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reasoning/bayesian.py

@@ -1,187 +1,325 @@
-"""Bayesian reasoning implementation."""
+"""Advanced Bayesian reasoning for probabilistic analysis."""
 
 import logging
-from typing import Dict, Any, List
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
 import json
-import re
+from dataclasses import dataclass, field
+from enum import Enum
 from datetime import datetime
+import numpy as np
+from collections import defaultdict
 
 from .base import ReasoningStrategy
 
+@dataclass
+class BayesianHypothesis:
+    """Bayesian hypothesis with probabilities."""
+    name: str
+    prior: float
+    likelihood: float
+    posterior: float = 0.0
+    evidence: List[Dict[str, Any]] = field(default_factory=list)
+
 class BayesianReasoning(ReasoningStrategy):
-    """Implements Bayesian reasoning for probabilistic analysis."""
+    """
+    Advanced Bayesian reasoning that:
+    1. Generates hypotheses
+    2. Calculates prior probabilities
+    3. Updates with evidence
+    4. Computes posteriors
+    5. Provides probabilistic analysis
+    """
     
-    def __init__(self, prior_weight: float = 0.3):
-        self.prior_weight = prior_weight
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize Bayesian reasoning."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Configure Bayesian parameters
+        self.prior_weight = self.config.get('prior_weight', 0.3)
+        self.evidence_threshold = self.config.get('evidence_threshold', 0.1)
+        self.min_likelihood = self.config.get('min_likelihood', 0.01)
     
     async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Apply Bayesian reasoning to analyze probabilities and update beliefs.
+        
+        Args:
+            query: The input query to reason about
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing reasoning results and confidence scores
+        """
         try:
             # Generate hypotheses
             hypotheses = await self._generate_hypotheses(query, context)
             
-            # Calculate prior probabilities
+            # Calculate priors
             priors = await self._calculate_priors(hypotheses, context)
             
             # Update with evidence
-            posteriors = await self._update_with_evidence(hypotheses, priors, context)
+            posteriors = await self._update_with_evidence(
+                hypotheses,
+                priors,
+                context
+            )
             
-            # Generate final analysis
+            # Generate analysis
             analysis = await self._generate_analysis(posteriors, context)
             
             return {
-                "success": True,
-                "answer": analysis["conclusion"],
-                "hypotheses": hypotheses,
-                "priors": priors,
-                "posteriors": posteriors,
-                "confidence": analysis["confidence"],
-                "reasoning_path": analysis["reasoning_path"]
+                'answer': self._format_analysis(analysis),
+                'confidence': self._calculate_confidence(posteriors),
+                'hypotheses': hypotheses,
+                'priors': priors,
+                'posteriors': posteriors,
+                'analysis': analysis
             }
+            
         except Exception as e:
-            return {"success": False, "error": str(e)}
+            logging.error(f"Bayesian reasoning failed: {str(e)}")
+            return {
+                'error': f"Bayesian reasoning failed: {str(e)}",
+                'confidence': 0.0
+            }
     
-    async def _generate_hypotheses(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
-        prompt = f"""
-        Generate 3-4 hypotheses for this problem:
-        Query: {query}
-        Context: {json.dumps(context)}
-        
-        For each hypothesis:
-        1. [Statement]: Clear statement of the hypothesis
-        2. [Assumptions]: Key assumptions made
-        3. [Testability]: How it could be tested/verified
-        
-        Format as:
-        [H1]
-        Statement: ...
-        Assumptions: ...
-        Testability: ...
-        """
-        
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_hypotheses(response["answer"])
-
-    async def _calculate_priors(self, hypotheses: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, float]:
-        prompt = f"""
-        Calculate prior probabilities for these hypotheses:
-        Context: {json.dumps(context)}
-        
-        Hypotheses:
-        {json.dumps(hypotheses, indent=2)}
+    async def _generate_hypotheses(
+        self,
+        query: str,
+        context: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """Generate plausible hypotheses."""
+        hypotheses = []
         
-        For each hypothesis, estimate its prior probability (0-1) based on:
-        1. Alignment with known principles
-        2. Historical precedent
-        3. Domain expertise
+        # Extract key terms for hypothesis generation
+        terms = set(query.lower().split())
         
-        Format: [H1]: 0.XX, [H2]: 0.XX, ...
-        """
+        # Generate hypotheses based on context and terms
+        if 'options' in context:
+            # Use provided options as hypotheses
+            for option in context['options']:
+                hypotheses.append({
+                    'name': option,
+                    'description': f"Hypothesis based on option: {option}",
+                    'factors': self._extract_factors(option, terms)
+                })
+        else:
+            # Generate default hypotheses
+            hypotheses.extend([
+                {
+                    'name': 'primary',
+                    'description': "Primary hypothesis based on direct interpretation",
+                    'factors': self._extract_factors(query, terms)
+                },
+                {
+                    'name': 'alternative',
+                    'description': "Alternative hypothesis considering other factors",
+                    'factors': self._generate_alternative_factors(terms)
+                }
+            ])
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_probabilities(response["answer"])
-
-    async def _update_with_evidence(self, hypotheses: List[Dict[str, Any]], priors: Dict[str, float], 
-                                  context: Dict[str, Any]) -> Dict[str, float]:
-        prompt = f"""
-        Update probabilities with available evidence:
-        Context: {json.dumps(context)}
+        return hypotheses
+    
+    async def _calculate_priors(
+        self,
+        hypotheses: List[Dict[str, Any]],
+        context: Dict[str, Any]
+    ) -> Dict[str, float]:
+        """Calculate prior probabilities."""
+        priors = {}
         
-        Hypotheses and Priors:
-        {json.dumps(list(zip(hypotheses, priors.values())), indent=2)}
+        # Get historical data if available
+        history = context.get('history', {})
+        total_cases = sum(history.values()) if history else len(hypotheses)
         
-        Consider:
-        1. How well each hypothesis explains the evidence
-        2. Any new evidence from the context
-        3. Potential conflicts or support between hypotheses
+        for hypothesis in hypotheses:
+            name = hypothesis['name']
+            
+            # Calculate prior from history or use uniform prior
+            if name in history:
+                priors[name] = history[name] / total_cases
+            else:
+                priors[name] = 1.0 / len(hypotheses)
+            
+            # Adjust prior based on factors
+            factor_weight = len(hypothesis['factors']) / 10  # Normalize factor count
+            priors[name] = (
+                priors[name] * (1 - self.prior_weight) +
+                factor_weight * self.prior_weight
+            )
         
-        Format: [H1]: 0.XX, [H2]: 0.XX, ...
-        """
+        # Normalize priors
+        total_prior = sum(priors.values())
+        if total_prior > 0:
+            priors = {
+                name: prob / total_prior
+                for name, prob in priors.items()
+            }
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_probabilities(response["answer"])
-
-    async def _generate_analysis(self, posteriors: Dict[str, float], context: Dict[str, Any]) -> Dict[str, Any]:
-        prompt = f"""
-        Generate final Bayesian analysis:
-        Context: {json.dumps(context)}
-        
-        Posterior Probabilities:
-        {json.dumps(posteriors, indent=2)}
-        
-        Provide:
-        1. Main conclusion based on highest probability hypotheses
-        2. Confidence level (0-1)
-        3. Key reasoning steps taken
-        """
+        return priors
+    
+    async def _update_with_evidence(
+        self,
+        hypotheses: List[Dict[str, Any]],
+        priors: Dict[str, float],
+        context: Dict[str, Any]
+    ) -> Dict[str, float]:
+        """Update probabilities with evidence."""
+        posteriors = priors.copy()
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_analysis(response["answer"])
-
-    def _parse_hypotheses(self, response: str) -> List[Dict[str, Any]]:
-        """Parse hypotheses from response."""
-        hypotheses = []
-        current = None
+        # Get evidence from context
+        evidence = context.get('evidence', [])
+        if not evidence:
+            return posteriors
         
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
+        for e in evidence:
+            # Calculate likelihood for each hypothesis
+            likelihoods = {}
+            for hypothesis in hypotheses:
+                name = hypothesis['name']
+                likelihood = self._calculate_likelihood(hypothesis, e)
+                likelihoods[name] = max(likelihood, self.min_likelihood)
+            
+            # Update posteriors using Bayes' rule
+            total_probability = sum(
+                likelihoods[name] * posteriors[name]
+                for name in posteriors
+            )
             
-            if line.startswith('[H'):
-                if current:
-                    hypotheses.append(current)
-                current = {
-                    "statement": "",
-                    "assumptions": "",
-                    "testability": ""
+            if total_probability > 0:
+                posteriors = {
+                    name: (likelihoods[name] * posteriors[name]) / total_probability
+                    for name in posteriors
                 }
-            elif current:
-                if line.startswith('Statement:'):
-                    current["statement"] = line[10:].strip()
-                elif line.startswith('Assumptions:'):
-                    current["assumptions"] = line[12:].strip()
-                elif line.startswith('Testability:'):
-                    current["testability"] = line[12:].strip()
         
-        if current:
-            hypotheses.append(current)
+        return posteriors
+    
+    def _calculate_likelihood(
+        self,
+        hypothesis: Dict[str, Any],
+        evidence: Dict[str, Any]
+    ) -> float:
+        """Calculate likelihood of evidence given hypothesis."""
+        # Extract evidence factors
+        evidence_factors = set(
+            str(v).lower()
+            for v in evidence.values()
+            if isinstance(v, (str, int, float))
+        )
         
-        return hypotheses
-
-    def _parse_probabilities(self, response: str) -> Dict[str, float]:
-        """Parse probabilities from response."""
-        probs = {}
-        pattern = r'\[H(\d+)\]:\s*(0\.\d+)'
+        # Compare with hypothesis factors
+        common_factors = evidence_factors.intersection(hypothesis['factors'])
         
-        for match in re.finditer(pattern, response):
-            h_num = int(match.group(1))
-            prob = float(match.group(2))
-            probs[f"H{h_num}"] = prob
+        if not evidence_factors:
+            return 0.5  # Neutral likelihood if no factors
         
-        return probs
-
-    def _parse_analysis(self, response: str) -> Dict[str, Any]:
-        """Parse analysis from response."""
-        lines = response.split('\n')
-        analysis = {
-            "conclusion": "",
-            "confidence": 0.0,
-            "reasoning_path": []
+        return len(common_factors) / len(evidence_factors)
+    
+    async def _generate_analysis(
+        self,
+        posteriors: Dict[str, float],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Generate probabilistic analysis."""
+        # Sort hypotheses by posterior probability
+        ranked_hypotheses = sorted(
+            posteriors.items(),
+            key=lambda x: x[1],
+            reverse=True
+        )
+        
+        # Calculate statistics
+        mean = np.mean(list(posteriors.values()))
+        std = np.std(list(posteriors.values()))
+        entropy = -sum(
+            p * np.log2(p) if p > 0 else 0
+            for p in posteriors.values()
+        )
+        
+        return {
+            'top_hypothesis': ranked_hypotheses[0][0],
+            'probability': ranked_hypotheses[0][1],
+            'alternatives': [
+                {'name': name, 'probability': prob}
+                for name, prob in ranked_hypotheses[1:]
+            ],
+            'statistics': {
+                'mean': mean,
+                'std': std,
+                'entropy': entropy
+            }
         }
+    
+    def _format_analysis(self, analysis: Dict[str, Any]) -> str:
+        """Format analysis into readable text."""
+        sections = []
         
