#include <stdio.h>#include <ctype.h>#include <stdlib.h>#include <string.h>#include "svm.h"char* line;int max_line_len = 1024;struct svm_node *x;int max_nr_attr = 64;struct svm_model* model;int predict_probability=0;void predict(FILE *input, FILE *output){	int correct = 0;	int total = 0;	double error = 0;	double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;	int svm_type=svm_get_svm_type(model);	int nr_class=svm_get_nr_class(model);	int *labels=(int *) malloc(nr_class*sizeof(int));	double *prob_estimates=NULL;	int j;	if(predict_probability)	{		if (svm_type==NU_SVR || svm_type==EPSILON_SVR)			printf("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		{			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");		}	}	while(1)	{		int i = 0;		int c;		double target,v;		if (fscanf(input,"%lf",&target)==EOF)			break;		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));			}			do {				c = getc(input);				if(c=='\n' || c==EOF) goto out2;			} while(isspace(c));			ungetc(c,input);			fscanf(input,"%d:%lf",&x[i].index,&x[i].value);			++i;		}	out2:		x[i++].index = -1;		if (predict_probability && (svm_type==C_SVC || svm_type==NU_SVC))		{			v = svm_predict_probability(model,x,prob_estimates);			fprintf(output,"%g ",v);			for(j=0;j<nr_class;j++)				fprintf(output,"%g ",prob_estimates[j]);			fprintf(output,"\n");		}		else		{			v = svm_predict(model,x);			fprintf(output,"%g\n",v);		}		if(v == target)			++correct;		error += (v-target)*(v-target);		sumv += v;		sumy += target;		sumvv += v*v;		sumyy += target*target;		sumvy += v*target;		++total;	}	printf("Accuracy = %g%% (%d/%d) (classification)\n",		(double)correct/total*100,correct,total);	printf("Mean squared error = %g (regression)\n",error/total);	printf("Squared correlation coefficient = %g (regression)\n",		((total*sumvy-sumv*sumy)*(total*sumvy-sumv*sumy))/		((total*sumvv-sumv*sumv)*(total*sumyy-sumy*sumy))		);	if(predict_probability)	{		free(prob_estimates);		free(labels);	}}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"	);	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;			default:				fprintf(stderr,"unknown option\n");				exit_with_help();		}	}	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=svm_load_model(argv[i+1]))==0)	{		fprintf(stderr,"can't open model file %s\n",argv[i+1]);		exit(1);	}		line = (char *) malloc(max_line_len*sizeof(char));	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);		}	predict(input,output);	svm_destroy_model(model);	free(line);	free(x);	fclose(input);	fclose(output);	return 0;}