Struct CountVectorizer 

pub struct CountVectorizer { /* private fields */ }

Count-based document-feature matrix builder with N-gram support.

Implementations

impl CountVectorizer

pub fn new() -> Self

Create a new CountVectorizer with default settings (unigrams only).

pub fn with_max_features(self, n: usize) -> Self

Limit the vocabulary to the n most frequent features.

pub fn with_ngram_range(self, min: usize, max: usize) -> Self

Set N-gram range (min_n, max_n).

pub fn with_min_df(self, min_df: usize) -> Self

Set minimum document frequency (number of documents a token must appear in).

pub fn with_max_df_ratio(self, ratio: f64) -> Self

Set maximum document frequency as a fraction of the corpus (0.0–1.0).

pub fn fit(&mut self, corpus: &[String]) -> Result<()>

Fit the vocabulary from corpus.

pub fn transform(&self, texts: &[String]) -> Result<Vec<Vec<f64>>>

Transform texts into a count matrix.

pub fn fit_transform(&mut self, corpus: &[String]) -> Result<Vec<Vec<f64>>>

Fit then transform in one step.

pub fn vocabulary_size(&self) -> usize

Return the current vocabulary size.

pub fn vocabulary(&self) -> &HashMap<String, usize>

Borrow the vocabulary map.

Trait Implementations

impl Clone for CountVectorizer

fn clone(&self) -> CountVectorizer

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CountVectorizer

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for CountVectorizer

fn default() -> Self

Returns the “default value” for a type.