Struct GaussianNB 

pub struct GaussianNB<XT: RealNumber, YT: WholeNumber> { /* private fields */ }

Implementation of Gaussian Naive Bayes classifier.

This struct represents a Gaussian Naive Bayes classifier. It is used to fit a training dataset and make predictions on new data points. The classifier assumes that the features are independent and follow a Gaussian distribution.

Type Parameters

• XT: The type of the input features.
• YT: The type of the target labels.

Example

use rusty_ai::bayes::gaussian::GaussianNB;
use rusty_ai::data::dataset::Dataset;
use nalgebra::{DMatrix, DVector};

// Create a new Gaussian Naive Bayes classifier
let mut classifier = GaussianNB::new();

// Create a training dataset
let x = DMatrix::from_row_slice(3, 2, &[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
let y = DVector::from_vec(vec![0, 1, 0]);
let dataset = Dataset::new(x, y);

// Fit the classifier to the training dataset
let result = classifier.fit(&dataset);
assert!(result.is_ok());

// Make predictions on new data points
let x_test = DMatrix::from_row_slice(2, 2, &[1.0, 2.0, 3.0, 4.0]);
let predictions = classifier.predict(&x_test);
assert!(predictions.is_ok());

Implementations

impl<XT: RealNumber, YT: WholeNumber> GaussianNB<XT, YT>

pub fn new() -> Self

Creates a new Gaussian Naive Bayes classifier.

This function initializes the classifier with empty class frequency, mean, and variance maps.

Returns

A new instance of GaussianNB.

pub fn class_freq(&self) -> &HashMap<YT, XT>

Returns a reference to the class frequency map.

This function returns a reference to the map that stores the frequency of each class in the training dataset.

Returns

A reference to the class frequency map.

pub fn class_mean(&self) -> &HashMap<YT, DVector<XT>>

Returns a reference to the class mean map.

This function returns a reference to the map that stores the mean values of each feature for each class in the training dataset.

Returns

A reference to the class mean map.

pub fn class_variance(&self) -> &HashMap<YT, DVector<XT>>

Returns a reference to the class variance map.

This function returns a reference to the map that stores the variance values of each feature for each class in the training dataset.

Returns

A reference to the class variance map.

pub fn fit( &mut self, dataset: &Dataset<XT, YT>, ) -> Result<String, Box<dyn Error>>

Fits the classifier to a training dataset.

This function fits the classifier to the provided training dataset. It calculates the class frequency, mean, and variance for each class in the dataset.

Arguments

• dataset - The training dataset to fit the classifier to.

Returns

A Result indicating whether the fitting process was successful or an error occurred.

pub fn predict(&self, x: &DMatrix<XT>) -> Result<DVector<YT>, Box<dyn Error>>

Predicts the class labels for a given matrix of feature vectors.

This function predicts the class labels for each feature vector in the input matrix using the fitted Gaussian Naive Bayes classifier. It returns a vector of predicted class labels.

Arguments

• x - The matrix of feature vectors to predict the class labels for.

Returns

A Result containing a vector of predicted class labels or an error if the prediction process fails.

Trait Implementations

impl<XT: RealNumber, YT: WholeNumber> ClassificationMetrics<YT> for GaussianNB<XT, YT>

fn confusion_matrix( &self, y_true: &DVector<T>, y_pred: &DVector<T>, ) -> Result<DMatrix<usize>, Box<dyn Error>>

Computes the confusion matrix based on the true labels and predicted labels.

fn accuracy( &self, y_true: &DVector<T>, y_pred: &DVector<T>, ) -> Result<f64, Box<dyn Error>>

Computes the accuracy based on the true labels and predicted labels.

fn precision( &self, y_true: &DVector<T>, y_pred: &DVector<T>, ) -> Result<f64, Box<dyn Error>>

Computes the precision based on the true labels and predicted labels.

fn recall( &self, y_true: &DVector<T>, y_pred: &DVector<T>, ) -> Result<f64, Box<dyn Error>>

Computes the recall based on the true labels and predicted labels.

fn f1_score( &self, y_true: &DVector<T>, y_pred: &DVector<T>, ) -> Result<f64, Box<dyn Error>>

Computes the F1 score based on the true labels and predicted labels.

impl<XT: Clone + RealNumber, YT: Clone + WholeNumber> Clone for GaussianNB<XT, YT>

fn clone(&self) -> GaussianNB<XT, YT>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<XT: Debug + RealNumber, YT: Debug + WholeNumber> Debug for GaussianNB<XT, YT>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<XT: RealNumber, YT: WholeNumber> Default for GaussianNB<XT, YT>

fn default() -> Self

Returns the “default value” for a type.