DataPredict

API Reference - Models - SupportVectorMachine

SupportVectorMachine is a supervised machine learning model that predicts values of -1 and 1 only.

Stored Model Parameters

Contains a matrix.

• ModelParameters[I][J]: Value of matrix at row I and column J. The rows 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.

SupportVectorMachine.new(maximumNumberOfIterations: integer, cValue: number, kernelFunction: string, kernelParameters: table): ModelObject

Parameters:

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

• learningRate: The speed at which the model learns. Recommended that the value is set between (0 to 1).

• cValue: How strict should the model can classify the data correctly. Higher the cValue, the closer the data points to the decision boundary.

• kernelFunction: The kernel function to be used to train the model. Available options are:

  • Linear

  • Polynomial

  • RadialBasisFunction

  • Sigmoid

  • Cosine

• kernelParameters: A table containg the required parameters

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.

SupportVectorMachine:setParameters(maximumNumberOfIterations: integer, cValue: number, kernelFunction: string, kernelParameters: table)

Parameters:

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

• learningRate: The speed at which the model learns. Recommended that the value is set between (0 to 1).

• cValue: How strict should the model can classify the data correctly. Higher the cValue, the closer the data points to the decision boundary.

• kernelFunction: The kernel function to be used to train the model. Available options are:

  • Linear

  • Polynomial

  • RadialBasisFunction

  • Sigmoid

  • Cosine

• kernelParameters: A table containg the required parameters

setCValue()

Set how hard the margin should be.

SupportVectorMachine:setCValue(cValue: number)

Parameters:

• cValue: The value of c to be used.

train()

Train the model.

SupportVectorMachine: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.

SupportVectorMachine:predict(featureMatrix: Matrix, returnOriginalOutput): Matrix -OR- Matrix

Parameters:

• featureMatrix: Matrix containing all data.

Returns:

• predictedVector: A vector that is predicted by the model.

-OR-

• originalPredictedVector: A vector that contains the original predicted values.

Inherited From

• BaseModel

References

• Kernel Methods and Support Vector Machines by Zhiyuan Chen from University Of Nottingham Malaysia (2022/2023). It is the university that Aqwam got his Bachelor’s degree in.