Struct linfa_bayes::GaussianNbParams

pub struct GaussianNbParams<F, L>(_);

A hyper-parameter set during construction

The parameter set can be verified into a GaussianNbValidParams by calling ParamGuard::check. It is also possible to directly fit a model with Fit::fit or FitWith::fit_with which implicitely verifies the parameter set prior to the model estimation and forwards any error.

Parameters

Name	Default	Purpose	Range
var_smoothing	1e-9	Stabilize variance calculation if ratios are small in update step	[0, inf)

Errors

The following errors can come from invalid hyper-parameters:

Returns InvalidSmoothing if the smoothing parameter is negative.

Example

use linfa_bayes::{GaussianNbParams, GaussianNbValidParams, Result};
use linfa::prelude::*;
use ndarray::array;

let x = array![
    [-2., -1.],
    [-1., -1.],
    [-1., -2.],
    [1., 1.],
    [1., 2.],
    [2., 1.]
];
let y = array![1, 1, 1, 2, 2, 2];
let ds = DatasetView::new(x.view(), y.view());

// create a new parameter set with variance smoothing equals `1e-5`
let unchecked_params = GaussianNbParams::new()
    .var_smoothing(1e-5);

// fit model with unchecked parameter set
let model = unchecked_params.fit(&ds)?;

// transform into a verified parameter set
let checked_params = unchecked_params.check()?;

// update model with the verified parameters, this only returns
// errors originating from the fitting process
let model = checked_params.fit_with(Some(model), &ds)?;

Implementations

impl<F: Float, L> GaussianNbParams<F, L>

pub fn new() -> Self

Create new GaussianNbParams set with default values for its parameters

pub fn var_smoothing(self, var_smoothing: F) -> Self

Specifies the portion of the largest variance of all the features that is added to the variance for calculation stability

Trait Implementations

impl<F: Float, L> Default for GaussianNbParams<F, L>

fn default() -> Self

Returns the “default value” for a type.

impl<F: Float, L> ParamGuard for GaussianNbParams<F, L>

type Checked = GaussianNbValidParams<F, L>

The checked hyperparameters

type Error = NaiveBayesError

Error type resulting from failed hyperparameter checking

fn check_ref(&self) -> Result<&Self::Checked, Self::Error>

Checks the hyperparameters and returns a reference to the checked hyperparameters if successful

fn check(self) -> Result<Self::Checked, Self::Error>

Checks the hyperparameters and returns the checked hyperparameters if successful

fn check_unwrap(self) -> Self::Checked

Calls check() and unwraps the result