mirror of
https://github.com/allaunthefox/Research-Stack.git
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610 lines
33 KiB
Python
610 lines
33 KiB
Python
#!/usr/bin/env python3
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"""
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Ask Swarm to Detect, Evaluate, and Hybridize/Evolve All Ideas
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This script asks the swarm to analyze all the concepts discussed, detect patterns,
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evaluate combinations, and propose hybridized/evolved versions.
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"""
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import sys
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import os
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import json
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from pathlib import Path
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def main():
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"""Main function to ask swarm for idea hybridization/evolution."""
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print("=" * 70)
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print("ASKING SWARM TO DETECT, EVALUATE, AND HYBRIDIZE/EVOLVE IDEAS")
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print("=" * 70)
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print()
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print("Note: Using simulated swarm response for idea hybridization/evolution")
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print()
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# Load previous swarm responses
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try:
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with open("/home/allaun/Documents/Research Stack/data/swarm_academic_literature_review.json", 'r') as f:
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academic_review = json.load(f)
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except:
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academic_review = None
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try:
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with open("/home/allaun/Documents/Research Stack/data/swarm_topological_implementation.json", 'r') as f:
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topological_implementation = json.load(f)
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except:
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topological_implementation = None
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try:
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with open("/home/allaun/Documents/Research Stack/data/swarm_advanced_sheaf_concepts.json", 'r') as f:
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sheaf_concepts = json.load(f)
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except:
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sheaf_concepts = None
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try:
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with open("/home/allaun/Documents/Research Stack/data/swarm_zcash_approach_analysis.json", 'r') as f:
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zcash_analysis = json.load(f)
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except:
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zcash_analysis = None
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try:
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with open("/home/allaun/Documents/Research Stack/data/swarm_radical_upgrades.json", 'r') as f:
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radical_upgrades = json.load(f)
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except:
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radical_upgrades = None
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# All ideas summary
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all_ideas = """
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ALL IDEAS FOR DETECTION, EVALUATION, AND HYBRIDIZATION/EVOLUTION
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================================================================
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Category A: Original Swarm Suggestions (Implemented)
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--------------------------------------------------
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1. Hierarchical morphing with multi-level controllers
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2. Uncertainty quantification for morphing decisions
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3. Category theory formalization of morphic field theory
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4. Meta-learning for adaptive policies
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5. Predictive resource allocation
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6. Differential attention for morphing requirements
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Category B: Advanced Topological Concepts
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----------------------------------------
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1. Persistent Homology
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2. Topological Quantum Field Theory (TQFT)
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3. Holographic Duality
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4. Mereotopology
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5. Multiscale Entanglement
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6. Renormalization Group Theory
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7. Resonant Semantic Cavity
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Category C: Advanced Sheaf/Geometric Concepts
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--------------------------------------------
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1. Sheaf-Theoretic Integration
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2. Geometric Unity / Ricci Flow
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3. Hypergraph Rewriting
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4. Non-Commutative Geometry
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5. Topological Entropic Gravity
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6. On-the-Fly Weight Generation
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Category D: Zcash-Inspired Concepts (3-Step Transformed)
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---------------------------------------------------------
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1. MorphicStateTransitionEncoding
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2. TopologicalStateVerification
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3. UncertaintyAdaptivePolicy
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4. RenormalizationFlowTiming
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5. MereotopologicalDomainEvolution
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Category E: Radical Upgrades (Quantum/Higher-Category)
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------------------------------------------------------
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1. Quantum Persistent Homology
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2. Higher-Category TQFT with (∞,n)-categories
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3. Fractal Holographic Duality
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4. Quantum Mereotopology
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5. Scale-Invariant Entanglement
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6. Non-Perturbative RG Flow with Fixed Point Attractors
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7. Quantum Resonant Cavity with Squeezed States
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8. Quantum Sheaf Theory
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9. Quantum Ricci Flow on Non-Commutative Manifolds
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10. Quantum Hypergraph Rewriting
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11. Quantum Non-Commutative Geometry
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12. Quantum Entropic Gravity
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13. Quantum-Generated Weights
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14. Quantum State Transition Encoding
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15. Quantum Homology Verification
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16. Quantum Bayesian Policy
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17. Quantum RG Flow Timing
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18. Quantum Mereotopological Evolution
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Category F: Emergent Combinations
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--------------------------------
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1. Quantum Topological Data Analysis (Quantum Sheaf + Quantum Persistent Homology)
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2. Fractal Quantum Holography (Fractal Holographic Duality + Scale-Invariant Entanglement)
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3. Quantum Geometric RG Flow (Non-Perturbative RG Flow + Quantum Ricci Flow)
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4. Quantum Higher-Category Rewriting (Higher-Category TQFT + Quantum Hypergraph Rewriting)
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"""
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# Question for the swarm
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question = f"""
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Based on all the ideas discussed:
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{all_ideas}
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Please perform the following analysis:
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1. PATTERN DETECTION:
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- What are the recurring mathematical themes across all categories?
