forked from M-Labs/nalgebra
55 lines
1.5 KiB
Rust
55 lines
1.5 KiB
Rust
// This module implement several methods to fill some
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// missing features of num-complex when it comes to randomness.
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use na::RealField;
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use num_complex::Complex;
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use quickcheck::{Arbitrary, Gen};
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use rand::distributions::{Distribution, Standard};
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use rand::Rng;
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#[derive(Copy, Clone, Debug, PartialEq, Eq)]
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pub struct RandComplex<N>(pub Complex<N>);
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impl<N: Arbitrary + RealField> Arbitrary for RandComplex<N> {
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#[inline]
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fn arbitrary(rng: &mut Gen) -> Self {
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let im = Arbitrary::arbitrary(rng);
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let re = Arbitrary::arbitrary(rng);
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RandComplex(Complex::new(re, im))
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}
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}
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impl<N: RealField> Distribution<RandComplex<N>> for Standard
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where
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Standard: Distribution<N>,
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{
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#[inline]
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fn sample<'a, G: Rng + ?Sized>(&self, rng: &'a mut G) -> RandComplex<N> {
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let re = rng.gen();
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let im = rng.gen();
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RandComplex(Complex::new(re, im))
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}
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}
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// This is a wrapper similar to RandComplex, but for non-complex.
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// This exists only to make generic tests easier to write.
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#[derive(Copy, Clone, Debug, PartialEq, Eq)]
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pub struct RandScalar<N>(pub N);
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impl<N: Arbitrary> Arbitrary for RandScalar<N> {
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#[inline]
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fn arbitrary(rng: &mut Gen) -> Self {
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RandScalar(Arbitrary::arbitrary(rng))
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}
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}
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impl<N: RealField> Distribution<RandScalar<N>> for Standard
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where
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Standard: Distribution<N>,
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{
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#[inline]
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fn sample<'a, G: Rng + ?Sized>(&self, rng: &'a mut G) -> RandScalar<N> {
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RandScalar(self.sample(rng))
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}
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}
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