►CAbstractSampleQueueManager | This class manages input, output and pending queues for samples that need to be evaluated or have been evaluated |
►CDatasetDataSource | This class implements a dataset lookup sample evaluator |
CGriddedDatasetDataSource | This class implements a gridded dataset lookup sample evaluator |
CScatteredDatasetDataSource | This class implements a scattered dataset lookup sample evaluator |
►CDefaultSampleQueueManager | |
CBasicSampleQueueManager | This class implements a simple first-in-first-out queue |
CPrioritySampleQueueManager | This class implements a priority FIFO queue, points with the highest priority are fetched first |
►CBasicGaussianProcess | A kriging surrogate model (also known as a Gaussian Process) |
CCoKriging | A cokriging surrogate model |
►CKriging | A kriging surrogate model |
CBlindKriging | A blind kriging surrogate model |
►CCandidateGenerator | An abstract class that provides an easy and convenient interface for generating candidates, either by subclassing from this one, or by just calling it directly, through a function |
CDelaunayCandidateGenerator | Generates samples based on a Delaunay triangulation |
CProjectedDistanceGridCandidateGenerator | Generates all the local optima for the projected distance criterion exactly, by using the inherent properties of the surface |
CProjectedThresholdRandomCandidateGenerator | TODO |
CRandomCandidateGenerator | Generates candidates from the uniform distribution |
CRandomZoomCandidateGenerator | Generates candidates from the uniform distribution near existing maximin samples |
►CCandidateRanker | An interface that allows the object to score a set of candidates according to its own system |
CAggregateObjective | Aggregates several candidateRankers into one score (using a weighted average) |
CAudzeEglaisDistance | Selects the candidates with the highest AudzeEglais distance to existing points |
Ccrowdedness | Calculates the crowdedness at a given design x or in this case, for all designs in 'points') |
CdelaunayVolume | Compute the volume of the delaunay triangle containing each candidate |
CdimensionDistance | Calculate the non-collapsing factor of the candidates |
CEnsembleRanker | Calculates the expected improvement statistical infill criterion |
►CexpectedImprovement | Calculates the expected improvement statistical infill criterion |
CknowledgeGradient | Calculates the knowledge gradient statistical infill criterion |
CexpectedImprovementEuclidean | Calculates the expected improvement statistical infill criterion |
CexpectedImprovementHypervolume | Calculates the hypervolume-based expected improvement statistical infill criterion |
CfuzzySpaceFilling | Space-filling score based on fuzzy logic |
CgExpectedImprovement | Calculates the Generalized Expected Improvement |
CgProbabilityOfFeasibility | Calculates the generalized probability of feasibility for a point |
ChypervolumePoI | Quantifies the PoI with the hypervolume contribution (for MOSBO problems) |
Ckushner | Calculates the kushner criterion |
ClowerConfidenceBound | Calculates the lower confidence bound |
Clrm | The LRM candidate ranker |
CmaximinDistance | Selects the candidates with the highest maximin distance to existing points |
CmaximinManhattanDistance | Selects the candidates with the highest maximin distance to existing points |
CmodelDifference | Calculates the difference between the last nrModels sumo models |
CmodelEvaluate | Simple function that evaluates the model directly |
CmodelVariance | Calculates the prediction variance |
CphiDistance | Selects the candidates with the highest maximin distance to existing points |
CpredictiveEntropy | Calculates the expected improvement statistical infill criterion |
CprobabilityOfFeasibility | Calculates the probability of feasibility for a point |
CprobabilityOfImprovement | Calculates the probability of improvement for a point |
CProjectedDistanceThreshold | A criterion that ranks all points according to their projected distance score |
CpsiDistance | Selects the candidates with the highest psi distance to existing points |
CrationalPoleSupression | Promotes points that makes the denominator zero, thus finding poles |
Cwb1 | Calculates the threshold-bounded extreme |
Cwb2 | Locates the regional extreme |
CwExpectedImprovement | Weighted expected improvement |
