Apply cargo fmt

1 files changed, 346 insertions, 348 deletions

diff --git a/src/util/bipartite.rs b/src/util/bipartite.rs
index aec7b042..ade831a4 100644
--- a/src/util/bipartite.rs
+++ b/src/util/bipartite.rs
@@ -1,378 +1,376 @@
 /*
- * This module deals with graph algorithm in complete bipartite 
+ * This module deals with graph algorithm in complete bipartite
  * graphs. It is used in layout.rs to build the partition to node
  * assignation.
  * */
 
-use std::cmp::{min,max};
-use std::collections::VecDeque;
 use rand::prelude::SliceRandom;
+use std::cmp::{max, min};
+use std::collections::VecDeque;
 
 //Graph data structure for the flow algorithm.
-#[derive(Clone,Copy,Debug)]
-struct EdgeFlow{
-    c : i32,
-    flow : i32,
-    v : usize,
-    rev : usize,
+#[derive(Clone, Copy, Debug)]
+struct EdgeFlow {
+	c: i32,
+	flow: i32,
+	v: usize,
+	rev: usize,
 }
 
 //Graph data structure for the detection of positive cycles.
-#[derive(Clone,Copy,Debug)]
-struct WeightedEdge{
-    w : i32, 
-    u : usize,
-    v : usize,
+#[derive(Clone, Copy, Debug)]
+struct WeightedEdge {
+	w: i32,
+	u: usize,
+	v: usize,
 }
 
-
-/* This function takes two matchings (old_match and new_match) in a 
- * complete bipartite graph. It returns a matching that has the 
+/* This function takes two matchings (old_match and new_match) in a
+ * complete bipartite graph. It returns a matching that has the
  * same degree as new_match at every vertex, and that is as close
  * as possible to old_match.
  * */
-pub fn optimize_matching( old_match : &Vec<Vec<usize>> , 
-                          new_match : &Vec<Vec<usize>> , 
-                          nb_right : usize )
-                                    -> Vec<Vec<usize>> {
-    let nb_left = old_match.len();
-    let ed = WeightedEdge{w:-1,u:0,v:0};
-    let mut edge_vec = vec![ed ; nb_left*nb_right];
-    
-    //We build the complete bipartite graph structure, represented
-    //by the list of all edges.
-    for i in 0..nb_left {
-        for j in 0..nb_right{
-            edge_vec[i*nb_right + j].u = i;
-            edge_vec[i*nb_right + j].v = nb_left+j;
-        }
-    }
-
-    for i in 0..edge_vec.len() {
-        //We add the old matchings
-        if old_match[edge_vec[i].u].contains(&(edge_vec[i].v-nb_left)) {
-            edge_vec[i].w *= -1;
-        }
-        //We add the new matchings
-        if new_match[edge_vec[i].u].contains(&(edge_vec[i].v-nb_left)) {
-            (edge_vec[i].u,edge_vec[i].v) = 
-                            (edge_vec[i].v,edge_vec[i].u);
-            edge_vec[i].w *= -1;
-        }
-    }
-    //Now edge_vec is a graph where edges are oriented LR if we
-    //can add them to new_match, and RL otherwise. If 
-    //adding/removing them makes the matching closer to old_match
-    //they have weight 1; and -1 otherwise.
-    
-    //We shuffle the edge list so that there is no bias depending in
-    //partitions/zone label in the triplet dispersion 
-    let mut rng = rand::thread_rng(); 
-    edge_vec.shuffle(&mut rng);
-    
-    //Discovering and flipping a cycle with positive weight in this
-    //graph will make the matching closer to old_match.
-    //We use Bellman Ford algorithm to discover positive cycles 
-    loop{
-        if let Some(cycle) = positive_cycle(&edge_vec, nb_left, nb_right) {
-            for i in cycle {
-                //We flip the edges of the cycle.
-                (edge_vec[i].u,edge_vec[i].v) = 
-                            (edge_vec[i].v,edge_vec[i].u);
-                edge_vec[i].w *= -1;        
-            }
-        }
-        else {
-            //If there is no cycle, we return the optimal matching.
-            break;
-        }
-    }
-    
-    //The optimal matching is build from the graph structure.
-    let mut matching = vec![Vec::<usize>::new() ; nb_left];
-    for e in edge_vec {
-        if e.u > e.v {
-            matching[e.v].push(e.u-nb_left);
-        }
-    }
-    matching
+pub fn optimize_matching(
+	old_match: &Vec<Vec<usize>>,
+	new_match: &Vec<Vec<usize>>,
+	nb_right: usize,
+) -> Vec<Vec<usize>> {
+	let nb_left = old_match.len();
+	let ed = WeightedEdge { w: -1, u: 0, v: 0 };
+	let mut edge_vec = vec![ed; nb_left * nb_right];
+
+	//We build the complete bipartite graph structure, represented
+	//by the list of all edges.
+	for i in 0..nb_left {
+		for j in 0..nb_right {
+			edge_vec[i * nb_right + j].u = i;
+			edge_vec[i * nb_right + j].v = nb_left + j;
+		}
+	}
+
+	for i in 0..edge_vec.len() {
+		//We add the old matchings
+		if old_match[edge_vec[i].u].contains(&(edge_vec[i].v - nb_left)) {
+			edge_vec[i].w *= -1;
+		}
+		//We add the new matchings
+		if new_match[edge_vec[i].u].contains(&(edge_vec[i].v - nb_left)) {
+			(edge_vec[i].u, edge_vec[i].v) = (edge_vec[i].v, edge_vec[i].u);
+			edge_vec[i].w *= -1;
+		}
+	}
+	//Now edge_vec is a graph where edges are oriented LR if we
+	//can add them to new_match, and RL otherwise. If
+	//adding/removing them makes the matching closer to old_match
+	//they have weight 1; and -1 otherwise.
+
+	//We shuffle the edge list so that there is no bias depending in
+	//partitions/zone label in the triplet dispersion
+	let mut rng = rand::thread_rng();
+	edge_vec.shuffle(&mut rng);
+
+	//Discovering and flipping a cycle with positive weight in this
+	//graph will make the matching closer to old_match.
+	//We use Bellman Ford algorithm to discover positive cycles
+	loop {
+		if let Some(cycle) = positive_cycle(&edge_vec, nb_left, nb_right) {
+			for i in cycle {
+				//We flip the edges of the cycle.
+				(edge_vec[i].u, edge_vec[i].v) = (edge_vec[i].v, edge_vec[i].u);
+				edge_vec[i].w *= -1;
+			}
+		} else {
+			//If there is no cycle, we return the optimal matching.
+			break;
+		}
+	}
+
+	//The optimal matching is build from the graph structure.
+	let mut matching = vec![Vec::<usize>::new(); nb_left];
+	for e in edge_vec {
+		if e.u > e.v {
+			matching[e.v].push(e.u - nb_left);
+		}
+	}
+	matching
 }
 
