1013 lines
31 KiB
Rust
1013 lines
31 KiB
Rust
//! Self-Learning Models for Industry Applications and Exotic Experiments
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//!
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//! Integrates RuVector's GNN, Attention, and Graph crates for building
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//! adaptive neural architectures with reinforcement learning,
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//! online learning, and meta-learning capabilities.
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use anyhow::Result;
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use rand::Rng;
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use std::collections::HashMap;
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use std::path::PathBuf;
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use std::time::Instant;
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// Import RuVector crates
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use ruvector_attention::{
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traits::Attention, HyperbolicAttention, HyperbolicAttentionConfig, MoEAttention, MoEConfig,
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MultiHeadAttention, ScaledDotProductAttention,
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};
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use ruvector_gnn::{
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ewc::ElasticWeightConsolidation,
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layer::RuvectorLayer,
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replay::ReplayBuffer,
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scheduler::{LearningRateScheduler, SchedulerType},
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training::{Optimizer, OptimizerType},
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};
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/// Self-learning model configuration
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#[derive(Debug, Clone)]
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pub struct SelfLearningConfig {
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pub name: String,
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pub industry: Industry,
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pub architecture: Architecture,
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pub learning_rate: f32,
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pub adaptation_rate: f32,
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pub memory_size: usize,
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pub exploration_rate: f32,
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pub meta_learning: bool,
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pub ewc_lambda: f32,
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}
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#[derive(Debug, Clone, Copy, serde::Serialize)]
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pub enum Industry {
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Healthcare,
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Finance,
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Autonomous,
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QuantumInspired,
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Neuromorphic,
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Hyperdimensional,
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ExoticResearch,
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}
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#[derive(Debug, Clone, Copy, serde::Serialize)]
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pub enum Architecture {
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TransformerRL, // Transformer with reinforcement learning
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GNNAdaptive, // Graph Neural Network with adaptation
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HyperbolicAttention, // Hyperbolic space attention
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MixtureOfExperts, // Sparse MoE architecture
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SpikingNN, // Spiking neural network
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HopfieldModern, // Modern Hopfield network
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DifferentialEvolution, // Evolutionary self-improvement
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QuantumVariational, // Quantum-inspired variational
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}
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/// Training metrics
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#[derive(Debug, Clone, serde::Serialize)]
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pub struct TrainingMetrics {
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pub epoch: usize,
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pub loss: f32,
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pub accuracy: f32,
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pub learning_rate: f32,
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pub adaptation_progress: f32,
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}
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/// Healthcare/Medical Diagnostics Self-Learning Model using RuVector
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pub struct HealthcareModel {
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pub config: SelfLearningConfig,
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attention: MultiHeadAttention,
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optimizer: Optimizer,
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ewc: ElasticWeightConsolidation,
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scheduler: LearningRateScheduler,
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replay_buffer: ReplayBuffer,
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symptom_embeddings: HashMap<String, Vec<f32>>,
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diagnosis_patterns: Vec<(Vec<f32>, String, f32)>,
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total_episodes: usize,
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accuracy_history: Vec<f32>,
