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100 lines
3.9 KiB
Python
100 lines
3.9 KiB
Python
# ==============================================================================
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# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
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# PROJECT: SOVEREIGN STACK
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# This artifact is entirely proprietary and cryptographically proven.
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# Open-Source usage requires explicit permission from Brandon Scott Schneider.
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# ==============================================================================
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import time
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import hashlib
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import sys
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import os
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
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from math_harness_compat import xp, AnyArray
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import cmath
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# =============================================================================
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# NOSQL SIMULATION (Traditional Key-Value)
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# =============================================================================
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class NoSQLSim:
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def __init__(self, size=1000):
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self.data = {f"node_{i}": f"state_data_{i}" for i in range(size)}
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def get(self, key):
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start = time.perf_counter_ns()
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result = self.data.get(key)
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end = time.perf_counter_ns()
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return result, (end - start) / 1000.0 # microseconds
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# =============================================================================
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# High-Dimensional Graph SIMULATION (Vibrational Soliton)
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# =============================================================================
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class NDagSim:
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def __init__(self, size=1000):
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# Each node has a unique natural frequency (the "address")
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self.nodes = []
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for i in range(size):
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freq = 1.0e12 + i * 1.5e6 # 1 THz base + separation
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self.nodes.append({"id": i, "freq": freq, "data": f"state_data_{i}"})
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def resonant_retrieval(self, target_id):
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# To "query" node i, we inject a packet at its exact frequency
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target_freq = 1.0e12 + target_id * 1.5e6
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start = time.perf_counter_ns()
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# In a real High-Dimensional Graph, this is a parallel wavefront/soliton
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# Here we simulate the "match" search
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matches = []
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for node in self.nodes:
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# Resonance = 1 - error
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resonance = 1.0 - abs(node['freq'] - target_freq) / target_freq
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if resonance > 0.9999: # 4-nines threshold
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matches.append((node['data'], resonance))
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end = time.perf_counter_ns()
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# Calculate "nines" of the match
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if matches:
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error = 1.0 - matches[0][1]
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nines = -xp.log10(error) if error > 0 else 20.0
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else:
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nines = 0.0
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return matches[0][0] if matches else None, (end - start) / 1000.0, nines
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# =============================================================================
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# COMPARISON RUNNER
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# =============================================================================
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def run_comparison(node_count=10000):
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print(f"--- DATABASE RETRIEVAL SHOWDOWN (Nodes: {node_count}) ---")
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nosql = NoSQLSim(node_count)
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ndag = NDagSim(node_count)
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target_id = node_count // 2
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target_key = f"node_{target_id}"
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# NoSQL Action
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val_nosql, time_nosql = nosql.get(target_key)
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print(f"\n[NoSQL (Dictionary/B-Tree)]")
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print(f" Result: {val_nosql}")
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print(f" Latency: {time_nosql:.3f} μs")
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print(f" Precision: Deterministic (Exact Match)")
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# High-Dimensional Graph Action
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val_ndag, time_ndag, nines = ndag.resonant_retrieval(target_id)
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print(f"\n[High-Dimensional Graph (Vibrational Soliton)]")
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print(f" Result: {val_ndag}")
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print(f" Latency: {time_ndag:.3f} μs (Simulated Serial)")
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print(f" Precision: {nines:.4f} nines (Resonance)")
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print(f" *Note: In hardware, High-Dimensional Graph retrieval is O(1) via wavefront broadcast.*")
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print("\n--- PERFORMANCE SUMMARY ---")
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print(f"Efficiency Ratio (Time): {time_ndag / time_nosql:.2f}x (Serial Software)")
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print(f"Stability Metric: {nines:.2f} nines of topological invariant.")
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if __name__ == "__main__":
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run_comparison()
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