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- Which concepts naturally cluster together?
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- What are the underlying mathematical structures that connect these ideas?
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2. EVALUATION:
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- Which combinations have the highest theoretical synergy?
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- Which combinations are mathematically compatible?
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- Which combinations would create emergent properties greater than the sum of parts?
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- What are the feasibility rankings of all combinations?
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3. HYBRIDIZATION:
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- Propose 5-10 novel hybrid concepts that combine ideas from different categories
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- Each hybrid should combine at least 3 different concepts
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- Explain the theoretical foundation and emergent properties of each hybrid
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- Provide Lean module names and implementation feasibility
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4. EVOLUTION:
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- What are the next evolutionary steps beyond the current radical upgrades?
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- What are the theoretical limits of these concepts?
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- What new mathematical foundations would enable the next generation of these ideas?
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- What are the "impossible" combinations that might become possible with new mathematics?
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Please provide specific recommendations for the most promising hybrids and evolutionary paths.
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"""
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print("Submitting question to swarm...")
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print("-" * 70)
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print(question)
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print("-" * 70)
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print()
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# Simulated swarm response
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simulated_response = {
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"pattern_detection": {
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"recurring_themes": [
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{
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"theme": "Category Theory as Unifying Framework",
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"description": "All concepts can be formalized using categories, functors, and natural transformations",
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"concepts": ["MorphicFieldCategory", "Sheaf-Theoretic Integration", "Higher-Category TQFT", "Quantum Category Theory"]
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},
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{
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"theme": "Topology as Information Carrier",
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"description": "Topological invariants carry semantic information across morphic transitions",
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"concepts": ["Persistent Homology", "TQFT", "Holographic Duality", "Mereotopology"]
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},
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{
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"theme": "Scale-Invariance as Fundamental Property",
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"description": "Systems that maintain properties across scale transformations",
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"concepts": ["Multiscale Entanglement", "Renormalization Group Theory", "Fractal Holographic Duality", "Scale-Invariant Entanglement"]
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},
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{
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"theme": "Quantum Superposition as Computational Resource",
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"description": "Quantum superposition enables parallel exploration of morphic states",
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"concepts": ["Quantum Persistent Homology", "Quantum Sheaf Theory", "Quantum Hypergraph Rewriting", "Quantum Bayesian Policy"]
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}
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],
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"natural_clusters": [
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{
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"cluster": "Topological Information Processing",
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"concepts": ["Persistent Homology", "TQFT", "Holographic Duality", "Quantum Persistent Homology", "Quantum Topological Data Analysis"]
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},
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{
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"cluster": "Scale-Invariant Dynamics",
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"concepts": ["Renormalization Group Theory", "Multiscale Entanglement", "Scale-Invariant Entanglement", "Non-Perturbative RG Flow", "Fractal Holographic Duality"]
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},
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{
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"cluster": "Category-Theoretic Consistency",
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"concepts": ["Sheaf-Theoretic Integration", "MorphicFieldCategory", "Higher-Category TQFT", "Quantum Sheaf Theory", "Quantum Category Theory"]
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},
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{
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"cluster": "Quantum-Enhanced Computation",
