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NaiveBayesClassifier< ModelMatType > Class Template Reference

The simple Naive Bayes classifier. More...

Public Types

typedef ModelMatType::elem_type ElemType
 

Public Member Functions

template
<
typename
MatType
>
 NaiveBayesClassifier (const MatType &data, const arma::Row< size_t > &labels, const size_t numClasses, 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 numClasses=0)
 Initialize the Naive Bayes classifier without performing training. More...

 
template
<
typename
VecType
>
size_t Classify (const VecType &point) const
 Classify the given point, using the trained NaiveBayesClassifier model. More...

 
template
<
typename
VecType
,
typename
ProbabilitiesVecType
>
void Classify (const VecType &point, size_t &prediction, ProbabilitiesVecType &probabilities) const
 Classify the given point using the trained NaiveBayesClassifier model and also return estimates of the probability for each class in the given vector. More...

 
template
<
typename
MatType
>
void Classify (const MatType &data, arma::Row< size_t > &predictions) const
 Classify the given points using the trained NaiveBayesClassifier model. More...

 
template
<
typename
MatType
,
typename
ProbabilitiesMatType
>
void Classify (const MatType &data, arma::Row< size_t > &predictions, ProbabilitiesMatType &probabilities) const
 Classify the given points using the trained NaiveBayesClassifier model and also return estimates of the probabilities for each class in the given matrix. More...

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

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

 
const ModelMatType & Probabilities () const
 Get the prior probabilities for each class. More...

 
ModelMatType & Probabilities ()
 Modify the prior probabilities for each class. More...

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

 
template
<
typename
MatType
>
void Train (const MatType &data, const arma::Row< size_t > &labels, const size_t numClasses, 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 ModelMatType & Variances () const
 Get the sample variances for each class. More...

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

 

Detailed Description


template
<
typename
ModelMatType
=
arma::mat
>

class mlpack::naive_bayes::NaiveBayesClassifier< ModelMatType >

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. Along with 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);

The ModelMatType template parameter specifies the internal matrix type that NaiveBayesClassifier will use to hold the means, variances, and weights that make up the Naive Bayes model. This can be arma::mat, arma::fmat, or any other Armadillo (or Armadillo-compatible) object. Because ModelMatType may be different than the type of the data the model is trained on, now training is possible with subviews, sparse matrices, or anything else, while still storing the model as a ModelMatType internally.

Template Parameters
ModelMatTypeInternal matrix type to use to store the model.

Definition at line 58 of file naive_bayes_classifier.hpp.

Member Typedef Documentation

◆ ElemType

typedef ModelMatType::elem_type ElemType

Definition at line 62 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  numClasses,
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.
numClassesNumber 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  numClasses = 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() [1/4]

size_t Classify ( const VecType &  point) const

Classify the given point, using the trained NaiveBayesClassifier model.

The predicted label is returned.

Parameters
pointPoint to classify.

◆ Classify() [2/4]

void Classify ( const VecType &  point,
size_t &  prediction,
ProbabilitiesVecType &  probabilities 
) const

Classify the given point using the trained NaiveBayesClassifier model and also return estimates of the probability for each class in the given vector.

Parameters
pointPoint to classify.
predictionThis will be set to the predicted class of the point.
probabilitiesThis will be filled with class probabilities for the point.

◆ Classify() [3/4]

void Classify ( const MatType &  data,
arma::Row< size_t > &  predictions 
) const

Classify the given points using the trained NaiveBayesClassifier model.

The predicted labels for each point are stored in the given vector.

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.
predictionsVector that class predictions will be placed into.

◆ Classify() [4/4]

void Classify ( const MatType &  data,
arma::Row< size_t > &  predictions,
ProbabilitiesMatType &  probabilities 
) const

Classify the given points using the trained NaiveBayesClassifier model and also return estimates of the probabilities for each class in the given matrix.

The predicted labels for each point are stored in the given vector.

arma::mat test_data; // each column is a test point
arma::Row<size_t> results;
arma::mat resultsProbs;
...
nbc.Classify(test_data, results, resultsProbs);
Parameters
dataSet of points to classify.
predictionsThis will be filled with predictions for each point.
probabilitiesThis will be filled with class probabilities for each point. Each row represents a point.
Template Parameters
MatTypeType of data to be classified.
ProbabilitiesMatTypeType to store output probabilities in.

◆ Means() [1/2]

const ModelMatType& Means ( ) const
inline

Get the sample means for each class.

Definition at line 200 of file naive_bayes_classifier.hpp.

◆ Means() [2/2]

ModelMatType& Means ( )
inline

Modify the sample means for each class.

Definition at line 202 of file naive_bayes_classifier.hpp.

◆ Probabilities() [1/2]

const ModelMatType& Probabilities ( ) const
inline

Get the prior probabilities for each class.

Definition at line 210 of file naive_bayes_classifier.hpp.

◆ Probabilities() [2/2]

ModelMatType& Probabilities ( )
inline

Modify the prior probabilities for each class.

Definition at line 212 of file naive_bayes_classifier.hpp.

References NaiveBayesClassifier< ModelMatType >::Serialize().

◆ Serialize()

void Serialize ( Archive &  ar,
const unsigned  int 
)

Serialize the classifier.

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

◆ Train() [1/2]

void Train ( const MatType &  data,
const arma::Row< size_t > &  labels,
const size_t  numClasses,
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.
labelsThe labels for the dataset.
numClassesThe numbe of classes in the dataset.
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 ModelMatType& Variances ( ) const
inline

Get the sample variances for each class.

Definition at line 205 of file naive_bayes_classifier.hpp.

◆ Variances() [2/2]

ModelMatType& Variances ( )
inline

Modify the sample variances for each class.

Definition at line 207 of file naive_bayes_classifier.hpp.


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