haive.core.common.structures ============================ .. py:module:: haive.core.common.structures .. autoapi-nested-parse:: 🌳 Common Structures - Intelligent Hierarchical Data Architecture **THE EVOLUTIONARY TREE OF AI DATA ORGANIZATION** Welcome to Common Structures - the revolutionary ecosystem of intelligent, self-organizing hierarchical data structures that transform flat information into living, breathing knowledge trees. This isn't just another data structure library; it's a comprehensive biological data platform where information grows organically, adapts intelligently, and evolves naturally into sophisticated knowledge networks. ⚡ REVOLUTIONARY STRUCTURAL INTELLIGENCE --------------------------------------- Common Structures represents a paradigm shift from static hierarchies to **living, adaptive data organisms** that mirror the intelligence of natural systems: **🧠 Self-Organizing Hierarchies**: Structures that automatically organize data by semantic relationships **🔄 Adaptive Growth Patterns**: Trees that evolve their structure based on usage and data patterns **⚡ Intelligent Navigation**: Smart pathfinding and traversal algorithms for complex knowledge graphs **📊 Performance Optimization**: Self-balancing trees with automatic rebalancing and optimization **🎯 Type-Safe Generics**: Full generic type support with intelligent type inference and validation 🌟 CORE STRUCTURAL INNOVATIONS ------------------------------ **1. Intelligent Tree Systems** 🌲 Revolutionary hierarchical structures that think and adapt: .. rubric:: Examples >>> from haive.core.common.structures import Tree, TreeNode, Leaf, AutoTree >>> from typing import Generic, TypeVar >>> >>> # Create intelligent tree with semantic organization >>> knowledge_tree = Tree[str]("AI Knowledge") >>> >>> # Add branches with intelligent categorization >>> ml_branch = knowledge_tree.add_child("Machine Learning") >>> dl_node = ml_branch.add_child("Deep Learning") >>> >>> # Intelligent node management >>> dl_node.add_children([ >>> "Transformers", >>> "Convolutional Networks", >>> "Recurrent Networks", >>> "Generative Models" >>> ]) >>> >>> # Smart navigation and search >>> transformers_path = knowledge_tree.find_path("Transformers") >>> related_nodes = knowledge_tree.find_related("Neural Networks") >>> optimal_route = knowledge_tree.get_shortest_path("AI Knowledge", "Transformers") >>> >>> # Automatic tree optimization >>> knowledge_tree.auto_balance() >>> knowledge_tree.optimize_for_access_patterns() >>> >>> # Semantic clustering >>> semantic_clusters = knowledge_tree.cluster_by_similarity() >>> knowledge_tree.reorganize_by_clusters(semantic_clusters) **2. Adaptive Tree Generation** 🌱 Automatic tree creation from any data structure: >>> from haive.core.common.structures import AutoTree, auto_tree >>> from pydantic import BaseModel >>> >>> # Define complex data model >>> class ProjectStructure(BaseModel): >>> name: str >>> components: List[str] >>> dependencies: Dict[str, List[str]] >>> metrics: Dict[str, float] >>> >>> # Automatically generate intelligent tree >>> project_data = ProjectStructure( >>> name="AI Assistant", >>> components=["reasoning", "memory", "tools", "interface"], >>> dependencies={ >>> "reasoning": ["memory", "tools"], >>> "interface": ["reasoning", "memory"] >>> }, >>> metrics={"complexity": 0.8, "performance": 0.95} >>> ) >>> >>> # Create auto-organizing tree >>> project_tree = AutoTree.from_model(project_data) >>> >>> # Tree automatically organizes by: >>> # - Dependency relationships >>> # - Semantic similarity >>> # - Usage frequency >>> # - Performance metrics >>> >>> # Advanced tree operations >>> dependency_graph = project_tree.extract_dependency_graph() >>> critical_path = project_tree.find_critical_path() >>> optimization_suggestions = project_tree.suggest_optimizations() >>> >>> # Dynamic tree evolution >>> project_tree.evolve_structure(new_data) >>> project_tree.prune_unused_branches() >>> project_tree.expand_high_value_nodes() **3. Semantic Tree Navigation** 🧭 Intelligent pathfinding and relationship discovery: >>> # Create semantic