oxirs_embed/

novel_arch_impl.rs

//! Behaviour for [`NovelArchitectureModel`].
//!
//! Holds the inherent impl blocks (initialisation routines, per-architecture
//! training epochs, hyperbolic / Neural-ODE / quantum primitives) and the
//! `EmbeddingModel` trait implementation that exposes the model to the rest
//! of the embedding ecosystem.

use crate::novel_arch_types::{
    ArchitectureState, ArchitectureType, GeometricState, GraphTransformerState, HyperbolicInit,
    HyperbolicState, IntegrationStats, NeuralODEState, NovelArchitectureConfig,
    NovelArchitectureModel, QuantumState,
};
use crate::{EmbeddingModel, ModelConfig, ModelStats, TrainingStats, Triple, Vector};
use anyhow::{anyhow, Result};
use async_trait::async_trait;
use chrono::Utc;
use scirs2_core::ndarray_ext::{s, Array1, Array2, Array3};
use scirs2_core::random::{Random, RngExt};
use std::collections::HashMap;
use uuid::Uuid;

impl NovelArchitectureModel {
    /// Create a new novel architecture model
    pub fn new(config: NovelArchitectureConfig) -> Self {
        let model_id = Uuid::new_v4();
        let dimensions = config.base_config.dimensions;

        Self {
            config,
            model_id,
            entities: HashMap::new(),
            relations: HashMap::new(),
            entity_embeddings: Array2::zeros((0, dimensions)),
            relation_embeddings: Array2::zeros((0, dimensions)),
            architecture_state: ArchitectureState {
                transformer_state: None,
                ode_state: None,
                hyperbolic_state: None,
                geometric_state: None,
                quantum_state: None,
            },
            training_stats: None,
            is_trained: false,
        }
    }

    /// Initialize architecture-specific components
    pub fn initialize_architecture(&mut self) -> Result<()> {
        match &self.config.architecture {
            ArchitectureType::GraphTransformer => {
                self.initialize_graph_transformer()?;
            }
            ArchitectureType::NeuralODE => {
                self.initialize_neural_ode()?;
            }
            ArchitectureType::HyperbolicEmbedding => {
                self.initialize_hyperbolic()?;
            }
            ArchitectureType::GeometricDeepLearning => {
                self.initialize_geometric()?;
            }
            ArchitectureType::QuantumInspired => {
                self.initialize_quantum()?;
            }
            ArchitectureType::ContinuousNormalizingFlow => {
                self.initialize_cnf()?;
            }
        }
        Ok(())
    }

    /// Initialize Graph Transformer components
    fn initialize_graph_transformer(&mut self) -> Result<()> {
        let params = &self.config.architecture_params.transformer_params;
        let num_entities = self.entities.len();

        if num_entities > 0 {
            let attention_weights = Array3::zeros((params.num_layers, num_entities, num_entities));

            let mut random = Random::default();
            let structural_features =
                Array2::from_shape_fn((num_entities, params.structural_dim), |_| {
                    random.random::<f64>()
                });

            let position_encodings = if params.use_positional_encoding {
                Some(Array2::from_shape_fn(
                    (num_entities, params.attention_dim),
                    |_| random.random::<f64>(),
                ))
            } else {
                None
            };

            self.architecture_state.transformer_state = Some(GraphTransformerState {
                attention_weights,
                layer_outputs: Vec::new(),
                structural_features,
                position_encodings,
            });
        }

        Ok(())
    }

    /// Initialize Neural ODE components
    fn initialize_neural_ode(&mut self) -> Result<()> {
        let params = &self.config.architecture_params.ode_params;
        let dimensions = self.config.base_config.dimensions;

        let mut random = Random::default();
        let ode_params = Array2::from_shape_fn((dimensions, params.hidden_dims[0]), |_| {
            random.random::<f64>()
        });

        self.architecture_state.ode_state = Some(NeuralODEState {
            current_time: 0.0,
            trajectory: Vec::new(),
            ode_params,
            integration_stats: IntegrationStats {
                steps_taken: 0,
                function_evaluations: 0,
                jacobian_evaluations: 0,
                failed_steps: 0,
                final_error: 0.0,
            },
        });

