Remove svm classifiers

CMakeLists.txt

@@ -126,7 +126,6 @@ SET(${PROJECT_NAME}_HEADERS
   include/hpp/fcl/collision_func_matrix.h
   include/hpp/fcl/distance.h
   include/hpp/fcl/continuous_collision.h
-  include/hpp/fcl/math/vec_nf.h
   include/hpp/fcl/math/matrix_3f.h
   include/hpp/fcl/math/matrix_3fx.h
   include/hpp/fcl/math/vec_3f.h

test/CMakeLists.txt

@@ -39,7 +39,6 @@ add_fcl_test(test_fcl_capsule_box_1 test_fcl_capsule_box_1.cpp)
 add_fcl_test(test_fcl_capsule_box_2 test_fcl_capsule_box_2.cpp)
 #add_fcl_test(test_fcl_obb test_fcl_obb.cpp)
 
-#add_fcl_test(test_fcl_global_penetration test_fcl_global_penetration.cpp libsvm/svm.cpp test_fcl_utility.cpp)
 add_fcl_test(test_fcl_bvh_models test_fcl_bvh_models.cpp test_fcl_utility.cpp)
 
 if (FCL_HAVE_OCTOMAP)

test/libsvm/svm.h

-#ifndef _LIBSVM_H
-#define _LIBSVM_H
-
-#include <cstdlib>
-
-#define LIBSVM_VERSION 314
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-  extern int libsvm_version;
-
-  struct svm_node
-  {
-	int index;
-	double value;
-  };
-
-  struct svm_problem
-  {
-	int l;
-	double *y;
-	struct svm_node **x;
-	double *W; /* instance weight */
-  };
-
-  enum { C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR };	/* svm_type */
-  enum { LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED }; /* kernel_type */
-
-  struct svm_parameter
-  {
-	int svm_type;
-	int kernel_type;
-	int degree;	/* for poly */
-	double gamma;	/* for poly/rbf/sigmoid */
-	double coef0;	/* for poly/sigmoid */
-
-	/* these are for training only */
-	double cache_size; /* in MB */
-	double eps;	/* stopping criteria */
-	double C;	/* for C_SVC, EPSILON_SVR and NU_SVR */
-	int nr_weight;		/* for C_SVC */
-	int *weight_label;	/* for C_SVC */
-	double* weight;		/* for C_SVC */
-	double nu;	/* for NU_SVC, ONE_CLASS, and NU_SVR */
-	double p;	/* for EPSILON_SVR */
-	int shrinking;	/* use the shrinking heuristics */
-	int probability; /* do probability estimates */
-  };
-
-  //
-  // svm_model
-  // 
-  struct svm_model
-  {
-	struct svm_parameter param;	/* parameter */
-	int nr_class;		/* number of classes, = 2 in regression/one class svm */
-	int l;			/* total #SV */
-	struct svm_node **SV;		/* SVs (SV[l]) */
-	double **sv_coef;	/* coefficients for SVs in decision functions (sv_coef[k-1][l]) */
-	double *rho;		/* constants in decision functions (rho[k*(k-1)/2]) */
-	double *probA;		/* pairwise probability information */
-	double *probB;
-	int *sv_indices;        /* sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set */
-
-
-	/* for classification only */
-
-	int *label;		/* label of each class (label[k]) */
-	int *nSV;		/* number of SVs for each class (nSV[k]) */
-    /* nSV[0] + nSV[1] + ... + nSV[k-1] = l */
-	/* XXX */
-	int free_sv;		/* 1 if svm_model is created by svm_load_model*/
-    /* 0 if svm_model is created by svm_train */
-  };
-
-  struct svm_model *svm_train(const struct svm_problem *prob, const struct svm_parameter *param);
-  void svm_cross_validation(const struct svm_problem *prob, const struct svm_parameter *param, int nr_fold, double *target);
-
-  int svm_save_model(const char *model_file_name, const struct svm_model *model);
-  struct svm_model *svm_load_model(const char *model_file_name);
-
-  int svm_get_svm_type(const struct svm_model *model);
-  int svm_get_nr_class(const struct svm_model *model);
-  void svm_get_labels(const struct svm_model *model, int *label);
-  void svm_get_sv_indices(const struct svm_model *model, int *sv_indices);
-  int svm_get_nr_sv(const struct svm_model *model);
-  double svm_get_svr_probability(const struct svm_model *model);
-
-  double svm_predict_values(const struct svm_model *model, const struct svm_node *x, double* dec_values);
-  double svm_predict(const struct svm_model *model, const struct svm_node *x);
-  double svm_predict_probability(const struct svm_model *model, const struct svm_node *x, double* prob_estimates);
-
-  double svm_predict_values_twoclass(const struct svm_model* model, const struct svm_node* x);
-  double svm_hyper_w_normsqr_twoclass(const struct svm_model* model);
-
-
-  double k_function(const svm_node* x, const svm_node* y, const svm_parameter& param);
-
-
-  void svm_free_model_content(struct svm_model *model_ptr);
-  void svm_free_and_destroy_model(struct svm_model **model_ptr_ptr);
-  void svm_destroy_param(struct svm_parameter *param);
-
-  const char *svm_check_parameter(const struct svm_problem *prob, const struct svm_parameter *param);
-  int svm_check_probability_model(const struct svm_model *model);
-
-  void svm_set_print_string_function(void (*print_func)(const char *));
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif /* _LIBSVM_H */

