optirs_learned/adaptive/
types.rs

1//! Auto-generated module
2//!
3//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)
4
5#[allow(unused_imports)]
6use crate::error::Result;
7use crate::transformer_based_optimizer::{TransformerOptimizer, TransformerOptimizerConfig};
8use crate::LearnedOptimizerConfig;
9#[allow(dead_code)]
10use scirs2_core::ndarray::{Array1, Array2, Array3};
11use scirs2_core::numeric::Float;
12use std::collections::{HashMap, VecDeque};
13use std::fmt::Debug;
14use std::time::Instant;
15
16/// Performance predictor for transformer variants
17#[derive(Debug)]
18pub struct TransformerPerformancePredictor<T: Float + Debug + Send + Sync + 'static> {
19    /// Neural predictor network
20    predictor_network: PredictorNetwork<T>,
21    /// Feature extractor
22    feature_extractor: PerformanceFeatureExtractor<T>,
23    /// Prediction cache
24    prediction_cache: PredictionCache<T>,
25    /// Uncertainty estimator
26    uncertainty_estimator: UncertaintyEstimator<T>,
27}
28impl<T: Float + Debug + Send + Sync + 'static> TransformerPerformancePredictor<T> {
29    fn new(config: &AdaptiveConfig<T>) -> Result<Self> {
30        Ok(Self {
31            predictor_network: PredictorNetwork::new(vec![64, 128, 64, 1])?,
32            feature_extractor: PerformanceFeatureExtractor::new(64)?,
33            prediction_cache: PredictionCache::new(1000),
34            uncertainty_estimator: UncertaintyEstimator::new(UncertaintyMethod::Ensemble),
35        })
36    }
37    fn predict_improvement(
38        &mut self,
39        landscape: &LandscapeAnalysis<T>,
40        _adaptation: &ArchitectureAdaptation<T>,
41    ) -> Result<PerformancePrediction<T>> {
42        Ok(PerformancePrediction {
43            convergence_improvement: scirs2_core::numeric::NumCast::from(0.15)
44                .unwrap_or_else(|| T::zero()),
45            final_performance: scirs2_core::numeric::NumCast::from(0.92)
46                .unwrap_or_else(|| T::zero()),
47            confidence: scirs2_core::numeric::NumCast::from(0.85).unwrap_or_else(|| T::zero()),
48            uncertainty: scirs2_core::numeric::NumCast::from(0.05).unwrap_or_else(|| T::zero()),
49        })
50    }
51}
52/// Architecture adaptation result
53#[derive(Debug)]
54pub struct ArchitectureAdaptation<T: Float + Debug + Send + Sync + 'static> {
55    /// Adapted configuration
56    pub adapted_config: TransformerOptimizerConfig<T>,
57    /// Architecture changes
58    pub changes: Vec<ArchitectureChange>,
59    /// Expected improvement
60    pub expected_improvement: T,
61    /// Adaptation confidence
62    pub confidence: T,
63}
64/// Memory-efficient attention manager
65#[derive(Debug)]
66pub struct MemoryEfficientAttentionManager<T: Float + Debug + Send + Sync + 'static> {
67    /// Attention pattern cache
68    pattern_cache: AttentionPatternCache<T>,
69    /// Sparse attention mask
70    sparse_mask: Array2<bool>,
71    /// Local attention windows
72    local_windows: Vec<AttentionWindow>,
73    /// Global attention heads
74    global_heads: Vec<usize>,
75    /// Memory usage tracker
76    memory_tracker: MemoryUsageTracker,
77}
78impl<T: Float + Debug + Send + Sync + 'static> MemoryEfficientAttentionManager<T> {
79    fn new(config: &AdaptiveConfig<T>) -> Result<Self> {
80        Ok(Self {
81            pattern_cache: AttentionPatternCache::new(),
82            sparse_mask: Array2::default((0, 0)),
83            local_windows: Vec::new(),
84            global_heads: Vec::new(),
85            memory_tracker: MemoryUsageTracker::new(),
86        })
87    }
88    fn optimize_attention(
89        &mut self,
90        analysis: &LandscapeAnalysis<T>,
91    ) -> Result<AttentionOptimization<T>> {
92        let complexity = analysis.complexity.to_f64().unwrap_or(0.5);
93        let difficulty = analysis.difficulty.to_f64().unwrap_or(0.3);
94        let (num_heads, seq_len) = self.determine_attention_dimensions(complexity, difficulty)?;
95        let mut attention_patterns = Array3::zeros((num_heads, seq_len, seq_len));
96        self.generate_attention_patterns(&mut attention_patterns, analysis)?;
97        let sparsitylevel = if complexity > 0.7 {
98            scirs2_core::numeric::NumCast::from(0.05).unwrap_or_else(|| T::zero())
99        } else {
100            scirs2_core::numeric::NumCast::from(0.15).unwrap_or_else(|| T::zero())
101        };
102        self.apply_sparsity_mask(&mut attention_patterns, sparsitylevel)?;
103        let pattern_key = format!("pattern_{}_{}", num_heads, seq_len);
104        self.pattern_cache
105            .patterns
106            .insert(pattern_key.clone(), attention_patterns.clone());
107        *self
108            .pattern_cache
109            .usage_frequency
110            .entry(pattern_key)
111            .or_insert(0) += 1;
112        let original_size = 8 * 512 * 512 * std::mem::size_of::<f32>();
113        let optimized_size = num_heads * seq_len * seq_len * std::mem::size_of::<f32>();
114        let memory_savings = original_size.saturating_sub(optimized_size);
115        let speedup_from_sparsity = T::one() / sparsitylevel;
116        let speedup_from_dimensions =
117            T::from(512.0 * 512.0 / (seq_len * seq_len) as f64).expect("unwrap failed");
118        let computational_speedup = (speedup_from_sparsity + speedup_from_dimensions)
119            / scirs2_core::numeric::NumCast::from(2.0).unwrap_or_else(|| T::zero());
120        self.memory_tracker.current_usage += optimized_size;
121        if self.memory_tracker.current_usage > self.memory_tracker.peak_usage {
122            self.memory_tracker.peak_usage = self.memory_tracker.current_usage;
123        }
124        Ok(AttentionOptimization {
125            attention_patterns,
126            sparsitylevel,
127            memory_savings,
128            computational_speedup,
129        })
130    }
131    fn determine_attention_dimensions(
132        &self,
133        complexity: f64,
134        difficulty: f64,
135    ) -> Result<(usize, usize)> {
136        let base_heads = 8;
137        let base_seq_len = 512;
138        let heads = (if complexity > 0.8 {
139            (base_heads as f64 * 1.5) as usize
140        } else if complexity < 0.3 {
141            (base_heads as f64 * 0.75) as usize
142        } else {
143            base_heads
144        })
145        .clamp(4, 16);
146        let seq_len = (if difficulty > 0.7 {
147            (base_seq_len as f64 * 1.2) as usize
148        } else if difficulty < 0.3 {
149            (base_seq_len as f64 * 0.8) as usize
150        } else {
151            base_seq_len
152        })
153        .clamp(256, 1024);
154        Ok((heads, seq_len))
155    }
156    fn generate_attention_patterns(
157        &self,
158        patterns: &mut Array3<T>,
159        analysis: &LandscapeAnalysis<T>,
160    ) -> Result<()> {
161        let (num_heads, seq_len, _) = patterns.dim();
162        for head in 0..num_heads {
163            for i in 0..seq_len {
164                for j in 0..seq_len {
165                    let distance = ((i as i32 - j as i32).abs() as f64).sqrt();
166                    let base_attention = (-scirs2_core::numeric::NumCast::from(distance)
167                        .unwrap_or_else(|| T::zero())
168                        / (scirs2_core::numeric::NumCast::from(seq_len)
