clean up and moving iter and threads to params

This commit is contained in:
Niko Abeler 2022-11-20 16:57:31 +01:00
parent d897436ba1
commit d8b172c7fb
7 changed files with 149 additions and 223 deletions

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@ -1,3 +1,4 @@
use rand::Rng;
use std::sync::{Arc, RwLock}; use std::sync::{Arc, RwLock};
pub struct Node { pub struct Node {
pub x: f32, pub x: f32,
@ -10,3 +11,26 @@ pub struct Edge {
pub type EdgeMatrix = Arc<RwLock<Vec<Vec<Edge>>>>; pub type EdgeMatrix = Arc<RwLock<Vec<Vec<Edge>>>>;
pub type NodeVector = Arc<Vec<RwLock<Node>>>; pub type NodeVector = Arc<Vec<RwLock<Node>>>;
pub fn new_edge_matrix(size: usize) -> EdgeMatrix {
let mut matrix = Vec::with_capacity(size);
for _ in 0..size {
let mut row = Vec::with_capacity(size);
for _ in 0..size {
row.push(Edge { weight: 0.0 });
}
matrix.push(row);
}
Arc::new(RwLock::new(matrix))
}
pub fn new_node_vector(size: usize) -> NodeVector {
let mut nodes = Vec::with_capacity(size);
for _ in 0..size {
nodes.push(RwLock::new(Node {
x: rand::thread_rng().gen_range(-0.5..0.5),
y: rand::thread_rng().gen_range(-0.5..0.5),
}));
}
Arc::new(nodes)
}

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@ -1,94 +0,0 @@
use rand::Rng;
use std::sync::{Arc, RwLock};
use std::thread;
use crate::graph::{EdgeMatrix, NodeVector, Node, Edge};
use crate::my_model;
use crate::utils;
fn nodes_list(size: usize) -> NodeVector {
let mut nodes = Vec::new();
for _ in 0..size {
let node = RwLock::new(Node {
x: rand::thread_rng().gen_range(-0.5..0.5),
y: rand::thread_rng().gen_range(-0.5..0.5),
});
nodes.push(node);
}
Arc::new(nodes)
}
fn connection_matrix(size: usize) -> EdgeMatrix {
let mut matrix = Vec::with_capacity(size);
for _ in 0..size {
let mut row = Vec::with_capacity(size);
for _ in 0..size {
row.push(Edge { weight: 0.0 });
}
matrix.push(row);
}
Arc::new(RwLock::new(matrix))
}
fn edge_matrix_from_edge_list(number_of_nodes: usize, edge_list: Vec<(u32, u32)>) -> EdgeMatrix {
let matrix_ptr = connection_matrix(number_of_nodes as usize);
{
let mut matrix = matrix_ptr.write().unwrap();
for (node_a, node_b) in edge_list {
matrix[node_a as usize][node_b as usize].weight = 1.0;
matrix[node_b as usize][node_a as usize].weight = 1.0;
}
}
matrix_ptr
}
pub fn layout(number_of_nodes: usize, edge_list: Vec<(u32, u32)>) -> Vec<(f32, f32)> {
const ITER: usize = 5000;
const THREADS: usize = 8;
// let edges = connection_matrix(size);
let edges = edge_matrix_from_edge_list(number_of_nodes, edge_list);
let mut nodes = nodes_list(number_of_nodes);
let mut nodes_next = nodes_list(number_of_nodes);
// let model = Arc::new(RwLock::new(spring_model::InitialModel::new(edges, number_of_nodes)));
let model = Arc::new(RwLock::new(my_model::MyModel::new(edges, number_of_nodes, ITER)));
let chunks = utils::gen_chunks(number_of_nodes, THREADS);
for epoch in 0..ITER {
model.write().unwrap().prepare(&nodes);
let mut handles = vec![];
for i in 0..THREADS {
let nodes = nodes.clone();
let nodes_next = nodes_next.clone();
let chunk = chunks[i].clone();
let model = model.clone();
let handle = thread::spawn(move || {
for n in chunk {
let update = model.read().unwrap().step(&nodes,n);
let mut result = nodes_next[n].write().unwrap();
result.x = update.x;
result.y = update.y;
}
});
handles.push(handle);
}
for handle in handles {
handle.join().unwrap();
}
// swap nodes and nodes_next
let tmp = nodes.clone();
nodes = nodes_next.clone();
nodes_next = tmp.clone();
}
let mut result = vec![];
for node in nodes.iter() {
let node = node.read().unwrap();
result.push((node.x, node.y));
}
result
}

