Structured SVM（SSVM）
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Structured SVM（SSVM）
Dr Andrea Vedaldi 的SVM-struct MATLAB代码here
SSVM主页
通常的SVMs仅仅是二值决策，例如
svm_struct_learn_mex.c
/***********************************************************************//* *//* svm_struct_main.c *//* *//* Command line interface to the alignment learning module of the *//* Support Vector Machine. *//* *//* Author: Thorsten Joachims *//* Date: 03.07.04 *//* *//* Copyright (c) 2004 Thorsten Joachims - All rights reserved *//* *//* This software is available for non-commercial use only. It must *//* not be modified and distributed without prior permission of the *//* author. The author is not responsible for implications from the *//* use of this software. *//* *//***********************************************************************/#ifdef __cplusplusextern "C" {#endif#include "svm_light/svm_common.h"#include "svm_light/svm_learn.h"#ifdef __cplusplus}#endif# include "svm_struct/svm_struct_learn.h"# include "svm_struct/svm_struct_common.h"# include "svm_struct_api.h"#include <stdio.h>#include <string.h>#include <assert.h>void read_input_parameters (int, char **, long *, long *, STRUCT_LEARN_PARM *, LEARN_PARM *, KERNEL_PARM *, int *);void arg_split (char *string, int *argc, char ***argv) ;void init_qp_solver() ;void free_qp_solver() ;/** ------------------------------------------------------------------ ** @brief MEX entry point **/voidmexFunction (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} ; char arg [1024 + 1] ; int argc ; char ** argv ; mxArray const * sparm_array; mxArray const * patterns_array ; mxArray const * labels_array ; mxArray const * kernelFn_array ; int numExamples, ei ; mxArray * model_array; /* 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 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */ 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 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */ sparm_array = in [IN_SPARM] ; // jk remove 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 defined 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'. */ // jk change 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 () ;}/** ------------------------------------------------------------------ ** @brief Parse argument string **/voidread_input_parameters (int argc,char *argv[], long *verbosity,long *struct_verbosity, STRUCT_LEARN_PARM *struct_parm, LEARN_PARM *learn_parm, KERNEL_PARM *kernel_parm, int *alg_type){ long i ; (*alg_type)=DEFAULT_ALG_TYPE; /* SVM struct options */ (*struct_verbosity)=1; struct_parm->C=-0.01; struct_parm->slack_norm=1; struct_parm->epsilon=DEFAULT_EPS; struct_parm->custom_argc=0; struct_parm->loss_function=DEFAULT_LOSS_FCT; struct_parm->loss_type=DEFAULT_RESCALING; struct_parm->newconstretrain=100; struct_parm->ccache_size=5; struct_parm->batch_size=100; /* SVM light options */ (*verbosity)=0; strcpy (learn_parm->predfile, "trans_predictions"); strcpy (learn_parm->alphafile, ""); learn_parm->biased_hyperplane=1; learn_parm->remove_inconsistent=0; learn_parm->skip_final_opt_check=0; learn_parm->svm_maxqpsize=10; learn_parm->svm_newvarsinqp=0; learn_parm->svm_iter_to_shrink=-9999; learn_parm->maxiter=100000; learn_parm->kernel_cache_size=40; learn_parm->svm_c=99999999; /* overridden by struct_parm->C */ learn_parm->eps=0.001; /* overridden by struct_parm->epsilon */ learn_parm->transduction_posratio=-1.0; learn_parm->svm_costratio=1.0; learn_parm->svm_costratio_unlab=1.0; learn_parm->svm_unlabbound=1E-5; learn_parm->epsilon_crit=0.001; learn_parm->epsilon_a=1E-10; /* changed from 1e-15 */ learn_parm->compute_loo=0; learn_parm->rho=1.0; learn_parm->xa_depth=0; kernel_parm->kernel_type=0; kernel_parm->poly_degree=3; kernel_parm->rbf_gamma=1.0; kernel_parm->coef_lin=1; kernel_parm->coef_const=1; strcpy (kernel_parm->custom,"empty"); /* Parse -x options, delegat --x ones */ for(i=1;(i<argc) && ((argv[i])[0] == '-');i++) { switch ((argv[i])[1]) { case 'a': i++; strcpy(learn_parm->alphafile,argv[i]); break; case 'c': i++; struct_parm->C=atof(argv[i]); break; case 'p': i++; struct_parm->slack_norm=atol(argv[i]); break; case 'e': i++; struct_parm->epsilon=atof(argv[i]); break; case 'k': i++; struct_parm->newconstretrain=atol(argv[i]); break; case 'h': i++; learn_parm->svm_iter_to_shrink=atol(argv[i]); break; case '#': i++; learn_parm->maxiter=atol(argv[i]); break; case 'm': i++; learn_parm->kernel_cache_size=atol(argv[i]); break; case 'w': i++; (*alg_type)=atol(argv[i]); break; case 'o': i++; struct_parm->loss_type=atol(argv[i]); break; case 'n': i++; learn_parm->svm_newvarsinqp=atol(argv[i]); break; case 'q': i++; learn_parm->svm_maxqpsize=atol(argv[i]); break; case 'l': i++; struct_parm->loss_function=atol(argv[i]); break; case 'f': i++; struct_parm->ccache_size=atol(argv[i]); break; case 'b': i++; struct_parm->batch_size=atof(argv[i]); break; case 't': i++; kernel_parm->kernel_type=atol(argv[i]); break; case 'd': i++; kernel_parm->poly_degree=atol(argv[i]); break; case 'g': i++; kernel_parm->rbf_gamma=atof(argv[i]); break; case 's': i++; kernel_parm->coef_lin=atof(argv[i]); break; case 'r': i++; kernel_parm->coef_const=atof(argv[i]); break; case 'u': i++; strcpy(kernel_parm->custom,argv[i]); break; case 'v': i++; (*struct_verbosity)=atol(argv[i]); break; case 'y': i++; (*verbosity)=atol(argv[i]); break; case '-': strcpy(struct_parm->custom_argv[struct_parm->custom_argc++],argv[i]); i++; strcpy(struct_parm->custom_argv[struct_parm->custom_argc++],argv[i]); break; default: { char msg [1024+1] ; #ifndef WIN snprintf(msg, sizeof(msg)/sizeof(char), "Unrecognized option '%s'",argv[i]) ; #else sprintf(msg, sizeof(msg)/sizeof(char), "Unrecognized option '%s'",argv[i]) ; #endif mexErrMsgTxt(msg) ; } } } /* whatever is left is an error */ if (i < argc) { char msg [1024+1] ; #ifndef WIN snprintf(msg, sizeof(msg)/sizeof(char), "Unrecognized argument '%s'", argv[i]) ; #else sprintf(msg, sizeof(msg)/sizeof(char), "Unrecognized argument '%s'", argv[i]) ; #endif mexErrMsgTxt(msg) ; } /* Check parameter validity */ if(learn_parm->svm_iter_to_shrink == -9999) { learn_parm->svm_iter_to_shrink=100; } if((learn_parm->skip_final_opt_check) && (kernel_parm->kernel_type == LINEAR)) { mexWarnMsgTxt("It does not make sense to skip the final optimality check for linear kernels."); learn_parm->skip_final_opt_check=0; } if((learn_parm->skip_final_opt_check) && (learn_parm->remove_inconsistent)) { mexErrMsgTxt("It is necessary to do the final optimality check when removing inconsistent examples."); } if((learn_parm->svm_maxqpsize<2)) { char msg [1025] ; #ifndef WIN snprintf(msg, sizeof(msg)/sizeof(char), "Maximum size of QP-subproblems not in valid range: %ld [2..]",learn_parm->svm_maxqpsize) ; #else sprintf(msg, sizeof(msg)/sizeof(char), "Maximum size of QP-subproblems not in valid range: %ld [2..]",learn_parm->svm_maxqpsize) ; #endif mexErrMsgTxt(msg) ; } if((learn_parm->svm_maxqpsize<learn_parm->svm_newvarsinqp)) { char