SM-2 Algorithm - Advanced Spaced Repetition Implementation

class SM2Algorithm:
    def __init__(self):
        self.MIN_EASE_FACTOR = 1.3
        self.MAX_EASE_FACTOR = 2.5
        self.START_EASE_FACTOR = 2.5
        self.EASE_FACTOR_INCREASE = 0.1
        self.EASE_FACTOR_DECREASE_FACTOR = 0.08

    def calculate_next_review(self, card, quality_score):
        """
        Calculate next review interval using enhanced SM-2 algorithm

        Args:
            card: Card object with current state
            quality_score: int (0-5) quality of recall
                0 - Complete blackout
                1 - Incorrect response, but the correct one remembered
                2 - Incorrect response, but the correct one seemed easy to recall
                3 - Correct response recalled with serious difficulty
                4 - Correct response after a hesitation
                5 - Perfect response

        Returns:
            dict: Updated card state and next review information
        """

        # Validate quality score
        quality_score = max(0, min(5, quality_score))

        # Store review history
        card.review_history.append({
            'date': datetime.now(),
            'quality_score': quality_score,
            'previous_interval': card.repetition_interval,
            'previous_ease_factor': card.ease_factor
        })

        if quality_score < 3:
            # Failed review - reset to beginning
            card.repetition_count = 0
            card.repetition_interval = 1
        else:
            # Successful review - calculate new interval
            if card.repetition_count == 0:
                card.repetition_interval = 1
            elif card.repetition_count == 1:
                card.repetition_interval = 6
            else:
                card.repetition_interval = int(card.repetition_interval * card.ease_factor)

            card.repetition_count += 1

        # Update ease factor
        ease_factor_change = self.EASE_FACTOR_INCREASE - (5 - quality_score) * (
            self.EASE_FACTOR_DECREASE_FACTOR + (5 - quality_score) * 0.02
        )

        card.ease_factor = max(
            self.MIN_EASE_FACTOR,
            card.ease_factor + ease_factor_change
        )

        # Update next review date
        card.next_review_date = datetime.now() + timedelta(days=card.repetition_interval)
        card.last_review_date = datetime.now()

        return {
            'next_interval': card.repetition_interval,
            'next_review_date': card.next_review_date,
            'new_ease_factor': card.ease_factor,
            'repetition_count': card.repetition_count,
            'interval_change': self._calculate_interval_change(card),
            'ease_factor_change': ease_factor_change,
            'quality_received': quality_score
        }

    def _calculate_interval_change(self, card):
        """Calculate the change in interval from previous to current"""
        if len(card.review_history) < 2:
            return 0

        previous_interval = card.review_history[-2]['previous_interval']
        current_interval = card.repetition_interval

        if previous_interval == 0:
            return current_interval

        return ((current_interval - previous_interval) / previous_interval) * 100

class EnhancedSM2Algorithm(SM2Algorithm):
    def __init__(self):
        super().__init__()
        self.modifiers = {
            'difficulty': {
                'Very Hard': 0.7,
                'Hard': 0.85,
                'Medium': 1.0,
                'Easy': 1.2
            },
            'subject': {
                'Physics': 0.95,  # More frequent reviews due to conceptual complexity
                'Chemistry': 1.0,
                'Mathematics': 0.9,  # More frequent reviews for practice
                'Biology': 1.1   # Can be spaced further apart
            },
            'user_performance': {
                'high_performer': 1.15,  # Can handle longer intervals
                'average_performer': 1.0,
                'struggling_student': 0.85  # Needs more frequent reviews
            }
        }

    def calculate_enhanced_next_review(self, card, quality_score, user_context):
        """
        Enhanced SM-2 algorithm with modern optimizations

        Args:
            card: Card object with current state
            quality_score: int (0-5) quality of recall
            user_context: dict with user-specific information

        Returns:
            dict: Enhanced review calculation with optimizations
        """

        # Base SM-2 calculation
        base_result = self.calculate_next_review(card, quality_score)

        # Apply modifiers
        modified_interval = base_result['next_interval']

        # Difficulty modifier
        difficulty_modifier = self.modifiers['difficulty'].get(card.difficulty, 1.0)
        modified_interval = int(modified_interval * difficulty_modifier)

