From be943c14aa8b00982e9ed07e1dbe8b4c9cfef7af Mon Sep 17 00:00:00 2001
From: Joseph Mirabel <jmirabel@laas.fr>
Date: Fri, 3 Jun 2016 19:29:52 +0200
Subject: [PATCH] Remove svm classifiers
---
CMakeLists.txt | 1 -
test/CMakeLists.txt | 1 -
test/libsvm/svm.cpp | 3288 --------------------------------------
test/libsvm/svm.h | 115 --
test/libsvm_classifier.h | 212 ---
test/test_fcl_simple.cpp | 12 -
6 files changed, 3629 deletions(-)
delete mode 100644 test/libsvm/svm.cpp
delete mode 100644 test/libsvm/svm.h
delete mode 100644 test/libsvm_classifier.h
diff --git a/CMakeLists.txt b/CMakeLists.txt
index cd9636ee..d0e5356c 100644
--- a/CMakeLists.txt
+++ b/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
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index f0f09c65..d03acad6 100644
--- a/test/CMakeLists.txt
+++ b/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)
diff --git a/test/libsvm/svm.cpp b/test/libsvm/svm.cpp
deleted file mode 100644
index 219a352a..00000000
--- a/test/libsvm/svm.cpp
+++ /dev/null
@@ -1,3288 +0,0 @@
-#include <math.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <ctype.h>
-#include <float.h>
-#include <string.h>
-#include <stdarg.h>
-#include <limits.h>
-#include <locale.h>
-#include <assert.h>
-#include "svm.h"
-int libsvm_version = LIBSVM_VERSION;
-typedef float Qfloat;
-typedef signed char schar;
-#ifndef min
-template <class T> static inline T min(T x,T y) { return (x<y)?x:y; }
-#endif
-#ifndef max
-template <class T> static inline T max(T x,T y) { return (x>y)?x:y; }
-#endif
-template <class T> static inline void swap(T& x, T& y) { T t=x; x=y; y=t; }
-template <class S, class T> static inline void clone(T*& dst, S* src, int n)
-{
- dst = new T[n];
- memcpy((void *)dst,(void *)src,sizeof(T)*n);
-}
-static inline double powi(double base, int times)
-{
- double tmp = base, ret = 1.0;
-
- for(int t=times; t>0; t/=2)
- {
- if(t%2==1) ret*=tmp;
- tmp = tmp * tmp;
- }
- return ret;
-}
-#define INF HUGE_VAL
-#define TAU 1e-12
-#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
-
-static void print_string_stdout(const char *s)
-{
- fputs(s,stdout);
- fflush(stdout);
-}
-static void (*svm_print_string) (const char *) = &print_string_stdout;
-#if 0
-static void info(const char *fmt,...)
-{
- char buf[BUFSIZ];
- va_list ap;
- va_start(ap,fmt);
- vsprintf(buf,fmt,ap);
- va_end(ap);
- (*svm_print_string)(buf);
-}
-#else
-static void info(const char *fmt,...) {}
-#endif
-
-//
-// Kernel Cache
-//
-// l is the number of total data items
-// size is the cache size limit in bytes
-//
-class Cache
-{
-public:
- Cache(int l,long int size);
- ~Cache();
-
- // request data [0,len)
- // return some position p where [p,len) need to be filled
- // (p >= len if nothing needs to be filled)
- int get_data(const int index, Qfloat **data, int len);
- void swap_index(int i, int j);
-private:
- int l;
- long int size;
- struct head_t
- {
- head_t *prev, *next; // a circular list
- Qfloat *data;
- int len; // data[0,len) is cached in this entry
- };
-
- head_t *head;
- head_t lru_head;
- void lru_delete(head_t *h);
- void lru_insert(head_t *h);
-};
-
-Cache::Cache(int l_,long int size_):l(l_),size(size_)
-{
- head = (head_t *)calloc(l,sizeof(head_t)); // initialized to 0
- size /= sizeof(Qfloat);
- size -= l * sizeof(head_t) / sizeof(Qfloat);
- size = max(size, 2 * (long int) l); // cache must be large enough for two columns
- lru_head.next = lru_head.prev = &lru_head;
-}
-
-Cache::~Cache()
-{
- for(head_t *h = lru_head.next; h != &lru_head; h=h->next)
- free(h->data);
- free(head);
-}
-
-void Cache::lru_delete(head_t *h)
-{
- // delete from current location
- h->prev->next = h->next;
- h->next->prev = h->prev;
-}
-
-void Cache::lru_insert(head_t *h)
-{
- // insert to last position
- h->next = &lru_head;
- h->prev = lru_head.prev;
- h->prev->next = h;
- h->next->prev = h;
-}
-
-int Cache::get_data(const int index, Qfloat **data, int len)
-{
- head_t *h = &head[index];
- if(h->len) lru_delete(h);
- int more = len - h->len;
-
- if(more > 0)
- {
- // free old space
- while(size < more)
- {
- head_t *old = lru_head.next;
- lru_delete(old);
- free(old->data);
- size += old->len;
- old->data = 0;
- old->len = 0;
- }
-
- // allocate new space
- h->data = (Qfloat *)realloc(h->data,sizeof(Qfloat)*len);
- size -= more;
- swap(h->len,len);
- }
-
- lru_insert(h);
- *data = h->data;
- return len;
-}
-
-void Cache::swap_index(int i, int j)
-{
- if(i==j) return;
-
- if(head[i].len) lru_delete(&head[i]);
- if(head[j].len) lru_delete(&head[j]);
- swap(head[i].data,head[j].data);
- swap(head[i].len,head[j].len);
- if(head[i].len) lru_insert(&head[i]);
- if(head[j].len) lru_insert(&head[j]);
-
- if(i>j) swap(i,j);
- for(head_t *h = lru_head.next; h!=&lru_head; h=h->next)
- {
- if(h->len > i)
- {
- if(h->len > j)
- swap(h->data[i],h->data[j]);
- else
- {
- // give up
- lru_delete(h);
- free(h->data);
- size += h->len;
- h->data = 0;
- h->len = 0;
- }
- }
- }
-}
-
-//
-// Kernel evaluation
-//
-// the static method k_function is for doing single kernel evaluation
-// the constructor of Kernel prepares to calculate the l*l kernel matrix
-// the member function get_Q is for getting one column from the Q Matrix
-//
-class QMatrix {
-public:
- virtual Qfloat *get_Q(int column, int len) const = 0;
- virtual double *get_QD() const = 0;
- virtual void swap_index(int i, int j) const = 0;
- virtual ~QMatrix() {}
-};
-
-class Kernel: public QMatrix {
-public:
- Kernel(int l, svm_node * const * x, const svm_parameter& param);
- virtual ~Kernel();
-
- static double k_function(const svm_node *x, const svm_node *y,
- const svm_parameter& param);
-
- virtual Qfloat *get_Q(int column, int len) const = 0;
- virtual double *get_QD() const = 0;
- virtual void swap_index(int i, int j) const // no so const...
- {
- swap(x[i],x[j]);
- if(x_square) swap(x_square[i],x_square[j]);
- }
-protected:
-
- double (Kernel::*kernel_function)(int i, int j) const;
-
-private:
- const svm_node **x;
- double *x_square;
-
- // svm_parameter
- const int kernel_type;
- const int degree;
- const double gamma;
- const double coef0;
-
- static double dot(const svm_node *px, const svm_node *py);
- double kernel_linear(int i, int j) const
- {
- return dot(x[i],x[j]);
- }
- double kernel_poly(int i, int j) const
- {
- return powi(gamma*dot(x[i],x[j])+coef0,degree);
- }
- double kernel_rbf(int i, int j) const
- {
- return exp(-gamma*(x_square[i]+x_square[j]-2*dot(x[i],x[j])));
- }
- double kernel_sigmoid(int i, int j) const
- {
- return tanh(gamma*dot(x[i],x[j])+coef0);
- }
- double kernel_precomputed(int i, int j) const
- {
- return x[i][(int)(x[j][0].value)].value;
- }
-};
-
-
-double k_function(const svm_node* x, const svm_node* y, const svm_parameter& param)
-{
- return Kernel::k_function(x, y, param);
-}
-
-Kernel::Kernel(int l, svm_node * const * x_, const svm_parameter& param)
- :kernel_type(param.kernel_type), degree(param.degree),
- gamma(param.gamma), coef0(param.coef0)
-{
- switch(kernel_type)
- {
- case LINEAR:
- kernel_function = &Kernel::kernel_linear;
- break;
- case POLY:
- kernel_function = &Kernel::kernel_poly;
- break;
- case RBF:
- kernel_function = &Kernel::kernel_rbf;
- break;
- case SIGMOID:
- kernel_function = &Kernel::kernel_sigmoid;
- break;
- case PRECOMPUTED:
- kernel_function = &Kernel::kernel_precomputed;
- break;
- }
-
- clone(x,x_,l);
-
- if(kernel_type == RBF)
- {
- x_square = new double[l];
- for(int i=0;i<l;i++)
- x_square[i] = dot(x[i],x[i]);
- }
- else
- x_square = 0;
-}
-
-Kernel::~Kernel()
-{
- delete[] x;
- delete[] x_square;
-}
-
-double Kernel::dot(const svm_node *px, const svm_node *py)
-{
- double sum = 0;
- while(px->index != -1 && py->index != -1)
- {
- if(px->index == py->index)
- {
- sum += px->value * py->value;
- ++px;
- ++py;
- }
- else
- {
- if(px->index > py->index)
- ++py;
- else
- ++px;
- }
- }
- return sum;
-}
-
-double Kernel::k_function(const svm_node *x, const svm_node *y,
- const svm_parameter& param)
-{
- switch(param.kernel_type)
- {
- case LINEAR:
- return dot(x,y);
- case POLY:
- return powi(param.gamma*dot(x,y)+param.coef0,param.degree);
- case RBF:
- {
- double sum = 0;
- while(x->index != -1 && y->index !=-1)
- {
- if(x->index == y->index)
- {
- double d = x->value - y->value;
- sum += d*d;
- ++x;
- ++y;
- }
- else
- {
- if(x->index > y->index)
- {
- sum += y->value * y->value;
- ++y;
- }
- else
- {
- sum += x->value * x->value;
- ++x;
- }
- }
- }
-
- while(x->index != -1)
- {
- sum += x->value * x->value;
- ++x;
- }
-
