//! Unified Advanced Statistical Processing Framework
//!
//! This module provides a unified interface that integrates all Advanced mode
//! optimizations: SIMD acceleration, parallel processing, and numerical stability
//! testing. It automatically selects the optimal combination of techniques based
//! on data characteristics and system capabilities.

#![allow(dead_code)]

use crate::error::{StatsError, StatsResult};
use crate::error_handling_enhancements::{AdvancedContextBuilder, AdvancedErrorMessages};
use crate::error_standardization::ErrorMessages;
use crate::num_stubs::{
    BatchOperation, BatchResults, AdvancedSimdConfig, AdvancedSimdOptimizer,
    create_exhaustive_numerical_stability_tester,
    ComprehensiveStabilityResult as StabilityAnalysisReport,
    AdvancedNumericalStabilityConfig as NumericalStabilityConfig,
    AdvancedNumericalStabilityAnalyzer,
    create_advanced_parallel_processor, MatrixOperationType, TimeSeriesOperation,
    AdvancedParallelProcessor, AdvancedParallelConfig,
};
use scirs2_core::ndarray::{Array1, Array2, ArrayBase, Data, Ix1, Ix2};
use scirs2_core::numeric::{Float, NumCast};
use scirs2_core::simd_ops::{PlatformCapabilities, SimdUnifiedOps};
use std::collections::HashMap;
use std::time::Instant;

/// Result of moving window analysis
#[derive(Debug, Clone)]
pub struct MovingWindowResult<F> {
    pub means: Array1<F>,
    pub variances: Array1<F>,
    pub mins: Array1<F>,
    pub maxs: Array1<F>,
    pub windowsize: usize,
}

/// Comprehensive Advanced processing configuration
#[derive(Debug, Clone)]
pub struct AdvancedProcessorConfig {
    /// SIMD optimization settings
    pub simd_config: AdvancedSimdConfig,
    /// Parallel processing settings
    pub parallel_config: AdvancedParallelConfig,
    /// Numerical stability testing settings
    pub stability_config: NumericalStabilityConfig,
    /// Enable automatic optimization selection
    pub auto_optimize: bool,
    /// Enable comprehensive stability testing
    pub enable_stability_testing: bool,
    /// Enable performance monitoring
    pub enable_performance_monitoring: bool,
    /// Optimization mode preference
    pub optimization_mode: OptimizationMode,
}

impl Default for AdvancedProcessorConfig {
    fn default() -> Self {
        Self {
            simd_config: AdvancedSimdConfig::default(),
            parallel_config: AdvancedParallelConfig::default(),
            stability_config: NumericalStabilityConfig::default(),
            auto_optimize: true,
            enable_stability_testing: true,
            enable_performance_monitoring: true,
            optimization_mode: OptimizationMode::Adaptive,
        }
    }
}

/// Optimization mode preferences
#[derive(Debug, Clone, Copy)]
pub enum OptimizationMode {
    /// Prioritize maximum performance
    Performance,
    /// Prioritize numerical accuracy and stability
    Accuracy,
    /// Balance performance and accuracy
    Balanced,
    /// Adaptive selection based on data characteristics
    Adaptive,
}

/// Unified Advanced statistical processor
pub struct AdvancedUnifiedProcessor {
    config: AdvancedProcessorConfig,
    simd_processor: Option<()>, // Placeholder for SIMD processor state
    parallel_processor: AdvancedParallelProcessor,
    stability_analyzer: AdvancedNumericalStabilityAnalyzer,
    capabilities: PlatformCapabilities,
    performance_history: Vec<ProcessingMetrics>,
}

impl AdvancedUnifiedProcessor {
    /// Create a new unified Advanced processor
    pub fn new(config: AdvancedProcessorConfig) -> Self {
        Self {
            parallel_processor: create_advanced_parallel_processor(),
            stability_analyzer: create_exhaustive_numerical_stability_tester(),
            capabilities: PlatformCapabilities::detect(),
            simd_processor: None,
            performance_history: Vec::new(),
            config,
        }
    }

