voirs_spatial/position/

advanced_prediction.rs

//! Advanced Predictive Head Movement Compensation System
//!
//! This module provides sophisticated head movement prediction using machine learning,
//! motion modeling, and adaptive algorithms to minimize motion-to-sound latency
//! in VR/AR and gaming applications.

use crate::position::{HeadTracker, MotionSnapshot};
use crate::types::Position3D;
use crate::{Error, Result};
use candle_core::{Device, Tensor};
use candle_nn::{linear, Linear, Module, VarBuilder, VarMap};
use scirs2_core::ndarray::Array1;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

/// Advanced predictive head movement compensation system
pub struct AdvancedPredictiveTracker {
    /// Base head tracker
    base_tracker: HeadTracker,
    /// Prediction models
    prediction_models: PredictionModels,
    /// Motion pattern analyzer
    pattern_analyzer: MotionPatternAnalyzer,
    /// Adaptive prediction controller
    adaptive_controller: AdaptivePredictionController,
    /// Configuration
    config: PredictiveTrackingConfig,
    /// Performance metrics
    metrics: PredictionMetrics,
}

/// Collection of prediction models for different scenarios
pub struct PredictionModels {
    /// Linear motion model (baseline)
    linear_model: LinearMotionModel,
    /// Polynomial motion model for complex curves
    polynomial_model: PolynomialMotionModel,
    /// Neural network model for learned patterns
    neural_model: Option<NeuralPredictionModel>,
    /// Kalman filter for smooth prediction
    kalman_filter: KalmanMotionFilter,
    /// Currently active model
    active_model: PredictionModelType,
}

/// Motion pattern analysis for adaptive prediction
pub struct MotionPatternAnalyzer {
    /// Recent motion patterns
    recent_patterns: VecDeque<MotionPattern>,
    /// Known pattern library
    pattern_library: HashMap<String, MotionPatternTemplate>,
    /// Pattern recognition state
    recognition_state: PatternRecognitionState,
}

/// Adaptive controller for prediction parameters
pub struct AdaptivePredictionController {
    /// Prediction accuracy history
    accuracy_history: VecDeque<PredictionAccuracy>,
    /// Current adaptation state
    adaptation_state: AdaptationState,
    /// Learning rate for adaptation
    learning_rate: f32,
    /// Minimum confidence threshold
    min_confidence: f32,
}

/// Configuration for predictive tracking
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictiveTrackingConfig {
    /// Maximum prediction lookahead time
    pub max_prediction_time: Duration,
    /// Minimum samples required for prediction
    pub min_samples_for_prediction: usize,
    /// Model selection strategy
    pub model_selection_strategy: ModelSelectionStrategy,
    /// Enable adaptive learning
    pub enable_adaptive_learning: bool,
    /// Enable neural network prediction
    pub enable_neural_prediction: bool,
    /// Pattern recognition configuration
    pub pattern_recognition: PatternRecognitionConfig,
    /// Performance optimization settings
    pub performance_optimization: PerformanceOptimizationConfig,
}

/// Strategy for selecting prediction models
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ModelSelectionStrategy {
    /// Always use linear model (fastest)
    AlwaysLinear,
    /// Always use polynomial model
    AlwaysPolynomial,
    /// Always use neural model (if available)
    AlwaysNeural,
    /// Automatically select best model based on pattern
    Adaptive,
    /// Use ensemble of multiple models
    Ensemble,
}

/// Types of prediction models available
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum PredictionModelType {
    /// Linear extrapolation
    Linear,
    /// Polynomial curve fitting
    Polynomial,
    /// Neural network prediction
    Neural,
    /// Kalman filter
    Kalman,
    /// Ensemble combination
    Ensemble,
}

/// Motion pattern classification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MotionPattern {
    /// Pattern type
    pub pattern_type: MotionPatternType,
    /// Pattern parameters
    pub parameters: MotionPatternParameters,
    /// Confidence in pattern detection
    pub confidence: f32,
    /// Time window for this pattern
    pub time_window: Duration,
    /// Number of samples in pattern
    pub sample_count: usize,
}

