525 lines
13 KiB
C
525 lines
13 KiB
C
#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <ctype.h>
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#include "../svm.h"
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#include "mex.h"
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#include "svm_model_matlab.h"
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#ifdef MX_API_VER
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#if MX_API_VER < 0x07030000
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typedef int mwIndex;
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#endif
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#endif
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#define CMD_LEN 2048
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#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
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void print_null(const char *s) {}
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void print_string_matlab(const char *s) {mexPrintf(s);}
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void exit_with_help()
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{
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mexPrintf(
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"Usage: model = svmtrain(training_weight_vector, training_label_vector, training_instance_matrix, 'libsvm_options');\n"
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"libsvm_options:\n"
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"-s svm_type : set type of SVM (default 0)\n"
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" 0 -- C-SVC (multi-class classification)\n"
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" 1 -- nu-SVC (multi-class classification)\n"
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" 2 -- one-class SVM\n"
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" 3 -- epsilon-SVR (regression)\n"
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" 4 -- nu-SVR (regression)\n"
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"-t kernel_type : set type of kernel function (default 2)\n"
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" 0 -- linear: u'*v\n"
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" 1 -- polynomial: (gamma*u'*v + coef0)^degree\n"
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" 2 -- radial basis function: exp(-gamma*|u-v|^2)\n"
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" 3 -- sigmoid: tanh(gamma*u'*v + coef0)\n"
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" 4 -- precomputed kernel (kernel values in training_instance_matrix)\n"
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"-d degree : set degree in kernel function (default 3)\n"
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"-g gamma : set gamma in kernel function (default 1/num_features)\n"
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"-r coef0 : set coef0 in kernel function (default 0)\n"
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"-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)\n"
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"-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)\n"
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"-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n"
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"-m cachesize : set cache memory size in MB (default 100)\n"
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"-e epsilon : set tolerance of termination criterion (default 0.001)\n"
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"-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)\n"
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"-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)\n"
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"-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)\n"
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"-v n : n-fold cross validation mode\n"
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"-q : quiet mode (no outputs)\n"
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);
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}
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// svm arguments
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struct svm_parameter param; // set by parse_command_line
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struct svm_problem prob; // set by read_problem
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struct svm_model *model;
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struct svm_node *x_space;
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int cross_validation;
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int nr_fold;
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double do_cross_validation()
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{
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int i;
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int total_correct = 0;
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double total_error = 0;
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double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
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double *target = Malloc(double,prob.l);
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double retval = 0.0;
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svm_cross_validation(&prob,¶m,nr_fold,target);
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if(param.svm_type == EPSILON_SVR ||
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param.svm_type == NU_SVR)
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{
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for(i=0;i<prob.l;i++)
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{
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double y = prob.y[i];
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double v = target[i];
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total_error += (v-y)*(v-y);
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sumv += v;
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sumy += y;
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sumvv += v*v;
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sumyy += y*y;
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sumvy += v*y;
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}
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mexPrintf("Cross Validation Mean squared error = %g\n",total_error/prob.l);
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mexPrintf("Cross Validation Squared correlation coefficient = %g\n",
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((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/
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((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))
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);
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retval = total_error/prob.l;
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}
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else
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{
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for(i=0;i<prob.l;i++)
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if(target[i] == prob.y[i])
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++total_correct;
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mexPrintf("Cross Validation Accuracy = %g%%\n",100.0*total_correct/prob.l);
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retval = 100.0*total_correct/prob.l;
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}
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free(target);
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return retval;
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}
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// nrhs should be 4
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int parse_command_line(int nrhs, const mxArray *prhs[], char *model_file_name)
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{
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int i, argc = 1;
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char cmd[CMD_LEN];
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char *argv[CMD_LEN/2];
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void (*print_func)(const char *) = print_string_matlab; // default printing to stdout
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// default values
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param.svm_type = C_SVC;
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param.kernel_type = RBF;
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param.degree = 3;
