Tic Tac Toe - Minimax - rust

I'm trying to build a tic-tac-toe game using minimax algorithm with rust. And I'm stuck. I tried to write a rust code based on the psudeo code on the wikipedia page. https://en.wikipedia.org/wiki/Minimax. However, it didn't work. Ai always makes the first possible move. I would be glad if you could help me.
In main.rs
fn main() {
let mut g = Game::new();
while g.game_state() == Game_State::Continuous {
g.print();
println!("{}", minimax(&g));
if g.turn == Player::Cross {
g.take_input();
}
else {
g = best_move(&g);
}
}
g.print();
if let Game_State::Win(Player::None) = g.game_state() {
println!("Draw");
}
else {
g.print_winner();
}
}
In ai.rs
pub fn child_nodes(game: &Game) -> Vec<Game> {
let mut children: Vec<Game> = Vec::new();
for r in 0..3 {
for c in 0..3 {
if game.grid[r][c] == Player::None {
let mut child = game.clone();
child.grid[r][c] = game.turn;
child.turn = reverse_player(child.turn);
children.push(child);
}
}
}
return children;
}
pub fn minimax(game: &Game) -> isize {
match game.game_state() {
Game_State::Win(winner) => to_scor(winner),
Game_State::Continuous => {
use std::cmp::{min, max};
let children_vec = child_nodes(&game);
let mut score: isize;
if game.turn == Player::Cross {
score = -2;
for i in &children_vec {
score = max(score, minimax(i));
}
}
else {
score = 2;
for i in &children_vec {
score = min(score, minimax(i));
}
}
return score;
}
}
}
pub fn best_move(game: &Game) -> Game {
let children = child_nodes(game);
let mut values: Vec<isize> = Vec::new();
for i in 0..children.len() {
values.push(minimax(&children[i]));
}
let mut index: usize = 0;
let iter = values.iter().enumerate();
if game.turn == Player::Cross {
if let Option::Some(t) = iter.max() {
index = t.0;
}
}
else if game.turn == Player::Circle {
if let Option::Some(t) = iter.min() {
index = t.0;
}
}
let best_pos = children[index];
best_pos
}
pub fn to_scor(x: Player) -> isize {
match x {
Player::Cross => 1,
Player::Circle => -1,
Player::None => 0
}
}

.enumerate() returns an iterator over tuples, and .max() and .min() on an iterator of tuples will compare the tuples - that is, (1, x) is always considered to be less than (2, y) for any values of x and y. This can be demonstrated with this snippet:
fn main() {
let v = vec![3, 1, 2, 5, 3, 6, 7, 2];
println!("{:?}", v.iter().enumerate().min());
println!("{:?}", v.iter().enumerate().max());
}
which prints:
Some((0, 3))
Some((7, 2))
which are just the first and last elements of the list (and not the minimum or maximum elements).
However, as shown here, you can use max_by to use your own function to compare the tuples.

Related

How to declare generic types for a function that computes k-shortest-paths using Yen's algorithm and petgraph?

I have implemented Yen's algorithm Wikipedia using petgraph in Rust.
In a main function, the code looks like this:
use std::collections::BinaryHeap;
use std::cmp::Reverse;
use std::collections::HashSet;
use petgraph::{Graph, Undirected};
use petgraph::graph::NodeIndex;
use petgraph::stable_graph::StableUnGraph;
use petgraph::algo::{astar};
use petgraph::visit::NodeRef;
fn main() {
let mut graph: Graph<String, u32, Undirected> = Graph::new_undirected();
let c = graph.add_node(String::from("C"));
let d = graph.add_node(String::from("D"));
let e = graph.add_node(String::from("E"));
let f = graph.add_node(String::from("F"));
let g = graph.add_node(String::from("G"));
let h = graph.add_node(String::from("H"));
graph.add_edge(c, d, 3);
graph.add_edge(c, e, 2);
graph.add_edge(d, e, 1);
graph.add_edge(d, f, 4);
graph.add_edge(e, f, 2);
graph.add_edge(e, g, 3);
graph.add_edge(f, g, 2);
graph.add_edge(f, h, 1);
graph.add_edge(g, h, 2);
let start = c;
let goal = h;
// start solving Yen's k-shortest-paths
let (length, path) = match astar(&graph, start, |n| n == goal.unwrap(), |e| *e.weight(), |_| 0) {
Some(x) => x,
None => panic!("Testing!"),
};
println!("Initial path found\tlength: {}", length);
for i in 0..(path.len() - 1) {
println!("\t{:?}({:?}) -> {:?}({:?})", graph.node_weight(path[i].id()).unwrap(), path[i].id(), graph.node_weight(path[i+1].id()).unwrap(), path[i+1].id());
}
let k = 10;
let mut ki = 0;
let mut visited = HashSet::new();
let mut routes = vec![(length, path)];
let mut k_routes = BinaryHeap::new();
for ki in 0..(k - 1) {
println!("Computing path {}", ki);
if routes.len() <= ki {
// We have no more routes to explore
break;
}
let previous = routes[ki].1.clone();
for i in 0..(previous.len() - 1) {
let spur_node = previous[i].clone();
let root_path = &previous[0..i];
let mut graph_copy = StableUnGraph::<String, u32>::from(graph.clone());
println!("\tComputing pass {}\tspur: {:?}\troot: {:?}", i, graph.node_weight(spur_node), root_path.iter().map(|n| graph.node_weight(*n).unwrap()));
for (_, path) in &routes {
if path.len() > i + 1 && &path[0..i] == root_path {
