svm_destroy_model(submodel);		free(subprob.x);		free(subprob.y);	}			free(fold_start);	free(perm);	}int svm_get_svm_type(const svm_model *model){	return model->param.svm_type;}int svm_get_nr_class(const svm_model *model){	return model->nr_class;}void svm_get_labels(const svm_model *model, int* label){	if (model->label != NULL)		for(int i=0;i<model->nr_class;i++)			label[i] = model->label[i];}double svm_get_svr_probability(const svm_model *model){	if ((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) &&	    model->probA!=NULL)		return model->probA[0];	else	{		info("Model doesn't contain information for SVR probability inference\n");		return 0;	}}void svm_predict_values(const svm_model *model, const svm_node *x, double* dec_values){	if(model->param.svm_type == ONE_CLASS ||	   model->param.svm_type == EPSILON_SVR ||	   model->param.svm_type == NU_SVR)	{		double *sv_coef = model->sv_coef[0];		double sum = 0;		for(int i=0;i<model->l;i++)			sum += sv_coef[i] * Kernel::k_function(x,model->SV[i],model->param);		sum -= model->rho[0];		*dec_values = sum;	}	else	{		int i;		int nr_class = model->nr_class;		int l = model->l;				double *kvalue = Malloc(double,l);		for(i=0;i<l;i++)			kvalue[i] = Kernel::k_function(x,model->SV[i],model->param);		int *start = Malloc(int,nr_class);		start[0] = 0;		for(i=1;i<nr_class;i++)			start[i] = start[i-1]+model->nSV[i-1];		int p=0;		int pos=0;		for(i=0;i<nr_class;i++)			for(int j=i+1;j<nr_class;j++)			{				double sum = 0;				int si = start[i];				int sj = start[j];				int ci = model->nSV[i];				int cj = model->nSV[j];								int k;				double *coef1 = model->sv_coef[j-1];				double *coef2 = model->sv_coef[i];				for(k=0;k<ci;k++)					sum += coef1[si+k] * kvalue[si+k];				for(k=0;k<cj;k++)					sum += coef2[sj+k] * kvalue[sj+k];				sum -= model->rho[p++];				dec_values[pos++] = sum;			}		free(kvalue);		free(start);	}}double svm_predict(const svm_model *model, const svm_node *x){	if(model->param.svm_type == ONE_CLASS ||	   model->param.svm_type == EPSILON_SVR ||	   model->param.svm_type == NU_SVR)	{		double res;		svm_predict_values(model, x, &res);				if(model->param.svm_type == ONE_CLASS)			return (res>0)?1:-1;		else			return res;	}	else	{		int i;		int nr_class = model->nr_class;		double *dec_values = Malloc(double, nr_class*(nr_class-1)/2);		svm_predict_values(model, x, dec_values);		int *vote = Malloc(int,nr_class);		for(i=0;i<nr_class;i++)			vote[i] = 0;		int pos=0;		for(i=0;i<nr_class;i++)			for(int j=i+1;j<nr_class;j++)			{				if(dec_values[pos++] > 0)					++vote[i];				else					++vote[j];			}		int vote_max_idx = 0;		for(i=1;i<nr_class;i++)			if(vote[i] > vote[vote_max_idx])				vote_max_idx = i;		free(vote);		free(dec_values);		return model->label[vote_max_idx];	}}double svm_predict_probability(	const svm_model *model, const svm_node *x, double *prob_estimates){	if ((model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC) &&	    model->probA!=NULL && model->probB!