AbstractSingleObjectiveOptimizer. More...
Namespaces | |
| namespace | blas |
| namespace | cma |
| namespace | cmsa |
| namespace | detail |
| namespace | elitist_cma |
| namespace | example |
| namespace | glpk |
| namespace | mocma |
| namespace | moo |
| namespace | one_plus_one_es |
| namespace | soo |
| namespace | tag |
| namespace | tags |
| Tags are empty types which can be used as a function argument. | |
| namespace | test |
| namespace | traits |
Classes | |
| class | Oracle |
| Abstract base class for oracles. More... | |
| class | OracleOfDelphi |
| Implements the oracle of Delphi. More... | |
| class | HitchhikersGuideToTheGalaxy |
| Implements the oracle interface in terms of the answers given by the Hitchhiker's Guide to the Galaxy. More... | |
| class | AbstractMultiObjectiveOptimizer |
| base class for abstract multi-objective optimizers for arbitrary search spaces. More... | |
| class | TypeErasedMultiObjectiveOptimizer |
| Type erasure to integrate Optimizer adhering to the concept of a multi-objective optimizer with the inheritance hierarchy of AbstractOptimizer. More... | |
| struct | OptimizerTraits< TypeErasedMultiObjectiveOptimizer< S, O > > |
| Implements OptimizerTraits for a type erase MOO. More... | |
| class | AbstractOptimizer |
| An optimizer that optimizes general objective functions. More... | |
| class | AbstractSingleObjectiveOptimizer |
| Base class for all single objective optimizer. More... | |
| struct | OptimizerTraits< detail::AGE > |
| AGE specialization of optimizer traits. More... | |
| struct | OptimizerTraits< detail::AGE2 > |
| AGE specialization of optimizer traits. More... | |
| struct | BoundingBoxCalculator |
| Calculates the bounding of a d-dimensional point set. More... | |
| class | CMA |
| Implements the CMA-ES. More... | |
| class | CMSA |
| Implements the CMSA. More... | |
| class | ElitistCMA |
| Implements the elitist CMA-ES. More... | |
| struct | EvaluationCountStoppingCondition |
| Maximum number of evaluations stoppping condition. More... | |
| class | ExperimentBase |
| Single-objective and multi-objective experiments for evolutionary algorithms. More... | |
| struct | FrontStore |
| A store for Pareto-front approximations, for usage with class InterruptibleAlgorithmRunner. More... | |
| struct | BaseFastNonDominatedSort |
| Implements the well-known non-dominated sorting algorithm. More... | |
| struct | FitnessComparator |
| Fitness comparator for the single-objective case. More... | |
| struct | IndirectFitnessComparator |
| Indirect (pointer,iterator) fitness comparator for the single-objective case. More... | |
| struct | IdentityFitnessExtractor |
| Functor that returns its argument without conversion. More... | |
| struct | FitnessExtractor |
| Default fitness extractor. More... | |
| struct | CastingFitnessExtractor |
| Casting fitness extractor. More... | |
| class | GridSearch |
| Optimize by trying out a grid of configurations. More... | |
| class | NestedGridSearch |
| Nested grid search. More... | |
| class | PointSearch |
| Optimize by trying out predefined configurations. More... | |
| struct | HypervolumeApproximator |
| Implements an FPRAS for approximating the volume of a set of high-dimensional objects. More... | |
| struct | HypervolumeCalculator |
| Implementation of the exact hypervolume calculation in m dimensions. More... | |
| struct | HypervolumeIndicator |
| Calculates the hypervolume covered by a set of non-dominated points. More... | |
| struct | AdditiveEpsilonIndicator |
| Given a reference front R and an approximation F, calculates the additive approximation quality of F. More... | |
| struct | LocalitySensitiveAdditiveEpsilonIndicator |
| Binary performance indicator inspired by AGE-I. More... | |
| struct | InvertedGenerationalDistance |
| Inverted generational distance for comparing Pareto-front approximations. More... | |
| struct | MultiplicativeEpsilonIndicator |
| Given a reference front R and an approximation F, calculates the multiplicative approximation quality of F. More... | |
| struct | Sampler |
| Samples a random point. More... | |
| struct | BoundingBoxComputer |
| Calculates bounding boxes. More... | |
| struct | LeastContributorApproximator |
| Approximately determines the point of a set contributing the least hypervolume. More... | |
| struct | OptimizerTraits< detail::MOCMA< Indicator > > |
| \((\mu+1)\)-MO-CMA-ES specialization of optimizer traits. More... | |
| class | OnePlusOneES |
| Implements the (1+1)-ES. More... | |
| struct | BitflipMutator |
| Bitflip mutation operator. More... | |
| struct | PolynomialMutator |
| Polynomial mutation operator. More... | |
| struct | GlobalIntermediateRecombination |
| Recombinates a set of individuals given a weight vector. More... | |
| struct | TypedOnePointCrossover |
| Implements one-point crossover. More... | |
| struct | SimulatedBinaryCrossover |
| Simulated binary crossover operator. More... | |
| class | UniformCrossover |
| Uniform crossover of arbitrary individuals. More... | |
| struct | ApproximatedHypervolumeSelection |
| Implements an approximated hypervolume selection scheme. More... | |
| struct | BinaryTournamentSelection |
| Implements binary tournament selection with a user-selectable predicate. More... | |
| struct | EPTournamentSelection |
| Selects individuals from the range of parent and offspring individuals. More... | |
| struct | IndicatorBasedSelection |
| Implements the well-known indicator-based selection strategy. More... | |
| struct | LinearRankingSelection |
| Implements a fitness-proportional selection scheme that scales the fitness values linearly before carrying out the actual selection. More... | |
| struct | RouletteWheelSelection |
| Fitness-proportional selection operator. More... | |
| struct | SteadyStateIndicatorBasedSelection |
| Steady state (+1) Indicator-based selection strategy for multi-objective selection. More... | |
| struct | TournamentSelection |
| Tournament selection operator. More... | |
| struct | UniformRankingSelection |
| Selects individuals from the range of parent and offspring individuals. More... | |
| struct | ParetoDominanceComparator |
| Implementation of the Pareto-Dominance relation under the assumption of all objectives to be minimized. More... | |
| struct | RankShareComparator |
| Compares two individuals w.r.t. their level of non-dominance and w.r.t. the share they contribute to the front both of them belong to. More... | |
| struct | OptimizerTraits< detail::RealCodedNSGAII<> > |
| NSGA-II specialization of optimizer traits. More... | |
| struct | OptimizerTraits< detail::SMSEMOA > |
| SMS-EMOA specialization of optimizer traits. More... | |
| struct | OptimizerTraits< detail::SteadyStateMOCMA< Indicator > > |
| \((\mu+1)\)-MO-CMA-ES specialization of optimizer traits. More... | |
| struct | FitnessTraits |
| Abstracts extraction of fitness values from individuals. More... | |
| struct | QualityIndicatorTraits |
| Abstracts common properties of unary and binary quality indicators. More... | |
| struct | ChromosomeIndex |
| Explicitly wraps up a chromosome index. More... | |
| class | TypedIndividual |
| TypedIndividual is a templated class modelling an individual that acts as a candidate solution in an evolutionary algorithm. More... | |
| struct | FitnessTraits< TypedIndividual< T0, T1, T2, T3, T4, T5, T6, T7, T8, T9, T10 > > |
| Allows for extracting the penalized and unpenalized fitness of arbitrary TypedIndividuals based on fitness traits. More... | |
| class | BFGS |
| Broyden, Fletcher, Goldfarb, Shannon algorithm for unconstraint optimization. More... | |
| class | CG |
| Conjugate-gradient method for unconstraint optimization. More... | |
| class | ObjectiveFunctionDerivativeWrapper |
| A wrapper which wraps the evaluation of a model, given a set of parameters It can be used as glue to use errorfunctions together with the linesearch algorithms of LinAlg. More... | |
| class | IRLS |
| Iterated Reweightes Least Squares iteratively calculates a newton step and then performs a line search in that direction. More... | |
| class | LBFGS |
| Limited-Memory Broyden, Fletcher, Goldfarb, Shannon algorithm for unconstrained optimization. More... | |
| class | LineSearch |
| Wrapper for the linesearch class of functions in the linear algebra library. More... | |
| class | NoisyRprop |
| Rprop-like algorithm for noisy function evaluations. More... | |
| class | Quickprop |
| This class offers methods for using the popular heuristic "Quickprop" optimization algorithm. More... | |
| class | QuickpropOriginal |
| This class offers methods for using the popular heuristic "Quickprop" optimization algorithm. More... | |
| class | RpropMinus |
| This class offers methods for the usage of the Resilient-Backpropagation-algorithm without weight-backtracking. More... | |
| class | RpropPlus |
| This class offers methods for the usage of the Resilient-Backpropagation-algorithm with weight-backtracking. More... | |
| class | IRpropPlus |
| This class offers methods for the usage of the improved Resilient-Backpropagation-algorithm with weight-backtracking. More... | |
| class | IRpropMinus |
| This class offers methods for the usage of the improved Resilient-Backpropagation-algorithm without weight-backtracking. More... | |
| class | SteepestDescent |
| Standard steepest descent. More... | |
| class | JaakkolaHeuristic |
| Jaakkola's heuristic and related quantities for Gaussian kernel selection. More... | |
| class | LP |
| Linear Program Solver. More... | |
| class | AbstractNearestNeighbors |
| Interface for Narest Neighbor queries. More... | |
| class | SimpleNearestNeighbors |
| Brute force optimized nearest neighbor implementation. More... | |
| class | IterativeNNQuery |
| Iterative nearest neighbors query. More... | |
| class | TreeNearestNeighbors |
| Nearest Neighbors implementation using binary trees. More... | |
| class | Pegasos |
| Pegasos solver for linear (binary) support vector machines. More... | |
| class | McPegasos |
| Pegasos solver for linear multi-class support vector machines. More... | |
| struct | MaximumGradientCriterion |
| Working set selection by maximization of the projected gradient. More... | |
| struct | MaximumGainCriterion |
| Working set selection by maximization of the dual objective gain. More... | |
| class | BoxConstrainedProblem |
| Quadratic program with box constraints. More... | |
| struct | BoxConstrainedShrinkingProblem |
| Same as BoxConstrainedProblem, but including a shrinking heuristic. More... | |
| class | LRUCache |
| Implements an LRU-Caching Strategy for arbitrary Cache-Lines. More... | |
| class | QpBoxLinear |
| Quadratic program solver for box-constrained problems with linear kernel. More... | |
| class | QpMcDecomp |
| Quadratic program solver for multi class SVM problems. More... | |
| class | QpMcLinear |
| Generic solver skeleton for linear multi-class SVM problems. More... | |
| class | QpMcLinearWW |
| Solver for the multi-class SVM by Weston & Watkins. More... | |
| class | QpMcLinearLLW |
| Solver for the multi-class SVM by Lee, Lin & Wahba. More... | |
| class | QpMcLinearATS |
| Solver for the multi-class SVM with absolute margin and total sum loss. More... | |
| class | QpMcLinearMMR |
| Solver for the multi-class maximum margin regression SVM. More... | |
| class | QpMcLinearCS |
| Solver for the multi-class SVM by Crammer & Singer. More... | |
| class | QpMcLinearADM |
| Solver for the multi-class SVM with absolute margin and discriminative maximum loss. More... | |
| class | QpMcLinearATM |
| Solver for the multi-class SVM with absolute margin and total maximum loss. More... | |
| class | GeneralQuadraticProblem |
| Most gneral problem formualtion, needs to be configured by hand. More... | |
| class | BoxedSVMProblem |
| Boxed problem for alpha in [lower,upper]^n and equality constraints. More... | |
| class | CSVMProblem |
| Problem formulation for binary C-SVM problems. More... | |
| class | BaseShrinkingProblem |
| class | QpSolver |
| Quadratic program solver. More... | |
| class | QpSparseArray |
| specialized container class for multi-class SVM problems More... | |
| struct | QpStoppingCondition |
| stopping conditions for quadratic programming More... | |
| struct | QpSolutionProperties |
| properties of the solution of a quadratic program More... | |
| class | KernelMatrix |
| Kernel Gram matrix. More... | |
| class | KernelMatrix< blas::compressed_vector< T >, CacheType > |
| Specialization for sparse vectors which don't have a super ffective lookup. More... | |
| class | RegularizedKernelMatrix |
| Kernel Gram matrix with modified diagonal. More... | |
| class | ModifiedKernelMatrix |
| Modified Kernel Gram matrix. More... | |
| class | BlockMatrix2x2 |
| SVM regression matrix. More... | |
| class | CachedMatrix |
| Efficient quadratic matrix cache. More... | |
| class | PrecomputedMatrix |
| Precomputed version of a matrix for quadratic programming. More... | |
| class | ExampleModifiedKernelMatrix |
| struct | MVPSelectionCriterion |
| struct | LibSVMSelectionCriterion |
| class | HMGSelectionCriterion |
| class | SvmProblem |
| struct | SvmShrinkingProblem |
| class | AbstractStoppingCriterion |
| Base class for stopping criteria of optimization algorithms. More... | |
| class | GeneralizationLoss |
| The generalization loss calculates the relative increase of the validation error compared to the minimum training error. More... | |
| class | GeneralizationQuotient |
| SStopping criterion monitoring the quotient of generalization loss and training progress. More... | |
| class | MaxIterations |
| This stopping criterion stops after a fixed number of iterations. More... | |
| class | TrainingError |
| This stopping criterion tracks the improvement of the error function of the training error over an interval of iterations. More... | |
| class | TrainingProgress |
| This stopping criterion tracks the improvement of the training error over an interval of iterations. More... | |
| class | ValidatedStoppingCriterion |
| Given the current Result set of the optimizer, calculates the validation error using a validation function and hands the results over to the underlying stopping criterion. More... | |
| class | QpConfig |
| Super class of all support vector machine trainers. More... | |
| class | AbstractSvmTrainer |
| Super class of all kernelized (non-linear) SVM trainers. More... | |
| class | AbstractLinearSvmTrainer |
| Super class of all linear SVM trainers. More... | |
| class | AbstractTrainer |
| Superclass of supervised learning algorithms. More... | |
| class | AbstractUnsupervisedTrainer |
| Superclass of unsupervised learning algorithms. More... | |
| class | CARTTrainer |
| Classification And Regression Trees CART. More... | |
| class | CSvmTrainer |
| Training of C-SVMs for binary classification. More... | |
| class | LinearCSvmTrainer |
| class | DistTrainerContainer |
| Container for known distribution trainers. More... | |
| class | GenericDistTrainer |
| class | NormalTrainer |
| Trainer for normal distribution. More... | |
| class | EpsilonSvmTrainer |
| Training of Epsilon-SVMs for regression. More... | |
| class | FisherLDA |
| Fisher's Linear Discriminant Analysis for data compression. More... | |
| class | KernelMeanClassifier |
| Kernelized mean-classifier. More... | |
| class | LassoRegression |
| LASSO Regression. More... | |
| class | LDA |
| Linear Discriminant Analysis (LDA) More... | |
| class | LinearRegression |
| Linear Regression. More... | |
| class | McSvmADMTrainer |
| Training of ADM-SVMs for multi-category classification. More... | |
| class | LinearMcSvmADMTrainer |
| class | McSvmATMTrainer |
| Training of ATM-SVMs for multi-category classification. More... | |
| class | LinearMcSvmATMTrainer |
| class | McSvmATSTrainer |
| Training of ATS-SVMs for multi-category classification. More... | |
| class | LinearMcSvmATSTrainer |
| class | McSvmCSTrainer |
| Training of the multi-category SVM by Crammer and Singer (CS). More... | |
| class | LinearMcSvmCSTrainer |
| class | McSvmLLWTrainer |
| Training of the multi-category SVM by Lee, Lin and Wahba (LLW). More... | |
| class | LinearMcSvmLLWTrainer |
| class | McSvmMMRTrainer |
| Training of the maximum margin regression (MMR) multi-category SVM. More... | |
| class | LinearMcSvmMMRTrainer |
| class | McSvmOVATrainer |
| Training of a multi-category SVM by the one-versus-all (OVA) method. More... | |
| class | LinearMcSvmOVATrainer |
| class | McSvmWWTrainer |
| Training of the multi-category SVM by Weston and Watkins (WW). More... | |
| class | LinearMcSvmWWTrainer |
| class | MissingFeatureSvmTrainer |
| Trainer for binary SVMs natively supporting missing features. More... | |
| class | NBClassifierTrainer |
| Trainer for naive Bayes classifier. More... | |
| class | NormalizeComponentsUnitInterval |
| Train a model to normalize the components of a dataset to fit into the unit inverval. More... | |
| class | NormalizeComponentsUnitVariance |
| Train a linear model to normalize the components of a dataset to unit variance, and optionally to zero mean. More... | |
| class | NormalizeComponentsWhitening |
| Train a linear model to whiten the data. More... | |
| class | NormalizeKernelUnitVariance |
| Determine the scaling factor of a ScaledKernel so that it has unit variance in feature space one on a given dataset. More... | |
| class | OneClassSvmTrainer |
| Training of one-class SVMs. More... | |
| class | OptimizationTrainer |
| Wrapper for training schemes based on (iterative) optimization. More... | |
| class | PCA |
| Principal Component Analysis. More... | |
| class | Perceptron |
| Perceptron online learning algorithm. More... | |
| class | RegularizationNetworkTrainer |
| Training of a regularization network. More... | |
| class | RFTrainer |
| Random Forest. More... | |
| class | SigmoidFitRpropNLL |
| Optimizes the parameters of a sigmoid to fit a validation dataset via backpropagation on the negative log-likelihood. More... | |
| class | SigmoidFitPlatt |
| Optimizes the parameters of a sigmoid to fit a validation dataset via Platt's method. More... | |
| struct | AbstractFeasibleRegion |
| Models a feasible region. More... | |
| class | Chart |
| Models an abstract chart. More... | |
| struct | TypedFirstOrderDerivative |
| Encapsulation of the first order derivative information, consisting of the gradient only. More... | |
| struct | TypedSecondOrderDerivative |
| Encapsulation of the second order derivative information, consisting of the gradient and the Hessian. More... | |
| class | Exception |
| Top-level exception class of the shark library. More... | |
| class | Factory |
| Implements the factory pattern. More... | |
| struct | FactoryRegisterer |
| Helper structure to allow for automatic registration of types with the factory modelled by the template parameter. More... | |
| struct | TypeErasedAbstractFactory |
| Type erase to ease implementing factories for custom types. More... | |
| class | TypedFlags |
| Flexible and extensible mechanisms for holding flags. More... | |
| class | TypedFeatureNotAvailableException |
| Exception indicating the attempt to use a feature which is not supported. More... | |