        Ok(())
    }

    /// Initialize Hyperbolic components
    fn initialize_hyperbolic(&mut self) -> Result<()> {
        let params = &self.config.architecture_params.hyperbolic_params;
        let num_entities = self.entities.len();

        if num_entities > 0 {
            let mut random = Random::default();
            let manifold_embeddings = match params.initialization {
                HyperbolicInit::RandomNormal => {
                    Array2::from_shape_fn((num_entities, params.manifold_dim), |_| {
                        random.random::<f64>()
                    })
                }
                HyperbolicInit::UniformHyperbolic => {
                    // Initialize uniformly on hyperbolic space
                    let mut embeddings =
                        Array2::from_shape_fn((num_entities, params.manifold_dim), |_| {
                            random.random::<f64>() * 2.0 - 1.0
                        });
                    // Project to Poincaré ball
                    for mut row in embeddings.rows_mut() {
                        let norm = row.mapv(|x| x * x).sum().sqrt();
                        if norm >= 1.0 {
                            row *= 0.99 / norm;
                        }
                    }
                    embeddings
                }
                _ => Array2::from_shape_fn((num_entities, params.manifold_dim), |_| {
                    random.random::<f64>()
                }),
            };

            let tangent_vectors = Array2::zeros((num_entities, params.manifold_dim));
            let metric_tensor =
                Array3::zeros((num_entities, params.manifold_dim, params.manifold_dim));

            self.architecture_state.hyperbolic_state = Some(HyperbolicState {
                manifold_embeddings,
                curvature: params.curvature,
                tangent_vectors,
                metric_tensor,
            });
        }

        Ok(())
    }

    /// Initialize Geometric Deep Learning components
    fn initialize_geometric(&mut self) -> Result<()> {
        let _params = &self.config.architecture_params.geometric_params;
        let dimensions = self.config.base_config.dimensions;

        let mut random = Random::default();
        let connection = Array3::from_shape_fn((dimensions, dimensions, dimensions), |_| {
            random.random::<f64>()
        });

        let curvature_tensor = Array3::from_shape_fn((dimensions, dimensions, dimensions), |_| {
            random.random::<f64>()
        });

        self.architecture_state.geometric_state = Some(GeometricState {
            connection,
            curvature_tensor,
            transport_maps: HashMap::new(),
            equivariance_maps: Vec::new(),
        });

        Ok(())
    }

    /// Initialize Quantum components
    fn initialize_quantum(&mut self) -> Result<()> {
        let params = &self.config.architecture_params.quantum_params;
        let state_dim = 2_usize.pow(params.num_qubits as u32);

        // Initialize quantum state vector (deterministic for test reproducibility)
        let mut state_vector = Array1::from_shape_fn(state_dim, |i| {
            // Use a deterministic pattern based on index to ensure reproducible tests
            0.5 + 0.3 * ((i as f64 + 1.0).sin())
        });
        let norm = state_vector.mapv(|x| x * x).sum().sqrt();
        state_vector /= norm;

        // Initialize quantum gates
        let gates = vec![
            Array2::eye(state_dim), // Identity gate
                                    // Add more gates as needed
        ];

        self.architecture_state.quantum_state = Some(QuantumState {
            state_vector,
            gates,
            measurements: Vec::new(),
            entanglement: 0.0,
        });

        Ok(())
    }

    /// Initialize Continuous Normalizing Flow components
    fn initialize_cnf(&mut self) -> Result<()> {
        // Initialize CNF-specific components
        self.initialize_neural_ode()?;
        Ok(())
    }

    /// Compute hyperbolic distance in Poincaré ball
    pub fn poincare_distance(&self, x: &Array1<f64>, y: &Array1<f64>) -> f64 {
        let curvature = self
            .config
            .architecture_params
            .hyperbolic_params
            .curvature
            .abs();

        let diff = x - y;
        let norm_diff_sq = diff.mapv(|v| v * v).sum();
        let norm_x_sq = x.mapv(|v| v * v).sum();
        let norm_y_sq = y.mapv(|v| v * v).sum();

        let numerator = norm_diff_sq;
        let denominator = (1.0 - norm_x_sq) * (1.0 - norm_y_sq);

        if denominator <= 0.0 {
            return f64::INFINITY;
        }

        let ratio = numerator / denominator;
        (curvature.sqrt()) * (1.0 + 2.0 * ratio).ln()
    }

    /// Compute graph attention for Graph Transformer
    pub fn compute_graph_attention(
        &self,
        queries: &Array2<f64>,
        keys: &Array2<f64>,
        values: &Array2<f64>,
        adjacency: &Array2<f64>,
    ) -> Result<Array2<f64>> {
        let attention_scores = queries.dot(keys);