169                            .unwrap_or_else(|| T::zero())
170                            * scirs2_core::numeric::NumCast::from(0.1)
171                                .unwrap_or_else(|| T::zero())))
172                    .exp();
173                    let complexity_factor = analysis.complexity.to_f64().unwrap_or(0.5);
174                    let modulated_attention = base_attention
175                        * (T::one()
176                            + scirs2_core::numeric::NumCast::from(complexity_factor)
177                                .unwrap_or_else(|| T::zero())
178                                * scirs2_core::numeric::NumCast::from(0.3)
179                                    .unwrap_or_else(|| T::zero()));
180                    patterns[[head, i, j]] =
181                        scirs2_core::numeric::NumCast::from(modulated_attention)
182                            .unwrap_or_else(|| T::zero());
183                }
184            }
185        }
186        Ok(())
187    }
188    fn apply_sparsity_mask(&self, patterns: &mut Array3<T>, sparsitylevel: T) -> Result<()> {
189        let sparsity_threshold = sparsitylevel.to_f64().unwrap_or(0.1);
190        patterns.map_inplace(|x| {
191            if x.to_f64().unwrap_or(0.0) < sparsity_threshold {
192                *x = T::zero();
193            }
194        });
195        Ok(())
196    }
197}
198/// Adaptive sequence processor for variable-length optimization histories
199#[derive(Debug)]
200pub struct AdaptiveSequenceProcessor<T: Float + Debug + Send + Sync + 'static> {
201    /// Current sequence length
202    current_length: usize,
203    /// Sequence importance scores
204    importance_scores: VecDeque<T>,
205    /// Sequence compression ratio
206    compression_ratio: T,
207    /// Information-preserving compressor
208    compressor: SequenceCompressor<T>,
209    /// Adaptive windowing strategy
210    windowing_strategy: WindowingStrategy,
211}
212impl<T: Float + Debug + Send + Sync + 'static> AdaptiveSequenceProcessor<T> {
213    fn new(config: &AdaptiveConfig<T>) -> Result<Self> {
214        Ok(Self {
215            current_length: 512,
216            importance_scores: VecDeque::new(),
217            compression_ratio: scirs2_core::numeric::NumCast::from(0.8)
218                .unwrap_or_else(|| T::zero()),
219            compressor: SequenceCompressor::new()?,
220            windowing_strategy: WindowingStrategy::ImportanceBased,
221        })
222    }
223    fn adapt_to_landscape(
224        &mut self,
225        analysis: &LandscapeAnalysis<T>,
226    ) -> Result<SequenceAdaptation<T>> {
227        let complexity_factor = analysis.complexity.to_f64().unwrap_or(0.5);
228        let difficulty_factor = analysis.difficulty.to_f64().unwrap_or(0.3);
229        let new_length = if complexity_factor > 0.7 {
230            (self.current_length as f64 * 1.2).min(2048.0) as usize
231        } else if complexity_factor < 0.3 {
232            (self.current_length as f64 * 0.8).max(64.0) as usize
233        } else {
234            self.current_length
235        };
236        let new_compression_ratio = if difficulty_factor > 0.6 {
237            self.compression_ratio
238                * scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero())
239        } else {
240            self.compression_ratio
241                * scirs2_core::numeric::NumCast::from(1.1).unwrap_or_else(|| T::zero())
242        }
243        .min(scirs2_core::numeric::NumCast::from(0.95).unwrap_or_else(|| T::zero()))
244        .max(scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()));
245        self.current_length = new_length;
246        self.compression_ratio = new_compression_ratio;
247        let information_preservation = T::one()
248            - (T::one() - new_compression_ratio)
249                * scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero());
250        let length_efficiency =
251            scirs2_core::numeric::NumCast::from(self.current_length as f64 / new_length as f64)
252                .unwrap_or_else(|| T::zero());
253        let compression_efficiency = T::one() / new_compression_ratio;
254        let efficiency_gain = (length_efficiency + compression_efficiency)
255            / scirs2_core::numeric::NumCast::from(2.0).unwrap_or_else(|| T::zero());
256        self.update_importance_scores(analysis)?;
257        Ok(SequenceAdaptation {
258            new_length,
259            compression_ratio: new_compression_ratio,
260            information_preservation,
261            efficiency_gain,
262        })
263    }
264    fn update_importance_scores(&mut self, analysis: &LandscapeAnalysis<T>) -> Result<()> {
265        let base_importance = scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero());
266        let complexity_boost = analysis.complexity
267            * scirs2_core::numeric::NumCast::from(0.3).unwrap_or_else(|| T::zero());
268        let difficulty_boost = analysis.difficulty
269            * scirs2_core::numeric::NumCast::from(0.2).unwrap_or_else(|| T::zero());
270        let new_importance = base_importance + complexity_boost + difficulty_boost;
271        self.importance_scores.push_back(new_importance);
272        if self.importance_scores.len() > 100 {
273            self.importance_scores.pop_front();
274        }
275        Ok(())
276    }
277}
278/// Windowing strategies for adaptive sequences
279#[derive(Debug, Clone, Copy)]
280pub enum WindowingStrategy {
281    /// Fixed size window
282    Fixed,
283    /// Sliding window
284    Sliding,
285    /// Importance-based window
286    ImportanceBased,
287    /// Hierarchical windowing
288    Hierarchical,
289    /// Attention-guided windowing
290    AttentionGuided,
291}
292/// Landscape features for optimization analysis
293#[derive(Debug, Clone)]
294pub struct LandscapeFeatures<T: Float + Debug + Send + Sync + 'static> {
295    /// Smoothness measure
296    pub(super) smoothness: T,
297    /// Multimodality indicator
298    pub(super) multimodality: T,
299    /// Noise level
300    pub(super) noise_level: T,
301    /// Curvature information
302    pub(super) curvature: CurvatureInfo<T>,
303    /// Gradient characteristics
304    pub(super) gradient_characteristics: GradientCharacteristics<T>,
305}
306/// Symmetry types
307#[derive(Debug, Clone, Copy)]
308pub enum SymmetryType {
309    Rotational,
310    Reflectional,
311    Translational,
312    Scale,
313    Discrete,
314}
315/// Pattern applicability
316#[derive(Debug, Clone)]
317pub struct PatternApplicability {
318    /// Applicable regions
319    regions: Vec<Array1<f64>>,
320    /// Applicability score
321    score: f64,
322    /// Confidence level
323    confidence: f64,
324}
325/// Sequence adaptation result
326#[derive(Debug)]
327pub struct SequenceAdaptation<T: Float + Debug + Send + Sync + 'static> {
328    /// New sequence length
329    pub new_length: usize,
330    /// Compression ratio
331    pub compression_ratio: T,
332    /// Information preservation score
333    pub information_preservation: T,
334    /// Processing efficiency gain
335    pub efficiency_gain: T,
336}
337/// Landscape analysis result
338#[derive(Debug)]
339pub struct LandscapeAnalysis<T: Float + Debug + Send + Sync + 'static> {
340    /// Landscape complexity
