rustframe/docs/src/compute.md

Compute Features

The compute module hosts numerical routines for exploratory data analysis. It covers descriptive statistics, correlations, probability distributions and some basic inferential tests.

Basic Statistics

# extern crate rustframe;
use rustframe::compute::stats::{mean, mean_vertical, stddev, median};
use rustframe::matrix::Matrix;

let m = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
assert_eq!(mean(&m), 2.5);
assert_eq!(stddev(&m), 1.118033988749895);
assert_eq!(median(&m), 2.5);
// column averages returned as 1 x n matrix
let col_means = mean_vertical(&m);
assert_eq!(col_means.data(), &[1.5, 3.5]);

Correlation

Correlation functions help measure linear relationships between datasets.

# extern crate rustframe;
use rustframe::compute::stats::{pearson, covariance};
use rustframe::matrix::Matrix;

let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
let y = Matrix::from_vec(vec![2.0, 4.0, 6.0, 8.0], 2, 2);
let corr = pearson(&x, &y);
let cov = covariance(&x, &y);
assert!((corr - 1.0).abs() < 1e-8);
assert!((cov - 2.5).abs() < 1e-8);

Distributions

Probability distribution helpers are available for common PDFs and CDFs.

# extern crate rustframe;
use rustframe::compute::stats::distributions::normal_pdf;
use rustframe::matrix::Matrix;

let x = Matrix::from_vec(vec![0.0, 1.0], 1, 2);
let pdf = normal_pdf(x, 0.0, 1.0);
assert_eq!(pdf.data().len(), 2);

With the basics covered, explore predictive models in the machine learning chapter.