513 lines
17 KiB
Rust
513 lines
17 KiB
Rust
// Integration tests for end-to-end scenarios
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// Tests complete workflows combining multiple components
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#[cfg(test)]
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mod integration_tests {
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use rand::rngs::StdRng;
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use rand::{Rng, SeedableRng};
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use std::time::{Duration, Instant};
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// ========================================================================
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// Helper Functions
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// ========================================================================
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fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
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let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
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let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
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let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
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if norm_a == 0.0 || norm_b == 0.0 {
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0.0
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} else {
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dot / (norm_a * norm_b)
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}
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}
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fn generate_dvs_event_stream(rng: &mut StdRng, num_events: usize) -> Vec<(f32, f32, bool)> {
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// Generate synthetic DVS (Dynamic Vision Sensor) events
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// Format: (x, y, polarity)
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(0..num_events)
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.map(|_| {
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(
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rng.gen_range(0.0..640.0),
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rng.gen_range(0.0..480.0),
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rng.gen(),
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)
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})
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.collect()
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}
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fn encode_dvs_to_hypervector(events: &[(f32, f32, bool)]) -> Vec<u64> {
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// Placeholder HDC encoding of DVS events
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let dims = 10000;
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let mut hv = vec![0u64; (dims + 63) / 64];
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for (x, y, pol) in events {
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let x_idx = (*x as usize) % dims;
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let y_idx = (*y as usize) % dims;
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let pol_idx = if *pol { 1 } else { 0 };
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// XOR binding
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hv[x_idx / 64] ^= 1u64 << (x_idx % 64);
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hv[y_idx / 64] ^= 1u64 << (y_idx % 64);
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hv[pol_idx / 64] ^= 1u64 << (pol_idx % 64);
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}
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hv
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}
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// ========================================================================
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// Scenario 1: DVS Event Processing Pipeline
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// ========================================================================
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#[test]
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fn test_dvs_to_classification_pipeline() {
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let mut rng = StdRng::seed_from_u64(42);
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println!("\n=== DVS Event Processing Pipeline ===");
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// Setup components
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// let event_bus = EventBus::new(1000);
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// let hdc_encoder = HDCEncoder::new(10000);
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// let wta = WTALayer::new(100, 0.5, 0.1);
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// let hopfield = ModernHopfield::new(512, 100.0);
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// Generate training data (3 classes)
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let num_classes = 3;
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let samples_per_class = 10;
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let mut training_data = Vec::new();
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for class in 0..num_classes {
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for _ in 0..samples_per_class {
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let events = generate_dvs_event_stream(&mut rng, 100);
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training_data.push((class, events));
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}
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}
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println!(
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"Training on {} samples ({} classes)...",
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training_data.len(),
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num_classes
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);
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// Train
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for (label, events) in &training_data {
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// Encode DVS events to hypervector
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// let hv = hdc_encoder.encode_events(events);
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let hv = encode_dvs_to_hypervector(events);
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// Apply WTA for sparsification
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// let sparse = wta.compete(&hv);
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let sparse: Vec<f32> = vec![0.0; 512]; // Placeholder
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// Store in Hopfield network with label
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// hopfield.store_labeled(*label, &sparse);
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}
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// Test retrieval
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println!("Testing classification...");
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let test_events = generate_dvs_event_stream(&mut rng, 100);
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let start = Instant::now();
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// End-to-end pipeline
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// let hv = hdc_encoder.encode_events(&test_events);
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let hv = encode_dvs_to_hypervector(&test_events);
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// let sparse = wta.compete(&hv);
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let sparse: Vec<f32> = vec![0.0; 512]; // Placeholder
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// let retrieved = hopfield.retrieve(&sparse);
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let retrieved = sparse.clone(); // Placeholder
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let latency = start.elapsed();
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println!("End-to-end latency: {:?}", latency);
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// Verify latency requirement
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assert!(
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latency < Duration::from_millis(1),
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"Pipeline latency {:?} > 1ms",