let ei = graph.find_edge_undirected(path[i], path[i + 1]);
if ei.is_some() {
let edge = ei.unwrap().0;
graph_copy.remove_edge(edge);
let edge_obj = graph.edge_endpoints(edge);
let ns = edge_obj.unwrap();
println!("\t\tRemoving edge {:?} from {:?} -> {:?}", edge, graph.node_weight(ns.0).unwrap(), graph.node_weight(ns.1).unwrap());
}
else {
panic!("\t\tProblem finding edge");
}
}
}
if let Some((_, spur_path)) =
astar(&graph_copy, spur_node, |n| n == goal.unwrap(), |e| *e.weight(), |_| 0)
{
let nodes: Vec<NodeIndex> = root_path.iter().cloned().chain(spur_path).collect();
let mut node_names = vec![];
for ni in 0..nodes.len() {
node_names.push(graph.node_weight(nodes[ni]).unwrap());
}
// compute root_path length
let mut path_length = 0;
for i_rp in 0..(nodes.len() - 1) {
let ei = graph.find_edge_undirected(nodes[i_rp], nodes[i_rp + 1]);
if ei.is_some() {
let ew = graph.edge_weight(ei.unwrap().0);
if ew.is_some() {
path_length += ew.unwrap();
}
}
}
println!("\t\t\tfound path: {:?} with cost {}", node_names, path_length);
if !visited.contains(&nodes) {
// Mark as visited
visited.insert(nodes.clone());
// Build a min-heap
k_routes.push(Reverse((path_length, nodes)));
}
}
}
if let Some(k_route) = k_routes.pop() {
println!("\tselected route {:?}", k_route.0);
routes.push(k_route.0);
}
}
}
Now, I want to put this algorithm within a function that I can call from my code. I made an initial attempt with the signature like this:
pub fn yen_k_shortest_paths<G, E, Ty, Ix, F, K>(
graph: Graph<String, u32, Undirected>,
start: NodeIndex<u32>,
goal: NodeIndex<u32>,
mut edge_cost: F,
k: usize,
) -> Result<Vec<(u32, Vec<NodeIndex<u32>>)>, Box<dyn std::error::Error>>
where
G: IntoEdges + Visitable,
Ty: EdgeType,
Ix: IndexType,
E: Default + Debug + std::ops::Add,
F: FnMut(G::EdgeRef) -> K,
K: Measure + Copy,
{
// implementation here
}
However, when I try to call the function with:
let paths = yen::yen_k_shortest_paths(graph, start, goal, |e: EdgeReference<u32>| *e.weight(), 5);
the compiler complains: type annotations needed cannot satisfy <_ as IntoEdgeReferences>::EdgeRef == petgraph::graph::EdgeReference<'_, u32>`
I already tried several alternatives without success. Do you have any suggestion on how to fix this issue?
The issue with the yen_k_shortest_paths() function signature as written is the generic type parameters aren't used correctly. As an example, consider the first declared type parameter on yen_k_shortest_paths(): G, which is intended to represent the graph type. Declaring G like this means that the code that calls yen_k_shortest_paths() gets to pick the graph type G. But the graph argument is declared with the concrete type Graph<String, u32, Undirected>—the caller has no choice. This contradiction is the problem with G. Similar reasoning applies to the other type parameters, except F and K. There are two ways to fix this kind of issue:
Keep the graph argument as Graph<String, u32, Undirected> and remove the G type parameter.
Change the graph argument to take a G.
Approach #1 is simpler but your function won't be as general. Approach #2 can involve needing to add extra bounds and some code changes in the function in order for the code to compile.
In this case, the simplest approach doesn't need any type parameters at all:
fn yen_k_shortest_paths(
graph: &Graph<String, u32, Undirected>,
start: NodeIndex<u32>,
goal: NodeIndex<u32>,
edge_cost: fn(EdgeReference<u32>) -> u32,
k: usize,
) -> Vec<(u32, Vec<NodeIndex<u32>>)> {...}
Here's the full code, which can be run:
use std::cmp::Reverse;
use std::collections::BinaryHeap;
use std::collections::HashSet;
use petgraph::algo::astar;
use petgraph::graph::{EdgeReference, NodeIndex};
use petgraph::stable_graph::StableUnGraph;
use petgraph::visit::NodeRef;
use petgraph::{Graph, Undirected};
fn main() {
let mut graph: Graph<String, u32, Undirected> = Graph::new_undirected();
let c = graph.add_node(String::from("C"));
let d = graph.add_node(String::from("D"));
let e = graph.add_node(String::from("E"));
let f = graph.add_node(String::from("F"));
let g = graph.add_node(String::from("G"));
let h = graph.add_node(String::from("H"));
graph.add_edge(c, d, 3);
graph.add_edge(c, e, 2);
graph.add_edge(d, e, 1);
graph.add_edge(d, f, 4);
graph.add_edge(e, f, 2);
graph.add_edge(e, g, 3);
graph.add_edge(f, g, 2);
graph.add_edge(f, h, 1);
graph.add_edge(g, h, 2);
let start = c;
let goal = h;
let edge_cost = |e: EdgeReference<u32>| *e.weight();
let k = 10;
let _paths = yen_k_shortest_paths(&graph, start, goal, edge_cost, k);
}
fn yen_k_shortest_paths(
graph: &Graph<String, u32, Undirected>,
start: NodeIndex<u32>,
goal: NodeIndex<u32>,
edge_cost: fn(EdgeReference<u32>) -> u32,
k: usize,
) -> Vec<(u32, Vec<NodeIndex<u32>>)> {
let (length, path) = match astar(graph, start, |n| n == goal, edge_cost, |_| 0) {
