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