        // Apply structural bias
        let masked_scores = &attention_scores * adjacency;

        // Apply softmax
        let softmax_scores = self.softmax_2d(&masked_scores);

        // Apply to values
        Ok(softmax_scores.dot(values))
    }

    /// Apply softmax to 2D array
    pub(crate) fn softmax_2d(&self, x: &Array2<f64>) -> Array2<f64> {
        let mut result = x.clone();
        for mut row in result.rows_mut() {
            let max_val = row.fold(f64::NEG_INFINITY, |a, &b| a.max(b));
            row.mapv_inplace(|v| (v - max_val).exp());
            let sum = row.sum();
            if sum > 0.0 {
                row /= sum;
            }
        }
        result
    }

    /// Solve Neural ODE using Runge-Kutta method
    pub fn solve_neural_ode(
        &mut self,
        initial_state: &Array2<f64>,
        time_span: (f64, f64),
    ) -> Result<Array2<f64>> {
        let (t_start, t_end) = time_span;
        let params = &self.config.architecture_params.ode_params;
        let dt = (t_end - t_start) / params.time_steps as f64;

        let mut state = initial_state.clone();
        let mut t = t_start;

        // Store trajectory and update stats
        let mut trajectory = Vec::new();
        trajectory.push(state.clone());

        for _ in 0..params.time_steps {
            // Runge-Kutta 4th order step
            let k1 = self.ode_function(&state, t)?;
            let k2 = self.ode_function(&(&state + &(&k1 * (dt / 2.0))), t + dt / 2.0)?;
            let k3 = self.ode_function(&(&state + &(&k2 * (dt / 2.0))), t + dt / 2.0)?;
            let k4 = self.ode_function(&(&state + &(&k3 * dt)), t + dt)?;

            state = &state + &((&k1 + &(&k2 * 2.0) + &(&k3 * 2.0) + &k4) * (dt / 6.0));
            t += dt;

            trajectory.push(state.clone());
        }

        // Update ODE state after computation
        if let Some(ref mut ode_state) = self.architecture_state.ode_state {
            ode_state.trajectory = trajectory;
            ode_state.integration_stats.steps_taken += params.time_steps;
            ode_state.integration_stats.function_evaluations += params.time_steps * 4;
            ode_state.current_time = t;
        }

        Ok(state)
    }

    /// ODE function f(y, t) for dy/dt = f(y, t)
    pub(crate) fn ode_function(&self, state: &Array2<f64>, _t: f64) -> Result<Array2<f64>> {
        if let Some(ref ode_state) = self.architecture_state.ode_state {
            // Simple neural ODE function: tanh(Wy + b)
            let result = state.dot(&ode_state.ode_params);
            Ok(result.mapv(|x| x.tanh()))
        } else {
            Err(anyhow!("Neural ODE state not initialized"))
        }
    }

    /// Compute quantum-inspired output using classical simulation
    /// Note: Full quantum circuit implementation removed - awaiting quantum computing library stabilization
    pub fn quantum_forward(&self, input: &Array1<f64>) -> Result<Array1<f64>> {
        // Use a simple classical simulation that mimics quantum behavior
        // This provides a placeholder until a stable quantum computing library is available
        let mut output = Array1::zeros(input.len());

        // Apply a simple transformation inspired by quantum gates
        for (i, &val) in input.iter().enumerate() {
            // Simulate Hadamard-like superposition and phase rotation
            let angle = val * std::f64::consts::PI;
            output[i] = angle.cos().tanh(); // Bounded output in [-1, 1]
        }

        Ok(output)
    }
}

#[async_trait]
impl EmbeddingModel for NovelArchitectureModel {
    fn config(&self) -> &ModelConfig {
        &self.config.base_config
    }

    fn model_id(&self) -> &Uuid {
        &self.model_id
    }

    fn model_type(&self) -> &'static str {
        match self.config.architecture {
            ArchitectureType::GraphTransformer => "NovelArchitecture::GraphTransformer",
            ArchitectureType::NeuralODE => "NovelArchitecture::NeuralODE",
            ArchitectureType::HyperbolicEmbedding => "NovelArchitecture::HyperbolicEmbedding",
            ArchitectureType::GeometricDeepLearning => "NovelArchitecture::GeometricDeepLearning",
            ArchitectureType::QuantumInspired => "NovelArchitecture::QuantumInspired",
            ArchitectureType::ContinuousNormalizingFlow => {
                "NovelArchitecture::ContinuousNormalizingFlow"
            }
        }
    }

    fn add_triple(&mut self, triple: Triple) -> Result<()> {
        let subject_str = triple.subject.iri.clone();
        let predicate_str = triple.predicate.iri.clone();
        let object_str = triple.object.iri.clone();

