Struct LikelihoodEvaluator

pub struct LikelihoodEvaluator { /* private fields */ }

A structure to evaluate and minimize combinations of LikelihoodTerms.

Implementations

impl LikelihoodEvaluator

pub fn parameters(&self) -> Vec<String>

The parameter names used in LikelihoodEvaluator::evaluate’s input in order.

pub fn evaluate(&self, parameters: &[f64]) -> Result<f64, LadduError>

A function that can be called to evaluate the sum/product of the LikelihoodTerms contained by this LikelihoodEvaluator.

pub fn evaluate_gradient( &self, parameters: &[f64], ) -> Result<Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>, LadduError>

Evaluate the gradient of the stored LikelihoodExpression over the events in the Dataset stored by the LikelihoodEvaluator with the given values for free parameters.

pub fn minimize( &self, p0: &[f64], bounds: Option<Vec<(f64, f64)>>, options: Option<MinimizerOptions>, ) -> Result<Status, LadduError>

A function that can be called to minimize the sum/product of the LikelihoodTerms contained by this LikelihoodEvaluator.

See NLL::minimize for more details.

pub fn mcmc<T>( &self, p0: T, n_steps: usize, options: Option<MCMCOptions>, rng: Rng, ) -> Result<Ensemble, LadduError>

where T: AsRef<[Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>]>,

A function that can be called to perform Markov Chain Monte Carlo sampling of the sum/product of the LikelihoodTerms contained by this LikelihoodEvaluator.

See NLL::mcmc for more details.

Trait Implementations

impl Function<ThreadPool, LadduError> for LikelihoodEvaluator

fn evaluate( &self, parameters: &[f64], thread_pool: &mut ThreadPool, ) -> Result<f64, LadduError>

The evaluation of the function at a point x with the given arguments/user data.

fn gradient( &self, parameters: &[f64], thread_pool: &mut ThreadPool, ) -> Result<Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>, LadduError>

The evaluation of the gradient at a point x with the given arguments/user data.

fn evaluate_bounded( &self, x: &[f64], bounds: Option<&Vec<Bound>>, user_data: &mut U, ) -> Result<f64, E>

The evaluation of the function at a point x with the given arguments/user data. This function assumes x is an internal, unbounded vector, but performs a coordinate transform to bound x when evaluating the function.

fn gradient_bounded( &self, x: &[f64], bounds: Option<&Vec<Bound>>, user_data: &mut U, ) -> Result<Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>, E>

The evaluation of the gradient at a point x with the given arguments/user data. This function assumes x is an internal, unbounded vector, but performs a coordinate transform to bound x when evaluating the function.

fn hessian( &self, x: &[f64], user_data: &mut U, ) -> Result<Matrix<f64, Dyn, Dyn, VecStorage<f64, Dyn, Dyn>>, E>

The evaluation of the hessian at a point x with the given arguments/user data.

fn hessian_bounded( &self, x: &[f64], bounds: Option<&Vec<Bound>>, user_data: &mut U, ) -> Result<Matrix<f64, Dyn, Dyn, VecStorage<f64, Dyn, Dyn>>, E>

The evaluation of the hessian at a point x with the given arguments/user data. This function assumes x is an internal, unbounded vector, but performs a coordinate transform to bound x when evaluating the function.