CGHMM Class Reference


Detailed Description

class GHMM - this class is non-functional and was meant to implement a Generalize Hidden Markov Model (aka Semi Hidden Markov HMM).

Definition at line 22 of file GHMM.h.

Inheritance diagram for CGHMM:
Inheritance graph
[legend]

List of all members.

Public Member Functions

 CGHMM ()
virtual ~CGHMM ()
virtual bool train (CFeatures *data=NULL)
virtual int32_t get_num_model_parameters ()
virtual float64_t get_log_model_parameter (int32_t param_num)
virtual float64_t get_log_derivative (int32_t param_num, int32_t num_example)
virtual float64_t get_log_likelihood_example (int32_t num_example)

Constructor & Destructor Documentation

CGHMM (  ) 

default constructor

Definition at line 15 of file GHMM.cpp.

~CGHMM (  )  [virtual]

Definition at line 20 of file GHMM.cpp.


Member Function Documentation

float64_t get_log_derivative ( int32_t  param_num,
int32_t  num_example 
) [virtual]

get logarithm of one example's derivative's likelihood

Parameters:
param_num which example's param
num_example which example
Returns:
logarithm of example's derivative's likelihood

Implements CDistribution.

Definition at line 39 of file GHMM.cpp.

float64_t get_log_likelihood_example ( int32_t  num_example  )  [virtual]

get logarithm of one example's likelihood

Parameters:
num_example which example
Returns:
logarithm of example's likelihood

Implements CDistribution.

Definition at line 44 of file GHMM.cpp.

float64_t get_log_model_parameter ( int32_t  param_num  )  [virtual]

get logarithm of given model parameter

Parameters:
param_num which param
Returns:
logarithm of given model parameter

Implements CDistribution.

Definition at line 34 of file GHMM.cpp.

int32_t get_num_model_parameters (  )  [virtual]

get number of model parameters

Returns:
number of model parameters

Implements CDistribution.

Definition at line 29 of file GHMM.cpp.

bool train ( CFeatures data = NULL  )  [virtual]

learn distribution

Parameters:
data training data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data)
Returns:
whether training was successful

Implements CDistribution.

Definition at line 24 of file GHMM.cpp.


The documentation for this class was generated from the following files:

SHOGUN Machine Learning Toolbox - Documentation