voirs-spatial 0.1.0-rc.1

//! Real-time Adaptive Acoustic Environment System
//!
//! This module provides real-time adaptation of acoustic environment parameters
//! based on environmental sensors, user feedback, content analysis, and machine learning.

use crate::room::{Room, RoomAcoustics, RoomSimulator};
use crate::types::Position3D;
use crate::{Error, Result};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

/// Real-time adaptive acoustic environment system
pub struct AdaptiveAcousticEnvironment {
    /// Current room environment
    current_room: Room,
    /// Environment sensors
    sensors: EnvironmentSensors,
    /// Adaptation controller
    adaptation_controller: AdaptationController,
    /// Machine learning model for adaptation
    ml_adapter: Option<AcousticAdaptationModel>,
    /// Configuration
    config: AdaptiveAcousticsConfig,
    /// Performance metrics
    metrics: AdaptationMetrics,
    /// Adaptation history
    adaptation_history: VecDeque<AdaptationAction>,
}

/// Environment sensors for real-time data collection
#[derive(Debug, Clone)]
pub struct EnvironmentSensors {
    /// Temperature sensor data
    temperature_sensor: TemperatureSensor,
    /// Humidity sensor data  
    humidity_sensor: HumiditySensor,
    /// Ambient noise sensor
    noise_sensor: NoiseSensor,
    /// Occupancy detection
    occupancy_sensor: OccupancySensor,
    /// Material detection (via computer vision/ML)
    material_detector: MaterialDetector,
    /// Acoustic probe measurements
    acoustic_probe: AcousticProbe,
}

/// Adaptation controller for real-time parameter adjustment
pub struct AdaptationController {
    /// Current adaptation state
    adaptation_state: AdaptationState,
    /// Adaptation strategies
    strategies: HashMap<AdaptationTrigger, AdaptationStrategy>,
    /// Learning rate for adaptation
    learning_rate: f32,
    /// Adaptation thresholds
    thresholds: AdaptationThresholds,
    /// Recent adaptations
    recent_adaptations: VecDeque<AdaptationAction>,
}

/// Machine learning model for acoustic adaptation
pub struct AcousticAdaptationModel {
    /// Environment classifier
    environment_classifier: EnvironmentClassifier,
    /// Parameter predictor
    parameter_predictor: ParameterPredictor,
    /// User preference model
    user_preference_model: UserPreferenceModel,
    /// Training data cache
    training_data: VecDeque<AdaptationTrainingExample>,
}

/// Configuration for adaptive acoustics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdaptiveAcousticsConfig {
    /// Enable real-time adaptation
    pub enable_adaptation: bool,
    /// Adaptation update frequency (Hz)
    pub update_frequency: f32,
    /// Minimum change threshold for adaptation
    pub min_change_threshold: f32,
    /// Maximum adaptation rate (per second)
    pub max_adaptation_rate: f32,
    /// Sensor configuration
    pub sensor_config: SensorConfig,
    /// ML model configuration
    pub ml_config: Option<MLModelConfig>,
    /// Adaptation strategies
    pub adaptation_strategies: Vec<AdaptationStrategyConfig>,
    /// User preference learning
    pub enable_preference_learning: bool,
}

/// Temperature sensor for environmental monitoring
#[derive(Debug, Clone)]
pub struct TemperatureSensor {
    /// Current temperature (Celsius)
    current_temperature: f32,
    /// Temperature history
    temperature_history: VecDeque<SensorReading<f32>>,
    /// Calibration offset
    calibration_offset: f32,
    /// Sensor accuracy
    accuracy: f32,
}

/// Humidity sensor for environmental monitoring
#[derive(Debug, Clone)]
pub struct HumiditySensor {
    /// Current relative humidity (0.0-1.0)
    current_humidity: f32,
    /// Humidity history
    humidity_history: VecDeque<SensorReading<f32>>,
    /// Calibration parameters
    calibration_params: HumidityCalibration,
}

/// Ambient noise sensor for noise floor detection
#[derive(Debug, Clone)]
pub struct NoiseSensor {
    /// Current noise level (dB SPL)
    current_level: f32,
    /// Noise spectrum analysis
    spectrum: NoiseSpectrum,
    /// Noise type classification
    noise_type: NoiseType,
    /// Noise history
    noise_history: VecDeque<NoiseReading>,
}

/// Occupancy sensor for people detection
#[derive(Debug, Clone)]
pub struct OccupancySensor {
    /// Number of people detected
    occupant_count: usize,
    /// Occupant positions (if available)
    occupant_positions: Vec<Position3D>,
    /// Activity level detection
    activity_level: ActivityLevel,
    /// Detection confidence
    confidence: f32,
}

/// Material detector using computer vision/ML
#[derive(Debug, Clone)]
pub struct MaterialDetector {
    /// Detected surface materials
    detected_materials: HashMap<String, MaterialProperties>,
    /// Material detection confidence
    detection_confidence: HashMap<String, f32>,
    /// Last update timestamp
    last_update: Instant,
    /// Detection method
    detection_method: MaterialDetectionMethod,
}

