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CCHAIDTree Class Reference

Detailed Description

This class implements the CHAID algorithm proposed by Kass (1980) for decision tree learning. CHAID consists of three steps: merging, splitting and stopping. A tree is grown by repeatedly using these three steps on each node starting from the root node. CHAID accepts nominal or ordinal categorical predictors only. If predictors are continuous, they have to be transformed into ordinal predictors before tree growing.

CONVERTING CONTINUOUS PREDICTORS TO ORDINAL :
Continuous predictors are converted to ordinal by binning. The number of bins (K) has to be supplied by the user. Given K, a predictor is split in such a way that all the bins get the same number (more or less) of distinct predictor values. The maximum feature value in each bin is used as a breakpoint.

MERGING :
During the merging step, allowable pairs of categories of a predictor are evaluated for similarity. If the similarity of a pair is above a threshold, the categories constituting the pair are merged into a single category. The process is repeated until there is no pair left having high similarity between its categories. Similarity between categories is evaluated using the p_value

SPLITTING :
The splitting step selects which predictor to be used to best split the node. Selection is accomplished by comparing the adjusted p_value associated with each predictor. The predictor that has the smallest adjusted p_value is chosen for splitting the node.

STOPPING :
The tree growing process stops if any of the following conditions is satisfied :
.

  1. If a node becomes pure; that is, all cases in a node have identical values of the dependent variable, the node will not be split.
  2. If all cases in a node have identical values for each predictor, the node will not be split.
  3. If the current tree depth reaches the user specified maximum tree depth limit value, the tree growing process will stop.
  4. If the size of a node is less than the user-specified minimum node size value, the node will not be split.

    p_value CALCULATIONS FOR NOMINAL DEPENDENT VARIABLE:
    If the dependent variable is nominal categorical, a contingency (or count) table is formed using classes of Y as columns and categories of the predictor X as rows. The p_value is computed using the entries of this table and Pearson chi-squared statistic, For more details, please see : http://pic.dhe.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Falg_tree-chaid_pvalue_categorical.htm

    p_value CALCULATIONS FOR ORDINAL DEPENDENT VARIABLE:
    If the dependent variable Y is categorical ordinal, the null hypothesis of independence of X and Y is tested against the row effects model, with the rows being the categories of X and columns the classes of Y. Again Pearson chi- squared statistic is used (like nominal case) but two sets of expected cell frequencies are calculated. For more details : http://pic.dhe.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Falg_tree-chaid_pvalue_ordinal.htm

    p_value CALCULATIONS FOR CONTINUOUS DEPENDENT VARIABLE:
    If the dependent variable Y is continuous, an ANOVA F test is performed that tests if the means of Y for different categories of X are the same. For more details please see : http://pic.dhe.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Falg_tree-chaid_pvalue_scale.htm

Definition at line 90 of file CHAIDTree.h.

Inheritance diagram for CCHAIDTree:
Inheritance graph
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Public Types

typedef CTreeMachineNode
< CHAIDTreeNodeData
node_t
typedef CBinaryTreeMachineNode
< CHAIDTreeNodeData
bnode_t

