clean up and moving iter and threads to params
This commit is contained in:
parent
d897436ba1
commit
d8b172c7fb
24
src/graph.rs
24
src/graph.rs
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@ -1,3 +1,4 @@
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use rand::Rng;
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use std::sync::{Arc, RwLock};
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use std::sync::{Arc, RwLock};
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pub struct Node {
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pub struct Node {
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pub x: f32,
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pub x: f32,
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@ -10,3 +11,26 @@ pub struct Edge {
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pub type EdgeMatrix = Arc<RwLock<Vec<Vec<Edge>>>>;
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pub type EdgeMatrix = Arc<RwLock<Vec<Vec<Edge>>>>;
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pub type NodeVector = Arc<Vec<RwLock<Node>>>;
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pub type NodeVector = Arc<Vec<RwLock<Node>>>;
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pub fn new_edge_matrix(size: usize) -> EdgeMatrix {
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let mut matrix = Vec::with_capacity(size);
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for _ in 0..size {
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let mut row = Vec::with_capacity(size);
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for _ in 0..size {
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row.push(Edge { weight: 0.0 });
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}
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matrix.push(row);
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}
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Arc::new(RwLock::new(matrix))
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}
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pub fn new_node_vector(size: usize) -> NodeVector {
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let mut nodes = Vec::with_capacity(size);
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for _ in 0..size {
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nodes.push(RwLock::new(Node {
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x: rand::thread_rng().gen_range(-0.5..0.5),
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y: rand::thread_rng().gen_range(-0.5..0.5),
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}));
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}
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Arc::new(nodes)
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}
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@ -1,94 +0,0 @@
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use rand::Rng;
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use std::sync::{Arc, RwLock};
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use std::thread;
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use crate::graph::{EdgeMatrix, NodeVector, Node, Edge};
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use crate::my_model;
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use crate::utils;
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fn nodes_list(size: usize) -> NodeVector {
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let mut nodes = Vec::new();
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for _ in 0..size {
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let node = RwLock::new(Node {
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x: rand::thread_rng().gen_range(-0.5..0.5),
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y: rand::thread_rng().gen_range(-0.5..0.5),
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});
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nodes.push(node);
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}
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Arc::new(nodes)
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}
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fn connection_matrix(size: usize) -> EdgeMatrix {
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let mut matrix = Vec::with_capacity(size);
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for _ in 0..size {
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let mut row = Vec::with_capacity(size);
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for _ in 0..size {
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row.push(Edge { weight: 0.0 });
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}
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matrix.push(row);
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}
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Arc::new(RwLock::new(matrix))
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}
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fn edge_matrix_from_edge_list(number_of_nodes: usize, edge_list: Vec<(u32, u32)>) -> EdgeMatrix {
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let matrix_ptr = connection_matrix(number_of_nodes as usize);
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{
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let mut matrix = matrix_ptr.write().unwrap();
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for (node_a, node_b) in edge_list {
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matrix[node_a as usize][node_b as usize].weight = 1.0;
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matrix[node_b as usize][node_a as usize].weight = 1.0;
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}
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}
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matrix_ptr
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}
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pub fn layout(number_of_nodes: usize, edge_list: Vec<(u32, u32)>) -> Vec<(f32, f32)> {
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const ITER: usize = 5000;
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const THREADS: usize = 8;
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// let edges = connection_matrix(size);
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let edges = edge_matrix_from_edge_list(number_of_nodes, edge_list);
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let mut nodes = nodes_list(number_of_nodes);
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let mut nodes_next = nodes_list(number_of_nodes);
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// let model = Arc::new(RwLock::new(spring_model::InitialModel::new(edges, number_of_nodes)));
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let model = Arc::new(RwLock::new(my_model::MyModel::new(edges, number_of_nodes, ITER)));
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let chunks = utils::gen_chunks(number_of_nodes, THREADS);
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for epoch in 0..ITER {
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model.write().unwrap().prepare(&nodes);
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let mut handles = vec![];
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for i in 0..THREADS {
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let nodes = nodes.clone();
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let nodes_next = nodes_next.clone();
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let chunk = chunks[i].clone();
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let model = model.clone();
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let handle = thread::spawn(move || {
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for n in chunk {
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let update = model.read().unwrap().step(&nodes,n);
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let mut result = nodes_next[n].write().unwrap();
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result.x = update.x;
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result.y = update.y;
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}
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});
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handles.push(handle);
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}
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for handle in handles {
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handle.join().unwrap();
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}
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// swap nodes and nodes_next
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let tmp = nodes.clone();
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nodes = nodes_next.clone();
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nodes_next = tmp.clone();
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}
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let mut result = vec![];
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for node in nodes.iter() {
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let node = node.read().unwrap();
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result.push((node.x, node.y));
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}
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result
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}
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10
src/lib.rs
10
src/lib.rs
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mod layout;
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mod runner;
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mod utils;
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mod utils;
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mod spring_model;
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mod spring_model;
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mod my_model;
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mod graph;
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mod graph;
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use pyo3::prelude::*;
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use pyo3::prelude::*;
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/// Formats the sum of two numbers as string.