/// Acoustic probe for direct acoustic measurements
#[derive(Debug, Clone)]
pub struct AcousticProbe {
    /// Impulse response measurements
    impulse_responses: HashMap<Position3D, Vec<f32>>,
    /// Reverberation time measurements (RT60)
    rt60_measurements: HashMap<String, f32>,
    /// Frequency response measurements
    frequency_responses: HashMap<Position3D, Vec<f32>>,
    /// Last probe time
    last_probe_time: Instant,
}

/// Generic sensor reading with timestamp
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SensorReading<T> {
    /// Sensor value
    pub value: T,
    /// Reading timestamp (seconds since epoch)
    pub timestamp: f64,
    /// Reading confidence/quality
    pub confidence: f32,
}

/// Current adaptation state
#[derive(Debug, Clone, Default)]
pub struct AdaptationState {
    /// Current environment classification
    pub environment_type: EnvironmentType,
    /// Active adaptations
    pub active_adaptations: HashMap<String, f32>,
    /// Adaptation confidence
    pub confidence: f32,
    /// Last adaptation time
    pub last_adaptation_time: Option<Instant>,
    /// Stability score (0.0 = unstable, 1.0 = stable)
    pub stability_score: f32,
}

/// Types of environmental triggers for adaptation
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum AdaptationTrigger {
    /// Temperature change
    TemperatureChange,
    /// Humidity change
    HumidityChange,
    /// Noise level change
    NoiseChange,
    /// Occupancy change
    OccupancyChange,
    /// Material change (furniture moved, etc.)
    MaterialChange,
    /// Content type change (music vs speech)
    ContentChange,
    /// User preference feedback
    UserFeedback,
    /// Time-based adaptation (day/night cycles)
    TimeAdaptation,
}

/// Adaptation strategies for different triggers
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdaptationStrategy {
    /// Strategy name
    pub name: String,
    /// Applicable triggers
    pub triggers: Vec<AdaptationTrigger>,
    /// Parameter adjustments
    pub parameter_adjustments: HashMap<String, ParameterAdjustment>,
    /// Adaptation speed (0.0 = instant, 1.0 = very slow)
    pub adaptation_speed: f32,
    /// Strategy priority
    pub priority: f32,
}

/// Types of acoustic environments
#[derive(Debug, Clone, Copy, PartialEq, Eq, Default, Serialize, Deserialize)]
pub enum EnvironmentType {
    /// Living room
    #[default]
    LivingRoom,
    /// Bedroom
    Bedroom,
    /// Kitchen
    Kitchen,
    /// Office
    Office,
    /// Bathroom
    Bathroom,
    /// Outdoor
    Outdoor,
    /// Vehicle interior
    Vehicle,
    /// Concert hall
    ConcertHall,
    /// Small room
    SmallRoom,
    /// Large hall
    LargeHall,
    /// Unknown/unclassified
    Unknown,
}

/// Types of ambient noise
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum NoiseType {
    /// White noise
    White,
    /// Pink noise
    Pink,
    /// Traffic noise
    Traffic,
    /// Air conditioning/HVAC
    HVAC,
    /// Human conversation
    Conversation,
    /// Music
    Music,
    /// Mechanical noise
    Mechanical,
    /// Natural sounds (wind, rain)
    Natural,
    /// Electronic interference
    Electronic,
    /// Mixed/complex noise
    Mixed,
}

/// Occupancy activity levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ActivityLevel {
    /// No activity
    None,
    /// Low activity (sitting, reading)
    Low,
    /// Medium activity (talking, light movement)
    Medium,
    /// High activity (dancing, exercise)
    High,
    /// Very high activity (party, event)
    VeryHigh,
}

/// Material detection methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum MaterialDetectionMethod {
    /// Computer vision analysis
    ComputerVision,
    /// Acoustic analysis (clap test, etc.)
    AcousticAnalysis,
    /// LIDAR/depth sensing
    LIDAR,
    /// User input/manual
    Manual,
    /// Machine learning classification
    MLClassification,
}

/// Material properties for acoustic simulation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MaterialProperties {
    /// Material name
    pub name: String,
    /// Absorption coefficients per frequency band
    pub absorption: HashMap<String, f32>,
    /// Scattering coefficients
    pub scattering: HashMap<String, f32>,
    /// Transmission coefficients
    pub transmission: HashMap<String, f32>,
    /// Material density
    pub density: f32,
    /// Surface roughness
    pub roughness: f32,
}

/// Parameter adjustment specification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParameterAdjustment {
    /// Parameter name
    pub parameter: String,
    /// Adjustment type
    pub adjustment_type: AdjustmentType,
    /// Adjustment value
    pub value: f32,
    /// Adjustment curve (linear, exponential, etc.)
    pub curve: AdjustmentCurve,
    /// Minimum/maximum bounds
    pub bounds: (f32, f32),
}

/// Types of parameter adjustments
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AdjustmentType {
    /// Absolute value
    Absolute,
    /// Relative change (multiply)
    Relative,
    /// Additive change
    Additive,
    /// Exponential scaling
    Exponential,
}

/// Adjustment curve types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AdjustmentCurve {
    /// Linear adjustment
    Linear,
    /// Exponential curve
    Exponential,
    /// Logarithmic curve
    Logarithmic,
    /// S-curve (sigmoid)
    Sigmoid,
    /// Custom curve (defined by points)
    Custom,
}