| class | IConfigurable |
| Interface that abstracts a configurable component. More... | |
| class | INameable |
| This class is an interface for all objects which can have a name. More... | |
| class | IParameterizable |
| Top level interface for everything that holds parameters. More... | |
| class | ISerializable |
| Abstracts serializing functionality. More... | |
| class | PrintfLogFormatter |
| A log formatter that relies on externally defined printf-like formats. More... | |
| class | Logger |
| Implements a generic logging facility. More... | |
| class | LoggerPool |
| Singleton that manages named loggers. More... | |
| class | StreamHandlerBase |
| Implements a generic log handler for arbitrary streams. More... | |
| struct | ConsoleHandler |
| Defines a console handler based on tag dispatching. More... | |
| struct | ConsoleHandler< tag::cout > |
| Template specialization for std::cout. More... | |
| struct | ConsoleHandler< tag::clog > |
| Template specialization for std::clog. More... | |
| struct | ConsoleHandler< tag::cerr > |
| Template specialization for std::cerr. More... | |
| struct | HighchartRenderer |
| Models a renderer that renders charts using the Highcharts JS API (see http://www.highcharts.com). More... | |
| struct | ResultSet |
| struct | SingleObjectiveResultSet |
| Result set for single objective algorithm. More... | |
| struct | ValidatedSingleObjectiveResultSet |
| Result set for validated points. More... | |
| struct | VectorSpace |
| Models the concept of a vector space over. More... | |
| class | SharedVector |
| Vector container prepared for data sharing. More... | |
| class | Shark |
| Allows for querying compile settings at runtime. Provides the current command line arguments to the rest of the library. More... | |
| class | SignalTrap |
| Class that interferes with signals and allows for a graceful shutdown. More... | |
| class | Singleton |
| Models a singleton, i.e., enforces a single instance of a class. More... | |
| struct | State |
| Represents the State of an Object. More... | |
| struct | EmptyState |
| Default State of an Object which does not need a State. More... | |
| class | Timer |
| Timer abstraction with microsecond resolution///. More... | |
| struct | IsVector |
| IsVector is a traits class evaluating to true if T implements the rquirements of a vector. More... | |
| struct | IsVector< blas::vector< T > > |
| struct | IsVector< blas::compressed_vector< T > > |
| struct | IsVector< blas::matrix< T > > |
| struct | IsVector< blas::compressed_matrix< T > > |
| struct | MultiObjectiveFunctionTraits |
| Abstract traits specific to a multi-objective function type that are not modeled in interface AbstractObjectiveFunction. More... | |
| struct | OptimizerTraits |
| Abstract traits specific to an optimizer type that are not modeled in interface AbstractOptimizer. More... | |
| struct | ConstProxyReference |
| sets the type of ProxxyReference More... | |
| struct | ConstProxyReference< blas::vector< T > > |
| struct | ConstProxyReference< blas::vector< T > const > |
| struct | ConstProxyReference< blas::compressed_vector< T > > |
| struct | ConstProxyReference< blas::compressed_vector< T > const > |
| struct | ConstProxyReference< blas::matrix< T > > |
| struct | ConstProxyReference< blas::matrix< T > const > |
| struct | CanBeCalled |
| detects whether Functor(Argument) can be called. More... | |
| struct | CopyConst |
| If U is a const Type, than T is also made const. More... | |
| struct | CopyConst< T, U const > |
| class | IndexedIterator |
| creates an Indexed Iterator, an Iterator which also carries index information using index() More... | |
| class | ProxyIterator |
| Creates an iterator which reinterpretes an object as a range. More... | |
| class | MultiSequenceIterator |
| Iterator which iterates of the elements of a nested sequence. More... | |
| struct | KeyValuePair |
| Represents a Key-Value-Pair similar std::pair which is strictly ordered by it's key. More... | |
| struct | PairReference< KeyValuePair< Key, Value >, KeyIterator, ValueIterator > |
| Reference type used by zipKeyValuePair. More... | |
| struct | KeyValueRange |
| class | ScopedHandle |
| struct | PairReference |
| Given a type of pair and two iterators to zip together, returns the reference. More... | |
| struct | PairReference< std::pair< T, U >, Iterator1, Iterator2 > |
| class | PairIterator |
| A Pair-Iterator which gives a unified view of two ranges. More... | |
| struct | PairRangeType |
| struct | Batch |
| class which helps using different batch types More... | |
| struct | Batch< blas::vector< T > > |
| specialization for ublas vectors which should be matrices in batch mode! More... | |
| struct | Batch< shark::blas::compressed_vector< T > > |
| specialization for ublas compressed vectors which are compressed matrices in batch mode! More... | |
| class | CVFolds |
| class | DataDistribution |
| A DataDistribution defines an unsupervised learning problem. More... | |
| class | LabeledDataDistribution |
| A LabeledDataDistribution defines a supervised learning problem. More... | |
| class | Chessboard |
| "chess board" problem for binary classification More... | |
| class | Wave |
| Noisy sinc function: y = sin(x) / x + noise. More... | |
| class | PamiToy |
| class | CircleInSquare |
| class | DiagonalWithCircle |
| class | Data |
| Data container. More... | |
| class | UnlabeledData |
| data set for unbase_typevised learning More... | |
| class | LabeledData |
| Data set for base_typevised learning. More... | |
| struct | TransformedData |
| class | DataView |
| Constant time Element-Lookup for Datasets. More... | |
| struct | DataPair |
| The type used to mimic a pair of data. More... | |
| struct | PairReference< DataPair< I, L >, InputIterator, LabelIterator > |
| struct | DataBatchPair |
| The type used to mimic a pair of data batches. More... | |
| struct | Batch< DataPair< InputType, LabelType > > |
| struct | PairReference< DataBatchPair< InputBatchType, LabelBatchType >, OuterInputBatchIterator, OuterLabelBatchIterator > |
| struct | Batch< boost::fusion::vector< BOOST_PP_ENUM_PARAMS(FUSION_MAX_VECTOR_SIZE, T)> > |
| Default implementation for boost::fusion::vector. More... | |
| struct | ImageInformation |
| Stores name and size of image externally. More... | |
| class | CustomIM |
| An user defined inference machine. More... | |
| class | FuzzyControlLanguageParser |
| LL-Parser for the Fuzzy-Control-Language (see IEC 61131-7). More... | |
| class | FuzzyRelation |
| A fuzzy relation. More... | |
| class | FuzzySet |
| Abstract super class for specific fuzzy sets. More... | |
| class | BellFS |
| FuzzySet with a bell-shaped (Gaussian) membership function. More... | |
| class | ComposedFS |
| A composed FuzzySet. More... | |
| class | ComposedNDimFS |
| A composed n-dimensional FuzzySet. More... | |
| class | ConstantFS |
| FuzzySet with constant membership function. More... | |
| class | CustomizedFS |
| A FuzzySet with an user defined mambership function. More... | |
| class | GeneralizedBellFS |
| FuzzySet with a generalized bell-shaped membership function. More... | |
| class | HomogenousNDimFS |
| A homogenous n-dimensional fuzzy set. More... | |
| class | InfinityFS |
| FuzzySet with a step function as membership function. More... | |
| class | NDimFS |
| Base class for n-dimensional fuzzy sets. More... | |
| class | SigmoidalFS |
| FuzzySet with sigmoidal membership function. More... | |
| class | SingletonFS |
| FuzzySet with a single point of positive membership. More... | |
| class | TrapezoidFS |
| FuzzySet with trapezoid membership function. More... | |
| class | TriangularFS |
| FuzzySet with triangular membership function. More... | |
| class | Implication |
| Fuzzy implication. More... | |
| class | InferenceMachine |
| An inference machine. More... | |
| class | LinguisticTerm |
| A single linguistic term. More... | |
| class | BellLT |
| LinguisticTerm with a bell-shaped (Gaussian) membership function. More... | |
| class | ComposedLT |
| A composed LinguisticTerm. More... | |
| class | ConstantLT |
| LinguisticTerm with constant membership function. More... | |
| class | CustomizedLT |
| A LinguisticTerm with an user defined mambership function. More... | |
| class | GeneralizedBellLT |
| LinguisticTerm with a generalized bell-shaped membership function. More... | |
| class | InfinityLT |
| LinguisticTerm with a step function as membership function. More... | |
| class | SigmoidalLT |
| LinguisticTerm with sigmoidal membership function. More... | |
| class | SingletonLT |
| LinguisticTerm with a single point of positive membership. More... | |
| class | TrapezoidLT |
| LinguisticTerm with trapezoid membership function. More... | |
| class | TriangularLT |
| LinguisticTerm with triangular membership function. More... | |
| class | LinguisticVariable |
| A composite of linguistic terms. More... | |
| class | MamdaniIM |
| Mamdami inference machine. More... | |
| class | Operators |
| Operators and connective functions. More... | |
| class | Rule |
| A rule which is composed of premise and conclusion. More... | |
| class | RuleBase |
| A composite of rules. More... | |
| class | SugenoIM |
| Sugeno inference machine. More... | |
| class | SugenoRule |
| Sugeno rule. More... | |
| class | VectorRepeater |
| class | FixedDenseVectorProxy |
| class | FixedDenseMatrixProxy |
| class | FixedSparseVectorProxy |
| struct | ExpressionTraitsBase< FixedSparseVectorProxy< T, I > > |
| struct | ExpressionTraitsBase< FixedSparseVectorProxy< T, I > const > |
| class | Blocking |
| partitions the matrix in 4 blocks defined by one splitting point (i,j). More... | |
| struct | ExpressionTraitsBase< blas::compressed_matrix< T, blas::row_major, 0, IA, TA > > |
| struct | ExpressionTraitsBase< blas::compressed_matrix< T, blas::row_major, 0, IA, TA > const > |
| struct | ExpressionTraitsBase< blas::compressed_vector< T, IB, IA, TA > > |
| struct | ExpressionTraitsBase< blas::compressed_vector< T, IB, IA, TA > const > |
| struct | VectorMatrixTraits |
| Template which finds for every Vector type the best fitting Matrix. More... | |
| struct | SolveAXB |
| Flag indicating that a system AX=B is to be solved. More... | |
| struct | SolveXAB |
| Flag indicating that a system XA=B is to be solved. More... | |
| struct | Upper |
| Flag indicating that the matrix is Upper triangular. More... | |
| struct | UnitUpper |
| Flag indicating that the matrix is Upper triangular and diagonal elements are to be assumed as 1. More... | |
| struct | Lower |
| Flag indicating that the matrix is Lower triangular. More... | |
| struct | UnitLower |
| Flag indicating that the matrix is Lower triangular and diagonal elements are to be assumed as 1. More... | |
| class | AbstractModel |
| Base class for all Models. More... | |
| class | AbstractClustering |
| Base class for clustering. More... | |
| class | Centroids |
| Clusters defined by centroids. More... | |
| class | ClusteringModel |
| Abstract model with associated clustering object. More... | |
| class | HardClusteringModel |
| Model for "hard" clustering. More... | |
| class | HierarchicalClustering |
| Clusters defined by a binary space partitioning tree. More... | |
| class | SoftClusteringModel |
| Model for "soft" clustering. More... | |
| class | CMACMap |
| The CMACMap class represents a linear combination of piecewise constant functions. More... | |
| class | ConcatenatedModel |
| ConcatenatedModel concatenates two models such that the output of the first model is input to the second. More... | |
| class | ThresholdConverter |
| Convertion of real-valued outputs to classes 0 or 1. More... | |
| class | ThresholdVectorConverter |
| Convertion of real-vector outputs to vectors of class labels 0 or 1. More... | |
| class | ArgMaxConverter |
| Convertion of real-valued outputs to classes. More... | |
| class | OneHotConverter |
| Convertion of class indices to a one-hot encoding. More... | |
| class | FFNet |
| Offers the functions to create and to work with a feed-forward network. More... | |
| class | KalmanFilter |
| Standard linear Kalman Filter. More... | |
| class | AbstractKernelFunction |
| Base class of all Kernel functions. More... | |
| class | ARDKernelUnconstrained |
| Automatic relevance detection kernel for unconstrained parameter optimization. More... | |
| class | CSvmDerivative |
| This class provides two main member functions for computing the derivative of a C-SVM hypothesis w.r.t. its hyperparameters. The constructor takes a pointer to a KernelExpansion and an SvmTrainer, in the assumption that the former was trained by the latter. It heavily accesses their members to calculate the derivative of the alpha and offset values w.r.t. the SVM hyperparameters, that is, the regularization parameter C and the kernel parameters. This is done in the member function prepareCSvmParameterDerivative called by the constructor. After this initial, heavier computation step, modelCSvmParameterDerivative can be called on an input sample to the SVM model, and the method will yield the derivative of the hypothesis w.r.t. the SVM hyperparameters. More... | |
| class | DiscreteKernel |
| Kernel on a finite, discrete space. More... | |
| class | GaussianRbfKernel |
| Gaussian radial basis function kernel. More... | |
| class | KernelExpansion |
| Linear model in a kernel feature space. More... | |
| class | LinearKernel |
| Linear Kernel, parameter free. More... | |
| class | MissingFeaturesKernelExpansion |
| Kernel expansion with missing features support. More... | |
| class | MklKernel |
| Weighted sum of kernel functions. More... | |
| class | MonomialKernel |
| Monomial kernel. Calculates \( \left\langle x_1, x_2 \right\rangle^m_exponent \). More... | |
| struct | MultiTaskSample |
| Aggregation of input data and task index. More... | |
| class | NormalizedKernel |
| Normalized version of a kernel function. More... | |
| class | PolynomialKernel |
| Polynomial kernel. More... | |
| class | ProductKernel |
| Product of kernel functions. More... | |
| class | ScaledKernel |
| Scaled version of a kernel function. More... | |
| class | SubrangeKernel |
| Weighted sum of kernel functions. More... | |
| class | WeightedSumKernel |
| Weighted sum of kernel functions. More... | |
| class | LinearClassifier |
| Basic linear classifier. More... | |
| class | LinearModel |
| Linear Prediction. More... | |
| class | LinearNorm |
| Normalizes the (non-negative) input by dividing by the overall sum. More... | |
| class | NBClassifier |
| Naive Bayes classifier. More... | |
| class | NearestNeighborClassifier |
| Nearest Neighbor Classifier. More... | |
| class | NearestNeighborRegression |
| Nearest neighbor regression model. More... | |
| struct | LogisticNeuron |
| Neuron which computes the Logistic (logistic) function with range [0,1]. More... | |
| struct | TanhNeuron |
| Neuron which computes the hyperbolic tangenst with range [-1,1]. More... | |
| struct | LinearNeuron |
| Linear activation Neuron. More... | |
| struct | FastSigmoidNeuron |
| Fast sigmoidal function, which does not need to compute an exponential function. More... | |
| class | Normalizer |
| "Diagonal" linear model for data normalization. More... | |
| class | OneVersusOneClassifier |
| One-versus-one Classifier. More... | |
| class | OnlineRNNet |
| A recurrent neural network regression model optimized for online learning. More... | |
| class | RBFNet |
| Offers the functions to create and to work with radial basis function networks. More... | |
| class | RecurrentStructure |
| Offers a basic structure for recurrent networks. More... | |
| class | RNNet |
| A recurrent neural network regression model that learns with Back Propagation Through Time. More... | |
| class | SigmoidModel |
| Standard sigmoid function. More... | |
| class | SimpleSigmoidModel |
| Simple sigmoid function. More... | |
| class | TanhSigmoidModel |
| scaled Tanh sigmoid function More... | |
| class | Softmax |
| Softmax function. More... | |
| class | SoftNearestNeighborClassifier |
| SoftNearestNeighborClassifier returns a probabilistic classification by looking at the k nearest neighbors. More... | |
| class | TreeConstruction |
| Stopping criteria for tree construction. More... | |
| class | BinaryTree |
| Super class of binary space-partitioning trees. More... | |
| class | CARTClassifier |
| CART Classifier. More... | |
| class | KDTree |
| KD-tree, a binary space-partitioning tree. More... | |
| class | KHCTree |
| KHC-tree, a binary space-partitioning tree. More... | |
| class | LCTree |
| LC-tree, a binary space-partitioning tree. More... | |
| class | RFClassifier |
| Random Forest Classifier. More... | |
| class | AbstractConstraintHandler |
| Implements the base class for constraint handling. More... | |
| class | AbstractCost |
| Cost function interface. More... | |
| class | AbstractObjectiveFunction |
| Super class of all objective functions for optimization and learning. More... | |
| struct | Ackley |
| Convex quadratic benchmark function with single dominant axis. More... | |
| struct | Cigar |
| Convex quadratic benchmark function with single dominant axis. More... | |
| class | CigarDiscus |
| Convex quadratic benchmark function. More... | |
| struct | CIGTAB1 |
| Multi-objective optimization benchmark function CIGTAB 1. More... | |
| struct | CIGTAB2 |
| Multi-objective optimization benchmark function CIGTAB 2. More... | |
| struct | DiffPowers |
| struct | Discus |
| Convex quadratic benchmark function. More... | |
| struct | DTLZ1 |
| Implements the benchmark function DTLZ1. More... | |
| struct | DTLZ2 |
| Implements the benchmark function DTLZ2. More... | |
| struct | DTLZ3 |
| Implements the benchmark function DTLZ3. More... | |
| struct | DTLZ4 |
| Implements the benchmark function DTLZ4. More... | |
| struct | DTLZ5 |
| Implements the benchmark function DTLZ5. More... | |
| struct | DTLZ6 |
| Implements the benchmark function DTLZ6. More... | |
| struct | DTLZ7 |
| Implements the benchmark function DTLZ7. More... | |
| struct | ELLI1 |
| Multi-objective optimization benchmark function ELLI1. More... | |
| struct | ELLI2 |
| Multi-objective optimization benchmark function ELLI2. More... | |
| struct | Ellipsoid |
| Convex quadratic benchmark function. More... | |
| struct | Fonseca |
| Bi-objective real-valued benchmark function proposed by Fonseca and Flemming. More... | |
| struct | GSP |
| Real-valued benchmark function with two objectives. More... | |
| struct | Himmelblau |
| Multi-modal two-dimensional continuous Himmelblau benchmark function. More... | |
| struct | IHR1 |
| Multi-objective optimization benchmark function IHR1. More... | |
| struct | IHR2 |
| Multi-objective optimization benchmark function IHR 2. More... | |
| struct | IHR3 |
| Multi-objective optimization benchmark function IHR3. More... | |
| struct | IHR4 |
| Multi-objective optimization benchmark function IHR 4. More... | |
| struct | IHR6 |
| Multi-objective optimization benchmark function IHR 6. More... | |
| struct | LZ1 |
| Multi-objective optimization benchmark function LZ1. More... | |
| struct | LZ2 |
| Multi-objective optimization benchmark function LZ2. More... | |
