#include <stdio.h>#include <stdlib.h>#include <string.h>#include <ctype.h>#include "svm.h"#include <mex.h>#include "svm_model_matlab.h"#define BUF_LEN 2048#define Malloc(type,n) (type *)malloc((n)*sizeof(type))// svm argumentsstruct svm_parameter param;		// set by parse_command_linestruct svm_problem prob;		// set by read_problemstruct svm_model *model;struct svm_node *x_space;int cross_validation = 0;int nr_fold;void exit_with_help(){	mexPrintf(			"Usage: model = svmtrain(training_label_matrix, training_instance_matrix, 'libsvm_options');\n"			"libsvm_options:\n"			"-s svm_type : set type of SVM (default 0)\n"			"	0 -- C-SVC\n"			"	1 -- nu-SVC\n"			"	2 -- one-class SVM\n"			"	3 -- epsilon-SVR\n"			"	4 -- nu-SVR\n"			"-t kernel_type : set type of kernel function (default 2)\n"			"	0 -- linear: u'*v\n"			"	1 -- polynomial: (gamma*u'*v + coef0)^degree\n"			"	2 -- radial basis function: exp(-gamma*|u-v|^2)\n"			"	3 -- sigmoid: tanh(gamma*u'*v + coef0)\n"			"-d degree : set degree in kernel function (default 3)\n"			"-g gamma : set gamma in kernel function (default 1/k)\n"			"-r coef0 : set coef0 in kernel function (default 0)\n"			"-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)\n"			"-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)\n"			"-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n"			"-m cachesize : set cache memory size in MB (default 40)\n"			"-e epsilon : set tolerance of termination criterion (default 0.001)\n"			"-h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1)\n"			"-b probability_estimates: whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)\n"			"-wi weight: set the parameter C of class i to weight*C, for C-SVC (default 1)\n"			"-v n: n-fold cross validation mode\n"			);}double do_cross_validation(){	int i;	int total_correct = 0;	double total_error = 0;	double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;	double *target = Malloc(double,prob.l);	svm_cross_validation(&prob,&param,nr_fold,target);	if(param.svm_type == EPSILON_SVR ||			param.svm_type == NU_SVR)	{		for(i=0;i<prob.l;i++)		{			double y = prob.y[i];			double v = target[i];			total_error += (v-y)*(v-y);			sumv += v;			sumy += y;			sumvv += v*v;			sumyy += y*y;			sumvy += v*y;		}		mexPrintf("Cross Validation Mean squared error = %g\n",total_error/prob.l);		mexPrintf("Cross Validation Squared correlation coefficient = %g\n",				((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/				((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))				);		return total_error/prob.l;	}	else	{		for(i=0;i<prob.l;i++)			if(target[i] == prob.y[i])				++total_correct;		mexPrintf("Cross Validation Accuracy = %g%%\n",100.0*total_correct/prob.l);		return 100.0*total_correct/prob.l;	}	free(target);}// nrhs should be 3int parse_command_line(int nrhs, const mxArray *prhs[], char *model_file_name){	int i, flag = 0;	char cmd[BUF_LEN];	char *argv_1, *argv_2;	// default values	param.svm_type = C_SVC;	param.kernel_type = RBF;	param.degree = 3;	param.gamma = 0;	// 1/k	param.coef0 = 0;	param.nu = 0.5;	param.cache_size = 40;	param.C = 1;	param.eps = 1e-3;	param.p = 0.1;	param.shrinking = 1;	param.probability = 0;	param.nr_weight = 0;	param.weight_label = NULL;	param.weight = NULL;	cross_validation = 0;		// parse options	if(nrhs < 3) 		return(0);		/* Get options */	mxGetString(prhs[2], cmd,  mxGetN(prhs[2]) + 1);	while(1) 	{			if(flag == 0) 		{			argv_1 = strtok(cmd, " ");				flag = 1;		}		else 		{			argv_1 = strtok(NULL, " ");		}		if(argv_1 == NULL || argv_1[0] != '-') break;		++i;		argv_2 = strtok(NULL, " ");		if(argv_2 == NULL) 		{			exit_with_help();			return(1);			}		switch(argv_1[1])		{		case 's':			param.svm_type = atoi(argv_2);			break;		case 't':			param.kernel_type = atoi(argv_2);			break;		case 'd':			param.degree = atof(argv_2);			break;		case 'g':			param.gamma = atof(argv_2);			break;		case 'r':			param.coef0 = atof(argv_2);			break;		case 'n':			param.nu = atof(argv_2);			break;		case 'm':			param.cache_size = atof(argv_2);			break;		case 'c':			param.C = atof(argv_2);			break;		case 'e':			param.eps = atof(argv_2);			break;		case 'p':			param.p = atof(argv_2);			break;		case 'h':			param.shrinking = atoi(argv_2);			break;		case 'b':			param.probability = atoi(argv_2);			break;		