        # Subject modifier
        subject_modifier = self.modifiers['subject'].get(card.subject, 1.0)
        modified_interval = int(modified_interval * subject_modifier)

        # User performance modifier
        user_performance_level = self._assess_user_performance(user_context)
        performance_modifier = self.modifiers['user_performance'].get(user_performance_level, 1.0)
        modified_interval = int(modified_interval * performance_modifier)

        # Time-of-day modifier
        time_modifier = self._calculate_time_modifier(user_context.get('review_time'))
        modified_interval = int(modified_interval * time_modifier)

        # Fatigue modifier
        fatigue_modifier = self._calculate_fatigue_modifier(user_context)
        modified_interval = int(modified_interval * fatigue_modifier)

        # Apply bounds checking
        modified_interval = max(1, min(3650, modified_interval))  # 1 day to 10 years

        # Update card with enhanced values
        card.repetition_interval = modified_interval
        card.next_review_date = datetime.now() + timedelta(days=modified_interval)

        return {
            **base_result,
            'enhanced_interval': modified_interval,
            'modifiers_applied': {
                'difficulty': difficulty_modifier,
                'subject': subject_modifier,
                'performance': performance_modifier,
                'time': time_modifier,
                'fatigue': fatigue_modifier
            },
            'modifier_explanation': self._explain_modifiers(
                difficulty_modifier, subject_modifier, performance_modifier,
                time_modifier, fatigue_modifier
            )
        }

    def _assess_user_performance(self, user_context):
        """Assess user performance level for interval adjustment"""

        recent_performance = user_context.get('recent_performance', [])
        if not recent_performance:
            return 'average_performer'

        average_quality = sum(recent_performance) / len(recent_performance)

        if average_quality >= 4.5:
            return 'high_performer'
        elif average_quality >= 3.5:
            return 'average_performer'
        else:
            return 'struggling_student'

    def _calculate_time_modifier(self, review_time):
        """Calculate time-of-day modifier for optimal review timing"""

        if not review_time:
            return 1.0

        hour = review_time.hour

        # Optimal learning times (based on cognitive science research)
        if 9 <= hour <= 11:  # Morning peak
            return 1.1
        elif 14 <= hour <= 16:  # Afternoon dip
            return 0.95
        elif 19 <= hour <= 21:  # Evening recovery
            return 1.05
        else:  # Off-hours
            return 0.9

    def _calculate_fatigue_modifier(self, user_context):
        """Calculate fatigue modifier based on session metrics"""

        session_length = user_context.get('session_length', 0)  # minutes
        cards_reviewed = user_context.get('cards_reviewed', 0)
        recent_accuracy = user_context.get('recent_accuracy', 1.0)

        # Base fatigue calculation
        fatigue_factor = 1.0

        # Session length fatigue
        if session_length > 60:  # More than 1 hour
            fatigue_factor *= 0.95
        elif session_length > 120:  # More than 2 hours
            fatigue_factor *= 0.9

        # Mental fatigue from card volume
        if cards_reviewed > 50:
            fatigue_factor *= 0.95
        elif cards_reviewed > 100:
            fatigue_factor *= 0.9

        # Performance-based fatigue
        if recent_accuracy < 0.7:
            fatigue_factor *= 0.95  # Reduce interval if struggling

        return fatigue_factor

class PredictiveIntervalOptimizer:
    def __init__(self):
        self.ml_model = None  # Would be loaded with trained model
        self.feature_extractor = FeatureExtractor()
        self.performance_predictor = PerformancePredictor()

    def predict_optimal_interval(self, card, user_history, context):
        """
        Use machine learning to predict optimal review interval

        Args:
            card: Current card being reviewed
            user_history: User's historical performance data
            context: Current review context

        Returns:
            dict: Predicted optimal interval with confidence score
        """

        # Extract features for ML model
        features = self.feature_extractor.extract_features(
            card, user_history, context
        )

