//! Spatial Data Structures for Geographic Tree Algorithms
//!
//! This module provides specialized data structures and algorithms for handling
//! geospatial data in tree-based models, including spatial splits, geographic
//! partitioning, and location-aware tree construction.

use crate::SplitCriterion;
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::random::{Random, thread_rng};
use sklears_core::{
    error::{Result, SklearsError},
    traits::Untrained,
};
use std::collections::{HashMap, VecDeque};
use std::marker::PhantomData;

use crate::decision_tree::{DecisionTreeConfig, SplitCriterion};

/// Spatial coordinate systems
#[derive(Debug, Clone)]
pub enum CoordinateSystem {
    /// Geographic coordinates (latitude, longitude)
    Geographic,
    /// Projected coordinates (x, y)
    Projected { epsg_code: u32 },
    /// Universal Transverse Mercator
    UTM { zone: u8, hemisphere: Hemisphere },
    /// Web Mercator (EPSG:3857)
    WebMercator,
    /// Custom coordinate system
    Custom { name: String },
}

/// Hemisphere for UTM coordinates
#[derive(Debug, Clone, Copy)]
pub enum Hemisphere {
    North,
    South,
}

/// Spatial point representation
#[derive(Debug, Clone, Copy)]
pub struct SpatialPoint {
    /// X coordinate (longitude or easting)
    pub x: f64,
    /// Y coordinate (latitude or northing)
    pub y: f64,
    /// Optional Z coordinate (elevation)
    pub z: Option<f64>,
}

impl SpatialPoint {
    pub fn new(x: f64, y: f64) -> Self {
        Self { x, y, z: None }
    }

    pub fn new_3d(x: f64, y: f64, z: f64) -> Self {
        Self { x, y, z: Some(z) }
    }

    /// Calculate Euclidean distance to another point
    pub fn distance(&self, other: &SpatialPoint) -> f64 {
        let dx = self.x - other.x;
        let dy = self.y - other.y;

        match (self.z, other.z) {
            (Some(z1), Some(z2)) => {
                let dz = z1 - z2;
                (dx * dx + dy * dy + dz * dz).sqrt()
            }
            _ => (dx * dx + dy * dy).sqrt(),
        }
    }

    /// Calculate Haversine distance (for geographic coordinates)
    pub fn haversine_distance(&self, other: &SpatialPoint) -> f64 {
        const EARTH_RADIUS_KM: f64 = 6371.0;

        let lat1 = self.y.to_radians();
        let lat2 = other.y.to_radians();
        let delta_lat = (other.y - self.y).to_radians();
        let delta_lon = (other.x - self.x).to_radians();

        let a = (delta_lat / 2.0).sin().powi(2)
            + lat1.cos() * lat2.cos() * (delta_lon / 2.0).sin().powi(2);
        let c = 2.0 * a.sqrt().atan2((1.0 - a).sqrt());

        EARTH_RADIUS_KM * c
    }
}

/// Spatial bounding box
#[derive(Debug, Clone, Copy)]
pub struct BoundingBox {
    /// Minimum X coordinate
    pub min_x: f64,
    /// Maximum X coordinate
    pub max_x: f64,
    /// Minimum Y coordinate
    pub min_y: f64,
    /// Maximum Y coordinate
    pub max_y: f64,
}

impl BoundingBox {
    pub fn new(min_x: f64, max_x: f64, min_y: f64, max_y: f64) -> Self {
        Self {
            min_x,
            max_x,
            min_y,
            max_y,
        }
    }

    pub fn from_points(points: &[SpatialPoint]) -> Self {
        if points.is_empty() {
            return Self::new(0.0, 0.0, 0.0, 0.0);
        }

        let mut min_x = points[0].x;
        let mut max_x = points[0].x;
        let mut min_y = points[0].y;
        let mut max_y = points[0].y;

        for point in points.iter().skip(1) {
            min_x = min_x.min(point.x);
            max_x = max_x.max(point.x);
            min_y = min_y.min(point.y);
            max_y = max_y.max(point.y);
        }