| struct | LZ3 |
| Multi-objective optimization benchmark function LZ3. More... | |
| struct | LZ4 |
| Multi-objective optimization benchmark function LZ4. More... | |
| struct | LZ5 |
| Multi-objective optimization benchmark function LZ5. More... | |
| struct | LZ6 |
| Multi-objective optimization benchmark function LZ6. More... | |
| struct | LZ7 |
| Multi-objective optimization benchmark function LZ7. More... | |
| struct | LZ8 |
| Multi-objective optimization benchmark function LZ8. More... | |
| struct | LZ9 |
| struct | Rosenbrock |
| Generalized Rosenbrock benchmark function. More... | |
| struct | Sphere |
| Convex quadratic benchmark function. More... | |
| struct | ZDT1 |
| Multi-objective optimization benchmark function ZDT1. More... | |
| struct | ZDT2 |
| Multi-objective optimization benchmark function ZDT2. More... | |
| struct | ZDT3 |
| Multi-objective optimization benchmark function ZDT3. More... | |
| struct | ZDT4 |
| Multi-objective optimization benchmark function ZDT4. More... | |
| struct | ZDT6 |
| Multi-objective optimization benchmark function ZDT6. More... | |
| class | BoxConstraintHandler |
| class | CombinedObjectiveFunction |
| Linear combination of objective functions. More... | |
| class | CrossValidationError |
| Cross-validation error for selection of hyper-parameters. More... | |
| class | SupervisedObjectiveFunction |
| Data-dependent objective function for supervised learning. More... | |
| class | UnsupervisedObjectiveFunction |
| Data-dependent objective function for unsupervised learning. More... | |
| class | DenoisingAutoencoderError |
| Objective function for unsupervised training of denoising autoencoders. More... | |
| class | ErrorFunction |
| Objective function for supervised learning. More... | |
| class | KernelTargetAlignment |
| Kernel Target Alignment - a measure of alignment of a kernel Gram matrix with labels. More... | |
| class | LooError |
| Leave-one-out error objective function. More... | |
| class | LooErrorCSvm |
| Leave-one-out error, specifically optimized for C-SVMs. More... | |
| class | AbsoluteLoss |
| absolute loss More... | |
| class | AbstractLoss |
| Loss function interface. More... | |
| class | CrossEntropy |
| Error measure for classication tasks that can be used as the objective function for training. More... | |
| class | CrossEntropyIndependent |
| Error measure for classification tasks of non exclusive attributes that can be used for model training. More... | |
| class | DiscreteLoss |
| flexible loss for classification More... | |
| class | NegativeClassificationLogLikelihood |
| Negative logarithm of the likelihood of a classification model given labeled data. More... | |
| class | SquaredLoss |
| squared loss for regression and classification More... | |
| class | ZeroOneLoss |
| 0-1-loss for classification. More... | |
| class | ZeroOneLoss< unsigned int, RealVector > |
| 0-1-loss for classification. More... | |
| class | NegativeAUC |
| Negative area under the curve. More... | |
| class | NegativeWilcoxonMannWhitneyStatistic |
| Negative Wilcoxon-Mann-Whitney statistic. More... | |
| class | NegativeGaussianProcessEvidence |
| Evidence for model selection of a regularization network/Gaussian process. More... | |
| class | NoisyErrorFunction |
| Error Function which only uses a random fraction of data. More... | |
| class | RadiusMarginQuotient |
| radius margin quotions for binary SVMs More... | |
| class | OneNormRegularizer |
| One-norm of the input as an objective function. More... | |
| class | TwoNormRegularizer |
| Two-norm of the input as an objective function. More... | |
| class | ROC |
| ROC-Curve - false negatives over false positives. More... | |
| class | SpanBoundCSvm |
| Approximate version of the span-bound for C-SVMs. More... | |
| class | SparseFFNetError |
| Error Function for FFNets which should be trained with sparse activation of the hidden neurons. More... | |
| class | SvmLogisticInterpretation |
| Maximum-likelihood model selection score for binary support vector machines. More... | |
| class | AbstractDistribution |
| Abstract class for distributions. More... | |
| class | Bernoulli |
| This class simulates a "Bernoulli trial", which is like a coin toss. More... | |
| class | Binomial |
| Models a binomial distribution with parameters p and n. More... | |
| class | Cauchy |
| Cauchy distribution. More... | |
| class | DiffGeometric_distribution |
| Implements a diff geometric distribution. More... | |
| class | DiffGeometric |
| Random variable with diff geometric distribution. More... | |
| class | Dirichlet_distribution |
| Dirichlet distribution. More... | |
| class | Dirichlet |
| Implements a Dirichlet distribution. More... | |
| class | DiscreteUniform |
| Implements the discrete uniform distribution. More... | |
| class | Erlang_distribution |
| Implements an Erlang distribution. More... | |
| class | Erlang |
| Erlang distributed random variable. More... | |
| class | Gamma_distribution |
| Gamma distribution. More... | |
| class | Gamma |
| Gamma distributed random variable. More... | |
| class | Geometric |
| Implements the geometric distribution. More... | |
| class | BaseRng |
| Collection of different variate generators for different distributions. More... | |
| class | HyperGeometric_distribution |
| Hypergeometric distribution. More... | |
| class | HyperGeometric |
| Random variable with a hypergeometric distribution. More... | |
| class | LogNormal |
| Implements a log-normal distribution with parameters location m and Scale s. More... | |
| class | NegExponential |
| Implements the Negative exponential distribution. More... | |
| class | Normal |
| Implements a univariate normal (Gaussian) distribution. More... | |
| class | Poisson |
| Implements a Poisson distribution with parameter mean. More... | |
| class | TruncatedExponential_distribution |
| boost random suitable distribution for an truncated exponential. See TruncatedExponential for more details. More... | |
| class | TruncatedExponential |
| Implements a generator for the truncated exponential function. More... | |
| class | Uniform |
| Implements a continuous uniform distribution. More... | |
| class | Weibull_distribution |
| Weibull distribution. More... | |
| class | Weibull |
| Weibull distributed random variable. More... | |
| struct | Statistics |
| Calculate pre-defined statistics given a range of values. More... | |
| struct | WilcoxonRankSumTest |
| Wilcoxon rank-sum test / Mann–Whitney U test. More... | |
| struct | Energy |
| The Energy function determining the Gibbs distribution of an RBM. More... | |
| class | AverageEnergyGradient |
| The gradient of the energy averaged over a set of cumulative added samples. More... | |
| class | ContrastiveDivergence |
| Implements k-step Contrastive Divergence described by Hinton et all. (2006). More... | |
| class | ExactGradient |
| class | MultiChainApproximator |
| Approximates the gradient by taking samples from an ensemble of Markov chains running in parallel. More... | |
| class | SingleChainApproximator |
| Approximates the gradient by taking samples from a single Markov chain. More... | |
| struct | Batch< detail::BinarySufficientStatistics< VectorType > > |
| class | BinaryLayer |
| Layer of binary units taking values in {0,1}. More... | |
| struct | Batch< detail::GaussianSufficientStatistics< VectorType > > |
| class | GaussianLayer |
| A layer of Gaussian neurons. More... | |
| struct | Batch< detail::TruncatedExponentialSufficientStatistics< VectorType > > |
| class | TruncatedExponentialLayer |
| A layer of truncated exponential neurons. More... | |
| class | BarsAndStripes |
| Generates the Bars-And-Stripes problem. In this problem, a 4x4 image has either rows or columns of the same value. More... | |
| class | DistantModes |
| Creates a set of pattern (each later representing the "center" of a mode) which than are randomly perturbed to create the data set. add reference. More... | |
| class | MNIST |
| Reads in the famous MNIST data in possibly binarized form. The MNIST database itself is not included in Shark, this class just helps loading it. More... | |
| class | Shifter |
| Shifter problem. More... | |
| class | RBM |
| stub for the RBM class. at the moment it is just a holder of the parameter set and the Energy. More... | |
| class | GibbsOperator |
| Implements Gibbs Sampling related transition operators for various temperatures. More... | |
| struct | Batch< detail::GibbsSample< Statistics > > |
| struct | Batch< detail::MarkovChainSample< Hidden, Visible > > |
| class | MarkovChain |
| A single Markov chain. More... | |
| class | TemperedMarkovChain |
| struct | RealSpace |
| The RealSpace can't be enumerated. Infinite values are just too much. More... | |
| struct | TwoStateSpace |
| The TwoStateSpace is a discrete Space with only two values, for example {0,1} or {-1,1}. More... | |
| struct | Producer |
| Produces normally distributed values and reports them via a probe. More... | |
| struct | Consumer |
| Consumes floating point values via a probe. More... | |
| struct | Store |
| Helper class for storing the most recent value reported from a probe. More... | |
| struct | TspTourLength |
| Calculates the cost of a tour w.r.t. to a cost matrix. More... | |
| struct | PartiallyMappedCrossover |
| Implements partially mapped crossover. More... | |
Enumerations | |
| enum | AlphaStatus { AlphaFree = 0, AlphaLowerBound = 1, AlphaUpperBound = 2, AlphaDeactivated = 3 } |
| enum | QpStopType { QpNone = 0, QpAccuracyReached = 1, QpMaxIterationsReached = 4, QpTimeout = 8 } |
| enum | BuildType { RELEASE_BUILD_TYPE, DEBUG_BUILD_TYPE } |
| Models the build type. More... | |
| enum | Connective { AND, OR, PROD, PROBOR } |
| enum | SamplingFlagTypes { StoreHiddenStatistics = 1, StoreHiddenInput = 2, StoreHiddenState = 4, StoreHiddenFeatures = 8, StoreVisibleStatistics = 16, StoreVisibleInput = 32, StoreVisibleState = 64, StoreVisibleFeatures = 128, StoreEnergyComponents = 256 } |
| Possible values a Sampler might need to store. More... | |
| enum | GradientFlagTypes { RequiresStatistics = 1, RequiresInput = 2, RequiresState = 4, RequiresFeatures = 8 } |
| Values a gradient may require. More... | |
Functions | |
| ANNOUNCE_ORACLE (OracleOfDelphi, OracleFactory) | |
| ANNOUNCE_ORACLE (HitchhikersGuideToTheGalaxy, OracleFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_OPTIMIZER (AGE, shark::moo::RealValuedMultiObjectiveOptimizerFactory) | |
| ANNOUNCE_SINGLE_OBJECTIVE_OPTIMIZER (CMA, soo::RealValuedSingleObjectiveOptimizerFactory) | |
| Registers the CMA with the factory. More... | |
| ANNOUNCE_SINGLE_OBJECTIVE_OPTIMIZER (CMSA, soo::RealValuedSingleObjectiveOptimizerFactory) | |
| Registers the CMSA with the factory. More... | |
| ANNOUNCE_SINGLE_OBJECTIVE_OPTIMIZER (ElitistCMA, soo::RealValuedSingleObjectiveOptimizerFactory) | |
| Registers the elitist CMA with the factory. More... | |
| ANNOUNCE_SINGLE_OBJECTIVE_OPTIMIZER (OnePlusOneES, soo::RealValuedSingleObjectiveOptimizerFactory) | |
| Registers the (1+1)-ES with the factory. More... | |
| template<typename ParentsIterator , typename OffspringIterator > | |
| void | select_mu_komma_lambda (ParentsIterator beginParents, ParentsIterator endParents, OffspringIterator beginOffspring, OffspringIterator endOffspring) |
| Selects lambda offspring individuals from mu parents. More... | |
| template<typename ParentsIterator , typename OffspringIterator , typename Relation > | |
| void | select_mu_komma_lambda_p (ParentsIterator beginParents, ParentsIterator endParents, OffspringIterator beginOffspring, OffspringIterator endOffspring, Relation relation) |
| Selects lambda offspring individuals from mu parents. Relies on the supplied predicate for comparing individuals. More... | |
| template<typename ParentsIterator , typename OffspringIterator > | |
| void | select_mu_plus_lambda (ParentsIterator beginParents, ParentsIterator endParents, OffspringIterator beginOffspring, OffspringIterator endOffspring) |
| template<typename ParentsIterator , typename OffspringIterator , typename Relation > | |
| void | select_mu_plus_lambda_p (ParentsIterator beginParents, ParentsIterator endParents, OffspringIterator beginOffspring, OffspringIterator endOffspring, Relation relation) |
| template<typename Population > | |
| void | shuffle (Population &p) |
| template<typename FitnessTag > | |
| Population::const_iterator | best_individual (const Population &p) |
| template<typename FitnessTag > | |
| Population::const_iterator | worst_individual (const Population &p) |
| ANNOUNCE_MULTI_OBJECTIVE_OPTIMIZER (HypRealCodedNSGAII, moo::RealValuedMultiObjectiveOptimizerFactory) | |
| Registers the real-coded NSGA-II relying on the hypervolume indicator with the factory. More... | |
| ANNOUNCE_MULTI_OBJECTIVE_OPTIMIZER (SteadyStateMOCMA, moo::RealValuedMultiObjectiveOptimizerFactory) | |
| Injects the Steady-State MOCMA relying on the locality sensitive additive epsilon indicator into the inheritance hierarchy. More... | |
| ANNOUNCE_MULTI_OBJECTIVE_OPTIMIZER (EpsilonSteadyStateMOCMA, moo::RealValuedMultiObjectiveOptimizerFactory) | |
| Registers the Steady-State MOCMA relying on the additive epsilon indicator with the factory. More... | |
| template<typename SearchPointType > | |
| TypedIndividual< SearchPointType > | make_individual (const SearchPointType &p, const std::vector< double > &fitness, const std::vector< double > &unpenalizedFitness) |
| double | wlsCubicInterp (double t1, double t2, double f1, double f2, double gtd1, double gtd2) |
| template<class VectorT , class Function > | |
| void | wolfecubic (VectorT &point, const VectorT &searchDirection, double &fret, Function func, VectorT const &gradient) |
| Line search, using cubic interpolation, satisfying the strong Wolfe conditions. More... | |
| std::size_t | kMeans (Data< RealVector > const &data, std::size_t k, Centroids ¢roids, std::size_t maxIterations=0) |
| The k-means clustering algorithm. More... | |
| ANNOUNCE_LOG_FORMATTER (PlainTextLogFormatter, LogFormatterFactory) | |
| Make the plain text formatter known to the formatter factory. More... | |
| Logger & | operator<< (Logger &logger, const Logger::Record &record) |
| Pushes the record in the specified logger. More... | |
| ANNOUNCE_LOG_HANDLER (CoutLogHandler, LogHandlerFactory) | |
| Make the std::cout log handler known to the factory. More... | |
| ANNOUNCE_LOG_HANDLER (CerrLogHandler, LogHandlerFactory) | |
| Make the std::cerr log handler known to the factory. More... | |
| ANNOUNCE_LOG_HANDLER (ClogLogHandler, LogHandlerFactory) | |
| Make the std::clog log handler known to the factory. More... | |
| template<class T > | |
| T | maxExpInput () |
| Maximum allowed input value for exp. More... | |
| template<class T > | |
| T | minExpInput () |
| Minimum value for exp(x) allowed so that it is not 0. More... | |
| template<class T > | |
| boost::enable_if < boost::is_arithmetic< T >, T > ::type | sqr (const T &x) |
| Calculates x^2. More... | |
| template<class T > | |
| T | cube (const T &x) |
| Calculates x^3. More... | |
| template<class T > | |
| boost::enable_if < boost::is_arithmetic< T >, T > ::type | sigmoid (T x) |
| Logistic function/logistic function. More... | |
| template<class T > | |
| T | safeExp (T x) |
| Thresholded exp function, over- and underflow safe. More... | |
| template<class T > | |
| T | safeLog (T x) |
| Thresholded log function, over- and underflow safe. More... | |
| template<class T > | |
| boost::enable_if < boost::is_arithmetic< T >, T > ::type | softPlus (T x) |
| Numerically stable version of the function log(1+exp(x)). More... | |
| template<class T > | |
| T | copySign (T x, T y) |
| template<typename T , typename U > | |
| ResultSet< T, U > | makeResultSet (T const &t, U const &u) |
| Generates a typed solution given the search point and the corresponding objective function value. More... | |
| template<class SearchPoint , class Result > | |
| std::ostream & | operator<< (std::ostream &out, ResultSet< SearchPoint, Result > const &solution) |
| template<class SearchPoint > | |
| std::ostream & | operator<< (std::ostream &out, ValidatedSingleObjectiveResultSet< SearchPoint > const &solution) |
| template<class T > | |
| bool | operator== (const SharedVector< T > &op1, const SharedVector< T > &op2) |
| template<class T > | |
| bool | operator!= (const SharedVector< T > &op1, const SharedVector< T > &op2) |
| template<class RandomAccessIterator , class Rng > | |
| void | partial_shuffle (RandomAccessIterator begin, RandomAccessIterator middle, RandomAccessIterator end, Rng &rng) |
| random_shuffle algorithm which stops after acquiring the random subsequence for [begin,middle) More... | |
| template<class RandomAccessIterator > | |
| void | partial_shuffle (RandomAccessIterator begin, RandomAccessIterator middle, RandomAccessIterator end) |
| random_shuffle algorithm which stops after acquiring the random subsequence for [begin,middle) More... | |
| template<class Range > | |
| boost::range_iterator< Range > ::type | median_element (Range &range) |
| Returns the iterator to the median element. after this call, the range is partially ordered. More... | |
| template<class Range > | |
| boost::range_iterator< Range > ::type | median_element (Range const &rangeAdaptor) |
| template<class Range > | |
| boost::range_iterator< Range > ::type | partitionEqually (Range &range) |
| Partitions a range in two parts as equal in size as possible. More... | |
| template<class Range > | |
| boost::range_iterator< Range > ::type | partitionEqually (Range const &rangeAdaptor) |
| Partitions a range in two parts as equal in size as possible and returns it's result. More... | |
| template<class K , class V > | |
| void | swap (KeyValuePair< K, V > &pair1, KeyValuePair< K, V > &pair2) |
| Swaps the contents of two instances of KeyValuePair. More... | |
| template<class Key , class Value > | |
| KeyValuePair< Key, Value > | makeKeyValuePair (Key const &key, Value const &value) |
| Creates a KeyValuePair. More... | |
| template<class Iterator1 , class Iterator2 > | |
| KeyValueRange< Iterator1, Iterator2 > | zipKeyValuePairs (Iterator1 begin1, Iterator1 end1, Iterator2 begin2, Iterator2 end2) |
| Zips two ranges together, interpreting the first range as Key which can be sorted. More... | |
| template<class Range1 , class Range2 > | |
| KeyValueRange< typename boost::range_iterator< Range1 > ::type, typename boost::range_iterator< Range2 > ::type > | zipKeyValuePairs (Range1 &range1, Range2 &range2) |
| Zips two ranges together, interpreting the first range as Key which can be sorted. More... | |
| template<class Range > | |
| std::size_t | size (Range const &range) |
| returns the size of a range More... | |
| template<class Range > | |
| boost::range_reference< Range > ::type | get (Range &range, std::size_t i) |
| returns the i-th element of a range More... | |
| template<class Range > | |
| boost::range_reference< Range const >::type | get (Range const &range, std::size_t i) |
| template<class PairType , class Iterator1 , class Iterator2 > | |
| boost::iterator_range < PairIterator< PairType, Iterator1, Iterator2 >> | zipPairRange (Iterator1 begin1, Iterator1 end1, Iterator2 begin2, Iterator2 end2) |
