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from enum import Enum
from dataclasses import dataclass
from typing import Dict, List, Optional, Any
import numpy as np
import torch
import torch.nn as nn
class SignLevel(Enum):
SYNTACTIC = "syntactic"
SEMANTIC = "semantic"
PRAGMATIC = "pragmatic"
@dataclass
class SemioticState:
sign_level: SignLevel
meaning_vector: np.ndarray
context_relations: Dict[str, float]
interpretation_confidence: float
sign_vector: np.ndarray
context_embedding: np.ndarray
semantic_relations: Dict[str, float]
class SemioticProcessor:
def __init__(self):
self.sign_encoder = nn.Sequential(
nn.Linear(768, 256),
nn.ReLU(),
nn.Linear(256, 128)
)
self.network_builder = SemioticNetworkBuilder()
self.interpreter = SignInterpreter()
self.generator = SignGenerator()
self.meaning_network = {}
def process_signs(self, input_data: Dict[str, Any]) -> SemioticState:
network = self.network_builder.construct(input_data)
interpretation = self.interpreter.interpret(network)
if self._requires_generation(interpretation):
generated_signs = self.generator.create_signs(interpretation)
return self._integrate_semiotic_state(interpretation, generated_signs)
return self._create_semiotic_state(interpretation) |