CChangeEventListener | |
►CCloneable | |
CProfileRecord< Type > | A simple profile record holding a tuple of numeric data |
CComplex | Complex implements a complex number and defines complex arithmetic and mathematical functions |
CConfig | |
CConfigUtil | A class with configuration related helper functions |
►CConstraint | Abstract base class representing a constraint |
CLinearConstraint | Implementation of a linear constraint |
CMaximinDistanceConstraint | Implementation of a maximin distance constraint |
CNonlinearConstraint | Nonlinear constraint, accepts an arbitrary function handle that implements the constraint |
CContextConfig | An important class that holds globally important configuration information and plays a large role in bootstrapping the toolbox |
►CDataModifier | A DataModifier modifies data coming from a simulator, for example introducing noise, or taking the log |
CAssignLabels | Can be used to define labels on a continuous interval |
CFailure | Introduces failed simulations to the data (NaN's) |
CLogTransform | Takes the logarithm of the data |
CNoise | Introduces noise to the data |
COutlier | Introduces outliers to the data |
CScaleByConstant | Scales the data by a constant |
►CDataSource | Interface to an entity that is able to evaluate sample points |
CDatasetDataSource | This class implements a dataset lookup sample evaluator |
►CDefaultDataSource | A helper class for implementing DataSource |
►CBasicDataSource | This is class is an partial implementation of the interface DataSource |
►CThreadedBasicDataSource | This class simply calls evaluate() in a new thread |
►CDistributedDataSource | Evaluates samplepoints through a remote SGE administered cluster |
CRemoteSGEDataSource | Evaluates samplepoints through a remote SGE administered cluster |
►CBatchDataSource | Same as BasicDataSource, only now samples can be evaluated in batches |
►CThreadedBatchDataSource | Adds threading capabilities to BatchDataSource |
CLocalDataSource | This class implements a local sample evaluator |
CMatlabDataSource | |
CDataSourceStatus | |
CDegrees | Construct a degree class |
►CDianneNetworkSpecification | |
CDianneConvolutionalNetwork | |
CDianneFeedForwardNetwork | |
CDiophantineSolver | |
►CDistance | TODO |
CEuclideanDistance | TODO |
CFractionalDistance | TODO |
CSDPDistance | TODO |
►CDistributedBackend | This class is a baseclass for all distributed resource backends |
►CRemoteDistributedBackend | This class forms a baseclass for all distributed resources that must be contacted through a remote submit node (or front node) reachable through SSH |
CRemoteSGEBackend | This backend can submit jobs through a remote front end to a SGE cluster |
►CDockableHandler | |
CDockedViewHandler | |
CToJTableHandler | |
CDockedView | |
►CEndPoint | |
CDianneEndPoint | |
CSamplePoint.EqualType | |
CEvalResponse | |
CEvaluationUnit.EvaluationState | The EvaluationState includes all the different states a unit can be in: EVALUATED: The point was evaluated correctly |
CEvaluationUnit | An evaluation unit encapsulate a sample point for evaluation |
CEvaluationUnitBatch | |
CExtinctionPrevention | A helper class that takes a the current population, compares it with the previous population and ensures that the current population contains at least minCount individuals of each model type (copying them over from the previous population) |
►Chandle | |
CConstraintManager | Manages several constraint classes |
CDatasetDirectDataSource | A fast data source which reads data from files and doesn't use java code |
CMatlabDirectDataSource | A fast data source for Matlab functions which doesn't use java code |
CModelGridManager | Create a new empty model grid manager which can be used to store & get the evaluated grid of a model |
CTriangulation | A handle class that holds and updates a triangulation |
►CInitialDesign | Base class for all initial design generators |
CBoxBehnkenDesign | Choose an initial sampleset according to a Box-Behnken design |
CCentralCompositeDesign | Choose an initial sampleset according to a central composite design |
CCombinedDesign | Wrap 2 different Initial Designs Together |
CCrossCornerDesign | Cross Corner design - just generate 2 points at [-1,...-1] and [1,...,1] |
CDatasetDesign | Read an initial design from a dataset file dataset can also be specified using the id from a simulator xml |