- while(y->index != -1)
- {
- sum += y->value * y->value;
- ++y;
- }
-
- return exp(-param.gamma*sum);
- }
- case SIGMOID:
- return tanh(param.gamma*dot(x,y)+param.coef0);
- case PRECOMPUTED: //x: test (validation), y: SV
- return x[(int)(y->value)].value;
- default:
- return 0; // Unreachable
- }
-}
-
-// An SMO algorithm in Fan et al., JMLR 6(2005), p. 1889--1918
-// Solves:
-//
-// min 0.5(\alpha^T Q \alpha) + p^T \alpha
-//
-// y^T \alpha = \delta
-// y_i = +1 or -1
-// 0 <= alpha_i <= Cp for y_i = 1
-// 0 <= alpha_i <= Cn for y_i = -1
-//
-// Given:
-//
-// Q, p, y, Cp, Cn, and an initial feasible point \alpha
-// l is the size of vectors and matrices
-// eps is the stopping tolerance
-//
-// solution will be put in \alpha, objective value will be put in obj
-//
-class Solver {
-public:
- Solver() {};
- virtual ~Solver() {};
-
- struct SolutionInfo {
- double obj;
- double rho;
- double *upper_bound;
- double r; // for Solver_NU
- };
-
- void Solve(int l, const QMatrix& Q, const double *p_, const schar *y_,
- double *alpha_, const double* C_, double eps,
- SolutionInfo* si, int shrinking);
-protected:
- int active_size;
- schar *y;
- double *G; // gradient of objective function
- enum { LOWER_BOUND, UPPER_BOUND, FREE };
- char *alpha_status; // LOWER_BOUND, UPPER_BOUND, FREE
- double *alpha;
- const QMatrix *Q;
- const double *QD;
- double eps;
- double Cp,Cn;
- double *C;
- double *p;
- int *active_set;
- double *G_bar; // gradient, if we treat free variables as 0
- int l;
- bool unshrink; // XXX
-
- double get_C(int i)
- {
- return C[i];
- }
- void update_alpha_status(int i)
- {
- if(alpha[i] >= get_C(i))
- alpha_status[i] = UPPER_BOUND;
- else if(alpha[i] <= 0)
- alpha_status[i] = LOWER_BOUND;
- else alpha_status[i] = FREE;
- }
- bool is_upper_bound(int i) { return alpha_status[i] == UPPER_BOUND; }
- bool is_lower_bound(int i) { return alpha_status[i] == LOWER_BOUND; }
- bool is_free(int i) { return alpha_status[i] == FREE; }
- void swap_index(int i, int j);
- void reconstruct_gradient();
- virtual int select_working_set(int &i, int &j);
- virtual double calculate_rho();
- virtual void do_shrinking();
-private:
- bool be_shrunk(int i, double Gmax1, double Gmax2);
-};
-
-void Solver::swap_index(int i, int j)
-{
- Q->swap_index(i,j);
- swap(y[i],y[j]);
- swap(G[i],G[j]);
- swap(alpha_status[i],alpha_status[j]);
- swap(alpha[i],alpha[j]);
- swap(p[i],p[j]);
- swap(active_set[i],active_set[j]);
- swap(G_bar[i],G_bar[j]);
- swap(C[i],C[j]);
-}
-
-void Solver::reconstruct_gradient()
-{
- // reconstruct inactive elements of G from G_bar and free variables
-
- if(active_size == l) return;
-
- int i,j;
- int nr_free = 0;
-
- for(j=active_size;j<l;j++)
- G[j] = G_bar[j] + p[j];
-
- for(j=0;j<active_size;j++)
- if(is_free(j))
- nr_free++;
-
- if(2*nr_free < active_size)
- info("\nWARNING: using -h 0 may be faster\n");
-
- if (nr_free*l > 2*active_size*(l-active_size))
- {
- for(i=active_size;i<l;i++)
- {
- const Qfloat *Q_i = Q->get_Q(i,active_size);
- for(j=0;j<active_size;j++)
- if(is_free(j))
- G[i] += alpha[j] * Q_i[j];
- }
- }
- else
- {
- for(i=0;i<active_size;i++)
- if(is_free(i))
- {
- const Qfloat *Q_i = Q->get_Q(i,l);
- double alpha_i = alpha[i];
- for(j=active_size;j<l;j++)
- G[j] += alpha_i * Q_i[j];
- }
- }
-}
-
-void Solver::Solve(int l, const QMatrix& Q, const double *p_, const schar *y_,
- double *alpha_, const double* C_, double eps,
- SolutionInfo* si, int shrinking)
-{
- this->l = l;
- this->Q = &Q;
- QD=Q.get_QD();
- clone(p, p_,l);
- clone(y, y_,l);
- clone(alpha,alpha_,l);
- clone(C,C_,l);
- this->eps = eps;
- unshrink = false;
-
- // initialize alpha_status
- {
- alpha_status = new char[l];
- for(int i=0;i<l;i++)
- update_alpha_status(i);
- }
-
- // initialize active set (for shrinking)
- {
- active_set = new int[l];
- for(int i=0;i<l;i++)
- active_set[i] = i;
- active_size = l;
- }
-
- // initialize gradient
- {
- G = new double[l];
- G_bar = new double[l];
- int i;
- for(i=0;i<l;i++)
- {
- G[i] = p[i];
- G_bar[i] = 0;
- }
- for(i=0;i<l;i++)
- if(!is_lower_bound(i))
- {
- const Qfloat *Q_i = Q.get_Q(i,l);
- double alpha_i = alpha[i];
- int j;
- for(j=0;j<l;j++)
- G[j] += alpha_i*Q_i[j];
- if(is_upper_bound(i))
- for(j=0;j<l;j++)
- G_bar[j] += get_C(i) * Q_i[j];
- }
- }
-
- // optimization step
-
- int iter = 0;
- int max_iter = max(10000000, l>INT_MAX/100 ? INT_MAX : 100*l);
- int counter = min(l,1000)+1;
-
- while(iter < max_iter)
- {
- // show progress and do shrinking
-
- if(--counter == 0)
- {
- counter = min(l,1000);
- if(shrinking) do_shrinking();
- info(".");
- }
-
- int i,j;
- if(select_working_set(i,j)!=0)
- {
- // reconstruct the whole gradient
- reconstruct_gradient();
- // reset active set size and check
- active_size = l;
- info("*");
- if(select_working_set(i,j)!=0)
- break;
- else
- counter = 1; // do shrinking next iteration
- }
-
- ++iter;
-
- // update alpha[i] and alpha[j], handle bounds carefully
-
- const Qfloat *Q_i = Q.get_Q(i,active_size);
- const Qfloat *Q_j = Q.get_Q(j,active_size);
-
- double C_i = get_C(i);
- double C_j = get_C(j);
-
- double old_alpha_i = alpha[i];
- double old_alpha_j = alpha[j];
-
- if(y[i]!=y[j])
- {
- double quad_coef = QD[i]+QD[j]+2*Q_i[j];
- if (quad_coef <= 0)
- quad_coef = TAU;
- double delta = (-G[i]-G[j])/quad_coef;
- double diff = alpha[i] - alpha[j];
- alpha[i] += delta;
- alpha[j] += delta;
-
- if(diff > 0)
- {
- if(alpha[j] < 0)
- {
- alpha[j] = 0;
- alpha[i] = diff;
- }
- }
- else
- {
- if(alpha[i] < 0)
- {
- alpha[i] = 0;
- alpha[j] = -diff;
- }
- }
- if(diff > C_i - C_j)
- {
- if(alpha[i] > C_i)
- {
- alpha[i] = C_i;
- alpha[j] = C_i - diff;
- }
- }
- else
- {
- if(alpha[j] > C_j)
- {
- alpha[j] = C_j;
- alpha[i] = C_j + diff;
- }
- }
- }
- else
- {
- double quad_coef = QD[i]+QD[j]-2*Q_i[j];
- if (quad_coef <= 0)
- quad_coef = TAU;
- double delta = (G[i]-G[j])/quad_coef;
- double sum = alpha[i] + alpha[j];
- alpha[i] -= delta;
- alpha[j] += delta;
-
- if(sum > C_i)
- {
- if(alpha[i] > C_i)
- {
- alpha[i] = C_i;
- alpha[j] = sum - C_i;
- }
- }
- else
- {
- if(alpha[j] < 0)
- {
- alpha[j] = 0;
- alpha[i] = sum;
- }
- }
- if(sum > C_j)
- {
- if(alpha[j] > C_j)
- {
- alpha[j] = C_j;
- alpha[i] = sum - C_j;
- }
- }
- else
- {
- if(alpha[i] < 0)
- {
- alpha[i] = 0;
- alpha[j] = sum;
- }
- }
- }
-
- // update G
-
- double delta_alpha_i = alpha[i] - old_alpha_i;
- double delta_alpha_j = alpha[j] - old_alpha_j;
-
- for(int k=0;k<active_size;k++)
- {
- G[k] += Q_i[k]*delta_alpha_i + Q_j[k]*delta_alpha_j;
- }
-
- // update alpha_status and G_bar
-
- {
- bool ui = is_upper_bound(i);
- bool uj = is_upper_bound(j);
- update_alpha_status(i);
- update_alpha_status(j);
- int k;
- if(ui != is_upper_bound(i))
- {
- Q_i = Q.get_Q(i,l);
- if(ui)
- for(k=0;k<l;k++)
- G_bar[k] -= C_i * Q_i[k];
- else
- for(k=0;k<l;k++)
- G_bar[k] += C_i * Q_i[k];
- }
-
- if(uj != is_upper_bound(j))
- {
- Q_j = Q.get_Q(j,l);
- if(uj)
- for(k=0;k<l;k++)
- G_bar[k] -= C_j * Q_j[k];
- else
- for(k=0;k<l;k++)
- G_bar[k] += C_j * Q_j[k];
- }
- }
- }
-
- if(iter >= max_iter)
- {
- if(active_size < l)
- {
- // reconstruct the whole gradient to calculate objective value
- reconstruct_gradient();
- active_size = l;
- info("*");
- }
- fprintf(stderr,"\nWARNING: reaching max number of iterations\n");
- }
-
- // calculate rho
-
- si->rho = calculate_rho();
-
- // calculate objective value
- {
- double v = 0;
- int i;
- for(i=0;i<l;i++)
- v += alpha[i] * (G[i] + p[i]);
-
- si->obj = v/2;
- }
-
- // put back the solution
- {
- for(int i=0;i<l;i++)
- alpha_[active_set[i]] = alpha[i];
- }
-
- // juggle everything back
- /*{
- for(int i=0;i<l;i++)
- while(active_set[i] != i)
- swap_index(i,active_set[i]);
- // or Q.swap_index(i,active_set[i]);
- }*/
-
- for(int i=0;i<l;i++)
- si->upper_bound[i] = C[i];
-
- info("\noptimization finished, #iter = %d\n",iter);
-
- delete[] p;
- delete[] y;
- delete[] C;
- delete[] alpha;
- delete[] alpha_status;
- delete[] active_set;
- delete[] G;
- delete[] G_bar;
-}
-
-// return 1 if already optimal, return 0 otherwise
-int Solver::select_working_set(int &out_i, int &out_j)
-{
- // return i,j such that
- // i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
- // j: minimizes the decrease of obj value
- // (if quadratic coefficeint <= 0, replace it with tau)
- // -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
-
- double Gmax = -INF;
- double Gmax2 = -INF;
- int Gmax_idx = -1;
- int Gmin_idx = -1;
- double obj_diff_min = INF;
-
- for(int t=0;t<active_size;t++)
- if(y[t]==+1)
