The simple Naive Bayes classifier. More...

Public Member Functions
	NaiveBayesClassifier (const MatType &data, const arma::Row< size_t > &labels, const size_t classes, const bool incrementalVariance=false)
	Initializes the classifier as per the input and then trains it by calculating the sample mean and variances. More...

	NaiveBayesClassifier (const size_t dimensionality=0, const size_t classes=0)
	Initialize the Naive Bayes classifier without performing training. More...

void	Classify (const MatType &data, arma::Row< size_t > &results)
	Given a bunch of data points, this function evaluates the class of each of those data points, and puts it in the vector 'results'. More...

const MatType &	Means () const
	Get the sample means for each class. More...

MatType &	Means ()
	Modify the sample means for each class. More...

const arma::vec &	Probabilities () const
	Get the prior probabilities for each class. More...

arma::vec &	Probabilities ()
	Modify the prior probabilities for each class. More...

template<typename Archive >
void	Serialize (Archive &ar, const unsigned int)
	Serialize the classifier. More...

void	Train (const MatType &data, const arma::Row< size_t > &labels, const bool incremental=true)
	Train the Naive Bayes classifier on the given dataset. More...

template<typename VecType >
void	Train (const VecType &point, const size_t label)
	Train the Naive Bayes classifier on the given point. More...

const MatType &	Variances () const
	Get the sample variances for each class. More...

MatType &	Variances ()
	Modify the sample variances for each class. More...

Private Attributes
MatType	means
	Sample mean for each class. More...

arma::vec	probabilities
	Class probabilities. More...

size_t	trainingPoints
	Number of training points seen so far. More...

MatType	variances
	Sample variances for each class. More...

Detailed Description

template<typename MatType = arma::mat>
class mlpack::naive_bayes::NaiveBayesClassifier< MatType >

The simple Naive Bayes classifier.

This class trains on the data by calculating the sample mean and variance of the features with respect to each of the labels, and also the class probabilities. The class labels are assumed to be positive integers (starting with 0), and are expected to be the last row of the data input to the constructor.

Mathematically, it computes P(X_i = x_i | Y = y_j) for each feature X_i for each of the labels y_j. Alongwith this, it also computes the class probabilities P(Y = y_j).

For classifying a data point (x_1, x_2, ..., x_n), it computes the following: arg max_y(P(Y = y)*P(X_1 = x_1 | Y = y) * ... * P(X_n = x_n | Y = y))

Example use:

extern arma::mat training_data, testing_data;
NaiveBayesClassifier<> nbc(training_data, 5);
arma::vec results;
nbc.Classify(testing_data, results);

Definition at line 47 of file naive_bayes_classifier.hpp.

Constructor & Destructor Documentation

template<typename MatType = arma::mat>

mlpack::naive_bayes::NaiveBayesClassifier< MatType >::NaiveBayesClassifier	(	const MatType &	data,
		const arma::Row< size_t > &	labels,
		const size_t	classes,
		const bool	incrementalVariance = `false`
	)

Initializes the classifier as per the input and then trains it by calculating the sample mean and variances.

Example use:

extern arma::mat training_data, testing_data;
extern arma::Row<size_t> labels;
NaiveBayesClassifier nbc(training_data, labels, 5);

Parameters

data	Training data points.
labels	Labels corresponding to training data points.
classes	Number of classes in this classifier.
incrementalVariance	If true, an incremental algorithm is used to calculate the variance; this can prevent loss of precision in some cases, but will be somewhat slower to calculate.

template<typename MatType = arma::mat>

mlpack::naive_bayes::NaiveBayesClassifier< MatType >::NaiveBayesClassifier	(	const size_t	dimensionality = `0`,
		const size_t	classes = `0`
	)

Initialize the Naive Bayes classifier without performing training.

All of the parameters of the model will be initialized to zero. Be sure to use Train() before calling Classify(), otherwise the results may be meaningless.

Member Function Documentation

template<typename MatType = arma::mat>

void mlpack::naive_bayes::NaiveBayesClassifier< MatType >::Classify	(	const MatType &	data,
		arma::Row< size_t > &	results
	)

Given a bunch of data points, this function evaluates the class of each of those data points, and puts it in the vector 'results'.

arma::mat test_data; // each column is a test point
arma::Row<size_t> results;
...
nbc.Classify(test_data, &results);

Parameters

data	List of data points.
results	Vector that class predictions will be placed into.

template<typename MatType = arma::mat>

const MatType& mlpack::naive_bayes::NaiveBayesClassifier< MatType >::Means ( ) const

inline

Get the sample means for each class.

Definition at line 129 of file naive_bayes_classifier.hpp.

References mlpack::naive_bayes::NaiveBayesClassifier< MatType >::means.

template<typename MatType = arma::mat>

MatType& mlpack::naive_bayes::NaiveBayesClassifier< MatType >::Means ( )

inline

Modify the sample means for each class.

Definition at line 131 of file naive_bayes_classifier.hpp.

References mlpack::naive_bayes::NaiveBayesClassifier< MatType >::means.

template<typename MatType = arma::mat>

const arma::vec& mlpack::naive_bayes::NaiveBayesClassifier< MatType >::Probabilities ( ) const

inline

Get the prior probabilities for each class.

Definition at line 139 of file naive_bayes_classifier.hpp.

References mlpack::naive_bayes::NaiveBayesClassifier< MatType >::probabilities.

template<typename MatType = arma::mat>

arma::vec& mlpack::naive_bayes::NaiveBayesClassifier< MatType >::Probabilities ( )

inline

Modify the prior probabilities for each class.

Definition at line 141 of file naive_bayes_classifier.hpp.

References mlpack::naive_bayes::NaiveBayesClassifier< MatType >::probabilities, and mlpack::naive_bayes::NaiveBayesClassifier< MatType >::Serialize().

template<typename MatType = arma::mat>

template<typename Archive >

void mlpack::naive_bayes::NaiveBayesClassifier< MatType >::Serialize	(	Archive &	ar,
		const unsigned	int
	)

Serialize the classifier.

Referenced by mlpack::naive_bayes::NaiveBayesClassifier< MatType >::Probabilities().

template<typename MatType = arma::mat>

void mlpack::naive_bayes::NaiveBayesClassifier< MatType >::Train	(	const MatType &	data,
		const arma::Row< size_t > &	labels,
		const bool	incremental = `true`
	)

Train the Naive Bayes classifier on the given dataset.

If the incremental algorithm is used, the current model is used as a starting point (this is the default). If the incremental algorithm is not used, then the current model is ignored and the new model will be trained only on the given data. Note that even if the incremental algorithm is not used, the data must have the same dimensionality and number of classes that the model was initialized with. If you want to change the dimensionality or number of classes, either re-initialize or call Means(), Variances(), and Probabilities() individually to set them to the right size.

Parameters