Public Member Functions

 CCHAIDTree ()
 CCHAIDTree (int32_t dependent_vartype)
 CCHAIDTree (int32_t dependent_vartype, SGVector< int32_t > feature_types, int32_t num_breakpoints=0)
virtual ~CCHAIDTree ()
virtual const char * get_name () const
virtual EProblemType get_machine_problem_type () const
virtual bool is_label_valid (CLabels *lab) const
virtual CMulticlassLabelsapply_multiclass (CFeatures *data=NULL)
virtual CRegressionLabelsapply_regression (CFeatures *data=NULL)
void set_weights (SGVector< float64_t > w)
SGVector< float64_tget_weights () const
void clear_weights ()
void set_feature_types (SGVector< int32_t > ft)
SGVector< int32_t > get_feature_types () const
void clear_feature_types ()
void set_dependent_vartype (int32_t var)
int32_t get_dependent_vartype () const
void set_max_tree_depth (int32_t d)
int32_t get_specified_max_tree_depth () const
void set_min_node_size (int32_t size)
int32_t get_min_node_size () const
void set_alpha_merge (float64_t a)
float64_t get_alpha_merge () const
void set_alpha_split (float64_t a)
float64_t get_alpha_split () const
void set_num_breakpoints (int32_t b)
float64_t get_num_breakpoints () const
void set_root (CTreeMachineNode< CHAIDTreeNodeData > *root)
CTreeMachineNode
< CHAIDTreeNodeData > * 
get_root ()
CTreeMachineclone_tree ()
int32_t get_num_machines () const
virtual bool train (CFeatures *data=NULL)
virtual CLabelsapply (CFeatures *data=NULL)
virtual CBinaryLabelsapply_binary (CFeatures *data=NULL)
virtual CStructuredLabelsapply_structured (CFeatures *data=NULL)
virtual CLatentLabelsapply_latent (CFeatures *data=NULL)
virtual void set_labels (CLabels *lab)
virtual CLabelsget_labels ()
void set_max_train_time (float64_t t)
float64_t get_max_train_time ()
virtual EMachineType get_classifier_type ()
void set_solver_type (ESolverType st)
ESolverType get_solver_type ()
virtual void set_store_model_features (bool store_model)
virtual bool train_locked (SGVector< index_t > indices)
virtual float64_t apply_one (int32_t i)
virtual CLabelsapply_locked (SGVector< index_t > indices)
virtual CBinaryLabelsapply_locked_binary (SGVector< index_t > indices)
virtual CRegressionLabelsapply_locked_regression (SGVector< index_t > indices)
virtual CMulticlassLabelsapply_locked_multiclass (SGVector< index_t > indices)
virtual CStructuredLabelsapply_locked_structured (SGVector< index_t > indices)
virtual CLatentLabelsapply_locked_latent (SGVector< index_t > indices)
virtual void data_lock (CLabels *labs, CFeatures *features)
virtual void post_lock (CLabels *labs, CFeatures *features)
virtual void data_unlock ()
virtual bool supports_locking () const
bool is_data_locked () const
virtual CSGObjectshallow_copy () const
virtual CSGObjectdeep_copy () const
virtual bool is_generic (EPrimitiveType *generic) const
template<class T >
void set_generic ()
void unset_generic ()
virtual void print_serializable (const char *prefix="")
virtual bool save_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
virtual bool load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
DynArray< TParameter * > * load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="")
DynArray< TParameter * > * load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="")
void map_parameters (DynArray< TParameter * > *param_base, int32_t &base_version, DynArray< const SGParamInfo * > *target_param_infos)
void set_global_io (SGIO *io)
SGIOget_global_io ()
void set_global_parallel (Parallel *parallel)
Parallelget_global_parallel ()
void set_global_version (Version *version)
Versionget_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 CSGObjectclone ()

Public Attributes

SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
Parameterm_gradient_parameters
ParameterMapm_parameter_map
uint32_t m_hash

Static Public Attributes

static const float64_t MISSING = CMath::MAX_REAL_NUMBER

Protected Member Functions

virtual bool train_machine (CFeatures *data=NULL)
virtual void store_model_features ()
virtual bool train_require_labels () const
virtual TParametermigrate (DynArray< TParameter * > *param_base, const SGParamInfo *target)
virtual void one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL)
virtual void load_serializable_pre () throw (ShogunException)
virtual void load_serializable_post () throw (ShogunException)
virtual void save_serializable_pre () throw (ShogunException)
virtual void save_serializable_post () throw (ShogunException)

Protected Attributes

CTreeMachineNode
< CHAIDTreeNodeData > * 
m_root
CDynamicObjectArraym_machines
float64_t m_max_train_time
CLabelsm_labels
ESolverType m_solver_type
bool m_store_model_features
bool m_data_locked

Member Typedef Documentation

bnode_t type- Tree node with max 2 possible children

Definition at line 55 of file TreeMachine.h.

node_t type- Tree node with many possible children

Definition at line 52 of file TreeMachine.h.

Constructor & Destructor Documentation

default constructor

Definition at line 39 of file CHAIDTree.cpp.

CCHAIDTree ( int32_t  dependent_vartype)

constructor

Parameters
dependent_vartypefeature type for dependent variable (0-nominal, 1-ordinal or 2-continuous)

Definition at line 45 of file CHAIDTree.cpp.