- {
- if(!is_upper_bound(t))
- if(-G[t] >= Gmax)
- {
- Gmax = -G[t];
- Gmax_idx = t;
- }
- }
- else
- {
- if(!is_lower_bound(t))
- if(G[t] >= Gmax)
- {
- Gmax = G[t];
- Gmax_idx = t;
- }
- }
-
- int i = Gmax_idx;
- const Qfloat *Q_i = NULL;
- if(i != -1) // NULL Q_i not accessed: Gmax=-INF if i=-1
- Q_i = Q->get_Q(i,active_size);
-
- for(int j=0;j<active_size;j++)
- {
- if(y[j]==+1)
- {
- if (!is_lower_bound(j))
- {
- double grad_diff=Gmax+G[j];
- if (G[j] >= Gmax2)
- Gmax2 = G[j];
- if (grad_diff > 0)
- {
- double obj_diff;
- double quad_coef = QD[i]+QD[j]-2.0*y[i]*Q_i[j];
- if (quad_coef > 0)
- obj_diff = -(grad_diff*grad_diff)/quad_coef;
- else
- obj_diff = -(grad_diff*grad_diff)/TAU;
-
- if (obj_diff <= obj_diff_min)
- {
- Gmin_idx=j;
- obj_diff_min = obj_diff;
- }
- }
- }
- }
- else
- {
- if (!is_upper_bound(j))
- {
- double grad_diff= Gmax-G[j];
- if (-G[j] >= Gmax2)
- Gmax2 = -G[j];
- if (grad_diff > 0)
- {
- double obj_diff;
- double quad_coef = QD[i]+QD[j]+2.0*y[i]*Q_i[j];
- if (quad_coef > 0)
- obj_diff = -(grad_diff*grad_diff)/quad_coef;
- else
- obj_diff = -(grad_diff*grad_diff)/TAU;
-
- if (obj_diff <= obj_diff_min)
- {
- Gmin_idx=j;
- obj_diff_min = obj_diff;
- }
- }
- }
- }
- }
-
- if(Gmax+Gmax2 < eps)
- return 1;
-
- out_i = Gmax_idx;
- out_j = Gmin_idx;
- return 0;
-}
-
-bool Solver::be_shrunk(int i, double Gmax1, double Gmax2)
-{
- if(is_upper_bound(i))
- {
- if(y[i]==+1)
- return(-G[i] > Gmax1);
- else
- return(-G[i] > Gmax2);
- }
- else if(is_lower_bound(i))
- {
- if(y[i]==+1)
- return(G[i] > Gmax2);
- else
- return(G[i] > Gmax1);
- }
- else
- return(false);
-}
-
-void Solver::do_shrinking()
-{
- int i;
- double Gmax1 = -INF; // max { -y_i * grad(f)_i | i in I_up(\alpha) }
- double Gmax2 = -INF; // max { y_i * grad(f)_i | i in I_low(\alpha) }
-
- // find maximal violating pair first
- for(i=0;i<active_size;i++)
- {
- if(y[i]==+1)
- {
- if(!is_upper_bound(i))
- {
- if(-G[i] >= Gmax1)
- Gmax1 = -G[i];
- }
- if(!is_lower_bound(i))
- {
- if(G[i] >= Gmax2)
- Gmax2 = G[i];
- }
- }
- else
- {
- if(!is_upper_bound(i))
- {
- if(-G[i] >= Gmax2)
- Gmax2 = -G[i];
- }
- if(!is_lower_bound(i))
- {
- if(G[i] >= Gmax1)
- Gmax1 = G[i];
- }
- }
- }
-
- if(unshrink == false && Gmax1 + Gmax2 <= eps*10)
- {
- unshrink = true;
- reconstruct_gradient();
- active_size = l;
- info("*");
- }
-
- for(i=0;i<active_size;i++)
- if (be_shrunk(i, Gmax1, Gmax2))
- {
- active_size--;
- while (active_size > i)
- {
- if (!be_shrunk(active_size, Gmax1, Gmax2))
- {
- swap_index(i,active_size);
- break;
- }
- active_size--;
- }
- }
-}
-
-double Solver::calculate_rho()
-{
- double r;
- int nr_free = 0;
- double ub = INF, lb = -INF, sum_free = 0;
- for(int i=0;i<active_size;i++)
- {
- double yG = y[i]*G[i];
-
- if(is_upper_bound(i))
- {
- if(y[i]==-1)
- ub = min(ub,yG);
- else
- lb = max(lb,yG);
- }
- else if(is_lower_bound(i))
- {
- if(y[i]==+1)
- ub = min(ub,yG);
- else
- lb = max(lb,yG);
- }
- else
- {
- ++nr_free;
- sum_free += yG;
- }
- }
-
- if(nr_free>0)
- r = sum_free/nr_free;
- else
- r = (ub+lb)/2;
-
- return r;
-}
-
-//
-// Solver for nu-svm classification and regression
-//
-// additional constraint: e^T \alpha = constant
-//
-class Solver_NU : public Solver
-{
-public:
- Solver_NU() {}
- void Solve(int l, const QMatrix& Q, const double *p, const schar *y,
- double *alpha, double* C_, double eps,
- SolutionInfo* si, int shrinking)
- {
- this->si = si;
- Solver::Solve(l,Q,p,y,alpha,C_,eps,si,shrinking);
- }
-private:
- SolutionInfo *si;
- int select_working_set(int &i, int &j);
- double calculate_rho();
- bool be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4);
- void do_shrinking();
-};
-
-// return 1 if already optimal, return 0 otherwise
-int Solver_NU::select_working_set(int &out_i, int &out_j)
-{
- // return i,j such that y_i = y_j and
- // i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
- // j: minimizes the decrease of obj value
- // (if quadratic coefficeint <= 0, replace it with tau)
- // -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
-
- double Gmaxp = -INF;
- double Gmaxp2 = -INF;
- int Gmaxp_idx = -1;
-
- double Gmaxn = -INF;
- double Gmaxn2 = -INF;
- int Gmaxn_idx = -1;
-
- int Gmin_idx = -1;
- double obj_diff_min = INF;
-
- for(int t=0;t<active_size;t++)
- if(y[t]==+1)
- {
- if(!is_upper_bound(t))
- if(-G[t] >= Gmaxp)
- {
- Gmaxp = -G[t];
- Gmaxp_idx = t;
- }
- }
- else
- {
- if(!is_lower_bound(t))
- if(G[t] >= Gmaxn)
- {
- Gmaxn = G[t];
- Gmaxn_idx = t;
- }
- }
-
- int ip = Gmaxp_idx;
- int in = Gmaxn_idx;
- const Qfloat *Q_ip = NULL;
- const Qfloat *Q_in = NULL;
- if(ip != -1) // NULL Q_ip not accessed: Gmaxp=-INF if ip=-1
- Q_ip = Q->get_Q(ip,active_size);
- if(in != -1)
- Q_in = Q->get_Q(in,active_size);
-
- for(int j=0;j<active_size;j++)
- {
- if(y[j]==+1)
- {
- if (!is_lower_bound(j))
- {
- double grad_diff=Gmaxp+G[j];
- if (G[j] >= Gmaxp2)
- Gmaxp2 = G[j];
- if (grad_diff > 0)
- {
- double obj_diff;
- double quad_coef = QD[ip]+QD[j]-2*Q_ip[j];
- if (quad_coef > 0)
- obj_diff = -(grad_diff*grad_diff)/quad_coef;
- else
- obj_diff = -(grad_diff*grad_diff)/TAU;
-
- if (obj_diff <= obj_diff_min)
- {
- Gmin_idx=j;
- obj_diff_min = obj_diff;
- }
- }
- }
- }
- else
- {
- if (!is_upper_bound(j))
- {
- double grad_diff=Gmaxn-G[j];
- if (-G[j] >= Gmaxn2)
- Gmaxn2 = -G[j];
- if (grad_diff > 0)
- {
- double obj_diff;
- double quad_coef = QD[in]+QD[j]-2*Q_in[j];
- if (quad_coef > 0)
- obj_diff = -(grad_diff*grad_diff)/quad_coef;
- else
- obj_diff = -(grad_diff*grad_diff)/TAU;
-
- if (obj_diff <= obj_diff_min)
- {
- Gmin_idx=j;
- obj_diff_min = obj_diff;
- }
- }
- }
- }
- }
-
- if(max(Gmaxp+Gmaxp2,Gmaxn+Gmaxn2) < eps)
- return 1;
-
- if (y[Gmin_idx] == +1)
- out_i = Gmaxp_idx;
- else
- out_i = Gmaxn_idx;
- out_j = Gmin_idx;
-
- return 0;
-}
-
-bool Solver_NU::be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4)
-{
- if(is_upper_bound(i))
- {
- if(y[i]==+1)
- return(-G[i] > Gmax1);
- else
- return(-G[i] > Gmax4);
- }
- else if(is_lower_bound(i))
- {
- if(y[i]==+1)
- return(G[i] > Gmax2);
- else
- return(G[i] > Gmax3);
- }
- else
- return(false);
-}
-
-void Solver_NU::do_shrinking()
-{
- double Gmax1 = -INF; // max { -y_i * grad(f)_i | y_i = +1, i in I_up(\alpha) }
- double Gmax2 = -INF; // max { y_i * grad(f)_i | y_i = +1, i in I_low(\alpha) }
- double Gmax3 = -INF; // max { -y_i * grad(f)_i | y_i = -1, i in I_up(\alpha) }
- double Gmax4 = -INF; // max { y_i * grad(f)_i | y_i = -1, i in I_low(\alpha) }
-
- // find maximal violating pair first
- int i;
- for(i=0;i<active_size;i++)
- {
- if(!is_upper_bound(i))
- {
- if(y[i]==+1)
- {
- if(-G[i] > Gmax1) Gmax1 = -G[i];
- }
- else if(-G[i] > Gmax4) Gmax4 = -G[i];
- }
- if(!is_lower_bound(i))
- {
- if(y[i]==+1)
- {
- if(G[i] > Gmax2) Gmax2 = G[i];
- }
- else if(G[i] > Gmax3) Gmax3 = G[i];
- }
- }
-
- if(unshrink == false && max(Gmax1+Gmax2,Gmax3+Gmax4) <= eps*10)
- {
- unshrink = true;
- reconstruct_gradient();
- active_size = l;
- }
-
- for(i=0;i<active_size;i++)
- if (be_shrunk(i, Gmax1, Gmax2, Gmax3, Gmax4))
- {
- active_size--;
- while (active_size > i)
- {
- if (!be_shrunk(active_size, Gmax1, Gmax2, Gmax3, Gmax4))
- {
- swap_index(i,active_size);
- break;
- }
- active_size--;
- }
- }
-}
-
-double Solver_NU::calculate_rho()
-{
- int nr_free1 = 0,nr_free2 = 0;
- double ub1 = INF, ub2 = INF;
- double lb1 = -INF, lb2 = -INF;
- double sum_free1 = 0, sum_free2 = 0;
-
- for(int i=0;i<active_size;i++)
- {
- if(y[i]==+1)
- {
- if(is_upper_bound(i))
- lb1 = max(lb1,G[i]);
- else if(is_lower_bound(i))
- ub1 = min(ub1,G[i]);
- else
- {
- ++nr_free1;
- sum_free1 += G[i];
- }
- }
- else
- {
- if(is_upper_bound(i))
- lb2 = max(lb2,G[i]);
- else if(is_lower_bound(i))
- ub2 = min(ub2,G[i]);
- else
- {
- ++nr_free2;
- sum_free2 += G[i];
- }
- }
- }
-
- double r1,r2;
- if(nr_free1 > 0)
- r1 = sum_free1/nr_free1;
- else
- r1 = (ub1+lb1)/2;
-
- if(nr_free2 > 0)
- r2 = sum_free2/nr_free2;
- else
- r2 = (ub2+lb2)/2;
-
- si->r = (r1+r2)/2;
- return (r1-r2)/2;
-}
-
-//
-// Q matrices for various formulations
-//
-class SVC_Q: public Kernel
-{
-public:
- SVC_Q(const svm_problem& prob, const svm_parameter& param, const schar *y_)
- :Kernel(prob.l, prob.x, param)
- {
- clone(y,y_,prob.l);
- cache = new Cache(prob.l,(long int)(param.cache_size*(1<<20)));
- QD = new double[prob.l];
- for(int i=0;i<prob.l;i++)
- QD[i] = (this->*kernel_function)(i,i);
- }
-
- Qfloat *get_Q(int i, int len) const
- {
- Qfloat *data;
- int start, j;
- if((start = cache->get_data(i,&data,len)) < len)
- {
