Struct linfa_preprocessing::count_vectorization::FittedCountVectorizer

pub struct FittedCountVectorizer { /* fields omitted */ }

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.

Implementations

impl FittedCountVectorizer

pub fn nentries(&self) -> usize

Number of vocabulary entries learned during fitting

pub fn transform<T: ToString, D: Data<Elem = T>>(
    &self,
    x: &ArrayBase<D, Ix1>
) -> CsMat<usize>

Given a sequence of n documents, produces a sparse array of size (n, vocabulary_entries) where column j of row i is the number of occurrences of vocabulary entry j in the document of index i. Vocabulary entry j is the string at the j-th position in the vocabulary. If a vocabulary entry was not encountered in a document, then the relative cell in the sparse matrix will be set to None.

pub fn transform_files<P: AsRef<Path>>(
    &self,
    input: &[P],
    encoding: EncodingRef,
    trap: DecoderTrap
) -> CsMat<usize>

Given a sequence of n file names, produces a sparse array of size (n, vocabulary_entries) where column j of row i is the number of occurrences of vocabulary entry j in the document contained in the file of index i. Vocabulary entry j is the string at the j-th position in the vocabulary. If a vocabulary entry was not encountered in a document, then the relative cell in the sparse matrix will be set to None.

The files will be read using the specified encoding, and any sequence unrecognized by the encoding will be handled according to trap.

pub fn vocabulary(&self) -> &Vec<String>

Contains all vocabulary entries, in the same order used by the transform methods.