        Self::new(min_x, max_x, min_y, max_y)
    }

    /// Check if point is inside bounding box
    pub fn contains(&self, point: &SpatialPoint) -> bool {
        point.x >= self.min_x
            && point.x <= self.max_x
            && point.y >= self.min_y
            && point.y <= self.max_y
    }

    /// Calculate area of bounding box
    pub fn area(&self) -> f64 {
        (self.max_x - self.min_x) * (self.max_y - self.min_y)
    }

    /// Calculate intersection with another bounding box
    pub fn intersection(&self, other: &BoundingBox) -> Option<BoundingBox> {
        let min_x = self.min_x.max(other.min_x);
        let max_x = self.max_x.min(other.max_x);
        let min_y = self.min_y.max(other.min_y);
        let max_y = self.max_y.min(other.max_y);

        if min_x <= max_x && min_y <= max_y {
            Some(BoundingBox::new(min_x, max_x, min_y, max_y))
        } else {
            None
        }
    }
}

/// Spatial split criteria for geographic data
#[derive(Debug, Clone)]
pub enum SpatialSplitCriterion {
    /// Standard split criteria applied to spatial features
    Standard(SplitCriterion),
    /// Geographic distance-based splitting
    GeographicDistance {
        distance_metric: DistanceMetric,
        distance_weight: f64,
    },
    /// Spatial autocorrelation-aware splitting
    SpatialAutocorrelation {
        lag_distance: f64,
        autocorr_weight: f64,
    },
    /// Geographically weighted splitting
    GeographicallyWeighted {
        bandwidth: f64,
        kernel: SpatialKernel,
    },
    /// Quadtree-based spatial splitting
    Quadtree {
        max_depth: usize,
        min_points_per_cell: usize,
    },
    /// Voronoi diagram-based splitting
    Voronoi {
        n_sites: usize,
        site_selection: VoronoiSiteSelection,
    },
    /// Elevation-aware splitting
    ElevationAware {
        elevation_weight: f64,
        slope_threshold: f64,
    },
}

/// Distance metrics for spatial calculations
#[derive(Debug, Clone, Copy)]
pub enum DistanceMetric {
    /// Euclidean distance
    Euclidean,
    /// Manhattan distance
    Manhattan,
    /// Haversine distance (for geographic coordinates)
    Haversine,
    /// Great circle distance
    GreatCircle,
    /// Geodesic distance
    Geodesic,
}

/// Spatial kernels for weighted calculations
#[derive(Debug, Clone, Copy)]
pub enum SpatialKernel {
    /// Gaussian kernel
    Gaussian,
    /// Exponential kernel
    Exponential,
    /// Linear kernel
    Linear,
    /// Uniform kernel
    Uniform,
    /// Tricube kernel
    Tricube,
}

/// Voronoi site selection strategies
#[derive(Debug, Clone, Copy)]
pub enum VoronoiSiteSelection {
    /// Random selection
    Random,
    /// K-means clustering
    KMeans,
    /// Farthest point sampling
    FarthestPoint,
    /// Grid-based selection
    Grid,
}

/// Spatial data structure for geographic data
#[derive(Debug, Clone)]
pub struct SpatialDataStructure {
    /// Spatial points
    pub points: Vec<SpatialPoint>,
    /// Feature values at each point
    pub features: Array2<f64>,
    /// Target values
    pub targets: Array1<f64>,
    /// Coordinate system
    pub coordinate_system: CoordinateSystem,
    /// Spatial index for fast queries
    pub spatial_index: Option<SpatialIndex>,
    /// Spatial metadata
    pub metadata: SpatialMetadata,
}