- for(j=start;j<len;j++)
- data[j] = (Qfloat)(y[i]*y[j]*(this->*kernel_function)(i,j));
- }
- return data;
- }
-
- double *get_QD() const
- {
- return QD;
- }
-
- void swap_index(int i, int j) const
- {
- cache->swap_index(i,j);
- Kernel::swap_index(i,j);
- swap(y[i],y[j]);
- swap(QD[i],QD[j]);
- }
-
- ~SVC_Q()
- {
- delete[] y;
- delete cache;
- delete[] QD;
- }
-private:
- schar *y;
- Cache *cache;
- double *QD;
-};
-
-class ONE_CLASS_Q: public Kernel
-{
-public:
- ONE_CLASS_Q(const svm_problem& prob, const svm_parameter& param)
- :Kernel(prob.l, prob.x, param)
- {
- cache = new Cache(prob.l,(long int)(param.cache_size*(1<<20)));
- QD = new double[prob.l];
- for(int i=0;i<prob.l;i++)
- QD[i] = (this->*kernel_function)(i,i);
- }
-
- Qfloat *get_Q(int i, int len) const
- {
- Qfloat *data;
- int start, j;
- if((start = cache->get_data(i,&data,len)) < len)
- {
- for(j=start;j<len;j++)
- data[j] = (Qfloat)(this->*kernel_function)(i,j);
- }
- return data;
- }
-
- double *get_QD() const
- {
- return QD;
- }
-
- void swap_index(int i, int j) const
- {
- cache->swap_index(i,j);
- Kernel::swap_index(i,j);
- swap(QD[i],QD[j]);
- }
-
- ~ONE_CLASS_Q()
- {
- delete cache;
- delete[] QD;
- }
-private:
- Cache *cache;
- double *QD;
-};
-
-class SVR_Q: public Kernel
-{
-public:
- SVR_Q(const svm_problem& prob, const svm_parameter& param)
- :Kernel(prob.l, prob.x, param)
- {
- l = prob.l;
- cache = new Cache(l,(long int)(param.cache_size*(1<<20)));
- QD = new double[2*l];
- sign = new schar[2*l];
- index = new int[2*l];
- for(int k=0;k<l;k++)
- {
- sign[k] = 1;
- sign[k+l] = -1;
- index[k] = k;
- index[k+l] = k;
- QD[k] = (this->*kernel_function)(k,k);
- QD[k+l] = QD[k];
- }
- buffer[0] = new Qfloat[2*l];
- buffer[1] = new Qfloat[2*l];
- next_buffer = 0;
- }
-
- void swap_index(int i, int j) const
- {
- swap(sign[i],sign[j]);
- swap(index[i],index[j]);
- swap(QD[i],QD[j]);
- }
-
- Qfloat *get_Q(int i, int len) const
- {
- Qfloat *data;
- int j, real_i = index[i];
- if(cache->get_data(real_i,&data,l) < l)
- {
- for(j=0;j<l;j++)
- data[j] = (Qfloat)(this->*kernel_function)(real_i,j);
- }
-
- // reorder and copy
- Qfloat *buf = buffer[next_buffer];
- next_buffer = 1 - next_buffer;
- schar si = sign[i];
- for(j=0;j<len;j++)
- buf[j] = (Qfloat) si * (Qfloat) sign[j] * data[index[j]];
- return buf;
- }
-
- double *get_QD() const
- {
- return QD;
- }
-
- ~SVR_Q()
- {
- delete cache;
- delete[] sign;
- delete[] index;
- delete[] buffer[0];
- delete[] buffer[1];
- delete[] QD;
- }
-private:
- int l;
- Cache *cache;
- schar *sign;
- int *index;
- mutable int next_buffer;
- Qfloat *buffer[2];
- double *QD;
-};
-#include <iostream>
-//
-// construct and solve various formulations
-//
-static void solve_c_svc(
- const svm_problem *prob, const svm_parameter* param,
- double *alpha, Solver::SolutionInfo* si, double Cp, double Cn)
-{
- int l = prob->l;
- double *minus_ones = new double[l];
- schar *y = new schar[l];
- double *C = new double[l];
-
- int i;
-
- for(i=0;i<l;i++)
- {
- alpha[i] = 0;
- minus_ones[i] = -1;
- if(prob->y[i] > 0)
- {
- y[i] = +1;
- C[i] = prob->W[i]*Cp;
- }
- else
- {
- y[i] = -1;
- C[i] = prob->W[i]*Cn;
- }
- //std::cout << C[i] << " ";
- }
- //std::cout << std::endl;
-
- Solver s;
- s.Solve(l, SVC_Q(*prob,*param,y), minus_ones, y,
- alpha, C, param->eps, si, param->shrinking);
-
- /*
- double sum_alpha=0;
- for(i=0;i<l;i++)
- sum_alpha += alpha[i];
- if (Cp==Cn)
- info("nu = %f\n", sum_alpha/(Cp*prob->l));
- */
-
- for(i=0;i<l;i++)
- alpha[i] *= y[i];
-
- delete[] C;
- delete[] minus_ones;
- delete[] y;
-}
-
-static void solve_nu_svc(
- const svm_problem *prob, const svm_parameter *param,
- double *alpha, Solver::SolutionInfo* si)
-{
- int i;
- int l = prob->l;
- double nu = param->nu;
-
- schar *y = new schar[l];
- double *C = new double[l];
-
- for(i=0;i<l;i++)
- {
- if(prob->y[i]>0)
- y[i] = +1;
- else
- y[i] = -1;
- C[i] = prob->W[i];
- }
-
- double nu_l = 0;
- for(i=0;i<l;i++) nu_l += nu*C[i];
- double sum_pos = nu_l/2;
- double sum_neg = nu_l/2;
-
- for(i=0;i<l;i++)
- if(y[i] == +1)
- {
- alpha[i] = min(C[i],sum_pos);
- sum_pos -= alpha[i];
- }
- else
- {
- alpha[i] = min(C[i],sum_neg);
- sum_neg -= alpha[i];
- }
-
- double *zeros = new double[l];
-
- for(i=0;i<l;i++)
- zeros[i] = 0;
-
- Solver_NU s;
- s.Solve(l, SVC_Q(*prob,*param,y), zeros, y,
- alpha, C, param->eps, si, param->shrinking);
- double r = si->r;
-
- info("C = %f\n",1/r);
-
- for(i=0;i<l;i++)
- {
- alpha[i] *= y[i]/r;
- si->upper_bound[i] /= r;
- }
-
- si->rho /= r;
- si->obj /= (r*r);
-
- delete[] C;
- delete[] y;
- delete[] zeros;
-}
-
-static void solve_one_class(
- const svm_problem *prob, const svm_parameter *param,
- double *alpha, Solver::SolutionInfo* si)
-{
- int l = prob->l;
- double *zeros = new double[l];
- schar *ones = new schar[l];
- double *C = new double[l];
- int i;
-
- double nu_l = 0;
-
- for(i=0;i<l;i++)
- {
- C[i] = prob->W[i];
- nu_l += C[i] * param->nu;
- }
-
- i = 0;
- while(nu_l > 0)
- {
- alpha[i] = min(C[i],nu_l);
- nu_l -= alpha[i];
- ++i;
- }
- for(;i<l;i++)
- alpha[i] = 0;
-
- for(i=0;i<l;i++)
- {
- zeros[i] = 0;
- ones[i] = 1;
- }
-
- Solver s;
- s.Solve(l, ONE_CLASS_Q(*prob,*param), zeros, ones,
- alpha, C, param->eps, si, param->shrinking);
-
- delete[] C;
- delete[] zeros;
- delete[] ones;
-}
-
-static void solve_epsilon_svr(
- const svm_problem *prob, const svm_parameter *param,
- double *alpha, Solver::SolutionInfo* si)
-{
- int l = prob->l;
- double *alpha2 = new double[2*l];
- double *linear_term = new double[2*l];
- double *C = new double[2*l];
- schar *y = new schar[2*l];
- int i;
-
- for(i=0;i<l;i++)
- {
- alpha2[i] = 0;
- linear_term[i] = param->p - prob->y[i];
- y[i] = 1;
- C[i] = prob->W[i]*param->C;
-
- alpha2[i+l] = 0;
- linear_term[i+l] = param->p + prob->y[i];
- y[i+l] = -1;
- C[i+l] = prob->W[i]*param->C;
- }
-
- Solver s;
- s.Solve(2*l, SVR_Q(*prob,*param), linear_term, y,
- alpha2, C, param->eps, si, param->shrinking);
- double sum_alpha = 0;
- for(i=0;i<l;i++)
- {
- alpha[i] = alpha2[i] - alpha2[i+l];
- sum_alpha += fabs(alpha[i]);
- }
- //info("nu = %f\n",sum_alpha/(param->C*l));
- delete[] alpha2;
- delete[] linear_term;
- delete[] C;
- delete[] y;
-}
-
-static void solve_nu_svr(
- const svm_problem *prob, const svm_parameter *param,
- double *alpha, Solver::SolutionInfo* si)
-{
- int l = prob->l;
- double *C = new double[2*l];
- double *alpha2 = new double[2*l];
- double *linear_term = new double[2*l];
- schar *y = new schar[2*l];
- int i;
-
- double sum = 0;
- for(i=0;i<l;i++)
- {
- C[i] = C[i+l] = prob->W[i]*param->C;
- sum += C[i] * param->nu;
- }
- sum /= 2;
-
- for(i=0;i<l;i++)
- {
- alpha2[i] = alpha2[i+l] = min(sum,C[i]);
- sum -= alpha2[i];
-
- linear_term[i] = - prob->y[i];
- y[i] = 1;
-
- linear_term[i+l] = prob->y[i];
- y[i+l] = -1;
- }
-
- Solver_NU s;
- s.Solve(2*l, SVR_Q(*prob,*param), linear_term, y,
- alpha2, C, param->eps, si, param->shrinking);
-
- info("epsilon = %f\n",-si->r);
-
- for(i=0;i<l;i++)
- alpha[i] = alpha2[i] - alpha2[i+l];
-
- delete[] alpha2;
- delete[] linear_term;
- delete[] C;
- delete[] y;
-}
-
-//
-// decision_function
-//
-struct decision_function
-{
- double *alpha;
- double rho;
-};
-
-static decision_function svm_train_one(
- const svm_problem *prob, const svm_parameter *param,
- double Cp, double Cn)
-{
- double *alpha = Malloc(double,prob->l);
- Solver::SolutionInfo si;
- switch(param->svm_type)
- {
- case C_SVC:
- si.upper_bound = Malloc(double,prob->l);
- solve_c_svc(prob,param,alpha,&si,Cp,Cn);
- break;
- case NU_SVC:
- si.upper_bound = Malloc(double,prob->l);
- solve_nu_svc(prob,param,alpha,&si);
- break;
- case ONE_CLASS:
- si.upper_bound = Malloc(double,prob->l);
- solve_one_class(prob,param,alpha,&si);
- break;
- case EPSILON_SVR:
- si.upper_bound = Malloc(double,2*prob->l);
- solve_epsilon_svr(prob,param,alpha,&si);
- break;
- case NU_SVR:
- si.upper_bound = Malloc(double,2*prob->l);
- solve_nu_svr(prob,param,alpha,&si);
- break;
- }
-
- info("obj = %f, rho = %f\n",si.obj,si.rho);
-
- // output SVs
-
- int nSV = 0;
- int nBSV = 0;
- for(int i=0;i<prob->l;i++)
- {
- if(fabs(alpha[i]) > 0)
- {
- ++nSV;
- if(prob->y[i] > 0)
- {
- if(fabs(alpha[i]) >= si.upper_bound[i])
- ++nBSV;
- }
- else
- {
- if(fabs(alpha[i]) >= si.upper_bound[i])
- ++nBSV;
- }
- }
- }
-
- free(si.upper_bound);
-
- info("nSV = %d, nBSV = %d\n",nSV,nBSV);
-
- decision_function f;
- f.alpha = alpha;
- f.rho = si.rho;
- return f;
-}
-
-// Platt's binary SVM Probablistic Output: an improvement from Lin et al.