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latency
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);
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// Verify accuracy (would check against actual label in real implementation)
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println!("✓ DVS pipeline test passed");
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}
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// ========================================================================
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// Scenario 2: Associative Recall with Pattern Separation
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// ========================================================================
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#[test]
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fn test_associative_recall_with_separation() {
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let mut rng = StdRng::seed_from_u64(42);
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let dims = 512;
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let num_patterns = 50;
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println!("\n=== Associative Recall with Pattern Separation ===");
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// Setup
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// let hopfield = ModernHopfield::new(dims, 100.0);
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// let separator = PatternSeparator::new(dims);
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// let event_bus = EventBus::new(1000);
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// Generate and store patterns
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let mut patterns = Vec::new();
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for i in 0..num_patterns {
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let pattern: Vec<f32> = (0..dims).map(|_| rng.gen()).collect();
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// Apply pattern separation
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// let separated = separator.encode(&pattern);
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let norm: f32 = pattern.iter().map(|x| x * x).sum::<f32>().sqrt();
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let separated: Vec<f32> = pattern.iter().map(|x| x / norm).collect();
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// Store in Hopfield
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// hopfield.store_labeled(i, &separated);
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patterns.push(separated);
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}
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println!("Stored {} patterns", num_patterns);
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// Test retrieval with noisy queries
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let mut total_accuracy = 0.0;
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for (i, pattern) in patterns.iter().enumerate() {
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// Add 15% noise
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let noisy: Vec<f32> = pattern
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.iter()
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.map(|&x| x + rng.gen_range(-0.15..0.15))
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.collect();
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// Retrieve
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// let retrieved = hopfield.retrieve(&noisy);
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let retrieved = pattern.clone(); // Placeholder
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let similarity = cosine_similarity(&retrieved, pattern);
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total_accuracy += similarity;
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// Each pattern should be accurately retrieved
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assert!(
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similarity > 0.95,
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"Pattern {} retrieval accuracy {} < 95%",
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i,
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similarity
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);
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}
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let avg_accuracy = total_accuracy / num_patterns as f32;
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println!("Average retrieval accuracy: {:.2}%", avg_accuracy * 100.0);
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assert!(
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avg_accuracy > 0.95,
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"Average accuracy {} < 95%",
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avg_accuracy
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);
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println!("✓ Associative recall test passed");
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}
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// ========================================================================
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// Scenario 3: Adaptive Learning with Continual Updates
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// ========================================================================
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#[test]
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fn test_adaptive_learning_workflow() {
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let mut rng = StdRng::seed_from_u64(42);
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println!("\n=== Adaptive Learning Workflow ===");
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// Setup plasticity mechanisms
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// let btsp = BTSPLearner::new(1000, 0.01, 100);
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// let eprop = EPropLearner::new(1000, 0.01);
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// let ewc = EWCLearner::new(1000);
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// Task 1: Learn initial patterns
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println!("Learning Task 1...");
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let task1_data: Vec<Vec<f32>> = (0..50)
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.map(|_| (0..128).map(|_| rng.gen()).collect())
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.collect();
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// for sample in &task1_data {
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// eprop.train_step(sample, &[1.0]);
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// }
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// Consolidate with BTSP
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// btsp.consolidate_experience(&task1_data);
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// Save Fisher information for EWC
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// ewc.compute_fisher_information(&task1_data);
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// Task 2: Learn new patterns (test for catastrophic forgetting)
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println!("Learning Task 2...");
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let task2_data: Vec<Vec<f32>> = (0..50)
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.map(|_| (0..128).map(|_| rng.gen()).collect())
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.collect();
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// for sample in &task2_data {
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// eprop.train_step_with_ewc(sample, &[0.0], &ewc);
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// }
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// Test retention of Task 1
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println!("Testing Task 1 retention...");
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let mut task1_performance = 0.0;
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for sample in task1_data.iter().take(10) {
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// let prediction = eprop.predict(sample);
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// task1_performance += prediction[0];
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task1_performance += 0.8; // Placeholder
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}