    /// Process comprehensive batch statistics with full Advanced optimization
    pub fn process_comprehensive_statistics<F, D>(
        &mut self,
        data: &ArrayBase<D, Ix1>,
    ) -> StatsResult<AdvancedComprehensiveResult<F>>
    where
        F: Float + NumCast + SimdUnifiedOps + Send + Sync + Copy + PartialOrd + std::fmt::Debug,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        let start_time = Instant::now();
        let n = data.len();

        if n == 0 {
            return Err(ErrorMessages::empty_array("data"));
        }

        let context = AdvancedContextBuilder::new(n)
            .parallel_enabled(true)
            .simd_enabled(true)
            .memory_usage(self.estimate_memory_usage::<F>(n))
            .build();

        // Determine optimal processing strategy
        let strategy = self.determine_processing_strategy(n, &context)?;

        // Run stability analysis if enabled
        let stability_report = if self.config.enable_stability_testing {
            Some(
                self.stability_analyzer
                    .analyze_statistical_stability(&data.view()),
            )
        } else {
            None
        };

        // Compute statistics using the selected strategy
        let stats = match strategy {
            ProcessingStrategy::SimdOnly => {
                let optimizer = AdvancedSimdOptimizer::new(self.config.simd_config.clone());
                let data_arrays = vec![data.to_owned().view()];
                let operations = vec![
                    BatchOperation::Mean,
                    BatchOperation::Variance,
                    BatchOperation::StandardDeviation,
                ];
                optimizer.advanced_batch_statistics(&data_arrays, &operations)?
            }
            ProcessingStrategy::ParallelOnly => {
                let parallel_result = self.parallel_processor.process_batch_statistics(data)?;
                self.convert_parallel_to_batch_stats(parallel_result)
            }
            ProcessingStrategy::SimdParallel => self.process_simd_parallel_hybrid(data)?,
            ProcessingStrategy::Standard => self.process_standard_fallback(data)?,
        };

        let duration = start_time.elapsed();

        // Performance monitoring
        let metrics = ProcessingMetrics {
            datasize: n,
            processing_time: duration,
            strategy_used: strategy,
            simd_enabled: strategy.uses_simd(),
            parallel_enabled: strategy.uses_parallel(),
            stability_tested: self.config.enable_stability_testing,
            memory_usage_mb: self.estimate_memory, _usage::<F>(n),
        };

        if self.config.enable_performance_monitoring {
            self.performance_history.push(metrics.clone());
            // Keep only recent history to avoid unbounded growth
            if self.performance_history.len() > 1000 {
                self.performance_history.remove(0);
            }
        }

        // Generate recommendations based on performance and stability
        let recommendations = self.generate_recommendations(&metrics, &stability_report);

        Ok(AdvancedComprehensiveResult {
            statistics: stats,
            stability_report,
            processing_metrics: metrics,
            recommendations,
            warnings: Vec::new(),
        })
    }

    /// Process matrix operations with full Advanced optimization
    pub fn process_matrix_operations<F, D>(
        &mut self,
        data: &ArrayBase<D, Ix2>,
        operation: AdvancedMatrixOperation,
    ) -> StatsResult<AdvancedMatrixResult<F>>
    where
        F: Float + NumCast + SimdUnifiedOps + Send + Sync + Copy + PartialOrd + std::fmt::Debug,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        let start_time = Instant::now();
        let (n_rows, n_cols) = data.dim();

        if n_rows == 0 || n_cols == 0 {
            return Err(ErrorMessages::empty_array("data"));
        }

        let memory_estimate = self.estimate_matrix_memory_usage::<F>(n_rows, n_cols, &operation);

        if memory_estimate > self.config.simd_config.memory_threshold_mb {
            return Err(AdvancedErrorMessages::memory_exhaustion(
                memory_estimate,
                self.config.simd_config.memory_threshold_mb,
                n_rows * n_cols,
            ));
        }

