Parametric Naive Bayes Classifier

An implementation of the Naive Bayes Classifier, used for classification.

:- use_module('path/to/.../src/methods/naive_bayes_classifier/naive_bayes_classifier.pl').

%% usage example
TrainData = [5.1,3.5,1.4, 4.9,3.0,1.4, 4.7,3.2,1.3, 4.6,3.1,1.5],
TestData = [3,2,0, 5,1,4, 0,0,4, 3,3,5, 0,5,5, 2,5,5],
nbc_initModelWithTrain(TrainData, 3, [0,1,0,1], 2, 0, 0.000001),
nbc_classifyMatrix(TestData, 3, PredictionList, _, ProbabilitiesList).

Available Predicates

links/resources

nbc_initModelWithTrain/6

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

%% predicate definition
nbc_initModelWithTrain(DataList, DataRows, LabelsList, NumClasses, IncrementalVar, Epsilon) :-
        NumClasses >= 0,
        Epsilon >= 0,
        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
        convert_list_to_float_array(LabelsList, array(Ysize, Y)),
        initModelWithTrainI(X, Xsize, Xrownum, Y, Ysize, NumClasses, IncrementalVar, Epsilon).

%% foreign c++ predicate definition 
foreign(initModelWithTrain, c, initModelWithTrainI( +pointer(float_array), +integer, +integer, 
                                                    +pointer(float_array), +integer, 
                                                    +integer, 
                                                    +integer, 
                                                    +float32)).

Parameters

Name	Type	Description	Default
data	+matrix	Training data points.	-
labels	+vector	Labels corresponding to training data points.	-
numClasses	+integer	Number of classes in this classifier.	0
incrementalVariance	+integer(bool)	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.	(0)false
epsilon	+float	Small value to prevent log of zero.	1e-10

nbc_initModelNoTrain/3

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.

%% predicate definition
nbc_initModelNoTrain(Dimensionality, NumClasses, Epsilon) :-
        Dimensionality > 0,
        NumClasses >= 0,
        Epsilon >= 0,
        initModelNoTrainI(Dimensionality, NumClasses, Epsilon).

%% foreign c++ predicate definition 
foreign(initModelNoTrain, c, initModelNoTrainI( +integer, 
                                                +integer, 
                                                +float32)).

Parameters

Name	Type	Description	Default
dimensionality	+integer	Dimensionality of of the Model for the Datapoints	1
numClasses	+integer	Number of classes in this classifier.	0
epsilon	+float	Small value to prevent log of zero.	1e-10

nbc_classifyMatrix/5

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.

%% predicate definition
nbc_classifyMatrix(DataList, DataRows, PredictionList, ProbsList, ZCols) :-
        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
        classifyMatrixI(X, Xsize, Xrows, Y, Ysize, Z, ZCols, ZRows),
        convert_float_array_to_list(Y, Ysize, PredictionList),
        convert_float_array_to_2d_list(Z, ZCols, ZRows, ProbsList).

%% foreign c++ predicate definition 
foreign(classifyMatrix, c, classifyMatrixI( +pointer(float_array), +integer, +integer, 
                                            -pointer(float_array), -integer, 
                                            -pointer(float_array), -integer, -integer)).

Parameters

Name	Type	Description	Default
data	+matrix	Set of points to classify.	-
predictions	-vector	Filled with predictions for each point.	-
probabilities	-matrix	Filled with class probabilities for each point. Each row represents a point.	-

nbc_means/2

Get the sample means for each class.

%% predicate definition
nbc_means(MeansList, XCols) :-
        meansI(X, XCols, XRows),
        convert_float_array_to_2d_list(X, XCols, XRows, MeansList).

%% foreign c++ predicate definition 
foreign(means, c, meansI(-pointer(float_array), -integer, -integer)).

Parameters

Name	Type	Description	Default
means	-matrix	Get the sample means for each class.	-

nbc_probabilities/2

Get the prior probabilities for each class.

%% predicate definition
nbc_probabilities(ProbabilitiesList, XCols) :-
        probabilitiesI(X, XCols, XRows),
        convert_float_array_to_2d_list(X, XCols, XRows, ProbabilitiesList).

%% foreign c++ predicate definition 
foreign(probabilities, c, probabilitiesI(-pointer(float_array), -integer, -integer)).

Parameters

Name	Type	Description	Default
probabilities	-matrix	Get the prior probabilities for each class.	-

nbc_trainMatrix/5

Train the Naive Bayes classifier on the given dataset. The data must be the same dimensionality as the existing model parameters.

%% predicate definition
nbc_trainMatrix(DataList, DataRows, LabelsList, NumClasses, IncrementalVar) :-
        NumClasses >= 0,
        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
        convert_list_to_float_array(LabelsList, array(Ysize, Y)),
        trainMatrixI(X, Xsize, Xrownum, Y, Ysize, NumClasses, IncrementalVar).

%% foreign c++ predicate definition 
foreign(trainMatrix, c, trainMatrixI( +pointer(float_array), +integer, +integer, 
                                      +pointer(float_array), +integer, 
                                      +integer, 
                                      +integer)).

Parameters

Name	Type	Description	Default
data	+matrix	Training data points.	-
labels	+vector	Labels corresponding to training data points.	-
numClasses	+integer	Number of classes in this classifier.	0
incrementalVariance	+integer(bool)	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.	(1)true

nbc_trainPoint/2

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.

%% predicate definition 
nbc_trainPoint(DataList, Label) :-
        convert_list_to_float_array(DataList, array(Xsize, X)),
        trainPointI(X, Xsize, Label).

%% foreign c++ predicate definition 
foreign(trainPoint, c, trainPointI( +pointer(float_array), +integer, 
                                    +integer)).

Parameters

Name	Type	Description	Default
point	+vector	Data point to train on.	-
label	+integer	Label of data point.	-

nbc_variances/2

Get the sample variances for each class.

%% predicate definition 
nbc_variances(VariancesList, XCols) :-
        variancesI(X, XCols, XRows),
        convert_float_array_to_2d_list(X, XCols, XRows, VariancesList).

%% foreign c++ predicate definition 
foreign(variances, c, variancesI(-pointer(float_array), -integer, -integer)).

Parameters

Name	Type	Description	Default
variances	-matrix	Get the sample variances for each class.	-

Connected Links/Resources

If you want a more detailed explanation, then go to the python documentation. There is most of the time a good explanation on how the methods work and what the parameters do.

added some of the links from the python documentation

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