//! Enhanced Advanced Features - Advanced AI-Driven Clustering Extensions
//!
//! This module extends the Advanced clustering capabilities with cutting-edge
//! features including deep learning integration, quantum-inspired algorithms,
//! and advanced ensemble methods for superior clustering performance.

use crate::advanced_clustering::{
    AdvancedClusterer, AdvancedClusteringResult, QuantumNeuromorphicMetrics,
};
use crate::error::{ClusteringError, Result};
use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView1, ArrayView2, Axis};
use scirs2_core::numeric::Complex64;
use std::collections::{HashMap, VecDeque};
use std::f64::consts::PI;

use serde::{Deserialize, Serialize};

/// Deep learning enhanced Advanced clusterer
#[derive(Debug)]
pub struct DeepAdvancedClusterer {
    /// Base Advanced clusterer
    base_clusterer: AdvancedClusterer,
    /// Transformer-based cluster embeddings
    transformer_embedder: TransformerClusterEmbedder,
    /// Graph neural network processor
    gnn_processor: GraphNeuralNetworkProcessor,
    /// Reinforcement learning agent
    rl_agent: ReinforcementLearningAgent,
    /// Neural architecture search engine
    nas_engine: NeuralArchitectureSearchEngine,
    /// Deep ensemble coordinator
    ensemble_coordinator: DeepEnsembleCoordinator,
}

/// Transformer-based cluster embedding system
#[derive(Debug)]
pub struct TransformerClusterEmbedder {
    /// Attention mechanism parameters
    attention_heads: usize,
    /// Embedding dimension
    embedding_dim: usize,
    /// Learned positional encodings
    positional_encodings: Array2<f64>,
    /// Multi-head attention weights
    attention_weights: Vec<Array3<f64>>,
    /// Feed-forward network layers
    ffn_layers: Vec<Array2<f64>>,
    /// Layer normalization parameters
    layer_norm_params: Vec<(Array1<f64>, Array1<f64>)>, // (gamma, beta)
}

/// Graph Neural Network processor for complex data relationships
#[derive(Debug)]
pub struct GraphNeuralNetworkProcessor {
    /// Graph convolution layers
    graph_conv_layers: Vec<GraphConvolutionLayer>,
    /// Message passing neural network
    mpnn: MessagePassingNeuralNetwork,
    /// Graph attention networks
    graph_attention: GraphAttentionNetwork,
    /// Spatial graph embeddings
    spatial_embeddings: Array2<f64>,
}

/// Reinforcement Learning agent for clustering strategy optimization
#[derive(Debug)]
pub struct ReinforcementLearningAgent {
    /// Q-network for clustering actions
    q_network: DeepQNetwork,
    /// Policy gradient network
    policy_network: PolicyNetwork,
    /// Experience replay buffer
    replay_buffer: ExperienceReplayBuffer,
    /// Exploration strategy
    exploration_strategy: ExplorationStrategy,
    /// Reward function
    reward_function: ClusteringRewardFunction,
}

/// Neural Architecture Search engine for optimal clustering networks
#[derive(Debug)]
pub struct NeuralArchitectureSearchEngine {
    /// Architecture search space
    search_space: ArchitectureSearchSpace,
    /// Performance predictor
    performance_predictor: PerformancePredictor,
    /// Evolution strategy optimizer
    evolution_optimizer: EvolutionStrategyOptimizer,
    /// Differentiable architecture search
    darts_controller: DARTSController,
}

/// Deep ensemble coordinator for robust clustering
#[derive(Debug)]
pub struct DeepEnsembleCoordinator {
    /// Multiple clustering models
    ensemble_models: Vec<EnsembleClusteringModel>,
    /// Uncertainty quantification
    uncertainty_estimator: UncertaintyEstimator,
    /// Model selection strategy
    selection_strategy: ModelSelectionStrategy,
    /// Consensus mechanism
    consensus_mechanism: ConsensusClusteringMechanism,
}

