init

src/uniformity/uncorrelation.rs

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+use statrs::distribution::{Normal, ContinuousCDF};
+
+use crate::Onod;
+
+impl Onod {
+
+    /// UnCorrelation randomness test
+    /// Computes the Pearson correlation between the sequence and its shifted version, returning a p-value.
+    pub fn un_correlation(input: &[u8]) -> f64 {
+
+        let samples = input.iter().map(|&x| x as i32).collect::<Vec<i32>>();
+
+        if samples.len() < 2 {
+            return 0.0; // Default to perfect randomness for insufficient data
+        }
+
+        // Convert samples to f64 for correlation computation
+        let samples_a: Vec<f64> = samples.iter().map(|&x| x as f64).collect();
+
+        // Create a shifted version of the sequence
+        let mut samples_b = vec![0.0; samples.len()];
+        samples_b[0] = samples_a[samples.len() - 1]; // Wrap around
+        for i in 1..samples.len() {
+            samples_b[i] = samples_a[i - 1];
+        }
+
+        // Calculate mean of both sequences
+        let mean_a = samples_a.iter().sum::<f64>() / samples_a.len() as f64;
+        let mean_b = samples_b.iter().sum::<f64>() / samples_b.len() as f64;
+
+        // Compute Pearson correlation coefficient
+        let mut numerator = 0.0;
+        let mut denominator_a = 0.0;
+        let mut denominator_b = 0.0;
+
+        for i in 0..samples.len() {
+            let diff_a = samples_a[i] - mean_a;
+            let diff_b = samples_b[i] - mean_b;
+            numerator += diff_a * diff_b;
+            denominator_a += diff_a.powi(2);
+            denominator_b += diff_b.powi(2);
+        }
+
+        let correlation = numerator / (denominator_a.sqrt() * denominator_b.sqrt());
+
+        // Calculate p-value for null hypothesis of zero correlation
+        let n = samples.len() as f64;
+        let t_stat = correlation * ((n - 2.0) / (1.0 - correlation.powi(2))).sqrt();
+
+        // Use t-distribution approximation for large n
+        let normal_dist = Normal::new(0.0, 1.0).expect("Failed to create Normal distribution");
+        let p_value = 2.0 * (1.0 - normal_dist.cdf(t_stat.abs()));
+
+        p_value
+    }
+
+}