Repository: allenyangyl/Face_Liveness_Detection
Branch: master
Commit: 9e92d3353e8a
Files: 178
Total size: 35.0 MB

Directory structure:
gitextract_0t6fbsbr/

├── C++/
│   ├── README.txt
│   ├── lbp/
│   │   ├── CMakeLists.txt
│   │   ├── LICENSE
│   │   ├── histogram.cpp
│   │   ├── histogram.hpp
│   │   ├── lbp.cpp
│   │   ├── lbp.hpp
│   │   ├── main.cpp
│   │   ├── mapping_n16.txt
│   │   └── mapping_n8.txt
│   ├── liveness.cpp
│   ├── liveness.h
│   ├── svm_minmax_NUAA.txt
│   ├── svm_model_NUAA
│   └── test.cpp
├── Matlab/
│   ├── DoG.m
│   ├── DoGLBP_Test.m
│   ├── DoG_CASIA_Test.m
│   ├── DoG_CASIA_Train.m
│   ├── DoG_LBP_All_Train.m
│   ├── DoG_LBP_CASIA_Test.m
│   ├── DoG_LBP_CASIA_Train.m
│   ├── DoG_LBP_NUAA_Test.m
│   ├── DoG_LBP_NUAA_Train.m
│   ├── DoG_LBP_PRINT_ATTACK_Test.m
│   ├── DoG_LBP_PRINT_ATTACK_Train.m
│   ├── DoG_NUAA_Test.m
│   ├── DoG_NUAA_Train.m
│   ├── DoG_PRINT_ATTACK_Test.m
│   ├── DoG_PRINT_ATTACK_Train.m
│   ├── HFD.m
│   ├── HFDTest.m
│   ├── HFDTrain.m
│   ├── HOOF/
│   │   ├── README
│   │   ├── README~
│   │   ├── chiSquareDist.m
│   │   ├── computeDistanceModularHistogram.m
│   │   ├── computeDistanceOrdinalHistogram.m
│   │   ├── computeDistancesBetweenKPCASystems.m
│   │   ├── findSubspaceAnglesBetweenKPCASystems.m
│   │   ├── findSubspaceAnglesBetweenKPCASystemsSqrtHist.asv
│   │   ├── findSubspaceAnglesBetweenKPCASystemsSqrtHist.m
│   │   ├── gradientHistogram.m
│   │   ├── histogramKernel.m
│   │   ├── identifySystemUsingKPCA.m
│   │   ├── traceKernelKPCASystems.m
│   │   └── traceKernelKPCASystemsSqrtHist.m
│   ├── HOOF.m
│   ├── HSoptflow.m
│   ├── LBP_CASIA_Test.m
│   ├── LBP_CASIA_Train.m
│   ├── LBP_NUAA_Test.m
│   ├── LBP_NUAA_Train.m
│   ├── LBP_PRINT_ATTACK_Test.m
│   ├── LBP_PRINT_ATTACK_Train.m
│   ├── LBP_feature.m
│   ├── NUAA/
│   │   ├── client_test_normalized.txt
│   │   ├── client_train_normalized.txt
│   │   ├── imposter_test_normalized.txt
│   │   ├── imposter_train_normalized.txt
│   │   └── readme.txt
│   ├── OF_All_Train.m
│   ├── OF_CASIA_Test.m
│   ├── OF_CASIA_Train.m
│   ├── OF_PRINT_ATTACK_Test.m
│   ├── OF_PRINT_ATTACK_Train.m
│   ├── RealTime_DoGLBP.m
│   ├── RealTime_OF.m
│   ├── Save_CASIA_Test.m
│   ├── Save_CASIA_Train.m
│   ├── Save_NUAA.m
│   ├── Save_PRINT_ATTACK_Test.m
│   ├── Save_PRINT_ATTACK_Train.m
│   ├── lbp-0.3.3/
│   │   ├── getmapping.m
│   │   └── lbp.m
│   ├── liblinear-1.96/
│   │   ├── COPYRIGHT
│   │   ├── Makefile
│   │   ├── Makefile.win
│   │   ├── README
│   │   ├── blas/
│   │   │   ├── Makefile
│   │   │   ├── blas.h
│   │   │   ├── blasp.h
│   │   │   ├── daxpy.c
│   │   │   ├── ddot.c
│   │   │   ├── dnrm2.c
│   │   │   └── dscal.c
│   │   ├── heart_scale
│   │   ├── linear.cpp
│   │   ├── linear.def
│   │   ├── linear.h
│   │   ├── matlab/
│   │   │   ├── Makefile
│   │   │   ├── README
│   │   │   ├── libsvmread.c
│   │   │   ├── libsvmread.mexw64
│   │   │   ├── libsvmwrite.c
│   │   │   ├── libsvmwrite.mexw64
│   │   │   ├── linear_model_matlab.c
│   │   │   ├── linear_model_matlab.h
│   │   │   ├── make.m
│   │   │   ├── predict.c
│   │   │   ├── predict.mexw64
│   │   │   ├── train.c
│   │   │   └── train.mexw64
│   │   ├── predict.c
│   │   ├── python/
│   │   │   ├── Makefile
│   │   │   ├── README
│   │   │   ├── liblinear.py
│   │   │   └── liblinearutil.py
│   │   ├── train.c
│   │   ├── tron.cpp
│   │   ├── tron.h
│   │   └── windows/
│   │       ├── libsvmread.mexw64
│   │       ├── libsvmwrite.mexw64
│   │       ├── predict.mexw64
│   │       └── train.mexw64
│   └── libsvm-3.19/
│       ├── COPYRIGHT
│       ├── FAQ.html
│       ├── Makefile
│       ├── Makefile.win
│       ├── README
│       ├── heart_scale
│       ├── java/
│       │   ├── Makefile
│       │   ├── libsvm/
│       │   │   ├── svm.java
│       │   │   ├── svm.m4
│       │   │   ├── svm_model.java
│       │   │   ├── svm_node.java
│       │   │   ├── svm_parameter.java
│       │   │   ├── svm_print_interface.java
│       │   │   └── svm_problem.java
│       │   ├── libsvm.jar
│       │   ├── svm_predict.java
│       │   ├── svm_scale.java
│       │   ├── svm_toy.java
│       │   ├── svm_train.java
│       │   └── test_applet.html
│       ├── matlab/
│       │   ├── Makefile
│       │   ├── README
│       │   ├── libsvmread.c
│       │   ├── libsvmread.mexw64
│       │   ├── libsvmwrite.c
│       │   ├── libsvmwrite.mexw64
│       │   ├── make.m
│       │   ├── svm_model_matlab.c
│       │   ├── svm_model_matlab.h
│       │   ├── svmpredict.c
│       │   ├── svmpredict.mexw64
│       │   ├── svmtrain.c
│       │   └── svmtrain.mexw64
│       ├── python/
│       │   ├── Makefile
│       │   ├── README
│       │   ├── svm.py
│       │   └── svmutil.py
│       ├── svm-predict.c
│       ├── svm-scale.c
│       ├── svm-toy/
│       │   ├── gtk/
│       │   │   ├── Makefile
│       │   │   ├── callbacks.cpp
│       │   │   ├── callbacks.h
│       │   │   ├── interface.c
│       │   │   ├── interface.h
│       │   │   ├── main.c
│       │   │   └── svm-toy.glade
│       │   ├── qt/
│       │   │   ├── Makefile
│       │   │   └── svm-toy.cpp
│       │   └── windows/
│       │       └── svm-toy.cpp
│       ├── svm-train.c
│       ├── svm.cpp
│       ├── svm.def
│       ├── svm.h
│       ├── tools/
│       │   ├── README
│       │   ├── checkdata.py
│       │   ├── easy.py
│       │   ├── grid.py
│       │   └── subset.py
│       └── windows/
│           ├── libsvmread.mexw64
│           ├── libsvmwrite.mexw64
│           ├── svmpredict.mexw64
│           └── svmtrain.mexw64
└── README.md

================================================
FILE CONTENTS
================================================

================================================
FILE: C++/README.txt
================================================
ļ嵥
liveness.cpp
liveness.hͷļ
svm_modelѵsvmģ
svm_minmaxʱһIJ
./lbpLBPȡ
./libsvm-3.19LibSVM

ҪĿcppļ
liveness.cpp
./lbp/lbp.cpp
./lbp/histogram.cpp
./libsvm-3.19/svm.cpp
õͷļΪ
#include"liveness.h"

ʹõOpenCV汾Ϊ3.0.0-RC1

ʹõݿNUAA, PRINT-ATTACK, CASIA
Щⲻճ


class ThuVisionFaceLiveCheck{
public:
	// Initialization: successful - true; failed - false
	bool Init();
	// Liveness Predict: input image path or Mat. 
	// If it is a real face, return true; otherwise, return false
	bool Check(std::string imgPath);
	bool Check(cv::Mat img);
private:
	int img_size;
	int LBP_size;
	svm_model* model;
	double* Feature_Max;
	double* Feature_Min;
	vector<int> mapping_r8;
	vector<int> mapping_r16;
};

================================================
FILE: C++/lbp/CMakeLists.txt
================================================
PROJECT(githublbp)
SET(CMAKE_BUILD_TYPE Release)
CMAKE_MINIMUM_REQUIRED( VERSION 2.6 )
FIND_PACKAGE( OpenCV REQUIRED )
ADD_EXECUTABLE(lbp main.cpp lbp.cpp histogram.cpp)
TARGET_LINK_LIBRARIES(lbp ${OpenCV_LIBS})


================================================
FILE: C++/lbp/LICENSE
================================================
Copyright (c) 2011, philipp <bytefish[at]gmx.de>
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
    * Redistributions of source code must retain the above copyright
      notice, this list of conditions and the following disclaimer.
    * Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.
    * Neither the name of the organization nor the
      names of its contributors may be used to endorse or promote products
      derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL COPYRIGHT HOLDER BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.



================================================
FILE: C++/lbp/histogram.cpp
================================================
#include "histogram.hpp"
#include <vector>

template <typename _Tp>
void lbp::histogram_(const Mat& src, Mat& hist, int numPatterns) {
	hist = Mat::zeros(1, numPatterns, CV_32SC1);
	for(int i = 0; i < src.rows; i++) {
		for(int j = 0; j < src.cols; j++) {
			int bin = src.at<_Tp>(i,j);
			hist.at<int>(0,bin) += 1;
		}
	}
}

template <typename _Tp>
double lbp::chi_square_(const Mat& histogram0, const Mat& histogram1) {
	if(histogram0.type() != histogram1.type())
			CV_Error(CV_StsBadArg, "Histograms must be of equal type.");
	if(histogram0.rows != 1 || histogram0.rows != histogram1.rows || histogram0.cols != histogram1.cols)
			CV_Error(CV_StsBadArg, "Histograms must be of equal dimension.");
	double result = 0.0;
	for(int i=0; i < histogram0.cols; i++) {
		double a = histogram0.at<_Tp>(0,i) - histogram1.at<_Tp>(0,i);
		double b = histogram0.at<_Tp>(0,i) + histogram1.at<_Tp>(0,i);
		if(abs(b) > numeric_limits<double>::epsilon()) {
			result+=(a*a)/b;
		}
	}
	return result;
}


void lbp::spatial_histogram(const Mat& src, Mat& hist, int numPatterns, const Size& window, int overlap) {
	int width = src.cols;
	int height = src.rows;
	vector<Mat> histograms;
	for(int x=0; x < width - window.width + 1; x+=(window.width-overlap)) {
		for(int y=0; y < height-window.height + 1; y+=(window.height-overlap)) {
			Mat cell = Mat(src, Rect(x,y,window.width, window.height));
			histograms.push_back(histogram(cell, numPatterns));
		}
	}
	hist.create(1, histograms.size()*numPatterns, CV_32SC1);
	// i know this is a bit lame now... feel free to make this a bit more efficient...
	for(int histIdx=0; histIdx < histograms.size(); histIdx++) {
		for(int valIdx = 0; valIdx < numPatterns; valIdx++) {
			int y = histIdx*numPatterns+valIdx;
			hist.at<int>(0,y) = histograms[histIdx].at<int>(valIdx);
		}
	}
}

// wrappers
void lbp::histogram(const Mat& src, Mat& hist, int numPatterns) {
	switch(src.type()) {
		case CV_8SC1: histogram_<char>(src, hist, numPatterns); break;
		case CV_8UC1: histogram_<unsigned char>(src, hist, numPatterns); break;
		case CV_16SC1: histogram_<short>(src, hist, numPatterns); break;
		case CV_16UC1: histogram_<unsigned short>(src, hist, numPatterns); break;
		case CV_32SC1: histogram_<int>(src, hist, numPatterns); break;
	}
}

double lbp::chi_square(const Mat& histogram0, const Mat& histogram1) {
	switch(histogram0.type()) {
		case CV_8SC1: return chi_square_<char>(histogram0,histogram1); break;
		case CV_8UC1: return chi_square_<unsigned char>(histogram0,histogram1); break;
		case CV_16SC1: return chi_square_<short>(histogram0, histogram1); break;
		case CV_16UC1: return chi_square_<unsigned short>(histogram0,histogram1); break;
		case CV_32SC1: return chi_square_<int>(histogram0,histogram1); break;
	}
}

void lbp::spatial_histogram(const Mat& src, Mat& dst, int numPatterns, int gridx, int gridy, int overlap) {
	int width = static_cast<int>(floor(src.cols / gridx));
	int height = static_cast<int>(floor(src.rows / gridy));
	spatial_histogram(src, dst, numPatterns, Size_<int>(width, height), overlap);
}

// Mat return type functions
Mat lbp::histogram(const Mat& src, int numPatterns) {
	Mat hist;
	histogram(src, hist, numPatterns);
	return hist;
}


Mat lbp::spatial_histogram(const Mat& src, int numPatterns, const Size& window, int overlap) {
	Mat hist;
	spatial_histogram(src, hist, numPatterns, window, overlap);
	return hist;
}


Mat lbp::spatial_histogram(const Mat& src, int numPatterns, int gridx, int gridy, int overlap) {
	Mat hist;
	spatial_histogram(src, hist, numPatterns, gridx, gridy, overlap);
	return hist;
}


================================================
FILE: C++/lbp/histogram.hpp
================================================
#ifndef HISTOGRAM_HPP_
#define HISTOGRAM_HPP_

//! \author philipp <bytefish[at]gmx[dot]de>
//! \copyright BSD, see LICENSE.

#include <cv.h>
#include <limits>

using namespace cv;
using namespace std;

namespace lbp {

// templated functions
template <typename _Tp>
void histogram_(const Mat& src, Mat& hist, int numPatterns);

template <typename _Tp>
double chi_square_(const Mat& histogram0, const Mat& histogram1);

// non-templated functions
void spatial_histogram(const Mat& src, Mat& spatialhist, int numPatterns, const Size& window, int overlap=0);

// wrapper functions
void spatial_histogram(const Mat& src, Mat& spatialhist, int numPatterns, int gridx=8, int gridy=8, int overlap=0);
void histogram(const Mat& src, Mat& hist, int numPatterns);
double chi_square(const Mat& histogram0, const Mat& histogram1);

// Mat return type functions
Mat histogram(const Mat& src, int numPatterns);
Mat spatial_histogram(const Mat& src, int numPatterns, const Size& window, int overlap=0);
Mat spatial_histogram(const Mat& src, int numPatterns, int gridx=8, int gridy=8, int overlap=0);
}
#endif


================================================
FILE: C++/lbp/lbp.cpp
================================================
#include "lbp.hpp"
#define M_PI 3.14159265358979323846

using namespace cv;

template <typename _Tp>
void lbp::OLBP_(const Mat& src, Mat& dst) {
	dst = Mat::zeros(src.rows-2, src.cols-2, CV_8UC1);
	for(int i=1;i<src.rows-1;i++) {
		for(int j=1;j<src.cols-1;j++) {
			_Tp center = src.at<_Tp>(i,j);
			unsigned char code = 0;
			code |= (src.at<_Tp>(i-1,j-1) > center) << 7;
			code |= (src.at<_Tp>(i-1,j) > center) << 6;
			code |= (src.at<_Tp>(i-1,j+1) > center) << 5;
			code |= (src.at<_Tp>(i,j+1) > center) << 4;
			code |= (src.at<_Tp>(i+1,j+1) > center) << 3;
			code |= (src.at<_Tp>(i+1,j) > center) << 2;
			code |= (src.at<_Tp>(i+1,j-1) > center) << 1;
			code |= (src.at<_Tp>(i,j-1) > center) << 0;
			dst.at<unsigned char>(i-1,j-1) = code;
		}
	}
}

template <typename _Tp>
void lbp::ELBP_(const Mat& src, Mat& dst, int radius, int neighbors) {
	neighbors = max(min(neighbors,31),1); // set bounds...
	// Note: alternatively you can switch to the new OpenCV Mat_
	// type system to define an unsigned int matrix... I am probably
	// mistaken here, but I didn't see an unsigned int representation
	// in OpenCV's classic typesystem...
	dst = Mat::zeros(src.rows-2*radius, src.cols-2*radius, CV_32SC1);
	for(int n=0; n<neighbors; n++) {
		// sample points
		float x = static_cast<float>(radius) * cos(2.0*M_PI*n/static_cast<float>(neighbors));
		float y = static_cast<float>(radius) * -sin(2.0*M_PI*n/static_cast<float>(neighbors));
		// relative indices
		int fx = static_cast<int>(floor(x));
		int fy = static_cast<int>(floor(y));
		int cx = static_cast<int>(ceil(x));
		int cy = static_cast<int>(ceil(y));
		// fractional part
		float ty = y - fy;
		float tx = x - fx;
		// set interpolation weights
		float w1 = (1 - tx) * (1 - ty);
		float w2 =      tx  * (1 - ty);
		float w3 = (1 - tx) *      ty;
		float w4 =      tx  *      ty;
		// iterate through your data
		for(int i=radius; i < src.rows-radius;i++) {
			for(int j=radius;j < src.cols-radius;j++) {
				float t = w1*src.at<_Tp>(i+fy,j+fx) + w2*src.at<_Tp>(i+fy,j+cx) + w3*src.at<_Tp>(i+cy,j+fx) + w4*src.at<_Tp>(i+cy,j+cx);
				// we are dealing with floating point precision, so add some little tolerance
				dst.at<int>(i-radius,j-radius) += ((t > src.at<_Tp>(i,j)) && (abs(t-src.at<_Tp>(i,j)) > std::numeric_limits<float>::epsilon())) << n;
			}
		}
	}
}

template <typename _Tp>
void lbp::VARLBP_(const Mat& src, Mat& dst, int radius, int neighbors) {
	max(min(neighbors,31),1); // set bounds
	dst = Mat::zeros(src.rows-2*radius, src.cols-2*radius, CV_32FC1); //! result
	// allocate some memory for temporary on-line variance calculations
	Mat _mean = Mat::zeros(src.rows, src.cols, CV_32FC1);
	Mat _delta = Mat::zeros(src.rows, src.cols, CV_32FC1);
	Mat _m2 = Mat::zeros(src.rows, src.cols, CV_32FC1);
	for(int n=0; n<neighbors; n++) {
		// sample points
		float x = static_cast<float>(radius) * cos(2.0*M_PI*n/static_cast<float>(neighbors));
		float y = static_cast<float>(radius) * -sin(2.0*M_PI*n/static_cast<float>(neighbors));
		// relative indices
		int fx = static_cast<int>(floor(x));
		int fy = static_cast<int>(floor(y));
		int cx = static_cast<int>(ceil(x));
		int cy = static_cast<int>(ceil(y));
		// fractional part
		float ty = y - fy;
		float tx = x - fx;
		// set interpolation weights
		float w1 = (1 - tx) * (1 - ty);
		float w2 =      tx  * (1 - ty);
		float w3 = (1 - tx) *      ty;
		float w4 =      tx  *      ty;
		// iterate through your data
		for(int i=radius; i < src.rows-radius;i++) {
			for(int j=radius;j < src.cols-radius;j++) {
				float t = w1*src.at<_Tp>(i+fy,j+fx) + w2*src.at<_Tp>(i+fy,j+cx) + w3*src.at<_Tp>(i+cy,j+fx) + w4*src.at<_Tp>(i+cy,j+cx);
				_delta.at<float>(i,j) = t - _mean.at<float>(i,j);
				_mean.at<float>(i,j) = (_mean.at<float>(i,j) + (_delta.at<float>(i,j) / (1.0*(n+1)))); // i am a bit paranoid
				_m2.at<float>(i,j) = _m2.at<float>(i,j) + _delta.at<float>(i,j) * (t - _mean.at<float>(i,j));
			}
		}
	}
	// calculate result
	for(int i = radius; i < src.rows-radius; i++) {
		for(int j = radius; j < src.cols-radius; j++) {
			dst.at<float>(i-radius, j-radius) = _m2.at<float>(i,j) / (1.0*(neighbors-1));
		}
	}
}

// now the wrapper functions
void lbp::OLBP(const Mat& src, Mat& dst) {
	switch(src.type()) {
		case CV_8SC1: OLBP_<char>(src, dst); break;
		case CV_8UC1: OLBP_<unsigned char>(src, dst); break;
		case CV_16SC1: OLBP_<short>(src, dst); break;
		case CV_16UC1: OLBP_<unsigned short>(src, dst); break;
		case CV_32SC1: OLBP_<int>(src, dst); break;
		case CV_32FC1: OLBP_<float>(src, dst); break;
		case CV_64FC1: OLBP_<double>(src, dst); break;
	}
}

void lbp::ELBP(const Mat& src, Mat& dst, int radius, int neighbors) {
	switch(src.type()) {
		case CV_8SC1: ELBP_<char>(src, dst, radius, neighbors); break;
		case CV_8UC1: ELBP_<unsigned char>(src, dst, radius, neighbors); break;
		case CV_16SC1: ELBP_<short>(src, dst, radius, neighbors); break;
		case CV_16UC1: ELBP_<unsigned short>(src, dst, radius, neighbors); break;
		case CV_32SC1: ELBP_<int>(src, dst, radius, neighbors); break;
		case CV_32FC1: ELBP_<float>(src, dst, radius, neighbors); break;
		case CV_64FC1: ELBP_<double>(src, dst, radius, neighbors); break;
	}
}

void lbp::VARLBP(const Mat& src, Mat& dst, int radius, int neighbors) {
	switch(src.type()) {
		case CV_8SC1: VARLBP_<char>(src, dst, radius, neighbors); break;
		case CV_8UC1: VARLBP_<unsigned char>(src, dst, radius, neighbors); break;
		case CV_16SC1: VARLBP_<short>(src, dst, radius, neighbors); break;
		case CV_16UC1: VARLBP_<unsigned short>(src, dst, radius, neighbors); break;
		case CV_32SC1: VARLBP_<int>(src, dst, radius, neighbors); break;
		case CV_32FC1: VARLBP_<float>(src, dst, radius, neighbors); break;
		case CV_64FC1: VARLBP_<double>(src, dst, radius, neighbors); break;
	}
}

// now the Mat return functions
Mat lbp::OLBP(const Mat& src) { Mat dst; OLBP(src, dst); return dst; }
Mat lbp::ELBP(const Mat& src, int radius, int neighbors) { Mat dst; ELBP(src, dst, radius, neighbors); return dst; }
Mat lbp::VARLBP(const Mat& src, int radius, int neighbors) { Mat dst; VARLBP(src, dst, radius, neighbors); return dst; }






================================================
FILE: C++/lbp/lbp.hpp
================================================
#ifndef LBP_HPP_
#define LBP_HPP_

//! \author philipp <bytefish[at]gmx[dot]de>
//! \copyright BSD, see LICENSE.

#include <cv.h>
#include <limits>

using namespace cv;
using namespace std;

namespace lbp {

// templated functions
template <typename _Tp>
void OLBP_(const cv::Mat& src, cv::Mat& dst);

template <typename _Tp>
void ELBP_(const cv::Mat& src, cv::Mat& dst, int radius = 1, int neighbors = 8);

template <typename _Tp>
void VARLBP_(const cv::Mat& src, cv::Mat& dst, int radius = 1, int neighbors = 8);

// wrapper functions
void OLBP(const Mat& src, Mat& dst);
void ELBP(const Mat& src, Mat& dst, int radius = 1, int neighbors = 8);
void VARLBP(const Mat& src, Mat& dst, int radius = 1, int neighbors = 8);

// Mat return type functions
Mat OLBP(const Mat& src);
Mat ELBP(const Mat& src, int radius = 1, int neighbors = 8);
Mat VARLBP(const Mat& src, int radius = 1, int neighbors = 8);

}
#endif


================================================
FILE: C++/lbp/main.cpp
================================================
#include <cv.h>
#include <highgui.h>
#include "lbp.hpp"
#include "histogram.hpp"

using namespace cv;

int main(int argc, const char *argv[]) {
	int deviceId = 0;
	if(argc > 1)
		deviceId = atoi(argv[1]);

	VideoCapture cap(deviceId);

	if(!cap.isOpened()) {
    	cerr << "Capture Device ID " << deviceId << "cannot be opened." << endl;
    	return -1;
    }

	// initial values
    int radius = 1;
    int neighbors = 8;

    // windows
    namedWindow("original",CV_WINDOW_AUTOSIZE);
    namedWindow("lbp",CV_WINDOW_AUTOSIZE);

    // matrices used
    Mat frame; // always references the last frame
    Mat dst; // image after preprocessing
    Mat lbp; // lbp image

    // just to switch between possible lbp operators
    vector<string> lbp_names;
    lbp_names.push_back("Extended LBP"); // 0
    lbp_names.push_back("Fixed Sampling LBP"); // 1
    lbp_names.push_back("Variance-based LBP"); // 2
    int lbp_operator=0;

    bool running=true;
    while(running) {
    	cap >> frame;
    	cvtColor(frame, dst, CV_BGR2GRAY);
    	GaussianBlur(dst, dst, Size(7,7), 5, 3, BORDER_CONSTANT); // tiny bit of smoothing is always a good idea
    	// comment the following lines for original size
    	resize(frame, frame, Size(), 0.5, 0.5);
    	resize(dst,dst,Size(), 0.5, 0.5);
    	//
    	switch(lbp_operator) {
    	case 0:
    		lbp::ELBP(dst, lbp, radius, neighbors); // use the extended operator
    		break;
    	case 1:
    		lbp::OLBP(dst, lbp); // use the original operator
    		break;
    	case 2:
    		lbp::VARLBP(dst, lbp, radius, neighbors);
    		break;
    	}
    	// now to show the patterns a normalization is necessary
    	// a simple min-max norm will do the job...
    	normalize(lbp, lbp, 0, 255, NORM_MINMAX, CV_8UC1);

    	imshow("original", frame);
    	imshow("lbp", lbp);

    	char key = (char) waitKey(20);

    	// exit on escape
    	if(key == 27)
    		running=false;

    	// to make it a bit interactive, you can increase and decrease the parameters
    	switch(key) {
    	case 'q': case 'Q':
    		running=false;
    		break;
    	// lower case r decreases the radius (min 1)
    	case 'r':
    		radius-=1;
    		radius = std::max(radius,1);
    		cout << "radius=" << radius << endl;
    		break;
    	// upper case r increases the radius (there's no real upper bound)
    	case 'R':
    		radius+=1;
    		radius = std::min(radius,32);
    		cout << "radius=" << radius << endl;
    		break;
    	// lower case p decreases the number of sampling points (min 1)
    	case 'p':
    		neighbors-=1;
    		neighbors = std::max(neighbors,1);
    		cout << "sampling points=" << neighbors << endl;
    		break;
    	// upper case p increases the number of sampling points (max 31)
    	case 'P':
    		neighbors+=1;
    		neighbors = std::min(neighbors,31);
    		cout << "sampling points=" << neighbors << endl;
    		break;
    	// switch between operators
    	case 'o': case 'O':
    		lbp_operator = (lbp_operator + 1) % 3;
    		cout << "Switched to operator " << lbp_names[lbp_operator] << endl;
    		break;
    	case 's': case 'S':
    		imwrite("original.jpg", frame);
    		imwrite("lbp.jpg", lbp);
    		cout << "Screenshot (operator=" << lbp_names[lbp_operator] << ",radius=" << radius <<",points=" << neighbors << ")" << endl;
    		break;
    	default:
    		break;
    	}

    }
    	return 0; // success
}


================================================
FILE: C++/lbp/mapping_n16.txt
================================================
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================================================
FILE: C++/lbp/mapping_n8.txt
================================================
0	1	2	3	4	58	5	6	7	58	58	58	8	58	9	10	11	58	58	58	58	58	58	58	12	58	58	58	13	58	14	15	16	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	17	58	58	58	58	58	58	58	18	58	58	58	19	58	20	21	22	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	23	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	24	58	58	58	58	58	58	58	25	58	58	58	26	58	27	28	29	30	58	31	58	58	58	32	58	58	58	58	58	58	58	33	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	34	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	35	36	37	58	38	58	58	58	39	58	58	58	58	58	58	58	40	58	58	58	58	58	58	58	58	58	58	58	58	58	58	58	41	42	43	58	44	58	58	58	45	58	58	58	58	58	58	58	46	47	48	58	49	58	58	58	50	51	52	58	53	54	55	56	57

================================================
FILE: C++/liveness.cpp
================================================
#include"liveness.h"
	
// Sigmoid
double sigmoid(double input)
{
	return 1 / ( 1 + exp(-input) );
}

// DoG Features
Mat DoG(Mat input, double sigma1, double sigma2)
{
	Mat XF1, XF2, DXF, output;
	int size1, size2;
	// Filter Sizes
	size1 = 2 * (int)(3*sigma1) + 3;
	size2 = 2 * (int)(3*sigma2) + 3;
	// Gaussian Filter
	GaussianBlur(input, XF1, Size(size1, size1), sigma1, sigma1, BORDER_REPLICATE);
	GaussianBlur(input, XF2, Size(size2, size2), sigma2, sigma2, BORDER_REPLICATE);
	// Difference
	DXF = XF1 - XF2;
	// Discrete Fourier Transform
	DXF.convertTo(DXF, CV_64FC1);
	dft(DXF, output);
	return abs(output);
}

// LBP Features
Mat LBP(Mat input, vector<int> mapping_r8, vector<int> mapping_r16)
{

	// LBP(r2, n16)
	Mat LBP_16_2 = lbp::ELBP(input, 2, 16);
	for(int i = 0; i < LBP_16_2.rows; i++)
		for(int j = 0; j < LBP_16_2.cols; j++)
			LBP_16_2.at<int>(i, j) = mapping_r16[LBP_16_2.at<int>(i, j)];
	Mat hist_16_2 = lbp::histogram(LBP_16_2, 243);

	// LBP(r2, n8)
	Mat LBP_8_2 = lbp::ELBP(input, 2, 8);
	for(int i = 0; i < LBP_8_2.rows; i++)
		for(int j = 0; j < LBP_8_2.cols; j++)
			LBP_8_2.at<int>(i, j) = mapping_r8[LBP_8_2.at<int>(i, j)];
	Mat hist_8_2 = lbp::histogram(LBP_8_2, 59);

	// LBP(r1, n8) * 9
	Mat LBP_8_1 = lbp::ELBP(input, 1, 8);
	for(int i = 0; i < LBP_8_1.rows; i++)
		for(int j = 0; j < LBP_8_1.cols; j++)
			LBP_8_1.at<int>(i, j) = mapping_r8[LBP_8_1.at<int>(i, j)];
	Mat hist_8_1 = lbp::spatial_histogram(LBP_8_1, 59, Size_<int>(30, 30), 14);

	// Histogram Concatenate
	Mat hist_8, hist;
	hconcat(hist_8_2, hist_8_1, hist_8);
	hconcat(hist_16_2, hist_8, hist);
	return hist;
}

// Extract Features
svm_node* Liveness_Feature(Mat img, vector<int> mapping_r8, vector<int> mapping_r16, int img_size, int LBP_size)
{
	// Total feature size
	int fea_size = img_size * img_size + LBP_size;
	// Grayscale
	cvtColor(img, img, CV_BGR2GRAY);
	// Histogram Equlization - Ineffective
	//equalizeHist(img, img);
	// Image Resize
	resize(img, img, Size(img_size, img_size));
	// DoG Features
	Mat DoG_Feature = DoG(img);
	// LBP Features
	Mat LBP_Feature = LBP(img, mapping_r8, mapping_r16);
	// Filling Features in svm_node
	svm_node* features = new svm_node[fea_size+1];
	for(int i = 0; i < DoG_Feature.rows; i++)
		for(int j = 0; j < DoG_Feature.cols; j++)
		{
			features[i*img_size+j].index = i*img_size+j+1;
			features[i*img_size+j].value = DoG_Feature.at<double>(i, j);
		}
	for(int i = 0; i < LBP_Feature.cols; i++)
		{
			features[img_size*img_size+i].index = img_size*img_size+i+1;
			features[img_size*img_size+i].value = (double)LBP_Feature.at<int>(0, i);
		}
	features[fea_size].index = -1;
	// Return
	return features;
}

// Prediction
double Liveness_Predict(Mat img, svm_model* model, double* Feature_Max, double* Feature_Min, 
						vector<int> mapping_r8, vector<int> mapping_r16, int img_size, int LBP_size)
{
	int fea_size = img_size * img_size + LBP_size;
	svm_node* features = Liveness_Feature(img, mapping_r8, mapping_r16, img_size, LBP_size);
	for(int i = 0; i < fea_size; i++)
		features[i].value = (features[i].value - Feature_Min[i]) / (Feature_Max[i] - Feature_Min[i]);
	double Value;
	double Label = svm_predict_values(model, features, &Value);
	delete[] features;
	return Value;
}

// Initialization
bool ThuVisionFaceLiveCheck::Init()
{
	// Image size, LBP feature size and total feature size
	img_size = 64;
	LBP_size = 243 + 59 + 9*59;
	int fea_size = img_size * img_size + LBP_size;

	// Path
	char* svm_path = "svm_model";
	char* minmax_path = "svm_minmax.txt";
	string Mapping_r8_path = ".\\lbp\\mapping_n8.txt";
	string Mapping_r16_path = ".\\lbp\\mapping_n16.txt";

	// Load Model
	svm_model* SVM_Model = svm_load_model(svm_path);
	if(model == NULL)
	{
		std::cout << "Can not open file: " << svm_path << endl;
		return false;
	}
	model = SVM_Model;

	// Scaling
	ifstream feature_minmax_file;
	string feature_minmax_path = minmax_path;
	feature_minmax_file.open(feature_minmax_path);
	if(!feature_minmax_file.is_open())
	{
		std::cout << "Can not open file: " << feature_minmax_path << endl;
		return false;
	}
	double* SVM_Feature_Max = new double[fea_size];
	double* SVM_Feature_Min = new double[fea_size];
	for(int Feature_Idx = 0; Feature_Idx < fea_size; Feature_Idx++)
		feature_minmax_file >> SVM_Feature_Max[Feature_Idx] >> SVM_Feature_Min[Feature_Idx];
	Feature_Max = SVM_Feature_Max;
	Feature_Min = SVM_Feature_Min;
	
	int mapping;
	// LBP n=8 mapping
	vector<int> SVM_mapping_r8;
	ifstream Mapping_r8_file;
	Mapping_r8_file.open(Mapping_r8_path);
	if(!Mapping_r8_file.is_open())
	{
		std::cout << "Can not open file: " << Mapping_r8_path << endl;
		return false;
	}
	for(int i = 0; i < 256; i++)
	{
		Mapping_r8_file >> mapping;
		SVM_mapping_r8.push_back(mapping);
	}
	mapping_r8 = SVM_mapping_r8;

	// LBP n=16 mapping
	vector<int> SVM_mapping_r16;
	ifstream Mapping_r16_file;
	Mapping_r16_file.open(Mapping_r16_path);
	if(!Mapping_r16_file.is_open())
	{
		std::cout << "Can not open file: " << Mapping_r16_path << endl;
		return false;
	}
	for(int i = 0; i < 65536; i++)
	{
		Mapping_r16_file >> mapping;
		SVM_mapping_r16.push_back(mapping);
	}
	mapping_r16 = SVM_mapping_r16;

	return true;
}

// Check with image as an input
double ThuVisionFaceLiveCheck::Check(cv::Mat img)
{
	// Predict
	double value = Liveness_Predict(img, model, Feature_Max, Feature_Min, mapping_r8, mapping_r16, img_size, LBP_size);
	return sigmoid(value);
}

// Check with path as an input
double ThuVisionFaceLiveCheck::Check(std::string imgPath)
{
	// Load Image
	Mat img = imread(imgPath);
	if(img.empty())
	{
		std::cout << "Can not load image: " << imgPath << endl;
		return false;
	}

	return ThuVisionFaceLiveCheck::Check(img);
}

void Train()
{
	// Image size, LBP feature size and total feature size
	int img_size = 64;
	int LBP_size = 243 + 59 + 9*59;
	int fea_size = img_size * img_size + LBP_size;

	// Path
	char* svm_path = "svm_model";
	char* minmax_path = "svm_minmax.txt";
	string Mapping_r8_path = ".\\lbp\\mapping_n8.txt";
	string Mapping_r16_path = ".\\lbp\\mapping_n16.txt";

	// Train data set
	int real_train_count[] = {1742, 22274, 6322};
	int fake_train_count[] = {1748, 14064, 22080};
	string train_set[] = {"NUAA", "PRINT-ATTACK", "CASIA"};
	string real_train_name;
	string fake_train_name;
	vector<double> real_train_value;
	vector<double> fake_train_value;

	// LibSVM
	svm_problem svm_prob;
	svm_parameter svm_param;
	svm_prob.l = real_train_count[0] + real_train_count[1] + real_train_count[2] + 
		fake_train_count[0] + fake_train_count[1] + fake_train_count[2];
	svm_prob.y = new double[svm_prob.l];
	svm_prob.x = new svm_node*[svm_prob.l];
	svm_param.svm_type = C_SVC;
	svm_param.kernel_type = LINEAR;
	//svm_param.degree = 3;
	//svm_param.gamma = 0;
	//svm_param.coef0 = 0;
	svm_param.cache_size = 1;
	svm_param.eps = 1e-3;
	svm_param.C = 1;
	svm_param.shrinking = 1;
	svm_param.probability = 1;
	svm_param.nr_weight = NULL;
	svm_param.weight_label = NULL;
	svm_param.weight = NULL;
	
	// LBP n=8 mapping
	int mapping;
	vector<int> mapping_r8;
	ifstream Mapping_r8_file;
	Mapping_r8_file.open(Mapping_r8_path);
	for(int i = 0; i < 256; i++)
	{
		Mapping_r8_file >> mapping;
		mapping_r8.push_back(mapping);
	}
	// LBP n=16 mapping
	vector<int> mapping_r16;
	ifstream Mapping_r16_file;
	Mapping_r16_file.open(Mapping_r16_path);
	for(int i = 0; i < 65536; i++)
	{
		Mapping_r16_file >> mapping;
		mapping_r16.push_back(mapping);
	}

	// Extract Features
	int count = 0;
	for(int setIdx = 0; setIdx < 3; setIdx++)
	{
		// Real face training
		for(int imgIdx = 0; imgIdx < real_train_count[setIdx]; imgIdx++)
		{
			// Image Path
			stringstream real_train_stream;
			real_train_stream  << setw(5) << setfill('0') << imgIdx;
			real_train_name = ".\\" + train_set[setIdx] + "\\train\\real\\" + real_train_stream.str() + ".png";
			// Load Image
			Mat img = imread(real_train_name);
			if(img.empty())
			{
				std::cout << "Can not load image: " << real_train_name << endl;
				return;
			}
			// Extract Features
			svm_prob.y[count] = 1;
			svm_prob.x[count] = Liveness_Feature(img, mapping_r8, mapping_r16, img_size, LBP_size);
			std::cout << "Training data: " << train_set[setIdx] << " Real " << imgIdx + 1 << endl;
			count++;
		}

		// Fake face training
		for(int imgIdx = 0; imgIdx < fake_train_count[setIdx]; imgIdx++)
		{
			// Image Path
			stringstream fake_train_stream;
			fake_train_stream  << setw(5) << setfill('0') << imgIdx;
			fake_train_name = ".\\" + train_set[setIdx] + "\\train\\fake\\" + fake_train_stream.str() + ".png";
			// Load Image
			Mat img = imread(fake_train_name);
			if(img.empty())
			{
				std::cout << "Can not load image: " << fake_train_name << endl;
				return;
			}
			// Extract Features
			svm_prob.y[count] = -1;
			svm_prob.x[count] = Liveness_Feature(img, mapping_r8, mapping_r16, img_size, LBP_size);
			std::cout << "Training data: " << train_set[setIdx] << " Fake " << imgIdx + 1 << endl;
			count++;
		}
	}

	// Scaling
	ofstream minmax_file;
	minmax_file.open(minmax_path);
	for(int Feature_Idx = 0; Feature_Idx < fea_size; Feature_Idx++)
	{
		double Max = 0;
		double Min = 4294967296;
		// Get Max and Min
		for(int train_Idx = 0; train_Idx < svm_prob.l; train_Idx++)
		{
			Max = Max > svm_prob.x[train_Idx][Feature_Idx].value
				? Max : svm_prob.x[train_Idx][Feature_Idx].value;
			Min = Min < svm_prob.x[train_Idx][Feature_Idx].value
				? Min : svm_prob.x[train_Idx][Feature_Idx].value;
		}
		// Scaling Features
		for(int svm_train_Idx = 0; svm_train_Idx < svm_prob.l; svm_train_Idx++)
		{
			svm_prob.x[svm_train_Idx][Feature_Idx].value = 
				(svm_prob.x[svm_train_Idx][Feature_Idx].value - Min) / (Max - Min);
		}
		// Save Max and Min
		minmax_file << Max << ' ' << Min << endl;
	}

	// Training
	svm_model* svm_model = svm_train(&svm_prob, &svm_param);
	// Count correct rate
	int test_count = 0;
	for(int test_Idx = 0; test_Idx < svm_prob.l; test_Idx++)
	{
		double value = svm_predict(svm_model, svm_prob.x[test_Idx]);
		test_count += (value == svm_prob.y[test_Idx]);
	}
	cout << "Correct rate: " << (double) test_count / (svm_prob.l) << endl;
	// Free
	svm_save_model(svm_path, svm_model);
	svm_free_and_destroy_model(&svm_model);
	delete[] svm_prob.x;
	delete[] svm_prob.y;
	svm_destroy_param(&svm_param);

	return;
}

void Test()
{
	ThuVisionFaceLiveCheck liveness_test;
	bool Init_message = liveness_test.Init();
	if(Init_message == false)
	{
		std::cout << "Initialization failed. " << endl;
		return;
	}
	std::cout << "Initialized successfully. " << endl;

	// Test data set
	int real_test_count[] = {3362, 29562, 9966};
	int fake_test_count[] = {5761, 18636, 31683};
	string test_set[] = {"NUAA", "PRINT-ATTACK", "CASIA"};
	string real_test_name;
	string fake_test_name;
	vector<double> real_test_value[3];
	vector<double> fake_test_value[3];

	// Testing
	for(int setIdx = 0; setIdx < 3; setIdx++)
	{
		// Real face testing
		for(int imgIdx = 0; imgIdx < real_test_count[setIdx]; imgIdx++)
		{
			// Image Path
			stringstream real_test_stream;
			real_test_stream  << setw(5) << setfill('0') << imgIdx;
			real_test_name = ".\\" + test_set[setIdx] + "\\test\\real\\" + real_test_stream.str() + ".png";
			// Predict
			double value = liveness_test.Check(real_test_name);
			real_test_value[setIdx].push_back(value);
			std::cout << "Testing data: " << test_set[setIdx] << " Real " << imgIdx + 1 << "; "
				<< "Predict Value: " << value << "; "
				<< "True Label: 1. " << endl;
		}

		// Fake face testing
		for(int imgIdx = 0; imgIdx < fake_test_count[setIdx]; imgIdx++)
		{
			// Image Path
			stringstream fake_test_stream;
			fake_test_stream  << setw(5) << setfill('0') << imgIdx;
			fake_test_name = ".\\" + test_set[setIdx] + "\\test\\fake\\" + fake_test_stream.str() + ".png";
			// Predict
			double value = liveness_test.Check(fake_test_name);
			fake_test_value[setIdx].push_back(value);
			std::cout << "Testing data: " << test_set[setIdx] << " Fake " << imgIdx + 1 << "; "
				<< "Predict Value: " << value << "; "
				<< "True Label: 0. " << endl;
		}
	}

	// Result
	ofstream count_file[3];
	int correct_count;
	int* test_count = new int[3];
	for(int setIdx = 0; setIdx < 3; setIdx++)
	{
		count_file[setIdx].open("count_" + test_set[setIdx] + ".txt");
		test_count[setIdx] = real_test_count[setIdx] + fake_test_count[setIdx];
		for(double k = 0; k <= 1; k+=0.01)
		{
			correct_count = 0;
			for(int i = 0; i < real_test_value[setIdx].size(); i++)
				correct_count += (real_test_value[setIdx][i] >= k);
			for(int i = 0; i < fake_test_value[setIdx].size(); i++)
				correct_count += (fake_test_value[setIdx][i] < k );
			count_file[setIdx] << "k on " << test_set[setIdx] << ": " << k << endl;
			count_file[setIdx] << "Total data on " << test_set[setIdx] << ": " << test_count[setIdx] << endl;
			count_file[setIdx] << "Correct data on " << test_set[setIdx] << ": "  << correct_count << endl;
			count_file[setIdx] << "Correct rate on " << test_set[setIdx] << ": "  << (double) correct_count / test_count[setIdx] << endl;
		}
	}

	return;
}

================================================
FILE: C++/liveness.h
================================================
#include <iostream>
#include <fstream>
#include <sstream>
#include <iomanip>
#include <vector>
#include <opencv2\opencv.hpp>
#include ".\libsvm-3.19\svm.h" 
#include ".\lbp\lbp.hpp" 
#include ".\lbp\histogram.hpp" 
using namespace std;
using namespace cv;

// Sigmoid
double sigmoid(double input);

// DoG Features
Mat DoG(Mat input, double sigma1 = 0.5, double sigma2 = 1);

// LBP Features
Mat LBP(Mat input, vector<int> mapping_r8, vector<int> mapping_r16);

// Extract Features
svm_node* Liveness_Feature(Mat img, vector<int> mapping_r8, vector<int> mapping_r16, int img_size, int LBP_size);

// Predict
double Liveness_Predict(Mat img, svm_model* model, double* Feature_Max, double* Feature_Min, 
						vector<int> mapping_r8, vector<int> mapping_r16, int img_size, int LBP_size);

// Liveness Detection Class
class ThuVisionFaceLiveCheck{
public:
	// Initialization: successful - true; failed - false
	bool Init();
	// Liveness Predict: input image path or Mat. 
	double Check(std::string imgPath);
	double Check(cv::Mat img);
private:
	// Image size (DoG feature size = img_size * img_size)
	int img_size;
	// LBP feature size
	int LBP_size;
	// SVM Model
	svm_model* model;
	// Min and Max (for scaling)
	double* Feature_Max;
	double* Feature_Min;
	// LBP mapping
	vector<int> mapping_r8;
	vector<int> mapping_r16;
};

// Training
void Train();

// Testing
void Test();


================================================
FILE: C++/svm_minmax_NUAA.txt
================================================
4638 186
2292.61 0.258032
1968.03 0.0429906
1056.06 0.0884837
2106.64 0.168737
814.024 0.120735
1285.34 0.0165767
748.185 0.00170226
844.288 0.0834991
574.271 0.0353163
803.612 0.0325523
643.701 0.00572559
742.611 0.0302885
704.634 0.0885741
865.697 0.0245662
818.328 6.21725e-015
541.139 0.00862197
675.26 0.00923781
610.372 0.0268034
565.88 0.000325988
715.587 0.0847821
560.933 0.0062252
876.012 0.0324584
678.649 0.0121225
686.271 0.0135724
595.999 0.0301275
701.879 0.00912817
498 0.0122942
698.358 0.00511841
544.79 0.00583248
572.791 0.00293929
623 0
402 0
598.563 0.0133827
519.585 0.00705574
540.404 0.00250907
515.277 0.0203501
454.082 0.00123163
436.233 0.00668602
477.899 0.00114529
362.275 0.0183693
308.319 0.000746276
473.772 0.0128237
345.295 0.00651096
362.67 0.00298229
386.227 0.0299721
354.025 0.0127712
461.863 8.88178e-016
286.123 0.0121933
279.589 0.0347225
287.342 0.0131526
266.558 0.00168677
261.494 0.0094623
270.138 0.0233686
217.855 0.0465949
250.662 0.0586835
190.437 0.00332815
287.271 0.0090122
181.854 0.0142805
321.398 0.00207759
222.81 0.0178291
367.611 0.00210843
175.399 0.00537264
493 0
1191.26 0.0132862
1272.52 0.534385
1027.92 0.197807
1150.38 0.0308084
1442.69 0.710015
901.662 0.00559385
1053.75 0.0266603
982.157 0.0969134
861.551 0.00694012
706.16 0.00404798
887.749 0.0763378
917.746 0.0139738
684.177 0.0612228
639.437 0.00550469
735.161 0.00894135
803.885 0.0138206
681.999 0.0244332
600.548 0.013805
782.932 0.0200553
429.771 0.00947661
592.997 0.0029203
461.528 0.144719
646.107 0.0240248
418.455 0.026494
458.802 0.00240852
442.052 0.0161637
511.063 0.0530391
472.054 0.0373713
490.098 0.00491298
566.848 0.0233287
587.677 0.0151435
454.53 0.029391
572.913 0.0187844
381.813 0.0275307
399.031 0.00993181
367.964 0.0135449
372.675 0.0214892
538.768 0.0292419
505.79 0.0115374
558.371 0.0127384
419.524 0.00796698
503.93 0.00292722
542.759 0.0312349
466.166 0.00495845
452.066 0.0243236
328.651 0.0229813
402.096 0.0129765
336.259 0.00636526
390.83 0.0100214
339.709 0.0102316
437.798 0.00726407
306.812 0.00792195
329.633 0.00263265
361.918 0.00608831
368.935 0.0243964
269.51 0.00461483
343.242 0.0108465
243.192 0.0272887
213.837 9.45839e-005
270.461 0.000843973
241.703 0.0138016
256.421 0.000354027
228.683 0.00150489
273.223 0.000966426
1255.62 0.0727434
1100.56 0.0537289
903.181 0.0828493
776.817 0.000661446
966.542 0.135085
809.154 0.109499
624.316 0.082965
749.833 0.0363108
828.803 0.0794155
635.865 0.0482245
931.019 0.0283288
840.368 0.0196538
607.907 0.00296418
580.334 0.112362
559.817 0.147965
541.093 0.00536489
683.111 0.0256236
496.128 0.0937267
494.912 0.00267068
471.192 0.00175061
429.896 0.00650188
449.279 0.00390718
454.233 0.00620899
449.279 0.0239917
468.357 0.0206665
430.127 0.0363543
432.361 0.0185265
431.108 0.020497
509.963 0.0176186
455.382 0.00615107
425.749 0.0143736
459.278 0.00782328
531.453 0.00820711
387.278 0.0208447
666.226 0.0242608
490.683 0.0214072
629.575 0.00615557
480.968 0.0129884
595.965 0.0136598
456.217 0.024403
370.487 0.00750192
267.374 0.00623814
397.787 0.00449583
477 0.011655
370.061 0.00373504
320.224 0.00319924
367.329 0.0194477
432.823 0.0103622
261.002 0.00984015
300.032 0.000807762
317.769 0.0139045
301.103 0.000416534
276.367 0.0126043
406.312 0.00660743
252.666 0.00385511
376.192 0.0301101
222.33 0.0232183
273.377 0.00373185
343.889 0.00713145
263.738 0.00995165
403.621 0.0046526
282.603 0.00584146
363.619 0.0036613
227.406 0.0197452
1640.21 0.132571
722.763 0.122084
990.83 0.065559
805.847 0.0436376
753.1 0.0495231
479.096 0.0569496
740.235 0.00308668
633.875 0.00887367
552.75 0.042442
646.946 0.0548192
478.904 0.0485743
508.171 0.0427687
697.427 0.00264661
619.659 0.0123616
648.415 0.0118865
537.915 0.00734329
456.086 0.00852339
523.654 0.00316749
620.368 0.000245148
455.493 0.0387759
410.356 0.0307251
449.886 0.0313531
427.101 0.0107647
391.424 0.0558898
380.502 0.0498883
457.903 0.0188219
560.479 0.000388067
439.922 0.0110602
362.369 0.00428629
396.31 0.0107113
383.09 0.00160793
367.179 0.00999502
414.61 0.00484781
478.148 0.00928183
416.203 0.00669265
330.647 0.00346521
279.461 0.00226358
338.656 0.000528617
305.376 0.00373747
253.129 0.000763232
257.122 0.00917065
390.278 0.00259718
314.599 0.0207473
331.422 0.00119396
288.857 0.000947032
343.541 0.0198409
298.489 0.00622538
250.826 0.0137948
238.008 0.00186629
307.772 0.0639878
205.343 0.00331676
257.375 0.00866105
227.442 0.00405915
255.746 0.000996362
230.452 0.0737804
219.794 0.00685649
270.586 0.00934284
186.194 0.0085002
229.35 0.00523108
251.875 0.00362591
262.192 0.0354053
257.757 0.00168949
281.937 0.0161841
325.867 0.00893534
1440.7 0.0493222
757.073 0.0340349
628.744 0.18866
709.967 0.00938141
718.138 0.020334
571.921 0.0263652
613.958 0.0138249
663.748 0.0845945
503.802 0.0198822
663.536 0.00722288
502.657 0.0247267
561.044 0.0137579
529.707 0.0437586
501.141 0.0399298
567.409 0.0138549
573.914 0.0360973
581.831 0.0655676
625.943 0.00494519
551.08 0.0517938
485.526 0.0352965
554.985 0.00557072
621.947 0.0179256
431.818 0.0419553
442.927 0.0274282
413.022 0.0438075
334.606 0.0128968
449.513 0.071765
355.823 0.0130854
344.05 0.00532927
460.85 0.00802145
292.53 0.00735568
379.189 0.0147083
395.3 0.00252128
399.84 0.00366313
369.003 0.0266241
280.349 0.0120607
344.016 0.012053
344.18 0.0570188
255.704 0.0106002
257.373 0.014672
258.842 0.00993619
265.773 0.034679
254.952 0.0205942
240.952 0.00608497
208.36 0.00130549
279.619 0.00142747
244.542 0.0201925
242.114 0.0012693
258.541 0.00714668
183.148 0.00549695
238.453 0.0119491
204.33 0.00342101
218.185 0.0169233
212.844 0.0165412
246.974 0.0102653
198.999 0.00271908
193.838 0.0105552
185.461 0.0192424
222.376 0.0162523
208.189 0.00216341
215.107 0.0113122
219.601 0.00178331
177.656 0.0148194
275.611 0.068843
780.137 0.0596555
699.994 0.0173279
617.54 0.0702795
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16 0
5 0
27 0
32 0
92 11
223 9
29 0
24 0
36 1
9 0
20 0
60 2
23 0
20 0
116 10
193 21
31 0
28 1
56 0
219 19
38 4
21 0
24 0
60 0
50 0
45 1
12 0
13 0
18 0
37 0
60 0
20 0
16 0
3 0
21 0
35 0
84 0
90 0
50 2
10 0
14 0
18 0
71 2
85 1
32 0
19 0
4 0
99 9
132 19
58 4
22 0
33 1
177 13
33 2
15 0
3 0
37 4
12 0
13 0
19 0
5 0
28 1
27 1
106 9
743 3
34 0
14 0
28 0
10 0
11 0
32 0
15 0
14 0
67 1
100 1
32 0
30 0
44 0
140 5
29 0
21 0
33 0
109 1
114 1
39 0
12 0
15 0
23 0
64 1
123 3
29 0
14 0
5 0
23 0
35 0
67 0
74 0
41 0
8 0
14 0
26 0
54 1
75 0
22 0
18 0
4 0
110 7
150 3
59 1
20 0
28 1
167 8
32 2
12 0
5 0
84 5
13 0
15 0
20 0
4 0
27 0
28 1
74 3
696 7
21 0
27 0
29 0
12 0
23 0
45 0
35 0
24 0
68 3
118 5
28 0
35 0
66 0
186 1
24 0
25 0
31 0
141 0
122 0
69 1
11 0
14 0
22 0
47 1
120 2
27 0
17 0
5 0
36 0
35 0
122 0
109 1
66 4
12 0
15 0
28 0
41 1
136 2
40 0
18 0
4 0
103 5
116 4
58 7
19 0
26 0
156 12
31 1
16 0
4 0
51 5
12 0
17 0
14 0
4 0
22 0
35 1
90 8
663 8
18 0
38 0
35 0
15 0
27 0
70 0
36 0
33 0
132 7
155 13
22 0
36 0
116 0
230 2
33 0
22 0
26 0
88 0
146 0
95 0
14 0
12 0
16 0
31 0
68 0
19 0
14 0
4 0
33 0
32 0
135 0
119 2
64 4
17 0
17 0
23 0
40 0
247 2
52 1
16 0
5 0
68 2
92 5
67 4
17 0
25 1
97 1
18 0
11 0
5 0
34 0
10 0
14 0
13 0
3 0
22 0
31 0
108 8


================================================
FILE: C++/svm_model_NUAA
================================================
[File too large to display: 33.5 MB]

================================================
FILE: C++/test.cpp
================================================
#include"liveness.h"

// Demo of Liveness Detection
int main()
{
	Test();
	/*
	// Initialization
	ThuVisionFaceLiveCheck Check;
	bool Init_message = Check.Init();
	if(Init_message == false)
	{
		std::cout << "Initialization failed. " << endl;
		return -1;
	}
	std::cout << "Initialized successfully. " << endl;

	// Check with Image Path
	string imgPath0 = "00000.png";
	double value0 = Check.Check(imgPath0);
	cout << imgPath0 << " is a " << (value0 >= 0.5 ? "real" : "fake") << " face. " <<endl;

	// Check with Image Mat
	string imgPath1 = "00001.png";
	Mat img = imread(imgPath1);
	if(img.empty())
	{
		std::cout << "Can not load image: " << imgPath1 << endl;
		return -1;
	}
	double value1 = Check.Check(img);
	cout << "img" << " is a " << (value1 >= 0.5 ? "real" : "fake") << " face. " <<endl;
	*/

	return 0;
}


================================================
FILE: Matlab/DoG.m
================================================
function Y = DoG(X, sigma1, sigma2)
%% default parameter
if nargin == 1
    sigma1 = 0.5;
    sigma2 = 1;
end
%% Init. operations
F1 = fspecial('gaussian', 2*ceil(3*sigma1)+1, sigma1);
F2 = fspecial('gaussian', 2*ceil(3*sigma2)+1, sigma2);

%% Filtering
XF1 = imfilter(X, F1, 'replicate', 'same');
XF2 = imfilter(X, F2, 'replicate', 'same');
DXF = XF1 - XF2;

%% Fourier Spectra
Y = abs(fftshift(fft2(DXF)));
Y = Y(:);
end

================================================
FILE: Matlab/DoGLBP_Test.m
================================================
clear all, close all, clc

%% read client and imposter testing data name
ClientTestNormalizedName = importdata('./NUAA/client_test_normalized.txt');
ClientTestNormalizedNumber = size(ClientTestNormalizedName, 1);
ImposterTestNormalizedName = importdata('./NUAA/imposter_test_normalized.txt');
ImposterTestNormalizedNumber = size(ImposterTestNormalizedName, 1);
addpath('./libsvm-3.19/matlab');
addpath('./lbp-0.3.3');
load('LBP_SVM.mat');
Map_u2_16 = getmapping(16, 'u2');
Map_u2_8 = getmapping(8, 'u2');
model_LBP = model;
MinMax_LBP = MinMax;
load DoG_SVM.mat
model_DoG = model;
MinMax_DoG = MinMax;

%% client testing data
% tic
ClientFlag = zeros(ClientTestNormalizedNumber, 1);
for i = 1 : ClientTestNormalizedNumber
    ClientTest = imread(['./NUAA/ClientNormalized/' ClientTestNormalizedName{i}]);
    ClientTestFeature_LBP = LBP_feature(ClientTest, Map_u2_16, Map_u2_8);
    ClientTestFeature_LBP = (ClientTestFeature_LBP-MinMax_LBP(1,:))./(MinMax_LBP(2,:)-MinMax_LBP(1,:));
    [~, ~, TestValue_LBP] = svmpredict(1, ClientTestFeature_LBP, model_LBP, '-q');
    ClientTestFeature_DoG = DoG(ClientTest, 0.5, 1);
    ClientTestFeature_DoG = (ClientTestFeature_DoG'-MinMax_DoG(1,:))./(MinMax_DoG(2,:)-MinMax_DoG(1,:));
    [~, ~, TestValue_DoG] = svmpredict(1, ClientTestFeature_DoG, model_DoG, '-q');
    ClientFlag(i) = TestValue_LBP+TestValue_DoG;
%     disp(['frame:',num2str(i),'; flag:',num2str(ClientFlag(i))]);
%     toc
end

%% LBP feature of imposter testing data
ImposterFlag = zeros(ImposterTestNormalizedNumber, 1);
for i = 1 : ImposterTestNormalizedNumber
    ImposterTest = imread(['./NUAA/ImposterNormalized/' ImposterTestNormalizedName{i}]);  
    ImposterTestFeature_LBP = LBP_feature(ImposterTest, Map_u2_16, Map_u2_8);
    ImposterTestFeature_LBP = (ImposterTestFeature_LBP-MinMax_LBP(1,:))./(MinMax_LBP(2,:)-MinMax_LBP(1,:));
    [~, ~, TestValue_LBP] = svmpredict(1, ImposterTestFeature_LBP, model_LBP, '-q');
    ImposterTestFeature_DoG = DoG(ImposterTest, 0.5, 1);
    ImposterTestFeature_DoG = (ImposterTestFeature_DoG'-MinMax_DoG(1,:))./(MinMax_DoG(2,:)-MinMax_DoG(1,:));
    [~, ~, TestValue_DoG] = svmpredict(1, ImposterTestFeature_DoG, model_DoG, '-q');
    ImposterFlag(i) = TestValue_LBP+TestValue_DoG;
%     disp(['frame:',num2str(i),'; flag:',num2str(ImposterFlag(i))]);
%     toc
end


================================================
FILE: Matlab/DoG_CASIA_Test.m
================================================
% clear all, close all, clc
% 
% %% read client and imposter testing data name
% addpath('./libsvm-3.19/matlab');
% addpath('./lbp-0.3.3');
% Map_u2_16 = getmapping(16, 'u2');
% Map_u2_8 = getmapping(8, 'u2');
% load DoG_SVM_CASIA.mat
% 
% %% extract feature
% detector = vision.CascadeObjectDetector('MinSize', [100,100]);
% flagSet = cell(30, 8);
% for IdxSubject = 1 : 30
%     for IdxData = 1 : 8
%         if IdxData <= 8
%             Name = ['./CASIA/test_release/' num2str(IdxSubject) '/' num2str(IdxData) '.avi'];
%         else
%             Name = ['./CASIA/test_release/' num2str(IdxSubject) '/HR_' num2str(IdxData-8) '.avi'];
%         end
%         Mov = VideoReader(Name);
%         NumFrame = Mov.NumberOfFrames;
%         flag = [];
%         for IdxFrame = 1 : NumFrame
%             data = read(Mov, IdxFrame);
%             box = step(detector, data);
%             if size(box, 1) == 1
%                 frame_now = rgb2gray(imresize(data(box(2):box(2)+box(4), box(1):box(1)+box(3), :), [64,64]));
%                 TestFeature = DoG(frame_now, 0.5, 1)';
%                 TestFeature = (TestFeature-MinMax(1,:))./(MinMax(2,:)-MinMax(1,:));
%                 [TestLabel, TestAccuracy, TestValue] = svmpredict(1, TestFeature, model, '-q');
%                 flag = [flag,TestValue];
%             end
%         end
%         flagSet{IdxSubject, IdxData} = flag;
%         disp([num2str(IdxSubject) ', ' num2str(IdxData)])
%         clear Mov;
%     end
% end
% save DoG_SVM_CASIA_testfeature.mat flagSet

%% testing
load DoG_SVM_CASIA_testfeature.mat flagSet        
DataCorrectRate = zeros(3, 601);
CorrectRate = zeros(3, 601);
for Bias = -3:0.01:3;
    CorrectNoReal = 0;
    CorrectNoFake = 0;
    TotalNoReal = 0;
    TotalNoFake = 0;
    DataCorrectNoReal = 0;
    DataCorrectNoFake = 0;
    for IdxSubject = 1 : 30
        for IdxData = 1 : 8
            if IdxData <= 2
                CorrectNoReal = CorrectNoReal + sum(flagSet{IdxSubject, IdxData}>=Bias);
                DataCorrectNoReal = DataCorrectNoReal + (mean(flagSet{IdxSubject, IdxData})>=Bias);
                TotalNoReal = TotalNoReal + size(flagSet{IdxSubject, IdxData}, 2);
            else
                CorrectNoFake = CorrectNoFake + sum(flagSet{IdxSubject, IdxData}<Bias);
                DataCorrectNoFake = DataCorrectNoFake + (mean(flagSet{IdxSubject, IdxData})<Bias);
                TotalNoFake = TotalNoFake + size(flagSet{IdxSubject, IdxData}, 2);
            end
        end
    end
    DataCorrectRate(:, round((Bias+3.01)*100)) = [(DataCorrectNoReal+DataCorrectNoFake) / 240; DataCorrectNoReal / 60; DataCorrectNoFake / 180];
    CorrectRate(:, round((Bias+3.01)*100)) = [(CorrectNoReal+CorrectNoFake) / (TotalNoReal+TotalNoFake); CorrectNoReal / TotalNoReal; CorrectNoFake / TotalNoFake];
end



================================================
FILE: Matlab/DoG_CASIA_Train.m
================================================
clear all, close all, clc

%% read client and imposter testing data name
addpath('./libsvm-3.19/matlab');
addpath('./lbp-0.3.3');
Map_u2_16 = getmapping(16, 'u2');
Map_u2_8 = getmapping(8, 'u2');

%% extract features
detector = vision.CascadeObjectDetector('MinSize', [100,100]);
TrainFeatureSet = cell(20, 8);
NumFrame = zeros(20, 8);
for IdxSubject = 1 : 20
    for IdxData = 1 : 8
        if IdxData <= 8
            Name = ['./CASIA/train_release/' num2str(IdxSubject) '/' num2str(IdxData) '.avi'];
        else
            Name = ['./CASIA/train_release/' num2str(IdxSubject) '/HR_' num2str(IdxData-8) '.avi'];
        end
        Mov = VideoReader(Name);
        NumFrame(IdxSubject, IdxData) = Mov.NumberOfFrames;
        TrainFeature = [];
        for IdxFrame = 1 : NumFrame(IdxSubject, IdxData)
            data = read(Mov, IdxFrame);
            box = step(detector, data);
            if size(box, 1) == 1
                frame_now = rgb2gray(imresize(data(box(2):box(2)+box(4), box(1):box(1)+box(3), :), [64,64]));
                Feature = DoG(frame_now, 0.5, 1)';
                TrainFeature = [TrainFeature;Feature];
            end
        end
        TrainFeatureSet{IdxSubject, IdxData} = TrainFeature;
        disp([num2str(IdxSubject) ', ' num2str(IdxData)])
        clear Mov;
    end
end
save DoG_SVM_CASIA_raw.mat TrainFeatureSet

%% training
load DoG_SVM_CASIA_raw.mat
TrainFeatureAll = [];
TrainTruth = [];
for IdxSubject = 1 : 20
    for IdxData = 1 : 8
        TrainFeatureAll = [TrainFeatureAll;TrainFeatureSet{IdxSubject,IdxData}];
        if IdxData <= 2
            TrainTruth = [TrainTruth;ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        else
            TrainTruth = [TrainTruth;-ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        end
    end
    disp(num2str(IdxSubject));
end
length = size(TrainTruth, 1);
MinMax = minmax(TrainFeatureAll')';
TrainFeatureAll = (TrainFeatureAll-kron(MinMax(1,:),ones(length,1)))./(kron(MinMax(2,:),ones(length,1))-kron(MinMax(1,:),ones(length,1)));
model = svmtrain(TrainTruth, TrainFeatureAll, '-t 0');
[TrainLabel, TrainAccuracy, TrainValue] = svmpredict(ones(length,1), TrainFeatureAll, model);
save DoG_SVM_CASIA.mat model MinMax

================================================
FILE: Matlab/DoG_LBP_All_Train.m
================================================
clear all, close all, clc

%% add path
addpath('./libsvm-3.19/matlab');
addpath('./lbp-0.3.3');

%% NUAA
% load
ClientTrainNormalizedName = importdata('./NUAA/client_train_normalized.txt');
ClientTrainNormalizedNumber = size(ClientTrainNormalizedName, 1);
ImposterTrainNormalizedName = importdata('./NUAA/imposter_train_normalized.txt');
ImposterTrainNormalizedNumber = size(ImposterTrainNormalizedName, 1);
% mapping
Map_u2_16 = getmapping(16, 'u2');
Map_u2_8 = getmapping(8, 'u2');
% features of client training data
ClientTrainFeature = zeros(ClientTrainNormalizedNumber, 833+64*64);
for i = 1 : ClientTrainNormalizedNumber
    ClientTrain = imread(['./NUAA/ClientNormalized/' ClientTrainNormalizedName{i}]);
    ClientTrainFeature(i, :) = [LBP_feature(ClientTrain, Map_u2_16, Map_u2_8), DoG(ClientTrain, 0.5, 1)'];
end
% features of imposter training data
ImposterTrainFeature = zeros(ImposterTrainNormalizedNumber, 833+64*64);
for i = 1 : ImposterTrainNormalizedNumber
    ImposterTrain = imread(['./NUAA/ImposterNormalized/' ImposterTrainNormalizedName{i}]);  
    ImposterTrainFeature(i, :) = [LBP_feature(ImposterTrain, Map_u2_16, Map_u2_8), DoG(ImposterTrain, 0.5, 1)'];
end
% features and truths
TrainTruth = [ones(ClientTrainNormalizedNumber,1);-ones(ImposterTrainNormalizedNumber,1)];
TrainFeatureAll = [ClientTrainFeature;ImposterTrainFeature];
clear ClientTrainFeature ImposterTrainFeature

%% CASIA
load DoG&LBP_SVM_CASIA_raw.mat
for IdxSubject = 1 : 20
    for IdxData = 1 : 8
        TrainFeatureAll = [TrainFeatureAll;TrainFeatureSet{IdxSubject,IdxData}];
        if IdxData <= 2
            TrainTruth = [TrainTruth;ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        else
            TrainTruth = [TrainTruth;-ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        end
    end
    disp(num2str(IdxSubject));
end
clear TrainFeatureSet

%% PRINT-ATTACK
load DoG&LBP_SVM_PRINT_ATTACK_raw.mat
for IdxSubject = 1 : 15
    for IdxData = 1 : 8
        TrainFeatureAll = [TrainFeatureAll;TrainFeatureSet{IdxSubject,IdxData}];
        if IdxData <= 4
            TrainTruth = [TrainTruth;-ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        else
            TrainTruth = [TrainTruth;ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        end
    end
    disp(num2str(IdxSubject));
end
clear TrainFeatureSet

%% training
length = size(TrainTruth, 1);
MinMax = minmax(TrainFeatureAll')';
TrainFeatureAll = (TrainFeatureAll-kron(MinMax(1,:),ones(length,1)))./(kron(MinMax(2,:),ones(length,1))-kron(MinMax(1,:),ones(length,1)));
model = svmtrain(TrainTruth, TrainFeatureAll, '-t 0');
[TrainLabel, TrainAccuracy, TrainValue] = svmpredict(ones(length,1), TrainFeatureAll, model);
save DoG&LBP_SVM_All.mat model MinMax

================================================
FILE: Matlab/DoG_LBP_CASIA_Test.m
================================================
% clear all, close all, clc
% 
% %% read client and imposter testing data name
% addpath('./libsvm-3.19/matlab');
% addpath('./lbp-0.3.3');
% Map_u2_16 = getmapping(16, 'u2');
% Map_u2_8 = getmapping(8, 'u2');
% load DoG&LBP_SVM_CASIA.mat
% 
% %% extract feature
% detector = vision.CascadeObjectDetector('MinSize', [100,100]);
% flagSet = cell(30, 8);
% for IdxSubject = 1 : 30
%     for IdxData = 1 : 8
%         if IdxData <= 8
%             Name = ['./CASIA/test_release/' num2str(IdxSubject) '/' num2str(IdxData) '.avi'];
%         else
%             Name = ['./CASIA/test_release/' num2str(IdxSubject) '/HR_' num2str(IdxData-8) '.avi'];
%         end
%         Mov = VideoReader(Name);
%         NumFrame = Mov.NumberOfFrames;
%         flag = [];
%         for IdxFrame = 1 : NumFrame
%             data = read(Mov, IdxFrame);
%             box = step(detector, data);
%             if size(box, 1) == 1
%                 frame_now = rgb2gray(imresize(data(box(2):box(2)+box(4), box(1):box(1)+box(3), :), [64,64]));
%                 TestFeature = [LBP_feature(frame_now, Map_u2_16, Map_u2_8), DoG(frame_now, 0.5, 1)'];
%                 TestFeature = (TestFeature-MinMax(1,:))./(MinMax(2,:)-MinMax(1,:));
%                 [TestLabel, TestAccuracy, TestValue] = svmpredict(1, TestFeature, model, '-q');
%                 flag = [flag,TestValue];
%             end
%         end
%         flagSet{IdxSubject, IdxData} = flag;
%         disp([num2str(IdxSubject) ', ' num2str(IdxData)])
%         clear Mov;
%     end
% end
% save DoG&LBP_SVM_CASIA_testfeature.mat flagSet

%% testing
load DoG&LBP_SVM_CASIA_testfeature.mat flagSet
DataCorrectRate = zeros(3, 601);
CorrectRate = zeros(3, 601);
for Bias = -3:0.01:3;
    CorrectNoReal = 0;
    CorrectNoFake = 0;
    TotalNoReal = 0;
    TotalNoFake = 0;
    DataCorrectNoReal = 0;
    DataCorrectNoFake = 0;
    for IdxSubject = 1 : 30
        for IdxData = 1 : 8
            if IdxData <= 2
                CorrectNoReal = CorrectNoReal + sum(flagSet{IdxSubject, IdxData}>=Bias);
                DataCorrectNoReal = DataCorrectNoReal + (mean(flagSet{IdxSubject, IdxData})>=Bias);
                TotalNoReal = TotalNoReal + size(flagSet{IdxSubject, IdxData}, 2);
            else
                CorrectNoFake = CorrectNoFake + sum(flagSet{IdxSubject, IdxData}<Bias);
                DataCorrectNoFake = DataCorrectNoFake + (mean(flagSet{IdxSubject, IdxData})<Bias);
                TotalNoFake = TotalNoFake + size(flagSet{IdxSubject, IdxData}, 2);
            end
        end
    end
    DataCorrectRate(:, round((Bias+3.01)*100)) = [(DataCorrectNoReal+DataCorrectNoFake) / 240; DataCorrectNoReal / 60; DataCorrectNoFake / 180];
    CorrectRate(:, round((Bias+3.01)*100)) = [(CorrectNoReal+CorrectNoFake) / (TotalNoReal+TotalNoFake); CorrectNoReal / TotalNoReal; CorrectNoFake / TotalNoFake];
end      



================================================
FILE: Matlab/DoG_LBP_CASIA_Train.m
================================================
clear all, close all, clc

%% read client and imposter testing data name
addpath('./libsvm-3.19/matlab');
addpath('./lbp-0.3.3');
Map_u2_16 = getmapping(16, 'u2');
Map_u2_8 = getmapping(8, 'u2');

%% extract features
detector = vision.CascadeObjectDetector('MinSize', [100,100]);
TrainFeatureSet = cell(20, 8);
NumFrame = zeros(20, 8);
for IdxSubject = 1 : 20
    for IdxData = 1 : 8
        if IdxData <= 8
            Name = ['./CASIA/train_release/' num2str(IdxSubject) '/' num2str(IdxData) '.avi'];
        else
            Name = ['./CASIA/train_release/' num2str(IdxSubject) '/HR_' num2str(IdxData-8) '.avi'];
        end
        Mov = VideoReader(Name);
        NumFrame(IdxSubject, IdxData) = Mov.NumberOfFrames;
        TrainFeature = [];
        for IdxFrame = 1 : NumFrame(IdxSubject, IdxData)
            data = read(Mov, IdxFrame);
            box = step(detector, data);
            if size(box, 1) == 1
                frame_now = rgb2gray(imresize(data(box(2):box(2)+box(4), box(1):box(1)+box(3), :), [64,64]));
                Feature = [LBP_feature(frame_now, Map_u2_16, Map_u2_8), DoG(frame_now, 0.5, 1)'];
                TrainFeature = [TrainFeature;Feature];
            end
        end
        TrainFeatureSet{IdxSubject, IdxData} = TrainFeature;
        disp([num2str(IdxSubject) ', ' num2str(IdxData)])
        clear Mov;
    end
end
% save DoG&LBP_SVM_CASIA_raw.mat TrainFeatureSet

%% training
% load DoG&LBP_SVM_CASIA_raw.mat
TrainFeatureAll = [];
TrainTruth = [];
for IdxSubject = 1 : 20
    for IdxData = 1 : 8
        TrainFeatureAll = [TrainFeatureAll;TrainFeatureSet{IdxSubject,IdxData}];
        if IdxData <= 2
            TrainTruth = [TrainTruth;ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        else
            TrainTruth = [TrainTruth;-ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        end
    end
    disp(num2str(IdxSubject));
end
length = size(TrainTruth, 1);
MinMax = minmax(TrainFeatureAll')';
TrainFeatureAll = (TrainFeatureAll-kron(MinMax(1,:),ones(length,1)))./(kron(MinMax(2,:),ones(length,1))-kron(MinMax(1,:),ones(length,1)));
model = svmtrain(TrainTruth, TrainFeatureAll, '-t 0');
[TrainLabel, TrainAccuracy, TrainValue] = svmpredict(ones(length,1), TrainFeatureAll, model);
save DoG&LBP_SVM_CASIA.mat model MinMax

================================================
FILE: Matlab/DoG_LBP_NUAA_Test.m
================================================
clear all, close all, clc

%% read client and imposter testing data name
ClientTestNormalizedName = importdata('./NUAA/client_test_normalized.txt');
ClientTestNormalizedNumber = size(ClientTestNormalizedName, 1);
ImposterTestNormalizedName = importdata('./NUAA/imposter_test_normalized.txt');
ImposterTestNormalizedNumber = size(ImposterTestNormalizedName, 1);
addpath('./libsvm-3.19/matlab');
addpath('./lbp-0.3.3');
Map_u2_16 = getmapping(16, 'u2');
Map_u2_8 = getmapping(8, 'u2');
load DoG&LBP_SVM_NUAA.mat

%% client testing data
ClientTestFeature = zeros(ClientTestNormalizedNumber, 833+64*64);
for i = 1 : ClientTestNormalizedNumber
    ClientTest = imread(['./NUAA/ClientNormalized/' ClientTestNormalizedName{i}]);
    ClientTestFeature(i, :) = [LBP_feature(ClientTest, Map_u2_16, Map_u2_8), DoG(ClientTest, 0.5, 1)'];
    ClientTestFeature(i, :) = (ClientTestFeature(i, :)-MinMax(1,:))./(MinMax(2,:)-MinMax(1,:));
end

%% LBP feature of imposter testing data
ImposterTestFeature = zeros(ImposterTestNormalizedNumber, 833+64*64);
for i = 1 : ImposterTestNormalizedNumber
    ImposterTest = imread(['./NUAA/ImposterNormalized/' ImposterTestNormalizedName{i}]);  
    ImposterTestFeature(i, :) = [LBP_feature(ImposterTest, Map_u2_16, Map_u2_8), DoG(ImposterTest, 0.5, 1)'];
    ImposterTestFeature(i, :) = (ImposterTestFeature(i, :)-MinMax(1,:))./(MinMax(2,:)-MinMax(1,:));
end

%% SVM testing
[ClientTestLabel, ClientTestAccuracy, ClientTestValue] = svmpredict(ones(ClientTestNormalizedNumber,1), ClientTestFeature, model);
[ImposterTestLabel, ImposterTestAccuracy, ImposterTestValue] = svmpredict(-ones(ImposterTestNormalizedNumber,1), ImposterTestFeature, model);
accuracy = zeros(601, 3);
for i=-3:0.01:3
    accuracy(round(i*100+301),:)=[mean(ClientTestValue>=i),mean(ImposterTestValue<i),(sum(ClientTestValue>=i)+sum(ImposterTestValue<i))/(3362+5761)];
end

================================================
FILE: Matlab/DoG_LBP_NUAA_Train.m
================================================
clear all, close all, clc

%% read client and imposter training data name
ClientTrainNormalizedName = importdata('./NUAA/client_train_normalized.txt');
ClientTrainNormalizedNumber = size(ClientTrainNormalizedName, 1);
ImposterTrainNormalizedName = importdata('./NUAA/imposter_train_normalized.txt');
ImposterTrainNormalizedNumber = size(ImposterTrainNormalizedName, 1);
addpath('./libsvm-3.19/matlab');
addpath('./lbp-0.3.3');

%% mapping
Map_u2_16 = getmapping(16, 'u2');
Map_u2_8 = getmapping(8, 'u2');

%% LBP feature of client training data
ClientTrainFeature = zeros(ClientTrainNormalizedNumber, 833+64*64);
for i = 1 : ClientTrainNormalizedNumber
    ClientTrain = imread(['./NUAA/ClientNormalized/' ClientTrainNormalizedName{i}]);
    ClientTrainFeature(i, :) = [LBP_feature(ClientTrain, Map_u2_16, Map_u2_8), DoG(ClientTrain, 0.5, 1)'];
end

%% LBP feature of imposter training data
ImposterTrainFeature = zeros(ImposterTrainNormalizedNumber, 833+64*64);
for i = 1 : ImposterTrainNormalizedNumber
    ImposterTrain = imread(['./NUAA/ImposterNormalized/' ImposterTrainNormalizedName{i}]);  
    ImposterTrainFeature(i, :) = [LBP_feature(ImposterTrain, Map_u2_16, Map_u2_8), DoG(ImposterTrain, 0.5, 1)'];
end

%% SVM training
MinMax = minmax([ClientTrainFeature;ImposterTrainFeature]')';
ClientTrainFeature = (ClientTrainFeature-kron(MinMax(1,:),ones(ClientTrainNormalizedNumber,1)))./(kron(MinMax(2,:),ones(ClientTrainNormalizedNumber,1))-kron(MinMax(1,:),ones(ClientTrainNormalizedNumber,1)));
ImposterTrainFeature = (ImposterTrainFeature-kron(MinMax(1,:),ones(ImposterTrainNormalizedNumber,1)))./(kron(MinMax(2,:),ones(ImposterTrainNormalizedNumber,1))-kron(MinMax(1,:),ones(ImposterTrainNormalizedNumber,1)));
Truth = [ones(ClientTrainNormalizedNumber,1);-ones(ImposterTrainNormalizedNumber,1)];
model = svmtrain(Truth, [ClientTrainFeature;ImposterTrainFeature], '-t 0');
[ClientTrainLabel, ClientTrainAccuracy, ClientTrainValue] = svmpredict(ones(ClientTrainNormalizedNumber,1), ClientTrainFeature, model);
[ImposterTrainLabel, ImposterTrainAccuracy, ImposterTrainValue] = svmpredict(-ones(ImposterTrainNormalizedNumber,1), ImposterTrainFeature, model);
save DoG&LBP_SVM_NUAA.mat model MinMax

================================================
FILE: Matlab/DoG_LBP_PRINT_ATTACK_Test.m
================================================
% clear all, close all, clc
% 
% %% read client and imposter testing data name
% addpath('./libsvm-3.19/matlab');
% addpath('./lbp-0.3.3');
% Map_u2_16 = getmapping(16, 'u2');
% Map_u2_8 = getmapping(8, 'u2');
% load DoG&LBP_SVM_PRINT_ATTACK.mat
% 
% %% extract feature
% detector = vision.CascadeObjectDetector('MinSize', [50,50]);
% fileIndex = {'009', '011', '013', '014', '019', '020', '021', '023', '024', '026', '028', '031', '102', '104', '106', '107', '109', '112', '115', '117'};
% fileDirec = {'attack/fixed/attack_print_', 'attack/fixed/attack_print_', 'attack/hand/attack_print_', 'attack/hand/attack_print_', 'real/', 'real/', 'real/', 'real/'; ...
%     'highdef_photo_adverse', 'highdef_photo_controlled', 'highdef_photo_adverse', 'highdef_photo_controlled', 'webcam_authenticate_adverse_1', 'webcam_authenticate_adverse_2', 'webcam_authenticate_controlled_1', 'webcam_authenticate_controlled_2'};
% fileNo = size(fileIndex, 2);
% flagSet = cell(fileNo, 8);
% for IdxSubject = 1 : fileNo
%     for IdxData = 1 : 8
%         Name = ['./PRINT-ATTACK/test/' fileDirec{1, IdxData} 'client' fileIndex{IdxSubject} '_session01_' fileDirec{2, IdxData} '.mov'];
%         Mov = VideoReader(Name);
%         NumFrame = Mov.NumberOfFrames;
%         flag = [];
%         for IdxFrame = 1 : NumFrame
%             data = read(Mov, IdxFrame);
%             box = step(detector, data);
%             if size(box, 1) == 1
%                 frame_now = rgb2gray(imresize(data(box(2):box(2)+box(4), box(1):box(1)+box(3), :), [64,64]));
%                 TestFeature = [LBP_feature(frame_now, Map_u2_16, Map_u2_8), DoG(frame_now, 0.5, 1)'];
%                 TestFeature = (TestFeature-MinMax(1,:))./(MinMax(2,:)-MinMax(1,:));
%                 [TestLabel, TestAccuracy, TestValue] = svmpredict(1, TestFeature, model, '-q');
%                 flag = [flag,TestValue];
%             end
%         end
%         flagSet{IdxSubject, IdxData} = flag;
%         disp([num2str(IdxSubject) ', ' num2str(IdxData)])
%         clear Mov;
%     end
% end
% save DoG&LBP_SVM_PRINT_ATTACK_testfeature.mat flagSet

%% testing
load DoG&LBP_SVM_PRINT_ATTACK_testfeature.mat flagSet
DataCorrectRate = zeros(3, 601);
CorrectRate = zeros(3, 601);
for Bias = -3:0.01:3;
    CorrectNoReal = 0;
    CorrectNoFake = 0;
    TotalNoReal = 0;
    TotalNoFake = 0;
    DataCorrectNoReal = 0;
    DataCorrectNoFake = 0;
    for IdxSubject = 1 : 20
        for IdxData = 1 : 8
            if IdxData > 4
                CorrectNoReal = CorrectNoReal + sum(flagSet{IdxSubject, IdxData}>=Bias);
                DataCorrectNoReal = DataCorrectNoReal + (mean(flagSet{IdxSubject, IdxData})>=Bias);
                TotalNoReal = TotalNoReal + size(flagSet{IdxSubject, IdxData}, 2);
            else
                CorrectNoFake = CorrectNoFake + sum(flagSet{IdxSubject, IdxData}<Bias);
                DataCorrectNoFake = DataCorrectNoFake + (mean(flagSet{IdxSubject, IdxData})<Bias);
                TotalNoFake = TotalNoFake + size(flagSet{IdxSubject, IdxData}, 2);
            end
        end
    end
    DataCorrectRate(:, round((Bias+3.01)*100)) = [(DataCorrectNoReal+DataCorrectNoFake) / 160; DataCorrectNoReal / 80; DataCorrectNoFake / 80];
    CorrectRate(:, round((Bias+3.01)*100)) = [(CorrectNoReal+CorrectNoFake) / (TotalNoReal+TotalNoFake); CorrectNoReal / TotalNoReal; CorrectNoFake / TotalNoFake];
end

================================================
FILE: Matlab/DoG_LBP_PRINT_ATTACK_Train.m
================================================
clear all, close all, clc

%% read client and imposter testing data name
addpath('./libsvm-3.19/matlab');
addpath('./lbp-0.3.3');
Map_u2_16 = getmapping(16, 'u2');
Map_u2_8 = getmapping(8, 'u2');

%% extract feature
detector = vision.CascadeObjectDetector('MinSize', [50,50]);
fileIndex = {'001', '002', '004', '006', '007', '008', '012', '016', '018', '025', '027', '103', '105', '108', '110'};
fileDirec = {'attack/fixed/attack_print_', 'attack/fixed/attack_print_', 'attack/hand/attack_print_', 'attack/hand/attack_print_', 'real/', 'real/', 'real/', 'real/'; ...
    'highdef_photo_adverse', 'highdef_photo_controlled', 'highdef_photo_adverse', 'highdef_photo_controlled', 'webcam_authenticate_adverse_1', 'webcam_authenticate_adverse_2', 'webcam_authenticate_controlled_1', 'webcam_authenticate_controlled_2'};
fileNo = size(fileIndex, 2);
TrainFeatureSet = cell(fileNo, 8);
for IdxSubject = 1 : fileNo
    for IdxData = 1 : 8
        Name = ['./PRINT-ATTACK/train/' fileDirec{1, IdxData} 'client' fileIndex{IdxSubject} '_session01_' fileDirec{2, IdxData} '.mov'];
        Mov = VideoReader(Name);
        NumFrame = Mov.NumberOfFrames;
        TrainFeature = [];
        for IdxFrame = 1 : NumFrame
            data = read(Mov, IdxFrame);
            box = step(detector, data);
            if size(box, 1) == 1
                frame_now = rgb2gray(imresize(data(box(2):box(2)+box(4), box(1):box(1)+box(3), :), [64,64]));
                Feature = [LBP_feature(frame_now, Map_u2_16, Map_u2_8), DoG(frame_now, 0.5, 1)'];
                TrainFeature = [TrainFeature;Feature];
            end
        end
        TrainFeatureSet{IdxSubject, IdxData} = TrainFeature;
        disp([num2str(IdxSubject) ', ' num2str(IdxData)])
        clear Mov;
    end
end
% save DoG&LBP_SVM_PRINT_ATTACK_raw.mat TrainFeatureSet

%% training
% load DoG&LBP_SVM_PRINT_ATTACK_raw.mat
TrainFeatureAll = [];
TrainTruth = [];
for IdxSubject = 1 : 15
    for IdxData = 1 : 8
        TrainFeatureAll = [TrainFeatureAll;TrainFeatureSet{IdxSubject,IdxData}];
        if IdxData <= 4
            TrainTruth = [TrainTruth;-ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        else
            TrainTruth = [TrainTruth;ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        end
    end
    disp(num2str(IdxSubject));
end
length = size(TrainTruth, 1);
MinMax = minmax(TrainFeatureAll')';
TrainFeatureAll = (TrainFeatureAll-kron(MinMax(1,:),ones(length,1)))./(kron(MinMax(2,:),ones(length,1))-kron(MinMax(1,:),ones(length,1)));
model = svmtrain(TrainTruth, TrainFeatureAll, '-t 0');
[TrainLabel, TrainAccuracy, TrainValue] = svmpredict(ones(length,1), TrainFeatureAll, model);
save DoG&LBP_SVM_PRINT_ATTACK.mat model MinMax
        



================================================
FILE: Matlab/DoG_NUAA_Test.m
================================================
% clear all, close all, clc

%% read client and imposter testing data name
ClientTestNormalizedName = importdata('./NUAA/client_test_normalized.txt');
ClientTestNormalizedNumber = size(ClientTestNormalizedName, 1);
ImposterTestNormalizedName = importdata('./NUAA/imposter_test_normalized.txt');
ImposterTestNormalizedNumber = size(ImposterTestNormalizedName, 1);
addpath('./libsvm-3.19/matlab');
load DoG_SVM_NUAA.mat

%% DoG feature of client testing data
ClientTestFeature = zeros(ClientTestNormalizedNumber, 64*64);
for i = 1 : ClientTestNormalizedNumber
    ClientTest = imread(['./NUAA/ClientNormalized/' ClientTestNormalizedName{i}]);
    ClientTestFeature(i, :) = DoG(ClientTest, 0.5, 1);
    ClientTestFeature(i, :) = (ClientTestFeature(i, :)-MinMax(1,:))./(MinMax(2,:)-MinMax(1,:));
end

%% DoG feature of imposter testing data
ImposterTestFeature = zeros(ImposterTestNormalizedNumber, 64*64);
for i = 1 : ImposterTestNormalizedNumber
    ImposterTest = imread(['./NUAA/ImposterNormalized/' ImposterTestNormalizedName{i}]);  
    ImposterTestFeature(i, :) = DoG(ImposterTest, 0.5, 1);
    ImposterTestFeature(i, :) = (ImposterTestFeature(i, :)-MinMax(1,:))./(MinMax(2,:)-MinMax(1,:));
end

%% SVM testing
[ClientTestLabel, ClientTestAccuracy, ClientTestValue] = svmpredict(ones(ClientTestNormalizedNumber,1), ClientTestFeature, model);
[ImposterTestLabel, ImposterTestAccuracy, ImposterTestValue] = svmpredict(-ones(ImposterTestNormalizedNumber,1), ImposterTestFeature, model);
accuracy = zeros(601, 3);
for i=-3:0.01:3
    accuracy(round(i*100+301),:)=[mean(ClientTestValue>=i),mean(ImposterTestValue<i),(sum(ClientTestValue>=i)+sum(ImposterTestValue<i))/(3362+5761)];
end

================================================
FILE: Matlab/DoG_NUAA_Train.m
================================================
clear all, close all, clc

%% read client and imposter training data name
ClientTrainNormalizedName = importdata('./NUAA/client_train_normalized.txt');
ClientTrainNormalizedNumber = size(ClientTrainNormalizedName, 1);
ImposterTrainNormalizedName = importdata('./NUAA/imposter_train_normalized.txt');
ImposterTrainNormalizedNumber = size(ImposterTrainNormalizedName, 1);
addpath('./libsvm-3.19/matlab');

%% DoG feature of client training data
ClientTrainFeature = zeros(ClientTrainNormalizedNumber, 64*64);
for i = 1 : ClientTrainNormalizedNumber
    ClientTrain = imread(['./NUAA/ClientNormalized/' ClientTrainNormalizedName{i}]);
    ClientTrainFeature(i, :) = DoG(ClientTrain, 0.5, 1);
end

%% DoG feature of imposter training data
ImposterTrainFeature = zeros(ImposterTrainNormalizedNumber, 64*64);
for i = 1 : ImposterTrainNormalizedNumber
    ImposterTrain = imread(['./NUAA/ImposterNormalized/' ImposterTrainNormalizedName{i}]);  
    ImposterTrainFeature(i, :) = DoG(ImposterTrain, 0.5, 1);
end

%% SVM training
MinMax = minmax([ClientTrainFeature;ImposterTrainFeature]')';
ClientTrainFeature = (ClientTrainFeature-kron(MinMax(1,:),ones(ClientTrainNormalizedNumber,1)))./(kron(MinMax(2,:),ones(ClientTrainNormalizedNumber,1))-kron(MinMax(1,:),ones(ClientTrainNormalizedNumber,1)));
ImposterTrainFeature = (ImposterTrainFeature-kron(MinMax(1,:),ones(ImposterTrainNormalizedNumber,1)))./(kron(MinMax(2,:),ones(ImposterTrainNormalizedNumber,1))-kron(MinMax(1,:),ones(ImposterTrainNormalizedNumber,1)));
Truth = [ones(ClientTrainNormalizedNumber,1);-ones(ImposterTrainNormalizedNumber,1)];
model = svmtrain(Truth, [ClientTrainFeature;ImposterTrainFeature], '-t 0');
[ClientTrainLabel, ClientTrainAccuracy, ClientTrainValue] = svmpredict(ones(ClientTrainNormalizedNumber,1), ClientTrainFeature, model);
[ImposterTrainLabel, ImposterTrainAccuracy, ImposterTrainValue] = svmpredict(-ones(ImposterTrainNormalizedNumber,1), ImposterTrainFeature, model);
save DoG_SVM_NUAA.mat model MinMax

================================================
FILE: Matlab/DoG_PRINT_ATTACK_Test.m
================================================
% clear all, close all, clc
% 
% %% read client and imposter testing data name
% addpath('./libsvm-3.19/matlab');
% addpath('./lbp-0.3.3');
% Map_u2_16 = getmapping(16, 'u2');
% Map_u2_8 = getmapping(8, 'u2');
% load DoG_SVM_PRINT_ATTACK.mat
% 
% %% extract feature
% detector = vision.CascadeObjectDetector('MinSize', [50,50]);
% fileIndex = {'009', '011', '013', '014', '019', '020', '021', '023', '024', '026', '028', '031', '102', '104', '106', '107', '109', '112', '115', '117'};
% fileDirec = {'attack/fixed/attack_print_', 'attack/fixed/attack_print_', 'attack/hand/attack_print_', 'attack/hand/attack_print_', 'real/', 'real/', 'real/', 'real/'; ...
%     'highdef_photo_adverse', 'highdef_photo_controlled', 'highdef_photo_adverse', 'highdef_photo_controlled', 'webcam_authenticate_adverse_1', 'webcam_authenticate_adverse_2', 'webcam_authenticate_controlled_1', 'webcam_authenticate_controlled_2'};
% fileNo = size(fileIndex, 2);
% flagSet = cell(fileNo, 8);
% for IdxSubject = 1 : fileNo
%     for IdxData = 1 : 8
%         Name = ['./PRINT-ATTACK/test/' fileDirec{1, IdxData} 'client' fileIndex{IdxSubject} '_session01_' fileDirec{2, IdxData} '.mov'];
%         Mov = VideoReader(Name);
%         NumFrame = Mov.NumberOfFrames;
%         flag = [];
%         for IdxFrame = 1 : NumFrame
%             data = read(Mov, IdxFrame);
%             box = step(detector, data);
%             if size(box, 1) == 1
%                 frame_now = rgb2gray(imresize(data(box(2):box(2)+box(4), box(1):box(1)+box(3), :), [64,64]));
%                 TestFeature = DoG(frame_now, 0.5, 1)';
%                 TestFeature = (TestFeature-MinMax(1,:))./(MinMax(2,:)-MinMax(1,:));
%                 [TestLabel, TestAccuracy, TestValue] = svmpredict(1, TestFeature, model, '-q');
%                 flag = [flag,TestValue];
%             end
%         end
%         flagSet{IdxSubject, IdxData} = flag;
%         disp([num2str(IdxSubject) ', ' num2str(IdxData)])
%         clear Mov;
%     end
% end
% save DoG_SVM_PRINT_ATTACK_testfeature.mat flagSet

%% testing
load DoG_SVM_PRINT_ATTACK_testfeature.mat flagSet
DataCorrectRate = zeros(3, 601);
CorrectRate = zeros(3, 601);
for Bias = -3:0.01:3;
    CorrectNoReal = 0;
    CorrectNoFake = 0;
    TotalNoReal = 0;
    TotalNoFake = 0;
    DataCorrectNoReal = 0;
    DataCorrectNoFake = 0;
    for IdxSubject = 1 : 20
        for IdxData = 1 : 8
            if IdxData > 4
                CorrectNoReal = CorrectNoReal + sum(flagSet{IdxSubject, IdxData}>=Bias);
                DataCorrectNoReal = DataCorrectNoReal + (mean(flagSet{IdxSubject, IdxData})>=Bias);
                TotalNoReal = TotalNoReal + size(flagSet{IdxSubject, IdxData}, 2);
            else
                CorrectNoFake = CorrectNoFake + sum(flagSet{IdxSubject, IdxData}<Bias);
                DataCorrectNoFake = DataCorrectNoFake + (mean(flagSet{IdxSubject, IdxData})<Bias);
                TotalNoFake = TotalNoFake + size(flagSet{IdxSubject, IdxData}, 2);
            end
        end
    end
    DataCorrectRate(:, round((Bias+3.01)*100)) = [(DataCorrectNoReal+DataCorrectNoFake) / 160; DataCorrectNoReal / 80; DataCorrectNoFake / 80];
    CorrectRate(:, round((Bias+3.01)*100)) = [(CorrectNoReal+CorrectNoFake) / (TotalNoReal+TotalNoFake); CorrectNoReal / TotalNoReal; CorrectNoFake / TotalNoFake];
end

================================================
FILE: Matlab/DoG_PRINT_ATTACK_Train.m
================================================
clear all, close all, clc

%% read client and imposter testing data name
addpath('./libsvm-3.19/matlab');
addpath('./lbp-0.3.3');
Map_u2_16 = getmapping(16, 'u2');
Map_u2_8 = getmapping(8, 'u2');

%% extract feature
detector = vision.CascadeObjectDetector('MinSize', [50,50]);
fileIndex = {'001', '002', '004', '006', '007', '008', '012', '016', '018', '025', '027', '103', '105', '108', '110'};
fileDirec = {'attack/fixed/attack_print_', 'attack/fixed/attack_print_', 'attack/hand/attack_print_', 'attack/hand/attack_print_', 'real/', 'real/', 'real/', 'real/'; ...
    'highdef_photo_adverse', 'highdef_photo_controlled', 'highdef_photo_adverse', 'highdef_photo_controlled', 'webcam_authenticate_adverse_1', 'webcam_authenticate_adverse_2', 'webcam_authenticate_controlled_1', 'webcam_authenticate_controlled_2'};
fileNo = size(fileIndex, 2);
TrainFeatureSet = cell(fileNo, 8);
for IdxSubject = 1 : fileNo
    for IdxData = 1 : 8
        Name = ['./PRINT-ATTACK/train/' fileDirec{1, IdxData} 'client' fileIndex{IdxSubject} '_session01_' fileDirec{2, IdxData} '.mov'];
        Mov = VideoReader(Name);
        NumFrame = Mov.NumberOfFrames;
        TrainFeature = [];
        for IdxFrame = 1 : NumFrame
            data = read(Mov, IdxFrame);
            box = step(detector, data);
            if size(box, 1) == 1
                frame_now = rgb2gray(imresize(data(box(2):box(2)+box(4), box(1):box(1)+box(3), :), [64,64]));
                Feature = DoG(frame_now, 0.5, 1)';
                TrainFeature = [TrainFeature;Feature];
            end
        end
        TrainFeatureSet{IdxSubject, IdxData} = TrainFeature;
        disp([num2str(IdxSubject) ', ' num2str(IdxData)])
        clear Mov;
    end
end
save DoG_SVM_PRINT_ATTACK_raw.mat TrainFeatureSet

%% training
load DoG_SVM_PRINT_ATTACK_raw.mat
TrainFeatureAll = [];
TrainTruth = [];
for IdxSubject = 1 : 15
    for IdxData = 1 : 8
        TrainFeatureAll = [TrainFeatureAll;TrainFeatureSet{IdxSubject,IdxData}];
        if IdxData <= 4
            TrainTruth = [TrainTruth;-ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        else
            TrainTruth = [TrainTruth;ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        end
    end
    disp(num2str(IdxSubject));
end
length = size(TrainTruth, 1);
MinMax = minmax(TrainFeatureAll')';
TrainFeatureAll = (TrainFeatureAll-kron(MinMax(1,:),ones(length,1)))./(kron(MinMax(2,:),ones(length,1))-kron(MinMax(1,:),ones(length,1)));
model = svmtrain(TrainTruth, TrainFeatureAll, '-t 0');
[TrainLabel, TrainAccuracy, TrainValue] = svmpredict(ones(length,1), TrainFeatureAll, model);
save DoG_SVM_PRINT_ATTACK.mat model MinMax
        



================================================
FILE: Matlab/HFD.m
================================================
%% High Frequency Descriptor
function HFD = HFD(Image, ThresholdFrequency, ImageSize, HighFrequencyRadius)
%% default parameter
if nargin < 4
    if nargin < 3
        if nargin < 2
            ThresholdFrequency = 100;
        end
        ImageSize = 64;
    end
    HighFrequencyRadius = ImageSize / 3;
end
%% fourier spectra
ClientTrainFFT = abs(fftshift(fft2(Image)));
%% energy of high frequencies
HighFrequencyEnergy = 0;
for x = 1 : ImageSize
    for y = 1 : ImageSize
        if ((x-(1+ImageSize)/2)^2+(y-(1+ImageSize)/2)^2)>HighFrequencyRadius^2 && ClientTrainFFT(x,y)>=ThresholdFrequency
            HighFrequencyEnergy = HighFrequencyEnergy + ClientTrainFFT(x,y);
        end
    end
end
%% energy of all frequencies
TotalEnergy = sum(ClientTrainFFT(:)) - sum(Image(:));
%% high frequency descriptor
HFD = HighFrequencyEnergy / TotalEnergy;
end

================================================
FILE: Matlab/HFDTest.m
================================================
% clear all, close all, clc

%% read client and imposter testing data name
ClientTestNormalizedName = importdata('./NUAA/client_test_normalized.txt');
ClientTestNormalizedNumber = size(ClientTestNormalizedName, 1);
ImposterTestNormalizedName = importdata('./NUAA/imposter_test_normalized.txt');
ImposterTestNormalizedNumber = size(ImposterTestNormalizedName, 1);

figure;
color = 'brgymck';
ThresholdFrequency = [0, 10, 20, 50, 100, 200, 500];
for n = 1 : 7
    %% HFD of client Testing data
    ClientTestHFD = zeros(1, ClientTestNormalizedNumber);
    for i = 1 : ClientTestNormalizedNumber
        ClientTest = imread(['./NUAA/ClientNormalized/' ClientTestNormalizedName{i}]);
        ClientTestHFD(i) = HFD(ClientTest, ThresholdFrequency(n));
    end

    %% HFD of imposter Testing data
    ImposterTestHFD = zeros(1, ImposterTestNormalizedNumber);
    for i = 1 : ImposterTestNormalizedNumber
        ImposterTest = imread(['./NUAA/ImposterNormalized/' ImposterTestNormalizedName{i}]);  
        ImposterTestHFD(i) = HFD(ImposterTest, ThresholdFrequency(n));
    end

    %% ROC curve
    Thfd = 0 : 0.001 :0.5;
    sensitivity = [1, size(Thfd, 2)];
    specificity = [1, size(Thfd, 2)];
    for i = 1 : size(Thfd, 2)
        %% sensitivity and specificity
        true_positive = sum(ClientTestHFD >= Thfd(i));
        true_negative = sum(ImposterTestHFD < Thfd(i));
        false_positive = sum(ImposterTestHFD >= Thfd(i));
        false_negative = sum(ClientTestHFD < Thfd(i));
        sensitivity(i) = true_positive/(true_positive+false_negative);
        specificity(i) = true_negative/(false_positive+true_negative);
    end
    hold on
    plot(1-specificity, sensitivity, color(n))
end

================================================
FILE: Matlab/HFDTrain.m
================================================
clear all, close all, clc

%% read client and imposter training data name
ClientTrainNormalizedName = importdata('./NUAA/client_train_normalized.txt');
ClientTrainNormalizedNumber = size(ClientTrainNormalizedName, 1);
ImposterTrainNormalizedName = importdata('./NUAA/imposter_train_normalized.txt');
ImposterTrainNormalizedNumber = size(ImposterTrainNormalizedName, 1);
addpath('./libsvm-3.19/matlab');

figure;
color = 'brgymck';
ThresholdFrequency = [0, 10, 20, 50, 100, 200, 500];
for n = 1 : 7
    %% HFD of client training data
    ClientTrainHFD = zeros(1, ClientTrainNormalizedNumber);
    for i = 1 : ClientTrainNormalizedNumber
        ClientTrain = imread(['./NUAA/ClientNormalized/' ClientTrainNormalizedName{i}]);
        ClientTrainHFD(i) = HFD(ClientTrain, ThresholdFrequency(n));
    end

    %% HFD of imposter training data
    ImposterTrainHFD = zeros(1, ImposterTrainNormalizedNumber);
    for i = 1 : ImposterTrainNormalizedNumber
        ImposterTrain = imread(['./NUAA/ImposterNormalized/' ImposterTrainNormalizedName{i}]);  
        ImposterTrainHFD(i) = HFD(ImposterTrain, ThresholdFrequency(n));
    end

    %% ROC curve
    Thfd = 0 : 0.001 :0.5;
    sensitivity = [1, size(Thfd, 2)];
    specificity = [1, size(Thfd, 2)];
    for i = 1 : size(Thfd, 2)
        %% sensitivity and specificity
        true_positive = sum(ClientTrainHFD >= Thfd(i));
        true_negative = sum(ImposterTrainHFD < Thfd(i));
        false_positive = sum(ImposterTrainHFD >= Thfd(i));
        false_negative = sum(ClientTrainHFD < Thfd(i));
        sensitivity(i) = true_positive/(true_positive+false_negative);
        specificity(i) = true_negative/(false_positive+true_negative);
    end
    hold on
    plot(1-specificity, sensitivity, color(n))
end

================================================
FILE: Matlab/HOOF/README
================================================
This code has been provided only for research purposes. If you want to use this for commerical purposes, you will need an appropriate license. Contact the email address below. Although care has been taken to collect this code, we do not assume any liability for any errors or incomplete files. Moreover, we are not responsible for any loss, financial or physical, that  results from any use of whole or part of the provided code in any situation.

If you decide to use this code and report results in a publication, please refer to the publication:

R. Chaudhry, A. Ravichandran, G. Hager and R. Vidal
Histograms of Oriented Optical Flow and Binet-Cauchy Kernels on Nonlinear Dynamical Systems for the Recognition of Human Actions
CVPR 2009

(c) Rizwan Chaudhry - JHU Vision Lab

For questions and bug reports, contact Rizwan Chaudhry at rizwanch@cis.jhu.edu

Usage:

gradientHistogram.m 

  Extract HOOF features from a frame of optical flow

identifySestemUsingKPCA.m

  Use predefined kernels (in this case the ones provided for histograms) to compute the system parameters for the NLDS using KPCA.
  computeDistanceModularHistogram
    MDPA distance between two modular histograms.
  computeDistanceOrdinalHistogram
    MDPA distance between two ordinal (regular) histograms.
  chiSquareDist.m
    Chi-square distance between two histograms.
  histKernel.m
    Histogram intersection kernel

computeDistancesBetweenKPCASystems.m

  Compute several all-pair distances between NLDS identified using the appropriate kernels

  findSubspaceAnglesBetweenKPCASystems.m
    Compute subspace angles between NLDS to compute Martin distance
  traceKernelKPCASystems.m
    Compute the Binet-Cauchy trace kernel between NLDS



For faster implementation when the kernel used is the square-root representation, use PCA on the square-root representation and use regular metrics for LDS.

findSubspaceAnglesBetweenKPCASystemsSqrtHist.m
traceKernelKPCASystemsSqrtHist

================================================
FILE: Matlab/HOOF/README~
================================================
This code has been provided only for research purposes. If you want to use this for commerical purposes, you will need an appropriate license. Contact the email address below. Although care has been taken to collect this code, we do not assume any liability for any errors or incomplete files. Moreover, we are not responsible for any loss, financial or physical, that  results from any use of whole or part of the provided code in any situation.

If you decide to use this code and report results in a publication, please refer to the publication:

R. Chaudhry, A. Ravichandran, G. Hager and R. Vidal
Histograms of Oriented Optical Flow and Binet-Cauchy Kernels on Nonlinear Dynamical Systems for the Recognition of Human Actions
CVPR 2009

(c) Rizwan Chaudhry - JHU Vision Lab

Usage:

gradientHistogram.m 

  Extract HOOF features from a frame of optical flow

identifySestemUsingKPCA.m

  Use predefined kernels (in this case the ones provided for histograms) to compute the system parameters for the NLDS using KPCA.
  computeDistanceModularHistogram
    MDPA distance between two modular histograms.
  computeDistanceOrdinalHistogram
    MDPA distance between two ordinal (regular) histograms.
  chiSquareDist.m
    Chi-square distance between two histograms.
  histKernel.m
    Histogram intersection kernel

computeDistancesBetweenKPCASystems.m

  Compute several all-pair distances between NLDS identified using the appropriate kernels

  findSubspaceAnglesBetweenKPCASystems.m
    Compute subspace angles between NLDS to compute Martin distance
  traceKernelKPCASystems.m
    Compute the Binet-Cauchy trace kernel between NLDS



For faster implementation when the kernel used is the square-root representation, use PCA on the square-root representation and use regular metrics for LDS.

findSubspaceAnglesBetweenKPCASystemsSqrtHist.m
traceKernelKPCASystemsSqrtHist

================================================
FILE: Matlab/HOOF/chiSquareDist.m
================================================
function distance = chiSquareDist(A)

% Caclulate Chi Squared for a vector of Histograms


L = A;
t = size(L,2);

distance = zeros(t,t);

for i=1:t
   temp = repmat(L(:,i),1,t);
   G = (temp+L>0);
   distance(i,:) = sum(G.*[(temp - L).^2]./ (G.*[temp+L]+not(G)),1) ;
end

================================================
FILE: Matlab/HOOF/computeDistanceModularHistogram.m
================================================
function dist = computeDistanceModularHistogram(hist1, hist2)

% dist = computeDistanceModularHistogram(hist1, hist2)
%
% Computes the distance between two modular histograms. Modular histograms are histograms formed from 
% modular data such as angles etc
%
% Implementation of the MDPA (Minimum Distance of Pair Assignments) algorithm in
% S. -H. Cha, S.N. Srihari, On measuring the distance between histograms, Pattern Recognition, 35 (2002) 1355-1370
%
% (c) Rizwan Chaudhry - JHU Vision Lab

% Assuming both histograms have the same number of bins

histDiff = hist1-hist2;
prefixsum = cumsum(histDiff);
h_dist = sum(abs(prefixsum));

dist = h_dist;

dist_increased = 0;

while dist_increased == 0
    d = min(prefixsum(prefixsum > 0));
    if isempty(d) == 1
        break;
    end
    tempprefixsum = prefixsum - repmat(d,size(prefixsum));
    h_dist2 = sum(abs(tempprefixsum));
    if h_dist2 < h_dist
        h_dist = h_dist2;
        prefixsum = tempprefixsum;
    else
        dist_increased = 1;
    end
end
dist_increased = 0;
while dist_increased == 0
    d = max(prefixsum(prefixsum < 0));
    if isempty(d) == 1
        break;
    end
    tempprefixsum = prefixsum - repmat(d,size(prefixsum));
    h_dist2 = sum(abs(tempprefixsum));
    if h_dist2 < h_dist
        h_dist = h_dist2;
        prefixsum = tempprefixsum;
    else
        dist_increased = 1;
    end
end

dist = h_dist;

================================================
FILE: Matlab/HOOF/computeDistanceOrdinalHistogram.m
================================================
function dist = computeDistanceOrdinalHistogram(hist1, hist2)
% dist = computeDistanceOrdinalHistogram(hist1, hist2)
%
% Computes the distance between two ordinal histograms. Ordinal histograms are histograms formed from 
% ordinal data such as linear intervals etc
%
% Implementation of the MDPA (Minimum Distance of Pair Assignments) algorithm in
% S. -H. Cha, S.N. Srihari, On measuring the distance between histograms, Pattern Recognition, 35 (2002) 1355-1370
%
% (c) Rizwan Chaudhry - JHU Vision Lab

% Assuming both histograms have the same number of bins

histDiff = hist1-hist2;
prefixsum = cumsum(histDiff);
h_dist = sum(abs(prefixsum));

dist = h_dist;

================================================
FILE: Matlab/HOOF/computeDistancesBetweenKPCASystems.m
================================================
function distances = computeDistancesBetweenKPCASystems(sysParams,metric,kernel)

% function distances =
% computeDistancesBetweenKPCASystems(sysParams,metric,kernel)
%
% Computes the required kernel distance
%
% (c) Rizwan Chaudhry - JHU Vision Lab
%

kernelName{1} = 'Geodesic';
kernelName{2} = 'MDPA-ordinal';
kernelName{3} = 'ChiSquare';
kernelName{4} = 'HIST';

metricName{1} = 'Martin';
metricName{2} = 'BC-trace';
metricName{3} = 'BC-init-ind';

lambda = 0.9; % Parameter for Binet Cauchy kernels

% Compute all pairwise sequence kernels
%disp('Computing all pairwise sequence kernels');
for ind_i = 1:length(sysParams)
    for ind_j = 1:ind_i
        Y_12 = double([sysParams{ind_i}.Yoriginal, sysParams{ind_j}.Yoriginal]);
        % Evaluate the kernel on these vectors;
        kernel_12 = zeros(size(Y_12,2),'double');

        % Evaluate the kernel on these vectors;

        if strcmp(kernelName{kernel}, 'Geodesic')
            Y_12 = sqrt(Y_12);
            kernel_12 = Y_12'*Y_12;
        elseif strcmp(kernelName{kernel}, 'MDPA-ordinal')
            for i=1:size(Y_12,2)
               for j =1:i-1
                   kernel_12(i,j) = computeDistanceOrdinalHistogram(Y_12(:,i),Y_12(:,j));  % MDPA
               end
            end
            kernel_12 = kernel_12 + kernel_12';
            kernel_12 = exp(-(kernel_12.^2));
        elseif strcmp(kernelName{kernel}, 'ChiSquare')
            kernel_12 = exp(-(chiSquareDist(Y_12).^2));
        elseif strcmp(kernelName{kernel}, 'HIST')
            for i=1:size(Y_12,2)
                for j = 1:i
                    kernel_12(i,j) = histogramKernel(Y_12(:,i),Y_12(:,j));
                end
            end
            kdiag = diag(kernel_12);
            kernel_12 = kernel_12 + kernel_12' - diag(kdiag);
        end
        
        allPairWiseKernels{ind_i,ind_j} = kernel_12;
        
    end
end

distances = zeros(length(sysParams),length(sysParams),length(metric));

for m = 1:length(metric)
   if strcmp(metricName{metric(m)},'Martin') == 1
       
       %disp('Martin distance');
       
       for ind_i = 1:length(sysParams)
           for ind_j = 1:ind_i
               sysParams1 = sysParams{ind_i};
               sysParams2 = sysParams{ind_j};
               kernel_12 = allPairWiseKernels{ind_i,ind_j};
       
                N1 = size(sysParams1.Yoriginal,2);
                N2 = size(sysParams2.Yoriginal,2);

                order = min(sysParams1.order,sysParams2.order);
                
%                 alphaPrime1 = sysParams1.alpha;
%                 alphaPrime2 = sysParams2.alpha;
                alphaPrime1 = sysParams1.alpha-1/N1*(repmat(sum(sysParams1.alpha,1),size(sysParams1.alpha,1),1));
                alphaPrime2 = sysParams2.alpha-1/N2*(repmat(sum(sysParams2.alpha,1),size(sysParams2.alpha,1),1));

                F_11 = alphaPrime1'*kernel_12(1:N1,1:N1)*alphaPrime1;
                F_12 = alphaPrime1'*kernel_12(1:N1,N1+1:end)*alphaPrime2;
                F_21 = alphaPrime2'*kernel_12(N1+1:end,1:N1)*alphaPrime1;
                F_22 = alphaPrime2'*kernel_12(N1+1:end,N1+1:end)*alphaPrime2;
%                 
%                 M_11 = dlyap(sysParams1.A',sysParams1.A,F_11);
%                 M_12 = dlyap(sysParams1.A',sysParams2.A,F_12);
%                 M_21 = dlyap(sysParams2.A',sysParams1.A,F_21);
%                 M_22 = dlyap(sysParams2.A',sysParams2.A,F_22);
% 
%                 evals = eig(pinv(M_11)*M_12*pinv(M_22)*M_21);
%
%                 angles = real(acos(sqrt(evals(1:order))));
                

                Ft = [F_11,F_12;F_21,F_22];                
                At = [sysParams1.A,zeros(order);zeros(order),sysParams2.A];                
                
                M = dlyap(At',Ft);
                evals = eig([zeros(order) pinv(M(1:order,1:order))*M(1:order,order+1:2*order);...
                    pinv(M(order+1:2*order,order+1:2*order))*M(order+1:2*order,1:order) zeros(order)]);
                
                evals = real(evals);
                evals = max(-ones(size(evals)),evals);
                evals = min(ones(size(evals)),evals);
                evals = sort(evals,'descend');
                
                angles = real(acos(evals(1:order)));
                
                distances(ind_i,ind_j,m) = sqrt(-sum(log((cos(angles)).^2)));
                
                distances(ind_j,ind_i,m) = distances(ind_i,ind_j,m);
           end
       end
       
       
       
       
   elseif strcmp(metricName{metric(m)},'BC-trace') == 1
       
       %disp('Binet Cauchy Kernel');       
    
        for ind_i = 1:length(sysParams)
           for ind_j = 1:ind_i
               sysParams1 = sysParams{ind_i};
               sysParams2 = sysParams{ind_j};
               kernel_12 = allPairWiseKernels{ind_i,ind_j};

               N1 = size(sysParams1.Yoriginal,2);
               N2 = size(sysParams2.Yoriginal,2);

               alphaPrime1 = sysParams1.alpha-1/N1*(repmat(sum(sysParams1.alpha,1),size(sysParams1.alpha,1),1));
               alphaPrime2 = sysParams2.alpha-1/N2*(repmat(sum(sysParams2.alpha,1),size(sysParams2.alpha,1),1));

               F_12 = alphaPrime1'*kernel_12(1:N1,N1+1:end)*alphaPrime2;

               M_12 = dlyap(lambda*sysParams1.A',sysParams2.A,F_12);
               M_12 = real(M_12);

               % Check for numerical errors due to very small eigenvalues
               % of Q
%                [V1,D1] = eig(sysParams1.Q);
%                if any(diag(D1) <= 0)
%                    diag1 = diag(D1);
%                    diag1(diag1 <= 0) = min(abs(diag1));
%                    D1 = diag(diag1);
%                    sysParams1.Q = V1*D1*V1';
%                end
%                [V2,D2] = eig(sysParams2.Q);
%                if any(diag(D2) <= 0)                   
%                    diag2 = diag(D2);
%                    diag2(diag2 <= 0) = min(abs(diag2));
%                    D2 = diag(diag2);
%                    sysParams2.Q = V2*D2*V2';
%                end

               % Regularize Qs with 1
               sysParams1.Q = sysParams1.Q + eps*eye(size(sysParams1.Q));
               sysParams2.Q = sysParams2.Q + eps*eye(size(sysParams2.Q));               
               
               B1 = real(chol(sysParams1.Q))';
               B2 = real(chol(sysParams2.Q))';

               kernel = sysParams1.X(:,1)'*M_12*sysParams2.X(:,1) + lambda/(1-lambda)*trace(B1'*M_12*B2);

               distances(ind_i,ind_j,m) = kernel;
               distances(ind_j,ind_i,m) = kernel;
           end
        end

   elseif strcmp(metricName{metric(m)},'BC-init-ind') == 1
       
       %disp('Initial state independent Binet Cauchy Kernel');       

        for ind_i = 1:length(sysParams)
           for ind_j = 1:ind_i
               sysParams1 = sysParams{ind_i};
               sysParams2 = sysParams{ind_j};
               kernel_12 = allPairWiseKernels{ind_i,ind_j};

               N1 = size(sysParams1.Yoriginal,2);
               
               N2 = size(sysParams2.Yoriginal,2);

               alphaPrime1 = sysParams1.alpha-1/N1*(repmat(sum(sysParams1.alpha,1),size(sysParams1.alpha,1),1));
               alphaPrime2 = sysParams2.alpha-1/N2*(repmat(sum(sysParams2.alpha,1),size(sysParams2.alpha,1),1));

               F_12 = alphaPrime1'*kernel_12(1:N1,N1+1:end)*alphaPrime2;

               M_12 = dlyap(lambda*sysParams1.A',sysParams2.A,F_12);
               M_12 = real(M_12);
               
               kernel = max(svd(M_12));

               distances(ind_i,ind_j,m) = kernel;
               distances(ind_j,ind_i,m) = kernel;
           end
        end
        
   end
   
end

================================================
FILE: Matlab/HOOF/findSubspaceAnglesBetweenKPCASystems.m
================================================
function angles = findSubspaceAnglesBetweenKPCASystems(sysParams1, sysParams2)

% angles = findSubspaceAnglesBetweenKPCASystems(sysParams1,sysParams2)
%
% Finds subspace angles between two systems identified using KPCA
%
% (c) Rizwan Chaudhry - JHU Vision Lab

Y_12 = [sysParams1.Yoriginal, sysParams2.Yoriginal];

% Evaluate the kernel on these vectors;
kernel_12 = zeros(size(Y_12,2));

for i=1:length(Y_12)
   for j =1:i-1
      % Choose appropriate kernel here
       kernel_12(i,j) = computeDistanceModularHistogram(Y_12(:,i),Y_12(:,j));  % MDPA
   end
end

kernel_12 = kernel_12 + kernel_12';

kernel_12 = exp(-kernel_12.^2);

N1 = size(sysParams1.Yoriginal,2);
N2 = size(sysParams2.Yoriginal,2);

e1 = ones(1,N1)';
e2 = ones(1,N2)';

alphaPrime1 = sysParams1.alpha-1/N1*(repmat(sum(sysParams1.alpha,1),size(sysParams1.alpha,1),1));
alphaPrime2 = sysParams2.alpha-1/N2*(repmat(sum(sysParams2.alpha,1),size(sysParams2.alpha,1),1));

F_11 = alphaPrime1'*kernel_12(1:N1,1:N1)*alphaPrime1;
F_12 = alphaPrime1'*kernel_12(1:N1,N1+1:end)*alphaPrime2;
F_21 = alphaPrime2'*kernel_12(N1+1:end,1:N1)*alphaPrime1;
F_22 = alphaPrime2'*kernel_12(N1+1:end,N1+1:end)*alphaPrime2;

M_11 = dlyap(sysParams1.A',sysParams1.A,F_11);
M_12 = dlyap(sysParams1.A',sysParams2.A,F_12);
M_21 = dlyap(sysParams2.A',sysParams1.A,F_21);
M_22 = dlyap(sysParams2.A',sysParams2.A,F_22);

lambda = eig(pinv(M_11)*M_12*pinv(M_22)*M_21);
lambda = real(lambda);
lambda = max(zeros(size(lambda)),lambda);
lambda = min(ones(size(lambda)),lambda);
lambda = sort(lambda,'descend');

lambda = sqrt(lambda);

order = min(sysParams1.order,sysParams2.order);

angles = real(acos(lambda(1:order)));

================================================
FILE: Matlab/HOOF/findSubspaceAnglesBetweenKPCASystemsSqrtHist.asv
================================================
function angles = findSubspaceAnglesBetweenKPCASystemsSqrtHist(sysParams1, sysParams2)

% angles = findSubspaceAnglesBetweenKPCASystems(sysParams1,sysParams2)
%
% Finds subspace angles between two systems identified using KPCA
%
% (c) Rizwan Chaudhry - JHU Vision Lab

Y_12 = [sysParams1.Yoriginal, sysParams2.Yoriginal];

% Evaluate the kernel on these vectors;
Y_12 = sqrt(Y_12);
kernel_12 = Y_12'*Y_12;

N1 = size(sysParams1.Yoriginal,2);
N2 = size(sysParams2.Yoriginal,2);

e1 = ones(1,N1)';
e2 = ones(1,N2)';

alphaPrime1 = sysParams1.alpha-1/N1*(repmat(sum(sysParams1.alpha,1),size(sysParams1.alpha,1),1));
alphaPrime2 = sysParams2.alpha-1/N2*(repmat(sum(sysParams2.alpha,1),size(sysParams2.alpha,1),1));

F_11 = alphaPrime1'*kernel_12(1:N1,1:N1)*alphaPrime1;
F_12 = alphaPrime1'*kernel_12(1:N1,N1+1:end)*alphaPrime2;
F_21 = alphaPrime2'*kernel_12(N1+1:end,1:N1)*alphaPrime1;
F_22 = alphaPrime2'*kernel_12(N1+1:end,N1+1:end)*alphaPrime2;

M_11 = dlyap(sysParams1.A',sysParams1.A',F_11);
M_12 = dlyap(sysParams1.A',sysParams2.A',F_12);
M_21 = dlyap(sysParams2.A',sysParams1.A',F_21);
M_22 = dlyap(sysParams2.A',sysParams2.A',F_22);

lambda = eig(pinv(M_11)*M_12*pinv(M_22)*M_21);
lambda = real(lambda);
lambda = max(zeros(size(lambda)),lambda);
lambda = min(ones(size(lambda)),lambda);
lambda = sort(lambda,'descend');

lambda = sqrt(lambda);

order = min(sysParams1.order,sysParams2.order);

angles = real(acos(lambda(1:order)));

================================================
FILE: Matlab/HOOF/findSubspaceAnglesBetweenKPCASystemsSqrtHist.m
================================================
function angles = findSubspaceAnglesBetweenKPCASystemsSqrtHist(sysParams1, sysParams2)

% angles = findSubspaceAnglesBetweenKPCASystems(sysParams1,sysParams2)
%
% Finds subspace angles between two systems identified using KPCA
%
% (c) Rizwan Chaudhry - JHU Vision Lab

Y_12 = [sysParams1.Yoriginal, sysParams2.Yoriginal];

% Evaluate the kernel on these vectors;
Y_12 = sqrt(Y_12);
kernel_12 = Y_12'*Y_12;

N1 = size(sysParams1.Yoriginal,2);
N2 = size(sysParams2.Yoriginal,2);

e1 = ones(1,N1)';
e2 = ones(1,N2)';

alphaPrime1 = sysParams1.alpha-1/N1*(repmat(sum(sysParams1.alpha,1),size(sysParams1.alpha,1),1));
alphaPrime2 = sysParams2.alpha-1/N2*(repmat(sum(sysParams2.alpha,1),size(sysParams2.alpha,1),1));

F_11 = alphaPrime1'*kernel_12(1:N1,1:N1)*alphaPrime1;
F_12 = alphaPrime1'*kernel_12(1:N1,N1+1:end)*alphaPrime2;
F_21 = alphaPrime2'*kernel_12(N1+1:end,1:N1)*alphaPrime1;
F_22 = alphaPrime2'*kernel_12(N1+1:end,N1+1:end)*alphaPrime2;

M_11 = dlyap(sysParams1.A',sysParams1.A,F_11);
M_12 = dlyap(sysParams1.A',sysParams2.A,F_12);
M_21 = dlyap(sysParams2.A',sysParams1.A,F_21);
M_22 = dlyap(sysParams2.A',sysParams2.A,F_22);

lambda = eig(pinv(M_11)*M_12*pinv(M_22)*M_21);
lambda = real(lambda);
lambda = max(zeros(size(lambda)),lambda);
lambda = min(ones(size(lambda)),lambda);
lambda = sort(lambda,'descend');

lambda = sqrt(lambda);

order = min(sysParams1.order,sysParams2.order);

angles = real(acos(lambda(1:order)));

================================================
FILE: Matlab/HOOF/gradientHistogram.m
================================================
function ohog = gradientHistogram(Fx,Fy,binSize)
% Compute HOOF feature
% INPUTS
%   Fx      - X-flow 
%   Fy	    - Y-flow
%   binSize - number of bins used
%
% OUTPUTS
%   ohog    - output histogram of oriented optical flow
%
% EXAMPLE
%

%% Written by  : Rizwan Chaudhry and Avinash Ravichandran
%% $DATE       : 28-Aug-2008 11:00:58 $
%% $Revision   : 1.00 $
%% Matlab      : 7.4.0.287 (R2007a)
%% FILENAME    : gradientHistogram.m
%
% (c) Rizwan Chaudhry, Avinash Ravichandran - JHU Vision Lab

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%gradientHistogram.m
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


magnitudeImage   = (Fx.^2 + Fy.^2 ).^0.5;
orientationImage =  atan2(Fy,Fx);

greaterPiBy2Index = orientationImage > pi/2;
smallerMinusPiBy2Index = orientationImage < -pi/2;
remainingIndex = orientationImage <=pi/2 & orientationImage >= -pi/2;

greaterPiBy2Mat = greaterPiBy2Index.*orientationImage;
smallerMinusPiBy2Mat = smallerMinusPiBy2Index.*orientationImage;
remainingMat = remainingIndex.*orientationImage;

piMat = pi*ones(size(orientationImage));

convertGreaterPiBy2Mat = greaterPiBy2Index.*piMat - greaterPiBy2Mat;
convertSmallerMinusPiBy2Mat = smallerMinusPiBy2Index.*(-piMat) - smallerMinusPiBy2Mat;

newOrientationImage = convertGreaterPiBy2Mat + remainingMat + convertSmallerMinusPiBy2Mat;


% [hog,idx] =
% histc(reshape(orientationImage,1,[]),linspace(-pi,pi,binSize+1) );
[hog,idx] = histc(reshape(newOrientationImage,1,[]),linspace(-pi/2,pi/2,binSize+1) );
values = reshape(magnitudeImage,1,[]);

for k=1:binSize
    bin(k) = sum(values(find(idx==k)));
end

ohog = bin/sum(bin);

ohog = ohog';


================================================
FILE: Matlab/HOOF/histogramKernel.m
================================================
function [ output ] = histogramKernel( hist1, hist2 )
%HISTOGRAMKERNEL find the 'histogram kernel' between time series of
%histograms
%  This test code implements a so-called histogram intersection kernel for
%  histogram time series data by extending the original histogram kernel.
%
% Parameters:
%       hist1:  first normalized to 1 histogram of size d1 bins x N1 samples
%       hist2:  second normalized to 1 histogram of size d2 bins x N2 samples
% Outputs:
%       output: the value of the histogram kernel between two histogram
%       time series
%
% (c) Rizwan Chaudhry - JHU Vision Lab

[d1, N1] = size(hist1);
[d2, N2] = size(hist2);

if d1 ~= d2
    error('Number of bins in the histograms must be the same');
end

% TODO: check for normalization

N = min(N1,N2);

output = sum(sum(min(hist1(:,1:N), hist2(:,1:N)),1))/N;

================================================
FILE: Matlab/HOOF/identifySystemUsingKPCA.m
================================================
function sysParams = identifySystemUsingKPCA(Yoriginal,kernelMatrix, order)

% sysParams = identifySystemUsingKPCA(Yoriginal, kernelMatrix, order)
%
% Takes as input the kernel computed on the output trajectory of a sequence
% and computes the relevant system parameters as mentioned in A.B.Chan CVPR
% 07
% (c) Rizwan Chaudhry - JHU Vision Lab

K = double(kernelMatrix);
N = size(kernelMatrix,1);

e = ones(1,N)';

KTilde = (eye(N)-e*e'/N)*K*(eye(N)-e*e'/N);

% remove any numerical inconsistencies by making KTilde symmetric

KTilde = (KTilde + KTilde')/2;

[V,D] = eig(KTilde);

d = real(diag(D))';

% Format data such that the eigen values and corresponding eigen vectors
% are listed in descending order

V = V(:,end:-1:1);
d = d(end:-1:1);

alpha = V./sqrt(repmat(d,size(V,1),1));

X = alpha'*KTilde;

X = X(1:order,:);
alpha = alpha(:,1:order);

A = X(:,2:end)*pinv(X(:,1:end-1));
V = zeros(order,N);
V(:,2:end) = X(:,2:end)-A*X(:,1:end-1);
Q = zeros(size(V,1),size(V,1));
for i=1:N-1
    Q = Q + V(:,i)*V(:,i)';
end
Q = 1/(N-1)*Q;

% Y - minimum norm reconstruction
% R

sysParams.A = A;
sysParams.alpha = alpha;
sysParams.X = X;
sysParams.K = K;
sysParams.KTilde = KTilde;
sysParams.Yoriginal = Yoriginal;
sysParams.Q = Q;
sysParams.order = order;

================================================
FILE: Matlab/HOOF/traceKernelKPCASystems.m
================================================
function kernel = traceKernelKPCASystems(sysParams1, sysParams2, lambda)

% (c) Rizwan Chaudhry - JHU Vision Lab

Y_12 = [sysParams1.Yoriginal, sysParams2.Yoriginal];

% Evaluate the kernel on these vectors;
kernel_12 = zeros(size(Y_12,2));

for i=1:length(Y_12)
   for j =1:i-1
       % Choose appropriate kernel here
       kernel_12(i,j) = computeDistanceModularHistogram(Y_12(:,i),Y_12(:,j));  % MDPA
   end
end

kernel_12 = kernel_12 + kernel_12';

kernel_12 = exp(-kernel_12.^2);

N1 = size(sysParams1.Yoriginal,2);
N2 = size(sysParams2.Yoriginal,2);

e1 = ones(1,N1)';
e2 = ones(1,N2)';

alphaPrime1 = sysParams1.alpha-1/N1*(repmat(sum(sysParams1.alpha,1),size(sysParams1.alpha,1),1));
alphaPrime2 = sysParams2.alpha-1/N2*(repmat(sum(sysParams2.alpha,1),size(sysParams2.alpha,1),1));

F_12 = alphaPrime1'*kernel_12(1:N1,N1+1:end)*alphaPrime2;

M_12 = dlyap(lambda*sysParams1.A',sysParams2.A,F_12);
M_12 = real(M_12);

B1 = real(chol(sysParams1.Q,'lower'));
B2 = real(chol(sysParams2.Q,'lower'));

kernel = sysParams1.X(:,1)'*M_12*sysParams2.X(:,1) + lambda/(1-lambda)*trace(B1'*M_12*B2);

================================================
FILE: Matlab/HOOF/traceKernelKPCASystemsSqrtHist.m
================================================
function kernel = traceKernelKPCASystemsSqrtHist(sysParams1, sysParams2, lambda)

% (c) Rizwan Chaudhry - JHU Vision Lab

Y_12 = [sysParams1.Yoriginal, sysParams2.Yoriginal];

% Evaluate the kernel on these vectors;
Y_12 = sqrt(Y_12);
kernel_12 = Y_12'*Y_12;

N1 = size(sysParams1.Yoriginal,2);
N2 = size(sysParams2.Yoriginal,2);

e1 = ones(1,N1)';
e2 = ones(1,N2)';

alphaPrime1 = sysParams1.alpha-1/N1*(repmat(sum(sysParams1.alpha,1),size(sysParams1.alpha,1),1));
alphaPrime2 = sysParams2.alpha-1/N2*(repmat(sum(sysParams2.alpha,1),size(sysParams2.alpha,1),1));

F_12 = alphaPrime1'*kernel_12(1:N1,N1+1:end)*alphaPrime2;

M_12 = dlyap(lambda*sysParams1.A',sysParams2.A,F_12);
M_12 = real(M_12);

B1 = real(chol(sysParams1.Q,'lower'));
B2 = real(chol(sysParams2.Q,'lower'));

kernel = sysParams1.X(:,1)'*M_12*sysParams2.X(:,1) + lambda/(1-lambda)*trace(B1'*M_12*B2);

================================================
FILE: Matlab/HOOF.m
================================================
function Feature = HOOF(OF, bins, blocks)
Feature = zeros(1, bins * blocks * blocks);
[h, w] = size(OF);
OF(isnan(OF)) = 0;
for iBlock = 1 : blocks
    for jBlock = 1 : blocks
        Feature(((iBlock - 1) * blocks + jBlock - 1) * bins + 1 : ((iBlock - 1) * blocks + jBlock - 1) * bins + bins) = ...
            gradientHistogram(...
            real(OF(round(1+h*(iBlock-1)/(blocks+1)):round(h*(iBlock+1)/(blocks+1)), round(1+w*(jBlock-1)/(blocks+1)):round(w*(jBlock+1)/(blocks+1)))), ...
            imag(OF(round(1+h*(iBlock-1)/(blocks+1)):round(h*(iBlock+1)/(blocks+1)), round(1+w*(jBlock-1)/(blocks+1)):round(w*(jBlock+1)/(blocks+1)))), ...
            bins)';
    end
end
Feature(isnan(Feature)) = 0;
end

================================================
FILE: Matlab/HSoptflow.m
================================================
function [us,vs] = HSoptflow(Xrgb,n)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Author: Gregory Power gregory.power@wpafb.af.mil
% This MATLAB code shows a Motion Estimation map created by
% using a Horn and Schunck motion estimation technique on two
% consecutive frames.  Input requires.
%     Xrgb(h,w,d,N) where X is a frame sequence of a certain
%                height(h), width (w), depth (d=3 for color), 
%                and number of frames (N). 
%     n= is the starting frame number which is less than N 
%     V= the output variable which is a 2D velocity array
%
% Sample Call: V=HSoptflow(X,3);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[h,w,d,N]=size(Xrgb);
if n>N-1
   error(1,'requested file greater than frame number required');
end; 
%get two image frames
if d==1
    Xn=double(Xrgb(:,:,1,n));
    Xnp1=double(Xrgb(:,:,1,n+1));
elseif d==3
    Xn=double(Xrgb(:,:,1,n)*0.299+Xrgb(:,:,2,n)*0.587+Xrgb(:,:,3,n)*0.114);
    Xnp1=double(Xrgb(:,:,1,n+1)*0.299+Xrgb(:,:,2,n+1)*0.587+Xrgb(:,:,3,n+1)*0.114);
else
    error(2,'not an RGB or Monochrome image file');
end;

%get image size and adjust for border
size(Xn);
hm5=h-5; wm5=w-5;
z=zeros(h,w); v1=z; v2=z;

%initialize
dsx2=v1; dsx1=v1; dst=v1;
alpha2=625;
imax=20;

%Calculate gradients
dst(5:hm5,5:wm5) = ( Xnp1(6:hm5+1,6:wm5+1)-Xn(6:hm5+1,6:wm5+1) + Xnp1(6:hm5+1,5:wm5)-Xn(6:hm5+1,5:wm5)+ Xnp1(5:hm5,6:wm5+1)-Xn(5:hm5,6:wm5+1) +Xnp1(5:hm5,5:wm5)-Xn(5:hm5,5:wm5))/4;
dsx2(5:hm5,5:wm5) = ( Xnp1(6:hm5+1,6:wm5+1)-Xnp1(5:hm5,6:wm5+1) + Xnp1(6:hm5+1,5:wm5)-Xnp1(5:hm5,5:wm5)+ Xn(6:hm5+1,6:wm5+1)-Xn(5:hm5,6:wm5+1) +Xn(6:hm5+1,5:wm5)-Xn(5:hm5,5:wm5))/4;
dsx1(5:hm5,5:wm5) = ( Xnp1(6:hm5+1,6:wm5+1)-Xnp1(6:hm5+1,5:wm5) + Xnp1(5:hm5,6:wm5+1)-Xnp1(5:hm5,5:wm5)+ Xn(6:hm5+1,6:wm5+1)-Xn(6:hm5+1,5:wm5) +Xn(5:hm5,6:wm5+1)-Xn(5:hm5,5:wm5))/4;

for i=1:imax
   delta=(dsx1.*v1+dsx2.*v2+dst)./(alpha2+dsx1.^2+dsx2.^2);
   v1=v1-dsx1.*delta;
   v2=v2-dsx2.*delta;
end;
u=z; u(5:hm5,5:wm5)=v1(5:hm5,5:wm5);
v=z; v(5:hm5,5:wm5)=v2(5:hm5,5:wm5);

xskip=round(h/64);
[hs,ws]=size(u(1:xskip:h,1:xskip:w));
us=zeros(hs,ws); vs=us;

N=xskip^2;
for i=1:hs-1
  for j=1:ws-1
     hk=i*xskip-xskip+1;
     hl=i*xskip;
     wk=j*xskip-xskip+1;
     wl=j*xskip;
     us(i,j)=sum(sum(u(hk:hl,wk:wl)))/N;
     vs(i,j)=sum(sum(v(hk:hl,wk:wl)))/N;
   end;
end;

figure(1);
quiver(us,vs);
colormap('default');
axis ij;
axis tight;
axis equal;

end

================================================
FILE: Matlab/LBP_CASIA_Test.m
================================================
% clear all, close all, clc
% 
% %% read client and imposter testing data name
% addpath('./libsvm-3.19/matlab');
% addpath('./lbp-0.3.3');
% Map_u2_16 = getmapping(16, 'u2');
% Map_u2_8 = getmapping(8, 'u2');
% load LBP_SVM_CASIA.mat
% 
% %% extract feature
% detector = vision.CascadeObjectDetector('MinSize', [100,100]);
% flagSet = cell(30, 8);
% for IdxSubject = 1 : 30
%     for IdxData = 1 : 8
%         if IdxData <= 8
%             Name = ['./CASIA/test_release/' num2str(IdxSubject) '/' num2str(IdxData) '.avi'];
%         else
%             Name = ['./CASIA/test_release/' num2str(IdxSubject) '/HR_' num2str(IdxData-8) '.avi'];
%         end
%         Mov = VideoReader(Name);
%         NumFrame = Mov.NumberOfFrames;
%         flag = [];
%         for IdxFrame = 1 : NumFrame
%             data = read(Mov, IdxFrame);
%             box = step(detector, data);
%             if size(box, 1) == 1
%                 frame_now = rgb2gray(imresize(data(box(2):box(2)+box(4), box(1):box(1)+box(3), :), [64,64]));
%                 TestFeature = LBP_feature(frame_now, Map_u2_16, Map_u2_8);
%                 TestFeature = (TestFeature-MinMax(1,:))./(MinMax(2,:)-MinMax(1,:));
%                 [TestLabel, TestAccuracy, TestValue] = svmpredict(1, TestFeature, model, '-q');
%                 flag = [flag,TestValue];
%             end
%         end
%         flagSet{IdxSubject, IdxData} = flag;
%         disp([num2str(IdxSubject) ', ' num2str(IdxData)])
%         clear Mov;
%     end
% end
% save LBP_SVM_CASIA_testfeature.mat flagSet

%% testing
load LBP_SVM_CASIA_testfeature.mat flagSet        
DataCorrectRate = zeros(3, 601);
CorrectRate = zeros(3, 601);
for Bias = -3:0.01:3;
    CorrectNoReal = 0;
    CorrectNoFake = 0;
    TotalNoReal = 0;
    TotalNoFake = 0;
    DataCorrectNoReal = 0;
    DataCorrectNoFake = 0;
    for IdxSubject = 1 : 30
        for IdxData = 1 : 8
            if IdxData <= 2
                CorrectNoReal = CorrectNoReal + sum(flagSet{IdxSubject, IdxData}>=Bias);
                DataCorrectNoReal = DataCorrectNoReal + (mean(flagSet{IdxSubject, IdxData})>=Bias);
                TotalNoReal = TotalNoReal + size(flagSet{IdxSubject, IdxData}, 2);
            else
                CorrectNoFake = CorrectNoFake + sum(flagSet{IdxSubject, IdxData}<Bias);
                DataCorrectNoFake = DataCorrectNoFake + (mean(flagSet{IdxSubject, IdxData})<Bias);
                TotalNoFake = TotalNoFake + size(flagSet{IdxSubject, IdxData}, 2);
            end
        end
    end
    DataCorrectRate(:, round((Bias+3.01)*100)) = [(DataCorrectNoReal+DataCorrectNoFake) / 240; DataCorrectNoReal / 60; DataCorrectNoFake / 180];
    CorrectRate(:, round((Bias+3.01)*100)) = [(CorrectNoReal+CorrectNoFake) / (TotalNoReal+TotalNoFake); CorrectNoReal / TotalNoReal; CorrectNoFake / TotalNoFake];
end



================================================
FILE: Matlab/LBP_CASIA_Train.m
================================================
clear all, close all, clc

%% read client and imposter testing data name
addpath('./libsvm-3.19/matlab');
addpath('./lbp-0.3.3');
Map_u2_16 = getmapping(16, 'u2');
Map_u2_8 = getmapping(8, 'u2');

%% extract features
detector = vision.CascadeObjectDetector('MinSize', [100,100]);
TrainFeatureSet = cell(20, 8);
NumFrame = zeros(20, 8);
for IdxSubject = 1 : 20
    for IdxData = 1 : 8
        if IdxData <= 8
            Name = ['./CASIA/train_release/' num2str(IdxSubject) '/' num2str(IdxData) '.avi'];
        else
            Name = ['./CASIA/train_release/' num2str(IdxSubject) '/HR_' num2str(IdxData-8) '.avi'];
        end
        Mov = VideoReader(Name);
        NumFrame(IdxSubject, IdxData) = Mov.NumberOfFrames;
        TrainFeature = [];
        for IdxFrame = 1 : NumFrame(IdxSubject, IdxData)
            data = read(Mov, IdxFrame);
            box = step(detector, data);
            if size(box, 1) == 1
                frame_now = rgb2gray(imresize(data(box(2):box(2)+box(4), box(1):box(1)+box(3), :), [64,64]));
                Feature = LBP_feature(frame_now, Map_u2_16, Map_u2_8);
                TrainFeature = [TrainFeature;Feature];
            end
        end
        TrainFeatureSet{IdxSubject, IdxData} = TrainFeature;
        disp([num2str(IdxSubject) ', ' num2str(IdxData)])
        clear Mov;
    end
end
save LBP_SVM_CASIA_raw.mat TrainFeatureSet

%% training
load LBP_SVM_CASIA_raw.mat
TrainFeatureAll = [];
TrainTruth = [];
for IdxSubject = 1 : 20
    for IdxData = 1 : 8
        TrainFeatureAll = [TrainFeatureAll;TrainFeatureSet{IdxSubject,IdxData}];
        if IdxData <= 2
            TrainTruth = [TrainTruth;ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        else
            TrainTruth = [TrainTruth;-ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        end
    end
    disp(num2str(IdxSubject));
end
length = size(TrainTruth, 1);
MinMax = minmax(TrainFeatureAll')';
TrainFeatureAll = (TrainFeatureAll-kron(MinMax(1,:),ones(length,1)))./(kron(MinMax(2,:),ones(length,1))-kron(MinMax(1,:),ones(length,1)));
model = svmtrain(TrainTruth, TrainFeatureAll, '-t 0');
[TrainLabel, TrainAccuracy, TrainValue] = svmpredict(ones(length,1), TrainFeatureAll, model);
save LBP_SVM_CASIA.mat model MinMax

================================================
FILE: Matlab/LBP_NUAA_Test.m
================================================
clear all, close all, clc

%% read client and imposter testing data name
ClientTestNormalizedName = importdata('./NUAA/client_test_normalized.txt');
ClientTestNormalizedNumber = size(ClientTestNormalizedName, 1);
ImposterTestNormalizedName = importdata('./NUAA/imposter_test_normalized.txt');
ImposterTestNormalizedNumber = size(ImposterTestNormalizedName, 1);
addpath('./libsvm-3.19/matlab');
addpath('./lbp-0.3.3');
load LBP_SVM.mat

%% mapping
Map_u2_16 = getmapping(16, 'u2');
Map_u2_8 = getmapping(8, 'u2');

%% LBP feature of client testing data
ClientTestFeature = zeros(ClientTestNormalizedNumber, 833);
for i = 1 : ClientTestNormalizedNumber
    ClientTest = imread(['./NUAA/ClientNormalized/' ClientTestNormalizedName{i}]);
    ClientTestFeature(i, :) = LBP_feature(ClientTest, Map_u2_16, Map_u2_8);
    ClientTestFeature(i, :) = (ClientTestFeature(i, :)-MinMax(1,:))./(MinMax(2,:)-MinMax(1,:));
end

%% LBP feature of imposter testing data
ImposterTestFeature = zeros(ImposterTestNormalizedNumber, 833);
for i = 1 : ImposterTestNormalizedNumber
    ImposterTest = imread(['./NUAA/ImposterNormalized/' ImposterTestNormalizedName{i}]);  
    ImposterTestFeature(i, :) = LBP_feature(ImposterTest, Map_u2_16, Map_u2_8);
    ImposterTestFeature(i, :) = (ImposterTestFeature(i, :)-MinMax(1,:))./(MinMax(2,:)-MinMax(1,:));
end

%% SVM testing
[ClientTestLabel, ClientTestAccuracy, ClientTestValue] = svmpredict(ones(ClientTestNormalizedNumber,1), ClientTestFeature, model);
[ImposterTestLabel, ImposterTestAccuracy, ImposterTestValue] = svmpredict(-ones(ImposterTestNormalizedNumber,1), ImposterTestFeature, model);
accuracy = zeros(601, 3);
for i=-3:0.01:3
    accuracy(round(i*100+301),:)=[mean(ClientTestValue>=i),mean(ImposterTestValue<i),(sum(ClientTestValue>=i)+sum(ImposterTestValue<i))/(3362+5761)];
end

================================================
FILE: Matlab/LBP_NUAA_Train.m
================================================
clear all, close all, clc

%% read client and imposter training data name
ClientTrainNormalizedName = importdata('./NUAA/client_train_normalized.txt');
ClientTrainNormalizedNumber = size(ClientTrainNormalizedName, 1);
ImposterTrainNormalizedName = importdata('./NUAA/imposter_train_normalized.txt');
ImposterTrainNormalizedNumber = size(ImposterTrainNormalizedName, 1);
addpath('./libsvm-3.19/matlab');
addpath('./lbp-0.3.3');

%% mapping
Map_u2_16 = getmapping(16, 'u2');
Map_u2_8 = getmapping(8, 'u2');

%% LBP feature of client training data
ClientTrainFeature = zeros(ClientTrainNormalizedNumber, 833);
for i = 1 : ClientTrainNormalizedNumber
    ClientTrain = imread(['./NUAA/ClientNormalized/' ClientTrainNormalizedName{i}]);
    ClientTrainFeature(i, :) = LBP_feature(ClientTrain, Map_u2_16, Map_u2_8);
end

%% LBP feature of imposter training data
ImposterTrainFeature = zeros(ImposterTrainNormalizedNumber, 833);
for i = 1 : ImposterTrainNormalizedNumber
    ImposterTrain = imread(['./NUAA/ImposterNormalized/' ImposterTrainNormalizedName{i}]);  
    ImposterTrainFeature(i, :) = LBP_feature(ImposterTrain, Map_u2_16, Map_u2_8);
end

%% SVM training
MinMax = minmax([ClientTrainFeature;ImposterTrainFeature]')';
ClientTrainFeature = (ClientTrainFeature-kron(MinMax(1,:),ones(ClientTrainNormalizedNumber,1)))./(kron(MinMax(2,:),ones(ClientTrainNormalizedNumber,1))-kron(MinMax(1,:),ones(ClientTrainNormalizedNumber,1)));
ImposterTrainFeature = (ImposterTrainFeature-kron(MinMax(1,:),ones(ImposterTrainNormalizedNumber,1)))./(kron(MinMax(2,:),ones(ImposterTrainNormalizedNumber,1))-kron(MinMax(1,:),ones(ImposterTrainNormalizedNumber,1)));
Truth = [ones(ClientTrainNormalizedNumber,1);-ones(ImposterTrainNormalizedNumber,1)];
model = svmtrain(Truth, [ClientTrainFeature;ImposterTrainFeature]);
[ClientTrainLabel, ClientTrainAccuracy, ClientTrainValue] = svmpredict(ones(ClientTrainNormalizedNumber,1), ClientTrainFeature, model);
[ImposterTrainLabel, ImposterTrainAccuracy, ImposterTrainValue] = svmpredict(-ones(ImposterTrainNormalizedNumber,1), ImposterTrainFeature, model);
save LBP_SVM.mat model MinMax

================================================
FILE: Matlab/LBP_PRINT_ATTACK_Test.m
================================================
% clear all, close all, clc
% 
% %% read client and imposter testing data name
% addpath('./libsvm-3.19/matlab');
% addpath('./lbp-0.3.3');
% Map_u2_16 = getmapping(16, 'u2');
% Map_u2_8 = getmapping(8, 'u2');
% load LBP_SVM_PRINT_ATTACK.mat
% 
% %% extract feature
% detector = vision.CascadeObjectDetector('MinSize', [50,50]);
% fileIndex = {'009', '011', '013', '014', '019', '020', '021', '023', '024', '026', '028', '031', '102', '104', '106', '107', '109', '112', '115', '117'};
% fileDirec = {'attack/fixed/attack_print_', 'attack/fixed/attack_print_', 'attack/hand/attack_print_', 'attack/hand/attack_print_', 'real/', 'real/', 'real/', 'real/'; ...
%     'highdef_photo_adverse', 'highdef_photo_controlled', 'highdef_photo_adverse', 'highdef_photo_controlled', 'webcam_authenticate_adverse_1', 'webcam_authenticate_adverse_2', 'webcam_authenticate_controlled_1', 'webcam_authenticate_controlled_2'};
% fileNo = size(fileIndex, 2);
% flagSet = cell(fileNo, 8);
% for IdxSubject = 1 : fileNo
%     for IdxData = 1 : 8
%         Name = ['./PRINT-ATTACK/test/' fileDirec{1, IdxData} 'client' fileIndex{IdxSubject} '_session01_' fileDirec{2, IdxData} '.mov'];
%         Mov = VideoReader(Name);
%         NumFrame = Mov.NumberOfFrames;
%         flag = [];
%         for IdxFrame = 1 : NumFrame
%             data = read(Mov, IdxFrame);
%             box = step(detector, data);
%             if size(box, 1) == 1
%                 frame_now = rgb2gray(imresize(data(box(2):box(2)+box(4), box(1):box(1)+box(3), :), [64,64]));
%                 TestFeature = LBP_feature(frame_now, Map_u2_16, Map_u2_8);
%                 TestFeature = (TestFeature-MinMax(1,:))./(MinMax(2,:)-MinMax(1,:));
%                 [TestLabel, TestAccuracy, TestValue] = svmpredict(1, TestFeature, model, '-q');
%                 flag = [flag,TestValue];
%             end
%         end
%         flagSet{IdxSubject, IdxData} = flag;
%         disp([num2str(IdxSubject) ', ' num2str(IdxData)])
%         clear Mov;
%     end
% end
% save LBP_SVM_PRINT_ATTACK_testfeature.mat flagSet

%% testing
load LBP_SVM_PRINT_ATTACK_testfeature.mat flagSet
DataCorrectRate = zeros(3, 601);
CorrectRate = zeros(3, 601);
for Bias = -3:0.01:3;
    CorrectNoReal = 0;
    CorrectNoFake = 0;
    TotalNoReal = 0;
    TotalNoFake = 0;
    DataCorrectNoReal = 0;
    DataCorrectNoFake = 0;
    for IdxSubject = 1 : 20
        for IdxData = 1 : 8
            if IdxData > 4
                CorrectNoReal = CorrectNoReal + sum(flagSet{IdxSubject, IdxData}>=Bias);
                DataCorrectNoReal = DataCorrectNoReal + (mean(flagSet{IdxSubject, IdxData})>=Bias);
                TotalNoReal = TotalNoReal + size(flagSet{IdxSubject, IdxData}, 2);
            else
                CorrectNoFake = CorrectNoFake + sum(flagSet{IdxSubject, IdxData}<Bias);
                DataCorrectNoFake = DataCorrectNoFake + (mean(flagSet{IdxSubject, IdxData})<Bias);
                TotalNoFake = TotalNoFake + size(flagSet{IdxSubject, IdxData}, 2);
            end
        end
    end
    DataCorrectRate(:, round((Bias+3.01)*100)) = [(DataCorrectNoReal+DataCorrectNoFake) / 160; DataCorrectNoReal / 80; DataCorrectNoFake / 80];
    CorrectRate(:, round((Bias+3.01)*100)) = [(CorrectNoReal+CorrectNoFake) / (TotalNoReal+TotalNoFake); CorrectNoReal / TotalNoReal; CorrectNoFake / TotalNoFake];
end

================================================
FILE: Matlab/LBP_PRINT_ATTACK_Train.m
================================================
clear all, close all, clc

%% read client and imposter testing data name
addpath('./libsvm-3.19/matlab');
addpath('./lbp-0.3.3');
Map_u2_16 = getmapping(16, 'u2');
Map_u2_8 = getmapping(8, 'u2');

%% extract feature
detector = vision.CascadeObjectDetector('MinSize', [50,50]);
fileIndex = {'001', '002', '004', '006', '007', '008', '012', '016', '018', '025', '027', '103', '105', '108', '110'};
fileDirec = {'attack/fixed/attack_print_', 'attack/fixed/attack_print_', 'attack/hand/attack_print_', 'attack/hand/attack_print_', 'real/', 'real/', 'real/', 'real/'; ...
    'highdef_photo_adverse', 'highdef_photo_controlled', 'highdef_photo_adverse', 'highdef_photo_controlled', 'webcam_authenticate_adverse_1', 'webcam_authenticate_adverse_2', 'webcam_authenticate_controlled_1', 'webcam_authenticate_controlled_2'};
fileNo = size(fileIndex, 2);
TrainFeatureSet = cell(fileNo, 8);
for IdxSubject = 1 : fileNo
    for IdxData = 1 : 8
        Name = ['./PRINT-ATTACK/train/' fileDirec{1, IdxData} 'client' fileIndex{IdxSubject} '_session01_' fileDirec{2, IdxData} '.mov'];
        Mov = VideoReader(Name);
        NumFrame = Mov.NumberOfFrames;
        TrainFeature = [];
        for IdxFrame = 1 : NumFrame
            data = read(Mov, IdxFrame);
            box = step(detector, data);
            if size(box, 1) == 1
                frame_now = rgb2gray(imresize(data(box(2):box(2)+box(4), box(1):box(1)+box(3), :), [64,64]));
                Feature = LBP_feature(frame_now, Map_u2_16, Map_u2_8);
                TrainFeature = [TrainFeature;Feature];
            end
        end
        TrainFeatureSet{IdxSubject, IdxData} = TrainFeature;
        disp([num2str(IdxSubject) ', ' num2str(IdxData)])
        clear Mov;
    end
end
save LBP_SVM_PRINT_ATTACK_raw.mat TrainFeatureSet

%% training
load LBP_SVM_PRINT_ATTACK_raw.mat
TrainFeatureAll = [];
TrainTruth = [];
for IdxSubject = 1 : 15
    for IdxData = 1 : 8
        TrainFeatureAll = [TrainFeatureAll;TrainFeatureSet{IdxSubject,IdxData}];
        if IdxData <= 4
            TrainTruth = [TrainTruth;-ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        else
            TrainTruth = [TrainTruth;ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        end
    end
    disp(num2str(IdxSubject));
end
length = size(TrainTruth, 1);
MinMax = minmax(TrainFeatureAll')';
TrainFeatureAll = (TrainFeatureAll-kron(MinMax(1,:),ones(length,1)))./(kron(MinMax(2,:),ones(length,1))-kron(MinMax(1,:),ones(length,1)));
model = svmtrain(TrainTruth, TrainFeatureAll, '-t 0');
[TrainLabel, TrainAccuracy, TrainValue] = svmpredict(ones(length,1), TrainFeatureAll, model);
save LBP_SVM_PRINT_ATTACK.mat model MinMax
        



================================================
FILE: Matlab/LBP_feature.m
================================================
function Y = LBP_feature(X, Map_u2_16, Map_u2_8)
%% histogram
Hist_u2_16_2=lbp(X, 2, 16, Map_u2_16,'h');
Hist_u2_8_2=lbp(X, 2, 8, Map_u2_8,'h');
Hist_u2_8_1_1=lbp(X(1:26,1:26), 1, 8, Map_u2_8,'h');   
Hist_u2_8_1_2=lbp(X(1:26,20:45), 1, 8, Map_u2_8,'h'); 
Hist_u2_8_1_3=lbp(X(1:26,39:64), 1, 8, Map_u2_8,'h'); 
Hist_u2_8_1_4=lbp(X(20:45,1:26), 1, 8, Map_u2_8,'h');   
Hist_u2_8_1_5=lbp(X(20:45,20:45), 1, 8, Map_u2_8,'h'); 
Hist_u2_8_1_6=lbp(X(20:45,39:64), 1, 8, Map_u2_8,'h'); 
Hist_u2_8_1_7=lbp(X(39:64,1:26), 1, 8, Map_u2_8,'h');   
Hist_u2_8_1_8=lbp(X(39:64,20:45), 1, 8, Map_u2_8,'h'); 
Hist_u2_8_1_9=lbp(X(39:64,39:64), 1, 8, Map_u2_8,'h'); 
Y = [Hist_u2_16_2, Hist_u2_8_2, Hist_u2_8_1_1, Hist_u2_8_1_2, Hist_u2_8_1_3, Hist_u2_8_1_4, Hist_u2_8_1_5, Hist_u2_8_1_6, Hist_u2_8_1_7, Hist_u2_8_1_8, Hist_u2_8_1_9];
end

