diff --git a/lang/zh/gklearn/gedlib/lib/libsvm.3.22/matlab/svmtrain.c b/lang/zh/gklearn/gedlib/lib/libsvm.3.22/matlab/svmtrain.c
new file mode 100644
index 0000000..27a52b8
--- /dev/null
+++ b/lang/zh/gklearn/gedlib/lib/libsvm.3.22/matlab/svmtrain.c
@@ -0,0 +1,495 @@
+#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;
+	}
+}