Module vectorize 

Text vectorization for machine learning.

This module provides vectorization tools to convert text documents into numerical feature vectors suitable for machine learning models:

• CountVectorizer: Bag of Words representation (word counts)
• TfidfVectorizer: TF-IDF weighted features (term frequency-inverse document frequency)

Design Principles

• Zero unwrap() calls (Cloudflare-class safety)
• Result-based error handling
• Comprehensive test coverage (≥95%)
• Integration with tokenizers and stop words

Quick Start

use aprender::text::vectorize::CountVectorizer;
use aprender::text::tokenize::WhitespaceTokenizer;

let documents = vec![
    "the cat sat on the mat",
    "the dog sat on the log",
];

let mut vectorizer = CountVectorizer::new()
    .with_tokenizer(Box::new(WhitespaceTokenizer::new()));

let matrix = vectorizer.fit_transform(&documents).expect("vectorization should succeed");
// matrix shape: (2 documents, vocabulary_size features)

Structs

CountVectorizer

Bag of Words vectorizer that converts text to word count matrix.

HashingVectorizer

Stateless hashing vectorizer for streaming/large-scale text.

TfidfVectorizer

TF-IDF vectorizer that converts text to TF-IDF weighted matrix.