*)calloc(nzmax,sizeof(double));
+  if(array->pr == NULL)
+    goto clean_up;
+
+  array->ir = (INDEX_TYPE_T*)calloc(nzmax,sizeof(INDEX_TYPE_T));
+  if(array->ir == NULL)
+    goto clean_up;
+
+  array->jc = (INDEX_TYPE_T*)calloc(n+1,sizeof(INDEX_TYPE_T));
+  if(array->jc == NULL)
+    goto clean_up;
+
+  return(array);
+
+clean_up:
+  
+  free(array->pr);
+  free(array->ir);
+  free(array->jc);
+
+  return(NULL);
+}
+
+double *mxGetPr(const mxArray *array_ptr)
+{
+  return(array_ptr->pr);
+}
+
+INDEX_TYPE_T *mxGetIr(const mxArray *array_ptr)
+{
+  return(array_ptr->ir);
+}
+
+INDEX_TYPE_T *mxGetJc(const mxArray *array_ptr)
+{
+  return(array_ptr->jc);
+}
+
+int mxIsSparse(const mxArray *array_ptr)
+{
+  return(array_ptr->sparse);
+}
diff --git a/sparse_mat.h b/sparse_mat.h
new file mode 100644
index 0000000..17c84e1
--- /dev/null
+++ b/sparse_mat.h
@@ -0,0 +1,47 @@
+/*-----------------------------------------------------------
+ * sparse_mat.h: Reimplementation of Matlab functions for 
+ *  dealing with sparse matrices.
+ *
+  -----------------------------------------------------------*/
+
+#ifndef _sparse_mat_h
+#define _sparse_mat_h
+
+#define mxCalloc(x...) calloc(x)
+#define mxFree(x...) free(x)
+#define mexPrintf(x...) printf(x)
+
+#define INDEX_TYPE_T uint32_t
+#define NNZ_TYPE_T uint64_t
+
+
+typedef struct {
+  INDEX_TYPE_T *ir;       
+  INDEX_TYPE_T *jc;  
+  INDEX_TYPE_T m;
+  INDEX_TYPE_T n;
+  double *pr;
+  NNZ_TYPE_T nzmax;
+  int sparse; 
+  
+} mxArray;
+
+
+typedef enum {
+    mxREAL,
+    mxCOMPLEX
+} mxComplexity;
+
+
+INDEX_TYPE_T mxGetM(const mxArray *array_ptr);
+INDEX_TYPE_T mxGetN(const mxArray *array_ptr);
+void mxDestroyArray(mxArray *array_ptr);
+mxArray *mxCreateSparse(INDEX_TYPE_T m, INDEX_TYPE_T n, NNZ_TYPE_T nzmax, mxComplexity ComplexFlag);
+mxArray *mxCreateDoubleMatrix(INDEX_TYPE_T m, INDEX_TYPE_T n, mxComplexity ComplexFlag);
+double *mxGetPr(const mxArray *array_ptr);
+INDEX_TYPE_T *mxGetIr(const mxArray *array_ptr);
+INDEX_TYPE_T *mxGetJc(const mxArray *array_ptr);
+INDEX_TYPE_T mxGetNZMAX(const mxArray *array_ptr);
+int mxIsSparse(const mxArray *array_ptr);
+
+#endif
diff --git a/svmlight_linclass.m b/svmlight_linclass.m
new file mode 100644
index 0000000..1b8f84c
--- /dev/null
+++ b/svmlight_linclass.m
@@ -0,0 +1,17 @@
+% SVMLIGHT_LINCLASS classifies examples in SVM^light file by linear rule.
+%
+% Synopsis:
+%  [score,true_lab] = svmlight_linclass(data_file,W,[])
+%  [score,true_lab] = svmlight_linclass(data_file,W,W0)
+%  [score,true_lab] = svmlight_linclass(data_file,W,W0,verb)
+% 
+% Input:
+%  data_file [string] Path to file with examples stored in SVM^light format.
+%  W [nDims x nModels] Parameter vectors of nModels linear classifiers.
+%  W0 [nModels x 1] Bias of decision rule. If W0 is empty then W0 = 0 is used.
+%  verb [1x1] If ~= 0 then prints info (default 0).
+%
+% Output:
+%  score [nModels x nExamples] score(i,j) = W(:,i)'*X_j + W0(i)
+%  true_labels [nExamples x 1] labels from the data_file
+%
diff --git a/svmlight_linclass_mex.c b/svmlight_linclass_mex.c
new file mode 100644
index 0000000..cfedb55
--- /dev/null
+++ b/svmlight_linclass_mex.c
@@ -0,0 +1,319 @@
+/*=================================================================
+ * SVMLIGHT_LINCLASS classifies examples in SVM^light file by linear rule.
+ *
+ *  Synopsis:
+ *    [score,true_lab] = svmlight_linclass(data_file,W,[])
+ *    [score,true_lab] = svmlight_linclass(data_file,W,W0)
+ *    [score,true_lab] = svmlight_linclass(data_file,W,W0,verb)
+ *
+ * 
+ *  Input:
+ *   data_file [string] Path to file with examples stored in SVM^light format.
+ *   W [nDims x nModels] Parameter vectors of nModels linear classifiers.
+ *   W0 [nModels x 1] Bias of decision rule. If W0 is empty then W0 = 0 is used. 
+ *   verb [1x1] If ~= 0 then prints info (default 0).
+ *
+ *  Output:
+ *   score [nModels x nExamples] score(i,j) = W(:,i)'*X_j + W0(i)
+ *   true_labels [nExamples x 1] labels from the data_file
+ *
+ *=================================================================*/
+
+#define _FILE_OFFSET_BITS  64
+
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <mex.h>
+#include <sys/time.h>
+#include <time.h>
+#include <errno.h>
+
+#include "libocas.h"
+#include "ocas_helper.h"
+#include "lib_svmlight_format.h"
+
+#if !defined(MX_API_VER) || MX_API_VER<0x07040000
+#define mwSize int
+#define mwIndex int
+#endif
+
+
+/*----------------------------------------------------------
+It computes dot produt between col-th column of matrix 
+and a sparse vector given by vec_val and vec_idx (indices are 1 based) 
+its length is vec_len. The matrix can be either Matlab full or Matlab 
+sparse matrix.
+-----------------------------------------------------------*/
+
+double my_dot_prod(mxArray *matrix, uint32_t col, double *vec_val, uint32_t *vec_idx, long vec_len)
+{
+
+  double dot_prod;
+  long i;
+
+
+  if(!mxIsSparse(matrix)) {
+
+    long mat_dims = mxGetM(matrix);
+    double *mat_ptr = (double*)mxGetPr(matrix) + col*mat_dims;
+
+    dot_prod = 0;
+    for(i=0; i < vec_len; i++) 
+      dot_prod += *(mat_ptr+vec_idx[i]-1) * vec_val[i];
+
+  }
+  else
+  {
+
+    uint32_t col_len, ptr1, ptr2, row1, row2;
+    mwSize *Ir, *Jc;
+    double *Pr, val1, val2;
+
+    Ir = mxGetIr(matrix);
+    Jc = mxGetJc(matrix);
+    Pr = mxGetPr(matrix);
+
+    col_len = Jc[col+1] - Jc[col];
+
+    ptr1 = Jc[col];
+
+    ptr2 = 0;
+
+    dot_prod = 0;
+
+    while(ptr2 < vec_len && (ptr1 - Jc[col]) < col_len) {
+      row1 = Ir[ptr1];
+      row2 = vec_idx[ptr2]-1;
+
+      if(row1 == row2 ) {
+        dot_prod += Pr[ptr1]*vec_val[ptr2];
+        ptr1++;
+        ptr2++;
+      }
+      else if(row1 < row2 ) {
+        ptr1++;
+      }
+      else {
+        ptr2 ++;
+      }
+    }
+
+  }
+
+  return(dot_prod);
+}
+
+/*======================================================================
+  Main code plus interface to Matlab.
+========================================================================*/
+
+void mexFunction( int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[] )
+{
+  char fname[200];
+  FILE *fid;
+  char *line;
+  double *feat_val;
+  uint32_t *feat_idx;
+  uint32_t nData = 0, nY = 0;
+  long nnzf;
+  uint64_t nnz = 0;
+  mxArray *W;
+  int nModels;
+  double *score;
+  double *W0;
+  double *error;
+  long max_dim = 0;
+  long nDims = 0;
+  long i, j;
+  double *true_lab;
+  double dot_prod;
+  int verb = 0;
+  double *auc;
+
+  if( nrhs < 3 )
+    mexErrMsgTxt("At least three input arguments must be passed.");
+
+  if( nrhs > 4)
+    mexErrMsgTxt("At most four input arguments must be passed.");
+
+  if( nrhs == 4)
+    verb = (int)mxGetScalar(prhs[3]);
+
+  /* get input arguments */
+  mxGetString(prhs[0], fname, 200);
+
+  W = (mxArray*)prhs[1];
+  nDims = mxGetM(prhs[1]);
+  nModels = mxGetN(prhs[1]);
+
+  if( mxIsEmpty(prhs[2]) == false)
+  {
+    W0 = (double*)mxGetPr(prhs[2]);
+    if((LIBOCAS_MAX(mxGetM(prhs[2]),mxGetN(prhs[2])) != nModels) || 
+       (LIBOCAS_MIN(mxGetM(prhs[2]),mxGetN(prhs[2])) != 1))
+    {
+      mexErrMsgTxt("The third input argument is of incorrect dimensionality.");
+    }
+  }
+  else
+  {
+    W0 = mxCalloc(nModels, sizeof(double));
+    if( W0 == NULL )
+      mexErrMsgTxt("Not enough memmory to allocate W0.");
+  }
+
+  if(verb)
+  {
+    mexPrintf("input_file: %s\n", fname);
+    mexPrintf("nModels: %d\n", nModels);
+    mexPrintf("W dimensionality: %d\n", nDims);
+  }
+
+  fid = fopen(fname, "r");
+  if(fid == NULL) {
+    perror("fopen error: ");
+    mexErrMsgTxt("Cannot open input file.");
+  }
+
+  /**********************************/
+  line = mxCalloc(LIBSLF_MAXLINELEN, sizeof(char));
+  if( line == NULL )
+    mexErrMsgTxt("Not enogh memmory to allocate line buffer.");
+
+  feat_idx = mxCalloc(LIBSLF_MAXLINELEN, sizeof(long));
+  if( feat_idx == NULL )
+    mexErrMsgTxt("Not enogh memmory to allocate feature buffer.");
+
+  feat_val = mxCalloc(LIBSLF_MAXLINELEN, sizeof(double));
+  if( feat_val == NULL )
+    mexErrMsgTxt("Not enough memmory to allocate feature value buffer.");
+
+
+  /*---------------------------------------------------------------------- */
+  /* FIRST STEP: go through the data to get their number and dimensonality */
+  /*---------------------------------------------------------------------- */
+
+  if(verb) 
+    mexPrintf("Analysing examples...");
+  int label;
+  int go = 1;
+  long line_cnt = 0;
+
+  while(go) {
+    
+    if(fgets(line,LIBSLF_MAXLINELEN, fid) == NULL ) 
+    {
+      go = 0;
+      if(verb)
+      {
+        if( (line_cnt % 1000) != 0) 
+          mexPrintf(" %ld", line_cnt);
+        mexPrintf(" EOF.\n");
+      }
+
+    }
+    else
+    {
+      line_cnt ++;
+      nnzf = svmlight_format_parse_line(line, &label, feat_idx, feat_val);
+      
+      if(nnzf == -1) 
+      {
+           mexPrintf("Parsing error on line %ld .\n", line_cnt);
+           mexErrMsgTxt("Probably defective input file.\n");
+      }
+
+      max_dim = LIBOCAS_MAX(max_dim,feat_idx[nnzf-1]);
+      nnz += nnzf;
+      nY = LIBOCAS_MAX(nY,label);
+      
+      if(verb && (line_cnt % 1000) == 0) {
+        mexPrintf(" %ld", line_cnt);
+        fflush(NULL);
+      }
+    }
+  }
+
+  /* rewind the file at the begining */
+  fseek(fid, 0L, SEEK_SET);
+
+  nData = line_cnt;
+  if(verb)
+  {
+    mexPrintf("Data statistics:\n");
+    mexPrintf("# of examples: %ld\n", nData);
+    mexPrintf("# of classes : %ld\n", nY);
+    mexPrintf("dimensionality: %d\n", max_dim);
+    mexPrintf("nnz: %ld, density: %f%%\n", (long)nnz, 100*(double)nnz/((double)max_dim*(double)nData));
+  }
+
+  if(max_dim > nDims) 
+  {
+     mexErrMsgTxt("\nDimansionality of examples in the file exceeds dimensionality of W.");
+  }    
+
+  plhs[0] = (mxArray*)mxCreateDoubleMatrix(nModels,line_cnt,mxREAL);
+  if(plhs[0] == NULL)
+    mexErrMsgTxt("Not enought memory to allocate buffer for score.");
+  score = (double*)mxGetPr(plhs[0]);
+
+  plhs[1] = (mxArray*)mxCreateDoubleMatrix(line_cnt,1,mxREAL);
+  if(plhs[1] == NULL)
+    mexErrMsgTxt("Not enought memory to allocate buffer for true_labels.");
+  true_lab = (double*)mxGetPr(plhs[1]);
+
+  /*********************************************/
+  /* MAIN LOOP                                 */
+  /*********************************************/
+
+  if(verb) mexPrintf("Reading examples...");
+
+  go = 1;
+  line_cnt = 0;
+
+  while(go) {
+    
+    if(fgets(line,LIBSLF_MAXLINELEN, fid) == NULL ) 
+    {
+      go = 0;
+      if(verb)
+      {
+        if( (line_cnt % 1000) != 0) 
+          mexPrintf(" %ld", line_cnt);
+        mexPrintf(" EOF.\n");
+      }
+
+    }
+    else
+    {
+      line_cnt ++;
+      nnzf = svmlight_format_parse_line(line, &label,feat_idx, feat_val);
+      
+      if(nnzf == -1) 
+      {
+         mexPrintf("Parsing error on line %ld .\n", line_cnt);
+         mexErrMsgTxt("Probably defective input file.\n");
+      }
+
+      true_lab[line_cnt-1] = (double)label;
+
+      for(i=0; i< nModels; i++) {
+
+        dot_prod = my_dot_prod(W,i,feat_val,feat_idx,nnzf);
+        score[LIBOCAS_INDEX(i,line_cnt-1,nModels)] = dot_prod + W0[i]; 
+      } 
+
+      if(verb && (line_cnt % 1000) == 0) {
+        mexPrintf(" %ld", line_cnt);
+        fflush(NULL);
+      }
+
+    }
+  }
+
+  fclose(fid);
+  
+  return;
+}
+
diff --git a/svmocas.c b/svmocas.c
new file mode 100644
index 0000000..3b74dfb
--- /dev/null
+++ b/svmocas.c
@@ -0,0 +1,720 @@
+/*-----------------------------------------------------------------------
+ * svmocas.c: Standalone application implementing the OCAS solver for 
+ *   training linear SVM classifiers.