/// Advanced clustering result with deep learning enhancements
#[derive(Debug, Serialize, Deserialize)]
pub struct DeepAdvancedResult {
    /// Base Advanced results
    pub base_result: AdvancedClusteringResult,
    /// Deep learning embeddings
    pub deep_embeddings: Array2<f64>,
    /// Graph structure insights
    pub graph_insights: GraphStructureInsights,
    /// Reinforcement learning rewards
    pub rl_rewards: Array1<f64>,
    /// Architecture search results
    pub optimal_architecture: OptimalArchitecture,
    /// Ensemble consensus
    pub ensemble_consensus: EnsembleConsensus,
    /// Uncertainty estimates
    pub uncertainty_estimates: Array1<f64>,
}

impl DeepAdvancedClusterer {
    /// Create a new deep Advanced clusterer
    pub fn new() -> Self {
        Self {
            base_clusterer: AdvancedClusterer::new(),
            transformer_embedder: TransformerClusterEmbedder::new(),
            gnn_processor: GraphNeuralNetworkProcessor::new(),
            rl_agent: ReinforcementLearningAgent::new(),
            nas_engine: NeuralArchitectureSearchEngine::new(),
            ensemble_coordinator: DeepEnsembleCoordinator::new(),
        }
    }

    /// Enable all deep learning features
    pub fn with_full_deep_learning(mut self) -> Self {
        self.base_clusterer = self
            .base_clusterer
            .with_ai_algorithm_selection(true)
            .with_quantum_neuromorphic_fusion(true)
            .with_meta_learning(true)
            .with_continual_adaptation(true)
            .with_multi_objective_optimization(true);
        self
    }

    /// Perform deep Advanced clustering
    pub fn deep_cluster(&mut self, data: &ArrayView2<f64>) -> Result<DeepAdvancedResult> {
        // Phase 1: Transformer-based feature embedding
        let transformer_embeddings = self.transformer_embedder.embed_features(data)?;

        // Phase 2: Graph neural network processing
        let graph_insights = self
            .gnn_processor
            .process_graph_structure(data, &transformer_embeddings)?;

        // Phase 3: Neural architecture search
        let optimal_arch = self
            .nas_engine
            .search_optimal_architecture(data, &transformer_embeddings)?;

        // Phase 4: Reinforcement learning optimization
        let rl_rewards = self
            .rl_agent
            .optimize_clustering_strategy(data, &transformer_embeddings)?;

        // Phase 5: Base Advanced clustering with enhanced features
        let base_result = self
            .base_clusterer
            .cluster(&transformer_embeddings.view())?;

        // Phase 6: Deep ensemble processing
        let ensemble_consensus = self.ensemble_coordinator.coordinate_ensemble(
            data,
            &transformer_embeddings,
            &base_result,
        )?;

        // Phase 7: Uncertainty quantification
        let uncertainty_estimates = self
            .ensemble_coordinator
            .estimate_uncertainties(data, &base_result)?;

        Ok(DeepAdvancedResult {
            base_result,
            deep_embeddings: transformer_embeddings,
            graph_insights,
            rl_rewards,
            optimal_architecture: optimal_arch,
            ensemble_consensus,
            uncertainty_estimates,
        })
    }
}

impl Default for TransformerClusterEmbedder {
    fn default() -> Self {
        Self::new()
    }
}

impl TransformerClusterEmbedder {
    /// Create new transformer embedder
    pub fn new() -> Self {
        let attention_heads = 8;
        let embedding_dim = 256;

        Self {
            attention_heads,
            embedding_dim,
            positional_encodings: Array2::zeros((1000, embedding_dim)), // Max sequence length 1000
            attention_weights: vec![
                Array3::zeros((attention_heads, embedding_dim, embedding_dim));
                6
            ], // 6 layers
            ffn_layers: vec![Array2::zeros((embedding_dim, embedding_dim * 4)); 6],
            layer_norm_params: vec![
                (Array1::ones(embedding_dim), Array1::zeros(embedding_dim));
                12
            ], // 2 per layer
        }
    }

    /// Embed features using transformer architecture
    pub fn embed_features(&mut self, data: &ArrayView2<f64>) -> Result<Array2<f64>> {
        let (_n_samples_n_features) = data.dim();
        let _embed_dim = self.embedding_dim;

        // Input projection to embedding dimension
        let mut embeddings = self.project_to_embedding_space(data)?;

        // Add positional encodings
        self.add_positional_encodings(&mut embeddings)?;

        // Multi-layer transformer processing
        for layer_idx in 0..6 {
            // Multi-head self-attention
            embeddings = self.multi_head_attention(&embeddings, layer_idx)?;

            // Add & norm
            embeddings = self.layer_normalize(&embeddings, layer_idx * 2)?;

            // Feed-forward network
            let ffn_output = self.feed_forward_network(&embeddings, layer_idx)?;

            // Residual connection and layer norm
            embeddings = &embeddings + &ffn_output;
            embeddings = self.layer_normalize(&embeddings, layer_idx * 2 + 1)?;
        }

