Module neural_adaptive_sparse 

Neural-Adaptive Sparse Matrix Operations for Advanced Mode

This module implements neural network-inspired adaptive algorithms for sparse matrix operations that learn and optimize based on matrix characteristics and usage patterns.

Architecture

The neural adaptive system consists of several interconnected components:

• Neural Networks: Multi-layer perceptrons with attention mechanisms for pattern recognition
• Transformer Models: Advanced attention-based models for complex pattern understanding
• Reinforcement Learning: Agents that learn optimal strategies through trial and reward
• Pattern Memory: Efficient storage and retrieval of learned optimization patterns
• Configuration: Flexible configuration system for different use cases

Usage

use scirs2_sparse::neural_adaptive_sparse::{
    NeuralAdaptiveSparseProcessor, NeuralAdaptiveConfig, OptimizationStrategy
};

// Create a configuration
let config = NeuralAdaptiveConfig::default();

// Create the processor
let mut processor = NeuralAdaptiveSparseProcessor::new(config);

// Use the processor to optimize matrix operations
// (actual matrix features would be extracted from real sparse matrices)
let matrix_features = vec![1.0, 2.0, 3.0]; // Simplified example
let context = OperationContext {
    matrix_shape: (1000, 1000),
    nnz: 5000,
    operation_type: OperationType::MatVec,
    performance_target: PerformanceTarget::Speed,
};

let strategy = processor.optimize_operation::<f64>(&matrix_features, &context)?;

Performance Learning

The system learns from performance feedback to improve future optimizations:

use scirs2_sparse::neural_adaptive_sparse::{PerformanceMetrics, OptimizationStrategy};

// After executing the operation, provide performance feedback
let performance = PerformanceMetrics::new(
    0.1,  // execution_time
    0.8,  // cache_efficiency
    0.9,  // simd_utilization
    0.7,  // parallel_efficiency
    0.85, // memory_bandwidth
    OptimizationStrategy::SIMDVectorized,
);

processor.learn_from_performance(
    OptimizationStrategy::SIMDVectorized,
    performance,
    &matrix_features,
    &context,
)?;

Re-exports

pub use config::NeuralAdaptiveConfig;

pub use pattern_memory::OptimizationStrategy;

pub use processor::NeuralAdaptiveSparseProcessor;

pub use processor::NeuralProcessorStats;

pub use processor::OperationContext;

pub use processor::OperationType;

pub use processor::PerformanceTarget;

pub use processor::ProcessorState;

pub use reinforcement_learning::PerformanceMetrics;

pub use reinforcement_learning::RLAlgorithm;

pub use neural_network::ActivationFunction;

pub use transformer::LayerGradients;

pub use transformer::TransformerGradients;

Modules

config

Configuration for neural-adaptive sparse matrix processing

neural_network

Neural network components for sparse matrix optimization

pattern_memory

Pattern memory and fingerprinting for sparse matrix optimization

processor

Main neural-adaptive sparse matrix processor

reinforcement_learning

Reinforcement learning components for adaptive sparse matrix optimization

transformer

Transformer models for advanced pattern recognition in sparse matrices