=NULL)	{		int i;		int nr_class = model->nr_class;		double *dec_values = Malloc(double, nr_class*(nr_class-1)/2);		svm_predict_values(model, x, dec_values);		double min_prob=1e-7;		double **pairwise_prob=Malloc(double *,nr_class);		for(i=0;i<nr_class;i++)			pairwise_prob[i]=Malloc(double,nr_class);		int k=0;		for(i=0;i<nr_class;i++)			for(int j=i+1;j<nr_class;j++)			{				pairwise_prob[i][j]=min(max(sigmoid_predict(dec_values[k],model->probA[k],model->probB[k]),min_prob),1-min_prob);				pairwise_prob[j][i]=1-pairwise_prob[i][j];				k++;			}		multiclass_probability(nr_class,pairwise_prob,prob_estimates);		int prob_max_idx = 0;		for(i=1;i<nr_class;i++)			if(prob_estimates[i] > prob_estimates[prob_max_idx])				prob_max_idx = i;		for(i=0;i<nr_class;i++)			free(pairwise_prob[i]);		free(dec_values);                free(pairwise_prob);	     		return model->label[prob_max_idx];	}	else 		return svm_predict(model, x);}const char *svm_type_table[] ={	"c_svc","nu_svc","one_class","epsilon_svr","nu_svr",NULL};const char *kernel_type_table[]={	"linear","polynomial","rbf","sigmoid","precomputed",NULL};int svm_save_model(const char *model_file_name, const svm_model *model){	FILE *fp = fopen(model_file_name,"w");	if(fp==NULL) return -1;	const svm_parameter& param = model->param;	fprintf(fp,"svm_type %s\n", svm_type_table[param.svm_type]);	fprintf(fp,"kernel_type %s\n", kernel_type_table[param.kernel_type]);	if(param.kernel_type == POLY)		fprintf(fp,"degree %d\n", param.degree);	if(param.kernel_type == POLY || param.kernel_type == RBF || param.kernel_type == SIGMOID)		fprintf(fp,"gamma %g\n", param.gamma);	if(param.kernel_type == POLY || param.kernel_type == SIGMOID)		fprintf(fp,"coef0 %g\n", param.coef0);	int nr_class = model->nr_class;	int l = model->l;	fprintf(fp, "nr_class %d\n", nr_class);	fprintf(fp, "total_sv %d\n",l);		{		fprintf(fp, "rho");		for(int i=0;i<nr_class*(nr_class-1)/2;i++)			fprintf(fp," %g",model->rho[i]);		fprintf(fp, "\n");	}		if(model->label)	{		fprintf(fp, "label");		for(int i=0;i<nr_class;i++)			fprintf(fp," %d",model->label[i]);		fprintf(fp, "\n");	}	if(model->probA) // regression has probA only	{		fprintf(fp, "probA");		for(int i=0;i<nr_class*(nr_class-1)/2;i++)			fprintf(fp," %g",model->probA[i]);		fprintf(fp, "\n");	}	if(model->probB)	{		fprintf(fp, "probB");		for(int i=0;i<nr_class*(nr_class-1)/2;i++)			fprintf(fp," %g",model->probB[i]);		fprintf(fp, "\n");	}	if(model->nSV)	{		fprintf(fp, "nr_sv");		for(int i=0;i<nr_class;i++)			fprintf(fp," %d",model->nSV[i]);		fprintf(fp, "\n");	}	fprintf(fp, "SV\n");	const double * const *sv_coef = model->sv_coef;	const svm_node * const *SV = model->SV;	for(int i=0;i<l;i++)	{		for(int j=0;j<nr_class-1;j++)			fprintf(fp, "%.16g ",sv_coef[j][i]);		const svm_node *p = SV[i];		if(param.kernel_type == PRECOMPUTED)			fprintf(fp,"0:%d ",(int)(p->value));		else			while(p->index != -1)			{				fprintf(fp,"%d:%.8g ",p->index,p->value);				p++;			}		fprintf(fp, "\n");	}	fclose(fp);	return 0;}svm_model *svm_load_model(const char *model_file_name){	FILE *fp = fopen(model_file_name,"rb");	if(fp==NULL) return NULL;		// read parameters	svm_model *model = Malloc(svm_model,1);	svm_parameter& param = model->param;	model->rho = NULL;	model->probA = NULL;	model->probB = NULL;	model->label = NULL;	model->nSV = NULL;	char cmd[81];	while(1)	{		fscanf(fp,"%80s",cmd);		if(strcmp(cmd,"svm_type")==0)		{			fscanf(fp,"%80s",cmd);			int i;			for(i=0;svm_type_table[i];i++)			{				if(strcmp(svm_type_table[i],cmd)==0)				{					param.svm_type=i;					break;				}			}			if(svm_type_table[i] == NULL)			{				fprintf(stderr,"unknown svm type.