| returns a paired zip range using pair type Pair This class must be specialized for every Pair to be used More... | |
| template<class PairType , class Range1 , class Range2 > | |
| PairRangeType< PairType, Range1, Range2 >::type | zipPairRange (Range1 &range1, Range2 &range2) |
| returns a paired zip range using pair type Pair More... | |
| template<class PairType , class Range1 , class Range2 > | |
| PairRangeType< PairType, Range1 const, Range2 const > ::type | zipPairRange (Range1 const &range1, Range2 const &range2) |
| returns a paired zip range using pair type Pair More... | |
| template<class T , class Range > | |
| Batch< T >::type | createBatch (Range const &range) |
| creates a batch from a range of inputs More... | |
| template<class T > | |
| boost::range_iterator < shark::FixedDenseMatrixProxy < T > const >::type | range_begin (shark::FixedDenseMatrixProxy< T > const &m) |
| template<class T > | |
| boost::range_iterator < shark::FixedDenseMatrixProxy < T > >::type | range_begin (shark::FixedDenseMatrixProxy< T > &m) |
| template<class T > | |
| boost::range_iterator < shark::FixedDenseMatrixProxy < T > const >::type | range_end (shark::FixedDenseMatrixProxy< T > const &m) |
| template<class T > | |
| boost::range_iterator < shark::FixedDenseMatrixProxy < T > >::type | range_end (shark::FixedDenseMatrixProxy< T > &m) |
| template<class S > | |
| S & | fusionize (detail::FusionFacade< S > &facade) |
| template<class S > | |
| S const & | fusionize (detail::FusionFacade< S > const &facade) |
| template<class S > | |
| boost::disable_if < detail::isFusionFacade< S > , S & >::type | fusionize (S &facade) |
| template<class S > | |
| boost::disable_if < detail::isFusionFacade< S > , S const & >::type | fusionize (S const &facade) |
| template<class DatasetType , class IndexRange > | |
| DataView< DatasetType > | subset (DataView< DatasetType > const &view, IndexRange const &indizes) |
| creates a subset of a DataView with elements indexed by indices More... | |
| template<class DatasetType > | |
| DataView< DatasetType > | randomSubset (DataView< DatasetType > const &view, std::size_t size) |
| creates a random subset of a DataView with given size More... | |
| template<class DatasetType , class IndexRange > | |
| DataView< DatasetType >::batch_type | subBatch (DataView< DatasetType > const &view, IndexRange const &indizes) |
| Creates a batch given a set of indices. More... | |
| template<class DatasetType > | |
| DataView< DatasetType >::batch_type | randomSubBatch (DataView< DatasetType > const &view, std::size_t size) |
| Creates a random batch of a given size. More... | |
| template<class DatasetType > | |
| DataView< DatasetType > | toView (DatasetType &set) |
| Creates a View from a dataset. More... | |
| template<class T > | |
| DataView< T >::dataset_type | toDataset (DataView< T > const &view, std::size_t batchSize=DataView< T >::dataset_type::DefaultBatchSize) |
| Creates a new dataset from a View. More... | |
| template<class DatasetType > | |
| unsigned int | numberOfClasses (DataView< DatasetType > const &view) |
| template<class DatasetType > | |
| std::size_t | inputDimension (DataView< DatasetType > const &view) |
| Return the input dimensionality of the labeled dataset represented by the view. More... | |
| template<class DatasetType > | |
| std::size_t | labelDimension (DataView< DatasetType > const &view) |
| Return the label dimensionality of the labeled dataset represented by the view. More... | |
| template<class DatasetType > | |
| std::size_t | dataDimension (DataView< DatasetType > const &view) |
| Return the dimensionality of the dataset represented by the view. More... | |
| template<typename VectorType > | |
| void | importHDF5 (Data< VectorType > &data, const std::string &fileName, const std::string &datasetName) |
| Import data from a HDF5 file. More... | |
| template<typename VectorType , typename LabelType > | |
| void | importHDF5 (LabeledData< VectorType, LabelType > &labeledData, const std::string &fileName, const std::string &data, const std::string &label) |
| Import data to a LabeledData object from a HDF5 file. More... | |
| template<typename VectorType > | |
| void | importHDF5 (Data< VectorType > &data, const std::string &fileName, const std::vector< std::string > &cscDatasetName) |
| Import data from HDF5 dataset of compressed sparse column format. More... | |
| template<typename VectorType , typename LabelType > | |
| void | importHDF5 (LabeledData< VectorType, LabelType > &labeledData, const std::string &fileName, const std::vector< std::string > &cscDatasetName, const std::string &label) |
| Import data from HDF5 dataset of compressed sparse column format. More... | |
| template<class T > | |
| void | importPGM (const char *fileName, T &data, int &sx, int &sy) |
| Import a PGM image from file. More... | |
| template<class T > | |
| void | exportPGM (const char *fileName, const T &data, int sx, int sy, bool normalize=false) |
| Export a PGM image to file. More... | |
| template<class T > | |
| void | importPGMDir (const std::string &p, T &container, std::vector< ImageInformation > &info) |
| Import PGM images scanning a directory recursively. More... | |
| template<class T > | |
| void | importPGMSet (const std::string &p, Data< T > &set, Data< ImageInformation > &setInfo) |
| Import PGM images scanning a directory recursively. More... | |
| static std::string | connective_to_name (Connective c) |
| ANNOUNCE_FUZZY_SET (BellFS, FuzzySetFactory) | |
| ANNOUNCE_FUZZY_SET (ConstantFS, FuzzySetFactory) | |
| ANNOUNCE_FUZZY_SET (CustomizedFS, FuzzySetFactory) | |
| ANNOUNCE_FUZZY_SET (GeneralizedBellFS, FuzzySetFactory) | |
| ANNOUNCE_FUZZY_SET (InfinityFS, FuzzySetFactory) | |
| ANNOUNCE_FUZZY_SET (SigmoidalFS, FuzzySetFactory) | |
| ANNOUNCE_FUZZY_SET (SingletonFS, FuzzySetFactory) | |
| ANNOUNCE_FUZZY_SET (TrapezoidFS, FuzzySetFactory) | |
| ANNOUNCE_FUZZY_SET (TriangularFS, FuzzySetFactory) | |
| ANNOUNCE_LINGUISTIC_TERM (BellLT, LinguisticTermFactory) | |
| ANNOUNCE_LINGUISTIC_TERM (ComposedLT, LinguisticTermFactory) | |
| ANNOUNCE_LINGUISTIC_TERM (ConstantLT, LinguisticTermFactory) | |
| ANNOUNCE_LINGUISTIC_TERM (CustomizedLT, LinguisticTermFactory) | |
| ANNOUNCE_LINGUISTIC_TERM (GeneralizedBellLT, LinguisticTermFactory) | |
| ANNOUNCE_LINGUISTIC_TERM (InfinityLT, LinguisticTermFactory) | |
| ANNOUNCE_LINGUISTIC_TERM (SigmoidalLT, LinguisticTermFactory) | |
| ANNOUNCE_LINGUISTIC_TERM (SingletonLT, LinguisticTermFactory) | |
| ANNOUNCE_LINGUISTIC_TERM (TrapezoidLT, LinguisticTermFactory) | |
| ANNOUNCE_LINGUISTIC_TERM (TriangularLT, LinguisticTermFactory) | |
| template<class MatA , class MatB , class MatC , class ComputeKernel > | |
| void | generalPanelPanelOperation (blas::matrix_expression< MatA > const &matA, blas::matrix_expression< MatB > const &matB, blas::matrix_container< MatC > &matC, ComputeKernel kernel) |
| General building block for matrix-matrix-multiplication like operations using matrices with much more columns than rows(often called gepp in BLAS). More... | |
| template<class MatA , class MatB , class MatC , class ComputeKernel > | |
| void | generalMatrixMatrixOperation (blas::matrix_expression< MatA > const &matA, blas::matrix_expression< MatB > const &matB, blas::matrix_container< MatC > &matC, ComputeKernel kernel) |
| General building block for matrix-matrix-multiplication like operations using generla matrices(often called gemmin BLAS). More... | |
| template<class MatA , class MatB , class MatC > | |
| void | fast_prod (blas::matrix_expression< MatA > const &matA, blas::matrix_expression< MatB > const &matB, blas::matrix_expression< MatC > &matC, bool beta=false, double alpha=1.0) |
| Fast matrix/matrix product. More... | |
| template<class MatA , class MatB , class MatC > | |
| void | fast_prod (blas::matrix_expression< MatA > const &matA, blas::matrix_expression< MatB > const &matB, blas::matrix_range< MatC > matC, bool beta=false, double alpha=1.0) |
| Fast matrix/matrix product. More... | |
| template<class MatA , class VecB , class VecC > | |
| void | fast_prod (blas::matrix_expression< MatA > const &matA, blas::vector_expression< VecB > const &vecB, blas::vector_expression< VecC > &vecC, bool beta=false, double alpha=1.0) |
| Fast matrix/vector product. More... | |
| template<class MatA , class VecB , class VecC > | |
| void | fast_prod (blas::matrix_expression< MatA > const &matA, blas::vector_expression< VecB > const &vecB, blas::vector_range< VecC > vecC, bool beta=false, double alpha=1.0) |
| Special case of matrix/vector product for subrange result. More... | |
| template<class MatA , class VecB , class MatC > | |
| void | fast_prod (blas::matrix_expression< MatA > const &matA, blas::vector_expression< VecB > const &vecB, blas::matrix_row< MatC > vecC, bool beta=false, double alpha=1.0) |
| Special case of matrix/vector product for matrix row results. More... | |
| template<class MatA , class VecB , class MatC > | |
| void | fast_prod (blas::matrix_expression< MatA > const &matA, blas::vector_expression< VecB > const &vecB, blas::matrix_column< MatC > vecC, bool beta=false, double alpha=1.0) |
| Special case of matrix/vector product for matrix column results. More... | |
| template<class MatA , class VecB , class VecC > | |
| void | fast_prod (blas::vector_expression< VecB > const &vecB, blas::matrix_expression< MatA > const &matA, blas::vector_expression< VecC > &vecC, bool beta=false, double alpha=1.0) |
| Fast matrix/vector product. More... | |
| template<class MatA , class VecB , class VecC > | |
| void | fast_prod (blas::vector_expression< VecB > const &vecB, blas::matrix_expression< MatA > const &matA, blas::vector_range< VecC > &vecC, bool beta=false, double alpha=1.0) |
| Fast matrix/vector product. More... | |
| template<class MatA , class VecB , class VecC > | |
| void | fast_prod (blas::vector_expression< VecB > const &vecB, blas::matrix_expression< MatA > const &matA, blas::matrix_row< VecC > &vecC, bool beta=false, double alpha=1.0) |
| Fast matrix/vector product. More... | |
| template<class MatA , class VecB , class VecC > | |
| void | fast_prod (blas::vector_expression< VecB > const &vecB, blas::matrix_expression< MatA > const &matA, blas::matrix_column< VecC > &vecC, bool beta=false, double alpha=1.0) |
| Fast matrix/vector product. More... | |
| template<class MatA , class MatC > | |
| void | symmRankKUpdate (blas::matrix_expression< MatA > const &matA, blas::matrix_expression< MatC > &matC, bool beta=false, double alpha=1.0) |
| Fast rank k update to a symmetric matrix. More... | |
| template<class MatA , class VecB > | |
| void | sumColumns (blas::matrix_expression< MatA > const &A, blas::vector_container< VecB > &b) |
| calculates \( b+=A^1+A^2+A^n \) where \(A^i\) are the columns of A More... | |
| template<class MatA , class VecB > | |
| void | sumRows (blas::matrix_expression< MatA > const &A, blas::vector_container< VecB > &b) |
| calculates \( b+=A_1+A_2+A_n \) where \(A_i\) are the rows of A More... | |
| template<class MatA > | |
| blas::vector< typename MatA::value_type > | sumColumns (blas::matrix_expression< MatA > const &A) |
| calculates \( b+=A^1+A^2+A^n \) where \(A^i\) are the columns of A More... | |
| template<class MatA > | |
| blas::vector< typename MatA::value_type > | sumRows (blas::matrix_expression< MatA > const &A) |
| calculates \( b+=A_1+A_2+A_n \) where \(A_i\) are the rows of A More... | |
| template<class MatA > | |
| MatA::value_type | sumElements (blas::matrix_expression< MatA > const &A) |
| template<class MatA , class MatC > | |
| void | symmRankKUpdate (blas::matrix_expression< MatA > const &matA, blas::matrix_range< MatC > matC, bool beta=false, double alpha=1.0) |
| template<class MatA , class VecB , class VecC , class ComputeKernel > | |
| void | generalMatrixVectorOperation (blas::matrix_expression< MatA > const &matA, blas::vector_expression< VecB > const &vecB, blas::vector_expression< VecC > &vecC, ComputeKernel kernel) |
| implements an operation of the Form \( c_i = c_i + \sum_{j=1}^n k(A_{ij},b_j)\) for arbitrary kernels k. More... | |
| template<class MatA , class VecB , class VecC , class ComputeKernel > | |
| void | generalMatrixVectorOperation (blas::matrix_expression< MatA > const &matA, blas::vector_expression< VecB > const &vecB, blas::matrix_row< VecC > vecC, ComputeKernel kernel) |
| template<class MatA , class VecB , class VecC , class ComputeKernel > | |
| void | generalMatrixVectorOperation (blas::matrix_expression< MatA > const &matA, blas::vector_expression< VecB > const &vecB, blas::matrix_column< VecC > vecC, ComputeKernel kernel) |
| template<class MatA , class VecB , class VecC , class ComputeKernel > | |
| void | generalMatrixVectorOperation (blas::matrix_expression< MatA > const &matA, blas::vector_expression< VecB > const &vecB, blas::vector_range< VecC > vecC, ComputeKernel kernel) |
| template<class M , class Permutation > | |
| void | swapRows (Permutation const &P, blas::matrix_expression< M > &A) |
| implements row pivoting at matrix A using permutation P More... | |
| template<class V , class Permutation > | |
| void | swapRows (Permutation const &P, blas::vector_expression< V > &v) |
| implements column pivoting of vector A using permutation P More... | |
| template<class M , class Permutation > | |
| void | swapColumns (Permutation const &P, M &A) |
| implements column pivoting at matrix A using permutation P More... | |
| template<class M , class Permutation > | |
| void | swapRowsInverted (Permutation const &P, blas::matrix_expression< M > &A) |
| implements the inverse row pivoting at matrix A using permutation P More... | |
| template<class M , class Permutation > | |
| void | swapColumnsInverted (Permutation const &P, blas::matrix_expression< M > &A) |
| implements the inverse column pivoting at matrix A using permutation P More... | |
| template<class M , class Permutation > | |
| void | swapFull (Permutation const &P, blas::matrix_expression< M > &A) |
| Implements full pivoting at matrix A using permutation P. More... | |
| template<class M , class Permutation > | |
| void | swapFullInverted (Permutation const &P, blas::matrix_expression< M > &A) |
| implements the inverse full pivoting at matrix A using permutation P More... | |
| static storage | compressedStorage (type &v) |
| template<class T > | |
| FixedDenseVectorProxy< T > | makeVector (const std::size_t size, T *data) |
| converts a chunk of memory into a (readonly) usable ublas blas::vector. More... | |
| template<class T , std::size_t N> | |
| FixedDenseVectorProxy< T > | makeVector (T(&array)[N]) |
| template<class T > | |
| FixedDenseMatrixProxy< T > | makeMatrix (const std::size_t size1, const std::size_t size2, T *data) |
| converts a chunk of memory into a (readonly) usable dense blas::matrix. More... | |
| template<class T , std::size_t M, std::size_t N> | |
| FixedDenseMatrixProxy< T > | makeMatrix (T(&array)[M][N]) |
| converts a C-style 2D array into a (readonly) usable dense blas::matrix. More... | |
| template<class T , class BaseExpression > | |
| static SHARK_COMPRESSEDTRAITSSPEC(blas::compressed_matrix < T >) public storage | compressedStorage (type &m) |
| static storage | compressedStorage (type &v) |
| template<class T , class BaseExpression > | |
| static SHARK_COMPRESSEDTRAITSSPEC(blas::compressed_vector < T >) public storage | compressedStorage (type &v) |
| SHARK_VECTOR_MATRIX_TYPEDEFS (long double, BigReal) | |
| SHARK_VECTOR_MATRIX_ASSIGNMENT (BigReal) | |
| template<class MatT , class Vec1T , class Vec2T > | |
| void | solveSystem (blas::matrix_expression< MatT > const &A, blas::vector_expression< Vec1T > &x, blas::vector_expression< Vec2T > const &b) |
| System of linear equations solver. More... | |
| template<class MatT , class Vec1T , class Vec2T > | |
| void | solveSystem (blas::matrix_expression< MatT > const &A, blas::matrix_expression< Vec1T > &x, blas::matrix_expression< Vec2T > const &b) |
| System of linear equations solver. More... | |
| template<class System , class MatT , class VecT > | |
| void | solveSymmSystemInPlace (blas::matrix_expression< MatT > const &A, blas::vector_expression< VecT > &b) |
| System of symmetric linear equations solver. The result is stored in b. More... | |
| template<class System , class MatT , class Mat1T > | |
| void | solveSymmSystemInPlace (blas::matrix_expression< MatT > const &A, blas::matrix_expression< Mat1T > &B) |
| System of symmetric linear equations solver. More... | |
| template<class System , class MatT , class Vec1T , class Vec2T > | |
| void | solveSymmSystem (blas::matrix_expression< MatT > const &A, blas::vector_expression< Vec1T > &x, blas::vector_expression< Vec2T > const &b) |
| System of symmetric linear equations solver. More... | |
| template<class System , class MatT , class Mat1T , class Mat2T > | |
| void | solveSymmSystem (blas::matrix_expression< MatT > const &A, blas::matrix_expression< Mat1T > &X, blas::matrix_expression< Mat2T > const &B) |
| System of symmetric linear equations solver. More... | |
| template<class MatT , class VecT > | |
| void | approxSolveSymmSystem (blas::matrix_expression< MatT > const &A, blas::vector_expression< VecT > &x, blas::vector_expression< VecT > const &b, double epsilon=1.e-10, unsigned int maxIterations=0) |
| Approximates the solution of a linear system of equation Ax=b. More... | |
| template<class MatT , class VecT > | |
| void | approxSolveSymmSystemInPlace (blas::matrix_expression< MatT > const &A, blas::vector_expression< VecT > &b, double epsilon=1.e-10, unsigned int maxIterations=0) |
| Approximates the solution of a linear system of equation Ax=b, storing the solution in b. More... | |
| template<class System , class DiagType , class MatT , class VecT > | |
| void | solveTriangularSystemInPlace (const blas::matrix_expression< MatT > &A, blas::vector_expression< VecT > &b) |
| In-place triangular linear equation solver. More... | |
| template<class System , class DiagType , class MatA , class MatB > | |
| void | solveTriangularSystemInPlace (const blas::matrix_expression< MatA > &A, blas::matrix_expression< MatB > &B) |
| In-place triangular linear equation solver. More... | |
| template<class System , class MatA , class MatB > | |
| void | solveTriangularCholeskyInPlace (const blas::matrix_expression< MatA > &A, blas::matrix_expression< MatB > &B) |
| In-Place solver if A was already cholesky decomposed Solves multiple systems of linear equations Ax_1=b_1 Ax_1=b_2 ... =>AX=B or XA=B given an A which was already Cholesky-decomposed as A=LL^T where L is a lower triangular matrix. More... | |
| template<class System , class MatA , class MatB > | |
| void | solveTriangularCholeskyInPlace (const blas::matrix_expression< MatA > &A, blas::vector_expression< MatB > &B) |
| In-Place solver if A was already cholesky decomposed Solves system of linear equations Ax=b given an A which was already Cholesky-decomposed as A=LL^T where L is a lower triangular matrix. More... | |
| template<class InputT , class IntermediateT , class OutputT > | |
| detail::ConcatenatedModelWrapper < InputT, IntermediateT, OutputT > | operator>> (AbstractModel< InputT, IntermediateT > &firstModel, AbstractModel< IntermediateT, OutputT > &secondModel) |
