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from dataclasses import dataclass
from typing import Dict, List, Optional, Any
import networkx as nx
import numpy as np
class MeaningExtractor:
def process(self, input_data: Dict[str, Any]) -> List:
# Placeholder implementation
return []
class ContextAnalyzer:
def analyze(self, input_data: Dict[str, Any]) -> List:
# Placeholder implementation
return []
@dataclass
class SignNode:
id: str
level: str
meaning_vector: np.ndarray
context: Dict[str, float]
relations: List[str]
class SemioticNetworkBuilder:
def __init__(self):
self.graph = nx.MultiDiGraph()
self.meaning_extractor = MeaningExtractor()
self.context_analyzer = ContextAnalyzer()
def construct(self, input_data: Dict[str, Any]) -> nx.MultiDiGraph:
signs = self._extract_signs(input_data)
self._build_nodes(signs)
self._establish_relations()
return self._optimize_network()
def _extract_signs(self, input_data: Dict[str, Any]) -> List[SignNode]:
meanings = self.meaning_extractor.process(input_data)
contexts = self.context_analyzer.analyze(input_data)
return [self._create_sign_node(m, c) for m, c in zip(meanings, contexts)]
def _build_nodes(self, signs: List[SignNode]) -> None:
# Placeholder implementation
pass
def _establish_relations(self) -> None:
# Placeholder implementation
pass
def _optimize_network(self) -> nx.MultiDiGraph:
# Placeholder implementation
return self.graph
def _create_sign_node(self, meaning, context) -> SignNode:
# Placeholder implementation
return SignNode(
id="placeholder",
level="",
meaning_vector=np.array([]),
context={},
relations=[]
)
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