\n");				free(model->rho);				free(model->label);				free(model->nSV);				free(model);				return NULL;			}		}		else if(strcmp(cmd,"kernel_type")==0)		{					fscanf(fp,"%80s",cmd);			int i;			for(i=0;kernel_type_table[i];i++)			{				if(strcmp(kernel_type_table[i],cmd)==0)				{					param.kernel_type=i;					break;				}			}			if(kernel_type_table[i] == NULL)			{				fprintf(stderr,"unknown kernel function.\n");				free(model->rho);				free(model->label);				free(model->nSV);				free(model);				return NULL;			}		}		else if(strcmp(cmd,"degree")==0)			fscanf(fp,"%d",&param.degree);		else if(strcmp(cmd,"gamma")==0)			fscanf(fp,"%lf",&param.gamma);		else if(strcmp(cmd,"coef0")==0)			fscanf(fp,"%lf",&param.coef0);		else if(strcmp(cmd,"nr_class")==0)			fscanf(fp,"%d",&model->nr_class);		else if(strcmp(cmd,"total_sv")==0)			fscanf(fp,"%d",&model->l);		else if(strcmp(cmd,"rho")==0)		{			int n = model->nr_class * (model->nr_class-1)/2;			model->rho = Malloc(double,n);			for(int i=0;i<n;i++)				fscanf(fp,"%lf",&model->rho[i]);		}		else if(strcmp(cmd,"label")==0)		{			int n = model->nr_class;			model->label = Malloc(int,n);			for(int i=0;i<n;i++)				fscanf(fp,"%d",&model->label[i]);		}		else if(strcmp(cmd,"probA")==0)		{			int n = model->nr_class * (model->nr_class-1)/2;			model->probA = Malloc(double,n);			for(int i=0;i<n;i++)				fscanf(fp,"%lf",&model->probA[i]);		}		else if(strcmp(cmd,"probB")==0)		{			int n = model->nr_class * (model->nr_class-1)/2;			model->probB = Malloc(double,n);			for(int i=0;i<n;i++)				fscanf(fp,"%lf",&model->probB[i]);		}		else if(strcmp(cmd,"nr_sv")==0)		{			int n = model->nr_class;			model->nSV = Malloc(int,n);			for(int i=0;i<n;i++)				fscanf(fp,"%d",&model->nSV[i]);		}		else if(strcmp(cmd,"SV")==0)		{			while(1)			{				int c = getc(fp);				if(c==EOF || c=='\n') break;				}			break;		}		else		{			fprintf(stderr,"unknown text in model file\n");			free(model->rho);			free(model->label);			free(model->nSV);			free(model);			return NULL;		}	}	// read sv_coef and SV	int elements = 0;	long pos = ftell(fp);	while(1)	{		int c = fgetc(fp);		switch(c)		{			case '\n':				// count the '-1' element			case ':':				++elements;				break;			case EOF:				goto out;			default:				;		}	}out:	fseek(fp,pos,SEEK_SET);	int m = model->nr_class - 1;	int l = model->l;	model->sv_coef = Malloc(double *,m);	int i;	for(i=0;i<m;i++)		model->sv_coef[i] = Malloc(double,l);	model->SV = Malloc(svm_node*,l);	svm_node *x_space=NULL;	if(l>0) x_space = Malloc(svm_node,elements);	int j=0;	for(i=0;i<l;i++)	{		model->SV[i] = &x_space[j];		for(int k=0;k<m;k++)			fscanf(fp,"%lf",&model->sv_coef[k][i]);		while(1)		{			int c;			do {				c = getc(fp);				if(c=='\n') goto out2;			} while(isspace(c));			ungetc(c,fp);			