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//! # Common Interfaces and API
//!
//! This module provides interfaces and uniform API with simple conventions
//! that are used in other modules for supervised and unsupervised learning.
use crate::error::Failed;
/// An estimator for unsupervised learning, that provides method `fit` to learn from data
pub trait UnsupervisedEstimator<X, P> {
/// Fit a model to a training dataset, estimate model's parameters.
/// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
/// * `parameters` - hyperparameters of an algorithm
fn fit(x: &X, parameters: P) -> Result<Self, Failed>
where
Self: Sized,
P: Clone;
}
/// An estimator for supervised learning, that provides method `fit` to learn from data and training values
pub trait SupervisedEstimator<X, Y, P>: Predictor<X, Y> {
/// Empty constructor, instantiate an empty estimator. Object is dropped as soon as `fit()` is called.
/// used to pass around the correct `fit()` implementation.
/// by calling `::fit()`. mostly used to be used with `model_selection::cross_validate(...)`
fn new() -> Self;
/// Fit a model to a training dataset, estimate model's parameters.
/// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
/// * `y` - target training values of size _N_.
/// * `parameters` - hyperparameters of an algorithm
fn fit(x: &X, y: &Y, parameters: P) -> Result<Self, Failed>
where
Self: Sized,
P: Clone;
}
/// An estimator for supervised learning.
/// In this one parameters are borrowed instead of moved, this is useful for parameters that carry
/// references. Also to be used when there is no predictor attached to the estimator.
pub trait SupervisedEstimatorBorrow<'a, X, Y, P> {
/// Empty constructor, instantiate an empty estimator. Object is dropped as soon as `fit()` is called.
/// used to pass around the correct `fit()` implementation.
/// by calling `::fit()`. mostly used to be used with `model_selection::cross_validate(...)`
fn new() -> Self;
/// Fit a model to a training dataset, estimate model's parameters.
/// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
/// * `y` - target training values of size _N_.
/// * `¶meters` - hyperparameters of an algorithm
fn fit(x: &'a X, y: &'a Y, parameters: &'a P) -> Result<Self, Failed>
where
Self: Sized,
P: Clone;
}
/// Implements method predict that estimates target value from new data
pub trait Predictor<X, Y> {
/// Estimate target values from new data.
/// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
fn predict(&self, x: &X) -> Result<Y, Failed>;
}
/// Implements method predict that estimates target value from new data, with borrowing
pub trait PredictorBorrow<'a, X, T> {
/// Estimate target values from new data.
/// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
fn predict(&self, x: &'a X) -> Result<Vec<T>, Failed>;
}
/// Implements method transform that filters or modifies input data
pub trait Transformer<X> {
/// Transform data by modifying or filtering it
/// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
fn transform(&self, x: &X) -> Result<X, Failed>;
}
/// empty parameters for an estimator, see `BiasedEstimator`
pub trait NoParameters {}