up

semiotic_processor.py

@@ -0,0 +1,244 @@
+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})
+

src/core/semiotic_processor.py

@@ -6,12 +6,16 @@ import torch
 import torch.nn as nn
 
 class SignLevel(Enum):
-    SYNTACTIC = "syntactic"
-    SEMANTIC = "semantic"
-    PRAGMATIC = "pragmatic"
+    """Enumeration of different semiotic sign levels."""
+    ICONIC = "iconic"      # Direct representation
+    INDEXICAL = "indexical"  # Causal relationship
+    SYMBOLIC = "symbolic"  # Arbitrary convention
+    SEMANTIC = "semantic"  # Meaning-based
+    PRAGMATIC = "pragmatic"  # Context-based
 
 @dataclass
 class SemioticState:
+    """Represents the current state of semiotic processing."""
     sign_level: SignLevel
     meaning_vector: np.ndarray
     context_relations: Dict[str, float]
@@ -20,24 +24,223 @@ class SemioticState:
     context_embedding: np.ndarray
     semantic_relations: Dict[str, float]
 
+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:
+    """Processes semiotic signs to extract and generate meaning."""
+    
     def __init__(self):
         self.sign_encoder = nn.Sequential(
-            nn.Linear(768, 256),
+            nn.Linear(768, 256),  # Using proper input size (768)
             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:
+    async def process(self, input_data: Dict[str, Any]) -> SemioticState:
+        """
+        Process input data to extract semiotic meaning and generate new signs.
+        
+        Args:
+            input_data: Dictionary containing sign and context information
+            
+        Returns:
+            SemioticState representing the processed state
+        """
+        # 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)
+        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})