// DBSCAN 聚类 — 纯 Rust 手写实现
// 深度整合步骤 1：发现新主题和重复模式

use serde::{Deserialize, Serialize};

/// 聚类结果
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ClusteringResult {
    pub num_clusters: usize,
    pub num_noise: usize,
    pub cluster_sizes: Vec<usize>,
    pub labels: Vec<i32>,
}

/// DBSCAN 聚类器
pub struct Clusterer {
    epsilon: f64,
    min_points: usize,
}

impl Clusterer {
    pub fn new(epsilon: f64, min_points: usize) -> Self {
        Self { epsilon, min_points }
    }

    pub fn cluster(
        &self,
        vectors: &[Vec<f32>],
        _ids: &[String],
    ) -> Result<ClusteringResult, Box<dyn std::error::Error>> {
        if vectors.is_empty() {
            return Ok(ClusteringResult {
                num_clusters: 0, num_noise: 0,
                cluster_sizes: vec![],
                labels: vec![],
            });
        }

        let n = vectors.len();
        let mut labels = vec![-1_i32; n];
        let mut cluster_id = 0;

        // 朴� DBSCAN
        for i in 0..n {
            if labels[i] != -1 {
                continue;
            }
            let neighbors = self.region_query(vectors, i);
            if neighbors.len() < self.min_points {
                labels[i] = -1; // noise for now
                continue;
            }
            // 扩展簇
            labels[i] = cluster_id;
            let mut seed = neighbors;
            let mut idx = 0;
            while idx < seed.len() {
                let j = seed[idx];
                idx += 1;
                if labels[j] == -1 {
                    labels[j] = cluster_id;
                    let nbrs = self.region_query(vectors, j);
                    if nbrs.len() >= self.min_points {
                        for k in nbrs {
                            if labels[k] == -1 || labels[k] == -1 {
                                if labels[k] == -1 {
                                    seed.push(k);
                                }
                                labels[k] = cluster_id;
                            }
                        }
                    }
                }
            }
            cluster_id += 1;
        }

        let num_clusters = cluster_id as usize;
        let mut noise_count = 0_usize;
        let mut cluster_sizes = vec![0_usize; num_clusters];
        for &label in &labels {
            if label == -1 {
                noise_count += 1;
            } else {
                cluster_sizes[label as usize] += 1;
            }
        }

        eprintln!(
            "[cluster] DBSCAN eps={} min_pts={} → {} clusters + {} noise from {} items",
            self.epsilon, self.min_points, num_clusters, noise_count, n
        );

        Ok(ClusteringResult {
            num_clusters,
            num_noise: noise_count,
            cluster_sizes,
            labels,
        })
    }

    fn region_query(&self, vectors: &[Vec<f32>], idx: usize) -> Vec<usize> {
        let mut neighbors = Vec::new();
        let target = &vectors[idx];
        for (i, v) in vectors.iter().enumerate() {
            if i != idx && euclidean_sq(target, v) <= self.epsilon * self.epsilon {
                neighbors.push(i);
            }
        }
        neighbors
    }

    pub fn extract_centroids(
        vectors: &[Vec<f32>],
        labels: &[i32],
        num_clusters: usize,
    ) -> Vec<Vec<f32>> {
        let dim = vectors.first().map(|v| v.len()).unwrap_or(1024);
        let mut centroids = vec![vec![0.0_f32; dim]; num_clusters];
        let mut counts = vec![0_usize; num_clusters];
        for (vec, &label) in vectors.iter().zip(labels.iter()) {
            if label >= 0 {
                let ci = label as usize;
                if ci < num_clusters {
                    for (j, &val) in vec.iter().enumerate() {
                        centroids[ci][j] += val;
                    }
                    counts[ci] += 1;
                }
            }
        }
        for ci in 0..num_clusters {
            if counts[ci] > 0 {
                let inv = 1.0 / counts[ci] as f32;
                for val in centroids[ci].iter_mut() {
                    *val *= inv;
                }
            }
        }
        centroids
    }

    pub fn find_duplicates(centroids: &[Vec<f32>], cluster_sizes: &[usize]) -> Vec<(usize, usize)> {
        let mut pairs = Vec::new();
        for i in 0..centroids.len() {
            if cluster_sizes[i] < 2 { continue; }
            for j in (i + 1)..centroids.len() {
                if cluster_sizes[j] < 2 { continue; }
                let sim = cosine_similarity(&centroids[i], &centroids[j]);
                if sim > 0.95 {
                    pairs.push((i, j));
                }
            }
        }
        eprintln!("[cluster] found {} duplicate pairs (sim > 0.95)", pairs.len());
        pairs
    }
}

fn euclidean_sq(a: &[f32], b: &[f32]) -> f64 {
    let n = a.len().min(b.len());
    let mut sum = 0.0_f64;
    for i in 0..n {
        let diff = a[i] as f64 - b[i] as f64;
        sum += diff * diff;
    }
    sum
}

fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
    let n = a.len().min(b.len());
    let (mut dot, mut na, mut nb) = (0.0_f32, 0.0_f32, 0.0_f32);
    for i in 0..n {
        dot += a[i] * b[i];
        na += a[i] * a[i];
        nb += b[i] * b[i];
    }
    (dot / (na.sqrt() * nb.sqrt().max(1e-10))).max(0.0)
}