        # Make prediction using trained model
        prediction = self.ml_model.predict(features)

        # Calculate confidence score
        confidence = self._calculate_prediction_confidence(features, prediction)

        # Apply safety bounds and adjustments
        optimal_interval = self._apply_safety_bounds(prediction, confidence)

        # Generate alternative intervals for different risk levels
        alternatives = self._generate_alternative_intervals(
            optimal_interval, confidence, card
        )

        return {
            'predicted_interval': optimal_interval,
            'confidence_score': confidence,
            'risk_assessment': self._assess_interval_risk(optimal_interval, confidence),
            'alternatives': alternatives,
            'prediction_factors': self._identify_prediction_factors(features),
            'recommended_action': self._recommend_action(confidence, optimal_interval)
        }

    def _calculate_prediction_confidence(self, features, prediction):
        """Calculate confidence score for interval prediction"""

        # Base confidence from model
        model_confidence = prediction.get('confidence', 0.5)

        # Adjust based on data quality
        data_quality_factor = self._assess_data_quality(features)

        # Adjust based on user consistency
        consistency_factor = self._assess_user_consistency(features)

        # Adjust based on similar cards
        similarity_factor = self._assess_similarity_confidence(features)

        # Combined confidence score
        confidence = model_confidence * data_quality_factor * consistency_factor * similarity_factor

        return min(1.0, max(0.0, confidence))

    def _generate_alternative_intervals(self, optimal_interval, confidence, card):
        """Generate alternative intervals for different risk tolerances"""

        alternatives = {}

        # Conservative interval (shorter, safer)
        alternatives['conservative'] = {
            'interval': max(1, int(optimal_interval * 0.7)),
            'risk_level': 'low',
            'description': 'Safer option with higher review frequency',
            'expected_retention': 0.9,
            'review_efficiency': 0.7
        }

        # Balanced interval (moderate risk)
        alternatives['balanced'] = {
            'interval': optimal_interval,
            'risk_level': 'medium',
            'description': 'Balanced approach based on prediction',
            'expected_retention': 0.8,
            'review_efficiency': 0.85
        }

        # Aggressive interval (longer, higher risk)
        alternatives['aggressive'] = {
            'interval': min(365, int(optimal_interval * 1.3)),
            'risk_level': 'high',
            'description': 'Longer intervals for higher efficiency',
            'expected_retention': 0.65,
            'review_efficiency': 0.95
        }

        # Custom interval based on confidence
        if confidence > 0.8:
            alternatives['high_confidence'] = {
                'interval': min(180, int(optimal_interval * 1.1)),
                'risk_level': 'low',
                'description': 'Extended interval due to high confidence',
                'expected_retention': 0.85,
                'review_efficiency': 0.9
            }
        elif confidence < 0.5:
            alternatives['low_confidence'] = {
                'interval': max(1, int(optimal_interval * 0.8)),
                'risk_level': 'low',
                'description': 'Shorter interval due to low confidence',
                'expected_retention': 0.92,
                'review_efficiency': 0.75
            }

        return alternatives

class AdaptiveDifficultyManager:
    def __init__(self):
        self.difficulty_adjusters = {
            'performance_based': self._performance_based_adjustment,
            'time_based': self._time_based_adjustment,
            'consistency_based': self._consistency_based_adjustment,
            'progress_based': self._progress_based_adjustment
        }

    def assess_and_adjust_difficulty(self, card, review_history):
        """
        Assess card difficulty and adjust if necessary

        Args:
            card: Current card being evaluated
            review_history: Historical review data for this card

        Returns:
            dict: Difficulty assessment and adjustment recommendations
        """

        # Collect difficulty indicators
        difficulty_indicators = self._collect_difficulty_indicators(card, review_history)

        # Calculate difficulty score
        difficulty_score = self._calculate_difficulty_score(difficulty_indicators)

        # Determine if difficulty adjustment is needed
        adjustment_needed = self._assess_adjustment_need(difficulty_score, card.difficulty)

        if adjustment_needed['needed']:
            # Calculate new difficulty level
            new_difficulty = self._calculate_new_difficulty(
                difficulty_score, card.difficulty, adjustment_needed['reason']
            )