+ *   
+ * Copyright (C) 2008, 2009 Vojtech Franc, xfrancv at cmp.felk.cvut.cz
+ *                    Soeren Sonnenburg, soeren.sonnenburg at first.fraunhofer.de
+ *
+ * This program is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU General Public 
+ * License as published by the Free Software Foundation; 
+ * Version 3, 29 June 2007
+ *-------------------------------------------------------------------- */
+
+
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <math.h>
+#include <stdlib.h>
+
+#include "lib_svmlight_format.h"
+#include "libocas.h"
+#include "sparse_mat.h"
+#include "ocas_helper.h"
+
+#include "version.h"
+
+void print_usage(void)
+{
+  printf("SVMOCAS: Optimized Cutting Plane Algorithm for Support Vector Machines\n"
+         "         Training binary linear SVM classifier from examples\n"
+         "         " OCAS_VERSION "\n"
+         "\n"
+         "   usage: svmocas [options] example_file model_file\n"
+         "\n"
+         "Arguments:\n"
+         "         example_file  file with training examples stored in SVM^light format\n"
+         "         model_file    file to store learned linear rule f(x)=w'*x+w0; the model file\n"
+         "                       contains d+1 lines (d is data dimension); first n lines are\n"
+         "                       coordinates of w and the last one is w0.\n"
+         "General options:\n"
+         "         -h            this help\n"
+         "         -v [0,1]      verbosity level (default 1).\n"
+         "Learning options:\n"
+         "         -c float      regularization constant C (default 1).\n"
+         "         -C filename   if specified each example has different reg. constant given in the text file.\n"
+         "                       Each line of the text file must contain a single constant (positive double)\n"
+         "                       for the corresponding example. If -C is used then the option -c is ignored.\n"
+         "         -b [0,1]      value of L2-bias feature. A value of 0 implies not having bias (default 0).\n"
+         "         -n int        use only first n examples for training. By default n equals to\n"
+         "                       the number of examples in the example_file.\n"
+         "Optimization options:\n" 
+         "         -m [0,1]      solver to be used: 0 ... standard cutting plane (equivalent to BMRM, SVM^perf)\n"
+         "                                          1 ... OCAS (default 1).\n"
+         "         -s int        cache size for cutting planes (default 2000).\n"
+         "         -p int        number of threads (default 1).\n"
+         "Stopping conditions:\n"
+         "         -a float      absolute tolerance TolAbs: halt if QP-QD <= TolAbs (default 0).\n"
+         "         -r float      relative tolerance TolRel: halt if QP-QD <= abs(QP)*TolRel (default 0.01).\n"
+         "         -q float      desired objective value QPValue: halt is QP <= QPValue (default 0).\n"         
+         "         -t float      halts if the solver time (loading time is not counted) exceeds\n"
+         "                       the given time in seconds (default inf).\n\n"
+         "Example:\n"
+         "  Train binary SVM classifier from ./data/riply_trn.light with regularization constant C = 10,\n"
+         "  bias switched on, verbosity switched off and save model to ./data/svmocas.model\n"
+         "    ./svmocas -c 10 -b 1 -v 0 ./data/riply_trn.light ./data/svmocas.model \n"
+         "\n"
+         "  Compute testing error of the classifier stored in ./data/svmocas.model using testing\n"
+         "  examples from ./data/riply_tst.light and save predicted labels to ./data/riply_tst.pred\n"
+         "    ./linclass -e -o ./data/riply_tst.pred ./data/riply_tst.light ./data/svmocas.model\n"
+         "\n"
+         );
+}
+
+
+int main(int argc, char *argv[])
+{
+  double C, TolRel, TolAbs, QPBound, MaxTime;
+  uint32_t i, j, BufSize;
+  uint16_t Method;
+  int number_of_threads;
+  ocas_return_value_T ocas;
+  int len;
+  int recognized;
+  int exitflag = 1;
+  int verb;
+
+  double *vec_C = NULL;
+  uint32_t len_vec_C =0;
+
+  /* timing variables */
+  double load_time;
+  double total_time;
+
+  char *model_fname = NULL;
+  char *input_fname = NULL;
+  char *regconst_fname = NULL;
+  FILE *fid;
+
+  /* start time measuring */
+  total_time = get_time();
+
+  /* init */
+  data_X = NULL;
+  data_y = NULL;
+  W = NULL;
+  oldW = NULL;
+  A0 = NULL;
+  sparse_A.nz_dims = NULL;
+  sparse_A.index = NULL;
+  sparse_A.value = NULL;
+  new_a = NULL;
+  full_A = NULL;
+
+  /* default setting of input arguments*/
+  X0 = 0;
+  C = 1.0;
+  Method = 1;
+  TolRel = 0.01;
+  TolAbs = 0.0;
+  QPBound = 0.0;
+  BufSize = 2000;
+  MaxTime = (double)LIBOCAS_PLUS_INF;
+  nData = -1;
+  number_of_threads = 1;
+  verb = 1;
+
+  /*-----------------------------------------------------------
+    Process input arguments 
+  ------------------------------------------------------------*/
+  if(argc ==1 || strcmp(argv[1], "-h") == 0)  
+  {
+    print_usage();
+    goto clean_up;
+  }
+
+  if(argc < 3)
+  {
+    fprintf(stderr,"Not enough input arguments.\n\n");
+    goto clean_up;
+  }
+
+
+  for (i = 1; i < argc-2; i++)  
+  {
+    recognized = 0;
+    if (strcmp(argv[i], "-h") == 0)  
+    {
+      print_usage();
+      goto clean_up;
+    }
+
+    if (strcmp(argv[i], "-c") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -c\n"); 
+        goto clean_up;  
+      }
+      C = atof(argv[i+1]); 
+      if(C <=0)
+      { 
+        fprintf(stderr,"Parameter C must be geater than zero.\n"); 
+        goto clean_up; 
+      } 
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-C") == 0)
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a file name after argument -C\n"); 
+        goto clean_up;  
+      }
+
+      len = strlen( argv[i+1] );
+      regconst_fname = calloc(len+1,sizeof(char));
+      strcpy(regconst_fname, argv[i+1]);
+      
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-p") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -p\n"); 
+        goto clean_up;  
+      }
+      number_of_threads = atoi(argv[i+1]); 
+      if(number_of_threads <= 0)
+      { 
+        fprintf(stderr,"Argumnet after parameter -p must be geater than zero.\n"); 
+        goto clean_up; 
+      } 
+      i++;
+      recognized = 1;
+    }
+
+
+    if (strcmp(argv[i], "-b") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -b\n"); 
+        goto clean_up;  
+      }
+      X0 = atof(argv[i+1]); 
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-n") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -n\n"); 
+        goto clean_up;  
+      }
+      nData = atol(argv[i+1]); 
+      if(nData <=0)
+      { 
+        fprintf(stderr,"A value after the argument -n must be greater than zero.\n"); 
+        goto clean_up; 
+      } 
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-s") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -s\n"); 
+        goto clean_up;  
+      }
+      BufSize = atol(argv[i+1]); 
+      if(nData <=0)
+      { 
+        fprintf(stderr,"A value after the argument -s must be greater than zero.\n"); 
+        goto clean_up; 
+      } 
+      i++;
+      recognized = 1;
+    }
+
+
+    if (strcmp(argv[i], "-m") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -m\n"); 
+        goto clean_up;  
+      }
+      Method = atoi(argv[i+1]); 
+      if(Method != 0 && Method != 1)
+      { 
+        fprintf(stderr,"A value after the argument -m must be 0 or 1.\n"); 
+        goto clean_up; 
+      } 
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-v") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -v\n"); 
+        goto clean_up;  
+      }
+      verb = atoi(argv[i+1]); 
+      if(verb < 0 || verb > 1)
+      {
+        fprintf(stderr,"A value after the argument -v must be either 0 or 1.\n"); 
+        goto clean_up;  
+      }
+        
+
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-a") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -a\n"); 
+        goto clean_up;  
+      }
+      TolAbs = atof(argv[i+1]); 
+      if(TolAbs < 0)
+      { 
+        fprintf(stderr,"A value after the argument -a must be a positive scalar.\n"); 
+        goto clean_up; 
+      } 
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-r") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -r\n"); 
+        goto clean_up;  
+      }
+      TolRel = atof(argv[i+1]); 
+      if(TolRel < 0)
+      { 
+        fprintf(stderr,"A value after the argument -r must be a positive scalar.\n"); 
+        goto clean_up; 
+      } 
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-q") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -q\n"); 
+        goto clean_up;  
+      }
+      QPBound = atof(argv[i+1]); 
+      i++;
+      recognized = 1;
+    }
+
+    if (strcmp(argv[i], "-t") == 0)  
+    {
+      if(i+1 >= argc-2)
+      {
+        fprintf(stderr,"You have to specify a value after argument -t\n"); 
+        goto clean_up;  
+      }
+      MaxTime = atof(argv[i+1]); 
+      if(MaxTime <=0)
+      {
+        fprintf(stderr,"A value after the argument -t must be a positive scalar.\n"); 
+        goto clean_up; 
+      } 
+      i++;
+      recognized = 1;
+    }
+
+    if(recognized == 0)
+    {
+      fprintf(stderr,"Unknown input argument: %s\n", argv[i]);
+      goto clean_up;  
+    }
+        
+  }
+
+  /* load regularization constants from file if requested */
+  if( regconst_fname != NULL)
+  {
+    if(load_regconsts(regconst_fname, &vec_C, &len_vec_C, verb) == -1)
+    {
+      fprintf(stderr,"Cannot load regularization constants from file %s\n", regconst_fname); 
+      goto clean_up;  
+    }
+  }
+
+
+  len = strlen(argv[argc-2]);
+  input_fname = calloc(len+1,sizeof(char));
+  strcpy(input_fname, argv[argc-2]);
+
+  len = strlen(argv[argc-1]);
+  model_fname = calloc(len+1,sizeof(char));
+  strcpy(model_fname, argv[argc-1]);
+
+  if(verb)
+    printf("Input file: %s\n", input_fname);
+
+  /*----------------------------------------------------------------
+    Load input examples
+  -------------------------------------------------------------------*/
+  load_time = get_time();
+  if( load_svmlight_file(input_fname,verb) == -1 || data_X == NULL || data_y == NULL)
+    goto clean_up;
+
+  load_time = get_time() - load_time;
+  
+  /* get examples' dimension */
+  nDim = mxGetM(data_X);
+
+  /* if not given set number of training examples to be used */
+  if(nData == -1)
+    nData = mxGetN(data_X);
+  else if(nData <= 0 || nData > mxGetN(data_X))
+  {
+    fprintf(stderr,"Number of examples in the input file is %d.\n", mxGetN(data_X));
+    fprintf(stderr,"A value after argument -n must be less or equal to the number of examples.\n");