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param.gamma = 0; // 1/num_features
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param.coef0 = 0;
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param.nu = 0.5;
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param.cache_size = 100;
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param.C = 1;
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param.eps = 1e-3;
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param.p = 0.1;
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param.shrinking = 1;
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param.probability = 0;
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param.nr_weight = 0;
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param.weight_label = NULL;
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param.weight = NULL;
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cross_validation = 0;
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if(nrhs <= 1)
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return 1;
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if(nrhs > 3)
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{
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// put options in argv[]
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mxGetString(prhs[3], cmd, mxGetN(prhs[3]) + 1);
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if((argv[argc] = strtok(cmd, " ")) != NULL)
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while((argv[++argc] = strtok(NULL, " ")) != NULL)
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;
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}
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// parse options
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for(i=1;i<argc;i++)
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{
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if(argv[i][0] != '-') break;
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++i;
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if(i>=argc && argv[i-1][1] != 'q') // since option -q has no parameter
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return 1;
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switch(argv[i-1][1])
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{
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case 's':
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param.svm_type = atoi(argv[i]);
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break;
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case 't':
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param.kernel_type = atoi(argv[i]);
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break;
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case 'd':
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param.degree = atoi(argv[i]);
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break;
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case 'g':
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param.gamma = atof(argv[i]);
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break;
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case 'r':
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param.coef0 = atof(argv[i]);
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break;
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case 'n':
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param.nu = atof(argv[i]);
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break;
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case 'm':
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param.cache_size = atof(argv[i]);
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break;
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case 'c':
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param.C = atof(argv[i]);
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break;
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case 'e':
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param.eps = atof(argv[i]);
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break;
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case 'p':
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param.p = atof(argv[i]);
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break;
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case 'h':
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param.shrinking = atoi(argv[i]);
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break;
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case 'b':
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param.probability = atoi(argv[i]);
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break;
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case 'q':
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print_func = &print_null;
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i--;
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break;
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case 'v':
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cross_validation = 1;
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nr_fold = atoi(argv[i]);
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if(nr_fold < 2)
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{
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mexPrintf("n-fold cross validation: n must >= 2\n");
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return 1;
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}
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break;
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case 'w':
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++param.nr_weight;
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param.weight_label = (int *)realloc(param.weight_label,sizeof(int)*param.nr_weight);
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param.weight = (double *)realloc(param.weight,sizeof(double)*param.nr_weight);
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param.weight_label[param.nr_weight-1] = atoi(&argv[i-1][2]);
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param.weight[param.nr_weight-1] = atof(argv[i]);
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break;
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default:
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mexPrintf("Unknown option -%c\n", argv[i-1][1]);
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return 1;
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}
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}
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svm_set_print_string_function(print_func);
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return 0;
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}
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// read in a problem (in svmlight format)
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int read_problem_dense(const mxArray *weight_vec, const mxArray *label_vec, const mxArray *instance_mat)
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{
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size_t i, j, k, l;
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size_t elements, max_index, sc, label_vector_row_num, weight_vector_row_num;
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double *samples, *labels, *weights;
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prob.x = NULL;
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prob.y = NULL;
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prob.W = NULL;
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x_space = NULL;
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weights = mxGetPr(weight_vec);
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labels = mxGetPr(label_vec);
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samples = mxGetPr(instance_mat);
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sc = mxGetN(instance_mat);
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elements = 0;
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// the number of instance
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l = mxGetM(instance_mat);
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prob.l = (int)l;
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weight_vector_row_num = mxGetM(weight_vec);
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label_vector_row_num = mxGetM(label_vec);
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if(weight_vector_row_num == 0)
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mexPrintf("Warning: treat each instance with weight 1.0\n");
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else if(weight_vector_row_num!=prob.l)