            # Generate adjustment plan
            adjustment_plan = self._create_adjustment_plan(
                card.difficulty, new_difficulty, adjustment_needed['reason']
            )

            return {
                'current_difficulty': card.difficulty,
                'difficulty_score': difficulty_score,
                'indicators': difficulty_indicators,
                'adjustment_needed': True,
                'recommended_difficulty': new_difficulty,
                'adjustment_plan': adjustment_plan,
                'confidence': adjustment_needed['confidence']
            }
        else:
            return {
                'current_difficulty': card.difficulty,
                'difficulty_score': difficulty_score,
                'indicators': difficulty_indicators,
                'adjustment_needed': False,
                'validation_reason': adjustment_needed['reason'],
                'next_assessment': self._schedule_next_assessment(card)
            }

    def _collect_difficulty_indicators(self, card, review_history):
        """Collect various indicators of card difficulty"""

        indicators = {}

        if review_history:
            # Performance indicators
            recent_scores = [r['quality_score'] for r in review_history[-5:]]
            indicators['average_quality'] = sum(recent_scores) / len(recent_scores)
            indicators['quality_variance'] = statistics.variance(recent_scores) if len(recent_scores) > 1 else 0

            # Time indicators
            indicators['average_time'] = self._calculate_average_time(review_history)
            indicators['time_variance'] = self._calculate_time_variance(review_history)

            # Consistency indicators
            indicators['consecutive_correct'] = self._count_consecutive_correct(review_history)
            indicators['consecutive_incorrect'] = self._count_consecutive_incorrect(review_history)

            # Pattern indicators
            indicators['improvement_trend'] = self._calculate_improvement_trend(review_history)
            indicators['forgetting_pattern'] = self._analyze_forgetting_pattern(review_history)
        else:
            # New card indicators
            indicators['intrinsic_difficulty'] = self._assess_intrinsic_difficulty(card)
            indicators['subject_difficulty'] = self._assess_subject_difficulty(card)
            indicators['concept_complexity'] = self._assess_concept_complexity(card)

        return indicators

    def _calculate_difficulty_score(self, indicators):
        """Calculate overall difficulty score from indicators"""

        score_components = {}

        # Performance component (40% weight)
        if 'average_quality' in indicators:
            quality_score = (5 - indicators['average_quality']) / 5  # Invert: lower quality = higher difficulty
            score_components['performance'] = quality_score * 0.4
        else:
            score_components['performance'] = 0.2  # Default for new cards

        # Time component (25% weight)
        if 'average_time' in indicators:
            # Normalize time (assuming 1-10 minutes is normal range)
            time_score = min(1.0, indicators['average_time'] / 10)
            score_components['time'] = time_score * 0.25
        else:
            score_components['time'] = 0.125  # Default for new cards

        # Consistency component (20% weight)
        if 'quality_variance' in indicators:
            consistency_score = min(1.0, indicators['quality_variance'] / 4)  # Max variance of 4
            score_components['consistency'] = consistency_score * 0.2
        else:
            score_components['consistency'] = 0.1  # Default for new cards

        # Pattern component (15% weight)
        if 'improvement_trend' in indicators:
            pattern_score = max(0, -indicators['improvement_trend'])  # Negative trend = higher difficulty
            score_components['pattern'] = pattern_score * 0.15
        else:
            score_components['pattern'] = 0.075  # Default for new cards

        # Calculate total score
        total_score = sum(score_components.values())

        return {
            'total_score': min(1.0, max(0.0, total_score)),
            'components': score_components,
            'difficulty_level': self._score_to_difficulty_level(total_score)
        }

    def _score_to_difficulty_level(self, score):
        """Convert numerical score to difficulty level"""

        if score <= 0.25:
            return 'Easy'
        elif score <= 0.5:
            return 'Medium'
        elif score <= 0.75:
            return 'Hard'
        else:
            return 'Very Hard'

class AlgorithmPerformanceAnalyzer:
    def __init__(self):
        self.metrics_history = []
        self.benchmark_data = {}
        self.performance_thresholds = {
            'retention_rate': 0.85,
            'review_efficiency': 0.8,
            'interval_accuracy': 0.75,
            'user_satisfaction': 0.8
        }

    def analyze_algorithm_performance(self, user_id, time_period=30):
        """
        Comprehensive analysis of SM-2 algorithm performance