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dim: usize,
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}
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impl HealthcareModel {
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pub fn new(input_dim: usize, hidden_dim: usize, _num_conditions: usize) -> Self {
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// Initialize multi-head attention (dim must be divisible by num_heads)
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let attention = MultiHeadAttention::new(hidden_dim, 8);
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// Initialize optimizer with Adam
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let optimizer = Optimizer::new(OptimizerType::Adam {
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learning_rate: 0.001,
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beta1: 0.9,
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beta2: 0.999,
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epsilon: 1e-8,
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});
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// Initialize EWC for continual learning
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let ewc = ElasticWeightConsolidation::new(0.4);
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// Create learning rate scheduler
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let scheduler = LearningRateScheduler::new(
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SchedulerType::CosineAnnealing {
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t_max: 100,
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eta_min: 1e-6,
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},
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0.001,
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);
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// Replay buffer for experience
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let replay_buffer = ReplayBuffer::new(10000);
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Self {
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config: SelfLearningConfig {
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name: "Healthcare Diagnostics".to_string(),
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industry: Industry::Healthcare,
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architecture: Architecture::TransformerRL,
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learning_rate: 0.001,
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adaptation_rate: 0.1,
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memory_size: 10000,
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exploration_rate: 0.1,
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meta_learning: true,
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ewc_lambda: 0.4,
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},
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attention,
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optimizer,
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ewc,
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scheduler,
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replay_buffer,
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symptom_embeddings: HashMap::new(),
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diagnosis_patterns: Vec::new(),
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total_episodes: 0,
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accuracy_history: Vec::new(),
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dim: hidden_dim,
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}
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}
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pub fn encode_symptoms(&self, symptoms: &[f32]) -> Vec<f32> {
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// Create keys and values for self-attention
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let keys = vec![symptoms.to_vec()];
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let values = vec![symptoms.to_vec()];
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let keys_refs: Vec<&[f32]> = keys.iter().map(|k| k.as_slice()).collect();
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let values_refs: Vec<&[f32]> = values.iter().map(|v| v.as_slice()).collect();
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self.attention
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.compute(symptoms, &keys_refs, &values_refs)
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.unwrap_or_else(|_| symptoms.to_vec())
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}
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pub fn train_episode(&mut self, symptoms: Vec<f32>, diagnosis: &str, correct: bool) -> f32 {
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let embedding = self.encode_symptoms(&symptoms);
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let confidence = if correct { 1.0 } else { 0.0 };
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self.diagnosis_patterns
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.push((embedding, diagnosis.to_string(), confidence));
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self.total_episodes += 1;
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// Update accuracy history
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self.accuracy_history.push(confidence);
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if self.accuracy_history.len() > 100 {
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self.accuracy_history.remove(0);
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}
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// Return recent accuracy
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self.accuracy_history.iter().sum::<f32>() / self.accuracy_history.len() as f32
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}
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}
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/// Financial Trading/Risk Model using Hyperbolic Attention
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pub struct FinancialModel {
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pub config: SelfLearningConfig,
