DataPredict

API Reference - Others - DistributedModelParameters

DistributedTraining is a base class for distributed learning. The individual child models will calculate their own model parameters and these will create a new main model parameters using average.

Notes:

• The child models must be created separately. Then use addModel() to put it inside the DistributedLearning object.

Constructors

new()

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

DistributedModelParameters.new(totalNumberOfChildModelUpdatesToUpdateMainModel: number): DistributedLearningObject

Parameters:

• totalNumberOfChildModelUpdatesToUpdateMainModel: The required total number of reinforce() and train() function calls from all child models to update the main model.

Returns:

• DistributedLearningObject: The generated distributed learning object.

Functions

setParameters()

Set model’s parameters. When any of the arguments are nil, previous argument values for that argument will be used.

DistributedModelParameters:setParameters(totalNumberOfChildModelUpdatesToUpdateMainModel: number)

Parameters:

• totalNumberOfChildModelUpdatesToUpdateMainModel: The required total number of reinforce() and train() function calls from all child models to update the main model.

addModel()

DistributedModelParameters:addModel(Model: ModelObject)

Parameters:

• Model: The child model to be added to main model.

setModelParametersMerger()

Sets the ModelParametersMerger into the DistributedModelParameters.

DistributedModelParameters:setModelParametersMerger(ModelParametersMerger: ModelParametersMergerObject)

ModelParametersMerger: A ModelParametersMerger object to be used by the DistributedModelParameters object

setMainModelParameters()

DistributedModelParameters:setMainModelParameters(MainModelParameters: any)

Parameters:

• MainModelParameters: The model parameters for the main model.

getMainModelParameters()

DistributedModelParameters:getMainModelParameters(): any

Returns:

• MainModelParameters: The model parameters for the main model.

train()

Train a model for a given model number.

DistributedModelParameters:train(featureMatrix: Matrix, labelVector/labelMatrix: matrix, modelNumber: number): []

Parameters:

• featureMatrix: Matrix containing all data.

• labelVector / labelMatrix: A (n x 1) / (n x o) matrix containing values related to featureMatrix. When using the label matrix, the number of columns must be equal to number of classes.

• modelNumber: The model number for a model to be trained.

Returns:

• costArray: An array containing all the costs

predict()

Predict values for the given data using the specified model number.

DistributedModelParameters:predict(featureMatrix: Matrix, returnOriginalOutput: boolean,  modelNumber: number): 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.

• modelNumber: The model number for a model to return the prediction.

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 predicted values from all classes.

reinforce()

Reward or punish a model from a model number based on the current state of the environment.

DistributedModelParameters:reinforce(currentFeatureVector: Matrix, rewardValue: number, returnOriginalOutput: boolean, modelNumber: number): integer, number -OR- Matrix

Parameters:

• currentFeatureVector: Matrix containing data from the current state.

• rewardValue: The reward value added/subtracted from the current state (recommended value between -1 and 1, but can be larger than these values).

• returnOriginalOutput: Set whether or not to return predicted vector instead of value with highest probability.

• modelNumber: The model number for a model to be reinforced.

Returns:

• predictedLabel: A label that is predicted by the model.

• value: The value of predicted label.

-OR-

• predictedVector: A matrix containing all predicted values from all classes.

getCurrentTotalNumberOfChildModelUpdatesToUpdateMainModel()

DistributedModelParameters:getCurrentTotalNumberOfChildModelUpdatesToUpdateMainModel(): number

Returns:

• getCurrentTotalNumberOfChildModelUpdatesToUpdateMainModel: The current total number of reinforcements from all child models.

reset()

Reset the main model’s stored values (excluding the parameters).

DistributedModelParameters:reset()

destroy()

Destroys the model object.

DistributedModelParameters:destroy()