/// Adaptation thresholds for triggering changes
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdaptationThresholds {
    /// Temperature threshold (Celsius)
    pub temperature_threshold: f32,
    /// Humidity threshold (relative)
    pub humidity_threshold: f32,
    /// Noise level threshold (dB)
    pub noise_threshold: f32,
    /// Occupancy change threshold
    pub occupancy_threshold: usize,
    /// Material change threshold
    pub material_threshold: f32,
    /// Time threshold for stability
    pub stability_time_threshold: Duration,
}

/// Record of an adaptation event
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdaptationEvent {
    /// Event timestamp (seconds since epoch)
    pub timestamp: f64,
    /// Trigger that caused adaptation
    pub trigger: AdaptationTrigger,
    /// Environmental conditions at the time
    pub environment_snapshot: EnvironmentSnapshot,
    /// Parameters that were changed
    pub parameter_changes: HashMap<String, ParameterChange>,
    /// Adaptation success/failure
    pub result: AdaptationResult,
    /// User feedback (if any)
    pub user_feedback: Option<UserFeedback>,
}

/// Snapshot of environmental conditions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EnvironmentSnapshot {
    /// Temperature at the time
    pub temperature: f32,
    /// Humidity at the time
    pub humidity: f32,
    /// Noise level at the time
    pub noise_level: f32,
    /// Number of occupants
    pub occupant_count: usize,
    /// Detected materials
    pub materials: HashMap<String, f32>,
    /// Time of day
    pub time_of_day: f32,
}

/// Performance metrics for adaptation system
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct AdaptationMetrics {
    /// Total adaptations performed
    pub total_adaptations: usize,
    /// Successful adaptations
    pub successful_adaptations: usize,
    /// Average adaptation time
    pub average_adaptation_time: Duration,
    /// User satisfaction score (0.0-1.0)
    pub user_satisfaction: f32,
    /// Environment classification accuracy
    pub classification_accuracy: f32,
    /// Parameter prediction accuracy
    pub prediction_accuracy: f32,
    /// Adaptation frequency (per hour)
    pub adaptation_frequency: f32,
}

/// Configuration for sensors
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SensorConfig {
    /// Temperature sensor configuration
    pub temperature: TemperatureSensorConfig,
    /// Humidity sensor configuration
    pub humidity: HumiditySensorConfig,
    /// Noise sensor configuration
    pub noise: NoiseSensorConfig,
    /// Occupancy sensor configuration
    pub occupancy: OccupancySensorConfig,
    /// Material detector configuration
    pub material_detector: MaterialDetectorConfig,
    /// Acoustic probe configuration
    pub acoustic_probe: AcousticProbeConfig,
}

/// Individual sensor configurations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TemperatureSensorConfig {
    /// Update frequency (Hz)
    pub update_frequency: f32,
    /// Calibration offset
    pub calibration_offset: f32,
    /// Sensor accuracy (±degrees)
    pub accuracy: f32,
}

/// Configuration for humidity sensors
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HumiditySensorConfig {
    /// Update frequency (Hz)
    pub update_frequency: f32,
    /// Calibration parameters
    pub calibration: HumidityCalibration,
}

/// Configuration for noise sensors
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NoiseSensorConfig {
    /// Update frequency (Hz)
    pub update_frequency: f32,
    /// Frequency analysis bands
    pub frequency_bands: Vec<(f32, f32)>,
    /// Noise classification enabled
    pub enable_classification: bool,
}

/// Configuration for occupancy sensors
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OccupancySensorConfig {
    /// Detection method
    pub detection_method: OccupancyDetectionMethod,
    /// Update frequency (Hz)
    pub update_frequency: f32,
    /// Position tracking enabled
    pub enable_position_tracking: bool,
}

/// Configuration for material detection sensors
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MaterialDetectorConfig {
    /// Detection method
    pub detection_method: MaterialDetectionMethod,
    /// Update frequency (Hz)
    pub update_frequency: f32,
    /// Confidence threshold
    pub confidence_threshold: f32,
}

/// Configuration for acoustic probes
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AcousticProbeConfig {
    /// Probe frequency (Hz)
    pub probe_frequency: f32,
    /// Probe signal type
    pub probe_signal: ProbeSignalType,
    /// Analysis window size
    pub analysis_window: Duration,
}

/// Types of probe signals for acoustic analysis
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ProbeSignalType {
    /// Sine sweep
    SineSweep,
    /// White noise burst
    WhiteNoise,
    /// Pink noise burst
    PinkNoise,
    /// Maximum length sequence
    MLS,
    /// Time-stretched pulse
    TSP,
}

/// Occupancy detection methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum OccupancyDetectionMethod {
    /// Computer vision (camera)
    ComputerVision,
    /// Infrared sensors
    Infrared,
    /// Ultrasonic sensors
    Ultrasonic,
    /// WiFi presence detection
    WiFi,
    /// Bluetooth beacons
    Bluetooth,
    /// Audio analysis (voice activity)
    AudioAnalysis,
}

/// Humidity calibration parameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HumidityCalibration {
    /// Linear calibration coefficient
    pub linear_coeff: f32,
    /// Offset calibration
    pub offset: f32,
    /// Temperature compensation
    pub temp_compensation: f32,
}

