scirs2_series/advanced_fusion_intelligence/

meta_learning.rs

//! Meta-Learning Components for Advanced Fusion Intelligence
//!
//! This module contains all meta-learning related structures and implementations
//! for the advanced fusion intelligence system, including optimization models,
//! learning strategies, adaptation mechanisms, and knowledge transfer systems.

use scirs2_core::ndarray::Array1;
use scirs2_core::numeric::{Float, FromPrimitive};
use std::collections::HashMap;
use std::fmt::Debug;

use crate::error::Result;

/// Meta-optimization model for learning algorithms
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct MetaOptimizationModel<F: Float + Debug> {
    model_parameters: Vec<F>,
    optimization_strategy: OptimizationStrategy,
    adaptation_rate: F,
}

/// Available optimization strategies for meta-learning
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum OptimizationStrategy {
    /// Gradient-based optimization
    GradientBased,
    /// Evolutionary algorithm optimization
    EvolutionaryBased,
    /// Bayesian optimization approach
    BayesianOptimization,
    /// Reinforcement learning optimization
    ReinforcementLearning,
}

/// Library of available learning strategies
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct LearningStrategyLibrary<F: Float + Debug> {
    strategies: Vec<LearningStrategy<F>>,
    performance_history: HashMap<String, F>,
}

/// Individual learning strategy configuration
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct LearningStrategy<F: Float + Debug> {
    name: String,
    parameters: Vec<F>,
    applicability_score: F,
}

/// System for evaluating learning performance
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct LearningEvaluationSystem<F: Float + Debug> {
    evaluation_metrics: Vec<EvaluationMetric>,
    performance_threshold: F,
    validation_protocol: ValidationMethod,
}

/// Metrics for evaluating learning performance
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum EvaluationMetric {
    /// Accuracy metric
    Accuracy,
    /// Speed metric
    Speed,
    /// Efficiency metric
    Efficiency,
    /// Robustness metric
    Robustness,
    /// Interpretability metric
    Interpretability,
}

/// Methods for validating learning performance
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum ValidationMethod {
    /// Cross-validation method
    CrossValidation,
    /// Hold-out validation method
    HoldOut,
    /// Leave-one-out validation
    LeaveOneOut,
    /// Bootstrap validation
    Bootstrap,
}

/// Mechanism for meta-adaptation of learning strategies
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct MetaAdaptationMechanism<F: Float + Debug> {
    adaptation_rules: Vec<AdaptationRule<F>>,
    trigger_conditions: Vec<TriggerCondition<F>>,
    adaptation_history: HashMap<String, Vec<F>>,
}

/// Rule for adaptive behavior modification
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AdaptationRule<F: Float + Debug> {
    rule_id: String,
    condition: String,
    action: String,
    priority: F,
}

/// Condition that triggers adaptive behavior
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct TriggerCondition<F: Float + Debug> {
    metric_name: String,
    threshold: F,
    comparison: ComparisonDirection,
}

/// Direction for threshold comparisons
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum ComparisonDirection {
    /// Greater than threshold
    GreaterThan,
    /// Less than threshold
    LessThan,
    /// Equal to threshold
    EqualTo,
    /// Within range of threshold
    WithinRange,
}

/// System for transferring knowledge between tasks
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct KnowledgeTransferSystem<F: Float + Debug> {
    knowledge_base: Vec<KnowledgeItem<F>>,
    transfer_mechanisms: Vec<TransferMechanism>,
    similarity_metrics: HashMap<String, F>,
    transfer_efficiency: F,
}

/// Individual knowledge item for transfer
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct KnowledgeItem<F: Float + Debug> {
    item_id: String,
    knowledge_type: String,
    parameters: Vec<F>,
    source_task: String,
    applicability_score: F,
}

/// Mechanisms for knowledge transfer
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum TransferMechanism {
    /// Parameter transfer
    ParameterTransfer,
    /// Feature transfer
    FeatureTransfer,
    /// Model transfer
    ModelTransfer,
    /// Representation transfer
    RepresentationTransfer,
    /// Meta-transfer
    MetaTransfer,
}

impl<F: Float + Debug + Clone + FromPrimitive> MetaOptimizationModel<F> {
    /// Create new meta-optimization model
    pub fn new(strategy: OptimizationStrategy) -> Self {
        MetaOptimizationModel {
            model_parameters: vec![F::from_f64(0.1).unwrap(); 10],
            optimization_strategy: strategy,
            adaptation_rate: F::from_f64(0.01).unwrap(),
        }
    }