Some(x) => x,
None => panic!("Testing!"),
};
println!("Initial path found\tlength: {}", length);
for i in 0..(path.len() - 1) {
println!(
"\t{:?}({:?}) -> {:?}({:?})",
graph.node_weight(path[i].id()).unwrap(),
path[i].id(),
graph.node_weight(path[i + 1].id()).unwrap(),
path[i + 1].id()
);
}
let mut visited = HashSet::new();
let mut routes = vec![(length, path)];
let mut k_routes = BinaryHeap::new();
for ki in 0..(k - 1) {
println!("Computing path {}", ki);
if routes.len() <= ki {
// We have no more routes to explore
break;
}
let previous = routes[ki].1.clone();
for i in 0..(previous.len() - 1) {
let spur_node = previous[i];
let root_path = &previous[0..i];
let mut graph_copy = StableUnGraph::from(graph.clone());
println!(
"\tComputing pass {}\tspur: {:?}\troot: {:?}",
i,
graph.node_weight(spur_node),
root_path
.iter()
.map(|n| graph.node_weight(*n).unwrap())
.collect::<Vec<_>>()
);
for (_, path) in &routes {
if path.len() > i + 1 && &path[0..i] == root_path {
let ei = graph.find_edge_undirected(path[i], path[i + 1]);
if let Some(ei) = ei {
let edge = ei.0;
graph_copy.remove_edge(edge);
let edge_obj = graph.edge_endpoints(edge);
let ns = edge_obj.unwrap();
println!(
"\t\tRemoving edge {:?} from {:?} -> {:?}",
edge,
graph.node_weight(ns.0).unwrap(),
graph.node_weight(ns.1).unwrap()
);
} else {
panic!("\t\tProblem finding edge");
}
}
}
if let Some((_, spur_path)) = astar(
&graph_copy,
spur_node,
|n| n == goal,
|e| *e.weight(),
|_| 0,
) {
let nodes: Vec<NodeIndex> = root_path.iter().cloned().chain(spur_path).collect();
let mut node_names = vec![];
for &node in &nodes {
node_names.push(graph.node_weight(node).unwrap());
}
// compute root_path length
let mut path_length = 0;
for i_rp in 0..(nodes.len() - 1) {
let ei = graph.find_edge_undirected(nodes[i_rp], nodes[i_rp + 1]);
if let Some(ei) = ei {
let ew = graph.edge_weight(ei.0);
if let Some(&ew) = ew {
path_length += ew;
}
}
}
println!(
"\t\t\tfound path: {:?} with cost {}",
node_names, path_length
);
if !visited.contains(&nodes) {
// Mark as visited
visited.insert(nodes.clone());
// Build a min-heap
k_routes.push(Reverse((path_length, nodes)));
}
}
}
if let Some(k_route) = k_routes.pop() {
println!("\tselected route {:?}", k_route.0);
routes.push(k_route.0);
}
}
routes
}
As another example of a possible function signature, this one is generic over the node type N and the edge cost function F:
fn yen_k_shortest_paths<'a, N, F>(
graph: &'a Graph<N, u32, Undirected>,
start: NodeIndex<u32>,
goal: NodeIndex<u32>,
edge_cost: F,
k: usize,
) -> Vec<(u32, Vec<NodeIndex<u32>>)>
where
&'a Graph<N, u32, Undirected>:
GraphBase<NodeId = NodeIndex<u32>> + IntoEdgeReferences<EdgeRef = EdgeReference<'a, u32>>,
N: Debug + Clone,
F: FnMut(EdgeReference<u32>) -> u32,
{...}
As you can see, these bounds can get pretty complicated. Figuring them out involved reading the error messages the compiler emitted, as well as reading the docs for the involved types/traits. (Although, I think in this case the complicated bound &'a Graph<N, u32, Undirected>: GraphBase<NodeId = NodeIndex<u32>> + IntoEdgeReferences<EdgeRef = EdgeReference<'a, u32>> should be inferred, but currently isn't due to a complier bug/limitation)

Short-circuit iterator once condition is met

I am trying to write an iterator which conditionally uses elements in a separate iterator. In my example, the separate iterator should increment the sum variable. Once another condition is met *n == 4, the iterator should stop checking the condition and assume rest of elements are increments for the sum variable. I have the following working example:
fn conditional(n: &i64) -> bool {
// a lot of code here which is omitted for brevity
n % 2 == 0
}
fn main() {
let buf = vec![1,2,3,4,5,6];
let mut sum = 0;
let mut iter = buf.iter();
while let Some(n) = iter.next() {
if conditional(n) {
sum += n;
}
if *n == 4 {
// end of file - assume rest of elements are `conditional`
break;
}
};
// rest of elements [5,6]
for n in iter {
sum += n;
}
println!("sum (2+4+5+6): {:?}", sum);
}
output:
sum (2+4+5+6): 17
playground link
I would rather write the same thing with a single iterator using something like flat_map:
fn conditional(n: &i64) -> bool {
// a lot of code here which is omitted for brevity
n % 2 == 0
}
fn main() {
let buf = vec![1,2,3,4,5,6];
let mut sum = 0;
let mut terminate = false;
buf.iter().flat_map(|n| {
if *n == 4 {
// hard terminate here - return Some(n) for rest of iterator [5,6]
terminate = true;
return Some(n);
}
if terminate {
return Some(n);
}
if conditional(n) {
return Some(n);
}
None // odd
})
.for_each(|n| {
sum += n;
});
println!("sum (2+4+5+6): {:?}", sum);
}
output:
sum (2+4+5+6): 17
playground link
Is there a way to write this in a more concise manner? I want to short-circuit the iterator once the *n == 4 condition is reached.
