Struct GaussianNaiveBayes 

pub struct GaussianNaiveBayes<State = Unfit> {
    pub classes: Vec<String>,
    pub probability_of_class: HashMap<String, f64>,
    pub probability_of_feat_by_class: HashMap<String, Vec<(f64, f64)>>,
    /* private fields */
}

The GaussianNaiveBayes struct represents a Gaussian Naive Bayes classifier.

A Gaussian Naive Bayes classifier is a type of probabilistic machine learning model used for classification tasks. The Gaussian version assumes the features that it is learning from are distributed normally.

This struct has two possible states: Unfit and Fit. An Unfit model is one that has not yet been trained on data, while a Fit model has been trained and can be used for making predictions.

Fields

• classes - A vector of unique class labels (targets) that the model may predict.

• probability_of_class - A hashmap where keys are the class labels and the values are the corresponding prior probabilities of each class.

• probability_of_feat_by_class - A hashmap where keys are the class labels and the values are vectors of tuples. Each tuple represents the mean and standard deviation of a particular feature for that class.

• state - A marker for the model’s state. This is either Unfit (for a newly instantiated model) or Fit (for a model that has been trained on data).

Examples

use std::collections::HashMap;
use ducky_learn::naive_bayes::GaussianNaiveBayes;

let model = GaussianNaiveBayes::new();

let x_train: Vec<Vec<f64>> = vec![
    vec![1.0, 2.0],
    vec![2.0, 3.0],
    vec![3.0, 4.0],
    vec![4.0, 5.0],
];
let y_train: Vec<String> = vec![
    "class1".to_string(),
    "class2".to_string(),
    "class1".to_string(),
    "class2".to_string(),
];

let model = model.fit(&x_train, &y_train);

let x_test: Vec<Vec<f64>> = vec![
    vec![1.5, 2.5],
    vec![3.5, 4.5],
];

let predictions = model.predict(&x_test);

println!("{:?}", predictions);

Fields

classes: Vec<String>

probability_of_class: HashMap<String, f64>

probability_of_feat_by_class: HashMap<String, Vec<(f64, f64)>>

Implementations

impl GaussianNaiveBayes

pub fn new() -> Self

Creates a new GaussianNaiveBayes instance with an Unfit state.

Returns

• Self - A new instance of GaussianNaiveBayes.

Examples

use ducky_learn::naive_bayes::GaussianNaiveBayes;
let model = GaussianNaiveBayes::new();

pub fn fit(self, x: &Vec<Vec<f64>>, y: &Vec<String>) -> GaussianNaiveBayes<Fit>

Fits the model on the provided dataset, updating the model’s state to Fit.

Arguments

• x - A reference to a vector of vectors, where each inner vector represents the features of a data point.

• y - A reference to a vector of class labels for each data point in x.

Returns

• GaussianNaiveBayes<Fit> - The same model instance with updated fields and state set to Fit.

Examples

use ducky_learn::naive_bayes::GaussianNaiveBayes;

let model = GaussianNaiveBayes::new();

let x_train: Vec<Vec<f64>> = vec![
    vec![1.0, 2.0],
    vec![2.0, 3.0],
    vec![3.0, 4.0],
    vec![4.0, 5.0],
];
let y_train: Vec<String> = vec![
    "class1".to_string(),
    "class2".to_string(),
    "class1".to_string(),
    "class2".to_string(),
];

let model = model.fit(&x_train, &y_train);

impl GaussianNaiveBayes<Fit>

pub fn predict(&self, x: &Vec<Vec<f64>>) -> Vec<String>

Predicts the class of the provided data points.

Arguments

• x - A reference to a vector of vectors, where each inner vector represents the features of a data point.

Returns

• Vec<String> - A vector of predicted class labels for each data point in x.

Examples

use ducky_learn::naive_bayes::GaussianNaiveBayes;

let model = GaussianNaiveBayes::new().fit(
    &vec![vec![0.1, 0.5], vec![0.6, 0.6]],
    &vec!["class1".to_string(), "class2".to_string()]
);

let x_test: Vec<Vec<f64>> = vec![
    vec![1.5, 2.5],
    vec![3.5, 4.5],
];

let predictions = model.predict(&x_test);

println!("{:?}", predictions);