    /// Optimize model parameters based on performance feedback
    pub fn optimize_parameters(&mut self, performance_data: &Array1<F>) -> Result<()> {
        match self.optimization_strategy {
            OptimizationStrategy::GradientBased => {
                self.gradient_based_optimization(performance_data)?;
            }
            OptimizationStrategy::EvolutionaryBased => {
                self.evolutionary_optimization(performance_data)?;
            }
            OptimizationStrategy::BayesianOptimization => {
                self.bayesian_optimization(performance_data)?;
            }
            OptimizationStrategy::ReinforcementLearning => {
                self.reinforcement_learning_optimization(performance_data)?;
            }
        }
        Ok(())
    }

    /// Gradient-based parameter optimization
    fn gradient_based_optimization(&mut self, performance_data: &Array1<F>) -> Result<()> {
        if performance_data.is_empty() {
            return Ok(());
        }

        let performance_mean = performance_data.iter().fold(F::zero(), |acc, &x| acc + x)
            / F::from_usize(performance_data.len()).unwrap();

        // Simple gradient approximation
        for param in &mut self.model_parameters {
            let gradient = performance_mean - F::from_f64(0.5).unwrap();
            *param = *param + self.adaptation_rate * gradient;
        }
        Ok(())
    }

    /// Evolutionary algorithm optimization
    fn evolutionary_optimization(&mut self, _performance_data: &Array1<F>) -> Result<()> {
        // Simple mutation-based evolution
        for param in &mut self.model_parameters {
            let mutation = F::from_f64(0.01).unwrap()
                * (F::from_f64(scirs2_core::random::random::<f64>()).unwrap()
                    - F::from_f64(0.5).unwrap());
            *param = *param + mutation;
        }
        Ok(())
    }

    /// Bayesian optimization approach
    fn bayesian_optimization(&mut self, performance_data: &Array1<F>) -> Result<()> {
        // Simplified Bayesian update
        if performance_data.is_empty() {
            return Ok(());
        }

        let performance_variance = {
            let mean = performance_data.iter().fold(F::zero(), |acc, &x| acc + x)
                / F::from_usize(performance_data.len()).unwrap();
            performance_data
                .iter()
                .fold(F::zero(), |acc, &x| acc + (x - mean) * (x - mean))
                / F::from_usize(performance_data.len()).unwrap()
        };

        let uncertainty_factor =
            F::from_f64(1.0).unwrap() / (F::from_f64(1.0).unwrap() + performance_variance);

        for param in &mut self.model_parameters {
            *param = *param * uncertainty_factor;
        }
        Ok(())
    }

    /// Reinforcement learning optimization
    fn reinforcement_learning_optimization(&mut self, performance_data: &Array1<F>) -> Result<()> {
        if performance_data.is_empty() {
            return Ok(());
        }

        // Simple Q-learning inspired update
        let reward = performance_data.iter().fold(F::zero(), |acc, &x| acc + x)
            / F::from_usize(performance_data.len()).unwrap();

        let learning_rate = F::from_f64(0.1).unwrap();
        let discount_factor = F::from_f64(0.9).unwrap();

        for param in &mut self.model_parameters {
            *param = *param + learning_rate * reward * discount_factor;
        }
        Ok(())
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for LearningStrategyLibrary<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> LearningStrategyLibrary<F> {
    /// Create new learning strategy library
    pub fn new() -> Self {
        LearningStrategyLibrary {
            strategies: Vec::new(),
            performance_history: HashMap::new(),
        }
    }

    /// Add a new learning strategy
    pub fn add_strategy(&mut self, strategy: LearningStrategy<F>) {
        self.strategies.push(strategy);
    }

    /// Select best strategy based on performance history
    pub fn select_best_strategy(
        &self,
        taskcharacteristics: &Array1<F>,
    ) -> Option<&LearningStrategy<F>> {
        if self.strategies.is_empty() {
            return None;
        }

        // Find strategy with highest applicability score for given task
        self.strategies.iter().max_by(|a, b| {
            a.applicability_score
                .partial_cmp(&b.applicability_score)
                .unwrap()
        })
    }

    /// Update strategy performance
    pub fn update_performance(&mut self, strategy_name: &str, performance: F) {
        self.performance_history
            .insert(strategy_name.to_string(), performance);
    }

