diff --git a/src/bin/plain.rs b/src/bin/plain.rs
index 9bb62bd..d2f76c2 100644
--- a/src/bin/plain.rs
+++ b/src/bin/plain.rs
@@ -1,6 +1,9 @@
+#![allow(dead_code)]
 use anyhow::Result;
 use clap::Parser;
+use rand::Rng;
 use serde::{Deserialize, Serialize};
+use std::cmp::Ordering;
 use std::fs::File;
 use std::io::{BufRead, BufReader, Write};
 
@@ -8,9 +11,9 @@ use std::io::{BufRead, BufReader, Write};
 #[command(name = "hfe_knn")]
 #[command(about = "FHE-based KNN classifier")]
 struct Args {
-    #[arg(long)]
+    #[arg(long, default_value = "./dataset/train.jsonl")]
     dataset: String,
-    #[arg(long)]
+    #[arg(long, default_value = "./dataset/answer1.jsonl")]
     predictions: String,
 }
 
@@ -45,27 +48,268 @@ fn knn_classify(query: &[f64], data: &[Vec<f64>], k: usize) -> Vec<usize> {
     distances.into_iter().take(k).map(|(_, idx)| idx).collect()
 }
 
+#[derive(Clone)]
+struct HNSWNode {
+    vector: Vec<f64>,
+    level: usize,
+    neighbors: Vec<Vec<usize>>,
+}
+
+#[derive(Clone)]
+struct HNSWGraph {
+    nodes: Vec<HNSWNode>,
+    entry_point: Option<usize>,
+    max_level: usize,
+    max_connections: usize,
+}
+
+#[derive(Debug, Clone, Copy, PartialEq)]
+struct OrderedFloat(f64);
+
+impl Eq for OrderedFloat {}
+
+impl PartialOrd for OrderedFloat {
+    fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
+        Some(self.cmp(other))
+    }
+}
+
+impl Ord for OrderedFloat {
+    fn cmp(&self, other: &Self) -> Ordering {
+        self.0.partial_cmp(&other.0).unwrap_or(Ordering::Equal)
+    }
+}
+
+impl HNSWGraph {
+    fn new() -> Self {
+        Self {
+            nodes: Vec::new(),
+            entry_point: None,
+            max_level: 3,
+            max_connections: 16,
+        }
+    }
+
+    fn insert_node(&mut self, vector: Vec<f64>) -> usize {
+        let level = self.select_level();
+        let node_id = self.nodes.len();
+
+        let node = HNSWNode {
+            vector,
+            level,
+            neighbors: vec![Vec::new(); level + 1],
+        };
+
+        // 先添加节点到向量中
+        self.nodes.push(node);
+
+        if node_id == 0 {
+            self.entry_point = Some(node_id);
+            return node_id;
+        }
+
+        let mut current_candidates = vec![self.entry_point.unwrap()];
+
+        // 从最高层搜索到目标层+1
+        for lc in (level + 1..=self.max_level).rev() {
+            current_candidates =
+                self.search_layer(&self.nodes[node_id].vector, current_candidates, 1, lc);
+        }
+
+        // 从目标层到第0层建立连接
+        for lc in (0..=level).rev() {
+            current_candidates = self.search_layer(
+                &self.nodes[node_id].vector,
+                current_candidates,
+                self.max_connections,
+                lc,
+            );
+
+            for &candidate_id in &current_candidates {
+                self.connect_nodes(node_id, candidate_id, lc);
+            }
+        }
+
+        // 更新入口点
+        if level > self.nodes[self.entry_point.unwrap()].level {
+            self.entry_point = Some(node_id);
+        }
+
+        node_id
+    }
+
+    fn search_layer(
+        &self,
+        query: &[f64],
+        entry_points: Vec<usize>,
+        ef: usize,
+        layer: usize,
+    ) -> Vec<usize> {
+        let mut visited = std::collections::HashSet::new();
+        let mut candidates = std::collections::BinaryHeap::new();
+        let mut w = std::collections::BinaryHeap::new();
+
+        // 初始化候选点
+        for &ep in &entry_points {
+            if ep < self.nodes.len() && self.nodes[ep].level >= layer {
+                let dist = euclidean_distance(query, &self.nodes[ep].vector);
+                candidates.push(std::cmp::Reverse((OrderedFloat(dist), ep)));
+                w.push((OrderedFloat(dist), ep));
+                visited.insert(ep);
+            }
+        }
+
+        while let Some(std::cmp::Reverse((current_dist, current))) = candidates.pop() {
+            // 如果当前距离已经比最远的结果距离大，停止搜索
+            if let Some(&(farthest_dist, _)) = w.iter().max() {
+                if current_dist > farthest_dist && w.len() >= ef {
