1use crate::auto_feature_engineering::{
7 DatasetMetaFeatures, TransformationConfig, TransformationType,
8};
9use crate::error::{Result, TransformError};
10use scirs2_core::ndarray::{Array1, Array2};
11use scirs2_core::random::Rng;
12use scirs2_core::simd_ops::SimdUnifiedOps;
14use scirs2_core::validation::{check_not_empty, check_positive};
15use statrs::statistics::Statistics;
16use std::collections::{HashMap, VecDeque};
17
18#[derive(Debug, Clone)]
20pub struct SpikingNeuron {
21 membrane_potential: f64,
23 threshold: f64,
25 reset_potential: f64,
27 tau_membrane: f64,
29 refractory_period: f64,
31 refractory_counter: f64,
33 spike_history: VecDeque<f64>,
35 synaptic_weights: Array1<f64>,
37 learning_rate: f64,
39 ltp_trace: f64,
41 ltd_trace: f64,
43}
44
45impl SpikingNeuron {
46 pub fn new(_ninputs: usize, threshold: f64) -> Self {
48 let mut rng = scirs2_core::random::rng();
49
50 SpikingNeuron {
51 membrane_potential: 0.0,
52 threshold,
53 reset_potential: 0.0,
54 tau_membrane: 10.0,
55 refractory_period: 2.0,
56 refractory_counter: 0.0,
57 spike_history: VecDeque::with_capacity(100),
58 synaptic_weights: Array1::from_iter((0.._ninputs).map(|_| rng.gen_range(-0.5..0.5))),
59 learning_rate: 0.01,
60 ltp_trace: 0.0,
61 ltd_trace: 0.0,
62 }
63 }
64
65 pub fn update(&mut self, inputs: &Array1<f64>, dt: f64) -> bool {
67 if self.refractory_counter > 0.0 {
69 self.refractory_counter -= dt;
70 return false;
71 }
72
73 let synaptic_input = inputs.dot(&self.synaptic_weights);
75
76 let decay = (-dt / self.tau_membrane).exp();
78 self.membrane_potential = self.membrane_potential * decay + synaptic_input * (1.0 - decay);
79
80 if self.membrane_potential >= self.threshold {
82 self.membrane_potential = self.reset_potential;
84 self.refractory_counter = self.refractory_period;
85
86 if self.spike_history.len() >= 100 {
88 self.spike_history.pop_front();
89 }
90 self.spike_history.push_back(1.0);
91
92 self.ltp_trace += 1.0;
94
95 true
96 } else {
97 if self.spike_history.len() >= 100 {
99 self.spike_history.pop_front();
100 }
101 self.spike_history.push_back(0.0);
102
103 self.ltp_trace *= 0.95;
105 self.ltd_trace *= 0.95;
106
107 false
108 }
109 }
110
111 pub fn apply_stdp(&mut self, pre_spike_times: &[f64], post_spiketime: Option<f64>) {
113 if let Some(post_time) = post_spiketime {
114 for (i, &pre_time) in pre_spike_times.iter().enumerate() {
115 if i < self.synaptic_weights.len() {
116 let delta_t = post_time - pre_time;
117
118 let weight_change = if delta_t > 0.0 {
120 self.learning_rate * (-delta_t / 20.0).exp()
122 } else {
123 -self.learning_rate * (delta_t / 20.0).exp()
125 };
126
127 self.synaptic_weights[i] += weight_change;
128
129 self.synaptic_weights[i] = self.synaptic_weights[i].clamp(-1.0, 1.0);
131 }
132 }
133 }
134 }
135
136 pub fn get_spike_rate(&self) -> f64 {
138 if self.spike_history.is_empty() {
139 0.0
140 } else {
141 self.spike_history.iter().sum::<f64>() / self.spike_history.len() as f64
142 }
143 }
144}
145
146pub struct NeuromorphicAdaptationNetwork {
148 input_neurons: Vec<SpikingNeuron>,
150 hidden_neurons: Vec<SpikingNeuron>,
152 output_neurons: Vec<SpikingNeuron>,
154 connectivity: Array2<f64>,
156 #[allow(dead_code)]
158 homeostatic_scaling: Array1<f64>,
159 time_step: f64,
161 adaptation_rate: f64,
163 transformation_history: VecDeque<(DatasetMetaFeatures, Vec<TransformationConfig>, f64)>,
165}
166
167impl NeuromorphicAdaptationNetwork {
168 pub fn new(input_size: usize, hidden_size: usize, outputsize: usize) -> Self {
170 let mut rng = scirs2_core::random::rng();
171
172 let input_neurons: Vec<SpikingNeuron> = (0..input_size)
174 .map(|_| SpikingNeuron::new(1, 1.0))
175 .collect();
176
177 let hidden_neurons: Vec<SpikingNeuron> = (0..hidden_size)
178 .map(|_| SpikingNeuron::new(input_size, 1.5))
179 .collect();
180
181 let output_neurons: Vec<SpikingNeuron> = (0..outputsize)
182 .map(|_| SpikingNeuron::new(hidden_size, 2.0))
183 .collect();
184
185 let total_neurons = input_size + hidden_size + outputsize;
187 let mut connectivity = Array2::zeros((total_neurons, total_neurons));
188
189 for i in 0..input_size {
191 for j in input_size..(input_size + hidden_size) {
192 connectivity[[i, j]] = rng.gen_range(-0.3..0.3);
193 }
194 }
195
196 for i in input_size..(input_size + hidden_size) {
198 for j in (input_size + hidden_size)..total_neurons {
199 connectivity[[i, j]] = rng.gen_range(-0.3..0.3);
200 }
201 }
202
203 NeuromorphicAdaptationNetwork {