| Connects two AbstractModels so that the output of the first model is the input of the second. More... | |
| template<class InputT , class IntermediateT , class OutputT > | |
| detail::ConcatenatedModelList < InputT, IntermediateT, OutputT > | operator>> (const detail::ConcatenatedModelWrapperBase< InputT, IntermediateT > &firstModel, AbstractModel< IntermediateT, OutputT > &secondModel) |
| Connects another AbstractModel two a previously created connection of models. More... | |
| template<typename InputType , typename InputTypeT1 , typename InputTypeT2 > | |
| double | evalSkipMissingFeatures (const AbstractKernelFunction< InputType > &kernelFunction, const InputTypeT1 &inputA, const InputTypeT2 &inputB) |
| template<typename InputType , typename InputTypeT1 , typename InputTypeT2 , typename InputTypeT3 > | |
| double | evalSkipMissingFeatures (const AbstractKernelFunction< InputType > &kernelFunction, const InputTypeT1 &inputA, const InputTypeT2 &inputB, InputTypeT3 const &missingness) |
| template<class InputType > | |
| RealMatrix | calculateRegularizedKernelMatrix (AbstractKernelFunction< InputType >const &kernel, Data< InputType > const &dataset, double regularizer=0) |
| Calculates the regularized kernel gram matrix of the points stored inside a dataset. More... | |
| template<class InputType , class WeightMatrix > | |
| RealVector | calculateKernelMatrixParameterDerivative (AbstractKernelFunction< InputType > const &kernel, Data< InputType > const &dataset, WeightMatrix const &weights) |
| Efficiently calculates the weighted derivative of a Kernel Gram Matrix w.r.t the Kernel Parameters. More... | |
| ANNOUNCE_SINGLE_OBJECTIVE_FUNCTION (Ackley, shark::soo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_SINGLE_OBJECTIVE_FUNCTION (Cigar, shark::soo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_SINGLE_OBJECTIVE_FUNCTION (CigarDiscus, shark::soo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (CIGTAB1, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (CIGTAB2, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_SINGLE_OBJECTIVE_FUNCTION (DiffPowers, shark::soo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_SINGLE_OBJECTIVE_FUNCTION (Discus, shark::soo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (DTLZ1, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (DTLZ2, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (DTLZ3, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (DTLZ4, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (DTLZ5, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (DTLZ6, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (DTLZ7, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (ELLI1, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (ELLI2, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_SINGLE_OBJECTIVE_FUNCTION (Ellipsoid, shark::soo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_SINGLE_OBJECTIVE_FUNCTION (Himmelblau, shark::soo::RealValuedObjectiveFunctionFactory) | |
| Makes Himmelblau's function known to the factory. More... | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (IHR1, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (IHR2, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (IHR3, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (IHR4, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (IHR6, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (LZ1, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (LZ2, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (LZ3, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (LZ4, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (LZ5, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (LZ6, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (LZ7, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (LZ8, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (LZ9, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_SINGLE_OBJECTIVE_FUNCTION (Rosenbrock, shark::soo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_SINGLE_OBJECTIVE_FUNCTION (Sphere, shark::soo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (ZDT1, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (ZDT2, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (ZDT3, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (ZDT4, shark::moo::RealValuedObjectiveFunctionFactory) | |
| ANNOUNCE_MULTI_OBJECTIVE_FUNCTION (ZDT6, shark::moo::RealValuedObjectiveFunctionFactory) | |
| template<class InputType , class LabelType > | |
| void | swap (const ErrorFunction< InputType, LabelType > &op1, const ErrorFunction< InputType, LabelType > &op2) |
| template<class InputType , class LabelType > | |
| void | swap (const NoisyErrorFunction< InputType, LabelType > &op1, const NoisyErrorFunction< InputType, LabelType > &op2) |
| template<typename RngType > | |
| double | entropy (const Bernoulli< RngType > &coin) |
| Returns the entropy of the bernoulli distribution. More... | |
| template<typename RngType > | |
| double | entropy (const Binomial< RngType > &coin) |
| template<typename RngType > | |
| double | entropy (const Cauchy< RngType > &distribution) |
| Returns the entropy of the Cauchy distribution. More... | |
| template<typename RngType > | |
| double | entropy (const DiscreteUniform< RngType > &uniform) |
| template<typename Distribution > | |
| double | entropy (Distribution &d, std::size_t trials=10000) |
| Estimates the entropy of a distribution. The more trials the better is the estimate, but good estimates are slow. More... | |
| ANNOUNCE_SHARK_RNG (shark::FastRngType, FastRng) | |
| ANNOUNCE_SHARK_RNG (shark::DefaultRngType, Rng) | |
| template<typename DistributionP , typename DistributionQ > | |
| double | kullback_leiber_divergence (DistributionP &p, DistributionQ &q, std::size_t trials=10000) |
| Estimates the kullback-leibler-divergence between two distributions. The more trials the better is the estimate, but good estimates are slow. More... | |
| template<typename RngType > | |
| double | entropy (const Normal< RngType > &normal) |
| template<typename CharT , typename Traits > | |
| static std::basic_ostream < CharT, Traits > & | operator<< (std::basic_ostream< CharT, Traits > &s, const Statistics &stats) |
| Writes statistics to the supplied stream. More... | |
| template<typename Stream > | |
| Stream & | operator<< (Stream &s, const WilcoxonRankSumTest::Element &element) |
| template<class RBMType > | |
| double | logPartitionFunction (RBMType const &rbm, double beta=1.0) |
| Calculates the value of the partition function $Z$. More... | |
| template<class RBMType > | |
| double | negativeLogLikelihoodFromLogPartition (RBMType const &rbm, UnlabeledData< RealVector > const &inputs, double logPartition, double beta=1.0) |
| Estimates the negative log-likelihood of a set of input vectors under the models distribution using the partition function. More... | |
| template<class RBMType > | |
| double | negativeLogLikelihood (RBMType const &rbm, UnlabeledData< RealVector > const &inputs, double beta=1.0) |
| Estimates the negative log-likelihood of a set of input vectors under the models distribution. More... | |
| TypedFlags< SamplingFlagTypes > | convertToSamplingFlags (TypedFlags< GradientFlagTypes > hiddenFlags, TypedFlags< GradientFlagTypes > visibleFlags) |
| Transforms a set of visible and hidden gradient flags to a list of general sampling flags. More... | |
| template<typename Stream > | |
| FrontType | read_front (Stream &in, std::size_t noObjectives, const std::string &separator=" ", std::size_t headerLines=0) |
| template<typename FitnessTag > | |
| bool | compare_fitness (const Individual &a, const Individual &b) |
| template<class I , class L > | |
| CVFolds< LabeledData< I, L > > | createCVIID (LabeledData< I, L > &set, size_t numberOfPartitions, std::size_t batchSize=Data< I >::DefaultBatchSize) |
| Create a partition for cross validation. More... | |
| template<class I , class L > | |
| CVFolds< LabeledData< I, L > > | createCVSameSize (LabeledData< I, L > &set, std::size_t numberOfPartitions, std::size_t batchSize=LabeledData< I, L >::DefaultBatchSize) |
| Create a partition for cross validation. More... | |
| template<class I > | |
| CVFolds< LabeledData< I, unsigned int > > | createCVSameSizeBalanced (LabeledData< I, unsigned int > &set, size_t numberOfPartitions, std::size_t batchSize=Data< I >::DefaultBatchSize) |
| Create a partition for cross validation. More... | |
| template<class I > | |
| CVFolds< LabeledData< I, RealVector > > | createCVSameSizeBalanced (LabeledData< I, RealVector > &set, size_t numberOfPartitions, std::size_t batchSize=Data< I >::DefaultBatchSize) |
| Create a partition for cross validation. More... | |
| template<class I , class L > | |
| CVFolds< LabeledData< I, L > > | createCVIndexed (LabeledData< I, L > &set, size_t numberOfPartitions, std::vector< size_t > indices, std::size_t batchSize=Data< I >::DefaultBatchSize) |
| Create a partition for cross validation from indices. More... | |
| void | import_libsvm (LabeledData< RealVector, unsigned int > &dataset, std::istream &stream, int highestIndex=0) |
| Import data from a LIBSVM file. More... | |
| void | import_libsvm (LabeledData< CompressedRealVector, unsigned int > &dataset, std::istream &stream, int highestIndex=0) |
| Import data from a LIBSVM file. More... | |
| void | import_libsvm (LabeledData< RealVector, unsigned int > &dataset, std::string fn, int highestIndex=0) |
| Import data from a LIBSVM file. More... | |
| void | import_libsvm (LabeledData< CompressedRealVector, unsigned int > &dataset, std::string fn, int highestIndex=0) |
| Import data from a LIBSVM file. More... | |
| template<typename InputType > | |
| void | export_libsvm (LabeledData< InputType, unsigned int > &dataset, const std::string &fn, bool dense=false, bool oneMinusOne=true, bool sortLabels=false) |
| Export data to LIBSVM format. More... | |
| template<class VectorT , class Function > | |
| void | lnsrch (const VectorT &xold, double fold, VectorT &g, VectorT &p, VectorT &x, double &f, double stpmax, bool &check, Function func) |
| Does a line search, i.e. given a nonlinear function, a starting point and a direction, a new point is calculated where the function has decreased "sufficiently". More... | |
| template<class VectorT , class Function > | |
| void | linmin (VectorT &p, const VectorT &xi, double &fret, Function func, double ax=0.0, double bx=1.0) |
| Minimizes a function of "N" variables. More... | |
| template<class VectorT , class VectorU , class DifferentiableFunction > | |
| void | dlinmin (VectorT &p, const VectorU &xi, double &fret, DifferentiableFunction &func, double ax=0.0, double bx=1.0) |
| template<class Source > | |
| detail::ADLVector< Source & > | init (shark::blas::vector_container< Source > &source) |
| Starting-point for the initialization sequence. More... | |
| template<class Source > | |
| detail::ADLVector< const Source & > | init (const shark::blas::vector_container< Source > &source) |
| Starting-point for the initialization sequence. More... | |
| template<class Source > | |
| detail::ADLVector < shark::blas::vector_range < Source > > | init (const shark::blas::vector_range< Source > &source) |
| Starting-point for the initialization sequence when used for splitting the vector. More... | |
| template<class Source > | |
| detail::ADLVector < shark::blas::matrix_row < Source > > | init (const shark::blas::matrix_row< Source > &source) |
| Specialization for matrix rows. More... | |
| template<class Source > | |
| detail::ADLVector < shark::blas::matrix_column < Source > > | init (const shark::blas::matrix_row< Source > &source) |
| Specialization for matrix columns. More... | |
| template<class Matrix > | |
| detail::MatrixExpression < const Matrix > | toVector (const shark::blas::matrix_expression< Matrix > &matrix) |
| Linearizes a matrix as a set of row vectors and treats them as a set of vectors for initialization. More... | |
| template<class Matrix > | |
| detail::MatrixExpression< Matrix > | toVector (shark::blas::matrix_expression< Matrix > &matrix) |
| Linearizes a matrix as a set of row vectors and treats them as a set of vectors for initialization. More... | |
| template<class T > | |
| detail::ParameterizableExpression < const T > | parameters (const T &object) |
| Uses the parameters of a parameterizable object for initialization. More... | |
| template<class T > | |
| detail::ParameterizableExpression < T > | parameters (T &object) |
| Uses the parameters of a parameterizable object for initialization. More... | |
| template<class T > | |
| detail::InitializerRange < typename T::const_iterator, detail::VectorExpression < const typename T::value_type & > > | vectorSet (const T &range) |
| Uses a range of vectors for initialization. More... | |
| template<class T > | |
| detail::InitializerRange < typename T::iterator, detail::VectorExpression < typename T::value_type & > > | vectorSet (T &range) |
| Uses a range of vectors for splitting and initialization. More... | |
| template<class T > | |
| detail::InitializerRange < typename T::const_iterator, detail::MatrixExpression < const typename T::value_type > > | matrixSet (const T &range) |
| Uses a range of vectors for initialization. More... | |
| template<class T > | |
| detail::InitializerRange < typename T::iterator, detail::MatrixExpression < typename T::value_type > > | matrixSet (T &range) |
| Uses a range of vectors for splitting and initialization. More... | |
| template<class T > | |
| detail::InitializerRange < typename T::const_iterator, detail::ParameterizableExpression < const typename T::value_type > > | parameterSet (const T &range) |
| Uses a range of parametrizable objects for initialization. More... | |
| template<class T > | |
| detail::InitializerRange < typename T::iterator, detail::ParameterizableExpression < typename T::value_type > > | parameterSet (T &range) |
| Uses a range of parametrizable objects for splitting and initialization. More... | |
| template<class Matrix > | |
| blas::matrix_vector_range < Matrix const > | diag (blas::matrix_expression< Matrix > const &mat) |
| returns the diagonal of a constant square matrix as vector More... | |
| template<class Matrix > | |
| blas::matrix_vector_range< Matrix > | diag (blas::matrix_expression< Matrix > &mat) |
| returns the diagonal of a square matrix as vector More... | |
| template<class Matrix > | |
| void | zero (blas::matrix_expression< Matrix > &mat) |
| Zeros a matrix. If it is sparse, the structure is preserved. More... | |
| template<class Vector > | |
| void | zero (blas::vector_expression< Vector > &vec) |
| Zeros a matrix. If it is sparse, the structure is preserved. More... | |
| template<class Matrix > | |
| void | zero (blas::matrix_range< Matrix > mat) |
| Zeros a subrange of a matrix. If it is sparse, the structure is preserved. More... | |
| template<class Vector > | |
| void | zero (blas::vector_range< Vector > vec) |
| Zeros a subrange of a vector. If it is sparse, the structure is preserved. More... | |
| template<class Vector > | |
| void | zero (blas::matrix_row< Vector > vec) |
| Zeros a row of a matrix. If it is sparse, the structure is preserved. More... | |
| template<class Vector > | |
| void | zero (blas::matrix_column< Vector > vec) |
| Zeros a column of a matrix. If it is sparse, the structure is preserved. More... | |
| template<class V > | |
| std::size_t | nonzeroElements (blas::vector_expression< V > const &vec) |
| template<class Matrix > | |
| void | identity (blas::matrix_expression< Matrix > &mat) |
| Initializes the square matrix A to be the identity matrix. More... | |
| template<class Matrix > | |
| void | ensureSize (blas::matrix_expression< Matrix > &mat, std::size_t rows, std::size_t columns) |
| Ensures that the matrix has the right size. More... | |
| template<class Vector > | |
| void | ensureSize (blas::vector_expression< Vector > &vec, std::size_t size) |
| Ensures that the vector has the right size. More... | |
| template<class Matrix > | |
| blas::vector_range < blas::matrix_row< Matrix const > > | triangularRow (blas::matrix_expression< Matrix > const &mat, std::size_t i) |
| Returns the i-th row of an upper triangular matrix excluding the elements right of the diagonal. More... | |
| template<class Matrix > | |
| blas::vector_range < blas::matrix_row< Matrix > > | triangularRow (blas::matrix_expression< Matrix > &mat, std::size_t i) |
| Returns the i-th row of an upper triangular matrix excluding the elements right of the diagonal. More... | |
| template<class Matrix > | |
| blas::vector_range < blas::matrix_row< Matrix const > > | unitTriangularRow (blas::matrix_expression< Matrix > const &mat, std::size_t i) |
| Returns the elements in the i-th row of a lower triangular matrix left of the diagonal. More... | |
| template<class Matrix > | |
| blas::vector_range < blas::matrix_row< Matrix > > | unitTriangularRow (blas::matrix_expression< Matrix > &mat, std::size_t i) |
| Returns the elements in the i-th row of a lower triangular matrix left of the diagonal. More... | |
| template<class Matrix > | |
| blas::vector_range < blas::matrix_column< Matrix const > > | unitTriangularColumn (blas::matrix_expression< Matrix > const &mat, std::size_t i) |
| Returns the elements in the i-th column of the matrix below the diagonal. More... | |
| template<class Matrix > | |
| blas::vector_range < blas::matrix_column< Matrix > > | unitTriangularColumn (blas::matrix_expression< Matrix > &mat, std::size_t i) |
| Returns the elements in the i-th column of the matrix below the diagonal. More... | |
| template<class Matrix > | |
| blas::vector_range < blas::matrix_column< Matrix const > > | triangularColumn (blas::matrix_expression< Matrix > const &mat, std::size_t i) |
| Returns the elements in the i-th column of the matrix excluding the zero elements. More... | |
| template<class Matrix > | |
| blas::vector_range < blas::matrix_column< Matrix > > | triangularColumn (blas::matrix_expression< Matrix > &mat, std::size_t i) |
| Returns the elements in the i-th column of the matrix excluding the zero elements. More... | |
| template<class Vector > | |
| VectorRepeater< Vector > | repeat (blas::vector_expression< Vector > const &vector, std::size_t rows) |
| Creates a matrix from a vector by repeating the vector in every row of the matrix. More... | |
| template<class T > | |
| boost::enable_if < boost::is_arithmetic< T > , blas::scalar_vector< T > >::type | repeat (T scalar, std::size_t elements) |
| template<class T > | |
| boost::enable_if < boost::is_arithmetic< T > , blas::scalar_matrix< T > >::type | repeat (T scalar, std::size_t rows, std::size_t columns) |
| template<class Matrix > | |
| blas::matrix_range< Matrix const > | rows (blas::matrix_expression< Matrix > const &mat, std::size_t start, std::size_t end) |
| brief picks a subrange of rows from a matrix. much easier to use than subrange More... | |
| template<class Matrix > | |
| blas::matrix_range< Matrix > | rows (blas::matrix_expression< Matrix > &mat, std::size_t start, std::size_t end) |
| brief picks a subrange of rows from a matrix. much easier to use than subrange More... | |