 //This function finds a positive cycle in a bipartite wieghted graph.
-fn positive_cycle( edge_vec : &Vec<WeightedEdge>, nb_left : usize,
-                   nb_right : usize) -> Option<Vec<usize>> {
-    let nb_side_min = min(nb_left, nb_right);
-    let nb_vertices = nb_left+nb_right;
-    let weight_lowerbound = -((nb_left +nb_right) as i32) -1; 
-    let mut accessed = vec![false ; nb_left];
-   
-    //We try to find a positive cycle accessible from the left 
-    //vertex i.
-    for i in 0..nb_left{
-        if accessed[i] {
-            continue;
-        }
-        let mut weight =vec![weight_lowerbound ; nb_vertices];
-        let mut prev =vec![ edge_vec.len() ; nb_vertices];
-        weight[i] = 0;
-        //We compute largest weighted paths from i.
-        //Since the graph is bipartite, any simple cycle has length
-        //at most 2*nb_side_min. In the general Bellman-Ford 
-        //algorithm, the bound here is the number of vertices. Since
-        //the number of partitions can be much larger than the 
-        //number of nodes, we optimize that.
-        for _ in 0..(2*nb_side_min) {
-            for j in 0..edge_vec.len() {
-                let e = edge_vec[j];
-                if weight[e.v] < weight[e.u]+e.w {
-                    weight[e.v] = weight[e.u]+e.w;
-                    prev[e.v] = j;
-                }
-            }
-        }
-        //We update the accessed table
-        for i in 0..nb_left {
-            if weight[i] > weight_lowerbound {
-                accessed[i] = true;
-            }
-        }
-        //We detect positive cycle
-        for e in edge_vec {
-            if weight[e.v] < weight[e.u]+e.w {
-        //it means e is on a path branching from a positive cycle
-                let mut was_seen = vec![false ; nb_vertices];
-                let mut curr = e.u;
-                //We track back with prev until we reach the cycle.
-                while !was_seen[curr]{
-                    was_seen[curr] = true;
-                    curr = edge_vec[prev[curr]].u;
-                }
-                //Now curr is on the cycle. We collect the edges ids.
-                let mut cycle = Vec::<usize>::new();
-                cycle.push(prev[curr]);
-                let mut cycle_vert = edge_vec[prev[curr]].u;
-                while cycle_vert != curr {
-                    cycle.push(prev[cycle_vert]);
-                    cycle_vert = edge_vec[prev[cycle_vert]].u;
-                }
-
-                return Some(cycle);
-            }
-        }
-    }
-
-    None
-}
+fn positive_cycle(
+	edge_vec: &Vec<WeightedEdge>,
+	nb_left: usize,
+	nb_right: usize,
+) -> Option<Vec<usize>> {
+	let nb_side_min = min(nb_left, nb_right);
+	let nb_vertices = nb_left + nb_right;
+	let weight_lowerbound = -((nb_left + nb_right) as i32) - 1;
+	let mut accessed = vec![false; nb_left];
 
