//! Auto-generated module
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

use scirs2_special::*;
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

use std::collections::{VecDeque, HashMap};

#[derive(Debug, Clone)]
enum LearningStyle {
    Visual,
    Analytical,
    Intuitive,
    Practical,
    Historical,
}
#[derive(Debug, Clone)]
struct AssessmentResult {
    topic: String,
    score: f64,
    #[allow(dead_code)]
    time_taken: Duration,
    difficulty_level: u32,
    #[allow(dead_code)]
    mistakes: Vec<String>,
    #[allow(dead_code)]
    timestamp: Instant,
}
#[allow(dead_code)]
#[derive(Debug, Clone)]
struct AssessmentQuestion {
    pub(super) question_type: QuestionType,
    content: String,
    difficulty: u32,
    expected_time: Duration,
    pub(super) hints: Vec<String>,
    solution_steps: Vec<String>,
    common_mistakes: Vec<String>,
}
#[derive(Debug, Clone)]
enum VisualizationType {
    Graph2D {
        #[allow(dead_code)]
        x_range: (f64, f64),
        #[allow(dead_code)]
        y_range: (f64, f64),
    },
    Graph3D { #[allow(dead_code)] ranges: ((f64, f64), (f64, f64), (f64, f64)) },
    ComplexPlane { #[allow(dead_code)] radius: f64 },
    Contour { #[allow(dead_code)] levels: Vec<f64> },
    Animation {
        #[allow(dead_code)]
        frames: usize,
        #[allow(dead_code)]
        duration: Duration,
    },
    Interactive { #[allow(dead_code)] parameters: Vec<String> },
}
#[derive(Debug, Clone)]
struct LearningProfile {
    user_id: String,
    pub(super) skill_levels: HashMap<String, f64>,
    pub(super) learning_speed: f64,
    pub(super) preferred_learning_style: LearningStyle,
    completed_modules: Vec<String>,
    pub(super) time_spent: HashMap<String, Duration>,
    pub(super) assessment_scores: Vec<AssessmentResult>,
    pub(super) mistake_patterns: HashMap<String, u32>,
    pub(super) mastery_goals: Vec<String>,
    #[allow(dead_code)]
    last_session: Option<Instant>,
}
#[allow(dead_code)]
#[derive(Debug, Clone)]
struct AdaptiveLearningSession {
    pub(super) profile: LearningProfile,
    current_topic: String,
    pub(super) knowledge_graph: HashMap<String, ConceptNode>,
    session_start: Instant,
    pub(super) performance_history: VecDeque<(String, f64, Duration)>,
    difficulty_adjustment: f64,
    next_recommendations: Vec<String>,
}
impl AdaptiveLearningSession {
    fn new(profile: LearningProfile) -> Self {
        let mut knowledge_graph = HashMap::new();
        Self::initialize_knowledge_graph(&mut knowledge_graph);
        Self {
            profile,
            current_topic: String::new(),
            knowledge_graph,
            session_start: Instant::now(),
            performance_history: VecDeque::with_capacity(10),
            difficulty_adjustment: 0.0,
            next_recommendations: Vec::new(),
        }
    }
    fn initialize_knowledge_graph(graph: &mut HashMap<String, ConceptNode>) {
        graph
            .insert(
                "gamma_basics".to_string(),
                ConceptNode {
                    name: "Gamma Function Fundamentals".to_string(),
                    description: "Definition, basic properties, and simple evaluations"
                        .to_string(),
                    prerequisites: vec![
                        "calculus_integration".to_string(), "factorial_concept"
                        .to_string(),
                    ],
                    difficulty: 2,
                    estimated_time: Duration::from_secs(1800),
                    learning_objectives: vec![
                        "Understand the integral definition of the gamma function"
                        .to_string(), "Apply the recurrence relation Γ(z+1) = zΓ(z)"
                        .to_string(), "Evaluate Γ(n) for positive integers".to_string(),
                        "Recognize key values like Γ(1/2) = √π".to_string(),
                    ],
                    applications: vec![
                        "Probability distributions".to_string(),
                        "Stirling's approximation".to_string(),
                        "Beta function relationship".to_string(),
                    ],
                    visualizations: vec![
                        VisualizationType::Graph2D { x_range : (0.1, 5.0), y_range :
                        (0.0, 10.0), }, VisualizationType::ComplexPlane { radius : 3.0 },
                    ],
                    assessment_questions: create_gamma_basic_questions(),
                },
            );
        graph
            .insert(
                "gamma_advanced".to_string(),
                ConceptNode {
                    name: "Advanced Gamma Function Theory".to_string(),
                    description: "Reflection formula, duplication formula, and analytic continuation"
                        .to_string(),
