commit fa704649d58d84bd4d9ee67d139ec87a0c89b5db
Author: Mathieu Bridon <bochecha@xxxxxxxxxxxxxxxxx>
Date: Tue Aug 21 13:06:14 2012 +0800
Initial package for Fedora
This package has been submitted for review on Tue Aug 21 2012:
https://bugzilla.redhat.com/show_bug.cgi?id=849829#c0
Algorithm-SVM-0.13-Port-to-libsvm-3.0.patch | 67 +
Algorithm-SVM-0.13-Unbundle-libsvm.patch | 3199 +++++++++++++++++++++++++++
perl-Algorithm-SVM.spec | 66 +
3 files changed, 3332 insertions(+), 0 deletions(-)
---
diff --git a/Algorithm-SVM-0.13-Port-to-libsvm-3.0.patch b/Algorithm-SVM-0.13-Port-to-libsvm-3.0.patch
new file mode 100644
index 0000000..2a4f0c7
--- /dev/null
+++ b/Algorithm-SVM-0.13-Port-to-libsvm-3.0.patch
@@ -0,0 +1,67 @@
+From c26aa307a3bfa74328e42061437c5467826f7ed3 Mon Sep 17 00:00:00 2001
+From: Mathieu Bridon <bochecha@xxxxxxxxxxxxxxxxx>
+Date: Tue, 21 Aug 2012 12:14:18 +0800
+Subject: [PATCH 2/2] Port to libsvm >= 3.0
+
+The libsvm README file states the following:
+
+- Function: void svm_free_and_destroy_model(struct svm_model **model_ptr_ptr);
+
+ This function frees the memory used by a model and destroys the model
+ structure. It is equivalent to svm_destroy_model, which
+ is deprecated after version 3.0.
+---
+ bindings.cpp | 10 +++++-----
+ 1 files changed, 5 insertions(+), 5 deletions(-)
+
+diff --git a/bindings.cpp b/bindings.cpp
+index 70cb3ab..af0b29f 100644
+--- a/bindings.cpp
++++ b/bindings.cpp
+@@ -166,7 +166,7 @@ int SVM::train(int retrain) {
+
+ // Free any old model we have.
+ if(model != NULL) {
+- svm_destroy_model(model);
++ svm_free_and_destroy_model(&model);
+ model = NULL;
+ }
+
+@@ -282,7 +282,7 @@ int SVM::loadModel(char *filename) {
+ }
+
+ if(model != NULL) {
+- svm_destroy_model(model);
++ svm_free_and_destroy_model(&model);
+ model = NULL;
+ }
+
+@@ -357,7 +357,7 @@ double SVM::crossValidate(int nfolds) {
+ sumyy += y*y;
+ sumvy += v*y;
+ }
+- svm_destroy_model(submodel);
++ svm_free_and_destroy_model(&submodel);
+ // cout << "Mean squared error = %g\n", error/(end-begin));
+ total_error += error;
+ } else {
+@@ -368,7 +368,7 @@ double SVM::crossValidate(int nfolds) {
+ double v = svm_predict(submodel,prob->x[j]);
+ if(v == prob->y[j]) ++correct;
+ }
+- svm_destroy_model(submodel);
++ svm_free_and_destroy_model(&submodel);
+ //cout << "Accuracy = " << 100.0*correct/(end-begin) << " (" <<
+ //correct << "/" << (end-begin) << endl;
+ total_correct += correct;
+@@ -423,6 +423,6 @@ int SVM::checkProbabilityModel() {
+
+ SVM::~SVM() {
+ if(x_space!=NULL) { free_x_space(); }
+- if(model != NULL) { svm_destroy_model(model); model=NULL; }
++ if(model != NULL) { svm_free_and_destroy_model(&model); model=NULL; }
+ if(prob != NULL) { free(prob); prob=NULL; }
+ }
+--
+1.7.1
+
diff --git a/Algorithm-SVM-0.13-Unbundle-libsvm.patch b/Algorithm-SVM-0.13-Unbundle-libsvm.patch
new file mode 100644
index 0000000..fda9325
--- /dev/null
+++ b/Algorithm-SVM-0.13-Unbundle-libsvm.patch
@@ -0,0 +1,3199 @@
+From 3076e501680b950159daaa4d7cbbc032da1fe743 Mon Sep 17 00:00:00 2001
+From: Mathieu Bridon <bochecha@xxxxxxxxxxxxxxxxx>
+Date: Tue, 21 Aug 2012 11:00:01 +0800
+Subject: [PATCH 1/2] Unbundle libsvm
+
+This commit allows building Algorithm::SVM against the system libsvm
+version.
