Tanl::Classifier::LBFGS Class Reference
[Classifier]

Inheritance diagram for Tanl::Classifier::LBFGS:

List of all members.

Public Member Functions
	LBFGS (EventStream &es, int iterations, int cutoff=0, double eps=1E-05)
	Create and train a model from events from a single EventStream.
	LBFGS (int iterations=50, int cutoff=0, double eps=1E-05)
	The EventStream is supplied separately with method read().
void	train ()
	Train a model on events read with previous calls to read().
void	train (EventStream &es)
	Train a model on events read from.
void	save (char const *path)
	Save the model to file.
void	writeHeader (std::ofstream &ofs)
void	writeData (std::ofstream &ofs)
Protected Attributes
double *	lambda
	the model parameters

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Constructor & Destructor Documentation

Tanl::Classifier::LBFGS::LBFGS	(	EventStream &	es,
		int	iterations,
		int	cutoff = 0,
		double	eps = 1E-05
	)

Create and train a model from events from a single EventStream.

Parameters:

	es	the EventStream from which to read training events
	iterations	max number of iterations to perform
	cutoff	discard predicates that do not occur at least this many times in the training set
	eps	determines the terminating accuracy

References train().

Tanl::Classifier::LBFGS::LBFGS	(	int	iterations = 50,
		int	cutoff = 0,
		double	eps = 1E-05
	)

The EventStream is supplied separately with method read().

This is useful to supply several streams in turn (for instance data from several files).

Parameters:

	iterations	max number of iterations to perform
	cutoff	discard predicates that do not occur at least this many times in the training set
	eps	determines the terminating accuracy

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Member Function Documentation

void Tanl::Classifier::LBFGS::save

(

char const *

file

)

Save the model to file.

Format for the GIS maxent (.mem) files.

This format can be memory mapped.

1. GIS (model type identifier)

2. the correction constant (int)

3. the correction parameter (double)

4. # of outcomes (int)

• list of outcome names (string)

5. # of predicates (int)

• list of predicate names (string)

6. parameters

• # of groups (i.e. predicates with same set of outcomes)
• The following repeated for each group a. group size (gs), # group outcomes
  • The following repeated for each outcome: 1. outcome (i) 2. param[n + j, i], for 0 <= j < gs

Example of 5. and 6.: 7(# preds) Sunny(first pred. name) Happy Dry Humid Sad Cloudy Rainy 3(# groups) 1 1(group 1: 1 predicate, 1 outcome) 0(outcome 0) 2.4005893(param[0, 0]) 5 2(group 2: 5 predicates, 2 outcomes) 0(outcome 0) 2.1392054(param[1, 0]) -0.3270814(param[2, 0]) 0.2927261(param[3, 0]) -1.9319866(param[4, 0]) 1.1981091(param[5, 0]) 1(outcome 1) -4.7484765(param[1, 1]) 0.3342510(param[2, 1]) -0.3882752(param[3, 1]) 2.0065205(param[4, 1]) -2.1725304(param[5, 1]) 1 1(group 3: 1 predicate, 1 outcome) 1(outcome 1) 3.5907883(param[6, 1])

Reimplemented from Tanl::Classifier::MaxEnt.

void Tanl::Classifier::LBFGS::train

(

EventStream &

es

)

Train a model on events read from.

Parameters:

es.

References Tanl::Classifier::MaxEnt::read(), and train().

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

• classifier/LBFGS.h
• classifier/LBFGS.cpp