-static void sigmoid_train(
- int l, const double *dec_values, const double *labels,
- double& A, double& B)
-{
- double prior1=0, prior0 = 0;
- int i;
-
- for (i=0;i<l;i++)
- if (labels[i] > 0) prior1+=1;
- else prior0+=1;
-
- int max_iter=100; // Maximal number of iterations
- double min_step=1e-10; // Minimal step taken in line search
- double sigma=1e-12; // For numerically strict PD of Hessian
- double eps=1e-5;
- double hiTarget=(prior1+1.0)/(prior1+2.0);
- double loTarget=1/(prior0+2.0);
- double *t=Malloc(double,l);
- double fApB,p,q,h11,h22,h21,g1,g2,det,dA,dB,gd,stepsize;
- double newA,newB,newf,d1,d2;
- int iter;
-
- // Initial Point and Initial Fun Value
- A=0.0; B=log((prior0+1.0)/(prior1+1.0));
- double fval = 0.0;
-
- for (i=0;i<l;i++)
- {
- if (labels[i]>0) t[i]=hiTarget;
- else t[i]=loTarget;
- fApB = dec_values[i]*A+B;
- if (fApB>=0)
- fval += t[i]*fApB + log(1+exp(-fApB));
- else
- fval += (t[i] - 1)*fApB +log(1+exp(fApB));
- }
- for (iter=0;iter<max_iter;iter++)
- {
- // Update Gradient and Hessian (use H' = H + sigma I)
- h11=sigma; // numerically ensures strict PD
- h22=sigma;
- h21=0.0;g1=0.0;g2=0.0;
- for (i=0;i<l;i++)
- {
- fApB = dec_values[i]*A+B;
- if (fApB >= 0)
- {
- p=exp(-fApB)/(1.0+exp(-fApB));
- q=1.0/(1.0+exp(-fApB));
- }
- else
- {
- p=1.0/(1.0+exp(fApB));
- q=exp(fApB)/(1.0+exp(fApB));
- }
- d2=p*q;
- h11+=dec_values[i]*dec_values[i]*d2;
- h22+=d2;
- h21+=dec_values[i]*d2;
- d1=t[i]-p;
- g1+=dec_values[i]*d1;
- g2+=d1;
- }
-
- // Stopping Criteria
- if (fabs(g1)<eps && fabs(g2)<eps)
- break;
-
- // Finding Newton direction: -inv(H') * g
- det=h11*h22-h21*h21;
- dA=-(h22*g1 - h21 * g2) / det;
- dB=-(-h21*g1+ h11 * g2) / det;
- gd=g1*dA+g2*dB;
-
-
- stepsize = 1; // Line Search
- while (stepsize >= min_step)
- {
- newA = A + stepsize * dA;
- newB = B + stepsize * dB;
-
- // New function value
- newf = 0.0;
- for (i=0;i<l;i++)
- {
- fApB = dec_values[i]*newA+newB;
- if (fApB >= 0)
- newf += t[i]*fApB + log(1+exp(-fApB));
- else
- newf += (t[i] - 1)*fApB +log(1+exp(fApB));
- }
- // Check sufficient decrease
- if (newf<fval+0.0001*stepsize*gd)
- {
- A=newA;B=newB;fval=newf;
- break;
- }
- else
- stepsize = stepsize / 2.0;
- }
-
- if (stepsize < min_step)
- {
- info("Line search fails in two-class probability estimates\n");
- break;
- }
- }
-
- if (iter>=max_iter)
- info("Reaching maximal iterations in two-class probability estimates\n");
- free(t);
-}
-
-static double sigmoid_predict(double decision_value, double A, double B)
-{
- double fApB = decision_value*A+B;
- // 1-p used later; avoid catastrophic cancellation
- if (fApB >= 0)
- return exp(-fApB)/(1.0+exp(-fApB));
- else
- return 1.0/(1+exp(fApB)) ;
-}
-
-// Method 2 from the multiclass_prob paper by Wu, Lin, and Weng
-static void multiclass_probability(int k, double **r, double *p)
-{
- int t,j;
- int iter = 0, max_iter=max(100,k);
- double **Q=Malloc(double *,k);
- double *Qp=Malloc(double,k);
- double pQp, eps=0.005/k;
-
- for (t=0;t<k;t++)
- {
- p[t]=1.0/k; // Valid if k = 1
- Q[t]=Malloc(double,k);
- Q[t][t]=0;
- for (j=0;j<t;j++)
- {
- Q[t][t]+=r[j][t]*r[j][t];
- Q[t][j]=Q[j][t];
- }
- for (j=t+1;j<k;j++)
- {
- Q[t][t]+=r[j][t]*r[j][t];
- Q[t][j]=-r[j][t]*r[t][j];
- }
- }
- for (iter=0;iter<max_iter;iter++)
- {
- // stopping condition, recalculate QP,pQP for numerical accuracy
- pQp=0;
- for (t=0;t<k;t++)
- {
- Qp[t]=0;
- for (j=0;j<k;j++)
- Qp[t]+=Q[t][j]*p[j];
- pQp+=p[t]*Qp[t];
- }
- double max_error=0;
- for (t=0;t<k;t++)
- {
- double error=fabs(Qp[t]-pQp);
- if (error>max_error)
- max_error=error;
- }
- if (max_error<eps) break;
-
- for (t=0;t<k;t++)
- {
- double diff=(-Qp[t]+pQp)/Q[t][t];
- p[t]+=diff;
- pQp=(pQp+diff*(diff*Q[t][t]+2*Qp[t]))/(1+diff)/(1+diff);
- for (j=0;j<k;j++)
- {
- Qp[j]=(Qp[j]+diff*Q[t][j])/(1+diff);
- p[j]/=(1+diff);
- }
- }
- }
- if (iter>=max_iter)
- info("Exceeds max_iter in multiclass_prob\n");
- for(t=0;t<k;t++) free(Q[t]);
- free(Q);
- free(Qp);
-}
-
-// Cross-validation decision values for probability estimates
-static void svm_binary_svc_probability(
- const svm_problem *prob, const svm_parameter *param,
- double Cp, double Cn, double& probA, double& probB)
-{
- int i;
- int nr_fold = 5;
- int *perm = Malloc(int,prob->l);
- double *dec_values = Malloc(double,prob->l);
-
- // random shuffle
- for(i=0;i<prob->l;i++) perm[i]=i;
- for(i=0;i<prob->l;i++)
- {
- int j = i+rand()%(prob->l-i);
- swap(perm[i],perm[j]);
- }
- for(i=0;i<nr_fold;i++)
- {
- int begin = i*prob->l/nr_fold;
- int end = (i+1)*prob->l/nr_fold;
- int j,k;
- struct svm_problem subprob;
-
- subprob.l = prob->l-(end-begin);
- subprob.x = Malloc(struct svm_node*,subprob.l);
- subprob.y = Malloc(double,subprob.l);
- subprob.W = Malloc(double,subprob.l);
-
- k=0;
- for(j=0;j<begin;j++)
- {
- subprob.x[k] = prob->x[perm[j]];
- subprob.y[k] = prob->y[perm[j]];
- subprob.W[k] = prob->W[perm[j]];
- ++k;
- }
- for(j=end;j<prob->l;j++)
- {
- subprob.x[k] = prob->x[perm[j]];
- subprob.y[k] = prob->y[perm[j]];
- subprob.W[k] = prob->W[perm[j]];
- ++k;
- }
- int p_count=0,n_count=0;
- for(j=0;j<k;j++)
- if(subprob.y[j]>0)
- p_count++;
- else
- n_count++;
-
- if(p_count==0 && n_count==0)
- for(j=begin;j<end;j++)
- dec_values[perm[j]] = 0;
- else if(p_count > 0 && n_count == 0)
- for(j=begin;j<end;j++)
- dec_values[perm[j]] = 1;
- else if(p_count == 0 && n_count > 0)
- for(j=begin;j<end;j++)
- dec_values[perm[j]] = -1;
- else
- {
- svm_parameter subparam = *param;
- subparam.probability=0;
- subparam.C=1.0;
- subparam.nr_weight=2;
- subparam.weight_label = Malloc(int,2);
- subparam.weight = Malloc(double,2);
- subparam.weight_label[0]=+1;
- subparam.weight_label[1]=-1;
- subparam.weight[0]=Cp;
- subparam.weight[1]=Cn;
- struct svm_model *submodel = svm_train(&subprob,&subparam);
- for(j=begin;j<end;j++)
- {
- svm_predict_values(submodel,prob->x[perm[j]],&(dec_values[perm[j]]));
- // ensure +1 -1 order; reason not using CV subroutine
- dec_values[perm[j]] *= submodel->label[0];
- }
- svm_free_and_destroy_model(&submodel);
- svm_destroy_param(&subparam);
- }
- free(subprob.x);
- free(subprob.y);
- free(subprob.W);
- }
- sigmoid_train(prob->l,dec_values,prob->y,probA,probB);
- free(dec_values);
- free(perm);
-}
-
-// Return parameter of a Laplace distribution
-static double svm_svr_probability(
- const svm_problem *prob, const svm_parameter *param)
-{
- int i;
- int nr_fold = 5;
- double *ymv = Malloc(double,prob->l);
- double mae = 0;
-
- svm_parameter newparam = *param;
- newparam.probability = 0;
- svm_cross_validation(prob,&newparam,nr_fold,ymv);
- for(i=0;i<prob->l;i++)
- {
- ymv[i]=prob->y[i]-ymv[i];
- mae += fabs(ymv[i]);
- }
- mae /= prob->l;
- double std=sqrt(2*mae*mae);
- int count=0;
- mae=0;
- for(i=0;i<prob->l;i++)
- if (fabs(ymv[i]) > 5*std)
- count=count+1;
- else
- mae+=fabs(ymv[i]);
- mae /= (prob->l-count);
- info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma= %g\n",mae);
- free(ymv);
- return mae;
-}
-
-
-// label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data
-// perm, length l, must be allocated before calling this subroutine
-static void svm_group_classes(const svm_problem *prob, int *nr_class_ret, int **label_ret, int **start_ret, int **count_ret, int *perm)
-{
- int l = prob->l;
- int max_nr_class = 16;
- int nr_class = 0;
- int *label = Malloc(int,max_nr_class);
- int *count = Malloc(int,max_nr_class);
- int *data_label = Malloc(int,l);
- int i;
-
- for(i=0;i<l;i++)
- {
- int this_label = (int)prob->y[i];
- int j;
- for(j=0;j<nr_class;j++)
- {
- if(this_label == label[j])
- {
- ++count[j];
- break;
- }
- }
- data_label[i] = j;
- if(j == nr_class)
- {
- if(nr_class == max_nr_class)
- {
- max_nr_class *= 2;
- label = (int *)realloc(label,max_nr_class*sizeof(int));
- count = (int *)realloc(count,max_nr_class*sizeof(int));
- }
- label[nr_class] = this_label;
- count[nr_class] = 1;
- ++nr_class;
- }
- }
-
- int *start = Malloc(int,nr_class);
- start[0] = 0;
- for(i=1;i<nr_class;i++)
- start[i] = start[i-1]+count[i-1];
- for(i=0;i<l;i++)
- {
- perm[start[data_label[i]]] = i;
- ++start[data_label[i]];
- }
- start[0] = 0;
- for(i=1;i<nr_class;i++)
- start[i] = start[i-1]+count[i-1];
-
- *nr_class_ret = nr_class;
- *label_ret = label;
- *start_ret = start;
- *count_ret = count;
- free(data_label);
-}
-
-//
-// Remove zero weighed data as libsvm and some liblinear solvers require C > 0.