/// Spatial metadata
#[derive(Debug, Clone)]
pub struct SpatialMetadata {
    /// Bounding box of all points
    pub bounding_box: BoundingBox,
    /// Spatial resolution (if applicable)
    pub resolution: Option<f64>,
    /// Spatial clustering information
    pub clusters: Vec<SpatialCluster>,
    /// Elevation statistics
    pub elevation_stats: Option<ElevationStats>,
    /// Spatial autocorrelation statistics
    pub autocorrelation_stats: Option<AutocorrelationStats>,
}

/// Spatial cluster information
#[derive(Debug, Clone)]
pub struct SpatialCluster {
    /// Cluster ID
    pub id: usize,
    /// Center point
    pub center: SpatialPoint,
    /// Points in cluster
    pub point_indices: Vec<usize>,
    /// Cluster radius
    pub radius: f64,
}

/// Elevation statistics
#[derive(Debug, Clone)]
pub struct ElevationStats {
    /// Mean elevation
    pub mean: f64,
    /// Standard deviation of elevation
    pub std: f64,
    /// Minimum elevation
    pub min: f64,
    /// Maximum elevation
    pub max: f64,
    /// Slope distribution
    pub slope_distribution: Vec<f64>,
}

/// Spatial autocorrelation statistics
#[derive(Debug, Clone)]
pub struct AutocorrelationStats {
    /// Moran's I statistic
    pub morans_i: f64,
    /// Geary's C statistic
    pub gearys_c: f64,
    /// Local indicators of spatial association
    pub lisa: Vec<f64>,
    /// Spatial lag values
    pub spatial_lag: Vec<f64>,
}

/// Spatial index for fast spatial queries
#[derive(Debug, Clone)]
pub enum SpatialIndex {
    /// Quadtree index
    Quadtree(QuadTree),
    /// R-tree index
    RTree(RTree),
    /// Grid index
    Grid(GridIndex),
    /// KD-tree index
    KDTree(KDTree),
}

/// Quadtree spatial index
#[derive(Debug, Clone)]
pub struct QuadTree {
    /// Root node
    pub root: QuadTreeNode,
    /// Maximum depth
    pub max_depth: usize,
    /// Maximum points per leaf
    pub max_points_per_leaf: usize,
}

/// Quadtree node
#[derive(Debug, Clone)]
pub struct QuadTreeNode {
    /// Bounding box of this node
    pub bounds: BoundingBox,
    /// Points in this node (for leaf nodes)
    pub points: Vec<usize>,
    /// Child nodes (for internal nodes)
    pub children: Option<[Box<QuadTreeNode>; 4]>,
    /// Is this a leaf node?
    pub is_leaf: bool,
    /// Node depth
    pub depth: usize,
}

/// R-tree spatial index (simplified)
#[derive(Debug, Clone)]
pub struct RTree {
    /// Root node
    pub root: RTreeNode,
    /// Maximum entries per node
    pub max_entries: usize,
}

/// R-tree node (simplified)
#[derive(Debug, Clone)]
pub struct RTreeNode {
    /// Bounding box
    pub bounds: BoundingBox,
    /// Child entries
    pub entries: Vec<RTreeEntry>,
    /// Is leaf node
    pub is_leaf: bool,
}

/// R-tree entry
#[derive(Debug, Clone)]
pub struct RTreeEntry {
    /// Bounding box
    pub bounds: BoundingBox,
    /// Either point index (for leaf) or child node
    pub data: RTreeData,
}

/// R-tree data
#[derive(Debug, Clone)]
pub enum RTreeData {
    /// Point index (for leaf nodes)
    Point(usize),
    /// Child node (for internal nodes)
    Node(Box<RTreeNode>),
}

/// Grid-based spatial index
#[derive(Debug, Clone)]
pub struct GridIndex {
    /// Grid cells
    pub cells: Vec<Vec<GridCell>>,
    /// Grid resolution
    pub cell_size: f64,
    /// Grid bounds
    pub bounds: BoundingBox,
    /// Grid dimensions
    pub grid_width: usize,
    pub grid_height: usize,
}