        // Add entities
        let next_entity_id = self.entities.len();
        let subject_id = *self.entities.entry(subject_str).or_insert(next_entity_id);
        if subject_id == next_entity_id {
            self.entity_embeddings =
                self.resize_embeddings(&self.entity_embeddings, self.entities.len());
        }

        let next_entity_id = self.entities.len();
        let object_id = *self.entities.entry(object_str).or_insert(next_entity_id);
        if object_id == next_entity_id {
            self.entity_embeddings =
                self.resize_embeddings(&self.entity_embeddings, self.entities.len());
        }

        // Add relation
        let next_relation_id = self.relations.len();
        let _predicate_id = *self
            .relations
            .entry(predicate_str)
            .or_insert(next_relation_id);
        if _predicate_id == next_relation_id {
            self.relation_embeddings =
                self.resize_embeddings(&self.relation_embeddings, self.relations.len());
        }

        Ok(())
    }

    async fn train(&mut self, epochs: Option<usize>) -> Result<TrainingStats> {
        let epochs = epochs.unwrap_or(self.config.base_config.max_epochs);
        let start_time = std::time::Instant::now();

        // Initialize architecture-specific components
        self.initialize_architecture()?;

        // Training loop with architecture-specific updates
        let mut loss_history = Vec::new();

        for epoch in 0..epochs {
            let epoch_loss = match &self.config.architecture {
                ArchitectureType::GraphTransformer => self.train_graph_transformer_epoch()?,
                ArchitectureType::NeuralODE => self.train_neural_ode_epoch()?,
                ArchitectureType::HyperbolicEmbedding => self.train_hyperbolic_epoch()?,
                ArchitectureType::GeometricDeepLearning => self.train_geometric_epoch()?,
                ArchitectureType::QuantumInspired => self.train_quantum_epoch()?,
                ArchitectureType::ContinuousNormalizingFlow => self.train_cnf_epoch()?,
            };

            loss_history.push(epoch_loss);

            // Early stopping check
            if epoch > 10 && epoch_loss < 1e-6 {
                break;
            }
        }

        let training_time = start_time.elapsed().as_secs_f64();
        let final_loss = loss_history.last().copied().unwrap_or(0.0);

        let stats = TrainingStats {
            epochs_completed: loss_history.len(),
            final_loss,
            training_time_seconds: training_time,
            convergence_achieved: final_loss < 1e-4,
            loss_history,
        };

        self.training_stats = Some(stats.clone());
        self.is_trained = true;

        Ok(stats)
    }

    fn get_entity_embedding(&self, entity: &str) -> Result<Vector> {
        if let Some(&entity_id) = self.entities.get(entity) {
            if entity_id < self.entity_embeddings.nrows() {
                let embedding = self.entity_embeddings.row(entity_id);
                return Ok(Vector::new(embedding.mapv(|x| x as f32).to_vec()));
            }
        }
        Err(anyhow!("Entity not found: {}", entity))
    }

    fn get_relation_embedding(&self, relation: &str) -> Result<Vector> {
        if let Some(&relation_id) = self.relations.get(relation) {
            if relation_id < self.relation_embeddings.nrows() {
                let embedding = self.relation_embeddings.row(relation_id);
                return Ok(Vector::new(embedding.mapv(|x| x as f32).to_vec()));
            }
        }
        Err(anyhow!("Relation not found: {}", relation))
    }

    fn score_triple(&self, subject: &str, predicate: &str, object: &str) -> Result<f64> {
        let subject_emb = self.get_entity_embedding(subject)?;
        let predicate_emb = self.get_relation_embedding(predicate)?;
        let object_emb = self.get_entity_embedding(object)?;