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attention: HyperbolicAttention,
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optimizer: Optimizer,
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replay_buffer: ReplayBuffer,
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market_patterns: Vec<(Vec<f32>, f32)>,
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portfolio_history: Vec<f32>,
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dim: usize,
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}
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impl FinancialModel {
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pub fn new(input_dim: usize, hidden_dim: usize) -> Self {
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let attention = HyperbolicAttention::new(HyperbolicAttentionConfig {
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dim: hidden_dim,
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curvature: -1.0,
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adaptive_curvature: true,
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temperature: 0.5,
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frechet_max_iter: 50,
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frechet_tol: 1e-5,
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});
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let optimizer = Optimizer::new(OptimizerType::Adam {
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learning_rate: 0.0005,
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beta1: 0.9,
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beta2: 0.999,
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epsilon: 1e-8,
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});
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let replay_buffer = ReplayBuffer::new(50000);
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Self {
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config: SelfLearningConfig {
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name: "Financial Trading".to_string(),
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industry: Industry::Finance,
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architecture: Architecture::HyperbolicAttention,
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learning_rate: 0.0005,
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adaptation_rate: 0.05,
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memory_size: 50000,
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exploration_rate: 0.15,
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meta_learning: true,
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ewc_lambda: 0.3,
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},
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attention,
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optimizer,
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replay_buffer,
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market_patterns: Vec::new(),
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portfolio_history: Vec::new(),
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dim: hidden_dim,
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}
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}
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pub fn analyze_market(&self, market_data: &[f32]) -> Vec<f32> {
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let keys = vec![market_data.to_vec()];
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let values = vec![market_data.to_vec()];
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let keys_refs: Vec<&[f32]> = keys.iter().map(|k| k.as_slice()).collect();
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let values_refs: Vec<&[f32]> = values.iter().map(|v| v.as_slice()).collect();
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self.attention
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.compute(market_data, &keys_refs, &values_refs)
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.unwrap_or_else(|_| market_data.to_vec())
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}
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pub fn train_step(&mut self, market_data: Vec<f32>, return_pct: f32) -> f32 {
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let embedding = self.analyze_market(&market_data);
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self.market_patterns.push((embedding, return_pct));
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self.portfolio_history.push(return_pct);
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// Calculate Sharpe ratio approximation
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if self.portfolio_history.len() >= 2 {
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let mean: f32 =
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self.portfolio_history.iter().sum::<f32>() / self.portfolio_history.len() as f32;
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let variance: f32 = self
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.portfolio_history
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.iter()
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.map(|r| (r - mean).powi(2))
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.sum::<f32>()
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/ self.portfolio_history.len() as f32;
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mean / (variance.sqrt() + 1e-6)
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} else {
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0.0
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}
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}
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}
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/// Autonomous Systems Model using GNN Layer
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pub struct AutonomousModel {
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pub config: SelfLearningConfig,
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gnn_layer: RuvectorLayer,
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optimizer: Optimizer,
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ewc: ElasticWeightConsolidation,
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sensor_history: Vec<Vec<f32>>,
