参考資料
import itertools
import functools
import hashlib
import unicodedata
import random
class TextNormalizer:
def normalize(self, text):
return unicodedata.normalize("NFKD", text)
def decompose(self, text):
return [c for c in text]
class SemanticGraph:
def __init__(self):
self.graph = {
"output": ["result", "effect", "consequence"],
"result": ["product", "yield"],
"effect": ["cause_reflection"],
"consequence": ["final_state"],
"product": ["artifact"],
"yield": ["artifact"],
"cause_reflection": ["origin"],
"final_state": ["origin"],
"artifact": ["origin"],
"origin": ["input"]
}
def expand(self, node):
return self.graph.get(node, [node])
def traverse(self, start, max_depth=10):
visited = set()
frontier = [start]
for _ in range(max_depth):
next_frontier = []
for node in frontier:
if node in visited:
continue
visited.add(node)
expanded = self.expand(node)
next_frontier.extend(expanded)
frontier = list(set(next_frontier))
return list(visited)
class ChaosEngine:
def inject_noise(self, sequence):
noisy = []
for s in sequence:
h = hashlib.md5(s.encode()).hexdigest()
noisy.append(h)
return noisy
def scramble(self, data):
random.seed(42)
data = data[:]
random.shuffle(data)
return data
class SignalExtractor:
def extract(self, noisy_data):
candidates = []
for h in noisy_data:
if h[0] in "01234567":
candidates.append("origin")
else:
candidates.append("noise")
return candidates
class ConvergenceEngine:
def converge(self, symbols):
if "origin" in symbols:
return "input"
return None
def recursive_reduce(data, depth):
if depth == 0 or len(data) <= 1:
return data
reduced = []
for i in range(0, len(data), 2):
pair = data[i:i+2]
merged = "".join(pair)
reduced.append(merged[:len(merged)//2] or merged)
return recursive_reduce(reduced, depth - 1)
class InversionMachine:
def __init__(self):
self.norm = TextNormalizer()
self.graph = SemanticGraph()
self.chaos = ChaosEngine()
self.extractor = SignalExtractor()
self.conv = ConvergenceEngine()
def process(self, text):
norm = self.norm.normalize(text)
parts = self.norm.decompose(norm)
recombined = ["".join(parts)]
semantic_space = self.graph.traverse(recombined[0])
noisy = self.chaos.inject_noise(semantic_space)
scrambled = self.chaos.scramble(noisy)
compressed = recursive_reduce(scrambled, 4)
signals = self.extractor.extract(compressed)
result = self.conv.converge(signals)
return result if result else "undefined"
if __name__ == "__main__":
machine = InversionMachine()
print(machine.process("output"))