204 input_neurons,
205 hidden_neurons,
206 output_neurons,
207 connectivity,
208 homeostatic_scaling: Array1::ones(total_neurons),
209 time_step: 1.0,
210 adaptation_rate: 0.001,
211 transformation_history: VecDeque::with_capacity(1000),
212 }
213 }
214
215 pub fn process_input(
217 &mut self,
218 metafeatures: &DatasetMetaFeatures,
219 ) -> Result<Vec<TransformationConfig>> {
220 let inputpattern = self.meta_features_to_spikes(metafeatures)?;
222
223 let outputspikes = self.simulate_network_dynamics(&inputpattern)?;
225
226 self.spikes_to_transformations(&outputspikes)
228 }
229
230 fn meta_features_to_spikes(&self, metafeatures: &DatasetMetaFeatures) -> Result<Array1<f64>> {
232 let features = vec![
234 (metafeatures.n_samples as f64).ln().max(0.0) / 10.0,
235 (metafeatures.n_features as f64).ln().max(0.0) / 10.0,
236 metafeatures.sparsity,
237 metafeatures.mean_correlation.abs(),
238 metafeatures.std_correlation.min(1.0),
239 metafeatures.mean_skewness.abs().min(5.0) / 5.0,
240 metafeatures.mean_kurtosis.abs().min(5.0) / 5.0,
241 metafeatures.missing_ratio,
242 metafeatures.variance_ratio.min(1.0),
243 metafeatures.outlier_ratio,
244 ];
245
246 if features.len() != self.input_neurons.len() {
247 return Err(TransformError::InvalidInput(format!(
248 "Feature size mismatch: expected {}, got {}",
249 self.input_neurons.len(),
250 features.len()
251 )));
252 }
253
254 Ok(Array1::from_vec(features))
255 }
256
257 fn simulate_network_dynamics(&mut self, inputpattern: &Array1<f64>) -> Result<Array1<f64>> {
259 let simulation_steps = 100;
260 let mut output_accumulator = Array1::zeros(self.output_neurons.len());
261
262 for _step in 0..simulation_steps {
263 for (i, neuron) in self.input_neurons.iter_mut().enumerate() {
265 let input = Array1::from_elem(1, inputpattern[i]);
266 neuron.update(&input, self.time_step);
267 }
268
269 let input_spikes: Array1<f64> = self
271 .input_neurons
272 .iter()
273 .map(|n| if n.get_spike_rate() > 0.5 { 1.0 } else { 0.0 })
274 .collect();
275
276 for neuron in &mut self.hidden_neurons {
277 neuron.update(&input_spikes, self.time_step);
278 }
279
280 let hidden_spikes: Array1<f64> = self
282 .hidden_neurons
283 .iter()
284 .map(|n| if n.get_spike_rate() > 0.5 { 1.0 } else { 0.0 })
285 .collect();
286
287 for (i, neuron) in self.output_neurons.iter_mut().enumerate() {
288 let spike = neuron.update(&hidden_spikes, self.time_step);
289 if spike {
290 output_accumulator[i] += 1.0;
291 }
292 }
293
294 self.apply_homeostatic_scaling();
296 }
297
298 let max_spikes = simulation_steps as f64;
300 output_accumulator.mapv_inplace(|x| x / max_spikes);
301
302 Ok(output_accumulator)
303 }
304
305 fn spikes_to_transformations(
307 &self,
308 outputspikes: &Array1<f64>,
309 ) -> Result<Vec<TransformationConfig>> {
310 let mut transformations = Vec::new();
311 let threshold = 0.3; let transformation_types = [
314 TransformationType::StandardScaler,
315 TransformationType::MinMaxScaler,
316 TransformationType::RobustScaler,
317 TransformationType::PowerTransformer,
318 TransformationType::PolynomialFeatures,
319 TransformationType::PCA,
320 TransformationType::VarianceThreshold,
321 TransformationType::QuantileTransformer,
322 TransformationType::BinaryEncoder,
323 TransformationType::TargetEncoder,
324 ];
325
326 for (i, &spike_rate) in outputspikes.iter().enumerate() {
327 if spike_rate > threshold && i < transformation_types.len() {
328 let mut parameters = HashMap::new();
329
330 match &transformation_types[i] {
332 TransformationType::PCA => {
333 parameters.insert("n_components".to_string(), spike_rate);
334 }
335 TransformationType::PolynomialFeatures => {
336 let degree = (spike_rate * 4.0 + 1.0).round();
337 parameters.insert("degree".to_string(), degree);
338 }
339 TransformationType::VarianceThreshold => {
340 parameters.insert("threshold".to_string(), spike_rate * 0.1);
341 }
342 _ => {}
343 }
344
345 transformations.push(TransformationConfig {
346 transformation_type: transformation_types[i].clone(),
347 parameters,
348 expected_performance: spike_rate,
349 });
350 }
351 }
352
353 transformations.sort_by(|a, b| {
355 b.expected_performance
356 .partial_cmp(&a.expected_performance)
357 .unwrap_or(std::cmp::Ordering::Equal)
358 });
359
360 Ok(transformations)
361 }
362
363 fn apply_homeostatic_scaling(&mut self) {
365 let target_activity = 0.1; let scaling_rate = 0.001;
367
368 let input_activity = self
370 .input_neurons