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@ -1,16 +1,16 @@
mod layout; mod runner;
mod utils; mod utils;
mod spring_model; mod spring_model;
mod my_model;
mod graph; mod graph;
use pyo3::prelude::*; use pyo3::prelude::*;
/// Formats the sum of two numbers as string. /// Formats the sum of two numbers as string.
#[pyfunction] #[pyfunction(number_of_nodes, edges, "*", iter=500, threads=0)]
fn layout_from_edge_list(number_of_nodes: usize, edges: Vec<(u32, u32)>) -> PyResult<Vec<(f32, f32)>> { fn layout_from_edge_list(number_of_nodes: usize, edges: Vec<(u32, u32)>, iter: usize, threads: usize) -> PyResult<Vec<(f32, f32)>> {
let r = runner::Runner::new(iter, threads);
Ok( Ok(
layout::layout(number_of_nodes, edges) r.layout(number_of_nodes, edges)
) )
} }

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@ -1,97 +0,0 @@
use crate::graph::{EdgeMatrix, NodeVector, Node};
pub struct MyModel {
edges: EdgeMatrix,
ranks: Vec<f32>,
size: usize,
opt_dist: f32,
c: f32,
dc: f32,
}
impl MyModel {
pub fn new(edges: EdgeMatrix, size: usize, iterations: usize) -> MyModel {
let opt_dist = 1.0;
let c = 0.1;
let mut ranks = vec![0.0; size];
{
let edges = edges.read().unwrap();
for i in 0..size {
ranks[i] = edges[i].iter().map(|e| e.weight).sum();
}
}
MyModel{
edges,
ranks,
size,
opt_dist,
c: c,
dc: c / ((iterations + 1) as f32)
}
}
pub fn prepare(& mut self, _nodes: &NodeVector) {
self.c -= self.dc;
}
pub fn step(&self, nodes: &NodeVector, i_node: usize) -> Node {
let node = nodes[i_node].read().unwrap();
let edges = self.edges.read().unwrap();
let node_x = node.x;
let node_y = node.y;
let mut sum_x = 0.0;
let mut sum_y = 0.0;
for o in 0..self.size {
if o == i_node {
continue;
}
let o_x: f32;
let o_y: f32;
{
let other = nodes[o].read().unwrap();
o_x = other.x;
o_y = other.y;
}
let d_x = o_x - node_x;
let d_y = o_y - node_y;
let dist = (d_x * d_x + d_y * d_y).sqrt();
let unit_x = d_x / dist;
let unit_y = d_y / dist;
let edge = edges[i_node][o].weight;
if edge == 0.0 {
let f_rep = dist.powi(2).recip().min(self.opt_dist);
let f_rep_x = f_rep * unit_x;
let f_rep_y = f_rep * unit_y;
sum_x -= f_rep_x;
sum_y -= f_rep_y;
} else {
let f_spring = 0.5 * (dist - self.opt_dist);
let f_spring_x = f_spring * unit_x;
let f_spring_y = f_spring * unit_y;
sum_x += f_spring_x;
sum_y += f_spring_y;
}
}
// limit the movement
// TODO: find a good upper bound
let sum_l = (sum_x * sum_x + sum_y * sum_y).sqrt().max(1e-6).recip() * self.c;
let sum_x = sum_x * sum_l;
let sum_y = sum_y * sum_l;
Node {
x: node_x + sum_x,
y: node_y + sum_y,
}
}
}

80
src/runner.rs Normal file
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@ -0,0 +1,80 @@
use std::sync::{Arc, RwLock};
use std::thread;
use crate::graph::{EdgeMatrix, new_node_vector, new_edge_matrix};
use crate::spring_model;
use crate::utils;
pub struct Runner {
iterations: usize,
threads: usize,
}
impl Runner {
pub fn new(iterations: usize, threads: usize) -> Self {
Self {
iterations,
threads,
}
}
pub fn layout(self: &Self, number_of_nodes: usize, edge_list: Vec<(u32, u32)>) -> Vec<(f32, f32)> {
// let edges = connection_matrix(size);
let edges = edge_matrix_from_edge_list(number_of_nodes, edge_list);
let mut nodes = new_node_vector(number_of_nodes);
let mut nodes_next = new_node_vector(number_of_nodes);
// let model = Arc::new(RwLock::new(spring_model::InitialModel::new(edges, number_of_nodes)));
let model = Arc::new(RwLock::new(spring_model::MyModel::new(edges, number_of_nodes, self.iterations)));
let chunks = utils::gen_chunks(number_of_nodes, self.threads);
for _epoch in 0..self.iterations {
model.write().unwrap().prepare(&nodes);
let mut handles = vec![];
for i in 0..self.threads {
let nodes = nodes.clone();
let nodes_next = nodes_next.clone();
let chunk = chunks[i].clone();
let model = model.clone();
let handle = thread::spawn(move || {
for n in chunk {
let update = model.read().unwrap().step(&nodes,n);
let mut result = nodes_next[n].write().unwrap();
result.x = update.x;
result.y = update.y;
}
});
handles.push(handle);
}
for handle in handles {
handle.join().unwrap();
}
// swap nodes and nodes_next
let tmp = nodes.clone();
nodes = nodes_next.clone();
nodes_next = tmp.clone();
}
let mut result = vec![];
for node in nodes.iter() {
let node = node.read().unwrap();
result.push((node.x, node.y));
}
result
}
}
fn edge_matrix_from_edge_list(number_of_nodes: usize, edge_list: Vec<(u32, u32)>) -> EdgeMatrix {
let matrix_ptr = new_edge_matrix(number_of_nodes as usize);
{
let mut matrix = matrix_ptr.write().unwrap();
for (node_a, node_b) in edge_list {
matrix[node_a as usize][node_b as usize].weight = 1.0;
matrix[node_b as usize][node_a as usize].weight = 1.0;
}
}
matrix_ptr
}