-        for line in lines:
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('Conclusion:'):
-                analysis["conclusion"] = line[11:].strip()
-            elif line.startswith('Confidence:'):
-                try:
-                    analysis["confidence"] = float(line[11:].strip())
-                except:
-                    analysis["confidence"] = 0.5
-            elif line.startswith('- '):
-                analysis["reasoning_path"].append(line[2:].strip())
-        
-        return analysis
+        # Top hypothesis
+        sections.append(
+            f"Most likely hypothesis: {analysis['top_hypothesis']} "
+            f"(probability: {analysis['probability']:.2%})"
+        )
+        
+        # Alternative hypotheses
+        if analysis['alternatives']:
+            sections.append("\nAlternative hypotheses:")
+            for alt in analysis['alternatives']:
+                sections.append(
+                    f"- {alt['name']}: {alt['probability']:.2%}"
+                )
+        
+        # Statistics
+        stats = analysis['statistics']
+        sections.append("\nDistribution statistics:")
+        sections.append(f"- Mean probability: {stats['mean']:.2%}")
+        sections.append(f"- Standard deviation: {stats['std']:.2%}")
+        sections.append(f"- Entropy: {stats['entropy']:.2f} bits")
+        
+        return "\n".join(sections)
+    
+    def _calculate_confidence(self, posteriors: Dict[str, float]) -> float:
+        """Calculate overall confidence score."""
+        if not posteriors:
+            return 0.0
+        
+        # Base confidence
+        confidence = 0.5
+        
+        # Adjust based on probability distribution
+        probs = list(posteriors.values())
+        
+        # Strong leading hypothesis increases confidence
+        max_prob = max(probs)
+        if max_prob > 0.8:
+            confidence += 0.3
+        elif max_prob > 0.6:
+            confidence += 0.2
+        elif max_prob > 0.4:
+            confidence += 0.1
+        
+        # Low entropy (clear distinction) increases confidence
+        entropy = -sum(p * np.log2(p) if p > 0 else 0 for p in probs)
+        max_entropy = -np.log2(1/len(probs))  # Maximum possible entropy
+        
+        if entropy < 0.3 * max_entropy:
+            confidence += 0.2
+        elif entropy < 0.6 * max_entropy:
+            confidence += 0.1
+        
+        return min(confidence, 1.0)
+    
+    def _extract_factors(self, text: str, terms: Set[str]) -> Set[str]:
+        """Extract relevant factors from text."""
+        return set(word.lower() for word in text.split() if word.lower() in terms)
+    
+    def _generate_alternative_factors(self, terms: Set[str]) -> Set[str]:
+        """Generate factors for alternative hypothesis."""
+        # Simple approach: use terms not in primary hypothesis
+        return set(
+            word for word in terms
+            if not any(
+                similar in word or word in similar
+                for similar in terms
+            )
+        )

reasoning/meta_learning.py

@@ -1,412 +1,339 @@
-"""Meta-learning reasoning implementation with advanced adaptation capabilities."""
+"""Advanced meta-learning strategy for adaptive reasoning."""
 
 import logging
-from typing import Dict, Any, List, Optional, Set, Tuple, Callable
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
 import json
 from dataclasses import dataclass, field
 from enum import Enum
-from collections import defaultdict
-import numpy as np
 from datetime import datetime
+import numpy as np
+from collections import defaultdict
 
 from .base import ReasoningStrategy
 
-class MetaFeatureType(Enum):
-    """Types of meta-features for learning."""
-    PROBLEM_STRUCTURE = "problem_structure"
-    SOLUTION_PATTERN = "solution_pattern"
-    REASONING_STYLE = "reasoning_style"
-    ERROR_PATTERN = "error_pattern"
-    PERFORMANCE_METRIC = "performance_metric"
-    ADAPTATION_SIGNAL = "adaptation_signal"
-
 @dataclass
-class MetaFeature:
-    """Represents a meta-feature for learning."""
-    type: MetaFeatureType
+class MetaTask:
+    """Meta-learning task with parameters and performance metrics."""
     name: str
-    value: Any
-    confidence: float
-    timestamp: datetime
-    metadata: Dict[str, Any] = field(default_factory=dict)
-
-@dataclass
-class LearningEpisode:
-    """Represents a learning episode."""
-    id: str
-    query: str
-    features: List[MetaFeature]
-    outcome: Dict[str, Any]
-    performance: float
-    timestamp: datetime
-    metadata: Dict[str, Any] = field(default_factory=dict)
+    parameters: Dict[str, Any]
+    metrics: Dict[str, float]
+    history: List[Dict[str, Any]] = field(default_factory=list)
 
 class MetaLearningStrategy(ReasoningStrategy):
     """
-    Advanced Meta-Learning reasoning implementation with:
-    - Dynamic strategy adaptation
-    - Performance tracking
-    - Pattern recognition
-    - Automated optimization
-    - Cross-episode learning
+    Advanced meta-learning strategy that:
+    1. Adapts to new tasks
+    2. Learns from experience
+    3. Optimizes parameters
+    4. Transfers knowledge
+    5. Improves over time
     """
     
-    def __init__(self,
-                 learning_rate: float = 0.1,
-                 memory_size: int = 1000,
-                 adaptation_threshold: float = 0.7,
-                 exploration_rate: float = 0.2):
-        self.learning_rate = learning_rate
-        self.memory_size = memory_size
-        self.adaptation_threshold = adaptation_threshold
-        self.exploration_rate = exploration_rate
-        
-        # Learning components
-        self.episode_memory: List[LearningEpisode] = []
-        self.feature_patterns: Dict[str, Dict[str, float]] = defaultdict(lambda: defaultdict(float))
-        self.strategy_performance: Dict[str, List[float]] = defaultdict(list)
-        self.adaptation_history: List[Dict[str, Any]] = []
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize meta-learning strategy."""
+        super().__init__()
+        self.config = config or {}
         
-        # Performance tracking
-        self.success_rate: float = 0.0
-        self.adaptation_rate: float = 0.0
-        self.exploration_count: int = 0
+        # Configure parameters
+        self.learning_rate = self.config.get('learning_rate', 0.01)
+        self.memory_size = self.config.get('memory_size', 100)
+        self.adaptation_threshold = self.config.get('adaptation_threshold', 0.7)
         
+        # Initialize task memory
+        self.task_memory: List[MetaTask] = []
+    
     async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
-        """Main reasoning method implementing meta-learning."""
+        """
+        Apply meta-learning to adapt and optimize reasoning.
+        
+        Args:
+            query: The input query to reason about
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing reasoning results and confidence scores
+        """
         try:
-            # Extract meta-features
-            features = await self._extract_meta_features(query, context)
+            # Identify similar tasks
+            similar_tasks = await self._find_similar_tasks(query, context)
             
-            # Select optimal strategy
-            strategy = await self._select_strategy(features, context)
+            # Adapt parameters
+            adapted_params = await self._adapt_parameters(similar_tasks, context)
             
-            # Apply strategy with adaptation
-            result = await self._apply_strategy(strategy, query, features, context)
+            # Apply meta-learning
+            results = await self._apply_meta_learning(
+                query,
+                adapted_params,
+                context
+            )
             
-            # Learn from episode
-            episode = self._create_episode(query, features, result)
-            self._learn_from_episode(episode)
+            # Update memory
+            await self._update_memory(query, results, context)
             
-            # Optimize performance
-            self._optimize_performance()
+            # Generate analysis
+            analysis = await self._generate_analysis(results, context)
             
             return {
-                "success": True,
-                "answer": result["answer"],
-                "confidence": result["confidence"],
-                "meta_features": [self._feature_to_dict(f) for f in features],
-                "selected_strategy": strategy,
-                "adaptations": result["adaptations"],
-                "performance_metrics": result["performance_metrics"],
-                "meta_insights": result["meta_insights"]
+                'answer': self._format_analysis(analysis),
+                'confidence': self._calculate_confidence(results),
+                'similar_tasks': similar_tasks,
+                'adapted_params': adapted_params,
+                'results': results,
+                'analysis': analysis
             }
+            
         except Exception as e:
-            logging.error(f"Error in meta-learning reasoning: {str(e)}")
-            return {"success": False, "error": str(e)}
-
-    async def _extract_meta_features(self, query: str, context: Dict[str, Any]) -> List[MetaFeature]:
-        """Extract meta-features from query and context."""
-        prompt = f"""
-        Extract meta-features for learning:
-        Query: {query}
-        Context: {json.dumps(context)}
-        
-        For each feature type:
-        1. Problem Structure
-        2. Solution Patterns
-        3. Reasoning Style
-        4. Error Patterns
-        5. Performance Metrics
-        6. Adaptation Signals
-        
-        Format as:
-        [Type1]
-        Name: ...
-        Value: ...
-        Confidence: ...
-        Metadata: ...
-        
-        [Type2]
-        ...
-        """
-        
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_meta_features(response["answer"])
-
-    async def _select_strategy(self, features: List[MetaFeature], context: Dict[str, Any]) -> str:
-        """Select optimal reasoning strategy based on meta-features."""
-        prompt = f"""
-        Select optimal reasoning strategy:
-        Features: {json.dumps([self._feature_to_dict(f) for f in features])}
-        Context: {json.dumps(context)}
+            logging.error(f"Meta-learning failed: {str(e)}")
+            return {
+                'error': f"Meta-learning failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    async def _find_similar_tasks(
+        self,
+        query: str,
+        context: Dict[str, Any]
+    ) -> List[MetaTask]:
+        """Find similar tasks in memory."""
+        similar_tasks = []
         
-        Consider:
-        1. Past performance patterns
-        2. Feature relevance
-        3. Adaptation potential
-        4. Resource constraints
+        # Extract query features
+        query_features = self._extract_features(query)
         
-        Format as:
-        [Selection]
-        Strategy: ...
-        Rationale: ...
-        Confidence: ...
-        Adaptations: ...
-        """
+        for task in self.task_memory:
+            # Calculate similarity
+            similarity = self._calculate_similarity(
+                query_features,
+                self._extract_features(task.name)
+            )
+            
+            if similarity > self.adaptation_threshold:
+                similar_tasks.append(task)
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_strategy_selection(response["answer"])
-
-    async def _apply_strategy(self, strategy: str, query: str, features: List[MetaFeature], context: Dict[str, Any]) -> Dict[str, Any]:
-        """Apply selected strategy with dynamic adaptation."""
-        prompt = f"""
-        Apply strategy with meta-learning:
-        Strategy: {strategy}
-        Query: {query}
-        Features: {json.dumps([self._feature_to_dict(f) for f in features])}
-        Context: {json.dumps(context)}
+        # Sort by similarity
+        similar_tasks.sort(
+            key=lambda x: np.mean(list(x.metrics.values())),
+            reverse=True
+        )
         
-        Provide:
-        1. Main reasoning steps
-        2. Adaptation points
-        3. Performance metrics
-        4. Meta-insights
+        return similar_tasks
+    
+    def _extract_features(self, text: str) -> np.ndarray:
+        """Extract features from text."""
+        # Simple bag of words for now
+        words = set(text.lower().split())
+        return np.array([hash(word) % 100 for word in words])
+    
+    def _calculate_similarity(
+        self,
+        features1: np.ndarray,
+        features2: np.ndarray
+    ) -> float:
+        """Calculate similarity between feature sets."""
+        # Simple Jaccard similarity
+        intersection = np.intersect1d(features1, features2)
+        union = np.union1d(features1, features2)
         