| template<class Matrix > | |
| blas::matrix_range< Matrix const > | columns (blas::matrix_expression< Matrix > const &mat, std::size_t start, std::size_t end) |
| brief picks a subrange of columns from a matrix. much easier to use than subrange More... | |
| template<class Matrix > | |
| blas::matrix_range< Matrix > | columns (blas::matrix_expression< Matrix > &mat, std::size_t start, std::size_t end) |
| brief picks a subrange of columns from a matrix. much easier to use than subrange More... | |
| template<class MatrixT > | |
| MatrixT::value_type | trace (blas::matrix_expression< MatrixT > const &m) |
| Evaluates the sum of the values at the diagonal of matrix "v". More... | |
| template<class MatrixT , class MatrixL > | |
| void | choleskyDecomposition (blas::matrix_expression< MatrixT > const &A, blas::matrix_expression< MatrixL > &L) |
| Lower triangular Cholesky decomposition. More... | |
| template<class MatrixT , class VectorT > | |
| void | eigensort (MatrixT &vmatA, VectorT &dvecA) |
| Sorts the eigenvalues in vector "dvecA" and the corresponding eigenvectors in matrix "vmatA". More... | |
| template<class MatrixT , class MatrixU , class VectorT > | |
| void | eigensymmJacobi (MatrixT &amatA, MatrixU &vmatA, VectorT &dvecA) |
| template<class MatrixT , class MatrixU , class VectorT > | |
| void | eigensymmJacobi2 (MatrixT &amatA, MatrixU &vmatA, VectorT &dvecA) |
| template<class MatrixT , class MatrixU , class MatrixV , class VectorT > | |
| void | eigensymm_intermediate (const MatrixT &amatA, MatrixU &hmatA, MatrixV &vmatA, VectorT &dvecA) |
| Calculates the eigenvalues and the normalized eigenvectors of a symmetric matrix "amatA" using the Givens and Householder reduction. More... | |
| template<class MatrixT , class MatrixU , class VectorT > | |
| void | eigensymm (const MatrixT &A, MatrixU &G, VectorT &l) |
| Used as frontend for eigensymm for calculating the eigenvalues and the normalized eigenvectors of a symmetric matrix 'amatA' using the Givens and Householder reduction. Each time this frontend is called additional memory is allocated for intermediate results. More... | |
| template<class MatrixT , class MatrixU , class VectorT > | |
| void | eigensymm (const MatrixT &amatA, MatrixU &vmatA, VectorT &dvecA, VectorT &odvecA) |
| Calculates the eigenvalues and the normalized eigenvectors of a symmetric matrix "amatA" using the Givens and Householder reduction without corrupting "amatA" during application. More... | |
| template<class MatrixT , class MatrixU , class VectorT > | |
| double | eigenerr (const MatrixT &amatA, const MatrixU &vmatA, const VectorT &dvecA, unsigned c) |
| Calculates the relative error of eigenvalue no. "c". More... | |
| template<class MatrixT , class MatrixU , class VectorT > | |
| unsigned | rank (const MatrixT &amatA, const MatrixU &vmatA, const VectorT &dvecA) |
| Determines the rank of the symmetric matrix "amatA". More... | |
| template<class MatrixT , class MatrixU , class VectorT > | |
| double | detsymm (MatrixT &amatA, MatrixU &vmatA, VectorT &dvecA) |
| Calculates the determinant of the symmetric matrix "amatA". More... | |
| template<class MatrixT , class MatrixU , class VectorT > | |
| double | logdetsymm (MatrixT &amatA, MatrixU &vmatA, VectorT &dvecA) |
| Calculates the log of the determinant of the symmetric matrix "amatA". More... | |
| template<class MatrixT , class MatrixU , class MatrixV , class VectorT > | |
| unsigned | rankDecomp (MatrixT &amatA, MatrixU &vmatA, MatrixV &hmatA, VectorT &dvecA) |
| template<class MatrixT > | |
| void | fft (MatrixT &data, int isign) |
| template<class MatrixT > | |
| void | fft (MatrixT &data) |
| Replaces the "data" by its discrete Fourier transform. More... | |
| template<class MatrixT > | |
| void | ifft (MatrixT &data) |
| Replaces the "data" by its inverse discrete Fourier transform. More... | |
| template<class Vec1T , class Vec2T , class Vec3T > | |
| void | meanvar (Data< Vec1T > const &data, blas::vector_container< Vec2T > &meanVec, blas::vector_container< Vec3T > &varianceVec) |
| Calculates the mean and variance values of a dataset. More... | |
| template<class MatT , class Vec1T , class Vec2T > | |
| void | meanvar (blas::matrix_container< MatT > &data, blas::vector_container< Vec1T > &meanVec, blas::vector_container< Vec2T > &varianceVec) |
| Calculates the mean, variance and covariance values of the input data. More... | |
| template<class Vec1T , class Vec2T , class MatT > | |
| void | meanvar (const Data< Vec1T > &data, blas::vector_container< Vec2T > &meanVec, blas::matrix_container< MatT > &covariance) |
| Calculates the mean and covariance values of a set of data. More... | |
| template<class VectorType > | |
| VectorType | mean (Data< VectorType > const &data) |
| Calculates the mean vector of array "x". More... | |
| template<class MatrixType > | |
| blas::vector< typename MatrixType::value_type > | mean (const blas::matrix_container< MatrixType > &data) |
| Calculates the mean vector of the input vectors. More... | |
| template<class VectorType > | |
| VectorType | variance (const Data< VectorType > &data) |
| Calculates the variance vector of array "x". More... | |
| template<class VectorType > | |
| VectorMatrixTraits< VectorType > ::DenseMatrixType | covariance (const Data< VectorType > &data) |
| Calculates the covariance matrix of the data vectors stored in data. More... | |
| template<class VectorType > | |
| VectorMatrixTraits< VectorType > ::DenseMatrixType | corrcoef (const Data< VectorType > &data) |
| Calculates the coefficient of correlation matrix of the data vectors stored in data. More... | |
| template<class VectorType > | |
| VectorType | mean (UnlabeledData< VectorType > const &data) |
| template<class T > | |
| double | stl_median (std::vector< T > &v) |
| compute median More... | |
| template<class T > | |
| double | stl_percentile (std::vector< T > &v, double p=.25) |
| compute percentilee (Excel way) More... | |
| template<class T > | |
| double | nth (std::vector< T > &v, unsigned n) |
| return nth element after sorting More... | |
| template<class T > | |
| double | stl_mean (std::vector< T > v) |
| compute mean More... | |
| template<class T > | |
| double | stl_correlation (std::vector< T > &v1, std::vector< T > &v2) |
| compute variance More... | |
| template<class T > | |
| double | stl_variance (std::vector< T > &v, bool unbiased=true) |
| compute sample variance More... | |
| template<class MatrixT > | |
| RealMatrix | invert (const MatrixT &mat) |
| Inverts a matrix with full rank. More... | |
| template<class MatrixT , class MatrixU > | |
| void | invertSymmPositiveDefinite (MatrixT &I, const MatrixU &ArrSymm) |
| Inverts a symmetric positive definite matrix. More... | |
| template<class MatA , class MatU > | |
| void | decomposedGeneralInverse (blas::matrix_expression< MatA > const &matA, blas::matrix_expression< MatU > &matU) |
| For a given square matrix A computes a matrix U where A'=LU^T. More... | |
| template<class MatrixT > | |
| RealMatrix | g_inverse (blas::matrix_expression< MatrixT > const &matrixA) |
| Calculates the generalized inverse matrix of input matrix "matrixA". More... | |
| template<class MatrixT > | |
| void | orthoNormalize (blas::matrix_container< MatrixT > &matrixC) |
| template<class MatrixT , typename RngType > | |
| void | randomRotationMatrix (blas::matrix_container< MatrixT > &matrixC, RngType &rng) |
| Initializes a matrix such that it forms a random rotation matrix. More... | |
| template<class MatrixT > | |
| void | randomRotationMatrix (blas::matrix_container< MatrixT > &matrixC) |
| Initializes a matrix such that it forms a random rotation. More... | |
| template<typename RngType > | |
| RealMatrix | randomRotationMatrix (size_t size, RngType &rng) |
| Creates a random rotation matrix with a certain size using the random number generator rng. More... | |
| RealMatrix | randomRotationMatrix (size_t size) |
| Creates a random rotation matrix with a certain size using the global random number gneerator. More... | |
| template<class X , class R > | |
| X::value_type | createHouseholderReflection (blas::vector_expression< X > const &x, blas::vector_expression< R > &reflection) |
| Generates a Householder reflection from a vector to use with applyHouseholderLeft/Right. More... | |
| template<class Mat , class R , class T > | |
| void | applyHouseholderOnTheRight (blas::matrix_expression< Mat > &matrix, blas::vector_expression< R > const &reflection, T beta) |
| template<class Mat , class R , class T > | |
| void | applyHouseholderOnTheLeft (blas::matrix_expression< Mat > &matrix, blas::vector_expression< R > const &reflection, T const &beta) |
| template<class MatrixT , class Mat > | |
| std::size_t | pivotingRQ (blas::matrix_expression< MatrixT > const &matrixA, blas::matrix_container< Mat > &matrixR, blas::matrix_container< Mat > &matrixQ, blas::permutation_matrix< std::size_t > &permutation) |
| Calculates the RQ-Decomposition of A using pivoting. More... | |
| template<class MatrixT , class MatrixU > | |
| std::size_t | pivotingRQHouseholder (blas::matrix_expression< MatrixT > const &matrixA, blas::matrix_container< MatrixU > &matrixR, blas::matrix_container< MatrixU > &householderTransform, blas::permutation_matrix< std::size_t > &permutation) |
| Determines the RQ Decomposition of the matrix A using pivoting returning the housholder transformation instead of Q. More... | |
| template<class MatrixT , class MatrixU , class VectorT > | |
| unsigned | svdrank (const MatrixT &amatA, MatrixU &umatA, MatrixU &vmatA, VectorT &wvecA) |
| template<class MatrixT , class MatrixU , class VectorT > | |
| void | svd (const MatrixT &amatA, MatrixU &umatA, MatrixU &vmatA, VectorT &wvecA, unsigned maxIterations=200, bool ignoreThreshold=true) |
| Determines the singular value decomposition of a rectangular matrix "amatA". More... | |
| template<class MatrixU , class VectorT > | |
| void | svdsort (MatrixU &umatA, MatrixU &vmatA, VectorT &wvecA) |
| Sorts the singular values in vector "wvecA" by descending order. More... | |
| template<class InputType , class OutputType > | |
| void | initRandomNormal (AbstractModel< InputType, OutputType > &model, double s) |
| Initialize model parameters normally distributed. More... | |
| template<class InputType , class OutputType > | |
| void | initRandomUniform (AbstractModel< InputType, OutputType > &model, double l, double h) |
| Initialize model parameters uniformly at random. More... | |
Variables | |
| static const double | SQRT_2_PI = boost::math::constants::root_two_pi<double>() |
| Constant for sqrt( 2 * pi ). More... | |
| enum | LabelPosition { FIRST_COLUMN, LAST_COLUMN } |
| Position of the label in a CSV file. More... | |
| template<typename Type > | |
| void | import_csv (Data< Type > &data, std::string fn, std::string separator=",", std::string comment="", std::size_t batchSize=Data< Type >::DefaultBatchSize) |
| Import unlabeled data from a character-separated value file. More... | |
| template<typename Type > | |
| void | export_csv (Data< Type > const &set, std::string fn, std::string separator=",", bool sci=true, unsigned int width=0) |
| Format unlabeled data into a character-separated value file. More... | |
| template<typename InputType , typename LabelType > | |
| void | import_csv (LabeledData< InputType, LabelType > &dataset, std::string fn, LabelPosition lp, std::string separator=",", std::string comment="", bool allowMissingClasses=false, std::map< LabelType, LabelType > const *labelmap=NULL, std::size_t batchSize=LabeledData< InputType, LabelType >::DefaultBatchSize) |
| Import labeled data from a character-separated value file. More... | |
| void | import_csv (LabeledData< RealVector, RealVector > &dataset, std::string fn, LabelPosition lp, std::string separator=",", std::string comment="", std::size_t numberOfOutputs=1) |
| Import regression data from a character-separated value file. More... | |
| template<typename InputType , typename LabelType > | |
| void | export_csv (LabeledData< InputType, LabelType > const &dataset, std::string fn, LabelPosition lp, std::string separator=",", bool sci=true, unsigned int width=0) |
| Format labeled data into a character-separated value file. More... | |
| template<typename InputType > | |
| void | string2data (Data< InputType > &data, const std::string &dataInString, std::size_t batchSize=Data< InputType >::DefaultBatchSize) |
| Construct Shark data from a string. More... | |
| template<typename InputType , typename LabelType > | |
| void | string2data (LabeledData< InputType, LabelType > &dataset, const std::string &dataInString, LabelPosition labelPosition=LAST_COLUMN, bool allowMissingFeatures=false, bool allowMissingClasses=false, std::map< LabelType, LabelType > const *labelmap=NULL, std::size_t batchSize=LabeledData< InputType, LabelType >::DefaultBatchSize) |
| Construct Shark labeled data from a string. More... | |
| typedef LabeledData < RealVector, unsigned int > | ClassificationDataset |
| specialized template for classification with unsigned int labels More... | |
| typedef LabeledData < RealVector, RealVector > | RegressionDataset |
| specialized template for regression with RealVector labels More... | |
| typedef LabeledData < CompressedRealVector, unsigned int > | CompressedClassificationDataset |
| specialized template for classification with unsigned int labels and sparse data More... | |
| template<class T > | |
| std::ostream & | operator<< (std::ostream &stream, const Data< T > &d) |
| Outstream of elements. More... | |
| template<class Range > | |
| Data< typename boost::range_value< Range > ::type > | createDataFromRange (Range const &inputs, std::size_t maximumBatchSize=0) |
| creates a data object from a range of elements More... | |
| template<class Range1 , class Range2 > | |
| LabeledData< typename boost::range_value< Range1 > ::type, typename boost::range_value< Range2 > ::type > | createLabeledDataFromRange (Range1 const &inputs, Range2 const &labels, std::size_t batchSize=0) |
| creates a labeled data object from two ranges, representing inputs and labels More... | |
| template<class T , class U > | |
| std::ostream & | operator<< (std::ostream &stream, const LabeledData< T, U > &d) |
| brief Outstream of elements for labeled data. More... | |
| unsigned int | numberOfClasses (Data< unsigned int > const &labels) |
| Return the number of classes of a set of class labels with unsigned int label encoding. More... | |
| std::vector< std::size_t > | classSizes (Data< unsigned int > const &labels) |
| Returns the number of members of each class in the dataset. More... | |
| template<class InputType > | |
| unsigned int | dataDimension (Data< InputType > const &dataset) |
| Return the dimensionality of a dataset. More... | |
| template<class InputType , class LabelType > | |
| unsigned int | inputDimension (LabeledData< InputType, LabelType > const &dataset) |
| Return the input dimensionality of a labeled dataset. More... | |
| template<class InputType , class LabelType > | |
| unsigned int | labelDimension (LabeledData< InputType, LabelType > const &dataset) |
| Return the label/output dimensionality of a labeled dataset. More... | |
| template<class InputType > | |
| unsigned int | numberOfClasses (LabeledData< InputType, unsigned int > const &dataset) |
| Return the number of classes (highest label value +1) of a classification dataset with unsigned int label encoding. More... | |
| template<class InputType > | |
| unsigned int | numberOfClasses (LabeledData< InputType, RealVector > const &dataset) |
| template<class InputType , class LabelType > | |
| std::vector< std::size_t > | classSizes (LabeledData< InputType, LabelType > const &dataset) |
| Returns the number of members of each class in the dataset. More... | |
| template<class T , class Functor > | |
| boost::lazy_disable_if < CanBeCalled< Functor, typename Data< T >::batch_type > , TransformedData< Functor, T > >::type | transform (Data< T > const &data, Functor f) |
| Transforms a dataset using a Functor f and returns the transformed result. More... | |
| template<class T , class Functor > | |
| boost::lazy_enable_if < CanBeCalled< Functor, typename Data< T >::batch_type > , TransformedData< Functor, T > >::type | transform (Data< T > const &data, Functor const &f) |
| Transforms a dataset using a Functor f and returns the transformed result. More... | |
| template<class I , class L , class Functor > | |
| LabeledData< typename detail::TransformedDataElement < Functor, I >::type, L > | transformInputs (LabeledData< I, L > const &data, Functor const &f) |
| Transforms the inputs of a dataset and return the transformed result. More... | |
| template<class I , class L , class Functor > | |
| LabeledData< I, typename detail::TransformedDataElement < Functor, L >::type > | transformLabels (LabeledData< I, L > const &data, Functor const &f) |
| Transforms the labels of a dataset and returns the transformed result. More... | |
| template<class DatasetT > | |
| DatasetT | indexedSubset (DatasetT const &dataset, typename DatasetT::IndexSet const &indices) |
| template<class DatasetT > | |
| DatasetT | rangeSubset (DatasetT const &dataset, std::size_t start, std::size_t end) |
| Fill in the subset of batches [start,...,size+start[. More... | |
| template<class DatasetT > | |
| DatasetT | rangeSubset (DatasetT const &dataset, std::size_t size) |
| Fill in the subset of batches [0,...,size[. More... | |
| template<class DatasetT > | |
| DatasetT | splitAtElement (DatasetT &data, std::size_t elementIndex) |
| Removes the last part of a given dataset and returns a new split containing the removed elements. More... | |
| template<class I > | |
| void | repartitionByClass (LabeledData< I, unsigned int > &data, std::size_t batchSize=LabeledData< I, unsigned int >::DefaultBatchSize) |
| reorders the dataset such, that points are grouped by labels More... | |
| template<class I > | |
| LabeledData< I, unsigned int > | binarySubProblem (LabeledData< I, unsigned int >const &data, unsigned int zeroClass, unsigned int oneClass) |
| template<class I > | |
| LabeledData< I, unsigned int > | oneVersusRestProblem (LabeledData< I, unsigned int >const &data, unsigned int oneClass) |
| Construct a binary (two-class) one-versus-rest problem from a multi-class problem. More... | |
| enum | KernelMatrixNormalizationType { NONE, MULTIPLICATIVE_TRACE_ONE, MULTIPLICATIVE_TRACE_N, MULTIPLICATIVE_VARIANCE_ONE, CENTER_ONLY, CENTER_AND_MULTIPLICATIVE_TRACE_ONE } |
| template<typename InputType , typename LabelType > | |
| void | export_kernel_matrix (LabeledData< InputType, LabelType > const &dataset, AbstractKernelFunction< InputType > &kernel, std::ostream &out, KernelMatrixNormalizationType normalizer=NONE, bool scientific=false, unsigned int fieldwidth=0) |
| Write a kernel Gram matrix to stream. More... | |
| template<typename InputType , typename LabelType > | |
| void | export_kernel_matrix (LabeledData< InputType, LabelType > const &dataset, AbstractKernelFunction< InputType > &kernel, std::string fn, KernelMatrixNormalizationType normalizer=NONE, bool sci=false, unsigned int width=0) |
| Write a kernel Gram matrix to file. More... | |
AbstractSingleObjectiveOptimizer.