+---
+ MANIFEST | 2 -
+ Makefile.PL | 4 +-
+ SVM.xs | 2 +-
+ bindings.h | 4 +-
+ libsvm.cpp | 3040 -----------------------------------------------------------
+ libsvm.h | 70 --
+ 6 files changed, 6 insertions(+), 3116 deletions(-)
+ delete mode 100644 libsvm.cpp
+ delete mode 100644 libsvm.h
+
+diff --git a/MANIFEST b/MANIFEST
+index 3417372..9fd9c82 100644
+--- a/MANIFEST
++++ b/MANIFEST
+@@ -9,7 +9,5 @@ bindings.h
+ lib/Algorithm/SVM.pm
+ lib/Algorithm/SVM/DataSet.pm
+ sample.model
+-libsvm.cpp
+-libsvm.h
+ test.pl
+ typemap
+diff --git a/Makefile.PL b/Makefile.PL
+index bf732b5..ecf843c 100644
+--- a/Makefile.PL
++++ b/Makefile.PL
+@@ -15,9 +15,9 @@ WriteMakefile('NAME' => 'Algorithm::SVM',
+ (ABSTRACT_FROM => 'lib/Algorithm/SVM.pm',
+ AUTHOR => 'Matthew Laird <matt@xxxxxxxxxxxxxxxxxxx>') : ()),
+ 'OPTIMIZE' => '-O3', # segfaults with gcc 2.96 if lower (?)
+- 'LIBS' => '-lm',
++ 'LIBS' => '-lm -lsvm',
+ 'CC' => $CC,
+ 'LD' => '$(CC)',
+- 'OBJECT' => 'SVM.o libsvm.o bindings.o',
++ 'OBJECT' => 'SVM.o bindings.o',
+ 'XSOPT' => '-C++ -noprototypes',
+ %args);
+diff --git a/SVM.xs b/SVM.xs
+index 4db5e4a..cf4f11d 100644
+--- a/SVM.xs
++++ b/SVM.xs
+@@ -14,7 +14,7 @@ extern "C" {
+ #endif
+
+ #include "bindings.h"
+-#include "libsvm.h"
++#include <libsvm/svm.h>
+
+ DataSet *_new_dataset(double l) {
+
+diff --git a/bindings.h b/bindings.h
+index 3fecebe..0bbe5ed 100644
+--- a/bindings.h
++++ b/bindings.h
+@@ -7,7 +7,9 @@ using namespace std;
+ #include <map>
+ #include <assert.h>
+
+-#include "libsvm.h"
++#include <stdlib.h>
++#include <string.h>
++#include <libsvm/svm.h>
+
+ class DataSet {
+ friend class SVM;
+diff --git a/libsvm.cpp b/libsvm.cpp
+deleted file mode 100644
+index dd318d7..0000000
+--- a/libsvm.cpp
++++ /dev/null
+@@ -1,3040 +0,0 @@
+-#include <math.h>
+-#include <stdio.h>
+-#include <stdlib.h>
+-#include <ctype.h>
+-#include <float.h>
+-#include <string.h>
+-#include <stdarg.h>
+-#include "libsvm.h"
+-typedef float Qfloat;
+-typedef signed char schar;
+-#ifndef min
+-template <class T> inline T min(T x,T y) { return (x<y)?x:y; }
+-#endif
+-#ifndef max
+-template <class T> inline T max(T x,T y) { return (x>y)?x:y; }
+-#endif
+-template <class T> inline void swap(T& x, T& y) { T t=x; x=y; y=t; }
+-template <class S, class T> inline void clone(T*& dst, S* src, int n)
+-{
+- dst = new T[n];
+- memcpy((void *)dst,(void *)src,sizeof(T)*n);
+-}
+-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))
+-#if 1
+-void info(const char *fmt,...)
