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| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| from typing import Dict, List, Any, Optional | |
| from enum import Enum | |
| class SignLevel(Enum): | |
| ICONIC = 1 | |
| INDEXICAL = 2 | |
| SYMBOLIC = 3 | |
| SEMANTIC = 4 | |
| class SemioticState: | |
| """ | |
| Represents the state of semiotic processing with sign and meaning information. | |
| """ | |
| def __init__( | |
| self, | |
| 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] | |
| ): | |
| self.sign_level = sign_level | |
| self.meaning_vector = meaning_vector | |
| self.context_relations = context_relations | |
| self.interpretation_confidence = interpretation_confidence | |
| self.sign_vector = sign_vector | |
| self.context_embedding = context_embedding | |
| self.semantic_relations = semantic_relations | |
| class SemioticNetworkBuilder: | |
| """Builds semiotic networks from input data, representing sign relationships.""" | |
| def __init__(self): | |
| self.relation_encoder = nn.Sequential( | |
| nn.Linear(768, 256), | |
| nn.ReLU(), | |
| nn.Linear(256, 128) | |
| ) | |
| self.graph_state = {} | |
| def construct(self, input_data: Dict[str, Any]) -> Dict[str, Any]: | |
| """ | |
| Construct a semiotic network from input data. | |
| Args: | |
| input_data: Dictionary containing sign and context information | |
| Returns: | |
| Dictionary containing the constructed semiotic network | |
| """ | |
| encoded_signs = self._encode_signs(input_data.get("signs", [])) | |
| context_embedding = self._process_context(input_data.get("context", {})) | |
| relations = self._build_relations(encoded_signs, context_embedding) | |
| return { | |
| "signs": encoded_signs, | |
| "context": context_embedding, | |
| "relations": relations, | |
| "meta_info": self._extract_meta_information(input_data) | |
| } | |
| def _encode_signs(self, signs: List[Any]) -> Dict[str, torch.Tensor]: | |
| """Encode individual signs into vector representations.""" | |
| encoded = {} | |
| for sign in signs: | |
| sign_tensor = torch.randn(768) # Placeholder for actual encoding | |
| encoded[str(sign)] = self.relation_encoder(sign_tensor) | |
| return encoded | |
| def _process_context(self, context: Dict[str, Any]) -> torch.Tensor: | |
| """Process context information into an embedding.""" | |
| # Placeholder implementation | |
| return torch.randn(128) | |
| def _build_relations(self, signs: Dict[str, torch.Tensor], context: torch.Tensor) -> Dict[str, float]: | |
| """Build relationships between signs in the context.""" | |
| relations = {} | |
| for sign1 in signs: | |
| for sign2 in signs: | |
| if sign1 != sign2: | |
| relation_strength = torch.cosine_similarity(signs[sign1], signs[sign2], dim=0) | |
| relations[f"{sign1}-{sign2}"] = float(relation_strength) | |
| return relations | |
| def _extract_meta_information(self, input_data: Dict[str, Any]) -> Dict[str, Any]: | |
| """Extract meta-information about the semiotic network.""" | |
| return { | |
| "network_density": len(input_data.get("signs", [])) / 100, | |
| "context_richness": len(input_data.get("context", {})) / 100 | |
| } | |
| class SignInterpreter: | |
| """Interprets semiotic networks to extract meaning and relationships.""" | |
| def __init__(self): | |
| self.interpretation_network = nn.Sequential( | |
| nn.Linear(128, 64), | |
| nn.ReLU(), | |
| nn.Linear(64, 32) | |
| ) | |
| def interpret(self, network: Dict[str, Any]) -> Dict[str, Any]: | |
| """ | |
| Interpret a semiotic network to extract meaningful patterns. | |
| Args: | |
| network: The semiotic network to interpret | |
| Returns: | |
| Dictionary containing interpretation results | |
| """ | |
| signs = network["signs"] | |
| relations = network["relations"] | |
| context = network["context"] | |
| interpreted_meanings = self._interpret_meanings(signs, context) | |
| relation_patterns = self._analyze_relations(relations) | |
| contextual_insights = self._extract_contextual_insights(context) | |
| return { | |
| "meanings": interpreted_meanings, | |
| "patterns": relation_patterns, | |
| "contextual_insights": contextual_insights | |
| } | |
| def _interpret_meanings(self, signs: Dict[str, torch.Tensor], context: torch.Tensor) -> Dict[str, Any]: | |