================================================
FILE: Matlab/NUAA/client_test_normalized.txt
================================================
0010\0010_01_05_03_115.bmp
0010\0010_01_05_03_121.bmp
0010\0010_01_05_03_125.bmp
0010\0010_01_05_03_129.bmp
0010\0010_01_05_03_131.bmp
0010\0010_01_05_03_132.bmp
0010\0010_01_05_03_133.bmp
0010\0010_01_05_03_135.bmp
0010\0010_01_05_03_136.bmp
0010\0010_01_05_03_140.bmp
0010\0010_01_05_03_141.bmp
0010\0010_01_05_03_192.bmp
0010\0010_01_05_03_240.bmp
0010\0010_01_05_03_241.bmp
0010\0010_01_05_03_243.bmp
0010\0010_01_05_03_244.bmp
0010\0010_01_05_03_247.bmp
0010\0010_01_05_03_254.bmp
0010\0010_01_05_03_260.bmp
0010\0010_01_05_03_265.bmp
0010\0010_01_05_03_266.bmp
0010\0010_01_05_03_267.bmp
0010\0010_01_05_03_268.bmp
0010\0010_01_05_03_269.bmp
0010\0010_01_05_03_278.bmp
0010\0010_01_05_03_281.bmp
0010\0010_01_05_03_287.bmp
0010\0010_01_05_03_288.bmp
0010\0010_01_05_03_290.bmp
0010\0010_01_05_03_30.bmp
0010\0010_01_05_03_304.bmp
0010\0010_01_05_03_306.bmp
0010\0010_01_05_03_308.bmp
0010\0010_01_05_03_31.bmp
0010\0010_01_05_03_311.bmp
0010\0010_01_05_03_314.bmp
0010\0010_01_05_03_319.bmp
0010\0010_01_05_03_321.bmp
0010\0010_01_05_03_322.bmp
0010\0010_01_05_03_323.bmp
0010\0010_01_05_03_324.bmp
0010\0010_01_05_03_325.bmp
0010\0010_01_05_03_326.bmp
0010\0010_01_05_03_330.bmp
0010\0010_01_05_03_332.bmp
0010\0010_01_05_03_333.bmp
0010\0010_01_05_03_334.bmp
0010\0010_01_05_03_336.bmp
0010\0010_01_05_03_338.bmp
0010\0010_01_05_03_341.bmp
0010\0010_01_05_03_342.bmp
0010\0010_01_05_03_344.bmp
0010\0010_01_05_03_35.bmp
0010\0010_01_05_03_354.bmp
0010\0010_01_05_03_358.bmp
0010\0010_01_05_03_359.bmp
0010\0010_01_05_03_360.bmp
0010\0010_01_05_03_363.bmp
0010\0010_01_05_03_394.bmp
0010\0010_01_05_03_404.bmp
0010\0010_01_05_03_408.bmp
0010\0010_01_05_03_41.bmp
0010\0010_01_05_03_410.bmp
0010\0010_01_05_03_418.bmp
0010\0010_01_05_03_445.bmp
0010\0010_01_05_03_450.bmp
0010\0010_01_05_03_454.bmp
0010\0010_01_05_03_464.bmp
0010\0010_01_05_03_465.bmp
0010\0010_01_05_03_466.bmp
0010\0010_01_05_03_468.bmp
0010\0010_01_05_03_471.bmp
0010\0010_01_05_03_474.bmp
0010\0010_01_05_03_484.bmp
0010\0010_01_05_03_55.bmp
0010\0010_01_05_03_56.bmp
0015\0015_01_05_03_108.bmp
0015\0015_01_05_03_113.bmp
0015\0015_01_05_03_114.bmp
0015\0015_01_05_03_118.bmp
0015\0015_01_05_03_119.bmp
0015\0015_01_05_03_120.bmp
0015\0015_01_05_03_121.bmp
0015\0015_01_05_03_124.bmp
0015\0015_01_05_03_125.bmp
0015\0015_01_05_03_127.bmp
0015\0015_01_05_03_128.bmp
0015\0015_01_05_03_129.bmp
0015\0015_01_05_03_130.bmp
0015\0015_01_05_03_131.bmp
0015\0015_01_05_03_132.bmp
0015\0015_01_05_03_136.bmp
0015\0015_01_05_03_137.bmp
0015\0015_01_05_03_138.bmp
0015\0015_01_05_03_141.bmp
0015\0015_01_05_03_142.bmp
0015\0015_01_05_03_143.bmp
0015\0015_01_05_03_144.bmp
0015\0015_01_05_03_146.bmp
0015\0015_01_05_03_15.bmp
0015\0015_01_05_03_150.bmp
0015\0015_01_05_03_151.bmp
0015\0015_01_05_03_154.bmp
0015\0015_01_05_03_155.bmp
0015\0015_01_05_03_156.bmp
0015\0015_01_05_03_157.bmp
0015\0015_01_05_03_158.bmp
0015\0015_01_05_03_159.bmp
0015\0015_01_05_03_160.bmp
0015\0015_01_05_03_161.bmp
0015\0015_01_05_03_166.bmp
0015\0015_01_05_03_167.bmp
0015\0015_01_05_03_171.bmp
0015\0015_01_05_03_172.bmp
0015\0015_01_05_03_174.bmp
0015\0015_01_05_03_176.bmp
0015\0015_01_05_03_177.bmp
0015\0015_01_05_03_181.bmp
0015\0015_01_05_03_187.bmp
0015\0015_01_05_03_188.bmp
0015\0015_01_05_03_189.bmp
0015\0015_01_05_03_19.bmp
0015\0015_01_05_03_192.bmp
0015\0015_01_05_03_195.bmp
0015\0015_01_05_03_196.bmp
0015\0015_01_05_03_197.bmp
0015\0015_01_05_03_198.bmp
0015\0015_01_05_03_2.bmp
0015\0015_01_05_03_203.bmp
0015\0015_01_05_03_211.bmp
0015\0015_01_05_03_213.bmp
0015\0015_01_05_03_215.bmp
0015\0015_01_05_03_216.bmp
0015\0015_01_05_03_217.bmp
0015\0015_01_05_03_22.bmp
0015\0015_01_05_03_252.bmp
0015\0015_01_05_03_26.bmp
0015\0015_01_05_03_260.bmp
0015\0015_01_05_03_261.bmp
0015\0015_01_05_03_263.bmp
0015\0015_01_05_03_264.bmp
0015\0015_01_05_03_265.bmp
0015\0015_01_05_03_266.bmp
0015\0015_01_05_03_267.bmp
0015\0015_01_05_03_27.bmp
0015\0015_01_05_03_276.bmp
0015\0015_01_05_03_277.bmp
0015\0015_01_05_03_28.bmp
0015\0015_01_05_03_291.bmp
0015\0015_01_05_03_294.bmp
0015\0015_01_05_03_3.bmp
0015\0015_01_05_03_301.bmp
0015\0015_01_05_03_302.bmp
0015\0015_01_05_03_303.bmp
0015\0015_01_05_03_315.bmp
0015\0015_01_05_03_316.bmp
0015\0015_01_05_03_319.bmp
0015\0015_01_05_03_33.bmp
0015\0015_01_05_03_331.bmp
0015\0015_01_05_03_332.bmp
0015\0015_01_05_03_336.bmp
0015\0015_01_05_03_338.bmp
0015\0015_01_05_03_339.bmp
0015\0015_01_05_03_35.bmp
0015\0015_01_05_03_36.bmp
0015\0015_01_05_03_361.bmp
0015\0015_01_05_03_363.bmp
0015\0015_01_05_03_364.bmp
0015\0015_01_05_03_365.bmp
0015\0015_01_05_03_366.bmp
0015\0015_01_05_03_379.bmp
0015\0015_01_05_03_380.bmp
0015\0015_01_05_03_383.bmp
0015\0015_01_05_03_39.bmp
0015\0015_01_05_03_390.bmp
0015\0015_01_05_03_397.bmp
0015\0015_01_05_03_40.bmp
0015\0015_01_05_03_400.bmp
0015\0015_01_05_03_403.bmp
0015\0015_01_05_03_404.bmp
0015\0015_01_05_03_407.bmp
0015\0015_01_05_03_408.bmp
0015\0015_01_05_03_410.bmp
0015\0015_01_05_03_437.bmp
0015\0015_01_05_03_438.bmp
0015\0015_01_05_03_439.bmp
0015\0015_01_05_03_478.bmp
0015\0015_01_05_03_482.bmp
0015\0015_01_05_03_485.bmp
0015\0015_01_05_03_486.bmp
0015\0015_01_05_03_499.bmp
0015\0015_01_05_03_53.bmp
0015\0015_01_05_03_92.bmp
0015\0015_01_05_03_93.bmp
0007\0007_01_05_03_10.bmp
0007\0007_01_05_03_100.bmp
0007\0007_01_05_03_101.bmp
0007\0007_01_05_03_103.bmp
0007\0007_01_05_03_104.bmp
0007\0007_01_05_03_105.bmp
0007\0007_01_05_03_106.bmp
0007\0007_01_05_03_107.bmp
0007\0007_01_05_03_108.bmp
0007\0007_01_05_03_109.bmp
0007\0007_01_05_03_11.bmp
0007\0007_01_05_03_110.bmp
0007\0007_01_05_03_111.bmp
0007\0007_01_05_03_112.bmp
0007\0007_01_05_03_113.bmp
0007\0007_01_05_03_114.bmp
0007\0007_01_05_03_115.bmp
0007\0007_01_05_03_116.bmp
0007\0007_01_05_03_117.bmp
0007\0007_01_05_03_118.bmp
0007\0007_01_05_03_12.bmp
0007\0007_01_05_03_125.bmp
0007\0007_01_05_03_126.bmp
0007\0007_01_05_03_13.bmp
0007\0007_01_05_03_130.bmp
0007\0007_01_05_03_131.bmp
0007\0007_01_05_03_132.bmp
0007\0007_01_05_03_133.bmp
0007\0007_01_05_03_134.bmp
0007\0007_01_05_03_135.bmp
0007\0007_01_05_03_137.bmp
0007\0007_01_05_03_138.bmp
0007\0007_01_05_03_139.bmp
0007\0007_01_05_03_14.bmp
0007\0007_01_05_03_141.bmp
0007\0007_01_05_03_142.bmp
0007\0007_01_05_03_143.bmp
0007\0007_01_05_03_144.bmp
0007\0007_01_05_03_145.bmp
0007\0007_01_05_03_146.bmp
0007\0007_01_05_03_147.bmp
0007\0007_01_05_03_148.bmp
0007\0007_01_05_03_149.bmp
0007\0007_01_05_03_15.bmp
0007\0007_01_05_03_150.bmp
0007\0007_01_05_03_151.bmp
0007\0007_01_05_03_152.bmp
0007\0007_01_05_03_153.bmp
0007\0007_01_05_03_154.bmp
0007\0007_01_05_03_155.bmp
0007\0007_01_05_03_156.bmp
0007\0007_01_05_03_157.bmp
0007\0007_01_05_03_158.bmp
0007\0007_01_05_03_159.bmp
0007\0007_01_05_03_16.bmp
0007\0007_01_05_03_160.bmp
0007\0007_01_05_03_161.bmp
0007\0007_01_05_03_162.bmp
0007\0007_01_05_03_163.bmp
0007\0007_01_05_03_164.bmp
0007\0007_01_05_03_165.bmp
0007\0007_01_05_03_166.bmp
0007\0007_01_05_03_167.bmp
0007\0007_01_05_03_168.bmp
0007\0007_01_05_03_169.bmp
0007\0007_01_05_03_17.bmp
0007\0007_01_05_03_170.bmp
0007\0007_01_05_03_171.bmp
0007\0007_01_05_03_172.bmp
0007\0007_01_05_03_174.bmp
0007\0007_01_05_03_175.bmp
0007\0007_01_05_03_176.bmp
0007\0007_01_05_03_177.bmp
0007\0007_01_05_03_178.bmp
0007\0007_01_05_03_179.bmp
0007\0007_01_05_03_18.bmp
0007\0007_01_05_03_180.bmp
0007\0007_01_05_03_181.bmp
0007\0007_01_05_03_182.bmp
0007\0007_01_05_03_183.bmp
0007\0007_01_05_03_184.bmp
0007\0007_01_05_03_185.bmp
0007\0007_01_05_03_186.bmp
0007\0007_01_05_03_187.bmp
0007\0007_01_05_03_188.bmp
0007\0007_01_05_03_189.bmp
0007\0007_01_05_03_19.bmp
0007\0007_01_05_03_190.bmp
0007\0007_01_05_03_191.bmp
0007\0007_01_05_03_194.bmp
0007\0007_01_05_03_195.bmp
0007\0007_01_05_03_196.bmp
0007\0007_01_05_03_197.bmp
0007\0007_01_05_03_199.bmp
0007\0007_01_05_03_20.bmp
0007\0007_01_05_03_200.bmp
0007\0007_01_05_03_201.bmp
0007\0007_01_05_03_202.bmp
0007\0007_01_05_03_203.bmp
0007\0007_01_05_03_204.bmp
0007\0007_01_05_03_205.bmp
0007\0007_01_05_03_206.bmp
0007\0007_01_05_03_207.bmp
0007\0007_01_05_03_208.bmp
0007\0007_01_05_03_209.bmp
0007\0007_01_05_03_212.bmp
0007\0007_01_05_03_213.bmp
0007\0007_01_05_03_214.bmp
0007\0007_01_05_03_215.bmp
0007\0007_01_05_03_216.bmp
0007\0007_01_05_03_217.bmp
0007\0007_01_05_03_218.bmp
0007\0007_01_05_03_219.bmp
0007\0007_01_05_03_22.bmp
0007\0007_01_05_03_220.bmp
0007\0007_01_05_03_221.bmp
0007\0007_01_05_03_222.bmp
0007\0007_01_05_03_223.bmp
0007\0007_01_05_03_224.bmp
0007\0007_01_05_03_225.bmp
0007\0007_01_05_03_226.bmp
0007\0007_01_05_03_227.bmp
0007\0007_01_05_03_228.bmp
0007\0007_01_05_03_229.bmp
0007\0007_01_05_03_23.bmp
0007\0007_01_05_03_230.bmp
0007\0007_01_05_03_231.bmp
0007\0007_01_05_03_232.bmp
0007\0007_01_05_03_233.bmp
0007\0007_01_05_03_234.bmp
0007\0007_01_05_03_235.bmp
0007\0007_01_05_03_236.bmp
0007\0007_01_05_03_237.bmp
0007\0007_01_05_03_238.bmp
0007\0007_01_05_03_239.bmp
0007\0007_01_05_03_24.bmp
0007\0007_01_05_03_240.bmp
0007\0007_01_05_03_241.bmp
0007\0007_01_05_03_242.bmp
0007\0007_01_05_03_243.bmp
0007\0007_01_05_03_244.bmp
0007\0007_01_05_03_245.bmp
0007\0007_01_05_03_246.bmp
0007\0007_01_05_03_247.bmp
0007\0007_01_05_03_248.bmp
0007\0007_01_05_03_249.bmp
0007\0007_01_05_03_25.bmp
0007\0007_01_05_03_250.bmp
0007\0007_01_05_03_251.bmp
0007\0007_01_05_03_252.bmp
0007\0007_01_05_03_253.bmp
0007\0007_01_05_03_254.bmp
0007\0007_01_05_03_255.bmp
0007\0007_01_05_03_256.bmp
0007\0007_01_05_03_257.bmp
0007\0007_01_05_03_258.bmp
0007\0007_01_05_03_259.bmp
0007\0007_01_05_03_26.bmp
0007\0007_01_05_03_263.bmp
0007\0007_01_05_03_264.bmp
0007\0007_01_05_03_265.bmp
0007\0007_01_05_03_267.bmp
0007\0007_01_05_03_268.bmp
0007\0007_01_05_03_269.bmp
0007\0007_01_05_03_27.bmp
0007\0007_01_05_03_270.bmp
0007\0007_01_05_03_272.bmp
0007\0007_01_05_03_273.bmp
0007\0007_01_05_03_274.bmp
0007\0007_01_05_03_275.bmp
0007\0007_01_05_03_276.bmp
0007\0007_01_05_03_277.bmp
0007\0007_01_05_03_278.bmp
0007\0007_01_05_03_280.bmp
0007\0007_01_05_03_281.bmp
0007\0007_01_05_03_282.bmp
0007\0007_01_05_03_283.bmp
0007\0007_01_05_03_284.bmp
0007\0007_01_05_03_285.bmp
0007\0007_01_05_03_286.bmp
0007\0007_01_05_03_287.bmp
0007\0007_01_05_03_288.bmp
0007\0007_01_05_03_289.bmp
0007\0007_01_05_03_29.bmp
0007\0007_01_05_03_290.bmp
0007\0007_01_05_03_291.bmp
0007\0007_01_05_03_292.bmp
0007\0007_01_05_03_293.bmp
0007\0007_01_05_03_294.bmp
0007\0007_01_05_03_295.bmp
0007\0007_01_05_03_296.bmp
0007\0007_01_05_03_297.bmp
0007\0007_01_05_03_298.bmp
0007\0007_01_05_03_299.bmp
0007\0007_01_05_03_30.bmp
0007\0007_01_05_03_300.bmp
0007\0007_01_05_03_301.bmp
0007\0007_01_05_03_302.bmp
0007\0007_01_05_03_303.bmp
0007\0007_01_05_03_304.bmp
0007\0007_01_05_03_305.bmp
0007\0007_01_05_03_307.bmp
0007\0007_01_05_03_309.bmp
0007\0007_01_05_03_31.bmp
0007\0007_01_05_03_312.bmp
0007\0007_01_05_03_314.bmp
0007\0007_01_05_03_315.bmp
0007\0007_01_05_03_317.bmp
0007\0007_01_05_03_318.bmp
0007\0007_01_05_03_32.bmp
0007\0007_01_05_03_320.bmp
0007\0007_01_05_03_321.bmp
0007\0007_01_05_03_322.bmp
0007\0007_01_05_03_323.bmp
0007\0007_01_05_03_325.bmp
0007\0007_01_05_03_326.bmp
0007\0007_01_05_03_327.bmp
0007\0007_01_05_03_328.bmp
0007\0007_01_05_03_329.bmp
0007\0007_01_05_03_33.bmp
0007\0007_01_05_03_330.bmp
0007\0007_01_05_03_331.bmp
0007\0007_01_05_03_332.bmp
0007\0007_01_05_03_333.bmp
0007\0007_01_05_03_334.bmp
0007\0007_01_05_03_338.bmp
0007\0007_01_05_03_339.bmp
0007\0007_01_05_03_34.bmp
0007\0007_01_05_03_340.bmp
0007\0007_01_05_03_341.bmp
0007\0007_01_05_03_342.bmp
0007\0007_01_05_03_343.bmp
0007\0007_01_05_03_346.bmp
0007\0007_01_05_03_347.bmp
0007\0007_01_05_03_349.bmp
0007\0007_01_05_03_35.bmp
0007\0007_01_05_03_350.bmp
0007\0007_01_05_03_351.bmp
0007\0007_01_05_03_353.bmp
0007\0007_01_05_03_356.bmp
0007\0007_01_05_03_36.bmp
0007\0007_01_05_03_361.bmp
0007\0007_01_05_03_362.bmp
0007\0007_01_05_03_363.bmp
0007\0007_01_05_03_364.bmp
0007\0007_01_05_03_365.bmp
0007\0007_01_05_03_366.bmp
0007\0007_01_05_03_367.bmp
0007\0007_01_05_03_368.bmp
0007\0007_01_05_03_369.bmp
0007\0007_01_05_03_37.bmp
0007\0007_01_05_03_370.bmp
0007\0007_01_05_03_371.bmp
0007\0007_01_05_03_373.bmp
0007\0007_01_05_03_374.bmp
0007\0007_01_05_03_375.bmp
0007\0007_01_05_03_376.bmp
0007\0007_01_05_03_377.bmp
0007\0007_01_05_03_378.bmp
0007\0007_01_05_03_379.bmp
0007\0007_01_05_03_38.bmp
0007\0007_01_05_03_380.bmp
0007\0007_01_05_03_381.bmp
0007\0007_01_05_03_382.bmp
0007\0007_01_05_03_383.bmp
0007\0007_01_05_03_384.bmp
0007\0007_01_05_03_385.bmp
0007\0007_01_05_03_386.bmp
0007\0007_01_05_03_387.bmp
0007\0007_01_05_03_388.bmp
0007\0007_01_05_03_389.bmp
0007\0007_01_05_03_39.bmp
0007\0007_01_05_03_390.bmp
0007\0007_01_05_03_391.bmp
0007\0007_01_05_03_392.bmp
0007\0007_01_05_03_393.bmp
0007\0007_01_05_03_394.bmp
0007\0007_01_05_03_395.bmp
0007\0007_01_05_03_396.bmp
0007\0007_01_05_03_397.bmp
0007\0007_01_05_03_398.bmp
0007\0007_01_05_03_399.bmp
0007\0007_01_05_03_40.bmp
0007\0007_01_05_03_400.bmp
0007\0007_01_05_03_401.bmp
0007\0007_01_05_03_402.bmp
0007\0007_01_05_03_403.bmp
0007\0007_01_05_03_404.bmp
0007\0007_01_05_03_405.bmp
0007\0007_01_05_03_406.bmp
0007\0007_01_05_03_407.bmp
0007\0007_01_05_03_408.bmp
0007\0007_01_05_03_409.bmp
0007\0007_01_05_03_41.bmp
0007\0007_01_05_03_410.bmp
0007\0007_01_05_03_411.bmp
0007\0007_01_05_03_412.bmp
0007\0007_01_05_03_413.bmp
0007\0007_01_05_03_414.bmp
0007\0007_01_05_03_415.bmp
0007\0007_01_05_03_416.bmp
0007\0007_01_05_03_42.bmp
0007\0007_01_05_03_420.bmp
0007\0007_01_05_03_421.bmp
0007\0007_01_05_03_422.bmp
0007\0007_01_05_03_423.bmp
0007\0007_01_05_03_424.bmp
0007\0007_01_05_03_425.bmp
0007\0007_01_05_03_426.bmp
0007\0007_01_05_03_427.bmp
0007\0007_01_05_03_428.bmp
0007\0007_01_05_03_429.bmp
0007\0007_01_05_03_43.bmp
0007\0007_01_05_03_430.bmp
0007\0007_01_05_03_431.bmp
0007\0007_01_05_03_432.bmp
0007\0007_01_05_03_433.bmp
0007\0007_01_05_03_434.bmp
0007\0007_01_05_03_435.bmp
0007\0007_01_05_03_436.bmp
0007\0007_01_05_03_437.bmp
0007\0007_01_05_03_438.bmp
0007\0007_01_05_03_439.bmp
0007\0007_01_05_03_44.bmp
0007\0007_01_05_03_440.bmp
0007\0007_01_05_03_441.bmp
0007\0007_01_05_03_442.bmp
0007\0007_01_05_03_443.bmp
0007\0007_01_05_03_444.bmp
0007\0007_01_05_03_445.bmp
0007\0007_01_05_03_446.bmp
0007\0007_01_05_03_447.bmp
0007\0007_01_05_03_448.bmp
0007\0007_01_05_03_449.bmp
0007\0007_01_05_03_45.bmp
0007\0007_01_05_03_450.bmp
0007\0007_01_05_03_451.bmp
0007\0007_01_05_03_452.bmp
0007\0007_01_05_03_453.bmp
0007\0007_01_05_03_454.bmp
0007\0007_01_05_03_455.bmp
0007\0007_01_05_03_456.bmp
0007\0007_01_05_03_457.bmp
0007\0007_01_05_03_459.bmp
0007\0007_01_05_03_46.bmp
0007\0007_01_05_03_460.bmp
0007\0007_01_05_03_462.bmp
0007\0007_01_05_03_463.bmp
0007\0007_01_05_03_464.bmp
0007\0007_01_05_03_465.bmp
0007\0007_01_05_03_466.bmp
0007\0007_01_05_03_467.bmp
0007\0007_01_05_03_468.bmp
0007\0007_01_05_03_469.bmp
0007\0007_01_05_03_47.bmp
0007\0007_01_05_03_470.bmp
0007\0007_01_05_03_471.bmp
0007\0007_01_05_03_472.bmp
0007\0007_01_05_03_473.bmp
0007\0007_01_05_03_474.bmp
0007\0007_01_05_03_475.bmp
0007\0007_01_05_03_476.bmp
0007\0007_01_05_03_477.bmp
0007\0007_01_05_03_479.bmp
0007\0007_01_05_03_48.bmp
0007\0007_01_05_03_481.bmp
0007\0007_01_05_03_482.bmp
0007\0007_01_05_03_483.bmp
0007\0007_01_05_03_484.bmp
0007\0007_01_05_03_485.bmp
0007\0007_01_05_03_486.bmp
0007\0007_01_05_03_487.bmp
0007\0007_01_05_03_488.bmp
0007\0007_01_05_03_489.bmp
0007\0007_01_05_03_49.bmp
0007\0007_01_05_03_490.bmp
0007\0007_01_05_03_492.bmp
0007\0007_01_05_03_493.bmp
0007\0007_01_05_03_494.bmp
0007\0007_01_05_03_495.bmp
0007\0007_01_05_03_496.bmp
0007\0007_01_05_03_497.bmp
0007\0007_01_05_03_498.bmp
0007\0007_01_05_03_5.bmp
0007\0007_01_05_03_50.bmp
0007\0007_01_05_03_51.bmp
0007\0007_01_05_03_52.bmp
0007\0007_01_05_03_53.bmp
0007\0007_01_05_03_54.bmp
0007\0007_01_05_03_55.bmp
0007\0007_01_05_03_57.bmp
0007\0007_01_05_03_58.bmp
0007\0007_01_05_03_6.bmp
0007\0007_01_05_03_60.bmp
0007\0007_01_05_03_61.bmp
0007\0007_01_05_03_62.bmp
0007\0007_01_05_03_63.bmp
0007\0007_01_05_03_64.bmp
0007\0007_01_05_03_65.bmp
0007\0007_01_05_03_66.bmp
0007\0007_01_05_03_67.bmp
0007\0007_01_05_03_68.bmp
0007\0007_01_05_03_69.bmp
0007\0007_01_05_03_7.bmp
0007\0007_01_05_03_72.bmp
0007\0007_01_05_03_73.bmp
0007\0007_01_05_03_74.bmp
0007\0007_01_05_03_75.bmp
0007\0007_01_05_03_76.bmp
0007\0007_01_05_03_77.bmp
0007\0007_01_05_03_78.bmp
0007\0007_01_05_03_8.bmp
0007\0007_01_05_03_80.bmp
0007\0007_01_05_03_81.bmp
0007\0007_01_05_03_82.bmp
0007\0007_01_05_03_83.bmp
0007\0007_01_05_03_84.bmp
0007\0007_01_05_03_85.bmp
0007\0007_01_05_03_87.bmp
0007\0007_01_05_03_88.bmp
0007\0007_01_05_03_89.bmp
0007\0007_01_05_03_9.bmp
0007\0007_01_05_03_90.bmp
0007\0007_01_05_03_91.bmp
0007\0007_01_05_03_93.bmp
0007\0007_01_05_03_94.bmp
0007\0007_01_05_03_95.bmp
0007\0007_01_05_03_97.bmp
0007\0007_01_05_03_98.bmp
0007\0007_01_05_03_99.bmp
0014\0014_01_06_03_0.bmp
0014\0014_01_06_03_1.bmp
0014\0014_01_06_03_10.bmp
0014\0014_01_06_03_100.bmp
0014\0014_01_06_03_101.bmp
0014\0014_01_06_03_102.bmp
0014\0014_01_06_03_103.bmp
0014\0014_01_06_03_104.bmp
0014\0014_01_06_03_105.bmp
0014\0014_01_06_03_106.bmp
0014\0014_01_06_03_107.bmp
0014\0014_01_06_03_108.bmp
0014\0014_01_06_03_109.bmp
0014\0014_01_06_03_11.bmp
0014\0014_01_06_03_110.bmp
0014\0014_01_06_03_111.bmp
0014\0014_01_06_03_112.bmp
0014\0014_01_06_03_113.bmp
0014\0014_01_06_03_114.bmp
0014\0014_01_06_03_115.bmp
0014\0014_01_06_03_116.bmp
0014\0014_01_06_03_117.bmp
0014\0014_01_06_03_118.bmp
0014\0014_01_06_03_119.bmp
0014\0014_01_06_03_12.bmp
0014\0014_01_06_03_120.bmp
0014\0014_01_06_03_121.bmp
0014\0014_01_06_03_122.bmp
0014\0014_01_06_03_123.bmp
0014\0014_01_06_03_124.bmp
0014\0014_01_06_03_125.bmp
0014\0014_01_06_03_126.bmp
0014\0014_01_06_03_127.bmp
0014\0014_01_06_03_128.bmp
0014\0014_01_06_03_129.bmp
0014\0014_01_06_03_13.bmp
0014\0014_01_06_03_130.bmp
0014\0014_01_06_03_131.bmp
0014\0014_01_06_03_132.bmp
0014\0014_01_06_03_133.bmp
0014\0014_01_06_03_134.bmp
0014\0014_01_06_03_135.bmp
0014\0014_01_06_03_136.bmp
0014\0014_01_06_03_137.bmp
0014\0014_01_06_03_14.bmp
0014\0014_01_06_03_143.bmp
0014\0014_01_06_03_144.bmp
0014\0014_01_06_03_145.bmp
0014\0014_01_06_03_146.bmp
0014\0014_01_06_03_147.bmp
0014\0014_01_06_03_148.bmp
0014\0014_01_06_03_149.bmp
0014\0014_01_06_03_15.bmp
0014\0014_01_06_03_150.bmp
0014\0014_01_06_03_151.bmp
0014\0014_01_06_03_152.bmp
0014\0014_01_06_03_153.bmp
0014\0014_01_06_03_154.bmp
0014\0014_01_06_03_155.bmp
0014\0014_01_06_03_156.bmp
0014\0014_01_06_03_157.bmp
0014\0014_01_06_03_158.bmp
0014\0014_01_06_03_159.bmp
0014\0014_01_06_03_16.bmp
0014\0014_01_06_03_160.bmp
0014\0014_01_06_03_161.bmp
0014\0014_01_06_03_162.bmp
0014\0014_01_06_03_163.bmp
0014\0014_01_06_03_164.bmp
0014\0014_01_06_03_165.bmp
0014\0014_01_06_03_166.bmp
0014\0014_01_06_03_167.bmp
0014\0014_01_06_03_168.bmp
0014\0014_01_06_03_169.bmp
0014\0014_01_06_03_17.bmp
0014\0014_01_06_03_170.bmp
0014\0014_01_06_03_171.bmp
0014\0014_01_06_03_172.bmp
0014\0014_01_06_03_173.bmp
0014\0014_01_06_03_174.bmp
0014\0014_01_06_03_175.bmp
0014\0014_01_06_03_176.bmp
0014\0014_01_06_03_177.bmp
0014\0014_01_06_03_178.bmp
0014\0014_01_06_03_179.bmp
0014\0014_01_06_03_18.bmp
0014\0014_01_06_03_180.bmp
0014\0014_01_06_03_181.bmp
0014\0014_01_06_03_182.bmp
0014\0014_01_06_03_183.bmp
0014\0014_01_06_03_184.bmp
0014\0014_01_06_03_185.bmp
0014\0014_01_06_03_186.bmp
0014\0014_01_06_03_187.bmp
0014\0014_01_06_03_188.bmp
0014\0014_01_06_03_189.bmp
0014\0014_01_06_03_19.bmp
0014\0014_01_06_03_190.bmp
0014\0014_01_06_03_191.bmp
0014\0014_01_06_03_192.bmp
0014\0014_01_06_03_193.bmp
0014\0014_01_06_03_194.bmp
0014\0014_01_06_03_195.bmp
0014\0014_01_06_03_196.bmp
0014\0014_01_06_03_197.bmp
0014\0014_01_06_03_198.bmp
0014\0014_01_06_03_199.bmp
0014\0014_01_06_03_2.bmp
0014\0014_01_06_03_20.bmp
0014\0014_01_06_03_200.bmp
0014\0014_01_06_03_201.bmp
0014\0014_01_06_03_202.bmp
0014\0014_01_06_03_203.bmp
0014\0014_01_06_03_204.bmp
0014\0014_01_06_03_205.bmp
0014\0014_01_06_03_206.bmp
0014\0014_01_06_03_207.bmp
0014\0014_01_06_03_208.bmp
0014\0014_01_06_03_209.bmp
0014\0014_01_06_03_21.bmp
0014\0014_01_06_03_210.bmp
0014\0014_01_06_03_211.bmp
0014\0014_01_06_03_212.bmp
0014\0014_01_06_03_213.bmp
0014\0014_01_06_03_214.bmp
0014\0014_01_06_03_215.bmp
0014\0014_01_06_03_216.bmp
0014\0014_01_06_03_217.bmp
0014\0014_01_06_03_218.bmp
0014\0014_01_06_03_219.bmp
0014\0014_01_06_03_22.bmp
0014\0014_01_06_03_220.bmp
0014\0014_01_06_03_221.bmp
0014\0014_01_06_03_222.bmp
0014\0014_01_06_03_223.bmp
0014\0014_01_06_03_224.bmp
0014\0014_01_06_03_225.bmp
0014\0014_01_06_03_226.bmp
0014\0014_01_06_03_227.bmp
0014\0014_01_06_03_228.bmp
0014\0014_01_06_03_229.bmp
0014\0014_01_06_03_23.bmp
0014\0014_01_06_03_230.bmp
0014\0014_01_06_03_231.bmp
0014\0014_01_06_03_232.bmp
0014\0014_01_06_03_233.bmp
0014\0014_01_06_03_234.bmp
0014\0014_01_06_03_235.bmp
0014\0014_01_06_03_236.bmp
0014\0014_01_06_03_237.bmp
0014\0014_01_06_03_238.bmp
0014\0014_01_06_03_239.bmp
0014\0014_01_06_03_24.bmp
0014\0014_01_06_03_240.bmp
0014\0014_01_06_03_241.bmp
0014\0014_01_06_03_242.bmp
0014\0014_01_06_03_243.bmp
0014\0014_01_06_03_244.bmp
0014\0014_01_06_03_245.bmp
0014\0014_01_06_03_246.bmp
0014\0014_01_06_03_247.bmp
0014\0014_01_06_03_248.bmp
0014\0014_01_06_03_249.bmp
0014\0014_01_06_03_25.bmp
0014\0014_01_06_03_250.bmp
0014\0014_01_06_03_251.bmp
0014\0014_01_06_03_252.bmp
0014\0014_01_06_03_253.bmp
0014\0014_01_06_03_254.bmp
0014\0014_01_06_03_255.bmp
0014\0014_01_06_03_256.bmp
0014\0014_01_06_03_257.bmp
0014\0014_01_06_03_258.bmp
0014\0014_01_06_03_259.bmp
0014\0014_01_06_03_26.bmp
0014\0014_01_06_03_260.bmp
0014\0014_01_06_03_261.bmp
0014\0014_01_06_03_262.bmp
0014\0014_01_06_03_263.bmp
0014\0014_01_06_03_264.bmp
0014\0014_01_06_03_265.bmp
0014\0014_01_06_03_266.bmp
0014\0014_01_06_03_267.bmp
0014\0014_01_06_03_268.bmp
0014\0014_01_06_03_269.bmp
0014\0014_01_06_03_27.bmp
0014\0014_01_06_03_270.bmp
0014\0014_01_06_03_271.bmp
0014\0014_01_06_03_272.bmp
0014\0014_01_06_03_273.bmp
0014\0014_01_06_03_274.bmp
0014\0014_01_06_03_275.bmp
0014\0014_01_06_03_276.bmp
0014\0014_01_06_03_277.bmp
0014\0014_01_06_03_278.bmp
0014\0014_01_06_03_279.bmp
0014\0014_01_06_03_28.bmp
0014\0014_01_06_03_280.bmp
0014\0014_01_06_03_281.bmp
0014\0014_01_06_03_282.bmp
0014\0014_01_06_03_283.bmp
0014\0014_01_06_03_284.bmp
0014\0014_01_06_03_285.bmp
0014\0014_01_06_03_286.bmp
0014\0014_01_06_03_287.bmp
0014\0014_01_06_03_288.bmp
0014\0014_01_06_03_289.bmp
0014\0014_01_06_03_29.bmp
0014\0014_01_06_03_290.bmp
0014\0014_01_06_03_291.bmp
0014\0014_01_06_03_292.bmp
0014\0014_01_06_03_293.bmp
0014\0014_01_06_03_3.bmp
0014\0014_01_06_03_30.bmp
0014\0014_01_06_03_304.bmp
0014\0014_01_06_03_305.bmp
0014\0014_01_06_03_306.bmp
0014\0014_01_06_03_307.bmp
0014\0014_01_06_03_308.bmp
0014\0014_01_06_03_309.bmp
0014\0014_01_06_03_31.bmp
0014\0014_01_06_03_310.bmp
0014\0014_01_06_03_311.bmp
0014\0014_01_06_03_312.bmp
0014\0014_01_06_03_313.bmp
0014\0014_01_06_03_314.bmp
0014\0014_01_06_03_315.bmp
0014\0014_01_06_03_316.bmp
0014\0014_01_06_03_317.bmp
0014\0014_01_06_03_318.bmp
0014\0014_01_06_03_319.bmp
0014\0014_01_06_03_32.bmp
0014\0014_01_06_03_320.bmp
0014\0014_01_06_03_321.bmp
0014\0014_01_06_03_322.bmp
0014\0014_01_06_03_323.bmp
0014\0014_01_06_03_324.bmp
0014\0014_01_06_03_325.bmp
0014\0014_01_06_03_326.bmp
0014\0014_01_06_03_327.bmp
0014\0014_01_06_03_328.bmp
0014\0014_01_06_03_329.bmp
0014\0014_01_06_03_33.bmp
0014\0014_01_06_03_330.bmp
0014\0014_01_06_03_331.bmp
0014\0014_01_06_03_332.bmp
0014\0014_01_06_03_333.bmp
0014\0014_01_06_03_334.bmp
0014\0014_01_06_03_335.bmp
0014\0014_01_06_03_336.bmp
0014\0014_01_06_03_337.bmp
0014\0014_01_06_03_338.bmp
0014\0014_01_06_03_339.bmp
0014\0014_01_06_03_34.bmp
0014\0014_01_06_03_340.bmp
0014\0014_01_06_03_341.bmp
0014\0014_01_06_03_342.bmp
0014\0014_01_06_03_343.bmp
0014\0014_01_06_03_344.bmp
0014\0014_01_06_03_345.bmp
0014\0014_01_06_03_346.bmp
0014\0014_01_06_03_347.bmp
0014\0014_01_06_03_348.bmp
0014\0014_01_06_03_349.bmp
0014\0014_01_06_03_35.bmp
0014\0014_01_06_03_350.bmp
0014\0014_01_06_03_351.bmp
0014\0014_01_06_03_352.bmp
0014\0014_01_06_03_353.bmp
0014\0014_01_06_03_354.bmp
0014\0014_01_06_03_355.bmp
0014\0014_01_06_03_356.bmp
0014\0014_01_06_03_357.bmp
0014\0014_01_06_03_358.bmp
0014\0014_01_06_03_359.bmp
0014\0014_01_06_03_36.bmp
0014\0014_01_06_03_360.bmp
0014\0014_01_06_03_361.bmp
0014\0014_01_06_03_362.bmp
0014\0014_01_06_03_363.bmp
0014\0014_01_06_03_364.bmp
0014\0014_01_06_03_365.bmp
0014\0014_01_06_03_366.bmp
0014\0014_01_06_03_367.bmp
0014\0014_01_06_03_368.bmp
0014\0014_01_06_03_369.bmp
0014\0014_01_06_03_37.bmp
0014\0014_01_06_03_370.bmp
0014\0014_01_06_03_371.bmp
0014\0014_01_06_03_372.bmp
0014\0014_01_06_03_373.bmp
0014\0014_01_06_03_374.bmp
0014\0014_01_06_03_375.bmp
0014\0014_01_06_03_376.bmp
0014\0014_01_06_03_377.bmp
0014\0014_01_06_03_378.bmp
0014\0014_01_06_03_379.bmp
0014\0014_01_06_03_38.bmp
0014\0014_01_06_03_380.bmp
0014\0014_01_06_03_381.bmp
0014\0014_01_06_03_382.bmp
0014\0014_01_06_03_383.bmp
0014\0014_01_06_03_384.bmp
0014\0014_01_06_03_385.bmp
0014\0014_01_06_03_386.bmp
0014\0014_01_06_03_387.bmp
0014\0014_01_06_03_388.bmp
0014\0014_01_06_03_389.bmp
0014\0014_01_06_03_39.bmp
0014\0014_01_06_03_390.bmp
0014\0014_01_06_03_391.bmp
0014\0014_01_06_03_392.bmp
0014\0014_01_06_03_393.bmp
0014\0014_01_06_03_394.bmp
0014\0014_01_06_03_395.bmp
0014\0014_01_06_03_396.bmp
0014\0014_01_06_03_397.bmp
0014\0014_01_06_03_398.bmp
0014\0014_01_06_03_399.bmp
0014\0014_01_06_03_4.bmp
0014\0014_01_06_03_40.bmp
0014\0014_01_06_03_400.bmp
0014\0014_01_06_03_401.bmp
0014\0014_01_06_03_402.bmp
0014\0014_01_06_03_403.bmp
0014\0014_01_06_03_404.bmp
0014\0014_01_06_03_405.bmp
0014\0014_01_06_03_406.bmp
0014\0014_01_06_03_407.bmp
0014\0014_01_06_03_408.bmp
0014\0014_01_06_03_409.bmp
0014\0014_01_06_03_41.bmp
0014\0014_01_06_03_410.bmp
0014\0014_01_06_03_411.bmp
0014\0014_01_06_03_412.bmp
0014\0014_01_06_03_413.bmp
0014\0014_01_06_03_414.bmp
0014\0014_01_06_03_415.bmp
0014\0014_01_06_03_416.bmp
0014\0014_01_06_03_417.bmp
0014\0014_01_06_03_418.bmp
0014\0014_01_06_03_419.bmp
0014\0014_01_06_03_42.bmp
0014\0014_01_06_03_420.bmp
0014\0014_01_06_03_421.bmp
0014\0014_01_06_03_422.bmp
0014\0014_01_06_03_423.bmp
0014\0014_01_06_03_424.bmp
0014\0014_01_06_03_425.bmp
0014\0014_01_06_03_426.bmp
0014\0014_01_06_03_427.bmp
0014\0014_01_06_03_428.bmp
0014\0014_01_06_03_429.bmp
0014\0014_01_06_03_43.bmp
0014\0014_01_06_03_430.bmp
0014\0014_01_06_03_431.bmp
0014\0014_01_06_03_432.bmp
0014\0014_01_06_03_433.bmp
0014\0014_01_06_03_434.bmp
0014\0014_01_06_03_435.bmp
0014\0014_01_06_03_436.bmp
0014\0014_01_06_03_437.bmp
0014\0014_01_06_03_438.bmp
0014\0014_01_06_03_439.bmp
0014\0014_01_06_03_44.bmp
0014\0014_01_06_03_440.bmp
0014\0014_01_06_03_441.bmp
0014\0014_01_06_03_442.bmp
0014\0014_01_06_03_443.bmp
0014\0014_01_06_03_444.bmp
0014\0014_01_06_03_445.bmp
0014\0014_01_06_03_446.bmp
0014\0014_01_06_03_447.bmp
0014\0014_01_06_03_448.bmp
0014\0014_01_06_03_449.bmp
0014\0014_01_06_03_45.bmp
0014\0014_01_06_03_450.bmp
0014\0014_01_06_03_451.bmp
0014\0014_01_06_03_452.bmp
0014\0014_01_06_03_453.bmp
0014\0014_01_06_03_454.bmp
0014\0014_01_06_03_455.bmp
0014\0014_01_06_03_456.bmp
0014\0014_01_06_03_457.bmp
0014\0014_01_06_03_458.bmp
0014\0014_01_06_03_459.bmp
0014\0014_01_06_03_46.bmp
0014\0014_01_06_03_460.bmp
0014\0014_01_06_03_461.bmp
0014\0014_01_06_03_462.bmp
0014\0014_01_06_03_463.bmp
0014\0014_01_06_03_464.bmp
0014\0014_01_06_03_465.bmp
0014\0014_01_06_03_466.bmp
0014\0014_01_06_03_467.bmp
0014\0014_01_06_03_468.bmp
0014\0014_01_06_03_469.bmp
0014\0014_01_06_03_47.bmp
0014\0014_01_06_03_470.bmp
0014\0014_01_06_03_471.bmp
0014\0014_01_06_03_472.bmp
0014\0014_01_06_03_473.bmp
0014\0014_01_06_03_474.bmp
0014\0014_01_06_03_475.bmp
0014\0014_01_06_03_476.bmp
0014\0014_01_06_03_477.bmp
0014\0014_01_06_03_478.bmp
0014\0014_01_06_03_479.bmp
0014\0014_01_06_03_48.bmp
0014\0014_01_06_03_480.bmp
0014\0014_01_06_03_481.bmp
0014\0014_01_06_03_482.bmp
0014\0014_01_06_03_483.bmp
0014\0014_01_06_03_484.bmp
0014\0014_01_06_03_485.bmp
0014\0014_01_06_03_486.bmp
0014\0014_01_06_03_487.bmp
0014\0014_01_06_03_488.bmp
0014\0014_01_06_03_489.bmp
0014\0014_01_06_03_49.bmp
0014\0014_01_06_03_490.bmp
0014\0014_01_06_03_491.bmp
0014\0014_01_06_03_492.bmp
0014\0014_01_06_03_493.bmp
0014\0014_01_06_03_494.bmp
0014\0014_01_06_03_495.bmp
0014\0014_01_06_03_496.bmp
0014\0014_01_06_03_497.bmp
0014\0014_01_06_03_498.bmp
0014\0014_01_06_03_499.bmp
0014\0014_01_06_03_5.bmp
0014\0014_01_06_03_50.bmp
0014\0014_01_06_03_51.bmp
0014\0014_01_06_03_52.bmp
0014\0014_01_06_03_53.bmp
0014\0014_01_06_03_54.bmp
0014\0014_01_06_03_55.bmp
0014\0014_01_06_03_56.bmp
0014\0014_01_06_03_57.bmp
0014\0014_01_06_03_58.bmp
0014\0014_01_06_03_59.bmp
0014\0014_01_06_03_6.bmp
0014\0014_01_06_03_60.bmp
0014\0014_01_06_03_61.bmp
0014\0014_01_06_03_62.bmp
0014\0014_01_06_03_63.bmp
0014\0014_01_06_03_64.bmp
0014\0014_01_06_03_65.bmp
0014\0014_01_06_03_66.bmp
0014\0014_01_06_03_67.bmp
0014\0014_01_06_03_68.bmp
0014\0014_01_06_03_69.bmp
0014\0014_01_06_03_7.bmp
0014\0014_01_06_03_70.bmp
0014\0014_01_06_03_71.bmp
0014\0014_01_06_03_72.bmp
0014\0014_01_06_03_73.bmp
0014\0014_01_06_03_74.bmp
0014\0014_01_06_03_75.bmp
0014\0014_01_06_03_76.bmp
0014\0014_01_06_03_84.bmp
0014\0014_01_06_03_85.bmp
0014\0014_01_06_03_86.bmp
0014\0014_01_06_03_87.bmp
0014\0014_01_06_03_88.bmp
0014\0014_01_06_03_89.bmp
0014\0014_01_06_03_9.bmp
0014\0014_01_06_03_90.bmp
0014\0014_01_06_03_91.bmp
0014\0014_01_06_03_92.bmp
0014\0014_01_06_03_93.bmp
0014\0014_01_06_03_94.bmp
0014\0014_01_06_03_95.bmp
0014\0014_01_06_03_96.bmp
0014\0014_01_06_03_97.bmp
0014\0014_01_06_03_98.bmp
0014\0014_01_06_03_99.bmp
0004\0004_01_06_03_0.bmp
0004\0004_01_06_03_1.bmp
0004\0004_01_06_03_10.bmp
0004\0004_01_06_03_100.bmp
0004\0004_01_06_03_101.bmp
0004\0004_01_06_03_102.bmp
0004\0004_01_06_03_103.bmp
0004\0004_01_06_03_104.bmp
0004\0004_01_06_03_105.bmp
0004\0004_01_06_03_106.bmp
0004\0004_01_06_03_107.bmp
0004\0004_01_06_03_108.bmp
0004\0004_01_06_03_109.bmp
0004\0004_01_06_03_11.bmp
0004\0004_01_06_03_110.bmp
0004\0004_01_06_03_111.bmp
0004\0004_01_06_03_112.bmp
0004\0004_01_06_03_113.bmp
0004\0004_01_06_03_114.bmp
0004\0004_01_06_03_115.bmp
0004\0004_01_06_03_116.bmp
0004\0004_01_06_03_117.bmp
0004\0004_01_06_03_118.bmp
0004\0004_01_06_03_119.bmp
0004\0004_01_06_03_12.bmp
0004\0004_01_06_03_120.bmp
0004\0004_01_06_03_121.bmp
0004\0004_01_06_03_122.bmp
0004\0004_01_06_03_123.bmp
0004\0004_01_06_03_124.bmp
0004\0004_01_06_03_125.bmp
0004\0004_01_06_03_126.bmp
0004\0004_01_06_03_127.bmp
0004\0004_01_06_03_128.bmp
0004\0004_01_06_03_129.bmp
0004\0004_01_06_03_13.bmp
0004\0004_01_06_03_130.bmp
0004\0004_01_06_03_131.bmp
0004\0004_01_06_03_132.bmp
0004\0004_01_06_03_133.bmp
0004\0004_01_06_03_134.bmp
0004\0004_01_06_03_135.bmp
0004\0004_01_06_03_136.bmp
0004\0004_01_06_03_137.bmp
0004\0004_01_06_03_138.bmp
0004\0004_01_06_03_139.bmp
0004\0004_01_06_03_14.bmp
0004\0004_01_06_03_140.bmp
0004\0004_01_06_03_141.bmp
0004\0004_01_06_03_143.bmp
0004\0004_01_06_03_144.bmp
0004\0004_01_06_03_145.bmp
0004\0004_01_06_03_146.bmp
0004\0004_01_06_03_147.bmp
0004\0004_01_06_03_148.bmp
0004\0004_01_06_03_149.bmp
0004\0004_01_06_03_15.bmp
0004\0004_01_06_03_150.bmp
0004\0004_01_06_03_151.bmp
0004\0004_01_06_03_152.bmp
0004\0004_01_06_03_153.bmp
0004\0004_01_06_03_154.bmp
0004\0004_01_06_03_155.bmp
0004\0004_01_06_03_156.bmp
0004\0004_01_06_03_157.bmp
0004\0004_01_06_03_158.bmp
0004\0004_01_06_03_159.bmp
0004\0004_01_06_03_16.bmp
0004\0004_01_06_03_161.bmp
0004\0004_01_06_03_17.bmp
0004\0004_01_06_03_173.bmp
0004\0004_01_06_03_174.bmp
0004\0004_01_06_03_175.bmp
0004\0004_01_06_03_176.bmp
0004\0004_01_06_03_177.bmp
0004\0004_01_06_03_178.bmp
0004\0004_01_06_03_179.bmp
0004\0004_01_06_03_18.bmp
0004\0004_01_06_03_183.bmp
0004\0004_01_06_03_184.bmp
0004\0004_01_06_03_185.bmp
0004\0004_01_06_03_186.bmp
0004\0004_01_06_03_187.bmp
0004\0004_01_06_03_188.bmp
0004\0004_01_06_03_189.bmp
0004\0004_01_06_03_19.bmp
0004\0004_01_06_03_190.bmp
0004\0004_01_06_03_191.bmp
0004\0004_01_06_03_192.bmp
0004\0004_01_06_03_193.bmp
0004\0004_01_06_03_194.bmp
0004\0004_01_06_03_195.bmp
0004\0004_01_06_03_196.bmp
0004\0004_01_06_03_197.bmp
0004\0004_01_06_03_198.bmp
0004\0004_01_06_03_199.bmp
0004\0004_01_06_03_2.bmp
0004\0004_01_06_03_20.bmp
0004\0004_01_06_03_200.bmp
0004\0004_01_06_03_201.bmp
0004\0004_01_06_03_202.bmp
0004\0004_01_06_03_203.bmp
0004\0004_01_06_03_204.bmp
0004\0004_01_06_03_205.bmp
0004\0004_01_06_03_206.bmp
0004\0004_01_06_03_207.bmp
0004\0004_01_06_03_208.bmp
0004\0004_01_06_03_209.bmp
0004\0004_01_06_03_21.bmp
0004\0004_01_06_03_210.bmp
0004\0004_01_06_03_211.bmp
0004\0004_01_06_03_212.bmp
0004\0004_01_06_03_213.bmp
0004\0004_01_06_03_214.bmp
0004\0004_01_06_03_215.bmp
0004\0004_01_06_03_216.bmp
0004\0004_01_06_03_217.bmp
0004\0004_01_06_03_218.bmp
0004\0004_01_06_03_219.bmp
0004\0004_01_06_03_22.bmp
0004\0004_01_06_03_220.bmp
0004\0004_01_06_03_221.bmp
0004\0004_01_06_03_222.bmp
0004\0004_01_06_03_223.bmp
0004\0004_01_06_03_224.bmp
0004\0004_01_06_03_225.bmp
0004\0004_01_06_03_226.bmp
0004\0004_01_06_03_227.bmp
0004\0004_01_06_03_228.bmp
0004\0004_01_06_03_229.bmp
0004\0004_01_06_03_23.bmp
0004\0004_01_06_03_230.bmp
0004\0004_01_06_03_231.bmp
0004\0004_01_06_03_232.bmp
0004\0004_01_06_03_233.bmp
0004\0004_01_06_03_234.bmp
0004\0004_01_06_03_235.bmp
0004\0004_01_06_03_236.bmp
0004\0004_01_06_03_237.bmp
0004\0004_01_06_03_238.bmp
0004\0004_01_06_03_239.bmp
0004\0004_01_06_03_24.bmp
0004\0004_01_06_03_240.bmp
0004\0004_01_06_03_241.bmp
0004\0004_01_06_03_242.bmp
0004\0004_01_06_03_243.bmp
0004\0004_01_06_03_244.bmp
0004\0004_01_06_03_245.bmp
0004\0004_01_06_03_246.bmp
0004\0004_01_06_03_247.bmp
0004\0004_01_06_03_248.bmp
0004\0004_01_06_03_249.bmp
0004\0004_01_06_03_25.bmp
0004\0004_01_06_03_250.bmp
0004\0004_01_06_03_251.bmp
0004\0004_01_06_03_252.bmp
0004\0004_01_06_03_253.bmp
0004\0004_01_06_03_254.bmp
0004\0004_01_06_03_255.bmp
0004\0004_01_06_03_256.bmp
0004\0004_01_06_03_257.bmp
0004\0004_01_06_03_258.bmp
0004\0004_01_06_03_259.bmp
0004\0004_01_06_03_26.bmp
0004\0004_01_06_03_260.bmp
0004\0004_01_06_03_261.bmp
0004\0004_01_06_03_262.bmp
0004\0004_01_06_03_263.bmp
0004\0004_01_06_03_264.bmp
0004\0004_01_06_03_265.bmp
0004\0004_01_06_03_266.bmp
0004\0004_01_06_03_267.bmp
0004\0004_01_06_03_268.bmp
0004\0004_01_06_03_269.bmp
0004\0004_01_06_03_27.bmp
0004\0004_01_06_03_270.bmp
0004\0004_01_06_03_272.bmp
0004\0004_01_06_03_273.bmp
0004\0004_01_06_03_275.bmp
0004\0004_01_06_03_276.bmp
0004\0004_01_06_03_28.bmp
0004\0004_01_06_03_281.bmp
0004\0004_01_06_03_282.bmp
0004\0004_01_06_03_283.bmp
0004\0004_01_06_03_284.bmp
0004\0004_01_06_03_285.bmp
0004\0004_01_06_03_286.bmp
0004\0004_01_06_03_287.bmp
0004\0004_01_06_03_288.bmp
0004\0004_01_06_03_289.bmp
0004\0004_01_06_03_29.bmp
0004\0004_01_06_03_290.bmp
0004\0004_01_06_03_291.bmp
0004\0004_01_06_03_292.bmp
0004\0004_01_06_03_293.bmp
0004\0004_01_06_03_294.bmp
0004\0004_01_06_03_295.bmp
0004\0004_01_06_03_296.bmp
0004\0004_01_06_03_297.bmp
0004\0004_01_06_03_298.bmp
0004\0004_01_06_03_299.bmp
0004\0004_01_06_03_3.bmp
0004\0004_01_06_03_30.bmp
0004\0004_01_06_03_304.bmp
0004\0004_01_06_03_305.bmp
0004\0004_01_06_03_306.bmp
0004\0004_01_06_03_307.bmp
0004\0004_01_06_03_308.bmp
0004\0004_01_06_03_309.bmp
0004\0004_01_06_03_31.bmp
0004\0004_01_06_03_311.bmp
0004\0004_01_06_03_312.bmp
0004\0004_01_06_03_314.bmp
0004\0004_01_06_03_32.bmp
0004\0004_01_06_03_321.bmp
0004\0004_01_06_03_322.bmp
0004\0004_01_06_03_323.bmp
0004\0004_01_06_03_324.bmp
0004\0004_01_06_03_325.bmp
0004\0004_01_06_03_326.bmp
0004\0004_01_06_03_327.bmp
0004\0004_01_06_03_328.bmp
0004\0004_01_06_03_329.bmp
0004\0004_01_06_03_33.bmp
0004\0004_01_06_03_330.bmp
0004\0004_01_06_03_331.bmp
0004\0004_01_06_03_332.bmp
0004\0004_01_06_03_333.bmp
0004\0004_01_06_03_334.bmp
0004\0004_01_06_03_335.bmp
0004\0004_01_06_03_336.bmp
0004\0004_01_06_03_337.bmp
0004\0004_01_06_03_338.bmp
0004\0004_01_06_03_339.bmp
0004\0004_01_06_03_34.bmp
0004\0004_01_06_03_340.bmp
0004\0004_01_06_03_341.bmp
0004\0004_01_06_03_342.bmp
0004\0004_01_06_03_343.bmp
0004\0004_01_06_03_344.bmp
0004\0004_01_06_03_345.bmp
0004\0004_01_06_03_346.bmp
0004\0004_01_06_03_347.bmp
0004\0004_01_06_03_348.bmp
0004\0004_01_06_03_349.bmp
0004\0004_01_06_03_35.bmp
0004\0004_01_06_03_350.bmp
0004\0004_01_06_03_351.bmp
0004\0004_01_06_03_352.bmp
0004\0004_01_06_03_353.bmp
0004\0004_01_06_03_354.bmp
0004\0004_01_06_03_355.bmp
0004\0004_01_06_03_356.bmp
0004\0004_01_06_03_357.bmp
0004\0004_01_06_03_358.bmp
0004\0004_01_06_03_359.bmp
0004\0004_01_06_03_36.bmp
0004\0004_01_06_03_360.bmp
0004\0004_01_06_03_361.bmp
0004\0004_01_06_03_362.bmp
0004\0004_01_06_03_363.bmp
0004\0004_01_06_03_364.bmp
0004\0004_01_06_03_365.bmp
0004\0004_01_06_03_366.bmp
0004\0004_01_06_03_367.bmp
0004\0004_01_06_03_368.bmp
0004\0004_01_06_03_369.bmp
0004\0004_01_06_03_37.bmp
0004\0004_01_06_03_370.bmp
0004\0004_01_06_03_371.bmp
0004\0004_01_06_03_372.bmp
0004\0004_01_06_03_373.bmp
0004\0004_01_06_03_374.bmp
0004\0004_01_06_03_375.bmp
0004\0004_01_06_03_376.bmp
0004\0004_01_06_03_377.bmp
0004\0004_01_06_03_378.bmp
0004\0004_01_06_03_379.bmp
0004\0004_01_06_03_38.bmp
0004\0004_01_06_03_380.bmp
0004\0004_01_06_03_381.bmp
0004\0004_01_06_03_382.bmp
0004\0004_01_06_03_383.bmp
0004\0004_01_06_03_384.bmp
0004\0004_01_06_03_385.bmp
0004\0004_01_06_03_386.bmp
0004\0004_01_06_03_387.bmp
0004\0004_01_06_03_388.bmp
0004\0004_01_06_03_389.bmp
0004\0004_01_06_03_39.bmp
0004\0004_01_06_03_390.bmp
0004\0004_01_06_03_391.bmp
0004\0004_01_06_03_392.bmp
0004\0004_01_06_03_393.bmp
0004\0004_01_06_03_394.bmp
0004\0004_01_06_03_395.bmp
0004\0004_01_06_03_396.bmp
0004\0004_01_06_03_397.bmp
0004\0004_01_06_03_398.bmp
0004\0004_01_06_03_399.bmp
0004\0004_01_06_03_4.bmp
0004\0004_01_06_03_40.bmp
0004\0004_01_06_03_401.bmp
0004\0004_01_06_03_403.bmp
0004\0004_01_06_03_405.bmp
0004\0004_01_06_03_406.bmp
0004\0004_01_06_03_407.bmp
0004\0004_01_06_03_408.bmp
0004\0004_01_06_03_409.bmp
0004\0004_01_06_03_41.bmp
0004\0004_01_06_03_410.bmp
0004\0004_01_06_03_412.bmp
0004\0004_01_06_03_413.bmp
0004\0004_01_06_03_414.bmp
0004\0004_01_06_03_415.bmp
0004\0004_01_06_03_416.bmp
0004\0004_01_06_03_417.bmp
0004\0004_01_06_03_418.bmp
0004\0004_01_06_03_419.bmp
0004\0004_01_06_03_42.bmp
0004\0004_01_06_03_420.bmp
0004\0004_01_06_03_421.bmp
0004\0004_01_06_03_422.bmp
0004\0004_01_06_03_423.bmp
0004\0004_01_06_03_424.bmp
0004\0004_01_06_03_425.bmp
0004\0004_01_06_03_426.bmp
0004\0004_01_06_03_427.bmp
0004\0004_01_06_03_428.bmp
0004\0004_01_06_03_429.bmp
0004\0004_01_06_03_43.bmp
0004\0004_01_06_03_430.bmp
0004\0004_01_06_03_431.bmp
0004\0004_01_06_03_432.bmp
0004\0004_01_06_03_433.bmp
0004\0004_01_06_03_434.bmp
0004\0004_01_06_03_435.bmp
0004\0004_01_06_03_436.bmp
0004\0004_01_06_03_437.bmp
0004\0004_01_06_03_438.bmp
0004\0004_01_06_03_439.bmp
0004\0004_01_06_03_44.bmp
0004\0004_01_06_03_440.bmp
0004\0004_01_06_03_441.bmp
0004\0004_01_06_03_442.bmp
0004\0004_01_06_03_443.bmp
0004\0004_01_06_03_444.bmp
0004\0004_01_06_03_445.bmp
0004\0004_01_06_03_446.bmp
0004\0004_01_06_03_447.bmp
0004\0004_01_06_03_448.bmp
0004\0004_01_06_03_449.bmp
0004\0004_01_06_03_45.bmp
0004\0004_01_06_03_450.bmp
0004\0004_01_06_03_451.bmp
0004\0004_01_06_03_452.bmp
0004\0004_01_06_03_453.bmp
0004\0004_01_06_03_454.bmp
0004\0004_01_06_03_455.bmp
0004\0004_01_06_03_456.bmp
0004\0004_01_06_03_457.bmp
0004\0004_01_06_03_458.bmp
0004\0004_01_06_03_459.bmp
0004\0004_01_06_03_46.bmp
0004\0004_01_06_03_460.bmp
0004\0004_01_06_03_461.bmp
0004\0004_01_06_03_462.bmp
0004\0004_01_06_03_463.bmp
0004\0004_01_06_03_464.bmp
0004\0004_01_06_03_465.bmp
0004\0004_01_06_03_466.bmp
0004\0004_01_06_03_467.bmp
0004\0004_01_06_03_468.bmp
0004\0004_01_06_03_469.bmp
0004\0004_01_06_03_47.bmp
0004\0004_01_06_03_470.bmp
0004\0004_01_06_03_471.bmp
0004\0004_01_06_03_472.bmp
0004\0004_01_06_03_473.bmp
0004\0004_01_06_03_474.bmp
0004\0004_01_06_03_475.bmp
0004\0004_01_06_03_477.bmp
0004\0004_01_06_03_478.bmp
0004\0004_01_06_03_48.bmp
0004\0004_01_06_03_483.bmp
0004\0004_01_06_03_484.bmp
0004\0004_01_06_03_485.bmp
0004\0004_01_06_03_486.bmp
0004\0004_01_06_03_487.bmp
0004\0004_01_06_03_488.bmp
0004\0004_01_06_03_489.bmp
0004\0004_01_06_03_49.bmp
0004\0004_01_06_03_490.bmp
0004\0004_01_06_03_491.bmp
0004\0004_01_06_03_492.bmp
0004\0004_01_06_03_493.bmp
0004\0004_01_06_03_494.bmp
0004\0004_01_06_03_495.bmp
0004\0004_01_06_03_496.bmp
0004\0004_01_06_03_497.bmp
0004\0004_01_06_03_499.bmp
0004\0004_01_06_03_5.bmp
0004\0004_01_06_03_50.bmp
0004\0004_01_06_03_51.bmp
0004\0004_01_06_03_52.bmp
0004\0004_01_06_03_53.bmp
0004\0004_01_06_03_54.bmp
0004\0004_01_06_03_55.bmp
0004\0004_01_06_03_56.bmp
0004\0004_01_06_03_57.bmp
0004\0004_01_06_03_58.bmp
0004\0004_01_06_03_59.bmp
0004\0004_01_06_03_6.bmp
0004\0004_01_06_03_60.bmp
0004\0004_01_06_03_61.bmp
0004\0004_01_06_03_62.bmp
0004\0004_01_06_03_63.bmp
0004\0004_01_06_03_64.bmp
0004\0004_01_06_03_65.bmp
0004\0004_01_06_03_66.bmp
0004\0004_01_06_03_67.bmp
0004\0004_01_06_03_68.bmp
0004\0004_01_06_03_69.bmp
0004\0004_01_06_03_7.bmp
0004\0004_01_06_03_70.bmp
0004\0004_01_06_03_71.bmp
0004\0004_01_06_03_72.bmp
0004\0004_01_06_03_73.bmp
0004\0004_01_06_03_74.bmp
0004\0004_01_06_03_75.bmp
0004\0004_01_06_03_79.bmp
0004\0004_01_06_03_8.bmp
0004\0004_01_06_03_80.bmp
0004\0004_01_06_03_81.bmp
0004\0004_01_06_03_82.bmp
0004\0004_01_06_03_83.bmp
0004\0004_01_06_03_84.bmp
0004\0004_01_06_03_85.bmp
0004\0004_01_06_03_86.bmp
0004\0004_01_06_03_87.bmp
0004\0004_01_06_03_88.bmp
0004\0004_01_06_03_89.bmp
0004\0004_01_06_03_9.bmp
0004\0004_01_06_03_90.bmp
0004\0004_01_06_03_91.bmp
0004\0004_01_06_03_92.bmp
0004\0004_01_06_03_93.bmp
0004\0004_01_06_03_94.bmp
0004\0004_01_06_03_95.bmp
0004\0004_01_06_03_96.bmp
0004\0004_01_06_03_97.bmp
0004\0004_01_06_03_98.bmp
0004\0004_01_06_03_99.bmp
0006\0006_00_06_03_10.bmp
0006\0006_00_06_03_100.bmp
0006\0006_00_06_03_101.bmp
0006\0006_00_06_03_102.bmp
0006\0006_00_06_03_103.bmp
0006\0006_00_06_03_104.bmp
0006\0006_00_06_03_105.bmp
0006\0006_00_06_03_106.bmp
0006\0006_00_06_03_107.bmp
0006\0006_00_06_03_108.bmp
0006\0006_00_06_03_109.bmp
0006\0006_00_06_03_11.bmp
0006\0006_00_06_03_110.bmp
0006\0006_00_06_03_111.bmp
0006\0006_00_06_03_112.bmp
0006\0006_00_06_03_113.bmp
0006\0006_00_06_03_114.bmp
0006\0006_00_06_03_115.bmp
0006\0006_00_06_03_116.bmp
0006\0006_00_06_03_117.bmp
0006\0006_00_06_03_118.bmp
0006\0006_00_06_03_119.bmp
0006\0006_00_06_03_12.bmp
0006\0006_00_06_03_120.bmp
0006\0006_00_06_03_121.bmp
0006\0006_00_06_03_122.bmp
0006\0006_00_06_03_123.bmp
0006\0006_00_06_03_124.bmp
0006\0006_00_06_03_125.bmp
0006\0006_00_06_03_126.bmp
0006\0006_00_06_03_127.bmp
0006\0006_00_06_03_128.bmp
0006\0006_00_06_03_129.bmp
0006\0006_00_06_03_13.bmp
0006\0006_00_06_03_130.bmp
0006\0006_00_06_03_131.bmp
0006\0006_00_06_03_132.bmp
0006\0006_00_06_03_133.bmp
0006\0006_00_06_03_134.bmp
0006\0006_00_06_03_135.bmp
0006\0006_00_06_03_136.bmp
0006\0006_00_06_03_137.bmp
0006\0006_00_06_03_138.bmp
0006\0006_00_06_03_139.bmp
0006\0006_00_06_03_14.bmp
0006\0006_00_06_03_140.bmp
0006\0006_00_06_03_141.bmp
0006\0006_00_06_03_142.bmp
0006\0006_00_06_03_143.bmp
0006\0006_00_06_03_144.bmp
0006\0006_00_06_03_145.bmp
0006\0006_00_06_03_146.bmp
0006\0006_00_06_03_147.bmp
0006\0006_00_06_03_148.bmp
0006\0006_00_06_03_149.bmp
0006\0006_00_06_03_15.bmp
0006\0006_00_06_03_150.bmp
0006\0006_00_06_03_151.bmp
0006\0006_00_06_03_152.bmp
0006\0006_00_06_03_153.bmp
0006\0006_00_06_03_154.bmp
0006\0006_00_06_03_155.bmp
0006\0006_00_06_03_156.bmp
0006\0006_00_06_03_157.bmp
0006\0006_00_06_03_158.bmp
0006\0006_00_06_03_159.bmp
0006\0006_00_06_03_16.bmp
0006\0006_00_06_03_160.bmp
0006\0006_00_06_03_161.bmp
0006\0006_00_06_03_162.bmp
0006\0006_00_06_03_163.bmp
0006\0006_00_06_03_164.bmp
0006\0006_00_06_03_165.bmp
0006\0006_00_06_03_166.bmp
0006\0006_00_06_03_167.bmp
0006\0006_00_06_03_168.bmp
0006\0006_00_06_03_169.bmp
0006\0006_00_06_03_17.bmp
0006\0006_00_06_03_170.bmp
0006\0006_00_06_03_171.bmp
0006\0006_00_06_03_172.bmp
0006\0006_00_06_03_173.bmp
0006\0006_00_06_03_174.bmp
0006\0006_00_06_03_175.bmp
0006\0006_00_06_03_176.bmp
0006\0006_00_06_03_177.bmp
0006\0006_00_06_03_178.bmp
0006\0006_00_06_03_179.bmp
0006\0006_00_06_03_180.bmp
0006\0006_00_06_03_181.bmp
0006\0006_00_06_03_182.bmp
0006\0006_00_06_03_183.bmp
0006\0006_00_06_03_184.bmp
0006\0006_00_06_03_185.bmp
0006\0006_00_06_03_186.bmp
0006\0006_00_06_03_187.bmp
0006\0006_00_06_03_188.bmp
0006\0006_00_06_03_189.bmp
0006\0006_00_06_03_190.bmp
0006\0006_00_06_03_191.bmp
0006\0006_00_06_03_192.bmp
0006\0006_00_06_03_193.bmp
0006\0006_00_06_03_194.bmp
0006\0006_00_06_03_195.bmp
0006\0006_00_06_03_196.bmp
0006\0006_00_06_03_197.bmp
0006\0006_00_06_03_198.bmp
0006\0006_00_06_03_199.bmp
0006\0006_00_06_03_2.bmp
0006\0006_00_06_03_200.bmp
0006\0006_00_06_03_201.bmp
0006\0006_00_06_03_202.bmp
0006\0006_00_06_03_203.bmp
0006\0006_00_06_03_204.bmp
0006\0006_00_06_03_205.bmp
0006\0006_00_06_03_206.bmp
0006\0006_00_06_03_207.bmp
0006\0006_00_06_03_208.bmp
0006\0006_00_06_03_209.bmp
0006\0006_00_06_03_210.bmp
0006\0006_00_06_03_211.bmp
0006\0006_00_06_03_212.bmp
0006\0006_00_06_03_213.bmp
0006\0006_00_06_03_214.bmp
0006\0006_00_06_03_215.bmp
0006\0006_00_06_03_216.bmp
0006\0006_00_06_03_217.bmp
0006\0006_00_06_03_218.bmp
0006\0006_00_06_03_219.bmp
0006\0006_00_06_03_220.bmp
0006\0006_00_06_03_221.bmp
0006\0006_00_06_03_222.bmp
0006\0006_00_06_03_223.bmp
0006\0006_00_06_03_224.bmp
0006\0006_00_06_03_225.bmp
0006\0006_00_06_03_226.bmp
0006\0006_00_06_03_227.bmp
0006\0006_00_06_03_228.bmp
0006\0006_00_06_03_229.bmp
0006\0006_00_06_03_230.bmp
0006\0006_00_06_03_231.bmp
0006\0006_00_06_03_232.bmp
0006\0006_00_06_03_233.bmp
0006\0006_00_06_03_234.bmp
0006\0006_00_06_03_235.bmp
0006\0006_00_06_03_236.bmp
0006\0006_00_06_03_237.bmp
0006\0006_00_06_03_238.bmp
0006\0006_00_06_03_239.bmp
0006\0006_00_06_03_24.bmp
0006\0006_00_06_03_240.bmp
0006\0006_00_06_03_241.bmp
0006\0006_00_06_03_242.bmp
0006\0006_00_06_03_243.bmp
0006\0006_00_06_03_244.bmp
0006\0006_00_06_03_245.bmp
0006\0006_00_06_03_246.bmp
0006\0006_00_06_03_247.bmp
0006\0006_00_06_03_248.bmp
0006\0006_00_06_03_249.bmp
0006\0006_00_06_03_25.bmp
0006\0006_00_06_03_250.bmp
0006\0006_00_06_03_251.bmp
0006\0006_00_06_03_252.bmp
0006\0006_00_06_03_253.bmp
0006\0006_00_06_03_254.bmp
0006\0006_00_06_03_255.bmp
0006\0006_00_06_03_256.bmp
0006\0006_00_06_03_257.bmp
0006\0006_00_06_03_258.bmp
0006\0006_00_06_03_259.bmp
0006\0006_00_06_03_26.bmp
0006\0006_00_06_03_260.bmp
0006\0006_00_06_03_261.bmp
0006\0006_00_06_03_262.bmp
0006\0006_00_06_03_263.bmp
0006\0006_00_06_03_264.bmp
0006\0006_00_06_03_265.bmp
0006\0006_00_06_03_266.bmp
0006\0006_00_06_03_267.bmp
0006\0006_00_06_03_268.bmp
0006\0006_00_06_03_269.bmp
0006\0006_00_06_03_27.bmp
0006\0006_00_06_03_270.bmp
0006\0006_00_06_03_271.bmp
0006\0006_00_06_03_272.bmp
0006\0006_00_06_03_273.bmp
0006\0006_00_06_03_274.bmp
0006\0006_00_06_03_275.bmp
0006\0006_00_06_03_276.bmp
0006\0006_00_06_03_277.bmp
0006\0006_00_06_03_278.bmp
0006\0006_00_06_03_279.bmp
0006\0006_00_06_03_28.bmp
0006\0006_00_06_03_280.bmp
0006\0006_00_06_03_281.bmp
0006\0006_00_06_03_282.bmp
0006\0006_00_06_03_283.bmp
0006\0006_00_06_03_284.bmp
0006\0006_00_06_03_285.bmp
0006\0006_00_06_03_286.bmp
0006\0006_00_06_03_287.bmp
0006\0006_00_06_03_288.bmp
0006\0006_00_06_03_289.bmp
0006\0006_00_06_03_29.bmp
0006\0006_00_06_03_290.bmp
0006\0006_00_06_03_291.bmp
0006\0006_00_06_03_292.bmp
0006\0006_00_06_03_293.bmp
0006\0006_00_06_03_294.bmp
0006\0006_00_06_03_295.bmp
0006\0006_00_06_03_296.bmp
0006\0006_00_06_03_297.bmp
0006\0006_00_06_03_298.bmp
0006\0006_00_06_03_299.bmp
0006\0006_00_06_03_3.bmp
0006\0006_00_06_03_30.bmp
0006\0006_00_06_03_300.bmp
0006\0006_00_06_03_301.bmp
0006\0006_00_06_03_302.bmp
0006\0006_00_06_03_303.bmp
0006\0006_00_06_03_304.bmp
0006\0006_00_06_03_305.bmp
0006\0006_00_06_03_306.bmp
0006\0006_00_06_03_307.bmp
0006\0006_00_06_03_308.bmp
0006\0006_00_06_03_309.bmp
0006\0006_00_06_03_31.bmp
0006\0006_00_06_03_310.bmp
0006\0006_00_06_03_311.bmp
0006\0006_00_06_03_312.bmp
0006\0006_00_06_03_313.bmp
0006\0006_00_06_03_314.bmp
0006\0006_00_06_03_315.bmp
0006\0006_00_06_03_316.bmp
0006\0006_00_06_03_317.bmp
0006\0006_00_06_03_318.bmp
0006\0006_00_06_03_319.bmp
0006\0006_00_06_03_32.bmp
0006\0006_00_06_03_320.bmp
0006\0006_00_06_03_321.bmp
0006\0006_00_06_03_322.bmp
0006\0006_00_06_03_323.bmp
0006\0006_00_06_03_324.bmp
0006\0006_00_06_03_325.bmp
0006\0006_00_06_03_326.bmp
0006\0006_00_06_03_327.bmp
0006\0006_00_06_03_328.bmp
0006\0006_00_06_03_329.bmp
0006\0006_00_06_03_33.bmp
0006\0006_00_06_03_330.bmp
0006\0006_00_06_03_331.bmp
0006\0006_00_06_03_332.bmp
0006\0006_00_06_03_333.bmp
0006\0006_00_06_03_334.bmp
0006\0006_00_06_03_335.bmp
0006\0006_00_06_03_336.bmp
0006\0006_00_06_03_337.bmp
0006\0006_00_06_03_338.bmp
0006\0006_00_06_03_339.bmp
0006\0006_00_06_03_34.bmp
0006\0006_00_06_03_340.bmp
0006\0006_00_06_03_341.bmp
0006\0006_00_06_03_342.bmp
0006\0006_00_06_03_343.bmp
0006\0006_00_06_03_344.bmp
0006\0006_00_06_03_345.bmp
0006\0006_00_06_03_346.bmp
0006\0006_00_06_03_347.bmp
0006\0006_00_06_03_348.bmp
0006\0006_00_06_03_349.bmp
0006\0006_00_06_03_35.bmp
0006\0006_00_06_03_350.bmp
0006\0006_00_06_03_351.bmp
0006\0006_00_06_03_352.bmp
0006\0006_00_06_03_353.bmp
0006\0006_00_06_03_354.bmp
0006\0006_00_06_03_355.bmp
0006\0006_00_06_03_356.bmp
0006\0006_00_06_03_357.bmp
0006\0006_00_06_03_358.bmp
0006\0006_00_06_03_359.bmp
0006\0006_00_06_03_36.bmp
0006\0006_00_06_03_360.bmp
0006\0006_00_06_03_361.bmp
0006\0006_00_06_03_362.bmp
0006\0006_00_06_03_363.bmp
0006\0006_00_06_03_364.bmp
0006\0006_00_06_03_365.bmp
0006\0006_00_06_03_366.bmp
0006\0006_00_06_03_367.bmp
0006\0006_00_06_03_368.bmp
0006\0006_00_06_03_369.bmp
0006\0006_00_06_03_37.bmp
0006\0006_00_06_03_370.bmp
0006\0006_00_06_03_371.bmp
0006\0006_00_06_03_372.bmp
0006\0006_00_06_03_373.bmp
0006\0006_00_06_03_374.bmp
0006\0006_00_06_03_375.bmp
0006\0006_00_06_03_376.bmp
0006\0006_00_06_03_377.bmp
0006\0006_00_06_03_378.bmp
0006\0006_00_06_03_379.bmp
0006\0006_00_06_03_38.bmp
0006\0006_00_06_03_380.bmp
0006\0006_00_06_03_381.bmp
0006\0006_00_06_03_382.bmp
0006\0006_00_06_03_383.bmp
0006\0006_00_06_03_384.bmp
0006\0006_00_06_03_385.bmp
0006\0006_00_06_03_386.bmp
0006\0006_00_06_03_387.bmp
0006\0006_00_06_03_388.bmp
0006\0006_00_06_03_389.bmp
0006\0006_00_06_03_39.bmp
0006\0006_00_06_03_390.bmp
0006\0006_00_06_03_391.bmp
0006\0006_00_06_03_392.bmp
0006\0006_00_06_03_393.bmp
0006\0006_00_06_03_394.bmp
0006\0006_00_06_03_395.bmp
0006\0006_00_06_03_396.bmp
0006\0006_00_06_03_397.bmp
0006\0006_00_06_03_398.bmp
0006\0006_00_06_03_399.bmp
0006\0006_00_06_03_4.bmp
0006\0006_00_06_03_40.bmp
0006\0006_00_06_03_400.bmp
0006\0006_00_06_03_401.bmp
0006\0006_00_06_03_402.bmp
0006\0006_00_06_03_403.bmp
0006\0006_00_06_03_404.bmp
0006\0006_00_06_03_405.bmp
0006\0006_00_06_03_406.bmp
0006\0006_00_06_03_407.bmp
0006\0006_00_06_03_408.bmp
0006\0006_00_06_03_409.bmp
0006\0006_00_06_03_41.bmp
0006\0006_00_06_03_410.bmp
0006\0006_00_06_03_411.bmp
0006\0006_00_06_03_412.bmp
0006\0006_00_06_03_413.bmp
0006\0006_00_06_03_414.bmp
0006\0006_00_06_03_415.bmp
0006\0006_00_06_03_416.bmp
0006\0006_00_06_03_417.bmp
0006\0006_00_06_03_418.bmp
0006\0006_00_06_03_419.bmp
0006\0006_00_06_03_42.bmp
0006\0006_00_06_03_420.bmp
0006\0006_00_06_03_421.bmp
0006\0006_00_06_03_422.bmp
0006\0006_00_06_03_423.bmp
0006\0006_00_06_03_424.bmp
0006\0006_00_06_03_425.bmp
0006\0006_00_06_03_426.bmp
0006\0006_00_06_03_427.bmp
0006\0006_00_06_03_428.bmp
0006\0006_00_06_03_429.bmp
0006\0006_00_06_03_43.bmp
0006\0006_00_06_03_430.bmp
0006\0006_00_06_03_431.bmp
0006\0006_00_06_03_432.bmp
0006\0006_00_06_03_433.bmp
0006\0006_00_06_03_434.bmp
0006\0006_00_06_03_435.bmp
0006\0006_00_06_03_436.bmp
0006\0006_00_06_03_437.bmp
0006\0006_00_06_03_438.bmp
0006\0006_00_06_03_439.bmp
0006\0006_00_06_03_44.bmp
0006\0006_00_06_03_440.bmp
0006\0006_00_06_03_441.bmp
0006\0006_00_06_03_442.bmp
0006\0006_00_06_03_443.bmp
0006\0006_00_06_03_444.bmp
0006\0006_00_06_03_445.bmp
0006\0006_00_06_03_446.bmp
0006\0006_00_06_03_447.bmp
0006\0006_00_06_03_448.bmp
0006\0006_00_06_03_449.bmp
0006\0006_00_06_03_45.bmp
0006\0006_00_06_03_450.bmp
0006\0006_00_06_03_451.bmp
0006\0006_00_06_03_452.bmp
0006\0006_00_06_03_453.bmp
0006\0006_00_06_03_454.bmp
0006\0006_00_06_03_455.bmp
0006\0006_00_06_03_456.bmp
0006\0006_00_06_03_457.bmp
0006\0006_00_06_03_458.bmp
0006\0006_00_06_03_459.bmp
0006\0006_00_06_03_46.bmp
0006\0006_00_06_03_460.bmp
0006\0006_00_06_03_461.bmp
0006\0006_00_06_03_462.bmp
0006\0006_00_06_03_463.bmp
0006\0006_00_06_03_464.bmp
0006\0006_00_06_03_465.bmp
0006\0006_00_06_03_466.bmp
0006\0006_00_06_03_467.bmp
0006\0006_00_06_03_468.bmp
0006\0006_00_06_03_469.bmp
0006\0006_00_06_03_47.bmp
0006\0006_00_06_03_470.bmp
0006\0006_00_06_03_471.bmp
0006\0006_00_06_03_472.bmp
0006\0006_00_06_03_473.bmp
0006\0006_00_06_03_474.bmp
0006\0006_00_06_03_475.bmp
0006\0006_00_06_03_476.bmp
0006\0006_00_06_03_477.bmp
0006\0006_00_06_03_478.bmp
0006\0006_00_06_03_479.bmp
0006\0006_00_06_03_48.bmp
0006\0006_00_06_03_480.bmp
0006\0006_00_06_03_481.bmp
0006\0006_00_06_03_482.bmp
0006\0006_00_06_03_483.bmp
0006\0006_00_06_03_484.bmp
0006\0006_00_06_03_485.bmp
0006\0006_00_06_03_486.bmp
0006\0006_00_06_03_487.bmp
0006\0006_00_06_03_488.bmp
0006\0006_00_06_03_489.bmp
0006\0006_00_06_03_49.bmp
0006\0006_00_06_03_490.bmp
0006\0006_00_06_03_491.bmp
0006\0006_00_06_03_492.bmp
0006\0006_00_06_03_493.bmp
0006\0006_00_06_03_494.bmp
0006\0006_00_06_03_495.bmp
0006\0006_00_06_03_496.bmp
0006\0006_00_06_03_497.bmp
0006\0006_00_06_03_498.bmp
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0013\0013_01_07_03_64.bmp
0013\0013_01_07_03_65.bmp
0013\0013_01_07_03_66.bmp
0013\0013_01_07_03_67.bmp
0013\0013_01_07_03_68.bmp
0013\0013_01_07_03_69.bmp
0013\0013_01_07_03_7.bmp
0013\0013_01_07_03_70.bmp
0013\0013_01_07_03_71.bmp
0013\0013_01_07_03_72.bmp
0013\0013_01_07_03_73.bmp
0013\0013_01_07_03_74.bmp
0013\0013_01_07_03_75.bmp
0013\0013_01_07_03_76.bmp
0013\0013_01_07_03_8.bmp
0013\0013_01_07_03_84.bmp
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0013\0013_01_07_03_9.bmp
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0012\0012_01_07_03_264.bmp
0012\0012_01_07_03_266.bmp
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0012\0012_01_07_03_27.bmp
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0012\0012_01_07_03_297.bmp
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0012\0012_01_07_03_32.bmp
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0012\0012_01_07_03_342.bmp
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0011\0011_01_07_03_129.bmp
0011\0011_01_07_03_13.bmp
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0011\0011_01_07_03_136.bmp
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0011\0011_01_07_03_14.bmp
0011\0011_01_07_03_140.bmp
0011\0011_01_07_03_141.bmp
0011\0011_01_07_03_142.bmp
0011\0011_01_07_03_143.bmp
0011\0011_01_07_03_144.bmp
0011\0011_01_07_03_145.bmp
0011\0011_01_07_03_146.bmp
0011\0011_01_07_03_147.bmp
0011\0011_01_07_03_148.bmp
0011\0011_01_07_03_149.bmp
0011\0011_01_07_03_15.bmp
0011\0011_01_07_03_150.bmp
0011\0011_01_07_03_151.bmp
0011\0011_01_07_03_152.bmp
0011\0011_01_07_03_153.bmp
0011\0011_01_07_03_154.bmp
0011\0011_01_07_03_156.bmp
0011\0011_01_07_03_157.bmp
0011\0011_01_07_03_158.bmp
0011\0011_01_07_03_159.bmp
0011\0011_01_07_03_16.bmp
0011\0011_01_07_03_160.bmp
0011\0011_01_07_03_162.bmp
0011\0011_01_07_03_163.bmp
0011\0011_01_07_03_164.bmp
0011\0011_01_07_03_165.bmp
0011\0011_01_07_03_166.bmp
0011\0011_01_07_03_167.bmp
0011\0011_01_07_03_168.bmp
0011\0011_01_07_03_169.bmp
0011\0011_01_07_03_17.bmp
0011\0011_01_07_03_170.bmp
0011\0011_01_07_03_171.bmp
0011\0011_01_07_03_172.bmp
0011\0011_01_07_03_173.bmp
0011\0011_01_07_03_174.bmp
0011\0011_01_07_03_175.bmp
0011\0011_01_07_03_176.bmp
0011\0011_01_07_03_177.bmp
0011\0011_01_07_03_178.bmp
0011\0011_01_07_03_179.bmp
0011\0011_01_07_03_18.bmp
0011\0011_01_07_03_180.bmp
0011\0011_01_07_03_181.bmp
0011\0011_01_07_03_182.bmp
0011\0011_01_07_03_184.bmp
0011\0011_01_07_03_186.bmp
0011\0011_01_07_03_187.bmp
0011\0011_01_07_03_19.bmp
0011\0011_01_07_03_191.bmp
0011\0011_01_07_03_192.bmp
0011\0011_01_07_03_193.bmp
0011\0011_01_07_03_194.bmp
0011\0011_01_07_03_196.bmp
0011\0011_01_07_03_198.bmp
0011\0011_01_07_03_199.bmp
0011\0011_01_07_03_2.bmp
0011\0011_01_07_03_20.bmp
0011\0011_01_07_03_200.bmp
0011\0011_01_07_03_201.bmp
0011\0011_01_07_03_202.bmp
0011\0011_01_07_03_203.bmp
0011\0011_01_07_03_204.bmp
0011\0011_01_07_03_205.bmp
0011\0011_01_07_03_206.bmp
0011\0011_01_07_03_207.bmp
0011\0011_01_07_03_208.bmp
0011\0011_01_07_03_209.bmp
0011\0011_01_07_03_21.bmp
0011\0011_01_07_03_215.bmp
0011\0011_01_07_03_216.bmp
0011\0011_01_07_03_218.bmp
0011\0011_01_07_03_219.bmp
0011\0011_01_07_03_22.bmp
0011\0011_01_07_03_220.bmp
0011\0011_01_07_03_221.bmp
0011\0011_01_07_03_222.bmp
0011\0011_01_07_03_223.bmp
0011\0011_01_07_03_224.bmp
0011\0011_01_07_03_225.bmp
0011\0011_01_07_03_226.bmp
0011\0011_01_07_03_228.bmp
0011\0011_01_07_03_229.bmp
0011\0011_01_07_03_23.bmp
0011\0011_01_07_03_230.bmp
0011\0011_01_07_03_231.bmp
0011\0011_01_07_03_232.bmp
0011\0011_01_07_03_233.bmp
0011\0011_01_07_03_234.bmp
0011\0011_01_07_03_235.bmp
0011\0011_01_07_03_236.bmp
0011\0011_01_07_03_237.bmp
0011\0011_01_07_03_238.bmp
0011\0011_01_07_03_239.bmp
0011\0011_01_07_03_24.bmp
0011\0011_01_07_03_240.bmp
0011\0011_01_07_03_241.bmp
0011\0011_01_07_03_242.bmp
0011\0011_01_07_03_243.bmp
0011\0011_01_07_03_244.bmp
0011\0011_01_07_03_249.bmp
0011\0011_01_07_03_25.bmp
0011\0011_01_07_03_250.bmp
0011\0011_01_07_03_252.bmp
0011\0011_01_07_03_254.bmp
0011\0011_01_07_03_255.bmp
0011\0011_01_07_03_256.bmp
0011\0011_01_07_03_257.bmp
0011\0011_01_07_03_258.bmp
0011\0011_01_07_03_259.bmp
0011\0011_01_07_03_26.bmp
0011\0011_01_07_03_260.bmp
0011\0011_01_07_03_261.bmp
0011\0011_01_07_03_263.bmp
0011\0011_01_07_03_264.bmp
0011\0011_01_07_03_265.bmp
0011\0011_01_07_03_266.bmp
0011\0011_01_07_03_267.bmp
0011\0011_01_07_03_269.bmp
0011\0011_01_07_03_27.bmp
0011\0011_01_07_03_270.bmp
0011\0011_01_07_03_271.bmp
0011\0011_01_07_03_272.bmp
0011\0011_01_07_03_273.bmp
0011\0011_01_07_03_274.bmp
0011\0011_01_07_03_276.bmp
0011\0011_01_07_03_277.bmp
0011\0011_01_07_03_278.bmp
0011\0011_01_07_03_279.bmp
0011\0011_01_07_03_28.bmp
0011\0011_01_07_03_280.bmp
0011\0011_01_07_03_281.bmp
0011\0011_01_07_03_282.bmp
0011\0011_01_07_03_283.bmp
0011\0011_01_07_03_284.bmp
0011\0011_01_07_03_285.bmp
0011\0011_01_07_03_286.bmp
0011\0011_01_07_03_287.bmp
0011\0011_01_07_03_288.bmp
0011\0011_01_07_03_29.bmp
0011\0011_01_07_03_290.bmp
0011\0011_01_07_03_291.bmp
0011\0011_01_07_03_293.bmp
0011\0011_01_07_03_294.bmp
0011\0011_01_07_03_295.bmp
0011\0011_01_07_03_296.bmp
0011\0011_01_07_03_297.bmp
0011\0011_01_07_03_298.bmp
0011\0011_01_07_03_299.bmp
0011\0011_01_07_03_3.bmp
0011\0011_01_07_03_30.bmp
0011\0011_01_07_03_300.bmp
0011\0011_01_07_03_301.bmp
0011\0011_01_07_03_303.bmp
0011\0011_01_07_03_304.bmp
0011\0011_01_07_03_305.bmp
0011\0011_01_07_03_306.bmp
0011\0011_01_07_03_307.bmp
0011\0011_01_07_03_308.bmp
0011\0011_01_07_03_309.bmp
0011\0011_01_07_03_31.bmp
0011\0011_01_07_03_310.bmp
0011\0011_01_07_03_311.bmp
0011\0011_01_07_03_312.bmp
0011\0011_01_07_03_313.bmp
0011\0011_01_07_03_314.bmp
0011\0011_01_07_03_315.bmp
0011\0011_01_07_03_318.bmp
0011\0011_01_07_03_319.bmp
0011\0011_01_07_03_32.bmp
0011\0011_01_07_03_320.bmp
0011\0011_01_07_03_321.bmp
0011\0011_01_07_03_322.bmp
0011\0011_01_07_03_326.bmp
0011\0011_01_07_03_327.bmp
0011\0011_01_07_03_328.bmp
0011\0011_01_07_03_329.bmp
0011\0011_01_07_03_33.bmp
0011\0011_01_07_03_330.bmp
0011\0011_01_07_03_331.bmp
0011\0011_01_07_03_332.bmp
0011\0011_01_07_03_333.bmp
0011\0011_01_07_03_334.bmp
0011\0011_01_07_03_335.bmp
0011\0011_01_07_03_336.bmp
0011\0011_01_07_03_34.bmp
0011\0011_01_07_03_341.bmp
0011\0011_01_07_03_342.bmp
0011\0011_01_07_03_343.bmp
0011\0011_01_07_03_345.bmp
0011\0011_01_07_03_346.bmp
0011\0011_01_07_03_348.bmp
0011\0011_01_07_03_349.bmp
0011\0011_01_07_03_35.bmp
0011\0011_01_07_03_350.bmp
0011\0011_01_07_03_351.bmp
0011\0011_01_07_03_352.bmp
0011\0011_01_07_03_353.bmp
0011\0011_01_07_03_354.bmp
0011\0011_01_07_03_355.bmp
0011\0011_01_07_03_356.bmp
0011\0011_01_07_03_358.bmp
0011\0011_01_07_03_36.bmp
0011\0011_01_07_03_360.bmp
0011\0011_01_07_03_361.bmp
0011\0011_01_07_03_362.bmp
0011\0011_01_07_03_363.bmp
0011\0011_01_07_03_365.bmp
0011\0011_01_07_03_366.bmp
0011\0011_01_07_03_367.bmp
0011\0011_01_07_03_369.bmp
0011\0011_01_07_03_37.bmp
0011\0011_01_07_03_370.bmp
0011\0011_01_07_03_371.bmp
0011\0011_01_07_03_373.bmp
0011\0011_01_07_03_374.bmp
0011\0011_01_07_03_376.bmp
0011\0011_01_07_03_377.bmp
0011\0011_01_07_03_378.bmp
0011\0011_01_07_03_38.bmp
0011\0011_01_07_03_380.bmp
0011\0011_01_07_03_381.bmp
0011\0011_01_07_03_382.bmp
0011\0011_01_07_03_385.bmp
0011\0011_01_07_03_386.bmp
0011\0011_01_07_03_387.bmp
0011\0011_01_07_03_388.bmp
0011\0011_01_07_03_389.bmp
0011\0011_01_07_03_39.bmp
0011\0011_01_07_03_390.bmp
0011\0011_01_07_03_391.bmp
0011\0011_01_07_03_392.bmp
0011\0011_01_07_03_393.bmp
0011\0011_01_07_03_394.bmp
0011\0011_01_07_03_395.bmp
0011\0011_01_07_03_396.bmp
0011\0011_01_07_03_399.bmp
0011\0011_01_07_03_4.bmp
0011\0011_01_07_03_40.bmp
0011\0011_01_07_03_400.bmp
0011\0011_01_07_03_401.bmp
0011\0011_01_07_03_402.bmp
0011\0011_01_07_03_404.bmp
0011\0011_01_07_03_405.bmp
0011\0011_01_07_03_406.bmp
0011\0011_01_07_03_407.bmp
0011\0011_01_07_03_408.bmp
0011\0011_01_07_03_409.bmp
0011\0011_01_07_03_41.bmp
0011\0011_01_07_03_410.bmp
0011\0011_01_07_03_412.bmp
0011\0011_01_07_03_413.bmp
0011\0011_01_07_03_414.bmp
0011\0011_01_07_03_415.bmp
0011\0011_01_07_03_416.bmp
0011\0011_01_07_03_417.bmp
0011\0011_01_07_03_418.bmp
0011\0011_01_07_03_419.bmp
0011\0011_01_07_03_42.bmp
0011\0011_01_07_03_420.bmp
0011\0011_01_07_03_421.bmp
0011\0011_01_07_03_422.bmp
0011\0011_01_07_03_423.bmp
0011\0011_01_07_03_424.bmp
0011\0011_01_07_03_425.bmp
0011\0011_01_07_03_428.bmp
0011\0011_01_07_03_43.bmp
0011\0011_01_07_03_430.bmp
0011\0011_01_07_03_431.bmp
0011\0011_01_07_03_432.bmp
0011\0011_01_07_03_433.bmp
0011\0011_01_07_03_434.bmp
0011\0011_01_07_03_435.bmp
0011\0011_01_07_03_436.bmp
0011\0011_01_07_03_437.bmp
0011\0011_01_07_03_438.bmp
0011\0011_01_07_03_44.bmp
0011\0011_01_07_03_445.bmp
0011\0011_01_07_03_446.bmp
0011\0011_01_07_03_447.bmp
0011\0011_01_07_03_448.bmp
0011\0011_01_07_03_449.bmp
0011\0011_01_07_03_45.bmp
0011\0011_01_07_03_450.bmp
0011\0011_01_07_03_452.bmp
0011\0011_01_07_03_453.bmp
0011\0011_01_07_03_454.bmp
0011\0011_01_07_03_455.bmp
0011\0011_01_07_03_456.bmp
0011\0011_01_07_03_457.bmp
0011\0011_01_07_03_459.bmp
0011\0011_01_07_03_46.bmp
0011\0011_01_07_03_460.bmp
0011\0011_01_07_03_467.bmp
0011\0011_01_07_03_47.bmp
0011\0011_01_07_03_479.bmp
0011\0011_01_07_03_482.bmp
0011\0011_01_07_03_483.bmp
0011\0011_01_07_03_484.bmp
0011\0011_01_07_03_487.bmp
0011\0011_01_07_03_488.bmp
0011\0011_01_07_03_489.bmp
0011\0011_01_07_03_49.bmp
0011\0011_01_07_03_490.bmp
0011\0011_01_07_03_491.bmp
0011\0011_01_07_03_492.bmp
0011\0011_01_07_03_493.bmp
0011\0011_01_07_03_494.bmp
0011\0011_01_07_03_495.bmp
0011\0011_01_07_03_496.bmp
0011\0011_01_07_03_497.bmp
0011\0011_01_07_03_499.bmp
0011\0011_01_07_03_5.bmp
0011\0011_01_07_03_50.bmp
0011\0011_01_07_03_51.bmp
0011\0011_01_07_03_52.bmp
0011\0011_01_07_03_53.bmp
0011\0011_01_07_03_54.bmp
0011\0011_01_07_03_55.bmp
0011\0011_01_07_03_56.bmp
0011\0011_01_07_03_57.bmp
0011\0011_01_07_03_58.bmp
0011\0011_01_07_03_59.bmp
0011\0011_01_07_03_6.bmp
0011\0011_01_07_03_60.bmp
0011\0011_01_07_03_61.bmp
0011\0011_01_07_03_63.bmp
0011\0011_01_07_03_64.bmp
0011\0011_01_07_03_65.bmp
0011\0011_01_07_03_66.bmp
0011\0011_01_07_03_67.bmp
0011\0011_01_07_03_68.bmp
0011\0011_01_07_03_69.bmp
0011\0011_01_07_03_7.bmp
0011\0011_01_07_03_70.bmp
0011\0011_01_07_03_71.bmp
0011\0011_01_07_03_72.bmp
0011\0011_01_07_03_73.bmp
0011\0011_01_07_03_74.bmp
0011\0011_01_07_03_75.bmp
0011\0011_01_07_03_76.bmp
0011\0011_01_07_03_77.bmp
0011\0011_01_07_03_78.bmp
0011\0011_01_07_03_79.bmp
0011\0011_01_07_03_8.bmp
0011\0011_01_07_03_80.bmp
0011\0011_01_07_03_81.bmp
0011\0011_01_07_03_82.bmp
0011\0011_01_07_03_83.bmp
0011\0011_01_07_03_84.bmp
0011\0011_01_07_03_85.bmp
0011\0011_01_07_03_86.bmp
0011\0011_01_07_03_87.bmp
0011\0011_01_07_03_88.bmp
0011\0011_01_07_03_89.bmp
0011\0011_01_07_03_9.bmp
0011\0011_01_07_03_90.bmp
0011\0011_01_07_03_91.bmp
0011\0011_01_07_03_92.bmp
0011\0011_01_07_03_93.bmp
0011\0011_01_07_03_94.bmp
0011\0011_01_07_03_95.bmp
0011\0011_01_07_03_96.bmp
0011\0011_01_07_03_97.bmp
0011\0011_01_07_03_98.bmp
0011\0011_01_07_03_99.bmp


================================================
FILE: Matlab/NUAA/client_train_normalized.txt
================================================
0001\0001_00_00_01_0.bmp
0001\0001_00_00_01_101.bmp
0001\0001_00_00_01_105.bmp
0001\0001_00_00_01_109.bmp
0001\0001_00_00_01_112.bmp
0001\0001_00_00_01_116.bmp
0001\0001_00_00_01_12.bmp
0001\0001_00_00_01_123.bmp
0001\0001_00_00_01_127.bmp
0001\0001_00_00_01_130.bmp
0001\0001_00_00_01_134.bmp
0001\0001_00_00_01_138.bmp
0001\0001_00_00_01_141.bmp
0001\0001_00_00_01_145.bmp
0001\0001_00_00_01_149.bmp
0001\0001_00_00_01_152.bmp
0001\0001_00_00_01_156.bmp
0001\0001_00_00_01_16.bmp
0001\0001_00_00_01_163.bmp
0001\0001_00_00_01_167.bmp
0001\0001_00_00_01_170.bmp
0001\0001_00_00_01_174.bmp
0001\0001_00_00_01_178.bmp
0001\0001_00_00_01_181.bmp
0001\0001_00_00_01_185.bmp
0001\0001_00_00_01_189.bmp
0001\0001_00_00_01_192.bmp
0001\0001_00_00_01_196.bmp
0001\0001_00_00_01_2.bmp
0001\0001_00_00_01_202.bmp
0001\0001_00_00_01_206.bmp
0001\0001_00_00_01_21.bmp
0001\0001_00_00_01_213.bmp
0001\0001_00_00_01_217.bmp
0001\0001_00_00_01_220.bmp
0001\0001_00_00_01_224.bmp
0001\0001_00_00_01_228.bmp
0001\0001_00_00_01_231.bmp
0001\0001_00_00_01_235.bmp
0001\0001_00_00_01_239.bmp
0001\0001_00_00_01_242.bmp
0001\0001_00_00_01_246.bmp
0001\0001_00_00_01_25.bmp
0001\0001_00_00_01_253.bmp
0001\0001_00_00_01_257.bmp
0001\0001_00_00_01_260.bmp
0001\0001_00_00_01_264.bmp
0001\0001_00_00_01_268.bmp
0001\0001_00_00_01_271.bmp
0001\0001_00_00_01_275.bmp
0001\0001_00_00_01_279.bmp
0001\0001_00_00_01_282.bmp
0001\0001_00_00_01_286.bmp
0001\0001_00_00_01_29.bmp
0001\0001_00_00_01_293.bmp
0001\0001_00_00_01_297.bmp
0001\0001_00_00_01_30.bmp
0001\0001_00_00_01_303.bmp
0001\0001_00_00_01_307.bmp
0001\0001_00_00_01_310.bmp
0001\0001_00_00_01_314.bmp
0001\0001_00_00_01_318.bmp
0001\0001_00_00_01_321.bmp
0001\0001_00_00_01_325.bmp
0001\0001_00_00_01_329.bmp
0001\0001_00_00_01_332.bmp
0001\0001_00_00_01_336.bmp
0001\0001_00_00_01_34.bmp
0001\0001_00_00_01_343.bmp
0001\0001_00_00_01_347.bmp
0001\0001_00_00_01_350.bmp
0001\0001_00_00_01_354.bmp
0001\0001_00_00_01_358.bmp
0001\0001_00_00_01_361.bmp
0001\0001_00_00_01_365.bmp
0001\0001_00_00_01_369.bmp
0001\0001_00_00_01_372.bmp
0001\0001_00_00_01_376.bmp
0001\0001_00_00_01_38.bmp
0001\0001_00_00_01_383.bmp
0001\0001_00_00_01_387.bmp
0001\0001_00_00_01_390.bmp
0001\0001_00_00_01_394.bmp
0001\0001_00_00_01_398.bmp
0001\0001_00_00_01_400.bmp
0001\0001_00_00_01_404.bmp
0001\0001_00_00_01_408.bmp
0001\0001_00_00_01_411.bmp
0001\0001_00_00_01_415.bmp
0001\0001_00_00_01_419.bmp
0001\0001_00_00_01_422.bmp
0001\0001_00_00_01_426.bmp
0001\0001_00_00_01_43.bmp
0001\0001_00_00_01_433.bmp
0001\0001_00_00_01_437.bmp
0001\0001_00_00_01_440.bmp
0001\0001_00_00_01_444.bmp
0001\0001_00_00_01_448.bmp
0001\0001_00_00_01_451.bmp
0001\0001_00_00_01_455.bmp
0001\0001_00_00_01_459.bmp
0001\0001_00_00_01_462.bmp
0001\0001_00_00_01_466.bmp
0001\0001_00_00_01_47.bmp
0001\0001_00_00_01_473.bmp
0001\0001_00_00_01_477.bmp
0001\0001_00_00_01_480.bmp
0001\0001_00_00_01_484.bmp
0001\0001_00_00_01_488.bmp
0001\0001_00_00_01_491.bmp
0001\0001_00_00_01_495.bmp
0001\0001_00_00_01_499.bmp
0001\0001_00_00_01_52.bmp
0001\0001_00_00_01_56.bmp
0001\0001_00_00_01_6.bmp
0001\0001_00_00_01_63.bmp
0001\0001_00_00_01_67.bmp
0001\0001_00_00_01_70.bmp
0001\0001_00_00_01_74.bmp
0001\0001_00_00_01_78.bmp
0001\0001_00_00_01_81.bmp
0001\0001_00_00_01_85.bmp
0001\0001_00_00_01_89.bmp
0001\0001_00_00_01_92.bmp
0001\0001_00_00_01_96.bmp
0002\0002_01_00_01_100.bmp
0002\0002_01_00_01_109.bmp
0002\0002_01_00_01_116.bmp
0002\0002_01_00_01_120.bmp
0002\0002_01_00_01_124.bmp
0002\0002_01_00_01_128.bmp
0002\0002_01_00_01_132.bmp
0002\0002_01_00_01_136.bmp
0002\0002_01_00_01_14.bmp
0002\0002_01_00_01_143.bmp
0002\0002_01_00_01_147.bmp
0002\0002_01_00_01_150.bmp
0002\0002_01_00_01_154.bmp
0002\0002_01_00_01_158.bmp
0002\0002_01_00_01_17.bmp
0002\0002_01_00_01_192.bmp
0002\0002_01_00_01_200.bmp
0002\0002_01_00_01_206.bmp
0002\0002_01_00_01_212.bmp
0002\0002_01_00_01_217.bmp
0002\0002_01_00_01_221.bmp
0002\0002_01_00_01_227.bmp
0002\0002_01_00_01_25.bmp
0002\0002_01_00_01_29.bmp
0002\0002_01_00_01_303.bmp
0002\0002_01_00_01_308.bmp
0002\0002_01_00_01_311.bmp
0002\0002_01_00_01_315.bmp
0002\0002_01_00_01_319.bmp
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0003\0003_01_00_01_98.bmp
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0004\0004_01_00_01_9.bmp
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0004\0004_01_00_01_97.bmp
0005\0005_00_00_01_0.bmp
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0005\0005_00_00_01_12.bmp
0005\0005_00_00_01_13.bmp
0005\0005_00_00_01_136.bmp
0005\0005_00_00_01_152.bmp
0005\0005_00_00_01_16.bmp
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0005\0005_00_00_01_195.bmp
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0005\0005_00_00_01_206.bmp
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0005\0005_00_00_01_381.bmp
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0005\0005_00_00_01_47.bmp
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0005\0005_00_00_01_477.bmp
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0005\0005_00_00_01_488.bmp
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0005\0005_00_00_01_62.bmp
0005\0005_00_00_01_66.bmp
0005\0005_00_00_01_7.bmp
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0005\0005_00_00_01_88.bmp
0005\0005_00_00_01_91.bmp
0005\0005_00_00_01_95.bmp
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0006\0006_00_00_01_405.bmp
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0006\0006_00_00_01_416.bmp
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0006\0006_00_00_01_81.bmp
0006\0006_00_00_01_85.bmp
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0009\0009_01_00_01_158.bmp
0009\0009_01_00_01_161.bmp
0009\0009_01_00_01_165.bmp
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0009\0009_01_00_01_244.bmp
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0009\0009_01_00_01_256.bmp
0009\0009_01_00_01_26.bmp
0009\0009_01_00_01_263.bmp
0009\0009_01_00_01_268.bmp
0009\0009_01_00_01_271.bmp
0009\0009_01_00_01_275.bmp
0009\0009_01_00_01_279.bmp
0009\0009_01_00_01_282.bmp
0009\0009_01_00_01_286.bmp
0009\0009_01_00_01_29.bmp
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0009\0009_01_00_01_297.bmp
0009\0009_01_00_01_301.bmp
0009\0009_01_00_01_305.bmp
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0009\0009_01_00_01_312.bmp
0009\0009_01_00_01_316.bmp
0009\0009_01_00_01_32.bmp
0009\0009_01_00_01_323.bmp
0009\0009_01_00_01_327.bmp
0009\0009_01_00_01_332.bmp
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0009\0009_01_00_01_341.bmp
0009\0009_01_00_01_345.bmp
0009\0009_01_00_01_349.bmp
0009\0009_01_00_01_352.bmp
0009\0009_01_00_01_356.bmp
0009\0009_01_00_01_36.bmp
0009\0009_01_00_01_363.bmp
0009\0009_01_00_01_367.bmp
0009\0009_01_00_01_370.bmp
0009\0009_01_00_01_374.bmp
0009\0009_01_00_01_378.bmp
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0007\0007_00_00_01_316.bmp
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0008\0008_00_00_02_318.bmp
0008\0008_00_00_02_321.bmp
0008\0008_00_00_02_325.bmp
0008\0008_00_00_02_329.bmp
0008\0008_00_00_02_332.bmp
0008\0008_00_00_02_336.bmp
0008\0008_00_00_02_34.bmp
0008\0008_00_00_02_343.bmp
0008\0008_00_00_02_347.bmp
0008\0008_00_00_02_350.bmp
0008\0008_00_00_02_354.bmp
0008\0008_00_00_02_358.bmp
0008\0008_00_00_02_361.bmp
0008\0008_00_00_02_365.bmp
0008\0008_00_00_02_369.bmp
0008\0008_00_00_02_372.bmp
0008\0008_00_00_02_376.bmp
0008\0008_00_00_02_38.bmp
0008\0008_00_00_02_383.bmp
0008\0008_00_00_02_387.bmp
0008\0008_00_00_02_390.bmp
0008\0008_00_00_02_394.bmp
0008\0008_00_00_02_398.bmp
0008\0008_00_00_02_400.bmp
0008\0008_00_00_02_404.bmp
0008\0008_00_00_02_408.bmp
0008\0008_00_00_02_411.bmp
0008\0008_00_00_02_415.bmp
0008\0008_00_00_02_419.bmp
0008\0008_00_00_02_422.bmp
0008\0008_00_00_02_426.bmp
0008\0008_00_00_02_43.bmp
0008\0008_00_00_02_433.bmp
0008\0008_00_00_02_437.bmp
0008\0008_00_00_02_440.bmp
0008\0008_00_00_02_444.bmp
0008\0008_00_00_02_448.bmp
0008\0008_00_00_02_451.bmp
0008\0008_00_00_02_455.bmp
0008\0008_00_00_02_459.bmp
0008\0008_00_00_02_462.bmp
0008\0008_00_00_02_466.bmp
0008\0008_00_00_02_47.bmp
0008\0008_00_00_02_473.bmp
0008\0008_00_00_02_48.bmp
0008\0008_00_00_02_485.bmp
0008\0008_00_00_02_489.bmp
0008\0008_00_00_02_492.bmp
0008\0008_00_00_02_496.bmp
0008\0008_00_00_02_5.bmp
0008\0008_00_00_02_53.bmp
0008\0008_00_00_02_57.bmp
0008\0008_00_00_02_60.bmp
0008\0008_00_00_02_64.bmp
0008\0008_00_00_02_68.bmp
0008\0008_00_00_02_71.bmp
0008\0008_00_00_02_75.bmp
0008\0008_00_00_02_79.bmp
0008\0008_00_00_02_82.bmp
0008\0008_00_00_02_86.bmp
0008\0008_00_00_02_9.bmp
0008\0008_00_00_02_93.bmp
0008\0008_00_00_02_97.bmp