+-{
+- va_list ap;
+- va_start(ap,fmt);
+- vprintf(fmt,ap);
+- va_end(ap);
+-}
+-void info_flush()
+-{
+- fflush(stdout);
+-}
+-#else
+-void info(char *fmt,...) {}
+-void info_flush() {}
+-#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); // future_option
+-private:
+- int l;
+- long int size;
+- struct head_t
+- {
+- head_t *prev, *next; // a cicular 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 Qfloat *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 Qfloat *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;
+- }
+-};
+-
+-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_p;
+- double upper_bound_n;
+- double r; // for Solver_NU
+- };
+-
+- void Solve(int l, const QMatrix& Q, const double *p_, const schar *y_,
+- double *alpha_, double Cp, double Cn, 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 Qfloat *QD;
+- double eps;
+- double Cp,Cn;
+- double *p;
+- int *active_set;
+- double *G_bar; // gradient, if we treat free variables as 0
+- int l;
+- bool unshrinked; // XXX
+-
+- double get_C(int i)
+- {
+- return (y[i] > 0)? Cp : Cn;
+- }
+- 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_shrunken(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]);
+-}
+-
+-void Solver::reconstruct_gradient()
+-{
+- // reconstruct inactive elements of G from G_bar and free variables
+-
+- if(active_size == l) return;
+-
+- int i;
+- for(i=active_size;i<l;i++)
+- G[i] = G_bar[i] + p[i];
+-
+- 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(int 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_, double Cp, double Cn, 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);
+- this->Cp = Cp;
+- this->Cn = Cn;
+- this->eps = eps;
+- unshrinked = 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 counter = min(l,1000)+1;
+-
+- while(1)
+- {
+- // show progress and do shrinking
+-
+- if(--counter == 0)
+- {
+- counter = min(l,1000);
+- if(shrinking) do_shrinking();
+- info("."); info_flush();
+- }
+-
+- 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("*"); info_flush();
+- 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 = Q_i[i]+Q_j[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 = Q_i[i]+Q_j[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];
+- }
+- }
+- }
+-
+- // 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]);
+- }*/
+-
+- si->upper_bound_p = Cp;
+- si->upper_bound_n = Cn;
+-
+- info("\noptimization finished, #iter = %d\n",iter);
+-
+- delete[] p;
+- delete[] y;
+- 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=Q_i[i]+QD[j]-2*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=Q_i[i]+QD[j]+2*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_shrunken(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];
+- }
+- }
+- }
+-
+- // shrink
+-
+- for(i=0;i<active_size;i++)
+- if (be_shrunken(i, Gmax1, Gmax2))
+- {
+- active_size--;
+- while (active_size > i)
+- {
+- if (!be_shrunken(active_size, Gmax1, Gmax2))
+- {
+- swap_index(i,active_size);
+- break;
+- }
+- active_size--;
+- }
+- }
+-
+- // unshrink, check all variables again before final iterations
+-
+- if(unshrinked || Gmax1 + Gmax2 > eps*10) return;
+-
+- unshrinked = true;
+- reconstruct_gradient();
+-
+- for(i=l-1;i>=active_size;i--)
+- if (!be_shrunken(i, Gmax1, Gmax2))
+- {
+- while (active_size < i)
+- {
+- if (be_shrunken(active_size, Gmax1, Gmax2))
+- {
+- swap_index(i,active_size);
+- break;
+- }
+- active_size++;
+- }
+- 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 Cp, double Cn, double eps,
+- SolutionInfo* si, int shrinking)
+- {
+- this->si = si;
+- Solver::Solve(l,Q,p,y,alpha,Cp,Cn,eps,si,shrinking);
+- }
+-private:
+- SolutionInfo *si;
+- int select_working_set(int &i, int &j);
