further refinement. upper bound for movement
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@ -63,11 +63,11 @@ fn read_graph(file_name: &str) -> (usize, EdgeMatrix) {
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}
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fn main() -> std::io::Result<()> {
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const ITER: usize = 50;
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const ITER: usize = 600;
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const THREADS: usize = 8;
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// let edges = connection_matrix(size);
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let (size, edges): (usize, EdgeMatrix) = read_graph("../graph.bin");
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let (size, edges): (usize, EdgeMatrix) = read_graph("../debug_graph.bin");
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println!("Size: {}", size);
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let mut nodes = nodes_list(size);
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let mut nodes_next = nodes_list(size);
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@ -41,8 +41,12 @@ impl MyModel {
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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 mut node_x = node.x;
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let mut node_y = node.y;
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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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@ -64,23 +68,30 @@ impl MyModel {
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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 = (self.c / (dist).powi(2)).min(self.c);
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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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node_x -= f_rep_x;
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node_y -= f_rep_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 = self.c * 0.5 * (dist - self.opt_dist) / self.ranks[i_node];
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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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node_x += f_spring_x;
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node_y += f_spring_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,
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y: node_y,
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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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