98 lines
3.3 KiB
Rust
98 lines
3.3 KiB
Rust
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#![allow(dead_code)]
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use crate::{search::{Searcher, Task}, time::Timer};
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use ansi_term::Color::Yellow;
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use linregress::{FormulaRegressionBuilder, RegressionDataBuilder};
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use nalgebra::{DMatrix, DVector};
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/// struct representing the circle counting task to be used by `crate::search::Searcher` to compute
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/// the fractal dimension via the circle counting method
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struct CircleCountingTask {
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counts: Vec<usize>,
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maxes: Vec<f64>,
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}
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impl Task for CircleCountingTask {
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/// Implementation of `Task` for `CircleCountingTask` that counts circles, unsurprisingly.
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fn recurse(&mut self, circle: &DVector<f64>) -> bool {
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let mut too_big = true;
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for (i, max) in self.maxes.iter().enumerate() {
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if circle[1] < *max {
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too_big = false;
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self.counts[i] += 1;
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}
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}
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!too_big
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}
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}
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impl CircleCountingTask {
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/// Create a new `CircleCountingTask`. `n` is the number of data points (skewed to larger
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/// maximum curvatures) while `max_curvature` is the overall maximum curvature.
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fn new(n: usize, max_curvature: f64) -> Self {
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Self {
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counts: vec![0; n],
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maxes: (1..=n)
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.map(|x| (x as f64 * max_curvature / (2.0 * n as f64) + max_curvature / 2.0))
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.collect(),
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}
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}
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}
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/// Compute the fractal dimension of a polyhedral circle packing with generators given by
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/// `generators` and root tuple given by `root` by counting circles with curvature less than
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/// `max_curvature`, using `n` data points in the regression. The value of `debug` determines
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/// whether or not debug information is printed, and `max_depth` is an optional maximum recursion
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/// depth.
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pub fn fractal_dimension(
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generators: &[DMatrix<f64>],
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root: &DMatrix<f64>,
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max_curvature: f64,
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n: usize,
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debug: bool,
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max_depth: usize,
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timer: &Timer,
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) -> Result<f64, linregress::Error> {
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if debug {
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println!("{}", Yellow.paint("Counting circles"));
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}
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let mut circle_counter = CircleCountingTask::new(n, max_curvature);
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let mut searcher = Searcher::new(&mut circle_counter, generators, max_depth);
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searcher.search(root, debug, timer);
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if debug {
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println!("{}", Yellow.paint("Done counting circles!"));
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println!("sample points:\n{:?}", circle_counter.maxes);
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println!(
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"\nnumber of circles fewer than each of those sample points:\n{:?}",
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circle_counter.counts
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);
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}
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let xs: Vec<f64> = circle_counter.maxes.iter().map(|x| x.ln()).collect();
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let ys: Vec<f64> = circle_counter
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.counts
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.iter()
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.map(|x| (*x as f64).ln())
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.collect();
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let data = vec![("Y", ys), ("X", xs)];
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let data = RegressionDataBuilder::new().build_from(data)?;
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let formula = "Y ~ X";
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let model = FormulaRegressionBuilder::new()
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.data(&data)
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.formula(formula)
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.fit()?;
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if debug {
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println!("\n{}", Yellow.paint("Built regression model!"));
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println!("Model info:");
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println!("\tr^2:\t\t{}", model.rsquared);
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println!("\tp-value:\t{}", model.pvalues.pairs()[0].1);
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println!("\tintercept:\t{}", model.parameters.intercept_value);
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println!("\tslope:\t{}", model.parameters.regressor_values[0]);
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}
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Ok(model.parameters.regressor_values[0])
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}
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