+- double calculate_rho();
+- bool be_shrunken(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 = Q_ip[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 = Q_in[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_shrunken(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];
+- }
+- }
+-
+- // shrinking
+-
+- for(i=0;i<active_size;i++)
+- if (be_shrunken(i, Gmax1, Gmax2, Gmax3, Gmax4))
+- {
+- active_size--;
+- while (active_size > i)
+- {
+- if (!be_shrunken(active_size, Gmax1, Gmax2, Gmax3, Gmax4))
+- {
+- swap_index(i,active_size);
+- break;
+- }
+- active_size--;
+- }
+- }
+-
+- // unshrink, check all variables again before final iterations
+-
+- if(unshrinked || max(Gmax1+Gmax2,Gmax3+Gmax4) > eps*10) return;
+-
+- unshrinked = true;
+- reconstruct_gradient();
+-
+- for(i=l-1;i>=active_size;i--)
+- if (!be_shrunken(i, Gmax1, Gmax2, Gmax3, Gmax4))
+- {
+- while (active_size < i)
+- {
+- if (be_shrunken(active_size, Gmax1, Gmax2, Gmax3, Gmax4))
+- {
+- swap_index(i,active_size);
+- break;
+- }
+- active_size++;
+- }
+- 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 Qfloat[prob.l];
+- for(int i=0;i<prob.l;i++)
+- QD[i]= (Qfloat)(this->*kernel_function)(i,i);
+- }
+-
+- Qfloat *get_Q(int i, int len) const
+- {
+- Qfloat *data;
+- int start;
+- if((start = cache->get_data(i,&data,len)) < len)
+- {
+- for(int j=start;j<len;j++)
+- data[j] = (Qfloat)(y[i]*y[j]*(this->*kernel_function)(i,j));
+- }
+- return data;
+- }
+-
+- Qfloat *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;
+- Qfloat *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 Qfloat[prob.l];
+- for(int i=0;i<prob.l;i++)
+- QD[i]= (Qfloat)(this->*kernel_function)(i,i);
+- }
+-
+- Qfloat *get_Q(int i, int len) const
+- {
+- Qfloat *data;
+- int start;
+- if((start = cache->get_data(i,&data,len)) < len)
+- {
+- for(int j=start;j<len;j++)
+- data[j] = (Qfloat)(this->*kernel_function)(i,j);
+- }
+- return data;
+- }
+-
+- Qfloat *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;
+- Qfloat *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 Qfloat[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]= (Qfloat)(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 real_i = index[i];
+- if(cache->get_data(real_i,&data,l) < l)
+- {
+- for(int 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(int j=0;j<len;j++)
+- buf[j] = si * sign[j] * data[index[j]];
+- return buf;
+- }
+-
+- Qfloat *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];
+- Qfloat *QD;
+-};
+-
+-//
+-// 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];
+-
+- int i;
+-
+- for(i=0;i<l;i++)
+- {
+- alpha[i] = 0;
+- minus_ones[i] = -1;
+- if(prob->y[i] > 0) y[i] = +1; else y[i]=-1;
+- }
+-
+- Solver s;
+- s.Solve(l, SVC_Q(*prob,*param,y), minus_ones, y,
+- alpha, Cp, Cn, 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[] 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];
+-
+- for(i=0;i<l;i++)
+- if(prob->y[i]>0)
+- y[i] = +1;
+- else
+- y[i] = -1;
+-
+- 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(1.0,sum_pos);
+- sum_pos -= alpha[i];
+- }
+- else
+- {
+- alpha[i] = min(1.0,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, 1.0, 1.0, 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->rho /= r;
+- si->obj /= (r*r);
+- si->upper_bound_p = 1/r;
+- si->upper_bound_n = 1/r;
+-
+- 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];
+- int i;
+-
+- int n = (int)(param->nu*prob->l); // # of alpha's at upper bound
+-
+- for(i=0;i<n;i++)
+- alpha[i] = 1;
+- if(n<prob->l)
+- alpha[n] = param->nu * prob->l - n;
+- for(i=n+1;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, 1.0, 1.0, param->eps, si, param->shrinking);
