haive.core.common.structures

🌳 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:

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")

Submodules