Spaces:
Sleeping
Sleeping
File size: 2,413 Bytes
06fd561 5b429a2 06fd561 5b429a2 06fd561 5b429a2 06fd561 5b429a2 06fd561 5b429a2 06fd561 5b429a2 06fd561 5b429a2 06fd561 5b429a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | import hashlib
import json
class LinguisticParser:
"""
Law XII Component: The Advanced Linguistic Ingestor
Provides SVOC (Subject-Verb-Object-Context) shattering for complex reasoning traces.
Maps thought processes to Torus trajectories.
"""
def __init__(self, m=256, k=4):
self.m = m
self.k = k
def _get_coord(self, text, fiber=2):
h = hashlib.sha256(text.encode()).digest()
coords = [h[i % len(h)] % self.m for i in range(self.k - 1)]
w = (fiber - sum(coords)) % self.m
return tuple(coords + [w])
def generate_reasoning_trace(self, goal):
"""
Decomposes a goal into a series of SVOC-structured thought atoms.
Creates a 'Reasoning Trace' as a sequence of manifold coordinates.
"""
print(f"\n--- [LINGUISTIC PARSER]: Generating Reasoning Trace for '{goal}' ---")
# In a production system, this would use a Dependency Parser (e.g., spaCy)
# Here we simulate the structural shattering of a thought process
trace = []
# Thought 1: Perception (Subject: TGI, Verb: Identify, Object: Goal)
t1 = f"TGI identifies goal: {goal}"
# Thought 2: Retrieval (Subject: Manifold, Verb: Search, Object: Knowledge)
t2 = f"Manifold searches Fiber 2 for context."
# Thought 3: Action (Subject: Engine, Verb: Execute, Object: Solution)
t3 = f"Engine executes Hamiltonian path to target."
thoughts = [t1, t2, t3]
for i, thought in enumerate(thoughts):
coord = self._get_coord(thought)
trace.append({
"step": i + 1,
"svoc": thought,
"coord": coord,
"fiber": 2
})
print(f" [TRACE STEP {i+1}]: {thought} -> @ {coord}")
return trace
def ingest_language_spec(self, lang_name, spec_text):
"""Standard shattering of linguistic rules."""
atoms = []
units = spec_text.split(". ")
for unit in units:
if unit.strip():
coord = self._get_coord(unit)
atoms.append({"data": unit.strip(), "fiber": 2, "coord": coord, "type": "linguistic_atom"})
return atoms
if __name__ == "__main__":
parser = LinguisticParser()
parser.generate_reasoning_trace("Optimize topological routing.")
|