/// Types of head motion patterns
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum MotionPatternType {
    /// Static or minimal movement
    Static,
    /// Linear movement in one direction
    Linear,
    /// Circular or rotational movement
    Circular,
    /// Oscillatory movement (nodding, shaking)
    Oscillatory,
    /// Sudden/jerky movement
    Jerky,
    /// Smooth curved movement
    Curved,
    /// Complex/unpredictable movement
    Complex,
}

/// Parameters for motion patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MotionPatternParameters {
    /// Primary direction of movement
    pub primary_direction: Position3D,
    /// Movement frequency (for oscillatory patterns)
    pub frequency: f32,
    /// Movement amplitude
    pub amplitude: f32,
    /// Acceleration characteristics
    pub acceleration_profile: AccelerationProfile,
    /// Periodicity (if any)
    pub periodicity: Option<f32>,
}

/// Acceleration profile characteristics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AccelerationProfile {
    /// Average acceleration magnitude
    pub average_magnitude: f32,
    /// Peak acceleration
    pub peak_magnitude: f32,
    /// Jerk (rate of acceleration change)
    pub jerk: f32,
    /// Smoothness score (0.0 = jerky, 1.0 = smooth)
    pub smoothness: f32,
}

/// Template for known motion patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MotionPatternTemplate {
    /// Template name
    pub name: String,
    /// Pattern type
    pub pattern_type: MotionPatternType,
    /// Expected parameters
    pub expected_parameters: MotionPatternParameters,
    /// Matching tolerance
    pub tolerance: f32,
    /// Prediction model to use for this pattern
    pub preferred_model: PredictionModelType,
}

/// Linear motion prediction model
#[derive(Debug, Clone)]
pub struct LinearMotionModel {
    /// Last computed velocity
    last_velocity: Position3D,
    /// Velocity smoothing factor
    smoothing_factor: f32,
}

/// Polynomial motion prediction model
#[derive(Debug, Clone)]
pub struct PolynomialMotionModel {
    /// Polynomial degree
    degree: usize,
    /// Minimum samples needed
    min_samples: usize,
}

/// Neural network prediction model
pub struct NeuralPredictionModel {
    /// Neural network
    network: PredictionNetwork,
    /// Training data cache
    training_data: VecDeque<TrainingExample>,
    /// Model configuration
    config: NeuralModelConfig,
    /// Device for computation
    device: Device,
    /// Variable map
    var_map: VarMap,
}

/// Kalman filter for motion prediction
#[derive(Debug, Clone)]
pub struct KalmanMotionFilter {
    /// State vector (position, velocity, acceleration)
    state: [f32; 9], // 3D position + 3D velocity + 3D acceleration
    /// Covariance matrix (flattened)
    covariance: [f32; 81], // 9x9 matrix
    /// Process noise
    process_noise: f32,
    /// Measurement noise
    measurement_noise: f32,
    /// Time step
    dt: f32,
}

/// Neural network for motion prediction
pub struct PredictionNetwork {
    /// Input layer
    input_layer: Linear,
    /// Hidden layers
    hidden_layers: Vec<Linear>,
    /// Output layer
    output_layer: Linear,
}

/// Performance metrics for prediction system
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct PredictionMetrics {
    /// Total predictions made
    pub total_predictions: usize,
    /// Successful predictions (within error threshold)
    pub successful_predictions: usize,
    /// Average prediction error (meters)
    pub average_error: f32,
    /// Peak prediction error
    pub peak_error: f32,
    /// Prediction latency (microseconds)
    pub average_latency: f32,
    /// Model accuracy by type
    pub model_accuracies: HashMap<PredictionModelType, f32>,
    /// Pattern recognition accuracy
    pub pattern_recognition_accuracy: f32,
}

