Struct DocumentSimilarityKernel 

pub struct DocumentSimilarityKernel {
    pub vectorizer: TfIdfVectorizer,
    /* private fields */
}

Document similarity kernel based on cosine similarity

This kernel computes the cosine similarity between document vectors, which is commonly used in information retrieval and text classification.

K(d1, d2) = cos(θ) = (d1 · d2) / (||d1|| × ||d2||)

Fields

vectorizer: TfIdfVectorizer

TF-IDF vectorizer

Implementations

impl DocumentSimilarityKernel

pub fn new(vectorizer: TfIdfVectorizer) -> Self

Create a new document similarity kernel

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

Fit the kernel on a corpus

pub fn compute_document_similarity( &self, doc1_idx: usize, doc2_idx: usize, ) -> Result<f64>

Compute similarity between two document indices

pub fn get_document_vectors(&self) -> Option<&Array2<f64>>

Get the document vector matrix

Trait Implementations

impl Clone for DocumentSimilarityKernel

fn clone(&self) -> DocumentSimilarityKernel

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for DocumentSimilarityKernel

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

Formats the value using the given formatter.

impl Kernel for DocumentSimilarityKernel

fn compute(&self, x: ArrayView1<'_, f64>, y: ArrayView1<'_, f64>) -> f64

Compute kernel value between two vectors

fn parameters(&self) -> HashMap<String, f64>

Get kernel parameters for serialization

fn compute_matrix(&self, x: &Array2<f64>, y: &Array2<f64>) -> Array2<f64>

Compute kernel matrix for two datasets