371 .iter()
372 .map(|n| n.get_spike_rate())
373 .sum::<f64>()
374 / self.input_neurons.len() as f64;
375
376 let hidden_activity = self
377 .hidden_neurons
378 .iter()
379 .map(|n| n.get_spike_rate())
380 .sum::<f64>()
381 / self.hidden_neurons.len() as f64;
382
383 let output_activity = self
384 .output_neurons
385 .iter()
386 .map(|n| n.get_spike_rate())
387 .sum::<f64>()
388 / self.output_neurons.len() as f64;
389
390 if input_activity > target_activity * 2.0 {
392 for neuron in &mut self.input_neurons {
393 neuron.threshold *= 1.0 + scaling_rate;
394 }
395 } else if input_activity < target_activity * 0.5 {
396 for neuron in &mut self.input_neurons {
397 neuron.threshold *= 1.0 - scaling_rate;
398 }
399 }
400
401 if hidden_activity > target_activity * 2.0 {
402 for neuron in &mut self.hidden_neurons {
403 neuron.threshold *= 1.0 + scaling_rate;
404 }
405 } else if hidden_activity < target_activity * 0.5 {
406 for neuron in &mut self.hidden_neurons {
407 neuron.threshold *= 1.0 - scaling_rate;
408 }
409 }
410
411 if output_activity > target_activity * 2.0 {
412 for neuron in &mut self.output_neurons {
413 neuron.threshold *= 1.0 + scaling_rate;
414 }
415 } else if output_activity < target_activity * 0.5 {
416 for neuron in &mut self.output_neurons {
417 neuron.threshold *= 1.0 - scaling_rate;
418 }
419 }
420 }
421
422 pub fn learn_from_feedback(
424 &mut self,
425 metafeatures: DatasetMetaFeatures,
426 transformations: Vec<TransformationConfig>,
427 performance: f64,
428 ) -> Result<()> {
429 self.transformation_history
431 .push_back((metafeatures, transformations, performance));
432
433 if self.transformation_history.len() > 1000 {
435 self.transformation_history.pop_front();
436 }
437
438 self.apply_reinforcement_learning(performance)?;
440
441 Ok(())
442 }
443
444 fn apply_reinforcement_learning(&mut self, performance: f64) -> Result<()> {
446 let reward = (performance - 0.5) * 2.0; let learning_factor = self.adaptation_rate * reward;
450
451 for i in 0..self.connectivity.nrows() {
453 for j in 0..self.connectivity.ncols() {
454 if self.connectivity[[i, j]] != 0.0 {
455 self.connectivity[[i, j]] +=
456 learning_factor * self.connectivity[[i, j]].signum();
457
458 self.connectivity[[i, j]] = self.connectivity[[i, j]].clamp(-1.0, 1.0);
460 }
461 }
462 }
463
464 Ok(())
465 }
466
467 pub fn adaptive_reconfiguration(&mut self) -> Result<()> {
469 if self.transformation_history.len() < 10 {
470 return Ok(()); }
472
473 let recent_performances: Vec<f64> = self
475 .transformation_history
476 .iter()
477 .rev()
478 .take(10)
479 .map(|(_, _, perf)| *perf)
480 .collect();
481
482 let avg_performance =
483 recent_performances.iter().sum::<f64>() / recent_performances.len() as f64;
484
485 if avg_performance > 0.8 {
487 self.adaptation_rate *= 0.95;
489 for neuron in &mut self.hidden_neurons {
490 neuron.learning_rate *= 0.95;
491 }
492 } else if avg_performance < 0.4 {
493 self.adaptation_rate *= 1.05;
495 for neuron in &mut self.hidden_neurons {
496 neuron.learning_rate *= 1.05;
497 }
498 }
499
500 self.adaptation_rate = self.adaptation_rate.clamp(0.0001, 0.01);
502
503 Ok(())
504 }
505}
506
507pub struct NeuromorphicMemorySystem {
509 episodic_memory: Vec<TransformationEpisode>,
511 semantic_memory: HashMap<String, SemanticConcept>,
513 #[allow(dead_code)]
515 working_memory: VecDeque<TransformationConfig>,
516 consolidation_threshold: f64,
518 forgetting_rate: f64,
520}
521
522#[derive(Debug, Clone)]
524pub struct TransformationEpisode {
525 context: DatasetMetaFeatures,
527 transformation_sequence: Vec<TransformationConfig>,
529 outcome: f64,
531 #[allow(dead_code)]
533 timestamp: u64,
534 memory_strength: f64,
536}
537
538#[derive(Debug, Clone)]
540pub struct SemanticConcept {
541 #[allow(dead_code)]
543 name: String,
544 transformation_types: Vec<TransformationType>,
546 activation: f64,
548 #[allow(dead_code)]
550 associations: HashMap<String, f64>,
551}
552
553impl Default for NeuromorphicMemorySystem {
554 fn default() -> Self {
555 Self::new()
556 }
557}
558
559impl NeuromorphicMemorySystem {
560 pub fn new() -> Self {
562 let mut semantic_memory = HashMap::new();
563
564 semantic_memory.insert(
566 "normalization".to_string(),
567 SemanticConcept {
568 name: "normalization".to_string(),
569 transformation_types: vec![
570 TransformationType::StandardScaler,
571 TransformationType::MinMaxScaler,
572 TransformationType::RobustScaler,
573 ],
574 activation: 1.0,
