// 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(parse_command_line(nrhs, prhs, NULL)) { exit_with_help(); svm_destroy_param(¶m); 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(¶m); 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, ¶m); if(err || error_msg) { if (error_msg != NULL) mexPrintf("Error: %s\n", error_msg); svm_destroy_param(¶m); 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, ¶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(x_space); } else { exit_with_help(); fake_answer(nlhs, plhs); return; } }
// 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=NULL; // transform the input matrix to libspline format if(nrhs > 0 && nrhs < 5){ int err=0; if(!mxIsDouble(prhs[0]) || !mxIsDouble(prhs[1])) { mexPrintf("Error: label vector and instance matrix must be double\n"); fake_answer(plhs); return; } if(parse_command_line(nrhs, prhs, NULL)){ exit_with_help(); fake_answer(plhs); return; } if(!mxIsSparse(prhs[1])) err = read_problem_dense(prhs[0], prhs[1]); else{ mexPrintf("Error: test_instance_matrix must be dense\n"); fake_answer(plhs); return; } if(err || error_msg){ if (error_msg != NULL) mexPrintf("Error: %s\n", error_msg); fake_answer(plhs); delete [] x; return; } //initialize an empty model model = new additiveModel(); error_msg = matlab_matrix_to_model(model,prhs[2]); if(error_msg){ mexPrintf("Error: can't read model: %s\n", error_msg); delete [] x; delete model; fake_answer(plhs); return; } //compute predictions if(do_predict(plhs) < 0){ delete [] x; delete [] model; fake_answer(plhs); return; } delete [] x; delete model; }else{ exit_with_help(); fake_answer(plhs); return; } }