Module ml_optimization 

Machine learning-based spatial optimization

This module implements advanced machine learning techniques to automatically optimize spatial algorithms, including neural network-based parameter tuning, reinforcement learning for algorithm selection, and adaptive optimization strategies that learn from data patterns and computational environments.

Features

• Neural Algorithm Optimization: Deep learning models that optimize algorithm parameters
• Reinforcement Learning: RL agents that learn optimal algorithm selection strategies
• Meta-learning: Learn to learn new spatial patterns quickly
• AutoML for Spatial Computing: Automated machine learning for spatial problems
• Bayesian Optimization: Gaussian process-based hyperparameter optimization
• Ensemble Methods: Combine multiple algorithms intelligently
• Online Learning: Adapt to changing data distributions in real-time
• Transfer Learning: Apply knowledge from related spatial domains

Applications

• Automatic hyperparameter tuning for clustering algorithms
• Dynamic algorithm selection based on data characteristics
• Learned distance metrics optimized for specific tasks
• Adaptive spatial data structures that restructure based on access patterns
• Predictive preprocessing that optimizes data layout for better performance

Examples

use scirs2_spatial::ml_optimization::{NeuralSpatialOptimizer, ReinforcementLearningSelector};
use scirs2_core::ndarray::array;

// Neural network-based spatial optimizer
let optimizer_builder = NeuralSpatialOptimizer::new()
    .with_network_architecture([64, 128, 64, 32])
    .with_learning_rate(0.001)
    .with_adaptive_learning(true);
let mut optimizer = optimizer_builder;

let points = array![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]];
let optimized_params = optimizer.optimize_clustering_parameters(&points.view())?;
println!("Optimized k-means parameters: {:?}", optimized_params);

// Reinforcement learning algorithm selector
let rl_builder = ReinforcementLearningSelector::new()
    .with_epsilon_greedy(0.1)
    .with_experience_replay(true)
    .with_target_network(true);
let mut rl_selector = rl_builder;

let selected_algorithm = rl_selector.select_best_algorithm(&points.view())?;
println!("RL selected algorithm: {:?}", selected_algorithm);

Structs

ClusteringParameters

Clustering parameters optimized by neural network

ClusteringResult

Clustering result

DataState

Data state representation

Experience

Experience tuple for reinforcement learning

NeuralLayer

Neural network layer for spatial optimization

NeuralSpatialOptimizer

Neural spatial optimizer

ReinforcementLearningSelector

Reinforcement learning algorithm selector

StateAction

State-action pair for Q-learning

Enums

ActivationFunction

Activation functions for neural networks

ClusteringTendencyCategory

Clustering tendency categories

DensityCategory

Density categories

DimensionalityCategory

Dimensionality categories

DistanceMetric

Distance metrics for clustering

InitMethod

Initialization methods for clustering

SizeCategory

Data size categories

SpatialAlgorithm

Available spatial algorithms