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use fuzzy_logic_rs::{
    aggregations::Aggregations,
    defuzzifications::Defuzzifiers,
    fuzzy_inference_systems::MamdaniFIS,
    implications::Implications,
    membership_functions::{MFKind, Triangle, MF},
    membership_ranges::MembershipRange,
    rules::Rule,
    s_norms::SNorms,
    t_norms::TNorms,
    variables::{InputVariables, OutputVariables},
};

fn main() {
    let mut v1 = InputVariables::new("speed".to_string(), (0.0, 140.0));
    v1.add_membership(MF::new(
        "S".to_string(),
        MFKind::Triangle(Triangle::new(-58.3, 0.0, 58.3)),
    ));
    v1.add_membership(MF::new(
        "M".to_string(),
        MFKind::Triangle(Triangle::new(11.67, 70.0, 128.3)),
    ));
    v1.add_membership(MF::new(
        "L".to_string(),
        MFKind::Triangle(Triangle::new(81.67, 140.0, 198.3)),
    ));

    let mut v2 = InputVariables::new("Distance".to_string(), (0.0, 50.0));
    v2.add_membership(MF::new(
        "S".to_string(),
        MFKind::Triangle(Triangle::new(-20.83, 0.0, 20.83)),
    ));
    v2.add_membership(MF::new(
        "M".to_string(),
        MFKind::Triangle(Triangle::new(4.168, 25.0, 45.82)),
    ));
    v2.add_membership(MF::new(
        "L".to_string(),
        MFKind::Triangle(Triangle::new(29.17, 50.0, 70.82)),
    ));

    let mut o1 = OutputVariables::new(String::from("Acceleration"), (-1.0, 1.0), 100);
    o1.add_membership(MembershipRange::new_gaussian(
        o1.get_universe(),
        "NB".to_string(),
        -1.0,
        0.2123,
    ));
    o1.add_membership(MembershipRange::new_gaussian(
        o1.get_universe(),
        "NS".to_string(),
        -0.5,
        0.2123,
    ));
    o1.add_membership(MembershipRange::new_gaussian(
        o1.get_universe(),
        "ZR".to_string(),
        0.0,
        0.2123,
    ));
    o1.add_membership(MembershipRange::new_gaussian(
        o1.get_universe(),
        "PS".to_string(),
        0.5,
        0.2123,
    ));
    o1.add_membership(MembershipRange::new_gaussian(
        o1.get_universe(),
        "PB".to_string(),
        1.0,
        0.2123,
    ));

    let mut fis = MamdaniFIS::new(
        SNorms::Max,
        TNorms::Min,
        Implications::Min,
        Aggregations::Max,
        Defuzzifiers::Bisection,
    );
    fis.add_input(v1);
    fis.add_input(v2);
    fis.add_output(o1);

    fis.add_rule(Rule::new_and(vec![0, 0, 2], 1.0));
    fis.add_rule(Rule::new_and(vec![0, 1, 3], 1.0));
    fis.add_rule(Rule::new_and(vec![0, 2, 4], 1.0));

    fis.add_rule(Rule::new_and(vec![1, 0, 1], 1.0));
    fis.add_rule(Rule::new_and(vec![1, 1, 2], 1.0));
    fis.add_rule(Rule::new_and(vec![1, 2, 3], 1.0));

    fis.add_rule(Rule::new_and(vec![2, 0, 0], 1.0));
    fis.add_rule(Rule::new_and(vec![2, 1, 1], 1.0));
    fis.add_rule(Rule::new_and(vec![2, 2, 2], 1.0));

    let output = fis.compute_outputs(vec![40.0, 43.0]);
    println!("output is: {:#?}", output);
}