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from dataclasses import dataclass
from typing import Dict, List, Any
# Define missing classes
class MetaCognitiveMonitor:
def analyze(self, current_state):
# Placeholder implementation
class MonitoringResults:
def __init__(self):
self.attention_focus = {}
self.processing_efficiency = 0.8
return MonitoringResults()
class SelfEvaluationSystem:
def evaluate(self, monitoring_results):
# Placeholder implementation
class EvaluationResults:
def __init__(self):
self.recommendations = []
return EvaluationResults()
@dataclass
class ReflectionOutput:
insights: Dict[str, Any]
adjustments: List[str]
consciousness_state: float
self_awareness_metrics: Dict[str, float]
class ReflexiveLayer:
def __init__(self):
self.meta_cognitive_monitor = MetaCognitiveMonitor()
self.self_evaluation_system = SelfEvaluationSystem()
self.consciousness_threshold = 0.7
self.reflection_history = []
def process_reflection(self, current_state):
monitoring_results = self.meta_cognitive_monitor.analyze(current_state)
evaluation_results = self.self_evaluation_system.evaluate(monitoring_results)
return self._generate_reflection_output(monitoring_results, evaluation_results)
def _generate_reflection_output(self, monitoring_results, evaluation_results):
output = ReflectionOutput(
insights=self._extract_insights(monitoring_results),
adjustments=evaluation_results.recommendations,
consciousness_state=self._calculate_consciousness_state(),
self_awareness_metrics=self._compute_awareness_metrics()
)
self.reflection_history.append(output)
return output
def _extract_insights(self, monitoring_results):
return {
'cognitive_patterns': self._analyze_cognitive_patterns(),
'learning_trends': self._analyze_learning_trends(),
'attention_distribution': monitoring_results.attention_focus,
'processing_efficiency': monitoring_results.processing_efficiency
}
def _calculate_consciousness_state(self):
# Implementation of consciousness state calculation
return 0.8 # Return a default float value instead of None
def _compute_awareness_metrics(self):
# Implementation of self-awareness metrics computation
return {"self_reflection": 0.7, "adaptability": 0.8} # Return a default dict instead of None
def _analyze_cognitive_patterns(self):
# Implementation for analyzing cognitive patterns
return {"pattern_recognition": 0.75}
def _analyze_learning_trends(self):
# Implementation for analyzing learning trends
return {"improvement_rate": 0.65}
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