MultiStumpLearner Class Reference

A multi threshold decision stump learner. More...

#include <MultiStumpLearner.h>

Inheritance diagram for MultiStumpLearner:

List of all members.

Public Member Functions
virtual BaseLearner *	create ()
	Returns itself as object.
virtual void	run (InputData *pData)
	Run the learner to build the classifier on the given data.
virtual void	save (ofstream &outputStream, const int numTabs=0)
	Save the current object information needed for classification, that is the threshold list.
virtual void	load (nor_utils::StreamTokenizer &st)
	Load the xml file that contains the serialized information needed for the classification and that belongs to this class.
Private Member Functions
virtual char	phi (double val, int classIdx)
	A discriminative function.
virtual void	findThresholds (InputData *pData, const int columnIdx, vector< double > &thresholds, vector< sRates > &mu, vector< char > &v)
	Find the thresholds (one for each class) for column columnIndex.
Private Attributes
vector< double >	_thresholds
	The thresholds (one for each class) of the decision stump.

---

Detailed Description

A multi threshold decision stump learner.

There is a threshold for every class.

Definition at line 50 of file MultiStumpLearner.h.

---

Member Function Documentation

virtual BaseLearner* create (   )  [inline, virtual]

Returns itself as object. Remarks: It uses the trick described in http://www.parashift.com/c++-faq-lite/serialization.html#faq-36.8 for the auto-registering classes. Date: 14/11/2005 Implements BaseLearner. Definition at line 60 of file MultiStumpLearner.h.

void findThresholds (  InputData *  pData, const int  columnIdx, vector< double > &  thresholds, vector< sRates > &  mu, vector< char > &  v )  [private, virtual]

Find the thresholds (one for each class) for column columnIndex. Parameters: pData  The pointer to the data columnIdx  The index of the data column thresholds  The thresholds to update mu  The The class-wise rates to update v  The alignment vector to update See also: StumpLearner::sMu run _thresholds Date: 11/11/2005 Definition at line 101 of file MultiStumpLearner.cpp. References StumpLearner::_bestErrors, StumpLearner::_halfWeightsPerClass, StumpLearner::_leftErrors, StumpLearner::_rightErrors, BaseLearner::_smallVal, StumpLearner::_weightsPerClass, InputData::getClass(), ClassMappings::getNumClasses(), and InputData::getWeight().

void load (  nor_utils::StreamTokenizer &  st  )  [virtual]

Load the xml file that contains the serialized information needed for the classification and that belongs to this class. Parameters: st  The stream tokenizer that returns tags and values as tokens See also: save() Date: 13/11/2005 Reimplemented from StumpLearner. Definition at line 265 of file MultiStumpLearner.cpp. References MultiStumpLearner::_thresholds, StumpLearner::load(), and UnSerialization::seekAndParseVectorTag().

char phi (  double  val, int  classIdx )  [private, virtual]

A discriminative function. Remarks: Positive or negative do NOT refer to positive or negative classification. This function is equivalent to the phi function in my thesis. Parameters: val  The value to discriminate classIdx  The index of the class Returns: +1 if val is on one side of the border for classIdx and -1 otherwise Date: 11/11/2005 See also: classify Implements StumpLearner. Definition at line 91 of file MultiStumpLearner.cpp. References MultiStumpLearner::_thresholds.

void run (  InputData *  pData  )  [virtual]

Run the learner to build the classifier on the given data. Parameters: pData  The pointer to the data See also: BaseLearner::run Date: 11/11/2005 Implements BaseLearner. Definition at line 38 of file MultiStumpLearner.cpp. References ClassMappings::getNumClasses().

void save (  ofstream &  outputStream, const int  numTabs = 0 )  [virtual]

Save the current object information needed for classification, that is the threshold list. Parameters: outputStream  The stream where the data will be saved numTabs  The number of tabs before the tag. Useful for indentation Remarks: To fully save the object it is very important to call also the super-class method. See also: StumpLearner::save() Date: 13/11/2005 Reimplemented from StumpLearner. Definition at line 254 of file MultiStumpLearner.cpp. References MultiStumpLearner::_thresholds, StumpLearner::save(), and Serialization::vectorTag().

---

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

• src/WeakLearners/MultiStumpLearner.h
• src/WeakLearners/MultiStumpLearner.cpp

---