Struct statrs::distribution::Dirichlet [−][src]
pub struct Dirichlet { /* fields omitted */ }
Expand description
Implements the Dirichlet distribution
Examples
use statrs::distribution::{Dirichlet, Continuous}; use statrs::statistics::Distribution; use nalgebra::DVector; use statrs::statistics::MeanN; let n = Dirichlet::new(vec![1.0, 2.0, 3.0]).unwrap(); assert_eq!(n.mean().unwrap(), DVector::from_vec(vec![1.0 / 6.0, 1.0 / 3.0, 0.5])); assert_eq!(n.pdf(&DVector::from_vec(vec![0.33333, 0.33333, 0.33333])), 2.222155556222205);
Implementations
impl Dirichlet
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impl Dirichlet
[src]pub fn new(alpha: Vec<f64>) -> Result<Dirichlet>
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pub fn new(alpha: Vec<f64>) -> Result<Dirichlet>
[src]Constructs a new dirichlet distribution with the given concentration parameters (alpha)
Errors
Returns an error if any element x
in alpha exist
such that x < = 0.0
or x
is NaN
, or if the length of alpha is
less than 2
Examples
use statrs::distribution::Dirichlet; use nalgebra::DVector; let alpha_ok = vec![1.0, 2.0, 3.0]; let mut result = Dirichlet::new(alpha_ok); assert!(result.is_ok()); let alpha_err = vec![0.0]; result = Dirichlet::new(alpha_err); assert!(result.is_err());
pub fn new_with_param(alpha: f64, n: usize) -> Result<Dirichlet>
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pub fn new_with_param(alpha: f64, n: usize) -> Result<Dirichlet>
[src]Constructs a new dirichlet distribution with the given
concentration parameter (alpha) repeated n
times
Errors
Returns an error if alpha < = 0.0
or alpha
is NaN
,
or if n < 2
Examples
use statrs::distribution::Dirichlet; let mut result = Dirichlet::new_with_param(1.0, 3); assert!(result.is_ok()); result = Dirichlet::new_with_param(0.0, 1); assert!(result.is_err());
pub fn alpha(&self) -> &DVector<f64>
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pub fn alpha(&self) -> &DVector<f64>
[src]Returns the concentration parameters of the dirichlet distribution as a slice
Examples
use statrs::distribution::Dirichlet; use nalgebra::DVector; let n = Dirichlet::new(vec![1.0, 2.0, 3.0]).unwrap(); assert_eq!(n.alpha(), &DVector::from_vec(vec![1.0, 2.0, 3.0]));
pub fn entropy(&self) -> Option<f64>
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pub fn entropy(&self) -> Option<f64>
[src]Returns the entropy of the dirichlet distribution
Formula
ln(B(α)) - (K - α_0)ψ(α_0) - Σ((α_i - 1)ψ(α_i))
where
B(α) = Π(Γ(α_i)) / Γ(Σ(α_i))
α_0
is the sum of all concentration parameters,
K
is the number of concentration parameters, ψ
is the digamma
function, α_i
is the i
th concentration parameter, and Σ
is the sum from 1
to K
Trait Implementations
impl<'a> Continuous<&'a Matrix<f64, Dynamic, Const<1_usize>, VecStorage<f64, Dynamic, Const<1_usize>>>, f64> for Dirichlet
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impl<'a> Continuous<&'a Matrix<f64, Dynamic, Const<1_usize>, VecStorage<f64, Dynamic, Const<1_usize>>>, f64> for Dirichlet
[src]fn pdf(&self, x: &DVector<f64>) -> f64
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fn pdf(&self, x: &DVector<f64>) -> f64
[src]Calculates the probabiliy density function for the dirichlet
distribution
with given x
’s corresponding to the concentration parameters for this
distribution
Panics
If any element in x
is not in (0, 1)
, the elements in x
do not
sum to
1
with a tolerance of 1e-4
, or if x
is not the same length as
the vector of
concentration parameters for this distribution
Formula
(1 / B(α)) * Π(x_i^(α_i - 1))
where
B(α) = Π(Γ(α_i)) / Γ(Σ(α_i))
α
is the vector of concentration parameters, α_i
is the i
th
concentration parameter, x_i
is the i
th argument corresponding to
the i
th concentration parameter, Γ
is the gamma function,
Π
is the product from 1
to K
, Σ
is the sum from 1
to K
,
and K
is the number of concentration parameters
fn ln_pdf(&self, x: &DVector<f64>) -> f64
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fn ln_pdf(&self, x: &DVector<f64>) -> f64
[src]Calculates the log probabiliy density function for the dirichlet
distribution
with given x
’s corresponding to the concentration parameters for this
distribution
Panics
If any element in x
is not in (0, 1)
, the elements in x
do not
sum to
1
with a tolerance of 1e-4
, or if x
is not the same length as
the vector of
concentration parameters for this distribution
Formula
ln((1 / B(α)) * Π(x_i^(α_i - 1)))
where
B(α) = Π(Γ(α_i)) / Γ(Σ(α_i))
α
is the vector of concentration parameters, α_i
is the i
th
concentration parameter, x_i
is the i
th argument corresponding to
the i
th concentration parameter, Γ
is the gamma function,
Π
is the product from 1
to K
, Σ
is the sum from 1
to K
,
and K
is the number of concentration parameters
impl Distribution<Matrix<f64, Dynamic, Const<1_usize>, VecStorage<f64, Dynamic, Const<1_usize>>>> for Dirichlet
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impl Distribution<Matrix<f64, Dynamic, Const<1_usize>, VecStorage<f64, Dynamic, Const<1_usize>>>> for Dirichlet
[src]fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> DVector<f64>
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fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> DVector<f64>
[src]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,
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fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T> where
R: Rng,
[src]Create an iterator that generates random values of T
, using rng
as
the source of randomness. Read more
impl MeanN<Matrix<f64, Dynamic, Const<1_usize>, VecStorage<f64, Dynamic, Const<1_usize>>>> for Dirichlet
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impl MeanN<Matrix<f64, Dynamic, Const<1_usize>, VecStorage<f64, Dynamic, Const<1_usize>>>> for Dirichlet
[src]impl StructuralPartialEq for Dirichlet
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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
impl<T> BorrowMut<T> for T where
T: ?Sized,
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impl<T> BorrowMut<T> for T where
T: ?Sized,
[src]pub fn borrow_mut(&mut self) -> &mut T
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pub fn borrow_mut(&mut self) -> &mut T
[src]Mutably borrows from an owned value. Read more
impl<T> Same<T> for T
impl<T> Same<T> for T
type Output = T
type Output = T
Should always be Self
impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,
pub fn to_subset(&self) -> Option<SS>
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
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
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
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,
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impl<T> ToOwned for T where
T: Clone,
[src]type Owned = T
type Owned = T
The resulting type after obtaining ownership.
pub fn to_owned(&self) -> T
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pub fn to_owned(&self) -> T
[src]Creates owned data from borrowed data, usually by cloning. Read more
pub fn clone_into(&self, target: &mut T)
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pub fn clone_into(&self, target: &mut T)
[src]🔬 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<V, T> VZip<V> for T where
V: MultiLane<T>,
impl<V, T> VZip<V> for T where
V: MultiLane<T>,