/// Configuration for pattern recognition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PatternRecognitionConfig {
    /// Enable pattern recognition
    pub enable_recognition: bool,
    /// Minimum pattern duration
    pub min_pattern_duration: Duration,
    /// Pattern matching threshold
    pub matching_threshold: f32,
    /// Update frequency for pattern analysis
    pub analysis_frequency: f32,
}

/// Configuration for performance optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceOptimizationConfig {
    /// Target latency for predictions (microseconds)
    pub target_latency: f32,
    /// Maximum computation time per prediction
    pub max_computation_time: Duration,
    /// Enable SIMD optimizations
    pub enable_simd: bool,
    /// Enable GPU acceleration (if available)
    pub enable_gpu: bool,
}

/// Pattern recognition state
#[derive(Debug, Clone, Default)]
pub struct PatternRecognitionState {
    /// Currently detected pattern
    pub current_pattern: Option<MotionPattern>,
    /// Pattern confidence
    pub confidence: f32,
    /// Time since pattern detection
    pub time_since_detection: Duration,
    /// Pattern stability score
    pub stability_score: f32,
}

/// Adaptation state for prediction controller
#[derive(Debug, Clone, Default)]
pub struct AdaptationState {
    /// Learning phase (warm-up, adapting, stable)
    pub phase: AdaptationPhase,
    /// Adaptation rate
    pub adaptation_rate: f32,
    /// Model weights
    pub model_weights: HashMap<PredictionModelType, f32>,
    /// Recent performance trend
    pub performance_trend: f32,
}

/// Prediction accuracy measurement
#[derive(Debug, Clone)]
pub struct PredictionAccuracy {
    /// Predicted position
    pub predicted_position: Position3D,
    /// Actual position
    pub actual_position: Position3D,
    /// Prediction error (distance)
    pub error: f32,
    /// Timestamp
    pub timestamp: Instant,
    /// Model used for prediction
    pub model_used: PredictionModelType,
}

/// Training example for neural model
#[derive(Debug, Clone)]
pub struct TrainingExample {
    /// Input features (motion history)
    pub input_features: Vec<f32>,
    /// Target position
    pub target_position: Position3D,
    /// Time delta
    pub time_delta: f32,
}

/// Neural model configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuralModelConfig {
    /// Input feature dimension
    pub input_dim: usize,
    /// Hidden layer dimensions
    pub hidden_dims: Vec<usize>,
    /// Output dimension (3 for position)
    pub output_dim: usize,
    /// Learning rate
    pub learning_rate: f64,
    /// Training batch size
    pub batch_size: usize,
}

/// Adaptation phases
#[derive(Debug, Clone, Copy, PartialEq, Eq, Default, Serialize, Deserialize)]
pub enum AdaptationPhase {
    /// Initial warm-up phase
    #[default]
    WarmUp,
    /// Active adaptation phase
    Adapting,
    /// Stable operation phase
    Stable,
    /// Re-adaptation after performance drop
    ReAdapting,
}

impl Default for PredictiveTrackingConfig {
    fn default() -> Self {
        Self {
            max_prediction_time: Duration::from_millis(100),
            min_samples_for_prediction: 5,
            model_selection_strategy: ModelSelectionStrategy::Adaptive,
            enable_adaptive_learning: true,
            enable_neural_prediction: false, // Disabled by default for performance
            pattern_recognition: PatternRecognitionConfig {
                enable_recognition: true,
                min_pattern_duration: Duration::from_millis(200),
                matching_threshold: 0.8,
                analysis_frequency: 10.0, // 10 Hz
            },
            performance_optimization: PerformanceOptimizationConfig {
                target_latency: 1000.0, // 1ms target
                max_computation_time: Duration::from_micros(500),
                enable_simd: true,
                enable_gpu: false, // Disabled by default
            },
        }
    }
}

impl AdvancedPredictiveTracker {
    /// Create new advanced predictive tracker
    pub fn new(config: PredictiveTrackingConfig) -> Result<Self> {
        let base_tracker = HeadTracker::new();
        let prediction_models = PredictionModels::new(&config)?;
        let pattern_analyzer = MotionPatternAnalyzer::new(&config.pattern_recognition);
        let adaptive_controller = AdaptivePredictionController::new();