575 associations: HashMap::new(),
576 },
577 );
578
579 semantic_memory.insert(
580 "dimensionality_reduction".to_string(),
581 SemanticConcept {
582 name: "dimensionality_reduction".to_string(),
583 transformation_types: vec![
584 TransformationType::PCA,
585 TransformationType::VarianceThreshold,
586 ],
587 activation: 1.0,
588 associations: HashMap::new(),
589 },
590 );
591
592 NeuromorphicMemorySystem {
593 episodic_memory: Vec::new(),
594 semantic_memory,
595 working_memory: VecDeque::with_capacity(10),
596 consolidation_threshold: 0.8,
597 forgetting_rate: 0.99,
598 }
599 }
600
601 pub fn store_episode(
603 &mut self,
604 context: DatasetMetaFeatures,
605 transformations: Vec<TransformationConfig>,
606 outcome: f64,
607 ) -> Result<()> {
608 let episode = TransformationEpisode {
609 context,
610 transformation_sequence: transformations,
611 outcome,
612 timestamp: std::time::SystemTime::now()
613 .duration_since(std::time::UNIX_EPOCH)
614 .unwrap()
615 .as_secs(),
616 memory_strength: if outcome > self.consolidation_threshold {
617 1.0
618 } else {
619 0.5
620 },
621 };
622
623 self.episodic_memory.push(episode);
624
625 self.apply_memory_decay();
627
628 self.consolidate_memories()?;
630
631 Ok(())
632 }
633
634 pub fn retrieve_similar_episodes(
636 &self,
637 query_context: &DatasetMetaFeatures,
638 k: usize,
639 ) -> Result<Vec<&TransformationEpisode>> {
640 let mut similarities: Vec<(usize, f64)> = self
641 .episodic_memory
642 .iter()
643 .enumerate()
644 .map(|(i, episode)| {
645 let similarity = self.compute_context_similarity(query_context, &episode.context);
646 (i, similarity * episode.memory_strength)
647 })
648 .collect();
649
650 similarities.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
652
653 let retrieved_episodes: Vec<&TransformationEpisode> = similarities
654 .into_iter()
655 .take(k)
656 .map(|(i, _)| &self.episodic_memory[i])
657 .collect();
658
659 Ok(retrieved_episodes)
660 }
661
662 fn apply_memory_decay(&mut self) {
664 for episode in &mut self.episodic_memory {
665 episode.memory_strength *= self.forgetting_rate;
666 }
667
668 self.episodic_memory
670 .retain(|episode| episode.memory_strength > 0.1);
671 }
672
673 fn consolidate_memories(&mut self) -> Result<()> {
675 let successful_episodes: Vec<TransformationEpisode> = self
677 .episodic_memory
678 .iter()
679 .filter(|episode| episode.outcome > self.consolidation_threshold)
680 .cloned()
681 .collect();
682
683 for episode in successful_episodes {
685 self.extract_semantic_patterns(&episode)?;
686 }
687
688 Ok(())
689 }
690
691 fn extract_semantic_patterns(&mut self, episode: &TransformationEpisode) -> Result<()> {
693 let sequence_pattern =
695 self.analyze_transformation_sequence(&episode.transformation_sequence);
696
697 let pattern_matches: Vec<(String, f64)> = self
700 .semantic_memory
701 .iter()
702 .map(|(concept_name, concept)| {
703 let pattern_match =
704 self.compute_pattern_match(&sequence_pattern, &concept.transformation_types);
705 (concept_name.clone(), pattern_match)
706 })
707 .collect();
708
709 for (concept_name, pattern_match) in pattern_matches {
711 if pattern_match > 0.5 {
712 if let Some(concept) = self.semantic_memory.get_mut(&concept_name) {
713 concept.activation =
714 (concept.activation + episode.outcome * pattern_match) / 2.0;
715 }
716 }
717 }
718
719 Ok(())
720 }
721
722 fn analyze_transformation_sequence(
724 &self,
725 sequence: &[TransformationConfig],
726 ) -> Vec<TransformationType> {
727 sequence
728 .iter()
729 .map(|config| config.transformation_type.clone())
730 .collect()
731 }
732
733 fn compute_pattern_match(
735 &self,
736 sequence: &[TransformationType],
737 concept_types: &[TransformationType],
738 ) -> f64 {
739 let matches = sequence
740 .iter()
741 .filter(|&t| concept_types.contains(t))
742 .count();
743
744 if sequence.is_empty() {
745 0.0
746 } else {
747 matches as f64 / sequence.len() as f64
748 }
749 }
750
751 fn compute_context_similarity(
753 &self,
754 context1: &DatasetMetaFeatures,
755 context2: &DatasetMetaFeatures,
756 ) -> f64 {
757 let features1 = [
759 context1.sparsity,
760 context1.mean_correlation,
761 context1.mean_skewness,
762 context1.variance_ratio,
763 context1.outlier_ratio,
764 ];
765
766 let features2 = [
767 context2.sparsity,
768 context2.mean_correlation,
769 context2.mean_skewness,
770 context2.variance_ratio,
771 context2.outlier_ratio,
772 ];
773
774 let dot_product: f64 = features1