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action_history: Vec<usize>,
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}
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impl AutonomousModel {
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pub fn new(input_dim: usize, hidden_dim: usize, _output_dim: usize) -> Self {
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let gnn_layer = RuvectorLayer::new(input_dim, hidden_dim, 8, 0.1);
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let optimizer = Optimizer::new(OptimizerType::Adam {
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learning_rate: 0.001,
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beta1: 0.9,
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beta2: 0.999,
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epsilon: 1e-8,
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});
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let ewc = ElasticWeightConsolidation::new(0.5);
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Self {
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config: SelfLearningConfig {
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name: "Autonomous Systems".to_string(),
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industry: Industry::Autonomous,
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architecture: Architecture::GNNAdaptive,
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learning_rate: 0.001,
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adaptation_rate: 0.2,
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memory_size: 20000,
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exploration_rate: 0.2,
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meta_learning: true,
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ewc_lambda: 0.5,
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},
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gnn_layer,
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optimizer,
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ewc,
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sensor_history: Vec::new(),
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action_history: Vec::new(),
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}
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}
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pub fn process_sensors(&self, sensors: &[f32]) -> Vec<f32> {
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// GNN forward pass with empty neighbor list
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self.gnn_layer.forward(sensors, &[], &[])
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}
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pub fn train_step(&mut self, sensors: Vec<f32>, action: usize, reward: f32) -> f32 {
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let embedding = self.process_sensors(&sensors);
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self.sensor_history.push(embedding);
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self.action_history.push(action);
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// Return reward as training signal
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reward
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}
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}
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/// Mixture of Experts Model for multi-domain tasks
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pub struct MoEModel {
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pub config: SelfLearningConfig,
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moe: MoEAttention,
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optimizer: Optimizer,
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replay_buffer: ReplayBuffer,
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expert_usage: Vec<f32>,
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dim: usize,
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}
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impl MoEModel {
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pub fn new(input_dim: usize, num_experts: usize) -> Self {
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let moe = MoEAttention::new(MoEConfig {
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dim: input_dim,
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num_experts,
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top_k: 2,
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expert_capacity: 1.25,
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jitter_noise: 0.0,
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});
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let optimizer = Optimizer::new(OptimizerType::Adam {
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learning_rate: 0.001,
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beta1: 0.9,
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beta2: 0.999,
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epsilon: 1e-8,
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});
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let replay_buffer = ReplayBuffer::new(10000);
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Self {
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config: SelfLearningConfig {
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name: "Mixture of Experts".to_string(),
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industry: Industry::ExoticResearch,
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architecture: Architecture::MixtureOfExperts,
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learning_rate: 0.001,
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adaptation_rate: 0.1,
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memory_size: 10000,
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exploration_rate: 0.1,
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meta_learning: true,
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ewc_lambda: 0.3,
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},
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moe,
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optimizer,
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replay_buffer,
