References

summary
public	C ColumnVectorizer class creating sparse matrices from a corpus
public	C DataSet class for manipulating an array of objects, typically from CSV data
public	C ReinforcedLearningBase base interface class for reinforced learning
public	C ThompsonSampling Implementation of the Thompson Sampling algorithm
public	C UpperConfidenceBound Implementation of the Upper Confidence Bound algorithm
public	F assocationRuleLearning(transactions: Array, options: Object): Object returns association rule learning results
public	F getTransactions(data: Array, options: Object): Object Formats an array of transactions into a sparse matrix like format for Apriori/Eclat
public	F async loadCSV(filepath: string, options: Object): Object[] Asynchronously loads a CSV from either a filepath or remote URL and returns an array of objects
public	F async loadCSVURI(filepath: string, options: Object): Object[] Asynchronously loads a CSV from a remote URL and returns an array of objects
public	F async loadTSV(filepath: string, options: Object): Object[] Asynchronously loads a TSV from either a filepath or remote URL and returns an array of objects
public	V calc
public	V cross_validation
public	V PD
public	V calc
public	V cross_validation
public	V csv
public	V loadCSV
public	V loadCSVURI
public	V ml
public	V model_selection
public	V nlp
public	V preprocessing
public	V util
public	V ml
public	V nlp
public	V PD
public	V util