        Args:
            user_id: User identifier for analysis
            time_period: Analysis period in days

        Returns:
            dict: Comprehensive performance analysis
        """

        # Collect performance data
        performance_data = self._collect_performance_data(user_id, time_period)

        # Calculate key metrics
        metrics = {
            'retention_analysis': self._analyze_retention_performance(performance_data),
            'interval_effectiveness': self._analyze_interval_effectiveness(performance_data),
            'ease_factor_performance': self._analyze_ease_factor_performance(performance_data),
            'difficulty_assessment': self._analyze_difficulty_assessment(performance_data),
            'user_satisfaction': self._analyze_user_satisfaction(performance_data),
            'learning_velocity': self._analyze_learning_velocity(performance_data)
        }

        # Compare with benchmarks
        benchmark_comparison = self._compare_with_benchmarks(metrics)

        # Identify optimization opportunities
        optimization_opportunities = self._identify_optimization_opportunities(metrics)

        # Generate recommendations
        recommendations = self._generate_performance_recommendations(metrics, optimization_opportunities)

        return {
            'analysis_period': time_period,
            'performance_metrics': metrics,
            'benchmark_comparison': benchmark_comparison,
            'optimization_opportunities': optimization_opportunities,
            'recommendations': recommendations,
            'overall_score': self._calculate_overall_performance_score(metrics)
        }

    def _analyze_retention_performance(self, performance_data):
        """Analyze how well the algorithm maintains retention"""

        retention_data = {
            'overall_retention_rate': self._calculate_overall_retention(performance_data),
            'retention_by_interval': self._calculate_retention_by_intervals(performance_data),
            'retention_by_difficulty': self._calculate_retention_by_difficulty(performance_data),
            'retention_by_subject': self._calculate_retention_by_subject(performance_data),
            'retention_trends': self._analyze_retention_trends(performance_data)
        }

        # Evaluate retention effectiveness
        retention_effectiveness = {
            'meets_threshold': retention_data['overall_retention_rate'] >= self.performance_thresholds['retention_rate'],
            'performance_level': self._categorize_performance(retention_data['overall_retention_rate']),
            'strength_areas': self._identify_retention_strengths(retention_data),
            'improvement_areas': self._identify_retention_improvements(retention_data)
        }

        return {
            'data': retention_data,
            'effectiveness': retention_effectiveness,
            'recommendations': self._generate_retention_recommendations(retention_data)
        }

    def _analyze_interval_effectiveness(self, performance_data):
        """Analyze how well calculated intervals perform"""

        interval_data = {
            'interval_accuracy': self._calculate_interval_accuracy(performance_data),
            'optimal_interval_distribution': self._analyze_interval_distribution(performance_data),
            'interval_adjustment_effectiveness': self._analyze_interval_adjustments(performance_data),
            'predicted_vs_actual': self._compare_predicted_actual_performance(performance_data)
        }

        # Evaluate interval performance
        interval_performance = {
            'accuracy_score': interval_data['interval_accuracy'],
            'meets_threshold': interval_data['interval_accuracy'] >= self.performance_thresholds['interval_accuracy'],
            'distribution_optimality': self._assess_distribution_optimality(interval_data['optimal_interval_distribution']),
            'adjustment_effectiveness': self._assess_adjustment_effectiveness(interval_data['interval_adjustment_effectiveness'])
        }

        return {
            'data': interval_data,
            'performance': interval_performance,
            'optimization_suggestions': self._suggest_interval_optimizations(interval_data)
        }

class AlgorithmABTester:
    def __init__(self):
        self.test_groups = {}
        self.test_results = {}
        self.statistical_analyzer = StatisticalAnalyzer()

    def create_algorithm_comparison_test(self, test_config):
        """
        Create A/B test to compare algorithm variations