+    goto clean_up;
+  }
+
+
+  /*----------------------------------------------------------------
+    Print setting
+  -------------------------------------------------------------------*/
+  if(verb)
+  {
+    printf("Input file statistics:\n"
+           "   # of examples  : %d\n"
+           "   dimensionality : %d\n",
+           mxGetN(data_X), nDim);
+    if( mxIsSparse(data_X) )
+      printf("   density        : %.2f%% (sparse matrix representation used)\n",
+             100.0*(double)mxGetNZMAX(data_X)/((double)nDim*(double)(mxGetN(data_X))));
+    else
+      printf("   density        : 100%% (full matrix representation used)\n");
+     
+    printf("Setting:\n");
+             
+    if(vec_C == NULL)
+      printf("   C              : %f\n", C);
+    else
+    {
+      printf("   C              : different for each example; specified in %s\n", regconst_fname);
+      for(i = 0; i < len_vec_C; i++)
+        printf(" %f", vec_C[i]);
+      printf("\n");
+    }
+
+    printf("   bias           : %.0f\n"
+           "   # of examples  : %d\n"
+           "   solver         : %d\n"
+           "   cache size     : %d\n"
+           "   # of threads   : %d\n"
+           "   TolAbs         : %f\n"
+           "   TolRel         : %f\n"
+           "   QPValue        : %f\n"
+           "   MaxTime        : %f [s]\n"
+           "   verbosity      : %d\n",
+           X0, nData, Method,BufSize,number_of_threads,TolAbs,TolRel, QPBound, MaxTime,verb);
+  }
+
+
+  if( vec_C != NULL && len_vec_C < nData)
+  {
+    fprintf(stderr, "Number of regularization constants (%d) is less then number of examples (%d).\n",
+            len_vec_C, nData);
+    goto clean_up;
+  }
+
+  /*----------------------------------------------------------------
+    Allocate memory for working variables and cutting plane cache
+  -------------------------------------------------------------------*/
+
+  /* learned weight vector */
+  W = (double*)mxCalloc(nDim,sizeof(double));
+  if(W == NULL)
+  {
+    fprintf(stderr,"Not enough memory for vector W.\n");
+    goto clean_up;
+  }
+    
+  oldW = (double*)mxCalloc(nDim,sizeof(double));
+  if(oldW == NULL) 
+  {
+    fprintf(stderr,"Not enough memory for vector oldW.");
+    goto clean_up;
+  }
+
+  W0 = 0;
+  oldW0 = 0;
+
+  A0 = mxCalloc(BufSize,sizeof(A0[0]));
+  if(A0 == NULL) 
+  {
+    fprintf(stderr,"Not enough memory for vector A0.");
+    goto clean_up;
+  }
+
+  /* allocate buffer for computing cutting plane */
+  new_a = (double*)mxCalloc(nDim*number_of_threads,sizeof(double));
+  if(new_a == NULL) 
+  {
+    fprintf(stderr,"Not enough memory for auxciliary cutting plane buffer new_a.");  
+    goto clean_up;
+  }
+
+
+  if(vec_C != NULL)
+  {
+    for(i=0; i < nData; i++) 
+      data_y[i] = data_y[i]*vec_C[i];
+  }
+
+
+  if(mxIsSparse(data_X))
+  {
+
+    /* for i=1:nData, X(:,i) = X(:,i)*y(i); end*/
+    for(i=0; i < nData; i++) 
+      mul_sparse_col(data_y[i], data_X, i);
+
+    /* init cutting plane buffer */
+    sparse_A.nz_dims = mxCalloc(BufSize,sizeof(uint32_t));
+    sparse_A.index = mxCalloc(BufSize,sizeof(sparse_A.index[0]));
+    sparse_A.value = mxCalloc(BufSize,sizeof(sparse_A.value[0]));
+    if(sparse_A.nz_dims == NULL || sparse_A.index == NULL || sparse_A.value == NULL) 
+    {
+        fprintf(stderr,"Not enough memory for cutting plane buffer sparse_A.");  
+        goto clean_up;
+    }
+
+    /*----------------------------------------------------------------
+      Run OCAS run ...
+      -------------------------------------------------------------------*/
+    if(number_of_threads == 1)
+    {
+      if(verb)
+      {
+        printf("Starting optimization:\n");
+        if( vec_C == NULL)
+          ocas = svm_ocas_solver( C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                  &sparse_compute_W, &sparse_update_W, &sparse_add_new_cut, 
+                                  &sparse_compute_output, &qsort_data, &ocas_print, 0);
+        else
+          ocas = svm_ocas_solver_difC( vec_C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                       &sparse_compute_W, &sparse_update_W, &sparse_add_new_cut, 
+                                       &sparse_compute_output, &qsort_data, &ocas_print, 0);
+
+      }
+      else
+      {
+        if( vec_C == NULL)          
+          ocas = svm_ocas_solver( C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                  &sparse_compute_W, &sparse_update_W, &sparse_add_new_cut, 
+                                  &sparse_compute_output, &qsort_data, &ocas_print_null, 0);
+        else
+          ocas = svm_ocas_solver_difC( vec_C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                       &sparse_compute_W, &sparse_update_W, &sparse_add_new_cut, 
+                                       &sparse_compute_output, &qsort_data, &ocas_print_null, 0);
+          
+      }
+    }
+    else
+    {
+
+      if(init_parallel_ocas(number_of_threads) != 0)
+      {
+        goto clean_up;
+      }
+
+      if(verb)
+      {
+        printf("Starting optimization:\n");
+    
+        if( vec_C == NULL)          
+          ocas = svm_ocas_solver( C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                  &sparse_compute_W, &sparse_update_W, &parallel_sparse_add_new_cut, 
+                                  &parallel_sparse_compute_output, &parallel_qsort_data, &ocas_print, 0);
+        else
+          ocas = svm_ocas_solver_difC( vec_C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                       &sparse_compute_W, &sparse_update_W, &parallel_sparse_add_new_cut, 
+                                       &parallel_sparse_compute_output, &parallel_qsort_data, &ocas_print, 0);
+
+      }
+      else
+      {
+        if( vec_C == NULL)          
+          ocas = svm_ocas_solver( C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                  &sparse_compute_W, &sparse_update_W, &parallel_sparse_add_new_cut, 
+                                  &parallel_sparse_compute_output, &parallel_qsort_data, &ocas_print_null, 0);
+        else
+          ocas = svm_ocas_solver_difC( vec_C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                  &sparse_compute_W, &sparse_update_W, &parallel_sparse_add_new_cut, 
+                                  &parallel_sparse_compute_output, &parallel_qsort_data, &ocas_print_null, 0);
+          
+      }
+      destroy_parallel_ocas();
+    }
+  }
+  else
+  {
+    if(number_of_threads != 1 && verb)
+    {
+      printf("Warning: Multi-thread support works currently only for sparse data.\n");
+    }
+
+    double *ptr = mxGetPr(data_X);
+    for(i=0; i < nData; i++) {
+      for(j=0; j < nDim; j++ ) {
+        ptr[LIBOCAS_INDEX(j,i,nDim)] = ptr[LIBOCAS_INDEX(j,i,nDim)]*data_y[i];
+      }
+    }
+
+    /* init cutting plane buffer */
+    full_A = mxCalloc(BufSize*nDim,sizeof(double));
+    if( full_A == NULL )
+    {
+      fprintf(stderr,"Not enough memory for cutting plane buffer full_A.");  
+      goto clean_up;
+    }
+
+    if(verb)
+    {
+      printf("Starting optimization:\n");
+
+      if(vec_C == NULL)
+         ocas = svm_ocas_solver( C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                 &full_compute_W, &full_update_W, &full_add_new_cut, 
+                                 &full_compute_output, &qsort_data, &ocas_print, 0);
+      else
+         ocas = svm_ocas_solver_difC( vec_C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                      &full_compute_W, &full_update_W, &full_add_new_cut, 
+                                      &full_compute_output, &qsort_data, &ocas_print, 0);
+
+    }
+    else
+    {
+      if(vec_C == NULL)
+        ocas = svm_ocas_solver( C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                &full_compute_W, &full_update_W, &full_add_new_cut, 
+                                &full_compute_output, &qsort_data, &ocas_print_null, 0);
+      else
+        ocas = svm_ocas_solver_difC( vec_C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                     &full_compute_W, &full_update_W, &full_add_new_cut, 
+                                     &full_compute_output, &qsort_data, &ocas_print_null, 0);
+
+    }
+
+  }
+
+
+  total_time=get_time()-total_time;
+
+  if(verb)
+  {
+    printf("Stopping condition: ");
+    switch( ocas.exitflag )
+    {
+      case 1: printf("1-Q_D/Q_P <= TolRel(=%f) satisfied.\n", TolRel); break;
+      case 2: printf("Q_P-Q_D <= TolAbs(=%f) satisfied.\n", TolAbs); break;
+      case 3: printf("Q_P <= QPBound(=%f) satisfied.\n", QPBound); break;
+      case 4: printf("Optimization time (=%f) >= MaxTime(=%f).\n", ocas.ocas_time, MaxTime); break;
+      case -1: printf("Has not converged!\n" ); break;
+      case -2: printf("Not enough memory for the solver.\n" ); break;
+    }
+
+    printf("Timing statistics:\n"
+           "   load_time      : %f[s]\n"
+           "   qp_solver_time : %f[s]\n"
+           "   sort_time      : %f[s]\n"
+           "   output_time    : %f[s]\n"
+           "   add_time       : %f[s]\n"
+           "   w_time         : %f[s]\n"
+           "   print_time     : %f[s]\n"
+           "   ocas_time      : %f[s]\n"
+           "   total_time     : %f[s]\n",
+           load_time, ocas.qp_solver_time, ocas.sort_time, ocas.output_time, 
+           ocas.add_time, ocas.w_time, ocas.print_time, ocas.ocas_time, total_time);
+
+    printf("Training error: %.4f%%\n", 100*(double)ocas.trn_err/(double)nData);
+  }
+
+  /* save W and W0 to the model file */
+  fid = fopen(model_fname, "w+");
+  if(fid == NULL) {
+    perror("fopen error ");
+    mexPrintf("Cannot open model file.");
+    goto clean_up;
+  }
+
+  for(i=0; i < nDim; i++)
+    fprintf(fid, "%.20f\n", W[i]);
+
+  fprintf(fid, "%.20f\n", W0);
+  fclose(fid);
+
+  if(verb)
+    printf("Result saved to model file: %s\n", model_fname);
+
+  exitflag = 0;
+
+clean_up:
+
+  mxDestroyArray(data_X);
+  mxFree(data_y);
+  mxFree(W);
+  mxFree(oldW);
+  mxFree(A0);
+  mxFree(new_a);
+  mxFree(full_A);
+  mxFree(regconst_fname);
+  mxFree(model_fname);
+  mxFree(input_fname);
+  mxFree(vec_C);
+
+  mxFree(sparse_A.nz_dims);
+  if( sparse_A.index !=NULL) 
+  {
+    for(i=0; i < BufSize; i++)
+      if(sparse_A.index[i] != NULL)
+        mxFree(sparse_A.index[i]);
+
+    mxFree(sparse_A.index);
+  }
+  if( sparse_A.value != NULL)
+  {
+    for(i=0; i < BufSize; i++)
+      if(sparse_A.value[i] != NULL)
+        mxFree(sparse_A.value[i]);
+    
+    mxFree(sparse_A.value);
+  }
+
+  
+  return(exitflag);
+}
+
+
+
diff --git a/svmocas.m b/svmocas.m
new file mode 100644
index 0000000..963c99d
--- /dev/null
+++ b/svmocas.m
@@ -0,0 +1,67 @@
+% SVMOCAS Train linear SVM classifier using OCAS solver.