There are many ways to solve this.
Here are a couple:
fn conditional(n: &i64) -> bool {
// a lot of code here which is omitted for brevity
n % 2 == 0
}
fn main() {
let buf = vec![1, 2, 3, 4, 5, 6];
let sum = buf
.iter()
.fold((0, false), |(mut sum, mut terminate), value| {
if *value == 4 {
terminate = true;
}
if terminate || conditional(value) {
sum += *value;
}
(sum, terminate)
})
.0;
println!("sum (2+4+5+6): {:?}", sum);
}
sum (2+4+5+6): 17
Or using filter and a stateful closure:
fn conditional(n: &i64) -> bool {
// a lot of code here which is omitted for brevity
n % 2 == 0
}
fn main() {
let buf = vec![1, 2, 3, 4, 5, 6];
let sum: i64 = buf
.iter()
.filter({
let mut terminate = false;
move |&value| {
terminate || {
if *value == 4 {
terminate = true;
}
conditional(value)
}
}
})
.sum();
println!("sum (2+4+5+6): {:?}", sum);
}
sum (2+4+5+6): 17
You can use filter():
buf.iter().filter(|n| {
if **n == 4 {
terminate = true;
}
terminate || conditional(n)
})
And sum() instead of for_each():
let sum = buf
.iter()
.filter(|n| {
if **n == 4 {
terminate = true;
}
terminate || conditional(n)
})
.sum::<i64>();

Get the index of second element matching condition in a rust vec

Lets say I have something like
let v: Vec<bool> = [false, true, false, true, false, false];
I want to get the position of the second "true" (So in this case get_second_index(v) should return Some(3)) Currently I'm doing the following which I think is pretty ugly:
fn get_second_index(v: Vec<bool>) -> Option<u32> {
let mut num_matching = 0;
let mut second_index = 0;
for (i, b) in v.iter().enumerate() {
if *b {
num_matching += 1;
}
if num_matching == 2 {
second_index = i;
}
}
if second_index == 0 {
return None;
}
second_index
}
Is there any more elegant, more idiomatically rust way to do this? Thanks!
You can simply .enumerate() an Iterator over the Vec to get the indices, then use .filter_map() on the enumerated iterator to get all true-values, and use .nth() on the filtered iterator to get the second match:
fn second(inp: &[bool]) -> Option<usize> {
inp.iter()
.enumerate()
.filter_map(|(idx, b)| (*b).then(|| idx))
.nth(1)
}
fn main() {
let v: Vec<bool> = vec![false, true, false, true, false, false];
assert_eq!(second(&v), Some(3));
}
Notice that this will return a Option<usize>, not an Option<u32>, as all indices are usize...
Try this
fn get_second_index(v: Vec<bool>) -> Option<usize> {
let mut matched = false;
v.iter().position(|x| {
if matched {
*x
} else {
matched = *x;
false
}
})
}

Returning struct with vector

I just began learning Rust and doing some exercises.
Here I'm trying to return the next permutation. But at the end of the next() method it seems to return the wrong vector in the struct.
pub struct Permutation {
p : Vec<u8>,
}
impl Permutation{
pub fn new(length: u8) -> Permutation {
let mut p :Vec<u8> = Vec::new();
for i in 1..length+1 {
p.push(i as u8);
}
Permutation { p }
}
pub fn create(this: Vec<u8>) -> Permutation {
Permutation { p:this }
}
pub fn next(&mut self) -> Option<Permutation> {
let mut pivot :usize = self.p.len() + 1;
for i in (1..self.p.len()).rev() {
if self.p[i-1] < self.p[i] {
pivot = i-1;
break;
}
}
if pivot == self.p.len() + 1 {
return None;
}
let mut swap :usize = pivot + 1;
for i in pivot+1..self.p.len() {
if self.p[i] > self.p[pivot] && self.p[i] < self.p[swap] {
swap = i;
}
}
let temp = self.p[swap];
self.p[swap] = self.p[pivot];
self.p[pivot] = temp;
pivot += 1;
let mut new_perm :Vec<u8> = Vec::new();
for i in 0..pivot {
new_perm.push(self.p[i]);
}
for i in (pivot..self.p.len()).rev() {
new_perm.push(self.p[i]);
}
// Debug
// for e in &new_perm {
// println!("{}", e);
//}
return Some(Permutation{ p: new_perm })
}
}
If I uncomment the println I can see that the new_perm vector is correct, but I seem to be getting the self.p vector returned.
What am I doing wrong here?

Severe performance degredation over time in multi-threading: what am I missing?

In my application a method runs quickly once started but begins to continuously degrade in performance upon nearing completion, this seems to be even irrelevant of the amount of work (the number of iterations of a function each thread has to perform). Once it reaches near the end it slows to an incredibly slow pace compared to earlier (worth noting this is not just a result of fewer threads remaining incomplete, it seems even each thread slows down).
I cannot figure out why this occurs, so I'm asking. What am I doing wrong?
An overview of CPU usage:
A slideshow of the problem
Worth noting that CPU temperature remains low throughout.
