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94 lines
3.4 KiB
Rust
94 lines
3.4 KiB
Rust
use rustframe::compute::stats::{
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chi2_test, covariance, covariance_matrix, mean, median, pearson, percentile, stddev, t_test,
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};
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use rustframe::matrix::{Axis, Matrix};
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/// Demonstrates some of the statistics utilities in Rustframe.
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///
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/// The example is split into three parts:
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/// - Basic descriptive statistics on a small data set
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/// - Covariance and correlation calculations
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/// - Simple inferential tests (t-test and chi-square)
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fn main() {
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descriptive_demo();
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println!("\n-----\n");
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correlation_demo();
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println!("\n-----\n");
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inferential_demo();
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}
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fn descriptive_demo() {
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println!("Descriptive statistics\n----------------------");
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let data = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0], 1, 5);
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println!("mean : {:.2}", mean(&data));
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println!("std dev : {:.2}", stddev(&data));
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println!("median : {:.2}", median(&data));
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println!("25th percentile: {:.2}", percentile(&data, 25.0));
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}
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fn correlation_demo() {
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println!("Covariance and Correlation\n--------------------------");
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let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
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let y = Matrix::from_vec(vec![1.0, 2.0, 3.0, 5.0], 2, 2);
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let cov = covariance(&x, &y);
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let cov_mat = covariance_matrix(&x, Axis::Col);
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let corr = pearson(&x, &y);
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println!("covariance : {:.2}", cov);
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println!("cov matrix : {:?}", cov_mat.data());
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println!("pearson r : {:.2}", corr);
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}
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fn inferential_demo() {
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println!("Inferential statistics\n----------------------");
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let s1 = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0], 1, 5);
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let s2 = Matrix::from_vec(vec![6.0, 7.0, 8.0, 9.0, 10.0], 1, 5);
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let (t_stat, t_p) = t_test(&s1, &s2);
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println!("t statistic : {:.2}, p-value: {:.4}", t_stat, t_p);
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let observed = Matrix::from_vec(vec![12.0, 5.0, 8.0, 10.0], 2, 2);
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let (chi2, chi_p) = chi2_test(&observed);
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println!("chi^2 : {:.2}, p-value: {:.4}", chi2, chi_p);
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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const EPS: f64 = 1e-8;
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#[test]
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fn test_descriptive_demo() {
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let data = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0], 1, 5);
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assert!((mean(&data) - 3.0).abs() < EPS);
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assert!((stddev(&data) - 1.4142135623730951).abs() < EPS);
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assert!((median(&data) - 3.0).abs() < EPS);
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assert!((percentile(&data, 25.0) - 2.0).abs() < EPS);
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}
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#[test]
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fn test_correlation_demo() {
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let x = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
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let y = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 5.0], 2, 2);
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let cov = covariance(&x, &y);
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assert!((cov - 1.625).abs() < EPS);
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let cov_mat = covariance_matrix(&x, Axis::Col);
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assert!((cov_mat.get(0, 0) - 2.0).abs() < EPS);
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assert!((cov_mat.get(1, 1) - 2.0).abs() < EPS);
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let corr = pearson(&x, &y);
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assert!((corr - 0.9827076298239908).abs() < 1e-6);
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}
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#[test]
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fn test_inferential_demo() {
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let s1 = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0], 1, 5);
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let s2 = Matrix::from_rows_vec(vec![6.0, 7.0, 8.0, 9.0, 10.0], 1, 5);
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let (t_stat, p_value) = t_test(&s1, &s2);
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assert!((t_stat + 5.0).abs() < 1e-5);
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assert!(p_value > 0.0 && p_value < 1.0);
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let observed = Matrix::from_rows_vec(vec![12.0, 5.0, 8.0, 10.0], 2, 2);
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let (chi2, p) = chi2_test(&observed);
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assert!(chi2 > 0.0);
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assert!(p > 0.0 && p < 1.0);
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}
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}
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