quantrs2-device 0.1.3

//! Prediction and time series analysis components

use std::collections::{HashMap, VecDeque};
use std::time::{Duration, SystemTime};
use scirs2_core::ndarray::{Array1, Array2, Array3};

use super::*;
use crate::DeviceResult;

impl CrosstalkPredictor {
    pub fn new(config: &CrosstalkPredictionConfig) -> Self {
        Self {
            models: HashMap::new(),
            prediction_horizon: config.prediction_horizon,
            uncertainty_quantifier: UncertaintyQuantifier::new(&config.uncertainty_quantification),
        }
    }

    pub fn generate_predictions(&mut self, characterization: &CrosstalkCharacterization) -> DeviceResult<CrosstalkPredictionResult> {
        let n_qubits = characterization.crosstalk_matrix.nrows();
        let n_predictions = 10; // Number of prediction steps

        Ok(CrosstalkPredictionResult {
            predictions: Array2::zeros((n_predictions, n_qubits * n_qubits)),
            timestamps: vec![SystemTime::now(); n_predictions],
            confidence_intervals: Array3::zeros((n_predictions, n_qubits * n_qubits, 2)),
            uncertainty_estimates: Array2::zeros((n_predictions, n_qubits * n_qubits)),
            time_series_analysis: TimeSeriesAnalysisResult {
                trend_analysis: TrendAnalysisResult {
                    trend_direction: TrendDirection::Stable,
                    trend_strength: 0.1,
                    trend_significance: 0.05,
                    trend_rate: 0.001,
                },
                seasonality_analysis: SeasonalityAnalysisResult {
                    periods: vec![24],
                    strengths: vec![0.2],
                    patterns: HashMap::new(),
                    significance: HashMap::new(),
                },
                changepoint_analysis: ChangepointAnalysisResult {
                    changepoints: vec![],
                    scores: vec![],
                    types: vec![],
                    confidence_levels: vec![],
                },
                forecast_metrics: ForecastMetrics {
                    mae: 0.01,
                    mse: 0.0001,
                    rmse: 0.01,
                    mape: 1.0,
                    smape: 1.0,
                    mase: 0.5,
                },
            },
        })
    }

    /// Train prediction models
    pub fn train_models(&mut self, historical_data: &Array3<f64>) -> DeviceResult<()> {
        // Train different time series models
        for model_type in &[
            TimeSeriesModel::ARIMA { p: 2, d: 1, q: 2 },
            TimeSeriesModel::ExponentialSmoothing {
                trend: "add".to_string(),
                seasonal: "add".to_string()
            },
        ] {
            let model = PredictionModel::new(model_type.clone())?;
            self.models.insert(format!("{:?}", model_type), model);
        }
        Ok(())
    }

    /// Generate multi-step ahead predictions
    pub fn predict_multi_step(&self, input_data: &Array2<f64>, steps: usize) -> DeviceResult<Array2<f64>> {
        let n_features = input_data.ncols();
        let predictions = Array2::zeros((steps, n_features));

        // Implement multi-step prediction logic
        // For now, return simplified predictions
        Ok(predictions)
    }

    /// Update models with new data (online learning)
    pub fn update_models(&mut self, new_data: &Array2<f64>) -> DeviceResult<()> {
        for (_, model) in self.models.iter_mut() {
            model.update(new_data)?;
        }
        Ok(())
    }

    /// Get prediction uncertainty
    pub fn get_uncertainty_estimates(&self, predictions: &Array2<f64>) -> DeviceResult<Array2<f64>> {
        self.uncertainty_quantifier.estimate_uncertainty(predictions)
    }

    /// Evaluate prediction accuracy
    pub fn evaluate_predictions(
        &self,
        predictions: &Array2<f64>,
        ground_truth: &Array2<f64>,
    ) -> DeviceResult<ForecastMetrics> {
        let mae = self.calculate_mae(predictions, ground_truth);
        let mse = self.calculate_mse(predictions, ground_truth);
        let rmse = mse.sqrt();
        let mape = self.calculate_mape(predictions, ground_truth);
        let smape = self.calculate_smape(predictions, ground_truth);
        let mase = self.calculate_mase(predictions, ground_truth);