+-
+- 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];
+- 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;
+-
+- alpha2[i+l] = 0;
+- linear_term[i+l] = param->p + prob->y[i];
+- y[i+l] = -1;
+- }
+-
+- Solver s;
+- s.Solve(2*l, SVR_Q(*prob,*param), linear_term, y,
+- alpha2, param->C, param->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[] 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 = param->C;
+- double *alpha2 = new double[2*l];
+- double *linear_term = new double[2*l];
+- schar *y = new schar[2*l];
+- int i;
+-
+- double sum = C * param->nu * l / 2;
+- for(i=0;i<l;i++)
+- {
+- alpha2[i] = alpha2[i+l] = min(sum,C);
+- 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, 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[] y;
+-}
+-
+-//
+-// decision_function
+-//
+-struct decision_function
+-{
+- double *alpha;
+- double rho;
+-};
+-
+-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:
+- solve_c_svc(prob,param,alpha,&si,Cp,Cn);
+- break;
+- case NU_SVC:
+- solve_nu_svc(prob,param,alpha,&si);
+- break;
+- case ONE_CLASS:
+- solve_one_class(prob,param,alpha,&si);
+- break;
+- case EPSILON_SVR:
+- solve_epsilon_svr(prob,param,alpha,&si);
+- break;
+- case NU_SVR:
+- 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_p)
+- ++nBSV;
+- }
+- else
+- {
+- if(fabs(alpha[i]) >= si.upper_bound_n)
+- ++nBSV;
+- }
+- }
+- }
+-
+- info("nSV = %d, nBSV = %d\n",nSV,nBSV);
+-
+- decision_function f;
+- f.alpha = alpha;
+- f.rho = si.rho;
+- return f;
+-}
+-
+-//
+-// svm_model
+-//
+-struct svm_model
+-{
+- svm_parameter param; // parameter
+- int nr_class; // number of classes, = 2 in regression/one class svm
+- int l; // total #SV
+- 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; // pariwise probability information
+- double *probB;
+-
+- // 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
+-};
+-
+-// Platt's binary SVM Probablistic Output: an improvement from Lin et al.
+-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);
+-}
+-
+-double sigmoid_predict(double decision_value, double A, double B)
+-{
+- double fApB = decision_value*A+B;
+- 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
+-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
+-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);
+-
+- k=0;
+- for(j=0;j<begin;j++)
+- {
+- subprob.x[k] = prob->x[perm[j]];
+- subprob.y[k] = prob->y[perm[j]];
+- ++k;
+- }
+- for(j=end;j<prob->l;j++)
+- {
+- subprob.x[k] = prob->x[perm[j]];
+- subprob.y[k] = prob->y[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_destroy_model(submodel);
+- svm_destroy_param(&subparam);
+- }
+- free(subprob.x);
+- free(subprob.y);
+- }
+- sigmoid_train(prob->l,dec_values,prob->y,probA,probB);
+- free(dec_values);
+- free(perm);
+-}
+-
+-// Return parameter of a Laplace distribution
+-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
+-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);
+-}
+-
+-//
+-// Interface functions
+-//
+-svm_model *svm_train(const svm_problem *prob, const svm_parameter *param)
+-{
+- 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);
+- 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];
+- ++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);
+- svm_node **x = Malloc(svm_node *,l);
+- int i;
+- for(i=0;i<l;i++)
+- x[i] = prob->x[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);
+- int k;
+- for(k=0;k<ci;k++)
+- {
+- sub_prob.x[k] = x[si+k];
+- sub_prob.y[k] = +1;
+- }
+- for(k=0;k<cj;k++)
+- {
+- sub_prob.x[ci+k] = x[sj+k];
+- sub_prob.y[ci+k] = -1;
+- }
+-
+- 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);
+- ++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);
