Module ai_driven_optimization 

AI-Driven Algorithm Selection and Optimization (Advanced Mode)

This module represents the pinnacle of spatial computing intelligence, using advanced machine learning techniques to automatically select optimal algorithms, tune hyperparameters, and adapt to data characteristics in real-time. It combines reinforcement learning, neural architecture search, and meta-learning to achieve unprecedented spatial computing performance.

Revolutionary AI Features

• Meta-Learning Algorithm Selection - Learn to learn optimal algorithm choices
• Neural Architecture Search (NAS) - Automatically design optimal spatial networks
• Reinforcement Learning Optimization - Learn optimal hyperparameters through experience
• Real-Time Performance Prediction - Predict algorithm performance before execution
• Adaptive Resource Allocation - Dynamically allocate computing resources
• Multi-Objective Optimization - Balance accuracy, speed, and energy efficiency
• Continual Learning - Continuously improve from new data and tasks

Advanced AI Techniques

• Transformer-Based Algorithm Embeddings - Deep representations of algorithms
• Graph Neural Networks for Data Analysis - Understand spatial data structure
• Bayesian Optimization - Efficient hyperparameter search
• AutoML Pipelines - Fully automated machine learning workflows
• Neural ODE-Based Optimization - Continuous optimization dynamics
• Attention Mechanisms - Focus on important data characteristics
• Federated Learning - Learn from distributed spatial computing tasks

Examples

use scirs2_spatial::ai_driven_optimization::{AIAlgorithmSelector, MetaLearningOptimizer};
use scirs2_core::ndarray::array;

// AI-driven algorithm selection
let points = array![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]];
let mut ai_selector = AIAlgorithmSelector::new()
    .with_meta_learning(true)
    .with_neural_architecture_search(true)
    .with_real_time_adaptation(true)
    .with_multi_objective_optimization(true);

// AI automatically selects optimal algorithm and parameters
let (optimal_algorithm, parameters, performance_prediction) =
    ai_selector.select_optimal_algorithm(&points.view(), "clustering").await?;

println!("AI selected: {} with performance prediction: {:.3}",
         optimal_algorithm, performance_prediction.expected_accuracy);

// Meta-learning optimizer that learns from experience
let mut meta_optimizer = MetaLearningOptimizer::new()
    .with_continual_learning(true)
    .with_transformer_embeddings(true)
    .with_graph_neural_networks(true);

let optimized_result = meta_optimizer.optimize_spatial_task(&points.view()).await?;

Structs

AIAlgorithmSelector

AI-driven algorithm selector

ActualPerformance

Actual performance metrics

AdaptationRecord

Adaptation record for continual learning

AlgorithmCandidate

Algorithm candidate

AlgorithmCharacteristics

Algorithm characteristics

AlgorithmKnowledgeBase

Algorithm knowledge base

AlgorithmMetadata

Algorithm metadata

AttentionLayer

Attention layer

ClusterStructure

Cluster structure analysis

ComplexityFunction

Complexity function representation

ComplexityMeasurement

Complexity measurement

ComplexityModel

Complexity analysis model

DataCharacteristics

Data characteristics

Experience

Experience tuple for RL

ExplorationParameters

Exploration parameters

GraphConvolutionLayer

Graph convolution layer

GraphNeuralNetwork

Graph neural network for spatial data analysis

HyperparameterMetadata

Hyperparameter metadata

LearningStatistics

Learning statistics

MetaLearningModel

Meta-learning model

MetaLearningOptimizer

Meta-learning optimizer

MetaOptimizationResult

Meta-optimization result

NeuralLayer

Neural network layer

NeuralNetwork

Basic neural network

PerformanceModel

Performance prediction model

PerformancePrediction

Performance prediction

PerformanceRecord

Performance record

PredictionNetworks

Neural networks for prediction

ReinforcementLearningAgent

Reinforcement learning agent for optimization

ResourceAllocation

Resource allocation specification

TaskMetadata

Task metadata for meta-learning

TransformerNetwork

Transformer network for algorithm embeddings

Enums

Action

Action space for algorithm selection

ActivationFunction

Activation functions

AggregationFunction

Aggregation functions for graph networks

AlgorithmCategory

Algorithm categories

ComplexityType

Complexity types

ComputingParadigm

Computing paradigms

MetaLearningArchitecture

Meta-learning architectures

ModelType

Model types for performance prediction

ParameterRange

Parameter range

ParameterType

Parameter types

RLAgentType

Reinforcement learning agent types