CCHAIDTree ( int32_t  dependent_vartype,
SGVector< int32_t >  feature_types,
int32_t  num_breakpoints = 0 
)

constructor

Parameters
dependent_vartypefeature type for dependent variable (0-nominal, 1-ordinal or 2-continuous)
feature_typestype of various attributes (0-nominal, 1-ordinal or 2-continuous)
num_breakpointsnumber of breakpoints for continuous to ordinal conversion of attributes

Definition at line 52 of file CHAIDTree.cpp.

~CCHAIDTree ( )
virtual

destructor

Definition at line 61 of file CHAIDTree.cpp.

Member Function Documentation

CLabels * apply ( CFeatures data = NULL)
virtualinherited

apply machine to data if data is not specified apply to the current features

Parameters
data(test)data to be classified
Returns
classified labels

Definition at line 160 of file Machine.cpp.

CBinaryLabels * apply_binary ( CFeatures data = NULL)
virtualinherited

apply machine to data in means of binary classification problem

Reimplemented in CKernelMachine, COnlineLinearMachine, CWDSVMOcas, CNeuralNetwork, CLinearMachine, CGaussianProcessClassification, CDomainAdaptationSVMLinear, CDomainAdaptationSVM, CPluginEstimate, and CBaggingMachine.

Definition at line 216 of file Machine.cpp.

CLatentLabels * apply_latent ( CFeatures data = NULL)
virtualinherited

apply machine to data in means of latent problem

Reimplemented in CLinearLatentMachine.

Definition at line 240 of file Machine.cpp.

CLabels * apply_locked ( SGVector< index_t indices)
virtualinherited

Applies a locked machine on a set of indices. Error if machine is not locked

Parameters
indicesindex vector (of locked features) that is predicted

Definition at line 195 of file Machine.cpp.

CBinaryLabels * apply_locked_binary ( SGVector< index_t indices)
virtualinherited

applies a locked machine on a set of indices for binary problems

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 246 of file Machine.cpp.

CLatentLabels * apply_locked_latent ( SGVector< index_t indices)
virtualinherited

applies a locked machine on a set of indices for latent problems

Definition at line 274 of file Machine.cpp.

CMulticlassLabels * apply_locked_multiclass ( SGVector< index_t indices)
virtualinherited

applies a locked machine on a set of indices for multiclass problems

Definition at line 260 of file Machine.cpp.

CRegressionLabels * apply_locked_regression ( SGVector< index_t indices)
virtualinherited

applies a locked machine on a set of indices for regression problems

Reimplemented in CKernelMachine.

Definition at line 253 of file Machine.cpp.

CStructuredLabels * apply_locked_structured ( SGVector< index_t indices)
virtualinherited

applies a locked machine on a set of indices for structured problems

Definition at line 267 of file Machine.cpp.

CMulticlassLabels * apply_multiclass ( CFeatures data = NULL)
virtual

classify data using Classification Tree NOTE : This method replaces all values of continuous attributes in supplied data with the actual breakpoint values used for classification

Parameters
datadata to be classified
Returns
MulticlassLabels corresponding to labels of various test vectors

Reimplemented from CMachine.

Definition at line 99 of file CHAIDTree.cpp.

virtual float64_t apply_one ( int32_t  i)
virtualinherited
CRegressionLabels * apply_regression ( CFeatures data = NULL)
virtual

Get regression labels using Regression Tree NOTE : This method replaces all values of continuous attributes in supplied data with the actual breakpoint values used for classification

Parameters
datadata whose regression output is needed
Returns
Regression output for various test vectors

Reimplemented from CMachine.

Definition at line 106 of file CHAIDTree.cpp.

CStructuredLabels * apply_structured ( CFeatures data = NULL)
virtualinherited

apply machine to data in means of SO classification problem

Reimplemented in CLinearStructuredOutputMachine.

Definition at line 234 of file Machine.cpp.

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 1189 of file SGObject.cpp.

void clear_feature_types ( )

clear feature types of various features

Definition at line 143 of file CHAIDTree.cpp.

void clear_weights ( )

clear weights of data points

Definition at line 127 of file CHAIDTree.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 1306 of file SGObject.cpp.

