CLinearHMM Class Reference

Detailed Description

The class LinearHMM is for learning Higher Order Markov chains.

While learning the parameters \({\bf \theta}\) in

\begin{eqnarray*} P({\bf x}|{\bf \theta}^\pm)&=&P(x_1, \ldots, x_N|{\bf \theta}^\pm)\\ &=&P(x_1,\ldots,x_{d}|{\bf \theta}^\pm)\prod_{i=d+1}^N P(x_i|x_{i-1},\ldots,x_{i-d},{\bf \theta}^\pm) \end{eqnarray*}

are determined.

A more detailed description can be found in

Durbin et.al, Biological Sequence Analysis -Probabilistic Models of Proteins and Nucleic Acids, 1998

Definition at line 41 of file LinearHMM.h.

Inheritance diagram for CLinearHMM:

Public Member Functions
	CLinearHMM ()
	CLinearHMM (CStringFeatures< uint16_t > *f)
	CLinearHMM (int32_t p_num_features, int32_t p_num_symbols)
virtual	~CLinearHMM ()
virtual bool	train (CFeatures *data=NULL)
bool	train (const int32_t *indizes, int32_t num_indizes, float64_t pseudo_count)
float64_t	get_log_likelihood_example (uint16_t *vector, int32_t len)
float64_t	get_likelihood_example (uint16_t *vector, int32_t len)
float64_t	get_likelihood_example (int32_t num_example)
virtual float64_t	get_log_likelihood_example (int32_t num_example)
virtual float64_t	get_log_derivative (int32_t num_param, int32_t num_example)
virtual float64_t	get_log_derivative_obsolete (uint16_t obs, int32_t pos)
virtual float64_t	get_derivative_obsolete (uint16_t *vector, int32_t len, int32_t pos)
virtual int32_t	get_sequence_length ()
virtual int32_t	get_num_symbols ()
virtual int32_t	get_num_model_parameters ()
virtual float64_t	get_positional_log_parameter (uint16_t obs, int32_t position)
virtual float64_t	get_log_model_parameter (int32_t num_param)
virtual SGVector< float64_t >	get_log_transition_probs ()
virtual bool	set_log_transition_probs (const SGVector< float64_t > probs)
virtual SGVector< float64_t >	get_transition_probs ()
virtual bool	set_transition_probs (const SGVector< float64_t > probs)
virtual const char *	get_name () const
virtual int32_t	get_num_relevant_model_parameters ()
virtual float64_t	get_log_likelihood_sample ()
virtual SGVector< float64_t >	get_log_likelihood ()
virtual float64_t	get_model_parameter (int32_t num_param)
virtual float64_t	get_derivative (int32_t num_param, int32_t num_example)
virtual SGVector< float64_t >	get_likelihood_for_all_examples ()
virtual void	set_features (CFeatures *f)
virtual CFeatures *	get_features ()
virtual void	set_pseudo_count (float64_t pseudo)
virtual float64_t	get_pseudo_count ()
virtual float64_t	update_params_em (float64_t *alpha_k, int32_t len)
virtual CSGObject *	shallow_copy () const
virtual CSGObject *	deep_copy () const
virtual bool	is_generic (EPrimitiveType *generic) const
template<class T >
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
void	unset_generic ()
virtual void	print_serializable (const char *prefix="")
virtual bool	save_serializable (CSerializableFile *file, const char *prefix="")
virtual bool	load_serializable (CSerializableFile *file, const char *prefix="")
void	set_global_io (SGIO *io)
SGIO *	get_global_io ()
void	set_global_parallel (Parallel *parallel)
Parallel *	get_global_parallel ()
void	set_global_version (Version *version)
Version *	get_global_version ()
SGStringList< char >	get_modelsel_names ()
void	print_modsel_params ()
char *	get_modsel_param_descr (const char *param_name)
index_t	get_modsel_param_index (const char *param_name)
void	build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict)
bool	has (const std::string &name) const
template<typename T >
bool	has (const Tag< T > &tag) const
template<typename T , typename U = void>
bool	has (const std::string &name) const
template<typename T >
void	set (const Tag< T > &_tag, const T &value)
template<typename T , typename U = void>
void	set (const std::string &name, const T &value)
template<typename T >
T	get (const Tag< T > &_tag) const
template<typename T , typename U = void>
T	get (const std::string &name) const
virtual void	update_parameter_hash ()
virtual bool	parameter_hash_changed ()
virtual bool	equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)
virtual CSGObject *	clone ()

