chore: fine tune binomial_bounds.rs code (#103)

Signed-off-by: tison <wander4096@gmail.com>

Author: tisonkun

datasketches/src/common/binomial_bounds.rs

@@ -275,7 +275,7 @@ static UB_EQUIV_TABLE: [f64; 363] = [
 /// # Arguments
 ///
 /// * `num_samples`: The number of samples in the sample set.
-/// * `theta`: The sampling probability. Must be in the range (0.0, 1.0].
+/// * `theta`: The sampling probability. Must be in the range `(0.0, 1.0]`.
 /// * `num_std_dev`: The number of standard deviations for confidence bounds.
 ///
 /// # Returns
@@ -284,13 +284,17 @@ static UB_EQUIV_TABLE: [f64; 363] = [
 ///
 /// # Errors
 ///
-/// Returns an error if `theta` is not in the range (0.0, 1.0].
+/// Returns an error if `theta` is not in the range `(0.0, 1.0]`.
 pub(crate) fn lower_bound(
     num_samples: u64,
     theta: f64,
     num_std_dev: NumStdDev,
 ) -> Result<f64, Error> {
-    check_theta(theta)?;
+    if theta <= 0.0 || theta > 1.0 {
+        return Err(Error::invalid_argument(format!(
+            "theta must be in the range (0.0, 1.0], got {theta}"
+        )));
+    }
 
     let estimate = num_samples as f64 / theta;
     let lb = compute_approx_binomial_lower_bound(num_samples, theta, num_std_dev);
@@ -325,7 +329,12 @@ pub(crate) fn upper_bound(
     if no_data_seen {
         return Ok(0.0);
     }
-    check_theta(theta)?;
+
+    if theta <= 0.0 || theta > 1.0 {
+        return Err(Error::invalid_argument(format!(
+            "theta must be in the range (0.0, 1.0], got {theta}"
+        )));
+    }
 
     let estimate = num_samples as f64 / theta;
     let ub = compute_approx_binomial_upper_bound(num_samples, theta, num_std_dev);
@@ -360,6 +369,7 @@ fn cont_classic_ub(num_samples: u64, theta: f64, num_std_devs: f64) -> f64 {
 /// # Limitations
 ///
 /// Outside of the valid input range, two different bad things will happen:
+///
 /// 1. Because we are not using logarithms, the values of intermediate quantities will exceed the
 ///    dynamic range of doubles.
 /// 2. Even if that problem were fixed, the running time of this procedure is essentially linear in
@@ -548,21 +558,6 @@ fn compute_approx_binomial_upper_bound(
     special_n_prime_f(num_samples, theta, delta).unwrap_or(num_samples + 1) as f64 // no need to round
 }
 
-/// Validates that theta is in the valid range [0.0, 1.0].
-///
-/// # Errors
-///
-/// Returns an error if theta < 0.0 or theta > 1.0.
-fn check_theta(theta: f64) -> Result<(), Error> {
-    if (theta <= 0.0) || (theta > 1.0) {
-        return Err(Error::invalid_argument(format!(
-            "theta must be in the range [0.0, 1.0]: {}",
-            theta
-        )));
-    }
-    Ok(())
-}
-
 #[cfg(test)]
 mod tests {
     use super::*;
@@ -679,53 +674,34 @@ mod tests {
     fn check_bounds() {
         let mut i = 0;
 
+        fn assert_approx_equal(ci: NumStdDev, j: usize, expected: f64, actual: f64) {
+            let ratio = actual / expected;
+            assert!(
+                (ratio - 1.0).abs() < TOL,
+                "ci={ci:?}, j={j}: expected {expected}, got {actual}, ratio={ratio}",
+            );
+        }
+
         for ci in [NumStdDev::One, NumStdDev::Two, NumStdDev::Three] {
             let arr = run_test_aux(20, ci, 1e-3);
             for j in 0..5 {
-                let ratio = arr[j] / STD[i][j];
-                assert!(
-                    (ratio - 1.0).abs() < TOL,
-                    "ci={:?}, j={}: expected {}, got {}, ratio={}",
-                    ci,
-                    j,
-                    STD[i][j],
-                    arr[j],
-                    ratio
-                );
+                assert_approx_equal(ci, j, STD[i][j], arr[j]);
             }
             i += 1;
         }
 
         for ci in [NumStdDev::One, NumStdDev::Two, NumStdDev::Three] {
             let arr = run_test_aux(200, ci, 1e-5);
             for j in 0..5 {
-                let ratio = arr[j] / STD[i][j];
-                assert!(
-                    (ratio - 1.0) < TOL,
-                    "ci={:?}, j={}: expected {}, got {}, ratio={}",
-                    ci,
-                    j,
-                    STD[i][j],
-                    arr[j],
-                    ratio
-                );
+                assert_approx_equal(ci, j, STD[i][j], arr[j]);
             }
             i += 1;
         }
 
         for ci in [NumStdDev::One, NumStdDev::Two, NumStdDev::Three] {
             let arr = run_test_aux(2000, ci, 1e-7);
             for j in 0..5 {
-                let ratio = arr[j] / STD[i][j];
-                assert!(
-                    (ratio - 1.0).abs() < TOL,
-                    "ci={:?}, j={}: expected {}, got {}, ratio={}",
-                    ci,
-                    j,
-                    STD[i][j],
-                    arr[j],
-                    ratio
-                );
+                assert_approx_equal(ci, j, STD[i][j], arr[j]);
             }
             i += 1;
         }