CTreeMachine* clone_tree ( )
inherited

clone tree

Returns
clone of entire tree

Definition at line 97 of file TreeMachine.h.

void data_lock ( CLabels labs,
CFeatures features 
)
virtualinherited

Locks the machine on given labels and data. After this call, only train_locked and apply_locked may be called

Only possible if supports_locking() returns true

Parameters
labslabels used for locking
featuresfeatures used for locking

Reimplemented in CKernelMachine.

Definition at line 120 of file Machine.cpp.

void data_unlock ( )
virtualinherited

Unlocks a locked machine and restores previous state

Reimplemented in CKernelMachine.

Definition at line 151 of file Machine.cpp.

CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 146 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 1210 of file SGObject.cpp.

float64_t get_alpha_merge ( ) const

get alpha_merge

Returns
a alpha_merge

Definition at line 207 of file CHAIDTree.h.

float64_t get_alpha_split ( ) const

get alpha_split

Returns
a alpha_split

Definition at line 217 of file CHAIDTree.h.

EMachineType get_classifier_type ( )
virtualinherited
int32_t get_dependent_vartype ( ) const

get dependent variable type : 0 for nominal, 1 for ordinal and 2 for continuous

Returns
integer corresponding to the dependent variable type

Definition at line 177 of file CHAIDTree.h.

SGVector< int32_t > get_feature_types ( ) const

get feature types of various features

Returns
vector with feature types : 0-nominal, 1-ordinal or 2-continuous

Definition at line 138 of file CHAIDTree.cpp.

SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 183 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 224 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 237 of file SGObject.cpp.

CLabels * get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 84 of file Machine.cpp.

EProblemType get_machine_problem_type ( ) const
virtual

get problem type - multiclass classification or regression

Returns
PT_MULTICLASS or PT_REGRESSION

Reimplemented from CBaseMulticlassMachine.

Definition at line 65 of file CHAIDTree.cpp.

float64_t get_max_train_time ( )
inherited

get maximum training time

Returns
maximum training time

Definition at line 95 of file Machine.cpp.

int32_t get_min_node_size ( ) const

get minimum node size

Returns
size min node size

Definition at line 197 of file CHAIDTree.h.

SGStringList< char > get_modelsel_names ( )
inherited
Returns
vector of names of all parameters which are registered for model selection

Definition at line 1081 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 1105 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 1118 of file SGObject.cpp.

virtual const char* get_name ( ) const
virtual

get name

Returns
class name CHAIDTree

Reimplemented from CTreeMachine< CHAIDTreeNodeData >.

Definition at line 114 of file CHAIDTree.h.

float64_t get_num_breakpoints ( ) const

get number of breakpoints

Returns
number of breakpoints

Definition at line 227 of file CHAIDTree.h.

int32_t get_num_machines ( ) const
inherited

get number of machines

Returns
number of machines

Definition at line 27 of file BaseMulticlassMachine.cpp.

CTreeMachineNode<CHAIDTreeNodeData >* get_root ( )
inherited

get root

Returns
root the root node of the tree

Definition at line 88 of file TreeMachine.h.

ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 110 of file Machine.cpp.

int32_t get_specified_max_tree_depth ( ) const

get max tree depth

Returns
d max tree depth

Definition at line 187 of file CHAIDTree.h.

SGVector< float64_t > get_weights ( ) const

get weights of data points

Returns
vector of weights

Definition at line 119 of file CHAIDTree.cpp.

bool is_data_locked ( ) const
inherited
Returns
whether this machine is locked

Definition at line 294 of file Machine.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
genericset to the type of the generic if returning TRUE
Returns
TRUE if a class template.

Definition at line 243 of file SGObject.cpp.

bool is_label_valid ( CLabels lab) const
virtual

whether labels supplied are valid for current problem type

Parameters
lablabels supplied
Returns
true for valid labels, false for invalid labels

Reimplemented from CBaseMulticlassMachine.

Definition at line 82 of file CHAIDTree.cpp.

DynArray< TParameter * > * load_all_file_parameters ( int32_t  file_version,
int32_t  current_version,
CSerializableFile file,
const char *  prefix = "" 
)
inherited

maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)

Parameters
file_versionparameter version of the file
current_versionversion from which mapping begins (you want to use Version::get_version_parameter() for this in most cases)
filefile to load from
prefixprefix for members
Returns
(sorted) array of created TParameter instances with file data

Definition at line 650 of file SGObject.cpp.