-        Format as:
-        [Application]
-        Steps: ...
-        Adaptations: ...
-        Metrics: ...
-        Insights: ...
+        return len(intersection) / len(union) if len(union) > 0 else 0
+    
+    async def _adapt_parameters(
+        self,
+        similar_tasks: List[MetaTask],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Adapt parameters based on similar tasks."""
+        if not similar_tasks:
+            return self.config.copy()
         
-        [Result]
-        Answer: ...
-        Confidence: ...
-        """
+        adapted_params = {}
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_strategy_application(response["answer"])
-
-    def _create_episode(self, query: str, features: List[MetaFeature], result: Dict[str, Any]) -> LearningEpisode:
-        """Create a learning episode from the current interaction."""
-        return LearningEpisode(
-            id=f"episode_{len(self.episode_memory)}",
-            query=query,
-            features=features,
-            outcome=result,
-            performance=result.get("confidence", 0.0),
-            timestamp=datetime.now(),
-            metadata={
-                "adaptations": result.get("adaptations", []),
-                "metrics": result.get("performance_metrics", {})
-            }
+        # Weight tasks by performance
+        total_performance = sum(
+            np.mean(list(task.metrics.values()))
+            for task in similar_tasks
         )
-
-    def _learn_from_episode(self, episode: LearningEpisode):
-        """Learn from a completed episode."""
-        # Update episode memory
-        self.episode_memory.append(episode)
-        if len(self.episode_memory) > self.memory_size:
-            self.episode_memory.pop(0)
         
-        # Update feature patterns
-        for feature in episode.features:
-            pattern_key = f"{feature.type.value}:{feature.name}"
-            self.feature_patterns[pattern_key]["count"] += 1
-            self.feature_patterns[pattern_key]["success"] += episode.performance
+        if total_performance > 0:
+            # Weighted average of parameters
+            for param_name in self.config:
+                adapted_params[param_name] = sum(
+                    task.parameters.get(param_name, self.config[param_name]) *
+                    (np.mean(list(task.metrics.values())) / total_performance)
+                    for task in similar_tasks
+                )
+        else:
+            adapted_params = self.config.copy()
         
-        # Update strategy performance
-        strategy = episode.metadata.get("selected_strategy", "default")
-        self.strategy_performance[strategy].append(episode.performance)
+        return adapted_params
+    
+    async def _apply_meta_learning(
+        self,
+        query: str,
+        parameters: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Apply meta-learning with adapted parameters."""
+        results = {
+            'query': query,
+            'parameters': parameters,
+            'metrics': {}
+        }
         
-        # Track adaptations
-        self.adaptation_history.append({
-            "timestamp": episode.timestamp,
-            "adaptations": episode.metadata.get("adaptations", []),
-            "performance": episode.performance
-        })
+        # Apply learning rate
+        for param_name, value in parameters.items():
+            if isinstance(value, (int, float)):
+                results['parameters'][param_name] = (
+                    value * (1 - self.learning_rate) +
+                    self.config[param_name] * self.learning_rate
+                )
         
-        # Update performance metrics
-        self._update_performance_metrics(episode)
-
-    def _optimize_performance(self):
-        """Optimize meta-learning performance."""
-        # Adjust learning rate
-        recent_performance = [e.performance for e in self.episode_memory[-10:]]
-        if recent_performance:
-            avg_performance = sum(recent_performance) / len(recent_performance)
-            if avg_performance > 0.8:
-                self.learning_rate *= 0.9  # Reduce learning rate when performing well
-            elif avg_performance < 0.5:
-                self.learning_rate *= 1.1  # Increase learning rate when performing poorly
+        # Calculate performance metrics
+        results['metrics'] = {
+            'adaptation_score': np.mean([
+                p / self.config[name]
+                for name, p in results['parameters'].items()
+                if isinstance(p, (int, float)) and self.config[name] != 0
+            ]),
+            'novelty_score': 1 - max(
+                self._calculate_similarity(
+                    self._extract_features(query),
+                    self._extract_features(task.name)
+                )
+                for task in self.task_memory
+            ) if self.task_memory else 1.0
+        }
         
-        # Adjust exploration rate
-        self.exploration_rate = max(0.1, self.exploration_rate * 0.995)  # Gradually reduce exploration
+        return results
+    
+    async def _update_memory(
+        self,
+        query: str,
+        results: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> None:
+        """Update task memory."""
+        # Create new task
+        task = MetaTask(
+            name=query,
+            parameters=results['parameters'],
+            metrics=results['metrics'],
+            history=[{
+                'timestamp': datetime.now().isoformat(),
+                'context': context,
+                'results': results
+            }]
+        )
         
-        # Prune ineffective patterns
-        for pattern, stats in list(self.feature_patterns.items()):
-            if stats["count"] > 10 and stats["success"] / stats["count"] < 0.3:
-                del self.feature_patterns[pattern]
+        # Add to memory
+        self.task_memory.append(task)
         
-        # Update adaptation threshold
-        recent_adaptations = [a["performance"] for a in self.adaptation_history[-10:]]
-        if recent_adaptations:
-            self.adaptation_threshold = sum(recent_adaptations) / len(recent_adaptations)
-
-    def _update_performance_metrics(self, episode: LearningEpisode):
-        """Update performance tracking metrics."""
-        # Update success rate
-        self.success_rate = (self.success_rate * len(self.episode_memory) + episode.performance) / (len(self.episode_memory) + 1)
+        # Maintain memory size
+        if len(self.task_memory) > self.memory_size:
+            # Remove worst performing task
+            self.task_memory.sort(
+                key=lambda x: np.mean(list(x.metrics.values()))
+            )
+            self.task_memory.pop(0)
+    
+    async def _generate_analysis(
+        self,
+        results: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Generate meta-learning analysis."""
+        # Calculate statistics
+        param_stats = {
+            name: {
+                'value': value,
+                'adaptation': value / self.config[name]
+                if isinstance(value, (int, float)) and self.config[name] != 0
+                else 1.0
+            }
+            for name, value in results['parameters'].items()
+        }
         
-        # Update adaptation rate
-        adaptations = len(episode.metadata.get("adaptations", []))
-        self.adaptation_rate = (self.adaptation_rate * len(self.adaptation_history) + (adaptations > 0)) / (len(self.adaptation_history) + 1)
+        # Calculate overall metrics
+        metrics = {
+            'adaptation_score': results['metrics']['adaptation_score'],
+            'novelty_score': results['metrics']['novelty_score'],
+            'memory_usage': len(self.task_memory) / self.memory_size
+        }
         
-        # Track exploration
-        if episode.metadata.get("exploration", False):
-            self.exploration_count += 1
-
-    def _parse_meta_features(self, response: str) -> List[MetaFeature]:
-        """Parse meta-features from response."""
-        features = []
-        current_type = None
-        current_feature = None
+        return {
+            'parameters': param_stats,
+            'metrics': metrics,
+            'memory_size': len(self.task_memory),
+            'total_tasks_seen': len(self.task_memory)
+        }
+    
+    def _format_analysis(self, analysis: Dict[str, Any]) -> str:
+        """Format analysis into readable text."""
+        sections = []
         
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('[Type'):
-                if current_feature:
-                    features.append(current_feature)
-                current_feature = None
-                try:
-                    type_str = line[1:-1].lower()
-                    current_type = MetaFeatureType(type_str)
-                except ValueError:
-                    current_type = None
-            elif current_type and line.startswith('Name:'):
-                current_feature = MetaFeature(
-                    type=current_type,
-                    name=line[5:].strip(),
-                    value=None,
-                    confidence=0.0,
-                    timestamp=datetime.now(),
-                    metadata={}
-                )
-            elif current_feature:
-                if line.startswith('Value:'):
-                    current_feature.value = line[6:].strip()
-                elif line.startswith('Confidence:'):
-                    try:
-                        current_feature.confidence = float(line[11:].strip())
-                    except:
-                        pass
-                elif line.startswith('Metadata:'):
-                    try:
-                        current_feature.metadata = json.loads(line[9:].strip())
-                    except:
-                        pass
+        # Parameter adaptations
+        sections.append("Parameter adaptations:")
+        for name, stats in analysis['parameters'].items():
+            sections.append(
+                f"- {name}: {stats['value']:.2f} "
+                f"({stats['adaptation']:.1%} of original)"
+            )
         
-        if current_feature:
-            features.append(current_feature)
+        # Performance metrics
+        sections.append("\nPerformance metrics:")
+        metrics = analysis['metrics']
+        sections.append(f"- Adaptation score: {metrics['adaptation_score']:.1%}")
+        sections.append(f"- Novelty score: {metrics['novelty_score']:.1%}")
+        sections.append(f"- Memory usage: {metrics['memory_usage']:.1%}")
         
-        return features
-
-    def _parse_strategy_selection(self, response: str) -> str:
-        """Parse strategy selection from response."""
-        lines = response.split('\n')
-        strategy = "default"
+        # Memory statistics
+        sections.append("\nMemory statistics:")
+        sections.append(f"- Current tasks in memory: {analysis['memory_size']}")
+        sections.append(f"- Total tasks seen: {analysis['total_tasks_seen']}")
         
-        for line in lines:
-            if line.startswith('Strategy:'):
-                strategy = line[9:].strip()
-                break
+        return "\n".join(sections)
+    
+    def _calculate_confidence(self, results: Dict[str, Any]) -> float:
+        """Calculate overall confidence score."""
+        if not results.get('metrics'):
+            return 0.0
         
-        return strategy
-
-    def _parse_strategy_application(self, response: str) -> Dict[str, Any]:
-        """Parse strategy application results."""
-        result = {
-            "answer": "",
-            "confidence": 0.0,
-            "steps": [],
-            "adaptations": [],
-            "performance_metrics": {},
-            "meta_insights": []
-        }
+        # Base confidence
+        confidence = 0.5
         
-        section = None
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('[Application]'):
-                section = "application"
-            elif line.startswith('[Result]'):
-                section = "result"
-            elif section == "application":
-                if line.startswith('Steps:'):
-                    result["steps"] = [s.strip() for s in line[6:].split(',')]
-                elif line.startswith('Adaptations:'):
-                    result["adaptations"] = [a.strip() for a in line[12:].split(',')]
-                elif line.startswith('Metrics:'):
-                    try:
-                        result["performance_metrics"] = json.loads(line[8:].strip())
-                    except:
-                        pass
-                elif line.startswith('Insights:'):
-                    result["meta_insights"] = [i.strip() for i in line[9:].split(',')]
-            elif section == "result":
-                if line.startswith('Answer:'):
-                    result["answer"] = line[7:].strip()
-                elif line.startswith('Confidence:'):
-                    try:
-                        result["confidence"] = float(line[11:].strip())
-                    except:
-                        result["confidence"] = 0.5
+        # Adjust based on adaptation score
+        adaptation_score = results['metrics']['adaptation_score']
+        if adaptation_score > 0.8:
+            confidence += 0.3
+        elif adaptation_score > 0.6:
+            confidence += 0.2
+        elif adaptation_score > 0.4:
+            confidence += 0.1
         
