58 lines
1.8 KiB
Rust
58 lines
1.8 KiB
Rust
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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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use crate::search::Searcher;
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pub fn fractal_dimension(
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generators: Vec<DMatrix<f64>>,
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root: Vec<DVector<f64>>,
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upper_bound: f64,
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n: usize,
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debug: bool,
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generations: usize,
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orthogonal_generators: Vec<Vec<usize>>,
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) -> Result<f64, linregress::Error> {
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let xs: Vec<f64> = (1..=n)
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.map(|x| (x as f64 * upper_bound / (2.0 * n as f64) + upper_bound / 2.0))
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.collect();
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let mut searcher = Searcher::new(&xs, &generators);
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for circle in root {
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searcher.search(&circle, std::usize::MAX, 0, generations);
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}
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if debug {
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println!("{}", Yellow.paint("Done counting circles!"));
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println!("sample points:\n{:?}", xs);
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println!(
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"\nnumber of circles fewer than each of those sample points:\n{:?}",
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searcher.counts
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);
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}
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let xs: Vec<f64> = xs.iter().map(|x| x.ln()).collect();
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let ys: Vec<f64> = searcher.counts.iter().map(|x| (*x as f64).ln()).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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let parameters = model.parameters;
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Ok(parameters.regressor_values[0])
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}
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