Module parameter 

Parameter trait and five built-in parameter types.

The Parameter trait provides a unified way to define search-space dimensions and sample values from a Trial. Five implementations cover the most common hyperparameter types:

Type	Sampled value	Typical use
FloatParam	f64	Learning rate, dropout probability
IntParam	i64	Layer count, batch size
CategoricalParam	T: Clone	Optimizer name, activation function
BoolParam	bool	Feature toggle
EnumParam	T: Categorical	Typed enum variant selection

All five types support .name() for a human-readable label and .suggest(&mut trial) as a shorthand for trial.suggest_param(&param).

Example

use optimizer::Trial;
use optimizer::parameter::{BoolParam, FloatParam, IntParam, Parameter};

let mut trial = Trial::new(0);

let lr = FloatParam::new(1e-5, 1e-1)
    .log_scale()
    .name("learning_rate")
    .suggest(&mut trial)
    .unwrap();
let layers = IntParam::new(1, 10)
    .name("n_layers")
    .suggest(&mut trial)
    .unwrap();
let dropout = BoolParam::new()
    .name("use_dropout")
    .suggest(&mut trial)
    .unwrap();

Re-exports

pub use crate::param::ParamValue;

Structs

BoolParam

A boolean parameter (equivalent to a two-choice categorical: false / true).

CategoricalParam

A categorical parameter that selects from a list of choices.

EnumParam

A parameter that selects from the variants of an enum implementing Categorical.

FloatParam

A floating-point parameter with optional log-scale and step size.

IntParam

An integer parameter with optional log-scale and step size.

ParamId

A unique identifier for a parameter instance.

Traits

Categorical

Map an enum type to sequential indices for use as a categorical parameter.

Parameter

Define a parameter type that can be suggested by a Trial.