DataPredict

API Reference - Models - NaiveBayes

NaiveBayes 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]: probabilitiesMatrix. 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.

NaiveBayes.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.

NaiveBayes:setParameters(useLogProbabilities: boolean)

Parameters:

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

train()

Train the model.

NaiveBayes: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.

NaiveBayes: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()

NaiveBayes: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()

NaiveBayes: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

• BaseModel

Notes

• Untested. May give wrong model. Use at your own risk. (I am new at understanding this model)