        Args:
            test_config: Configuration for the comparison test

        Returns:
            dict: Test setup and monitoring information
        """

        test_id = self._generate_test_id()

        # Setup test groups
        control_group = {
            'name': 'control',
            'algorithm': 'standard_sm2',
            'parameters': test_config.get('control_parameters', {}),
            'users': []
        }

        test_groups = []
        for i, variation in enumerate(test_config.get('algorithm_variations', [])):
            group = {
                'name': f'test_group_{i+1}',
                'algorithm': variation['algorithm'],
                'parameters': variation.get('parameters', {}),
                'users': []
            }
            test_groups.append(group)

        # Setup test metrics
        test_metrics = {
            'primary_metrics': test_config.get('primary_metrics', ['retention_rate', 'review_efficiency']),
            'secondary_metrics': test_config.get('secondary_metrics', ['user_satisfaction', 'learning_velocity']),
            'test_duration': test_config.get('test_duration', 30),  # days
            'sample_size_required': self._calculate_sample_size(test_config),
            'statistical_significance': test_config.get('significance_level', 0.05)
        }

        # Store test configuration
        self.test_groups[test_id] = {
            'control': control_group,
            'test_groups': test_groups,
            'metrics': test_metrics,
            'start_date': datetime.now(),
            'status': 'ready'
        }

        return {
            'test_id': test_id,
            'setup': {
                'control_group': control_group,
                'test_groups': test_groups,
                'metrics': test_metrics
            },
            'monitoring_plan': self._create_monitoring_plan(test_metrics),
            'success_criteria': self._define_success_criteria(test_config),
            'risk_mitigation': self._create_risk_mitigation_plan(test_config)
        }

    def analyze_test_results(self, test_id):
        """Analyze results of algorithm comparison test"""

        test_data = self.test_groups.get(test_id)
        if not test_data:
            return {'error': 'Test not found'}

        # Collect performance data for all groups
        control_performance = self._collect_group_performance(test_data['control'])
        test_performances = [
            self._collect_group_performance(group) for group in test_data['test_groups']
        ]

        # Perform statistical analysis
        statistical_results = {}
        for metric in test_data['metrics']['primary_metrics']:
            metric_results = self.statistical_analyzer.compare_groups(
                control_data=control_performance[metric],
                test_data=[perf[metric] for perf in test_performances],
                significance_level=test_data['metrics']['statistical_significance']
            )
            statistical_results[metric] = metric_results

        # Analyze secondary metrics
        secondary_analysis = {}
        for metric in test_data['metrics']['secondary_metrics']:
            secondary_analysis[metric] = self._analyze_secondary_metric(
                control_performance[metric],
                [perf[metric] for perf in test_performances]
            )

        # Determine winning algorithm
        winner_analysis = self._determine_winner(statistical_results, secondary_analysis)

        # Generate recommendations
        recommendations = self._generate_ab_test_recommendations(
            statistical_results, secondary_analysis, winner_analysis
        )

        return {
            'test_id': test_id,
            'analysis_period': test_data['metrics']['test_duration'],
            'statistical_results': statistical_results,
            'secondary_analysis': secondary_analysis,
            'winner_analysis': winner_analysis,
            'recommendations': recommendations,
            'confidence_level': self._calculate_overall_confidence(statistical_results),
            'implementation_plan': self._create_implementation_plan(winner_analysis)
        }

class AlgorithmPersonalizer:
    def __init__(self):
        self.personalization_factors = {
            'learning_style': self._learning_style_personalization,
            'memory_capacity': self._memory_capacity_personalization,
            'study_schedule': self._study_schedule_personalization,
            'performance_patterns': self._performance_patterns_personalization,
            'subject_preferences': self._subject_preferences_personalization
        }

    def personalize_algorithm(self, user_id, user_profile):
        """
        Create personalized SM-2 algorithm parameters based on user profile

        Args:
            user_id: User identifier
            user_profile: User's learning profile and preferences