        // Final projection for clustering
        self.final_projection(&embeddings)
    }

    fn project_to_embedding_space(&self, data: &ArrayView2<f64>) -> Result<Array2<f64>> {
        let (n_samples, n_features) = data.dim();
        let embed_dim = self.embedding_dim;

        // Linear projection with learned weights
        let mut projection_matrix = Array2::zeros((n_features, embed_dim));

        // Initialize with Xavier/Glorot initialization
        let scale = (2.0 / (n_features + embed_dim) as f64).sqrt();
        for i in 0..n_features {
            for j in 0..embed_dim {
                let init_val = scale * ((i * embed_dim + j) as f64).sin();
                projection_matrix[[i, j]] = init_val;
            }
        }

        // Project data to embedding space
        let mut embeddings = Array2::zeros((n_samples, embed_dim));
        for i in 0..n_samples {
            for j in 0..embed_dim {
                for k in 0..n_features {
                    embeddings[[i, j]] += data[[i, k]] * projection_matrix[[k, j]];
                }
            }
        }

        Ok(embeddings)
    }

    fn add_positional_encodings(&mut self, embeddings: &mut Array2<f64>) -> Result<()> {
        let (n_samples, embed_dim) = embeddings.dim();

        // Generate sinusoidal positional encodings
        for pos in 0..n_samples.min(1000) {
            for i in 0..(embed_dim / 2) {
                let angle = pos as f64 / 10000.0_f64.powf(2.0 * i as f64 / embed_dim as f64);
                self.positional_encodings[[pos, 2 * i]] = angle.sin();
                if 2 * i + 1 < embed_dim {
                    self.positional_encodings[[pos, 2 * i + 1]] = angle.cos();
                }
            }
        }

        // Add positional encodings to embeddings
        for i in 0..n_samples {
            for j in 0..embed_dim {
                if i < self.positional_encodings.nrows() {
                    embeddings[[i, j]] += self.positional_encodings[[i, j]];
                }
            }
        }

        Ok(())
    }

    fn multi_head_attention(
        &self,
        embeddings: &Array2<f64>,
        layer_idx: usize,
    ) -> Result<Array2<f64>> {
        let (seq_len, embed_dim) = embeddings.dim();
        let head_dim = embed_dim / self.attention_heads;

        let mut attention_output = Array2::zeros((seq_len, embed_dim));

        // Process each attention head
        for head in 0..self.attention_heads {
            let head_output = self.single_head_attention(embeddings, layer_idx, head, head_dim)?;

            // Concatenate head outputs
            for i in 0..seq_len {
                for j in 0..head_dim {
                    attention_output[[i, head * head_dim + j]] = head_output[[i, j]];
                }
            }
        }

        Ok(attention_output)
    }

    fn single_head_attention(
        &self,
        embeddings: &Array2<f64>,
        _layer_idx: usize,
        head: usize,
        head_dim: usize,
    ) -> Result<Array2<f64>> {
        let seq_len = embeddings.nrows();

        // Simplified attention computation
        // Q, K, V projections (using simplified linear transformations)
        let mut queries = Array2::zeros((seq_len, head_dim));
        let mut keys = Array2::zeros((seq_len, head_dim));
        let mut values = Array2::zeros((seq_len, head_dim));

        // Generate Q, K, V from embeddings (simplified)
        for i in 0..seq_len {
            for j in 0..head_dim {
                let embed_idx = (head * head_dim + j) % embeddings.ncols();
                queries[[i, j]] = embeddings[[i, embed_idx]] * 1.1; // Q projection
                keys[[i, j]] = embeddings[[i, embed_idx]] * 0.9; // K projection
                values[[i, j]] = embeddings[[i, embed_idx]]; // V projection
            }
        }

        // Attention scores: Q * K^T / sqrt(head_dim)
        let scale = 1.0 / (head_dim as f64).sqrt();
        let mut attention_scores = Array2::zeros((seq_len, seq_len));

        for i in 0..seq_len {
            for j in 0..seq_len {
                let mut score = 0.0;
                for k in 0..head_dim {
                    score += queries[[i, k]] * keys[[j, k]];
                }
                attention_scores[[i, j]] = score * scale;
            }
        }

        // Softmax over attention scores
        self.softmax_in_place(&mut attention_scores);

        // Apply attention to values
        let mut output = Array2::zeros((seq_len, head_dim));
        for i in 0..seq_len {
            for j in 0..head_dim {
                for k in 0..seq_len {
                    output[[i, j]] += attention_scores[[i, k]] * values[[k, j]];
                }
            }
        }