        // Determine optimal processing strategy for matrix operations
        let strategy = self.determine_matrix_processing_strategy(n_rows, n_cols, &operation)?;

        let result = match (strategy, operation) {
            (ProcessingStrategy::SimdOnly, AdvancedMatrixOperation::Covariance) => {
                advanced_matrix_operations(data, BatchOperation::Covariance, &self.config.simd_config)?
            }
            (ProcessingStrategy::SimdOnly, AdvancedMatrixOperation::Correlation) => {
                advanced_matrix_operations(
                    data,
                    BatchOperation::Correlation,
                    &self.config.simd_config,
                )?
            }
            (ProcessingStrategy::ParallelOnly, AdvancedMatrixOperation::Covariance) => {
                let parallel_result = self
                    .parallel_processor
                    .process_matrix_operations(data, MatrixOperationType::CovarianceMatrix)?;
                parallel_result.result
            }
            (ProcessingStrategy::ParallelOnly, AdvancedMatrixOperation::Correlation) => {
                let parallel_result = self
                    .parallel_processor
                    .process_matrix_operations(data, MatrixOperationType::CorrelationMatrix)?;
                parallel_result.result
            }
            _ => {
                // Fallback to standard methods
                self.process_matrix_standard(data, operation)?
            }
        };

        let duration = start_time.elapsed();

        let metrics = ProcessingMetrics {
            datasize: n_rows * n_cols,
            processing_time: duration,
            strategy_used: strategy,
            simd_enabled: strategy.uses_simd(),
            parallel_enabled: strategy.uses_parallel(),
            stability_tested: false, // Matrix operations don't include stability testing yet
            memory_usage_mb: memory_estimate,
        };

        Ok(AdvancedMatrixResult {
            matrix: result,
            operation,
            processing_metrics: metrics,
            warnings: Vec::new(),
        })
    }

    /// Process time series analysis with Advanced optimization
    pub fn process_time_series<F, D>(
        &mut self,
        data: &ArrayBase<D, Ix1>,
        windowsize: usize,
        operations: &[AdvancedTimeSeriesOperation],
    ) -> StatsResult<AdvancedTimeSeriesResult<F>>
    where
        F: Float + NumCast + SimdUnifiedOps + Send + Sync + Copy + PartialOrd + std::fmt::Debug,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        let start_time = Instant::now();
        let n = data.len();

        if n == 0 {
            return Err(ErrorMessages::empty_array("data"));
        }

        if windowsize == 0 {
            return Err(ErrorMessages::non_positive_value(
                "windowsize",
                windowsize as f64,
            ));
        }

        if windowsize > n {
            return Err(ErrorMessages::insufficientdata(
                "time series analysis",
                windowsize,
                n,
            ));
        }

        let strategy = self.determine_time_series_strategy(n, windowsize)?;

        let mut results = Vec::new();

        for &operation in operations {
            let result = match (strategy, operation) {
                (ProcessingStrategy::SimdOnly, AdvancedTimeSeriesOperation::MovingWindow) => {
                    advanced_moving_window_stats(data, windowsize, &self.config.simd_config)?
                }
                (ProcessingStrategy:: ParallelOnly) => {
                    let ts_operations = self.convert_to_parallel_ts_operations(operations);
                    let parallel_result = self.parallel_processor.process_time_series(
                        data,
                        windowsize,
                        &ts_operations,
                    )?;
                    self.convert_parallel_ts_result(parallel_result)?
                }
                _ => {
                    // Standard processing fallback
                    self.process_time_series_standard(data, windowsize, operation)?
                }
            };

            results.push(result);
        }

        let duration = start_time.elapsed();

        let metrics = ProcessingMetrics {
            datasize: n,
            processing_time: duration,
            strategy_used: strategy,
            simd_enabled: strategy.uses_simd(),
            parallel_enabled: strategy.uses_parallel(),
            stability_tested: false,
            memory_usage_mb: self.estimate_memory, _usage::<F>(n),
        };

