//! Scalability Features for Large-Scale Baseline Methods
//!
//! This module provides scalability enhancements for handling large datasets,
//! distributed computation, streaming updates, and approximate methods for
//! baseline dummy estimators.
//!
//! Features:
//! - Large-scale baseline methods optimized for massive datasets
//! - Distributed baseline computation across multiple nodes
//! - Streaming baseline updates for incremental learning
//! - Approximate baseline methods with bounded error guarantees
//! - Sampling-based baselines for efficient large-scale processing

use rayon::prelude::*;
use scirs2_core::ndarray::{s, Array1, Array2, ArrayView1, ArrayView2};
use scirs2_core::random::{thread_rng, Distribution, RngExt};
use sklears_core::error::{Result, SklearsError};
use sklears_core::traits::Estimator;
use std::collections::HashMap;
use std::sync::RwLock;
use std::time::Instant;

/// Configuration for large-scale baseline methods
#[derive(Debug, Clone)]
pub struct LargeScaleConfig {
    /// Maximum memory usage in bytes
    pub max_memory_bytes: usize,
    /// Chunk size for batch processing
    pub chunk_size: usize,
    /// Number of parallel workers
    pub n_workers: usize,
    /// Use memory mapping for large datasets
    pub use_memory_mapping: bool,
    /// Enable compression for intermediate results
    pub enable_compression: bool,
}

impl Default for LargeScaleConfig {
    fn default() -> Self {
        Self {
            max_memory_bytes: 1_073_741_824, // 1GB
            chunk_size: 10_000,
            n_workers: num_cpus::get(),
            use_memory_mapping: true,
            enable_compression: true,
        }
    }
}

/// Strategy for large-scale baseline computation
#[derive(Debug, Clone, PartialEq)]
pub enum LargeScaleStrategy {
    /// Chunked processing with configurable chunk size
    ChunkedProcessing { chunk_size: usize, overlap: usize },
    /// Memory-mapped processing for very large datasets
    MemoryMapped {
        block_size: usize,
        prefetch_blocks: usize,
    },
    /// Reservoir sampling for approximate statistics
    ReservoirSampling {
        reservoir_size: usize,
        replacement_rate: f64,
    },
    /// Sketching algorithms for approximate computation
    SketchBased {
        sketch_size: usize,
        hash_functions: usize,
    },
    /// Distributed processing across multiple nodes
    Distributed {
        node_id: usize,
        total_nodes: usize,
        coordinator_address: String,
    },
}

/// Large-scale dummy estimator for massive datasets
pub struct LargeScaleDummyEstimator {
    strategy: LargeScaleStrategy,
    config: LargeScaleConfig,
    state: RwLock<LargeScaleState>,
}

#[derive(Debug, Clone)]
struct LargeScaleState {
    /// Accumulated statistics
    sample_count: usize,
    running_sum: f64,
    running_sum_squares: f64,
    /// Reservoir sample for approximate methods
    reservoir: Vec<f64>,
    /// Sketch data structures
    sketches: HashMap<usize, Vec<f64>>,
    /// Distributed state
    node_statistics: HashMap<usize, NodeStatistics>,
    /// Memory usage tracking
    current_memory_usage: usize,
}

#[derive(Debug, Clone)]
struct NodeStatistics {
    sample_count: usize,
    mean: f64,
    variance: f64,
    last_update: Instant,
}

impl LargeScaleDummyEstimator {
    /// Create new large-scale dummy estimator
    pub fn new(strategy: LargeScaleStrategy) -> Self {
        Self::with_config(strategy, LargeScaleConfig::default())
    }

    /// Create with custom configuration
    pub fn with_config(strategy: LargeScaleStrategy, config: LargeScaleConfig) -> Self {
        Self {
            strategy,
            config,
            state: RwLock::new(LargeScaleState {
                sample_count: 0,
                running_sum: 0.0,
                running_sum_squares: 0.0,
                reservoir: Vec::new(),
                sketches: HashMap::new(),
                node_statistics: HashMap::new(),
                current_memory_usage: 0,
            }),
        }
    }

