AbstractMultiObjectiveOptimizer. More...
Namespaces  
blas  
random  
statistics  
tags  
Tags are empty types which can be used as a function argument.  
Classes  
class  AbsoluteLoss 
absolute loss More...  
class  AbstractBudgetMaintenanceStrategy 
This is the abstract interface for any budget maintenance strategy. More...  
class  AbstractClustering 
Base class for clustering. More...  
class  AbstractConstraintHandler 
Implements the base class for constraint handling. More...  
class  AbstractCost 
Cost function interface. More...  
class  AbstractKernelFunction 
Base class of all Kernel functions. More...  
class  AbstractLinearSvmTrainer 
Super class of all linear SVM trainers. More...  
class  AbstractLineSearchOptimizer 
Basis class for line search methods. More...  
class  AbstractLoss 
Loss function interface. More...  
class  AbstractMetric 
class  AbstractModel 
Base class for all Models. More...  
class  AbstractMultiObjectiveOptimizer 
base class for abstract multiobjective optimizers for arbitrary search spaces. More...  
class  AbstractNearestNeighbors 
Interface for Nearest Neighbor queries. More...  
class  AbstractObjectiveFunction 
Super class of all objective functions for optimization and learning. More...  
class  AbstractOptimizer 
An optimizer that optimizes general objective functions. More...  
class  AbstractSingleObjectiveOptimizer 
Base class for all single objective optimizer. More...  
class  AbstractStoppingCriterion 
Base class for stopping criteria of optimization algorithms. More...  
class  AbstractSvmTrainer 
Super class of all kernelized (nonlinear) SVM trainers. More...  
class  AbstractTrainer 
Superclass of supervised learning algorithms. More...  
class  AbstractUnsupervisedTrainer 
Superclass of unsupervised learning algorithms. More...  
class  AbstractWeightedTrainer 
Superclass of weighted supervised learning algorithms. More...  
class  AbstractWeightedUnsupervisedTrainer 
Superclass of weighted unsupervised learning algorithms. More...  
struct  Ackley 
Convex quadratic benchmark function with single dominant axis. More...  
class  Adam 
Adaptive Moment Estimation Algorithm (ADAM) More...  
struct  AdditiveEpsilonIndicator 
Implements the Additive approximation properties of sets. More...  
class  ARDKernelUnconstrained 
Automatic relevance detection kernel for unconstrained parameter optimization. More...  
class  BarsAndStripes 
Generates the BarsAndStripes problem. In this problem, a 4x4 image has either rows or columns of the same value. More...  
class  BaseDCNonDominatedSort 
Divideandconquer algorithm for nondominated sorting. More...  
struct  Batch 
class which helps using different batch types More...  
struct  Batch< blas::vector< T > > 
specialization for vectors which should be matrices in batch mode! More...  
struct  Batch< detail::MatrixRowReference< M > > 
struct  Batch< shark::blas::compressed_vector< T > > 
specialization for ublas compressed vectors which are compressed matrices in batch mode! More...  
struct  Batch< WeightedDataPair< DataType, WeightType > > 
struct  BatchTraits 
struct  BatchTraits< blas::compressed_matrix< T > > 
struct  BatchTraits< blas::dense_matrix_adaptor< T, blas::row_major > > 
struct  BatchTraits< blas::matrix< T > > 
struct  BatchTraits< WeightedDataBatch< DataType, WeightType > > 
class  BFGS 
Broyden, Fletcher, Goldfarb, Shannon algorithm for unconstraint optimization. More...  
class  BiasSolver 
class  BiasSolverSimplex 
class  BinaryLayer 
Layer of binary units taking values in {0,1}. More...  
class  BinaryTree 
Super class of binary spacepartitioning trees. More...  
class  BipolarLayer 
Layer of bipolar units taking values in {1,1}. More...  
struct  BitflipMutator 
Bitflip mutation operator. More...  
class  BlockMatrix2x2 
SVM regression matrix. More...  
struct  BoxBasedShrinkingStrategy 
Takes q boxx constrained QPtype problem and implements shrinking on it. More...  
class  BoxConstrainedProblem 
Quadratic program with box constraints. More...  
class  BoxConstrainedShrinkingProblem 
class  BoxConstraintHandler 
class  BoxedSVMProblem 
Boxed problem for alpha in [lower,upper]^n and equality constraints. More...  
class  CachedMatrix 
Efficient quadratic matrix cache. More...  
struct  CanBeCalled 
detects whether Functor(Argument) can be called. More...  
struct  CanBeCalled< R(*)(T), Argument > 
struct  CanBeCalled< R(T), Argument > 
class  CARTree 
Classification and Regression Tree. More...  
class  Centroids 
Clusters defined by centroids. More...  
class  CG 
Conjugategradient method for unconstrained optimization. More...  
class  Chessboard 
"chess board" problem for binary classification More...  
struct  Cigar 
Convex quadratic benchmark function with single dominant axis. More...  
class  CigarDiscus 
Convex quadratic benchmark function. More...  
struct  CIGTAB1 
Multiobjective optimization benchmark function CIGTAB 1. More...  
struct  CIGTAB2 
Multiobjective optimization benchmark function CIGTAB 2. More...  
class  CircleInSquare 
class  Classifier 
Conversion of realvalued or vector valued outputs to class labels. More...  
class  ClusteringModel 
Abstract model with associated clustering object. More...  
class  CMA 
Implements the CMAES. More...  
struct  CMAChromosome 
Models a CMAChromosomeof the elitist (MO)CMAES that encodes strategy parameters. More...  
class  CMACMap 
The CMACMap class represents a linear combination of piecewise constant functions. More...  
class  CMAIndividual 
class  CMSA 
Implements the CMSA. More...  
class  CombinedObjectiveFunction 
Linear combination of objective functions. More...  
class  ConcatenatedModel 
ConcatenatedModel concatenates two models such that the output of the first model is input to the second. More...  
struct  ConstProxyReference 
sets the type of ProxxyReference More...  
struct  ConstrainedSphere 
Constrained Sphere function. More...  
class  ContrastiveDivergence 
Implements kstep Contrastive Divergence described by Hinton et al. (2006). More...  
class  Conv2DModel 
Convolutional Model for 2D image data. More...  
class  CrossEntropy 
Error measure for classification tasks that can be used as the objective function for training. More...  
class  CrossEntropyMethod 
Implements the Cross Entropy Method. More...  
class  CrossValidationError 
Crossvalidation error for selection of hyperparameters. More...  
struct  CrowdingDistance 
Implements the Crowding Distance of a pareto front. More...  
class  CSvmDerivative 
This class provides two main member functions for computing the derivative of a CSVM hypothesis w.r.t. its hyperparameters. The constructor takes a pointer to a KernelClassifier 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  CSVMProblem 
Problem formulation for binary CSVM problems. More...  
class  CSvmTrainer 
Training of CSVMs for binary classification. More...  
class  CVFolds 
class  Data 
Data container. More...  
class  DataDistribution 
A DataDistribution defines an unsupervised learning problem. More...  
class  DataView 
Constant time ElementLookup for Datasets. More...  
class  DiagonalWithCircle 
class  DifferenceKernelMatrix 
SVM ranking matrix. More...  
struct  DiffPowers 
class  DiscreteKernel 
Kernel on a finite, discrete space. More...  
class  DiscreteLoss 
flexible loss for classification More...  
struct  Discus 
Convex quadratic benchmark function. More...  
class  DistantModes 
Creates a set of pattern (each later representing a mode) which than are randomly perturbed to create the data set. The dataset was introduced in Desjardins et al. (2010) (Parallel Tempering for training restricted Boltzmann machines, AISTATS 2010) More...  
class  DoublePole 
class  DropoutLayer 
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...  
class  ElitistCMA 
Implements the elitist CMAES. More...  
struct  ElitistSelection 
Survival selection to find the next parent set. More...  
struct  ELLI1 
Multiobjective optimization benchmark function ELLI1. More...  
struct  ELLI2 
Multiobjective optimization benchmark function ELLI2. More...  
struct  Ellipsoid 
Convex quadratic benchmark function. More...  
struct  EmptyState 
Default State of an Object which does not need a State. More...  
struct  Energy 
The Energy function determining the Gibbs distribution of an RBM. More...  
class  EnergyStoringTemperedMarkovChain 
Implements parallel tempering but also stores additional statistics on the energy differences. More...  
class  EpsilonHingeLoss 
Hingeloss for large margin regression. More...  
class  EpsilonSvmTrainer 
Training of EpsilonSVMs for regression. More...  
struct  EPTournamentSelection 
Survival and mating selection to find the next parent set. More...  
class  ErrorFunction 
Objective function for supervised learning. More...  
class  EvaluationArchive 
Objective function wrapper storing all function evaluations. More...  
class  ExactGradient 
class  ExampleModifiedKernelMatrix 
class  Exception 
Toplevel exception class of the shark library. More...  
struct  FastSigmoidNeuron 
Fast sigmoidal function, which does not need to compute an exponential function. More...  
class  FisherLDA 
Fisher's Linear Discriminant Analysis for data compression. More...  
struct  Fonseca 
Biobjective realvalued benchmark function proposed by Fonseca and Flemming. More...  
class  GaussianKernelMatrix 
Efficient special case if the kernel is Gaussian and the inputs are sparse vectors. More...  
class  GaussianLayer 
A layer of Gaussian neurons. More...  
class  GaussianRbfKernel 
Gaussian radial basis function kernel. More...  
class  GaussianTaskKernel 
Special "Gaussianlike" kernel function on tasks. 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  GeneralQuadraticProblem 
Quadratic Problem with only BoxConstraints Let K the kernel matrix, than the problem has the form. More...  
class  GibbsOperator 
Implements Block Gibbs Sampling related transition operators for various temperatures. More...  
class  GridSearch 
Optimize by trying out a grid of configurations. More...  
struct  GSP 
Realvalued benchmark function with two objectives. More...  
class  HardClusteringModel 
Model for "hard" clustering. More...  
class  HierarchicalClustering 
Clusters defined by a binary space partitioning tree. More...  
struct  Himmelblau 
Multimodal twodimensional continuous Himmelblau benchmark function. More...  
class  HingeLoss 
Hingeloss for large margin classification. More...  
class  HMGSelectionCriterion 
class  HuberLoss 
Huberloss for for robust regression. More...  
struct  HypervolumeApproximator 
Implements an FPRAS for approximating the volume of a set of highdimensional objects. The algorithm is described in. More...  
struct  HypervolumeCalculator 
Frontend for hypervolume calculation algorithms in m dimensions. More...  
struct  HypervolumeCalculator2D 
Implementation of the exact hypervolume calculation in 2 dimensions. More...  
struct  HypervolumeCalculator3D 
Implementation of the exact hypervolume calculation in 3 dimensions. More...  
struct  HypervolumeCalculatorMDHOY 
Implementation of the exact hypervolume calculation in m dimensions. More...  
struct  HypervolumeCalculatorMDWFG 
Implementation of the exact hypervolume calculation in m dimensions. More...  
struct  HypervolumeContribution 
Frontend for hypervolume contribution algorithms in m dimensions. More...  
struct  HypervolumeContribution2D 
Finds the smallest/largest Contributors given 2D points. More...  
struct  HypervolumeContribution3D 
Finds the hypervolume contribution for points in 3DD. More...  
struct  HypervolumeContributionApproximator 
Approximately determines the point of a set contributing the least hypervolume. More...  
struct  HypervolumeContributionMD 
Finds the hypervolume contribution for points in MD. More...  
struct  HypervolumeIndicator 
Calculates the hypervolume covered by a front of nondominated points. More...  
struct  HypervolumeSubsetSelection2D 
Implementation of the exact hypervolume subset selection algorithm in 2 dimensions. More...  
struct  IHR1 
Multiobjective optimization benchmark function IHR1. More...  
struct  IHR2 
Multiobjective optimization benchmark function IHR 2. More...  
struct  IHR3 
Multiobjective optimization benchmark function IHR3. More...  
struct  IHR4 
Multiobjective optimization benchmark function IHR 4. More...  
struct  IHR6 
Multiobjective optimization benchmark function IHR 6. More...  
class  ImagePatches 
Given a set of images, draws a set of image patches of a given size. More...  
class  INameable 
This class is an interface for all objects which can have a name. More...  
class  IndexedIterator 
creates an Indexed Iterator, an Iterator which also carries index information using index() More...  
class  IndexingIterator 
class  IndicatorBasedMOCMA 
Implements the generational MOCMAES. More...  
class  IndicatorBasedRealCodedNSGAII 
Implements the NSGAII. More...  
struct  IndicatorBasedSelection 
Implements the wellknown indicatorbased selection strategy. More...  
class  IndicatorBasedSteadyStateMOCMA 
Implements the \((\mu+1)\)MOCMAES. More...  
class  Individual 
Individual is a simple templated class modelling an individual that acts as a candidate solution in an evolutionary algorithm. More...  
class  IParameterizable 
Top level interface for everything that holds parameters. More...  
class  IRpropMinus 
This class offers methods for the usage of the improved ResilientBackpropagationalgorithm without weightbacktracking. More...  
class  IRpropPlus 
This class offers methods for the usage of the improved ResilientBackpropagationalgorithm with weightbacktracking. More...  
class  IRpropPlusFull 
class  ISerializable 
Abstracts serializing functionality. More...  
class  IterativeNNQuery 
Iterative nearest neighbors query. More...  
class  JaakkolaHeuristic 
Jaakkola's heuristic and related quantities for Gaussian kernel selection. More...  
class  KDTree 
KDtree, a binary spacepartitioning tree. More...  
class  KernelBudgetedSGDTrainer 
Budgeted stochastic gradient descent training for kernelbased models. More...  
struct  KernelClassifier 
Linear classifier in a kernel feature space. More...  
class  KernelExpansion 
Linear model in a kernel feature space. More...  
class  KernelMatrix 
Kernel Gram matrix. More...  
class  KernelMeanClassifier 
Kernelized meanclassifier. More...  
class  KernelSGDTrainer 
Generic stochastic gradient descent training for kernelbased models. More...  
class  KernelTargetAlignment 
Kernel Target Alignment  a measure of alignment of a kernel Gram matrix with labels. More...  
struct  KeyValuePair 
Represents a KeyValuePair similar std::pair which is strictly ordered by it's key. More...  
class  KHCTree 
KHCtree, a binary spacepartitioning tree. More...  
class  LabeledData 
Data set for supervised learning. More...  
class  LabeledDataDistribution 
A LabeledDataDistribution defines a supervised learning problem. More...  
class  LabelOrder 
This will normalize the labels of a given dataset to 0..N1. More...  
class  LassoRegression 
LASSO Regression. More...  
class  LBFGS 
LimitedMemory Broyden, Fletcher, Goldfarb, Shannon algorithm. More...  
class  LCTree 
LCtree, a binary spacepartitioning tree. More...  
class  LDA 
Linear Discriminant Analysis (LDA) More...  
struct  LibSVMSelectionCriterion 
Computes the maximum gian solution. More...  
class  LinearClassifier 
Basic linear classifier. More...  
class  LinearCSvmTrainer 
class  LinearKernel 
Linear Kernel, parameter free. More...  
class  LinearModel 
Linear Prediction with optional activation function. More...  
struct  LinearNeuron 
Linear activation Neuron. More...  
struct  LinearRankingSelection 
Implements a fitnessproportional selection scheme for mating selection that scales the fitness values linearly before carrying out the actual selection. More...  
class  LinearRegression 
Linear Regression. More...  
class  LinearSAGTrainer 
Stochastic Average Gradient Method for training of linear models,. More...  
class  LineSearch 
Wrapper for the linesearch class of functions in the linear algebra library. More...  
class  LMCMA 
Implements a LimitedMemoryCMA. More...  
struct  LogisticNeuron 
Neuron which computes the Logistic (logistic) function with range [0,1]. More...  
class  LogisticRegression 
Trainer for Logistic regression. More...  
class  LooError 
Leaveoneout error objective function. More...  
class  LooErrorCSvm 
Leaveoneout error, specifically optimized for CSVMs. More...  
class  LRUCache 
Implements an LRUCaching Strategy for arbitrary CacheLines. More...  
struct  LZ1 
Multiobjective optimization benchmark function LZ1. More...  
struct  LZ2 
Multiobjective optimization benchmark function LZ2. More...  
struct  LZ3 
Multiobjective optimization benchmark function LZ3. More...  
struct  LZ4 
Multiobjective optimization benchmark function LZ4. More...  
struct  LZ5 
Multiobjective optimization benchmark function LZ5. More...  
struct  LZ6 
Multiobjective optimization benchmark function LZ6. More...  
struct  LZ7 
Multiobjective optimization benchmark function LZ7. More...  
struct  LZ8 
Multiobjective optimization benchmark function LZ8. More...  
struct  LZ9 
class  MarkovChain 
A single Markov chain. More...  
class  MarkovPole 
struct  MaximumGainCriterion 
Working set selection by maximization of the dual objective gain. More...  
struct  MaximumGradientCriterion 
Working set selection by maximization of the projected gradient. More...  
class  MaxIterations 
This stopping criterion stops after a fixed number of iterations. More...  
