meta::classify::winnow Class Reference

Implements the Winnow classifier, a simplistic linear classifier for linearly-separable data. More...

#include <winnow.h>

Inheritance diagram for meta::classify::winnow:

Public Member Functions
	winnow (std::shared_ptr< index::forward_index > idx, double m=default_m, double gamma=default_gamma, size_t max_iter=default_max_iter)
	Constructs a winnow classifier with the given multiplier, error threshold, and maximum iterations. More...
void	train (const std::vector< doc_id > &docs) override
	Trains the winnow on the given training documents. More...
class_label	classify (doc_id d_id) override
	Classifies the given document. More...
void	reset () override
	Resets all learned information for this winnow so it may be re-learned.
Public Member Functions inherited from meta::classify::classifier
	classifier (std::shared_ptr< index::forward_index > idx)
virtual confusion_matrix	test (const std::vector< doc_id > &docs)
	Classifies a collection document into specific groups, as determined by training data; this function will make repeated calls to classify(). More...
virtual confusion_matrix	cross_validate (const std::vector< doc_id > &input_docs, size_t k, bool even_split=false, int seed=1)
	Performs k-fold cross-validation on a set of documents. More...

Static Public Attributes
static const constexpr double	default_m = 1.5
	The default \(m\) parameter.
static const constexpr double	default_gamma = 0.05
	The default \(gamma\) parameter.
static const constexpr size_t	default_max_iter = 100
	The default number of allowed iterations.
static const std::string	id = "winnow"
	The identifier for this classifier.

Private Member Functions
double	get_weight (const class_label &label, const term_id &term) const
void	zero_weights (const std::vector< doc_id > &docs)
	Initializes the weight vectors to zero for every class label. More...

Private Attributes
std::unordered_map< class_label, std::unordered_map< term_id, double > >	weights_
	The weight vectors for each class label.
const double	m_
	\(m\), the multiplicative learning rate.
const double	gamma_
	\(\gamma\), the error threshold.
const size_t	max_iter_
	The maximum number of iterations for training.

Additional Inherited Members
Protected Attributes inherited from meta::classify::classifier
std::shared_ptr< index::forward_index >	idx_
	the index that the classifer is run on

Detailed Description

Implements the Winnow classifier, a simplistic linear classifier for linearly-separable data.

As opposed to winnow (which uses an additive update rule), winnow uses a multiplicative update rule.

Constructor & Destructor Documentation

meta::classify::winnow::winnow	(	std::shared_ptr< index::forward_index >	idx,
		double	m = default_m,
		double	gamma = default_gamma,
		size_t	max_iter = default_max_iter
	)

Constructs a winnow classifier with the given multiplier, error threshold, and maximum iterations.

Parameters

idx	The index to run the classifier on
m	\(m\), the multiplicative learning rate
gamma	\(gamma\), the error threshold
max_iter	The maximum number of iterations for training

Member Function Documentation

void meta::classify::winnow::train ( const std::vector< doc_id > &  docs )

overridevirtual

Trains the winnow on the given training documents.

Maintains a set of weight vectors \(w_1,\ldots,w_K\) where \(K\) is the number of classes and updates them for each training document seen in each iteration. This continues until the error threshold is met or the maximum number of iterations is completed.

Parameters

docs	The training set

Implements meta::classify::classifier.

class_label meta::classify::winnow::classify ( doc_id  d_id )

overridevirtual

Classifies the given document.

The class label returned is \(\argmax_k(w_k^\intercal x_n + b)\)—in other words, the class whose associated weight vector gives the highest result.

Parameters

doc	The document to be classified

Returns

the class label determined for the document

Implements meta::classify::classifier.

double meta::classify::winnow::get_weight ( const class_label &  label, const term_id &  term  ) const

private

Returns

the given term's weight in the weight vector for the given class

Parameters

label	The class label for the weight vector we want
term	The term whose weight should be returned

void meta::classify::winnow::zero_weights ( const std::vector< doc_id > &  docs )

private

Initializes the weight vectors to zero for every class label.

Parameters

docs	The set of documents to collect class labels from.

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

• /home/chase/projects/meta/include/classify/classifier/winnow.h
• /home/chase/projects/meta/src/classify/classifier/winnow.cpp