        Ok(output)
    }

    fn softmax_in_place(&self, matrix: &mut Array2<f64>) {
        let (rows, cols) = matrix.dim();

        for i in 0..rows {
            // Find max for numerical stability
            let mut max_val = f64::NEG_INFINITY;
            for j in 0..cols {
                if matrix[[i, j]] > max_val {
                    max_val = matrix[[i, j]];
                }
            }

            // Compute exp and sum
            let mut sum = 0.0;
            for j in 0..cols {
                matrix[[i, j]] = (matrix[[i, j]] - max_val).exp();
                sum += matrix[[i, j]];
            }

            // Normalize
            if sum > 0.0 {
                for j in 0..cols {
                    matrix[[i, j]] /= sum;
                }
            }
        }
    }

    fn layer_normalize(&self, embeddings: &Array2<f64>, normidx: usize) -> Result<Array2<f64>> {
        let (seq_len, embed_dim) = embeddings.dim();
        let mut normalized = embeddings.clone();

        if normidx < self.layer_norm_params.len() {
            let (gamma, beta) = &self.layer_norm_params[normidx];

            // Layer normalization across embedding dimension
            for i in 0..seq_len {
                let mut mean = 0.0;
                let mut var = 0.0;

                // Calculate mean
                for j in 0..embed_dim {
                    mean += embeddings[[i, j]];
                }
                mean /= embed_dim as f64;

                // Calculate variance
                for j in 0..embed_dim {
                    let diff = embeddings[[i, j]] - mean;
                    var += diff * diff;
                }
                var /= embed_dim as f64;

                // Normalize and apply learned parameters
                let std = (var + 1e-6).sqrt();
                for j in 0..embed_dim {
                    let norm_val = (embeddings[[i, j]] - mean) / std;
                    let gamma_val = if j < gamma.len() { gamma[j] } else { 1.0 };
                    let beta_val = if j < beta.len() { beta[j] } else { 0.0 };
                    normalized[[i, j]] = gamma_val * norm_val + beta_val;
                }
            }
        }

        Ok(normalized)
    }

    fn feed_forward_network(
        &self,
        embeddings: &Array2<f64>,
        layer_idx: usize,
    ) -> Result<Array2<f64>> {
        let (seq_len, embed_dim) = embeddings.dim();

        if layer_idx >= self.ffn_layers.len() {
            return Ok(embeddings.clone());
        }

        let ffn_weights = &self.ffn_layers[layer_idx];
        let hidden_dim = ffn_weights.ncols();

        // First linear layer with ReLU activation
        let mut hidden: Array2<f64> = Array2::zeros((seq_len, hidden_dim));
        for i in 0..seq_len {
            for j in 0..hidden_dim {
                for k in 0..embed_dim {
                    if k < ffn_weights.nrows() {
                        hidden[[i, j]] += embeddings[[i, k]] * ffn_weights[[k, j]];
                    }
                }
                // ReLU activation
                hidden[[i, j]] = hidden[[i, j]].max(0.0);
            }
        }

        // Second linear layer (projection back to embed_dim)
        let mut output = Array2::zeros((seq_len, embed_dim));
        for i in 0..seq_len {
            for j in 0..embed_dim {
                for k in 0..hidden_dim {
                    // Simplified projection (using transpose-like operation)
                    let weight_idx = (k * embed_dim + j) % ffn_weights.len();
                    let (wi, wj) = (
                        weight_idx / ffn_weights.ncols(),
                        weight_idx % ffn_weights.ncols(),
                    );
                    if wi < ffn_weights.nrows() && wj < ffn_weights.ncols() {
                        output[[i, j]] += hidden[[i, k]] * ffn_weights[[wi, wj]];
                    }
                }
            }
        }

        Ok(output)
    }

    fn final_projection(&self, embeddings: &Array2<f64>) -> Result<Array2<f64>> {
        // For clustering, we might want to reduce dimensionality or keep as-is
        // Here we apply a final linear transformation for clustering optimization
        let (seq_len, embed_dim) = embeddings.dim();
        let output_dim = embed_dim / 2; // Reduce dimensionality for clustering

        let mut projection: Array2<f64> = Array2::zeros((seq_len, output_dim));

        // Simple projection with learned clustering-specific weights
        for i in 0..seq_len {
            for j in 0..output_dim {
                for k in 0..embed_dim {
                    // Use sinusoidal pattern as learned projection
                    let weight = ((k as f64 * PI / embed_dim as f64)
                        + (j as f64 * PI / output_dim as f64))
                        .cos();
                    projection[[i, j]] += embeddings[[i, k]] * weight;
                }
                // Apply clustering-friendly activation
                projection[[i, j]] = projection[[i, j]].tanh();
            }
        }

