Struct Dirichlet Copy item path

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());

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());

Constructs a new distribution with the given vector for alpha Does not clone the vector it takes ownership of

§Error

Returns an error if vector has length less than 2 or if any element of alpha is NOT finite positive

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]));

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 ith concentration parameter, and Σ is the sum from 1 to K

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 ith concentration parameter, x_i is the ith argument corresponding to the ith 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

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 ith concentration parameter, x_i is the ith argument corresponding to the ith 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

Returns the means of the dirichlet distribution

§Formula

α_i / α_0

for the ith element where α_i is the ith concentration parameter and α_0 is the sum of all concentration parameters

Returns the variances of the dirichlet distribution

§Formula

(α_i * (α_0 - α_i)) / (α_0^2 * (α_0 + 1))

for the ith element where α_i is the ith concentration parameter and α_0 is the sum of all concentration parameters

Returns the argument unchanged.

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.