scirs2_linalg/simd_ops/neural/
training.rs

1//! Training and optimization logic for neural memory patterns.
2
3use super::cache::DenseLayer;
4use super::patterns::{MemoryAccessPattern, PatternDatabase};
5use super::types::*;
6use crate::error::{LinalgError, LinalgResult};
7use scirs2_core::ndarray::{Array1, Array2};
8use scirs2_core::numeric::{Float, NumAssign, Zero};
9use std::collections::{HashMap, VecDeque};
10use std::fmt::Debug;
11
12/// Advanced memory pattern learning agent
13#[derive(Debug)]
14#[allow(dead_code)]
15pub struct AdvancedMemoryPatternLearning<T> {
16    /// Pattern recognition neural network
17    pattern_recognition_nn: ConvolutionalPatternNetwork<T>,
18    /// Prefetch learning agent
19    prefetch_learning_agent: ReinforcementLearningAgent<T>,
20    /// Memory layout optimizer
21    memory_layout_optimizer: GeneticLayoutOptimizer<T>,
22    /// Pattern database
23    pattern_database: PatternDatabase<T>,
24}
25
26/// Convolutional pattern network for memory access patterns
27#[derive(Debug)]
28#[allow(dead_code)]
29pub struct ConvolutionalPatternNetwork<T> {
30    /// Convolutional layers
31    conv_layers: Vec<ConvolutionalLayer<T>>,
32    /// Pooling layers
33    pooling_layers: Vec<PoolingLayer>,
34    /// Pattern embedding layer
35    embedding_layer: EmbeddingLayer<T>,
36    /// Classification head
37    classification_head: ClassificationHead<T>,
38}
39
40/// Convolutional layer for spatial pattern recognition
41#[derive(Debug)]
42pub struct ConvolutionalLayer<T> {
43    /// Kernel weights
44    pub kernels: Array2<T>,
45    /// Bias terms
46    pub biases: Array1<T>,
47    /// Stride
48    pub stride: (usize, usize),
49    /// Padding
50    pub padding: (usize, usize),
51    /// Activation function
52    pub activation: ActivationFunction,
53}
54
55/// Pooling layer for dimension reduction
56#[derive(Debug)]
57pub struct PoolingLayer {
58    /// Pooling type
59    pub pooling_type: PoolingType,
60    /// Kernel size
61    pub kernelsize: (usize, usize),
62    /// Stride
63    pub stride: (usize, usize),
64}
65
66/// Types of pooling operations
67#[derive(Debug, Clone, PartialEq)]
68pub enum PoolingType {
69    Max,
70    Average,
71    AdaptiveMax,
72    AdaptiveAverage,
73    GlobalMax,
74    GlobalAverage,
75}
76
77/// Embedding layer for pattern representation
78#[derive(Debug)]
79pub struct EmbeddingLayer<T> {
80    /// Embedding weights
81    pub weights: Array2<T>,
82    /// Embedding dimension
83    pub embedding_dim: usize,
84    /// Vocabulary size
85    pub vocabsize: usize,
86}
87
88/// Classification head for pattern classification
89#[derive(Debug)]
90#[allow(dead_code)]
91pub struct ClassificationHead<T> {
92    /// Dense layers
93    dense_layers: Vec<DenseLayer<T>>,
94    /// Output layer
95    output_layer: DenseLayer<T>,
96    /// Number of classes
97    num_classes: usize,
98}
99
100/// Reinforcement learning agent for prefetch optimization
101#[derive(Debug)]
102#[allow(dead_code)]
103pub struct ReinforcementLearningAgent<T> {
104    /// Q-network for value estimation
105    q_network: QNetwork<T>,
106    /// Policy network
107    policy_network: PolicyNetwork<T>,
108    /// Experience replay buffer
109    replay_buffer: ExperienceReplayBuffer<T>,
110    /// Learning parameters
111    learning_params: RLLearningParameters,
112}
113
114/// Q-network for value function approximation
115#[derive(Debug)]
116#[allow(dead_code)]
117pub struct QNetwork<T> {
118    /// Network layers
119    layers: Vec<DenseLayer<T>>,
120    /// Target network
121    target_network: Vec<DenseLayer<T>>,
122    /// Update frequency for target network
123    target_update_freq: usize,
124}
125
126/// Policy network for action selection
127#[derive(Debug)]
128#[allow(dead_code)]
129pub struct PolicyNetwork<T> {
130    /// Actor network
131    actor: Vec<DenseLayer<T>>,
132    /// Critic network
133    critic: Vec<DenseLayer<T>>,
134    /// Action space dimension
135    action_dim: usize,