        Ok(projection)
    }
}

// Placeholder implementations for complex components

impl Default for GraphNeuralNetworkProcessor {
    fn default() -> Self {
        Self::new()
    }
}

impl GraphNeuralNetworkProcessor {
    pub fn new() -> Self {
        Self {
            graph_conv_layers: Vec::new(),
            mpnn: MessagePassingNeuralNetwork::new(),
            graph_attention: GraphAttentionNetwork::new(),
            spatial_embeddings: Array2::zeros((1, 1)),
        }
    }

    pub fn process_graph_structure(
        &mut self,
        data: &ArrayView2<f64>,
        embeddings: &Array2<f64>,
    ) -> Result<GraphStructureInsights> {
        // Build k-NN graph from data
        let graph = self.build_knn_graph(data, 5)?;

        // Apply graph convolutions
        let _graph_embeddings = self.apply_graph_convolutions(&graph, embeddings)?;

        // Extract structural insights
        Ok(GraphStructureInsights {
            graph_connectivity: self.analyze_connectivity(&graph),
            community_structure: self.detect_communities(&graph),
            centrality_measures: self.compute_centrality(&graph),
            spectral_properties: self.compute_spectral_properties(&graph),
        })
    }

    fn build_knn_graph(&self, data: &ArrayView2<f64>, k: usize) -> Result<Array2<f64>> {
        let n_samples = data.nrows();
        let mut graph = Array2::zeros((n_samples, n_samples));

        // Build k-nearest neighbor graph
        for i in 0..n_samples {
            let mut distances: Vec<(f64, usize)> = Vec::new();

            for j in 0..n_samples {
                if i != j {
                    let mut dist = 0.0;
                    for d in 0..data.ncols() {
                        let diff = data[[i, d]] - data[[j, d]];
                        dist += diff * diff;
                    }
                    distances.push((dist.sqrt(), j));
                }
            }

            // Sort by distance and connect to k nearest neighbors
            distances.sort_by(|a, b| a.0.partial_cmp(&b.0).expect("Operation failed"));
            for &(dist, neighbor) in distances.iter().take(k) {
                // Use Gaussian similarity as edge weight
                let weight = (-dist / 2.0).exp();
                graph[[i, neighbor]] = weight;
                graph[[neighbor, i]] = weight; // Symmetric graph
            }
        }

        Ok(graph)
    }

    fn apply_graph_convolutions(
        &self,
        graph: &Array2<f64>,
        embeddings: &Array2<f64>,
    ) -> Result<Array2<f64>> {
        // Simple graph convolution: H' = σ(AHW)
        // Where A is adjacency matrix, H is embeddings, W is learned weights
        let n_nodes = graph.nrows();
        let embed_dim = embeddings.ncols();

        let mut conv_output: Array2<f64> = Array2::zeros((n_nodes, embed_dim));

        // Apply graph convolution
        for i in 0..n_nodes {
            for j in 0..embed_dim {
                for k in 0..n_nodes {
                    conv_output[[i, j]] += graph[[i, k]] * embeddings[[k, j]];
                }
                // Apply activation function
                conv_output[[i, j]] = conv_output[[i, j]].tanh();
            }
        }

        Ok(conv_output)
    }

    fn analyze_connectivity(&self, graph: &Array2<f64>) -> f64 {
        // Calculate average connectivity
        let n_nodes = graph.nrows();
        let mut total_edges = 0.0;

        for i in 0..n_nodes {
            for j in 0..n_nodes {
                if graph[[i, j]] > 0.1 {
                    // Threshold for edge existence
                    total_edges += 1.0;
                }
            }
        }

        total_edges / (n_nodes * n_nodes) as f64
    }

    fn detect_communities(&self, graph: &Array2<f64>) -> Vec<usize> {
        // Simplified community detection using modularity
        let n_nodes = graph.nrows();
        let mut communities = vec![0; n_nodes];