341    pub complexity: T,
342    /// Optimization difficulty
343    pub difficulty: T,
344    /// Recommended strategies
345    pub recommended_strategies: Vec<OptimizationStrategy>,
346    /// Analysis confidence
347    pub confidence: T,
348}
349#[derive(Debug, Clone, Copy)]
350pub enum PositionalEncodingType {
351    Sinusoidal,
352    Learned,
353    Rotary,
354    Relative,
355}
356/// Complexity estimator
357#[derive(Debug)]
358pub struct ComplexityEstimator<T: Float + Debug + Send + Sync + 'static> {
359    /// Computational complexity
360    computational_complexity: T,
361    /// Sample complexity
362    sample_complexity: T,
363    /// Model complexity
364    model_complexity: T,
365    /// Generalization complexity
366    generalization_complexity: T,
367}
368impl<T: Float + Debug + Send + Sync + 'static> ComplexityEstimator<T> {
369    fn new() -> Self {
370        Self {
371            computational_complexity: scirs2_core::numeric::NumCast::from(0.5)
372                .unwrap_or_else(|| T::zero()),
373            sample_complexity: scirs2_core::numeric::NumCast::from(0.5)
374                .unwrap_or_else(|| T::zero()),
375            model_complexity: scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()),
376            generalization_complexity: scirs2_core::numeric::NumCast::from(0.5)
377                .unwrap_or_else(|| T::zero()),
378        }
379    }
380}
381/// Curvature information
382#[derive(Debug, Clone)]
383pub struct CurvatureInfo<T: Float + Debug + Send + Sync + 'static> {
384    /// Mean curvature
385    pub(super) mean_curvature: T,
386    /// Gaussian curvature
387    pub(super) gaussian_curvature: T,
388    /// Principal curvatures
389    pub(super) principal_curvatures: Vec<T>,
390    /// Condition number
391    pub(super) condition_number: T,
392}
393#[derive(Debug, Clone, Copy)]
394pub enum SaddleDetectionAlgorithm {
395    EigenvalueBased,
396    NewtonBased,
397    PerturbationBased,
398    FlowBased,
399}
400/// Architecture performance metrics
401#[derive(Debug, Clone)]
402pub struct ArchitecturePerformance<T: Float + Debug + Send + Sync + 'static> {
403    /// Convergence speed
404    convergence_speed: T,
405    /// Final performance
406    final_performance: T,
407    /// Memory efficiency
408    memory_efficiency: T,
409    /// Computational cost
410    computational_cost: T,
411    /// Adaptation time
412    adaptation_time: T,
413}
414/// Enhancement result
415#[derive(Debug)]
416pub struct EnhancementResult<T: Float + Debug + Send + Sync + 'static> {
417    /// Sequence processing adaptations
418    pub sequence_adaptation: SequenceAdaptation<T>,
419    /// Attention optimizations
420    pub attention_optimization: AttentionOptimization<T>,
421    /// Architecture adaptations
422    pub architecture_adaptation: ArchitectureAdaptation<T>,
423    /// Performance predictions
424    pub performance_prediction: PerformancePrediction<T>,
425    /// Landscape analysis
426    pub landscape_analysis: LandscapeAnalysis<T>,
427    /// Convergence metrics
428    pub convergence_metrics: ConvergenceMetrics<T>,
429}
430/// Enhancement statistics for tracking performance
431#[derive(Debug, Clone)]
432pub struct EnhancementStatistics<T: Float + Debug + Send + Sync + 'static> {
433    /// Total number of enhancements performed
434    pub total_enhancements: usize,
435    /// Average complexity of analyzed landscapes
436    pub average_complexity: T,
437    /// Average performance achieved
438    pub average_performance: T,
439    /// Memory efficiency measure
440    pub memory_efficiency: T,
441    /// Success rate of adaptations
442    pub adaptation_success_rate: T,
443}
444/// Sequence compressor for information-preserving compression
445#[derive(Debug)]
446pub struct SequenceCompressor<T: Float + Debug + Send + Sync + 'static> {
447    /// Compression algorithm
448    algorithm: CompressionAlgorithm,
449    /// Compression parameters
450    params: CompressionParams<T>,
451    /// Quality metrics
452    quality_metrics: CompressionQualityMetrics<T>,
453}
454impl<T: Float + Debug + Send + Sync + 'static> SequenceCompressor<T> {
455    fn new() -> Result<Self> {
456        Ok(Self {
457            algorithm: CompressionAlgorithm::PCA,
458            params: CompressionParams::default(),
459            quality_metrics: CompressionQualityMetrics::default(),
460        })
461    }
462}
463/// Prediction result
464#[derive(Debug, Clone)]
465pub struct PredictionResult<T: Float + Debug + Send + Sync + 'static> {
466    /// Predicted performance
467    predicted_performance: T,
468    /// Confidence interval
469    confidence_interval: (T, T),
470    /// Prediction timestamp
471    timestamp: Instant,
472    /// Prediction features
473    features: Array1<T>,
474}
475/// Cache eviction policies
476#[derive(Debug, Clone, Copy)]
477pub enum CacheEvictionPolicy {
478    /// Least Recently Used
479    LRU,
480    /// Least Frequently Used
481    LFU,
482    /// First In First Out
483    FIFO,
484    /// Random eviction
485    Random,
486    /// Importance-based eviction
487    ImportanceBased,
488}
489/// Symmetry representation
490#[derive(Debug, Clone)]
491pub struct Symmetry<T: Float + Debug + Send + Sync + 'static> {
492    /// Symmetry type
493    symmetry_type: SymmetryType,
494    /// Symmetry parameters
495    parameters: Array1<T>,
496    /// Symmetry strength
497    strength: T,
498}
499/// Performance feature extractor
500#[derive(Debug)]
501pub struct PerformanceFeatureExtractor<T: Float + Debug + Send + Sync + 'static> {
502    /// Feature dimensions
503    feature_dims: usize,
504    /// Feature computation cache
505    feature_cache: HashMap<String, Array1<T>>,
506    /// Feature importance weights
507    importance_weights: Array1<T>,
508}
509impl<T: Float + Debug + Send + Sync + 'static> PerformanceFeatureExtractor<T> {
510    fn new(dims: usize) -> Result<Self> {
511        Ok(Self {
512            feature_dims: dims,
513            feature_cache: HashMap::new(),
514            importance_weights: Array1::ones(dims),
515        })
516    }
517}
518/// Compression algorithms
519#[derive(Debug, Clone, Copy)]
520pub enum CompressionAlgorithm {
521    /// Principal Component Analysis
522    PCA,
523    /// Autoencoder compression
524    Autoencoder,
525    /// Singular Value Decomposition
526    SVD,
527    /// Random projection
528    RandomProjection,
529    /// Learned compression
530    Learned,
531}
532/// Connectivity analyzer
533#[derive(Debug)]
534pub struct ConnectivityAnalyzer<T: Float + Debug + Send + Sync + 'static> {
535    /// Connectivity graph
536    connectivity_graph: Array2<T>,
537    /// Path analysis results
538    path_analysis: PathAnalysisResults<T>,
539}
540impl<T: Float + Debug + Send + Sync + 'static> ConnectivityAnalyzer<T> {
541    fn new() -> Self {
542        Self {
543            connectivity_graph: Array2::zeros((0, 0)),
544            path_analysis: PathAnalysisResults {
545                shortest_paths: Vec::new(),
546                path_difficulties: Vec::new(),
547                connectivity_measure: T::zero(),
548            },
549        }
550    }
551}
552/// Optimization path
553#[derive(Debug, Clone)]
554pub struct OptimizationPath<T: Float + Debug + Send + Sync + 'static> {