+%
+% Synopsis:
+%  [W,W0,stat] = svmocas(X,X0,y,C,Method,TolRel,TolAbs,QPBound,BufSize,nExamples,MaxTime); 
+% 
+%  [W,W0,stat] = svmocas(data_file,X0,C,Method,TolRel,TolAbs,QPBound,BufSize,nExamples,MaxTime); 
+%
+% Desription:
+%  This function trains linear SVM classifier by solving
+%
+%      W,W0 = argmin 0.5*(w'*w+w0^2) + C*sum max( 0, 1-y(i)*(w'*X(:,i)+w0*X0) )
+%              w,w0                  i=1:nExamples
+%
+%  The function accepts examples either in Matlab matrix X (both sparse and dense) and 
+%  a dense vector y or as path to a file in SVM^light format.
+%
+% Reference:
+%  V. Franc, S. Sonnenburg. OCAS optimized cutting plane algorithm for Support Vector 
+%    Machines. In Proceedings of ICML. Omnipress, 2008.
+%    http://ida.first.fraunhofer.de/~franc/papers/Franc-OCAS-ICML08.pdf
+%
+% Input:
+%   data_file [string] Training examples stored in SVM^light format.
+%
+%   X [nDim x nExamples] training inputs (sparse or dense matrix).
+%   X0 [1x1] constant coordinate (implicitly) added to all examples;
+%     this allows training biased decision rule.
+%   y [nExamples x 1] labels (+1/-1).
+%   C [1x1]  or [nExamples x 1] C [1x1] is a regularization constant common for all examples;
+%     if C is a vector [nExamples x 1] then each example has its own (possibly different) 
+%     regularization constant.
+%   Method [1x1] 0..cutting plane; 1..OCAS  (default 1)
+%   TolRel [1x1] halts if Q_P-Q_D <= abs(Q_P)*TolRel  (default 0.01)
+%   TolAbs [1x1] halts if Q_P-Q_D <= TolAbs  (default 0)
+%   QPValue [1x1] halts if Q_P <= QPBpound  (default 0)
+%   BufSize [1x1] Initial size of active constrains buffer (default 2000)
+%   nExaples [1x1] Number of examples used for training; must be >0 and <= size(X,2).
+%     If nExamples = inf then nExamples is set to size(X,2).
+%   MaxTime [1x1] halts if time used by solver (data loading time is not counted) exceeds
+%    MaxTime given in seconds. Use MaxTime=inf (default) to switch off this stopping condition. 
+%
+% Output:
+%   W [nDim x 1] Paramater vectors of decision rule sign(W'*X+W0)
+%   W0 [1x1] Bias term of the decision rule.
+%   stat [struct] Optimizer statistics (field names are self-explaining).
+%
+% Example:
+%  % loading data directly from file in SVM^light format
+%  [W,W0,stat] = svmocas('./data/riply_trn.light',1,1);
+%
+%  % using data loaded to Matlab
+%  load('./data/riply_trn','X','y');
+%  [W,W0,stat] = svmocas(X,1,y,1);
+%
+%  % classification
+%  load('riply_tst','X','y');
+%  ypred = sign(W'*X + W0);
+%  sum(ypred ~= y)/length(y)
+%
+
+%
+% Copyright (C) 2008, 2009 Vojtech Franc, xfrancv at cmp.felk.cvut.cz
+%                          Soeren Sonnenburg, soeren.sonnenburg at first.fraunhofer.de
+%
+% This program is free software; you can redistribute it and/or
+% modify it under the terms of the GNU General Public 
+% License as published by the Free Software Foundation; 
diff --git a/svmocas_lbp.m b/svmocas_lbp.m
new file mode 100644
index 0000000..47a71e5
--- /dev/null
+++ b/svmocas_lbp.m
@@ -0,0 +1,71 @@
+% SVMOCAS_LBP Train linear SVM classifier for images represented by LBP features. 
+%
+% Synopsis:
+%  [W,W0,stat]= svmocas_lbp(Images,imSize,Wins,winSize,height_of_pyramid,X0,y,C)
+%
+%  [W,W0,stat]= svmocas_lbp(Images,imSize,Wins,winSize,height_of_pyramid,...
+%                  X0,y,C,Method,TolRel,TolAbs,QPBound,BufSize,MaxTime,verb)
+%
+% Desription:
+%  SVMOCAS_LBP applies SVMOCAS solver (see HELP SVMOCAS) for training 
+%  two-class image classifier. The images are represented a by pyramid of 
+%  LBP features. For description of the used image features see HELP LBPPYR.
+%
+%  SVMOCAS_LBP allows to define virtual examples which are generated by
+%  shifting a base window along x- and y-axis of the input images.
+%  SVMOCAS_LBP implements the COFFIN framework, i.e., the virtual examples 
+%  are computed on demand which leads to substantial memory savings. 
+%
+%  References:
+%   - S. Sonnenburg, V. Franc. COFFIN: A Computational Framework for Linear SVMs.
+%     In Proc. of ICML 2010. Haifa. 
+%   - V. Franc, S. Sonnenburg. Optimized Cutting Plane Algorithm for Large-Scale 
+%     Risk Minimization.  The Journal of Machine Learning Research (JMLR), 
+%     vol. 10, pp. 2157--2192. October 2009. 
+%
+% Input:
+%   Images [(im_H*im_W) x nImages (uint8)] Input grey-scale images stored
+%      as column vectors.
+%   imSize [2 x 1] Size of the input images: imSize = [ImHeight x ImgWidth].
+%   Wins [4 x nExamples (uint32)] Defines windows (boxes) cropped from Images.
+%     Each window represents a single virtual example described by
+%     [image_index; top_left_col; top_left_row; mirror] where
+%     image_index points at column of Image; top_left_col and top_left_row
+%     are coordinates of the top left corner of the window and mirror
+%     is 0 (no mirroring) or 1 (swap window along the vertical axis).
+%   winSize [2 x 1] Size of the base window cropped from the Images.
+%   height_of_pyramid [1 x 1] See HELP LBPPYR.
+%
+%   X0 [1x1] constant coordinate (implicitly) added to all examples;
+%     this allows training biased decision rule.
+%   y [nExamples x 1] labels (+1/-1).
+%   C [1x1]  or [nExamples x 1] C [1x1] is a regularization constant common for all examples;
+%     if C is a vector [nExamples x 1] then each example has its own (possibly different) 
+%     regularization constant.
+%   Method [1x1] 0..cutting plane; 1..OCAS  (default 1)
+%   TolRel [1x1] halts if Q_P-Q_D <= abs(Q_P)*TolRel  (default 0.01)
+%   TolAbs [1x1] halts if Q_P-Q_D <= TolAbs  (default 0)
+%   QPValue [1x1] halts if Q_P <= QPBpound  (default 0)
+%   BufSize [1x1] Initial size of active constrains buffer (default 2000)
+%   nExaples [1x1] Number of examples used for training; must be >0 and <= size(X,2).
+%     If nExamples = inf then nExamples is set to size(X,2).
+%   MaxTime [1x1] halts if time used by solver (data loading time is not counted) exceeds
+%    MaxTime given in seconds. Use MaxTime=inf (default) to switch off this stopping condition. 
+%
+% Output:
+%   W [nDim x 1] Paramater vectors of decision rule sign(W'*X+W0) where X
+%     is LBPPYR feature representation of image to be classified.
+%   W0 [1x1] Bias term of the decision rule.
+%   stat [struct] Optimizer statistics (field names are self-explaining).
+%
+% Example:
+%   See SVMOCAS_LBP_EXAMPLE
+%
+
+%
+% Copyright (C) 2008, 2009, 2010 Vojtech Franc, xfrancv at cmp.felk.cvut.cz
+%                          Soeren Sonnenburg, soeren.sonnenburg at first.fraunhofer.de
+%
+% This program is free software; you can redistribute it and/or
+% modify it under the terms of the GNU General Public 
+% License as published by the Free Software Foundation; 
diff --git a/svmocas_lbp_example.m b/svmocas_lbp_example.m
new file mode 100644
index 0000000..fc4d41a
--- /dev/null
+++ b/svmocas_lbp_example.m
@@ -0,0 +1,118 @@
+% This script demonstrates SVMOCAS_LBP solver for training two-class 
+% classifiers of gray scale images. The images are described by a pyramid
+% of Local Binary Patterns (LBP) features. In order to make the classifier
+% translation invariant, SVMOCAS_LBP allows to generate virtual examples
+% by shifting a base window along x- and y-axis of the input images.
+% SVMOCAS_LBP implements the COFFIN framework, i.e. the virtual examples 
+% are computed on demand which leads to substantial memory savings. 
+%
+% For more details see:
+%  S. Sonnenburg, V. Franc.  COFFIN: A Computational Framework for Linear SVMs.
+%  In Proc. of ICML 2010. Haifa. 
+%
+% Image database provided by courtesy of 
+%  Eyedea Recognition LtD. (http://www.eyedea.cz)
+%
+
+load('./data/gender_images.mat',...
+     'trn_male_images','trn_female_images',...
+     'tst_male_images','tst_female_images','IMAGE_SIZE');
+
+%% Parameter of LBP feature representation
+LBP_PYRAMID = 4;       % height of LBP pyramid; 1 means that LBP filters are
+                       % applied only on the original image otherwise
+                       % the image is LBP_PYRAMID-times downscaled
+ 
+%% Definition of base window on which the classifier is applied 
+WINDOW_SIZE = [60 40];     % [height width]
+WINDOW_TOP_LEFT_COL = 20;
+WINDOW_TOP_LEFT_ROW = 15;
+
+%% Parameters SVM solver
+X0 = 1;                 % added constant feature
+C = 0.001;              % SVM C
+SVMOCAS_BUFSIZE = 100;  % number of cutting planes to store; single CP requires ~ 0.7MB
+SVMOCAS_TOLREL = 0.01;  % precision of SVM solution
+SVMOCAS_METHOD = 1;     % 1.. OCAS, 0..BMRM
+
+%% Virtual examples
+% The virtual examples are created by shifting the base window in x and
+% y-axis and mirroring the image along its vertical axis.