class  McPegasos 
Pegasos solver for linear multiclass support vector machines. More...  
class  MeanModel 
Calculates the weighted mean of a set of models. More...  
class  MergeBudgetMaintenanceStrategy 
Budget maintenance strategy that merges two vectors. More...  
class  MergeBudgetMaintenanceStrategy< RealVector > 
Budget maintenance strategy merging vectors. More...  
class  MissingFeaturesKernelExpansion 
Kernel expansion with missing features support. More...  
class  MissingFeatureSvmTrainer 
Trainer for binary SVMs natively supporting missing features. More...  
class  MklKernel 
Weighted sum of kernel functions. 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  ModelKernel 
Kernel function that uses a Model as transformation function for another kernel. More...  
class  ModifiedKernelMatrix 
Modified Kernel Gram matrix. More...  
class  MOEAD 
Implements the MOEA/D algorithm. More...  
class  MonomialKernel 
Monomial kernel. Calculates \( \left\langle x_1, x_2 \right\rangle^m_exponent \). More...  
class  MultiChainApproximator 
Approximates the gradient by taking samples from an ensemble of Markov chains running in parallel. More...  
class  MultiNomialDistribution 
Implements a multinomial distribution. More...  
class  MultiObjectiveBenchmark 
Creates a multiobjective Benchmark from a set of given single objective functions. More...  
class  MultiTaskKernel 
Special kernel function for multitask and transfer learning. More...  
struct  MultiTaskSample 
Aggregation of input data and task index. More...  
class  MultiVariateNormalDistribution 
Implements a multivariate normal distribution with zero mean. More...  
class  MultiVariateNormalDistributionCholesky 
Multivariate normal distribution with zero mean using a cholesky decomposition. More...  
struct  MVPSelectionCriterion 
Computes the most violating pair of the problem. More...  
class  NearestNeighborModel 
NearestNeighbor model for classification and regression. More...  
class  NearestNeighborModel< InputType, unsigned int > 
class  NegativeAUC 
Negative area under the curve. More...  
class  NegativeGaussianProcessEvidence 
Evidence for model selection of a regularization network/Gaussian process. More...  
class  NegativeLogLikelihood 
Computes the negative log likelihood of a dataset under a model. More...  
class  NegativeWilcoxonMannWhitneyStatistic 
Negative WilcoxonMannWhitney statistic. More...  
class  NestedGridSearch 
Nested grid search. More...  
class  NeuronLayer 
class  NonMarkovPole 
Objective function for single and double nonMarkov poles. More...  
class  NormalDistributedPoints 
Generates a set of normally distributed points. 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  NormalizeComponentsZCA 
Train a linear model to whiten the data. More...  
class  NormalizedKernel 
Normalized version of a kernel function. 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  Normalizer 
"Diagonal" linear model for data normalization. More...  
struct  NormalizerNeuron 
struct  NSGA3Indicator 
class  OneClassSvmTrainer 
Training of oneclass SVMs. More...  
class  OneNormRegularizer 
Onenorm of the input as an objective function. More...  
struct  OnePointCrossover 
Implements onepoint crossover. More...  
class  OneVersusOneClassifier 
Oneversusone Classifier. More...  
class  OptimizationTrainer 
Wrapper for training schemes based on (iterative) optimization. More...  
class  PamiToy 
struct  PartiallyMappedCrossover 
Implements partially mapped crossover. More...  
class  PartlyPrecomputedMatrix 
Partly Precomputed version of a matrix for quadratic programming. More...  
class  PCA 
Principal Component Analysis. More...  
class  Pegasos 
Pegasos solver for linear (binary) support vector machines. More...  
struct  PenalizingEvaluator 
Penalizing evaluator for scalar objective functions. More...  
class  Perceptron 
Perceptron online learning algorithm. More...  
class  PointSearch 
Optimize by trying out predefined configurations. More...  
class  PointSetKernel 
Normalized version of a kernel function. More...  
class  PolynomialKernel 
Polynomial kernel. More...  
struct  PolynomialMutator 
Polynomial mutation operator. More...  
class  PopulationBasedStepSizeAdaptation 
Step size adaptation based on the success of the new population compared to the old. More...  
class  PrecomputedMatrix 
Precomputed version of a matrix for quadratic programming. More...  
class  ProductKernel 
Product of kernel functions. More...  
class  ProjectBudgetMaintenanceStrategy 
Budget maintenance strategy that projects a vector. More...  
class  ProjectBudgetMaintenanceStrategy< RealVector > 
Budget maintenance strategy that projects a vector. More...  
class  ProxyIterator 
Creates an iterator which reinterpretes an object as a range. More...  
class  QpBoxLinear 
Quadratic program solver for boxconstrained problems with linear kernel. More...  
class  QpConfig 
Super class of all support vector machine trainers. More...  
class  QpMcBoxDecomp 
class  QpMcLinear 
Generic solver skeleton for linear multiclass SVM problems. More...  
class  QpMcLinearADM 
Solver for the multiclass SVM with absolute margin and discriminative maximum loss. More...  
class  QpMcLinearATM 
Solver for the multiclass SVM with absolute margin and total maximum loss. More...  
class  QpMcLinearATS 
Solver for the multiclass SVM with absolute margin and total sum loss. More...  
class  QpMcLinearCS 
Solver for the multiclass SVM by Crammer & Singer. More...  
class  QpMcLinearLLW 
Solver for the multiclass SVM by Lee, Lin & Wahba. More...  
class  QpMcLinearMMR 
Solver for the multiclass maximum margin regression SVM. More...  
class  QpMcLinearReinforced 
Solver for the "reinforced" multiclass SVM. More...  
class  QpMcLinearWW 
Solver for the multiclass SVM by Weston & Watkins. More...  
class  QpMcSimplexDecomp 
struct  QpSolutionProperties 
properties of the solution of a quadratic program More...  
class  QpSolver 
Quadratic program solver. More...  
class  QpSparseArray 
specialized container class for multiclass SVM problems More...  
struct  QpStoppingCondition 
stopping conditions for quadratic programming More...  
class  RadiusMarginQuotient 
radius margin quotions for binary SVMs More...  
class  RankingSvmTrainer 
Training of an SVM for ranking. More...  
class  RBFLayer 
Implements a layer of radial basis functions in a neural network. 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  RealCodedNSGAIII 
Implements the NSGAIII. More...  
struct  RealSpace 
The RealSpace can't be enumerated. Infinite values are just too much. More...  
struct  RectifierNeuron 
Rectifier Neuron f(x) = max(0,x) More...  
struct  ReferenceVectorAdaptation 
Reference vector adaptation for the RVEA algorithm. More...  
struct  ReferenceVectorGuidedSelection 
Implements the reference vector selection for the RVEA algorithm. More...  
class  RegularizationNetworkTrainer 
Training of a regularization network. More...  
class  RegularizedKernelMatrix 
Kernel Gram matrix with modified diagonal. More...  
class  RemoveBudgetMaintenanceStrategy 
Budget maintenance strategy that removes a vector. More...  
struct  ResultSet 
class  RFClassifier 
Random Forest Classifier. More...  
class  RFTrainer 
Random Forest. More...  
class  RFTrainer< RealVector > 
class  RFTrainer< unsigned int > 
class  ROC 
ROCCurve  false negatives over false positives. More...  
struct  Rosenbrock 
Generalized Rosenbrock benchmark function. More...  
struct  RotatedObjectiveFunction 
Rotates an objective function using a randomly initialized rotation. More...  
struct  RouletteWheelSelection 
Fitnessproportional selection operator. More...  
class  RpropMinus 
This class offers methods for the usage of the ResilientBackpropagationalgorithm without weightbacktracking. More...  
class  RpropPlus 
This class offers methods for the usage of the ResilientBackpropagationalgorithm with weightbacktracking. More...  
class  RVEA 
Implements the RVEA algorithm. More...  
class  ScaledKernel 
Scaled version of a kernel function. More...  
struct  Schwefel 
Convex benchmark function. More...  
class  ScopedHandle 
class  Shape 
Represents the Shape of an input or output. More...  
class  Shark 
Allows for querying compile settings at runtime. Provides the current command line arguments to the rest of the library. More...  
class  Shifter 
Shifter problem. More...  
class  SimpleNearestNeighbors 
Brute force optimized nearest neighbor implementation. More...  
class  SimplexDownhill 
Simplex Downhill Method. More...  
struct  SimulatedBinaryCrossover 
Simulated binary crossover operator. More...  
class  SingleChainApproximator 
Approximates the gradient by taking samples from a single Markov chain. More...  
struct  SingleObjectiveResultSet 
Result set for single objective algorithm. More...  
class  SinglePole 
class  SMSEMOA 
Implements the SMSEMOA. More...  
class  SoftClusteringModel 
Model for "soft" clustering. More...  
struct  SoftmaxNeuron 
struct  Sphere 
Convex quadratic benchmark function. More...  
class  SquaredEpsilonHingeLoss 
Hingeloss for large margin regression using th squared twonorm. More...  
class  SquaredHingeCSvmTrainer 
class  SquaredHingeLinearCSvmTrainer 
class  SquaredHingeLoss 
Squared Hingeloss for large margin classification. More...  
class  SquaredLoss 
squared loss for regression and classification More...  
class  SquaredLoss< OutputType, unsigned int > 
class  SquaredLoss< Sequence, Sequence > 
struct  State 
Represents the State of an Object. More...  
class  SteepestDescent 
Standard steepest descent. More...  
class  SubrangeKernel 
Weighted sum of kernel functions. More...  
class  SvmLogisticInterpretation 
Maximumlikelihood model selection score for binary support vector machines. More...  
class  SvmProblem 
class  SvmShrinkingProblem 
struct  TanhNeuron 
Neuron which computes the hyperbolic tangenst with range [1,1]. More...  
class  TemperedMarkovChain 
class  Timer 
Timer abstraction with microsecond resolution. More...  
struct  TournamentSelection 
Tournament selection operator. 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...  
struct  TransformedData 
class  TreeConstruction 
Stopping criteria for tree construction. More...  
class  TreeNearestNeighbors 
Nearest Neighbors implementation using binary trees. More...  
class  TruncatedExponentialLayer 
A layer of truncated exponential neurons. More...  
class  TrustRegionNewton 
Simple TrustRegion method based on the full Hessian matrix. More...  
class  TukeyBiweightLoss 
Tukey's Biweightloss for robust regression. More...  
class  TwoNormRegularizer 
Twonorm of the input as an objective function. More...  
class  TwoPointStepSizeAdaptation 
Step size adaptation based on the success of the new population compared to the old. More...  
struct  TwoStateSpace 
The TwoStateSpace is a discrete Space with only two values, for example {0,1} or {1,1}. More...  
class  TypedFeatureNotAvailableException 
Exception indicating the attempt to use a feature which is not supported. More...  
class  TypedFlags 
Flexible and extensible mechanisms for holding flags. More...  
class  UniformCrossover 
Uniform crossover of arbitrary individuals. More...  
struct  UniformRankingSelection 
Selects individuals from the range of individual and offspring individuals. More...  
class  UnlabeledData 
Data set for unsupervised learning. More...  
struct  ValidatedSingleObjectiveResultSet 
Result set for validated points. 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  VariationalAutoencoderError 
Computes the variational autoencoder error function. More...  
class  VDCMA 
class  Wave 
Noisy sinc function: y = sin(x) / x + noise. More...  
struct  WeightedDataBatch 
struct  WeightedDataPair 
InputLabel pair of data. More...  
class  WeightedLabeledData 
Weighted data set for supervised learning. More...  
class  WeightedSumKernel 
Weighted sum of kernel functions. More...  
class  WeightedUnlabeledData 
Weighted data set for unsupervised learning. More...  
struct  WS2MaximumGradientCriterion 
Working set selection by maximization of the projected gradient. More...  
struct  ZDT1 
Multiobjective optimization benchmark function ZDT1. More...  
struct  ZDT2 
Multiobjective optimization benchmark function ZDT2. More...  
struct  ZDT3 
Multiobjective optimization benchmark function ZDT3. More...  
struct  ZDT4 
Multiobjective optimization benchmark function ZDT4. More...  
struct  ZDT6 
Multiobjective optimization benchmark function ZDT6. More...  
class  ZeroOneLoss 
01loss for classification. More...  
class  ZeroOneLoss< unsigned int, RealVector > 
01loss for classification. More...  
Enumerations  
enum  DominanceRelation { INCOMPARABLE = 0, LHS_DOMINATES_RHS = 1, RHS_DOMINATES_LHS = 2, EQUIVALENT = 3 } 
Result of comparing two objective vectors w.r.t. Pareto dominance. More...  
enum  AlphaStatus { AlphaFree = 0, AlphaLowerBound = 1, AlphaUpperBound = 2, AlphaDeactivated = 3 } 
enum  QpStopType { QpNone = 0, QpAccuracyReached = 1, QpMaxIterationsReached = 4, QpTimeout = 8 } 
enum  McSvm { McSvm::WW, McSvm::CS, McSvm::LLW, McSvm::ATM, McSvm::ATS, McSvm::ADM, McSvm::OVA, McSvm::MMR, McSvm::ReinforcedSvm } 
enum  BuildType { RELEASE_BUILD_TYPE, DEBUG_BUILD_TYPE } 
Models the build type. More...  
enum  Convolution { Convolution::Valid, Convolution::ZeroPad } 
enum  PartitionEstimationAlgorithm { AIS, AISMean, TwoSidedAISMean, AcceptanceRatio, AcceptanceRatioMean } 
Functions  
SHARK_EXPORT_SYMBOL KernelExpansion< RealVector >  approximateKernelExpansion (random::rng_type &rng, KernelExpansion< RealVector > const &model, std::size_t k, double precision=1.e8) 
Approximates a kernel expansion by a smaller one using an optimized basis. More...  
template<class IndividualRange >  
auto  penalizedFitness (IndividualRange &range) > decltype(boost::adaptors::transform(range, detail::IndividualPenalizedFitnessFunctor())) 
template<class IndividualRange >  
auto  unpenalizedFitness (IndividualRange &range) > decltype(boost::adaptors::transform(range, detail::IndividualUnpenalizedFitnessFunctor())) 
template<class IndividualRange >  
auto  ranks (IndividualRange &range) > decltype(boost::adaptors::transform(range, detail::IndividualRankFunctor())) 
template<class IndividualRange >  
auto  searchPoint (IndividualRange &range) > decltype(boost::adaptors::transform(range, detail::IndividualSearchPointFunctor())) 
template<class PointRange , class RankRange >  
void  dcNonDominatedSort (PointRange const &points, RankRange &ranks) 
template<class PointRange , class RankRange >  
void  fastNonDominatedSort (PointRange const &points, RankRange &ranks) 
Implements the wellknown nondominated sorting algorithm. More...  
template<class PointRange , class RankRange >  
void  nonDominatedSort (PointRange const &points, RankRange &ranks) 
Frontend for nondominated sorting algorithms. More...  
template<class PointRange , class RankRange >  
void  nonDominatedSort (PointRange const &points, RankRange const &ranks) 
template<class VectorTypeA , class VectorTypeB >  
DominanceRelation  dominance (VectorTypeA const &lhs, VectorTypeB const &rhs) 
Pareto dominance relation for two objective vectors. More...  
template<typename Matrix , typename randomType = shark::random::rng_type>  
Matrix  sampleLatticeUniformly (randomType &rng, Matrix const &matrix, std::size_t const n, bool const keep_corners=true) 
RealMatrix  preferenceAdjustedUnitVectors (std::size_t const n, std::size_t const sum, std::vector< Preference > const &preferences) 
Return a set of evenly spaced ndimensional points on the unit sphere clustered around the specified preference points. More...  
RealMatrix  preferenceAdjustedWeightVectors (std::size_t const n, std::size_t const sum, std::vector< Preference > const &preferences) 
Return a set of of evenly spaced ndimensional points on the "unit
simplex" clustered around the specified preference points. More...  
std::size_t  computeOptimalLatticeTicks (std::size_t const n, std::size_t const target_count) 
Computes the number of Ticks for a grid of a certain size. More...  
RealMatrix  weightLattice (std::size_t const n, std::size_t const sum) 
Returns a set of evenly spaced ndimensional points on the "unit simplex". More...  
RealMatrix  unitVectorsOnLattice (std::size_t const n, std::size_t const sum) 
Return a set of evenly spaced ndimensional points on the unit sphere. More...  
template<typename Matrix >  
UIntMatrix  computeClosestNeighbourIndicesOnLattice (Matrix const &m, std::size_t const n) 
double  tchebycheffScalarizer (RealVector const &fitness, RealVector const &weights, RealVector const &optimalPointFitness) 
SHARK_EXPORT_SYMBOL std::size_t  kMeans (Data< RealVector > const &data, std::size_t k, Centroids ¢roids, std::size_t maxIterations=0) 
The kmeans clustering algorithm. More...  
SHARK_EXPORT_SYMBOL std::size_t  kMeans (Data< RealVector > const &data, RBFLayer &model, std::size_t maxIterations=0) 
The kmeans clustering algorithm for initializing an RBF Layer. More...  
template<class InputType >  
KernelExpansion< InputType >  kMeans (Data< InputType > const &dataset, std::size_t k, AbstractKernelFunction< InputType > &kernel, std::size_t maxIterations=0) 
The kernel kmeans clustering algorithm. 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< std::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< std::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< std::is_arithmetic< T >, T >::type  softPlus (T x) 
Numerically stable version of the function log(1+exp(x)). More...  
