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 | |