Struct classifier::NaiveBayes

pub struct NaiveBayes { /* fields omitted */ }

Naive Bayes classifier

Methods

impl Classifier

fn new() -> Classifier

Creates a new classifier

fn add_document_tokenized(&mut self, document: &Vec<String>, label: &String)

Takes a document that has been tokenized into a vector of strings and a label and adds the document to the list of documents that the classifier is aware of and will train on next time the train() method is called

fn add_document(&mut self, document: &String, label: &String)

Takes a document and a label and tokenizes the document by breaking on whitespace characters. The document is added to the list of documents that the classifier is aware of and will train on next time the train() method is called

fn add_documents(&mut self, examples: &Vec<(String, String)>)

Adds a list of (document, label) tuples to the classifier

fn add_documents_tokenized(&mut self, examples: &Vec<(Vec<String>, String)>)

Adds a list of (tokenized document, label) tuples to the classifier

fn get_labels(&self) -> Vec<String>

Gets a vector of all of the labels that the classifier has seen so far

fn set_smoothing(&mut self, smoothing: f64)

Sets the smoothing value (must be greater than 0.0)

fn train(&mut self)

Trains the classifier on the documents that have been observed so far

fn classify_tokenized(&self, document: &Vec<String>) -> String

Takes an unlabeled document that has been tokenized into a vector of strings and then computes a classifying label for the document

fn classify(&self, document: &String) -> String

Takes an unlabeled document and tokenizes it by breaking on spaces and then computes a classifying label for the document

fn get_document_probabilities_tokenized(
    &self,
    document: &Vec<String>
) -> Vec<(String, f64)>

Similar to classify but instead of returning a single label, returns all labels and the probabilities of each one given the document

fn get_document_probabilities(&self, document: &String) -> Vec<(String, f64)>

Similar to classify but instead of returning a single label, returns all labels and the probabilities of each one given the document

fn to_json(&self) -> String

Encodes the classifier as a JSON string.

fn from_json(encoded: &str) -> Classifier

Builds a new classifier from a JSON string

Trait Implementations

impl Debug for Classifier

fn fmt(&self, __arg_0: &mut Formatter) -> Result

Formats the value using the given formatter.

impl Clone for Classifier

fn clone(&self) -> Classifier

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Decodable for Classifier

fn decode<__D: Decoder>(__arg_0: &mut __D) -> Result<Classifier, __D::Error>

Deserialize a value using a Decoder.

impl Encodable for Classifier

fn encode<__S: Encoder>(&self, __arg_0: &mut __S) -> Result<(), __S::Error>

Serialize a value using an Encoder.