CEmptyDesign | Choose no samples at all |
CFactorialDesign | Factorial design |
CHilbertCurve | Choose points so that they form a Hilbert curve in 2 dimensions Ref: http://blogs.mathworks.com/steve/2012/01/25/generating-hilbert-curves/ |
CLatinHypercubeDesign | Choose an initial sampleset in such a way that they form a latin hypercube |
CQuasiRandomDesign | Generates a space-filling initial design by generating the first of a set of quasi-random numbers |
CRandomDesign | Choose samples randomly |
CSequentialInitialDesign | Uses a space filling sequential design to create an initial design |
CTPLatinHypercubeDesign | Choose an initial sampleset in such a way that they form a latin hypercube |
►CInputConfig | |
CBasicInputConfig | |
CFilteredInputConfig | |
CInputDescription | |
►CinputParser | |
CinputParserSUMO | SUMO version of an inputParser |
CJob | This class represents the typical distributed Job |
CLevelPlot | The class holds the data necessary to generate LevelPlots |
CMathUtil | |
►CMeasure | Abstract base class for a measure |
CAIC | Calculates Akaike's information criteria (AIC) |
CCrossValidation | This measure uses k-fold crossvalidation to gauge the accuracy of the model |
CEvaluationTimeMeasure | Measures the evaluation time for a model |
CLeaveOneOut | This measure performs Leave-One-Out crossvalidation to gauge the accuracy of the model |
CLRMMeasure | Return a score based on how much a model approaches a linear fit (the more linear the lower the score) LRM: Linear Reference Model |
CMinMax | This measure enforces the minimum and maximum that is defined for the output |
CModelDifference | Estimate the accuracy of the model by comparing it with all the other models and assuming that, when models are similar, the algorithm is converging to a stable model, which should be the correct one |
CSampleError | This measure simply compares the values of the model at the locations of all the samples used for constructing it with the actual values of these samples |
CSensitivityCrossValidation | This measure uses k-fold crossvalidation to gauge the accuracy of the model |
CTestMinimum | This measure is used for validition purpose of the optimization framework The true minimum (function value!) is compared to the current minimum found |
CTimeMeasure | Measure based on the current time |
CTrainingTimeMeasure | Measures the training time of a model |
CValidationSet | This measure uses a set of validation samples, not used for construction of the model, to estimate the accuracy of the model |
►CMergeCriterion | Implement this interface if you want to be able to select a set of samples that have to be evaluated from a set of candidates, based on one or more rankings provided by other objects |
CClosenessThreshold | Selects a set of new samples from the candidates by selecting the best scoring n candidates, but avoiding samples that lie too close to each other |
CDelaunayMerger | DelaunaydMerger performs a merging of the candidates for each simplex (generated using a Delaunay triangulation) |
CSeparateCriteria | Merge criterion that select the top samples of each measure independently, in a cyclic fashion |
►CWeightedAverage | WeightedAverage performs a weighted averaged merging of the different scores |
CWeightedAverageSnapToBorder | WeightedAverage performs a weighted averaged merging of the different scores |
CWeightedAverageOnePerSimplex | Weighted averaging of the candidateRankers |
►CModelBuilder | Adaptive model builder base class |
CCombinedModelBuilder | Runs multiple modelbuilders in sequence |
CEGOModelBuilder | Uses the Efficient Global Optimization (EGO) algorithm to optimize in hyperparameter space |
CEmptyModelBuilder | Generates a zero model by default |
CGeneticModelBuilder | Uses a Genetic Algorithm (GA) to select the best model parameters |
COptimizerModelBuilder | Optimizes the model parameters using one the Optimizers available in src/matalb/tools/Optimizers |
CParetoModelBuilder | Uses a Multiobjective GA to select the best model parameters |
CRandomModelBuilder | Generate random models, usefull as a baseline benchmark |
CSequentialModelBuilder | Adaptive model builder subclass that builds models sequentially |
►CModelFactory | Base class for all model factories |
CDIANNEFactory | A very simple class that generates ELMModel objects |