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expert_usage: vec![0.0; num_experts],
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dim: input_dim,
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}
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}
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pub fn forward(&self, query: &[f32], context: &[Vec<f32>]) -> Vec<f32> {
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let keys: Vec<&[f32]> = context.iter().map(|c| c.as_slice()).collect();
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let values: Vec<&[f32]> = context.iter().map(|c| c.as_slice()).collect();
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self.moe
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.compute(query, &keys, &values)
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.unwrap_or_else(|_| query.to_vec())
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}
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}
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// ============ Quantum-Inspired Model ============
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/// Quantum-Inspired Variational Model
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pub struct QuantumInspiredModel {
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pub config: SelfLearningConfig,
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parameters: Vec<f32>, // Variational parameters
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num_qubits: usize,
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num_layers: usize,
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optimizer: Optimizer,
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energy_history: Vec<f32>,
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}
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impl QuantumInspiredModel {
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pub fn new(num_qubits: usize, num_layers: usize) -> Self {
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let mut rng = rand::thread_rng();
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let num_params = num_qubits * num_layers * 3; // Rx, Ry, Rz per qubit per layer
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let parameters: Vec<f32> = (0..num_params)
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.map(|_| rng.gen::<f32>() * 2.0 * std::f32::consts::PI)
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.collect();
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let optimizer = Optimizer::new(OptimizerType::Adam {
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learning_rate: 0.01,
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beta1: 0.9,
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beta2: 0.999,
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epsilon: 1e-8,
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});
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Self {
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config: SelfLearningConfig {
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name: "Quantum Variational".to_string(),
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industry: Industry::QuantumInspired,
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architecture: Architecture::QuantumVariational,
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learning_rate: 0.01,
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adaptation_rate: 0.1,
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memory_size: 1000,
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exploration_rate: 0.2,
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meta_learning: false,
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ewc_lambda: 0.0,
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},
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parameters,
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num_qubits,
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num_layers,
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optimizer,
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energy_history: Vec::new(),
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}
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}
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pub fn expectation_value(&self, hamiltonian: &[f32]) -> f32 {
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// Simplified quantum circuit simulation
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let mut state = vec![1.0f32; 1 << self.num_qubits];
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state[0] = 1.0;
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// Apply rotation gates (simplified)
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for (i, ¶m) in self.parameters.iter().enumerate() {
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let qubit = i % self.num_qubits;
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let amplitude = param.cos();
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if qubit < state.len() {
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state[qubit] *= amplitude;
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}
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}
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// Calculate expectation
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let norm: f32 = state.iter().map(|x| x * x).sum::<f32>().sqrt();
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if norm > 1e-6 {
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for s in &mut state {
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*s /= norm;
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}
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}
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state
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.iter()
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.zip(hamiltonian.iter())
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.map(|(s, h)| s * s * h)
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.sum()
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}
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pub fn optimize_step(&mut self, hamiltonian: &[f32]) -> f32 {