msg [1025] ; #ifndef WIN snprintf(msg, sizeof(msg)/sizeof(char), "Maximum size of QP-subproblems [%ld] must be larger than the number of" " new variables [%ld] entering the working set in each iteration.", learn_parm->svm_maxqpsize, learn_parm->svm_newvarsinqp) ; #else sprintf(msg, sizeof(msg)/sizeof(char), "Maximum size of QP-subproblems [%ld] must be larger than the number of" " new variables [%ld] entering the working set in each iteration.", learn_parm->svm_maxqpsize, learn_parm->svm_newvarsinqp) ; #endif mexErrMsgTxt(msg) ; } if(learn_parm->svm_iter_to_shrink<1) { char msg [1025] ; #ifndef WIN snprintf(msg, sizeof(msg)/sizeof(char), "Maximum number of iterations for shrinking not in valid range: %ld [1,..]", learn_parm->svm_iter_to_shrink); #else sprintf(msg, sizeof(msg)/sizeof(char), "Maximum number of iterations for shrinking not in valid range: %ld [1,..]", learn_parm->svm_iter_to_shrink); #endif mexErrMsgTxt(msg) ; } if(struct_parm->C<0) { mexErrMsgTxt("You have to specify a value for the parameter '-c' (C>0)!"); } if(((*alg_type) < 0) || (((*alg_type) > 5) && ((*alg_type) != 9))) { mexErrMsgTxt("Algorithm type must be either '0', '1', '2', '3', '4', or '9'!"); } if(learn_parm->transduction_posratio>1) { mexErrMsgTxt("The fraction of unlabeled examples to classify as positives must " "be less than 1.0 !!!"); } if(learn_parm->svm_costratio<=0) { mexErrMsgTxt("The COSTRATIO parameter must be greater than zero!"); } if(struct_parm->epsilon<=0) { mexErrMsgTxt("The epsilon parameter must be greater than zero!"); } if((struct_parm->ccache_size<=0) && ((*alg_type) == 4)) { mexErrMsgTxt("The cache size must be at least 1!"); } if(((struct_parm->batch_size<=0) || (struct_parm->batch_size>100)) && ((*alg_type) == 4)) { mexErrMsgTxt("The batch size must be in the interval ]0,100]!"); } if((struct_parm->slack_norm<1) || (struct_parm->slack_norm>2)) { mexErrMsgTxt("The norm of the slacks must be either 1 (L1-norm) or 2 (L2-norm)!"); } if((struct_parm->loss_type != SLACK_RESCALING) && (struct_parm->loss_type != MARGIN_RESCALING)) { mexErrMsgTxt("The loss type must be either 1 (slack rescaling) or 2 (margin rescaling)!"); } if(learn_parm->rho<0) { mexErrMsgTxt("The parameter rho for xi/alpha-estimates and leave-one-out pruning must" " be greater than zero (typically 1.0 or 2.0, see T. Joachims, Estimating the" " Generalization Performance of an SVM Efficiently, ICML, 2000.)!"); } if((learn_parm->xa_depth<0) || (learn_parm->xa_depth>100)) { mexErrMsgTxt("The parameter depth for ext. xi/alpha-estimates must be in [0..100] (zero" "for switching to the conventional xa/estimates described in T. Joachims," "Estimating the Generalization Performance of an SVM Efficiently, ICML, 2000.)") ; } parse_struct_parameters (struct_parm) ;}voidarg_split (char *string, int *argc, char ***argv){ size_t size; char *d, *p; for (size = 1, p = string; *p; p++) { if (isspace((int) *p)) { size++; } } size++; /* leave space for final NULL pointer. */ *argv = (char **) my_malloc(((size * sizeof(char *)) + (p - string) + 1)); for (*argc = 0, p = string, d = ((char *) *argv) + size*sizeof(char *); *p != 0; ) { (*argv)[*argc] = NULL; while (*p && isspace((int) *p)) p++; if (*argc == 0 && *p == '#') { break; } if (*p) { char *s = p; (*argv)[(*argc)++] = d; while (*p && !isspace((int) *p)) p++; memcpy(d, s, p-s); d += p-s; *d++ = 0; while (*p && isspace((int) *p)) p++; } }} 0 0
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