/// Grid cell
#[derive(Debug, Clone)]
pub struct GridCell {
    /// Cell bounds
    pub bounds: BoundingBox,
    /// Points in this cell
    pub point_indices: Vec<usize>,
}

/// KD-tree spatial index (simplified)
#[derive(Debug, Clone)]
pub struct KDTree {
    /// Root node
    pub root: Option<KDTreeNode>,
    /// Dimensionality
    pub dimensions: usize,
}

/// KD-tree node
#[derive(Debug, Clone)]
pub struct KDTreeNode {
    /// Point index
    pub point_index: usize,
    /// Split dimension
    pub split_dim: usize,
    /// Left child
    pub left: Option<Box<KDTreeNode>>,
    /// Right child
    pub right: Option<Box<KDTreeNode>>,
}

impl SpatialDataStructure {
    pub fn new(
        points: Vec<SpatialPoint>,
        features: Array2<f64>,
        targets: Array1<f64>,
        coordinate_system: CoordinateSystem,
    ) -> Self {
        let bounding_box = BoundingBox::from_points(&points);

        Self {
            points,
            features,
            targets,
            coordinate_system,
            spatial_index: None,
            metadata: SpatialMetadata {
                bounding_box,
                resolution: None,
                clusters: Vec::new(),
                elevation_stats: None,
                autocorrelation_stats: None,
            },
        }
    }

    /// Build spatial index for fast queries
    pub fn build_spatial_index(&mut self, index_type: SpatialIndexType) -> Result<()> {
        match index_type {
            SpatialIndexType::Quadtree {
                max_depth,
                max_points_per_leaf,
            } => {
                let quadtree = self.build_quadtree(max_depth, max_points_per_leaf)?;
                self.spatial_index = Some(SpatialIndex::Quadtree(quadtree));
            }
            SpatialIndexType::Grid { cell_size } => {
                let grid = self.build_grid_index(cell_size)?;
                self.spatial_index = Some(SpatialIndex::Grid(grid));
            }
            SpatialIndexType::KDTree => {
                let kdtree = self.build_kdtree()?;
                self.spatial_index = Some(SpatialIndex::KDTree(kdtree));
            }
        }

        Ok(())
    }

    /// Build quadtree index
    fn build_quadtree(&self, max_depth: usize, max_points_per_leaf: usize) -> Result<QuadTree> {
        let point_indices: Vec<usize> = (0..self.points.len()).collect();
        let root = self.build_quadtree_node(
            &point_indices,
            self.metadata.bounding_box,
            0,
            max_depth,
            max_points_per_leaf,
        )?;

        Ok(QuadTree {
            root,
            max_depth,
            max_points_per_leaf,
        })
    }

    /// Build quadtree node recursively
    fn build_quadtree_node(
        &self,
        point_indices: &[usize],
        bounds: BoundingBox,
        depth: usize,
        max_depth: usize,
        max_points_per_leaf: usize,
    ) -> Result<QuadTreeNode> {
        if depth >= max_depth || point_indices.len() <= max_points_per_leaf {
            // Create leaf node
            return Ok(QuadTreeNode {
                bounds,
                points: point_indices.to_vec(),
                children: None,
                is_leaf: true,
                depth,
            });
        }

        // Split into quadrants
        let mid_x = (bounds.min_x + bounds.max_x) / 2.0;
        let mid_y = (bounds.min_y + bounds.max_y) / 2.0;

        let quadrants = [
            BoundingBox::new(bounds.min_x, mid_x, bounds.min_y, mid_y), // SW
            BoundingBox::new(mid_x, bounds.max_x, bounds.min_y, mid_y), // SE
            BoundingBox::new(bounds.min_x, mid_x, mid_y, bounds.max_y), // NW
            BoundingBox::new(mid_x, bounds.max_x, mid_y, bounds.max_y), // NE
        ];

        let mut children = Vec::new();

        for quadrant in &quadrants {
            let quadrant_points: Vec<usize> = point_indices
                .iter()
                .copied()
                .filter(|&idx| quadrant.contains(&self.points[idx]))
                .collect();