double  softPlus (double x) 
Numerically stable version of the function log(1+exp(x)). calculated with float precision to save some time. 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) 
bool  operator== (Shape const &shape1, Shape const &shape2) 
bool  operator!= (Shape const &shape1, Shape const &shape2) 
template<class E , class T >  
std::basic_ostream< E, T > &  operator<< (std::basic_ostream< E, T > &os, Shape const &shape) 
template<class Iterator , class Rng >  
void  shuffle (Iterator begin, Iterator end, Rng &rng) 
random_shuffle algorithm which stops after acquiring the random subsequence for [begin,middle) More...  
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 T , class Range >  
Batch< T >::type  createBatch (Range const &range) 
creates a batch from a range of inputs More...  
template<class Range >  
Batch< typename Range::value_type >::type  createBatch (Range const &range) 
creates a batch from a range of inputs More...  
template<class T , class Iterator >  
Batch< T >::type  createBatch (Iterator const &begin, Iterator const &end) 
template<class BatchT >  
auto  getBatchElement (BatchT &batch, std::size_t i) > decltype(BatchTraits< BatchT >::type::get(std::declval< BatchT &>(), i)) 
template<class BatchT >  
auto  getBatchElement (BatchT const &batch, std::size_t i) > decltype(BatchTraits< BatchT >::type::get(std::declval< BatchT const &>(), i)) 
template<class BatchT >  
std::size_t  batchSize (BatchT const &batch) 
template<class BatchT >  
auto  batchBegin (BatchT &batch) > decltype(BatchTraits< BatchT >::type::begin(batch)) 
template<class BatchT >  
auto  batchBegin (BatchT const &batch) > decltype(BatchTraits< BatchT >::type::begin(batch)) 
template<class BatchT >  
auto  batchEnd (BatchT &batch) > decltype(BatchTraits< BatchT >::type::end(batch)) 
template<class BatchT >  
auto  batchEnd (BatchT const &batch) > decltype(BatchTraits< BatchT >::type::end(batch)) 
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 Range >  
Data< typename Range::value_type >  createDataFromRange (Range const &inputs, std::size_t maximumBatchSize=0) 
creates a data object from a range of elements More...  
template<class Range >  
UnlabeledData< typename boost::range_value< Range >::type >  createUnlabeledDataFromRange (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 maximumBatchSize=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 >  
std::size_t  dataDimension (Data< InputType > const &dataset) 
Return the dimensionality of a dataset. More...  
template<class InputType , class LabelType >  
std::size_t  inputDimension (LabeledData< InputType, LabelType > const &dataset) 
Return the input dimensionality of a labeled dataset. More...  
template<class InputType , class LabelType >  
std::size_t  labelDimension (LabeledData< InputType, LabelType > const &dataset) 
Return the label/output dimensionality of a labeled dataset. More...  
template<class InputType >  
std::size_t  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 , 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 T , class FeatureSet >  
Data< blas::vector< T > >  selectFeatures (Data< blas::vector< T > > const &data, FeatureSet const &features) 
Creates a copy of a dataset selecting only a certain set of features. More...  
template<class T , class FeatureSet >  
LabeledData< RealVector, T >  selectInputFeatures (LabeledData< RealVector, T > const &data, FeatureSet const &features) 
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 (twoclass) oneversusrest problem from a multiclass problem. More...  
template<typename RowType >  
RowType  getColumn (Data< RowType > const &data, std::size_t columnID) 
template<typename RowType >  
void  setColumn (Data< RowType > &data, std::size_t columnID, RowType newColumn) 
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 >  
std::size_t  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 (std::string const &fileName, T &data, std::size_t &sx, std::size_t &sy) 
Import a PGM image from file. More...  
template<class T >  
void  exportPGM (std::string const &fileName, T const &data, std::size_t sx, std::size_t sy, bool normalize=false) 
Export a PGM image to file. More...  
void  exportFiltersToPGMGrid (std::string const &basename, RealMatrix const &filters, std::size_t width, std::size_t height) 
Exports a set of filters as a grid image. More...  
void  exportFiltersToPGMGrid (std::string const &basename, Data< RealVector > const &filters, std::size_t width, std::size_t height) 
Exports a set of filters as a grid image. More...  
template<class T >  
void  importPGMSet (std::string const &p, Data< T > &set) 
Import PGM images scanning a directory recursively. More...  
template<class Vec1T , class Vec2T , class Vec3T , class Device >  
void  meanvar (Data< Vec1T > const &data, blas::vector_container< Vec2T, Device > &mean, blas::vector_container< Vec3T, Device > &variance) 
Calculates the mean and variance values of the input data. More...  
template<class Vec1T , class Vec2T , class MatT , class Device >  
void  meanvar (Data< Vec1T > const &data, blas::vector_container< Vec2T, Device > &mean, blas::matrix_container< MatT, Device > &variance) 
Calculates the mean, variance and covariance values of the input data. More...  
template<class VectorType >  
VectorType  mean (Data< VectorType > const &data) 
Calculates the mean vector of the input vectors. More...  
template<class VectorType >  
VectorType  mean (UnlabeledData< VectorType > const &data) 
template<class VectorType >  
VectorType  variance (Data< VectorType > const &data) 
Calculates the variance vector of the input vectors. More...  
template<class VectorType >  
blas::matrix< typename VectorType::value_type >  covariance (Data< VectorType > const &data) 
Calculates the covariance matrix of the data vectors. More...  
template<class D1 , class W1 , class D2 , class W2 >  
void  swap (WeightedDataPair< D1, W1 > &&p1, WeightedDataPair< D2, W2 > &&p2) 
template<class D1 , class W1 , class D2 , class W2 >  
void  swap (WeightedDataBatch< D1, W1 > &p1, WeightedDataBatch< D2, W2 > &p2) 
template<class T >  
std::ostream &  operator<< (std::ostream &stream, const WeightedUnlabeledData< T > &d) 
brief Outstream of elements for weighted data. More...  
template<class DataRange , class WeightRange >  
boost::disable_if< boost::is_arithmetic< WeightRange >, WeightedUnlabeledData< typename boost::range_value< DataRange >::type > >::type  createUnlabeledDataFromRange (DataRange const &data, WeightRange const &weights, std::size_t batchSize=0) 
creates a weighted unweighted data object from two ranges, representing data and weights More...  
template<class T , class U >  
std::ostream &  operator<< (std::ostream &stream, const WeightedLabeledData< T, U > &d) 
brief Outstream of elements for weighted labeled data. More...  
std::size_t  numberOfClasses (WeightedUnlabeledData< unsigned int > const &labels) 
std::vector< std::size_t >  classSizes (WeightedUnlabeledData< unsigned int > const &labels) 
Returns the number of members of each class in the dataset. More...  
template<class InputType >  
std::size_t  dataDimension (WeightedUnlabeledData< InputType > const &dataset) 
Return the dimnsionality of points of a weighted dataset. More...  
template<class InputType , class LabelType >  
std::size_t  inputDimension (WeightedLabeledData< InputType, LabelType > const &dataset) 
Return the input dimensionality of a weighted labeled dataset. More...  
template<class InputType , class LabelType >  
std::size_t  labelDimension (WeightedLabeledData< InputType, LabelType > const &dataset) 
Return the label/output dimensionality of a labeled dataset. More...  
template<class InputType >  
std::size_t  numberOfClasses (WeightedLabeledData< 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 , class LabelType >  
std::vector< std::size_t >  classSizes (WeightedLabeledData< InputType, LabelType > const &dataset) 
Returns the number of members of each class in the dataset. More...  
template<class InputType >  
double  sumOfWeights (WeightedUnlabeledData< InputType > const &dataset) 
Returns the total sum of weights. More...  
template<class InputType , class LabelType >  
double  sumOfWeights (WeightedLabeledData< InputType, LabelType > const &dataset) 
Returns the total sum of weights. More...  
template<class InputType >  
RealVector  classWeight (WeightedLabeledData< InputType, unsigned int > const &dataset) 
Computes the cumulative weight of every class. More...  
template<class InputRange , class LabelRange , class WeightRange >  
boost::disable_if< boost::is_arithmetic< WeightRange >, WeightedLabeledData< typename boost::range_value< InputRange >::type, typename boost::range_value< LabelRange >::type >>::type  createLabeledDataFromRange (InputRange const &inputs, LabelRange const &labels, WeightRange const &weights, std::size_t batchSize=0) 
creates a weighted unweighted data object from two ranges, representing data and weights More...  
template<class InputType , class LabelType >  
WeightedLabeledData< InputType, LabelType >  bootstrap (LabeledData< InputType, LabelType > const &dataset, std::size_t bootStrapSize=0) 
Creates a bootstrap partition of a labeled dataset and returns it using weighting. More...  
template<class InputType >  
WeightedUnlabeledData< InputType >  bootstrap (UnlabeledData< InputType > const &dataset, std::size_t bootStrapSize=0) 
Creates a bootstrap partition of an unlabeled dataset and returns it using weighting. More...  
SHARK_VECTOR_MATRIX_TYPEDEFS (long double, BigReal)  
SHARK_VECTOR_MATRIX_TYPEDEFS (bool, Bool)  
template<class VectorType >  
ConcatenatedModel< VectorType >  operator>> (AbstractModel< VectorType, VectorType, VectorType > &firstModel, AbstractModel< VectorType, VectorType, VectorType > &secondModel) 
Connects two AbstractModels so that the output of the first model is the input of the second. More...  
template<class VectorType >  
ConcatenatedModel< VectorType >  operator>> (ConcatenatedModel< VectorType > const &firstModel, AbstractModel< VectorType, VectorType, VectorType > &secondModel) 
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 , class M , class Device >  
void  calculateRegularizedKernelMatrix (AbstractKernelFunction< InputType >const &kernel, Data< InputType > const &dataset, blas::matrix_expression< M, Device > &matrix, double regularizer=0) 
Calculates the regularized kernel gram matrix of the points stored inside a dataset. More...  
template<class InputType , class M , class Device >  
void  calculateMixedKernelMatrix (AbstractKernelFunction< InputType >const &kernel, Data< InputType > const &dataset1, Data< InputType > const &dataset2, blas::matrix_expression< M, Device > &matrix) 
Calculates the kernel gram matrix between two data sets. More...  
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 >  
RealMatrix  calculateMixedKernelMatrix (AbstractKernelFunction< InputType >const &kernel, Data< InputType > const &dataset1, Data< InputType > const &dataset2) 
Calculates the kernel gram matrix between two data sets. 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...  
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 loglikelihood 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 loglikelihood of a set of input vectors under the models distribution. More...  
double  estimateLogFreeEnergyFromEnergySamples (RealMatrix const &energyDiffUp, RealMatrix const &energyDiffDown, PartitionEstimationAlgorithm algorithm=AIS) 
template<class RBMType >  
double  estimateLogFreeEnergy (RBMType &rbm, Data< RealVector > const &initDataset, RealVector const &beta, std::size_t samples, PartitionEstimationAlgorithm algorithm=AcceptanceRatioMean, float burnInPercentage=0.1) 
template<class RBMType >  
double  annealedImportanceSampling (RBMType &rbm, RealVector const &beta, std::size_t samples) 
template<class RBMType >  
double  estimateLogFreeEnergy (RBMType &rbm, Data< RealVector > const &initDataset, std::size_t chains, std::size_t samples, PartitionEstimationAlgorithm algorithm=AIS, float burnInPercentage=0.1) 
template<class RBMType >  
double  annealedImportanceSampling (RBMType &rbm, std::size_t chains, std::size_t samples) 
template<typename Stream >  
FrontType  read_front (Stream &in, std::size_t noObjectives, const std::string &separator=" ", std::size_t headerLines=0) 
template<class I , class L >  
CVFolds< LabeledData< I, L > >  createCVIID (LabeledData< I, L > &set, std::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, std::size_t numberOfPartitions, std::size_t batchSize=Data< I >::DefaultBatchSize, RecreationIndices *cv_indices=NULL) 
Create a partition for cross validation. More...  
template<class I , class L >  
CVFolds< LabeledData< I, L > >  createCVBatch (LabeledData< I, L > const &set, std::size_t numberOfPartitions) 
Create a partition for cross validation without changing the dataset. More...  
template<class I , class L >  
CVFolds< LabeledData< I, L > >  createCVIndexed (LabeledData< I, L > &set, std::size_t numberOfPartitions, std::vector< std::size_t > indices, std::size_t batchSize=Data< I >::DefaultBatchSize) 
Create a partition for cross validation from indices. More...  
template<class I , class L >  
CVFolds< LabeledData< I, L > >  createCVFullyIndexed (LabeledData< I, L > &set, std::size_t numberOfPartitions, RecreationIndices indices, std::size_t batchSize=Data< I >::DefaultBatchSize) 
Create a partition for cross validation from indices for both ordering and partitioning. More...  
template<class T >  
std::ostream &  operator<< (std::ostream &stream, const Data< T > &d) 
Outstream of elements. More...  
SHARK_EXPORT_SYMBOL std::pair< std::string, std::string >  splitUrl (std::string const &url) 
Split a URL into its domain and resource parts. More...  
SHARK_EXPORT_SYMBOL std::string  download (std::string const &url, unsigned short port=80) 
Download a document with the HTTP protocol. More...  
template<class InputType , class LabelType >  
void  downloadSparseData (LabeledData< InputType, LabelType > &dataset, std::string const &url, unsigned short port=80, unsigned int highestIndex=0, std::size_t batchSize=LabeledData< RealVector, unsigned int >::DefaultBatchSize) 
Download and import a sparse data (libSVM) file. More...  
template<class InputType , class LabelType >  
void  downloadFromMLData (LabeledData< InputType, LabelType > &dataset, std::string const &name, std::size_t batchSize=LabeledData< RealVector, unsigned int >::DefaultBatchSize) 
Download a data set from mldata.org. More...  
template<class InputType >  
void  downloadCsvData (LabeledData< InputType, unsigned int > &dataset, std::string const &url, LabelPosition lp, char separator=',', char comment='#', unsigned short port=80, std::size_t maximumBatchSize=LabeledData< RealVector, RealVector >::DefaultBatchSize) 
Download and import a dense data (CSV) file for classification. More...  
template<class InputType >  
void  downloadCsvData (LabeledData< InputType, RealVector > &dataset, std::string const &url, LabelPosition lp, std::size_t numberOfOutputs=1, char separator=',', char comment='#', unsigned short port=80, std::size_t maximumBatchSize=LabeledData< RealVector, RealVector >::DefaultBatchSize) 
Download and import a dense data (CSV) file for regression. More...  
void  import_libsvm (LabeledData< RealVector, unsigned int > &dataset, std::istream &stream, unsigned int highestIndex=0, std::size_t batchSize=LabeledData< RealVector, unsigned int >::DefaultBatchSize) 
Import data from a LIBSVM file. More...  
void  import_libsvm (LabeledData< CompressedRealVector, unsigned int > &dataset, std::istream &stream, unsigned int highestIndex=0, std::size_t batchSize=LabeledData< RealVector, unsigned int >::DefaultBatchSize) 
Import data from a LIBSVM file. More...  
void  import_libsvm (LabeledData< RealVector, unsigned int > &dataset, std::string fn, unsigned int highestIndex=0, std::size_t batchSize=LabeledData< RealVector, unsigned int >::DefaultBatchSize) 
Import data from a LIBSVM file. More...  
void  import_libsvm (LabeledData< CompressedRealVector, unsigned int > &dataset, std::string fn, unsigned int highestIndex=0, std::size_t batchSize=LabeledData< RealVector, unsigned int >::DefaultBatchSize) 
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, bool append=false) 
Export data to LIBSVM format. More...  
SHARK_EXPORT_SYMBOL void  importSparseData (LabeledData< RealVector, unsigned int > &dataset, std::istream &stream, unsigned int highestIndex=0, std::size_t batchSize=LabeledData< RealVector, unsigned int >::DefaultBatchSize) 
Import classification data from a sparse data (libSVM) file. More...  
SHARK_EXPORT_SYMBOL void  importSparseData (LabeledData< RealVector, RealVector > &dataset, std::istream &stream, unsigned int highestIndex=0, std::size_t batchSize=LabeledData< RealVector, RealVector >::DefaultBatchSize) 
Import regression data from a sparse data (libSVM) file. More...  
SHARK_EXPORT_SYMBOL void  importSparseData (LabeledData< CompressedRealVector, unsigned int > &dataset, std::istream &stream, unsigned int highestIndex=0, std::size_t batchSize=LabeledData< RealVector, unsigned int >::DefaultBatchSize) 
Import classification data from a sparse data (libSVM) file. More...  
SHARK_EXPORT_SYMBOL void  importSparseData (LabeledData< CompressedRealVector, RealVector > &dataset, std::istream &stream, unsigned int highestIndex=0, std::size_t batchSize=LabeledData< RealVector, RealVector >::DefaultBatchSize) 
Import regression data from a sparse data (libSVM) file. More...  