776 .iter()
777 .zip(features2.iter())
778 .map(|(&a, &b)| a * b)
779 .sum();
780 let norm1: f64 = features1.iter().map(|&x| x * x).sum::<f64>().sqrt();
781 let norm2: f64 = features2.iter().map(|&x| x * x).sum::<f64>().sqrt();
782
783 if norm1 < f64::EPSILON || norm2 < f64::EPSILON {
784 0.0
785 } else {
786 (dot_product / (norm1 * norm2)).clamp(0.0, 1.0)
787 }
788 }
789}
790
791pub struct NeuromorphicTransformationSystem {
793 adaptation_network: NeuromorphicAdaptationNetwork,
795 memory_system: NeuromorphicMemorySystem,
797 system_state: SystemState,
799}
800
801#[derive(Debug, Clone)]
803pub struct SystemState {
804 performance_level: f64,
806 adaptation_rate: f64,
808 memory_utilization: f64,
810 energy_level: f64,
812}
813
814impl Default for NeuromorphicTransformationSystem {
815 fn default() -> Self {
816 Self::new()
817 }
818}
819
820impl NeuromorphicTransformationSystem {
821 pub fn new() -> Self {
823 NeuromorphicTransformationSystem {
824 adaptation_network: NeuromorphicAdaptationNetwork::new(10, 20, 10),
825 memory_system: NeuromorphicMemorySystem::new(),
826 system_state: SystemState {
827 performance_level: 0.5,
828 adaptation_rate: 0.01,
829 memory_utilization: 0.0,
830 energy_level: 1.0,
831 },
832 }
833 }
834
835 pub fn recommend_transformations(
837 &mut self,
838 metafeatures: &DatasetMetaFeatures,
839 ) -> Result<Vec<TransformationConfig>> {
840 let similar_episodes = self
842 .memory_system
843 .retrieve_similar_episodes(metafeatures, 5)?;
844
845 let mut network_recommendations = self.adaptation_network.process_input(metafeatures)?;
847
848 if !similar_episodes.is_empty() {
850 let memory_recommendations = self.extract_memory_recommendations(&similar_episodes);
851 network_recommendations =
852 self.integrate_recommendations(network_recommendations, memory_recommendations)?;
853 }
854
855 self.update_system_state();
857
858 Ok(network_recommendations)
859 }
860
861 pub fn learn_from_performance(
863 &mut self,
864 metafeatures: DatasetMetaFeatures,
865 transformations: Vec<TransformationConfig>,
866 performance: f64,
867 ) -> Result<()> {
868 self.memory_system.store_episode(
870 metafeatures.clone(),
871 transformations.clone(),
872 performance,
873 )?;
874
875 self.adaptation_network
877 .learn_from_feedback(metafeatures, transformations, performance)?;
878
879 if performance < 0.3 {
881 self.adaptation_network.adaptive_reconfiguration()?;
882 }
883
884 self.system_state.performance_level =
886 (self.system_state.performance_level * 0.9) + (performance * 0.1);
887
888 Ok(())
889 }
890
891 fn extract_memory_recommendations(
893 &self,
894 episodes: &[&TransformationEpisode],
895 ) -> Vec<TransformationConfig> {
896 let mut transformation_votes: HashMap<TransformationType, (f64, usize)> = HashMap::new();
897
898 for episode in episodes {
899 let weight = episode.memory_strength * episode.outcome;
900
901 for transformation in &episode.transformation_sequence {
902 let entry = transformation_votes
903 .entry(transformation.transformation_type.clone())
904 .or_insert((0.0, 0));
905 entry.0 += weight;
906 entry.1 += 1;
907 }
908 }
909
910 let mut recommendations: Vec<_> = transformation_votes
912 .into_iter()
913 .map(|(t_type, (total_weight, count))| TransformationConfig {
914 transformation_type: t_type,
915 parameters: HashMap::new(),
916 expected_performance: total_weight / count as f64,
917 })
918 .collect();
919
920 recommendations.sort_by(|a, b| {
921 b.expected_performance
922 .partial_cmp(&a.expected_performance)
923 .unwrap_or(std::cmp::Ordering::Equal)
924 });
925
926 recommendations
927 }
928
929 fn integrate_recommendations(
931 &self,
932 network_recs: Vec<TransformationConfig>,
933 memory_recs: Vec<TransformationConfig>,
934 ) -> Result<Vec<TransformationConfig>> {
935 let mut integrated = HashMap::new();
936 let network_weight = 0.6;
937 let memory_weight = 0.4;
938
939 for rec in network_recs {
941 integrated.insert(
942 rec.transformation_type.clone(),
943 TransformationConfig {
944 transformation_type: rec.transformation_type,
945 parameters: rec.parameters,
946 expected_performance: rec.expected_performance * network_weight,
947 },
948 );
949 }
950
951 for rec in memory_recs {
953 if let Some(existing) = integrated.get_mut(&rec.transformation_type) {
954 existing.expected_performance += rec.expected_performance * memory_weight;
955 } else {
956 integrated.insert(
957 rec.transformation_type.clone(),
958 TransformationConfig {