        Returns:
            dict: Personalized algorithm configuration
        """

        personalized_config = {
            'base_algorithm': 'enhanced_sm2',
            'personalization_factors': {},
            'parameter_adjustments': {},
            'expected_impact': {}
        }

        # Apply personalization factors
        for factor_name, factor_function in self.personalization_factors.items():
            factor_result = factor_function(user_profile)
            personalized_config['personalization_factors'][factor_name] = factor_result

            # Apply parameter adjustments
            parameter_adjustments = self._apply_factor_adjustments(factor_result)
            personalized_config['parameter_adjustments'].update(parameter_adjustments)

        # Create final personalized parameters
        final_parameters = self._create_final_parameters(
            personalized_config['parameter_adjustments']
        )

        # Predict impact of personalization
        impact_prediction = self._predict_personalization_impact(
            user_profile, final_parameters
        )

        personalized_config.update({
            'final_parameters': final_parameters,
            'impact_prediction': impact_prediction,
            'validation_plan': self._create_validation_plan(user_id, final_parameters),
            'adjustment_schedule': self._create_adjustment_schedule(user_profile)
        })

        return personalized_config

    def _learning_style_personalization(self, user_profile):
        """Personalize based on learning style"""

        learning_style = user_profile.get('learning_style', 'balanced')

        style_adjustments = {
            'visual': {
                'ease_factor_modifier': 1.05,  # Visual learners often have better retention
                'interval_modifier': 1.1,      # Can handle slightly longer intervals
                'difficulty_threshold': 0.9    # May underestimate difficulty
            },
            'auditory': {
                'ease_factor_modifier': 1.0,
                'interval_modifier': 1.0,
                'difficulty_threshold': 1.0
            },
            'kinesthetic': {
                'ease_factor_modifier': 0.95,  # May need more frequent practice
                'interval_modifier': 0.9,      # Shorter intervals for practice
                'difficulty_threshold': 1.1    # May find concepts more challenging
            },
            'reading_writing': {
                'ease_factor_modifier': 1.02,
                'interval_modifier': 1.05,
                'difficulty_threshold': 0.95
            },
            'balanced': {
                'ease_factor_modifier': 1.0,
                'interval_modifier': 1.0,
                'difficulty_threshold': 1.0
            }
        }

        return {
            'learning_style': learning_style,
            'adjustments': style_adjustments.get(learning_style, style_adjustments['balanced']),
            'confidence': 0.8,
            'rationale': f'Adjustments based on {learning_style} learning preferences'
        }

    def _memory_capacity_personalization(self, user_profile):
        """Personalize based on memory capacity and working memory"""

        memory_profile = user_profile.get('memory_profile', {})
        working_memory_score = memory_profile.get('working_memory', 0.5)  # 0-1 scale
        long_term_memory_score = memory_profile.get('long_term_memory', 0.5)

        # Calculate memory capacity adjustments
        if working_memory_score > 0.7:  # High working memory
            working_memory_adjustments = {
                'max_new_cards_per_day': 25,
                'session_length': 60,
                'cognitive_load_threshold': 0.8,
                'complexity_handling': 'high'
            }
        elif working_memory_score > 0.4:  # Average working memory
            working_memory_adjustments = {
                'max_new_cards_per_day': 20,
                'session_length': 45,
                'cognitive_load_threshold': 0.7,
                'complexity_handling': 'medium'
            }
        else:  # Lower working memory
            working_memory_adjustments = {
                'max_new_cards_per_day': 15,
                'session_length': 30,
                'cognitive_load_threshold': 0.6,
                'complexity_handling': 'low'
            }

