#include?
#include?
#include?
#include?
#include?“svm.h“

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）;
	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
		{
			int?*labels=（int?*）?malloc（nr_class*sizeof（int））;
			svm_get_labels（modellabels）;
			prob_estimates?=?（double?*）?malloc（nr_class*sizeof（double））;
			fprintf（output“l(fā)abels“）;		
			for（j=0;j				fprintf（output“?%d“l(fā)abels[j]）;
			fprintf（output“\n“）;
			free（labels）;
		}
	}
	while（1）
	{
		int?i?=?0;
		int?c;
		double?targetv;

		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（xmax_nr_attr*sizeof（struct?svm_node））;
			}

			do?{
				c?=?getc（input）;
				if（c==‘\n‘?||?c==EOF）?goto?out2;
			}?while（isspace（c））;
			ungetc（cinput）;
			if?（fscanf（input“%d:%lf“&x[i].index&x[i].value）?			{
				fprintf（stderr“Wrong?input?format?at?line?%d\n“?total+1）;
				exit（1）;
			}
			++i;
		}	

out2:
		x[i].index?=?-1;

		if?（predict_probability?&&?（svm_type==C_SVC?||?svm_type==NU_SVC））
		{
			v?=?svm_predict_probability（modelxprob_estimates）;
			fprintf（output“%g“v）;
			for（j=0;j				fprintf（output“?%g“prob_estimates[j]）;
			fprintf（output“\n“）;
		}
		else
		{
			v?=?svm_predict（modelx）;
			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;
	}
	if?（svm_type==NU_SVR?||?svm_type==EPSILON_SVR）
	{
		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））
		???????）;
	}
	else
		printf（“Accuracy?=?%g%%?（%d/%d）?（classification）\n“
		???????（double）correct/total*100correcttotal）;
	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“
	）;
	exit（1）;
}

int?main（int?argc?char?**argv）
{
	FILE?*input?*output;
	int?i;

	//?parse?options
	for（i=1;i	{
		if（argv[i][0]?!=?‘-‘）?break;
		++i;
		switch（argv

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----

?????文件??????61804??2008-10-29?11:16??libsvm-2.88\svm.cpp

?????文件???????2184??2008-10-15?11:38??libsvm-2.88\svm.h

?????文件??????22334??2008-10-15?11:38??libsvm-2.88\README

?????文件???????1304??2008-02-09?09:36??libsvm-2.88\Makefile.win

?????文件???????7362??2008-10-12?07:25??libsvm-2.88\svm-train.c

?????文件???????1497??2008-10-29?22:51??libsvm-2.88\COPYRIGHT

?????文件???????4062??2008-10-15?11:38??libsvm-2.88\svm-predict.c

?????文件??????60637??2008-10-30?07:43??libsvm-2.88\FAQ.html

?????文件??????27670??2003-07-12?12:07??libsvm-2.88\heart_scale

?????文件???????6700??2008-09-15?12:36??libsvm-2.88\svm-scale.c

?????文件????????497??2008-09-15?12:36??libsvm-2.88\Makefile

?????文件??????10787??2006-03-04?15:40??libsvm-2.88\svm-toy\windows\svm-toy.cpp

?????文件???????9702??2006-03-04?15:40??libsvm-2.88\svm-toy\gtk\callbacks.cpp

?????文件???????6457??2003-07-12?12:07??libsvm-2.88\svm-toy\gtk\interface.c

?????文件???????1765??2003-07-12?12:07??libsvm-2.88\svm-toy\gtk\callbacks.h

?????文件????????398??2003-07-12?12:07??libsvm-2.88\svm-toy\gtk\main.c

?????文件????????203??2003-07-12?12:07??libsvm-2.88\svm-toy\gtk\interface.h

?????文件???????6402??2003-07-12?12:07??libsvm-2.88\svm-toy\gtk\svm-toy.glade

?????文件????????530??2007-10-14?14:29??libsvm-2.88\svm-toy\gtk\Makefile

?????文件???????9133??2008-05-01?20:42??libsvm-2.88\svm-toy\qt\svm-toy.cpp

?????文件????????366??2008-05-01?20:42??libsvm-2.88\svm-toy\qt\Makefile

?????文件???????3034??2005-11-16?20:58??libsvm-2.88\tools\subset.py

?????文件???????2525??2008-02-10?20:12??libsvm-2.88\tools\easy.py

?????文件??????11701??2008-08-08?18:41??libsvm-2.88\tools\grid.py

?????文件???????4642??2008-03-09?22:33??libsvm-2.88\tools\README

?????文件???????2371??2007-11-15?15:57??libsvm-2.88\tools\checkdata.py

?????文件?????147456??2008-10-29?22:44??libsvm-2.88\windows\svm-train.exe

?????文件??????90112??2008-10-29?22:44??libsvm-2.88\windows\svm-scale.exe

?????文件?????110592??2008-10-29?22:44??libsvm-2.88\windows\svm-predict.exe

?????文件?????151552??2008-10-29?22:44??libsvm-2.88\windows\svm-toy.exe

............此處省略37個文件信息