-        return result
-
-    def _feature_to_dict(self, feature: MetaFeature) -> Dict[str, Any]:
-        """Convert feature to dictionary for serialization."""
-        return {
-            "type": feature.type.value,
-            "name": feature.name,
-            "value": feature.value,
-            "confidence": feature.confidence,
-            "timestamp": feature.timestamp.isoformat(),
-            "metadata": feature.metadata
-        }
+        # Adjust based on novelty
+        novelty_score = results['metrics']['novelty_score']
+        if novelty_score < 0.2:  # Very similar to known tasks
+            confidence += 0.2
+        elif novelty_score < 0.4:
+            confidence += 0.1
+        
+        return min(confidence, 1.0)
 
     def get_performance_metrics(self) -> Dict[str, Any]:
         """Get current performance metrics."""
         return {
-            "success_rate": self.success_rate,
-            "adaptation_rate": self.adaptation_rate,
-            "exploration_count": self.exploration_count,
-            "episode_count": len(self.episode_memory),
-            "pattern_count": len(self.feature_patterns),
+            "success_rate": 0.0,
+            "adaptation_rate": 0.0,
+            "exploration_count": 0,
+            "episode_count": len(self.task_memory),
+            "pattern_count": 0,
             "learning_rate": self.learning_rate,
-            "exploration_rate": self.exploration_rate
+            "exploration_rate": 0.0
         }
 
     def get_top_patterns(self, n: int = 10) -> List[Tuple[str, float]]:
         """Get top performing patterns."""
-        pattern_scores = []
-        for pattern, stats in self.feature_patterns.items():
-            if stats["count"] > 0:
-                score = stats["success"] / stats["count"]
-                pattern_scores.append((pattern, score))
-        
-        return sorted(pattern_scores, key=lambda x: x[1], reverse=True)[:n]
+        return []
 
     def clear_memory(self):
         """Clear learning memory."""
-        self.episode_memory.clear()
-        self.feature_patterns.clear()
-        self.strategy_performance.clear()
-        self.adaptation_history.clear()
+        self.task_memory.clear()

reasoning/monetization.py

@@ -9,6 +9,8 @@ from datetime import datetime
 import numpy as np
 from collections import defaultdict
 
+from .base import ReasoningStrategy
+
 @dataclass
 class MonetizationModel:
     """Monetization model configuration."""
@@ -276,3 +278,142 @@ class MonetizationOptimizer:
                 ) if self.streams else 0
             }
         }
+
+class MonetizationStrategy(ReasoningStrategy):
+    """
+    Advanced monetization strategy that:
+    1. Designs optimal pricing models
+    2. Optimizes revenue streams
+    3. Maximizes customer lifetime value
+    4. Reduces churn
+    5. Increases profitability
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize monetization strategy."""
+        super().__init__()
+        self.config = config or {}
+        self.optimizer = MonetizationOptimizer()
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Generate monetization strategy based on query and context.
+        
+        Args:
+            query: The monetization query
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing monetization strategy and confidence scores
+        """
+        try:
+            # Extract venture type
+            venture_type = self._extract_venture_type(query, context)
+            
+            # Optimize monetization
+            optimization_result = await self.optimizer.optimize_monetization(
+                venture_type=venture_type,
+                context=context
+            )
+            
+            # Format results
+            formatted_result = self._format_strategy(optimization_result)
+            
+            return {
+                'answer': formatted_result,
+                'confidence': self._calculate_confidence(optimization_result),
+                'optimization': optimization_result
+            }
+            
+        except Exception as e:
+            logging.error(f"Monetization strategy generation failed: {str(e)}")
+            return {
+                'error': f"Monetization strategy generation failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    def _extract_venture_type(self, query: str, context: Dict[str, Any]) -> str:
+        """Extract venture type from query and context."""
+        # Use context if available
+        if 'venture_type' in context:
+            return context['venture_type']
+        
+        # Simple keyword matching
+        query_lower = query.lower()
+        if any(term in query_lower for term in ['ai', 'ml', 'model']):
+            return 'ai_startup'
+        elif any(term in query_lower for term in ['saas', 'software']):
+            return 'saas'
+        elif any(term in query_lower for term in ['api', 'service']):
+            return 'api_service'
+        elif any(term in query_lower for term in ['data', 'analytics']):
+            return 'data_analytics'
+        
+        # Default to SaaS if unclear
+        return 'saas'
+    
+    def _calculate_confidence(self, result: Dict[str, Any]) -> float:
+        """Calculate confidence score based on optimization quality."""
+        # Base confidence
+        confidence = 0.5
+        
+        # Adjust based on optimization completeness
+        if result.get('models'):
+            confidence += 0.1
+        if result.get('pricing'):
+            confidence += 0.1
+        if result.get('revenue'):
+            confidence += 0.1
+        if result.get('value'):
+            confidence += 0.1
+            
+        # Adjust based on projected performance
+        performance = result.get('performance', {})
+        if performance.get('roi', 0) > 2.0:
+            confidence += 0.1
+        if performance.get('ltv', 0) > 1000:
+            confidence += 0.1
+            
+        return min(confidence, 1.0)
+    
+    def _format_strategy(self, result: Dict[str, Any]) -> str:
+        """Format monetization strategy into readable text."""
+        sections = []
+        
+        # Monetization models
+        if 'models' in result:
+            models = result['models']
+            sections.append("Monetization Models:")
+            for model in models:
+                sections.append(f"- {model['name']}: {model['type']}")
+                if 'pricing_tiers' in model:
+                    sections.append("  Pricing Tiers:")
+                    for tier in model['pricing_tiers']:
+                        sections.append(f"  * {tier['name']}: ${tier['price']}/mo")
+        
+        # Revenue optimization
+        if 'revenue' in result:
+            revenue = result['revenue']
+            sections.append("\nRevenue Optimization:")
+            for stream, details in revenue.items():
+                sections.append(f"- {stream.replace('_', ' ').title()}:")
+                sections.append(f"  * Projected Revenue: ${details['projected_revenue']:,.2f}")
+                sections.append(f"  * Growth Rate: {details['growth_rate']*100:.1f}%")
+        
+        # Customer value optimization
+        if 'value' in result:
+            value = result['value']
+            sections.append("\nCustomer Value Optimization:")
+            sections.append(f"- Customer Acquisition Cost: ${value['cac']:,.2f}")
+            sections.append(f"- Lifetime Value: ${value['ltv']:,.2f}")
+            sections.append(f"- Churn Rate: {value['churn_rate']*100:.1f}%")
+        
+        # Performance projections
+        if 'performance' in result:
+            perf = result['performance']
+            sections.append("\nPerformance Projections:")
+            sections.append(f"- ROI: {perf['roi']*100:.1f}%")
+            sections.append(f"- Payback Period: {perf['payback_months']:.1f} months")
+            sections.append(f"- Break-even Point: ${perf['breakeven']:,.2f}")
+        
+        return "\n".join(sections)

reasoning/multimodal.py

@@ -1,268 +1,278 @@
-"""Multi-modal reasoning implementation."""
+"""Advanced multimodal reasoning combining different types of information."""
 
 import logging
-from typing import Dict, Any, List
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
 import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
 
 from .base import ReasoningStrategy
 
+@dataclass
+class ModalityFeatures:
+    """Features extracted from different modalities."""
+    text: List[Dict[str, Any]]
+    image: Optional[List[Dict[str, Any]]] = None
+    audio: Optional[List[Dict[str, Any]]] = None
+    video: Optional[List[Dict[str, Any]]] = None
+    structured: Optional[List[Dict[str, Any]]] = None
+
 class MultiModalReasoning(ReasoningStrategy):
-    """Implements multi-modal reasoning across different types of information."""
+    """
+    Advanced multimodal reasoning that:
+    1. Processes different types of information
+    2. Aligns cross-modal features
+    3. Integrates multimodal context
+    4. Generates coherent responses
+    5. Handles uncertainty
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize multimodal reasoning."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Configure modality weights
+        self.weights = {
+            'text': 0.4,
+            'image': 0.3,
+            'audio': 0.1,
+            'video': 0.1,
+            'structured': 0.1
+        }
     
     async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Apply multimodal reasoning to process and integrate different types of information.
+        
+        Args:
+            query: The input query to reason about
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing reasoning results and confidence scores
+        """
         try:
-            # Process different modalities
+            # Process across modalities
             modalities = await self._process_modalities(query, context)
             
-            # Cross-modal alignment
+            # Align cross-modal information
             alignment = await self._cross_modal_alignment(modalities, context)
             
-            # Integrated analysis
+            # Integrate aligned information
             integration = await self._integrated_analysis(alignment, context)
             
-            # Generate unified response
+            # Generate final response
             response = await self._generate_response(integration, context)
             
             return {
-                "success": True,
-                "answer": response["conclusion"],
-                "modalities": modalities,
-                "alignment": alignment,
-                "integration": integration,
-                "confidence": response["confidence"]
+                'answer': response.get('text', ''),
+                'confidence': self._calculate_confidence(integration),
+                'modalities': modalities,
+                'alignment': alignment,
+                'integration': integration
             }
+            
         except Exception as e:
-            logging.error(f"Error in multi-modal reasoning: {str(e)}")
-            return {"success": False, "error": str(e)}
-
-    async def _process_modalities(self, query: str, context: Dict[str, Any]) -> Dict[str, List[Dict[str, Any]]]:
+            logging.error(f"Multimodal reasoning failed: {str(e)}")
+            return {
+                'error': f"Multimodal reasoning failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    async def _process_modalities(
+        self,
+        query: str,
+        context: Dict[str, Any]
+    ) -> Dict[str, List[Dict[str, Any]]]:
         """Process query across different modalities."""
-        prompt = f"""
-        Process query across modalities:
-        Query: {query}
-        Context: {json.dumps(context)}
-        
-        For each modality extract:
-        1. [Type]: Modality type
-        2. [Content]: Relevant content
-        3. [Features]: Key features
-        4. [Quality]: Content quality
-        
-        Format as:
-        [M1]
-        Type: ...
-        Content: ...
-        Features: ...
-        Quality: ...
-        """
+        modalities = {}
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_modalities(response["answer"])
-
-    async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """Align information across different modalities."""
-        try:
-            # Extract modality types
-            modal_types = list(modalities.keys())
-            
-            # Initialize alignment results
-            alignments = []
-            
-            # Process each modality pair
-            for i in range(len(modal_types)):
-                for j in range(i + 1, len(modal_types)):
-                    type1, type2 = modal_types[i], modal_types[j]
-                    
-                    # Get items from each modality
-                    items1 = modalities[type1]
-                    items2 = modalities[type2]
-                    
-                    # Find alignments between items
-                    for item1 in items1:
-                        for item2 in items2:
-                            similarity = self._calculate_similarity(item1, item2)
-                            if similarity > 0.5:  # Threshold for alignment
-                                alignments.append({
-                                    "type1": type1,
-                                    "type2": type2,
-                                    "item1": item1,
-                                    "item2": item2,
-                                    "similarity": similarity
-                                })
+        # Process text
+        if 'text' in context:
+            modalities['text'] = self._process_text(context['text'])
             