SHARK_EXPORT_SYMBOL void  importSparseData (LabeledData< RealVector, unsigned int > &dataset, std::string fn, unsigned int highestIndex=0, std::size_t batchSize=LabeledData< RealVector, unsigned int >::DefaultBatchSize) 
Import classification data from a sparse data (libSVM) file. More...  
SHARK_EXPORT_SYMBOL void  importSparseData (LabeledData< RealVector, RealVector > &dataset, std::string fn, unsigned int highestIndex=0, std::size_t batchSize=LabeledData< RealVector, RealVector >::DefaultBatchSize) 
Import regression data from a sparse data (libSVM) file. More...  
SHARK_EXPORT_SYMBOL void  importSparseData (LabeledData< CompressedRealVector, unsigned int > &dataset, std::string fn, unsigned int highestIndex=0, std::size_t batchSize=LabeledData< RealVector, unsigned int >::DefaultBatchSize) 
Import classification data from a sparse data (libSVM) file. More...  
SHARK_EXPORT_SYMBOL void  importSparseData (LabeledData< CompressedRealVector, RealVector > &dataset, std::string fn, unsigned int highestIndex=0, std::size_t batchSize=LabeledData< RealVector, RealVector >::DefaultBatchSize) 
Import regression data from a sparse data (libSVM) file. More...  
template<typename InputType >  
void  exportSparseData (LabeledData< InputType, unsigned int > const &dataset, std::ostream &stream, bool oneMinusOne=true, bool sortLabels=false) 
Export classification data to sparse data (libSVM) format. More...  
template<typename InputType >  
void  exportSparseData (LabeledData< InputType, unsigned int > const &dataset, const std::string &fn, bool oneMinusOne=true, bool sortLabels=false, bool append=false) 
Export classification data to sparse data (libSVM) format. More...  
template<typename InputType >  
void  exportSparseData (LabeledData< InputType, RealVector > const &dataset, std::ostream &stream) 
Export regression data to sparse data (libSVM) format. More...  
template<typename InputType >  
void  exportSparseData (LabeledData< InputType, RealVector > const &dataset, const std::string &fn, bool append=false) 
Export regression data to sparse data (libSVM) format. 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 lower, double upper) 
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...  
SHARK_EXPORT_SYMBOL void  csvStringToData (Data< FloatVector > &data, std::string const &contents, char separator=',', char comment='#', std::size_t maximumBatchSize=Data< RealVector >::DefaultBatchSize) 
Import unlabeled vectors from a readin characterseparated value file. More...  
SHARK_EXPORT_SYMBOL void  csvStringToData (Data< RealVector > &data, std::string const &contents, char separator=',', char comment='#', std::size_t maximumBatchSize=Data< RealVector >::DefaultBatchSize) 
Import unlabeled vectors from a readin characterseparated value file. More...  
SHARK_EXPORT_SYMBOL void  csvStringToData (Data< unsigned int > &data, std::string const &contents, char separator=',', char comment='#', std::size_t maximumBatchSize=Data< unsigned int >::DefaultBatchSize) 
Import "csv" from string consisting only of a single unsigned int per row. More...  
SHARK_EXPORT_SYMBOL void  csvStringToData (Data< int > &data, std::string const &contents, char separator=',', char comment='#', std::size_t maximumBatchSize=Data< int >::DefaultBatchSize) 
Import "csv" from string consisting only of a single int per row. More...  
SHARK_EXPORT_SYMBOL void  csvStringToData (Data< float > &data, std::string const &contents, char separator=',', char comment='#', std::size_t maximumBatchSize=Data< double >::DefaultBatchSize) 
Import "csv" from string consisting only of a single double per row. More...  
SHARK_EXPORT_SYMBOL void  csvStringToData (Data< double > &data, std::string const &contents, char separator=',', char comment='#', std::size_t maximumBatchSize=Data< double >::DefaultBatchSize) 
Import "csv" from string consisting only of a single double per row. More...  
SHARK_EXPORT_SYMBOL void  csvStringToData (LabeledData< RealVector, unsigned int > &dataset, std::string const &contents, LabelPosition lp, char separator=',', char comment='#', std::size_t maximumBatchSize=LabeledData< RealVector, unsigned int >::DefaultBatchSize) 
Import labeled data from a characterseparated value file. More...  
SHARK_EXPORT_SYMBOL void  csvStringToData (LabeledData< FloatVector, unsigned int > &dataset, std::string const &contents, LabelPosition lp, char separator=',', char comment='#', std::size_t maximumBatchSize=LabeledData< RealVector, unsigned int >::DefaultBatchSize) 
Import labeled data from a characterseparated value file. More...  
SHARK_EXPORT_SYMBOL void  csvStringToData (LabeledData< RealVector, RealVector > &dataset, std::string const &contents, LabelPosition lp, std::size_t numberOfOutputs=1, char separator=',', char comment='#', std::size_t maximumBatchSize=LabeledData< RealVector, RealVector >::DefaultBatchSize) 
Import regression data from a readin characterseparated value file. More...  
SHARK_EXPORT_SYMBOL void  csvStringToData (LabeledData< FloatVector, FloatVector > &dataset, std::string const &contents, LabelPosition lp, std::size_t numberOfOutputs=1, char separator=',', char comment='#', std::size_t maximumBatchSize=LabeledData< RealVector, RealVector >::DefaultBatchSize) 
Import regression data from a readin characterseparated value file. More...  
template<class T >  
void  importCSV (Data< T > &data, std::string fn, char separator=',', char comment='#', std::size_t maximumBatchSize=Data< T >::DefaultBatchSize, std::size_t titleLines=0) 
Import a Dataset from a csv file. More...  
template<class T >  
void  importCSV (LabeledData< blas::vector< T >, unsigned int > &data, std::string fn, LabelPosition lp, char separator=',', char comment='#', std::size_t maximumBatchSize=LabeledData< RealVector, unsigned int >::DefaultBatchSize) 
Import a labeled Dataset from a csv file. More...  
template<class T >  
void  importCSV (LabeledData< blas::vector< T >, blas::vector< T > > &data, std::string fn, LabelPosition lp, std::size_t numberOfOutputs=1, char separator=',', char comment='#', std::size_t maximumBatchSize=LabeledData< RealVector, RealVector >::DefaultBatchSize) 
Import a labeled Dataset from a csv file. More...  
template<typename Type >  
void  exportCSV (Data< Type > const &set, std::string fn, char separator=',', bool sci=true, unsigned int width=0) 
Format unlabeled data into a characterseparated value file. More...  
template<typename InputType , typename LabelType >  
void  exportCSV (LabeledData< InputType, LabelType > const &dataset, std::string fn, LabelPosition lp, char separator=',', bool sci=true, unsigned int width=0) 
Format labeled data into a characterseparated value file. 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  exportKernelMatrix (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  exportKernelMatrix (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...  
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) 
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) 
AbstractMultiObjectiveOptimizer.
Implements Block Gibbs Sampling.
Implements the Shifter benchmark problem.
Loads the MNIST benchmark problem.
Implements the DistantModes/ArtificialModes benchmark problem.
Implements the Bars & Stripes benchmark problem.
Implements the bipolar (1,1) state neuron layer.
Typedefs for the Bipolar RBM.
Typedefs for the BinaryBinary RBM.
Calculate statistics given a range of values.
ROC.
Implements a multivariate normal distribution with zero mean.
Implements a multinomial distribution.
Variationalautoencoder error function.
Maximumlikelihood model selection for binary support vector machines.
Regularizer.
Radius Margin Quotient for SVM model selection.
Negative Log Likelihood error function.
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 Tukey's Biweightloss function for robust regression.
Implements the Squared Error Loss function for regression.
Implements the Squared Hinge Loss function for maximum margin classification.
Implements the squard Hinge Loss function for maximum margin regression.
Implements the Huber loss function for robust regression.
Implements the Hinge Loss function for maximum margin classification.
Implements the Hinge Loss function for maximum margin regression.
Flexible error measure for classication tasks.
Error measure for classification 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
Leaveoneout error for CSVMs.
Leaveoneout error.
Kernel Target Alignment  a measure of alignment of a kernel Gram matrix with labels.
Archive of evaluated points as an objective function wrapper.
error function for supervised learning
crossvalidation error for selection of hyperparameters
Multiobjective optimization benchmark function ZDT6.
Multiobjective optimization benchmark function ZDT4.
Multiobjective optimization benchmark function ZDT3.
Multiobjective optimization benchmark function ZDT2.
Multiobjective optimization benchmark function ZDT1.
Convex benchmark function.
Implements a wrapper over an m_objective function which just rotates its inputs.
Generalized Rosenbrock benchmark function.
Pole balancing simulation for double pole.
Pole balancing simulation for double poles.
Objective function for single and double poles with partial state information (nonMarkovian task)
Objective function for single and double poles with full state information (Markovian task)
Multiobjective optimization benchmark function LZ9.
Multiobjective optimization benchmark function LZ8.
Multiobjective optimization benchmark function LZ7.
Multiobjective optimization benchmark function LZ6.
Multiobjective optimization benchmark function LZ5.
Multiobjective optimization benchmark function LZ4.
Multiobjective optimization benchmark function LZ3.
Multiobjective optimization benchmark function LZ2.
Multiobjective optimization benchmark function LZ1.
Multiobjective optimization benchmark function IHR 6.
Multiobjective optimization benchmark function IHR 4.
Multiobjective optimization benchmark function IHR 3.
Multiobjective optimization benchmark function IHR 2.
Multiobjective optimization benchmark function IHR 1.
Twodimensional, realvalued Himmelblau function.
GSP benchmark function for multiobjective optimization.
Biobjective realvalued benchmark function proposed by Fonseca and Flemming.
Multiobjective optimization benchmark function ELLI 2.
Multiobjective optimization benchmark function ELLI 1.
Objective function DTLZ7.
Objective function DTLZ6.
Objective function DTLZ5.
Objective function DTLZ4.
Objective function DTLZ3.
Objective function DTLZ2.
Objective function DTLZ1.
Multiobjective optimization benchmark function CIGTAB 2.
Multiobjective 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 kernelinduced feature spaces.
Tree for nearest neighbor search in low dimensions.
Cart Classifier.
Binary spacepartitioning tree of data points.
Implements a radial basis function layer.
Oneversusone Classifier.
Model for scaling and translation of data vectors.
NEarest neighbor model for classification and regression.
Implements the Mean Model that can be used for ensemble classifiers.
Implements a Model using a linear function.
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.
Applies a kernel to two pointsets and comptues the average response.
Normalization of a kernel function.
Special kernel classes for multitask 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.
Radial Gaussian kernel.
Do special kernel evaluation by skipping missing features.
Kernel on a finite, discrete space.
Derivative of a CSVM hypothesis w.r.t. its hyperparameters.
Gaussian automatic relevance detection (ARD) kernel.
abstract super class of all metrics
abstract super class of all kernel functions
Implements a model applying a convolution to an image.
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.
Model for conversion of real valued output to class labels.
base class for all models, as well as a specialized differentiable model
Some operations for creating rotation matrices.
Kernel Gram matrix with modified diagonal.
Precomputed version of a matrix for quadratic programming.
Partly Precomputed version of a matrix for quadratic programming.
Modified Kernel Gram matrix.
Cache implementing an LeastRecentlyUsed Strategy.
Kernel Gram matrix.
Efficient special case if the kernel is gaussian and the inputs are sparse vectors.
Kernel matrix which supports kernel evaluations on data with missing features.
Kernel matrix for SVM ranking.
Efficient quadratic matrix cache.
Kernel matrix for SVM regression.
Entry Point for all Basic Linear Algebra(BLAS) in shark.
Weighted data sets for (un)supervised learning.
some functions for vector valued statistics like mean, variance and covariance
Support for importing and exporting data from and to sparse data (libSVM) formatted data files.
Importing and exporting PGM images.
Deprecated import_libsvm and export_libsvm functions.
This will relabel a given dataset to have labels 0..N1 (and vice versa)
Support for importing data from HDF5 file.
export precomputed kernel matrices (using libsvm format)
Support for downloading data sets from online sources.
Fast lookup for elements in constant datasets.
Data for (un)supervised learning.
Learning problems given by analytic distributions.
Tools for crossvalidation.
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.
A scoped_ptr like container for C type handles.
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.
Timer abstraction with microsecond resolution.
Class which externalizes the state of an Object.
Class Describing the Shape of an Input.
Result sets for algorithms.
Shark Random number generation.
Very basic math abstraction layer.
ISerializable interface.
IParameterizable interface.
INameable interface.
Flexible and extensible mechanisms for holding flags.
Random Forest Trainer.
Trainer for a Regularization Network or a Gaussian Process.
Support Vector Machine Trainer for the rankingSVM.
Principal Component Analysis.
Model training by means of a general purpose optimization procedure.
Trainer for OneClass 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, unit variance and zero covariance while keping the original coordinate system.
Data normalization to zero mean, unit variance and zero covariance.
Data normalization to zero mean and unit variance.
Data normalization to the unit interval.
Trainer for binary SVMs natively supporting missing features.
Logistic Regression.
Generic Stochastic Average Gradient Descent training for linear models.
Linear Regression.
LDA.
LASSO Regression.
Generic stochastic gradient descent training for kernelbased models.
Trainer for the EpsilonSupport Vector Machine for Regression.
Remove budget maintenance strategy.
Project budget maintenance strategy.
Merge budget maintenance strategy.
Budgeted stochastic gradient descent training for kernelbased models.
Abstract Budget maintenance strategy.
Abstract Trainer Interface for trainers that support weighting.
Abstract Trainer Interface.
Abstract Support Vector Machine Trainer, general and linear case.
Stopping Criterion which evaluates the validation error and hands the result over to another stopping criterion.
Stopping Criterion which stops, when the training error seems to converge.
Stopping Criterion which stops, when the trainign error seems to converge.
Stopping Criterion which stops after a fixed number of iterations.
Stopping criterion monitoring the quotient of generalization loss and training progress.
Stopping Criterion which stops, when the generalization of the solution gets worse.
Defines a base class for stopping criteria of optimization algorithms.
Quadratic programming for Support Vector Machines.
Special container for certain coefficients describing multiclass SVMs.
General and specialized quadratic program classes and a generic solver.
Quadratic programming problem for multiclass SVMs.
Quadratic programming solvers for linear multiclass SVM training without bias.
Quadratic programming m_problem for multiclass SVMs.
Quadratic programming solver linear SVM training without bias.
Quadratic program definitions.
Shrinking strategy based on box constraints.
Pegasos solvers for linear SVMs.
Efficient Nearest neighbor queries.
Efficient brute force implementation of nearest neighbors.
Interface for nearest Neighbor queries.
Jaakkola's heuristic and related quantities for Gaussian kernel selection.
TrustRegion NewtonStep Method.
implements different versions of Resilient Backpropagation of error.
CG.
BFGS.
Adam.
Base class for Line Search Optimizer.
Implements the VDCMAES Algorithm.
SteadyStateMOCMA.h.
Implements the SMSEMOA.
NelderMead Simplex Downhill Method.
Implements the RVEA algorithm.
RealCodedNSGAIIII.h.
IndicatorBasedRealCodedNSGAII.h.
Implements a two point step size adaptation rule based on a linesearch type of approach.
RouletteWheelSelection using uniform selection probability assignment.
Implements tournament selection.
Implements fitness proportional selection.
Implements the reference vector selection for RVEA.
RouletteWheelSelection based on fitnessrankbased selection probability assignment.
Indicatorbased selection strategy for multiobjective selection.
EPTournament selection operator.
Elitist Selection Operator suitable for (mu,lambda) and (mu+lambda) selection.
Implements the Tchebycheff scalarizer.
Reference vector adaptation for the RVEA algorithm.
Uniform crossover of arbitrary individuals.
Simulated binary crossover operator.
Implements onepoint crossover operator.
Implements the tep size adaptation based on the success of the new population compared to the old.