Example for examining characteristics of the CMA with the help of the Probe framework.
Calculate statistics given a range of values.
Implements a multi-variate normal distribution with zero mean.
Implements a Weibull distribution.
Implements a uniform distribution.
Implements a truncated exponential.
Basic types and definitions of the Rng component.
Implements a poisson distribution.
Implements a univariate normal distribution.
Implements the negative exponential distribution.
Provides a log-normal distribution.
Provides a function for estimating the kullback-leibler-divergence.
Hypergeometric distribution.
This class subsumes several often used random number generators.
Implements a geometric distribution.
Implements a Gamma distribution.
Implements an Erlang distribution.
Provides a function for estimating the entropy of a distribution.
Implements a dirichlet distribution.
Diff geometric distribution.
Standard Cauchy distribution.
Implements a binomial distribution.
Implements a Bernoulli distribution.
Abstract class for statistical distributions.
Specific error function for Feed-Forward-Networks which enforces it to have sparse hidden neuron activation.
Approximate version of the span-bound for C-SVMs.
Regularizer.
Radius Margin Quotient for SVM model selection.
implements an error fucntion which only uses a random portion of the data for training
Evidence for model selection of a regularization network/Gaussian process.
Functions for measuring the area under the (ROC) curve.
Error measure for classication tasks, typically used for evaluation of results.
Implements the Squared Error Loss function for regression.
Negative logarithm of the likelihood of a probabilistic binary classification model.
Flexible error measure for classication tasks.
Error measure for classification tasks of non exclusive attributes that can be used for model training.
Error measure for classication tasks that can be used as the objective function for training.
super class of all loss functions
implements the absolute loss, which is the distance between labels and predictions
Leave-one-out error for C-SVMs.
Leave-one-out error.
Kernel Target Alignment - a measure of alignment of a kernel Gram matrix with labels.
Base class for type erasure in error functions.
error function for supervised learning
Error Function used for training Denoising Autoencoders.
data-dependent objective functions for learning
cross-validation error for selection of hyper-parameters
Multi-objective optimization benchmark function ZDT6.
Multi-objective optimization benchmark function ZDT4.
Multi-objective optimization benchmark function ZDT3.
Multi-objective optimization benchmark function ZDT2.
Multi-objective optimization benchmark function ZDT1.
Generalized Rosenbrock benchmark function.
Multi-objective optimization benchmark function LZ9.
Multi-objective optimization benchmark function LZ1.
Multi-objective optimization benchmark function IHR 6.
Multi-objective optimization benchmark function IHR 4.
Multi-objective optimization benchmark function IHR 3.
Multi-objective optimization benchmark function IHR 2.
Multi-objective optimization benchmark function IHR 1.
Two-dimensional, real-valued Himmelblau function.
Bi-objective real-valued benchmark function proposed by Fonseca and Flemming.
Objective function DTLZ7.
Objective function DTLZ6.
Objective function DTLZ5.
Objective function DTLZ4.
Objective function DTLZ3.
Objective function DTLZ2.
Objective function DTLZ1.
Multi-objective optimization benchmark function CIGTAB 2.
Multi-objective optimization benchmark function CIGTAB 1.
Convex quadratic benchmark function.
Convex quadratic benchmark function with single dominant axis.
cost function for quantitative judgement of deviations of predictions from target values
Base class for constraints.
Random Forest Classifier.
Tree for nearest neighbor search in data with low embedding dimension.
Tree for nearest neighbor search in kernel-induced feature spaces.
Tree for nearest neighbor search in low dimensions.
Cart Classifier.
Binary space-partitioning tree of data points.
Soft/probabilistic nearest neighbor classifier for vector-valued data.
Soft-max transformation.
Offers a basic structure for recurrent networks.
Offers the functions to create and to work with a radial basis fucntion network.
Offers the functions to create and to work with a recurrent neural network.
One-versus-one Classifier.
Model for scaling and translation of data vectors.
Nearest neighbor regression.
Nearest neighbor classification.
Implementation of Naive Bayes classifier.
Weighted sum of m_base kernels.
Variant of WeightedSumKernel which works on subranges of Vector inputs.
A kernel function that wraps a member kernel and multiplies it by a scalar.
Product of kernel functions.
Polynomial kernel.
Normalization of a kernel function.
Special kernel classes for multi-task and transfer learning.
monomial (polynomial) kernel
Weighted sum of base kernels, each acting on a subset of features only.
A kernel expansion with support of missing features.
linear kernel (standard inner product)
Collection of functions dealing with typical tasks of kernels.
Affine linear kernel function expansion.
Defines a helper class which assigns to every element of a tuple of points a kernel of a tuple of kernels.
Radial Gaussian kernel.
Do special kernel evaluation by skipping missing features.
Kernel on a finite, discrete space.
Derivative of a C-SVM hypothesis w.r.t. its hyperparameters.
Gaussian automatic relevance detection (ARD) kernel.
abstract super class of all kernel functions
Implements a Model using a linear function.
Implements a Feef-Forward multilayer perceptron.
Format conversion models.
concatenation of two models, with type erasure
Model for "soft" clustering.
Hierarchical Clustering.
Model for "hard" clustering.
Super class for clustering models.
Clusters defined by centroids.
Super class for clustering definitions.
base class for all models, as well as a specialized differentiable model
Algorithm for Singular Value Decomposition.
Some operations for matrices.
Algorithm for Triangular-Quadratic-Decomposition.
Some operations for creating rotation matrices.
Matrix inverses.
Algorithms for Eigenvalue decompositions.
Cholesky Decompositions for a Matrix A = LL^T.
Convenience macros for defining vector and matrix types.
Proxycontainer for Dense Vectors.
Helper functions for linear algebra component.
Helper functions to calculate several norms and distances.
Optimized generalized matrix-vector operations for Linear Algebra.
Constructs a matrix as a repetition of a single row vector.
solves systems of triangular matrices
Easy initialization of vectors.
Optimized operations for Linear Algebra.
Entry Point for all Basic Linear Algebra(BLAS) in shark.
Read and write precomputed kernel matrices (using libsvm format)
Importing and exporting PGM images.
Batch definitions for fusion::vectors which are used in MKL learning.
Support for importing and exporting data from and to LIBSVM formatted data files.
Support for importing data from HDF5 file.
Fast lookup for elements in constant datasets.
Data for (un-)base_typevised learning.
Learning problems given by analytic distributions.
Tools for cross-validation.
Support for importing and exporting data from and to character separated value (CSV) files.
Defines an batch adptor for structures.
Defines the Batch Interface for a type, e.g., for every type a container with optimal structure.
Range which zips two ranges together while allowing a custom pair type.
A scoped_ptr like container for C type handles.
Helper Methods to use with boost range.
Provides a pair of Key and Value, as well as functions working with them.
Small Iterator collection.
Small General algorithm collection.
Template class checking whether for a functor F and Argument U, F(U) can be called.
Traits which allow to define ProxyReferences for types.
Traits wich specifies whether a type is part of a vector space.
Timer abstraction with microsecond resolution.
Class which externalises the state of an Object.
Class that interferes with signals and allows for graceful shutdowns.
Result sets for algorithms.
Implements a generic log handler for arbitrary streams.
Implements a generic logging facility.
Implements a generic log formatter that relies on an externally defined printf-like format.
Implements the factory pattern.
Random Forest Trainer.
Trainer for a Regularization Network or a Gaussian Process.
Principal Component Analysis.
Model training by means of a general purpose optimization procedure.
Trainer for One-Class Support Vector Machines.
Determine the scaling factor of a ScaledKernel so that it has unit variance in feature space one on a given dataset.
Data normalization to zero mean and unit variance.
Data normalization to the unit interval.
Trainer of Naive Bayes classifier.
Trainer for binary SVMs natively supporting missing features.
Trainer for the Multi-class Support Vector Machine by Weston and Watkins.
Trainer for One-versus-all (one-versus-rest) Multi-class Support Vector Machines.
Trainer for the Maximum Margin Regression Multi-class Support Vector Machine.
Trainer for the Multi-class Support Vector Machine by Lee, Lin, and Wahba.
Trainer for the Multi-class Support Vector Machine by Crammer and Singer.
Trainer for the ATS Multi-class Support Vector Machine.
Trainer for the ATM Multi-class Support Vector Machine.
Trainer for the ADM Multi-class Support Vector Machine.
LASSO Regression.
Trainer for the Epsilon-Support Vector Machine for Regression.
Trainer for normal distribution.
Implementations of various distribution trainers.
Container for known distribution trainers.
Support Vector Machine Trainer for the standard C-SVM.
CART.
Abstract Trainer Interface.
Abstract Support Vector Machine Trainer, general and linear case.
Special container for certain coefficients describing multi-class SVMs.
General and specialized quadratic program classes and a generic solver.
Quadratic programming solvers for linear multi-class SVM training without bias.
Quadratic programming solver for multi-class SVMs.
Quadratic programming solver linear SVM training without bias.
Cache implementing an Least-Recently-Used Strategy.
Quadratic program definitions.
Pegasos solvers for linear SVMs.
Efficient Nearest neighbor queries.
Efficient brute force implementation of nearest neighbors.
Interface for nearest Neighbor queries.
The k-means clustering algorithm.
Jaakkola's heuristic and related quantities for Gaussian kernel selection.
Summarizes common properties of unary and binary quality indicators.
Models extraction of fitness values.
Implements the SMS-EMOA.
Population on an Evolutionary Algorithm.
Implementation of the Pareto-Dominance relation.
Roulette-Wheel-Selection using uniform selection probability assignment.
Implements tournament selection.
Steady state (+1) Indicator-based selection strategy for multi-objective selection.
Implements fitness proportional selection.
Implements \((\mu,\lambda)\) selection.
Roulette-Wheel-Selection based on fitness-rank-based selection probability assignment.
Indicator-based selection strategy for multi-objective selection.
EP-Tournament selection operator.
Implements binary tournament selection.
Hypervolume selection based on an approximation scheme.
Uniform crossover of arbitrary individuals.
Simulated binary crossover operator.
Implements one-point crossover operator.
Recombinates a set of individuals given a weight vector.
Polynomial mutation operator.
Implements the default variation operator of the multi-objective covariance matrix ES.
Bit flip mutation operator.
Initializer for chromosomes/individuals of the CMA-ES.
Initializer for chromosomes/individuals of the (MO-)CMA-ES.
PenalizingEvaluator.
Implements the (1+1)-ES.
Implements the generational Multi-objective Covariance Matrix Adapation ES.
Approximately determines the individual contributing the least hypervolume.
Executes multi-objective optimizers.
Calculates the multiplicate approximation quality of a Pareto-front approximation.
Inverted generational distance for comparing Pareto-front approximations.
Calculates the additive approximation quality of a Pareto-front approximation.
Calculates the hypervolume covered by a set of non-dominated points.
Implementation of the exact hypervolume calculation in m dimensions.
Determine the volume of the union of objects by an FPRAS.
Explicit traits for extracting fitness values from arbitrary types.
Fitness comparator for the single-objective case.
Stores Pareto-fronts.
Summarizes definitions and tags common to the EA/DirectSearch component.
Chromosome of the CMA-ES.
Bounding box calculator.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
The algorithm is described in
Nicola Beume und G�nter Rudolph. Faster S-Metric Calculation by Considering Dominated Hypervolume as Klee's Measure Problem. In: B. Kovalerchuk (ed.): Proceedings of the Second IASTED Conference on Computational Intelligence (CI 2006), pp. 231-236. ACTA Press: Anaheim, 2006.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
The algorithm is described in
http://www.scholarpedia.org/article/Evolution_strategies
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
The algorithm is presented in: K. Bringmann, T. Friedrich. Approximating the least hypervolume contributor: NP-hard in general, but fast in practice. Proc. of the 5th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2009), Vol. 5467 of LNCS, pages 6-20, Springer-Verlag, 2009.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
See http://en.wikipedia.org/wiki/Tournament_selection
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
The algorithm is described in: James E. Baker. Adaptive Selection Methods for Genetic Algorithms. In John J. Grefenstette (ed.): Proceedings of the 1st International Conference on Genetic Algorithms (ICGA), pp. 101-111, Lawrence Erlbaum Associates, 1985
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
See http://en.wikipedia.org/wiki/Fitness_proportionate_selection
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
The function is described in
Christian Igel, Nikolaus Hansen, and Stefan Roth. Covariance Matrix Adaptation for Multi-objective Optimization. Evolutionary Computation 15(1), pp. 1-28, 2007
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
See Nicola Beume, Boris Naujoks, and Michael Emmerich. SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research, 181(3):1653-1669, 2007.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
A ProxyReference can be used in the context of abstract functions to bind several related types of arguments to a single proxy type. Main use are ublas expression templates so that vectors, matrix rows and subvectors can be treated as one argument type
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/. Shark linear algebra definitions
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR matA PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received matA copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR matA PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received matA copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received matA copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
Often, you want to initialize a vector using already available data or you know a fixed initialization. In this case, this header helps, since it is possible to initialize a vector using init(vec) << a,b,c,...; also if you want to split a vector into several smaller parts, you can write init(vec) >> a,b,c,...; a,b,c are allowed to be vectors, vector expressions or single values. However, all vectors needs to be initialized to the correct size. It is checked in debug mode, that size(vec) equals size(a,b,c,...). The usage is not restricted to initialization, but can be used for any assignment where a vector is constructed from several parts. For example the construction of parameter vectors in AbstractModel::parameterVector.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
Sometimes we have to use self_types in the context of virtual context. Since virtual templates are not allowed, a wrapper is needed which hides the type. However type erasure is slow regarding element access, but for self_type types with actual storage, like RealVector, one can emulate a general self_type by directly accessing the memory.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or(at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.for complex vectors (currently not supported very well)
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
Multi-modal benchmark function.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
The function is described in
H. Li and Q. Zhang. Multiobjective Optimization Problems with Complicated Pareto Sets, MOEA/D and NSGA-II, IEEE Trans on Evolutionary Computation, 2(12):284-302, April 2009.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This non-convex benchmark function for real-valued optimization is a generalization from two to multiple dimensions of a classic function first proposed in:
H. H. Rosenbrock. An automatic method for finding the greatest or least value of a function. The Computer Journal 3: 175–184, 1960
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
The function is described in
Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2):173-195, 2000
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This class offers convenience functions to generate numbers using a global random number generator from the following distributions:
Additionally this class offers a global random number generator of Type #RngType. The Default of this Is the Mersenne Twister with a cycle length of $2^19937$. This Generator can be used to construct additional distributions. The seed can be used using #Rng::seed
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
This file is part of Shark. This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this library; if not, see http://www.gnu.org/licenses/.
| typedef TypeErasedMultiObjectiveOptimizer< VectorSpace<double>, detail::AGE > shark::AGE |
Definition at line 50 of file BinaryRBM.h.
