Gaussian Process

This solver uses lazy learning and a measure of the similarity between points (the kernel function) to predict the value for an unseen point from training data. The prediction is not just an estimate for that point, but also has uncertainty information — it is a one-dimensional Gaussian distribution.

Usage

e["Solver"]["Type"] = "Learner/GaussianProcess"

Results

These are the results produced by this solver:

Variable-Specific Settings

These are settings required by this module that are added to each of the experiment’s variables when this module is selected.

Configuration

These are settings required by this module.

Covariance Function

  • Usage: e[“Solver”][“Covariance Function”] = string

  • Description: Covariance function for the libgp library.

Default Hyperparameter

  • Usage: e[“Solver”][“Default Hyperparameter”] = float

  • Description: Default value of the hyperparameters, used to initialize the Gaussian Processes.

Optimizer

  • Usage: e[“Solver”][“Optimizer”] = knlohmann::json

  • Description: Represents the state and configuration of the optimization algorithm.

Termination Criteria

These are the customizable criteria that indicates whether the solver should continue or finish execution. Korali will stop when at least one of these conditions are met. The criteria is expressed in C++ since it is compiled and evaluated as seen here in the engine.

Terminate With Optimizer

  • Usage: e[“Solver”][“Terminate With Optimizer”] = True/False

  • Description: Execution will end as soon as the internal optimizer reaches one of its termination criteria.

  • Criteria: _terminateWithOptimizer && _koraliExperiment._solver->checkTermination()

Max Model Evaluations

  • Usage: e[“Solver”][“Max Model Evaluations”] = unsigned integer

  • Description: Specifies the maximum allowed evaluations of the computational model.

  • Criteria: _maxModelEvaluations <= _modelEvaluationCount

Max Generations

  • Usage: e[“Solver”][“Max Generations”] = unsigned integer

  • Description: Determines how many solver generations to run before stopping execution. Execution can be resumed at a later moment.

  • Criteria: _k->_currentGeneration > _maxGenerations

Default Configuration

These following configuration will be assigned by default. Any settings defined by the user will override the given settings specified in these defaults.

{
"Default Hyperparameter": 0.1,
"Model Evaluation Count": 0,
"Termination Criteria": {
    "Max Generations": 10000000000,
    "Max Model Evaluations": 1000000000,
    "Terminate With Optimizer": true
    },
"Variable Count": 0
}

Variable Defaults

These following configuration will be assigned to each of the experiment variables by default. Any settings defined by the user will override the given settings specified in these defaults.

{    }