libitofin 0.27.0

A ground-up Rust port of QuantLib: quantitative-finance primitives for pricing, risk, and numerical methods.
Documentation 
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use super::*;
use crate::math::distributions::normal::InverseCumulativeNormal;
use crate::math::distributions::poisson::InverseCumulativePoisson;
use crate::methods::montecarlo::Sample;

#[test]
fn quantlib_rngtraits_seed_oracles() {
    let mut gaussian = PseudoRandom::make_sequence_generator(100, 1234).unwrap();
    let mut scalar_normal = PseudoRandom::make_scalar_generator(1234);
    let values = gaussian.next_sequence().value.clone();
    for &value in &values {
        assert_eq!(scalar_normal.next_sample().unwrap().value, value);
    }
    let sum: f64 = values.iter().sum();
    assert!((sum - 4.09916).abs() <= 1e-5);
    for (lambda, expected) in [(1.0, 108.0), (4.0, 409.0)] {
        let mut sequence = PoissonPseudoRandom::with_lambda(100, 1234, lambda).unwrap();
        let mut scalar = PoissonPseudoRandom::make_scalar_generator(1234, lambda).unwrap();
        let sample = sequence.next_sequence().unwrap();
        assert_eq!(sample.value.iter().sum::<f64>(), expected);
        assert_eq!(sample.weight, 1.0);
        for &value in &sample.value {
            assert_eq!(scalar.next_sample().unwrap().value, value);
        }
        let mut copy = sequence.clone();
        assert_eq!(
            sequence.next_sequence().unwrap().value,
            copy.next_sequence().unwrap().value
        );
    }
    let mut default = PoissonPseudoRandom::make_sequence_generator(100, 1234).unwrap();
    assert_eq!(
        default.next_sequence().unwrap().value.iter().sum::<f64>(),
        108.0
    );
}

#[test]
fn sobol_policy_skips_zero_and_preserves_copied_state() {
    let mut sequence = LowDiscrepancy::make_sequence_generator(16, 42).unwrap();
    assert_eq!(sequence.next_sequence().value, vec![0.0; 16]);
    for _ in 0..4094 {
        assert!(sequence.next_sequence().value.iter().all(|x| x.is_finite()));
    }
    let mut copy = sequence.clone();
    assert_eq!(copy.next_sequence().value, sequence.next_sequence().value);
    for gray in [true, false] {
        let mut sobol =
            sobol::SobolRsg::with_gray_code(16, 42, sobol::DirectionIntegers::Jaeckel, gray);
        sobol.skip_to(4093);
        let mut a = SobolSequence::new(sobol.clone());
        assert_eq!(a.next_sequence().value, sobol.next_sequence());
        assert!(a.last_sequence().value.iter().all(|&x| x > 0.0 && x < 1.0));
    }
    assert!(LowDiscrepancy::make_sequence_generator(0, 0).is_err());
    assert!(LowDiscrepancy::make_sequence_generator(sobol::PPMT_MAX_DIM + 1, 0).is_err());
}

#[derive(Clone)]
struct Uniforms(Vec<f64>);
impl UniformRng for Uniforms {
    fn next_real(&mut self) -> f64 {
        self.0.remove(0)
    }
}

#[test]
fn scalar_transform_errors_recover_without_panics() {
    let mut normal = InverseCumulativeRng::new(
        Uniforms(vec![0.0, 1.0, f64::NAN, 0.5]),
        InverseCumulativeNormal::standard(),
    );
    for _ in 0..3 {
        assert!(normal.next_sample().is_err());
    }
    assert_eq!(normal.next_sample().unwrap().value, 0.0);
    let poisson = InverseCumulativePoisson::new(700.0).unwrap();
    assert!(poisson.try_evaluate(1.0).is_err());
    assert!(poisson.try_evaluate(-0.1).is_err());
    assert!(poisson.try_evaluate(f64::NAN).is_err());
    assert_eq!(poisson.try_evaluate(0.0).unwrap(), 0.0);
    assert!(PoissonPseudoRandom::with_lambda(1, 42, f64::INFINITY).is_err());
    assert!(PoissonPseudoRandom::with_lambda(0, 42, 1.0).is_err());
    assert!(PoissonPseudoRandom::with_lambda(1, 42, 750.0).is_err());
}

struct Weighted {
    sample: Sample<Vec<f64>>,
    next: Vec<f64>,
    next_weight: f64,
}
impl SequenceGenerator for Weighted {
    fn next_sequence(&mut self) -> &Sample<Vec<f64>> {
        std::mem::swap(&mut self.sample.value, &mut self.next);
        std::mem::swap(&mut self.sample.weight, &mut self.next_weight);
        &self.sample
    }
    fn last_sequence(&self) -> &Sample<Vec<f64>> {
        &self.sample
    }
    fn dimension(&self) -> usize {
        self.sample.value.len()
    }
}

#[test]
fn fallible_sequence_preserves_weights_and_last_success_on_error() {
    let source = Weighted {
        sample: Sample::new(vec![0.9, 1.0], 0.75),
        next: vec![0.25, 0.75],
        next_weight: 0.25,
    };
    let mut generator =
        FallibleInverseCumulativeRsg::new(source, InverseCumulativeNormal::standard());
    let first = generator.next_sequence().unwrap().clone();
    assert_eq!(first.weight, 0.25);
    assert!(first.value.iter().all(|x| x.is_finite() && *x != 0.0));
    assert!(generator.next_sequence().is_err());
    assert_eq!(generator.last_sequence().value, first.value);
    assert_eq!(generator.last_sequence().weight, first.weight);
    assert_eq!(generator.next_sequence().unwrap().value, first.value);
}