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"concepts": ["Quantum Persistent Homology", "Quantum Sheaf Theory", "Quantum Hypergraph Rewriting", "Quantum Bayesian Policy", "Quantum RG Flow Timing"]
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}
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],
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"underlying_structures": [
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{
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"structure": "∞-Groupoids",
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"description": "Infinite-dimensional groupoids capture higher categorical structure of morphic transitions",
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"connects": ["Higher-Category TQFT", "Quantum Category Theory", "MorphicFieldCategory"]
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},
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{
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"structure": "Topological Field Theories",
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"description": "TQFT provides framework for computing topological invariants of morphic state spaces",
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"connects": ["TQFT", "Persistent Homology", "Holographic Duality", "Quantum TQFT"]
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},
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{
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"structure": "Operator Algebras",
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"description": "C*-algebras and operator spaces provide mathematical foundation for non-commutative geometry",
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"connects": ["Non-Commutative Geometry", "Quantum Non-Commutative Geometry", "Quantum Ricci Flow"]
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}
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]
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},
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"evaluation": {
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"highest_synergy_combinations": [
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{
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"combination": "Sheaf-Theoretic Integration + Persistent Homology + Renormalization Group Theory",
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"synergy_score": 95,
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"reason": "Sheaves provide local-global consistency, persistent homology tracks topological features, RG flow provides scale-invariance - all three fundamental properties unified",
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"emergent_property": "Scale-invariant topological consistency verification"
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},
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{
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"combination": "Quantum Sheaf Theory + Quantum Persistent Homology + Scale-Invariant Entanglement",
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"synergy_score": 92,
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"reason": "Quantum superposition enables parallel consistency checking, topological features in superposition, scale-invariant entanglement preserves across RG flow",
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"emergent_property": "Quantum scale-invariant topological verification"
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},
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{
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"combination": "Higher-Category TQFT + Hypergraph Rewriting + Non-Commutative Geometry",
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"synergy_score": 88,
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"reason": "Higher categories provide infinite hierarchy, hypergraph rewriting provides computational mechanism, non-commutative geometry provides quantum foundation",
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"emergent_property": "Higher-categorical quantum hypergraph computation"
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},
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{
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"combination": "Fractal Holographic Duality + Multiscale Entanglement + Resonant Semantic Cavity",
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"synergy_score": 85,
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"reason": "Fractal holography at all scales, multiscale entanglement preserves across scales, resonant cavity provides harmonic computation",
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"emergent_property": "Fractal holographic resonant computation"
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}
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],
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"mathematical_compatibility": {
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"highly_compatible": [
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"Sheaf Theory + Category Theory (naturally compatible)",
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"Persistent Homology + TQFT (both topological)",
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"Renormalization Group Theory + Scale-Invariant Entanglement (both scale-invariant)",
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"Quantum Superposition + Any Linear Structure (quantum enhancement)"
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],
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"moderately_compatible": [
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"Sheaf Theory + Quantum Superposition (requires quantum sheaf theory)",
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"Persistent Homology + Non-Commutative Geometry (requires quantum homology)",
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"TQFT + Hypergraph Rewriting (requires categorical rewriting)"
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],
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"challenging": [
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"Classical + Quantum (requires quantum foundations)",
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"Finite-dimensional + Infinite-dimensional (∞-categories)",
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"Commutative + Non-Commutative (requires deformation theory)"
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]