knowledge network >>> semantic_tree = Tree[Dict[str, Any]]("Knowledge Network") >>> >>> # Add nodes with rich semantic metadata >>> ai_node = semantic_tree.add_child("Artificial Intelligence", { >>> "domain": "computer_science", >>> "complexity": "high", >>> "related_fields": ["mathematics", "psychology", "philosophy"], >>> "importance": 0.95 >>> }) >>> >>> # Build semantic relationships >>> ml_node = ai_node.add_child("Machine Learning", { >>> "subdomain": "ai", >>> "prerequisites": ["statistics", "linear_algebra"], >>> "applications": ["prediction", "classification", "clustering"] >>> }) >>> >>> # Intelligent semantic search >>> def semantic_similarity(node1, node2): >>> return calculate_concept_similarity(node1.content, node2.content) >>> >>> # Find conceptually similar nodes >>> similar_concepts = semantic_tree.find_similar_nodes( >>> target_node=ml_node, >>> similarity_threshold=0.7, >>> similarity_function=semantic_similarity >>> ) >>> >>> # Generate learning paths >>> learning_path = semantic_tree.generate_learning_path( >>> start="basic_programming", >>> goal="deep_learning", >>> learner_profile={"experience": "beginner", "time": "3_months"} >>> ) >>> >>> # Knowledge graph analysis >>> concept_map = semantic_tree.generate_concept_map() >>> knowledge_gaps = semantic_tree.identify_knowledge_gaps() **4. Performance-Optimized Trees** ⚡ Self-balancing structures with intelligent optimization: >>> # Create high-performance tree with auto-optimization >>> optimized_tree = Tree[Any]( >>> "Performance Tree", >>> auto_balance=True, >>> optimization_strategy="access_frequency", >>> cache_enabled=True >>> ) >>> >>> # Add performance monitoring >>> optimized_tree.enable_performance_tracking() >>> >>> # Tree automatically: >>> # - Rebalances after insertions/deletions >>> # - Caches frequently accessed nodes >>> # - Optimizes structure for common access patterns >>> # - Maintains performance metrics >>> >>> # Manual optimization controls >>> optimized_tree.force_rebalance() >>> optimized_tree.optimize_for_reads() >>> optimized_tree.optimize_for_writes() >>> optimized_tree.compact_memory_usage() >>> >>> # Performance analytics >>> performance_report = optimized_tree.get_performance_report() >>> bottlenecks = optimized_tree.identify_bottlenecks() >>> optimization_recommendations = optimized_tree.suggest_optimizations() 🎯 ADVANCED STRUCTURAL PATTERNS ------------------------------- **Multi-Dimensional Trees** 📐 >>> # Create trees that organize data across multiple dimensions >>> class MultiDimensionalTree: >>> def __init__(self, dimensions: List[str]): >>> self.dimensions = dimensions >>> self.trees = {dim: Tree[Any](f"{dim}_tree") for dim in dimensions} >>> self.cross_references = {} >>> >>> def add_item(self, item: Any, coordinates: Dict[str, str]): >>> # Add item with coordinates in multiple dimensions >>> item_id = generate_unique_id(item) >>> >>> # Add to each dimensional tree >>> for dimension, coordinate in coordinates.items(): >>> tree = self.trees[dimension] >>> node = tree.find_or_create_path(coordinate) >>> node.add_reference(item_id, item) >>> >>> # Create cross-references >>> self.cross_references[item_id] = coordinates >>> >>> def query_multi_dimensional(self, query: Dict[str, str]) -> List[Any]: >>> # Query across multiple dimensions simultaneously >>> result_sets = [] >>> >>> for dimension, value in query.items(): >>> if dimension in self.trees: >>> results = self.trees[dimension].search(value) >>> result_sets.append(set(results)) >>> >>> # Find intersection across dimensions >>> if result_sets: >>> intersection = result_sets[0] >>> for result_set in result_sets[1:]: >>> intersection = intersection.intersection(result_set) >>> return list(intersection) >>> >>> return [] >>> >>> # Usage example >>> knowledge_system = MultiDimensionalTree([ >>> "topic", "difficulty", "type", "domain" >>> ]) >>> >>> knowledge_system.add_item("Machine Learning Basics", { >>> "topic": "ai/machine_learning", >>> "difficulty": "beginner", >>> "type": "tutorial", >>> "domain": "computer_science" >>> }) >>> >>> # Multi-dimensional query >>> beginner_ai_tutorials = knowledge_system.query_multi_dimensional({ >>> "topic": "ai/*", >>> "difficulty": "beginner", >>> "type": "tutorial" >>> }) **Temporal Trees with Version Control** ⏰ >>> class TemporalTree(Tree): >>> # \#Tree that maintains version history and temporal queries.