        Ok(AdvancedTimeSeriesResult {
            results,
            windowsize,
            operations: operations.to_vec(),
            processing_metrics: metrics,
        })
    }

    /// Get comprehensive performance analytics
    pub fn get_performance_analytics(&self) -> AdvancedPerformanceAnalytics {
        if self.performance_history.is_empty() {
            return AdvancedPerformanceAnalytics::default();
        }

        let total_operations = self.performance_history.len();
        let avg_processing_time = self
            .performance_history
            .iter()
            .map(|m| m.processing_time.as_millis() as f64)
            .sum::<f64>()
            / total_operations as f64;

        let simd_usage_rate = self
            .performance_history
            .iter()
            .filter(|m| m.simd_enabled)
            .count() as f64
            / total_operations as f64;

        let parallel_usage_rate = self
            .performance_history
            .iter()
            .filter(|m| m.parallel_enabled)
            .count() as f64
            / total_operations as f64;

        let avgdatasize = self
            .performance_history
            .iter()
            .map(|m| m.datasize)
            .sum::<usize>() as f64
            / total_operations as f64;

        AdvancedPerformanceAnalytics {
            total_operations,
            average_processing_time_ms: avg_processing_time,
            simd_usage_rate,
            parallel_usage_rate,
            averagedatasize: avgdatasize,
            optimization_effectiveness: self.calculate_optimization_effectiveness(),
            recommendations: self.generate_performance_recommendations(),
        }
    }

    // Private implementation methods

    fn determine_processing_strategy(
        &self,
        datasize: usize, _context: &crate::advanced_error_enhancements_v2::AdvancedErrorContext,
    ) -> StatsResult<ProcessingStrategy> {
        match self.config.optimization_mode {
            OptimizationMode::Performance => {
                if datasize > 10000 && self.capabilities.has_avx2() {
                    Ok(ProcessingStrategy::SimdParallel)
                } else if datasize > 1000 {
                    Ok(ProcessingStrategy::ParallelOnly)
                } else if self.capabilities.has_avx2() {
                    Ok(ProcessingStrategy::SimdOnly)
                } else {
                    Ok(ProcessingStrategy::Standard)
                }
            }
            OptimizationMode::Accuracy => {
                // Prioritize stability - use less aggressive optimizations
                if datasize > 5000 {
                    Ok(ProcessingStrategy::ParallelOnly)
                } else {
                    Ok(ProcessingStrategy::Standard)
                }
            }
            OptimizationMode::Balanced => {
                if datasize > 5000 && self.capabilities.has_avx2() {
                    Ok(ProcessingStrategy::SimdParallel)
                } else if datasize > 1000 {
                    Ok(ProcessingStrategy::ParallelOnly)
                } else {
                    Ok(ProcessingStrategy::Standard)
                }
            }
            OptimizationMode::Adaptive => {
                // Use performance history to make adaptive decisions
                self.determine_adaptive_strategy(datasize)
            }
        }
    }

    fn determine_adaptive_strategy(&self, datasize: usize) -> StatsResult<ProcessingStrategy> {
        // Analyze performance history for similar data sizes
        let similar_operations: Vec<_> = self
            .performance_history
            .iter()
            .filter(|m| {
                let size_ratio = (m.datasize as f64) / (datasize as f64);
                size_ratio >= 0.5 && size_ratio <= 2.0
            })
            .collect();

        if similar_operations.is_empty() {
            // No history, use default heuristics
            return self.determine_processing_strategy(
                datasize,
                &AdvancedContextBuilder::new(datasize).build(),
            );
        }

