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OpenStudioCore:analysis |
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OpenStudioCore:analysis |
| ▼Nopenstudio | |
| ▼Nanalysis | |
| CAlgorithm | Algorithm is an abstract AnalysisObject that is the base class for all OpenStudioAlgorithms and DakotaAlgorithms |
| CAlgorithmOptions | AlgorithmOptions defines the basic options that all Algorithms are assumed to have, that is, a maximum number of iterations and a maximum number of simulations |
| CAnalysis | Analysis is a AnalysisObject that contains an entire analysis |
| CAnalysisJSONLoadResult | |
| CAnalysisObject | AnalysisObject is the base class for all major classes in the openstudio::analysis namespace |
| CAnalysisSerializationOptions | |
| CAnalysisSerializationScope | Enum to indicate how much of the analysis should be written out to JSON |
| CBetaDistribution | BetaDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a continuous variable |
| CBinomialDistribution | BinomialDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a discrete variable |
| CContinuousVariable | A ContinuousVariable is an InputVariable whose value is a real number, perhaps restricted to lie within some bounds |
| CDakotaAlgorithm | DakotaAlgorithm is an Algorithm that works with the third-party software package DAKOTA (http://dakota.sandia.gov/software.html) to generate new data points |
| CDakotaAlgorithmOptions | DakotaAlgorithmOptions is an AlgorithmOptions class for DakotaAlgorithms |
| CDakotaFunctionType | List of function types DAKOTA expects us to evaluate |
| CDakotaParametersFile | DakotaParametersFile is an interface to the files that DAKOTA drops on the filesystem to indicate that we should create and run a new DataPoint |
| CDataPoint | DataPoint is an AnalysisObject that describes a single simulation run/to be run for a given Analysis |
| CDataPointRunType | List of DataPoint run types |
| CDDACEAlgorithm | DDACEAlgorithm is a DakotaAlgorithm that implements dakotaInFileDescription so as to specify a particular Distributed Design and Analysis for Computer Experiments (DDACE) method and related options |
| CDDACEAlgorithmOptions | DDACEAlgorithmOptions is a DakotaAlgorithmOptions class that defines options specific to DDACEAlgorithm |
| CDDACEAlgorithmType | Lists the types of sampling methods offered by the DDACE libary |
| CDesignOfExperiments | DesignOfExperiments is an OpenStudioAlgorithm |
| CDesignOfExperimentsOptions | DesignOfExperimentsOptions is an AlgorithmOptions class for use with DesignOfExperiments |
| CDesignOfExperimentsType | |
| CDiscreteVariable | A DiscreteVariable is an InputVariable whose value is a finite list of values mappable to a range of integers |
| CExponentialDistribution | ExponentialDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a continuous variable |
| CFrechetDistribution | FrechetDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a continuous variable |
| CFSUDaceAlgorithm | FSUDaceAlgorithm is a DakotaAlgorithm that implements dakotaInFileDescription so as to specify a particular Florida State University Design and Analysis of Computer Experiments method and related options |
| CFSUDaceAlgorithmOptions | FSUDaceAlgorithmOptions is a DakotaAlgorithmOptions class that defines options specific to FSUDaceAlgorithm |
| CFSUDaceAlgorithmType | Lists the types of sampling methods offered by the FSU Dace libary |
| CFSUDaceCvtTrialType | Lists the types of trials that may be used with the FSUDaceAlgorithmType::cvt |
| CFunction | Function is an abstract AnalysisObject that registers a set of Variables whose values can be retrieved from a DataPoint, and requires classes derived from it to implement Function::getValue |
| CGammaDistribution | GammaDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a continuous variable |
| CGenericUncertaintyDescription | GenericUncertaintyDescription is an UncertaintyDescription interface for use by APIs and for serialization/deserialization |
| CGeometricDistribution | GeometricDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a discrete variable |
| CGumbelDistribution | GumbelDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a continuous variable |
| CHistogramBinDistribution | HistogramBinDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a continuous variable |
| CHistogramPointDistribution | HistogramPointDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a discrete variable |
| CHypergeometricDistribution | HypergeometricDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a discrete variable |
| CInputVariable | |
| CLinearFunction | LinearFunction is a Function of the form a1*x1 + a2*x2 + .. |
| CLognormalDistribution | LognormalDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a continuous variable |