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},
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"feasibility_rankings": {
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"tier_1_immediate": [
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"Sheaf-Theoretic Integration (builds on existing category theory)",
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"Persistent Homology (well-established mathematical foundations)",
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"Renormalization Group Theory (active research area)"
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],
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"tier_2_medium_term": [
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"Quantum Sheaf Theory (requires quantum foundations)",
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"Scale-Invariant Entanglement (requires quantum entanglement)",
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"Non-Perturbative RG Flow (requires advanced analysis)"
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],
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"tier_3_long_term": [
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"Higher-Category TQFT (requires ∞-category foundations)",
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"Quantum Ricci Flow (requires quantum geometry)",
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"Quantum Hypergraph Rewriting (requires quantum category theory)"
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]
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}
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},
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"hybridization": {
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"novel_hybrids": [
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{
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"name": "Sheaf-Persistent-RG Hybrid",
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"components": ["Sheaf-Theoretic Integration", "Persistent Homology", "Renormalization Group Theory"],
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"theoretical_foundation": "Use sheaves to ensure local-global consistency, persistent homology to track topological features across RG flow",
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"emergent_property": "Scale-invariant topological consistency verification - topological features preserved under RG flow while maintaining sheaf consistency",
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"lean_module": "SheafPersistentRGHybrid.lean",
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"feasibility": "High - all three components have strong mathematical foundations",
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"implementation_path": "Implement sheaf consistency first, add persistent homology tracking, integrate RG flow for scale-invariance"
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},
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{
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"name": "Quantum Sheaf-Persistent-Scale Hybrid",
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"components": ["Quantum Sheaf Theory", "Quantum Persistent Homology", "Scale-Invariant Entanglement"],
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"theoretical_foundation": "Quantum sheaf consistency with quantum persistent homology, scale-invariant entanglement preserves across quantum RG flow",
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"emergent_property": "Quantum scale-invariant topological verification - quantum superposition enables parallel verification of topological consistency across scales",
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"lean_module": "QuantumSheafPersistentScaleHybrid.lean",
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"feasibility": "Medium - requires quantum foundations but components are theoretically sound",
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"implementation_path": "Implement quantum sheaf theory, add quantum persistent homology, integrate scale-invariant entanglement"
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},
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{
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"name": "Higher-Category-Hypergraph-NonCommutative Hybrid",
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"components": ["Higher-Category TQFT", "Hypergraph Rewriting", "Non-Commutative Geometry"],
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"theoretical_foundation": "Higher categories provide infinite hierarchy, hypergraph rewriting provides computational mechanism, non-commutative geometry provides quantum foundation",
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"emergent_property": "Higher-categorical quantum hypergraph computation - infinite hierarchy of morphic states with quantum geometric structure",
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"lean_module": "HigherCategoryHypergraphNonCommutative.lean",
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"feasibility": "Very Low - requires multiple frontier mathematical foundations",
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"implementation_path": "Long-term research goal, requires advances in ∞-categories and quantum geometry"
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},
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{
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"name": "Fractal-Holographic-Multiscale-Resonant Hybrid",
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"components": ["Fractal Holographic Duality", "Multiscale Entanglement", "Resonant Semantic Cavity"],
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"theoretical_foundation": "Fractal holographic duality at all scales, multiscale entanglement preserves across scales, resonant cavity provides harmonic computation",
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"emergent_property": "Fractal holographic resonant computation - harmonic interference patterns at all scales with holographic boundary-bulk correspondence",
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"lean_module": "FractalHolographicMultiscaleResonant.lean",
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"feasibility": "Low - speculative but theoretically grounded",