            let child = self.build_quadtree_node(
                &quadrant_points,
                *quadrant,
                depth + 1,
                max_depth,
                max_points_per_leaf,
            )?;

            children.push(Box::new(child));
        }

        if children.len() != 4 {
            return Err(SklearsError::InvalidInput(
                "Failed to create quadtree children".to_string(),
            ));
        }

        let children_array: [Box<QuadTreeNode>; 4] = [
            children.remove(0),
            children.remove(0),
            children.remove(0),
            children.remove(0),
        ];

        Ok(QuadTreeNode {
            bounds,
            points: Vec::new(),
            children: Some(children_array),
            is_leaf: false,
            depth,
        })
    }

    /// Build grid index
    fn build_grid_index(&self, cell_size: f64) -> Result<GridIndex> {
        let bounds = self.metadata.bounding_box;
        let grid_width = ((bounds.max_x - bounds.min_x) / cell_size).ceil() as usize;
        let grid_height = ((bounds.max_y - bounds.min_y) / cell_size).ceil() as usize;

        let mut cells = vec![
            vec![
                GridCell {
                    bounds: BoundingBox::new(0.0, 0.0, 0.0, 0.0),
                    point_indices: Vec::new(),
                };
                grid_height
            ];
            grid_width
        ];

        // Initialize cell bounds
        for i in 0..grid_width {
            for j in 0..grid_height {
                let min_x = bounds.min_x + i as f64 * cell_size;
                let max_x = min_x + cell_size;
                let min_y = bounds.min_y + j as f64 * cell_size;
                let max_y = min_y + cell_size;

                cells[i][j].bounds = BoundingBox::new(min_x, max_x, min_y, max_y);
            }
        }

        // Assign points to cells
        for (point_idx, point) in self.points.iter().enumerate() {
            let grid_x = ((point.x - bounds.min_x) / cell_size).floor() as usize;
            let grid_y = ((point.y - bounds.min_y) / cell_size).floor() as usize;

            if grid_x < grid_width && grid_y < grid_height {
                cells[grid_x][grid_y].point_indices.push(point_idx);
            }
        }

        Ok(GridIndex {
            cells,
            cell_size,
            bounds,
            grid_width,
            grid_height,
        })
    }

    /// Build KD-tree index
    fn build_kdtree(&self) -> Result<KDTree> {
        let mut point_indices: Vec<usize> = (0..self.points.len()).collect();
        let root = self.build_kdtree_node(&mut point_indices, 0, 2)?; // 2D for now

        Ok(KDTree {
            root,
            dimensions: 2,
        })
    }

    /// Build KD-tree node recursively
    fn build_kdtree_node(
        &self,
        point_indices: &mut [usize],
        depth: usize,
        dimensions: usize,
    ) -> Result<Option<KDTreeNode>> {
        if point_indices.is_empty() {
            return Ok(None);
        }

        if point_indices.len() == 1 {
            return Ok(Some(KDTreeNode {
                point_index: point_indices[0],
                split_dim: depth % dimensions,
                left: None,
                right: None,
            }));
        }

        let split_dim = depth % dimensions;

        // Sort points by the split dimension
        point_indices.sort_by(|&a, &b| {
            let coord_a = if split_dim == 0 {
                self.points[a].x
            } else {
                self.points[a].y
            };
            let coord_b = if split_dim == 0 {
                self.points[b].x
            } else {
                self.points[b].y
            };
            coord_a.partial_cmp(&coord_b).unwrap()
        });

        let median = point_indices.len() / 2;
        let median_index = point_indices[median];

        let left_child =
            self.build_kdtree_node(&mut point_indices[..median], depth + 1, dimensions)?;
        let right_child =
            self.build_kdtree_node(&mut point_indices[median + 1..], depth + 1, dimensions)?;