+	//We try to find a positive cycle accessible from the left
+	//vertex i.
+	for i in 0..nb_left {
+		if accessed[i] {
+			continue;
+		}
+		let mut weight = vec![weight_lowerbound; nb_vertices];
+		let mut prev = vec![edge_vec.len(); nb_vertices];
+		weight[i] = 0;
+		//We compute largest weighted paths from i.
+		//Since the graph is bipartite, any simple cycle has length
+		//at most 2*nb_side_min. In the general Bellman-Ford
+		//algorithm, the bound here is the number of vertices. Since
+		//the number of partitions can be much larger than the
+		//number of nodes, we optimize that.
+		for _ in 0..(2 * nb_side_min) {
+			for j in 0..edge_vec.len() {
+				let e = edge_vec[j];
+				if weight[e.v] < weight[e.u] + e.w {
+					weight[e.v] = weight[e.u] + e.w;
+					prev[e.v] = j;
+				}
+			}
+		}
+		//We update the accessed table
+		for i in 0..nb_left {
+			if weight[i] > weight_lowerbound {
+				accessed[i] = true;
+			}
+		}
+		//We detect positive cycle
+		for e in edge_vec {
+			if weight[e.v] < weight[e.u] + e.w {
+				//it means e is on a path branching from a positive cycle
+				let mut was_seen = vec![false; nb_vertices];
+				let mut curr = e.u;
+				//We track back with prev until we reach the cycle.
+				while !was_seen[curr] {
+					was_seen[curr] = true;
+					curr = edge_vec[prev[curr]].u;
+				}
+				//Now curr is on the cycle. We collect the edges ids.
+				let mut cycle = Vec::<usize>::new();
+				cycle.push(prev[curr]);
+				let mut cycle_vert = edge_vec[prev[curr]].u;
+				while cycle_vert != curr {
+					cycle.push(prev[cycle_vert]);
+					cycle_vert = edge_vec[prev[cycle_vert]].u;
+				}
 