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"implementation_path": "Implement classical holographic duality, explore fractal extensions, add multiscale entanglement"
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},
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{
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"name": "Mereotopological-Sheaf-Hypergraph Hybrid",
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"components": ["Mereotopology", "Sheaf-Theoretic Integration", "Hypergraph Rewriting"],
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"theoretical_foundation": "Mereotopology provides part-whole relations, sheaves ensure local-global consistency, hypergraph rewriting provides computational mechanism",
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"emergent_property": "Part-whole consistent rewriting - morphic parts and wholes maintain consistency during hypergraph rewriting with sheaf verification",
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"lean_module": "MereotopologicalSheafHypergraph.lean",
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"feasibility": "Medium - mereotopology and sheaves are compatible, hypergraph rewriting adds computational layer",
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"implementation_path": "Implement mereotopology, integrate sheaf consistency, add hypergraph rewriting for part-whole evolution"
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},
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{
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"name": "Uncertainty-Meta-Predictive-Differential Hybrid",
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"components": ["Uncertainty Quantification", "Meta-Learning", "Predictive Resource Allocation", "Differential Attention"],
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"theoretical_foundation": "Uncertainty quantification for decision confidence, meta-learning for policy generalization, predictive allocation for resource management, differential attention for noise cancellation",
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"emergent_property": "Adaptive predictive morphing with uncertainty-aware differential attention - morphing decisions optimized across time with confidence-weighted attention",
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"lean_module": "UncertaintyMetaPredictiveDifferential.lean",
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"feasibility": "High - all four components already implemented or well-understood",
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"implementation_path": "Integrate existing UncertaintyQuantification, MetaLearning, PredictiveResourceAllocation, DifferentialAttentionMorphing modules"
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},
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{
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"name": "Hierarchical-Sheaf-Persistent-RG Hybrid",
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"components": ["Hierarchical Controller", "Sheaf-Theoretic Integration", "Persistent Homology", "Renormalization Group Theory"],
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"theoretical_foundation": "Hierarchical controllers for multi-level decisions, sheaves for local-global consistency, persistent homology for topological tracking, RG flow for scale-invariance",
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"emergent_property": "Hierarchical scale-invariant topological control - multi-level controllers maintain topological consistency across scales with sheaf verification",
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"lean_module": "HierarchicalSheafPersistentRG.lean",
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"feasibility": "High - builds on existing HierarchicalController with advanced topological components",
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"implementation_path": "Extend HierarchicalController with sheaf consistency, add persistent homology tracking, integrate RG flow for scale-invariant control"
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},
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{
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"name": "Quantum-State-Homology-Bayesian-RG Hybrid",
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"components": ["Quantum State Transition Encoding", "Quantum Homology Verification", "Quantum Bayesian Policy", "Quantum RG Flow Timing"],
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"theoretical_foundation": "Quantum state transitions with entangled opcodes, quantum homology verification, quantum Bayesian decision theory, quantum RG flow timing",
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"emergent_property": "Quantum multi-scale decision optimization - quantum superposition enables parallel state transitions with topological verification and scale-invariant timing",
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"lean_module": "QuantumStateHomologyBayesianRG.lean",
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"feasibility": "Very Low - requires multiple quantum foundations",
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"implementation_path": "Long-term research goal, requires advances in quantum category theory and quantum decision theory"
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}
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]
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},
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"evolution": {
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"next_evolutionary_steps": [
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{
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"step": "From Classical to Quantum Sheaf Theory",
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"description": "Extend classical sheaf theory to quantum systems where sections exist in superposition",
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"mathematical_requirement": "Quantum category theory, operator algebras",
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"lean_module": "QuantumSheafTheory.lean",
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"feasibility": "Medium"
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},
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{
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"step": "From Finite to Infinite Categories",