136}
137
138/// Experience replay buffer for RL training
139#[derive(Debug)]
140#[allow(dead_code)]
141pub struct ExperienceReplayBuffer<T> {
142    /// Buffer of experiences
143    buffer: VecDeque<Experience<T>>,
144    /// Buffer capacity
145    capacity: usize,
146    /// Current size
147    currentsize: usize,
148}
149
150/// Experience tuple for reinforcement learning
151#[derive(Debug, Clone)]
152pub struct Experience<T> {
153    /// State
154    pub state: Array1<T>,
155    /// Action
156    pub action: usize,
157    /// Reward
158    pub reward: f64,
159    /// Next state
160    pub next_state: Array1<T>,
161    /// Done flag
162    pub done: bool,
163}
164
165/// Reinforcement learning parameters
166#[derive(Debug, Clone)]
167pub struct RLLearningParameters {
168    /// Learning rate
169    pub learning_rate: f64,
170    /// Discount factor
171    pub discount_factor: f64,
172    /// Exploration rate
173    pub exploration_rate: f64,
174    /// Exploration decay
175    pub exploration_decay: f64,
176    /// Minimum exploration rate
177    pub min_exploration_rate: f64,
178    /// Batch size
179    pub batchsize: usize,
180    /// Update frequency
181    pub update_frequency: usize,
182}
183
184/// Genetic algorithm for memory layout optimization
185#[derive(Debug)]
186#[allow(dead_code)]
187pub struct GeneticLayoutOptimizer<T> {
188    /// Population of layout solutions
189    population: Vec<AdvancedMemoryLayout<T>>,
190    /// Population size
191    populationsize: usize,
192    /// Genetic algorithm parameters
193    ga_params: GeneticAlgorithmParameters,
194    /// Fitness evaluator
195    fitness_evaluator: FitnessEvaluator<T>,
196}
197
198/// Memory layout representation
199#[derive(Debug, Clone)]
200pub struct AdvancedMemoryLayout<T> {
201    /// Layout type
202    pub layout_type: LayoutType,
203    /// Block sizes
204    pub blocksizes: Vec<usize>,
205    /// Alignment requirements
206    pub alignments: Vec<usize>,
207    /// Padding strategies
208    pub padding: PaddingStrategy,
209    /// Cache-friendly ordering
210    pub ordering: DataOrdering,
211    /// Fitness score
212    pub fitness: f64,
213    /// Custom parameters
214    pub custom_params: HashMap<String, T>,
215}
216
217/// Genetic algorithm parameters
218#[derive(Debug, Clone)]
219pub struct GeneticAlgorithmParameters {
220    /// Population size
221    pub populationsize: usize,
222    /// Number of generations
223    pub generations: usize,
224    /// Crossover rate
225    pub crossover_rate: f64,
226    /// Mutation rate
227    pub mutation_rate: f64,
228    /// Selection method
229    pub selection_method: SelectionMethod,
230    /// Elitism percentage
231    pub elitism_rate: f64,
232}
233
234/// Selection methods for genetic algorithm
235#[derive(Debug, Clone, PartialEq)]
236pub enum SelectionMethod {
237    Tournament(usize),
238    Roulette,
239    Rank,
240    Stochastic,
241    Custom(String),
242}
243
244/// Fitness evaluator for memory layouts
245#[derive(Debug)]
246#[allow(dead_code)]
247pub struct FitnessEvaluator<T> {
248    /// Evaluation metrics
249    metrics: Vec<FitnessMetric<T>>,
250    /// Metric weights
251    weights: Array1<f64>,
252    /// Benchmark suite
253    benchmark_suite: BenchmarkSuite<T>,
254}
255
256/// Fitness metrics for layout evaluation
257pub enum FitnessMetric<T> {
258    CacheHitRate,
259    MemoryBandwidthUtilization,
260    AccessLatency,
261    EnergyEfficiency,
262    #[allow(clippy::type_complexity)]
263    Custom(Box<dyn Fn(&AdvancedMemoryLayout<T>) -> f64 + Send + Sync>),
264}
265
266impl<T> std::fmt::Debug for FitnessMetric<T> {
267    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
268        match self {
269            FitnessMetric::CacheHitRate => write!(f, "CacheHitRate"),
270            FitnessMetric::MemoryBandwidthUtilization => write!(f, "MemoryBandwidthUtilization"),
271            FitnessMetric::AccessLatency => write!(f, "AccessLatency"),
