Start to implement clustering big assignment

src/k_clustering.rs

@@ -27,12 +27,12 @@ pub fn k_clustering(graph: &mut WeightedGraph) -> usize {
         let cluster_id_b = node_id_to_cluster_id[edge.target_id];
 
         if cluster_id_a != cluster_id_b {
-	        let mut cluster_b = std::mem::take(&mut clusters[cluster_id_b]);
-	        for node_id in &cluster_b {
-	        	node_id_to_cluster_id[*node_id] = cluster_id_a;
-	        }
-	        clusters[cluster_id_a].append(&mut cluster_b);
-	        cluster_count -= 1;
+            let mut cluster_b = std::mem::take(&mut clusters[cluster_id_b]);
+            for node_id in &cluster_b {
+                node_id_to_cluster_id[*node_id] = cluster_id_a;
+            }
+            clusters[cluster_id_a].append(&mut cluster_b);
+            cluster_count -= 1;
         }
 
         if cluster_count <= k {
@@ -45,9 +45,9 @@ pub fn k_clustering(graph: &mut WeightedGraph) -> usize {
         let cluster_id_a = node_id_to_cluster_id[edge.source_id];
         let cluster_id_b = node_id_to_cluster_id[edge.target_id];
         if cluster_id_a != cluster_id_b {
-        	if edge.weight < min_spacing {
-        		min_spacing = edge.weight;
-        	}
+            if edge.weight < min_spacing {
+                min_spacing = edge.weight;
+            }
         }
     }
 

src/k_clustering_big.rs

@@ -0,0 +1,19 @@
+pub struct ImpliciteGraph {
+    pub nodes: Vec<Vec<usize>>,
+}
+
+fn distance(a: &Vec<usize>, b: &Vec<usize>) -> u16 {
+    let mut distance = 0;
+
+    for i in 0..a.len() {
+        if a[i] != b[i] {
+            distance += 1;
+        }
+    }
+    distance
+}
+
+pub fn k_clustering_big(g: &ImpliciteGraph) -> usize {
+    println!("distance: {:?}", distance(&g.nodes[0], &g.nodes[1]));
+    g.nodes.len()
+}

src/main.rs

@@ -2,6 +2,7 @@ mod dijkstra;
 mod heap;
 mod jobs;
 mod k_clustering;
+mod k_clustering_big;
 mod merge_sort;
 mod min_cut;
 mod prims;
@@ -14,6 +15,7 @@ use crate::dijkstra::dijkstra;
 use crate::heap::heap;
 use crate::jobs::{jobs_1, jobs_2};
 use crate::k_clustering::k_clustering;
+use crate::k_clustering_big::k_clustering_big;
 use crate::merge_sort::merge_sort_inversions;
 use crate::min_cut::min_cut;
 use crate::prims::prims;
@@ -108,6 +110,9 @@ fn c3a2() {
     let mut graph = util::read_weighted_graph_clustering("data/c3a2_clustering.txt").unwrap();
     let r1 = k_clustering(&mut graph);
     println!("r1 = {:?}", r1);
+    let graph = util::read_k_cluster_big("data/c3a2_clustering_big.txt").unwrap();
+    let r2 = k_clustering_big(&graph);
+    println!("r2 = {:?}", r2);
 }
 
 fn main() {

src/util.rs

@@ -1,5 +1,6 @@
 use crate::jobs;
 use crate::k_clustering;
+use crate::k_clustering_big;
 use crate::min_cut;
 use crate::prims;
 use crate::ssc;
@@ -222,3 +223,31 @@ pub fn read_weighted_graph_clustering(
 
     Ok(g)
 }
+
+pub fn read_k_cluster_big(path: &str) -> Result<k_clustering_big::ImpliciteGraph, io::Error> {
+    let file = File::open(path)?;
+    let mut lines = BufReader::new(file).lines();
+    let line = lines.next().unwrap().unwrap();
+
+    let mut fields = line.split_whitespace();
+    let total_nodes: usize = fields.next().unwrap().parse().unwrap();
+    let bits_per_node: usize = fields.next().unwrap().parse().unwrap();
+
+    println!(
+        "total_nodes = {:?}\nbits_per_node = {:?}",
+        total_nodes, bits_per_node
+    );
+
+    let mut g = k_clustering_big::ImpliciteGraph { nodes: vec![] };
+
+    for line in lines {
+        let line = line?;
+        let v: Vec<usize> = line
+            .split_whitespace()
+            .map(|s| s.parse().unwrap())
+            .collect();
+        g.nodes.push(v);
+    }
+
+    Ok(g)
+}