        Ok(Some(KDTreeNode {
            point_index: median_index,
            split_dim,
            left: left_child.map(Box::new),
            right: right_child.map(Box::new),
        }))
    }

    /// Calculate spatial autocorrelation
    pub fn calculate_spatial_autocorrelation(&mut self, lag_distance: f64) -> Result<()> {
        let n = self.points.len();
        let mut weights = Array2::zeros((n, n));

        // Create spatial weights matrix
        for i in 0..n {
            for j in 0..n {
                if i != j {
                    let distance = match self.coordinate_system {
                        CoordinateSystem::Geographic => {
                            self.points[i].haversine_distance(&self.points[j])
                        }
                        _ => self.points[i].distance(&self.points[j]),
                    };

                    if distance <= lag_distance {
                        weights[[i, j]] = 1.0 / distance.max(1e-8);
                    }
                }
            }
        }

        // Normalize weights
        for i in 0..n {
            let row_sum: f64 = weights.row(i).sum();
            if row_sum > 0.0 {
                for j in 0..n {
                    weights[[i, j]] /= row_sum;
                }
            }
        }

        // Calculate Moran's I
        let y_mean = self.targets.mean().unwrap_or(0.0);
        let mut numerator = 0.0;
        let mut denominator = 0.0;
        let mut w_sum = 0.0;

        for i in 0..n {
            for j in 0..n {
                let yi = self.targets[i] - y_mean;
                let yj = self.targets[j] - y_mean;
                numerator += weights[[i, j]] * yi * yj;
                w_sum += weights[[i, j]];
            }
            denominator += (self.targets[i] - y_mean).powi(2);
        }

        let morans_i = if denominator > 0.0 && w_sum > 0.0 {
            (n as f64 / w_sum) * (numerator / denominator)
        } else {
            0.0
        };

        // Calculate Geary's C (simplified)
        let mut geary_numerator = 0.0;
        for i in 0..n {
            for j in 0..n {
                geary_numerator += weights[[i, j]] * (self.targets[i] - self.targets[j]).powi(2);
            }
        }

        let gearys_c = if denominator > 0.0 && w_sum > 0.0 {
            ((n - 1) as f64 / (2.0 * w_sum)) * (geary_numerator / denominator)
        } else {
            1.0
        };

        // Calculate spatial lag
        let mut spatial_lag = Vec::with_capacity(n);
        for i in 0..n {
            let mut lag_value = 0.0;
            for j in 0..n {
                lag_value += weights[[i, j]] * self.targets[j];
            }
            spatial_lag.push(lag_value);
        }

        // Calculate LISA (Local Indicators of Spatial Association) - simplified
        let lisa: Vec<f64> = (0..n)
            .map(|i| {
                let yi = self.targets[i] - y_mean;
                let mut lisa_value = 0.0;
                for j in 0..n {
                    let yj = self.targets[j] - y_mean;
                    lisa_value += weights[[i, j]] * yj;
                }
                yi * lisa_value
            })
            .collect();

        self.metadata.autocorrelation_stats = Some(AutocorrelationStats {
            morans_i,
            gearys_c,
            lisa,
            spatial_lag,
        });

        Ok(())
    }

    /// Perform spatial clustering
    pub fn spatial_clustering(
        &mut self,
        n_clusters: usize,
        method: SpatialClusteringMethod,
    ) -> Result<()> {
        match method {
            SpatialClusteringMethod::KMeans => self.kmeans_clustering(n_clusters),
            SpatialClusteringMethod::DBSCAN { eps, min_points } => {
                self.dbscan_clustering(eps, min_points)
            }
            SpatialClusteringMethod::Hierarchical => self.hierarchical_clustering(n_clusters),
        }
    }

    /// K-means spatial clustering
    fn kmeans_clustering(&mut self, n_clusters: usize) -> Result<()> {
        // Simple K-means implementation
        let mut rng = scirs2_core::random::thread_rng();