272            FitnessMetric::EnergyEfficiency => write!(f, "EnergyEfficiency"),
273            FitnessMetric::Custom(_) => write!(f, "Custom(<function>)"),
274        }
275    }
276}
277
278/// Benchmark suite for layout evaluation
279#[derive(Debug)]
280#[allow(dead_code)]
281pub struct BenchmarkSuite<T> {
282    /// Benchmark tests
283    benchmarks: Vec<MemoryBenchmark<T>>,
284    /// Test data sets
285    test_datasets: Vec<Array2<T>>,
286    /// Performance baseline
287    baseline_performance: f64,
288}
289
290/// Memory benchmark test
291#[derive(Debug)]
292pub struct MemoryBenchmark<T> {
293    /// Benchmark name
294    pub name: String,
295    /// Test function
296    pub test_fn: fn(&AdvancedMemoryLayout<T>, &Array2<T>) -> BenchmarkResult,
297    /// Weight in overall score
298    pub weight: f64,
299}
300
301/// Benchmark result
302#[derive(Debug, Clone)]
303pub struct BenchmarkResult {
304    /// Execution time
305    pub execution_time: std::time::Duration,
306    /// Memory usage
307    pub memory_usage: usize,
308    /// Cache hit rate
309    pub cache_hit_rate: f64,
310    /// Bandwidth utilization
311    pub bandwidth_utilization: f64,
312    /// Energy consumption
313    pub energy_consumption: f64,
314}
315
316/// Optimization recommendations from pattern learning
317#[derive(Debug)]
318pub struct OptimizationRecommendations<T> {
319    /// Prefetch strategies
320    pub prefetch_strategies: Vec<PrefetchStrategy>,
321    /// Memory layout recommendations
322    pub layout_recommendations: Vec<AdvancedMemoryLayout<T>>,
323    /// Access pattern optimizations
324    pub pattern_optimizations: Vec<PatternOptimization>,
325    /// Overall improvement estimate
326    pub improvement_estimate: f64,
327}
328
329/// Prefetch strategies
330#[derive(Debug, Clone)]
331pub struct PrefetchStrategy {
332    /// Strategy type
333    pub strategy_type: PrefetchType,
334    /// Prefetch distance
335    pub prefetch_distance: usize,
336    /// Confidence threshold
337    pub confidence_threshold: f64,
338    /// Expected benefit
339    pub expected_benefit: f64,
340}
341
342/// Types of prefetch strategies
343#[derive(Debug, Clone, PartialEq)]
344pub enum PrefetchType {
345    Sequential,
346    Strided,
347    Indirect,
348    Adaptive,
349    MLGuided,
350}
351
352/// Pattern optimization suggestions
353#[derive(Debug, Clone)]
354pub struct PatternOptimization {
355    /// Optimization type
356    pub optimization_type: OptimizationType,
357    /// Description
358    pub description: String,
359    /// Expected improvement
360    pub expected_improvement: f64,
361    /// Implementation effort
362    pub implementation_effort: EffortLevel,
363}
364
365/// Types of optimizations
366#[derive(Debug, Clone, PartialEq)]
367pub enum OptimizationType {
368    AccessReordering,
369    DataRestructuring,
370    CacheBlocking,
371    LoopTiling,
372    Vectorization,
373    Parallelization,
374}
375
376/// Implementation effort levels
377#[derive(Debug, Clone, PartialEq)]
378pub enum EffortLevel {
379    Low,
380    Medium,
381    High,
382    Expert,
383}
384
385// Implementations
386impl<T> AdvancedMemoryPatternLearning<T>
387where
388    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
389{
390    pub fn new() -> LinalgResult<Self> {
391        Ok(Self {
392            pattern_recognition_nn: ConvolutionalPatternNetwork::new()?,
393            prefetch_learning_agent: ReinforcementLearningAgent::new()?,
394            memory_layout_optimizer: GeneticLayoutOptimizer::new()?,
395            pattern_database: PatternDatabase::new(),
396        })
397    }
398
399    pub fn learn_patterns(
400        &self,
401        _access_traces: &[MemoryAccessPattern<T>],
402    ) -> LinalgResult<OptimizationRecommendations<T>> {
403        Ok(OptimizationRecommendations {
404            prefetch_strategies: Vec::new(),
405            layout_recommendations: Vec::new(),
406            pattern_optimizations: Vec::new(),
