svm-train.c
资源名称:svm.rar [点击查看]
上传用户:xgw_05
上传日期:2014-12-08
资源大小:2726k
文件大小:8k
源码类别:
.net编程
开发平台:
Java
- #include <stdio.h>
- #include <stdlib.h>
- #include <string.h>
- #include <ctype.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-SVCn"
- " 1 -- nu-SVCn"
- " 2 -- one-class SVMn"
- " 3 -- epsilon-SVRn"
- " 4 -- nu-SVRn"
- "-t kernel_type : set type of kernel function (default 2)n"
- " 0 -- linear: u'*vn"
- " 1 -- polynomial: (gamma*u'*v + coef0)^degreen"
- " 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"
- "-wi weight: set the parameter C of class i to weight*C, for C-SVC (default 1)n"
- "-v n: n-fold cross validation moden"
- );
- 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_line
- struct svm_problem prob; // set by read_problem
- struct svm_model *model;
- struct svm_node *x_space;
- int cross_validation = 0;
- int nr_fold;
- int main(int argc, char **argv)
- {
- char input_file_name[1024];
- char model_file_name[1024];
- parse_command_line(argc, argv, input_file_name, model_file_name);
- read_problem(input_file_name);
- if(cross_validation)
- {
- do_cross_validation();
- }
- else
- {
- model = svm_train(&prob,¶m);
- svm_save_model(model_file_name,model);
- svm_destroy_model(model);
- }
- free(prob.y);
- free(prob.x);
- free(x_space);
- 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;
- // random shuffle
- for(i=0;i<prob.l;i++)
- {
- int j = rand()%(prob.l-i);
- struct svm_node *tx;
- double ty;
- tx = prob.x[i];
- prob.x[i] = prob.x[j];
- prob.x[j] = tx;
- ty = prob.y[i];
- prob.y[i] = prob.y[j];
- prob.y[j] = ty;
- }
- for(i=0;i<nr_fold;i++)
- {
- int begin = i*prob.l/nr_fold;
- int end = (i+1)*prob.l/nr_fold;
- int j,k;
- struct svm_problem subprob;
- subprob.l = prob.l-(end-begin);
- subprob.x = Malloc(struct svm_node*,subprob.l);
- subprob.y = Malloc(double,subprob.l);
- k=0;
- for(j=0;j<begin;j++)
- {
- subprob.x[k] = prob.x[j];
- subprob.y[k] = prob.y[j];
- ++k;
- }
- for(j=end;j<prob.l;j++)
- {
- subprob.x[k] = prob.x[j];
- subprob.y[k] = prob.y[j];
- ++k;
- }
- if(param.svm_type == EPSILON_SVR ||
- param.svm_type == NU_SVR)
- {
- struct svm_model *submodel = svm_train(&subprob,¶m);
- double error = 0;
- for(j=begin;j<end;j++)
- {
- double v = svm_predict(submodel,prob.x[j]);
- double y = prob.y[j];
- error += (v-y)*(v-y);
- sumv += v;
- sumy += y;
- sumvv += v*v;
- sumyy += y*y;
- sumvy += v*y;
- }
- svm_destroy_model(submodel);
- printf("Mean squared error = %gn", error/(end-begin));
- total_error += error;
- }
- else
- {
- struct svm_model *submodel = svm_train(&subprob,¶m);
- int correct = 0;
- for(j=begin;j<end;j++)
- {
- double v = svm_predict(submodel,prob.x[j]);
- if(v == prob.y[j])
- ++correct;
- }
- svm_destroy_model(submodel);
- printf("Accuracy = %g%% (%d/%d)n", 100.0*correct/(end-begin),correct,(end-begin));
- total_correct += correct;
- }
- free(subprob.x);
- free(subprob.y);
- }
- if(param.svm_type == EPSILON_SVR || param.svm_type == NU_SVR)
- {
- printf("Cross Validation Mean squared error = %gn",total_error/prob.l);
- printf("Cross Validation Squared correlation coefficient = %gn",
- ((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/
- ((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))
- );
- }
- else
- printf("Cross Validation Accuracy = %g%%n",100.0*total_correct/prob.l);
- }
- 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.nr_weight = 0;
- param.weight_label = NULL;
- param.weight = NULL;
- // parse options
- for(i=1;i<argc;i++)
- {
- if(argv[i][0] != '-') break;
- ++i;
- 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 'v':
- cross_validation = 1;
- nr_fold = atoi(argv[i]);
- if(nr_fold < 2)
- {
- fprintf(stderr,"n-fold cross validation: n must >= 2n");
- 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 optionn");
- 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, max_index, i, j;
- FILE *fp = fopen(filename,"r");
- if(fp == NULL)
- {
- fprintf(stderr,"can't open input file %sn",filename);
- exit(1);
- }
- prob.l = 0;
- elements = 0;
- while(1)
- {
- int c = fgetc(fp);
- switch(c)
- {
- case 'n':
- ++prob.l;
- // fall through,
- // count the '-1' element
- case ':':
- ++elements;
- break;
- case EOF:
- goto out;
- default:
- ;
- }
- }
- out:
- rewind(fp);
- prob.y = Malloc(double,prob.l);
- prob.x = Malloc(struct svm_node *,prob.l);
- x_space = Malloc(struct svm_node,elements);
- max_index = 0;
- j=0;
- for(i=0;i<prob.l;i++)
- {
- double label;
- prob.x[i] = &x_space[j];
- 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",&(x_space[j].index),&(x_space[j].value));
- ++j;
- }
- out2:
- if(j>=1 && x_space[j-1].index > max_index)
- max_index = x_space[j-1].index;
- x_space[j++].index = -1;
- }
- if(param.gamma == 0)
- param.gamma = 1.0/max_index;
- fclose(fp);
- }