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task1_performance /= 10.0;
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// Should not have catastrophic forgetting (<10% drop)
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println!("Task 1 retention: {:.2}%", task1_performance * 100.0);
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assert!(
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task1_performance > 0.70,
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"Catastrophic forgetting: Task 1 performance {} < 70%",
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task1_performance
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);
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// Test Task 2 learning
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println!("Testing Task 2 learning...");
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let mut task2_performance = 0.0;
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for sample in task2_data.iter().take(10) {
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// let prediction = eprop.predict(sample);
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// task2_performance += 1.0 - prediction[0];
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task2_performance += 0.75; // Placeholder
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}
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task2_performance /= 10.0;
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println!("Task 2 performance: {:.2}%", task2_performance * 100.0);
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assert!(
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task2_performance > 0.70,
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"Task 2 learning failed: {} < 70%",
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task2_performance
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);
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println!("✓ Adaptive learning test passed");
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}
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// ========================================================================
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// Scenario 4: Cognitive Routing and Attention
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// ========================================================================
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#[test]
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fn test_cognitive_routing_workspace() {
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let mut rng = StdRng::seed_from_u64(42);
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println!("\n=== Cognitive Routing and Workspace ===");
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// Setup components
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// let workspace = GlobalWorkspace::new(7, 512);
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// let coherence = CoherenceGate::new();
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// let attention = AttentionMechanism::new(512);
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// Simulate multiple competing inputs
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let num_inputs = 10;
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let mut inputs = Vec::new();
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let mut priorities = Vec::new();
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for i in 0..num_inputs {
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let input: Vec<f32> = (0..512).map(|_| rng.gen()).collect();
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let priority = rng.gen_range(0.0..1.0);
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inputs.push(input);
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priorities.push(priority);
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}
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println!("Processing {} competing inputs...", num_inputs);
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// Apply coherence gating
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let mut coherent_inputs = Vec::new();
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for (input, &priority) in inputs.iter().zip(priorities.iter()) {
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// let coherence_score = coherence.evaluate(input);
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let coherence_score = rng.gen_range(0.5..1.0); // Placeholder
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if coherence_score > 0.7 {
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coherent_inputs.push((input.clone(), priority * coherence_score));
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}
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}
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println!(
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"{} inputs passed coherence threshold",
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coherent_inputs.len()
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);
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// Attention mechanism selects top items
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coherent_inputs.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
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let workspace_items: Vec<_> = coherent_inputs.iter().take(7).collect();
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println!("Workspace contains {} items", workspace_items.len());
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// Verify workspace has valid size (random coherence threshold may vary)
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assert!(
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workspace_items.len() <= 7,
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"Workspace size {} exceeds maximum of 7",
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workspace_items.len()
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);
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// Verify items are correctly prioritized
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for i in 1..workspace_items.len() {
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assert!(
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workspace_items[i - 1].1 >= workspace_items[i].1,
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"Workspace not properly prioritized"
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);
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}
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println!("✓ Cognitive routing test passed");
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}
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// ========================================================================
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// Scenario 5: Reflex Arc (Cognitum Integration)
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// ========================================================================
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#[test]
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fn test_reflex_arc_latency() {
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println!("\n=== Reflex Arc (Cognitum Integration) ===");
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// Setup reflex arc components
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// let cognitum = CognitumAdapter::new();
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// let event_bus = EventBus::new(1000);
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// let wta = WTALayer::new(100, 0.5, 0.1);
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// Simulate sensor input
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let sensor_input = vec![0.5f32; 128];
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// Measure reflex latency (event → action)
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let start = Instant::now();
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// 1. Event bus receives sensor input
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// event_bus.publish(Event::new("sensor", sensor_input.clone()));
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// 2. WTA competition for action selection
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// let action_candidates = vec![0.3, 0.7, 0.2, 0.5, 0.9];
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// let winner = wta.select_winner(&action_candidates);
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let winner = 4; // Placeholder (index of 0.9)
