use crate::graph::{EdgeMatrix, NodeVector, Node}; const C_REP: f32 = 0.1; const C_SPRING: f32 = 0.1; pub struct InitialModel { edges: EdgeMatrix, size: usize, opt_dist: f32, t: f32, } impl InitialModel { pub fn new(edges: EdgeMatrix, size: usize) -> InitialModel { let opt_dist = 1.0 / (size as f32).sqrt(); InitialModel{ edges, size, opt_dist, t: 0.1 } } pub fn prepare(& mut self, nodes: &NodeVector) { self.t = 0.1 * f32::max( 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 { let node = nodes[i_node].read().unwrap(); let edges = self.edges.read().unwrap(); let mut node_x = node.x; let mut node_y = node.y; 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().max(0.01); 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 = (C_REP / (dist).powi(2)).min(self.t); let f_rep_x = f_rep * unit_x; let f_rep_y = f_rep * unit_y; node_x -= f_rep_x; node_y -= f_rep_y; } else { let f_spring = (C_SPRING * (dist / self.opt_dist).log(2.0)).min(self.t); let f_spring_x = f_spring * unit_x; let f_spring_y = f_spring * unit_y; node_x += f_spring_x; node_y += f_spring_y; } } Node { x: node_x, y: node_y, } } }