-// This function takes two arrays of capacity and computes the 
-// maximal matching in the complete bipartite graph such that the 
+				return Some(cycle);
+			}
+		}
+	}
+
+	None
+}
+
+// This function takes two arrays of capacity and computes the
+// maximal matching in the complete bipartite graph such that the
 // left vertex i is matched to left_cap_vec[i] right vertices, and
 // the right vertex j is matched to right_cap_vec[j] left vertices.
 // To do so, we use Dinic's maximum flow algorithm.
-pub fn dinic_compute_matching( left_cap_vec : Vec<u32>,
-        right_cap_vec : Vec<u32>) -> Vec< Vec<usize> >
-{
-    let mut graph = Vec::<Vec::<EdgeFlow>  >::new();
-    let ed = EdgeFlow{c:0,flow:0,v:0, rev:0};
-
-    // 0 will be the source
-    graph.push(vec![ed ; left_cap_vec.len()]);
-    for i in 0..left_cap_vec.len()
-    {
-        graph[0][i].c = left_cap_vec[i] as i32;
-        graph[0][i].v = i+2;
-        graph[0][i].rev = 0;
-    }
-
-    //1 will be the sink
-    graph.push(vec![ed ; right_cap_vec.len()]);
-    for i in 0..right_cap_vec.len()
-    {
-        graph[1][i].c = right_cap_vec[i] as i32;
-        graph[1][i].v = i+2+left_cap_vec.len();
-        graph[1][i].rev = 0;
-    }
-    
-    //we add left vertices
-    for i in 0..left_cap_vec.len() {
-        graph.push(vec![ed ; 1+right_cap_vec.len()]);
-        graph[i+2][0].c = 0; //directed
-        graph[i+2][0].v = 0;
-        graph[i+2][0].rev = i;
-
-        for j in 0..right_cap_vec.len() {
-            graph[i+2][j+1].c = 1;
-            graph[i+2][j+1].v = 2+left_cap_vec.len()+j;
-            graph[i+2][j+1].rev = i+1;
-        }
-    }
-
-    //we add right vertices
-    for i in 0..right_cap_vec.len() {
-        let lft_ln = left_cap_vec.len();
-        graph.push(vec![ed ; 1+lft_ln]);
-        graph[i+lft_ln+2][0].c = graph[1][i].c;
-        graph[i+lft_ln+2][0].v = 1;
-        graph[i+lft_ln+2][0].rev = i;
-
-        for j in 0..left_cap_vec.len() {
-            graph[i+2+lft_ln][j+1].c = 0; //directed
-            graph[i+2+lft_ln][j+1].v = j+2;
-            graph[i+2+lft_ln][j+1].rev = i+1;
-        }
-    }
-
-    //To ensure the dispersion of the triplets generated by the 
-    //assignation, we shuffle the neighbours of the nodes. Hence,
-    //left vertices do not consider the right ones in the same order.
-    let mut rng = rand::thread_rng(); 
-    for i in 0..graph.len() {
-        graph[i].shuffle(&mut rng);
-        //We need to update the ids of the reverse edges.
-        for j in 0..graph[i].len() {
-            let target_v = graph[i][j].v;
-            let target_rev = graph[i][j].rev;
-            graph[target_v][target_rev].rev = j;
-        }
-    }
-
-    let nb_vertices = graph.len();
-    
-    //We run Dinic's max flow algorithm
-    loop{
-        //We build the level array from Dinic's algorithm.
-        let mut level = vec![-1; nb_vertices];
-
-        let mut fifo = VecDeque::new();
-        fifo.push_back((0,0));
-        while !fifo.is_empty() {
-            if let Some((id,lvl)) = fifo.pop_front(){
-                if level[id] == -1 {
-                    level[id] = lvl;
-                    for e in graph[id].iter(){
-                        if e.c-e.flow > 0{
-                            fifo.push_back((e.v,lvl+1));
-                        }
-                    }
-                }
-            }
-        }
-        if level[1] == -1 {
-            //There is no residual flow
-            break;
-        }
-
-        //Now we run DFS respecting the level array
-        let mut next_nbd = vec![0; nb_vertices];
-        let mut lifo = VecDeque::new();
-        
-        let flow_upper_bound;
-        if let Some(x) = left_cap_vec.iter().max() {
-            flow_upper_bound=*x as i32;
-        }
-        else {
-            flow_upper_bound = 0;
-            assert!(false);
-        }
-        
-        lifo.push_back((0,flow_upper_bound));
-        