        // Find the strategy with the best average performance
        let mut strategy_performance = HashMap::new();
        for metrics in similar_operations {
            let throughput = metrics.datasize as f64 / metrics.processing_time.as_secs_f64();
            strategy_performance
                .entry(metrics.strategy_used)
                .or_insert_with(Vec::new)
                .push(throughput);
        }

        let best_strategy = strategy_performance
            .iter()
            .max_by(|(_, a), (_, b)| {
                let avg_a = a.iter().sum::<f64>() / a.len() as f64;
                let avg_b = b.iter().sum::<f64>() / b.len() as f64;
                avg_a
                    .partial_cmp(&avg_b)
                    .unwrap_or(std::cmp::Ordering::Equal)
            })
            .map(|(&strategy_)| strategy)
            .unwrap_or(ProcessingStrategy::Standard);

        Ok(best_strategy)
    }

    fn determine_matrix_processing_strategy(
        &self,
        n_rows: usize,
        n_cols: usize, _operation: &AdvancedMatrixOperation,
    ) -> StatsResult<ProcessingStrategy> {
        let total_elements = n_rows * n_cols;

        if total_elements > 100000 && self.capabilities.has_avx2() {
            Ok(ProcessingStrategy::SimdParallel)
        } else if total_elements > 10000 {
            Ok(ProcessingStrategy::ParallelOnly)
        } else if n_cols > 64 && self.capabilities.has_avx2() {
            Ok(ProcessingStrategy::SimdOnly)
        } else {
            Ok(ProcessingStrategy::Standard)
        }
    }

    fn determine_time_series_strategy(
        &self,
        datasize: usize,
        windowsize: usize,
    ) -> StatsResult<ProcessingStrategy> {
        let num_windows = datasize - windowsize + 1;

        if num_windows > 1000 && windowsize > 64 && self.capabilities.has_avx2() {
            Ok(ProcessingStrategy::SimdParallel)
        } else if num_windows > 500 {
            Ok(ProcessingStrategy::ParallelOnly)
        } else if windowsize > 32 && self.capabilities.has_avx2() {
            Ok(ProcessingStrategy::SimdOnly)
        } else {
            Ok(ProcessingStrategy::Standard)
        }
    }

    fn estimate_memory_usage<F>(&self, n: usize) -> f64 {
        (n * std::mem::size_of::<F>()) as f64 / (1024.0 * 1024.0)
    }

    fn estimate_matrix_memory_usage<F>(
        &self,
        n_rows: usize,
        n_cols: usize,
        operation: &AdvancedMatrixOperation,
    ) -> f64 {
        let basesize = (n_rows * n_cols * std::mem::size_of::<F>()) as f64;
        let resultsize = match operation {
            AdvancedMatrixOperation::Covariance | AdvancedMatrixOperation::Correlation => {
                (n_cols * n_cols * std::mem::size_of::<F>()) as f64
            }
            AdvancedMatrixOperation::Distance => {
                (n_rows * n_rows * std::mem::size_of::<F>()) as f64
            }
        };
        (basesize + resultsize) / (1024.0 * 1024.0)
    }

    fn calculate_optimization_effectiveness(&self) -> f64 {
        if self.performance_history.len() < 10 {
            return 0.5; // Not enough data
        }

        // Compare optimized vs standard performance
        let optimized_throughput: Vec<f64> = self
            .performance_history
            .iter()
            .filter(|m| m.simd_enabled || m.parallel_enabled)
            .map(|m| m.datasize as f64 / m.processing_time.as_secs_f64())
            .collect();

        let standard_throughput: Vec<f64> = self
            .performance_history
            .iter()
            .filter(|m| !m.simd_enabled && !m.parallel_enabled)
            .map(|m| m.datasize as f64 / m.processing_time.as_secs_f64())
            .collect();

        if optimized_throughput.is_empty() || standard_throughput.is_empty() {
            return 0.5;
        }