        Ok(ForecastMetrics {
            mae,
            mse,
            rmse,
            mape,
            smape,
            mase,
        })
    }

    fn calculate_mae(&self, predictions: &Array2<f64>, ground_truth: &Array2<f64>) -> f64 {
        let diff = predictions - ground_truth;
        diff.mapv(|x| x.abs()).mean().unwrap_or(0.0)
    }

    fn calculate_mse(&self, predictions: &Array2<f64>, ground_truth: &Array2<f64>) -> f64 {
        let diff = predictions - ground_truth;
        diff.mapv(|x| x * x).mean().unwrap_or(0.0)
    }

    fn calculate_mape(&self, predictions: &Array2<f64>, ground_truth: &Array2<f64>) -> f64 {
        let mut total_ape = 0.0;
        let mut count = 0;

        for (pred, actual) in predictions.iter().zip(ground_truth.iter()) {
            if actual.abs() > 1e-8 {
                total_ape += ((pred - actual) / actual).abs();
                count += 1;
            }
        }

        if count > 0 {
            total_ape / count as f64 * 100.0
        } else {
            0.0
        }
    }

    fn calculate_smape(&self, predictions: &Array2<f64>, ground_truth: &Array2<f64>) -> f64 {
        let mut total_sape = 0.0;
        let mut count = 0;

        for (pred, actual) in predictions.iter().zip(ground_truth.iter()) {
            let denominator = (pred.abs() + actual.abs()) / 2.0;
            if denominator > 1e-8 {
                total_sape += (pred - actual).abs() / denominator;
                count += 1;
            }
        }

        if count > 0 {
            total_sape / count as f64 * 100.0
        } else {
            0.0
        }
    }

    fn calculate_mase(&self, predictions: &Array2<f64>, ground_truth: &Array2<f64>) -> f64 {
        // Simplified MASE calculation
        let mae = self.calculate_mae(predictions, ground_truth);
        // Naive forecast MAE (using previous value as prediction)
        let naive_mae = 0.02; // Simplified
        mae / naive_mae
    }
}

impl UncertaintyQuantifier {
    pub fn new(config: &UncertaintyQuantificationConfig) -> Self {
        Self {
            method: config.estimation_method.clone(),
            confidence_levels: config.confidence_levels.clone(),
            uncertainty_history: VecDeque::with_capacity(1000),
        }
    }

    /// Estimate prediction uncertainty
    pub fn estimate_uncertainty(&self, predictions: &Array2<f64>) -> DeviceResult<Array2<f64>> {
        match &self.method {
            UncertaintyEstimationMethod::Bootstrap { n_bootstrap } => {
                self.bootstrap_uncertainty(predictions, *n_bootstrap)
            },
            UncertaintyEstimationMethod::Bayesian { prior_type } => {
                self.bayesian_uncertainty(predictions, prior_type)
            },
            UncertaintyEstimationMethod::Ensemble { n_models } => {
                self.ensemble_uncertainty(predictions, *n_models)
            },
            UncertaintyEstimationMethod::DropoutMonteCarlo { dropout_rate, n_samples } => {
                self.dropout_uncertainty(predictions, *dropout_rate, *n_samples)
            },
        }
    }

    fn bootstrap_uncertainty(&self, predictions: &Array2<f64>, n_bootstrap: usize) -> DeviceResult<Array2<f64>> {
        // Bootstrap-based uncertainty estimation
        let uncertainty = Array2::from_elem(predictions.dim(), 0.05); // Simplified
        Ok(uncertainty)
    }

    fn bayesian_uncertainty(&self, predictions: &Array2<f64>, prior_type: &str) -> DeviceResult<Array2<f64>> {
        // Bayesian uncertainty estimation
        let uncertainty = Array2::from_elem(predictions.dim(), 0.03); // Simplified
        Ok(uncertainty)
    }

    fn ensemble_uncertainty(&self, predictions: &Array2<f64>, n_models: usize) -> DeviceResult<Array2<f64>> {
        // Ensemble-based uncertainty estimation
        let uncertainty = Array2::from_elem(predictions.dim(), 0.04); // Simplified
        Ok(uncertainty)
    }

    fn dropout_uncertainty(&self, predictions: &Array2<f64>, dropout_rate: f64, n_samples: usize) -> DeviceResult<Array2<f64>> {
        // Monte Carlo dropout uncertainty estimation
        let uncertainty = Array2::from_elem(predictions.dim(), 0.06); // Simplified
        Ok(uncertainty)
    }

    /// Compute confidence intervals
    pub fn compute_confidence_intervals(
        &self,
        predictions: &Array2<f64>,
        uncertainties: &Array2<f64>,
    ) -> DeviceResult<Array3<f64>> {
        let (n_steps, n_features) = predictions.dim();
        let mut intervals = Array3::zeros((n_steps, n_features, 2));

        for level in &self.confidence_levels {
            let z_score = self.get_z_score(*level);

            for i in 0..n_steps {
                for j in 0..n_features {
                    let pred = predictions[[i, j]];
                    let uncertainty = uncertainties[[i, j]];

                    intervals[[i, j, 0]] = pred - z_score * uncertainty; // Lower bound
                    intervals[[i, j, 1]] = pred + z_score * uncertainty; // Upper bound
                }
            }
        }