Static Public Member Functions
static CDistribution *	obtain_from_generic (CSGObject *object)

Public Attributes
SGIO *	io
Parallel *	parallel
Version *	version
Parameter *	m_parameters
Parameter *	m_model_selection_parameters
Parameter *	m_gradient_parameters
uint32_t	m_hash

Protected Member Functions
virtual void	load_serializable_post () throw (ShogunException)
virtual void	load_serializable_pre () throw (ShogunException)
virtual void	save_serializable_pre () throw (ShogunException)
virtual void	save_serializable_post () throw (ShogunException)
template<typename T >
void	register_param (Tag< T > &_tag, const T &value)
template<typename T >
void	register_param (const std::string &name, const T &value)

Protected Attributes
int32_t	sequence_length
int32_t	num_symbols
int32_t	num_params
float64_t *	transition_probs
float64_t *	log_transition_probs
CFeatures *	features
float64_t	pseudo_count

Constructor & Destructor Documentation

CLinearHMM

(

)

default constructor

Definition at line 22 of file LinearHMM.cpp.

CLinearHMM

(

CStringFeatures< uint16_t > *

f

)

constructor

Parameters

f	features to use

Definition at line 27 of file LinearHMM.cpp.

CLinearHMM	(	int32_t	p_num_features,
		int32_t	p_num_symbols
	)

constructor

Parameters

p_num_features	number of features
p_num_symbols	number of symbols in features

Definition at line 38 of file LinearHMM.cpp.

~CLinearHMM ( )

virtual

Definition at line 48 of file LinearHMM.cpp.

Member Function Documentation

void build_gradient_parameter_dictionary ( CMap< TParameter *, CSGObject * > *  dict )

inherited

Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.

Parameters

dict	dictionary of parameters to be built.

Definition at line 630 of file SGObject.cpp.

CSGObject * clone ( )

virtualinherited

Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.

Returns

an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed

Definition at line 747 of file SGObject.cpp.

CSGObject * deep_copy ( ) const

virtualinherited

A deep copy. All the instance variables will also be copied.

Definition at line 231 of file SGObject.cpp.

bool equals ( CSGObject *  other, float64_t  accuracy = 0.0, bool  tolerant = false  )

virtualinherited

Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!

May be overwritten but please do with care! Should not be necessary in most cases.

Parameters

other	object to compare with
accuracy	accuracy to use for comparison (optional)
tolerant	allows linient check on float equality (within accuracy)

Returns

true if all parameters were equal, false if not

Definition at line 651 of file SGObject.cpp.

T get ( const Tag< T > &  _tag ) const

inherited

Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters

_tag	name and type information of parameter

Returns

value of the parameter identified by the input tag

Definition at line 367 of file SGObject.h.

T get ( const std::string &  name ) const

inherited

Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters

name	name of the parameter

Returns

value of the parameter corresponding to the input name and type

Definition at line 388 of file SGObject.h.

virtual float64_t get_derivative ( int32_t  num_param, int32_t  num_example  )

virtualinherited

get partial derivative of likelihood function

Parameters

num_param	partial derivative against which param
num_example	which example

Returns

derivative of likelihood function

Definition at line 134 of file Distribution.h.

virtual float64_t get_derivative_obsolete ( uint16_t *  vector, int32_t  len, int32_t  pos  )

virtual

obsolete get one example's derivative

Parameters

vector	vector
len	length
pos	position

Definition at line 140 of file LinearHMM.h.

virtual CFeatures* get_features ( )

virtualinherited

get feature vectors

Returns

feature vectors

Definition at line 171 of file Distribution.h.

SGIO * get_global_io ( )

inherited

get the io object

Returns

io object

Definition at line 268 of file SGObject.cpp.

Parallel * get_global_parallel ( )

inherited

get the parallel object

Returns

parallel object

Definition at line 310 of file SGObject.cpp.