-        loop
-        {
-            if let Some((id_tmp, f_tmp)) = lifo.back() {
-                let id = *id_tmp;
-                let f = *f_tmp;
-                if id == 1  {
-                    //The DFS reached the sink, we can add a 
-                    //residual flow.
-                    lifo.pop_back();
-                    while !lifo.is_empty() {
-                        if let Some((id,_)) = lifo.pop_back(){
-                            let nbd=next_nbd[id];
-                            graph[id][nbd].flow += f;
-                            let id_v = graph[id][nbd].v;
-                            let nbd_v = graph[id][nbd].rev;
-                            graph[id_v][nbd_v].flow -= f;
-                        }
-                    }
-                    lifo.push_back((0,flow_upper_bound));
-                    continue;
-                }
-               //else we did not reach the sink
-                let nbd = next_nbd[id];
-                if nbd >= graph[id].len() {
-                    //There is nothing to explore from id anymore
-                    lifo.pop_back();
-                    if let Some((parent, _)) = lifo.back(){
-                        next_nbd[*parent] +=1;
-                    }
-                    continue;
-                }
-                //else we can try to send flow from id to its nbd
-                let new_flow = min(f,graph[id][nbd].c
-                                        - graph[id][nbd].flow);
-                if level[graph[id][nbd].v] <= level[id] || 
-                                                   new_flow == 0 {
-                    //We cannot send flow to nbd.
-                    next_nbd[id] += 1;
-                    continue;
-                }
-                //otherwise, we send flow to nbd.
-                lifo.push_back((graph[id][nbd].v, new_flow));
-            }
-            else {
-                break;
-            }
-        }
-    }
-    
-    //We return the association
-    let assoc_table = (0..left_cap_vec.len()).map(
-        |id| graph[id+2].iter()
-                    .filter(|e| e.flow > 0)
-                    .map( |e| e.v-2-left_cap_vec.len())
-                    .collect()).collect();
-
-    //consistency check
-    
-    //it is a flow
-    for i in 3..graph.len(){
-        assert!( graph[i].iter().map(|e| e.flow).sum::<i32>() == 0);
-        for e in graph[i].iter(){
-            assert!(e.flow + graph[e.v][e.rev].flow == 0);
-        }
-    }
-    
-    //it solves the matching problem
-    for i in 0..left_cap_vec.len(){
-        assert!(left_cap_vec[i] as i32 == 
-            graph[i+2].iter().map(|e| max(0,e.flow)).sum::<i32>());
-    }
-    for i in 0..right_cap_vec.len(){
-        assert!(right_cap_vec[i] as i32 == 
-            graph[i+2+left_cap_vec.len()].iter()
-                    .map(|e| max(0,e.flow)).sum::<i32>());
-    }
-
-
-    assoc_table
-}
+pub fn dinic_compute_matching(left_cap_vec: Vec<u32>, right_cap_vec: Vec<u32>) -> Vec<Vec<usize>> {
+	let mut graph = Vec::<Vec<EdgeFlow>>::new();
+	let ed = EdgeFlow {
+		c: 0,
+		flow: 0,
+		v: 0,
+		rev: 0,
+	};
+
+	// 0 will be the source
+	graph.push(vec![ed; left_cap_vec.len()]);
+	for i in 0..left_cap_vec.len() {
+		graph[0][i].c = left_cap_vec[i] as i32;
+		graph[0][i].v = i + 2;
+		graph[0][i].rev = 0;
+	}
+
+	//1 will be the sink
+	graph.push(vec![ed; right_cap_vec.len()]);
+	for i in 0..right_cap_vec.len() {
+		graph[1][i].c = right_cap_vec[i] as i32;
+		graph[1][i].v = i + 2 + left_cap_vec.len();
+		graph[1][i].rev = 0;
+	}
+
+	//we add left vertices
+	for i in 0..left_cap_vec.len() {
+		graph.push(vec![ed; 1 + right_cap_vec.len()]);
+		graph[i + 2][0].c = 0; //directed
+		graph[i + 2][0].v = 0;
+		graph[i + 2][0].rev = i;
+
+		for j in 0..right_cap_vec.len() {
+			graph[i + 2][j + 1].c = 1;
+			graph[i + 2][j + 1].v = 2 + left_cap_vec.len() + j;
+			graph[i + 2][j + 1].rev = i + 1;
+		}
+	}
 