-            # Sort alignments by similarity
-            alignments.sort(key=lambda x: x["similarity"], reverse=True)
+        # Process images
+        if 'images' in context:
+            modalities['image'] = self._process_images(context['images'])
             
-            return alignments
+        # Process audio
+        if 'audio' in context:
+            modalities['audio'] = self._process_audio(context['audio'])
             
-        except Exception as e:
-            logging.error(f"Error in cross-modal alignment: {str(e)}")
-            return []
-
-    def _calculate_similarity(self, item1: Dict[str, Any], item2: Dict[str, Any]) -> float:
-        """Calculate similarity between two items from different modalities."""
-        try:
-            # Extract content from items
-            content1 = str(item1.get("content", ""))
-            content2 = str(item2.get("content", ""))
+        # Process video
+        if 'video' in context:
+            modalities['video'] = self._process_video(context['video'])
             
-            # Calculate basic similarity (can be enhanced with more sophisticated methods)
-            common_words = set(content1.lower().split()) & set(content2.lower().split())
-            total_words = set(content1.lower().split()) | set(content2.lower().split())
+        # Process structured data
+        if 'structured' in context:
+            modalities['structured'] = self._process_structured(context['structured'])
             
-            if not total_words:
-                return 0.0
-                
-            return len(common_words) / len(total_words)
+        return modalities
+    
+    async def _cross_modal_alignment(
+        self,
+        modalities: Dict[str, List[Dict[str, Any]]],
+        context: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """Align information across different modalities."""
+        alignments = []
+        
+        # Get all modality pairs
+        modality_pairs = [
+            (m1, m2) for i, m1 in enumerate(modalities.keys())
+            for m2 in list(modalities.keys())[i+1:]
+        ]
+        
+        # Align each pair
+        for mod1, mod2 in modality_pairs:
+            items1 = modalities[mod1]
+            items2 = modalities[mod2]
             
-        except Exception as e:
-            logging.error(f"Error calculating similarity: {str(e)}")
-            return 0.0
-
-    async def _integrated_analysis(self, alignment: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
-        prompt = f"""
-        Perform integrated multi-modal analysis:
-        Alignment: {json.dumps(alignment)}
-        Context: {json.dumps(context)}
+            # Calculate cross-modal similarities
+            for item1 in items1:
+                for item2 in items2:
+                    similarity = self._calculate_similarity(item1, item2)
+                    if similarity > 0.7:  # Alignment threshold
+                        alignments.append({
+                            'modality1': mod1,
+                            'modality2': mod2,
+                            'item1': item1,
+                            'item2': item2,
+                            'similarity': similarity
+                        })
         
-        For each insight:
-        1. [Insight]: Key finding
-        2. [Sources]: Contributing modalities
-        3. [Support]: Supporting evidence
-        4. [Confidence]: Confidence level
+        return alignments
+    
+    def _calculate_similarity(
+        self,
+        item1: Dict[str, Any],
+        item2: Dict[str, Any]
+    ) -> float:
+        """Calculate similarity between two items from different modalities."""
+        # Simple feature overlap for now
+        features1 = set(str(v) for v in item1.values())
+        features2 = set(str(v) for v in item2.values())
         
-        Format as:
-        [I1]
-        Insight: ...
-        Sources: ...
-        Support: ...
-        Confidence: ...
-        """
+        if not features1 or not features2:
+            return 0.0
+            
+        overlap = len(features1.intersection(features2))
+        total = len(features1.union(features2))
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_integration(response["answer"])
-
-    async def _generate_response(self, integration: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
-        prompt = f"""
-        Generate unified multi-modal response:
-        Integration: {json.dumps(integration)}
-        Context: {json.dumps(context)}
+        return overlap / total if total > 0 else 0.0
+    
+    async def _integrated_analysis(
+        self,
+        alignment: List[Dict[str, Any]],
+        context: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """Perform integrated analysis of aligned information."""
+        integrated = []
         
-        Provide:
-        1. Main conclusion
-        2. Modal contributions
-        3. Integration benefits
-        4. Confidence level (0-1)
-        """
+        # Group alignments by similarity
+        similarity_groups = defaultdict(list)
+        for align in alignment:
+            similarity_groups[align['similarity']].append(align)
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_response(response["answer"])
-
-    def _parse_modalities(self, response: str) -> Dict[str, List[Dict[str, Any]]]:
-        """Parse modalities from response."""
-        modalities = {}
-        current_modality = None
+        # Process groups in order of similarity
+        for similarity, group in sorted(
+            similarity_groups.items(),
+            key=lambda x: x[0],
+            reverse=True
+        ):
+            # Combine aligned features
+            for align in group:
+                integrated.append({
+                    'features': {
+                        **align['item1'],
+                        **align['item2']
+                    },
+                    'modalities': [align['modality1'], align['modality2']],
+                    'confidence': align['similarity']
+                })
         
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('[M'):
-                if current_modality:
-                    if current_modality["type"] not in modalities:
-                        modalities[current_modality["type"]] = []
-                    modalities[current_modality["type"]].append(current_modality)
-                current_modality = {
-                    "type": "",
-                    "content": "",
-                    "features": "",
-                    "quality": ""
-                }
-            elif current_modality:
-                if line.startswith('Type:'):
-                    current_modality["type"] = line[5:].strip()
-                elif line.startswith('Content:'):
-                    current_modality["content"] = line[8:].strip()
-                elif line.startswith('Features:'):
-                    current_modality["features"] = line[9:].strip()
-                elif line.startswith('Quality:'):
-                    current_modality["quality"] = line[8:].strip()
+        return integrated
+    
+    async def _generate_response(
+        self,
+        integration: List[Dict[str, Any]],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Generate coherent response from integrated analysis."""
+        if not integration:
+            return {'text': '', 'confidence': 0.0}
         
-        if current_modality:
-            if current_modality["type"] not in modalities:
-                modalities[current_modality["type"]] = []
-            modalities[current_modality["type"]].append(current_modality)
+        # Combine all integrated features
+        all_features = {}
+        for item in integration:
+            all_features.update(item['features'])
         
-        return modalities
-
-    def _parse_integration(self, response: str) -> List[Dict[str, Any]]:
-        """Parse integration from response."""
-        integration = []
-        current_insight = None
+        # Generate response text
+        response_text = []
         
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('[I'):
-                if current_insight:
-                    integration.append(current_insight)
-                current_insight = {
-                    "insight": "",
-                    "sources": "",
-                    "support": "",
-                    "confidence": 0.0
-                }
-            elif current_insight:
-                if line.startswith('Insight:'):
-                    current_insight["insight"] = line[8:].strip()
-                elif line.startswith('Sources:'):
-                    current_insight["sources"] = line[8:].strip()
-                elif line.startswith('Support:'):
-                    current_insight["support"] = line[8:].strip()
-                elif line.startswith('Confidence:'):
-                    try:
-                        current_insight["confidence"] = float(line[11:].strip())
-                    except:
-                        pass
+        # Add main findings
+        response_text.append("Main findings across modalities:")
+        for feature, value in all_features.items():
+            response_text.append(f"- {feature}: {value}")
         
-        if current_insight:
-            integration.append(current_insight)
+        # Add confidence
+        confidence = sum(item['confidence'] for item in integration) / len(integration)
+        response_text.append(f"\nOverall confidence: {confidence:.2f}")
         
-        return integration
-
-    def _parse_response(self, response: str) -> Dict[str, Any]:
-        """Parse response from response."""
-        response_dict = {
-            "conclusion": "",
-            "modal_contributions": [],
-            "integration_benefits": [],
-            "confidence": 0.0
+        return {
+            'text': "\n".join(response_text),
+            'confidence': confidence
         }
+    
+    def _calculate_confidence(self, integration: List[Dict[str, Any]]) -> float:
+        """Calculate overall confidence score."""
+        if not integration:
+            return 0.0
+        
+        # Base confidence
+        confidence = 0.5
         
-        mode = None
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('Conclusion:'):
-                response_dict["conclusion"] = line[11:].strip()
-            elif line.startswith('Modal Contributions:'):
-                mode = "modal"
-            elif line.startswith('Integration Benefits:'):
-                mode = "integration"
-            elif line.startswith('Confidence:'):
-                try:
-                    response_dict["confidence"] = float(line[11:].strip())
-                except:
-                    response_dict["confidence"] = 0.5
-                mode = None
-            elif mode == "modal" and line.startswith('- '):
-                response_dict["modal_contributions"].append(line[2:].strip())
-            elif mode == "integration" and line.startswith('- '):
-                response_dict["integration_benefits"].append(line[2:].strip())
+        # Adjust based on number of modalities
+        unique_modalities = set()
+        for item in integration:
+            unique_modalities.update(item['modalities'])
         
-        return response_dict
+        modality_bonus = len(unique_modalities) * 0.1
+        confidence += min(modality_bonus, 0.3)
+        
+        # Adjust based on integration quality
+        avg_similarity = sum(
+            item['confidence'] for item in integration
+        ) / len(integration)
+        confidence += avg_similarity * 0.2
+        
+        return min(confidence, 1.0)
+
+    def _process_text(self, text: str) -> List[Dict[str, Any]]:
+        """Process text modality."""
+        # Simple text processing for now
+        return [{'text': text}]
+
+    def _process_images(self, images: List[str]) -> List[Dict[str, Any]]:
+        """Process image modality."""
+        # Simple image processing for now
+        return [{'image': image} for image in images]
+
+    def _process_audio(self, audio: List[str]) -> List[Dict[str, Any]]:
+        """Process audio modality."""
+        # Simple audio processing for now
+        return [{'audio': audio_file} for audio_file in audio]
+
+    def _process_video(self, video: List[str]) -> List[Dict[str, Any]]:
+        """Process video modality."""
+        # Simple video processing for now
+        return [{'video': video_file} for video_file in video]
+
+    def _process_structured(self, structured: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Process structured data modality."""
+        # Simple structured data processing for now
+        return [{'structured': structured}]

reasoning/neurosymbolic.py

@@ -1,264 +1,305 @@
-"""Neurosymbolic reasoning implementation."""
+"""Advanced neurosymbolic reasoning combining neural and symbolic approaches."""
 
 import logging
-from typing import Dict, Any, List, Tuple
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
 import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
 
 from .base import ReasoningStrategy
 
+@dataclass
+class NeuralFeature:
+    """Neural features extracted from data."""
+    name: str
+    values: np.ndarray
+    importance: float
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class SymbolicRule:
+    """Symbolic rule with conditions and confidence."""
+    name: str
+    conditions: List[str]
+    conclusion: str
+    confidence: float
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
 class NeurosymbolicReasoning(ReasoningStrategy):
-    """Implements neurosymbolic reasoning combining neural and symbolic approaches."""
+    """
+    Advanced neurosymbolic reasoning that:
+    1. Extracts neural features
+    2. Generates symbolic rules
+    3. Combines approaches
+    4. Handles uncertainty
+    5. Provides interpretable results
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize neurosymbolic reasoning."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Configure parameters
+        self.feature_threshold = self.config.get('feature_threshold', 0.1)
+        self.rule_confidence_threshold = self.config.get('rule_confidence', 0.7)
+        self.max_rules = self.config.get('max_rules', 10)
     
     async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Apply neurosymbolic reasoning to combine neural and symbolic approaches.
+        
+        Args:
+            query: The input query to reason about
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing reasoning results and confidence scores
+        """
         try:
-            # Neural processing
-            neural_features = await self._neural_processing(query, context)
+            # Extract neural features
+            features = await self._extract_features(query, context)
             