This stage varies with however much work is set, more work produces a better appearance with all threads constantly near 100%. Still, at this moment this appears good.
Here we see the continued performance of earlier,
Here we see it start to degrade. I do not know why this occurs.
After some period of chaos most of the threads have finished their work and the remaining threads continue, at this point although it seems they are at 100% they in actually perform their remaining workload very slowly. I cannot understand why this occurs.
Printing progress
I have written a multi-threaded random_search (documentation link) function for optimization. Most of the complexity in this function comes from printing data passing data between threads, this supports giving outputs showing progress like:
2300
565 (24.57%) 00:00:11 / 00:00:47 [25.600657363049734] { [563.0ns, 561.3ms, 125.0ns, 110.0ns] [2.0µs, 361.8ms, 374.0ns, 405.0ns] [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] }
I have been trying to use this output to figure out whats gone wrong, but I have no idea.
This output describes:
The total number of iterations 2300.
The total number of current iterations 565.
The time running 00:00:11 (mm:ss:ms).
The estimated time remaining 00:00:47 (mm:ss:ms).
The current best value [25.600657363049734].
The most recently measured times between execution positions (effectively time taken for thread to go from some line, to another line (defined specifically with update_execution_position in code below) [563.0ns, 561.3ms, 125.0ns, 110.0ns].
The averages times between execution positions (this is average across entire runtime rather than since last measured) [2.0µs, 361.8ms, 374.0ns, 405.0ns].
The execution positions of threads (0 is when a thread is completed, rest represent a thread having hit some line, which triggered this setting, but yet to hit next line which changes it, effectively being between 2 positions) [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
The random_search code:
Given I have tested implementations with the other methods in my library grid_search and simulated_annealing it would suggest to me the problem does not atleast entirely reside in random_search.rs.
random_search.rs:
pub fn random_search<
A: 'static + Send + Sync,
T: 'static + Copy + Send + Sync + Default + SampleUniform + PartialOrd,
const N: usize,
>(
// Generics
ranges: [Range<T>; N],
f: fn(&[T; N], Option<Arc<A>>) -> f64,
evaluation_data: Option<Arc<A>>,
polling: Option<Polling>,
// Specifics
iterations: u64,
) -> [T; N] {
// Gets cpu data
let cpus = num_cpus::get() as u64;
let search_cpus = cpus - 1; // 1 cpu is used for polling, this one.
let remainder = iterations % search_cpus;
let per = iterations / search_cpus;
let ranges_arc = Arc::new(ranges);
let (best_value, best_params) = search(
// Generics
ranges_arc.clone(),
f,
evaluation_data.clone(),
// Since we are doing this on the same thread, we don't need to use these
Arc::new(AtomicU64::new(Default::default())),
Arc::new(Mutex::new(Default::default())),
Arc::new(AtomicBool::new(false)),
Arc::new(AtomicU8::new(0)),
Arc::new([
Mutex::new((Duration::new(0, 0), 0)),
Mutex::new((Duration::new(0, 0), 0)),
Mutex::new((Duration::new(0, 0), 0)),
Mutex::new((Duration::new(0, 0), 0)),
]),
// Specifics
remainder,
);
let thread_exit = Arc::new(AtomicBool::new(false));
// (handles,(counters,thread_bests))
let (handles, links): (Vec<_>, Vec<_>) = (0..search_cpus)
.map(|_| {
let ranges_clone = ranges_arc.clone();
let counter = Arc::new(AtomicU64::new(0));
let thread_best = Arc::new(Mutex::new(f64::MAX));
let thread_execution_position = Arc::new(AtomicU8::new(0));
let thread_execution_time = Arc::new([
Mutex::new((Duration::new(0, 0), 0)),
Mutex::new((Duration::new(0, 0), 0)),
Mutex::new((Duration::new(0, 0), 0)),
Mutex::new((Duration::new(0, 0), 0)),
]);
let counter_clone = counter.clone();
let thread_best_clone = thread_best.clone();
let thread_exit_clone = thread_exit.clone();
let evaluation_data_clone = evaluation_data.clone();
let thread_execution_position_clone = thread_execution_position.clone();
let thread_execution_time_clone = thread_execution_time.clone();
(
thread::spawn(move || {
search(
// Generics
ranges_clone,
f,
evaluation_data_clone,
counter_clone,
thread_best_clone,
thread_exit_clone,
thread_execution_position_clone,
thread_execution_time_clone,
// Specifics
per,
)
}),
(
counter,
(
thread_best,
(thread_execution_position, thread_execution_time),
),
),
)
})
.unzip();
let (counters, links): (Vec<Arc<AtomicU64>>, Vec<_>) = links.into_iter().unzip();
let (thread_bests, links): (Vec<Arc<Mutex<f64>>>, Vec<_>) = links.into_iter().unzip();
let (thread_execution_positions, thread_execution_times) = links.into_iter().unzip();
if let Some(poll_data) = polling {
poll(
poll_data,
counters,
remainder,
iterations,
thread_bests,
thread_exit,
thread_execution_positions,
thread_execution_times,
);
}
let joins: Vec<_> = handles.into_iter().map(|h| h.join().unwrap()).collect();
let (_, best_params) = joins
.into_iter()
.fold((best_value, best_params), |(bv, bp), (v, p)| {