Polynomial mutation operator.
Bit flip mutation operator.
Various functions for generating ndimensional grids (simplexlattices).
Algorithm selecting front based on their crowding distance.
Calculates the hypervolume covered by a front of nondominated points.
Algorithm selecting points based on their crowding distance.
Calculates the additive approximation quality of a Paretofront approximation.
Approximately determines the individual contributing the least hypervolume.
Implements the frontend for the HypervolumeContribution algorithms, including the approximations.
Implementation of the exact hypervolume calculation in m dimensions.
Implementation of the exact hypervolume calculation in 3 dimensions.
Implementation of the exact hypervolume calculation in 2 dimensions.
Implements the frontend for the HypervolumeCalculator algorithms, including the approximations.
Determine the volume of the union of objects by an FPRAS.
Implementation of the ParetoDominance relation.
Swapper method for nondominated sorting.
Implements the fast nondominated sort algorithm.
Implements a divideandconquer nondominated sorting algorithm.
Implements the MOEA/D algorithm.
Implements the generational Multiobjective Covariance Matrix Adapation ES.
Implements the most recent version of the elitist CMAES.
Implements the Cross Entropy Algorithm.
Implements the CMSA.
Implements the most recent version of the nonelitist CMAES.
TypedIndividual.
CMAChromosomeof the CMAES.
The kmeans clustering algorithm.
AbstractSingleObjectiveOptimizer.
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Hansen, N. The CMA Evolution Startegy: A Tutorial, June 28, 2011 and the eqation numbers refer to this publication (retrieved April 2014).
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The algorithm is described in
H. G. Beyer, B. Sendhoff (2008). Covariance Matrix Adaptation Revisited: The CMSA Evolution Strategy In Proceedings of the Tenth International Conference on Parallel Problem Solving from Nature (PPSN X), pp. 123132, LNCS, SpringerVerlag
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Christophe Thiery, Bruno Scherrer. Improvements on Learning Tetris with Cross Entropy. International Computer Games Association Journal, ICGA, 2009, 32. <inria00418930>
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The algorithm is based on
C. Igel, T. Suttorp, and N. Hansen. A Computational Efficient Covariance Matrix Update and a (1+1)CMA for Evolution Strategies. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006), pp. 453460, ACM Press, 2006
D. V. Arnold and N. Hansen: Active covariance matrix adaptation for the (1+1)CMAES. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2010): pp 385392, ACM Press 2010
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This file is part of Shark. http://sharkml.org/
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This file is part of Shark. http://sharkml.org/
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This file is part of Shark. http://sharkml.org/
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This file is part of Shark. http://sharkml.org/
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This file is part of Shark. http://sharkml.org/
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Shark 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 Lesser General Public License for more details.
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This file is part of Shark. http://sharkml.org/
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Shark 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 Lesser General Public License for more details.
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This file is part of Shark. http://sharkml.org/
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Shark 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 Lesser General Public License for more details.
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This file is part of Shark. http://sharkml.org/
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This file is part of Shark. http://sharkml.org/
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This file is part of Shark. http://sharkml.org/
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Shark 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 Lesser General Public License for more details.
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This file is part of Shark. http://sharkml.org/
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This file is part of Shark. http://sharkml.org/
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This file is part of Shark. http://sharkml.org/
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This file is part of Shark. http://sharkml.org/
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K. Miettinen, "Nonlinear Multiobjective Optimization", International Series in Operations Research and Management Science (12) DOI: 10.1007/9781461555636
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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. 101111, Lawrence Erlbaum Associates, 1985
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See http://en.wikipedia.org/wiki/Fitness_proportionate_selection
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See http://en.wikipedia.org/wiki/Tournament_selection
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See Nicola Beume, Boris Naujoks, and Michael Emmerich. SMSEMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research, 181(3):16531669, 2007.
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You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.Implements the VDCMAES Algorithm
The VDCMAES implements a restricted form of the CMAES where the covariance matrix is restriced to be (D+vv^T) where D is a diagonal matrix and v a single vector. Therefore this variant is capable of largescale optimisation
For more reference, see the paper Akimoto, Y., A. Auger, and N. Hansen (2014). ComparisonBased Natural Gradient Optimization in High Dimension. To appear in Genetic and Evolutionary Computation Conference (GECCO 2014), Proceedings, ACM
The implementation differs from the paper to be closer to the reference implementation and to have better numerical accuracy.
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This file is part of Shark. http://sharkml.org/
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The Broyden, Fletcher, Goldfarb, Shannon (BFGS) algorithm is a quasiNewton method for unconstrained realvalued optimization.
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Conjugategradient method for unconstraint optimization.
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The LimitedMemory Broyden, Fletcher, Goldfarb, Shannon (BFGS) algorithm is a quasiNewton method for unconstrained realvalued optimization. See: http://en.wikipedia.org/wiki/LBFGS for details.
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This file is part of Shark. http://sharkml.org/
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This file is part of Shark. http://sharkml.org/
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Shark 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 Lesser General Public License for more details.
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This file is part of Shark. http://sharkml.org/
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This file is part of Shark. http://sharkml.org/
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This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
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This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
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This file is part of Shark. http://sharkml.org/
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This file is part of Shark. http://sharkml.org/
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This file is part of Shark. http://sharkml.org/
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Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. 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. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/. Shark linear algebra definitions
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
The function is described in
Christian Igel, Nikolaus Hansen, and Stefan Roth. Covariance Matrix Adaptation for Multiobjective Optimization. Evolutionary Computation 15(1), pp. 128, 2007
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
Multimodal benchmark function.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. 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 NSGAII, IEEE Trans on Evolutionary Computation, 2(12):284302, April 2009.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
Class for balancing one or two poles on a cart using a fitness function that decreases the longer the pole(s) balance(s). Based on code written by Verena HeidrichMeisner for the paper
V. HeidrichMeisner and C. Igel. Neuroevolution strategies for episodic reinforcement learning. Journal of Algorithms, 64(4):152â€“168, 2009.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
Class for simulating two poles balancing on a cart. Based on code written by Verena HeidrichMeisner for the paper
V. HeidrichMeisner and C. Igel. Neuroevolution strategies for episodic reinforcement learning. Journal of Algorithms, 64(4):152â€“168, 2009.
which was in turn based on code available at http://webdocs.cs.ualberta.ca/~sutton/book/code/pole.c as of 2015/4/19, written by Rich Sutton and Chuck Anderson and later modified. Faustino Gomez wrote the physics code using the differential equations from Alexis Weiland's paper and added the RungeKutta solver.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
Class for simulating a single pole balancing on a cart. Based on code written by Verena HeidrichMeisner for the paper
V. HeidrichMeisner and C. Igel. Neuroevolution strategies for episodic reinforcement learning. Journal of Algorithms, 64(4):152â€“168, 2009.
which was in turn based on code available at http://webdocs.cs.ualberta.ca/~sutton/book/code/pole.c as of 2015/4/19, written by Rich Sutton and Chuck Anderson and later modified. Faustino Gomez wrote the physics code using the differential equations from Alexis Weiland's paper and added the RungeKutta solver.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This nonconvex benchmark function for realvalued 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: 175184, 1960
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. 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):173195, 2000
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
This file is part of Shark. http://sharkml.org/
Shark is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Shark 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 Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with Shark. If not, see http://www.gnu.org/licenses/.
Definition at line 53 of file BinaryRBM.h.
Definition at line 49 of file BinaryRBM.h.
Definition at line 48 of file BinaryRBM.h.
Definition at line 54 of file BinaryRBM.h.
Definition at line 52 of file BinaryRBM.h.
Definition at line 50 of file BinaryRBM.h.
typedef RBM<BinaryLayer,BinaryLayer, random::rng_type> shark::BinaryRBM 
Definition at line 47 of file BinaryRBM.h.
typedef TwoStateSpace<0,1> shark::BinarySpace 
Definition at line 82 of file TwoStateSpace.h.
Definition at line 53 of file BipolarRBM.h.
Definition at line 49 of file BipolarRBM.h.
Definition at line 48 of file BipolarRBM.h.
Definition at line 54 of file BipolarRBM.h.
Definition at line 52 of file BipolarRBM.h.
Definition at line 50 of file BipolarRBM.h.
typedef RBM<BipolarLayer,BipolarLayer, random::rng_type> shark::BipolarRBM 
Definition at line 47 of file BipolarRBM.h.
typedef LabeledData<RealVector, unsigned int> shark::ClassificationDataset 
typedef ARDKernelUnconstrained<CompressedRealVector> shark::CompressedARDKernel 
Definition at line 263 of file ArdKernel.h.
typedef LabeledData<CompressedRealVector, unsigned int> shark::CompressedClassificationDataset 
typedef LinearKernel<CompressedRealVector> shark::CompressedLinearKernel 
Definition at line 133 of file LinearKernel.h.
typedef ModelKernel<CompressedRealVector> shark::CompressedModelKernel 
Definition at line 275 of file ModelKernel.h.
typedef MonomialKernel<CompressedRealVector> shark::CompressedMonomialKernel 
Definition at line 193 of file MonomialKernel.h.
typedef NormalizedKernel<CompressedRealVector> shark::CompressedNormalizedKernel 
Definition at line 277 of file NormalizedKernel.h.
typedef PolynomialKernel<CompressedRealVector> shark::CompressedPolynomialKernel 
Definition at line 303 of file PolynomialKernel.h.
typedef GaussianRbfKernel<CompressedRealVector> shark::CompressedRbfKernel 
Definition at line 279 of file GaussianRbfKernel.h.
typedef ScaledKernel<CompressedRealVector> shark::CompressedScaledKernel 
Definition at line 160 of file ScaledKernel.h.
typedef WeightedSumKernel<CompressedRealVector> shark::CompressedWeightedSumKernel 
Definition at line 399 of file WeightedSumKernel.h.
typedef SubrangeKernel<CompressedRealVector> shark::CompressesSubrangeKernel 
Definition at line 208 of file SubrangeKernel.h.
Definition at line 293 of file RealCodedNSGAII.h.
Definition at line 262 of file ArdKernel.h.
typedef LinearKernel shark::DenseLinearKernel 
Definition at line 132 of file LinearKernel.h.
typedef ModelKernel<RealVector> shark::DenseModelKernel 
Definition at line 274 of file ModelKernel.h.
Definition at line 192 of file MonomialKernel.h.
Definition at line 276 of file NormalizedKernel.h.
Definition at line 302 of file PolynomialKernel.h.
Definition at line 278 of file GaussianRbfKernel.h.
typedef ScaledKernel shark::DenseScaledKernel 
Definition at line 159 of file ScaledKernel.h.
typedef SubrangeKernel<RealVector> shark::DenseSubrangeKernel 
Definition at line 207 of file SubrangeKernel.h.
Definition at line 398 of file WeightedSumKernel.h.
Definition at line 286 of file SteadyStateMOCMA.h.
Definition at line 292 of file RealCodedNSGAII.h.
typedef std::vector< shark::RealVector > shark::FrontType 
Definition at line 14 of file AdditiveEpsilonIndicatorMain.cpp.
Definition at line 54 of file GaussianBinaryRBM.h.
Definition at line 50 of file GaussianBinaryRBM.h.
Definition at line 49 of file GaussianBinaryRBM.h.
Definition at line 55 of file GaussianBinaryRBM.h.
Definition at line 53 of file GaussianBinaryRBM.h.
Definition at line 51 of file GaussianBinaryRBM.h.
typedef RBM<GaussianLayer,BinaryLayer, random::rng_type> shark::GaussianBinaryRBM 
Definition at line 48 of file GaussianBinaryRBM.h.
typedef boost::archive::polymorphic_iarchive shark::InArchive 
Type of an archive to read from.
Definition at line 74 of file ISerializable.h.
typedef IndicatorBasedMOCMA< HypervolumeIndicator > shark::MOCMA 
typedef AbstractObjectiveFunction< RealVector, RealVector > shark::MultiObjectiveFunction 
Definition at line 318 of file AbstractObjectiveFunction.h.
typedef boost::archive::polymorphic_oarchive shark::OutArchive 
Type of an archive to write to.
Definition at line 80 of file ISerializable.h.
typedef blas::permutation_matrix shark::PermutationMatrix 
typedef std::pair<double, RealVector> shark::Preference 
A preferred region in a latticesampled unit sphere.
A preferred region is a pair with a radius and a vector. The intersection of the vector and the unit sphere denotes the center of the preferred region, and the radius denotes the radius of a sphere constructed such that the center point (intersection of vector and unit sphere) is on its periphery. Points are then sampled on this smaller sphere and projected up on the unit sphere, thus restricting the covered segment/area/volume/hypervolume of the unit sphere. See Figure 1 in "Evolutionary Manyobjective Optimization of Hybrid Electric Vehicle Control: From General Optimization to Preference Articulation"
Definition at line 291 of file RealCodedNSGAII.h.
typedef std::pair< std::vector<std::size_t> , std::vector<std::size_t> > shark::RecreationIndices 
auxiliary typedef for createCVSameSizeBalanced and createCVFullyIndexed, stores location index in the first and partition index in the second
Definition at line 127 of file CVDatasetTools.h.
typedef LabeledData<RealVector, RealVector> shark::RegressionDataset 
typedef RpropPlus shark::Rprop93 
typedef RpropMinus shark::Rprop94 
typedef IRpropPlus shark::Rprop99 
typedef IRpropMinus shark::Rprop99d 
typedef std::deque<RealVector> shark::Sequence 
typedef AbstractObjectiveFunction< RealVector, double > shark::SingleObjectiveFunction 
Definition at line 317 of file AbstractObjectiveFunction.h.
Definition at line 285 of file SteadyStateMOCMA.h.
typedef TwoStateSpace<1,1> shark::SymmetricBinarySpace 
Definition at line 83 of file TwoStateSpace.h.
typedef boost::archive::polymorphic_text_iarchive shark::TextInArchive 
Definition at line 75 of file ISerializable.h.
typedef boost::archive::polymorphic_text_oarchive shark::TextOutArchive 
Definition at line 81 of file ISerializable.h.
Definition at line 53 of file TruncExpBinaryRBM.h.
Definition at line 49 of file TruncExpBinaryRBM.h.
Definition at line 48 of file TruncExpBinaryRBM.h.
Definition at line 54 of file TruncExpBinaryRBM.h.
Definition at line 52 of file TruncExpBinaryRBM.h.
Definition at line 50 of file TruncExpBinaryRBM.h.
typedef RBM<TruncExpBinaryEnergy, random::rng_type> shark::TruncExpBinaryRBM 
Definition at line 47 of file TruncExpBinaryRBM.h.
enum shark::AlphaStatus 
Enumerator  

