pub struct Dirichlet { /* private fields */ }
Expand description
Dirichlet distribution over points on the k-simplex.
Implementations§
source§impl Dirichlet
impl Dirichlet
sourcepub fn new(alphas: Vec<f64>) -> Result<Self, DirichletError>
pub fn new(alphas: Vec<f64>) -> Result<Self, DirichletError>
Creates a Dirichlet
with a given alphas
vector
sourcepub fn new_unchecked(alphas: Vec<f64>) -> Self
pub fn new_unchecked(alphas: Vec<f64>) -> Self
Creates a new Dirichlet without checking whether the parameters are valid.
sourcepub fn symmetric(alpha: f64, k: usize) -> Result<Self, DirichletError>
pub fn symmetric(alpha: f64, k: usize) -> Result<Self, DirichletError>
Creates a Dirichlet
where all alphas are identical.
Notes
SymmetricDirichlet
if faster and more compact, and is the preferred
way to represent a Dirichlet symmetric weights.
Examples
let dir = Dirichlet::symmetric(1.0, 4).unwrap();
assert_eq!(*dir.alphas(), vec![1.0, 1.0, 1.0, 1.0]);
// Equivalent to SymmetricDirichlet
let symdir = SymmetricDirichlet::new(1.0, 4).unwrap();
let x: Vec<f64> = vec![0.1, 0.4, 0.3, 0.2];
assert::close(dir.ln_f(&x), symdir.ln_f(&x), 1E-12);
sourcepub fn jeffreys(k: usize) -> Result<Self, DirichletError>
pub fn jeffreys(k: usize) -> Result<Self, DirichletError>
Creates a Dirichlet
with all alphas = 0.5 (Jeffreys prior)
Notes
SymmetricDirichlet
if faster and more compact, and is the preferred
way to represent a Dirichlet symmetric weights.
Examples
let dir = Dirichlet::jeffreys(3).unwrap();
assert_eq!(*dir.alphas(), vec![0.5, 0.5, 0.5]);
// Equivalent to SymmetricDirichlet::jeffreys
let symdir = SymmetricDirichlet::jeffreys(3).unwrap();
let x: Vec<f64> = vec![0.1, 0.4, 0.5];
assert::close(dir.ln_f(&x), symdir.ln_f(&x), 1E-12);
Trait Implementations§
source§impl<X: CategoricalDatum> ConjugatePrior<X, Categorical> for Dirichlet
impl<X: CategoricalDatum> ConjugatePrior<X, Categorical> for Dirichlet
source§fn posterior(&self, x: &CategoricalData<'_, X>) -> Self::Posterior
fn posterior(&self, x: &CategoricalData<'_, X>) -> Self::Posterior
Computes the posterior distribution from the data
source§fn ln_m_cache(&self) -> Self::LnMCache
fn ln_m_cache(&self) -> Self::LnMCache
Compute the cache for the log marginal likelihood.
source§fn ln_m_with_cache(
&self,
cache: &Self::LnMCache,
x: &CategoricalData<'_, X>
) -> f64
fn ln_m_with_cache( &self, cache: &Self::LnMCache, x: &CategoricalData<'_, X> ) -> f64
Log marginal likelihood with supplied cache.
source§fn ln_pp_cache(&self, x: &CategoricalData<'_, X>) -> Self::LnPpCache
fn ln_pp_cache(&self, x: &CategoricalData<'_, X>) -> Self::LnPpCache
Compute the cache for the Log posterior predictive of y given x. Read more
source§fn ln_pp_with_cache(&self, cache: &Self::LnPpCache, y: &X) -> f64
fn ln_pp_with_cache(&self, cache: &Self::LnPpCache, y: &X) -> f64
Log posterior predictive of y given x with supplied ln(norm)
source§fn ln_m(&self, x: &DataOrSuffStat<'_, X, Fx>) -> f64
fn ln_m(&self, x: &DataOrSuffStat<'_, X, Fx>) -> f64
The log marginal likelihood
source§fn ln_pp(&self, y: &X, x: &DataOrSuffStat<'_, X, Fx>) -> f64
fn ln_pp(&self, y: &X, x: &DataOrSuffStat<'_, X, Fx>) -> f64
Log posterior predictive of y given x
source§fn m(&self, x: &DataOrSuffStat<'_, X, Fx>) -> f64
fn m(&self, x: &DataOrSuffStat<'_, X, Fx>) -> f64
Marginal likelihood of x
source§impl<'de> Deserialize<'de> for Dirichlet
impl<'de> Deserialize<'de> for Dirichlet
source§fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where __D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
source§impl From<&SymmetricDirichlet> for Dirichlet
impl From<&SymmetricDirichlet> for Dirichlet
source§fn from(symdir: &SymmetricDirichlet) -> Self
fn from(symdir: &SymmetricDirichlet) -> Self
Converts to this type from the input type.
source§impl From<SymmetricDirichlet> for Dirichlet
impl From<SymmetricDirichlet> for Dirichlet
source§fn from(symdir: SymmetricDirichlet) -> Self
fn from(symdir: SymmetricDirichlet) -> Self
Converts to this type from the input type.
source§impl PartialEq<Dirichlet> for Dirichlet
impl PartialEq<Dirichlet> for Dirichlet
source§impl Rv<Categorical> for Dirichlet
impl Rv<Categorical> for Dirichlet
impl StructuralPartialEq for Dirichlet
Auto Trait Implementations§
impl RefUnwindSafe for Dirichlet
impl Send for Dirichlet
impl Sync for Dirichlet
impl Unpin for Dirichlet
impl UnwindSafe for Dirichlet
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
source§impl<Fx> Rv<Datum> for Fxwhere
Fx: RvDatum,
impl<Fx> Rv<Datum> for Fxwhere Fx: RvDatum,
§impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SPwhere SS: SubsetOf<SP>,
§fn to_subset(&self) -> Option<SS>
fn to_subset(&self) -> Option<SS>
The inverse inclusion map: attempts to construct
self
from the equivalent element of its
superset. Read more§fn is_in_subset(&self) -> bool
fn is_in_subset(&self) -> bool
Checks if
self
is actually part of its subset T
(and can be converted to it).§fn to_subset_unchecked(&self) -> SS
fn to_subset_unchecked(&self) -> SS
Use with care! Same as
self.to_subset
but without any property checks. Always succeeds.§fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
The inclusion map: converts
self
to the equivalent element of its superset.