Struct linfa_bayes::GaussianNbParams [−][src]
pub struct GaussianNbParams { /* fields omitted */ }
Gaussian Naive Bayes (GaussianNB)
The Gaussian Naive Bayes is a classification algorithm where the likelihood of the feature P(x_i | y) is assumed to be Gaussian, features are assumed to be independent, and the mean and variance are estimated using maximum likelihood.
Implementations
impl GaussianNbParams
[src]
impl GaussianNbParams
[src]pub fn params() -> Self
[src]
Create new GaussianNB model with default values for its parameters
pub fn var_smoothing(self, var_smoothing: f64) -> Self
[src]
Specifies the portion of the largest variance of all the features that is added to the variance for calculation stability
Trait Implementations
impl Default for GaussianNbParams
[src]
impl Default for GaussianNbParams
[src]impl<F, D, L> Fit<ArrayBase<D, Dim<[usize; 2]>>, L, BayesError> for GaussianNbParams where
F: Float,
D: Data<Elem = F>,
L: AsTargets<Elem = usize> + Labels<Elem = usize>,
[src]
impl<F, D, L> Fit<ArrayBase<D, Dim<[usize; 2]>>, L, BayesError> for GaussianNbParams where
F: Float,
D: Data<Elem = F>,
L: AsTargets<Elem = usize> + Labels<Elem = usize>,
[src]type Object = GaussianNb<F>
fn fit(
&self,
dataset: &DatasetBase<ArrayBase<D, Ix2>, L>
) -> Result<Self::Object>
[src]
&self,
dataset: &DatasetBase<ArrayBase<D, Ix2>, L>
) -> Result<Self::Object>
Fit the model
Example
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 data = Dataset::new(x, y); let model = GaussianNbParams::params().fit(&data)?; let pred = model.predict(&data); assert_abs_diff_eq!(pred, data.try_single_target()?);
impl<F, D, L> IncrementalFit<'_, ArrayBase<D, Dim<[usize; 2]>>, L> for GaussianNbParams where
F: Float,
D: Data<Elem = F>,
L: AsTargets<Elem = usize> + Labels<Elem = usize>,
[src]
impl<F, D, L> IncrementalFit<'_, ArrayBase<D, Dim<[usize; 2]>>, L> for GaussianNbParams where
F: Float,
D: Data<Elem = F>,
L: AsTargets<Elem = usize> + Labels<Elem = usize>,
[src]type ObjectIn = Option<GaussianNb<F>>
type ObjectOut = Result<Option<GaussianNb<F>>>
fn fit_with(
&self,
model_in: Self::ObjectIn,
dataset: &DatasetBase<ArrayBase<D, Ix2>, L>
) -> Self::ObjectOut
[src]
&self,
model_in: Self::ObjectIn,
dataset: &DatasetBase<ArrayBase<D, Ix2>, L>
) -> Self::ObjectOut
Incrementally fit on a batch of samples
Example
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 mut clf = GaussianNbParams::params(); let mut model = None; for (x, y) in x .axis_chunks_iter(Axis(0), 2) .zip(y.axis_chunks_iter(Axis(0), 2)) { model = clf.fit_with(model, &DatasetView::new(x, y))?; } let pred = model.as_ref().unwrap().predict(&x); assert_abs_diff_eq!(pred, y);
Auto Trait Implementations
impl RefUnwindSafe for GaussianNbParams
impl RefUnwindSafe for GaussianNbParams
impl Send for GaussianNbParams
impl Send for GaussianNbParams
impl Sync for GaussianNbParams
impl Sync for GaussianNbParams
impl Unpin for GaussianNbParams
impl Unpin for GaussianNbParams
impl UnwindSafe for GaussianNbParams
impl UnwindSafe for GaussianNbParams