linfa-preprocessing is a crate in the
linfa ecosystem, an effort to create a toolkit for classical Machine Learning implemented in pure Rust, akin to Python’s
linfa-preprocessing provides a pure Rust implementation of:
- Standard scaling
- Min-max scaling
- Max Abs Scaling
- Normalization (l1, l2 and max norm)
- Count vectorization
- Term frequency - inverse document frequency count vectorization
Error definitions for preprocessing
Linear Scaling methods
Sample normalization methods
Term frequency - inverse document frequency vectorization methods
Methods for uncorrelating data
Counts the occurrences of each vocabulary entry, learned during fitting, in a sequence of documents. Each vocabulary entry is mapped to an integer value that is used to index the count in the result.
Count vectorizer: learns a vocabulary from a sequence of documents (or file paths) and maps each vocabulary entry to an integer value, producing a FittedCountVectorizer that can be used to count the occurrences of each vocabulary entry in any sequence of documents. Alternatively a user-specified vocabulary can be used for fitting.