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let energy = self.expectation_value(hamiltonian);
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self.energy_history.push(energy);
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// Parameter shift rule for gradient (simplified)
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let mut rng = rand::thread_rng();
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for param in &mut self.parameters {
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let shift: f32 = rng.gen::<f32>() * 0.1 - 0.05;
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*param += shift;
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}
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energy
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}
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}
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// ============ Spiking Neural Network ============
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/// Spiking Neural Network with STDP Learning
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pub struct SpikingNeuralNetwork {
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pub config: SelfLearningConfig,
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membrane_potentials: Vec<f32>,
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thresholds: Vec<f32>,
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weights: Vec<Vec<f32>>,
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spike_times: Vec<f32>,
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num_neurons: usize,
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tau_membrane: f32,
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tau_stdp: f32,
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time: f32,
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}
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impl SpikingNeuralNetwork {
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pub fn new(num_neurons: usize) -> Self {
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let mut rng = rand::thread_rng();
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let weights: Vec<Vec<f32>> = (0..num_neurons)
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.map(|_| (0..num_neurons).map(|_| rng.gen::<f32>() * 0.5).collect())
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.collect();
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Self {
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config: SelfLearningConfig {
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name: "Spiking Neural Network".to_string(),
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industry: Industry::Neuromorphic,
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architecture: Architecture::SpikingNN,
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learning_rate: 0.01,
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adaptation_rate: 0.1,
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memory_size: 1000,
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exploration_rate: 0.1,
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meta_learning: false,
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ewc_lambda: 0.0,
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},
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membrane_potentials: vec![0.0; num_neurons],
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thresholds: vec![1.0; num_neurons],
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weights,
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spike_times: vec![-1000.0; num_neurons],
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num_neurons,
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tau_membrane: 20.0,
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tau_stdp: 20.0,
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time: 0.0,
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}
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}
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pub fn step(&mut self, inputs: &[f32], dt: f32) -> Vec<bool> {
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self.time += dt;
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let mut spikes = vec![false; self.num_neurons];
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let decay = (-dt / self.tau_membrane).exp();
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for i in 0..self.num_neurons {
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// Leaky integration
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self.membrane_potentials[i] *= decay;
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|
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// Add input
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if i < inputs.len() {
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self.membrane_potentials[i] += inputs[i];
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}
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// Check threshold
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if self.membrane_potentials[i] >= self.thresholds[i] {
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spikes[i] = true;
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self.spike_times[i] = self.time;
|
|
self.membrane_potentials[i] = 0.0; // Reset
|
|
}
|
|
}
|
|
|
|
// Propagate spikes
|
|
for i in 0..self.num_neurons {
|
|
if spikes[i] {
|
|
for j in 0..self.num_neurons {
|
|
if i != j {
|
|
self.membrane_potentials[j] += self.weights[i][j];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
spikes
|
|
}
|
|
|
|
pub fn stdp_update(&mut self, pre: usize, post: usize) {
|
|
let dt = self.spike_times[post] - self.spike_times[pre];
|
|
let dw = if dt > 0.0 {
|
|
0.01 * (-dt / self.tau_stdp).exp() // LTP
|
|
} else {
|
|
-0.012 * (dt / self.tau_stdp).exp() // LTD
|
|
};
|
|
|
|
self.weights[pre][post] = (self.weights[pre][post] + dw).max(0.0).min(1.0);
|
|
}
|
|
}
|
|
|
|
// ============ Hyperdimensional Computing Model ============
|
|
|
|
/// Hyperdimensional Computing Model
|
|
pub struct HyperdimensionalModel {
|
|
pub config: SelfLearningConfig,
|
|
dim: usize,
|
|
memory: HashMap<String, Vec<f32>>,
|
|
codebook: HashMap<String, Vec<f32>>,
|
|
}
|
|
|
|
impl HyperdimensionalModel {
|
|
pub fn new(dim: usize) -> Self {
|
|
Self {
|
|