        match &self.config.architecture {
            ArchitectureType::HyperbolicEmbedding => {
                // Use hyperbolic distance for scoring
                let subject_arr = Array1::from_vec(
                    subject_emb
                        .values
                        .iter()
                        .copied()
                        .map(|x| x as f64)
                        .collect(),
                );
                let object_arr = Array1::from_vec(
                    object_emb
                        .values
                        .iter()
                        .copied()
                        .map(|x| x as f64)
                        .collect(),
                );
                let distance = self.poincare_distance(&subject_arr, &object_arr);
                Ok(-distance) // Negative distance as score
            }
            _ => {
                // Standard TransE-like scoring
                let subject_arr = Array1::from_vec(
                    subject_emb
                        .values
                        .iter()
                        .copied()
                        .map(|x| x as f64)
                        .collect(),
                );
                let predicate_arr = Array1::from_vec(
                    predicate_emb
                        .values
                        .iter()
                        .copied()
                        .map(|x| x as f64)
                        .collect(),
                );
                let object_arr = Array1::from_vec(
                    object_emb
                        .values
                        .iter()
                        .copied()
                        .map(|x| x as f64)
                        .collect(),
                );

                let predicted = &subject_arr + &predicate_arr;
                let diff = &predicted - &object_arr;
                let distance = diff.mapv(|x| x * x).sum().sqrt();
                Ok(-distance)
            }
        }
    }

    fn predict_objects(
        &self,
        subject: &str,
        predicate: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        let mut scores = Vec::new();

        for entity in self.entities.keys() {
            if entity != subject {
                let score = self.score_triple(subject, predicate, entity)?;
                scores.push((entity.clone(), score));
            }
        }

        scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        scores.truncate(k);

        Ok(scores)
    }

    fn predict_subjects(
        &self,
        predicate: &str,
        object: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        let mut scores = Vec::new();

        for entity in self.entities.keys() {
            if entity != object {
                let score = self.score_triple(entity, predicate, object)?;
                scores.push((entity.clone(), score));
            }
        }

        scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        scores.truncate(k);

        Ok(scores)
    }

    fn predict_relations(
        &self,
        subject: &str,
        object: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        let mut scores = Vec::new();

        for relation in self.relations.keys() {
            let score = self.score_triple(subject, relation, object)?;
            scores.push((relation.clone(), score));
        }

        scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        scores.truncate(k);

        Ok(scores)
    }

    fn get_entities(&self) -> Vec<String> {
        self.entities.keys().cloned().collect()
    }

    fn get_relations(&self) -> Vec<String> {
        self.relations.keys().cloned().collect()
    }

    fn get_stats(&self) -> ModelStats {
        ModelStats {
            num_entities: self.entities.len(),
            num_relations: self.relations.len(),
            num_triples: 0, // Would need to track this
            dimensions: self.config.base_config.dimensions,
            is_trained: self.is_trained,
            model_type: self.model_type().to_string(),
            creation_time: Utc::now(),
            last_training_time: if self.is_trained {
                Some(Utc::now())
            } else {
                None
            },
        }
    }

    fn save(&self, _path: &str) -> Result<()> {
        // Implementation would serialize the model state
        Ok(())
    }

    fn load(&mut self, _path: &str) -> Result<()> {
        // Implementation would deserialize the model state
        Ok(())
    }

    fn clear(&mut self) {
        self.entities.clear();
        self.relations.clear();
        self.entity_embeddings = Array2::zeros((0, self.config.base_config.dimensions));
        self.relation_embeddings = Array2::zeros((0, self.config.base_config.dimensions));
        self.is_trained = false;
        self.training_stats = None;
    }

    fn is_trained(&self) -> bool {
        self.is_trained
    }

    async fn encode(&self, texts: &[String]) -> Result<Vec<Vec<f32>>> {
        // Simple encoding for novel architectures
        let mut results = Vec::new();

        for text in texts {
            match &self.config.architecture {
                ArchitectureType::QuantumInspired => {
                    // Use quantum encoding
                    let input = Array1::from_vec(
                        text.chars()
                            .take(self.config.base_config.dimensions)
                            .map(|c| (c as u8 as f64) / 255.0)
                            .collect(),
                    );

                    // Pad or truncate to required dimension
                    let mut padded_input = Array1::zeros(self.config.base_config.dimensions);
                    let copy_len = input.len().min(self.config.base_config.dimensions);
                    padded_input
                        .slice_mut(s![..copy_len])
                        .assign(&input.slice(s![..copy_len]));

                    match self.quantum_forward(&padded_input) {
                        Ok(quantum_output) => {
                            results.push(quantum_output.mapv(|x| x as f32).to_vec());
                        }
                        _ => {
                            results.push(vec![0.0; self.config.base_config.dimensions]);
                        }
                    }
                }
                _ => {
                    // Standard text encoding
                    let mut embedding = vec![0.0f32; self.config.base_config.dimensions];
                    for (i, c) in text.chars().enumerate() {
                        if i >= self.config.base_config.dimensions {
                            break;
                        }
                        embedding[i] = (c as u8 as f32) / 255.0;
                    }
                    results.push(embedding);
                }
            }
        }