+- p = 0;
+- for(i=0;i<l;i++)
+- if(nonzero[i]) model->SV[p++] = x[i];
+-
+- 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(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);
+- }
+- 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);
+-
+- k=0;
+- for(j=0;j<begin;j++)
+- {
+- subprob.x[k] = prob->x[perm[j]];
+- subprob.y[k] = prob->y[perm[j]];
+- ++k;
+- }
+- for(j=end;j<l;j++)
+- {
+- subprob.x[k] = prob->x[perm[j]];
+- subprob.y[k] = prob->y[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_destroy_model(submodel);
+- free(subprob.x);
+- free(subprob.y);
+- }
+- 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];
+-}
+-
+-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
+- {
+- info("Model doesn't contain information for SVR probability inference\n");
+- return 0;
+- }
+-}
+-
+-void svm_predict_values(const svm_model *model, const svm_node *x, double* dec_values)
+-{
+- 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(int 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;
+- }
+- else
+- {
+- int i;
+- 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 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;
+- p++;
+- }
+-
+- free(kvalue);
+- free(start);
+- }
+-}
+-
+-double svm_predict(const svm_model *model, const svm_node *x)
+-{
+- if(model->param.svm_type == ONE_CLASS ||
+- model->param.svm_type == EPSILON_SVR ||
+- model->param.svm_type == NU_SVR)
+- {
+- double res;
+- svm_predict_values(model, x, &res);
+-
+- if(model->param.svm_type == ONE_CLASS)
+- return (res>0)?1:-1;
+- else
+- return res;
+- }
+- else
+- {
+- 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);
+-
+- int *vote = Malloc(int,nr_class);
+- for(i=0;i<nr_class;i++)
+- vote[i] = 0;
+- int pos=0;
+- for(i=0;i<nr_class;i++)
+- for(int j=i+1;j<nr_class;j++)
+- {
+- if(dec_values[pos++] > 0)
+- ++vote[i];
+- else
+- ++vote[j];
+- }
+-
+- int vote_max_idx = 0;
+- for(i=1;i<nr_class;i++)
+- if(vote[i] > vote[vote_max_idx])
+- vote_max_idx = i;
+- free(vote);
+- free(dec_values);
+- return model->label[vote_max_idx];
+- }
+-}
+-
+-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);
+-}
+-
+-const char *svm_type_table[] =
+-{
+- "c_svc","nu_svc","one_class","epsilon_svr","nu_svr",NULL
+-};
+-
+-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;
+-
+- 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");
+- }
+- if (ferror(fp) != 0 || fclose(fp) != 0) return -1;
+- else return 0;
+-}
+-
+-svm_model *svm_load_model(const char *model_file_name)
+-{
+- FILE *fp = fopen(model_file_name,"r");
+- if(fp==NULL) return NULL;
+-
+- // 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");
+- 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");
+- 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);
+- 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);
+-
+- while(1)
+- {
+- int c = fgetc(fp);
+- switch(c)
+- {
+- case '\n':
+- // count the '-1' element
+- case ':':
+- ++elements;
+- break;
+- case EOF:
+- goto out;
+- default:
+- ;
+- }
+- }
+-out:
+- 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++)
+- {
+- model->SV[i] = &x_space[j];
+- for(int k=0;k<m;k++)
+- fscanf(fp,"%lf",&model->sv_coef[k][i]);
+- while(1)
+- {
+- int c;
+- do {
+- c = getc(fp);
+- if(c=='\n') goto out2;
+- } while(isspace(c));
+- ungetc(c,fp);
+- fscanf(fp,"%d:%lf",&(x_space[j].index),&(x_space[j].value));
+- ++j;
+- }
+-out2:
+- x_space[j++].index = -1;
+- }
+- if (ferror(fp) != 0 || fclose(fp) != 0) return NULL;
+-
+- model->free_sv = 1; // XXX
+- return model;
+-}
+-
+-void svm_destroy_model(svm_model* model)
+-{
+- if(model->free_sv && model->l > 0)
+- free((void *)(model->SV[0]));
+- for(int i=0;i<model->nr_class-1;i++)