================================================
FILE: Matlab/NUAA/imposter_test_normalized.txt
================================================
0001\0001_00_03_03_0.bmp
0001\0001_00_03_03_103.bmp
0001\0001_00_03_03_109.bmp
0001\0001_00_03_03_114.bmp
0001\0001_00_03_03_12.bmp
0001\0001_00_03_03_125.bmp
0001\0001_00_03_03_130.bmp
0001\0001_00_03_03_136.bmp
0001\0001_00_03_03_141.bmp
0001\0001_00_03_03_147.bmp
0001\0001_00_03_03_152.bmp
0001\0001_00_03_03_161.bmp
0001\0001_00_03_03_167.bmp
0001\0001_00_03_03_172.bmp
0001\0001_00_03_03_178.bmp
0001\0001_00_03_03_183.bmp
0001\0001_00_03_03_189.bmp
0001\0001_00_03_03_194.bmp
0001\0001_00_03_03_2.bmp
0001\0001_00_03_03_204.bmp
0001\0001_00_03_03_21.bmp
0001\0001_00_03_03_215.bmp
0001\0001_00_03_03_220.bmp
0001\0001_00_03_03_226.bmp
0001\0001_00_03_03_231.bmp
0001\0001_00_03_03_237.bmp
0001\0001_00_03_03_242.bmp
0001\0001_00_03_03_248.bmp
0001\0001_00_03_03_253.bmp
0001\0001_00_03_03_259.bmp
0001\0001_00_03_03_264.bmp
0001\0001_00_03_03_27.bmp
0001\0001_00_03_03_275.bmp
0001\0001_00_03_03_280.bmp
0001\0001_00_03_03_286.bmp
0001\0001_00_03_03_291.bmp
0001\0001_00_03_03_297.bmp
0001\0001_00_03_03_301.bmp
0001\0001_00_03_03_307.bmp
0001\0001_00_03_03_312.bmp
0001\0001_00_03_03_318.bmp
0001\0001_00_03_03_323.bmp
0001\0001_00_03_03_329.bmp
0001\0001_00_03_03_34.bmp
0001\0001_00_03_03_345.bmp
0001\0001_00_03_03_350.bmp
0001\0001_00_03_03_356.bmp
0001\0001_00_03_03_361.bmp
0001\0001_00_03_03_367.bmp
0001\0001_00_03_03_372.bmp
0001\0001_00_03_03_378.bmp
0001\0001_00_03_03_383.bmp
0001\0001_00_03_03_389.bmp
0001\0001_00_03_03_394.bmp
0001\0001_00_03_03_4.bmp
0001\0001_00_03_03_404.bmp
0001\0001_00_03_03_41.bmp
0001\0001_00_03_03_42.bmp
0001\0001_00_03_03_429.bmp
0001\0001_00_03_03_434.bmp
0001\0001_00_03_03_44.bmp
0001\0001_00_03_03_445.bmp
0001\0001_00_03_03_450.bmp
0001\0001_00_03_03_456.bmp
0001\0001_00_03_03_461.bmp
0001\0001_00_03_03_467.bmp
0001\0001_00_03_03_472.bmp
0001\0001_00_03_03_478.bmp
0001\0001_00_03_03_483.bmp
0001\0001_00_03_03_489.bmp
0001\0001_00_03_03_496.bmp
0001\0001_00_03_03_54.bmp
0001\0001_00_03_03_6.bmp
0001\0001_00_03_03_65.bmp
0001\0001_00_03_03_70.bmp
0001\0001_00_03_03_76.bmp
0001\0001_00_03_03_81.bmp
0001\0001_00_03_03_87.bmp
0001\0001_00_03_03_92.bmp
0001\0001_00_03_03_98.bmp
0001\0001_00_04_03_0.bmp
0001\0001_00_04_03_103.bmp
0001\0001_00_04_03_109.bmp
0001\0001_00_04_03_114.bmp
0001\0001_00_04_03_12.bmp
0001\0001_00_04_03_125.bmp
0001\0001_00_04_03_130.bmp
0001\0001_00_04_03_136.bmp
0001\0001_00_04_03_141.bmp
0001\0001_00_04_03_147.bmp
0001\0001_00_04_03_152.bmp
0001\0001_00_04_03_158.bmp
0001\0001_00_04_03_163.bmp
0001\0001_00_04_03_169.bmp
0001\0001_00_04_03_174.bmp
0001\0001_00_04_03_18.bmp
0001\0001_00_04_03_185.bmp
0001\0001_00_04_03_190.bmp
0001\0001_00_04_03_196.bmp
0001\0001_00_04_03_200.bmp
0001\0001_00_04_03_206.bmp
0001\0001_00_04_03_211.bmp
0001\0001_00_04_03_217.bmp
0001\0001_00_04_03_224.bmp
0001\0001_00_04_03_23.bmp
0001\0001_00_04_03_235.bmp
0001\0001_00_04_03_240.bmp
0001\0001_00_04_03_246.bmp
0001\0001_00_04_03_251.bmp
0001\0001_00_04_03_257.bmp
0001\0001_00_04_03_262.bmp
0001\0001_00_04_03_268.bmp
0001\0001_00_04_03_273.bmp
0001\0001_00_04_03_279.bmp
0001\0001_00_04_03_284.bmp
0001\0001_00_04_03_29.bmp
0001\0001_00_04_03_295.bmp
0001\0001_00_04_03_30.bmp
0001\0001_00_04_03_305.bmp
0001\0001_00_04_03_310.bmp
0001\0001_00_04_03_316.bmp
0001\0001_00_04_03_321.bmp
0001\0001_00_04_03_327.bmp
0001\0001_00_04_03_332.bmp
0001\0001_00_04_03_338.bmp
0001\0001_00_04_03_343.bmp
0001\0001_00_04_03_349.bmp
0001\0001_00_04_03_354.bmp
0001\0001_00_04_03_36.bmp
0001\0001_00_04_03_365.bmp
0001\0001_00_04_03_370.bmp
0001\0001_00_04_03_376.bmp
0001\0001_00_04_03_381.bmp
0001\0001_00_04_03_387.bmp
0001\0001_00_04_03_392.bmp
0001\0001_00_04_03_398.bmp
0001\0001_00_04_03_402.bmp
0001\0001_00_04_03_408.bmp
0001\0001_00_04_03_413.bmp
0001\0001_00_04_03_419.bmp
0001\0001_00_04_03_424.bmp
0001\0001_00_04_03_43.bmp
0001\0001_00_04_03_435.bmp
0001\0001_00_04_03_440.bmp
0001\0001_00_04_03_446.bmp
0001\0001_00_04_03_451.bmp
0001\0001_00_04_03_457.bmp
0001\0001_00_04_03_462.bmp
0001\0001_00_04_03_468.bmp
0001\0001_00_04_03_473.bmp
0001\0001_00_04_03_479.bmp
0001\0001_00_04_03_484.bmp
0001\0001_00_04_03_49.bmp
0001\0001_00_04_03_495.bmp
0001\0001_00_04_03_50.bmp
0001\0001_00_04_03_56.bmp
0001\0001_00_04_03_61.bmp
0001\0001_00_04_03_67.bmp
0001\0001_00_04_03_72.bmp
0001\0001_00_04_03_78.bmp
0001\0001_00_04_03_83.bmp
0001\0001_00_04_03_89.bmp
0001\0001_00_04_03_94.bmp
0001\0001_00_01_03_0.bmp
0001\0001_00_01_03_103.bmp
0001\0001_00_01_03_109.bmp
0001\0001_00_01_03_114.bmp
0001\0001_00_01_03_12.bmp
0001\0001_00_01_03_125.bmp
0001\0001_00_01_03_130.bmp
0001\0001_00_01_03_136.bmp
0001\0001_00_01_03_141.bmp
0001\0001_00_01_03_147.bmp
0001\0001_00_01_03_152.bmp
0001\0001_00_01_03_158.bmp
0001\0001_00_01_03_163.bmp
0001\0001_00_01_03_169.bmp
0001\0001_00_01_03_174.bmp
0001\0001_00_01_03_18.bmp
0001\0001_00_01_03_185.bmp
0001\0001_00_01_03_190.bmp
0001\0001_00_01_03_196.bmp
0001\0001_00_01_03_200.bmp
0001\0001_00_01_03_206.bmp
0001\0001_00_01_03_211.bmp
0001\0001_00_01_03_217.bmp
0001\0001_00_01_03_222.bmp
0001\0001_00_01_03_228.bmp
0001\0001_00_01_03_233.bmp
0001\0001_00_01_03_239.bmp
0001\0001_00_01_03_244.bmp
0001\0001_00_01_03_25.bmp
0001\0001_00_01_03_255.bmp
0001\0001_00_01_03_260.bmp
0001\0001_00_01_03_266.bmp
0001\0001_00_01_03_271.bmp
0001\0001_00_01_03_277.bmp
0001\0001_00_01_03_282.bmp
0001\0001_00_01_03_288.bmp
0001\0001_00_01_03_293.bmp
0001\0001_00_01_03_299.bmp
0001\0001_00_01_03_303.bmp
0001\0001_00_01_03_309.bmp
0001\0001_00_01_03_314.bmp
0001\0001_00_01_03_32.bmp
0001\0001_00_01_03_325.bmp
0001\0001_00_01_03_330.bmp
0001\0001_00_01_03_336.bmp
0001\0001_00_01_03_341.bmp
0001\0001_00_01_03_347.bmp
0001\0001_00_01_03_352.bmp
0001\0001_00_01_03_358.bmp
0001\0001_00_01_03_363.bmp
0001\0001_00_01_03_369.bmp
0001\0001_00_01_03_374.bmp
0001\0001_00_01_03_38.bmp
0001\0001_00_01_03_385.bmp
0001\0001_00_01_03_390.bmp
0001\0001_00_01_03_396.bmp
0001\0001_00_01_03_400.bmp
0001\0001_00_01_03_406.bmp
0001\0001_00_01_03_411.bmp
0001\0001_00_01_03_417.bmp
0001\0001_00_01_03_422.bmp
0001\0001_00_01_03_428.bmp
0001\0001_00_01_03_434.bmp
0001\0001_00_01_03_44.bmp
0001\0001_00_01_03_445.bmp
0001\0001_00_01_03_450.bmp
0001\0001_00_01_03_456.bmp
0001\0001_00_01_03_461.bmp
0001\0001_00_01_03_467.bmp
0001\0001_00_01_03_472.bmp
0001\0001_00_01_03_478.bmp
0001\0001_00_01_03_483.bmp
0001\0001_00_01_03_489.bmp
0001\0001_00_01_03_494.bmp
0001\0001_00_01_03_5.bmp
0001\0001_00_01_03_55.bmp
0001\0001_00_01_03_60.bmp
0001\0001_00_01_03_66.bmp
0001\0001_00_01_03_71.bmp
0001\0001_00_01_03_77.bmp
0001\0001_00_01_03_82.bmp
0001\0001_00_01_03_88.bmp
0001\0001_00_01_03_93.bmp
0001\0001_00_01_03_99.bmp
0001\0001_00_02_03_0.bmp
0001\0001_00_02_03_103.bmp
0001\0001_00_02_03_109.bmp
0001\0001_00_02_03_114.bmp
0001\0001_00_02_03_12.bmp
0001\0001_00_02_03_125.bmp
0001\0001_00_02_03_134.bmp
0001\0001_00_02_03_14.bmp
0001\0001_00_02_03_145.bmp
0001\0001_00_02_03_150.bmp
0001\0001_00_02_03_156.bmp
0001\0001_00_02_03_164.bmp
0001\0001_00_02_03_17.bmp
0001\0001_00_02_03_175.bmp
0001\0001_00_02_03_180.bmp
0001\0001_00_02_03_186.bmp
0001\0001_00_02_03_191.bmp
0001\0001_00_02_03_197.bmp
0001\0001_00_02_03_201.bmp
0001\0001_00_02_03_207.bmp
0001\0001_00_02_03_212.bmp
0001\0001_00_02_03_218.bmp
0001\0001_00_02_03_223.bmp
0001\0001_00_02_03_229.bmp
0001\0001_00_02_03_234.bmp
0001\0001_00_02_03_24.bmp
0001\0001_00_02_03_245.bmp
0001\0001_00_02_03_250.bmp
0001\0001_00_02_03_256.bmp
0001\0001_00_02_03_261.bmp
0001\0001_00_02_03_267.bmp
0001\0001_00_02_03_272.bmp
0001\0001_00_02_03_278.bmp
0001\0001_00_02_03_283.bmp
0001\0001_00_02_03_289.bmp
0001\0001_00_02_03_294.bmp
0001\0001_00_02_03_3.bmp
0001\0001_00_02_03_304.bmp
0001\0001_00_02_03_31.bmp
0001\0001_00_02_03_315.bmp
0001\0001_00_02_03_320.bmp
0001\0001_00_02_03_326.bmp
0001\0001_00_02_03_331.bmp
0001\0001_00_02_03_337.bmp
0001\0001_00_02_03_342.bmp
0001\0001_00_02_03_348.bmp
0001\0001_00_02_03_353.bmp
0001\0001_00_02_03_359.bmp
0001\0001_00_02_03_364.bmp
0001\0001_00_02_03_37.bmp
0001\0001_00_02_03_375.bmp
0001\0001_00_02_03_380.bmp
0001\0001_00_02_03_386.bmp
0001\0001_00_02_03_391.bmp
0001\0001_00_02_03_397.bmp
0001\0001_00_02_03_401.bmp
0001\0001_00_02_03_407.bmp
0001\0001_00_02_03_412.bmp
0001\0001_00_02_03_418.bmp
0001\0001_00_02_03_423.bmp
0001\0001_00_02_03_429.bmp
0001\0001_00_02_03_434.bmp
0001\0001_00_02_03_44.bmp
0001\0001_00_02_03_445.bmp
0001\0001_00_02_03_450.bmp
0001\0001_00_02_03_456.bmp
0001\0001_00_02_03_461.bmp
0001\0001_00_02_03_467.bmp
0001\0001_00_02_03_472.bmp
0001\0001_00_02_03_478.bmp
0001\0001_00_02_03_483.bmp
0001\0001_00_02_03_489.bmp
0001\0001_00_02_03_494.bmp
0001\0001_00_02_03_5.bmp
0001\0001_00_02_03_55.bmp
0001\0001_00_02_03_60.bmp
0001\0001_00_02_03_66.bmp
0001\0001_00_02_03_71.bmp
0001\0001_00_02_03_77.bmp
0001\0001_00_02_03_82.bmp
0001\0001_00_02_03_88.bmp
0001\0001_00_02_03_93.bmp
0001\0001_00_02_03_99.bmp
0001\0001_00_08_03_0.bmp
0001\0001_00_08_03_103.bmp
0001\0001_00_08_03_109.bmp
0001\0001_00_08_03_114.bmp
0001\0001_00_08_03_12.bmp
0001\0001_00_08_03_125.bmp
0001\0001_00_08_03_130.bmp
0001\0001_00_08_03_136.bmp
0001\0001_00_08_03_141.bmp
0001\0001_00_08_03_147.bmp
0001\0001_00_08_03_152.bmp
0001\0001_00_08_03_158.bmp
0001\0001_00_08_03_163.bmp
0001\0001_00_08_03_169.bmp
0001\0001_00_08_03_174.bmp
0001\0001_00_08_03_18.bmp
0001\0001_00_08_03_185.bmp
0001\0001_00_08_03_190.bmp
0001\0001_00_08_03_196.bmp
0001\0001_00_08_03_200.bmp
0001\0001_00_08_03_206.bmp
0001\0001_00_08_03_211.bmp
0001\0001_00_08_03_217.bmp
0001\0001_00_08_03_222.bmp
0001\0001_00_08_03_228.bmp
0001\0001_00_08_03_233.bmp
0001\0001_00_08_03_239.bmp
0001\0001_00_08_03_244.bmp
0001\0001_00_08_03_25.bmp
0001\0001_00_08_03_255.bmp
0001\0001_00_08_03_260.bmp
0001\0001_00_08_03_266.bmp
0001\0001_00_08_03_271.bmp
0001\0001_00_08_03_277.bmp
0001\0001_00_08_03_282.bmp
0001\0001_00_08_03_288.bmp
0001\0001_00_08_03_293.bmp
0001\0001_00_08_03_299.bmp
0001\0001_00_08_03_303.bmp
0001\0001_00_08_03_309.bmp
0001\0001_00_08_03_314.bmp
0001\0001_00_08_03_32.bmp
0001\0001_00_08_03_325.bmp
0001\0001_00_08_03_330.bmp
0001\0001_00_08_03_336.bmp
0001\0001_00_08_03_341.bmp
0001\0001_00_08_03_347.bmp
0001\0001_00_08_03_352.bmp
0001\0001_00_08_03_358.bmp
0001\0001_00_08_03_363.bmp
0001\0001_00_08_03_369.bmp
0001\0001_00_08_03_374.bmp
0001\0001_00_08_03_38.bmp
0001\0001_00_08_03_385.bmp
0001\0001_00_08_03_390.bmp
0001\0001_00_08_03_396.bmp
0001\0001_00_08_03_400.bmp
0001\0001_00_08_03_406.bmp
0001\0001_00_08_03_411.bmp
0001\0001_00_08_03_417.bmp
0001\0001_00_08_03_422.bmp
0001\0001_00_08_03_428.bmp
0001\0001_00_08_03_433.bmp
0001\0001_00_08_03_439.bmp
0001\0001_00_08_03_444.bmp
0001\0001_00_08_03_45.bmp
0001\0001_00_08_03_455.bmp
0001\0001_00_08_03_460.bmp
0001\0001_00_08_03_466.bmp
0001\0001_00_08_03_471.bmp
0001\0001_00_08_03_477.bmp
0001\0001_00_08_03_482.bmp
0001\0001_00_08_03_488.bmp
0001\0001_00_08_03_493.bmp
0001\0001_00_08_03_499.bmp
0001\0001_00_08_03_54.bmp
0001\0001_00_08_03_6.bmp
0001\0001_00_08_03_65.bmp
0001\0001_00_08_03_70.bmp
0001\0001_00_08_03_76.bmp
0001\0001_00_08_03_81.bmp
0001\0001_00_08_03_87.bmp
0001\0001_00_08_03_92.bmp
0001\0001_00_08_03_98.bmp
0002\0002_01_03_03_0.bmp
0002\0002_01_03_03_103.bmp
0002\0002_01_03_03_109.bmp
0002\0002_01_03_03_114.bmp
0002\0002_01_03_03_12.bmp
0002\0002_01_03_03_13.bmp
0002\0002_01_03_03_139.bmp
0002\0002_01_03_03_144.bmp
0002\0002_01_03_03_15.bmp
0002\0002_01_03_03_156.bmp
0002\0002_01_03_03_161.bmp
0002\0002_01_03_03_167.bmp
0002\0002_01_03_03_175.bmp
0002\0002_01_03_03_180.bmp
0002\0002_01_03_03_186.bmp
0002\0002_01_03_03_191.bmp
0002\0002_01_03_03_197.bmp
0002\0002_01_03_03_201.bmp
0002\0002_01_03_03_207.bmp
0002\0002_01_03_03_212.bmp
0002\0002_01_03_03_218.bmp
0002\0002_01_03_03_223.bmp
0002\0002_01_03_03_229.bmp
0002\0002_01_03_03_24.bmp
0002\0002_01_03_03_248.bmp
0002\0002_01_03_03_253.bmp
0002\0002_01_03_03_259.bmp
0002\0002_01_03_03_264.bmp
0002\0002_01_03_03_27.bmp
0002\0002_01_03_03_275.bmp
0002\0002_01_03_03_280.bmp
0002\0002_01_03_03_286.bmp
0002\0002_01_03_03_291.bmp
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0002\0002_01_03_03_301.bmp
0002\0002_01_03_03_307.bmp
0002\0002_01_03_03_312.bmp
0002\0002_01_03_03_318.bmp
0002\0002_01_03_03_323.bmp
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0002\0002_01_03_03_350.bmp
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0002\0002_01_03_03_394.bmp
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0002\0002_01_03_03_404.bmp
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0002\0002_01_03_03_415.bmp
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0002\0002_01_03_03_431.bmp
0002\0002_01_03_03_440.bmp
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0002\0002_01_03_03_457.bmp
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0002\0002_01_03_03_473.bmp
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0002\0002_01_03_03_495.bmp
0002\0002_01_03_03_50.bmp
0002\0002_01_03_03_56.bmp
0002\0002_01_03_03_61.bmp
0002\0002_01_03_03_67.bmp
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0002\0002_01_03_03_78.bmp
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0002\0002_01_04_03_175.bmp
0002\0002_01_04_03_180.bmp
0002\0002_01_04_03_186.bmp
0002\0002_01_04_03_191.bmp
0002\0002_01_04_03_197.bmp
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0002\0002_01_04_03_3.bmp
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0002\0002_01_04_03_31.bmp
0002\0002_01_04_03_315.bmp
0002\0002_01_04_03_321.bmp
0002\0002_01_04_03_327.bmp
0002\0002_01_04_03_333.bmp
0002\0002_01_04_03_339.bmp
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0002\0002_01_04_03_4.bmp
0002\0002_01_04_03_404.bmp
0002\0002_01_04_03_410.bmp
0002\0002_01_04_03_416.bmp
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0002\0002_01_04_03_46.bmp
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0002\0002_01_04_03_53.bmp
0002\0002_01_04_03_59.bmp
0002\0002_01_04_03_64.bmp
0002\0002_01_04_03_7.bmp
0002\0002_01_04_03_77.bmp
0002\0002_01_04_03_88.bmp
0002\0002_01_04_03_93.bmp
0002\0002_01_04_03_99.bmp
0002\0002_01_01_03_0.bmp
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0002\0002_01_01_03_163.bmp
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0002\0002_01_01_03_18.bmp
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0002\0002_01_01_03_200.bmp
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0002\0002_01_01_03_211.bmp
0002\0002_01_01_03_217.bmp
0002\0002_01_01_03_222.bmp
0002\0002_01_01_03_228.bmp
0002\0002_01_01_03_233.bmp
0002\0002_01_01_03_239.bmp
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0002\0002_01_01_03_25.bmp
0002\0002_01_01_03_255.bmp
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0002\0002_01_01_03_271.bmp
0002\0002_01_01_03_277.bmp
0002\0002_01_01_03_282.bmp
0002\0002_01_01_03_288.bmp
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0002\0002_01_01_03_309.bmp
0002\0002_01_01_03_314.bmp
0002\0002_01_01_03_32.bmp
0002\0002_01_01_03_325.bmp
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0002\0002_01_01_03_336.bmp
0002\0002_01_01_03_341.bmp
0002\0002_01_01_03_347.bmp
0002\0002_01_01_03_352.bmp
0002\0002_01_01_03_358.bmp
0002\0002_01_01_03_363.bmp
0002\0002_01_01_03_369.bmp
0002\0002_01_01_03_374.bmp
0002\0002_01_01_03_38.bmp
0002\0002_01_01_03_385.bmp
0002\0002_01_01_03_390.bmp
0002\0002_01_01_03_396.bmp
0002\0002_01_01_03_400.bmp
0002\0002_01_01_03_406.bmp
0002\0002_01_01_03_411.bmp
0002\0002_01_01_03_417.bmp
0002\0002_01_01_03_422.bmp
0002\0002_01_01_03_428.bmp
0002\0002_01_01_03_433.bmp
0002\0002_01_01_03_439.bmp
0002\0002_01_01_03_444.bmp
0002\0002_01_01_03_45.bmp
0002\0002_01_01_03_455.bmp
0002\0002_01_01_03_460.bmp
0002\0002_01_01_03_466.bmp
0002\0002_01_01_03_471.bmp
0002\0002_01_01_03_477.bmp
0002\0002_01_01_03_482.bmp
0002\0002_01_01_03_488.bmp
0002\0002_01_01_03_493.bmp
0002\0002_01_01_03_499.bmp
0002\0002_01_01_03_54.bmp
0002\0002_01_01_03_6.bmp
0002\0002_01_01_03_65.bmp
0002\0002_01_01_03_70.bmp
0002\0002_01_01_03_76.bmp
0002\0002_01_01_03_81.bmp
0002\0002_01_01_03_87.bmp
0002\0002_01_01_03_92.bmp
0002\0002_01_01_03_98.bmp
0002\0002_01_02_03_0.bmp
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0002\0002_01_02_03_109.bmp
0002\0002_01_02_03_114.bmp
0002\0002_01_02_03_12.bmp
0002\0002_01_02_03_125.bmp
0002\0002_01_02_03_130.bmp
0002\0002_01_02_03_136.bmp
0002\0002_01_02_03_141.bmp
0002\0002_01_02_03_147.bmp
0002\0002_01_02_03_152.bmp
0002\0002_01_02_03_158.bmp
0002\0002_01_02_03_163.bmp
0002\0002_01_02_03_169.bmp
0002\0002_01_02_03_174.bmp
0002\0002_01_02_03_18.bmp
0002\0002_01_02_03_185.bmp
0002\0002_01_02_03_190.bmp
0002\0002_01_02_03_196.bmp
0002\0002_01_02_03_200.bmp
0002\0002_01_02_03_206.bmp
0002\0002_01_02_03_211.bmp
0002\0002_01_02_03_217.bmp
0002\0002_01_02_03_222.bmp
0002\0002_01_02_03_228.bmp
0002\0002_01_02_03_233.bmp
0002\0002_01_02_03_239.bmp
0002\0002_01_02_03_244.bmp
0002\0002_01_02_03_25.bmp
0002\0002_01_02_03_255.bmp
0002\0002_01_02_03_260.bmp
0002\0002_01_02_03_266.bmp
0002\0002_01_02_03_271.bmp
0002\0002_01_02_03_277.bmp
0002\0002_01_02_03_282.bmp
0002\0002_01_02_03_288.bmp
0002\0002_01_02_03_293.bmp
0002\0002_01_02_03_299.bmp
0002\0002_01_02_03_303.bmp
0002\0002_01_02_03_309.bmp
0002\0002_01_02_03_314.bmp
0002\0002_01_02_03_32.bmp
0002\0002_01_02_03_325.bmp
0002\0002_01_02_03_330.bmp
0002\0002_01_02_03_336.bmp
0002\0002_01_02_03_341.bmp
0002\0002_01_02_03_349.bmp
0002\0002_01_02_03_354.bmp
0002\0002_01_02_03_362.bmp
0002\0002_01_02_03_368.bmp
0002\0002_01_02_03_373.bmp
0002\0002_01_02_03_379.bmp
0002\0002_01_02_03_384.bmp
0002\0002_01_02_03_39.bmp
0002\0002_01_02_03_395.bmp
0002\0002_01_02_03_40.bmp
0002\0002_01_02_03_405.bmp
0002\0002_01_02_03_410.bmp
0002\0002_01_02_03_416.bmp
0002\0002_01_02_03_421.bmp
0002\0002_01_02_03_427.bmp
0002\0002_01_02_03_432.bmp
0002\0002_01_02_03_438.bmp
0002\0002_01_02_03_443.bmp
0002\0002_01_02_03_449.bmp
0002\0002_01_02_03_454.bmp
0002\0002_01_02_03_46.bmp
0002\0002_01_02_03_465.bmp
0002\0002_01_02_03_470.bmp
0002\0002_01_02_03_476.bmp
0002\0002_01_02_03_481.bmp
0002\0002_01_02_03_487.bmp
0002\0002_01_02_03_492.bmp
0002\0002_01_02_03_498.bmp
0002\0002_01_02_03_53.bmp
0002\0002_01_02_03_59.bmp
0002\0002_01_02_03_64.bmp
0002\0002_01_02_03_7.bmp
0002\0002_01_02_03_75.bmp
0002\0002_01_02_03_80.bmp
0002\0002_01_02_03_86.bmp
0002\0002_01_02_03_91.bmp
0002\0002_01_02_03_97.bmp
0002\0002_01_08_03_0.bmp
0002\0002_01_08_03_103.bmp
0002\0002_01_08_03_109.bmp
0002\0002_01_08_03_114.bmp
0002\0002_01_08_03_12.bmp
0002\0002_01_08_03_125.bmp
0002\0002_01_08_03_130.bmp
0002\0002_01_08_03_136.bmp
0002\0002_01_08_03_141.bmp
0002\0002_01_08_03_147.bmp
0002\0002_01_08_03_152.bmp
0002\0002_01_08_03_158.bmp
0002\0002_01_08_03_163.bmp
0002\0002_01_08_03_169.bmp
0002\0002_01_08_03_174.bmp
0002\0002_01_08_03_18.bmp
0002\0002_01_08_03_185.bmp
0002\0002_01_08_03_190.bmp
0002\0002_01_08_03_196.bmp
0002\0002_01_08_03_200.bmp
0002\0002_01_08_03_206.bmp
0002\0002_01_08_03_211.bmp
0002\0002_01_08_03_217.bmp
0002\0002_01_08_03_222.bmp
0002\0002_01_08_03_228.bmp
0002\0002_01_08_03_233.bmp
0002\0002_01_08_03_239.bmp
0002\0002_01_08_03_244.bmp
0002\0002_01_08_03_25.bmp
0002\0002_01_08_03_255.bmp
0002\0002_01_08_03_260.bmp
0002\0002_01_08_03_266.bmp
0002\0002_01_08_03_271.bmp
0002\0002_01_08_03_277.bmp
0002\0002_01_08_03_282.bmp
0002\0002_01_08_03_288.bmp
0002\0002_01_08_03_293.bmp
0002\0002_01_08_03_299.bmp
0002\0002_01_08_03_303.bmp
0002\0002_01_08_03_309.bmp
0002\0002_01_08_03_314.bmp
0002\0002_01_08_03_32.bmp
0002\0002_01_08_03_325.bmp
0002\0002_01_08_03_330.bmp
0002\0002_01_08_03_336.bmp
0002\0002_01_08_03_341.bmp
0002\0002_01_08_03_347.bmp
0002\0002_01_08_03_352.bmp
0002\0002_01_08_03_358.bmp
0002\0002_01_08_03_363.bmp
0002\0002_01_08_03_369.bmp
0002\0002_01_08_03_374.bmp
0002\0002_01_08_03_38.bmp
0002\0002_01_08_03_385.bmp
0002\0002_01_08_03_390.bmp
0002\0002_01_08_03_396.bmp
0002\0002_01_08_03_400.bmp
0002\0002_01_08_03_406.bmp
0002\0002_01_08_03_411.bmp
0002\0002_01_08_03_417.bmp
0002\0002_01_08_03_422.bmp
0002\0002_01_08_03_428.bmp
0002\0002_01_08_03_433.bmp
0002\0002_01_08_03_439.bmp
0002\0002_01_08_03_444.bmp
0002\0002_01_08_03_45.bmp
0002\0002_01_08_03_455.bmp
0002\0002_01_08_03_460.bmp
0002\0002_01_08_03_466.bmp
0002\0002_01_08_03_471.bmp
0002\0002_01_08_03_477.bmp
0002\0002_01_08_03_482.bmp
0002\0002_01_08_03_488.bmp
0002\0002_01_08_03_493.bmp
0002\0002_01_08_03_499.bmp
0002\0002_01_08_03_54.bmp
0002\0002_01_08_03_6.bmp
0002\0002_01_08_03_65.bmp
0002\0002_01_08_03_70.bmp
0002\0002_01_08_03_76.bmp
0002\0002_01_08_03_81.bmp
0002\0002_01_08_03_87.bmp
0002\0002_01_08_03_92.bmp
0002\0002_01_08_03_98.bmp
0003\0003_01_03_03_100.bmp
0003\0003_01_03_03_106.bmp
0003\0003_01_03_03_112.bmp
0003\0003_01_03_03_118.bmp
0003\0003_01_03_03_124.bmp
0003\0003_01_03_03_130.bmp
0003\0003_01_03_03_136.bmp
0003\0003_01_03_03_142.bmp
0003\0003_01_03_03_148.bmp
0003\0003_01_03_03_154.bmp
0003\0003_01_03_03_160.bmp
0003\0003_01_03_03_166.bmp
0003\0003_01_03_03_172.bmp
0003\0003_01_03_03_178.bmp
0003\0003_01_03_03_184.bmp
0003\0003_01_03_03_190.bmp
0003\0003_01_03_03_196.bmp
0003\0003_01_03_03_201.bmp
0003\0003_01_03_03_207.bmp
0003\0003_01_03_03_212.bmp
0003\0003_01_03_03_218.bmp
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0003\0003_01_03_03_261.bmp
0003\0003_01_03_03_267.bmp
0003\0003_01_03_03_272.bmp
0003\0003_01_03_03_278.bmp
0003\0003_01_03_03_283.bmp
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0003\0003_01_03_03_30.bmp
0003\0003_01_03_03_305.bmp
0003\0003_01_03_03_310.bmp
0003\0003_01_03_03_316.bmp
0003\0003_01_03_03_321.bmp
0003\0003_01_03_03_327.bmp
0003\0003_01_03_03_332.bmp
0003\0003_01_03_03_338.bmp
0003\0003_01_03_03_343.bmp
0003\0003_01_03_03_349.bmp
0003\0003_01_03_03_354.bmp
0003\0003_01_03_03_36.bmp
0003\0003_01_03_03_365.bmp
0003\0003_01_03_03_370.bmp
0003\0003_01_03_03_376.bmp
0003\0003_01_03_03_381.bmp
0003\0003_01_03_03_387.bmp
0003\0003_01_03_03_392.bmp
0003\0003_01_03_03_398.bmp
0003\0003_01_03_03_403.bmp
0003\0003_01_03_03_409.bmp
0003\0003_01_03_03_414.bmp
0003\0003_01_03_03_42.bmp
0003\0003_01_03_03_428.bmp
0003\0003_01_03_03_433.bmp
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0003\0003_01_03_03_444.bmp
0003\0003_01_03_03_45.bmp
0003\0003_01_03_03_455.bmp
0003\0003_01_03_03_460.bmp
0003\0003_01_03_03_466.bmp
0003\0003_01_03_03_471.bmp
0003\0003_01_03_03_477.bmp
0003\0003_01_03_03_482.bmp
0003\0003_01_03_03_488.bmp
0003\0003_01_03_03_493.bmp
0003\0003_01_03_03_499.bmp
0003\0003_01_03_03_55.bmp
0003\0003_01_03_03_61.bmp
0003\0003_01_03_03_67.bmp
0003\0003_01_03_03_73.bmp
0003\0003_01_03_03_79.bmp
0003\0003_01_03_03_85.bmp
0003\0003_01_03_03_91.bmp
0003\0003_01_03_03_97.bmp
0003\0003_01_04_03_10.bmp
0003\0003_01_04_03_105.bmp
0003\0003_01_04_03_110.bmp
0003\0003_01_04_03_116.bmp
0003\0003_01_04_03_121.bmp
0003\0003_01_04_03_127.bmp
0003\0003_01_04_03_133.bmp
0003\0003_01_04_03_139.bmp
0003\0003_01_04_03_145.bmp
0003\0003_01_04_03_150.bmp
0003\0003_01_04_03_156.bmp
0003\0003_01_04_03_161.bmp
0003\0003_01_04_03_167.bmp
0003\0003_01_04_03_172.bmp
0003\0003_01_04_03_178.bmp
0003\0003_01_04_03_183.bmp
0003\0003_01_04_03_189.bmp
0003\0003_01_04_03_194.bmp
0003\0003_01_04_03_20.bmp
0003\0003_01_04_03_205.bmp
0003\0003_01_04_03_210.bmp
0003\0003_01_04_03_216.bmp
0003\0003_01_04_03_221.bmp
0003\0003_01_04_03_227.bmp
0003\0003_01_04_03_232.bmp
0003\0003_01_04_03_238.bmp
0003\0003_01_04_03_243.bmp
0003\0003_01_04_03_249.bmp
0003\0003_01_04_03_254.bmp
0003\0003_01_04_03_26.bmp
0003\0003_01_04_03_265.bmp
0003\0003_01_04_03_270.bmp
0003\0003_01_04_03_276.bmp
0003\0003_01_04_03_281.bmp
0003\0003_01_04_03_287.bmp
0003\0003_01_04_03_292.bmp
0003\0003_01_04_03_298.bmp
0003\0003_01_04_03_302.bmp
0003\0003_01_04_03_308.bmp
0003\0003_01_04_03_313.bmp
0003\0003_01_04_03_319.bmp
0003\0003_01_04_03_325.bmp
0003\0003_01_04_03_331.bmp
0003\0003_01_04_03_337.bmp
0003\0003_01_04_03_343.bmp
0003\0003_01_04_03_349.bmp
0003\0003_01_04_03_355.bmp
0003\0003_01_04_03_360.bmp
0003\0003_01_04_03_366.bmp
0003\0003_01_04_03_371.bmp
0003\0003_01_04_03_377.bmp
0003\0003_01_04_03_382.bmp
0003\0003_01_04_03_388.bmp
0003\0003_01_04_03_393.bmp
0003\0003_01_04_03_399.bmp
0003\0003_01_04_03_404.bmp
0003\0003_01_04_03_41.bmp
0003\0003_01_04_03_415.bmp
0003\0003_01_04_03_420.bmp
0003\0003_01_04_03_426.bmp
0003\0003_01_04_03_431.bmp
0003\0003_01_04_03_437.bmp
0003\0003_01_04_03_442.bmp
0003\0003_01_04_03_448.bmp
0003\0003_01_04_03_453.bmp
0003\0003_01_04_03_459.bmp
0003\0003_01_04_03_464.bmp
0003\0003_01_04_03_47.bmp
0003\0003_01_04_03_475.bmp
0003\0003_01_04_03_480.bmp
0003\0003_01_04_03_487.bmp
0003\0003_01_04_03_492.bmp
0003\0003_01_04_03_498.bmp
0003\0003_01_04_03_53.bmp
0003\0003_01_04_03_59.bmp
0003\0003_01_04_03_64.bmp
0003\0003_01_04_03_70.bmp
0003\0003_01_04_03_76.bmp
0003\0003_01_04_03_81.bmp
0003\0003_01_04_03_87.bmp
0003\0003_01_04_03_92.bmp
0003\0003_01_04_03_98.bmp
0003\0003_01_01_03_100.bmp
0003\0003_01_01_03_106.bmp
0003\0003_01_01_03_112.bmp
0003\0003_01_01_03_118.bmp
0003\0003_01_01_03_124.bmp
0003\0003_01_01_03_130.bmp
0003\0003_01_01_03_136.bmp
0003\0003_01_01_03_142.bmp
0003\0003_01_01_03_148.bmp
0003\0003_01_01_03_154.bmp
0003\0003_01_01_03_160.bmp
0003\0003_01_01_03_166.bmp
0003\0003_01_01_03_172.bmp
0003\0003_01_01_03_178.bmp
0003\0003_01_01_03_184.bmp
0003\0003_01_01_03_190.bmp
0003\0003_01_01_03_196.bmp
0003\0003_01_01_03_201.bmp
0003\0003_01_01_03_207.bmp
0003\0003_01_01_03_212.bmp
0003\0003_01_01_03_218.bmp
0003\0003_01_01_03_223.bmp
0003\0003_01_01_03_229.bmp
0003\0003_01_01_03_234.bmp
0003\0003_01_01_03_24.bmp
0003\0003_01_01_03_245.bmp
0003\0003_01_01_03_252.bmp
0003\0003_01_01_03_258.bmp
0003\0003_01_01_03_263.bmp
0003\0003_01_01_03_269.bmp
0003\0003_01_01_03_274.bmp
0003\0003_01_01_03_28.bmp
0003\0003_01_01_03_285.bmp
0003\0003_01_01_03_290.bmp
0003\0003_01_01_03_296.bmp
0003\0003_01_01_03_301.bmp
0003\0003_01_01_03_307.bmp
0003\0003_01_01_03_313.bmp
0003\0003_01_01_03_319.bmp
0003\0003_01_01_03_325.bmp
0003\0003_01_01_03_331.bmp
0003\0003_01_01_03_337.bmp
0003\0003_01_01_03_343.bmp
0003\0003_01_01_03_349.bmp
0003\0003_01_01_03_354.bmp
0003\0003_01_01_03_36.bmp
0003\0003_01_01_03_365.bmp
0003\0003_01_01_03_372.bmp
0003\0003_01_01_03_378.bmp
0003\0003_01_01_03_383.bmp
0003\0003_01_01_03_389.bmp
0003\0003_01_01_03_394.bmp
0003\0003_01_01_03_40.bmp
0003\0003_01_01_03_405.bmp
0003\0003_01_01_03_410.bmp
0003\0003_01_01_03_416.bmp
0003\0003_01_01_03_421.bmp
0003\0003_01_01_03_43.bmp
0003\0003_01_01_03_435.bmp
0003\0003_01_01_03_440.bmp
0003\0003_01_01_03_446.bmp
0003\0003_01_01_03_451.bmp
0003\0003_01_01_03_457.bmp
0003\0003_01_01_03_462.bmp
0003\0003_01_01_03_468.bmp
0003\0003_01_01_03_473.bmp
0003\0003_01_01_03_479.bmp
0003\0003_01_01_03_484.bmp
0003\0003_01_01_03_49.bmp
0003\0003_01_01_03_495.bmp
0003\0003_01_01_03_51.bmp
0003\0003_01_01_03_57.bmp
0003\0003_01_01_03_63.bmp
0003\0003_01_01_03_69.bmp
0003\0003_01_01_03_75.bmp
0003\0003_01_01_03_82.bmp
0003\0003_01_01_03_88.bmp
0003\0003_01_01_03_94.bmp
0003\0003_01_02_03_100.bmp
0003\0003_01_02_03_106.bmp
0003\0003_01_02_03_112.bmp
0003\0003_01_02_03_118.bmp
0003\0003_01_02_03_124.bmp
0003\0003_01_02_03_130.bmp
0003\0003_01_02_03_136.bmp
0003\0003_01_02_03_142.bmp
0003\0003_01_02_03_148.bmp
0003\0003_01_02_03_154.bmp
0003\0003_01_02_03_160.bmp
0003\0003_01_02_03_166.bmp
0003\0003_01_02_03_172.bmp
0003\0003_01_02_03_178.bmp
0003\0003_01_02_03_184.bmp
0003\0003_01_02_03_190.bmp
0003\0003_01_02_03_196.bmp
0003\0003_01_02_03_201.bmp
0003\0003_01_02_03_207.bmp
0003\0003_01_02_03_212.bmp
0003\0003_01_02_03_218.bmp
0003\0003_01_02_03_223.bmp
0003\0003_01_02_03_229.bmp
0003\0003_01_02_03_234.bmp
0003\0003_01_02_03_24.bmp
0003\0003_01_02_03_245.bmp
0003\0003_01_02_03_250.bmp
0003\0003_01_02_03_256.bmp
0003\0003_01_02_03_261.bmp
0003\0003_01_02_03_267.bmp
0003\0003_01_02_03_272.bmp
0003\0003_01_02_03_278.bmp
0003\0003_01_02_03_283.bmp
0003\0003_01_02_03_289.bmp
0003\0003_01_02_03_294.bmp
0003\0003_01_02_03_30.bmp
0003\0003_01_02_03_305.bmp
0003\0003_01_02_03_310.bmp
0003\0003_01_02_03_316.bmp
0003\0003_01_02_03_321.bmp
0003\0003_01_02_03_327.bmp
0003\0003_01_02_03_332.bmp
0003\0003_01_02_03_338.bmp
0003\0003_01_02_03_343.bmp
0003\0003_01_02_03_349.bmp
0003\0003_01_02_03_355.bmp
0003\0003_01_02_03_360.bmp
0003\0003_01_02_03_366.bmp
0003\0003_01_02_03_371.bmp
0003\0003_01_02_03_377.bmp
0003\0003_01_02_03_382.bmp
0003\0003_01_02_03_388.bmp
0003\0003_01_02_03_393.bmp
0003\0003_01_02_03_399.bmp
0003\0003_01_02_03_404.bmp
0003\0003_01_02_03_41.bmp
0003\0003_01_02_03_415.bmp
0003\0003_01_02_03_420.bmp
0003\0003_01_02_03_426.bmp
0003\0003_01_02_03_431.bmp
0003\0003_01_02_03_437.bmp
0003\0003_01_02_03_442.bmp
0003\0003_01_02_03_448.bmp
0003\0003_01_02_03_453.bmp
0003\0003_01_02_03_459.bmp
0003\0003_01_02_03_464.bmp
0003\0003_01_02_03_47.bmp
0003\0003_01_02_03_475.bmp
0003\0003_01_02_03_480.bmp
0003\0003_01_02_03_486.bmp
0003\0003_01_02_03_492.bmp
0003\0003_01_02_03_498.bmp
0003\0003_01_02_03_55.bmp
0003\0003_01_02_03_61.bmp
0003\0003_01_02_03_67.bmp
0003\0003_01_02_03_74.bmp
0003\0003_01_02_03_80.bmp
0003\0003_01_02_03_86.bmp
0003\0003_01_02_03_92.bmp
0003\0003_01_02_03_98.bmp
0003\0003_01_08_03_0.bmp
0003\0003_01_08_03_103.bmp
0003\0003_01_08_03_109.bmp
0003\0003_01_08_03_114.bmp
0003\0003_01_08_03_12.bmp
0003\0003_01_08_03_125.bmp
0003\0003_01_08_03_130.bmp
0003\0003_01_08_03_136.bmp
0003\0003_01_08_03_141.bmp
0003\0003_01_08_03_147.bmp
0003\0003_01_08_03_152.bmp
0003\0003_01_08_03_158.bmp
0003\0003_01_08_03_163.bmp
0003\0003_01_08_03_169.bmp
0003\0003_01_08_03_174.bmp
0003\0003_01_08_03_18.bmp
0003\0003_01_08_03_185.bmp
0003\0003_01_08_03_190.bmp
0003\0003_01_08_03_196.bmp
0003\0003_01_08_03_200.bmp
0003\0003_01_08_03_206.bmp
0003\0003_01_08_03_211.bmp
0003\0003_01_08_03_217.bmp
0003\0003_01_08_03_222.bmp
0003\0003_01_08_03_228.bmp
0003\0003_01_08_03_233.bmp
0003\0003_01_08_03_239.bmp
0003\0003_01_08_03_244.bmp
0003\0003_01_08_03_25.bmp
0003\0003_01_08_03_255.bmp
0003\0003_01_08_03_260.bmp
0003\0003_01_08_03_266.bmp
0003\0003_01_08_03_271.bmp
0003\0003_01_08_03_277.bmp
0003\0003_01_08_03_282.bmp
0003\0003_01_08_03_288.bmp
0003\0003_01_08_03_293.bmp
0003\0003_01_08_03_299.bmp
0003\0003_01_08_03_303.bmp
0003\0003_01_08_03_309.bmp
0003\0003_01_08_03_314.bmp
0003\0003_01_08_03_32.bmp
0003\0003_01_08_03_325.bmp
0003\0003_01_08_03_330.bmp
0003\0003_01_08_03_336.bmp
0003\0003_01_08_03_341.bmp
0003\0003_01_08_03_347.bmp
0003\0003_01_08_03_352.bmp
0003\0003_01_08_03_358.bmp
0003\0003_01_08_03_363.bmp
0003\0003_01_08_03_369.bmp
0003\0003_01_08_03_374.bmp
0003\0003_01_08_03_38.bmp
0003\0003_01_08_03_385.bmp
0003\0003_01_08_03_390.bmp
0003\0003_01_08_03_396.bmp
0003\0003_01_08_03_400.bmp
0003\0003_01_08_03_406.bmp
0003\0003_01_08_03_411.bmp
0003\0003_01_08_03_417.bmp
0003\0003_01_08_03_422.bmp
0003\0003_01_08_03_428.bmp
0003\0003_01_08_03_433.bmp
0003\0003_01_08_03_439.bmp
0003\0003_01_08_03_444.bmp
0003\0003_01_08_03_45.bmp
0003\0003_01_08_03_455.bmp
0003\0003_01_08_03_460.bmp
0003\0003_01_08_03_466.bmp
0003\0003_01_08_03_471.bmp
0003\0003_01_08_03_477.bmp
0003\0003_01_08_03_482.bmp
0003\0003_01_08_03_488.bmp
0003\0003_01_08_03_493.bmp
0003\0003_01_08_03_499.bmp
0003\0003_01_08_03_54.bmp
0003\0003_01_08_03_6.bmp
0003\0003_01_08_03_65.bmp
0003\0003_01_08_03_70.bmp
0003\0003_01_08_03_76.bmp
0003\0003_01_08_03_81.bmp
0003\0003_01_08_03_87.bmp
0003\0003_01_08_03_92.bmp
0003\0003_01_08_03_98.bmp
0004\0004_01_03_03_0.bmp
0004\0004_01_03_03_103.bmp
0004\0004_01_03_03_109.bmp
0004\0004_01_03_03_114.bmp
0004\0004_01_03_03_12.bmp
0004\0004_01_03_03_125.bmp
0004\0004_01_03_03_130.bmp
0004\0004_01_03_03_136.bmp
0004\0004_01_03_03_141.bmp
0004\0004_01_03_03_147.bmp
0004\0004_01_03_03_152.bmp
0004\0004_01_03_03_158.bmp
0004\0004_01_03_03_163.bmp
0004\0004_01_03_03_169.bmp
0004\0004_01_03_03_175.bmp
0004\0004_01_03_03_180.bmp
0004\0004_01_03_03_186.bmp
0004\0004_01_03_03_191.bmp
0004\0004_01_03_03_197.bmp
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0004\0004_01_03_03_212.bmp
0004\0004_01_03_03_218.bmp
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0004\0004_01_03_03_245.bmp
0004\0004_01_03_03_250.bmp
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0004\0004_01_03_03_261.bmp
0004\0004_01_03_03_267.bmp
0004\0004_01_03_03_272.bmp
0004\0004_01_03_03_278.bmp
0004\0004_01_03_03_283.bmp
0004\0004_01_03_03_289.bmp
0004\0004_01_03_03_294.bmp
0004\0004_01_03_03_3.bmp
0004\0004_01_03_03_304.bmp
0004\0004_01_03_03_31.bmp
0004\0004_01_03_03_315.bmp
0004\0004_01_03_03_320.bmp
0004\0004_01_03_03_326.bmp
0004\0004_01_03_03_331.bmp
0004\0004_01_03_03_337.bmp
0004\0004_01_03_03_344.bmp
0004\0004_01_03_03_355.bmp
0004\0004_01_03_03_360.bmp
0004\0004_01_03_03_366.bmp
0004\0004_01_03_03_371.bmp
0004\0004_01_03_03_377.bmp
0004\0004_01_03_03_382.bmp
0004\0004_01_03_03_388.bmp
0004\0004_01_03_03_398.bmp
0004\0004_01_03_03_402.bmp
0004\0004_01_03_03_408.bmp
0004\0004_01_03_03_413.bmp
0004\0004_01_03_03_419.bmp
0004\0004_01_03_03_424.bmp
0004\0004_01_03_03_43.bmp
0004\0004_01_03_03_435.bmp
0004\0004_01_03_03_440.bmp
0004\0004_01_03_03_446.bmp
0004\0004_01_03_03_451.bmp
0004\0004_01_03_03_457.bmp
0004\0004_01_03_03_462.bmp
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0004\0004_01_03_03_473.bmp
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0004\0004_01_03_03_484.bmp
0004\0004_01_03_03_49.bmp
0004\0004_01_03_03_496.bmp
0004\0004_01_03_03_51.bmp
0004\0004_01_03_03_57.bmp
0004\0004_01_03_03_62.bmp
0004\0004_01_03_03_69.bmp
0004\0004_01_03_03_74.bmp
0004\0004_01_03_03_8.bmp
0004\0004_01_03_03_85.bmp
0004\0004_01_03_03_90.bmp
0004\0004_01_03_03_96.bmp
0004\0004_01_04_03_0.bmp
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0004\0004_01_04_03_12.bmp
0004\0004_01_04_03_125.bmp
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0004\0004_01_04_03_141.bmp
0004\0004_01_04_03_147.bmp
0004\0004_01_04_03_152.bmp
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0004\0004_01_04_03_164.bmp
0004\0004_01_04_03_17.bmp
0004\0004_01_04_03_175.bmp
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0004\0004_01_04_03_201.bmp
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0004\0004_01_04_03_212.bmp
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0004\0004_01_04_03_257.bmp
0004\0004_01_04_03_262.bmp
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0004\0004_01_04_03_273.bmp
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0004\0004_01_04_03_284.bmp
0004\0004_01_04_03_29.bmp
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0004\0004_01_04_03_310.bmp
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0004\0004_01_04_03_321.bmp
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0004\0004_01_04_03_332.bmp
0004\0004_01_04_03_338.bmp
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0004\0004_01_04_03_370.bmp
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0004\0004_01_04_03_381.bmp
0004\0004_01_04_03_387.bmp
0004\0004_01_04_03_393.bmp
0004\0004_01_04_03_399.bmp
0004\0004_01_04_03_404.bmp
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0004\0004_01_04_03_415.bmp
0004\0004_01_04_03_420.bmp
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0004\0004_01_04_03_481.bmp
0004\0004_01_04_03_49.bmp
0004\0004_01_04_03_495.bmp
0004\0004_01_04_03_50.bmp
0004\0004_01_04_03_56.bmp
0004\0004_01_04_03_62.bmp
0004\0004_01_04_03_68.bmp
0004\0004_01_04_03_74.bmp
0004\0004_01_04_03_8.bmp
0004\0004_01_04_03_85.bmp
0004\0004_01_04_03_90.bmp
0004\0004_01_04_03_96.bmp
0004\0004_01_01_03_0.bmp
0004\0004_01_01_03_103.bmp
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0004\0004_01_01_03_114.bmp
0004\0004_01_01_03_12.bmp
0004\0004_01_01_03_125.bmp
0004\0004_01_01_03_130.bmp
0004\0004_01_01_03_136.bmp
0004\0004_01_01_03_141.bmp
0004\0004_01_01_03_147.bmp
0004\0004_01_01_03_152.bmp
0004\0004_01_01_03_158.bmp
0004\0004_01_01_03_163.bmp
0004\0004_01_01_03_169.bmp
0004\0004_01_01_03_174.bmp
0004\0004_01_01_03_18.bmp
0004\0004_01_01_03_185.bmp
0004\0004_01_01_03_190.bmp
0004\0004_01_01_03_196.bmp
0004\0004_01_01_03_200.bmp
0004\0004_01_01_03_206.bmp
0004\0004_01_01_03_211.bmp
0004\0004_01_01_03_217.bmp
0004\0004_01_01_03_222.bmp
0004\0004_01_01_03_228.bmp
0004\0004_01_01_03_233.bmp
0004\0004_01_01_03_239.bmp
0004\0004_01_01_03_244.bmp
0004\0004_01_01_03_25.bmp
0004\0004_01_01_03_259.bmp
0004\0004_01_01_03_264.bmp
0004\0004_01_01_03_27.bmp
0004\0004_01_01_03_275.bmp
0004\0004_01_01_03_280.bmp
0004\0004_01_01_03_286.bmp
0004\0004_01_01_03_291.bmp
0004\0004_01_01_03_297.bmp
0004\0004_01_01_03_301.bmp
0004\0004_01_01_03_307.bmp
0004\0004_01_01_03_312.bmp
0004\0004_01_01_03_318.bmp
0004\0004_01_01_03_323.bmp
0004\0004_01_01_03_329.bmp
0004\0004_01_01_03_334.bmp
0004\0004_01_01_03_34.bmp
0004\0004_01_01_03_345.bmp
0004\0004_01_01_03_350.bmp
0004\0004_01_01_03_356.bmp
0004\0004_01_01_03_361.bmp
0004\0004_01_01_03_367.bmp
0004\0004_01_01_03_372.bmp
0004\0004_01_01_03_378.bmp
0004\0004_01_01_03_383.bmp
0004\0004_01_01_03_389.bmp
0004\0004_01_01_03_394.bmp
0004\0004_01_01_03_4.bmp
0004\0004_01_01_03_404.bmp
0004\0004_01_01_03_41.bmp
0004\0004_01_01_03_415.bmp
0004\0004_01_01_03_420.bmp
0004\0004_01_01_03_426.bmp
0004\0004_01_01_03_431.bmp
0004\0004_01_01_03_437.bmp
0004\0004_01_01_03_442.bmp
0004\0004_01_01_03_448.bmp
0004\0004_01_01_03_453.bmp
0004\0004_01_01_03_459.bmp
0004\0004_01_01_03_464.bmp
0004\0004_01_01_03_47.bmp
0004\0004_01_01_03_475.bmp
0004\0004_01_01_03_480.bmp
0004\0004_01_01_03_486.bmp
0004\0004_01_01_03_491.bmp
0004\0004_01_01_03_497.bmp
0004\0004_01_01_03_52.bmp
0004\0004_01_01_03_58.bmp
0004\0004_01_01_03_63.bmp
0004\0004_01_01_03_69.bmp
0004\0004_01_01_03_74.bmp
0004\0004_01_01_03_8.bmp
0004\0004_01_01_03_85.bmp
0004\0004_01_01_03_90.bmp
0004\0004_01_01_03_96.bmp
0004\0004_01_02_03_0.bmp
0004\0004_01_02_03_103.bmp
0004\0004_01_02_03_109.bmp
0004\0004_01_02_03_114.bmp
0004\0004_01_02_03_12.bmp
0004\0004_01_02_03_125.bmp
0004\0004_01_02_03_130.bmp
0004\0004_01_02_03_136.bmp
0004\0004_01_02_03_141.bmp
0004\0004_01_02_03_147.bmp
0004\0004_01_02_03_152.bmp
0004\0004_01_02_03_158.bmp
0004\0004_01_02_03_163.bmp
0004\0004_01_02_03_169.bmp
0004\0004_01_02_03_174.bmp
0004\0004_01_02_03_18.bmp
0004\0004_01_02_03_186.bmp
0004\0004_01_02_03_191.bmp
0004\0004_01_02_03_20.bmp
0004\0004_01_02_03_205.bmp
0004\0004_01_02_03_211.bmp
0004\0004_01_02_03_217.bmp
0004\0004_01_02_03_222.bmp
0004\0004_01_02_03_228.bmp
0004\0004_01_02_03_233.bmp
0004\0004_01_02_03_239.bmp
0004\0004_01_02_03_25.bmp
0004\0004_01_02_03_261.bmp
0004\0004_01_02_03_267.bmp
0004\0004_01_02_03_272.bmp
0004\0004_01_02_03_279.bmp
0004\0004_01_02_03_284.bmp
0004\0004_01_02_03_29.bmp
0004\0004_01_02_03_295.bmp
0004\0004_01_02_03_30.bmp
0004\0004_01_02_03_305.bmp
0004\0004_01_02_03_311.bmp
0004\0004_01_02_03_317.bmp
0004\0004_01_02_03_322.bmp
0004\0004_01_02_03_328.bmp
0004\0004_01_02_03_333.bmp
0004\0004_01_02_03_339.bmp
0004\0004_01_02_03_344.bmp
0004\0004_01_02_03_35.bmp
0004\0004_01_02_03_355.bmp
0004\0004_01_02_03_360.bmp
0004\0004_01_02_03_366.bmp
0004\0004_01_02_03_371.bmp
0004\0004_01_02_03_377.bmp
0004\0004_01_02_03_382.bmp
0004\0004_01_02_03_388.bmp
0004\0004_01_02_03_393.bmp
0004\0004_01_02_03_399.bmp
0004\0004_01_02_03_403.bmp
0004\0004_01_02_03_409.bmp
0004\0004_01_02_03_414.bmp
0004\0004_01_02_03_42.bmp
0004\0004_01_02_03_425.bmp
0004\0004_01_02_03_430.bmp
0004\0004_01_02_03_436.bmp
0004\0004_01_02_03_441.bmp
0004\0004_01_02_03_447.bmp
0004\0004_01_02_03_452.bmp
0004\0004_01_02_03_459.bmp
0004\0004_01_02_03_464.bmp
0004\0004_01_02_03_472.bmp
0004\0004_01_02_03_478.bmp
0004\0004_01_02_03_483.bmp
0004\0004_01_02_03_49.bmp
0004\0004_01_02_03_495.bmp
0004\0004_01_02_03_50.bmp
0004\0004_01_02_03_56.bmp
0004\0004_01_02_03_61.bmp
0004\0004_01_02_03_67.bmp
0004\0004_01_02_03_72.bmp
0004\0004_01_02_03_78.bmp
0004\0004_01_02_03_83.bmp
0004\0004_01_02_03_89.bmp
0004\0004_01_02_03_94.bmp
0004\0004_01_08_03_0.bmp
0004\0004_01_08_03_103.bmp
0004\0004_01_08_03_109.bmp
0004\0004_01_08_03_114.bmp
0004\0004_01_08_03_12.bmp
0004\0004_01_08_03_125.bmp
0004\0004_01_08_03_130.bmp
0004\0004_01_08_03_136.bmp
0004\0004_01_08_03_141.bmp
0004\0004_01_08_03_147.bmp
0004\0004_01_08_03_152.bmp
0004\0004_01_08_03_158.bmp
0004\0004_01_08_03_163.bmp
0004\0004_01_08_03_169.bmp
0004\0004_01_08_03_174.bmp
0004\0004_01_08_03_18.bmp
0004\0004_01_08_03_185.bmp
0004\0004_01_08_03_190.bmp
0004\0004_01_08_03_196.bmp
0004\0004_01_08_03_200.bmp
0004\0004_01_08_03_206.bmp
0004\0004_01_08_03_211.bmp
0004\0004_01_08_03_217.bmp
0004\0004_01_08_03_222.bmp
0004\0004_01_08_03_228.bmp
0004\0004_01_08_03_233.bmp
0004\0004_01_08_03_239.bmp
0004\0004_01_08_03_244.bmp
0004\0004_01_08_03_25.bmp
0004\0004_01_08_03_255.bmp
0004\0004_01_08_03_260.bmp
0004\0004_01_08_03_266.bmp
0004\0004_01_08_03_271.bmp
0004\0004_01_08_03_277.bmp
0004\0004_01_08_03_282.bmp
0004\0004_01_08_03_288.bmp
0004\0004_01_08_03_293.bmp
0004\0004_01_08_03_299.bmp
0004\0004_01_08_03_303.bmp
0004\0004_01_08_03_309.bmp
0004\0004_01_08_03_314.bmp
0004\0004_01_08_03_32.bmp
0004\0004_01_08_03_325.bmp
0004\0004_01_08_03_330.bmp
0004\0004_01_08_03_336.bmp
0004\0004_01_08_03_341.bmp
0004\0004_01_08_03_347.bmp
0004\0004_01_08_03_352.bmp
0004\0004_01_08_03_358.bmp
0004\0004_01_08_03_363.bmp
0004\0004_01_08_03_369.bmp
0004\0004_01_08_03_374.bmp
0004\0004_01_08_03_38.bmp
0004\0004_01_08_03_385.bmp
0004\0004_01_08_03_390.bmp
0004\0004_01_08_03_397.bmp
0004\0004_01_08_03_401.bmp
0004\0004_01_08_03_407.bmp
0004\0004_01_08_03_412.bmp
0004\0004_01_08_03_418.bmp
0004\0004_01_08_03_423.bmp
0004\0004_01_08_03_429.bmp
0004\0004_01_08_03_434.bmp
0004\0004_01_08_03_44.bmp
0004\0004_01_08_03_445.bmp
0004\0004_01_08_03_450.bmp
0004\0004_01_08_03_456.bmp
0004\0004_01_08_03_461.bmp
0004\0004_01_08_03_467.bmp
0004\0004_01_08_03_472.bmp
0004\0004_01_08_03_478.bmp
0004\0004_01_08_03_483.bmp
0004\0004_01_08_03_489.bmp
0004\0004_01_08_03_494.bmp
0004\0004_01_08_03_5.bmp
0004\0004_01_08_03_55.bmp
0004\0004_01_08_03_60.bmp
0004\0004_01_08_03_66.bmp
0004\0004_01_08_03_71.bmp
0004\0004_01_08_03_77.bmp
0004\0004_01_08_03_82.bmp
0004\0004_01_08_03_88.bmp
0004\0004_01_08_03_93.bmp
0004\0004_01_08_03_99.bmp
0005\0005_00_03_03_10.bmp
0005\0005_00_03_03_107.bmp
0005\0005_00_03_03_112.bmp
0005\0005_00_03_03_118.bmp
0005\0005_00_03_03_123.bmp
0005\0005_00_03_03_129.bmp
0005\0005_00_03_03_134.bmp
0005\0005_00_03_03_14.bmp
0005\0005_00_03_03_145.bmp
0005\0005_00_03_03_150.bmp
0005\0005_00_03_03_156.bmp
0005\0005_00_03_03_161.bmp
0005\0005_00_03_03_167.bmp
0005\0005_00_03_03_172.bmp
0005\0005_00_03_03_178.bmp
0005\0005_00_03_03_183.bmp
0005\0005_00_03_03_189.bmp
0005\0005_00_03_03_194.bmp
0005\0005_00_03_03_20.bmp
0005\0005_00_03_03_205.bmp
0005\0005_00_03_03_210.bmp
0005\0005_00_03_03_216.bmp
0005\0005_00_03_03_221.bmp
0005\0005_00_03_03_227.bmp
0005\0005_00_03_03_232.bmp
0005\0005_00_03_03_239.bmp
0005\0005_00_03_03_244.bmp
0005\0005_00_03_03_25.bmp
0005\0005_00_03_03_255.bmp
0005\0005_00_03_03_260.bmp
0005\0005_00_03_03_266.bmp
0005\0005_00_03_03_271.bmp
0005\0005_00_03_03_277.bmp
0005\0005_00_03_03_282.bmp
0005\0005_00_03_03_288.bmp
0005\0005_00_03_03_293.bmp
0005\0005_00_03_03_299.bmp
0005\0005_00_03_03_304.bmp
0005\0005_00_03_03_31.bmp
0005\0005_00_03_03_315.bmp
0005\0005_00_03_03_320.bmp
0005\0005_00_03_03_326.bmp
0005\0005_00_03_03_331.bmp
0005\0005_00_03_03_337.bmp
0005\0005_00_03_03_342.bmp
0005\0005_00_03_03_348.bmp
0005\0005_00_03_03_353.bmp
0005\0005_00_03_03_359.bmp
0005\0005_00_03_03_364.bmp
0005\0005_00_03_03_37.bmp
0005\0005_00_03_03_375.bmp
0005\0005_00_03_03_380.bmp
0005\0005_00_03_03_386.bmp
0005\0005_00_03_03_392.bmp
0005\0005_00_03_03_398.bmp
0005\0005_00_03_03_403.bmp
0005\0005_00_03_03_409.bmp
0005\0005_00_03_03_415.bmp
0005\0005_00_03_03_420.bmp
0005\0005_00_03_03_426.bmp
0005\0005_00_03_03_431.bmp
0005\0005_00_03_03_437.bmp
0005\0005_00_03_03_442.bmp
0005\0005_00_03_03_448.bmp
0005\0005_00_03_03_453.bmp
0005\0005_00_03_03_459.bmp
0005\0005_00_03_03_464.bmp
0005\0005_00_03_03_47.bmp
0005\0005_00_03_03_475.bmp
0005\0005_00_03_03_480.bmp
0005\0005_00_03_03_486.bmp
0005\0005_00_03_03_491.bmp
0005\0005_00_03_03_497.bmp
0005\0005_00_03_03_53.bmp
0005\0005_00_03_03_59.bmp
0005\0005_00_03_03_65.bmp
0005\0005_00_03_03_70.bmp
0005\0005_00_03_03_76.bmp
0005\0005_00_03_03_81.bmp
0005\0005_00_03_03_88.bmp
0005\0005_00_03_03_99.bmp
0005\0005_00_04_03_0.bmp
0005\0005_00_04_03_103.bmp
0005\0005_00_04_03_109.bmp
0005\0005_00_04_03_114.bmp
0005\0005_00_04_03_120.bmp
0005\0005_00_04_03_126.bmp
0005\0005_00_04_03_132.bmp
0005\0005_00_04_03_138.bmp
0005\0005_00_04_03_145.bmp
0005\0005_00_04_03_151.bmp
0005\0005_00_04_03_160.bmp
0005\0005_00_04_03_169.bmp
0005\0005_00_04_03_174.bmp
0005\0005_00_04_03_180.bmp
0005\0005_00_04_03_187.bmp
0005\0005_00_04_03_193.bmp
0005\0005_00_04_03_199.bmp
0005\0005_00_04_03_203.bmp
0005\0005_00_04_03_209.bmp
0005\0005_00_04_03_214.bmp
0005\0005_00_04_03_22.bmp
0005\0005_00_04_03_226.bmp
0005\0005_00_04_03_231.bmp
0005\0005_00_04_03_237.bmp
0005\0005_00_04_03_242.bmp
0005\0005_00_04_03_248.bmp
0005\0005_00_04_03_253.bmp
0005\0005_00_04_03_259.bmp
0005\0005_00_04_03_264.bmp
0005\0005_00_04_03_27.bmp
0005\0005_00_04_03_275.bmp
0005\0005_00_04_03_280.bmp
0005\0005_00_04_03_286.bmp
0005\0005_00_04_03_292.bmp
0005\0005_00_04_03_298.bmp
0005\0005_00_04_03_302.bmp
0005\0005_00_04_03_308.bmp
0005\0005_00_04_03_313.bmp
0005\0005_00_04_03_321.bmp
0005\0005_00_04_03_329.bmp
0005\0005_00_04_03_334.bmp
0005\0005_00_04_03_34.bmp
0005\0005_00_04_03_345.bmp
0005\0005_00_04_03_355.bmp
0005\0005_00_04_03_372.bmp
0005\0005_00_04_03_378.bmp
0005\0005_00_04_03_384.bmp
0005\0005_00_04_03_39.bmp
0005\0005_00_04_03_399.bmp
0005\0005_00_04_03_403.bmp
0005\0005_00_04_03_409.bmp
0005\0005_00_04_03_414.bmp
0005\0005_00_04_03_42.bmp
0005\0005_00_04_03_425.bmp
0005\0005_00_04_03_430.bmp
0005\0005_00_04_03_436.bmp
0005\0005_00_04_03_441.bmp
0005\0005_00_04_03_449.bmp
0005\0005_00_04_03_456.bmp
0005\0005_00_04_03_464.bmp
0005\0005_00_04_03_470.bmp
0005\0005_00_04_03_476.bmp
0005\0005_00_04_03_481.bmp
0005\0005_00_04_03_488.bmp
0005\0005_00_04_03_493.bmp
0005\0005_00_04_03_5.bmp
0005\0005_00_04_03_55.bmp
0005\0005_00_04_03_60.bmp
0005\0005_00_04_03_7.bmp
0005\0005_00_04_03_82.bmp
0005\0005_00_04_03_88.bmp
0005\0005_00_04_03_93.bmp
0005\0005_00_04_03_99.bmp
0005\0005_00_01_03_0.bmp
0005\0005_00_01_03_104.bmp
0005\0005_00_01_03_11.bmp
0005\0005_00_01_03_115.bmp
0005\0005_00_01_03_120.bmp
0005\0005_00_01_03_126.bmp
0005\0005_00_01_03_131.bmp
0005\0005_00_01_03_137.bmp
0005\0005_00_01_03_142.bmp
0005\0005_00_01_03_148.bmp
0005\0005_00_01_03_153.bmp
0005\0005_00_01_03_159.bmp
0005\0005_00_01_03_164.bmp
0005\0005_00_01_03_17.bmp
0005\0005_00_01_03_175.bmp
0005\0005_00_01_03_180.bmp
0005\0005_00_01_03_186.bmp
0005\0005_00_01_03_192.bmp
0005\0005_00_01_03_198.bmp
0005\0005_00_01_03_202.bmp
0005\0005_00_01_03_208.bmp
0005\0005_00_01_03_213.bmp
0005\0005_00_01_03_219.bmp
0005\0005_00_01_03_224.bmp
0005\0005_00_01_03_23.bmp
0005\0005_00_01_03_235.bmp
0005\0005_00_01_03_240.bmp
0005\0005_00_01_03_246.bmp
0005\0005_00_01_03_251.bmp
0005\0005_00_01_03_257.bmp
0005\0005_00_01_03_262.bmp
0005\0005_00_01_03_268.bmp
0005\0005_00_01_03_273.bmp
0005\0005_00_01_03_279.bmp
0005\0005_00_01_03_284.bmp
0005\0005_00_01_03_29.bmp
0005\0005_00_01_03_295.bmp
0005\0005_00_01_03_30.bmp
0005\0005_00_01_03_305.bmp
0005\0005_00_01_03_310.bmp
0005\0005_00_01_03_316.bmp
0005\0005_00_01_03_321.bmp
0005\0005_00_01_03_327.bmp
0005\0005_00_01_03_332.bmp
0005\0005_00_01_03_338.bmp
0005\0005_00_01_03_343.bmp
0005\0005_00_01_03_349.bmp
0005\0005_00_01_03_354.bmp
0005\0005_00_01_03_36.bmp
0005\0005_00_01_03_365.bmp
0005\0005_00_01_03_370.bmp
0005\0005_00_01_03_376.bmp
0005\0005_00_01_03_381.bmp
0005\0005_00_01_03_387.bmp
0005\0005_00_01_03_392.bmp
0005\0005_00_01_03_398.bmp
0005\0005_00_01_03_402.bmp
0005\0005_00_01_03_408.bmp
0005\0005_00_01_03_413.bmp
0005\0005_00_01_03_419.bmp
0005\0005_00_01_03_424.bmp
0005\0005_00_01_03_43.bmp
0005\0005_00_01_03_435.bmp
0005\0005_00_01_03_440.bmp
0005\0005_00_01_03_446.bmp
0005\0005_00_01_03_451.bmp
0005\0005_00_01_03_457.bmp
0005\0005_00_01_03_462.bmp
0005\0005_00_01_03_468.bmp
0005\0005_00_01_03_473.bmp
0005\0005_00_01_03_479.bmp
0005\0005_00_01_03_484.bmp
0005\0005_00_01_03_49.bmp
0005\0005_00_01_03_495.bmp
0005\0005_00_01_03_50.bmp
0005\0005_00_01_03_56.bmp
0005\0005_00_01_03_61.bmp
0005\0005_00_01_03_67.bmp
0005\0005_00_01_03_72.bmp
0005\0005_00_01_03_78.bmp
0005\0005_00_01_03_83.bmp
0005\0005_00_01_03_89.bmp
0005\0005_00_01_03_94.bmp
0005\0005_00_02_03_0.bmp
0005\0005_00_02_03_103.bmp
0005\0005_00_02_03_109.bmp
0005\0005_00_02_03_114.bmp
0005\0005_00_02_03_12.bmp
0005\0005_00_02_03_125.bmp
0005\0005_00_02_03_130.bmp
0005\0005_00_02_03_136.bmp
0005\0005_00_02_03_141.bmp
0005\0005_00_02_03_147.bmp
0005\0005_00_02_03_152.bmp
0005\0005_00_02_03_158.bmp
0005\0005_00_02_03_163.bmp
0005\0005_00_02_03_169.bmp
0005\0005_00_02_03_174.bmp
0005\0005_00_02_03_18.bmp
0005\0005_00_02_03_185.bmp
0005\0005_00_02_03_190.bmp
0005\0005_00_02_03_196.bmp
0005\0005_00_02_03_200.bmp
0005\0005_00_02_03_206.bmp
0005\0005_00_02_03_211.bmp
0005\0005_00_02_03_217.bmp
0005\0005_00_02_03_222.bmp
0005\0005_00_02_03_228.bmp
0005\0005_00_02_03_233.bmp
0005\0005_00_02_03_239.bmp
0005\0005_00_02_03_244.bmp
0005\0005_00_02_03_25.bmp
0005\0005_00_02_03_255.bmp
0005\0005_00_02_03_260.bmp
0005\0005_00_02_03_266.bmp
0005\0005_00_02_03_271.bmp
0005\0005_00_02_03_277.bmp
0005\0005_00_02_03_282.bmp
0005\0005_00_02_03_288.bmp
0005\0005_00_02_03_293.bmp
0005\0005_00_02_03_299.bmp
0005\0005_00_02_03_303.bmp
0005\0005_00_02_03_309.bmp
0005\0005_00_02_03_314.bmp
0005\0005_00_02_03_32.bmp
0005\0005_00_02_03_325.bmp
0005\0005_00_02_03_330.bmp
0005\0005_00_02_03_336.bmp
0005\0005_00_02_03_341.bmp
0005\0005_00_02_03_347.bmp
0005\0005_00_02_03_352.bmp
0005\0005_00_02_03_358.bmp
0005\0005_00_02_03_363.bmp
0005\0005_00_02_03_369.bmp
0005\0005_00_02_03_374.bmp
0005\0005_00_02_03_38.bmp
0005\0005_00_02_03_385.bmp
0005\0005_00_02_03_390.bmp
0005\0005_00_02_03_396.bmp
0005\0005_00_02_03_400.bmp
0005\0005_00_02_03_406.bmp
0005\0005_00_02_03_411.bmp
0005\0005_00_02_03_417.bmp
0005\0005_00_02_03_422.bmp
0005\0005_00_02_03_428.bmp
0005\0005_00_02_03_433.bmp
0005\0005_00_02_03_439.bmp
0005\0005_00_02_03_444.bmp
0005\0005_00_02_03_45.bmp
0005\0005_00_02_03_455.bmp
0005\0005_00_02_03_460.bmp
0005\0005_00_02_03_466.bmp
0005\0005_00_02_03_471.bmp
0005\0005_00_02_03_477.bmp
0005\0005_00_02_03_482.bmp
0005\0005_00_02_03_488.bmp
0005\0005_00_02_03_493.bmp
0005\0005_00_02_03_499.bmp
0005\0005_00_02_03_54.bmp
0005\0005_00_02_03_6.bmp
0005\0005_00_02_03_65.bmp
0005\0005_00_02_03_70.bmp
0005\0005_00_02_03_76.bmp
0005\0005_00_02_03_81.bmp
0005\0005_00_02_03_87.bmp
0005\0005_00_02_03_92.bmp
0005\0005_00_02_03_99.bmp
0005\0005_00_08_03_0.bmp
0005\0005_00_08_03_103.bmp
0005\0005_00_08_03_109.bmp
0005\0005_00_08_03_114.bmp
0005\0005_00_08_03_12.bmp
0005\0005_00_08_03_125.bmp
0005\0005_00_08_03_130.bmp
0005\0005_00_08_03_136.bmp
0005\0005_00_08_03_141.bmp
0005\0005_00_08_03_147.bmp
0005\0005_00_08_03_152.bmp
0005\0005_00_08_03_158.bmp
0005\0005_00_08_03_163.bmp
0005\0005_00_08_03_169.bmp
0005\0005_00_08_03_174.bmp
0005\0005_00_08_03_18.bmp
0005\0005_00_08_03_185.bmp
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0005\0005_00_08_03_196.bmp
0005\0005_00_08_03_200.bmp
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0005\0005_00_08_03_217.bmp
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0005\0005_00_08_03_239.bmp
0005\0005_00_08_03_244.bmp
0005\0005_00_08_03_25.bmp
0005\0005_00_08_03_255.bmp
0005\0005_00_08_03_260.bmp
0005\0005_00_08_03_266.bmp
0005\0005_00_08_03_271.bmp
0005\0005_00_08_03_277.bmp
0005\0005_00_08_03_282.bmp
0005\0005_00_08_03_288.bmp
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0005\0005_00_08_03_309.bmp
0005\0005_00_08_03_314.bmp
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0005\0005_00_08_03_38.bmp
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0005\0005_00_08_03_390.bmp
0005\0005_00_08_03_396.bmp
0005\0005_00_08_03_400.bmp
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0005\0005_00_08_03_411.bmp
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0005\0005_00_08_03_6.bmp
0005\0005_00_08_03_65.bmp
0005\0005_00_08_03_70.bmp
0005\0005_00_08_03_76.bmp
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0005\0005_00_08_03_87.bmp
0005\0005_00_08_03_92.bmp
0005\0005_00_08_03_98.bmp
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0006\0006_00_03_03_214.bmp
0006\0006_00_03_03_220.bmp
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0006\0006_00_04_03_160.bmp
0006\0006_00_04_03_170.bmp
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0006\0006_00_04_03_186.bmp
0006\0006_00_04_03_193.bmp
0006\0006_00_04_03_199.bmp
0006\0006_00_04_03_208.bmp
0006\0006_00_04_03_216.bmp
0006\0006_00_04_03_23.bmp
0006\0006_00_04_03_237.bmp
0006\0006_00_04_03_242.bmp
0006\0006_00_04_03_248.bmp
0006\0006_00_04_03_256.bmp
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0006\0006_00_04_03_311.bmp
0006\0006_00_04_03_319.bmp
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0006\0006_00_01_03_139.bmp
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0006\0006_00_01_03_151.bmp
0006\0006_00_01_03_159.bmp
0006\0006_00_01_03_164.bmp
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0006\0006_00_01_03_232.bmp
0006\0006_00_01_03_240.bmp
0006\0006_00_01_03_25.bmp
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0006\0006_00_01_03_265.bmp
0006\0006_00_01_03_270.bmp
0006\0006_00_01_03_278.bmp
0006\0006_00_01_03_284.bmp
0006\0006_00_01_03_292.bmp
0006\0006_00_01_03_303.bmp
0006\0006_00_01_03_310.bmp
0006\0006_00_01_03_316.bmp
0006\0006_00_01_03_321.bmp
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0007\0007_01_03_03_152.bmp
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0007\0007_01_04_03_192.bmp
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0007\0007_01_04_03_202.bmp
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0007\0007_01_04_03_69.bmp
0007\0007_01_04_03_74.bmp
0007\0007_01_04_03_8.bmp
0007\0007_01_04_03_87.bmp
0007\0007_01_04_03_92.bmp
0007\0007_01_04_03_98.bmp
0007\0007_01_01_03_0.bmp
0007\0007_01_01_03_103.bmp
0007\0007_01_01_03_109.bmp
0007\0007_01_01_03_114.bmp
0007\0007_01_01_03_12.bmp
0007\0007_01_01_03_125.bmp
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0007\0007_01_01_03_311.bmp
0007\0007_01_01_03_317.bmp
0007\0007_01_01_03_322.bmp
0007\0007_01_01_03_328.bmp
0007\0007_01_01_03_333.bmp
0007\0007_01_01_03_339.bmp
0007\0007_01_01_03_344.bmp
0007\0007_01_01_03_35.bmp
0007\0007_01_01_03_355.bmp
0007\0007_01_01_03_360.bmp
0007\0007_01_01_03_366.bmp
0007\0007_01_01_03_371.bmp
0007\0007_01_01_03_377.bmp
0007\0007_01_01_03_382.bmp
0007\0007_01_01_03_388.bmp
0007\0007_01_01_03_393.bmp
0007\0007_01_01_03_399.bmp
0007\0007_01_01_03_403.bmp
0007\0007_01_01_03_409.bmp
0007\0007_01_01_03_414.bmp
0007\0007_01_01_03_42.bmp
0007\0007_01_01_03_425.bmp
0007\0007_01_01_03_430.bmp
0007\0007_01_01_03_436.bmp
0007\0007_01_01_03_441.bmp
0007\0007_01_01_03_447.bmp
0007\0007_01_01_03_452.bmp
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0007\0007_01_01_03_463.bmp
0007\0007_01_01_03_469.bmp
0007\0007_01_01_03_474.bmp
0007\0007_01_01_03_48.bmp
0007\0007_01_01_03_485.bmp
0007\0007_01_01_03_490.bmp
0007\0007_01_01_03_496.bmp
0007\0007_01_01_03_51.bmp
0007\0007_01_01_03_57.bmp
0007\0007_01_01_03_62.bmp
0007\0007_01_01_03_68.bmp
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0007\0007_01_01_03_79.bmp
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0007\0007_01_01_03_9.bmp
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0007\0007_01_02_03_120.bmp
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0007\0007_01_02_03_142.bmp
0007\0007_01_02_03_148.bmp
0007\0007_01_02_03_154.bmp
0007\0007_01_02_03_16.bmp
0007\0007_01_02_03_165.bmp
0007\0007_01_02_03_170.bmp
0007\0007_01_02_03_176.bmp
0007\0007_01_02_03_181.bmp
0007\0007_01_02_03_187.bmp
0007\0007_01_02_03_192.bmp
0007\0007_01_02_03_198.bmp
0007\0007_01_02_03_202.bmp
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0007\0007_01_02_03_214.bmp
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0007\0007_01_02_03_231.bmp
0007\0007_01_02_03_237.bmp
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0007\0007_01_02_03_316.bmp
0007\0007_01_02_03_321.bmp
0007\0007_01_02_03_327.bmp
0007\0007_01_02_03_332.bmp
0007\0007_01_02_03_338.bmp
0007\0007_01_02_03_343.bmp
0007\0007_01_02_03_349.bmp
0007\0007_01_02_03_354.bmp
0007\0007_01_02_03_36.bmp
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0007\0007_01_02_03_370.bmp
0007\0007_01_02_03_376.bmp
0007\0007_01_02_03_381.bmp
0007\0007_01_02_03_387.bmp
0007\0007_01_02_03_392.bmp
0007\0007_01_02_03_398.bmp
0007\0007_01_02_03_402.bmp
0007\0007_01_02_03_408.bmp
0007\0007_01_02_03_413.bmp
0007\0007_01_02_03_419.bmp
0007\0007_01_02_03_424.bmp
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0007\0007_01_02_03_457.bmp
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0007\0007_01_02_03_479.bmp
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0007\0007_01_02_03_491.bmp
0007\0007_01_02_03_497.bmp
0007\0007_01_02_03_52.bmp
0007\0007_01_02_03_58.bmp
0007\0007_01_02_03_63.bmp
0007\0007_01_02_03_69.bmp
0007\0007_01_02_03_74.bmp
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0008\0008_00_08_03_250.bmp
0008\0008_00_08_03_256.bmp
0008\0008_00_08_03_262.bmp
0008\0008_00_08_03_268.bmp
0008\0008_00_08_03_274.bmp
0008\0008_00_08_03_280.bmp
0008\0008_00_08_03_286.bmp
0008\0008_00_08_03_292.bmp
0008\0008_00_08_03_298.bmp
0008\0008_00_08_03_304.bmp
0008\0008_00_08_03_310.bmp
0008\0008_00_08_03_316.bmp
0008\0008_00_08_03_322.bmp
0008\0008_00_08_03_328.bmp
0008\0008_00_08_03_334.bmp
0008\0008_00_08_03_340.bmp
0008\0008_00_08_03_346.bmp
0008\0008_00_08_03_352.bmp
0008\0008_00_08_03_358.bmp
0008\0008_00_08_03_364.bmp
0008\0008_00_08_03_370.bmp
0008\0008_00_08_03_376.bmp
0008\0008_00_08_03_382.bmp
0008\0008_00_08_03_388.bmp
0008\0008_00_08_03_394.bmp
0008\0008_00_08_03_400.bmp
0008\0008_00_08_03_406.bmp
0008\0008_00_08_03_412.bmp
0008\0008_00_08_03_418.bmp
0008\0008_00_08_03_424.bmp
0008\0008_00_08_03_430.bmp
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0008\0008_00_08_03_442.bmp
0008\0008_00_08_03_448.bmp
0008\0008_00_08_03_454.bmp
0008\0008_00_08_03_460.bmp
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0008\0008_00_08_03_478.bmp
0008\0008_00_08_03_484.bmp
0008\0008_00_08_03_490.bmp
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0008\0008_00_08_03_82.bmp
0008\0008_00_08_03_88.bmp
0008\0008_00_08_03_94.bmp
0014\0014_01_03_03_0.bmp
0014\0014_01_03_03_102.bmp
0014\0014_01_03_03_107.bmp
0014\0014_01_03_03_111.bmp
0014\0014_01_03_03_116.bmp
0014\0014_01_03_03_120.bmp
0014\0014_01_03_03_125.bmp
0014\0014_01_03_03_13.bmp
0014\0014_01_03_03_134.bmp
0014\0014_01_03_03_139.bmp
0014\0014_01_03_03_143.bmp
0014\0014_01_03_03_148.bmp
0014\0014_01_03_03_152.bmp
0014\0014_01_03_03_157.bmp
0014\0014_01_03_03_161.bmp
0014\0014_01_03_03_166.bmp
0014\0014_01_03_03_170.bmp
0014\0014_01_03_03_175.bmp
0014\0014_01_03_03_18.bmp
0014\0014_01_03_03_184.bmp
0014\0014_01_03_03_189.bmp
0014\0014_01_03_03_193.bmp
0014\0014_01_03_03_198.bmp
0014\0014_01_03_03_201.bmp
0014\0014_01_03_03_206.bmp
0014\0014_01_03_03_210.bmp
0014\0014_01_03_03_215.bmp
0014\0014_01_03_03_22.bmp
0014\0014_01_03_03_224.bmp
0014\0014_01_03_03_229.bmp
0014\0014_01_03_03_233.bmp
0014\0014_01_03_03_238.bmp
0014\0014_01_03_03_242.bmp
0014\0014_01_03_03_247.bmp
0014\0014_01_03_03_251.bmp
0014\0014_01_03_03_256.bmp
0014\0014_01_03_03_260.bmp
0014\0014_01_03_03_265.bmp
0014\0014_01_03_03_27.bmp
0014\0014_01_03_03_274.bmp
0014\0014_01_03_03_279.bmp
0014\0014_01_03_03_283.bmp
0014\0014_01_03_03_288.bmp
0014\0014_01_03_03_292.bmp
0014\0014_01_03_03_297.bmp
0014\0014_01_03_03_300.bmp
0014\0014_01_03_03_305.bmp
0014\0014_01_03_03_31.bmp
0014\0014_01_03_03_314.bmp
0014\0014_01_03_03_319.bmp
0014\0014_01_03_03_323.bmp
0014\0014_01_03_03_328.bmp
0014\0014_01_03_03_332.bmp
0014\0014_01_03_03_337.bmp
0014\0014_01_03_03_341.bmp
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0014\0014_01_03_03_350.bmp
0014\0014_01_03_03_355.bmp
0014\0014_01_03_03_36.bmp
0014\0014_01_03_03_364.bmp
0014\0014_01_03_03_369.bmp
0014\0014_01_03_03_373.bmp
0014\0014_01_03_03_378.bmp
0014\0014_01_03_03_382.bmp
0014\0014_01_03_03_387.bmp
0014\0014_01_03_03_391.bmp
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0014\0014_01_03_03_413.bmp
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0014\0014_01_03_03_490.bmp
0014\0014_01_03_03_495.bmp
0014\0014_01_03_03_5.bmp
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0014\0014_01_03_03_59.bmp
0014\0014_01_03_03_63.bmp
0014\0014_01_03_03_68.bmp
0014\0014_01_03_03_72.bmp
0014\0014_01_03_03_77.bmp
0014\0014_01_03_03_81.bmp
0014\0014_01_03_03_86.bmp
0014\0014_01_03_03_90.bmp
0014\0014_01_03_03_95.bmp
0014\0014_01_04_03_0.bmp
0014\0014_01_04_03_103.bmp
0014\0014_01_04_03_108.bmp
0014\0014_01_04_03_112.bmp
0014\0014_01_04_03_117.bmp
0014\0014_01_04_03_121.bmp
0014\0014_01_04_03_126.bmp
0014\0014_01_04_03_130.bmp
0014\0014_01_04_03_135.bmp
0014\0014_01_04_03_14.bmp
0014\0014_01_04_03_144.bmp
0014\0014_01_04_03_149.bmp
0014\0014_01_04_03_153.bmp
0014\0014_01_04_03_158.bmp
0014\0014_01_04_03_162.bmp
0014\0014_01_04_03_167.bmp
0014\0014_01_04_03_171.bmp
0014\0014_01_04_03_176.bmp
0014\0014_01_04_03_180.bmp
0014\0014_01_04_03_185.bmp
0014\0014_01_04_03_19.bmp
0014\0014_01_04_03_194.bmp
0014\0014_01_04_03_2.bmp
0014\0014_01_04_03_203.bmp
0014\0014_01_04_03_208.bmp
0014\0014_01_04_03_212.bmp
0014\0014_01_04_03_217.bmp
0014\0014_01_04_03_222.bmp
0014\0014_01_04_03_227.bmp
0014\0014_01_04_03_232.bmp
0014\0014_01_04_03_237.bmp
0014\0014_01_04_03_241.bmp
0014\0014_01_04_03_246.bmp
0014\0014_01_04_03_250.bmp
0014\0014_01_04_03_255.bmp
0014\0014_01_04_03_26.bmp
0014\0014_01_04_03_264.bmp
0014\0014_01_04_03_269.bmp
0014\0014_01_04_03_273.bmp
0014\0014_01_04_03_278.bmp
0014\0014_01_04_03_282.bmp
0014\0014_01_04_03_287.bmp
0014\0014_01_04_03_291.bmp
0014\0014_01_04_03_296.bmp
0014\0014_01_04_03_30.bmp
0014\0014_01_04_03_304.bmp
0014\0014_01_04_03_309.bmp
0014\0014_01_04_03_313.bmp
0014\0014_01_04_03_318.bmp
0014\0014_01_04_03_323.bmp
0014\0014_01_04_03_328.bmp
0014\0014_01_04_03_332.bmp
0014\0014_01_04_03_337.bmp
0014\0014_01_04_03_341.bmp
0014\0014_01_04_03_346.bmp
0014\0014_01_04_03_350.bmp
0014\0014_01_04_03_355.bmp
0014\0014_01_04_03_361.bmp
0014\0014_01_04_03_366.bmp
0014\0014_01_04_03_370.bmp
0014\0014_01_04_03_375.bmp
0014\0014_01_04_03_38.bmp
0014\0014_01_04_03_384.bmp
0014\0014_01_04_03_389.bmp
0014\0014_01_04_03_393.bmp
0014\0014_01_04_03_398.bmp
0014\0014_01_04_03_401.bmp
0014\0014_01_04_03_406.bmp
0014\0014_01_04_03_410.bmp
0014\0014_01_04_03_415.bmp
0014\0014_01_04_03_42.bmp
0014\0014_01_04_03_424.bmp
0014\0014_01_04_03_429.bmp
0014\0014_01_04_03_433.bmp
0014\0014_01_04_03_438.bmp
0014\0014_01_04_03_442.bmp
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0014\0014_01_04_03_452.bmp
0014\0014_01_04_03_457.bmp
0014\0014_01_04_03_461.bmp
0014\0014_01_04_03_466.bmp
0014\0014_01_04_03_470.bmp
0014\0014_01_04_03_475.bmp
0014\0014_01_04_03_480.bmp
0014\0014_01_04_03_485.bmp
0014\0014_01_04_03_49.bmp
0014\0014_01_04_03_494.bmp
0014\0014_01_04_03_499.bmp
0014\0014_01_04_03_54.bmp
0014\0014_01_04_03_59.bmp
0014\0014_01_04_03_63.bmp
0014\0014_01_04_03_68.bmp
0014\0014_01_04_03_72.bmp
0014\0014_01_04_03_77.bmp
0014\0014_01_04_03_81.bmp
0014\0014_01_04_03_86.bmp
0014\0014_01_04_03_90.bmp
0014\0014_01_04_03_95.bmp
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0014\0014_01_01_03_13.bmp
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0014\0014_01_01_03_163.bmp
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0014\0014_01_01_03_172.bmp
0014\0014_01_01_03_177.bmp
0014\0014_01_01_03_181.bmp
0014\0014_01_01_03_186.bmp
0014\0014_01_01_03_190.bmp
0014\0014_01_01_03_195.bmp
0014\0014_01_01_03_2.bmp
0014\0014_01_01_03_203.bmp
0014\0014_01_01_03_208.bmp
0014\0014_01_01_03_212.bmp
0014\0014_01_01_03_217.bmp
0014\0014_01_01_03_221.bmp
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0014\0014_01_01_03_231.bmp
0014\0014_01_01_03_236.bmp
0014\0014_01_01_03_240.bmp
0014\0014_01_01_03_245.bmp
0014\0014_01_01_03_25.bmp
0014\0014_01_01_03_254.bmp
0014\0014_01_01_03_259.bmp
0014\0014_01_01_03_263.bmp
0014\0014_01_01_03_268.bmp
0014\0014_01_01_03_272.bmp
0014\0014_01_01_03_277.bmp
0014\0014_01_01_03_281.bmp
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0014\0014_01_01_03_290.bmp
0014\0014_01_01_03_295.bmp
0014\0014_01_01_03_3.bmp
0014\0014_01_01_03_303.bmp
0014\0014_01_01_03_308.bmp
0014\0014_01_01_03_314.bmp
0014\0014_01_01_03_32.bmp
0014\0014_01_01_03_325.bmp
0014\0014_01_01_03_33.bmp
0014\0014_01_01_03_336.bmp
0014\0014_01_01_03_340.bmp
0014\0014_01_01_03_346.bmp
0014\0014_01_01_03_350.bmp
0014\0014_01_01_03_355.bmp
0014\0014_01_01_03_360.bmp
0014\0014_01_01_03_365.bmp
0014\0014_01_01_03_370.bmp
0014\0014_01_01_03_375.bmp
0014\0014_01_01_03_380.bmp
0014\0014_01_01_03_388.bmp
0014\0014_01_01_03_392.bmp
0014\0014_01_01_03_398.bmp
0014\0014_01_01_03_401.bmp
0014\0014_01_01_03_407.bmp
0014\0014_01_01_03_412.bmp
0014\0014_01_01_03_417.bmp
0014\0014_01_01_03_422.bmp
0014\0014_01_01_03_428.bmp
0014\0014_01_01_03_432.bmp
0014\0014_01_01_03_437.bmp
0014\0014_01_01_03_441.bmp
0014\0014_01_01_03_446.bmp
0014\0014_01_01_03_450.bmp
0014\0014_01_01_03_455.bmp
0014\0014_01_01_03_460.bmp
0014\0014_01_01_03_465.bmp
0014\0014_01_01_03_47.bmp
0014\0014_01_01_03_474.bmp
0014\0014_01_01_03_479.bmp
0014\0014_01_01_03_484.bmp
0014\0014_01_01_03_49.bmp
0014\0014_01_01_03_498.bmp
0014\0014_01_01_03_52.bmp
0014\0014_01_01_03_57.bmp
0014\0014_01_01_03_61.bmp
0014\0014_01_01_03_68.bmp
0014\0014_01_01_03_74.bmp
0014\0014_01_01_03_8.bmp
0014\0014_01_01_03_84.bmp
0014\0014_01_01_03_89.bmp
0014\0014_01_01_03_93.bmp
0014\0014_01_01_03_98.bmp
0014\0014_01_02_03_102.bmp
0014\0014_01_02_03_107.bmp
0014\0014_01_02_03_117.bmp
0014\0014_01_02_03_126.bmp
0014\0014_01_02_03_133.bmp
0014\0014_01_02_03_139.bmp
0014\0014_01_02_03_145.bmp
0014\0014_01_02_03_15.bmp
0014\0014_01_02_03_157.bmp
0014\0014_01_02_03_162.bmp
0014\0014_01_02_03_168.bmp
0014\0014_01_02_03_173.bmp
0014\0014_01_02_03_178.bmp
0014\0014_01_02_03_185.bmp
0014\0014_01_02_03_190.bmp
0014\0014_01_02_03_205.bmp
0014\0014_01_02_03_216.bmp
0014\0014_01_02_03_225.bmp
0014\0014_01_02_03_23.bmp
0014\0014_01_02_03_236.bmp
0014\0014_01_02_03_240.bmp
0014\0014_01_02_03_248.bmp
0014\0014_01_02_03_257.bmp
0014\0014_01_02_03_265.bmp
0014\0014_01_02_03_276.bmp
0014\0014_01_02_03_285.bmp
0014\0014_01_02_03_290.bmp
0014\0014_01_02_03_306.bmp
0014\0014_01_02_03_317.bmp
0014\0014_01_02_03_321.bmp
0014\0014_01_02_03_33.bmp
0014\0014_01_02_03_34.bmp
0014\0014_01_02_03_35.bmp
0014\0014_01_02_03_361.bmp
0014\0014_01_02_03_366.bmp
0014\0014_01_02_03_374.bmp
0014\0014_01_02_03_383.bmp
0014\0014_01_02_03_392.bmp
0014\0014_01_02_03_40.bmp
0014\0014_01_02_03_408.bmp
0014\0014_01_02_03_418.bmp
0014\0014_01_02_03_43.bmp
0014\0014_01_02_03_44.bmp
0014\0014_01_02_03_451.bmp
0014\0014_01_02_03_461.bmp
0014\0014_01_02_03_471.bmp
0014\0014_01_02_03_487.bmp
0014\0014_01_02_03_492.bmp
0014\0014_01_02_03_52.bmp
0014\0014_01_02_03_62.bmp
0014\0014_01_02_03_68.bmp
0014\0014_01_02_03_73.bmp
0014\0014_01_02_03_84.bmp
0014\0014_01_02_03_91.bmp
0014\0014_01_02_03_96.bmp
0014\0014_01_08_03_0.bmp
0014\0014_01_08_03_102.bmp
0014\0014_01_08_03_107.bmp
0014\0014_01_08_03_111.bmp
0014\0014_01_08_03_117.bmp
0014\0014_01_08_03_122.bmp
0014\0014_01_08_03_127.bmp
0014\0014_01_08_03_131.bmp
0014\0014_01_08_03_136.bmp
0014\0014_01_08_03_140.bmp
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0014\0014_01_08_03_15.bmp
0014\0014_01_08_03_155.bmp
0014\0014_01_08_03_16.bmp
0014\0014_01_08_03_164.bmp
0014\0014_01_08_03_169.bmp
0014\0014_01_08_03_173.bmp
0014\0014_01_08_03_178.bmp
0014\0014_01_08_03_182.bmp
0014\0014_01_08_03_187.bmp
0014\0014_01_08_03_191.bmp
0014\0014_01_08_03_199.bmp
0014\0014_01_08_03_202.bmp
0014\0014_01_08_03_207.bmp
0014\0014_01_08_03_211.bmp
0014\0014_01_08_03_216.bmp
0014\0014_01_08_03_220.bmp
0014\0014_01_08_03_226.bmp
0014\0014_01_08_03_230.bmp
0014\0014_01_08_03_24.bmp
0014\0014_01_08_03_244.bmp
0014\0014_01_08_03_249.bmp
0014\0014_01_08_03_253.bmp
0014\0014_01_08_03_258.bmp
0014\0014_01_08_03_262.bmp
0014\0014_01_08_03_267.bmp
0014\0014_01_08_03_272.bmp
0014\0014_01_08_03_277.bmp
0014\0014_01_08_03_281.bmp
0014\0014_01_08_03_287.bmp
0014\0014_01_08_03_292.bmp
0014\0014_01_08_03_30.bmp
0014\0014_01_08_03_306.bmp
0014\0014_01_08_03_310.bmp
0014\0014_01_08_03_315.bmp
0014\0014_01_08_03_323.bmp
0014\0014_01_08_03_328.bmp
0014\0014_01_08_03_332.bmp
0014\0014_01_08_03_337.bmp
0014\0014_01_08_03_341.bmp
0014\0014_01_08_03_350.bmp
0014\0014_01_08_03_356.bmp
0014\0014_01_08_03_360.bmp
0014\0014_01_08_03_365.bmp
0014\0014_01_08_03_37.bmp
0014\0014_01_08_03_374.bmp
0014\0014_01_08_03_381.bmp
0014\0014_01_08_03_391.bmp
0014\0014_01_08_03_396.bmp
0014\0014_01_08_03_40.bmp
0014\0014_01_08_03_404.bmp
0014\0014_01_08_03_409.bmp
0014\0014_01_08_03_413.bmp
0014\0014_01_08_03_418.bmp
0014\0014_01_08_03_422.bmp
0014\0014_01_08_03_427.bmp
0014\0014_01_08_03_431.bmp
0014\0014_01_08_03_436.bmp
0014\0014_01_08_03_440.bmp
0014\0014_01_08_03_445.bmp
0014\0014_01_08_03_45.bmp
0014\0014_01_08_03_454.bmp
0014\0014_01_08_03_459.bmp
0014\0014_01_08_03_463.bmp
0014\0014_01_08_03_468.bmp
0014\0014_01_08_03_472.bmp
0014\0014_01_08_03_477.bmp
0014\0014_01_08_03_481.bmp
0014\0014_01_08_03_488.bmp
0014\0014_01_08_03_492.bmp
0014\0014_01_08_03_497.bmp
0014\0014_01_08_03_51.bmp
0014\0014_01_08_03_56.bmp
0014\0014_01_08_03_60.bmp
0014\0014_01_08_03_65.bmp
0014\0014_01_08_03_7.bmp
0014\0014_01_08_03_74.bmp
0014\0014_01_08_03_79.bmp
0014\0014_01_08_03_83.bmp
0014\0014_01_08_03_88.bmp
0014\0014_01_08_03_92.bmp
0014\0014_01_08_03_97.bmp
0016\0016_01_03_03_0.bmp
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0016\0016_01_03_03_109.bmp
0016\0016_01_03_03_113.bmp
0016\0016_01_03_03_118.bmp
0016\0016_01_03_03_122.bmp
0016\0016_01_03_03_127.bmp
0016\0016_01_03_03_131.bmp
0016\0016_01_03_03_136.bmp
0016\0016_01_03_03_140.bmp
0016\0016_01_03_03_145.bmp
0016\0016_01_03_03_15.bmp
0016\0016_01_03_03_154.bmp
0016\0016_01_03_03_159.bmp
0016\0016_01_03_03_163.bmp
0016\0016_01_03_03_168.bmp
0016\0016_01_03_03_172.bmp
0016\0016_01_03_03_177.bmp
0016\0016_01_03_03_181.bmp
0016\0016_01_03_03_186.bmp
0016\0016_01_03_03_190.bmp
0016\0016_01_03_03_195.bmp
0016\0016_01_03_03_2.bmp
0016\0016_01_03_03_203.bmp
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0015\0015_01_08_03_29.bmp
0015\0015_01_08_03_294.bmp
0015\0015_01_08_03_299.bmp
0015\0015_01_08_03_302.bmp
0015\0015_01_08_03_307.bmp
0015\0015_01_08_03_311.bmp
0015\0015_01_08_03_316.bmp
0015\0015_01_08_03_321.bmp
0015\0015_01_08_03_326.bmp
0015\0015_01_08_03_330.bmp
0015\0015_01_08_03_335.bmp
0015\0015_01_08_03_34.bmp
0015\0015_01_08_03_344.bmp
0015\0015_01_08_03_349.bmp
0015\0015_01_08_03_353.bmp
0015\0015_01_08_03_358.bmp
0015\0015_01_08_03_362.bmp
0015\0015_01_08_03_367.bmp
0015\0015_01_08_03_375.bmp
0015\0015_01_08_03_38.bmp
0015\0015_01_08_03_39.bmp
0015\0015_01_08_03_396.bmp
0015\0015_01_08_03_40.bmp
0015\0015_01_08_03_407.bmp
0015\0015_01_08_03_416.bmp
0015\0015_01_08_03_420.bmp
0015\0015_01_08_03_425.bmp
0015\0015_01_08_03_43.bmp
0015\0015_01_08_03_434.bmp
0015\0015_01_08_03_44.bmp
0015\0015_01_08_03_446.bmp
0015\0015_01_08_03_456.bmp
0015\0015_01_08_03_461.bmp
0015\0015_01_08_03_474.bmp
0015\0015_01_08_03_479.bmp
0015\0015_01_08_03_483.bmp
0015\0015_01_08_03_488.bmp
0015\0015_01_08_03_492.bmp
0015\0015_01_08_03_5.bmp
0015\0015_01_08_03_54.bmp
0015\0015_01_08_03_59.bmp
0015\0015_01_08_03_63.bmp
0015\0015_01_08_03_68.bmp
0015\0015_01_08_03_73.bmp
0015\0015_01_08_03_80.bmp
0015\0015_01_08_03_85.bmp
0015\0015_01_08_03_9.bmp
0015\0015_01_08_03_94.bmp
0012\0012_01_03_03_10.bmp
0012\0012_01_03_03_105.bmp
0012\0012_01_03_03_11.bmp
0012\0012_01_03_03_114.bmp
0012\0012_01_03_03_12.bmp
0012\0012_01_03_03_124.bmp
0012\0012_01_03_03_129.bmp
0012\0012_01_03_03_133.bmp
0012\0012_01_03_03_138.bmp
0012\0012_01_03_03_142.bmp
0012\0012_01_03_03_147.bmp
0012\0012_01_03_03_151.bmp
0012\0012_01_03_03_156.bmp
0012\0012_01_03_03_160.bmp
0012\0012_01_03_03_165.bmp
0012\0012_01_03_03_17.bmp
0012\0012_01_03_03_174.bmp
0012\0012_01_03_03_179.bmp
0012\0012_01_03_03_183.bmp
0012\0012_01_03_03_188.bmp
0012\0012_01_03_03_192.bmp
0012\0012_01_03_03_197.bmp
0012\0012_01_03_03_201.bmp
0012\0012_01_03_03_206.bmp
0012\0012_01_03_03_211.bmp
0012\0012_01_03_03_216.bmp
0012\0012_01_03_03_220.bmp
0012\0012_01_03_03_225.bmp
0012\0012_01_03_03_230.bmp
0012\0012_01_03_03_235.bmp
0012\0012_01_03_03_24.bmp
0012\0012_01_03_03_245.bmp
0012\0012_01_03_03_25.bmp
0012\0012_01_03_03_254.bmp
0012\0012_01_03_03_26.bmp
0012\0012_01_03_03_264.bmp
0012\0012_01_03_03_269.bmp
0012\0012_01_03_03_273.bmp
0012\0012_01_03_03_278.bmp
0012\0012_01_03_03_282.bmp
0012\0012_01_03_03_287.bmp
0012\0012_01_03_03_292.bmp
0012\0012_01_03_03_297.bmp
0012\0012_01_03_03_301.bmp
0012\0012_01_03_03_306.bmp
0012\0012_01_03_03_310.bmp
0012\0012_01_03_03_315.bmp
0012\0012_01_03_03_32.bmp
0012\0012_01_03_03_324.bmp
0012\0012_01_03_03_329.bmp
0012\0012_01_03_03_333.bmp
0012\0012_01_03_03_338.bmp
0012\0012_01_03_03_343.bmp
0012\0012_01_03_03_348.bmp
0012\0012_01_03_03_353.bmp
0012\0012_01_03_03_358.bmp
0012\0012_01_03_03_362.bmp
0012\0012_01_03_03_368.bmp
0012\0012_01_03_03_374.bmp
0012\0012_01_03_03_38.bmp
0012\0012_01_03_03_384.bmp
0012\0012_01_03_03_389.bmp
0012\0012_01_03_03_393.bmp
0012\0012_01_03_03_399.bmp
0012\0012_01_03_03_402.bmp
0012\0012_01_03_03_407.bmp
0012\0012_01_03_03_411.bmp
0012\0012_01_03_03_416.bmp
0012\0012_01_03_03_421.bmp
0012\0012_01_03_03_426.bmp
0012\0012_01_03_03_431.bmp
0012\0012_01_03_03_437.bmp
0012\0012_01_03_03_441.bmp
0012\0012_01_03_03_446.bmp
0012\0012_01_03_03_450.bmp
0012\0012_01_03_03_456.bmp
0012\0012_01_03_03_460.bmp
0012\0012_01_03_03_465.bmp
0012\0012_01_03_03_47.bmp
0012\0012_01_03_03_475.bmp
0012\0012_01_03_03_480.bmp
0012\0012_01_03_03_485.bmp
0012\0012_01_03_03_490.bmp
0012\0012_01_03_03_495.bmp
0012\0012_01_03_03_5.bmp
0012\0012_01_03_03_54.bmp
0012\0012_01_03_03_59.bmp
0012\0012_01_03_03_63.bmp
0012\0012_01_03_03_68.bmp
0012\0012_01_03_03_72.bmp
0012\0012_01_03_03_77.bmp
0012\0012_01_03_03_82.bmp
0012\0012_01_03_03_87.bmp
0012\0012_01_03_03_91.bmp
0012\0012_01_03_03_96.bmp
0012\0012_01_04_03_0.bmp
0012\0012_01_04_03_103.bmp
0012\0012_01_04_03_108.bmp
0012\0012_01_04_03_112.bmp
0012\0012_01_04_03_120.bmp
0012\0012_01_04_03_125.bmp
0012\0012_01_04_03_13.bmp
0012\0012_01_04_03_134.bmp
0012\0012_01_04_03_139.bmp
0012\0012_01_04_03_143.bmp
0012\0012_01_04_03_149.bmp
0012\0012_01_04_03_153.bmp
0012\0012_01_04_03_158.bmp
0012\0012_01_04_03_163.bmp
0012\0012_01_04_03_168.bmp
0012\0012_01_04_03_172.bmp
0012\0012_01_04_03_178.bmp
0012\0012_01_04_03_183.bmp
0012\0012_01_04_03_188.bmp
0012\0012_01_04_03_192.bmp
0012\0012_01_04_03_197.bmp
0012\0012_01_04_03_203.bmp
0012\0012_01_04_03_21.bmp
0012\0012_01_04_03_214.bmp
0012\0012_01_04_03_222.bmp
0012\0012_01_04_03_228.bmp
0012\0012_01_04_03_232.bmp
0012\0012_01_04_03_237.bmp
0012\0012_01_04_03_247.bmp
0012\0012_01_04_03_258.bmp
0012\0012_01_04_03_263.bmp
0012\0012_01_04_03_268.bmp
0012\0012_01_04_03_272.bmp
0012\0012_01_04_03_277.bmp
0012\0012_01_04_03_281.bmp
0012\0012_01_04_03_287.bmp
0012\0012_01_04_03_291.bmp
0012\0012_01_04_03_296.bmp
0012\0012_01_04_03_30.bmp
0012\0012_01_04_03_304.bmp
0012\0012_01_04_03_309.bmp
0012\0012_01_04_03_313.bmp
0012\0012_01_04_03_318.bmp
0012\0012_01_04_03_322.bmp
0012\0012_01_04_03_327.bmp
0012\0012_01_04_03_331.bmp
0012\0012_01_04_03_336.bmp
0012\0012_01_04_03_340.bmp
0012\0012_01_04_03_347.bmp
0012\0012_01_04_03_351.bmp
0012\0012_01_04_03_357.bmp
0012\0012_01_04_03_361.bmp
0012\0012_01_04_03_368.bmp
0012\0012_01_04_03_375.bmp
0012\0012_01_04_03_38.bmp
0012\0012_01_04_03_384.bmp
0012\0012_01_04_03_389.bmp
0012\0012_01_04_03_393.bmp
0012\0012_01_04_03_398.bmp
0012\0012_01_04_03_401.bmp
0012\0012_01_04_03_406.bmp
0012\0012_01_04_03_410.bmp
0012\0012_01_04_03_415.bmp
0012\0012_01_04_03_420.bmp
0012\0012_01_04_03_425.bmp
0012\0012_01_04_03_430.bmp
0012\0012_01_04_03_435.bmp
0012\0012_01_04_03_44.bmp
0012\0012_01_04_03_444.bmp
0012\0012_01_04_03_449.bmp
0012\0012_01_04_03_453.bmp
0012\0012_01_04_03_459.bmp
0012\0012_01_04_03_463.bmp
0012\0012_01_04_03_468.bmp
0012\0012_01_04_03_472.bmp
0012\0012_01_04_03_477.bmp
0012\0012_01_04_03_481.bmp
0012\0012_01_04_03_486.bmp
0012\0012_01_04_03_490.bmp
0012\0012_01_04_03_495.bmp
0012\0012_01_04_03_5.bmp
0012\0012_01_04_03_55.bmp
0012\0012_01_04_03_60.bmp
0012\0012_01_04_03_65.bmp
0012\0012_01_04_03_70.bmp
0012\0012_01_04_03_75.bmp
0012\0012_01_04_03_80.bmp
0012\0012_01_04_03_85.bmp
0012\0012_01_04_03_9.bmp
0012\0012_01_04_03_95.bmp
0012\0012_01_01_03_0.bmp
0012\0012_01_01_03_104.bmp
0012\0012_01_01_03_11.bmp
0012\0012_01_01_03_114.bmp
0012\0012_01_01_03_120.bmp
0012\0012_01_01_03_127.bmp
0012\0012_01_01_03_131.bmp
0012\0012_01_01_03_138.bmp
0012\0012_01_01_03_143.bmp
0012\0012_01_01_03_149.bmp
0012\0012_01_01_03_155.bmp
0012\0012_01_01_03_163.bmp
0012\0012_01_01_03_168.bmp
0012\0012_01_01_03_173.bmp
0012\0012_01_01_03_18.bmp
0012\0012_01_01_03_185.bmp
0012\0012_01_01_03_190.bmp
0012\0012_01_01_03_195.bmp
0012\0012_01_01_03_20.bmp
0012\0012_01_01_03_205.bmp
0012\0012_01_01_03_212.bmp
0012\0012_01_01_03_217.bmp
0012\0012_01_01_03_221.bmp
0012\0012_01_01_03_226.bmp
0012\0012_01_01_03_231.bmp
0012\0012_01_01_03_236.bmp
0012\0012_01_01_03_243.bmp
0012\0012_01_01_03_248.bmp
0012\0012_01_01_03_253.bmp
0012\0012_01_01_03_258.bmp
0012\0012_01_01_03_263.bmp
0012\0012_01_01_03_27.bmp
0012\0012_01_01_03_274.bmp
0012\0012_01_01_03_28.bmp
0012\0012_01_01_03_286.bmp
0012\0012_01_01_03_291.bmp
0012\0012_01_01_03_30.bmp
0012\0012_01_01_03_304.bmp
0012\0012_01_01_03_310.bmp
0012\0012_01_01_03_316.bmp
0012\0012_01_01_03_320.bmp
0012\0012_01_01_03_327.bmp
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0012\0012_01_01_03_336.bmp
0012\0012_01_01_03_341.bmp
0012\0012_01_01_03_346.bmp
0012\0012_01_01_03_350.bmp
0012\0012_01_01_03_357.bmp
0012\0012_01_01_03_363.bmp
0012\0012_01_01_03_368.bmp
0012\0012_01_01_03_372.bmp
0012\0012_01_01_03_377.bmp
0012\0012_01_01_03_381.bmp
0012\0012_01_01_03_387.bmp
0012\0012_01_01_03_391.bmp
0012\0012_01_01_03_397.bmp
0012\0012_01_01_03_400.bmp
0012\0012_01_01_03_406.bmp
0012\0012_01_01_03_410.bmp
0012\0012_01_01_03_415.bmp
0012\0012_01_01_03_42.bmp
0012\0012_01_01_03_425.bmp
0012\0012_01_01_03_432.bmp
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0012\0012_01_01_03_456.bmp
0012\0012_01_01_03_461.bmp
0012\0012_01_01_03_468.bmp
0012\0012_01_01_03_473.bmp
0012\0012_01_01_03_479.bmp
0012\0012_01_01_03_484.bmp
0012\0012_01_01_03_489.bmp
0012\0012_01_01_03_496.bmp
0012\0012_01_01_03_50.bmp
0012\0012_01_01_03_56.bmp
0012\0012_01_01_03_60.bmp
0012\0012_01_01_03_69.bmp
0012\0012_01_01_03_73.bmp
0012\0012_01_01_03_78.bmp
0012\0012_01_01_03_84.bmp
0012\0012_01_01_03_9.bmp
0012\0012_01_01_03_95.bmp
0012\0012_01_02_03_10.bmp
0012\0012_01_02_03_104.bmp
0012\0012_01_02_03_112.bmp
0012\0012_01_02_03_121.bmp
0012\0012_01_02_03_132.bmp
0012\0012_01_02_03_140.bmp
0012\0012_01_02_03_15.bmp
0012\0012_01_02_03_160.bmp
0012\0012_01_02_03_171.bmp
0012\0012_01_02_03_182.bmp
0012\0012_01_02_03_194.bmp
0012\0012_01_02_03_202.bmp
0012\0012_01_02_03_213.bmp
0012\0012_01_02_03_228.bmp
0012\0012_01_02_03_238.bmp
0012\0012_01_02_03_243.bmp
0012\0012_01_02_03_259.bmp
0012\0012_01_02_03_267.bmp
0012\0012_01_02_03_281.bmp
0012\0012_01_02_03_298.bmp
0012\0012_01_02_03_309.bmp
0012\0012_01_02_03_319.bmp
0012\0012_01_02_03_328.bmp
0012\0012_01_02_03_333.bmp
0012\0012_01_02_03_340.bmp
0012\0012_01_02_03_350.bmp
0012\0012_01_02_03_355.bmp
0012\0012_01_02_03_363.bmp
0012\0012_01_02_03_373.bmp
0012\0012_01_02_03_384.bmp
0012\0012_01_02_03_396.bmp
0012\0012_01_02_03_406.bmp
0012\0012_01_02_03_416.bmp
0012\0012_01_02_03_421.bmp
0012\0012_01_02_03_430.bmp
0012\0012_01_02_03_438.bmp
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0012\0012_01_02_03_457.bmp
0012\0012_01_02_03_462.bmp
0012\0012_01_02_03_471.bmp
0012\0012_01_02_03_482.bmp
0012\0012_01_02_03_494.bmp
0012\0012_01_02_03_6.bmp
0012\0012_01_02_03_74.bmp
0012\0012_01_02_03_84.bmp
0012\0012_01_02_03_94.bmp
0012\0012_01_08_03_0.bmp
0012\0012_01_08_03_102.bmp
0012\0012_01_08_03_107.bmp
0012\0012_01_08_03_117.bmp
0012\0012_01_08_03_121.bmp
0012\0012_01_08_03_129.bmp
0012\0012_01_08_03_133.bmp
0012\0012_01_08_03_147.bmp
0012\0012_01_08_03_151.bmp
0012\0012_01_08_03_156.bmp
0012\0012_01_08_03_160.bmp
0012\0012_01_08_03_165.bmp
0012\0012_01_08_03_17.bmp
0012\0012_01_08_03_174.bmp
0012\0012_01_08_03_179.bmp
0012\0012_01_08_03_183.bmp
0012\0012_01_08_03_188.bmp
0012\0012_01_08_03_196.bmp
0012\0012_01_08_03_200.bmp
0012\0012_01_08_03_205.bmp
0012\0012_01_08_03_21.bmp
0012\0012_01_08_03_214.bmp
0012\0012_01_08_03_219.bmp
0012\0012_01_08_03_227.bmp
0012\0012_01_08_03_232.bmp
0012\0012_01_08_03_237.bmp
0012\0012_01_08_03_247.bmp
0012\0012_01_08_03_254.bmp
0012\0012_01_08_03_26.bmp
0012\0012_01_08_03_267.bmp
0012\0012_01_08_03_276.bmp
0012\0012_01_08_03_297.bmp
0012\0012_01_08_03_300.bmp
0012\0012_01_08_03_305.bmp
0012\0012_01_08_03_319.bmp
0012\0012_01_08_03_324.bmp
0012\0012_01_08_03_331.bmp
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0012\0012_01_08_03_35.bmp
0012\0012_01_08_03_363.bmp
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0012\0012_01_08_03_377.bmp
0012\0012_01_08_03_387.bmp
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0012\0012_01_08_03_40.bmp
0012\0012_01_08_03_414.bmp
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0012\0012_01_08_03_444.bmp
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0012\0012_01_08_03_49.bmp
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0012\0012_01_08_03_5.bmp
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0012\0012_01_08_03_59.bmp
0012\0012_01_08_03_63.bmp
0012\0012_01_08_03_78.bmp
0012\0012_01_08_03_85.bmp
0012\0012_01_08_03_9.bmp
0012\0012_01_08_03_94.bmp
0011\0011_01_03_03_0.bmp
0011\0011_01_03_03_103.bmp
0011\0011_01_03_03_112.bmp
0011\0011_01_03_03_118.bmp
0011\0011_01_03_03_125.bmp
0011\0011_01_03_03_131.bmp
0011\0011_01_03_03_137.bmp
0011\0011_01_03_03_143.bmp
0011\0011_01_03_03_151.bmp
0011\0011_01_03_03_156.bmp
0011\0011_01_03_03_161.bmp
0011\0011_01_03_03_168.bmp
0011\0011_01_03_03_173.bmp
0011\0011_01_03_03_179.bmp
0011\0011_01_03_03_183.bmp
0011\0011_01_03_03_19.bmp
0011\0011_01_03_03_196.bmp
0011\0011_01_03_03_202.bmp
0011\0011_01_03_03_207.bmp
0011\0011_01_03_03_212.bmp
0011\0011_01_03_03_221.bmp
0011\0011_01_03_03_227.bmp
0011\0011_01_03_03_231.bmp
0011\0011_01_03_03_236.bmp
0011\0011_01_03_03_241.bmp
0011\0011_01_03_03_246.bmp
0011\0011_01_03_03_250.bmp
0011\0011_01_03_03_255.bmp
0011\0011_01_03_03_260.bmp
0011\0011_01_03_03_268.bmp
0011\0011_01_03_03_275.bmp
0011\0011_01_03_03_28.bmp
0011\0011_01_03_03_287.bmp
0011\0011_01_03_03_292.bmp
0011\0011_01_03_03_297.bmp
0011\0011_01_03_03_300.bmp
0011\0011_01_03_03_305.bmp
0011\0011_01_03_03_310.bmp
0011\0011_01_03_03_317.bmp
0011\0011_01_03_03_321.bmp
0011\0011_01_03_03_327.bmp
0011\0011_01_03_03_331.bmp
0011\0011_01_03_03_336.bmp
0011\0011_01_03_03_341.bmp
0011\0011_01_03_03_346.bmp
0011\0011_01_03_03_353.bmp
0011\0011_01_03_03_358.bmp
0011\0011_01_03_03_362.bmp
0011\0011_01_03_03_367.bmp
0011\0011_01_03_03_372.bmp
0011\0011_01_03_03_382.bmp
0011\0011_01_03_03_390.bmp
0011\0011_01_03_03_395.bmp
0011\0011_01_03_03_4.bmp
0011\0011_01_03_03_404.bmp
0011\0011_01_03_03_409.bmp
0011\0011_01_03_03_414.bmp
0011\0011_01_03_03_419.bmp
0011\0011_01_03_03_423.bmp
0011\0011_01_03_03_428.bmp
0011\0011_01_03_03_432.bmp
0011\0011_01_03_03_437.bmp
0011\0011_01_03_03_441.bmp
0011\0011_01_03_03_446.bmp
0011\0011_01_03_03_451.bmp
0011\0011_01_03_03_456.bmp
0011\0011_01_03_03_460.bmp
0011\0011_01_03_03_468.bmp
0011\0011_01_03_03_475.bmp
0011\0011_01_03_03_480.bmp
0011\0011_01_03_03_486.bmp
0011\0011_01_03_03_491.bmp
0011\0011_01_03_03_496.bmp
0011\0011_01_03_03_51.bmp
0011\0011_01_03_03_6.bmp
0011\0011_01_03_03_67.bmp
0011\0011_01_03_03_71.bmp
0011\0011_01_03_03_77.bmp
0011\0011_01_03_03_82.bmp
0011\0011_01_03_03_89.bmp
0011\0011_01_03_03_94.bmp
0011\0011_01_04_03_0.bmp
0011\0011_01_04_03_105.bmp
0011\0011_01_04_03_11.bmp
0011\0011_01_04_03_121.bmp
0011\0011_01_04_03_127.bmp
0011\0011_01_04_03_137.bmp
0011\0011_01_04_03_146.bmp
0011\0011_01_04_03_151.bmp
0011\0011_01_04_03_156.bmp
0011\0011_01_04_03_161.bmp
0011\0011_01_04_03_167.bmp
0011\0011_01_04_03_171.bmp
0011\0011_01_04_03_176.bmp
0011\0011_01_04_03_180.bmp
0011\0011_01_04_03_185.bmp
0011\0011_01_04_03_190.bmp
0011\0011_01_04_03_195.bmp
0011\0011_01_04_03_20.bmp
0011\0011_01_04_03_205.bmp
0011\0011_01_04_03_210.bmp
0011\0011_01_04_03_215.bmp
0011\0011_01_04_03_226.bmp
0011\0011_01_04_03_235.bmp
0011\0011_01_04_03_24.bmp
0011\0011_01_04_03_245.bmp
0011\0011_01_04_03_25.bmp
0011\0011_01_04_03_255.bmp
0011\0011_01_04_03_26.bmp
0011\0011_01_04_03_264.bmp
0011\0011_01_04_03_27.bmp
0011\0011_01_04_03_275.bmp
0011\0011_01_04_03_28.bmp
0011\0011_01_04_03_288.bmp
0011\0011_01_04_03_296.bmp
0011\0011_01_04_03_301.bmp
0011\0011_01_04_03_307.bmp
0011\0011_01_04_03_311.bmp
0011\0011_01_04_03_317.bmp
0011\0011_01_04_03_322.bmp
0011\0011_01_04_03_327.bmp
0011\0011_01_04_03_340.bmp
0011\0011_01_04_03_346.bmp
0011\0011_01_04_03_350.bmp
0011\0011_01_04_03_357.bmp
0011\0011_01_04_03_362.bmp
0011\0011_01_04_03_372.bmp
0011\0011_01_04_03_378.bmp
0011\0011_01_04_03_384.bmp
0011\0011_01_04_03_390.bmp
0011\0011_01_04_03_396.bmp
0011\0011_01_04_03_403.bmp
0011\0011_01_04_03_408.bmp
0011\0011_01_04_03_413.bmp
0011\0011_01_04_03_418.bmp
0011\0011_01_04_03_422.bmp
0011\0011_01_04_03_427.bmp
0011\0011_01_04_03_432.bmp
0011\0011_01_04_03_438.bmp
0011\0011_01_04_03_443.bmp
0011\0011_01_04_03_448.bmp
0011\0011_01_04_03_452.bmp
0011\0011_01_04_03_457.bmp
0011\0011_01_04_03_461.bmp
0011\0011_01_04_03_468.bmp
0011\0011_01_04_03_476.bmp
0011\0011_01_04_03_480.bmp
0011\0011_01_04_03_486.bmp
0011\0011_01_04_03_491.bmp
0011\0011_01_04_03_496.bmp
0011\0011_01_04_03_51.bmp
0011\0011_01_04_03_61.bmp
0011\0011_01_04_03_67.bmp
0011\0011_01_04_03_72.bmp
0011\0011_01_04_03_78.bmp
0011\0011_01_04_03_84.bmp
0011\0011_01_04_03_9.bmp
0011\0011_01_04_03_98.bmp
0011\0011_01_01_03_0.bmp
0011\0011_01_01_03_105.bmp
0011\0011_01_01_03_114.bmp
0011\0011_01_01_03_120.bmp
0011\0011_01_01_03_132.bmp
0011\0011_01_01_03_138.bmp
0011\0011_01_01_03_149.bmp
0011\0011_01_01_03_157.bmp
0011\0011_01_01_03_168.bmp
0011\0011_01_01_03_176.bmp
0011\0011_01_01_03_185.bmp
0011\0011_01_01_03_191.bmp
0011\0011_01_01_03_20.bmp
0011\0011_01_01_03_208.bmp
0011\0011_01_01_03_217.bmp
0011\0011_01_01_03_221.bmp
0011\0011_01_01_03_23.bmp
0011\0011_01_01_03_234.bmp
0011\0011_01_01_03_243.bmp
0011\0011_01_01_03_25.bmp
0011\0011_01_01_03_257.bmp
0011\0011_01_01_03_262.bmp
0011\0011_01_01_03_269.bmp
0011\0011_01_01_03_276.bmp
0011\0011_01_01_03_281.bmp
0011\0011_01_01_03_290.bmp
0011\0011_01_01_03_300.bmp
0011\0011_01_01_03_310.bmp
0011\0011_01_01_03_316.bmp
0011\0011_01_01_03_321.bmp
0011\0011_01_01_03_328.bmp
0011\0011_01_01_03_333.bmp
0011\0011_01_01_03_340.bmp
0011\0011_01_01_03_346.bmp
0011\0011_01_01_03_356.bmp
0011\0011_01_01_03_360.bmp
0011\0011_01_01_03_367.bmp
0011\0011_01_01_03_375.bmp
0011\0011_01_01_03_380.bmp
0011\0011_01_01_03_389.bmp
0011\0011_01_01_03_397.bmp
0011\0011_01_01_03_404.bmp
0011\0011_01_01_03_411.bmp
0011\0011_01_01_03_417.bmp
0011\0011_01_01_03_423.bmp
0011\0011_01_01_03_431.bmp
0011\0011_01_01_03_436.bmp
0011\0011_01_01_03_442.bmp
0011\0011_01_01_03_447.bmp
0011\0011_01_01_03_453.bmp
0011\0011_01_01_03_460.bmp
0011\0011_01_01_03_472.bmp
0011\0011_01_01_03_481.bmp
0011\0011_01_01_03_489.bmp
0011\0011_01_01_03_498.bmp
0011\0011_01_01_03_60.bmp
0011\0011_01_01_03_67.bmp
0011\0011_01_01_03_74.bmp
0011\0011_01_01_03_83.bmp
0011\0011_01_01_03_89.bmp
0011\0011_01_01_03_96.bmp
0011\0011_01_02_03_106.bmp
0011\0011_01_02_03_119.bmp
0011\0011_01_02_03_137.bmp
0011\0011_01_02_03_165.bmp
0011\0011_01_02_03_174.bmp
0011\0011_01_02_03_184.bmp
0011\0011_01_02_03_193.bmp
0011\0011_01_02_03_203.bmp
0011\0011_01_02_03_214.bmp
0011\0011_01_02_03_236.bmp
0011\0011_01_02_03_248.bmp
0011\0011_01_02_03_260.bmp
0011\0011_01_02_03_27.bmp
0011\0011_01_02_03_287.bmp
0011\0011_01_02_03_30.bmp
0011\0011_01_02_03_310.bmp
0011\0011_01_02_03_331.bmp
0011\0011_01_02_03_337.bmp
0011\0011_01_02_03_360.bmp
0011\0011_01_02_03_374.bmp
0011\0011_01_02_03_386.bmp
0011\0011_01_02_03_395.bmp
0011\0011_01_02_03_407.bmp
0011\0011_01_02_03_418.bmp
0011\0011_01_02_03_429.bmp
0011\0011_01_02_03_441.bmp
0011\0011_01_02_03_455.bmp
0011\0011_01_02_03_47.bmp
0011\0011_01_02_03_49.bmp
0011\0011_01_02_03_57.bmp
0011\0011_01_02_03_66.bmp
0011\0011_01_02_03_77.bmp
0011\0011_01_02_03_89.bmp
0011\0011_01_02_03_99.bmp
0011\0011_01_08_03_0.bmp
0011\0011_01_08_03_102.bmp
0011\0011_01_08_03_108.bmp
0011\0011_01_08_03_112.bmp
0011\0011_01_08_03_119.bmp
0011\0011_01_08_03_136.bmp
0011\0011_01_08_03_143.bmp
0011\0011_01_08_03_152.bmp
0011\0011_01_08_03_159.bmp
0011\0011_01_08_03_174.bmp
0011\0011_01_08_03_179.bmp
0011\0011_01_08_03_187.bmp
0011\0011_01_08_03_22.bmp
0011\0011_01_08_03_239.bmp
0011\0011_01_08_03_244.bmp
0011\0011_01_08_03_25.bmp
0011\0011_01_08_03_256.bmp
0011\0011_01_08_03_260.bmp
0011\0011_01_08_03_266.bmp
0011\0011_01_08_03_270.bmp
0011\0011_01_08_03_279.bmp
0011\0011_01_08_03_283.bmp
0011\0011_01_08_03_288.bmp
0011\0011_01_08_03_293.bmp
0011\0011_01_08_03_307.bmp
0011\0011_01_08_03_311.bmp
0011\0011_01_08_03_326.bmp
0011\0011_01_08_03_345.bmp
0011\0011_01_08_03_354.bmp
0011\0011_01_08_03_369.bmp
0011\0011_01_08_03_380.bmp
0011\0011_01_08_03_386.bmp
0011\0011_01_08_03_390.bmp
0011\0011_01_08_03_423.bmp
0011\0011_01_08_03_428.bmp
0011\0011_01_08_03_434.bmp
0011\0011_01_08_03_44.bmp
0011\0011_01_08_03_447.bmp
0011\0011_01_08_03_453.bmp
0011\0011_01_08_03_458.bmp
0011\0011_01_08_03_463.bmp
0011\0011_01_08_03_476.bmp
0011\0011_01_08_03_482.bmp
0011\0011_01_08_03_495.bmp
0011\0011_01_08_03_51.bmp
0011\0011_01_08_03_58.bmp
0011\0011_01_08_03_67.bmp
0011\0011_01_08_03_79.bmp
0011\0011_01_08_03_91.bmp
0011\0011_01_08_03_96.bmp


================================================
FILE: Matlab/NUAA/imposter_train_normalized.txt
================================================
0001\0001_00_00_01_0.bmp
0001\0001_00_00_01_102.bmp
0001\0001_00_00_01_107.bmp
0001\0001_00_00_01_111.bmp
0001\0001_00_00_01_116.bmp
0001\0001_00_00_01_120.bmp
0001\0001_00_00_01_125.bmp
0001\0001_00_00_01_13.bmp
0001\0001_00_00_01_134.bmp
0001\0001_00_00_01_139.bmp
0001\0001_00_00_01_143.bmp
0001\0001_00_00_01_148.bmp
0001\0001_00_00_01_152.bmp
0001\0001_00_00_01_157.bmp
0001\0001_00_00_01_161.bmp
0001\0001_00_00_01_166.bmp
0001\0001_00_00_01_170.bmp
0001\0001_00_00_01_175.bmp
0001\0001_00_00_01_18.bmp
0001\0001_00_00_01_184.bmp
0001\0001_00_00_01_189.bmp
0001\0001_00_00_01_193.bmp
0001\0001_00_00_01_198.bmp
0001\0001_00_00_01_201.bmp
0001\0001_00_00_01_206.bmp
0001\0001_00_00_01_210.bmp
0001\0001_00_00_01_215.bmp
0001\0001_00_00_01_22.bmp
0001\0001_00_00_01_224.bmp
0001\0001_00_00_01_229.bmp
0001\0001_00_00_01_233.bmp
0001\0001_00_00_01_238.bmp
0001\0001_00_00_01_242.bmp
0001\0001_00_00_01_247.bmp
0001\0001_00_00_01_251.bmp
0001\0001_00_00_01_256.bmp
0001\0001_00_00_01_260.bmp
0001\0001_00_00_01_265.bmp
0001\0001_00_00_01_27.bmp
0001\0001_00_00_01_274.bmp
0001\0001_00_00_01_279.bmp
0001\0001_00_00_01_283.bmp
0001\0001_00_00_01_288.bmp
0001\0001_00_00_01_292.bmp
0001\0001_00_00_01_297.bmp
0001\0001_00_00_01_300.bmp
0001\0001_00_00_01_305.bmp
0001\0001_00_00_01_31.bmp
0001\0001_00_00_01_314.bmp
0001\0001_00_00_01_319.bmp
0001\0001_00_00_01_323.bmp
0001\0001_00_00_01_328.bmp
0001\0001_00_00_01_332.bmp
0001\0001_00_00_01_337.bmp
0001\0001_00_00_01_341.bmp
0001\0001_00_00_01_346.bmp
0001\0001_00_00_01_350.bmp
0001\0001_00_00_01_355.bmp
0001\0001_00_00_01_36.bmp
0001\0001_00_00_01_364.bmp
0001\0001_00_00_01_369.bmp
0001\0001_00_00_01_373.bmp
0001\0001_00_00_01_378.bmp
0001\0001_00_00_01_382.bmp
0001\0001_00_00_01_387.bmp
0001\0001_00_00_01_391.bmp
0001\0001_00_00_01_396.bmp
0001\0001_00_00_01_40.bmp
0001\0001_00_00_01_404.bmp
0001\0001_00_00_01_409.bmp
0001\0001_00_00_01_413.bmp
0001\0001_00_00_01_418.bmp
0001\0001_00_00_01_422.bmp
0001\0001_00_00_01_427.bmp
0001\0001_00_00_01_431.bmp
0001\0001_00_00_01_436.bmp
0001\0001_00_00_01_440.bmp
0001\0001_00_00_01_445.bmp
0001\0001_00_00_01_45.bmp
0001\0001_00_00_01_454.bmp
0001\0001_00_00_01_459.bmp
0001\0001_00_00_01_463.bmp
0001\0001_00_00_01_468.bmp
0001\0001_00_00_01_472.bmp
0001\0001_00_00_01_477.bmp
0001\0001_00_00_01_481.bmp
0001\0001_00_00_01_486.bmp
0001\0001_00_00_01_490.bmp
0001\0001_00_00_01_495.bmp
0001\0001_00_00_01_5.bmp
0001\0001_00_00_01_54.bmp
0001\0001_00_00_01_59.bmp
0001\0001_00_00_01_63.bmp
0001\0001_00_00_01_68.bmp
0001\0001_00_00_01_72.bmp
0001\0001_00_00_01_77.bmp
0001\0001_00_00_01_81.bmp
0001\0001_00_00_01_86.bmp
0001\0001_00_00_01_90.bmp
0001\0001_00_00_01_95.bmp
0002\0002_01_00_01_0.bmp
0002\0002_01_00_01_102.bmp
0002\0002_01_00_01_107.bmp
0002\0002_01_00_01_111.bmp
0002\0002_01_00_01_116.bmp
0002\0002_01_00_01_120.bmp
0002\0002_01_00_01_125.bmp
0002\0002_01_00_01_13.bmp
0002\0002_01_00_01_134.bmp
0002\0002_01_00_01_139.bmp
0002\0002_01_00_01_143.bmp
0002\0002_01_00_01_148.bmp
0002\0002_01_00_01_152.bmp
0002\0002_01_00_01_157.bmp
0002\0002_01_00_01_161.bmp
0002\0002_01_00_01_166.bmp
0002\0002_01_00_01_170.bmp
0002\0002_01_00_01_175.bmp
0002\0002_01_00_01_18.bmp
0002\0002_01_00_01_184.bmp
0002\0002_01_00_01_189.bmp
0002\0002_01_00_01_193.bmp
0002\0002_01_00_01_198.bmp
0002\0002_01_00_01_201.bmp
0002\0002_01_00_01_206.bmp
0002\0002_01_00_01_210.bmp
0002\0002_01_00_01_215.bmp
0002\0002_01_00_01_22.bmp
0002\0002_01_00_01_224.bmp
0002\0002_01_00_01_229.bmp
0002\0002_01_00_01_233.bmp
0002\0002_01_00_01_238.bmp
0002\0002_01_00_01_242.bmp
0002\0002_01_00_01_247.bmp
0002\0002_01_00_01_251.bmp
0002\0002_01_00_01_256.bmp
0002\0002_01_00_01_260.bmp
0002\0002_01_00_01_265.bmp
0002\0002_01_00_01_27.bmp
0002\0002_01_00_01_274.bmp
0002\0002_01_00_01_279.bmp
0002\0002_01_00_01_283.bmp
0002\0002_01_00_01_288.bmp
0002\0002_01_00_01_292.bmp
0002\0002_01_00_01_297.bmp
0002\0002_01_00_01_300.bmp
0002\0002_01_00_01_305.bmp
0002\0002_01_00_01_31.bmp
0002\0002_01_00_01_314.bmp
0002\0002_01_00_01_319.bmp
0002\0002_01_00_01_323.bmp
0002\0002_01_00_01_328.bmp
0002\0002_01_00_01_332.bmp
0002\0002_01_00_01_337.bmp
0002\0002_01_00_01_341.bmp
0002\0002_01_00_01_346.bmp
0002\0002_01_00_01_350.bmp
0002\0002_01_00_01_355.bmp
0002\0002_01_00_01_36.bmp
0002\0002_01_00_01_364.bmp
0002\0002_01_00_01_369.bmp
0002\0002_01_00_01_373.bmp
0002\0002_01_00_01_378.bmp
0002\0002_01_00_01_382.bmp
0002\0002_01_00_01_387.bmp
0002\0002_01_00_01_391.bmp
0002\0002_01_00_01_396.bmp
0002\0002_01_00_01_40.bmp
0002\0002_01_00_01_404.bmp
0002\0002_01_00_01_409.bmp
0002\0002_01_00_01_413.bmp
0002\0002_01_00_01_418.bmp
0002\0002_01_00_01_422.bmp
0002\0002_01_00_01_427.bmp
0002\0002_01_00_01_431.bmp
0002\0002_01_00_01_436.bmp
0002\0002_01_00_01_440.bmp
0002\0002_01_00_01_445.bmp
0002\0002_01_00_01_45.bmp
0002\0002_01_00_01_454.bmp
0002\0002_01_00_01_459.bmp
0002\0002_01_00_01_463.bmp
0002\0002_01_00_01_468.bmp
0002\0002_01_00_01_472.bmp
0002\0002_01_00_01_477.bmp
0002\0002_01_00_01_481.bmp
0002\0002_01_00_01_486.bmp
0002\0002_01_00_01_490.bmp
0002\0002_01_00_01_495.bmp
0002\0002_01_00_01_50.bmp
0002\0002_01_00_01_55.bmp
0002\0002_01_00_01_6.bmp
0002\0002_01_00_01_64.bmp
0002\0002_01_00_01_69.bmp
0002\0002_01_00_01_73.bmp
0002\0002_01_00_01_78.bmp
0002\0002_01_00_01_82.bmp
0002\0002_01_00_01_87.bmp
0002\0002_01_00_01_91.bmp
0002\0002_01_00_01_96.bmp
0003\0003_01_00_01_10.bmp
0003\0003_01_00_01_104.bmp
0003\0003_01_00_01_109.bmp
0003\0003_01_00_01_113.bmp
0003\0003_01_00_01_118.bmp
0003\0003_01_00_01_122.bmp
0003\0003_01_00_01_127.bmp
0003\0003_01_00_01_131.bmp
0003\0003_01_00_01_136.bmp
0003\0003_01_00_01_140.bmp
0003\0003_01_00_01_145.bmp
0003\0003_01_00_01_15.bmp
0003\0003_01_00_01_154.bmp
0003\0003_01_00_01_159.bmp
0003\0003_01_00_01_163.bmp
0003\0003_01_00_01_168.bmp
0003\0003_01_00_01_172.bmp
0003\0003_01_00_01_177.bmp
0003\0003_01_00_01_181.bmp
0003\0003_01_00_01_186.bmp
0003\0003_01_00_01_190.bmp
0003\0003_01_00_01_195.bmp
0003\0003_01_00_01_20.bmp
0003\0003_01_00_01_204.bmp
0003\0003_01_00_01_209.bmp
0003\0003_01_00_01_213.bmp
0003\0003_01_00_01_218.bmp
0003\0003_01_00_01_222.bmp
0003\0003_01_00_01_227.bmp
0003\0003_01_00_01_231.bmp
0003\0003_01_00_01_236.bmp
0003\0003_01_00_01_240.bmp
0003\0003_01_00_01_245.bmp
0003\0003_01_00_01_25.bmp
0003\0003_01_00_01_254.bmp
0003\0003_01_00_01_259.bmp
0003\0003_01_00_01_263.bmp
0003\0003_01_00_01_268.bmp
0003\0003_01_00_01_272.bmp
0003\0003_01_00_01_277.bmp
0003\0003_01_00_01_281.bmp
0003\0003_01_00_01_286.bmp
0003\0003_01_00_01_290.bmp
0003\0003_01_00_01_295.bmp
0003\0003_01_00_01_30.bmp
0003\0003_01_00_01_304.bmp
0003\0003_01_00_01_309.bmp
0003\0003_01_00_01_313.bmp
0003\0003_01_00_01_318.bmp
0003\0003_01_00_01_322.bmp
0003\0003_01_00_01_327.bmp
0003\0003_01_00_01_331.bmp
0003\0003_01_00_01_336.bmp
0003\0003_01_00_01_340.bmp
0003\0003_01_00_01_345.bmp
0003\0003_01_00_01_35.bmp
0003\0003_01_00_01_354.bmp
0003\0003_01_00_01_359.bmp
0003\0003_01_00_01_363.bmp
0003\0003_01_00_01_368.bmp
0003\0003_01_00_01_372.bmp
0003\0003_01_00_01_377.bmp
0003\0003_01_00_01_381.bmp
0003\0003_01_00_01_386.bmp
0003\0003_01_00_01_390.bmp
0003\0003_01_00_01_395.bmp
0003\0003_01_00_01_40.bmp
0003\0003_01_00_01_404.bmp
0003\0003_01_00_01_409.bmp
0003\0003_01_00_01_413.bmp
0003\0003_01_00_01_418.bmp
0003\0003_01_00_01_422.bmp
0003\0003_01_00_01_427.bmp
0003\0003_01_00_01_431.bmp
0003\0003_01_00_01_436.bmp
0003\0003_01_00_01_440.bmp
0003\0003_01_00_01_445.bmp
0003\0003_01_00_01_45.bmp
0003\0003_01_00_01_454.bmp
0003\0003_01_00_01_459.bmp
0003\0003_01_00_01_463.bmp
0003\0003_01_00_01_468.bmp
0003\0003_01_00_01_472.bmp
0003\0003_01_00_01_477.bmp
0003\0003_01_00_01_481.bmp
0003\0003_01_00_01_486.bmp
0003\0003_01_00_01_490.bmp
0003\0003_01_00_01_495.bmp
0003\0003_01_00_01_50.bmp
0003\0003_01_00_01_55.bmp
0003\0003_01_00_01_6.bmp
0003\0003_01_00_01_64.bmp
0003\0003_01_00_01_69.bmp
0003\0003_01_00_01_73.bmp
0003\0003_01_00_01_78.bmp
0003\0003_01_00_01_82.bmp
0003\0003_01_00_01_87.bmp
0003\0003_01_00_01_91.bmp
0003\0003_01_00_01_96.bmp
0004\0004_01_00_01_0.bmp
0004\0004_01_00_01_102.bmp
0004\0004_01_00_01_107.bmp
0004\0004_01_00_01_111.bmp
0004\0004_01_00_01_116.bmp
0004\0004_01_00_01_120.bmp
0004\0004_01_00_01_125.bmp
0004\0004_01_00_01_13.bmp
0004\0004_01_00_01_134.bmp
0004\0004_01_00_01_139.bmp
0004\0004_01_00_01_143.bmp
0004\0004_01_00_01_148.bmp
0004\0004_01_00_01_154.bmp
0004\0004_01_00_01_16.bmp
0004\0004_01_00_01_164.bmp
0004\0004_01_00_01_169.bmp
0004\0004_01_00_01_173.bmp
0004\0004_01_00_01_178.bmp
0004\0004_01_00_01_182.bmp
0004\0004_01_00_01_187.bmp
0004\0004_01_00_01_191.bmp
0004\0004_01_00_01_196.bmp
0004\0004_01_00_01_20.bmp
0004\0004_01_00_01_204.bmp
0004\0004_01_00_01_209.bmp
0004\0004_01_00_01_213.bmp
0004\0004_01_00_01_218.bmp
0004\0004_01_00_01_222.bmp
0004\0004_01_00_01_227.bmp
0004\0004_01_00_01_231.bmp
0004\0004_01_00_01_236.bmp
0004\0004_01_00_01_240.bmp
0004\0004_01_00_01_245.bmp
0004\0004_01_00_01_25.bmp
0004\0004_01_00_01_254.bmp
0004\0004_01_00_01_259.bmp
0004\0004_01_00_01_263.bmp
0004\0004_01_00_01_268.bmp
0004\0004_01_00_01_272.bmp
0004\0004_01_00_01_277.bmp
0004\0004_01_00_01_281.bmp
0004\0004_01_00_01_286.bmp
0004\0004_01_00_01_290.bmp
0004\0004_01_00_01_295.bmp
0004\0004_01_00_01_3.bmp
0004\0004_01_00_01_303.bmp
0004\0004_01_00_01_308.bmp
0004\0004_01_00_01_312.bmp
0004\0004_01_00_01_317.bmp
0004\0004_01_00_01_321.bmp
0004\0004_01_00_01_326.bmp
0004\0004_01_00_01_330.bmp
0004\0004_01_00_01_335.bmp
0004\0004_01_00_01_34.bmp
0004\0004_01_00_01_344.bmp
0004\0004_01_00_01_349.bmp
0004\0004_01_00_01_353.bmp
0004\0004_01_00_01_358.bmp
0004\0004_01_00_01_362.bmp
0004\0004_01_00_01_367.bmp
0004\0004_01_00_01_371.bmp
0004\0004_01_00_01_376.bmp
0004\0004_01_00_01_380.bmp
0004\0004_01_00_01_385.bmp
0004\0004_01_00_01_39.bmp
0004\0004_01_00_01_394.bmp
0004\0004_01_00_01_399.bmp
0004\0004_01_00_01_402.bmp
0004\0004_01_00_01_407.bmp
0004\0004_01_00_01_411.bmp
0004\0004_01_00_01_416.bmp
0004\0004_01_00_01_420.bmp
0004\0004_01_00_01_425.bmp
0004\0004_01_00_01_43.bmp
0004\0004_01_00_01_434.bmp
0004\0004_01_00_01_439.bmp
0004\0004_01_00_01_443.bmp
0004\0004_01_00_01_448.bmp
0004\0004_01_00_01_452.bmp
0004\0004_01_00_01_457.bmp
0004\0004_01_00_01_461.bmp
0004\0004_01_00_01_466.bmp
0004\0004_01_00_01_470.bmp
0004\0004_01_00_01_475.bmp
0004\0004_01_00_01_48.bmp
0004\0004_01_00_01_484.bmp
0004\0004_01_00_01_489.bmp
0004\0004_01_00_01_493.bmp
0004\0004_01_00_01_498.bmp
0004\0004_01_00_01_53.bmp
0004\0004_01_00_01_58.bmp
0004\0004_01_00_01_63.bmp
0004\0004_01_00_01_68.bmp
0004\0004_01_00_01_72.bmp
0004\0004_01_00_01_77.bmp
0004\0004_01_00_01_82.bmp
0004\0004_01_00_01_87.bmp
0004\0004_01_00_01_91.bmp
0004\0004_01_00_01_96.bmp
0005\0005_00_00_01_10.bmp
0005\0005_00_00_01_104.bmp
0005\0005_00_00_01_109.bmp
0005\0005_00_00_01_113.bmp
0005\0005_00_00_01_118.bmp
0005\0005_00_00_01_122.bmp
0005\0005_00_00_01_127.bmp
0005\0005_00_00_01_131.bmp
0005\0005_00_00_01_136.bmp
0005\0005_00_00_01_140.bmp
0005\0005_00_00_01_145.bmp
0005\0005_00_00_01_151.bmp
0005\0005_00_00_01_156.bmp
0005\0005_00_00_01_160.bmp
0005\0005_00_00_01_165.bmp
0005\0005_00_00_01_17.bmp
0005\0005_00_00_01_174.bmp
0005\0005_00_00_01_179.bmp
0005\0005_00_00_01_183.bmp
0005\0005_00_00_01_188.bmp
0005\0005_00_00_01_192.bmp
0005\0005_00_00_01_197.bmp
0005\0005_00_00_01_201.bmp
0005\0005_00_00_01_206.bmp
0005\0005_00_00_01_210.bmp
0005\0005_00_00_01_215.bmp
0005\0005_00_00_01_22.bmp
0005\0005_00_00_01_224.bmp
0005\0005_00_00_01_229.bmp
0005\0005_00_00_01_233.bmp
0005\0005_00_00_01_238.bmp
0005\0005_00_00_01_242.bmp
0005\0005_00_00_01_247.bmp
0005\0005_00_00_01_251.bmp
0005\0005_00_00_01_256.bmp
0005\0005_00_00_01_260.bmp
0005\0005_00_00_01_265.bmp
0005\0005_00_00_01_27.bmp
0005\0005_00_00_01_274.bmp
0005\0005_00_00_01_279.bmp
0005\0005_00_00_01_283.bmp
0005\0005_00_00_01_30.bmp
0005\0005_00_00_01_328.bmp
0005\0005_00_00_01_332.bmp
0005\0005_00_00_01_337.bmp
0005\0005_00_00_01_341.bmp
0005\0005_00_00_01_346.bmp
0005\0005_00_00_01_356.bmp
0005\0005_00_00_01_360.bmp
0005\0005_00_00_01_365.bmp
0005\0005_00_00_01_37.bmp
0005\0005_00_00_01_374.bmp
0005\0005_00_00_01_379.bmp
0005\0005_00_00_01_383.bmp
0005\0005_00_00_01_388.bmp
0005\0005_00_00_01_392.bmp
0005\0005_00_00_01_397.bmp
0005\0005_00_00_01_401.bmp
0005\0005_00_00_01_406.bmp
0005\0005_00_00_01_410.bmp
0005\0005_00_00_01_415.bmp
0005\0005_00_00_01_42.bmp
0005\0005_00_00_01_425.bmp
0005\0005_00_00_01_43.bmp
0005\0005_00_00_01_434.bmp
0005\0005_00_00_01_439.bmp
0005\0005_00_00_01_443.bmp
0005\0005_00_00_01_448.bmp
0005\0005_00_00_01_452.bmp
0005\0005_00_00_01_457.bmp
0005\0005_00_00_01_461.bmp
0005\0005_00_00_01_466.bmp
0005\0005_00_00_01_470.bmp
0005\0005_00_00_01_475.bmp
0005\0005_00_00_01_48.bmp
0005\0005_00_00_01_484.bmp
0005\0005_00_00_01_489.bmp
0005\0005_00_00_01_493.bmp
0005\0005_00_00_01_498.bmp
0005\0005_00_00_01_52.bmp
0005\0005_00_00_01_57.bmp
0005\0005_00_00_01_61.bmp
0005\0005_00_00_01_66.bmp
0005\0005_00_00_01_70.bmp
0005\0005_00_00_01_75.bmp
0005\0005_00_00_01_8.bmp
0005\0005_00_00_01_84.bmp
0005\0005_00_00_01_89.bmp
0005\0005_00_00_01_93.bmp
0005\0005_00_00_01_98.bmp
0006\0006_00_00_01_100.bmp
0006\0006_00_00_01_105.bmp
0006\0006_00_00_01_110.bmp
0006\0006_00_00_01_115.bmp
0006\0006_00_00_01_13.bmp
0006\0006_00_00_01_136.bmp
0006\0006_00_00_01_140.bmp
0006\0006_00_00_01_145.bmp
0006\0006_00_00_01_15.bmp
0006\0006_00_00_01_154.bmp
0006\0006_00_00_01_159.bmp
0006\0006_00_00_01_164.bmp
0006\0006_00_00_01_170.bmp
0006\0006_00_00_01_175.bmp
0006\0006_00_00_01_180.bmp
0006\0006_00_00_01_185.bmp
0006\0006_00_00_01_190.bmp
0006\0006_00_00_01_195.bmp
0006\0006_00_00_01_2.bmp
0006\0006_00_00_01_205.bmp
0006\0006_00_00_01_211.bmp
0006\0006_00_00_01_224.bmp
0006\0006_00_00_01_229.bmp
0006\0006_00_00_01_233.bmp
0006\0006_00_00_01_239.bmp
0006\0006_00_00_01_243.bmp
0006\0006_00_00_01_248.bmp
0006\0006_00_00_01_252.bmp
0006\0006_00_00_01_257.bmp
0006\0006_00_00_01_263.bmp
0006\0006_00_00_01_270.bmp
0006\0006_00_00_01_281.bmp
0006\0006_00_00_01_286.bmp
0006\0006_00_00_01_290.bmp
0006\0006_00_00_01_295.bmp
0006\0006_00_00_01_30.bmp
0006\0006_00_00_01_304.bmp
0006\0006_00_00_01_309.bmp
0006\0006_00_00_01_313.bmp
0006\0006_00_00_01_318.bmp
0006\0006_00_00_01_326.bmp
0006\0006_00_00_01_330.bmp
0006\0006_00_00_01_335.bmp
0006\0006_00_00_01_34.bmp
0006\0006_00_00_01_344.bmp
0006\0006_00_00_01_349.bmp
0006\0006_00_00_01_354.bmp
0006\0006_00_00_01_359.bmp
0006\0006_00_00_01_363.bmp
0006\0006_00_00_01_368.bmp
0006\0006_00_00_01_372.bmp
0006\0006_00_00_01_377.bmp
0006\0006_00_00_01_381.bmp
0006\0006_00_00_01_386.bmp
0006\0006_00_00_01_390.bmp
0006\0006_00_00_01_395.bmp
0006\0006_00_00_01_40.bmp
0006\0006_00_00_01_404.bmp
0006\0006_00_00_01_409.bmp
0006\0006_00_00_01_413.bmp
0006\0006_00_00_01_418.bmp
0006\0006_00_00_01_422.bmp
0006\0006_00_00_01_427.bmp
0006\0006_00_00_01_431.bmp
0006\0006_00_00_01_436.bmp
0006\0006_00_00_01_440.bmp
0006\0006_00_00_01_445.bmp
0006\0006_00_00_01_45.bmp
0006\0006_00_00_01_454.bmp
0006\0006_00_00_01_46.bmp
0006\0006_00_00_01_465.bmp
0006\0006_00_00_01_47.bmp
0006\0006_00_00_01_474.bmp
0006\0006_00_00_01_479.bmp
0006\0006_00_00_01_483.bmp
0006\0006_00_00_01_488.bmp
0006\0006_00_00_01_492.bmp
0006\0006_00_00_01_497.bmp
0006\0006_00_00_01_52.bmp
0006\0006_00_00_01_57.bmp
0006\0006_00_00_01_62.bmp
0006\0006_00_00_01_67.bmp
0006\0006_00_00_01_72.bmp
0006\0006_00_00_01_78.bmp
0006\0006_00_00_01_83.bmp
0006\0006_00_00_01_88.bmp
0006\0006_00_00_01_93.bmp
0006\0006_00_00_01_98.bmp
0007\0007_01_00_01_0.bmp
0007\0007_01_00_01_102.bmp
0007\0007_01_00_01_107.bmp
0007\0007_01_00_01_111.bmp
0007\0007_01_00_01_116.bmp
0007\0007_01_00_01_120.bmp
0007\0007_01_00_01_125.bmp
0007\0007_01_00_01_13.bmp
0007\0007_01_00_01_134.bmp
0007\0007_01_00_01_139.bmp
0007\0007_01_00_01_144.bmp
0007\0007_01_00_01_149.bmp
0007\0007_01_00_01_154.bmp
0007\0007_01_00_01_159.bmp
0007\0007_01_00_01_164.bmp
0007\0007_01_00_01_171.bmp
0007\0007_01_00_01_176.bmp
0007\0007_01_00_01_180.bmp
0007\0007_01_00_01_185.bmp
0007\0007_01_00_01_19.bmp
0007\0007_01_00_01_194.bmp
0007\0007_01_00_01_199.bmp
0007\0007_01_00_01_204.bmp
0007\0007_01_00_01_209.bmp
0007\0007_01_00_01_214.bmp
0007\0007_01_00_01_219.bmp
0007\0007_01_00_01_223.bmp
0007\0007_01_00_01_228.bmp
0007\0007_01_00_01_233.bmp
0007\0007_01_00_01_238.bmp
0007\0007_01_00_01_242.bmp
0007\0007_01_00_01_247.bmp
0007\0007_01_00_01_251.bmp
0007\0007_01_00_01_256.bmp
0007\0007_01_00_01_260.bmp
0007\0007_01_00_01_265.bmp
0007\0007_01_00_01_27.bmp
0007\0007_01_00_01_274.bmp
0007\0007_01_00_01_279.bmp
0007\0007_01_00_01_283.bmp
0007\0007_01_00_01_288.bmp
0007\0007_01_00_01_292.bmp
0007\0007_01_00_01_297.bmp
0007\0007_01_00_01_301.bmp
0007\0007_01_00_01_306.bmp
0007\0007_01_00_01_311.bmp
0007\0007_01_00_01_316.bmp
0007\0007_01_00_01_320.bmp
0007\0007_01_00_01_325.bmp
0007\0007_01_00_01_33.bmp
0007\0007_01_00_01_334.bmp
0007\0007_01_00_01_339.bmp
0007\0007_01_00_01_343.bmp
0007\0007_01_00_01_348.bmp
0007\0007_01_00_01_352.bmp
0007\0007_01_00_01_358.bmp
0007\0007_01_00_01_362.bmp
0007\0007_01_00_01_367.bmp
0007\0007_01_00_01_371.bmp
0007\0007_01_00_01_376.bmp
0007\0007_01_00_01_380.bmp
0007\0007_01_00_01_385.bmp
0007\0007_01_00_01_39.bmp
0007\0007_01_00_01_394.bmp
0007\0007_01_00_01_399.bmp
0007\0007_01_00_01_403.bmp
0007\0007_01_00_01_41.bmp
0007\0007_01_00_01_414.bmp
0007\0007_01_00_01_419.bmp
0007\0007_01_00_01_423.bmp
0007\0007_01_00_01_428.bmp
0007\0007_01_00_01_432.bmp
0007\0007_01_00_01_437.bmp
0007\0007_01_00_01_441.bmp
0007\0007_01_00_01_446.bmp
0007\0007_01_00_01_450.bmp
0007\0007_01_00_01_455.bmp
0007\0007_01_00_01_46.bmp
0007\0007_01_00_01_464.bmp
0007\0007_01_00_01_469.bmp
0007\0007_01_00_01_473.bmp
0007\0007_01_00_01_478.bmp
0007\0007_01_00_01_482.bmp
0007\0007_01_00_01_487.bmp
0007\0007_01_00_01_491.bmp
0007\0007_01_00_01_496.bmp
0007\0007_01_00_01_51.bmp
0007\0007_01_00_01_56.bmp
0007\0007_01_00_01_61.bmp
0007\0007_01_00_01_66.bmp
0007\0007_01_00_01_70.bmp
0007\0007_01_00_01_75.bmp
0007\0007_01_00_01_8.bmp
0007\0007_01_00_01_84.bmp
0007\0007_01_00_01_89.bmp
0007\0007_01_00_01_93.bmp
0007\0007_01_00_01_98.bmp
0009\0009_01_00_01_100.bmp
0009\0009_01_00_01_105.bmp
0009\0009_01_00_01_11.bmp
0009\0009_01_00_01_114.bmp
0009\0009_01_00_01_119.bmp
0009\0009_01_00_01_123.bmp
0009\0009_01_00_01_128.bmp
0009\0009_01_00_01_132.bmp
0009\0009_01_00_01_137.bmp
0009\0009_01_00_01_141.bmp
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0009\0009_01_00_01_150.bmp
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0009\0009_01_00_01_16.bmp
0009\0009_01_00_01_164.bmp
0009\0009_01_00_01_169.bmp
0009\0009_01_00_01_173.bmp
0009\0009_01_00_01_18.bmp
0009\0009_01_00_01_184.bmp
0009\0009_01_00_01_189.bmp
0009\0009_01_00_01_193.bmp
0009\0009_01_00_01_198.bmp
0009\0009_01_00_01_202.bmp
0009\0009_01_00_01_207.bmp
0009\0009_01_00_01_211.bmp
0009\0009_01_00_01_216.bmp
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0009\0009_01_00_01_225.bmp
0009\0009_01_00_01_23.bmp
0009\0009_01_00_01_234.bmp
0009\0009_01_00_01_239.bmp
0009\0009_01_00_01_244.bmp
0009\0009_01_00_01_249.bmp
0009\0009_01_00_01_253.bmp
0009\0009_01_00_01_258.bmp
0009\0009_01_00_01_262.bmp
0009\0009_01_00_01_267.bmp
0009\0009_01_00_01_271.bmp
0009\0009_01_00_01_276.bmp
0009\0009_01_00_01_280.bmp
0009\0009_01_00_01_285.bmp
0009\0009_01_00_01_29.bmp
0009\0009_01_00_01_294.bmp
0009\0009_01_00_01_299.bmp
0009\0009_01_00_01_310.bmp
0009\0009_01_00_01_317.bmp
0009\0009_01_00_01_321.bmp
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0009\0009_01_00_01_341.bmp
0009\0009_01_00_01_346.bmp
0009\0009_01_00_01_350.bmp
0009\0009_01_00_01_355.bmp
0009\0009_01_00_01_36.bmp
0009\0009_01_00_01_364.bmp
0009\0009_01_00_01_369.bmp
0009\0009_01_00_01_374.bmp
0009\0009_01_00_01_379.bmp
0009\0009_01_00_01_383.bmp
0009\0009_01_00_01_388.bmp
0009\0009_01_00_01_392.bmp
0009\0009_01_00_01_397.bmp
0009\0009_01_00_01_401.bmp
0009\0009_01_00_01_408.bmp
0009\0009_01_00_01_415.bmp
0009\0009_01_00_01_422.bmp
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0009\0009_01_00_01_431.bmp
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0009\0009_01_00_01_441.bmp
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0009\0009_01_00_01_450.bmp
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0009\0009_01_00_01_460.bmp
0009\0009_01_00_01_465.bmp
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0009\0009_01_00_01_475.bmp
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0009\0009_01_00_01_490.bmp
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0009\0009_01_00_01_60.bmp
0009\0009_01_00_01_65.bmp
0009\0009_01_00_01_70.bmp
0009\0009_01_00_01_75.bmp
0009\0009_01_00_01_80.bmp
0009\0009_01_00_01_85.bmp
0009\0009_01_00_01_99.bmp
0008\0008_00_00_01_10.bmp
0008\0008_00_00_01_104.bmp
0008\0008_00_00_01_109.bmp
0008\0008_00_00_01_113.bmp
0008\0008_00_00_01_118.bmp
0008\0008_00_00_01_122.bmp
0008\0008_00_00_01_127.bmp
0008\0008_00_00_01_131.bmp
0008\0008_00_00_01_136.bmp
0008\0008_00_00_01_140.bmp
0008\0008_00_00_01_145.bmp
0008\0008_00_00_01_15.bmp
0008\0008_00_00_01_154.bmp
0008\0008_00_00_01_159.bmp
0008\0008_00_00_01_163.bmp
0008\0008_00_00_01_168.bmp
0008\0008_00_00_01_172.bmp
0008\0008_00_00_01_18.bmp
0008\0008_00_00_01_191.bmp
0008\0008_00_00_01_196.bmp
0008\0008_00_00_01_200.bmp
0008\0008_00_00_01_205.bmp
0008\0008_00_00_01_21.bmp
0008\0008_00_00_01_214.bmp
0008\0008_00_00_01_219.bmp
0008\0008_00_00_01_223.bmp
0008\0008_00_00_01_228.bmp
0008\0008_00_00_01_233.bmp
0008\0008_00_00_01_238.bmp
0008\0008_00_00_01_243.bmp
0008\0008_00_00_01_248.bmp
0008\0008_00_00_01_252.bmp
0008\0008_00_00_01_257.bmp
0008\0008_00_00_01_261.bmp
0008\0008_00_00_01_266.bmp
0008\0008_00_00_01_270.bmp
0008\0008_00_00_01_275.bmp
0008\0008_00_00_01_280.bmp
0008\0008_00_00_01_285.bmp
0008\0008_00_00_01_290.bmp
0008\0008_00_00_01_295.bmp
0008\0008_00_00_01_300.bmp
0008\0008_00_00_01_305.bmp
0008\0008_00_00_01_310.bmp
0008\0008_00_00_01_315.bmp
0008\0008_00_00_01_32.bmp
0008\0008_00_00_01_324.bmp
0008\0008_00_00_01_329.bmp
0008\0008_00_00_01_333.bmp
0008\0008_00_00_01_338.bmp
0008\0008_00_00_01_342.bmp
0008\0008_00_00_01_347.bmp
0008\0008_00_00_01_352.bmp
0008\0008_00_00_01_357.bmp
0008\0008_00_00_01_362.bmp
0008\0008_00_00_01_367.bmp
0008\0008_00_00_01_372.bmp
0008\0008_00_00_01_377.bmp
0008\0008_00_00_01_382.bmp
0008\0008_00_00_01_387.bmp
0008\0008_00_00_01_392.bmp
0008\0008_00_00_01_397.bmp
0008\0008_00_00_01_402.bmp
0008\0008_00_00_01_407.bmp
0008\0008_00_00_01_412.bmp
0008\0008_00_00_01_417.bmp
0008\0008_00_00_01_421.bmp
0008\0008_00_00_01_426.bmp
0008\0008_00_00_01_430.bmp
0008\0008_00_00_01_435.bmp
0008\0008_00_00_01_44.bmp
0008\0008_00_00_01_444.bmp
0008\0008_00_00_01_449.bmp
0008\0008_00_00_01_453.bmp
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0008\0008_00_00_01_462.bmp
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0008\0008_00_00_01_471.bmp
0008\0008_00_00_01_476.bmp
0008\0008_00_00_01_480.bmp
0008\0008_00_00_01_485.bmp
0008\0008_00_00_01_49.bmp
0008\0008_00_00_01_494.bmp
0008\0008_00_00_01_499.bmp
0008\0008_00_00_01_54.bmp
0008\0008_00_00_01_59.bmp
0008\0008_00_00_01_64.bmp
0008\0008_00_00_01_69.bmp
0008\0008_00_00_01_74.bmp
0008\0008_00_00_01_79.bmp
0008\0008_00_00_01_83.bmp
0008\0008_00_00_01_88.bmp
0008\0008_00_00_01_92.bmp
0008\0008_00_00_01_97.bmp
0001\0001_00_01_02_0.bmp
0001\0001_00_01_02_116.bmp
0001\0001_00_01_02_134.bmp
0001\0001_00_01_02_152.bmp
0001\0001_00_01_02_170.bmp
0001\0001_00_01_02_189.bmp
0001\0001_00_01_02_206.bmp
0001\0001_00_01_02_224.bmp
0001\0001_00_01_02_242.bmp
0001\0001_00_01_02_260.bmp
0001\0001_00_01_02_279.bmp
0001\0001_00_01_02_297.bmp
0001\0001_00_01_02_315.bmp
0001\0001_00_01_02_333.bmp
0001\0001_00_01_02_351.bmp
0001\0001_00_01_02_37.bmp
0001\0001_00_01_02_388.bmp
0001\0001_00_01_02_405.bmp
0001\0001_00_01_02_423.bmp
0001\0001_00_01_02_441.bmp
0001\0001_00_01_02_46.bmp
0001\0001_00_01_02_478.bmp
0001\0001_00_01_02_496.bmp
0001\0001_00_01_02_64.bmp
0001\0001_00_01_02_82.bmp
0001\0001_00_02_02_0.bmp
0001\0001_00_02_02_116.bmp
0001\0001_00_02_02_134.bmp
0001\0001_00_02_02_152.bmp
0001\0001_00_02_02_170.bmp
0001\0001_00_02_02_189.bmp
0001\0001_00_02_02_206.bmp
0001\0001_00_02_02_224.bmp
0001\0001_00_02_02_242.bmp
0001\0001_00_02_02_260.bmp
0001\0001_00_02_02_279.bmp
0001\0001_00_02_02_297.bmp
0001\0001_00_02_02_314.bmp
0001\0001_00_02_02_332.bmp
0001\0001_00_02_02_350.bmp
0001\0001_00_02_02_369.bmp
0001\0001_00_02_02_387.bmp
0001\0001_00_02_02_404.bmp
0001\0001_00_02_02_422.bmp
0001\0001_00_02_02_440.bmp
0001\0001_00_02_02_459.bmp
0001\0001_00_02_02_477.bmp
0001\0001_00_02_02_495.bmp
0001\0001_00_02_02_63.bmp
0001\0001_00_02_02_81.bmp
0001\0001_00_03_02_0.bmp
0001\0001_00_03_02_116.bmp
0001\0001_00_03_02_134.bmp
0001\0001_00_03_02_152.bmp
0001\0001_00_03_02_170.bmp
0001\0001_00_03_02_189.bmp
0001\0001_00_03_02_206.bmp
0001\0001_00_03_02_224.bmp
0001\0001_00_03_02_242.bmp
0001\0001_00_03_02_260.bmp
0001\0001_00_03_02_280.bmp
0001\0001_00_03_02_3.bmp
0001\0001_00_03_02_320.bmp
0001\0001_00_03_02_339.bmp
0001\0001_00_03_02_357.bmp
0001\0001_00_03_02_376.bmp
0001\0001_00_03_02_394.bmp
0001\0001_00_03_02_411.bmp
0001\0001_00_03_02_431.bmp
0001\0001_00_03_02_45.bmp
0001\0001_00_03_02_468.bmp
0001\0001_00_03_02_486.bmp
0001\0001_00_03_02_54.bmp
0001\0001_00_03_02_72.bmp
0001\0001_00_03_02_90.bmp
0001\0001_00_04_02_0.bmp
0001\0001_00_04_02_116.bmp
0001\0001_00_04_02_134.bmp
0001\0001_00_04_02_152.bmp
0001\0001_00_04_02_170.bmp
0001\0001_00_04_02_189.bmp
0001\0001_00_04_02_206.bmp
0001\0001_00_04_02_224.bmp
0001\0001_00_04_02_242.bmp
0001\0001_00_04_02_260.bmp
0001\0001_00_04_02_279.bmp
0001\0001_00_04_02_297.bmp
0001\0001_00_04_02_314.bmp
0001\0001_00_04_02_332.bmp
0001\0001_00_04_02_350.bmp
0001\0001_00_04_02_369.bmp
0001\0001_00_04_02_387.bmp
0001\0001_00_04_02_404.bmp
0001\0001_00_04_02_422.bmp
0001\0001_00_04_02_440.bmp
0001\0001_00_04_02_459.bmp
0001\0001_00_04_02_477.bmp
0001\0001_00_04_02_495.bmp
0001\0001_00_04_02_63.bmp
0001\0001_00_04_02_81.bmp
0002\0002_01_01_02_0.bmp
0002\0002_01_01_02_116.bmp
0002\0002_01_01_02_134.bmp
0002\0002_01_01_02_152.bmp
0002\0002_01_01_02_170.bmp
0002\0002_01_01_02_189.bmp
0002\0002_01_01_02_206.bmp
0002\0002_01_01_02_224.bmp
0002\0002_01_01_02_242.bmp
0002\0002_01_01_02_260.bmp
0002\0002_01_01_02_279.bmp
0002\0002_01_01_02_297.bmp
0002\0002_01_01_02_314.bmp
0002\0002_01_01_02_332.bmp
0002\0002_01_01_02_350.bmp
0002\0002_01_01_02_369.bmp
0002\0002_01_01_02_387.bmp
0002\0002_01_01_02_404.bmp
0002\0002_01_01_02_422.bmp
0002\0002_01_01_02_440.bmp
0002\0002_01_01_02_459.bmp
0002\0002_01_01_02_477.bmp
0002\0002_01_01_02_495.bmp
0002\0002_01_01_02_63.bmp
0002\0002_01_01_02_81.bmp
0002\0002_01_02_02_0.bmp
0002\0002_01_02_02_117.bmp
0002\0002_01_02_02_136.bmp
0002\0002_01_02_02_154.bmp
0002\0002_01_02_02_172.bmp
0002\0002_01_02_02_190.bmp
0002\0002_01_02_02_208.bmp
0002\0002_01_02_02_226.bmp
0002\0002_01_02_02_244.bmp
0002\0002_01_02_02_262.bmp
0002\0002_01_02_02_280.bmp
0002\0002_01_02_02_299.bmp
0002\0002_01_02_02_316.bmp
0002\0002_01_02_02_334.bmp
0002\0002_01_02_02_352.bmp
0002\0002_01_02_02_370.bmp
0002\0002_01_02_02_389.bmp
0002\0002_01_02_02_406.bmp
0002\0002_01_02_02_424.bmp
0002\0002_01_02_02_442.bmp
0002\0002_01_02_02_460.bmp
0002\0002_01_02_02_479.bmp
0002\0002_01_02_02_497.bmp
0002\0002_01_02_02_66.bmp
0002\0002_01_02_02_84.bmp
0002\0002_01_03_02_0.bmp
0002\0002_01_03_02_116.bmp
0002\0002_01_03_02_134.bmp
0002\0002_01_03_02_152.bmp
0002\0002_01_03_02_170.bmp
0002\0002_01_03_02_189.bmp
0002\0002_01_03_02_206.bmp
0002\0002_01_03_02_224.bmp
0002\0002_01_03_02_242.bmp
0002\0002_01_03_02_260.bmp
0002\0002_01_03_02_279.bmp
0002\0002_01_03_02_297.bmp
0002\0002_01_03_02_314.bmp
0002\0002_01_03_02_332.bmp
0002\0002_01_03_02_350.bmp
0002\0002_01_03_02_369.bmp
0002\0002_01_03_02_387.bmp
0002\0002_01_03_02_404.bmp
0002\0002_01_03_02_422.bmp
0002\0002_01_03_02_440.bmp
0002\0002_01_03_02_459.bmp
0002\0002_01_03_02_477.bmp
0002\0002_01_03_02_495.bmp
0002\0002_01_03_02_63.bmp
0002\0002_01_03_02_81.bmp
0002\0002_01_04_02_0.bmp
0002\0002_01_04_02_116.bmp
0002\0002_01_04_02_134.bmp
0002\0002_01_04_02_152.bmp
0002\0002_01_04_02_170.bmp
0002\0002_01_04_02_189.bmp
0002\0002_01_04_02_206.bmp
0002\0002_01_04_02_224.bmp
0002\0002_01_04_02_242.bmp
0002\0002_01_04_02_260.bmp
0002\0002_01_04_02_279.bmp
0002\0002_01_04_02_297.bmp
0002\0002_01_04_02_314.bmp
0002\0002_01_04_02_332.bmp
0002\0002_01_04_02_350.bmp
0002\0002_01_04_02_369.bmp
0002\0002_01_04_02_387.bmp
0002\0002_01_04_02_404.bmp
0002\0002_01_04_02_422.bmp
0002\0002_01_04_02_440.bmp
0002\0002_01_04_02_459.bmp
0002\0002_01_04_02_477.bmp
0002\0002_01_04_02_495.bmp
0002\0002_01_04_02_63.bmp
0002\0002_01_04_02_81.bmp
0003\0003_01_01_02_10.bmp
0003\0003_01_01_02_118.bmp
0003\0003_01_01_02_136.bmp
0003\0003_01_01_02_154.bmp
0003\0003_01_01_02_172.bmp
0003\0003_01_01_02_190.bmp
0003\0003_01_01_02_209.bmp
0003\0003_01_01_02_227.bmp
0003\0003_01_01_02_245.bmp
0003\0003_01_01_02_263.bmp
0003\0003_01_01_02_281.bmp
0003\0003_01_01_02_30.bmp
0003\0003_01_01_02_318.bmp
0003\0003_01_01_02_336.bmp
0003\0003_01_01_02_354.bmp
0003\0003_01_01_02_372.bmp
0003\0003_01_01_02_390.bmp
0003\0003_01_01_02_409.bmp
0003\0003_01_01_02_427.bmp
0003\0003_01_01_02_445.bmp
0003\0003_01_01_02_463.bmp
0003\0003_01_01_02_481.bmp
0003\0003_01_01_02_50.bmp
0003\0003_01_01_02_7.bmp
0003\0003_01_01_02_88.bmp
0003\0003_01_02_02_0.bmp
0003\0003_01_02_02_117.bmp
0003\0003_01_02_02_137.bmp
0003\0003_01_02_02_155.bmp
0003\0003_01_02_02_173.bmp
0003\0003_01_02_02_191.bmp
0003\0003_01_02_02_210.bmp
0003\0003_01_02_02_230.bmp
0003\0003_01_02_02_249.bmp
0003\0003_01_02_02_267.bmp
0003\0003_01_02_02_285.bmp
0003\0003_01_02_02_302.bmp
0003\0003_01_02_02_320.bmp
0003\0003_01_02_02_339.bmp
0003\0003_01_02_02_357.bmp
0003\0003_01_02_02_375.bmp
0003\0003_01_02_02_393.bmp
0003\0003_01_02_02_410.bmp
0003\0003_01_02_02_429.bmp
0003\0003_01_02_02_447.bmp
0003\0003_01_02_02_465.bmp
0003\0003_01_02_02_483.bmp
0003\0003_01_02_02_51.bmp
0003\0003_01_02_02_7.bmp
0003\0003_01_02_02_88.bmp
0003\0003_01_03_02_10.bmp
0003\0003_01_03_02_118.bmp
0003\0003_01_03_02_136.bmp
0003\0003_01_03_02_154.bmp
0003\0003_01_03_02_172.bmp
0003\0003_01_03_02_190.bmp
0003\0003_01_03_02_209.bmp
0003\0003_01_03_02_227.bmp
0003\0003_01_03_02_245.bmp
0003\0003_01_03_02_263.bmp
0003\0003_01_03_02_281.bmp
0003\0003_01_03_02_30.bmp
0003\0003_01_03_02_318.bmp
0003\0003_01_03_02_336.bmp
0003\0003_01_03_02_354.bmp
0003\0003_01_03_02_372.bmp
0003\0003_01_03_02_390.bmp
0003\0003_01_03_02_409.bmp
0003\0003_01_03_02_427.bmp
0003\0003_01_03_02_445.bmp
0003\0003_01_03_02_463.bmp
0003\0003_01_03_02_481.bmp
0003\0003_01_03_02_5.bmp
0003\0003_01_03_02_68.bmp
0003\0003_01_03_02_86.bmp
0003\0003_01_04_02_0.bmp
0003\0003_01_04_02_117.bmp
0003\0003_01_04_02_137.bmp
0003\0003_01_04_02_157.bmp
0003\0003_01_04_02_175.bmp
0003\0003_01_04_02_193.bmp
0003\0003_01_04_02_211.bmp
0003\0003_01_04_02_23.bmp
0003\0003_01_04_02_248.bmp
0003\0003_01_04_02_266.bmp
0003\0003_01_04_02_284.bmp
0003\0003_01_04_02_302.bmp
0003\0003_01_04_02_327.bmp
0003\0003_01_04_02_345.bmp
0003\0003_01_04_02_363.bmp
0003\0003_01_04_02_381.bmp
0003\0003_01_04_02_40.bmp
0003\0003_01_04_02_418.bmp
0003\0003_01_04_02_436.bmp
0003\0003_01_04_02_454.bmp
0003\0003_01_04_02_472.bmp
0003\0003_01_04_02_490.bmp
0003\0003_01_04_02_60.bmp
0003\0003_01_04_02_8.bmp
0003\0003_01_04_02_98.bmp
0004\0004_01_01_02_0.bmp
0004\0004_01_01_02_116.bmp
0004\0004_01_01_02_134.bmp
0004\0004_01_01_02_152.bmp
0004\0004_01_01_02_170.bmp
0004\0004_01_01_02_189.bmp
0004\0004_01_01_02_207.bmp
0004\0004_01_01_02_225.bmp
0004\0004_01_01_02_243.bmp
0004\0004_01_01_02_269.bmp
0004\0004_01_01_02_287.bmp
0004\0004_01_01_02_304.bmp
0004\0004_01_01_02_322.bmp
0004\0004_01_01_02_341.bmp
0004\0004_01_01_02_360.bmp
0004\0004_01_01_02_379.bmp
0004\0004_01_01_02_397.bmp
0004\0004_01_01_02_414.bmp
0004\0004_01_01_02_432.bmp
0004\0004_01_01_02_450.bmp
0004\0004_01_01_02_469.bmp
0004\0004_01_01_02_487.bmp
0004\0004_01_01_02_56.bmp
0004\0004_01_01_02_74.bmp
0004\0004_01_01_02_92.bmp
0004\0004_01_02_02_0.bmp
0004\0004_01_02_02_116.bmp
0004\0004_01_02_02_134.bmp
0004\0004_01_02_02_152.bmp
0004\0004_01_02_02_170.bmp
0004\0004_01_02_02_189.bmp
0004\0004_01_02_02_206.bmp
0004\0004_01_02_02_224.bmp
0004\0004_01_02_02_243.bmp
0004\0004_01_02_02_261.bmp
0004\0004_01_02_02_28.bmp
0004\0004_01_02_02_298.bmp
0004\0004_01_02_02_316.bmp
0004\0004_01_02_02_334.bmp
0004\0004_01_02_02_352.bmp
0004\0004_01_02_02_370.bmp
0004\0004_01_02_02_389.bmp
0004\0004_01_02_02_406.bmp
0004\0004_01_02_02_424.bmp
0004\0004_01_02_02_442.bmp
0004\0004_01_02_02_460.bmp
0004\0004_01_02_02_479.bmp
0004\0004_01_02_02_497.bmp
0004\0004_01_02_02_66.bmp
0004\0004_01_02_02_84.bmp
0004\0004_01_03_02_0.bmp
0004\0004_01_03_02_118.bmp
0004\0004_01_03_02_136.bmp
0004\0004_01_03_02_159.bmp
0004\0004_01_03_02_177.bmp
0004\0004_01_03_02_195.bmp
0004\0004_01_03_02_212.bmp
0004\0004_01_03_02_230.bmp
0004\0004_01_03_02_249.bmp
0004\0004_01_03_02_267.bmp
0004\0004_01_03_02_285.bmp
0004\0004_01_03_02_303.bmp
0004\0004_01_03_02_322.bmp
0004\0004_01_03_02_340.bmp
0004\0004_01_03_02_359.bmp
0004\0004_01_03_02_377.bmp
0004\0004_01_03_02_395.bmp
0004\0004_01_03_02_412.bmp
0004\0004_01_03_02_430.bmp
0004\0004_01_03_02_449.bmp
0004\0004_01_03_02_467.bmp
0004\0004_01_03_02_485.bmp
0004\0004_01_03_02_53.bmp
0004\0004_01_03_02_71.bmp
0004\0004_01_03_02_9.bmp
0004\0004_01_04_02_0.bmp
0004\0004_01_04_02_116.bmp
0004\0004_01_04_02_134.bmp
0004\0004_01_04_02_152.bmp
0004\0004_01_04_02_170.bmp
0004\0004_01_04_02_189.bmp
0004\0004_01_04_02_206.bmp
0004\0004_01_04_02_225.bmp
0004\0004_01_04_02_243.bmp
0004\0004_01_04_02_261.bmp
0004\0004_01_04_02_280.bmp
0004\0004_01_04_02_30.bmp
0004\0004_01_04_02_32.bmp
0004\0004_01_04_02_339.bmp
0004\0004_01_04_02_357.bmp
0004\0004_01_04_02_375.bmp
0004\0004_01_04_02_394.bmp
0004\0004_01_04_02_411.bmp
0004\0004_01_04_02_43.bmp
0004\0004_01_04_02_449.bmp
0004\0004_01_04_02_467.bmp
0004\0004_01_04_02_485.bmp
0004\0004_01_04_02_54.bmp
0004\0004_01_04_02_73.bmp
0004\0004_01_04_02_91.bmp
0005\0005_00_01_02_0.bmp
0005\0005_00_01_02_116.bmp
0005\0005_00_01_02_134.bmp
0005\0005_00_01_02_152.bmp
0005\0005_00_01_02_170.bmp
0005\0005_00_01_02_189.bmp
0005\0005_00_01_02_206.bmp
0005\0005_00_01_02_224.bmp
0005\0005_00_01_02_246.bmp
0005\0005_00_01_02_264.bmp
0005\0005_00_01_02_283.bmp
0005\0005_00_01_02_300.bmp
0005\0005_00_01_02_319.bmp
0005\0005_00_01_02_337.bmp
0005\0005_00_01_02_355.bmp
0005\0005_00_01_02_373.bmp
0005\0005_00_01_02_391.bmp
0005\0005_00_01_02_41.bmp
0005\0005_00_01_02_428.bmp
0005\0005_00_01_02_446.bmp
0005\0005_00_01_02_464.bmp
0005\0005_00_01_02_482.bmp
0005\0005_00_01_02_50.bmp
0005\0005_00_01_02_69.bmp
0005\0005_00_01_02_87.bmp
0005\0005_00_02_02_0.bmp
0005\0005_00_02_02_116.bmp
0005\0005_00_02_02_134.bmp
0005\0005_00_02_02_152.bmp
0005\0005_00_02_02_170.bmp
0005\0005_00_02_02_189.bmp
0005\0005_00_02_02_206.bmp
0005\0005_00_02_02_225.bmp
0005\0005_00_02_02_243.bmp
0005\0005_00_02_02_261.bmp
0005\0005_00_02_02_28.bmp
0005\0005_00_02_02_298.bmp
0005\0005_00_02_02_318.bmp
0005\0005_00_02_02_336.bmp
0005\0005_00_02_02_355.bmp
0005\0005_00_02_02_373.bmp
0005\0005_00_02_02_394.bmp
0005\0005_00_02_02_411.bmp
0005\0005_00_02_02_430.bmp
0005\0005_00_02_02_449.bmp
0005\0005_00_02_02_467.bmp
0005\0005_00_02_02_485.bmp
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0007\0007_01_01_02_405.bmp
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0007\0007_01_02_02_0.bmp
0007\0007_01_02_02_116.bmp
0007\0007_01_02_02_134.bmp
0007\0007_01_02_02_152.bmp
0007\0007_01_02_02_171.bmp
0007\0007_01_02_02_19.bmp
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0007\0007_01_02_02_225.bmp
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0007\0007_01_03_02_170.bmp
0007\0007_01_03_02_189.bmp
0007\0007_01_03_02_206.bmp
0007\0007_01_03_02_224.bmp
0007\0007_01_03_02_242.bmp
0007\0007_01_03_02_260.bmp
0007\0007_01_03_02_279.bmp
0007\0007_01_03_02_297.bmp
0007\0007_01_03_02_314.bmp
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0007\0007_01_03_02_350.bmp
0007\0007_01_03_02_369.bmp
0007\0007_01_03_02_387.bmp
0007\0007_01_03_02_404.bmp
0007\0007_01_03_02_422.bmp
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0007\0007_01_03_02_461.bmp
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0007\0007_01_04_02_0.bmp
0007\0007_01_04_02_116.bmp
0007\0007_01_04_02_134.bmp
0007\0007_01_04_02_152.bmp
0007\0007_01_04_02_170.bmp
0007\0007_01_04_02_189.bmp
0007\0007_01_04_02_206.bmp
0007\0007_01_04_02_224.bmp
0007\0007_01_04_02_242.bmp
0007\0007_01_04_02_260.bmp
0007\0007_01_04_02_279.bmp
0007\0007_01_04_02_297.bmp
0007\0007_01_04_02_314.bmp
0007\0007_01_04_02_332.bmp
0007\0007_01_04_02_350.bmp
0007\0007_01_04_02_369.bmp
0007\0007_01_04_02_387.bmp
0007\0007_01_04_02_404.bmp
0007\0007_01_04_02_422.bmp
0007\0007_01_04_02_440.bmp
0007\0007_01_04_02_459.bmp
0007\0007_01_04_02_477.bmp
0007\0007_01_04_02_495.bmp
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0007\0007_01_04_02_81.bmp
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0009\0009_01_01_02_260.bmp
0009\0009_01_01_02_279.bmp
0009\0009_01_01_02_297.bmp
0009\0009_01_01_02_314.bmp
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0009\0009_01_01_02_350.bmp
0009\0009_01_01_02_369.bmp
0009\0009_01_01_02_387.bmp
0009\0009_01_01_02_404.bmp
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0009\0009_01_01_02_477.bmp
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0009\0009_01_02_02_463.bmp
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0009\0009_01_02_02_5.bmp
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0009\0009_01_03_02_152.bmp
0009\0009_01_03_02_170.bmp
0009\0009_01_03_02_189.bmp
0009\0009_01_03_02_206.bmp
0009\0009_01_03_02_224.bmp
0009\0009_01_03_02_242.bmp
0009\0009_01_03_02_260.bmp
0009\0009_01_03_02_279.bmp
0009\0009_01_03_02_297.bmp
0009\0009_01_03_02_314.bmp
0009\0009_01_03_02_332.bmp
0009\0009_01_03_02_350.bmp
0009\0009_01_03_02_369.bmp
0009\0009_01_03_02_387.bmp
0009\0009_01_03_02_404.bmp
0009\0009_01_03_02_422.bmp
0009\0009_01_03_02_440.bmp
0009\0009_01_03_02_459.bmp
0009\0009_01_03_02_477.bmp
0009\0009_01_03_02_495.bmp
0009\0009_01_03_02_63.bmp
0009\0009_01_03_02_81.bmp
0009\0009_01_04_02_0.bmp
0009\0009_01_04_02_116.bmp
0009\0009_01_04_02_134.bmp
0009\0009_01_04_02_152.bmp
0009\0009_01_04_02_170.bmp
0009\0009_01_04_02_189.bmp
0009\0009_01_04_02_206.bmp
0009\0009_01_04_02_224.bmp
0009\0009_01_04_02_242.bmp
0009\0009_01_04_02_260.bmp
0009\0009_01_04_02_279.bmp
0009\0009_01_04_02_297.bmp
0009\0009_01_04_02_314.bmp
0009\0009_01_04_02_332.bmp
0009\0009_01_04_02_350.bmp
0009\0009_01_04_02_369.bmp
0009\0009_01_04_02_387.bmp
0009\0009_01_04_02_404.bmp
0009\0009_01_04_02_422.bmp
0009\0009_01_04_02_440.bmp
0009\0009_01_04_02_459.bmp
0009\0009_01_04_02_477.bmp
0009\0009_01_04_02_495.bmp
0009\0009_01_04_02_63.bmp
0009\0009_01_04_02_81.bmp
0008\0008_00_01_02_0.bmp
0008\0008_00_01_02_116.bmp
0008\0008_00_01_02_134.bmp
0008\0008_00_01_02_152.bmp
0008\0008_00_01_02_170.bmp
0008\0008_00_01_02_189.bmp
0008\0008_00_01_02_206.bmp
0008\0008_00_01_02_224.bmp
0008\0008_00_01_02_242.bmp
0008\0008_00_01_02_260.bmp
0008\0008_00_01_02_279.bmp
0008\0008_00_01_02_297.bmp
0008\0008_00_01_02_314.bmp
0008\0008_00_01_02_332.bmp
0008\0008_00_01_02_350.bmp
0008\0008_00_01_02_369.bmp
0008\0008_00_01_02_387.bmp
0008\0008_00_01_02_404.bmp
0008\0008_00_01_02_422.bmp
0008\0008_00_01_02_440.bmp
0008\0008_00_01_02_463.bmp
0008\0008_00_01_02_486.bmp
0008\0008_00_01_02_54.bmp
0008\0008_00_01_02_72.bmp
0008\0008_00_01_02_90.bmp
0008\0008_00_02_02_0.bmp
0008\0008_00_02_02_116.bmp
0008\0008_00_02_02_135.bmp
0008\0008_00_02_02_153.bmp
0008\0008_00_02_02_171.bmp
0008\0008_00_02_02_19.bmp
0008\0008_00_02_02_207.bmp
0008\0008_00_02_02_225.bmp
0008\0008_00_02_02_243.bmp
0008\0008_00_02_02_261.bmp
0008\0008_00_02_02_28.bmp
0008\0008_00_02_02_298.bmp
0008\0008_00_02_02_315.bmp
0008\0008_00_02_02_333.bmp
0008\0008_00_02_02_351.bmp
0008\0008_00_02_02_37.bmp
0008\0008_00_02_02_388.bmp
0008\0008_00_02_02_405.bmp
0008\0008_00_02_02_423.bmp
0008\0008_00_02_02_441.bmp
0008\0008_00_02_02_46.bmp
0008\0008_00_02_02_478.bmp
0008\0008_00_02_02_496.bmp
0008\0008_00_02_02_64.bmp
0008\0008_00_02_02_82.bmp
0008\0008_00_03_02_1.bmp
0008\0008_00_03_02_117.bmp
0008\0008_00_03_02_135.bmp
0008\0008_00_03_02_153.bmp
0008\0008_00_03_02_171.bmp
0008\0008_00_03_02_19.bmp
0008\0008_00_03_02_207.bmp
0008\0008_00_03_02_225.bmp
0008\0008_00_03_02_243.bmp
0008\0008_00_03_02_261.bmp
0008\0008_00_03_02_28.bmp
0008\0008_00_03_02_298.bmp
0008\0008_00_03_02_315.bmp
0008\0008_00_03_02_333.bmp
0008\0008_00_03_02_351.bmp
0008\0008_00_03_02_37.bmp
0008\0008_00_03_02_388.bmp
0008\0008_00_03_02_405.bmp
0008\0008_00_03_02_423.bmp
0008\0008_00_03_02_441.bmp
0008\0008_00_03_02_46.bmp
0008\0008_00_03_02_478.bmp
0008\0008_00_03_02_496.bmp
0008\0008_00_03_02_64.bmp
0008\0008_00_03_02_82.bmp
0008\0008_00_04_02_0.bmp
0008\0008_00_04_02_116.bmp
0008\0008_00_04_02_134.bmp
0008\0008_00_04_02_152.bmp
0008\0008_00_04_02_170.bmp
0008\0008_00_04_02_189.bmp
0008\0008_00_04_02_206.bmp
0008\0008_00_04_02_224.bmp
0008\0008_00_04_02_242.bmp
0008\0008_00_04_02_260.bmp
0008\0008_00_04_02_279.bmp
0008\0008_00_04_02_297.bmp
0008\0008_00_04_02_314.bmp
0008\0008_00_04_02_333.bmp
0008\0008_00_04_02_351.bmp
0008\0008_00_04_02_370.bmp
0008\0008_00_04_02_389.bmp
0008\0008_00_04_02_406.bmp
0008\0008_00_04_02_424.bmp
0008\0008_00_04_02_444.bmp
0008\0008_00_04_02_462.bmp
0008\0008_00_04_02_485.bmp
0008\0008_00_04_02_55.bmp
0008\0008_00_04_02_73.bmp
0008\0008_00_04_02_91.bmp


================================================
FILE: Matlab/NUAA/readme.txt
================================================
short description

This directory contains the geometrically normalized face images based on the two eyes' coordinates. There are 5105 client images and 7509 imposter images in total. Images from each subject are stored in a separate directory.

1. for evaluation
training set:
client   images: stored in the ClientNormalized directory and indexed in client_train_Normalized.txt
imposter images: stored in the ImposterNormalized directory and indexed in imposter_train_Normalized.txt

testing set:
client   images: stored in the ClientNormalized directory and indexed in client_test_Normalized.txt
imposter images: stored in the ImposterNormalized directory and indexed in imposter_test_Normalized.txt


2. The file name format of the indexed images: 

ID_galss_pos_session_picNo 

E.g. 0010_01_05_03_115.jpg
ID0001~0016 picture number
Glasses00~01
 	00with glasses
	01no glasses
Pos   01~08 the location and light conditions of images
	01: up-down-rotate
        02: up-down-twist
        03: left-right-rotate
        04: left-right-twist
        05: close--window-open-lights
        07: open-window-open-lights
        08: open-widow-shut-lights
        08: still
Session01~03
picNopicture number





================================================
FILE: Matlab/OF_All_Train.m
================================================
clear all, close all, clc

%% load libsvm
addpath('./libsvm-3.19/matlab');
TrainFeatureAll = [];
TrainTruth = [];

%% PRINT-ATTACK
load OF_SVM_PRINT_ATTACK_raw.mat
for IdxSubject = 1 : 15
    for IdxData = 1 : 8
        TrainFeatureAll = [TrainFeatureAll;TrainFeatureSet{IdxSubject,IdxData}];
        if IdxData <= 4
            TrainTruth = [TrainTruth;-ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        else
            TrainTruth = [TrainTruth;ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        end
    end
    disp(num2str(IdxSubject));
end

%% CASIA
load OF_SVM_CASIA_raw.mat
for IdxSubject = 1 : 20
    for IdxData = 1 : 8
        TrainFeatureAll = [TrainFeatureAll;TrainFeatureSet{IdxSubject,IdxData}];
        if IdxData <= 2
            TrainTruth = [TrainTruth;ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        else
            TrainTruth = [TrainTruth;-ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        end
    end
    disp(num2str(IdxSubject));
end

%% Training
length = size(TrainTruth, 1);
TrainFeatureAll(isnan(TrainFeatureAll)) = 0;
model = svmtrain(TrainTruth, TrainFeatureAll, '-t 0');
[TrainLabel, TrainAccuracy, TrainValue] = svmpredict(ones(length,1), TrainFeatureAll, model);
save OF_SVM_All.mat model

================================================
FILE: Matlab/OF_CASIA_Test.m
================================================
% clear all, close all, clc
% 
% %% read client and imposter testing data name
% addpath('./libsvm-3.19/matlab');
% addpath('./HOOF');
% % load OF_SVM_CASIA.mat
% % load OF_SVM_PRINT_ATTACK.mat
% load OF_SVM_All.mat
% 
% %% extract feature
% detector = vision.CascadeObjectDetector('MinSize', [100,100]);
% flagSet = cell(30, 8);
% bins = 9;
% blocks = 10;
% for IdxSubject = 1 : 30
%     for IdxData = 1 : 8
%         if IdxData <= 8
%             Name = ['./CASIA/test_release/' num2str(IdxSubject) '/' num2str(IdxData) '.avi'];
%         else
%             Name = ['./CASIA/test_release/' num2str(IdxSubject) '/HR_' num2str(IdxData-8) '.avi'];
%         end
%         Mov = VideoReader(Name);
%         NumFrame = Mov.NumberOfFrames;
%         opticalFlow = vision.OpticalFlow('ReferenceFrameSource', 'Input Port', 'OutputValue', 'Horizontal and vertical components in complex form', 'Method', 'Lucas-Kanade');
%         flag = [];
%         frame_now = rgb2gray(read(Mov, 1));
%         for IdxFrame = 2 : NumFrame
%             frame_pre = frame_now;
%             frame_now = rgb2gray(read(Mov, IdxFrame));
%             box = step(detector, frame_now);
%             if size(box, 1) == 1
%                 OF = step(opticalFlow, double(frame_now), double(frame_pre));
%                 Feature = [];
%                 for iBlock = 1 : blocks
%                     for jBlock = 1 : blocks
%                         Feature = [Feature, gradientHistogram(...
%                             real(OF(round(box(2)+box(4)*(iBlock-1)/(blocks+1)):round(box(2)+box(4)*(iBlock+1)/(blocks+1)), round(box(1)+box(3)*(jBlock-1)/(blocks+1)):round(box(1)+box(3)*(jBlock+1)/(blocks+1)))), ...
%                             imag(OF(round(box(2)+box(4)*(iBlock-1)/(blocks+1)):round(box(2)+box(4)*(iBlock+1)/(blocks+1)), round(box(1)+box(3)*(jBlock-1)/(blocks+1)):round(box(1)+box(3)*(jBlock+1)/(blocks+1)))), ...
%                             bins)'];
%                     end
%                 end
%                 Feature(isnan(Feature)) = 0;
%                 TestFeature = Feature;
%                 [TestLabel, TestAccuracy, TestValue] = svmpredict(1, TestFeature, model, '-q');
%                 flag = [flag, TestValue];
%             end
%         end
%         flagSet{IdxSubject, IdxData} = flag;
%         disp([num2str(IdxSubject) ', ' num2str(IdxData) ', ' num2str(mean(flag))])
%         clear Mov;
%     end
% end
% % save OF_SVM_CASIA_testfeature.mat flagSet

%% testing
load OF_SVM_CASIA_testfeature.mat flagSet
DataCorrectRate = zeros(3, 1001);
CorrectRate = zeros(3, 1001);
for Bias = -5:0.01:5;
    CorrectNoReal = 0;
    CorrectNoFake = 0;
    TotalNoReal = 0;
    TotalNoFake = 0;
    DataCorrectNoReal = 0;
    DataCorrectNoFake = 0;
    for IdxSubject = 1 : 30
        for IdxData = 1 : 8
            if IdxData <= 2
                CorrectNoReal = CorrectNoReal + sum(flagSet{IdxSubject, IdxData}>=Bias);
                DataCorrectNoReal = DataCorrectNoReal + (mean(flagSet{IdxSubject, IdxData})>=Bias);
                TotalNoReal = TotalNoReal + size(flagSet{IdxSubject, IdxData}, 2);
            else
                CorrectNoFake = CorrectNoFake + sum(flagSet{IdxSubject, IdxData}<Bias);
                DataCorrectNoFake = DataCorrectNoFake + (mean(flagSet{IdxSubject, IdxData})<Bias);
                TotalNoFake = TotalNoFake + size(flagSet{IdxSubject, IdxData}, 2);
            end
        end
    end
    DataCorrectRate(:, round((Bias+5.01)*100)) = [(DataCorrectNoReal+DataCorrectNoFake) / 240; DataCorrectNoReal / 60; DataCorrectNoFake / 180];
    CorrectRate(:, round((Bias+5.01)*100)) = [(CorrectNoReal+CorrectNoFake) / (TotalNoReal+TotalNoFake); CorrectNoReal / TotalNoReal; CorrectNoFake / TotalNoFake];
end      

================================================
FILE: Matlab/OF_CASIA_Train.m
================================================
clear all, close all, clc

%% read client and imposter testing data name
addpath('./libsvm-3.19/matlab');
addpath('./HOOF');

%% extract features
detector = vision.CascadeObjectDetector('MinSize', [100,100]);
TrainFeatureSet = cell(20, 8);
NumFrame = zeros(20, 8);
bins = 9;
blocks = 10;
for IdxSubject = 1 : 20
    for IdxData = 1 : 8
        if IdxData <= 8
            Name = ['./CASIA/train_release/' num2str(IdxSubject) '/' num2str(IdxData) '.avi'];
        else
            Name = ['./CASIA/train_release/' num2str(IdxSubject) '/HR_' num2str(IdxData-8) '.avi'];
        end
        Mov = VideoReader(Name);
        NumFrame(IdxSubject, IdxData) = Mov.NumberOfFrames;
        opticalFlow = vision.OpticalFlow('ReferenceFrameSource', 'Input Port', 'OutputValue', 'Horizontal and vertical components in complex form', 'Method', 'Lucas-Kanade');
        TrainFeature = [];
        frame_now = rgb2gray(read(Mov, 1));
        for IdxFrame = 2 : NumFrame(IdxSubject, IdxData)
            frame_pre = frame_now;
            frame_now = rgb2gray(read(Mov, IdxFrame));
            box = step(detector, frame_now);
            if size(box, 1) == 1
                OF = step(opticalFlow, double(frame_now), double(frame_pre));
                Feature = [];
                for iBlock = 1 : blocks
                    for jBlock = 1 : blocks
                        Feature = [Feature, gradientHistogram(...
                            real(OF(round(box(2)+box(4)*(iBlock-1)/(blocks+1)):round(box(2)+box(4)*(iBlock+1)/(blocks+1)), round(box(1)+box(3)*(jBlock-1)/(blocks+1)):round(box(1)+box(3)*(jBlock+1)/(blocks+1)))), ...
                            imag(OF(round(box(2)+box(4)*(iBlock-1)/(blocks+1)):round(box(2)+box(4)*(iBlock+1)/(blocks+1)), round(box(1)+box(3)*(jBlock-1)/(blocks+1)):round(box(1)+box(3)*(jBlock+1)/(blocks+1)))), ...
                            bins)'];
                    end
                end
                TrainFeature = [TrainFeature;Feature];
            end
        end
        TrainFeatureSet{IdxSubject, IdxData} = TrainFeature;
        disp([num2str(IdxSubject) ', ' num2str(IdxData)])
        clear Mov;
    end
end
save OF_SVM_CASIA_raw.mat TrainFeatureSet

%% training
load OF_SVM_CASIA_raw.mat
TrainFeatureAll = [];
TrainTruth = [];
for IdxSubject = 1 : 20
    for IdxData = 1 : 8
        TrainFeatureAll = [TrainFeatureAll;TrainFeatureSet{IdxSubject,IdxData}];
        if IdxData <= 2
            TrainTruth = [TrainTruth;ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        else
            TrainTruth = [TrainTruth;-ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        end
    end
    disp(num2str(IdxSubject));
end
length = size(TrainTruth, 1);
TrainFeatureAll(isnan(TrainFeatureAll)) = 0;
% MinMax = minmax(TrainFeatureAll')';
% TrainFeatureAll = (TrainFeatureAll-kron(MinMax(1,:),ones(length,1)))./(kron(MinMax(2,:),ones(length,1))-kron(MinMax(1,:),ones(length,1)));
model = svmtrain(TrainTruth, TrainFeatureAll, '-t 0');
[TrainLabel, TrainAccuracy, TrainValue] = svmpredict(ones(length,1), TrainFeatureAll, model);
% save OF_SVM_CASIA.mat model MinMax
save OF_SVM_CASIA.mat model

================================================
FILE: Matlab/OF_PRINT_ATTACK_Test.m
================================================
% clear all, close all, clc
% 
% %% read client and imposter testing data name
% addpath('./libsvm-3.19/matlab');
% addpath('./HOOF');
% % load OF_SVM_PRINT_ATTACK.mat
% % load OF_SVM_CASIA.mat
% load OF_SVM_All.mat
% 
% %% extract feature
% detector = vision.CascadeObjectDetector('MinSize', [50,50]);
% fileIndex = {'009', '011', '013', '014', '019', '020', '021', '023', '024', '026', '028', '031', '102', '104', '106', '107', '109', '112', '115', '117'};
% fileDirec = {'attack/fixed/attack_print_', 'attack/fixed/attack_print_', 'attack/hand/attack_print_', 'attack/hand/attack_print_', 'real/', 'real/', 'real/', 'real/'; ...
%     'highdef_photo_adverse', 'highdef_photo_controlled', 'highdef_photo_adverse', 'highdef_photo_controlled', 'webcam_authenticate_adverse_1', 'webcam_authenticate_adverse_2', 'webcam_authenticate_controlled_1', 'webcam_authenticate_controlled_2'};
% fileNo = size(fileIndex, 2);
% flagSet = cell(fileNo, 8);
% bins = 9;
% blocks = 10;
% for IdxSubject = 1 : fileNo
%     for IdxData = 1 : 8
%         Name = ['./PRINT-ATTACK/test/' fileDirec{1, IdxData} 'client' fileIndex{IdxSubject} '_session01_' fileDirec{2, IdxData} '.mov'];
%         Mov = VideoReader(Name);
%         NumFrame = Mov.NumberOfFrames;
%         opticalFlow = vision.OpticalFlow('ReferenceFrameSource', 'Input Port', 'OutputValue', 'Horizontal and vertical components in complex form', 'Method', 'Lucas-Kanade');
%         flag = [];
%         frame_now = rgb2gray(read(Mov, 1));
%         for IdxFrame = 2 : NumFrame
%             frame_pre = frame_now;
%             frame_now = rgb2gray(read(Mov, IdxFrame));
%             box = step(detector, frame_now);
%             if size(box, 1) == 1
%                 OF = step(opticalFlow, double(frame_now), double(frame_pre));
%                 Feature = [];
%                 for iBlock = 1 : blocks
%                     for jBlock = 1 : blocks
%                         Feature = [Feature, gradientHistogram(...
%                             real(OF(round(box(2)+box(4)*(iBlock-1)/(blocks+1)):round(box(2)+box(4)*(iBlock+1)/(blocks+1)), round(box(1)+box(3)*(jBlock-1)/(blocks+1)):round(box(1)+box(3)*(jBlock+1)/(blocks+1)))), ...
%                             imag(OF(round(box(2)+box(4)*(iBlock-1)/(blocks+1)):round(box(2)+box(4)*(iBlock+1)/(blocks+1)), round(box(1)+box(3)*(jBlock-1)/(blocks+1)):round(box(1)+box(3)*(jBlock+1)/(blocks+1)))), ...
%                             bins)'];
%                     end
%                 end
%                 Feature(isnan(Feature)) = 0;
%                 TestFeature = Feature;
%                 [TestLabel, TestAccuracy, TestValue] = svmpredict(1, TestFeature, model, '-q');
%                 flag = [flag, TestValue];
%             end
%         end
%         flagSet{IdxSubject, IdxData} = flag;
%         disp([num2str(IdxSubject) ', ' num2str(IdxData) ', ' num2str(mean(flag))])
%         clear Mov;
%     end
% end
% save OF_SVM_PRINT_ATTACK_testfeature.mat flagSet

%% testing
load OF_SVM_PRINT_ATTACK_testfeature.mat flagSet
DataCorrectRate = zeros(3, 1001);
CorrectRate = zeros(3, 1001);
for Bias = -5:0.01:5;
    CorrectNoReal = 0;
    CorrectNoFake = 0;
    TotalNoReal = 0;
    TotalNoFake = 0;
    DataCorrectNoReal = 0;
    DataCorrectNoFake = 0;
    for IdxSubject = 1 : 20
        for IdxData = 1 : 8
            if IdxData > 4
                CorrectNoReal = CorrectNoReal + sum(flagSet{IdxSubject, IdxData}>=Bias);
                DataCorrectNoReal = DataCorrectNoReal + (mean(flagSet{IdxSubject, IdxData})>=Bias);
                TotalNoReal = TotalNoReal + size(flagSet{IdxSubject, IdxData}, 2);
            else
                CorrectNoFake = CorrectNoFake + sum(flagSet{IdxSubject, IdxData}<Bias);
                DataCorrectNoFake = DataCorrectNoFake + (mean(flagSet{IdxSubject, IdxData})<Bias);
                TotalNoFake = TotalNoFake + size(flagSet{IdxSubject, IdxData}, 2);
            end
        end
    end
    DataCorrectRate(:, round((Bias+5.01)*100)) = [(DataCorrectNoReal+DataCorrectNoFake) / 160; DataCorrectNoReal / 80; DataCorrectNoFake / 80];
    CorrectRate(:, round((Bias+5.01)*100)) = [(CorrectNoReal+CorrectNoFake) / (TotalNoReal+TotalNoFake); CorrectNoReal / TotalNoReal; CorrectNoFake / TotalNoFake];
end

================================================
FILE: Matlab/OF_PRINT_ATTACK_Train.m
================================================
% clear all, close all, clc
% 
% %% read client and imposter testing data name
% addpath('./libsvm-3.19/matlab');
% addpath('./HOOF');
% 
% %% extract feature
% detector = vision.CascadeObjectDetector('MinSize', [50,50]);
% fileIndex = {'001', '002', '004', '006', '007', '008', '012', '016', '018', '025', '027', '103', '105', '108', '110'};
% fileDirec = {'attack/fixed/attack_print_', 'attack/fixed/attack_print_', 'attack/hand/attack_print_', 'attack/hand/attack_print_', 'real/', 'real/', 'real/', 'real/'; ...
%     'highdef_photo_adverse', 'highdef_photo_controlled', 'highdef_photo_adverse', 'highdef_photo_controlled', 'webcam_authenticate_adverse_1', 'webcam_authenticate_adverse_2', 'webcam_authenticate_controlled_1', 'webcam_authenticate_controlled_2'};
% fileNo = size(fileIndex, 2);
% TrainFeatureSet = cell(fileNo, 8);
% bins = 9;
% blocks = 10;
% for IdxSubject = 1 : fileNo
%     for IdxData = 1 : 8
%         Name = ['./PRINT-ATTACK/train/' fileDirec{1, IdxData} 'client' fileIndex{IdxSubject} '_session01_' fileDirec{2, IdxData} '.mov'];
%         Mov = VideoReader(Name);
%         NumFrame(IdxSubject, IdxData) = Mov.NumberOfFrames;
%         opticalFlow = vision.OpticalFlow('ReferenceFrameSource', 'Input Port', 'OutputValue', 'Horizontal and vertical components in complex form', 'Method', 'Lucas-Kanade');
%         TrainFeature = [];
%         frame_now = rgb2gray(read(Mov, 1));
%         for IdxFrame = 2 : NumFrame(IdxSubject, IdxData)
%             frame_pre = frame_now;
%             frame_now = rgb2gray(read(Mov, IdxFrame));
%             box = step(detector, frame_now);
%             if size(box, 1) == 1
%                 OF = step(opticalFlow, double(frame_now), double(frame_pre));
%                 Feature = [];
%                 for iBlock = 1 : blocks
%                     for jBlock = 1 : blocks
%                         Feature = [Feature, gradientHistogram(...
%                             real(OF(round(box(2)+box(4)*(iBlock-1)/(blocks+1)):round(box(2)+box(4)*(iBlock+1)/(blocks+1)), round(box(1)+box(3)*(jBlock-1)/(blocks+1)):round(box(1)+box(3)*(jBlock+1)/(blocks+1)))), ...
%                             imag(OF(round(box(2)+box(4)*(iBlock-1)/(blocks+1)):round(box(2)+box(4)*(iBlock+1)/(blocks+1)), round(box(1)+box(3)*(jBlock-1)/(blocks+1)):round(box(1)+box(3)*(jBlock+1)/(blocks+1)))), ...
%                             bins)'];
%                     end
%                 end
%                 TrainFeature = [TrainFeature;Feature];
%             end
%         end
%         TrainFeatureSet{IdxSubject, IdxData} = TrainFeature;
%         disp([num2str(IdxSubject) ', ' num2str(IdxData)])
%         clear Mov;
%     end
% end
% save OF_SVM_PRINT_ATTACK_raw.mat TrainFeatureSet

%% training
load OF_SVM_PRINT_ATTACK_raw.mat
TrainFeatureAll = [];
TrainTruth = [];
for IdxSubject = 1 : 15
    for IdxData = 1 : 8
        TrainFeatureAll = [TrainFeatureAll;TrainFeatureSet{IdxSubject,IdxData}];
        if IdxData <= 4
            TrainTruth = [TrainTruth;-ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        else
            TrainTruth = [TrainTruth;ones(size(TrainFeatureSet{IdxSubject,IdxData},1),1)];
        end
    end
    disp(num2str(IdxSubject));
end
length = size(TrainTruth, 1);
TrainFeatureAll(isnan(TrainFeatureAll)) = 0;
% MinMax = minmax(TrainFeatureAll')';
% TrainFeatureAll = (TrainFeatureAll-kron(MinMax(1,:),ones(length,1)))./(kron(MinMax(2,:),ones(length,1))-kron(MinMax(1,:),ones(length,1)));
model = svmtrain(TrainTruth, TrainFeatureAll, '-t 0');
[TrainLabel, TrainAccuracy, TrainValue] = svmpredict(ones(length,1), TrainFeatureAll, model);
save OF_SVM_PRINT_ATTACK.mat model

================================================
FILE: Matlab/RealTime_DoGLBP.m
================================================
clear all, close all, clc

%% camera
adaptorname = 'winvideo';
info = imaqhwinfo(adaptorname);
deviceID = info.DeviceIDs{1};
format = info.DeviceInfo(deviceID).SupportedFormats{2};
video = videoinput(adaptorname, deviceID);
%set(video, 'ReturnedColorSpace', 'rgb');
vidRes = get(video, 'VideoResolution');
nBands = get(video, 'NumberOfBands'); 
hImage = image( zeros(vidRes(2), vidRes(1), nBands) ); 
preview(video, hImage); 

%% SVM classifier
addpath('./libsvm-3.19/matlab');
addpath('./lbp-0.3.3');
load('DoG&LBP_SVM_All.mat');
Map_u2_16 = getmapping(16, 'u2');
Map_u2_8 = getmapping(8, 'u2');

%% test
detector = vision.CascadeObjectDetector('MinSize', [64,64]);
bias = 0.5;
threshold = 5;
max_frame = 1000;
i = 0;
flag = zeros(1, max_frame);
tic;
while(1)    
    i = i + 1;
    data = getsnapshot(video);
    box = step(detector, data);
    if size(box, 1) >= 1
        imshow(data);
        frame_now = rgb2gray(imresize(data(box(1,2):box(1,2)+box(1,4), box(1,1):box(1,1)+box(1,3), :), [64,64]));
        rectangle('Position',box(1,:),'LineWidth',4,'EdgeColor','r');
        TestFeature = [LBP_feature(frame_now, Map_u2_16, Map_u2_8), DoG(frame_now, 0.5, 1)'];
        TestFeature = (TestFeature-MinMax(1,:))./(MinMax(2,:)-MinMax(1,:));
        [TestLabel, TestAccuracy, TestValue] = svmpredict(1, TestFeature, model, '-q');
        flag = [flag(2:size(flag,2)), TestValue+bias];
        disp(['frame:',num2str(i),'; time:',num2str(toc),'; flag:',num2str(TestValue+bias)]);
        text(20, 20, {['Frame: ',num2str(i)];['Time: ',num2str(toc),'s'];['Flag: ',num2str(TestValue+bias)];['TotalFlag: ',num2str(sum(flag))]}, 'Color', 'r', 'FontSize', 12, 'VerticalAlignment', 'top');
    end
    if abs(sum(flag)) > threshold || i > max_frame;
        break;
    end
end
if sum(flag)>0
    disp('real face');
    text(box(1,1)+box(1,3)/2, box(1,2), 'Real Face', 'Color', 'r', 'FontSize', 30, 'HorizontalAlignment', 'center', 'VerticalAlignment', 'bottom');
else
    disp('fake face');
    text(box(1,1)+box(1,3)/2, box(1,2), 'Fake Face', 'Color', 'r', 'FontSize', 30, 'HorizontalAlignment', 'center', 'VerticalAlignment', 'bottom');
end

================================================
FILE: Matlab/RealTime_OF.m
================================================
clear all, close all, clc

%% camera
adaptorname = 'winvideo';
info = imaqhwinfo(adaptorname);
deviceID = info.DeviceIDs{1};
format = info.DeviceInfo(deviceID).SupportedFormats{5};
video = videoinput(adaptorname, deviceID, format);
vidRes = get(video, 'VideoResolution');
nBands = get(video, 'NumberOfBands'); 
hImage = image( zeros(vidRes(2), vidRes(1), nBands) ); 
preview(video, hImage); 

%% SVM classifier
addpath('./libsvm-3.19/matlab');
addpath('./HOOF');
load('OF_SVM_All.mat');

%% test
opticalFlow = vision.OpticalFlow('ReferenceFrameSource', 'Input Port', 'OutputValue', 'Horizontal and vertical components in complex form', 'Method', 'Lucas-Kanade');
detector = vision.CascadeObjectDetector('MinSize', [64,64]);
bins = 9;
blocks = 10;
bias = 0;
threshold = 5;
max_frame = 1000;
i = 0;
flag = zeros(1, 50);
tic;
while(1)
    i = i + 1;
    frame_now = rgb2gray(getsnapshot(video));    
    box = step(detector, frame_now);
    if size(box, 1) == 1 && i > 1
        OF = step(opticalFlow, double(frame_now), double(frame_pre));
        HOOF_Feature = HOOF(OF(box(2):box(2)+box(4), box(1):box(1)+box(3), :), bins, blocks);
        [TestLabel, TestAccuracy, TestValue] = svmpredict(1, HOOF_Feature, model, '-q');
        flag = [flag(2:size(flag,2)), TestValue+bias];
        disp(['frame:',num2str(i),'; time:',num2str(toc),'; flag:',num2str(TestValue+bias)]);
        rectangle('Position',box(1,:),'LineWidth',4,'EdgeColor','r');
        text(20, 20, {['Frame: ',num2str(i)];['Time: ',num2str(toc),'s'];['Flag: ',num2str(TestValue+bias)];['TotalFlag: ',num2str(sum(flag))]}, 'Color', 'r', 'FontSize', 12, 'VerticalAlignment', 'top');
    end
    frame_pre = frame_now;    
    if abs(sum(flag)) > threshold || i > max_frame;
        break;
    end
end
if sum(flag)>0
    disp('real face');
    text(box(1,1)+box(1,3)/2, box(1,2), 'Real Face', 'Color', 'r', 'FontSize', 30, 'HorizontalAlignment', 'center', 'VerticalAlignment', 'bottom');
else
    disp('fake face');
    text(box(1,1)+box(1,3)/2, box(1,2), 'Fake Face', 'Color', 'r', 'FontSize', 30, 'HorizontalAlignment', 'center', 'VerticalAlignment', 'bottom');
end

================================================
FILE: Matlab/Save_CASIA_Test.m
================================================
clear all, close all, clc

detector = vision.CascadeObjectDetector('MinSize', [100,100]);
count_real = 0;
count_fake = 0;
for IdxSubject = 1 : 30
    for IdxData = 1 : 8
        if IdxData <= 8
            Name = ['./CASIA/test_release/' num2str(IdxSubject) '/' num2str(IdxData) '.avi'];
        else
            Name = ['./CASIA/test_release/' num2str(IdxSubject) '/HR_' num2str(IdxData-8) '.avi'];
        end
        Mov = VideoReader(Name);
        for IdxFrame = 1 : Mov.NumberOfFrames
            data = read(Mov, IdxFrame);
            box = step(detector, data);
            if size(box, 1) == 1
                frame_now = data(box(2):box(2)+box(4), box(1):box(1)+box(3), :);
                if IdxData <= 2
                    imwrite(frame_now, ['.\CASIA\test\real\' num2str(count_real, '%05d') '.png']);
                    count_real = count_real + 1;
                else
                    imwrite(frame_now, ['.\CASIA\test\fake\' num2str(count_fake, '%05d') '.png']);
                    count_fake = count_fake + 1;
                end
            end
        end
        disp([num2str(IdxSubject) ', ' num2str(IdxData)])
        clear Mov;
    end
end

================================================
FILE: Matlab/Save_CASIA_Train.m
================================================
clear all, close all, clc

detector = vision.CascadeObjectDetector('MinSize', [100,100]);
count_real = 0;
count_fake = 0;
for IdxSubject = 1 : 20
    for IdxData = 1 : 8
        if IdxData <= 8
            Name = ['./CASIA/train_release/' num2str(IdxSubject) '/' num2str(IdxData) '.avi'];
        else
            Name = ['./CASIA/train_release/' num2str(IdxSubject) '/HR_' num2str(IdxData-8) '.avi'];
        end
        Mov = VideoReader(Name);
        for IdxFrame = 1 : Mov.NumberOfFrames
            data = read(Mov, IdxFrame);
            box = step(detector, data);
            if size(box, 1) == 1
                frame_now = data(box(2):box(2)+box(4), box(1):box(1)+box(3), :);
                if IdxData <= 2
                    imwrite(frame_now, ['.\CASIA\train\real\' num2str(count_real, '%05d') '.png']);
                    count_real = count_real + 1;
                else
                    imwrite(frame_now, ['.\CASIA\train\fake\' num2str(count_fake, '%05d') '.png']);
                    count_fake = count_fake + 1;
                end
            end
        end
        disp([num2str(IdxSubject) ', ' num2str(IdxData)])
        clear Mov;
    end
end

================================================
FILE: Matlab/Save_NUAA.m
================================================
clear all, close all, clc

ClientTrainNormalizedName = importdata('./NUAA/client_train_normalized.txt');
ClientTrainNormalizedNumber = size(ClientTrainNormalizedName, 1);
ImposterTrainNormalizedName = importdata('./NUAA/imposter_train_normalized.txt');
ImposterTrainNormalizedNumber = size(ImposterTrainNormalizedName, 1);
ClientTestNormalizedName = importdata('./NUAA/client_test_normalized.txt');
ClientTestNormalizedNumber = size(ClientTestNormalizedName, 1);
ImposterTestNormalizedName = importdata('./NUAA/imposter_test_normalized.txt');
ImposterTestNormalizedNumber = size(ImposterTestNormalizedName, 1);

for i = 1 : ClientTrainNormalizedNumber
    ClientTrain = imread(['./NUAA/ClientNormalized/' ClientTrainNormalizedName{i}]);
    imwrite(ClientTrain, ['.\NUAA\train\real\' num2str(i-1, '%05d') '.png']);
end

for i = 1 : ImposterTrainNormalizedNumber
    ImposterTrain = imread(['./NUAA/ImposterNormalized/' ImposterTrainNormalizedName{i}]);  
    imwrite(ImposterTrain, ['.\NUAA\train\fake\' num2str(i-1, '%05d') '.png']);
end

for i = 1 : ClientTestNormalizedNumber
    ClientTest = imread(['./NUAA/ClientNormalized/' ClientTestNormalizedName{i}]);
    imwrite(ClientTest, ['.\NUAA\test\real\' num2str(i-1, '%05d') '.png']);
end

count_fake = 0;
for i = 1 : ImposterTestNormalizedNumber
    ImposterTest = imread(['./NUAA/ImposterNormalized/' ImposterTestNormalizedName{i}]);  
    imwrite(ImposterTest, ['.\NUAA\test\fake\' num2str(i-1, '%05d') '.png']);
end

================================================
FILE: Matlab/Save_PRINT_ATTACK_Test.m
================================================
clear all, close all, clc

detector = vision.CascadeObjectDetector('MinSize', [50,50]);
fileIndex = {'009', '011', '013', '014', '019', '020', '021', '023', '024', '026', '028', '031', '102', '104', '106', '107', '109', '112', '115', '117'};
fileDirec = {'attack/fixed/attack_print_', 'attack/fixed/attack_print_', 'attack/hand/attack_print_', 'attack/hand/attack_print_', 'real/', 'real/', 'real/', 'real/'; ...
    'highdef_photo_adverse', 'highdef_photo_controlled', 'highdef_photo_adverse', 'highdef_photo_controlled', 'webcam_authenticate_adverse_1', 'webcam_authenticate_adverse_2', 'webcam_authenticate_controlled_1', 'webcam_authenticate_controlled_2'};
fileNo = size(fileIndex, 2);
count_real = 0;
count_fake = 0;
for IdxSubject = 1 : fileNo
    for IdxData = 1 : 8
        Name = ['./PRINT-ATTACK/test/' fileDirec{1, IdxData} 'client' fileIndex{IdxSubject} '_session01_' fileDirec{2, IdxData} '.mov'];
        Mov = VideoReader(Name);
        NumFrame = Mov.NumberOfFrames;
        for IdxFrame = 1 : NumFrame
            data = read(Mov, IdxFrame);
            box = step(detector, data);
            if size(box, 1) == 1
                frame_now = data(box(2):box(2)+box(4), box(1):box(1)+box(3), :);
                if IdxData > 4
                    imwrite(frame_now, ['.\PRINT-ATTACK\test_\real\' num2str(count_real, '%05d') '.png']);
                    count_real = count_real + 1;
                else
                    imwrite(frame_now, ['.\PRINT-ATTACK\test_\fake\' num2str(count_fake, '%05d') '.png']);
                    count_fake = count_fake + 1;
                end
            end
        end
        disp([num2str(IdxSubject) ', ' num2str(IdxData)])
        clear Mov;
    end
end

================================================
FILE: Matlab/Save_PRINT_ATTACK_Train.m
================================================
clear all, close all, clc

detector = vision.CascadeObjectDetector('MinSize', [50,50]);
fileIndex = {'001', '002', '004', '006', '007', '008', '012', '016', '018', '025', '027', '103', '105', '108', '110'};
fileDirec = {'attack/fixed/attack_print_', 'attack/fixed/attack_print_', 'attack/hand/attack_print_', 'attack/hand/attack_print_', 'real/', 'real/', 'real/', 'real/'; ...
    'highdef_photo_adverse', 'highdef_photo_controlled', 'highdef_photo_adverse', 'highdef_photo_controlled', 'webcam_authenticate_adverse_1', 'webcam_authenticate_adverse_2', 'webcam_authenticate_controlled_1', 'webcam_authenticate_controlled_2'};
fileNo = size(fileIndex, 2);
count_real = 0;
count_fake = 0;
for IdxSubject = 1 : fileNo
    for IdxData = 1 : 8
        Name = ['./PRINT-ATTACK/train/' fileDirec{1, IdxData} 'client' fileIndex{IdxSubject} '_session01_' fileDirec{2, IdxData} '.mov'];
        Mov = VideoReader(Name);
        NumFrame = Mov.NumberOfFrames;
        for IdxFrame = 1 : NumFrame
            data = read(Mov, IdxFrame);
            box = step(detector, data);
            if size(box, 1) == 1
                frame_now = data(box(2):box(2)+box(4), box(1):box(1)+box(3), :);
                if IdxData > 4
                    imwrite(frame_now, ['.\PRINT-ATTACK\train_\real\' num2str(count_real, '%05d') '.png']);
                    count_real = count_real + 1;
                else
                    imwrite(frame_now, ['.\PRINT-ATTACK\train_\fake\' num2str(count_fake, '%05d') '.png']);
                    count_fake = count_fake + 1;
                end
            end
        end
        disp([num2str(IdxSubject) ', ' num2str(IdxData)])
        clear Mov;
    end
end

================================================
FILE: Matlab/lbp-0.3.3/getmapping.m
================================================
%GETMAPPING returns a structure containing a mapping table for LBP codes.
%  MAPPING = GETMAPPING(SAMPLES,MAPPINGTYPE) returns a 
%  structure containing a mapping table for
%  LBP codes in a neighbourhood of SAMPLES sampling
%  points. Possible values for MAPPINGTYPE are
%       'u2'   for uniform LBP
%       'ri'   for rotation-invariant LBP
%       'riu2' for uniform rotation-invariant LBP.
%
%  Example:
%       I=imread('rice.tif');
%       MAPPING=getmapping(16,'riu2');
%       LBPHIST=lbp(I,2,16,MAPPING,'hist');
%  Now LBPHIST contains a rotation-invariant uniform LBP
%  histogram in a (16,2) neighbourhood.
%

function mapping = getmapping(samples,mappingtype)
% Version 0.1.1
% Authors: Marko Heikkil and Timo Ahonen

% Changelog
% 0.1.1 Changed output to be a structure
% Fixed a bug causing out of memory errors when generating rotation 
% invariant mappings with high number of sampling points.
% Lauge Sorensen is acknowledged for spotting this problem.



table = 0:2^samples-1;
newMax  = 0; %number of patterns in the resulting LBP code
index   = 0;

if strcmp(mappingtype,'u2') %Uniform 2
  newMax = samples*(samples-1) + 3; 
  for i = 0:2^samples-1
    j = bitset(bitshift(i,1,samples),1,bitget(i,samples)); %rotate left
    numt = sum(bitget(bitxor(i,j),1:samples)); %number of 1->0 and
                                               %0->1 transitions
                                               %in binary string 
                                               %x is equal to the
                                               %number of 1-bits in
                                               %XOR(x,Rotate left(x)) 
    if numt <= 2
      table(i+1) = index;
      index = index + 1;
    else
      table(i+1) = newMax - 1;
    end
  end
end

if strcmp(mappingtype,'ri') %Rotation invariant
  tmpMap = zeros(2^samples,1) - 1;
  for i = 0:2^samples-1
    rm = i;
    r  = i;
    for j = 1:samples-1
      r = bitset(bitshift(r,1,samples),1,bitget(r,samples)); %rotate
                                                             %left
      if r < rm
        rm = r;
      end
    end
    if tmpMap(rm+1) < 0
      tmpMap(rm+1) = newMax;
      newMax = newMax + 1;
    end
    table(i+1) = tmpMap(rm+1);
  end
end

if strcmp(mappingtype,'riu2') %Uniform & Rotation invariant
  newMax = samples + 2;
  for i = 0:2^samples - 1
    j = bitset(bitshift(i,1,samples),1,bitget(i,samples)); %rotate left
    numt = sum(bitget(bitxor(i,j),1:samples));
    if numt <= 2
      table(i+1) = sum(bitget(i,1:samples));
    else
      table(i+1) = samples+1;
    end
  end
end

mapping.table=table;
mapping.samples=samples;
mapping.num=newMax;


================================================
FILE: Matlab/lbp-0.3.3/lbp.m
================================================
%LBP returns the local binary pattern image or LBP histogram of an image.
%  J = LBP(I,R,N,MAPPING,MODE) returns either a local binary pattern
%  coded image or the local binary pattern histogram of an intensity
%  image I. The LBP codes are computed using N sampling points on a 
%  circle of radius R and using mapping table defined by MAPPING. 
%  See the getmapping function for different mappings and use 0 for
%  no mapping. Possible values for MODE are
%       'h' or 'hist'  to get a histogram of LBP codes
%       'nh'           to get a normalized histogram
%  Otherwise an LBP code image is returned.
%
%  J = LBP(I) returns the original (basic) LBP histogram of image I
%
%  J = LBP(I,SP,MAPPING,MODE) computes the LBP codes using n sampling
%  points defined in (n * 2) matrix SP. The sampling points should be
%  defined around the origin (coordinates (0,0)).
%
%  Examples
%  --------
%       I=imread('rice.png');
%       mapping=getmapping(8,'u2'); 
%       H1=LBP(I,1,8,mapping,'h'); %LBP histogram in (8,1) neighborhood
%                                  %using uniform patterns
%       subplot(2,1,1),stem(H1);
%
%       H2=LBP(I);
%       subplot(2,1,2),stem(H2);
%
%       SP=[-1 -1; -1 0; -1 1; 0 -1; -0 1; 1 -1; 1 0; 1 1];
%       I2=LBP(I,SP,0,'i'); %LBP code image using sampling points in SP
%                           %and no mapping. Now H2 is equal to histogram
%                           %of I2.

function result = lbp(varargin) % image,radius,neighbors,mapping,mode)
% Version 0.3.3
% Authors: Marko Heikkil?and Timo Ahonen

% Changelog
% Version 0.3.2: A bug fix to enable using mappings together with a
% predefined spoints array
% Version 0.3.1: Changed MAPPING input to be a struct containing the mapping
% table and the number of bins to make the function run faster with high number
% of sampling points. Lauge Sorensen is acknowledged for spotting this problem.