Version * get_global_version ( )

inherited

get the version object

Returns

version object

Definition at line 323 of file SGObject.cpp.

float64_t get_likelihood_example	(	uint16_t *	vector,
		int32_t	len
	)

get one example's likelihood

Parameters

vector	the example
len	length of vector

Returns

likelihood

Definition at line 210 of file LinearHMM.cpp.

float64_t get_likelihood_example ( int32_t  num_example )

virtual

compute likelihood for example

Parameters

num_example

which example

Returns

likelihood for example

Reimplemented from CDistribution.

Definition at line 220 of file LinearHMM.cpp.

SGVector< float64_t > get_likelihood_for_all_examples ( )

virtualinherited

compute likelihood for all vectors in sample

Returns

likelihood vector for all examples

Definition at line 65 of file Distribution.cpp.

float64_t get_log_derivative ( int32_t  num_param, int32_t  num_example  )

virtual

get logarithm of one example's derivative's likelihood

Parameters

num_param	which example's param
num_example	which example

Returns

logarithm of example's derivative

Implements CDistribution.

Definition at line 235 of file LinearHMM.cpp.

virtual float64_t get_log_derivative_obsolete ( uint16_t  obs, int32_t  pos  )

virtual

obsolete get logarithm of one example's derivative's likelihood

Parameters

obs	observation
pos	position

Definition at line 128 of file LinearHMM.h.

SGVector< float64_t > get_log_likelihood ( )

virtualinherited

compute log likelihood for each example

Returns

log likelihood vector

Definition at line 39 of file Distribution.cpp.

float64_t get_log_likelihood_example	(	uint16_t *	vector,
		int32_t	len
	)

get logarithm of one example's likelihood

Parameters

vector	the example
len	length of vector

Returns

logarithm of likelihood

Definition at line 186 of file LinearHMM.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 196 of file LinearHMM.cpp.

float64_t get_log_likelihood_sample ( )

virtualinherited

compute log likelihood for whole sample

Returns

log likelihood for whole sample

Definition at line 28 of file Distribution.cpp.

virtual float64_t get_log_model_parameter ( int32_t  num_param )

virtual

get logarithm of given model parameter

Parameters

num_param

which param

Returns

logarithm of given model parameter

Implements CDistribution.

Definition at line 182 of file LinearHMM.h.

SGVector< float64_t > get_log_transition_probs ( )

virtual

get logarithm of all transition probs

Returns

logarithm of transition probs vector

Definition at line 278 of file LinearHMM.cpp.

virtual float64_t get_model_parameter ( int32_t  num_param )

virtualinherited

get model parameter

Parameters

num_param

which param

Returns

model parameter

Definition at line 123 of file Distribution.h.

SGStringList< char > get_modelsel_names ( )

inherited

Returns

vector of names of all parameters which are registered for model selection

Definition at line 531 of file SGObject.cpp.

char * get_modsel_param_descr ( const char *  param_name )

inherited

Returns description of a given parameter string, if it exists. SG_ERROR otherwise

Parameters

param_name

name of the parameter

Returns

description of the parameter

Definition at line 555 of file SGObject.cpp.

index_t get_modsel_param_index ( const char *  param_name )

inherited

Returns index of model selection parameter with provided index

Parameters

param_name

name of model selection parameter

Returns

index of model selection parameter with provided name, -1 if there is no such

Definition at line 568 of file SGObject.cpp.

virtual const char* get_name ( ) const

virtual

Returns

object name

Implements CSGObject.

Definition at line 217 of file LinearHMM.h.

virtual int32_t get_num_model_parameters ( )

virtual

get number of model parameters

Returns

number of model parameters

Implements CDistribution.

Definition at line 163 of file LinearHMM.h.

int32_t get_num_relevant_model_parameters ( )

virtualinherited

get number of parameters in model that are relevant, i.e. > ALMOST_NEG_INFTY

Returns

number of relevant model parameters

Definition at line 52 of file Distribution.cpp.

virtual int32_t get_num_symbols ( )

virtual

get number of symbols in examples

Returns

number of symbols in examples

Definition at line 157 of file LinearHMM.h.

virtual float64_t get_positional_log_parameter ( uint16_t  obs, int32_t  position  )

virtual

get positional log parameter

Parameters

obs	observation
position	position

Returns

positional log parameter

Definition at line 171 of file LinearHMM.h.

virtual float64_t get_pseudo_count ( )

virtualinherited

get pseudo count

Returns

pseudo count

Definition at line 187 of file Distribution.h.

virtual int32_t get_sequence_length ( )

virtual

get sequence length of each example

Returns

sequence length of each example

Definition at line 151 of file LinearHMM.h.