test/libsvm_classifier.h

-#ifndef FCL_TEST_LIBSVM_CLASSIFIER_H
-#define FCL_TEST_LIBSVM_CLASSIFIER_H
-
-#include <hpp/fcl/learning/classifier.h>
-#include <libsvm/svm.h>
-
-namespace fcl
-{
-
-
-template<std::size_t N>
-class LibSVMClassifier : public SVMClassifier<N>
-{
-public:
-  LibSVMClassifier()
-  {
-    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; // can change
-    param.C = 1;
-    param.eps = 1e-3;
-    param.p = 0.1;
-    param.shrinking = 1;    // use shrinking
-    param.probability = 0;
-    param.nr_weight = 0;
-    param.weight_label = NULL;
-    param.weight = NULL;
-			
-    param.nr_weight = 2;
-    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[0] = -1;
-    param.weight_label[1] = 1;
-    param.weight[0] = 1;
-    param.weight[1] = 1;
-			
-    model = NULL;
-    x_space = NULL;
-    problem.x = NULL;
-    problem.y = NULL;
-    problem.W = NULL;
-  }
-
-
-  void setCSVM() { param.svm_type = C_SVC; }
-  void setNuSVM() { param.svm_type = NU_SVC; }
-  void setC(FCL_REAL C) { param.C = C; }
-  void setNu(FCL_REAL nu) { param.nu = nu; }
-  void setLinearClassifier() { param.kernel_type = LINEAR; }
-  void setNonLinearClassifier() { param.kernel_type = RBF; }
-  void setProbability(bool use_probability) { param.probability = use_probability; }
-  virtual void setScaler(const Scaler<N>& scaler_)
-  {
-    scaler = scaler_;
-  }
-
-  void setNegativeWeight(FCL_REAL c)
-  {
-    param.weight[0] = c;
-  }
-
-  void setPositiveWeight(FCL_REAL c)
-  {
-    param.weight[1] = c;
-  }
-
-  void setEPS(FCL_REAL e)
-  {
-    param.eps = e;
-  }
-
-  void setGamma(FCL_REAL gamma)
-  {
-    param.gamma = gamma;
-  }
-
-  ~LibSVMClassifier()
-  {
-    svm_destroy_param(&param);
-    svm_free_and_destroy_model(&model);
-    delete [] x_space;
-    delete [] problem.x;
-    delete [] problem.y;
-    delete [] problem.W;
-  }
-
-  virtual void learn(const std::vector<Item<N> >& data)
-  {
-    if(data.size() == 0) return;
-
-    if(model) svm_free_and_destroy_model(&model);
-    if(param.gamma == 0) param.gamma = 1.0 / N;
-
-    problem.l = data.size();
-    if(problem.y) delete [] problem.y;
-    problem.y = new double [problem.l];
-    if(problem.x) delete [] problem.x;
-    problem.x = new svm_node* [problem.l];
-    if(problem.W) delete [] problem.W;
-    problem.W = new double [problem.l];
-    if(x_space) delete [] x_space;
-    x_space = new svm_node [(N + 1) * problem.l];
-
-    for(std::size_t i = 0; i < data.size(); ++i)
-    {
-      svm_node* cur_x_space = x_space + (N + 1) * i;
-      Vecnf<N> q_scaled = scaler.scale(data[i].q);
-      for(std::size_t j = 0; j < N; ++j)
-      {
-        cur_x_space[j].index = j + 1;
-        cur_x_space[j].value = q_scaled[j];
-      }
-      cur_x_space[N].index = -1;