% Check number of input arguments.
error(nargchk(1,5,nargin));

image=varargin{1};
d_image=double(image);

if nargin==1
    spoints=[-1 -1; -1 0; -1 1; 0 -1; -0 1; 1 -1; 1 0; 1 1];
    neighbors=8;
    mapping=0;
    mode='h';
end

if (nargin == 2) && (length(varargin{2}) == 1)
    error('Input arguments');
end

if (nargin > 2) && (length(varargin{2}) == 1)
    radius=varargin{2};
    neighbors=varargin{3};
    
    spoints=zeros(neighbors,2);

    % Angle step.
    a = 2*pi/neighbors;
    
    for i = 1:neighbors
        spoints(i,1) = -radius*sin((i-1)*a);
        spoints(i,2) = radius*cos((i-1)*a);
    end
    
    if(nargin >= 4)
        mapping=varargin{4};
        if(isstruct(mapping) && mapping.samples ~= neighbors)
            error('Incompatible mapping');
        end
    else
        mapping=0;
    end
    
    if(nargin >= 5)
        mode=varargin{5};
    else
        mode='h';
    end
end

if (nargin > 1) && (length(varargin{2}) > 1)
    spoints=varargin{2};
    neighbors=size(spoints,1);
    
    if(nargin >= 3)
        mapping=varargin{3};
        if(isstruct(mapping) && mapping.samples ~= neighbors)
            error('Incompatible mapping');
        end
    else
        mapping=0;
    end
    
    if(nargin >= 4)
        mode=varargin{4};
    else
        mode='h';
    end   
end

% Determine the dimensions of the input image.
[ysize xsize] = size(image);



miny=min(spoints(:,1));
maxy=max(spoints(:,1));
minx=min(spoints(:,2));
maxx=max(spoints(:,2));

% Block size, each LBP code is computed within a block of size bsizey*bsizex
bsizey=ceil(max(maxy,0))-floor(min(miny,0))+1;
bsizex=ceil(max(maxx,0))-floor(min(minx,0))+1;

% Coordinates of origin (0,0) in the block
origy=1-floor(min(miny,0));
origx=1-floor(min(minx,0));

% Minimum allowed size for the input image depends
% on the radius of the used LBP operator.
if(xsize < bsizex || ysize < bsizey)
  error('Too small input image. Should be at least (2*radius+1) x (2*radius+1)');
end

% Calculate dx and dy;
dx = xsize - bsizex;
dy = ysize - bsizey;

% Fill the center pixel matrix C.
C = image(origy:origy+dy,origx:origx+dx);
d_C = double(C);

bins = 2^neighbors;

% Initialize the result matrix with zeros.
result=zeros(dy+1,dx+1);

%Compute the LBP code image

for i = 1:neighbors
  y = spoints(i,1)+origy;
  x = spoints(i,2)+origx;
  % Calculate floors, ceils and rounds for the x and y.
  fy = floor(y); cy = ceil(y); ry = round(y);
  fx = floor(x); cx = ceil(x); rx = round(x);
  % Check if interpolation is needed.
  if (abs(x - rx) < 1e-6) && (abs(y - ry) < 1e-6)
    % Interpolation is not needed, use original datatypes
    N = image(ry:ry+dy,rx:rx+dx);
    D = N >= C; 
  else
    % Interpolation needed, use double type images 
    ty = y - fy;
    tx = x - fx;

    % Calculate the interpolation weights.
    w1 = roundn((1 - tx) * (1 - ty),-6);
    w2 = roundn(tx * (1 - ty),-6);
    w3 = roundn((1 - tx) * ty,-6) ;
    % w4 = roundn(tx * ty,-6) ;
    w4 = roundn(1 - w1 - w2 - w3, -6);
            
    % Compute interpolated pixel values
    N = w1*d_image(fy:fy+dy,fx:fx+dx) + w2*d_image(fy:fy+dy,cx:cx+dx) + ...
w3*d_image(cy:cy+dy,fx:fx+dx) + w4*d_image(cy:cy+dy,cx:cx+dx);
    N = roundn(N,-4);
    D = N >= d_C; 
  end  
  % Update the result matrix.
  v = 2^(i-1);
  result = result + v*D;
end

%Apply mapping if it is defined
if isstruct(mapping)
    bins = mapping.num;
    for i = 1:size(result,1)
        for j = 1:size(result,2)
            result(i,j) = mapping.table(result(i,j)+1);
        end
    end
end

if (strcmp(mode,'h') || strcmp(mode,'hist') || strcmp(mode,'nh'))
    % Return with LBP histogram if mode equals 'hist'.
    result=hist(result(:),0:(bins-1));
    if (strcmp(mode,'nh'))
        result=result/sum(result);
    end
else
    %Otherwise return a matrix of unsigned integers
    if ((bins-1)<=intmax('uint8'))
        result=uint8(result);
    elseif ((bins-1)<=intmax('uint16'))
        result=uint16(result);
    else
        result=uint32(result);
    end
end

end

function x = roundn(x, n)

error(nargchk(2, 2, nargin, 'struct'))
validateattributes(x, {'single', 'double'}, {}, 'ROUNDN', 'X')
validateattributes(n, ...
    {'numeric'}, {'scalar', 'real', 'integer'}, 'ROUNDN', 'N')

if n < 0
    p = 10 ^ -n;
    x = round(p * x) / p;
elseif n > 0
    p = 10 ^ n;
    x = p * round(x / p);
else
    x = round(x);
end

end




================================================
FILE: Matlab/liblinear-1.96/COPYRIGHT
================================================

Copyright (c) 2007-2014 The LIBLINEAR Project.
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:

1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.

3. Neither name of copyright holders nor the names of its contributors
may be used to endorse or promote products derived from this software
without specific prior written permission.


THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE REGENTS OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


================================================
FILE: Matlab/liblinear-1.96/Makefile
================================================
CXX ?= g++
CC ?= gcc
CFLAGS = -Wall -Wconversion -O3 -fPIC
LIBS = blas/blas.a
SHVER = 2
OS = $(shell uname)
#LIBS = -lblas

all: train predict

lib: linear.o tron.o blas/blas.a
	if [ "$(OS)" = "Darwin" ]; then \
		SHARED_LIB_FLAG="-dynamiclib -Wl,-install_name,liblinear.so.$(SHVER)"; \
	else \
		SHARED_LIB_FLAG="-shared -Wl,-soname,liblinear.so.$(SHVER)"; \
	fi; \
	$(CXX) $${SHARED_LIB_FLAG} linear.o tron.o blas/blas.a -o liblinear.so.$(SHVER)

train: tron.o linear.o train.c blas/blas.a
	$(CXX) $(CFLAGS) -o train train.c tron.o linear.o $(LIBS)

predict: tron.o linear.o predict.c blas/blas.a
	$(CXX) $(CFLAGS) -o predict predict.c tron.o linear.o $(LIBS)

tron.o: tron.cpp tron.h
	$(CXX) $(CFLAGS) -c -o tron.o tron.cpp

linear.o: linear.cpp linear.h
	$(CXX) $(CFLAGS) -c -o linear.o linear.cpp

blas/blas.a: blas/*.c blas/*.h
	make -C blas OPTFLAGS='$(CFLAGS)' CC='$(CC)';

clean:
	make -C blas clean
	make -C matlab clean
	rm -f *~ tron.o linear.o train predict liblinear.so.$(SHVER)


================================================
FILE: Matlab/liblinear-1.96/Makefile.win
================================================
#You must ensure nmake.exe, cl.exe, link.exe are in system path.
#VCVARS32.bat
#Under dosbox prompt
#nmake -f Makefile.win

##########################################
CXX = cl.exe
CFLAGS = /nologo /O2 /EHsc /I. /D _WIN32 /D _CRT_SECURE_NO_DEPRECATE
TARGET = windows

all: $(TARGET)\train.exe $(TARGET)\predict.exe

$(TARGET)\train.exe: tron.obj linear.obj train.c blas\*.c
	$(CXX) $(CFLAGS) -Fe$(TARGET)\train.exe tron.obj linear.obj train.c blas\*.c

$(TARGET)\predict.exe: tron.obj linear.obj predict.c blas\*.c
	$(CXX) $(CFLAGS) -Fe$(TARGET)\predict.exe tron.obj linear.obj predict.c blas\*.c

linear.obj: linear.cpp linear.h
	$(CXX) $(CFLAGS) -c linear.cpp

tron.obj: tron.cpp tron.h
	$(CXX) $(CFLAGS) -c tron.cpp

lib: linear.cpp linear.h linear.def tron.obj
	$(CXX) $(CFLAGS) -LD linear.cpp tron.obj blas\*.c -Fe$(TARGET)\liblinear -link -DEF:linear.def 

clean:
	 -erase /Q *.obj $(TARGET)\.



================================================
FILE: Matlab/liblinear-1.96/README
================================================
LIBLINEAR is a simple package for solving large-scale regularized linear 
classification and regression. It currently supports 
- L2-regularized logistic regression/L2-loss support vector classification/L1-loss support vector classification
- L1-regularized L2-loss support vector classification/L1-regularized logistic regression
- L2-regularized L2-loss support vector regression/L1-loss support vector regression. 
This document explains the usage of LIBLINEAR.

To get started, please read the ``Quick Start'' section first.
For developers, please check the ``Library Usage'' section to learn
how to integrate LIBLINEAR in your software.

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

- When to use LIBLINEAR but not LIBSVM
- Quick Start
- Installation
- `train' Usage
- `predict' Usage
- Examples
- Library Usage
- Building Windows Binaries
- Additional Information
- MATLAB/OCTAVE interface
- PYTHON interface

When to use LIBLINEAR but not LIBSVM
====================================

There are some large data for which with/without nonlinear mappings
gives similar performances.  Without using kernels, one can
efficiently train a much larger set via linear classification/regression.  
These data usually have a large number of features. Document classification
is an example.

Warning: While generally liblinear is very fast, its default solver
may be slow under certain situations (e.g., data not scaled or C is
large). See Appendix B of our SVM guide about how to handle such
cases.
http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

Warning: If you are a beginner and your data sets are not large, you
should consider LIBSVM first.

LIBSVM page:
http://www.csie.ntu.edu.tw/~cjlin/libsvm


Quick Start
===========

See the section ``Installation'' for installing LIBLINEAR.

After installation, there are programs `train' and `predict' for
training and testing, respectively.

About the data format, please check the README file of LIBSVM. Note
that feature index must start from 1 (but not 0).

A sample classification data included in this package is `heart_scale'.

Type `train heart_scale', and the program will read the training
data and output the model file `heart_scale.model'. If you have a test
set called heart_scale.t, then type `predict heart_scale.t
heart_scale.model output' to see the prediction accuracy. The `output'
file contains the predicted class labels.

For more information about `train' and `predict', see the sections
`train' Usage and `predict' Usage.

To obtain good performances, sometimes one needs to scale the
data. Please check the program `svm-scale' of LIBSVM. For large and
sparse data, use `-l 0' to keep the sparsity.

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

On Unix systems, type `make' to build the `train' and `predict'
programs. Run them without arguments to show the usages.

On other systems, consult `Makefile' to build them (e.g., see
'Building Windows binaries' in this file) or use the pre-built
binaries (Windows binaries are in the directory `windows').

This software uses some level-1 BLAS subroutines. The needed functions are
included in this package.  If a BLAS library is available on your
machine, you may use it by modifying the Makefile: Unmark the following line

        #LIBS ?= -lblas

and mark

        LIBS ?= blas/blas.a

`train' Usage
=============

Usage: train [options] training_set_file [model_file]
options:
-s type : set type of solver (default 1)
  for multi-class classification
	 0 -- L2-regularized logistic regression (primal)
	 1 -- L2-regularized L2-loss support vector classification (dual)
	 2 -- L2-regularized L2-loss support vector classification (primal)
	 3 -- L2-regularized L1-loss support vector classification (dual)
	 4 -- support vector classification by Crammer and Singer
	 5 -- L1-regularized L2-loss support vector classification
	 6 -- L1-regularized logistic regression
	 7 -- L2-regularized logistic regression (dual)
  for regression
	11 -- L2-regularized L2-loss support vector regression (primal)
	12 -- L2-regularized L2-loss support vector regression (dual)
	13 -- L2-regularized L1-loss support vector regression (dual)
-c cost : set the parameter C (default 1)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
-e epsilon : set tolerance of termination criterion
	-s 0 and 2
		|f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,
		where f is the primal function and pos/neg are # of
		positive/negative data (default 0.01)
	-s 11
		|f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001) 
	-s 1, 3, 4 and 7
		Dual maximal violation <= eps; similar to libsvm (default 0.1)
	-s 5 and 6
		|f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,
		where f is the primal function (default 0.01)
	-s 12 and 13\n"
		|f'(alpha)|_1 <= eps |f'(alpha0)|,
		where f is the dual function (default 0.1)
-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)
-wi weight: weights adjust the parameter C of different classes (see README for details)
-v n: n-fold cross validation mode
-q : quiet mode (no outputs)

Option -v randomly splits the data into n parts and calculates cross
validation accuracy on them.

Formulations:

For L2-regularized logistic regression (-s 0), we solve

min_w w^Tw/2 + C \sum log(1 + exp(-y_i w^Tx_i))

For L2-regularized L2-loss SVC dual (-s 1), we solve

min_alpha  0.5(alpha^T (Q + I/2/C) alpha) - e^T alpha
    s.t.   0 <= alpha_i,

For L2-regularized L2-loss SVC (-s 2), we solve

min_w w^Tw/2 + C \sum max(0, 1- y_i w^Tx_i)^2

For L2-regularized L1-loss SVC dual (-s 3), we solve

min_alpha  0.5(alpha^T Q alpha) - e^T alpha
    s.t.   0 <= alpha_i <= C,

For L1-regularized L2-loss SVC (-s 5), we solve

min_w \sum |w_j| + C \sum max(0, 1- y_i w^Tx_i)^2

For L1-regularized logistic regression (-s 6), we solve

min_w \sum |w_j| + C \sum log(1 + exp(-y_i w^Tx_i))

For L2-regularized logistic regression (-s 7), we solve

min_alpha  0.5(alpha^T Q alpha) + \sum alpha_i*log(alpha_i) + \sum (C-alpha_i)*log(C-alpha_i) - a constant
    s.t.   0 <= alpha_i <= C,

where

Q is a matrix with Q_ij = y_i y_j x_i^T x_j.

For L2-regularized L2-loss SVR (-s 11), we solve

min_w w^Tw/2 + C \sum max(0, |y_i-w^Tx_i|-epsilon)^2

For L2-regularized L2-loss SVR dual (-s 12), we solve

min_beta  0.5(beta^T (Q + lambda I/2/C) beta) - y^T beta + \sum |beta_i|

For L2-regularized L1-loss SVR dual (-s 13), we solve

min_beta  0.5(beta^T Q beta) - y^T beta + \sum |beta_i|
    s.t.   -C <= beta_i <= C,

where

Q is a matrix with Q_ij = x_i^T x_j.

If bias >= 0, w becomes [w; w_{n+1}] and x becomes [x; bias].

The primal-dual relationship implies that -s 1 and -s 2 give the same
model, -s 0 and -s 7 give the same, and -s 11 and -s 12 give the same.

We implement 1-vs-the rest multi-class strategy for classification. 
In training i vs. non_i, their C parameters are (weight from -wi)*C 
and C, respectively. If there are only two classes, we train only one
model. Thus weight1*C vs. weight2*C is used. See examples below.

We also implement multi-class SVM by Crammer and Singer (-s 4):

min_{w_m, \xi_i}  0.5 \sum_m ||w_m||^2 + C \sum_i \xi_i
    s.t.  w^T_{y_i} x_i - w^T_m x_i >= \e^m_i - \xi_i \forall m,i

where e^m_i = 0 if y_i  = m,
      e^m_i = 1 if y_i != m,

Here we solve the dual problem:

min_{\alpha}  0.5 \sum_m ||w_m(\alpha)||^2 + \sum_i \sum_m e^m_i alpha^m_i
    s.t.  \alpha^m_i <= C^m_i \forall m,i , \sum_m \alpha^m_i=0 \forall i

where w_m(\alpha) = \sum_i \alpha^m_i x_i,
and C^m_i = C if m  = y_i,
    C^m_i = 0 if m != y_i.

`predict' Usage
===============

Usage: predict [options] test_file model_file output_file
options:
-b probability_estimates: whether to output probability estimates, 0 or 1 (default 0); currently for logistic regression only
-q : quiet mode (no outputs)

Note that -b is only needed in the prediction phase. This is different
from the setting of LIBSVM.

Examples
========

> train data_file

Train linear SVM with L2-loss function.

> train -s 0 data_file

Train a logistic regression model.

> train -v 5 -e 0.001 data_file

Do five-fold cross-validation using L2-loss svm.
Use a smaller stopping tolerance 0.001 than the default
0.1 if you want more accurate solutions.

> train -c 10 -w1 2 -w2 5 -w3 2 four_class_data_file

Train four classifiers:
positive        negative        Cp      Cn
class 1         class 2,3,4.    20      10
class 2         class 1,3,4.    50      10
class 3         class 1,2,4.    20      10
class 4         class 1,2,3.    10      10

> train -c 10 -w3 1 -w2 5 two_class_data_file

If there are only two classes, we train ONE model.
The C values for the two classes are 10 and 50.

> predict -b 1 test_file data_file.model output_file

Output probability estimates (for logistic regression only).

Library Usage
=============

- Function: model* train(const struct problem *prob,
                const struct parameter *param);

    This function constructs and returns a linear classification 
    or regression model according to the given training data and 
    parameters.

    struct problem describes the problem:

        struct problem
        {
            int l, n;
            int *y;
            struct feature_node **x;
            double bias;
        };

    where `l' is the number of training data. If bias >= 0, we assume
    that one additional feature is added to the end of each data
    instance. `n' is the number of feature (including the bias feature
    if bias >= 0). `y' is an array containing the target values. (integers 
    in classification, real numbers in regression) And `x' is an array 
    of pointers, each of which points to a sparse representation (array 
    of feature_node) of one training vector.

    For example, if we have the following training data:

    LABEL       ATTR1   ATTR2   ATTR3   ATTR4   ATTR5
    -----       -----   -----   -----   -----   -----
    1           0       0.1     0.2     0       0
    2           0       0.1     0.3    -1.2     0
    1           0.4     0       0       0       0
    2           0       0.1     0       1.4     0.5
    3          -0.1    -0.2     0.1     1.1     0.1

    and bias = 1, then the components of problem are:

    l = 5
    n = 6

    y -> 1 2 1 2 3

    x -> [ ] -> (2,0.1) (3,0.2) (6,1) (-1,?)
         [ ] -> (2,0.1) (3,0.3) (4,-1.2) (6,1) (-1,?)
         [ ] -> (1,0.4) (6,1) (-1,?)
         [ ] -> (2,0.1) (4,1.4) (5,0.5) (6,1) (-1,?)
         [ ] -> (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (6,1) (-1,?)

    struct parameter describes the parameters of a linear classification 
    or regression model:

        struct parameter
        {
                int solver_type;

                /* these are for training only */
                double eps;             /* stopping criteria */
                double C;
                int nr_weight;
                int *weight_label;
                double* weight;
                double p;
        };

    solver_type can be one of L2R_LR, L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L1R_L2LOSS_SVC, L1R_LR, L2R_LR_DUAL, L2R_L2LOSS_SVR, L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL.
  for classification
    L2R_LR                L2-regularized logistic regression (primal)
    L2R_L2LOSS_SVC_DUAL   L2-regularized L2-loss support vector classification (dual)
    L2R_L2LOSS_SVC        L2-regularized L2-loss support vector classification (primal)
    L2R_L1LOSS_SVC_DUAL   L2-regularized L1-loss support vector classification (dual)
    MCSVM_CS              support vector classification by Crammer and Singer
    L1R_L2LOSS_SVC        L1-regularized L2-loss support vector classification
    L1R_LR                L1-regularized logistic regression
    L2R_LR_DUAL           L2-regularized logistic regression (dual)
  for regression
    L2R_L2LOSS_SVR        L2-regularized L2-loss support vector regression (primal)
    L2R_L2LOSS_SVR_DUAL   L2-regularized L2-loss support vector regression (dual)
    L2R_L1LOSS_SVR_DUAL   L2-regularized L1-loss support vector regression (dual)

    C is the cost of constraints violation.
    p is the sensitiveness of loss of support vector regression. 
    eps is the stopping criterion.

    nr_weight, weight_label, and weight are used to change the penalty
    for some classes (If the weight for a class is not changed, it is
    set to 1). This is useful for training classifier using unbalanced
    input data or with asymmetric misclassification cost.

    nr_weight is the number of elements in the array weight_label and
    weight. Each weight[i] corresponds to weight_label[i], meaning that
    the penalty of class weight_label[i] is scaled by a factor of weight[i].

    If you do not want to change penalty for any of the classes,
    just set nr_weight to 0.

    *NOTE* To avoid wrong parameters, check_parameter() should be
    called before train().

    struct model stores the model obtained from the training procedure:

        struct model
        {
                struct parameter param;
                int nr_class;           /* number of classes */
                int nr_feature;
                double *w;
                int *label;             /* label of each class */
                double bias;
        };

     param describes the parameters used to obtain the model.

     nr_class and nr_feature are the number of classes and features, 
     respectively. nr_class = 2 for regression. 

     The nr_feature*nr_class array w gives feature weights. We use one
     against the rest for multi-class classification, so each feature
     index corresponds to nr_class weight values. Weights are
     organized in the following way

     +------------------+------------------+------------+
     | nr_class weights | nr_class weights |  ...
     | for 1st feature  | for 2nd feature  |
     +------------------+------------------+------------+

     If bias >= 0, x becomes [x; bias]. The number of features is
     increased by one, so w is a (nr_feature+1)*nr_class array. The
     value of bias is stored in the variable bias.

     The array label stores class labels.

- Function: void cross_validation(const problem *prob, const parameter *param, int nr_fold, double *target);

    This function conducts cross validation. Data are separated to
    nr_fold folds. Under given parameters, sequentially each fold is
    validated using the model from training the remaining. Predicted
    labels in the validation process are stored in the array called
    target.

    The format of prob is same as that for train().

- Function: double predict(const model *model_, const feature_node *x);

    For a classification model, the predicted class for x is returned.
    For a regression model, the function value of x calculated using
    the model is returned. 

- Function: double predict_values(const struct model *model_,
            const struct feature_node *x, double* dec_values);

    This function gives nr_w decision values in the array dec_values. 
    nr_w=1 if regression is applied or the number of classes is two. An exception is
    multi-class svm by Crammer and Singer (-s 4), where nr_w = 2 if there are two classes. For all other situations, nr_w is the 
    number of classes.

    We implement one-vs-the rest multi-class strategy (-s 0,1,2,3,5,6,7) 
    and multi-class svm by Crammer and Singer (-s 4) for multi-class SVM.
    The class with the highest decision value is returned.

- Function: double predict_probability(const struct model *model_,
            const struct feature_node *x, double* prob_estimates);

    This function gives nr_class probability estimates in the array
    prob_estimates. nr_class can be obtained from the function
    get_nr_class. The class with the highest probability is
    returned. Currently, we support only the probability outputs of
    logistic regression.

- Function: int get_nr_feature(const model *model_);

    The function gives the number of attributes of the model.

- Function: int get_nr_class(const model *model_);

    The function gives the number of classes of the model.
    For a regression model, 2 is returned.

- Function: void get_labels(const model *model_, int* label);

    This function outputs the name of labels into an array called label.
    For a regression model, label is unchanged.

- Function: double get_decfun_coef(const struct model *model_, int feat_idx,
            int label_idx);

    This function gives the coefficient for the feature with feature index =
    feat_idx and the class with label index = label_idx. Note that feat_idx
    starts from 1, while label_idx starts from 0. If feat_idx is not in the
    valid range (1 to nr_feature), then a zero value will be returned. For
    classification models, if label_idx is not in the valid range (0 to
    nr_class-1), then a zero value will be returned; for regression models,
    label_idx is ignored.

- Function: double get_decfun_bias(const struct model *model_, int label_idx);

    This function gives the bias term corresponding to the class with the
    label_idx. For classification models, if label_idx is not in a valid range
    (0 to nr_class-1), then a zero value will be returned; for regression
    models, label_idx is ignored.

- Function: const char *check_parameter(const struct problem *prob,
            const struct parameter *param);

    This function checks whether the parameters are within the feasible
    range of the problem. This function should be called before calling
    train() and cross_validation(). It returns NULL if the
    parameters are feasible, otherwise an error message is returned.

- Function: int check_probability_model(const struct model *model);

    This function returns 1 if the model supports probability output;
    otherwise, it returns 0.

- Function: int check_regression_model(const struct model *model);

    This function returns 1 if the model is a regression model; otherwise
    it returns 0.

- Function: int save_model(const char *model_file_name,
            const struct model *model_);

    This function saves a model to a file; returns 0 on success, or -1
    if an error occurs.

- Function: struct model *load_model(const char *model_file_name);

    This function returns a pointer to the model read from the file,
    or a null pointer if the model could not be loaded.

- Function: void free_model_content(struct model *model_ptr);

    This function frees the memory used by the entries in a model structure.

- Function: void free_and_destroy_model(struct model **model_ptr_ptr);

    This function frees the memory used by a model and destroys the model
    structure.

- Function: void destroy_param(struct parameter *param);

    This function frees the memory used by a parameter set.

- Function: void set_print_string_function(void (*print_func)(const char *));

    Users can specify their output format by a function. Use
        set_print_string_function(NULL); 
    for default printing to stdout.

Building Windows Binaries
=========================

Windows binaries are in the directory `windows'. To build them via
Visual C++, use the following steps:

1. Open a dos command box and change to liblinear directory. If
environment variables of VC++ have not been set, type

"C:\Program Files\Microsoft Visual Studio 10.0\VC\bin\vcvars32.bat"

You may have to modify the above command according which version of
VC++ or where it is installed.

2. Type

nmake -f Makefile.win clean all

2. (Optional) To build 64-bit windows binaries, you must
	(1) Setup vcvars64.bat instead of vcvars32.bat
	(2) Change CFLAGS in Makefile.win: /D _WIN32 to /D _WIN64

MATLAB/OCTAVE Interface
=======================

Please check the file README in the directory `matlab'.

PYTHON Interface
================

Please check the file README in the directory `python'.

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

If you find LIBLINEAR helpful, please cite it as

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

For any questions and comments, please send your email to
cjlin@csie.ntu.edu.tw




================================================
FILE: Matlab/liblinear-1.96/blas/Makefile
================================================
AR     = ar rcv
RANLIB = ranlib 

HEADERS = blas.h blasp.h
FILES = dnrm2.o daxpy.o ddot.o dscal.o 

CFLAGS = $(OPTFLAGS) 
FFLAGS = $(OPTFLAGS)

blas: $(FILES) $(HEADERS)
	$(AR) blas.a $(FILES)  
	$(RANLIB) blas.a

clean:
	- rm -f *.o
	- rm -f *.a
	- rm -f *~

.c.o:
	$(CC) $(CFLAGS) -c $*.c




================================================
FILE: Matlab/liblinear-1.96/blas/blas.h
================================================
/* blas.h  --  C header file for BLAS                         Ver 1.0 */
/* Jesse Bennett                                       March 23, 2000 */

/**  barf  [ba:rf]  2.  "He suggested using FORTRAN, and everybody barfed."

	- From The Shogakukan DICTIONARY OF NEW ENGLISH (Second edition) */

#ifndef BLAS_INCLUDE
#define BLAS_INCLUDE

/* Data types specific to BLAS implementation */
typedef struct { float r, i; } fcomplex;
typedef struct { double r, i; } dcomplex;
typedef int blasbool;

#include "blasp.h"    /* Prototypes for all BLAS functions */

#define FALSE 0
#define TRUE  1

/* Macro functions */
#define MIN(a,b) ((a) <= (b) ? (a) : (b))
#define MAX(a,b) ((a) >= (b) ? (a) : (b))

#endif


================================================
FILE: Matlab/liblinear-1.96/blas/blasp.h
================================================
/* blasp.h  --  C prototypes for BLAS                         Ver 1.0 */
/* Jesse Bennett                                       March 23, 2000 */

/* Functions  listed in alphabetical order */

#ifdef __cplusplus
extern "C" {
#endif

#ifdef F2C_COMPAT

void cdotc_(fcomplex *dotval, int *n, fcomplex *cx, int *incx,
            fcomplex *cy, int *incy);

void cdotu_(fcomplex *dotval, int *n, fcomplex *cx, int *incx,
            fcomplex *cy, int *incy);

double sasum_(int *n, float *sx, int *incx);

double scasum_(int *n, fcomplex *cx, int *incx);

double scnrm2_(int *n, fcomplex *x, int *incx);

double sdot_(int *n, float *sx, int *incx, float *sy, int *incy);

double snrm2_(int *n, float *x, int *incx);

void zdotc_(dcomplex *dotval, int *n, dcomplex *cx, int *incx,
            dcomplex *cy, int *incy);

void zdotu_(dcomplex *dotval, int *n, dcomplex *cx, int *incx,
            dcomplex *cy, int *incy);

#else

fcomplex cdotc_(int *n, fcomplex *cx, int *incx, fcomplex *cy, int *incy);

fcomplex cdotu_(int *n, fcomplex *cx, int *incx, fcomplex *cy, int *incy);

float sasum_(int *n, float *sx, int *incx);

float scasum_(int *n, fcomplex *cx, int *incx);

float scnrm2_(int *n, fcomplex *x, int *incx);

float sdot_(int *n, float *sx, int *incx, float *sy, int *incy);

float snrm2_(int *n, float *x, int *incx);

dcomplex zdotc_(int *n, dcomplex *cx, int *incx, dcomplex *cy, int *incy);

dcomplex zdotu_(int *n, dcomplex *cx, int *incx, dcomplex *cy, int *incy);

#endif

/* Remaining functions listed in alphabetical order */

int caxpy_(int *n, fcomplex *ca, fcomplex *cx, int *incx, fcomplex *cy,
           int *incy);

int ccopy_(int *n, fcomplex *cx, int *incx, fcomplex *cy, int *incy);

int cgbmv_(char *trans, int *m, int *n, int *kl, int *ku,
           fcomplex *alpha, fcomplex *a, int *lda, fcomplex *x, int *incx,
           fcomplex *beta, fcomplex *y, int *incy);

int cgemm_(char *transa, char *transb, int *m, int *n, int *k,
           fcomplex *alpha, fcomplex *a, int *lda, fcomplex *b, int *ldb,
           fcomplex *beta, fcomplex *c, int *ldc);

int cgemv_(char *trans, int *m, int *n, fcomplex *alpha, fcomplex *a,
           int *lda, fcomplex *x, int *incx, fcomplex *beta, fcomplex *y,
           int *incy);

int cgerc_(int *m, int *n, fcomplex *alpha, fcomplex *x, int *incx,
           fcomplex *y, int *incy, fcomplex *a, int *lda);

int cgeru_(int *m, int *n, fcomplex *alpha, fcomplex *x, int *incx,
           fcomplex *y, int *incy, fcomplex *a, int *lda);

int chbmv_(char *uplo, int *n, int *k, fcomplex *alpha, fcomplex *a,
           int *lda, fcomplex *x, int *incx, fcomplex *beta, fcomplex *y,
           int *incy);

int chemm_(char *side, char *uplo, int *m, int *n, fcomplex *alpha,
           fcomplex *a, int *lda, fcomplex *b, int *ldb, fcomplex *beta,
           fcomplex *c, int *ldc);

int chemv_(char *uplo, int *n, fcomplex *alpha, fcomplex *a, int *lda,
           fcomplex *x, int *incx, fcomplex *beta, fcomplex *y, int *incy);

int cher_(char *uplo, int *n, float *alpha, fcomplex *x, int *incx,
          fcomplex *a, int *lda);

int cher2_(char *uplo, int *n, fcomplex *alpha, fcomplex *x, int *incx,
           fcomplex *y, int *incy, fcomplex *a, int *lda);

int cher2k_(char *uplo, char *trans, int *n, int *k, fcomplex *alpha,
            fcomplex *a, int *lda, fcomplex *b, int *ldb, float *beta,
            fcomplex *c, int *ldc);

int cherk_(char *uplo, char *trans, int *n, int *k, float *alpha,
           fcomplex *a, int *lda, float *beta, fcomplex *c, int *ldc);

int chpmv_(char *uplo, int *n, fcomplex *alpha, fcomplex *ap, fcomplex *x,
           int *incx, fcomplex *beta, fcomplex *y, int *incy);

int chpr_(char *uplo, int *n, float *alpha, fcomplex *x, int *incx,
          fcomplex *ap);

int chpr2_(char *uplo, int *n, fcomplex *alpha, fcomplex *x, int *incx,
           fcomplex *y, int *incy, fcomplex *ap);

int crotg_(fcomplex *ca, fcomplex *cb, float *c, fcomplex *s);

int cscal_(int *n, fcomplex *ca, fcomplex *cx, int *incx);

int csscal_(int *n, float *sa, fcomplex *cx, int *incx);

int cswap_(int *n, fcomplex *cx, int *incx, fcomplex *cy, int *incy);

int csymm_(char *side, char *uplo, int *m, int *n, fcomplex *alpha,
           fcomplex *a, int *lda, fcomplex *b, int *ldb, fcomplex *beta,
           fcomplex *c, int *ldc);

int csyr2k_(char *uplo, char *trans, int *n, int *k, fcomplex *alpha,
            fcomplex *a, int *lda, fcomplex *b, int *ldb, fcomplex *beta,
            fcomplex *c, int *ldc);

int csyrk_(char *uplo, char *trans, int *n, int *k, fcomplex *alpha,
           fcomplex *a, int *lda, fcomplex *beta, fcomplex *c, int *ldc);

int ctbmv_(char *uplo, char *trans, char *diag, int *n, int *k,
           fcomplex *a, int *lda, fcomplex *x, int *incx);

int ctbsv_(char *uplo, char *trans, char *diag, int *n, int *k,
           fcomplex *a, int *lda, fcomplex *x, int *incx);

int ctpmv_(char *uplo, char *trans, char *diag, int *n, fcomplex *ap,
           fcomplex *x, int *incx);

int ctpsv_(char *uplo, char *trans, char *diag, int *n, fcomplex *ap,
           fcomplex *x, int *incx);

int ctrmm_(char *side, char *uplo, char *transa, char *diag, int *m,
           int *n, fcomplex *alpha, fcomplex *a, int *lda, fcomplex *b,
           int *ldb);

int ctrmv_(char *uplo, char *trans, char *diag, int *n, fcomplex *a,
           int *lda, fcomplex *x, int *incx);

int ctrsm_(char *side, char *uplo, char *transa, char *diag, int *m,
           int *n, fcomplex *alpha, fcomplex *a, int *lda, fcomplex *b,
           int *ldb);

int ctrsv_(char *uplo, char *trans, char *diag, int *n, fcomplex *a,
           int *lda, fcomplex *x, int *incx);

int daxpy_(int *n, double *sa, double *sx, int *incx, double *sy,
           int *incy);

int dcopy_(int *n, double *sx, int *incx, double *sy, int *incy);

int dgbmv_(char *trans, int *m, int *n, int *kl, int *ku,
           double *alpha, double *a, int *lda, double *x, int *incx,
           double *beta, double *y, int *incy);

int dgemm_(char *transa, char *transb, int *m, int *n, int *k,
           double *alpha, double *a, int *lda, double *b, int *ldb,
           double *beta, double *c, int *ldc);

int dgemv_(char *trans, int *m, int *n, double *alpha, double *a,
           int *lda, double *x, int *incx, double *beta, double *y, 
           int *incy);

int dger_(int *m, int *n, double *alpha, double *x, int *incx,
          double *y, int *incy, double *a, int *lda);

int drot_(int *n, double *sx, int *incx, double *sy, int *incy,
          double *c, double *s);

int drotg_(double *sa, double *sb, double *c, double *s);

int dsbmv_(char *uplo, int *n, int *k, double *alpha, double *a,
           int *lda, double *x, int *incx, double *beta, double *y, 
           int *incy);

int dscal_(int *n, double *sa, double *sx, int *incx);

int dspmv_(char *uplo, int *n, double *alpha, double *ap, double *x,
           int *incx, double *beta, double *y, int *incy);

int dspr_(char *uplo, int *n, double *alpha, double *x, int *incx,
          double *ap);

int dspr2_(char *uplo, int *n, double *alpha, double *x, int *incx,
           double *y, int *incy, double *ap);

int dswap_(int *n, double *sx, int *incx, double *sy, int *incy);

int dsymm_(char *side, char *uplo, int *m, int *n, double *alpha,
           double *a, int *lda, double *b, int *ldb, double *beta,
           double *c, int *ldc);

int dsymv_(char *uplo, int *n, double *alpha, double *a, int *lda,
           double *x, int *incx, double *beta, double *y, int *incy);

int dsyr_(char *uplo, int *n, double *alpha, double *x, int *incx,
          double *a, int *lda);

int dsyr2_(char *uplo, int *n, double *alpha, double *x, int *incx,
           double *y, int *incy, double *a, int *lda);

int dsyr2k_(char *uplo, char *trans, int *n, int *k, double *alpha,
            double *a, int *lda, double *b, int *ldb, double *beta,
            double *c, int *ldc);

int dsyrk_(char *uplo, char *trans, int *n, int *k, double *alpha,
           double *a, int *lda, double *beta, double *c, int *ldc);

int dtbmv_(char *uplo, char *trans, char *diag, int *n, int *k,
           double *a, int *lda, double *x, int *incx);

int dtbsv_(char *uplo, char *trans, char *diag, int *n, int *k,
           double *a, int *lda, double *x, int *incx);

int dtpmv_(char *uplo, char *trans, char *diag, int *n, double *ap,
           double *x, int *incx);

int dtpsv_(char *uplo, char *trans, char *diag, int *n, double *ap,
           double *x, int *incx);

int dtrmm_(char *side, char *uplo, char *transa, char *diag, int *m,
           int *n, double *alpha, double *a, int *lda, double *b, 
           int *ldb);

int dtrmv_(char *uplo, char *trans, char *diag, int *n, double *a,
           int *lda, double *x, int *incx);

int dtrsm_(char *side, char *uplo, char *transa, char *diag, int *m,
           int *n, double *alpha, double *a, int *lda, double *b, 
           int *ldb);

int dtrsv_(char *uplo, char *trans, char *diag, int *n, double *a,
           int *lda, double *x, int *incx);


int saxpy_(int *n, float *sa, float *sx, int *incx, float *sy, int *incy);

int scopy_(int *n, float *sx, int *incx, float *sy, int *incy);

int sgbmv_(char *trans, int *m, int *n, int *kl, int *ku,
           float *alpha, float *a, int *lda, float *x, int *incx,
           float *beta, float *y, int *incy);

int sgemm_(char *transa, char *transb, int *m, int *n, int *k,
           float *alpha, float *a, int *lda, float *b, int *ldb,
           float *beta, float *c, int *ldc);

int sgemv_(char *trans, int *m, int *n, float *alpha, float *a,
           int *lda, float *x, int *incx, float *beta, float *y, 
           int *incy);

int sger_(int *m, int *n, float *alpha, float *x, int *incx,
          float *y, int *incy, float *a, int *lda);

int srot_(int *n, float *sx, int *incx, float *sy, int *incy,
          float *c, float *s);

int srotg_(float *sa, float *sb, float *c, float *s);

int ssbmv_(char *uplo, int *n, int *k, float *alpha, float *a,
           int *lda, float *x, int *incx, float *beta, float *y, 
           int *incy);

int sscal_(int *n, float *sa, float *sx, int *incx);

int sspmv_(char *uplo, int *n, float *alpha, float *ap, float *x,
           int *incx, float *beta, float *y, int *incy);

int sspr_(char *uplo, int *n, float *alpha, float *x, int *incx,
          float *ap);

int sspr2_(char *uplo, int *n, float *alpha, float *x, int *incx,
           float *y, int *incy, float *ap);

int sswap_(int *n, float *sx, int *incx, float *sy, int *incy);

int ssymm_(char *side, char *uplo, int *m, int *n, float *alpha,
           float *a, int *lda, float *b, int *ldb, float *beta,
           float *c, int *ldc);

int ssymv_(char *uplo, int *n, float *alpha, float *a, int *lda,
           float *x, int *incx, float *beta, float *y, int *incy);

int ssyr_(char *uplo, int *n, float *alpha, float *x, int *incx,
          float *a, int *lda);

int ssyr2_(char *uplo, int *n, float *alpha, float *x, int *incx,
           float *y, int *incy, float *a, int *lda);

int ssyr2k_(char *uplo, char *trans, int *n, int *k, float *alpha,
            float *a, int *lda, float *b, int *ldb, float *beta,
            float *c, int *ldc);

int ssyrk_(char *uplo, char *trans, int *n, int *k, float *alpha,
           float *a, int *lda, float *beta, float *c, int *ldc);

int stbmv_(char *uplo, char *trans, char *diag, int *n, int *k,
           float *a, int *lda, float *x, int *incx);

int stbsv_(char *uplo, char *trans, char *diag, int *n, int *k,
           float *a, int *lda, float *x, int *incx);

int stpmv_(char *uplo, char *trans, char *diag, int *n, float *ap,
           float *x, int *incx);

int stpsv_(char *uplo, char *trans, char *diag, int *n, float *ap,
           float *x, int *incx);

int strmm_(char *side, char *uplo, char *transa, char *diag, int *m,
           int *n, float *alpha, float *a, int *lda, float *b, 
           int *ldb);

int strmv_(char *uplo, char *trans, char *diag, int *n, float *a,
           int *lda, float *x, int *incx);

int strsm_(char *side, char *uplo, char *transa, char *diag, int *m,
           int *n, float *alpha, float *a, int *lda, float *b, 
           int *ldb);

int strsv_(char *uplo, char *trans, char *diag, int *n, float *a,
           int *lda, float *x, int *incx);

int zaxpy_(int *n, dcomplex *ca, dcomplex *cx, int *incx, dcomplex *cy,
           int *incy);

int zcopy_(int *n, dcomplex *cx, int *incx, dcomplex *cy, int *incy);

int zdscal_(int *n, double *sa, dcomplex *cx, int *incx);

int zgbmv_(char *trans, int *m, int *n, int *kl, int *ku,
           dcomplex *alpha, dcomplex *a, int *lda, dcomplex *x, int *incx,
           dcomplex *beta, dcomplex *y, int *incy);

int zgemm_(char *transa, char *transb, int *m, int *n, int *k,
           dcomplex *alpha, dcomplex *a, int *lda, dcomplex *b, int *ldb,
           dcomplex *beta, dcomplex *c, int *ldc);

int zgemv_(char *trans, int *m, int *n, dcomplex *alpha, dcomplex *a,
           int *lda, dcomplex *x, int *incx, dcomplex *beta, dcomplex *y,
           int *incy);

int zgerc_(int *m, int *n, dcomplex *alpha, dcomplex *x, int *incx,
           dcomplex *y, int *incy, dcomplex *a, int *lda);

int zgeru_(int *m, int *n, dcomplex *alpha, dcomplex *x, int *incx,
           dcomplex *y, int *incy, dcomplex *a, int *lda);

int zhbmv_(char *uplo, int *n, int *k, dcomplex *alpha, dcomplex *a,
           int *lda, dcomplex *x, int *incx, dcomplex *beta, dcomplex *y,
           int *incy);

int zhemm_(char *side, char *uplo, int *m, int *n, dcomplex *alpha,
           dcomplex *a, int *lda, dcomplex *b, int *ldb, dcomplex *beta,
           dcomplex *c, int *ldc);

int zhemv_(char *uplo, int *n, dcomplex *alpha, dcomplex *a, int *lda,
           dcomplex *x, int *incx, dcomplex *beta, dcomplex *y, int *incy);

int zher_(char *uplo, int *n, double *alpha, dcomplex *x, int *incx,
          dcomplex *a, int *lda);

int zher2_(char *uplo, int *n, dcomplex *alpha, dcomplex *x, int *incx,
           dcomplex *y, int *incy, dcomplex *a, int *lda);

int zher2k_(char *uplo, char *trans, int *n, int *k, dcomplex *alpha,
            dcomplex *a, int *lda, dcomplex *b, int *ldb, double *beta,
            dcomplex *c, int *ldc);

int zherk_(char *uplo, char *trans, int *n, int *k, double *alpha,
           dcomplex *a, int *lda, double *beta, dcomplex *c, int *ldc);

int zhpmv_(char *uplo, int *n, dcomplex *alpha, dcomplex *ap, dcomplex *x,
           int *incx, dcomplex *beta, dcomplex *y, int *incy);

int zhpr_(char *uplo, int *n, double *alpha, dcomplex *x, int *incx,
          dcomplex *ap);

int zhpr2_(char *uplo, int *n, dcomplex *alpha, dcomplex *x, int *incx,
           dcomplex *y, int *incy, dcomplex *ap);

int zrotg_(dcomplex *ca, dcomplex *cb, double *c, dcomplex *s);

int zscal_(int *n, dcomplex *ca, dcomplex *cx, int *incx);

int zswap_(int *n, dcomplex *cx, int *incx, dcomplex *cy, int *incy);

int zsymm_(char *side, char *uplo, int *m, int *n, dcomplex *alpha,
           dcomplex *a, int *lda, dcomplex *b, int *ldb, dcomplex *beta,
           dcomplex *c, int *ldc);

int zsyr2k_(char *uplo, char *trans, int *n, int *k, dcomplex *alpha,
            dcomplex *a, int *lda, dcomplex *b, int *ldb, dcomplex *beta,
            dcomplex *c, int *ldc);

int zsyrk_(char *uplo, char *trans, int *n, int *k, dcomplex *alpha,
           dcomplex *a, int *lda, dcomplex *beta, dcomplex *c, int *ldc);

int ztbmv_(char *uplo, char *trans, char *diag, int *n, int *k,
           dcomplex *a, int *lda, dcomplex *x, int *incx);

int ztbsv_(char *uplo, char *trans, char *diag, int *n, int *k,
           dcomplex *a, int *lda, dcomplex *x, int *incx);

int ztpmv_(char *uplo, char *trans, char *diag, int *n, dcomplex *ap,
           dcomplex *x, int *incx);

int ztpsv_(char *uplo, char *trans, char *diag, int *n, dcomplex *ap,
           dcomplex *x, int *incx);

int ztrmm_(char *side, char *uplo, char *transa, char *diag, int *m,
           int *n, dcomplex *alpha, dcomplex *a, int *lda, dcomplex *b,
           int *ldb);

int ztrmv_(char *uplo, char *trans, char *diag, int *n, dcomplex *a,
           int *lda, dcomplex *x, int *incx);

int ztrsm_(char *side, char *uplo, char *transa, char *diag, int *m,
           int *n, dcomplex *alpha, dcomplex *a, int *lda, dcomplex *b,
           int *ldb);

int ztrsv_(char *uplo, char *trans, char *diag, int *n, dcomplex *a,
           int *lda, dcomplex *x, int *incx);

#ifdef __cplusplus
}
#endif


================================================
FILE: Matlab/liblinear-1.96/blas/daxpy.c
================================================
#include "blas.h"

#ifdef __cplusplus
extern "C" {
#endif

int daxpy_(int *n, double *sa, double *sx, int *incx, double *sy,
           int *incy)
{
  long int i, m, ix, iy, nn, iincx, iincy;
  register double ssa;

  /* constant times a vector plus a vector.   
     uses unrolled loop for increments equal to one.   
     jack dongarra, linpack, 3/11/78.   
     modified 12/3/93, array(1) declarations changed to array(*) */

  /* Dereference inputs */
  nn = *n;
  ssa = *sa;
  iincx = *incx;
  iincy = *incy;

  if( nn > 0 && ssa != 0.0 )
  {
    if (iincx == 1 && iincy == 1) /* code for both increments equal to 1 */
    {
      m = nn-3;
      for (i = 0; i < m; i += 4)
      {
        sy[i] += ssa * sx[i];
        sy[i+1] += ssa * sx[i+1];
        sy[i+2] += ssa * sx[i+2];
        sy[i+3] += ssa * sx[i+3];
      }
      for ( ; i < nn; ++i) /* clean-up loop */
        sy[i] += ssa * sx[i];
    }
    else /* code for unequal increments or equal increments not equal to 1 */
    {
      ix = iincx >= 0 ? 0 : (1 - nn) * iincx;
      iy = iincy >= 0 ? 0 : (1 - nn) * iincy;
      for (i = 0; i < nn; i++)
      {
        sy[iy] += ssa * sx[ix];
        ix += iincx;
        iy += iincy;
      }
    }
  }

  return 0;
} /* daxpy_ */

#ifdef __cplusplus
}
#endif


================================================
FILE: Matlab/liblinear-1.96/blas/ddot.c
================================================
#include "blas.h"

#ifdef __cplusplus
extern "C" {
#endif

double ddot_(int *n, double *sx, int *incx, double *sy, int *incy)
{
  long int i, m, nn, iincx, iincy;
  double stemp;
  long int ix, iy;

  /* forms the dot product of two vectors.   
     uses unrolled loops for increments equal to one.   
     jack dongarra, linpack, 3/11/78.   
     modified 12/3/93, array(1) declarations changed to array(*) */

  /* Dereference inputs */
  nn = *n;
  iincx = *incx;
  iincy = *incy;

  stemp = 0.0;
  if (nn > 0)
  {
    if (iincx == 1 && iincy == 1) /* code for both increments equal to 1 */
    {
      m = nn-4;
      for (i = 0; i < m; i += 5)
        stemp += sx[i] * sy[i] + sx[i+1] * sy[i+1] + sx[i+2] * sy[i+2] +
                 sx[i+3] * sy[i+3] + sx[i+4] * sy[i+4];

      for ( ; i < nn; i++)        /* clean-up loop */
        stemp += sx[i] * sy[i];
    }
    else /* code for unequal increments or equal increments not equal to 1 */
    {
      ix = 0;
      iy = 0;
      if (iincx < 0)
        ix = (1 - nn) * iincx;
      if (iincy < 0)
        iy = (1 - nn) * iincy;
      for (i = 0; i < nn; i++)
      {
        stemp += sx[ix] * sy[iy];
        ix += iincx;
        iy += iincy;
      }
    }
  }

  return stemp;
} /* ddot_ */

#ifdef __cplusplus
}
#endif


================================================
FILE: Matlab/liblinear-1.96/blas/dnrm2.c
================================================
#include <math.h>  /* Needed for fabs() and sqrt() */
#include "blas.h"

#ifdef __cplusplus
extern "C" {
#endif

double dnrm2_(int *n, double *x, int *incx)
{
  long int ix, nn, iincx;
  double norm, scale, absxi, ssq, temp;

/*  DNRM2 returns the euclidean norm of a vector via the function   
    name, so that   

       DNRM2 := sqrt( x'*x )   

    -- This version written on 25-October-1982.   
       Modified on 14-October-1993 to inline the call to SLASSQ.   
       Sven Hammarling, Nag Ltd.   */

  /* Dereference inputs */
  nn = *n;
  iincx = *incx;

  if( nn > 0 && iincx > 0 )
  {
    if (nn == 1)
    {
      norm = fabs(x[0]);
    }  
    else
    {
      scale = 0.0;
      ssq = 1.0;

      /* The following loop is equivalent to this call to the LAPACK 
         auxiliary routine:   CALL SLASSQ( N, X, INCX, SCALE, SSQ ) */

      for (ix=(nn-1)*iincx; ix>=0; ix-=iincx)
      {
        if (x[ix] != 0.0)
        {
          absxi = fabs(x[ix]);
          if (scale < absxi)
          {
            temp = scale / absxi;
            ssq = ssq * (temp * temp) + 1.0;
            scale = absxi;
          }
          else
          {
            temp = absxi / scale;
            ssq += temp * temp;
          }
        }
      }
      norm = scale * sqrt(ssq);
    }
  }
  else
    norm = 0.0;

  return norm;

} /* dnrm2_ */

#ifdef __cplusplus
}
#endif


================================================
FILE: Matlab/liblinear-1.96/blas/dscal.c
================================================
#include "blas.h"

#ifdef __cplusplus
extern "C" {
#endif

int dscal_(int *n, double *sa, double *sx, int *incx)
{
  long int i, m, nincx, nn, iincx;
  double ssa;

  /* scales a vector by a constant.   
     uses unrolled loops for increment equal to 1.   
     jack dongarra, linpack, 3/11/78.   
     modified 3/93 to return if incx .le. 0.   
     modified 12/3/93, array(1) declarations changed to array(*) */

  /* Dereference inputs */
  nn = *n;
  iincx = *incx;
  ssa = *sa;

  if (nn > 0 && iincx > 0)
  {
    if (iincx == 1) /* code for increment equal to 1 */
    {
      m = nn-4;
      for (i = 0; i < m; i += 5)
      {
        sx[i] = ssa * sx[i];
        sx[i+1] = ssa * sx[i+1];
        sx[i+2] = ssa * sx[i+2];
        sx[i+3] = ssa * sx[i+3];
        sx[i+4] = ssa * sx[i+4];
      }
      for ( ; i < nn; ++i) /* clean-up loop */
        sx[i] = ssa * sx[i];
    }
    else /* code for increment not equal to 1 */
    {
      nincx = nn * iincx;
      for (i = 0; i < nincx; i += iincx)
        sx[i] = ssa * sx[i];
    }
  }

  return 0;
} /* dscal_ */

#ifdef __cplusplus
}
#endif


================================================
FILE: Matlab/liblinear-1.96/heart_scale
================================================
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================================================
FILE: Matlab/liblinear-1.96/linear.cpp
================================================
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <stdarg.h>
#include <locale.h>
#include "linear.h"
#include "tron.h"
typedef signed char schar;
template <class T> static inline void swap(T& x, T& y) { T t=x; x=y; y=t; }
#ifndef min
template <class T> static inline T min(T x,T y) { return (x<y)?x:y; }
#endif
#ifndef max
template <class T> static inline T max(T x,T y) { return (x>y)?x:y; }
#endif
template <class S, class T> static inline void clone(T*& dst, S* src, int n)
{
	dst = new T[n];
	memcpy((void *)dst,(void *)src,sizeof(T)*n);
}
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
#define INF HUGE_VAL

static void print_string_stdout(const char *s)
{
	fputs(s,stdout);
	fflush(stdout);
}

static void (*liblinear_print_string) (const char *) = &print_string_stdout;

#if 1
static void info(const char *fmt,...)
{
	char buf[BUFSIZ];
	va_list ap;
	va_start(ap,fmt);
	vsprintf(buf,fmt,ap);
	va_end(ap);
	(*liblinear_print_string)(buf);
}
#else
static void info(const char *fmt,...) {}
#endif

class l2r_lr_fun: public function
{
public:
	l2r_lr_fun(const problem *prob, double *C);
	~l2r_lr_fun();

	double fun(double *w);
	void grad(double *w, double *g);
	void Hv(double *s, double *Hs);

	int get_nr_variable(void);

private:
	void Xv(double *v, double *Xv);
	void XTv(double *v, double *XTv);

	double *C;
	double *z;
	double *D;
	const problem *prob;
};

l2r_lr_fun::l2r_lr_fun(const problem *prob, double *C)
{
	int l=prob->l;

	this->prob = prob;

	z = new double[l];
	D = new double[l];
	this->C = C;
}

l2r_lr_fun::~l2r_lr_fun()
{
	delete[] z;
	delete[] D;
}


double l2r_lr_fun::fun(double *w)
{
	int i;
	double f=0;
	double *y=prob->y;
	int l=prob->l;
	int w_size=get_nr_variable();

	Xv(w, z);

	for(i=0;i<w_size;i++)
		f += w[i]*w[i];
	f /= 2.0;
	for(i=0;i<l;i++)
	{
		double yz = y[i]*z[i];
		if (yz >= 0)
			f += C[i]*log(1 + exp(-yz));
		else
			f += C[i]*(-yz+log(1 + exp(yz)));
	}

	return(f);
}

void l2r_lr_fun::grad(double *w, double *g)
{
	int i;
	double *y=prob->y;
	int l=prob->l;
	int w_size=get_nr_variable();

	for(i=0;i<l;i++)
	{
		z[i] = 1/(1 + exp(-y[i]*z[i]));
		D[i] = z[i]*(1-z[i]);
		z[i] = C[i]*(z[i]-1)*y[i];
	}
	XTv(z, g);

	for(i=0;i<w_size;i++)
		g[i] = w[i] + g[i];
}

int l2r_lr_fun::get_nr_variable(void)
{
	return prob->n;
}

void l2r_lr_fun::Hv(double *s, double *Hs)
{
	int i;
	int l=prob->l;
	int w_size=get_nr_variable();
	double *wa = new double[l];

	Xv(s, wa);
	for(i=0;i<l;i++)
		wa[i] = C[i]*D[i]*wa[i];

	XTv(wa, Hs);
	for(i=0;i<w_size;i++)
		Hs[i] = s[i] + Hs[i];
	delete[] wa;
}

void l2r_lr_fun::Xv(double *v, double *Xv)
{
	int i;
	int l=prob->l;
	feature_node **x=prob->x;

	for(i=0;i<l;i++)
	{
		feature_node *s=x[i];
		Xv[i]=0;
		while(s->index!=-1)
		{
			Xv[i]+=v[s->index-1]*s->value;
			s++;
		}
	}
}

void l2r_lr_fun::XTv(double *v, double *XTv)
{
	int i;
	int l=prob->l;
	int w_size=get_nr_variable();
	feature_node **x=prob->x;

	for(i=0;i<w_size;i++)
		XTv[i]=0;
	for(i=0;i<l;i++)
	{
		feature_node *s=x[i];
		while(s->index!=-1)
		{
			XTv[s->index-1]+=v[i]*s->value;
			s++;
		}
	}
}

class l2r_l2_svc_fun: public function
{
public:
	l2r_l2_svc_fun(const problem *prob, double *C);
	~l2r_l2_svc_fun();

	double fun(double *w);
	void grad(double *w, double *g);
	void Hv(double *s, double *Hs);

	int get_nr_variable(void);

protected:
	void Xv(double *v, double *Xv);
	void subXv(double *v, double *Xv);
	void subXTv(double *v, double *XTv);

	double *C;
	double *z;
	double *D;
	int *I;
	int sizeI;
	const problem *prob;
};

l2r_l2_svc_fun::l2r_l2_svc_fun(const problem *prob, double *C)
{
	int l=prob->l;

	this->prob = prob;

	z = new double[l];
	D = new double[l];
	I = new int[l];
	this->C = C;
}

l2r_l2_svc_fun::~l2r_l2_svc_fun()
{
	delete[] z;
	delete[] D;
	delete[] I;
}

double l2r_l2_svc_fun::fun(double *w)
{
	int i;
	double f=0;
	double *y=prob->y;
	int l=prob->l;
	int w_size=get_nr_variable();

	Xv(w, z);

	for(i=0;i<w_size;i++)
		f += w[i]*w[i];
	f /= 2.0;
	for(i=0;i<l;i++)
	{
		z[i] = y[i]*z[i];
		double d = 1-z[i];
		if (d > 0)
			f += C[i]*d*d;
	}

	return(f);
}

void l2r_l2_svc_fun::grad(double *w, double *g)
{
	int i;
	double *y=prob->y;
	int l=prob->l;
	int w_size=get_nr_variable();

	sizeI = 0;
	for (i=0;i<l;i++)
		if (z[i] < 1)
		{
			z[sizeI] = C[i]*y[i]*(z[i]-1);
			I[sizeI] = i;
			sizeI++;
		}
	subXTv(z, g);

	for(i=0;i<w_size;i++)
		g[i] = w[i] + 2*g[i];
}

int l2r_l2_svc_fun::get_nr_variable(void)
{
	return prob->n;
}

void l2r_l2_svc_fun::Hv(double *s, double *Hs)
{
	int i;
	int w_size=get_nr_variable();
	double *wa = new double[sizeI];

	subXv(s, wa);
	for(i=0;i<sizeI;i++)
		wa[i] = C[I[i]]*wa[i];

	subXTv(wa, Hs);
	for(i=0;i<w_size;i++)
		Hs[i] = s[i] + 2*Hs[i];
	delete[] wa;
}

void l2r_l2_svc_fun::Xv(double *v, double *Xv)
{
	int i;
	int l=prob->l;
	feature_node **x=prob->x;

	for(i=0;i<l;i++)
	{
		feature_node *s=x[i];
		Xv[i]=0;
		while(s->index!=-1)
		{
			Xv[i]+=v[s->index-1]*s->value;
			s++;
		}
	}
}

void l2r_l2_svc_fun::subXv(double *v, double *Xv)
{
	int i;
	feature_node **x=prob->x;

	for(i=0;i<sizeI;i++)
	{
		feature_node *s=x[I[i]];
		Xv[i]=0;
		while(s->index!=-1)
		{
			Xv[i]+=v[s->index-1]*s->value;
			s++;
		}
	}
}

void l2r_l2_svc_fun::subXTv(double *v, double *XTv)
{
	int i;
	int w_size=get_nr_variable();
	feature_node **x=prob->x;

	for(i=0;i<w_size;i++)
		XTv[i]=0;
	for(i=0;i<sizeI;i++)
	{
		feature_node *s=x[I[i]];
		while(s->index!=-1)
		{
			XTv[s->index-1]+=v[i]*s->value;
			s++;
		}
	}
}

class l2r_l2_svr_fun: public l2r_l2_svc_fun
{
public:
	l2r_l2_svr_fun(const problem *prob, double *C, double p);

	double fun(double *w);
	void grad(double *w, double *g);

private:
	double p;
};

l2r_l2_svr_fun::l2r_l2_svr_fun(const problem *prob, double *C, double p):
	l2r_l2_svc_fun(prob, C)
{
	this->p = p;
}

double l2r_l2_svr_fun::fun(double *w)
{
	int i;
	double f=0;
	double *y=prob->y;
	int l=prob->l;
	int w_size=get_nr_variable();
	double d;

	Xv(w, z);

	for(i=0;i<w_size;i++)
		f += w[i]*w[i];
	f /= 2;
	for(i=0;i<l;i++)
	{
		d = z[i] - y[i];
		if(d < -p)
			f += C[i]*(d+p)*(d+p);
		else if(d > p)
			f += C[i]*(d-p)*(d-p);
	}

	return(f);
}

void l2r_l2_svr_fun::grad(double *w, double *g)
{
	int i;
	double *y=prob->y;
	int l=prob->l;
	int w_size=get_nr_variable();
	double d;

	sizeI = 0;
	for(i=0;i<l;i++)
	{
		d = z[i] - y[i];

		// generate index set I
		if(d < -p)
		{
			z[sizeI] = C[i]*(d+p);
			I[sizeI] = i;
			sizeI++;
		}
		else if(d > p)
		{
			z[sizeI] = C[i]*(d-p);
			I[sizeI] = i;
			sizeI++;
		}

	}
	subXTv(z, g);

	for(i=0;i<w_size;i++)
		g[i] = w[i] + 2*g[i];
}

// A coordinate descent algorithm for 
// multi-class support vector machines by Crammer and Singer
//
//  min_{\alpha}  0.5 \sum_m ||w_m(\alpha)||^2 + \sum_i \sum_m e^m_i alpha^m_i
//    s.t.     \alpha^m_i <= C^m_i \forall m,i , \sum_m \alpha^m_i=0 \forall i
// 
//  where e^m_i = 0 if y_i  = m,
//        e^m_i = 1 if y_i != m,
//  C^m_i = C if m  = y_i, 
//  C^m_i = 0 if m != y_i, 
//  and w_m(\alpha) = \sum_i \alpha^m_i x_i 
//
// Given: 
// x, y, C
// eps is the stopping tolerance
//
// solution will be put in w
//
// See Appendix of LIBLINEAR paper, Fan et al. (2008)

#define GETI(i) ((int) prob->y[i])
// To support weights for instances, use GETI(i) (i)

class Solver_MCSVM_CS
{
	public:
		Solver_MCSVM_CS(const problem *prob, int nr_class, double *C, double eps=0.1, int max_iter=100000);
		~Solver_MCSVM_CS();
		void Solve(double *w);
	private:
		void solve_sub_problem(double A_i, int yi, double C_yi, int active_i, double *alpha_new);
		bool be_shrunk(int i, int m, int yi, double alpha_i, double minG);
		double *B, *C, *G;
		int w_size, l;
		int nr_class;
		int max_iter;
		double eps;
		const problem *prob;
};

Solver_MCSVM_CS::Solver_MCSVM_CS(const problem *prob, int nr_class, double *weighted_C, double eps, int max_iter)
{
	this->w_size = prob->n;
	this->l = prob->l;
	this->nr_class = nr_class;
	this->eps = eps;
	this->max_iter = max_iter;
	this->prob = prob;
	this->B = new double[nr_class];
	this->G = new double[nr_class];
	this->C = weighted_C;
}

Solver_MCSVM_CS::~Solver_MCSVM_CS()
{
	delete[] B;
	delete[] G;
}

int compare_double(const void *a, const void *b)
{
	if(*(double *)a > *(double *)b)
		return -1;
	if(*(double *)a < *(double *)b)
		return 1;
	return 0;
}

void Solver_MCSVM_CS::solve_sub_problem(double A_i, int yi, double C_yi, int active_i, double *alpha_new)
{
	int r;
	double *D;

	clone(D, B, active_i);
	if(yi < active_i)
		D[yi] += A_i*C_yi;
	qsort(D, active_i, sizeof(double), compare_double);

	double beta = D[0] - A_i*C_yi;
	for(r=1;r<active_i && beta<r*D[r];r++)
		beta += D[r];
	beta /= r;

	for(r=0;r<active_i;r++)
	{
		if(r == yi)
			alpha_new[r] = min(C_yi, (beta-B[r])/A_i);
		else
			alpha_new[r] = min((double)0, (beta - B[r])/A_i);
	}
	delete[] D;
}

bool Solver_MCSVM_CS::be_shrunk(int i, int m, int yi, double alpha_i, double minG)
{
	double bound = 0;
	if(m == yi)
		bound = C[GETI(i)];
	if(alpha_i == bound && G[m] < minG)
		return true;
	return false;
}

void Solver_MCSVM_CS::Solve(double *w)
{
	int i, m, s;
	int iter = 0;
	double *alpha =  new double[l*nr_class];
	double *alpha_new = new double[nr_class];
	int *index = new int[l];
	double *QD = new double[l];
	int *d_ind = new int[nr_class];
	double *d_val = new double[nr_class];
	int *alpha_index = new int[nr_class*l];
	int *y_index = new int[l];
	int active_size = l;
	int *active_size_i = new int[l];
	double eps_shrink = max(10.0*eps, 1.0); // stopping tolerance for shrinking
	bool start_from_all = true;

	// Initial alpha can be set here. Note that 
	// sum_m alpha[i*nr_class+m] = 0, for all i=1,...,l-1
	// alpha[i*nr_class+m] <= C[GETI(i)] if prob->y[i] == m
	// alpha[i*nr_class+m] <= 0 if prob->y[i] != m
	// If initial alpha isn't zero, uncomment the for loop below to initialize w
	for(i=0;i<l*nr_class;i++)
		alpha[i] = 0;

	for(i=0;i<w_size*nr_class;i++)
		w[i] = 0;
	for(i=0;i<l;i++)
	{
		for(m=0;m<nr_class;m++)
			alpha_index[i*nr_class+m] = m;
		feature_node *xi = prob->x[i];
		QD[i] = 0;
		while(xi->index != -1)
		{
			double val = xi->value;
			QD[i] += val*val;

			// Uncomment the for loop if initial alpha isn't zero
			// for(m=0; m<nr_class; m++)
			//	w[(xi->index-1)*nr_class+m] += alpha[i*nr_class+m]*val;
			xi++;
		}
		active_size_i[i] = nr_class;
		y_index[i] = (int)prob->y[i];
		index[i] = i;
	}

	while(iter < max_iter)
	{
		double stopping = -INF;
		for(i=0;i<active_size;i++)
		{
			int j = i+rand()%(active_size-i);
			swap(index[i], index[j]);
		}
		for(s=0;s<active_size;s++)
		{
			i = index[s];
			double Ai = QD[i];
			double *alpha_i = &alpha[i*nr_class];
			int *alpha_index_i = &alpha_index[i*nr_class];

			if(Ai > 0)
			{
				for(m=0;m<active_size_i[i];m++)
					G[m] = 1;
				if(y_index[i] < active_size_i[i])
					G[y_index[i]] = 0;

				feature_node *xi = prob->x[i];
				while(xi->index!= -1)
				{
					double *w_i = &w[(xi->index-1)*nr_class];
					for(m=0;m<active_size_i[i];m++)
						G[m] += w_i[alpha_index_i[m]]*(xi->value);
					xi++;
				}

				double minG = INF;
				double maxG = -INF;
				for(m=0;m<active_size_i[i];m++)
				{
					if(alpha_i[alpha_index_i[m]] < 0 && G[m] < minG)
						minG = G[m];
					if(G[m] > maxG)
						maxG = G[m];
				}
				if(y_index[i] < active_size_i[i])
					if(alpha_i[(int) prob->y[i]] < C[GETI(i)] && G[y_index[i]] < minG)
						minG = G[y_index[i]];

				for(m=0;m<active_size_i[i];m++)
				{
					if(be_shrunk(i, m, y_index[i], alpha_i[alpha_index_i[m]], minG))
					{
						active_size_i[i]--;
						while(active_size_i[i]>m)
						{
							if(!be_shrunk(i, active_size_i[i], y_index[i],
											alpha_i[alpha_index_i[active_size_i[i]]], minG))
							{
								swap(alpha_index_i[m], alpha_index_i[active_size_i[i]]);
								swap(G[m], G[active_size_i[i]]);
								if(y_index[i] == active_size_i[i])
									y_index[i] = m;
								else if(y_index[i] == m)
									y_index[i] = active_size_i[i];
								break;
							}
							active_size_i[i]--;
						}
					}
				}

				if(active_size_i[i] <= 1)
				{
					active_size--;
					swap(index[s], index[active_size]);
					s--;
					continue;
				}

				if(maxG-minG <= 1e-12)
					continue;
				else
					stopping = max(maxG - minG, stopping);

				for(m=0;m<active_size_i[i];m++)
					B[m] = G[m] - Ai*alpha_i[alpha_index_i[m]] ;

				solve_sub_problem(Ai, y_index[i], C[GETI(i)], active_size_i[i], alpha_new);
				int nz_d = 0;
				for(m=0;m<active_size_i[i];m++)
				{
					double d = alpha_new[m] - alpha_i[alpha_index_i[m]];
					alpha_i[alpha_index_i[m]] = alpha_new[m];
					if(fabs(d) >= 1e-12)
					{
						d_ind[nz_d] = alpha_index_i[m];
						d_val[nz_d] = d;
						nz_d++;
					}
				}

				xi = prob->x[i];
				while(xi->index != -1)
				{
					double *w_i = &w[(xi->index-1)*nr_class];
					for(m=0;m<nz_d;m++)
						w_i[d_ind[m]] += d_val[m]*xi->value;
					xi++;
				}
			}
		}

		iter++;
		if(iter % 10 == 0)
		{
			info(".");
		}

		if(stopping < eps_shrink)
		{
			if(stopping < eps && start_from_all == true)
				break;
			else
			{
				active_size = l;
				for(i=0;i<l;i++)
					active_size_i[i] = nr_class;
				info("*");
				eps_shrink = max(eps_shrink/2, eps);
				start_from_all = true;
			}
		}
		else
			start_from_all = false;
	}

	info("\noptimization finished, #iter = %d\n",iter);
	if (iter >= max_iter)
		info("\nWARNING: reaching max number of iterations\n");

	// calculate objective value
	double v = 0;
	int nSV = 0;
	for(i=0;i<w_size*nr_class;i++)
		v += w[i]*w[i];
	v = 0.5*v;
	for(i=0;i<l*nr_class;i++)
	{
		v += alpha[i];
		if(fabs(alpha[i]) > 0)
			nSV++;
	}
	for(i=0;i<l;i++)
		v -= alpha[i*nr_class+(int)prob->y[i]];
	info("Objective value = %lf\n",v);
	info("nSV = %d\n",nSV);

	delete [] alpha;
	delete [] alpha_new;
	delete [] index;
	delete [] QD;
	delete [] d_ind;
	delete [] d_val;
	delete [] alpha_index;
	delete [] y_index;
	delete [] active_size_i;
}

// A coordinate descent algorithm for 
// L1-loss and L2-loss SVM dual problems
//
//  min_\alpha  0.5(\alpha^T (Q + D)\alpha) - e^T \alpha,
//    s.t.      0 <= \alpha_i <= upper_bound_i,
// 
//  where Qij = yi yj xi^T xj and
//  D is a diagonal matrix 
//
// In L1-SVM case:
// 		upper_bound_i = Cp if y_i = 1
// 		upper_bound_i = Cn if y_i = -1
// 		D_ii = 0
// In L2-SVM case:
// 		upper_bound_i = INF
// 		D_ii = 1/(2*Cp)	if y_i = 1
// 		D_ii = 1/(2*Cn)	if y_i = -1
//
// Given: 
// x, y, Cp, Cn
// eps is the stopping tolerance
//
// solution will be put in w
// 
// See Algorithm 3 of Hsieh et al., ICML 2008

#undef GETI
#define GETI(i) (y[i]+1)
// To support weights for instances, use GETI(i) (i)

static void solve_l2r_l1l2_svc(
	const problem *prob, double *w, double eps,
	double Cp, double Cn, int solver_type)
{
	int l = prob->l;
	int w_size = prob->n;
	int i, s, iter = 0;
	double C, d, G;
	double *QD = new double[l];
	int max_iter = 1000;
	int *index = new int[l];
	double *alpha = new double[l];
	schar *y = new schar[l];
	int active_size = l;

	// PG: projected gradient, for shrinking and stopping
	double PG;
	double PGmax_old = INF;
	double PGmin_old = -INF;
	double PGmax_new, PGmin_new;

	// default solver_type: L2R_L2LOSS_SVC_DUAL
	double diag[3] = {0.5/Cn, 0, 0.5/Cp};
	double upper_bound[3] = {INF, 0, INF};
	if(solver_type == L2R_L1LOSS_SVC_DUAL)
	{
		diag[0] = 0;
		diag[2] = 0;
		upper_bound[0] = Cn;
		upper_bound[2] = Cp;
	}

	for(i=0; i<l; i++)
	{
		if(prob->y[i] > 0)
		{
			y[i] = +1;
		}
		else
		{
			y[i] = -1;
		}
	}

	// Initial alpha can be set here. Note that
	// 0 <= alpha[i] <= upper_bound[GETI(i)]
	for(i=0; i<l; i++)
		alpha[i] = 0;

	for(i=0; i<w_size; i++)
		w[i] = 0;
	for(i=0; i<l; i++)
	{
		QD[i] = diag[GETI(i)];

		feature_node *xi = prob->x[i];
		while (xi->index != -1)
		{
			double val = xi->value;
			QD[i] += val*val;
			w[xi->index-1] += y[i]*alpha[i]*val;
			xi++;
		}
		index[i] = i;
	}

	while (iter < max_iter)
	{
		PGmax_new = -INF;
		PGmin_new = INF;

		for (i=0; i<active_size; i++)
		{
			int j = i+rand()%(active_size-i);
			swap(index[i], index[j]);
		}

		for (s=0; s<active_size; s++)
		{
			i = index[s];
			G = 0;
			schar yi = y[i];

			feature_node *xi = prob->x[i];
			while(xi->index!= -1)
			{
				G += w[xi->index-1]*(xi->value);
				xi++;
			}
			G = G*yi-1;

			C = upper_bound[GETI(i)];
			G += alpha[i]*diag[GETI(i)];

			PG = 0;
			if (alpha[i] == 0)
			{
				if (G > PGmax_old)
				{
					active_size--;
					swap(index[s], index[active_size]);
					s--;
					continue;
				}
				else if (G < 0)
					PG = G;
			}
			else if (alpha[i] == C)
			{
				if (G < PGmin_old)
				{
					active_size--;
					swap(index[s], index[active_size]);
					s--;
					continue;
				}
				else if (G > 0)
					PG = G;
			}
			else
				PG = G;

			PGmax_new = max(PGmax_new, PG);
			PGmin_new = min(PGmin_new, PG);

			if(fabs(PG) > 1.0e-12)
			{
				double alpha_old = alpha[i];
				alpha[i] = min(max(alpha[i] - G/QD[i], 0.0), C);
				d = (alpha[i] - alpha_old)*yi;
				xi = prob->x[i];
				while (xi->index != -1)
				{
					w[xi->index-1] += d*xi->value;
					xi++;
				}
			}
		}

		iter++;
		if(iter % 10 == 0)
			info(".");

		if(PGmax_new - PGmin_new <= eps)
		{
			if(active_size == l)
				break;
			else
			{
				active_size = l;
				info("*");
				PGmax_old = INF;
				PGmin_old = -INF;
				continue;
			}
		}
		PGmax_old = PGmax_new;
		PGmin_old = PGmin_new;
		if (PGmax_old <= 0)
			PGmax_old = INF;
		if (PGmin_old >= 0)
			PGmin_old = -INF;
	}

	info("\noptimization finished, #iter = %d\n",iter);
	if (iter >= max_iter)
		info("\nWARNING: reaching max number of iterations\nUsing -s 2 may be faster (also see FAQ)\n\n");

	// calculate objective value

	double v = 0;
	int nSV = 0;
	for(i=0; i<w_size; i++)
		v += w[i]*w[i];
	for(i=0; i<l; i++)
	{
		v += alpha[i]*(alpha[i]*diag[GETI(i)] - 2);
		if(alpha[i] > 0)
			++nSV;
	}
	info("Objective value = %lf\n",v/2);
	info("nSV = %d\n",nSV);

	delete [] QD;
	delete [] alpha;
	delete [] y;
	delete [] index;
}


// A coordinate descent algorithm for 
// L1-loss and L2-loss epsilon-SVR dual problem
//
//  min_\beta  0.5\beta^T (Q + diag(lambda)) \beta - p \sum_{i=1}^l|\beta_i| + \sum_{i=1}^l yi\beta_i,
//    s.t.      -upper_bound_i <= \beta_i <= upper_bound_i,
// 
//  where Qij = xi^T xj and
//  D is a diagonal matrix 
//
// In L1-SVM case:
// 		upper_bound_i = C
// 		lambda_i = 0
// In L2-SVM case:
// 		upper_bound_i = INF
// 		lambda_i = 1/(2*C)
//
// Given: 
// x, y, p, C
// eps is the stopping tolerance
//
// solution will be put in w
//
// See Algorithm 4 of Ho and Lin, 2012   

#undef GETI
#define GETI(i) (0)
// To support weights for instances, use GETI(i) (i)

static void solve_l2r_l1l2_svr(
	const problem *prob, double *w, const parameter *param,
	int solver_type)
{
	int l = prob->l;
	double C = param->C;
	double p = param->p;
	int w_size = prob->n;
	double eps = param->eps;
	int i, s, iter = 0;
	int max_iter = 1000;
	int active_size = l;
	int *index = new int[l];

	double d, G, H;
	double Gmax_old = INF;
	double Gmax_new, Gnorm1_new;
	double Gnorm1_init = -1.0; // Gnorm1_init is initialized at the first iteration
	double *beta = new double[l];
	double *QD = new double[l];
	double *y = prob->y;

	// L2R_L2LOSS_SVR_DUAL
	double lambda[1], upper_bound[1];
	lambda[0] = 0.5/C;
	upper_bound[0] = INF;

	if(solver_type == L2R_L1LOSS_SVR_DUAL)
	{
		lambda[0] = 0;
		upper_bound[0] = C;
	}

	// Initial beta can be set here. Note that
	// -upper_bound <= beta[i] <= upper_bound
	for(i=0; i<l; i++)
		beta[i] = 0;

	for(i=0; i<w_size; i++)
		w[i] = 0;
	for(i=0; i<l; i++)
	{
		QD[i] = 0;
		feature_node *xi = prob->x[i];
		while(xi->index != -1)
		{
			double val = xi->value;
			QD[i] += val*val;
			w[xi->index-1] += beta[i]*val;
			xi++;
		}

		index[i] = i;
	}


	while(iter < max_iter)
	{
		Gmax_new = 0;
		Gnorm1_new = 0;

		for(i=0; i<active_size; i++)
		{
			int j = i+rand()%(active_size-i);
			swap(index[i], index[j]);
		}

		for(s=0; s<active_size; s++)
		{
			i = index[s];
			G = -y[i] + lambda[GETI(i)]*beta[i];
			H = QD[i] + lambda[GETI(i)];

			feature_node *xi = prob->x[i];
			while(xi->index != -1)
			{
				int ind = xi->index-1;
				double val = xi->value;
				G += val*w[ind];
				xi++;
			}

			double Gp = G+p;
			double Gn = G-p;
			double violation = 0;
			if(beta[i] == 0)
			{
				if(Gp < 0)
					violation = -Gp;
				else if(Gn > 0)
					violation = Gn;
				else if(Gp>Gmax_old && Gn<-Gmax_old)
				{
					active_size--;
					swap(index[s], index[active_size]);
					s--;
					continue;
				}
			}
			else if(beta[i] >= upper_bound[GETI(i)])
			{
				if(Gp > 0)
					violation = Gp;
				else if(Gp < -Gmax_old)
				{
					active_size--;
					swap(index[s], index[active_size]);
					s--;
					continue;
				}
			}
			else if(beta[i] <= -upper_bound[GETI(i)])
			{
				if(Gn < 0)
					violation = -Gn;
				else if(Gn > Gmax_old)
				{
					active_size--;
					swap(index[s], index[active_size]);
					s--;
					continue;
				}
			}
			else if(beta[i] > 0)
				violation = fabs(Gp);
			else
				violation = fabs(Gn);

			Gmax_new = max(Gmax_new, violation);
			Gnorm1_new += violation;

			// obtain Newton direction d
			if(Gp < H*beta[i])
				d = -Gp/H;
			else if(Gn > H*beta[i])
				d = -Gn/H;
			else
				d = -beta[i];

			if(fabs(d) < 1.0e-12)
				continue;

			double beta_old = beta[i];
			beta[i] = min(max(beta[i]+d, -upper_bound[GETI(i)]), upper_bound[GETI(i)]);
			d = beta[i]-beta_old;

			if(d != 0)
			{
				xi = prob->x[i];
				while(xi->index != -1)
				{
					w[xi->index-1] += d*xi->value;
					xi++;
				}
			}
		}

		if(iter == 0)
			Gnorm1_init = Gnorm1_new;
		iter++;
		if(iter % 10 == 0)
			info(".");

		if(Gnorm1_new <= eps*Gnorm1_init)
		{
			if(active_size == l)
				break;
			else
			{
				active_size = l;
				info("*");
				Gmax_old = INF;
				continue;
			}
		}

		Gmax_old = Gmax_new;
	}

	info("\noptimization finished, #iter = %d\n", iter);
	if(iter >= max_iter)
		info("\nWARNING: reaching max number of iterations\nUsing -s 11 may be faster\n\n");

	// calculate objective value
	double v = 0;
	int nSV = 0;
	for(i=0; i<w_size; i++)
		v += w[i]*w[i];
	v = 0.5*v;
	for(i=0; i<l; i++)
	{
		v += p*fabs(beta[i]) - y[i]*beta[i] + 0.5*lambda[GETI(i)]*beta[i]*beta[i];
		if(beta[i] != 0)
			nSV++;
	}

	info("Objective value = %lf\n", v);
	info("nSV = %d\n",nSV);

	delete [] beta;
	delete [] QD;
	delete [] index;
}


// A coordinate descent algorithm for 
// the dual of L2-regularized logistic regression problems
//
//  min_\alpha  0.5(\alpha^T Q \alpha) + \sum \alpha_i log (\alpha_i) + (upper_bound_i - \alpha_i) log (upper_bound_i - \alpha_i),
//    s.t.      0 <= \alpha_i <= upper_bound_i,
// 
//  where Qij = yi yj xi^T xj and 
//  upper_bound_i = Cp if y_i = 1
//  upper_bound_i = Cn if y_i = -1
//
// Given: 
// x, y, Cp, Cn
// eps is the stopping tolerance
//
// solution will be put in w
//
// See Algorithm 5 of Yu et al., MLJ 2010

#undef GETI
#define GETI(i) (y[i]+1)
// To support weights for instances, use GETI(i) (i)

void solve_l2r_lr_dual(const problem *prob, double *w, double eps, double Cp, double Cn)
{
	int l = prob->l;
	int w_size = prob->n;
	int i, s, iter = 0;
	double *xTx = new double[l];
	int max_iter = 1000;
	int *index = new int[l];	
	double *alpha = new double[2*l]; // store alpha and C - alpha
	schar *y = new schar[l];
	int max_inner_iter = 100; // for inner Newton
	double innereps = 1e-2;
	double innereps_min = min(1e-8, eps);
	double upper_bound[3] = {Cn, 0, Cp};

	for(i=0; i<l; i++)
	{
		if(prob->y[i] > 0)
		{
			y[i] = +1;
		}
		else
		{
			y[i] = -1;
		}
	}
	
	// Initial alpha can be set here. Note that
	// 0 < alpha[i] < upper_bound[GETI(i)]
	// alpha[2*i] + alpha[2*i+1] = upper_bound[GETI(i)]
	for(i=0; i<l; i++)
	{
		alpha[2*i] = min(0.001*upper_bound[GETI(i)], 1e-8);
		alpha[2*i+1] = upper_bound[GETI(i)] - alpha[2*i];
	}

	for(i=0; i<w_size; i++)
		w[i] = 0;
	for(i=0; i<l; i++)
	{
		xTx[i] = 0;
		feature_node *xi = prob->x[i];
		while (xi->index != -1)
		{
			double val = xi->value;
			xTx[i] += val*val;
			w[xi->index-1] += y[i]*alpha[2*i]*val;
			xi++;
		}
		index[i] = i;
	}

	while (iter < max_iter)
	{
		for (i=0; i<l; i++)
		{
			int j = i+rand()%(l-i);
			swap(index[i], index[j]);
		}
		int newton_iter = 0;
		double Gmax = 0;
		for (s=0; s<l; s++)
		{
			i = index[s];
			schar yi = y[i];
			double C = upper_bound[GETI(i)];
			double ywTx = 0, xisq = xTx[i];
			feature_node *xi = prob->x[i];
			while (xi->index != -1)
			{
				ywTx += w[xi->index-1]*xi->value;
				xi++;
			}
			ywTx *= y[i];
			double a = xisq, b = ywTx;

			// Decide to minimize g_1(z) or g_2(z)
			int ind1 = 2*i, ind2 = 2*i+1, sign = 1;
			if(0.5*a*(alpha[ind2]-alpha[ind1])+b < 0)
			{
				ind1 = 2*i+1;
				ind2 = 2*i;
				sign = -1;
			}

			//  g_t(z) = z*log(z) + (C-z)*log(C-z) + 0.5a(z-alpha_old)^2 + sign*b(z-alpha_old)
			double alpha_old = alpha[ind1];
			double z = alpha_old;
			if(C - z < 0.5 * C)
				z = 0.1*z;
			double gp = a*(z-alpha_old)+sign*b+log(z/(C-z));
			Gmax = max(Gmax, fabs(gp));

			// Newton method on the sub-problem
			const double eta = 0.1; // xi in the paper
			int inner_iter = 0;
			while (inner_iter <= max_inner_iter)
			{
				if(fabs(gp) < innereps)
					break;
				double gpp = a + C/(C-z)/z;
				double tmpz = z - gp/gpp;
				if(tmpz <= 0)
					z *= eta;
				else // tmpz in (0, C)
					z = tmpz;
				gp = a*(z-alpha_old)+sign*b+log(z/(C-z));
				newton_iter++;
				inner_iter++;
			}

			if(inner_iter > 0) // update w
			{
				alpha[ind1] = z;
				alpha[ind2] = C-z;
				xi = prob->x[i];
				while (xi->index != -1)
				{
					w[xi->index-1] += sign*(z-alpha_old)*yi*xi->value;
					xi++;
				}
			}
		}

		iter++;
		if(iter % 10 == 0)
			info(".");

		if(Gmax < eps)
			break;

		if(newton_iter <= l/10)
			innereps = max(innereps_min, 0.1*innereps);

	}

	info("\noptimization finished, #iter = %d\n",iter);
	if (iter >= max_iter)
		info("\nWARNING: reaching max number of iterations\nUsing -s 0 may be faster (also see FAQ)\n\n");

	// calculate objective value

	double v = 0;
	for(i=0; i<w_size; i++)
		v += w[i] * w[i];
	v *= 0.5;
	for(i=0; i<l; i++)
		v += alpha[2*i] * log(alpha[2*i]) + alpha[2*i+1] * log(alpha[2*i+1])
			- upper_bound[GETI(i)] * log(upper_bound[GETI(i)]);
	info("Objective value = %lf\n", v);

	delete [] xTx;
	delete [] alpha;
	delete [] y;
	delete [] index;
}

// A coordinate descent algorithm for 
// L1-regularized L2-loss support vector classification
//
//  min_w \sum |wj| + C \sum max(0, 1-yi w^T xi)^2,
//
// Given: 
// x, y, Cp, Cn
// eps is the stopping tolerance
//
// solution will be put in w
//
// See Yuan et al. (2010) and appendix of LIBLINEAR paper, Fan et al. (2008)

#undef GETI
#define GETI(i) (y[i]+1)
// To support weights for instances, use GETI(i) (i)

static void solve_l1r_l2_svc(
	problem *prob_col, double *w, double eps,
	double Cp, double Cn)
{
	int l = prob_col->l;
	int w_size = prob_col->n;
	int j, s, iter = 0;
	int max_iter = 1000;
	int active_size = w_size;
	int max_num_linesearch = 20;

	double sigma = 0.01;
	double d, G_loss, G, H;
	double Gmax_old = INF;
	double Gmax_new, Gnorm1_new;
	double Gnorm1_init = -1.0; // Gnorm1_init is initialized at the first iteration
	double d_old, d_diff;
	double loss_old, loss_new;
	double appxcond, cond;

	int *index = new int[w_size];
	schar *y = new schar[l];
	double *b = new double[l]; // b = 1-ywTx
	double *xj_sq = new double[w_size];
	feature_node *x;

	double C[3] = {Cn,0,Cp};

	// Initial w can be set here.
	for(j=0; j<w_size; j++)
		w[j] = 0;

	for(j=0; j<l; j++)
	{
		b[j] = 1;
		if(prob_col->y[j] > 0)
			y[j] = 1;
		else
			y[j] = -1;
	}
	for(j=0; j<w_size; j++)
	{
		index[j] = j;
		xj_sq[j] = 0;
		x = prob_col->x[j];
		while(x->index != -1)
		{
			int ind = x->index-1;
			x->value *= y[ind]; // x->value stores yi*xij
			double val = x->value;
			b[ind] -= w[j]*val;
			xj_sq[j] += C[GETI(ind)]*val*val;
			x++;
		}
	}

	while(iter < max_iter)
	{
		Gmax_new = 0;
		Gnorm1_new = 0;

		for(j=0; j<active_size; j++)
		{
			int i = j+rand()%(active_size-j);
			swap(index[i], index[j]);
		}

		for(s=0; s<active_size; s++)
		{
			j = index[s];
			G_loss = 0;
			H = 0;

			x = prob_col->x[j];
			while(x->index != -1)
			{
				int ind = x->index-1;
				if(b[ind] > 0)
				{
					double val = x->value;
					double tmp = C[GETI(ind)]*val;
					G_loss -= tmp*b[ind];
					H += tmp*val;
				}
				x++;
			}
			G_loss *= 2;

			G = G_loss;
			H *= 2;
			H = max(H, 1e-12);

			double Gp = G+1;
			double Gn = G-1;
			double violation = 0;
			if(w[j] == 0)
			{
				if(Gp < 0)
					violation = -Gp;
				else if(Gn > 0)
					violation = Gn;
				else if(Gp>Gmax_old/l && Gn<-Gmax_old/l)
				{
					active_size--;
					swap(index[s], index[active_size]);
					s--;
					continue;
				}
			}
			else if(w[j] > 0)
				violation = fabs(Gp);
			else
				violation = fabs(Gn);

			Gmax_new = max(Gmax_new, violation);
			Gnorm1_new += violation;

			// obtain Newton direction d
			if(Gp < H*w[j])
				d = -Gp/H;
			else if(Gn > H*w[j])
				d = -Gn/H;
			else
				d = -w[j];

			if(fabs(d) < 1.0e-12)
				continue;

			double delta = fabs(w[j]+d)-fabs(w[j]) + G*d;
			d_old = 0;
			int num_linesearch;
			for(num_linesearch=0; num_linesearch < max_num_linesearch; num_linesearch++)
			{
				d_diff = d_old - d;
				cond = fabs(w[j]+d)-fabs(w[j]) - sigma*delta;

				appxcond = xj_sq[j]*d*d + G_loss*d + cond;
				if(appxcond <= 0)
				{
					x = prob_col->x[j];
					while(x->index != -1)
					{
						b[x->index-1] += d_diff*x->value;
						x++;
					}
					break;
				}

				if(num_linesearch == 0)
				{
					loss_old = 0;
					loss_new = 0;
					x = prob_col->x[j];
					while(x->index != -1)
					{
						int ind = x->index-1;
						if(b[ind] > 0)
							loss_old += C[GETI(ind)]*b[ind]*b[ind];
						double b_new = b[ind] + d_diff*x->value;
						b[ind] = b_new;
						if(b_new > 0)
							loss_new += C[GETI(ind)]*b_new*b_new;
						x++;
					}
				}
				else
				{
					loss_new = 0;
					x = prob_col->x[j];
					while(x->index != -1)
					{
						int ind = x->index-1;
						double b_new = b[ind] + d_diff*x->value;
						b[ind] = b_new;
						if(b_new > 0)
							loss_new += C[GETI(ind)]*b_new*b_new;
						x++;
					}
				}

				cond = cond + loss_new - loss_old;
				if(cond <= 0)
					break;
				else
				{
					d_old = d;
					d *= 0.5;
					delta *= 0.5;
				}
			}

			w[j] += d;

			// recompute b[] if line search takes too many steps
			if(num_linesearch >= max_num_linesearch)
			{
				info("#");
				for(int i=0; i<l; i++)
					b[i] = 1;

				for(int i=0; i<w_size; i++)
				{
					if(w[i]==0) continue;
					x = prob_col->x[i];
					while(x->index != -1)
					{
						b[x->index-1] -= w[i]*x->value;
						x++;
					}
				}
			}
		}

		if(iter == 0)
			Gnorm1_init = Gnorm1_new;
		iter++;
		if(iter % 10 == 0)
			info(".");

		if(Gnorm1_new <= eps*Gnorm1_init)
		{
			if(active_size == w_size)
				break;
			else
			{
				active_size = w_size;
				info("*");
				Gmax_old = INF;
				continue;
			}
		}

		Gmax_old = Gmax_new;
	}

	info("\noptimization finished, #iter = %d\n", iter);
	if(iter >= max_iter)
		info("\nWARNING: reaching max number of iterations\n");

	// calculate objective value

	double v = 0;
	int nnz = 0;
	for(j=0; j<w_size; j++)
	{
		x = prob_col->x[j];
		while(x->index != -1)
		{
			x->value *= prob_col->y[x->index-1]; // restore x->value
			x++;
		}
		if(w[j] != 0)
		{
			v += fabs(w[j]);
			nnz++;
		}
	}
	for(j=0; j<l; j++)
		if(b[j] > 0)
			v += C[GETI(j)]*b[j]*b[j];

	info("Objective value = %lf\n", v);
	info("#nonzeros/#features = %d/%d\n", nnz, w_size);

	delete [] index;
	delete [] y;
	delete [] b;
	delete [] xj_sq;
}

// A coordinate descent algorithm for 
// L1-regularized logistic regression problems
//
//  min_w \sum |wj| + C \sum log(1+exp(-yi w^T xi)),
//
// Given: 
// x, y, Cp, Cn
// eps is the stopping tolerance
//
// solution will be put in w
//
// See Yuan et al. (2011) and appendix of LIBLINEAR paper, Fan et al. (2008)

#undef GETI
#define GETI(i) (y[i]+1)
// To support weights for instances, use GETI(i) (i)

static void solve_l1r_lr(
	const problem *prob_col, double *w, double eps,
	double Cp, double Cn)
{
	int l = prob_col->l;
	int w_size = prob_col->n;
	int j, s, newton_iter=0, iter=0;
	int max_newton_iter = 100;
	int max_iter = 1000;
	int max_num_linesearch = 20;
	int active_size;
	int QP_active_size;

	double nu = 1e-12;
	double inner_eps = 1;
	double sigma = 0.01;
	double w_norm, w_norm_new;
	double z, G, H;
	double Gnorm1_init = -1.0; // Gnorm1_init is initialized at the first iteration
	double Gmax_old = INF;
	double Gmax_new, Gnorm1_new;
	double QP_Gmax_old = INF;
	double QP_Gmax_new, QP_Gnorm1_new;
	double delta, negsum_xTd, cond;

	int *index = new int[w_size];
	schar *y = new schar[l];
	double *Hdiag = new double[w_size];
	double *Grad = new double[w_size];
	double *wpd = new double[w_size];
	double *xjneg_sum = new double[w_size];
	double *xTd = new double[l];
	double *exp_wTx = new double[l];
	double *exp_wTx_new = new double[l];
	double *tau = new double[l];
	double *D = new double[l];
	feature_node *x;

	double C[3] = {Cn,0,Cp};

	// Initial w can be set here.
	for(j=0; j<w_size; j++)
		w[j] = 0;

	for(j=0; j<l; j++)
	{
		if(prob_col->y[j] > 0)
			y[j] = 1;
		else
			y[j] = -1;

		exp_wTx[j] = 0;
	}

	w_norm = 0;
	for(j=0; j<w_size; j++)
	{
		w_norm += fabs(w[j]);
		wpd[j] = w[j];
		index[j] = j;
		xjneg_sum[j] = 0;
		x = prob_col->x[j];
		while(x->index != -1)
		{
			int ind = x->index-1;
			double val = x->value;
			exp_wTx[ind] += w[j]*val;
			if(y[ind] == -1)
				xjneg_sum[j] += C[GETI(ind)]*val;
			x++;
		}
	}
	for(j=0; j<l; j++)
	{
		exp_wTx[j] = exp(exp_wTx[j]);
		double tau_tmp = 1/(1+exp_wTx[j]);
		tau[j] = C[GETI(j)]*tau_tmp;
		D[j] = C[GETI(j)]*exp_wTx[j]*tau_tmp*tau_tmp;
	}

	while(newton_iter < max_newton_iter)
	{
		Gmax_new = 0;
		Gnorm1_new = 0;
		active_size = w_size;

		for(s=0; s<active_size; s++)
		{
			j = index[s];
			Hdiag[j] = nu;
			Grad[j] = 0;

			double tmp = 0;
			x = prob_col->x[j];
			while(x->index != -1)
			{
				int ind = x->index-1;
				Hdiag[j] += x->value*x->value*D[ind];
				tmp += x->value*tau[ind];
				x++;
			}
			Grad[j] = -tmp + xjneg_sum[j];

			double Gp = Grad[j]+1;
			double Gn = Grad[j]-1;
			double violation = 0;
			if(w[j] == 0)
			{
				if(Gp < 0)
					violation = -Gp;
				else if(Gn > 0)
					violation = Gn;
				//outer-level shrinking
				else if(Gp>Gmax_old/l && Gn<-Gmax_old/l)
				{
					active_size--;
					swap(index[s], index[active_size]);
					s--;
					continue;
				}
			}
			else if(w[j] > 0)
				violation = fabs(Gp);
			else
				violation = fabs(Gn);

			Gmax_new = max(Gmax_new, violation);
			Gnorm1_new += violation;
		}

		if(newton_iter == 0)
			Gnorm1_init = Gnorm1_new;

		if(Gnorm1_new <= eps*Gnorm1_init)
			break;

		iter = 0;
		QP_Gmax_old = INF;
		QP_active_size = active_size;

		for(int i=0; i<l; i++)
			xTd[i] = 0;

		// optimize QP over wpd
		while(iter < max_iter)
		{
			QP_Gmax_new = 0;
			QP_Gnorm1_new = 0;

			for(j=0; j<QP_active_size; j++)
			{
				int i = j+rand()%(QP_active_size-j);
				swap(index[i], index[j]);
			}

			for(s=0; s<QP_active_size; s++)
			{
				j = index[s];
				H = Hdiag[j];

				x = prob_col->x[j];
				G = Grad[j] + (wpd[j]-w[j])*nu;
				while(x->index != -1)
				{
					int ind = x->index-1;
					G += x->value*D[ind]*xTd[ind];
					x++;
				}

				double Gp = G+1;
				double Gn = G-1;
				double violation = 0;
				if(wpd[j] == 0)
				{
					if(Gp < 0)
						violation = -Gp;
					else if(Gn > 0)
						violation = Gn;
					//inner-level shrinking
					else if(Gp>QP_Gmax_old/l && Gn<-QP_Gmax_old/l)
					{
						QP_active_size--;
						swap(index[s], index[QP_active_size]);
						s--;
						continue;
					}
				}
				else if(wpd[j] > 0)
					violation = fabs(Gp);
				else
					violation = fabs(Gn);

				QP_Gmax_new = max(QP_Gmax_new, violation);
				QP_Gnorm1_new += violation;

				// obtain solution of one-variable problem
				if(Gp < H*wpd[j])
					z = -Gp/H;
				else if(Gn > H*wpd[j])
					z = -Gn/H;
				else
					z = -wpd[j];

				if(fabs(z) < 1.0e-12)
					continue;
				z = min(max(z,-10.0),10.0);

				wpd[j] += z;

				x = prob_col->x[j];
				while(x->index != -1)
				{
					int ind = x->index-1;
					xTd[ind] += x->value*z;
					x++;
				}
			}

			iter++;

			if(QP_Gnorm1_new <= inner_eps*Gnorm1_init)
			{
				//inner stopping
				if(QP_active_size == active_size)
					break;
				//active set reactivation
				else
				{
					QP_active_size = active_size;
					QP_Gmax_old = INF;
					continue;
				}
			}

			QP_Gmax_old = QP_Gmax_new;
		}

		if(iter >= max_iter)
			info("WARNING: reaching max number of inner iterations\n");

		delta = 0;
		w_norm_new = 0;
		for(j=0; j<w_size; j++)
		{
			delta += Grad[j]*(wpd[j]-w[j]);
			if(wpd[j] != 0)
				w_norm_new += fabs(wpd[j]);
		}
		delta += (w_norm_new-w_norm);

		negsum_xTd = 0;
		for(int i=0; i<l; i++)
			if(y[i] == -1)
				negsum_xTd += C[GETI(i)]*xTd[i];

		int num_linesearch;
		for(num_linesearch=0; num_linesearch < max_num_linesearch; num_linesearch++)
		{
			cond = w_norm_new - w_norm + negsum_xTd - sigma*delta;

			for(int i=0; i<l; i++)
			{
				double exp_xTd = exp(xTd[i]);
				exp_wTx_new[i] = exp_wTx[i]*exp_xTd;
				cond += C[GETI(i)]*log((1+exp_wTx_new[i])/(exp_xTd+exp_wTx_new[i]));
			}

			if(cond <= 0)
			{
				w_norm = w_norm_new;
				for(j=0; j<w_size; j++)
					w[j] = wpd[j];
				for(int i=0; i<l; i++)
				{
					exp_wTx[i] = exp_wTx_new[i];
					double tau_tmp = 1/(1+exp_wTx[i]);
					tau[i] = C[GETI(i)]*tau_tmp;
					D[i] = C[GETI(i)]*exp_wTx[i]*tau_tmp*tau_tmp;
				}
				break;
			}
			else
			{
				w_norm_new = 0;
				for(j=0; j<w_size; j++)
				{
					wpd[j] = (w[j]+wpd[j])*0.5;
					if(wpd[j] != 0)
						w_norm_new += fabs(wpd[j]);
				}
				delta *= 0.5;
				negsum_xTd *= 0.5;
				for(int i=0; i<l; i++)
					xTd[i] *= 0.5;
			}
		}

		// Recompute some info due to too many line search steps
		if(num_linesearch >= max_num_linesearch)
		{
			for(int i=0; i<l; i++)
				exp_wTx[i] = 0;

			for(int i=0; i<w_size; i++)
			{
				if(w[i]==0) continue;
				x = prob_col->x[i];
				while(x->index != -1)
				{
					exp_wTx[x->index-1] += w[i]*x->value;
					x++;
				}
			}

			for(int i=0; i<l; i++)
				exp_wTx[i] = exp(exp_wTx[i]);
		}

		if(iter == 1)
			inner_eps *= 0.25;

		newton_iter++;
		Gmax_old = Gmax_new;

		info("iter %3d  #CD cycles %d\n", newton_iter, iter);
	}

	info("=========================\n");
	info("optimization finished, #iter = %d\n", newton_iter);
	if(newton_iter >= max_newton_iter)
		info("WARNING: reaching max number of iterations\n");

	// calculate objective value

	double v = 0;
	int nnz = 0;
	for(j=0; j<w_size; j++)
		if(w[j] != 0)
		{
			v += fabs(w[j]);
			nnz++;
		}
	for(j=0; j<l; j++)
		if(y[j] == 1)
			v += C[GETI(j)]*log(1+1/exp_wTx[j]);
		else
			v += C[GETI(j)]*log(1+exp_wTx[j]);

	info("Objective value = %lf\n", v);
	info("#nonzeros/#features = %d/%d\n", nnz, w_size);

	delete [] index;
	delete [] y;
	delete [] Hdiag;
	delete [] Grad;
	delete [] wpd;
	delete [] xjneg_sum;
	delete [] xTd;
	delete [] exp_wTx;
	delete [] exp_wTx_new;
	delete [] tau;
	delete [] D;
}

// transpose matrix X from row format to column format
static void transpose(const problem *prob, feature_node **x_space_ret, problem *prob_col)
{
	int i;
	int l = prob->l;
	int n = prob->n;
	size_t nnz = 0;
	size_t *col_ptr = new size_t [n+1];
	feature_node *x_space;
	prob_col->l = l;
	prob_col->n = n;
	prob_col->y = new double[l];
	prob_col->x = new feature_node*[n];

	for(i=0; i<l; i++)
		prob_col->y[i] = prob->y[i];

	for(i=0; i<n+1; i++)
		col_ptr[i] = 0;
	for(i=0; i<l; i++)
	{
		feature_node *x = prob->x[i];
		while(x->index != -1)
		{
			nnz++;
			col_ptr[x->index]++;
			x++;
		}
	}
	for(i=1; i<n+1; i++)
		col_ptr[i] += col_ptr[i-1] + 1;

	x_space = new feature_node[nnz+n];
	for(i=0; i<n; i++)
		prob_col->x[i] = &x_space[col_ptr[i]];

	for(i=0; i<l; i++)
	{
		feature_node *x = prob->x[i];
		while(x->index != -1)
		{
			int ind = x->index-1;
			x_space[col_ptr[ind]].index = i+1; // starts from 1
			x_space[col_ptr[ind]].value = x->value;
			col_ptr[ind]++;
			x++;
		}
	}
	for(i=0; i<n; i++)
		x_space[col_ptr[i]].index = -1;

	*x_space_ret = x_space;

	delete [] col_ptr;
}

// label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data
// perm, length l, must be allocated before calling this subroutine
static void group_classes(const problem *prob, int *nr_class_ret, int **label_ret, int **start_ret, int **count_ret, int *perm)
{
	int l = prob->l;
	int max_nr_class = 16;
	int nr_class = 0;
	int *label = Malloc(int,max_nr_class);
	int *count = Malloc(int,max_nr_class);
	int *data_label = Malloc(int,l);
	int i;

	for(i=0;i<l;i++)
	{
		int this_label = (int)prob->y[i];
		int j;
		for(j=0;j<nr_class;j++)
		{
			if(this_label == label[j])
			{
				++count[j];
				break;
			}
		}
		data_label[i] = j;
		if(j == nr_class)
		{
			if(nr_class == max_nr_class)
			{
				max_nr_class *= 2;
				label = (int *)realloc(label,max_nr_class*sizeof(int));
				count = (int *)realloc(count,max_nr_class*sizeof(int));
			}
			label[nr_class] = this_label;
			count[nr_class] = 1;
			++nr_class;
		}
	}

	//
	// Labels are ordered by their first occurrence in the training set. 
	// However, for two-class sets with -1/+1 labels and -1 appears first, 
	// we swap labels to ensure that internally the binary SVM has positive data corresponding to the +1 instances.
	//
	if (nr_class == 2 && label[0] == -1 && label[1] == 1)
	{
		swap(label[0],label[1]);
		swap(count[0],count[1]);
		for(i=0;i<l;i++)
		{
			if(data_label[i] == 0)
				data_label[i] = 1;
			else
				data_label[i] = 0;
		}
	}

	int *start = Malloc(int,nr_class);
	start[0] = 0;
	for(i=1;i<nr_class;i++)
		start[i] = start[i-1]+count[i-1];
	for(i=0;i<l;i++)
	{
		perm[start[data_label[i]]] = i;
		++start[data_label[i]];
	}
	start[0] = 0;
	for(i=1;i<nr_class;i++)
		start[i] = start[i-1]+count[i-1];

	*nr_class_ret = nr_class;
	*label_ret = label;
	*start_ret = start;
	*count_ret = count;
	free(data_label);
}

static void train_one(const problem *prob, const parameter *param, double *w, double Cp, double Cn)
{
	double eps=param->eps;
	int pos = 0;
	int neg = 0;
	for(int i=0;i<prob->l;i++)
		if(prob->y[i] > 0)
			pos++;
	neg = prob->l - pos;

	double primal_solver_tol = eps*max(min(pos,neg), 1)/prob->l;

	function *fun_obj=NULL;
	switch(param->solver_type)
	{
		case L2R_LR:
		{
			double *C = new double[prob->l];
			for(int i = 0; i < prob->l; i++)
			{
				if(prob->y[i] > 0)
					C[i] = Cp;
				else
					C[i] = Cn;
			}
			fun_obj=new l2r_lr_fun(prob, C);
			TRON tron_obj(fun_obj, primal_solver_tol);
			tron_obj.set_print_string(liblinear_print_string);
			tron_obj.tron(w);
			delete fun_obj;
			delete[] C;
			break;
		}
		case L2R_L2LOSS_SVC:
		{
			double *C = new double[prob->l];
			for(int i = 0; i < prob->l; i++)
			{
				if(prob->y[i] > 0)
					C[i] = Cp;
				else
					C[i] = Cn;
			}
			fun_obj=new l2r_l2_svc_fun(prob, C);
			TRON tron_obj(fun_obj, primal_solver_tol);
			tron_obj.set_print_string(liblinear_print_string);
			tron_obj.tron(w);
			delete fun_obj;
			delete[] C;
			break;
		}
		case L2R_L2LOSS_SVC_DUAL:
			solve_l2r_l1l2_svc(prob, w, eps, Cp, Cn, L2R_L2LOSS_SVC_DUAL);
			break;
		case L2R_L1LOSS_SVC_DUAL:
			solve_l2r_l1l2_svc(prob, w, eps, Cp, Cn, L2R_L1LOSS_SVC_DUAL);
			break;
		case L1R_L2LOSS_SVC:
		{
			problem prob_col;
			feature_node *x_space = NULL;
			transpose(prob, &x_space ,&prob_col);
			solve_l1r_l2_svc(&prob_col, w, primal_solver_tol, Cp, Cn);
			delete [] prob_col.y;
			delete [] prob_col.x;
			delete [] x_space;
			break;
		}
		case L1R_LR:
		{
			problem prob_col;
			feature_node *x_space = NULL;
			transpose(prob, &x_space ,&prob_col);
			solve_l1r_lr(&prob_col, w, primal_solver_tol, Cp, Cn);
			delete [] prob_col.y;
			delete [] prob_col.x;
			delete [] x_space;
			break;
		}
		case L2R_LR_DUAL:
			solve_l2r_lr_dual(prob, w, eps, Cp, Cn);
			break;
		case L2R_L2LOSS_SVR:
		{
			double *C = new double[prob->l];
			for(int i = 0; i < prob->l; i++)
				C[i] = param->C;

			fun_obj=new l2r_l2_svr_fun(prob, C, param->p);
			TRON tron_obj(fun_obj, param->eps);
			tron_obj.set_print_string(liblinear_print_string);
			tron_obj.tron(w);
			delete fun_obj;
			delete[] C;
			break;

		}
		case L2R_L1LOSS_SVR_DUAL:
			solve_l2r_l1l2_svr(prob, w, param, L2R_L1LOSS_SVR_DUAL);
			break;
		case L2R_L2LOSS_SVR_DUAL:
			solve_l2r_l1l2_svr(prob, w, param, L2R_L2LOSS_SVR_DUAL);
			break;
		default:
			fprintf(stderr, "ERROR: unknown solver_type\n");
			break;
	}
}

//
// Interface functions
//
model* train(const problem *prob, const parameter *param)
{
	int i,j;
	int l = prob->l;
	int n = prob->n;
	int w_size = prob->n;
	model *model_ = Malloc(model,1);

	if(prob->bias>=0)
		model_->nr_feature=n-1;
	else
		model_->nr_feature=n;
	model_->param = *param;
	model_->bias = prob->bias;

	if(check_regression_model(model_))
	{
		model_->w = Malloc(double, w_size);
		model_->nr_class = 2;
		model_->label = NULL;
		train_one(prob, param, &model_->w[0], 0, 0);
	}
	else
	{
		int nr_class;
		int *label = NULL;
		int *start = NULL;
		int *count = NULL;
		int *perm = Malloc(int,l);

		// group training data of the same class
		group_classes(prob,&nr_class,&label,&start,&count,perm);

		model_->nr_class=nr_class;
		model_->label = Malloc(int,nr_class);
		for(i=0;i<nr_class;i++)
			model_->label[i] = label[i];

		// calculate weighted C
		double *weighted_C = Malloc(double, nr_class);
		for(i=0;i<nr_class;i++)
			weighted_C[i] = param->C;
		for(i=0;i<param->nr_weight;i++)
		{
			for(j=0;j<nr_class;j++)
				if(param->weight_label[i] == label[j])
					break;
			if(j == nr_class)
				fprintf(stderr,"WARNING: class label %d specified in weight is not found\n", param->weight_label[i]);
			else
				weighted_C[j] *= param->weight[i];
		}

		// constructing the subproblem
		feature_node **x = Malloc(feature_node *,l);
		for(i=0;i<l;i++)
			x[i] = prob->x[perm[i]];

		int k;
		problem sub_prob;
		sub_prob.l = l;
		sub_prob.n = n;
		sub_prob.x = Malloc(feature_node *,sub_prob.l);
		sub_prob.y = Malloc(double,sub_prob.l);

		for(k=0; k<sub_prob.l; k++)
			sub_prob.x[k] = x[k];

		// multi-class svm by Crammer and Singer
		if(param->solver_type == MCSVM_CS)
		{
			model_->w=Malloc(double, n*nr_class);
			for(i=0;i<nr_class;i++)
				for(j=start[i];j<start[i]+count[i];j++)
					sub_prob.y[j] = i;
			Solver_MCSVM_CS Solver(&sub_prob, nr_class, weighted_C, param->eps);
			Solver.Solve(model_->w);
		}
		else
		{
			if(nr_class == 2)
			{
				model_->w=Malloc(double, w_size);

				int e0 = start[0]+count[0];
				k=0;
				for(; k<e0; k++)
					sub_prob.y[k] = +1;
				for(; k<sub_prob.l; k++)
					sub_prob.y[k] = -1;

				train_one(&sub_prob, param, &model_->w[0], weighted_C[0], weighted_C[1]);
			}
			else
			{
				model_->w=Malloc(double, w_size*nr_class);
				double *w=Malloc(double, w_size);
				for(i=0;i<nr_class;i++)
				{
					int si = start[i];
					int ei = si+count[i];

					k=0;
					for(; k<si; k++)
						sub_prob.y[k] = -1;
					for(; k<ei; k++)
						sub_prob.y[k] = +1;
					for(; k<sub_prob.l; k++)
						sub_prob.y[k] = -1;

					train_one(&sub_prob, param, w, weighted_C[i], param->C);

					for(int j=0;j<w_size;j++)
						model_->w[j*nr_class+i] = w[j];
				}
				free(w);
			}

		}

		free(x);
		free(label);
		free(start);
		free(count);
		free(perm);
		free(sub_prob.x);
		free(sub_prob.y);
		free(weighted_C);
	}
	return model_;
}

void cross_validation(const problem *prob, const parameter *param, int nr_fold, double *target)
{
	int i;
	int *fold_start;
	int l = prob->l;
	int *perm = Malloc(int,l);
	if (nr_fold > l)
	{
		nr_fold = l;
		fprintf(stderr,"WARNING: # folds > # data. Will use # folds = # data instead (i.e., leave-one-out cross validation)\n");
	}
	fold_start = Malloc(int,nr_fold+1);
	for(i=0;i<l;i++) perm[i]=i;
	for(i=0;i<l;i++)
	{
		int j = i+rand()%(l-i);
		swap(perm[i],perm[j]);
	}
	for(i=0;i<=nr_fold;i++)
		fold_start[i]=i*l/nr_fold;

	for(i=0;i<nr_fold;i++)
	{
		int begin = fold_start[i];
		int end = fold_start[i+1];
		int j,k;
		struct problem subprob;

		subprob.bias = prob->bias;
		subprob.n = prob->n;
		subprob.l = l-(end-begin);
		subprob.x = Malloc(struct feature_node*,subprob.l);
		subprob.y = Malloc(double,subprob.l);

		k=0;
		for(j=0;j<begin;j++)
		{
			subprob.x[k] = prob->x[perm[j]];
			subprob.y[k] = prob->y[perm[j]];
			++k;
		}
		for(j=end;j<l;j++)
		{
			subprob.x[k] = prob->x[perm[j]];
			subprob.y[k] = prob->y[perm[j]];
			++k;
		}
		struct model *submodel = train(&subprob,param);
		for(j=begin;j<end;j++)
			target[perm[j]] = predict(submodel,prob->x[perm[j]]);
		free_and_destroy_model(&submodel);
		free(subprob.x);
		free(subprob.y);
	}
	free(fold_start);
	free(perm);
}

double predict_values(const struct model *model_, const struct feature_node *x, double *dec_values)
{
	int idx;
	int n;
	if(model_->bias>=0)
		n=model_->nr_feature+1;
	else
		n=model_->nr_feature;
	double *w=model_->w;
	int nr_class=model_->nr_class;
	int i;
	int nr_w;
	if(nr_class==2 && model_->param.solver_type != MCSVM_CS)
		nr_w = 1;
	else
		nr_w = nr_class;

	const feature_node *lx=x;
	for(i=0;i<nr_w;i++)
		dec_values[i] = 0;
	for(; (idx=lx->index)!=-1; lx++)
	{
		// the dimension of testing data may exceed that of training
		if(idx<=n)
			for(i=0;i<nr_w;i++)
				dec_values[i] += w[(idx-1)*nr_w+i]*lx->value;
	}

	if(nr_class==2)
	{
		if(check_regression_model(model_))
			return dec_values[0];
		else
			return (dec_values[0]>0)?model_->label[0]:model_->label[1];
	}
	else
	{
		int dec_max_idx = 0;
		for(i=1;i<nr_class;i++)
		{
			if(dec_values[i] > dec_values[dec_max_idx])
				dec_max_idx = i;
		}
		return model_->label[dec_max_idx];
	}
}

double predict(const model *model_, const feature_node *x)
{
	double *dec_values = Malloc(double, model_->nr_class);
	double label=predict_values(model_, x, dec_values);
	free(dec_values);
	return label;
}

double predict_probability(const struct model *model_, const struct feature_node *x, double* prob_estimates)
{
	if(check_probability_model(model_))
	{
		int i;
		int nr_class=model_->nr_class;
		int nr_w;
		if(nr_class==2)
			nr_w = 1;
		else
			nr_w = nr_class;

		double label=predict_values(model_, x, prob_estimates);
		for(i=0;i<nr_w;i++)
			prob_estimates[i]=1/(1+exp(-prob_estimates[i]));

		if(nr_class==2) // for binary classification
			prob_estimates[1]=1.-prob_estimates[0];
		else
		{
			double sum=0;
			for(i=0; i<nr_class; i++)
				sum+=prob_estimates[i];

			for(i=0; i<nr_class; i++)
				prob_estimates[i]=prob_estimates[i]/sum;
		}

		return label;
	}
	else
		return 0;
}

static const char *solver_type_table[]=
{
	"L2R_LR", "L2R_L2LOSS_SVC_DUAL", "L2R_L2LOSS_SVC", "L2R_L1LOSS_SVC_DUAL", "MCSVM_CS",
	"L1R_L2LOSS_SVC", "L1R_LR", "L2R_LR_DUAL",
	"", "", "",
	"L2R_L2LOSS_SVR", "L2R_L2LOSS_SVR_DUAL", "L2R_L1LOSS_SVR_DUAL", NULL
};

int save_model(const char *model_file_name, const struct model *model_)
{
	int i;
	int nr_feature=model_->nr_feature;
	int n;
	const parameter& param = model_->param;

	if(model_->bias>=0)
		n=nr_feature+1;
	else
		n=nr_feature;
	int w_size = n;
	FILE *fp = fopen(model_file_name,"w");
	if(fp==NULL) return -1;

	char *old_locale = strdup(setlocale(LC_ALL, NULL));
	setlocale(LC_ALL, "C");

	int nr_w;
	if(model_->nr_class==2 && model_->param.solver_type != MCSVM_CS)
		nr_w=1;
	else
		nr_w=model_->nr_class;

	fprintf(fp, "solver_type %s\n", solver_type_table[param.solver_type]);
	fprintf(fp, "nr_class %d\n", model_->nr_class);

	if(model_->label)
	{
		fprintf(fp, "label");
		for(i=0; i<model_->nr_class; i++)
			fprintf(fp, " %d", model_->label[i]);
		fprintf(fp, "\n");
	}

	fprintf(fp, "nr_feature %d\n", nr_feature);

	fprintf(fp, "bias %.16g\n", model_->bias);

	fprintf(fp, "w\n");
	for(i=0; i<w_size; i++)
	{
		int j;
		for(j=0; j<nr_w; j++)
			fprintf(fp, "%.16g ", model_->w[i*nr_w+j]);
		fprintf(fp, "\n");
	}

	setlocale(LC_ALL, old_locale);
	free(old_locale);

	if (ferror(fp) != 0 || fclose(fp) != 0) return -1;
	else return 0;
}

struct model *load_model(const char *model_file_name)
{
	FILE *fp = fopen(model_file_name,"r");
	if(fp==NULL) return NULL;

	int i;
	int nr_feature;
	int n;
	int nr_class;
	double bias;
	model *model_ = Malloc(model,1);
	parameter& param = model_->param;

	model_->label = NULL;

	char *old_locale = strdup(setlocale(LC_ALL, NULL));
	setlocale(LC_ALL, "C");

	char cmd[81];
	while(1)
	{
		fscanf(fp,"%80s",cmd);
		if(strcmp(cmd,"solver_type")==0)
		{
			fscanf(fp,"%80s",cmd);
			int i;
			for(i=0;solver_type_table[i];i++)
			{
				if(strcmp(solver_type_table[i],cmd)==0)
				{
					param.solver_type=i;
					break;
				}
			}
			if(solver_type_table[i] == NULL)
			{
				fprintf(stderr,"unknown solver type.\n");

				setlocale(LC_ALL, old_locale);
				free(model_->label);
				free(model_);
				free(old_locale);
				return NULL;
			}
		}
		else if(strcmp(cmd,"nr_class")==0)
		{
			fscanf(fp,"%d",&nr_class);
			model_->nr_class=nr_class;
		}
		else if(strcmp(cmd,"nr_feature")==0)
		{
			fscanf(fp,"%d",&nr_feature);
			model_->nr_feature=nr_feature;
		}
		else if(strcmp(cmd,"bias")==0)
		{
			fscanf(fp,"%lf",&bias);
			model_->bias=bias;
		}
		else if(strcmp(cmd,"w")==0)
		{
			break;
		}
		else if(strcmp(cmd,"label")==0)
		{
			int nr_class = model_->nr_class;
			model_->label = Malloc(int,nr_class);
			for(int i=0;i<nr_class;i++)
				fscanf(fp,"%d",&model_->label[i]);
		}
		else
		{
			fprintf(stderr,"unknown text in model file: [%s]\n",cmd);
			setlocale(LC_ALL, old_locale);
			free(model_->label);
			free(model_);
			free(old_locale);
			return NULL;
		}
	}

	nr_feature=model_->nr_feature;
	if(model_->bias>=0)
		n=nr_feature+1;
	else
		n=nr_feature;
	int w_size = n;
	int nr_w;
	if(nr_class==2 && param.solver_type != MCSVM_CS)
		nr_w = 1;
	else
		nr_w = nr_class;

	model_->w=Malloc(double, w_size*nr_w);
	for(i=0; i<w_size; i++)
	{
		int j;
		for(j=0; j<nr_w; j++)
			fscanf(fp, "%lf ", &model_->w[i*nr_w+j]);
		fscanf(fp, "\n");
	}

	setlocale(LC_ALL, old_locale);
	free(old_locale);

	if (ferror(fp) != 0 || fclose(fp) != 0) return NULL;

	return model_;
}

int get_nr_feature(const model *model_)
{
	return model_->nr_feature;
}

int get_nr_class(const model *model_)
{
	return model_->nr_class;
}

void get_labels(const model *model_, int* label)
{
	if (model_->label != NULL)
		for(int i=0;i<model_->nr_class;i++)
			label[i] = model_->label[i];
}

// use inline here for better performance (around 20% faster than the non-inline one)
static inline double get_w_value(const struct model *model_, int idx, int label_idx) 
{
	int nr_class = model_->nr_class;
	int solver_type = model_->param.solver_type;
	const double *w = model_->w;

	if(idx < 0 || idx > model_->nr_feature)
		return 0;
	if(check_regression_model(model_))
		return w[idx];
	else 
	{
		if(label_idx < 0 || label_idx >= nr_class)
			return 0;
		if(nr_class == 2 && solver_type != MCSVM_CS)
		{
			if(label_idx == 0)
				return w[idx];
			else
				return -w[idx];
		}
		else
			return w[idx*nr_class+label_idx];
	}
}

// feat_idx: starting from 1 to nr_feature
// label_idx: starting from 0 to nr_class-1 for classification models;
//            for regression models, label_idx is ignored.
double get_decfun_coef(const struct model *model_, int feat_idx, int label_idx)
{
	if(feat_idx > model_->nr_feature)
		return 0;
	return get_w_value(model_, feat_idx-1, label_idx);
}

double get_decfun_bias(const struct model *model_, int label_idx)
{
	int bias_idx = model_->nr_feature;
	double bias = model_->bias;
	if(bias <= 0)
		return 0;
	else
		return bias*get_w_value(model_, bias_idx, label_idx);
}

void free_model_content(struct model *model_ptr)
{
	if(model_ptr->w != NULL)
		free(model_ptr->w);
	if(model_ptr->label != NULL)
		free(model_ptr->label);
}

void free_and_destroy_model(struct model **model_ptr_ptr)
{
	struct model *model_ptr = *model_ptr_ptr;
	if(model_ptr != NULL)
	{
		free_model_content(model_ptr);
		free(model_ptr);
	}
}

void destroy_param(parameter* param)
{
	if(param->weight_label != NULL)
		free(param->weight_label);
	if(param->weight != NULL)
		free(param->weight);
}

const char *check_parameter(const problem *prob, const parameter *param)
{
	if(param->eps <= 0)
		return "eps <= 0";

	if(param->C <= 0)
		return "C <= 0";

	if(param->p < 0)
		return "p < 0";

	if(param->solver_type != L2R_LR
		&& param->solver_type != L2R_L2LOSS_SVC_DUAL
		&& param->solver_type != L2R_L2LOSS_SVC
		&& param->solver_type != L2R_L1LOSS_SVC_DUAL
		&& param->solver_type != MCSVM_CS
		&& param->solver_type != L1R_L2LOSS_SVC
		&& param->solver_type != L1R_LR
		&& param->solver_type != L2R_LR_DUAL
		&& param->solver_type != L2R_L2LOSS_SVR
		&& param->solver_type != L2R_L2LOSS_SVR_DUAL
		&& param->solver_type != L2R_L1LOSS_SVR_DUAL)
		return "unknown solver type";

	return NULL;
}

int check_probability_model(const struct model *model_)
{
	return (model_->param.solver_type==L2R_LR ||
			model_->param.solver_type==L2R_LR_DUAL ||
			model_->param.solver_type==L1R_LR);
}

int check_regression_model(const struct model *model_)
{
	return (model_->param.solver_type==L2R_L2LOSS_SVR ||
			model_->param.solver_type==L2R_L1LOSS_SVR_DUAL ||
			model_->param.solver_type==L2R_L2LOSS_SVR_DUAL);
}

void set_print_string_function(void (*print_func)(const char*))
{
	if (print_func == NULL)
		liblinear_print_string = &print_string_stdout;
	else
		liblinear_print_string = print_func;
}



================================================
FILE: Matlab/liblinear-1.96/linear.def
================================================
LIBRARY liblinear
EXPORTS
	train	@1
	cross_validation	@2
	save_model	@3
	load_model	@4
	get_nr_feature	@5
	get_nr_class	@6
	get_labels	@7
	predict_values	@8
	predict	@9
	predict_probability	@10
	free_and_destroy_model	@11
	free_model_content	@12
	destroy_param	@13
	check_parameter	@14
	check_probability_model	@15
	set_print_string_function	@16
    get_decfun_coef @17
    get_decfun_bias @18
    check_regression_model  @19


================================================
FILE: Matlab/liblinear-1.96/linear.h
================================================
#ifndef _LIBLINEAR_H
#define _LIBLINEAR_H

#ifdef __cplusplus
extern "C" {
#endif

struct feature_node
{
	int index;
	double value;
};

struct problem
{
	int l, n;
	double *y;
	struct feature_node **x;
	double bias;            /* < 0 if no bias term */  
};

enum { L2R_LR, L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L1R_L2LOSS_SVC, L1R_LR, L2R_LR_DUAL, L2R_L2LOSS_SVR = 11, L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL }; /* solver_type */

struct parameter
{
	int solver_type;

	/* these are for training only */
	double eps;	        /* stopping criteria */
	double C;
	int nr_weight;
	int *weight_label;
	double* weight;
	double p;
};

struct model
{
	struct parameter param;
	int nr_class;		/* number of classes */
	int nr_feature;
	double *w;
	int *label;		/* label of each class */
	double bias;
};

struct model* train(const struct problem *prob, const struct parameter *param);
void cross_validation(const struct problem *prob, const struct parameter *param, int nr_fold, double *target);

double predict_values(const struct model *model_, const struct feature_node *x, double* dec_values);
double predict(const struct model *model_, const struct feature_node *x);
double predict_probability(const struct model *model_, const struct feature_node *x, double* prob_estimates);

int save_model(const char *model_file_name, const struct model *model_);
struct model *load_model(const char *model_file_name);

int get_nr_feature(const struct model *model_);
int get_nr_class(const struct model *model_);
void get_labels(const struct model *model_, int* label);
double get_decfun_coef(const struct model *model_, int feat_idx, int label_idx);
double get_decfun_bias(const struct model *model_, int label_idx);

void free_model_content(struct model *model_ptr);
void free_and_destroy_model(struct model **model_ptr_ptr);
void destroy_param(struct parameter *param);

const char *check_parameter(const struct problem *prob, const struct parameter *param);
int check_probability_model(const struct model *model);
int check_regression_model(const struct model *model);
void set_print_string_function(void (*print_func) (const char*));

#ifdef __cplusplus
}
#endif

#endif /* _LIBLINEAR_H */



================================================
FILE: Matlab/liblinear-1.96/matlab/Makefile
================================================
# This Makefile is used under Linux

MATLABDIR ?= /usr/local/matlab
CXX ?= g++
#CXX = g++-3.3
CC ?= gcc
CFLAGS = -Wall -Wconversion -O3 -fPIC -I$(MATLABDIR)/extern/include -I..

MEX = $(MATLABDIR)/bin/mex
MEX_OPTION = CC="$(CXX)" CXX="$(CXX)" CFLAGS="$(CFLAGS)" CXXFLAGS="$(CFLAGS)"
# comment the following line if you use MATLAB on a 32-bit computer
MEX_OPTION += -largeArrayDims
MEX_EXT = $(shell $(MATLABDIR)/bin/mexext)

all:	matlab

matlab:	binary

octave:
	@echo "please type make under Octave"
binary: train.$(MEX_EXT) predict.$(MEX_EXT) libsvmread.$(MEX_EXT) libsvmwrite.$(MEX_EXT)

train.$(MEX_EXT): train.c ../linear.h ../tron.o ../linear.o linear_model_matlab.o ../blas/blas.a
	$(MEX) $(MEX_OPTION) train.c ../tron.o ../linear.o linear_model_matlab.o ../blas/blas.a

predict.$(MEX_EXT): predict.c ../linear.h ../tron.o ../linear.o linear_model_matlab.o ../blas/blas.a
	$(MEX) $(MEX_OPTION) predict.c ../tron.o ../linear.o linear_model_matlab.o ../blas/blas.a

libsvmread.$(MEX_EXT):	libsvmread.c
	$(MEX) $(MEX_OPTION) libsvmread.c

libsvmwrite.$(MEX_EXT):	libsvmwrite.c
	$(MEX) $(MEX_OPTION) libsvmwrite.c

linear_model_matlab.o: linear_model_matlab.c ../linear.h
	$(CXX) $(CFLAGS) -c linear_model_matlab.c

../linear.o: ../linear.cpp ../linear.h
	make -C .. linear.o

../tron.o: ../tron.cpp ../tron.h 
	make -C .. tron.o

../blas/blas.a: ../blas/*.c ../blas/*.h
	make -C ../blas OPTFLAGS='$(CFLAGS)' CC='$(CC)';

clean:
	make -C ../blas clean
	rm -f *~ *.o *.mex* *.obj ../linear.o ../tron.o


================================================
FILE: Matlab/liblinear-1.96/matlab/README
================================================
--------------------------------------------
--- MATLAB/OCTAVE interface of LIBLINEAR ---
--------------------------------------------

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

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


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

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

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

On Windows systems, pre-built binary files are already in the
directory '..\windows', so no need to conduct installation. Now we
provide binary files only for 64bit MATLAB on Windows. If you would
like to re-build the package, please rely on the following steps.

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

On MATLAB or Octave:

        >> make

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

Example:

        matlab>> mex -setup
        (ps: MATLAB will show the following messages to setup default compiler.)
        Please choose your compiler for building external interface (MEX) files:
        Would you like mex to locate installed compilers [y]/n? y
        Select a compiler:
        [1] Microsoft Visual C/C++ version 7.1 in C:\Program Files\Microsoft Visual Studio
        [0] None
        Compiler: 1
        Please verify your choices:
        Compiler: Microsoft Visual C/C++ 7.1
        Location: C:\Program Files\Microsoft Visual Studio
        Are these correct?([y]/n): y

        matlab>> make

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

Example:
        linux> make

To use octave, type 'make octave':

Example:
        linux> make octave

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

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

Usage
=====

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

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

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

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

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

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

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

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

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

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

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

A matlab function libsvmread reads files in LIBSVM format: 

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

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

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

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

The instance_matrix must be a sparse matrix. (type must be double)
For windows, `libsvmread.mexw64' and `libsvmwrite.mexw64' are ready in 
the directory `..\windows'.