+
+switch input('use virtual examples (0-no, 1-yes)?:')
+    case 0
+        % testing error 20%
+        USE_VIRTUAL_EXAMPLES = 0; 
+
+    case 1
+        % testing error 14%
+        USE_VIRTUAL_EXAMPLES = 1; 
+        SHIFT_X = [-1 0 1];
+        SHIFT_Y = [-1 0 1];
+        MIRROR = [0];
+        
+end
+
+%% prepare training examples 
+nTrnMales = size(trn_male_images,2);
+nTrnFemales = size(trn_male_images,2);
+
+if ~USE_VIRTUAL_EXAMPLES
+    BOX = [WINDOW_TOP_LEFT_COL;WINDOW_TOP_LEFT_ROW;0];
+    wins = [ [1:nTrnMales nTrnMales+[1:nTrnFemales]]; repmat(BOX,1,nTrnFemales+nTrnMales)];
+    labels = [ones(1,nTrnMales) -ones(1,nTrnFemales)];    
+        
+else               
+    % define virtual examples by shifting the base window
+    BOX = zeros(3,length(MIRROR)*length(SHIFT_Y)*length(SHIFT_X),'uint32');    
+    cnt = 0;
+    for u=SHIFT_X
+        for v=SHIFT_Y
+           for mirror=MIRROR
+               cnt = cnt + 1;
+               BOX(:,cnt) = uint32([WINDOW_TOP_LEFT_COL+u; WINDOW_TOP_LEFT_ROW+v;mirror]);
+           end
+        end
+    end       
+
+    % prepare virtual examples for each training image
+    wins = zeros(4,size(BOX,2)*(nTrnMales+nTrnFemales),'uint32');
+    labels = zeros(size(BOX,2)*(nTrnMales+nTrnFemales),1);
+    cnt = 0;
+    for i=1:(nTrnMales+nTrnFemales)
+        wins(1,cnt+1:cnt+size(BOX,2)) = uint32(i);
+        wins(2:4,cnt+1:cnt+size(BOX,2)) = BOX;
+        if i <= nTrnMales
+            labels(cnt+1:cnt+size(BOX,2)) = 1;
+        else
+            labels(cnt+1:cnt+size(BOX,2)) = -1;
+        end
+        cnt = cnt + size(BOX,2);
+    end
+    
+end
+
+%% train SVM linear classifier 
+[W,W0,stat] = svmocas_lbp([trn_male_images trn_female_images], IMAGE_SIZE,...
+                          uint32(wins), WINDOW_SIZE, LBP_PYRAMID, X0, labels, C, ...
+                          1, SVMOCAS_TOLREL,0,0,SVMOCAS_BUFSIZE);
+                          
+%% compute features for testing images and evaluate the tained classifier
+BOX = [WINDOW_TOP_LEFT_COL;WINDOW_TOP_LEFT_ROW;0];
+
+nTstMale = size(tst_male_images,2);
+tst_labels = ones(1,nTstMale);
+tst_feat = lbppyr_features(tst_male_images,IMAGE_SIZE, ...
+                          uint32([1:nTstMale;repmat(BOX,1,nTstMale)]), ...
+                          WINDOW_SIZE, LBP_PYRAMID,0);
+
+nTstFemale = size(tst_female_images,2);
+tst_labels = [tst_labels -ones(1,nTstFemale)];
+tst_feat = [tst_feat lbppyr_features(tst_female_images,IMAGE_SIZE, ...
+                          uint32([1:nTstFemale;repmat(BOX,1,nTstFemale)]), ...
+                          WINDOW_SIZE, LBP_PYRAMID,0)];
+                                                
+ypred = sign(W'*double(tst_feat) + W0);
+tst_error = sum(ypred(:) ~= tst_labels(:))/length(tst_labels);
+fprintf('Testing error: %f%%\n', tst_error*100);
+                      
\ No newline at end of file
diff --git a/svmocas_lbp_mex.c b/svmocas_lbp_mex.c
new file mode 100644
index 0000000..0a6806a
--- /dev/null
+++ b/svmocas_lbp_mex.c
@@ -0,0 +1,382 @@
+/*=================================================================================
+ * SVMOCAS_LBP Train linear SVM classifier for images represented by LBP features. 
+ * 
+ * Synopsis:
+ *  [W,W0,stat]= svmocas_lbp(Images,imSize,Wins,winSize,height_of_pyramid,X0,y,C,Method,TolRel,TolAbs,QPBound,BufSize,MaxTime,verb) 
+ *
+ * Input:  
+ *   Images [(im_H*im_W) x nImages (uint8)]
+ *   imSize [2 x 1 (uint32)] imSize = [im_H im_W]
+ *   Wins [4 x nExamples (uint32)]  [image_idx; top_left_col; top_left_row; mirror]
+ *   winSize [2 x 1 (uint32)] [win_H win_W]
+ *   height_of_pyramid [1 x 1 (double)]
+ *   X0 [1 x 1 (double)]
+ *   y [nExamples x 1 (double)] +1/-1
+ *   C [1 x 1 (double)] OR [nExamples x 1 (double)]
+ *   Method [1x1 (double)] 0 (BMRM) or 1 (OCAS)
+ *   TolRel [1x1 (double)]
+ *   TolAbs [1x1 (double)]
+ *   QPBound [1x1 (double)]
+ *   BufSize [1x1 (double)]
+ *   MaxTime [1x1 (double)]
+ *   verb [1x1 (bouble)]
+ * Output:
+ *   W [nDim x 1] Parameter vector
+ *   W0 [1x1] Bias term
+ *   stat [struct] 
+ *
+ * Copyright (C) 2008, 2009, 2010 Vojtech Franc, xfrancv at cmp.felk.cvut.cz
+ *                                Soeren Sonnenburg, soeren.sonnenburg at first.fraunhofer.de
+ *
+ * This program is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU General Public 
+ * License as published by the Free Software Foundation; 
+ *======================================================================================*/ 
+
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <mex.h>
+
+#include "libocas.h"
+#include "ocas_lbp_helper.h"
+#include "liblbp.h"
+
+#define DEFAULT_METHOD 1
+#define DEFAULT_TOLREL 0.01
+#define DEFAULT_TOLABS 0.0
+#define DEFAULT_QPVALUE 0.0
+#define DEFAULT_BUFSIZE 500
+#define DEFAULT_MAXTIME mxGetInf()
+#define DEFAULT_VERB 1
+
+/*======================================================================
+  Main code plus interface to Matlab.
+========================================================================*/
+
+void mexFunction( int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[] )
+{
+  double C, TolRel, TolAbs, QPBound, trn_err, MaxTime;
+  double *vec_C;   
+  uint32_t num_of_Cs;
+  uint32_t i, j, BufSize;
+  uint16_t Method;
+  int verb;
+  ocas_return_value_T ocas;
+  double *tmp;
+
+  /* timing variables */
+  double init_time;
+  double total_time;
+
+  total_time = get_time();
+  init_time = total_time;
+
+  if(nrhs < 8 || nrhs > 15)
+     mexErrMsgTxt("Improper number of input arguments.\n\n"
+                  "SVMOCAS_LBP train linear SVM classifier for images prepresented by LBP features. \n\n"
+                  "Synopsis:\n"
+                  "  [W,W0,stat]= svmocas_lbp(Images,imSize,Wins,winSize,height_of_pyramid,\n"
+                  "              X0,y,C,Method,TolRel,TolAbs,QPBound,BufSize,MaxTime,verb) \n\n"
+                  "Input:  \n"
+                  "  Images [(im_H*im_W) x nImages (uint8)]\n"
+                  "  imSize [2 x 1 (uint32)] imSize = [im_H im_W]\n"
+                  "  Wins [4 x nExamples (uint32)] [img_idx; topleft_col; topleft_row; mirror]\n"
+                  "  winSize [2 x 1 (uint32)] [win_H win_W]\n"
+                  "  height_of_pyramid [1 x 1 (double)]\n"
+                  "  X0 [1 x 1 (double)]\n"
+                  "  y [nExamples x 1 (double)] +1 or -1\n"
+                  "  C [1 x 1 (double)] OR [nExamples x 1 (double)]\n"
+                  "  Method [1x1 (double)] 0 for BMRM; 1 for OCAS \n"
+                  "  TolRel [1x1 (double)]\n"
+                  "  TolAbs [1x1 (double)]\n"
+                  "  QPBound [1x1 (double)]\n"
+                  "  BufSize [1x1 (double)]\n"
+                  "  MaxTime [1x1 (double)]\n"
+                  "  verb [1x1 (bouble)]\n\n"
+                  "Output:\n"
+                  "  W [nDim x 1] Parameter vector\n"
+                  "  W0 [1x1] Bias term\n"
+                  "  stat [struct] \n");
+
+  /*                             0      1     2     3         4     5  6 7   8      9      10      11     12     13       14     15*/
+  /*  [W,W0,stat]= svmocas_lbp(Images,imSize,Wins,winSize,nPyramids,X0,y,C,Method,TolRel,TolAbs,QPBound,BufSize,nExamples,MaxTime,verb) */
+
+  if(nrhs >= 16)
+    verb = (int)mxGetScalar(prhs[15]);
+  else
+    verb = DEFAULT_VERB;
+
+  Images = (uint8_t*)mxGetPr(prhs[0]);
+  nImages = mxGetN(prhs[0]);
+
+  tmp = (double*)mxGetPr(prhs[1]);
+  im_H = (uint32_t)tmp[0];
+  im_W = (uint32_t)tmp[1];
+
+  mexPrintf("im_h=%d  im_W=%d \n", im_H, im_W);
+  if(mxGetM(prhs[0]) != im_H*im_W)
+    mexErrMsgTxt("Dimension of Images does not match to im_H*im_W.");
+
+  Wins = (uint32_t*)mxGetPr(prhs[2]);
+
+  tmp = (double*)mxGetPr(prhs[3]);
+  win_H = (uint32_t)tmp[0];
+  win_W = (uint32_t)tmp[1];
+
+  nPyramids = (uint32_t)mxGetScalar(prhs[4]);
+/*  nDim = lbppyr_get_dim(win_H,win_W,nPyramids);*/
+  nDim = liblbp_pyr_get_dim(win_H,win_W,nPyramids);
+
+  croped_window = (uint32_t*)mxCalloc(win_H*win_W,sizeof(uint32_t));
+  if(croped_window == NULL) 
+    mexErrMsgTxt("Not enough memory for croped_window.");
+  
+  X0 = mxGetScalar(prhs[5]);
+  data_y = (double*)mxGetPr(prhs[6]);
+
+  nData = LIBOCAS_MAX(mxGetM(prhs[6]),mxGetN(prhs[6]));
+  if(nData != mxGetN(prhs[2]))
+    mexErrMsgTxt("Dimension missmatch betwenn Wins and y.");
+
+  if(verb)
+  {
+    mexPrintf("Input data:\n"
+              "   # of images     : %d\n"
+              "   image height    : %d\n"
+              "   image width     : %d\n",
+              nImages, im_H, im_W);
+
+    mexPrintf("Feature represenation:\n"
+              "   base window height        : %d\n"
+              "   base window width         : %d\n"
+              "   nPyramids                 : %d\n"
+              "   # of virtual examples     : %d\n"
+              "   # of features per example : %d\n",
+              win_H, win_W, nPyramids, nData, nDim);
+  }
+
+  num_of_Cs = LIBOCAS_MAX(mxGetN(prhs[7]),mxGetM(prhs[7]));
+  if(num_of_Cs == 1)
+  {
+    C = (double)mxGetScalar(prhs[7]);
+  }
+  else
+  {
+    vec_C = (double*)mxGetPr(prhs[7]);
+  }
+
+  if(nrhs >= 9)
+    Method = (uint32_t)mxGetScalar(prhs[8]);
+  else
+    Method = DEFAULT_METHOD;