-            # Symbolic reasoning
-            symbolic_rules = await self._symbolic_reasoning(neural_features, context)
+            # Generate symbolic rules
+            rules = await self._generate_rules(features, context)
             
-            # Integration
-            integrated = await self._neurosymbolic_integration(neural_features, symbolic_rules, context)
+            # Combine approaches
+            combined = await self._combine_approaches(features, rules, context)
             
-            # Final inference
-            conclusion = await self._final_inference(integrated, context)
+            # Generate analysis
+            analysis = await self._generate_analysis(combined, context)
             
             return {
-                "success": True,
-                "answer": conclusion["answer"],
-                "neural_features": neural_features,
-                "symbolic_rules": symbolic_rules,
-                "integrated_reasoning": integrated,
-                "confidence": conclusion["confidence"],
-                "explanation": conclusion["explanation"]
+                'answer': self._format_analysis(analysis),
+                'confidence': self._calculate_confidence(combined),
+                'features': features,
+                'rules': rules,
+                'combined': combined,
+                'analysis': analysis
             }
+            
         except Exception as e:
-            return {"success": False, "error": str(e)}
-
-    async def _neural_processing(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
-        prompt = f"""
-        Extract neural features from query:
-        Query: {query}
-        Context: {json.dumps(context)}
+            logging.error(f"Neurosymbolic reasoning failed: {str(e)}")
+            return {
+                'error': f"Neurosymbolic reasoning failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    async def _extract_features(
+        self,
+        query: str,
+        context: Dict[str, Any]
+    ) -> List[NeuralFeature]:
+        """Extract neural features from input."""
+        features = []
         
-        For each feature:
-        1. [Type]: Feature type
-        2. [Value]: Extracted value
-        3. [Confidence]: Extraction confidence
-        4. [Relations]: Related concepts
+        # Extract key terms
+        terms = query.lower().split()
         
-        Format as:
-        [F1]
-        Type: ...
-        Value: ...
-        Confidence: ...
-        Relations: ...
-        """
+        # Process each term
+        for term in terms:
+            # Simple feature extraction for now
+            values = np.random.randn(10)  # Placeholder for real feature extraction
+            importance = np.abs(values).mean()
+            
+            if importance > self.feature_threshold:
+                features.append(NeuralFeature(
+                    name=term,
+                    values=values,
+                    importance=importance,
+                    metadata={'source': 'term_extraction'}
+                ))
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_features(response["answer"])
-
-    async def _symbolic_reasoning(self, features: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
-        prompt = f"""
-        Generate symbolic rules from features:
-        Features: {json.dumps(features)}
-        Context: {json.dumps(context)}
+        # Sort by importance
+        features.sort(key=lambda x: x.importance, reverse=True)
         
-        For each rule:
-        1. [Condition]: Rule condition
-        2. [Implication]: What it implies
-        3. [Certainty]: Rule certainty
-        4. [Source]: Derivation source
+        return features
+    
+    async def _generate_rules(
+        self,
+        features: List[NeuralFeature],
+        context: Dict[str, Any]
+    ) -> List[SymbolicRule]:
+        """Generate symbolic rules from features."""
+        rules = []
         
-        Format as:
-        [R1]
-        Condition: ...
-        Implication: ...
-        Certainty: ...
-        Source: ...
-        """
+        # Process feature combinations
+        for i, feature1 in enumerate(features):
+            for j, feature2 in enumerate(features[i+1:], i+1):
+                # Calculate correlation
+                correlation = np.corrcoef(feature1.values, feature2.values)[0, 1]
+                
+                if abs(correlation) > self.rule_confidence_threshold:
+                    # Create rule based on correlation
+                    if correlation > 0:
+                        condition = f"{feature1.name} AND {feature2.name}"
+                        conclusion = "positively_correlated"
+                    else:
+                        condition = f"{feature1.name} XOR {feature2.name}"
+                        conclusion = "negatively_correlated"
+                    
+                    rules.append(SymbolicRule(
+                        name=f"rule_{len(rules)}",
+                        conditions=[condition],
+                        conclusion=conclusion,
+                        confidence=abs(correlation),
+                        metadata={
+                            'features': [feature1.name, feature2.name],
+                            'correlation': correlation
+                        }
+                    ))
+                
+                if len(rules) >= self.max_rules:
+                    break
+            
+            if len(rules) >= self.max_rules:
+                break
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_rules(response["answer"])
-
-    async def _neurosymbolic_integration(self, features: List[Dict[str, Any]], rules: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
-        prompt = f"""
-        Integrate neural and symbolic components:
-        Features: {json.dumps(features)}
-        Rules: {json.dumps(rules)}
-        Context: {json.dumps(context)}
+        return rules
+    
+    async def _combine_approaches(
+        self,
+        features: List[NeuralFeature],
+        rules: List[SymbolicRule],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Combine neural and symbolic approaches."""
+        combined = {
+            'neural_weights': {},
+            'symbolic_weights': {},
+            'combined_scores': {}
+        }
         
-        For each integration:
-        1. [Components]: What is being integrated
-        2. [Method]: How they are combined
-        3. [Result]: Integration outcome
-        4. [Confidence]: Integration confidence
+        # Calculate neural weights
+        total_importance = sum(f.importance for f in features)
+        if total_importance > 0:
+            combined['neural_weights'] = {
+                f.name: f.importance / total_importance
+                for f in features
+            }
         
-        Format as:
-        [I1]
-        Components: ...
-        Method: ...
-        Result: ...
-        Confidence: ...
-        """
+        # Calculate symbolic weights
+        total_confidence = sum(r.confidence for r in rules)
+        if total_confidence > 0:
+            combined['symbolic_weights'] = {
+                r.name: r.confidence / total_confidence
+                for r in rules
+            }
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_integration(response["answer"])
-
-    async def _final_inference(self, integrated: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
-        prompt = f"""
-        Draw final conclusions from integrated reasoning:
-        Integrated: {json.dumps(integrated)}
-        Context: {json.dumps(context)}
+        # Combine scores
+        all_elements = set(
+            list(combined['neural_weights'].keys()) +
+            list(combined['symbolic_weights'].keys())
+        )
         
-        Provide:
-        1. Final answer/conclusion
-        2. Confidence level (0-1)
-        3. Explanation of reasoning
-        4. Key factors considered
-        """
+        for element in all_elements:
+            neural_score = combined['neural_weights'].get(element, 0)
+            symbolic_score = combined['symbolic_weights'].get(element, 0)
+            
+            # Simple weighted average
+            combined['combined_scores'][element] = (
+                neural_score * 0.6 +  # Favor neural slightly
+                symbolic_score * 0.4
+            )
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_conclusion(response["answer"])
-
-    def _parse_features(self, response: str) -> List[Dict[str, Any]]:
-        """Parse neural features from response."""
-        features = []
-        current = None
+        return combined
+    
+    async def _generate_analysis(
+        self,
+        combined: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Generate neurosymbolic analysis."""
+        # Sort elements by combined score
+        ranked_elements = sorted(
+            combined['combined_scores'].items(),
+            key=lambda x: x[1],
+            reverse=True
+        )
         
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('[F'):
-                if current:
-                    features.append(current)
-                current = {
-                    "type": "",
-                    "value": "",
-                    "confidence": 0.0,
-                    "relations": []
-                }
-            elif current:
-                if line.startswith('Type:'):
-                    current["type"] = line[5:].strip()
-                elif line.startswith('Value:'):
-                    current["value"] = line[6:].strip()
-                elif line.startswith('Confidence:'):
-                    try:
-                        current["confidence"] = float(line[11:].strip())
-                    except:
-                        pass
-                elif line.startswith('Relations:'):
-                    current["relations"] = [r.strip() for r in line[10:].split(',')]
+        # Calculate statistics
+        scores = list(combined['combined_scores'].values())
+        mean = np.mean(scores) if scores else 0
+        std = np.std(scores) if scores else 0
         
-        if current:
-            features.append(current)
+        # Calculate entropy
+        entropy = -sum(
+            s * np.log2(s) if s > 0 else 0
+            for s in combined['combined_scores'].values()
+        )
         
-        return features
-
-    def _parse_rules(self, response: str) -> List[Dict[str, Any]]:
-        """Parse symbolic rules from response."""
-        rules = []
-        current = None
+        return {
+            'top_element': ranked_elements[0][0] if ranked_elements else '',
+            'score': ranked_elements[0][1] if ranked_elements else 0,
+            'alternatives': [
+                {'name': name, 'score': score}
+                for name, score in ranked_elements[1:]
+            ],
+            'statistics': {
+                'mean': mean,
+                'std': std,
+                'entropy': entropy
+            }
+        }
+    
+    def _format_analysis(self, analysis: Dict[str, Any]) -> str:
+        """Format analysis into readable text."""
+        sections = []
         
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('[R'):
-                if current:
-                    rules.append(current)
-                current = {
-                    "condition": "",
-                    "implication": "",
-                    "certainty": 0.0,
-                    "source": ""
-                }
-            elif current:
-                if line.startswith('Condition:'):
-                    current["condition"] = line[10:].strip()
-                elif line.startswith('Implication:'):
-                    current["implication"] = line[12:].strip()
-                elif line.startswith('Certainty:'):
-                    try:
-                        current["certainty"] = float(line[10:].strip())
-                    except:
-                        pass
-                elif line.startswith('Source:'):
-                    current["source"] = line[7:].strip()
+        # Top element
+        if analysis['top_element']:
+            sections.append(
+                f"Most significant element: {analysis['top_element']} "
+                f"(score: {analysis['score']:.2%})"
+            )
         
-        if current:
-            rules.append(current)
+        # Alternative elements
+        if analysis['alternatives']:
+            sections.append("\nAlternative elements:")
+            for alt in analysis['alternatives']:
+                sections.append(
+                    f"- {alt['name']}: {alt['score']:.2%}"
+                )
         
-        return rules
-
-    def _parse_integration(self, response: str) -> List[Dict[str, Any]]:
-        """Parse integration results from response."""
-        integrations = []
-        current = None
+        # Statistics
+        stats = analysis['statistics']
+        sections.append("\nAnalysis statistics:")
+        sections.append(f"- Mean score: {stats['mean']:.2%}")
+        sections.append(f"- Standard deviation: {stats['std']:.2%}")
+        sections.append(f"- Information entropy: {stats['entropy']:.2f} bits")
         
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('[I'):
-                if current:
-                    integrations.append(current)
-                current = {
-                    "components": "",
-                    "method": "",
-                    "result": "",
-                    "confidence": 0.0
-                }
-            elif current:
-                if line.startswith('Components:'):
-                    current["components"] = line[11:].strip()
-                elif line.startswith('Method:'):
-                    current["method"] = line[7:].strip()
-                elif line.startswith('Result:'):
-                    current["result"] = line[7:].strip()
-                elif line.startswith('Confidence:'):
-                    try:
-                        current["confidence"] = float(line[11:].strip())
-                    except:
-                        pass
+        return "\n".join(sections)
+    
+    def _calculate_confidence(self, combined: Dict[str, Any]) -> float:
+        """Calculate overall confidence score."""
+        if not combined['combined_scores']:
+            return 0.0
         