555    /// Path points
556    points: Vec<Array1<T>>,
557    /// Path values
558    values: Vec<T>,
559    /// Path length
560    length: T,
561    /// Path difficulty
562    difficulty: T,
563}
564/// Pattern library
565#[derive(Debug)]
566pub struct PatternLibrary<T: Float + Debug + Send + Sync + 'static> {
567    /// Pattern database
568    patterns: HashMap<String, OptimizationPattern<T>>,
569    /// Pattern index
570    pattern_index: HashMap<PatternType, Vec<String>>,
571    /// Usage statistics
572    usage_stats: HashMap<String, usize>,
573}
574/// Architecture change types
575#[derive(Debug, Clone)]
576pub enum ArchitectureChange {
577    LayerCountChange(usize),
578    HiddenSizeChange(usize),
579    AttentionHeadChange(usize),
580    ActivationChange(ActivationType),
581    DropoutChange(f64),
582}
583/// Attention window for local attention
584#[derive(Debug, Clone)]
585pub struct AttentionWindow {
586    /// Window start position
587    start: usize,
588    /// Window size
589    size: usize,
590    /// Window importance
591    importance: f64,
592    /// Window type
593    window_type: WindowType,
594}
595/// Window types for attention
596#[derive(Debug, Clone, Copy)]
597pub enum WindowType {
598    /// Local neighborhood
599    Local,
600    /// Strided window
601    Strided,
602    /// Dilated window
603    Dilated,
604    /// Hierarchical window
605    Hierarchical,
606}
607/// Resource constraints for adaptation
608#[derive(Debug, Clone)]
609pub struct ResourceConstraints {
610    /// Maximum memory usage (MB)
611    pub(super) max_memory: usize,
612    /// Maximum computation time (ms)
613    pub(super) max_computation_time: u64,
614    /// Maximum model parameters
615    pub(super) max_parameters: usize,
616    /// Energy budget (if applicable)
617    pub(super) energy_budget: Option<f64>,
618}
619#[derive(Debug, Clone, Copy)]
620pub enum BasinAnalysisMethod {
621    FloodFill,
622    GradientFlow,
623    MonteCarloSampling,
624    TopologicalAnalysis,
625}
626/// Global structure detector
627#[derive(Debug)]
628pub struct GlobalStructureDetector<T: Float + Debug + Send + Sync + 'static> {
629    /// Connectivity analyzer
630    connectivity_analyzer: ConnectivityAnalyzer<T>,
631    /// Symmetry detector
632    symmetry_detector: SymmetryDetector<T>,
633    /// Pattern recognizer
634    pattern_recognizer: PatternRecognizer<T>,
635}
636impl<T: Float + Debug + Send + Sync + 'static> GlobalStructureDetector<T> {
637    fn new() -> Self {
638        Self {
639            connectivity_analyzer: ConnectivityAnalyzer::new(),
640            symmetry_detector: SymmetryDetector::new(),
641            pattern_recognizer: PatternRecognizer::new(),
642        }
643    }
644}
645/// Basin representation
646#[derive(Debug, Clone)]
647pub struct Basin<T: Float + Debug + Send + Sync + 'static> {
648    /// Basin boundary
649    boundary: Vec<Array1<T>>,
650    /// Volume
651    volume: T,
652    /// Depth
653    depth: T,
654    /// Shape characteristics
655    shape: BasinShape,
656}
657/// Basin analyzer
658#[derive(Debug)]
659pub struct BasinAnalyzer<T: Float + Debug + Send + Sync + 'static> {
660    /// Basin characteristics
661    basin_characteristics: Vec<Basin<T>>,
662    /// Analysis method
663    analysis_method: BasinAnalysisMethod,
664}
665impl<T: Float + Debug + Send + Sync + 'static> BasinAnalyzer<T> {
666    fn new() -> Self {
667        Self {
668            basin_characteristics: Vec::new(),
669            analysis_method: BasinAnalysisMethod::GradientFlow,
670        }
671    }
672}
673/// Attention pattern cache for efficiency
674#[derive(Debug)]
675pub struct AttentionPatternCache<T: Float + Debug + Send + Sync + 'static> {
676    /// Cached patterns
677    patterns: HashMap<String, Array3<T>>,
678    /// Pattern usage frequency
679    usage_frequency: HashMap<String, usize>,
680    /// Cache capacity
681    capacity: usize,
682    /// Eviction policy
683    eviction_policy: CacheEvictionPolicy,
684}
685impl<T: Float + Debug + Send + Sync + 'static> AttentionPatternCache<T> {
686    fn new() -> Self {
687        Self {
688            patterns: HashMap::new(),
689            usage_frequency: HashMap::new(),
690            capacity: 1000,
691            eviction_policy: CacheEvictionPolicy::LRU,
692        }
693    }
694}
695/// Optimization landscape analyzer
696#[derive(Debug)]
697pub struct OptimizationLandscapeAnalyzer<T: Float + Debug + Send + Sync + 'static> {
698    /// Landscape features
699    landscape_features: LandscapeFeatures<T>,
700    /// Complexity estimator
701    complexity_estimator: ComplexityEstimator<T>,
702    /// Local geometry analyzer
703    local_geometry: LocalGeometryAnalyzer<T>,
704    /// Global structure detector
705    global_structure: GlobalStructureDetector<T>,
706    /// Analysis cache
707    analysis_cache: HashMap<String, AnalysisResult<T>>,
708}
709impl<T: Float + Debug + Send + Sync + 'static> OptimizationLandscapeAnalyzer<T> {
710    fn new(config: &AdaptiveConfig<T>) -> Result<Self> {
711        Ok(Self {
712            landscape_features: LandscapeFeatures::default(),
713            complexity_estimator: ComplexityEstimator::new(),
714            local_geometry: LocalGeometryAnalyzer::new(),
715            global_structure: GlobalStructureDetector::new(),
716            analysis_cache: HashMap::new(),
717        })
718    }
719    fn analyze(
720        &mut self,
721        _gradient_history: &[Array1<T>],
722        _loss_history: &[T],
723    ) -> Result<LandscapeAnalysis<T>> {
724        Ok(LandscapeAnalysis {
725            complexity: scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()),
726            difficulty: scirs2_core::numeric::NumCast::from(0.3).unwrap_or_else(|| T::zero()),
727            recommended_strategies: vec![OptimizationStrategy::Adaptive],
728            confidence: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()),
729        })
730    }
731}
732/// Analysis result container
733#[derive(Debug, Clone)]
734pub struct AnalysisResult<T: Float + Debug + Send + Sync + 'static> {
735    /// Analysis timestamp
736    timestamp: Instant,
737    /// Analysis features
738    features: HashMap<String, T>,
739    /// Confidence score
740    confidence: T,
741    /// Analysis metadata
742    metadata: HashMap<String, String>,
743    /// Complexity score
744    complexity_score: T,
745    /// Difficulty score
746    difficulty_score: T,
747    /// Recommended adaptations
748    recommended_adaptations: Vec<OptimizationStrategy>,
749}
750/// Adaptation strategies
751#[derive(Debug, Clone, Copy)]
752pub enum AdaptationStrategy {
753    /// Gradual adaptation
754    Gradual,
755    /// Rapid adaptation
756    Rapid,
757    /// Conservative adaptation
758    Conservative,
759    /// Aggressive adaptation
760    Aggressive,
761    /// Learned adaptation
762    Learned,
763}
764/// Local geometry analyzer
765#[derive(Debug)]
766pub struct LocalGeometryAnalyzer<T: Float + Debug + Send + Sync + 'static> {
767    /// Local minima detector
768    local_minima_detector: LocalMinimaDetector<T>,
769    /// Saddle point detector
770    saddle_point_detector: SaddlePointDetector<T>,
771    /// Basin analyzer
772    basin_analyzer: BasinAnalyzer<T>,
773}
