| ▼Cxmm::AttributeBase | Base Class for Generic Attributes |
| Cxmm::Attribute< bool > | |
| Cxmm::Attribute< CovarianceMode > | |
| Cxmm::Attribute< double > | |
| Cxmm::Attribute< float > | |
| Cxmm::Attribute< std::string > | |
| Cxmm::Attribute< unsigned int > | |
| Cxmm::Attribute< xmm::GaussianDistribution::CovarianceMode > | |
| Cxmm::Attribute< xmm::HMM::RegressionEstimator > | |
| Cxmm::Attribute< xmm::HMM::TransitionMode > | |
| Cxmm::Attribute< T > | Generic Attribute |
| Cxmm::Attribute< std::vector< std::string > > | Generic Attribute (Vector Specialization) |
| Cxmm::Attribute< std::vector< T > > | Generic Attribute (Vector Specialization) |
| Cxmm::CircularBuffer< T, channels > | Simple CircularBuffer Class |
| Cxmm::CircularBuffer< double > | |
| ▼Cxmm::ClassParameters< HMM > | Parameters specific to each class of a Hidden Markov Model |
| Cxmm::Configuration< HMM > | |
| Cxmm::ClassResults< ModelType > | Class-specific Results of the filtering/inference process |
| Cxmm::ClassResults< HMM > | Results of Hidden Markov Models for a single class |
| Cxmm::ClassResults< xmm::GMM > | |
| Cxmm::ClassResults< xmm::HMM > | |
| Cxmm::Ellipse | Structure for storing Ellipse parameters |
| Cxmm::EventGenerator< EventType > | Generator class for a specific type of events |
| Cxmm::EventGenerator< xmm::PhraseEvent > | |
| Cxmm::EventGenerator< xmm::TrainingEvent > | |
| ▼Cexception | |
| Cxmm::JsonException | Exception class for handling JSON parsing errors |
| Cxmm::HMM | |
| Cxmm::Matrix< T > | Dirty and very incomplete Matrix Class |
| Cxmm::PhraseEvent | Event that can be thrown by a phrase to a training set |
| Cxmm::Results< ModelType > | Results of the filtering/inference process (for a Model with multiple classes) |
| Cxmm::Results< KMeans > | Results of the clustering process |
| Cxmm::Results< xmm::GMM > | |
| Cxmm::Results< xmm::HMM > | |
| Cxmm::Results< xmm::KMeans > | |
| Cxmm::TrainingEvent | Event for monitoring the training process |
| ▼Cxmm::Writable | Abstract class for handling JSON + File I/O |
| Cxmm::ClassParameters< xmm::GMM > | |
| Cxmm::ClassParameters< xmm::HMM > | |
| ▼Cxmm::ClassParameters< xmm::KMeans > | |
| Cxmm::Configuration< xmm::KMeans > | |
| ▼Cxmm::Model< SingleClassGMM, GMM > | |
| Cxmm::GMM | Gaussian Mixture Model for Continuous Recognition and Regression (Multi-class) |
| ▼Cxmm::Model< SingleClassHMM, HMM > | |
| Cxmm::HierarchicalHMM | Hierarchical Hidden Markov Model for Continuous Recognition and Regression (Multi-class) |
| ▼Cxmm::ClassParameters< ModelType > | Class-specific Model Parameters |
| Cxmm::Configuration< ModelType > | Model configuration |
| ▼Cxmm::ClassParameters< GMM > | Parameters specific to each class of a Gaussian Mixture Model |
| Cxmm::Configuration< GMM > | |
| Cxmm::ClassParameters< KMeans > | Parameters specific to each class of a K-Means Algorithm |
| Cxmm::GaussianDistribution | Multivariate Gaussian Distribution |
| Cxmm::KMeans | K-Means Clustering algorithm |
| Cxmm::Model< SingleClassModel, ModelType > | Probabilistic machine learning model for multiclass recognition and regression |
| Cxmm::Phrase | Data phrase |
| Cxmm::SharedParameters | Shared Parameters for models with multiple classes |
| ▼Cxmm::SingleClassProbabilisticModel | Generic Template for Machine Learning Probabilistic models based on the EM algorithm |
| Cxmm::SingleClassGMM | Single-Class Gaussian Mixture Model |
| Cxmm::SingleClassHMM | Single-Class Hidden Markov Model |
| Cxmm::TrainingSet | Base class for the definition of training sets |