| CLoguniformDistribution | LoguniformDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a continuous variable |
| CMeasure | Measure is an AnalysisObject that defines one particular value of a DiscreteVariable |
| CMeasureGroup | MeasureGroup is an DiscreteVariable that takes on discrete values, each of which is described by a Measure |
| CNegativeBinomialDistribution | NegativeBinomialDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a discrete variable |
| CNormalDistribution | NormalDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a continuous variable |
| CNullMeasure | NullMeasure is a Measure that does not change the input model in any way |
| COpenStudioAlgorithm | OpenStudioAlgorithm is an Algorithm whose logic is directly encoded in the implementation of createNextIteration |
| COptimizationDataPoint | OptimizationDataPoint is a DataPoint for use with OptimizationProblems |
| COptimizationDataPointObjectiveFunctionLess | Predicate struct for comparing OptimizationDataPoints by objective function value |
| COptimizationProblem | OptimizationProblem is a Problem that has objective functions in addition to variables, response functions, and a simulation workflow |
| COutputAttributeVariable | OutputAttributeVariable is an OutputVariable that accesses attributes written to XML as part of a simulation post-processing step |
| COutputVariable | |
| CParameterStudyAlgorithm | ParameterStudyAlgorithm is a DakotaAlgorithm that implements dakotaInFileDescription so as to specify a particular Parameter Study method and related options |
| CParameterStudyAlgorithmOptions | ParameterStudyAlgorithmOptions is a DakotaAlgorithmOptions that defines options specific to ParameterStudyAlgorithm |
| CParameterStudyAlgorithmType | Lists the types of sampling methods offered by the Parameter Study library |
| CPoissonDistribution | PoissonDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a discrete variable |
| CProblem | Problem is an AnalysisObject that contains a (building energy) problem formulation stated as a vector of workflow steps (input variables or runmanager::WorkItems), and a vector of response functions |
| CPSUADEDaceAlgorithm | PSUADEDaceAlgorithm is a DakotaAlgorithm that implements dakotaInFileDescription so as to specify a particular Problem Solving Environment for Uncertainty Analysis and Design Exploration Morris One-At-A-Time method and related options |
| CPSUADEDaceAlgorithmOptions | PSUADEDaceAlgorithmOptions is a DakotaAlgorithmOptions that defines options specific to PSUADEDaceAlgorithm |
| CRubyContinuousVariable | RubyContinuousVariable is a ContinuousVariable |
| CRubyMeasure | RubyMeasure is a Measure that modifies a model (OSM or IDF file) using a Ruby script |
| CSamplingAlgorithm | SamplingAlgorithm is a DakotaAlgorithm that implements dakotaInFileDescription so as to specify a particular Sampling method and related options |
| CSamplingAlgorithmOptions | SamplingAlgorithmOptions is a DakotaAlgorithmOptions class that defines options specific to SamplingAlgorithm |
| CSamplingAlgorithmRNGType | Lists the types of pseudo-random number generators that may be used with the Sampling library |
| CSamplingAlgorithmSampleType | Lists the types of sampling methods offered by the Sampling libary |
| CSequentialSearch | SequentialSearch is an OpenStudioAlgorithm that can be used to solve OptimizationProblems with two objective functions and discrete variables |
| CSequentialSearchOptions | SequentialSearchOptions is an options class for SequentialSearch, derived from AlgorithmOptions |
| CTriangularDistribution | TriangularDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a continuous variable |
| CUncertaintyDescription | UncertaintyDescription is an abstract base class that can be used to append uncertainty information to a Variable |
| CUncertaintyDescriptionType | List of all the uncertainty types supported by DAKOTA |
| CUncertaintyType | List of uncertainty types |
| CUniformDistribution | UniformDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a continuous variable |
| CVariable | Variable is an AnalysisObject that serves as a base class for InputVariable and OutputVariable |
| CVariableValueType | List of variable numeric types |
| CWeibullDistribution | WeibullDistribution is an UncertaintyDescription that can apply aleatory uncertainty to a continuous variable |
| CWorkflowStep | WorkflowStep is an AnalysisObject that describes an individual step in a Problem's overall Workflow |
| CWorkflowStepJob | WorkflowStepJob is a structure for articulating the detailed runtime results associated with each WorkflowStep as executed for a particular DataPoint |
| CEnumBase | |
| CQObject |