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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:
Inheritance graph
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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)
 
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)
 

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
ffeatures 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_featuresnumber of features
p_num_symbolsnumber 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
dictdictionary of parameters to be built.

Definition at line 597 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 714 of file SGObject.cpp.

CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 198 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
otherobject to compare with
accuracyaccuracy to use for comparison (optional)
tolerantallows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 618 of file SGObject.cpp.

virtual float64_t get_derivative ( int32_t  num_param,
int32_t  num_example 
)
virtualinherited

get partial derivative of likelihood function

Parameters
num_parampartial derivative against which param
num_examplewhich 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
vectorvector
lenlength
posposition

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 235 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 277 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 290 of file SGObject.cpp.

float64_t get_likelihood_example ( uint16_t *  vector,
int32_t  len 
)

get one example's likelihood

Parameters
vectorthe example
lenlength 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_examplewhich 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_paramwhich example's param
num_examplewhich 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
obsobservation
posposition

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
vectorthe example
lenlength 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_examplewhich 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_paramwhich 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_paramwhich 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 498 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_namename of the parameter
Returns
description of the parameter

Definition at line 522 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_namename of model selection parameter
Returns
index of model selection parameter with provided name, -1 if there is no such

Definition at line 535 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
obsobservation
positionposition
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 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
genericset to the type of the generic if returning TRUE
Returns
TRUE if a class template.

Definition at line 296 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
filewhere to load from
prefixprefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 369 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
ShogunExceptionwill 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
ShogunExceptionwill 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 421 of file SGObject.cpp.

CDistribution * obtain_from_generic ( CSGObject *  object)
staticinherited

obtain from generic

Parameters
objectgeneric 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 262 of file SGObject.cpp.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 474 of file SGObject.cpp.

void print_serializable ( const char *  prefix = "")
virtualinherited

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 308 of file SGObject.cpp.

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

Save this object to file.

Parameters
filewhere to save the object; will be closed during returning if PREFIX is an empty string.
prefixprefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 314 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
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 436 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
ShogunExceptionwill 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 431 of file SGObject.cpp.

virtual void set_features ( CFeatures *  f)
virtualinherited

set feature vectors

Parameters
fnew feature vectors

Definition at line 160 of file Distribution.h.

void set_generic ( )
inherited

Definition at line 41 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 46 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 51 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 56 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 61 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 66 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 71 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 76 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 81 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 86 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 91 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 96 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 101 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 106 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 111 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
ioio object to use

Definition at line 228 of file SGObject.cpp.

void set_global_parallel ( Parallel *  parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 241 of file SGObject.cpp.

void set_global_version ( Version *  version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 283 of file SGObject.cpp.

bool set_log_transition_probs ( const SGVector< float64_t >  probs)
virtual

set logarithm of all transition probs

Parameters
probsnew 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
pseudonew 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
probsnew 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 192 of file SGObject.cpp.

bool train ( CFeatures *  data = NULL)
virtual

estimate LinearHMM distribution

Parameters
datatraining 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
indizesindices
num_indizesnumber of indices
pseudo_countpseudo 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 303 of file SGObject.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 248 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
lenlength 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 369 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 384 of file SGObject.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 387 of file SGObject.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 381 of file SGObject.h.

Parameter* m_parameters
inherited

parameters

Definition at line 378 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 372 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 375 of file SGObject.h.

The documentation for this class was generated from the following files:
SHOGUN Machine Learning Toolbox - Documentation