c语言版本的支持向量机SVM

#include <stdio.h> #include <stdlib.h> #include <string.h> #include <ctype.h> #include "svm.h" #include "mex.h" #include "svm_model_matlab.h" #if MX_API_VER < 0x07030000 typedef int mwIndex; #endif #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" " 4 -- precomputed kernel (kernel values in training_instance_matrix)\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 100)\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 arguments 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; 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; 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 3 int 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 = 100; 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 = 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) { 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) int 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); if(param.kernel_type == PRECOMPUTED) elements = prob.l * (sc + 1); else { 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(param.kernel_type == PRECOMPUTED || 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; if(param.kernel_type == PRECOMPUTED) for(i=0;i<prob.l;i++) { if((int)prob.x[i][0].value <= 0 || (int)prob.x[i][0].value > max_index) { mexPrintf("Wrong input format: sample_serial_number out of range\n"); return -1; } } return 0; } int read_problem_sparse(const mxArray *label_vec, const mxArray *instance_mat) { int i, j, k, low, high; mwIndex *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 -1; } instance_mat_tr = plhs[0]; mxDestroyArray(prhs[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; return 0; } static voi