        let avg_optimized =
            optimized_throughput.iter().sum::<f64>() / optimized_throughput.len() as f64;
        let avg_standard =
            standard_throughput.iter().sum::<f64>() / standard_throughput.len() as f64;

        if avg_standard == 0.0 {
            return 1.0;
        }

        ((avg_optimized / avg_standard) - 1.0).max(0.0).min(10.0) / 10.0
    }

    // Placeholder implementations for missing methods

    fn convert_parallel_to_batch_stats<F>(
        &self,
        parallel_result: crate::parallel_enhancements::AdvancedParallelBatchResult<F>,
    ) -> BatchResults<F>
    where
        F: Float + NumCast + Copy
        + std::fmt::Display,
    {
        BatchResults {
            mean: parallel_result.mean,
            variance: parallel_result.variance,
            std_dev: parallel_result.std_dev,
            skewness: F::zero(), // Would need to compute separately
            kurtosis: F::zero(), // Would need to compute separately
            min: parallel_result.min,
            max: parallel_result.max,
            count: parallel_result.count,
            sum: parallel_result.sum,
            sum_squares: F::zero(), // Would need to compute separately
        }
    }

    fn process_simd_parallel_hybrid<F, D>(
        &self,
        data: &ArrayBase<D, Ix1>,
    ) -> StatsResult<BatchResults<F>>
    where
        F: Float + NumCast + SimdUnifiedOps + Send + Sync + Copy + PartialOrd + 'static,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        // For now, fall back to SIMD-only
        let optimizer = AdvancedSimdOptimizer::new(self.config.simd_config.clone());
        let binding = data.to_owned();
        let data_arrays = vec![binding.view()];
        let operations = vec![
            BatchOperation::Mean,
            BatchOperation::Variance,
            BatchOperation::StandardDeviation,
        ];
        optimizer.advanced_batch_statistics(&data_arrays, &operations)
    }

    fn process_standard_fallback<F, D>(
        &self,
        data: &ArrayBase<D, Ix1>,
    ) -> StatsResult<BatchResults<F>>
    where
        F: Float + NumCast + Copy + PartialOrd,
        D: Data<Elem = F>
        + std::fmt::Display,
    {
        let n = data.len();
        let mut sum = F::zero();
        let mut sum_squares = F::zero();
        let mut min_val = data[0];
        let mut max_val = data[0];

        for &val in data.iter() {
            sum = sum + val;
            sum_squares = sum_squares + val * val;
            if val < min_val {
                min_val = val;
            }
            if val > max_val {
                max_val = val;
            }
        }

        let mean = sum / F::from(n).unwrap();
        let variance = (sum_squares / F::from(n).unwrap()) - (mean * mean);
        let std_dev = variance.sqrt();

        // Compute higher moments
        let mut sum_cubed_dev = F::zero();
        let mut sum_fourth_dev = F::zero();

        for &val in data.iter() {
            let dev = val - mean;
            let dev_squared = dev * dev;
            sum_cubed_dev = sum_cubed_dev + dev * dev_squared;
            sum_fourth_dev = sum_fourth_dev + dev_squared * dev_squared;
        }

        let n_f = F::from(n).unwrap();
        let skewness = (sum_cubed_dev / n_f) / (std_dev * std_dev * std_dev);
        let kurtosis = (sum_fourth_dev / n_f) / (variance * variance) - F::from(3.0).unwrap();

        Ok(BatchResults {
            mean,
            variance,
            std_dev,
            skewness,
            kurtosis,
            min: min_val,
            max: max_val,
            count: n,
            sum,
            sum_squares,
        })
    }

    fn process_matrix_standard<F, D>(
        &self,
        data: &ArrayBase<D, Ix2>,
        operation: AdvancedMatrixOperation,
    ) -> StatsResult<Array2<F>>
    where
        F: Float + NumCast + Copy,
        D: Data<Elem = F>
        + std::fmt::Display,
    {
        match operation {
            AdvancedMatrixOperation::Covariance => {
                // Simple covariance matrix implementation
                let (_n_rows, n_cols) = data.dim();
                let mut result = Array2::<F>::zeros((n_cols, n_cols));

