16 #ifndef _VOWPALWABBIT_H__
17 #define _VOWPALWABBIT_H__
198 virtual const char*
get_name()
const {
return "VowpalWabbit"; }
251 virtual void output_example(
VwExample* &ex);
258 virtual void print_update(
VwExample* &ex);
275 void set_verbose(
bool verbose);
310 bool save_predictions;
312 int32_t prediction_fd;
316 #endif // _VOWPALWABBIT_H__
uint32_t vw_size_t
vw_size_t typedef to work across platforms
CVwRegressor * reg
Regressor.
Class OnlineLinearMachine is a generic interface for linear machines like classifiers which work thro...
void set_adaptive(bool adaptive_learning)
virtual CVwEnvironment * get_env()
void set_prediction_out(char *file_name)
Class CVwEnvironment is the environment used by VW.
CVwLearner * get_learner()
void set_num_passes(int32_t passes)
Class v_array taken directly from JL's implementation.
CVwEnvironment * env
Environment for VW, i.e., globals.
float32_t compute_exact_norm_quad(float32_t *weights, VwFeature &page_feature, v_array< VwFeature > &offer_features, vw_size_t mask, float32_t g, float32_t &sum_abs_x)
void load_regressor(char *file_name)
virtual void set_learner()
MACHINE_PROBLEM_TYPE(PT_BINARY)
float32_t compute_exact_norm(VwExample *&ex, float32_t &sum_abs_x)
Base class for all VW learners.
vw_size_t num_passes
Number of passes.
void set_exact_adaptive_norm(bool exact_adaptive)
virtual float32_t predict_and_finalize(VwExample *ex)
This class implements streaming features for use with VW.
void set_regressor_out(char *file_name, bool is_text=true)
virtual const char * get_name() const
void set_no_training(bool dont_train)
virtual bool train_machine(CFeatures *feat=NULL)
all of classes and functions are contained in the shogun namespace
CStreamingVwFeatures * features
Features.
The class Features is the base class of all feature objects.
void add_quadratic_pair(char *pair)
void reinitialize_weights()
Class CVowpalWabbit is the implementation of the online learning algorithm used in Vowpal Wabbit...
CVwLearner * learner
Learner to use.