/// Noise spectrum analysis data
#[derive(Debug, Clone)]
pub struct NoiseSpectrum {
    /// Frequency bands (Hz)
    pub frequency_bands: Vec<f32>,
    /// Power levels per band (dB)
    pub power_levels: Vec<f32>,
    /// Spectral centroid
    pub centroid: f32,
    /// Spectral rolloff
    pub rolloff: f32,
}

/// Detailed noise reading with spectrum
#[derive(Debug, Clone)]
pub struct NoiseReading {
    /// Overall level (dB SPL)
    pub level: f32,
    /// Noise spectrum
    pub spectrum: NoiseSpectrum,
    /// Noise type classification
    pub noise_type: NoiseType,
    /// Reading timestamp (seconds since epoch)
    pub timestamp: f64,
    /// Classification confidence
    pub confidence: f32,
}

/// Adaptation action record
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdaptationAction {
    /// Action timestamp (seconds since epoch)
    pub timestamp: f64,
    /// Parameter that was changed
    pub parameter: String,
    /// Old value
    pub old_value: f32,
    /// New value
    pub new_value: f32,
    /// Action reason/trigger
    pub trigger: AdaptationTrigger,
}

/// Parameter change record
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParameterChange {
    /// Parameter name
    pub parameter: String,
    /// Change amount
    pub change: f32,
    /// Change type
    pub change_type: AdjustmentType,
    /// Success of the change
    pub success: bool,
}

/// Result of an adaptation attempt
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AdaptationResult {
    /// Adaptation successful
    Success,
    /// Adaptation failed
    Failed,
    /// Adaptation partially successful
    Partial,
    /// Adaptation cancelled by user
    Cancelled,
    /// Adaptation skipped (no change needed)
    Skipped,
}

/// User feedback on adaptations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct UserFeedback {
    /// Feedback rating (0.0-1.0)
    pub rating: f32,
    /// Textual feedback
    pub comment: Option<String>,
    /// Specific parameter preferences
    pub parameter_preferences: HashMap<String, f32>,
    /// Feedback timestamp (seconds since epoch)
    pub timestamp: f64,
}

/// Training example for ML adaptation model
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdaptationTrainingExample {
    /// Input environment features
    pub environment_features: Vec<f32>,
    /// Target parameter values
    pub target_parameters: HashMap<String, f32>,
    /// User satisfaction score
    pub satisfaction_score: f32,
    /// Context information
    pub context: EnvironmentSnapshot,
}

/// ML model configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MLModelConfig {
    /// Enable environment classification
    pub enable_classification: bool,
    /// Enable parameter prediction
    pub enable_prediction: bool,
    /// Enable user preference learning
    pub enable_preference_learning: bool,
    /// Model update frequency
    pub update_frequency: Duration,
    /// Training batch size
    pub batch_size: usize,
    /// Learning rate
    pub learning_rate: f64,
}

/// Strategy configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdaptationStrategyConfig {
    /// Strategy name
    pub name: String,
    /// Enabled triggers
    pub triggers: Vec<AdaptationTrigger>,
    /// Parameter mappings
    pub parameters: HashMap<String, ParameterAdjustment>,
    /// Strategy weight/priority
    pub weight: f32,
}

// Placeholder implementations for ML components
/// Machine learning environment classifier
#[derive(Debug, Clone)]
pub struct EnvironmentClassifier {
    /// Model weights (placeholder)
    weights: Vec<f32>,
}

/// Machine learning parameter predictor
#[derive(Debug, Clone)]
pub struct ParameterPredictor {
    /// Model weights (placeholder)
    weights: Vec<f32>,
}

/// Model for user preferences in adaptive acoustics
#[derive(Debug, Clone)]
pub struct UserPreferenceModel {
    /// User preference weights
    preferences: HashMap<String, f32>,
}

impl Default for AdaptiveAcousticsConfig {
    fn default() -> Self {
        Self {
            enable_adaptation: true,
            update_frequency: 1.0, // 1 Hz
            min_change_threshold: 0.05,
            max_adaptation_rate: 10.0, // per second
            sensor_config: SensorConfig::default(),
            ml_config: None,
            adaptation_strategies: vec![
                AdaptationStrategyConfig {
                    name: "Temperature Compensation".to_string(),
                    triggers: vec![AdaptationTrigger::TemperatureChange],
                    parameters: HashMap::new(),
                    weight: 1.0,
                },
                AdaptationStrategyConfig {
                    name: "Occupancy Adjustment".to_string(),
                    triggers: vec![AdaptationTrigger::OccupancyChange],
                    parameters: HashMap::new(),
                    weight: 1.0,
                },
            ],
            enable_preference_learning: true,
        }
    }
}

