lace 0.9.1

A probabilistic cross-categorization engine
Documentation 
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// Compute the mutual information and dependence probability for every pair of
// variables in the satellites dataset.
use std::convert::TryInto;

use lace::examples::satellites::Column;
use lace::examples::Example;
use lace::prelude::*;
use rayon::prelude::*;

fn main() {
    // Load the satellites example
    let oracle = Example::Satellites.oracle().unwrap();

    let mut rng = rand::rng();
    let xs = oracle.simulate(
        &["Class_of_Orbit"],
        &Given::Conditions(vec![(
            "Type_of_Orbit",
            Datum::Categorical("Sun-Synchronous".into()),
        )]),
        10,
        None,
        &mut rng,
    );

    println!(
        "simulate from f(Class_of_Orbit | Type_of_Orbit = Sun-Synchronous)"
    );
    println!("{:#?}", xs);

    let mut col_pairs: Vec<(usize, usize)> = Vec::new();
    for i in 0..20 {
        for j in i..20 {
            col_pairs.push((i, j));
        }
    }

    println!(
        " {:>26} │ {:>26} │ {:>10.5} │ {:>10.5} ",
        "A", "B", "MI", "DepProb"
    );
    println!(" {:─>28}{:─>29}{:─>13}{:─>12}", "┼", "┼", "┼", "");

    col_pairs.par_iter().for_each(|(col_a, col_b)| {
        // Use samples to approximate mutual information between pairs of
        // continuous variables. Negative mutual information indicates an
        // approximation error which can often be fixed with more samples
        // (longer compute time).
        let mi = oracle.mi(*col_a, *col_b, 1_000, MiType::UnNormed).unwrap();

        let depprob = oracle.depprob(*col_a, *col_b).unwrap();

        let col_name_a: Column = (*col_a).try_into().unwrap();
        let col_name_b: Column = (*col_b).try_into().unwrap();
        let label_a = format!("{:?}", col_name_a);
        let label_b = format!("{:?}", col_name_b);

        println!(
            " {:>26} │ {:>26} │ {:>10.5} │ {:10.5}",
            label_a, label_b, mi, depprob
        );
    });
}