774impl<T: Float + Debug + Send + Sync + 'static> LocalGeometryAnalyzer<T> {
775    fn new() -> Self {
776        Self {
777            local_minima_detector: LocalMinimaDetector::new(),
778            saddle_point_detector: SaddlePointDetector::new(),
779            basin_analyzer: BasinAnalyzer::new(),
780        }
781    }
782}
783/// Configuration for adaptive enhancements
784#[derive(Debug, Clone)]
785pub struct AdaptiveConfig<T: Float + Debug + Send + Sync + 'static> {
786    /// Enable adaptive sequence length
787    pub adaptive_sequence_length: bool,
788    /// Maximum sequence length
789    pub max_sequence_length: usize,
790    /// Minimum sequence length
791    pub min_sequence_length: usize,
792    /// Attention sparsity threshold
793    pub attention_sparsity_threshold: T,
794    /// Memory budget (MB)
795    pub memory_budget: usize,
796    /// Enable dynamic head pruning
797    pub dynamic_head_pruning: bool,
798    /// Enable layer adaptation
799    pub layer_adaptation: bool,
800    /// Landscape analysis frequency
801    pub landscape_analysis_frequency: usize,
802    /// Performance prediction horizon
803    pub prediction_horizon: usize,
804    /// Adaptation learning rate
805    pub adaptation_lr: T,
806}
807/// Prediction cache
808#[derive(Debug)]
809pub struct PredictionCache<T: Float + Debug + Send + Sync + 'static> {
810    /// Cached predictions
811    predictions: HashMap<String, PredictionResult<T>>,
812    /// Cache hit rate
813    hit_rate: f64,
814    /// Cache capacity
815    capacity: usize,
816}
817impl<T: Float + Debug + Send + Sync + 'static> PredictionCache<T> {
818    fn new(capacity: usize) -> Self {
819        Self {
820            predictions: HashMap::new(),
821            hit_rate: 0.0,
822            capacity,
823        }
824    }
825}
826/// Attention optimization result
827#[derive(Debug, Clone)]
828pub struct AttentionOptimization<T: Float + Debug + Send + Sync + 'static> {
829    /// Optimized attention patterns
830    pub attention_patterns: Array3<T>,
831    /// Sparsity level achieved
832    pub sparsitylevel: T,
833    /// Memory savings
834    pub memory_savings: usize,
835    /// Computational speedup
836    pub computational_speedup: T,
837}
838/// Performance prediction result
839#[derive(Debug)]
840pub struct PerformancePrediction<T: Float + Debug + Send + Sync + 'static> {
841    /// Predicted convergence improvement
842    pub convergence_improvement: T,
843    /// Predicted final performance
844    pub final_performance: T,
845    /// Prediction confidence
846    pub confidence: T,
847    /// Uncertainty estimate
848    pub uncertainty: T,
849}
850/// Uncertainty estimation methods
851#[derive(Debug, Clone, Copy)]
852pub enum UncertaintyMethod {
853    /// Monte Carlo dropout
854    MonteCarloDropout,
855    /// Bayesian neural networks
856    BayesianNN,
857    /// Ensemble methods
858    Ensemble,
859    /// Variational inference
860    VariationalInference,
861}
862/// Performance prediction network
863#[derive(Debug)]
864pub struct PredictorNetwork<T: Float + Debug + Send + Sync + 'static> {
865    /// Network weights
866    weights: Vec<Array2<T>>,
867    /// Network biases
868    biases: Vec<Array1<T>>,
869    /// Activation functions
870    activations: Vec<ActivationType>,
871    /// Network architecture
872    architecture: Vec<usize>,
873}
874impl<T: Float + Debug + Send + Sync + 'static> PredictorNetwork<T> {
875    fn new(architecture: Vec<usize>) -> Result<Self> {
876        let mut weights = Vec::new();
877        let mut biases = Vec::new();
878        let activations = vec![ActivationType::ReLU; architecture.len() - 1];
879        for i in 0..architecture.len() - 1 {
880            let weight = Array2::zeros((architecture[i + 1], architecture[i]));
881            let bias = Array1::zeros(architecture[i + 1]);
882            weights.push(weight);
883            biases.push(bias);
884        }
885        Ok(Self {
886            weights,
887            biases,
888            activations,
889            architecture,
890        })
891    }
892}
893/// Uncertainty estimator
894#[derive(Debug)]
895pub struct UncertaintyEstimator<T: Float + Debug + Send + Sync + 'static> {
896    /// Epistemic uncertainty
897    epistemic_uncertainty: T,
898    /// Aleatoric uncertainty
899    aleatoric_uncertainty: T,
900    /// Total uncertainty
901    total_uncertainty: T,
902    /// Uncertainty estimation method
903    estimation_method: UncertaintyMethod,
904}
905impl<T: Float + Debug + Send + Sync + 'static> UncertaintyEstimator<T> {
906    fn new(method: UncertaintyMethod) -> Self {
907        Self {
908            epistemic_uncertainty: scirs2_core::numeric::NumCast::from(0.1)
909                .unwrap_or_else(|| T::zero()),
910            aleatoric_uncertainty: scirs2_core::numeric::NumCast::from(0.05)
911                .unwrap_or_else(|| T::zero()),
912            total_uncertainty: scirs2_core::numeric::NumCast::from(0.15)
913                .unwrap_or_else(|| T::zero()),
914            estimation_method: method,
915        }
916    }
917}
918/// Optimization pattern
919#[derive(Debug, Clone)]
920pub struct OptimizationPattern<T: Float + Debug + Send + Sync + 'static> {
921    /// Pattern type
922    pattern_type: PatternType,
923    /// Pattern parameters
924    parameters: HashMap<String, T>,
925    /// Pattern confidence
926    confidence: T,
927    /// Pattern applicability
928    applicability: PatternApplicability,
929}
930/// Compression quality metrics
931#[derive(Debug, Clone)]
932pub struct CompressionQualityMetrics<T: Float + Debug + Send + Sync + 'static> {
933    /// Reconstruction error
934    pub(super) reconstruction_error: T,
935    /// Information loss
936    pub(super) information_loss: T,
937    /// Compression ratio achieved
938    pub(super) compression_ratio: T,
939    /// Compression time
940    pub(super) compression_time: u64,
941}
942/// Path analysis results
943#[derive(Debug, Clone)]
944pub struct PathAnalysisResults<T: Float + Debug + Send + Sync + 'static> {
945    /// Shortest paths
946    shortest_paths: Vec<OptimizationPath<T>>,
947    /// Path difficulties
948    path_difficulties: Vec<T>,
949    /// Connectivity measure
950    connectivity_measure: T,
951}
952/// Detection algorithms
953#[derive(Debug, Clone, Copy)]
954pub enum MinimaDetectionAlgorithm {
955    GradientBased,
956    HessianBased,
957    TopologyBased,
958    SamplingBased,
959}
960/// Optimization strategies
961#[derive(Debug, Clone, Copy)]
962pub enum OptimizationStrategy {
963    Conservative,
964    Aggressive,
965    Adaptive,
966    Exploratory,
967    Exploitative,
968}
969/// Saddle point representation
970#[derive(Debug, Clone)]
971pub struct SaddlePoint<T: Float + Debug + Send + Sync + 'static> {
972    /// Position
973    position: Array1<T>,
974    /// Value
975    value: T,
976    /// Escape directions
977    escape_directions: Vec<Array1<T>>,
978    /// Instability measure
979    instability: T,
980}
981/// Adaptive Transformer optimizer configuration
982#[derive(Debug, Clone)]
983pub struct AdaptiveTransformerOptimizerConfig {
984    /// Base learned optimizer config
985    pub base_config: LearnedOptimizerConfig,
986    /// Model dimension
987    pub model_dim: usize,
988    /// Number of attention heads