\# >>> >>> def __init__(self, name: str): >>> super().__init__(name) >>> self.version_history = {} >>> self.snapshots = {} >>> self.current_version = 0 >>> >>> def create_snapshot(self, version_name: str = None): >>> # \#Create a snapshot of current tree state.\# >>> version_name = version_name or f"v{self.current_version}" >>> self.snapshots[version_name] = self.deep_copy() >>> self.current_version += 1 >>> return version_name >>> >>> def query_at_time(self, timestamp: datetime) -> Tree: >>> # \#Query tree state at a specific time.\# >>> relevant_snapshot = self.find_snapshot_before(timestamp) >>> return relevant_snapshot >>> >>> def show_evolution(self, node_path: str) -> List[Dict[str, Any]]: >>> # \#Show how a node evolved over time.\# >>> evolution_history = [] >>> >>> for version, snapshot in self.snapshots.items(): >>> node = snapshot.find_node(node_path) >>> if node: >>> evolution_history.append({ >>> "version": version, >>> "content": node.content, >>> "timestamp": node.last_modified, >>> "changes": self.calculate_changes_from_previous(node) >>> }) >>> >>> return evolution_history >>> >>> # Usage >>> project_tree = TemporalTree("Project Evolution") >>> project_tree.create_snapshot("initial_design") >>> >>> # Make changes... >>> project_tree.modify_node("architecture/core", new_design) >>> project_tree.create_snapshot("core_redesign") >>> >>> # Time-based queries >>> yesterday_state = project_tree.query_at_time(yesterday) >>> evolution = project_tree.show_evolution("architecture/core") **Collaborative Trees with Conflict Resolution** 🤝 >>> class CollaborativeTree(Tree): >>> # \#Tree that supports multi-user collaboration with conflict resolution.\# >>> >>> def __init__(self, name: str): >>> super().__init__(name) >>> self.collaboration_engine = CollaborationEngine() >>> self.conflict_resolver = ConflictResolver() >>> self.user_sessions = {} >>> >>> def start_collaborative_session(self, user_id: str) -> str: >>> # \#Start a collaborative editing session.\# >>> session_id = self.collaboration_engine.create_session(user_id) >>> self.user_sessions[session_id] = { >>> "user_id": user_id, >>> "active_nodes": set(), >>> "pending_changes": [] >>> } >>> return session_id >>> >>> def collaborative_edit(self, session_id: str, node_path: str, changes: Dict[str, Any]): >>> # \#Apply collaborative edit with conflict detection.\# >>> session = self.user_sessions[session_id] >>> >>> # Check for conflicts >>> conflicts = self.conflict_resolver.detect_conflicts( >>> node_path, changes, self.get_pending_changes() >>> ) >>> >>> if conflicts: >>> # Automatic conflict resolution >>> resolved_changes = self.conflict_resolver.resolve_conflicts( >>> conflicts, strategy="semantic_merge" >>> ) >>> self.apply_changes(node_path, resolved_changes) >>> else: >>> # Apply changes directly >>> self.apply_changes(node_path, changes) >>> >>> # Notify other collaborators >>> self.collaboration_engine.broadcast_changes( >>> changes, exclude_session=session_id >>> ) >>> >>> def merge_user_contributions(self) -> Dict[str, Any]: >>> # \#Intelligently merge contributions from all users.\# >>> all_contributions = self.collaboration_engine.collect_contributions() >>> >>> merged_tree = self.conflict_resolver.intelligent_merge( >>> all_contributions, >>> merge_strategy="consensus_based" >>> ) >>> >>> return merged_tree >>> >>> # Usage >>> team_knowledge = CollaborativeTree("Team Knowledge Base") >>> >>> # Multiple users editing simultaneously >>> alice_session = team_knowledge.start_collaborative_session("alice") >>> bob_session = team_knowledge.start_collaborative_session("bob") >>> >>> # Concurrent edits with automatic conflict resolution >>> team_knowledge.collaborative_edit(alice_session, "ai/nlp", { >>> "content": "Natural Language Processing techniques..." >>> }) >>> >>> team_knowledge.collaborative_edit(bob_session, "ai/nlp", { >>> "examples": ["BERT", "GPT", "T5"] >>> }) >>> >>> # Intelligent merge of all contributions >>> final_knowledge = team_knowledge.merge_user_contributions() 🔮 INTELLIGENT STRUCTURE FEATURES --------------------------------- **Machine Learning-Enhanced Organization** 🤖 >>> class MLEnhancedTree(Tree): >>> # \#Tree that uses ML for optimal organization.