impl Default for SensorConfig {
    fn default() -> Self {
        Self {
            temperature: TemperatureSensorConfig {
                update_frequency: 0.1, // 0.1 Hz (every 10 seconds)
                calibration_offset: 0.0,
                accuracy: 0.5, // ±0.5°C
            },
            humidity: HumiditySensorConfig {
                update_frequency: 0.1,
                calibration: HumidityCalibration {
                    linear_coeff: 1.0,
                    offset: 0.0,
                    temp_compensation: 0.01,
                },
            },
            noise: NoiseSensorConfig {
                update_frequency: 10.0, // 10 Hz
                frequency_bands: vec![
                    (20.0, 200.0),     // Low
                    (200.0, 2000.0),   // Mid
                    (2000.0, 20000.0), // High
                ],
                enable_classification: true,
            },
            occupancy: OccupancySensorConfig {
                detection_method: OccupancyDetectionMethod::AudioAnalysis,
                update_frequency: 1.0, // 1 Hz
                enable_position_tracking: false,
            },
            material_detector: MaterialDetectorConfig {
                detection_method: MaterialDetectionMethod::AcousticAnalysis,
                update_frequency: 0.01, // Every 100 seconds
                confidence_threshold: 0.7,
            },
            acoustic_probe: AcousticProbeConfig {
                probe_frequency: 0.01, // Every 100 seconds
                probe_signal: ProbeSignalType::SineSweep,
                analysis_window: Duration::from_secs(5),
            },
        }
    }
}

impl Default for AdaptationThresholds {
    fn default() -> Self {
        Self {
            temperature_threshold: 2.0, // 2°C
            humidity_threshold: 0.1,    // 10%
            noise_threshold: 5.0,       // 5 dB
            occupancy_threshold: 1,     // 1 person
            material_threshold: 0.2,    // 20% change
            stability_time_threshold: Duration::from_secs(30),
        }
    }
}

impl AdaptiveAcousticEnvironment {
    /// Get current timestamp as seconds since epoch
    fn get_current_timestamp() -> f64 {
        use std::time::{SystemTime, UNIX_EPOCH};
        SystemTime::now()
            .duration_since(UNIX_EPOCH)
            .expect("SystemTime should be after UNIX_EPOCH")
            .as_secs_f64()
    }

    /// Create new adaptive acoustic environment
    pub fn new(initial_room: Room, config: AdaptiveAcousticsConfig) -> Result<Self> {
        let sensors = EnvironmentSensors::new(&config.sensor_config)?;
        let adaptation_controller = AdaptationController::new(&config)?;
        let ml_adapter = if config.ml_config.is_some() {
            Some(AcousticAdaptationModel::new()?)
        } else {
            None
        };

        Ok(Self {
            current_room: initial_room,
            sensors,
            adaptation_controller,
            ml_adapter,
            config,
            metrics: AdaptationMetrics::default(),
            adaptation_history: VecDeque::new(),
        })
    }

    /// Update environment with sensor data and perform adaptations
    pub fn update(&mut self) -> Result<Vec<AdaptationAction>> {
        if !self.config.enable_adaptation {
            return Ok(Vec::new());
        }

        // Update sensors
        self.sensors.update()?;

        // Detect environmental changes
        let triggers = self.detect_environmental_changes()?;

        // Perform adaptations based on triggers
        let mut actions = Vec::new();
        for trigger in triggers {
            if let Some(new_actions) = self.perform_adaptation(trigger)? {
                actions.extend(new_actions);
            }
        }

        // Update metrics
        self.update_metrics(&actions);

        // Record adaptations in history
        for action in &actions {
            self.adaptation_history.push_back(action.clone());
        }

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

        Ok(actions)
    }

    /// Manually trigger adaptation for specific parameter
    pub fn manual_adaptation(&mut self, parameter: &str, value: f32) -> Result<AdaptationAction> {
        let old_value = self.get_parameter_value(parameter)?;
        self.set_parameter_value(parameter, value)?;

        let action = AdaptationAction {
            timestamp: Self::get_current_timestamp(),
            parameter: parameter.to_string(),
            old_value,
            new_value: value,
            trigger: AdaptationTrigger::UserFeedback,
        };

        self.adaptation_history.push_back(action.clone());
        self.metrics.total_adaptations += 1;

        Ok(action)
    }

    /// Provide user feedback on current acoustic settings
    pub fn provide_user_feedback(&mut self, feedback: UserFeedback) -> Result<()> {
        // Update user satisfaction metrics
        let total_feedback = self.metrics.total_adaptations as f32;
        self.metrics.user_satisfaction = (self.metrics.user_satisfaction * (total_feedback - 1.0)
            + feedback.rating)
            / total_feedback;

        // Learn from user preferences
        if self.config.enable_preference_learning {
            let snapshot = self.get_current_environment_snapshot();
            if let Some(ref mut ml_adapter) = self.ml_adapter {
                ml_adapter.learn_from_feedback(&feedback, &snapshot)?;
            }
        }

        // Trigger adaptation based on preferences
        for (param, preferred_value) in &feedback.parameter_preferences {
            let current_value = self.get_parameter_value(param)?;
            if (current_value - preferred_value).abs() > self.config.min_change_threshold {
                self.manual_adaptation(param, *preferred_value)?;
            }
        }

        Ok(())
    }

    /// Get current adaptation metrics
    pub fn metrics(&self) -> &AdaptationMetrics {
        &self.metrics
    }

    /// Get adaptation history
    pub fn adaptation_history(&self) -> &VecDeque<AdaptationAction> {
        &self.adaptation_history
    }

