class LogRBF

LogRBF layer for GradientMachine.

Inheritance:

Public Fields
EMTrainer* initial_kmeans_trainer: optional initialization using a Kmeans

Public Methods
LogRBF(int n_inputs_, int n_outputs_, EMTrainer* kmeans_trainer=NULL)

Inherited from GradientMachine:

Public Fields
int n_inputs
int n_outputs
Parameters* params
Parameters* der_params
Sequence* beta

Public Methods
virtual void iterInitialize()
virtual void forward(Sequence* inputs)
virtual void backward(Sequence* inputs, Sequence* alpha)
virtual void setPartialBackprop(bool flag=true)
virtual void frameForward(int t, real* f_inputs, real* f_outputs)
virtual void frameBackward(int t, real* f_inputs, real* beta_, real* f_outputs, real* alpha_)
virtual void loadXFile(XFile* file)
virtual void saveXFile(XFile* file)

Inherited from Machine:

Public Fields
Sequence* outputs

Public Methods
virtual void reset()
virtual void setDataSet(DataSet* dataset_)

Inherited from Object:

Public Fields
Allocator* allocator

Public Methods
void addOption(const char* name, int size, void* ptr, const char* help="")
void addIOption(const char* name, int* ptr, int init_value, const char* help="")
void addROption(const char* name, real* ptr, real init_value, const char* help="")
void addBOption(const char* name, bool* ptr, bool init_value, const char* help="")
void addOOption(const char* name, Object** ptr, Object* init_value, const char* help="")
void setOption(const char* name, void* ptr)
void setIOption(const char* name, int option)
void setROption(const char* name, real option)
void setBOption(const char* name, bool option)
void setOOption(const char* name, Object* option)
void load(const char* filename)
void save(const char* filename)
void* operator new(size_t size, Allocator* allocator_=NULL)
void* operator new(size_t size, Allocator* allocator_, void* ptr_)
void operator delete(void* ptr)

Documentation

LogRBF layer for GradientMachine. Formally speaking, .
and are in params, with the following structure:

For a better initialization, one can provide a EMTrainer using a Kmeans distribution that will be used to initialize the means and gamma.