Struct nalgebra_mvn::MultivariateNormal[−][src]

pub struct MultivariateNormal<Real, N> where
    Real: RealField,
    N: Dim + DimMin<N, Output = N>,
    DefaultAllocator: Allocator<Real, N>,
    DefaultAllocator: Allocator<Real, N, N>,
    DefaultAllocator: Allocator<Real, U1, N>,
    DefaultAllocator: Allocator<(usize, usize), <N as DimMin<N>>::Output>,  { /* fields omitted */ }

Expand description

An N-dimensional multivariate normal distribution

See the crate-level docs for example usage.

Implementations

impl<Real, N> MultivariateNormal<Real, N> where
    Real: RealField,
    N: Dim + DimMin<N, Output = N> + DimSub<Dynamic>,
    DefaultAllocator: Allocator<Real, N>,
    DefaultAllocator: Allocator<Real, N, N>,
    DefaultAllocator: Allocator<Real, U1, N>,
    DefaultAllocator: Allocator<(usize, usize ), <N as DimMin<N>>::Output>,

pub fn from_mean_and_precision(
mu: &OVector<Real, N>,
precision: &OMatrix<Real, N, N>
) -> Self

Create a multivariate normal distribution from a mean and precision

The mean vector mu is N dimensional and the precision matrix is N x N.

pub fn from_mean_and_covariance(
mu: &OVector<Real, N>,
covariance: &OMatrix<Real, N, N>
) -> Result<Self, Error>

Create a multivariate normal distribution from a mean and covariance

The mean vector mu is N dimensional and the covariance matrix is N x N.

The precision matrix is calculated by inverting the covariance matrix using a Cholesky decomposition. This can fail if the covariance matrix is not definite positive.

pub fn mean(&self) -> OVector<Real, N>

Get the mean of the distribution

pub fn precision(&self) -> &OMatrix<Real, N, N>

Get the precision of the distribution

pub fn pdf<Count>(&self, xs: &OMatrix<Real, Count, N>) -> OVector<Real, Count> where
    Count: Dim,
    DefaultAllocator: Allocator<Real, Count>,
    DefaultAllocator: Allocator<Real, N, Count>,
    DefaultAllocator: Allocator<Real, Count, N>,
    DefaultAllocator: Allocator<Real, Count, Count>,

Probability density function

Evaluate the probability density at locations xs.

pub fn logpdf<Count>(
    &self,
    xs: &OMatrix<Real, Count, N>
) -> OVector<Real, Count> where
    Count: Dim,
    DefaultAllocator: Allocator<Real, Count>,
    DefaultAllocator: Allocator<Real, N, Count>,
    DefaultAllocator: Allocator<Real, Count, N>,
    DefaultAllocator: Allocator<Real, Count, Count>,

Log of the probability density function

Evaluate the log probability density at locations xs.

Trait Implementations

impl<Real: Clone, N: Clone> Clone for MultivariateNormal<Real, N> where
    Real: RealField,
    N: Dim + DimMin<N, Output = N>,
    DefaultAllocator: Allocator<Real, N>,
    DefaultAllocator: Allocator<Real, N, N>,
    DefaultAllocator: Allocator<Real, U1, N>,
    DefaultAllocator: Allocator<(usize, usize ), <N as DimMin<N>>::Output>,

fn clone(&self) -> MultivariateNormal<Real, N>

Returns a copy of the value. Read more

1.0.0[src]

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more

impl<Real: Debug, N: Debug> Debug for MultivariateNormal<Real, N> where
    Real: RealField,
    N: Dim + DimMin<N, Output = N>,
    DefaultAllocator: Allocator<Real, N>,
    DefaultAllocator: Allocator<Real, N, N>,
    DefaultAllocator: Allocator<Real, U1, N>,
    DefaultAllocator: Allocator<(usize, usize ), <N as DimMin<N>>::Output>,

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more

Auto Trait Implementations

impl<Real, N> !RefUnwindSafe for MultivariateNormal<Real, N>

impl<Real, N> !Send for MultivariateNormal<Real, N>

impl<Real, N> !Sync for MultivariateNormal<Real, N>

impl<Real, N> !Unpin for MultivariateNormal<Real, N>

impl<Real, N> !UnwindSafe for MultivariateNormal<Real, N>

Blanket Implementations

impl<T> Any for T where
T: 'static + ?Sized,

pub fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more

impl<T> Borrow<T> for T where
T: ?Sized,

pub fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more

impl<T> BorrowMut<T> for T where
T: ?Sized,

pub fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more

impl<T> From<T> for T

pub fn from(t: T) -> T

Performs the conversion.

impl<T, U> Into<U> for T where
U: From<T>,

pub fn into(self) -> U

Performs the conversion.

impl<T> Same<T> for T

type Output = T

Should always be Self

impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,

pub fn to_subset(&self) -> Option<SS>

The inverse inclusion map: attempts to construct self from the equivalent element of its superset. Read more

pub fn is_in_subset(&self) -> bool

Checks if self is actually part of its subset T (and can be converted to it).

pub fn to_subset_unchecked(&self) -> SS

Use with care! Same as self.to_subset but without any property checks. Always succeeds.

pub fn from_subset(element: &SS) -> SP

The inclusion map: converts self to the equivalent element of its superset.

impl<T> ToOwned for T where
T: Clone,

type Owned = T

The resulting type after obtaining ownership.

pub fn to_owned(&self) -> T

Creates owned data from borrowed data, usually by cloning. Read more

pub fn clone_into(&self, target: &mut T)

🔬 This is a nightly-only experimental API. (toowned_clone_into)

recently added

Uses borrowed data to replace owned data, usually by cloning. Read more

impl<T, U> TryFrom<U> for T where
U: Into<T>,

type Error = Infallible

The type returned in the event of a conversion error.

pub fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.

impl<T, U> TryInto<U> for T where
U: TryFrom<T>,

type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.

pub fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.