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

Examples
========

Train and test on the provided data heart_scale:

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

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

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

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

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

Please cite LIBLINEAR as follows

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

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



================================================
FILE: Matlab/liblinear-1.96/matlab/libsvmread.c
================================================
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <ctype.h>
#include <errno.h>

#include "mex.h"

#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif 
#endif 
#ifndef max
#define max(x,y) (((x)>(y))?(x):(y))
#endif
#ifndef min
#define min(x,y) (((x)<(y))?(x):(y))
#endif

void exit_with_help()
{
	mexPrintf(
	"Usage: [label_vector, instance_matrix] = libsvmread('filename');\n"
	);
}

static void fake_answer(int nlhs, mxArray *plhs[])
{
	int i;
	for(i=0;i<nlhs;i++)
		plhs[i] = mxCreateDoubleMatrix(0, 0, mxREAL);
}

static char *line;
static int max_line_len;

static char* readline(FILE *input)
{
	int len;
	
	if(fgets(line,max_line_len,input) == NULL)
		return NULL;

	while(strrchr(line,'\n') == NULL)
	{
		max_line_len *= 2;
		line = (char *) realloc(line, max_line_len);
		len = (int) strlen(line);
		if(fgets(line+len,max_line_len-len,input) == NULL)
			break;
	}
	return line;
}

// read in a problem (in libsvm format)
void read_problem(const char *filename, int nlhs, mxArray *plhs[])
{
	int max_index, min_index, inst_max_index;
	size_t elements, k, i, l=0;
	FILE *fp = fopen(filename,"r");
	char *endptr;
	mwIndex *ir, *jc;
	double *labels, *samples;

	if(fp == NULL)
	{
		mexPrintf("can't open input file %s\n",filename);
		fake_answer(nlhs, plhs);
		return;
	}

	max_line_len = 1024;
	line = (char *) malloc(max_line_len*sizeof(char));

	max_index = 0;
	min_index = 1; // our index starts from 1
	elements = 0;
	while(readline(fp) != NULL)
	{
		char *idx, *val;
		// features
		int index = 0;

		inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0
		strtok(line," \t"); // label
		while (1)
		{
			idx = strtok(NULL,":"); // index:value
			val = strtok(NULL," \t");
			if(val == NULL)
				break;

			errno = 0;
			index = (int) strtol(idx,&endptr,10);
			if(endptr == idx || errno != 0 || *endptr != '\0' || index <= inst_max_index)
			{
				mexPrintf("Wrong input format at line %d\n",l+1);
				fake_answer(nlhs, plhs);
				return;
			}
			else
				inst_max_index = index;

			min_index = min(min_index, index);
			elements++;
		}
		max_index = max(max_index, inst_max_index);
		l++;
	}
	rewind(fp);

	// y
	plhs[0] = mxCreateDoubleMatrix(l, 1, mxREAL);
	// x^T
	if (min_index <= 0)
		plhs[1] = mxCreateSparse(max_index-min_index+1, l, elements, mxREAL);
	else
		plhs[1] = mxCreateSparse(max_index, l, elements, mxREAL);

	labels = mxGetPr(plhs[0]);
	samples = mxGetPr(plhs[1]);
	ir = mxGetIr(plhs[1]);
	jc = mxGetJc(plhs[1]);

	k=0;
	for(i=0;i<l;i++)
	{
		char *idx, *val, *label;
		jc[i] = k;

		readline(fp);

		label = strtok(line," \t\n");
		if(label == NULL)
		{
			mexPrintf("Empty line at line %d\n",i+1);
			fake_answer(nlhs, plhs);
			return;
		}
		labels[i] = strtod(label,&endptr);
		if(endptr == label || *endptr != '\0')
		{
			mexPrintf("Wrong input format at line %d\n",i+1);
			fake_answer(nlhs, plhs);
			return;
		}

		// features
		while(1)
		{
			idx = strtok(NULL,":");
			val = strtok(NULL," \t");
			if(val == NULL)
				break;

			ir[k] = (mwIndex) (strtol(idx,&endptr,10) - min_index); // precomputed kernel has <index> start from 0

			errno = 0;
			samples[k] = strtod(val,&endptr);
			if (endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
			{
				mexPrintf("Wrong input format at line %d\n",i+1);
				fake_answer(nlhs, plhs);
				return;
			}
			++k;
		}
	}
	jc[l] = k;

	fclose(fp);
	free(line);

	{
		mxArray *rhs[1], *lhs[1];
		rhs[0] = plhs[1];
		if(mexCallMATLAB(1, lhs, 1, rhs, "transpose"))
		{
			mexPrintf("Error: cannot transpose problem\n");
			fake_answer(nlhs, plhs);
			return;
		}
		plhs[1] = lhs[0];
	}
}

void mexFunction( int nlhs, mxArray *plhs[],
		int nrhs, const mxArray *prhs[] )
{
	char filename[256];

	if(nrhs != 1 || nlhs != 2)
	{
		exit_with_help();
		fake_answer(nlhs, plhs);
		return;
	}

	mxGetString(prhs[0], filename, mxGetN(prhs[0]) + 1);

	if(filename == NULL)
	{
		mexPrintf("Error: filename is NULL\n");
		return;
	}

	read_problem(filename, nlhs, plhs);

	return;
}



================================================
FILE: Matlab/liblinear-1.96/matlab/libsvmwrite.c
================================================
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "mex.h"

#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif

void exit_with_help()
{
	mexPrintf(
	"Usage: libsvmwrite('filename', label_vector, instance_matrix);\n"
	);
}

static void fake_answer(int nlhs, mxArray *plhs[])
{
	int i;
	for(i=0;i<nlhs;i++)
		plhs[i] = mxCreateDoubleMatrix(0, 0, mxREAL);
}

void libsvmwrite(const char *filename, const mxArray *label_vec, const mxArray *instance_mat)
{
	FILE *fp = fopen(filename,"w");
	mwIndex *ir, *jc, k, low, high;
	size_t i, l, label_vector_row_num;
	double *samples, *labels;
	mxArray *instance_mat_col; // instance sparse matrix in column format

	if(fp ==NULL)
	{
		mexPrintf("can't open output file %s\n",filename);			
		return;
	}

	// transpose instance matrix
	{
		mxArray *prhs[1], *plhs[1];
		prhs[0] = mxDuplicateArray(instance_mat);
		if(mexCallMATLAB(1, plhs, 1, prhs, "transpose"))
		{
			mexPrintf("Error: cannot transpose instance matrix\n");
			return;
		}
		instance_mat_col = plhs[0];
		mxDestroyArray(prhs[0]);
	}

	// the number of instance
	l = mxGetN(instance_mat_col);
	label_vector_row_num = mxGetM(label_vec);

	if(label_vector_row_num!=l)
	{
		mexPrintf("Length of label vector does not match # of instances.\n");
		return;
	}

	// each column is one instance
	labels = mxGetPr(label_vec);
	samples = mxGetPr(instance_mat_col);
	ir = mxGetIr(instance_mat_col);
	jc = mxGetJc(instance_mat_col);

	for(i=0;i<l;i++)
	{
		fprintf(fp,"%g", labels[i]);

		low = jc[i], high = jc[i+1];
		for(k=low;k<high;k++)
			fprintf(fp," %lu:%g", (size_t)ir[k]+1, samples[k]);		

		fprintf(fp,"\n");
	}

	fclose(fp);
	return;
}

void mexFunction( int nlhs, mxArray *plhs[],
		int nrhs, const mxArray *prhs[] )
{
	if(nlhs > 0)
	{
		exit_with_help();
		fake_answer(nlhs, plhs);
		return;
	}
	
	// Transform the input Matrix to libsvm format
	if(nrhs == 3)
	{
		char filename[256];
		if(!mxIsDouble(prhs[1]) || !mxIsDouble(prhs[2]))
		{
			mexPrintf("Error: label vector and instance matrix must be double\n");			
			return;
		}
		
		mxGetString(prhs[0], filename, mxGetN(prhs[0])+1);		

		if(mxIsSparse(prhs[2]))
			libsvmwrite(filename, prhs[1], prhs[2]);
		else
		{
			mexPrintf("Instance_matrix must be sparse\n");			
			return;
		}
	}
	else
	{
		exit_with_help();		
		return;
	}
}


================================================
FILE: Matlab/liblinear-1.96/matlab/linear_model_matlab.c
================================================
#include <stdlib.h>
#include <string.h>
#include "../linear.h"

#include "mex.h"

#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif

#define Malloc(type,n) (type *)malloc((n)*sizeof(type))

#define NUM_OF_RETURN_FIELD 6

static const char *field_names[] = {
	"Parameters",
	"nr_class",
	"nr_feature",
	"bias",
	"Label",
	"w",
};

const char *model_to_matlab_structure(mxArray *plhs[], struct model *model_)
{
	int i;
	int nr_w;
	double *ptr;
	mxArray *return_model, **rhs;
	int out_id = 0;
	int n, w_size;

	rhs = (mxArray **)mxMalloc(sizeof(mxArray *)*NUM_OF_RETURN_FIELD);

	// Parameters
	// for now, only solver_type is needed
	rhs[out_id] = mxCreateDoubleMatrix(1, 1, mxREAL);
	ptr = mxGetPr(rhs[out_id]);
	ptr[0] = model_->param.solver_type;
	out_id++;

	// nr_class
	rhs[out_id] = mxCreateDoubleMatrix(1, 1, mxREAL);
	ptr = mxGetPr(rhs[out_id]);
	ptr[0] = model_->nr_class;
	out_id++;

	if(model_->nr_class==2 && model_->param.solver_type != MCSVM_CS)
		nr_w=1;
	else
		nr_w=model_->nr_class;

	// nr_feature
	rhs[out_id] = mxCreateDoubleMatrix(1, 1, mxREAL);
	ptr = mxGetPr(rhs[out_id]);
	ptr[0] = model_->nr_feature;
	out_id++;

	// bias
	rhs[out_id] = mxCreateDoubleMatrix(1, 1, mxREAL);
	ptr = mxGetPr(rhs[out_id]);
	ptr[0] = model_->bias;
	out_id++;

	if(model_->bias>=0)
		n=model_->nr_feature+1;
	else
		n=model_->nr_feature;

	w_size = n;
	// Label
	if(model_->label)
	{
		rhs[out_id] = mxCreateDoubleMatrix(model_->nr_class, 1, mxREAL);
		ptr = mxGetPr(rhs[out_id]);
		for(i = 0; i < model_->nr_class; i++)
			ptr[i] = model_->label[i];
	}
	else
		rhs[out_id] = mxCreateDoubleMatrix(0, 0, mxREAL);
	out_id++;

	// w
	rhs[out_id] = mxCreateDoubleMatrix(nr_w, w_size, mxREAL);
	ptr = mxGetPr(rhs[out_id]);
	for(i = 0; i < w_size*nr_w; i++)
		ptr[i]=model_->w[i];
	out_id++;

	/* Create a struct matrix contains NUM_OF_RETURN_FIELD fields */
	return_model = mxCreateStructMatrix(1, 1, NUM_OF_RETURN_FIELD, field_names);

	/* Fill struct matrix with input arguments */
	for(i = 0; i < NUM_OF_RETURN_FIELD; i++)
		mxSetField(return_model,0,field_names[i],mxDuplicateArray(rhs[i]));
	/* return */
	plhs[0] = return_model;
	mxFree(rhs);

	return NULL;
}

const char *matlab_matrix_to_model(struct model *model_, const mxArray *matlab_struct)
{
	int i, num_of_fields;
	int nr_w;
	double *ptr;
	int id = 0;
	int n, w_size;
	mxArray **rhs;

	num_of_fields = mxGetNumberOfFields(matlab_struct);
	rhs = (mxArray **) mxMalloc(sizeof(mxArray *)*num_of_fields);

	for(i=0;i<num_of_fields;i++)
		rhs[i] = mxGetFieldByNumber(matlab_struct, 0, i);

	model_->nr_class=0;
	nr_w=0;
	model_->nr_feature=0;
	model_->w=NULL;
	model_->label=NULL;

	// Parameters
	ptr = mxGetPr(rhs[id]);
	model_->param.solver_type = (int)ptr[0];
	id++;

	// nr_class
	ptr = mxGetPr(rhs[id]);
	model_->nr_class = (int)ptr[0];
	id++;

	if(model_->nr_class==2 && model_->param.solver_type != MCSVM_CS)
		nr_w=1;
	else
		nr_w=model_->nr_class;

	// nr_feature
	ptr = mxGetPr(rhs[id]);
	model_->nr_feature = (int)ptr[0];
	id++;

	// bias
	ptr = mxGetPr(rhs[id]);
	model_->bias = ptr[0];
	id++;

	if(model_->bias>=0)
		n=model_->nr_feature+1;
	else
		n=model_->nr_feature;
	w_size = n;

	// Label
	if(mxIsEmpty(rhs[id]) == 0)
	{
		model_->label = Malloc(int, model_->nr_class);
		ptr = mxGetPr(rhs[id]);
		for(i=0;i<model_->nr_class;i++)
			model_->label[i] = (int)ptr[i];
	}
	id++;

	ptr = mxGetPr(rhs[id]);
	model_->w=Malloc(double, w_size*nr_w);
	for(i = 0; i < w_size*nr_w; i++)
		model_->w[i]=ptr[i];
	id++;
	mxFree(rhs);

	return NULL;
}



================================================
FILE: Matlab/liblinear-1.96/matlab/linear_model_matlab.h
================================================
const char *model_to_matlab_structure(mxArray *plhs[], struct model *model_);
const char *matlab_matrix_to_model(struct model *model_, const mxArray *matlab_struct);


================================================
FILE: Matlab/liblinear-1.96/matlab/make.m
================================================
% This make.m is for MATLAB and OCTAVE under Windows, Mac, and Unix

try
	Type = ver;
	% This part is for OCTAVE
	if(strcmp(Type(1).Name, 'Octave') == 1)
		mex libsvmread.c
		mex libsvmwrite.c
		mex train.c linear_model_matlab.c ../linear.cpp ../tron.cpp ../blas/daxpy.c ../blas/ddot.c ../blas/dnrm2.c ../blas/dscal.c
		mex predict.c linear_model_matlab.c ../linear.cpp ../tron.cpp ../blas/daxpy.c ../blas/ddot.c ../blas/dnrm2.c ../blas/dscal.c
	% This part is for MATLAB
	% Add -largeArrayDims on 64-bit machines of MATLAB
	else
		mex CFLAGS="\$CFLAGS -std=c99" -largeArrayDims libsvmread.c
		mex CFLAGS="\$CFLAGS -std=c99" -largeArrayDims libsvmwrite.c
		mex CFLAGS="\$CFLAGS -std=c99" -largeArrayDims train.c linear_model_matlab.c ../linear.cpp ../tron.cpp ../blas/daxpy.c ../blas/ddot.c ../blas/dnrm2.c ../blas/dscal.c
		mex CFLAGS="\$CFLAGS -std=c99" -largeArrayDims predict.c linear_model_matlab.c ../linear.cpp ../tron.cpp ../blas/daxpy.c ../blas/ddot.c ../blas/dnrm2.c ../blas/dscal.c
	end
catch
	fprintf('If make.m fails, please check README about detailed instructions.\n');
end


================================================
FILE: Matlab/liblinear-1.96/matlab/predict.c
================================================
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "../linear.h"

#include "mex.h"
#include "linear_model_matlab.h"

#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif

#define CMD_LEN 2048

#define Malloc(type,n) (type *)malloc((n)*sizeof(type))

int print_null(const char *s,...) {}
int (*info)(const char *fmt,...);

int col_format_flag;

void read_sparse_instance(const mxArray *prhs, int index, struct feature_node *x, int feature_number, double bias)
{
	int j;
	mwIndex *ir, *jc, low, high, i;
	double *samples;

	ir = mxGetIr(prhs);
	jc = mxGetJc(prhs);
	samples = mxGetPr(prhs);

	// each column is one instance
	j = 0;
	low = jc[index], high = jc[index+1];
	for(i=low; i<high && (int) (ir[i])<feature_number; i++)
	{
		x[j].index = (int) ir[i]+1;
		x[j].value = samples[i];
		j++;
	}
	if(bias>=0)
	{
		x[j].index = feature_number+1;
		x[j].value = bias;
		j++;
	}
	x[j].index = -1;
}

static void fake_answer(int nlhs, mxArray *plhs[])
{
	int i;
	for(i=0;i<nlhs;i++)
		plhs[i] = mxCreateDoubleMatrix(0, 0, mxREAL);
}

void do_predict(int nlhs, mxArray *plhs[], const mxArray *prhs[], struct model *model_, const int predict_probability_flag)
{
	int label_vector_row_num, label_vector_col_num;
	int feature_number, testing_instance_number;
	int instance_index;
	double *ptr_label, *ptr_predict_label;
	double *ptr_prob_estimates, *ptr_dec_values, *ptr;
	struct feature_node *x;
	mxArray *pplhs[1]; // instance sparse matrix in row format
	mxArray *tplhs[3]; // temporary storage for plhs[]

	int correct = 0;
	int total = 0;
	double error = 0;
	double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;

	int nr_class=get_nr_class(model_);
	int nr_w;
	double *prob_estimates=NULL;

	if(nr_class==2 && model_->param.solver_type!=MCSVM_CS)
		nr_w=1;
	else
		nr_w=nr_class;

	// prhs[1] = testing instance matrix
	feature_number = get_nr_feature(model_);
	testing_instance_number = (int) mxGetM(prhs[1]);
	if(col_format_flag)
	{
		feature_number = (int) mxGetM(prhs[1]);
		testing_instance_number = (int) mxGetN(prhs[1]);
	}

	label_vector_row_num = (int) mxGetM(prhs[0]);
	label_vector_col_num = (int) mxGetN(prhs[0]);

	if(label_vector_row_num!=testing_instance_number)
	{
		mexPrintf("Length of label vector does not match # of instances.\n");
		fake_answer(nlhs, plhs);
		return;
	}
	if(label_vector_col_num!=1)
	{
		mexPrintf("label (1st argument) should be a vector (# of column is 1).\n");
		fake_answer(nlhs, plhs);
		return;
	}

	ptr_label    = mxGetPr(prhs[0]);

	// transpose instance matrix
	if(col_format_flag)
		pplhs[0] = (mxArray *)prhs[1];
	else
	{
		mxArray *pprhs[1];
		pprhs[0] = mxDuplicateArray(prhs[1]);
		if(mexCallMATLAB(1, pplhs, 1, pprhs, "transpose"))
		{
			mexPrintf("Error: cannot transpose testing instance matrix\n");
			fake_answer(nlhs, plhs);
			return;
		}
	}


	prob_estimates = Malloc(double, nr_class);

	tplhs[0] = mxCreateDoubleMatrix(testing_instance_number, 1, mxREAL);
	if(predict_probability_flag)
		tplhs[2] = mxCreateDoubleMatrix(testing_instance_number, nr_class, mxREAL);
	else
		tplhs[2] = mxCreateDoubleMatrix(testing_instance_number, nr_w, mxREAL);

	ptr_predict_label = mxGetPr(tplhs[0]);
	ptr_prob_estimates = mxGetPr(tplhs[2]);
	ptr_dec_values = mxGetPr(tplhs[2]);
	x = Malloc(struct feature_node, feature_number+2);
	for(instance_index=0;instance_index<testing_instance_number;instance_index++)
	{
		int i;
		double target_label, predict_label;

		target_label = ptr_label[instance_index];

		// prhs[1] and prhs[1]^T are sparse
		read_sparse_instance(pplhs[0], instance_index, x, feature_number, model_->bias);

		if(predict_probability_flag)
		{
			predict_label = predict_probability(model_, x, prob_estimates);
			ptr_predict_label[instance_index] = predict_label;
			for(i=0;i<nr_class;i++)
				ptr_prob_estimates[instance_index + i * testing_instance_number] = prob_estimates[i];
		}
		else
		{
			double *dec_values = Malloc(double, nr_class);
			predict_label = predict_values(model_, x, dec_values);
			ptr_predict_label[instance_index] = predict_label;

			for(i=0;i<nr_w;i++)
				ptr_dec_values[instance_index + i * testing_instance_number] = dec_values[i];
			free(dec_values);
		}

		if(predict_label == target_label)
			++correct;
		error += (predict_label-target_label)*(predict_label-target_label);
		sump += predict_label;
		sumt += target_label;
		sumpp += predict_label*predict_label;
		sumtt += target_label*target_label;
		sumpt += predict_label*target_label;

		++total;
	}

	if(check_regression_model(model_))
	{
		info("Mean squared error = %g (regression)\n",error/total);
		info("Squared correlation coefficient = %g (regression)\n",
			((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
			((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt))
			);
	}
	else
		info("Accuracy = %g%% (%d/%d)\n", (double) correct/total*100,correct,total);

	// return accuracy, mean squared error, squared correlation coefficient
	tplhs[1] = mxCreateDoubleMatrix(3, 1, mxREAL);
	ptr = mxGetPr(tplhs[1]);
	ptr[0] = (double)correct/total*100;
	ptr[1] = error/total;
	ptr[2] = ((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
				((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt));

	free(x);
	if(prob_estimates != NULL)
		free(prob_estimates);

	switch(nlhs)
	{
		case 3:
			plhs[2] = tplhs[2];
			plhs[1] = tplhs[1];
		case 1:
		case 0:
			plhs[0] = tplhs[0];
	}
}

void exit_with_help()
{
	mexPrintf(
			"Usage: [predicted_label, accuracy, decision_values/prob_estimates] = predict(testing_label_vector, testing_instance_matrix, model, 'liblinear_options','col')\n"
			"       [predicted_label] = predict(testing_label_vector, testing_instance_matrix, model, 'liblinear_options','col')\n"
			"liblinear_options:\n"
			"-b probability_estimates: whether to output probability estimates, 0 or 1 (default 0); currently for logistic regression only\n"
			"-q quiet mode (no outputs)\n"
			"col: if 'col' is setted testing_instance_matrix is parsed in column format, otherwise is in row format\n"
			"Returns:\n"
			"  predicted_label: prediction output vector.\n"
			"  accuracy: a vector with accuracy, mean squared error, squared correlation coefficient.\n"
			"  prob_estimates: If selected, probability estimate vector.\n"
			);
}

void mexFunction( int nlhs, mxArray *plhs[],
		int nrhs, const mxArray *prhs[] )
{
	int prob_estimate_flag = 0;
	struct model *model_;
	char cmd[CMD_LEN];
	info = &mexPrintf;
	col_format_flag = 0;

	if(nlhs == 2 || nlhs > 3 || nrhs > 5 || nrhs < 3)
	{
		exit_with_help();
		fake_answer(nlhs, plhs);
		return;
	}
	if(nrhs == 5)
	{
		mxGetString(prhs[4], cmd, mxGetN(prhs[4])+1);
		if(strcmp(cmd, "col") == 0)
		{
			col_format_flag = 1;
		}
	}

	if(!mxIsDouble(prhs[0]) || !mxIsDouble(prhs[1])) {
		mexPrintf("Error: label vector and instance matrix must be double\n");
		fake_answer(nlhs, plhs);
		return;
	}

	if(mxIsStruct(prhs[2]))
	{
		const char *error_msg;

		// parse options
		if(nrhs>=4)
		{
			int i, argc = 1;
			char *argv[CMD_LEN/2];

			// put options in argv[]
			mxGetString(prhs[3], cmd,  mxGetN(prhs[3]) + 1);
			if((argv[argc] = strtok(cmd, " ")) != NULL)
				while((argv[++argc] = strtok(NULL, " ")) != NULL)
					;

			for(i=1;i<argc;i++)
			{
				if(argv[i][0] != '-') break;
				++i;
				if(i>=argc && argv[i-1][1] != 'q')
				{
					exit_with_help();
					fake_answer(nlhs, plhs);
					return;
				}
				switch(argv[i-1][1])
				{
					case 'b':
						prob_estimate_flag = atoi(argv[i]);
						break;
					case 'q':
						info = &print_null;
						i--;
						break;
					default:
						mexPrintf("unknown option\n");
						exit_with_help();
						fake_answer(nlhs, plhs);
						return;
				}
			}
		}

		model_ = Malloc(struct model, 1);
		error_msg = matlab_matrix_to_model(model_, prhs[2]);
		if(error_msg)
		{
			mexPrintf("Error: can't read model: %s\n", error_msg);
			free_and_destroy_model(&model_);
			fake_answer(nlhs, plhs);
			return;
		}

		if(prob_estimate_flag)
		{
			if(!check_probability_model(model_))
			{
				mexPrintf("probability output is only supported for logistic regression\n");
				prob_estimate_flag=0;
			}
		}

		if(mxIsSparse(prhs[1]))
			do_predict(nlhs, plhs, prhs, model_, prob_estimate_flag);
		else
		{
			mexPrintf("Testing_instance_matrix must be sparse; "
				"use sparse(Testing_instance_matrix) first\n");
			fake_answer(nlhs, plhs);
		}

		// destroy model_
		free_and_destroy_model(&model_);
	}
	else
	{
		mexPrintf("model file should be a struct array\n");
		fake_answer(nlhs, plhs);
	}

	return;
}


================================================
FILE: Matlab/liblinear-1.96/matlab/train.c
================================================
#include <stdio.h>
#include <math.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include "../linear.h"

#include "mex.h"
#include "linear_model_matlab.h"

#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif

#define CMD_LEN 2048
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
#define INF HUGE_VAL

void print_null(const char *s) {}
void print_string_matlab(const char *s) {mexPrintf(s);}

void exit_with_help()
{
	mexPrintf(
	"Usage: model = train(training_label_vector, training_instance_matrix, 'liblinear_options', 'col');\n"
	"liblinear_options:\n"
	"-s type : set type of solver (default 1)\n"
	"  for multi-class classification\n"
	"	 0 -- L2-regularized logistic regression (primal)\n"
	"	 1 -- L2-regularized L2-loss support vector classification (dual)\n"	
	"	 2 -- L2-regularized L2-loss support vector classification (primal)\n"
	"	 3 -- L2-regularized L1-loss support vector classification (dual)\n"
	"	 4 -- support vector classification by Crammer and Singer\n"
	"	 5 -- L1-regularized L2-loss support vector classification\n"
	"	 6 -- L1-regularized logistic regression\n"
	"	 7 -- L2-regularized logistic regression (dual)\n"
	"  for regression\n"
	"	11 -- L2-regularized L2-loss support vector regression (primal)\n"
	"	12 -- L2-regularized L2-loss support vector regression (dual)\n"
	"	13 -- L2-regularized L1-loss support vector regression (dual)\n"
	"-c cost : set the parameter C (default 1)\n"
	"-p epsilon : set the epsilon in loss function of SVR (default 0.1)\n"
	"-e epsilon : set tolerance of termination criterion\n"
	"	-s 0 and 2\n" 
	"		|f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,\n" 
	"		where f is the primal function and pos/neg are # of\n" 
	"		positive/negative data (default 0.01)\n"
	"	-s 11\n"
	"		|f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001)\n" 
	"	-s 1, 3, 4 and 7\n"
	"		Dual maximal violation <= eps; similar to libsvm (default 0.1)\n"
	"	-s 5 and 6\n"
	"		|f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,\n"
	"		where f is the primal function (default 0.01)\n"
	"	-s 12 and 13\n"
	"		|f'(alpha)|_1 <= eps |f'(alpha0)|,\n"
	"		where f is the dual function (default 0.1)\n"
	"-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)\n"
	"-wi weight: weights adjust the parameter C of different classes (see README for details)\n"
	"-v n: n-fold cross validation mode\n"
	"-q : quiet mode (no outputs)\n"
	"col:\n"
	"	if 'col' is setted, training_instance_matrix is parsed in column format, otherwise is in row format\n"
	);
}

// liblinear arguments
struct parameter param;		// set by parse_command_line
struct problem prob;		// set by read_problem
struct model *model_;
struct feature_node *x_space;
int cross_validation_flag;
int col_format_flag;
int nr_fold;
double bias;

double do_cross_validation()
{
	int i;
	int total_correct = 0;
	double total_error = 0;
	double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
	double *target = Malloc(double, prob.l);
	double retval = 0.0;

	cross_validation(&prob,&param,nr_fold,target);
	if(param.solver_type == L2R_L2LOSS_SVR || 
	   param.solver_type == L2R_L1LOSS_SVR_DUAL || 
	   param.solver_type == L2R_L2LOSS_SVR_DUAL)
	{
		for(i=0;i<prob.l;i++)
                {
                        double y = prob.y[i];
                        double v = target[i];
                        total_error += (v-y)*(v-y);
                        sumv += v;
                        sumy += y;
                        sumvv += v*v;
                        sumyy += y*y;
                        sumvy += v*y;
                }
                printf("Cross Validation Mean squared error = %g\n",total_error/prob.l);
                printf("Cross Validation Squared correlation coefficient = %g\n",
                        ((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/
                        ((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))
                        );
		retval = total_error/prob.l;
	}
	else
	{
		for(i=0;i<prob.l;i++)
			if(target[i] == prob.y[i])
				++total_correct;
		printf("Cross Validation Accuracy = %g%%\n",100.0*total_correct/prob.l);
		retval = 100.0*total_correct/prob.l;
	}

	free(target);
	return retval;
}

// nrhs should be 3
int parse_command_line(int nrhs, const mxArray *prhs[], char *model_file_name)
{
	int i, argc = 1;
	char cmd[CMD_LEN];
	char *argv[CMD_LEN/2];
	void (*print_func)(const char *) = print_string_matlab;	// default printing to matlab display

	// default values
	param.solver_type = L2R_L2LOSS_SVC_DUAL;
	param.C = 1;
	param.eps = INF; // see setting below
	param.p = 0.1;
	param.nr_weight = 0;
	param.weight_label = NULL;
	param.weight = NULL;
	cross_validation_flag = 0;
	col_format_flag = 0;
	bias = -1;


	if(nrhs <= 1)
		return 1;

	if(nrhs == 4)
	{
		mxGetString(prhs[3], cmd, mxGetN(prhs[3])+1);
		if(strcmp(cmd, "col") == 0)
			col_format_flag = 1;
	}

	// put options in argv[]
	if(nrhs > 2)
	{
		mxGetString(prhs[2], cmd,  mxGetN(prhs[2]) + 1);
		if((argv[argc] = strtok(cmd, " ")) != NULL)
			while((argv[++argc] = strtok(NULL, " ")) != NULL)
				;
	}

	// parse options
	for(i=1;i<argc;i++)
	{
		if(argv[i][0] != '-') break;
		++i;
		if(i>=argc && argv[i-1][1] != 'q') // since option -q has no parameter
			return 1;
		switch(argv[i-1][1])
		{
			case 's':
				param.solver_type = atoi(argv[i]);
				break;
			case 'c':
				param.C = atof(argv[i]);
				break;
			case 'p':
				param.p = atof(argv[i]);
				break;
			case 'e':
				param.eps = atof(argv[i]);
				break;
			case 'B':
				bias = atof(argv[i]);
				break;
			case 'v':
				cross_validation_flag = 1;
				nr_fold = atoi(argv[i]);
				if(nr_fold < 2)
				{
					mexPrintf("n-fold cross validation: n must >= 2\n");
					return 1;
				}
				break;
			case 'w':
				++param.nr_weight;
				param.weight_label = (int *) realloc(param.weight_label,sizeof(int)*param.nr_weight);
				param.weight = (double *) realloc(param.weight,sizeof(double)*param.nr_weight);
				param.weight_label[param.nr_weight-1] = atoi(&argv[i-1][2]);
				param.weight[param.nr_weight-1] = atof(argv[i]);
				break;
			case 'q':
				print_func = &print_null;
				i--;
				break;
			default:
				mexPrintf("unknown option\n");
				return 1;
		}
	}

	set_print_string_function(print_func);

	if(param.eps == INF)
	{
		switch(param.solver_type)
		{
			case L2R_LR: 
			case L2R_L2LOSS_SVC:
				param.eps = 0.01;
				break;
			case L2R_L2LOSS_SVR:
				param.eps = 0.001;
				break;
			case L2R_L2LOSS_SVC_DUAL: 
			case L2R_L1LOSS_SVC_DUAL: 
			case MCSVM_CS: 
			case L2R_LR_DUAL: 
				param.eps = 0.1;
				break;
			case L1R_L2LOSS_SVC: 
			case L1R_LR:
				param.eps = 0.01;
				break;
			case L2R_L1LOSS_SVR_DUAL:
			case L2R_L2LOSS_SVR_DUAL:
				param.eps = 0.1;
				break;
		}
	}
	return 0;
}

static void fake_answer(int nlhs, mxArray *plhs[])
{
	int i;
	for(i=0;i<nlhs;i++)
		plhs[i] = mxCreateDoubleMatrix(0, 0, mxREAL);
}

int read_problem_sparse(const mxArray *label_vec, const mxArray *instance_mat)
{
	mwIndex *ir, *jc, low, high, k;
	// using size_t due to the output type of matlab functions
	size_t i, j, l, elements, max_index, label_vector_row_num;
	mwSize num_samples;
	double *samples, *labels;
	mxArray *instance_mat_col; // instance sparse matrix in column format

	prob.x = NULL;
	prob.y = NULL;
	x_space = NULL;

	if(col_format_flag)
		instance_mat_col = (mxArray *)instance_mat;
	else
	{
		// transpose instance matrix
		mxArray *prhs[1], *plhs[1];
		prhs[0] = mxDuplicateArray(instance_mat);
		if(mexCallMATLAB(1, plhs, 1, prhs, "transpose"))
		{
			mexPrintf("Error: cannot transpose training instance matrix\n");
			return -1;
		}
		instance_mat_col = plhs[0];
		mxDestroyArray(prhs[0]);
	}

	// the number of instance
	l = mxGetN(instance_mat_col);
	label_vector_row_num = mxGetM(label_vec);
	prob.l = (int) l;

	if(label_vector_row_num!=l)
	{
		mexPrintf("Length of label vector does not match # of instances.\n");
		return -1;
	}
	
	// each column is one instance
	labels = mxGetPr(label_vec);
	samples = mxGetPr(instance_mat_col);
	ir = mxGetIr(instance_mat_col);
	jc = mxGetJc(instance_mat_col);

	num_samples = mxGetNzmax(instance_mat_col);

	elements = num_samples + l*2;
	max_index = mxGetM(instance_mat_col);

	prob.y = Malloc(double, l);
	prob.x = Malloc(struct feature_node*, l);
	x_space = Malloc(struct feature_node, elements);

	prob.bias=bias;

	j = 0;
	for(i=0;i<l;i++)
	{
		prob.x[i] = &x_space[j];
		prob.y[i] = labels[i];
		low = jc[i], high = jc[i+1];
		for(k=low;k<high;k++)
		{
			x_space[j].index = (int) ir[k]+1;
			x_space[j].value = samples[k];
			j++;
	 	}
		if(prob.bias>=0)
		{
			x_space[j].index = (int) max_index+1;
			x_space[j].value = prob.bias;
			j++;
		}
		x_space[j++].index = -1;
	}

	if(prob.bias>=0)
		prob.n = (int) max_index+1;
	else
		prob.n = (int) max_index;

	return 0;
}

// Interface function of matlab
// now assume prhs[0]: label prhs[1]: features
void mexFunction( int nlhs, mxArray *plhs[],
		int nrhs, const mxArray *prhs[] )
{
	const char *error_msg;
	// fix random seed to have same results for each run
	// (for cross validation)
	srand(1);

	if(nlhs > 1)
	{
		exit_with_help();
		fake_answer(nlhs, plhs);
		return;
	}

	// Transform the input Matrix to libsvm format
	if(nrhs > 1 && nrhs < 5)
	{
		int err=0;

		if(!mxIsDouble(prhs[0]) || !mxIsDouble(prhs[1]))
		{
			mexPrintf("Error: label vector and instance matrix must be double\n");
			fake_answer(nlhs, plhs);
			return;
		}

		if(mxIsSparse(prhs[0]))
		{
			mexPrintf("Error: label vector should not be in sparse format");
			fake_answer(nlhs, plhs);
			return;
		}

		if(parse_command_line(nrhs, prhs, NULL))
		{
			exit_with_help();
			destroy_param(&param);
			fake_answer(nlhs, plhs);
			return;
		}

		if(mxIsSparse(prhs[1]))
			err = read_problem_sparse(prhs[0], prhs[1]);
		else
		{
			mexPrintf("Training_instance_matrix must be sparse; "
				"use sparse(Training_instance_matrix) first\n");
			destroy_param(&param);
			fake_answer(nlhs, plhs);
			return;
		}

		// train's original code
		error_msg = check_parameter(&prob, &param);

		if(err || error_msg)
		{
			if (error_msg != NULL)
				mexPrintf("Error: %s\n", error_msg);
			destroy_param(&param);
			free(prob.y);
			free(prob.x);
			free(x_space);
			fake_answer(nlhs, plhs);
			return;
		}

		if(cross_validation_flag)
		{
			double *ptr;
			plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL);
			ptr = mxGetPr(plhs[0]);
			ptr[0] = do_cross_validation();
		}
		else
		{
			const char *error_msg;

			model_ = train(&prob, &param);
			error_msg = model_to_matlab_structure(plhs, model_);
			if(error_msg)
				mexPrintf("Error: can't convert libsvm model to matrix structure: %s\n", error_msg);
			free_and_destroy_model(&model_);
		}
		destroy_param(&param);
		free(prob.y);
		free(prob.x);
		free(x_space);
	}
	else
	{
		exit_with_help();
		fake_answer(nlhs, plhs);
		return;
	}
}


================================================
FILE: Matlab/liblinear-1.96/predict.c
================================================
#include <stdio.h>
#include <ctype.h>
#include <stdlib.h>
#include <string.h>
#include <errno.h>
#include "linear.h"

int print_null(const char *s,...) {return 0;}

static int (*info)(const char *fmt,...) = &printf;

struct feature_node *x;
int max_nr_attr = 64;

struct model* model_;
int flag_predict_probability=0;

void exit_input_error(int line_num)
{
	fprintf(stderr,"Wrong input format at line %d\n", line_num);
	exit(1);
}

static char *line = NULL;
static int max_line_len;

static char* readline(FILE *input)
{
	int len;

	if(fgets(line,max_line_len,input) == NULL)
		return NULL;

	while(strrchr(line,'\n') == NULL)
	{
		max_line_len *= 2;
		line = (char *) realloc(line,max_line_len);
		len = (int) strlen(line);
		if(fgets(line+len,max_line_len-len,input) == NULL)
			break;
	}
	return line;
}

void do_predict(FILE *input, FILE *output)
{
	int correct = 0;
	int total = 0;
	double error = 0;
	double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;

	int nr_class=get_nr_class(model_);
	double *prob_estimates=NULL;
	int j, n;
	int nr_feature=get_nr_feature(model_);
	if(model_->bias>=0)
		n=nr_feature+1;
	else
		n=nr_feature;

	if(flag_predict_probability)
	{
		int *labels;

		if(!check_probability_model(model_))
		{
			fprintf(stderr, "probability output is only supported for logistic regression\n");
			exit(1);
		}

		labels=(int *) malloc(nr_class*sizeof(int));
		get_labels(model_,labels);
		prob_estimates = (double *) malloc(nr_class*sizeof(double));
		fprintf(output,"labels");
		for(j=0;j<nr_class;j++)
			fprintf(output," %d",labels[j]);
		fprintf(output,"\n");
		free(labels);
	}

	max_line_len = 1024;
	line = (char *)malloc(max_line_len*sizeof(char));
	while(readline(input) != NULL)
	{
		int i = 0;
		double target_label, predict_label;
		char *idx, *val, *label, *endptr;
		int inst_max_index = 0; // strtol gives 0 if wrong format

		label = strtok(line," \t\n");
		if(label == NULL) // empty line
			exit_input_error(total+1);

		target_label = strtod(label,&endptr);
		if(endptr == label || *endptr != '\0')
			exit_input_error(total+1);

		while(1)
		{
			if(i>=max_nr_attr-2)	// need one more for index = -1
			{
				max_nr_attr *= 2;
				x = (struct feature_node *) realloc(x,max_nr_attr*sizeof(struct feature_node));
			}

			idx = strtok(NULL,":");
			val = strtok(NULL," \t");

			if(val == NULL)
				break;
			errno = 0;
			x[i].index = (int) strtol(idx,&endptr,10);
			if(endptr == idx || errno != 0 || *endptr != '\0' || x[i].index <= inst_max_index)
				exit_input_error(total+1);
			else
				inst_max_index = x[i].index;

			errno = 0;
			x[i].value = strtod(val,&endptr);
			if(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
				exit_input_error(total+1);

			// feature indices larger than those in training are not used
			if(x[i].index <= nr_feature)
				++i;
		}

		if(model_->bias>=0)
		{
			x[i].index = n;
			x[i].value = model_->bias;
			i++;
		}
		x[i].index = -1;

		if(flag_predict_probability)
		{
			int j;
			predict_label = predict_probability(model_,x,prob_estimates);
			fprintf(output,"%g",predict_label);
			for(j=0;j<model_->nr_class;j++)
				fprintf(output," %g",prob_estimates[j]);
			fprintf(output,"\n");
		}
		else
		{
			predict_label = predict(model_,x);
			fprintf(output,"%g\n",predict_label);
		}

		if(predict_label == target_label)
			++correct;
		error += (predict_label-target_label)*(predict_label-target_label);
		sump += predict_label;
		sumt += target_label;
		sumpp += predict_label*predict_label;
		sumtt += target_label*target_label;
		sumpt += predict_label*target_label;
		++total;
	}
	if(check_regression_model(model_))
	{
		info("Mean squared error = %g (regression)\n",error/total);
		info("Squared correlation coefficient = %g (regression)\n",
			((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
			((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt))
			);
	}
	else
		info("Accuracy = %g%% (%d/%d)\n",(double) correct/total*100,correct,total);
	if(flag_predict_probability)
		free(prob_estimates);
}

void exit_with_help()
{
	printf(
	"Usage: predict [options] test_file model_file output_file\n"
	"options:\n"
	"-b probability_estimates: whether to output probability estimates, 0 or 1 (default 0); currently for logistic regression only\n"
	"-q : quiet mode (no outputs)\n"
	);
	exit(1);
}

int main(int argc, char **argv)
{
	FILE *input, *output;
	int i;

	// parse options
	for(i=1;i<argc;i++)
	{
		if(argv[i][0] != '-') break;
		++i;
		switch(argv[i-1][1])
		{
			case 'b':
				flag_predict_probability = atoi(argv[i]);
				break;
			case 'q':
				info = &print_null;
				i--;
				break;
			default:
				fprintf(stderr,"unknown option: -%c\n", argv[i-1][1]);
				exit_with_help();
				break;
		}
	}
	if(i>=argc)
		exit_with_help();

	input = fopen(argv[i],"r");
	if(input == NULL)
	{
		fprintf(stderr,"can't open input file %s\n",argv[i]);
		exit(1);
	}

	output = fopen(argv[i+2],"w");
	if(output == NULL)
	{
		fprintf(stderr,"can't open output file %s\n",argv[i+2]);
		exit(1);
	}

	if((model_=load_model(argv[i+1]))==0)
	{
		fprintf(stderr,"can't open model file %s\n",argv[i+1]);
		exit(1);
	}

	x = (struct feature_node *) malloc(max_nr_attr*sizeof(struct feature_node));
	do_predict(input, output);
	free_and_destroy_model(&model_);
	free(line);
	free(x);
	fclose(input);
	fclose(output);
	return 0;
}



================================================
FILE: Matlab/liblinear-1.96/python/Makefile
================================================
all = lib

lib:
	make -C .. lib


================================================
FILE: Matlab/liblinear-1.96/python/README
================================================
-------------------------------------
--- Python interface of LIBLINEAR ---
-------------------------------------

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

- Introduction
- Installation
- Quick Start
- Design Description
- Data Structures
- Utility Functions
- Additional Information

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

Python (http://www.python.org/) is a programming language suitable for rapid
development. This tool provides a simple Python interface to LIBLINEAR, a library
for support vector machines (http://www.csie.ntu.edu.tw/~cjlin/liblinear). The
interface is very easy to use as the usage is the same as that of LIBLINEAR. The
interface is developed with the built-in Python library "ctypes."

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

On Unix systems, type

> make

The interface needs only LIBLINEAR shared library, which is generated by
the above command. We assume that the shared library is on the LIBLINEAR
main directory or in the system path.

For windows, the shared library liblinear.dll is ready in the directory
`..\windows'. You can also copy it to the system directory (e.g.,
`C:\WINDOWS\system32\' for Windows XP). To regenerate the shared library,
please follow the instruction of building windows binaries in LIBLINEAR README.

Quick Start
===========

There are two levels of usage. The high-level one uses utility functions
in liblinearutil.py and the usage is the same as the LIBLINEAR MATLAB interface.

>>> from liblinearutil import *
# Read data in LIBSVM format
>>> y, x = svm_read_problem('../heart_scale')
>>> m = train(y[:200], x[:200], '-c 4')
>>> p_label, p_acc, p_val = predict(y[200:], x[200:], m)

# Construct problem in python format
# Dense data
>>> y, x = [1,-1], [[1,0,1], [-1,0,-1]]
# Sparse data
>>> y, x = [1,-1], [{1:1, 3:1}, {1:-1,3:-1}]
>>> prob  = problem(y, x)
>>> param = parameter('-s 0 -c 4 -B 1')
>>> m = train(prob, param)

# Other utility functions
>>> save_model('heart_scale.model', m)
>>> m = load_model('heart_scale.model')
>>> p_label, p_acc, p_val = predict(y, x, m, '-b 1')
>>> ACC, MSE, SCC = evaluations(y, p_label)

# Getting online help
>>> help(train)

The low-level use directly calls C interfaces imported by liblinear.py. Note that
all arguments and return values are in ctypes format. You need to handle them
carefully.

>>> from liblinear import *
>>> prob = problem([1,-1], [{1:1, 3:1}, {1:-1,3:-1}])
>>> param = parameter('-c 4')
>>> m = liblinear.train(prob, param) # m is a ctype pointer to a model
# Convert a Python-format instance to feature_nodearray, a ctypes structure
>>> x0, max_idx = gen_feature_nodearray({1:1, 3:1})
>>> label = liblinear.predict(m, x0)

Design Description
==================

There are two files liblinear.py and liblinearutil.py, which respectively correspond to
low-level and high-level use of the interface.

In liblinear.py, we adopt the Python built-in library "ctypes," so that
Python can directly access C structures and interface functions defined
in linear.h.

While advanced users can use structures/functions in liblinear.py, to
avoid handling ctypes structures, in liblinearutil.py we provide some easy-to-use
functions. The usage is similar to LIBLINEAR MATLAB interface.

Data Structures
===============

Three data structures derived from linear.h are node, problem, and
parameter. They all contain fields with the same names in
linear.h. Access these fields carefully because you directly use a C structure
instead of a Python object. The following description introduces additional
fields and methods.

Before using the data structures, execute the following command to load the
LIBLINEAR shared library:

    >>> from liblinear import *

- class feature_node:

    Construct a feature_node.

    >>> node = feature_node(idx, val)

    idx: an integer indicates the feature index.

    val: a float indicates the feature value.

    Show the index and the value of a node.

    >>> print(node) 

- Function: gen_feature_nodearray(xi [,feature_max=None [,issparse=True]])

    Generate a feature vector from a Python list/tuple or a dictionary:

    >>> xi, max_idx = gen_feature_nodearray({1:1, 3:1, 5:-2})

    xi: the returned feature_nodearray (a ctypes structure)

    max_idx: the maximal feature index of xi

    issparse: if issparse == True, zero feature values are removed. The default
              value is True for the sparsity.

    feature_max: if feature_max is assigned, features with indices larger than
                 feature_max are removed.

- class problem:

    Construct a problem instance

    >>> prob = problem(y, x [,bias=-1])

    y: a Python list/tuple of l labels (type must be int/double).

    x: a Python list/tuple of l data instances. Each element of x must be
       an instance of list/tuple/dictionary type.

    bias: if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term 
          added (default -1)

    You can also modify the bias value by

    >>> prob.set_bias(1)

    Note that if your x contains sparse data (i.e., dictionary), the internal
    ctypes data format is still sparse.

- class parameter:

    Construct a parameter instance

    >>> param = parameter('training_options')

    If 'training_options' is empty, LIBLINEAR default values are applied.

    Set param to LIBLINEAR default values.

    >>> param.set_to_default_values()

    Parse a string of options.

    >>> param.parse_options('training_options')

    Show values of parameters.

    >>> print(param)

- class model:

    There are two ways to obtain an instance of model:

    >>> model_ = train(y, x)
    >>> model_ = load_model('model_file_name')

    Note that the returned structure of interface functions
    liblinear.train and liblinear.load_model is a ctypes pointer of
    model, which is different from the model object returned
    by train and load_model in liblinearutil.py. We provide a
    function toPyModel for the conversion:

    >>> model_ptr = liblinear.train(prob, param)
    >>> model_ = toPyModel(model_ptr)

    If you obtain a model in a way other than the above approaches,
    handle it carefully to avoid memory leak or segmentation fault.

    Some interface functions to access LIBLINEAR models are wrapped as
    members of the class model:

    >>> nr_feature =  model_.get_nr_feature()
    >>> nr_class = model_.get_nr_class()
    >>> class_labels = model_.get_labels()
    >>> is_prob_model = model_.is_probability_model()
    >>> is_regression_model = model_.is_regression_model()

    The decision function is W*x + b, where
        W is an nr_class-by-nr_feature matrix, and
        b is a vector of size nr_class.
    To access W_kj (i.e., coefficient for the k-th class and the j-th feature)
    and b_k (i.e., bias for the k-th class), use the following functions.

    >>> W_kj = model_.get_decfun_coef(feat_idx=j, label_idx=k)
    >>> b_k = model_.get_decfun_bias(label_idx=k)

    We also provide a function to extract w_k (i.e., the k-th row of W) and
    b_k directly as follows.

    >>> [w_k, b_k] = model_.get_decfun(label_idx=k)

    Note that w_k is a Python list of length nr_feature, which means that
        w_k[0] = W_k1.
    For regression models, W is just a vector of length nr_feature. Either
	set label_idx=0 or omit the label_idx parameter to access the coefficients.

    >>> W_j = model_.get_decfun_coef(feat_idx=j)
    >>> b = model_.get_decfun_bias()
    >>> [W, b] = model_.get_decfun()

    Note that in get_decfun_coef, get_decfun_bias, and get_decfun, feat_idx
    starts from 1, while label_idx starts from 0. If label_idx is not in the
    valid range (0 to nr_class-1), then a NaN will be returned; and if feat_idx
    is not in the valid range (1 to nr_feature), then a zero value will be
    returned. For regression models, label_idx is ignored.

Utility Functions
=================

To use utility functions, type

    >>> from liblinearutil import *

The above command loads
    train()            : train a linear model
    predict()          : predict testing data
    svm_read_problem() : read the data from a LIBSVM-format file.
    load_model()       : load a LIBLINEAR model.
    save_model()       : save model to a file.
    evaluations()      : evaluate prediction results.

- Function: train

    There are three ways to call train()

    >>> model = train(y, x [, 'training_options'])
    >>> model = train(prob [, 'training_options'])
    >>> model = train(prob, param)

    y: a list/tuple of l training labels (type must be int/double).

    x: a list/tuple of l training instances. The feature vector of
       each training instance is an instance of list/tuple or dictionary.

    training_options: a string in the same form as that for LIBLINEAR command
                      mode.

    prob: a problem instance generated by calling
          problem(y, x).

    param: a parameter instance generated by calling
           parameter('training_options')

    model: the returned model instance. See linear.h for details of this
           structure. If '-v' is specified, cross validation is
           conducted and the returned model is just a scalar: cross-validation
           accuracy for classification and mean-squared error for regression.

    To train the same data many times with different
    parameters, the second and the third ways should be faster..

    Examples:

    >>> y, x = svm_read_problem('../heart_scale')
    >>> prob = problem(y, x)
    >>> param = parameter('-s 3 -c 5 -q')
    >>> m = train(y, x, '-c 5')
    >>> m = train(prob, '-w1 5 -c 5')
    >>> m = train(prob, param)
    >>> CV_ACC = train(y, x, '-v 3')

- Function: predict

    To predict testing data with a model, use

    >>> p_labs, p_acc, p_vals = predict(y, x, model [,'predicting_options'])

    y: a list/tuple of l true labels (type must be int/double). It is used
       for calculating the accuracy. Use [] if true labels are
       unavailable.

    x: a list/tuple of l predicting instances. The feature vector of
       each predicting instance is an instance of list/tuple or dictionary.

    predicting_options: a string of predicting options in the same format as
                        that of LIBLINEAR.

    model: a model instance.

    p_labels: a list of predicted labels

    p_acc: a tuple including accuracy (for classification), mean
           squared error, and squared correlation coefficient (for
           regression).

    p_vals: a list of decision values or probability estimates (if '-b 1' 
            is specified). If k is the number of classes, for decision values,
            each element includes results of predicting k binary-class
            SVMs. If k = 2 and solver is not MCSVM_CS, only one decision value 
            is returned. For probabilities, each element contains k values 
            indicating the probability that the testing instance is in each class.
            Note that the order of classes here is the same as 'model.label'
            field in the model structure.

    Example:

    >>> m = train(y, x, '-c 5')
    >>> p_labels, p_acc, p_vals = predict(y, x, m)

- Functions: svm_read_problem/load_model/save_model

    See the usage by examples:

    >>> y, x = svm_read_problem('data.txt')
    >>> m = load_model('model_file')
    >>> save_model('model_file', m)

- Function: evaluations

    Calculate some evaluations using the true values (ty) and predicted
    values (pv):

    >>> (ACC, MSE, SCC) = evaluations(ty, pv)

    ty: a list of true values.

    pv: a list of predict values.

    ACC: accuracy.

    MSE: mean squared error.

    SCC: squared correlation coefficient.


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

This interface was written by Hsiang-Fu Yu from Department of Computer
Science, National Taiwan University. If you find this tool useful, please
cite LIBLINEAR as follows

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

For any question, please contact Chih-Jen Lin <cjlin@csie.ntu.edu.tw>,
or check the FAQ page:

http://www.csie.ntu.edu.tw/~cjlin/liblinear/faq.html


================================================
FILE: Matlab/liblinear-1.96/python/liblinear.py
================================================
#!/usr/bin/env python

from ctypes import *
from ctypes.util import find_library
from os import path
import sys

__all__ = ['liblinear', 'feature_node', 'gen_feature_nodearray', 'problem',
           'parameter', 'model', 'toPyModel', 'L2R_LR', 'L2R_L2LOSS_SVC_DUAL',
           'L2R_L2LOSS_SVC', 'L2R_L1LOSS_SVC_DUAL', 'MCSVM_CS', 
           'L1R_L2LOSS_SVC', 'L1R_LR', 'L2R_LR_DUAL', 'L2R_L2LOSS_SVR', 
           'L2R_L2LOSS_SVR_DUAL', 'L2R_L1LOSS_SVR_DUAL', 'print_null']

try:
	dirname = path.dirname(path.abspath(__file__))
	if sys.platform == 'win32':
		liblinear = CDLL(path.join(dirname, r'..\windows\liblinear.dll'))
	else:
		liblinear = CDLL(path.join(dirname, '../liblinear.so.2'))
except:
# For unix the prefix 'lib' is not considered.
	if find_library('linear'):
		liblinear = CDLL(find_library('linear'))
	elif find_library('liblinear'):
		liblinear = CDLL(find_library('liblinear'))
	else:
		raise Exception('LIBLINEAR library not found.')

L2R_LR = 0
L2R_L2LOSS_SVC_DUAL = 1 
L2R_L2LOSS_SVC = 2 
L2R_L1LOSS_SVC_DUAL = 3
MCSVM_CS = 4 
L1R_L2LOSS_SVC = 5 
L1R_LR = 6 
L2R_LR_DUAL = 7  
L2R_L2LOSS_SVR = 11
L2R_L2LOSS_SVR_DUAL = 12
L2R_L1LOSS_SVR_DUAL = 13

PRINT_STRING_FUN = CFUNCTYPE(None, c_char_p)
def print_null(s): 
	return 

def genFields(names, types): 
	return list(zip(names, types))

def fillprototype(f, restype, argtypes): 
	f.restype = restype
	f.argtypes = argtypes

class feature_node(Structure):
	_names = ["index", "value"]
	_types = [c_int, c_double]
	_fields_ = genFields(_names, _types)

	def __str__(self):
		return '%d:%g' % (self.index, self.value)

def gen_feature_nodearray(xi, feature_max=None, issparse=True):
	if isinstance(xi, dict):
		index_range = xi.keys()
	elif isinstance(xi, (list, tuple)):
		xi = [0] + xi  # idx should start from 1
		index_range = range(1, len(xi))
	else:
		raise TypeError('xi should be a dictionary, list or tuple')

	if feature_max:
		assert(isinstance(feature_max, int))
		index_range = filter(lambda j: j <= feature_max, index_range)
	if issparse: 
		index_range = filter(lambda j:xi[j] != 0, index_range)

	index_range = sorted(index_range)
	ret = (feature_node * (len(index_range)+2))()
	ret[-1].index = -1 # for bias term
	ret[-2].index = -1
	for idx, j in enumerate(index_range):
		ret[idx].index = j
		ret[idx].value = xi[j]
	max_idx = 0
	if index_range : 
		max_idx = index_range[-1]
	return ret, max_idx

class problem(Structure):
	_names = ["l", "n", "y", "x", "bias"]
	_types = [c_int, c_int, POINTER(c_double), POINTER(POINTER(feature_node)), c_double]
	_fields_ = genFields(_names, _types)

	def __init__(self, y, x, bias = -1):
		if len(y) != len(x) :
			raise ValueError("len(y) != len(x)")
		self.l = l = len(y)
		self.bias = -1

		max_idx = 0
		x_space = self.x_space = []
		for i, xi in enumerate(x):
			tmp_xi, tmp_idx = gen_feature_nodearray(xi)
			x_space += [tmp_xi]
			max_idx = max(max_idx, tmp_idx)
		self.n = max_idx

		self.y = (c_double * l)()
		for i, yi in enumerate(y): self.y[i] = y[i]

		self.x = (POINTER(feature_node) * l)() 
		for i, xi in enumerate(self.x_space): self.x[i] = xi

		self.set_bias(bias)

	def set_bias(self, bias):
		if self.bias == bias:
			return 
		if bias >= 0 and self.bias < 0: 
			self.n += 1
			node = feature_node(self.n, bias)
		if bias < 0 and self.bias >= 0: 
			self.n -= 1
			node = feature_node(-1, bias)

		for xi in self.x_space:
			xi[-2] = node
		self.bias = bias


class parameter(Structure):
	_names = ["solver_type", "eps", "C", "nr_weight", "weight_label", "weight", "p"]
	_types = [c_int, c_double, c_double, c_int, POINTER(c_int), POINTER(c_double), c_double]
	_fields_ = genFields(_names, _types)

	def __init__(self, options = None):
		if options == None:
			options = ''
		self.parse_options(options)

	def __str__(self):
		s = ''
		attrs = parameter._names + list(self.__dict__.keys())
		values = map(lambda attr: getattr(self, attr), attrs) 
		for attr, val in zip(attrs, values):
			s += (' %s: %s\n' % (attr, val))
		s = s.strip()

		return s

	def set_to_default_values(self):
		self.solver_type = L2R_L2LOSS_SVC_DUAL
		self.eps = float('inf')
		self.C = 1
		self.p = 0.1
		self.nr_weight = 0
		self.weight_label = (c_int * 0)()
		self.weight = (c_double * 0)()
		self.bias = -1
		self.cross_validation = False
		self.nr_fold = 0
		self.print_func = cast(None, PRINT_STRING_FUN)

	def parse_options(self, options):
		if isinstance(options, list):
			argv = options
		elif isinstance(options, str):
			argv = options.split()
		else:
			raise TypeError("arg 1 should be a list or a str.")
		self.set_to_default_values()
		self.print_func = cast(None, PRINT_STRING_FUN)
		weight_label = []
		weight = []

		i = 0
		while i < len(argv) :
			if argv[i] == "-s":
				i = i + 1
				self.solver_type = int(argv[i])
			elif argv[i] == "-c":
				i = i + 1
				self.C = float(argv[i])
			elif argv[i] == "-p":
				i = i + 1
				self.p = float(argv[i])
			elif argv[i] == "-e":
				i = i + 1
				self.eps = float(argv[i])
			elif argv[i] == "-B":
				i = i + 1
				self.bias = float(argv[i])
			elif argv[i] == "-v":
				i = i + 1
				self.cross_validation = 1
				self.nr_fold = int(argv[i])
				if self.nr_fold < 2 :
					raise ValueError("n-fold cross validation: n must >= 2")
			elif argv[i].startswith("-w"):
				i = i + 1
				self.nr_weight += 1
				nr_weight = self.nr_weight
				weight_label += [int(argv[i-1][2:])]
				weight += [float(argv[i])]
			elif argv[i] == "-q":
				self.print_func = PRINT_STRING_FUN(print_null)
			else :
				raise ValueError("Wrong options")
			i += 1

		liblinear.set_print_string_function(self.print_func)
		self.weight_label = (c_int*self.nr_weight)()
		self.weight = (c_double*self.nr_weight)()
		for i in range(self.nr_weight): 
			self.weight[i] = weight[i]
			self.weight_label[i] = weight_label[i]

		if self.eps == float('inf'):
			if self.solver_type in [L2R_LR, L2R_L2LOSS_SVC]:
				self.eps = 0.01
			elif self.solver_type in [L2R_L2LOSS_SVR]:
				self.eps = 0.001
			elif self.solver_type in [L2R_L2LOSS_SVC_DUAL, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L2R_LR_DUAL]:
				self.eps = 0.1
			elif self.solver_type in [L1R_L2LOSS_SVC, L1R_LR]:
				self.eps = 0.01
			elif self.solver_type in [L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL]:
				self.eps = 0.1

class model(Structure):
	_names = ["param", "nr_class", "nr_feature", "w", "label", "bias"]
	_types = [parameter, c_int, c_int, POINTER(c_double), POINTER(c_int), c_double]
	_fields_ = genFields(_names, _types)

	def __init__(self):
		self.__createfrom__ = 'python'

	def __del__(self):
		# free memory created by C to avoid memory leak
		if hasattr(self, '__createfrom__') and self.__createfrom__ == 'C':
			liblinear.free_and_destroy_model(pointer(self))

	def get_nr_feature(self):
		return liblinear.get_nr_feature(self)

	def get_nr_class(self):
		return liblinear.get_nr_class(self)

	def get_labels(self):
		nr_class = self.get_nr_class()
		labels = (c_int * nr_class)()
		liblinear.get_labels(self, labels)
		return labels[:nr_class]

	def get_decfun_coef(self, feat_idx, label_idx=0):
		return liblinear.get_decfun_coef(self, feat_idx, label_idx)

	def get_decfun_bias(self, label_idx=0):
		return liblinear.get_decfun_bias(self, label_idx)

	def get_decfun(self, label_idx=0):
		w = [liblinear.get_decfun_coef(self, feat_idx, label_idx) for feat_idx in range(1, self.nr_feature+1)]
		b = liblinear.get_decfun_bias(self, label_idx)
		return (w, b)

	def is_probability_model(self):
		return (liblinear.check_probability_model(self) == 1)

	def is_regression_model(self):
		return (liblinear.check_regression_model(self) == 1)

def toPyModel(model_ptr):
	"""
	toPyModel(model_ptr) -> model

	Convert a ctypes POINTER(model) to a Python model
	"""
	if bool(model_ptr) == False:
		raise ValueError("Null pointer")
	m = model_ptr.contents
	m.__createfrom__ = 'C'
	return m

fillprototype(liblinear.train, POINTER(model), [POINTER(problem), POINTER(parameter)])
fillprototype(liblinear.cross_validation, None, [POINTER(problem), POINTER(parameter), c_int, POINTER(c_double)])

fillprototype(liblinear.predict_values, c_double, [POINTER(model), POINTER(feature_node), POINTER(c_double)])
fillprototype(liblinear.predict, c_double, [POINTER(model), POINTER(feature_node)])
fillprototype(liblinear.predict_probability, c_double, [POINTER(model), POINTER(feature_node), POINTER(c_double)])

fillprototype(liblinear.save_model, c_int, [c_char_p, POINTER(model)])
fillprototype(liblinear.load_model, POINTER(model), [c_char_p])

fillprototype(liblinear.get_nr_feature, c_int, [POINTER(model)])
fillprototype(liblinear.get_nr_class, c_int, [POINTER(model)])
fillprototype(liblinear.get_labels, None, [POINTER(model), POINTER(c_int)])
fillprototype(liblinear.get_decfun_coef, c_double, [POINTER(model), c_int, c_int])
fillprototype(liblinear.get_decfun_bias, c_double, [POINTER(model), c_int])

fillprototype(liblinear.free_model_content, None, [POINTER(model)])
fillprototype(liblinear.free_and_destroy_model, None, [POINTER(POINTER(model))])
fillprototype(liblinear.destroy_param, None, [POINTER(parameter)])
fillprototype(liblinear.check_parameter, c_char_p, [POINTER(problem), POINTER(parameter)])
fillprototype(liblinear.check_probability_model, c_int, [POINTER(model)])
fillprototype(liblinear.check_regression_model, c_int, [POINTER(model)])
fillprototype(liblinear.set_print_string_function, None, [CFUNCTYPE(None, c_char_p)])


================================================
FILE: Matlab/liblinear-1.96/python/liblinearutil.py
================================================
#!/usr/bin/env python

import os, sys
sys.path = [os.path.dirname(os.path.abspath(__file__))] + sys.path 
from liblinear import *
from liblinear import __all__ as liblinear_all
from ctypes import c_double

__all__ = ['svm_read_problem', 'load_model', 'save_model', 'evaluations',
           'train', 'predict'] + liblinear_all


def svm_read_problem(data_file_name):
	"""
	svm_read_problem(data_file_name) -> [y, x]

	Read LIBSVM-format data from data_file_name and return labels y
	and data instances x.
	"""
	prob_y = []
	prob_x = []
	for line in open(data_file_name):
		line = line.split(None, 1)
		# In case an instance with all zero features
		if len(line) == 1: line += ['']
		label, features = line
		xi = {}
		for e in features.split():
			ind, val = e.split(":")
			xi[int(ind)] = float(val)
		prob_y += [float(label)]
		prob_x += [xi]
	return (prob_y, prob_x)

def load_model(model_file_name):
	"""
	load_model(model_file_name) -> model

	Load a LIBLINEAR model from model_file_name and return.
	"""
	model = liblinear.load_model(model_file_name.encode())
	if not model:
		print("can't open model file %s" % model_file_name)
		return None
	model = toPyModel(model)
	return model

def save_model(model_file_name, model):
	"""
	save_model(model_file_name, model) -> None

	Save a LIBLINEAR model to the file model_file_name.
	"""
	liblinear.save_model(model_file_name.encode(), model)

def evaluations(ty, pv):
	"""
	evaluations(ty, pv) -> (ACC, MSE, SCC)

	Calculate accuracy, mean squared error and squared correlation coefficient
	using the true values (ty) and predicted values (pv).
	"""
	if len(ty) != len(pv):
		raise ValueError("len(ty) must equal to len(pv)")
	total_correct = total_error = 0
	sumv = sumy = sumvv = sumyy = sumvy = 0
	for v, y in zip(pv, ty):
		if y == v:
			total_correct += 1
		total_error += (v-y)*(v-y)
		sumv += v
		sumy += y
		sumvv += v*v
		sumyy += y*y
		sumvy += v*y
	l = len(ty)
	ACC = 100.0*total_correct/l
	MSE = total_error/l
	try:
		SCC = ((l*sumvy-sumv*sumy)*(l*sumvy-sumv*sumy))/((l*sumvv-sumv*sumv)*(l*sumyy-sumy*sumy))
	except:
		SCC = float('nan')
	return (ACC, MSE, SCC)

def train(arg1, arg2=None, arg3=None):
	"""
	train(y, x [, options]) -> model | ACC
	train(prob [, options]) -> model | ACC
	train(prob, param) -> model | ACC

	Train a model from data (y, x) or a problem prob using
	'options' or a parameter param.
	If '-v' is specified in 'options' (i.e., cross validation)
	either accuracy (ACC) or mean-squared error (MSE) is returned.

	options:
		-s type : set type of solver (default 1)
		  for multi-class classification
			 0 -- L2-regularized logistic regression (primal)
			 1 -- L2-regularized L2-loss support vector classification (dual)
			 2 -- L2-regularized L2-loss support vector classification (primal)
			 3 -- L2-regularized L1-loss support vector classification (dual)
			 4 -- support vector classification by Crammer and Singer
			 5 -- L1-regularized L2-loss support vector classification
			 6 -- L1-regularized logistic regression
			 7 -- L2-regularized logistic regression (dual)
		  for regression
			11 -- L2-regularized L2-loss support vector regression (primal)
			12 -- L2-regularized L2-loss support vector regression (dual)
			13 -- L2-regularized L1-loss support vector regression (dual)
		-c cost : set the parameter C (default 1)
		-p epsilon : set the epsilon in loss function of SVR (default 0.1)
		-e epsilon : set tolerance of termination criterion
			-s 0 and 2
				|f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,
				where f is the primal function, (default 0.01)
			-s 11
				|f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001)
			-s 1, 3, 4, and 7
				Dual maximal violation <= eps; similar to liblinear (default 0.)
			-s 5 and 6
				|f'(w)|_inf <= eps*min(pos,neg)/l*|f'(w0)|_inf,
				where f is the primal function (default 0.01)
			-s 12 and 13
				|f'(alpha)|_1 <= eps |f'(alpha0)|,
				where f is the dual function (default 0.1)
		-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)
		-wi weight: weights adjust the parameter C of different classes (see README for details)
		-v n: n-fold cross validation mode
	    -q : quiet mode (no outputs)
	"""
	prob, param = None, None
	if isinstance(arg1, (list, tuple)):
		assert isinstance(arg2, (list, tuple))
		y, x, options = arg1, arg2, arg3
		prob = problem(y, x)
		param = parameter(options)
	elif isinstance(arg1, problem):
		prob = arg1
		if isinstance(arg2, parameter):
			param = arg2
		else :
			param = parameter(arg2)
	if prob == None or param == None :
		raise TypeError("Wrong types for the arguments")

	prob.set_bias(param.bias)
	liblinear.set_print_string_function(param.print_func)
	err_msg = liblinear.check_parameter(prob, param)
	if err_msg :
		raise ValueError('Error: %s' % err_msg)

	if param.cross_validation:
		l, nr_fold = prob.l, param.nr_fold
		target = (c_double * l)()
		liblinear.cross_validation(prob, param, nr_fold, target)
		ACC, MSE, SCC = evaluations(prob.y[:l], target[:l])
		if param.solver_type in [L2R_L2LOSS_SVR, L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL]:
			print("Cross Validation Mean squared error = %g" % MSE)
			print("Cross Validation Squared correlation coefficient = %g" % SCC)
			return MSE
		else:
			print("Cross Validation Accuracy = %g%%" % ACC)
			return ACC
	else :
		m = liblinear.train(prob, param)
		m = toPyModel(m)

		return m

def predict(y, x, m, options=""):
	"""
	predict(y, x, m [, options]) -> (p_labels, p_acc, p_vals)

	Predict data (y, x) with the SVM model m.
	options:
	    -b probability_estimates: whether to output probability estimates, 0 or 1 (default 0); currently for logistic regression only
	    -q quiet mode (no outputs)

	The return tuple contains
	p_labels: a list of predicted labels
	p_acc: a tuple including  accuracy (for classification), mean-squared
	       error, and squared correlation coefficient (for regression).
	p_vals: a list of decision values or probability estimates (if '-b 1'
	        is specified). If k is the number of classes, for decision values,
	        each element includes results of predicting k binary-class
	        SVMs. if k = 2 and solver is not MCSVM_CS, only one decision value
	        is returned. For probabilities, each element contains k values
	        indicating the probability that the testing instance is in each class.
	        Note that the order of classes here is the same as 'model.label'
	        field in the model structure.
	"""

	def info(s):
		print(s)

	predict_probability = 0
	argv = options.split()
	i = 0
	while i < len(argv):
		if argv[i] == '-b':
			i += 1
			predict_probability = int(argv[i])
		elif argv[i] == '-q':
			info = print_null
		else:
			raise ValueError("Wrong options")
		i+=1

	solver_type = m.param.solver_type
	nr_class = m.get_nr_class()
	nr_feature = m.get_nr_feature()
	is_prob_model = m.is_probability_model()
	bias = m.bias
	if bias >= 0:
		biasterm = feature_node(nr_feature+1, bias)
	else:
		biasterm = feature_node(-1, bias)
	pred_labels = []
	pred_values = []

	if predict_probability:
		if not is_prob_model:
			raise TypeError('probability output is only supported for logistic regression')
		prob_estimates = (c_double * nr_class)()
		for xi in x:
			xi, idx = gen_feature_nodearray(xi, feature_max=nr_feature)
			xi[-2] = biasterm
			label = liblinear.predict_probability(m, xi, prob_estimates)
			values = prob_estimates[:nr_class]
			pred_labels += [label]
			pred_values += [values]
	else:
		if nr_class <= 2:
			nr_classifier = 1
		else:
			nr_classifier = nr_class
		dec_values = (c_double * nr_classifier)()
		for xi in x:
			xi, idx = gen_feature_nodearray(xi, feature_max=nr_feature)
			xi[-2] = biasterm
			label = liblinear.predict_values(m, xi, dec_values)
			values = dec_values[:nr_classifier]
			pred_labels += [label]
			pred_values += [values]
	if len(y) == 0:
		y = [0] * len(x)
	ACC, MSE, SCC = evaluations(y, pred_labels)
	l = len(y)
	if m.is_regression_model():
		info("Mean squared error = %g (regression)" % MSE)
		info("Squared correlation coefficient = %g (regression)" % SCC)
	else:
		info("Accuracy = %g%% (%d/%d) (classification)" % (ACC, int(l*ACC/100), l))

	return pred_labels, (ACC, MSE, SCC), pred_values


================================================
FILE: Matlab/liblinear-1.96/train.c
================================================
#include <stdio.h>
#include <math.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include <errno.h>
#include "linear.h"
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
#define INF HUGE_VAL

void print_null(const char *s) {}

void exit_with_help()
{
	printf(
	"Usage: train [options] training_set_file [model_file]\n"
	"options:\n"
	"-s type : set type of solver (default 1)\n"
	"  for multi-class classification\n"
	"	 0 -- L2-regularized logistic regression (primal)\n"
	"	 1 -- L2-regularized L2-loss support vector classification (dual)\n"
	"	 2 -- L2-regularized L2-loss support vector classification (primal)\n"
	"	 3 -- L2-regularized L1-loss support vector classification (dual)\n"
	"	 4 -- support vector classification by Crammer and Singer\n"
	"	 5 -- L1-regularized L2-loss support vector classification\n"
	"	 6 -- L1-regularized logistic regression\n"
	"	 7 -- L2-regularized logistic regression (dual)\n"
	"  for regression\n"
	"	11 -- L2-regularized L2-loss support vector regression (primal)\n"
	"	12 -- L2-regularized L2-loss support vector regression (dual)\n"
	"	13 -- L2-regularized L1-loss support vector regression (dual)\n"
	"-c cost : set the parameter C (default 1)\n"
	"-p epsilon : set the epsilon in loss function of SVR (default 0.1)\n"
	"-e epsilon : set tolerance of termination criterion\n"
	"	-s 0 and 2\n"
	"		|f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,\n"
	"		where f is the primal function and pos/neg are # of\n"
	"		positive/negative data (default 0.01)\n"
	"	-s 11\n"
	"		|f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001)\n"
	"	-s 1, 3, 4, and 7\n"
	"		Dual maximal violation <= eps; similar to libsvm (default 0.1)\n"
	"	-s 5 and 6\n"
	"		|f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,\n"
	"		where f is the primal function (default 0.01)\n"
	"	-s 12 and 13\n"
	"		|f'(alpha)|_1 <= eps |f'(alpha0)|,\n"
	"		where f is the dual function (default 0.1)\n"
	"-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)\n"
	"-wi weight: weights adjust the parameter C of different classes (see README for details)\n"
	"-v n: n-fold cross validation mode\n"
	"-q : quiet mode (no outputs)\n"
	);
	exit(1);
}

void exit_input_error(int line_num)
{
	fprintf(stderr,"Wrong input format at line %d\n", line_num);
	exit(1);
}

static char *line = NULL;
static int max_line_len;

static char* readline(FILE *input)
{
	int len;

	if(fgets(line,max_line_len,input) == NULL)
		return NULL;

	while(strrchr(line,'\n') == NULL)
	{
		max_line_len *= 2;
		line = (char *) realloc(line,max_line_len);
		len = (int) strlen(line);
		if(fgets(line+len,max_line_len-len,input) == NULL)
			break;
	}
	return line;
}

void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name);
void read_problem(const char *filename);
void do_cross_validation();

struct feature_node *x_space;
struct parameter param;
struct problem prob;
struct model* model_;
int flag_cross_validation;
int nr_fold;
double bias;

int main(int argc, char **argv)
{
	char input_file_name[1024];
	char model_file_name[1024];
	const char *error_msg;

	parse_command_line(argc, argv, input_file_name, model_file_name);
	read_problem(input_file_name);
	error_msg = check_parameter(&prob,&param);

	if(error_msg)
	{
		fprintf(stderr,"ERROR: %s\n",error_msg);
		exit(1);
	}

	if(flag_cross_validation)
	{
		do_cross_validation();
	}
	else
	{
		model_=train(&prob, &param);
		if(save_model(model_file_name, model_))
		{
			fprintf(stderr,"can't save model to file %s\n",model_file_name);
			exit(1);
		}
		free_and_destroy_model(&model_);
	}
	destroy_param(&param);
	free(prob.y);
	free(prob.x);
	free(x_space);
	free(line);

	return 0;
}

void do_cross_validation()
{
	int i;
	int total_correct = 0;
	double total_error = 0;
	double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
	double *target = Malloc(double, prob.l);

	cross_validation(&prob,&param,nr_fold,target);
	if(param.solver_type == L2R_L2LOSS_SVR ||
	   param.solver_type == L2R_L1LOSS_SVR_DUAL ||
	   param.solver_type == L2R_L2LOSS_SVR_DUAL)
	{
		for(i=0;i<prob.l;i++)
		{
			double y = prob.y[i];
			double v = target[i];
			total_error += (v-y)*(v-y);
			sumv += v;
			sumy += y;
			sumvv += v*v;
			sumyy += y*y;
			sumvy += v*y;
		}
		printf("Cross Validation Mean squared error = %g\n",total_error/prob.l);
		printf("Cross Validation Squared correlation coefficient = %g\n",
				((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/
				((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))
			  );
	}
	else
	{
		for(i=0;i<prob.l;i++)
			if(target[i] == prob.y[i])
				++total_correct;
		printf("Cross Validation Accuracy = %g%%\n",100.0*total_correct/prob.l);
	}

	free(target);
}

void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name)
{
	int i;
	void (*print_func)(const char*) = NULL;	// default printing to stdout

	// default values
	param.solver_type = L2R_L2LOSS_SVC_DUAL;
	param.C = 1;
	param.eps = INF; // see setting below
	param.p = 0.1;
	param.nr_weight = 0;
	param.weight_label = NULL;
	param.weight = NULL;
	flag_cross_validation = 0;
	bias = -1;

	// parse options
	for(i=1;i<argc;i++)
	{
		if(argv[i][0] != '-') break;
		if(++i>=argc)
			exit_with_help();
		switch(argv[i-1][1])
		{
			case 's':
				param.solver_type = atoi(argv[i]);
				break;

			case 'c':
				param.C = atof(argv[i]);
				break;

			case 'p':
				param.p = atof(argv[i]);
				break;

			case 'e':
				param.eps = atof(argv[i]);
				break;

			case 'B':
				bias = atof(argv[i]);
				break;

			case 'w':
				++param.nr_weight;
				param.weight_label = (int *) realloc(param.weight_label,sizeof(int)*param.nr_weight);
				param.weight = (double *) realloc(param.weight,sizeof(double)*param.nr_weight);
				param.weight_label[param.nr_weight-1] = atoi(&argv[i-1][2]);
				param.weight[param.nr_weight-1] = atof(argv[i]);
				break;

			case 'v':
				flag_cross_validation = 1;
				nr_fold = atoi(argv[i]);
				if(nr_fold < 2)
				{
					fprintf(stderr,"n-fold cross validation: n must >= 2\n");
					exit_with_help();
				}
				break;

			case 'q':
				print_func = &print_null;
				i--;
				break;

			default:
				fprintf(stderr,"unknown option: -%c\n", argv[i-1][1]);
				exit_with_help();
				break;
		}
	}

	set_print_string_function(print_func);

	// determine filenames
	if(i>=argc)
		exit_with_help();

	strcpy(input_file_name, argv[i]);

	if(i<argc-1)
		strcpy(model_file_name,argv[i+1]);
	else
	{
		char *p = strrchr(argv[i],'/');
		if(p==NULL)
			p = argv[i];
		else
			++p;
		sprintf(model_file_name,"%s.model",p);
	}

	if(param.eps == INF)
	{
		switch(param.solver_type)
		{
			case L2R_LR:
			case L2R_L2LOSS_SVC:
				param.eps = 0.01;
				break;
			case L2R_L2LOSS_SVR:
				param.eps = 0.001;
				break;
			case L2R_L2LOSS_SVC_DUAL:
			case L2R_L1LOSS_SVC_DUAL:
			case MCSVM_CS:
			case L2R_LR_DUAL:
				param.eps = 0.1;
				break;
			case L1R_L2LOSS_SVC:
			case L1R_LR:
				param.eps = 0.01;
				break;
			case L2R_L1LOSS_SVR_DUAL:
			case L2R_L2LOSS_SVR_DUAL:
				param.eps = 0.1;
				break;
		}
	}
}

// read in a problem (in libsvm format)
void read_problem(const char *filename)
{
	int max_index, inst_max_index, i;
	size_t elements, j;
	FILE *fp = fopen(filename,"r");
	char *endptr;
	char *idx, *val, *label;

	if(fp == NULL)
	{
		fprintf(stderr,"can't open input file %s\n",filename);
		exit(1);
	}

	prob.l = 0;
	elements = 0;
	max_line_len = 1024;
	line = Malloc(char,max_line_len);
	while(readline(fp)!=NULL)
	{
		char *p = strtok(line," \t"); // label

		// features
		while(1)
		{
			p = strtok(NULL," \t");
			if(p == NULL || *p == '\n') // check '\n' as ' ' may be after the last feature
				break;
			elements++;
		}
		elements++; // for bias term
		prob.l++;
	}
	rewind(fp);

	prob.bias=bias;

	prob.y = Malloc(double,prob.l);
	prob.x = Malloc(struct feature_node *,prob.l);
	x_space = Malloc(struct feature_node,elements+prob.l);

	max_index = 0;
	j=0;
	for(i=0;i<prob.l;i++)
	{
		inst_max_index = 0; // strtol gives 0 if wrong format
		readline(fp);
		prob.x[i] = &x_space[j];
		label = strtok(line," \t\n");
		if(label == NULL) // empty line
			exit_input_error(i+1);

		prob.y[i] = strtod(label,&endptr);
		if(endptr == label || *endptr != '\0')
			exit_input_error(i+1);

		while(1)
		{
			idx = strtok(NULL,":");
			val = strtok(NULL," \t");

			if(val == NULL)
				break;

			errno = 0;
			x_space[j].index = (int) strtol(idx,&endptr,10);
			if(endptr == idx || errno != 0 || *endptr != '\0' || x_space[j].index <= inst_max_index)
				exit_input_error(i+1);
			else
				inst_max_index = x_space[j].index;

			errno = 0;
			x_space[j].value = strtod(val,&endptr);
			if(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
				exit_input_error(i+1);

			++j;
		}

		if(inst_max_index > max_index)
			max_index = inst_max_index;

		if(prob.bias >= 0)
			x_space[j++].value = prob.bias;

		x_space[j++].index = -1;
	}

	if(prob.bias >= 0)
	{
		prob.n=max_index+1;
		for(i=1;i<prob.l;i++)
			(prob.x[i]-2)->index = prob.n;
		x_space[j-2].index = prob.n;
	}
	else
		prob.n=max_index;

	fclose(fp);
}


================================================
FILE: Matlab/liblinear-1.96/tron.cpp
================================================
#include <math.h>
#include <stdio.h>
#include <string.h>
#include <stdarg.h>
#include "tron.h"

#ifndef min
template <class T> static inline T min(T x,T y) { return (x<y)?x:y; }
#endif

#ifndef max
template <class T> static inline T max(T x,T y) { return (x>y)?x:y; }
#endif

#ifdef __cplusplus
extern "C" {
#endif

extern double dnrm2_(int *, double *, int *);
extern double ddot_(int *, double *, int *, double *, int *);
extern int daxpy_(int *, double *, double *, int *, double *, int *);
extern int dscal_(int *, double *, double *, int *);

#ifdef __cplusplus
}
#endif

static void default_print(const char *buf)
{
	fputs(buf,stdout);
	fflush(stdout);
}

void TRON::info(const char *fmt,...)
{
	char buf[BUFSIZ];
	va_list ap;
	va_start(ap,fmt);
	vsprintf(buf,fmt,ap);
	va_end(ap);
	(*tron_print_string)(buf);
}

TRON::TRON(const function *fun_obj, double eps, int max_iter)
{
	this->fun_obj=const_cast<function *>(fun_obj);
	this->eps=eps;
	this->max_iter=max_iter;
	tron_print_string = default_print;
}

TRON::~TRON()
{
}

void TRON::tron(double *w)
{
	// Parameters for updating the iterates.
	double eta0 = 1e-4, eta1 = 0.25, eta2 = 0.75;

	// Parameters for updating the trust region size delta.
	double sigma1 = 0.25, sigma2 = 0.5, sigma3 = 4;

	int n = fun_obj->get_nr_variable();
	int i, cg_iter;
	double delta, snorm, one=1.0;
	double alpha, f, fnew, prered, actred, gs;
	int search = 1, iter = 1, inc = 1;
	double *s = new double[n];
	double *r = new double[n];
	double *w_new = new double[n];
	double *g = new double[n];

	for (i=0; i<n; i++)
		w[i] = 0;

	f = fun_obj->fun(w);
	fun_obj->grad(w, g);
	delta = dnrm2_(&n, g, &inc);
	double gnorm1 = delta;
	double gnorm = gnorm1;

	if (gnorm <= eps*gnorm1)
		search = 0;

	iter = 1;

	while (iter <= max_iter && search)
	{
		cg_iter = trcg(delta, g, s, r);

		memcpy(w_new, w, sizeof(double)*n);
		daxpy_(&n, &one, s, &inc, w_new, &inc);

		gs = ddot_(&n, g, &inc, s, &inc);
		prered = -0.5*(gs-ddot_(&n, s, &inc, r, &inc));
		fnew = fun_obj->fun(w_new);

		// Compute the actual reduction.
		actred = f - fnew;

		// On the first iteration, adjust the initial step bound.
		snorm = dnrm2_(&n, s, &inc);
		if (iter == 1)
			delta = min(delta, snorm);

		// Compute prediction alpha*snorm of the step.
		if (fnew - f - gs <= 0)
			alpha = sigma3;
		else
			alpha = max(sigma1, -0.5*(gs/(fnew - f - gs)));

		// Update the trust region bound according to the ratio of actual to predicted reduction.
		if (actred < eta0*prered)
			delta = min(max(alpha, sigma1)*snorm, sigma2*delta);
		else if (actred < eta1*prered)
			delta = max(sigma1*delta, min(alpha*snorm, sigma2*delta));
		else if (actred < eta2*prered)
			delta = max(sigma1*delta, min(alpha*snorm, sigma3*delta));
		else
			delta = max(delta, min(alpha*snorm, sigma3*delta));

		info("iter %2d act %5.3e pre %5.3e delta %5.3e f %5.3e |g| %5.3e CG %3d\n", iter, actred, prered, delta, f, gnorm, cg_iter);

		if (actred > eta0*prered)
		{
			iter++;
			memcpy(w, w_new, sizeof(double)*n);
			f = fnew;
			fun_obj->grad(w, g);

			gnorm = dnrm2_(&n, g, &inc);
			if (gnorm <= eps*gnorm1)
				break;
		}
		if (f < -1.0e+32)
		{
			info("WARNING: f < -1.0e+32\n");
			break;
		}
		if (fabs(actred) <= 0 && prered <= 0)
		{
			info("WARNING: actred and prered <= 0\n");
			break;
		}
		if (fabs(actred) <= 1.0e-12*fabs(f) &&
		    fabs(prered) <= 1.0e-12*fabs(f))
		{
			info("WARNING: actred and prered too small\n");
			break;
		}
	}

	delete[] g;
	delete[] r;
	delete[] w_new;
	delete[] s;
}

int TRON::trcg(double delta, double *g, double *s, double *r)
{
	int i, inc = 1;
	int n = fun_obj->get_nr_variable();
	double one = 1;
	double *d = new double[n];
	double *Hd = new double[n];
	double rTr, rnewTrnew, alpha, beta, cgtol;

	for (i=0; i<n; i++)
	{
		s[i] = 0;
		r[i] = -g[i];
		d[i] = r[i];
	}
	cgtol = 0.1*dnrm2_(&n, g, &inc);

	int cg_iter = 0;
	rTr = ddot_(&n, r, &inc, r, &inc);
	while (1)
	{
		if (dnrm2_(&n, r, &inc) <= cgtol)
			break;
		cg_iter++;
		fun_obj->Hv(d, Hd);

		alpha = rTr/ddot_(&n, d, &inc, Hd, &inc);
		daxpy_(&n, &alpha, d, &inc, s, &inc);
		if (dnrm2_(&n, s, &inc) > delta)
		{
			info("cg reaches trust region boundary\n");
			alpha = -alpha;
			daxpy_(&n, &alpha, d, &inc, s, &inc);

			double std = ddot_(&n, s, &inc, d, &inc);
			double sts = ddot_(&n, s, &inc, s, &inc);
			double dtd = ddot_(&n, d, &inc, d, &inc);
			double dsq = delta*delta;
			double rad = sqrt(std*std + dtd*(dsq-sts));
			if (std >= 0)
				alpha = (dsq - sts)/(std + rad);
			else
				alpha = (rad - std)/dtd;
			daxpy_(&n, &alpha, d, &inc, s, &inc);
			alpha = -alpha;
			daxpy_(&n, &alpha, Hd, &inc, r, &inc);
			break;
		}
		alpha = -alpha;
		daxpy_(&n, &alpha, Hd, &inc, r, &inc);
		rnewTrnew = ddot_(&n, r, &inc, r, &inc);
		beta = rnewTrnew/rTr;
		dscal_(&n, &beta, d, &inc);
		daxpy_(&n, &one, r, &inc, d, &inc);
		rTr = rnewTrnew;
	}

	delete[] d;
	delete[] Hd;

	return(cg_iter);
}

double TRON::norm_inf(int n, double *x)
{
	double dmax = fabs(x[0]);
	for (int i=1; i<n; i++)
		if (fabs(x[i]) >= dmax)
			dmax = fabs(x[i]);
	return(dmax);
}

void TRON::set_print_string(void (*print_string) (const char *buf))
{
	tron_print_string = print_string;
}


================================================
FILE: Matlab/liblinear-1.96/tron.h
================================================
#ifndef _TRON_H
#define _TRON_H

class function
{
public:
	virtual double fun(double *w) = 0 ;
	virtual void grad(double *w, double *g) = 0 ;
	virtual void Hv(double *s, double *Hs) = 0 ;

	virtual int get_nr_variable(void) = 0 ;
	virtual ~function(void){}
};

class TRON
{
public:
	TRON(const function *fun_obj, double eps = 0.1, int max_iter = 1000);
	~TRON();

	void tron(double *w);
	void set_print_string(void (*i_print) (const char *buf));

private:
	int trcg(double delta, double *g, double *s, double *r);
	double norm_inf(int n, double *x);

	double eps;
	int max_iter;
	function *fun_obj;
	void info(const char *fmt,...);
	void (*tron_print_string)(const char *buf);
};
#endif


================================================
FILE: Matlab/libsvm-3.19/COPYRIGHT
================================================

Copyright (c) 2000-2014 Chih-Chung Chang and Chih-Jen Lin
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:

1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.

3. Neither name of copyright holders nor the names of its contributors
may be used to endorse or promote products derived from this software
without specific prior written permission.


THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE REGENTS OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


================================================
FILE: Matlab/libsvm-3.19/FAQ.html
================================================


<html>
<head>
<title>LIBSVM FAQ</title>
</head>
<body bgcolor="#ffffcc">

<a name="_TOP"><b><h1><a
href=http://www.csie.ntu.edu.tw/~cjlin/libsvm>LIBSVM</a>  FAQ </h1></b></a>
<b>last modified : </b>
Thu, 20 Mar 2014 16:05:14 GMT
<class="categories">
<li><a
href="#_TOP">All Questions</a>(81)</li>
<ul><b>
<li><a
href="#/Q01:_Some_sample_uses_of_libsvm">Q01:_Some_sample_uses_of_libsvm</a>(2)</li>
<li><a
href="#/Q02:_Installation_and_running_the_program">Q02:_Installation_and_running_the_program</a>(13)</li>
<li><a
href="#/Q03:_Data_preparation">Q03:_Data_preparation</a>(7)</li>
<li><a
href="#/Q04:_Training_and_prediction">Q04:_Training_and_prediction</a>(28)</li>
<li><a
href="#/Q05:_Cross_validation_and_parameter_selection">Q05:_Cross_validation_and_parameter_selection</a>(8)</li>
<li><a
href="#/Q06:_Probability_outputs">Q06:_Probability_outputs</a>(3)</li>
<li><a
href="#/Q07:_Graphic_interface">Q07:_Graphic_interface</a>(3)</li>
<li><a
href="#/Q08:_Java_version_of_libsvm">Q08:_Java_version_of_libsvm</a>(4)</li>
<li><a
href="#/Q09:_Python_interface">Q09:_Python_interface</a>(1)</li>
<li><a
href="#/Q10:_MATLAB_interface">Q10:_MATLAB_interface</a>(12)</li>
</b></ul>
</li>

<ul><ul class="headlines">
<li class="headlines_item"><a href="#faq101">Some courses which have used libsvm as a tool</a></li>
<li class="headlines_item"><a href="#faq102">Some applications/tools which have used libsvm </a></li>
<li class="headlines_item"><a href="#f201">Where can I find documents/videos of libsvm ?</a></li>
<li class="headlines_item"><a href="#f202">Where are change log and earlier versions?</a></li>
<li class="headlines_item"><a href="#f203">How to cite LIBSVM?</a></li>
<li class="headlines_item"><a href="#f204">I would like to use libsvm in my software. Is there any license problem?</a></li>
<li class="headlines_item"><a href="#f205">Is there a repository of additional tools based on libsvm?</a></li>
<li class="headlines_item"><a href="#f206">On unix machines, I got "error in loading shared libraries" or "cannot open shared object file." What happened ? </a></li>
<li class="headlines_item"><a href="#f207">I have modified the source and would like to build the graphic interface "svm-toy" on MS windows. How should I do it ?</a></li>
<li class="headlines_item"><a href="#f208">I am an MS windows user but why only one (svm-toy) of those precompiled .exe actually runs ?  </a></li>
<li class="headlines_item"><a href="#f209">What is the difference between "." and "*" outputed during training? </a></li>
<li class="headlines_item"><a href="#f210">Why occasionally the program (including MATLAB or other interfaces) crashes and gives a segmentation fault?</a></li>
<li class="headlines_item"><a href="#f211">How to build a dynamic library (.dll file) on MS windows?</a></li>
<li class="headlines_item"><a href="#f212">On some systems (e.g., Ubuntu), compiling LIBSVM gives many warning messages. Is this a problem and how to disable the warning message?</a></li>
<li class="headlines_item"><a href="#f213">In LIBSVM, why you don't use certain C/C++ library functions to make the code shorter?</a></li>
<li class="headlines_item"><a href="#f301">Why sometimes not all attributes of a data appear in the training/model files ?</a></li>
<li class="headlines_item"><a href="#f302">What if my data are non-numerical ?</a></li>
<li class="headlines_item"><a href="#f303">Why do you consider sparse format ? Will the training of dense data be much slower ?</a></li>
<li class="headlines_item"><a href="#f304">Why sometimes the last line of my data is not read by svm-train?</a></li>
<li class="headlines_item"><a href="#f305">Is there a program to check if my data are in the correct format?</a></li>
<li class="headlines_item"><a href="#f306">May I put comments in data files?</a></li>
<li class="headlines_item"><a href="#f307">How to convert other data formats to LIBSVM format?</a></li>
<li class="headlines_item"><a href="#f401">The output of training C-SVM is like the following. What do they mean?</a></li>
<li class="headlines_item"><a href="#f402">Can you explain more about the model file?</a></li>
<li class="headlines_item"><a href="#f403">Should I use float or double to store numbers in the cache ?</a></li>
<li class="headlines_item"><a href="#f405">Does libsvm have special treatments for linear SVM?</a></li>
<li class="headlines_item"><a href="#f406">The number of free support vectors is large. What should I do?</a></li>
<li class="headlines_item"><a href="#f407">Should I scale training and testing data in a similar way?</a></li>
<li class="headlines_item"><a href="#f408">Does it make a big difference  if I scale each attribute to [0,1] instead of [-1,1]?</a></li>
<li class="headlines_item"><a href="#f409">The prediction rate is low. How could I improve it?</a></li>
<li class="headlines_item"><a href="#f410">My data are unbalanced. Could libsvm handle such problems?</a></li>
<li class="headlines_item"><a href="#f411">What is the difference between nu-SVC and C-SVC?</a></li>
<li class="headlines_item"><a href="#f412">The program keeps running (without showing any output). What should I do?</a></li>
<li class="headlines_item"><a href="#f413">The program keeps running (with output, i.e. many dots). What should I do?</a></li>
<li class="headlines_item"><a href="#f414">The training time is too long. What should I do?</a></li>
<li class="headlines_item"><a href="#f4141">Does shrinking always help?</a></li>
<li class="headlines_item"><a href="#f415">How do I get the decision value(s)?</a></li>
<li class="headlines_item"><a href="#f4151">How do I get the distance between a point and the hyperplane?</a></li>
<li class="headlines_item"><a href="#f416">On 32-bit machines, if I use a large cache (i.e. large -m) on a linux machine, why sometimes I get "segmentation fault ?"</a></li>
<li class="headlines_item"><a href="#f417">How do I disable screen output of svm-train?</a></li>
<li class="headlines_item"><a href="#f418">I would like to use my own kernel. Any example? In svm.cpp, there are two subroutines for kernel evaluations: k_function() and kernel_function(). Which one should I modify ?</a></li>
<li class="headlines_item"><a href="#f419">What method does libsvm use for multi-class SVM ? Why don't you use the "1-against-the rest" method?</a></li>
<li class="headlines_item"><a href="#f422">I would like to solve L2-loss SVM (i.e., error term is quadratic). How should I modify the code ?</a></li>
<li class="headlines_item"><a href="#f425">In one-class SVM, parameter nu should be an upper bound of the training error rate. Why sometimes I get a training error rate bigger than nu?</a></li>
<li class="headlines_item"><a href="#f427">Why the code gives NaN (not a number) results?</a></li>
<li class="headlines_item"><a href="#f430">Why the sign of predicted labels and decision values are sometimes reversed?</a></li>
<li class="headlines_item"><a href="#f431">I don't know class labels of test data. What should I put in the first column of the test file?</a></li>
<li class="headlines_item"><a href="#f432">How can I use OpenMP to parallelize LIBSVM on a multicore/shared-memory computer?</a></li>
<li class="headlines_item"><a href="#f433">How could I know which training instances are support vectors?</a></li>
<li class="headlines_item"><a href="#f434">Why sv_indices (indices of support vectors) are not stored in the saved model file?</a></li>
<li class="headlines_item"><a href="#f501">After doing cross validation, why there is no model file outputted ?</a></li>
<li class="headlines_item"><a href="#f502">Why my cross-validation results are different from those in the Practical Guide?</a></li>
<li class="headlines_item"><a href="#f503">On some systems CV accuracy is the same in several runs. How could I use different data partitions? In other words, how do I set random seed in LIBSVM?</a></li>
<li class="headlines_item"><a href="#f504">Why on windows sometimes grid.py fails?</a></li>
<li class="headlines_item"><a href="#f505">Why grid.py/easy.py sometimes generates the following warning message?</a></li>
<li class="headlines_item"><a href="#f506">How do I choose the kernel?</a></li>
<li class="headlines_item"><a href="#f507">How does LIBSVM perform parameter selection for multi-class problems? </a></li>
<li class="headlines_item"><a href="#f508">How do I choose parameters for one-class SVM as training data are in only one class?</a></li>
<li class="headlines_item"><a href="#f425">Why training a probability model (i.e., -b 1) takes a longer time?</a></li>
<li class="headlines_item"><a href="#f426">Why using the -b option does not give me better accuracy?</a></li>
<li class="headlines_item"><a href="#f427">Why using svm-predict -b 0 and -b 1 gives different accuracy values?</a></li>
<li class="headlines_item"><a href="#f501">How can I save images drawn by svm-toy?</a></li>
<li class="headlines_item"><a href="#f502">I press the "load" button to load data points but why svm-toy does not draw them ?</a></li>
<li class="headlines_item"><a href="#f503">I would like svm-toy to handle more than three classes of data, what should I do ?</a></li>
<li class="headlines_item"><a href="#f601">What is the difference between Java version and C++ version of libsvm?</a></li>
<li class="headlines_item"><a href="#f602">Is the Java version significantly slower than the C++ version?</a></li>
<li class="headlines_item"><a href="#f603">While training I get the following error message: java.lang.OutOfMemoryError. What is wrong?</a></li>
<li class="headlines_item"><a href="#f604">Why you have the main source file svm.m4 and then transform it to svm.java?</a></li>
<li class="headlines_item"><a href="#f704">Except the python-C++ interface provided, could I use Jython to call libsvm ?</a></li>
<li class="headlines_item"><a href="#f801">I compile the MATLAB interface without problem, but why errors occur while running it?</a></li>
<li class="headlines_item"><a href="#f8011">On 64bit Windows I compile the MATLAB interface without problem, but why errors occur while running it?</a></li>
<li class="headlines_item"><a href="#f802">Does the MATLAB interface provide a function to do scaling?</a></li>
<li class="headlines_item"><a href="#f803">How could I use MATLAB interface for parameter selection?</a></li>
<li class="headlines_item"><a href="#f8031">I use MATLAB parallel programming toolbox on a multi-core environment for parameter selection. Why the program is even slower?</a></li>
<li class="headlines_item"><a href="#f8032">How do I use LIBSVM with OpenMP under MATLAB?</a></li>
<li class="headlines_item"><a href="#f804">How could I generate the primal variable w of linear SVM?</a></li>
<li class="headlines_item"><a href="#f805">Is there an OCTAVE interface for libsvm?</a></li>
<li class="headlines_item"><a href="#f806">How to handle the name conflict between svmtrain in the libsvm matlab interface and that in MATLAB bioinformatics toolbox?</a></li>
<li class="headlines_item"><a href="#f807">On Windows I got an error message "Invalid MEX-file: Specific module not found" when running the pre-built MATLAB interface in the windows sub-directory. What should I do?</a></li>
<li class="headlines_item"><a href="#f808">LIBSVM supports 1-vs-1 multi-class classification. If instead I would like to use 1-vs-rest, how to implement it using MATLAB interface?</a></li>
<li class="headlines_item"><a href="#f809">I tried to install matlab interface on mac, but failed. What should I do?</a></li>
</ul></ul>


<hr size="5" noshade />
<p/>
  
<a name="/Q01:_Some_sample_uses_of_libsvm"></a>
<a name="faq101"><b>Q: Some courses which have used libsvm as a tool</b></a>
<br/>                                                                                
<ul>
<li><a href=http://lmb.informatik.uni-freiburg.de/lectures/svm_seminar/>Institute for Computer Science,           
Faculty of Applied Science, University of Freiburg, Germany 
</a>
<li> <a href=http://www.cs.vu.nl/~elena/ml.html>
Division of Mathematics and Computer Science. 
Faculteit der Exacte Wetenschappen 
Vrije Universiteit, The Netherlands. </a>
<li>
<a href=http://www.cae.wisc.edu/~ece539/matlab/>
Electrical and Computer Engineering Department, 
University of Wisconsin-Madison 
</a>
<li>
<a href=http://www.hpl.hp.com/personal/Carl_Staelin/cs236601/project.html>
Technion (Israel Institute of Technology), Israel.
<li>
<a href=http://www.cise.ufl.edu/~fu/learn.html>
Computer and Information Sciences Dept., University of Florida</a>
<li>
<a href=http://www.uonbi.ac.ke/acad_depts/ics/course_material/machine_learning/ML_and_DM_Resources.html>
The Institute of Computer Science,
University of Nairobi, Kenya.</a>
<li>
<a href=http://cerium.raunvis.hi.is/~tpr/courseware/svm/hugbunadur.html>
Applied Mathematics and Computer Science, University of Iceland.
<li>
<a href=http://chicago05.mlss.cc/tiki/tiki-read_article.php?articleId=2>
SVM tutorial in machine learning
summer school, University of Chicago, 2005.
</a>
</ul>
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q01:_Some_sample_uses_of_libsvm"></a>
<a name="faq102"><b>Q: Some applications/tools which have used libsvm </b></a>
<br/>                                                                                
(and maybe liblinear).
<ul>
<li>
<a href=http://people.csail.mit.edu/jjl/libpmk/>LIBPMK: A Pyramid Match Toolkit</a>
</li>
<li><a href=http://maltparser.org/>Maltparser</a>:
a system for data-driven dependency parsing
</li>
<li>
<a href=http://www.pymvpa.org/>PyMVPA: python tool for classifying neuroimages</a>
</li>
<li>
<a href=http://solpro.proteomics.ics.uci.edu/>
SOLpro: protein solubility predictor
</a>
</li>
<li>
<a href=http://bdval.campagnelab.org>
BDVal</a>: biomarker discovery in high-throughput datasets.
</li>
<li><a href=http://johel.m.free.fr/demo_045.htm>
Realtime object recognition</a>
</li>
<li><a href=http://scikit-learn.sourceforge.net/>
scikits.learn: machine learning in Python</a>
</li>
</ul>
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f201"><b>Q: Where can I find documents/videos of libsvm ?</b></a>
<br/>                                                                                
<p>

<ul>
<li>
Official implementation document:
<br>
C.-C. Chang and
C.-J. Lin.
LIBSVM
: a library for support vector machines.
ACM Transactions on Intelligent
Systems and Technology, 2:27:1--27:27, 2011.
<a href="http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf">pdf</a>, <a href=http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.ps.gz>ps.gz</a>,
<a href=http://portal.acm.org/citation.cfm?id=1961199&CFID=29950432&CFTOKEN=30974232>ACM digital lib</a>.


<li> Instructions for using LIBSVM are in the README files in the main directory and some sub-directories.
<br>
README in the main directory: details all options, data format, and library calls.
<br>
tools/README: parameter selection and other tools
<li>
A guide for beginners:
<br>
C.-W. Hsu, C.-C. Chang, and
C.-J. Lin.
<A HREF="http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf">
A practical guide to support vector classification
</A> 
<li> An <a href=http://www.youtube.com/watch?v=gePWtNAQcK8>introductory video</a>
for windows users.

</ul>
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f202"><b>Q: Where are change log and earlier versions?</b></a>
<br/>                                                                                
<p>See <a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm/log">the change log</a>.

<p> You can download earlier versions 
<a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm/oldfiles">here</a>.
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f203"><b>Q: How to cite LIBSVM?</b></a>
<br/>                                                                                
<p>
Please cite the following paper:
<p>
Chih-Chung Chang and Chih-Jen Lin, LIBSVM
: a library for support vector machines.
ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011.
Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
<p>
The bibtex format is 
<pre>
@article{CC01a,
 author = {Chang, Chih-Chung and Lin, Chih-Jen},
 title = {{LIBSVM}: A library for support vector machines},
 journal = {ACM Transactions on Intelligent Systems and Technology},
 volume = {2},
 issue = {3},
 year = {2011},
 pages = {27:1--27:27},
 note =	 {Software available at \url{http://www.csie.ntu.edu.tw/~cjlin/libsvm}}
}
</pre>
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f204"><b>Q: I would like to use libsvm in my software. Is there any license problem?</b></a>
<br/>                                                                                
<p>
The libsvm license ("the modified BSD license")
is compatible with many
free software licenses such as GPL. Hence, it is very easy to
use libsvm in your software.
Please check the COPYRIGHT file in detail. Basically
you need to 
<ol>
<li>
Clearly indicate that LIBSVM is used.
</li>
<li>
Retain the LIBSVM COPYRIGHT file in your software.
</li>
</ol>
It can also be used in commercial products.
<p align="right">
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<hr/>
  <a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f205"><b>Q: Is there a repository of additional tools based on libsvm?</b></a>
<br/>                                                                                
<p>
Yes, see <a href="http://www.csie.ntu.edu.tw/~cjlin/libsvmtools">libsvm 
tools</a>
<p align="right">
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<hr/>
  <a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f206"><b>Q: On unix machines, I got "error in loading shared libraries" or "cannot open shared object file." What happened ? </b></a>
<br/>                                                                                

<p>
This usually happens if you compile the code
on one machine and run it on another which has incompatible
libraries.
Try to recompile the program on that machine or use static linking.
<p align="right">
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<hr/>
  <a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f207"><b>Q: I have modified the source and would like to build the graphic interface "svm-toy" on MS windows. How should I do it ?</b></a>
<br/>                                                                                

<p>
Build it as a project by choosing "Win32 Project."
On the other hand, for "svm-train" and "svm-predict"
you want to choose "Win32 Console Project."
After libsvm 2.5, you can also use the file Makefile.win.
See details in README.


<p>
If you are not using Makefile.win and see the following 
link error
<pre>
LIBCMTD.lib(wwincrt0.obj) : error LNK2001: unresolved external symbol
_wWinMain@16
</pre>
you may have selected a wrong project type.
<p align="right">
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<hr/>
  <a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f208"><b>Q: I am an MS windows user but why only one (svm-toy) of those precompiled .exe actually runs ?  </b></a>
<br/>                                                                                

<p>
You need to open a command window 
and type  svmtrain.exe to see all options.
Some examples are in README file.
<p align="right">
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<hr/>
  <a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f209"><b>Q: What is the difference between "." and "*" outputed during training? </b></a>
<br/>                                                                                

<p>
"." means every 1,000 iterations (or every #data 
iterations is your #data is less than 1,000).
"*" means that after iterations of using
a smaller shrunk problem, 
we reset to use the whole set. See the 
<a href=../papers/libsvm.pdf>implementation document</a> for details.
<p align="right">
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<hr/>
  <a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f210"><b>Q: Why occasionally the program (including MATLAB or other interfaces) crashes and gives a segmentation fault?</b></a>
<br/>                                                                                

<p>
Very likely the program consumes too much memory than what the 
operating system can provide. Try a smaller data and see if the 
program still crashes.
<p align="right">
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<hr/>
  <a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f211"><b>Q: How to build a dynamic library (.dll file) on MS windows?</b></a>
<br/>                                                                                
<p>

The easiest way is to use Makefile.win.
See details in README.

Alternatively, you can use Visual C++. Here is 
the example using Visual Studio .Net 2008:
<ol>
<li>Create a Win32 empty DLL project and set (in Project->$Project_Name
Properties...->Configuration) to "Release."
   About how to create a new dynamic link library, please refer to
<a href=http://msdn2.microsoft.com/en-us/library/ms235636(VS.80).aspx>http://msdn2.microsoft.com/en-us/library/ms235636(VS.80).aspx</a>

<li> Add svm.cpp, svm.h to your project.
<li> Add __WIN32__ and _CRT_SECURE_NO_DEPRECATE to Preprocessor definitions (in
Project->$Project_Name Properties...->C/C++->Preprocessor)
<li> Set Create/Use Precompiled Header to Not Using Precompiled Headers
(in Project->$Project_Name Properties...->C/C++->Precompiled Headers)
<li> Set the path for the Modulation Definition File svm.def (in 
Project->$Project_Name Properties...->Linker->input
<li> Build the DLL.
<li> Rename the dll file to libsvm.dll and move it to the correct path.
</ol>


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<hr/>
  <a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f212"><b>Q: On some systems (e.g., Ubuntu), compiling LIBSVM gives many warning messages. Is this a problem and how to disable the warning message?</b></a>
<br/>                                                                                

<p>
If you are using a version before 3.18, probably you see
a warning message like
<pre>
svm.cpp:2730: warning: ignoring return value of int fscanf(FILE*, const char*, ...), declared with attribute warn_unused_result
</pre>
This is not a problem; see <a href=https://wiki.ubuntu.com/CompilerFlags#-D_FORTIFY_SOURCE=2>this page</a> for more 
details of ubuntu systems.
To disable the warning message you can replace
<pre>
CFLAGS = -Wall -Wconversion -O3 -fPIC
</pre>
with
<pre>
CFLAGS = -Wall -Wconversion -O3 -fPIC -U_FORTIFY_SOURCE
</pre>
in Makefile.
<p> After version 3.18, we have a better setting so that such warning messages do not appear.
<p align="right">
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<hr/>
  <a name="/Q02:_Installation_and_running_the_program"></a>
<a name="f213"><b>Q: In LIBSVM, why you don't use certain C/C++ library functions to make the code shorter?</b></a>
<br/>                                                                                

<p>
For portability, we use only features defined in ISO C89. Note that features in ISO C99 may not be available everywhere. 
Even the newest gcc lacks some features in C99 (see <a href=http://gcc.gnu.org/c99status.html>http://gcc.gnu.org/c99status.html</a> for details).
If the situation changes in the future, 
we might consider using these newer features.
<p align="right">
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<hr/>
  <a name="/Q03:_Data_preparation"></a>
<a name="f301"><b>Q: Why sometimes not all attributes of a data appear in the training/model files ?</b></a>
<br/>                                                                                
<p>
libsvm uses the so called "sparse" format where zero
values do not need to be stored. Hence a data with attributes
<pre>
1 0 2 0
</pre>
is represented as
<pre>
1:1 3:2
</pre>
<p align="right">
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<hr/>
  <a name="/Q03:_Data_preparation"></a>
<a name="f302"><b>Q: What if my data are non-numerical ?</b></a>
<br/>                                                                                
<p>
Currently libsvm supports only numerical data.
You may have to change non-numerical data to 
numerical. For example, you can use several
binary attributes to represent a categorical
attribute.
<p align="right">
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<hr/>
  <a name="/Q03:_Data_preparation"></a>
<a name="f303"><b>Q: Why do you consider sparse format ? Will the training of dense data be much slower ?</b></a>
<br/>                                                                                
<p>
This is a controversial issue. The kernel
evaluation (i.e. inner product) of sparse vectors is slower 
so the total training time can be at least twice or three times
of that using the dense format.
However, we cannot support only dense format as then we CANNOT
handle extremely sparse cases. Simplicity of the code is another
concern. Right now we decide to support
the sparse format only.
<p align="right">
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<hr/>
  <a name="/Q03:_Data_preparation"></a>
<a name="f304"><b>Q: Why sometimes the last line of my data is not read by svm-train?</b></a>
<br/>                                                                                

<p>
We assume that you have '\n' in the end of
each line. So please press enter in the end
of your last line.
<p align="right">
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<hr/>
  <a name="/Q03:_Data_preparation"></a>
<a name="f305"><b>Q: Is there a program to check if my data are in the correct format?</b></a>
<br/>                                                                                

<p>
The svm-train program in libsvm conducts only a simple check of the input data. To do a
detailed check, after libsvm 2.85, you can use the python script tools/checkdata.py. See tools/README for details.
<p align="right">
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<hr/>
  <a name="/Q03:_Data_preparation"></a>
<a name="f306"><b>Q: May I put comments in data files?</b></a>
<br/>                                                                                

<p>
We don't officially support this. But, currently LIBSVM
is able to process data in the following
format:
<pre>
1 1:2 2:1 # your comments
</pre>
Note that the character ":" should not appear in your
comments.
<!--
No, for simplicity we don't support that.
However, you can easily preprocess your data before
using libsvm. For example,
if you have the following data
<pre>
test.txt
1 1:2 2:1 # proten A
</pre>
then on unix machines you can do
<pre>
cut -d '#' -f 1 < test.txt > test.features
cut -d '#' -f 2 < test.txt > test.comments
svm-predict test.feature train.model test.predicts
paste -d '#' test.predicts test.comments | sed 's/#/ #/' > test.results
</pre>
-->
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<hr/>
  <a name="/Q03:_Data_preparation"></a>
<a name="f307"><b>Q: How to convert other data formats to LIBSVM format?</b></a>
<br/>                                                                                

<p>
It depends on your data format. A simple way is to use
libsvmwrite in the libsvm matlab/octave interface.

Take a CSV (comma-separated values) file
in UCI machine learning repository as an example.
We download <a href=http://archive.ics.uci.edu/ml/machine-learning-databases/spect/SPECTF.train>SPECTF.train</a>. 
Labels are in the first column. The following steps produce
a file in the libsvm format.
<pre>
matlab> SPECTF = csvread('SPECTF.train'); % read a csv file
matlab> labels = SPECTF(:, 1); % labels from the 1st column
matlab> features = SPECTF(:, 2:end); 
matlab> features_sparse = sparse(features); % features must be in a sparse matrix
matlab> libsvmwrite('SPECTFlibsvm.train', labels, features_sparse);
</pre>
The tranformed data are stored in SPECTFlibsvm.train.

<p>
Alternatively, you can use <a href="./faqfiles/convert.c">convert.c</a> 
to convert CSV format to libsvm format.
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f401"><b>Q: The output of training C-SVM is like the following. What do they mean?</b></a>
<br/>                                                                                
<br>optimization finished, #iter = 219
<br>nu = 0.431030
<br>obj = -100.877286, rho = 0.424632
<br>nSV = 132, nBSV = 107
<br>Total nSV = 132
<p>
obj is the optimal objective value of the dual SVM problem.
rho is the bias term in the decision function
sgn(w^Tx - rho).
nSV and nBSV are number of support vectors and bounded support
vectors (i.e., alpha_i = C). nu-svm is a somewhat equivalent
form of C-SVM where C is replaced by nu. nu simply shows the
corresponding parameter. More details are in
<a href="http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf">
libsvm document</a>.
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f402"><b>Q: Can you explain more about the model file?</b></a>
<br/>                                                                                

<p>
In the model file, after parameters and other informations such as labels , each line represents a support vector.
Support vectors are listed in the order of "labels" shown earlier.
(i.e., those from the first class in the "labels" list are
grouped first, and so on.) 
If k is the total number of classes,
in front of a support vector in class j, there are
k-1 coefficients 
y*alpha where alpha are dual solution of the
following two class problems:
<br>
1 vs j, 2 vs j, ..., j-1 vs j, j vs j+1, j vs j+2, ..., j vs k
<br>
and y=1 in first j-1 coefficients, y=-1 in the remaining
k-j coefficients.

For example, if there are 4 classes, the file looks like:

<pre>
+-+-+-+--------------------+
|1|1|1|                    |
|v|v|v|  SVs from class 1  |
|2|3|4|                    |
+-+-+-+--------------------+
|1|2|2|                    |
|v|v|v|  SVs from class 2  |
|2|3|4|                    |
+-+-+-+--------------------+
|1|2|3|                    |
|v|v|v|  SVs from class 3  |
|3|3|4|                    |
+-+-+-+--------------------+
|1|2|3|                    |
|v|v|v|  SVs from class 4  |
|4|4|4|                    |
+-+-+-+--------------------+
</pre>
See also
<a href="#f804"> an illustration using
MATLAB/OCTAVE.</a>
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f403"><b>Q: Should I use float or double to store numbers in the cache ?</b></a>
<br/>                                                                                

<p>
We have float as the default as you can store more numbers
in the cache. 
In general this is good enough but for few difficult
cases (e.g. C very very large) where solutions are huge
numbers, it might be possible that the numerical precision is not
enough using only float.
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f405"><b>Q: Does libsvm have special treatments for linear SVM?</b></a>
<br/>                                                                                

<p>

No, libsvm solves linear/nonlinear SVMs by the
same way.
Some tricks may save training/testing time if the
linear kernel is used,
so libsvm is <b>NOT</b> particularly efficient for linear SVM,
especially when
C is large and
the number of data is much larger
than the number of attributes.
You can either
<ul>
<li>
Use small C only. We have shown in the following paper
that after C is larger than a certain threshold,
the decision function is the same. 
<p>
<a href="http://guppy.mpe.nus.edu.sg/~mpessk/">S. S. Keerthi</a>
and
<B>C.-J. Lin</B>.
<A HREF="papers/limit.pdf">
Asymptotic behaviors of support vector machines with 
Gaussian kernel
</A>
.
<I><A HREF="http://mitpress.mit.edu/journal-home.tcl?issn=08997667">Neural Computation</A></I>, 15(2003), 1667-1689.


<li>
Check <a href=http://www.csie.ntu.edu.tw/~cjlin/liblinear>liblinear</a>,
which is designed for large-scale linear classification.
</ul>

<p> Please also see our <a href=../papers/guide/guide.pdf>SVM guide</a>
on the discussion of using RBF and linear
kernels.
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f406"><b>Q: The number of free support vectors is large. What should I do?</b></a>
<br/>                                                                                
 <p>
This usually happens when the data are overfitted.
If attributes of your data are in large ranges,
try to scale them. Then the region
of appropriate parameters may be larger.
Note that there is a scale program
in libsvm. 
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f407"><b>Q: Should I scale training and testing data in a similar way?</b></a>
<br/>                                                                                
<p>
Yes, you can do the following:
<pre>
> svm-scale -s scaling_parameters train_data > scaled_train_data
> svm-scale -r scaling_parameters test_data > scaled_test_data
</pre>
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f408"><b>Q: Does it make a big difference  if I scale each attribute to [0,1] instead of [-1,1]?</b></a>
<br/>                                                                                

<p>
For the linear scaling method, if the RBF kernel is
used and parameter selection is conducted, there
is no difference. Assume Mi and mi are 
respectively the maximal and minimal values of the
ith attribute. Scaling to [0,1] means
<pre>
                x'=(x-mi)/(Mi-mi)
</pre>
For [-1,1],
<pre>
                x''=2(x-mi)/(Mi-mi)-1.
</pre>
In the RBF kernel,
<pre>
                x'-y'=(x-y)/(Mi-mi), x''-y''=2(x-y)/(Mi-mi).
</pre>
Hence, using (C,g) on the [0,1]-scaled data is the
same as (C,g/2) on the [-1,1]-scaled data.

<p> Though the performance is the same, the computational
time may be different. For data with many zero entries,
[0,1]-scaling keeps the sparsity of input data and hence
may save the time.
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f409"><b>Q: The prediction rate is low. How could I improve it?</b></a>
<br/>                                                                                
<p>
Try to use the model selection tool grid.py in the tools
directory find
out good parameters. To see the importance of model selection,
please 
see our guide for beginners:
<A HREF="http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf">
A practical guide to support vector 
classification 
</A>
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f410"><b>Q: My data are unbalanced. Could libsvm handle such problems?</b></a>
<br/>                                                                                
<p>
Yes, there is a -wi options. For example, if you use
<pre>
> svm-train -s 0 -c 10 -w1 1 -w-1 5 data_file
</pre>
<p>
the penalty for class "-1" is larger.
Note that this -w option is for C-SVC only.
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f411"><b>Q: What is the difference between nu-SVC and C-SVC?</b></a>
<br/>                                                                                
<p>
Basically they are the same thing but with different
parameters. The range of C is from zero to infinity
but nu is always between [0,1]. A nice property
of nu is that it is related to the ratio of 
support vectors and the ratio of the training
error.
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f412"><b>Q: The program keeps running (without showing any output). What should I do?</b></a>
<br/>                                                                                
<p>
You may want to check your data. Each training/testing
data must be in one line. It cannot be separated.
In addition, you have to remove empty lines.
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f413"><b>Q: The program keeps running (with output, i.e. many dots). What should I do?</b></a>
<br/>                                                                                
<p>
In theory libsvm guarantees to converge.
Therefore, this means you are
handling ill-conditioned situations
(e.g. too large/small parameters) so numerical
difficulties occur.
<p>
You may get better numerical stability by replacing
<pre>
typedef float Qfloat;
</pre>
in svm.cpp with
<pre>
typedef double Qfloat;
</pre>
That is, elements in the kernel cache are stored
in double instead of single. However, this means fewer elements
can be put in the kernel cache.
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f414"><b>Q: The training time is too long. What should I do?</b></a>
<br/>                                                                                
<p>
For large problems, please specify enough cache size (i.e.,
-m).
Slow convergence may happen for some difficult cases (e.g. -c is large).
You can try to use a looser stopping tolerance with -e.
If that still doesn't work, you may train only a subset of the data.
You can use the program subset.py in the directory "tools" 
to obtain a random subset.

<p>
If you have extremely large data and face this difficulty, please
contact us. We will be happy to discuss possible solutions.

<p> When using large -e, you may want to check if -h 0 (no shrinking) or -h 1 (shrinking) is faster.
See a related question below.

<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f4141"><b>Q: Does shrinking always help?</b></a>
<br/>                                                                                
<p>
If the number of iterations is high, then shrinking
often helps.
However, if the number of iterations is small
(e.g., you specify a large -e), then
probably using -h 0 (no shrinking) is better.
See the 
<a href=../papers/libsvm.pdf>implementation document</a> for details.
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f415"><b>Q: How do I get the decision value(s)?</b></a>
<br/>                                                                                
<p>
We print out decision values for regression. For classification,
we solve several binary SVMs for multi-class cases. You
can obtain values by easily calling the subroutine
svm_predict_values. Their corresponding labels
can be obtained from svm_get_labels. 
Details are in 
README of libsvm package. 

<p>
If you are using MATLAB/OCTAVE interface, svmpredict can directly
give you decision values. Please see matlab/README for details.

<p>
We do not recommend the following. But if you would
like to get values for 
TWO-class classification with labels +1 and -1
(note: +1 and -1 but not things like 5 and 10)
in the easiest way, simply add 
<pre>
		printf("%f\n", dec_values[0]*model->label[0]);
</pre>
after the line
<pre>
		svm_predict_values(model, x, dec_values);
</pre>
of the file svm.cpp.
Positive (negative)
decision values correspond to data predicted as +1 (-1).


<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f4151"><b>Q: How do I get the distance between a point and the hyperplane?</b></a>
<br/>                                                                                
<p>
The distance is |decision_value| / |w|. 
We have |w|^2 = w^Tw = alpha^T Q alpha = 2*(dual_obj + sum alpha_i). 
Thus in svm.cpp please find the place 
where we calculate the dual objective value
(i.e., the subroutine Solve())
and add a statement to print w^Tw.

<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f416"><b>Q: On 32-bit machines, if I use a large cache (i.e. large -m) on a linux machine, why sometimes I get "segmentation fault ?"</b></a>
<br/>                                                                                
<p>

On 32-bit machines, the maximum addressable
memory is 4GB. The Linux kernel uses 3:1
split which means user space is 3G and
kernel space is 1G. Although there are
3G user space, the maximum dynamic allocation
memory is 2G. So, if you specify -m near 2G,
the memory will be exhausted. And svm-train
will fail when it asks more memory.
For more details, please read 
<a href=http://groups.google.com/groups?hl=en&lr=&ie=UTF-8&selm=3BA164F6.BAFA4FB%40daimi.au.dk>
this article</a>.
<p>
The easiest solution is to switch to a
 64-bit machine.
Otherwise, there are two ways to solve this. If your
machine supports Intel's PAE (Physical Address
Extension), you can turn on the option HIGHMEM64G
in Linux kernel which uses 4G:4G split for
kernel and user space. If you don't, you can
try a software `tub' which can eliminate the 2G
boundary for dynamic allocated memory. The `tub'
is available at 
<a href=http://www.bitwagon.com/tub.html>http://www.bitwagon.com/tub.html</a>.


<!--

This may happen only  when the cache is large, but each cached row is
not large enough. <b>Note:</b> This problem is specific to 
gnu C library which is used in linux.
The solution is as follows:

<p>
In our program we have malloc() which uses two methods 
to allocate memory from kernel. One is
sbrk() and another is mmap(). sbrk is faster, but mmap 
has a larger address
space. So malloc uses mmap only if the wanted memory size is larger
than some threshold (default 128k).
In the case where each row is not large enough (#elements < 128k/sizeof(float)) but we need a large cache ,
the address space for sbrk can be exhausted. The solution is to
lower the threshold to force malloc to use mmap
and increase the maximum number of chunks to allocate
with mmap.

<p>
Therefore, in the main program (i.e. svm-train.c) you want
to have
<pre>
      #include &lt;malloc.h&gt;
</pre>
and then in main():
<pre>
      mallopt(M_MMAP_THRESHOLD, 32768);
      mallopt(M_MMAP_MAX,1000000);
</pre>
You can also set the environment variables instead
of writing them in the program:
<pre>
$ M_MMAP_MAX=1000000 M_MMAP_THRESHOLD=32768 ./svm-train .....
</pre>
More information can be found by 
<pre>
$ info libc "Malloc Tunable Parameters"
</pre>
-->
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f417"><b>Q: How do I disable screen output of svm-train?</b></a>
<br/>                                                                                
<p>
For commend-line users, use the -q option:
<pre>
> ./svm-train -q heart_scale
</pre>
<p>
For library users, set the global variable
<pre>
extern void (*svm_print_string) (const char *);
</pre>
to specify the output format. You can disable the output by the following steps:
<ol>
<li>
Declare a function to output nothing:
<pre>
void print_null(const char *s) {}
</pre>
</li>
<li>
Assign the output function of libsvm by
<pre>
svm_print_string = &print_null;
</pre>
</li>
</ol>
Finally, a way used in earlier libsvm
is by updating svm.cpp from
<pre>
#if 1
void info(const char *fmt,...)
</pre>
to
<pre>
#if 0
void info(const char *fmt,...)
</pre>
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f418"><b>Q: I would like to use my own kernel. Any example? In svm.cpp, there are two subroutines for kernel evaluations: k_function() and kernel_function(). Which one should I modify ?</b></a>
<br/>                                                                                
<p>
An example is "LIBSVM for string data" in LIBSVM Tools.
<p>
The reason why we have two functions is as follows.
For the RBF kernel exp(-g |xi - xj|^2), if we calculate
xi - xj first and then the norm square, there are 3n operations.
Thus we consider exp(-g (|xi|^2 - 2dot(xi,xj) +|xj|^2))
and by calculating all |xi|^2 in the beginning, 
the number of operations is reduced to 2n.
This is for the training.  For prediction we cannot
do this so a regular subroutine using that 3n operations is
needed.

The easiest way to have your own kernel is
to  put the same code in these two
subroutines by replacing any kernel.
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f419"><b>Q: What method does libsvm use for multi-class SVM ? Why don't you use the "1-against-the rest" method?</b></a>
<br/>                                                                                
<p>
It is one-against-one. We chose it after doing the following
comparison:
C.-W. Hsu and C.-J. Lin.
<A HREF="http://www.csie.ntu.edu.tw/~cjlin/papers/multisvm.pdf">
A comparison of methods 
for multi-class support vector machines
</A>, 
<I>IEEE Transactions on Neural Networks</A></I>, 13(2002), 415-425.

<p>
"1-against-the rest" is a good method whose performance
is comparable to "1-against-1." We do the latter
simply because its training time is shorter.
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f422"><b>Q: I would like to solve L2-loss SVM (i.e., error term is quadratic). How should I modify the code ?</b></a>
<br/>                                                                                
<p>
It is extremely easy. Taking c-svc for example, to solve
<p>
min_w w^Tw/2 + C \sum max(0, 1- (y_i w^Tx_i+b))^2,
<p>
only two 
places of svm.cpp have to be changed. 
First, modify the following line of 
solve_c_svc from 
<pre>
	s.Solve(l, SVC_Q(*prob,*param,y), minus_ones, y,
		alpha, Cp, Cn, param->eps, si, param->shrinking);
</pre>
to
<pre>
	s.Solve(l, SVC_Q(*prob,*param,y), minus_ones, y,
		alpha, INF, INF, param->eps, si, param->shrinking);
</pre>
Second, in  the class  of SVC_Q, declare C as 
a private variable:
<pre>
	double C;
</pre> 
In the constructor replace
<pre>
	for(int i=0;i&lt;prob.l;i++)
		QD[i]= (Qfloat)(this->*kernel_function)(i,i);
</pre>
with
<pre>
        this->C = param.C;
	for(int i=0;i&lt;prob.l;i++)
		QD[i]= (Qfloat)(this->*kernel_function)(i,i)+0.5/C;
</pre>
Then in the subroutine get_Q, after the for loop, add
<pre>
        if(i >= start && i < len) 
		data[i] += 0.5/C;
</pre>

<p>
For one-class svm, the modification is exactly the same. For SVR, you don't need an if statement like the above. Instead, you only need a simple assignment:
<pre>
	data[real_i] += 0.5/C;
</pre>


<p>
For large linear L2-loss SVM, please use
<a href=../liblinear>LIBLINEAR</a>.
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f425"><b>Q: In one-class SVM, parameter nu should be an upper bound of the training error rate. Why sometimes I get a training error rate bigger than nu?</b></a>
<br/>                                                                                

<p>
At optimum, some training instances should satisfy
w^Tx - rho = 0. However, numerically they may be slightly
smaller than zero
Then they are wrongly counted
as training errors. You can use a smaller stopping tolerance
(by the -e option) to make this problem less serious.

<p> 
This issue <b>does not occur</b> for nu-SVC for 
two-class classification.
We have that
<ol>
<li>nu is an upper bound on the ratio of training points
on the wrong side of the hyperplane, and
<li>therefore, nu is also an upper bound on the training error rate.
</ol>
Numerical issues occur in calculating the first case
because some training points satisfying y(w^Tx + b) - rho = 0
become negative. 
However, we have no numerical  problems for the second case because
we compare y(w^Tx + b) and 0 for counting training errors.
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f427"><b>Q: Why the code gives NaN (not a number) results?</b></a>
<br/>                                                                                
<p>
This rarely happens, but few users reported the problem.
It seems that their 
computers for training libsvm have the VPN client
running. The VPN software has some bugs and causes this
problem. Please try to close or disconnect the VPN client.
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f430"><b>Q: Why the sign of predicted labels and decision values are sometimes reversed?</b></a>
<br/>                                                                                
<p>

This situation may occur <b>before version 3.17</b>.
Nothing is wrong. Very likely you have two labels +1/-1 and the first instance in your data
has -1. We give the following explanation.

<p>
Internally class labels are ordered by their first occurrence in the training set. For a k-class data, internally labels
are 0, ..., k-1, and each two-class SVM considers pair
(i, j) with i < j. Then class i is treated as positive (+1)
and j as negative (-1).
For example, if the data set has labels +5/+10 and +10 appears
first, then internally the +5 versus +10 SVM problem
has +10 as positive (+1) and +5 as negative (-1).

<p>
By this setting, if you have labels +1 and -1,
it's possible that internally they correspond to -1 and +1,
respectively. Some new users have been confused about
this, so <b>after version 3.17</b>, if the data set has only
two labels +1 and -1,
internally we ensure +1 to be before -1. Then class +1 
is always treated as positive in the SVM problem.
Note that this is for <b>two-class data only.</b>
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f431"><b>Q: I don't know class labels of test data. What should I put in the first column of the test file?</b></a>
<br/>                                                                                
<p>Any value is ok. In this situation, what you will use is the output file of svm-predict, which gives predicted class labels.


<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f432"><b>Q: How can I use OpenMP to parallelize LIBSVM on a multicore/shared-memory computer?</b></a>
<br/>                                                                                

<p>It is very easy if you are using GCC 4.2
or after. 

<p> In Makefile, add -fopenmp  to CFLAGS.

<p> In class SVC_Q of svm.cpp, modify the for loop
of get_Q to:
<pre>
#pragma omp parallel for private(j) 
			for(j=start;j&lt;len;j++)
</pre>
<p> In the subroutine svm_predict_values of svm.cpp, add one line to the for loop:
<pre>
#pragma omp parallel for private(i) 
		for(i=0;i&lt;l;i++)
			kvalue[i] = Kernel::k_function(x,model-&gt;SV[i],model-&gt;param);
</pre>
For regression, you need to modify
class SVR_Q instead. The loop in svm_predict_values
is also different because you need
a reduction clause for the variable sum:
<pre>
#pragma omp parallel for private(i) reduction(+:sum) 
		for(i=0;i&lt;model->l;i++)
			sum += sv_coef[i] * Kernel::k_function(x,model-&gt;SV[i],model-&gt;param);
</pre>

<p> Then rebuild the package. Kernel evaluations in training/testing will be parallelized. An example of running this modification on
an 8-core machine using the data set
<a href=../libsvmtools/datasets/binary/ijcnn1.bz2>ijcnn1</a>:

<p> 8 cores:
<pre>
%setenv OMP_NUM_THREADS 8
%time svm-train -c 16 -g 4 -m 400 ijcnn1
27.1sec
</pre>
1 core:
<pre>
%setenv OMP_NUM_THREADS 1
%time svm-train -c 16 -g 4 -m 400 ijcnn1
79.8sec
</pre>
For this data, kernel evaluations take 80% of training time. In the above example, we assume you use csh. For bash, use
<pre>
export OMP_NUM_THREADS=8
</pre>
instead.

<p> For Python interface, you need to add the -lgomp link option:
<pre>
$(CXX) -lgomp -shared -dynamiclib svm.o -o libsvm.so.$(SHVER)
</pre>

<p> For MS Windows, you need to add /openmp in CFLAGS of Makefile.win

<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f433"><b>Q: How could I know which training instances are support vectors?</b></a>
<br/>                                                                                

<p>
It's very simple. Since version 3.13, you can use the function
<pre>
void svm_get_sv_indices(const struct svm_model *model, int *sv_indices)
</pre>
to get indices of support vectors. For example, in svm-train.c, after
<pre>
		model = svm_train(&amp;prob, &amp;param);
</pre>
you can add
<pre>
		int nr_sv = svm_get_nr_sv(model);
		int *sv_indices = Malloc(int, nr_sv);
		svm_get_sv_indices(model, sv_indices);
		for (int i=0; i&lt;nr_sv; i++)
			printf("instance %d is a support vector\n", sv_indices[i]);
</pre>

<p> If you use matlab interface, you can directly check
<pre>
model.sv_indices
</pre>
<p align="right">
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<hr/>
  <a name="/Q04:_Training_and_prediction"></a>
<a name="f434"><b>Q: Why sv_indices (indices of support vectors) are not stored in the saved model file?</b></a>
<br/>                                                                                

<p>
Although sv_indices is a member of the model structure
to 
indicate support vectors in the training set,
we do not store its contents in the model file.
The model file is mainly used in the future for
prediction, so it is basically <b>independent</b>
from training data. Thus 
storing sv_indices is not necessary.
Users should find support vectors right after
the training process. See the previous FAQ.
<p align="right">
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<hr/>
  <a name="/Q05:_Cross_validation_and_parameter_selection"></a>
<a name="f501"><b>Q: After doing cross validation, why there is no model file outputted ?</b></a>
<br/>                                                                                
<p>
Cross validation is used for selecting good parameters.
After finding them, you want to re-train the whole
data without the -v option.
<p align="right">
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<hr/>
  <a name="/Q05:_Cross_validation_and_parameter_selection"></a>
<a name="f502"><b>Q: Why my cross-validation results are different from those in the Practical Guide?</b></a>
<br/>                                                                                
<p>

Due to random partitions of
the data, on different systems CV accuracy values
may be different.
<p align="right">
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<hr/>
  <a name="/Q05:_Cross_validation_and_parameter_selection"></a>
<a name="f503"><b>Q: On some systems CV accuracy is the same in several runs. How could I use different data partitions? In other words, how do I set random seed in LIBSVM?</b></a>
<br/>                                                                                
<p>
If you use GNU C library,
the default seed 1 is considered. Thus you always
get the same result of running svm-train -v.
To have different seeds, you can add the following code
in svm-train.c:
<pre>
#include &lt;time.h&gt;
</pre>
and in the beginning of main(),
<pre>
srand(time(0));
</pre>
Alternatively, if you are not using GNU C library
and would like to use a fixed seed, you can have
<pre>
srand(1);
</pre>

<p>
For Java, the random number generator
is initialized using the time information.
So results of two CV runs are different.
To fix the seed, after version 3.1 (released
in mid 2011), you can add
<pre>
svm.rand.setSeed(0);
</pre>
in the main() function of svm_train.java.

<p>
If you use CV to select parameters, it is recommended to use identical folds
under different parameters. In this case, you can consider fixing the seed.
<p align="right">
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<hr/>
  <a name="/Q05:_Cross_validation_and_parameter_selection"></a>
<a name="f504"><b>Q: Why on windows sometimes grid.py fails?</b></a>
<br/>                                                                                
<p>

This problem shouldn't happen after version
2.85. If you are using earlier versions,
please download the latest one.

<!--
<p>
If you are using earlier 
versions, the error message is probably
<pre>
Traceback (most recent call last):
  File "grid.py", line 349, in ?
    main()
  File "grid.py", line 344, in main
    redraw(db)
  File "grid.py", line 132, in redraw
    gnuplot.write("set term windows\n")
IOError: [Errno 22] Invalid argument
</pre>

<p>Please try to close gnuplot windows and rerun.
If the problem still occurs, comment the following
two lines in grid.py by inserting "#" in the beginning:
<pre>
        redraw(db)
        redraw(db,1)
</pre>
Then you get accuracy only but not cross validation contours.
-->
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<hr/>
  <a name="/Q05:_Cross_validation_and_parameter_selection"></a>
<a name="f505"><b>Q: Why grid.py/easy.py sometimes generates the following warning message?</b></a>
<br/>                                                                                
<pre>
Warning: empty z range [62.5:62.5], adjusting to [61.875:63.125]
Notice: cannot contour non grid data!
</pre>
<p>Nothing is wrong and please disregard the 
message. It is from gnuplot when drawing
the contour.  
<p align="right">
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<hr/>
  <a name="/Q05:_Cross_validation_and_parameter_selection"></a>
<a name="f506"><b>Q: How do I choose the kernel?</b></a>
<br/>                                                                                

<p>
In general we suggest you to try the RBF kernel first.
A recent result by Keerthi and Lin
(<a href=http://www.csie.ntu.edu.tw/~cjlin/papers/limit.pdf>
download paper here</a>)
shows that if RBF is used with model selection,
then there is no need to consider the linear kernel.
The kernel matrix using sigmoid may not be positive definite
and in general it's accuracy is not better than RBF.
(see the paper by Lin and Lin
(<a href=http://www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf>
download paper here</a>).
Polynomial kernels are ok but if a high degree is used,
numerical difficulties tend to happen
(thinking about dth power of (<1) goes to 0
and (>1) goes to infinity).
<p align="right">
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<hr/>
  <a name="/Q05:_Cross_validation_and_parameter_selection"></a>
<a name="f507"><b>Q: How does LIBSVM perform parameter selection for multi-class problems? </b></a>
<br/>                                                                                

<p>
LIBSVM implements "one-against-one" multi-class method, so there are 
k(k-1)/2 binary models, where k is the number of classes.

<p>
We can consider two ways to conduct parameter selection.

<ol>
<li>
For any two classes of data, a parameter selection procedure is conducted. Finally,
each decision function has its own optimal parameters.
</li>
<li>
The same parameters are used for all k(k-1)/2 binary classification problems.
We select parameters that achieve the highest overall performance.
</li>
</ol>

Each has its own advantages. A
single parameter set may not be uniformly good for all k(k-1)/2 decision functions.
However, as the overall accuracy is the final consideration, one parameter set 
for one decision function may lead to over-fitting. In the paper
<p>
Chen, Lin, and Sch&ouml;lkopf,
<A HREF="../papers/nusvmtutorial.pdf">
A tutorial on nu-support vector machines.
</A> 
Applied Stochastic Models in Business and Industry, 21(2005), 111-136,

<p>
they have experimentally
shown that the two methods give similar performance.
Therefore, currently the parameter selection in LIBSVM
takes the second approach by considering the same parameters for
all k(k-1)/2 models.
<p align="right">
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<hr/>
  <a name="/Q05:_Cross_validation_and_parameter_selection"></a>
<a name="f508"><b>Q: How do I choose parameters for one-class SVM as training data are in only one class?</b></a>
<br/>                                                                                
<p>
You have pre-specified true positive rate in mind and then search for
parameters which achieve similar cross-validation accuracy.
<p align="right">
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<hr/>
  <a name="/Q06:_Probability_outputs"></a>
<a name="f425"><b>Q: Why training a probability model (i.e., -b 1) takes a longer time?</b></a>
<br/>                                                                                
<p>
To construct this probability model, we internally conduct a 
cross validation, which is more time consuming than
a regular training.
Hence, in general you do parameter selection first without
-b 1. You only use -b 1 when good parameters have been
selected. In other words, you avoid using -b 1 and -v
together.
<p align="right">
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<hr/>
  <a name="/Q06:_Probability_outputs"></a>
<a name="f426"><b>Q: Why using the -b option does not give me better accuracy?</b></a>
<br/>                                                                                
<p>
There is absolutely no reason the probability outputs guarantee
you better accuracy. The main purpose of this option is
to provide you the probability estimates, but not to boost
prediction accuracy. From our experience, 
after proper parameter selections, in general with
and without -b have similar accuracy. Occasionally there
are some differences.
It is not recommended to compare the two under 
just a fixed parameter
set as more differences will be observed.
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<hr/>
  <a name="/Q06:_Probability_outputs"></a>
<a name="f427"><b>Q: Why using svm-predict -b 0 and -b 1 gives different accuracy values?</b></a>
<br/>                                                                                
<p>
Let's just consider two-class classification here. After probability information is obtained in training,
we do not have
<p>
prob > = 0.5 if and only if decision value >= 0.
<p>
So predictions may be different with -b 0 and 1.
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<hr/>
  <a name="/Q07:_Graphic_interface"></a>
<a name="f501"><b>Q: How can I save images drawn by svm-toy?</b></a>
<br/>                                                                                
<p>
For Microsoft windows, first press the "print screen" key on the keyboard.
Open "Microsoft Paint" 
(included in Windows) 
and press "ctrl-v." Then you can clip
the part of picture which you want.
For X windows, you can 
use the program "xv" or "import" to grab the picture of the svm-toy window.
<p align="right">
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<hr/>
  <a name="/Q07:_Graphic_interface"></a>
<a name="f502"><b>Q: I press the "load" button to load data points but why svm-toy does not draw them ?</b></a>
<br/>                                                                                
<p>
The program svm-toy assumes both attributes (i.e. x-axis and y-axis
values) are in (0,1). Hence you want to scale your 
data to between a small positive number and 
a number less than but very close to 1.
Moreover, class labels must be 1, 2, or 3
(not 1.0, 2.0 or anything else).
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<hr/>
  <a name="/Q07:_Graphic_interface"></a>
<a name="f503"><b>Q: I would like svm-toy to handle more than three classes of data, what should I do ?</b></a>
<br/>                                                                                
<p>
Taking windows/svm-toy.cpp as an example, you need to
modify it and  the difference
from the original file is as the following: (for five classes of
data)
<pre>
30,32c30
< 	RGB(200,0,200),
< 	RGB(0,160,0),
< 	RGB(160,0,0)
---
> 	RGB(200,0,200)
39c37
< HBRUSH brush1, brush2, brush3, brush4, brush5;
---
> HBRUSH brush1, brush2, brush3;
113,114d110
< 	brush4 = CreateSolidBrush(colors[7]);
< 	brush5 = CreateSolidBrush(colors[8]);
155,157c151
< 	else if(v==3) return brush3;
< 	else if(v==4) return brush4;
< 	else return brush5;
---
> 	else return brush3;
325d318
< 	  int colornum = 5;
327c320
< 		svm_node *x_space = new svm_node[colornum * prob.l];
---
> 		svm_node *x_space = new svm_node[3 * prob.l];
333,338c326,331
< 			x_space[colornum * i].index = 1;
< 			x_space[colornum * i].value = q->x;
< 			x_space[colornum * i + 1].index = 2;
< 			x_space[colornum * i + 1].value = q->y;
< 			x_space[colornum * i + 2].index = -1;
< 			prob.x[i] = &x_space[colornum * i];
---
> 			x_space[3 * i].index = 1;
> 			x_space[3 * i].value = q->x;
> 			x_space[3 * i + 1].index = 2;
> 			x_space[3 * i + 1].value = q->y;
> 			x_space[3 * i + 2].index = -1;
> 			prob.x[i] = &x_space[3 * i];
397c390
< 				if(current_value > 5) current_value = 1;
---
> 				if(current_value > 3) current_value = 1;
</pre>
<p align="right">
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<hr/>
  <a name="/Q08:_Java_version_of_libsvm"></a>
<a name="f601"><b>Q: What is the difference between Java version and C++ version of libsvm?</b></a>
<br/>                                                                                
<p>
They are the same thing. We just rewrote the C++ code
in Java.
<p align="right">
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<hr/>
  <a name="/Q08:_Java_version_of_libsvm"></a>
<a name="f602"><b>Q: Is the Java version significantly slower than the C++ version?</b></a>
<br/>                                                                                
<p>
This depends on the VM you used. We have seen good
VM which leads the Java version to be quite competitive with
the C++ code. (though still slower)
<p align="right">
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<hr/>
  <a name="/Q08:_Java_version_of_libsvm"></a>
<a name="f603"><b>Q: While training I get the following error message: java.lang.OutOfMemoryError. What is wrong?</b></a>
<br/>                                                                                
<p>
You should try to increase the maximum Java heap size.
For example,
<pre>
java -Xmx2048m -classpath libsvm.jar svm_train ...
</pre>
sets the maximum heap size to 2048M.
<p align="right">
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<hr/>
  <a name="/Q08:_Java_version_of_libsvm"></a>
<a name="f604"><b>Q: Why you have the main source file svm.m4 and then transform it to svm.java?</b></a>
<br/>                                                                                
<p>
Unlike C, Java does not have a preprocessor built-in.
However,  we need some macros (see first 3 lines of svm.m4).

</ul>
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<hr/>
  <a name="/Q09:_Python_interface"></a>
<a name="f704"><b>Q: Except the python-C++ interface provided, could I use Jython to call libsvm ?</b></a>
<br/>                                                                                
<p> Yes, here are some examples:

<pre>
$ export CLASSPATH=$CLASSPATH:~/libsvm-2.91/java/libsvm.jar
$ ./jython
Jython 2.1a3 on java1.3.0 (JIT: jitc)
Type "copyright", "credits" or "license" for more information.
>>> from libsvm import *
>>> dir()
['__doc__', '__name__', 'svm', 'svm_model', 'svm_node', 'svm_parameter',
'svm_problem']
>>> x1 = [svm_node(index=1,value=1)]
>>> x2 = [svm_node(index=1,value=-1)]
>>> param = svm_parameter(svm_type=0,kernel_type=2,gamma=1,cache_size=40,eps=0.001,C=1,nr_weight=0,shrinking=1)
>>> prob = svm_problem(l=2,y=[1,-1],x=[x1,x2])
>>> model = svm.svm_train(prob,param)
*
optimization finished, #iter = 1
nu = 1.0
obj = -1.018315639346838, rho = 0.0
nSV = 2, nBSV = 2
Total nSV = 2
>>> svm.svm_predict(model,x1)
1.0
>>> svm.svm_predict(model,x2)
-1.0
>>> svm.svm_save_model("test.model",model)

</pre>

<p align="right">
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<hr/>
  <a name="/Q10:_MATLAB_interface"></a>
<a name="f801"><b>Q: I compile the MATLAB interface without problem, but why errors occur while running it?</b></a>
<br/>                                                                                
<p>
Your compiler version may not be supported/compatible for MATLAB.
Please check <a href=http://www.mathworks.com/support/compilers/current_release>this MATLAB page</a> first and then specify the version
number. For example, if g++ X.Y is supported, replace
<pre>
CXX = g++
</pre>
in the Makefile with
<pre>
CXX = g++-X.Y
</pre>
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<hr/>
  <a name="/Q10:_MATLAB_interface"></a>
<a name="f8011"><b>Q: On 64bit Windows I compile the MATLAB interface without problem, but why errors occur while running it?</b></a>
<br/>                                                                                
<p>


Please make sure that you use
the -largeArrayDims option in make.m. For example,
<pre>
mex -largeArrayDims -O -c svm.cpp
</pre>

Moreover, if you use Microsoft Visual Studio, 
probabally it is not properly installed. 
See the explanation 
<a href=http://www.mathworks.com/support/compilers/current_release/win64.html#n7>here</a>.
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<hr/>
  <a name="/Q10:_MATLAB_interface"></a>
<a name="f802"><b>Q: Does the MATLAB interface provide a function to do scaling?</b></a>
<br/>                                                                                
<p>
It is extremely easy to do scaling under MATLAB.
The following one-line code scale each feature to the range
of [0,1]:
<pre>
(data - repmat(min(data,[],1),size(data,1),1))*spdiags(1./(max(data,[],1)-min(data,[],1))',0,size(data,2),size(data,2))
</pre>
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<hr/>
  <a name="/Q10:_MATLAB_interface"></a>
<a name="f803"><b>Q: How could I use MATLAB interface for parameter selection?</b></a>
<br/>                                                                                
<p>
One can do this by a simple loop. 
See the following example:
<pre>
bestcv = 0;
for log2c = -1:3,
  for log2g = -4:1,
    cmd = ['-v 5 -c ', num2str(2^log2c), ' -g ', num2str(2^log2g)];
    cv = svmtrain(heart_scale_label, heart_scale_inst, cmd);
    if (cv >= bestcv),
      bestcv = cv; bestc = 2^log2c; bestg = 2^log2g;
    end
    fprintf('%g %g %g (best c=%g, g=%g, rate=%g)\n', log2c, log2g, cv, bestc, bestg, bestcv);
  end
end
</pre>
You may adjust the parameter range in the above loops.
<p align="right">
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<hr/>
  <a name="/Q10:_MATLAB_interface"></a>
<a name="f8031"><b>Q: I use MATLAB parallel programming toolbox on a multi-core environment for parameter selection. Why the program is even slower?</b></a>
<br/>                                                                                
<p>
Fabrizio Lacalandra of University of Pisa reported this issue.
It seems the problem is caused by the screen output.
If you disable the <b>info</b> function
using <pre>#if 0,</pre> then the problem
may be solved.
<p align="right">
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<hr/>
  <a name="/Q10:_MATLAB_interface"></a>
<a name="f8032"><b>Q: How do I use LIBSVM with OpenMP under MATLAB?</b></a>
<br/>                                                                                
<p>
In Makefile,
you need to add -fopenmp to CFLAGS and -lgomp to MEX_OPTION. For Octave, you need the same modification.

<p> However, a minor problem is that
the number of threads cannot
be specified in MATLAB. We tried Version 7.12 (R2011a) and gcc-4.6.1.

<pre>
% export OMP_NUM_THREADS=4; matlab
>> setenv('OMP_NUM_THREADS', '1');
</pre>

Then OMP_NUM_THREADS is still 4 while running the program. Please contact us if you 
see how to solve this problem. You can, however,
specify the number in the source code (thanks
to comments from Ricardo Santiago-mozos):
<pre>
#pragma omp parallel  for private(i) num_threads(4)
</pre>
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<hr/>
  <a name="/Q10:_MATLAB_interface"></a>
<a name="f804"><b>Q: How could I generate the primal variable w of linear SVM?</b></a>
<br/>                                                                                
<p>
Let's start from the binary class and
assume you have two labels -1 and +1.
After obtaining the model from calling svmtrain,
do the following to have w and b:
<pre>
w = model.SVs' * model.sv_coef;
b = -model.rho;

if model.Label(1) == -1
  w = -w;
  b = -b;
end
</pre>
If you do regression or one-class SVM, then the if statement is not needed.

<p> For multi-class SVM, we illustrate the setting
in the following example of running the iris
data, which have 3 classes
<pre>  
> [y, x] = libsvmread('../../htdocs/libsvmtools/datasets/multiclass/iris.scale');
> m = svmtrain(y, x, '-t 0')

m = 

    Parameters: [5x1 double]
      nr_class: 3
       totalSV: 42
           rho: [3x1 double]
         Label: [3x1 double]
         ProbA: []
         ProbB: []
           nSV: [3x1 double]
       sv_coef: [42x2 double]
           SVs: [42x4 double]
</pre>
sv_coef is like:
<pre>
+-+-+--------------------+
|1|1|                    |
|v|v|  SVs from class 1  |
|2|3|                    |
+-+-+--------------------+
|1|2|                    |
|v|v|  SVs from class 2  |
|2|3|                    |
+-+-+--------------------+
|1|2|                    |
|v|v|  SVs from class 3  |
|3|3|                    |
+-+-+--------------------+
</pre>
so we need to see nSV of each classes.
<pre>  
> m.nSV

ans =

     3
    21
    18
</pre>
Suppose the goal is to find the vector w of classes 
1 vs 3. Then
y_i alpha_i of training 1 vs 3 are
<pre>  
> coef = [m.sv_coef(1:3,2); m.sv_coef(25:42,1)];
</pre>
and SVs are:
<pre>  
> SVs = [m.SVs(1:3,:); m.SVs(25:42,:)];
</pre>
Hence, w is
<pre>
> w = SVs'*coef;
</pre>  
For rho,
<pre>
> m.rho

ans =

    1.1465
    0.3682
   -1.9969
> b = -m.rho(2);
</pre>
because rho is arranged by 1vs2 1vs3 2vs3.


  
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<hr/>
  <a name="/Q10:_MATLAB_interface"></a>
<a name="f805"><b>Q: Is there an OCTAVE interface for libsvm?</b></a>
<br/>                                                                                
<p>
Yes, after libsvm 2.86, the matlab interface
works on OCTAVE as well. Please use make.m by typing
<pre>
>> make 
</pre>
under OCTAVE.
<p align="right">
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<hr/>
  <a name="/Q10:_MATLAB_interface"></a>
<a name="f806"><b>Q: How to handle the name conflict between svmtrain in the libsvm matlab interface and that in MATLAB bioinformatics toolbox?</b></a>
<br/>                                                                                
<p>
The easiest way is to rename the svmtrain binary 
file (e.g., svmtrain.mexw32 on 32-bit windows) 
to a different
name (e.g., svmtrain2.mexw32).
<p align="right">
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<hr/>
  <a name="/Q10:_MATLAB_interface"></a>
<a name="f807"><b>Q: On Windows I got an error message "Invalid MEX-file: Specific module not found" when running the pre-built MATLAB interface in the windows sub-directory. What should I do?</b></a>
<br/>                                                                                
<p>

The error usually happens
when there are missing runtime components
such as MSVCR100.dll on your Windows platform.
You can use tools such as 
<a href=http://www.dependencywalker.com/>Dependency 
Walker</a> to find missing library files.

<p>
For example, if the pre-built MEX files are compiled by
Visual C++ 2010,
you must have installed
Microsoft Visual C++ Redistributable Package 2010
(vcredist_x86.exe). You can easily find the freely
available file from Microsoft's web site. 

<p>
For 64bit Windows, the situation is similar. If
the pre-built files are by
Visual C++ 2008, then you must have
Microsoft Visual C++ Redistributable Package 2008
(vcredist_x64.exe).
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<hr/>
  <a name="/Q10:_MATLAB_interface"></a>
<a name="f808"><b>Q: LIBSVM supports 1-vs-1 multi-class classification. If instead I would like to use 1-vs-rest, how to implement it using MATLAB interface?</b></a>
<br/>                                                                                

<p>
Please use code in the following <a href=../libsvmtools/ovr_multiclass>directory</a>. The following example shows how to
train and test the problem dna (<a href=../libsvmtools/datasets/multiclass/dna.scale>training</a> and <a href=../libsvmtools/datasets/multiclass/dna.scale.t>testing</a>).

<p> Load, train and predict data:
<pre>
[trainY trainX] = libsvmread('./dna.scale');
[testY testX] = libsvmread('./dna.scale.t');
model = ovrtrain(trainY, trainX, '-c 8 -g 4');
[pred ac decv] = ovrpredict(testY, testX, model);
fprintf('Accuracy = %g%%\n', ac * 100);
</pre>
Conduct CV on a grid of parameters 
<pre>
bestcv = 0;
for log2c = -1:2:3,
  for log2g = -4:2:1,
    cmd = ['-q -c ', num2str(2^log2c), ' -g ', num2str(2^log2g)];
    cv = get_cv_ac(trainY, trainX, cmd, 3);
    if (cv >= bestcv),
      bestcv = cv; bestc = 2^log2c; bestg = 2^log2g;
    end
    fprintf('%g %g %g (best c=%g, g=%g, rate=%g)\n', log2c, log2g, cv, bestc, bestg, bestcv);
  end
end
</pre>
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<hr/>
  <a name="/Q10:_MATLAB_interface"></a>
<a name="f809"><b>Q: I tried to install matlab interface on mac, but failed. What should I do?</b></a>
<br/>                                                                                

<p>
We assume that in a matlab command window you change directory to libsvm/matlab and type
<pre>
>> make
</pre>
We discuss the following situations.

<ol>
<li>An error message like "libsvmread.c:1:19: fatal error:
stdio.h: No such file or directory" appears.

<p>
Reason: "make" looks for a C++ compiler, but 
no compiler is found. To get one, you can
<ul>
<li> Install XCode offered by Apple Inc.
<li> Install XCode Command Line Tools.
</ul>

<p>
<li> On OS X with Xcode 4.2+, I got an error message like "llvm-gcc-4.2:
command not found." 

<p>
Reason: Since Apple Inc. only ships llsvm-gcc instead of gcc-4.2, 
llvm-gcc-4.2 cannot be found.

<p>
If you are using Xcode 4.2-4.6,
a related solution is offered at
<a href=http://www.mathworks.com/matlabcentral/answers/94092>http://www.mathworks.com/matlabcentral/answers/94092</a>.

<p>
On the other hand, for Xcode 5 (including Xcode 4.2-4.6), in a Matlab command window, enter
<ul>
<li> cd (matlabroot)
<li> cd bin
<li> Backup your mexopts.sh first
<li> edit mexopts.sh
<li> Scroll down to "maci64" section. Change
<pre>
		CC='llvm-gcc-4.2'
		CXX='llvm-g++-4.2'
</pre>
to
<pre>
		CC='llvm-gcc'
		CXX='llvm-g++'
</pre>
</ul>

Please also ensure that SDKROOT corresponds to the SDK version you are using.

<p>
<li> Other errors: you may check <a href=http://www.mathworks.com/matlabcentral/answers/94092>http://www.mathworks.com/matlabcentral/answers/94092</a>.

</ol>
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <p align="middle">
<a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm">LIBSVM home page</a>
</p>
</body>
</html>


================================================
FILE: Matlab/libsvm-3.19/Makefile
================================================
CXX ?= g++
CFLAGS = -Wall -Wconversion -O3 -fPIC
SHVER = 2
OS = $(shell uname)

all: svm-train svm-predict svm-scale

lib: svm.o
	if [ "$(OS)" = "Darwin" ]; then \
		SHARED_LIB_FLAG="-dynamiclib -Wl,-install_name,libsvm.so.$(SHVER)"; \
	else \
		SHARED_LIB_FLAG="-shared -Wl,-soname,libsvm.so.$(SHVER)"; \
	fi; \
	$(CXX) $${SHARED_LIB_FLAG} svm.o -o libsvm.so.$(SHVER)

svm-predict: svm-predict.c svm.o
	$(CXX) $(CFLAGS) svm-predict.c svm.o -o svm-predict -lm
svm-train: svm-train.c svm.o
	$(CXX) $(CFLAGS) svm-train.c svm.o -o svm-train -lm
svm-scale: svm-scale.c
	$(CXX) $(CFLAGS) svm-scale.c -o svm-scale
svm.o: svm.cpp svm.h
	$(CXX) $(CFLAGS) -c svm.cpp
clean:
	rm -f *~ svm.o svm-train svm-predict svm-scale libsvm.so.$(SHVER)


================================================
FILE: Matlab/libsvm-3.19/Makefile.win
================================================
#You must ensure nmake.exe, cl.exe, link.exe are in system path.
#VCVARS32.bat
#Under dosbox prompt
#nmake -f Makefile.win

##########################################
CXX = cl.exe
CFLAGS = /nologo /O2 /EHsc /I. /D _WIN32 /D _CRT_SECURE_NO_DEPRECATE
TARGET = windows

all: $(TARGET)\svm-train.exe $(TARGET)\svm-predict.exe $(TARGET)\svm-scale.exe $(TARGET)\svm-toy.exe lib

$(TARGET)\svm-predict.exe: svm.h svm-predict.c svm.obj
	$(CXX) $(CFLAGS) svm-predict.c svm.obj -Fe$(TARGET)\svm-predict.exe

$(TARGET)\svm-train.exe: svm.h svm-train.c svm.obj
	$(CXX) $(CFLAGS) svm-train.c svm.obj -Fe$(TARGET)\svm-train.exe

$(TARGET)\svm-scale.exe: svm.h svm-scale.c
	$(CXX) $(CFLAGS) svm-scale.c -Fe$(TARGET)\svm-scale.exe

$(TARGET)\svm-toy.exe: svm.h svm.obj svm-toy\windows\svm-toy.cpp
	$(CXX) $(CFLAGS) svm-toy\windows\svm-toy.cpp svm.obj user32.lib gdi32.lib comdlg32.lib  -Fe$(TARGET)\svm-toy.exe

svm.obj: svm.cpp svm.h
	$(CXX) $(CFLAGS) -c svm.cpp

lib: svm.cpp svm.h svm.def
	$(CXX) $(CFLAGS) -LD svm.cpp -Fe$(TARGET)\libsvm -link -DEF:svm.def 

clean:
	-erase /Q *.obj $(TARGET)\.



================================================
FILE: Matlab/libsvm-3.19/README
================================================
Libsvm is a simple, easy-to-use, and efficient software for SVM
classification and regression. It solves C-SVM classification, nu-SVM
classification, one-class-SVM, epsilon-SVM regression, and nu-SVM
regression. It also provides an automatic model selection tool for
C-SVM classification. This document explains the use of libsvm.

Libsvm is available at 
http://www.csie.ntu.edu.tw/~cjlin/libsvm
Please read the COPYRIGHT file before using libsvm.

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

- Quick Start
- Installation and Data Format
- `svm-train' Usage
- `svm-predict' Usage
- `svm-scale' Usage
- Tips on Practical Use
- Examples
- Precomputed Kernels 
- Library Usage
- Java Version
- Building Windows Binaries
- Additional Tools: Sub-sampling, Parameter Selection, Format checking, etc.
- MATLAB/OCTAVE Interface
- Python Interface
- Additional Information

Quick Start
===========

If you are new to SVM and if the data is not large, please go to 
`tools' directory and use easy.py after installation. It does 
everything automatic -- from data scaling to parameter selection.

Usage: easy.py training_file [testing_file]

More information about parameter selection can be found in
`tools/README.'

Installation and Data Format
============================

On Unix systems, type `make' to build the `svm-train' and `svm-predict'
programs. Run them without arguments to show the usages of them.

On other systems, consult `Makefile' to build them (e.g., see
'Building Windows binaries' in this file) or use the pre-built
binaries (Windows binaries are in the directory `windows').

The format of training and testing data file is:

<label> <index1>:<value1> <index2>:<value2> ...
.
.
.

Each line contains an instance and is ended by a '\n' character.  For
classification, <label> is an integer indicating the class label
(multi-class is supported). For regression, <label> is the target
value which can be any real number. For one-class SVM, it's not used
so can be any number.  The pair <index>:<value> gives a feature
(attribute) value: <index> is an integer starting from 1 and <value>
is a real number. The only exception is the precomputed kernel, where
<index> starts from 0; see the section of precomputed kernels. Indices
must be in ASCENDING order. Labels in the testing file are only used
to calculate accuracy or errors. If they are unknown, just fill the
first column with any numbers.

A sample classification data included in this package is
`heart_scale'. To check if your data is in a correct form, use
`tools/checkdata.py' (details in `tools/README').

Type `svm-train heart_scale', and the program will read the training
data and output the model file `heart_scale.model'. If you have a test
set called heart_scale.t, then type `svm-predict heart_scale.t
heart_scale.model output' to see the prediction accuracy. The `output'
file contains the predicted class labels.

For classification, if training data are in only one class (i.e., all
labels are the same), then `svm-train' issues a warning message:
`Warning: training data in only one class. See README for details,'
which means the training data is very unbalanced. The label in the
training data is directly returned when testing.

There are some other useful programs in this package.

svm-scale:

	This is a tool for scaling input data file.

svm-toy:

	This is a simple graphical interface which shows how SVM
	separate data in a plane. You can click in the window to 
	draw data points. Use "change" button to choose class 
	1, 2 or 3 (i.e., up to three classes are supported), "load"
	button to load data from a file, "save" button to save data to
	a file, "run" button to obtain an SVM model, and "clear"
	button to clear the window.

	You can enter options in the bottom of the window, the syntax of
	options is the same as `svm-train'.

	Note that "load" and "save" consider dense data format both in
	classification and the regression cases. For classification,
	each data point has one label (the color) that must be 1, 2,
	or 3 and two attributes (x-axis and y-axis values) in
	[0,1). For regression, each data point has one target value
	(y-axis) and one attribute (x-axis values) in [0, 1).

	Type `make' in respective directories to build them.

	You need Qt library to build the Qt version.
	(available from http://www.trolltech.com)

	You need GTK+ library to build the GTK version.
	(available from http://www.gtk.org)
	
	The pre-built Windows binaries are in the `windows'
	directory. We use Visual C++ on a 32-bit machine, so the
	maximal cache size is 2GB.

`svm-train' Usage
=================

Usage: svm-train [options] training_set_file [model_file]
options:
-s svm_type : set type of SVM (default 0)
	0 -- C-SVC		(multi-class classification)
	1 -- nu-SVC		(multi-class classification)
	2 -- one-class SVM	
	3 -- epsilon-SVR	(regression)
	4 -- nu-SVR		(regression)
-t kernel_type : set type of kernel function (default 2)
	0 -- linear: u'*v
	1 -- polynomial: (gamma*u'*v + coef0)^degree
	2 -- radial basis function: exp(-gamma*|u-v|^2)
	3 -- sigmoid: tanh(gamma*u'*v + coef0)
	4 -- precomputed kernel (kernel values in training_set_file)
-d degree : set degree in kernel function (default 3)
-g gamma : set gamma in kernel function (default 1/num_features)
-r coef0 : set coef0 in kernel function (default 0)
-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
-m cachesize : set cache memory size in MB (default 100)
-e epsilon : set tolerance of termination criterion (default 0.001)
-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)
-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)
-v n: n-fold cross validation mode
-q : quiet mode (no outputs)


The k in the -g option means the number of attributes in the input data.

option -v randomly splits the data into n parts and calculates cross
validation accuracy/mean squared error on them.

See libsvm FAQ for the meaning of outputs.

`svm-predict' Usage
===================

Usage: svm-predict [options] test_file model_file output_file
options:
-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); for one-class SVM only 0 is supported

model_file is the model file generated by svm-train.
test_file is the test data you want to predict.
svm-predict will produce output in the output_file.

`svm-scale' Usage
=================

Usage: svm-scale [options] data_filename
options:
-l lower : x scaling lower limit (default -1)
-u upper : x scaling upper limit (default +1)
-y y_lower y_upper : y scaling limits (default: no y scaling)
-s save_filename : save scaling parameters to save_filename
-r restore_filename : restore scaling parameters from restore_filename

See 'Examples' in this file for examples.

Tips on Practical Use
=====================

* Scale your data. For example, scale each attribute to [0,1] or [-1,+1].
* For C-SVC, consider using the model selection tool in the tools directory.
* nu in nu-SVC/one-class-SVM/nu-SVR approximates the fraction of training
  errors and support vectors.
* If data for classification are unbalanced (e.g. many positive and
  few negative), try different penalty parameters C by -wi (see
  examples below).
* Specify larger cache size (i.e., larger -m) for huge problems.

Examples
========

> svm-scale -l -1 -u 1 -s range train > train.scale
> svm-scale -r range test > test.scale

Scale each feature of the training data to be in [-1,1]. Scaling
factors are stored in the file range and then used for scaling the
test data.

> svm-train -s 0 -c 5 -t 2 -g 0.5 -e 0.1 data_file 

Train a classifier with RBF kernel exp(-0.5|u-v|^2), C=10, and
stopping tolerance 0.1.

> svm-train -s 3 -p 0.1 -t 0 data_file

Solve SVM regression with linear kernel u'v and epsilon=0.1
in the loss function.

> svm-train -c 10 -w1 1 -w-2 5 -w4 2 data_file

Train a classifier with penalty 10 = 1 * 10 for class 1, penalty 50 =
5 * 10 for class -2, and penalty 20 = 2 * 10 for class 4.

> svm-train -s 0 -c 100 -g 0.1 -v 5 data_file

Do five-fold cross validation for the classifier using
the parameters C = 100 and gamma = 0.1

> svm-train -s 0 -b 1 data_file
> svm-predict -b 1 test_file data_file.model output_file

Obtain a model with probability information and predict test data with
probability estimates

Precomputed Kernels 
===================

Users may precompute kernel values and input them as training and
testing files.  Then libsvm does not need the original
training/testing sets.

Assume there are L training instances x1, ..., xL and. 
Let K(x, y) be the kernel
value of two instances x and y. The input formats
are:

New training instance for xi:

<label> 0:i 1:K(xi,x1) ... L:K(xi,xL) 

New testing instance for any x:

<label> 0:? 1:K(x,x1) ... L:K(x,xL) 

That is, in the training file the first column must be the "ID" of
xi. In testing, ? can be any value.

All kernel values including ZEROs must be explicitly provided.  Any
permutation or random subsets of the training/testing files are also
valid (see examples below).

Note: the format is slightly different from the precomputed kernel
package released in libsvmtools earlier.

Examples:

	Assume the original training data has three four-feature
	instances and testing data has one instance:

	15  1:1 2:1 3:1 4:1
	45      2:3     4:3
	25          3:1

	15  1:1     3:1

	If the linear kernel is used, we have the following new
	training/testing sets:

	15  0:1 1:4 2:6  3:1
	45  0:2 1:6 2:18 3:0 
	25  0:3 1:1 2:0  3:1
 
	15  0:? 1:2 2:0  3:1

	? can be any value.

	Any subset of the above training file is also valid. For example,

	25  0:3 1:1 2:0  3:1
	45  0:2 1:6 2:18 3:0 

	implies that the kernel matrix is

		[K(2,2) K(2,3)] = [18 0]
		[K(3,2) K(3,3)] = [0  1]

Library Usage
=============

These functions and structures are declared in the header file
`svm.h'.  You need to #include "svm.h" in your C/C++ source files and
link your program with `svm.cpp'. You can see `svm-train.c' and
`svm-predict.c' for examples showing how to use them. We define
LIBSVM_VERSION and declare `extern int libsvm_version; ' in svm.h, so
you can check the version number.

Before you classify test data, you need to construct an SVM model
(`svm_model') using training data. A model can also be saved in
a file for later use. Once an SVM model is available, you can use it
to classify new data.

- Function: struct svm_model *svm_train(const struct svm_problem *prob,
					const struct svm_parameter *param);

    This function constructs and returns an SVM model according to
    the given training data and parameters.

    struct svm_problem describes the problem:
	
	struct svm_problem
	{
		int l;
		double *y;
		struct svm_node **x;
	};
 
    where `l' is the number of training data, and `y' is an array containing
    their target values. (integers in classification, real numbers in
    regression) `x' is an array of pointers, each of which points to a sparse
    representation (array of svm_node) of one training vector. 

    For example, if we have the following training data:

    LABEL	ATTR1	ATTR2	ATTR3	ATTR4	ATTR5
    -----	-----	-----	-----	-----	-----
      1		  0	  0.1	  0.2	  0	  0
      2		  0	  0.1	  0.3	 -1.2	  0
      1		  0.4	  0	  0	  0	  0
      2		  0	  0.1	  0	  1.4	  0.5
      3		 -0.1	 -0.2	  0.1	  1.1	  0.1

    then the components of svm_problem are:

    l = 5

    y -> 1 2 1 2 3

    x -> [ ] -> (2,0.1) (3,0.2) (-1,?)
	 [ ] -> (2,0.1) (3,0.3) (4,-1.2) (-1,?)
	 [ ] -> (1,0.4) (-1,?)
	 [ ] -> (2,0.1) (4,1.4) (5,0.5) (-1,?)
	 [ ] -> (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (-1,?)

    where (index,value) is stored in the structure `svm_node':

	struct svm_node
	{
		int index;
		double value;
	};

    index = -1 indicates the end of one vector. Note that indices must
    be in ASCENDING order.
 
    struct svm_parameter describes the parameters of an SVM model:

	struct svm_parameter
	{
		int svm_type;
		int kernel_type;
		int degree;	/* for poly */
		double gamma;	/* for poly/rbf/sigmoid */
		double coef0;	/* for poly/sigmoid */

		/* these are for training only */
		double cache_size; /* in MB */
		double eps;	/* stopping criteria */
		double C;	/* for C_SVC, EPSILON_SVR, and NU_SVR */
		int nr_weight;		/* for C_SVC */
		int *weight_label;	/* for C_SVC */
		double* weight;		/* for C_SVC */
		double nu;	/* for NU_SVC, ONE_CLASS, and NU_SVR */
		double p;	/* for EPSILON_SVR */
		int shrinking;	/* use the shrinking heuristics */
		int probability; /* do probability estimates */
	};

    svm_type can be one of C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR.

    C_SVC:		C-SVM classification
    NU_SVC:		nu-SVM classification
    ONE_CLASS:		one-class-SVM
    EPSILON_SVR:	epsilon-SVM regression
    NU_SVR:		nu-SVM regression

    kernel_type can be one of LINEAR, POLY, RBF, SIGMOID.

    LINEAR:	u'*v
    POLY:	(gamma*u'*v + coef0)^degree
    RBF:	exp(-gamma*|u-v|^2)
    SIGMOID:	tanh(gamma*u'*v + coef0)
    PRECOMPUTED: kernel values in training_set_file

    cache_size is the size of the kernel cache, specified in megabytes.
    C is the cost of constraints violation. 
    eps is the stopping criterion. (we usually use 0.00001 in nu-SVC,
    0.001 in others). nu is the parameter in nu-SVM, nu-SVR, and
    one-class-SVM. p is the epsilon in epsilon-insensitive loss function
    of epsilon-SVM regression. shrinking = 1 means shrinking is conducted;
    = 0 otherwise. probability = 1 means model with probability
    information is obtained; = 0 otherwise.

    nr_weight, weight_label, and weight are used to change the penalty
    for some classes (If the weight for a class is not changed, it is
    set to 1). This is useful for training classifier using unbalanced
    input data or with asymmetric misclassification cost.

    nr_weight is the number of elements in the array weight_label and
    weight. Each weight[i] corresponds to weight_label[i], meaning that
    the penalty of class weight_label[i] is scaled by a factor of weight[i].
    
    If you do not want to change penalty for any of the classes,
    just set nr_weight to 0.

    *NOTE* Because svm_model contains pointers to svm_problem, you can
    not free the memory used by svm_problem if you are still using the
    svm_model produced by svm_train(). 

    *NOTE* To avoid wrong parameters, svm_check_parameter() should be
    called before svm_train().

    struct svm_model stores the model obtained from the training procedure.
    It is not recommended to directly access entries in this structure.
    Programmers should use the interface functions to get the values.

	struct svm_model
	{
		struct svm_parameter param;	/* parameter */
		int nr_class;		/* number of classes, = 2 in regression/one class svm */
		int l;			/* total #SV */
		struct svm_node **SV;		/* SVs (SV[l]) */
		double **sv_coef;	/* coefficients for SVs in decision functions (sv_coef[k-1][l]) */
		double *rho;		/* constants in decision functions (rho[k*(k-1)/2]) */
		double *probA;		/* pairwise probability information */
		double *probB;
		int *sv_indices;        /* sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set */

		/* for classification only */

		int *label;		/* label of each class (label[k]) */
		int *nSV;		/* number of SVs for each class (nSV[k]) */
					/* nSV[0] + nSV[1] + ... + nSV[k-1] = l */
		/* XXX */
		int free_sv;		/* 1 if svm_model is created by svm_load_model*/
					/* 0 if svm_model is created by svm_train */
	};

    param describes the parameters used to obtain the model.

    nr_class is the number of classes. It is 2 for regression and one-class SVM.

    l is the number of support vectors. SV and sv_coef are support
    vectors and the corresponding coefficients, respectively. Assume there are
    k classes. For data in class j, the corresponding sv_coef includes (k-1) y*alpha vectors,
    where alpha's are solutions of the following two class problems:
    1 vs j, 2 vs j, ..., j-1 vs j, j vs j+1, j vs j+2, ..., j vs k
    and y=1 for the first j-1 vectors, while y=-1 for the remaining k-j 
    vectors. For example, if there are 4 classes, sv_coef and SV are like:

        +-+-+-+--------------------+
        |1|1|1|                    |
        |v|v|v|  SVs from class 1  |
        |2|3|4|                    |
        +-+-+-+--------------------+
        |1|2|2|                    |
        |v|v|v|  SVs from class 2  |
        |2|3|4|                    |
        +-+-+-+--------------------+
        |1|2|3|                    |
        |v|v|v|  SVs from class 3  |
        |3|3|4|                    |
        +-+-+-+--------------------+
        |1|2|3|                    |
        |v|v|v|  SVs from class 4  |
        |4|4|4|                    |
        +-+-+-+--------------------+

    See svm_train() for an example of assigning values to sv_coef.

    rho is the bias term (-b). probA and probB are parameters used in
    probability outputs. If there are k classes, there are k*(k-1)/2
    binary problems as well as rho, probA, and probB values. They are
    aligned in the order of binary problems:
    1 vs 2, 1 vs 3, ..., 1 vs k, 2 vs 3, ..., 2 vs k, ..., k-1 vs k.

    sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to
    indicate support vectors in the training set.

    label contains labels in the training data.

    nSV is the number of support vectors in each class.

    free_sv is a flag used to determine whether the space of SV should 
    be released in free_model_content(struct svm_model*) and 
    free_and_destroy_model(struct svm_model**). If the model is
    generated by svm_train(), then SV points to data in svm_problem
    and should not be removed. For example, free_sv is 0 if svm_model
    is created by svm_train, but is 1 if created by svm_load_model.