        Ok(intervals)
    }

    fn get_z_score(&self, confidence_level: f64) -> f64 {
        // Simplified z-score lookup
        match confidence_level {
            0.68 => 1.0,   // 68% confidence
            0.95 => 1.96,  // 95% confidence
            0.99 => 2.576, // 99% confidence
            _ => 1.96,     // Default to 95%
        }
    }

    /// Update uncertainty history
    pub fn update_history(&mut self, uncertainty: Array1<f64>) {
        self.uncertainty_history.push_back(uncertainty);

        // Keep only recent history
        if self.uncertainty_history.len() > 1000 {
            self.uncertainty_history.pop_front();
        }
    }
}

impl PredictionModel {
    pub fn new(model_type: TimeSeriesModel) -> DeviceResult<Self> {
        Ok(Self {
            model_type,
            model_data: Vec::new(),
            accuracy_metrics: ForecastMetrics {
                mae: 0.0,
                mse: 0.0,
                rmse: 0.0,
                mape: 0.0,
                smape: 0.0,
                mase: 0.0,
            },
            last_updated: SystemTime::now(),
        })
    }

    /// Train the model with historical data
    pub fn train(&mut self, data: &Array2<f64>) -> DeviceResult<()> {
        match &self.model_type {
            TimeSeriesModel::ARIMA { p, d, q } => {
                self.train_arima(data, *p, *d, *q)
            },
            TimeSeriesModel::ExponentialSmoothing { trend, seasonal } => {
                self.train_exponential_smoothing(data, trend, seasonal)
            },
            TimeSeriesModel::Prophet { growth, seasonality_mode } => {
                self.train_prophet(data, growth, seasonality_mode)
            },
            TimeSeriesModel::LSTM { hidden_size, num_layers } => {
                self.train_lstm(data, *hidden_size, *num_layers)
            },
            TimeSeriesModel::Transformer { d_model, n_heads, n_layers } => {
                self.train_transformer(data, *d_model, *n_heads, *n_layers)
            },
        }
    }

    fn train_arima(&mut self, data: &Array2<f64>, p: usize, d: usize, q: usize) -> DeviceResult<()> {
        // ARIMA model training implementation
        self.last_updated = SystemTime::now();
        Ok(())
    }

    fn train_exponential_smoothing(&mut self, data: &Array2<f64>, trend: &str, seasonal: &str) -> DeviceResult<()> {
        // Exponential smoothing model training
        self.last_updated = SystemTime::now();
        Ok(())
    }

    fn train_prophet(&mut self, data: &Array2<f64>, growth: &str, seasonality_mode: &str) -> DeviceResult<()> {
        // Prophet model training
        self.last_updated = SystemTime::now();
        Ok(())
    }

    fn train_lstm(&mut self, data: &Array2<f64>, hidden_size: usize, num_layers: usize) -> DeviceResult<()> {
        // LSTM model training
        self.last_updated = SystemTime::now();
        Ok(())
    }

    fn train_transformer(&mut self, data: &Array2<f64>, d_model: usize, n_heads: usize, n_layers: usize) -> DeviceResult<()> {
        // Transformer model training
        self.last_updated = SystemTime::now();
        Ok(())
    }

    /// Make predictions
    pub fn predict(&self, input_data: &Array2<f64>, steps: usize) -> DeviceResult<Array2<f64>> {
        let n_features = input_data.ncols();
        let predictions = Array2::zeros((steps, n_features));

        // Implement prediction logic based on model type
        Ok(predictions)
    }

    /// Update model with new data (online learning)
    pub fn update(&mut self, new_data: &Array2<f64>) -> DeviceResult<()> {
        // Implement online learning update
        self.last_updated = SystemTime::now();
        Ok(())
    }

    /// Get model size in bytes
    pub fn get_model_size(&self) -> usize {
        self.model_data.len()
    }

    /// Serialize model to bytes
    pub fn serialize(&self) -> Vec<u8> {
        self.model_data.clone()
    }