    /// Process large dataset in chunks
    pub fn fit_chunked(&self, x: &ArrayView2<f64>, y: &ArrayView1<f64>) -> Result<()> {
        match &self.strategy {
            LargeScaleStrategy::ChunkedProcessing {
                chunk_size,
                overlap,
            } => self.process_chunked(x, y, *chunk_size, *overlap),
            LargeScaleStrategy::MemoryMapped {
                block_size,
                prefetch_blocks,
            } => self.process_memory_mapped(x, y, *block_size, *prefetch_blocks),
            LargeScaleStrategy::ReservoirSampling {
                reservoir_size,
                replacement_rate,
            } => self.process_reservoir_sampling(x, y, *reservoir_size, *replacement_rate),
            LargeScaleStrategy::SketchBased {
                sketch_size,
                hash_functions,
            } => self.process_sketch_based(x, y, *sketch_size, *hash_functions),
            LargeScaleStrategy::Distributed {
                node_id,
                total_nodes,
                coordinator_address,
            } => self.process_distributed(x, y, *node_id, *total_nodes, coordinator_address),
        }
    }

    /// Chunked processing implementation
    fn process_chunked(
        &self,
        x: &ArrayView2<f64>,
        y: &ArrayView1<f64>,
        chunk_size: usize,
        overlap: usize,
    ) -> Result<()> {
        let n_samples = x.nrows();
        let mut start_idx = 0;

        while start_idx < n_samples {
            let end_idx = (start_idx + chunk_size).min(n_samples);
            let chunk_x = x.slice(s![start_idx..end_idx, ..]);
            let chunk_y = y.slice(s![start_idx..end_idx]);

            // Process chunk
            self.update_statistics(&chunk_x, &chunk_y)?;

            // Move to next chunk with overlap
            start_idx += chunk_size - overlap;
        }

        Ok(())
    }

    /// Memory-mapped processing implementation
    fn process_memory_mapped(
        &self,
        x: &ArrayView2<f64>,
        y: &ArrayView1<f64>,
        block_size: usize,
        prefetch_blocks: usize,
    ) -> Result<()> {
        if !self.config.use_memory_mapping {
            return self.process_chunked(x, y, block_size, 0);
        }

        // Create memory-mapped arrays for large datasets
        let n_samples = x.nrows();
        let n_features = x.ncols();

        // Process in memory-mapped blocks
        for block_start in (0..n_samples).step_by(block_size) {
            let block_end = (block_start + block_size).min(n_samples);

            // Simulate memory-mapped access with prefetching
            let block_x = x.slice(s![block_start..block_end, ..]);
            let block_y = y.slice(s![block_start..block_end]);

            // Prefetch next blocks in background
            if block_end + prefetch_blocks * block_size < n_samples {
                // In real implementation, this would prefetch from disk/memory
            }

            self.update_statistics(&block_x, &block_y)?;
        }

        Ok(())
    }

    /// Reservoir sampling implementation
    fn process_reservoir_sampling(
        &self,
        x: &ArrayView2<f64>,
        y: &ArrayView1<f64>,
        reservoir_size: usize,
        replacement_rate: f64,
    ) -> Result<()> {
        let mut state = self.state.write().expect("operation should succeed");
        let mut rng = thread_rng();

        // Initialize reservoir if needed
        if state.reservoir.is_empty() {
            state.reservoir.reserve(reservoir_size);
        }

        for &value in y.iter() {
            state.sample_count += 1;

            if state.reservoir.len() < reservoir_size {
                // Fill reservoir
                state.reservoir.push(value);
            } else {
                // Reservoir sampling algorithm
                let k = rng.random_range(0..state.sample_count);
                if k < reservoir_size {
                    state.reservoir[k] = value;
                } else if rng.random::<f64>() < replacement_rate {
                    // Occasional replacement to handle concept drift
                    let idx = rng.random_range(0..reservoir_size);
                    state.reservoir[idx] = value;
                }
            }
        }

        // Update statistics from reservoir
        if !state.reservoir.is_empty() {
            state.running_sum = state.reservoir.iter().sum();
            state.running_sum_squares = state.reservoir.iter().map(|&x| x * x).sum();
        }

        Ok(())
    }

    /// Sketch-based processing implementation
    fn process_sketch_based(
        &self,
        x: &ArrayView2<f64>,
        y: &ArrayView1<f64>,
        sketch_size: usize,
        hash_functions: usize,
    ) -> Result<()> {
        let mut state = self.state.write().expect("operation should succeed");

        // Initialize sketches
        for h in 0..hash_functions {
            state
                .sketches
                .entry(h)
                .or_insert_with(|| vec![0.0; sketch_size]);
        }

        // Count-Min sketch for frequency estimation
        for &value in y.iter() {
            for h in 0..hash_functions {
                let hash = self.hash_function(value, h) % sketch_size;
                if let Some(sketch) = state.sketches.get_mut(&h) {
                    sketch[hash] += 1.0;
                }
            }
            state.sample_count += 1;
        }

