diff --git a/lang/zh/gklearn/gedlib/lib/libsvm.3.22/svm-predict.c b/lang/zh/gklearn/gedlib/lib/libsvm.3.22/svm-predict.c
new file mode 100644
index 0000000..859c9ff
--- /dev/null
+++ b/lang/zh/gklearn/gedlib/lib/libsvm.3.22/svm-predict.c
@@ -0,0 +1,239 @@
+#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;
+}