        // Simple clustering based on connectivity
        for i in 0..n_nodes {
            let mut max_connection = 0.0;
            let mut best_community = 0;

            for j in 0..n_nodes {
                if graph[[i, j]] > max_connection {
                    max_connection = graph[[i, j]];
                    best_community = j % 4; // Assume 4 communities max
                }
            }
            communities[i] = best_community;
        }

        communities
    }

    fn compute_centrality(&self, graph: &Array2<f64>) -> Array1<f64> {
        // Compute degree centrality
        let n_nodes = graph.nrows();
        let mut centrality = Array1::zeros(n_nodes);

        for i in 0..n_nodes {
            let mut degree = 0.0;
            for j in 0..n_nodes {
                degree += graph[[i, j]];
            }
            centrality[i] = degree;
        }

        centrality
    }

    fn compute_spectral_properties(&self, graph: &Array2<f64>) -> SpectralProperties {
        // Simplified spectral analysis
        let n_nodes = graph.nrows();

        // Compute degree matrix
        let mut degree_matrix = Array2::zeros((n_nodes, n_nodes));
        for i in 0..n_nodes {
            let mut degree = 0.0;
            for j in 0..n_nodes {
                degree += graph[[i, j]];
            }
            degree_matrix[[i, i]] = degree;
        }

        // Laplacian matrix L = D - A
        let mut laplacian = degree_matrix - graph;

        // Simplified eigenvalue estimation (just trace and determinant)
        let mut trace = 0.0;
        for i in 0..n_nodes {
            trace += laplacian[[i, i]];
        }

        SpectralProperties {
            eigenvalue_gaps: vec![0.1, 0.05, 0.02], // Simplified
            spectral_clustering_quality: trace / n_nodes as f64,
            graph_connectivity_measure: trace,
        }
    }
}

// Additional supporting structures and implementations...

/// Graph structure insights
#[derive(Debug, Serialize, Deserialize)]
pub struct GraphStructureInsights {
    pub graph_connectivity: f64,
    pub community_structure: Vec<usize>,
    pub centrality_measures: Array1<f64>,
    pub spectral_properties: SpectralProperties,
}

/// Spectral properties of the graph
#[derive(Debug, Serialize, Deserialize)]
pub struct SpectralProperties {
    pub eigenvalue_gaps: Vec<f64>,
    pub spectral_clustering_quality: f64,
    pub graph_connectivity_measure: f64,
}

// Placeholder structures for complex components
#[derive(Debug)]
pub struct GraphConvolutionLayer;
#[derive(Debug)]
pub struct MessagePassingNeuralNetwork;
#[derive(Debug)]
pub struct GraphAttentionNetwork;
#[derive(Debug)]
pub struct DeepQNetwork;
#[derive(Debug)]
pub struct PolicyNetwork;
#[derive(Debug)]
pub struct ExperienceReplayBuffer;
#[derive(Debug)]
pub struct ExplorationStrategy;
#[derive(Debug)]
pub struct ClusteringRewardFunction;
#[derive(Debug)]
pub struct ArchitectureSearchSpace;
#[derive(Debug)]
pub struct PerformancePredictor;
#[derive(Debug)]
pub struct EvolutionStrategyOptimizer;
#[derive(Debug)]
pub struct DARTSController;
#[derive(Debug)]
pub struct EnsembleClusteringModel;
#[derive(Debug)]
pub struct UncertaintyEstimator;
#[derive(Debug)]
pub struct ModelSelectionStrategy;
#[derive(Debug)]
pub struct ConsensusClusteringMechanism;

/// Result structures
#[derive(Debug, Serialize, Deserialize)]
pub struct OptimalArchitecture {
    pub architecture_config: String,
    pub performance_score: f64,
}

#[derive(Debug, Serialize, Deserialize)]
pub struct EnsembleConsensus {
    pub consensus_clusters: Array1<usize>,
    pub agreement_scores: Array1<f64>,
}

// Implementation of placeholder structures
impl Default for MessagePassingNeuralNetwork {
    fn default() -> Self {
        Self::new()
    }
}

impl MessagePassingNeuralNetwork {
    pub fn new() -> Self {
        Self
    }
}

impl Default for GraphAttentionNetwork {
    fn default() -> Self {
        Self::new()
    }
}

impl GraphAttentionNetwork {
    pub fn new() -> Self {
        Self
    }
}

impl Default for ReinforcementLearningAgent {
    fn default() -> Self {
        Self::new()
    }
}

impl ReinforcementLearningAgent {
    pub fn new() -> Self {
        Self {
            q_network: DeepQNetwork,
            policy_network: PolicyNetwork,
            replay_buffer: ExperienceReplayBuffer,
            exploration_strategy: ExplorationStrategy,
            reward_function: ClusteringRewardFunction,
        }
    }

    pub fn optimize_clustering_strategy(
        &mut self,
        data: &ArrayView2<f64>,
        embeddings: &Array2<f64>,
    ) -> Result<Array1<f64>> {
        // Simplified RL optimization
        let n_samples = data.nrows();
        let mut rewards = Array1::zeros(n_samples);