AlphaFree  
AlphaLowerBound  
AlphaUpperBound  
AlphaDeactivated 
Definition at line 391 of file QpSolver.h.
enum shark::BuildType 

strong 
Enumerator  

Valid  
ZeroPad 
Definition at line 42 of file ConvolutionalModel.h.
Result of comparing two objective vectors w.r.t. Pareto dominance.
Enumerator  

INCOMPARABLE  
LHS_DOMINATES_RHS  
RHS_DOMINATES_LHS  
EQUIVALENT 
Definition at line 42 of file ParetoDominance.h.

strong 
Enumerator  

WW  
CS  
LLW  
ATM  
ATS  
ADM  
OVA  
MMR  
ReinforcedSvm 
Definition at line 27 of file CSvmTrainer.h.
Enumerator  

AIS  
AISMean  
TwoSidedAISMean  
AcceptanceRatio  
AcceptanceRatioMean 
Definition at line 111 of file analytics.h.
enum shark::QpStopType 
reason for the quadratic programming solver to stop the iterative optimization process
Enumerator  

QpNone  
QpAccuracyReached  
QpMaxIterationsReached  
QpTimeout 
Definition at line 63 of file QuadraticProgram.h.
double shark::annealedImportanceSampling  (  RBMType &  rbm, 
RealVector const &  beta,  
std::size_t  samples  
) 
Definition at line 171 of file analytics.h.
Referenced by annealedImportanceSampling().
double shark::annealedImportanceSampling  (  RBMType &  rbm, 
std::size_t  chains,  
std::size_t  samples  
) 
Definition at line 196 of file analytics.h.
References annealedImportanceSampling(), and beta.
SHARK_EXPORT_SYMBOL KernelExpansion<RealVector> shark::approximateKernelExpansion  (  random::rng_type &  rng, 
KernelExpansion< RealVector > const &  model,  
std::size_t  k,  
double  precision = 1.e8 

) 
Approximates a kernel expansion by a smaller one using an optimized basis.
often, a kernel expansion can be represented much more compactly when the points defining the basis of the kernel expansion are not fixed. The resulting kernel expansion can be evaluated much quicker than the original when k is small compared to the number of the nonzero elements in the weight vector of the supplied kernel expansion.
Given a kernel expansion with weight matrix alpha and Basis B of size m, finds a new weight matrix beta and Basis Z with k vectors so that the difference of the resulting decision vectors is small in the RKHS defined by the supplied kernel.
The algorithm proceeds by first performing a kMeans clustering as a good initialization. This initial guess is then optimized by finding the closest weight vector to the original vector representable by the basis. Using this estimate, the basis can then be optimized.
The supplied kernel must be dereferentiable wrt its input parameters which excludes all kernels not defined on RealVector
The algorithms is O(k^3 + k m) in each iteration.
rng  the Rng used for the kMeans clustering 
model  the kernel expansion to approximate 
k  the number of basis vectors to be used by the approximation 
precision  target precision of the gradient to be reached during optimization 
auto shark::batchBegin  (  BatchT &  batch  )  > decltype(BatchTraits<BatchT>::type::begin(batch)) 
Definition at line 420 of file BatchInterface.h.
Referenced by transform().
auto shark::batchBegin  (  BatchT const &  batch  )  > decltype(BatchTraits<BatchT>::type::begin(batch)) 
Definition at line 425 of file BatchInterface.h.
auto shark::batchEnd  (  BatchT &  batch  )  > decltype(BatchTraits<BatchT>::type::end(batch)) 
Definition at line 430 of file BatchInterface.h.
Referenced by shark::ContrastiveDivergence< Operator >::evalDerivative(), and transform().
auto shark::batchEnd  (  BatchT const &  batch  )  > decltype(BatchTraits<BatchT>::type::end(batch)) 
Definition at line 435 of file BatchInterface.h.
std::size_t shark::batchSize  (  BatchT const &  batch  ) 
Definition at line 415 of file BatchInterface.h.
Referenced by calculateKernelMatrixParameterDerivative(), calculateMixedKernelMatrix(), shark::KHCTree< Container, CuttingAccuracy >::calculateNormal(), calculateRegularizedKernelMatrix(), createCVFullyIndexed(), createCVIID(), createCVIndexed(), createCVSameSize(), createCVSameSizeBalanced(), createLabeledDataFromRange(), shark::GibbsOperator< RBMType >::createSample(), createUnlabeledDataFromRange(), shark::DataView< shark::Data< LabelType > const >::DataView(), downloadFromMLData(), downloadSparseData(), shark::Energy< RBM >::energy(), shark::Energy< RBM >::energyFromHiddenInput(), shark::Energy< RBM >::energyFromVisibleInput(), shark::GaussianLayer::energyTerm(), shark::MissingFeaturesKernelExpansion< InputType >::eval(), shark::PointSetKernel< InputType >::eval(), shark::Classifier< KernelExpansion< InputType > >::eval(), shark::DiscreteKernel::eval(), shark::NormalizedKernel< InputType >::eval(), shark::ProductKernel< MultiTaskSample< InputTypeT > >::eval(), shark::OneVersusOneClassifier< InputType, VectorType >::eval(), shark::WeightedSumKernel< InputType >::eval(), shark::KernelExpansion< InputType >::eval(), shark::KernelTargetAlignment< InputType, LabelType >::evalDerivative(), shark::TruncatedExponentialLayer::expectedPhiValue(), exportSparseData(), shark::AbstractKernelFunction< MultiTaskSample< InputTypeT > >::featureDistanceSqr(), shark::ExactGradient< RBMType >::getLogPartition(), shark::SimpleNearestNeighbors< InputType, LabelType >::getNeighbors(), shark::TreeNearestNeighbors< InputType, LabelType >::getNeighbors(), shark::HierarchicalClustering< InputT >::hardMembership(), shark::AbstractClustering< RealVector >::hardMembership(), import_libsvm(), shark::Energy< RBM >::logUnnormalizedProbabilityHidden(), shark::Energy< RBM >::logUnnormalizedProbabilityVisible(), main(), repartitionByClass(), shark::MultiChainApproximator< MarkovChainType >::setBatchSize(), splitAtElement(), toDataset(), shark::ScaledKernel< InputType >::weightedInputDerivative(), shark::NormalizedKernel< InputType >::weightedInputDerivative(), shark::WeightedSumKernel< InputType >::weightedInputDerivative(), shark::PointSetKernel< InputType >::weightedParameterDerivative(), and shark::NormalizedKernel< InputType >::weightedParameterDerivative().
WeightedLabeledData< InputType, LabelType> shark::bootstrap  (  LabeledData< InputType, LabelType > const &  dataset, 
std::size_t  bootStrapSize = 0 

) 
Creates a bootstrap partition of a labeled dataset and returns it using weighting.
Bootstrapping resamples the dataset by drawing a set of points with replacement. Thus the sampled set will contain some points multiple times and some points not at all. Bootstrapping is usefull to obtain unbiased measurements of the mean and variance of an estimator.
Optionally the size of the bootstrap (that is, the number of sampled points) can be set. By default it is 0, which indicates that it is the same size as the original dataset.
Definition at line 801 of file WeightedDataset.h.
References shark::random::discrete(), shark::random::globalRng, shark::LabeledData< InputT, LabelT >::inputShape(), shark::WeightedLabeledData< InputT, LabelT >::inputShape(), shark::LabeledData< InputT, LabelT >::labelShape(), shark::WeightedLabeledData< InputT, LabelT >::labelShape(), and shark::LabeledData< InputT, LabelT >::numberOfElements().
WeightedUnlabeledData<InputType> shark::bootstrap  (  UnlabeledData< InputType > const &  dataset, 
std::size_t  bootStrapSize = 0 

) 
Creates a bootstrap partition of an unlabeled dataset and returns it using weighting.
Bootstrapping resamples the dataset by drawing a set of points with replacement. Thus the sampled set will contain some points multiple times and some points not at all. Bootstrapping is usefull to obtain unbiased measurements of the mean and variance of an estimator.
Optionally the size of the bootstrap (that is, the number of sampled points) can be set. By default it is 0, which indicates that it is the same size as the original dataset.
Definition at line 829 of file WeightedDataset.h.
References shark::random::discrete(), shark::random::globalRng, shark::Data< Type >::numberOfElements(), shark::Data< Type >::shape(), and shark::WeightedUnlabeledData< DataT >::shape().
RealVector shark::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.
The formula is \( \sum_i \sum_j w_{ij} k(x_i,x_j)\) where w_ij are the weights of the gradient and x_i x_j are the datapoints defining the gram matrix and k is the kernel. For efficiency it is assumd that w_ij = w_ji. This method is only useful when the whole Kernel Gram Matrix neds to be computed to get the weights w_ij and only computing smaller blocks is not sufficient.
kernel  the kernel for which to calculate the kernel gram matrix 
dataset  the set of points used in the gram matrix 
weights  the weights of the derivative, they must be symmetric! 
Definition at line 181 of file KernelHelpers.h.
References shark::Data< Type >::batch(), batchSize(), shark::AbstractKernelFunction< InputTypeT >::createState(), shark::AbstractKernelFunction< InputTypeT >::eval(), shark::Data< Type >::numberOfBatches(), shark::IParameterizable< VectorType >::numberOfParameters(), and shark::AbstractKernelFunction< InputTypeT >::weightedParameterDerivative().
Referenced by shark::RadiusMarginQuotient< InputType, CacheType >::evalDerivative().
void shark::calculateMixedKernelMatrix  (  AbstractKernelFunction< InputType >const &  kernel, 
Data< InputType > const &  dataset1,  
Data< InputType > const &  dataset2,  
blas::matrix_expression< M, Device > &  matrix  
) 
Calculates the kernel gram matrix between two data sets.
kernel  the kernel for which to calculate the kernel gram matrix 
dataset1  the set of points corresponding to rows of the Gram matrix 
dataset2  the set of points corresponding to columns of the Gram matrix 
matrix  the target kernel matrix 
Definition at line 95 of file KernelHelpers.h.
References shark::Data< Type >::batch(), batchSize(), shark::Data< Type >::numberOfBatches(), shark::Data< Type >::numberOfElements(), SHARK_PARALLEL_FOR, and SIZE_CHECK.
Referenced by calculateMixedKernelMatrix().
RealMatrix shark::calculateMixedKernelMatrix  (  AbstractKernelFunction< InputType >const &  kernel, 
Data< InputType > const &  dataset1,  
Data< InputType > const &  dataset2  
) 
Calculates the kernel gram matrix between two data sets.
kernel  the kernel for which to calculate the kernel gram matrix 
dataset1  the set of points corresponding to rows of the Gram matrix 
dataset2  the set of points corresponding to columns of the Gram matrix 
Definition at line 159 of file KernelHelpers.h.
References calculateMixedKernelMatrix().
void shark::calculateRegularizedKernelMatrix  (  AbstractKernelFunction< InputType >const &  kernel, 
Data< InputType > const &  dataset,  
blas::matrix_expression< M, Device > &  matrix,  
double  regularizer = 0 

) 
Calculates the regularized kernel gram matrix of the points stored inside a dataset.
Regularization is applied by adding the regularizer on the diagonal
kernel  the kernel for which to calculate the kernel gram matrix 
dataset  the set of points used in the gram matrix 
matrix  the target kernel matrix 
regularizer  the regularizer of the matrix which is always >= 0. default is 0. 
Definition at line 52 of file KernelHelpers.h.
References shark::Data< Type >::batch(), batchSize(), shark::Data< Type >::numberOfBatches(), shark::Data< Type >::numberOfElements(), SHARK_PARALLEL_FOR, SHARK_RUNTIME_CHECK, and SIZE_CHECK.
Referenced by calculateRegularizedKernelMatrix(), shark::KernelMatrix< InputType, CacheType >::matrix(), and shark::RegularizationNetworkTrainer< InputType >::train().
RealMatrix shark::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.
Regularization is applied by adding the regularizer on the diagonal
kernel  the kernel for which to calculate the kernel gram matrix 
dataset  the set of points used in the gram matrix 
regularizer  the regularizer of the matrix which is always >= 0. default is 0. 
Definition at line 141 of file KernelHelpers.h.
References calculateRegularizedKernelMatrix(), and SHARK_RUNTIME_CHECK.

inline 
Returns the number of members of each class in the dataset.
Definition at line 701 of file WeightedDataset.h.
References classSizes().

inline 
Returns the number of members of each class in the dataset.
Definition at line 730 of file WeightedDataset.h.
References classSizes(), and shark::WeightedLabeledData< InputT, LabelT >::labels().
RealVector shark::classWeight  (  WeightedLabeledData< InputType, unsigned int > const &  dataset  ) 
Computes the cumulative weight of every class.
Definition at line 755 of file WeightedDataset.h.
References numberOfClasses().
Referenced by shark::KernelMeanClassifier< InputType >::train().
UIntMatrix shark::computeClosestNeighbourIndicesOnLattice  (  Matrix const &  m, 
std::size_t const  n  
) 
std::size_t shark::computeOptimalLatticeTicks  (  std::size_t const  n, 
std::size_t const  target_count  
) 
Computes the number of Ticks for a grid of a certain size.
Computes the least number of ticks in each dimension required to make an ndimensional simplex grid at least as many points as a target number points. For example, the points in a twodimensional grid – a line – with size n are the points (0,n1), (1,n2), ... (n1,0).
Referenced by shark::NSGA3Indicator::init().
blas::matrix<typename VectorType::value_type> shark::covariance  (  Data< VectorType > const &  data  ) 
Calculates the covariance matrix of the data vectors.
Referenced by createData(), mean(), shark::NormalDistributedPoints::NormalDistributedPoints(), and shark::NormalizeComponentsZCA::train().
Batch<T>::type shark::createBatch  (  Range const &  range  ) 
creates a batch from a range of inputs
Definition at line 389 of file BatchInterface.h.
Referenced by shark::AbstractLoss< unsigned int, RealVector >::eval(), shark::AbstractKernelFunction< MultiTaskSample< InputTypeT > >::eval(), shark::NormalizedKernel< InputType >::eval(), shark::AbstractModel< InputT, unsigned int >::eval(), shark::AbstractLoss< unsigned int, RealVector >::evalDerivative(), shark::AbstractClustering< RealVector >::hardMembership(), shark::CSvmDerivative< InputType, CacheType >::modelCSvmParameterDerivative(), subBatch(), shark::KernelSGDTrainer< InputType, CacheType >::train(), shark::KernelBudgetedSGDTrainer< InputType, CacheType >::train(), and shark::CSvmDerivative< InputType, CacheType >::write().
Batch<typename Range::value_type>::type shark::createBatch  (  Range const &  range  ) 
creates a batch from a range of inputs
Definition at line 395 of file BatchInterface.h.
Batch<T>::type shark::createBatch  (  Iterator const &  begin, 
Iterator const &  end  
) 
Definition at line 400 of file BatchInterface.h.
boost::disable_if< boost::is_arithmetic<WeightRange>, WeightedLabeledData< typename boost::range_value<InputRange>::type, typename boost::range_value<LabelRange>::type >>::type shark::createLabeledDataFromRange  (  InputRange const &  inputs, 
LabelRange const &  labels,  
WeightRange const &  weights,  
std::size_t  batchSize = 0 

