| //! Rand implementations for complex numbers |
| |
| use rand::distributions::Standard; |
| use rand::prelude::*; |
| use traits::Num; |
| use Complex; |
| |
| impl<T> Distribution<Complex<T>> for Standard |
| where |
| T: Num + Clone, |
| Standard: Distribution<T>, |
| { |
| fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Complex<T> { |
| Complex::new(self.sample(rng), self.sample(rng)) |
| } |
| } |
| |
| /// A generic random value distribution for complex numbers. |
| #[derive(Clone, Copy, Debug)] |
| pub struct ComplexDistribution<Re, Im = Re> { |
| re: Re, |
| im: Im, |
| } |
| |
| impl<Re, Im> ComplexDistribution<Re, Im> { |
| /// Creates a complex distribution from independent |
| /// distributions of the real and imaginary parts. |
| pub fn new(re: Re, im: Im) -> Self { |
| ComplexDistribution { re, im } |
| } |
| } |
| |
| impl<T, Re, Im> Distribution<Complex<T>> for ComplexDistribution<Re, Im> |
| where |
| T: Num + Clone, |
| Re: Distribution<T>, |
| Im: Distribution<T>, |
| { |
| fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Complex<T> { |
| Complex::new(self.re.sample(rng), self.im.sample(rng)) |
| } |
| } |
| |
| #[cfg(test)] |
| fn test_rng() -> SmallRng { |
| SmallRng::from_seed([42; 16]) |
| } |
| |
| #[test] |
| fn standard_f64() { |
| let mut rng = test_rng(); |
| for _ in 0..100 { |
| let c: Complex<f64> = rng.gen(); |
| assert!(c.re >= 0.0 && c.re < 1.0); |
| assert!(c.im >= 0.0 && c.im < 1.0); |
| } |
| } |
| |
| #[test] |
| fn generic_standard_f64() { |
| let mut rng = test_rng(); |
| let dist = ComplexDistribution::new(Standard, Standard); |
| for _ in 0..100 { |
| let c: Complex<f64> = rng.sample(&dist); |
| assert!(c.re >= 0.0 && c.re < 1.0); |
| assert!(c.im >= 0.0 && c.im < 1.0); |
| } |
| } |
| |
| #[test] |
| fn generic_uniform_f64() { |
| use rand::distributions::Uniform; |
| |
| let mut rng = test_rng(); |
| let re = Uniform::new(-100.0, 0.0); |
| let im = Uniform::new(0.0, 100.0); |
| let dist = ComplexDistribution::new(re, im); |
| for _ in 0..100 { |
| // no type annotation required, since `Uniform` only produces one type. |
| let c = rng.sample(&dist); |
| assert!(c.re >= -100.0 && c.re < 0.0); |
| assert!(c.im >= 0.0 && c.im < 100.0); |
| } |
| } |
| |
| #[test] |
| fn generic_mixed_f64() { |
| use rand::distributions::Uniform; |
| |
| let mut rng = test_rng(); |
| let re = Uniform::new(-100.0, 0.0); |
| let dist = ComplexDistribution::new(re, Standard); |
| for _ in 0..100 { |
| // no type annotation required, since `Uniform` only produces one type. |
| let c = rng.sample(&dist); |
| assert!(c.re >= -100.0 && c.re < 0.0); |
| assert!(c.im >= 0.0 && c.im < 1.0); |
| } |
| } |
| |
| #[test] |
| fn generic_uniform_i32() { |
| use rand::distributions::Uniform; |
| |
| let mut rng = test_rng(); |
| let re = Uniform::new(-100, 0); |
| let im = Uniform::new(0, 100); |
| let dist = ComplexDistribution::new(re, im); |
| for _ in 0..100 { |
| // no type annotation required, since `Uniform` only produces one type. |
| let c = rng.sample(&dist); |
| assert!(c.re >= -100 && c.re < 0); |
| assert!(c.im >= 0 && c.im < 100); |
| } |
| } |