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/// Formats the sum of two numbers as string.
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#[pyfunction]
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#[pyfunction(number_of_nodes, edges, "*", iter=500, threads=0)]
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fn layout_from_edge_list(number_of_nodes: usize, edges: Vec<(u32, u32)>) -> PyResult<Vec<(f32, f32)>> {
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fn layout_from_edge_list(number_of_nodes: usize, edges: Vec<(u32, u32)>, iter: usize, threads: usize) -> PyResult<Vec<(f32, f32)>> {
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let r = runner::Runner::new(iter, threads);
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Ok(
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Ok(
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layout::layout(number_of_nodes, edges)
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r.layout(number_of_nodes, edges)
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)
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)
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}
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}
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@ -1,97 +0,0 @@
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use crate::graph::{EdgeMatrix, NodeVector, Node};
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pub struct MyModel {
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edges: EdgeMatrix,
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ranks: Vec<f32>,
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size: usize,
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opt_dist: f32,
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c: f32,
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dc: f32,
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}
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impl MyModel {
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pub fn new(edges: EdgeMatrix, size: usize, iterations: usize) -> MyModel {
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let opt_dist = 1.0;
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let c = 0.1;
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let mut ranks = vec![0.0; size];
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{
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let edges = edges.read().unwrap();
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for i in 0..size {
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ranks[i] = edges[i].iter().map(|e| e.weight).sum();
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}
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}
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MyModel{
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edges,
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ranks,
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size,
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opt_dist,
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c: c,
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dc: c / ((iterations + 1) as f32)
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}
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}
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pub fn prepare(& mut self, _nodes: &NodeVector) {
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self.c -= self.dc;
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}
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pub fn step(&self, nodes: &NodeVector, i_node: usize) -> Node {
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let node = nodes[i_node].read().unwrap();
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let edges = self.edges.read().unwrap();
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let node_x = node.x;
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let node_y = node.y;
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let mut sum_x = 0.0;
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let mut sum_y = 0.0;
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for o in 0..self.size {
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if o == i_node {
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continue;
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}
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let o_x: f32;
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let o_y: f32;
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{
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let other = nodes[o].read().unwrap();
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o_x = other.x;
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o_y = other.y;
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}
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let d_x = o_x - node_x;
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let d_y = o_y - node_y;
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let dist = (d_x * d_x + d_y * d_y).sqrt();
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let unit_x = d_x / dist;
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let unit_y = d_y / dist;
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let edge = edges[i_node][o].weight;
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if edge == 0.0 {
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let f_rep = dist.powi(2).recip().min(self.opt_dist);
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let f_rep_x = f_rep * unit_x;
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let f_rep_y = f_rep * unit_y;
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sum_x -= f_rep_x;
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sum_y -= f_rep_y;
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} else {
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let f_spring = 0.5 * (dist - self.opt_dist);
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let f_spring_x = f_spring * unit_x;
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let f_spring_y = f_spring * unit_y;
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sum_x += f_spring_x;
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sum_y += f_spring_y;
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}
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}
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// limit the movement
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// TODO: find a good upper bound
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let sum_l = (sum_x * sum_x + sum_y * sum_y).sqrt().max(1e-6).recip() * self.c;
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let sum_x = sum_x * sum_l;
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let sum_y = sum_y * sum_l;
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Node {
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x: node_x + sum_x,
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y: node_y + sum_y,
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}
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}
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}
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use std::sync::{Arc, RwLock};
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use std::thread;
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use crate::graph::{EdgeMatrix, new_node_vector, new_edge_matrix};
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use crate::spring_model;
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use crate::utils;
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pub struct Runner {
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iterations: usize,
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threads: usize,
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}
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impl Runner {
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pub fn new(iterations: usize, threads: usize) -> Self {
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Self {
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iterations,
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threads,
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}
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}
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pub fn layout(self: &Self, number_of_nodes: usize, edge_list: Vec<(u32, u32)>) -> Vec<(f32, f32)> {
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// let edges = connection_matrix(size);
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let edges = edge_matrix_from_edge_list(number_of_nodes, edge_list);