| """Extract meanings from signs in context.""" | |
| return {sign: {"salience": 0.8, "certainty": 0.7} for sign in signs} | |
| def _analyze_relations(self, relations: Dict[str, float]) -> Dict[str, float]: | |
| """Analyze patterns in sign relations.""" | |
| return {"coherence": 0.8, "complexity": 0.6} | |
| def _extract_contextual_insights(self, context: torch.Tensor) -> Dict[str, float]: | |
| """Extract insights from contextual information.""" | |
| return {"relevance": 0.75, "specificity": 0.65} | |
| class SignGenerator: | |
| """Generates new signs based on interpretations and patterns.""" | |
| def __init__(self): | |
| self.generator_network = nn.Sequential( | |
| nn.Linear(32, 64), | |
| nn.ReLU(), | |
| nn.Linear(64, 128) | |
| ) | |
| def create_signs(self, interpretation: Dict[str, Any]) -> Dict[str, Any]: | |
| """ | |
| Generate new signs based on interpretation. | |
| Args: | |
| interpretation: The interpretation to base generation on | |
| Returns: | |
| Dictionary containing generated signs and their properties | |
| """ | |
| meanings = interpretation["meanings"] | |
| patterns = interpretation["patterns"] | |
| generated = self._generate_from_patterns(patterns) | |
| refined = self._refine_generated_signs(generated, meanings) | |
| return { | |
| "signs": refined, | |
| "confidence": self._assess_generation_quality(refined) | |
| } | |
| def _generate_from_patterns(self, patterns: Dict[str, float]) -> List[torch.Tensor]: | |
| """Generate initial signs from observed patterns.""" | |
| return [torch.randn(128) for _ in range(3)] # Generate 3 new signs | |
| def _refine_generated_signs(self, signs: List[torch.Tensor], meanings: Dict[str, Any]) -> List[Dict[str, Any]]: | |
| """Refine generated signs based on existing meanings.""" | |
| return [{"vector": sign, "quality": 0.7} for sign in signs] | |
| def _assess_generation_quality(self, signs: List[Dict[str, Any]]) -> float: | |
| """Assess the quality of generated signs.""" | |
| return sum(sign["quality"] for sign in signs) / len(signs) | |
| class SemioticProcessor: | |
| def __init__(self): | |
| self.sign_encoder = nn.Sequential( | |
| nn.Linear(512, 256), | |
| nn.ReLU(), | |
| nn.Linear(256, 128) | |
| ) | |
| self.network_builder = SemioticNetworkBuilder() | |
| self.interpreter = SignInterpreter() | |
| self.generator = SignGenerator() | |
| async def process(self, input_data: Dict[str, Any]) -> SemioticState: | |
| # Build semiotic network | |
| network = self.network_builder.construct(input_data) | |
| # Interpret the network | |
| interpretation = self.interpreter.interpret(network) | |
| # Generate new signs if needed | |
| 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) | |
| def _requires_generation(self, interpretation: Dict[str, Any]) -> bool: | |
| """ | |
| Determine if new sign generation is required based on interpretation. | |
| Args: | |
| interpretation: The current interpretation state | |
| Returns: | |
| Boolean indicating if generation is needed | |
| """ | |
| patterns = interpretation.get("patterns", {}) | |
| return patterns.get("coherence", 0) < 0.5 or len(interpretation.get("meanings", {})) < 3 | |
| def _integrate_semiotic_state(self, interpretation: Dict[str, Any], generated_signs: Dict[str, Any]) -> SemioticState: | |
| """ | |
| Integrate interpretation and generated signs into a semiotic state. | |
| """ | |
| meaning_vector = np.random.rand(128) # Placeholder for actual meaning vector | |
| sign_vector = np.random.rand(128) # Placeholder for actual sign vector | |
| return SemioticState( | |
| sign_level=SignLevel.SEMANTIC, | |
| meaning_vector=meaning_vector, | |
| context_relations=interpretation.get("patterns", {}), | |
| interpretation_confidence=generated_signs.get("confidence", 0.5), | |
| sign_vector=sign_vector, | |
| context_embedding=np.random.rand(128), | |
| semantic_relations=interpretation.get("contextual_insights", {}) | |
| ) | |
| def _create_semiotic_state(self, interpretation: Dict[str, Any]) -> SemioticState: | |
| """Create a semiotic state from interpretation without generation.""" | |
| return self._integrate_semiotic_state(interpretation, {"confidence": 0.8}) | |