        Ok(Self {
            base_tracker,
            prediction_models,
            pattern_analyzer,
            adaptive_controller,
            config,
            metrics: PredictionMetrics::default(),
        })
    }

    /// Update head position with advanced prediction
    pub fn update_position(&mut self, position: Position3D, timestamp: Instant) -> Result<()> {
        // Update base tracker
        self.base_tracker.update_position(position, timestamp);

        // Analyze motion patterns
        self.analyze_motion_patterns()?;

        // Update adaptive controller
        self.update_adaptive_controller()?;

        // Update neural model if enabled
        if self.config.enable_neural_prediction {
            self.update_neural_model()?;
        }

        Ok(())
    }

    /// Get advanced prediction using best available model
    pub fn predict_position(&self, lookahead_time: Duration) -> Result<PredictedPosition> {
        let start_time = Instant::now();

        // Select best model based on current pattern and performance
        let selected_model = self.select_prediction_model()?;

        // Generate prediction
        let prediction = match selected_model {
            PredictionModelType::Linear => self
                .prediction_models
                .linear_model
                .predict(self.base_tracker.position_history(), lookahead_time)?,
            PredictionModelType::Polynomial => self
                .prediction_models
                .polynomial_model
                .predict(self.base_tracker.position_history(), lookahead_time)?,
            PredictionModelType::Neural => {
                if let Some(ref neural_model) = self.prediction_models.neural_model {
                    neural_model.predict(self.base_tracker.position_history(), lookahead_time)?
                } else {
                    // Fallback to linear
                    self.prediction_models
                        .linear_model
                        .predict(self.base_tracker.position_history(), lookahead_time)?
                }
            }
            PredictionModelType::Kalman => self
                .prediction_models
                .kalman_filter
                .predict(lookahead_time)?,
            PredictionModelType::Ensemble => self.ensemble_prediction(lookahead_time)?,
        };

        let computation_time = start_time.elapsed();

        Ok(PredictedPosition {
            position: prediction.position,
            confidence: prediction.confidence,
            model_used: selected_model,
            computation_time,
            pattern_type: self
                .pattern_analyzer
                .recognition_state
                .current_pattern
                .as_ref()
                .map(|p| p.pattern_type),
        })
    }

    /// Update prediction accuracy based on actual observed position
    pub fn update_accuracy(&mut self, predicted: &PredictedPosition, actual: Position3D) {
        let error = predicted.position.distance_to(&actual);

        let accuracy = PredictionAccuracy {
            predicted_position: predicted.position,
            actual_position: actual,
            error,
            timestamp: Instant::now(),
            model_used: predicted.model_used,
        };

        self.adaptive_controller
            .accuracy_history
            .push_back(accuracy);

        // Update metrics
        self.metrics.total_predictions += 1;
        if error < 0.05 {
            // 5cm threshold
            self.metrics.successful_predictions += 1;
        }

        // Update running averages
        let total = self.metrics.total_predictions as f32;
        self.metrics.average_error = (self.metrics.average_error * (total - 1.0) + error) / total;
        self.metrics.peak_error = self.metrics.peak_error.max(error);

        // Update model-specific accuracy
        let model_accuracy = self
            .metrics
            .model_accuracies
            .entry(predicted.model_used)
            .or_insert(0.0);
        *model_accuracy =
            (*model_accuracy * (total - 1.0) + if error < 0.05 { 1.0 } else { 0.0 }) / total;

        // Limit history size
        if self.adaptive_controller.accuracy_history.len() > 1000 {
            self.adaptive_controller.accuracy_history.pop_front();
        }
    }

    /// Get prediction performance metrics
    pub fn metrics(&self) -> &PredictionMetrics {
        &self.metrics
    }