        // Estimate statistics from sketches
        self.estimate_from_sketches(&mut state, y)?;

        Ok(())
    }

    /// Distributed processing implementation
    fn process_distributed(
        &self,
        x: &ArrayView2<f64>,
        y: &ArrayView1<f64>,
        node_id: usize,
        total_nodes: usize,
        coordinator_address: &str,
    ) -> Result<()> {
        let mut state = self.state.write().expect("operation should succeed");

        // Compute local statistics
        let local_count = y.len();
        let local_sum: f64 = y.iter().sum();
        let local_mean = if local_count > 0 {
            local_sum / local_count as f64
        } else {
            0.0
        };
        let local_variance = if local_count > 1 {
            y.iter().map(|&x| (x - local_mean).powi(2)).sum::<f64>() / (local_count - 1) as f64
        } else {
            0.0
        };

        // Store local node statistics
        state.node_statistics.insert(
            node_id,
            NodeStatistics {
                sample_count: local_count,
                mean: local_mean,
                variance: local_variance,
                last_update: Instant::now(),
            },
        );

        // In a real distributed system, this would communicate with coordinator
        // For now, we simulate by combining all available node statistics
        self.combine_distributed_statistics(&mut state)?;

        Ok(())
    }

    /// Update running statistics
    fn update_statistics(&self, x: &ArrayView2<f64>, y: &ArrayView1<f64>) -> Result<()> {
        let mut state = self.state.write().expect("operation should succeed");

        let chunk_count = y.len();
        let chunk_sum: f64 = y.iter().sum();
        let chunk_sum_squares: f64 = y.iter().map(|&x| x * x).sum();

        // Online update of statistics
        let old_count = state.sample_count;
        let new_count = old_count + chunk_count;

        if new_count > 0 {
            let delta = chunk_sum - state.running_sum * (chunk_count as f64 / old_count as f64);
            state.running_sum += chunk_sum;
            state.running_sum_squares += chunk_sum_squares;
            state.sample_count = new_count;
        }

        // Update memory usage
        state.current_memory_usage += std::mem::size_of_val(x) + std::mem::size_of_val(y);

        Ok(())
    }

    /// Simple hash function for sketching
    fn hash_function(&self, value: f64, seed: usize) -> usize {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        value.to_bits().hash(&mut hasher);
        seed.hash(&mut hasher);
        hasher.finish() as usize
    }

    /// Estimate statistics from sketches
    fn estimate_from_sketches(
        &self,
        state: &mut LargeScaleState,
        y: &ArrayView1<f64>,
    ) -> Result<()> {
        // Simple estimation: use minimum values across all sketches for robustness
        if let Some(sketch_0) = state.sketches.get(&0) {
            // Estimate mean from sketch frequencies
            let total_frequency: f64 = sketch_0.iter().sum();
            if total_frequency > 0.0 {
                // This is a simplified estimation - in practice you'd use more sophisticated methods
                state.running_sum = y.iter().sum();
                state.running_sum_squares = y.iter().map(|&x| x * x).sum();
            }
        }
        Ok(())
    }

    /// Combine statistics from distributed nodes
    fn combine_distributed_statistics(&self, state: &mut LargeScaleState) -> Result<()> {
        let mut total_count = 0;
        let mut weighted_sum = 0.0;
        let mut weighted_sum_squares = 0.0;

        for node_stats in state.node_statistics.values() {
            total_count += node_stats.sample_count;
            weighted_sum += node_stats.mean * node_stats.sample_count as f64;
            weighted_sum_squares += (node_stats.variance + node_stats.mean * node_stats.mean)
                * node_stats.sample_count as f64;
        }

        if total_count > 0 {
            state.sample_count = total_count;
            state.running_sum = weighted_sum;
            state.running_sum_squares = weighted_sum_squares;
        }

        Ok(())
    }

    /// Get current mean estimate
    pub fn get_mean(&self) -> f64 {
        let state = self.state.read().expect("operation should succeed");
        if state.sample_count > 0 {
            state.running_sum / state.sample_count as f64
        } else {
            0.0
        }
    }

    /// Get current variance estimate
    pub fn get_variance(&self) -> f64 {
        let state = self.state.read().expect("operation should succeed");
        if state.sample_count > 1 {
            let mean = state.running_sum / state.sample_count as f64;
            let variance = state.running_sum_squares / state.sample_count as f64 - mean * mean;
            variance * state.sample_count as f64 / (state.sample_count - 1) as f64
        // Bessel's correction
        } else {
            0.0
        }
    }

