- Generation of predicates
- Use respective B model classes of ProB2
- subsitute definitions
- Generation of predicate lists
- Generation of training set
- From directory of machine files
- From predicate lists
- Training data formats
- Loading of samples from the format
- Create DB
- Create features and labels
- Skip already existing data
- naively by timestamp
- optional by versioned generation step
- Generation statistics
- Return statistics after creation of data
- Wrap StateSpaces
- CLI Option to create data
- Incorporate BPredicate/BElement classes more
- Training set manipulation
- Split training set
- upsample training set
- downsample training set
- shuffle training set
- shuffling of big data sets that do not fit totally into memory
- Training set analysis
- Classification analysis tool
- Regression analysis tool
- PredicateDb analysis
- Data Base translation
- Migrate from old pdump to new JSON
- Translate Db to Training Format
- Training of neural networks
- Enhance versioning of Backends
- Appending to a format
- enhanced documentation
- documentation of JSON entries, (legacy) Predicate dumps, other formats
- usage examples
- Training data generation
- Training data migration
- Training/using neural networks
- RNN support
- set training set structure
- set/implement appropriate RecordReader
- create RNNTrainingDataGenerator(s)
- create RNN features
- raw predicate features
- predicate AST features
- set training set structure
- Data augmentation utilities
- PCA: whitening
- add type information to identifiers
- hungarian notation: "x + y" -> "xInt + yInt"
- joshua notation: "x + y" -> "i1 + i2"
- Normalisation of predicates
- Enhanced analysis of feature sets
- feature dimensionality reduction
- PCA
- RBM
- by decision trees
- t-SNE
- feature dimensionality reduction
- Decision trees
- Random forests
- Deep Forest