diff --git a/lang/fr/gklearn/gedlib/lib/libsvm.3.22/java/svm_predict.java b/lang/fr/gklearn/gedlib/lib/libsvm.3.22/java/svm_predict.java
new file mode 100644
index 0000000..d714c5b
--- /dev/null
+++ b/lang/fr/gklearn/gedlib/lib/libsvm.3.22/java/svm_predict.java
@@ -0,0 +1,194 @@
+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();
+		}
+	}
+}