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StochasticGBMachine.h
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30 
31 #ifndef _StochasticGBMachine_H__
32 #define _StochasticGBMachine_H__
33 
34 
35 #include <shogun/lib/config.h>
36 
37 #include <shogun/machine/Machine.h>
40 
41 namespace shogun
42 {
43 
53 {
54 public:
63  CStochasticGBMachine(CMachine* machine=NULL, CLossFunction* loss=NULL, int32_t num_iterations=100,
64  float64_t learning_rate=1.0, float64_t subset_fraction=0.6);
65 
67  virtual ~CStochasticGBMachine();
68 
73  virtual const char* get_name() const { return "StochasticGBMachine"; }
74 
79  void set_machine(CMachine* machine);
80 
85  CMachine* get_machine() const;
86 
91  virtual void set_loss_function(CLossFunction* f);
92 
97  virtual CLossFunction* get_loss_function() const;
98 
103  void set_num_iterations(int32_t iter);
104 
109  int32_t get_num_iterations() const;
110 
115  void set_subset_fraction(float64_t frac);
116 
122 
127  void set_learning_rate(float64_t lr);
128 
134 
140  virtual CRegressionLabels* apply_regression(CFeatures* data=NULL);
141 
142 protected:
148  virtual bool train_machine(CFeatures* data=NULL);
149 
157 
165 
172 
179 
181  void initialize_learners();
182 
188  float64_t get_gamma(void* instance);
189 
198  static float64_t lbfgs_evaluate(void *obj, const float64_t *parameters, float64_t *gradient, const int dim, const float64_t step);
199 
201  void init();
202 
203 protected:
206 
209 
211  int32_t m_num_iter;
212 
215 
218 
221 
224 };
225 }/* shogun */
226 
227 #endif /* _StochasticGBMachine_H__ */
virtual CRegressionLabels * apply_regression(CFeatures *data=NULL)
Real Labels are real-valued labels.
Class CLossFunction is the base class of all loss functions.
Definition: LossFunction.h:57
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
virtual void set_loss_function(CLossFunction *f)
A generic learning machine interface.
Definition: Machine.h:143
This class implements the stochastic gradient boosting algorithm for ensemble learning invented by Je...
CRegressionLabels * compute_pseudo_residuals(CRegressionLabels *inter_f)
double float64_t
Definition: common.h:50
static float64_t lbfgs_evaluate(void *obj, const float64_t *parameters, float64_t *gradient, const int dim, const float64_t step)
float64_t compute_multiplier(CRegressionLabels *f, CRegressionLabels *hm)
void set_machine(CMachine *machine)
CMachine * fit_model(CDenseFeatures< float64_t > *feats, CRegressionLabels *labels)
Dynamic array class for CSGObject pointers that creates an array that can be used like a list or an a...
void set_subset_fraction(float64_t frac)
virtual const char * get_name() const
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
CStochasticGBMachine(CMachine *machine=NULL, CLossFunction *loss=NULL, int32_t num_iterations=100, float64_t learning_rate=1.0, float64_t subset_fraction=0.6)
CDynamicArray< float64_t > * m_gamma
The class Features is the base class of all feature objects.
Definition: Features.h:68
float64_t get_gamma(void *instance)
virtual bool train_machine(CFeatures *data=NULL)
void apply_subset(CDenseFeatures< float64_t > *f, CLabels *interf)
virtual CLossFunction * get_loss_function() const
CDynamicObjectArray * m_weak_learners

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