959 transformation_type: rec.transformation_type,
960 parameters: rec.parameters,
961 expected_performance: rec.expected_performance * memory_weight,
962 },
963 );
964 }
965 }
966
967 let mut result: Vec<_> = integrated.into_values().collect();
968 result.sort_by(|a, b| {
969 b.expected_performance
970 .partial_cmp(&a.expected_performance)
971 .unwrap_or(std::cmp::Ordering::Equal)
972 });
973
974 Ok(result)
975 }
976
977 fn update_system_state(&mut self) {
979 self.system_state.memory_utilization =
981 self.memory_system.episodic_memory.len() as f64 / 1000.0;
982
983 self.system_state.energy_level *= 0.999; if self.system_state.energy_level < 0.5 {
986 self.system_state.energy_level = 1.0; }
988
989 if self.system_state.performance_level > 0.8 {
991 self.system_state.adaptation_rate *= 0.95; } else if self.system_state.performance_level < 0.3 {
993 self.system_state.adaptation_rate *= 1.05; }
995
996 self.system_state.adaptation_rate = self.system_state.adaptation_rate.clamp(0.001, 0.1);
997 }
998
999 pub const fn get_system_state(&self) -> &SystemState {
1001 &self.system_state
1002 }
1003}
1004
1005pub struct AdvancedNeuromorphicProcessor {
1011 network: NeuromorphicAdaptationNetwork,
1013 spike_buffer: Array2<f64>,
1015 batch_size: usize,
1017 processing_chunks: usize,
1019 performance_metrics: AdvancedNeuromorphicMetrics,
1021 adaptive_thresholds: Array1<f64>,
1023 memory_pool: Vec<Array1<f64>>,
1025}
1026
1027#[derive(Debug, Clone)]
1029pub struct AdvancedNeuromorphicMetrics {
1030 pub throughput: f64,
1032 pub memory_efficiency: f64,
1034 pub network_utilization: f64,
1036 pub adaptation_success_rate: f64,
1038 pub energy_efficiency: f64,
1040 pub real_time_satisfaction: f64,
1042}
1043
1044impl AdvancedNeuromorphicProcessor {
1045 pub fn new(_input_size: usize, hidden_size: usize, outputsize: usize) -> Self {
1047 let network = NeuromorphicAdaptationNetwork::new(_input_size, hidden_size, outputsize);
1048 let batch_size = 64; let processing_chunks = num_cpus::get().min(8); AdvancedNeuromorphicProcessor {
1052 network,
1053 spike_buffer: Array2::zeros((batch_size, _input_size + hidden_size + outputsize)),
1054 batch_size,
1055 processing_chunks,
1056 performance_metrics: AdvancedNeuromorphicMetrics {
1057 throughput: 0.0,
1058 memory_efficiency: 1.0,
1059 network_utilization: 0.0,
1060 adaptation_success_rate: 0.0,
1061 energy_efficiency: 1.0,
1062 real_time_satisfaction: 1.0,
1063 },
1064 adaptive_thresholds: Array1::ones(outputsize),
1065 memory_pool: Vec::with_capacity(32),
1066 }
1067 }
1068
1069 pub fn process_batch(
1071 &mut self,
1072 meta_features_batch: &[DatasetMetaFeatures],
1073 ) -> Result<Vec<Vec<TransformationConfig>>> {
1074 check_not_empty(
1075 &Array1::from_iter(meta_features_batch.iter().map(|_| 1.0)),
1076 "_batch",
1077 )?;
1078
1079 let start_time = std::time::Instant::now();
1080 let mut results = Vec::with_capacity(meta_features_batch.len());
1081
1082 for metafeatures in meta_features_batch {
1084 let configs = self.process_single_advanced(metafeatures)?;
1085 results.push(configs);
1086 }
1087
1088 let processing_time = start_time.elapsed().as_secs_f64();
1092 self.performance_metrics.throughput = meta_features_batch.len() as f64 / processing_time;
1093 self.update_advanced_metrics();
1094
1095 Ok(results)
1096 }
1097
1098 fn process_single_advanced(
1100 &mut self,
1101 metafeatures: &DatasetMetaFeatures,
1102 ) -> Result<Vec<TransformationConfig>> {
1103 let inputpattern = self.advanced_feature_encoding(metafeatures)?;
1105
1106 let outputspikes = self.advanced_network_simulation(&inputpattern)?;
1108
1109 self.adapt_thresholds_realtime(&outputspikes);
1111
1112 self.advanced_transformation_generation(&outputspikes)
1114 }
1115
1116 fn advanced_feature_encoding(&self, metafeatures: &DatasetMetaFeatures) -> Result<Array1<f64>> {
1118 let raw_features = vec![
1120 (metafeatures.n_samples as f64).ln().max(0.0),
1121 (metafeatures.n_features as f64).ln().max(0.0),
1122 metafeatures.sparsity * 10.0,
1123 metafeatures.mean_correlation.abs() * 10.0,
1124 metafeatures.std_correlation * 10.0,
1125 metafeatures.mean_skewness.abs(),
1126 metafeatures.mean_kurtosis.abs(),
1127 metafeatures.missing_ratio * 10.0,
1128 metafeatures.variance_ratio * 10.0,
1129 metafeatures.outlier_ratio * 10.0,
1130 ];
1131
1132 let features = Array1::from_vec(raw_features);
1134 let norm = f64::simd_norm(&features.view());
1135 let normalized = if norm > 1e-8 {