CEureqaFactory | ModelFactory for the Eureqa Symbolic Regression Tool |
►CGeneticFactory | Factories that support the Genetic Model Builder must derive from this class |
CANNFactory | This class is responsible for generating ANN models based on the Matlab ANN toolbox |
CELMFactory | A very simple class that generates ELMModel objects |
CEnsembleFactory | Responsible for generating weighted ensemble models |
CFANNFactory | This class is responsible for generating neural network models as implemented in the Fast Artificial Neural Network Libary (FANN) http://leenissen.dk/fann/ |
CHeterogeneousFactory | This is a meta-Factory that wraps other factories as part of the heterogeneous evolution for model type selection |
CRationalFactory | This class generates Rational models |
CRBFFactory | This class is responsible for generating RBF models |
CRBFNNFactory | This class is responsible for generating Radial Basis Function Neural Networks as implemented in the Matlab NN toolbox |
►CSliceSampleFactory | Base class for all model factories |
CGaussianProcessFactory | This class is responsible for generating Gaussian Process Models |
CKrigingFactory | This class is responsible for generating Kriging Models |
CSplineFactory | This class is responsible for generating Smoothing Spline models (1D and 2D only), based on the Matlab splines toolbox |
►CSVMFactory | The class serves as a kind of base class for SVM based modelers, it takes care of parsing and holding the basic configuration options that any SVM modeler needs |
CLSSVMFactory | The class serves as a kind of base class for LS-SVM based modelers, it takes care of parsing and holding the basic configuration options that any LSSVM modeler needs |
CInterpolationFactory | A very simple class that generates InterpolationModel objects |
CPolynomialFactory | This class is responsible for generating Polynomial Models |
►CModelInterface | The model interface provides the set of abstract functions that must be supported by any model, be it real or wrapper |
►CModel | Constructs an abstract Model object |
CANNModel | Constructs a new feedforward Artificial Neural Network (ANN) |
CComplexWrapper | A utility class to wrap 2 models for a complex output |
CDataModel | Wraps a dataset in a SUMO Model object |
CDIANNEModel | Constructs a new Extreme Learning Machine (ELM) |
CELMModel | Constructs a new Extreme Learning Machine (ELM) |
CEmptyModel | Empty zero-model |
CEnsembleModel | Constructs a basic weighted ensemble object |
CEureqaModel | Wrapper for the Eureqa Symbolic Regression Tool |
CExpressionModel | Wraps any matlab expression in a SUMO Model object |
CFANNModel | Constructs a new neural network backed by the FANN library |
CGaussianProcessModel | Represents a Gaussian Process surrogate model |
CInterpolationModel | Constructs an interpolation based on Matlabs griddata (to allow for scattered data) |
CLSSVMModel | Constructs a new Least Squares Support Vector Machine (SVM) model using the given configuration |
CModelContainer | Merges one or more sumo Models together in one Model |
CPolynomialModel | Construct a `PolynomialModel' object |
CRationalModel | Construct a `RationalModel' object |
CRBFModel | Constructs a radial basis function model object |
CRBFNNModel | Constructs a new Radial Basis Function Neural Network (RBFNN) |
CSplineModel | Constructs a new Spline model based on the smoothing spline implementation from the Matlab Splines toolbox |
CSVMModel | Constructs a new Support Vector Machine (SVM) model using the given configuration |
►CWrappedModel | This model is a wrapper around another model |
COutputFilterWrapper | This class wraps another model, hiding one or more outputs |
►CObservable | An Observable is an object that is able to extract model parameter values from a model so they can be monitored (plotted) during the modeling process |
CANNGenerationObservable | This observable monitors how often each learning rule is used in a population of ANN models |
CBatchObservable | This observable works on a set of models, instead of on a single model |
CCategoriesObservable | This observable monitors how often each model type occurs in a population |
CEnsembleObservable | This observable tracks the composition of the best performing ensemble |
CSimpleObservable | This is the simplest form of observable |