- Function: double svm_predict(const struct svm_model *model,
                               const struct svm_node *x);

    This function does classification or regression on a test vector x
    given a model.

    For a classification model, the predicted class for x is returned.
    For a regression model, the function value of x calculated using
    the model is returned. For an one-class model, +1 or -1 is
    returned.

- Function: void svm_cross_validation(const struct svm_problem *prob,
	const struct svm_parameter *param, int nr_fold, double *target);

    This function conducts cross validation. Data are separated to
    nr_fold folds. Under given parameters, sequentially each fold is
    validated using the model from training the remaining. Predicted
    labels (of all prob's instances) in the validation process are
    stored in the array called target.

    The format of svm_prob is same as that for svm_train(). 

- Function: int svm_get_svm_type(const struct svm_model *model);

    This function gives svm_type of the model. Possible values of
    svm_type are defined in svm.h.

- Function: int svm_get_nr_class(const svm_model *model);

    For a classification model, this function gives the number of
    classes. For a regression or an one-class model, 2 is returned.

- Function: void svm_get_labels(const svm_model *model, int* label)
    
    For a classification model, this function outputs the name of
    labels into an array called label. For regression and one-class
    models, label is unchanged.

- Function: void svm_get_sv_indices(const struct svm_model *model, int *sv_indices)

    This function outputs indices of support vectors into an array called sv_indices. 
    The size of sv_indices is the number of support vectors and can be obtained by calling svm_get_nr_sv. 
    Each sv_indices[i] is in the range of [1, ..., num_traning_data].

- Function: int svm_get_nr_sv(const struct svm_model *model) 

    This function gives the number of total support vector.

- Function: double svm_get_svr_probability(const struct svm_model *model);

    For a regression model with probability information, this function
    outputs a value sigma > 0. For test data, we consider the
    probability model: target value = predicted value + z, z: Laplace
    distribution e^(-|z|/sigma)/(2sigma)

    If the model is not for svr or does not contain required
    information, 0 is returned.

- Function: double svm_predict_values(const svm_model *model, 
				    const svm_node *x, double* dec_values)

    This function gives decision values on a test vector x given a
    model, and return the predicted label (classification) or
    the function value (regression).

    For a classification model with nr_class classes, this function
    gives nr_class*(nr_class-1)/2 decision values in the array
    dec_values, where nr_class can be obtained from the function
    svm_get_nr_class. The order is label[0] vs. label[1], ...,
    label[0] vs. label[nr_class-1], label[1] vs. label[2], ...,
    label[nr_class-2] vs. label[nr_class-1], where label can be
    obtained from the function svm_get_labels. The returned value is
    the predicted class for x. Note that when nr_class = 1, this 
    function does not give any decision value.

    For a regression model, dec_values[0] and the returned value are
    both the function value of x calculated using the model. For a
    one-class model, dec_values[0] is the decision value of x, while
    the returned value is +1/-1.

- Function: double svm_predict_probability(const struct svm_model *model, 
	    const struct svm_node *x, double* prob_estimates);
    
    This function does classification or regression on a test vector x
    given a model with probability information.

    For a classification model with probability information, this
    function gives nr_class probability estimates in the array
    prob_estimates. nr_class can be obtained from the function
    svm_get_nr_class. The class with the highest probability is
    returned. For regression/one-class SVM, the array prob_estimates
    is unchanged and the returned value is the same as that of
    svm_predict.

- Function: const char *svm_check_parameter(const struct svm_problem *prob,
                                            const struct svm_parameter *param);

    This function checks whether the parameters are within the feasible
    range of the problem. This function should be called before calling
    svm_train() and svm_cross_validation(). It returns NULL if the
    parameters are feasible, otherwise an error message is returned.

- Function: int svm_check_probability_model(const struct svm_model *model);

    This function checks whether the model contains required
    information to do probability estimates. If so, it returns
    +1. Otherwise, 0 is returned. This function should be called
    before calling svm_get_svr_probability and
    svm_predict_probability.

- Function: int svm_save_model(const char *model_file_name,
			       const struct svm_model *model);

    This function saves a model to a file; returns 0 on success, or -1
    if an error occurs.

- Function: struct svm_model *svm_load_model(const char *model_file_name);

    This function returns a pointer to the model read from the file,
    or a null pointer if the model could not be loaded.

- Function: void svm_free_model_content(struct svm_model *model_ptr);

    This function frees the memory used by the entries in a model structure.

- Function: void svm_free_and_destroy_model(struct svm_model **model_ptr_ptr);

    This function frees the memory used by a model and destroys the model
    structure. It is equivalent to svm_destroy_model, which
    is deprecated after version 3.0.

- Function: void svm_destroy_param(struct svm_parameter *param);

    This function frees the memory used by a parameter set.

- Function: void svm_set_print_string_function(void (*print_func)(const char *));

    Users can specify their output format by a function. Use
        svm_set_print_string_function(NULL); 
    for default printing to stdout.

Java Version
============

The pre-compiled java class archive `libsvm.jar' and its source files are
in the java directory. To run the programs, use

java -classpath libsvm.jar svm_train <arguments>
java -classpath libsvm.jar svm_predict <arguments>
java -classpath libsvm.jar svm_toy
java -classpath libsvm.jar svm_scale <arguments>

Note that you need Java 1.5 (5.0) or above to run it.

You may need to add Java runtime library (like classes.zip) to the classpath.
You may need to increase maximum Java heap size.

Library usages are similar to the C version. These functions are available:

public class svm {
	public static final int LIBSVM_VERSION=319; 
	public static svm_model svm_train(svm_problem prob, svm_parameter param);
	public static void svm_cross_validation(svm_problem prob, svm_parameter param, int nr_fold, double[] target);
	public static int svm_get_svm_type(svm_model model);
	public static int svm_get_nr_class(svm_model model);
	public static void svm_get_labels(svm_model model, int[] label);
	public static void svm_get_sv_indices(svm_model model, int[] indices);
	public static int svm_get_nr_sv(svm_model model);
	public static double svm_get_svr_probability(svm_model model);
	public static double svm_predict_values(svm_model model, svm_node[] x, double[] dec_values);
	public static double svm_predict(svm_model model, svm_node[] x);
	public static double svm_predict_probability(svm_model model, svm_node[] x, double[] prob_estimates);
	public static void svm_save_model(String model_file_name, svm_model model) throws IOException
	public static svm_model svm_load_model(String model_file_name) throws IOException
	public static String svm_check_parameter(svm_problem prob, svm_parameter param);
	public static int svm_check_probability_model(svm_model model);
	public static void svm_set_print_string_function(svm_print_interface print_func);
}

The library is in the "libsvm" package.
Note that in Java version, svm_node[] is not ended with a node whose index = -1.

Users can specify their output format by

	your_print_func = new svm_print_interface()
	{ 
		public void print(String s)
		{
			// your own format
		}
	};
	svm.svm_set_print_string_function(your_print_func);

Building Windows Binaries
=========================

Windows binaries are in the directory `windows'. To build them via
Visual C++, use the following steps:

1. Open a DOS command box (or Visual Studio Command Prompt) and change
to libsvm directory. If environment variables of VC++ have not been
set, type

"C:\Program Files\Microsoft Visual Studio 10.0\VC\bin\vcvars32.bat"

You may have to modify the above command according which version of
VC++ or where it is installed.

2. Type

nmake -f Makefile.win clean all

3. (optional) To build shared library libsvm.dll, type

nmake -f Makefile.win lib

4. (optional) To build 64-bit windows binaries, you must
	(1) Run vcvars64.bat instead of vcvars32.bat. Note that 
	vcvars64.bat is located at "C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\bin\amd64\"
	(2) Change CFLAGS in Makefile.win: /D _WIN32 to /D _WIN64	
	
Another way is to build them from Visual C++ environment. See details
in libsvm FAQ.

- Additional Tools: Sub-sampling, Parameter Selection, Format checking, etc.
============================================================================

See the README file in the tools directory.

MATLAB/OCTAVE Interface
=======================

Please check the file README in the directory `matlab'.

Python Interface
================

See the README file in python directory.

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

If you find LIBSVM helpful, please cite it as

Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support
vector machines. ACM Transactions on Intelligent Systems and
Technology, 2:27:1--27:27, 2011. Software available at
http://www.csie.ntu.edu.tw/~cjlin/libsvm

LIBSVM implementation document is available at
http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf

For any questions and comments, please email cjlin@csie.ntu.edu.tw

Acknowledgments:
This work was supported in part by the National Science 
Council of Taiwan via the grant NSC 89-2213-E-002-013.
The authors thank their group members and users
for many helpful discussions and comments. They are listed in
http://www.csie.ntu.edu.tw/~cjlin/libsvm/acknowledgements



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================================================
FILE: Matlab/libsvm-3.19/java/Makefile
================================================
.SUFFIXES: .class .java
FILES = libsvm/svm.class libsvm/svm_model.class libsvm/svm_node.class \
		libsvm/svm_parameter.class libsvm/svm_problem.class \
		libsvm/svm_print_interface.class \
		svm_train.class svm_predict.class svm_toy.class svm_scale.class

#JAVAC = jikes
JAVAC_FLAGS = -target 1.5 -source 1.5
JAVAC = javac
# JAVAC_FLAGS =

all: $(FILES)
	jar cvf libsvm.jar *.class libsvm/*.class

.java.class:
	$(JAVAC) $(JAVAC_FLAGS) $<

libsvm/svm.java: libsvm/svm.m4
	m4 libsvm/svm.m4 > libsvm/svm.java

clean:
	rm -f libsvm/*.class *.class *.jar libsvm/*~ *~ libsvm/svm.java

dist: clean all
	rm *.class libsvm/*.class


================================================
FILE: Matlab/libsvm-3.19/java/libsvm/svm.java
================================================





package libsvm;
import java.io.*;
import java.util.*;

//
// Kernel Cache
//
// l is the number of total data items
// size is the cache size limit in bytes
//
class Cache {
	private final int l;
	private long size;
	private final class head_t
	{
		head_t prev, next;	// a cicular list
		float[] data;
		int len;		// data[0,len) is cached in this entry
	}
	private final head_t[] head;
	private head_t lru_head;

	Cache(int l_, long size_)
	{
		l = l_;
		size = size_;
		head = new head_t[l];
		for(int i=0;i<l;i++) head[i] = new head_t();
		size /= 4;
		size -= l * (16/4);	// sizeof(head_t) == 16
		size = Math.max(size, 2* (long) l);  // cache must be large enough for two columns
		lru_head = new head_t();
		lru_head.next = lru_head.prev = lru_head;
	}

	private void lru_delete(head_t h)
	{
		// delete from current location
		h.prev.next = h.next;
		h.next.prev = h.prev;
	}

	private void lru_insert(head_t h)
	{
		// insert to last position
		h.next = lru_head;
		h.prev = lru_head.prev;
		h.prev.next = h;
		h.next.prev = h;
	}

	// request data [0,len)
	// return some position p where [p,len) need to be filled
	// (p >= len if nothing needs to be filled)
	// java: simulate pointer using single-element array
	int get_data(int index, float[][] data, int len)
	{
		head_t h = head[index];
		if(h.len > 0) lru_delete(h);
		int more = len - h.len;

		if(more > 0)
		{
			// free old space
			while(size < more)
			{
				head_t old = lru_head.next;
				lru_delete(old);
				size += old.len;
				old.data = null;
				old.len = 0;
			}

			// allocate new space
			float[] new_data = new float[len];
			if(h.data != null) System.arraycopy(h.data,0,new_data,0,h.len);
			h.data = new_data;
			size -= more;
			do {int _=h.len; h.len=len; len=_;} while(false);
		}

		lru_insert(h);
		data[0] = h.data;
		return len;
	}

	void swap_index(int i, int j)
	{
		if(i==j) return;
		
		if(head[i].len > 0) lru_delete(head[i]);
		if(head[j].len > 0) lru_delete(head[j]);
		do {float[] _=head[i].data; head[i].data=head[j].data; head[j].data=_;} while(false);
		do {int _=head[i].len; head[i].len=head[j].len; head[j].len=_;} while(false);
		if(head[i].len > 0) lru_insert(head[i]);
		if(head[j].len > 0) lru_insert(head[j]);

		if(i>j) do {int _=i; i=j; j=_;} while(false);
		for(head_t h = lru_head.next; h!=lru_head; h=h.next)
		{
			if(h.len > i)
			{
				if(h.len > j)
					do {float _=h.data[i]; h.data[i]=h.data[j]; h.data[j]=_;} while(false);
				else
				{
					// give up
					lru_delete(h);
					size += h.len;
					h.data = null;
					h.len = 0;
				}
			}
		}
	}
}

//
// Kernel evaluation
//
// the static method k_function is for doing single kernel evaluation
// the constructor of Kernel prepares to calculate the l*l kernel matrix
// the member function get_Q is for getting one column from the Q Matrix
//
abstract class QMatrix {
	abstract float[] get_Q(int column, int len);
	abstract double[] get_QD();
	abstract void swap_index(int i, int j);
};

abstract class Kernel extends QMatrix {
	private svm_node[][] x;
	private final double[] x_square;

	// svm_parameter
	private final int kernel_type;
	private final int degree;
	private final double gamma;
	private final double coef0;

	abstract float[] get_Q(int column, int len);
	abstract double[] get_QD();

	void swap_index(int i, int j)
	{
		do {svm_node[] _=x[i]; x[i]=x[j]; x[j]=_;} while(false);
		if(x_square != null) do {double _=x_square[i]; x_square[i]=x_square[j]; x_square[j]=_;} while(false);
	}

	private static double powi(double base, int times)
	{
		double tmp = base, ret = 1.0;

		for(int t=times; t>0; t/=2)
		{
			if(t%2==1) ret*=tmp;
			tmp = tmp * tmp;
		}
		return ret;
	}

	double kernel_function(int i, int j)
	{
		switch(kernel_type)
		{
			case svm_parameter.LINEAR:
				return dot(x[i],x[j]);
			case svm_parameter.POLY:
				return powi(gamma*dot(x[i],x[j])+coef0,degree);
			case svm_parameter.RBF:
				return Math.exp(-gamma*(x_square[i]+x_square[j]-2*dot(x[i],x[j])));
			case svm_parameter.SIGMOID:
				return Math.tanh(gamma*dot(x[i],x[j])+coef0);
			case svm_parameter.PRECOMPUTED:
				return x[i][(int)(x[j][0].value)].value;
			default:
				return 0;	// java
		}
	}

	Kernel(int l, svm_node[][] x_, svm_parameter param)
	{
		this.kernel_type = param.kernel_type;
		this.degree = param.degree;
		this.gamma = param.gamma;
		this.coef0 = param.coef0;

		x = (svm_node[][])x_.clone();

		if(kernel_type == svm_parameter.RBF)
		{
			x_square = new double[l];
			for(int i=0;i<l;i++)
				x_square[i] = dot(x[i],x[i]);
		}
		else x_square = null;
	}

	static double dot(svm_node[] x, svm_node[] y)
	{
		double sum = 0;
		int xlen = x.length;
		int ylen = y.length;
		int i = 0;
		int j = 0;
		while(i < xlen && j < ylen)
		{
			if(x[i].index == y[j].index)
				sum += x[i++].value * y[j++].value;
			else
			{
				if(x[i].index > y[j].index)
					++j;
				else
					++i;
			}
		}
		return sum;
	}

	static double k_function(svm_node[] x, svm_node[] y,
					svm_parameter param)
	{
		switch(param.kernel_type)
		{
			case svm_parameter.LINEAR:
				return dot(x,y);
			case svm_parameter.POLY:
				return powi(param.gamma*dot(x,y)+param.coef0,param.degree);
			case svm_parameter.RBF:
			{
				double sum = 0;
				int xlen = x.length;
				int ylen = y.length;
				int i = 0;
				int j = 0;
				while(i < xlen && j < ylen)
				{
					if(x[i].index == y[j].index)
					{
						double d = x[i++].value - y[j++].value;
						sum += d*d;
					}
					else if(x[i].index > y[j].index)
					{
						sum += y[j].value * y[j].value;
						++j;
					}
					else
					{
						sum += x[i].value * x[i].value;
						++i;
					}
				}

				while(i < xlen)
				{
					sum += x[i].value * x[i].value;
					++i;
				}

				while(j < ylen)
				{
					sum += y[j].value * y[j].value;
					++j;
				}

				return Math.exp(-param.gamma*sum);
			}
			case svm_parameter.SIGMOID:
				return Math.tanh(param.gamma*dot(x,y)+param.coef0);
			case svm_parameter.PRECOMPUTED:
				return	x[(int)(y[0].value)].value;
			default:
				return 0;	// java
		}
	}
}

// An SMO algorithm in Fan et al., JMLR 6(2005), p. 1889--1918
// Solves:
//
//	min 0.5(\alpha^T Q \alpha) + p^T \alpha
//
//		y^T \alpha = \delta
//		y_i = +1 or -1
//		0 <= alpha_i <= Cp for y_i = 1
//		0 <= alpha_i <= Cn for y_i = -1
//
// Given:
//
//	Q, p, y, Cp, Cn, and an initial feasible point \alpha
//	l is the size of vectors and matrices
//	eps is the stopping tolerance
//
// solution will be put in \alpha, objective value will be put in obj
//
class Solver {
	int active_size;
	byte[] y;
	double[] G;		// gradient of objective function
	static final byte LOWER_BOUND = 0;
	static final byte UPPER_BOUND = 1;
	static final byte FREE = 2;
	byte[] alpha_status;	// LOWER_BOUND, UPPER_BOUND, FREE
	double[] alpha;
	QMatrix Q;
	double[] QD;
	double eps;
	double Cp,Cn;
	double[] p;
	int[] active_set;
	double[] G_bar;		// gradient, if we treat free variables as 0
	int l;
	boolean unshrink;	// XXX
	
	static final double INF = java.lang.Double.POSITIVE_INFINITY;

	double get_C(int i)
	{
		return (y[i] > 0)? Cp : Cn;
	}
	void update_alpha_status(int i)
	{
		if(alpha[i] >= get_C(i))
			alpha_status[i] = UPPER_BOUND;
		else if(alpha[i] <= 0)
			alpha_status[i] = LOWER_BOUND;
		else alpha_status[i] = FREE;
	}
	boolean is_upper_bound(int i) { return alpha_status[i] == UPPER_BOUND; }
	boolean is_lower_bound(int i) { return alpha_status[i] == LOWER_BOUND; }
	boolean is_free(int i) {  return alpha_status[i] == FREE; }

	// java: information about solution except alpha,
	// because we cannot return multiple values otherwise...
	static class SolutionInfo {
		double obj;
		double rho;
		double upper_bound_p;
		double upper_bound_n;
		double r;	// for Solver_NU
	}

	void swap_index(int i, int j)
	{
		Q.swap_index(i,j);
		do {byte _=y[i]; y[i]=y[j]; y[j]=_;} while(false);
		do {double _=G[i]; G[i]=G[j]; G[j]=_;} while(false);
		do {byte _=alpha_status[i]; alpha_status[i]=alpha_status[j]; alpha_status[j]=_;} while(false);
		do {double _=alpha[i]; alpha[i]=alpha[j]; alpha[j]=_;} while(false);
		do {double _=p[i]; p[i]=p[j]; p[j]=_;} while(false);
		do {int _=active_set[i]; active_set[i]=active_set[j]; active_set[j]=_;} while(false);
		do {double _=G_bar[i]; G_bar[i]=G_bar[j]; G_bar[j]=_;} while(false);
	}

	void reconstruct_gradient()
	{
		// reconstruct inactive elements of G from G_bar and free variables

		if(active_size == l) return;

		int i,j;
		int nr_free = 0;

		for(j=active_size;j<l;j++)
			G[j] = G_bar[j] + p[j];

		for(j=0;j<active_size;j++)
			if(is_free(j))
				nr_free++;

		if(2*nr_free < active_size)
			svm.info("\nWARNING: using -h 0 may be faster\n");

		if (nr_free*l > 2*active_size*(l-active_size))
		{
			for(i=active_size;i<l;i++)
			{
				float[] Q_i = Q.get_Q(i,active_size);
				for(j=0;j<active_size;j++)
					if(is_free(j))
						G[i] += alpha[j] * Q_i[j];
			}	
		}
		else
		{
			for(i=0;i<active_size;i++)
				if(is_free(i))
				{
					float[] Q_i = Q.get_Q(i,l);
					double alpha_i = alpha[i];
					for(j=active_size;j<l;j++)
						G[j] += alpha_i * Q_i[j];
				}
		}
	}

	void Solve(int l, QMatrix Q, double[] p_, byte[] y_,
		   double[] alpha_, double Cp, double Cn, double eps, SolutionInfo si, int shrinking)
	{
		this.l = l;
		this.Q = Q;
		QD = Q.get_QD();
		p = (double[])p_.clone();
		y = (byte[])y_.clone();
		alpha = (double[])alpha_.clone();
		this.Cp = Cp;
		this.Cn = Cn;
		this.eps = eps;
		this.unshrink = false;

		// initialize alpha_status
		{
			alpha_status = new byte[l];
			for(int i=0;i<l;i++)
				update_alpha_status(i);
		}

		// initialize active set (for shrinking)
		{
			active_set = new int[l];
			for(int i=0;i<l;i++)
				active_set[i] = i;
			active_size = l;
		}

		// initialize gradient
		{
			G = new double[l];
			G_bar = new double[l];
			int i;
			for(i=0;i<l;i++)
			{
				G[i] = p[i];
				G_bar[i] = 0;
			}
			for(i=0;i<l;i++)
				if(!is_lower_bound(i))
				{
					float[] Q_i = Q.get_Q(i,l);
					double alpha_i = alpha[i];
					int j;
					for(j=0;j<l;j++)
						G[j] += alpha_i*Q_i[j];
					if(is_upper_bound(i))
						for(j=0;j<l;j++)
							G_bar[j] += get_C(i) * Q_i[j];
				}
		}

		// optimization step

		int iter = 0;
		int max_iter = Math.max(10000000, l>Integer.MAX_VALUE/100 ? Integer.MAX_VALUE : 100*l);
		int counter = Math.min(l,1000)+1;
		int[] working_set = new int[2];

		while(iter < max_iter)
		{
			// show progress and do shrinking

			if(--counter == 0)
			{
				counter = Math.min(l,1000);
				if(shrinking!=0) do_shrinking();
				svm.info(".");
			}

			if(select_working_set(working_set)!=0)
			{
				// reconstruct the whole gradient
				reconstruct_gradient();
				// reset active set size and check
				active_size = l;
				svm.info("*");
				if(select_working_set(working_set)!=0)
					break;
				else
					counter = 1;	// do shrinking next iteration
			}
			
			int i = working_set[0];
			int j = working_set[1];

			++iter;

			// update alpha[i] and alpha[j], handle bounds carefully

			float[] Q_i = Q.get_Q(i,active_size);
			float[] Q_j = Q.get_Q(j,active_size);

			double C_i = get_C(i);
			double C_j = get_C(j);

			double old_alpha_i = alpha[i];
			double old_alpha_j = alpha[j];

			if(y[i]!=y[j])
			{
				double quad_coef = QD[i]+QD[j]+2*Q_i[j];
				if (quad_coef <= 0)
					quad_coef = 1e-12;
				double delta = (-G[i]-G[j])/quad_coef;
				double diff = alpha[i] - alpha[j];
				alpha[i] += delta;
				alpha[j] += delta;
			
				if(diff > 0)
				{
					if(alpha[j] < 0)
					{
						alpha[j] = 0;
						alpha[i] = diff;
					}
				}
				else
				{
					if(alpha[i] < 0)
					{
						alpha[i] = 0;
						alpha[j] = -diff;
					}
				}
				if(diff > C_i - C_j)
				{
					if(alpha[i] > C_i)
					{
						alpha[i] = C_i;
						alpha[j] = C_i - diff;
					}
				}
				else
				{
					if(alpha[j] > C_j)
					{
						alpha[j] = C_j;
						alpha[i] = C_j + diff;
					}
				}
			}
			else
			{
				double quad_coef = QD[i]+QD[j]-2*Q_i[j];
				if (quad_coef <= 0)
					quad_coef = 1e-12;
				double delta = (G[i]-G[j])/quad_coef;
				double sum = alpha[i] + alpha[j];
				alpha[i] -= delta;
				alpha[j] += delta;

				if(sum > C_i)
				{
					if(alpha[i] > C_i)
					{
						alpha[i] = C_i;
						alpha[j] = sum - C_i;
					}
				}
				else
				{
					if(alpha[j] < 0)
					{
						alpha[j] = 0;
						alpha[i] = sum;
					}
				}
				if(sum > C_j)
				{
					if(alpha[j] > C_j)
					{
						alpha[j] = C_j;
						alpha[i] = sum - C_j;
					}
				}
				else
				{
					if(alpha[i] < 0)
					{
						alpha[i] = 0;
						alpha[j] = sum;
					}
				}
			}

			// update G

			double delta_alpha_i = alpha[i] - old_alpha_i;
			double delta_alpha_j = alpha[j] - old_alpha_j;

			for(int k=0;k<active_size;k++)
			{
				G[k] += Q_i[k]*delta_alpha_i + Q_j[k]*delta_alpha_j;
			}

			// update alpha_status and G_bar

			{
				boolean ui = is_upper_bound(i);
				boolean uj = is_upper_bound(j);
				update_alpha_status(i);
				update_alpha_status(j);
				int k;
				if(ui != is_upper_bound(i))
				{
					Q_i = Q.get_Q(i,l);
					if(ui)
						for(k=0;k<l;k++)
							G_bar[k] -= C_i * Q_i[k];
					else
						for(k=0;k<l;k++)
							G_bar[k] += C_i * Q_i[k];
				}

				if(uj != is_upper_bound(j))
				{
					Q_j = Q.get_Q(j,l);
					if(uj)
						for(k=0;k<l;k++)
							G_bar[k] -= C_j * Q_j[k];
					else
						for(k=0;k<l;k++)
							G_bar[k] += C_j * Q_j[k];
				}
			}

		}
		
		if(iter >= max_iter)
		{
			if(active_size < l)
			{
				// reconstruct the whole gradient to calculate objective value
				reconstruct_gradient();
				active_size = l;
				svm.info("*");
			}
			System.err.print("\nWARNING: reaching max number of iterations\n");
		}

		// calculate rho

		si.rho = calculate_rho();

		// calculate objective value
		{
			double v = 0;
			int i;
			for(i=0;i<l;i++)
				v += alpha[i] * (G[i] + p[i]);

			si.obj = v/2;
		}

		// put back the solution
		{
			for(int i=0;i<l;i++)
				alpha_[active_set[i]] = alpha[i];
		}

		si.upper_bound_p = Cp;
		si.upper_bound_n = Cn;

		svm.info("\noptimization finished, #iter = "+iter+"\n");
	}

	// return 1 if already optimal, return 0 otherwise
	int select_working_set(int[] working_set)
	{
		// return i,j such that
		// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
		// j: mimimizes the decrease of obj value
		//    (if quadratic coefficeint <= 0, replace it with tau)
		//    -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
		
		double Gmax = -INF;
		double Gmax2 = -INF;
		int Gmax_idx = -1;
		int Gmin_idx = -1;
		double obj_diff_min = INF;
	
		for(int t=0;t<active_size;t++)
			if(y[t]==+1)	
			{
				if(!is_upper_bound(t))
					if(-G[t] >= Gmax)
					{
						Gmax = -G[t];
						Gmax_idx = t;
					}
			}
			else
			{
				if(!is_lower_bound(t))
					if(G[t] >= Gmax)
					{
						Gmax = G[t];
						Gmax_idx = t;
					}
			}
	
		int i = Gmax_idx;
		float[] Q_i = null;
		if(i != -1) // null Q_i not accessed: Gmax=-INF if i=-1
			Q_i = Q.get_Q(i,active_size);
	
		for(int j=0;j<active_size;j++)
		{
			if(y[j]==+1)
			{
				if (!is_lower_bound(j))
				{
					double grad_diff=Gmax+G[j];
					if (G[j] >= Gmax2)
						Gmax2 = G[j];
					if (grad_diff > 0)
					{
						double obj_diff; 
						double quad_coef = QD[i]+QD[j]-2.0*y[i]*Q_i[j];
						if (quad_coef > 0)
							obj_diff = -(grad_diff*grad_diff)/quad_coef;
						else
							obj_diff = -(grad_diff*grad_diff)/1e-12;
	
						if (obj_diff <= obj_diff_min)
						{
							Gmin_idx=j;
							obj_diff_min = obj_diff;
						}
					}
				}
			}
			else
			{
				if (!is_upper_bound(j))
				{
					double grad_diff= Gmax-G[j];
					if (-G[j] >= Gmax2)
						Gmax2 = -G[j];
					if (grad_diff > 0)
					{
						double obj_diff; 
						double quad_coef = QD[i]+QD[j]+2.0*y[i]*Q_i[j];
						if (quad_coef > 0)
							obj_diff = -(grad_diff*grad_diff)/quad_coef;
						else
							obj_diff = -(grad_diff*grad_diff)/1e-12;
	
						if (obj_diff <= obj_diff_min)
						{
							Gmin_idx=j;
							obj_diff_min = obj_diff;
						}
					}
				}
			}
		}

		if(Gmax+Gmax2 < eps)
			return 1;

		working_set[0] = Gmax_idx;
		working_set[1] = Gmin_idx;
		return 0;
	}

	private boolean be_shrunk(int i, double Gmax1, double Gmax2)
	{	
		if(is_upper_bound(i))
		{
			if(y[i]==+1)
				return(-G[i] > Gmax1);
			else
				return(-G[i] > Gmax2);
		}
		else if(is_lower_bound(i))
		{
			if(y[i]==+1)
				return(G[i] > Gmax2);
			else	
				return(G[i] > Gmax1);
		}
		else
			return(false);
	}

	void do_shrinking()
	{
		int i;
		double Gmax1 = -INF;		// max { -y_i * grad(f)_i | i in I_up(\alpha) }
		double Gmax2 = -INF;		// max { y_i * grad(f)_i | i in I_low(\alpha) }

		// find maximal violating pair first
		for(i=0;i<active_size;i++)
		{
			if(y[i]==+1)
			{
				if(!is_upper_bound(i))	
				{
					if(-G[i] >= Gmax1)
						Gmax1 = -G[i];
				}
				if(!is_lower_bound(i))
				{
					if(G[i] >= Gmax2)
						Gmax2 = G[i];
				}
			}
			else		
			{
				if(!is_upper_bound(i))	
				{
					if(-G[i] >= Gmax2)
						Gmax2 = -G[i];
				}
				if(!is_lower_bound(i))	
				{
					if(G[i] >= Gmax1)
						Gmax1 = G[i];
				}
			}
		}

		if(unshrink == false && Gmax1 + Gmax2 <= eps*10) 
		{
			unshrink = true;
			reconstruct_gradient();
			active_size = l;
		}

		for(i=0;i<active_size;i++)
			if (be_shrunk(i, Gmax1, Gmax2))
			{
				active_size--;
				while (active_size > i)
				{
					if (!be_shrunk(active_size, Gmax1, Gmax2))
					{
						swap_index(i,active_size);
						break;
					}
					active_size--;
				}
			}
	}

	double calculate_rho()
	{
		double r;
		int nr_free = 0;
		double ub = INF, lb = -INF, sum_free = 0;
		for(int i=0;i<active_size;i++)
		{
			double yG = y[i]*G[i];

			if(is_lower_bound(i))
			{
				if(y[i] > 0)
					ub = Math.min(ub,yG);
				else
					lb = Math.max(lb,yG);
			}
			else if(is_upper_bound(i))
			{
				if(y[i] < 0)
					ub = Math.min(ub,yG);
				else
					lb = Math.max(lb,yG);
			}
			else
			{
				++nr_free;
				sum_free += yG;
			}
		}

		if(nr_free>0)
			r = sum_free/nr_free;
		else
			r = (ub+lb)/2;

		return r;
	}

}

//
// Solver for nu-svm classification and regression
//
// additional constraint: e^T \alpha = constant
//
final class Solver_NU extends Solver
{
	private SolutionInfo si;

	void Solve(int l, QMatrix Q, double[] p, byte[] y,
		   double[] alpha, double Cp, double Cn, double eps,
		   SolutionInfo si, int shrinking)
	{
		this.si = si;
		super.Solve(l,Q,p,y,alpha,Cp,Cn,eps,si,shrinking);
	}

	// return 1 if already optimal, return 0 otherwise
	int select_working_set(int[] working_set)
	{
		// return i,j such that y_i = y_j and
		// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
		// j: minimizes the decrease of obj value
		//    (if quadratic coefficeint <= 0, replace it with tau)
		//    -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
	
		double Gmaxp = -INF;
		double Gmaxp2 = -INF;
		int Gmaxp_idx = -1;
	
		double Gmaxn = -INF;
		double Gmaxn2 = -INF;
		int Gmaxn_idx = -1;
	
		int Gmin_idx = -1;
		double obj_diff_min = INF;
	
		for(int t=0;t<active_size;t++)
			if(y[t]==+1)
			{
				if(!is_upper_bound(t))
					if(-G[t] >= Gmaxp)
					{
						Gmaxp = -G[t];
						Gmaxp_idx = t;
					}
			}
			else
			{
				if(!is_lower_bound(t))
					if(G[t] >= Gmaxn)
					{
						Gmaxn = G[t];
						Gmaxn_idx = t;
					}
			}
	
		int ip = Gmaxp_idx;
		int in = Gmaxn_idx;
		float[] Q_ip = null;
		float[] Q_in = null;
		if(ip != -1) // null Q_ip not accessed: Gmaxp=-INF if ip=-1
			Q_ip = Q.get_Q(ip,active_size);
		if(in != -1)
			Q_in = Q.get_Q(in,active_size);
	
		for(int j=0;j<active_size;j++)
		{
			if(y[j]==+1)
			{
				if (!is_lower_bound(j))	
				{
					double grad_diff=Gmaxp+G[j];
					if (G[j] >= Gmaxp2)
						Gmaxp2 = G[j];
					if (grad_diff > 0)
					{
						double obj_diff; 
						double quad_coef = QD[ip]+QD[j]-2*Q_ip[j];
						if (quad_coef > 0)
							obj_diff = -(grad_diff*grad_diff)/quad_coef;
						else
							obj_diff = -(grad_diff*grad_diff)/1e-12;
	
						if (obj_diff <= obj_diff_min)
						{
							Gmin_idx=j;
							obj_diff_min = obj_diff;
						}
					}
				}
			}
			else
			{
				if (!is_upper_bound(j))
				{
					double grad_diff=Gmaxn-G[j];
					if (-G[j] >= Gmaxn2)
						Gmaxn2 = -G[j];
					if (grad_diff > 0)
					{
						double obj_diff; 
						double quad_coef = QD[in]+QD[j]-2*Q_in[j];
						if (quad_coef > 0)
							obj_diff = -(grad_diff*grad_diff)/quad_coef;
						else
							obj_diff = -(grad_diff*grad_diff)/1e-12;
	
						if (obj_diff <= obj_diff_min)
						{
							Gmin_idx=j;
							obj_diff_min = obj_diff;
						}
					}
				}
			}
		}

		if(Math.max(Gmaxp+Gmaxp2,Gmaxn+Gmaxn2) < eps)
			return 1;
	
		if(y[Gmin_idx] == +1)
			working_set[0] = Gmaxp_idx;
		else
			working_set[0] = Gmaxn_idx;
		working_set[1] = Gmin_idx;
	
		return 0;
	}

	private boolean be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4)
	{
		if(is_upper_bound(i))
		{
			if(y[i]==+1)
				return(-G[i] > Gmax1);
			else	
				return(-G[i] > Gmax4);
		}
		else if(is_lower_bound(i))
		{
			if(y[i]==+1)
				return(G[i] > Gmax2);
			else	
				return(G[i] > Gmax3);
		}
		else
			return(false);
	}

	void do_shrinking()
	{
		double Gmax1 = -INF;	// max { -y_i * grad(f)_i | y_i = +1, i in I_up(\alpha) }
		double Gmax2 = -INF;	// max { y_i * grad(f)_i | y_i = +1, i in I_low(\alpha) }
		double Gmax3 = -INF;	// max { -y_i * grad(f)_i | y_i = -1, i in I_up(\alpha) }
		double Gmax4 = -INF;	// max { y_i * grad(f)_i | y_i = -1, i in I_low(\alpha) }
 
		// find maximal violating pair first
		int i;
		for(i=0;i<active_size;i++)
		{
			if(!is_upper_bound(i))
			{
				if(y[i]==+1)
				{
					if(-G[i] > Gmax1) Gmax1 = -G[i];
				}
				else	if(-G[i] > Gmax4) Gmax4 = -G[i];
			}
			if(!is_lower_bound(i))
			{
				if(y[i]==+1)
				{	
					if(G[i] > Gmax2) Gmax2 = G[i];
				}
				else	if(G[i] > Gmax3) Gmax3 = G[i];
			}
		}

		if(unshrink == false && Math.max(Gmax1+Gmax2,Gmax3+Gmax4) <= eps*10) 
		{
			unshrink = true;
			reconstruct_gradient();
			active_size = l;
		}

		for(i=0;i<active_size;i++)
			if (be_shrunk(i, Gmax1, Gmax2, Gmax3, Gmax4))
			{
				active_size--;
				while (active_size > i)
				{
					if (!be_shrunk(active_size, Gmax1, Gmax2, Gmax3, Gmax4))
					{
						swap_index(i,active_size);
						break;
					}
					active_size--;
				}
			}
	}
	
	double calculate_rho()
	{
		int nr_free1 = 0,nr_free2 = 0;
		double ub1 = INF, ub2 = INF;
		double lb1 = -INF, lb2 = -INF;
		double sum_free1 = 0, sum_free2 = 0;

		for(int i=0;i<active_size;i++)
		{
			if(y[i]==+1)
			{
				if(is_lower_bound(i))
					ub1 = Math.min(ub1,G[i]);
				else if(is_upper_bound(i))
					lb1 = Math.max(lb1,G[i]);
				else
				{
					++nr_free1;
					sum_free1 += G[i];
				}
			}
			else
			{
				if(is_lower_bound(i))
					ub2 = Math.min(ub2,G[i]);
				else if(is_upper_bound(i))
					lb2 = Math.max(lb2,G[i]);
				else
				{
					++nr_free2;
					sum_free2 += G[i];
				}
			}
		}

		double r1,r2;
		if(nr_free1 > 0)
			r1 = sum_free1/nr_free1;
		else
			r1 = (ub1+lb1)/2;

		if(nr_free2 > 0)
			r2 = sum_free2/nr_free2;
		else
			r2 = (ub2+lb2)/2;

		si.r = (r1+r2)/2;
		return (r1-r2)/2;
	}
}

//
// Q matrices for various formulations
//
class SVC_Q extends Kernel
{
	private final byte[] y;
	private final Cache cache;
	private final double[] QD;

	SVC_Q(svm_problem prob, svm_parameter param, byte[] y_)
	{
		super(prob.l, prob.x, param);
		y = (byte[])y_.clone();
		cache = new Cache(prob.l,(long)(param.cache_size*(1<<20)));
		QD = new double[prob.l];
		for(int i=0;i<prob.l;i++)
			QD[i] = kernel_function(i,i);
	}

	float[] get_Q(int i, int len)
	{
		float[][] data = new float[1][];
		int start, j;
		if((start = cache.get_data(i,data,len)) < len)
		{
			for(j=start;j<len;j++)
				data[0][j] = (float)(y[i]*y[j]*kernel_function(i,j));
		}
		return data[0];
	}

	double[] get_QD()
	{
		return QD;
	}

	void swap_index(int i, int j)
	{
		cache.swap_index(i,j);
		super.swap_index(i,j);
		do {byte _=y[i]; y[i]=y[j]; y[j]=_;} while(false);
		do {double _=QD[i]; QD[i]=QD[j]; QD[j]=_;} while(false);
	}
}

class ONE_CLASS_Q extends Kernel
{
	private final Cache cache;
	private final double[] QD;

	ONE_CLASS_Q(svm_problem prob, svm_parameter param)
	{
		super(prob.l, prob.x, param);
		cache = new Cache(prob.l,(long)(param.cache_size*(1<<20)));
		QD = new double[prob.l];
		for(int i=0;i<prob.l;i++)
			QD[i] = kernel_function(i,i);
	}

	float[] get_Q(int i, int len)
	{
		float[][] data = new float[1][];
		int start, j;
		if((start = cache.get_data(i,data,len)) < len)
		{
			for(j=start;j<len;j++)
				data[0][j] = (float)kernel_function(i,j);
		}
		return data[0];
	}

	double[] get_QD()
	{
		return QD;
	}

	void swap_index(int i, int j)
	{
		cache.swap_index(i,j);
		super.swap_index(i,j);
		do {double _=QD[i]; QD[i]=QD[j]; QD[j]=_;} while(false);
	}
}

class SVR_Q extends Kernel
{
	private final int l;
	private final Cache cache;
	private final byte[] sign;
	private final int[] index;
	private int next_buffer;
	private float[][] buffer;
	private final double[] QD;

	SVR_Q(svm_problem prob, svm_parameter param)
	{
		super(prob.l, prob.x, param);
		l = prob.l;
		cache = new Cache(l,(long)(param.cache_size*(1<<20)));
		QD = new double[2*l];
		sign = new byte[2*l];
		index = new int[2*l];
		for(int k=0;k<l;k++)
		{
			sign[k] = 1;
			sign[k+l] = -1;
			index[k] = k;
			index[k+l] = k;
			QD[k] = kernel_function(k,k);
			QD[k+l] = QD[k];
		}
		buffer = new float[2][2*l];
		next_buffer = 0;
	}

	void swap_index(int i, int j)
	{
		do {byte _=sign[i]; sign[i]=sign[j]; sign[j]=_;} while(false);
		do {int _=index[i]; index[i]=index[j]; index[j]=_;} while(false);
		do {double _=QD[i]; QD[i]=QD[j]; QD[j]=_;} while(false);
	}

	float[] get_Q(int i, int len)
	{
		float[][] data = new float[1][];
		int j, real_i = index[i];
		if(cache.get_data(real_i,data,l) < l)
		{
			for(j=0;j<l;j++)
				data[0][j] = (float)kernel_function(real_i,j);
		}

		// reorder and copy
		float buf[] = buffer[next_buffer];
		next_buffer = 1 - next_buffer;
		byte si = sign[i];
		for(j=0;j<len;j++)
			buf[j] = (float) si * sign[j] * data[0][index[j]];
		return buf;
	}

	double[] get_QD()
	{
		return QD;
	}
}

public class svm {
	//
	// construct and solve various formulations
	//
	public static final int LIBSVM_VERSION=319; 
	public static final Random rand = new Random();

	private static svm_print_interface svm_print_stdout = new svm_print_interface()
	{
		public void print(String s)
		{
			System.out.print(s);
			System.out.flush();
		}
	};

	private static svm_print_interface svm_print_string = svm_print_stdout;

	static void info(String s) 
	{
		svm_print_string.print(s);
	}

	private static void solve_c_svc(svm_problem prob, svm_parameter param,
					double[] alpha, Solver.SolutionInfo si,
					double Cp, double Cn)
	{
		int l = prob.l;
		double[] minus_ones = new double[l];
		byte[] y = new byte[l];

		int i;

		for(i=0;i<l;i++)
		{
			alpha[i] = 0;
			minus_ones[i] = -1;
			if(prob.y[i] > 0) y[i] = +1; else y[i] = -1;
		}

		Solver s = new Solver();
		s.Solve(l, new SVC_Q(prob,param,y), minus_ones, y,
			alpha, Cp, Cn, param.eps, si, param.shrinking);

		double sum_alpha=0;
		for(i=0;i<l;i++)
			sum_alpha += alpha[i];

		if (Cp==Cn)
			svm.info("nu = "+sum_alpha/(Cp*prob.l)+"\n");

		for(i=0;i<l;i++)
			alpha[i] *= y[i];
	}

	private static void solve_nu_svc(svm_problem prob, svm_parameter param,
					double[] alpha, Solver.SolutionInfo si)
	{
		int i;
		int l = prob.l;
		double nu = param.nu;

		byte[] y = new byte[l];

		for(i=0;i<l;i++)
			if(prob.y[i]>0)
				y[i] = +1;
			else
				y[i] = -1;

		double sum_pos = nu*l/2;
		double sum_neg = nu*l/2;

		for(i=0;i<l;i++)
			if(y[i] == +1)
			{
				alpha[i] = Math.min(1.0,sum_pos);
				sum_pos -= alpha[i];
			}
			else
			{
				alpha[i] = Math.min(1.0,sum_neg);
				sum_neg -= alpha[i];
			}

		double[] zeros = new double[l];

		for(i=0;i<l;i++)
			zeros[i] = 0;

		Solver_NU s = new Solver_NU();
		s.Solve(l, new SVC_Q(prob,param,y), zeros, y,
			alpha, 1.0, 1.0, param.eps, si, param.shrinking);
		double r = si.r;

		svm.info("C = "+1/r+"\n");

		for(i=0;i<l;i++)
			alpha[i] *= y[i]/r;

		si.rho /= r;
		si.obj /= (r*r);
		si.upper_bound_p = 1/r;
		si.upper_bound_n = 1/r;
	}

	private static void solve_one_class(svm_problem prob, svm_parameter param,
					double[] alpha, Solver.SolutionInfo si)
	{
		int l = prob.l;
		double[] zeros = new double[l];
		byte[] ones = new byte[l];
		int i;

		int n = (int)(param.nu*prob.l);	// # of alpha's at upper bound

		for(i=0;i<n;i++)
			alpha[i] = 1;
		if(n<prob.l)
			alpha[n] = param.nu * prob.l - n;
		for(i=n+1;i<l;i++)
			alpha[i] = 0;

		for(i=0;i<l;i++)
		{
			zeros[i] = 0;
			ones[i] = 1;
		}

		Solver s = new Solver();
		s.Solve(l, new ONE_CLASS_Q(prob,param), zeros, ones,
			alpha, 1.0, 1.0, param.eps, si, param.shrinking);
	}

	private static void solve_epsilon_svr(svm_problem prob, svm_parameter param,
					double[] alpha, Solver.SolutionInfo si)
	{
		int l = prob.l;
		double[] alpha2 = new double[2*l];
		double[] linear_term = new double[2*l];
		byte[] y = new byte[2*l];
		int i;

		for(i=0;i<l;i++)
		{
			alpha2[i] = 0;
			linear_term[i] = param.p - prob.y[i];
			y[i] = 1;

			alpha2[i+l] = 0;
			linear_term[i+l] = param.p + prob.y[i];
			y[i+l] = -1;
		}

		Solver s = new Solver();
		s.Solve(2*l, new SVR_Q(prob,param), linear_term, y,
			alpha2, param.C, param.C, param.eps, si, param.shrinking);

		double sum_alpha = 0;
		for(i=0;i<l;i++)
		{
			alpha[i] = alpha2[i] - alpha2[i+l];
			sum_alpha += Math.abs(alpha[i]);
		}
		svm.info("nu = "+sum_alpha/(param.C*l)+"\n");
	}

	private static void solve_nu_svr(svm_problem prob, svm_parameter param,
					double[] alpha, Solver.SolutionInfo si)
	{
		int l = prob.l;
		double C = param.C;
		double[] alpha2 = new double[2*l];
		double[] linear_term = new double[2*l];
		byte[] y = new byte[2*l];
		int i;

		double sum = C * param.nu * l / 2;
		for(i=0;i<l;i++)
		{
			alpha2[i] = alpha2[i+l] = Math.min(sum,C);
			sum -= alpha2[i];
			
			linear_term[i] = - prob.y[i];
			y[i] = 1;

			linear_term[i+l] = prob.y[i];
			y[i+l] = -1;
		}

		Solver_NU s = new Solver_NU();
		s.Solve(2*l, new SVR_Q(prob,param), linear_term, y,
			alpha2, C, C, param.eps, si, param.shrinking);

		svm.info("epsilon = "+(-si.r)+"\n");
		
		for(i=0;i<l;i++)
			alpha[i] = alpha2[i] - alpha2[i+l];
	}

	//
	// decision_function
	//
	static class decision_function
	{
		double[] alpha;
		double rho;	
	};

	static decision_function svm_train_one(
		svm_problem prob, svm_parameter param,
		double Cp, double Cn)
	{
		double[] alpha = new double[prob.l];
		Solver.SolutionInfo si = new Solver.SolutionInfo();
		switch(param.svm_type)
		{
			case svm_parameter.C_SVC:
				solve_c_svc(prob,param,alpha,si,Cp,Cn);
				break;
			case svm_parameter.NU_SVC:
				solve_nu_svc(prob,param,alpha,si);
				break;
			case svm_parameter.ONE_CLASS:
				solve_one_class(prob,param,alpha,si);
				break;
			case svm_parameter.EPSILON_SVR:
				solve_epsilon_svr(prob,param,alpha,si);
				break;
			case svm_parameter.NU_SVR:
				solve_nu_svr(prob,param,alpha,si);
				break;
		}

		svm.info("obj = "+si.obj+", rho = "+si.rho+"\n");

		// output SVs

		int nSV = 0;
		int nBSV = 0;
		for(int i=0;i<prob.l;i++)
		{
			if(Math.abs(alpha[i]) > 0)
			{
				++nSV;
				if(prob.y[i] > 0)
				{
					if(Math.abs(alpha[i]) >= si.upper_bound_p)
					++nBSV;
				}
				else
				{
					if(Math.abs(alpha[i]) >= si.upper_bound_n)
						++nBSV;
				}
			}
		}

		svm.info("nSV = "+nSV+", nBSV = "+nBSV+"\n");

		decision_function f = new decision_function();
		f.alpha = alpha;
		f.rho = si.rho;
		return f;
	}

	// Platt's binary SVM Probablistic Output: an improvement from Lin et al.
	private static void sigmoid_train(int l, double[] dec_values, double[] labels, 
				  double[] probAB)
	{
		double A, B;
		double prior1=0, prior0 = 0;
		int i;

		for (i=0;i<l;i++)
			if (labels[i] > 0) prior1+=1;
			else prior0+=1;
	
		int max_iter=100;	// Maximal number of iterations
		double min_step=1e-10;	// Minimal step taken in line search
		double sigma=1e-12;	// For numerically strict PD of Hessian
		double eps=1e-5;
		double hiTarget=(prior1+1.0)/(prior1+2.0);
		double loTarget=1/(prior0+2.0);
		double[] t= new double[l];
		double fApB,p,q,h11,h22,h21,g1,g2,det,dA,dB,gd,stepsize;
		double newA,newB,newf,d1,d2;
		int iter; 
	
		// Initial Point and Initial Fun Value
		A=0.0; B=Math.log((prior0+1.0)/(prior1+1.0));
		double fval = 0.0;

		for (i=0;i<l;i++)
		{
			if (labels[i]>0) t[i]=hiTarget;
			else t[i]=loTarget;
			fApB = dec_values[i]*A+B;
			if (fApB>=0)
				fval += t[i]*fApB + Math.log(1+Math.exp(-fApB));
			else
				fval += (t[i] - 1)*fApB +Math.log(1+Math.exp(fApB));
		}
		for (iter=0;iter<max_iter;iter++)
		{
			// Update Gradient and Hessian (use H' = H + sigma I)
			h11=sigma; // numerically ensures strict PD
			h22=sigma;
			h21=0.0;g1=0.0;g2=0.0;
			for (i=0;i<l;i++)
			{
				fApB = dec_values[i]*A+B;
				if (fApB >= 0)
				{
					p=Math.exp(-fApB)/(1.0+Math.exp(-fApB));
					q=1.0/(1.0+Math.exp(-fApB));
				}
				else
				{
					p=1.0/(1.0+Math.exp(fApB));
					q=Math.exp(fApB)/(1.0+Math.exp(fApB));
				}
				d2=p*q;
				h11+=dec_values[i]*dec_values[i]*d2;
				h22+=d2;
				h21+=dec_values[i]*d2;
				d1=t[i]-p;
				g1+=dec_values[i]*d1;
				g2+=d1;
			}

			// Stopping Criteria
			if (Math.abs(g1)<eps && Math.abs(g2)<eps)
				break;
			
			// Finding Newton direction: -inv(H') * g
			det=h11*h22-h21*h21;
			dA=-(h22*g1 - h21 * g2) / det;
			dB=-(-h21*g1+ h11 * g2) / det;
			gd=g1*dA+g2*dB;


			stepsize = 1;		// Line Search
			while (stepsize >= min_step)
			{
				newA = A + stepsize * dA;
				newB = B + stepsize * dB;

				// New function value
				newf = 0.0;
				for (i=0;i<l;i++)
				{
					fApB = dec_values[i]*newA+newB;
					if (fApB >= 0)
						newf += t[i]*fApB + Math.log(1+Math.exp(-fApB));
					else
						newf += (t[i] - 1)*fApB +Math.log(1+Math.exp(fApB));
				}
				// Check sufficient decrease
				if (newf<fval+0.0001*stepsize*gd)
				{
					A=newA;B=newB;fval=newf;
					break;
				}
				else
					stepsize = stepsize / 2.0;
			}
			
			if (stepsize < min_step)
			{
				svm.info("Line search fails in two-class probability estimates\n");
				break;
			}
		}
		
		if (iter>=max_iter)
			svm.info("Reaching maximal iterations in two-class probability estimates\n");
		probAB[0]=A;probAB[1]=B;
	}

	private static double sigmoid_predict(double decision_value, double A, double B)
	{
		double fApB = decision_value*A+B;
		if (fApB >= 0)
			return Math.exp(-fApB)/(1.0+Math.exp(-fApB));
		else
			return 1.0/(1+Math.exp(fApB)) ;
	}

	// Method 2 from the multiclass_prob paper by Wu, Lin, and Weng
	private static void multiclass_probability(int k, double[][] r, double[] p)
	{
		int t,j;
		int iter = 0, max_iter=Math.max(100,k);
		double[][] Q=new double[k][k];
		double[] Qp=new double[k];
		double pQp, eps=0.005/k;
	
		for (t=0;t<k;t++)
		{
			p[t]=1.0/k;  // Valid if k = 1
			Q[t][t]=0;
			for (j=0;j<t;j++)
			{
				Q[t][t]+=r[j][t]*r[j][t];
				Q[t][j]=Q[j][t];
			}
			for (j=t+1;j<k;j++)
			{
				Q[t][t]+=r[j][t]*r[j][t];
				Q[t][j]=-r[j][t]*r[t][j];
			}
		}
		for (iter=0;iter<max_iter;iter++)
		{
			// stopping condition, recalculate QP,pQP for numerical accuracy
			pQp=0;
			for (t=0;t<k;t++)
			{
				Qp[t]=0;
				for (j=0;j<k;j++)
					Qp[t]+=Q[t][j]*p[j];
				pQp+=p[t]*Qp[t];
			}
			double max_error=0;
			for (t=0;t<k;t++)
			{
				double error=Math.abs(Qp[t]-pQp);
				if (error>max_error)
					max_error=error;
			}
			if (max_error<eps) break;
		
			for (t=0;t<k;t++)
			{
				double diff=(-Qp[t]+pQp)/Q[t][t];
				p[t]+=diff;
				pQp=(pQp+diff*(diff*Q[t][t]+2*Qp[t]))/(1+diff)/(1+diff);
				for (j=0;j<k;j++)
				{
					Qp[j]=(Qp[j]+diff*Q[t][j])/(1+diff);
					p[j]/=(1+diff);
				}
			}
		}
		if (iter>=max_iter)
			svm.info("Exceeds max_iter in multiclass_prob\n");
	}

	// Cross-validation decision values for probability estimates
	private static void svm_binary_svc_probability(svm_problem prob, svm_parameter param, double Cp, double Cn, double[] probAB)
	{
		int i;
		int nr_fold = 5;
		int[] perm = new int[prob.l];
		double[] dec_values = new double[prob.l];

		// random shuffle
		for(i=0;i<prob.l;i++) perm[i]=i;
		for(i=0;i<prob.l;i++)
		{
			int j = i+rand.nextInt(prob.l-i);
			do {int _=perm[i]; perm[i]=perm[j]; perm[j]=_;} while(false);
		}
		for(i=0;i<nr_fold;i++)
		{
			int begin = i*prob.l/nr_fold;
			int end = (i+1)*prob.l/nr_fold;
			int j,k;
			svm_problem subprob = new svm_problem();

			subprob.l = prob.l-(end-begin);
			subprob.x = new svm_node[subprob.l][];
			subprob.y = new double[subprob.l];
			
			k=0;
			for(j=0;j<begin;j++)
			{
				subprob.x[k] = prob.x[perm[j]];
				subprob.y[k] = prob.y[perm[j]];
				++k;
			}
			for(j=end;j<prob.l;j++)
			{
				subprob.x[k] = prob.x[perm[j]];
				subprob.y[k] = prob.y[perm[j]];
				++k;
			}
			int p_count=0,n_count=0;
			for(j=0;j<k;j++)
				if(subprob.y[j]>0)
					p_count++;
				else
					n_count++;
			
			if(p_count==0 && n_count==0)
				for(j=begin;j<end;j++)
					dec_values[perm[j]] = 0;
			else if(p_count > 0 && n_count == 0)
				for(j=begin;j<end;j++)
					dec_values[perm[j]] = 1;
			else if(p_count == 0 && n_count > 0)
				for(j=begin;j<end;j++)
					dec_values[perm[j]] = -1;
			else
			{
				svm_parameter subparam = (svm_parameter)param.clone();
				subparam.probability=0;
				subparam.C=1.0;
				subparam.nr_weight=2;
				subparam.weight_label = new int[2];
				subparam.weight = new double[2];
				subparam.weight_label[0]=+1;
				subparam.weight_label[1]=-1;
				subparam.weight[0]=Cp;
				subparam.weight[1]=Cn;
				svm_model submodel = svm_train(subprob,subparam);
				for(j=begin;j<end;j++)
				{
					double[] dec_value=new double[1];
					svm_predict_values(submodel,prob.x[perm[j]],dec_value);
					dec_values[perm[j]]=dec_value[0];
					// ensure +1 -1 order; reason not using CV subroutine
					dec_values[perm[j]] *= submodel.label[0];
				}		
			}
		}		
		sigmoid_train(prob.l,dec_values,prob.y,probAB);
	}

	// Return parameter of a Laplace distribution 
	private static double svm_svr_probability(svm_problem prob, svm_parameter param)
	{
		int i;
		int nr_fold = 5;
		double[] ymv = new double[prob.l];
		double mae = 0;

		svm_parameter newparam = (svm_parameter)param.clone();
		newparam.probability = 0;
		svm_cross_validation(prob,newparam,nr_fold,ymv);
		for(i=0;i<prob.l;i++)
		{
			ymv[i]=prob.y[i]-ymv[i];
			mae += Math.abs(ymv[i]);
		}		
		mae /= prob.l;
		double std=Math.sqrt(2*mae*mae);
		int count=0;
		mae=0;
		for(i=0;i<prob.l;i++)
			if (Math.abs(ymv[i]) > 5*std) 
				count=count+1;
			else 
				mae+=Math.abs(ymv[i]);
		mae /= (prob.l-count);
		svm.info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma="+mae+"\n");
		return mae;
	}

	// label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data
	// perm, length l, must be allocated before calling this subroutine
	private static void svm_group_classes(svm_problem prob, int[] nr_class_ret, int[][] label_ret, int[][] start_ret, int[][] count_ret, int[] perm)
	{
		int l = prob.l;
		int max_nr_class = 16;
		int nr_class = 0;
		int[] label = new int[max_nr_class];
		int[] count = new int[max_nr_class];
		int[] data_label = new int[l];
		int i;

		for(i=0;i<l;i++)
		{
			int this_label = (int)(prob.y[i]);
			int j;
			for(j=0;j<nr_class;j++)
			{
				if(this_label == label[j])
				{
					++count[j];
					break;
				}
			}
			data_label[i] = j;
			if(j == nr_class)
			{
				if(nr_class == max_nr_class)
				{
					max_nr_class *= 2;
					int[] new_data = new int[max_nr_class];
					System.arraycopy(label,0,new_data,0,label.length);
					label = new_data;
					new_data = new int[max_nr_class];
					System.arraycopy(count,0,new_data,0,count.length);
					count = new_data;					
				}
				label[nr_class] = this_label;
				count[nr_class] = 1;
				++nr_class;
			}
		}

		//
		// Labels are ordered by their first occurrence in the training set. 
		// However, for two-class sets with -1/+1 labels and -1 appears first, 
		// we swap labels to ensure that internally the binary SVM has positive data corresponding to the +1 instances.
		//
		if (nr_class == 2 && label[0] == -1 && label[1] == +1)
		{
			do {int _=label[0]; label[0]=label[1]; label[1]=_;} while(false);
			do {int _=count[0]; count[0]=count[1]; count[1]=_;} while(false);
			for(i=0;i<l;i++)
			{
				if(data_label[i] == 0)
					data_label[i] = 1;
				else
					data_label[i] = 0;
			}
		}

		int[] start = new int[nr_class];
		start[0] = 0;
		for(i=1;i<nr_class;i++)
			start[i] = start[i-1]+count[i-1];
		for(i=0;i<l;i++)
		{
			perm[start[data_label[i]]] = i;
			++start[data_label[i]];
		}
		start[0] = 0;
		for(i=1;i<nr_class;i++)
			start[i] = start[i-1]+count[i-1];

		nr_class_ret[0] = nr_class;
		label_ret[0] = label;
		start_ret[0] = start;
		count_ret[0] = count;
	}

	//
	// Interface functions
	//
	public static svm_model svm_train(svm_problem prob, svm_parameter param)
	{
		svm_model model = new svm_model();
		model.param = param;

		if(param.svm_type == svm_parameter.ONE_CLASS ||
		   param.svm_type == svm_parameter.EPSILON_SVR ||
		   param.svm_type == svm_parameter.NU_SVR)
		{
			// regression or one-class-svm
			model.nr_class = 2;
			model.label = null;
			model.nSV = null;
			model.probA = null; model.probB = null;
			model.sv_coef = new double[1][];

			if(param.probability == 1 &&
			   (param.svm_type == svm_parameter.EPSILON_SVR ||
			    param.svm_type == svm_parameter.NU_SVR))
			{
				model.probA = new double[1];
				model.probA[0] = svm_svr_probability(prob,param);
			}

			decision_function f = svm_train_one(prob,param,0,0);
			model.rho = new double[1];
			model.rho[0] = f.rho;

			int nSV = 0;
			int i;
			for(i=0;i<prob.l;i++)
				if(Math.abs(f.alpha[i]) > 0) ++nSV;
			model.l = nSV;
			model.SV = new svm_node[nSV][];
			model.sv_coef[0] = new double[nSV];
			model.sv_indices = new int[nSV];
			int j = 0;
			for(i=0;i<prob.l;i++)
				if(Math.abs(f.alpha[i]) > 0)
				{
					model.SV[j] = prob.x[i];
					model.sv_coef[0][j] = f.alpha[i];
					model.sv_indices[j] = i+1;
					++j;
				}
		}
		else
		{
			// classification
			int l = prob.l;
			int[] tmp_nr_class = new int[1];
			int[][] tmp_label = new int[1][];
			int[][] tmp_start = new int[1][];
			int[][] tmp_count = new int[1][];			
			int[] perm = new int[l];

			// group training data of the same class
			svm_group_classes(prob,tmp_nr_class,tmp_label,tmp_start,tmp_count,perm);
			int nr_class = tmp_nr_class[0];			
			int[] label = tmp_label[0];
			int[] start = tmp_start[0];
			int[] count = tmp_count[0];
 			
			if(nr_class == 1) 
				svm.info("WARNING: training data in only one class. See README for details.\n");
			
			svm_node[][] x = new svm_node[l][];
			int i;
			for(i=0;i<l;i++)
				x[i] = prob.x[perm[i]];

			// calculate weighted C

			double[] weighted_C = new double[nr_class];
			for(i=0;i<nr_class;i++)
				weighted_C[i] = param.C;
			for(i=0;i<param.nr_weight;i++)
			{
				int j;
				for(j=0;j<nr_class;j++)
					if(param.weight_label[i] == label[j])
						break;
				if(j == nr_class)
					System.err.print("WARNING: class label "+param.weight_label[i]+" specified in weight is not found\n");
				else
					weighted_C[j] *= param.weight[i];
			}

			// train k*(k-1)/2 models

			boolean[] nonzero = new boolean[l];
			for(i=0;i<l;i++)
				nonzero[i] = false;
			decision_function[] f = new decision_function[nr_class*(nr_class-1)/2];

			double[] probA=null,probB=null;
			if (param.probability == 1)
			{
				probA=new double[nr_class*(nr_class-1)/2];
				probB=new double[nr_class*(nr_class-1)/2];
			}

			int p = 0;
			for(i=0;i<nr_class;i++)
				for(int j=i+1;j<nr_class;j++)
				{
					svm_problem sub_prob = new svm_problem();
					int si = start[i], sj = start[j];
					int ci = count[i], cj = count[j];
					sub_prob.l = ci+cj;
					sub_prob.x = new svm_node[sub_prob.l][];
					sub_prob.y = new double[sub_prob.l];
					int k;
					for(k=0;k<ci;k++)
					{
						sub_prob.x[k] = x[si+k];
						sub_prob.y[k] = +1;
					}
					for(k=0;k<cj;k++)
					{
						sub_prob.x[ci+k] = x[sj+k];
						sub_prob.y[ci+k] = -1;
					}

					if(param.probability == 1)
					{
						double[] probAB=new double[2];
						svm_binary_svc_probability(sub_prob,param,weighted_C[i],weighted_C[j],probAB);
						probA[p]=probAB[0];
						probB[p]=probAB[1];
					}

					f[p] = svm_train_one(sub_prob,param,weighted_C[i],weighted_C[j]);
					for(k=0;k<ci;k++)
						if(!nonzero[si+k] && Math.abs(f[p].alpha[k]) > 0)
							nonzero[si+k] = true;
					for(k=0;k<cj;k++)
						if(!nonzero[sj+k] && Math.abs(f[p].alpha[ci+k]) > 0)
							nonzero[sj+k] = true;
					++p;
				}

			// build output

			model.nr_class = nr_class;

			model.label = new int[nr_class];
			for(i=0;i<nr_class;i++)
				model.label[i] = label[i];

			model.rho = new double[nr_class*(nr_class-1)/2];
			for(i=0;i<nr_class*(nr_class-1)/2;i++)
				model.rho[i] = f[i].rho;

			if(param.probability == 1)
			{
				model.probA = new double[nr_class*(nr_class-1)/2];
				model.probB = new double[nr_class*(nr_class-1)/2];
				for(i=0;i<nr_class*(nr_class-1)/2;i++)
				{
					model.probA[i] = probA[i];
					model.probB[i] = probB[i];
				}
			}
			else
			{
				model.probA=null;
				model.probB=null;
			}

			int nnz = 0;
			int[] nz_count = new int[nr_class];
			model.nSV = new int[nr_class];
			for(i=0;i<nr_class;i++)
			{
				int nSV = 0;
				for(int j=0;j<count[i];j++)
					if(nonzero[start[i]+j])
					{
						++nSV;
						++nnz;
					}
				model.nSV[i] = nSV;
				nz_count[i] = nSV;
			}

			svm.info("Total nSV = "+nnz+"\n");

			model.l = nnz;
			model.SV = new svm_node[nnz][];
			model.sv_indices = new int[nnz];
			p = 0;
			for(i=0;i<l;i++)
				if(nonzero[i])
				{
					model.SV[p] = x[i];
					model.sv_indices[p++] = perm[i] + 1;
				}

			int[] nz_start = new int[nr_class];
			nz_start[0] = 0;
			for(i=1;i<nr_class;i++)
				nz_start[i] = nz_start[i-1]+nz_count[i-1];

			model.sv_coef = new double[nr_class-1][];
			for(i=0;i<nr_class-1;i++)
				model.sv_coef[i] = new double[nnz];

			p = 0;
			for(i=0;i<nr_class;i++)
				for(int j=i+1;j<nr_class;j++)
				{
					// classifier (i,j): coefficients with
					// i are in sv_coef[j-1][nz_start[i]...],
					// j are in sv_coef[i][nz_start[j]...]

					int si = start[i];
					int sj = start[j];
					int ci = count[i];
					int cj = count[j];

					int q = nz_start[i];
					int k;
					for(k=0;k<ci;k++)
						if(nonzero[si+k])
							model.sv_coef[j-1][q++] = f[p].alpha[k];
					q = nz_start[j];
					for(k=0;k<cj;k++)
						if(nonzero[sj+k])
							model.sv_coef[i][q++] = f[p].alpha[ci+k];
					++p;
				}
		}
		return model;
	}
	
	// Stratified cross validation
	public static void svm_cross_validation(svm_problem prob, svm_parameter param, int nr_fold, double[] target)
	{
		int i;
		int[] fold_start = new int[nr_fold+1];
		int l = prob.l;
		int[] perm = new int[l];
		
		// stratified cv may not give leave-one-out rate
		// Each class to l folds -> some folds may have zero elements
		if((param.svm_type == svm_parameter.C_SVC ||
		    param.svm_type == svm_parameter.NU_SVC) && nr_fold < l)
		{
			int[] tmp_nr_class = new int[1];
			int[][] tmp_label = new int[1][];
			int[][] tmp_start = new int[1][];
			int[][] tmp_count = new int[1][];

			svm_group_classes(prob,tmp_nr_class,tmp_label,tmp_start,tmp_count,perm);

			int nr_class = tmp_nr_class[0];
			int[] start = tmp_start[0];
			int[] count = tmp_count[0];		

			// random shuffle and then data grouped by fold using the array perm
			int[] fold_count = new int[nr_fold];
			int c;
			int[] index = new int[l];
			for(i=0;i<l;i++)
				index[i]=perm[i];
			for (c=0; c<nr_class; c++)
				for(i=0;i<count[c];i++)
				{
					int j = i+rand.nextInt(count[c]-i);
					do {int _=index[start[c]+j]; index[start[c]+j]=index[start[c]+i]; index[start[c]+i]=_;} while(false);
				}
			for(i=0;i<nr_fold;i++)
			{
				fold_count[i] = 0;
				for (c=0; c<nr_class;c++)
					fold_count[i]+=(i+1)*count[c]/nr_fold-i*count[c]/nr_fold;
			}
			fold_start[0]=0;
			for (i=1;i<=nr_fold;i++)
				fold_start[i] = fold_start[i-1]+fold_count[i-1];
			for (c=0; c<nr_class;c++)
				for(i=0;i<nr_fold;i++)
				{
					int begin = start[c]+i*count[c]/nr_fold;
					int end = start[c]+(i+1)*count[c]/nr_fold;
					for(int j=begin;j<end;j++)
					{
						perm[fold_start[i]] = index[j];
						fold_start[i]++;
					}
				}
			fold_start[0]=0;
			for (i=1;i<=nr_fold;i++)
				fold_start[i] = fold_start[i-1]+fold_count[i-1];
		}
		else
		{
			for(i=0;i<l;i++) perm[i]=i;
			for(i=0;i<l;i++)
			{
				int j = i+rand.nextInt(l-i);
				do {int _=perm[i]; perm[i]=perm[j]; perm[j]=_;} while(false);
			}
			for(i=0;i<=nr_fold;i++)
				fold_start[i]=i*l/nr_fold;
		}

		for(i=0;i<nr_fold;i++)
		{
			int begin = fold_start[i];
			int end = fold_start[i+1];
			int j,k;
			svm_problem subprob = new svm_problem();

			subprob.l = l-(end-begin);
			subprob.x = new svm_node[subprob.l][];
			subprob.y = new double[subprob.l];

			k=0;
			for(j=0;j<begin;j++)
			{
				subprob.x[k] = prob.x[perm[j]];
				subprob.y[k] = prob.y[perm[j]];
				++k;
			}
			for(j=end;j<l;j++)
			{
				subprob.x[k] = prob.x[perm[j]];
				subprob.y[k] = prob.y[perm[j]];
				++k;
			}
			svm_model submodel = svm_train(subprob,param);
			if(param.probability==1 &&
			   (param.svm_type == svm_parameter.C_SVC ||
			    param.svm_type == svm_parameter.NU_SVC))
			{
				double[] prob_estimates= new double[svm_get_nr_class(submodel)];
				for(j=begin;j<end;j++)
					target[perm[j]] = svm_predict_probability(submodel,prob.x[perm[j]],prob_estimates);
			}
			else
				for(j=begin;j<end;j++)
					target[perm[j]] = svm_predict(submodel,prob.x[perm[j]]);
		}
	}

	public static int svm_get_svm_type(svm_model model)
	{
		return model.param.svm_type;
	}

	public static int svm_get_nr_class(svm_model model)
	{
		return model.nr_class;
	}

	public static void svm_get_labels(svm_model model, int[] label)
	{
		if (model.label != null)
			for(int i=0;i<model.nr_class;i++)
				label[i] = model.label[i];
	}

	public static void svm_get_sv_indices(svm_model model, int[] indices)
	{
		if (model.sv_indices != null)
			for(int i=0;i<model.l;i++)
				indices[i] = model.sv_indices[i];
	}

	public static int svm_get_nr_sv(svm_model model)
	{
		return model.l;
	}

	public static double svm_get_svr_probability(svm_model model)
	{
		if ((model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR) &&
		    model.probA!=null)
		return model.probA[0];
		else
		{
			System.err.print("Model doesn't contain information for SVR probability inference\n");
			return 0;
		}
	}

	public static double svm_predict_values(svm_model model, svm_node[] x, double[] dec_values)
	{
		int i;
		if(model.param.svm_type == svm_parameter.ONE_CLASS ||
		   model.param.svm_type == svm_parameter.EPSILON_SVR ||
		   model.param.svm_type == svm_parameter.NU_SVR)
		{
			double[] sv_coef = model.sv_coef[0];
			double sum = 0;
			for(i=0;i<model.l;i++)
				sum += sv_coef[i] * Kernel.k_function(x,model.SV[i],model.param);
			sum -= model.rho[0];
			dec_values[0] = sum;

			if(model.param.svm_type == svm_parameter.ONE_CLASS)
				return (sum>0)?1:-1;
			else
				return sum;
		}
		else
		{
			int nr_class = model.nr_class;
			int l = model.l;
		
			double[] kvalue = new double[l];
			for(i=0;i<l;i++)
				kvalue[i] = Kernel.k_function(x,model.SV[i],model.param);

			int[] start = new int[nr_class];
			start[0] = 0;
			for(i=1;i<nr_class;i++)
				start[i] = start[i-1]+model.nSV[i-1];

			int[] vote = new int[nr_class];
			for(i=0;i<nr_class;i++)
				vote[i] = 0;

			int p=0;
			for(i=0;i<nr_class;i++)
				for(int j=i+1;j<nr_class;j++)
				{
					double sum = 0;
					int si = start[i];
					int sj = start[j];
					int ci = model.nSV[i];
					int cj = model.nSV[j];
				
					int k;
					double[] coef1 = model.sv_coef[j-1];
					double[] coef2 = model.sv_coef[i];
					for(k=0;k<ci;k++)
						sum += coef1[si+k] * kvalue[si+k];
					for(k=0;k<cj;k++)
						sum += coef2[sj+k] * kvalue[sj+k];
					sum -= model.rho[p];
					dec_values[p] = sum;					

					if(dec_values[p] > 0)
						++vote[i];
					else
						++vote[j];
					p++;
				}

			int vote_max_idx = 0;
			for(i=1;i<nr_class;i++)
				if(vote[i] > vote[vote_max_idx])
					vote_max_idx = i;

			return model.label[vote_max_idx];
		}
	}

	public static double svm_predict(svm_model model, svm_node[] x)
	{
		int nr_class = model.nr_class;
		double[] dec_values;
		if(model.param.svm_type == svm_parameter.ONE_CLASS ||
				model.param.svm_type == svm_parameter.EPSILON_SVR ||
				model.param.svm_type == svm_parameter.NU_SVR)
			dec_values = new double[1];
		else
			dec_values = new double[nr_class*(nr_class-1)/2];
		double pred_result = svm_predict_values(model, x, dec_values);
		return pred_result;
	}

	public static double svm_predict_probability(svm_model model, svm_node[] x, double[] prob_estimates)
	{
		if ((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) &&
		    model.probA!=null && model.probB!=null)
		{
			int i;
			int nr_class = model.nr_class;
			double[] dec_values = new double[nr_class*(nr_class-1)/2];
			svm_predict_values(model, x, dec_values);

			double min_prob=1e-7;
			double[][] pairwise_prob=new double[nr_class][nr_class];
			
			int k=0;
			for(i=0;i<nr_class;i++)
				for(int j=i+1;j<nr_class;j++)
				{
					pairwise_prob[i][j]=Math.min(Math.max(sigmoid_predict(dec_values[k],model.probA[k],model.probB[k]),min_prob),1-min_prob);
					pairwise_prob[j][i]=1-pairwise_prob[i][j];
					k++;
				}
			multiclass_probability(nr_class,pairwise_prob,prob_estimates);

			int prob_max_idx = 0;
			for(i=1;i<nr_class;i++)
				if(prob_estimates[i] > prob_estimates[prob_max_idx])
					prob_max_idx = i;
			return model.label[prob_max_idx];
		}
		else 
			return svm_predict(model, x);
	}

	static final String svm_type_table[] =
	{
		"c_svc","nu_svc","one_class","epsilon_svr","nu_svr",
	};

	static final String kernel_type_table[]=
	{
		"linear","polynomial","rbf","sigmoid","precomputed"
	};

	public static void svm_save_model(String model_file_name, svm_model model) throws IOException
	{
		DataOutputStream fp = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(model_file_name)));

		svm_parameter param = model.param;

		fp.writeBytes("svm_type "+svm_type_table[param.svm_type]+"\n");
		fp.writeBytes("kernel_type "+kernel_type_table[param.kernel_type]+"\n");

		if(param.kernel_type == svm_parameter.POLY)
			fp.writeBytes("degree "+param.degree+"\n");

		if(param.kernel_type == svm_parameter.POLY ||
		   param.kernel_type == svm_parameter.RBF ||
		   param.kernel_type == svm_parameter.SIGMOID)
			fp.writeBytes("gamma "+param.gamma+"\n");

		if(param.kernel_type == svm_parameter.POLY ||
		   param.kernel_type == svm_parameter.SIGMOID)
			fp.writeBytes("coef0 "+param.coef0+"\n");

		int nr_class = model.nr_class;
		int l = model.l;
		fp.writeBytes("nr_class "+nr_class+"\n");
		fp.writeBytes("total_sv "+l+"\n");
	
		{
			fp.writeBytes("rho");
			for(int i=0;i<nr_class*(nr_class-1)/2;i++)
				fp.writeBytes(" "+model.rho[i]);
			fp.writeBytes("\n");
		}
	
		if(model.label != null)
		{
			fp.writeBytes("label");
			for(int i=0;i<nr_class;i++)
				fp.writeBytes(" "+model.label[i]);
			fp.writeBytes("\n");
		}

		if(model.probA != null) // regression has probA only
		{
			fp.writeBytes("probA");
			for(int i=0;i<nr_class*(nr_class-1)/2;i++)
				fp.writeBytes(" "+model.probA[i]);
			fp.writeBytes("\n");
		}
		if(model.probB != null) 
		{
			fp.writeBytes("probB");
			for(int i=0;i<nr_class*(nr_class-1)/2;i++)
				fp.writeBytes(" "+model.probB[i]);
			fp.writeBytes("\n");
		}

		if(model.nSV != null)
		{
			fp.writeBytes("nr_sv");
			for(int i=0;i<nr_class;i++)
				fp.writeBytes(" "+model.nSV[i]);
			fp.writeBytes("\n");
		}

		fp.writeBytes("SV\n");
		double[][] sv_coef = model.sv_coef;
		svm_node[][] SV = model.SV;

		for(int i=0;i<l;i++)
		{
			for(int j=0;j<nr_class-1;j++)
				fp.writeBytes(sv_coef[j][i]+" ");

			svm_node[] p = SV[i];
			if(param.kernel_type == svm_parameter.PRECOMPUTED)
				fp.writeBytes("0:"+(int)(p[0].value));
			else	
				for(int j=0;j<p.length;j++)
					fp.writeBytes(p[j].index+":"+p[j].value+" ");
			fp.writeBytes("\n");
		}

		fp.close();
	}

	private static double atof(String s)
	{
		return Double.valueOf(s).doubleValue();
	}

	private static int atoi(String s)
	{
		return Integer.parseInt(s);
	}

	private static boolean read_model_header(BufferedReader fp, svm_model model)
	{
		svm_parameter param = new svm_parameter();
		model.param = param;
		try
		{
			while(true)
			{
				String cmd = fp.readLine();
				String arg = cmd.substring(cmd.indexOf(' ')+1);

				if(cmd.startsWith("svm_type"))
				{
					int i;
					for(i=0;i<svm_type_table.length;i++)
					{
						if(arg.indexOf(svm_type_table[i])!=-1)
						{
							param.svm_type=i;
							break;
						}
					}
					if(i == svm_type_table.length)
					{
						System.err.print("unknown svm type.\n");
						return false;
					}
				}
				else if(cmd.startsWith("kernel_type"))
				{
					int i;
					for(i=0;i<kernel_type_table.length;i++)
					{
						if(arg.indexOf(kernel_type_table[i])!=-1)
						{
							param.kernel_type=i;
							break;
						}
					}
					if(i == kernel_type_table.length)
					{
						System.err.print("unknown kernel function.\n");
						return false;
					}
				}
				else if(cmd.startsWith("degree"))
					param.degree = atoi(arg);
				else if(cmd.startsWith("gamma"))
					param.gamma = atof(arg);
				else if(cmd.startsWith("coef0"))
					param.coef0 = atof(arg);
				else if(cmd.startsWith("nr_class"))
					model.nr_class = atoi(arg);
				else if(cmd.startsWith("total_sv"))
					model.l = atoi(arg);
				else if(cmd.startsWith("rho"))
				{
					int n = model.nr_class * (model.nr_class-1)/2;
					model.rho = new double[n];
					StringTokenizer st = new StringTokenizer(arg);
					for(int i=0;i<n;i++)
						model.rho[i] = atof(st.nextToken());
				}
				else if(cmd.startsWith("label"))
				{
					int n = model.nr_class;
					model.label = new int[n];
					StringTokenizer st = new StringTokenizer(arg);
					for(int i=0;i<n;i++)
						model.label[i] = atoi(st.nextToken());					
				}
				else if(cmd.startsWith("probA"))
				{
					int n = model.nr_class*(model.nr_class-1)/2;
					model.probA = new double[n];
					StringTokenizer st = new StringTokenizer(arg);
					for(int i=0;i<n;i++)
						model.probA[i] = atof(st.nextToken());					
				}
				else if(cmd.startsWith("probB"))
				{
					int n = model.nr_class*(model.nr_class-1)/2;
					model.probB = new double[n];
					StringTokenizer st = new StringTokenizer(arg);
					for(int i=0;i<n;i++)
						model.probB[i] = atof(st.nextToken());					
				}
				else if(cmd.startsWith("nr_sv"))
				{
					int n = model.nr_class;
					model.nSV = new int[n];
					StringTokenizer st = new StringTokenizer(arg);
					for(int i=0;i<n;i++)
						model.nSV[i] = atoi(st.nextToken());
				}
				else if(cmd.startsWith("SV"))
				{
					break;
				}
				else
				{
					System.err.print("unknown text in model file: ["+cmd+"]\n");
					return false;
				}
			}
		}
		catch(Exception e)
		{
			return false;
		}
		return true;
	}

	public static svm_model svm_load_model(String model_file_name) throws IOException
	{
		return svm_load_model(new BufferedReader(new FileReader(model_file_name)));
	}

	public static svm_model svm_load_model(BufferedReader fp) throws IOException
	{
		// read parameters

		svm_model model = new svm_model();
		model.rho = null;
		model.probA = null;
		model.probB = null;
		model.label = null;
		model.nSV = null;

		if (read_model_header(fp, model) == false)
		{
			System.err.print("ERROR: failed to read model\n");
			return null;
		}

		// read sv_coef and SV

		int m = model.nr_class - 1;
		int l = model.l;
		model.sv_coef = new double[m][l];
		model.SV = new svm_node[l][];

		for(int i=0;i<l;i++)
		{
			String line = fp.readLine();
			StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");

			for(int k=0;k<m;k++)
				model.sv_coef[k][i] = atof(st.nextToken());
			int n = st.countTokens()/2;
			model.SV[i] = new svm_node[n];
			for(int j=0;j<n;j++)
			{
				model.SV[i][j] = new svm_node();
				model.SV[i][j].index = atoi(st.nextToken());
				model.SV[i][j].value = atof(st.nextToken());
			}
		}

		fp.close();
		return model;
	}

	public static String svm_check_parameter(svm_problem prob, svm_parameter param)
	{
		// svm_type

		int svm_type = param.svm_type;
		if(svm_type != svm_parameter.C_SVC &&
		   svm_type != svm_parameter.NU_SVC &&
		   svm_type != svm_parameter.ONE_CLASS &&
		   svm_type != svm_parameter.EPSILON_SVR &&
		   svm_type != svm_parameter.NU_SVR)
		return "unknown svm type";

		// kernel_type, degree
	
		int kernel_type = param.kernel_type;
		if(kernel_type != svm_parameter.LINEAR &&
		   kernel_type != svm_parameter.POLY &&
		   kernel_type != svm_parameter.RBF &&
		   kernel_type != svm_parameter.SIGMOID &&
		   kernel_type != svm_parameter.PRECOMPUTED)
			return "unknown kernel type";

		if(param.gamma < 0)
			return "gamma < 0";

		if(param.degree < 0)
			return "degree of polynomial kernel < 0";

		// cache_size,eps,C,nu,p,shrinking

		if(param.cache_size <= 0)
			return "cache_size <= 0";

		if(param.eps <= 0)
			return "eps <= 0";

		if(svm_type == svm_parameter.C_SVC ||
		   svm_type == svm_parameter.EPSILON_SVR ||
		   svm_type == svm_parameter.NU_SVR)
			if(param.C <= 0)
				return "C <= 0";

		if(svm_type == svm_parameter.NU_SVC ||
		   svm_type == svm_parameter.ONE_CLASS ||
		   svm_type == svm_parameter.NU_SVR)
			if(param.nu <= 0 || param.nu > 1)
				return "nu <= 0 or nu > 1";

		if(svm_type == svm_parameter.EPSILON_SVR)
			if(param.p < 0)
				return "p < 0";

		if(param.shrinking != 0 &&
		   param.shrinking != 1)
			return "shrinking != 0 and shrinking != 1";

		if(param.probability != 0 &&
		   param.probability != 1)
			return "probability != 0 and probability != 1";

		if(param.probability == 1 &&
		   svm_type == svm_parameter.ONE_CLASS)
			return "one-class SVM probability output not supported yet";
		
		// check whether nu-svc is feasible
	
		if(svm_type == svm_parameter.NU_SVC)
		{
			int l = prob.l;
			int max_nr_class = 16;
			int nr_class = 0;
			int[] label = new int[max_nr_class];
			int[] count = new int[max_nr_class];

			int i;
			for(i=0;i<l;i++)
			{
				int this_label = (int)prob.y[i];
				int j;
				for(j=0;j<nr_class;j++)
					if(this_label == label[j])
					{
						++count[j];
						break;
					}

				if(j == nr_class)
				{
					if(nr_class == max_nr_class)
					{
						max_nr_class *= 2;
						int[] new_data = new int[max_nr_class];
						System.arraycopy(label,0,new_data,0,label.length);
						label = new_data;
						
						new_data = new int[max_nr_class];
						System.arraycopy(count,0,new_data,0,count.length);
						count = new_data;
					}
					label[nr_class] = this_label;
					count[nr_class] = 1;
					++nr_class;
				}
			}

			for(i=0;i<nr_class;i++)
			{
				int n1 = count[i];
				for(int j=i+1;j<nr_class;j++)
				{
					int n2 = count[j];
					if(param.nu*(n1+n2)/2 > Math.min(n1,n2))
						return "specified nu is infeasible";
				}
			}
		}

		return null;
	}

	public static int svm_check_probability_model(svm_model model)
	{
		if (((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) &&
		model.probA!=null && model.probB!=null) ||
		((model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR) &&
		 model.probA!=null))
			return 1;
		else
			return 0;
	}

	public static void svm_set_print_string_function(svm_print_interface print_func)
	{
		if (print_func == null)
			svm_print_string = svm_print_stdout;
		else 
			svm_print_string = print_func;
	}
}


================================================
FILE: Matlab/libsvm-3.19/java/libsvm/svm.m4
================================================
define(`swap',`do {$1 _=$2; $2=$3; $3=_;} while(false)')
define(`Qfloat',`float')
define(`SIZE_OF_QFLOAT',4)
define(`TAU',1e-12)
changecom(`//', )
package libsvm;
import java.io.*;
import java.util.*;

//
// Kernel Cache
//
// l is the number of total data items
// size is the cache size limit in bytes
//
class Cache {
	private final int l;
	private long size;
	private final class head_t
	{
		head_t prev, next;	// a cicular list
		Qfloat[] data;
		int len;		// data[0,len) is cached in this entry
	}
	private final head_t[] head;
	private head_t lru_head;

	Cache(int l_, long size_)
	{
		l = l_;
		size = size_;
		head = new head_t[l];
		for(int i=0;i<l;i++) head[i] = new head_t();
		size /= SIZE_OF_QFLOAT;
		size -= l * (16/SIZE_OF_QFLOAT);	// sizeof(head_t) == 16
		size = Math.max(size, 2* (long) l);  // cache must be large enough for two columns
		lru_head = new head_t();
		lru_head.next = lru_head.prev = lru_head;
	}

	private void lru_delete(head_t h)
	{
		// delete from current location
		h.prev.next = h.next;
		h.next.prev = h.prev;
	}

	private void lru_insert(head_t h)
	{
		// insert to last position
		h.next = lru_head;
		h.prev = lru_head.prev;
		h.prev.next = h;
		h.next.prev = h;
	}

	// request data [0,len)
	// return some position p where [p,len) need to be filled
	// (p >= len if nothing needs to be filled)
	// java: simulate pointer using single-element array
	int get_data(int index, Qfloat[][] data, int len)
	{
		head_t h = head[index];
		if(h.len > 0) lru_delete(h);
		int more = len - h.len;

		if(more > 0)
		{
			// free old space
			while(size < more)
			{
				head_t old = lru_head.next;
				lru_delete(old);
				size += old.len;
				old.data = null;
				old.len = 0;
			}

			// allocate new space
			Qfloat[] new_data = new Qfloat[len];
			if(h.data != null) System.arraycopy(h.data,0,new_data,0,h.len);
			h.data = new_data;
			size -= more;
			swap(int,h.len,len);
		}

		lru_insert(h);
		data[0] = h.data;
		return len;
	}

	void swap_index(int i, int j)
	{
		if(i==j) return;
		
		if(head[i].len > 0) lru_delete(head[i]);
		if(head[j].len > 0) lru_delete(head[j]);
		swap(Qfloat[],head[i].data,head[j].data);
		swap(int,head[i].len,head[j].len);
		if(head[i].len > 0) lru_insert(head[i]);
		if(head[j].len > 0) lru_insert(head[j]);

		if(i>j) swap(int,i,j);
		for(head_t h = lru_head.next; h!=lru_head; h=h.next)
		{
			if(h.len > i)
			{
				if(h.len > j)
					swap(Qfloat,h.data[i],h.data[j]);
				else
				{
					// give up
					lru_delete(h);
					size += h.len;
					h.data = null;
					h.len = 0;
				}
			}
		}
	}
}

//
// Kernel evaluation
//
// the static method k_function is for doing single kernel evaluation
// the constructor of Kernel prepares to calculate the l*l kernel matrix
// the member function get_Q is for getting one column from the Q Matrix
//
abstract class QMatrix {
	abstract Qfloat[] get_Q(int column, int len);
	abstract double[] get_QD();
	abstract void swap_index(int i, int j);
};

abstract class Kernel extends QMatrix {
	private svm_node[][] x;
	private final double[] x_square;

	// svm_parameter
	private final int kernel_type;
	private final int degree;
	private final double gamma;
	private final double coef0;

	abstract Qfloat[] get_Q(int column, int len);
	abstract double[] get_QD();

	void swap_index(int i, int j)
	{
		swap(svm_node[],x[i],x[j]);
		if(x_square != null) swap(double,x_square[i],x_square[j]);
	}

	private static double powi(double base, int times)
	{
		double tmp = base, ret = 1.0;

		for(int t=times; t>0; t/=2)
		{
			if(t%2==1) ret*=tmp;
			tmp = tmp * tmp;
		}
		return ret;
	}

	double kernel_function(int i, int j)
	{
		switch(kernel_type)
		{
			case svm_parameter.LINEAR:
				return dot(x[i],x[j]);
			case svm_parameter.POLY:
				return powi(gamma*dot(x[i],x[j])+coef0,degree);
			case svm_parameter.RBF:
				return Math.exp(-gamma*(x_square[i]+x_square[j]-2*dot(x[i],x[j])));
			case svm_parameter.SIGMOID:
				return Math.tanh(gamma*dot(x[i],x[j])+coef0);
			case svm_parameter.PRECOMPUTED:
				return x[i][(int)(x[j][0].value)].value;
			default:
				return 0;	// java
		}
	}

	Kernel(int l, svm_node[][] x_, svm_parameter param)
	{
		this.kernel_type = param.kernel_type;
		this.degree = param.degree;
		this.gamma = param.gamma;
		this.coef0 = param.coef0;

		x = (svm_node[][])x_.clone();

		if(kernel_type == svm_parameter.RBF)
		{
			x_square = new double[l];
			for(int i=0;i<l;i++)
				x_square[i] = dot(x[i],x[i]);
		}
		else x_square = null;
	}

	static double dot(svm_node[] x, svm_node[] y)
	{
		double sum = 0;
		int xlen = x.length;
		int ylen = y.length;
		int i = 0;
		int j = 0;
		while(i < xlen && j < ylen)
		{
			if(x[i].index == y[j].index)
				sum += x[i++].value * y[j++].value;
			else
			{
				if(x[i].index > y[j].index)
					++j;
				else
					++i;
			}
		}
		return sum;
	}

	static double k_function(svm_node[] x, svm_node[] y,
					svm_parameter param)
	{
		switch(param.kernel_type)
		{
			case svm_parameter.LINEAR:
				return dot(x,y);
			case svm_parameter.POLY:
				return powi(param.gamma*dot(x,y)+param.coef0,param.degree);
			case svm_parameter.RBF:
			{
				double sum = 0;
				int xlen = x.length;
				int ylen = y.length;
				int i = 0;
				int j = 0;
				while(i < xlen && j < ylen)
				{
					if(x[i].index == y[j].index)
					{
						double d = x[i++].value - y[j++].value;
						sum += d*d;
					}
					else if(x[i].index > y[j].index)
					{
						sum += y[j].value * y[j].value;
						++j;
					}
					else
					{
						sum += x[i].value * x[i].value;
						++i;
					}
				}

				while(i < xlen)
				{
					sum += x[i].value * x[i].value;
					++i;
				}

				while(j < ylen)
				{
					sum += y[j].value * y[j].value;
					++j;
				}

				return Math.exp(-param.gamma*sum);
			}
			case svm_parameter.SIGMOID:
				return Math.tanh(param.gamma*dot(x,y)+param.coef0);
			case svm_parameter.PRECOMPUTED:
				return	x[(int)(y[0].value)].value;
			default:
				return 0;	// java
		}
	}
}

// An SMO algorithm in Fan et al., JMLR 6(2005), p. 1889--1918
// Solves:
//
//	min 0.5(\alpha^T Q \alpha) + p^T \alpha
//
//		y^T \alpha = \delta
//		y_i = +1 or -1
//		0 <= alpha_i <= Cp for y_i = 1
//		0 <= alpha_i <= Cn for y_i = -1
//
// Given:
//
//	Q, p, y, Cp, Cn, and an initial feasible point \alpha
//	l is the size of vectors and matrices
//	eps is the stopping tolerance
//
// solution will be put in \alpha, objective value will be put in obj
//
class Solver {
	int active_size;
	byte[] y;
	double[] G;		// gradient of objective function
	static final byte LOWER_BOUND = 0;
	static final byte UPPER_BOUND = 1;
	static final byte FREE = 2;
	byte[] alpha_status;	// LOWER_BOUND, UPPER_BOUND, FREE
	double[] alpha;
	QMatrix Q;
	double[] QD;
	double eps;
	double Cp,Cn;
	double[] p;
	int[] active_set;
	double[] G_bar;		// gradient, if we treat free variables as 0
	int l;
	boolean unshrink;	// XXX
	
	static final double INF = java.lang.Double.POSITIVE_INFINITY;

	double get_C(int i)
	{
		return (y[i] > 0)? Cp : Cn;
	}
	void update_alpha_status(int i)
	{
		if(alpha[i] >= get_C(i))
			alpha_status[i] = UPPER_BOUND;
		else if(alpha[i] <= 0)
			alpha_status[i] = LOWER_BOUND;
		else alpha_status[i] = FREE;
	}
	boolean is_upper_bound(int i) { return alpha_status[i] == UPPER_BOUND; }
	boolean is_lower_bound(int i) { return alpha_status[i] == LOWER_BOUND; }
	boolean is_free(int i) {  return alpha_status[i] == FREE; }

	// java: information about solution except alpha,
	// because we cannot return multiple values otherwise...
	static class SolutionInfo {
		double obj;
		double rho;
		double upper_bound_p;
		double upper_bound_n;
		double r;	// for Solver_NU
	}

	void swap_index(int i, int j)
	{
		Q.swap_index(i,j);
		swap(byte,	y[i],y[j]);
		swap(double,	G[i],G[j]);
		swap(byte,	alpha_status[i],alpha_status[j]);
		swap(double,	alpha[i],alpha[j]);
		swap(double,	p[i],p[j]);
		swap(int,	active_set[i],active_set[j]);
		swap(double,	G_bar[i],G_bar[j]);
	}

	void reconstruct_gradient()
	{
		// reconstruct inactive elements of G from G_bar and free variables

		if(active_size == l) return;

		int i,j;
		int nr_free = 0;

		for(j=active_size;j<l;j++)
			G[j] = G_bar[j] + p[j];

		for(j=0;j<active_size;j++)
			if(is_free(j))
				nr_free++;

		if(2*nr_free < active_size)
			svm.info("\nWARNING: using -h 0 may be faster\n");

		if (nr_free*l > 2*active_size*(l-active_size))
		{
			for(i=active_size;i<l;i++)
			{
				Qfloat[] Q_i = Q.get_Q(i,active_size);
				for(j=0;j<active_size;j++)
					if(is_free(j))
						G[i] += alpha[j] * Q_i[j];
			}	
		}
		else
		{
			for(i=0;i<active_size;i++)
				if(is_free(i))
				{
					Qfloat[] Q_i = Q.get_Q(i,l);
					double alpha_i = alpha[i];
					for(j=active_size;j<l;j++)
						G[j] += alpha_i * Q_i[j];
				}
		}
	}

	void Solve(int l, QMatrix Q, double[] p_, byte[] y_,
		   double[] alpha_, double Cp, double Cn, double eps, SolutionInfo si, int shrinking)
	{
		this.l = l;
		this.Q = Q;
		QD = Q.get_QD();
		p = (double[])p_.clone();
		y = (byte[])y_.clone();
		alpha = (double[])alpha_.clone();
		this.Cp = Cp;
		this.Cn = Cn;
		this.eps = eps;
		this.unshrink = false;

		// initialize alpha_status
		{
			alpha_status = new byte[l];
			for(int i=0;i<l;i++)
				update_alpha_status(i);
		}

		// initialize active set (for shrinking)
		{
			active_set = new int[l];
			for(int i=0;i<l;i++)
				active_set[i] = i;
			active_size = l;
		}

		// initialize gradient
		{
			G = new double[l];
			G_bar = new double[l];
			int i;
			for(i=0;i<l;i++)
			{
				G[i] = p[i];
				G_bar[i] = 0;
			}
			for(i=0;i<l;i++)
				if(!is_lower_bound(i))
				{
					Qfloat[] Q_i = Q.get_Q(i,l);
					double alpha_i = alpha[i];
					int j;
					for(j=0;j<l;j++)
						G[j] += alpha_i*Q_i[j];
					if(is_upper_bound(i))
						for(j=0;j<l;j++)
							G_bar[j] += get_C(i) * Q_i[j];
				}
		}

		// optimization step

		int iter = 0;
		int max_iter = Math.max(10000000, l>Integer.MAX_VALUE/100 ? Integer.MAX_VALUE : 100*l);
		int counter = Math.min(l,1000)+1;
		int[] working_set = new int[2];

		while(iter < max_iter)
		{
			// show progress and do shrinking

			if(--counter == 0)
			{
				counter = Math.min(l,1000);
				if(shrinking!=0) do_shrinking();
				svm.info(".");
			}

			if(select_working_set(working_set)!=0)
			{
				// reconstruct the whole gradient
				reconstruct_gradient();
				// reset active set size and check
				active_size = l;
				svm.info("*");
				if(select_working_set(working_set)!=0)
					break;
				else
					counter = 1;	// do shrinking next iteration
			}
			
			int i = working_set[0];
			int j = working_set[1];

			++iter;

			// update alpha[i] and alpha[j], handle bounds carefully

			Qfloat[] Q_i = Q.get_Q(i,active_size);
			Qfloat[] Q_j = Q.get_Q(j,active_size);

			double C_i = get_C(i);
			double C_j = get_C(j);

			double old_alpha_i = alpha[i];
			double old_alpha_j = alpha[j];

			if(y[i]!=y[j])
			{
				double quad_coef = QD[i]+QD[j]+2*Q_i[j];
				if (quad_coef <= 0)
					quad_coef = TAU;
				double delta = (-G[i]-G[j])/quad_coef;
				double diff = alpha[i] - alpha[j];
				alpha[i] += delta;
				alpha[j] += delta;
			
				if(diff > 0)
				{
					if(alpha[j] < 0)
					{
						alpha[j] = 0;
						alpha[i] = diff;
					}
				}
				else
				{
					if(alpha[i] < 0)
					{
						alpha[i] = 0;
						alpha[j] = -diff;
					}
				}
				if(diff > C_i - C_j)
				{
					if(alpha[i] > C_i)
					{
						alpha[i] = C_i;
						alpha[j] = C_i - diff;
					}
				}
				else
				{
					if(alpha[j] > C_j)
					{
						alpha[j] = C_j;
						alpha[i] = C_j + diff;
					}
				}
			}
			else
			{
				double quad_coef = QD[i]+QD[j]-2*Q_i[j];
				if (quad_coef <= 0)
					quad_coef = TAU;
				double delta = (G[i]-G[j])/quad_coef;
				double sum = alpha[i] + alpha[j];
				alpha[i] -= delta;
				alpha[j] += delta;

				if(sum > C_i)
				{
					if(alpha[i] > C_i)
					{
						alpha[i] = C_i;
						alpha[j] = sum - C_i;
					}
				}
				else
				{
					if(alpha[j] < 0)
					{
						alpha[j] = 0;
						alpha[i] = sum;
					}
				}
				if(sum > C_j)
				{
					if(alpha[j] > C_j)
					{
						alpha[j] = C_j;
						alpha[i] = sum - C_j;
					}
				}
				else
				{
					if(alpha[i] < 0)
					{
						alpha[i] = 0;
						alpha[j] = sum;
					}
				}
			}

			// update G

			double delta_alpha_i = alpha[i] - old_alpha_i;
			double delta_alpha_j = alpha[j] - old_alpha_j;

			for(int k=0;k<active_size;k++)
			{
				G[k] += Q_i[k]*delta_alpha_i + Q_j[k]*delta_alpha_j;
			}

			// update alpha_status and G_bar

			{
				boolean ui = is_upper_bound(i);
				boolean uj = is_upper_bound(j);
				update_alpha_status(i);
				update_alpha_status(j);
				int k;
				if(ui != is_upper_bound(i))
				{
					Q_i = Q.get_Q(i,l);
					if(ui)
						for(k=0;k<l;k++)
							G_bar[k] -= C_i * Q_i[k];
					else
						for(k=0;k<l;k++)
							G_bar[k] += C_i * Q_i[k];
				}

				if(uj != is_upper_bound(j))
				{
					Q_j = Q.get_Q(j,l);
					if(uj)
						for(k=0;k<l;k++)
							G_bar[k] -= C_j * Q_j[k];
					else
						for(k=0;k<l;k++)
							G_bar[k] += C_j * Q_j[k];
				}
			}

		}
		
		if(iter >= max_iter)
		{
			if(active_size < l)
			{
				// reconstruct the whole gradient to calculate objective value
				reconstruct_gradient();
				active_size = l;
				svm.info("*");
			}
			System.err.print("\nWARNING: reaching max number of iterations\n");
		}

		// calculate rho

		si.rho = calculate_rho();

		// calculate objective value
		{
			double v = 0;
			int i;
			for(i=0;i<l;i++)
				v += alpha[i] * (G[i] + p[i]);

			si.obj = v/2;
		}

		// put back the solution
		{
			for(int i=0;i<l;i++)
				alpha_[active_set[i]] = alpha[i];
		}

		si.upper_bound_p = Cp;
		si.upper_bound_n = Cn;

		svm.info("\noptimization finished, #iter = "+iter+"\n");
	}

	// return 1 if already optimal, return 0 otherwise
	int select_working_set(int[] working_set)
	{
		// return i,j such that
		// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
		// j: mimimizes the decrease of obj value
		//    (if quadratic coefficeint <= 0, replace it with tau)
		//    -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
		
		double Gmax = -INF;
		double Gmax2 = -INF;
		int Gmax_idx = -1;
		int Gmin_idx = -1;
		double obj_diff_min = INF;
	
		for(int t=0;t<active_size;t++)
			if(y[t]==+1)	
			{
				if(!is_upper_bound(t))
					if(-G[t] >= Gmax)
					{
						Gmax = -G[t];
						Gmax_idx = t;
					}
			}
			else
			{
				if(!is_lower_bound(t))
					if(G[t] >= Gmax)
					{
						Gmax = G[t];
						Gmax_idx = t;
					}
			}
	
		int i = Gmax_idx;
		Qfloat[] Q_i = null;
		if(i != -1) // null Q_i not accessed: Gmax=-INF if i=-1
			Q_i = Q.get_Q(i,active_size);
	
		for(int j=0;j<active_size;j++)
		{
			if(y[j]==+1)
			{
				if (!is_lower_bound(j))
				{
					double grad_diff=Gmax+G[j];
					if (G[j] >= Gmax2)
						Gmax2 = G[j];
					if (grad_diff > 0)
					{
						double obj_diff; 
						double quad_coef = QD[i]+QD[j]-2.0*y[i]*Q_i[j];
						if (quad_coef > 0)
							obj_diff = -(grad_diff*grad_diff)/quad_coef;
						else
							obj_diff = -(grad_diff*grad_diff)/TAU;
	
						if (obj_diff <= obj_diff_min)
						{
							Gmin_idx=j;
							obj_diff_min = obj_diff;
						}
					}
				}
			}
			else
			{
				if (!is_upper_bound(j))
				{
					double grad_diff= Gmax-G[j];
					if (-G[j] >= Gmax2)
						Gmax2 = -G[j];
					if (grad_diff > 0)
					{
						double obj_diff; 
						double quad_coef = QD[i]+QD[j]+2.0*y[i]*Q_i[j];
						if (quad_coef > 0)
							obj_diff = -(grad_diff*grad_diff)/quad_coef;
						else
							obj_diff = -(grad_diff*grad_diff)/TAU;
	
						if (obj_diff <= obj_diff_min)
						{
							Gmin_idx=j;
							obj_diff_min = obj_diff;
						}
					}
				}
			}
		}

		if(Gmax+Gmax2 < eps)
			return 1;

		working_set[0] = Gmax_idx;
		working_set[1] = Gmin_idx;
		return 0;
	}

	private boolean be_shrunk(int i, double Gmax1, double Gmax2)
	{	
		if(is_upper_bound(i))
		{
			if(y[i]==+1)
				return(-G[i] > Gmax1);
			else
				return(-G[i] > Gmax2);
		}
		else if(is_lower_bound(i))
		{
			if(y[i]==+1)
				return(G[i] > Gmax2);
			else	
				return(G[i] > Gmax1);
		}
		else
			return(false);
	}

	void do_shrinking()
	{
		int i;
		double Gmax1 = -INF;		// max { -y_i * grad(f)_i | i in I_up(\alpha) }
		double Gmax2 = -INF;		// max { y_i * grad(f)_i | i in I_low(\alpha) }

		// find maximal violating pair first
		for(i=0;i<active_size;i++)
		{
			if(y[i]==+1)
			{
				if(!is_upper_bound(i))	
				{
					if(-G[i] >= Gmax1)
						Gmax1 = -G[i];
				}
				if(!is_lower_bound(i))
				{
					if(G[i] >= Gmax2)
						Gmax2 = G[i];
				}
			}
			else		
			{
				if(!is_upper_bound(i))	
				{
					if(-G[i] >= Gmax2)
						Gmax2 = -G[i];
				}
				if(!is_lower_bound(i))	
				{
					if(G[i] >= Gmax1)
						Gmax1 = G[i];
				}
			}
		}

		if(unshrink == false && Gmax1 + Gmax2 <= eps*10) 
		{
			unshrink = true;
			reconstruct_gradient();
			active_size = l;
		}

		for(i=0;i<active_size;i++)
			if (be_shrunk(i, Gmax1, Gmax2))
			{
				active_size--;
				while (active_size > i)
				{
					if (!be_shrunk(active_size, Gmax1, Gmax2))
					{
						swap_index(i,active_size);
						break;
					}
					active_size--;
				}
			}
	}

	double calculate_rho()
	{
		double r;
		int nr_free = 0;
		double ub = INF, lb = -INF, sum_free = 0;
		for(int i=0;i<active_size;i++)
		{
			double yG = y[i]*G[i];

			if(is_lower_bound(i))
			{
				if(y[i] > 0)
					ub = Math.min(ub,yG);
				else
					lb = Math.max(lb,yG);
			}
			else if(is_upper_bound(i))
			{
				if(y[i] < 0)
					ub = Math.min(ub,yG);
				else
					lb = Math.max(lb,yG);
			}
			else
			{
				++nr_free;
				sum_free += yG;
			}
		}

		if(nr_free>0)
			r = sum_free/nr_free;
		else
			r = (ub+lb)/2;

		return r;
	}

}

//
// Solver for nu-svm classification and regression
//
// additional constraint: e^T \alpha = constant
//
final class Solver_NU extends Solver
{
	private SolutionInfo si;

	void Solve(int l, QMatrix Q, double[] p, byte[] y,
		   double[] alpha, double Cp, double Cn, double eps,
		   SolutionInfo si, int shrinking)
	{
		this.si = si;
		super.Solve(l,Q,p,y,alpha,Cp,Cn,eps,si,shrinking);
	}

	// return 1 if already optimal, return 0 otherwise
	int select_working_set(int[] working_set)
	{
		// return i,j such that y_i = y_j and
		// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
		// j: minimizes the decrease of obj value
		//    (if quadratic coefficeint <= 0, replace it with tau)
		//    -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
	
		double Gmaxp = -INF;
		double Gmaxp2 = -INF;
		int Gmaxp_idx = -1;
	
		double Gmaxn = -INF;
		double Gmaxn2 = -INF;
		int Gmaxn_idx = -1;
	
		int Gmin_idx = -1;
		double obj_diff_min = INF;
	
		for(int t=0;t<active_size;t++)
			if(y[t]==+1)
			{
				if(!is_upper_bound(t))
					if(-G[t] >= Gmaxp)
					{
						Gmaxp = -G[t];
						Gmaxp_idx = t;
					}
			}
			else
			{
				if(!is_lower_bound(t))
					if(G[t] >= Gmaxn)
					{
						Gmaxn = G[t];
						Gmaxn_idx = t;
					}
			}
	
		int ip = Gmaxp_idx;
		int in = Gmaxn_idx;
		Qfloat[] Q_ip = null;
		Qfloat[] Q_in = null;
		if(ip != -1) // null Q_ip not accessed: Gmaxp=-INF if ip=-1
			Q_ip = Q.get_Q(ip,active_size);
		if(in != -1)
			Q_in = Q.get_Q(in,active_size);
	
		for(int j=0;j<active_size;j++)
		{
			if(y[j]==+1)
			{
				if (!is_lower_bound(j))	
				{
					double grad_diff=Gmaxp+G[j];
					if (G[j] >= Gmaxp2)
						Gmaxp2 = G[j];
					if (grad_diff > 0)
					{
						double obj_diff; 
						double quad_coef = QD[ip]+QD[j]-2*Q_ip[j];
						if (quad_coef > 0)
							obj_diff = -(grad_diff*grad_diff)/quad_coef;
						else
							obj_diff = -(grad_diff*grad_diff)/TAU;
	
						if (obj_diff <= obj_diff_min)
						{
							Gmin_idx=j;
							obj_diff_min = obj_diff;
						}
					}
				}
			}
			else
			{
				if (!is_upper_bound(j))
				{
					double grad_diff=Gmaxn-G[j];
					if (-G[j] >= Gmaxn2)
						Gmaxn2 = -G[j];
					if (grad_diff > 0)
					{
						double obj_diff; 
						double quad_coef = QD[in]+QD[j]-2*Q_in[j];
						if (quad_coef > 0)
							obj_diff = -(grad_diff*grad_diff)/quad_coef;
						else
							obj_diff = -(grad_diff*grad_diff)/TAU;
	
						if (obj_diff <= obj_diff_min)
						{
							Gmin_idx=j;
							obj_diff_min = obj_diff;
						}
					}
				}
			}
		}

		if(Math.max(Gmaxp+Gmaxp2,Gmaxn+Gmaxn2) < eps)
			return 1;
	
		if(y[Gmin_idx] == +1)
			working_set[0] = Gmaxp_idx;
		else
			working_set[0] = Gmaxn_idx;
		working_set[1] = Gmin_idx;
	
		return 0;
	}

	private boolean be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4)
	{
		if(is_upper_bound(i))
		{
			if(y[i]==+1)
				return(-G[i] > Gmax1);
			else	
				return(-G[i] > Gmax4);
		}
		else if(is_lower_bound(i))
		{
			if(y[i]==+1)
				return(G[i] > Gmax2);
			else	
				return(G[i] > Gmax3);
		}
		else
			return(false);
	}

	void do_shrinking()
	{
		double Gmax1 = -INF;	// max { -y_i * grad(f)_i | y_i = +1, i in I_up(\alpha) }
		double Gmax2 = -INF;	// max { y_i * grad(f)_i | y_i = +1, i in I_low(\alpha) }
		double Gmax3 = -INF;	// max { -y_i * grad(f)_i | y_i = -1, i in I_up(\alpha) }
		double Gmax4 = -INF;	// max { y_i * grad(f)_i | y_i = -1, i in I_low(\alpha) }
 
		// find maximal violating pair first
		int i;
		for(i=0;i<active_size;i++)
		{
			if(!is_upper_bound(i))
			{
				if(y[i]==+1)
				{
					if(-G[i] > Gmax1) Gmax1 = -G[i];
				}
				else	if(-G[i] > Gmax4) Gmax4 = -G[i];
			}
			if(!is_lower_bound(i))
			{
				if(y[i]==+1)
				{	
					if(G[i] > Gmax2) Gmax2 = G[i];
				}
				else	if(G[i] > Gmax3) Gmax3 = G[i];
			}
		}

		if(unshrink == false && Math.max(Gmax1+Gmax2,Gmax3+Gmax4) <= eps*10) 
		{
			unshrink = true;
			reconstruct_gradient();
			active_size = l;
		}

		for(i=0;i<active_size;i++)
			if (be_shrunk(i, Gmax1, Gmax2, Gmax3, Gmax4))
			{
				active_size--;
				while (active_size > i)
				{
					if (!be_shrunk(active_size, Gmax1, Gmax2, Gmax3, Gmax4))
					{
						swap_index(i,active_size);
						break;
					}
					active_size--;
				}
			}
	}
	
	double calculate_rho()
	{
		int nr_free1 = 0,nr_free2 = 0;
		double ub1 = INF, ub2 = INF;
		double lb1 = -INF, lb2 = -INF;
		double sum_free1 = 0, sum_free2 = 0;

		for(int i=0;i<active_size;i++)
		{
			if(y[i]==+1)
			{
				if(is_lower_bound(i))
					ub1 = Math.min(ub1,G[i]);
				else if(is_upper_bound(i))
					lb1 = Math.max(lb1,G[i]);
				else
				{
					++nr_free1;
					sum_free1 += G[i];
				}
			}
			else
			{
				if(is_lower_bound(i))
					ub2 = Math.min(ub2,G[i]);
				else if(is_upper_bound(i))
					lb2 = Math.max(lb2,G[i]);
				else
				{
					++nr_free2;
					sum_free2 += G[i];
				}
			}
		}

		double r1,r2;
		if(nr_free1 > 0)
			r1 = sum_free1/nr_free1;
		else
			r1 = (ub1+lb1)/2;

		if(nr_free2 > 0)
			r2 = sum_free2/nr_free2;
		else
			r2 = (ub2+lb2)/2;

		si.r = (r1+r2)/2;
		return (r1-r2)/2;
	}
}

//
// Q matrices for various formulations
//
class SVC_Q extends Kernel
{
	private final byte[] y;
	private final Cache cache;
	private final double[] QD;

	SVC_Q(svm_problem prob, svm_parameter param, byte[] y_)
	{
		super(prob.l, prob.x, param);
		y = (byte[])y_.clone();
		cache = new Cache(prob.l,(long)(param.cache_size*(1<<20)));
		QD = new double[prob.l];
		for(int i=0;i<prob.l;i++)
			QD[i] = kernel_function(i,i);
	}

	Qfloat[] get_Q(int i, int len)
	{
		Qfloat[][] data = new Qfloat[1][];
		int start, j;
		if((start = cache.get_data(i,data,len)) < len)
		{
			for(j=start;j<len;j++)
				data[0][j] = (Qfloat)(y[i]*y[j]*kernel_function(i,j));
		}
		return data[0];
	}

	double[] get_QD()
	{
		return QD;
	}

	void swap_index(int i, int j)
	{
		cache.swap_index(i,j);
		super.swap_index(i,j);
		swap(byte,y[i],y[j]);
		swap(double,QD[i],QD[j]);
	}
}

class ONE_CLASS_Q extends Kernel
{
	private final Cache cache;
	private final double[] QD;

	ONE_CLASS_Q(svm_problem prob, svm_parameter param)
	{
		super(prob.l, prob.x, param);
		cache = new Cache(prob.l,(long)(param.cache_size*(1<<20)));
		QD = new double[prob.l];
		for(int i=0;i<prob.l;i++)
			QD[i] = kernel_function(i,i);
	}

	Qfloat[] get_Q(int i, int len)
	{
		Qfloat[][] data = new Qfloat[1][];
		int start, j;
		if((start = cache.get_data(i,data,len)) < len)
		{
			for(j=start;j<len;j++)
				data[0][j] = (Qfloat)kernel_function(i,j);
		}
		return data[0];
	}

	double[] get_QD()
	{
		return QD;
	}

	void swap_index(int i, int j)
	{
		cache.swap_index(i,j);
		super.swap_index(i,j);
		swap(double,QD[i],QD[j]);
	}
}

class SVR_Q extends Kernel
{
	private final int l;
	private final Cache cache;
	private final byte[] sign;
	private final int[] index;
	private int next_buffer;
	private Qfloat[][] buffer;
	private final double[] QD;

	SVR_Q(svm_problem prob, svm_parameter param)
	{
		super(prob.l, prob.x, param);
		l = prob.l;
		cache = new Cache(l,(long)(param.cache_size*(1<<20)));
		QD = new double[2*l];
		sign = new byte[2*l];
		index = new int[2*l];
		for(int k=0;k<l;k++)
		{
			sign[k] = 1;
			sign[k+l] = -1;
			index[k] = k;
			index[k+l] = k;
			QD[k] = kernel_function(k,k);
			QD[k+l] = QD[k];
		}
		buffer = new Qfloat[2][2*l];
		next_buffer = 0;
	}

	void swap_index(int i, int j)
	{
		swap(byte,sign[i],sign[j]);
		swap(int,index[i],index[j]);
		swap(double,QD[i],QD[j]);
	}

	Qfloat[] get_Q(int i, int len)
	{
		Qfloat[][] data = new Qfloat[1][];
		int j, real_i = index[i];
		if(cache.get_data(real_i,data,l) < l)
		{
			for(j=0;j<l;j++)
				data[0][j] = (Qfloat)kernel_function(real_i,j);
		}

		// reorder and copy
		Qfloat buf[] = buffer[next_buffer];
		next_buffer = 1 - next_buffer;
		byte si = sign[i];
		for(j=0;j<len;j++)
			buf[j] = (Qfloat) si * sign[j] * data[0][index[j]];
		return buf;
	}

	double[] get_QD()
	{
		return QD;
	}
}

public class svm {
	//
	// construct and solve various formulations
	//
	public static final int LIBSVM_VERSION=319; 
	public static final Random rand = new Random();

	private static svm_print_interface svm_print_stdout = new svm_print_interface()
	{
		public void print(String s)
		{
			System.out.print(s);
			System.out.flush();
		}
	};

	private static svm_print_interface svm_print_string = svm_print_stdout;

	static void info(String s) 
	{
		svm_print_string.print(s);
	}

	private static void solve_c_svc(svm_problem prob, svm_parameter param,
					double[] alpha, Solver.SolutionInfo si,
					double Cp, double Cn)
	{
		int l = prob.l;
		double[] minus_ones = new double[l];
		byte[] y = new byte[l];

		int i;

		for(i=0;i<l;i++)
		{
			alpha[i] = 0;
			minus_ones[i] = -1;
			if(prob.y[i] > 0) y[i] = +1; else y[i] = -1;
		}

		Solver s = new Solver();
		s.Solve(l, new SVC_Q(prob,param,y), minus_ones, y,
			alpha, Cp, Cn, param.eps, si, param.shrinking);

		double sum_alpha=0;
		for(i=0;i<l;i++)
			sum_alpha += alpha[i];

		if (Cp==Cn)
			svm.info("nu = "+sum_alpha/(Cp*prob.l)+"\n");

		for(i=0;i<l;i++)
			alpha[i] *= y[i];
	}

	private static void solve_nu_svc(svm_problem prob, svm_parameter param,
					double[] alpha, Solver.SolutionInfo si)
	{
		int i;
		int l = prob.l;
		double nu = param.nu;

		byte[] y = new byte[l];

		for(i=0;i<l;i++)
			if(prob.y[i]>0)
				y[i] = +1;
			else
				y[i] = -1;

		double sum_pos = nu*l/2;
		double sum_neg = nu*l/2;

		for(i=0;i<l;i++)
			if(y[i] == +1)
			{
				alpha[i] = Math.min(1.0,sum_pos);
				sum_pos -= alpha[i];
			}
			else
			{
				alpha[i] = Math.min(1.0,sum_neg);
				sum_neg -= alpha[i];
			}

		double[] zeros = new double[l];

		for(i=0;i<l;i++)
			zeros[i] = 0;

		Solver_NU s = new Solver_NU();
		s.Solve(l, new SVC_Q(prob,param,y), zeros, y,
			alpha, 1.0, 1.0, param.eps, si, param.shrinking);
		double r = si.r;

		svm.info("C = "+1/r+"\n");

		for(i=0;i<l;i++)
			alpha[i] *= y[i]/r;

		si.rho /= r;
		si.obj /= (r*r);
		si.upper_bound_p = 1/r;
		si.upper_bound_n = 1/r;
	}

	private static void solve_one_class(svm_problem prob, svm_parameter param,
					double[] alpha, Solver.SolutionInfo si)
	{
		int l = prob.l;
		double[] zeros = new double[l];
		byte[] ones = new byte[l];
		int i;

		int n = (int)(param.nu*prob.l);	// # of alpha's at upper bound

		for(i=0;i<n;i++)
			alpha[i] = 1;
		if(n<prob.l)
			alpha[n] = param.nu * prob.l - n;
		for(i=n+1;i<l;i++)
			alpha[i] = 0;

		for(i=0;i<l;i++)
		{
			zeros[i] = 0;
			ones[i] = 1;
		}

		Solver s = new Solver();
		s.Solve(l, new ONE_CLASS_Q(prob,param), zeros, ones,
			alpha, 1.0, 1.0, param.eps, si, param.shrinking);
	}

	private static void solve_epsilon_svr(svm_problem prob, svm_parameter param,
					double[] alpha, Solver.SolutionInfo si)
	{
		int l = prob.l;
		double[] alpha2 = new double[2*l];
		double[] linear_term = new double[2*l];
		byte[] y = new byte[2*l];
		int i;

		for(i=0;i<l;i++)
		{
			alpha2[i] = 0;
			linear_term[i] = param.p - prob.y[i];
			y[i] = 1;

			alpha2[i+l] = 0;
			linear_term[i+l] = param.p + prob.y[i];
			y[i+l] = -1;
		}

		Solver s = new Solver();
		s.Solve(2*l, new SVR_Q(prob,param), linear_term, y,
			alpha2, param.C, param.C, param.eps, si, param.shrinking);

		double sum_alpha = 0;
		for(i=0;i<l;i++)
		{
			alpha[i] = alpha2[i] - alpha2[i+l];
			sum_alpha += Math.abs(alpha[i]);
		}
		svm.info("nu = "+sum_alpha/(param.C*l)+"\n");
	}

	private static void solve_nu_svr(svm_problem prob, svm_parameter param,
					double[] alpha, Solver.SolutionInfo si)
	{
		int l = prob.l;
		double C = param.C;
		double[] alpha2 = new double[2*l];
		double[] linear_term = new double[2*l];
		byte[] y = new byte[2*l];
		int i;

		double sum = C * param.nu * l / 2;
		for(i=0;i<l;i++)
		{
			alpha2[i] = alpha2[i+l] = Math.min(sum,C);
			sum -= alpha2[i];
			
			linear_term[i] = - prob.y[i];
			y[i] = 1;

			linear_term[i+l] = prob.y[i];
			y[i+l] = -1;
		}

		Solver_NU s = new Solver_NU();
		s.Solve(2*l, new SVR_Q(prob,param), linear_term, y,
			alpha2, C, C, param.eps, si, param.shrinking);

		svm.info("epsilon = "+(-si.r)+"\n");
		
		for(i=0;i<l;i++)
			alpha[i] = alpha2[i] - alpha2[i+l];
	}

	//
	// decision_function
	//
	static class decision_function
	{
		double[] alpha;
		double rho;	
	};

	static decision_function svm_train_one(
		svm_problem prob, svm_parameter param,
		double Cp, double Cn)
	{
		double[] alpha = new double[prob.l];
		Solver.SolutionInfo si = new Solver.SolutionInfo();
		switch(param.svm_type)
		{
			case svm_parameter.C_SVC:
				solve_c_svc(prob,param,alpha,si,Cp,Cn);
				break;
			case svm_parameter.NU_SVC:
				solve_nu_svc(prob,param,alpha,si);
				break;
			case svm_parameter.ONE_CLASS:
				solve_one_class(prob,param,alpha,si);
				break;
			case svm_parameter.EPSILON_SVR:
				solve_epsilon_svr(prob,param,alpha,si);
				break;
			case svm_parameter.NU_SVR:
				solve_nu_svr(prob,param,alpha,si);
				break;
		}

		svm.info("obj = "+si.obj+", rho = "+si.rho+"\n");

		// output SVs

		int nSV = 0;
		int nBSV = 0;
		for(int i=0;i<prob.l;i++)
		{
			if(Math.abs(alpha[i]) > 0)
			{
				++nSV;
				if(prob.y[i] > 0)
				{
					if(Math.abs(alpha[i]) >= si.upper_bound_p)
					++nBSV;
				}
				else
				{
					if(Math.abs(alpha[i]) >= si.upper_bound_n)
						++nBSV;
				}
			}
		}

		svm.info("nSV = "+nSV+", nBSV = "+nBSV+"\n");

		decision_function f = new decision_function();
		f.alpha = alpha;
		f.rho = si.rho;
		return f;
	}

	// Platt's binary SVM Probablistic Output: an improvement from Lin et al.
	private static void sigmoid_train(int l, double[] dec_values, double[] labels, 
				  double[] probAB)
	{
		double A, B;
		double prior1=0, prior0 = 0;
		int i;

		for (i=0;i<l;i++)
			if (labels[i] > 0) prior1+=1;
			else prior0+=1;
	
		int max_iter=100;	// Maximal number of iterations
		double min_step=1e-10;	// Minimal step taken in line search
		double sigma=1e-12;	// For numerically strict PD of Hessian
		double eps=1e-5;
		double hiTarget=(prior1+1.0)/(prior1+2.0);
		double loTarget=1/(prior0+2.0);
		double[] t= new double[l];
		double fApB,p,q,h11,h22,h21,g1,g2,det,dA,dB,gd,stepsize;
		double newA,newB,newf,d1,d2;
		int iter; 
	
		// Initial Point and Initial Fun Value
		A=0.0; B=Math.log((prior0+1.0)/(prior1+1.0));
		double fval = 0.0;

		for (i=0;i<l;i++)
		{
			if (labels[i]>0) t[i]=hiTarget;
			else t[i]=loTarget;
			fApB = dec_values[i]*A+B;
			if (fApB>=0)
				fval += t[i]*fApB + Math.log(1+Math.exp(-fApB));
			else
				fval += (t[i] - 1)*fApB +Math.log(1+Math.exp(fApB));
		}
		for (iter=0;iter<max_iter;iter++)
		{
			// Update Gradient and Hessian (use H' = H + sigma I)
			h11=sigma; // numerically ensures strict PD
			h22=sigma;
			h21=0.0;g1=0.0;g2=0.0;
			for (i=0;i<l;i++)
			{
				fApB = dec_values[i]*A+B;
				if (fApB >= 0)
				{
					p=Math.exp(-fApB)/(1.0+Math.exp(-fApB));
					q=1.0/(1.0+Math.exp(-fApB));
				}
				else
				{
					p=1.0/(1.0+Math.exp(fApB));
					q=Math.exp(fApB)/(1.0+Math.exp(fApB));
				}
				d2=p*q;
				h11+=dec_values[i]*dec_values[i]*d2;
				h22+=d2;
				h21+=dec_values[i]*d2;
				d1=t[i]-p;
				g1+=dec_values[i]*d1;
				g2+=d1;
			}

			// Stopping Criteria
			if (Math.abs(g1)<eps && Math.abs(g2)<eps)
				break;
			
			// Finding Newton direction: -inv(H') * g
			det=h11*h22-h21*h21;
			dA=-(h22*g1 - h21 * g2) / det;
			dB=-(-h21*g1+ h11 * g2) / det;
			gd=g1*dA+g2*dB;


			stepsize = 1;		// Line Search
			while (stepsize >= min_step)
			{
				newA = A + stepsize * dA;
				newB = B + stepsize * dB;

				// New function value
				newf = 0.0;
				for (i=0;i<l;i++)
				{
					fApB = dec_values[i]*newA+newB;
					if (fApB >= 0)
						newf += t[i]*fApB + Math.log(1+Math.exp(-fApB));
					else
						newf += (t[i] - 1)*fApB +Math.log(1+Math.exp(fApB));
				}
				// Check sufficient decrease
				if (newf<fval+0.0001*stepsize*gd)
				{
					A=newA;B=newB;fval=newf;
					break;
				}
				else
					stepsize = stepsize / 2.0;
			}
			
			if (stepsize < min_step)
			{
				svm.info("Line search fails in two-class probability estimates\n");
				break;
			}
		}
		
		if (iter>=max_iter)
			svm.info("Reaching maximal iterations in two-class probability estimates\n");
		probAB[0]=A;probAB[1]=B;
	}

	private static double sigmoid_predict(double decision_value, double A, double B)
	{
		double fApB = decision_value*A+B;
		if (fApB >= 0)
			return Math.exp(-fApB)/(1.0+Math.exp(-fApB));
		else
			return 1.0/(1+Math.exp(fApB)) ;
	}

	// Method 2 from the multiclass_prob paper by Wu, Lin, and Weng
	private static void multiclass_probability(int k, double[][] r, double[] p)
	{
		int t,j;
		int iter = 0, max_iter=Math.max(100,k);
		double[][] Q=new double[k][k];
		double[] Qp=new double[k];
		double pQp, eps=0.005/k;
	
		for (t=0;t<k;t++)
		{
			p[t]=1.0/k;  // Valid if k = 1
			Q[t][t]=0;
			for (j=0;j<t;j++)
			{
				Q[t][t]+=r[j][t]*r[j][t];
				Q[t][j]=Q[j][t];
			}
			for (j=t+1;j<k;j++)
			{
				Q[t][t]+=r[j][t]*r[j][t];
				Q[t][j]=-r[j][t]*r[t][j];
			}
		}
		for (iter=0;iter<max_iter;iter++)
		{
			// stopping condition, recalculate QP,pQP for numerical accuracy
			pQp=0;
			for (t=0;t<k;t++)
			{
				Qp[t]=0;
				for (j=0;j<k;j++)
					Qp[t]+=Q[t][j]*p[j];
				pQp+=p[t]*Qp[t];
			}
			double max_error=0;
			for (t=0;t<k;t++)
			{
				double error=Math.abs(Qp[t]-pQp);
				if (error>max_error)
					max_error=error;
			}
			if (max_error<eps) break;
		
			for (t=0;t<k;t++)
			{
				double diff=(-Qp[t]+pQp)/Q[t][t];
				p[t]+=diff;
				pQp=(pQp+diff*(diff*Q[t][t]+2*Qp[t]))/(1+diff)/(1+diff);
				for (j=0;j<k;j++)
				{
					Qp[j]=(Qp[j]+diff*Q[t][j])/(1+diff);
					p[j]/=(1+diff);
				}
			}
		}
		if (iter>=max_iter)
			svm.info("Exceeds max_iter in multiclass_prob\n");
	}

	// Cross-validation decision values for probability estimates
	private static void svm_binary_svc_probability(svm_problem prob, svm_parameter param, double Cp, double Cn, double[] probAB)
	{
		int i;
		int nr_fold = 5;
		int[] perm = new int[prob.l];
		double[] dec_values = new double[prob.l];

		// random shuffle
		for(i=0;i<prob.l;i++) perm[i]=i;
		for(i=0;i<prob.l;i++)
		{
			int j = i+rand.nextInt(prob.l-i);
			swap(int,perm[i],perm[j]);
		}
		for(i=0;i<nr_fold;i++)
		{
			int begin = i*prob.l/nr_fold;
			int end = (i+1)*prob.l/nr_fold;
			int j,k;
			svm_problem subprob = new svm_problem();

			subprob.l = prob.l-(end-begin);
			subprob.x = new svm_node[subprob.l][];
			subprob.y = new double[subprob.l];
			
			k=0;
			for(j=0;j<begin;j++)
			{
				subprob.x[k] = prob.x[perm[j]];
				subprob.y[k] = prob.y[perm[j]];
				++k;
			}
			for(j=end;j<prob.l;j++)
			{
				subprob.x[k] = prob.x[perm[j]];
				subprob.y[k] = prob.y[perm[j]];
				++k;
			}
			int p_count=0,n_count=0;
			for(j=0;j<k;j++)
				if(subprob.y[j]>0)
					p_count++;
				else
					n_count++;
			
			if(p_count==0 && n_count==0)
				for(j=begin;j<end;j++)
					dec_values[perm[j]] = 0;
			else if(p_count > 0 && n_count == 0)
				for(j=begin;j<end;j++)
					dec_values[perm[j]] = 1;
			else if(p_count == 0 && n_count > 0)
				for(j=begin;j<end;j++)
					dec_values[perm[j]] = -1;
			else
			{
				svm_parameter subparam = (svm_parameter)param.clone();
				subparam.probability=0;
				subparam.C=1.0;
				subparam.nr_weight=2;
				subparam.weight_label = new int[2];
				subparam.weight = new double[2];
				subparam.weight_label[0]=+1;
				subparam.weight_label[1]=-1;
				subparam.weight[0]=Cp;
				subparam.weight[1]=Cn;
				svm_model submodel = svm_train(subprob,subparam);
				for(j=begin;j<end;j++)
				{
					double[] dec_value=new double[1];
					svm_predict_values(submodel,prob.x[perm[j]],dec_value);
					dec_values[perm[j]]=dec_value[0];
					// ensure +1 -1 order; reason not using CV subroutine
					dec_values[perm[j]] *= submodel.label[0];
				}		
			}
		}		
		sigmoid_train(prob.l,dec_values,prob.y,probAB);
	}

	// Return parameter of a Laplace distribution 
	private static double svm_svr_probability(svm_problem prob, svm_parameter param)
	{
		int i;
		int nr_fold = 5;
		double[] ymv = new double[prob.l];
		double mae = 0;

		svm_parameter newparam = (svm_parameter)param.clone();
		newparam.probability = 0;
		svm_cross_validation(prob,newparam,nr_fold,ymv);
		for(i=0;i<prob.l;i++)
		{
			ymv[i]=prob.y[i]-ymv[i];
			mae += Math.abs(ymv[i]);
		}		
		mae /= prob.l;
		double std=Math.sqrt(2*mae*mae);
		int count=0;
		mae=0;
		for(i=0;i<prob.l;i++)
			if (Math.abs(ymv[i]) > 5*std) 
				count=count+1;
			else 
				mae+=Math.abs(ymv[i]);
		mae /= (prob.l-count);
		svm.info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma="+mae+"\n");
		return mae;
	}

	// label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data
	// perm, length l, must be allocated before calling this subroutine
	private static void svm_group_classes(svm_problem prob, int[] nr_class_ret, int[][] label_ret, int[][] start_ret, int[][] count_ret, int[] perm)
	{
		int l = prob.l;
		int max_nr_class = 16;
		int nr_class = 0;
		int[] label = new int[max_nr_class];
		int[] count = new int[max_nr_class];
		int[] data_label = new int[l];
		int i;

		for(i=0;i<l;i++)
		{
			int this_label = (int)(prob.y[i]);
			int j;
			for(j=0;j<nr_class;j++)
			{
				if(this_label == label[j])
				{
					++count[j];
					break;
				}
			}
			data_label[i] = j;
			if(j == nr_class)
			{
				if(nr_class == max_nr_class)
				{
					max_nr_class *= 2;
					int[] new_data = new int[max_nr_class];
					System.arraycopy(label,0,new_data,0,label.length);
					label = new_data;
					new_data = new int[max_nr_class];
					System.arraycopy(count,0,new_data,0,count.length);
					count = new_data;					
				}
				label[nr_class] = this_label;
				count[nr_class] = 1;
				++nr_class;
			}
		}

		//
		// Labels are ordered by their first occurrence in the training set. 
		// However, for two-class sets with -1/+1 labels and -1 appears first, 
		// we swap labels to ensure that internally the binary SVM has positive data corresponding to the +1 instances.
		//
		if (nr_class == 2 && label[0] == -1 && label[1] == +1)
		{
			swap(int,label[0],label[1]);
			swap(int,count[0],count[1]);
			for(i=0;i<l;i++)
			{
				if(data_label[i] == 0)
					data_label[i] = 1;
				else
					data_label[i] = 0;
			}
		}

		int[] start = new int[nr_class];
		start[0] = 0;
		for(i=1;i<nr_class;i++)
			start[i] = start[i-1]+count[i-1];
		for(i=0;i<l;i++)
		{
			perm[start[data_label[i]]] = i;
			++start[data_label[i]];
		}
		start[0] = 0;
		for(i=1;i<nr_class;i++)
			start[i] = start[i-1]+count[i-1];

		nr_class_ret[0] = nr_class;
		label_ret[0] = label;
		start_ret[0] = start;
		count_ret[0] = count;
	}

	//
	// Interface functions
	//
	public static svm_model svm_train(svm_problem prob, svm_parameter param)
	{
		svm_model model = new svm_model();
		model.param = param;

		if(param.svm_type == svm_parameter.ONE_CLASS ||
		   param.svm_type == svm_parameter.EPSILON_SVR ||
		   param.svm_type == svm_parameter.NU_SVR)
		{
			// regression or one-class-svm
			model.nr_class = 2;
			model.label = null;
			model.nSV = null;
			model.probA = null; model.probB = null;
			model.sv_coef = new double[1][];

			if(param.probability == 1 &&
			   (param.svm_type == svm_parameter.EPSILON_SVR ||
			    param.svm_type == svm_parameter.NU_SVR))
			{
				model.probA = new double[1];
				model.probA[0] = svm_svr_probability(prob,param);
			}

			decision_function f = svm_train_one(prob,param,0,0);
			model.rho = new double[1];
			model.rho[0] = f.rho;

			int nSV = 0;
			int i;
			for(i=0;i<prob.l;i++)
				if(Math.abs(f.alpha[i]) > 0) ++nSV;
			model.l = nSV;
			model.SV = new svm_node[nSV][];
			model.sv_coef[0] = new double[nSV];
			model.sv_indices = new int[nSV];
			int j = 0;
			for(i=0;i<prob.l;i++)
				if(Math.abs(f.alpha[i]) > 0)
				{
					model.SV[j] = prob.x[i];
					model.sv_coef[0][j] = f.alpha[i];
					model.sv_indices[j] = i+1;
					++j;
				}
		}
		else
		{
			// classification
			int l = prob.l;
			int[] tmp_nr_class = new int[1];
			int[][] tmp_label = new int[1][];
			int[][] tmp_start = new int[1][];
			int[][] tmp_count = new int[1][];			
			int[] perm = new int[l];

			// group training data of the same class
			svm_group_classes(prob,tmp_nr_class,tmp_label,tmp_start,tmp_count,perm);
			int nr_class = tmp_nr_class[0];			
			int[] label = tmp_label[0];
			int[] start = tmp_start[0];
			int[] count = tmp_count[0];
 			
			if(nr_class == 1) 
				svm.info("WARNING: training data in only one class. See README for details.\n");
			
			svm_node[][] x = new svm_node[l][];
			int i;
			for(i=0;i<l;i++)
				x[i] = prob.x[perm[i]];

			// calculate weighted C

			double[] weighted_C = new double[nr_class];
			for(i=0;i<nr_class;i++)
				weighted_C[i] = param.C;
			for(i=0;i<param.nr_weight;i++)
			{
				int j;
				for(j=0;j<nr_class;j++)
					if(param.weight_label[i] == label[j])
						break;
				if(j == nr_class)
					System.err.print("WARNING: class label "+param.weight_label[i]+" specified in weight is not found\n");
				else
					weighted_C[j] *= param.weight[i];
			}

			// train k*(k-1)/2 models

			boolean[] nonzero = new boolean[l];
			for(i=0;i<l;i++)
				nonzero[i] = false;
			decision_function[] f = new decision_function[nr_class*(nr_class-1)/2];

			double[] probA=null,probB=null;
			if (param.probability == 1)
			{
				probA=new double[nr_class*(nr_class-1)/2];
				probB=new double[nr_class*(nr_class-1)/2];
			}

			int p = 0;
			for(i=0;i<nr_class;i++)
				for(int j=i+1;j<nr_class;j++)
				{
					svm_problem sub_prob = new svm_problem();
					int si = start[i], sj = start[j];
					int ci = count[i], cj = count[j];
					sub_prob.l = ci+cj;
					sub_prob.x = new svm_node[sub_prob.l][];
					sub_prob.y = new double[sub_prob.l];
					int k;
					for(k=0;k<ci;k++)
					{
						sub_prob.x[k] = x[si+k];
						sub_prob.y[k] = +1;
					}
					for(k=0;k<cj;k++)
					{
						sub_prob.x[ci+k] = x[sj+k];
						sub_prob.y[ci+k] = -1;
					}

					if(param.probability == 1)
					{
						double[] probAB=new double[2];
						svm_binary_svc_probability(sub_prob,param,weighted_C[i],weighted_C[j],probAB);
						probA[p]=probAB[0];
						probB[p]=probAB[1];
					}

					f[p] = svm_train_one(sub_prob,param,weighted_C[i],weighted_C[j]);
					for(k=0;k<ci;k++)
						if(!nonzero[si+k] && Math.abs(f[p].alpha[k]) > 0)
							nonzero[si+k] = true;
					for(k=0;k<cj;k++)
						if(!nonzero[sj+k] && Math.abs(f[p].alpha[ci+k]) > 0)
							nonzero[sj+k] = true;
					++p;
				}

			// build output

			model.nr_class = nr_class;

			model.label = new int[nr_class];
			for(i=0;i<nr_class;i++)
				model.label[i] = label[i];

			model.rho = new double[nr_class*(nr_class-1)/2];
			for(i=0;i<nr_class*(nr_class-1)/2;i++)
				model.rho[i] = f[i].rho;

			if(param.probability == 1)
			{
				model.probA = new double[nr_class*(nr_class-1)/2];
				model.probB = new double[nr_class*(nr_class-1)/2];
				for(i=0;i<nr_class*(nr_class-1)/2;i++)
				{
					model.probA[i] = probA[i];
					model.probB[i] = probB[i];
				}
			}
			else
			{
				model.probA=null;
				model.probB=null;
			}

			int nnz = 0;
			int[] nz_count = new int[nr_class];
			model.nSV = new int[nr_class];
			for(i=0;i<nr_class;i++)
			{
				int nSV = 0;
				for(int j=0;j<count[i];j++)
					if(nonzero[start[i]+j])
					{
						++nSV;
						++nnz;
					}
				model.nSV[i] = nSV;
				nz_count[i] = nSV;
			}

			svm.info("Total nSV = "+nnz+"\n");

			model.l = nnz;
			model.SV = new svm_node[nnz][];
			model.sv_indices = new int[nnz];
			p = 0;
			for(i=0;i<l;i++)
				if(nonzero[i])
				{
					model.SV[p] = x[i];
					model.sv_indices[p++] = perm[i] + 1;
				}

			int[] nz_start = new int[nr_class];
			nz_start[0] = 0;
			for(i=1;i<nr_class;i++)
				nz_start[i] = nz_start[i-1]+nz_count[i-1];

			model.sv_coef = new double[nr_class-1][];
			for(i=0;i<nr_class-1;i++)
				model.sv_coef[i] = new double[nnz];

			p = 0;
			for(i=0;i<nr_class;i++)
				for(int j=i+1;j<nr_class;j++)
				{
					// classifier (i,j): coefficients with
					// i are in sv_coef[j-1][nz_start[i]...],
					// j are in sv_coef[i][nz_start[j]...]

					int si = start[i];
					int sj = start[j];
					int ci = count[i];
					int cj = count[j];

					int q = nz_start[i];
					int k;
					for(k=0;k<ci;k++)
						if(nonzero[si+k])
							model.sv_coef[j-1][q++] = f[p].alpha[k];
					q = nz_start[j];
					for(k=0;k<cj;k++)
						if(nonzero[sj+k])
							model.sv_coef[i][q++] = f[p].alpha[ci+k];
					++p;
				}
		}
		return model;
	}
	
	// Stratified cross validation
	public static void svm_cross_validation(svm_problem prob, svm_parameter param, int nr_fold, double[] target)
	{
		int i;
		int[] fold_start = new int[nr_fold+1];
		int l = prob.l;
		int[] perm = new int[l];
		
		// stratified cv may not give leave-one-out rate
		// Each class to l folds -> some folds may have zero elements
		if((param.svm_type == svm_parameter.C_SVC ||
		    param.svm_type == svm_parameter.NU_SVC) && nr_fold < l)
		{
			int[] tmp_nr_class = new int[1];
			int[][] tmp_label = new int[1][];
			int[][] tmp_start = new int[1][];
			int[][] tmp_count = new int[1][];

			svm_group_classes(prob,tmp_nr_class,tmp_label,tmp_start,tmp_count,perm);

			int nr_class = tmp_nr_class[0];
			int[] start = tmp_start[0];
			int[] count = tmp_count[0];		

			// random shuffle and then data grouped by fold using the array perm
			int[] fold_count = new int[nr_fold];
			int c;
			int[] index = new int[l];
			for(i=0;i<l;i++)
				index[i]=perm[i];
			for (c=0; c<nr_class; c++)
				for(i=0;i<count[c];i++)
				{
					int j = i+rand.nextInt(count[c]-i);
					swap(int,index[start[c]+j],index[start[c]+i]);
				}
			for(i=0;i<nr_fold;i++)
			{
				fold_count[i] = 0;
				for (c=0; c<nr_class;c++)
					fold_count[i]+=(i+1)*count[c]/nr_fold-i*count[c]/nr_fold;
			}
			fold_start[0]=0;
			for (i=1;i<=nr_fold;i++)
				fold_start[i] = fold_start[i-1]+fold_count[i-1];
			for (c=0; c<nr_class;c++)
				for(i=0;i<nr_fold;i++)
				{
					int begin = start[c]+i*count[c]/nr_fold;
					int end = start[c]+(i+1)*count[c]/nr_fold;
					for(int j=begin;j<end;j++)
					{
						perm[fold_start[i]] = index[j];
						fold_start[i]++;
					}
				}
			fold_start[0]=0;
			for (i=1;i<=nr_fold;i++)
				fold_start[i] = fold_start[i-1]+fold_count[i-1];
		}
		else
		{
			for(i=0;i<l;i++) perm[i]=i;
			for(i=0;i<l;i++)
			{
				int j = i+rand.nextInt(l-i);
				swap(int,perm[i],perm[j]);
			}
			for(i=0;i<=nr_fold;i++)
				fold_start[i]=i*l/nr_fold;
		}

		for(i=0;i<nr_fold;i++)
		{
			int begin = fold_start[i];
			int end = fold_start[i+1];
			int j,k;
			svm_problem subprob = new svm_problem();

			subprob.l = l-(end-begin);
			subprob.x = new svm_node[subprob.l][];
			subprob.y = new double[subprob.l];

			k=0;
			for(j=0;j<begin;j++)
			{
				subprob.x[k] = prob.x[perm[j]];
				subprob.y[k] = prob.y[perm[j]];
				++k;
			}
			for(j=end;j<l;j++)
			{
				subprob.x[k] = prob.x[perm[j]];
				subprob.y[k] = prob.y[perm[j]];
				++k;
			}
			svm_model submodel = svm_train(subprob,param);
			if(param.probability==1 &&
			   (param.svm_type == svm_parameter.C_SVC ||
			    param.svm_type == svm_parameter.NU_SVC))
			{
				double[] prob_estimates= new double[svm_get_nr_class(submodel)];
				for(j=begin;j<end;j++)
					target[perm[j]] = svm_predict_probability(submodel,prob.x[perm[j]],prob_estimates);
			}
			else
				for(j=begin;j<end;j++)
					target[perm[j]] = svm_predict(submodel,prob.x[perm[j]]);
		}
	}

	public static int svm_get_svm_type(svm_model model)
	{
		return model.param.svm_type;
	}

	public static int svm_get_nr_class(svm_model model)
	{
		return model.nr_class;
	}

	public static void svm_get_labels(svm_model model, int[] label)
	{
		if (model.label != null)
			for(int i=0;i<model.nr_class;i++)
				label[i] = model.label[i];
	}

	public static void svm_get_sv_indices(svm_model model, int[] indices)
	{
		if (model.sv_indices != null)
			for(int i=0;i<model.l;i++)
				indices[i] = model.sv_indices[i];
	}

	public static int svm_get_nr_sv(svm_model model)
	{
		return model.l;
	}

	public static double svm_get_svr_probability(svm_model model)
	{
		if ((model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR) &&
		    model.probA!=null)
		return model.probA[0];
		else
		{
			System.err.print("Model doesn't contain information for SVR probability inference\n");
			return 0;
		}
	}

	public static double svm_predict_values(svm_model model, svm_node[] x, double[] dec_values)
	{
		int i;
		if(model.param.svm_type == svm_parameter.ONE_CLASS ||
		   model.param.svm_type == svm_parameter.EPSILON_SVR ||
		   model.param.svm_type == svm_parameter.NU_SVR)
		{
			double[] sv_coef = model.sv_coef[0];
			double sum = 0;
			for(i=0;i<model.l;i++)
				sum += sv_coef[i] * Kernel.k_function(x,model.SV[i],model.param);
			sum -= model.rho[0];
			dec_values[0] = sum;

			if(model.param.svm_type == svm_parameter.ONE_CLASS)
				return (sum>0)?1:-1;
			else
				return sum;
		}
		else
		{
			int nr_class = model.nr_class;
			int l = model.l;
		
			double[] kvalue = new double[l];
			for(i=0;i<l;i++)
				kvalue[i] = Kernel.k_function(x,model.SV[i],model.param);

			int[] start = new int[nr_class];
			start[0] = 0;
			for(i=1;i<nr_class;i++)
				start[i] = start[i-1]+model.nSV[i-1];

			int[] vote = new int[nr_class];
			for(i=0;i<nr_class;i++)
				vote[i] = 0;

			int p=0;
			for(i=0;i<nr_class;i++)
				for(int j=i+1;j<nr_class;j++)
				{
					double sum = 0;
					int si = start[i];
					int sj = start[j];
					int ci = model.nSV[i];
					int cj = model.nSV[j];
				
					int k;
					double[] coef1 = model.sv_coef[j-1];
					double[] coef2 = model.sv_coef[i];
					for(k=0;k<ci;k++)
						sum += coef1[si+k] * kvalue[si+k];
					for(k=0;k<cj;k++)
						sum += coef2[sj+k] * kvalue[sj+k];
					sum -= model.rho[p];
					dec_values[p] = sum;					

					if(dec_values[p] > 0)
						++vote[i];
					else
						++vote[j];
					p++;
				}

			int vote_max_idx = 0;
			for(i=1;i<nr_class;i++)
				if(vote[i] > vote[vote_max_idx])
					vote_max_idx = i;

			return model.label[vote_max_idx];
		}
	}

	public static double svm_predict(svm_model model, svm_node[] x)
	{
		int nr_class = model.nr_class;
		double[] dec_values;
		if(model.param.svm_type == svm_parameter.ONE_CLASS ||
				model.param.svm_type == svm_parameter.EPSILON_SVR ||
				model.param.svm_type == svm_parameter.NU_SVR)
			dec_values = new double[1];
		else
			dec_values = new double[nr_class*(nr_class-1)/2];
		double pred_result = svm_predict_values(model, x, dec_values);
		return pred_result;
	}

	public static double svm_predict_probability(svm_model model, svm_node[] x, double[] prob_estimates)
	{
		if ((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) &&
		    model.probA!=null && model.probB!=null)
		{
			int i;
			int nr_class = model.nr_class;
			double[] dec_values = new double[nr_class*(nr_class-1)/2];
			svm_predict_values(model, x, dec_values);

			double min_prob=1e-7;
			double[][] pairwise_prob=new double[nr_class][nr_class];
			
			int k=0;
			for(i=0;i<nr_class;i++)
				for(int j=i+1;j<nr_class;j++)
				{
					pairwise_prob[i][j]=Math.min(Math.max(sigmoid_predict(dec_values[k],model.probA[k],model.probB[k]),min_prob),1-min_prob);
					pairwise_prob[j][i]=1-pairwise_prob[i][j];
					k++;
				}
			multiclass_probability(nr_class,pairwise_prob,prob_estimates);

			int prob_max_idx = 0;
			for(i=1;i<nr_class;i++)
				if(prob_estimates[i] > prob_estimates[prob_max_idx])
					prob_max_idx = i;
			return model.label[prob_max_idx];
		}
		else 
			return svm_predict(model, x);
	}

	static final String svm_type_table[] =
	{
		"c_svc","nu_svc","one_class","epsilon_svr","nu_svr",
	};

	static final String kernel_type_table[]=
	{
		"linear","polynomial","rbf","sigmoid","precomputed"
	};

	public static void svm_save_model(String model_file_name, svm_model model) throws IOException
	{
		DataOutputStream fp = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(model_file_name)));

		svm_parameter param = model.param;

		fp.writeBytes("svm_type "+svm_type_table[param.svm_type]+"\n");
		fp.writeBytes("kernel_type "+kernel_type_table[param.kernel_type]+"\n");

		if(param.kernel_type == svm_parameter.POLY)
			fp.writeBytes("degree "+param.degree+"\n");

		if(param.kernel_type == svm_parameter.POLY ||
		   param.kernel_type == svm_parameter.RBF ||
		   param.kernel_type == svm_parameter.SIGMOID)
			fp.writeBytes("gamma "+param.gamma+"\n");

		if(param.kernel_type == svm_parameter.POLY ||
		   param.kernel_type == svm_parameter.SIGMOID)
			fp.writeBytes("coef0 "+param.coef0+"\n");

		int nr_class = model.nr_class;
		int l = model.l;
		fp.writeBytes("nr_class "+nr_class+"\n");
		fp.writeBytes("total_sv "+l+"\n");
	
		{
			fp.writeBytes("rho");
			for(int i=0;i<nr_class*(nr_class-1)/2;i++)
				fp.writeBytes(" "+model.rho[i]);
			fp.writeBytes("\n");
		}
	
		if(model.label != null)
		{
			fp.writeBytes("label");
			for(int i=0;i<nr_class;i++)
				fp.writeBytes(" "+model.label[i]);
			fp.writeBytes("\n");
		}

		if(model.probA != null) // regression has probA only
		{
			fp.writeBytes("probA");
			for(int i=0;i<nr_class*(nr_class-1)/2;i++)
				fp.writeBytes(" "+model.probA[i]);
			fp.writeBytes("\n");
		}
		if(model.probB != null) 
		{
			fp.writeBytes("probB");
			for(int i=0;i<nr_class*(nr_class-1)/2;i++)
				fp.writeBytes(" "+model.probB[i]);
			fp.writeBytes("\n");
		}

		if(model.nSV != null)
		{
			fp.writeBytes("nr_sv");
			for(int i=0;i<nr_class;i++)
				fp.writeBytes(" "+model.nSV[i]);
			fp.writeBytes("\n");
		}

		fp.writeBytes("SV\n");
		double[][] sv_coef = model.sv_coef;
		svm_node[][] SV = model.SV;

		for(int i=0;i<l;i++)
		{
			for(int j=0;j<nr_class-1;j++)
				fp.writeBytes(sv_coef[j][i]+" ");

			svm_node[] p = SV[i];
			if(param.kernel_type == svm_parameter.PRECOMPUTED)
				fp.writeBytes("0:"+(int)(p[0].value));
			else	
				for(int j=0;j<p.length;j++)
					fp.writeBytes(p[j].index+":"+p[j].value+" ");
			fp.writeBytes("\n");
		}

		fp.close();
	}

	private static double atof(String s)
	{
		return Double.valueOf(s).doubleValue();
	}

	private static int atoi(String s)
	{
		return Integer.parseInt(s);
	}

	private static boolean read_model_header(BufferedReader fp, svm_model model)
	{
		svm_parameter param = new svm_parameter();
		model.param = param;
		try
		{
			while(true)
			{
				String cmd = fp.readLine();
				String arg = cmd.substring(cmd.indexOf(' ')+1);

				if(cmd.startsWith("svm_type"))
				{
					int i;
					for(i=0;i<svm_type_table.length;i++)
					{
						if(arg.indexOf(svm_type_table[i])!=-1)
						{
							param.svm_type=i;
							break;
						}
					}
					if(i == svm_type_table.length)
					{
						System.err.print("unknown svm type.\n");
						return false;
					}
				}
				else if(cmd.startsWith("kernel_type"))
				{
					int i;
					for(i=0;i<kernel_type_table.length;i++)
					{
						if(arg.indexOf(kernel_type_table[i])!=-1)
						{
							param.kernel_type=i;
							break;
						}
					}
					if(i == kernel_type_table.length)
					{
						System.err.print("unknown kernel function.\n");
						return false;
					}
				}
				else if(cmd.startsWith("degree"))
					param.degree = atoi(arg);
				else if(cmd.startsWith("gamma"))
					param.gamma = atof(arg);
				else if(cmd.startsWith("coef0"))
					param.coef0 = atof(arg);
				else if(cmd.startsWith("nr_class"))
					model.nr_class = atoi(arg);
				else if(cmd.startsWith("total_sv"))
					model.l = atoi(arg);
				else if(cmd.startsWith("rho"))
				{
					int n = model.nr_class * (model.nr_class-1)/2;
					model.rho = new double[n];
					StringTokenizer st = new StringTokenizer(arg);
					for(int i=0;i<n;i++)
						model.rho[i] = atof(st.nextToken());
				}
				else if(cmd.startsWith("label"))
				{
					int n = model.nr_class;
					model.label = new int[n];
					StringTokenizer st = new StringTokenizer(arg);
					for(int i=0;i<n;i++)
						model.label[i] = atoi(st.nextToken());					
				}
				else if(cmd.startsWith("probA"))
				{
					int n = model.nr_class*(model.nr_class-1)/2;
					model.probA = new double[n];
					StringTokenizer st = new StringTokenizer(arg);
					for(int i=0;i<n;i++)
						model.probA[i] = atof(st.nextToken());					
				}
				else if(cmd.startsWith("probB"))
				{
					int n = model.nr_class*(model.nr_class-1)/2;
					model.probB = new double[n];
					StringTokenizer st = new StringTokenizer(arg);
					for(int i=0;i<n;i++)
						model.probB[i] = atof(st.nextToken());					
				}
				else if(cmd.startsWith("nr_sv"))
				{
					int n = model.nr_class;
					model.nSV = new int[n];
					StringTokenizer st = new StringTokenizer(arg);
					for(int i=0;i<n;i++)
						model.nSV[i] = atoi(st.nextToken());
				}
				else if(cmd.startsWith("SV"))
				{
					break;
				}
				else
				{
					System.err.print("unknown text in model file: ["+cmd+"]\n");
					return false;
				}
			}
		}
		catch(Exception e)
		{
			return false;
		}
		return true;
	}

	public static svm_model svm_load_model(String model_file_name) throws IOException
	{
		return svm_load_model(new BufferedReader(new FileReader(model_file_name)));
	}

	public static svm_model svm_load_model(BufferedReader fp) throws IOException
	{
		// read parameters

		svm_model model = new svm_model();
		model.rho = null;
		model.probA = null;
		model.probB = null;
		model.label = null;
		model.nSV = null;

		if (read_model_header(fp, model) == false)
		{
			System.err.print("ERROR: failed to read model\n");
			return null;
		}

		// read sv_coef and SV

		int m = model.nr_class - 1;
		int l = model.l;
		model.sv_coef = new double[m][l];
		model.SV = new svm_node[l][];

		for(int i=0;i<l;i++)
		{
			String line = fp.readLine();
			StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");

			for(int k=0;k<m;k++)
				model.sv_coef[k][i] = atof(st.nextToken());
			int n = st.countTokens()/2;
			model.SV[i] = new svm_node[n];
			for(int j=0;j<n;j++)
			{
				model.SV[i][j] = new svm_node();
				model.SV[i][j].index = atoi(st.nextToken());
				model.SV[i][j].value = atof(st.nextToken());
			}
		}

		fp.close();
		return model;
	}

	public static String svm_check_parameter(svm_problem prob, svm_parameter param)
	{
		// svm_type

		int svm_type = param.svm_type;
		if(svm_type != svm_parameter.C_SVC &&
		   svm_type != svm_parameter.NU_SVC &&
		   svm_type != svm_parameter.ONE_CLASS &&
		   svm_type != svm_parameter.EPSILON_SVR &&
		   svm_type != svm_parameter.NU_SVR)
		return "unknown svm type";

		// kernel_type, degree
	
		int kernel_type = param.kernel_type;
		if(kernel_type != svm_parameter.LINEAR &&
		   kernel_type != svm_parameter.POLY &&
		   kernel_type != svm_parameter.RBF &&
		   kernel_type != svm_parameter.SIGMOID &&
		   kernel_type != svm_parameter.PRECOMPUTED)
			return "unknown kernel type";

		if(param.gamma < 0)
			return "gamma < 0";

		if(param.degree < 0)
			return "degree of polynomial kernel < 0";

		// cache_size,eps,C,nu,p,shrinking

		if(param.cache_size <= 0)
			return "cache_size <= 0";

		if(param.eps <= 0)
			return "eps <= 0";

		if(svm_type == svm_parameter.C_SVC ||
		   svm_type == svm_parameter.EPSILON_SVR ||
		   svm_type == svm_parameter.NU_SVR)
			if(param.C <= 0)
				return "C <= 0";

		if(svm_type == svm_parameter.NU_SVC ||
		   svm_type == svm_parameter.ONE_CLASS ||
		   svm_type == svm_parameter.NU_SVR)
			if(param.nu <= 0 || param.nu > 1)
				return "nu <= 0 or nu > 1";

		if(svm_type == svm_parameter.EPSILON_SVR)
			if(param.p < 0)
				return "p < 0";

		if(param.shrinking != 0 &&
		   param.shrinking != 1)
			return "shrinking != 0 and shrinking != 1";

		if(param.probability != 0 &&
		   param.probability != 1)
			return "probability != 0 and probability != 1";

		if(param.probability == 1 &&
		   svm_type == svm_parameter.ONE_CLASS)
			return "one-class SVM probability output not supported yet";
		
		// check whether nu-svc is feasible
	
		if(svm_type == svm_parameter.NU_SVC)
		{
			int l = prob.l;
			int max_nr_class = 16;
			int nr_class = 0;
			int[] label = new int[max_nr_class];
			int[] count = new int[max_nr_class];

			int i;
			for(i=0;i<l;i++)
			{
				int this_label = (int)prob.y[i];
				int j;
				for(j=0;j<nr_class;j++)
					if(this_label == label[j])
					{
						++count[j];
						break;
					}

				if(j == nr_class)
				{
					if(nr_class == max_nr_class)
					{
						max_nr_class *= 2;
						int[] new_data = new int[max_nr_class];
						System.arraycopy(label,0,new_data,0,label.length);
						label = new_data;
						
						new_data = new int[max_nr_class];
						System.arraycopy(count,0,new_data,0,count.length);
						count = new_data;
					}
					label[nr_class] = this_label;
					count[nr_class] = 1;
					++nr_class;
				}
			}

			for(i=0;i<nr_class;i++)
			{
				int n1 = count[i];
				for(int j=i+1;j<nr_class;j++)
				{
					int n2 = count[j];
					if(param.nu*(n1+n2)/2 > Math.min(n1,n2))
						return "specified nu is infeasible";
				}
			}
		}

		return null;
	}

	public static int svm_check_probability_model(svm_model model)
	{
		if (((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) &&
		model.probA!=null && model.probB!=null) ||
		((model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR) &&
		 model.probA!=null))
			return 1;
		else
			return 0;
	}

	public static void svm_set_print_string_function(svm_print_interface print_func)
	{
		if (print_func == null)
			svm_print_string = svm_print_stdout;
		else 
			svm_print_string = print_func;
	}
}