    /// Get memory usage statistics
    pub fn get_memory_usage(&self) -> usize {
        self.state
            .read()
            .expect("operation should succeed")
            .current_memory_usage
    }

    /// Get processing statistics
    pub fn get_processing_stats(&self) -> ProcessingStats {
        let state = self.state.read().expect("operation should succeed");
        ProcessingStats {
            total_samples_processed: state.sample_count,
            current_memory_usage: state.current_memory_usage,
            max_memory_limit: self.config.max_memory_bytes,
            reservoir_size: state.reservoir.len(),
            sketch_count: state.sketches.len(),
            distributed_nodes: state.node_statistics.len(),
        }
    }
}

/// Processing statistics for monitoring
#[derive(Debug, Clone)]
pub struct ProcessingStats {
    /// total_samples_processed
    pub total_samples_processed: usize,
    /// current_memory_usage
    pub current_memory_usage: usize,
    /// max_memory_limit
    pub max_memory_limit: usize,
    /// reservoir_size
    pub reservoir_size: usize,
    /// sketch_count
    pub sketch_count: usize,
    /// distributed_nodes
    pub distributed_nodes: usize,
}

/// Streaming baseline updater for incremental learning
pub struct StreamingBaselineUpdater {
    /// Current statistics
    count: usize,
    mean: f64,
    m2: f64, // For Welford's algorithm
    /// Decay factor for exponential weighting
    decay_factor: f64,
    /// Minimum samples before making predictions
    min_samples: usize,
}

impl StreamingBaselineUpdater {
    /// Create new streaming updater
    pub fn new(decay_factor: f64, min_samples: usize) -> Self {
        Self {
            count: 0,
            mean: 0.0,
            m2: 0.0,
            decay_factor,
            min_samples,
        }
    }

    /// Update with new sample using Welford's online algorithm
    pub fn update(&mut self, value: f64) {
        self.count += 1;

        if self.decay_factor < 1.0 && self.count > 1 {
            // Exponential decay
            let effective_count = (self.count as f64 * self.decay_factor).max(1.0);
            let delta = value - self.mean;
            self.mean += delta / effective_count;
            let delta2 = value - self.mean;
            self.m2 += delta * delta2;
        } else {
            // Standard Welford's algorithm
            let delta = value - self.mean;
            self.mean += delta / self.count as f64;
            let delta2 = value - self.mean;
            self.m2 += delta * delta2;
        }
    }

    /// Get current mean
    pub fn mean(&self) -> f64 {
        self.mean
    }

    /// Get current variance
    pub fn variance(&self) -> f64 {
        if self.count > 1 {
            self.m2 / (self.count - 1) as f64
        } else {
            0.0
        }
    }

    /// Get current standard deviation
    pub fn std_dev(&self) -> f64 {
        self.variance().sqrt()
    }

    /// Check if ready for predictions
    pub fn is_ready(&self) -> bool {
        self.count >= self.min_samples
    }

    /// Get sample count
    pub fn count(&self) -> usize {
        self.count
    }

    /// Reset statistics
    pub fn reset(&mut self) {
        self.count = 0;
        self.mean = 0.0;
        self.m2 = 0.0;
    }

    /// Get prediction for new sample
    pub fn predict(&self) -> Result<f64> {
        if !self.is_ready() {
            return Err(SklearsError::InvalidInput(format!(
                "Need at least {} samples before making predictions",
                self.min_samples
            )));
        }
        Ok(self.mean)
    }

    /// Get prediction with confidence interval
    pub fn predict_with_confidence(&self, confidence_level: f64) -> Result<(f64, f64, f64)> {
        if !self.is_ready() {
            return Err(SklearsError::InvalidInput(format!(
                "Need at least {} samples before making predictions",
                self.min_samples
            )));
        }

        let prediction = self.mean;
        let std_err = self.std_dev() / (self.count as f64).sqrt();

        // Approximate z-score for confidence interval
        let z_score = match confidence_level {
            level if level >= 0.99 => 2.576,
            level if level >= 0.95 => 1.96,
            level if level >= 0.90 => 1.645,
            _ => 1.0,
        };

        let margin = z_score * std_err;
        Ok((prediction, prediction - margin, prediction + margin))
    }
}

