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added observation and z-score to results, emojis

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randogoth 2025-01-17 13:27:34 +02:00
parent 10ff2123ac
commit a8c74caf90
19 changed files with 121 additions and 98 deletions
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69
src/bin/main.rs
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@ -1,7 +1,6 @@
use std::fs::File;
use std::io::{self, Read};
use std::path::Path;
use statrs::distribution::{Normal, ContinuousCDF};
use onod3000::Onod;
@ -36,12 +35,16 @@ fn main() -> io::Result<()> {
std::process::exit(1);
}
println!("\nTesting {}", source);
println!("Testing {} bytes.", input_data.len());
println!("--------------------------------------");
println!("\nTesting {} bytes from {}.", input_data.len(), source);
println!("-----------------------------------------------------");
println!("Randomness Test Observation Z-Score P-Value Pass");
println!("-----------------------------------------------------");
let alpha = 0.01;
let mut passed_tests = 0;
let alpha = 0.01;
// Create vectors to store results
let (mut observations, mut z_scores, mut p_values) = (Vec::new(), Vec::new(), Vec::new());
// Run each test with the appropriate handling
let tests = [
@ -52,7 +55,7 @@ fn main() -> io::Result<()> {
("ChiByte", Onod::chi_byte(&input_data)),
("MeanByte", Onod::mean_byte(&input_data)),
("Compression", Onod::compression(&input_data)),
("KS", Onod::ks(&input_data)),
("Kolm.-Smirnov", Onod::ks(&input_data)),
("Pi", Onod::pi(&input_data)),
("Shells", Onod::shells(&input_data)),
("Gaps", Onod::gaps(&input_data)),
@ -63,47 +66,31 @@ fn main() -> io::Result<()> {
("UnCorrelation", Onod::un_correlation(&input_data)),
];
let mut p_values = Vec::new();
for (test_name, p_value) in tests.iter() {
let result = if *p_value >= alpha {
for (test_name, (observation, z_score, p_value)) in &tests {
let result = if *p_value >= alpha && *observation != -1.0 {
passed_tests += 1;
"PASS"
"✅"
} else {
"FAIL"
if *observation == -1.0 {
"SKIP"
} else {
"❌"
}
};
println!("{:<20} p = {:.4}, {}", test_name, p_value, result);
p_values.push(*p_value); // Dereference the value and push it
}
// Calculate combined p-value using Fisher's method
let (combined_z_score, combined_p_value) = combined_score_stouffer(&p_values);
let overall_result = if combined_p_value >= alpha { "PASS" } else { "FAIL" };
println!(
"{:<15} {:>15.3} {:>8.4} {:.4} {}",
test_name, observation, z_score, p_value, result
);
println!("--------------------------------------");
observations.push(*observation);
z_scores.push(*z_score);
p_values.push(*p_value);
}
println!("-----------------------------------------------------");
println!("{}/{} tests passed.", passed_tests, tests.len());
println!("--------------------------------------");
println!("Combined Z-Score = {:.6}\nCombined P-Value = {:.6}\nOverall Result: {}", combined_z_score.abs(), combined_p_value, overall_result);
println!("-----------------------------------------------------");
Ok(())
}
pub fn combined_score_stouffer(p_values: &[f64]) -> (f64, f64) {
let normal_dist = Normal::new(0.0, 1.0).expect("Failed to create Normal distribution");
// Calculate Z-scores for each p-value
let z_scores: Vec<f64> = p_values
.iter()
.map(|&p| normal_dist.inverse_cdf(1.0 - p))
.collect();
// Combine Z-scores
let combined_z = z_scores.iter().sum::<f64>() / (p_values.len() as f64).sqrt();
// Convert combined Z-score back to a p-value
let p_value = 2.0 * (1.0 - normal_dist.cdf(combined_z.abs())); // Two-tailed
(combined_z, p_value)
}