        # Long-term memory adjustments
        if long_term_memory_score > 0.7:
            retention_adjustments = {
                'base_interval_multiplier': 1.15,
                'ease_factor_bonus': 0.1,
                'review_frequency_modifier': 0.9
            }
        elif long_term_memory_score > 0.4:
            retention_adjustments = {
                'base_interval_multiplier': 1.0,
                'ease_factor_bonus': 0.0,
                'review_frequency_modifier': 1.0
            }
        else:
            retention_adjustments = {
                'base_interval_multiplier': 0.85,
                'ease_factor_bonus': -0.05,
                'review_frequency_modifier': 1.1
            }

        return {
            'memory_profile': memory_profile,
            'working_memory_adjustments': working_memory_adjustments,
            'retention_adjustments': retention_adjustments,
            'confidence': 0.75,
            'rationale': 'Personalization based on memory capacity assessment'
        }

class AlgorithmEvolution:
    def __init__(self):
        self.research_pipeline = ResearchPipeline()
        self.performance_monitor = PerformanceMonitor()
        self.improvement_engine = ImprovementEngine()

    def evolve_algorithm(self, current_algorithm, performance_data, research_findings):
        """
        Continuously evolve and improve the SM-2 algorithm

        Args:
            current_algorithm: Current algorithm implementation
            performance_data: Real-world performance data
            research_findings: Latest cognitive science research

        Returns:
            dict: Improved algorithm version and evolution report
        """

        # Analyze current performance
        performance_analysis = self.performance_monitor.analyze_performance(performance_data)

        # Identify improvement opportunities
        improvement_opportunities = self._identify_improvement_opportunities(
            performance_analysis, research_findings
        )

        # Develop algorithm improvements
        algorithm_improvements = self.improvement_engine.develop_improvements(
            current_algorithm, improvement_opportunities
        )

        # Validate improvements
        validation_results = self._validate_improvements(
            algorithm_improvements, performance_data
        )

        # Create evolved algorithm
        evolved_algorithm = self._create_evolved_algorithm(
            current_algorithm, algorithm_improvements, validation_results
        )

        return {
            'evolved_algorithm': evolved_algorithm,
            'improvements_made': algorithm_improvements,
            'validation_results': validation_results,
            'expected_improvements': self._predict_improvements(algorithm_improvements),
            'monitoring_plan': self._create_evolution_monitoring_plan(evolved_algorithm),
            'rollback_plan': self._create_rollback_plan(current_algorithm)
        }

    def _identify_improvement_opportunities(self, performance_analysis, research_findings):
        """Identify areas where algorithm can be improved"""

        opportunities = []

        # Performance-based opportunities
        if performance_analysis['retention_rate'] < 0.85:
            opportunities.append({
                'area': 'retention_optimization',
                'priority': 'high',
                'description': 'Improve retention rate through interval adjustments',
                'potential_impact': 0.15
            })

        if performance_analysis['review_efficiency'] < 0.8:
            opportunities.append({
                'area': 'efficiency_improvement',
                'priority': 'medium',
                'description': 'Optimize review scheduling for better efficiency',
                'potential_impact': 0.10
            })

        # Research-based opportunities
        for finding in research_findings:
            if finding['applicable_to_algorithm']:
                opportunities.append({
                    'area': 'research_integration',
                    'priority': finding['priority'],
                    'description': finding['description'],
                    'potential_impact': finding['estimated_impact'],
                    'research_reference': finding['reference']
                })

        # User feedback opportunities
        user_feedback_gaps = self._analyze_user_feedback_gaps(performance_analysis)
        opportunities.extend(user_feedback_gaps)

        return opportunities

    def _create_evolved_algorithm(self, current_algorithm, improvements, validation):
        """Create the next version of the algorithm"""

        evolved_algorithm = copy.deepcopy(current_algorithm)

        # Apply validated improvements
        for improvement in improvements:
            if validation[improvement['id']]['validated']:
                self._apply_improvement(evolved_algorithm, improvement)

        # Update algorithm version
        evolved_algorithm['version'] = self._increment_version(current_algorithm['version'])
        evolved_algorithm['evolution_history'] = current_algorithm.get('evolution_history', [])
        evolved_algorithm['evolution_history'].append({
            'version': evolved_algorithm['version'],
            'date': datetime.now(),
            'improvements': [imp['id'] for imp in improvements],
            'validation_summary': validation
        })

        # Set new performance baselines
        evolved_algorithm['performance_baselines'] = self._set_new_baselines(validation)

        return evolved_algorithm