-//
-static void remove_zero_weight(svm_problem *newprob, const svm_problem *prob)
-{
- int i;
- int l = 0;
- for(i=0;i<prob->l;i++)
- if(prob->W[i] > 0) l++;
- *newprob = *prob;
- newprob->l = l;
- newprob->x = Malloc(svm_node*,l);
- newprob->y = Malloc(double,l);
- newprob->W = Malloc(double,l);
-
- int j = 0;
- for(i=0;i<prob->l;i++)
- if(prob->W[i] > 0)
- {
- newprob->x[j] = prob->x[i];
- newprob->y[j] = prob->y[i];
- newprob->W[j] = prob->W[i];
- j++;
- }
-}
-
-//
-// Interface functions
-//
-svm_model *svm_train(const svm_problem *prob, const svm_parameter *param)
-{
- svm_problem newprob;
- remove_zero_weight(&newprob, prob);
- prob = &newprob;
-
- svm_model *model = Malloc(svm_model,1);
- model->param = *param;
- model->free_sv = 0; // XXX
-
- if(param->svm_type == ONE_CLASS ||
- param->svm_type == EPSILON_SVR ||
- param->svm_type == NU_SVR)
- {
- // regression or one-class-svm
- model->nr_class = 2;
- model->label = NULL;
- model->nSV = NULL;
- model->probA = NULL; model->probB = NULL;
- model->sv_coef = Malloc(double *,1);
-
- if(param->probability &&
- (param->svm_type == EPSILON_SVR ||
- param->svm_type == NU_SVR))
- {
- model->probA = Malloc(double,1);
- model->probA[0] = svm_svr_probability(prob,param);
- }
-
- decision_function f = svm_train_one(prob,param,0,0);
- model->rho = Malloc(double,1);
- model->rho[0] = f.rho;
-
- int nSV = 0;
- int i;
- for(i=0;i<prob->l;i++)
- if(fabs(f.alpha[i]) > 0) ++nSV;
- model->l = nSV;
- model->SV = Malloc(svm_node *,nSV);
- model->sv_coef[0] = Malloc(double,nSV);
- model->sv_indices = Malloc(int,nSV);
- int j = 0;
- for(i=0;i<prob->l;i++)
- if(fabs(f.alpha[i]) > 0)
- {
- model->SV[j] = prob->x[i];
- model->sv_coef[0][j] = f.alpha[i];
- model->sv_indices[j] = i+1;
- ++j;
- }
-
- free(f.alpha);
- }
- else
- {
- // classification
- int l = prob->l;
- int nr_class;
- int *label = NULL;
- int *start = NULL;
- int *count = NULL;
- int *perm = Malloc(int,l);
-
- // group training data of the same class
- svm_group_classes(prob,&nr_class,&label,&start,&count,perm);
- if(nr_class == 1)
- info("WARNING: training data in only one class. See README for details.\n");
-
- svm_node **x = Malloc(svm_node *,l);
- double *W;
- W = Malloc(double,l);
-
- int i;
- for(i=0;i<l;i++)
- {
- x[i] = prob->x[perm[i]];
- W[i] = prob->W[perm[i]];
- }
-
- // calculate weighted C
-
- double *weighted_C = Malloc(double, nr_class);
- for(i=0;i<nr_class;i++)
- weighted_C[i] = param->C;
- for(i=0;i<param->nr_weight;i++)
- {
- int j;
- for(j=0;j<nr_class;j++)
- if(param->weight_label[i] == label[j])
- break;
- if(j == nr_class)
- fprintf(stderr,"WARNING: class label %d specified in weight is not found\n", param->weight_label[i]);
- else
- weighted_C[j] *= param->weight[i];
- }
-
- // train k*(k-1)/2 models
-
- bool *nonzero = Malloc(bool,l);
- for(i=0;i<l;i++)
- nonzero[i] = false;
- decision_function *f = Malloc(decision_function,nr_class*(nr_class-1)/2);
-
- double *probA=NULL,*probB=NULL;
- if (param->probability)
- {
- probA=Malloc(double,nr_class*(nr_class-1)/2);
- probB=Malloc(double,nr_class*(nr_class-1)/2);
- }
-
- int p = 0;
- for(i=0;i<nr_class;i++)
- for(int j=i+1;j<nr_class;j++)
- {
- svm_problem sub_prob;
- int si = start[i], sj = start[j];
- int ci = count[i], cj = count[j];
- sub_prob.l = ci+cj;
- sub_prob.x = Malloc(svm_node *,sub_prob.l);
- sub_prob.y = Malloc(double,sub_prob.l);
- sub_prob.W = Malloc(double,sub_prob.l);
- int k;
- for(k=0;k<ci;k++)
- {
- sub_prob.x[k] = x[si+k];
- sub_prob.y[k] = +1;
- sub_prob.W[k] = W[si+k];
- }
- for(k=0;k<cj;k++)
- {
- sub_prob.x[ci+k] = x[sj+k];
- sub_prob.y[ci+k] = -1;
- sub_prob.W[ci+k] = W[sj+k];
- }
-
- if(param->probability)
- svm_binary_svc_probability(&sub_prob,param,weighted_C[i],weighted_C[j],probA[p],probB[p]);
-
- f[p] = svm_train_one(&sub_prob,param,weighted_C[i],weighted_C[j]);
- for(k=0;k<ci;k++)
- if(!nonzero[si+k] && fabs(f[p].alpha[k]) > 0)
- nonzero[si+k] = true;
- for(k=0;k<cj;k++)
- if(!nonzero[sj+k] && fabs(f[p].alpha[ci+k]) > 0)
- nonzero[sj+k] = true;
- free(sub_prob.x);
- free(sub_prob.y);
- free(sub_prob.W);
- ++p;
- }
-
- // build output
-
- model->nr_class = nr_class;
-
- model->label = Malloc(int,nr_class);
- for(i=0;i<nr_class;i++)
- model->label[i] = label[i];
-
- model->rho = Malloc(double,nr_class*(nr_class-1)/2);
- for(i=0;i<nr_class*(nr_class-1)/2;i++)
- model->rho[i] = f[i].rho;
-
- if(param->probability)
- {
- model->probA = Malloc(double,nr_class*(nr_class-1)/2);
- model->probB = Malloc(double,nr_class*(nr_class-1)/2);
- for(i=0;i<nr_class*(nr_class-1)/2;i++)
- {
- model->probA[i] = probA[i];
- model->probB[i] = probB[i];
- }
- }
- else
- {
- model->probA=NULL;
- model->probB=NULL;
- }
-
- int total_sv = 0;
- int *nz_count = Malloc(int,nr_class);
- model->nSV = Malloc(int,nr_class);
- for(i=0;i<nr_class;i++)
- {
- int nSV = 0;
- for(int j=0;j<count[i];j++)
- if(nonzero[start[i]+j])
- {
- ++nSV;
- ++total_sv;
- }
- model->nSV[i] = nSV;
- nz_count[i] = nSV;
- }
-
- info("Total nSV = %d\n",total_sv);
-
- model->l = total_sv;
- model->SV = Malloc(svm_node *,total_sv);
- model->sv_indices = Malloc(int,total_sv);
- p = 0;
- for(i=0;i<l;i++)
- if(nonzero[i])
- {
- model->SV[p] = x[i];
- model->sv_indices[p++] = perm[i] + 1;
- }
-
- int *nz_start = Malloc(int,nr_class);
- nz_start[0] = 0;
- for(i=1;i<nr_class;i++)
- nz_start[i] = nz_start[i-1]+nz_count[i-1];
-
- model->sv_coef = Malloc(double *,nr_class-1);
- for(i=0;i<nr_class-1;i++)
- model->sv_coef[i] = Malloc(double,total_sv);
-
- p = 0;
- for(i=0;i<nr_class;i++)
- for(int j=i+1;j<nr_class;j++)
- {
- // classifier (i,j): coefficients with
- // i are in sv_coef[j-1][nz_start[i]...],
- // j are in sv_coef[i][nz_start[j]...]