DynArray< TParameter * > * load_file_parameters ( const SGParamInfo param_info,
int32_t  file_version,
CSerializableFile file,
const char *  prefix = "" 
)
inherited

loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned

Parameters
param_infoinformation of parameter
file_versionparameter version of the file, must be <= provided parameter version
filefile to load from
prefixprefix for members
Returns
new array with TParameter instances with the attached data

Definition at line 491 of file SGObject.cpp.

bool load_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = Version::get_version_parameter() 
)
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
param_version(optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 320 of file SGObject.cpp.

void load_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::LOAD_SERIALIZABLE_POST is called.

Exceptions
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.

Definition at line 1008 of file SGObject.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 1003 of file SGObject.cpp.

void map_parameters ( DynArray< TParameter * > *  param_base,
int32_t &  base_version,
DynArray< const SGParamInfo * > *  target_param_infos 
)
inherited

Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match

Parameters
param_baseset of TParameter instances that are mapped to the provided target parameter infos
base_versionversion of the parameter base
target_param_infosset of SGParamInfo instances that specify the target parameter base

Definition at line 688 of file SGObject.cpp.

TParameter * migrate ( DynArray< TParameter * > *  param_base,
const SGParamInfo target 
)
protectedvirtualinherited

creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.

If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass

Parameters
param_baseset of TParameter instances to use for migration
targetparameter info for the resulting TParameter
Returns
a new TParameter instance with migrated data from the base of the type which is specified by the target parameter

Definition at line 895 of file SGObject.cpp.

void one_to_one_migration_prepare ( DynArray< TParameter * > *  param_base,
const SGParamInfo target,
TParameter *&  replacement,
TParameter *&  to_migrate,
char *  old_name = NULL 
)
protectedvirtualinherited

This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)

Parameters
param_baseset of TParameter instances to use for migration
targetparameter info for the resulting TParameter
replacement(used as output) here the TParameter instance which is returned by migration is created into
to_migratethe only source that is used for migration
old_namewith this parameter, a name change may be specified

Definition at line 835 of file SGObject.cpp.

bool parameter_hash_changed ( )
virtualinherited
Returns
whether parameter combination has changed since last update

Definition at line 209 of file SGObject.cpp.

virtual void post_lock ( CLabels labs,
CFeatures features 
)
virtualinherited

post lock

Reimplemented in CMultitaskLinearMachine.

Definition at line 285 of file Machine.h.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 1057 of file SGObject.cpp.

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

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 255 of file SGObject.cpp.

bool save_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = Version::get_version_parameter() 
)
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
param_version(optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 261 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 1018 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 1013 of file SGObject.cpp.

void set_alpha_merge ( float64_t  a)

set alpha_merge

Parameters
aalpha_merge

Definition at line 202 of file CHAIDTree.h.

void set_alpha_split ( float64_t  a)

set alpha_split

Parameters
aalpha_split

Definition at line 212 of file CHAIDTree.h.

void set_dependent_vartype ( int32_t  var)

set dependent variable type : 0 for nominal, 1 for ordinal and 2 for continuous

Parameters
varinteger corresponding to the dependent variable type

Definition at line 148 of file CHAIDTree.cpp.

void set_feature_types ( SGVector< int32_t >  ft)

set feature types of various features

Parameters
ftvector with feature types : 0-nominal, 1-ordinal or 2-continuous

Definition at line 133 of file CHAIDTree.cpp.

void set_generic< complex128_t > ( )
inherited

set generic type to T

Definition at line 38 of file SGObject.cpp.

void set_global_io ( SGIO io)
inherited

set the io object

Parameters
ioio object to use

Definition at line 176 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 189 of file SGObject.cpp.

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 230 of file SGObject.cpp.

void set_labels ( CLabels lab)
virtualinherited

set labels

Parameters
lablabels

Reimplemented in CNeuralNetwork, CGaussianProcessMachine, CCARTree, CStructuredOutputMachine, CRelaxedTree, and CMulticlassMachine.