1136 f64::simd_scalar_mul(&features.view(), 1.0 / norm)
1137 } else {
1138 features.clone()
1139 };
1140
1141 Ok(normalized)
1142 }
1143
1144 fn advanced_network_simulation(&mut self, inputpattern: &Array1<f64>) -> Result<Array1<f64>> {
1146 let simulation_steps = 50; let mut output_accumulator = self.get_pooled_array(self.network.output_neurons.len());
1148
1149 for _step in 0..simulation_steps {
1151 let input_spikes =
1153 self.compute_layer_spikes_simd(&self.network.input_neurons, inputpattern)?;
1154 let hidden_spikes =
1155 self.compute_layer_spikes_simd(&self.network.hidden_neurons, &input_spikes)?;
1156 let outputspikes =
1157 self.compute_layer_spikes_simd(&self.network.output_neurons, &hidden_spikes)?;
1158
1159 output_accumulator = f64::simd_add(&output_accumulator.view(), &outputspikes.view());
1161 }
1162
1163 let max_spikes = simulation_steps as f64;
1165 output_accumulator = f64::simd_scalar_mul(&output_accumulator.view(), 1.0 / max_spikes);
1166
1167 Ok(output_accumulator)
1168 }
1169
1170 fn compute_layer_spikes_simd(
1172 &self,
1173 neurons: &[SpikingNeuron],
1174 inputs: &Array1<f64>,
1175 ) -> Result<Array1<f64>> {
1176 let mut spikes = Array1::zeros(neurons.len());
1177
1178 for (i, neuron) in neurons.iter().enumerate() {
1180 let membrane_potential = inputs.dot(&neuron.synaptic_weights);
1182 spikes[i] = if membrane_potential > neuron.threshold {
1183 1.0
1184 } else {
1185 0.0
1186 };
1187 }
1188
1189 Ok(spikes)
1190 }
1191
1192 fn adapt_thresholds_realtime(&mut self, outputspikes: &Array1<f64>) {
1194 let target_activity = 0.3; let adaptation_rate = 0.01;
1197
1198 for i in 0..self.adaptive_thresholds.len().min(outputspikes.len()) {
1199 let activity_error = outputspikes[i] - target_activity;
1200 self.adaptive_thresholds[i] += adaptation_rate * activity_error;
1201 self.adaptive_thresholds[i] = self.adaptive_thresholds[i].clamp(0.1, 2.0);
1202 }
1203
1204 let average_activity = outputspikes.mean().unwrap_or(0.0);
1206 self.performance_metrics.network_utilization =
1207 (average_activity / target_activity).min(1.0);
1208 }
1209
1210 fn advanced_transformation_generation(
1212 &self,
1213 outputspikes: &Array1<f64>,
1214 ) -> Result<Vec<TransformationConfig>> {
1215 let mut transformations = Vec::with_capacity(outputspikes.len());
1216
1217 let transformation_types = [
1218 TransformationType::StandardScaler,
1219 TransformationType::MinMaxScaler,
1220 TransformationType::RobustScaler,
1221 TransformationType::PowerTransformer,
1222 TransformationType::PolynomialFeatures,
1223 TransformationType::PCA,
1224 TransformationType::VarianceThreshold,
1225 TransformationType::QuantileTransformer,
1226 TransformationType::BinaryEncoder,
1227 TransformationType::TargetEncoder,
1228 ];
1229
1230 for (i, &spike_rate) in outputspikes.iter().enumerate() {
1232 let adjusted_threshold = self.adaptive_thresholds.get(i).copied().unwrap_or(0.3);
1233
1234 if spike_rate > adjusted_threshold && i < transformation_types.len() {
1235 let mut parameters = HashMap::new();
1236
1237 match &transformation_types[i] {
1239 TransformationType::PCA => {
1240 let n_components = (spike_rate * 0.95).max(0.1);
1241 parameters.insert("n_components".to_string(), n_components);
1242 parameters.insert(
1243 "whiten".to_string(),
1244 if spike_rate > 0.7 { 1.0 } else { 0.0 },
1245 );
1246 }
1247 TransformationType::PolynomialFeatures => {
1248 let degree = (spike_rate * 3.0 + 1.0).round().min(4.0);
1249 parameters.insert("degree".to_string(), degree);
1250 parameters.insert(
1251 "include_bias".to_string(),
1252 if spike_rate > 0.6 { 1.0 } else { 0.0 },
1253 );
1254 }
1255 TransformationType::PowerTransformer => {
1256 let lambda = spike_rate * 2.0 - 1.0; parameters.insert("lambda".to_string(), lambda);
1258 parameters.insert("standardize".to_string(), 1.0);
1259 }
1260 TransformationType::VarianceThreshold => {
1261 let threshold = spike_rate * 0.1;
1262 parameters.insert("threshold".to_string(), threshold);
1263 }
1264 _ => {}
1265 }
1266
1267 transformations.push(TransformationConfig {
1268 transformation_type: transformation_types[i].clone(),
1269 parameters,
1270 expected_performance: spike_rate
1271 * self.performance_metrics.adaptation_success_rate,
1272 });
1273 }
1274 }
1275
1276 transformations.sort_by(|a, b| {
1278 b.expected_performance
1279 .partial_cmp(&a.expected_performance)
1280 .unwrap_or(std::cmp::Ordering::Equal)
1281 });
1282
1283 Ok(transformations)
1284 }
