API Reference - Models - IterativeMethodBaseModel
The base model for all machine and deep learning models.
Constructors
new()
Creates a new machine learning base model.
IterativeMethodBaseModel.new(): BaseModelObject
Functions
setNumberOfIterationsPerCostCalculation()
Set the number of iterations needed to calculate the costs. This is to save computational time.
BaseModel:setModelParameters(numberOfIterationsPerCostCalculation: number)
Parameters
- numberOfIterationsPerCostCalculation: The number of iterations for each cost calculation.
setNumberOfIterationsToCheckIfConverged()
Set the number of iterations needed to confirm convergence.
IterativeMethodBaseModel:setNumberOfIterationsToCheckIfConverged(numberOfIterations: number)
Parameters
- numberOfIterations: The number of iterations for confirming convergence.
setTargetCost()
Set the upper bound and lower bounds of the target cost.
IterativeMethodBaseModel:setTargetCost(upperBound: number, lowerBound: number)
Parameters
-
upperBound: The upper bound of target cost.
-
lowerBound: The lower bound of target cost.
setWaitDurations()
Set wait durations inside the models to avoid exhausting script running time.
IterativeMethodBaseModel:setWaitDurations(iterationWaitDuration: number, dataWaitDuration: number, sequenceWaitDuration: number)
Parameters:
-
iterationWaitDuration: The wait duration between the iterations.
-
dataWaitDuration: The wait duration between the data calculations.
-
sequenceWaitDuration: The wait duration between the token sequence.