+	//we add right vertices
+	for i in 0..right_cap_vec.len() {
+		let lft_ln = left_cap_vec.len();
+		graph.push(vec![ed; 1 + lft_ln]);
+		graph[i + lft_ln + 2][0].c = graph[1][i].c;
+		graph[i + lft_ln + 2][0].v = 1;
+		graph[i + lft_ln + 2][0].rev = i;
+
+		for j in 0..left_cap_vec.len() {
+			graph[i + 2 + lft_ln][j + 1].c = 0; //directed
+			graph[i + 2 + lft_ln][j + 1].v = j + 2;
+			graph[i + 2 + lft_ln][j + 1].rev = i + 1;
+		}
+	}
+
+	//To ensure the dispersion of the triplets generated by the
+	//assignation, we shuffle the neighbours of the nodes. Hence,
+	//left vertices do not consider the right ones in the same order.
+	let mut rng = rand::thread_rng();
+	for i in 0..graph.len() {
+		graph[i].shuffle(&mut rng);
+		//We need to update the ids of the reverse edges.
+		for j in 0..graph[i].len() {
+			let target_v = graph[i][j].v;
+			let target_rev = graph[i][j].rev;
+			graph[target_v][target_rev].rev = j;
+		}
+	}
+
+	let nb_vertices = graph.len();
+
+	//We run Dinic's max flow algorithm
+	loop {
+		//We build the level array from Dinic's algorithm.
+		let mut level = vec![-1; nb_vertices];
+
+		let mut fifo = VecDeque::new();
+		fifo.push_back((0, 0));
+		while !fifo.is_empty() {
+			if let Some((id, lvl)) = fifo.pop_front() {
+				if level[id] == -1 {
+					level[id] = lvl;
+					for e in graph[id].iter() {
+						if e.c - e.flow > 0 {
+							fifo.push_back((e.v, lvl + 1));
+						}
+					}
+				}
+			}
+		}
+		if level[1] == -1 {
+			//There is no residual flow
+			break;
+		}
+
+		//Now we run DFS respecting the level array
+		let mut next_nbd = vec![0; nb_vertices];
+		let mut lifo = VecDeque::new();
+
+		let flow_upper_bound;
+		if let Some(x) = left_cap_vec.iter().max() {
+			flow_upper_bound = *x as i32;
+		} else {
+			flow_upper_bound = 0;
+			assert!(false);
+		}
+
+		lifo.push_back((0, flow_upper_bound));
+
+		loop {
+			if let Some((id_tmp, f_tmp)) = lifo.back() {
+				let id = *id_tmp;
+				let f = *f_tmp;
+				if id == 1 {
+					//The DFS reached the sink, we can add a
+					//residual flow.
+					lifo.pop_back();
+					while !lifo.is_empty() {
+						if let Some((id, _)) = lifo.pop_back() {
+							let nbd = next_nbd[id];
+							graph[id][nbd].flow += f;
+							let id_v = graph[id][nbd].v;
+							let nbd_v = graph[id][nbd].rev;
+							graph[id_v][nbd_v].flow -= f;
+						}
+					}
+					lifo.push_back((0, flow_upper_bound));
+					continue;
+				}
+				//else we did not reach the sink
+				let nbd = next_nbd[id];
+				if nbd >= graph[id].len() {
+					//There is nothing to explore from id anymore
+					lifo.pop_back();
+					if let Some((parent, _)) = lifo.back() {
+						next_nbd[*parent] += 1;
+					}
+					continue;
+				}
+				//else we can try to send flow from id to its nbd
+				let new_flow = min(f, graph[id][nbd].c - graph[id][nbd].flow);
+				if level[graph[id][nbd].v] <= level[id] || new_flow == 0 {
+					//We cannot send flow to nbd.
+					next_nbd[id] += 1;
+					continue;
+				}
+				//otherwise, we send flow to nbd.
+				lifo.push_back((graph[id][nbd].v, new_flow));
+			} else {
+				break;
+			}
+		}
+	}
+
+	//We return the association
+	let assoc_table = (0..left_cap_vec.len())
+		.map(|id| {
+			graph[id + 2]
+				.iter()
+				.filter(|e| e.flow > 0)
+				.map(|e| e.v - 2 - left_cap_vec.len())
+				.collect()
+		})
+		.collect();
+
+	//consistency check
+
+	//it is a flow
+	for i in 3..graph.len() {
+		assert!(graph[i].iter().map(|e| e.flow).sum::<i32>() == 0);
+		for e in graph[i].iter() {
+			assert!(e.flow + graph[e.v][e.rev].flow == 0);
+		}
+	}
+
+	//it solves the matching problem
+	for i in 0..left_cap_vec.len() {
+		assert!(left_cap_vec[i] as i32 == graph[i + 2].iter().map(|e| max(0, e.flow)).sum::<i32>());
+	}
+	for i in 0..right_cap_vec.len() {
+		assert!(
+			right_cap_vec[i] as i32
+				== graph[i + 2 + left_cap_vec.len()]
+					.iter()
+					.map(|e| max(0, e.flow))
+					.sum::<i32>()
+		);
+	}
+
+	assoc_table
+}
 
 #[cfg(test)]
 mod tests {
-    use super::*;
-    
-    #[test]
-    fn test_flow() {
-        let left_vec = vec![3;8];
-        let right_vec = vec![0,4,8,4,8];
-        //There are asserts in the function that computes the flow
-        let _ = dinic_compute_matching(left_vec, right_vec);
-    }
-
-    //maybe add tests relative to the matching optilization ?
-}
+	use super::*;
 
+	#[test]
+	fn test_flow() {
+		let left_vec = vec![3; 8];
+		let right_vec = vec![0, 4, 8, 4, 8];
+		//There are asserts in the function that computes the flow
+		let _ = dinic_compute_matching(left_vec, right_vec);
+	}
 
+	//maybe add tests relative to the matching optilization ?
+}