Definition at line 46 of file BinaryRBM.h.
Definition at line 45 of file BinaryRBM.h.
Definition at line 51 of file BinaryRBM.h.
Definition at line 49 of file BinaryRBM.h.
Definition at line 47 of file BinaryRBM.h.
| typedef RBM<BinaryLayer,BinaryLayer, Rng::rng_type> shark::BinaryRBM |
Definition at line 44 of file BinaryRBM.h.
| typedef TwoStateSpace<0,1> shark::BinarySpace |
Definition at line 81 of file TwoStateSpace.h.
| typedef ConsoleHandler< tag::cerr > shark::CerrLogHandler |
Log handler outputting to std::cerr.
Definition at line 143 of file StreamHandler.h.
| typedef LabeledData<RealVector, unsigned int> shark::ClassificationDataset |
| typedef ConsoleHandler< tag::clog > shark::ClogLogHandler |
Log handler outputting to std::clog.
Definition at line 146 of file StreamHandler.h.
| typedef boost::property_map<Graph, boost::edge_color_t>::type shark::ColorMap |
| typedef ARDKernelUnconstrained<CompressedRealVector> shark::CompressedARDKernel |
Definition at line 280 of file ArdKernel.h.
| typedef LabeledData<CompressedRealVector, unsigned int> shark::CompressedClassificationDataset |
| typedef LinearKernel<CompressedRealVector> shark::CompressedLinearKernel |
Definition at line 135 of file LinearKernel.h.
| typedef MonomialKernel<CompressedRealVector> shark::CompressedMonomialKernel |
Definition at line 201 of file MonomialKernel.h.
| typedef NormalizedKernel<CompressedRealVector> shark::CompressedNormalizedKernel |
Definition at line 339 of file NormalizedKernel.h.
| typedef PolynomialKernel<CompressedRealVector> shark::CompressedPolynomialKernel |
Definition at line 304 of file PolynomialKernel.h.
| typedef GaussianRbfKernel<CompressedRealVector> shark::CompressedRbfKernel |
Definition at line 276 of file GaussianRbfKernel.h.
| typedef ScaledKernel<CompressedRealVector> shark::CompressedScaledKernel |
Definition at line 159 of file ScaledKernel.h.
| typedef WeightedSumKernel<CompressedRealVector> shark::CompressedWeightedSumKernel |
Definition at line 385 of file WeightedSumKernel.h.
| typedef SubrangeKernel<CompressedRealVector> shark::CompressesSubrangeKernel |
Definition at line 196 of file SubrangeKernel.h.
| typedef ConsoleHandler< tag::cout > shark::CoutLogHandler |
Log handler outputting to std::cout.
Definition at line 140 of file StreamHandler.h.
| typedef boost::mt19937 shark::DefaultRngType |
Definition at line 279 of file ArdKernel.h.
| typedef ARDKernelUnconstrained<ConstRealVectorRange> shark::DenseARDMklKernel |
Definition at line 281 of file ArdKernel.h.
| typedef LinearKernel shark::DenseLinearKernel |
Definition at line 134 of file LinearKernel.h.
Definition at line 200 of file MonomialKernel.h.
Definition at line 338 of file NormalizedKernel.h.
Definition at line 303 of file PolynomialKernel.h.
Definition at line 275 of file GaussianRbfKernel.h.
| typedef ScaledKernel shark::DenseScaledKernel |
Definition at line 158 of file ScaledKernel.h.
| typedef ScaledKernel<ConstRealVectorRange> shark::DenseScaledMklKernel |
Definition at line 160 of file ScaledKernel.h.
| typedef SubrangeKernel<RealVector> shark::DenseSubrangeKernel |
Definition at line 195 of file SubrangeKernel.h.
Definition at line 384 of file WeightedSumKernel.h.
| typedef boost::graph_traits<Graph>::edge_descriptor shark::Edge |
| typedef TypeErasedMultiObjectiveOptimizer< VectorSpace<double>, detail::MOCMA< AdditiveEpsilonIndicator > > shark::EpsilonMOCMA |
| typedef TypeErasedMultiObjectiveOptimizer< VectorSpace<double>, detail::SteadyStateMOCMA< AdditiveEpsilonIndicator > > shark::EpsilonSteadyStateMOCMA |
Injects the Steady-State MOCMA relying on the additive epsilon indicator into the inheritance hierarchy.
Definition at line 544 of file SteadyStateMOCMA.h.
| typedef TypeErasedMultiObjectiveOptimizer< VectorSpace<double>, detail::RealCodedNSGAII< shark::AdditiveEpsilonIndicator > > shark::EpsRealCodedNSGAII |
Injects the NSGA-II into the inheritance hierarchy.
Definition at line 306 of file RealCodedNSGAII.h.
| typedef BaseFastNonDominatedSort< ParetoDominanceComparator<tag::PenalizedFitness> > shark::FastNonDominatedSort |
Default fast non-dominated sorting based on the Pareto-dominance relation.
Definition at line 198 of file FastNonDominatedSort.h.
| typedef boost::rand48 shark::FastRngType |
| typedef std::vector< shark::RealVector > shark::FrontType |
Definition at line 14 of file AdditiveEpsilonIndicatorMain.cpp.
| typedef Factory< FuzzySet, std::string > shark::FuzzySetFactory |
Defines the default factory type for real-valued single-objective optimization problems.
Definition at line 380 of file FuzzySet.h.
Definition at line 51 of file GaussianBinaryRBM.h.
Definition at line 47 of file GaussianBinaryRBM.h.
Definition at line 46 of file GaussianBinaryRBM.h.
Definition at line 52 of file GaussianBinaryRBM.h.
Definition at line 50 of file GaussianBinaryRBM.h.
Definition at line 48 of file GaussianBinaryRBM.h.
| typedef RBM<GaussianLayer,BinaryLayer, Rng::rng_type> shark::GaussianBinaryRBM |
Definition at line 45 of file GaussianBinaryRBM.h.
| typedef boost::adjacency_matrix< boost::undirectedS, boost::property< boost::vertex_color_t, std::string >, boost::property< boost::edge_weight_t, double, boost::property< boost::edge_color_t, std::string > >, boost::no_property > shark::Graph |
| typedef TypeErasedMultiObjectiveOptimizer< VectorSpace<double>, detail::RealCodedNSGAII<> > shark::HypRealCodedNSGAII |
Injects the NSGA-II into the inheritance hierarchy.
Definition at line 303 of file RealCodedNSGAII.h.
| typedef boost::archive::polymorphic_iarchive shark::InArchive |
Type of an archive to read from.
Definition at line 48 of file ISerializable.h.
| typedef TypedIndividual< Tour > shark::Individual |
| typedef SharedVector<double> shark::Intermediate |
Definition at line 209 of file SharedVector.h.
Tags a JSON formatter.
Definition at line 151 of file PrintfLogFormatter.h.
| typedef Factory< LinguisticTerm, std::string > shark::LinguisticTermFactory |
Defines the default factory type for real-valued single-objective optimization problems.
Definition at line 101 of file LinguisticTerm.h.
| typedef FFNet<TanhNeuron,LinearNeuron> shark::LinOutFFNet |
| typedef shark::Factory< shark::Logger::AbstractFormatter, std::string > shark::LogFormatterFactory |
| typedef shark::Factory< shark::Logger::AbstractHandler, std::string > shark::LogHandlerFactory |
| typedef TypeErasedMultiObjectiveOptimizer< VectorSpace<double>, detail::MOCMA< HypervolumeIndicator > > shark::MOCMA |
| typedef AbstractObjectiveFunction< VectorSpace< double >, RealVector > shark::MultiObjectiveFunction |
Definition at line 310 of file AbstractObjectiveFunction.h.
| typedef detail::TypedMultiVariateNormalDistribution<Rng,RealMatrix,RealVector> shark::MultiVariateNormalDistribution |
Injects a multi-variate normal distribution in the shark namespace.
Definition at line 227 of file MultiVariateNormalDistribution.h.
| typedef Factory< Oracle, std::string > shark::OracleFactory |
Definition at line 64 of file Oracle.cpp.
| typedef boost::archive::polymorphic_oarchive shark::OutArchive |
Type of an archive to write to.
Definition at line 53 of file ISerializable.h.
Convenience typedef for comparing penalized fitness values.
Definition at line 76 of file FitnessComparator.h.
| typedef IndirectFitnessComparator< tag::PenalizedFitness > shark::PenalizedIndirectFitnessComparator |
Convenience typedef for comparing penalized fitness values.
Definition at line 83 of file FitnessComparator.h.
| typedef blas::permutation_matrix<std::size_t> shark::PermutationMatrix |
Definition at line 144 of file VectorMatrixType.h.
Tags a plain text formatter.
Definition at line 145 of file PrintfLogFormatter.h.
| typedef std::vector< Individual > shark::Population |
Definition at line 36 of file Population.h.
| typedef boost::property_tree::ptree shark::PropertyTree |
Type of a property tree.
Definition at line 45 of file IConfigurable.h.
| typedef blas::range shark::Range |
Definition at line 143 of file VectorMatrixType.h.
| typedef LabeledData<RealVector, RealVector> shark::RegressionDataset |
| typedef RpropPlus shark::Rprop93 |
| typedef RpropMinus shark::Rprop94 |
| typedef IRpropPlus shark::Rprop99 |
| typedef IRpropMinus shark::Rprop99d |
Convenience typedef for comparing scaled fitness values.
Definition at line 80 of file FitnessComparator.h.
Convenience typedef for comparing scaled fitness values.
Definition at line 87 of file FitnessComparator.h.
| typedef std::deque<RealVector> shark::Sequence |
| typedef FFNet<TanhNeuron,TanhNeuron> shark::SimpleFFNet |
| typedef AbstractObjectiveFunction< VectorSpace< double >, double > shark::SingleObjectiveFunction |
Definition at line 309 of file AbstractObjectiveFunction.h.
| typedef TypeErasedMultiObjectiveOptimizer< VectorSpace<double>, detail::SMSEMOA > shark::SMSEMOA |
Injects the SMS-EMOA into the inheritance hierarchy.
Definition at line 408 of file SMS-EMOA.h.
| typedef TypeErasedMultiObjectiveOptimizer< VectorSpace<double>, detail::SteadyStateMOCMA< HypervolumeIndicator > > shark::SteadyStateMOCMA |
Injects the Steady-State MOCMA relying on the hypervolume indicator into the inheritance hierarchy.
Definition at line 541 of file SteadyStateMOCMA.h.
| typedef StreamHandlerBase< std::ostream > shark::StlStreamHandler |
Tags a stream handler for STL ostreams.
Definition at line 92 of file StreamHandler.h.
| typedef Traits::storage shark::storage |
| typedef TwoStateSpace<-1,1> shark::SymmetricBinarySpace |
Definition at line 82 of file TwoStateSpace.h.
| typedef CompressedTraits<typename CopyConst<V,BaseExpression>::type> shark::Traits |
Definition at line 168 of file metafunctions.h.
Definition at line 50 of file TruncExpBinaryRBM.h.
Definition at line 46 of file TruncExpBinaryRBM.h.
Definition at line 45 of file TruncExpBinaryRBM.h.
Definition at line 51 of file TruncExpBinaryRBM.h.
Definition at line 49 of file TruncExpBinaryRBM.h.
Definition at line 47 of file TruncExpBinaryRBM.h.
| typedef RBM<TruncExpBinaryEnergy, Rng::rng_type> shark::TruncExpBinaryRBM |
Definition at line 44 of file TruncExpBinaryRBM.h.
Convenience typedef for comparing unpenalized fitness values.
Definition at line 78 of file FitnessComparator.h.
| typedef IndirectFitnessComparator< tag::UnpenalizedFitness > shark::UnpenalizedIndirectFitnessComparator |
Convenience typedef for comparing unpenalized fitness values.
Definition at line 85 of file FitnessComparator.h.
| typedef boost::graph_traits<Graph>::vertex_descriptor shark::Vertex |
| typedef boost::property_map<Graph, boost::edge_weight_t>::type shark::WeightMap |
Tags an XML formatter.
Definition at line 148 of file PrintfLogFormatter.h.
| enum shark::AlphaStatus |
| Enumerator | |
|---|---|
| AlphaFree | |
| AlphaLowerBound | |
| AlphaUpperBound | |
| AlphaDeactivated | |
Definition at line 325 of file QpSolver.h.
| enum shark::BuildType |
| enum shark::Connective |
| enum shark::QpStopType |
reason for the quadratic programming solver to stop the iterative optimization process
| Enumerator | |
|---|---|
| QpNone | |
| QpAccuracyReached | |
| QpMaxIterationsReached | |
| QpTimeout | |
Definition at line 73 of file QuadraticProgram.h.
| shark::ANNOUNCE_FUZZY_SET | ( | GeneralizedBellFS | , |
| FuzzySetFactory | |||
| ) |
| shark::ANNOUNCE_FUZZY_SET | ( | SingletonFS | , |
| FuzzySetFactory | |||
| ) |
| shark::ANNOUNCE_FUZZY_SET | ( | ConstantFS | , |
| FuzzySetFactory | |||
| ) |
| shark::ANNOUNCE_FUZZY_SET | ( | CustomizedFS | , |
| FuzzySetFactory | |||
| ) |
| shark::ANNOUNCE_FUZZY_SET | ( | TriangularFS | , |
| FuzzySetFactory | |||
| ) |
| shark::ANNOUNCE_FUZZY_SET | ( | TrapezoidFS | , |
| FuzzySetFactory | |||
| ) |
| shark::ANNOUNCE_FUZZY_SET | ( | InfinityFS | , |
| FuzzySetFactory | |||
| ) |
| shark::ANNOUNCE_FUZZY_SET | ( | SigmoidalFS | , |
| FuzzySetFactory | |||
| ) |
| shark::ANNOUNCE_FUZZY_SET | ( | BellFS | , |
| FuzzySetFactory | |||
| ) |
| shark::ANNOUNCE_LINGUISTIC_TERM | ( | CustomizedLT | , |
| LinguisticTermFactory | |||
| ) |
| shark::ANNOUNCE_LINGUISTIC_TERM | ( | SigmoidalLT | , |
| LinguisticTermFactory | |||
| ) |
| shark::ANNOUNCE_LINGUISTIC_TERM | ( | ConstantLT | , |
| LinguisticTermFactory | |||
| ) |
| shark::ANNOUNCE_LINGUISTIC_TERM | ( | GeneralizedBellLT | , |
| LinguisticTermFactory | |||
| ) |
| shark::ANNOUNCE_LINGUISTIC_TERM | ( | SingletonLT | , |
| LinguisticTermFactory | |||
| ) |
| shark::ANNOUNCE_LINGUISTIC_TERM | ( | TriangularLT | , |
| LinguisticTermFactory | |||
| ) |
| shark::ANNOUNCE_LINGUISTIC_TERM | ( | InfinityLT | , |
| LinguisticTermFactory | |||
| ) |
| shark::ANNOUNCE_LINGUISTIC_TERM | ( | TrapezoidLT | , |
| LinguisticTermFactory | |||
| ) |
| shark::ANNOUNCE_LINGUISTIC_TERM | ( | BellLT | , |
| LinguisticTermFactory | |||
| ) |
| shark::ANNOUNCE_LINGUISTIC_TERM | ( | ComposedLT | , |
| LinguisticTermFactory | |||
| ) |
| shark::ANNOUNCE_LOG_FORMATTER | ( | PlainTextLogFormatter | , |
| LogFormatterFactory | |||
| ) |
Make the plain text formatter known to the formatter factory.
Make the json formatter known to the formatter factory.
Make the xml formatter known to the formatter factory.
| shark::ANNOUNCE_LOG_HANDLER | ( | CoutLogHandler | , |
| LogHandlerFactory | |||
| ) |
Make the std::cout log handler known to the factory.
| shark::ANNOUNCE_LOG_HANDLER | ( | CerrLogHandler | , |
| LogHandlerFactory | |||
| ) |
Make the std::cerr log handler known to the factory.
| shark::ANNOUNCE_LOG_HANDLER | ( | ClogLogHandler | , |
| LogHandlerFactory | |||
| ) |
Make the std::clog log handler known to the factory.
| shark::ANNOUNCE_MULTI_OBJECTIVE_FUNCTION | ( | LZ9 | , |
| shark::moo::RealValuedObjectiveFunctionFactory | |||
| ) |
| shark::ANNOUNCE_MULTI_OBJECTIVE_FUNCTION | ( | ZDT3 | , |
| shark::moo::RealValuedObjectiveFunctionFactory | |||
| ) |
| shark::ANNOUNCE_MULTI_OBJECTIVE_FUNCTION | ( | ZDT1 | , |
| shark::moo::RealValuedObjectiveFunctionFactory | |||
| ) |
| shark::ANNOUNCE_MULTI_OBJECTIVE_FUNCTION | ( | ZDT2 | , |
| shark::moo::RealValuedObjectiveFunctionFactory | |||
| ) |
| shark::ANNOUNCE_MULTI_OBJECTIVE_FUNCTION | ( | ZDT6 | , |
| shark::moo::RealValuedObjectiveFunctionFactory | |||
| ) |
| shark::ANNOUNCE_MULTI_OBJECTIVE_FUNCTION | ( | LZ1 | , |
| shark::moo::RealValuedObjectiveFunctionFactory | |||
| ) |
| shark::ANNOUNCE_MULTI_OBJECTIVE_FUNCTION | ( | ZDT4 | , |
| shark::moo::RealValuedObjectiveFunctionFactory | |||
| ) |
| shark::ANNOUNCE_MULTI_OBJECTIVE_FUNCTION | ( | LZ3 | , |
| shark::moo::RealValuedObjectiveFunctionFactory | |||