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})