    /// Get currently detected motion pattern
    pub fn current_pattern(&self) -> Option<&MotionPattern> {
        self.pattern_analyzer
            .recognition_state
            .current_pattern
            .as_ref()
    }

    /// Configure prediction parameters
    pub fn configure(&mut self, config: PredictiveTrackingConfig) {
        self.config = config;
        // Reconfigure subsystems as needed
    }

    // Private helper methods

    fn analyze_motion_patterns(&mut self) -> Result<()> {
        let position_history = self.base_tracker.position_history();

        if position_history.len() < self.config.min_samples_for_prediction {
            return Ok(());
        }

        // Analyze recent motion for patterns
        let pattern = self.pattern_analyzer.analyze_motion(position_history)?;

        if let Some(detected_pattern) = pattern {
            // Update recognition state
            self.pattern_analyzer.recognition_state.current_pattern = Some(detected_pattern);
            self.pattern_analyzer.recognition_state.confidence = self
                .pattern_analyzer
                .recognition_state
                .current_pattern
                .as_ref()
                .map(|p| p.confidence)
                .unwrap_or(0.0);
        }

        Ok(())
    }

    fn update_adaptive_controller(&mut self) -> Result<()> {
        if !self.config.enable_adaptive_learning {
            return Ok(());
        }

        // Analyze recent prediction accuracy
        if let Some(recent_accuracy) = self.adaptive_controller.accuracy_history.back() {
            // Update model weights based on performance
            let current_weight = self
                .adaptive_controller
                .adaptation_state
                .model_weights
                .entry(recent_accuracy.model_used)
                .or_insert(1.0);

            // Adjust weight based on accuracy (higher accuracy = higher weight)
            let accuracy_factor = if recent_accuracy.error < 0.02 {
                1.1
            } else {
                0.9
            };
            *current_weight = (*current_weight * accuracy_factor).clamp(0.1, 2.0);
        }

        Ok(())
    }

    fn update_neural_model(&mut self) -> Result<()> {
        if self.prediction_models.neural_model.is_some() {
            // Add recent motion history as training data
            let position_history = self.base_tracker.position_history();

            if position_history.len() >= 10 {
                let training_example = self.create_training_example(position_history)?;

                if let Some(ref mut neural_model) = self.prediction_models.neural_model {
                    neural_model.training_data.push_back(training_example);

                    // Limit training data size
                    if neural_model.training_data.len() > 1000 {
                        neural_model.training_data.pop_front();
                    }

                    // Retrain if we have enough new data
                    if neural_model.training_data.len() % 100 == 0 {
                        neural_model.retrain()?;
                    }
                }
            }
        }

        Ok(())
    }

    fn select_prediction_model(&self) -> Result<PredictionModelType> {
        match self.config.model_selection_strategy {
            ModelSelectionStrategy::AlwaysLinear => Ok(PredictionModelType::Linear),
            ModelSelectionStrategy::AlwaysPolynomial => Ok(PredictionModelType::Polynomial),
            ModelSelectionStrategy::AlwaysNeural => {
                if self.prediction_models.neural_model.is_some() {
                    Ok(PredictionModelType::Neural)
                } else {
                    Ok(PredictionModelType::Linear) // Fallback
                }
            }
            ModelSelectionStrategy::Adaptive => {
                // Select model based on current pattern and performance
                if let Some(ref pattern) = self.pattern_analyzer.recognition_state.current_pattern {
                    match pattern.pattern_type {
                        MotionPatternType::Static => Ok(PredictionModelType::Linear),
                        MotionPatternType::Linear => Ok(PredictionModelType::Linear),
                        MotionPatternType::Circular | MotionPatternType::Curved => {
                            Ok(PredictionModelType::Polynomial)
                        }
                        MotionPatternType::Oscillatory => Ok(PredictionModelType::Kalman),
                        MotionPatternType::Complex => {
                            if self.prediction_models.neural_model.is_some() {
                                Ok(PredictionModelType::Neural)
                            } else {
                                Ok(PredictionModelType::Polynomial)
                            }
                        }
                        _ => Ok(PredictionModelType::Linear),
                    }
                } else {
                    Ok(PredictionModelType::Linear) // Default
                }
            }
            ModelSelectionStrategy::Ensemble => Ok(PredictionModelType::Ensemble),
        }
    }

    fn ensemble_prediction(&self, lookahead_time: Duration) -> Result<PredictionResult> {
        let position_history = self.base_tracker.position_history();