+                    break;
+                }
+            }
+
+            // 探索当前节点的邻居
+            if current < self.nodes.len() && layer < self.nodes[current].neighbors.len() {
+                for &neighbor in &self.nodes[current].neighbors[layer] {
+                    if !visited.contains(&neighbor) && neighbor < self.nodes.len() {
+                        visited.insert(neighbor);
+                        let dist = euclidean_distance(query, &self.nodes[neighbor].vector);
+                        let ordered_dist = OrderedFloat(dist);
+
+                        if w.len() < ef {
+                            candidates.push(std::cmp::Reverse((ordered_dist, neighbor)));
+                            w.push((ordered_dist, neighbor));
+                        } else if let Some(&(farthest_dist, _)) = w.iter().max() {
+                            if ordered_dist < farthest_dist {
+                                candidates.push(std::cmp::Reverse((ordered_dist, neighbor)));
+                                w.push((ordered_dist, neighbor));
+
+                                // 移除最远的点
+                                if let Some(max_item) = w.iter().max().copied() {
+                                    w.retain(|&x| x != max_item);
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+
+        // 返回按距离排序的结果
+        let mut results: Vec<_> = w.into_iter().collect();
+        results.sort_by(|a, b| a.0.cmp(&b.0));
+        results.into_iter().map(|(_, idx)| idx).collect()
+    }
+
+    fn select_level(&self) -> usize {
+        let mut rng = rand::rng();
+        let mut level = 0;
+        while level < self.max_level && rng.random::<f32>() < 0.5 {
+            level += 1;
+        }
+        level
+    }
+
+    fn connect_nodes(&mut self, node1: usize, node2: usize, layer: usize) {
+        self.nodes[node1].neighbors[layer].push(node2);
+        self.nodes[node2].neighbors[layer].push(node1);
+
+        if self.nodes[node1].neighbors[layer].len() > self.max_connections {
+            self.prune_node_connections(node1, layer);
+        }
+        if self.nodes[node2].neighbors[layer].len() > self.max_connections {
+            self.prune_node_connections(node2, layer);
+        }
+    }
+
+    fn prune_node_connections(&mut self, node_id: usize, _layer: usize) {
+        let node_vector = self.nodes[node_id].vector.clone();
+        let nodes = self.nodes.clone(); // Create immutable reference before mutable borrow
+
+        // 计算所有邻居的距离并排序
+        let neighbors = &mut self.nodes[node_id].neighbors[_layer];
+        let mut neighbor_distances: Vec<(f64, usize)> = neighbors
+            .iter()
+            .map(|&neighbor_id| {
+                let dist = euclidean_distance(&node_vector, &nodes[neighbor_id].vector);
+                (dist, neighbor_id)
+            })
+            .collect();
+
+        // 按距离排序
+        neighbor_distances.sort_by(|a, b| a.0.partial_cmp(&b.0).unwrap());
+
+        // 更新邻居列表，只保留距离最近的
+        *neighbors = neighbor_distances
+            .into_iter()
+            .take(self.max_connections)
+            .map(|(_, neighbor_id)| neighbor_id)
+            .collect();
+    }
+}
+
+// fn main() -> Result<()> {
+//     let args = Args::parse();
+//
+//     let file = File::open(&args.dataset)?;
+//     let reader = BufReader::new(file);
+//
+//     let mut results = Vec::new();
+//
+//     for line in reader.lines() {
+//         let line = line?;
+//         let dataset: Dataset = serde_json::from_str(&line)?;
+//
+//         let nearest = knn_classify(&dataset.query, &dataset.data, 10);
+//         results.push(Prediction { answer: nearest });
+//     }
+//
+//     let mut output_file = File::create(&args.predictions)?;
+//     for result in results {
+//         writeln!(output_file, "{}", serde_json::to_string(&result)?)?;
+//     }
+//
+//     Ok(())
+// }
 fn main() -> Result<()> {
     let args = Args::parse();
-
     let file = File::open(&args.dataset)?;
     let reader = BufReader::new(file);
-
     let mut results = Vec::new();
 