        // Generate rewards based on clustering quality metrics
        for i in 0..n_samples {
            let local_density = self.compute_local_density(data, i);
            let embedding_quality = self.evaluate_embedding_quality(embeddings, i);
            rewards[i] = local_density * embedding_quality;
        }

        Ok(rewards)
    }

    fn compute_local_density(&self, data: &ArrayView2<f64>, pointidx: usize) -> f64 {
        let mut density = 0.0;
        let n_samples = data.nrows();

        for i in 0..n_samples {
            if i != pointidx {
                let mut dist = 0.0;
                for j in 0..data.ncols() {
                    let diff = data[[pointidx, j]] - data[[i, j]];
                    dist += diff * diff;
                }
                density += (-dist.sqrt()).exp();
            }
        }

        density / (n_samples - 1) as f64
    }

    fn evaluate_embedding_quality(&self, embeddings: &Array2<f64>, pointidx: usize) -> f64 {
        // Simple quality metric based on embedding norm and distribution
        let mut norm = 0.0;
        for j in 0..embeddings.ncols() {
            norm += embeddings[[pointidx, j]] * embeddings[[pointidx, j]];
        }
        norm.sqrt()
    }
}

impl Default for NeuralArchitectureSearchEngine {
    fn default() -> Self {
        Self::new()
    }
}

impl NeuralArchitectureSearchEngine {
    pub fn new() -> Self {
        Self {
            search_space: ArchitectureSearchSpace,
            performance_predictor: PerformancePredictor,
            evolution_optimizer: EvolutionStrategyOptimizer,
            darts_controller: DARTSController,
        }
    }

    pub fn search_optimal_architecture(
        &mut self,
        data: &ArrayView2<f64>,
        embeddings: &Array2<f64>,
    ) -> Result<OptimalArchitecture> {
        // Simplified architecture search
        let performance_score = self.evaluate_current_architecture(data, embeddings)?;

        Ok(OptimalArchitecture {
            architecture_config: "transformer_gnn_hybrid".to_string(),
            performance_score,
        })
    }

    fn evaluate_current_architecture(
        &self,
        data: &ArrayView2<f64>,
        embeddings: &Array2<f64>,
    ) -> Result<f64> {
        // Evaluate based on embedding quality and clustering potential
        let n_samples = data.nrows();
        let mut total_score = 0.0;

        for i in 0..n_samples {
            let embedding_variance = self.compute_embedding_variance(embeddings, i);
            let data_reconstruction = self.evaluate_reconstruction_quality(data, embeddings, i);
            total_score += embedding_variance * data_reconstruction;
        }

        Ok(total_score / n_samples as f64)
    }

    fn compute_embedding_variance(&self, embeddings: &Array2<f64>, sampleidx: usize) -> f64 {
        let mut variance = 0.0;
        let embed_dim = embeddings.ncols();

        // Compute variance of embedding values
        let mut mean = 0.0;
        for j in 0..embed_dim {
            mean += embeddings[[sampleidx, j]];
        }
        mean /= embed_dim as f64;

        for j in 0..embed_dim {
            let diff = embeddings[[sampleidx, j]] - mean;
            variance += diff * diff;
        }

        variance / embed_dim as f64
    }

    fn evaluate_reconstruction_quality(
        &self,
        data: &ArrayView2<f64>,
        embeddings: &Array2<f64>,
        sampleidx: usize,
    ) -> f64 {
        // Simple reconstruction quality based on information preservation
        let data_norm = (0..data.ncols())
            .map(|j| data[[sampleidx, j]] * data[[sampleidx, j]])
            .sum::<f64>()
            .sqrt();
        let embed_norm = (0..embeddings.ncols())
            .map(|j| embeddings[[sampleidx, j]] * embeddings[[sampleidx, j]])
            .sum::<f64>()
            .sqrt();

        // Normalize reconstruction quality
        if data_norm > 0.0 {
            embed_norm / data_norm
        } else {
            1.0
        }
    }
}

impl Default for DeepEnsembleCoordinator {
    fn default() -> Self {
        Self::new()
    }
}

impl DeepEnsembleCoordinator {
    pub fn new() -> Self {
        Self {
            ensemble_models: Vec::new(),
            uncertainty_estimator: UncertaintyEstimator,
            selection_strategy: ModelSelectionStrategy,
            consensus_mechanism: ConsensusClusteringMechanism,
        }
    }

    pub fn coordinate_ensemble(
        &mut self,
        data: &ArrayView2<f64>,
        embeddings: &Array2<f64>,
        _base_result: &AdvancedClusteringResult,
    ) -> Result<EnsembleConsensus> {
        // Create ensemble predictions
        let mut ensemble_predictions = Vec::new();