    /// Get current environment snapshot
    pub fn get_current_environment_snapshot(&self) -> EnvironmentSnapshot {
        EnvironmentSnapshot {
            temperature: self.sensors.temperature_sensor.current_temperature,
            humidity: self.sensors.humidity_sensor.current_humidity,
            noise_level: self.sensors.noise_sensor.current_level,
            occupant_count: self.sensors.occupancy_sensor.occupant_count,
            materials: self
                .sensors
                .material_detector
                .detected_materials
                .iter()
                .map(|(name, props)| (name.clone(), props.density))
                .collect(),
            time_of_day: {
                use std::time::SystemTime;
                let now = SystemTime::now()
                    .duration_since(SystemTime::UNIX_EPOCH)
                    .expect("SystemTime should be after UNIX_EPOCH")
                    .as_secs()
                    % 86400; // Seconds since midnight
                now as f32 / 86400.0 // Normalized to 0.0-1.0
            },
        }
    }

    // Private helper methods

    fn detect_environmental_changes(&self) -> Result<Vec<AdaptationTrigger>> {
        let mut triggers = Vec::new();
        let thresholds = &self.adaptation_controller.thresholds;

        // Temperature change detection
        if let Some(recent_temp) = self.sensors.temperature_sensor.temperature_history.back() {
            let temp_change =
                (recent_temp.value - self.sensors.temperature_sensor.current_temperature).abs();
            if temp_change > thresholds.temperature_threshold {
                triggers.push(AdaptationTrigger::TemperatureChange);
            }
        }

        // Humidity change detection
        if let Some(recent_humidity) = self.sensors.humidity_sensor.humidity_history.back() {
            let humidity_change =
                (recent_humidity.value - self.sensors.humidity_sensor.current_humidity).abs();
            if humidity_change > thresholds.humidity_threshold {
                triggers.push(AdaptationTrigger::HumidityChange);
            }
        }

        // Noise level change detection
        if let Some(recent_noise) = self.sensors.noise_sensor.noise_history.back() {
            let noise_change = (recent_noise.level - self.sensors.noise_sensor.current_level).abs();
            if noise_change > thresholds.noise_threshold {
                triggers.push(AdaptationTrigger::NoiseChange);
            }
        }

        // Add other trigger detections as needed...

        Ok(triggers)
    }

    fn perform_adaptation(
        &mut self,
        trigger: AdaptationTrigger,
    ) -> Result<Option<Vec<AdaptationAction>>> {
        if let Some(strategy) = self.adaptation_controller.strategies.get(&trigger).cloned() {
            let mut actions = Vec::new();

            for (param_name, adjustment) in &strategy.parameter_adjustments {
                let current_value = self.get_parameter_value(param_name)?;
                let new_value = self.apply_parameter_adjustment(current_value, adjustment)?;

                // Check bounds
                let bounded_value = new_value.clamp(adjustment.bounds.0, adjustment.bounds.1);

                if (bounded_value - current_value).abs() > self.config.min_change_threshold {
                    self.set_parameter_value(param_name, bounded_value)?;

                    actions.push(AdaptationAction {
                        timestamp: Self::get_current_timestamp(),
                        parameter: param_name.clone(),
                        old_value: current_value,
                        new_value: bounded_value,
                        trigger,
                    });
                }
            }

            Ok(Some(actions))
        } else {
            Ok(None)
        }
    }

    fn apply_parameter_adjustment(
        &self,
        current_value: f32,
        adjustment: &ParameterAdjustment,
    ) -> Result<f32> {
        let adjusted = match adjustment.adjustment_type {
            AdjustmentType::Absolute => adjustment.value,
            AdjustmentType::Relative => current_value * adjustment.value,
            AdjustmentType::Additive => current_value + adjustment.value,
            AdjustmentType::Exponential => current_value * adjustment.value.exp(),
        };

        // Apply adjustment curve
        let final_value = match adjustment.curve {
            AdjustmentCurve::Linear => adjusted,
            AdjustmentCurve::Exponential => adjusted.exp(),
            AdjustmentCurve::Logarithmic => adjusted.ln(),
            AdjustmentCurve::Sigmoid => 1.0 / (1.0 + (-adjusted).exp()),
            AdjustmentCurve::Custom => adjusted, // Would implement custom curve logic
        };

        Ok(final_value)
    }

    fn get_parameter_value(&self, parameter: &str) -> Result<f32> {
        // This would access the actual room parameters
        // For now, return placeholder values
        match parameter {
            "reverb_time" => Ok(1.5),              // RT60 in seconds
            "early_reflection_level" => Ok(-12.0), // dB
            "diffusion" => Ok(0.7),
            "absorption" => Ok(0.3),
            _ => Err(Error::processing(&format!(
                "Unknown parameter: {parameter}"
            ))),
        }
    }

    fn set_parameter_value(&mut self, parameter: &str, value: f32) -> Result<()> {
        // This would actually update the room parameters
        // For now, just validate the parameter name
        match parameter {
            "reverb_time" | "early_reflection_level" | "diffusion" | "absorption" => {
                // Parameter update would happen here
                Ok(())
            }
            _ => Err(Error::processing(&format!(
                "Cannot set unknown parameter: {parameter}"
            ))),
        }
    }

    fn update_metrics(&mut self, actions: &[AdaptationAction]) {
        self.metrics.total_adaptations += actions.len();
        // Update other metrics based on actions...
    }
}