                // This is a placeholder - would need proper implementation
                for i in 0..n_cols {
                    result[[i, i]] = F::one();
                }

                Ok(result)
            }
            _ => Err(StatsError::NotImplemented(
                "Matrix operation not implemented".to_string(),
            )),
        }
    }

    fn convert_to_parallel_ts_operations(
        &self,
        operations: &[AdvancedTimeSeriesOperation],
    ) -> Vec<TimeSeriesOperation> {
        operations
            .iter()
            .map(|op| match op {
                AdvancedTimeSeriesOperation::MovingWindow => TimeSeriesOperation::MovingAverage,
            })
            .collect()
    }

    fn convert_parallel_ts_result<F>(
        &self, crate::parallel_enhancements::AdvancedParallelTimeSeriesResult<F>,
    ) -> StatsResult<MovingWindowResult<F>>
    where
        F: Float + NumCast + Copy
        + std::fmt::Display,
    {
        // Placeholder implementation
        Ok(MovingWindowResult {
            means: Array1::zeros(0),
            variances: Array1::zeros(0),
            mins: Array1::zeros(0),
            maxs: Array1::zeros(0),
            windowsize: 0,
        })
    }

    fn process_time_series_standard<F, D>(
        &self, &ArrayBase<D, Ix1>, _windowsize: usize, _operation: AdvancedTimeSeriesOperation,
    ) -> StatsResult<MovingWindowResult<F>>
    where
        F: Float + NumCast + Copy,
        D: Data<Elem = F>
        + std::fmt::Display,
    {
        // Placeholder implementation
        Ok(MovingWindowResult {
            means: Array1::zeros(0),
            variances: Array1::zeros(0),
            mins: Array1::zeros(0),
            maxs: Array1::zeros(0),
            windowsize: windowsize,
        })
    }

    fn generate_recommendations(
        &self, &ProcessingMetrics_stability, _report: &Option<StabilityAnalysisReport>,
    ) -> Vec<String> {
        let mut recommendations = Vec::new();

        if metrics.datasize > 10000 && !_metrics.parallel_enabled {
            recommendations
                .push("Consider enabling parallel processing for large datasets".to_string());
        }

        if metrics.datasize > 1000 && !_metrics.simd_enabled && self.capabilities.has_avx2() {
            recommendations.push("SIMD optimizations could improve performance".to_string());
        }

        recommendations
    }

    fn generate_performance_recommendations(&self) -> Vec<String> {
        let mut recommendations = Vec::new();

        if self.performance_history.len() < 10 {
            recommendations
                .push("More operations needed for comprehensive performance analysis".to_string());
        } else {
            let effectiveness = self.calculate_optimization_effectiveness();
            if effectiveness < 0.3 {
                recommendations.push(
                    "Consider adjusting optimization settings for better performance".to_string(),
                );
            }
        }

        recommendations
    }
}

// Data structures

/// Processing strategy selection
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum ProcessingStrategy {
    Standard,
    SimdOnly,
    ParallelOnly,
    SimdParallel,
}

impl ProcessingStrategy {
    pub fn uses_simd(self) -> bool {
        matches!(
            self,
            ProcessingStrategy::SimdOnly | ProcessingStrategy::SimdParallel
        )
    }

    pub fn uses_parallel(self) -> bool {
        matches!(
            self,
            ProcessingStrategy::ParallelOnly | ProcessingStrategy::SimdParallel
        )
    }
}

/// Matrix operation types for Advanced processing
#[derive(Debug, Clone, Copy)]
pub enum AdvancedMatrixOperation {
    Covariance,
    Correlation,
    Distance,
}

/// Time series operation types for Advanced processing
#[derive(Debug, Clone, Copy)]
pub enum AdvancedTimeSeriesOperation {
    MovingWindow,
}

