Struct BiologicalEmbeddingModel 

pub struct BiologicalEmbeddingModel { /* private fields */ }

Main biological computing embedding model

Implementations

impl BiologicalEmbeddingModel

pub fn new(config: ModelConfig, bio_config: BiologicalComputingConfig) -> Self

Create new biological embedding model

pub fn encode_entity_dna(&mut self, entity: &str) -> DNASequence

Encode entity as DNA sequence

pub fn compute_dna_embedding(&self, entity1: &str, entity2: &str) -> f64

Compute embedding using DNA hybridization

pub fn generate_ca_embedding(&mut self, input_embedding: &Vector) -> Vector

Generate embedding using cellular automaton

pub fn optimize_enzymatic_embedding(&mut self, embedding: &Vector) -> Vector

Optimize embedding using enzymatic network

pub fn evolve_gene_embedding(&mut self, embedding: &Vector) -> Vector

Evolve embedding using gene regulatory network

pub fn assemble_embedding(&mut self, embedding: &Vector) -> Vector

Self-assemble embedding structure

Trait Implementations

impl Clone for BiologicalEmbeddingModel

fn clone(&self) -> BiologicalEmbeddingModel

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for BiologicalEmbeddingModel

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

Formats the value using the given formatter.

impl EmbeddingModel for BiologicalEmbeddingModel

fn config(&self) -> &ModelConfig

fn model_id(&self) -> &Uuid

fn model_type(&self) -> &'static str

fn add_triple(&mut self, triple: Triple) -> Result<()>

fn train<'life0, 'async_trait>( &'life0 mut self, epochs: Option<usize>, ) -> Pin<Box<dyn Future<Output = Result<TrainingStats>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait,

fn get_entity_embedding(&self, entity: &str) -> Result<Vector>

fn get_relation_embedding(&self, relation: &str) -> Result<Vector>

fn score_triple( &self, subject: &str, predicate: &str, object: &str, ) -> Result<f64>

fn predict_objects( &self, subject: &str, predicate: &str, k: usize, ) -> Result<Vec<(String, f64)>>

fn predict_subjects( &self, predicate: &str, object: &str, k: usize, ) -> Result<Vec<(String, f64)>>

fn predict_relations( &self, subject: &str, object: &str, k: usize, ) -> Result<Vec<(String, f64)>>

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

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

fn get_stats(&self) -> ModelStats

fn save(&self, _path: &str) -> Result<()>

fn load(&mut self, _path: &str) -> Result<()>

fn clear(&mut self)

fn is_trained(&self) -> bool

fn encode<'life0, 'life1, 'async_trait>( &'life0 self, texts: &'life1 [String], ) -> Pin<Box<dyn Future<Output = Result<Vec<Vec<f32>>>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Encode text strings into embeddings