        // Get predictions from multiple models
        let linear_pred = self
            .prediction_models
            .linear_model
            .predict(position_history, lookahead_time)?;
        let poly_pred = self
            .prediction_models
            .polynomial_model
            .predict(position_history, lookahead_time)?;
        let kalman_pred = self
            .prediction_models
            .kalman_filter
            .predict(lookahead_time)?;

        // Weight predictions based on model performance
        let weights = &self.adaptive_controller.adaptation_state.model_weights;
        let linear_weight = weights.get(&PredictionModelType::Linear).unwrap_or(&1.0);
        let poly_weight = weights
            .get(&PredictionModelType::Polynomial)
            .unwrap_or(&1.0);
        let kalman_weight = weights.get(&PredictionModelType::Kalman).unwrap_or(&1.0);

        let total_weight = linear_weight + poly_weight + kalman_weight;

        // Weighted average of predictions
        let ensemble_position = Position3D::new(
            (linear_pred.position.x * linear_weight
                + poly_pred.position.x * poly_weight
                + kalman_pred.position.x * kalman_weight)
                / total_weight,
            (linear_pred.position.y * linear_weight
                + poly_pred.position.y * poly_weight
                + kalman_pred.position.y * kalman_weight)
                / total_weight,
            (linear_pred.position.z * linear_weight
                + poly_pred.position.z * poly_weight
                + kalman_pred.position.z * kalman_weight)
                / total_weight,
        );

        // Average confidence
        let ensemble_confidence = (linear_pred.confidence * linear_weight
            + poly_pred.confidence * poly_weight
            + kalman_pred.confidence * kalman_weight)
            / total_weight;

        Ok(PredictionResult {
            position: ensemble_position,
            confidence: ensemble_confidence,
        })
    }

    fn create_training_example(
        &self,
        position_history: &[crate::position::PositionSnapshot],
    ) -> Result<TrainingExample> {
        // Extract features from recent position history
        let mut features = Vec::new();

        // Use last 8 positions as features
        let start_idx = position_history.len().saturating_sub(8);
        for snapshot in &position_history[start_idx..] {
            features.push(snapshot.position.x);
            features.push(snapshot.position.y);
            features.push(snapshot.position.z);
            features.push(snapshot.velocity.x);
            features.push(snapshot.velocity.y);
            features.push(snapshot.velocity.z);
        }

        // Pad if not enough features
        while features.len() < 48 {
            // 8 snapshots * 6 values each
            features.push(0.0);
        }

        // Target is the next position (if available)
        let target_position = if let Some(latest) = position_history.last() {
            latest.position
        } else {
            Position3D::default()
        };

        Ok(TrainingExample {
            input_features: features,
            target_position,
            time_delta: 0.1, // 100ms prediction
        })
    }
}

/// Result of a prediction operation
#[derive(Debug, Clone)]
pub struct PredictionResult {
    /// Predicted position
    pub position: Position3D,
    /// Confidence in prediction (0.0-1.0)
    pub confidence: f32,
}

/// Extended prediction result with metadata
#[derive(Debug, Clone)]
pub struct PredictedPosition {
    /// Predicted position
    pub position: Position3D,
    /// Confidence in prediction
    pub confidence: f32,
    /// Model used for prediction
    pub model_used: PredictionModelType,
    /// Computation time
    pub computation_time: Duration,
    /// Detected pattern type (if any)
    pub pattern_type: Option<MotionPatternType>,
}

