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use text_io::read;
mod data;
use data::{Person, Party::republican};
mod probabilities;
use probabilities::PMaps;
fn test_single(prob: &PMaps, person: &Person) -> bool {
let is_republican = P!(Party = republican | person, prob) >= 0.5;
is_republican == (person.party == republican)
}
fn test(prob: &PMaps, people: &Vec<Person>) -> f64 {
let mut count_succ = 0.0;
for p in people {
if test_single(&prob, &p) {
count_succ += 1.0;
}
}
count_succ / people.len() as f64
}
static folds: usize = 10;
fn main() {
let fill: u8 = read!();
let fill = fill == 1;
let (training, testing) = data::get_data();
let prob = if fill {
probabilities::value_2_probabilities(&training)
} else {
probabilities::value_3_probabilities(&training)
};
// No laplace, so that's probably why it's not 92.80%
println!("1. Train Set Accuracy:");
println!(" Accuracy: {:.2}%", test(&prob, &training) * 100.0);
println!("");
println!("{}-Fold Cross-Validation Results:", folds);
let mut accuracy = Vec::new();
for i in 1..=folds {
let (training, testing) = data::get_data_fold(i-1, folds, &training);
accuracy.push(test(&prob, &testing));
println!(" Accuracy Fold {}: {:.2}%", i, accuracy.last().unwrap() * 100.0);
}
println!("");
let avg = accuracy.clone().into_iter().sum::<f64>();
println!(" Average Accuracy: {:.2}%", avg / folds as f64 * 100.0);
println!(" Standard Deviation: {:.2}%", (accuracy.into_iter().map(|x| (x - avg).powi(2)).sum::<f64>() / folds as f64).sqrt());
println!("");
println!("2. Test Set Accuracy:");
println!(" Accuracy: {:.2}%", test(&prob, &testing) * 100.0);
}
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