                    prerequisites: vec![
                        "gamma_basics".to_string(), "complex_analysis".to_string()
                    ],
                    difficulty: 4,
                    estimated_time: Duration::from_secs(3600),
                    learning_objectives: vec![
                        "Derive and apply the reflection formula".to_string(),
                        "Understand the duplication formula".to_string(),
                        "Grasp analytic continuation concepts".to_string(),
                        "Work with gamma function poles and residues".to_string(),
                    ],
                    applications: vec![
                        "Special function identities".to_string(), "Asymptotic analysis"
                        .to_string(), "Number theory".to_string(),
                    ],
                    visualizations: vec![
                        VisualizationType::ComplexPlane { radius : 5.0 },
                        VisualizationType::Graph3D { ranges : ((- 3.0, 3.0), (- 3.0,
                        3.0), (- 10.0, 10.0)), },
                    ],
                    assessment_questions: create_gamma_advanced_questions(),
                },
            );
        graph
            .insert(
                "bessel_basics".to_string(),
                ConceptNode {
                    name: "Bessel Functions Introduction".to_string(),
                    description: "Bessel's equation, series solutions, and basic properties"
                        .to_string(),
                    prerequisites: vec![
                        "differential_equations".to_string(), "series_solutions"
                        .to_string(),
                    ],
                    difficulty: 3,
                    estimated_time: Duration::from_secs(2700),
                    learning_objectives: vec![
                        "Understand Bessel's differential equation".to_string(),
                        "Derive series solutions for J_n(x)".to_string(),
                        "Explore orthogonality properties".to_string(),
                        "Calculate zeros and oscillations".to_string(),
                    ],
                    applications: vec![
                        "Wave equations in cylindrical coordinates".to_string(),
                        "Vibrating membranes".to_string(), "Heat conduction in cylinders"
                        .to_string(), "Antenna radiation patterns".to_string(),
                    ],
                    visualizations: vec![
                        VisualizationType::Graph2D { x_range : (0.0, 20.0), y_range : (-
                        0.5, 1.0), }, VisualizationType::Animation { frames : 60,
                        duration : Duration::from_secs(10), },
                        VisualizationType::Interactive { parameters : vec!["order"
                        .to_string(), "argument".to_string()], },
                    ],
                    assessment_questions: create_bessel_basic_questions(),
                },
            );
        Self::add_advanced_concepts(graph);
    }
    fn add_advanced_concepts(graph: &mut HashMap<String, ConceptNode>) {
        graph
            .insert(
                "hypergeometric".to_string(),
                ConceptNode {
                    name: "Hypergeometric Functions".to_string(),
                    description: "Generalized hypergeometric series and their properties"
                        .to_string(),
                    prerequisites: vec![
                        "gamma_advanced".to_string(), "series_convergence".to_string(),
                    ],
                    difficulty: 4,
                    estimated_time: Duration::from_secs(4500),
                    learning_objectives: vec![
                        "Understand the general hypergeometric series".to_string(),
                        "Derive integral representations".to_string(),
                        "Apply transformation formulas".to_string(),
                        "Connect to other special functions".to_string(),
                    ],
                    applications: vec![
                        "Elliptic integrals".to_string(), "Appell functions".to_string(),
                        "Mathematical physics".to_string(),
                    ],
                    visualizations: vec![
                        VisualizationType::ComplexPlane { radius : 2.0 },
                        VisualizationType::Contour { levels : vec![- 2.0, - 1.0, 0.0,
                        1.0, 2.0], },
                    ],
                    assessment_questions: create_hypergeometric_questions(),
                },
            );
        graph
            .insert(
                "wright_functions".to_string(),
                ConceptNode {
                    name: "Wright Functions and Fractional Calculus".to_string(),