-        if current:
-            integrations.append(current)
+        # Base confidence
+        confidence = 0.5
         
-        return integrations
-
-    def _parse_conclusion(self, response: str) -> Dict[str, Any]:
-        """Parse final conclusion from response."""
-        conclusion = {
-            "answer": "",
-            "confidence": 0.0,
-            "explanation": "",
-            "factors": []
-        }
+        # Get scores
+        scores = list(combined['combined_scores'].values())
         
-        mode = None
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('Answer:'):
-                conclusion["answer"] = line[7:].strip()
-            elif line.startswith('Confidence:'):
-                try:
-                    conclusion["confidence"] = float(line[11:].strip())
-                except:
-                    conclusion["confidence"] = 0.5
-            elif line.startswith('Explanation:'):
-                conclusion["explanation"] = line[12:].strip()
-            elif line.startswith('Factors:'):
-                mode = "factors"
-            elif mode == "factors" and line.startswith('- '):
-                conclusion["factors"].append(line[2:].strip())
+        # Strong leading score increases confidence
+        max_score = max(scores)
+        if max_score > 0.8:
+            confidence += 0.3
+        elif max_score > 0.6:
+            confidence += 0.2
+        elif max_score > 0.4:
+            confidence += 0.1
+        
+        # Low entropy (clear distinction) increases confidence
+        entropy = -sum(s * np.log2(s) if s > 0 else 0 for s in scores)
+        max_entropy = -np.log2(1/len(scores))  # Maximum possible entropy
+        
+        if entropy < 0.3 * max_entropy:
+            confidence += 0.2
+        elif entropy < 0.6 * max_entropy:
+            confidence += 0.1
         
-        return conclusion
+        return min(confidence, 1.0)

reasoning/portfolio_optimization.py

@@ -9,6 +9,8 @@ from datetime import datetime
 import numpy as np
 from collections import defaultdict
 
+from .base import ReasoningStrategy
+
 @dataclass
 class VentureMetrics:
     """Venture performance metrics."""
@@ -350,3 +352,198 @@ class PortfolioOptimizer:
             })
         
         return opportunities
+
+class PortfolioOptimizationStrategy(ReasoningStrategy):
+    """
+    Advanced portfolio optimization strategy that:
+    1. Analyzes venture metrics
+    2. Optimizes resource allocation
+    3. Balances risk-reward
+    4. Maximizes portfolio synergies
+    5. Provides actionable recommendations
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize portfolio optimization strategy."""
+        super().__init__()
+        self.config = config or {}
+        self.optimizer = PortfolioOptimizer()
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Generate portfolio optimization strategy based on query and context.
+        
+        Args:
+            query: The portfolio optimization query
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing optimization strategy and confidence scores
+        """
+        try:
+            # Extract portfolio parameters
+            params = self._extract_parameters(query, context)
+            
+            # Optimize portfolio
+            optimization_result = self.optimizer.optimize_portfolio(
+                ventures=params.get('ventures', []),
+                constraints=params.get('constraints', []),
+                objectives=params.get('objectives', [])
+            )
+            
+            # Get metrics
+            metrics = self.optimizer.get_portfolio_metrics()
+            
+            # Generate recommendations
+            recommendations = self._generate_recommendations(
+                optimization_result,
+                metrics
+            )
+            
+            return {
+                'answer': self._format_strategy(optimization_result, metrics, recommendations),
+                'confidence': self._calculate_confidence(optimization_result),
+                'optimization': optimization_result,
+                'metrics': metrics,
+                'recommendations': recommendations
+            }
+            
+        except Exception as e:
+            logging.error(f"Portfolio optimization failed: {str(e)}")
+            return {
+                'error': f"Portfolio optimization failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    def _extract_parameters(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Extract optimization parameters from query and context."""
+        params = {}
+        
+        # Extract ventures
+        if 'ventures' in context:
+            params['ventures'] = context['ventures']
+        else:
+            # Default empty portfolio
+            params['ventures'] = []
+        
+        # Extract constraints
+        if 'constraints' in context:
+            params['constraints'] = context['constraints']
+        else:
+            # Default constraints
+            params['constraints'] = [
+                'budget_limit',
+                'risk_tolerance',
+                'resource_capacity'
+            ]
+        
+        # Extract objectives
+        if 'objectives' in context:
+            params['objectives'] = context['objectives']
+        else:
+            # Default objectives
+            params['objectives'] = [
+                'maximize_returns',
+                'minimize_risk',
+                'maximize_synergies'
+            ]
+        
+        return params
+    
+    def _generate_recommendations(
+        self,
+        optimization_result: Dict[str, Any],
+        metrics: Dict[str, Any]
+    ) -> List[str]:
+        """Generate actionable recommendations."""
+        recommendations = []
+        
+        # Portfolio composition recommendations
+        if 'allocation' in optimization_result:
+            allocation = optimization_result['allocation']
+            recommendations.extend([
+                f"Allocate {alloc['percentage']:.1f}% to {alloc['venture']}"
+                for alloc in allocation
+            ])
+        
+        # Risk management recommendations
+        if 'risk_analysis' in metrics:
+            risk = metrics['risk_analysis']
+            if risk.get('total_risk', 0) > 0.7:
+                recommendations.append(
+                    "Consider reducing exposure to high-risk ventures"
+                )
+            if risk.get('correlation', 0) > 0.8:
+                recommendations.append(
+                    "Increase portfolio diversification to reduce correlation"
+                )
+        
+        # Performance optimization recommendations
+        if 'performance' in metrics:
+            perf = metrics['performance']
+            if perf.get('sharpe_ratio', 0) < 1.0:
+                recommendations.append(
+                    "Optimize risk-adjusted returns through better venture selection"
+                )
+            if perf.get('efficiency', 0) < 0.8:
+                recommendations.append(
+                    "Improve resource allocation efficiency across ventures"
+                )
+        
+        return recommendations
+    
+    def _calculate_confidence(self, optimization_result: Dict[str, Any]) -> float:
+        """Calculate confidence score based on optimization quality."""
+        # Base confidence
+        confidence = 0.5
+        
+        # Adjust based on optimization completeness
+        if optimization_result.get('allocation'):
+            confidence += 0.1
+        if optimization_result.get('risk_analysis'):
+            confidence += 0.1
+        if optimization_result.get('performance_metrics'):
+            confidence += 0.1
+            
+        # Adjust based on solution quality
+        if optimization_result.get('convergence_status') == 'optimal':
+            confidence += 0.2
+        elif optimization_result.get('convergence_status') == 'suboptimal':
+            confidence += 0.1
+            
+        return min(confidence, 1.0)
+    
+    def _format_strategy(
+        self,
+        optimization_result: Dict[str, Any],
+        metrics: Dict[str, Any],
+        recommendations: List[str]
+    ) -> str:
+        """Format optimization strategy into readable text."""
+        sections = []
+        
+        # Portfolio allocation
+        if 'allocation' in optimization_result:
+            allocation = optimization_result['allocation']
+            sections.append("Portfolio Allocation:")
+            for alloc in allocation:
+                sections.append(
+                    f"- {alloc['venture']}: {alloc['percentage']:.1f}%"
+                )
+        
+        # Key metrics
+        if metrics:
+            sections.append("\nKey Metrics:")
+            for key, value in metrics.items():
+                if isinstance(value, (int, float)):
+                    sections.append(f"- {key.replace('_', ' ').title()}: {value:.2f}")
+                else:
+                    sections.append(f"- {key.replace('_', ' ').title()}: {value}")
+        
+        # Recommendations
+        if recommendations:
+            sections.append("\nKey Recommendations:")
+            for rec in recommendations:
+                sections.append(f"- {rec}")
+        
+        return "\n".join(sections)

reasoning/quantum.py

@@ -1,268 +1,276 @@
 """Quantum-inspired reasoning implementations."""
 
 import logging
-from typing import Dict, Any, List
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
 import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
 
 from .base import ReasoningStrategy
 
+@dataclass
+class QuantumState:
+    """Quantum state with superposition and entanglement."""
+    name: str
+    amplitude: complex
+    phase: float
+    entangled_states: List[str] = field(default_factory=list)
+
 class QuantumReasoning(ReasoningStrategy):
-    """Implements quantum-inspired reasoning using superposition and entanglement principles."""
+    """
+    Advanced quantum reasoning that:
+    1. Creates quantum states
+    2. Applies quantum operations
+    3. Measures outcomes
+    4. Handles superposition
+    5. Models entanglement
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize quantum reasoning."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Configure quantum parameters
+        self.num_qubits = self.config.get('num_qubits', 3)
+        self.measurement_threshold = self.config.get('measurement_threshold', 0.1)
+        self.decoherence_rate = self.config.get('decoherence_rate', 0.01)
     
     async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Apply quantum reasoning to analyze complex decisions.
+        
+        Args:
+            query: The input query to reason about
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing reasoning results and confidence scores
+        """
         try:
-            # Create superposition of possibilities
-            superposition = await self._create_superposition(query, context)
+            # Initialize quantum states
+            states = await self._initialize_states(query, context)
             
-            # Analyze entanglements
-            entanglements = await self._analyze_entanglements(superposition, context)
+            # Apply quantum operations
+            evolved_states = await self._apply_operations(states, context)
             
-            # Perform quantum interference
-            interference = await self._quantum_interference(superposition, entanglements, context)
+            # Measure outcomes
+            measurements = await self._measure_states(evolved_states, context)
             
-            # Collapse to solution
-            solution = await self._collapse_to_solution(interference, context)
+            # Generate analysis
+            analysis = await self._generate_analysis(measurements, context)
             
             return {
-                "success": True,
-                "answer": solution["conclusion"],
-                "superposition": superposition,
-                "entanglements": entanglements,
-                "interference_patterns": interference,
-                "measurement": solution["measurement"],
-                "confidence": solution["confidence"]
+                'answer': self._format_analysis(analysis),
+                'confidence': self._calculate_confidence(measurements),
+                'states': states,
+                'evolved_states': evolved_states,
+                'measurements': measurements,
+                'analysis': analysis
             }
+            
         except Exception as e:
-            return {"success": False, "error": str(e)}
-
-    async def _create_superposition(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
-        prompt = f"""
-        Create superposition of possible solutions:
-        Query: {query}
-        Context: {json.dumps(context)}
+            logging.error(f"Quantum reasoning failed: {str(e)}")
+            return {
+                'error': f"Quantum reasoning failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    async def _initialize_states(
+        self,
+        query: str,
+        context: Dict[str, Any]
+    ) -> List[QuantumState]:
+        """Initialize quantum states."""
+        states = []
         
-        For each possibility state:
-        1. [State]: Description of possibility
-        2. [Amplitude]: Relative strength (0-1)
-        3. [Phase]: Relationship to other states
-        4. [Basis]: Underlying assumptions
+        # Extract key terms for state initialization
+        terms = set(query.lower().split())
         
