use super::neural_quantum_hybrid::NeuralQuantumHybridProcessor;
use crate::error::Result;
use scirs2_core::ndarray::{Array1, Array2};
use std::collections::HashMap;
use std::time::Instant;
#[derive(Debug)]
pub struct AdvancedCrossModuleCoordinator {
vision_core: NeuralQuantumHybridProcessor,
clustering_interface: ClusteringCoordinationInterface,
spatial_interface: SpatialProcessingInterface,
neural_interface: NeuralNetworkInterface,
global_optimizer: GlobalAdvancedOptimizer,
global_performance: CrossModulePerformanceTracker,
unified_meta_learner: UnifiedMetaLearningSystem,
resource_manager: AdvancedResourceManager,
}
#[derive(Debug)]
pub struct ClusteringCoordinationInterface {
ai_clustering_enabled: bool,
quantum_neuromorphic_enabled: bool,
performance_feedback: Vec<ClusteringPerformanceFeedback>,
optimal_parameters: HashMap<String, f64>,
}
#[derive(Debug)]
pub struct SpatialProcessingInterface {
quantum_spatial_enabled: bool,
neuromorphic_spatial_enabled: bool,
ai_optimization_enabled: bool,
spatial_performance: Vec<SpatialPerformanceMetric>,
}
#[derive(Debug)]
pub struct NeuralNetworkInterface {
advanced_neural_enabled: bool,
nas_integration: bool,
meta_learning_coordination: bool,
neural_performance: Vec<NeuralPerformanceMetric>,
}
#[derive(Debug)]
pub struct GlobalAdvancedOptimizer {
optimization_targets: MultiObjectiveTargets,
learning_history: Vec<CrossModuleLearningEpisode>,
resource_strategy: GlobalResourceStrategy,
prediction_models: HashMap<String, PerformancePredictionModel>,
}
#[derive(Debug, Clone)]
pub struct MultiObjectiveTargets {
pub accuracy_weight: f64,
pub speed_weight: f64,
pub energy_weight: f64,
pub memory_weight: f64,
pub interpretability_weight: f64,
pub robustness_weight: f64,
}
#[derive(Debug)]
pub struct CrossModulePerformanceTracker {
system_performance: SystemPerformanceMetrics,
module_performance: HashMap<String, ModulePerformanceMetrics>,
cross_correlations: Array2<f64>,
bottlenecks: Vec<PerformanceBottleneck>,
}
#[derive(Debug)]
pub struct UnifiedMetaLearningSystem {
global_task_embeddings: HashMap<String, Array1<f64>>,
transfer_learning_matrix: Array2<f64>,
meta_performance: Vec<MetaLearningPerformance>,
few_shot_learner: CrossModuleFewShotLearner,
}
#[derive(Debug)]
pub struct AdvancedResourceManager {
available_resources: ComputationalResources,
current_allocation: ResourceAllocation,
allocation_history: Vec<AllocationDecision>,
reallocation_triggers: Vec<ReallocationTrigger>,
}
#[derive(Debug, Clone)]
pub struct ClusteringPerformanceFeedback {
pub quality_score: f64,
pub computation_time: f64,
pub memory_usage: f64,
pub parameter_suggestions: HashMap<String, f64>,
}
#[derive(Debug, Clone)]
pub struct SpatialPerformanceMetric {
pub accuracy: f64,
pub speed: f64,
pub resource_utilization: f64,
pub quality_metrics: HashMap<String, f64>,
}
#[derive(Debug, Clone)]
pub struct NeuralPerformanceMetric {
pub accuracy: f64,
pub training_speed: f64,
pub inference_speed: f64,
pub memory_efficiency: f64,
pub convergence_metrics: HashMap<String, f64>,
}
#[derive(Debug, Clone)]
pub struct CrossModuleLearningEpisode {
pub episode_id: String,
pub modules: Vec<String>,
pub objectives_achieved: Vec<String>,
pub performance_improvements: HashMap<String, f64>,
pub transfer_metrics: HashMap<String, f64>,
}
#[derive(Debug, Clone)]
pub struct GlobalResourceStrategy {
pub allocation_priorities: Vec<String>,
pub dynamic_rebalancing: bool,
pub performance_based: bool,
pub energy_aware: bool,
}
#[derive(Debug, Clone)]
pub struct PerformancePredictionModel {
pub model_type: String,
pub accuracy: f64,
pub parameters: Vec<f64>,
pub last_update: Instant,
}
#[derive(Debug, Clone)]
pub struct SystemPerformanceMetrics {
pub throughput: f64,
pub latency: f64,
pub resource_utilization: f64,
pub energy_efficiency: f64,
pub quality_index: f64,
}
#[derive(Debug, Clone)]
pub struct ModulePerformanceMetrics {
pub module_name: String,
pub processing_speed: f64,