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{
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mexPrintf("Length of weight vector does not match # of instances.\n");
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return -1;
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}
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if(label_vector_row_num!=l)
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{
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mexPrintf("Length of label vector does not match # of instances.\n");
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return -1;
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}
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if(param.kernel_type == PRECOMPUTED)
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elements = l * (sc + 1);
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else
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{
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for(i = 0; i < l; i++)
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{
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for(k = 0; k < sc; k++)
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if(samples[k * l + i] != 0)
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elements++;
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// count the '-1' element
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elements++;
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}
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}
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prob.y = Malloc(double,l);
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prob.x = Malloc(struct svm_node *,l);
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prob.W = Malloc(double,l);
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x_space = Malloc(struct svm_node, elements);
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max_index = sc;
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j = 0;
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for(i = 0; i < l; i++)
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{
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prob.x[i] = &x_space[j];
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prob.y[i] = labels[i];
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prob.W[i] = 1;
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if(weight_vector_row_num == prob.l)
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prob.W[i] *= (double) weights[i];
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for(k = 0; k < sc; k++)
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{
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if(param.kernel_type == PRECOMPUTED || samples[k * l + i] != 0)
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{
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x_space[j].index = (int)k + 1;
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x_space[j].value = samples[k * l + i];
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j++;
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}
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}
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x_space[j++].index = -1;
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}
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if(param.gamma == 0 && max_index > 0)
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param.gamma = (double)(1.0/max_index);
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if(param.kernel_type == PRECOMPUTED)
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for(i=0;i<l;i++)
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{
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if((int)prob.x[i][0].value <= 0 || (int)prob.x[i][0].value > (int)max_index)
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{
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mexPrintf("Wrong input format: sample_serial_number out of range\n");
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return -1;
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}
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}
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return 0;
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}
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int read_problem_sparse(const mxArray *weight_vec, const mxArray *label_vec, const mxArray *instance_mat)
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{
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mwIndex *ir, *jc, low, high, k;
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// using size_t due to the output type of matlab functions
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size_t i, j, l, elements, max_index, label_vector_row_num, weight_vector_row_num;
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mwSize num_samples;
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double *samples, *labels, *weights;
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mxArray *instance_mat_col; // transposed instance sparse matrix
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prob.x = NULL;
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prob.y = NULL;
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prob.W = NULL;
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x_space = NULL;
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// transpose instance matrix
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{
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mxArray *prhs[1], *plhs[1];
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prhs[0] = mxDuplicateArray(instance_mat);
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if(mexCallMATLAB(1, plhs, 1, prhs, "transpose"))
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{
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mexPrintf("Error: cannot transpose training instance matrix\n");
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return -1;
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}
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instance_mat_col = plhs[0];
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mxDestroyArray(prhs[0]);
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}
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// each column is one instance
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weights = mxGetPr(weight_vec);
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labels = mxGetPr(label_vec);
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samples = mxGetPr(instance_mat_col);
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ir = mxGetIr(instance_mat_col);
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jc = mxGetJc(instance_mat_col);
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num_samples = mxGetNzmax(instance_mat_col);
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// the number of instance
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l = mxGetN(instance_mat_col);
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prob.l = (int) l;
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label_vector_row_num = mxGetM(label_vec);
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weight_vector_row_num = mxGetM(weight_vec);
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if(weight_vector_row_num == 0)
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mexPrintf("Warning: treat each instance with weight 1.0\n");
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else if(weight_vector_row_num!=prob.l)
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{
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mexPrintf("Length of weight vector does not match # of instances.\n");
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return -1;
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}
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if(label_vector_row_num!=l)
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{
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mexPrintf("Length of label vector does not match # of instances.\n");
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return -1;
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}
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elements = num_samples + l;
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max_index = mxGetM(instance_mat_col);
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prob.y = Malloc(double,l);
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prob.x = Malloc(struct svm_node *,l);
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prob.W = Malloc(double,l);
