API Reference - Models - GaussianNaiveBayes

GaussianNaiveBayes is an supervised machine learning model that predicts which classes that the input belongs to using probability.

Stored Model Parameters

Contains a table of matrices.

  • ModelParameters[1]: meanMatrix. The columns are the features.

  • ModelParameters[2]: standardDeviationMatrix. The columns are the features.

  • ModelParameters[3]: priorProbabilityMatrix. The columns are the features.

Constructors

new()

Create new model object. If any of the arguments are nil, default argument values for that argument will be used.

GaussianNaiveBayes.new(useLogProbabilities: boolean): ModelObject

Parameters:

  • useLogProbabilities: Convert the probabilities to larger values using log function.

Returns:

  • ModelObject: The generated model object.

Functions

setParameters()

Set model’s parameters. When any of the arguments are nil, previous argument values for that argument will be used.

GaussianNaiveBayes:setParameters(useLogProbabilities: boolean)

Parameters:

  • useLogProbabilities: Convert the probabilities to larger values using log function.

train()

Train the model.

GaussianNaiveBayes:train(featureMatrix: Matrix, labelVector: Matrix)

Parameters:

  • featureMatrix: Matrix containing all data.

  • labelVector: A (n x 1) matrix containing values related to featureMatrix.

Returns:

  • costArray: An array containing cost values.

predict()

Predict which cluster does it belong to for a given data.

GaussianNaiveBayes:predict(featureMatrix: Matrix, returnOriginalOutput: boolean): Matrix, Matrix -OR- Matrix

Parameters:

  • featureMatrix: Matrix containing all data.

  • returnOriginalOutput: Set whether or not to return predicted matrix instead of value with highest probability.

Returns:

  • predictedVector: A vector that is predicted by the model.

  • probabilityVector: A vector that contains the probability of predicted values in predictedVector.

-OR-

  • predictedMatrix: A matrix containing all predicted values from all classes.

getClassesList() 

GaussianNaiveBayes:getClassesList(): []

Returns:

  • ClassesList: A list of classes. The index of the class relates to which the neuron at output layer belong to. For example, {3, 1} means that the output for 3 is at first neuron, and the output for 1 is at second neuron.

setClassesList() 

GaussianNaiveBayes:setClassesList(ClassesList: [])

Parameters:

  • ClassesList: A list of classes. The index of the class relates to which the neuron at output layer belong to. For example, {3, 1} means that the output for 3 is at first neuron, and the output for 1 is at second neuron.

Inherited From