#include <stdio.h>#include <stdlib.h>#include <string.h>#include <ctype.h>#include "svm.h"#include "mex.h"#include "svm_model_matlab.h"#define CMD_LEN 2048#define Malloc(type,n) (type *)malloc((n)*sizeof(type))void exit_with_help(){	mexPrintf(	"Usage: model = svmtrain(training_label_vector, 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"	);}// 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;int nr_fold;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);	double retval = 0.0;	// fix random seed to have same results for each run	srand(1);	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))			);		retval = 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);		retval = 100.0*total_correct/prob.l;	}	free(target);	return retval;}// nrhs should be 3int parse_command_line(int nrhs, const mxArray *prhs[], char *model_file_name){	int i, argc = 1;	char cmd[CMD_LEN];	char *argv[CMD_LEN/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;	if(nrhs <= 1)		return 1;	if(nrhs == 2)		return 0;	// put options in argv[]	mxGetString(prhs[2], cmd,  mxGetN(prhs[2]) + 1);	if((argv[argc] = strtok(cmd, " ")) == NULL)		return 0;	while((argv[++argc] = strtok(NULL, " ")) != NULL)		;	// parse options	for(i=1;i<argc;i++)	{		if(argv[i][0] != '-') break;		if(++i>=argc)			return 1;		switch(argv[i-1][1])		{			case 's':				param.svm_type = atoi(argv[i]);				break;			case 't':				param.kernel_type = atoi(argv[i]);				break;			case 'd':				param.degree = atof(argv[i]);				break;			case 'g':				param.gamma = atof(argv[i]);				break;			case 'r':				param.coef0 = atof(argv[i]);				break;			case 'n':				param.nu = atof(argv[i]);				break;			case 'm':				param.cache_size = atof(argv[i]);				break;			case 'c':				param.C = atof(argv[i]);				break;			case 'e':				param.eps = atof(argv[i]);				break;			case 'p':				param.p = atof(argv[i]);				break;			case 'h':				param.shrinking = atoi(argv[i]);				break;			case 'b':				param.probability = atoi(argv[i]);				break;			case 'v':				cross_validation = 1;				nr_fold = atoi(argv[i]);				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[i-1][2]);				param.weight[param.nr_weight-1] = atof(argv[i]);				break;			default:				mexPrintf("unknown option\n");				return 1;		}	}	return 0;}// read in a problem (in svmlight format)void read_problem_dense(const mxArray *label_vec, const mxArray *instance_mat){	int i, j, k;	int elements, max_index, sc;	double *samples, *labels;	labels = mxGetPr(label_vec);	samples = mxGetPr(instance_mat);	sc = mxGetN(instance_mat);	elements = 0;	// the number of instance	prob.l = mxGetM(instance_mat);	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 *label_vec, const mxArray *instance_mat){	int i, j, k, low, high;	int *ir, *jc;	int elements, max_index, num_samples;	double *samples, *labels;	mxArray *instance_mat_tr; // transposed instance sparse matrix	// transpose instance matrix	{		mxArray *prhs[1], *plhs[1];		prhs[0] = mxDuplicateArray(instance_mat);		if (mexCallMATLAB(1, plhs, 1, prhs, "transpose")) {			mexPrintf("Error: cannot transpose training instance matrix\n");			return;		}		instance_mat_tr = plhs[0];	}	// each column is one instance	labels = mxGetPr(label_vec);	samples = mxGetPr(instance_mat_tr);	ir = mxGetIr(instance_mat_tr);	jc = mxGetJc(instance_mat_tr);	num_samples = mxGetNzmax(instance_mat_tr);	// the number of instance	prob.l = mxGetN(instance_mat_tr);	elements = num_samples + prob.l;	max_index = mxGetM(instance_mat_tr);	prob.y = Malloc(double,prob.l);	prob.x = Malloc(struct svm_node *,prob.l);	x_space = Malloc(struct svm_node, elements);	j = 0;	for(i=0;i<prob.l;i++)	{		prob.x[i] = &x_space[j];		prob.y[i] = labels[i];		low = jc[i], high = jc[i+1];		for(k=low;k<high;k++)		{			x_space[j].index = ir[k] + 1;			x_space[j].value = samples[k];			j++;	 	}		x_space[j++].index = -1;	}	if(param.gamma == 0)		param.gamma = 1.0/max_index;}static void fake_answer(mxArray *plhs[]){	plhs[0] = mxCreateDoubleMatrix(0, 0, mxREAL);}// Interface function of matlab// now assume prhs[0]: label prhs[1]: featuresvoid mexFunction( int nlhs, mxArray *plhs[],		int nrhs, const mxArray *prhs[] ){	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, NULL))		{			exit_with_help();			svm_destroy_param(&param);			fake_answer(plhs);			return;		}		if(mxIsSparse(prhs[1]))			read_problem_sparse(prhs[0], prhs[1]);		else			read_problem_dense(prhs[0], prhs[1]);		// svmtrain's original code		error_msg = svm_check_parameter(&prob, &param);		if(error_msg)		{			mexPrintf("Error: %s\n", error_msg);			svm_destroy_param(&param);			free(prob.y);			free(prob.x);			free(x_space);			fake_answer(plhs);			return;		}		if(cross_validation)		{			double *ptr;			plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL);			ptr = mxGetPr(plhs[0]);			ptr[0] = do_cross_validation();		}		else		{			int nr_feat = mxGetN(prhs[1]);			const char *error_msg;			model = svm_train(&prob, &param);			error_msg = model_to_matlab_structure(plhs, nr_feat, model);			if (error_msg)				mexPrintf("Error: can't convert libsvm model to matrix structure: %s\n", error_msg);			svm_destroy_model(model);		}		svm_destroy_param(&param);		free(prob.y);		free(prob.x);		free(x_space);	}	else	{		exit_with_help();		fake_answer(plhs);	}}