// Implement placeholder methods for components
impl PredictionModels {
    fn new(_config: &PredictiveTrackingConfig) -> Result<Self> {
        Ok(Self {
            linear_model: LinearMotionModel::new(),
            polynomial_model: PolynomialMotionModel::new(),
            neural_model: None, // Created on demand
            kalman_filter: KalmanMotionFilter::new(),
            active_model: PredictionModelType::Linear,
        })
    }
}

impl LinearMotionModel {
    fn new() -> Self {
        Self {
            last_velocity: Position3D::default(),
            smoothing_factor: 0.3,
        }
    }

    fn predict(
        &self,
        _history: &[crate::position::PositionSnapshot],
        lookahead: Duration,
    ) -> Result<PredictionResult> {
        // Simple linear extrapolation based on last velocity
        let dt = lookahead.as_secs_f32();
        let predicted_pos = Position3D::new(
            self.last_velocity.x * dt,
            self.last_velocity.y * dt,
            self.last_velocity.z * dt,
        );

        Ok(PredictionResult {
            position: predicted_pos,
            confidence: 0.8, // Fixed confidence for now
        })
    }
}

impl PolynomialMotionModel {
    fn new() -> Self {
        Self {
            degree: 3,
            min_samples: 5,
        }
    }

    fn predict(
        &self,
        history: &[crate::position::PositionSnapshot],
        lookahead: Duration,
    ) -> Result<PredictionResult> {
        if history.len() < self.min_samples {
            return Err(Error::processing(
                "Insufficient data for polynomial prediction",
            ));
        }

        // Simplified polynomial prediction (would be more sophisticated in practice)
        let last_pos = history
            .last()
            .ok_or_else(|| Error::processing("history is empty"))?
            .position;
        let dt = lookahead.as_secs_f32();

        // For now, just add some curvature to linear prediction
        let predicted_pos = Position3D::new(
            last_pos.x + dt * 0.1,
            last_pos.y + dt * 0.1,
            last_pos.z + dt * 0.1,
        );

        Ok(PredictionResult {
            position: predicted_pos,
            confidence: 0.7,
        })
    }
}

impl KalmanMotionFilter {
    fn new() -> Self {
        Self {
            state: [0.0; 9],
            covariance: [0.0; 81],
            process_noise: 0.01,
            measurement_noise: 0.1,
            dt: 0.01,
        }
    }

    fn predict(&self, lookahead: Duration) -> Result<PredictionResult> {
        let dt = lookahead.as_secs_f32();

        // Simple Kalman prediction (position + velocity * time)
        let predicted_pos = Position3D::new(
            self.state[0] + self.state[3] * dt,
            self.state[1] + self.state[4] * dt,
            self.state[2] + self.state[5] * dt,
        );

        Ok(PredictionResult {
            position: predicted_pos,
            confidence: 0.9, // Kalman filters are generally confident
        })
    }
}

impl MotionPatternAnalyzer {
    fn new(_config: &PatternRecognitionConfig) -> Self {
        Self {
            recent_patterns: VecDeque::new(),
            pattern_library: HashMap::new(),
            recognition_state: PatternRecognitionState::default(),
        }
    }

    fn analyze_motion(
        &mut self,
        history: &[crate::position::PositionSnapshot],
    ) -> Result<Option<MotionPattern>> {
        if history.len() < 5 {
            return Ok(None);
        }

        // Simple pattern detection based on velocity characteristics
        let mut total_velocity = 0.0;
        let mut direction_changes = 0;

        for window in history.windows(2) {
            let vel_mag = window[1].velocity.magnitude();
            total_velocity += vel_mag;

            // Detect direction changes
            if window.len() >= 2 {
                let dot_product = window[0].velocity.dot(&window[1].velocity);
                if dot_product < 0.0 {
                    direction_changes += 1;
                }
            }
        }

        let avg_velocity = total_velocity / (history.len() - 1) as f32;