►COptimizer | Abstract base class for an optimizer |
CCMAESOptimizer | Wrapper around the CMA-ES optimization algorithm |
CCombiOptimizer | Wrapper allowing usage of several optimizer together |
CDifferentialEvolution | Differential Evolution (DE) algorithm |
CDirectOptimizer | Wrapper around the DIRECT optimization algorithm |
CDiscreteOptimizer | Discrete optimizer |
CHillClimberOptimizer | Simple hill climbing algorithm, starting from a large initial population |
CLHDOptimizer | A quasi-LHD optimizer |
CLocalPatternSearch | Optimizer which generates quasi-latin hypercubes through genetic algorithm optimization |
CMatlabGA | Wrapper around the matlab optimizers |
CMatlabOptimizer | Wrapper around the matlab optimizers |
CMatlabPatternSearch | Wrapper around the matlab optimizers |
CMatlabSimAnnealing | Wrapper around the matlab optimizers |
CNLOPTOptimizer | Wrapper for the NLOPT optimization library |
CPCTOptimizer | An optimizer which handles multiple points in parallel and makes sure they are far away from each other (as much as possible) using ClosenessThreshold |
CProjectedDistanceGridOptimizer | Optimizes the points generated by the ProjectedDistanceGridCandidateGenerator towards a particular criterion |
CPSOOptimizer | Wrapper around the Another PSO library (Particle Swarm Optimization) |
CPSOtOptimizer | Wrapper around the PSOt library (Particle Swarm Optimization) |
CSQPLabOptimizer | Wrapper around the SQPLab optimization package |
►COutputConfig | |
CBasicOutputConfig | |
CFilteredOutputConfig | |
COutputDescription | Class that describes one input dimension This object represents a task for the DataSource: for each given set of input parameters, the DataSource has to calculate the output parameters |
CPair< A, B > | |
CParetoFront | Updates the cells (integral bounds) for the pareto front |
CProfilerManager | This class creates and manages all the Profilers |
CRawDataset | |
►CResultProcessor | An instance of this class is able to handle finished polled results |
CRemoteSGEDataSource | Evaluates samplepoints through a remote SGE administered cluster |
CResultRepresentation | |
►CRunnable | |
►CJobPoller | Distributed backends need their own poller to check for job status information |
CSSHResultPoller | This class will poll for result files (files with doubles) through ssh on a remote machine |
CThreadedBasicDataSource | This class simply calls evaluate() in a new thread |
CProcessInputStream | A ProcessInputStream simply buffers all incoming data using a separate thread to poll the original input stream |
CSampleManager | This class processes new samples and stores them, so that they can later be requested in both simulator and toolbox space |
►CSampleRanker | Ranks existing samples |
CFLOLASampleRanker | A class that rates samples according to the Fuzzy-LOLA non-linearity criterion |
►CSequentialDesign | An abstract base class for every SequentialDesign |
CCombinedSequentialDesign | Just a class to wrap together 2 different sample selectors |
CEmptySequentialDesign | This sample selector always return an empty selection |
CLOLASampleRanker | A class that rates samples according to the LOLA non-linearity criterion |
CLOLAVoronoiSequentialDesign | Uses the LOLASampleRanker and VoronoiSampleRanker to balance exploration (searching the input space) and exploitation (focussing on regions of non-linearity) |
COptimizeCriterion | This sample selector selects one or more samples that optimizes a certain candidateRanker |
CPipelineSequentialDesign | The PipeLineSequentialDesign generates a number of points using a CandidateGenerator, then evaluates all these points on one or more criteria (CandidateRanker), and then a MergeCriterion is used to merge these scores and select the samples from them |
CRandomSequentialDesign | Chooses datapoints in random locations |
CVoronoiEdgeTraversalSequentialDesign | Selects points along the Voronoi edges of the Tessellation of existing samples |
CVoronoiSampleRanker | Ranks samples based on a voronoi diagram |
CSequentialDesignTypes | Enumeration class to represent whether a sequential design is input, output or model based |
►CSimulator | |
CExternalSimulator | |
►CAbstractSimulator | |
CAcademic2DSimulator | Sample simulator for the Academic 2D test case |
CAcademic3DSimulator | Sample simulator for the Academic 3D test case |
