semiotic_processor.py

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})