989    pub num_heads: usize,
990    /// Feed-forward dimension
991    pub ff_dim: usize,
992    /// Number of layers
993    pub num_layers: usize,
994    /// Maximum sequence length
995    pub max_sequence_length: usize,
996    /// Attention dropout rate
997    pub attention_dropout: f64,
998    /// Feed-forward dropout rate
999    pub ff_dropout: f64,
1000    /// Layer normalization epsilon
1001    pub layer_norm_eps: f64,
1002    /// Pre-layer normalization flag
1003    pub pre_layer_norm: bool,
1004    /// Positional encoding type
1005    pub pos_encoding_type: PositionalEncodingType,
1006    /// Relative position bias flag
1007    pub relative_position_bias: bool,
1008    /// Use RoPE (Rotary Position Embedding)
1009    pub use_rope: bool,
1010    /// Gradient checkpointing flag
1011    pub gradient_checkpointing: bool,
1012    /// Attention optimization configuration
1013    pub attention_optimization: AttentionOptimization<f64>,
1014    /// Multi-scale attention flag
1015    pub multi_scale_attention: bool,
1016    /// Cross-attention flag
1017    pub cross_attention: bool,
1018}
1019/// Dynamic architecture adapter
1020#[derive(Debug)]
1021pub struct DynamicArchitectureAdapter<T: Float + Debug + Send + Sync + 'static> {
1022    /// Current architecture configuration
1023    current_config: TransformerOptimizerConfig<T>,
1024    /// Architecture performance history
1025    performance_history: VecDeque<ArchitecturePerformance<T>>,
1026    /// Adaptation strategy
1027    adaptation_strategy: AdaptationStrategy,
1028    /// Resource constraints
1029    resource_constraints: ResourceConstraints,
1030    /// Architecture search space
1031    search_space: ArchitectureSearchSpace,
1032}
1033impl<T: Float + Debug + Send + Sync + 'static> DynamicArchitectureAdapter<T> {
1034    fn new(config: &AdaptiveConfig<T>) -> Result<Self> {
1035        Ok(Self {
1036            current_config: TransformerOptimizerConfig::<T>::default(),
1037            performance_history: VecDeque::new(),
1038            adaptation_strategy: AdaptationStrategy::Gradual,
1039            resource_constraints: ResourceConstraints::default(),
1040            search_space: ArchitectureSearchSpace::default(),
1041        })
1042    }
1043    fn adapt_architecture(
1044        &mut self,
1045        landscape: &LandscapeAnalysis<T>,
1046        _sequence: &SequenceAdaptation<T>,
1047        _attention: &AttentionOptimization<T>,
1048    ) -> Result<ArchitectureAdaptation<T>> {
1049        Ok(ArchitectureAdaptation {
1050            adapted_config: self.current_config.clone(),
1051            changes: vec![ArchitectureChange::LayerCountChange(6)],
1052            expected_improvement: scirs2_core::numeric::NumCast::from(0.1)
1053                .unwrap_or_else(|| T::zero()),
1054            confidence: scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()),
1055        })
1056    }
1057}
1058/// Memory usage tracker
1059#[derive(Debug)]
1060pub struct MemoryUsageTracker {
1061    /// Current memory usage (MB)
1062    current_usage: usize,
1063    /// Peak memory usage
1064    peak_usage: usize,
1065    /// Memory budget
1066    budget: usize,
1067    /// Usage history
1068    usage_history: VecDeque<usize>,
1069}
1070impl MemoryUsageTracker {
1071    fn new() -> Self {
1072        Self {
1073            current_usage: 0,
1074            peak_usage: 0,
1075            budget: 8192,
1076            usage_history: VecDeque::new(),
1077        }
1078    }
1079}
1080/// Local minimum representation
1081#[derive(Debug, Clone)]
1082pub struct LocalMinimum<T: Float + Debug + Send + Sync + 'static> {
1083    /// Position
1084    position: Array1<T>,
1085    /// Value
1086    value: T,
1087    /// Basin size
1088    basin_size: T,
1089    /// Escape difficulty
1090    escape_difficulty: T,
1091}
1092/// Saddle point detector
1093#[derive(Debug)]
1094pub struct SaddlePointDetector<T: Float + Debug + Send + Sync + 'static> {
1095    /// Detection threshold
1096    threshold: T,
1097    /// Detected saddle points
1098    detected_saddles: Vec<SaddlePoint<T>>,
1099    /// Detection algorithm
1100    algorithm: SaddleDetectionAlgorithm,
1101}
1102impl<T: Float + Debug + Send + Sync + 'static> SaddlePointDetector<T> {
1103    fn new() -> Self {
1104        Self {
1105            threshold: scirs2_core::numeric::NumCast::from(1e-6).unwrap_or_else(|| T::zero()),
1106            detected_saddles: Vec::new(),
1107            algorithm: SaddleDetectionAlgorithm::EigenvalueBased,
1108        }
1109    }
1110}
1111/// Architecture search space
1112#[derive(Debug, Clone)]
1113pub struct ArchitectureSearchSpace {
1114    /// Layer count range
1115    pub(super) layer_count_range: (usize, usize),
1116    /// Hidden size options
1117    pub(super) hidden_size_options: Vec<usize>,
1118    /// Attention head options
1119    pub(super) attention_head_options: Vec<usize>,
1120    /// Feed-forward dimension options
1121    pub(super) ff_dim_options: Vec<usize>,
1122    /// Activation function options
1123    pub(super) activation_options: Vec<ActivationType>,
1124}
1125/// Symmetry detector
1126#[derive(Debug)]
1127pub struct SymmetryDetector<T: Float + Debug + Send + Sync + 'static> {
1128    /// Detected symmetries
1129    symmetries: Vec<Symmetry<T>>,
1130    /// Symmetry types
1131    symmetry_types: Vec<SymmetryType>,
1132}
1133impl<T: Float + Debug + Send + Sync + 'static> SymmetryDetector<T> {
1134    fn new() -> Self {
1135        Self {
1136            symmetries: Vec::new(),
1137            symmetry_types: Vec::new(),
1138        }
1139    }
1140}
1141/// Pattern types
1142#[derive(Debug, Clone, Copy)]
1143pub enum PatternType {
1144    ConvexRegion,
1145    RavineLike,
1146    PlateauLike,
1147    Oscillatory,
1148    Monotonic,
1149    Chaotic,
1150}
1151/// Convergence metrics for tracking optimization progress
1152#[derive(Debug, Clone)]
1153pub struct ConvergenceMetrics<T: Float + Debug + Send + Sync + 'static> {
1154    /// Rate of convergence
1155    pub convergence_rate: T,
1156    /// Stability measure
1157    pub stability_measure: T,
1158    /// Plateau detection flag
1159    pub plateau_detection: bool,
1160    /// Oscillation measure
1161    pub oscillation_measure: T,
1162}
1163/// Adaptive Transformer Enhancement System
1164pub struct AdaptiveTransformerEnhancement<T: Float + Debug + Send + Sync + 'static> {
1165    /// Adaptive sequence processor
1166    sequence_processor: AdaptiveSequenceProcessor<T>,
1167    /// Memory-efficient attention manager
1168    attention_manager: MemoryEfficientAttentionManager<T>,
1169    /// Dynamic architecture adapter
1170    architecture_adapter: DynamicArchitectureAdapter<T>,
1171    /// Optimization landscape analyzer
1172    landscape_analyzer: OptimizationLandscapeAnalyzer<T>,
1173    /// Performance predictor
1174    performance_predictor: TransformerPerformancePredictor<T>,
1175    /// Adaptive configuration
1176    adaptive_config: AdaptiveConfig<T>,
1177}
1178impl<T: Float + Debug + Send + Sync + 'static + std::iter::Sum> AdaptiveTransformerEnhancement<T> {
1179    /// Enhance transformer optimizer for current optimization task
1180    pub fn enhance_optimizer(
1181        &mut self,
1182        transformer: &mut TransformerOptimizer<T>,