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x_space = Malloc(struct svm_node, elements);
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j = 0;
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for(i=0;i<l;i++)
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{
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prob.x[i] = &x_space[j];
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prob.y[i] = labels[i];
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prob.W[i] = 1;
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if(weight_vector_row_num == prob.l)
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prob.W[i] *= (double) weights[i];
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low = jc[i], high = jc[i+1];
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for(k=low;k<high;k++)
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{
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x_space[j].index = (int)ir[k] + 1;
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x_space[j].value = samples[k];
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j++;
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}
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x_space[j++].index = -1;
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}
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if(param.gamma == 0 && max_index > 0)
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param.gamma = (double)(1.0/max_index);
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return 0;
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}
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static void fake_answer(int nlhs, mxArray *plhs[])
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{
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int i;
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for(i=0;i<nlhs;i++)
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plhs[i] = mxCreateDoubleMatrix(0, 0, mxREAL);
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}
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// Interface function of matlab
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// now assume prhs[0]: label prhs[1]: features
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void mexFunction( int nlhs, mxArray *plhs[],
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int nrhs, const mxArray *prhs[] )
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{
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const char *error_msg;
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// fix random seed to have same results for each run
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// (for cross validation and probability estimation)
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srand(1);
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if(nlhs > 1)
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{
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exit_with_help();
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fake_answer(nlhs, plhs);
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return;
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}
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// Transform the input Matrix to libsvm format
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if(nrhs > 2 && nrhs < 5)
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{
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int err;
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if(!mxIsDouble(prhs[0]) || !mxIsDouble(prhs[1]))
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{
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mexPrintf("Error: label vector and instance matrix must be double\n");
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fake_answer(nlhs, plhs);
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return;
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}
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if(mxIsSparse(prhs[0]))
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{
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mexPrintf("Error: label vector should not be in sparse format\n");
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fake_answer(nlhs, plhs);
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return;
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}
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if(parse_command_line(nrhs, prhs, NULL))
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{
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exit_with_help();
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svm_destroy_param(¶m);
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fake_answer(nlhs, plhs);
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return;
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}
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if(mxIsSparse(prhs[2]))
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{
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if(param.kernel_type == PRECOMPUTED)
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{
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// precomputed kernel requires dense matrix, so we make one
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mxArray *rhs[1], *lhs[1];
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rhs[0] = mxDuplicateArray(prhs[2]);
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if(mexCallMATLAB(1, lhs, 1, rhs, "full"))
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{
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mexPrintf("Error: cannot generate a full training instance matrix\n");
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svm_destroy_param(¶m);
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fake_answer(nlhs, plhs);
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return;
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}
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err = read_problem_dense(prhs[0], prhs[1], lhs[0]);
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mxDestroyArray(lhs[0]);
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mxDestroyArray(rhs[0]);
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}
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else
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err = read_problem_sparse(prhs[0], prhs[1], prhs[2]);
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}
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else
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err = read_problem_dense(prhs[0], prhs[1], prhs[2]);
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// svmtrain's original code
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error_msg = svm_check_parameter(&prob, ¶m);
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if(err || error_msg)
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{
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if (error_msg != NULL)
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mexPrintf("Error: %s\n", error_msg);
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svm_destroy_param(¶m);
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free(prob.y);
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free(prob.x);
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free(prob.W);
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free(x_space);
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fake_answer(nlhs, plhs);
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return;
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}
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if(cross_validation)
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{
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double *ptr;
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plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL);
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ptr = mxGetPr(plhs[0]);
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ptr[0] = do_cross_validation();
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}
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else
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{
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int nr_feat = (int)mxGetN(prhs[2]);
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const char *error_msg;
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model = svm_train(&prob, ¶m);
|
|
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_free_and_destroy_model(&model);
|
|
}
|
|
svm_destroy_param(¶m);
|
|
free(prob.y);
|
|
free(prob.x);
|
|
free(prob.W);
|
|
free(x_space);
|
|
}
|
|
else
|
|
{
|
|
exit_with_help();
|
|
fake_answer(nlhs, plhs);
|
|
return;
|
|
}
|
|
}
|