mlpack  2.2.5
NaiveBayesClassifier< MatType > Class Template Reference

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...

 

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

◆ NaiveBayesClassifier() [1/2]

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
dataTraining data points.
labelsLabels corresponding to training data points.
classesNumber of classes in this classifier.
incrementalVarianceIf 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.

◆ NaiveBayesClassifier() [2/2]

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

◆ Classify()

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'.

arma::mat test_data; // each column is a test point
arma::Row<size_t> results;
...
nbc.Classify(test_data, &results);
Parameters
dataList of data points.
resultsVector that class predictions will be placed into.

◆ Means() [1/2]

const MatType& Means ( ) const
inline

Get the sample means for each class.

Definition at line 129 of file naive_bayes_classifier.hpp.

◆ Means() [2/2]

MatType& Means ( )
inline

Modify the sample means for each class.

Definition at line 131 of file naive_bayes_classifier.hpp.

◆ Probabilities() [1/2]

const arma::vec& Probabilities ( ) const
inline

Get the prior probabilities for each class.

Definition at line 139 of file naive_bayes_classifier.hpp.

◆ Probabilities() [2/2]

arma::vec& Probabilities ( )
inline

Modify the prior probabilities for each class.

Definition at line 141 of file naive_bayes_classifier.hpp.

References NaiveBayesClassifier< MatType >::Serialize().

◆ Serialize()

void Serialize ( Archive &  ar,
const unsigned  int 
)

Serialize the classifier.

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

◆ Train() [1/2]

void 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
dataThe dataset to train on.
incrementalWhether or not to use the incremental algorithm for training.

◆ Train() [2/2]

void Train ( const VecType &  point,
const size_t  label 
)

Train the Naive Bayes classifier on the given point.

This will use the incremental algorithm for updating the model parameters. The data must be the same dimensionality as the existing model parameters.

Parameters
pointData point to train on.
labelLabel of data point.

◆ Variances() [1/2]

const MatType& Variances ( ) const
inline

Get the sample variances for each class.

Definition at line 134 of file naive_bayes_classifier.hpp.

◆ Variances() [2/2]

MatType& Variances ( )
inline

Modify the sample variances for each class.

Definition at line 136 of file naive_bayes_classifier.hpp.


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