SGVector< float64_t > get_transition_probs ( )

virtual

get all transition probs

Returns

vector of transition probs

Definition at line 254 of file LinearHMM.cpp.

bool has ( const std::string &  name ) const

inherited

Checks if object has a class parameter identified by a name.

Parameters

name	name of the parameter

Returns

true if the parameter exists with the input name

Definition at line 289 of file SGObject.h.

bool has ( const Tag< T > &  tag ) const

inherited

Checks if object has a class parameter identified by a Tag.

Parameters

tag	tag of the parameter containing name and type information

Returns

true if the parameter exists with the input tag

Definition at line 301 of file SGObject.h.

bool has ( const std::string &  name ) const

inherited

Checks if a type exists for a class parameter identified by a name.

Parameters

name	name of the parameter

Returns

true if the parameter exists with the input name and type

Definition at line 312 of file SGObject.h.

bool is_generic ( EPrimitiveType *  generic ) const

virtualinherited

If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.

Parameters

generic

set to the type of the generic if returning TRUE

Returns

TRUE if a class template.

Definition at line 329 of file SGObject.cpp.

bool load_serializable ( CSerializableFile *  file, const char *  prefix = ""  )

virtualinherited

Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!

Parameters

file	where to load from
prefix	prefix for members

Returns

TRUE if done, otherwise FALSE

Definition at line 402 of file SGObject.cpp.

void load_serializable_post ( ) throw ( ShogunException )

protectedvirtual

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.

Exceptions

ShogunException

will be thrown if an error occurs.

Reimplemented from CSGObject.

Definition at line 302 of file LinearHMM.cpp.

void load_serializable_pre ( ) throw ( ShogunException )

protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.

Exceptions

ShogunException

will be thrown if an error occurs.

Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.

Definition at line 454 of file SGObject.cpp.

CDistribution * obtain_from_generic ( CSGObject *  object )

staticinherited

obtain from generic

Parameters

object

generic object

Returns

Distribution object

Definition at line 85 of file Distribution.cpp.

bool parameter_hash_changed ( )

virtualinherited

Returns

whether parameter combination has changed since last update

Definition at line 295 of file SGObject.cpp.

void print_modsel_params ( )

inherited

prints all parameter registered for model selection and their type

Definition at line 507 of file SGObject.cpp.

void print_serializable ( const char *  prefix = "" )

virtualinherited

prints registered parameters out

Parameters

prefix

prefix for members

Definition at line 341 of file SGObject.cpp.

void register_param ( Tag< T > &  _tag, const T &  value  )

protectedinherited

Registers a class parameter which is identified by a tag. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters

_tag	name and type information of parameter
value	value of the parameter

Definition at line 439 of file SGObject.h.

void register_param ( const std::string &  name, const T &  value  )

protectedinherited

Registers a class parameter which is identified by a name. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters

name	name of the parameter
value	value of the parameter along with type information

Definition at line 452 of file SGObject.h.

bool save_serializable ( CSerializableFile *  file, const char *  prefix = ""  )

virtualinherited

Save this object to file.

Parameters

file	where to save the object; will be closed during returning if PREFIX is an empty string.
prefix	prefix for members

Returns

TRUE if done, otherwise FALSE

Definition at line 347 of file SGObject.cpp.

void save_serializable_post ( ) throw ( ShogunException )

protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.

Exceptions

ShogunException

will be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 469 of file SGObject.cpp.

void save_serializable_pre ( ) throw ( ShogunException )

protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.

Exceptions

ShogunException

will be thrown if an error occurs.

Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.