-        Format as:
-        [S1]
-        State: ...
-        Amplitude: ...
-        Phase: ...
-        Basis: ...
-        """
+        # Create quantum states based on terms
+        for i, term in enumerate(terms):
+            if i >= self.num_qubits:
+                break
+                
+            # Calculate initial amplitude and phase
+            amplitude = 1.0 / np.sqrt(len(terms[:self.num_qubits]))
+            phase = 2 * np.pi * i / len(terms[:self.num_qubits])
+            
+            states.append(QuantumState(
+                name=term,
+                amplitude=complex(amplitude * np.cos(phase), amplitude * np.sin(phase)),
+                phase=phase
+            ))
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_superposition(response["answer"])
-
-    async def _analyze_entanglements(self, superposition: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
-        prompt = f"""
-        Analyze entanglements between possibilities:
-        Superposition: {json.dumps(superposition)}
-        Context: {json.dumps(context)}
+        # Create entangled states if specified
+        if context.get('entangle', False):
+            self._entangle_states(states)
         
-        For each entanglement describe:
-        1. [States]: Entangled states
-        2. [Type]: Nature of entanglement
-        3. [Strength]: Correlation strength
-        4. [Impact]: Effect on outcomes
+        return states
+    
+    async def _apply_operations(
+        self,
+        states: List[QuantumState],
+        context: Dict[str, Any]
+    ) -> List[QuantumState]:
+        """Apply quantum operations to states."""
+        evolved_states = []
         
-        Format as:
-        [E1]
-        States: ...
-        Type: ...
-        Strength: ...
-        Impact: ...
-        """
+        # Get operation parameters
+        rotation = context.get('rotation', 0.0)
+        phase_shift = context.get('phase_shift', 0.0)
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_entanglements(response["answer"])
-
-    async def _quantum_interference(self, superposition: List[Dict[str, Any]], entanglements: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
-        prompt = f"""
-        Calculate quantum interference patterns:
-        Superposition: {json.dumps(superposition)}
-        Entanglements: {json.dumps(entanglements)}
-        Context: {json.dumps(context)}
+        for state in states:
+            # Apply rotation
+            rotated_amplitude = state.amplitude * np.exp(1j * rotation)
+            
+            # Apply phase shift
+            shifted_phase = (state.phase + phase_shift) % (2 * np.pi)
+            
+            # Apply decoherence
+            decohered_amplitude = rotated_amplitude * (1 - self.decoherence_rate)
+            
+            evolved_states.append(QuantumState(
+                name=state.name,
+                amplitude=decohered_amplitude,
+                phase=shifted_phase,
+                entangled_states=state.entangled_states.copy()
+            ))
         
-        For each interference pattern:
-        1. [Pattern]: Description
-        2. [Amplitude]: Combined strength
-        3. [Phase]: Combined phase
-        4. [Effect]: Impact on solution space
+        return evolved_states
+    
+    async def _measure_states(
+        self,
+        states: List[QuantumState],
+        context: Dict[str, Any]
+    ) -> Dict[str, float]:
+        """Measure quantum states."""
+        measurements = {}
         
-        Format as:
-        [I1]
-        Pattern: ...
-        Amplitude: ...
-        Phase: ...
-        Effect: ...
-        """
+        # Calculate total probability
+        total_probability = sum(
+            abs(state.amplitude) ** 2
+            for state in states
+        )
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_interference(response["answer"])
-
-    async def _collapse_to_solution(self, interference: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
-        prompt = f"""
-        Collapse quantum state to final solution:
-        Interference: {json.dumps(interference)}
-        Context: {json.dumps(context)}
+        if total_probability > 0:
+            # Normalize and store measurements
+            for state in states:
+                probability = (abs(state.amplitude) ** 2) / total_probability
+                if probability > self.measurement_threshold:
+                    measurements[state.name] = probability
         
-        Provide:
-        1. Final measured state
-        2. Measurement confidence
-        3. Key quantum effects utilized
-        4. Overall conclusion
-        5. Confidence level (0-1)
-        """
+        return measurements
+    
+    def _entangle_states(self, states: List[QuantumState]) -> None:
+        """Create entanglement between states."""
+        if len(states) < 2:
+            return
+            
+        # Simple entanglement: connect adjacent states
+        for i in range(len(states) - 1):
+            states[i].entangled_states.append(states[i + 1].name)
+            states[i + 1].entangled_states.append(states[i].name)
+    
+    async def _generate_analysis(
+        self,
+        measurements: Dict[str, float],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Generate quantum analysis."""
+        # Sort states by measurement probability
+        ranked_states = sorted(
+            measurements.items(),
+            key=lambda x: x[1],
+            reverse=True
+        )
         
-        response = await context["groq_api"].predict(prompt)
-        return self._parse_collapse(response["answer"])
-
-    def _parse_superposition(self, response: str) -> List[Dict[str, Any]]:
-        """Parse superposition states from response."""
-        superposition = []
-        current_state = None
+        # Calculate quantum statistics
+        amplitudes = list(measurements.values())
+        mean = np.mean(amplitudes) if amplitudes else 0
+        std = np.std(amplitudes) if amplitudes else 0
         
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('[S'):
-                if current_state:
-                    superposition.append(current_state)
-                current_state = {
-                    "state": "",
-                    "amplitude": 0.0,
-                    "phase": "",
-                    "basis": ""
-                }
-            elif current_state:
-                if line.startswith('State:'):
-                    current_state["state"] = line[6:].strip()
-                elif line.startswith('Amplitude:'):
-                    try:
-                        current_state["amplitude"] = float(line[10:].strip())
-                    except:
-                        pass
-                elif line.startswith('Phase:'):
-                    current_state["phase"] = line[6:].strip()
-                elif line.startswith('Basis:'):
-                    current_state["basis"] = line[6:].strip()
+        # Calculate quantum entropy
+        entropy = -sum(
+            p * np.log2(p) if p > 0 else 0
+            for p in measurements.values()
+        )
+        
+        return {
+            'top_state': ranked_states[0][0] if ranked_states else '',
+            'probability': ranked_states[0][1] if ranked_states else 0,
+            'alternatives': [
+                {'name': name, 'probability': prob}
+                for name, prob in ranked_states[1:]
+            ],
+            'statistics': {
+                'mean': mean,
+                'std': std,
+                'entropy': entropy
+            }
+        }
+    
+    def _format_analysis(self, analysis: Dict[str, Any]) -> str:
+        """Format analysis into readable text."""
+        sections = []
         
-        if current_state:
-            superposition.append(current_state)
+        # Top quantum state
+        if analysis['top_state']:
+            sections.append(
+                f"Most probable quantum state: {analysis['top_state']} "
+                f"(probability: {analysis['probability']:.2%})"
+            )
         
-        return superposition
-
-    def _parse_entanglements(self, response: str) -> List[Dict[str, Any]]:
-        """Parse entanglements from response."""
-        entanglements = []
-        current_entanglement = None
+        # Alternative states
+        if analysis['alternatives']:
+            sections.append("\nAlternative quantum states:")
+            for alt in analysis['alternatives']:
+                sections.append(
+                    f"- {alt['name']}: {alt['probability']:.2%}"
+                )
         
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('[E'):
-                if current_entanglement:
-                    entanglements.append(current_entanglement)
-                current_entanglement = {
-                    "states": "",
-                    "type": "",
-                    "strength": 0.0,
-                    "impact": ""
-                }
-            elif current_entanglement:
-                if line.startswith('States:'):
-                    current_entanglement["states"] = line[7:].strip()
-                elif line.startswith('Type:'):
-                    current_entanglement["type"] = line[5:].strip()
-                elif line.startswith('Strength:'):
-                    try:
-                        current_entanglement["strength"] = float(line[9:].strip())
-                    except:
-                        pass
-                elif line.startswith('Impact:'):
-                    current_entanglement["impact"] = line[7:].strip()
+        # Quantum statistics
+        stats = analysis['statistics']
+        sections.append("\nQuantum statistics:")
+        sections.append(f"- Mean amplitude: {stats['mean']:.2%}")
+        sections.append(f"- Standard deviation: {stats['std']:.2%}")
+        sections.append(f"- Quantum entropy: {stats['entropy']:.2f} bits")
         
-        if current_entanglement:
-            entanglements.append(current_entanglement)
+        return "\n".join(sections)
+    
+    def _calculate_confidence(self, measurements: Dict[str, float]) -> float:
+        """Calculate overall confidence score."""
+        if not measurements:
+            return 0.0
         
-        return entanglements
-
-    def _parse_interference(self, response: str) -> List[Dict[str, Any]]:
-        """Parse interference patterns from response."""
-        interference = []
-        current_pattern = None
+        # Base confidence
+        confidence = 0.5
         
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('[I'):
-                if current_pattern:
-                    interference.append(current_pattern)
-                current_pattern = {
-                    "pattern": "",
-                    "amplitude": 0.0,
-                    "phase": "",
-                    "effect": ""
-                }
-            elif current_pattern:
-                if line.startswith('Pattern:'):
-                    current_pattern["pattern"] = line[8:].strip()
-                elif line.startswith('Amplitude:'):
-                    try:
-                        current_pattern["amplitude"] = float(line[10:].strip())
-                    except:
-                        pass
-                elif line.startswith('Phase:'):
-                    current_pattern["phase"] = line[6:].strip()
-                elif line.startswith('Effect:'):
-                    current_pattern["effect"] = line[7:].strip()
+        # Adjust based on measurement distribution
+        probs = list(measurements.values())
         
-        if current_pattern:
-            interference.append(current_pattern)
+        # Strong leading measurement increases confidence
+        max_prob = max(probs)
+        if max_prob > 0.8:
+            confidence += 0.3
+        elif max_prob > 0.6:
+            confidence += 0.2
+        elif max_prob > 0.4:
+            confidence += 0.1
         
-        return interference
-
-    def _parse_collapse(self, response: str) -> Dict[str, Any]:
-        """Parse collapse to solution from response."""
-        collapse = {
-            "measurement": "",
-            "confidence": 0.0,
-            "quantum_effects": [],
-            "conclusion": ""
-        }
+        # Low entropy (clear distinction) increases confidence
+        entropy = -sum(p * np.log2(p) if p > 0 else 0 for p in probs)
+        max_entropy = -np.log2(1/len(probs))  # Maximum possible entropy
         
-        mode = None
-        for line in response.split('\n'):
-            line = line.strip()
-            if not line:
-                continue
-                
-            if line.startswith('Measurement:'):
-                collapse["measurement"] = line[12:].strip()
-            elif line.startswith('Confidence:'):
-                try:
-                    collapse["confidence"] = float(line[11:].strip())
-                except:
-                    collapse["confidence"] = 0.5
-            elif line.startswith('Quantum Effects:'):
-                mode = "effects"
-            elif mode == "effects" and line.startswith('- '):
-                collapse["quantum_effects"].append(line[2:].strip())
-            elif line.startswith('Conclusion:'):
-                collapse["conclusion"] = line[11:].strip()
+        if entropy < 0.3 * max_entropy:
+            confidence += 0.2
+        elif entropy < 0.6 * max_entropy:
+            confidence += 0.1
         
-        return collapse
+        return min(confidence, 1.0)
 
 
 class QuantumInspiredStrategy(ReasoningStrategy):

requirements.txt

@@ -1,7 +1,7 @@
 fastapi>=0.68.0
 uvicorn>=0.15.0
 pydantic>=2.0.0
-gradio>=4.16.0
+gradio==5.11.0
 llama-cpp-python>=0.2.23
 huggingface-hub>=0.19.4
 numpy>=1.24.0