-
-      problem.x[i] = cur_x_space;
-      problem.y[i] = (data[i].label ? 1 : -1);
-      problem.W[i] = data[i].w;
-    }
-
-    model = svm_train(&problem, &param);
-    hyperw_normsqr = svm_hyper_w_normsqr_twoclass(model);
-  }
-
-  virtual std::vector<PredictResult> predict(const std::vector<Vecnf<N> >& qs) const
-  {
-    std::vector<PredictResult> predict_results;
-
-    int nr_class = svm_get_nr_class(model);
-    double* prob_estimates = NULL;
-
-    svm_node* x = (svm_node*)malloc((N + 1) * sizeof(svm_node));
-    if(param.probability)
-      prob_estimates = (double*)malloc(nr_class * sizeof(double));
-
-    Vecnf<N> v;
-    for(std::size_t i = 0; i < qs.size(); ++i)
-    {
-      v = scaler.scale(qs[i]);
-      for(std::size_t j = 0; j < N; ++j)
-      {
-        x[j].index = j + 1;
-        x[j].value = v[j];
-      }
-      x[N].index = -1;
-
-      double predict_label;
-
-      if(param.probability)
-      {
-        predict_label = svm_predict_probability(model, x, prob_estimates);
-        predict_label = (predict_label > 0) ? 1 : 0;
-        predict_results.push_back(PredictResult(predict_label, *prob_estimates));
-      }
-      else
-      {
-        predict_label = svm_predict(model, x);
-        predict_label = (predict_label > 0) ? 1 : 0;
-        predict_results.push_back(PredictResult(predict_label));
-      } 
-    }
-
-    if(param.probability) free(prob_estimates);
-    free(x);
-
-    return predict_results;
-  }
-
-  virtual PredictResult predict(const Vecnf<N>& q) const
-  {
-    return (predict(std::vector<Vecnf<N> >(1, q)))[0];
-  }
-
-  void save(const std::string& filename) const
-  {
-    if(model)
-      svm_save_model(filename.c_str(), model);
-  }
-
-  virtual std::vector<Item<N> > getSupportVectors() const
-  {
-    std::vector<Item<N> > results;
-    Item<N> item;
-    for(std::size_t i = 0; i < (std::size_t)model->l; ++i)
-    {
-      for(std::size_t j = 0; j < N; ++j)
-        item.q[j] = model->SV[i][j].value;
-      item.q = scaler.unscale(item.q);
-      int id = model->sv_indices[i] - 1;
-      item.label = (problem.y[id] > 0);
-      results.push_back(item);
-    }
-
-    return results;
-  }
-
-  svm_parameter param;		
-  svm_problem problem;
-  svm_node* x_space;
-  svm_model* model;
-  double hyperw_normsqr;
-
-  Scaler<N> scaler;
-};
-
-
-}
-
-#endif

test/test_fcl_simple.cpp

@@ -19,18 +19,6 @@ static bool approx(FCL_REAL x, FCL_REAL y)
   return std::abs(x - y) < epsilon;
 }
 
-
-
-template<std::size_t N>
-double distance_Vecnf(const Vecnf<N>& a, const Vecnf<N>& b)
-{
-  double d = 0;
-  for(std::size_t i = 0; i < N; ++i)
-    d += (a[i] - b[i]) * (a[i] - b[i]);
-
-  return d;
-}
-
 BOOST_AUTO_TEST_CASE(projection_test_line)
 {
   Vec3f v1(0, 0, 0);