DataPredict

API Reference - Models - MeanShift

MeanShift is a an unsupervised machine learning model that finds cluster centers by moving points towards higher density regions.

Stored Model Parameters

Contains a matrix.

• ModelParameters[I][J]: Value of matrix at row I and column J. The rows represent the clusters. The columns represent the features.

Constructors

new()

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

MeanShift.new(maximumNumberOfIterations: integer, bandwidth: number, distanceFunction: string, kernelFunction: string, kernelParameters: table): ModelObject

Parameters:

• maximumNumberOfIterations: How many times should the model needed to be trained.

• bandwidth: The size of the area around each data point.

• distanceFunction: The function to calculate the distance between the data points and the centroids. Available options are:

  • Euclidean (Default)

  • Manhattan

  • Cosine

• kernelFunction: The function used to kernelize the distance between the data points and the centroids. Available options are:

• Gaussian (Default)

• Linear

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.

MeanShift:setParameters(maximumNumberOfIterations: integer, bandwidth: number, distanceFunction: string, kernelFunction: string, kernelParameters: table)

Parameters:

• maximumNumberOfIterations: How many times should the model needed to be trained.

• bandwidth: The size of the area around each data point.

• bandwidthStep: The size of the update for each clusters.

• distanceFunction: The function to calculate the distance between the data points and the centroids. Available options are:

  • Euclidean (Default)

  • Manhattan

  • Cosine

• kernelFunction: The function used to kernelize the distance between the data points and the centroids. Available options are:

• Gaussian (Default)

• Linear

train()

Train the model.

MeanShift:train(featureMatrix: Matrix)

Parameters:

• featureMatrix: Matrix containing all data.

Returns:

• costArray: An array containing cost values.

predict()

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

MeanShift:predict(featureMatrix: Matrix): integer, number

Parameters:

• featureMatrix: Matrix containing data.

Returns:

• clusterNumber: The cluster which the data belongs to.

• shortestDistance: The distance between the datapoint and the center of the cluster (centroids).

Inherited From

• BaseModel