case 'v':			cross_validation = 1;			nr_fold = atoi(argv_2);			if(nr_fold < 2)			{				mexPrintf("n-fold cross validation: n must >= 2\n");				return(1);			}			break;		case 'w':			++param.nr_weight;			param.weight_label = (int *)realloc(param.weight_label,sizeof(int)*param.nr_weight);			param.weight = (double *)realloc(param.weight,sizeof(double)*param.nr_weight);			param.weight_label[param.nr_weight-1] = atoi(&argv_1[2]);			param.weight[param.nr_weight-1] = atof(argv_2);			break;		default:			mexPrintf("unknown option\n");			return(1);		}	}}// read in a problem (in svmlight format)void read_problem_dense(double *labels, double *samples, int lr, int lc, int sr, int sc){	int elements, max_index, i, j, k;	elements = 0;	// the number of instance	prob.l = sr;	for(i = 0; i < prob.l; i++) 	{		for(k = 0; k < sc; k++) 		{			if(samples[k * prob.l + i] != 0) 			{				elements++;			}			}		// count the '-1' element		elements++;	}	prob.y = Malloc(double,prob.l);	prob.x = Malloc(struct svm_node *,prob.l);	x_space = Malloc(struct svm_node, elements);	max_index = sc;	j = 0;	for(i = 0; i < prob.l; i++)	{		prob.x[i] = &x_space[j];		prob.y[i] = labels[i];							for(k = 0; k < sc; k++) 		{			if(samples[k * prob.l + i] != 0) 			{				x_space[j].index = k + 1;				x_space[j].value = samples[k * prob.l + i];				++j;				}			}		x_space[j++].index = -1;	}	if(param.gamma == 0)		param.gamma = 1.0/max_index;		}void read_problem_sparse(const mxArray *prhs[]){	int elements, max_index, i, j, k;	int sr, sc, lr, lc;	int *ir, *jc, *tmp_row_index;	int tmp_index_sum, tmp_jc_num, pr_ir_index;	int num_samples, num_ir, num_jc;	double *samples, *labels;	lr = mxGetM(prhs[0]);	lc = mxGetN(prhs[0]);	sr = mxGetM(prhs[1]);	sc = mxGetN(prhs[1]);	labels = mxGetPr(prhs[0]);	samples = mxGetPr(prhs[1]);	ir = mxGetIr(prhs[1]);	jc = mxGetJc(prhs[1]);	num_jc = sc + 1;	num_samples = num_ir = jc[num_jc-1];	tmp_row_index = (int *)calloc(sr, sizeof(int));	// the number of instance	prob.l = sr;	elements = num_samples + prob.l;	prob.y = Malloc(double,prob.l);	prob.x = Malloc(struct svm_node *,prob.l);	x_space = Malloc(struct svm_node, elements);	max_index = sc;	for(i = 0; i < num_ir; i++)		tmp_row_index[ir[i]]++;	for(i = tmp_index_sum = 0; i < prob.l; i++) {		prob.x[i] = &x_space[tmp_index_sum];		prob.y[i] = labels[i];			tmp_index_sum += (tmp_row_index[i] + 1);		x_space[tmp_index_sum - 1].index = -1;	}	pr_ir_index = 0;	memset(tmp_row_index, 0, sr * sizeof(int));	for(i = 1; i < num_jc; i++)	{		tmp_jc_num = jc[i] - jc[i-1];		for(j = 0; j < tmp_jc_num; j++)		{			prob.x[ir[pr_ir_index]][tmp_row_index[ir[pr_ir_index]]].index = i;			prob.x[ir[pr_ir_index]][tmp_row_index[ir[pr_ir_index]]].value = samples[pr_ir_index];			tmp_row_index[ir[pr_ir_index]]++;			pr_ir_index++;		}	}	if(param.gamma == 0)		param.gamma = 1.0/max_index;		free(tmp_row_index);}// Interface function of matlab// now assume prhs[0]: label prhs[1]: featuresvoid mexFunction( int nlhs, mxArray *plhs[], 		int nrhs, const mxArray *prhs[] ){ 	int m1, n1, m2, n2, i, j;	double *labels, *samples, *ptr;	char input_file_name[1024];	char model_file_name[1024];	const char *error_msg;	// Translate the input Matrix to the format such that svmtrain.exe can recognize it	if(nrhs > 0 && nrhs < 4) 	{		if (parse_command_line(nrhs, prhs, model_file_name)==1)		{			plhs[0] = mxCreateDoubleMatrix(0, 0, mxREAL);			return;		}					m1 = mxGetM(prhs[0]);		n1 = mxGetN(prhs[0]);		m2 = mxGetM(prhs[1]);		n2 = mxGetN(prhs[1]);				if(mxIsSparse(prhs[1]))			read_problem_sparse(prhs);		else 		{			labels = mxGetPr(prhs[0]);			samples = mxGetPr(prhs[1]);			read_problem_dense(labels, samples, m1, n1, m2, n2);		}		// svmtrain's original code		error_msg = svm_check_parameter(&prob, &param);		if(error_msg)		{			mexPrintf("Error: %s\n", error_msg);			plhs[0] = mxCreateDoubleMatrix(0, 0, mxREAL);			return;		}		if(cross_validation)		{			plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL);			ptr = mxGetPr(plhs[0]);			ptr[0] = do_cross_validation();		}		else		{			model = svm_train(&prob, &param);			model_to_matlab_structure(plhs, n2, model);			svm_destroy_model(model);		}		svm_destroy_param(&param);		free(prob.y);		free(prob.x);		free(x_space);	}	else 	{		exit_with_help();		plhs[0] = mxCreateDoubleMatrix(0, 0, mxREAL);	}	return;}