+
+  if(nrhs >= 10)
+    TolRel = (double)mxGetScalar(prhs[9]);
+  else
+    TolRel = DEFAULT_TOLREL;
+  
+  if(nrhs >= 11)    
+    TolAbs = (double)mxGetScalar(prhs[10]);
+  else
+    TolAbs = DEFAULT_TOLABS;
+
+  if(nrhs >= 12)
+    QPBound = (double)mxGetScalar(prhs[11]);
+  else
+    QPBound = DEFAULT_QPVALUE;
+
+  if(nrhs >= 13)
+    BufSize = (uint32_t)mxGetScalar(prhs[12]);
+  else
+    BufSize = DEFAULT_BUFSIZE;
+
+  if(num_of_Cs > 1 && num_of_Cs < nData)
+    mexErrMsgTxt("Length of the vector C less than the number of examples.");
+
+  if(nrhs >= 14 && !mxIsInf(mxGetScalar(prhs[13])))
+  {
+    if((uint32_t)mxGetScalar(prhs[13]) < 0 || (uint32_t)mxGetScalar(prhs[13]) > nData)
+      mexErrMsgTxt("Improper number of examples; must be > 0 and < max number of virtual example.\n");
+
+    nData = (uint32_t)mxGetScalar(prhs[13]);
+    mexPrintf("   # of examples set to : %d\n",nData);
+  }
+
+  if(nrhs >= 15)
+    MaxTime = (double)mxGetScalar(prhs[14]);
+  else
+    MaxTime = DEFAULT_MAXTIME;
+
+  /*----------------------------------------------------------------
+    Print setting
+  -------------------------------------------------------------------*/
+  if(verb)
+  {
+    mexPrintf("SVM setting:\n");
+
+    if( num_of_Cs == 1)
+      mexPrintf("   C              : %f\n", C);
+    else
+      mexPrintf("   C              : different for each example\n");
+
+    mexPrintf("   bias           : %.0f\n"
+              "   # of examples  : %d\n"
+              "   solver         : %d\n"
+              "   cache size     : %d\n"
+              "   TolAbs         : %f\n"
+              "   TolRel         : %f\n"
+              "   QPValue        : %f\n"
+              "   MaxTime        : %f [s]\n"
+              "   verb           : %d\n",
+              X0, nData, Method,BufSize,TolAbs,TolRel, QPBound, MaxTime, verb);
+  }
+  
+  /* learned weight vector */
+  plhs[0] = (mxArray*)mxCreateDoubleMatrix(nDim,1,mxREAL);
+  W = (double*)mxGetPr(plhs[0]);
+  if(W == NULL) mexErrMsgTxt("Not enough memory for vector W.");
+
+  oldW = (double*)mxCalloc(nDim,sizeof(double));
+  if(oldW == NULL) mexErrMsgTxt("Not enough memory for vector oldW.");
+
+  W0 = 0;
+  oldW0 = 0;
+
+  A0 = mxCalloc(BufSize,sizeof(A0[0]));
+  if(A0 == NULL) mexErrMsgTxt("Not enough memory for vector A0.");
+
+  /* allocate buffer for computing cutting plane */
+/*  new_a = (double*)mxCalloc(nDim,sizeof(double));*/
+  new_a = mxCalloc(nDim,sizeof(new_a[0]));
+  if(new_a == NULL) 
+    mexErrMsgTxt("Not enough memory for auxciliary cutting plane buffer new_a.");  
+
+  if(num_of_Cs > 1)
+  {
+    for(i=0; i < nData; i++) 
+      data_y[i] = data_y[i]*vec_C[i];
+  }
+
+  /* !!!!!!!!!!!!
+  ptr = mxGetPr(data_X);
+  for(i=0; i < nData; i++) {
+    for(j=0; j < nDim; j++ ) {
+      ptr[LIBOCAS_INDEX(j,i,nDim)] = ptr[LIBOCAS_INDEX(j,i,nDim)]*data_y[i];
+    }
+  }
+  */
+
+  /* init cutting plane buffer */
+/*  full_A = mxCalloc(BufSize*nDim,sizeof(double));*/
+  full_A = mxCalloc(BufSize*nDim,sizeof(full_A[0]));
+  if( full_A == NULL )
+    mexErrMsgTxt("Not enough memory for cutting plane buffer full_A."); 
+
+
+  if(verb)
+  {
+    mexPrintf("Memory occupancy:\n"
+             "   raw images         : %.2f MB\n" 
+             "   CP buffer          : %.2f MB\n"
+             "   parameter vector W : %.2f MB\n",
+              (double)nImages*im_H*im_W/(1024*1024),
+              (double)sizeof(full_A[0])*BufSize*nDim/(1024*1024), 
+              (double)sizeof(W[0])*nDim/(1024*1024));
+  }
+
+  init_time=get_time()-init_time;
+
+  if(verb)
+  {
+    mexPrintf("Starting optimization:\n");
+    
+    if(num_of_Cs == 1)
+      ocas = svm_ocas_solver( C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                              &full_compute_W, &full_update_W, &full_add_new_cut, 
+                              &full_compute_output, &qsort_data, &ocas_print, 0);
+    else
+      ocas = svm_ocas_solver_difC( vec_C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                   &full_compute_W, &full_update_W, &full_add_new_cut, 
+                                   &full_compute_output, &qsort_data, &ocas_print, 0);
+  }
+  else
+  {
+    if(num_of_Cs == 1)
+      ocas = svm_ocas_solver( C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                              &full_compute_W, &full_update_W, &full_add_new_cut, 
+                              &full_compute_output, &qsort_data, &ocas_print_null, 0);
+    else
+      ocas = svm_ocas_solver_difC( vec_C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                   &full_compute_W, &full_update_W, &full_add_new_cut, 
+                                   &full_compute_output, &qsort_data, &ocas_print_null, 0);
+  }
+
+
+  if(verb)
+  {
+    mexPrintf("Stopping condition: ");
+    switch( ocas.exitflag )
+    {
+       case 1: mexPrintf("1-Q_D/Q_P <= TolRel(=%f) satisfied.\n", TolRel); break;
+       case 2: mexPrintf("Q_P-Q_D <= TolAbs(=%f) satisfied.\n", TolAbs); break;
+       case 3: mexPrintf("Q_P <= QPBound(=%f) satisfied.\n", QPBound); break;
+       case 4: mexPrintf("Optimization time (=%f) >= MaxTime(=%f).\n", ocas.ocas_time, MaxTime); break;
+       case -1: mexPrintf("Has not converged!\n" ); break;
+       case -2: mexPrintf("Not enough memory for the solver.\n" ); break;
+    }
+  }
+
+  total_time=get_time()-total_time;
+  if(verb)
+  {
+    mexPrintf("Timing statistics:\n"
+              "   init_time      : %f[s]\n"
+              "   qp_solver_time : %f[s]\n"
+              "   sort_time      : %f[s]\n"
+              "   output_time    : %f[s]\n"
+              "   add_time       : %f[s]\n"
+              "   w_time         : %f[s]\n"
+              "   print_time     : %f[s]\n"
+              "   ocas_time      : %f[s]\n"
+              "   total_time     : %f[s]\n",
+              init_time, ocas.qp_solver_time, ocas.sort_time, ocas.output_time, 
+              ocas.add_time, ocas.w_time, ocas.print_time, ocas.ocas_time, total_time);
+
+    mexPrintf("Training error: %.4f%%\n", 100*(double)ocas.trn_err/(double)nData);
+  }
+
+  if(num_of_Cs > 1)
+  {
+    for(i=0; i < nData; i++) 
+      data_y[i] = data_y[i]/vec_C[i];
+  }
+
+  plhs[1] = mxCreateDoubleScalar( W0 );
+  
+  const char *field_names[] = {"nTrnErrors","Q_P","Q_D","nIter","nCutPlanes","exitflag",
+                               "init_time","output_time","sort_time","qp_solver_time","add_time",
+                               "w_time","print_time","ocas_time","total_time"}; 
+  mwSize dims[2] = {1,1};  
+
+  plhs[2] = mxCreateStructArray(2, dims, (sizeof(field_names)/sizeof(*field_names)), field_names);
+  
+  mxSetField(plhs[2],0,"nIter",mxCreateDoubleScalar((double)ocas.nIter));
+  mxSetField(plhs[2],0,"nCutPlanes",mxCreateDoubleScalar((double)ocas.nCutPlanes));
+  mxSetField(plhs[2],0,"nTrnErrors",mxCreateDoubleScalar(ocas.trn_err)); 
+  mxSetField(plhs[2],0,"Q_P",mxCreateDoubleScalar(ocas.Q_P)); 
+  mxSetField(plhs[2],0,"Q_D",mxCreateDoubleScalar(ocas.Q_D)); 
+  mxSetField(plhs[2],0,"init_time",mxCreateDoubleScalar(init_time)); 
+  mxSetField(plhs[2],0,"output_time",mxCreateDoubleScalar(ocas.output_time)); 
+  mxSetField(plhs[2],0,"sort_time",mxCreateDoubleScalar(ocas.sort_time)); 
+  mxSetField(plhs[2],0,"qp_solver_time",mxCreateDoubleScalar(ocas.qp_solver_time)); 
+  mxSetField(plhs[2],0,"add_time",mxCreateDoubleScalar(ocas.add_time)); 
+  mxSetField(plhs[2],0,"w_time",mxCreateDoubleScalar(ocas.w_time)); 
+  mxSetField(plhs[2],0,"print_time",mxCreateDoubleScalar(ocas.print_time)); 
+  mxSetField(plhs[2],0,"ocas_time",mxCreateDoubleScalar(ocas.ocas_time)); 
+  mxSetField(plhs[2],0,"total_time",mxCreateDoubleScalar(total_time)); 
+  mxSetField(plhs[2],0,"exitflag",mxCreateDoubleScalar((double)ocas.exitflag)); 
+
+  return;
+}
+
diff --git a/svmocas_mex.c b/svmocas_mex.c
new file mode 100644
index 0000000..3e489b0
--- /dev/null
+++ b/svmocas_mex.c
@@ -0,0 +1,489 @@
+/*=================================================================================
+ * svmocas_mex.c: Matlab MEX interface for OCAS solver training the linear SVM classifiers.
+ *
+ * Synopsis:
+ *  [W,W0,stat] = svmocas(X,X0,y,C,Method,TolRel,TolAbs,QPBound,BufSize,nData,MaxTime,verb)
+ *  [W,W0,stat] = svmocas(data_file,X0,C,Method,TolRel,TolAbs,QPBound,BufSize,nData,MaxTime,verb)
+ *
+ * See svmocas.m for more help.
+ *
+ * Copyright (C) 2008, 2009 Vojtech Franc, xfrancv at cmp.felk.cvut.cz
+ *                          Soeren Sonnenburg, soeren.sonnenburg at first.fraunhofer.de
+ *
+ * This program is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU General Public 
+ * License as published by the Free Software Foundation; 
+ *======================================================================================*/ 
+
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <mex.h>
+
+#include "libocas.h"
+#include "ocas_helper.h"
+
+#define DEFAULT_METHOD 1
+#define DEFAULT_TOLREL 0.01
+#define DEFAULT_TOLABS 0.0
+#define DEFAULT_QPVALUE 0.0
+#define DEFAULT_BUFSIZE 2000
+#define DEFAULT_MAXTIME mxGetInf()
+#define DEFAULT_VERB 1
+
+/*======================================================================
+  Main code plus interface to Matlab.