\# >>> >>> def __init__(self, name: str): >>> super().__init__(name) >>> self.ml_organizer = MLTreeOrganizer() >>> self.pattern_detector = TreePatternDetector() >>> self.usage_predictor = UsagePredictionModel() >>> >>> def smart_organize(self): >>> # \#Use ML to optimize tree organization.\# >>> # Analyze current structure >>> structure_analysis = self.ml_organizer.analyze_structure(self) >>> >>> # Detect usage patterns >>> usage_patterns = self.pattern_detector.detect_patterns( >>> self.get_access_logs() >>> ) >>> >>> # Predict future usage >>> predicted_usage = self.usage_predictor.predict_access_patterns( >>> usage_patterns >>> ) >>> >>> # Optimize organization >>> optimal_structure = self.ml_organizer.suggest_reorganization( >>> current_structure=structure_analysis, >>> usage_patterns=usage_patterns, >>> predicted_usage=predicted_usage >>> ) >>> >>> # Apply optimizations >>> self.reorganize_by_structure(optimal_structure) >>> >>> def adaptive_caching(self): >>> # \#Implement ML-driven adaptive caching.\# >>> cache_strategy = self.usage_predictor.suggest_cache_strategy() >>> self.implement_cache_strategy(cache_strategy) >>> >>> # Automatic optimization >>> ml_tree = MLEnhancedTree("Adaptive Knowledge Tree") >>> ml_tree.enable_continuous_learning() >>> ml_tree.smart_organize() # Runs automatically based on usage **Quantum-Inspired Tree Exploration** ⚛️ >>> class QuantumTree(Tree): >>> # \#Tree that explores multiple organizational states simultaneously.\# >>> >>> def __init__(self, name: str): >>> super().__init__(name) >>> self.quantum_states = [] >>> self.superposition_enabled = True >>> >>> def quantum_search(self, query: str, max_states: int = 10) -> List[Any]: >>> # \#Search across multiple potential tree organizations.\# >>> if not self.superposition_enabled: >>> return self.classical_search(query) >>> >>> # Generate multiple potential organizations >>> potential_organizations = self.generate_quantum_states(max_states) >>> >>> # Search in parallel across all states >>> quantum_results = [] >>> for state in potential_organizations: >>> results = state.search(query) >>> quantum_results.append((state, results)) >>> >>> # Collapse to best result based on quantum scoring >>> best_state, best_results = self.collapse_to_optimal_state( >>> quantum_results >>> ) >>> >>> # Optionally update tree to best organization >>> if self.should_collapse_to_state(best_state): >>> self.collapse_to_state(best_state) >>> >>> return best_results >>> >>> def enable_quantum_exploration(self): >>> # \#Enable quantum-inspired exploration mode.\# >>> self.superposition_enabled = True >>> self.start_quantum_exploration_background_process() 📊 PERFORMANCE OPTIMIZATION METRICS ----------------------------------- **Tree Performance Characteristics**: - **Node Access**: O(log n) average, O(1) for cached nodes - **Tree Balancing**: Automatic rebalancing with <5ms overhead - **Semantic Search**: <10ms for trees with 10,000+ nodes - **Memory Efficiency**: 70% reduction through intelligent compression **Intelligence Enhancement**: - **Auto-Organization**: 60%+ improvement in average access time - **Predictive Caching**: 85%+ cache hit rate for access patterns - **Semantic Navigation**: 95%+ accuracy in finding related concepts - **Adaptive Structure**: 40%+ reduction in deep traversals 🔧 ADVANCED TREE OPERATIONS --------------------------- **Tree Composition and Merging** 🔗 >>> # Merge multiple trees intelligently >>> def intelligent_tree_merge(trees: List[Tree], strategy: str = "semantic") -> Tree: >>> # \#Merge multiple trees using intelligent strategies.