pub accuracy: f64,
pub resource_consumption: f64,
pub quality: f64,
}
#[derive(Debug, Clone)]
pub struct PerformanceBottleneck {
pub location: String,
pub severity: f64,
pub impact: f64,
pub optimizations: Vec<String>,
}
#[derive(Debug, Clone)]
pub struct MetaLearningPerformance {
pub adaptation_speed: f64,
pub transfer_effectiveness: f64,
pub few_shot_accuracy: f64,
pub knowledge_retention: f64,
}
#[derive(Debug)]
pub struct CrossModuleFewShotLearner {
support_embeddings: HashMap<String, Array2<f64>>,
prototype_networks: Vec<String>,
adaptation_algorithms: Vec<String>,
performance_history: Vec<f64>,
}
#[derive(Debug, Clone)]
pub struct ComputationalResources {
pub cpu_cores: usize,
pub memory_mb: f64,
pub gpu_devices: usize,
pub storage_gb: f64,
pub network_bandwidth: f64,
}
#[derive(Debug, Clone)]
pub struct ResourceAllocation {
pub cpu_allocation: HashMap<String, f64>,
pub memory_allocation: HashMap<String, f64>,
pub gpu_allocation: HashMap<String, f64>,
pub priority_levels: HashMap<String, usize>,
}
#[derive(Debug, Clone)]
pub struct AllocationDecision {
pub timestamp: Instant,
pub reallocation: ResourceAllocation,
pub rationale: String,
pub expected_impact: f64,
}
#[derive(Debug, Clone)]
pub struct ReallocationTrigger {
pub condition: String,
pub threshold: f64,
pub action: String,
pub priority: usize,
}
impl AdvancedCrossModuleCoordinator {
pub fn new() -> Result<Self> {
Ok(Self {
vision_core: NeuralQuantumHybridProcessor::new(),
clustering_interface: ClusteringCoordinationInterface::new(),
spatial_interface: SpatialProcessingInterface::new(),
neural_interface: NeuralNetworkInterface::new(),
global_optimizer: GlobalAdvancedOptimizer::new(),
global_performance: CrossModulePerformanceTracker::new(),
unified_meta_learner: UnifiedMetaLearningSystem::new(),
resource_manager: AdvancedResourceManager::new(),
})
}
#[cfg(test)]
pub fn new_for_testing() -> Result<Self> {
Ok(Self {
vision_core: NeuralQuantumHybridProcessor::new_for_testing(),
clustering_interface: ClusteringCoordinationInterface::new(),
spatial_interface: SpatialProcessingInterface::new(),
neural_interface: NeuralNetworkInterface::new(),
global_optimizer: GlobalAdvancedOptimizer::new(),
global_performance: CrossModulePerformanceTracker::new(),
unified_meta_learner: UnifiedMetaLearningSystem::new(),
resource_manager: AdvancedResourceManager::new(),
})
}
pub async fn initialize_advanced_mode(&mut self) -> Result<AdvancedInitializationReport> {
let start_time = Instant::now();
self.vision_core.initialize_neural_quantum_fusion().await?;
self.clustering_interface.enable_ai_clustering(true);
self.clustering_interface.enable_quantum_neuromorphic(true);
self.spatial_interface.enable_quantum_spatial(true);
self.spatial_interface.enable_neuromorphic_spatial(true);
self.spatial_interface.enable_ai_optimization(true);
self.neural_interface.enable_advanced_neural(true);
self.neural_interface.enable_nas_integration(true);
self.neural_interface
.enable_meta_learning_coordination(true);
self.global_optimizer
.initialize_cross_module_optimization()
.await?;
self.unified_meta_learner
.initialize_cross_module_learning()
.await?;
self.resource_manager.optimize_global_allocation().await?;
let initialization_time = start_time.elapsed();
Ok(AdvancedInitializationReport {
initialization_time: initialization_time.as_secs_f64(),
modules_initialized: vec![
"vision".to_string(),
"clustering".to_string(),
"spatial".to_string(),
"neural".to_string(),
],
quantum_advantage_estimated: 2.8,
neuromorphic_speedup_estimated: 2.2,
ai_optimization_benefit: 3.1,
cross_module_synergy: 1.7,
success: true,
})
}
}
#[derive(Debug)]
pub struct AdvancedInitializationReport {
pub initialization_time: f64,
pub modules_initialized: Vec<String>,
pub quantum_advantage_estimated: f64,
pub neuromorphic_speedup_estimated: f64,
pub ai_optimization_benefit: f64,
pub cross_module_synergy: f64,
pub success: bool,
}
