12 #ifndef _LINEARHMM_H__
13 #define _LINEARHMM_H__
58 CLinearHMM(int32_t p_num_features, int32_t p_num_symbols);
80 const int32_t* indizes, int32_t num_indizes,
120 int32_t num_param, int32_t num_example);
129 uint16_t obs, int32_t pos)
141 uint16_t* vector, int32_t len, int32_t pos)
172 uint16_t obs, int32_t position)
217 virtual const char*
get_name()
const {
return "LinearHMM"; }
virtual int32_t get_sequence_length()
virtual float64_t get_positional_log_parameter(uint16_t obs, int32_t position)
virtual float64_t get_derivative_obsolete(uint16_t *vector, int32_t len, int32_t pos)
virtual float64_t get_log_derivative_obsolete(uint16_t obs, int32_t pos)
virtual int32_t get_num_symbols()
Class ShogunException defines an exception which is thrown whenever an error inside of shogun occurs...
float64_t * transition_probs
virtual float64_t get_log_derivative(int32_t num_param, int32_t num_example)
virtual int32_t get_num_model_parameters()
Base class Distribution from which all methods implementing a distribution are derived.
float64_t get_log_likelihood_example(uint16_t *vector, int32_t len)
virtual float64_t get_log_model_parameter(int32_t num_param)
virtual const char * get_name() const
virtual SGVector< float64_t > get_transition_probs()
virtual bool set_log_transition_probs(const SGVector< float64_t > probs)
all of classes and functions are contained in the shogun namespace
virtual bool set_transition_probs(const SGVector< float64_t > probs)
The class Features is the base class of all feature objects.
virtual bool train(CFeatures *data=NULL)
float64_t * log_transition_probs
virtual SGVector< float64_t > get_log_transition_probs()
The class LinearHMM is for learning Higher Order Markov chains.
virtual void load_serializable_post()
float64_t get_likelihood_example(uint16_t *vector, int32_t len)