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#[cfg(test)]
mod tests {
use crate::*;
#[test]
fn test_hra_rank_accuracy() {
let mut sketch = ReqSketch::new(); // HRA mode
let n = 50_000;
// Add uniformly distributed values
for i in 0..n {
sketch.update(i as f64);
}
// Verify basic sketch properties
assert_eq!(sketch.total_n, n as u64);
assert!(sketch.len() <= n as u64);
// Focus on high ranks for HRA - this is where HRA should be most accurate
let test_ranks = [0.5, 0.9, 0.95, 0.99, 0.999];
for &rank in &test_ranks {
let true_quantile = rank * (n - 1) as f64;
let estimated_rank = sketch.rank(&true_quantile, SearchCriteria::Inclusive).unwrap();
let abs_error = (estimated_rank - rank).abs();
// Use absolute error thresholds appropriate for HRA
// Tighter bounds for higher ranks where HRA should excel
let max_abs_error = if rank >= 0.99 {
0.005 // Very tight for top 1%
} else if rank >= 0.9 {
0.01 // Reasonable for top 10%
} else {
0.02 // Looser for middle ranks
};
assert!(abs_error <= max_abs_error);
}
// Test that bounds functions are self-consistent for high ranks
for &rank in &[0.9, 0.99, 0.999] {
let lb3 = sketch.get_rank_lower_bound(rank, 3);
let ub3 = sketch.get_rank_upper_bound(rank, 3);
// For uniform data, the true rank should lie within the bounds
assert!(rank >= lb3 && rank <= ub3);
}
// Verify overall sketch health
assert!(sketch.compactors.len() > 0);
assert!(sketch.total_n > 0);
}
}