Struct rusty_machine::learning::k_means::KMeansClassifier [−] [src]

pub struct KMeansClassifier {
    pub iters: usize,
    pub k: usize,
    pub centroids: Option<Matrix<f64>>,
    pub init_algorithm: InitAlgorithm,
}

K-Means Classification model.

Contains option for centroids. Specifies iterations and number of classes.

Fields

iters: usize

Max iterations of algorithm to run.

k: usize

The number of classes.

centroids: Option<Matrix<f64>>

The fitted centroids .

init_algorithm: InitAlgorithm

The initial algorithm to use.

Methods

impl KMeansClassifier
[src]

fn new(k: usize) -> KMeansClassifier

Constructs untrained k-means classifier model.

Requires number of classes to be specified. Defaults to 100 iterations and kmeans++ initialization.

Examples

use rusty_machine::learning::k_means::KMeansClassifier;

let model = KMeansClassifier::new(5);

fn new_specified(k: usize, iters: usize, algo: InitAlgorithm) -> KMeansClassifier

Constructs untrained k-means classifier model.

Requires number of classes, number of iterations, and the initialization algorithm to use.

Examples

use rusty_machine::learning::k_means::KMeansClassifier;
use rusty_machine::learning::k_means::InitAlgorithm;

let model = KMeansClassifier::new_specified(5, 42, InitAlgorithm::Forgy);

fn k(&self) -> usize

Get the number of classes

fn iters(&self) -> usize

Get the number of iterations

fn init_algorithm(&self) -> InitAlgorithm

Get the initialization algorithm

Trait Implementations

impl Debug for KMeansClassifier
[src]

fn fmt(&self, __arg_0: &mut Formatter) -> Result

Formats the value using the given formatter.

impl UnSupModel<Matrix<f64>, Vector<usize>> for KMeansClassifier
[src]

fn predict(&self, inputs: &Matrix<f64>) -> Vector<usize>

Predict classes from data.

Model must be trained.

fn train(&mut self, inputs: &Matrix<f64>)

Train the classifier using input data.