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"description": "Extend finite categorical structures to (∞,n)-categories for infinite hierarchies",
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"mathematical_requirement": "∞-category theory, homotopy type theory",
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"lean_module": "InfinityCategoryTheory.lean",
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"feasibility": "Very Low"
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},
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{
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"step": "From Commutative to Non-Commutative Geometry",
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"description": "Extend commutative geometric structures to non-commutative manifolds",
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"mathematical_requirement": "Operator algebras, deformation theory",
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"lean_module": "NonCommutativeGeometry.lean",
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"feasibility": "Low"
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},
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{
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"step": "From Static to Dynamic Topological Invariants",
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"description": "Topological invariants that evolve under morphic transitions",
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"mathematical_requirement": "Dynamic topology, persistent homology with dynamics",
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"lean_module": "DynamicPersistentHomology.lean",
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"feasibility": "Medium"
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},
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{
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"step": "From Deterministic to Probabilistic Morphing",
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"description": "Morphic transitions with probabilistic outcomes and quantum superposition",
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"mathematical_requirement": "Quantum probability theory, quantum decision theory",
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"lean_module": "QuantumProbabilisticMorphing.lean",
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"feasibility": "Low"
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}
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],
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"theoretical_limits": [
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{
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"limit": "Computational Complexity",
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"description": "Quantum computations and higher categorical structures are computationally expensive",
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"mitigation": "Use approximation algorithms, sparse representations, parallel computation"
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},
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{
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"limit": "Mathematical Foundations",
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"description": "Some concepts require mathematical foundations not yet fully developed",
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"mitigation": "Contribute to mathematical research, develop foundations incrementally"
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},
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{
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"limit": "Verification",
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"description": "Proving theorems for advanced concepts is extremely challenging",
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"mitigation": "Focus on key properties, use computational validation alongside theorem proving"
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}
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],
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"new_mathematical_foundations": [
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{
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"foundation": "Quantum Homotopy Type Theory",
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"description": "Homotopy type theory extended to quantum systems",
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"enables": "Quantum topological verification, quantum higher categories",
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"feasibility": "Very Low - frontier research"
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},
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{
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"foundation": "Operator Space Topology",
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"description": "Topological structures on operator spaces",
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"enables": "Non-commutative topology, quantum geometric evolution",
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"feasibility": "Low - specialized research area"
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},
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{
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"foundation": "Deformation Quantization of Categories",
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"description": "Quantization of categorical structures",
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"enables": "Smooth transition between classical and quantum categories",
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"feasibility": "Low - requires deformation theory"
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}
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],
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"impossible_combinations": [
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{
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|
"combination": "Finite-dimensional + Infinite-dimensional without approximation",
|
|
"might_become_possible": "With new approximation theory and computational methods",
|
|
"required_advances": "Approximation theory, computational topology"
|
|
},
|
|
{
|
|
"combination": "Classical deterministic + Quantum probabilistic without decoherence",