================================================
FILE: Matlab/libsvm-3.19/java/libsvm/svm_model.java
================================================
//
// svm_model
//
package libsvm;
public class svm_model implements java.io.Serializable
{
	public svm_parameter param;	// parameter
	public int nr_class;		// number of classes, = 2 in regression/one class svm
	public int l;			// total #SV
	public svm_node[][] SV;	// SVs (SV[l])
	public double[][] sv_coef;	// coefficients for SVs in decision functions (sv_coef[k-1][l])
	public double[] rho;		// constants in decision functions (rho[k*(k-1)/2])
	public double[] probA;         // pariwise probability information
	public double[] probB;
	public int[] sv_indices;       // sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set

	// for classification only

	public int[] label;		// label of each class (label[k])
	public int[] nSV;		// number of SVs for each class (nSV[k])
				// nSV[0] + nSV[1] + ... + nSV[k-1] = l
};


================================================
FILE: Matlab/libsvm-3.19/java/libsvm/svm_node.java
================================================
package libsvm;
public class svm_node implements java.io.Serializable
{
	public int index;
	public double value;
}


================================================
FILE: Matlab/libsvm-3.19/java/libsvm/svm_parameter.java
================================================
package libsvm;
public class svm_parameter implements Cloneable,java.io.Serializable
{
	/* svm_type */
	public static final int C_SVC = 0;
	public static final int NU_SVC = 1;
	public static final int ONE_CLASS = 2;
	public static final int EPSILON_SVR = 3;
	public static final int NU_SVR = 4;

	/* kernel_type */
	public static final int LINEAR = 0;
	public static final int POLY = 1;
	public static final int RBF = 2;
	public static final int SIGMOID = 3;
	public static final int PRECOMPUTED = 4;

	public int svm_type;
	public int kernel_type;
	public int degree;	// for poly
	public double gamma;	// for poly/rbf/sigmoid
	public double coef0;	// for poly/sigmoid

	// these are for training only
	public double cache_size; // in MB
	public double eps;	// stopping criteria
	public double C;	// for C_SVC, EPSILON_SVR and NU_SVR
	public int nr_weight;		// for C_SVC
	public int[] weight_label;	// for C_SVC
	public double[] weight;		// for C_SVC
	public double nu;	// for NU_SVC, ONE_CLASS, and NU_SVR
	public double p;	// for EPSILON_SVR
	public int shrinking;	// use the shrinking heuristics
	public int probability; // do probability estimates

	public Object clone() 
	{
		try 
		{
			return super.clone();
		} catch (CloneNotSupportedException e) 
		{
			return null;
		}
	}

}


================================================
FILE: Matlab/libsvm-3.19/java/libsvm/svm_print_interface.java
================================================
package libsvm;
public interface svm_print_interface
{
	public void print(String s);
}


================================================
FILE: Matlab/libsvm-3.19/java/libsvm/svm_problem.java
================================================
package libsvm;
public class svm_problem implements java.io.Serializable
{
	public int l;
	public double[] y;
	public svm_node[][] x;
}


================================================
FILE: Matlab/libsvm-3.19/java/svm_predict.java
================================================
import libsvm.*;
import java.io.*;
import java.util.*;

class svm_predict {
	private static svm_print_interface svm_print_null = new svm_print_interface()
	{
		public void print(String s) {}
	};

	private static svm_print_interface svm_print_stdout = new svm_print_interface()
	{
		public void print(String s)
		{
			System.out.print(s);
		}
	};

	private static svm_print_interface svm_print_string = svm_print_stdout;

	static void info(String s) 
	{
		svm_print_string.print(s);
	}

	private static double atof(String s)
	{
		return Double.valueOf(s).doubleValue();
	}

	private static int atoi(String s)
	{
		return Integer.parseInt(s);
	}

	private static void predict(BufferedReader input, DataOutputStream output, svm_model model, int predict_probability) throws IOException
	{
		int correct = 0;
		int total = 0;
		double error = 0;
		double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;

		int svm_type=svm.svm_get_svm_type(model);
		int nr_class=svm.svm_get_nr_class(model);
		double[] prob_estimates=null;

		if(predict_probability == 1)
		{
			if(svm_type == svm_parameter.EPSILON_SVR ||
			   svm_type == svm_parameter.NU_SVR)
			{
				svm_predict.info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma="+svm.svm_get_svr_probability(model)+"\n");
			}
			else
			{
				int[] labels=new int[nr_class];
				svm.svm_get_labels(model,labels);
				prob_estimates = new double[nr_class];
				output.writeBytes("labels");
				for(int j=0;j<nr_class;j++)
					output.writeBytes(" "+labels[j]);
				output.writeBytes("\n");
			}
		}
		while(true)
		{
			String line = input.readLine();
			if(line == null) break;

			StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");

			double target = atof(st.nextToken());
			int m = st.countTokens()/2;
			svm_node[] x = new svm_node[m];
			for(int j=0;j<m;j++)
			{
				x[j] = new svm_node();
				x[j].index = atoi(st.nextToken());
				x[j].value = atof(st.nextToken());
			}

			double v;
			if (predict_probability==1 && (svm_type==svm_parameter.C_SVC || svm_type==svm_parameter.NU_SVC))
			{
				v = svm.svm_predict_probability(model,x,prob_estimates);
				output.writeBytes(v+" ");
				for(int j=0;j<nr_class;j++)
					output.writeBytes(prob_estimates[j]+" ");
				output.writeBytes("\n");
			}
			else
			{
				v = svm.svm_predict(model,x);
				output.writeBytes(v+"\n");
			}

			if(v == target)
				++correct;
			error += (v-target)*(v-target);
			sumv += v;
			sumy += target;
			sumvv += v*v;
			sumyy += target*target;
			sumvy += v*target;
			++total;
		}
		if(svm_type == svm_parameter.EPSILON_SVR ||
		   svm_type == svm_parameter.NU_SVR)
		{
			svm_predict.info("Mean squared error = "+error/total+" (regression)\n");
			svm_predict.info("Squared correlation coefficient = "+
				 ((total*sumvy-sumv*sumy)*(total*sumvy-sumv*sumy))/
				 ((total*sumvv-sumv*sumv)*(total*sumyy-sumy*sumy))+
				 " (regression)\n");
		}
		else
			svm_predict.info("Accuracy = "+(double)correct/total*100+
				 "% ("+correct+"/"+total+") (classification)\n");
	}

	private static void exit_with_help()
	{
		System.err.print("usage: svm_predict [options] test_file model_file output_file\n"
		+"options:\n"
		+"-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet\n"
		+"-q : quiet mode (no outputs)\n");
		System.exit(1);
	}

	public static void main(String argv[]) throws IOException
	{
		int i, predict_probability=0;
        	svm_print_string = svm_print_stdout;

		// parse options
		for(i=0;i<argv.length;i++)
		{
			if(argv[i].charAt(0) != '-') break;
			++i;
			switch(argv[i-1].charAt(1))
			{
				case 'b':
					predict_probability = atoi(argv[i]);
					break;
				case 'q':
					svm_print_string = svm_print_null;
					i--;
					break;
				default:
					System.err.print("Unknown option: " + argv[i-1] + "\n");
					exit_with_help();
			}
		}
		if(i>=argv.length-2)
			exit_with_help();
		try 
		{
			BufferedReader input = new BufferedReader(new FileReader(argv[i]));
			DataOutputStream output = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(argv[i+2])));
			svm_model model = svm.svm_load_model(argv[i+1]);
			if (model == null)
			{
				System.err.print("can't open model file "+argv[i+1]+"\n");
				System.exit(1);
			}
			if(predict_probability == 1)
			{
				if(svm.svm_check_probability_model(model)==0)
				{
					System.err.print("Model does not support probabiliy estimates\n");
					System.exit(1);
				}
			}
			else
			{
				if(svm.svm_check_probability_model(model)!=0)
				{
					svm_predict.info("Model supports probability estimates, but disabled in prediction.\n");
				}
			}
			predict(input,output,model,predict_probability);
			input.close();
			output.close();
		} 
		catch(FileNotFoundException e) 
		{
			exit_with_help();
		}
		catch(ArrayIndexOutOfBoundsException e) 
		{
			exit_with_help();
		}
	}
}


================================================
FILE: Matlab/libsvm-3.19/java/svm_scale.java
================================================
import libsvm.*;
import java.io.*;
import java.util.*;
import java.text.DecimalFormat;

class svm_scale
{
	private String line = null;
	private double lower = -1.0;
	private double upper = 1.0;
	private double y_lower;
	private double y_upper;
	private boolean y_scaling = false;
	private double[] feature_max;
	private double[] feature_min;
	private double y_max = -Double.MAX_VALUE;
	private double y_min = Double.MAX_VALUE;
	private int max_index;
	private long num_nonzeros = 0;
	private long new_num_nonzeros = 0;

	private static void exit_with_help()
	{
		System.out.print(
		 "Usage: svm-scale [options] data_filename\n"
		+"options:\n"
		+"-l lower : x scaling lower limit (default -1)\n"
		+"-u upper : x scaling upper limit (default +1)\n"
		+"-y y_lower y_upper : y scaling limits (default: no y scaling)\n"
		+"-s save_filename : save scaling parameters to save_filename\n"
		+"-r restore_filename : restore scaling parameters from restore_filename\n"
		);
		System.exit(1);
	}

	private BufferedReader rewind(BufferedReader fp, String filename) throws IOException
	{
		fp.close();
		return new BufferedReader(new FileReader(filename));
	}

	private void output_target(double value)
	{
		if(y_scaling)
		{
			if(value == y_min)
				value = y_lower;
			else if(value == y_max)
				value = y_upper;
			else
				value = y_lower + (y_upper-y_lower) *
				(value-y_min) / (y_max-y_min);
		}

		System.out.print(value + " ");
	}

	private void output(int index, double value)
	{
		/* skip single-valued attribute */
		if(feature_max[index] == feature_min[index])
			return;

		if(value == feature_min[index])
			value = lower;
		else if(value == feature_max[index])
			value = upper;
		else
			value = lower + (upper-lower) * 
				(value-feature_min[index])/
				(feature_max[index]-feature_min[index]);

		if(value != 0)
		{
			System.out.print(index + ":" + value + " ");
			new_num_nonzeros++;
		}
	}

	private String readline(BufferedReader fp) throws IOException
	{
		line = fp.readLine();
		return line;
	}

	private void run(String []argv) throws IOException
	{
		int i,index;
		BufferedReader fp = null, fp_restore = null;
		String save_filename = null;
		String restore_filename = null;
		String data_filename = null;


		for(i=0;i<argv.length;i++)
		{
			if (argv[i].charAt(0) != '-')	break;
			++i;
			switch(argv[i-1].charAt(1))
			{
				case 'l': lower = Double.parseDouble(argv[i]);	break;
				case 'u': upper = Double.parseDouble(argv[i]);	break;
				case 'y':
					  y_lower = Double.parseDouble(argv[i]);
					  ++i;
					  y_upper = Double.parseDouble(argv[i]);
					  y_scaling = true;
					  break;
				case 's': save_filename = argv[i];	break;
				case 'r': restore_filename = argv[i];	break;
				default:
					  System.err.println("unknown option");
					  exit_with_help();
			}
		}

		if(!(upper > lower) || (y_scaling && !(y_upper > y_lower)))
		{
			System.err.println("inconsistent lower/upper specification");
			System.exit(1);
		}
		if(restore_filename != null && save_filename != null)
		{
			System.err.println("cannot use -r and -s simultaneously");
			System.exit(1);
		}

		if(argv.length != i+1)
			exit_with_help();

		data_filename = argv[i];
		try {
			fp = new BufferedReader(new FileReader(data_filename));
		} catch (Exception e) {
			System.err.println("can't open file " + data_filename);
			System.exit(1);
		}

		/* assumption: min index of attributes is 1 */
		/* pass 1: find out max index of attributes */
		max_index = 0;

		if(restore_filename != null)
		{
			int idx, c;

			try {
				fp_restore = new BufferedReader(new FileReader(restore_filename));
			}
			catch (Exception e) {
				System.err.println("can't open file " + restore_filename);
				System.exit(1);
			}
			if((c = fp_restore.read()) == 'y')
			{
				fp_restore.readLine();
				fp_restore.readLine();		
				fp_restore.readLine();		
			}
			fp_restore.readLine();
			fp_restore.readLine();

			String restore_line = null;
			while((restore_line = fp_restore.readLine())!=null)
			{
				StringTokenizer st2 = new StringTokenizer(restore_line);
				idx = Integer.parseInt(st2.nextToken());
				max_index = Math.max(max_index, idx);
			}
			fp_restore = rewind(fp_restore, restore_filename);
		}

		while (readline(fp) != null)
		{
			StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");
			st.nextToken();
			while(st.hasMoreTokens())
			{
				index = Integer.parseInt(st.nextToken());
				max_index = Math.max(max_index, index);
				st.nextToken();
				num_nonzeros++;
			}
		}

		try {
			feature_max = new double[(max_index+1)];
			feature_min = new double[(max_index+1)];
		} catch(OutOfMemoryError e) {
			System.err.println("can't allocate enough memory");
			System.exit(1);
		}

		for(i=0;i<=max_index;i++)
		{
			feature_max[i] = -Double.MAX_VALUE;
			feature_min[i] = Double.MAX_VALUE;
		}

		fp = rewind(fp, data_filename);

		/* pass 2: find out min/max value */
		while(readline(fp) != null)
		{
			int next_index = 1;
			double target;
			double value;

			StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");
			target = Double.parseDouble(st.nextToken());
			y_max = Math.max(y_max, target);
			y_min = Math.min(y_min, target);

			while (st.hasMoreTokens())
			{
				index = Integer.parseInt(st.nextToken());
				value = Double.parseDouble(st.nextToken());

				for (i = next_index; i<index; i++)
				{
					feature_max[i] = Math.max(feature_max[i], 0);
					feature_min[i] = Math.min(feature_min[i], 0);
				}

				feature_max[index] = Math.max(feature_max[index], value);
				feature_min[index] = Math.min(feature_min[index], value);
				next_index = index + 1;
			}

			for(i=next_index;i<=max_index;i++)
			{
				feature_max[i] = Math.max(feature_max[i], 0);
				feature_min[i] = Math.min(feature_min[i], 0);
			}
		}

		fp = rewind(fp, data_filename);

		/* pass 2.5: save/restore feature_min/feature_max */
		if(restore_filename != null)
		{
			// fp_restore rewinded in finding max_index 
			int idx, c;
			double fmin, fmax;

			fp_restore.mark(2);				// for reset
			if((c = fp_restore.read()) == 'y')
			{
				fp_restore.readLine();		// pass the '\n' after 'y'
				StringTokenizer st = new StringTokenizer(fp_restore.readLine());
				y_lower = Double.parseDouble(st.nextToken());
				y_upper = Double.parseDouble(st.nextToken());
				st = new StringTokenizer(fp_restore.readLine());
				y_min = Double.parseDouble(st.nextToken());
				y_max = Double.parseDouble(st.nextToken());
				y_scaling = true;
			}
			else
				fp_restore.reset();

			if(fp_restore.read() == 'x') {
				fp_restore.readLine();		// pass the '\n' after 'x'
				StringTokenizer st = new StringTokenizer(fp_restore.readLine());
				lower = Double.parseDouble(st.nextToken());
				upper = Double.parseDouble(st.nextToken());
				String restore_line = null;
				while((restore_line = fp_restore.readLine())!=null)
				{
					StringTokenizer st2 = new StringTokenizer(restore_line);
					idx = Integer.parseInt(st2.nextToken());
					fmin = Double.parseDouble(st2.nextToken());
					fmax = Double.parseDouble(st2.nextToken());
					if (idx <= max_index)
					{
						feature_min[idx] = fmin;
						feature_max[idx] = fmax;
					}
				}
			}
			fp_restore.close();
		}

		if(save_filename != null)
		{
			Formatter formatter = new Formatter(new StringBuilder());
			BufferedWriter fp_save = null;

			try {
				fp_save = new BufferedWriter(new FileWriter(save_filename));
			} catch(IOException e) {
				System.err.println("can't open file " + save_filename);
				System.exit(1);
			}

			if(y_scaling)
			{
				formatter.format("y\n");
				formatter.format("%.16g %.16g\n", y_lower, y_upper);
				formatter.format("%.16g %.16g\n", y_min, y_max);
			}
			formatter.format("x\n");
			formatter.format("%.16g %.16g\n", lower, upper);
			for(i=1;i<=max_index;i++)
			{
				if(feature_min[i] != feature_max[i]) 
					formatter.format("%d %.16g %.16g\n", i, feature_min[i], feature_max[i]);
			}
			fp_save.write(formatter.toString());
			fp_save.close();
		}

		/* pass 3: scale */
		while(readline(fp) != null)
		{
			int next_index = 1;
			double target;
			double value;

			StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");
			target = Double.parseDouble(st.nextToken());
			output_target(target);
			while(st.hasMoreElements())
			{
				index = Integer.parseInt(st.nextToken());
				value = Double.parseDouble(st.nextToken());
				for (i = next_index; i<index; i++)
					output(i, 0);
				output(index, value);
				next_index = index + 1;
			}

			for(i=next_index;i<= max_index;i++)
				output(i, 0);
			System.out.print("\n");
		}
		if (new_num_nonzeros > num_nonzeros)
			System.err.print(
			 "WARNING: original #nonzeros " + num_nonzeros+"\n"
			+"         new      #nonzeros " + new_num_nonzeros+"\n"
			+"Use -l 0 if many original feature values are zeros\n");

		fp.close();
	}

	public static void main(String argv[]) throws IOException
	{
		svm_scale s = new svm_scale();
		s.run(argv);
	}
}


================================================
FILE: Matlab/libsvm-3.19/java/svm_toy.java
================================================
import libsvm.*;
import java.applet.*;
import java.awt.*;
import java.util.*;
import java.awt.event.*;
import java.io.*;

public class svm_toy extends Applet {

	static final String DEFAULT_PARAM="-t 2 -c 100";
	int XLEN;
	int YLEN;

	// off-screen buffer

	Image buffer;
	Graphics buffer_gc;

	// pre-allocated colors

	final static Color colors[] =
	{
	  new Color(0,0,0),
	  new Color(0,120,120),
	  new Color(120,120,0),
	  new Color(120,0,120),
	  new Color(0,200,200),
	  new Color(200,200,0),
	  new Color(200,0,200)
	};

	class point {
		point(double x, double y, byte value)
		{
			this.x = x;
			this.y = y;
			this.value = value;
		}
		double x, y;
		byte value;
	}

	Vector<point> point_list = new Vector<point>();
	byte current_value = 1;

	public void init()
	{
		setSize(getSize());

		final Button button_change = new Button("Change");
		Button button_run = new Button("Run");
		Button button_clear = new Button("Clear");
		Button button_save = new Button("Save");
		Button button_load = new Button("Load");
		final TextField input_line = new TextField(DEFAULT_PARAM);

		BorderLayout layout = new BorderLayout();
		this.setLayout(layout);

		Panel p = new Panel();
		GridBagLayout gridbag = new GridBagLayout();
		p.setLayout(gridbag);

		GridBagConstraints c = new GridBagConstraints();
		c.fill = GridBagConstraints.HORIZONTAL;
		c.weightx = 1;
		c.gridwidth = 1;
		gridbag.setConstraints(button_change,c);
		gridbag.setConstraints(button_run,c);
		gridbag.setConstraints(button_clear,c);
		gridbag.setConstraints(button_save,c);
		gridbag.setConstraints(button_load,c);
		c.weightx = 5;
		c.gridwidth = 5;
		gridbag.setConstraints(input_line,c);

		button_change.setBackground(colors[current_value]);

		p.add(button_change);
		p.add(button_run);
		p.add(button_clear);
		p.add(button_save);
		p.add(button_load);
		p.add(input_line);
		this.add(p,BorderLayout.SOUTH);

		button_change.addActionListener(new ActionListener()
		{ public void actionPerformed (ActionEvent e)
		  { button_change_clicked(); button_change.setBackground(colors[current_value]); }});

		button_run.addActionListener(new ActionListener()
		{ public void actionPerformed (ActionEvent e)
		  { button_run_clicked(input_line.getText()); }});

		button_clear.addActionListener(new ActionListener()
		{ public void actionPerformed (ActionEvent e)
		  { button_clear_clicked(); }});

		button_save.addActionListener(new ActionListener()
		{ public void actionPerformed (ActionEvent e)
		  { button_save_clicked(input_line.getText()); }});

		button_load.addActionListener(new ActionListener()
		{ public void actionPerformed (ActionEvent e)
		  { button_load_clicked(); }});

		input_line.addActionListener(new ActionListener()
		{ public void actionPerformed (ActionEvent e)
		  { button_run_clicked(input_line.getText()); }});

		this.enableEvents(AWTEvent.MOUSE_EVENT_MASK);
	}

	void draw_point(point p)
	{
		Color c = colors[p.value+3];

		Graphics window_gc = getGraphics();
		buffer_gc.setColor(c);
		buffer_gc.fillRect((int)(p.x*XLEN),(int)(p.y*YLEN),4,4);
		window_gc.setColor(c);
		window_gc.fillRect((int)(p.x*XLEN),(int)(p.y*YLEN),4,4);
	}

	void clear_all()
	{
		point_list.removeAllElements();
		if(buffer != null)
		{
			buffer_gc.setColor(colors[0]);
			buffer_gc.fillRect(0,0,XLEN,YLEN);
		}
		repaint();
	}

	void draw_all_points()
	{
		int n = point_list.size();
		for(int i=0;i<n;i++)
			draw_point(point_list.elementAt(i));
	}

	void button_change_clicked()
	{
		++current_value;
		if(current_value > 3) current_value = 1;
	}

	private static double atof(String s)
	{
		return Double.valueOf(s).doubleValue();
	}

	private static int atoi(String s)
	{
		return Integer.parseInt(s);
	}

	void button_run_clicked(String args)
	{
		// guard
		if(point_list.isEmpty()) return;

		svm_parameter param = new svm_parameter();

		// default values
		param.svm_type = svm_parameter.C_SVC;
		param.kernel_type = svm_parameter.RBF;
		param.degree = 3;
		param.gamma = 0;
		param.coef0 = 0;
		param.nu = 0.5;
		param.cache_size = 40;
		param.C = 1;
		param.eps = 1e-3;
		param.p = 0.1;
		param.shrinking = 1;
		param.probability = 0;
		param.nr_weight = 0;
		param.weight_label = new int[0];
		param.weight = new double[0];

		// parse options
		StringTokenizer st = new StringTokenizer(args);
		String[] argv = new String[st.countTokens()];
		for(int i=0;i<argv.length;i++)
			argv[i] = st.nextToken();

		for(int i=0;i<argv.length;i++)
		{
			if(argv[i].charAt(0) != '-') break;
			if(++i>=argv.length)
			{
				System.err.print("unknown option\n");
				break;
			}
			switch(argv[i-1].charAt(1))
			{
				case 's':
					param.svm_type = atoi(argv[i]);
					break;
				case 't':
					param.kernel_type = atoi(argv[i]);
					break;
				case 'd':
					param.degree = atoi(argv[i]);
					break;
				case 'g':
					param.gamma = atof(argv[i]);
					break;
				case 'r':
					param.coef0 = atof(argv[i]);
					break;
				case 'n':
					param.nu = atof(argv[i]);
					break;
				case 'm':
					param.cache_size = atof(argv[i]);
					break;
				case 'c':
					param.C = atof(argv[i]);
					break;
				case 'e':
					param.eps = atof(argv[i]);
					break;
				case 'p':
					param.p = atof(argv[i]);
					break;
				case 'h':
					param.shrinking = atoi(argv[i]);
					break;
				case 'b':
					param.probability = atoi(argv[i]);
					break;
				case 'w':
					++param.nr_weight;
					{
						int[] old = param.weight_label;
						param.weight_label = new int[param.nr_weight];
						System.arraycopy(old,0,param.weight_label,0,param.nr_weight-1);
					}

					{
						double[] old = param.weight;
						param.weight = new double[param.nr_weight];
						System.arraycopy(old,0,param.weight,0,param.nr_weight-1);
					}

					param.weight_label[param.nr_weight-1] = atoi(argv[i-1].substring(2));
					param.weight[param.nr_weight-1] = atof(argv[i]);
					break;
				default:
					System.err.print("unknown option\n");
			}
		}

		// build problem
		svm_problem prob = new svm_problem();
		prob.l = point_list.size();
		prob.y = new double[prob.l];

		if(param.kernel_type == svm_parameter.PRECOMPUTED)
		{
		}
		else if(param.svm_type == svm_parameter.EPSILON_SVR ||
			param.svm_type == svm_parameter.NU_SVR)
		{
			if(param.gamma == 0) param.gamma = 1;
			prob.x = new svm_node[prob.l][1];
			for(int i=0;i<prob.l;i++)
			{
				point p = point_list.elementAt(i);
				prob.x[i][0] = new svm_node();
				prob.x[i][0].index = 1;
				prob.x[i][0].value = p.x;
				prob.y[i] = p.y;
			}

			// build model & classify
			svm_model model = svm.svm_train(prob, param);
			svm_node[] x = new svm_node[1];
			x[0] = new svm_node();
			x[0].index = 1;
			int[] j = new int[XLEN];

			Graphics window_gc = getGraphics();
			for (int i = 0; i < XLEN; i++)
			{
				x[0].value = (double) i / XLEN;
				j[i] = (int)(YLEN*svm.svm_predict(model, x));
			}
			
			buffer_gc.setColor(colors[0]);
			buffer_gc.drawLine(0,0,0,YLEN-1);
			window_gc.setColor(colors[0]);
			window_gc.drawLine(0,0,0,YLEN-1);
			
			int p = (int)(param.p * YLEN);
			for(int i=1;i<XLEN;i++)
			{
				buffer_gc.setColor(colors[0]);
				buffer_gc.drawLine(i,0,i,YLEN-1);
				window_gc.setColor(colors[0]);
				window_gc.drawLine(i,0,i,YLEN-1);

				buffer_gc.setColor(colors[5]);
				window_gc.setColor(colors[5]);
				buffer_gc.drawLine(i-1,j[i-1],i,j[i]);
				window_gc.drawLine(i-1,j[i-1],i,j[i]);

				if(param.svm_type == svm_parameter.EPSILON_SVR)
				{
					buffer_gc.setColor(colors[2]);
					window_gc.setColor(colors[2]);
					buffer_gc.drawLine(i-1,j[i-1]+p,i,j[i]+p);
					window_gc.drawLine(i-1,j[i-1]+p,i,j[i]+p);

					buffer_gc.setColor(colors[2]);
					window_gc.setColor(colors[2]);
					buffer_gc.drawLine(i-1,j[i-1]-p,i,j[i]-p);
					window_gc.drawLine(i-1,j[i-1]-p,i,j[i]-p);
				}
			}
		}
		else
		{
			if(param.gamma == 0) param.gamma = 0.5;
			prob.x = new svm_node [prob.l][2];
			for(int i=0;i<prob.l;i++)
			{
				point p = point_list.elementAt(i);
				prob.x[i][0] = new svm_node();
				prob.x[i][0].index = 1;
				prob.x[i][0].value = p.x;
				prob.x[i][1] = new svm_node();
				prob.x[i][1].index = 2;
				prob.x[i][1].value = p.y;
				prob.y[i] = p.value;
			}

			// build model & classify
			svm_model model = svm.svm_train(prob, param);
			svm_node[] x = new svm_node[2];
			x[0] = new svm_node();
			x[1] = new svm_node();
			x[0].index = 1;
			x[1].index = 2;

			Graphics window_gc = getGraphics();
			for (int i = 0; i < XLEN; i++)
				for (int j = 0; j < YLEN ; j++) {
					x[0].value = (double) i / XLEN;
					x[1].value = (double) j / YLEN;
					double d = svm.svm_predict(model, x);
					if (param.svm_type == svm_parameter.ONE_CLASS && d<0) d=2;
					buffer_gc.setColor(colors[(int)d]);
					window_gc.setColor(colors[(int)d]);
					buffer_gc.drawLine(i,j,i,j);
					window_gc.drawLine(i,j,i,j);
			}
		}

		draw_all_points();
	}

	void button_clear_clicked()
	{
		clear_all();
	}

	void button_save_clicked(String args)
	{
		FileDialog dialog = new FileDialog(new Frame(),"Save",FileDialog.SAVE);
		dialog.setVisible(true);
		String filename = dialog.getDirectory() + dialog.getFile();
		if (filename == null) return;
		try {
			DataOutputStream fp = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(filename)));

			int svm_type = svm_parameter.C_SVC;
			int svm_type_idx = args.indexOf("-s ");
			if(svm_type_idx != -1)
			{
				StringTokenizer svm_str_st = new StringTokenizer(args.substring(svm_type_idx+2).trim());
				svm_type = atoi(svm_str_st.nextToken());
			}

			int n = point_list.size();
			if(svm_type == svm_parameter.EPSILON_SVR || svm_type == svm_parameter.NU_SVR)
			{
				for(int i=0;i<n;i++)
				{
					point p = point_list.elementAt(i);
					fp.writeBytes(p.y+" 1:"+p.x+"\n");
				}
			}
			else
			{
				for(int i=0;i<n;i++)
				{
					point p = point_list.elementAt(i);
					fp.writeBytes(p.value+" 1:"+p.x+" 2:"+p.y+"\n");
				}
			}
			fp.close();
		} catch (IOException e) { System.err.print(e); }
	}

	void button_load_clicked()
	{
		FileDialog dialog = new FileDialog(new Frame(),"Load",FileDialog.LOAD);
		dialog.setVisible(true);
		String filename = dialog.getDirectory() + dialog.getFile();
		if (filename == null) return;
		clear_all();
		try {
			BufferedReader fp = new BufferedReader(new FileReader(filename));
			String line;
			while((line = fp.readLine()) != null)
			{
				StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");
				if(st.countTokens() == 5)
				{
					byte value = (byte)atoi(st.nextToken());
					st.nextToken();
					double x = atof(st.nextToken());
					st.nextToken();
					double y = atof(st.nextToken());
					point_list.addElement(new point(x,y,value));
				}
				else if(st.countTokens() == 3)
				{
					double y = atof(st.nextToken());
					st.nextToken();
					double x = atof(st.nextToken());
					point_list.addElement(new point(x,y,current_value));
				}else
					break;
			}
			fp.close();
		} catch (IOException e) { System.err.print(e); }
		draw_all_points();
	}
	
	protected void processMouseEvent(MouseEvent e)
	{
		if(e.getID() == MouseEvent.MOUSE_PRESSED)
		{
			if(e.getX() >= XLEN || e.getY() >= YLEN) return;
			point p = new point((double)e.getX()/XLEN,
					    (double)e.getY()/YLEN,
					    current_value);
			point_list.addElement(p);
			draw_point(p);
		}
	}

	public void paint(Graphics g)
	{
		// create buffer first time
		if(buffer == null) {
			buffer = this.createImage(XLEN,YLEN);
			buffer_gc = buffer.getGraphics();
			buffer_gc.setColor(colors[0]);
			buffer_gc.fillRect(0,0,XLEN,YLEN);
		}
		g.drawImage(buffer,0,0,this);
	}

	public Dimension getPreferredSize() { return new Dimension(XLEN,YLEN+50); }

	public void setSize(Dimension d) { setSize(d.width,d.height); }
	public void setSize(int w,int h) {
		super.setSize(w,h);
		XLEN = w;
		YLEN = h-50;
		clear_all();
	}

	public static void main(String[] argv)
	{
		new AppletFrame("svm_toy",new svm_toy(),500,500+50);
	}
}

class AppletFrame extends Frame {
	AppletFrame(String title, Applet applet, int width, int height)
	{
		super(title);
		this.addWindowListener(new WindowAdapter() {
			public void windowClosing(WindowEvent e) {
				System.exit(0);
			}
		});
		applet.init();
		applet.setSize(width,height);
		applet.start();
		this.add(applet);
		this.pack();
		this.setVisible(true);
	}
}


================================================
FILE: Matlab/libsvm-3.19/java/svm_train.java
================================================
import libsvm.*;
import java.io.*;
import java.util.*;

class svm_train {
	private svm_parameter param;		// set by parse_command_line
	private svm_problem prob;		// set by read_problem
	private svm_model model;
	private String input_file_name;		// set by parse_command_line
	private String model_file_name;		// set by parse_command_line
	private String error_msg;
	private int cross_validation;
	private int nr_fold;

	private static svm_print_interface svm_print_null = new svm_print_interface()
	{
		public void print(String s) {}
	};

	private static void exit_with_help()
	{
		System.out.print(
		 "Usage: svm_train [options] training_set_file [model_file]\n"
		+"options:\n"
		+"-s svm_type : set type of SVM (default 0)\n"
		+"	0 -- C-SVC		(multi-class classification)\n"
		+"	1 -- nu-SVC		(multi-class classification)\n"
		+"	2 -- one-class SVM\n"
		+"	3 -- epsilon-SVR	(regression)\n"
		+"	4 -- nu-SVR		(regression)\n"
		+"-t kernel_type : set type of kernel function (default 2)\n"
		+"	0 -- linear: u'*v\n"
		+"	1 -- polynomial: (gamma*u'*v + coef0)^degree\n"
		+"	2 -- radial basis function: exp(-gamma*|u-v|^2)\n"
		+"	3 -- sigmoid: tanh(gamma*u'*v + coef0)\n"
		+"	4 -- precomputed kernel (kernel values in training_set_file)\n"
		+"-d degree : set degree in kernel function (default 3)\n"
		+"-g gamma : set gamma in kernel function (default 1/num_features)\n"
		+"-r coef0 : set coef0 in kernel function (default 0)\n"
		+"-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)\n"
		+"-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)\n"
		+"-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n"
		+"-m cachesize : set cache memory size in MB (default 100)\n"
		+"-e epsilon : set tolerance of termination criterion (default 0.001)\n"
		+"-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)\n"
		+"-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)\n"
		+"-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)\n"
		+"-v n : n-fold cross validation mode\n"
		+"-q : quiet mode (no outputs)\n"
		);
		System.exit(1);
	}

	private void do_cross_validation()
	{
		int i;
		int total_correct = 0;
		double total_error = 0;
		double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
		double[] target = new double[prob.l];

		svm.svm_cross_validation(prob,param,nr_fold,target);
		if(param.svm_type == svm_parameter.EPSILON_SVR ||
		   param.svm_type == svm_parameter.NU_SVR)
		{
			for(i=0;i<prob.l;i++)
			{
				double y = prob.y[i];
				double v = target[i];
				total_error += (v-y)*(v-y);
				sumv += v;
				sumy += y;
				sumvv += v*v;
				sumyy += y*y;
				sumvy += v*y;
			}
			System.out.print("Cross Validation Mean squared error = "+total_error/prob.l+"\n");
			System.out.print("Cross Validation Squared correlation coefficient = "+
				((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/
				((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))+"\n"
				);
		}
		else
		{
			for(i=0;i<prob.l;i++)
				if(target[i] == prob.y[i])
					++total_correct;
			System.out.print("Cross Validation Accuracy = "+100.0*total_correct/prob.l+"%\n");
		}
	}
	
	private void run(String argv[]) throws IOException
	{
		parse_command_line(argv);
		read_problem();
		error_msg = svm.svm_check_parameter(prob,param);

		if(error_msg != null)
		{
			System.err.print("ERROR: "+error_msg+"\n");
			System.exit(1);
		}

		if(cross_validation != 0)
		{
			do_cross_validation();
		}
		else
		{
			model = svm.svm_train(prob,param);
			svm.svm_save_model(model_file_name,model);
		}
	}

	public static void main(String argv[]) throws IOException
	{
		svm_train t = new svm_train();
		t.run(argv);
	}

	private static double atof(String s)
	{
		double d = Double.valueOf(s).doubleValue();
		if (Double.isNaN(d) || Double.isInfinite(d))
		{
			System.err.print("NaN or Infinity in input\n");
			System.exit(1);
		}
		return(d);
	}

	private static int atoi(String s)
	{
		return Integer.parseInt(s);
	}

	private void parse_command_line(String argv[])
	{
		int i;
		svm_print_interface print_func = null;	// default printing to stdout

		param = new svm_parameter();
		// default values
		param.svm_type = svm_parameter.C_SVC;
		param.kernel_type = svm_parameter.RBF;
		param.degree = 3;
		param.gamma = 0;	// 1/num_features
		param.coef0 = 0;
		param.nu = 0.5;
		param.cache_size = 100;
		param.C = 1;
		param.eps = 1e-3;
		param.p = 0.1;
		param.shrinking = 1;
		param.probability = 0;
		param.nr_weight = 0;
		param.weight_label = new int[0];
		param.weight = new double[0];
		cross_validation = 0;

		// parse options
		for(i=0;i<argv.length;i++)
		{
			if(argv[i].charAt(0) != '-') break;
			if(++i>=argv.length)
				exit_with_help();
			switch(argv[i-1].charAt(1))
			{
				case 's':
					param.svm_type = atoi(argv[i]);
					break;
				case 't':
					param.kernel_type = atoi(argv[i]);
					break;
				case 'd':
					param.degree = atoi(argv[i]);
					break;
				case 'g':
					param.gamma = atof(argv[i]);
					break;
				case 'r':
					param.coef0 = atof(argv[i]);
					break;
				case 'n':
					param.nu = atof(argv[i]);
					break;
				case 'm':
					param.cache_size = atof(argv[i]);
					break;
				case 'c':
					param.C = atof(argv[i]);
					break;
				case 'e':
					param.eps = atof(argv[i]);
					break;
				case 'p':
					param.p = atof(argv[i]);
					break;
				case 'h':
					param.shrinking = atoi(argv[i]);
					break;
				case 'b':
					param.probability = atoi(argv[i]);
					break;
				case 'q':
					print_func = svm_print_null;
					i--;
					break;
				case 'v':
					cross_validation = 1;
					nr_fold = atoi(argv[i]);
					if(nr_fold < 2)
					{
						System.err.print("n-fold cross validation: n must >= 2\n");
						exit_with_help();
					}
					break;
				case 'w':
					++param.nr_weight;
					{
						int[] old = param.weight_label;
						param.weight_label = new int[param.nr_weight];
						System.arraycopy(old,0,param.weight_label,0,param.nr_weight-1);
					}

					{
						double[] old = param.weight;
						param.weight = new double[param.nr_weight];
						System.arraycopy(old,0,param.weight,0,param.nr_weight-1);
					}

					param.weight_label[param.nr_weight-1] = atoi(argv[i-1].substring(2));
					param.weight[param.nr_weight-1] = atof(argv[i]);
					break;
				default:
					System.err.print("Unknown option: " + argv[i-1] + "\n");
					exit_with_help();
			}
		}

		svm.svm_set_print_string_function(print_func);

		// determine filenames

		if(i>=argv.length)
			exit_with_help();

		input_file_name = argv[i];

		if(i<argv.length-1)
			model_file_name = argv[i+1];
		else
		{
			int p = argv[i].lastIndexOf('/');
			++p;	// whew...
			model_file_name = argv[i].substring(p)+".model";
		}
	}

	// read in a problem (in svmlight format)

	private void read_problem() throws IOException
	{
		BufferedReader fp = new BufferedReader(new FileReader(input_file_name));
		Vector<Double> vy = new Vector<Double>();
		Vector<svm_node[]> vx = new Vector<svm_node[]>();
		int max_index = 0;

		while(true)
		{
			String line = fp.readLine();
			if(line == null) break;

			StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");

			vy.addElement(atof(st.nextToken()));
			int m = st.countTokens()/2;
			svm_node[] x = new svm_node[m];
			for(int j=0;j<m;j++)
			{
				x[j] = new svm_node();
				x[j].index = atoi(st.nextToken());
				x[j].value = atof(st.nextToken());
			}
			if(m>0) max_index = Math.max(max_index, x[m-1].index);
			vx.addElement(x);
		}

		prob = new svm_problem();
		prob.l = vy.size();
		prob.x = new svm_node[prob.l][];
		for(int i=0;i<prob.l;i++)
			prob.x[i] = vx.elementAt(i);
		prob.y = new double[prob.l];
		for(int i=0;i<prob.l;i++)
			prob.y[i] = vy.elementAt(i);

		if(param.gamma == 0 && max_index > 0)
			param.gamma = 1.0/max_index;

		if(param.kernel_type == svm_parameter.PRECOMPUTED)
			for(int i=0;i<prob.l;i++)
			{
				if (prob.x[i][0].index != 0)
				{
					System.err.print("Wrong kernel matrix: first column must be 0:sample_serial_number\n");
					System.exit(1);
				}
				if ((int)prob.x[i][0].value <= 0 || (int)prob.x[i][0].value > max_index)
				{
					System.err.print("Wrong input format: sample_serial_number out of range\n");
					System.exit(1);
				}
			}

		fp.close();
	}
}


================================================
FILE: Matlab/libsvm-3.19/java/test_applet.html
================================================
<APPLET code="svm_toy.class" archive="libsvm.jar" width=300 height=350></APPLET>


================================================
FILE: Matlab/libsvm-3.19/matlab/Makefile
================================================
# This Makefile is used under Linux

MATLABDIR ?= /usr/local/matlab
# for Mac
# MATLABDIR ?= /opt/local/matlab

CXX ?= g++
#CXX = g++-4.1
CFLAGS = -Wall -Wconversion -O3 -fPIC -I$(MATLABDIR)/extern/include -I..

MEX = $(MATLABDIR)/bin/mex
MEX_OPTION = CC="$(CXX)" CXX="$(CXX)" CFLAGS="$(CFLAGS)" CXXFLAGS="$(CFLAGS)"
# comment the following line if you use MATLAB on 32-bit computer
MEX_OPTION += -largeArrayDims
MEX_EXT = $(shell $(MATLABDIR)/bin/mexext)

all:	matlab

matlab:	binary

octave:
	@echo "please type make under Octave"

binary: svmpredict.$(MEX_EXT) svmtrain.$(MEX_EXT) libsvmread.$(MEX_EXT) libsvmwrite.$(MEX_EXT)

svmpredict.$(MEX_EXT):     svmpredict.c ../svm.h ../svm.o svm_model_matlab.o
	$(MEX) $(MEX_OPTION) svmpredict.c ../svm.o svm_model_matlab.o

svmtrain.$(MEX_EXT):       svmtrain.c ../svm.h ../svm.o svm_model_matlab.o
	$(MEX) $(MEX_OPTION) svmtrain.c ../svm.o svm_model_matlab.o

libsvmread.$(MEX_EXT):	libsvmread.c
	$(MEX) $(MEX_OPTION) libsvmread.c

libsvmwrite.$(MEX_EXT):	libsvmwrite.c
	$(MEX) $(MEX_OPTION) libsvmwrite.c

svm_model_matlab.o:     svm_model_matlab.c ../svm.h
	$(CXX) $(CFLAGS) -c svm_model_matlab.c

../svm.o: ../svm.cpp ../svm.h
	make -C .. svm.o

clean:
	rm -f *~ *.o *.mex* *.obj ../svm.o


================================================
FILE: Matlab/libsvm-3.19/matlab/README
================================================
-----------------------------------------
--- MATLAB/OCTAVE interface of LIBSVM ---
-----------------------------------------

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

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


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

This tool provides a simple interface to LIBSVM, a library for support vector
machines (http://www.csie.ntu.edu.tw/~cjlin/libsvm). It is very easy to use as
the usage and the way of specifying parameters are the same as that of LIBSVM.

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

On Windows systems, pre-built binary files are already in the 
directory '..\windows', so no need to conduct installation. Now we 
provide binary files only for 64bit MATLAB on Windows. If you would 
like to re-build the package, please rely on the following steps.

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

On MATLAB or Octave:

        >> make

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

Example:

	matlab>> mex -setup
	(ps: MATLAB will show the following messages to setup default compiler.)
	Please choose your compiler for building external interface (MEX) files:
	Would you like mex to locate installed compilers [y]/n? y
	Select a compiler:
	[1] Microsoft Visual C/C++ version 7.1 in C:\Program Files\Microsoft Visual Studio
	[0] None
	Compiler: 1
	Please verify your choices:
	Compiler: Microsoft Visual C/C++ 7.1
	Location: C:\Program Files\Microsoft Visual Studio
	Are these correct?([y]/n): y

	matlab>> make

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

Example:
        linux> make

To use octave, type 'make octave':

Example:
	linux> make octave

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

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

Usage
=====

matlab> model = svmtrain(training_label_vector, training_instance_matrix [, 'libsvm_options']);

        -training_label_vector:
            An m by 1 vector of training labels (type must be double).
        -training_instance_matrix:
            An m by n matrix of m training instances with n features.
            It can be dense or sparse (type must be double).
        -libsvm_options:
            A string of training options in the same format as that of LIBSVM.

matlab> [predicted_label, accuracy, decision_values/prob_estimates] = svmpredict(testing_label_vector, testing_instance_matrix, model [, 'libsvm_options']);
matlab> [predicted_label] = svmpredict(testing_label_vector, testing_instance_matrix, model [, 'libsvm_options']);

        -testing_label_vector:
            An m by 1 vector of prediction labels. If labels of test
            data are unknown, simply use any random values. (type must be double)
        -testing_instance_matrix:
            An m by n matrix of m testing instances with n features.
            It can be dense or sparse. (type must be double)
        -model:
            The output of svmtrain.
        -libsvm_options:
            A string of testing options in the same format as that of LIBSVM.

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

The 'svmtrain' function returns a model which can be used for future
prediction.  It is a structure and is organized as [Parameters, nr_class,
totalSV, rho, Label, ProbA, ProbB, nSV, sv_coef, SVs]:

        -Parameters: parameters
        -nr_class: number of classes; = 2 for regression/one-class svm
        -totalSV: total #SV
        -rho: -b of the decision function(s) wx+b
        -Label: label of each class; empty for regression/one-class SVM
        -sv_indices: values in [1,...,num_traning_data] to indicate SVs in the training set
        -ProbA: pairwise probability information; empty if -b 0 or in one-class SVM
        -ProbB: pairwise probability information; empty if -b 0 or in one-class SVM
        -nSV: number of SVs for each class; empty for regression/one-class SVM
        -sv_coef: coefficients for SVs in decision functions
        -SVs: support vectors

If you do not use the option '-b 1', ProbA and ProbB are empty
matrices. If the '-v' option is specified, cross validation is
conducted and the returned model is just a scalar: cross-validation
accuracy for classification and mean-squared error for regression.

More details about this model can be found in LIBSVM FAQ
(http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html) and LIBSVM
implementation document
(http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf).

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

The function 'svmpredict' has three outputs. The first one,
predictd_label, is a vector of predicted labels. The second output,
accuracy, is a vector including accuracy (for classification), mean
squared error, and squared correlation coefficient (for regression).
The third is a matrix containing decision values or probability
estimates (if '-b 1' is specified). If k is the number of classes
in training data, for decision values, each row includes results of 
predicting k(k-1)/2 binary-class SVMs. For classification, k = 1 is a
special case. Decision value +1 is returned for each testing instance,
instead of an empty vector. For probabilities, each row contains k values
indicating the probability that the testing instance is in each class.
Note that the order of classes here is the same as 'Label' field
in the model structure.

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

A matlab function libsvmread reads files in LIBSVM format: 

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

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

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

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

The instance_matrix must be a sparse matrix. (type must be double)
For 32bit and 64bit MATLAB on Windows, pre-built binary files are ready 
in the directory `..\windows', but in future releases, we will only 
include 64bit MATLAB binary files.

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

Examples
========

Train and test on the provided data heart_scale:

matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07');
matlab> [predict_label, accuracy, dec_values] = svmpredict(heart_scale_label, heart_scale_inst, model); % test the training data

For probability estimates, you need '-b 1' for training and testing:

matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07 -b 1');
matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
matlab> [predict_label, accuracy, prob_estimates] = svmpredict(heart_scale_label, heart_scale_inst, model, '-b 1');

To use precomputed kernel, you must include sample serial number as
the first column of the training and testing data (assume your kernel
matrix is K, # of instances is n):

matlab> K1 = [(1:n)', K]; % include sample serial number as first column
matlab> model = svmtrain(label_vector, K1, '-t 4');
matlab> [predict_label, accuracy, dec_values] = svmpredict(label_vector, K1, model); % test the training data

We give the following detailed example by splitting heart_scale into
150 training and 120 testing data.  Constructing a linear kernel
matrix and then using the precomputed kernel gives exactly the same
testing error as using the LIBSVM built-in linear kernel.

matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
matlab>
matlab> % Split Data
matlab> train_data = heart_scale_inst(1:150,:);
matlab> train_label = heart_scale_label(1:150,:);
matlab> test_data = heart_scale_inst(151:270,:);
matlab> test_label = heart_scale_label(151:270,:);
matlab>
matlab> % Linear Kernel
matlab> model_linear = svmtrain(train_label, train_data, '-t 0');
matlab> [predict_label_L, accuracy_L, dec_values_L] = svmpredict(test_label, test_data, model_linear);
matlab>
matlab> % Precomputed Kernel
matlab> model_precomputed = svmtrain(train_label, [(1:150)', train_data*train_data'], '-t 4');
matlab> [predict_label_P, accuracy_P, dec_values_P] = svmpredict(test_label, [(1:120)', test_data*train_data'], model_precomputed);
matlab>
matlab> accuracy_L % Display the accuracy using linear kernel
matlab> accuracy_P % Display the accuracy using precomputed kernel

Note that for testing, you can put anything in the
testing_label_vector.  For more details of precomputed kernels, please
read the section ``Precomputed Kernels'' in the README of the LIBSVM
package.

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

This interface was initially written by Jun-Cheng Chen, Kuan-Jen Peng,
Chih-Yuan Yang and Chih-Huai Cheng from Department of Computer
Science, National Taiwan University. The current version was prepared
by Rong-En Fan and Ting-Fan Wu. If you find this tool useful, please
cite LIBSVM as follows

Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support
vector machines. ACM Transactions on Intelligent Systems and
Technology, 2:27:1--27:27, 2011. Software available at
http://www.csie.ntu.edu.tw/~cjlin/libsvm

For any question, please contact Chih-Jen Lin <cjlin@csie.ntu.edu.tw>,
or check the FAQ page:

http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#/Q10:_MATLAB_interface


================================================
FILE: Matlab/libsvm-3.19/matlab/libsvmread.c
================================================
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <ctype.h>
#include <errno.h>

#include "mex.h"

#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif 
#endif 
#ifndef max
#define max(x,y) (((x)>(y))?(x):(y))
#endif
#ifndef min
#define min(x,y) (((x)<(y))?(x):(y))
#endif

void exit_with_help()
{
	mexPrintf(
	"Usage: [label_vector, instance_matrix] = libsvmread('filename');\n"
	);
}

static void fake_answer(int nlhs, mxArray *plhs[])
{
	int i;
	for(i=0;i<nlhs;i++)
		plhs[i] = mxCreateDoubleMatrix(0, 0, mxREAL);
}

static char *line;
static int max_line_len;

static char* readline(FILE *input)
{
	int len;
	
	if(fgets(line,max_line_len,input) == NULL)
		return NULL;

	while(strrchr(line,'\n') == NULL)
	{
		max_line_len *= 2;
		line = (char *) realloc(line, max_line_len);
		len = (int) strlen(line);
		if(fgets(line+len,max_line_len-len,input) == NULL)
			break;
	}
	return line;
}

// read in a problem (in libsvm format)
void read_problem(const char *filename, int nlhs, mxArray *plhs[])
{
	int max_index, min_index, inst_max_index;
	size_t elements, k, i, l=0;
	FILE *fp = fopen(filename,"r");
	char *endptr;
	mwIndex *ir, *jc;
	double *labels, *samples;

	if(fp == NULL)
	{
		mexPrintf("can't open input file %s\n",filename);
		fake_answer(nlhs, plhs);
		return;
	}

	max_line_len = 1024;
	line = (char *) malloc(max_line_len*sizeof(char));

	max_index = 0;
	min_index = 1; // our index starts from 1
	elements = 0;
	while(readline(fp) != NULL)
	{
		char *idx, *val;
		// features
		int index = 0;

		inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0
		strtok(line," \t"); // label
		while (1)
		{
			idx = strtok(NULL,":"); // index:value
			val = strtok(NULL," \t");
			if(val == NULL)
				break;

			errno = 0;
			index = (int) strtol(idx,&endptr,10);
			if(endptr == idx || errno != 0 || *endptr != '\0' || index <= inst_max_index)
			{
				mexPrintf("Wrong input format at line %d\n",l+1);
				fake_answer(nlhs, plhs);
				return;
			}
			else
				inst_max_index = index;

			min_index = min(min_index, index);
			elements++;
		}
		max_index = max(max_index, inst_max_index);
		l++;
	}
	rewind(fp);

	// y
	plhs[0] = mxCreateDoubleMatrix(l, 1, mxREAL);
	// x^T
	if (min_index <= 0)
		plhs[1] = mxCreateSparse(max_index-min_index+1, l, elements, mxREAL);
	else
		plhs[1] = mxCreateSparse(max_index, l, elements, mxREAL);

	labels = mxGetPr(plhs[0]);
	samples = mxGetPr(plhs[1]);
	ir = mxGetIr(plhs[1]);
	jc = mxGetJc(plhs[1]);

	k=0;
	for(i=0;i<l;i++)
	{
		char *idx, *val, *label;
		jc[i] = k;

		readline(fp);

		label = strtok(line," \t\n");
		if(label == NULL)
		{
			mexPrintf("Empty line at line %d\n",i+1);
			fake_answer(nlhs, plhs);
			return;
		}
		labels[i] = strtod(label,&endptr);
		if(endptr == label || *endptr != '\0')
		{
			mexPrintf("Wrong input format at line %d\n",i+1);
			fake_answer(nlhs, plhs);
			return;
		}

		// features
		while(1)
		{
			idx = strtok(NULL,":");
			val = strtok(NULL," \t");
			if(val == NULL)
				break;

			ir[k] = (mwIndex) (strtol(idx,&endptr,10) - min_index); // precomputed kernel has <index> start from 0

			errno = 0;
			samples[k] = strtod(val,&endptr);
			if (endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
			{
				mexPrintf("Wrong input format at line %d\n",i+1);
				fake_answer(nlhs, plhs);
				return;
			}
			++k;
		}
	}
	jc[l] = k;

	fclose(fp);
	free(line);

	{
		mxArray *rhs[1], *lhs[1];
		rhs[0] = plhs[1];
		if(mexCallMATLAB(1, lhs, 1, rhs, "transpose"))
		{
			mexPrintf("Error: cannot transpose problem\n");
			fake_answer(nlhs, plhs);
			return;
		}
		plhs[1] = lhs[0];
	}
}

void mexFunction( int nlhs, mxArray *plhs[],
		int nrhs, const mxArray *prhs[] )
{
	char filename[256];

	if(nrhs != 1 || nlhs != 2)
	{
		exit_with_help();
		fake_answer(nlhs, plhs);
		return;
	}

	mxGetString(prhs[0], filename, mxGetN(prhs[0]) + 1);

	if(filename == NULL)
	{
		mexPrintf("Error: filename is NULL\n");
		return;
	}

	read_problem(filename, nlhs, plhs);

	return;
}



================================================
FILE: Matlab/libsvm-3.19/matlab/libsvmwrite.c
================================================
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "mex.h"

#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif

void exit_with_help()
{
	mexPrintf(
	"Usage: libsvmwrite('filename', label_vector, instance_matrix);\n"
	);
}

static void fake_answer(int nlhs, mxArray *plhs[])
{
	int i;
	for(i=0;i<nlhs;i++)
		plhs[i] = mxCreateDoubleMatrix(0, 0, mxREAL);
}

void libsvmwrite(const char *filename, const mxArray *label_vec, const mxArray *instance_mat)
{
	FILE *fp = fopen(filename,"w");
	mwIndex *ir, *jc, k, low, high;
	size_t i, l, label_vector_row_num;
	double *samples, *labels;
	mxArray *instance_mat_col; // instance sparse matrix in column format

	if(fp ==NULL)
	{
		mexPrintf("can't open output file %s\n",filename);			
		return;
	}

	// transpose instance matrix
	{
		mxArray *prhs[1], *plhs[1];
		prhs[0] = mxDuplicateArray(instance_mat);
		if(mexCallMATLAB(1, plhs, 1, prhs, "transpose"))
		{
			mexPrintf("Error: cannot transpose instance matrix\n");
			return;
		}
		instance_mat_col = plhs[0];
		mxDestroyArray(prhs[0]);
	}

	// the number of instance
	l = mxGetN(instance_mat_col);
	label_vector_row_num = mxGetM(label_vec);

	if(label_vector_row_num!=l)
	{
		mexPrintf("Length of label vector does not match # of instances.\n");
		return;
	}

	// each column is one instance
	labels = mxGetPr(label_vec);
	samples = mxGetPr(instance_mat_col);
	ir = mxGetIr(instance_mat_col);
	jc = mxGetJc(instance_mat_col);

	for(i=0;i<l;i++)
	{
		fprintf(fp,"%g", labels[i]);

		low = jc[i], high = jc[i+1];
		for(k=low;k<high;k++)
			fprintf(fp," %zu:%g", (size_t)ir[k]+1, samples[k]);		

		fprintf(fp,"\n");
	}

	fclose(fp);
	return;
}

void mexFunction( int nlhs, mxArray *plhs[],
		int nrhs, const mxArray *prhs[] )
{
	if(nlhs > 0)
	{
		exit_with_help();
		fake_answer(nlhs, plhs);
		return;
	}
	
	// Transform the input Matrix to libsvm format
	if(nrhs == 3)
	{
		char filename[256];
		if(!mxIsDouble(prhs[1]) || !mxIsDouble(prhs[2]))
		{
			mexPrintf("Error: label vector and instance matrix must be double\n");			
			return;
		}
		
		mxGetString(prhs[0], filename, mxGetN(prhs[0])+1);		

		if(mxIsSparse(prhs[2]))
			libsvmwrite(filename, prhs[1], prhs[2]);
		else
		{
			mexPrintf("Instance_matrix must be sparse\n");			
			return;
		}
	}
	else
	{
		exit_with_help();		
		return;
	}
}


================================================
FILE: Matlab/libsvm-3.19/matlab/make.m
================================================
% This make.m is for MATLAB and OCTAVE under Windows, Mac, and Unix

try
	Type = ver;
	% This part is for OCTAVE
	if(strcmp(Type(1).Name, 'Octave') == 1)
		mex libsvmread.c
		mex libsvmwrite.c
		mex svmtrain.c ../svm.cpp svm_model_matlab.c
		mex svmpredict.c ../svm.cpp svm_model_matlab.c
	% This part is for MATLAB
	% Add -largeArrayDims on 64-bit machines of MATLAB
	else
		mex CFLAGS="\$CFLAGS -std=c99" -largeArrayDims libsvmread.c
		mex CFLAGS="\$CFLAGS -std=c99" -largeArrayDims libsvmwrite.c
		mex CFLAGS="\$CFLAGS -std=c99" -largeArrayDims svmtrain.c ../svm.cpp svm_model_matlab.c
		mex CFLAGS="\$CFLAGS -std=c99" -largeArrayDims svmpredict.c ../svm.cpp svm_model_matlab.c
	end
catch
	fprintf('If make.m fails, please check README about detailed instructions.\n');
end


================================================
FILE: Matlab/libsvm-3.19/matlab/svm_model_matlab.c
================================================
#include <stdlib.h>
#include <string.h>
#include "../svm.h"

#include "mex.h"

#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif

#define NUM_OF_RETURN_FIELD 11

#define Malloc(type,n) (type *)malloc((n)*sizeof(type))

static const char *field_names[] = {
	"Parameters",
	"nr_class",
	"totalSV",
	"rho",
	"Label",
	"sv_indices",
	"ProbA",
	"ProbB",
	"nSV",
	"sv_coef",
	"SVs"
};

const char *model_to_matlab_structure(mxArray *plhs[], int num_of_feature, struct svm_model *model)
{
	int i, j, n;
	double *ptr;
	mxArray *return_model, **rhs;
	int out_id = 0;

	rhs = (mxArray **)mxMalloc(sizeof(mxArray *)*NUM_OF_RETURN_FIELD);

	// Parameters
	rhs[out_id] = mxCreateDoubleMatrix(5, 1, mxREAL);
	ptr = mxGetPr(rhs[out_id]);
	ptr[0] = model->param.svm_type;
	ptr[1] = model->param.kernel_type;
	ptr[2] = model->param.degree;
	ptr[3] = model->param.gamma;
	ptr[4] = model->param.coef0;
	out_id++;

	// nr_class
	rhs[out_id] = mxCreateDoubleMatrix(1, 1, mxREAL);
	ptr = mxGetPr(rhs[out_id]);
	ptr[0] = model->nr_class;
	out_id++;

	// total SV
	rhs[out_id] = mxCreateDoubleMatrix(1, 1, mxREAL);
	ptr = mxGetPr(rhs[out_id]);
	ptr[0] = model->l;
	out_id++;

	// rho
	n = model->nr_class*(model->nr_class-1)/2;
	rhs[out_id] = mxCreateDoubleMatrix(n, 1, mxREAL);
	ptr = mxGetPr(rhs[out_id]);
	for(i = 0; i < n; i++)
		ptr[i] = model->rho[i];
	out_id++;

	// Label
	if(model->label)
	{
		rhs[out_id] = mxCreateDoubleMatrix(model->nr_class, 1, mxREAL);
		ptr = mxGetPr(rhs[out_id]);
		for(i = 0; i < model->nr_class; i++)
			ptr[i] = model->label[i];
	}
	else
		rhs[out_id] = mxCreateDoubleMatrix(0, 0, mxREAL);
	out_id++;

	// sv_indices
	if(model->sv_indices)
	{
		rhs[out_id] = mxCreateDoubleMatrix(model->l, 1, mxREAL);
		ptr = mxGetPr(rhs[out_id]);
		for(i = 0; i < model->l; i++)
			ptr[i] = model->sv_indices[i];
	}
	else
		rhs[out_id] = mxCreateDoubleMatrix(0, 0, mxREAL);
	out_id++;

	// probA
	if(model->probA != NULL)
	{
		rhs[out_id] = mxCreateDoubleMatrix(n, 1, mxREAL);
		ptr = mxGetPr(rhs[out_id]);
		for(i = 0; i < n; i++)
			ptr[i] = model->probA[i];
	}
	else
		rhs[out_id] = mxCreateDoubleMatrix(0, 0, mxREAL);
	out_id ++;

	// probB
	if(model->probB != NULL)
	{
		rhs[out_id] = mxCreateDoubleMatrix(n, 1, mxREAL);
		ptr = mxGetPr(rhs[out_id]);
		for(i = 0; i < n; i++)
			ptr[i] = model->probB[i];
	}
	else
		rhs[out_id] = mxCreateDoubleMatrix(0, 0, mxREAL);
	out_id++;

	// nSV
	if(model->nSV)
	{
		rhs[out_id] = mxCreateDoubleMatrix(model->nr_class, 1, mxREAL);
		ptr = mxGetPr(rhs[out_id]);
		for(i = 0; i < model->nr_class; i++)
			ptr[i] = model->nSV[i];
	}
	else
		rhs[out_id] = mxCreateDoubleMatrix(0, 0, mxREAL);
	out_id++;

	// sv_coef
	rhs[out_id] = mxCreateDoubleMatrix(model->l, model->nr_class-1, mxREAL);
	ptr = mxGetPr(rhs[out_id]);
	for(i = 0; i < model->nr_class-1; i++)
		for(j = 0; j < model->l; j++)
			ptr[(i*(model->l))+j] = model->sv_coef[i][j];
	out_id++;

	// SVs
	{
		int ir_index, nonzero_element;
		mwIndex *ir, *jc;
		mxArray *pprhs[1], *pplhs[1];	

		if(model->param.kernel_type == PRECOMPUTED)
		{
			nonzero_element = model->l;
			num_of_feature = 1;
		}
		else
		{
			nonzero_element = 0;
			for(i = 0; i < model->l; i++) {
				j = 0;
				while(model->SV[i][j].index != -1) 
				{
					nonzero_element++;
					j++;
				}
			}
		}

		// SV in column, easier accessing
		rhs[out_id] = mxCreateSparse(num_of_feature, model->l, nonzero_element, mxREAL);
		ir = mxGetIr(rhs[out_id]);
		jc = mxGetJc(rhs[out_id]);
		ptr = mxGetPr(rhs[out_id]);
		jc[0] = ir_index = 0;		
		for(i = 0;i < model->l; i++)
		{
			if(model->param.kernel_type == PRECOMPUTED)
			{
				// make a (1 x model->l) matrix
				ir[ir_index] = 0; 
				ptr[ir_index] = model->SV[i][0].value;
				ir_index++;
				jc[i+1] = jc[i] + 1;
			}
			else
			{
				int x_index = 0;
				while (model->SV[i][x_index].index != -1)
				{
					ir[ir_index] = model->SV[i][x_index].index - 1; 
					ptr[ir_index] = model->SV[i][x_index].value;
					ir_index++, x_index++;
				}
				jc[i+1] = jc[i] + x_index;
			}
		}
		// transpose back to SV in row
		pprhs[0] = rhs[out_id];
		if(mexCallMATLAB(1, pplhs, 1, pprhs, "transpose"))
			return "cannot transpose SV matrix";
		rhs[out_id] = pplhs[0];
		out_id++;
	}

	/* Create a struct matrix contains NUM_OF_RETURN_FIELD fields */
	return_model = mxCreateStructMatrix(1, 1, NUM_OF_RETURN_FIELD, field_names);

	/* Fill struct matrix with input arguments */
	for(i = 0; i < NUM_OF_RETURN_FIELD; i++)
		mxSetField(return_model,0,field_names[i],mxDuplicateArray(rhs[i]));
	/* return */
	plhs[0] = return_model;
	mxFree(rhs);

	return NULL;
}

struct svm_model *matlab_matrix_to_model(const mxArray *matlab_struct, const char **msg)
{
	int i, j, n, num_of_fields;
	double *ptr;
	int id = 0;
	struct svm_node *x_space;
	struct svm_model *model;
	mxArray **rhs;

	num_of_fields = mxGetNumberOfFields(matlab_struct);
	if(num_of_fields != NUM_OF_RETURN_FIELD) 
	{
		*msg = "number of return field is not correct";
		return NULL;
	}
	rhs = (mxArray **) mxMalloc(sizeof(mxArray *)*num_of_fields);

	for(i=0;i<num_of_fields;i++)
		rhs[i] = mxGetFieldByNumber(matlab_struct, 0, i);

	model = Malloc(struct svm_model, 1);
	model->rho = NULL;
	model->probA = NULL;
	model->probB = NULL;
	model->label = NULL;
	model->sv_indices = NULL;
	model->nSV = NULL;
	model->free_sv = 1; // XXX

	ptr = mxGetPr(rhs[id]);
	model->param.svm_type = (int)ptr[0];
	model->param.kernel_type  = (int)ptr[1];
	model->param.degree	  = (int)ptr[2];
	model->param.gamma	  = ptr[3];
	model->param.coef0	  = ptr[4];
	id++;

	ptr = mxGetPr(rhs[id]);
	model->nr_class = (int)ptr[0];
	id++;

	ptr = mxGetPr(rhs[id]);
	model->l = (int)ptr[0];
	id++;

	// rho
	n = model->nr_class * (model->nr_class-1)/2;
	model->rho = (double*) malloc(n*sizeof(double));
	ptr = mxGetPr(rhs[id]);
	for(i=0;i<n;i++)
		model->rho[i] = ptr[i];
	id++;

	// label
	if(mxIsEmpty(rhs[id]) == 0)
	{
		model->label = (int*) malloc(model->nr_class*sizeof(int));
		ptr = mxGetPr(rhs[id]);
		for(i=0;i<model->nr_class;i++)
			model->label[i] = (int)ptr[i];
	}
	id++;

	// sv_indices
	if(mxIsEmpty(rhs[id]) == 0)
	{
		model->sv_indices = (int*) malloc(model->l*sizeof(int));
		ptr = mxGetPr(rhs[id]);
		for(i=0;i<model->l;i++)
			model->sv_indices[i] = (int)ptr[i];
	}
	id++;

	// probA
	if(mxIsEmpty(rhs[id]) == 0)
	{
		model->probA = (double*) malloc(n*sizeof(double));
		ptr = mxGetPr(rhs[id]);
		for(i=0;i<n;i++)
			model->probA[i] = ptr[i];
	}
	id++;

	// probB
	if(mxIsEmpty(rhs[id]) == 0)
	{
		model->probB = (double*) malloc(n*sizeof(double));
		ptr = mxGetPr(rhs[id]);
		for(i=0;i<n;i++)
			model->probB[i] = ptr[i];
	}
	id++;

	// nSV
	if(mxIsEmpty(rhs[id]) == 0)
	{
		model->nSV = (int*) malloc(model->nr_class*sizeof(int));
		ptr = mxGetPr(rhs[id]);
		for(i=0;i<model->nr_class;i++)
			model->nSV[i] = (int)ptr[i];
	}
	id++;

	// sv_coef
	ptr = mxGetPr(rhs[id]);
	model->sv_coef = (double**) malloc((model->nr_class-1)*sizeof(double));
	for( i=0 ; i< model->nr_class -1 ; i++ )
		model->sv_coef[i] = (double*) malloc((model->l)*sizeof(double));
	for(i = 0; i < model->nr_class - 1; i++)
		for(j = 0; j < model->l; j++)
			model->sv_coef[i][j] = ptr[i*(model->l)+j];
	id++;

	// SV
	{
		int sr, elements;
		int num_samples;
		mwIndex *ir, *jc;
		mxArray *pprhs[1], *pplhs[1];

		// transpose SV
		pprhs[0] = rhs[id];
		if(mexCallMATLAB(1, pplhs, 1, pprhs, "transpose")) 
		{
			svm_free_and_destroy_model(&model);
			*msg = "cannot transpose SV matrix";
			return NULL;
		}
		rhs[id] = pplhs[0];

		sr = (int)mxGetN(rhs[id]);

		ptr = mxGetPr(rhs[id]);
		ir = mxGetIr(rhs[id]);
		jc = mxGetJc(rhs[id]);

		num_samples = (int)mxGetNzmax(rhs[id]);

		elements = num_samples + sr;

		model->SV = (struct svm_node **) malloc(sr * sizeof(struct svm_node *));
		x_space = (struct svm_node *)malloc(elements * sizeof(struct svm_node));

		// SV is in column
		for(i=0;i<sr;i++)
		{
			int low = (int)jc[i], high = (int)jc[i+1];
			int x_index = 0;
			model->SV[i] = &x_space[low+i];
			for(j=low;j<high;j++)
			{
				model->SV[i][x_index].index = (int)ir[j] + 1; 
				model->SV[i][x_index].value = ptr[j];
				x_index++;
			}
			model->SV[i][x_index].index = -1;
		}

		id++;
	}
	mxFree(rhs);

	return model;
}


================================================
FILE: Matlab/libsvm-3.19/matlab/svm_model_matlab.h
================================================
const char *model_to_matlab_structure(mxArray *plhs[], int num_of_feature, struct svm_model *model);
struct svm_model *matlab_matrix_to_model(const mxArray *matlab_struct, const char **error_message);


================================================
FILE: Matlab/libsvm-3.19/matlab/svmpredict.c
================================================
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "../svm.h"

#include "mex.h"
#include "svm_model_matlab.h"

#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif

#define CMD_LEN 2048

int print_null(const char *s,...) {}
int (*info)(const char *fmt,...) = &mexPrintf;

void read_sparse_instance(const mxArray *prhs, int index, struct svm_node *x)
{
	int i, j, low, high;
	mwIndex *ir, *jc;
	double *samples;

	ir = mxGetIr(prhs);
	jc = mxGetJc(prhs);
	samples = mxGetPr(prhs);

	// each column is one instance
	j = 0;
	low = (int)jc[index], high = (int)jc[index+1];
	for(i=low;i<high;i++)
	{
		x[j].index = (int)ir[i] + 1;
		x[j].value = samples[i];
		j++;
	}
	x[j].index = -1;
}

static void fake_answer(int nlhs, mxArray *plhs[])
{
	int i;
	for(i=0;i<nlhs;i++)
		plhs[i] = mxCreateDoubleMatrix(0, 0, mxREAL);
}

void predict(int nlhs, mxArray *plhs[], const mxArray *prhs[], struct svm_model *model, const int predict_probability)
{
	int label_vector_row_num, label_vector_col_num;
	int feature_number, testing_instance_number;
	int instance_index;
	double *ptr_instance, *ptr_label, *ptr_predict_label; 
	double *ptr_prob_estimates, *ptr_dec_values, *ptr;
	struct svm_node *x;
	mxArray *pplhs[1]; // transposed instance sparse matrix
	mxArray *tplhs[3]; // temporary storage for plhs[]

	int correct = 0;
	int total = 0;
	double error = 0;
	double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;

	int svm_type=svm_get_svm_type(model);
	int nr_class=svm_get_nr_class(model);
	double *prob_estimates=NULL;

	// prhs[1] = testing instance matrix
	feature_number = (int)mxGetN(prhs[1]);
	testing_instance_number = (int)mxGetM(prhs[1]);
	label_vector_row_num = (int)mxGetM(prhs[0]);
	label_vector_col_num = (int)mxGetN(prhs[0]);

	if(label_vector_row_num!=testing_instance_number)
	{
		mexPrintf("Length of label vector does not match # of instances.\n");
		fake_answer(nlhs, plhs);
		return;
	}
	if(label_vector_col_num!=1)
	{
		mexPrintf("label (1st argument) should be a vector (# of column is 1).\n");
		fake_answer(nlhs, plhs);
		return;
	}

	ptr_instance = mxGetPr(prhs[1]);
	ptr_label    = mxGetPr(prhs[0]);

	// transpose instance matrix
	if(mxIsSparse(prhs[1]))
	{
		if(model->param.kernel_type == PRECOMPUTED)
		{
			// precomputed kernel requires dense matrix, so we make one
			mxArray *rhs[1], *lhs[1];
			rhs[0] = mxDuplicateArray(prhs[1]);
			if(mexCallMATLAB(1, lhs, 1, rhs, "full"))
			{
				mexPrintf("Error: cannot full testing instance matrix\n");
				fake_answer(nlhs, plhs);
				return;
			}
			ptr_instance = mxGetPr(lhs[0]);
			mxDestroyArray(rhs[0]);
		}
		else
		{
			mxArray *pprhs[1];
			pprhs[0] = mxDuplicateArray(prhs[1]);
			if(mexCallMATLAB(1, pplhs, 1, pprhs, "transpose"))
			{
				mexPrintf("Error: cannot transpose testing instance matrix\n");
				fake_answer(nlhs, plhs);
				return;
			}
		}
	}

	if(predict_probability)
	{
		if(svm_type==NU_SVR || svm_type==EPSILON_SVR)
			info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g\n",svm_get_svr_probability(model));
		else
			prob_estimates = (double *) malloc(nr_class*sizeof(double));
	}

	tplhs[0] = mxCreateDoubleMatrix(testing_instance_number, 1, mxREAL);
	if(predict_probability)
	{
		// prob estimates are in plhs[2]
		if(svm_type==C_SVC || svm_type==NU_SVC)
			tplhs[2] = mxCreateDoubleMatrix(testing_instance_number, nr_class, mxREAL);
		else
			tplhs[2] = mxCreateDoubleMatrix(0, 0, mxREAL);
	}
	else
	{
		// decision values are in plhs[2]
		if(svm_type == ONE_CLASS ||
		   svm_type == EPSILON_SVR ||
		   svm_type == NU_SVR ||
		   nr_class == 1) // if only one class in training data, decision values are still returned.
			tplhs[2] = mxCreateDoubleMatrix(testing_instance_number, 1, mxREAL);
		else
			tplhs[2] = mxCreateDoubleMatrix(testing_instance_number, nr_class*(nr_class-1)/2, mxREAL);
	}

	ptr_predict_label = mxGetPr(tplhs[0]);
	ptr_prob_estimates = mxGetPr(tplhs[2]);
	ptr_dec_values = mxGetPr(tplhs[2]);
	x = (struct svm_node*)malloc((feature_number+1)*sizeof(struct svm_node) );
	for(instance_index=0;instance_index<testing_instance_number;instance_index++)
	{
		int i;
		double target_label, predict_label;

		target_label = ptr_label[instance_index];

		if(mxIsSparse(prhs[1]) && model->param.kernel_type != PRECOMPUTED) // prhs[1]^T is still sparse
			read_sparse_instance(pplhs[0], instance_index, x);
		else
		{
			for(i=0;i<feature_number;i++)
			{
				x[i].index = i+1;
				x[i].value = ptr_instance[testing_instance_number*i+instance_index];
			}
			x[feature_number].index = -1;
		}

		if(predict_probability)
		{
			if(svm_type==C_SVC || svm_type==NU_SVC)
			{
				predict_label = svm_predict_probability(model, x, prob_estimates);
				ptr_predict_label[instance_index] = predict_label;
				for(i=0;i<nr_class;i++)
					ptr_prob_estimates[instance_index + i * testing_instance_number] = prob_estimates[i];
			} else {
				predict_label = svm_predict(model,x);
				ptr_predict_label[instance_index] = predict_label;
			}
		}
		else
		{
			if(svm_type == ONE_CLASS ||
			   svm_type == EPSILON_SVR ||
			   svm_type == NU_SVR)
			{
				double res;
				predict_label = svm_predict_values(model, x, &res);
				ptr_dec_values[instance_index] = res;
			}
			else
			{
				double *dec_values = (double *) malloc(sizeof(double) * nr_class*(nr_class-1)/2);
				predict_label = svm_predict_values(model, x, dec_values);
				if(nr_class == 1) 
					ptr_dec_values[instance_index] = 1;
				else
					for(i=0;i<(nr_class*(nr_class-1))/2;i++)
						ptr_dec_values[instance_index + i * testing_instance_number] = dec_values[i];
				free(dec_values);
			}
			ptr_predict_label[instance_index] = predict_label;
		}

		if(predict_label == target_label)
			++correct;
		error += (predict_label-target_label)*(predict_label-target_label);
		sump += predict_label;
		sumt += target_label;
		sumpp += predict_label*predict_label;
		sumtt += target_label*target_label;
		sumpt += predict_label*target_label;
		++total;
	}
	if(svm_type==NU_SVR || svm_type==EPSILON_SVR)
	{
		info("Mean squared error = %g (regression)\n",error/total);
		info("Squared correlation coefficient = %g (regression)\n",
			((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
			((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt))
			);
	}
	else
		info("Accuracy = %g%% (%d/%d) (classification)\n",
			(double)correct/total*100,correct,total);

	// return accuracy, mean squared error, squared correlation coefficient
	tplhs[1] = mxCreateDoubleMatrix(3, 1, mxREAL);
	ptr = mxGetPr(tplhs[1]);
	ptr[0] = (double)correct/total*100;
	ptr[1] = error/total;
	ptr[2] = ((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
				((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt));

	free(x);
	if(prob_estimates != NULL)
		free(prob_estimates);

	switch(nlhs)
	{
		case 3:
			plhs[2] = tplhs[2];
			plhs[1] = tplhs[1];
		case 1:
		case 0:
			plhs[0] = tplhs[0];
	}
}

void exit_with_help()
{
	mexPrintf(
		"Usage: [predicted_label, accuracy, decision_values/prob_estimates] = svmpredict(testing_label_vector, testing_instance_matrix, model, 'libsvm_options')\n"
		"       [predicted_label] = svmpredict(testing_label_vector, testing_instance_matrix, model, 'libsvm_options')\n"
		"Parameters:\n"
		"  model: SVM model structure from svmtrain.\n"
		"  libsvm_options:\n"
		"    -b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet\n"
		"    -q : quiet mode (no outputs)\n"
		"Returns:\n"
		"  predicted_label: SVM prediction output vector.\n"
		"  accuracy: a vector with accuracy, mean squared error, squared correlation coefficient.\n"
		"  prob_estimates: If selected, probability estimate vector.\n"
	);
}

void mexFunction( int nlhs, mxArray *plhs[],
		 int nrhs, const mxArray *prhs[] )
{
	int prob_estimate_flag = 0;
	struct svm_model *model;
	info = &mexPrintf;

	if(nlhs == 2 || nlhs > 3 || nrhs > 4 || nrhs < 3)
	{
		exit_with_help();
		fake_answer(nlhs, plhs);
		return;
	}

	if(!mxIsDouble(prhs[0]) || !mxIsDouble(prhs[1])) {
		mexPrintf("Error: label vector and instance matrix must be double\n");
		fake_answer(nlhs, plhs);
		return;
	}

	if(mxIsStruct(prhs[2]))
	{
		const char *error_msg;

		// parse options
		if(nrhs==4)
		{
			int i, argc = 1;
			char cmd[CMD_LEN], *argv[CMD_LEN/2];

			// put options in argv[]
			mxGetString(prhs[3], cmd,  mxGetN(prhs[3]) + 1);
			if((argv[argc] = strtok(cmd, " ")) != NULL)
				while((argv[++argc] = strtok(NULL, " ")) != NULL)
					;

			for(i=1;i<argc;i++)
			{
				if(argv[i][0] != '-') break;
				if((++i>=argc) && argv[i-1][1] != 'q')
				{
					exit_with_help();
					fake_answer(nlhs, plhs);
					return;
				}
				switch(argv[i-1][1])
				{
					case 'b':
						prob_estimate_flag = atoi(argv[i]);
						break;
					case 'q':
						i--;
						info = &print_null;
						break;
					default:
						mexPrintf("Unknown option: -%c\n", argv[i-1][1]);
						exit_with_help();
						fake_answer(nlhs, plhs);
						return;
				}
			}
		}

		model = matlab_matrix_to_model(prhs[2], &error_msg);
		if (model == NULL)
		{
			mexPrintf("Error: can't read model: %s\n", error_msg);
			fake_answer(nlhs, plhs);
			return;
		}

		if(prob_estimate_flag)
		{
			if(svm_check_probability_model(model)==0)
			{
				mexPrintf("Model does not support probabiliy estimates\n");
				fake_answer(nlhs, plhs);
				svm_free_and_destroy_model(&model);
				return;
			}
		}
		else
		{
			if(svm_check_probability_model(model)!=0)
				info("Model supports probability estimates, but disabled in predicton.\n");
		}

		predict(nlhs, plhs, prhs, model, prob_estimate_flag);
		// destroy model
		svm_free_and_destroy_model(&model);
	}
	else
	{
		mexPrintf("model file should be a struct array\n");
		fake_answer(nlhs, plhs);
	}

	return;
}


================================================
FILE: Matlab/libsvm-3.19/matlab/svmtrain.c
================================================
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include "../svm.h"

#include "mex.h"
#include "svm_model_matlab.h"

#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif

#define CMD_LEN 2048
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))

void print_null(const char *s) {}
void print_string_matlab(const char *s) {mexPrintf(s);}

void exit_with_help()
{
	mexPrintf(
	"Usage: model = svmtrain(training_label_vector, training_instance_matrix, 'libsvm_options');\n"
	"libsvm_options:\n"
	"-s svm_type : set type of SVM (default 0)\n"
	"	0 -- C-SVC		(multi-class classification)\n"
	"	1 -- nu-SVC		(multi-class classification)\n"
	"	2 -- one-class SVM\n"
	"	3 -- epsilon-SVR	(regression)\n"
	"	4 -- nu-SVR		(regression)\n"
	"-t kernel_type : set type of kernel function (default 2)\n"
	"	0 -- linear: u'*v\n"
	"	1 -- polynomial: (gamma*u'*v + coef0)^degree\n"
	"	2 -- radial basis function: exp(-gamma*|u-v|^2)\n"
	"	3 -- sigmoid: tanh(gamma*u'*v + coef0)\n"
	"	4 -- precomputed kernel (kernel values in training_instance_matrix)\n"
	"-d degree : set degree in kernel function (default 3)\n"
	"-g gamma : set gamma in kernel function (default 1/num_features)\n"
	"-r coef0 : set coef0 in kernel function (default 0)\n"
	"-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)\n"
	"-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)\n"
	"-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n"
	"-m cachesize : set cache memory size in MB (default 100)\n"
	"-e epsilon : set tolerance of termination criterion (default 0.001)\n"
	"-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)\n"
	"-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)\n"
	"-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)\n"
	"-v n : n-fold cross validation mode\n"
	"-q : quiet mode (no outputs)\n"
	);
}

// svm arguments
struct svm_parameter param;		// set by parse_command_line
struct svm_problem prob;		// set by read_problem
struct svm_model *model;
struct svm_node *x_space;
int cross_validation;
int nr_fold;


double do_cross_validation()
{
	int i;
	int total_correct = 0;
	double total_error = 0;
	double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
	double *target = Malloc(double,prob.l);
	double retval = 0.0;

	svm_cross_validation(&prob,&param,nr_fold,target);
	if(param.svm_type == EPSILON_SVR ||
	   param.svm_type == NU_SVR)
	{
		for(i=0;i<prob.l;i++)
		{
			double y = prob.y[i];
			double v = target[i];
			total_error += (v-y)*(v-y);
			sumv += v;
			sumy += y;
			sumvv += v*v;
			sumyy += y*y;
			sumvy += v*y;
		}
		mexPrintf("Cross Validation Mean squared error = %g\n",total_error/prob.l);
		mexPrintf("Cross Validation Squared correlation coefficient = %g\n",
			((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/
			((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))
			);
		retval = total_error/prob.l;
	}
	else
	{
		for(i=0;i<prob.l;i++)
			if(target[i] == prob.y[i])
				++total_correct;
		mexPrintf("Cross Validation Accuracy = %g%%\n",100.0*total_correct/prob.l);
		retval = 100.0*total_correct/prob.l;
	}
	free(target);
	return retval;
}

// nrhs should be 3
int parse_command_line(int nrhs, const mxArray *prhs[], char *model_file_name)
{
	int i, argc = 1;
	char cmd[CMD_LEN];
	char *argv[CMD_LEN/2];
	void (*print_func)(const char *) = print_string_matlab;	// default printing to matlab display

	// default values
	param.svm_type = C_SVC;
	param.kernel_type = RBF;
	param.degree = 3;
	param.gamma = 0;	// 1/num_features
	param.coef0 = 0;
	param.nu = 0.5;
	param.cache_size = 100;
	param.C = 1;
	param.eps = 1e-3;
	param.p = 0.1;
	param.shrinking = 1;
	param.probability = 0;
	param.nr_weight = 0;
	param.weight_label = NULL;
	param.weight = NULL;
	cross_validation = 0;

	if(nrhs <= 1)
		return 1;

	if(nrhs > 2)
	{
		// put options in argv[]
		mxGetString(prhs[2], cmd, mxGetN(prhs[2]) + 1);
		if((argv[argc] = strtok(cmd, " ")) != NULL)
			while((argv[++argc] = strtok(NULL, " ")) != NULL)
				;
	}

	// parse options
	for(i=1;i<argc;i++)
	{
		if(argv[i][0] != '-') break;
		++i;
		if(i>=argc && argv[i-1][1] != 'q')	// since option -q has no parameter
			return 1;
		switch(argv[i-1][1])
		{
			case 's':
				param.svm_type = atoi(argv[i]);
				break;
			case 't':
				param.kernel_type = atoi(argv[i]);
				break;
			case 'd':
				param.degree = atoi(argv[i]);
				break;
			case 'g':
				param.gamma = atof(argv[i]);
				break;
			case 'r':
				param.coef0 = atof(argv[i]);
				break;
			case 'n':
				param.nu = atof(argv[i]);
				break;
			case 'm':
				param.cache_size = atof(argv[i]);
				break;
			case 'c':
				param.C = atof(argv[i]);
				break;
			case 'e':
				param.eps = atof(argv[i]);
				break;
			case 'p':
				param.p = atof(argv[i]);
				break;
			case 'h':
				param.shrinking = atoi(argv[i]);
				break;
			case 'b':
				param.probability = atoi(argv[i]);
				break;
			case 'q':
				print_func = &print_null;
				i--;
				break;
			case 'v':
				cross_validation = 1;
				nr_fold = atoi(argv[i]);
				if(nr_fold < 2)
				{
					mexPrintf("n-fold cross validation: n must >= 2\n");
					return 1;
				}
				break;
			case 'w':
				++param.nr_weight;
				param.weight_label = (int *)realloc(param.weight_label,sizeof(int)*param.nr_weight);
				param.weight = (double *)realloc(param.weight,sizeof(double)*param.nr_weight);
				param.weight_label[param.nr_weight-1] = atoi(&argv[i-1][2]);
				param.weight[param.nr_weight-1] = atof(argv[i]);
				break;
			default:
				mexPrintf("Unknown option -%c\n", argv[i-1][1]);
				return 1;
		}
	}

	svm_set_print_string_function(print_func);

	return 0;
}

// read in a problem (in svmlight format)
int read_problem_dense(const mxArray *label_vec, const mxArray *instance_mat)
{
	// using size_t due to the output type of matlab functions
	size_t i, j, k, l;
	size_t elements, max_index, sc, label_vector_row_num;
	double *samples, *labels;

	prob.x = NULL;
	prob.y = NULL;
	x_space = NULL;

	labels = mxGetPr(label_vec);
	samples = mxGetPr(instance_mat);
	sc = mxGetN(instance_mat);

	elements = 0;
	// number of instances
	l = mxGetM(instance_mat);
	label_vector_row_num = mxGetM(label_vec);
	prob.l = (int)l;

	if(label_vector_row_num!=l)
	{
		mexPrintf("Length of label vector does not match # of instances.\n");
		return -1;
	}

	if(param.kernel_type == PRECOMPUTED)
		elements = l * (sc + 1);
	else
	{
		for(i = 0; i < l; i++)
		{
			for(k = 0; k < sc; k++)
				if(samples[k * l + i] != 0)
					elements++;
			// count the '-1' element
			elements++;
		}
	}

	prob.y = Malloc(double,l);
	prob.x = Malloc(struct svm_node *,l);
	x_space = Malloc(struct svm_node, elements);

	max_index = sc;
	j = 0;
	for(i = 0; i < l; i++)
	{
		prob.x[i] = &x_space[j];
		prob.y[i] = labels[i];

		for(k = 0; k < sc; k++)
		{
			if(param.kernel_type == PRECOMPUTED || samples[k * l + i] != 0)
			{
				x_space[j].index = (int)k + 1;
				x_space[j].value = samples[k * l + i];
				j++;
			}
		}
		x_space[j++].index = -1;
	}

	if(param.gamma == 0 && max_index > 0)
		param.gamma = (double)(1.0/max_index);

	if(param.kernel_type == PRECOMPUTED)
		for(i=0;i<l;i++)
		{
			if((int)prob.x[i][0].value <= 0 || (int)prob.x[i][0].value > (int)max_index)
			{
				mexPrintf("Wrong input format: sample_serial_number out of range\n");
				return -1;
			}
		}

	return 0;
}

int read_problem_sparse(const mxArray *label_vec, const mxArray *instance_mat)
{
	mwIndex *ir, *jc, low, high, k;
	// using size_t due to the output type of matlab functions
	size_t i, j, l, elements, max_index, label_vector_row_num;
	mwSize num_samples;
	double *samples, *labels;
	mxArray *instance_mat_col; // transposed instance sparse matrix

	prob.x = NULL;
	prob.y = NULL;
	x_space = NULL;

	// transpose instance matrix
	{
		mxArray *prhs[1], *plhs[1];
		prhs[0] = mxDuplicateArray(instance_mat);
		if(mexCallMATLAB(1, plhs, 1, prhs, "transpose"))
		{
			mexPrintf("Error: cannot transpose training instance matrix\n");
			return -1;
		}
		instance_mat_col = plhs[0];
		mxDestroyArray(prhs[0]);
	}

	// each column is one instance
	labels = mxGetPr(label_vec);
	samples = mxGetPr(instance_mat_col);
	ir = mxGetIr(instance_mat_col);
	jc = mxGetJc(instance_mat_col);

	num_samples = mxGetNzmax(instance_mat_col);

	// number of instances
	l = mxGetN(instance_mat_col);
	label_vector_row_num = mxGetM(label_vec);
	prob.l = (int) l;

	if(label_vector_row_num!=l)
	{
		mexPrintf("Length of label vector does not match # of instances.\n");
		return -1;
	}

	elements = num_samples + l;
	max_index = mxGetM(instance_mat_col);

	prob.y = Malloc(double,l);
	prob.x = Malloc(struct svm_node *,l);
	x_space = Malloc(struct svm_node, elements);

	j = 0;
	for(i=0;i<l;i++)
	{
		prob.x[i] = &x_space[j];
		prob.y[i] = labels[i];
		low = jc[i], high = jc[i+1];
		for(k=low;k<high;k++)
		{
			x_space[j].index = (int)ir[k] + 1;
			x_space[j].value = samples[k];
			j++;
	 	}
		x_space[j++].index = -1;
	}

	if(param.gamma == 0 && max_index > 0)
		param.gamma = (double)(1.0/max_index);

	return 0;
}

static void fake_answer(int nlhs, mxArray *plhs[])
{
	int i;
	for(i=0;i<nlhs;i++)
		plhs[i] = mxCreateDoubleMatrix(0, 0, mxREAL);
}

// Interface function of matlab
// now assume prhs[0]: label prhs[1]: features
void mexFunction( int nlhs, mxArray *plhs[],
		int nrhs, const mxArray *prhs[] )
{
	const char *error_msg;

	// fix random seed to have same results for each run
	// (for cross validation and probability estimation)
	srand(1);

	if(nlhs > 1)
	{
		exit_with_help();
		fake_answer(nlhs, plhs);
		return;
	}

	// Transform the input Matrix to libsvm format
	if(nrhs > 1 && nrhs < 4)
	{
		int err;

		if(!mxIsDouble(prhs[0]) || !mxIsDouble(prhs[1]))
		{
			mexPrintf("Error: label vector and instance matrix must be double\n");
			fake_answer(nlhs, plhs);
			return;
		}

		if(mxIsSparse(prhs[0]))
		{
			mexPrintf("Error: label vector should not be in sparse format\n");
			fake_answer(nlhs, plhs);
			return;
		}

		if(parse_command_line(nrhs, prhs, NULL))
		{
			exit_with_help();
			svm_destroy_param(&param);
			fake_answer(nlhs, plhs);
			return;
		}

		if(mxIsSparse(prhs[1]))
		{
			if(param.kernel_type == PRECOMPUTED)
			{
				// precomputed kernel requires dense matrix, so we make one
				mxArray *rhs[1], *lhs[1];

				rhs[0] = mxDuplicateArray(prhs[1]);
				if(mexCallMATLAB(1, lhs, 1, rhs, "full"))
				{
					mexPrintf("Error: cannot generate a full training instance matrix\n");
					svm_destroy_param(&param);
					fake_answer(nlhs, plhs);
					return;
				}
				err = read_problem_dense(prhs[0], lhs[0]);
				mxDestroyArray(lhs[0]);
				mxDestroyArray(rhs[0]);
			}
			else
				err = read_problem_sparse(prhs[0], prhs[1]);
		}
		else
			err = read_problem_dense(prhs[0], prhs[1]);

		// svmtrain's original code
		error_msg = svm_check_parameter(&prob, &param);

		if(err || error_msg)
		{
			if (error_msg != NULL)
				mexPrintf("Error: %s\n", error_msg);
			svm_destroy_param(&param);
			free(prob.y);
			free(prob.x);
			free(x_space);
			fake_answer(nlhs, plhs);
			return;
		}

		if(cross_validation)
		{
			double *ptr;
			plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL);
			ptr = mxGetPr(plhs[0]);
			ptr[0] = do_cross_validation();
		}
		else
		{
			int nr_feat = (int)mxGetN(prhs[1]);
			const char *error_msg;
			model = svm_train(&prob, &param);
			error_msg = model_to_matlab_structure(plhs, nr_feat, model);
			if(error_msg)
				mexPrintf("Error: can't convert libsvm model to matrix structure: %s\n", error_msg);
			svm_free_and_destroy_model(&model);
		}
		svm_destroy_param(&param);
		free(prob.y);
		free(prob.x);
		free(x_space);
	}
	else
	{
		exit_with_help();
		fake_answer(nlhs, plhs);
		return;
	}
}


================================================
FILE: Matlab/libsvm-3.19/python/Makefile
================================================
all = lib

lib:
	make -C .. lib


================================================
FILE: Matlab/libsvm-3.19/python/README
================================================
----------------------------------
--- Python interface of LIBSVM ---
----------------------------------

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

- Introduction
- Installation
- Quick Start
- Design Description
- Data Structures
- Utility Functions
- Additional Information

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

Python (http://www.python.org/) is a programming language suitable for rapid
development. This tool provides a simple Python interface to LIBSVM, a library
for support vector machines (http://www.csie.ntu.edu.tw/~cjlin/libsvm). The
interface is very easy to use as the usage is the same as that of LIBSVM. The
interface is developed with the built-in Python library "ctypes."

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

On Unix systems, type

> make

The interface needs only LIBSVM shared library, which is generated by
the above command. We assume that the shared library is on the LIBSVM
main directory or in the system path.

For windows, the shared library libsvm.dll for 32-bit python is ready
in the directory `..\windows'. You can also copy it to the system
directory (e.g., `C:\WINDOWS\system32\' for Windows XP). To regenerate
the shared library, please follow the instruction of building windows
binaries in LIBSVM README.

Quick Start
===========

There are two levels of usage. The high-level one uses utility functions
in svmutil.py and the usage is the same as the LIBSVM MATLAB interface.

>>> from svmutil import *
# Read data in LIBSVM format
>>> y, x = svm_read_problem('../heart_scale')
>>> m = svm_train(y[:200], x[:200], '-c 4')
>>> p_label, p_acc, p_val = svm_predict(y[200:], x[200:], m)

# Construct problem in python format
# Dense data
>>> y, x = [1,-1], [[1,0,1], [-1,0,-1]]
# Sparse data
>>> y, x = [1,-1], [{1:1, 3:1}, {1:-1,3:-1}]
>>> prob  = svm_problem(y, x)
>>> param = svm_parameter('-t 0 -c 4 -b 1')
>>> m = svm_train(prob, param)

# Precomputed kernel data (-t 4)
# Dense data
>>> y, x = [1,-1], [[1, 2, -2], [2, -2, 2]]
# Sparse data
>>> y, x = [1,-1], [{0:1, 1:2, 2:-2}, {0:2, 1:-2, 2:2}]
# isKernel=True must be set for precomputer kernel
>>> prob  = svm_problem(y, x, isKernel=True)
>>> param = svm_parameter('-t 4 -c 4 -b 1')
>>> m = svm_train(prob, param)
# For the format of precomputed kernel, please read LIBSVM README.


# Other utility functions
>>> svm_save_model('heart_scale.model', m)
>>> m = svm_load_model('heart_scale.model')
>>> p_label, p_acc, p_val = svm_predict(y, x, m, '-b 1')
>>> ACC, MSE, SCC = evaluations(y, p_label)

# Getting online help
>>> help(svm_train)

The low-level use directly calls C interfaces imported by svm.py. Note that
all arguments and return values are in ctypes format. You need to handle them
carefully.

>>> from svm import *
>>> prob = svm_problem([1,-1], [{1:1, 3:1}, {1:-1,3:-1}])
>>> param = svm_parameter('-c 4')
>>> m = libsvm.svm_train(prob, param) # m is a ctype pointer to an svm_model
# Convert a Python-format instance to svm_nodearray, a ctypes structure
>>> x0, max_idx = gen_svm_nodearray({1:1, 3:1})
>>> label = libsvm.svm_predict(m, x0)

Design Description
==================

There are two files svm.py and svmutil.py, which respectively correspond to
low-level and high-level use of the interface.

In svm.py, we adopt the Python built-in library "ctypes," so that
Python can directly access C structures and interface functions defined
in svm.h.

While advanced users can use structures/functions in svm.py, to
avoid handling ctypes structures, in svmutil.py we provide some easy-to-use
functions. The usage is similar to LIBSVM MATLAB interface.

Data Structures
===============

Four data structures derived from svm.h are svm_node, svm_problem, svm_parameter, 
and svm_model. They all contain fields with the same names in svm.h. Access 
these fields carefully because you directly use a C structure instead of a 
Python object. For svm_model, accessing the field directly is not recommanded. 
Programmers should use the interface functions or methods of svm_model class
in Python to get the values. The following description introduces additional
fields and methods.

Before using the data structures, execute the following command to load the
LIBSVM shared library:

    >>> from svm import *

- class svm_node:

    Construct an svm_node.

    >>> node = svm_node(idx, val)

    idx: an integer indicates the feature index.

    val: a float indicates the feature value.

    Show the index and the value of a node.

    >>> print(node) 

- Function: gen_svm_nodearray(xi [,feature_max=None [,isKernel=False]])

    Generate a feature vector from a Python list/tuple or a dictionary:

    >>> xi, max_idx = gen_svm_nodearray({1:1, 3:1, 5:-2})

    xi: the returned svm_nodearray (a ctypes structure)

    max_idx: the maximal feature index of xi

    feature_max: if feature_max is assigned, features with indices larger than
                 feature_max are removed.
    
    isKernel: if isKernel == True, the list index starts from 0 for precomputed
              kernel. Otherwise, the list index starts from 1. The default 
	      value is False.

- class svm_problem:

    Construct an svm_problem instance

    >>> prob = svm_problem(y, x)

    y: a Python list/tuple of l labels (type must be int/double).

    x: a Python list/tuple of l data instances. Each element of x must be
       an instance of list/tuple/dictionary type.

    Note that if your x contains sparse data (i.e., dictionary), the internal
    ctypes data format is still sparse.

    For pre-computed kernel, the isKernel flag should be set to True:

    >>> prob = svm_problem(y, x, isKernel=True)

    Please read LIBSVM README for more details of pre-computed kernel.

- class svm_parameter:

    Construct an svm_parameter instance

    >>> param = svm_parameter('training_options')

    If 'training_options' is empty, LIBSVM default values are applied.

    Set param to LIBSVM default values.

    >>> param.set_to_default_values()

    Parse a string of options.

    >>> param.parse_options('training_options')

    Show values of parameters.

    >>> print(param)

- class svm_model:

    There are two ways to obtain an instance of svm_model:

    >>> model = svm_train(y, x)
    >>> model = svm_load_model('model_file_name')

    Note that the returned structure of interface functions
    libsvm.svm_train and libsvm.svm_load_model is a ctypes pointer of
    svm_model, which is different from the svm_model object returned
    by svm_train and svm_load_model in svmutil.py. We provide a
    function toPyModel for the conversion:

    >>> model_ptr = libsvm.svm_train(prob, param)
    >>> model = toPyModel(model_ptr)

    If you obtain a model in a way other than the above approaches,
    handle it carefully to avoid memory leak or segmentation fault.

    Some interface functions to access LIBSVM models are wrapped as
    members of the class svm_model:

    >>> svm_type = model.get_svm_type()
    >>> nr_class = model.get_nr_class()
    >>> svr_probability = model.get_svr_probability()
    >>> class_labels = model.get_labels()
    >>> sv_indices = model.get_sv_indices()
    >>> nr_sv = model.get_nr_sv()
    >>> is_prob_model = model.is_probability_model()
    >>> support_vector_coefficients = model.get_sv_coef()
    >>> support_vectors = model.get_SV() 

Utility Functions
=================

To use utility functions, type

    >>> from svmutil import *

The above command loads
    svm_train()        : train an SVM model
    svm_predict()      : predict testing data
    svm_read_problem() : read the data from a LIBSVM-format file.
    svm_load_model()   : load a LIBSVM model.
    svm_save_model()   : save model to a file.
    evaluations()      : evaluate prediction results.

- Function: svm_train

    There are three ways to call svm_train()

    >>> model = svm_train(y, x [, 'training_options'])
    >>> model = svm_train(prob [, 'training_options'])
    >>> model = svm_train(prob, param)

    y: a list/tuple of l training labels (type must be int/double).

    x: a list/tuple of l training instances. The feature vector of
       each training instance is an instance of list/tuple or dictionary.

    training_options: a string in the same form as that for LIBSVM command
                      mode.

    prob: an svm_problem instance generated by calling
          svm_problem(y, x).
	  For pre-computed kernel, you should use
	  svm_problem(y, x, isKernel=True)

    param: an svm_parameter instance generated by calling
           svm_parameter('training_options')

    model: the returned svm_model instance. See svm.h for details of this
           structure. If '-v' is specified, cross validation is
           conducted and the returned model is just a scalar: cross-validation
           accuracy for classification and mean-squared error for regression.

    To train the same data many times with different
    parameters, the second and the third ways should be faster..

    Examples:

    >>> y, x = svm_read_problem('../heart_scale')
    >>> prob = svm_problem(y, x)
    >>> param = svm_parameter('-s 3 -c 5 -h 0')
    >>> m = svm_train(y, x, '-c 5')
    >>> m = svm_train(prob, '-t 2 -c 5')
    >>> m = svm_train(prob, param)
    >>> CV_ACC = svm_train(y, x, '-v 3')

- Function: svm_predict

    To predict testing data with a model, use

    >>> p_labs, p_acc, p_vals = svm_predict(y, x, model [,'predicting_options'])

    y: a list/tuple of l true labels (type must be int/double). It is used
       for calculating the accuracy. Use [0]*len(x) if true labels are
       unavailable.

    x: a list/tuple of l predicting instances. The feature vector of
       each predicting instance is an instance of list/tuple or dictionary.

    predicting_options: a string of predicting options in the same format as
                        that of LIBSVM.

    model: an svm_model instance.

    p_labels: a list of predicted labels

    p_acc: a tuple including accuracy (for classification), mean
           squared error, and squared correlation coefficient (for
           regression).

    p_vals: a list of decision values or probability estimates (if '-b 1'
            is specified). If k is the number of classes in training data, 
	    for decision values, each element includes results of predicting 
	    k(k-1)/2 binary-class SVMs. For classification, k = 1 is a 
	    special case. Decision value [+1] is returned for each testing
	    instance, instead of an empty list.
	    For probabilities, each element contains k values indicating
            the probability that the testing instance is in each class.
            Note that the order of classes is the same as the 'model.label'
            field in the model structure.

    Example:

    >>> m = svm_train(y, x, '-c 5')
    >>> p_labels, p_acc, p_vals = svm_predict(y, x, m)

- Functions:  svm_read_problem/svm_load_model/svm_save_model

    See the usage by examples:

    >>> y, x = svm_read_problem('data.txt')
    >>> m = svm_load_model('model_file')
    >>> svm_save_model('model_file', m)

- Function: evaluations

    Calculate some evaluations using the true values (ty) and predicted
    values (pv):

    >>> (ACC, MSE, SCC) = evaluations(ty, pv)

    ty: a list of true values.

    pv: a list of predict values.

    ACC: accuracy.

    MSE: mean squared error.

    SCC: squared correlation coefficient.


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

This interface was written by Hsiang-Fu Yu from Department of Computer
Science, National Taiwan University. If you find this tool useful, please
cite LIBSVM as follows

Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support
vector machines. ACM Transactions on Intelligent Systems and
Technology, 2:27:1--27:27, 2011. Software available at
http://www.csie.ntu.edu.tw/~cjlin/libsvm

For any question, please contact Chih-Jen Lin <cjlin@csie.ntu.edu.tw>,
or check the FAQ page:

http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html


================================================
FILE: Matlab/libsvm-3.19/python/svm.py
================================================
#!/usr/bin/env python

from ctypes import *
from ctypes.util import find_library
from os import path
import sys

__all__ = ['libsvm', 'svm_problem', 'svm_parameter',
           'toPyModel', 'gen_svm_nodearray', 'print_null', 'svm_node', 'C_SVC',
           'EPSILON_SVR', 'LINEAR', 'NU_SVC', 'NU_SVR', 'ONE_CLASS',
           'POLY', 'PRECOMPUTED', 'PRINT_STRING_FUN', 'RBF',
           'SIGMOID', 'c_double', 'svm_model']

try:
	dirname = path.dirname(path.abspath(__file__))
	if sys.platform == 'win32':
		libsvm = CDLL(path.join(dirname, r'..\windows\libsvm.dll'))
	else:
		libsvm = CDLL(path.join(dirname, '../libsvm.so.2'))
except:
# For unix the prefix 'lib' is not considered.
	if find_library('svm'):
		libsvm = CDLL(find_library('svm'))
	elif find_library('libsvm'):
		libsvm = CDLL(find_library('libsvm'))
	else:
		raise Exception('LIBSVM library not found.')

C_SVC = 0
NU_SVC = 1
ONE_CLASS = 2
EPSILON_SVR = 3
NU_SVR = 4

LINEAR = 0
POLY = 1
RBF = 2
SIGMOID = 3
PRECOMPUTED = 4

PRINT_STRING_FUN = CFUNCTYPE(None, c_char_p)
def print_null(s):
	return

def genFields(names, types):
	return list(zip(names, types))

def fillprototype(f, restype, argtypes):
	f.restype = restype
	f.argtypes = argtypes

class svm_node(Structure):
	_names = ["index", "value"]
	_types = [c_int, c_double]
	_fields_ = genFields(_names, _types)

	def __str__(self):
		return '%d:%g' % (self.index, self.value)

def gen_svm_nodearray(xi, feature_max=None, isKernel=None):
	if isinstance(xi, dict):
		index_range = xi.keys()
	elif isinstance(xi, (list, tuple)):
		if not isKernel:
			xi = [0] + xi  # idx should start from 1
		index_range = range(len(xi))
	else:
		raise TypeError('xi should be a dictionary, list or tuple')

	if feature_max:
		assert(isinstance(feature_max, int))
		index_range = filter(lambda j: j <= feature_max, index_range)
	if not isKernel:
		index_range = filter(lambda j:xi[j] != 0, index_range)

	index_range = sorted(index_range)
	ret = (svm_node * (len(index_range)+1))()
	ret[-1].index = -1
	for idx, j in enumerate(index_range):
		ret[idx].index = j
		ret[idx].value = xi[j]
	max_idx = 0
	if index_range:
		max_idx = index_range[-1]
	return ret, max_idx

class svm_problem(Structure):
	_names = ["l", "y", "x"]
	_types = [c_int, POINTER(c_double), POINTER(POINTER(svm_node))]
	_fields_ = genFields(_names, _types)

	def __init__(self, y, x, isKernel=None):
		if len(y) != len(x):
			raise ValueError("len(y) != len(x)")
		self.l = l = len(y)

		max_idx = 0
		x_space = self.x_space = []
		for i, xi in enumerate(x):
			tmp_xi, tmp_idx = gen_svm_nodearray(xi,isKernel=isKernel)
			x_space += [tmp_xi]
			max_idx = max(max_idx, tmp_idx)
		self.n = max_idx

		self.y = (c_double * l)()
		for i, yi in enumerate(y): self.y[i] = yi

		self.x = (POINTER(svm_node) * l)()
		for i, xi in enumerate(self.x_space): self.x[i] = xi

class svm_parameter(Structure):
	_names = ["svm_type", "kernel_type", "degree", "gamma", "coef0",
			"cache_size", "eps", "C", "nr_weight", "weight_label", "weight",
			"nu", "p", "shrinking", "probability"]
	_types = [c_int, c_int, c_int, c_double, c_double,
			c_double, c_double, c_double, c_int, POINTER(c_int), POINTER(c_double),
			c_double, c_double, c_int, c_int]
	_fields_ = genFields(_names, _types)

	def __init__(self, options = None):
		if options == None:
			options = ''
		self.parse_options(options)

	def __str__(self):
		s = ''
		attrs = svm_parameter._names + list(self.__dict__.keys())
		values = map(lambda attr: getattr(self, attr), attrs)
		for attr, val in zip(attrs, values):
			s += (' %s: %s\n' % (attr, val))
		s = s.strip()

		return s

	def set_to_default_values(self):
		self.svm_type = C_SVC;
		self.kernel_type = RBF
		self.degree = 3
		self.gamma = 0
		self.coef0 = 0
		self.nu = 0.5
		self.cache_size = 100
		self.C = 1
		self.eps = 0.001
		self.p = 0.1
		self.shrinking = 1
		self.probability = 0
		self.nr_weight = 0
		self.weight_label = (c_int*0)()
		self.weight = (c_double*0)()
		self.cross_validation = False
		self.nr_fold = 0
		self.print_func = cast(None, PRINT_STRING_FUN)

	def parse_options(self, options):
		if isinstance(options, list):
			argv = options
		elif isinstance(options, str):
			argv = options.split()
		else:
			raise TypeError("arg 1 should be a list or a str.")
		self.set_to_default_values()
		self.print_func = cast(None, PRINT_STRING_FUN)
		weight_label = []
		weight = []

		i = 0
		while i < len(argv):
			if argv[i] == "-s":
				i = i + 1
				self.svm_type = int(argv[i])
			elif argv[i] == "-t":
				i = i + 1
				self.kernel_type = int(argv[i])
			elif argv[i] == "-d":
				i = i + 1
				self.degree = int(argv[i])
			elif argv[i] == "-g":
				i = i + 1
				self.gamma = float(argv[i])
			elif argv[i] == "-r":
				i = i + 1
				self.coef0 = float(argv[i])
			elif argv[i] == "-n":
				i = i + 1
				self.nu = float(argv[i])
			elif argv[i] == "-m":
				i = i + 1
				self.cache_size = float(argv[i])
			elif argv[i] == "-c":
				i = i + 1
				self.C = float(argv[i])
			elif argv[i] == "-e":
				i = i + 1
				self.eps = float(argv[i])
			elif argv[i] == "-p":
				i = i + 1
				self.p = float(argv[i])
			elif argv[i] == "-h":
				i = i + 1
				self.shrinking = int(argv[i])
			elif argv[i] == "-b":
				i = i + 1
				self.probability = int(argv[i])
			elif argv[i] == "-q":
				self.print_func = PRINT_STRING_FUN(print_null)
			elif argv[i] == "-v":
				i = i + 1
				self.cross_validation = 1
				self.nr_fold = int(argv[i])
				if self.nr_fold < 2:
					raise ValueError("n-fold cross validation: n must >= 2")
			elif argv[i].startswith("-w"):
				i = i + 1
				self.nr_weight += 1
				nr_weight = self.nr_weight
				weight_label += [int(argv[i-1][2:])]
				weight += [float(argv[i])]
			else:
				raise ValueError("Wrong options")
			i += 1

		libsvm.svm_set_print_string_function(self.print_func)
		self.weight_label = (c_int*self.nr_weight)()
		self.weight = (c_double*self.nr_weight)()
		for i in range(self.nr_weight):
			self.weight[i] = weight[i]
			self.weight_label[i] = weight_label[i]

class svm_model(Structure):
	_names = ['param', 'nr_class', 'l', 'SV', 'sv_coef', 'rho',
			'probA', 'probB', 'sv_indices', 'label', 'nSV', 'free_sv']
	_types = [svm_parameter, c_int, c_int, POINTER(POINTER(svm_node)),
			POINTER(POINTER(c_double)), POINTER(c_double),
			POINTER(c_double), POINTER(c_double), POINTER(c_int),
			POINTER(c_int), POINTER(c_int), c_int]
	_fields_ = genFields(_names, _types)

	def __init__(self):
		self.__createfrom__ = 'python'

	def __del__(self):
		# free memory created by C to avoid memory leak
		if hasattr(self, '__createfrom__') and self.__createfrom__ == 'C':
			libsvm.svm_free_and_destroy_model(pointer(self))

	def get_svm_type(self):
		return libsvm.svm_get_svm_type(self)

	def get_nr_class(self):
		return libsvm.svm_get_nr_class(self)

	def get_svr_probability(self):
		return libsvm.svm_get_svr_probability(self)

	def get_labels(self):
		nr_class = self.get_nr_class()
		labels = (c_int * nr_class)()
		libsvm.svm_get_labels(self, labels)
		return labels[:nr_class]

	def get_sv_indices(self):
		total_sv = self.get_nr_sv()
		sv_indices = (c_int * total_sv)()
		libsvm.svm_get_sv_indices(self, sv_indices)
		return sv_indices[:total_sv]

	def get_nr_sv(self):
		return libsvm.svm_get_nr_sv(self)

	def is_probability_model(self):
		return (libsvm.svm_check_probability_model(self) == 1)

	def get_sv_coef(self):
		return [tuple(self.sv_coef[j][i] for j in xrange(self.nr_class - 1))
				for i in xrange(self.l)]

	def get_SV(self):
		result = []
		for sparse_sv in self.SV[:self.l]:
			row = dict()

			i = 0
			while True:
				row[sparse_sv[i].index] = sparse_sv[i].value
				if sparse_sv[i].index == -1:
					break
				i += 1

			result.append(row)
		return result

def toPyModel(model_ptr):
	"""
	toPyModel(model_ptr) -> svm_model

	Convert a ctypes POINTER(svm_model) to a Python svm_model
	"""
	if bool(model_ptr) == False:
		raise ValueError("Null pointer")
	m = model_ptr.contents
	m.__createfrom__ = 'C'
	return m

fillprototype(libsvm.svm_train, POINTER(svm_model), [POINTER(svm_problem), POINTER(svm_parameter)])
fillprototype(libsvm.svm_cross_validation, None, [POINTER(svm_problem), POINTER(svm_parameter), c_int, POINTER(c_double)])

fillprototype(libsvm.svm_save_model, c_int, [c_char_p, POINTER(svm_model)])
fillprototype(libsvm.svm_load_model, POINTER(svm_model), [c_char_p])

fillprototype(libsvm.svm_get_svm_type, c_int, [POINTER(svm_model)])
fillprototype(libsvm.svm_get_nr_class, c_int, [POINTER(svm_model)])
fillprototype(libsvm.svm_get_labels, None, [POINTER(svm_model), POINTER(c_int)])
fillprototype(libsvm.svm_get_sv_indices, None, [POINTER(svm_model), POINTER(c_int)])
fillprototype(libsvm.svm_get_nr_sv, c_int, [POINTER(svm_model)])
fillprototype(libsvm.svm_get_svr_probability, c_double, [POINTER(svm_model)])

fillprototype(libsvm.svm_predict_values, c_double, [POINTER(svm_model), POINTER(svm_node), POINTER(c_double)])
fillprototype(libsvm.svm_predict, c_double, [POINTER(svm_model), POINTER(svm_node)])
fillprototype(libsvm.svm_predict_probability, c_double, [POINTER(svm_model), POINTER(svm_node), POINTER(c_double)])

fillprototype(libsvm.svm_free_model_content, None, [POINTER(svm_model)])
fillprototype(libsvm.svm_free_and_destroy_model, None, [POINTER(POINTER(svm_model))])
fillprototype(libsvm.svm_destroy_param, None, [POINTER(svm_parameter)])

fillprototype(libsvm.svm_check_parameter, c_char_p, [POINTER(svm_problem), POINTER(svm_parameter)])
fillprototype(libsvm.svm_check_probability_model, c_int, [POINTER(svm_model)])
fillprototype(libsvm.svm_set_print_string_function, None, [PRINT_STRING_FUN])


================================================
FILE: Matlab/libsvm-3.19/python/svmutil.py
================================================
#!/usr/bin/env python

import os
import sys
from svm import *
from svm import __all__ as svm_all


__all__ = ['evaluations', 'svm_load_model', 'svm_predict', 'svm_read_problem',
           'svm_save_model', 'svm_train'] + svm_all

sys.path = [os.path.dirname(os.path.abspath(__file__))] + sys.path

def svm_read_problem(data_file_name):
	"""
	svm_read_problem(data_file_name) -> [y, x]

	Read LIBSVM-format data from data_file_name and return labels y
	and data instances x.
	"""
	prob_y = []
	prob_x = []
	for line in open(data_file_name):
		line = line.split(None, 1)
		# In case an instance with all zero features
		if len(line) == 1: line += ['']
		label, features = line
		xi = {}
		for e in features.split():
			ind, val = e.split(":")
			xi[int(ind)] = float(val)
		prob_y += [float(label)]
		prob_x += [xi]
	return (prob_y, prob_x)

def svm_load_model(model_file_name):
	"""
	svm_load_model(model_file_name) -> model

	Load a LIBSVM model from model_file_name and return.
	"""
	model = libsvm.svm_load_model(model_file_name.encode())
	if not model:
		print("can't open model file %s" % model_file_name)
		return None
	model = toPyModel(model)
	return model

def svm_save_model(model_file_name, model):
	"""
	svm_save_model(model_file_name, model) -> None

	Save a LIBSVM model to the file model_file_name.
	"""
	libsvm.svm_save_model(model_file_name.encode(), model)

def evaluations(ty, pv):
	"""
	evaluations(ty, pv) -> (ACC, MSE, SCC)

	Calculate accuracy, mean squared error and squared correlation coefficient
	using the true values (ty) and predicted values (pv).
	"""
	if len(ty) != len(pv):
		raise ValueError("len(ty) must equal to len(pv)")
	total_correct = total_error = 0
	sumv = sumy = sumvv = sumyy = sumvy = 0
	for v, y in zip(pv, ty):
		if y == v:
			total_correct += 1
		total_error += (v-y)*(v-y)
		sumv += v
		sumy += y
		sumvv += v*v
		sumyy += y*y
		sumvy += v*y
	l = len(ty)
	ACC = 100.0*total_correct/l
	MSE = total_error/l
	try:
		SCC = ((l*sumvy-sumv*sumy)*(l*sumvy-sumv*sumy))/((l*sumvv-sumv*sumv)*(l*sumyy-sumy*sumy))
	except:
		SCC = float('nan')
	return (ACC, MSE, SCC)

def svm_train(arg1, arg2=None, arg3=None):
	"""
	svm_train(y, x [, options]) -> model | ACC | MSE
	svm_train(prob [, options]) -> model | ACC | MSE
	svm_train(prob, param) -> model | ACC| MSE

	Train an SVM model from data (y, x) or an svm_problem prob using
	'options' or an svm_parameter param.
	If '-v' is specified in 'options' (i.e., cross validation)
	either accuracy (ACC) or mean-squared error (MSE) is returned.
	options:
	    -s svm_type : set type of SVM (default 0)
	        0 -- C-SVC		(multi-class classification)
	        1 -- nu-SVC		(multi-class classification)
	        2 -- one-class SVM
	        3 -- epsilon-SVR	(regression)
	        4 -- nu-SVR		(regression)
	    -t kernel_type : set type of kernel function (default 2)
	        0 -- linear: u'*v
	        1 -- polynomial: (gamma*u'*v + coef0)^degree
	        2 -- radial basis function: exp(-gamma*|u-v|^2)
	        3 -- sigmoid: tanh(gamma*u'*v + coef0)
	        4 -- precomputed kernel (kernel values in training_set_file)
	    -d degree : set degree in kernel function (default 3)
	    -g gamma : set gamma in kernel function (default 1/num_features)
	    -r coef0 : set coef0 in kernel function (default 0)
	    -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
	    -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
	    -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
	    -m cachesize : set cache memory size in MB (default 100)
	    -e epsilon : set tolerance of termination criterion (default 0.001)
	    -h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)
	    -b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
	    -wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)
	    -v n: n-fold cross validation mode
	    -q : quiet mode (no outputs)
	"""
	prob, param = None, None
	if isinstance(arg1, (list, tuple)):
		assert isinstance(arg2, (list, tuple))
		y, x, options = arg1, arg2, arg3
		param = svm_parameter(options)
		prob = svm_problem(y, x, isKernel=(param.kernel_type == PRECOMPUTED))
	elif isinstance(arg1, svm_problem):
		prob = arg1
		if isinstance(arg2, svm_parameter):
			param = arg2
		else:
			param = svm_parameter(arg2)
	if prob == None or param == None:
		raise TypeError("Wrong types for the arguments")

	if param.kernel_type == PRECOMPUTED:
		for xi in prob.x_space:
			idx, val = xi[0].index, xi[0].value
			if xi[0].index != 0:
				raise ValueError('Wrong input format: first column must be 0:sample_serial_number')
			if val <= 0 or val > prob.n:
				raise ValueError('Wrong input format: sample_serial_number out of range')

	if param.gamma == 0 and prob.n > 0:
		param.gamma = 1.0 / prob.n
	libsvm.svm_set_print_string_function(param.print_func)
	err_msg = libsvm.svm_check_parameter(prob, param)
	if err_msg:
		raise ValueError('Error: %s' % err_msg)

	if param.cross_validation:
		l, nr_fold = prob.l, param.nr_fold
		target = (c_double * l)()
		libsvm.svm_cross_validation(prob, param, nr_fold, target)
		ACC, MSE, SCC = evaluations(prob.y[:l], target[:l])
		if param.svm_type in [EPSILON_SVR, NU_SVR]:
			print("Cross Validation Mean squared error = %g" % MSE)
			print("Cross Validation Squared correlation coefficient = %g" % SCC)
			return MSE
		else:
			print("Cross Validation Accuracy = %g%%" % ACC)
			return ACC
	else:
		m = libsvm.svm_train(prob, param)
		m = toPyModel(m)

		# If prob is destroyed, data including SVs pointed by m can remain.
		m.x_space = prob.x_space
		return m

def svm_predict(y, x, m, options=""):
	"""
	svm_predict(y, x, m [, options]) -> (p_labels, p_acc, p_vals)

	Predict data (y, x) with the SVM model m.
	options:
	    -b probability_estimates: whether to predict probability estimates,
	        0 or 1 (default 0); for one-class SVM only 0 is supported.
	    -q : quiet mode (no outputs).

	The return tuple contains
	p_labels: a list of predicted labels
	p_acc: a tuple including  accuracy (for classification), mean-squared
	       error, and squared correlation coefficient (for regression).
	p_vals: a list of decision values or probability estimates (if '-b 1'
	        is specified). If k is the number of classes, for decision values,
	        each element includes results of predicting k(k-1)/2 binary-class
	        SVMs. For probabilities, each element contains k values indicating
	        the probability that the testing instance is in each class.
	        Note that the order of classes here is the same as 'model.label'
	        field in the model structure.
	"""

	def info(s):
		print(s)

	predict_probability = 0
	argv = options.split()
	i = 0
	while i < len(argv):
		if argv[i] == '-b':
			i += 1
			predict_probability = int(argv[i])
		elif argv[i] == '-q':
			info = print_null
		else:
			raise ValueError("Wrong options")
		i+=1

	svm_type = m.get_svm_type()
	is_prob_model = m.is_probability_model()
	nr_class = m.get_nr_class()
	pred_labels = []
	pred_values = []

	if predict_probability:
		if not is_prob_model:
			raise ValueError("Model does not support probabiliy estimates")

		if svm_type in [NU_SVR, EPSILON_SVR]:
			info("Prob. model for test data: target value = predicted value + z,\n"
			"z: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g" % m.get_svr_probability());
			nr_class = 0

		prob_estimates = (c_double * nr_class)()
		for xi in x:
			xi, idx = gen_svm_nodearray(xi, isKernel=(m.param.kernel_type == PRECOMPUTED))
			label = libsvm.svm_predict_probability(m, xi, prob_estimates)
			values = prob_estimates[:nr_class]
			pred_labels += [label]
			pred_values += [values]
	else:
		if is_prob_model:
			info("Model supports probability estimates, but disabled in predicton.")
		if svm_type in (ONE_CLASS, EPSILON_SVR, NU_SVC):
			nr_classifier = 1
		else:
			nr_classifier = nr_class*(nr_class-1)//2
		dec_values = (c_double * nr_classifier)()
		for xi in x:
			xi, idx = gen_svm_nodearray(xi, isKernel=(m.param.kernel_type == PRECOMPUTED))
			label = libsvm.svm_predict_values(m, xi, dec_values)
			if(nr_class == 1):
				values = [1]
			else:
				values = dec_values[:nr_classifier]
			pred_labels += [label]
			pred_values += [values]

	ACC, MSE, SCC = evaluations(y, pred_labels)
	l = len(y)
	if svm_type in [EPSILON_SVR, NU_SVR]:
		info("Mean squared error = %g (regression)" % MSE)
		info("Squared correlation coefficient = %g (regression)" % SCC)
	else:
		info("Accuracy = %g%% (%d/%d) (classification)" % (ACC, int(l*ACC/100), l))

	return pred_labels, (ACC, MSE, SCC), pred_values




================================================
FILE: Matlab/libsvm-3.19/svm-predict.c
================================================
#include <stdio.h>
#include <ctype.h>
#include <stdlib.h>
#include <string.h>
#include <errno.h>
#include "svm.h"

int print_null(const char *s,...) {return 0;}

static int (*info)(const char *fmt,...) = &printf;

struct svm_node *x;
int max_nr_attr = 64;

struct svm_model* model;
int predict_probability=0;

static char *line = NULL;
static int max_line_len;

static char* readline(FILE *input)
{
	int len;

	if(fgets(line,max_line_len,input) == NULL)
		return NULL;

	while(strrchr(line,'\n') == NULL)
	{
		max_line_len *= 2;
		line = (char *) realloc(line,max_line_len);
		len = (int) strlen(line);
		if(fgets(line+len,max_line_len-len,input) == NULL)
			break;
	}
	return line;
}

void exit_input_error(int line_num)
{
	fprintf(stderr,"Wrong input format at line %d\n", line_num);
	exit(1);
}

void predict(FILE *input, FILE *output)
{
	int correct = 0;
	int total = 0;
	double error = 0;
	double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;

	int svm_type=svm_get_svm_type(model);
	int nr_class=svm_get_nr_class(model);
	double *prob_estimates=NULL;
	int j;

	if(predict_probability)
	{
		if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
			info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g\n",svm_get_svr_probability(model));
		else
		{
			int *labels=(int *) malloc(nr_class*sizeof(int));
			svm_get_labels(model,labels);
			prob_estimates = (double *) malloc(nr_class*sizeof(double));
			fprintf(output,"labels");		
			for(j=0;j<nr_class;j++)
				fprintf(output," %d",labels[j]);
			fprintf(output,"\n");
			free(labels);
		}
	}

	max_line_len = 1024;
	line = (char *)malloc(max_line_len*sizeof(char));
	while(readline(input) != NULL)
	{
		int i = 0;
		double target_label, predict_label;
		char *idx, *val, *label, *endptr;
		int inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0

		label = strtok(line," \t\n");
		if(label == NULL) // empty line
			exit_input_error(total+1);

		target_label = strtod(label,&endptr);
		if(endptr == label || *endptr != '\0')
			exit_input_error(total+1);

		while(1)
		{
			if(i>=max_nr_attr-1)	// need one more for index = -1
			{
				max_nr_attr *= 2;
				x = (struct svm_node *) realloc(x,max_nr_attr*sizeof(struct svm_node));
			}

			idx = strtok(NULL,":");
			val = strtok(NULL," \t");

			if(val == NULL)
				break;
			errno = 0;
			x[i].index = (int) strtol(idx,&endptr,10);
			if(endptr == idx || errno != 0 || *endptr != '\0' || x[i].index <= inst_max_index)
				exit_input_error(total+1);
			else
				inst_max_index = x[i].index;

			errno = 0;
			x[i].value = strtod(val,&endptr);
			if(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
				exit_input_error(total+1);

			++i;
		}
		x[i].index = -1;

		if (predict_probability && (svm_type==C_SVC || svm_type==NU_SVC))
		{
			predict_label = svm_predict_probability(model,x,prob_estimates);
			fprintf(output,"%g",predict_label);
			for(j=0;j<nr_class;j++)
				fprintf(output," %g",prob_estimates[j]);
			fprintf(output,"\n");
		}
		else
		{
			predict_label = svm_predict(model,x);
			fprintf(output,"%g\n",predict_label);
		}

		if(predict_label == target_label)
			++correct;
		error += (predict_label-target_label)*(predict_label-target_label);
		sump += predict_label;
		sumt += target_label;
		sumpp += predict_label*predict_label;
		sumtt += target_label*target_label;
		sumpt += predict_label*target_label;
		++total;
	}
	if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
	{
		info("Mean squared error = %g (regression)\n",error/total);
		info("Squared correlation coefficient = %g (regression)\n",
			((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
			((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt))
			);
	}
	else
		info("Accuracy = %g%% (%d/%d) (classification)\n",
			(double)correct/total*100,correct,total);
	if(predict_probability)
		free(prob_estimates);
}

void exit_with_help()
{
	printf(
	"Usage: svm-predict [options] test_file model_file output_file\n"
	"options:\n"
	"-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); for one-class SVM only 0 is supported\n"
	"-q : quiet mode (no outputs)\n"
	);
	exit(1);
}

int main(int argc, char **argv)
{
	FILE *input, *output;
	int i;
	// parse options
	for(i=1;i<argc;i++)
	{
		if(argv[i][0] != '-') break;
		++i;
		switch(argv[i-1][1])
		{
			case 'b':
				predict_probability = atoi(argv[i]);
				break;
			case 'q':
				info = &print_null;
				i--;
				break;
			default:
				fprintf(stderr,"Unknown option: -%c\n", argv[i-1][1]);
				exit_with_help();
		}
	}

	if(i>=argc-2)
		exit_with_help();

	input = fopen(argv[i],"r");
	if(input == NULL)
	{
		fprintf(stderr,"can't open input file %s\n",argv[i]);
		exit(1);
	}

	output = fopen(argv[i+2],"w");
	if(output == NULL)
	{
		fprintf(stderr,"can't open output file %s\n",argv[i+2]);
		exit(1);
	}

	if((model=svm_load_model(argv[i+1]))==0)
	{
		fprintf(stderr,"can't open model file %s\n",argv[i+1]);
		exit(1);
	}

	x = (struct svm_node *) malloc(max_nr_attr*sizeof(struct svm_node));
	if(predict_probability)
	{
		if(svm_check_probability_model(model)==0)
		{
			fprintf(stderr,"Model does not support probabiliy estimates\n");
			exit(1);
		}
	}
	else
	{
		if(svm_check_probability_model(model)!=0)
			info("Model supports probability estimates, but disabled in prediction.\n");
	}

	predict(input,output);
	svm_free_and_destroy_model(&model);
	free(x);
	free(line);
	fclose(input);
	fclose(output);
	return 0;
}


================================================
FILE: Matlab/libsvm-3.19/svm-scale.c
================================================
#include <float.h>
#include <stdio.h>
#include <stdlib.h>
#include <ctype.h>
#include <string.h>

void exit_with_help()
{
	printf(
	"Usage: svm-scale [options] data_filename\n"
	"options:\n"
	"-l lower : x scaling lower limit (default -1)\n"
	"-u upper : x scaling upper limit (default +1)\n"
	"-y y_lower y_upper : y scaling limits (default: no y scaling)\n"
	"-s save_filename : save scaling parameters to save_filename\n"
	"-r restore_filename : restore scaling parameters from restore_filename\n"
	);
	exit(1);
}

char *line = NULL;
int max_line_len = 1024;
double lower=-1.0,upper=1.0,y_lower,y_upper;
int y_scaling = 0;
double *feature_max;
double *feature_min;
double y_max = -DBL_MAX;
double y_min = DBL_MAX;
int max_index;
int min_index;
long int num_nonzeros = 0;
long int new_num_nonzeros = 0;

#define max(x,y) (((x)>(y))?(x):(y))
#define min(x,y) (((x)<(y))?(x):(y))

void output_target(double value);
void output(int index, double value);
char* readline(FILE *input);
int clean_up(FILE *fp_restore, FILE *fp, const char *msg);

int main(int argc,char **argv)
{
	int i,index;
	FILE *fp, *fp_restore = NULL;
	char *save_filename = NULL;
	char *restore_filename = NULL;

	for(i=1;i<argc;i++)
	{
		if(argv[i][0] != '-') break;
		++i;
		switch(argv[i-1][1])
		{
			case 'l': lower = atof(argv[i]); break;
			case 'u': upper = atof(argv[i]); break;
			case 'y':
				y_lower = atof(argv[i]);
				++i;
				y_upper = atof(argv[i]);
				y_scaling = 1;
				break;
			case 's': save_filename = argv[i]; break;
			case 'r': restore_filename = argv[i]; break;
			default:
				fprintf(stderr,"unknown option\n");
				exit_with_help();
		}
	}

	if(!(upper > lower) || (y_scaling && !(y_upper > y_lower)))
	{
		fprintf(stderr,"inconsistent lower/upper specification\n");
		exit(1);
	}
	
	if(restore_filename && save_filename)
	{
		fprintf(stderr,"cannot use -r and -s simultaneously\n");
		exit(1);
	}

	if(argc != i+1) 
		exit_with_help();

	fp=fopen(argv[i],"r");
	
	if(fp==NULL)
	{
		fprintf(stderr,"can't open file %s\n", argv[i]);
		exit(1);
	}

	line = (char *) malloc(max_line_len*sizeof(char));

#define SKIP_TARGET\
	while(isspace(*p)) ++p;\
	while(!isspace(*p)) ++p;

#define SKIP_ELEMENT\
	while(*p!=':') ++p;\
	++p;\
	while(isspace(*p)) ++p;\
	while(*p && !isspace(*p)) ++p;
	
	/* assumption: min index of attributes is 1 */
	/* pass 1: find out max index of attributes */
	max_index = 0;
	min_index = 1;

	if(restore_filename)
	{
		int idx, c;

		fp_restore = fopen(restore_filename,"r");
		if(fp_restore==NULL)
		{
			fprintf(stderr,"can't open file %s\n", restore_filename);
			exit(1);
		}

		c = fgetc(fp_restore);
		if(c == 'y')
		{
			readline(fp_restore);
			readline(fp_restore);
			readline(fp_restore);
		}
		readline(fp_restore);
		readline(fp_restore);

		while(fscanf(fp_restore,"%d %*f %*f\n",&idx) == 1)
			max_index = max(idx,max_index);
		rewind(fp_restore);
	}

	while(readline(fp)!=NULL)
	{
		char *p=line;

		SKIP_TARGET

		while(sscanf(p,"%d:%*f",&index)==1)
		{
			max_index = max(max_index, index);
			min_index = min(min_index, index);
			SKIP_ELEMENT
			num_nonzeros++;
		}
	}

	if(min_index < 1)
		fprintf(stderr,
			"WARNING: minimal feature index is %d, but indices should start from 1\n", min_index);

	rewind(fp);

	feature_max = (double *)malloc((max_index+1)* sizeof(double));
	feature_min = (double *)malloc((max_index+1)* sizeof(double));

	if(feature_max == NULL || feature_min == NULL)
	{
		fprintf(stderr,"can't allocate enough memory\n");
		exit(1);
	}

	for(i=0;i<=max_index;i++)
	{
		feature_max[i]=-DBL_MAX;
		feature_min[i]=DBL_MAX;
	}

	/* pass 2: find out min/max value */
	while(readline(fp)!=NULL)
	{
		char *p=line;
		int next_index=1;
		double target;
		double value;

		if (sscanf(p,"%lf",&target) != 1)
			return clean_up(fp_restore, fp, "ERROR: failed to read labels\n");
		y_max = max(y_max,target);
		y_min = min(y_min,target);
		
		SKIP_TARGET

		while(sscanf(p,"%d:%lf",&index,&value)==2)
		{
			for(i=next_index;i<index;i++)
			{
				feature_max[i]=max(feature_max[i],0);
				feature_min[i]=min(feature_min[i],0);
			}
			
			feature_max[index]=max(feature_max[index],value);
			feature_min[index]=min(feature_min[index],value);

			SKIP_ELEMENT
			next_index=index+1;
		}		

		for(i=next_index;i<=max_index;i++)
		{
			feature_max[i]=max(feature_max[i],0);
			feature_min[i]=min(feature_min[i],0);
		}	
	}

	rewind(fp);

	/* pass 2.5: save/restore feature_min/feature_max */
	
	if(restore_filename)
	{
		/* fp_restore rewinded in finding max_index */
		int idx, c;
		double fmin, fmax;
		int next_index = 1;
		
		if((c = fgetc(fp_restore)) == 'y')
		{
			if(fscanf(fp_restore, "%lf %lf\n", &y_lower, &y_upper) != 2 ||
			   fscanf(fp_restore, "%lf %lf\n", &y_min, &y_max) != 2)
				return clean_up(fp_restore, fp, "ERROR: failed to read scaling parameters\n");
			y_scaling = 1;
		}
		else
			ungetc(c, fp_restore);

		if (fgetc(fp_restore) == 'x') 
		{
			if(fscanf(fp_restore, "%lf %lf\n", &lower, &upper) != 2)
				return clean_up(fp_restore, fp, "ERROR: failed to read scaling parameters\n");
			while(fscanf(fp_restore,"%d %lf %lf\n",&idx,&fmin,&fmax)==3)
			{
				for(i = next_index;i<idx;i++)
					if(feature_min[i] != feature_max[i])
						fprintf(stderr,
							"WARNING: feature index %d appeared in file %s was not seen in the scaling factor file %s.\n",
							i, argv[argc-1], restore_filename);

				feature_min[idx] = fmin;
				feature_max[idx] = fmax;

				next_index = idx + 1;
			}
			
			for(i=next_index;i<=max_index;i++)
				if(feature_min[i] != feature_max[i])
					fprintf(stderr,
						"WARNING: feature index %d appeared in file %s was not seen in the scaling factor file %s.\n",
						i, argv[argc-1], restore_filename);
		}
		fclose(fp_restore);
	}

	if(save_filename)
	{
		FILE *fp_save = fopen(save_filename,"w");
		if(fp_save==NULL)
		{
			fprintf(stderr,"can't open file %s\n", save_filename);
			exit(1);
		}
		if(y_scaling)
		{
			fprintf(fp_save, "y\n");
			fprintf(fp_save, "%.16g %.16g\n", y_lower, y_upper);
			fprintf(fp_save, "%.16g %.16g\n", y_min, y_max);
		}
		fprintf(fp_save, "x\n");
		fprintf(fp_save, "%.16g %.16g\n", lower, upper);
		for(i=1;i<=max_index;i++)
		{
			if(feature_min[i]!=feature_max[i])
				fprintf(fp_save,"%d %.16g %.16g\n",i,feature_min[i],feature_max[i]);
		}

		if(min_index < 1)
			fprintf(stderr,
				"WARNING: scaling factors with indices smaller than 1 are not stored to the file %s.\n", save_filename);

		fclose(fp_save);
	}
	
	/* pass 3: scale */
	while(readline(fp)!=NULL)
	{
		char *p=line;
		int next_index=1;
		double target;
		double value;
		
		if (sscanf(p,"%lf",&target) != 1)
			return clean_up(NULL, fp, "ERROR: failed to read labels\n");
		output_target(target);

		SKIP_TARGET

		while(sscanf(p,"%d:%lf",&index,&value)==2)
		{
			for(i=next_index;i<index;i++)
				output(i,0);
			
			output(index,value);

			SKIP_ELEMENT
			next_index=index+1;
		}		

		for(i=next_index;i<=max_index;i++)
			output(i,0);

		printf("\n");
	}

	if (new_num_nonzeros > num_nonzeros)
		fprintf(stderr, 
			"WARNING: original #nonzeros %ld\n"
			"         new      #nonzeros %ld\n"
			"Use -l 0 if many original feature values are zeros\n",
			num_nonzeros, new_num_nonzeros);

	free(line);
	free(feature_max);
	free(feature_min);
	fclose(fp);
	return 0;
}

char* readline(FILE *input)
{
	int len;
	
	if(fgets(line,max_line_len,input) == NULL)
		return NULL;

	while(strrchr(line,'\n') == NULL)
	{
		max_line_len *= 2;
		line = (char *) realloc(line, max_line_len);
		len = (int) strlen(line);
		if(fgets(line+len,max_line_len-len,input) == NULL)
			break;
	}
	return line;
}

void output_target(double value)
{
	if(y_scaling)
	{
		if(value == y_min)
			value = y_lower;
		else if(value == y_max)
			value = y_upper;
		else value = y_lower + (y_upper-y_lower) *
			     (value - y_min)/(y_max-y_min);
	}
	printf("%g ",value);
}

void output(int index, double value)
{
	/* skip single-valued attribute */
	if(feature_max[index] == feature_min[index])
		return;

	if(value == feature_min[index])
		value = lower;
	else if(value == feature_max[index])
		value = upper;
	else
		value = lower + (upper-lower) * 
			(value-feature_min[index])/
			(feature_max[index]-feature_min[index]);

	if(value != 0)
	{
		printf("%d:%g ",index, value);
		new_num_nonzeros++;
	}
}

int clean_up(FILE *fp_restore, FILE *fp, const char* msg)
{
	fprintf(stderr,	"%s", msg);
	free(line);
	free(feature_max);
	free(feature_min);
	fclose(fp);
	if (fp_restore)
		fclose(fp_restore);
	return -1;
}



================================================
FILE: Matlab/libsvm-3.19/svm-toy/gtk/Makefile
================================================
CC? = gcc
CXX? = g++
CFLAGS = -Wall -O3 -g `pkg-config --cflags gtk+-2.0`
LIBS = `pkg-config --libs gtk+-2.0`

svm-toy: main.o interface.o callbacks.o ../../svm.o
	$(CXX) $(CFLAGS) main.o interface.o callbacks.o ../../svm.o -o svm-toy $(LIBS)

main.o: main.c
	$(CC) $(CFLAGS) -c main.c

interface.o: interface.c interface.h
	$(CC) $(CFLAGS) -c interface.c

callbacks.o: callbacks.cpp callbacks.h
	$(CXX) $(CFLAGS) -c callbacks.cpp

../../svm.o: ../../svm.cpp ../../svm.h
	make -C ../.. svm.o

clean:
	rm -f *~ callbacks.o svm-toy main.o interface.o callbacks.o ../../svm.o


================================================
FILE: Matlab/libsvm-3.19/svm-toy/gtk/callbacks.cpp
================================================
#include <gtk/gtk.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include <list>
#include "callbacks.h"
#include "interface.h"
#include "../../svm.h"
using namespace std;

#define DEFAULT_PARAM "-t 2 -c 100"
#define XLEN 500
#define YLEN 500

GdkColor colors[] = 
{
	{0,0,0,0},
	{0,0,120<<8,120<<8},
	{0,120<<8,120<<8,0},
	{0,120<<8,0,120<<8},
	{0,0,200<<8,200<<8},
	{0,200<<8,200<<8,0},
	{0,200<<8,0,200<<8},
};

GdkGC *gc;
GdkPixmap *pixmap;
extern "C" GtkWidget *draw_main;
GtkWidget *draw_main;
extern "C" GtkWidget *entry_option;
GtkWidget *entry_option;

typedef struct {
	double x, y;
	signed char value;
} point;

list<point> point_list;
int current_value = 1;

extern "C" void svm_toy_initialize()
{
	gboolean success[7];

	gdk_colormap_alloc_colors(
		gdk_colormap_get_system(),
		colors,
		7,
		FALSE,
		TRUE,
		success);

	gc = gdk_gc_new(draw_main->window);
	pixmap = gdk_pixmap_new(draw_main->window,XLEN,YLEN,-1);
	gdk_gc_set_foreground(gc,&colors[0]);
	gdk_draw_rectangle(pixmap,gc,TRUE,0,0,XLEN,YLEN);
	gtk_entry_set_text(GTK_ENTRY(entry_option),DEFAULT_PARAM);
}

void redraw_area(GtkWidget* widget, int x, int y, int w, int h)
{
	gdk_draw_pixmap(widget->window,
			gc,
			pixmap,
			x,y,x,y,w,h);
}

void draw_point(const point& p)
{
	gdk_gc_set_foreground(gc,&colors[p.value+3]);
	gdk_draw_rectangle(pixmap, gc, TRUE,int(p.x*XLEN),int(p.y*YLEN),4,4);
	gdk_draw_rectangle(draw_main->window, gc, TRUE,int(p.x*XLEN),int(p.y*YLEN),4,4);
}

void draw_all_points()
{
	for(list<point>::iterator p = point_list.begin(); p != point_list.end();p++)
		draw_point(*p);
}

void clear_all()
{
	point_list.clear();
	gdk_gc_set_foreground(gc,&colors[0]);
	gdk_draw_rectangle(pixmap,gc,TRUE,0,0,XLEN,YLEN);
	redraw_area(draw_main,0,0,XLEN,YLEN);
}

void
on_button_change_clicked               (GtkButton       *button,
                                        gpointer         user_data)
{
	++current_value;
	if(current_value > 3) current_value = 1;
}

void
on_button_run_clicked                  (GtkButton       *button,
                                        gpointer         user_data)
{
	// guard
	if(point_list.empty()) return;

	svm_parameter param;
	int i,j;	

	// default values
	param.svm_type = C_SVC;
	param.kernel_type = RBF;
	param.degree = 3;
	param.gamma = 0;
	param.coef0 = 0;
	param.nu = 0.5;
	param.cache_size = 100;
	param.C = 1;
	param.eps = 1e-3;
	param.p = 0.1;
	param.shrinking = 1;
	param.probability = 0;
	param.nr_weight = 0;
	param.weight_label = NULL;
	param.weight = NULL;

	// parse options
	const char *p = gtk_entry_get_text(GTK_ENTRY(entry_option));

	while (1) {
		while (*p && *p != '-')
			p++;

		if (*p == '\0')
			break;

		p++;
		switch (*p++) {
			case 's':
				param.svm_type = atoi(p);
				break;
			case 't':
				param.kernel_type = atoi(p);
				break;
			case 'd':
				param.degree = atoi(p);
				break;
			case 'g':
				param.gamma = atof(p);
				break;
			case 'r':
				param.coef0 = atof(p);
				break;
			case 'n':
				param.nu = atof(p);
				break;
			case 'm':
				param.cache_size = atof(p);
				break;
			case 'c':
				param.C = atof(p);
				break;
			case 'e':
				param.eps = atof(p);
				break;
			case 'p':
				param.p = atof(p);
				break;
			case 'h':
				param.shrinking = atoi(p);
				break;
			case 'b':
				param.probability = atoi(p);
				break;
			case 'w':
				++param.nr_weight;
				param.weight_label = (int *)realloc(param.weight_label,sizeof(int)*param.nr_weight);
				param.weight = (double *)realloc(param.weight,sizeof(double)*param.nr_weight);
				param.weight_label[param.nr_weight-1] = atoi(p);
				while(*p && !isspace(*p)) ++p;
				param.weight[param.nr_weight-1] = atof(p);
				break;
		}
	}
	
	// build problem
	svm_problem prob;

	prob.l = point_list.size();
	prob.y = new double[prob.l];

	if(param.kernel_type == PRECOMPUTED)
	{
	}
	else if(param.svm_type == EPSILON_SVR ||
		param.svm_type == NU_SVR)
	{
		if(param.gamma == 0) param.gamma = 1;
		svm_node *x_space = new svm_node[2 * prob.l];
		prob.x = new svm_node *[prob.l];

		i = 0;
		for (list <point>::iterator q = point_list.begin(); q != point_list.end(); q++, i++)
		{
			x_space[2 * i].index = 1;
			x_space[2 * i].value = q->x;
			x_space[2 * i + 1].index = -1;
			prob.x[i] = &x_space[2 * i];
			prob.y[i] = q->y;
		}

		// build model & classify
		svm_model *model = svm_train(&prob, &param);
		svm_node x[2];
		x[0].index = 1;
		x[1].index = -1;
		int *j = new int[XLEN];
	
		for (i = 0; i < XLEN; i++) 
		{
			x[0].value = (double) i / XLEN;
			j[i] = (int)(YLEN*svm_predict(model, x));
		}

		gdk_gc_set_foreground(gc,&colors[0]);
		gdk_draw_line(pixmap,gc,0,0,0,YLEN-1);
		gdk_draw_line(draw_main->window,gc,0,0,0,YLEN-1);
		
		int p = (int)(param.p * YLEN);
		for(i = 1; i < XLEN; i++)
		{
			gdk_gc_set_foreground(gc,&colors[0]);
			gdk_draw_line(pixmap,gc,i,0,i,YLEN-1);
			gdk_draw_line(draw_main->window,gc,i,0,i,YLEN-1);
			
			gdk_gc_set_foreground(gc,&colors[5]);
			gdk_draw_line(pixmap,gc,i-1,j[i-1],i,j[i]);
			gdk_draw_line(draw_main->window,gc,i-1,j[i-1],i,j[i]);
			
			if(param.svm_type == EPSILON_SVR)
			{
				gdk_gc_set_foreground(gc,&colors[2]);
				gdk_draw_line(pixmap,gc,i-1,j[i-1]+p,i,j[i]+p);
				gdk_draw_line(draw_main->window,gc,i-1,j[i-1]+p,i,j[i]+p);
			
				gdk_gc_set_foreground(gc,&colors[2]);
				gdk_draw_line(pixmap,gc,i-1,j[i-1]-p,i,j[i]-p);
				gdk_draw_line(draw_main->window,gc,i-1,j[i-1]-p,i,j[i]-p);
			}
		}

		svm_free_and_destroy_model(&model);
		delete[] j;
		delete[] x_space;
		delete[] prob.x;
		delete[] prob.y;
	}
	else
	{
		if(param.gamma == 0) param.gamma = 0.5;
		svm_node *x_space = new svm_node[3 * prob.l];
		prob.x = new svm_node *[prob.l];

		i = 0;
		for (list <point>::iterator q = point_list.begin(); q != point_list.end(); q++, i++)
		{
			x_space[3 * i].index = 1;
			x_space[3 * i].value = q->x;
			x_space[3 * i + 1].index = 2;
			x_space[3 * i + 1].value = q->y;
			x_space[3 * i + 2].index = -1;
			prob.x[i] = &x_space[3 * i];
			prob.y[i] = q->value;
		}

		// build model & classify
		svm_model *model = svm_train(&prob, &param);
		svm_node x[3];
		x[0].index = 1;
		x[1].index = 2;
		x[2].index = -1;
	
		for (i = 0; i < XLEN; i++) 
			for (j = 0; j < YLEN; j++) {
				x[0].value = (double) i / XLEN;
				x[1].value = (double) j / YLEN;
				double d = svm_predict(model, x);
				if (param.svm_type == ONE_CLASS && d<0) d=2;
				gdk_gc_set_foreground(gc,&colors[(int)d]);
				gdk_draw_point(pixmap,gc,i,j);
				gdk_draw_point(draw_main->window,gc,i,j);
			}

		svm_free_and_destroy_model(&model);
		delete[] x_space;
		delete[] prob.x;
		delete[] prob.y;
	}
	free(param.weight_label);
	free(param.weight);
	draw_all_points();
}

void
on_button_clear_clicked                (GtkButton       *button,
                                        gpointer         user_data)
{
	clear_all();
}

void
on_window1_destroy                     (GtkObject       *object,
                                        gpointer         user_data)
{
	gtk_exit(0);
}

gboolean
on_draw_main_button_press_event        (GtkWidget       *widget,
                                        GdkEventButton  *event,
                                        gpointer         user_data)
{
	point p = {(double)event->x/XLEN, (double)event->y/YLEN, current_value};
	point_list.push_back(p);
	draw_point(p);
	return FALSE;
}

gboolean
on_draw_main_expose_event              (GtkWidget       *widget,
                                        GdkEventExpose  *event,
                                        gpointer         user_data)
{
	redraw_area(widget,
		    event->area.x, event->area.y,
		    event->area.width, event->area.height);
	return FALSE;
}

GtkWidget *fileselection;
static enum { SAVE, LOAD } fileselection_flag;

void show_fileselection()
{
	fileselection = create_fileselection();
	gtk_signal_connect_object(
		GTK_OBJECT(GTK_FILE_SELECTION(fileselection)->ok_button),
		"clicked", GTK_SIGNAL_FUNC(gtk_widget_destroy),
		(GtkObject *) fileselection);
	
	gtk_signal_connect_object (GTK_OBJECT
		(GTK_FILE_SELECTION(fileselection)->cancel_button),
		"clicked", GTK_SIGNAL_FUNC(gtk_widget_destroy),
		(GtkObject *) fileselection);

	gtk_widget_show(fileselection);
}

void
on_button_save_clicked                 (GtkButton       *button,
                                        gpointer         user_data)
{
	fileselection_flag = SAVE;
	show_fileselection();
}


void
on_button_load_clicked                 (GtkButton       *button,
                                        gpointer         user_data)
{
	fileselection_flag = LOAD;
	show_fileselection();
}

void
on_filesel_ok_clicked                  (GtkButton       *button,
                                        gpointer         user_data)
{
	gtk_widget_hide(fileselection);
	const char *filename = gtk_file_selection_get_filename(GTK_FILE_SELECTION(fileselection));

	if(fileselection_flag == SAVE)
	{
		FILE *fp = fopen(filename,"w");
		
		const char *p = gtk_entry_get_text(GTK_ENTRY(entry_option));
		const char* svm_type_str = strstr(p, "-s ");
		int svm_type = C_SVC;
		if(svm_type_str != NULL)
			sscanf(svm_type_str, "-s %d", &svm_type);
		
		if(fp)
		{
			if(svm_type == EPSILON_SVR || svm_type == NU_SVR)
			{
				for(list<point>::iterator p = point_list.begin(); p != point_list.end();p++)
					fprintf(fp,"%f 1:%f\n", p->y, p->x);
			}
			else
			{
				for(list<point>::iterator p = point_list.begin(); p != point_list.end();p++)
					fprintf(fp,"%d 1:%f 2:%f\n", p->value, p->x, p->y);
			}
			fclose(fp);
		}

	}
	else if(fileselection_flag == LOAD)
	{
		FILE *fp = fopen(filename,"r");
		if(fp)
		{
			clear_all();
			char buf[4096];
			while(fgets(buf,sizeof(buf),fp))
			{
				int v;
				double x,y;
				if(sscanf(buf,"%d%*d:%lf%*d:%lf",&v,&x,&y)==3)
				{
					point p = {x,y,v};
					point_list.push_back(p);
				}
				else if(sscanf(buf,"%lf%*d:%lf",&y,&x)==2)
				{
					point p = {x,y,current_value};
					point_list.push_back(p);
				}
				else
					break;
			}
			fclose(fp);
			draw_all_points();
		}				
	}
}

void
on_fileselection_destroy               (GtkObject       *object,
                                        gpointer         user_data)
{
}

void
on_filesel_cancel_clicked              (GtkButton       *button,
                                        gpointer         user_data)
{
}


================================================
FILE: Matlab/libsvm-3.19/svm-toy/gtk/callbacks.h
================================================
#include <gtk/gtk.h>

#ifdef __cplusplus
extern "C" {
#endif

void
on_window1_destroy                     (GtkObject       *object,
                                        gpointer         user_data);

gboolean
on_draw_main_button_press_event        (GtkWidget       *widget,
                                        GdkEventButton  *event,
                                        gpointer         user_data);

gboolean
on_draw_main_expose_event              (GtkWidget       *widget,
                                        GdkEventExpose  *event,
                                        gpointer         user_data);

void
on_button_change_clicked               (GtkButton       *button,
                                        gpointer         user_data);

void
on_button_run_clicked                  (GtkButton       *button,
                                        gpointer         user_data);

void
on_button_clear_clicked                (GtkButton       *button,
                                        gpointer         user_data);

void
on_button_save_clicked                 (GtkButton       *button,
                                        gpointer         user_data);

void
on_button_load_clicked                 (GtkButton       *button,
                                        gpointer         user_data);

void
on_fileselection_destroy               (GtkObject       *object,
                                        gpointer         user_data);

void
on_filesel_ok_clicked                  (GtkButton       *button,
                                        gpointer         user_data);

void
on_filesel_cancel_clicked              (GtkButton       *button,
                                        gpointer         user_data);
#ifdef __cplusplus
}
#endif


================================================
FILE: Matlab/libsvm-3.19/svm-toy/gtk/interface.c
================================================
/*
 * DO NOT EDIT THIS FILE - it is generated by Glade.
 */

#include <sys/types.h>
#include <sys/stat.h>
#include <unistd.h>
#include <string.h>

#include <gdk/gdkkeysyms.h>
#include <gtk/gtk.h>

#include "callbacks.h"
#include "interface.h"

GtkWidget*
create_window (void)
{
  GtkWidget *window;
  GtkWidget *vbox1;
  extern GtkWidget *draw_main;
  GtkWidget *hbox1;
  GtkWidget *button_change;
  GtkWidget *button_run;
  GtkWidget *button_clear;
  GtkWidget *button_save;
  GtkWidget *button_load;
  extern GtkWidget *entry_option;

  window = gtk_window_new (GTK_WINDOW_TOPLEVEL);
  gtk_object_set_data (GTK_OBJECT (window), "window", window);
  gtk_window_set_title (GTK_WINDOW (window), "SVM Toy");

  vbox1 = gtk_vbox_new (FALSE, 0);
  gtk_widget_ref (vbox1);
  gtk_object_set_data_full (GTK_OBJECT (window), "vbox1", vbox1,
                            (GtkDestroyNotify) gtk_widget_unref);
  gtk_widget_show (vbox1);
  gtk_container_add (GTK_CONTAINER (window), vbox1);

  draw_main = gtk_drawing_area_new ();
  gtk_widget_ref (draw_main);
  gtk_object_set_data_full (GTK_OBJECT (window), "draw_main", draw_main,
                            (GtkDestroyNotify) gtk_widget_unref);
  gtk_widget_show (draw_main);
  gtk_box_pack_start (GTK_BOX (vbox1), draw_main, TRUE, TRUE, 0);
  gtk_widget_set_usize (draw_main, 500, 500);
  gtk_widget_set_events (draw_main, GDK_EXPOSURE_MASK | GDK_BUTTON_PRESS_MASK);

  hbox1 = gtk_hbox_new (FALSE, 0);
  gtk_widget_ref (hbox1);
  gtk_object_set_data_full (GTK_OBJECT (window), "hbox1", hbox1,
                            (GtkDestroyNotify) gtk_widget_unref);
  gtk_widget_show (hbox1);
  gtk_box_pack_start (GTK_BOX (vbox1), hbox1, FALSE, FALSE, 0);

  button_change = gtk_button_new_with_label ("Change");
  gtk_widget_ref (button_change);
  gtk_object_set_data_full (GTK_OBJECT (window), "button_change", button_change,
                            (GtkDestroyNotify) gtk_widget_unref);
  gtk_widget_show (button_change);
  gtk_box_pack_start (GTK_BOX (hbox1), button_change, FALSE, FALSE, 0);

  button_run = gtk_button_new_with_label ("Run");
  gtk_widget_ref (button_run);
  gtk_object_set_data_full (GTK_OBJECT (window), "button_run", button_run,
                            (GtkDestroyNotify) gtk_widget_unref);
  gtk_widget_show (button_run);
  gtk_box_pack_start (GTK_BOX (hbox1), button_run, FALSE, FALSE, 0);

  button_clear = gtk_button_new_with_label ("Clear");
  gtk_widget_ref (button_clear);
  gtk_object_set_data_full (GTK_OBJECT (window), "button_clear", button_clear,
                            (GtkDestroyNotify) gtk_widget_unref);
  gtk_widget_show (button_clear);
  gtk_box_pack_start (GTK_BOX (hbox1), button_clear, FALSE, FALSE, 0);

  button_save = gtk_button_new_with_label ("Save");
  gtk_widget_ref (button_save);
  gtk_object_set_data_full (GTK_OBJECT (window), "button_save", button_save,
                            (GtkDestroyNotify) gtk_widget_unref);
  gtk_widget_show (button_save);
  gtk_box_pack_start (GTK_BOX (hbox1), button_save, FALSE, FALSE, 0);

  button_load = gtk_button_new_with_label ("Load");
  gtk_widget_ref (button_load);
  gtk_object_set_data_full (GTK_OBJECT (window), "button_load", button_load,
                            (GtkDestroyNotify) gtk_widget_unref);
  gtk_widget_show (button_load);
  gtk_box_pack_start (GTK_BOX (hbox1), button_load, FALSE, FALSE, 0);

  entry_option = gtk_entry_new ();
  gtk_widget_ref (entry_option);
  gtk_object_set_data_full (GTK_OBJECT (window), "entry_option", entry_option,
                            (GtkDestroyNotify) gtk_widget_unref);
  gtk_widget_show (entry_option);
  gtk_box_pack_start (GTK_BOX (hbox1), entry_option, TRUE, TRUE, 0);

  gtk_signal_connect (GTK_OBJECT (window), "destroy",
                      GTK_SIGNAL_FUNC (on_window1_destroy),
                      NULL);
  gtk_signal_connect (GTK_OBJECT (draw_main), "button_press_event",
                      GTK_SIGNAL_FUNC (on_draw_main_button_press_event),
                      NULL);
  gtk_signal_connect (GTK_OBJECT (draw_main), "expose_event",
                      GTK_SIGNAL_FUNC (on_draw_main_expose_event),
                      NULL);
  gtk_signal_connect (GTK_OBJECT (button_change), "clicked",
                      GTK_SIGNAL_FUNC (on_button_change_clicked),
                      NULL);
  gtk_signal_connect (GTK_OBJECT (button_run), "clicked",
                      GTK_SIGNAL_FUNC (on_button_run_clicked),
                      NULL);
  gtk_signal_connect (GTK_OBJECT (button_clear), "clicked",
                      GTK_SIGNAL_FUNC (on_button_clear_clicked),
                      NULL);
  gtk_signal_connect (GTK_OBJECT (button_save), "clicked",
                      GTK_SIGNAL_FUNC (on_button_save_clicked),
                      NULL);
  gtk_signal_connect (GTK_OBJECT (button_load), "clicked",
                      GTK_SIGNAL_FUNC (on_button_load_clicked),
                      NULL);
  gtk_signal_connect (GTK_OBJECT (entry_option), "activate",
                      GTK_SIGNAL_FUNC (on_button_run_clicked),
                      NULL);

  return window;
}

GtkWidget*
create_fileselection (void)
{
  GtkWidget *fileselection;
  GtkWidget *filesel_ok;
  GtkWidget *filesel_cancel;

  fileselection = gtk_file_selection_new ("Select File");
  gtk_object_set_data (GTK_OBJECT (fileselection), "fileselection", fileselection);
  gtk_container_set_border_width (GTK_CONTAINER (fileselection), 10);
  gtk_window_set_modal (GTK_WINDOW (fileselection), TRUE);

  filesel_ok = GTK_FILE_SELECTION (fileselection)->ok_button;
  gtk_object_set_data (GTK_OBJECT (fileselection), "filesel_ok", filesel_ok);
  gtk_widget_show (filesel_ok);
  GTK_WIDGET_SET_FLAGS (filesel_ok, GTK_CAN_DEFAULT);

  filesel_cancel = GTK_FILE_SELECTION (fileselection)->cancel_button;
  gtk_object_set_data (GTK_OBJECT (fileselection), "filesel_cancel", filesel_cancel);
  gtk_widget_show (filesel_cancel);
  GTK_WIDGET_SET_FLAGS (filesel_cancel, GTK_CAN_DEFAULT);

  gtk_signal_connect (GTK_OBJECT (fileselection), "destroy",
                      GTK_SIGNAL_FUNC (on_fileselection_destroy),
                      NULL);
  gtk_signal_connect (GTK_OBJECT (filesel_ok), "clicked",
                      GTK_SIGNAL_FUNC (on_filesel_ok_clicked),
                      NULL);
  gtk_signal_connect (GTK_OBJECT (filesel_cancel), "clicked",
                      GTK_SIGNAL_FUNC (on_filesel_cancel_clicked),
                      NULL);

  return fileselection;
}



================================================
FILE: Matlab/libsvm-3.19/svm-toy/gtk/interface.h
================================================
/*
 * DO NOT EDIT THIS FILE - it is generated by Glade.
 */

#ifdef __cplusplus
extern "C" {
#endif

GtkWidget* create_window (void);
GtkWidget* create_fileselection (void);

#ifdef __cplusplus
}
#endif


================================================
FILE: Matlab/libsvm-3.19/svm-toy/gtk/main.c
================================================
/*
 * Initial main.c file generated by Glade. Edit as required.
 * Glade will not overwrite this file.
 */

