#include <stdio.h>#include <ctype.h>#include <stdlib.h>#include <string.h>#include <errno.h>#include "svm.h"int print_null(const char *s,...) {return 0;}static int (*info)(const char *fmt,...) = &printf;struct svm_node *x;int max_nr_attr = 64;struct svm_model* model;int predict_probability=0;static char *line = NULL;static int max_line_len;static char* readline(FILE *input){int len;if(fgets(line,max_line_len,input) == NULL)return NULL;while(strrchr(line,'\n') == NULL){max_line_len *= 2;line = (char *) realloc(line,max_line_len);len = (int) strlen(line);if(fgets(line+len,max_line_len-len,input) == NULL)break;}return line;}void exit_input_error(int line_num){fprintf(stderr,"Wrong input format at line %d\n", line_num);exit(1);}void predict(FILE *input, FILE *output){int correct = 0;int total = 0;double error = 0;double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;int svm_type=svm_get_svm_type(model);int nr_class=svm_get_nr_class(model);double *prob_estimates=NULL;int j;if(predict_probability){if (svm_type==NU_SVR || svm_type==EPSILON_SVR)info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g\n",svm_get_svr_probability(model));else{int *labels=(int *) malloc(nr_class*sizeof(int));svm_get_labels(model,labels);prob_estimates = (double *) malloc(nr_class*sizeof(double));fprintf(output,"labels");for(j=0;j<nr_class;j++)fprintf(output," %d",labels[j]);fprintf(output,"\n");free(labels);}}max_line_len = 1024;line = (char *)malloc(max_line_len*sizeof(char));while(readline(input) != NULL){int i = 0;double target_label, predict_label;char *idx, *val, *label, *endptr;int inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0label = strtok(line," \t\n");if(label == NULL) // empty lineexit_input_error(total+1);target_label = strtod(label,&endptr);if(endptr == label || *endptr != '\0')exit_input_error(total+1);while(1){if(i>=max_nr_attr-1)// need one more for index = -1{max_nr_attr *= 2;x = (struct svm_node *) realloc(x,max_nr_attr*sizeof(struct svm_node));}idx = strtok(NULL,":");val = strtok(NULL," \t");if(val == NULL)break;errno = 0;x[i].index = (int) strtol(idx,&endptr,10);if(endptr == idx || errno != 0 || *endptr != '\0' || x[i].index <= inst_max_index)exit_input_error(total+1);elseinst_max_index = x[i].index;errno = 0;x[i].value = strtod(val,&endptr);if(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))exit_input_error(total+1);++i;}x[i].index = -1;if (predict_probability && (svm_type==C_SVC || svm_type==NU_SVC)){predict_label = svm_predict_probability(model,x,prob_estimates);fprintf(output,"%g",predict_label);for(j=0;j<nr_class;j++)fprintf(output," %g",prob_estimates[j]);fprintf(output,"\n");}else{predict_label = svm_predict(model,x);fprintf(output,"%g\n",predict_label);}if(predict_label == target_label)++correct;error += (predict_label-target_label)*(predict_label-target_label);sump += predict_label;sumt += target_label;sumpp += predict_label*predict_label;sumtt += target_label*target_label;sumpt += predict_label*target_label;++total;}if (svm_type==NU_SVR || svm_type==EPSILON_SVR){info("Mean squared error = %g (regression)\n",error/total);info("Squared correlation coefficient = %g (regression)\n",((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt)));}elseinfo("Accuracy = %g%% (%d/%d) (classification)\n",(double)correct/total*100,correct,total);if(predict_probability)free(prob_estimates);}/*void exit_with_help(){printf("Usage: svm-predict [options] test_file model_file output_file\n""options:\n""-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); for one-class SVM only 0 is supported\n""-q : quiet mode (no outputs)\n");exit(1);}*/int main()//int argc, char **argv){FILE *input, *output;int i;predict_probability=0;// parse options/*for(i=1;i<argc;i++){if(argv[i][0] != '-') break;++i;switch(argv[i-1][1]){case 'b':predict_probability = atoi(argv[i]);break;case 'q':info = &print_null;i--;break;default:fprintf(stderr,"Unknown option: -%c\n", argv[i-1][1]);exit_with_help();}}if(i>=argc-2)exit_with_help();*//*input = fopen("formatdata1.txt","r");if(input == NULL){//fprintf(stderr,"can't open input file %s\n",argv[i]);printf("can't open input file");exit(1);}output = fopen("libSVMPredictResult1.txt","w");if(output == NULL){//fprintf(stderr,"can't open output file %s\n",argv[i+2]);printf("can't open output file");exit(1);}if((model=svm_load_model("Trained.model"))==0){//fprintf(stderr,"can't open model file %s\n",argv[i+1]);printf("can't open model file");exit(1);}x = (struct svm_node *) malloc(max_nr_attr*sizeof(struct svm_node));if(predict_probability){if(svm_check_probability_model(model)==0){fprintf(stderr,"Model does not support probabiliy estimates\n");exit(1);}}else{if(svm_check_probability_model(model)!=0)info("Model supports probability estimates, but disabled in prediction.\n");}predict(input,output); //svm_free_and_destroy_model(&model);free(x);free(line);fclose(input);fclose(output);return 0;}