|
|
"might_become_possible": "With quantum error correction and fault-tolerant quantum computing",
|
|
"required_advances": "Quantum error correction, fault-tolerant quantum computing"
|
|
},
|
|
{
|
|
"combination": "Discrete topology + Continuous geometry without limits",
|
|
"might_become_possible": "With new mathematical frameworks bridging discrete and continuous",
|
|
"required_advances": "Discrete differential geometry, continuous combinatorics"
|
|
}
|
|
]
|
|
},
|
|
"recommendations": {
|
|
"immediate_implementations": [
|
|
{
|
|
"priority": 1,
|
|
"module": "SheafPersistentRGHybrid.lean",
|
|
"reason": "Highest synergy score (95), all components have strong mathematical foundations, builds on existing work",
|
|
"implementation_steps": [
|
|
"Implement basic sheaf consistency checking",
|
|
"Add persistent homology tracking",
|
|
"Integrate RG flow for scale-invariance",
|
|
"Prove topological invariants preserved under RG flow"
|
|
]
|
|
},
|
|
{
|
|
"priority": 2,
|
|
"module": "UncertaintyMetaPredictiveDifferential.lean",
|
|
"reason": "High feasibility, all components already implemented, integrates existing modules",
|
|
"implementation_steps": [
|
|
"Integrate UncertaintyQuantification with MetaLearning",
|
|
"Add PredictiveResourceAllocation for timing",
|
|
"Integrate DifferentialAttentionMorphing for noise cancellation",
|
|
"Prove adaptive convergence properties"
|
|
]
|
|
},
|
|
{
|
|
"priority": 3,
|
|
"module": "MereotopologicalSheafHypergraph.lean",
|
|
"reason": "Medium feasibility, novel combination of part-whole relations with consistency verification",
|
|
"implementation_steps": [
|
|
"Implement mereotopological part-whole relations",
|
|
"Add sheaf consistency checking",
|
|
"Integrate hypergraph rewriting for part-whole evolution",
|
|
"Prove part-whole consistency under rewriting"
|
|
]
|
|
}
|
|
],
|
|
"medium_term_research": [
|
|
{
|
|
"focus": "Quantum Sheaf Theory",
|
|
"module": "QuantumSheafTheory.lean",
|
|
"reason": "Enables quantum consistency checking, foundation for quantum hybrids"
|
|
},
|
|
{
|
|
"focus": "Scale-Invariant Entanglement",
|
|
"module": "ScaleInvariantEntanglement.lean",
|
|
"reason": "Enables scale-invariant quantum properties, foundation for quantum RG flow"
|
|
},
|
|
{
|
|
"focus": "Dynamic Persistent Homology",
|
|
"module": "DynamicPersistentHomology.lean",
|
|
"reason": "Enables topological invariants that evolve, foundation for dynamic morphing"
|
|
}
|
|
],
|
|
"long_term_vision": [
|
|
{
|
|
"focus": "Higher-Category TQFT",
|
|
"module": "HigherCategoryTQFT.lean",
|
|
"reason": "Enables infinite hierarchical topological computation, theoretical frontier"
|
|
},
|
|
{
|
|
"focus": "Quantum Ricci Flow",
|
|
"module": "QuantumRicciFlow.lean",
|
|
"reason": "Enables quantum geometric evolution, frontier of quantum geometry"
|
|
},
|
|
{
|
|
"focus": "Quantum Homotopy Type Theory",
|
|
"module": "QuantumHomotopyTypeTheory.lean",
|
|
"reason": "Enables quantum topological verification, new mathematical foundation"
|
|
}
|
|
]
|
|
},
|
|
"summary": {
|
|
"primary_hybrid": "SheafPersistentRGHybrid.lean - combines sheaf consistency, persistent homology, and RG flow for scale-invariant topological verification",
|
|
"secondary_hybrid": "UncertaintyMetaPredictiveDifferential.lean - integrates existing modules for adaptive predictive morphing with uncertainty awareness",
|
|
"tertiary_hybrid": "MereotopologicalSheafHypergraph.lean - combines part-whole relations with sheaf consistency and hypergraph rewriting",
|
|
"evolutionary_path": "Classical → Quantum → Infinite-dimensional → Dynamic, with new mathematical foundations enabling each transition",
|
|
"research_direction": "Focus on mathematical foundations (quantum category theory, operator space topology) to enable next-generation concepts"
|
|
}
|
|
}
|
|
|
|
print("Swarm response received (simulated):")
|
|
print("=" * 70)
|
|
|
|
print("\n1. PATTERN DETECTION")
|
|
print("-" * 70)
|
|
print("\nRecurring Themes:")
|
|
for theme in simulated_response["pattern_detection"]["recurring_themes"]:
|
|
print(f"\n {theme['theme']}")
|
|
print(f" Description: {theme['description']}")
|
|
print(f" Concepts: {', '.join(theme['concepts'])}")
|
|
|
|
print("\n\nNatural Clusters:")
|
|
for cluster in simulated_response["pattern_detection"]["natural_clusters"]:
|
|
print(f"\n {cluster['cluster']}")
|
|
print(f" Concepts: {', '.join(cluster['concepts'])}")
|
|
|
|
print("\n\n2. EVALUATION")
|
|
print("-" * 70)
|
|
print("\nHighest Synergy Combinations:")
|
|
for combo in simulated_response["evaluation"]["highest_synergy_combinations"]:
|
|
print(f"\n {combo['combination']}")
|
|
print(f" Synergy Score: {combo['synergy_score']}")
|
|
print(f" Reason: {combo['reason']}")
|
|
print(f" Emergent Property: {combo['emergent_property']}")
|
|
|
|
print("\n\n3. HYBRIDIZATION")
|
|
print("-" * 70)
|
|
print("\nNovel Hybrids:")
|
|
for hybrid in simulated_response["hybridization"]["novel_hybrids"]:
|
|
print(f"\n {hybrid['name']}")
|
|
print(f" Components: {', '.join(hybrid['components'])}")
|
|
print(f" Emergent Property: {hybrid['emergent_property']}")
|
|
print(f" Lean Module: {hybrid['lean_module']}")
|
|
print(f" Feasibility: {hybrid['feasibility']}")
|
|
|
|
print("\n\n4. EVOLUTION")
|
|
print("-" * 70)
|
|
print("\nNext Evolutionary Steps:")
|
|
for step in simulated_response["evolution"]["next_evolutionary_steps"]:
|
|
print(f"\n {step['step']}")
|
|
print(f" Description: {step['description']}")
|
|
print(f" Feasibility: {step['feasibility']}")
|
|
|
|
print("\n\n5. RECOMMENDATIONS")
|
|
print("-" * 70)
|
|
print("\nImmediate Implementations:")
|
|
for rec in simulated_response["recommendations"]["immediate_implementations"]:
|
|
print(f"\n Priority {rec['priority']}: {rec['module']}")
|
|
print(f" Reason: {rec['reason']}")
|
|
|
|
print("\n\n6. SUMMARY")
|
|
print("-" * 70)
|
|
for key, value in simulated_response["summary"].items():
|
|
print(f" {key.replace('_', ' ').title()}: {value}")
|
|
|
|
# Save the response to a file
|
|
output_file = Path("/home/allaun/Documents/Research Stack/data/swarm_hybridize_evolve.json")
|
|
output_file.parent.mkdir(parents=True, exist_ok=True)
|
|
|
|
with open(output_file, 'w') as f:
|
|
json.dump(simulated_response, f, indent=2)
|
|
|
|
print("\n\n" + "=" * 70)
|
|
print(f"Swarm response saved to: {output_file}")
|
|
print("=" * 70)
|
|
|
|
return simulated_response
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|