src/core/semiotic_network.py

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=[]
        )