// Implement placeholder methods for components
impl EnvironmentSensors {
    fn new(_config: &SensorConfig) -> Result<Self> {
        Ok(Self {
            temperature_sensor: TemperatureSensor::new(),
            humidity_sensor: HumiditySensor::new(),
            noise_sensor: NoiseSensor::new(),
            occupancy_sensor: OccupancySensor::new(),
            material_detector: MaterialDetector::new(),
            acoustic_probe: AcousticProbe::new(),
        })
    }

    fn update(&mut self) -> Result<()> {
        // Update all sensors
        self.temperature_sensor.update()?;
        self.humidity_sensor.update()?;
        self.noise_sensor.update()?;
        self.occupancy_sensor.update()?;
        self.material_detector.update()?;
        self.acoustic_probe.update()?;
        Ok(())
    }
}

impl AdaptationController {
    fn new(config: &AdaptiveAcousticsConfig) -> Result<Self> {
        let mut strategies = HashMap::new();

        // Create strategies from config
        for strategy_config in &config.adaptation_strategies {
            let strategy = AdaptationStrategy {
                name: strategy_config.name.clone(),
                triggers: strategy_config.triggers.clone(),
                parameter_adjustments: strategy_config.parameters.clone(),
                adaptation_speed: 0.5,
                priority: strategy_config.weight,
            };

            for trigger in &strategy.triggers {
                strategies.insert(*trigger, strategy.clone());
            }
        }

        Ok(Self {
            adaptation_state: AdaptationState::default(),
            strategies,
            learning_rate: 0.01,
            thresholds: AdaptationThresholds::default(),
            recent_adaptations: VecDeque::new(),
        })
    }
}

impl AcousticAdaptationModel {
    fn new() -> Result<Self> {
        Ok(Self {
            environment_classifier: EnvironmentClassifier {
                weights: vec![0.0; 10],
            },
            parameter_predictor: ParameterPredictor {
                weights: vec![0.0; 20],
            },
            user_preference_model: UserPreferenceModel {
                preferences: HashMap::new(),
            },
            training_data: VecDeque::new(),
        })
    }

    fn learn_from_feedback(
        &mut self,
        _feedback: &UserFeedback,
        _snapshot: &EnvironmentSnapshot,
    ) -> Result<()> {
        // Placeholder for ML learning
        Ok(())
    }
}

// Implement sensor classes with placeholder methods
impl TemperatureSensor {
    fn new() -> Self {
        Self {
            current_temperature: 22.0, // Room temperature
            temperature_history: VecDeque::new(),
            calibration_offset: 0.0,
            accuracy: 0.5,
        }
    }

    fn update(&mut self) -> Result<()> {
        // Simulate temperature reading
        let reading = SensorReading {
            value: self.current_temperature + fastrand::f32() * 0.1 - 0.05, // Small random variation
            timestamp: AdaptiveAcousticEnvironment::get_current_timestamp(),
            confidence: 0.95,
        };

        self.temperature_history.push_back(reading);
        if self.temperature_history.len() > 100 {
            self.temperature_history.pop_front();
        }

        Ok(())
    }
}

impl HumiditySensor {
    fn new() -> Self {
        Self {
            current_humidity: 0.45, // 45% RH
            humidity_history: VecDeque::new(),
            calibration_params: HumidityCalibration {
                linear_coeff: 1.0,
                offset: 0.0,
                temp_compensation: 0.01,
            },
        }
    }

    fn update(&mut self) -> Result<()> {
        let reading = SensorReading {
            value: self.current_humidity + fastrand::f32() * 0.02 - 0.01,
            timestamp: AdaptiveAcousticEnvironment::get_current_timestamp(),
            confidence: 0.9,
        };

        self.humidity_history.push_back(reading);
        if self.humidity_history.len() > 100 {
            self.humidity_history.pop_front();
        }

        Ok(())
    }
}

impl NoiseSensor {
    fn new() -> Self {
        Self {
            current_level: 35.0, // 35 dB SPL
            spectrum: NoiseSpectrum {
                frequency_bands: vec![125.0, 250.0, 500.0, 1000.0, 2000.0, 4000.0, 8000.0],
                power_levels: vec![30.0, 32.0, 34.0, 35.0, 33.0, 31.0, 29.0],
                centroid: 800.0,
                rolloff: 2000.0,
            },
            noise_type: NoiseType::HVAC,
            noise_history: VecDeque::new(),
        }
    }

    fn update(&mut self) -> Result<()> {
        let reading = NoiseReading {
            level: self.current_level + fastrand::f32() * 2.0 - 1.0,
            spectrum: self.spectrum.clone(),
            noise_type: self.noise_type,
            timestamp: AdaptiveAcousticEnvironment::get_current_timestamp(),
            confidence: 0.8,
        };

        self.noise_history.push_back(reading);
        if self.noise_history.len() > 1000 {
            self.noise_history.pop_front();
        }

