DataPredict

API Reference - Others - ModelChecker

Constructors

new()

Creates a new ModelParametersMerger object. If any of the arguments are not given, default argument values for that argument will be used.

ModelChecker.new(Model: ModelObject, modelType: string, maxNumberOfIterations: integer, maxGeneralizationError: number): ModelCheckerObject

Parameters:

• Model: The model object needed to perform testing and validation.

• modelType: The type where the model falls under. Available options are “Regression” and “Classification”.

• maxNumberOfIterations: How many times should the model needed to be trained if it is being validated.

• maxGeneralizationError: The maximum generalization error so that it stops training if it is being validated. It is calculated by subtracting the validation cost and the training cost.

Returns:

• ModelCheckerObject: The generated ModelParametersMerger object.

Functions

setParameters()

Set ModelParametersMerger’s parameters. When any of the arguments are not given, previous argument values for that argument will be used.

ModelChecker:setParameters(Model: ModelObject, modelType: string, maxNumberOfIterations: integer, maxGeneralizationError: number)

Parameters:

• Model: The model object needed to perform testing and validation.

• modelType: The type where the model falls under. Available options are “Regression” and “Classification”.

• maxNumberOfIterations: How many times should the model needed to be trained if it is being validated.

• maxGeneralizationError: The maximum generalization error so that it stops training if it is being validated. It is calculated by subtracting the validation cost and the training cost.

setClassesList()

Set the feature matrix and the label vector to perform certain merges.

ModelChecker: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.

test()

Test the model.

ModelChecker:test(testFeatureMatrix: Matrix, testLabelVector: Matrix): number, Matrix, Matrix

Parameters:

• testFeatureMatrix: Matrix containing all data for testing.

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

Returns:

• calculatedError: An array containing all the testing cost.

• errorVector: A vector that contains the error values during the testing.

• predictedLabelMatrix: A matrix that contains all the predicted values generated by the model.

validate()

Validate the model.

ModelChecker:validate(trainFeatureMatrix: Matrix, trainLabelVector: Matrix, validationFeatureMatrix: Matrix, validationLabelVector: Matrix): number[], number[]

Parameters:

• trainFeatureMatrix: Matrix containing all data for training.

• trainLabelVector: A (n x 1) matrix containing values related to trainFeatureMatrix.

• testFeatureMatrix: Matrix containing all data for validation.

• testLabelVector: A (n x 1) matrix containing values related to testFeatureMatrix.

Returns:

• trainErrorArray: An array containing all the training cost.

• validationErrorArray: An array containing all the validation cost.