) 
creates a weighted unweighted data object from two ranges, representing data and weights
Definition at line 773 of file WeightedDataset.h.
References batchSize(), createDataFromRange(), createLabeledDataFromRange(), and SHARK_RUNTIME_CHECK.
boost::disable_if< boost::is_arithmetic<WeightRange>, WeightedUnlabeledData< typename boost::range_value<DataRange>::type > >::type shark::createUnlabeledDataFromRange  (  DataRange const &  data, 
WeightRange const &  weights,  
std::size_t  batchSize = 0 

) 
creates a weighted unweighted data object from two ranges, representing data and weights
Definition at line 544 of file WeightedDataset.h.
References batchSize(), createDataFromRange(), createUnlabeledDataFromRange(), and SHARK_RUNTIME_CHECK.
std::size_t shark::dataDimension  (  DataView< DatasetType > const &  view  ) 
Return the dimensionality of the dataset represented by the view.
Definition at line 344 of file DataView.h.
References dataDimension(), and shark::DataView< DatasetType >::dataset().
std::size_t shark::dataDimension  (  WeightedUnlabeledData< InputType > const &  dataset  ) 
Return the dimnsionality of points of a weighted dataset.
Definition at line 707 of file WeightedDataset.h.
References dataDimension().
void shark::dcNonDominatedSort  (  PointRange const &  points, 
RankRange &  ranks  
) 
Definition at line 466 of file DCNonDominatedSort.h.
Referenced by nonDominatedSort().

inline 
Pareto dominance relation for two objective vectors.
Definition at line 52 of file ParetoDominance.h.
References EQUIVALENT, INCOMPARABLE, LHS_DOMINATES_RHS, RHS_DOMINATES_LHS, and SHARK_ASSERT.
Referenced by fastNonDominatedSort(), and shark::BaseDCNonDominatedSort::operator()().
double shark::estimateLogFreeEnergy  (  RBMType &  rbm, 
Data< RealVector > const &  initDataset,  
RealVector const &  beta,  
std::size_t  samples,  
PartitionEstimationAlgorithm  algorithm = AcceptanceRatioMean , 

float  burnInPercentage = 0.1 

) 
Definition at line 154 of file analytics.h.
References estimateLogFreeEnergyFromEnergySamples().
Referenced by estimateLogFreeEnergy().
double shark::estimateLogFreeEnergy  (  RBMType &  rbm, 
Data< RealVector > const &  initDataset,  
std::size_t  chains,  
std::size_t  samples,  
PartitionEstimationAlgorithm  algorithm = AIS , 

float  burnInPercentage = 0.1 

) 
Definition at line 182 of file analytics.h.
References beta, and estimateLogFreeEnergy().

inline 
double shark::evalSkipMissingFeatures  (  const AbstractKernelFunction< InputType > &  kernelFunction, 
const InputTypeT1 &  inputA,  
const InputTypeT2 &  inputB  
) 
Does a kernel function evaluation with Missing features in the inputs
kernelFunction  The kernel function used to do evaluation 
inputA  a input 
inputB  another input 
The kernel k(x,y) is evaluated taking missing features into account. For this it is checked whether a feature of x or y is nan and in this case the corresponding features in inputA and inputB won't be considered.
Definition at line 58 of file EvalSkipMissingFeatures.h.
References shark::AbstractKernelFunction< InputTypeT >::eval(), SHARK_RUNTIME_CHECK, SIZE_CHECK, and shark::AbstractKernelFunction< InputTypeT >::supportsVariableInputSize().
Referenced by shark::ExampleModifiedKernelMatrix< InputType, CacheType >::entry().
double shark::evalSkipMissingFeatures  (  const AbstractKernelFunction< InputType > &  kernelFunction, 
const InputTypeT1 &  inputA,  
const InputTypeT2 &  inputB,  
InputTypeT3 const &  missingness  
) 
Do kernel function evaluation while Missing features in the inputs
kernelFunction  The kernel function used to do evaluation 
inputA  a input 
inputB  another input 
missingness  used to decide which features in the inputs to take into consideration for the purpose of evaluation. If a feature is NaN, then the corresponding features in inputA and inputB won't be considered. 
Definition at line 104 of file EvalSkipMissingFeatures.h.
References shark::AbstractKernelFunction< InputTypeT >::eval(), SHARK_RUNTIME_CHECK, SIZE_CHECK, and shark::AbstractKernelFunction< InputTypeT >::supportsVariableInputSize().

inline 
Exports a set of filters as a grid image.
It is assumed that the filters each form a row in the filtermatrix. Moreover, the sizes of the filter images has to be given and it must gold width*height=W.size2(). The filters a re printed on a single image as a grid. The grid will be close to square. And the image are separated by a black 1 pixel wide line. The output will be normalized so that all images are on the same scale.
basename  File to write to. ".pgm" is appended to the filename 
filters  Matrix storing the filters row by row 
width  Width of the filter image 
height  Height of th filter image 
Definition at line 146 of file Pgm.h.
References SIZE_CHECK.
Referenced by main().

inline 
Exports a set of filters as a grid image.
It is assumed that the filters each form a row in the filtermatrix. Moreover, the sizes of the filter images has to be given and it must gold width*height=W.size2(). The filters a re printed on a single image as a grid. The grid will be close to square. And the image are separated by a black 1 pixel wide line. The output will be normalized so that all images are on the same scale.
basename  File to write to. ".pgm" is appended to the filename 
filters  Matrix storing the filters row by row 
width  Width of the filter image 
height  Height of th filter image 
Definition at line 183 of file Pgm.h.
References dataDimension(), shark::Data< Type >::numberOfElements(), and SIZE_CHECK.
void shark::exportPGM  (  std::string const &  fileName, 
T const &  data,  
std::size_t  sx,  
std::size_t  sy,  
bool  normalize = false 

) 
Export a PGM image to file.
fileName  File to write to 
data  Linear object storing image 
sx  Width of image 
sy  Height of image 
normalize  Adjust values to [0,255], default false 
Definition at line 118 of file Pgm.h.
References SIZE_CHECK.
Referenced by main().
void shark::fastNonDominatedSort  (  PointRange const &  points, 
RankRange &  ranks  
) 
Implements the wellknown nondominated sorting algorithm.
Assembles subsets/fronts of mututally nondominating individuals. Afterwards every individual is assigned a rank by points[i].rank() = frontNumber. The front of dominating points has the value 1.
The algorithm is dscribed in Deb et al, A Fast and Elitist Multiobjective Genetic Algorithm: NSGAII IEEE Transactions on Evolutionary Computation, 2002
points  [in] pointsulation to subdivide into fronts of nondominated individuals. 
ranks  [out] Set of integers storing the rank of the ith point. must have the same size 
Definition at line 49 of file FastNonDominatedSort.h.
References dominance(), LHS_DOMINATES_RHS, RHS_DOMINATES_LHS, and SIZE_CHECK.
Referenced by nonDominatedSort().
Definition at line 147 of file BatchInterfaceAdaptStruct.h.
References S.
Definition at line 151 of file BatchInterfaceAdaptStruct.h.
References S.
boost::disable_if<detail::isFusionFacade<S>,S&>::type shark::fusionize  (  S &  facade  ) 
Definition at line 157 of file BatchInterfaceAdaptStruct.h.
References S.
boost::disable_if<detail::isFusionFacade<S>,S const& >::type shark::fusionize  (  S const &  facade  ) 
Definition at line 162 of file BatchInterfaceAdaptStruct.h.
auto shark::getBatchElement  (  BatchT &  batch, 
std::size_t  i  
)  > decltype(BatchTraits<BatchT>::type::get(std::declval<BatchT&>(),i)) 
Definition at line 405 of file BatchInterface.h.
Referenced by shark::DifferenceKernelMatrix< InputType, CacheType >::entry(), shark::AbstractLoss< unsigned int, RealVector >::eval(), shark::PointSetKernel< InputType >::eval(), shark::AbstractKernelFunction< MultiTaskSample< InputTypeT > >::eval(), shark::NormalizedKernel< InputType >::eval(), shark::AbstractModel< InputT, unsigned int >::eval(), shark::AbstractLoss< unsigned int, RealVector >::evalDerivative(), exportSparseData(), shark::AbstractClustering< RealVector >::hardMembership(), main(), shark::CSvmDerivative< InputType, CacheType >::modelCSvmParameterDerivative(), shark::WeightedDataBatch< DataBatchType, WeightBatchType >::operator[](), shark::DataView< shark::Data< LabelType > const >::operator[](), shark::PointSetKernel< InputType >::weightedParameterDerivative(), and shark::CSvmDerivative< InputType, CacheType >::write().
auto shark::getBatchElement  (  BatchT const &  batch, 
std::size_t  i  
)  > decltype(BatchTraits<BatchT>::type::get(std::declval<BatchT const&>(),i)) 
Definition at line 410 of file BatchInterface.h.
void shark::importHDF5  (  Data< VectorType > &  data, 
const std::string &  fileName,  
const std::string &  datasetName  
) 
void shark::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.
labeledData  Container storing the loaded data 
fileName  The name of HDF5 file to be read from 
data  the HDF5 dataset name for data 
label  the HDF5 dataset name for label 
VectorType  Type of object stored in Shark data container 
LableType  Type of label 
void shark::importHDF5  (  Data< VectorType > &  data, 
const std::string &  fileName,  
const std::vector< std::string > &  cscDatasetName  
) 
Import data from HDF5 dataset of compressed sparse column format.
data  Container storing the loaded data 
fileName  The name of HDF5 file to be read from 
cscDatasetName  the CSC dataset names used to construct a matrix 
VectorType  Type of object stored in Shark data container 
void shark::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.
labeledData  Container storing the loaded data 
fileName  The name of HDF5 file to be read from 
cscDatasetName  the CSC dataset names used to construct a matrix 
label  the HDF5 dataset name for label 
VectorType  Type of object stored in Shark data container 
LabelType  Type of label 
void shark::importPGM  (  std::string const &  fileName, 
T &  data,  
std::size_t &  sx,  
std::size_t &  sy  
) 
Import a PGM image from file.
fileName  The file to read from 
data  Linear object for storing image 
sx  Width of imported image 
sy  Height of imported image 
Definition at line 103 of file Pgm.h.
Referenced by importPGMSet().
void shark::importPGMSet  (  std::string const &  p, 
Data< T > &  set  
) 
Import PGM images scanning a directory recursively.
All images are required to have the same size. the shape of the images is stored in set.shape()
p  Directory 
set  Set storing images 
Definition at line 222 of file Pgm.h.
References createDataFromRange(), importPGM(), and SHARKEXCEPTION.
Referenced by main().
std::size_t shark::inputDimension  (  DataView< DatasetType > const &  view  ) 
Return the input dimensionality of the labeled dataset represented by the view.
Definition at line 333 of file DataView.h.
References shark::DataView< DatasetType >::dataset(), and inputDimension().
std::size_t shark::inputDimension  (  WeightedLabeledData< InputType, LabelType > const &  dataset  ) 
Return the input dimensionality of a weighted labeled dataset.
Definition at line 713 of file WeightedDataset.h.
References dataDimension(), and shark::WeightedLabeledData< InputT, LabelT >::inputs().
SHARK_EXPORT_SYMBOL std::size_t shark::kMeans  (  Data< RealVector > const &  data, 
std::size_t  k,  
Centroids &  centroids,  
std::size_t  maxIterations = 0 

) 
The kmeans clustering algorithm.
data  vectorvalued data to be clustered 
k  number of clusters 
centroids  centroids input/output 
maxIterations  maximum number of kmeans iterations; 0: unlimited 
Referenced by main().
SHARK_EXPORT_SYMBOL std::size_t shark::kMeans  (  Data< RealVector > const &  data, 
RBFLayer &  model,  
std::size_t  maxIterations = 0 

) 
The kmeans clustering algorithm for initializing an RBF Layer.
data  vectorvalued data to be clustered 
model  RBFLayer input/output 
maxIterations  maximum number of kmeans iterations; 0: unlimited 
KernelExpansion<InputType> shark::kMeans  (  Data< InputType > const &  dataset, 
std::size_t  k,  
AbstractKernelFunction< InputType > &  kernel,  
std::size_t  maxIterations = 0 

) 
The kernel kmeans clustering algorithm.
dataset  vectorvalued data to be clustered 
k  number of clusters 
kernel  kernel function object 
maxIterations  maximum number of kmeans iterations; 0: unlimited 
std::size_t shark::labelDimension  (  DataView< DatasetType > const &  view  ) 
Return the label dimensionality of the labeled dataset represented by the view.
Definition at line 338 of file DataView.h.
References shark::DataView< DatasetType >::dataset(), and labelDimension().
std::size_t shark::labelDimension  (  WeightedLabeledData< InputType, LabelType > const &  dataset  ) 
Return the label/output dimensionality of a labeled dataset.
Definition at line 719 of file WeightedDataset.h.
References dataDimension(), and shark::WeightedLabeledData< InputT, LabelT >::labels().
double shark::logPartitionFunction  (  RBMType const &  rbm, 
double  beta = 1.0 

) 
Calculates the value of the partition function $Z$.
Only useful for small input and theoretical analysis
rbm  the RBM for which to calculate the function 
beta  the inverse temperature of the RBM. default is 1 
Definition at line 48 of file analytics.h.
References beta.
Referenced by negativeLogLikelihood().
KeyValuePair<Key,Value> shark::makeKeyValuePair  (  Key const &  key, 
Value const &  value  
) 
Creates a KeyValuePair.
Definition at line 96 of file KeyValuePair.h.
References shark::KeyValuePair< Key, Value >::key, and shark::KeyValuePair< Key, Value >::value.
ResultSet<T,U> shark::makeResultSet  (  T const &  t, 
U const &  u  
) 
Generates a typed solution given the search point and the corresponding objective function value.
[in]  t  The search point. 
[in]  u  The objective function value. 
Definition at line 71 of file ResultSets.h.
VectorType shark::mean  (  Data< VectorType > const &  data  ) 
Calculates the mean vector of the input vectors.
Referenced by createData(), shark::ZDT6::eval(), shark::TruncatedExponentialLayer::expectedPhiValue(), shark::random::gauss(), getSamples(), shark::McPegasos< VectorType >::lossGradientADS(), shark::McPegasos< VectorType >::lossGradientATS(), mean(), shark::FisherLDA::setWhitening(), shark::statistics::Variance::statistics(), shark::NormalizeComponentsZCA::train(), shark::NormalizeComponentsUnitVariance< DataType >::train(), shark::RegularizationNetworkTrainer< InputType >::train(), shark::QpMcLinearATS< InputT >::updateWeightVectors(), shark::QpMcLinearATM< InputT >::updateWeightVectors(), and shark::QpMcLinearReinforced< InputT >::updateWeightVectors().
VectorType shark::mean  (  UnlabeledData< VectorType > const &  data  ) 
Definition at line 69 of file Statistics.h.
References covariance(), mean(), and variance().
void shark::meanvar  (  Data< Vec1T > const &  data, 
blas::vector_container< Vec2T, Device > &  mean,  
blas::vector_container< Vec3T, Device > &  variance  
) 
Calculates the mean and variance values of the input data.
Referenced by main(), shark::NormalizeComponentsZCA::train(), and shark::NormalizeComponentsUnitVariance< DataType >::train().
void shark::meanvar  (  Data< Vec1T > const &  data, 
blas::vector_container< Vec2T, Device > &  mean,  
blas::matrix_container< MatT, Device > &  variance  
) 
Calculates the mean, variance and covariance values of the input data.
boost::range_iterator<Range>::type shark::median_element  (  Range &  range  ) 
Returns the iterator to the median element. after this call, the range is partially ordered.
After the call, all elements left of the median element are guaranteed to be <= median and all element on the right are >= median.
Definition at line 80 of file functional.h.
Referenced by median_element(), and partitionEqually().
boost::range_iterator<Range>::type shark::median_element  (  Range const &  rangeAdaptor  ) 
Definition at line 91 of file functional.h.
References median_element().
double shark::negativeLogLikelihood  (  RBMType const &  rbm, 
UnlabeledData< RealVector > const &  inputs,  
double  beta = 1.0 

