52 lines
1.7 KiB
TypeScript
52 lines
1.7 KiB
TypeScript
/**
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* Neural Augmentation for HNSW
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*
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* Based on: hnsw-neural-augmentation.md
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* Simulates GNN-guided edge selection, learned navigation functions,
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* embedding-topology co-optimization, and attention-based layer transitions.
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*
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* Research Foundation:
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* - GNN-guided edge selection for adaptive connectivity
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* - Learned navigation functions (RL-based)
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* - Embedding-topology joint optimization
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* - Attention-based hierarchical layer routing
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*/
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import type { SimulationScenario } from '../../types';
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export interface NeuralAugmentationMetrics {
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edgeSelectionQuality: number;
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adaptiveConnectivity: number;
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avgDegree: number;
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sparsityGain: number;
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navigationEfficiency: number;
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avgHopsReduction: number;
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rlConvergenceEpochs: number;
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policyQuality: number;
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jointOptimizationGain: number;
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embeddingQuality: number;
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topologyQuality: number;
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layerSkipRate: number;
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routingAccuracy: number;
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speedupFromRouting: number;
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}
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export interface NeuralStrategy {
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name: 'baseline' | 'gnn-edges' | 'rl-nav' | 'joint-opt' | 'full-neural';
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parameters: {
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gnnLayers?: number;
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hiddenDim?: number;
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rlEpisodes?: number;
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learningRate?: number;
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};
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}
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/**
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* Neural Augmentation Scenario
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*
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* This simulation:
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* 1. Tests GNN-based adaptive edge selection
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* 2. Compares RL navigation vs greedy search
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* 3. Analyzes joint embedding-topology optimization
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* 4. Measures attention-based layer routing benefits
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* 5. Evaluates full neural augmentation pipeline
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*/
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export declare const neuralAugmentationScenario: SimulationScenario;
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export default neuralAugmentationScenario;
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//# sourceMappingURL=neural-augmentation.d.ts.map
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