1285
1286 fn get_pooled_array(&mut self, size: usize) -> Array1<f64> {
1288 for (i, arr) in self.memory_pool.iter().enumerate() {
1290 if arr.len() == size {
1291 let mut reused = self.memory_pool.swap_remove(i);
1292 reused.fill(0.0);
1293 return reused;
1294 }
1295 }
1296
1297 Array1::zeros(size)
1299 }
1300
1301 #[allow(dead_code)]
1303 fn return_to_pool(&mut self, array: Array1<f64>) {
1304 if self.memory_pool.len() < 32 {
1305 self.memory_pool.push(array);
1307 }
1308 }
1309
1310 fn update_advanced_metrics(&mut self) {
1312 let pool_hit_rate = self.memory_pool.len() as f64 / 32.0;
1314 self.performance_metrics.memory_efficiency = pool_hit_rate;
1315
1316 let computational_intensity =
1318 self.performance_metrics.throughput * self.performance_metrics.network_utilization;
1319 self.performance_metrics.energy_efficiency =
1320 (1.0 / (computational_intensity + 1.0)).max(0.1);
1321
1322 let target_throughput = 1000.0; self.performance_metrics.real_time_satisfaction =
1325 (self.performance_metrics.throughput / target_throughput).min(1.0);
1326
1327 let quality_score = self.performance_metrics.network_utilization
1329 * self.performance_metrics.memory_efficiency;
1330 self.performance_metrics.adaptation_success_rate = quality_score;
1331 }
1332
1333 pub const fn get_advanced_diagnostics(&self) -> &AdvancedNeuromorphicMetrics {
1335 &self.performance_metrics
1336 }
1337
1338 pub fn tune_for_workload(&mut self, expected_load: f64, latencyrequirements: f64) {
1340 if latencyrequirements < 0.01 {
1342 self.batch_size = 1;
1344 self.processing_chunks = num_cpus::get();
1345 } else if expected_load > 1000.0 {
1346 self.batch_size = 128;
1348 self.processing_chunks = (num_cpus::get() / 2).max(1);
1349 } else {
1350 self.batch_size = 64;
1352 self.processing_chunks = num_cpus::get().min(8);
1353 }
1354
1355 let total_neurons = self.network.input_neurons.len()
1357 + self.network.hidden_neurons.len()
1358 + self.network.output_neurons.len();
1359 self.spike_buffer = Array2::zeros((self.batch_size, total_neurons));
1360 }
1361
1362 pub fn learn_from_feedback(
1364 &mut self,
1365 metafeatures: &DatasetMetaFeatures,
1366 applied_configs: &[TransformationConfig],
1367 performance_score: f64,
1368 ) -> Result<()> {
1369 check_positive(performance_score, "performance_score")?;
1370
1371 if performance_score > 0.8 {
1373 self.reinforce_successful_pattern(metafeatures, applied_configs)?;
1374 } else if performance_score < 0.3 {
1375 self.suppress_unsuccessful_pattern(metafeatures, applied_configs)?;
1376 }
1377
1378 let feedback_strength = (performance_score - 0.5).abs() * 2.0; self.network.adaptation_rate *= 1.0 + feedback_strength * 0.1;
1381 self.network.adaptation_rate = self.network.adaptation_rate.clamp(0.001, 0.1);
1382
1383 Ok(())
1384 }
1385
1386 fn reinforce_successful_pattern(
1388 &mut self,
1389 metafeatures: &DatasetMetaFeatures,
1390 #[allow(unused_variables)] _configs: &[TransformationConfig],
1391 ) -> Result<()> {
1392 let inputpattern = self.advanced_feature_encoding(metafeatures)?;
1393
1394 for (i, &activation) in inputpattern.iter().enumerate() {
1396 if i < self.network.input_neurons.len() && activation > 0.5 {
1397 for neuron in &mut self.network.hidden_neurons {
1399 if i < neuron.synaptic_weights.len() {
1400 neuron.synaptic_weights[i] *= 1.02;
1401 neuron.synaptic_weights[i] = neuron.synaptic_weights[i].min(1.0);
1402 }
1403 }
1404 }
1405 }
1406
1407 Ok(())
1408 }
1409
1410 fn suppress_unsuccessful_pattern(
1412 &mut self,
1413 metafeatures: &DatasetMetaFeatures,
1414 #[allow(unused_variables)] _configs: &[TransformationConfig],
1415 ) -> Result<()> {
1416 let inputpattern = self.advanced_feature_encoding(metafeatures)?;
1417
1418 for (i, &activation) in inputpattern.iter().enumerate() {
1420 if i < self.network.input_neurons.len() && activation > 0.5 {
1421 for neuron in &mut self.network.hidden_neurons {
1423 if i < neuron.synaptic_weights.len() {
1424 neuron.synaptic_weights[i] *= 0.98;
1425 neuron.synaptic_weights[i] = neuron.synaptic_weights[i].max(-1.0);
1426 }
1427 }
1428 }
1429 }
1430
1431 Ok(())
1432 }
1433}
1434
1435#[allow(dead_code)]
1436impl Default for AdvancedNeuromorphicMetrics {
1437 fn default() -> Self {
1438 AdvancedNeuromorphicMetrics {
1439 throughput: 0.0,
1440 memory_efficiency: 1.0,
1441 network_utilization: 0.0,
1442 adaptation_success_rate: 0.0,
1443 energy_efficiency: 1.0,
1444 real_time_satisfaction: 1.0,
1445 }
1446 }
1447}