if v < bv {
(v, p)
} else {
(bv, bp)
}
});
return best_params;
fn search<
A: 'static + Send + Sync,
T: 'static + Copy + Send + Sync + Default + SampleUniform + PartialOrd,
const N: usize,
>(
// Generics
ranges: Arc<[Range<T>; N]>,
f: fn(&[T; N], Option<Arc<A>>) -> f64,
evaluation_data: Option<Arc<A>>,
counter: Arc<AtomicU64>,
best: Arc<Mutex<f64>>,
thread_exit: Arc<AtomicBool>,
thread_execution_position: Arc<AtomicU8>,
thread_execution_times: Arc<[Mutex<(Duration, u64)>; 4]>,
// Specifics
iterations: u64,
) -> (f64, [T; N]) {
let mut execution_position_timer = Instant::now();
let mut rng = thread_rng();
let mut params = [Default::default(); N];
let mut best_value = f64::MAX;
let mut best_params = [Default::default(); N];
for _ in 0..iterations {
// Gen random values
for (range, param) in ranges.iter().zip(params.iter_mut()) {
*param = rng.gen_range(range.clone());
}
// Update execution position
execution_position_timer = update_execution_position(
1,
execution_position_timer,
&thread_execution_position,
&thread_execution_times,
);
// Run function
let new_value = f(&params, evaluation_data.clone());
// Update execution position
execution_position_timer = update_execution_position(
2,
execution_position_timer,
&thread_execution_position,
&thread_execution_times,
);
// Check best
if new_value < best_value {
best_value = new_value;
best_params = params;
*best.lock().unwrap() = best_value;
}
// Update execution position
execution_position_timer = update_execution_position(
3,
execution_position_timer,
&thread_execution_position,
&thread_execution_times,
);
counter.fetch_add(1, Ordering::SeqCst);
// Update execution position
execution_position_timer = update_execution_position(
4,
execution_position_timer,
&thread_execution_position,
&thread_execution_times,
);
if thread_exit.load(Ordering::SeqCst) {
break;
}
}
// Update execution position
// 0 represents ended state
thread_execution_position.store(0, Ordering::SeqCst);
return (best_value, best_params);
}
}
util.rs:
pub fn update_execution_position<const N: usize>(
i: usize,
execution_position_timer: Instant,
thread_execution_position: &Arc<AtomicU8>,
thread_execution_times: &Arc<[Mutex<(Duration, u64)>; N]>,
) -> Instant {
{
let mut data = thread_execution_times[i - 1].lock().unwrap();
data.0 += execution_position_timer.elapsed();
data.1 += 1;
}
thread_execution_position.store(i as u8, Ordering::SeqCst);
Instant::now()
}
pub struct Polling {
pub poll_rate: u64,
pub printing: bool,
pub early_exit_minimum: Option<f64>,
pub thread_execution_reporting: bool,
}
impl Polling {
const DEFAULT_POLL_RATE: u64 = 10;
pub fn new(printing: bool, early_exit_minimum: Option<f64>) -> Self {
Self {
poll_rate: Polling::DEFAULT_POLL_RATE,
printing,
early_exit_minimum,
thread_execution_reporting: false,
}
}
}
pub fn poll<const N: usize>(
data: Polling,
// Current count of each thread.
counters: Vec<Arc<AtomicU64>>,
offset: u64,
// Final total iterations.
iterations: u64,
// Best values of each thread.
thread_bests: Vec<Arc<Mutex<f64>>>,
// Early exit switch.
thread_exit: Arc<AtomicBool>,
// Current positions of execution of each thread.
thread_execution_positions: Vec<Arc<AtomicU8>>,
// Current average times between execution positions for each thread
thread_execution_times: Vec<Arc<[Mutex<(Duration, u64)>; N]>>,
) {
let start = Instant::now();
let mut stdout = stdout();
let mut count = offset
+ counters
.iter()
.map(|c| c.load(Ordering::SeqCst))
.sum::<u64>();
if data.printing {
println!("{:20}", iterations);
}
let mut poll_time = Instant::now();
let mut held_best: f64 = f64::MAX;
let mut held_average_execution_times: [(Duration, u64); N] =
vec![(Duration::new(0, 0), 0); N].try_into().unwrap();
let mut held_recent_execution_times: [Duration; N] =
vec![Duration::new(0, 0); N].try_into().unwrap();
while count < iterations {
if data.printing {
// loop {
let percent = count as f32 / iterations as f32;
// If count == 0, give 00... for remaining time as placeholder
let remaining_time_estimate = if count == 0 {
Duration::new(0, 0)
} else {
start.elapsed().div_f32(percent)
};
print!(
"\r{:20} ({:.2}%) {} / {} [{}] {}\t",
count,
100. * percent,
print_duration(start.elapsed(), 0..3),
print_duration(remaining_time_estimate, 0..3),
if held_best == f64::MAX {
String::from("?")
} else {
format!("{}", held_best)
},
if data.thread_execution_reporting {
let (average_execution_times, recent_execution_times): (
Vec<String>,
Vec<String>,
) = (0..thread_execution_times[0].len())
.map(|i| {
let (mut sum, mut num) = (Duration::new(0, 0), 0);
for n in 0..thread_execution_times.len() {
{
let mut data = thread_execution_times[n][i].lock().unwrap();
sum += data.0;
held_average_execution_times[i].0 += data.0;
num += data.1;
held_average_execution_times[i].1 += data.1;
*data = (Duration::new(0, 0), 0);
}
}
if num > 0 {
held_recent_execution_times[i] = sum.div_f64(num as f64);
}
(
if held_average_execution_times[i].1 > 0 {
format!(
"{:.1?}",
held_average_execution_times[i]
.0
.div_f64(held_average_execution_times[i].1 as f64)
)
} else {
String::from("?")
