#include <stdio.h>
#include <math.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include "../linear.h"
#include "mex.h"
#include "linear_model_matlab.h"
#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif
#define CMD_LEN 2048
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
#define INF HUGE_VAL
void print_null(const char *s) {}
void print_string_matlab(const char *s) {mexPrintf(s);}
void exit_with_help()
{
mexPrintf(
"Usage: model = train(training_label_vector, training_instance_matrix, 'liblinear_options', 'col');\n"
"liblinear_options:\n"
"-s type : set type of solver (default 1)\n"
" for multi-class classification\n"
" 0 -- L2-regularized logistic regression (primal)\n"
" 1 -- L2-regularized L2-loss support vector classification (dual)\n"
" 2 -- L2-regularized L2-loss support vector classification (primal)\n"
" 3 -- L2-regularized L1-loss support vector classification (dual)\n"
" 4 -- support vector classification by Crammer and Singer\n"
" 5 -- L1-regularized L2-loss support vector classification\n"
" 6 -- L1-regularized logistic regression\n"
" 7 -- L2-regularized logistic regression (dual)\n"
" for regression\n"
" 11 -- L2-regularized L2-loss support vector regression (primal)\n"
" 12 -- L2-regularized L2-loss support vector regression (dual)\n"
" 13 -- L2-regularized L1-loss support vector regression (dual)\n"
"-c cost : set the parameter C (default 1)\n"
"-p epsilon : set the epsilon in loss function of SVR (default 0.1)\n"
"-e epsilon : set tolerance of termination criterion\n"
" -s 0 and 2\n"
" |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,\n"
" where f is the primal function and pos/neg are # of\n"
" positive/negative data (default 0.01)\n"
" -s 11\n"
" |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001)\n"
" -s 1, 3, 4 and 7\n"
" Dual maximal violation <= eps; similar to libsvm (default 0.1)\n"
" -s 5 and 6\n"
" |f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,\n"
" where f is the primal function (default 0.01)\n"
" -s 12 and 13\n"
" |f'(alpha)|_1 <= eps |f'(alpha0)|,\n"
" where f is the dual function (default 0.1)\n"
"-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)\n"
"-wi weight: weights adjust the parameter C of different classes (see README for details)\n"
"-v n: n-fold cross validation mode\n"
"-q : quiet mode (no outputs)\n"
"col:\n"
" if 'col' is setted, training_instance_matrix is parsed in column format, otherwise is in row format\n"
);
}
// liblinear arguments
struct parameter param; // set by parse_command_line
struct problem prob; // set by read_problem
struct model *model_;
struct feature_node *x_space;
int cross_validation_flag;
int col_format_flag;
int nr_fold;
double bias;
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;
cross_validation(&prob,¶m,nr_fold,target);
if(param.solver_type == L2R_L2LOSS_SVR ||
param.solver_type == L2R_L1LOSS_SVR_DUAL ||
param.solver_type == L2R_L2LOSS_SVR_DUAL)
{
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))
);
retval = total_error/prob.l;
}
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);
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];
void (*print_func)(const char *) = print_string_matlab; // default printing to matlab display
// default values
param.solver_type = L2R_L2LOSS_SVC_DUAL;
param.C = 1;
param.eps = INF; // see setting below
param.p = 0.1;
param.nr_weight = 0;
param.weight_label = NULL;
param.weight = NULL;
cross_validation_flag = 0;
col_format_flag = 0;
bias = -1;
if(nrhs <= 1)
return 1;
if(nrhs == 4)
{
mxGetString(prhs[3], cmd, mxGetN(prhs[3])+1);
if(strcmp(cmd, "col") == 0)
col_format_flag = 1;
}
// put options in argv[]
if(nrhs > 2)
{
mxGetString(prhs[2], cmd, mxGetN(prhs[2]) + 1);
if((argv[argc] = strtok(cmd, " ")) != NULL)
while((argv[++argc] = strtok(NULL, " ")) != NULL)
;
}
// parse options
for(i=1;i<argc;i++)
{
if(argv[i][0] != '-') break;
++i;
if(i>=argc && argv[i-1][1] != 'q') // since option -q has no parameter
return 1;
switch(argv[i-1][1])
{
case 's':
param.solver_type = atoi(argv[i]);
break;
case 'c':
param.C = atof(argv[i]);
break;
case 'p':
param.p = atof(argv[i]);
break;
case 'e':
param.eps = atof(argv[i]);
break;
case 'B':
bias = atof(argv[i]);
break;
case 'v':
cross_validation_flag = 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;
case 'q':
print_func = &print_null;
i--;
break;
default:
mexPrintf("unknown option\n");
return 1;
}
}
set_print_string_function(print_func);
if(param.eps == INF)
{
switch(param.solver_type)
{
case L2R_LR:
case L2R_L2LOSS_SVC:
param.eps = 0.01;
break;
case L2R_L2LOSS_SVR:
param.eps = 0.001;
break;
case L2R_L2LOSS_SVC_DUAL:
case L2R_L1LOSS_SVC_DUAL:
case MCSVM_CS:
case L2R_LR_DUAL:
param.eps = 0.1;
break;
case L1R_L2LOSS_SVC:
case L1R_LR:
param.eps = 0.01;
break;
case L2R_L1LOSS_SVR_DUAL:
case L2R_L2LOSS_SVR_DUAL:
param.eps = 0.1;
break;
}
}
return 0;
}
static void fake_answer(mxArray *plhs[])
{
plhs[0] = mxCreateDoubleMatrix(0, 0, mxREAL);
}
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, label_vector_row_num;
double *samples, *labels;
mxArray *instance_mat_col; // instance sparse matrix in column format
prob.x = NULL;
prob.y = NULL;
x_space = NULL;
if(col_format_flag)
instance_mat_col = (mxArray *)instance_mat;
else
{
// 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_col = plhs[0];
mxDestroyArray(prhs[0]);
}
// the number of instance
prob.l = (int) mxGetN(instance_mat_col);
label_vector_row_num = (int) mxGetM(label_vec);
if(label_vector_r
评论0