CAckleySimulator | Implements Ackley's Path function |
CButterflyFunction | The Butterfly function |
CGaussusSimulator | Sample simulator for some function containing a gauss function and a sine function |
CGoldsteinPrice | Implements the Goldstein-Price function |
CHumanSimulator | Simulator where a human fills in the responses for the requested points |
CKotanchekSimulator | Simulator for a Kotanchekfunction |
CMandelbrotSimulator | Sample simulator for the Mandelbrot set |
CMichalewiczSimulator | Simulator for a noisy version of Michalewicz's function |
CRidgesFunction | The Ridges benchmark function |
CRosenbrockSimulator | Simulator for Rosenbrocks function |
CSincSimulator | Implements a sinc function with one clean output and one noisy output |
CSixHumpCamelBack | Implements the 6 Hump CamelBack function |
CStepSimulator | Simulator for a StepFunction |
CTestFunction1 | Simulator from the paper: |
CTestFunction2 | Example function taken from Matlab (ps_example.m) |
CTestFunction3 | A simple, discontinuous test function |
CTestFunction4 | Test function from the paper: |
CSobol | Helper class to estimate sobol indices (any order) |
CSSHWrapper | A wrapper class for all kinds of shell commands that need to be executed on a remote host over ssh |
CSUMO | The main class of the SUMO Toolbox |
CTestCase | This object defines a test case for the unit testing framework |
CTestEngine | Runs test cases (regression tests) |
CToFileHandler | Saves profiled data to a text file |
CToImageHandler | This outputhandlers dumps it's jcharted image to a png file |
CToPanelHandler | |
CTriple< A, B, C > | A simple class representing a tuple of size 3 |
CUtil | Misc |
CValidator | This class validates if the toolbox is used within the limits of the license |
CVandermondeMatrix | |
►CAbstractListModel | |
CFilteredListModel | |
►CAbstractTableModel | |
CProfiler | This class can be seen as a logger class for numeric data in tuple form (like java.util.logging is for text messages) |
►CControllerListener | |
CJpegImagesToMovie | This program takes a list of JPEG and/or PNG image files and converts them into a QuickTime movie |
►CDataSinkListener | |
CJpegImagesToMovie | This program takes a list of JPEG and/or PNG image files and converts them into a QuickTime movie |
►CEventListener | |
►CJobFinishedEventListener | Objects implementing this interface can be notified that a job is finished |
CRemoteSGEBackend | This backend can submit jobs through a remote front end to a SGE cluster |
►CEventObject | |
CJobEvent | This event is used to signal that a job has been finished |
►CException | |
►CSUMOException | Basic exception class |
CConfigureException | Configuration exception class |
CDataSourceException | |
►CFileFilter | |
CExtensionFileFilter | |
►CFilenameFilter | |
CHiddenFileFilter | |
CPrefixFilenameFilter | Filters out all files whose name starts with 'prefix' |
►CFilter | |
CSUMOLogFilter | |
►CFormatter | |
CSUMOFileFormatter | A custom logging formatter for the SUMO Toolbox |
CSUMOFormatter | A custom logging formatter for the SUMO Toolbox |
►CHandler | |
CSUMOWSHandler | A custom logging handler for the SUMO Toolbox, relaying logdata to a WebSocket |
►CInputStream | |
CProcessInputStream | A ProcessInputStream simply buffers all incoming data using a separate thread to poll the original input stream |
►CIterator | |
CGriddedDatasetIterator | Class that can iterate over a gridded dataset |
►CKDTree | |
CSamplePointKDTree | |
►CSerializable | |
CBuildInfo | |
CNodeConfig | |
►CDataset | Interface class that contains functions that a dataset should support |
CGriddedDataset | A gridded dataset contains of a single column of data, the inputvalues can be obtained by the relative position of the row |
CScatteredDataset | A scattered dataset contains of a series of unsorted columns of data |
CScatteredDataset | A scattered dataset contains of a series of unsorted columns of data |
CSamplePoint | SamplePoint keeps track of all the info related to one point that is submitted for simulation/evaluation |
CChartType | This class maintains a list of possible ways profiler data may be plotted/visualized |
CProfileRecord< Type > | A simple profile record holding a tuple of numeric data |
CSystemArchitecture | |
CSystemPlatform | |
►CSocket | |
CDianneEndPoint | |