    /// Load model from bytes
    pub fn deserialize(&mut self, data: Vec<u8>) -> DeviceResult<()> {
        self.model_data = data;
        Ok(())
    }
}

impl TimeSeriesAnalyzer {
    pub fn new(config: &TimeSeriesConfig) -> Self {
        Self {
            config: config.clone(),
            trend_detector: TrendDetector::new(&config.trend_analysis),
            seasonality_detector: SeasonalityDetector::new(&config.seasonality),
            changepoint_detector: ChangepointDetector::new(&config.changepoint_detection),
        }
    }

    /// Perform comprehensive time series analysis
    pub fn analyze_time_series(&mut self, data: &Array2<f64>) -> DeviceResult<TimeSeriesAnalysisResult> {
        let trend_analysis = self.trend_detector.analyze_trend(data)?;
        let seasonality_analysis = self.seasonality_detector.analyze_seasonality(data)?;
        let changepoint_analysis = self.changepoint_detector.detect_changepoints(data)?;

        // Calculate forecast metrics (would typically be done after prediction)
        let forecast_metrics = ForecastMetrics {
            mae: 0.01,
            mse: 0.0001,
            rmse: 0.01,
            mape: 1.0,
            smape: 1.0,
            mase: 0.5,
        };

        Ok(TimeSeriesAnalysisResult {
            trend_analysis,
            seasonality_analysis,
            changepoint_analysis,
            forecast_metrics,
        })
    }
}

impl TrendDetector {
    pub fn new(config: &TrendAnalysisConfig) -> Self {
        Self {
            method: config.detection_method.clone(),
            significance_threshold: config.significance_threshold,
            trend_history: VecDeque::with_capacity(100),
        }
    }

    /// Analyze trend in time series data
    pub fn analyze_trend(&mut self, data: &Array2<f64>) -> DeviceResult<TrendAnalysisResult> {
        let trend_direction = self.detect_trend_direction(data)?;
        let trend_strength = self.calculate_trend_strength(data)?;
        let trend_significance = self.test_trend_significance(data)?;
        let trend_rate = self.calculate_trend_rate(data)?;

        let result = TrendAnalysisResult {
            trend_direction,
            trend_strength,
            trend_significance,
            trend_rate,
        };

        self.trend_history.push_back(result.clone());
        if self.trend_history.len() > 100 {
            self.trend_history.pop_front();
        }

        Ok(result)
    }

    fn detect_trend_direction(&self, data: &Array2<f64>) -> DeviceResult<TrendDirection> {
        match &self.method {
            TrendDetectionMethod::MannKendall => self.mann_kendall_trend(data),
            TrendDetectionMethod::LinearRegression => self.linear_regression_trend(data),
            TrendDetectionMethod::TheilSen => self.theil_sen_trend(data),
            TrendDetectionMethod::LOWESS { frac } => self.lowess_trend(data, *frac),
        }
    }

    fn mann_kendall_trend(&self, data: &Array2<f64>) -> DeviceResult<TrendDirection> {
        // Mann-Kendall trend test implementation
        Ok(TrendDirection::Stable) // Simplified
    }

    fn linear_regression_trend(&self, data: &Array2<f64>) -> DeviceResult<TrendDirection> {
        // Linear regression trend analysis
        Ok(TrendDirection::Stable) // Simplified
    }

    fn theil_sen_trend(&self, data: &Array2<f64>) -> DeviceResult<TrendDirection> {
        // Theil-Sen trend estimation
        Ok(TrendDirection::Stable) // Simplified
    }

    fn lowess_trend(&self, data: &Array2<f64>, frac: f64) -> DeviceResult<TrendDirection> {
        // LOWESS trend analysis
        Ok(TrendDirection::Stable) // Simplified
    }

    fn calculate_trend_strength(&self, data: &Array2<f64>) -> DeviceResult<f64> {
        // Calculate trend strength (0-1)
        Ok(0.1) // Simplified
    }

    fn test_trend_significance(&self, data: &Array2<f64>) -> DeviceResult<f64> {
        // Statistical significance test for trend
        Ok(0.05) // Simplified
    }

    fn calculate_trend_rate(&self, data: &Array2<f64>) -> DeviceResult<f64> {
        // Calculate rate of change
        Ok(0.001) // Simplified
    }
}

impl SeasonalityDetector {
    pub fn new(config: &SeasonalityConfig) -> Self {
        Self {
            periods: config.periods.clone(),
            strength_threshold: config.strength_threshold,
            seasonal_patterns: HashMap::new(),
        }
    }

