| CLUS::BasicBinomialStatistics | |
| CLUS::BinaryDecisionTree< T_Splitter > | Implements the binary decision tree |
| CLUS::BinaryDecisionTreeNode< T_Splitter > | Implements a node of the binary decision tree |
| CLUS::BinaryMultiClassificationSplitter | |
| CLUS::BinaryObliqueProbabilisticSplitter | The class is completely redesigned as of May 27/2003 to incorporate fluctuations in splits not to use normal distributions to determine the probability functions |
| CLUS::BinaryObliqueSplitter | |
| CLUS::BinaryProbabilisticDecisionTree< T_Splitter > | |
| CLUS::BinaryProbabilisticDecisionTreeNode< T_Splitter > | |
| CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter > | |
| CLUS::BinaryProbabilisticRegressionTreeNode< T_Distribution, T_Regressor, T_Splitter > | Class used in building regression trees |
| CLUS::BinaryProbabilisticSplitter | |
| CLUS::BinaryRegressionTree< T_Distribution, T_Regressor, T_Splitter > | |
| CLUS::BinaryRegressionTreeNode< T_Distribution, T_Regressor, T_Splitter > | Class used in building regression trees |
| CLUS::BinarySplitter | Base class for all the splitters |
| CLUS::BinomialStatistics | |
| CLUS::Cluster | Cluster is the abstract base class for cluster hierarchy |
| CLUS::ContinuousLinearTransformation | Applies linear shifts on continuous data |
| CLUS::DataConsumer | |
| CLUS::DataProducer | |
| CLUS::DCTrainingData | Ancestor of all Training Data generators that can manipulate both discrete and continuous entries |
| CLUS::DiscretePermutationTransformation | |
| CLUS::Distribution | Base class for all the continuous distributions that have sufficient statistics |
| CLUS::DynamicBuffer | Class to keep data temporarily that can grow automatically, only doubles can be stored inside |
| elemList | |
| CLUS::EMHiperPlan | |
| CLUS::ErrMsg | |
| CLUS::FileDataConsumer | |
| CLUS::FileDataProducer | |
| CLUS::Filter | |
| CLUS::GridInputProducer | |
| CLUS::HiperPlanCluster | This class implements hiperclusters with only one possible output |
| CLUS::IndexedValue | |
| CLUS::LinearRegressor | |
| CLUS::Machine | Every machine has an input vector, an output one and a real output one should provide a constructor from file |
| CLUS::MulticlassContinuousDistribution< T_Distribution > | The class is a repository of continuous sistributions that each predict one of the class labels of a discrete variable |
| CLUS::MulticlassDistribution | Base class for all distributions that can predict a discrete variable |
| CLUS::MultiDecisionTree< T_Splitter > | |
| CLUS::MultiDecisionTreeNode< T_Splitter > | |
| CLUS::MultiDimNormal | Implements a multidimentional normal distribution |
| CLUS::MultidimNormalStatistics | Class implements a multidimentional normal distribution |
| CLUS::NormalStatistics | |
| CLUS::Permutation | Permutation[i] is the permuted value of i |
| CLUS::ProbabilisticBinomialStatistics | |
| CLUS::Regressor | |
| CLUS::RPMSConsumer | |
| CLUS::Scale | The following structure is used for scaling the inputs and the outputs newVal=adit+mult*oldVal |
| CLUS::SimpleBinarySplitter | Splitter for decision trees |
| CLUS::SimpleNormalDistribution | Implements a unidimensional normal distribution but the "active" dimension can be specified |
| CLUS::SkinyMultiDimNormal | For now make EMHiperPlanCluster look like a Distribution |
| CLUS::SphericCluster | Class that describes Spheric Clusters |
| CLUS::StreamDataConsumer | |
| CLUS::StreamDataProducer | |
| CLUS::StreamDCTrainingData | |
| CLUS::SyncObj | |
| CLUS::SyncObjList | |
| CLUS::SyncObjList::listel | |
| CLUS::T_array | Auxiliary type |
| CLUS::TrainingData | |
