Struct DistMult 

pub struct DistMult {
    pub entity_embeddings: Vec<Vec<f64>>,
    pub relation_embeddings: Vec<Vec<f64>>,
    pub dim: usize,
}

DistMult knowledge graph embedding model.

Score: Σ_k h_k · r_k · t_k (element-wise bilinear product).

Fields

entity_embeddings: Vec<Vec<f64>>

Entity embedding table [n_entities, dim]

relation_embeddings: Vec<Vec<f64>>

Relation (diagonal) embedding table [n_relations, dim]

dim: usize

Embedding dimension

Implementations

impl DistMult

pub fn new(n_entities: usize, n_relations: usize, dim: usize) -> Result<Self>

Create a DistMult model with random initialisation.

pub fn score(&self, h: usize, r: usize, t: usize) -> Result<f64>

Score triple (h, r, t): Σ h_k r_k t_k.

pub fn link_prediction_score(&self, h: usize, r: usize, t: usize) -> Result<f64>

Link prediction score.

pub fn predict_tails(&self, h: usize, r: usize, k: usize) -> Result<Vec<usize>>

Return the top-k entity indices most likely to complete (h, r, ?).

pub fn train_epoch(&mut self, dataset: &KGDataset, lr: f64, margin: f64) -> f64

Train one epoch with margin-based negative sampling.

Trait Implementations

impl Clone for DistMult

fn clone(&self) -> DistMult

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for DistMult

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

Formats the value using the given formatter.