-
- int si = start[i];
- int sj = start[j];
- int ci = count[i];
- int cj = count[j];
-
- int q = nz_start[i];
- int k;
- for(k=0;k<ci;k++)
- if(nonzero[si+k])
- model->sv_coef[j-1][q++] = f[p].alpha[k];
- q = nz_start[j];
- for(k=0;k<cj;k++)
- if(nonzero[sj+k])
- model->sv_coef[i][q++] = f[p].alpha[ci+k];
- ++p;
- }
-
- free(label);
- free(probA);
- free(probB);
- free(count);
- free(perm);
- free(start);
- free(W);
- free(x);
- free(weighted_C);
- free(nonzero);
- for(i=0;i<nr_class*(nr_class-1)/2;i++)
- free(f[i].alpha);
- free(f);
- free(nz_count);
- free(nz_start);
- }
- free(newprob.x);
- free(newprob.y);
- free(newprob.W);
- return model;
-}
-
-// Stratified cross validation
-void svm_cross_validation(const svm_problem *prob, const svm_parameter *param, int nr_fold, double *target)
-{
- int i;
- int *fold_start = Malloc(int,nr_fold+1);
- int l = prob->l;
- int *perm = Malloc(int,l);
- int nr_class;
-
- // stratified cv may not give leave-one-out rate
- // Each class to l folds -> some folds may have zero elements
- if((param->svm_type == C_SVC ||
- param->svm_type == NU_SVC) && nr_fold < l)
- {
- int *start = NULL;
- int *label = NULL;
- int *count = NULL;
- svm_group_classes(prob,&nr_class,&label,&start,&count,perm);
-
- // random shuffle and then data grouped by fold using the array perm
- int *fold_count = Malloc(int,nr_fold);
- int c;
- int *index = Malloc(int,l);
- for(i=0;i<l;i++)
- index[i]=perm[i];
- for (c=0; c<nr_class; c++)
- for(i=0;i<count[c];i++)
- {
- int j = i+rand()%(count[c]-i);
- swap(index[start[c]+j],index[start[c]+i]);
- }
- for(i=0;i<nr_fold;i++)
- {
- fold_count[i] = 0;
- for (c=0; c<nr_class;c++)
- fold_count[i]+=(i+1)*count[c]/nr_fold-i*count[c]/nr_fold;
- }
- fold_start[0]=0;
- for (i=1;i<=nr_fold;i++)
- fold_start[i] = fold_start[i-1]+fold_count[i-1];
- for (c=0; c<nr_class;c++)
- for(i=0;i<nr_fold;i++)
- {
- int begin = start[c]+i*count[c]/nr_fold;
- int end = start[c]+(i+1)*count[c]/nr_fold;
- for(int j=begin;j<end;j++)
- {
- perm[fold_start[i]] = index[j];
- fold_start[i]++;
- }
- }
- fold_start[0]=0;
- for (i=1;i<=nr_fold;i++)
- fold_start[i] = fold_start[i-1]+fold_count[i-1];
- free(start);
- free(label);
- free(count);
- free(index);
- free(fold_count);
- }
- else
- {
- for(i=0;i<l;i++) perm[i]=i;
- for(i=0;i<l;i++)
- {
- int j = i+rand()%(l-i);
- swap(perm[i],perm[j]);
- }
- for(i=0;i<=nr_fold;i++)
- fold_start[i]=i*l/nr_fold;
- }
-
- for(i=0;i<nr_fold;i++)
- {
- int begin = fold_start[i];
- int end = fold_start[i+1];
- int j,k;
- struct svm_problem subprob;
-
- subprob.l = l-(end-begin);
- subprob.x = Malloc(struct svm_node*,subprob.l);
- subprob.y = Malloc(double,subprob.l);
-
- subprob.W = Malloc(double,subprob.l);
- k=0;
- for(j=0;j<begin;j++)
- {
- subprob.x[k] = prob->x[perm[j]];
- subprob.y[k] = prob->y[perm[j]];
- subprob.W[k] = prob->W[perm[j]];
- ++k;
- }
- for(j=end;j<l;j++)
- {
- subprob.x[k] = prob->x[perm[j]];
- subprob.y[k] = prob->y[perm[j]];
- subprob.W[k] = prob->W[perm[j]];
- ++k;
- }
- struct svm_model *submodel = svm_train(&subprob,param);
- if(param->probability &&
- (param->svm_type == C_SVC || param->svm_type == NU_SVC))
- {
- double *prob_estimates=Malloc(double,svm_get_nr_class(submodel));
- for(j=begin;j<end;j++)
- target[perm[j]] = svm_predict_probability(submodel,prob->x[perm[j]],prob_estimates);
- free(prob_estimates);
- }
- else
- for(j=begin;j<end;j++)
- target[perm[j]] = svm_predict(submodel,prob->x[perm[j]]);
- svm_free_and_destroy_model(&submodel);
- free(subprob.x);
- free(subprob.y);
- free(subprob.W);
- }
- free(fold_start);
- free(perm);
-}
-
-
-int svm_get_svm_type(const svm_model *model)
-{
- return model->param.svm_type;
-}
-
-int svm_get_nr_class(const svm_model *model)
-{
- return model->nr_class;
-}
-
-void svm_get_labels(const svm_model *model, int* label)
-{
- if (model->label != NULL)
- for(int i=0;i<model->nr_class;i++)
- label[i] = model->label[i];
-}
-
-void svm_get_sv_indices(const svm_model *model, int* indices)
-{
- if (model->sv_indices != NULL)
- for(int i=0;i<model->l;i++)
- indices[i] = model->sv_indices[i];
-}
-
-int svm_get_nr_sv(const svm_model *model)
-{
- return model->l;
-}
-
-double svm_get_svr_probability(const svm_model *model)
-{
- if ((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) &&
- model->probA!=NULL)
- return model->probA[0];
- else
- {
- fprintf(stderr,"Model doesn't contain information for SVR probability inference\n");
- return 0;
- }
-}
-
-double svm_hyper_w_normsqr_twoclass(const struct svm_model* model)
-{
- assert(model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC);
- int i, j;
- // int nr_class = model->nr_class;
- // assert(nr_class == 2);
- int l = model->l;
-
- double sum = 0;
- double *coef = model->sv_coef[0];
-
- for(i=0;i<l;++i)
- for(j=0;j<l;++j)
- {
- double kv = Kernel::k_function(model->SV[i],model->SV[j],model->param);
- sum += kv * coef[i] * coef[j];
- }
-
- return sum;
-}
-
-double svm_predict_values_twoclass(const struct svm_model* model, const struct svm_node* x)
-{
-
- assert(model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC);
- int i;
- // int nr_class = model->nr_class;
- // assert(nr_class == 2);
- int l = model->l;
-
- double *kvalue = Malloc(double,l);
- for(i=0;i<l;i++)
- kvalue[i] = Kernel::k_function(x,model->SV[i],model->param);
-
-
- double sum = 0;
- double *coef = model->sv_coef[0];
- for(i=0;i<l;++i)
- sum += coef[i] * kvalue[i];
- sum -= model->rho[0];
-
- free(kvalue);
-
- return sum * model->label[0];
-}
-
-double svm_predict_values(const svm_model *model, const svm_node *x, double* dec_values)
-{
- int i;
- if(model->param.svm_type == ONE_CLASS ||
- model->param.svm_type == EPSILON_SVR ||
- model->param.svm_type == NU_SVR)
- {
- double *sv_coef = model->sv_coef[0];
- double sum = 0;
- for(i=0;i<model->l;i++)
- sum += sv_coef[i] * Kernel::k_function(x,model->SV[i],model->param);
- sum -= model->rho[0];
- *dec_values = sum;
-
- if(model->param.svm_type == ONE_CLASS)
- return (sum>0)?1:-1;
- else
- return sum;
- }
- else
- {
- int nr_class = model->nr_class;
- int l = model->l;
-
- double *kvalue = Malloc(double,l);
- for(i=0;i<l;i++)
- kvalue[i] = Kernel::k_function(x,model->SV[i],model->param);
-
- int *start = Malloc(int,nr_class);
- start[0] = 0;
- for(i=1;i<nr_class;i++)
- start[i] = start[i-1]+model->nSV[i-1];
-
- int *vote = Malloc(int,nr_class);
- for(i=0;i<nr_class;i++)
- vote[i] = 0;
-
- int p=0;
- for(i=0;i<nr_class;i++)
- for(int j=i+1;j<nr_class;j++)
- {
- double sum = 0;
- int si = start[i];
- int sj = start[j];
- int ci = model->nSV[i];
- int cj = model->nSV[j];
-
- int k;
- double *coef1 = model->sv_coef[j-1];
- double *coef2 = model->sv_coef[i];
- for(k=0;k<ci;k++)
- sum += coef1[si+k] * kvalue[si+k];
- for(k=0;k<cj;k++)
- sum += coef2[sj+k] * kvalue[sj+k];
- sum -= model->rho[p];
- dec_values[p] = sum;
-
- if(dec_values[p] > 0)
- ++vote[i];
- else
- ++vote[j];
- p++;
- }
-
- int vote_max_idx = 0;
- for(i=1;i<nr_class;i++)
- if(vote[i] > vote[vote_max_idx])
- vote_max_idx = i;
-
- free(kvalue);
- free(start);
- free(vote);
- return model->label[vote_max_idx];
- }
-}
-
-double svm_predict(const svm_model *model, const svm_node *x)
-{
- int nr_class = model->nr_class;
- double *dec_values;
- if(model->param.svm_type == ONE_CLASS ||
- model->param.svm_type == EPSILON_SVR ||
- model->param.svm_type == NU_SVR)
- dec_values = Malloc(double, 1);
- else
- dec_values = Malloc(double, nr_class*(nr_class-1)/2);
- double pred_result = svm_predict_values(model, x, dec_values);
- free(dec_values);
- return pred_result;
-}
-
-double svm_predict_probability(
- const svm_model *model, const svm_node *x, double *prob_estimates)
-{
- if ((model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC) &&
- model->probA!=NULL && model->probB!=NULL)
- {
- int i;
- int nr_class = model->nr_class;
- double *dec_values = Malloc(double, nr_class*(nr_class-1)/2);
- svm_predict_values(model, x, dec_values);
-
- double min_prob=1e-7;
- double **pairwise_prob=Malloc(double *,nr_class);
- for(i=0;i<nr_class;i++)
- pairwise_prob[i]=Malloc(double,nr_class);
- int k=0;
- for(i=0;i<nr_class;i++)
- for(int j=i+1;j<nr_class;j++)
- {
- pairwise_prob[i][j]=min(max(sigmoid_predict(dec_values[k],model->probA[k],model->probB[k]),min_prob),1-min_prob);
- pairwise_prob[j][i]=1-pairwise_prob[i][j];
- k++;
- }
- multiclass_probability(nr_class,pairwise_prob,prob_estimates);
-
- int prob_max_idx = 0;
- for(i=1;i<nr_class;i++)
- if(prob_estimates[i] > prob_estimates[prob_max_idx])
- prob_max_idx = i;
- for(i=0;i<nr_class;i++)
- free(pairwise_prob[i]);
- free(dec_values);
- free(pairwise_prob);
- return model->label[prob_max_idx];
- }
- else
- return svm_predict(model, x);
-}
-
-static const char *svm_type_table[] =
- {
- "c_svc","nu_svc","one_class","epsilon_svr","nu_svr",NULL
- };
-
-static const char *kernel_type_table[]=
- {
- "linear","polynomial","rbf","sigmoid","precomputed",NULL
- };
-
-int svm_save_model(const char *model_file_name, const svm_model *model)
-{
- FILE *fp = fopen(model_file_name,"w");
- if(fp==NULL) return -1;
-
- char *old_locale = strdup(setlocale(LC_ALL, NULL));
- setlocale(LC_ALL, "C");
-
- const svm_parameter& param = model->param;
-
- fprintf(fp,"svm_type %s\n", svm_type_table[param.svm_type]);
- fprintf(fp,"kernel_type %s\n", kernel_type_table[param.kernel_type]);
-
- if(param.kernel_type == POLY)
- fprintf(fp,"degree %d\n", param.degree);
-
- if(param.kernel_type == POLY || param.kernel_type == RBF || param.kernel_type == SIGMOID)
- fprintf(fp,"gamma %g\n", param.gamma);
-
- if(param.kernel_type == POLY || param.kernel_type == SIGMOID)
- fprintf(fp,"coef0 %g\n", param.coef0);
-
- int nr_class = model->nr_class;
- int l = model->l;
- fprintf(fp, "nr_class %d\n", nr_class);
- fprintf(fp, "total_sv %d\n",l);
-
- {
- fprintf(fp, "rho");
- for(int i=0;i<nr_class*(nr_class-1)/2;i++)
- fprintf(fp," %g",model->rho[i]);
- fprintf(fp, "\n");
- }
-
- if(model->label)
- {
- fprintf(fp, "label");
- for(int i=0;i<nr_class;i++)
- fprintf(fp," %d",model->label[i]);
- fprintf(fp, "\n");
- }
-
- if(model->probA) // regression has probA only
- {
- fprintf(fp, "probA");
- for(int i=0;i<nr_class*(nr_class-1)/2;i++)