Definition at line 464 of file SGObject.cpp.

void set ( const Tag< T > &  _tag, const T &  value  )

inherited

Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters

_tag	name and type information of parameter
value	value of the parameter

Definition at line 328 of file SGObject.h.

void set ( const std::string &  name, const T &  value  )

inherited

Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters

name	name of the parameter
value	value of the parameter along with type information

Definition at line 354 of file SGObject.h.

virtual void set_features ( CFeatures *  f )

virtualinherited

set feature vectors

Parameters

f	new feature vectors

Definition at line 160 of file Distribution.h.

void set_generic ( )

inherited

Definition at line 74 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 79 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 84 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 89 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 94 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 99 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 104 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 109 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 114 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 119 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 124 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 129 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 134 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 139 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 144 of file SGObject.cpp.

void set_generic ( )

inherited

set generic type to T

void set_global_io ( SGIO *  io )

inherited

set the io object

Parameters

io	io object to use

Definition at line 261 of file SGObject.cpp.

void set_global_parallel ( Parallel *  parallel )

inherited

set the parallel object

Parameters

parallel

parallel object to use

Definition at line 274 of file SGObject.cpp.

void set_global_version ( Version *  version )

inherited

set the version object

Parameters

version

version object to use

Definition at line 316 of file SGObject.cpp.

bool set_log_transition_probs ( const SGVector< float64_t >  probs )

virtual

set logarithm of all transition probs

Parameters

probs

new logarithm transition probs

Returns

if setting was successful

Definition at line 283 of file LinearHMM.cpp.

virtual void set_pseudo_count ( float64_t  pseudo )

virtualinherited

set pseudo count

Parameters

pseudo

new pseudo count

Definition at line 181 of file Distribution.h.

bool set_transition_probs ( const SGVector< float64_t >  probs )

virtual

set all transition probs

Parameters

probs

new transition probs

Returns

if setting was successful

Definition at line 259 of file LinearHMM.cpp.

CSGObject * shallow_copy ( ) const

virtualinherited

A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.

Reimplemented in CGaussianKernel.

Definition at line 225 of file SGObject.cpp.

bool train ( CFeatures *  data = NULL )

virtual

estimate LinearHMM 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 54 of file LinearHMM.cpp.

bool train	(	const int32_t *	indizes,
		int32_t	num_indizes,
		float64_t	pseudo_count
	)

alternative train distribution

Parameters

indizes	indices
num_indizes	number of indices
pseudo_count	pseudo count

Returns

if training was successful

Definition at line 123 of file LinearHMM.cpp.

void unset_generic ( )

inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 336 of file SGObject.cpp.

void update_parameter_hash ( )

virtualinherited

Updates the hash of current parameter combination

Definition at line 281 of file SGObject.cpp.

float64_t update_params_em ( float64_t *  alpha_k, int32_t  len  )

virtualinherited

update parameters in the em maximization step for mixture model of which this distribution is a part

abstract base method

Parameters

alpha_k	"belongingness" values of various data points
len	length of alpha_k array

Returns

sum of alpha_k values

Reimplemented in CGaussian.

Definition at line 78 of file Distribution.cpp.

Member Data Documentation

CFeatures* features

protectedinherited

feature vectors

Definition at line 209 of file Distribution.h.

SGIO* io

inherited

io

Definition at line 537 of file SGObject.h.

float64_t* log_transition_probs

protected

logarithm of transition probs

Definition at line 235 of file LinearHMM.h.

Parameter* m_gradient_parameters

inherited

parameters wrt which we can compute gradients

Definition at line 552 of file SGObject.h.

uint32_t m_hash

inherited

Hash of parameter values

Definition at line 555 of file SGObject.h.

Parameter* m_model_selection_parameters

inherited

model selection parameters

Definition at line 549 of file SGObject.h.

Parameter* m_parameters

inherited

parameters

Definition at line 546 of file SGObject.h.

int32_t num_params

protected

number of parameters

Definition at line 231 of file LinearHMM.h.

int32_t num_symbols

protected

number of symbols in examples

Definition at line 229 of file LinearHMM.h.

Parallel* parallel

inherited

parallel

Definition at line 540 of file SGObject.h.

float64_t pseudo_count

protectedinherited

pseudo count

Definition at line 211 of file Distribution.h.

int32_t sequence_length

protected

examples' sequence length

Definition at line 227 of file LinearHMM.h.

float64_t* transition_probs

protected

transition probs

Definition at line 233 of file LinearHMM.h.

Version* version

inherited

version

Definition at line 543 of file SGObject.h.

---

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

• LinearHMM.h
• LinearHMM.cpp