svmtrain.c

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#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include "svm.h"

#include "mex.h"
#include "svm_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))

void print_null(const char *s) {}
void print_string_matlab(const char *s) {mexPrintf(s);}

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              (multi-class classification)\n"
        "       1 -- nu-SVC             (multi-class classification)\n"
        "       2 -- one-class SVM\n"
        "       3 -- epsilon-SVR        (regression)\n"
        "       4 -- nu-SVR             (regression)\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/num_features)\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"
        "-q : quiet mode (no outputs)\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];
        void (*print_func)(const char *) = print_string_matlab; // default printing to matlab display

        // default values
        param.svm_type = C_SVC;
        param.kernel_type = RBF;
        param.degree = 3;
        param.gamma = 0;        // 1/num_features
        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)
        {
                // put options in argv[]
                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.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 'q':
                                print_func = &print_null;
                                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 -%c\n", argv[i-1][1]);
                                return 1;
                }
        }

        svm_set_print_string_function(print_func);

        return 0;
}

// read in a problem (in svmlight format)
int read_problem_dense(const mxArray *label_vec, const mxArray *instance_mat)
{
        // using size_t due to the output type of matlab functions
        size_t i, j, k, l;
        size_t elements, max_index, sc, label_vector_row_num;
        double *samples, *labels;

        prob.x = NULL;
        prob.y = NULL;
        x_space = NULL;

        labels = mxGetPr(label_vec);
        samples = mxGetPr(instance_mat);
        sc = mxGetN(instance_mat);

        elements = 0;
        // number of instances
        l = mxGetM(instance_mat);
        label_vector_row_num = mxGetM(label_vec);
        prob.l = (int)l;

        if(label_vector_row_num!=l)
        {
                mexPrintf("Length of label vector does not match # of instances.\n");
                return -1;
        }

        if(param.kernel_type == PRECOMPUTED)
                elements = l * (sc + 1);
        else
        {
                for(i = 0; i < l; i++)
                {
                        for(k = 0; k < sc; k++)
                                if(samples[k * l + i] != 0)
                                        elements++;
                        // count the '-1' element
                        elements++;
                }
        }

        prob.y = Malloc(double,l);
        prob.x = Malloc(struct svm_node *,l);
        x_space = Malloc(struct svm_node, elements);

        max_index = sc;
        j = 0;
        for(i = 0; i < 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 * l + i] != 0)
                        {
                                x_space[j].index = (int)k + 1;
                                x_space[j].value = samples[k * l + i];
                                j++;
                        }
                }
                x_space[j++].index = -1;
        }

        if(param.gamma == 0 && max_index > 0)
                param.gamma = (double)(1.0/max_index);

        if(param.kernel_type == PRECOMPUTED)
                for(i=0;i<l;i++)
                {
                        if((int)prob.x[i][0].value <= 0 || (int)prob.x[i][0].value > (int)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)
{
        mwIndex *ir, *jc, low, high, k;
        // using size_t due to the output type of matlab functions
        size_t i, j, l, elements, max_index, label_vector_row_num;
        mwSize num_samples;
        double *samples, *labels;
        mxArray *instance_mat_col; // transposed instance sparse matrix

        prob.x = NULL;
        prob.y = NULL;
        x_space = NULL;

        // 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]);
        }

        // each column is one instance
        labels = mxGetPr(label_vec);
        samples = mxGetPr(instance_mat_col);
        ir = mxGetIr(instance_mat_col);
        jc = mxGetJc(instance_mat_col);

        num_samples = mxGetNzmax(instance_mat_col);

        // number of instances
        l = mxGetN(instance_mat_col);
        label_vector_row_num = mxGetM(label_vec);
        prob.l = (int) l;

        if(label_vector_row_num!=l)
        {
                mexPrintf("Length of label vector does not match # of instances.\n");
                return -1;
        }

        elements = num_samples + l;
        max_index = mxGetM(instance_mat_col);

        prob.y = Malloc(double,l);
        prob.x = Malloc(struct svm_node *,l);
        x_space = Malloc(struct svm_node, elements);

        j = 0;
        for(i=0;i<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 = (int)ir[k] + 1;
                        x_space[j].value = samples[k];
                        j++;
                }
                x_space[j++].index = -1;
        }

        if(param.gamma == 0 && max_index > 0)
                param.gamma = (double)(1.0/max_index);

        return 0;
}

static void fake_answer(int nlhs, mxArray *plhs[])
{
        int i;
        for(i=0;i<nlhs;i++)
                plhs[i] = mxCreateDoubleMatrix(0, 0, mxREAL);
}

// Interface function of matlab
// now assume prhs[0]: label prhs[1]: features
void mexFunction( int nlhs, mxArray *plhs[],
                int nrhs, const mxArray *prhs[] )
{
        const char *error_msg;

        // fix random seed to have same results for each run
        // (for cross validation and probability estimation)
        srand(1);

        if(nlhs > 1)
        {
                exit_with_help();
                fake_answer(nlhs, plhs);
                return;
        }

        // Transform the input Matrix to libsvm format
        if(nrhs > 1 && nrhs < 4)
        {
                int err;

                if(!mxIsDouble(prhs[0]) || !mxIsDouble(prhs[1]))
                {
                        mexPrintf("Error: label vector and instance matrix must be double\n");
                        fake_answer(nlhs, plhs);
                        return;
                }

                if(mxIsSparse(prhs[0]))
                {
                        mexPrintf("Error: label vector should not be in sparse format\n");
                        fake_answer(nlhs, plhs);
                        return;
                }

                if(parse_command_line(nrhs, prhs, NULL))
                {
                        exit_with_help();
                        svm_destroy_param(&param);
                        fake_answer(nlhs, plhs);
                        return;
                }

                if(mxIsSparse(prhs[1]))
                {
                        if(param.kernel_type == PRECOMPUTED)
                        {
                                // precomputed kernel requires dense matrix, so we make one
                                mxArray *rhs[1], *lhs[1];

                                rhs[0] = mxDuplicateArray(prhs[1]);
                                if(mexCallMATLAB(1, lhs, 1, rhs, "full"))
                                {
                                        mexPrintf("Error: cannot generate a full training instance matrix\n");
                                        svm_destroy_param(&param);
                                        fake_answer(nlhs, plhs);
                                        return;
                                }
                                err = read_problem_dense(prhs[0], lhs[0]);
                                mxDestroyArray(lhs[0]);
                                mxDestroyArray(rhs[0]);
                        }
                        else
                                err = read_problem_sparse(prhs[0], prhs[1]);
                }
                else
                        err = read_problem_dense(prhs[0], prhs[1]);

                // svmtrain's original code
                error_msg = svm_check_parameter(&prob, &param);

                if(err || error_msg)
                {
                        if (error_msg != NULL)
                                mexPrintf("Error: %s\n", error_msg);
                        svm_destroy_param(&param);
                        free(prob.y);
                        free(prob.x);
                        free(x_space);
                        fake_answer(nlhs, plhs);
                        return;
                }

                if(cross_validation)
                {
                        double *ptr;
                        plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL);
                        ptr = mxGetPr(plhs[0]);
                        ptr[0] = do_cross_validation();
                }
                else
                {
                        int nr_feat = (int)mxGetN(prhs[1]);
                        const char *error_msg;
                        model = svm_train(&prob, &param);
                        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(&param);
                free(prob.y);
                free(prob.x);
                free(x_space);
        }
        else
        {
                exit_with_help();
                fake_answer(nlhs, plhs);
                return;
        }
}