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@ -1,29 +1,30 @@
use crate::graph::{EdgeMatrix, NodeVector, Node}; use crate::graph::{EdgeMatrix, NodeVector, Node};
pub struct MyModel {
const C_REP: f32 = 0.1;
const C_SPRING: f32 = 0.1;
pub struct InitialModel {
edges: EdgeMatrix, edges: EdgeMatrix,
size: usize, size: usize,
opt_dist: f32, opt_dist: f32,
t: f32, c: f32,
dc: f32,
} }
impl InitialModel { impl MyModel {
pub fn new(edges: EdgeMatrix, size: usize) -> InitialModel { pub fn new(edges: EdgeMatrix, size: usize, iterations: usize) -> MyModel {
let opt_dist = 1.0 / (size as f32).sqrt(); let opt_dist = 1.0;
InitialModel{ edges, size, opt_dist, t: 0.1 } let c = 0.1;
MyModel{
edges,
size,
opt_dist,
c: c,
dc: c / ((iterations + 1) as f32)
}
} }
pub fn prepare(& mut self, nodes: &NodeVector) { pub fn prepare(& mut self, _nodes: &NodeVector) {
self.t = 0.1 * f32::max( self.c -= self.dc;
nodes.iter().map(|n| n.read().unwrap().x.abs()).reduce(|a, b| a.max(b)).unwrap(),
nodes.iter().map(|n| n.read().unwrap().y.abs()).reduce(|a, b| a.max(b)).unwrap()
);
} }
pub fn step(&self, nodes: &NodeVector, i_node: usize) -> Node { pub fn step(&self, nodes: &NodeVector, i_node: usize) -> Node {
@ -31,8 +32,12 @@ impl InitialModel {
let node = nodes[i_node].read().unwrap(); let node = nodes[i_node].read().unwrap();
let edges = self.edges.read().unwrap(); let edges = self.edges.read().unwrap();
let mut node_x = node.x; let node_x = node.x;
let mut node_y = node.y; let node_y = node.y;
let mut sum_x = 0.0;
let mut sum_y = 0.0;
for o in 0..self.size { for o in 0..self.size {
if o == i_node { if o == i_node {
continue; continue;
@ -47,30 +52,37 @@ impl InitialModel {
let d_x = o_x - node_x; let d_x = o_x - node_x;
let d_y = o_y - node_y; let d_y = o_y - node_y;
let dist = (d_x * d_x + d_y * d_y).sqrt().max(0.01); let dist = (d_x * d_x + d_y * d_y).sqrt();
let unit_x = d_x / dist; let unit_x = d_x / dist;
let unit_y = d_y / dist; let unit_y = d_y / dist;
let edge = edges[i_node][o].weight; let edge = edges[i_node][o].weight;
if edge == 0.0 { if edge == 0.0 {
let f_rep = (C_REP / (dist).powi(2)).min(self.t); let f_rep = dist.powi(2).recip().min(self.opt_dist);
let f_rep_x = f_rep * unit_x; let f_rep_x = f_rep * unit_x;
let f_rep_y = f_rep * unit_y; let f_rep_y = f_rep * unit_y;
node_x -= f_rep_x; sum_x -= f_rep_x;
node_y -= f_rep_y; sum_y -= f_rep_y;
} else { } else {
let f_spring = (C_SPRING * (dist / self.opt_dist).log(2.0)).min(self.t); let f_spring = 0.5 * (dist - self.opt_dist);
let f_spring_x = f_spring * unit_x; let f_spring_x = f_spring * unit_x;
let f_spring_y = f_spring * unit_y; let f_spring_y = f_spring * unit_y;
node_x += f_spring_x; sum_x += f_spring_x;
node_y += f_spring_y; sum_y += f_spring_y;
} }
} }
// limit the movement
// TODO: find a good upper bound
let sum_l = (sum_x * sum_x + sum_y * sum_y).sqrt().max(1e-6).recip() * self.c;
let sum_x = sum_x * sum_l;
let sum_y = sum_y * sum_l;
Node { Node {
x: node_x, x: node_x + sum_x,
y: node_y, y: node_y + sum_y,
} }
} }
} }

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@ -16,6 +16,7 @@ pub fn gen_chunks(n: usize, chunks: usize) -> Vec<Range<usize>> {
} }
#[cfg(test)]
mod test { mod test {
use super::*; use super::*;