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| """ | |
| This module implements a neuro-symbolic architecture combining neural networks with symbolic reasoning. | |
| """ | |
| from typing import Dict, Any, List, Optional | |
| try: | |
| import numpy as np | |
| except Exception: | |
| np = None | |
| class NeuroSymbolicEngine: | |
| def __init__(self): | |
| self.symbolic_rules = [] | |
| self.neural_state = {} | |
| self.reasoning_cache = {} | |
| def add_symbolic_rule(self, rule: Dict[str, Any]): | |
| """ | |
| Add a symbolic reasoning rule. | |
| Args: | |
| rule: A dictionary defining the rule structure | |
| """ | |
| self.symbolic_rules.append(rule) | |
| def process_neural_input(self, input_data: Dict[str, Any]) -> Dict[str, Any]: | |
| """ | |
| Process input through the neural component. | |
| Args: | |
| input_data: Data to be processed by neural networks | |
| Returns: | |
| Processed neural outputs | |
| """ | |
| # Simulate neural processing | |
| if np is not None: | |
| processed_data = { | |
| "features": np.random.random(10).tolist(), # Simulated feature extraction | |
| "confidence": float(np.random.random()), | |
| "embedding": np.random.random(128).tolist() # Simulated embedding | |
| } | |
| else: | |
| import random | |
| processed_data = { | |
| "features": [random.random() for _ in range(10)], | |
| "confidence": float(random.random()), | |
| "embedding": [random.random() for _ in range(128)] | |
| } | |
| self.neural_state.update(processed_data) | |
| return processed_data | |
| def apply_symbolic_reasoning(self, context: Dict[str, Any]) -> Dict[str, Any]: | |
| """ | |
| Apply symbolic reasoning rules to the context. | |
| Args: | |
| context: The context to apply reasoning to | |
| Returns: | |
| Results of symbolic reasoning | |
| """ | |
| results = { | |
| "applied_rules": [], | |
| "inferences": [], | |
| "confidence": 0.0 | |
| } | |
| for rule in self.symbolic_rules: | |
| if self._check_rule_applicability(rule, context): | |
| inference = self._apply_rule(rule, context) | |
| results["applied_rules"].append(rule) | |
| results["inferences"].append(inference) | |
| if np is not None: | |
| results["confidence"] = float(np.mean([inf.get("confidence", 0) for inf in results["inferences"]])) if results["inferences"] else 0.0 | |
| else: | |
| vals = [inf.get("confidence", 0) for inf in results["inferences"]] | |
| results["confidence"] = float(sum(vals)/len(vals)) if vals else 0.0 | |
| return results | |
| def combine_neural_symbolic(self, neural_output: Dict[str, Any], symbolic_output: Dict[str, Any]) -> Dict[str, Any]: | |
| """ | |
| Combine neural and symbolic processing results. | |
| Args: | |
| neural_output: Results from neural processing | |
| symbolic_output: Results from symbolic reasoning | |
| Returns: | |
| Combined neuro-symbolic output | |
| """ | |
| if np is not None: | |
| confidence = float(np.mean([ | |
| neural_output.get("confidence", 0), | |
| symbolic_output.get("confidence", 0) | |
| ])) | |
| else: | |
| confidence = float((neural_output.get("confidence", 0) + symbolic_output.get("confidence", 0))/2) | |
| combined = { | |
| "neural_features": neural_output.get("features", []), | |
| "symbolic_inferences": symbolic_output.get("inferences", []), | |
| "confidence": confidence, | |
| "combined_embedding": self._combine_embeddings( | |
| neural_output.get("embedding", []), | |
| symbolic_output.get("embedding", []) | |
| ) | |
| } | |
| return combined | |
| def reason(self, input_data: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]: | |
| """ | |
| Perform complete neuro-symbolic reasoning. | |
| Args: | |
| input_data: Input data to process | |
| context: Context for reasoning | |
| Returns: | |
| Complete reasoning results | |
| """ | |
| # Process through neural component | |
| neural_output = self.process_neural_input(input_data) | |
| # Apply symbolic reasoning | |
| symbolic_output = self.apply_symbolic_reasoning(context) | |
| # Combine results | |
| result = self.combine_neural_symbolic(neural_output, symbolic_output) | |
| # Cache results | |
| cache_key = str(hash(str(input_data) + str(context))) | |
| self.reasoning_cache[cache_key] = result | |
| return result | |
| def _check_rule_applicability(self, rule: Dict[str, Any], context: Dict[str, Any]) -> bool: | |
| """ | |
| Check if a symbolic rule is applicable in the given context. | |
| """ | |
| # Simplified rule checking | |
| conditions = rule.get("conditions", []) | |
| return all( | |
| context.get(cond["key"]) == cond["value"] | |
| for cond in conditions | |
| ) | |
| def _apply_rule(self, rule: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]: | |
| """ | |
| Apply a symbolic rule to derive new knowledge. | |
| """ | |
| return { | |
| "rule_id": rule.get("id"), | |
| "inference": rule.get("conclusion", ""), | |
| "confidence": np.random.random() # Simplified confidence | |
| } | |
| def _combine_embeddings(self, neural_embedding: List[float], symbolic_embedding: List[float]) -> List[float]: | |
| """ | |
| Combine neural and symbolic embeddings. | |
| """ | |
| # If either embedding is empty, return the other | |
| if not neural_embedding: | |
| return symbolic_embedding | |
| if not symbolic_embedding: | |
| return neural_embedding | |
| # If lengths differ, pad shorter one with zeros | |
| max_len = max(len(neural_embedding), len(symbolic_embedding)) | |
| neural_embedding = neural_embedding + [0] * (max_len - len(neural_embedding)) | |
| symbolic_embedding = symbolic_embedding + [0] * (max_len - len(symbolic_embedding)) | |
| # Combine with equal weights | |
| return [(n + s) / 2 for n, s in zip(neural_embedding, symbolic_embedding)] | |
| def get_cached_reasoning(self, input_data: Dict[str, Any], context: Dict[str, Any]) -> Optional[Dict[str, Any]]: | |
| """ | |
| Retrieve cached reasoning results if available. | |
| """ | |
| cache_key = str(hash(str(input_data) + str(context))) | |
| return self.reasoning_cache.get(cache_key) | |
| def clear_cache(self): | |
| """ | |
| Clear the reasoning cache. | |
| """ | |
| self.reasoning_cache = {} |