     for line in reader.lines() {
         let line = line?;
         let dataset: Dataset = serde_json::from_str(&line)?;
 
-        let nearest = knn_classify(&dataset.query, &dataset.data, 10);
+        let mut hnsw = HNSWGraph::new();
+        for data_point in &dataset.data {
+            hnsw.insert_node(data_point.clone());
+        }
+
+        let nearest = if let Some(entry_point) = hnsw.entry_point {
+            let mut search_results = vec![entry_point];
+
+            for layer in (1..=hnsw.nodes[entry_point].level).rev() {
+                search_results = hnsw.search_layer(&dataset.query, search_results, 1, layer);
+            }
+
+            let final_results = hnsw.search_layer(&dataset.query, search_results, 16, 0);
+            final_results
+                .into_iter()
+                .take(10)
+                .map(|idx| idx + 1)
+                .collect()
+        } else {
+            Vec::new()
+        };
+
         results.push(Prediction { answer: nearest });
     }
 
     let mut output_file = File::create(&args.predictions)?;
     for result in results {
         writeln!(output_file, "{}", serde_json::to_string(&result)?)?;
+        println!("{}", serde_json::to_string(&result)?);
     }
 
     Ok(())
 }
-
diff --git a/testhnsw.py b/testhnsw.py
new file mode 100644
index 0000000..83a9aa5
--- /dev/null
+++ b/testhnsw.py
@@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+import subprocess
+import json
+from collections import Counter
+
+
+def load_answers(filepath):
+    with open(filepath, "r") as f:
+        data = json.load(f)
+        return data["answer"]
+
+
+def run_plain_binary():
+    result = subprocess.run(
+        ["cargo", "r", "-r", "--bin", "plain"], capture_output=True, text=True, cwd="."
+    )
+    if result.returncode == 0:
+        # The program outputs the same results as answer1.jsonl
+        return load_answers("dataset/answer1.jsonl")
+    return None
+
+
+def compare_answers(predictions, ground_truth):
+    if not predictions or len(predictions) != len(ground_truth):
+        return 0
+    return sum(1 for p, gt in zip(predictions, ground_truth) if p == gt)
+
+
+def main():
+    ground_truth = load_answers("dataset/answer.jsonl")
+
+    num_runs = 100
+    accuracies = []
+
+    for i in range(num_runs):
+        predictions = run_plain_binary()
+        if predictions is not None:
+            accuracy = compare_answers(predictions, ground_truth)
+            accuracies.append(accuracy)
+
+    print(f"\nResults ({len(accuracies)} runs):")
+    print(
+        f"Min: {min(accuracies)}, Max: {max(accuracies)}, Mean: {sum(accuracies)/len(accuracies):.2f}"
+    )
+
+    counter = Counter(accuracies)
+    print("Distribution:")
+    for correct_count in sorted(counter.keys()):
+        print(f"  {correct_count} correct: {counter[correct_count]} times")
+
+
+if __name__ == "__main__":
+    main()