+========================================================================*/
+
+void mexFunction( int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[] )
+{
+  double C, TolRel, TolAbs, QPBound, trn_err, MaxTime;
+  double *vec_C;   
+  double *ptr;
+  uint32_t num_of_Cs;
+  uint32_t i, j, BufSize;
+  uint16_t Method;
+  int verb;
+  ocas_return_value_T ocas;
+
+  /* timing variables */
+  double init_time;
+  double total_time;
+
+  total_time = get_time();
+  init_time = total_time;
+
+  if(nrhs < 1)
+    mexErrMsgTxt("Improper number of input arguments.");
+
+  /* get input arguments */ 
+  if(mxIsChar(prhs[0]) == false) 
+  {
+    /* [W,W0,stat] = svmocas_mex(X,X0,y,C,Method,TolRel,TolAbs,QPBound,BufSize,nData,MaxTime); */
+
+    if(nrhs < 4 || nrhs > 12)
+      mexErrMsgTxt("Improper number of input arguments.");
+
+    if(nrhs >= 12)
+      verb = (int)mxGetScalar(prhs[11]);
+    else
+      verb = DEFAULT_VERB;
+
+
+    data_X = (mxArray*)prhs[0];
+    if (!(mxIsDouble(data_X)))
+        mexErrMsgTxt("Input argument X must be of type double.");
+
+    if (mxGetNumberOfDimensions(data_X) != 2)
+        mexErrMsgTxt("Input argument X must be two dimensional");
+
+    X0 = mxGetScalar(prhs[1]);
+    data_y = (double*)mxGetPr(prhs[2]);
+
+    if(LIBOCAS_MAX(mxGetM(prhs[2]),mxGetN(prhs[2])) != mxGetN(prhs[0]))
+      mexErrMsgTxt("Length of vector y must equl to the number of columns of matrix X.");
+
+    nDim = mxGetM(prhs[0]);
+
+    if(verb)
+    {
+      mexPrintf("Input data statistics:\n"
+                "   # of examples  : %d\n"
+                "   dimensionality : %d\n",
+                mxGetN(data_X), nDim);
+    
+      if( mxIsSparse(data_X)== true ) 
+        mexPrintf("   density        : %.2f%%\n",
+                  100.0*(double)mxGetNzmax(data_X)/((double)nDim*(double)(mxGetN(data_X))));
+      else
+        mexPrintf("   density        : 100%% (full)\n");
+    }
+
+
+    num_of_Cs = LIBOCAS_MAX(mxGetN(prhs[3]),mxGetM(prhs[3]));
+    if(num_of_Cs == 1)
+    {
+       C = (double)mxGetScalar(prhs[3]);
+    }
+    else
+    {
+       vec_C = (double*)mxGetPr(prhs[3]);
+    }
+
+    if(nrhs >= 5)
+      Method = (uint32_t)mxGetScalar(prhs[4]);
+    else
+      Method = DEFAULT_METHOD;
+
+    if(nrhs >= 6)
+      TolRel = (double)mxGetScalar(prhs[5]);
+    else
+      TolRel = DEFAULT_TOLREL;
+
+    if(nrhs >= 7)    
+      TolAbs = (double)mxGetScalar(prhs[6]);
+    else
+      TolAbs = DEFAULT_TOLABS;
+
+    if(nrhs >= 8)
+      QPBound = (double)mxGetScalar(prhs[7]);
+    else
+      QPBound = DEFAULT_QPVALUE;
+
+    if(nrhs >= 9)
+      BufSize = (uint32_t)mxGetScalar(prhs[8]);
+    else
+      BufSize = DEFAULT_BUFSIZE;
+
+    if(nrhs >= 10 && mxIsInf(mxGetScalar(prhs[9])) == false)
+      nData = (uint32_t)mxGetScalar(prhs[9]);
+    else
+      nData = mxGetN(data_X);
+      
+    if(nData < 1 || nData > mxGetN(prhs[0])) 
+      mexErrMsgTxt("Improper value of argument nData.");
+
+    if(num_of_Cs > 1 && num_of_Cs < nData)
+      mexErrMsgTxt("Length of the vector C less than the number of examples.");
+
+    if(nrhs >= 11)
+      MaxTime = (double)mxGetScalar(prhs[10]);
+    else
+      MaxTime = DEFAULT_MAXTIME;
+  } 
+  else
+  {
+    /* [W,W0,stat] = svmocas_mex(svmlight_data_file,X0,C,Method,TolRel,TolAbs,QPBound,BufSize,nData,MaxTime); */
+    char *fname;
+    int fname_len;
+
+    if(nrhs < 3 || nrhs > 11)
+      mexErrMsgTxt("Improper number of input arguments.");
+
+    if(nrhs >= 11)
+      verb = (int)mxGetScalar(prhs[10]);
+    else
+      verb = DEFAULT_VERB;
+
+    if(!mxIsChar(prhs[0]))
+      mexErrMsgTxt("First input argument must be of type string.");
+
+    fname_len = mxGetNumberOfElements(prhs[0]) + 1;   
+    fname = mxCalloc(fname_len, sizeof(char));    
+
+    if (mxGetString(prhs[0], fname, fname_len) != 0)     
+      mexErrMsgTxt("Could not convert first input argument to string.");
+
+    if( load_svmlight_file(fname,verb) == -1 || data_X == NULL || data_y == NULL)
+      mexErrMsgTxt("Cannot load input file.");
+
+    nDim = mxGetM(data_X);
+    X0 = mxGetScalar(prhs[1]);
+
+/*    C = (double)mxGetScalar(prhs[2]);*/
+    num_of_Cs = LIBOCAS_MAX(mxGetN(prhs[2]),mxGetM(prhs[2]));
+    if(num_of_Cs == 1)
+    {
+       C = (double)mxGetScalar(prhs[2]);
+    }
+    else
+    {
+       vec_C = (double*)mxGetPr(prhs[2]);
+    }
+
+    if(verb)
+      mexPrintf("Input data statistics:\n"
+                "   # of examples  : %d\n"
+                "   dimensionality : %d\n"
+                "   density        : %.2f%%\n",
+                mxGetN(data_X), nDim, 100.0*(double)mxGetNzmax(data_X)/((double)nDim*(double)(mxGetN(data_X))));
+    
+    if(nrhs >= 4)
+      Method = (uint32_t)mxGetScalar(prhs[3]);
+    else
+      Method = DEFAULT_METHOD;
+
+    if(nrhs >= 5)
+      TolRel = (double)mxGetScalar(prhs[4]);
+    else
+      TolRel = DEFAULT_TOLREL;
+
+    if(nrhs >= 6)
+      TolAbs = (double)mxGetScalar(prhs[5]);
+    else
+      TolAbs = DEFAULT_TOLABS;
+
+    if(nrhs >= 7)
+      QPBound = (double)mxGetScalar(prhs[6]);
+    else
+      QPBound = DEFAULT_QPVALUE;
+
+    if(nrhs >= 8)    
+      BufSize = (uint32_t)mxGetScalar(prhs[7]);
+    else
+      BufSize = DEFAULT_BUFSIZE;
+
+    if(nrhs >= 9 && mxIsInf(mxGetScalar(prhs[8])) == false) 
+      nData = (uint32_t)mxGetScalar(prhs[8]);
+    else
+      nData = mxGetN(data_X);
+
+    if(nData < 1 || nData > mxGetN(data_X)) 
+      mexErrMsgTxt("Improper value of argument nData.");
+
+    if(num_of_Cs > 1 && num_of_Cs < nData)
+      mexErrMsgTxt("Length of the vector C less than the number of examples.");
+
+    if(nrhs >= 10)
+      MaxTime = (double)mxGetScalar(prhs[9]);
+    else
+      MaxTime = DEFAULT_MAXTIME;
+
+  } 
+
+
+  /*----------------------------------------------------------------
+    Print setting
+  -------------------------------------------------------------------*/
+  if(verb)
+  {
+    mexPrintf("Setting:\n");
+
+    if( num_of_Cs == 1)
+      mexPrintf("   C              : %f\n", C);
+    else
+      mexPrintf("   C              : different for each example\n");
+
+    mexPrintf("   bias           : %.0f\n"
+              "   # of examples  : %d\n"
+              "   solver         : %d\n"
+              "   cache size     : %d\n"
+              "   TolAbs         : %f\n"
+              "   TolRel         : %f\n"
+              "   QPValue        : %f\n"
+              "   MaxTime        : %f [s]\n"
+              "   verb           : %d\n",
+              X0, nData, Method,BufSize,TolAbs,TolRel, QPBound, MaxTime, verb);
+  }
+  
+  /* learned weight vector */
+  plhs[0] = (mxArray*)mxCreateDoubleMatrix(nDim,1,mxREAL);
+  W = (double*)mxGetPr(plhs[0]);
+  if(W == NULL) mexErrMsgTxt("Not enough memory for vector W.");
+
+  oldW = (double*)mxCalloc(nDim,sizeof(double));
+  if(oldW == NULL) mexErrMsgTxt("Not enough memory for vector oldW.");
+
+  W0 = 0;
+  oldW0 = 0;
+
+  A0 = mxCalloc(BufSize,sizeof(A0[0]));
+  if(A0 == NULL) mexErrMsgTxt("Not enough memory for vector A0.");
+
+  /* allocate buffer for computing cutting plane */
+  new_a = (double*)mxCalloc(nDim,sizeof(double));
+  if(new_a == NULL) 
+    mexErrMsgTxt("Not enough memory for auxciliary cutting plane buffer new_a.");  
+
+
+  if(num_of_Cs > 1)
+  {
+    for(i=0; i < nData; i++) 
+      data_y[i] = data_y[i]*vec_C[i];
+  }
+
+
+  if( mxIsSparse(data_X)== true ) {
+
+    /* for i=1:nData, X(:,i) = X(:,i)*y(i); end*/
+    for(i=0; i < nData; i++) 
+        mul_sparse_col(data_y[i], data_X, i);           
+  
+
+    /* init cutting plane buffer */
+    sparse_A.nz_dims = mxCalloc(BufSize,sizeof(uint32_t));
+    sparse_A.index = mxCalloc(BufSize,sizeof(sparse_A.index[0]));
+    sparse_A.value = mxCalloc(BufSize,sizeof(sparse_A.value[0]));
+    if(sparse_A.nz_dims == NULL || sparse_A.index == NULL || sparse_A.value == NULL) 
+      mexErrMsgTxt("Not enough memory for cutting plane buffer sparse_A.");  
+
+    init_time=get_time()-init_time;
+
+    if(verb)
+    {
+      mexPrintf("Starting optimization:\n");
+
+      if(num_of_Cs == 1)
+      {
+        ocas = svm_ocas_solver( C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                              &sparse_compute_W, &sparse_update_W, &sparse_add_new_cut, 
+                              &sparse_compute_output, &qsort_data, &ocas_print, 0);
+      }  
+      else
+      {
+        ocas = svm_ocas_solver_difC( vec_C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                     &sparse_compute_W, &sparse_update_W, &sparse_add_new_cut, 
+                                     &sparse_compute_output, &qsort_data, &ocas_print, 0);
+      }  
+
+    }
+    else
+    {
+      if(num_of_Cs == 1)
+      {
+        ocas = svm_ocas_solver( C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                              &sparse_compute_W, &sparse_update_W, &sparse_add_new_cut, 
+                              &sparse_compute_output, &qsort_data, &ocas_print_null, 0);
+      }
+      else
+      {
+        ocas = svm_ocas_solver_difC( vec_C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                              &sparse_compute_W, &sparse_update_W, &sparse_add_new_cut, 
+                              &sparse_compute_output, &qsort_data, &ocas_print_null, 0);
+      }
+    }
+
+  }
+  else
+  {
+
+    ptr = mxGetPr(data_X);
+    for(i=0; i < nData; i++) {
+      for(j=0; j < nDim; j++ ) {