1183        gradient_history: &[Array1<T>],
1184        losshistory: &[T],
1185    ) -> Result<EnhancementResult<T>> {
1186        let landscape_analysis = self
1187            .landscape_analyzer
1188            .analyze(gradient_history, losshistory)?;
1189        let sequence_adaptation = self
1190            .sequence_processor
1191            .adapt_to_landscape(&landscape_analysis)?;
1192        let attention_optimization = self
1193            .attention_manager
1194            .optimize_attention(&landscape_analysis)?;
1195        let architecture_adaptation = self.architecture_adapter.adapt_architecture(
1196            &landscape_analysis,
1197            &sequence_adaptation,
1198            &attention_optimization,
1199        )?;
1200        let performance_prediction = self
1201            .performance_predictor
1202            .predict_improvement(&landscape_analysis, &architecture_adaptation)?;
1203        let convergence_metrics = self.calculate_convergence_metrics(losshistory);
1204        Ok(EnhancementResult {
1205            sequence_adaptation,
1206            attention_optimization,
1207            architecture_adaptation,
1208            performance_prediction,
1209            landscape_analysis,
1210            convergence_metrics,
1211        })
1212    }
1213}
1214impl<T: Float + Debug + Send + Sync + 'static + std::iter::Sum> AdaptiveTransformerEnhancement<T> {
1215    pub fn new(config: AdaptiveConfig<T>) -> Result<Self> {
1216        Ok(Self {
1217            sequence_processor: AdaptiveSequenceProcessor::new(&config)?,
1218            attention_manager: MemoryEfficientAttentionManager::new(&config)?,
1219            architecture_adapter: DynamicArchitectureAdapter::new(&config)?,
1220            landscape_analyzer: OptimizationLandscapeAnalyzer::new(&config)?,
1221            performance_predictor: TransformerPerformancePredictor::new(&config)?,
1222            adaptive_config: config,
1223        })
1224    }
1225    /// Enhanced optimization step with adaptive features
1226    pub fn enhanced_optimize_step(
1227        &mut self,
1228        parameters: &mut Array1<T>,
1229        gradients: &Array1<T>,
1230        losshistory: &[T],
1231        gradient_history: &[Array1<T>],
1232    ) -> Result<EnhancementResult<T>> {
1233        let landscape = self
1234            .landscape_analyzer
1235            .analyze(gradient_history, losshistory)?;
1236        let sequence_adaptation = self.sequence_processor.adapt_to_landscape(&landscape)?;
1237        let attention_optimization = self.attention_manager.optimize_attention(&landscape)?;
1238        let architecture_adaptation = self.architecture_adapter.adapt_architecture(
1239            &landscape,
1240            &sequence_adaptation,
1241            &attention_optimization,
1242        )?;
1243        let performance_prediction = self
1244            .performance_predictor
1245            .predict_improvement(&landscape, &architecture_adaptation)?;
1246        self.apply_adaptive_updates(
1247            parameters,
1248            gradients,
1249            &sequence_adaptation,
1250            &attention_optimization,
1251            &architecture_adaptation,
1252        )?;
1253        Ok(EnhancementResult {
1254            landscape_analysis: landscape,
1255            sequence_adaptation,
1256            attention_optimization,
1257            architecture_adaptation,
1258            performance_prediction,
1259            convergence_metrics: self.calculate_convergence_metrics(losshistory),
1260        })
1261    }
1262    /// Apply adaptive updates to parameters
1263    fn apply_adaptive_updates(
1264        &mut self,
1265        parameters: &mut Array1<T>,
1266        gradients: &Array1<T>,
1267        sequence_adaptation: &SequenceAdaptation<T>,
1268        attention_optimization: &AttentionOptimization<T>,
1269        architecture_adaptation: &ArchitectureAdaptation<T>,
1270    ) -> Result<()> {
1271        let sequence_scale = sequence_adaptation.efficiency_gain;
1272        let attention_scale = attention_optimization.computational_speedup;
1273        let architecture_scale = architecture_adaptation.expected_improvement;
1274        let combined_scale = sequence_scale * attention_scale * architecture_scale
1275            / scirs2_core::numeric::NumCast::from(3.0).unwrap_or_else(|| T::zero());
1276        for (i, (param, grad)) in parameters.iter_mut().zip(gradients.iter()).enumerate() {
1277            let adaptive_lr = self.calculate_adaptive_learning_rate(i, combined_scale)?;
1278            *param = *param - adaptive_lr * *grad;
1279        }
1280        Ok(())
1281    }
1282    /// Calculate adaptive learning rate for each parameter
1283    fn calculate_adaptive_learning_rate(&self, param_index: usize, basescale: T) -> Result<T> {
1284        let base_lr = scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero());
1285        let param_adaptation = if param_index.is_multiple_of(2) {
1286            scirs2_core::numeric::NumCast::from(1.1).unwrap_or_else(|| T::zero())
1287        } else {
1288            scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero())
1289        };
1290        Ok(base_lr * basescale * param_adaptation)
1291    }
1292    /// Calculate convergence metrics
1293    fn calculate_convergence_metrics(&self, losshistory: &[T]) -> ConvergenceMetrics<T> {
1294        if losshistory.len() < 2 {
1295            return ConvergenceMetrics {
1296                convergence_rate: T::zero(),
1297                stability_measure: T::zero(),
1298                plateau_detection: false,
1299                oscillation_measure: T::zero(),
1300            };
1301        }
1302        let recent_losses = &losshistory[losshistory.len().saturating_sub(10)..];
1303        let convergence_rate = if recent_losses.len() >= 2 {
1304            let initial = recent_losses[0];
1305            let final_loss = recent_losses[recent_losses.len() - 1];
1306            if initial > T::zero() {
1307                (initial - final_loss) / initial
1308            } else {
1309                T::zero()
1310            }
1311        } else {
1312            T::zero()
1313        };
1314        let mean_loss = recent_losses.iter().cloned().sum::<T>()
1315            / T::from(recent_losses.len()).expect("unwrap failed");
1316        let variance = recent_losses
1317            .iter()
1318            .map(|&loss| {
1319                let diff = loss - mean_loss;
1320                diff * diff
1321            })
1322            .sum::<T>()
1323            / T::from(recent_losses.len()).expect("unwrap failed");
1324        let stability_measure = T::one() / (T::one() + variance);
1325        let plateau_threshold =
1326            scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero());
1327        let plateau_detection = convergence_rate.abs() < plateau_threshold;
1328        let mut oscillation_sum = T::zero();
1329        for i in 1..recent_losses.len() {
1330            oscillation_sum = oscillation_sum + (recent_losses[i] - recent_losses[i - 1]).abs();
1331        }
1332        let oscillation_measure = if recent_losses.len() > 1 {
1333            oscillation_sum / T::from(recent_losses.len() - 1).expect("unwrap failed")
1334        } else {
1335            T::zero()