                    description: "Advanced Wright functions and their role in fractional differential equations"
                        .to_string(),
                    prerequisites: vec![
                        "hypergeometric".to_string(), "fractional_calculus".to_string(),
                    ],
                    difficulty: 5,
                    estimated_time: Duration::from_secs(5400),
                    learning_objectives: vec![
                        "Understand Wright function definitions".to_string(),
                        "Explore asymptotic behavior".to_string(),
                        "Apply to fractional differential equations".to_string(),
                        "Connect to Mittag-Leffler functions".to_string(),
                    ],
                    applications: vec![
                        "Anomalous diffusion".to_string(), "Fractional kinetics"
                        .to_string(), "Memory effects in materials".to_string(),
                    ],
                    visualizations: vec![
                        VisualizationType::Graph3D { ranges : ((- 5.0, 5.0), (- 5.0,
                        5.0), (- 2.0, 10.0)), }, VisualizationType::Animation { frames :
                        120, duration : Duration::from_secs(20), },
                    ],
                    assessment_questions: create_wright_function_questions(),
                },
            );
    }
    fn recommend_next_topic(&mut self) -> Option<String> {
        let current_skills = &self.profile.skill_levels;
        let mut candidates = Vec::new();
        for (topic, node) in &self.knowledge_graph {
            let prerequisites_met = node
                .prerequisites
                .iter()
                .all(|prereq| current_skills.get(prereq).unwrap_or(&0.0) >= &0.7);
            if prerequisites_met && !self.profile.completed_modules.contains(topic) {
                let current_skill = current_skills.get(topic).unwrap_or(&0.0);
                let adjusted_difficulty = (node.difficulty as f64)
                    + self.difficulty_adjustment;
                let score = self
                    .calculate_topic_score(
                        topic,
                        node,
                        *current_skill,
                        adjusted_difficulty,
                    );
                candidates.push((topic.clone(), score));
            }
        }
        candidates.sort_by(|a, b| b.1.partial_cmp(&a.1).expect("Operation failed"));
        candidates.first().map(|(topic_, _)| topic_.clone())
    }
    fn calculate_topic_score(
        &self,
        topic: &str,
        node: &ConceptNode,
        skill: f64,
        difficulty: f64,
    ) -> f64 {
        let difficulty_score = 1.0 - (difficulty - 3.0).abs() / 5.0;
        let knowledge_score = if skill < 0.3 {
            1.0 - skill
        } else if skill > 0.8 {
            0.2
        } else {
            1.0
        };
        let style_score = match self.profile.preferred_learning_style {
            LearningStyle::Visual => {
                if node.visualizations.len() > 2 { 1.2 } else { 1.0 }
            }
            LearningStyle::Practical => {
                if node.applications.len() > 3 { 1.2 } else { 1.0 }
            }
            LearningStyle::Analytical => if node.difficulty >= 3 { 1.1 } else { 1.0 }
            _ => 1.0,
        };
        let performance_score = if self.performance_history.len() >= 3 {
            let recent_avg = self
                .performance_history
                .iter()
                .rev()
                .take(3)
                .map(|(_, score_, _)| *score_)
                .sum::<f64>() / 3.0;
            if recent_avg > 0.8 { 1.1 } else if recent_avg < 0.6 { 0.9 } else { 1.0 }
        } else {
            1.0
        };
        difficulty_score * knowledge_score * style_score * performance_score
    }
    fn adapt_difficulty_based_on_performance(&mut self) {
        if self.performance_history.len() >= 3 {
            let recent_scores: Vec<f64> = self
                .performance_history
                .iter()
                .rev()
                .take(3)
                .map(|(_, score_, _)| *score_)
                .collect();
            let avg_score = recent_scores.iter().sum::<f64>()
                / recent_scores.len() as f64;
            if avg_score > 0.85 {
                self.difficulty_adjustment = (self.difficulty_adjustment + 0.2).min(1.0);
                println!("🚀 Excellent performance! Increasing challenge level.");
            } else if avg_score < 0.65 {
                self.difficulty_adjustment = (self.difficulty_adjustment - 0.2)
                    .max(-1.0);
                println!(
                    "💪 Adjusting difficulty to better match your current level."
