diff --git a/lang/fr/gklearn/gedlib/lib/libsvm.3.22/java/svm_train.java b/lang/fr/gklearn/gedlib/lib/libsvm.3.22/java/svm_train.java
new file mode 100644
index 0000000..22ee043
--- /dev/null
+++ b/lang/fr/gklearn/gedlib/lib/libsvm.3.22/java/svm_train.java
@@ -0,0 +1,318 @@
+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();
+	}
+}