- fprintf(fp," %g",model->probA[i]);
- fprintf(fp, "\n");
- }
- if(model->probB)
- {
- fprintf(fp, "probB");
- for(int i=0;i<nr_class*(nr_class-1)/2;i++)
- fprintf(fp," %g",model->probB[i]);
- fprintf(fp, "\n");
- }
-
- if(model->nSV)
- {
- fprintf(fp, "nr_sv");
- for(int i=0;i<nr_class;i++)
- fprintf(fp," %d",model->nSV[i]);
- fprintf(fp, "\n");
- }
-
- fprintf(fp, "SV\n");
- const double * const *sv_coef = model->sv_coef;
- const svm_node * const *SV = model->SV;
-
- for(int i=0;i<l;i++)
- {
- for(int j=0;j<nr_class-1;j++)
- fprintf(fp, "%.16g ",sv_coef[j][i]);
-
- const svm_node *p = SV[i];
-
- if(param.kernel_type == PRECOMPUTED)
- fprintf(fp,"0:%d ",(int)(p->value));
- else
- while(p->index != -1)
- {
- fprintf(fp,"%d:%.8g ",p->index,p->value);
- p++;
- }
- fprintf(fp, "\n");
- }
-
- setlocale(LC_ALL, old_locale);
- free(old_locale);
-
- if (ferror(fp) != 0 || fclose(fp) != 0) return -1;
- else return 0;
-}
-
-static char *line = NULL;
-static int max_line_len;
-
-static char* readline(FILE *input)
-{
- int len;
-
- if(fgets(line,max_line_len,input) == NULL)
- return NULL;
-
- while(strrchr(line,'\n') == NULL)
- {
- max_line_len *= 2;
- line = (char *) realloc(line,max_line_len);
- len = (int) strlen(line);
- if(fgets(line+len,max_line_len-len,input) == NULL)
- break;
- }
- return line;
-}
-
-svm_model *svm_load_model(const char *model_file_name)
-{
- FILE *fp = fopen(model_file_name,"rb");
- if(fp==NULL) return NULL;
-
- char *old_locale = strdup(setlocale(LC_ALL, NULL));
- setlocale(LC_ALL, "C");
-
- // read parameters
-
- svm_model *model = Malloc(svm_model,1);
- svm_parameter& param = model->param;
- model->rho = NULL;
- model->probA = NULL;
- model->probB = NULL;
- model->label = NULL;
- model->nSV = NULL;
-
- char cmd[81];
- while(1)
- {
- fscanf(fp,"%80s",cmd);
-
- if(strcmp(cmd,"svm_type")==0)
- {
- fscanf(fp,"%80s",cmd);
- int i;
- for(i=0;svm_type_table[i];i++)
- {
- if(strcmp(svm_type_table[i],cmd)==0)
- {
- param.svm_type=i;
- break;
- }
- }
- if(svm_type_table[i] == NULL)
- {
- fprintf(stderr,"unknown svm type.\n");
-
- setlocale(LC_ALL, old_locale);
- free(old_locale);
- free(model->rho);
- free(model->label);
- free(model->nSV);
- free(model);
- return NULL;
- }
- }
- else if(strcmp(cmd,"kernel_type")==0)
- {
- fscanf(fp,"%80s",cmd);
- int i;
- for(i=0;kernel_type_table[i];i++)
- {
- if(strcmp(kernel_type_table[i],cmd)==0)
- {
- param.kernel_type=i;
- break;
- }
- }
- if(kernel_type_table[i] == NULL)
- {
- fprintf(stderr,"unknown kernel function.\n");
-
- setlocale(LC_ALL, old_locale);
- free(old_locale);
- free(model->rho);
- free(model->label);
- free(model->nSV);
- free(model);
- return NULL;
- }
- }
- else if(strcmp(cmd,"degree")==0)
- fscanf(fp,"%d",¶m.degree);
- else if(strcmp(cmd,"gamma")==0)
- fscanf(fp,"%lf",¶m.gamma);
- else if(strcmp(cmd,"coef0")==0)
- fscanf(fp,"%lf",¶m.coef0);
- else if(strcmp(cmd,"nr_class")==0)
- fscanf(fp,"%d",&model->nr_class);
- else if(strcmp(cmd,"total_sv")==0)
- fscanf(fp,"%d",&model->l);
- else if(strcmp(cmd,"rho")==0)
- {
- int n = model->nr_class * (model->nr_class-1)/2;
- model->rho = Malloc(double,n);
- for(int i=0;i<n;i++)
- fscanf(fp,"%lf",&model->rho[i]);
- }
- else if(strcmp(cmd,"label")==0)
- {
- int n = model->nr_class;
- model->label = Malloc(int,n);
- for(int i=0;i<n;i++)
- fscanf(fp,"%d",&model->label[i]);
- }
- else if(strcmp(cmd,"probA")==0)
- {
- int n = model->nr_class * (model->nr_class-1)/2;
- model->probA = Malloc(double,n);
- for(int i=0;i<n;i++)
- fscanf(fp,"%lf",&model->probA[i]);
- }
- else if(strcmp(cmd,"probB")==0)
- {
- int n = model->nr_class * (model->nr_class-1)/2;
- model->probB = Malloc(double,n);
- for(int i=0;i<n;i++)
- fscanf(fp,"%lf",&model->probB[i]);
- }
- else if(strcmp(cmd,"nr_sv")==0)
- {
- int n = model->nr_class;
- model->nSV = Malloc(int,n);
- for(int i=0;i<n;i++)
- fscanf(fp,"%d",&model->nSV[i]);
- }
- else if(strcmp(cmd,"SV")==0)
- {
- while(1)
- {
- int c = getc(fp);
- if(c==EOF || c=='\n') break;
- }
- break;
- }
- else
- {
- fprintf(stderr,"unknown text in model file: [%s]\n",cmd);
-
- setlocale(LC_ALL, old_locale);
- free(old_locale);
- free(model->rho);
- free(model->label);
- free(model->nSV);
- free(model);
- return NULL;
- }
- }
-
- // read sv_coef and SV
-
- int elements = 0;
- long pos = ftell(fp);
-
- max_line_len = 1024;
- line = Malloc(char,max_line_len);
- char *p,*endptr,*idx,*val;
-
- while(readline(fp)!=NULL)
- {
- p = strtok(line,":");
- while(1)
- {
- p = strtok(NULL,":");
- if(p == NULL)
- break;
- ++elements;
- }
- }
- elements += model->l;
-
- fseek(fp,pos,SEEK_SET);
-
- int m = model->nr_class - 1;
- int l = model->l;
- model->sv_coef = Malloc(double *,m);
- int i;
- for(i=0;i<m;i++)
- model->sv_coef[i] = Malloc(double,l);
- model->SV = Malloc(svm_node*,l);
- svm_node *x_space = NULL;
- if(l>0) x_space = Malloc(svm_node,elements);
-
- int j=0;
- for(i=0;i<l;i++)
- {
- readline(fp);
- model->SV[i] = &x_space[j];
-
- p = strtok(line, " \t");
- model->sv_coef[0][i] = strtod(p,&endptr);
- for(int k=1;k<m;k++)
- {
- p = strtok(NULL, " \t");
- model->sv_coef[k][i] = strtod(p,&endptr);
- }
-
- while(1)
- {
- idx = strtok(NULL, ":");
- val = strtok(NULL, " \t");
-
- if(val == NULL)
- break;
- x_space[j].index = (int) strtol(idx,&endptr,10);
- x_space[j].value = strtod(val,&endptr);
-
- ++j;
- }
- x_space[j++].index = -1;
- }
- free(line);
-
- setlocale(LC_ALL, old_locale);
- free(old_locale);
-
- if (ferror(fp) != 0 || fclose(fp) != 0)
- return NULL;
-
- model->free_sv = 1; // XXX
- return model;
-}
-
-void svm_free_model_content(svm_model* model_ptr)
-{
- if(model_ptr->free_sv && model_ptr->l > 0 && model_ptr->SV != NULL)
- free((void *)(model_ptr->SV[0]));
- if(model_ptr->sv_coef)
- {
- for(int i=0;i<model_ptr->nr_class-1;i++)
- free(model_ptr->sv_coef[i]);
- }
-
- free(model_ptr->SV);
- model_ptr->SV = NULL;
-
- free(model_ptr->sv_coef);
- model_ptr->sv_coef = NULL;
-
- free(model_ptr->rho);
- model_ptr->rho = NULL;
-
- free(model_ptr->label);
- model_ptr->label= NULL;
-
- free(model_ptr->probA);
- model_ptr->probA = NULL;
-
- free(model_ptr->probB);
- model_ptr->probB= NULL;
-
- free(model_ptr->nSV);
- model_ptr->nSV = NULL;
-}
-
-void svm_free_and_destroy_model(svm_model** model_ptr_ptr)
-{
- if(model_ptr_ptr != NULL && *model_ptr_ptr != NULL)
- {
- svm_free_model_content(*model_ptr_ptr);
- free(*model_ptr_ptr);
- *model_ptr_ptr = NULL;
- }
-}
-
-void svm_destroy_param(svm_parameter* param)
-{
- free(param->weight_label);
- free(param->weight);
-}
-
-const char *svm_check_parameter(const svm_problem *prob, const svm_parameter *param)
-{
- // svm_type
-
- int svm_type = param->svm_type;
- if(svm_type != C_SVC &&
- svm_type != NU_SVC &&
- svm_type != ONE_CLASS &&
- svm_type != EPSILON_SVR &&
- svm_type != NU_SVR)
- return "unknown svm type";
-
- // kernel_type, degree
-
- int kernel_type = param->kernel_type;
- if(kernel_type != LINEAR &&
- kernel_type != POLY &&
- kernel_type != RBF &&
- kernel_type != SIGMOID &&
- kernel_type != PRECOMPUTED)
- return "unknown kernel type";
-
- if(param->gamma < 0)
- return "gamma < 0";
-
- if(param->degree < 0)
- return "degree of polynomial kernel < 0";
-
- // cache_size,eps,C,nu,p,shrinking
-
- if(param->cache_size <= 0)
- return "cache_size <= 0";
-
- if(param->eps <= 0)
- return "eps <= 0";
-
- if(svm_type == C_SVC ||
- svm_type == EPSILON_SVR ||
- svm_type == NU_SVR)
- if(param->C <= 0)
- return "C <= 0";
-
- if(svm_type == NU_SVC ||
- svm_type == ONE_CLASS ||
- svm_type == NU_SVR)
- if(param->nu <= 0 || param->nu > 1)
- return "nu <= 0 or nu > 1";
-
- if(svm_type == EPSILON_SVR)
- if(param->p < 0)
- return "p < 0";
-
- if(param->shrinking != 0 &&
- param->shrinking != 1)
- return "shrinking != 0 and shrinking != 1";
-
- if(param->probability != 0 &&
- param->probability != 1)
- return "probability != 0 and probability != 1";
-
- if(param->probability == 1 &&
- svm_type == ONE_CLASS)
- return "one-class SVM probability output not supported yet";
-
-
- // check whether nu-svc is feasible
-
- if(svm_type == NU_SVC)
- {
- int l = prob->l;
- int max_nr_class = 16;
- int nr_class = 0;
- int *label = Malloc(int,max_nr_class);
- double *count = Malloc(double,max_nr_class);
-
- int i;
- for(i=0;i<l;i++)
- {
- int this_label = (int)prob->y[i];
- int j;
- for(j=0;j<nr_class;j++)
- if(this_label == label[j])
- {
- count[j] += prob->W[i];
- break;
- }
- if(j == nr_class)
- {
- if(nr_class == max_nr_class)
- {
- max_nr_class *= 2;
- label = (int *)realloc(label,max_nr_class*sizeof(int));
- count = (double *)realloc(count,max_nr_class*sizeof(double));
- }
- label[nr_class] = this_label;
- count[nr_class] = prob->W[i];
- ++nr_class;
- }
- }
-
- for(i=0;i<nr_class;i++)
- {
- double n1 = count[i];
- for(int j=i+1;j<nr_class;j++)
- {
- double n2 = count[j];
- if(param->nu*(n1+n2)/2 > min(n1,n2))
- {
- free(label);
- free(count);
- return "specified nu is infeasible";
- }
- }
- }
- free(label);
- free(count);
- }
-
- return NULL;
-}
-
-int svm_check_probability_model(const svm_model *model)
-{
- return ((model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC) &&
- model->probA!=NULL && model->probB!=NULL) ||
- ((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) &&
- model->probA!=NULL);
-}
-
-void svm_set_print_string_function(void (*print_func)(const char *))
-{
- if(print_func == NULL)
- svm_print_string = &print_string_stdout;
- else
- svm_print_string = print_func;
-}
diff --git a/test/libsvm/svm.h b/test/libsvm/svm.h
deleted file mode 100644
index 0b42202d..00000000
--- a/test/libsvm/svm.h
+++ /dev/null
@@ -1,115 +0,0 @@
-#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 */
diff --git a/test/libsvm_classifier.h b/test/libsvm_classifier.h
deleted file mode 100644
index 31e81eca..00000000
--- a/test/libsvm_classifier.h
+++ /dev/null
@@ -1,212 +0,0 @@
-#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(¶m);
- 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, ¶m);
- 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
diff --git a/test/test_fcl_simple.cpp b/test/test_fcl_simple.cpp
index 039b774b..8d88975d 100644
--- a/test/test_fcl_simple.cpp
+++ b/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);
--
GitLab