        // Classify pattern
        let pattern_type = if avg_velocity < 0.01 {
            MotionPatternType::Static
        } else if direction_changes == 0 {
            MotionPatternType::Linear
        } else if direction_changes > history.len() / 3 {
            MotionPatternType::Oscillatory
        } else {
            MotionPatternType::Curved
        };

        let pattern = MotionPattern {
            pattern_type,
            parameters: MotionPatternParameters {
                primary_direction: Position3D::new(1.0, 0.0, 0.0), // Placeholder
                frequency: 0.0,
                amplitude: avg_velocity,
                acceleration_profile: AccelerationProfile {
                    average_magnitude: 0.1,
                    peak_magnitude: 0.2,
                    jerk: 0.05,
                    smoothness: 0.8,
                },
                periodicity: None,
            },
            confidence: 0.7,
            time_window: Duration::from_millis(500),
            sample_count: history.len(),
        };

        Ok(Some(pattern))
    }
}

impl AdaptivePredictionController {
    fn new() -> Self {
        Self {
            accuracy_history: VecDeque::new(),
            adaptation_state: AdaptationState::default(),
            learning_rate: 0.01,
            min_confidence: 0.5,
        }
    }
}

impl NeuralPredictionModel {
    fn predict(
        &self,
        _history: &[crate::position::PositionSnapshot],
        _lookahead: Duration,
    ) -> Result<PredictionResult> {
        // Placeholder neural prediction
        Ok(PredictionResult {
            position: Position3D::default(),
            confidence: 0.6,
        })
    }

    fn retrain(&mut self) -> Result<()> {
        // Placeholder for neural model retraining
        Ok(())
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_predictive_tracker_creation() {
        let config = PredictiveTrackingConfig::default();
        let tracker = AdvancedPredictiveTracker::new(config);
        assert!(tracker.is_ok());
    }

    #[test]
    fn test_linear_model_prediction() {
        let model = LinearMotionModel::new();
        let prediction = model.predict(&[], Duration::from_millis(100));
        assert!(prediction.is_ok());
    }

    #[test]
    fn test_pattern_analysis() {
        let config = PatternRecognitionConfig {
            enable_recognition: true,
            min_pattern_duration: Duration::from_millis(100),
            matching_threshold: 0.8,
            analysis_frequency: 10.0,
        };
        let mut analyzer = MotionPatternAnalyzer::new(&config);
        let pattern = analyzer.analyze_motion(&[]);
        assert!(pattern.is_ok());
    }

    #[test]
    fn test_model_selection_strategies() {
        let config = PredictiveTrackingConfig::default();
        let tracker = AdvancedPredictiveTracker::new(config).unwrap();

        // Test different selection strategies
        let linear_model = tracker.select_prediction_model();
        assert!(linear_model.is_ok());
    }

    #[test]
    fn test_prediction_metrics() {
        let mut metrics = PredictionMetrics::default();
        metrics.total_predictions = 100;
        metrics.successful_predictions = 85;

        let accuracy = metrics.successful_predictions as f32 / metrics.total_predictions as f32;
        assert_eq!(accuracy, 0.85);
    }

    #[test]
    fn test_kalman_filter() {
        let filter = KalmanMotionFilter::new();
        let prediction = filter.predict(Duration::from_millis(50));
        assert!(prediction.is_ok());
    }

    #[test]
    fn test_motion_pattern_types() {
        let pattern = MotionPattern {
            pattern_type: MotionPatternType::Oscillatory,
            parameters: MotionPatternParameters {
                primary_direction: Position3D::new(1.0, 0.0, 0.0),
                frequency: 2.0,
                amplitude: 0.1,
                acceleration_profile: AccelerationProfile {
                    average_magnitude: 0.05,
                    peak_magnitude: 0.1,
                    jerk: 0.02,
                    smoothness: 0.9,
                },
                periodicity: Some(0.5),
            },
            confidence: 0.8,
            time_window: Duration::from_millis(1000),
            sample_count: 20,
        };

        assert_eq!(pattern.pattern_type, MotionPatternType::Oscillatory);
        assert_eq!(pattern.parameters.frequency, 2.0);
    }
}