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// Property-based tests for ferray-random
//
// Tests mathematical invariants of random number generation using proptest.
use ferray_random::default_rng_seeded;
use proptest::prelude::*;
proptest! {
#![proptest_config(ProptestConfig::with_cases(256))]
// -----------------------------------------------------------------------
// 1. Determinism: same seed produces same output
// -----------------------------------------------------------------------
#[test]
fn prop_determinism(seed in 0u64..1_000_000) {
let mut rng1 = default_rng_seeded(seed);
let mut rng2 = default_rng_seeded(seed);
let a = rng1.random(100).unwrap();
let b = rng2.random(100).unwrap();
let a_data: Vec<f64> = a.iter().copied().collect();
let b_data: Vec<f64> = b.iter().copied().collect();
prop_assert_eq!(a_data, b_data, "same seed {} produced different output", seed);
}
// -----------------------------------------------------------------------
// 2. Shape: output shape matches requested shape
// -----------------------------------------------------------------------
#[test]
fn prop_random_shape(n in 1usize..=1000) {
let mut rng = default_rng_seeded(42);
let arr = rng.random(n).unwrap();
prop_assert_eq!(arr.shape(), &[n]);
prop_assert_eq!(arr.size(), n);
}
// -----------------------------------------------------------------------
// 3. Range: uniform samples in [0, 1)
// -----------------------------------------------------------------------
#[test]
fn prop_random_range(seed in 0u64..100_000) {
let mut rng = default_rng_seeded(seed);
let arr = rng.random(500).unwrap();
for &v in arr.iter() {
prop_assert!(
(0.0..1.0).contains(&v),
"random() value {} outside [0, 1)",
v
);
}
}
// -----------------------------------------------------------------------
// 4. Range: uniform(low, high) samples in [low, high)
// -----------------------------------------------------------------------
#[test]
fn prop_uniform_range(
seed in 0u64..100_000,
low in -100.0f64..100.0,
span in 0.01f64..100.0,
) {
let high = low + span;
let mut rng = default_rng_seeded(seed);
let arr = rng.uniform(low, high, 500).unwrap();
for &v in arr.iter() {
prop_assert!(
v >= low && v < high,
"uniform({}, {}) value {} outside range",
low, high, v
);
}
}
// -----------------------------------------------------------------------
// 5. Range: integers(low, high) in [low, high)
// -----------------------------------------------------------------------
#[test]
fn prop_integers_range(
seed in 0u64..100_000,
low in -50i64..50,
span in 1i64..50,
) {
let high = low + span;
let mut rng = default_rng_seeded(seed);
let arr = rng.integers(low, high, 500).unwrap();
for &v in arr.iter() {
prop_assert!(
v >= low && v < high,
"integers({}, {}) value {} outside range",
low, high, v
);
}
}
// -----------------------------------------------------------------------
// 6. Shape: uniform output shape matches requested
// -----------------------------------------------------------------------
#[test]
fn prop_uniform_shape(n in 1usize..=1000) {
let mut rng = default_rng_seeded(42);
let arr = rng.uniform(0.0, 1.0, n).unwrap();
prop_assert_eq!(arr.shape(), &[n]);
}
// -----------------------------------------------------------------------
// 7. Shape: integers output shape matches requested
// -----------------------------------------------------------------------
#[test]
fn prop_integers_shape(n in 1usize..=1000) {
let mut rng = default_rng_seeded(42);
let arr = rng.integers(0, 100, n).unwrap();
prop_assert_eq!(arr.shape(), &[n]);
}
// -----------------------------------------------------------------------
// 8. Normal moments: mean ~= loc, var ~= scale^2 for large samples
// -----------------------------------------------------------------------
#[test]
fn prop_normal_moments(seed in 0u64..1000) {
let loc = 3.0;
let scale = 2.0;
let n = 50_000;
let mut rng = default_rng_seeded(seed);
let arr = rng.normal(loc, scale, n).unwrap();
let slice = arr.as_slice().unwrap();
let sample_mean: f64 = slice.iter().sum::<f64>() / n as f64;
let sample_var: f64 = slice.iter()
.map(|&x| (x - sample_mean).powi(2))
.sum::<f64>() / n as f64;
// Use generous tolerance for statistical tests (5 sigma)
let se_mean = (scale * scale / n as f64).sqrt();
prop_assert!(
(sample_mean - loc).abs() < 5.0 * se_mean,
"normal mean {} too far from {} (se={})",
sample_mean, loc, se_mean
);
let expected_var = scale * scale;
prop_assert!(
(sample_var - expected_var).abs() < 0.3,
"normal var {} too far from {}",
sample_var, expected_var
);
}
// -----------------------------------------------------------------------
// 9. Standard normal has zero mean approximately
// -----------------------------------------------------------------------
#[test]
fn prop_standard_normal_zero_mean(seed in 0u64..1000) {
let n = 50_000;
let mut rng = default_rng_seeded(seed);
let arr = rng.standard_normal(n).unwrap();
let slice = arr.as_slice().unwrap();
let sample_mean: f64 = slice.iter().sum::<f64>() / n as f64;
let se = (1.0 / n as f64).sqrt();
prop_assert!(
sample_mean.abs() < 5.0 * se,
"standard_normal mean {} too far from 0 (se={})",
sample_mean, se
);
}
// -----------------------------------------------------------------------
// 10. Different seeds produce different outputs (with high probability)
// -----------------------------------------------------------------------
#[test]
fn prop_different_seeds_differ(seed in 1u64..1_000_000) {
let mut rng1 = default_rng_seeded(seed);
let mut rng2 = default_rng_seeded(seed + 1);
let a = rng1.random(100).unwrap();
let b = rng2.random(100).unwrap();
let a_data: Vec<f64> = a.iter().copied().collect();
let b_data: Vec<f64> = b.iter().copied().collect();
// They should differ (extremely unlikely to be equal by chance)
prop_assert_ne!(a_data, b_data);
}
// -----------------------------------------------------------------------
// 11. Deterministic integers
// -----------------------------------------------------------------------
#[test]
fn prop_integers_deterministic(seed in 0u64..1_000_000) {
let mut rng1 = default_rng_seeded(seed);
let mut rng2 = default_rng_seeded(seed);
let a = rng1.integers(0, 100, 200).unwrap();
let b = rng2.integers(0, 100, 200).unwrap();
let a_data: Vec<i64> = a.iter().copied().collect();
let b_data: Vec<i64> = b.iter().copied().collect();
prop_assert_eq!(a_data, b_data);
}
// -----------------------------------------------------------------------
// 12. Uniform mean is approximately (low + high) / 2
// -----------------------------------------------------------------------
#[test]
fn prop_uniform_mean(
seed in 0u64..1000,
low in -50.0f64..50.0,
span in 1.0f64..50.0,
) {
let high = low + span;
let n = 50_000;
let mut rng = default_rng_seeded(seed);
let arr = rng.uniform(low, high, n).unwrap();
let slice = arr.as_slice().unwrap();
let sample_mean: f64 = slice.iter().sum::<f64>() / n as f64;
let expected_mean = (low + high) / 2.0;
let expected_var = (high - low).powi(2) / 12.0;
let se = (expected_var / n as f64).sqrt();
prop_assert!(
(sample_mean - expected_mean).abs() < 5.0 * se,
"uniform({},{}) mean {} too far from {} (se={})",
low, high, sample_mean, expected_mean, se
);
}
}