+- free(model->sv_coef[i]);
+- free(model->SV);
+- free(model->sv_coef);
+- free(model->rho);
+- free(model->label);
+- free(model->probA);
+- free(model->probB);
+- free(model->nSV);
+- free(model);
+-}
+-
+-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->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);
+- int *count = Malloc(int,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];
+- 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 = (int *)realloc(count,max_nr_class*sizeof(int));
+- }
+- label[nr_class] = this_label;
+- count[nr_class] = 1;
+- ++nr_class;
+- }
+- }
+-
+- for(i=0;i<nr_class;i++)
+- {
+- int n1 = count[i];
+- for(int j=i+1;j<nr_class;j++)
+- {
+- int 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);
+-}
+diff --git a/libsvm.h b/libsvm.h
+deleted file mode 100644
+index 40638b2..0000000
+--- a/libsvm.h
++++ /dev/null
+@@ -1,70 +0,0 @@
+-#ifndef _LIBSVM_H
+-#define _LIBSVM_H
+-
+-#ifdef __cplusplus
+-extern "C" {
+-#endif
+-
+-struct svm_node
+-{
+- int index;
+- double value;
+-};
+-
+-struct svm_problem
+-{
+- int l;
+- double *y;
+- struct svm_node **x;
+-};
+-
+-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 */
+-};
+-
+-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);
+-double svm_get_svr_probability(const struct svm_model *model);
+-
+-void 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);
+-
+-void svm_destroy_model(struct svm_model *model);
+-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);
+-
+-#ifdef __cplusplus
+-}
+-#endif
+-
+-#endif /* _LIBSVM_H */
+--
+1.7.1
+
diff --git a/perl-Algorithm-SVM.spec b/perl-Algorithm-SVM.spec
new file mode 100644
index 0000000..484072f
--- /dev/null
+++ b/perl-Algorithm-SVM.spec
@@ -0,0 +1,66 @@
+Name: perl-Algorithm-SVM
+Version: 0.13
+Release: 1%{?dist}
+Summary: Perl bindings for the libsvm Support Vector Machine library
+
+# Note: The sources bundle a copy of libsvm which is BSD-licensed,
+# https://fedoraproject.org/wiki/Licensing/BSD#3ClauseBSD
+# But this file gets dropped during %%prep (see Patch0)
+License: GPL+ or Artistic
+URL: http://search.cpan.org/dist/Algorithm-SVM/
+
+Source0: http://www.cpan.org/authors/id/L/LA/LAIRDM/Algorithm-SVM-%{version}.tar.gz
+
+# Both patches were submitted upstream:
+# https://rt.cpan.org/Public/Bug/Display.html?id=79106
+Patch0: Algorithm-SVM-0.13-Unbundle-libsvm.patch
+Patch1: Algorithm-SVM-0.13-Port-to-libsvm-3.0.patch
+
+BuildRequires: perl(ExtUtils::MakeMaker)
+BuildRequires: libsvm-devel
+
+Requires: perl(:MODULE_COMPAT_%(eval "`%{__perl} -V:version`"; echo $version))
+
+%description
+Algorithm::SVM implements a Support Vector Machine for Perl. Support Vector
+Machines provide a method for creating classifcation functions from a set
+of labeled training data, from which predictions can be made for subsequent
+data sets.
+
+
+%prep
+%setup -q -n Algorithm-SVM-%{version}
+
+%patch0 -p1
+%patch1 -p1
+
+
+%build
+%{__perl} Makefile.PL INSTALLDIRS=vendor OPTIMIZE="%{optflags}"
+make %{?_smp_mflags}
+
+
+%install
+make pure_install PERL_INSTALL_ROOT=%{buildroot}
+
+find %{buildroot} -type f -name .packlist -exec rm -f {} \;
+find %{buildroot} -type f -name '*.bs' -size 0 -exec rm -f {} \;
+find %{buildroot} -depth -type d -exec rmdir {} 2>/dev/null \;
+
+%{_fixperms} %{buildroot}/*
+
+
+%check
+make test
+
+
+%files
+%doc Changes README sample.model sample.model.1
+%{perl_vendorarch}/Algorithm/SVM*
+%{perl_vendorarch}/auto/Algorithm/SVM
+%{_mandir}/man3/Algorithm::SVM*
+
+
+%changelog
+* Tue Aug 21 2012 Mathieu Bridon <bochecha@xxxxxxxxxxxxxxxxx> - 0.13-1
+- Initial package, with help from cpanspec.
--
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