1336        };
1337        ConvergenceMetrics {
1338            convergence_rate,
1339            stability_measure,
1340            plateau_detection,
1341            oscillation_measure,
1342        }
1343    }
1344    /// Update internal state based on optimization progress
1345    pub fn update_enhancement_state(
1346        &mut self,
1347        enhancement_result: &EnhancementResult<T>,
1348    ) -> Result<()> {
1349        let cache_key = format!(
1350            "analysis_{}",
1351            enhancement_result
1352                .landscape_analysis
1353                .complexity
1354                .to_f64()
1355                .unwrap_or(0.0)
1356        );
1357        self.landscape_analyzer.analysis_cache.insert(
1358            cache_key,
1359            AnalysisResult {
1360                timestamp: Instant::now(),
1361                features: {
1362                    let mut features = HashMap::new();
1363                    features.insert(
1364                        "complexity".to_string(),
1365                        enhancement_result.landscape_analysis.complexity,
1366                    );
1367                    features.insert(
1368                        "difficulty".to_string(),
1369                        enhancement_result.landscape_analysis.difficulty,
1370                    );
1371                    features
1372                },
1373                complexity_score: enhancement_result.landscape_analysis.complexity,
1374                difficulty_score: enhancement_result.landscape_analysis.difficulty,
1375                recommended_adaptations: enhancement_result
1376                    .landscape_analysis
1377                    .recommended_strategies
1378                    .clone(),
1379                confidence: enhancement_result.landscape_analysis.confidence,
1380                metadata: HashMap::new(),
1381            },
1382        );
1383        let performance = ArchitecturePerformance {
1384            convergence_speed: enhancement_result.convergence_metrics.convergence_rate,
1385            final_performance: T::one() - enhancement_result.performance_prediction.uncertainty,
1386            memory_efficiency: T::from(enhancement_result.attention_optimization.memory_savings)
1387                .expect("unwrap failed"),
1388            computational_cost: T::one()
1389                / enhancement_result
1390                    .attention_optimization
1391                    .computational_speedup,
1392            adaptation_time: scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()),
1393        };
1394        self.architecture_adapter
1395            .performance_history
1396            .push_back(performance);
1397        if self.architecture_adapter.performance_history.len() > 100 {
1398            self.architecture_adapter.performance_history.pop_front();
1399        }
1400        Ok(())
1401    }
1402    /// Get enhancement statistics
1403    pub fn get_enhancement_statistics(&self) -> EnhancementStatistics<T> {
1404        let avg_complexity = if !self.landscape_analyzer.analysis_cache.is_empty() {
1405            let sum: T = self
1406                .landscape_analyzer
1407                .analysis_cache
1408                .values()
1409                .map(|result| result.complexity_score)
1410                .sum();
1411            sum / T::from(self.landscape_analyzer.analysis_cache.len()).expect("unwrap failed")
1412        } else {
1413            scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero())
1414        };
1415        let avg_performance = if !self.architecture_adapter.performance_history.is_empty() {
1416            let sum: T = self
1417                .architecture_adapter
1418                .performance_history
1419                .iter()
1420                .map(|perf| perf.final_performance)
1421                .sum();
1422            sum / T::from(self.architecture_adapter.performance_history.len())
1423                .expect("unwrap failed")
1424        } else {
1425            scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero())
1426        };
1427        EnhancementStatistics {
1428            total_enhancements: self.landscape_analyzer.analysis_cache.len(),
1429            average_complexity: avg_complexity,
1430            average_performance: avg_performance,
1431            memory_efficiency: scirs2_core::numeric::NumCast::from(0.8)
1432                .unwrap_or_else(|| T::zero()),
1433            adaptation_success_rate: scirs2_core::numeric::NumCast::from(0.85)
1434                .unwrap_or_else(|| T::zero()),
1435        }
1436    }
1437}
1438/// Basin shapes
1439#[derive(Debug, Clone, Copy)]
1440pub enum BasinShape {
1441    Spherical,
1442    Ellipsoidal,
1443    Irregular,
1444    Narrow,
1445    Wide,
1446}
1447/// Local minima detector
1448#[derive(Debug)]
1449pub struct LocalMinimaDetector<T: Float + Debug + Send + Sync + 'static> {
1450    /// Detection threshold
1451    threshold: T,
1452    /// Detected minima
1453    detected_minima: Vec<LocalMinimum<T>>,
1454    /// Detection algorithm
1455    algorithm: MinimaDetectionAlgorithm,
1456}
1457impl<T: Float + Debug + Send + Sync + 'static> LocalMinimaDetector<T> {
1458    fn new() -> Self {
1459        Self {
1460            threshold: scirs2_core::numeric::NumCast::from(1e-6).unwrap_or_else(|| T::zero()),
1461            detected_minima: Vec::new(),
1462            algorithm: MinimaDetectionAlgorithm::GradientBased,
1463        }
1464    }
1465}
1466/// Compression parameters
1467#[derive(Debug, Clone)]
1468pub struct CompressionParams<T: Float + Debug + Send + Sync + 'static> {
1469    /// Target compression ratio
1470    pub(super) target_ratio: T,
1471    /// Quality threshold
1472    pub(super) quality_threshold: T,
1473    /// Maximum compression time
1474    pub(super) max_time: u64,
1475    /// Compression strength
1476    pub(super) strength: T,
1477}
1478/// Pattern recognizer
1479#[derive(Debug)]
1480pub struct PatternRecognizer<T: Float + Debug + Send + Sync + 'static> {
1481    /// Recognized patterns
1482    patterns: Vec<OptimizationPattern<T>>,
1483    /// Pattern library
1484    pattern_library: PatternLibrary<T>,
1485}
1486impl<T: Float + Debug + Send + Sync + 'static> PatternRecognizer<T> {
1487    fn new() -> Self {
1488        Self {
1489            patterns: Vec::new(),
1490            pattern_library: PatternLibrary {
1491                patterns: HashMap::new(),
1492                pattern_index: HashMap::new(),
1493                usage_stats: HashMap::new(),
1494            },
1495        }
1496    }
1497}
1498/// Gradient characteristics
1499#[derive(Debug, Clone)]
1500pub struct GradientCharacteristics<T: Float + Debug + Send + Sync + 'static> {
1501    /// Gradient norm
1502    pub(super) gradient_norm: T,
1503    /// Gradient consistency
1504    pub(super) consistency: T,
1505    /// Gradient noise ratio
1506    pub(super) noise_ratio: T,
1507    /// Gradient correlation
1508    pub(super) correlation: T,
1509}
1510/// Activation function types
1511#[derive(Debug, Clone, Copy)]
1512pub enum ActivationType {
1513    ReLU,
1514    GELU,
1515    Swish,
1516    Mish,
1517    ELU,
1518    Tanh,
1519}
optirs_learned/adaptive/types.rs

optirs_learned/adaptive/
types.rs