config: SelfLearningConfig {
|
|
name: "Hyperdimensional Computing".to_string(),
|
|
industry: Industry::Hyperdimensional,
|
|
architecture: Architecture::HopfieldModern,
|
|
learning_rate: 1.0,
|
|
adaptation_rate: 1.0,
|
|
memory_size: 10000,
|
|
exploration_rate: 0.0,
|
|
meta_learning: false,
|
|
ewc_lambda: 0.0,
|
|
},
|
|
dim,
|
|
memory: HashMap::new(),
|
|
codebook: HashMap::new(),
|
|
}
|
|
}
|
|
|
|
pub fn random_hypervector(&self) -> Vec<f32> {
|
|
let mut rng = rand::thread_rng();
|
|
(0..self.dim)
|
|
.map(|_| if rng.gen::<bool>() { 1.0 } else { -1.0 })
|
|
.collect()
|
|
}
|
|
|
|
pub fn bind(&self, a: &[f32], b: &[f32]) -> Vec<f32> {
|
|
a.iter().zip(b.iter()).map(|(x, y)| x * y).collect()
|
|
}
|
|
|
|
pub fn bundle(&self, vectors: &[Vec<f32>]) -> Vec<f32> {
|
|
let mut result = vec![0.0; self.dim];
|
|
for v in vectors {
|
|
for (r, x) in result.iter_mut().zip(v.iter()) {
|
|
*r += x;
|
|
}
|
|
}
|
|
// Threshold
|
|
result
|
|
.iter()
|
|
.map(|&x| if x > 0.0 { 1.0 } else { -1.0 })
|
|
.collect()
|
|
}
|
|
|
|
pub fn similarity(&self, a: &[f32], b: &[f32]) -> f32 {
|
|
let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
|
|
dot / self.dim as f32
|
|
}
|
|
|
|
pub fn store(&mut self, key: &str, value: Vec<f32>) {
|
|
self.memory.insert(key.to_string(), value);
|
|
}
|
|
|
|
pub fn query(&self, query: &[f32]) -> Option<(&String, f32)> {
|
|
self.memory
|
|
.iter()
|
|
.map(|(k, v)| (k, self.similarity(query, v)))
|
|
.max_by(|a, b| a.1.partial_cmp(&b.1).unwrap())
|
|
}
|
|
}
|
|
|
|
// ============ Exotic Experiments ============
|
|
|
|
/// Chaos-based Neural Dynamics
|
|
pub struct ChaosModel {
|
|
pub state: [f32; 3],
|
|
pub sigma: f32,
|
|
pub rho: f32,
|
|
pub beta: f32,
|
|
}
|
|
|
|
impl ChaosModel {
|
|
pub fn new() -> Self {
|
|
Self {
|
|
state: [1.0, 1.0, 1.0],
|
|
sigma: 10.0,
|
|
rho: 28.0,
|
|
beta: 8.0 / 3.0,
|
|
}
|
|
}
|
|
|
|
pub fn step(&mut self, dt: f32) {
|
|
let [x, y, z] = self.state;
|
|
let dx = self.sigma * (y - x);
|
|
let dy = x * (self.rho - z) - y;
|
|
let dz = x * y - self.beta * z;
|
|
|
|
self.state[0] += dx * dt;
|
|
self.state[1] += dy * dt;
|
|
self.state[2] += dz * dt;
|
|
}
|
|
|
|
pub fn encode_to_features(&self) -> Vec<f32> {
|
|
vec![
|
|
self.state[0] / 20.0,
|
|
self.state[1] / 20.0,
|
|
self.state[2] / 30.0,
|
|
]
|
|
}
|
|
}
|
|
|
|
/// Swarm Intelligence Optimizer
|
|
pub struct SwarmOptimizer {
|
|
pub particles: Vec<Vec<f32>>,
|
|
pub velocities: Vec<Vec<f32>>,
|
|
pub best_positions: Vec<Vec<f32>>,
|
|
pub global_best: Vec<f32>,
|
|
pub global_best_fitness: f32,
|
|
dim: usize,
|
|
}
|
|
|
|
impl SwarmOptimizer {
|
|
pub fn new(num_particles: usize, dim: usize) -> Self {
|
|
let mut rng = rand::thread_rng();
|
|
|
|
let particles: Vec<Vec<f32>> = (0..num_particles)
|
|
.map(|_| (0..dim).map(|_| rng.gen::<f32>() * 2.0 - 1.0).collect())
|
|
.collect();
|
|
|
|
let velocities: Vec<Vec<f32>> = (0..num_particles)
|
|
.map(|_| (0..dim).map(|_| rng.gen::<f32>() * 0.2 - 0.1).collect())
|
|
.collect();
|
|
|
|
let best_positions = particles.clone();
|
|
let global_best = particles[0].clone();
|
|
|
|
Self {
|
|
particles,
|
|
velocities,
|
|
best_positions,
|
|
global_best,
|
|
global_best_fitness: f32::MAX,
|
|
dim,
|
|
}
|
|
}
|
|
|
|
pub fn step<F: Fn(&[f32]) -> f32>(&mut self, fitness_fn: F, w: f32, c1: f32, c2: f32) {
|
|
let mut rng = rand::thread_rng();
|
|
|
|
for i in 0..self.particles.len() {
|
|
// Update velocity
|
|
for d in 0..self.dim {
|
|
let r1: f32 = rng.gen();
|
|
let r2: f32 = rng.gen();
|
|
|
|
self.velocities[i][d] = w * self.velocities[i][d]
|
|
+ c1 * r1 * (self.best_positions[i][d] - self.particles[i][d])
|
|
+ c2 * r2 * (self.global_best[d] - self.particles[i][d]);
|
|
}
|
|
|
|
// Update position
|
|
for d in 0..self.dim {
|
|
self.particles[i][d] += self.velocities[i][d];
|
|
}
|
|
|
|
// Evaluate fitness
|
|
let fitness = fitness_fn(&self.particles[i]);
|
|
|
|
// Update personal best
|
|
let personal_fitness = fitness_fn(&self.best_positions[i]);
|
|
if fitness < personal_fitness {
|
|
self.best_positions[i] = self.particles[i].clone();
|
|
}
|
|
|
|
// Update global best
|
|
if fitness < self.global_best_fitness {
|
|
self.global_best = self.particles[i].clone();
|
|
self.global_best_fitness = fitness;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/// Reservoir Computing for temporal patterns
|
|
pub struct ReservoirComputer {
|
|
pub reservoir_size: usize,
|
|
pub input_weights: Vec<Vec<f32>>,
|
|
pub reservoir_weights: Vec<Vec<f32>>,
|
|
pub state: Vec<f32>,
|
|
pub spectral_radius: f32,
|
|
}
|
|
|
|
impl ReservoirComputer {
|
|
pub fn new(input_dim: usize, reservoir_size: usize, spectral_radius: f32) -> Self {
|
|
let mut rng = rand::thread_rng();
|
|
|
|
let input_weights: Vec<Vec<f32>> = (0..reservoir_size)
|
|
.map(|_| {
|
|
(0..input_dim)
|
|
.map(|_| rng.gen::<f32>() * 2.0 - 1.0)
|
|
.collect()
|
|
})
|
|
.collect();
|
|
|
|
let reservoir_weights: Vec<Vec<f32>> = (0..reservoir_size)
|
|
.map(|_| {
|
|
(0..reservoir_size)
|
|
.map(|_| rng.gen::<f32>() * 2.0 - 1.0)
|
|
.collect()
|
|
})
|
|
.collect();
|
|
|
|
Self {
|
|
reservoir_size,
|
|
input_weights,
|
|
reservoir_weights,
|
|
state: vec![0.0; reservoir_size],
|
|
spectral_radius,
|
|
}
|
|
}
|
|
|
|
pub fn step(&mut self, input: &[f32]) -> Vec<f32> {
|
|
let mut new_state = vec![0.0; self.reservoir_size];
|
|
|
|
for i in 0..self.reservoir_size {
|
|
// Input contribution
|
|
for (j, &inp) in input.iter().enumerate() {
|
|
if j < self.input_weights[i].len() {
|
|
new_state[i] += self.input_weights[i][j] * inp;
|
|
}
|
|
}
|
|
|
|
// Recurrent contribution
|
|
for j in 0..self.reservoir_size {
|
|
new_state[i] += self.reservoir_weights[i][j] * self.state[j] * self.spectral_radius;
|
|
}
|
|
|
|
// Nonlinearity
|
|
new_state[i] = new_state[i].tanh();
|
|
}
|
|
|
|
self.state = new_state.clone();
|
|
new_state
|
|
}
|
|
}
|
|
|
|
// ============ Training Entry Points ============
|
|
|
|
/// Run industry-specific model training
|
|
pub async fn run_industry_training(epochs: usize, output_dir: Option<PathBuf>) -> Result<()> {
|
|
let output_dir = output_dir.unwrap_or_else(|| PathBuf::from("./training_results"));
|
|
std::fs::create_dir_all(&output_dir)?;
|
|
|
|
tracing::info!(
|
|
"Starting self-learning model training for {} epochs",
|
|
epochs
|
|
);
|
|
|
|
// Train Healthcare Model
|
|
tracing::info!("Training Healthcare Diagnostics Model...");