        // Initialize centroids randomly
        let mut centroids: Vec<SpatialPoint> = Vec::new();
        for _ in 0..n_clusters {
            if !self.points.is_empty() {
                let idx = rng.gen_range(0..self.points.len());
                centroids.push(self.points[idx].clone());
            }
        }

        let mut clusters = vec![
            SpatialCluster {
                id: 0,
                center: SpatialPoint::new(0.0, 0.0),
                point_indices: Vec::new(),
                radius: 0.0,
            };
            n_clusters
        ];

        for iteration in 0..100 {
            // Max iterations
            // Assign points to nearest centroid
            for cluster in &mut clusters {
                cluster.point_indices.clear();
            }

            for (point_idx, point) in self.points.iter().enumerate() {
                let mut min_distance = f64::INFINITY;
                let mut closest_cluster = 0;

                for (cluster_idx, centroid) in centroids.iter().enumerate() {
                    let distance = point.distance(centroid);
                    if distance < min_distance {
                        min_distance = distance;
                        closest_cluster = cluster_idx;
                    }
                }

                clusters[closest_cluster].point_indices.push(point_idx);
            }

            // Update centroids
            let mut converged = true;
            for (cluster_idx, cluster) in clusters.iter_mut().enumerate() {
                if cluster.point_indices.is_empty() {
                    continue;
                }

                let mut sum_x = 0.0;
                let mut sum_y = 0.0;
                for &point_idx in &cluster.point_indices {
                    sum_x += self.points[point_idx].x;
                    sum_y += self.points[point_idx].y;
                }

                let new_centroid = SpatialPoint::new(
                    sum_x / cluster.point_indices.len() as f64,
                    sum_y / cluster.point_indices.len() as f64,
                );

                if centroids[cluster_idx].distance(&new_centroid) > 1e-6 {
                    converged = false;
                }

                centroids[cluster_idx] = new_centroid;
                cluster.center = new_centroid;
                cluster.id = cluster_idx;

                // Calculate cluster radius
                let mut max_distance: f64 = 0.0;
                for &point_idx in &cluster.point_indices {
                    let distance = self.points[point_idx].distance(&cluster.center);
                    max_distance = max_distance.max(distance);
                }
                cluster.radius = max_distance;
            }

            if converged {
                break;
            }
        }

        self.metadata.clusters = clusters;
        Ok(())
    }

    /// DBSCAN spatial clustering
    fn dbscan_clustering(&mut self, eps: f64, min_points: usize) -> Result<()> {
        let n = self.points.len();
        let mut cluster_labels = vec![-1i32; n]; // -1 means unassigned
        let mut cluster_id = 0i32;

        for i in 0..n {
            if cluster_labels[i] != -1 {
                continue; // Already processed
            }

            // Find neighbors
            let neighbors = self.find_neighbors(i, eps);

            if neighbors.len() < min_points {
                cluster_labels[i] = -2; // Mark as noise
                continue;
            }

            // Start new cluster
            cluster_labels[i] = cluster_id;
            let mut seed_set = VecDeque::from(neighbors);

            while let Some(neighbor) = seed_set.pop_front() {
                if cluster_labels[neighbor] == -2 {
                    cluster_labels[neighbor] = cluster_id; // Change noise to border point
                } else if cluster_labels[neighbor] == -1 {
                    cluster_labels[neighbor] = cluster_id;
                    let neighbor_neighbors = self.find_neighbors(neighbor, eps);
                    if neighbor_neighbors.len() >= min_points {
                        seed_set.extend(neighbor_neighbors);
                    }
                }
            }

            cluster_id += 1;
        }

        // Convert to cluster structure
        let mut clusters = HashMap::new();
        for (point_idx, &label) in cluster_labels.iter().enumerate() {
            if label >= 0 {
                clusters
                    .entry(label as usize)
                    .or_insert_with(Vec::new)
                    .push(point_idx);
            }
        }

        self.metadata.clusters = clusters
            .into_iter()
            .map(|(id, point_indices)| {
                // Calculate cluster center
                let mut sum_x = 0.0;
                let mut sum_y = 0.0;
                for &point_idx in &point_indices {
                    sum_x += self.points[point_idx].x;
                    sum_y += self.points[point_idx].y;
                }
                let center = SpatialPoint::new(
                    sum_x / point_indices.len() as f64,
                    sum_y / point_indices.len() as f64,
                );