+        ptr[LIBOCAS_INDEX(j,i,nDim)] = ptr[LIBOCAS_INDEX(j,i,nDim)]*data_y[i];
+      }
+    }
+
+    /* init cutting plane buffer */
+    full_A = mxCalloc(BufSize*nDim,sizeof(double));
+    if( full_A == NULL )
+      mexErrMsgTxt("Not enough memory for cutting plane buffer full_A.");    
+
+    init_time=get_time()-init_time;
+
+    if(verb)
+    {
+      mexPrintf("Starting optimization:\n");
+    
+      if(num_of_Cs == 1)
+        ocas = svm_ocas_solver( C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                &full_compute_W, &full_update_W, &full_add_new_cut, 
+                                &full_compute_output, &qsort_data, &ocas_print, 0);
+      else
+        ocas = svm_ocas_solver_difC( vec_C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                &full_compute_W, &full_update_W, &full_add_new_cut, 
+                                &full_compute_output, &qsort_data, &ocas_print, 0);
+
+    }
+    else
+    {
+      if(num_of_Cs == 1)
+        ocas = svm_ocas_solver( C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                &full_compute_W, &full_update_W, &full_add_new_cut, 
+                                &full_compute_output, &qsort_data, &ocas_print_null, 0);
+      else
+        ocas = svm_ocas_solver_difC( vec_C, nData, TolRel, TolAbs, QPBound, MaxTime,BufSize, Method, 
+                                     &full_compute_W, &full_update_W, &full_add_new_cut, 
+                                     &full_compute_output, &qsort_data, &ocas_print_null, 0);
+    }
+  }
+
+  if(verb)
+  {
+    mexPrintf("Stopping condition: ");
+    switch( ocas.exitflag )
+    {
+       case 1: mexPrintf("1-Q_D/Q_P <= TolRel(=%f) satisfied.\n", TolRel); break;
+       case 2: mexPrintf("Q_P-Q_D <= TolAbs(=%f) satisfied.\n", TolAbs); break;
+       case 3: mexPrintf("Q_P <= QPBound(=%f) satisfied.\n", QPBound); break;
+       case 4: mexPrintf("Optimization time (=%f) >= MaxTime(=%f).\n", ocas.ocas_time, MaxTime); break;
+       case -1: mexPrintf("Has not converged!\n" ); break;
+       case -2: mexPrintf("Not enough memory for the solver.\n" ); break;
+    }
+  }
+
+  total_time=get_time()-total_time;
+  if(verb)
+  {
+    mexPrintf("Timing statistics:\n"
+              "   init_time      : %f[s]\n"
+              "   qp_solver_time : %f[s]\n"
+              "   sort_time      : %f[s]\n"
+              "   output_time    : %f[s]\n"
+              "   add_time       : %f[s]\n"
+              "   w_time         : %f[s]\n"
+              "   print_time     : %f[s]\n"
+              "   ocas_time      : %f[s]\n"
+              "   total_time     : %f[s]\n",
+              init_time, ocas.qp_solver_time, ocas.sort_time, ocas.output_time, 
+              ocas.add_time, ocas.w_time, ocas.print_time, ocas.ocas_time, total_time);
+
+    mexPrintf("Training error: %.4f%%\n", 100*(double)ocas.trn_err/(double)nData);
+  }
+
+  /* multiply data ba labels as it was at the begining */
+  if( mxIsSparse(data_X)== true ) {
+    /* for i=1:nData, X(:,i) = X(:,i)*y(i); end*/
+    for(i=0; i < nData; i++) 
+    {
+        mul_sparse_col(1/data_y[i], data_X, i);
+    }
+  }
+  else
+  {
+    ptr = mxGetPr(data_X);
+    for(i=0; i < nData; i++) {
+      for(j=0; j < nDim; j++ ) {
+        ptr[LIBOCAS_INDEX(j,i,nDim)] = ptr[LIBOCAS_INDEX(j,i,nDim)]/data_y[i];
+      }
+    }
+  }
+
+  if(num_of_Cs > 1)
+  {
+    for(i=0; i < nData; i++) 
+      data_y[i] = data_y[i]/vec_C[i];
+  }
+
+
+  plhs[1] = mxCreateDoubleScalar( W0 );
+  
+  /* return ocas optimizer statistics */
+  /* typedef struct {
+     uint32_t nIter;    
+     uint32_t nCutPlanes;
+     double trn_err;      
+     double Q_P;          
+     double Q_D;
+     double output_time;
+     double sort_time;
+     double solver_time;
+     int8_t exitflag;       
+     } ocas_return_value_T; */
+
+  const char *field_names[] = {"nTrnErrors","Q_P","Q_D","nIter","nCutPlanes","exitflag",
+                               "init_time","output_time","sort_time","qp_solver_time","add_time",
+                               "w_time","print_time","ocas_time","total_time"}; 
+  mwSize dims[2] = {1,1};  
+
+  plhs[2] = mxCreateStructArray(2, dims, (sizeof(field_names)/sizeof(*field_names)), field_names);
+  
+  mxSetField(plhs[2],0,"nIter",mxCreateDoubleScalar((double)ocas.nIter));
+  mxSetField(plhs[2],0,"nCutPlanes",mxCreateDoubleScalar((double)ocas.nCutPlanes));
+  mxSetField(plhs[2],0,"nTrnErrors",mxCreateDoubleScalar(ocas.trn_err)); 
+  mxSetField(plhs[2],0,"Q_P",mxCreateDoubleScalar(ocas.Q_P)); 
+  mxSetField(plhs[2],0,"Q_D",mxCreateDoubleScalar(ocas.Q_D)); 
+  mxSetField(plhs[2],0,"init_time",mxCreateDoubleScalar(init_time)); 
+  mxSetField(plhs[2],0,"output_time",mxCreateDoubleScalar(ocas.output_time)); 
+  mxSetField(plhs[2],0,"sort_time",mxCreateDoubleScalar(ocas.sort_time)); 
+  mxSetField(plhs[2],0,"qp_solver_time",mxCreateDoubleScalar(ocas.qp_solver_time)); 
+  mxSetField(plhs[2],0,"add_time",mxCreateDoubleScalar(ocas.add_time)); 
+  mxSetField(plhs[2],0,"w_time",mxCreateDoubleScalar(ocas.w_time)); 
+  mxSetField(plhs[2],0,"print_time",mxCreateDoubleScalar(ocas.print_time)); 
+  mxSetField(plhs[2],0,"ocas_time",mxCreateDoubleScalar(ocas.ocas_time)); 
+  mxSetField(plhs[2],0,"total_time",mxCreateDoubleScalar(total_time)); 
+  mxSetField(plhs[2],0,"exitflag",mxCreateDoubleScalar((double)ocas.exitflag)); 
+
+  return;
+}
+
diff --git a/svmocas_parseout.m b/svmocas_parseout.m
new file mode 100644
index 0000000..fc4a9c7
--- /dev/null
+++ b/svmocas_parseout.m
@@ -0,0 +1,131 @@
+function [iter,timing,trn_err] = svmocas_parseout(fname)
+% SVMOCAS_PARSEOUT parses text output of SVMOCAS solver.
+% 
+% Synopsis:
+%   [iter,timing,trn_err] = svmocas_parseout(fname)
+%
+% Inputs:
+%  fname [string] file which contains OCAS output; e.g. created by running
+%
+%    ./svmocas -c 10 -b 1 riply_trn.light ocas.model > out.txt
+%
+% Outputs:
+%  iter [1 x nIter (struct)] progress info printed by OCAS solver:
+%   .time [1x1] runtime
+%   .Q_P [1x1] primal objective value
+%   .Q_D [1x1] dual (reduced) objective value
+%   .trn_err [1x1] training error
+%   .nza [1x1] number of active cutting planes
+%   .qpf [1x1] return flag of inner QP solver
+%
+%  timing [struct] Timing statistics.
+%  trn_err [1x1] Training error.
+% 
+% Example:  
+%  iter = svmocas_parseout('out.txt');
+%  figure; hold on;
+%  plot([iter.time],[iter.Q_P]); 
+%  plot([iter.time],[iter.Q_D],'r')
+% 
+
+
+lines = textread(fname,'%s','delimiter','\n','bufsize',100000);
+
+from = strmatch('Starting optimization:',lines,'exact')+1;
+to = strmatch('Stopping condition:',lines)-1;
+
+for i=from:to
+   cnt = i-from+1;
+   l = lines{i};
+
+   b = strfind(l,'tim=');
+   e = min(strfind(l(b:end),','));
+   iter(cnt).time = str2num(l(b+length('tim='):b+e-2));
+
+   b = strfind(l,'Q_P=');
+   e = min(strfind(l(b:end),','));
+   iter(cnt).Q_P = str2num(l(b+length('Q_P='):b+e-2));
+
+   b = strfind(l,'Q_D=');
+   e = min(strfind(l(b:end),','));
+   iter(cnt).Q_D = str2num(l(b+length('Q_D='):b+e-2));
+
+   if i > from
+       b = strfind(l,'nza');
+       e = min(strfind(l(b:end),','));
+       iter(cnt).nza = str2num(l(b+length('nza'):b+e-2));
+
+       b = strfind(l,'err');
+       e = min(strfind(l(b:end),','));
+       iter(cnt).trn_err = str2num(l(b+length('err'):b+e-2));
+
+       b = strfind(l,'qpf');
+       iter(cnt).qpf = str2num(l(b+length('nza'):end));
+   else
+       iter(cnt).nza = [];
+       iter(cnt).trn_err = [];
+       iter(cnt).qpf = [];
+   end
+end
+
+i = strmatch('load_time',lines);
+if isempty(i)
+    i = strmatch('init_time',lines);
+end
+l = lines{i};
+b = strfind(l,':');
+e = min(strfind(l(b:end),'[s]'));
+timing.load_time = str2num(l(b+length(':'):b+e-2));
+
+i = strmatch('qp_solver_time',lines);
+l = lines{i};
+b = strfind(l,':');
+e = min(strfind(l(b:end),'[s]'));
+timing.qp_solver_time = str2num(l(b+length(':'):b+e-2));
+
+i = strmatch('sort_time',lines);
+l = lines{i};
+b = strfind(l,':');
+e = min(strfind(l(b:end),'[s]'));
+timing.sort_time = str2num(l(b+length(':'):b+e-2));
+
+i = strmatch('output_time',lines);
+l = lines{i};
+b = strfind(l,':');
+e = min(strfind(l(b:end),'[s]'));
+timing.output_time = str2num(l(b+length(':'):b+e-2));
+
+i = strmatch('add_time',lines);
+l = lines{i};
+b = strfind(l,':');
+e = min(strfind(l(b:end),'[s]'));
+timing.add_time = str2num(l(b+length(':'):b+e-2));
+
+i = strmatch('w_time',lines);
+l = lines{i};
+b = strfind(l,':');
+e = min(strfind(l(b:end),'[s]'));
+timing.w_time = str2num(l(b+length(':'):b+e-2));
+
+i = strmatch('ocas_time',lines);
+l = lines{i};
+b = strfind(l,':');
+e = min(strfind(l(b:end),'[s]'));
+timing.ocas_time = str2num(l(b+length(':'):b+e-2));
+
+i = strmatch('total_time',lines);
+l = lines{i};
+b = strfind(l,':');
+e = min(strfind(l(b:end),'[s]'));
+timing.total_time = str2num(l(b+length(':'):b+e-2));
+
+i = strmatch('Training error:',lines);
+l = lines{i};
+b = strfind(l,':');
+e = min(strfind(l(b:end),'%'));
+trn_err = str2num(l(b+length(':'):b+e-2))/100;
+
+
+
+return;
+% EOF
\ No newline at end of file
diff --git a/version.h b/version.h
new file mode 100644
index 0000000..ed306ef
--- /dev/null
+++ b/version.h
@@ -0,0 +1,3 @@
+/* this text will be printed in all programs */
+
+#define OCAS_VERSION "v0.93, (C) 2008, 2009, 2010 Vojtech Franc, Soeren Sonnenburg"

-- 
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