#include <gtk/gtk.h>
#include "interface.h"
void svm_toy_initialize();

int main (int argc, char *argv[])
{
  GtkWidget *window;

  gtk_set_locale ();
  gtk_init (&argc, &argv);

  window = create_window ();
  gtk_widget_show (window);

  svm_toy_initialize();
  gtk_main ();
  return 0;
}


================================================
FILE: Matlab/libsvm-3.19/svm-toy/gtk/svm-toy.glade
================================================
<?xml version="1.0"?>
<GTK-Interface>

<project>
  <name>svm-toy</name>
  <program_name>svm-toy</program_name>
  <directory></directory>
  <source_directory>src</source_directory>
  <pixmaps_directory>pixmaps</pixmaps_directory>
  <language>C</language>
  <gnome_support>False</gnome_support>
  <gettext_support>False</gettext_support>
  <use_widget_names>False</use_widget_names>
  <output_main_file>True</output_main_file>
  <output_support_files>True</output_support_files>
  <output_build_files>True</output_build_files>
  <backup_source_files>False</backup_source_files>
  <main_source_file>interface.c</main_source_file>
  <main_header_file>interface.h</main_header_file>
  <handler_source_file>callbacks.c</handler_source_file>
  <handler_header_file>callbacks.h</handler_header_file>
  <support_source_file>support.c</support_source_file>
  <support_header_file>support.h</support_header_file>
  <translatable_strings_file></translatable_strings_file>
</project>

<widget>
  <class>GtkWindow</class>
  <name>window</name>
  <signal>
    <name>destroy</name>
    <handler>on_window1_destroy</handler>
    <last_modification_time>Sun, 16 Apr 2000 09:47:10 GMT</last_modification_time>
  </signal>
  <title>SVM Toy</title>
  <type>GTK_WINDOW_TOPLEVEL</type>
  <position>GTK_WIN_POS_NONE</position>
  <modal>False</modal>
  <allow_shrink>False</allow_shrink>
  <allow_grow>True</allow_grow>
  <auto_shrink>False</auto_shrink>

  <widget>
    <class>GtkVBox</class>
    <name>vbox1</name>
    <homogeneous>False</homogeneous>
    <spacing>0</spacing>

    <widget>
      <class>GtkDrawingArea</class>
      <name>draw_main</name>
      <width>500</width>
      <height>500</height>
      <events>GDK_EXPOSURE_MASK | GDK_BUTTON_PRESS_MASK</events>
      <signal>
	<name>button_press_event</name>
	<handler>on_draw_main_button_press_event</handler>
	<last_modification_time>Sun, 16 Apr 2000 13:02:05 GMT</last_modification_time>
      </signal>
      <signal>
	<name>expose_event</name>
	<handler>on_draw_main_expose_event</handler>
	<last_modification_time>Sun, 16 Apr 2000 14:27:05 GMT</last_modification_time>
      </signal>
      <child>
	<padding>0</padding>
	<expand>True</expand>
	<fill>True</fill>
      </child>
    </widget>

    <widget>
      <class>GtkHBox</class>
      <name>hbox1</name>
      <homogeneous>False</homogeneous>
      <spacing>0</spacing>
      <child>
	<padding>0</padding>
	<expand>False</expand>
	<fill>False</fill>
      </child>

      <widget>
	<class>GtkButton</class>
	<name>button_change</name>
	<can_focus>True</can_focus>
	<signal>
	  <name>clicked</name>
	  <handler>on_button_change_clicked</handler>
	  <last_modification_time>Sun, 16 Apr 2000 09:40:18 GMT</last_modification_time>
	</signal>
	<label>Change</label>
	<child>
	  <padding>0</padding>
	  <expand>False</expand>
	  <fill>False</fill>
	</child>
      </widget>

      <widget>
	<class>GtkButton</class>
	<name>button_run</name>
	<can_focus>True</can_focus>
	<signal>
	  <name>clicked</name>
	  <handler>on_button_run_clicked</handler>
	  <last_modification_time>Sun, 16 Apr 2000 09:40:37 GMT</last_modification_time>
	</signal>
	<label>Run</label>
	<child>
	  <padding>0</padding>
	  <expand>False</expand>
	  <fill>False</fill>
	</child>
      </widget>

      <widget>
	<class>GtkButton</class>
	<name>button_clear</name>
	<can_focus>True</can_focus>
	<signal>
	  <name>clicked</name>
	  <handler>on_button_clear_clicked</handler>
	  <last_modification_time>Sun, 16 Apr 2000 09:40:44 GMT</last_modification_time>
	</signal>
	<label>Clear</label>
	<child>
	  <padding>0</padding>
	  <expand>False</expand>
	  <fill>False</fill>
	</child>
      </widget>

      <widget>
	<class>GtkButton</class>
	<name>button_save</name>
	<can_focus>True</can_focus>
	<signal>
	  <name>clicked</name>
	  <handler>on_button_save_clicked</handler>
	  <last_modification_time>Fri, 16 Jun 2000 18:23:46 GMT</last_modification_time>
	</signal>
	<label>Save</label>
	<child>
	  <padding>0</padding>
	  <expand>False</expand>
	  <fill>False</fill>
	</child>
      </widget>

      <widget>
	<class>GtkButton</class>
	<name>button_load</name>
	<can_focus>True</can_focus>
	<signal>
	  <name>clicked</name>
	  <handler>on_button_load_clicked</handler>
	  <last_modification_time>Fri, 16 Jun 2000 18:23:56 GMT</last_modification_time>
	</signal>
	<label>Load</label>
	<child>
	  <padding>0</padding>
	  <expand>False</expand>
	  <fill>False</fill>
	</child>
      </widget>

      <widget>
	<class>GtkEntry</class>
	<name>entry_option</name>
	<can_focus>True</can_focus>
	<signal>
	  <name>activate</name>
	  <handler>on_button_run_clicked</handler>
	  <last_modification_time>Sun, 16 Apr 2000 09:42:46 GMT</last_modification_time>
	</signal>
	<editable>True</editable>
	<text_visible>True</text_visible>
	<text_max_length>0</text_max_length>
	<text></text>
	<child>
	  <padding>0</padding>
	  <expand>True</expand>
	  <fill>True</fill>
	</child>
      </widget>
    </widget>
  </widget>
</widget>

<widget>
  <class>GtkFileSelection</class>
  <name>fileselection</name>
  <border_width>10</border_width>
  <signal>
    <name>destroy</name>
    <handler>on_fileselection_destroy</handler>
    <last_modification_time>Fri, 16 Jun 2000 18:11:28 GMT</last_modification_time>
  </signal>
  <title>Select File</title>
  <type>GTK_WINDOW_TOPLEVEL</type>
  <position>GTK_WIN_POS_NONE</position>
  <modal>True</modal>
  <allow_shrink>False</allow_shrink>
  <allow_grow>True</allow_grow>
  <auto_shrink>False</auto_shrink>
  <show_file_op_buttons>True</show_file_op_buttons>

  <widget>
    <class>GtkButton</class>
    <child_name>FileSel:ok_button</child_name>
    <name>filesel_ok</name>
    <can_default>True</can_default>
    <can_focus>True</can_focus>
    <signal>
      <name>clicked</name>
      <handler>on_filesel_ok_clicked</handler>
      <last_modification_time>Fri, 16 Jun 2000 18:09:56 GMT</last_modification_time>
    </signal>
    <label>OK</label>
  </widget>

  <widget>
    <class>GtkButton</class>
    <child_name>FileSel:cancel_button</child_name>
    <name>filesel_cancel</name>
    <can_default>True</can_default>
    <can_focus>True</can_focus>
    <signal>
      <name>clicked</name>
      <handler>on_filesel_cancel_clicked</handler>
      <last_modification_time>Fri, 16 Jun 2000 18:09:46 GMT</last_modification_time>
    </signal>
    <label>Cancel</label>
  </widget>
</widget>

</GTK-Interface>


================================================
FILE: Matlab/libsvm-3.19/svm-toy/qt/Makefile
================================================
CXX? = g++
CFLAGS = -Wall -O3 -I$(INCLUDE) -I$(INCLUDE)/QtGui -lQtGui
INCLUDE = /usr/include/qt4
MOC = /usr/bin/moc-qt4

svm-toy: svm-toy.cpp svm-toy.moc ../../svm.o
	$(CXX) $(CFLAGS) svm-toy.cpp ../../svm.o -o svm-toy

svm-toy.moc: svm-toy.cpp
	$(MOC) svm-toy.cpp -o svm-toy.moc

../../svm.o: ../../svm.cpp ../../svm.h
	make -C ../.. svm.o

clean:
	rm -f *~ svm-toy svm-toy.moc ../../svm.o



================================================
FILE: Matlab/libsvm-3.19/svm-toy/qt/svm-toy.cpp
================================================
#include <QtGui>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include <list>
#include "../../svm.h"
using namespace std;

#define DEFAULT_PARAM "-t 2 -c 100"
#define XLEN 500
#define YLEN 500

QRgb colors[] =
{
	qRgb(0,0,0),
	qRgb(0,120,120),
	qRgb(120,120,0),
	qRgb(120,0,120),
	qRgb(0,200,200),
	qRgb(200,200,0),
	qRgb(200,0,200)
};

class SvmToyWindow : public QWidget
{

Q_OBJECT

public:
	SvmToyWindow();
	~SvmToyWindow();
protected:
	virtual void mousePressEvent( QMouseEvent* );
	virtual void paintEvent( QPaintEvent* );

private:
	QPixmap buffer;
	QPixmap icon1;
	QPixmap icon2;
	QPixmap icon3;
	QPushButton button_change_icon;
	QPushButton button_run;
	QPushButton button_clear;
	QPushButton button_save;
	QPushButton button_load;
	QLineEdit input_line;
	QPainter buffer_painter;
	struct point {
		double x, y;
		signed char value;
	};
	list<point> point_list;
	int current_value;
	const QPixmap& choose_icon(int v)
	{
		if(v==1) return icon1;
		else if(v==2) return icon2;
		else return icon3;
	}
	void clear_all()
	{
		point_list.clear();
		buffer.fill(Qt::black);
		repaint();
	}
	void draw_point(const point& p)
	{
		const QPixmap& icon = choose_icon(p.value);
		buffer_painter.drawPixmap((int)(p.x*XLEN),(int)(p.y*YLEN),icon);
		repaint();
	}
	void draw_all_points()
	{
		for(list<point>::iterator p = point_list.begin(); p != point_list.end();p++)
			draw_point(*p);	
	}
private slots: 
	void button_change_icon_clicked()
	{
		++current_value;
		if(current_value > 3) current_value = 1;
		button_change_icon.setIcon(choose_icon(current_value));
	}
	void button_run_clicked()
	{
		// guard
		if(point_list.empty()) return;

		svm_parameter param;
		int i,j;	

		// default values
		param.svm_type = C_SVC;
		param.kernel_type = RBF;
		param.degree = 3;
		param.gamma = 0;
		param.coef0 = 0;
		param.nu = 0.5;
		param.cache_size = 100;
		param.C = 1;
		param.eps = 1e-3;
		param.p = 0.1;
		param.shrinking = 1;
		param.probability = 0;
		param.nr_weight = 0;
		param.weight_label = NULL;
		param.weight = NULL;

		// parse options
		const char *p = input_line.text().toAscii().constData();

		while (1) {
			while (*p && *p != '-')
				p++;

			if (*p == '\0')
				break;

			p++;
			switch (*p++) {
				case 's':
					param.svm_type = atoi(p);
					break;
				case 't':
					param.kernel_type = atoi(p);
					break;
				case 'd':
					param.degree = atoi(p);
					break;
				case 'g':
					param.gamma = atof(p);
					break;
				case 'r':
					param.coef0 = atof(p);
					break;
				case 'n':
					param.nu = atof(p);
					break;
				case 'm':
					param.cache_size = atof(p);
					break;
				case 'c':
					param.C = atof(p);
					break;
				case 'e':
					param.eps = atof(p);
					break;
				case 'p':
					param.p = atof(p);
					break;
				case 'h':
					param.shrinking = atoi(p);
					break;
			        case 'b':
					param.probability = atoi(p);
					break;
				case 'w':
					++param.nr_weight;
					param.weight_label = (int *)realloc(param.weight_label,sizeof(int)*param.nr_weight);
					param.weight = (double *)realloc(param.weight,sizeof(double)*param.nr_weight);
					param.weight_label[param.nr_weight-1] = atoi(p);
					while(*p && !isspace(*p)) ++p;
					param.weight[param.nr_weight-1] = atof(p);
					break;
			}
		}
	
		// build problem
		svm_problem prob;

		prob.l = point_list.size();
		prob.y = new double[prob.l];

		if(param.kernel_type == PRECOMPUTED)
		{
		}
		else if(param.svm_type == EPSILON_SVR ||
			param.svm_type == NU_SVR)
		{
			if(param.gamma == 0) param.gamma = 1;
			svm_node *x_space = new svm_node[2 * prob.l];
			prob.x = new svm_node *[prob.l];

			i = 0;
			for (list <point>::iterator q = point_list.begin(); q != point_list.end(); q++, i++)
			{
				x_space[2 * i].index = 1;
				x_space[2 * i].value = q->x;
				x_space[2 * i + 1].index = -1;
				prob.x[i] = &x_space[2 * i];
				prob.y[i] = q->y;
			}

			// build model & classify
			svm_model *model = svm_train(&prob, &param);
			svm_node x[2];
			x[0].index = 1;
			x[1].index = -1;
			int *j = new int[XLEN];

			for (i = 0; i < XLEN; i++)
			{
				x[0].value = (double) i / XLEN;
				j[i] = (int)(YLEN*svm_predict(model, x));
			}
			
			buffer_painter.setPen(colors[0]);
			buffer_painter.drawLine(0,0,0,YLEN-1);

			int p = (int)(param.p * YLEN);
			for(i = 1; i < XLEN; i++)
			{
				buffer_painter.setPen(colors[0]);
				buffer_painter.drawLine(i,0,i,YLEN-1);
			
				buffer_painter.setPen(colors[5]);
				buffer_painter.drawLine(i-1,j[i-1],i,j[i]);
				
				if(param.svm_type == EPSILON_SVR)
				{
					buffer_painter.setPen(colors[2]);
					buffer_painter.drawLine(i-1,j[i-1]+p,i,j[i]+p);

					buffer_painter.setPen(colors[2]);
					buffer_painter.drawLine(i-1,j[i-1]-p,i,j[i]-p);
				}
			}

			svm_free_and_destroy_model(&model);
			delete[] j;
			delete[] x_space;
			delete[] prob.x;
			delete[] prob.y;
		}
		else
		{
			if(param.gamma == 0) param.gamma = 0.5;
			svm_node *x_space = new svm_node[3 * prob.l];
			prob.x = new svm_node *[prob.l];

			i = 0;
			for (list <point>::iterator q = point_list.begin(); q != point_list.end(); q++, i++)
			{
				x_space[3 * i].index = 1;
				x_space[3 * i].value = q->x;
				x_space[3 * i + 1].index = 2;
				x_space[3 * i + 1].value = q->y;
				x_space[3 * i + 2].index = -1;
				prob.x[i] = &x_space[3 * i];
				prob.y[i] = q->value;
			}

			// build model & classify
			svm_model *model = svm_train(&prob, &param);
			svm_node x[3];
			x[0].index = 1;
			x[1].index = 2;
			x[2].index = -1;

			for (i = 0; i < XLEN; i++)
				for (j = 0; j < YLEN ; j++) {
					x[0].value = (double) i / XLEN;
					x[1].value = (double) j / YLEN;
					double d = svm_predict(model, x);
					if (param.svm_type == ONE_CLASS && d<0) d=2;
					buffer_painter.setPen(colors[(int)d]);
					buffer_painter.drawPoint(i,j);
			}

			svm_free_and_destroy_model(&model);
			delete[] x_space;
			delete[] prob.x;
			delete[] prob.y;
		}
		free(param.weight_label);
		free(param.weight);
		draw_all_points();
	}
	void button_clear_clicked()
	{
		clear_all();
	}
	void button_save_clicked()
	{
		QString filename = QFileDialog::getSaveFileName();
		if(!filename.isNull())
		{
			FILE *fp = fopen(filename.toAscii().constData(),"w");
			
			const char *p = input_line.text().toAscii().constData();
			const char* svm_type_str = strstr(p, "-s ");
			int svm_type = C_SVC;
			if(svm_type_str != NULL)
				sscanf(svm_type_str, "-s %d", &svm_type);
		
			if(fp)
			{
				if(svm_type == EPSILON_SVR || svm_type == NU_SVR)
				{
					for(list<point>::iterator p = point_list.begin(); p != point_list.end();p++)
						fprintf(fp,"%f 1:%f\n", p->y, p->x);
				}
				else
				{
					for(list<point>::iterator p = point_list.begin(); p != point_list.end();p++)
						fprintf(fp,"%d 1:%f 2:%f\n", p->value, p->x, p->y);
				}
				fclose(fp);
			}
		}
	}
	void button_load_clicked()
	{
		QString filename = QFileDialog::getOpenFileName();
		if(!filename.isNull())
		{
			FILE *fp = fopen(filename.toAscii().constData(),"r");
			if(fp)
			{
				clear_all();
				char buf[4096];
				while(fgets(buf,sizeof(buf),fp))
				{
					int v;
					double x,y;
					if(sscanf(buf,"%d%*d:%lf%*d:%lf",&v,&x,&y)==3)
					{
						point p = {x,y,v};
						point_list.push_back(p);
					}
					else if(sscanf(buf,"%lf%*d:%lf",&y,&x)==2)
					{
						point p = {x,y,current_value};
						point_list.push_back(p);
					}
					else
						break;
				}
				fclose(fp);
				draw_all_points();
			}				
		}
		
	}
};

#include "svm-toy.moc"

SvmToyWindow::SvmToyWindow()
:button_change_icon(this)
,button_run("Run",this)
,button_clear("Clear",this)
,button_save("Save",this)
,button_load("Load",this)
,input_line(this)
,current_value(1)
{
	buffer = QPixmap(XLEN,YLEN);
	buffer.fill(Qt::black);

	buffer_painter.begin(&buffer);

	QObject::connect(&button_change_icon, SIGNAL(clicked()), this,
			 SLOT(button_change_icon_clicked()));
	QObject::connect(&button_run, SIGNAL(clicked()), this,
			 SLOT(button_run_clicked()));
	QObject::connect(&button_clear, SIGNAL(clicked()), this,
			 SLOT(button_clear_clicked()));
	QObject::connect(&button_save, SIGNAL(clicked()), this,
			 SLOT(button_save_clicked()));
	QObject::connect(&button_load, SIGNAL(clicked()), this,
			 SLOT(button_load_clicked()));
	QObject::connect(&input_line, SIGNAL(returnPressed()), this,
			 SLOT(button_run_clicked()));

  	// don't blank the window before repainting
	setAttribute(Qt::WA_NoBackground);
  
	icon1 = QPixmap(4,4);
	icon2 = QPixmap(4,4);
	icon3 = QPixmap(4,4);
	
	
	QPainter painter;
	painter.begin(&icon1);
	painter.fillRect(0,0,4,4,QBrush(colors[4]));
	painter.end();

	painter.begin(&icon2);
	painter.fillRect(0,0,4,4,QBrush(colors[5]));
	painter.end();

	painter.begin(&icon3);
	painter.fillRect(0,0,4,4,QBrush(colors[6]));
	painter.end();

	button_change_icon.setGeometry( 0, YLEN, 50, 25 );
	button_run.setGeometry( 50, YLEN, 50, 25 );
	button_clear.setGeometry( 100, YLEN, 50, 25 );
	button_save.setGeometry( 150, YLEN, 50, 25);
	button_load.setGeometry( 200, YLEN, 50, 25);
	input_line.setGeometry( 250, YLEN, 250, 25);
	
	input_line.setText(DEFAULT_PARAM);
	button_change_icon.setIcon(icon1);
}

SvmToyWindow::~SvmToyWindow()
{
	buffer_painter.end();
}

void SvmToyWindow::mousePressEvent( QMouseEvent* event )
{
	point p = {(double)event->x()/XLEN, (double)event->y()/YLEN, current_value};
	point_list.push_back(p);
	draw_point(p);
}

void SvmToyWindow::paintEvent( QPaintEvent* )
{
	// copy the image from the buffer pixmap to the window
	QPainter p(this);
	p.drawPixmap(0, 0, buffer);
}

int main( int argc, char* argv[] )
{
	QApplication myapp( argc, argv );

	SvmToyWindow* mywidget = new SvmToyWindow();
	mywidget->setGeometry( 100, 100, XLEN, YLEN+25 );

	mywidget->show();
	return myapp.exec();
}


================================================
FILE: Matlab/libsvm-3.19/svm-toy/windows/svm-toy.cpp
================================================
#include <windows.h>
#include <windowsx.h>
#include <stdio.h>
#include <string.h>
#include <ctype.h>
#include <list>
#include "../../svm.h"
using namespace std;

#define DEFAULT_PARAM "-t 2 -c 100"
#define XLEN 500
#define YLEN 500
#define DrawLine(dc,x1,y1,x2,y2,c) \
	do { \
		HPEN hpen = CreatePen(PS_SOLID,0,c); \
		HPEN horig = SelectPen(dc,hpen); \
		MoveToEx(dc,x1,y1,NULL); \
		LineTo(dc,x2,y2); \
		SelectPen(dc,horig); \
		DeletePen(hpen); \
	} while(0)

using namespace std;

COLORREF colors[] =
{
	RGB(0,0,0),
	RGB(0,120,120),
	RGB(120,120,0),
	RGB(120,0,120),
	RGB(0,200,200),
	RGB(200,200,0),
	RGB(200,0,200)
};

HWND main_window;
HBITMAP buffer;
HDC window_dc;
HDC buffer_dc;
HBRUSH brush1, brush2, brush3;
HWND edit;

enum {
	ID_BUTTON_CHANGE, ID_BUTTON_RUN, ID_BUTTON_CLEAR,
	ID_BUTTON_LOAD, ID_BUTTON_SAVE, ID_EDIT
};

struct point {
	double x, y;
	signed char value;
};

list<point> point_list;
int current_value = 1;

LRESULT CALLBACK WndProc(HWND, UINT, WPARAM, LPARAM);

int WINAPI WinMain(HINSTANCE hInstance, HINSTANCE hPrevInstance,
		   PSTR szCmdLine, int iCmdShow)
{
	static char szAppName[] = "SvmToy";
	MSG msg;
	WNDCLASSEX wndclass;

	wndclass.cbSize = sizeof(wndclass);
	wndclass.style = CS_HREDRAW | CS_VREDRAW;
	wndclass.lpfnWndProc = WndProc;
	wndclass.cbClsExtra = 0;
	wndclass.cbWndExtra = 0;
	wndclass.hInstance = hInstance;
	wndclass.hIcon = LoadIcon(NULL, IDI_APPLICATION);
	wndclass.hCursor = LoadCursor(NULL, IDC_ARROW);
	wndclass.hbrBackground = (HBRUSH) GetStockObject(BLACK_BRUSH);
	wndclass.lpszMenuName = NULL;
	wndclass.lpszClassName = szAppName;
	wndclass.hIconSm = LoadIcon(NULL, IDI_APPLICATION);

	RegisterClassEx(&wndclass);

	main_window = CreateWindow(szAppName,	// window class name
				    "SVM Toy",	// window caption
				    WS_OVERLAPPEDWINDOW,// window style
				    CW_USEDEFAULT,	// initial x position
				    CW_USEDEFAULT,	// initial y position
				    XLEN,	// initial x size
				    YLEN+52,	// initial y size
				    NULL,	// parent window handle
				    NULL,	// window menu handle
				    hInstance,	// program instance handle
				    NULL);	// creation parameters

	ShowWindow(main_window, iCmdShow);
	UpdateWindow(main_window);

	CreateWindow("button", "Change", WS_CHILD | WS_VISIBLE | BS_PUSHBUTTON,
		     0, YLEN, 50, 25, main_window, (HMENU) ID_BUTTON_CHANGE, hInstance, NULL);
	CreateWindow("button", "Run", WS_CHILD | WS_VISIBLE | BS_PUSHBUTTON,
		     50, YLEN, 50, 25, main_window, (HMENU) ID_BUTTON_RUN, hInstance, NULL);
	CreateWindow("button", "Clear", WS_CHILD | WS_VISIBLE | BS_PUSHBUTTON,
		     100, YLEN, 50, 25, main_window, (HMENU) ID_BUTTON_CLEAR, hInstance, NULL);
	CreateWindow("button", "Save", WS_CHILD | WS_VISIBLE | BS_PUSHBUTTON,
		     150, YLEN, 50, 25, main_window, (HMENU) ID_BUTTON_SAVE, hInstance, NULL);
	CreateWindow("button", "Load", WS_CHILD | WS_VISIBLE | BS_PUSHBUTTON,
		     200, YLEN, 50, 25, main_window, (HMENU) ID_BUTTON_LOAD, hInstance, NULL);

	edit = CreateWindow("edit", NULL, WS_CHILD | WS_VISIBLE,
		            250, YLEN, 250, 25, main_window, (HMENU) ID_EDIT, hInstance, NULL);

	Edit_SetText(edit,DEFAULT_PARAM);

	brush1 = CreateSolidBrush(colors[4]);
	brush2 = CreateSolidBrush(colors[5]);
	brush3 = CreateSolidBrush(colors[6]);

	window_dc = GetDC(main_window);
	buffer = CreateCompatibleBitmap(window_dc, XLEN, YLEN);
	buffer_dc = CreateCompatibleDC(window_dc);
	SelectObject(buffer_dc, buffer);
	PatBlt(buffer_dc, 0, 0, XLEN, YLEN, BLACKNESS);

	while (GetMessage(&msg, NULL, 0, 0)) {
		TranslateMessage(&msg);
		DispatchMessage(&msg);
	}
	return msg.wParam;
}

int getfilename( HWND hWnd , char *filename, int len, int save) 
{ 	
	OPENFILENAME OpenFileName; 	
	memset(&OpenFileName,0,sizeof(OpenFileName));
	filename[0]='\0';
 	
	OpenFileName.lStructSize       = sizeof(OPENFILENAME); 
	OpenFileName.hwndOwner         = hWnd; 	
	OpenFileName.lpstrFile         = filename; 
	OpenFileName.nMaxFile          = len; 
	OpenFileName.Flags             = 0;
 
	return save?GetSaveFileName(&OpenFileName):GetOpenFileName(&OpenFileName);		
}

void clear_all()
{
	point_list.clear();
	PatBlt(buffer_dc, 0, 0, XLEN, YLEN, BLACKNESS);
	InvalidateRect(main_window, 0, 0);
}

HBRUSH choose_brush(int v)
{
	if(v==1) return brush1;
	else if(v==2) return brush2;
	else return brush3;
}

void draw_point(const point & p)
{
	RECT rect;
	rect.left = int(p.x*XLEN);
	rect.top = int(p.y*YLEN);
	rect.right = int(p.x*XLEN) + 3;
	rect.bottom = int(p.y*YLEN) + 3;
	FillRect(window_dc, &rect, choose_brush(p.value));
	FillRect(buffer_dc, &rect, choose_brush(p.value));
}

void draw_all_points()
{
	for(list<point>::iterator p = point_list.begin(); p != point_list.end(); p++)
		draw_point(*p);
}

void button_run_clicked()
{
	// guard
	if(point_list.empty()) return;

	svm_parameter param;
	int i,j;
	
	// default values
	param.svm_type = C_SVC;
	param.kernel_type = RBF;
	param.degree = 3;
	param.gamma = 0;
	param.coef0 = 0;
	param.nu = 0.5;
	param.cache_size = 100;
	param.C = 1;
	param.eps = 1e-3;
	param.p = 0.1;
	param.shrinking = 1;
	param.probability = 0;
	param.nr_weight = 0;
	param.weight_label = NULL;
	param.weight = NULL;

	// parse options
	char str[1024];
	Edit_GetLine(edit, 0, str, sizeof(str));
	const char *p = str;

	while (1) {
		while (*p && *p != '-')
			p++;

		if (*p == '\0')
			break;

		p++;
		switch (*p++) {
			case 's':
				param.svm_type = atoi(p);
				break;
			case 't':
				param.kernel_type = atoi(p);
				break;
			case 'd':
				param.degree = atoi(p);
				break;
			case 'g':
				param.gamma = atof(p);
				break;
			case 'r':
				param.coef0 = atof(p);
				break;
			case 'n':
				param.nu = atof(p);
				break;
			case 'm':
				param.cache_size = atof(p);
				break;
			case 'c':
				param.C = atof(p);
				break;
			case 'e':
				param.eps = atof(p);
				break;
			case 'p':
				param.p = atof(p);
				break;
			case 'h':
				param.shrinking = atoi(p);
				break;
			case 'b':
				param.probability = atoi(p);
				break;
			case 'w':
				++param.nr_weight;
				param.weight_label = (int *)realloc(param.weight_label,sizeof(int)*param.nr_weight);
				param.weight = (double *)realloc(param.weight,sizeof(double)*param.nr_weight);
				param.weight_label[param.nr_weight-1] = atoi(p);
				while(*p && !isspace(*p)) ++p;
				param.weight[param.nr_weight-1] = atof(p);
				break;
		}
	}
	
	// build problem
	svm_problem prob;

	prob.l = point_list.size();
	prob.y = new double[prob.l];

	if(param.kernel_type == PRECOMPUTED)
	{
	}
	else if(param.svm_type == EPSILON_SVR ||
		param.svm_type == NU_SVR)
	{
		if(param.gamma == 0) param.gamma = 1;
		svm_node *x_space = new svm_node[2 * prob.l];
		prob.x = new svm_node *[prob.l];

		i = 0;
		for (list<point>::iterator q = point_list.begin(); q != point_list.end(); q++, i++)
		{
			x_space[2 * i].index = 1;
			x_space[2 * i].value = q->x;
			x_space[2 * i + 1].index = -1;
			prob.x[i] = &x_space[2 * i];
			prob.y[i] = q->y;
		}

		// build model & classify
		svm_model *model = svm_train(&prob, &param);
		svm_node x[2];
		x[0].index = 1;
		x[1].index = -1;
		int *j = new int[XLEN];

		for (i = 0; i < XLEN; i++)
		{
			x[0].value = (double) i / XLEN;
			j[i] = (int)(YLEN*svm_predict(model, x));
		}
		
		DrawLine(buffer_dc,0,0,0,YLEN,colors[0]);
		DrawLine(window_dc,0,0,0,YLEN,colors[0]);
		
		int p = (int)(param.p * YLEN);
		for(int i=1; i < XLEN; i++)
		{
			DrawLine(buffer_dc,i,0,i,YLEN,colors[0]);
			DrawLine(window_dc,i,0,i,YLEN,colors[0]);
			
			DrawLine(buffer_dc,i-1,j[i-1],i,j[i],colors[5]);
			DrawLine(window_dc,i-1,j[i-1],i,j[i],colors[5]);

			if(param.svm_type == EPSILON_SVR)
			{			
				DrawLine(buffer_dc,i-1,j[i-1]+p,i,j[i]+p,colors[2]);
				DrawLine(window_dc,i-1,j[i-1]+p,i,j[i]+p,colors[2]);

				DrawLine(buffer_dc,i-1,j[i-1]-p,i,j[i]-p,colors[2]);
				DrawLine(window_dc,i-1,j[i-1]-p,i,j[i]-p,colors[2]);
			}
		}
		
		svm_free_and_destroy_model(&model);
		delete[] j;
		delete[] x_space;
		delete[] prob.x;
		delete[] prob.y;
	}
	else
	{
		if(param.gamma == 0) param.gamma = 0.5;
		svm_node *x_space = new svm_node[3 * prob.l];
		prob.x = new svm_node *[prob.l];

		i = 0;
		for (list<point>::iterator q = point_list.begin(); q != point_list.end(); q++, i++)
		{
			x_space[3 * i].index = 1;
			x_space[3 * i].value = q->x;
			x_space[3 * i + 1].index = 2;
			x_space[3 * i + 1].value = q->y;
			x_space[3 * i + 2].index = -1;
			prob.x[i] = &x_space[3 * i];
			prob.y[i] = q->value;
		}

		// build model & classify
		svm_model *model = svm_train(&prob, &param);
		svm_node x[3];
		x[0].index = 1;
		x[1].index = 2;
		x[2].index = -1;

		for (i = 0; i < XLEN; i++)
			for (j = 0; j < YLEN; j++) {
				x[0].value = (double) i / XLEN;
				x[1].value = (double) j / YLEN;
				double d = svm_predict(model, x);
				if (param.svm_type == ONE_CLASS && d<0) d=2;
				SetPixel(window_dc, i, j, colors[(int)d]);
				SetPixel(buffer_dc, i, j, colors[(int)d]);
			}

		svm_free_and_destroy_model(&model);
		delete[] x_space;
		delete[] prob.x;
		delete[] prob.y;
	}
	free(param.weight_label);
	free(param.weight);
	draw_all_points();
}

LRESULT CALLBACK WndProc(HWND hwnd, UINT iMsg, WPARAM wParam, LPARAM lParam)
{
	HDC hdc;
	PAINTSTRUCT ps;

	switch (iMsg) {
	case WM_LBUTTONDOWN:
		{
			int x = LOWORD(lParam);
			int y = HIWORD(lParam);
			point p = {(double)x/XLEN, (double)y/YLEN, current_value};
			point_list.push_back(p);
			draw_point(p);
		}
		return 0;
	case WM_PAINT:
		{
			hdc = BeginPaint(hwnd, &ps);
			BitBlt(hdc, 0, 0, XLEN, YLEN, buffer_dc, 0, 0, SRCCOPY);
			EndPaint(hwnd, &ps);
		}
		return 0;
	case WM_COMMAND:
		{
			int id = LOWORD(wParam);
			switch (id) {
			case ID_BUTTON_CHANGE:
				++current_value;
				if(current_value > 3) current_value = 1;
				break;
			case ID_BUTTON_RUN:
				button_run_clicked();
				break;
			case ID_BUTTON_CLEAR:
				clear_all();				
				break;
			case ID_BUTTON_SAVE:
				{
					char filename[1024];
					if(getfilename(hwnd,filename,1024,1))
					{
						FILE *fp = fopen(filename,"w");

						char str[1024];
						Edit_GetLine(edit, 0, str, sizeof(str));
						const char *p = str;
						const char* svm_type_str = strstr(p, "-s ");
						int svm_type = C_SVC;
						if(svm_type_str != NULL)
							sscanf(svm_type_str, "-s %d", &svm_type);

						if(fp)
						{
							if(svm_type == EPSILON_SVR || svm_type == NU_SVR)
							{
								for(list<point>::iterator p = point_list.begin(); p != point_list.end();p++)
									fprintf(fp,"%f 1:%f\n", p->y, p->x);
							}
							else
							{
								for(list<point>::iterator p = point_list.begin(); p != point_list.end();p++)
									fprintf(fp,"%d 1:%f 2:%f\n", p->value, p->x, p->y);
							}
							fclose(fp);
						}
					}					
				}
				break;
			case ID_BUTTON_LOAD:
				{
					char filename[1024];
					if(getfilename(hwnd,filename,1024,0))					
					{
						FILE *fp = fopen(filename,"r");
						if(fp)
						{
							clear_all();
							char buf[4096];
							while(fgets(buf,sizeof(buf),fp))
							{
								int v;
								double x,y;
								if(sscanf(buf,"%d%*d:%lf%*d:%lf",&v,&x,&y)==3)
								{
									point p = {x,y,v};
									point_list.push_back(p);
								}
								else if(sscanf(buf,"%lf%*d:%lf",&y,&x)==2)
								{
									point p = {x,y,current_value};
									point_list.push_back(p);
								}
								else
									break;
							}
							fclose(fp);
							draw_all_points();
						}
					}
				}
				break;
			}
		}
		return 0;
	case WM_DESTROY:
		PostQuitMessage(0);
		return 0;
	}

	return DefWindowProc(hwnd, iMsg, wParam, lParam);
}


================================================
FILE: Matlab/libsvm-3.19/svm-train.c
================================================
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include <errno.h>
#include "svm.h"
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))

void print_null(const char *s) {}

void exit_with_help()
{
	printf(
	"Usage: svm-train [options] training_set_file [model_file]\n"
	"options:\n"
	"-s svm_type : set type of SVM (default 0)\n"
	"	0 -- C-SVC		(multi-class classification)\n"
	"	1 -- nu-SVC		(multi-class classification)\n"
	"	2 -- one-class SVM\n"
	"	3 -- epsilon-SVR	(regression)\n"
	"	4 -- nu-SVR		(regression)\n"
	"-t kernel_type : set type of kernel function (default 2)\n"
	"	0 -- linear: u'*v\n"
	"	1 -- polynomial: (gamma*u'*v + coef0)^degree\n"
	"	2 -- radial basis function: exp(-gamma*|u-v|^2)\n"
	"	3 -- sigmoid: tanh(gamma*u'*v + coef0)\n"
	"	4 -- precomputed kernel (kernel values in training_set_file)\n"
	"-d degree : set degree in kernel function (default 3)\n"
	"-g gamma : set gamma in kernel function (default 1/num_features)\n"
	"-r coef0 : set coef0 in kernel function (default 0)\n"
	"-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)\n"
	"-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)\n"
	"-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n"
	"-m cachesize : set cache memory size in MB (default 100)\n"
	"-e epsilon : set tolerance of termination criterion (default 0.001)\n"
	"-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)\n"
	"-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)\n"
	"-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)\n"
	"-v n: n-fold cross validation mode\n"
	"-q : quiet mode (no outputs)\n"
	);
	exit(1);
}

void exit_input_error(int line_num)
{
	fprintf(stderr,"Wrong input format at line %d\n", line_num);
	exit(1);
}

void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name);
void read_problem(const char *filename);
void do_cross_validation();

struct svm_parameter param;		// set by parse_command_line
struct svm_problem prob;		// set by read_problem
struct svm_model *model;
struct svm_node *x_space;
int cross_validation;
int nr_fold;

static char *line = NULL;
static int max_line_len;

static char* readline(FILE *input)
{
	int len;
	
	if(fgets(line,max_line_len,input) == NULL)
		return NULL;

	while(strrchr(line,'\n') == NULL)
	{
		max_line_len *= 2;
		line = (char *) realloc(line,max_line_len);
		len = (int) strlen(line);
		if(fgets(line+len,max_line_len-len,input) == NULL)
			break;
	}
	return line;
}

int main(int argc, char **argv)
{
	char input_file_name[1024];
	char model_file_name[1024];
	const char *error_msg;

	parse_command_line(argc, argv, input_file_name, model_file_name);
	read_problem(input_file_name);
	error_msg = svm_check_parameter(&prob,&param);

	if(error_msg)
	{
		fprintf(stderr,"ERROR: %s\n",error_msg);
		exit(1);
	}

	if(cross_validation)
	{
		do_cross_validation();
	}
	else
	{
		model = svm_train(&prob,&param);
		if(svm_save_model(model_file_name,model))
		{
			fprintf(stderr, "can't save model to file %s\n", model_file_name);
			exit(1);
		}
		svm_free_and_destroy_model(&model);
	}
	svm_destroy_param(&param);
	free(prob.y);
	free(prob.x);
	free(x_space);
	free(line);

	return 0;
}

void do_cross_validation()
{
	int i;
	int total_correct = 0;
	double total_error = 0;
	double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
	double *target = Malloc(double,prob.l);

	svm_cross_validation(&prob,&param,nr_fold,target);
	if(param.svm_type == EPSILON_SVR ||
	   param.svm_type == NU_SVR)
	{
		for(i=0;i<prob.l;i++)
		{
			double y = prob.y[i];
			double v = target[i];
			total_error += (v-y)*(v-y);
			sumv += v;
			sumy += y;
			sumvv += v*v;
			sumyy += y*y;
			sumvy += v*y;
		}
		printf("Cross Validation Mean squared error = %g\n",total_error/prob.l);
		printf("Cross Validation Squared correlation coefficient = %g\n",
			((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/
			((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))
			);
	}
	else
	{
		for(i=0;i<prob.l;i++)
			if(target[i] == prob.y[i])
				++total_correct;
		printf("Cross Validation Accuracy = %g%%\n",100.0*total_correct/prob.l);
	}
	free(target);
}

void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name)
{
	int i;
	void (*print_func)(const char*) = NULL;	// default printing to stdout

	// default values
	param.svm_type = C_SVC;
	param.kernel_type = RBF;
	param.degree = 3;
	param.gamma = 0;	// 1/num_features
	param.coef0 = 0;
	param.nu = 0.5;
	param.cache_size = 100;
	param.C = 1;
	param.eps = 1e-3;
	param.p = 0.1;
	param.shrinking = 1;
	param.probability = 0;
	param.nr_weight = 0;
	param.weight_label = NULL;
	param.weight = NULL;
	cross_validation = 0;

	// parse options
	for(i=1;i<argc;i++)
	{
		if(argv[i][0] != '-') break;
		if(++i>=argc)
			exit_with_help();
		switch(argv[i-1][1])
		{
			case 's':
				param.svm_type = atoi(argv[i]);
				break;
			case 't':
				param.kernel_type = atoi(argv[i]);
				break;
			case 'd':
				param.degree = atoi(argv[i]);
				break;
			case 'g':
				param.gamma = atof(argv[i]);
				break;
			case 'r':
				param.coef0 = atof(argv[i]);
				break;
			case 'n':
				param.nu = atof(argv[i]);
				break;
			case 'm':
				param.cache_size = atof(argv[i]);
				break;
			case 'c':
				param.C = atof(argv[i]);
				break;
			case 'e':
				param.eps = atof(argv[i]);
				break;
			case 'p':
				param.p = atof(argv[i]);
				break;
			case 'h':
				param.shrinking = atoi(argv[i]);
				break;
			case 'b':
				param.probability = atoi(argv[i]);
				break;
			case 'q':
				print_func = &print_null;
				i--;
				break;
			case 'v':
				cross_validation = 1;
				nr_fold = atoi(argv[i]);
				if(nr_fold < 2)
				{
					fprintf(stderr,"n-fold cross validation: n must >= 2\n");
					exit_with_help();
				}
				break;
			case 'w':
				++param.nr_weight;
				param.weight_label = (int *)realloc(param.weight_label,sizeof(int)*param.nr_weight);
				param.weight = (double *)realloc(param.weight,sizeof(double)*param.nr_weight);
				param.weight_label[param.nr_weight-1] = atoi(&argv[i-1][2]);
				param.weight[param.nr_weight-1] = atof(argv[i]);
				break;
			default:
				fprintf(stderr,"Unknown option: -%c\n", argv[i-1][1]);
				exit_with_help();
		}
	}

	svm_set_print_string_function(print_func);

	// determine filenames

	if(i>=argc)
		exit_with_help();

	strcpy(input_file_name, argv[i]);

	if(i<argc-1)
		strcpy(model_file_name,argv[i+1]);
	else
	{
		char *p = strrchr(argv[i],'/');
		if(p==NULL)
			p = argv[i];
		else
			++p;
		sprintf(model_file_name,"%s.model",p);
	}
}

// read in a problem (in svmlight format)

void read_problem(const char *filename)
{
	int max_index, inst_max_index, i;
	size_t elements, j;
	FILE *fp = fopen(filename,"r");
	char *endptr;
	char *idx, *val, *label;

	if(fp == NULL)
	{
		fprintf(stderr,"can't open input file %s\n",filename);
		exit(1);
	}

	prob.l = 0;
	elements = 0;

	max_line_len = 1024;
	line = Malloc(char,max_line_len);
	while(readline(fp)!=NULL)
	{
		char *p = strtok(line," \t"); // label

		// features
		while(1)
		{
			p = strtok(NULL," \t");
			if(p == NULL || *p == '\n') // check '\n' as ' ' may be after the last feature
				break;
			++elements;
		}
		++elements;
		++prob.l;
	}
	rewind(fp);

	prob.y = Malloc(double,prob.l);
	prob.x = Malloc(struct svm_node *,prob.l);
	x_space = Malloc(struct svm_node,elements);

	max_index = 0;
	j=0;
	for(i=0;i<prob.l;i++)
	{
		inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0
		readline(fp);
		prob.x[i] = &x_space[j];
		label = strtok(line," \t\n");
		if(label == NULL) // empty line
			exit_input_error(i+1);

		prob.y[i] = strtod(label,&endptr);
		if(endptr == label || *endptr != '\0')
			exit_input_error(i+1);

		while(1)
		{
			idx = strtok(NULL,":");
			val = strtok(NULL," \t");

			if(val == NULL)
				break;

			errno = 0;
			x_space[j].index = (int) strtol(idx,&endptr,10);
			if(endptr == idx || errno != 0 || *endptr != '\0' || x_space[j].index <= inst_max_index)
				exit_input_error(i+1);
			else
				inst_max_index = x_space[j].index;

			errno = 0;
			x_space[j].value = strtod(val,&endptr);
			if(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
				exit_input_error(i+1);

			++j;
		}

		if(inst_max_index > max_index)
			max_index = inst_max_index;
		x_space[j++].index = -1;
	}

	if(param.gamma == 0 && max_index > 0)
		param.gamma = 1.0/max_index;

	if(param.kernel_type == PRECOMPUTED)
		for(i=0;i<prob.l;i++)
		{
			if (prob.x[i][0].index != 0)
			{
				fprintf(stderr,"Wrong input format: first column must be 0:sample_serial_number\n");
				exit(1);
			}
			if ((int)prob.x[i][0].value <= 0 || (int)prob.x[i][0].value > max_index)
			{
				fprintf(stderr,"Wrong input format: sample_serial_number out of range\n");
				exit(1);
			}
		}

	fclose(fp);
}


================================================
FILE: Matlab/libsvm-3.19/svm.cpp
================================================
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <ctype.h>
#include <float.h>
#include <string.h>
#include <stdarg.h>
#include <limits.h>
#include <locale.h>
#include "svm.h"
int libsvm_version = LIBSVM_VERSION;
typedef float Qfloat;
typedef signed char schar;
#ifndef min
template <class T> static inline T min(T x,T y) { return (x<y)?x:y; }
#endif
#ifndef max
template <class T> static inline T max(T x,T y) { return (x>y)?x:y; }
#endif
template <class T> static inline void swap(T& x, T& y) { T t=x; x=y; y=t; }
template <class S, class T> static inline void clone(T*& dst, S* src, int n)
{
	dst = new T[n];
	memcpy((void *)dst,(void *)src,sizeof(T)*n);
}
static inline double powi(double base, int times)
{
	double tmp = base, ret = 1.0;

	for(int t=times; t>0; t/=2)
	{
		if(t%2==1) ret*=tmp;
		tmp = tmp * tmp;
	}
	return ret;
}
#define INF HUGE_VAL
#define TAU 1e-12
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))

static void print_string_stdout(const char *s)
{
	fputs(s,stdout);
	fflush(stdout);
}
static void (*svm_print_string) (const char *) = &print_string_stdout;
#if 1
static void info(const char *fmt,...)
{
	char buf[BUFSIZ];
	va_list ap;
	va_start(ap,fmt);
	vsprintf(buf,fmt,ap);
	va_end(ap);
	(*svm_print_string)(buf);
}
#else
static void info(const char *fmt,...) {}
#endif

//
// Kernel Cache
//
// l is the number of total data items
// size is the cache size limit in bytes
//
class Cache
{
public:
	Cache(int l,long int size);
	~Cache();

	// request data [0,len)
	// return some position p where [p,len) need to be filled
	// (p >= len if nothing needs to be filled)
	int get_data(const int index, Qfloat **data, int len);
	void swap_index(int i, int j);
private:
	int l;
	long int size;
	struct head_t
	{
		head_t *prev, *next;	// a circular list
		Qfloat *data;
		int len;		// data[0,len) is cached in this entry
	};

	head_t *head;
	head_t lru_head;
	void lru_delete(head_t *h);
	void lru_insert(head_t *h);
};

Cache::Cache(int l_,long int size_):l(l_),size(size_)
{
	head = (head_t *)calloc(l,sizeof(head_t));	// initialized to 0
	size /= sizeof(Qfloat);
	size -= l * sizeof(head_t) / sizeof(Qfloat);
	size = max(size, 2 * (long int) l);	// cache must be large enough for two columns
	lru_head.next = lru_head.prev = &lru_head;
}

Cache::~Cache()
{
	for(head_t *h = lru_head.next; h != &lru_head; h=h->next)
		free(h->data);
	free(head);
}

void Cache::lru_delete(head_t *h)
{
	// delete from current location
	h->prev->next = h->next;
	h->next->prev = h->prev;
}

void Cache::lru_insert(head_t *h)
{
	// insert to last position
	h->next = &lru_head;
	h->prev = lru_head.prev;
	h->prev->next = h;
	h->next->prev = h;
}

int Cache::get_data(const int index, Qfloat **data, int len)
{
	head_t *h = &head[index];
	if(h->len) lru_delete(h);
	int more = len - h->len;

	if(more > 0)
	{
		// free old space
		while(size < more)
		{
			head_t *old = lru_head.next;
			lru_delete(old);
			free(old->data);
			size += old->len;
			old->data = 0;
			old->len = 0;
		}

		// allocate new space
		h->data = (Qfloat *)realloc(h->data,sizeof(Qfloat)*len);
		size -= more;
		swap(h->len,len);
	}

	lru_insert(h);
	*data = h->data;
	return len;
}

void Cache::swap_index(int i, int j)
{
	if(i==j) return;

	if(head[i].len) lru_delete(&head[i]);
	if(head[j].len) lru_delete(&head[j]);
	swap(head[i].data,head[j].data);
	swap(head[i].len,head[j].len);
	if(head[i].len) lru_insert(&head[i]);
	if(head[j].len) lru_insert(&head[j]);

	if(i>j) swap(i,j);
	for(head_t *h = lru_head.next; h!=&lru_head; h=h->next)
	{
		if(h->len > i)
		{
			if(h->len > j)
				swap(h->data[i],h->data[j]);
			else
			{
				// give up
				lru_delete(h);
				free(h->data);
				size += h->len;
				h->data = 0;
				h->len = 0;
			}
		}
	}
}

//
// Kernel evaluation
//
// the static method k_function is for doing single kernel evaluation
// the constructor of Kernel prepares to calculate the l*l kernel matrix
// the member function get_Q is for getting one column from the Q Matrix
//
class QMatrix {
public:
	virtual Qfloat *get_Q(int column, int len) const = 0;
	virtual double *get_QD() const = 0;
	virtual void swap_index(int i, int j) const = 0;
	virtual ~QMatrix() {}
};

class Kernel: public QMatrix {
public:
	Kernel(int l, svm_node * const * x, const svm_parameter& param);
	virtual ~Kernel();

	static double k_function(const svm_node *x, const svm_node *y,
				 const svm_parameter& param);
	virtual Qfloat *get_Q(int column, int len) const = 0;
	virtual double *get_QD() const = 0;
	virtual void swap_index(int i, int j) const	// no so const...
	{
		swap(x[i],x[j]);
		if(x_square) swap(x_square[i],x_square[j]);
	}
protected:

	double (Kernel::*kernel_function)(int i, int j) const;

private:
	const svm_node **x;
	double *x_square;

	// svm_parameter
	const int kernel_type;
	const int degree;
	const double gamma;
	const double coef0;

	static double dot(const svm_node *px, const svm_node *py);
	double kernel_linear(int i, int j) const
	{
		return dot(x[i],x[j]);
	}
	double kernel_poly(int i, int j) const
	{
		return powi(gamma*dot(x[i],x[j])+coef0,degree);
	}
	double kernel_rbf(int i, int j) const
	{
		return exp(-gamma*(x_square[i]+x_square[j]-2*dot(x[i],x[j])));
	}
	double kernel_sigmoid(int i, int j) const
	{
		return tanh(gamma*dot(x[i],x[j])+coef0);
	}
	double kernel_precomputed(int i, int j) const
	{
		return x[i][(int)(x[j][0].value)].value;
	}
};

Kernel::Kernel(int l, svm_node * const * x_, const svm_parameter& param)
:kernel_type(param.kernel_type), degree(param.degree),
 gamma(param.gamma), coef0(param.coef0)
{
	switch(kernel_type)
	{
		case LINEAR:
			kernel_function = &Kernel::kernel_linear;
			break;
		case POLY:
			kernel_function = &Kernel::kernel_poly;
			break;
		case RBF:
			kernel_function = &Kernel::kernel_rbf;
			break;
		case SIGMOID:
			kernel_function = &Kernel::kernel_sigmoid;
			break;
		case PRECOMPUTED:
			kernel_function = &Kernel::kernel_precomputed;
			break;
	}

	clone(x,x_,l);

	if(kernel_type == RBF)
	{
		x_square = new double[l];
		for(int i=0;i<l;i++)
			x_square[i] = dot(x[i],x[i]);
	}
	else
		x_square = 0;
}

Kernel::~Kernel()
{
	delete[] x;
	delete[] x_square;
}

double Kernel::dot(const svm_node *px, const svm_node *py)
{
	double sum = 0;
	while(px->index != -1 && py->index != -1)
	{
		if(px->index == py->index)
		{
			sum += px->value * py->value;
			++px;
			++py;
		}
		else
		{
			if(px->index > py->index)
				++py;
			else
				++px;
		}			
	}
	return sum;
}

double Kernel::k_function(const svm_node *x, const svm_node *y,
			  const svm_parameter& param)
{
	switch(param.kernel_type)
	{
		case LINEAR:
			return dot(x,y);
		case POLY:
			return powi(param.gamma*dot(x,y)+param.coef0,param.degree);
		case RBF:
		{
			double sum = 0;
			while(x->index != -1 && y->index !=-1)
			{
				if(x->index == y->index)
				{
					double d = x->value - y->value;
					sum += d*d;
					++x;
					++y;
				}
				else
				{
					if(x->index > y->index)
					{	
						sum += y->value * y->value;
						++y;
					}
					else
					{
						sum += x->value * x->value;
						++x;
					}
				}
			}

			while(x->index != -1)
			{
				sum += x->value * x->value;
				++x;
			}

			while(y->index != -1)
			{
				sum += y->value * y->value;
				++y;
			}
			
			return exp(-param.gamma*sum);
		}
		case SIGMOID:
			return tanh(param.gamma*dot(x,y)+param.coef0);
		case PRECOMPUTED:  //x: test (validation), y: SV
			return x[(int)(y->value)].value;
		default:
			return 0;  // Unreachable 
	}
}

// An SMO algorithm in Fan et al., JMLR 6(2005), p. 1889--1918
// Solves:
//
//	min 0.5(\alpha^T Q \alpha) + p^T \alpha
//
//		y^T \alpha = \delta
//		y_i = +1 or -1
//		0 <= alpha_i <= Cp for y_i = 1
//		0 <= alpha_i <= Cn for y_i = -1
//
// Given:
//
//	Q, p, y, Cp, Cn, and an initial feasible point \alpha
//	l is the size of vectors and matrices
//	eps is the stopping tolerance
//
// solution will be put in \alpha, objective value will be put in obj
//
class Solver {
public:
	Solver() {};
	virtual ~Solver() {};

	struct SolutionInfo {
		double obj;
		double rho;
		double upper_bound_p;
		double upper_bound_n;
		double r;	// for Solver_NU
	};

	void Solve(int l, const QMatrix& Q, const double *p_, const schar *y_,
		   double *alpha_, double Cp, double Cn, double eps,
		   SolutionInfo* si, int shrinking);
protected:
	int active_size;
	schar *y;
	double *G;		// gradient of objective function
	enum { LOWER_BOUND, UPPER_BOUND, FREE };
	char *alpha_status;	// LOWER_BOUND, UPPER_BOUND, FREE
	double *alpha;
	const QMatrix *Q;
	const double *QD;
	double eps;
	double Cp,Cn;
	double *p;
	int *active_set;
	double *G_bar;		// gradient, if we treat free variables as 0
	int l;
	bool unshrink;	// XXX

	double get_C(int i)
	{
		return (y[i] > 0)? Cp : Cn;
	}
	void update_alpha_status(int i)
	{
		if(alpha[i] >= get_C(i))
			alpha_status[i] = UPPER_BOUND;
		else if(alpha[i] <= 0)
			alpha_status[i] = LOWER_BOUND;
		else alpha_status[i] = FREE;
	}
	bool is_upper_bound(int i) { return alpha_status[i] == UPPER_BOUND; }
	bool is_lower_bound(int i) { return alpha_status[i] == LOWER_BOUND; }
	bool is_free(int i) { return alpha_status[i] == FREE; }
	void swap_index(int i, int j);
	void reconstruct_gradient();
	virtual int select_working_set(int &i, int &j);
	virtual double calculate_rho();
	virtual void do_shrinking();
private:
	bool be_shrunk(int i, double Gmax1, double Gmax2);
};

void Solver::swap_index(int i, int j)
{
	Q->swap_index(i,j);
	swap(y[i],y[j]);
	swap(G[i],G[j]);
	swap(alpha_status[i],alpha_status[j]);
	swap(alpha[i],alpha[j]);
	swap(p[i],p[j]);
	swap(active_set[i],active_set[j]);
	swap(G_bar[i],G_bar[j]);
}

void Solver::reconstruct_gradient()
{
	// reconstruct inactive elements of G from G_bar and free variables

	if(active_size == l) return;

	int i,j;
	int nr_free = 0;

	for(j=active_size;j<l;j++)
		G[j] = G_bar[j] + p[j];

	for(j=0;j<active_size;j++)
		if(is_free(j))
			nr_free++;

	if(2*nr_free < active_size)
		info("\nWARNING: using -h 0 may be faster\n");

	if (nr_free*l > 2*active_size*(l-active_size))
	{
		for(i=active_size;i<l;i++)
		{
			const Qfloat *Q_i = Q->get_Q(i,active_size);
			for(j=0;j<active_size;j++)
				if(is_free(j))
					G[i] += alpha[j] * Q_i[j];
		}
	}
	else
	{
		for(i=0;i<active_size;i++)
			if(is_free(i))
			{
				const Qfloat *Q_i = Q->get_Q(i,l);
				double alpha_i = alpha[i];
				for(j=active_size;j<l;j++)
					G[j] += alpha_i * Q_i[j];
			}
	}
}

void Solver::Solve(int l, const QMatrix& Q, const double *p_, const schar *y_,
		   double *alpha_, double Cp, double Cn, double eps,
		   SolutionInfo* si, int shrinking)
{
	this->l = l;
	this->Q = &Q;
	QD=Q.get_QD();
	clone(p, p_,l);
	clone(y, y_,l);
	clone(alpha,alpha_,l);
	this->Cp = Cp;
	this->Cn = Cn;
	this->eps = eps;
	unshrink = false;

	// initialize alpha_status
	{
		alpha_status = new char[l];
		for(int i=0;i<l;i++)
			update_alpha_status(i);
	}

	// initialize active set (for shrinking)
	{
		active_set = new int[l];
		for(int i=0;i<l;i++)
			active_set[i] = i;
		active_size = l;
	}

	// initialize gradient
	{
		G = new double[l];
		G_bar = new double[l];
		int i;
		for(i=0;i<l;i++)
		{
			G[i] = p[i];
			G_bar[i] = 0;
		}
		for(i=0;i<l;i++)
			if(!is_lower_bound(i))
			{
				const Qfloat *Q_i = Q.get_Q(i,l);
				double alpha_i = alpha[i];
				int j;
				for(j=0;j<l;j++)
					G[j] += alpha_i*Q_i[j];
				if(is_upper_bound(i))
					for(j=0;j<l;j++)
						G_bar[j] += get_C(i) * Q_i[j];
			}
	}

	// optimization step

	int iter = 0;
	int max_iter = max(10000000, l>INT_MAX/100 ? INT_MAX : 100*l);
	int counter = min(l,1000)+1;
	
	while(iter < max_iter)
	{
		// show progress and do shrinking

		if(--counter == 0)
		{
			counter = min(l,1000);
			if(shrinking) do_shrinking();
			info(".");
		}

		int i,j;
		if(select_working_set(i,j)!=0)
		{
			// reconstruct the whole gradient
			reconstruct_gradient();
			// reset active set size and check
			active_size = l;
			info("*");
			if(select_working_set(i,j)!=0)
				break;
			else
				counter = 1;	// do shrinking next iteration
		}
		
		++iter;

		// update alpha[i] and alpha[j], handle bounds carefully
		
		const Qfloat *Q_i = Q.get_Q(i,active_size);
		const Qfloat *Q_j = Q.get_Q(j,active_size);

		double C_i = get_C(i);
		double C_j = get_C(j);

		double old_alpha_i = alpha[i];
		double old_alpha_j = alpha[j];

		if(y[i]!=y[j])
		{
			double quad_coef = QD[i]+QD[j]+2*Q_i[j];
			if (quad_coef <= 0)
				quad_coef = TAU;
			double delta = (-G[i]-G[j])/quad_coef;
			double diff = alpha[i] - alpha[j];
			alpha[i] += delta;
			alpha[j] += delta;
			
			if(diff > 0)
			{
				if(alpha[j] < 0)
				{
					alpha[j] = 0;
					alpha[i] = diff;
				}
			}
			else
			{
				if(alpha[i] < 0)
				{
					alpha[i] = 0;
					alpha[j] = -diff;
				}
			}
			if(diff > C_i - C_j)
			{
				if(alpha[i] > C_i)
				{
					alpha[i] = C_i;
					alpha[j] = C_i - diff;
				}
			}
			else
			{
				if(alpha[j] > C_j)
				{
					alpha[j] = C_j;
					alpha[i] = C_j + diff;
				}
			}
		}
		else
		{
			double quad_coef = QD[i]+QD[j]-2*Q_i[j];
			if (quad_coef <= 0)
				quad_coef = TAU;
			double delta = (G[i]-G[j])/quad_coef;
			double sum = alpha[i] + alpha[j];
			alpha[i] -= delta;
			alpha[j] += delta;

			if(sum > C_i)
			{
				if(alpha[i] > C_i)
				{
					alpha[i] = C_i;
					alpha[j] = sum - C_i;
				}
			}
			else
			{
				if(alpha[j] < 0)
				{
					alpha[j] = 0;
					alpha[i] = sum;
				}
			}
			if(sum > C_j)
			{
				if(alpha[j] > C_j)
				{
					alpha[j] = C_j;
					alpha[i] = sum - C_j;
				}
			}
			else
			{
				if(alpha[i] < 0)
				{
					alpha[i] = 0;
					alpha[j] = sum;
				}
			}
		}

		// update G

		double delta_alpha_i = alpha[i] - old_alpha_i;
		double delta_alpha_j = alpha[j] - old_alpha_j;
		
		for(int k=0;k<active_size;k++)
		{
			G[k] += Q_i[k]*delta_alpha_i + Q_j[k]*delta_alpha_j;
		}

		// update alpha_status and G_bar

		{
			bool ui = is_upper_bound(i);
			bool uj = is_upper_bound(j);
			update_alpha_status(i);
			update_alpha_status(j);
			int k;
			if(ui != is_upper_bound(i))
			{
				Q_i = Q.get_Q(i,l);
				if(ui)
					for(k=0;k<l;k++)
						G_bar[k] -= C_i * Q_i[k];
				else
					for(k=0;k<l;k++)
						G_bar[k] += C_i * Q_i[k];
			}

			if(uj != is_upper_bound(j))
			{
				Q_j = Q.get_Q(j,l);
				if(uj)
					for(k=0;k<l;k++)
						G_bar[k] -= C_j * Q_j[k];
				else
					for(k=0;k<l;k++)
						G_bar[k] += C_j * Q_j[k];
			}
		}
	}

	if(iter >= max_iter)
	{
		if(active_size < l)
		{
			// reconstruct the whole gradient to calculate objective value
			reconstruct_gradient();
			active_size = l;
			info("*");
		}
		fprintf(stderr,"\nWARNING: reaching max number of iterations\n");
	}

	// calculate rho

	si->rho = calculate_rho();

	// calculate objective value
	{
		double v = 0;
		int i;
		for(i=0;i<l;i++)
			v += alpha[i] * (G[i] + p[i]);

		si->obj = v/2;
	}

	// put back the solution
	{
		for(int i=0;i<l;i++)
			alpha_[active_set[i]] = alpha[i];
	}

	// juggle everything back
	/*{
		for(int i=0;i<l;i++)
			while(active_set[i] != i)
				swap_index(i,active_set[i]);
				// or Q.swap_index(i,active_set[i]);
	}*/

	si->upper_bound_p = Cp;
	si->upper_bound_n = Cn;

	info("\noptimization finished, #iter = %d\n",iter);

	delete[] p;
	delete[] y;
	delete[] alpha;
	delete[] alpha_status;
	delete[] active_set;
	delete[] G;
	delete[] G_bar;
}

// return 1 if already optimal, return 0 otherwise
int Solver::select_working_set(int &out_i, int &out_j)
{
	// return i,j such that
	// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
	// j: minimizes the decrease of obj value
	//    (if quadratic coefficeint <= 0, replace it with tau)
	//    -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
	
	double Gmax = -INF;
	double Gmax2 = -INF;
	int Gmax_idx = -1;
	int Gmin_idx = -1;
	double obj_diff_min = INF;

	for(int t=0;t<active_size;t++)
		if(y[t]==+1)	
		{
			if(!is_upper_bound(t))
				if(-G[t] >= Gmax)
				{
					Gmax = -G[t];
					Gmax_idx = t;
				}
		}
		else
		{
			if(!is_lower_bound(t))
				if(G[t] >= Gmax)
				{
					Gmax = G[t];
					Gmax_idx = t;
				}
		}

	int i = Gmax_idx;
	const Qfloat *Q_i = NULL;
	if(i != -1) // NULL Q_i not accessed: Gmax=-INF if i=-1
		Q_i = Q->get_Q(i,active_size);

	for(int j=0;j<active_size;j++)
	{
		if(y[j]==+1)
		{
			if (!is_lower_bound(j))
			{
				double grad_diff=Gmax+G[j];
				if (G[j] >= Gmax2)
					Gmax2 = G[j];
				if (grad_diff > 0)
				{
					double obj_diff;
					double quad_coef = QD[i]+QD[j]-2.0*y[i]*Q_i[j];
					if (quad_coef > 0)
						obj_diff = -(grad_diff*grad_diff)/quad_coef;
					else
						obj_diff = -(grad_diff*grad_diff)/TAU;

					if (obj_diff <= obj_diff_min)
					{
						Gmin_idx=j;
						obj_diff_min = obj_diff;
					}
				}
			}
		}
		else
		{
			if (!is_upper_bound(j))
			{
				double grad_diff= Gmax-G[j];
				if (-G[j] >= Gmax2)
					Gmax2 = -G[j];
				if (grad_diff > 0)
				{
					double obj_diff;
					double quad_coef = QD[i]+QD[j]+2.0*y[i]*Q_i[j];
					if (quad_coef > 0)
						obj_diff = -(grad_diff*grad_diff)/quad_coef;
					else
						obj_diff = -(grad_diff*grad_diff)/TAU;

					if (obj_diff <= obj_diff_min)
					{
						Gmin_idx=j;
						obj_diff_min = obj_diff;
					}
				}
			}
		}
	}

	if(Gmax+Gmax2 < eps)
		return 1;

	out_i = Gmax_idx;
	out_j = Gmin_idx;
	return 0;
}

bool Solver::be_shrunk(int i, double Gmax1, double Gmax2)
{
	if(is_upper_bound(i))
	{
		if(y[i]==+1)
			return(-G[i] > Gmax1);
		else
			return(-G[i] > Gmax2);
	}
	else if(is_lower_bound(i))
	{
		if(y[i]==+1)
			return(G[i] > Gmax2);
		else	
			return(G[i] > Gmax1);
	}
	else
		return(false);
}

void Solver::do_shrinking()
{
	int i;
	double Gmax1 = -INF;		// max { -y_i * grad(f)_i | i in I_up(\alpha) }
	double Gmax2 = -INF;		// max { y_i * grad(f)_i | i in I_low(\alpha) }

	// find maximal violating pair first
	for(i=0;i<active_size;i++)
	{
		if(y[i]==+1)	
		{
			if(!is_upper_bound(i))	
			{
				if(-G[i] >= Gmax1)
					Gmax1 = -G[i];
			}
			if(!is_lower_bound(i))	
			{
				if(G[i] >= Gmax2)
					Gmax2 = G[i];
			}
		}
		else	
		{
			if(!is_upper_bound(i))	
			{
				if(-G[i] >= Gmax2)
					Gmax2 = -G[i];
			}
			if(!is_lower_bound(i))	
			{
				if(G[i] >= Gmax1)
					Gmax1 = G[i];
			}
		}
	}

	if(unshrink == false && Gmax1 + Gmax2 <= eps*10) 
	{
		unshrink = true;
		reconstruct_gradient();
		active_size = l;
		info("*");
	}

	for(i=0;i<active_size;i++)
		if (be_shrunk(i, Gmax1, Gmax2))
		{
			active_size--;
			while (active_size > i)
			{
				if (!be_shrunk(active_size, Gmax1, Gmax2))
				{
					swap_index(i,active_size);
					break;
				}
				active_size--;
			}
		}
}

double Solver::calculate_rho()
{
	double r;
	int nr_free = 0;
	double ub = INF, lb = -INF, sum_free = 0;
	for(int i=0;i<active_size;i++)
	{
		double yG = y[i]*G[i];

		if(is_upper_bound(i))
		{
			if(y[i]==-1)
				ub = min(ub,yG);
			else
				lb = max(lb,yG);
		}
		else if(is_lower_bound(i))
		{
			if(y[i]==+1)
				ub = min(ub,yG);
			else
				lb = max(lb,yG);
		}
		else
		{
			++nr_free;
			sum_free += yG;
		}
	}

	if(nr_free>0)
		r = sum_free/nr_free;
	else
		r = (ub+lb)/2;

	return r;
}

//
// Solver for nu-svm classification and regression
//
// additional constraint: e^T \alpha = constant
//
class Solver_NU: public Solver
{
public:
	Solver_NU() {}
	void Solve(int l, const QMatrix& Q, const double *p, const schar *y,
		   double *alpha, double Cp, double Cn, double eps,
		   SolutionInfo* si, int shrinking)
	{
		this->si = si;
		Solver::Solve(l,Q,p,y,alpha,Cp,Cn,eps,si,shrinking);
	}
private:
	SolutionInfo *si;
	int select_working_set(int &i, int &j);
	double calculate_rho();
	bool be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4);
	void do_shrinking();
};

// return 1 if already optimal, return 0 otherwise
int Solver_NU::select_working_set(int &out_i, int &out_j)
{
	// return i,j such that y_i = y_j and
	// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
	// j: minimizes the decrease of obj value
	//    (if quadratic coefficeint <= 0, replace it with tau)
	//    -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)

	double Gmaxp = -INF;
	double Gmaxp2 = -INF;
	int Gmaxp_idx = -1;

	double Gmaxn = -INF;
	double Gmaxn2 = -INF;
	int Gmaxn_idx = -1;

	int Gmin_idx = -1;
	double obj_diff_min = INF;

	for(int t=0;t<active_size;t++)
		if(y[t]==+1)
		{
			if(!is_upper_bound(t))
				if(-G[t] >= Gmaxp)
				{
					Gmaxp = -G[t];
					Gmaxp_idx = t;
				}
		}
		else
		{
			if(!is_lower_bound(t))
				if(G[t] >= Gmaxn)
				{
					Gmaxn = G[t];
					Gmaxn_idx = t;
				}
		}

	int ip = Gmaxp_idx;
	int in = Gmaxn_idx;
	const Qfloat *Q_ip = NULL;
	const Qfloat *Q_in = NULL;
	if(ip != -1) // NULL Q_ip not accessed: Gmaxp=-INF if ip=-1
		Q_ip = Q->get_Q(ip,active_size);
	if(in != -1)
		Q_in = Q->get_Q(in,active_size);

	for(int j=0;j<active_size;j++)
	{
		if(y[j]==+1)
		{
			if (!is_lower_bound(j))	
			{
				double grad_diff=Gmaxp+G[j];
				if (G[j] >= Gmaxp2)
					Gmaxp2 = G[j];
				if (grad_diff > 0)
				{
					double obj_diff;
					double quad_coef = QD[ip]+QD[j]-2*Q_ip[j];
					if (quad_coef > 0)
						obj_diff = -(grad_diff*grad_diff)/quad_coef;
					else
						obj_diff = -(grad_diff*grad_diff)/TAU;

					if (obj_diff <= obj_diff_min)
					{
						Gmin_idx=j;
						obj_diff_min = obj_diff;
					}
				}
			}
		}
		else
		{
			if (!is_upper_bound(j))
			{
				double grad_diff=Gmaxn-G[j];
				if (-G[j] >= Gmaxn2)
					Gmaxn2 = -G[j];
				if (grad_diff > 0)
				{
					double obj_diff;
					double quad_coef = QD[in]+QD[j]-2*Q_in[j];
					if (quad_coef > 0)
						obj_diff = -(grad_diff*grad_diff)/quad_coef;
					else
						obj_diff = -(grad_diff*grad_diff)/TAU;

					if (obj_diff <= obj_diff_min)
					{
						Gmin_idx=j;
						obj_diff_min = obj_diff;
					}
				}
			}
		}
	}

	if(max(Gmaxp+Gmaxp2,Gmaxn+Gmaxn2) < eps)
		return 1;

	if (y[Gmin_idx] == +1)
		out_i = Gmaxp_idx;
	else
		out_i = Gmaxn_idx;
	out_j = Gmin_idx;

	return 0;
}

bool Solver_NU::be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4)
{
	if(is_upper_bound(i))
	{
		if(y[i]==+1)
			return(-G[i] > Gmax1);
		else	
			return(-G[i] > Gmax4);
	}
	else if(is_lower_bound(i))
	{
		if(y[i]==+1)
			return(G[i] > Gmax2);
		else	
			return(G[i] > Gmax3);
	}
	else
		return(false);
}

void Solver_NU::do_shrinking()
{
	double Gmax1 = -INF;	// max { -y_i * grad(f)_i | y_i = +1, i in I_up(\alpha) }
	double Gmax2 = -INF;	// max { y_i * grad(f)_i | y_i = +1, i in I_low(\alpha) }
	double Gmax3 = -INF;	// max { -y_i * grad(f)_i | y_i = -1, i in I_up(\alpha) }
	double Gmax4 = -INF;	// max { y_i * grad(f)_i | y_i = -1, i in I_low(\alpha) }

	// find maximal violating pair first
	int i;
	for(i=0;i<active_size;i++)
	{
		if(!is_upper_bound(i))
		{
			if(y[i]==+1)
			{
				if(-G[i] > Gmax1) Gmax1 = -G[i];
			}
			else	if(-G[i] > Gmax4) Gmax4 = -G[i];
		}
		if(!is_lower_bound(i))
		{
			if(y[i]==+1)
			{	
				if(G[i] > Gmax2) Gmax2 = G[i];
			}
			else	if(G[i] > Gmax3) Gmax3 = G[i];
		}
	}

	if(unshrink == false && max(Gmax1+Gmax2,Gmax3+Gmax4) <= eps*10) 
	{
		unshrink = true;
		reconstruct_gradient();
		active_size = l;
	}

	for(i=0;i<active_size;i++)
		if (be_shrunk(i, Gmax1, Gmax2, Gmax3, Gmax4))
		{
			active_size--;
			while (active_size > i)
			{
				if (!be_shrunk(active_size, Gmax1, Gmax2, Gmax3, Gmax4))
				{
					swap_index(i,active_size);
					break;
				}
				active_size--;
			}
		}
}

double Solver_NU::calculate_rho()
{
	int nr_free1 = 0,nr_free2 = 0;
	double ub1 = INF, ub2 = INF;
	double lb1 = -INF, lb2 = -INF;
	double sum_free1 = 0, sum_free2 = 0;

	for(int i=0;i<active_size;i++)
	{
		if(y[i]==+1)
		{
			if(is_upper_bound(i))
				lb1 = max(lb1,G[i]);
			else if(is_lower_bound(i))
				ub1 = min(ub1,G[i]);
			else
			{
				++nr_free1;
				sum_free1 += G[i];
			}
		}
		else
		{
			if(is_upper_bound(i))
				lb2 = max(lb2,G[i]);
			else if(is_lower_bound(i))
				ub2 = min(ub2,G[i]);
			else
			{
				++nr_free2;
				sum_free2 += G[i];
			}
		}
	}

	double r1,r2;
	if(nr_free1 > 0)
		r1 = sum_free1/nr_free1;
	else
		r1 = (ub1+lb1)/2;
	
	if(nr_free2 > 0)
		r2 = sum_free2/nr_free2;
	else
		r2 = (ub2+lb2)/2;
	
	si->r = (r1+r2)/2;
	return (r1-r2)/2;
}

//
// Q matrices for various formulations
//
class SVC_Q: public Kernel
{ 
public:
	SVC_Q(const svm_problem& prob, const svm_parameter& param, const schar *y_)
	:Kernel(prob.l, prob.x, param)
	{
		clone(y,y_,prob.l);
		cache = new Cache(prob.l,(long int)(param.cache_size*(1<<20)));
		QD = new double[prob.l];
		for(int i=0;i<prob.l;i++)
			QD[i] = (this->*kernel_function)(i,i);
	}
	
	Qfloat *get_Q(int i, int len) const
	{
		Qfloat *data;
		int start, j;
		if((start = cache->get_data(i,&data,len)) < len)
		{
			for(j=start;j<len;j++)
				data[j] = (Qfloat)(y[i]*y[j]*(this->*kernel_function)(i,j));
		}
		return data;
	}

	double *get_QD() const
	{
		return QD;
	}

	void swap_index(int i, int j) const
	{
		cache->swap_index(i,j);
		Kernel::swap_index(i,j);
		swap(y[i],y[j]);
		swap(QD[i],QD[j]);
	}

	~SVC_Q()
	{
		delete[] y;
		delete cache;
		delete[] QD;
	}
private:
	schar *y;
	Cache *cache;
	double *QD;
};

class ONE_CLASS_Q: public Kernel
{
public:
	ONE_CLASS_Q(const svm_problem& prob, const svm_parameter& param)
	:Kernel(prob.l, prob.x, param)
	{
		cache = new Cache(prob.l,(long int)(param.cache_size*(1<<20)));
		QD = new double[prob.l];
		for(int i=0;i<prob.l;i++)
			QD[i] = (this->*kernel_function)(i,i);
	}
	
	Qfloat *get_Q(int i, int len) const
	{
		Qfloat *data;
		int start, j;
		if((start = cache->get_data(i,&data,len)) < len)
		{
			for(j=start;j<len;j++)
				data[j] = (Qfloat)(this->*kernel_function)(i,j);
		}
		return data;
	}

	double *get_QD() const
	{
		return QD;
	}

	void swap_index(int i, int j) const
	{
		cache->swap_index(i,j);
		Kernel::swap_index(i,j);
		swap(QD[i],QD[j]);
	}

	~ONE_CLASS_Q()
	{
		delete cache;
		delete[] QD;
	}
private:
	Cache *cache;
	double *QD;
};

class SVR_Q: public Kernel
{ 
public:
	SVR_Q(const svm_problem& prob, const svm_parameter& param)
	:Kernel(prob.l, prob.x, param)
	{
		l = prob.l;
		cache = new Cache(l,(long int)(param.cache_size*(1<<20)));
		QD = new double[2*l];
		sign = new schar[2*l];
		index = new int[2*l];
		for(int k=0;k<l;k++)
		{
			sign[k] = 1;
			sign[k+l] = -1;
			index[k] = k;
			index[k+l] = k;
			QD[k] = (this->*kernel_function)(k,k);
			QD[k+l] = QD[k];
		}
		buffer[0] = new Qfloat[2*l];
		buffer[1] = new Qfloat[2*l];
		next_buffer = 0;
	}

	void swap_index(int i, int j) const
	{
		swap(sign[i],sign[j]);
		swap(index[i],index[j]);
		swap(QD[i],QD[j]);
	}
	
	Qfloat *get_Q(int i, int len) const
	{
		Qfloat *data;
		int j, real_i = index[i];
		if(cache->get_data(real_i,&data,l) < l)
		{
			for(j=0;j<l;j++)
				data[j] = (Qfloat)(this->*kernel_function)(real_i,j);
		}

		// reorder and copy
		Qfloat *buf = buffer[next_buffer];
		next_buffer = 1 - next_buffer;
		schar si = sign[i];
		for(j=0;j<len;j++)
			buf[j] = (Qfloat) si * (Qfloat) sign[j] * data[index[j]];
		return buf;
	}

	double *get_QD() const
	{
		return QD;
	}

	~SVR_Q()
	{
		delete cache;
		delete[] sign;
		delete[] index;
		delete[] buffer[0];
		delete[] buffer[1];
		delete[] QD;
	}
private:
	int l;
	Cache *cache;
	schar *sign;
	int *index;
	mutable int next_buffer;
	Qfloat *buffer[2];
	double *QD;
};

//
// construct and solve various formulations
//
static void solve_c_svc(
	const svm_problem *prob, const svm_parameter* param,
	double *alpha, Solver::SolutionInfo* si, double Cp, double Cn)
{
	int l = prob->l;
	double *minus_ones = new double[l];
	schar *y = new schar[l];

	int i;

	for(i=0;i<l;i++)
	{
		alpha[i] = 0;
		minus_ones[i] = -1;
		if(prob->y[i] > 0) y[i] = +1; else y[i] = -1;
	}

	Solver s;
	s.Solve(l, SVC_Q(*prob,*param,y), minus_ones, y,
		alpha, Cp, Cn, param->eps, si, param->shrinking);

	double sum_alpha=0;
	for(i=0;i<l;i++)
		sum_alpha += alpha[i];

	if (Cp==Cn)
		info("nu = %f\n", sum_alpha/(Cp*prob->l));

	for(i=0;i<l;i++)
		alpha[i] *= y[i];

	delete[] minus_ones;
	delete[] y;
}

static void solve_nu_svc(
	const svm_problem *prob, const svm_parameter *param,
	double *alpha, Solver::SolutionInfo* si)
{
	int i;
	int l = prob->l;
	double nu = param->nu;

	schar *y = new schar[l];

	for(i=0;i<l;i++)
		if(prob->y[i]>0)
			y[i] = +1;
		else
			y[i] = -1;

	double sum_pos = nu*l/2;
	double sum_neg = nu*l/2;

	for(i=0;i<l;i++)
		if(y[i] == +1)
		{
			alpha[i] = min(1.0,sum_pos);
			sum_pos -= alpha[i];
		}
		else
		{
			alpha[i] = min(1.0,sum_neg);
			sum_neg -= alpha[i];
		}

	double *zeros = new double[l];

	for(i=0;i<l;i++)
		zeros[i] = 0;

	Solver_NU s;
	s.Solve(l, SVC_Q(*prob,*param,y), zeros, y,
		alpha, 1.0, 1.0, param->eps, si,  param->shrinking);
	double r = si->r;

	info("C = %f\n",1/r);

	for(i=0;i<l;i++)
		alpha[i] *= y[i]/r;

	si->rho /= r;
	si->obj /= (r*r);
	si->upper_bound_p = 1/r;
	si->upper_bound_n = 1/r;

	delete[] y;
	delete[] zeros;
}

static void solve_one_class(
	const svm_problem *prob, const svm_parameter *param,
	double *alpha, Solver::SolutionInfo* si)
{
	int l = prob->l;
	double *zeros = new double[l];
	schar *ones = new schar[l];
	int i;

	int n = (int)(param->nu*prob->l);	// # of alpha's at upper bound

	for(i=0;i<n;i++)
		alpha[i] = 1;
	if(n<prob->l)
		alpha[n] = param->nu * prob->l - n;
	for(i=n+1;i<l;i++)
		alpha[i] = 0;

	for(i=0;i<l;i++)
	{
		zeros[i] = 0;
		ones[i] = 1;
	}

	Solver s;
	s.Solve(l, ONE_CLASS_Q(*prob,*param), zeros, ones,
		alpha, 1.0, 1.0, param->eps, si, param->shrinking);

	delete[] zeros;
	delete[] ones;
}

static void solve_epsilon_svr(
	const svm_problem *prob, const svm_parameter *param,
	double *alpha, Solver::SolutionInfo* si)
{
	int l = prob->l;
	double *alpha2 = new double[2*l];
	double *linear_term = new double[2*l];
	schar *y = new schar[2*l];
	int i;

	for(i=0;i<l;i++)
	{
		alpha2[i] = 0;
		linear_term[i] = param->p - prob->y[i];
		y[i] = 1;

		alpha2[i+l] = 0;
		linear_term[i+l] = param->p + prob->y[i];
		y[i+l] = -1;
	}

	Solver s;
	s.Solve(2*l, SVR_Q(*prob,*param), linear_term, y,
		alpha2, param->C, param->C, param->eps, si, param->shrinking);

	double sum_alpha = 0;
	for(i=0;i<l;i++)
	{
		alpha[i] = alpha2[i] - alpha2[i+l];
		sum_alpha += fabs(alpha[i]);
	}
	info("nu = %f\n",sum_alpha/(param->C*l));

	delete[] alpha2;
	delete[] linear_term;
	delete[] y;
}

static void solve_nu_svr(
	const svm_problem *prob, const svm_parameter *param,
	double *alpha, Solver::SolutionInfo* si)
{
	int l = prob->l;
	double C = param->C;
	double *alpha2 = new double[2*l];
	double *linear_term = new double[2*l];
	schar *y = new schar[2*l];
	int i;

	double sum = C * param->nu * l / 2;
	for(i=0;i<l;i++)
	{
		alpha2[i] = alpha2[i+l] = min(sum,C);
		sum -= alpha2[i];

		linear_term[i] = - prob->y[i];
		y[i] = 1;

		linear_term[i+l] = prob->y[i];
		y[i+l] = -1;
	}

	Solver_NU s;
	s.Solve(2*l, SVR_Q(*prob,*param), linear_term, y,
		alpha2, C, C, param->eps, si, param->shrinking);

	info("epsilon = %f\n",-si->r);

	for(i=0;i<l;i++)
		alpha[i] = alpha2[i] - alpha2[i+l];

	delete[] alpha2;
	delete[] linear_term;
	delete[] y;
}

//
// decision_function
//
struct decision_function
{
	double *alpha;
	double rho;
};

static decision_function svm_train_one(
	const svm_problem *prob, const svm_parameter *param,
	double Cp, double Cn)
{
	double *alpha = Malloc(double,prob->l);
	Solver::SolutionInfo si;
	switch(param->svm_type)
	{
		case C_SVC:
			solve_c_svc(prob,param,alpha,&si,Cp,Cn);
			break;
		case NU_SVC:
			solve_nu_svc(prob,param,alpha,&si);
			break;
		case ONE_CLASS:
			solve_one_class(prob,param,alpha,&si);
			break;
		case EPSILON_SVR:
			solve_epsilon_svr(prob,param,alpha,&si);
			break;
		case NU_SVR:
			solve_nu_svr(prob,param,alpha,&si);
			break;
	}

	info("obj = %f, rho = %f\n",si.obj,si.rho);

	// output SVs

	int nSV = 0;
	int nBSV = 0;
	for(int i=0;i<prob->l;i++)
	{
		if(fabs(alpha[i]) > 0)
		{
			++nSV;
			if(prob->y[i] > 0)
			{
				if(fabs(alpha[i]) >= si.upper_bound_p)
					++nBSV;
			}
			else
			{
				if(fabs(alpha[i]) >= si.upper_bound_n)
					++nBSV;
			}
		}
	}

	info("nSV = %d, nBSV = %d\n",nSV,nBSV);

	decision_function f;
	f.alpha = alpha;
	f.rho = si.rho;
	return f;
}

// Platt's binary SVM Probablistic Output: an improvement from Lin et al.
static void sigmoid_train(
	int l, const double *dec_values, const double *labels, 
	double& A, double& B)
{
	double prior1=0, prior0 = 0;
	int i;

	for (i=0;i<l;i++)
		if (labels[i] > 0) prior1+=1;
		else prior0+=1;
	
	int max_iter=100;	// Maximal number of iterations
	double min_step=1e-10;	// Minimal step taken in line search
	double sigma=1e-12;	// For numerically strict PD of Hessian
	double eps=1e-5;
	double hiTarget=(prior1+1.0)/(prior1+2.0);
	double loTarget=1/(prior0+2.0);
	double *t=Malloc(double,l);
	double fApB,p,q,h11,h22,h21,g1,g2,det,dA,dB,gd,stepsize;
	double newA,newB,newf,d1,d2;
	int iter;
	
	// Initial Point and Initial Fun Value
	A=0.0; B=log((prior0+1.0)/(prior1+1.0));
	double fval = 0.0;

	for (i=0;i<l;i++)
	{
		if (labels[i]>0) t[i]=hiTarget;
		else t[i]=loTarget;
		fApB = dec_values[i]*A+B;
		if (fApB>=0)
			fval += t[i]*fApB + log(1+exp(-fApB));
		else
			fval += (t[i] - 1)*fApB +log(1+exp(fApB));
	}
	for (iter=0;iter<max_iter;iter++)
	{
		// Update Gradient and Hessian (use H' = H + sigma I)
		h11=sigma; // numerically ensures strict PD
		h22=sigma;
		h21=0.0;g1=0.0;g2=0.0;
		for (i=0;i<l;i++)
		{
			fApB = dec_values[i]*A+B;
			if (fApB >= 0)
			{
				p=exp(-fApB)/(1.0+exp(-fApB));
				q=1.0/(1.0+exp(-fApB));
			}
			else
			{
				p=1.0/(1.0+exp(fApB));
				q=exp(fApB)/(1.0+exp(fApB));
			}
			d2=p*q;
			h11+=dec_values[i]*dec_values[i]*d2;
			h22+=d2;
			h21+=dec_values[i]*d2;
			d1=t[i]-p;
			g1+=dec_values[i]*d1;
			g2+=d1;
		}

		// Stopping Criteria
		if (fabs(g1)<eps && fabs(g2)<eps)
			break;

		// Finding Newton direction: -inv(H') * g
		det=h11*h22-h21*h21;
		dA=-(h22*g1 - h21 * g2) / det;
		dB=-(-h21*g1+ h11 * g2) / det;
		gd=g1*dA+g2*dB;


		stepsize = 1;		// Line Search
		while (stepsize >= min_step)
		{
			newA = A + stepsize * dA;
			newB = B + stepsize * dB;

			// New function value
			newf = 0.0;
			for (i=0;i<l;i++)
			{
				fApB = dec_values[i]*newA+newB;
				if (fApB >= 0)
					newf += t[i]*fApB + log(1+exp(-fApB));
				else
					newf += (t[i] - 1)*fApB +log(1+exp(fApB));
			}
			// Check sufficient decrease
			if (newf<fval+0.0001*stepsize*gd)
			{
				A=newA;B=newB;fval=newf;
				break;
			}
			else
				stepsize = stepsize / 2.0;
		}

		if (stepsize < min_step)
		{
			info("Line search fails in two-class probability estimates\n");
			break;
		}
	}

	if (iter>=max_iter)
		info("Reaching maximal iterations in two-class probability estimates\n");
	free(t);
}

static double sigmoid_predict(double decision_value, double A, double B)
{
	double fApB = decision_value*A+B;
	// 1-p used later; avoid catastrophic cancellation
	if (fApB >= 0)
		return exp(-fApB)/(1.0+exp(-fApB));
	else
		return 1.0/(1+exp(fApB)) ;
}

// Method 2 from the multiclass_prob paper by Wu, Lin, and Weng
static void multiclass_probability(int k, double **r, double *p)
{
	int t,j;
	int iter = 0, max_iter=max(100,k);
	double **Q=Malloc(double *,k);
	double *Qp=Malloc(double,k);
	double pQp, eps=0.005/k;
	
	for (t=0;t<k;t++)
	{
		p[t]=1.0/k;  // Valid if k = 1
		Q[t]=Malloc(double,k);
		Q[t][t]=0;
		for (j=0;j<t;j++)
		{
			Q[t][t]+=r[j][t]*r[j][t];
			Q[t][j]=Q[j][t];
		}
		for (j=t+1;j<k;j++)
		{
			Q[t][t]+=r[j][t]*r[j][t];
			Q[t][j]=-r[j][t]*r[t][j];
		}
	}
	for (iter=0;iter<max_iter;iter++)
	{
		// stopping condition, recalculate QP,pQP for numerical accuracy
		pQp=0;
		for (t=0;t<k;t++)
		{
			Qp[t]=0;
			for (j=0;j<k;j++)
				Qp[t]+=Q[t][j]*p[j];
			pQp+=p[t]*Qp[t];
		}
		double max_error=0;
		for (t=0;t<k;t++)
		{
			double error=fabs(Qp[t]-pQp);
			if (error>max_error)
				max_error=error;
		}
		if (max_error<eps) break;
		
		for (t=0;t<k;t++)
		{
			double diff=(-Qp[t]+pQp)/Q[t][t];
			p[t]+=diff;
			pQp=(pQp+diff*(diff*Q[t][t]+2*Qp[t]))/(1+diff)/(1+diff);
			for (j=0;j<k;j++)
			{
				Qp[j]=(Qp[j]+diff*Q[t][j])/(1+diff);
				p[j]/=(1+diff);
			}
		}
	}
	if (iter>=max_iter)
		info("Exceeds max_iter in multiclass_prob\n");
	for(t=0;t<k;t++) free(Q[t]);
	free(Q);
	free(Qp);
}

// Cross-validation decision values for probability estimates
static void svm_binary_svc_probability(
	const svm_problem *prob, const svm_parameter *param,
	double Cp, double Cn, double& probA, double& probB)
{
	int i;
	int nr_fold = 5;
	int *perm = Malloc(int,prob->l);
	double *dec_values = Malloc(double,prob->l);

	// random shuffle
	for(i=0;i<prob->l;i++) perm[i]=i;
	for(i=0;i<prob->l;i++)
	{
		int j = i+rand()%(prob->l-i);
		swap(perm[i],perm[j]);
	}
	for(i=0;i<nr_fold;i++)
	{
		int begin = i*prob->l/nr_fold;
		int end = (i+1)*prob->l/nr_fold;
		int j,k;
		struct svm_problem subprob;

		subprob.l = prob->l-(end-begin);
		subprob.x = Malloc(struct svm_node*,subprob.l);
		subprob.y = Malloc(double,subprob.l);
			
		k=0;
		for(j=0;j<begin;j++)
		{
			subprob.x[k] = prob->x[perm[j]];
			subprob.y[k] = prob->y[perm[j]];
			++k;
		}
		for(j=end;j<prob->l;j++)
		{
			subprob.x[k] = prob->x[perm[j]];
			subprob.y[k] = prob->y[perm[j]];
			++k;
		}
		int p_count=0,n_count=0;
		for(j=0;j<k;j++)
			if(subprob.y[j]>0)
				p_count++;
			else
				n_count++;

		if(p_count==0 && n_count==0)
			for(j=begin;j<end;j++)
				dec_values[perm[j]] = 0;
		else if(p_count > 0 && n_count == 0)
			for(j=begin;j<end;j++)
				dec_values[perm[j]] = 1;
		else if(p_count == 0 && n_count > 0)
			for(j=begin;j<end;j++)
				dec_values[perm[j]] = -1;
		else
		{
			svm_parameter subparam = *param;
			subparam.probability=0;
			subparam.C=1.0;
			subparam.nr_weight=2;
			subparam.weight_label = Malloc(int,2);
			subparam.weight = Malloc(double,2);
			subparam.weight_label[0]=+1;
			subparam.weight_label[1]=-1;
			subparam.weight[0]=Cp;
			subparam.weight[1]=Cn;
			struct svm_model *submodel = svm_train(&subprob,&subparam);
			for(j=begin;j<end;j++)
			{
				svm_predict_values(submodel,prob->x[perm[j]],&(dec_values[perm[j]]));
				// ensure +1 -1 order; reason not using CV subroutine
				dec_values[perm[j]] *= submodel->label[0];
			}		
			svm_free_and_destroy_model(&submodel);
			svm_destroy_param(&subparam);
		}
		free(subprob.x);
		free(subprob.y);
	}		
	sigmoid_train(prob->l,dec_values,prob->y,probA,probB);
	free(dec_values);
	free(perm);
}

// Return parameter of a Laplace distribution 
static double svm_svr_probability(
	const svm_problem *prob, const svm_parameter *param)
{
	int i;
	int nr_fold = 5;
	double *ymv = Malloc(double,prob->l);
	double mae = 0;

	svm_parameter newparam = *param;
	newparam.probability = 0;
	svm_cross_validation(prob,&newparam,nr_fold,ymv);
	for(i=0;i<prob->l;i++)
	{
		ymv[i]=prob->y[i]-ymv[i];
		mae += fabs(ymv[i]);
	}		
	mae /= prob->l;
	double std=sqrt(2*mae*mae);
	int count=0;
	mae=0;
	for(i=0;i<prob->l;i++)
		if (fabs(ymv[i]) > 5*std) 
			count=count+1;
		else 
			mae+=fabs(ymv[i]);
	mae /= (prob->l-count);
	info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma= %g\n",mae);
	free(ymv);
	return mae;
}


// label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data
// perm, length l, must be allocated before calling this subroutine
static void svm_group_classes(const svm_problem *prob, int *nr_class_ret, int **label_ret, int **start_ret, int **count_ret, int *perm)
{
	int l = prob->l;
	int max_nr_class = 16;
	int nr_class = 0;
	int *label = Malloc(int,max_nr_class);
	int *count = Malloc(int,max_nr_class);
	int *data_label = Malloc(int,l);
	int i;

	for(i=0;i<l;i++)
	{
		int this_label = (int)prob->y[i];
		int j;
		for(j=0;j<nr_class;j++)
		{
			if(this_label == label[j])
			{
				++count[j];
				break;
			}
		}
		data_label[i] = j;
		if(j == nr_class)
		{
			if(nr_class == max_nr_class)
			{
				max_nr_class *= 2;
				label = (int *)realloc(label,max_nr_class*sizeof(int));
				count = (int *)realloc(count,max_nr_class*sizeof(int));
			}
			label[nr_class] = this_label;
			count[nr_class] = 1;
			++nr_class;
		}
	}

	//
	// Labels are ordered by their first occurrence in the training set. 
	// However, for two-class sets with -1/+1 labels and -1 appears first, 
	// we swap labels to ensure that internally the binary SVM has positive data corresponding to the +1 instances.
	//
	if (nr_class == 2 && label[0] == -1 && label[1] == 1)
	{
		swap(label[0],label[1]);
		swap(count[0],count[1]);
		for(i=0;i<l;i++)
		{
			if(data_label[i] == 0)
				data_label[i] = 1;
			else
				data_label[i] = 0;
		}
	}

	int *start = Malloc(int,nr_class);
	start[0] = 0;
	for(i=1;i<nr_class;i++)
		start[i] = start[i-1]+count[i-1];
	for(i=0;i<l;i++)
	{
		perm[start[data_label[i]]] = i;
		++start[data_label[i]];
	}
	start[0] = 0;
	for(i=1;i<nr_class;i++)
		start[i] = start[i-1]+count[i-1];

	*nr_class_ret = nr_class;
	*label_ret = label;
	*start_ret = start;
	*count_ret = count;
	free(data_label);
}

//
// Interface functions
//
svm_model *svm_train(const svm_problem *prob, const svm_parameter *param)
{
	svm_model *model = Malloc(svm_model,1);
	model->param = *param;
	model->free_sv = 0;	// XXX

	if(param->svm_type == ONE_CLASS ||
	   param->svm_type == EPSILON_SVR ||
	   param->svm_type == NU_SVR)
	{
		// regression or one-class-svm
		model->nr_class = 2;
		model->label = NULL;
		model->nSV = NULL;
		model->probA = NULL; model->probB = NULL;
		model->sv_coef = Malloc(double *,1);

		if(param->probability && 
		   (param->svm_type == EPSILON_SVR ||
		    param->svm_type == NU_SVR))
		{
			model->probA = Malloc(double,1);
			model->probA[0] = svm_svr_probability(prob,param);
		}

		decision_function f = svm_train_one(prob,param,0,0);
		model->rho = Malloc(double,1);
		model->rho[0] = f.rho;

		int nSV = 0;
		int i;
		for(i=0;i<prob->l;i++)
			if(fabs(f.alpha[i]) > 0) ++nSV;
		model->l = nSV;
		model->SV = Malloc(svm_node *,nSV);
		model->sv_coef[0] = Malloc(double,nSV);
		model->sv_indices = Malloc(int,nSV);
		int j = 0;
		for(i=0;i<prob->l;i++)
			if(fabs(f.alpha[i]) > 0)
			{
				model->SV[j] = prob->x[i];
				model->sv_coef[0][j] = f.alpha[i];
				model->sv_indices[j] = i+1;
				++j;
			}		

		free(f.alpha);
	}
	else
	{
		// classification
		int l = prob->l;
		int nr_class;
		int *label = NULL;
		int *start = NULL;
		int *count = NULL;
		int *perm = Malloc(int,l);

		// group training data of the same class
		svm_group_classes(prob,&nr_class,&label,&start,&count,perm);
		if(nr_class == 1) 
			info("WARNING: training data in only one class. See README for details.\n");
		
		svm_node **x = Malloc(svm_node *,l);
		int i;
		for(i=0;i<l;i++)
			x[i] = prob->x[perm[i]];

		// calculate weighted C

		double *weighted_C = Malloc(double, nr_class);
		for(i=0;i<nr_class;i++)
			weighted_C[i] = param->C;
		for(i=0;i<param->nr_weight;i++)
		{	
			int j;
			for(j=0;j<nr_class;j++)
				if(param->weight_label[i] == label[j])
					break;
			if(j == nr_class)
				fprintf(stderr,"WARNING: class label %d specified in weight is not found\n", param->weight_label[i]);
			else
				weighted_C[j] *= param->weight[i];
		}

		// train k*(k-1)/2 models
		
		bool *nonzero = Malloc(bool,l);
		for(i=0;i<l;i++)
			nonzero[i] = false;
		decision_function *f = Malloc(decision_function,nr_class*(nr_class-1)/2);

		double *probA=NULL,*probB=NULL;
		if (param->probability)
		{
			probA=Malloc(double,nr_class*(nr_class-1)/2);
			probB=Malloc(double,nr_class*(nr_class-1)/2);
		}

		int p = 0;
		for(i=0;i<nr_class;i++)
			for(int j=i+1;j<nr_class;j++)
			{
				svm_problem sub_prob;
				int si = start[i], sj = start[j];
				int ci = count[i], cj = count[j];
				sub_prob.l = ci+cj;
				sub_prob.x = Malloc(svm_node *,sub_prob.l);
				sub_prob.y = Malloc(double,sub_prob.l);
				int k;
				for(k=0;k<ci;k++)
				{
					sub_prob.x[k] = x[si+k];
					sub_prob.y[k] = +1;
				}
				for(k=0;k<cj;k++)
				{
					sub_prob.x[ci+k] = x[sj+k];
					sub_prob.y[ci+k] = -1;
				}

				if(param->probability)
					svm_binary_svc_probability(&sub_prob,param,weighted_C[i],weighted_C[j],probA[p],probB[p]);

				f[p] = svm_train_one(&sub_prob,param,weighted_C[i],weighted_C[j]);
				for(k=0;k<ci;k++)
					if(!nonzero[si+k] && fabs(f[p].alpha[k]) > 0)
						nonzero[si+k] = true;
				for(k=0;k<cj;k++)
					if(!nonzero[sj+k] && fabs(f[p].alpha[ci+k]) > 0)
						nonzero[sj+k] = true;
				free(sub_prob.x);
				free(sub_prob.y);
				++p;
			}

		// build output

		model->nr_class = nr_class;
		
		model->label = Malloc(int,nr_class);
		for(i=0;i<nr_class;i++)
			model->label[i] = label[i];
		
		model->rho = Malloc(double,nr_class*(nr_class-1)/2);
		for(i=0;i<nr_class*(nr_class-1)/2;i++)
			model->rho[i] = f[i].rho;

		if(param->probability)
		{
			model->probA = Malloc(double,nr_class*(nr_class-1)/2);
			model->probB = Malloc(double,nr_class*(nr_class-1)/2);
			for(i=0;i<nr_class*(nr_class-1)/2;i++)
			{
				model->probA[i] = probA[i];
				model->probB[i] = probB[i];
			}
		}
		else
		{
			model->probA=NULL;
			model->probB=NULL;
		}

		int total_sv = 0;
		int *nz_count = Malloc(int,nr_class);
		model->nSV = Malloc(int,nr_class);
		for(i=0;i<nr_class;i++)
		{
			int nSV = 0;
			for(int j=0;j<count[i];j++)
				if(nonzero[start[i]+j])
				{	
					++nSV;
					++total_sv;
				}
			model->nSV[i] = nSV;
			nz_count[i] = nSV;
		}
		
		info("Total nSV = %d\n",total_sv);

		model->l = total_sv;
		model->SV = Malloc(svm_node *,total_sv);
		model->sv_indices = Malloc(int,total_sv);
		p = 0;
		for(i=0;i<l;i++)
			if(nonzero[i])
			{
				model->SV[p] = x[i];
				model->sv_indices[p++] = perm[i] + 1;
			}

		int *nz_start = Malloc(int,nr_class);
		nz_start[0] = 0;
		for(i=1;i<nr_class;i++)
			nz_start[i] = nz_start[i-1]+nz_count[i-1];

		model->sv_coef = Malloc(double *,nr_class-1);
		for(i=0;i<nr_class-1;i++)
			model->sv_coef[i] = Malloc(double,total_sv);

		p = 0;
		for(i=0;i<nr_class;i++)
			for(int j=i+1;j<nr_class;j++)
			{
				// classifier (i,j): coefficients with
				// i are in sv_coef[j-1][nz_start[i]...],
				// j are in sv_coef[i][nz_start[j]...]

				int si = start[i];
				int sj = start[j];
				int ci = count[i];
				int cj = count[j];
				
				int q = nz_start[i];
				int k;
				for(k=0;k<ci;k++)
					if(nonzero[si+k])
						model->sv_coef[j-1][q++] = f[p].alpha[k];
				q = nz_start[j];
				for(k=0;k<cj;k++)
					if(nonzero[sj+k])
						model->sv_coef[i][q++] = f[p].alpha[ci+k];
				++p;
			}
		
		free(label);
		free(probA);
		free(probB);
		free(count);
		free(perm);
		free(start);
		free(x);
		free(weighted_C);
		free(nonzero);
		for(i=0;i<nr_class*(nr_class-1)/2;i++)
			free(f[i].alpha);
		free(f);
		free(nz_count);
		free(nz_start);
	}
	return model;
}

// Stratified cross validation
void svm_cross_validation(const svm_problem *prob, const svm_parameter *param, int nr_fold, double *target)
{
	int i;
	int *fold_start;
	int l = prob->l;
	int *perm = Malloc(int,l);
	int nr_class;
	if (nr_fold > l)
	{
		nr_fold = l;
		fprintf(stderr,"WARNING: # folds > # data. Will use # folds = # data instead (i.e., leave-one-out cross validation)\n");
	}
	fold_start = Malloc(int,nr_fold+1);
	// stratified cv may not give leave-one-out rate
	// Each class to l folds -> some folds may have zero elements
	if((param->svm_type == C_SVC ||
	    param->svm_type == NU_SVC) && nr_fold < l)
	{
		int *start = NULL;
		int *label = NULL;
		int *count = NULL;
		svm_group_classes(prob,&nr_class,&label,&start,&count,perm);

		// random shuffle and then data grouped by fold using the array perm
		int *fold_count = Malloc(int,nr_fold);
		int c;
		int *index = Malloc(int,l);
		for(i=0;i<l;i++)
			index[i]=perm[i];
		for (c=0; c<nr_class; c++) 
			for(i=0;i<count[c];i++)
			{
				int j = i+rand()%(count[c]-i);
				swap(index[start[c]+j],index[start[c]+i]);
			}
		for(i=0;i<nr_fold;i++)
		{
			fold_count[i] = 0;
			for (c=0; c<nr_class;c++)
				fold_count[i]+=(i+1)*count[c]/nr_fold-i*count[c]/nr_fold;
		}
		fold_start[0]=0;
		for (i=1;i<=nr_fold;i++)
			fold_start[i] = fold_start[i-1]+fold_count[i-1];
		for (c=0; c<nr_class;c++)
			for(i=0;i<nr_fold;i++)
			{
				int begin = start[c]+i*count[c]/nr_fold;
				int end = start[c]+(i+1)*count[c]/nr_fold;
				for(int j=begin;j<end;j++)
				{
					perm[fold_start[i]] = index[j];
					fold_start[i]++;
				}
			}
		fold_start[0]=0;
		for (i=1;i<=nr_fold;i++)
			fold_start[i] = fold_start[i-1]+fold_count[i-1];
		free(start);
		free(label);
		free(count);
		free(index);
		free(fold_count);
	}
	else
	{
		for(i=0;i<l;i++) perm[i]=i;
		for(i=0;i<l;i++)
		{
			int j = i+rand()%(l-i);
			swap(perm[i],perm[j]);
		}
		for(i=0;i<=nr_fold;i++)
			fold_start[i]=i*l/nr_fold;
	}

	for(i=0;i<nr_fold;i++)
	{
		int begin = fold_start[i];
		int end = fold_start[i+1];
		int j,k;
		struct svm_problem subprob;

		subprob.l = l-(end-begin);
		subprob.x = Malloc(struct svm_node*,subprob.l);
		subprob.y = Malloc(double,subprob.l);
			
		k=0;
		for(j=0;j<begin;j++)
		{
			subprob.x[k] = prob->x[perm[j]];
			subprob.y[k] = prob->y[perm[j]];
			++k;
		}
		for(j=end;j<l;j++)
		{
			subprob.x[k] = prob->x[perm[j]];
			subprob.y[k] = prob->y[perm[j]];
			++k;
		}
		struct svm_model *submodel = svm_train(&subprob,param);
		if(param->probability && 
		   (param->svm_type == C_SVC || param->svm_type == NU_SVC))
		{
			double *prob_estimates=Malloc(double,svm_get_nr_class(submodel));
			for(j=begin;j<end;j++)
				target[perm[j]] = svm_predict_probability(submodel,prob->x[perm[j]],prob_estimates);
			free(prob_estimates);
		}
		else
			for(j=begin;j<end;j++)
				target[perm[j]] = svm_predict(submodel,prob->x[perm[j]]);
		svm_free_and_destroy_model(&submodel);
		free(subprob.x);
		free(subprob.y);
	}		
	free(fold_start);
	free(perm);
}


int svm_get_svm_type(const svm_model *model)
{
	return model->param.svm_type;
}

int svm_get_nr_class(const svm_model *model)
{
	return model->nr_class;
}

void svm_get_labels(const svm_model *model, int* label)
{
	if (model->label != NULL)
		for(int i=0;i<model->nr_class;i++)
			label[i] = model->label[i];
}

void svm_get_sv_indices(const svm_model *model, int* indices)
{
	if (model->sv_indices != NULL)
		for(int i=0;i<model->l;i++)
			indices[i] = model->sv_indices[i];
}

int svm_get_nr_sv(const svm_model *model)
{
	return model->l;
}

double svm_get_svr_probability(const svm_model *model)
{
	if ((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) &&
	    model->probA!=NULL)
		return model->probA[0];
	else
	{
		fprintf(stderr,"Model doesn't contain information for SVR probability inference\n");
		return 0;
	}
}

double svm_predict_values(const svm_model *model, const svm_node *x, double* dec_values)
{
	int i;
	if(model->param.svm_type == ONE_CLASS ||
	   model->param.svm_type == EPSILON_SVR ||
	   model->param.svm_type == NU_SVR)
	{
		double *sv_coef = model->sv_coef[0];
		double sum = 0;
		for(i=0;i<model->l;i++)
			sum += sv_coef[i] * Kernel::k_function(x,model->SV[i],model->param);
		sum -= model->rho[0];
		*dec_values = sum;

		if(model->param.svm_type == ONE_CLASS)
			return (sum>0)?1:-1;
		else
			return sum;
	}
	else
	{
		int nr_class = model->nr_class;
		int l = model->l;
		
		double *kvalue = Malloc(double,l);
		for(i=0;i<l;i++)
			kvalue[i] = Kernel::k_function(x,model->SV[i],model->param);

		int *start = Malloc(int,nr_class);
		start[0] = 0;
		for(i=1;i<nr_class;i++)
			start[i] = start[i-1]+model->nSV[i-1];

		int *vote = Malloc(int,nr_class);
		for(i=0;i<nr_class;i++)
			vote[i] = 0;

		int p=0;
		for(i=0;i<nr_class;i++)
			for(int j=i+1;j<nr_class;j++)
			{
				double sum = 0;
				int si = start[i];
				int sj = start[j];
				int ci = model->nSV[i];
				int cj = model->nSV[j];
				
				int k;
				double *coef1 = model->sv_coef[j-1];
				double *coef2 = model->sv_coef[i];
				for(k=0;k<ci;k++)
					sum += coef1[si+k] * kvalue[si+k];
				for(k=0;k<cj;k++)
					sum += coef2[sj+k] * kvalue[sj+k];
				sum -= model->rho[p];
				dec_values[p] = sum;

				if(dec_values[p] > 0)
					++vote[i];
				else
					++vote[j];
				p++;
			}

		int vote_max_idx = 0;
		for(i=1;i<nr_class;i++)
			if(vote[i] > vote[vote_max_idx])
				vote_max_idx = i;

		free(kvalue);
		free(start);
		free(vote);
		return model->label[vote_max_idx];
	}
}

double svm_predict(const svm_model *model, const svm_node *x)
{
	int nr_class = model->nr_class;
	double *dec_values;
	if(model->param.svm_type == ONE_CLASS ||
	   model->param.svm_type == EPSILON_SVR ||
	   model->param.svm_type == NU_SVR)
		dec_values = Malloc(double, 1);
	else 
		dec_values = Malloc(double, nr_class*(nr_class-1)/2);
	double pred_result = svm_predict_values(model, x, dec_values);
	free(dec_values);
	return pred_result;
}

double svm_predict_probability(
	const svm_model *model, const svm_node *x, double *prob_estimates)
{
	if ((model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC) &&
	    model->probA!=NULL && model->probB!=NULL)
	{
		int i;
		int nr_class = model->nr_class;
		double *dec_values = Malloc(double, nr_class*(nr_class-1)/2);
		svm_predict_values(model, x, dec_values);

		double min_prob=1e-7;
		double **pairwise_prob=Malloc(double *,nr_class);
		for(i=0;i<nr_class;i++)
			pairwise_prob[i]=Malloc(double,nr_class);
		int k=0;
		for(i=0;i<nr_class;i++)
			for(int j=i+1;j<nr_class;j++)
			{
				pairwise_prob[i][j]=min(max(sigmoid_predict(dec_values[k],model->probA[k],model->probB[k]),min_prob),1-min_prob);
				pairwise_prob[j][i]=1-pairwise_prob[i][j];
				k++;
			}
		multiclass_probability(nr_class,pairwise_prob,prob_estimates);

		int prob_max_idx = 0;
		for(i=1;i<nr_class;i++)
			if(prob_estimates[i] > prob_estimates[prob_max_idx])
				prob_max_idx = i;
		for(i=0;i<nr_class;i++)
			free(pairwise_prob[i]);
		free(dec_values);
		free(pairwise_prob);
		return model->label[prob_max_idx];
	}
	else 
		return svm_predict(model, x);
}

static const char *svm_type_table[] =
{
	"c_svc","nu_svc","one_class","epsilon_svr","nu_svr",NULL
};

static const char *kernel_type_table[]=
{
	"linear","polynomial","rbf","sigmoid","precomputed",NULL
};

int svm_save_model(const char *model_file_name, const svm_model *model)
{
	FILE *fp = fopen(model_file_name,"w");
	if(fp==NULL) return -1;

	char *old_locale = strdup(setlocale(LC_ALL, NULL));
	setlocale(LC_ALL, "C");

	const svm_parameter& param = model->param;

	fprintf(fp,"svm_type %s\n", svm_type_table[param.svm_type]);
	fprintf(fp,"kernel_type %s\n", kernel_type_table[param.kernel_type]);

	if(param.kernel_type == POLY)
		fprintf(fp,"degree %d\n", param.degree);

	if(param.kernel_type == POLY || param.kernel_type == RBF || param.kernel_type == SIGMOID)
		fprintf(fp,"gamma %g\n", param.gamma);

	if(param.kernel_type == POLY || param.kernel_type == SIGMOID)
		fprintf(fp,"coef0 %g\n", param.coef0);

	int nr_class = model->nr_class;
	int l = model->l;
	fprintf(fp, "nr_class %d\n", nr_class);
	fprintf(fp, "total_sv %d\n",l);
	
	{
		fprintf(fp, "rho");
		for(int i=0;i<nr_class*(nr_class-1)/2;i++)
			fprintf(fp," %g",model->rho[i]);
		fprintf(fp, "\n");
	}
	
	if(model->label)
	{
		fprintf(fp, "label");
		for(int i=0;i<nr_class;i++)
			fprintf(fp," %d",model->label[i]);
		fprintf(fp, "\n");
	}

	if(model->probA) // regression has probA only
	{
		fprintf(fp, "probA");
		for(int i=0;i<nr_class*(nr_class-1)/2;i++)
			fprintf(fp," %g",model->probA[i]);
		fprintf(fp, "\n");
	}
	if(model->probB)
	{
		fprintf(fp, "probB");
		for(int i=0;i<nr_class*(nr_class-1)/2;i++)
			fprintf(fp," %g",model->probB[i]);
		fprintf(fp, "\n");
	}

	if(model->nSV)
	{
		fprintf(fp, "nr_sv");
		for(int i=0;i<nr_class;i++)
			fprintf(fp," %d",model->nSV[i]);
		fprintf(fp, "\n");
	}

	fprintf(fp, "SV\n");
	const double * const *sv_coef = model->sv_coef;
	const svm_node * const *SV = model->SV;

	for(int i=0;i<l;i++)
	{
		for(int j=0;j<nr_class-1;j++)
			fprintf(fp, "%.16g ",sv_coef[j][i]);

		const svm_node *p = SV[i];

		if(param.kernel_type == PRECOMPUTED)
			fprintf(fp,"0:%d ",(int)(p->value));
		else
			while(p->index != -1)
			{
				fprintf(fp,"%d:%.8g ",p->index,p->value);
				p++;
			}
		fprintf(fp, "\n");
	}

	setlocale(LC_ALL, old_locale);
	free(old_locale);

	if (ferror(fp) != 0 || fclose(fp) != 0) return -1;
	else return 0;
}

static char *line = NULL;
static int max_line_len;

static char* readline(FILE *input)
{
	int len;

	if(fgets(line,max_line_len,input) == NULL)
		return NULL;

	while(strrchr(line,'\n') == NULL)
	{
		max_line_len *= 2;
		line = (char *) realloc(line,max_line_len);
		len = (int) strlen(line);
		if(fgets(line+len,max_line_len-len,input) == NULL)
			break;
	}
	return line;
}

//
// FSCANF helps to handle fscanf failures.
// Its do-while block avoids the ambiguity when
// if (...)
//    FSCANF();
// is used
//
#define FSCANF(_stream, _format, _var) do{ if (fscanf(_stream, _format, _var) != 1) return false; }while(0)
bool read_model_header(FILE *fp, svm_model* model)
{
	svm_parameter& param = model->param;
	char cmd[81];
	while(1)
	{
		FSCANF(fp,"%80s",cmd);

		if(strcmp(cmd,"svm_type")==0)
		{
			FSCANF(fp,"%80s",cmd);
			int i;
			for(i=0;svm_type_table[i];i++)
			{
				if(strcmp(svm_type_table[i],cmd)==0)
				{
					param.svm_type=i;
					break;
				}
			}
			if(svm_type_table[i] == NULL)
			{
				fprintf(stderr,"unknown svm type.\n");
				return false;
			}
		}
		else if(strcmp(cmd,"kernel_type")==0)
		{		
			FSCANF(fp,"%80s",cmd);
			int i;
			for(i=0;kernel_type_table[i];i++)
			{
				if(strcmp(kernel_type_table[i],cmd)==0)
				{
					param.kernel_type=i;
					break;
				}
			}
			if(kernel_type_table[i] == NULL)
			{
				fprintf(stderr,"unknown kernel function.\n");	
				return false;
			}
		}
		else if(strcmp(cmd,"degree")==0)
			FSCANF(fp,"%d",&param.degree);
		else if(strcmp(cmd,"gamma")==0)
			FSCANF(fp,"%lf",&param.gamma);
		else if(strcmp(cmd,"coef0")==0)
			FSCANF(fp,"%lf",&param.coef0);
		else if(strcmp(cmd,"nr_class")==0)
			FSCANF(fp,"%d",&model->nr_class);
		else if(strcmp(cmd,"total_sv")==0)
			FSCANF(fp,"%d",&model->l);
		else if(strcmp(cmd,"rho")==0)
		{
			int n = model->nr_class * (model->nr_class-1)/2;
			model->rho = Malloc(double,n);
			for(int i=0;i<n;i++)
				FSCANF(fp,"%lf",&model->rho[i]);
		}
		else if(strcmp(cmd,"label")==0)
		{
			int n = model->nr_class;
			model->label = Malloc(int,n);
			for(int i=0;i<n;i++)
				FSCANF(fp,"%d",&model->label[i]);
		}
		else if(strcmp(cmd,"probA")==0)
		{
			int n = model->nr_class * (model->nr_class-1)/2;
			model->probA = Malloc(double,n);
			for(int i=0;i<n;i++)
				FSCANF(fp,"%lf",&model->probA[i]);
		}
		else if(strcmp(cmd,"probB")==0)
		{
			int n = model->nr_class * (model->nr_class-1)/2;
			model->probB = Malloc(double,n);
			for(int i=0;i<n;i++)
				FSCANF(fp,"%lf",&model->probB[i]);
		}
		else if(strcmp(cmd,"nr_sv")==0)
		{
			int n = model->nr_class;
			model->nSV = Malloc(int,n);
			for(int i=0;i<n;i++)
				FSCANF(fp,"%d",&model->nSV[i]);
		}
		else if(strcmp(cmd,"SV")==0)
		{
			while(1)
			{
				int c = getc(fp);
				if(c==EOF || c=='\n') break;
			}
			break;
		}
		else
		{
			fprintf(stderr,"unknown text in model file: [%s]\n",cmd);
			return false;
		}
	}

	return true;

}

svm_model *svm_load_model(const char *model_file_name)
{
	FILE *fp = fopen(model_file_name,"rb");
	if(fp==NULL) return NULL;

	char *old_locale = strdup(setlocale(LC_ALL, NULL));
	setlocale(LC_ALL, "C");

	// read parameters

	svm_model *model = Malloc(svm_model,1);
	model->rho = NULL;
	model->probA = NULL;
	model->probB = NULL;
	model->sv_indices = NULL;
	model->label = NULL;
	model->nSV = NULL;
	
	// read header
	if (!read_model_header(fp, model))
	{
		fprintf(stderr, "ERROR: fscanf failed to read model\n");
		setlocale(LC_ALL, old_locale);
		free(old_locale);
		free(model->rho);
		free(model->label);
		free(model->nSV);
		free(model);
		return NULL;
	}
	
	// read sv_coef and SV

	int elements = 0;
	long pos = ftell(fp);

	max_line_len = 1024;
	line = Malloc(char,max_line_len);
	char *p,*endptr,*idx,*val;

	while(readline(fp)!=NULL)
	{
		p = strtok(line,":");
		while(1)
		{
			p = strtok(NULL,":");
			if(p == NULL)
				break;
			++elements;
		}
	}
	elements += model->l;

	fseek(fp,pos,SEEK_SET);

	int m = model->nr_class - 1;
	int l = model->l;
	model->sv_coef = Malloc(double *,m);
	int i;
	for(i=0;i<m;i++)
		model->sv_coef[i] = Malloc(double,l);
	model->SV = Malloc(svm_node*,l);
	svm_node *x_space = NULL;
	if(l>0) x_space = Malloc(svm_node,elements);

	int j=0;
	for(i=0;i<l;i++)
	{
		readline(fp);
		model->SV[i] = &x_space[j];

		p = strtok(line, " \t");
		model->sv_coef[0][i] = strtod(p,&endptr);
		for(int k=1;k<m;k++)
		{
			p = strtok(NULL, " \t");
			model->sv_coef[k][i] = strtod(p,&endptr);
		}

		while(1)
		{
			idx = strtok(NULL, ":");
			val = strtok(NULL, " \t");

			if(val == NULL)
				break;
			x_space[j].index = (int) strtol(idx,&endptr,10);
			x_space[j].value = strtod(val,&endptr);

			++j;
		}
		x_space[j++].index = -1;
	}
	free(line);

	setlocale(LC_ALL, old_locale);
	free(old_locale);

	if (ferror(fp) != 0 || fclose(fp) != 0)
		return NULL;

	model->free_sv = 1;	// XXX
	return model;
}

void svm_free_model_content(svm_model* model_ptr)
{
	if(model_ptr->free_sv && model_ptr->l > 0 && model_ptr->SV != NULL)
		free((void *)(model_ptr->SV[0]));
	if(model_ptr->sv_coef)
	{
		for(int i=0;i<model_ptr->nr_class-1;i++)
			free(model_ptr->sv_coef[i]);
	}

	free(model_ptr->SV);
	model_ptr->SV = NULL;

	free(model_ptr->sv_coef);
	model_ptr->sv_coef = NULL;

	free(model_ptr->rho);
	model_ptr->rho = NULL;

	free(model_ptr->label);
	model_ptr->label= NULL;

	free(model_ptr->probA);
	model_ptr->probA = NULL;

	free(model_ptr->probB);
	model_ptr->probB= NULL;

	free(model_ptr->sv_indices);
	model_ptr->sv_indices = NULL;

	free(model_ptr->nSV);
	model_ptr->nSV = NULL;
}

void svm_free_and_destroy_model(svm_model** model_ptr_ptr)
{
	if(model_ptr_ptr != NULL && *model_ptr_ptr != NULL)
	{
		svm_free_model_content(*model_ptr_ptr);
		free(*model_ptr_ptr);
		*model_ptr_ptr = NULL;
	}
}

void svm_destroy_param(svm_parameter* param)
{
	free(param->weight_label);
	free(param->weight);
}

const char *svm_check_parameter(const svm_problem *prob, const svm_parameter *param)
{
	// svm_type

	int svm_type = param->svm_type;
	if(svm_type != C_SVC &&
	   svm_type != NU_SVC &&
	   svm_type != ONE_CLASS &&
	   svm_type != EPSILON_SVR &&
	   svm_type != NU_SVR)
		return "unknown svm type";
	
	// kernel_type, degree
	
	int kernel_type = param->kernel_type;
	if(kernel_type != LINEAR &&
	   kernel_type != POLY &&
	   kernel_type != RBF &&
	   kernel_type != SIGMOID &&
	   kernel_type != PRECOMPUTED)
		return "unknown kernel type";

	if(param->gamma < 0)
		return "gamma < 0";

	if(param->degree < 0)
		return "degree of polynomial kernel < 0";

	// cache_size,eps,C,nu,p,shrinking

	if(param->cache_size <= 0)
		return "cache_size <= 0";

	if(param->eps <= 0)
		return "eps <= 0";

	if(svm_type == C_SVC ||
	   svm_type == EPSILON_SVR ||
	   svm_type == NU_SVR)
		if(param->C <= 0)
			return "C <= 0";

	if(svm_type == NU_SVC ||
	   svm_type == ONE_CLASS ||
	   svm_type == NU_SVR)
		if(param->nu <= 0 || param->nu > 1)
			return "nu <= 0 or nu > 1";

	if(svm_type == EPSILON_SVR)
		if(param->p < 0)
			return "p < 0";

	if(param->shrinking != 0 &&
	   param->shrinking != 1)
		return "shrinking != 0 and shrinking != 1";

	if(param->probability != 0 &&
	   param->probability != 1)
		return "probability != 0 and probability != 1";

	if(param->probability == 1 &&
	   svm_type == ONE_CLASS)
		return "one-class SVM probability output not supported yet";


	// check whether nu-svc is feasible
	
	if(svm_type == NU_SVC)
	{
		int l = prob->l;
		int max_nr_class = 16;
		int nr_class = 0;
		int *label = Malloc(int,max_nr_class);
		int *count = Malloc(int,max_nr_class);

		int i;
		for(i=0;i<l;i++)
		{
			int this_label = (int)prob->y[i];
			int j;
			for(j=0;j<nr_class;j++)
				if(this_label == label[j])
				{
					++count[j];
					break;
				}
			if(j == nr_class)
			{
				if(nr_class == max_nr_class)
				{
					max_nr_class *= 2;
					label = (int *)realloc(label,max_nr_class*sizeof(int));
					count = (int *)realloc(count,max_nr_class*sizeof(int));
				}
				label[nr_class] = this_label;
				count[nr_class] = 1;
				++nr_class;
			}
		}
	
		for(i=0;i<nr_class;i++)
		{
			int n1 = count[i];
			for(int j=i+1;j<nr_class;j++)
			{
				int n2 = count[j];
				if(param->nu*(n1+n2)/2 > min(n1,n2))
				{
					free(label);
					free(count);
					return "specified nu is infeasible";
				}
			}
		}
		free(label);
		free(count);
	}

	return NULL;
}

int svm_check_probability_model(const svm_model *model)
{
	return ((model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC) &&
		model->probA!=NULL && model->probB!=NULL) ||
		((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) &&
		 model->probA!=NULL);
}

void svm_set_print_string_function(void (*print_func)(const char *))
{
	if(print_func == NULL)
		svm_print_string = &print_string_stdout;
	else
		svm_print_string = print_func;
}


================================================
FILE: Matlab/libsvm-3.19/svm.def
================================================
LIBRARY libsvm
EXPORTS
	svm_train	@1
	svm_cross_validation	@2
	svm_save_model	@3
	svm_load_model	@4
	svm_get_svm_type	@5
	svm_get_nr_class	@6
	svm_get_labels	@7
	svm_get_svr_probability	@8
	svm_predict_values	@9
	svm_predict	@10
	svm_predict_probability	@11
	svm_free_model_content	@12
	svm_free_and_destroy_model	@13
	svm_destroy_param	@14
	svm_check_parameter	@15
	svm_check_probability_model	@16
	svm_set_print_string_function	@17
	svm_get_sv_indices	@18
	svm_get_nr_sv	@19


================================================
FILE: Matlab/libsvm-3.19/svm.h
================================================
#ifndef _LIBSVM_H
#define _LIBSVM_H

#define LIBSVM_VERSION 319

#ifdef __cplusplus
extern "C" {
#endif

extern int libsvm_version;

struct svm_node
{
	int index;
	double value;
};

struct svm_problem
{
	int l;
	double *y;
	struct svm_node **x;
};

enum { C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR };	/* svm_type */
enum { LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED }; /* kernel_type */

struct svm_parameter
{
	int svm_type;
	int kernel_type;
	int degree;	/* for poly */
	double gamma;	/* for poly/rbf/sigmoid */
	double coef0;	/* for poly/sigmoid */

	/* these are for training only */
	double cache_size; /* in MB */
	double eps;	/* stopping criteria */
	double C;	/* for C_SVC, EPSILON_SVR and NU_SVR */
	int nr_weight;		/* for C_SVC */
	int *weight_label;	/* for C_SVC */
	double* weight;		/* for C_SVC */
	double nu;	/* for NU_SVC, ONE_CLASS, and NU_SVR */
	double p;	/* for EPSILON_SVR */
	int shrinking;	/* use the shrinking heuristics */
	int probability; /* do probability estimates */
};

//
// svm_model
// 
struct svm_model
{
	struct svm_parameter param;	/* parameter */
	int nr_class;		/* number of classes, = 2 in regression/one class svm */
	int l;			/* total #SV */
	struct svm_node **SV;		/* SVs (SV[l]) */
	double **sv_coef;	/* coefficients for SVs in decision functions (sv_coef[k-1][l]) */
	double *rho;		/* constants in decision functions (rho[k*(k-1)/2]) */
	double *probA;		/* pariwise probability information */
	double *probB;
	int *sv_indices;        /* sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set */

	/* for classification only */

	int *label;		/* label of each class (label[k]) */
	int *nSV;		/* number of SVs for each class (nSV[k]) */
				/* nSV[0] + nSV[1] + ... + nSV[k-1] = l */
	/* XXX */
	int free_sv;		/* 1 if svm_model is created by svm_load_model*/
				/* 0 if svm_model is created by svm_train */
};

struct svm_model *svm_train(const struct svm_problem *prob, const struct svm_parameter *param);
void svm_cross_validation(const struct svm_problem *prob, const struct svm_parameter *param, int nr_fold, double *target);

int svm_save_model(const char *model_file_name, const struct svm_model *model);
struct svm_model *svm_load_model(const char *model_file_name);

int svm_get_svm_type(const struct svm_model *model);
int svm_get_nr_class(const struct svm_model *model);
void svm_get_labels(const struct svm_model *model, int *label);
void svm_get_sv_indices(const struct svm_model *model, int *sv_indices);
int svm_get_nr_sv(const struct svm_model *model);
double svm_get_svr_probability(const struct svm_model *model);

double svm_predict_values(const struct svm_model *model, const struct svm_node *x, double* dec_values);
double svm_predict(const struct svm_model *model, const struct svm_node *x);
double svm_predict_probability(const struct svm_model *model, const struct svm_node *x, double* prob_estimates);

void svm_free_model_content(struct svm_model *model_ptr);
void svm_free_and_destroy_model(struct svm_model **model_ptr_ptr);
void svm_destroy_param(struct svm_parameter *param);

const char *svm_check_parameter(const struct svm_problem *prob, const struct svm_parameter *param);
int svm_check_probability_model(const struct svm_model *model);

void svm_set_print_string_function(void (*print_func)(const char *));

#ifdef __cplusplus
}
#endif

#endif /* _LIBSVM_H */


================================================
FILE: Matlab/libsvm-3.19/tools/README
================================================
This directory includes some useful codes:

1. subset selection tools.
2. parameter selection tools.
3. LIBSVM format checking tools

Part I: Subset selection tools

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

Training large data is time consuming. Sometimes one should work on a
smaller subset first. The python script subset.py randomly selects a
specified number of samples. For classification data, we provide a
stratified selection to ensure the same class distribution in the
subset.

Usage: subset.py [options] dataset number [output1] [output2]

This script selects a subset of the given data set.

options:
-s method : method of selection (default 0)
     0 -- stratified selection (classification only)
     1 -- random selection

output1 : the subset (optional)
output2 : the rest of data (optional)

If output1 is omitted, the subset will be printed on the screen.

Example
=======

> python subset.py heart_scale 100 file1 file2

From heart_scale 100 samples are randomly selected and stored in
file1. All remaining instances are stored in file2.


Part II: Parameter Selection Tools

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

grid.py is a parameter selection tool for C-SVM classification using
the RBF (radial basis function) kernel. It uses cross validation (CV)
technique to estimate the accuracy of each parameter combination in
the specified range and helps you to decide the best parameters for
your problem.

grid.py directly executes libsvm binaries (so no python binding is needed)
for cross validation and then draw contour of CV accuracy using gnuplot.
You must have libsvm and gnuplot installed before using it. The package
gnuplot is available at http://www.gnuplot.info/

On Mac OSX, the precompiled gnuplot file needs the library Aquarterm,
which thus must be installed as well. In addition, this version of
gnuplot does not support png, so you need to change "set term png
transparent small" and use other image formats. For example, you may
have "set term pbm small color".

Usage: grid.py [grid_options] [svm_options] dataset

grid_options :
-log2c {begin,end,step | "null"} : set the range of c (default -5,15,2)
    begin,end,step -- c_range = 2^{begin,...,begin+k*step,...,end}
    "null"         -- do not grid with c
-log2g {begin,end,step | "null"} : set the range of g (default 3,-15,-2)
    begin,end,step -- g_range = 2^{begin,...,begin+k*step,...,end}
    "null"         -- do not grid with g
-v n : n-fold cross validation (default 5)
-svmtrain pathname : set svm executable path and name
-gnuplot {pathname | "null"} :
    pathname -- set gnuplot executable path and name
    "null"   -- do not plot 
-out {pathname | "null"} : (default dataset.out)
    pathname -- set output file path and name
    "null"   -- do not output file
-png pathname : set graphic output file path and name (default dataset.png)
-resume [pathname] : resume the grid task using an existing output file (default pathname is dataset.out)
    Use this option only if some parameters have been checked for the SAME data.

svm_options : additional options for svm-train

The program conducts v-fold cross validation using parameter C (and gamma)
= 2^begin, 2^(begin+step), ..., 2^end.

You can specify where the libsvm executable and gnuplot are using the
-svmtrain and -gnuplot parameters.

For windows users, please use pgnuplot.exe. If you are using gnuplot
3.7.1, please upgrade to version 3.7.3 or higher. The version 3.7.1
has a bug. If you use cygwin on windows, please use gunplot-x11.

If the task is terminated accidentally or you would like to change the
range of parameters, you can apply '-resume' to save time by re-using
previous results.  You may specify the output file of a previous run
or use the default (i.e., dataset.out) without giving a name. Please
note that the same condition must be used in two runs. For example,
you cannot use '-v 10' earlier and resume the task with '-v 5'.

The value of some options can be "null." For example, `-log2c -1,0,1
-log2 "null"' means that C=2^-1,2^0,2^1 and g=LIBSVM's default gamma
value. That is, you do not conduct parameter selection on gamma.

Example
=======

> python grid.py -log2c -5,5,1 -log2g -4,0,1 -v 5 -m 300 heart_scale

Users (in particular MS Windows users) may need to specify the path of
executable files. You can either change paths in the beginning of
grid.py or specify them in the command line. For example,

> grid.py -log2c -5,5,1 -svmtrain "c:\Program Files\libsvm\windows\svm-train.exe" -gnuplot c:\tmp\gnuplot\binary\pgnuplot.exe -v 10 heart_scale

Output: two files
dataset.png: the CV accuracy contour plot generated by gnuplot
dataset.out: the CV accuracy at each (log2(C),log2(gamma))

The following example saves running time by loading the output file of a previous run.

> python grid.py -log2c -7,7,1 -log2g -5,2,1 -v 5 -resume heart_scale.out heart_scale

Parallel grid search
====================

You can conduct a parallel grid search by dispatching jobs to a
cluster of computers which share the same file system. First, you add
machine names in grid.py:

ssh_workers = ["linux1", "linux5", "linux5"]

and then setup your ssh so that the authentication works without
asking a password.

The same machine (e.g., linux5 here) can be listed more than once if
it has multiple CPUs or has more RAM. If the local machine is the
best, you can also enlarge the nr_local_worker. For example:

nr_local_worker = 2

Example:

> python grid.py heart_scale
[local] -1 -1 78.8889  (best c=0.5, g=0.5, rate=78.8889)
[linux5] -1 -7 83.3333  (best c=0.5, g=0.0078125, rate=83.3333)
[linux5] 5 -1 77.037  (best c=0.5, g=0.0078125, rate=83.3333)
[linux1] 5 -7 83.3333  (best c=0.5, g=0.0078125, rate=83.3333)
.
.
.

If -log2c, -log2g, or -v is not specified, default values are used.

If your system uses telnet instead of ssh, you list the computer names
in telnet_workers.

Calling grid in Python
======================

In addition to using grid.py as a command-line tool, you can use it as a
Python module. 

>>> rate, param = find_parameters(dataset, options)

You need to specify `dataset' and `options' (default ''). See the following example.

> python

>>> from grid import *
>>> rate, param = find_parameters('../heart_scale', '-log2c -1,1,1 -log2g -1,1,1')
[local] 0.0 0.0 rate=74.8148 (best c=1.0, g=1.0, rate=74.8148)
[local] 0.0 -1.0 rate=77.037 (best c=1.0, g=0.5, rate=77.037)
.
.
[local] -1.0 -1.0 rate=78.8889 (best c=0.5, g=0.5, rate=78.8889)
.
.
>>> rate
78.8889
>>> param
{'c': 0.5, 'g': 0.5}


Part III: LIBSVM format checking tools

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

`svm-train' conducts only a simple check of the input data. To do a
detailed check, we provide a python script `checkdata.py.'

Usage: checkdata.py dataset

Exit status (returned value): 1 if there are errors, 0 otherwise.

This tool is written by Rong-En Fan at National Taiwan University.

Example
=======

> cat bad_data
1 3:1 2:4
> python checkdata.py bad_data
line 1: feature indices must be in an ascending order, previous/current features 3:1 2:4
Found 1 lines with error.




================================================
FILE: Matlab/libsvm-3.19/tools/checkdata.py
================================================
#!/usr/bin/env python

#
# A format checker for LIBSVM
#

#
# Copyright (c) 2007, Rong-En Fan
#
# All rights reserved.
#
# This program is distributed under the same license of the LIBSVM package.
# 

from sys import argv, exit
import os.path

def err(line_no, msg):
	print("line {0}: {1}".format(line_no, msg))

# works like float() but does not accept nan and inf
def my_float(x):
	if x.lower().find("nan") != -1 or x.lower().find("inf") != -1:
		raise ValueError

	return float(x)

def main():
	if len(argv) != 2:
		print("Usage: {0} dataset".format(argv[0]))
		exit(1)

	dataset = argv[1]

	if not os.path.exists(dataset):
		print("dataset {0} not found".format(dataset))
		exit(1)

	line_no = 1
	error_line_count = 0
	for line in open(dataset, 'r'):
		line_error = False

		# each line must end with a newline character
		if line[-1] != '\n':
			err(line_no, "missing a newline character in the end")
			line_error = True

		nodes = line.split()

		# check label
		try:
			label = nodes.pop(0)
			
			if label.find(',') != -1:
				# multi-label format
				try:
					for l in label.split(','):
						l = my_float(l)
				except:
					err(line_no, "label {0} is not a valid multi-label form".format(label))
					line_error = True
			else:
				try:
					label = my_float(label)
				except:
					err(line_no, "label {0} is not a number".format(label))
					line_error = True
		except:
			err(line_no, "missing label, perhaps an empty line?")
			line_error = True

		# check features
		prev_index = -1
		for i in range(len(nodes)):
			try:
				(index, value) =  nodes[i].split(':')

				index = int(index)
				value = my_float(value)

				# precomputed kernel's index starts from 0 and LIBSVM
				# checks it. Hence, don't treat index 0 as an error.
				if index < 0:
					err(line_no, "feature index must be positive; wrong feature {0}".format(nodes[i]))
					line_error = True
				elif index <= prev_index:
					err(line_no, "feature indices must be in an ascending order, previous/current features {0} {1}".format(nodes[i-1], nodes[i]))
					line_error = True
				prev_index = index
			except:
				err(line_no, "feature '{0}' not an <index>:<value> pair, <index> integer, <value> real number ".format(nodes[i]))
				line_error = True

		line_no += 1

		if line_error:
			error_line_count += 1
	
	if error_line_count > 0:
		print("Found {0} lines with error.".format(error_line_count))
		return 1
	else:
		print("No error.")
		return 0

if __name__ == "__main__":
	exit(main())


================================================
FILE: Matlab/libsvm-3.19/tools/easy.py
================================================
#!/usr/bin/env python

import sys
import os
from subprocess import *

if len(sys.argv) <= 1:
	print('Usage: {0} training_file [testing_file]'.format(sys.argv[0]))
	raise SystemExit

# svm, grid, and gnuplot executable files

is_win32 = (sys.platform == 'win32')
if not is_win32:
	svmscale_exe = "../svm-scale"
	svmtrain_exe = "../svm-train"
	svmpredict_exe = "../svm-predict"
	grid_py = "./grid.py"
	gnuplot_exe = "/usr/bin/gnuplot"
else:
        # example for windows
	svmscale_exe = r"..\windows\svm-scale.exe"
	svmtrain_exe = r"..\windows\svm-train.exe"
	svmpredict_exe = r"..\windows\svm-predict.exe"
	gnuplot_exe = r"c:\tmp\gnuplot\binary\pgnuplot.exe"
	grid_py = r".\grid.py"

assert os.path.exists(svmscale_exe),"svm-scale executable not found"
assert os.path.exists(svmtrain_exe),"svm-train executable not found"
assert os.path.exists(svmpredict_exe),"svm-predict executable not found"
assert os.path.exists(gnuplot_exe),"gnuplot executable not found"
assert os.path.exists(grid_py),"grid.py not found"

train_pathname = sys.argv[1]
assert os.path.exists(train_pathname),"training file not found"
file_name = os.path.split(train_pathname)[1]
scaled_file = file_name + ".scale"
model_file = file_name + ".model"
range_file = file_name + ".range"

if len(sys.argv) > 2:
	test_pathname = sys.argv[2]
	file_name = os.path.split(test_pathname)[1]
	assert os.path.exists(test_pathname),"testing file not found"
	scaled_test_file = file_name + ".scale"
	predict_test_file = file_name + ".predict"

cmd = '{0} -s "{1}" "{2}" > "{3}"'.format(svmscale_exe, range_file, train_pathname, scaled_file)
print('Scaling training data...')
Popen(cmd, shell = True, stdout = PIPE).communicate()	

cmd = '{0} -svmtrain "{1}" -gnuplot "{2}" "{3}"'.format(grid_py, svmtrain_exe, gnuplot_exe, scaled_file)
print('Cross validation...')
f = Popen(cmd, shell = True, stdout = PIPE).stdout

line = ''
while True:
	last_line = line
	line = f.readline()
	if not line: break
c,g,rate = map(float,last_line.split())

print('Best c={0}, g={1} CV rate={2}'.format(c,g,rate))

cmd = '{0} -c {1} -g {2} "{3}" "{4}"'.format(svmtrain_exe,c,g,scaled_file,model_file)
print('Training...')
Popen(cmd, shell = True, stdout = PIPE).communicate()

print('Output model: {0}'.format(model_file))
if len(sys.argv) > 2:
	cmd = '{0} -r "{1}" "{2}" > "{3}"'.format(svmscale_exe, range_file, test_pathname, scaled_test_file)
	print('Scaling testing data...')
	Popen(cmd, shell = True, stdout = PIPE).communicate()	

	cmd = '{0} "{1}" "{2}" "{3}"'.format(svmpredict_exe, scaled_test_file, model_file, predict_test_file)
	print('Testing...')
	Popen(cmd, shell = True).communicate()	

	print('Output prediction: {0}'.format(predict_test_file))


================================================
FILE: Matlab/libsvm-3.19/tools/grid.py
================================================
#!/usr/bin/env python
__all__ = ['find_parameters']

import os, sys, traceback, getpass, time, re
from threading import Thread
from subprocess import *

if sys.version_info[0] < 3:
	from Queue import Queue
else:
	from queue import Queue

telnet_workers = []
ssh_workers = []
nr_local_worker = 1

class GridOption:
	def __init__(self, dataset_pathname, options):
		dirname = os.path.dirname(__file__)
		if sys.platform != 'win32':
			self.svmtrain_pathname = os.path.join(dirname, '../svm-train')
			self.gnuplot_pathname = '/usr/bin/gnuplot'
		else:
			# example for windows
			self.svmtrain_pathname = os.path.join(dirname, r'..\windows\svm-train.exe')
			# svmtrain_pathname = r'c:\Program Files\libsvm\windows\svm-train.exe'
			self.gnuplot_pathname = r'c:\tmp\gnuplot\binary\pgnuplot.exe'
		self.fold = 5
		self.c_begin, self.c_end, self.c_step = -5,  15,  2
		self.g_begin, self.g_end, self.g_step =  3, -15, -2
		self.grid_with_c, self.grid_with_g = True, True
		self.dataset_pathname = dataset_pathname
		self.dataset_title = os.path.split(dataset_pathname)[1]
		self.out_pathname = '{0}.out'.format(self.dataset_title)
		self.png_pathname = '{0}.png'.format(self.dataset_title)
		self.pass_through_string = ' '
		self.resume_pathname = None
		self.parse_options(options)

	def parse_options(self, options):
		if type(options) == str:
			options = options.split()
		i = 0
		pass_through_options = []
		
		while i < len(options):
			if options[i] == '-log2c':
				i = i + 1
				if options[i] == 'null':
					self.grid_with_c = False
				else:
					self.c_begin, self.c_end, self.c_step = map(float,options[i].split(','))
			elif options[i] == '-log2g':
				i = i + 1
				if options[i] == 'null':
					self.grid_with_g = False
				else:
					self.g_begin, self.g_end, self.g_step = map(float,options[i].split(','))
			elif options[i] == '-v':
				i = i + 1
				self.fold = options[i]
			elif options[i] in ('-c','-g'):
				raise ValueError('Use -log2c and -log2g.')
			elif options[i] == '-svmtrain':
				i = i + 1
				self.svmtrain_pathname = options[i]
			elif options[i] == '-gnuplot':
				i = i + 1
				if options[i] == 'null':
					self.gnuplot_pathname = None
				else:	
					self.gnuplot_pathname = options[i]
			elif options[i] == '-out':
				i = i + 1
				if options[i] == 'null':
					self.out_pathname = None
				else:
					self.out_pathname = options[i]
			elif options[i] == '-png':
				i = i + 1
				self.png_pathname = options[i]
			elif options[i] == '-resume':
				if i == (len(options)-1) or options[i+1].startswith('-'):
					self.resume_pathname = self.dataset_title + '.out'
				else:
					i = i + 1
					self.resume_pathname = options[i]
			else:
				pass_through_options.append(options[i])
			i = i + 1

		self.pass_through_string = ' '.join(pass_through_options)
		if not os.path.exists(self.svmtrain_pathname):
			raise IOError('svm-train executable not found')
		if not os.path.exists(self.dataset_pathname):
			raise IOError('dataset not found')
		if self.resume_pathname and not os.path.exists(self.resume_pathname):
			raise IOError('file for resumption not found')
		if not self.grid_with_c and not self.grid_with_g:
			raise ValueError('-log2c and -log2g should not be null simultaneously')
		if self.gnuplot_pathname and not os.path.exists(self.gnuplot_pathname):
			sys.stderr.write('gnuplot executable not found\n')
			self.gnuplot_pathname = None

def redraw(db,best_param,gnuplot,options,tofile=False):
	if len(db) == 0: return
	begin_level = round(max(x[2] for x in db)) - 3
	step_size = 0.5

	best_log2c,best_log2g,best_rate = best_param

	# if newly obtained c, g, or cv values are the same,
	# then stop redrawing the contour.
	if all(x[0] == db[0][0]  for x in db): return
	if all(x[1] == db[0][1]  for x in db): return
	if all(x[2] == db[0][2]  for x in db): return

	if tofile:
		gnuplot.write(b"set term png transparent small linewidth 2 medium enhanced\n")
		gnuplot.write("set output \"{0}\"\n".format(options.png_pathname.replace('\\','\\\\')).encode())
		#gnuplot.write(b"set term postscript color solid\n")
		#gnuplot.write("set output \"{0}.ps\"\n".format(options.dataset_title).encode().encode())
	elif sys.platform == 'win32':
		gnuplot.write(b"set term windows\n")
	else:
		gnuplot.write( b"set term x11\n")
	gnuplot.write(b"set xlabel \"log2(C)\"\n")
	gnuplot.write(b"set ylabel \"log2(gamma)\"\n")
	gnuplot.write("set xrange [{0}:{1}]\n".format(options.c_begin,options.c_end).encode())
	gnuplot.write("set yrange [{0}:{1}]\n".format(options.g_begin,options.g_end).encode())
	gnuplot.write(b"set contour\n")
	gnuplot.write("set cntrparam levels incremental {0},{1},100\n".format(begin_level,step_size).encode())
	gnuplot.write(b"unset surface\n")
	gnuplot.write(b"unset ztics\n")
	gnuplot.write(b"set view 0,0\n")
	gnuplot.write("set title \"{0}\"\n".format(options.dataset_title).encode())
	gnuplot.write(b"unset label\n")
	gnuplot.write("set label \"Best log2(C) = {0}  log2(gamma) = {1}  accuracy = {2}%\" \
				  at screen 0.5,0.85 center\n". \
				  format(best_log2c, best_log2g, best_rate).encode())
	gnuplot.write("set label \"C = {0}  gamma = {1}\""
				  " at screen 0.5,0.8 center\n".format(2**best_log2c, 2**best_log2g).encode())
	gnuplot.write(b"set key at screen 0.9,0.9\n")
	gnuplot.write(b"splot \"-\" with lines\n")
	
	db.sort(key = lambda x:(x[0], -x[1]))

	prevc = db[0][0]
	for line in db:
		if prevc != line[0]:
			gnuplot.write(b"\n")
			prevc = line[0]
		gnuplot.write("{0[0]} {0[1]} {0[2]}\n".format(line).encode())
	gnuplot.write(b"e\n")
	gnuplot.write(b"\n") # force gnuplot back to prompt when term set failure
	gnuplot.flush()


def calculate_jobs(options):
	
	def range_f(begin,end,step):
		# like range, but works on non-integer too
		seq = []
		while True:
			if step > 0 and begin > end: break
			if step < 0 and begin < end: break
			seq.append(begin)
			begin = begin + step
		return seq
	
	def permute_sequence(seq):
		n = len(seq)
		if n <= 1: return seq
	
		mid = int(n/2)
		left = permute_sequence(seq[:mid])
		right = permute_sequence(seq[mid+1:])
	
		ret = [seq[mid]]
		while left or right:
			if left: ret.append(left.pop(0))
			if right: ret.append(right.pop(0))
			
		return ret	

	
	c_seq = permute_sequence(range_f(options.c_begin,options.c_end,options.c_step))
	g_seq = permute_sequence(range_f(options.g_begin,options.g_end,options.g_step))

	if not options.grid_with_c:
		c_seq = [None]
	if not options.grid_with_g:
		g_seq = [None] 
	
	nr_c = float(len(c_seq))
	nr_g = float(len(g_seq))
	i, j = 0, 0
	jobs = []

	while i < nr_c or j < nr_g:
		if i/nr_c < j/nr_g:
			# increase C resolution
			line = []
			for k in range(0,j):
				line.append((c_seq[i],g_seq[k]))
			i = i + 1
			jobs.append(line)
		else:
			# increase g resolution
			line = []
			for k in range(0,i):
				line.append((c_seq[k],g_seq[j]))
			j = j + 1
			jobs.append(line)

	resumed_jobs = {}
	
	if options.resume_pathname is None:
		return jobs, resumed_jobs

	for line in open(options.resume_pathname, 'r'):
		line = line.strip()
		rst = re.findall(r'rate=([0-9.]+)',line)
		if not rst: 
			continue
		rate = float(rst[0])

		c, g = None, None 
		rst = re.findall(r'log2c=([0-9.-]+)',line)
		if rst: 
			c = float(rst[0])
		rst = re.findall(r'log2g=([0-9.-]+)',line)
		if rst: 
			g = float(rst[0])

		resumed_jobs[(c,g)] = rate

	return jobs, resumed_jobs

	
class WorkerStopToken:  # used to notify the worker to stop or if a worker is dead
	pass

class Worker(Thread):
	def __init__(self,name,job_queue,result_queue,options):
		Thread.__init__(self)
		self.name = name
		self.job_queue = job_queue
		self.result_queue = result_queue
		self.options = options
		
	def run(self):
		while True:
			(cexp,gexp) = self.job_queue.get()
			if cexp is WorkerStopToken:
				self.job_queue.put((cexp,gexp))
				# print('worker {0} stop.'.format(self.name))
				break
			try:
				c, g = None, None
				if cexp != None:
					c = 2.0**cexp
				if gexp != None:
					g = 2.0**gexp
				rate = self.run_one(c,g)
				if rate is None: raise RuntimeError('get no rate')
			except:
				# we failed, let others do that and we just quit
			
				traceback.print_exception(sys.exc_info()[0], sys.exc_info()[1], sys.exc_info()[2])
				
				self.job_queue.put((cexp,gexp))
				sys.stderr.write('worker {0} quit.\n'.format(self.name))
				break
			else:
				self.result_queue.put((self.name,cexp,gexp,rate))

	def get_cmd(self,c,g):
		options=self.options
		cmdline = '"' + options.svmtrain_pathname + '"'
		if options.grid_with_c: 
			cmdline += ' -c {0} '.format(c)
		if options.grid_with_g: 
			cmdline += ' -g {0} '.format(g)
		cmdline += ' -v {0} {1} {2} '.format\
			(options.fold,options.pass_through_string,options.dataset_pathname)
		return cmdline
		
class LocalWorker(Worker):
	def run_one(self,c,g):
		cmdline = self.get_cmd(c,g)
		result = Popen(cmdline,shell=True,stdout=PIPE,stderr=PIPE,stdin=PIPE).stdout
		for line in result.readlines():
			if str(line).find('Cross') != -1:
				return float(line.split()[-1][0:-1])

class SSHWorker(Worker):
	def __init__(self,name,job_queue,result_queue,host,options):
		Worker.__init__(self,name,job_queue,result_queue,options)
		self.host = host
		self.cwd = os.getcwd()
	def run_one(self,c,g):
		cmdline = 'ssh -x -t -t {0} "cd {1}; {2}"'.format\
			(self.host,self.cwd,self.get_cmd(c,g))
		result = Popen(cmdline,shell=True,stdout=PIPE,stderr=PIPE,stdin=PIPE).stdout
		for line in result.readlines():
			if str(line).find('Cross') != -1:
				return float(line.split()[-1][0:-1])

class TelnetWorker(Worker):
	def __init__(self,name,job_queue,result_queue,host,username,password,options):
		Worker.__init__(self,name,job_queue,result_queue,options)
		self.host = host
		self.username = username
		self.password = password		
	def run(self):
		import telnetlib
		self.tn = tn = telnetlib.Telnet(self.host)
		tn.read_until('login: ')
		tn.write(self.username + '\n')
		tn.read_until('Password: ')
		tn.write(self.password + '\n')

		# XXX: how to know whether login is successful?
		tn.read_until(self.username)
		# 
		print('login ok', self.host)
		tn.write('cd '+os.getcwd()+'\n')
		Worker.run(self)
		tn.write('exit\n')			   
	def run_one(self,c,g):
		cmdline = self.get_cmd(c,g)
		result = self.tn.write(cmdline+'\n')
		(idx,matchm,output) = self.tn.expect(['Cross.*\n'])
		for line in output.split('\n'):
			if str(line).find('Cross') != -1:
				return float(line.split()[-1][0:-1])
			
def find_parameters(dataset_pathname, options=''):
	
	def update_param(c,g,rate,best_c,best_g,best_rate,worker,resumed):
		if (rate > best_rate) or (rate==best_rate and g==best_g and c<best_c):
			best_rate,best_c,best_g = rate,c,g
		stdout_str = '[{0}] {1} {2} (best '.format\
			(worker,' '.join(str(x) for x in [c,g] if x is not None),rate)
		output_str = ''
		if c != None:
			stdout_str += 'c={0}, '.format(2.0**best_c)
			output_str += 'log2c={0} '.format(c)
		if g != None:
			stdout_str += 'g={0}, '.format(2.0**best_g)
			output_str += 'log2g={0} '.format(g)
		stdout_str += 'rate={0})'.format(best_rate)
		print(stdout_str)
		if options.out_pathname and not resumed:
			output_str += 'rate={0}\n'.format(rate)
			result_file.write(output_str)
			result_file.flush()
		
		return best_c,best_g,best_rate
		
	options = GridOption(dataset_pathname, options);

	if options.gnuplot_pathname:
		gnuplot = Popen(options.gnuplot_pathname,stdin = PIPE,stdout=PIPE,stderr=PIPE).stdin
	else:
		gnuplot = None
		
	# put jobs in queue

	jobs,resumed_jobs = calculate_jobs(options)
	job_queue = Queue(0)
	result_queue = Queue(0)

	for (c,g) in resumed_jobs:
		result_queue.put(('resumed',c,g,resumed_jobs[(c,g)]))

	for line in jobs:
		for (c,g) in line:
			if (c,g) not in resumed_jobs:
				job_queue.put((c,g))

	# hack the queue to become a stack --
	# this is important when some thread
	# failed and re-put a job. It we still
	# use FIFO, the job will be put
	# into the end of the queue, and the graph
	# will only be updated in the end
 
	job_queue._put = job_queue.queue.appendleft

	# fire telnet workers

	if telnet_workers:
		nr_telnet_worker = len(telnet_workers)
		username = getpass.getuser()
		password = getpass.getpass()
		for host in telnet_workers:
			worker = TelnetWorker(host,job_queue,result_queue,
					 host,username,password,options)
			worker.start()

	# fire ssh workers

	if ssh_workers:
		for host in ssh_workers:
			worker = SSHWorker(host,job_queue,result_queue,host,options)
			worker.start()

	# fire local workers

	for i in range(nr_local_worker):
		worker = LocalWorker('local',job_queue,result_queue,options)
		worker.start()

	# gather results

	done_jobs = {}

	if options.out_pathname:
		if options.resume_pathname:
			result_file = open(options.out_pathname, 'a')
		else:
			result_file = open(options.out_pathname, 'w')


	db = []
	best_rate = -1
	best_c,best_g = None,None  

	for (c,g) in resumed_jobs:
		rate = resumed_jobs[(c,g)]
		best_c,best_g,best_rate = update_param(c,g,rate,best_c,best_g,best_rate,'resumed',True)

	for line in jobs:
		for (c,g) in line:
			while (c,g) not in done_jobs:
				(worker,c1,g1,rate1) = result_queue.get()
				done_jobs[(c1,g1)] = rate1
				if (c1,g1) not in resumed_jobs:
					best_c,best_g,best_rate = update_param(c1,g1,rate1,best_c,best_g,best_rate,worker,False)
			db.append((c,g,done_jobs[(c,g)]))
		if gnuplot and options.grid_with_c and options.grid_with_g:
			redraw(db,[best_c, best_g, best_rate],gnuplot,options)
			redraw(db,[best_c, best_g, best_rate],gnuplot,options,True)


	if options.out_pathname:
		result_file.close()
	job_queue.put((WorkerStopToken,None))
	best_param, best_cg  = {}, []
	if best_c != None:
		best_param['c'] = 2.0**best_c
		best_cg += [2.0**best_c]
	if best_g != None:
		best_param['g'] = 2.0**best_g
		best_cg += [2.0**best_g]
	print('{0} {1}'.format(' '.join(map(str,best_cg)), best_rate))

	return best_rate, best_param


if __name__ == '__main__':

	def exit_with_help():
		print("""\
Usage: grid.py [grid_options] [svm_options] dataset

grid_options :
-log2c {begin,end,step | "null"} : set the range of c (default -5,15,2)
    begin,end,step -- c_range = 2^{begin,...,begin+k*step,...,end}
    "null"         -- do not grid with c
-log2g {begin,end,step | "null"} : set the range of g (default 3,-15,-2)
    begin,end,step -- g_range = 2^{begin,...,begin+k*step,...,end}
    "null"         -- do not grid with g
-v n : n-fold cross validation (default 5)
-svmtrain pathname : set svm executable path and name
-gnuplot {pathname | "null"} :
    pathname -- set gnuplot executable path and name
    "null"   -- do not plot 
-out {pathname | "null"} : (default dataset.out)
    pathname -- set output file path and name
    "null"   -- do not output file
-png pathname : set graphic output file path and name (default dataset.png)
-resume [pathname] : resume the grid task using an existing output file (default pathname is dataset.out)
    This is experimental. Try this option only if some parameters have been checked for the SAME data.

svm_options : additional options for svm-train""")
		sys.exit(1)
	
	if len(sys.argv) < 2:
		exit_with_help()
	dataset_pathname = sys.argv[-1]
	options = sys.argv[1:-1]
	try:
		find_parameters(dataset_pathname, options)
	except (IOError,ValueError) as e:
		sys.stderr.write(str(e) + '\n')
		sys.stderr.write('Try "grid.py" for more information.\n')
		sys.exit(1)


================================================
FILE: Matlab/libsvm-3.19/tools/subset.py
================================================
#!/usr/bin/env python

import os, sys, math, random
from collections import defaultdict

if sys.version_info[0] >= 3:
	xrange = range

def exit_with_help(argv):
	print("""\
Usage: {0} [options] dataset subset_size [output1] [output2]

This script randomly selects a subset of the dataset.

options:
-s method : method of selection (default 0)
     0 -- stratified selection (classification only)
     1 -- random selection

output1 : the subset (optional)
output2 : rest of the data (optional)
If output1 is omitted, the subset will be printed on the screen.""".format(argv[0]))
	exit(1)

def process_options(argv):
	argc = len(argv)
	if argc < 3:
		exit_with_help(argv)

	# default method is stratified selection
	method = 0  
	subset_file = sys.stdout
	rest_file = None

	i = 1
	while i < argc:
		if argv[i][0] != "-":
			break
		if argv[i] == "-s":
			i = i + 1
			method = int(argv[i])
			if method not in [0,1]:
				print("Unknown selection method {0}".format(method))
				exit_with_help(argv)
		i = i + 1

	dataset = argv[i]
	subset_size = int(argv[i+1])
	if i+2 < argc:
		subset_file = open(argv[i+2],'w')
	if i+3 < argc:
		rest_file = open(argv[i+3],'w')

	return dataset, subset_size, method, subset_file, rest_file

def random_selection(dataset, subset_size):
	l = sum(1 for line in open(dataset,'r'))
	return sorted(random.sample(xrange(l), subset_size))

def stratified_selection(dataset, subset_size):
	labels = [line.split(None,1)[0] for line in open(dataset)]
	label_linenums = defaultdict(list)
	for i, label in enumerate(labels):
		label_linenums[label] += [i]

	l = len(labels)
	remaining = subset_size
	ret = []

	# classes with fewer data are sampled first; otherwise
	# some rare classes may not be selected
	for label in sorted(label_linenums, key=lambda x: len(label_linenums[x])):
		linenums = label_linenums[label]
		label_size = len(linenums) 
		# at least one instance per class
		s = int(min(remaining, max(1, math.ceil(label_size*(float(subset_size)/l)))))
		if s == 0:
			sys.stderr.write('''\
Error: failed to have at least one instance per class
    1. You may have regression data.
    2. Your classification data is unbalanced or too small.
Please use -s 1.
''')
			sys.exit(-1)
		remaining -= s
		ret += [linenums[i] for i in random.sample(xrange(label_size), s)]
	return sorted(ret)

def main(argv=sys.argv):
	dataset, subset_size, method, subset_file, rest_file = process_options(argv)
	#uncomment the following line to fix the random seed 
	#random.seed(0)
	selected_lines = []

	if method == 0:
		selected_lines = stratified_selection(dataset, subset_size)
	elif method == 1:
		selected_lines = random_selection(dataset, subset_size)

	#select instances based on selected_lines
	dataset = open(dataset,'r')
	prev_selected_linenum = -1
	for i in xrange(len(selected_lines)):
		for cnt in xrange(selected_lines[i]-prev_selected_linenum-1):
			line = dataset.readline()
			if rest_file: 
				rest_file.write(line)
		subset_file.write(dataset.readline())
		prev_selected_linenum = selected_lines[i]
	subset_file.close()

	if rest_file:
		for line in dataset: 
			rest_file.write(line)
		rest_file.close()
	dataset.close()

if __name__ == '__main__':
	main(sys.argv)



================================================
FILE: README.md
================================================
# Face_Liveness_Detection

## Matlab

NUAA database is included in Matlab folder. PRINT-ATTACK database can be downloaded at http://www.idiap.ch/dataset/printattack. CASIA database can be downloaded at http://www.cbsr.ia.ac.cn/english/FaceAntiSpoofDatabases.asp. 

LibSVM, LBP and HOOF toolboxes are included in Matlab folder. 

All *Train.m files are used for training and all *Test.m files are used for testing. Models are not uploaded. 

## C++

LBP toolbox is included

SVM model for NUAA dataset is included