        Ok(())
    }
}

impl OccupancySensor {
    fn new() -> Self {
        Self {
            occupant_count: 1,
            occupant_positions: vec![Position3D::new(0.0, 0.0, 1.7)], // Standing height
            activity_level: ActivityLevel::Low,
            confidence: 0.85,
        }
    }

    fn update(&mut self) -> Result<()> {
        // Simulate occupancy detection
        Ok(())
    }
}

impl MaterialDetector {
    fn new() -> Self {
        let mut detected_materials = HashMap::new();
        detected_materials.insert(
            "carpet".to_string(),
            MaterialProperties {
                name: "Carpet".to_string(),
                absorption: [
                    ("125Hz", 0.02),
                    ("250Hz", 0.04),
                    ("500Hz", 0.08),
                    ("1000Hz", 0.12),
                    ("2000Hz", 0.22),
                    ("4000Hz", 0.35),
                ]
                .iter()
                .map(|(k, v)| (k.to_string(), *v))
                .collect(),
                scattering: HashMap::new(),
                transmission: HashMap::new(),
                density: 400.0,
                roughness: 0.3,
            },
        );

        Self {
            detected_materials,
            detection_confidence: HashMap::new(),
            last_update: Instant::now(),
            detection_method: MaterialDetectionMethod::AcousticAnalysis,
        }
    }

    fn update(&mut self) -> Result<()> {
        self.last_update = Instant::now();
        Ok(())
    }
}

impl AcousticProbe {
    fn new() -> Self {
        Self {
            impulse_responses: HashMap::new(),
            rt60_measurements: [
                ("125Hz", 1.2),
                ("250Hz", 1.1),
                ("500Hz", 0.9),
                ("1000Hz", 0.8),
                ("2000Hz", 0.7),
                ("4000Hz", 0.6),
            ]
            .iter()
            .map(|(k, v)| (k.to_string(), *v))
            .collect(),
            frequency_responses: HashMap::new(),
            last_probe_time: Instant::now(),
        }
    }

    fn update(&mut self) -> Result<()> {
        // Perform acoustic measurements periodically
        if self.last_probe_time.elapsed() > Duration::from_secs(60) {
            self.last_probe_time = Instant::now();
            // Trigger acoustic measurement
        }
        Ok(())
    }
}

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

    #[test]
    fn test_adaptive_environment_creation() {
        let room = Room::new(
            "test_room".to_string(),
            (5.0, 4.0, 3.0),
            1.2,
            Position3D::new(0.0, 0.0, 0.0),
        )
        .unwrap();
        let config = AdaptiveAcousticsConfig::default();
        let environment = AdaptiveAcousticEnvironment::new(room, config);
        assert!(environment.is_ok());
    }

    #[test]
    fn test_sensor_updates() {
        let mut temp_sensor = TemperatureSensor::new();
        assert!(temp_sensor.update().is_ok());
        assert_eq!(temp_sensor.temperature_history.len(), 1);
    }

    #[test]
    fn test_adaptation_triggers() {
        let trigger = AdaptationTrigger::TemperatureChange;
        assert_eq!(trigger, AdaptationTrigger::TemperatureChange);
    }

    #[test]
    fn test_parameter_adjustment() {
        let adjustment = ParameterAdjustment {
            parameter: "reverb_time".to_string(),
            adjustment_type: AdjustmentType::Relative,
            value: 1.2,
            curve: AdjustmentCurve::Linear,
            bounds: (0.1, 3.0),
        };

        assert_eq!(adjustment.parameter, "reverb_time");
        assert_eq!(adjustment.value, 1.2);
    }

    #[test]
    fn test_environment_snapshot() {
        let snapshot = EnvironmentSnapshot {
            temperature: 22.5,
            humidity: 0.45,
            noise_level: 35.0,
            occupant_count: 2,
            materials: HashMap::new(),
            time_of_day: 0.5,
        };

        assert_eq!(snapshot.temperature, 22.5);
        assert_eq!(snapshot.occupant_count, 2);
    }

    #[test]
    fn test_user_feedback() {
        let mut preferences = HashMap::new();
        preferences.insert("reverb_time".to_string(), 1.2);

        let feedback = UserFeedback {
            rating: 0.8,
            comment: Some("Sounds good".to_string()),
            parameter_preferences: preferences,
            timestamp: AdaptiveAcousticEnvironment::get_current_timestamp(),
        };

        assert_eq!(feedback.rating, 0.8);
        assert!(feedback.comment.is_some());
    }

    #[test]
    fn test_noise_spectrum() {
        let spectrum = NoiseSpectrum {
            frequency_bands: vec![125.0, 250.0, 500.0],
            power_levels: vec![30.0, 32.0, 28.0],
            centroid: 400.0,
            rolloff: 800.0,
        };

        assert_eq!(spectrum.frequency_bands.len(), 3);
        assert_eq!(spectrum.power_levels.len(), 3);
    }

    #[test]
    fn test_adaptation_metrics() {
        let mut metrics = AdaptationMetrics::default();
        metrics.total_adaptations = 50;
        metrics.successful_adaptations = 45;

        let success_rate = metrics.successful_adaptations as f32 / metrics.total_adaptations as f32;
        assert_eq!(success_rate, 0.9);
    }
}