                // Calculate radius
                let mut max_distance: f64 = 0.0;
                for &point_idx in &point_indices {
                    let distance = self.points[point_idx].distance(&center);
                    max_distance = max_distance.max(distance);
                }

                SpatialCluster {
                    id,
                    center,
                    point_indices,
                    radius: max_distance,
                }
            })
            .collect();

        Ok(())
    }

    /// Find neighbors within distance
    fn find_neighbors(&self, point_idx: usize, eps: f64) -> Vec<usize> {
        let mut neighbors = Vec::new();
        let query_point = &self.points[point_idx];

        for (i, point) in self.points.iter().enumerate() {
            if i != point_idx && query_point.distance(point) <= eps {
                neighbors.push(i);
            }
        }

        neighbors
    }

    /// Hierarchical spatial clustering (simplified)
    fn hierarchical_clustering(&mut self, n_clusters: usize) -> Result<()> {
        // This is a placeholder for hierarchical clustering
        // In practice, you'd implement a proper hierarchical clustering algorithm
        self.kmeans_clustering(n_clusters)
    }
}

/// Spatial index types
#[derive(Debug, Clone)]
pub enum SpatialIndexType {
    /// Quadtree index
    Quadtree {
        max_depth: usize,
        max_points_per_leaf: usize,
    },
    /// Grid-based index
    Grid { cell_size: f64 },
    /// KD-tree index
    KDTree,
}

/// Spatial clustering methods
#[derive(Debug, Clone)]
pub enum SpatialClusteringMethod {
    /// K-means clustering
    KMeans,
    /// DBSCAN clustering
    DBSCAN { eps: f64, min_points: usize },
    /// Hierarchical clustering
    Hierarchical,
}

/// Spatial Decision Tree for geographic data
pub struct SpatialDecisionTree<State = Untrained> {
    /// Base tree configuration
    config: DecisionTreeConfig,
    /// Spatial-specific configuration
    spatial_config: SpatialTreeConfig,
    /// Spatial data structure
    spatial_data: Option<SpatialDataStructure>,
    /// Trained model
    model: Option<Box<dyn SpatialTreeModel>>,
    /// State marker
    state: PhantomData<State>,
}

/// Configuration for spatial decision trees
#[derive(Debug, Clone)]
pub struct SpatialTreeConfig {
    /// Spatial split criterion
    pub split_criterion: SpatialSplitCriterion,
    /// Coordinate system
    pub coordinate_system: CoordinateSystem,
    /// Enable spatial autocorrelation consideration
    pub consider_autocorrelation: bool,
    /// Spatial regularization strength
    pub spatial_regularization: f64,
    /// Minimum spatial cluster size
    pub min_spatial_cluster_size: usize,
}

impl Default for SpatialTreeConfig {
    fn default() -> Self {
        Self {
            split_criterion: SpatialSplitCriterion::Standard(SplitCriterion::MSE),
            coordinate_system: CoordinateSystem::Geographic,
            consider_autocorrelation: false,
            spatial_regularization: 0.01,
            min_spatial_cluster_size: 5,
        }
    }
}

/// Trait for spatial tree models
pub trait SpatialTreeModel: Send + Sync {
    /// Predict at spatial locations
    fn predict(&self, points: &[SpatialPoint], features: &Array2<f64>) -> Result<Array1<f64>>;

    /// Update model with new spatial data
    fn update(&mut self, spatial_data: &SpatialDataStructure) -> Result<()>;

    /// Get spatial feature importance
    fn get_spatial_importance(&self) -> Result<HashMap<String, f64>>;
}
