Struct nyx_space::mc::MultivariateNormal

pub struct MultivariateNormal<S: State>where
    DefaultAllocator: Allocator<f64, S::Size> + Allocator<f64, S::Size, S::Size> + Allocator<usize, S::Size, S::Size> + Allocator<f64, S::VecLength> + Allocator<f64, <S::Size as DimMin<S::Size>>::Output> + Allocator<f64, <<S::Size as DimMin<S::Size>>::Output as DimSub<Const<1>>>::Output> + Allocator<f64, S::Size, <S::Size as DimMin<S::Size>>::Output> + Allocator<f64, <S::Size as DimMin<S::Size>>::Output, S::Size> + Allocator<f64, <S::Size as DimSub<Const<1>>>::Output> + Allocator<f64, S::Size, <S::Size as DimSub<Const<1>>>::Output>,
    <DefaultAllocator as Allocator<f64, S::VecLength>>::Buffer: Send,
    S::Size: DimMin<S::Size> + DimSub<Const<1>>,
    <S::Size as DimMin<S::Size>>::Output: DimSub<Const<1>>,{
    pub template: S,
    pub params: Vec<StateParameter>,
    pub mean: OVector<f64, DimMinimum<S::Size, S::Size>>,
    pub sqrt_s_v: OMatrix<f64, S::Size, DimMinimum<S::Size, S::Size>>,
    pub std_norm_distr: Normal<f64>,
}

A state generator for Monte Carlo analyses.

Fields

template: S

The template state

params: Vec<StateParameter>

The ordered vector of parameters to which the mean and covariance correspond to.

mean: OVector<f64, DimMinimum<S::Size, S::Size>>

The mean of the multivariate normal distribution

sqrt_s_v: OMatrix<f64, S::Size, DimMinimum<S::Size, S::Size>>

The dot product \sqrt{\vec s} \cdot \vec v, where S is the singular values and V the V matrix from the SVD decomp of the covariance of multivariate normal distribution

std_norm_distr: Normal<f64>

The standard normal distribution used to seed the multivariate normal distribution

Implementations

impl<S: State> MultivariateNormal<S>where DefaultAllocator: Allocator<f64, S::Size> + Allocator<f64, S::Size, S::Size> + Allocator<usize, S::Size, S::Size> + Allocator<f64, S::VecLength> + Allocator<f64, <S::Size as DimMin<S::Size>>::Output> + Allocator<f64, <<S::Size as DimMin<S::Size>>::Output as DimSub<Const<1>>>::Output> + Allocator<f64, S::Size, <S::Size as DimMin<S::Size>>::Output> + Allocator<f64, <S::Size as DimMin<S::Size>>::Output, S::Size> + Allocator<f64, <S::Size as DimSub<Const<1>>>::Output> + Allocator<f64, S::Size, <S::Size as DimSub<Const<1>>>::Output>, <DefaultAllocator as Allocator<f64, S::VecLength>>::Buffer: Send, S::Size: DimMin<S::Size> + DimSub<Const<1>>, <S::Size as DimMin<S::Size>>::Output: DimSub<Const<1>>,

pub fn new( template: S, params: Vec<StateParameter>, mean: OVector<f64, DimMinimum<S::Size, S::Size>>, cov: OMatrix<f64, S::Size, S::Size> ) -> Result<Self, NyxError>

Creates a new Monte Carlos state generator from a mean and covariance which must be of the same size as the state vector The covariance must be positive semi definite. The algorithm is the one from numpy https://github.com/numpy/numpy/blob/6c16f23c30fe490422959d30c2e22345211a2fe3/numpy/random/mtrand.pyx#L3979

pub fn zero_mean( template: S, params: Vec<StateParameter>, cov: OMatrix<f64, S::Size, S::Size> ) -> Result<Self, NyxError>where <S::Size as DimMin<S::Size>>::Output: DimName,

Same as new but with a zero mean

Trait Implementations

impl<S: State> Distribution<DispersedState<S>> for MultivariateNormal<S>where DefaultAllocator: Allocator<f64, S::Size> + Allocator<f64, S::Size, S::Size> + Allocator<usize, S::Size, S::Size> + Allocator<f64, S::VecLength> + Allocator<f64, <S::Size as DimMin<S::Size>>::Output> + Allocator<f64, <<S::Size as DimMin<S::Size>>::Output as DimSub<Const<1>>>::Output> + Allocator<f64, S::Size, <S::Size as DimMin<S::Size>>::Output> + Allocator<f64, <S::Size as DimMin<S::Size>>::Output, S::Size> + Allocator<f64, <S::Size as DimSub<Const<1>>>::Output> + Allocator<f64, S::Size, <S::Size as DimSub<Const<1>>>::Output>, <DefaultAllocator as Allocator<f64, S::VecLength>>::Buffer: Send, S::Size: DimMin<S::Size> + DimSub<Const<1>>, <S::Size as DimMin<S::Size>>::Output: DimSub<Const<1>> + DimName,

fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> DispersedState<S>

Generate a random value of T, using rng as the source of randomness.

fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>where R: Rng, Self: Sized,

Create an iterator that generates random values of T, using rng as the source of randomness.

fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S>where F: Fn(T) -> S, Self: Sized,

Create a distribution of values of ‘S’ by mapping the output of Self through the closure F