},
if held_recent_execution_times[i] > Duration::new(0, 0) {
format!("{:.1?}", held_recent_execution_times[i])
} else {
String::from("?")
},
)
})
.unzip();
let execution_positions: Vec<u8> = thread_execution_positions
.iter()
.map(|pos| pos.load(Ordering::SeqCst))
.collect();
format!(
"{{ [{}] [{}] {:.?} }}",
recent_execution_times.join(", "),
average_execution_times.join(", "),
execution_positions
)
} else {
String::from("")
}
);
stdout.flush().unwrap();
}
// Updates best and does early exiting
match (data.early_exit_minimum, data.printing) {
(Some(early_exit), true) => {
for thread_best in thread_bests.iter() {
let thread_best_temp = *thread_best.lock().unwrap();
if thread_best_temp < held_best {
held_best = thread_best_temp;
if thread_best_temp <= early_exit {
thread_exit.store(true, Ordering::SeqCst);
println!();
return;
}
}
}
}
(None, true) => {
for thread_best in thread_bests.iter() {
let thread_best_temp = *thread_best.lock().unwrap();
if thread_best_temp < held_best {
held_best = thread_best_temp;
}
}
}
(Some(early_exit), false) => {
for thread_best in thread_bests.iter() {
if *thread_best.lock().unwrap() <= early_exit {
thread_exit.store(true, Ordering::SeqCst);
return;
}
}
}
(None, false) => {}
}
thread::sleep(saturating_sub(
Duration::from_millis(data.poll_rate),
poll_time.elapsed(),
));
poll_time = Instant::now();
count = offset
+ counters
.iter()
.map(|c| c.load(Ordering::SeqCst))
.sum::<u64>();
}
if data.printing {
println!(
"\r{:20} (100.00%) {} / {} [{}] {}\t",
count,
print_duration(start.elapsed(), 0..3),
print_duration(start.elapsed(), 0..3),
held_best,
if data.thread_execution_reporting {
let (average_execution_times, recent_execution_times): (Vec<String>, Vec<String>) =
(0..thread_execution_times[0].len())
.map(|i| {
let (mut sum, mut num) = (Duration::new(0, 0), 0);
for n in 0..thread_execution_times.len() {
{
let mut data = thread_execution_times[n][i].lock().unwrap();
sum += data.0;
held_average_execution_times[i].0 += data.0;
num += data.1;
held_average_execution_times[i].1 += data.1;
*data = (Duration::new(0, 0), 0);
}
}
if num > 0 {
held_recent_execution_times[i] = sum.div_f64(num as f64);
}
(
if held_average_execution_times[i].1 > 0 {
format!(
"{:.1?}",
held_average_execution_times[i]
.0
.div_f64(held_average_execution_times[i].1 as f64)
)
} else {
String::from("?")
},
if held_recent_execution_times[i] > Duration::new(0, 0) {
format!("{:.1?}", held_recent_execution_times[i])
} else {
String::from("?")
},
)
})
.unzip();
let execution_positions: Vec<u8> = thread_execution_positions
.iter()
.map(|pos| pos.load(Ordering::SeqCst))
.collect();
format!(
"{{ [{}] [{}] {:.?} }}",
recent_execution_times.join(", "),
average_execution_times.join(", "),
execution_positions
)
} else {
String::from("")
}
);
stdout.flush().unwrap();
}
}
// Since `Duration::saturating_sub` is unstable this is an alternative.
fn saturating_sub(a: Duration, b: Duration) -> Duration {
if let Some(dur) = a.checked_sub(b) {
dur
} else {
Duration::new(0, 0)
}
}
main.rs
use std::{cmp,sync::Arc};
type Image = Vec<Vec<Pixel>>;
#[derive(Clone)]
pub struct Pixel {
pub luma: u8,
}
impl From<&u8> for Pixel {
fn from(x: &u8) -> Pixel {
Pixel { luma: *x }
}
}
fn main() {
// Setup
// -------------------------------------------
fn open_image(path: &str) -> Image {
let example = image::open(path).unwrap().to_rgb8();
let dims = example.dimensions();
let size = (dims.0 as usize, dims.1 as usize);
let example_vec = example.into_raw();
// Binarizes image
let img_vec = from_raw(&example_vec, size);
img_vec
}
println!("Started ...");
let example: Image = open_image("example.jpg");
let target: Image = open_image("target.jpg");
// let first_image = Some(Arc::new((examples[0].clone(), targets[0].clone())));
println!("Opened...");
let image = Some(Arc::new((example, target)));
// Running the optimization
// -------------------------------------------
println!("Started opt...");
let best = simple_optimization::random_search(
[0..255, 0..255, 0..255, 1..255, 1..255],
eval_one,
image,
Some(simple_optimization::Polling {
poll_rate: 100,
printing: true,
early_exit_minimum: None,
thread_execution_reporting: true,
}),
2300,
);
println!("{:.?}", best); // [34, 220, 43, 253, 168]
assert!(false);
fn eval_one(arr: &[u8; 5], opt: Option<Arc<(Image, Image)>>) -> f64 {
let bin_params = (
arr[0] as u8,
arr[1] as u8,
arr[2] as u8,
arr[3] as usize,
arr[4] as usize,
);
let arc = opt.unwrap();
// Gets average mean-squared-error
let binary_pixels = binarize_buffer(arc.0.clone(), bin_params);
mse(binary_pixels, &arc.1)
}
// Mean-squared-error
fn mse(prediction: Image, target: &Image) -> f64 {
let n = target.len() * target[0].len();
prediction
.iter()
.flatten()
.zip(target.iter().flatten())
.map(|(p, t)| difference(p, t).powf(2.))