                );
            }
        }
    }
    fn provide_personalized_feedback(
        &self,
        topic: &str,
        score: f64,
        timetaken: Duration,
    ) {
        let node = self.knowledge_graph.get(topic).expect("Operation failed");
        let expected_time = node.estimated_time;
        println!("\n📊 Performance Analysis for {}:", node.name);
        println!("Score: {:.1}% ({}/10)", score * 100.0, (score * 10.0) as u32);
        if timetaken <= expected_time {
            println!(
                "⏱️ Excellent time management! Completed in {:.1} minutes (expected: {:.1})",
                timetaken.as_secs_f64() / 60.0, expected_time.as_secs_f64() / 60.0
            );
        } else {
            println!(
                "⏱️ Took {:.1} minutes (expected: {:.1}). Consider reviewing fundamentals.",
                timetaken.as_secs_f64() / 60.0, expected_time.as_secs_f64() / 60.0
            );
        }
        match score {
            s if s >= 0.9 => {
                println!("🌟 Outstanding mastery! You're ready for advanced topics.");
                println!(
                    "💡 Consider exploring: {}", self
                    .get_advanced_recommendations(topic).join(", ")
                );
            }
            s if s >= 0.8 => {
                println!(
                    "✅ Good understanding! Minor review might help solidify concepts."
                );
            }
            s if s >= 0.7 => {
                println!("👍 Satisfactory progress. Focus on the challenging areas:");
                self.suggest_review_areas(topic, score);
            }
            s if s >= 0.6 => {
                println!("📚 Needs more practice. Let's review the fundamentals:");
                self.suggest_prerequisite_review(topic);
            }
            _ => {
                println!("🔄 Let's take a step back and strengthen the foundation:");
                println!("Consider reviewing: {}", node.prerequisites.join(", "));
            }
        }
    }
    fn get_advanced_recommendations(&self, currenttopic: &str) -> Vec<String> {
        let mut recommendations = Vec::new();
        for (_topic, node) in &self.knowledge_graph {
            if node.prerequisites.contains(&currenttopic.to_string())
                && !self.profile.completed_modules.contains(_topic)
            {
                recommendations.push(node.name.clone());
            }
        }
        if recommendations.is_empty() {
            recommendations
                .push("Advanced applications and research topics".to_string());
        }
        recommendations
    }
    fn suggest_review_areas(&self, topic: &str, score: f64) {
        match topic {
            "gamma_basics" => {
                if score < 0.75 {
                    println!(
                        "  • Review integral definition and evaluation techniques"
                    );
                    println!("  • Practice with the recurrence relation");
                    println!("  • Work through more numerical examples");
                }
            }
            "bessel_basics" => {
                if score < 0.75 {
                    println!("  • Review the differential equation derivation");
                    println!("  • Practice series solution methods");
                    println!("  • Study orthogonality properties");
                }
            }
            _ => {
                println!("  • Review key theorems and their applications");
                println!("  • Practice computational examples");
            }
        }
    }
    fn suggest_prerequisite_review(&self, topic: &str) {
        if let Some(node) = self.knowledge_graph.get(topic) {
            println!("Recommended prerequisite review:");
            for prereq in &node.prerequisites {
                if let Some(skill_level) = self.profile.skill_levels.get(prereq) {
                    if *skill_level < 0.7 {
                        println!(
                            "  • {} (current level: {:.1}%)", prereq, skill_level *
                            100.0
                        );
                    }
                }
            }
        }
    }
}
#[allow(dead_code)]
#[derive(Debug, Clone)]
enum QuestionType {
    MultipleChoice { options: Vec<String>, correct: usize },
    NumericalAnswer { expected: f64, tolerance: f64 },
    ProofCompletion { steps: Vec<String>, blanks: Vec<usize> },
    ConceptMapping { concepts: Vec<String>, relationships: Vec<(usize, usize)> },
    CodeCompletion { template: String, solution: String },
    GraphicalInterpretation { data: Vec<(f64, f64)> },
}
#[derive(Debug, Clone)]
struct ConceptNode {
    name: String,
    description: String,
    prerequisites: Vec<String>,
    difficulty: u32,
    estimated_time: Duration,
    pub(super) learning_objectives: Vec<String>,
    pub(super) applications: Vec<String>,
    visualizations: Vec<VisualizationType>,
    pub(super) assessment_questions: Vec<AssessmentQuestion>,
}
