DataPredict

API Reference - Models - KNearestNeighbours (KNN)

LinearRegression is a supervised machine learning model that predicts continuous values (e.g. 1.2, -32, 90, -1.2 and etc. ). It uses iterative calculations to find the best model parameters.

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.

KNearestNeighbours.new(kValue: integer, distanceFunction: string): 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

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.

KNearestNeighbours:setParameters(kValue: integer, distanceFunction: string)

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.

KNearestNeighbours: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.

KNearestNeighbours: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

• BaseModel