82 lines
No EOL
3.3 KiB
Rust
82 lines
No EOL
3.3 KiB
Rust
// This file is a Rust port of the original Java implementation by Paul Uszak.
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// Original Java code:
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// http://www.reallyreallyrandom.com/gitbucketlabhub/
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//
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// Copyright (c) 2023 Paul Uszak. Port (C) 2025 by Tobias Raayoni Last (@randogoth)
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//
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// Permission is hereby granted, free of charge, to any person obtaining a copy
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// of this software and associated documentation files (the "Software"), to deal
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// in the Software without restriction, including without limitation the rights
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// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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// copies of the Software, and to permit persons to whom the Software is
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// furnished to do so, subject to the following conditions:
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//
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// The above copyright notice and this permission notice shall be included in all
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// copies or substantial portions of the Software.
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//
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// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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// SOFTWARE.
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use statrs::distribution::{Normal, ContinuousCDF};
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use crate::Onod;
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impl Onod {
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/// UnCorrelation randomness test
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/// Computes the Pearson correlation between the sequence and its shifted version, returning a p-value.
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pub fn uncorrelation(input: &[u8]) -> (f64, f64, f64) {
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let samples = input.iter().map(|&x| x as i32).collect::<Vec<i32>>();
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if samples.len() < 2 {
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return (-1.0, 0.0, 1.0); // Default to perfect randomness for insufficient data
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}
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// Convert samples to f64 for correlation computation
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let samples_a: Vec<f64> = samples.iter().map(|&x| x as f64).collect();
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// Create a shifted version of the sequence
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let mut samples_b = vec![0.0; samples.len()];
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samples_b[0] = samples_a[samples.len() - 1]; // Wrap around
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for i in 1..samples.len() {
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samples_b[i] = samples_a[i - 1];
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}
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// Calculate mean of both sequences
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let mean_a = samples_a.iter().sum::<f64>() / samples_a.len() as f64;
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let mean_b = samples_b.iter().sum::<f64>() / samples_b.len() as f64;
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// Compute Pearson correlation coefficient
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let mut numerator = 0.0;
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let mut denominator_a = 0.0;
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let mut denominator_b = 0.0;
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for i in 0..samples.len() {
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let diff_a = samples_a[i] - mean_a;
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let diff_b = samples_b[i] - mean_b;
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numerator += diff_a * diff_b;
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denominator_a += diff_a.powi(2);
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denominator_b += diff_b.powi(2);
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}
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let correlation = numerator / (denominator_a.sqrt() * denominator_b.sqrt());
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// Calculate p-value for null hypothesis of zero correlation
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let n = samples.len() as f64;
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let t_stat = correlation * ((n - 2.0) / (1.0 - correlation.powi(2))).sqrt();
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// Use t-distribution approximation for large n
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let normal_dist = Normal::new(0.0, 1.0).expect("Failed to create Normal distribution");
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let p_value = 2.0 * (1.0 - normal_dist.cdf(t_stat.abs()));
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(correlation, t_stat, p_value)
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}
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} |