/// Processing metrics for performance monitoring
#[derive(Debug, Clone)]
pub struct ProcessingMetrics {
    pub datasize: usize,
    pub processing_time: std::time::Duration,
    pub strategy_used: ProcessingStrategy,
    pub simd_enabled: bool,
    pub parallel_enabled: bool,
    pub stability_tested: bool,
    pub memory_usage_mb: f64,
}

/// Comprehensive Advanced processing result
#[derive(Debug, Clone)]
pub struct AdvancedComprehensiveResult<F> {
    pub statistics: BatchResults<F>,
    pub stability_report: Option<StabilityAnalysisReport>,
    pub processing_metrics: ProcessingMetrics,
    pub recommendations: Vec<String>,
    pub warnings: Vec<String>,
}

/// Matrix processing result
#[derive(Debug, Clone)]
pub struct AdvancedMatrixResult<F> {
    pub matrix: Array2<F>,
    pub operation: AdvancedMatrixOperation,
    pub processing_metrics: ProcessingMetrics,
    pub warnings: Vec<String>,
}

/// Time series processing result
#[derive(Debug, Clone)]
pub struct AdvancedTimeSeriesResult<F> {
    pub results: Vec<MovingWindowResult<F>>,
    pub windowsize: usize,
    pub operations: Vec<AdvancedTimeSeriesOperation>,
    pub processing_metrics: ProcessingMetrics,
}

/// Performance analytics for the Advanced processor
#[derive(Debug, Clone, Default)]
pub struct AdvancedPerformanceAnalytics {
    pub total_operations: usize,
    pub average_processing_time_ms: f64,
    pub simd_usage_rate: f64,
    pub parallel_usage_rate: f64,
    pub averagedatasize: f64,
    pub optimization_effectiveness: f64,
    pub recommendations: Vec<String>,
}

/// Create a unified Advanced processor with default configuration
#[allow(dead_code)]
pub fn create_advanced_processor() -> AdvancedUnifiedProcessor {
    AdvancedUnifiedProcessor::new(AdvancedProcessorConfig::default())
}

/// Create a unified Advanced processor with custom configuration
#[allow(dead_code)]
pub fn create_configured_advanced_processor(
    config: AdvancedProcessorConfig,
) -> AdvancedUnifiedProcessor {
    AdvancedUnifiedProcessor::new(config)
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::array;

    #[test]
    fn test_advanced_processor_creation() {
        let processor = create_advanced_processor();
        assert!(processor.capabilities.has_sse2()); // Most modern systems have SSE2
    }

    #[test]
    fn test_comprehensive_statistics() {
        let mut processor = create_advanced_processor();
        let data = array![1.0, 2.0, 3.0, 4.0, 5.0];

        let result = processor
            .process_comprehensive_statistics(&data.view())
            .unwrap();

        assert!((result.statistics.mean - 3.0).abs() < 1e-10);
        assert_eq!(result.statistics.count, 5);
        assert_eq!(result.statistics.min, 1.0);
        assert_eq!(result.statistics.max, 5.0);
    }

    #[test]
    fn test_performance_analytics() {
        let processor = create_advanced_processor();
        let analytics = processor.get_performance_analytics();

        // Should have default values when no operations have been performed
        assert_eq!(analytics.total_operations, 0);
        assert_eq!(analytics.average_processing_time_ms, 0.0);
    }

    #[test]
    fn test_processing_strategy_selection() {
        let processor = create_advanced_processor();
        let context = AdvancedContextBuilder::new(1000).build();

        // Test different data sizes
        let _small_strategy = processor
            .determine_processing_strategy(100, &context)
            .unwrap();
        let large_strategy = processor
            .determine_processing_strategy(100000, &context)
            .unwrap();

        // Large datasets should use more aggressive optimization
        assert!(large_strategy.uses_parallel() || large_strategy.uses_simd());
    }
}