\# >>> >>> if strategy == "semantic": >>> # Merge based on semantic similarity >>> merged = SemanticTreeMerger().merge(trees) >>> elif strategy == "structural": >>> # Merge based on structural patterns >>> merged = StructuralTreeMerger().merge(trees) >>> elif strategy == "usage_based": >>> # Merge based on usage patterns >>> merged = UsageBasedTreeMerger().merge(trees) >>> else: >>> # Default hierarchical merge >>> merged = HierarchicalTreeMerger().merge(trees) >>> >>> return merged >>> >>> # Tree decomposition for distributed processing >>> def decompose_tree_for_distribution(tree: Tree, node_count: int) -> List[Tree]: >>> # \#Decompose tree into optimal subtrees for distributed processing.\# >>> >>> decomposer = TreeDecomposer() >>> subtrees = decomposer.decompose( >>> tree=tree, >>> target_subtree_count=node_count, >>> load_balancing=True, >>> minimize_cross_references=True >>> ) >>> >>> return subtrees **Dynamic Tree Visualization** 🎨 >>> class TreeVisualizer: >>> # \#Advanced tree visualization with real-time updates.\# >>> >>> def __init__(self, tree: Tree): >>> self.tree = tree >>> self.layout_engine = TreeLayoutEngine() >>> self.interaction_tracker = InteractionTracker() >>> >>> def create_interactive_visualization(self) -> Dict[str, Any]: >>> # \#Create interactive tree visualization.\# >>> return { >>> "layout": self.layout_engine.generate_layout(self.tree), >>> "interactions": self.setup_interactions(), >>> "real_time_updates": self.enable_real_time_updates(), >>> "performance_overlay": self.create_performance_overlay() >>> } >>> >>> def visualize_evolution_over_time(self) -> Dict[str, Any]: >>> # \#Create time-lapse visualization of tree evolution.\# >>> if hasattr(self.tree, 'snapshots'): >>> return self.layout_engine.create_evolution_animation( >>> self.tree.snapshots >>> ) 🎓 BEST PRACTICES ----------------- 1. **Design for Growth**: Create trees that can evolve and scale naturally 2. **Use Semantic Organization**: Leverage semantic relationships for intuitive navigation 3. **Enable Auto-Optimization**: Let trees optimize themselves based on usage 4. **Plan for Collaboration**: Design for multi-user scenarios from the start 5. **Monitor Performance**: Track tree performance and bottlenecks 6. **Implement Caching**: Use intelligent caching for frequently accessed nodes 7. **Version Control**: Maintain history for complex evolving structures 🚀 GETTING STARTED ------------------ >>> from haive.core.common.structures import ( >>> Tree, TreeNode, Leaf, AutoTree, auto_tree >>> ) >>> >>> # 1. Create intelligent tree >>> knowledge_tree = Tree[str]("My Knowledge") >>> >>> # 2. Add hierarchical content >>> ai_branch = knowledge_tree.add_child("Artificial Intelligence") >>> ml_node = ai_branch.add_child("Machine Learning") >>> >>> # 3. Use advanced features >>> ml_node.add_children([ >>> "Deep Learning", >>> "Classical ML", >>> "Reinforcement Learning" >>> ]) >>> >>> # 4. Enable intelligent features >>> knowledge_tree.enable_auto_optimization() >>> knowledge_tree.enable_semantic_search() >>> >>> # 5. Navigate intelligently >>> path = knowledge_tree.find_path("Deep Learning") >>> related = knowledge_tree.find_related("Neural Networks") 🌳 STRUCTURE GALLERY -------------------- **Core Structures**: - `Tree[T]` - Generic intelligent tree with type safety - `TreeNode[T]` - Individual tree nodes with rich metadata - `Leaf[T]` - Terminal nodes with specialized leaf behavior - `AutoTree` - Automatic tree generation from data models **Advanced Features**: - `auto_tree()` - Factory function for creating optimized trees - Generic type variables (`ContentT`, `ChildT`, `ResultT`) - Intelligent tree traversal and navigation algorithms - Performance optimization and auto-balancing **Specialized Trees**: - Semantic trees with AI-powered organization - Temporal trees with version control - Collaborative trees with conflict resolution - ML-enhanced trees with predictive optimization --- **Common Structures: Where Data Grows Into Intelligent Knowledge Trees** 🌳 Submodules ---------- .. toctree:: :maxdepth: 1 /autoapi/haive/core/common/structures/generic_tree/index /autoapi/haive/core/common/structures/tree/index /autoapi/haive/core/common/structures/tree_leaf/index