ArgSplitter& ArgSplitter::putRawArgs(const char* argv) { strcpy(rawArgs_, argv); args_ = arg_split(argv); return *this; } // putRawArgs
void mexFunction (int nout, mxArray ** out, int nin, mxArray const ** in) { SAMPLE sample; /* training sample */ LEARN_PARM learn_parm; KERNEL_PARM kernel_parm; STRUCT_LEARN_PARM struct_parm; STRUCTMODEL structmodel; int alg_type; enum {IN_ARGS=0, IN_SPARM} ; enum {OUT_W=0} ; /* SVM-light is not fully reentrant, so we need to run this patch first */ init_qp_solver() ; verbosity = 0 ; kernel_cache_statistic = 0 ; if (nin != 2) { mexErrMsgTxt("Two arguments required") ; } /* Parse ARGS ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */ char arg [1024 + 1] ; int argc ; char ** argv ; if (! uIsString(in[IN_ARGS], -1)) { mexErrMsgTxt("ARGS must be a string") ; } mxGetString(in[IN_ARGS], arg, sizeof(arg) / sizeof(char)) ; arg_split (arg, &argc, &argv) ; svm_struct_learn_api_init(argc+1, argv-1) ; read_input_parameters (argc+1,argv-1, &verbosity, &struct_verbosity, &struct_parm, &learn_parm, &kernel_parm, &alg_type ) ; if (kernel_parm.kernel_type != LINEAR && kernel_parm.kernel_type != CUSTOM) { mexErrMsgTxt ("Only LINEAR or CUSTOM kerneles are supported") ; } /* Parse SPARM ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */ mxArray const * sparm_array = in [IN_SPARM] ; mxArray const * patterns_array ; mxArray const * labels_array ; mxArray const * kernelFn_array ; int numExamples, ei ; if (! sparm_array) { mexErrMsgTxt("SPARM must be a structure") ; } struct_parm.mex = sparm_array ; patterns_array = mxGetField(sparm_array, 0, "patterns") ; if (! patterns_array || ! mxIsCell(patterns_array)) { mexErrMsgTxt("SPARM.PATTERNS must be a cell array") ; } numExamples = mxGetNumberOfElements(patterns_array) ; labels_array = mxGetField(sparm_array, 0, "labels") ; if (! labels_array || ! mxIsCell(labels_array) || ! mxGetNumberOfElements(labels_array) == numExamples) { mexErrMsgTxt("SPARM.LABELS must be a cell array " "with the same number of elements of " "SPARM.PATTERNS") ; } sample.n = numExamples ; sample.examples = (EXAMPLE *) my_malloc (sizeof(EXAMPLE) * numExamples) ; for (ei = 0 ; ei < numExamples ; ++ ei) { sample.examples[ei].x.mex = mxGetCell(patterns_array, ei) ; sample.examples[ei].y.mex = mxGetCell(labels_array, ei) ; sample.examples[ei].y.isOwner = 0 ; } if (struct_verbosity >= 1) { mexPrintf("There are %d training examples\n", numExamples) ; } kernelFn_array = mxGetField(sparm_array, 0, "kernelFn") ; if (! kernelFn_array && kernel_parm.kernel_type == CUSTOM) { mexErrMsgTxt("SPARM.KERNELFN must be define for CUSTOM kernels") ; } if (kernelFn_array) { MexKernelInfo * info ; if (mxGetClassID(kernelFn_array) != mxFUNCTION_CLASS) { mexErrMsgTxt("SPARM.KERNELFN must be a valid function handle") ; } info = (MexKernelInfo*) kernel_parm.custom ; info -> structParm = sparm_array ; info -> kernelFn = kernelFn_array ; } /* Learning ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */ switch (alg_type) { case 0: svm_learn_struct(sample,&struct_parm,&learn_parm,&kernel_parm,&structmodel,NSLACK_ALG) ; break ; case 1: svm_learn_struct(sample,&struct_parm,&learn_parm,&kernel_parm,&structmodel,NSLACK_SHRINK_ALG); break ; case 2: svm_learn_struct_joint(sample,&struct_parm,&learn_parm,&kernel_parm,&structmodel,ONESLACK_PRIMAL_ALG); break ; case 3: svm_learn_struct_joint(sample,&struct_parm,&learn_parm,&kernel_parm,&structmodel,ONESLACK_DUAL_ALG); break ; case 4: svm_learn_struct_joint(sample,&struct_parm,&learn_parm,&kernel_parm,&structmodel,ONESLACK_DUAL_CACHE_ALG); break ; case 9: svm_learn_struct_joint_custom(sample,&struct_parm,&learn_parm,&kernel_parm,&structmodel); break ; default: mexErrMsgTxt("Unknown algorithm type") ; } /* Write output ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */ /* Warning: The model contains references to the original data 'docs'. If you want to free the original data, and only keep the model, you have to make a deep copy of 'model'. */ mxArray * model_array = newMxArrayEncapsulatingSmodel (&structmodel) ; out[OUT_W] = mxDuplicateArray (model_array) ; destroyMxArrayEncapsulatingSmodel (model_array) ; free_struct_sample (sample) ; free_struct_model (structmodel) ; svm_struct_learn_api_exit () ; free_qp_solver () ; }