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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}) | |