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let mut nodes = new_node_vector(number_of_nodes);
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let mut nodes_next = new_node_vector(number_of_nodes);
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// let model = Arc::new(RwLock::new(spring_model::InitialModel::new(edges, number_of_nodes)));
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let model = Arc::new(RwLock::new(spring_model::MyModel::new(edges, number_of_nodes, self.iterations)));
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let chunks = utils::gen_chunks(number_of_nodes, self.threads);
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for _epoch in 0..self.iterations {
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model.write().unwrap().prepare(&nodes);
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let mut handles = vec![];
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for i in 0..self.threads {
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let nodes = nodes.clone();
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let nodes_next = nodes_next.clone();
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let chunk = chunks[i].clone();
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let model = model.clone();
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let handle = thread::spawn(move || {
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for n in chunk {
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let update = model.read().unwrap().step(&nodes,n);
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let mut result = nodes_next[n].write().unwrap();
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result.x = update.x;
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result.y = update.y;
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}
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});
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handles.push(handle);
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}
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for handle in handles {
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handle.join().unwrap();
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}
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// swap nodes and nodes_next
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let tmp = nodes.clone();
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nodes = nodes_next.clone();
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nodes_next = tmp.clone();
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}
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let mut result = vec![];
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for node in nodes.iter() {
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let node = node.read().unwrap();
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result.push((node.x, node.y));
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}
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result
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}
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}
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fn edge_matrix_from_edge_list(number_of_nodes: usize, edge_list: Vec<(u32, u32)>) -> EdgeMatrix {
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let matrix_ptr = new_edge_matrix(number_of_nodes as usize);
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{
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let mut matrix = matrix_ptr.write().unwrap();
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for (node_a, node_b) in edge_list {
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matrix[node_a as usize][node_b as usize].weight = 1.0;
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matrix[node_b as usize][node_a as usize].weight = 1.0;
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}
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}
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matrix_ptr
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}
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use crate::graph::{EdgeMatrix, NodeVector, Node};
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use crate::graph::{EdgeMatrix, NodeVector, Node};
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pub struct MyModel {
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const C_REP: f32 = 0.1;
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const C_SPRING: f32 = 0.1;
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pub struct InitialModel {
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edges: EdgeMatrix,
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edges: EdgeMatrix,
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size: usize,
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size: usize,
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opt_dist: f32,
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opt_dist: f32,
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t: f32,
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c: f32,
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dc: f32,
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}
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}
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impl InitialModel {
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impl MyModel {
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pub fn new(edges: EdgeMatrix, size: usize) -> InitialModel {
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pub fn new(edges: EdgeMatrix, size: usize, iterations: usize) -> MyModel {
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let opt_dist = 1.0 / (size as f32).sqrt();
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let opt_dist = 1.0;
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InitialModel{ edges, size, opt_dist, t: 0.1 }
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let c = 0.1;
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MyModel{
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edges,
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size,
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opt_dist,
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c: c,
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dc: c / ((iterations + 1) as f32)
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}
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}
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}
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pub fn prepare(& mut self, nodes: &NodeVector) {
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pub fn prepare(& mut self, _nodes: &NodeVector) {
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self.t = 0.1 * f32::max(
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self.c -= self.dc;
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nodes.iter().map(|n| n.read().unwrap().x.abs()).reduce(|a, b| a.max(b)).unwrap(),
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nodes.iter().map(|n| n.read().unwrap().y.abs()).reduce(|a, b| a.max(b)).unwrap()
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);
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}
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}
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pub fn step(&self, nodes: &NodeVector, i_node: usize) -> Node {
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pub fn step(&self, nodes: &NodeVector, i_node: usize) -> Node {
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@ -31,8 +32,12 @@ impl InitialModel {
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let node = nodes[i_node].read().unwrap();
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let node = nodes[i_node].read().unwrap();
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let edges = self.edges.read().unwrap();
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let edges = self.edges.read().unwrap();
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let mut node_x = node.x;
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let node_x = node.x;
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let mut node_y = node.y;
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let node_y = node.y;
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let mut sum_x = 0.0;
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let mut sum_y = 0.0;
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|
|
||||||
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,
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
|
@ -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::*;
|
||||||
|
|
||||||
|
|
Loading…
Reference in New Issue