fscanf(fp,"%d:%lf",&(x_space[j].index),&(x_space[j].value));			++j;		}	out2:		x_space[j++].index = -1;	}	fclose(fp);	model->free_sv = 1;	// XXX	return model;}void svm_destroy_model(svm_model* model){	if(model->free_sv && model->l > 0)		free((void *)(model->SV[0]));	for(int i=0;i<model->nr_class-1;i++)		free(model->sv_coef[i]);	free(model->SV);	free(model->sv_coef);	free(model->rho);	free(model->label);	free(model->probA);	free(model->probB);	free(model->nSV);	free(model);}void svm_destroy_param(svm_parameter* param){	free(param->weight_label);	free(param->weight);}const char *svm_check_parameter(const svm_problem *prob, const svm_parameter *param){	// svm_type	int svm_type = param->svm_type;	if(svm_type != C_SVC &&	   svm_type != NU_SVC &&	   svm_type != ONE_CLASS &&	   svm_type != EPSILON_SVR &&	   svm_type != NU_SVR)		return "unknown svm type";		// kernel_type, degree		int kernel_type = param->kernel_type;	if(kernel_type != LINEAR &&	   kernel_type != POLY &&	   kernel_type != RBF &&	   kernel_type != SIGMOID &&	   kernel_type != PRECOMPUTED)		return "unknown kernel type";	if(param->degree < 0)		return "degree of polynomial kernel < 0";	// cache_size,eps,C,nu,p,shrinking	if(param->cache_size <= 0)		return "cache_size <= 0";	if(param->eps <= 0)		return "eps <= 0";	if(svm_type == C_SVC ||	   svm_type == EPSILON_SVR ||	   svm_type == NU_SVR)		if(param->C <= 0)			return "C <= 0";	if(svm_type == NU_SVC ||	   svm_type == ONE_CLASS ||	   svm_type == NU_SVR)		if(param->nu <= 0 || param->nu > 1)			return "nu <= 0 or nu > 1";	if(svm_type == EPSILON_SVR)		if(param->p < 0)			return "p < 0";	if(param->shrinking != 0 &&	   param->shrinking != 1)		return "shrinking != 0 and shrinking != 1";	if(param->probability != 0 &&	   param->probability != 1)		return "probability != 0 and probability != 1";	if(param->probability == 1 &&	   svm_type == ONE_CLASS)		return "one-class SVM probability output not supported yet";	// check whether nu-svc is feasible		if(svm_type == NU_SVC)	{		int l = prob->l;		int max_nr_class = 16;		int nr_class = 0;		int *label = Malloc(int,max_nr_class);		int *count = Malloc(int,max_nr_class);		int i;		for(i=0;i<l;i++)		{			int this_label = (int)prob->y[i];			int j;			for(j=0;j<nr_class;j++)				if(this_label == label[j])				{					++count[j];					break;				}			if(j == nr_class)			{				if(nr_class == max_nr_class)				{					max_nr_class *= 2;					label = (int *)realloc(label,max_nr_class*sizeof(int));					count = (int *)realloc(count,max_nr_class*sizeof(int));				}				label[nr_class] = this_label;				count[nr_class] = 1;				++nr_class;			}		}			for(i=0;i<nr_class;i++)		{			int n1 = count[i];			for(int j=i+1;j<nr_class;j++)			{				int n2 = count[j];				if(param->nu*(n1+n2)/2 > min(n1,n2))				{					free(label);					free(count);					return "specified nu is infeasible";				}			}		}		free(label);		free(count);	}	return NULL;}int svm_check_probability_model(const svm_model *model){	return ((model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC) &&		model->probA!=NULL && model->probB!=NULL) ||		((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) &&		 model->probA!=NULL);}