Struct std_dev::regression::binary_search::Options

pub struct Options {
    pub iterations: usize,
    pub precision: usize,
    pub max: f64,
    pub randomness_factor: f64,
    pub random_subset_regression: Option<Config>,
}

Available on crate feature regression only.

Options for the binary search optimization.

Fields

iterations: usize

Number of iterations to search for the optimal value

precision: usize

How fine values you can get. 59 covers the whole range of f64 30 seems to get you ~7 significant digits

max: f64

The assumed max value. Use f64::MAX to cover the whole range of f64.

randomness_factor: f64

Available on crate feature binary_search_rng only.

The factor for the randomness introduced when binary searching. Higher values result in finding more optimal values, but can also make it hard for the algorithm to find a good value.

random_subset_regression: Option<Config>

Available on crate feature random_subset_regression only.

Config for using random_subset_regression.

Implementations

impl Options

pub fn max_precision(&self) -> Self

Get max precision of every variable.

pub fn n_variable_optimization_no_rng<NV: NVariableStorage>( &self, fitness_function: impl Fn(NV::Given<'_>) -> f64, data: NV::Data ) -> NV

Like Options::n_variable_optimization but without random variation. More easily falls into local maxima (a variables thought to be the best). Useful for independent variables.

Faster than Options::n_variable_optimization.

pub fn n_variable_optimization<NV: NVariableStorage>( &self, fitness_function: impl Fn(NV::Given<'_>) -> f64, data: NV::Data, rng: &mut impl Rng ) -> NV

Available on crate feature binary_search_rng only.

Optimize n variables to fitness_function. Will return a set of values which (hopefully) minimize fitness_function.

Trait Implementations

impl Clone for Options

fn clone(&self) -> Options

Returns a copy of the value.

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

Performs copy-assignment from source.

impl CosecantEstimator for Options

fn model_cosecant( &self, predictors: &[f64], outcomes: &[f64], max_frequency: f64 ) -> CosecantCoefficients

Model the CosecantCoefficients from predictors and outcomes.

fn boxed_cosecant(self) -> Box<dyn CosecantEstimator>where Self: Sized + 'static,

Put this estimator in a box. This is useful for conditionally choosing different estimators.

impl CosineEstimator for Options

fn model_cosine( &self, predictors: &[f64], outcomes: &[f64], max_frequency: f64 ) -> CosineCoefficients

Model the CosineCoefficients from predictors and outcomes.

fn boxed_cosine(self) -> Box<dyn CosineEstimator>where Self: Sized + 'static,

Put this estimator in a box. This is useful for conditionally choosing different estimators.

impl CotangentEstimator for Options

fn model_cotangent( &self, predictors: &[f64], outcomes: &[f64], max_frequency: f64 ) -> CotangentCoefficients

Model the CotangentCoefficients from predictors and outcomes.

fn boxed_cotangent(self) -> Box<dyn CotangentEstimator>where Self: Sized + 'static,

Put this estimator in a box. This is useful for conditionally choosing different estimators.

impl Debug for Options

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

Formats the value using the given formatter.

impl Default for Options

fn default() -> Self

Returns the “default value” for a type.

impl ExponentialEstimator for Options

fn model_exponential( &self, predictors: &[f64], outcomes: &[f64] ) -> ExponentialCoefficients

Model the ExponentialCoefficients from predictors and outcomes.

fn boxed_exponential(self) -> Box<dyn ExponentialEstimator>where Self: Sized + 'static,

Put this estimator in a box. This is useful for conditionally choosing different estimators.

impl LinearEstimator for Options

fn model_linear( &self, predictors: &[f64], outcomes: &[f64] ) -> LinearCoefficients

Model the LinearCoefficients from predictors and outcomes.

fn boxed_linear(self) -> Box<dyn LinearEstimator>where Self: Sized + 'static,

Put this estimator in a box. This is useful for conditionally choosing different estimators.

impl LogisticEstimator for Options

fn model_logistic( &self, predictors: &[f64], outcomes: &[f64] ) -> LogisticCoefficients

Model the LogisticCoefficients from predictors and outcomes.

fn boxed_logistic(self) -> Box<dyn LogisticEstimator>where Self: Sized + 'static,

Put this estimator in a box. This is useful for conditionally choosing different estimators.

impl PartialEq<Options> for Options

fn eq(&self, other: &Options) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl PolynomialEstimator for Options

Available on crate feature binary_search_rng only.

fn model_polynomial( &self, predictors: &[f64], outcomes: &[f64], degree: usize ) -> PolynomialCoefficients

Model the PolynomialCoefficients from predictors and outcomes. Also takes a degree of the target polynomial. Some estimators may panic when degree is out of their range.

fn boxed_polynomial(self) -> Box<dyn PolynomialEstimator>where Self: Sized + 'static,

Put this estimator in a box. This is useful for conditionally choosing different estimators.

impl PowerEstimator for Options

fn model_power(&self, predictors: &[f64], outcomes: &[f64]) -> PowerCoefficients

Model the PowerCoefficients from predictors and outcomes.

fn boxed_power(self) -> Box<dyn PowerEstimator>where Self: Sized + 'static,

Put this estimator in a box. This is useful for conditionally choosing different estimators.

impl SecantEstimator for Options

fn model_secant( &self, predictors: &[f64], outcomes: &[f64], max_frequency: f64 ) -> SecantCoefficients

Model the SecantCoefficients from predictors and outcomes.

fn boxed_sesecant(self) -> Box<dyn SecantEstimator>where Self: Sized + 'static,

Put this estimator in a box. This is useful for conditionally choosing different estimators.

impl SineEstimator for Options

fn model_sine( &self, predictors: &[f64], outcomes: &[f64], max_frequency: f64 ) -> SineCoefficients

Model the SineCoefficients from predictors and outcomes.

fn boxed_sine(self) -> Box<dyn SineEstimator>where Self: Sized + 'static,

Put this estimator in a box. This is useful for conditionally choosing different estimators.

impl TangentEstimator for Options

fn model_tangent( &self, predictors: &[f64], outcomes: &[f64], max_frequency: f64 ) -> TangentCoefficients

Model the TangentCoefficients from predictors and outcomes.

fn boxed_tangent(self) -> Box<dyn TangentEstimator>where Self: Sized + 'static,

Put this estimator in a box. This is useful for conditionally choosing different estimators.

impl Copy for Options

impl StructuralPartialEq for Options