Definition at line 73 of file Machine.cpp.

void set_max_train_time ( float64_t  t)
inherited

set maximum training time

Parameters
tmaximimum training time

Definition at line 90 of file Machine.cpp.

void set_max_tree_depth ( int32_t  d)

set max tree depth

Parameters
dmax tree depth

Definition at line 182 of file CHAIDTree.h.

void set_min_node_size ( int32_t  size)

set minimum node size

Parameters
sizemin node size

Definition at line 192 of file CHAIDTree.h.

void set_num_breakpoints ( int32_t  b)

set number of breakpoints

Parameters
bnumber of breakpoints

Definition at line 222 of file CHAIDTree.h.

void set_root ( CTreeMachineNode< CHAIDTreeNodeData > *  root)
inherited

set root

Parameters
rootthe root node of the tree

Definition at line 78 of file TreeMachine.h.

void set_solver_type ( ESolverType  st)
inherited

set solver type

Parameters
stsolver type

Definition at line 105 of file Machine.cpp.

void set_store_model_features ( bool  store_model)
virtualinherited

Setter for store-model-features-after-training flag

Parameters
store_modelwhether model should be stored after training

Definition at line 115 of file Machine.cpp.

void set_weights ( SGVector< float64_t w)

set weights of data points

Parameters
wvector of weights

Definition at line 113 of file CHAIDTree.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 140 of file SGObject.cpp.

virtual void store_model_features ( )
protectedvirtualinherited

enable unlocked cross-validation - no model features to store

Reimplemented from CMachine.

Definition at line 152 of file TreeMachine.h.

virtual bool supports_locking ( ) const
virtualinherited
Returns
whether this machine supports locking

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 291 of file Machine.h.

bool train ( CFeatures data = NULL)
virtualinherited

train machine

Parameters
datatraining data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data). If flag is set, model features will be stored after training.
Returns
whether training was successful

Reimplemented in CRelaxedTree, CAutoencoder, CSGDQN, and COnlineSVMSGD.

Definition at line 47 of file Machine.cpp.

virtual bool train_locked ( SGVector< index_t indices)
virtualinherited

Trains a locked machine on a set of indices. Error if machine is not locked

NOT IMPLEMENTED

Parameters
indicesindex vector (of locked features) that is used for training
Returns
whether training was successful

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 237 of file Machine.h.

bool train_machine ( CFeatures data = NULL)
protectedvirtual

train machine - build CHAID from training data

Parameters
datatraining data
Returns
true

Reimplemented from CMachine.

Definition at line 154 of file CHAIDTree.cpp.

virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

Reimplemented in COnlineLinearMachine, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree, and CLibSVMOneClass.

Definition at line 352 of file Machine.h.

void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 250 of file SGObject.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 196 of file SGObject.cpp.

Member Data Documentation

SGIO* io
inherited

io

Definition at line 457 of file SGObject.h.

bool m_data_locked
protectedinherited

whether data is locked

Definition at line 368 of file Machine.h.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 472 of file SGObject.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 478 of file SGObject.h.

CLabels* m_labels
protectedinherited

labels

Definition at line 359 of file Machine.h.

CDynamicObjectArray* m_machines
protectedinherited

machines

Definition at line 56 of file BaseMulticlassMachine.h.

float64_t m_max_train_time
protectedinherited

maximum training time

Definition at line 356 of file Machine.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 469 of file SGObject.h.

ParameterMap* m_parameter_map
inherited

map for different parameter versions

Definition at line 475 of file SGObject.h.

Parameter* m_parameters
inherited

parameters

Definition at line 466 of file SGObject.h.

CTreeMachineNode<CHAIDTreeNodeData >* m_root
protectedinherited

tree root

Definition at line 156 of file TreeMachine.h.

ESolverType m_solver_type
protectedinherited

solver type

Definition at line 362 of file Machine.h.

bool m_store_model_features
protectedinherited

whether model features should be stored after training

Definition at line 365 of file Machine.h.

const float64_t MISSING = CMath::MAX_REAL_NUMBER
static

denotes that a feature in a vector is missing MISSING = MAX_REAL_NUMBER

Definition at line 393 of file CHAIDTree.h.

Parallel* parallel
inherited

parallel

Definition at line 460 of file SGObject.h.

Version* version
inherited

version

Definition at line 463 of file SGObject.h.


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

SHOGUN Machine Learning Toolbox - Documentation