        Ok(results)
    }
}

impl NovelArchitectureModel {
    /// Helper function to resize embedding matrices
    fn resize_embeddings(&self, embeddings: &Array2<f64>, new_size: usize) -> Array2<f64> {
        let dimensions = self.config.base_config.dimensions;
        let mut random = Random::default();
        let mut new_embeddings =
            Array2::from_shape_fn((new_size, dimensions), |_| random.random_range(-1.0..1.0));

        let copy_rows = embeddings.nrows().min(new_size);
        if copy_rows > 0 {
            new_embeddings
                .slice_mut(s![..copy_rows, ..])
                .assign(&embeddings.slice(s![..copy_rows, ..]));
        }

        new_embeddings
    }

    /// Training epoch for Graph Transformer
    fn train_graph_transformer_epoch(&mut self) -> Result<f64> {
        if self.entities.is_empty() {
            return Ok(0.0);
        }

        // Simulate graph transformer training
        let num_entities = self.entities.len();
        let adjacency = Array2::eye(num_entities); // Simple identity for now

        if let Some(ref mut transformer_state) = self.architecture_state.transformer_state {
            // Update attention weights
            for layer in 0..transformer_state.attention_weights.shape()[0] {
                let mut layer_attention =
                    transformer_state
                        .attention_weights
                        .slice_mut(s![layer, .., ..]);
                layer_attention.assign(&adjacency);
            }

            // Compute layer outputs
            transformer_state.layer_outputs.clear();
            transformer_state
                .layer_outputs
                .push(self.entity_embeddings.clone());
        }

        Ok(0.1) // Return mock loss
    }

    /// Training epoch for Neural ODE
    fn train_neural_ode_epoch(&mut self) -> Result<f64> {
        if self.entities.is_empty() {
            return Ok(0.0);
        }

        // Simulate Neural ODE training by solving ODE
        let embeddings = self.entity_embeddings.clone();
        let _final_state = self.solve_neural_ode(&embeddings, (0.0, 1.0))?;

        Ok(0.1) // Return mock loss
    }

    /// Training epoch for Hyperbolic embedding
    fn train_hyperbolic_epoch(&mut self) -> Result<f64> {
        if self.entities.is_empty() {
            return Ok(0.0);
        }

        // Simulate hyperbolic training
        if let Some(ref mut hyperbolic_state) = self.architecture_state.hyperbolic_state {
            // Project embeddings to Poincaré ball
            for mut row in hyperbolic_state.manifold_embeddings.rows_mut() {
                let norm = row.mapv(|x| x * x).sum().sqrt();
                if norm >= 1.0 {
                    row *= 0.99 / norm;
                }
            }
        }

        Ok(0.1) // Return mock loss
    }

    /// Training epoch for Geometric Deep Learning
    fn train_geometric_epoch(&mut self) -> Result<f64> {
        if self.entities.is_empty() {
            return Ok(0.0);
        }

        // Simulate geometric training
        if let Some(ref mut geometric_state) = self.architecture_state.geometric_state {
            // Update connection coefficients
            geometric_state.connection *= 0.99; // Simple decay
        }

        Ok(0.1) // Return mock loss
    }

    /// Training epoch for Quantum-inspired model
    fn train_quantum_epoch(&mut self) -> Result<f64> {
        if self.entities.is_empty() {
            return Ok(0.0);
        }

        // Simulate quantum training
        if let Some(ref mut quantum_state) = self.architecture_state.quantum_state {
            // Normalize quantum state
            let norm = quantum_state.state_vector.mapv(|x| x * x).sum().sqrt();
            if norm > 0.0 {
                quantum_state.state_vector /= norm;
            }
        }

        Ok(0.1) // Return mock loss
    }

    /// Training epoch for Continuous Normalizing Flow
    fn train_cnf_epoch(&mut self) -> Result<f64> {
        // CNF training similar to Neural ODE
        self.train_neural_ode_epoch()
    }
}