/// Approximate baseline methods with error bounds
pub struct ApproximateBaseline {
    method: ApproximateMethod,
    error_bound: f64,
    confidence_level: f64,
}

#[derive(Debug, Clone)]
pub enum ApproximateMethod {
    /// Random sampling with replacement
    Bootstrap { n_samples: usize },
    /// Stratified sampling
    Stratified {
        n_strata: usize,
        samples_per_stratum: usize,
    },
    /// Systematic sampling
    Systematic { sampling_interval: usize },
    /// Cluster sampling
    Cluster { n_clusters: usize },
}

impl ApproximateBaseline {
    /// Create new approximate baseline
    pub fn new(method: ApproximateMethod, error_bound: f64, confidence_level: f64) -> Result<Self> {
        if !(0.0..=1.0).contains(&error_bound) {
            return Err(SklearsError::InvalidInput(
                "Error bound must be between 0 and 1".to_string(),
            ));
        }
        if !(0.0..=1.0).contains(&confidence_level) {
            return Err(SklearsError::InvalidInput(
                "Confidence level must be between 0 and 1".to_string(),
            ));
        }

        Ok(Self {
            method,
            error_bound,
            confidence_level,
        })
    }

    /// Compute approximate statistics
    pub fn compute_approximate_stats(&self, y: &ArrayView1<f64>) -> Result<ApproximateStats> {
        match &self.method {
            ApproximateMethod::Bootstrap { n_samples } => self.bootstrap_stats(y, *n_samples),
            ApproximateMethod::Stratified {
                n_strata,
                samples_per_stratum,
            } => self.stratified_stats(y, *n_strata, *samples_per_stratum),
            ApproximateMethod::Systematic { sampling_interval } => {
                self.systematic_stats(y, *sampling_interval)
            }
            ApproximateMethod::Cluster { n_clusters } => self.cluster_stats(y, *n_clusters),
        }
    }

    /// Bootstrap sampling statistics
    fn bootstrap_stats(&self, y: &ArrayView1<f64>, n_samples: usize) -> Result<ApproximateStats> {
        let mut rng = thread_rng();
        let total_samples = y.len();
        let mut bootstrap_means = Vec::with_capacity(n_samples);
        let sample_size = (total_samples as f64 * 0.632).ceil() as usize; // ~63.2% for bootstrap

        for _ in 0..n_samples {
            let mut sample_sum = 0.0;

            for _ in 0..sample_size {
                let idx = rng.random_range(0..total_samples);
                sample_sum += y[idx];
            }

            bootstrap_means.push(sample_sum / sample_size as f64);
        }

        let estimated_mean = bootstrap_means.iter().sum::<f64>() / bootstrap_means.len() as f64;
        let estimated_variance = bootstrap_means
            .iter()
            .map(|&x| (x - estimated_mean).powi(2))
            .sum::<f64>()
            / (bootstrap_means.len() - 1) as f64;

        Ok(ApproximateStats {
            estimated_mean,
            estimated_variance,
            confidence_interval: self.compute_confidence_interval(
                estimated_mean,
                estimated_variance.sqrt(),
                bootstrap_means.len(),
            ),
            sample_size_used: sample_size * n_samples,
            method_info: format!("Bootstrap with {} resamples", n_samples),
        })
    }

    /// Stratified sampling statistics
    fn stratified_stats(
        &self,
        y: &ArrayView1<f64>,
        n_strata: usize,
        samples_per_stratum: usize,
    ) -> Result<ApproximateStats> {
        let mut rng = thread_rng();
        let total_samples = y.len();

        // Sort data for stratification
        let mut indexed_data: Vec<(usize, f64)> =
            y.iter().enumerate().map(|(i, &v)| (i, v)).collect();
        indexed_data.sort_by(|a, b| a.1.partial_cmp(&b.1).expect("operation should succeed"));

        let stratum_size = total_samples / n_strata;
        let mut stratum_means = Vec::new();
        let mut total_sampled = 0;

        for stratum in 0..n_strata {
            let start = stratum * stratum_size;
            let end = if stratum == n_strata - 1 {
                total_samples
            } else {
                (stratum + 1) * stratum_size
            };
            let stratum_data = &indexed_data[start..end];

            if stratum_data.is_empty() {
                continue;
            }

            let actual_samples = samples_per_stratum.min(stratum_data.len());
            let mut stratum_sum = 0.0;

            for _ in 0..actual_samples {
                let idx = rng.random_range(0..stratum_data.len());
                stratum_sum += stratum_data[idx].1;
                total_sampled += 1;
            }

            stratum_means.push(stratum_sum / actual_samples as f64);
        }

        let estimated_mean = stratum_means.iter().sum::<f64>() / stratum_means.len() as f64;
        let estimated_variance = if stratum_means.len() > 1 {
            stratum_means
                .iter()
                .map(|&x| (x - estimated_mean).powi(2))
                .sum::<f64>()
                / (stratum_means.len() - 1) as f64
        } else {
            0.0
        };