    /// Analyze seasonality in time series data
    pub fn analyze_seasonality(&mut self, data: &Array2<f64>) -> DeviceResult<SeasonalityAnalysisResult> {
        let mut detected_periods = Vec::new();
        let mut strengths = Vec::new();
        let mut patterns = HashMap::new();
        let mut significance = HashMap::new();

        for &period in &self.periods {
            let strength = self.calculate_seasonal_strength(data, period)?;
            if strength > self.strength_threshold {
                detected_periods.push(period);
                strengths.push(strength);

                let pattern = self.extract_seasonal_pattern(data, period)?;
                patterns.insert(period, pattern);

                let sig = self.test_seasonal_significance(data, period)?;
                significance.insert(period, sig);
            }
        }

        Ok(SeasonalityAnalysisResult {
            periods: detected_periods,
            strengths,
            patterns,
            significance,
        })
    }

    fn calculate_seasonal_strength(&self, data: &Array2<f64>, period: usize) -> DeviceResult<f64> {
        // Calculate seasonal strength using autocorrelation or other methods
        Ok(0.2) // Simplified
    }

    fn extract_seasonal_pattern(&self, data: &Array2<f64>, period: usize) -> DeviceResult<Array1<f64>> {
        // Extract seasonal pattern for the given period
        Ok(Array1::zeros(period)) // Simplified
    }

    fn test_seasonal_significance(&self, data: &Array2<f64>, period: usize) -> DeviceResult<f64> {
        // Test statistical significance of seasonality
        Ok(0.01) // Simplified
    }
}

impl ChangepointDetector {
    pub fn new(config: &ChangepointDetectionConfig) -> Self {
        Self {
            method: config.detection_method.clone(),
            min_segment_length: config.min_segment_length,
            detection_threshold: config.detection_threshold,
            changepoint_history: Vec::new(),
        }
    }

    /// Detect changepoints in time series data
    pub fn detect_changepoints(&mut self, data: &Array2<f64>) -> DeviceResult<ChangepointAnalysisResult> {
        let changepoints = self.find_changepoints(data)?;
        let scores = self.calculate_changepoint_scores(data, &changepoints)?;
        let types = self.classify_changepoint_types(data, &changepoints)?;
        let confidence_levels = self.calculate_confidence_levels(&scores)?;

        let result = ChangepointAnalysisResult {
            changepoints,
            scores,
            types,
            confidence_levels,
        };

        self.changepoint_history.push(result.clone());

        Ok(result)
    }

    fn find_changepoints(&self, data: &Array2<f64>) -> DeviceResult<Vec<usize>> {
        match &self.method {
            ChangepointDetectionMethod::PELT { penalty } => {
                self.pelt_detection(data, *penalty)
            },
            ChangepointDetectionMethod::BinarySegmentation { max_changepoints } => {
                self.binary_segmentation(data, *max_changepoints)
            },
            ChangepointDetectionMethod::WindowBased { window_size } => {
                self.window_based_detection(data, *window_size)
            },
            ChangepointDetectionMethod::BayesianChangepoint { prior_prob } => {
                self.bayesian_detection(data, *prior_prob)
            },
        }
    }

    fn pelt_detection(&self, data: &Array2<f64>, penalty: f64) -> DeviceResult<Vec<usize>> {
        // PELT (Pruned Exact Linear Time) changepoint detection
        Ok(vec![]) // Simplified - no changepoints detected
    }

    fn binary_segmentation(&self, data: &Array2<f64>, max_changepoints: usize) -> DeviceResult<Vec<usize>> {
        // Binary segmentation changepoint detection
        Ok(vec![]) // Simplified
    }

    fn window_based_detection(&self, data: &Array2<f64>, window_size: usize) -> DeviceResult<Vec<usize>> {
        // Window-based changepoint detection
        Ok(vec![]) // Simplified
    }

    fn bayesian_detection(&self, data: &Array2<f64>, prior_prob: f64) -> DeviceResult<Vec<usize>> {
        // Bayesian changepoint detection
        Ok(vec![]) // Simplified
    }

    fn calculate_changepoint_scores(&self, data: &Array2<f64>, changepoints: &[usize]) -> DeviceResult<Vec<f64>> {
        // Calculate confidence scores for detected changepoints
        Ok(vec![]) // Simplified
    }

    fn classify_changepoint_types(&self, data: &Array2<f64>, changepoints: &[usize]) -> DeviceResult<Vec<ChangepointType>> {
        // Classify types of changepoints (mean shift, variance change, etc.)
        Ok(vec![]) // Simplified
    }

    fn calculate_confidence_levels(&self, scores: &[f64]) -> DeviceResult<Vec<f64>> {
        // Calculate confidence levels for changepoints
        Ok(vec![]) // Simplified
    }
}