Module meta_learning 

Meta-Learning Module for Advanced Fusion Algorithms

This module provides sophisticated meta-learning capabilities that enable the system to learn how to learn, adapting processing strategies based on input patterns, temporal contexts, and performance feedback. It implements:

Key Features

• Temporal Memory Fusion: Integrates short-term and long-term memory patterns
• Hierarchical Learning: Multi-level learning abstraction and strategy development
• Strategy Evolution: Evolutionary optimization of learning strategies
• Adaptive Memory Consolidation: Intelligent memory management and consolidation
• Performance Tracking: Comprehensive learning curve analysis and strategy effectiveness

Meta-Learning Components

• Pattern analysis for optimal strategy adaptation
• Memory attention mechanisms for relevant experience retrieval
• Hierarchical learning structures with cross-level communication
• Evolutionary strategy optimization with multiple selection mechanisms
• Adaptive parameter updates based on performance feedback

Usage

The module provides both basic meta-learning adaptation (meta_learning_adaptation) and advanced temporal fusion capabilities (enhanced_meta_learning_with_temporal_fusion) for different complexity requirements.

Functions

analyze_input_patterns

Analyze Input Patterns for Meta-Learning Strategy Selection

apply_evolved_strategies

Apply Evolved Strategies

apply_hierarchical_learning

Apply Hierarchical Learning

apply_memory_fusion

Apply Memory Fusion with Temporal Decay

apply_meta_learning_update

Apply Meta-Learning Update

apply_temporal_memory_fusion

Apply Temporal Memory Fusion

calculate_adaptive_fusion_weights

Calculate Adaptive Fusion Weights

consolidate_to_long_term_memory

Consolidate Memory Trace to Long-Term Storage

create_memory_trace

Create Memory Trace from Processing Results

determine_optimal_weights

Determine Optimal Combination Weights

enhanced_meta_learning_with_temporal_fusion

Enhanced Meta-Learning with Temporal Memory Fusion

evolve_learning_strategies

Evolve Learning Strategies

meta_learning_adaptation

Basic Meta-Learning Adaptation

perform_adaptive_memory_consolidation

Perform Adaptive Memory Consolidation

retrieve_relevant_memories

Retrieve Relevant Memories for Current Context

update_memory_attention

Update Memory Attention Mechanism

update_meta_learning_parameters

Update Meta-Learning Parameters

update_meta_learning_parameters_enhanced

Enhanced Meta-Learning Parameter Update

update_meta_learning_performance

Update Meta-Learning Performance Tracking