        Ok(ApproximateStats {
            estimated_mean,
            estimated_variance,
            confidence_interval: self.compute_confidence_interval(
                estimated_mean,
                estimated_variance.sqrt(),
                stratum_means.len(),
            ),
            sample_size_used: total_sampled,
            method_info: format!(
                "Stratified sampling with {} strata, {} samples per stratum",
                n_strata, samples_per_stratum
            ),
        })
    }

    /// Systematic sampling statistics
    fn systematic_stats(
        &self,
        y: &ArrayView1<f64>,
        sampling_interval: usize,
    ) -> Result<ApproximateStats> {
        let mut rng = thread_rng();
        let total_samples = y.len();

        if sampling_interval >= total_samples {
            return Err(SklearsError::InvalidInput(
                "Sampling interval too large".to_string(),
            ));
        }

        let start = rng.random_range(0..sampling_interval);
        let mut sample_sum = 0.0;
        let mut sample_count = 0;

        for i in (start..total_samples).step_by(sampling_interval) {
            sample_sum += y[i];
            sample_count += 1;
        }

        let estimated_mean = if sample_count > 0 {
            sample_sum / sample_count as f64
        } else {
            0.0
        };

        // Estimate variance using systematic sampling
        let mut variance_sum = 0.0;
        for i in (start..total_samples).step_by(sampling_interval) {
            variance_sum += (y[i] - estimated_mean).powi(2);
        }
        let estimated_variance = if sample_count > 1 {
            variance_sum / (sample_count - 1) as f64
        } else {
            0.0
        };

        Ok(ApproximateStats {
            estimated_mean,
            estimated_variance,
            confidence_interval: self.compute_confidence_interval(
                estimated_mean,
                estimated_variance.sqrt(),
                sample_count,
            ),
            sample_size_used: sample_count,
            method_info: format!("Systematic sampling with interval {}", sampling_interval),
        })
    }

    /// Cluster sampling statistics
    fn cluster_stats(&self, y: &ArrayView1<f64>, n_clusters: usize) -> Result<ApproximateStats> {
        let mut rng = thread_rng();
        let total_samples = y.len();
        let cluster_size = total_samples / n_clusters;

        if cluster_size == 0 {
            return Err(SklearsError::InvalidInput(
                "Too many clusters for dataset size".to_string(),
            ));
        }

        // Randomly select clusters
        let selected_clusters = n_clusters / 2; // Select half of the clusters
        let mut cluster_means = Vec::new();
        let mut total_sampled = 0;

        for _ in 0..selected_clusters {
            let cluster_id = rng.random_range(0..n_clusters);
            let start = cluster_id * cluster_size;
            let end = if cluster_id == n_clusters - 1 {
                total_samples
            } else {
                (cluster_id + 1) * cluster_size
            };

            let cluster_sum: f64 = y.slice(s![start..end]).iter().sum();
            let cluster_mean = cluster_sum / (end - start) as f64;
            cluster_means.push(cluster_mean);
            total_sampled += end - start;
        }

        let estimated_mean = cluster_means.iter().sum::<f64>() / cluster_means.len() as f64;
        let estimated_variance = if cluster_means.len() > 1 {
            cluster_means
                .iter()
                .map(|&x| (x - estimated_mean).powi(2))
                .sum::<f64>()
                / (cluster_means.len() - 1) as f64
        } else {
            0.0
        };

        Ok(ApproximateStats {
            estimated_mean,
            estimated_variance,
            confidence_interval: self.compute_confidence_interval(
                estimated_mean,
                estimated_variance.sqrt(),
                cluster_means.len(),
            ),
            sample_size_used: total_sampled,
            method_info: format!(
                "Cluster sampling with {} selected from {} clusters",
                selected_clusters, n_clusters
            ),
        })
    }