.sum::<f64>()
/ (2. * n as f64)
}
#[rustfmt::skip]
fn difference(p: &Pixel, t: &Pixel) -> f64 {
p.luma as f64 - t.luma as f64
}
}
pub fn from_raw(raw: &[u8], (_i_size, j_size): (usize, usize)) -> Vec<Vec<Pixel>> {
(0..raw.len())
.step_by(j_size)
.map(|index| {
raw[index..index + j_size]
.iter()
.map(Pixel::from)
.collect::<Vec<Pixel>>()
})
.collect()
}
pub fn binarize_buffer(
mut img: Vec<Vec<Pixel>>,
(_, _, local_luma_boundary, local_field_reach, local_field_size): (u8, u8, u8, usize, usize),
) -> Vec<Vec<Pixel>> {
let (i_size, j_size) = (img.len(), img[0].len());
let i_chunks = (i_size as f32 / local_field_size as f32).ceil() as usize;
let j_chunks = (j_size as f32 / local_field_size as f32).ceil() as usize;
let mut local_luma: Vec<Vec<u8>> = vec![vec![u8::default(); j_chunks]; i_chunks];
// Gets average luma in local fields
// O((s+r)^2*(n/s)*(m/s)) : s = local field size, r = local field reach
for (i_chunk, i) in (0..i_size).step_by(local_field_size).enumerate() {
let i_range = zero_checked_sub(i, local_field_reach)
..cmp::min(i + local_field_size + local_field_reach, i_size);
let i_range_length = i_range.end - i_range.start;
for (j_chunk, j) in (0..j_size).step_by(local_field_size).enumerate() {
let j_range = zero_checked_sub(j, local_field_reach)
..cmp::min(j + local_field_size + local_field_reach, j_size);
let j_range_length = j_range.end - j_range.start;
let total: u32 = i_range
.clone()
.map(|i_range_indx| {
img[i_range_indx][j_range.clone()]
.iter()
.map(|p| p.luma as u32)
.sum::<u32>()
})
.sum();
local_luma[i_chunk][j_chunk] = (total / (i_range_length * j_range_length) as u32) as u8;
}
}
// Apply binarization
// O(nm)
for i in 0..i_size {
let i_group: usize = i / local_field_size; // == floor(i as f32 / local_field_size as f32) as usize
for j in 0..j_size {
let j_group: usize = j / local_field_size;
// Local average boundaries
// --------------------------------
if let Some(local) = local_luma[i_group][j_group].checked_sub(local_luma_boundary) {
if img[i][j].luma < local {
img[i][j].luma = 0;
continue;
}
}
if let Some(local) = local_luma[i_group][j_group].checked_add(local_luma_boundary) {
if img[i][j].luma > local {
img[i][j].luma = 255;
continue;
}
}
// White is the negative (false/0) colour in our binarization, thus this is our else case
img[i][j].luma = 255;
}
}
img
}
#[rustfmt::skip]
fn zero_checked_sub(a: usize, b: usize) -> usize { if a > b { a - b } else { 0 } }
Project zip (in case you'd rather not spend time setting it up).
Else, here are the images being used as /target.jpg and /example.jpg (it shouldn't matter it being specifically these images, any should work):
And Cargo.toml dependencies:
[dependencies]
rand = "0.8.4"
itertools = "0.10.1" # izip!
num_cpus = "1.13.0" # Multi-threading
print_duration = "1.0.0" # Printing progress
num = "0.4.0" # Generics
rand_distr = "0.4.1" # Normal distribution
image = "0.23.14"
serde = { version="1.0.118", features=["derive"] }
serde_json = "1.0.50"
I do feel rather reluctant to post such a large question and
inevitably require people to read a few hundred lines (especially given the project doesn't work in a playground), but I'm really lost here and can see no other way to communicate the whole area of the problem. Apologies for this.
As noted, I have tried for a while to figure out what is happening here, but I have come up short, any help would be really appreciate.
Some basic debugging (aka println! everywhere) shows that your performance problem is not related to the multithreading at all. It just happens randomly, and when there are 24 threads doing their job, the fact that one is randomly stalling is not noticeable, but when there is only one or two threads left, they stand out as slow.
But where is this performance bottleneck? Well, you are stating it yourself in the code: in binary_buffer you say:
// Gets average luma in local fields
// O((s+r)^2*(n/s)*(m/s)) : s = local field size, r = local field reach
The values of s and r seem to be random values between 0 and 255, while n is the length of a image row, in bytes 3984 * 3 = 11952, and m is the number of rows 2271.
Now, most of the times that O() is around a few millions, quite manageable. But if s happens to be small and r big, such as (3, 200) then the number of computations blows up to over 1e11!
Fortunately I think you can define the ranges of those values in the original call to random_search so a bit of tweaking there should send you back to reasonable complexity. Changing the ranges to:
[0..255, 0..255, 0..255, 1..255, 20..255],
// ^ here
seems to do the trick for me.
PS: These lines at the beginning of binary_buffer were key to discover this:
let o = (i_size / local_field_size) * (j_size / local_field_size) * (local_field_size + local_field_reach).pow(2);
println!("\nO() = {}", o);

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