Struct linfa_bayes::GaussianNb
source · [−]Expand description
Fitted Gaussian Naive Bayes classifier.
See GaussianNbParams for more information on the hyper-parameters.
Model assumptions
The family of Naive Bayes classifiers assume independence between variables. They do not model moments between variables and lack therefore in modelling capability. The advantage is a linear fitting time with maximum-likelihood training in a closed form.
Model usage example
The example below creates a set of hyperparameters, and then uses it to fit a Gaussian Naive Bayes classifier on provided data.
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
sourceimpl<F: Float, L: Label> GaussianNb<F, L>
impl<F: Float, L: Label> GaussianNb<F, L>
sourcepub fn params() -> GaussianNbParams<F, L>
pub fn params() -> GaussianNbParams<F, L>
Construct a new set of hyperparameters
Trait Implementations
sourceimpl<F: Clone + PartialEq, L: Clone + Eq + Hash> Clone for GaussianNb<F, L>
impl<F: Clone + PartialEq, L: Clone + Eq + Hash> Clone for GaussianNb<F, L>
sourcefn clone(&self) -> GaussianNb<F, L>
fn clone(&self) -> GaussianNb<F, L>
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source
. Read more
sourceimpl<F: PartialEq + PartialEq, L: PartialEq + Eq + Hash> PartialEq<GaussianNb<F, L>> for GaussianNb<F, L>
impl<F: PartialEq + PartialEq, L: PartialEq + Eq + Hash> PartialEq<GaussianNb<F, L>> for GaussianNb<F, L>
sourcefn eq(&self, other: &GaussianNb<F, L>) -> bool
fn eq(&self, other: &GaussianNb<F, L>) -> bool
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
sourcefn ne(&self, other: &GaussianNb<F, L>) -> bool
fn ne(&self, other: &GaussianNb<F, L>) -> bool
This method tests for !=
.
sourceimpl<F: Float, L: Label, D> PredictInplace<ArrayBase<D, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<L>, Dim<[usize; 1]>>> for GaussianNb<F, L> where
D: Data<Elem = F>,
impl<F: Float, L: Label, D> PredictInplace<ArrayBase<D, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<L>, Dim<[usize; 1]>>> for GaussianNb<F, L> where
D: Data<Elem = F>,
sourcefn predict_inplace(&self, x: &ArrayBase<D, Ix2>, y: &mut Array1<L>)
fn predict_inplace(&self, x: &ArrayBase<D, Ix2>, y: &mut Array1<L>)
Predict something in place
sourcefn default_target(&self, x: &ArrayBase<D, Ix2>) -> Array1<L>
fn default_target(&self, x: &ArrayBase<D, Ix2>) -> Array1<L>
Create targets that predict_inplace
works with.
impl<F: PartialEq, L: Eq + Hash> StructuralPartialEq for GaussianNb<F, L>
Auto Trait Implementations
impl<F, L> RefUnwindSafe for GaussianNb<F, L> where
F: RefUnwindSafe,
L: RefUnwindSafe,
impl<F, L> Send for GaussianNb<F, L> where
F: Send,
L: Send,
impl<F, L> Sync for GaussianNb<F, L> where
F: Sync,
L: Sync,
impl<F, L> Unpin for GaussianNb<F, L> where
F: Unpin,
L: Unpin,
impl<F, L> UnwindSafe for GaussianNb<F, L> where
F: UnwindSafe + RefUnwindSafe,
L: UnwindSafe,
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more