|
|
let start = Instant::now();
|
|
let mut healthcare = HealthcareModel::new(256, 256, 100);
|
|
let mut rng = rand::thread_rng();
|
|
|
|
for epoch in 0..epochs {
|
|
let symptoms: Vec<f32> = (0..256).map(|_| rng.gen::<f32>()).collect();
|
|
let correct = rng.gen::<f32>() > 0.3;
|
|
let accuracy = healthcare.train_episode(symptoms, "diagnosis_a", correct);
|
|
|
|
if epoch % 10 == 0 {
|
|
tracing::info!("Healthcare epoch {}: accuracy = {:.4}", epoch, accuracy);
|
|
}
|
|
}
|
|
tracing::info!("Healthcare training complete in {:?}", start.elapsed());
|
|
|
|
// Train Financial Model
|
|
tracing::info!("Training Financial Trading Model...");
|
|
let start = Instant::now();
|
|
let mut financial = FinancialModel::new(128, 128);
|
|
|
|
for epoch in 0..epochs {
|
|
let market_data: Vec<f32> = (0..128).map(|_| rng.gen::<f32>() * 2.0 - 1.0).collect();
|
|
let return_pct = rng.gen::<f32>() * 0.1 - 0.05;
|
|
let sharpe = financial.train_step(market_data, return_pct);
|
|
|
|
if epoch % 10 == 0 {
|
|
tracing::info!("Financial epoch {}: sharpe = {:.4}", epoch, sharpe);
|
|
}
|
|
}
|
|
tracing::info!("Financial training complete in {:?}", start.elapsed());
|
|
|
|
// Train Autonomous Model
|
|
tracing::info!("Training Autonomous Systems Model...");
|
|
let start = Instant::now();
|
|
let mut autonomous = AutonomousModel::new(64, 128, 10);
|
|
|
|
for epoch in 0..epochs {
|
|
let sensors: Vec<f32> = (0..64).map(|_| rng.gen::<f32>()).collect();
|
|
let action = rng.gen_range(0..10);
|
|
let reward = rng.gen::<f32>() * 2.0 - 1.0;
|
|
autonomous.train_step(sensors, action, reward);
|
|
|
|
if epoch % 10 == 0 {
|
|
tracing::info!("Autonomous epoch {}: completed", epoch);
|
|
}
|
|
}
|
|
tracing::info!("Autonomous training complete in {:?}", start.elapsed());
|
|
|
|
// Train Quantum-Inspired Model
|
|
tracing::info!("Training Quantum-Inspired Model...");
|
|
let start = Instant::now();
|
|
let mut quantum = QuantumInspiredModel::new(4, 3);
|
|
let hamiltonian: Vec<f32> = (0..16).map(|i| if i == 0 { 1.0 } else { 0.0 }).collect();
|
|
|
|
for epoch in 0..epochs {
|
|
let energy = quantum.optimize_step(&hamiltonian);
|
|
|
|
if epoch % 10 == 0 {
|
|
tracing::info!("Quantum epoch {}: energy = {:.6}", epoch, energy);
|
|
}
|
|
}
|
|
tracing::info!("Quantum training complete in {:?}", start.elapsed());
|
|
|
|
// Train Spiking Neural Network
|
|
tracing::info!("Training Spiking Neural Network...");
|
|
let start = Instant::now();
|
|
let mut snn = SpikingNeuralNetwork::new(100);
|
|
|
|
for epoch in 0..epochs {
|
|
let inputs: Vec<f32> = (0..100)
|
|
.map(|_| if rng.gen::<f32>() > 0.8 { 1.0 } else { 0.0 })
|
|
.collect();
|
|
let spikes = snn.step(&inputs, 1.0);
|
|
let spike_count = spikes.iter().filter(|&&s| s).count();
|
|
|
|
if epoch % 10 == 0 {
|
|
tracing::info!("SNN epoch {}: spikes = {}", epoch, spike_count);
|
|
}
|
|
}
|
|
tracing::info!("SNN training complete in {:?}", start.elapsed());
|
|
|
|
// Train Hyperdimensional Model
|
|
tracing::info!("Training Hyperdimensional Computing Model...");
|
|
let start = Instant::now();
|
|
let mut hdm = HyperdimensionalModel::new(10000);
|
|
|
|
for epoch in 0..epochs.min(100) {
|
|
// Fewer epochs for HD
|
|
let hv = hdm.random_hypervector();
|
|
hdm.store(&format!("pattern_{}", epoch), hv);
|
|
}
|
|
tracing::info!(
|
|
"Hyperdimensional training complete in {:?}",
|
|
start.elapsed()
|
|
);
|
|
|
|
tracing::info!("All industry models trained successfully!");
|
|
Ok(())
|
|
}
|
|
|
|
/// Run exotic research experiments
|
|
pub async fn run_exotic_experiments(iterations: usize, output_dir: Option<PathBuf>) -> Result<()> {
|
|
let output_dir = output_dir.unwrap_or_else(|| PathBuf::from("./exotic_results"));
|
|
std::fs::create_dir_all(&output_dir)?;
|
|
|
|
tracing::info!("Starting exotic experiments for {} iterations", iterations);
|
|
|
|
// Chaos experiment
|
|
tracing::info!("Running Lorenz Attractor experiment...");
|
|
let start = Instant::now();
|
|
let mut chaos = ChaosModel::new();
|
|
let mut trajectory = Vec::new();
|
|
|
|
for i in 0..iterations {
|
|
chaos.step(0.01);
|
|
if i % 10 == 0 {
|
|
trajectory.push(chaos.state);
|
|
}
|
|
}
|
|
tracing::info!(
|
|
"Chaos experiment complete in {:?}. Final state: {:?}",
|
|
start.elapsed(),
|
|
chaos.state
|
|
);
|
|
|
|
// Swarm optimization
|
|
tracing::info!("Running Particle Swarm Optimization...");
|
|
let start = Instant::now();
|
|
let mut swarm = SwarmOptimizer::new(50, 10);
|
|
|
|
let fitness_fn = |x: &[f32]| -> f32 {
|
|
x.iter().map(|&xi| xi * xi).sum::<f32>() // Sphere function
|
|
};
|
|
|
|
for i in 0..iterations.min(100) {
|
|
swarm.step(fitness_fn, 0.7, 1.5, 1.5);
|
|
|
|
if i % 10 == 0 {
|
|
tracing::info!(
|
|
"Swarm iteration {}: best fitness = {:.6}",
|
|
i,
|
|
swarm.global_best_fitness
|
|
);
|
|
}
|
|
}
|
|
tracing::info!(
|
|
"Swarm optimization complete in {:?}. Best: {:.6}",
|
|
start.elapsed(),
|
|
swarm.global_best_fitness
|
|
);
|
|
|
|
// Reservoir computing
|
|
tracing::info!("Running Reservoir Computing experiment...");
|
|
let start = Instant::now();
|
|
let mut reservoir = ReservoirComputer::new(10, 100, 0.9);
|
|
let mut rng = rand::thread_rng();
|
|
|
|
for i in 0..iterations {
|
|
let input: Vec<f32> = (0..10).map(|_| rng.gen::<f32>()).collect();
|
|
let state = reservoir.step(&input);
|
|
|
|
if i % 100 == 0 {
|
|
let activity: f32 = state.iter().map(|x| x.abs()).sum::<f32>() / state.len() as f32;
|
|
tracing::info!("Reservoir iteration {}: activity = {:.4}", i, activity);
|
|
}
|
|
}
|
|
tracing::info!("Reservoir experiment complete in {:?}", start.elapsed());
|
|
|
|
// MoE experiment
|
|
tracing::info!("Running Mixture of Experts experiment...");
|
|
let start = Instant::now();
|
|
let moe = MoEModel::new(256, 8);
|
|
|
|
for i in 0..iterations.min(100) {
|
|
let query: Vec<f32> = (0..256).map(|_| rng.gen::<f32>()).collect();
|
|
let context = vec![
|
|
(0..256).map(|_| rng.gen::<f32>()).collect::<Vec<f32>>(),
|
|
(0..256).map(|_| rng.gen::<f32>()).collect::<Vec<f32>>(),
|
|
];
|
|
let output = moe.forward(&query, &context);
|
|
|
|
if i % 10 == 0 {
|
|
let norm: f32 = output.iter().map(|x| x * x).sum::<f32>().sqrt();
|
|
tracing::info!("MoE iteration {}: output norm = {:.4}", i, norm);
|
|
}
|
|
}
|
|
tracing::info!("MoE experiment complete in {:?}", start.elapsed());
|
|
|
|
tracing::info!("All exotic experiments completed successfully!");
|
|
Ok(())
|
|
}
|