) 
Estimates the negative loglikelihood of a set of input vectors under the models distribution.
Only useful for small input and theoretical analysis
rbm  the Restricted Boltzmann machine for which the negative log likelihood of the data is to be calculated 
inputs  the input vectors 
beta  the inverse temperature of the RBM. default is 1 
Definition at line 102 of file analytics.h.
References beta, logPartitionFunction(), and negativeLogLikelihoodFromLogPartition().
Referenced by shark::ExactGradient< RBMType >::eval(), and main().
double shark::negativeLogLikelihoodFromLogPartition  (  RBMType const &  rbm, 
UnlabeledData< RealVector > const &  inputs,  
double  logPartition,  
double  beta = 1.0 

) 
Estimates the negative loglikelihood of a set of input vectors under the models distribution using the partition function.
Only useful for small input and theoretical analysis
rbm  the Restricted Boltzmann machine for which the negative log likelihood of the data is to be calculated 
inputs  the input vectors 
logPartition  the logarithmic value of the partition function of the RBM. 
beta  the inverse temperature of the RBM. default is 1 
Definition at line 79 of file analytics.h.
References shark::Data< Type >::batches().
Referenced by negativeLogLikelihood().
void shark::nonDominatedSort  (  PointRange const &  points, 
RankRange &  ranks  
) 
Frontend for nondominated sorting algorithms.
Assembles subsets/fronts of mutually nondominated individuals. Afterwards every individual is assigned a rank by pop[i].rank() = frontIndex. The front of nondominated points has the value 1.
Depending on dimensionality m and number of points n, either the fastNonDominatedSort algorithm with O(n^2 m) or the dcNonDominatedSort alforithm with complexity O(n log(n)^m) is called.
Definition at line 47 of file NonDominatedSort.h.
References dcNonDominatedSort(), fastNonDominatedSort(), and SIZE_CHECK.
Referenced by nonDominatedSort(), and shark::IndicatorBasedSelection< NSGA3Indicator >::operator()().
void shark::nonDominatedSort  (  PointRange const &  points, 
RankRange const &  ranks  
) 
Definition at line 68 of file NonDominatedSort.h.
References nonDominatedSort(), and ranks().
std::size_t shark::numberOfClasses  (  DataView< DatasetType > const &  view  ) 
Return the number of classes (size of the label vector) of a classification dataset with RealVector label encoding.
Definition at line 327 of file DataView.h.
References shark::DataView< DatasetType >::dataset(), and numberOfClasses().

inline 
Definition at line 696 of file WeightedDataset.h.
References numberOfClasses().
std::size_t shark::numberOfClasses  (  WeightedLabeledData< InputType, unsigned int > const &  dataset  ) 
Return the number of classes (highest label value +1) of a classification dataset with unsigned int label encoding.
Definition at line 724 of file WeightedDataset.h.
References shark::WeightedLabeledData< InputT, LabelT >::labels(), and numberOfClasses().
std::ostream& shark::operator<<  (  std::ostream &  out, 
ResultSet< SearchPoint, Result > const &  solution  
) 
Definition at line 75 of file ResultSets.h.
References shark::ResultSet< SearchPointT, ResultT >::value.
std::basic_ostream<E, T>& shark::operator<<  (  std::basic_ostream< E, T > &  os, 
Shape const &  shape  
) 
std::ostream& shark::operator<<  (  std::ostream &  out, 
ValidatedSingleObjectiveResultSet< SearchPoint > const &  solution  
) 
Definition at line 131 of file ResultSets.h.
std::ostream& shark::operator<<  (  std::ostream &  stream, 
const WeightedUnlabeledData< T > &  d  
) 
brief Outstream of elements for weighted data.
Definition at line 531 of file WeightedDataset.h.
std::ostream& shark::operator<<  (  std::ostream &  stream, 
const WeightedLabeledData< T, U > &  d  
) 
brief Outstream of elements for weighted labeled data.
Definition at line 688 of file WeightedDataset.h.
Definition at line 102 of file Shape.h.
References shark::Shape::size().
ConcatenatedModel<VectorType> shark::operator>>  (  AbstractModel< VectorType, VectorType, VectorType > &  firstModel, 
AbstractModel< VectorType, VectorType, VectorType > &  secondModel  
) 
Connects two AbstractModels so that the output of the first model is the input of the second.
Definition at line 308 of file ConcatenatedModel.h.
References shark::ConcatenatedModel< VectorType >::add().
ConcatenatedModel<VectorType> shark::operator>>  (  ConcatenatedModel< VectorType > const &  firstModel, 
AbstractModel< VectorType, VectorType, VectorType > &  secondModel  
) 
Definition at line 319 of file ConcatenatedModel.h.
References shark::ConcatenatedModel< VectorType >::add().
void shark::partial_shuffle  (  RandomAccessIterator  begin, 
RandomAccessIterator  middle,  
RandomAccessIterator  end,  
Rng &  rng  
) 
random_shuffle algorithm which stops after acquiring the random subsequence for [begin,middle)
Definition at line 55 of file functional.h.
References shuffle().
Referenced by partial_shuffle(), randomSubBatch(), and randomSubset().
void shark::partial_shuffle  (  RandomAccessIterator  begin, 
RandomAccessIterator  middle,  
RandomAccessIterator  end  
) 
random_shuffle algorithm which stops after acquiring the random subsequence for [begin,middle)
Definition at line 71 of file functional.h.
References shark::random::globalRng, and partial_shuffle().
boost::range_iterator<Range>::type shark::partitionEqually  (  Range &  range  ) 
Partitions a range in two parts as equal in size as possible.
The Algorithm partitions the range and returns the splitpoint. The elements in the range are ordered such that all elements in [begin,splitpoint) < [splitpoint,end). This partition is done such that the ranges are as equally sized as possible. It is guaranteed that the left range is not empty. However, if the range consists only of equal elements, the return value will be the end iterator indicating that there is no split possible. The whole algorithm runs in linear time by iterating 2 times over the sequence.
Definition at line 105 of file functional.h.
References median_element().
Referenced by partitionEqually(), and shark::BinaryTree< VectorType >::splitList().
boost::range_iterator<Range>::type shark::partitionEqually  (  Range const &  rangeAdaptor  ) 
Partitions a range in two parts as equal in size as possible and returns it's result.
This the verison for adapted ranges.
Definition at line 133 of file functional.h.
References partitionEqually().
auto shark::penalizedFitness  (  IndividualRange &  range  )  > decltype( boost::adaptors::transform(range,detail::IndividualPenalizedFitnessFunctor()) ) 
Definition at line 228 of file Individual.h.
References transform().
Referenced by shark::IndicatorBasedSelection< NSGA3Indicator >::indicator(), shark::IndicatorBasedSelection< NSGA3Indicator >::operator()(), and shark::PopulationBasedStepSizeAdaptation::update().
RealMatrix shark::preferenceAdjustedUnitVectors  (  std::size_t const  n, 
std::size_t const  sum,  
std::vector< Preference > const &  preferences  
) 
Return a set of evenly spaced ndimensional points on the unit sphere clustered around the specified preference points.
Referenced by shark::NSGA3Indicator::init().
RealMatrix shark::preferenceAdjustedWeightVectors  (  std::size_t const  n, 
std::size_t const  sum,  
std::vector< Preference > const &  preferences  
) 
Return a set of of evenly spaced ndimensional points on the "unit simplex" clustered around the specified preference points.
DataView<DatasetType>::batch_type shark::randomSubBatch  (  DataView< DatasetType > const &  view, 
std::size_t  size  
) 
Creates a random batch of a given size.
view  the view from which the batch is to be created 
size  the size of the batch 
Definition at line 285 of file DataView.h.
References partial_shuffle(), shark::DataView< DatasetType >::size(), and subBatch().
DataView<DatasetType> shark::randomSubset  (  DataView< DatasetType > const &  view, 
std::size_t  size  
) 
creates a random subset of a DataView with given size
view  the view for which the subset is to be created 
size  the size of the subset 
Definition at line 257 of file DataView.h.
References partial_shuffle(), shark::DataView< DatasetType >::size(), and subset().
Referenced by main().
auto shark::ranks  (  IndividualRange &  range  )  > decltype( boost::adaptors::transform(range,detail::IndividualRankFunctor()) ) 
Definition at line 242 of file Individual.h.
References transform().
Referenced by nonDominatedSort(), and shark::IndicatorBasedSelection< NSGA3Indicator >::operator()().
FrontType shark::read_front  (  Stream &  in, 
std::size_t  noObjectives,  
const std::string &  separator = " " , 

std::size_t  headerLines = 0 

) 
Definition at line 17 of file AdditiveEpsilonIndicatorMain.cpp.
Referenced by main().
Matrix shark::sampleLatticeUniformly  (  randomType &  rng, 
Matrix const &  matrix,  
std::size_t const  n,  
bool const  keep_corners = true 

) 
Definition at line 90 of file Lattice.h.
Referenced by shark::NSGA3Indicator::init().
auto shark::searchPoint  (  IndividualRange &  range  )  > decltype( boost::adaptors::transform(range,detail::IndividualSearchPointFunctor()) ) 
Definition at line 250 of file Individual.h.
References transform().
Referenced by shark::LineSearch::init(), main(), shark::VDCMA::step(), and shark::LMCMA::step().
shark::SHARK_VECTOR_MATRIX_TYPEDEFS  (  long  double, 
BigReal  
) 
shark::SHARK_VECTOR_MATRIX_TYPEDEFS  (  bool  , 
Bool  
) 
void shark::shuffle  (  Iterator  begin, 
Iterator  end,  
Rng &  rng  
) 
random_shuffle algorithm which stops after acquiring the random subsequence for [begin,middle)
Definition at line 44 of file functional.h.
References shark::random::discrete(), and swap().
Referenced by createCVBatch(), shark::ContrastiveDivergence< Operator >::evalDerivative(), partial_shuffle(), shark::UnlabeledData< InputType >::shuffle(), and shark::LabeledData< InputType, LabelType >::shuffle().
DataView<DatasetType>::batch_type shark::subBatch  (  DataView< DatasetType > const &  view, 
IndexRange const &  indizes  
) 
Creates a batch given a set of indices.
view  the view from which the batch is to be created 
indizes  the set of indizes defining the batch 
Definition at line 269 of file DataView.h.
References createBatch(), and subset().
Referenced by shark::RFClassifier< LabelType >::computeFeatureImportances(), createCVFullyIndexed(), createCVIndexed(), and randomSubBatch().
DataView<DatasetType> shark::subset  (  DataView< DatasetType > const &  view, 
IndexRange const &  indizes  
) 
creates a subset of a DataView with elements indexed by indices
view  the view for which the subset is to be created 
indizes  the index of the elements to be stored in the view 
Definition at line 247 of file DataView.h.
Referenced by shark::Data< LabelT >::indexedSubset(), main(), randomSubset(), and subBatch().
double shark::sumOfWeights  (  WeightedUnlabeledData< InputType > const &  dataset  ) 
Returns the total sum of weights.
Definition at line 736 of file WeightedDataset.h.
double shark::sumOfWeights  (  WeightedLabeledData< InputType, LabelType > const &  dataset  ) 
Returns the total sum of weights.
Definition at line 745 of file WeightedDataset.h.
void shark::swap  (  WeightedDataPair< D1, W1 > &&  p1, 
WeightedDataPair< D2, W2 > &&  p2  
) 
Definition at line 84 of file WeightedDataset.h.
References swap().
void shark::swap  (  KeyValuePair< K, V > &  pair1, 
KeyValuePair< K, V > &  pair2  
) 
Swaps the contents of two instances of KeyValuePair.
Definition at line 88 of file KeyValuePair.h.
References shark::KeyValuePair< Key, Value >::key, and shark::KeyValuePair< Key, Value >::value.
Referenced by createCVFullyIndexed(), createCVIndexed(), shark::QpMcBoxDecomp< Matrix >::deactivateExample(), shark::QpMcSimplexDecomp< Matrix >::deactivateExample(), shark::QpMcBoxDecomp< Matrix >::deactivateVariable(), shark::QpMcSimplexDecomp< Matrix >::deactivateVariable(), shark::ConcatenatedModel< VectorType >::eval(), shark::MultiChainApproximator< MarkovChainType >::evalDerivative(), shark::ExampleModifiedKernelMatrix< InputType, CacheType >::flipColumnsAndRows(), shark::BlockMatrix2x2< Matrix >::flipColumnsAndRows(), shark::RegularizedKernelMatrix< InputType, CacheType >::flipColumnsAndRows(), shark::GaussianKernelMatrix< T, CacheType >::flipColumnsAndRows(), shark::KernelMatrix< InputType, CacheType >::flipColumnsAndRows(), shark::ModifiedKernelMatrix< InputType, CacheType >::flipColumnsAndRows(), shark::DifferenceKernelMatrix< InputType, CacheType >::flipColumnsAndRows(), shark::CachedMatrix< Matrix >::flipColumnsAndRows(), shark::GeneralQuadraticProblem< MatrixT >::flipCoordinates(), shark::BoxedSVMProblem< MatrixT >::flipCoordinates(), shark::BoxBasedShrinkingStrategy< BoxConstrainedProblem< Problem > >::flipCoordinates(), shark::BoxConstrainedProblem< Problem >::flipCoordinates(), shark::CSVMProblem< MatrixT >::flipCoordinates(), shark::SvmProblem< Problem >::flipCoordinates(), main(), shark::WS2MaximumGradientCriterion::operator()(), shark::SimulatedBinaryCrossover< RealVector >::operator()(), shark::HMGSelectionCriterion::operator()(), repartitionByClass(), shark::MultiChainApproximator< MarkovChainType >::setData(), shark::BoxBasedShrinkingStrategy< BoxConstrainedProblem< Problem > >::shrink(), shuffle(), swap(), shark::LRUCache< QpFloatType >::swapLineIndices(), shark::ConcatenatedModel< VectorType >::weightedDerivatives(), shark::ConcatenatedModel< VectorType >::weightedInputDerivative(), and shark::ConcatenatedModel< VectorType >::weightedParameterDerivative().
void shark::swap  (  WeightedDataBatch< D1, W1 > &  p1, 
WeightedDataBatch< D2, W2 > &  p2  
) 
Definition at line 159 of file WeightedDataset.h.
References shark::WeightedDataBatch< DataBatchType, WeightBatchType >::data, swap(), and shark::WeightedDataBatch< DataBatchType, WeightBatchType >::weight.
Referenced by swap().
double shark::tchebycheffScalarizer  (  RealVector const &  fitness, 
RealVector const & 