#include <stdio.h>#include <stdlib.h>#include <string.h>#include <ctype.h>#include <mpi.h>#include <time.h>#include "svm.h"#define Malloc(type,n) (type *)malloc((n)*sizeof(type))void exit_with_help(){  printf(	 "Usage: svm-train [options] training_set_file [model_file]\n"	 "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"	 "-o n: max. size of working set\n"	 "-q n: max. number of new variables entering working set\n"	 );  exit(1);}void parse_command_line(int argc, char **argv, char *input_file_name, 			char *model_file_name);void read_problem(const char *filename);void do_cross_validation();struct svm_parameter param;		// set by parse_command_linestruct svm_problem prob;		// set by read_problemstruct svm_model *model;Xfloat *x_space;int *nz_idx_space;int cross_validation;int nr_fold;int main(int argc, char **argv){  char input_file_name[1024];  char model_file_name[1024];  const char *error_msg;  double time = 0;  MPI_Init(&argc, &argv);  parse_command_line(argc, argv, input_file_name, model_file_name);  time = MPI_Wtime();  read_problem(input_file_name);  time = MPI_Wtime() - time;  error_msg = svm_check_parameter(&prob,&param);  if(error_msg)    {      fprintf(stderr,"Error: %s\n",error_msg);      exit(1);    }  if(cross_validation)    {      do_cross_validation();    }  else    {      model = svm_train(&prob,&param);      svm_save_model(model_file_name,model);      svm_destroy_model(model);    }  svm_destroy_param(&param);  printf("I/O time = %.2lf\n", time);  free(prob.y);  free(prob.x);  free(prob.nz_idx);  free(prob.x_len);  free(x_space);  free(nz_idx_space);  MPI_Finalize();  return 0;}void 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;	}      printf("Cross Validation Mean squared error = %g\n",total_error/prob.l);      printf("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))	     );    }  else    {      for(i=0;i<prob.l;i++)	if(target[i] == prob.y[i])	  ++total_correct;      printf("Cross Validation Accuracy = %g%%\n",100.0*total_correct/prob.l);    }  free(target);}void parse_command_line(int argc, char **argv, char *input_file_name, 			char *model_file_name){  int i;  // 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;  param.o = 2; // safe defaults  param.q = 2;  cross_validation = 0;  // parse options  for(i=1;i<argc;i++)    {      if(argv[i][0] != '-') break;      if(++i>=argc)	exit_with_help();      switch(argv[i-1][1])	{	case 'o':	  param.o = atoi(argv[i]);	  break;	case 'q':	  param.q = atoi(argv[i]);	  break;	case 's':	  param.svm_type = atoi(argv[i]);	  break;	case 't':	  param.kernel_type = atoi(argv[i]);	  break;	case 'd':	  param.degree = atoi(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)	    {	      fprintf(stderr,"n-fold cross validation: n must >= 2\n");	      exit_with_help();	    }	  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:	  fprintf(stderr,"unknown option\n");	  exit_with_help();	}    }  // determine filenames  if(i>=argc)    exit_with_help();  strcpy(input_file_name, argv[i]);  if(i<argc-1)    strcpy(model_file_name,argv[i+1]);  else    {      char *p = strrchr(argv[i],'/');      if(p==NULL)	p = argv[i];      else	++p;      sprintf(model_file_name,"%s.model",p);    }}// read in a problem (in svmlight format)void read_problem(const char *filename){  int elements, i, j;  FILE *fp = fopen(filename,"r");	  if(fp == NULL)    {      fprintf(stderr,"can't open input file %s\n",filename);      exit(1);    }  prob.l = 0;  elements = 0;  while(1)    {      int c = fgetc(fp);      switch(c)	{	case '\n':	  ++prob.l;	  break;	case ':':	  ++elements;	  break;	case EOF:	  goto out;	default:	  ;	}    } out:  rewind(fp);  prob.y = Malloc(double,prob.l);  prob.x = Malloc(Xfloat *, prob.l);  prob.nz_idx = Malloc(int *, prob.l);  prob.x_len = Malloc(int, prob.l);  memset(prob.x_len, 0, sizeof(int)*prob.l);  x_space = Malloc(Xfloat,elements);  nz_idx_space = Malloc(int,elements);  prob.max_idx = 0;  j=0;  for(i=0;i<prob.l;i++)    {      double label;      prob.x[i] = &x_space[j];      prob.nz_idx[i] = &nz_idx_space[j];      prob.x_len[i] = 0;      fscanf(fp,"%lf",&label);      prob.y[i] = label;      while(1)	{	  int c;	  do {	    c = getc(fp);	    if(c=='\n') goto out2;	  } while(isspace(c));	  ungetc(c,fp);	  //	  fscanf(fp,"%d:%lf",&nz_idx_space[j],&x_space[j]);	  fscanf(fp,"%d:%f",&nz_idx_space[j],&x_space[j]);	  --nz_idx_space[j]; // we need zero based indices	  ++prob.x_len[i];	  ++j;	}    out2:      if(j>=1 && nz_idx_space[j-1]+1 > prob.max_idx)	{	  prob.max_idx = nz_idx_space[j-1]+1;	}    }  if(param.gamma == 0)    param.gamma = 1.0/prob.max_idx;  fclose(fp);}