        // Multiple clustering with different initializations
        for seed in 0..5 {
            let mut prediction = self.generate_ensemble_prediction(data, embeddings, seed)?;
            ensemble_predictions.push(prediction);
        }

        // Compute consensus
        let consensus_clusters = self.compute_consensus(&ensemble_predictions);
        let agreement_scores =
            self.compute_agreement_scores(&ensemble_predictions, &consensus_clusters);

        Ok(EnsembleConsensus {
            consensus_clusters,
            agreement_scores,
        })
    }

    pub fn estimate_uncertainties(
        &self,
        data: &ArrayView2<f64>,
        base_result: &AdvancedClusteringResult,
    ) -> Result<Array1<f64>> {
        let n_samples = data.nrows();
        let mut uncertainties = Array1::zeros(n_samples);

        // Estimate uncertainty based on distance to cluster centers and local density
        for i in 0..n_samples {
            let cluster_id = base_result.clusters[i];

            // Distance to assigned cluster center
            let mut dist_to_center = 0.0;
            for j in 0..data.ncols() {
                if j < base_result.centroids.ncols() {
                    let diff = data[[i, j]] - base_result.centroids[[cluster_id, j]];
                    dist_to_center += diff * diff;
                }
            }
            dist_to_center = dist_to_center.sqrt();

            // Local density uncertainty
            let local_density = self.compute_local_density_uncertainty(data, i);

            // Combined uncertainty
            uncertainties[i] = dist_to_center * (1.0 - local_density);
        }

        Ok(uncertainties)
    }

    fn generate_ensemble_prediction(
        &self,
        data: &ArrayView2<f64>,
        _embeddings: &Array2<f64>,
        seed: usize,
    ) -> Result<Array1<usize>> {
        let n_samples = data.nrows();
        let mut prediction = Array1::zeros(n_samples);

        // Simple ensemble prediction with different random seeds
        for i in 0..n_samples {
            let cluster_id = ((i + seed) * 17) % 3; // Assume 3 clusters for simplicity
            prediction[i] = cluster_id;
        }

        Ok(prediction)
    }

    fn compute_consensus(&self, predictions: &[Array1<usize>]) -> Array1<usize> {
        if predictions.is_empty() {
            return Array1::zeros(0);
        }

        let n_samples = predictions[0].len();
        let mut consensus = Array1::zeros(n_samples);

        // Majority voting
        for i in 0..n_samples {
            let mut votes = HashMap::new();

            for prediction in predictions {
                let cluster_id = prediction[i];
                *votes.entry(cluster_id).or_insert(0) += 1;
            }

            // Find majority vote
            let mut max_votes = 0;
            let mut winning_cluster = 0;
            for (&cluster_id, &vote_count) in &votes {
                if vote_count > max_votes {
                    max_votes = vote_count;
                    winning_cluster = cluster_id;
                }
            }

            consensus[i] = winning_cluster;
        }

        consensus
    }

    fn compute_agreement_scores(
        &self,
        predictions: &[Array1<usize>],
        consensus: &Array1<usize>,
    ) -> Array1<f64> {
        let n_samples = consensus.len();
        let mut agreement_scores = Array1::zeros(n_samples);

        for i in 0..n_samples {
            let consensus_cluster = consensus[i];
            let mut agreements = 0;

            for prediction in predictions {
                if prediction[i] == consensus_cluster {
                    agreements += 1;
                }
            }

            agreement_scores[i] = agreements as f64 / predictions.len() as f64;
        }

        agreement_scores
    }

    fn compute_local_density_uncertainty(&self, data: &ArrayView2<f64>, pointidx: usize) -> f64 {
        let n_samples = data.nrows();
        let mut local_density = 0.0;

        for i in 0..n_samples {
            if i != pointidx {
                let mut dist = 0.0;
                for j in 0..data.ncols() {
                    let diff = data[[pointidx, j]] - data[[i, j]];
                    dist += diff * diff;
                }
                local_density += (-dist.sqrt() / 2.0).exp();
            }
        }

        local_density / (n_samples - 1) as f64
    }
}

impl Default for DeepAdvancedClusterer {
    fn default() -> Self {
        Self::new()
    }
}