    /// Compute confidence interval
    fn compute_confidence_interval(&self, mean: f64, std_error: f64, n: usize) -> (f64, f64) {
        // Use t-distribution for small samples, normal for large samples
        let t_value = if n > 30 {
            // Normal approximation
            match self.confidence_level {
                level if level >= 0.99 => 2.576,
                level if level >= 0.95 => 1.96,
                level if level >= 0.90 => 1.645,
                _ => 1.0,
            }
        } else {
            // t-distribution (simplified lookup)
            match self.confidence_level {
                level if level >= 0.95 => 2.0,
                _ => 1.5,
            }
        };

        let margin = t_value * std_error / (n as f64).sqrt();
        (mean - margin, mean + margin)
    }
}

/// Approximate statistics result
#[derive(Debug, Clone)]
pub struct ApproximateStats {
    /// estimated_mean
    pub estimated_mean: f64,
    /// estimated_variance
    pub estimated_variance: f64,
    /// confidence_interval
    pub confidence_interval: (f64, f64),
    /// sample_size_used
    pub sample_size_used: usize,
    /// method_info
    pub method_info: String,
}

/// Sampling-based baseline for efficient processing
pub struct SamplingBasedBaseline {
    sampling_rate: f64,
    min_samples: usize,
    max_samples: usize,
    adaptive: bool,
}

impl SamplingBasedBaseline {
    /// Create new sampling-based baseline
    pub fn new(sampling_rate: f64, min_samples: usize, max_samples: usize) -> Result<Self> {
        if !(0.0..=1.0).contains(&sampling_rate) {
            return Err(SklearsError::InvalidInput(
                "Sampling rate must be between 0 and 1".to_string(),
            ));
        }
        if min_samples > max_samples {
            return Err(SklearsError::InvalidInput(
                "Min samples cannot exceed max samples".to_string(),
            ));
        }

        Ok(Self {
            sampling_rate,
            min_samples,
            max_samples,
            adaptive: true,
        })
    }

    /// Compute baseline using sampling
    pub fn compute_sampled_baseline(&self, y: &ArrayView1<f64>) -> Result<SampledBaselineResult> {
        let total_samples = y.len();
        let target_samples = (total_samples as f64 * self.sampling_rate) as usize;
        let actual_samples =
            target_samples.clamp(self.min_samples, self.max_samples.min(total_samples));

        if actual_samples == 0 {
            return Err(SklearsError::InvalidInput(
                "No samples to process".to_string(),
            ));
        }

        // Reservoir sampling for unbiased sample
        let mut rng = thread_rng();
        let mut sample = Vec::with_capacity(actual_samples);

        for (i, &value) in y.iter().enumerate() {
            if sample.len() < actual_samples {
                sample.push(value);
            } else {
                let j = rng.random_range(0..i + 1);
                if j < actual_samples {
                    sample[j] = value;
                }
            }
        }

        // Compute statistics from sample
        let mean = sample.iter().sum::<f64>() / sample.len() as f64;
        let variance = if sample.len() > 1 {
            sample.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / (sample.len() - 1) as f64
        } else {
            0.0
        };

        // Estimate error bounds
        let standard_error = variance.sqrt() / (sample.len() as f64).sqrt();
        let confidence_95 = (mean - 1.96 * standard_error, mean + 1.96 * standard_error);

        Ok(SampledBaselineResult {
            mean,
            variance,
            standard_error,
            confidence_interval: confidence_95,
            sample_size: sample.len(),
            total_size: total_samples,
            sampling_efficiency: sample.len() as f64 / total_samples as f64,
        })
    }

    /// Adaptive sampling that adjusts rate based on variance
    pub fn adaptive_sample(&mut self, y: &ArrayView1<f64>) -> Result<SampledBaselineResult> {
        if !self.adaptive {
            return self.compute_sampled_baseline(y);
        }

        // Start with initial sample to estimate variance
        let initial_result = self.compute_sampled_baseline(y)?;

        // Adjust sampling rate based on variance
        let cv = initial_result.standard_error / initial_result.mean.abs();

        if cv > 0.1 {
            // High variance, increase sampling rate
            self.sampling_rate = (self.sampling_rate * 1.5).min(1.0);
        } else if cv < 0.05 {
            // Low variance, can reduce sampling rate
            self.sampling_rate = (self.sampling_rate * 0.8).max(0.01);
        }

        // Recompute with adjusted rate
        self.compute_sampled_baseline(y)
    }
}

/// Sampled baseline result
#[derive(Debug, Clone)]
pub struct SampledBaselineResult {
    /// mean
    pub mean: f64,
    /// variance
    pub variance: f64,
    /// standard_error
    pub standard_error: f64,
    /// confidence_interval
    pub confidence_interval: (f64, f64),
    /// sample_size
    pub sample_size: usize,
    /// total_size
    pub total_size: usize,
    /// sampling_efficiency
    pub sampling_efficiency: f64,
}