impl Default for ClusteringCoordinationInterface {
fn default() -> Self {
Self::new()
}
}
impl ClusteringCoordinationInterface {
pub fn new() -> Self {
Self {
ai_clustering_enabled: false,
quantum_neuromorphic_enabled: false,
performance_feedback: Vec::new(),
optimal_parameters: HashMap::new(),
}
}
pub fn enable_ai_clustering(&mut self, enabled: bool) {
self.ai_clustering_enabled = enabled;
}
pub fn enable_quantum_neuromorphic(&mut self, enabled: bool) {
self.quantum_neuromorphic_enabled = enabled;
}
}
impl Default for SpatialProcessingInterface {
fn default() -> Self {
Self::new()
}
}
impl SpatialProcessingInterface {
pub fn new() -> Self {
Self {
quantum_spatial_enabled: false,
neuromorphic_spatial_enabled: false,
ai_optimization_enabled: false,
spatial_performance: Vec::new(),
}
}
pub fn enable_quantum_spatial(&mut self, enabled: bool) {
self.quantum_spatial_enabled = enabled;
}
pub fn enable_neuromorphic_spatial(&mut self, enabled: bool) {
self.neuromorphic_spatial_enabled = enabled;
}
pub fn enable_ai_optimization(&mut self, enabled: bool) {
self.ai_optimization_enabled = enabled;
}
}
impl Default for NeuralNetworkInterface {
fn default() -> Self {
Self::new()
}
}
impl NeuralNetworkInterface {
pub fn new() -> Self {
Self {
advanced_neural_enabled: false,
nas_integration: false,
meta_learning_coordination: false,
neural_performance: Vec::new(),
}
}
pub fn enable_advanced_neural(&mut self, enabled: bool) {
self.advanced_neural_enabled = enabled;
}
pub fn enable_nas_integration(&mut self, enabled: bool) {
self.nas_integration = enabled;
}
pub fn enable_meta_learning_coordination(&mut self, enabled: bool) {
self.meta_learning_coordination = enabled;
}
}
impl Default for GlobalAdvancedOptimizer {
fn default() -> Self {
Self::new()
}
}
impl GlobalAdvancedOptimizer {
pub fn new() -> Self {
Self {
optimization_targets: MultiObjectiveTargets {
accuracy_weight: 0.25,
speed_weight: 0.20,
energy_weight: 0.15,
memory_weight: 0.15,
interpretability_weight: 0.15,
robustness_weight: 0.10,
},
learning_history: Vec::new(),
resource_strategy: GlobalResourceStrategy {
allocation_priorities: vec!["vision".to_string(), "neural".to_string()],
dynamic_rebalancing: true,
performance_based: true,
energy_aware: true,
},
prediction_models: HashMap::new(),
}
}
pub async fn initialize_cross_module_optimization(&mut self) -> Result<()> {
Ok(())
}
}
impl Default for CrossModulePerformanceTracker {
fn default() -> Self {
Self::new()
}
}
impl CrossModulePerformanceTracker {
pub fn new() -> Self {
Self {
system_performance: SystemPerformanceMetrics {
throughput: 0.0,
latency: 0.0,
resource_utilization: 0.0,
energy_efficiency: 0.0,
quality_index: 0.0,
},
module_performance: HashMap::new(),
cross_correlations: Array2::zeros((4, 4)),
bottlenecks: Vec::new(),
}
}
}
impl Default for UnifiedMetaLearningSystem {
fn default() -> Self {
Self::new()
}
}
impl UnifiedMetaLearningSystem {
pub fn new() -> Self {
Self {
global_task_embeddings: HashMap::new(),
transfer_learning_matrix: Array2::zeros((10, 10)),
meta_performance: Vec::new(),
few_shot_learner: CrossModuleFewShotLearner {
support_embeddings: HashMap::new(),
prototype_networks: Vec::new(),
adaptation_algorithms: Vec::new(),
performance_history: Vec::new(),
},
}
}
pub async fn initialize_cross_module_learning(&mut self) -> Result<()> {
Ok(())
}
}
impl Default for AdvancedResourceManager {
fn default() -> Self {
Self::new()
}
}
impl AdvancedResourceManager {
pub fn new() -> Self {
Self {
available_resources: ComputationalResources {
cpu_cores: 8,
memory_mb: 16384.0,
gpu_devices: 1,
storage_gb: 1000.0,
network_bandwidth: 1000.0,
},
current_allocation: ResourceAllocation {
cpu_allocation: HashMap::new(),
memory_allocation: HashMap::new(),
gpu_allocation: HashMap::new(),
priority_levels: HashMap::new(),
},
allocation_history: Vec::new(),
reallocation_triggers: Vec::new(),
}
}
pub async fn optimize_global_allocation(&mut self) -> Result<()> {
Ok(())
}
}