    /// Recommend strategy adaptation
    pub fn recommend_adaptation(&self, current_performance: F) -> Vec<String> {
        let mut recommendations = Vec::new();

        let performance_threshold = F::from_f64(0.7).unwrap();
        if current_performance < performance_threshold {
            recommendations.push("Consider increasing learning rate".to_string());
            recommendations.push("Try different optimization strategy".to_string());
            recommendations.push("Add regularization".to_string());
        }

        recommendations
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> LearningEvaluationSystem<F> {
    /// Create new learning evaluation system
    pub fn new(threshold: F) -> Self {
        LearningEvaluationSystem {
            evaluation_metrics: vec![
                EvaluationMetric::Accuracy,
                EvaluationMetric::Speed,
                EvaluationMetric::Efficiency,
            ],
            performance_threshold: threshold,
            validation_protocol: ValidationMethod::CrossValidation,
        }
    }

    /// Evaluate learning performance
    pub fn evaluate_performance(
        &self,
        predictions: &Array1<F>,
        ground_truth: &Array1<F>,
    ) -> Result<HashMap<String, F>> {
        let mut results = HashMap::new();

        for metric in &self.evaluation_metrics {
            let score = match metric {
                EvaluationMetric::Accuracy => self.calculate_accuracy(predictions, ground_truth)?,
                EvaluationMetric::Speed => {
                    // Placeholder for speed measurement
                    F::from_f64(0.8).unwrap()
                }
                EvaluationMetric::Efficiency => self.calculate_efficiency(predictions)?,
                EvaluationMetric::Robustness => self.calculate_robustness(predictions)?,
                EvaluationMetric::Interpretability => {
                    // Placeholder for interpretability measurement
                    F::from_f64(0.6).unwrap()
                }
            };

            results.insert(format!("{:?}", metric), score);
        }

        Ok(results)
    }

    /// Calculate accuracy metric
    fn calculate_accuracy(&self, predictions: &Array1<F>, ground_truth: &Array1<F>) -> Result<F> {
        if predictions.len() != ground_truth.len() {
            return Ok(F::zero());
        }

        let mut correct = 0;
        let threshold = F::from_f64(0.5).unwrap();

        for (pred, truth) in predictions.iter().zip(ground_truth.iter()) {
            let pred_binary = if *pred > threshold {
                F::from_f64(1.0).unwrap()
            } else {
                F::zero()
            };
            let truth_binary = if *truth > threshold {
                F::from_f64(1.0).unwrap()
            } else {
                F::zero()
            };

            if (pred_binary - truth_binary).abs() < F::from_f64(0.1).unwrap() {
                correct += 1;
            }
        }

        let accuracy = F::from_usize(correct).unwrap() / F::from_usize(predictions.len()).unwrap();
        Ok(accuracy)
    }

    /// Calculate efficiency metric
    fn calculate_efficiency(&self, predictions: &Array1<F>) -> Result<F> {
        if predictions.is_empty() {
            return Ok(F::zero());
        }

        // Simple efficiency based on prediction confidence
        let confidence_sum = predictions.iter().fold(F::zero(), |acc, &x| acc + x.abs());
        let efficiency = confidence_sum / F::from_usize(predictions.len()).unwrap();

        Ok(efficiency)
    }

    /// Calculate robustness metric
    fn calculate_robustness(&self, predictions: &Array1<F>) -> Result<F> {
        if predictions.len() < 2 {
            return Ok(F::zero());
        }

        // Robustness based on prediction stability
        let mean = predictions.iter().fold(F::zero(), |acc, &x| acc + x)
            / F::from_usize(predictions.len()).unwrap();
        let variance = predictions
            .iter()
            .fold(F::zero(), |acc, &x| acc + (x - mean) * (x - mean))
            / F::from_usize(predictions.len()).unwrap();

        // Lower variance indicates higher robustness
        let robustness = F::from_f64(1.0).unwrap() / (F::from_f64(1.0).unwrap() + variance);
        Ok(robustness)
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for MetaAdaptationMechanism<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> MetaAdaptationMechanism<F> {
    /// Create new meta-adaptation mechanism
    pub fn new() -> Self {
        MetaAdaptationMechanism {
            adaptation_rules: Vec::new(),
            trigger_conditions: Vec::new(),
            adaptation_history: HashMap::new(),
        }
    }

    /// Add adaptation rule
    pub fn add_rule(&mut self, rule: AdaptationRule<F>) {
        self.adaptation_rules.push(rule);
    }

    /// Add trigger condition
    pub fn add_trigger(&mut self, condition: TriggerCondition<F>) {
        self.trigger_conditions.push(condition);
    }

