VwNonAdaptiveLearner uses a standard gradient descent weight update rule.
The weight vector is updated by adding the corresponding feature multiplied by the update factor for the example.
Definition at line 30 of file VwNonAdaptiveLearner.h.
Public Member Functions | |
CVwNonAdaptiveLearner () | |
CVwNonAdaptiveLearner (CVwRegressor *regressor, CVwEnvironment *vw_env) | |
virtual | ~CVwNonAdaptiveLearner () |
virtual void | train (VwExample *&ex, float32_t update) |
virtual const char * | get_name () const |
Default constructor
Definition at line 20 of file VwNonAdaptiveLearner.cpp.
CVwNonAdaptiveLearner | ( | CVwRegressor * | regressor, | |
CVwEnvironment * | vw_env | |||
) |
Constructor, initializes regressor and environment
regressor | regressor to use | |
vw_env | environment to use |
Definition at line 25 of file VwNonAdaptiveLearner.cpp.
~CVwNonAdaptiveLearner | ( | ) | [virtual] |
Destructor
Definition at line 30 of file VwNonAdaptiveLearner.cpp.
virtual const char* get_name | ( | ) | const [virtual] |
Return the name of the object
Reimplemented from CVwLearner.
Definition at line 64 of file VwNonAdaptiveLearner.h.
Train on one example, given the update
ex | example | |
update | the update |
Implements CVwLearner.
Definition at line 34 of file VwNonAdaptiveLearner.cpp.