API Reference - Models - KNearestNeighboursRegressor (KNN-R)

Stored Model Parameters

Contains a table of matrices.

  • ModelParameters[1]: Feature Matrix

  • ModelParameters[2]: Label Vector / Label Matrix

Constructors

new()

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

KNearestNeighboursRegressor.new(kValue: integer, distanceFunction: string, use, useWeightedDistance: boolean): ModelObject

Parameters:

  • kValue: The number of closest data points taken into consideration for majority voting to determine the class of a given data point.

  • distanceFunction: The distance function to be used to measure the similarity between two data points. Available options are:

    • Euclidean

    • Manhattan

    • Cosine

  • useWeightedDistance: Set whether or not to use distance as a factor for prediction.

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.

KNearestNeighboursRegressor:setParameters(kValue: integer, distanceFunction: string, useWeightedDistance: boolean)

Parameters:

  • kValue: The number of closest data points taken into consideration for majority voting to determine the class of a given data point.

  • distanceFunction: The distance function to be used to measure the similarity between two data points. Available options are:

    • Euclidean

    • Manhattan

    • Cosine

train()

Train the model.

KNearestNeighboursRegressor:train(featureMatrix: Matrix, labelVector: Matrix): number[]

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 the values for given data.

KNearestNeighboursRegressor: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:

  • predictedlabelVector: A vector tcontaining predicted labels generated from the model.

  • valueVector: A vector that contains the values of predicted labels.

-OR-

  • predictedMatrix: A matrix containing all distances between stored and given data points.

Inherited From