    /// Check if adaptation should be triggered
    pub fn should_adapt(&self, current_metrics: &HashMap<String, F>) -> bool {
        for condition in &self.trigger_conditions {
            if let Some(&metric_value) = current_metrics.get(&condition.metric_name) {
                let triggered = match condition.comparison {
                    ComparisonDirection::GreaterThan => metric_value > condition.threshold,
                    ComparisonDirection::LessThan => metric_value < condition.threshold,
                    ComparisonDirection::EqualTo => {
                        (metric_value - condition.threshold).abs() < F::from_f64(0.01).unwrap()
                    }
                    ComparisonDirection::WithinRange => {
                        let range = F::from_f64(0.1).unwrap();
                        (metric_value - condition.threshold).abs() <= range
                    }
                };

                if triggered {
                    return true;
                }
            }
        }
        false
    }

    /// Apply adaptation rules
    pub fn apply_adaptation(&mut self, current_metrics: &HashMap<String, F>) -> Vec<String> {
        let mut applied_actions = Vec::new();

        if self.should_adapt(current_metrics) {
            for rule in &self.adaptation_rules {
                // Simple rule application - in practice would be more sophisticated
                applied_actions.push(rule.action.clone());

                // Update adaptation history
                let history_key = format!("rule_{}", rule.rule_id);
                let history_entry = self.adaptation_history.entry(history_key).or_default();
                history_entry.push(rule.priority);
            }
        }

        applied_actions
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for KnowledgeTransferSystem<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> KnowledgeTransferSystem<F> {
    /// Create new knowledge transfer system
    pub fn new() -> Self {
        KnowledgeTransferSystem {
            knowledge_base: Vec::new(),
            transfer_mechanisms: vec![
                TransferMechanism::ParameterTransfer,
                TransferMechanism::FeatureTransfer,
            ],
            similarity_metrics: HashMap::new(),
            transfer_efficiency: F::from_f64(0.8).unwrap(),
        }
    }

    /// Add knowledge item to the base
    pub fn add_knowledge(&mut self, item: KnowledgeItem<F>) {
        self.knowledge_base.push(item);
    }

    /// Transfer knowledge from source to target task
    pub fn transfer_knowledge(
        &self,
        source_task: &str,
        target_task: &str,
        task_similarity: F,
    ) -> Result<Vec<KnowledgeItem<F>>> {
        let mut transferred_knowledge = Vec::new();

        // Find relevant knowledge from source task
        for item in &self.knowledge_base {
            if item.source_task == source_task {
                // Create adapted knowledge item for target task
                let mut adapted_item = item.clone();
                adapted_item.source_task = target_task.to_string();

                // Adjust applicability based on task similarity
                adapted_item.applicability_score =
                    adapted_item.applicability_score * task_similarity * self.transfer_efficiency;

                // Apply transfer mechanism adaptations
                for mechanism in &self.transfer_mechanisms {
                    match mechanism {
                        TransferMechanism::ParameterTransfer => {
                            // Scale parameters based on similarity
                            for param in &mut adapted_item.parameters {
                                *param = *param * task_similarity;
                            }
                        }
                        TransferMechanism::FeatureTransfer => {
                            // Feature-based adaptation
                            adapted_item.applicability_score =
                                adapted_item.applicability_score * F::from_f64(0.9).unwrap();
                        }
                        _ => {
                            // Other transfer mechanisms
                        }
                    }
                }

                transferred_knowledge.push(adapted_item);
            }
        }

        Ok(transferred_knowledge)
    }

    /// Calculate task similarity
    pub fn calculate_similarity(
        &self,
        task1_features: &Array1<F>,
        task2_features: &Array1<F>,
    ) -> Result<F> {
        if task1_features.len() != task2_features.len() {
            return Ok(F::zero());
        }

        // Cosine similarity
        let dot_product = task1_features
            .iter()
            .zip(task2_features.iter())
            .fold(F::zero(), |acc, (&a, &b)| acc + a * b);

        let norm1 = task1_features
            .iter()
            .fold(F::zero(), |acc, &x| acc + x * x)
            .sqrt();

        let norm2 = task2_features
            .iter()
            .fold(F::zero(), |acc, &x| acc + x * x)
            .sqrt();

        if norm1 == F::zero() || norm2 == F::zero() {
            return Ok(F::zero());
        }

        let similarity = dot_product / (norm1 * norm2);
        Ok(similarity)
    }
}