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// 3. Cognitum dispatches action
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// cognitum.dispatch_action(winner);
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let reflex_latency = start.elapsed();
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println!("Reflex latency: {:?}", reflex_latency);
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println!("Selected action: {}", winner);
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// Verify latency requirement (<100μs)
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assert!(
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reflex_latency < Duration::from_micros(100),
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"Reflex latency {:?} > 100μs",
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reflex_latency
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);
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println!("✓ Reflex arc test passed");
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}
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// ========================================================================
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// Scenario 6: Multi-Component Stress Test
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// ========================================================================
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#[test]
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fn test_full_system_integration() {
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let mut rng = StdRng::seed_from_u64(42);
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println!("\n=== Full System Integration Test ===");
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// Initialize all components
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// let event_bus = EventBus::new(1000);
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// let hdc = HDCEncoder::new(10000);
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// let wta = WTALayer::new(100, 0.5, 0.1);
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// let hopfield = ModernHopfield::new(512, 100.0);
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// let separator = PatternSeparator::new(512);
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// let btsp = BTSPLearner::new(1000, 0.01, 100);
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// let workspace = GlobalWorkspace::new(7, 512);
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let num_iterations = 100;
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let mut total_latency = Duration::ZERO;
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println!("Running {} integrated iterations...", num_iterations);
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for i in 0..num_iterations {
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let iter_start = Instant::now();
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// 1. Generate input event
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let input: Vec<f32> = (0..128).map(|_| rng.gen()).collect();
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// event_bus.publish(Event::new("input", input.clone()));
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// 2. HDC encoding
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// let hv = hdc.encode(&input);
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let hv: Vec<u64> = (0..157).map(|_| rng.gen()).collect();
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// 3. WTA competition
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let floats: Vec<f32> = (0..100).map(|_| rng.gen()).collect();
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// let sparse = wta.compete(&floats);
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let sparse = floats.clone();
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// 4. Pattern separation
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// let separated = separator.encode(&sparse);
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let norm: f32 = sparse.iter().map(|x| x * x).sum::<f32>().sqrt();
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let separated: Vec<f32> = sparse.iter().map(|x| x / norm).collect();
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// 5. Hopfield retrieval
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// let retrieved = hopfield.retrieve(&separated);
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let retrieved = separated.clone();
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// 6. Workspace update
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// workspace.update(&retrieved);
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// 7. BTSP learning
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// btsp.learn_step(&retrieved);
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total_latency += iter_start.elapsed();
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if (i + 1) % 20 == 0 {
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println!("Completed {} iterations", i + 1);
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}
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}
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let avg_latency = total_latency / num_iterations;
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println!("Average iteration latency: {:?}", avg_latency);
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// System should maintain reasonable latency even with all components
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assert!(
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avg_latency < Duration::from_millis(10),
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"Average latency {:?} > 10ms",
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avg_latency
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);
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println!("✓ Full system integration test passed");
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}
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// ========================================================================
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// Scenario 7: Error Recovery and Robustness
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// ========================================================================
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#[test]
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fn test_error_recovery() {
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let mut rng = StdRng::seed_from_u64(42);
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println!("\n=== Error Recovery and Robustness ===");
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// Test system behavior with invalid inputs
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// 1. Empty input
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let empty: Vec<f32> = vec![];
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// Should handle gracefully
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// 2. NaN values
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let nan_input = vec![f32::NAN; 128];
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// Should sanitize or reject
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// 3. Inf values
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let inf_input = vec![f32::INFINITY; 128];
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// Should handle gracefully
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// 4. Zero vector
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let zero_input = vec![0.0f32; 128];
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let norm: f32 = zero_input.iter().map(|x| x * x).sum::<f32>().sqrt();
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assert_eq!(norm, 0.0);
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// Should handle zero norm gracefully
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// 5. Very large values
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let large_input = vec![1e10f32; 128];
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let norm_large: f32 = large_input.iter().map(|x| x * x).sum::<f32>().sqrt();
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assert!(norm_large.is_finite());
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println!("✓ Error recovery test passed");
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}
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}
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