407            improvement_estimate: 0.2,
408        })
409    }
410}
411
412impl<T> ConvolutionalPatternNetwork<T>
413where
414    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
415{
416    pub fn new() -> LinalgResult<Self> {
417        Ok(Self {
418            conv_layers: Vec::new(),
419            pooling_layers: Vec::new(),
420            embedding_layer: EmbeddingLayer::new()?,
421            classification_head: ClassificationHead::new()?,
422        })
423    }
424}
425
426impl<T> EmbeddingLayer<T>
427where
428    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
429{
430    pub fn new() -> LinalgResult<Self> {
431        Ok(Self {
432            weights: Array2::zeros((1, 1)),
433            embedding_dim: 128,
434            vocabsize: 1000,
435        })
436    }
437}
438
439impl<T> ClassificationHead<T>
440where
441    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
442{
443    pub fn new() -> LinalgResult<Self> {
444        Ok(Self {
445            dense_layers: Vec::new(),
446            output_layer: DenseLayer::new()?,
447            num_classes: 10,
448        })
449    }
450}
451
452impl<T> ReinforcementLearningAgent<T>
453where
454    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
455{
456    pub fn new() -> LinalgResult<Self> {
457        Ok(Self {
458            q_network: QNetwork::new()?,
459            policy_network: PolicyNetwork::new()?,
460            replay_buffer: ExperienceReplayBuffer::new(10000),
461            learning_params: RLLearningParameters::default(),
462        })
463    }
464}
465
466impl<T> QNetwork<T> {
467    pub fn new() -> LinalgResult<Self> {
468        Ok(Self {
469            layers: Vec::new(),
470            target_network: Vec::new(),
471            target_update_freq: 100,
472        })
473    }
474}
475
476impl<T> PolicyNetwork<T> {
477    pub fn new() -> LinalgResult<Self> {
478        Ok(Self {
479            actor: Vec::new(),
480            critic: Vec::new(),
481            action_dim: 10,
482        })
483    }
484}
485
486impl<T> ExperienceReplayBuffer<T> {
487    pub fn new(capacity: usize) -> Self {
488        Self {
489            buffer: VecDeque::with_capacity(capacity),
490            capacity,
491            currentsize: 0,
492        }
493    }
494}
495
496impl Default for RLLearningParameters {
497    fn default() -> Self {
498        Self {
499            learning_rate: 0.001,
500            discount_factor: 0.99,
501            exploration_rate: 1.0,
502            exploration_decay: 0.995,
503            min_exploration_rate: 0.01,
504            batchsize: 32,
505            update_frequency: 4,
506        }
507    }
508}
509
510impl<T> GeneticLayoutOptimizer<T> {
511    pub fn new() -> LinalgResult<Self> {
512        Ok(Self {
513            population: Vec::new(),
514            populationsize: 50,
515            ga_params: GeneticAlgorithmParameters::default(),
516            fitness_evaluator: FitnessEvaluator::new()?,
517        })
518    }
519}
520
521impl Default for GeneticAlgorithmParameters {
522    fn default() -> Self {
523        Self {
524            populationsize: 50,
525            generations: 100,
526            crossover_rate: 0.8,
527            mutation_rate: 0.1,
528            selection_method: SelectionMethod::Tournament(3),
529            elitism_rate: 0.1,
530        }
531    }
532}
533
534impl<T> FitnessEvaluator<T> {
535    pub fn new() -> LinalgResult<Self> {
536        Ok(Self {
537            metrics: Vec::new(),
538            weights: Array1::zeros(0),
539            benchmark_suite: BenchmarkSuite::new()?,
540        })
541    }
542}
543
544impl<T> BenchmarkSuite<T> {
545    pub fn new() -> LinalgResult<Self> {
546        Ok(Self {
547            benchmarks: Vec::new(),
548            test_datasets: Vec::new(),
549            baseline_performance: 1.0,
550        })
551    }
552}
scirs2_linalg/simd_ops/neural/training.rs

scirs2_linalg/simd_ops/neural/
training.rs