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

    #[test]
    fn test_large_scale_chunked_processing() {
        let x = Array2::from_shape_vec((1000, 5), (0..5000).map(|i| i as f64).collect())
            .expect("shape and data length should match");
        let y = Array1::from_shape_vec(1000, (0..1000).map(|i| (i % 10) as f64).collect())
            .expect("shape and data length should match");

        let estimator = LargeScaleDummyEstimator::new(LargeScaleStrategy::ChunkedProcessing {
            chunk_size: 100,
            overlap: 10,
        });

        let result = estimator.fit_chunked(&x.view(), &y.view());
        assert!(result.is_ok());

        let mean = estimator.get_mean();
        assert!(mean >= 0.0 && mean <= 10.0);
    }

    #[test]
    fn test_streaming_baseline_updater() {
        let mut updater = StreamingBaselineUpdater::new(0.95, 5);

        // Add some samples
        for value in [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] {
            updater.update(value);
        }

        assert!(updater.is_ready());
        assert!((updater.mean() - 3.5).abs() < 0.1);
        assert!(updater.variance() > 0.0);
    }

    #[test]
    fn test_reservoir_sampling() {
        let x = Array2::zeros((1000, 5));
        let y = Array1::from_shape_vec(1000, (0..1000).map(|i| i as f64).collect())
            .expect("shape and data length should match");

        let estimator = LargeScaleDummyEstimator::new(LargeScaleStrategy::ReservoirSampling {
            reservoir_size: 100,
            replacement_rate: 0.1,
        });

        let result = estimator.fit_chunked(&x.view(), &y.view());
        assert!(result.is_ok());

        let stats = estimator.get_processing_stats();
        assert_eq!(stats.total_samples_processed, 1000);
        assert_eq!(stats.reservoir_size, 100);
    }

    #[test]
    fn test_approximate_bootstrap() {
        let y = array![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0];

        let approx =
            ApproximateBaseline::new(ApproximateMethod::Bootstrap { n_samples: 50 }, 0.05, 0.95)
                .expect("operation should succeed");

        let result = approx.compute_approximate_stats(&y.view());
        assert!(result.is_ok());

        let stats = result.expect("operation should succeed");
        assert!(stats.estimated_mean > 0.0);
        assert!(stats.estimated_variance >= 0.0);
        assert!(stats.confidence_interval.0 < stats.confidence_interval.1);
    }

    #[test]
    fn test_sampling_based_baseline() {
        let y = Array1::from_shape_vec(1000, (0..1000).map(|i| i as f64).collect())
            .expect("shape and data length should match");

        let baseline = SamplingBasedBaseline::new(0.1, 50, 200).expect("operation should succeed");
        let result = baseline.compute_sampled_baseline(&y.view());

        assert!(result.is_ok());
        let stats = result.expect("operation should succeed");
        assert!(stats.sample_size >= 50 && stats.sample_size <= 200);
        assert!(stats.sampling_efficiency > 0.0 && stats.sampling_efficiency <= 1.0);
    }

    #[test]
    fn test_distributed_processing() {
        let x = Array2::zeros((100, 3));
        let y = array![1.0, 2.0, 3.0, 4.0, 5.0];

        let estimator = LargeScaleDummyEstimator::new(LargeScaleStrategy::Distributed {
            node_id: 0,
            total_nodes: 3,
            coordinator_address: "localhost:8080".to_string(),
        });

        let result = estimator.fit_chunked(&x.slice(s![..5, ..]).view(), &y.view());
        assert!(result.is_ok());

        let stats = estimator.get_processing_stats();
        assert_eq!(stats.distributed_nodes, 1);
    }

    #[test]
    fn test_sketch_based_processing() {
        let x = Array2::zeros((200, 4));
        let y = Array1::from_shape_vec(200, (0..200).map(|i| (i % 20) as f64).collect())
            .expect("shape and data length should match");

        let estimator = LargeScaleDummyEstimator::new(LargeScaleStrategy::SketchBased {
            sketch_size: 32,
            hash_functions: 4,
        });

        let result = estimator.fit_chunked(&x.view(), &y.view());
        assert!(result.is_ok());

        let stats = estimator.get_processing_stats();
        assert_eq!(stats.sketch_count, 4);
    }
}
