Cluster Kmeans Module

Rust module: cuvs::cluster::kmeans

Source: rust/cuvs/src/cluster/kmeans/mod.rs

Kmeans clustering API’s

Example:

use cuvs::cluster::kmeans;
use cuvs::{ManagedTensor, Resources, Result};
use ndarray_rand::rand_distr::Uniform;
use ndarray_rand::RandomExt;
fn kmeans_example() -> Result<()> {
let res = Resources::new()?;
// Create a new random dataset to index
let n_datapoints = 65536;
let n_features = 512;
let n_clusters = 8;
let dataset =
ndarray::Array::<f32, _>::random((n_datapoints, n_features), Uniform::new(0., 1.0));
let dataset = ManagedTensor::from(&dataset).to_device(&res)?;
let centroids_host = ndarray::Array::<f32, _>::zeros((n_clusters, n_features));
let mut centroids = ManagedTensor::from(&centroids_host).to_device(&res)?;
// find the centroids with the kmeans index
let kmeans_params = kmeans::Params::new()?.set_n_clusters(n_clusters as i32);
let (inertia, n_iter) = kmeans::fit(&res, &kmeans_params, &dataset, &None, &mut centroids)?;
Ok(())
}

params::Params

1
pub use params::Params;

Source: rust/cuvs/src/cluster/kmeans/mod.rs:40

fit

1
pub fn fit(
2
res: &Resources,
3
params: &Params,
4
x: &ManagedTensor,
5
sample_weight: &Option<ManagedTensor>,
6
centroids: &mut ManagedTensor,
7
) -> Result<(f64, i32)> { ... }

Find clusters with the k-means algorithm

Arguments

• res - Resources to use
• params - Parameters to use to fit KMeans model
• x - A matrix in device memory - shape (m, k)
• sample_weight - Optional device matrix shape (n_clusters, 1)
• centroids - Output device matrix, that has the centroids for each cluster shape (n_clusters, k)

Source: rust/cuvs/src/cluster/kmeans/mod.rs:56

predict

1
pub fn predict(
2
res: &Resources,
3
params: &Params,
4
x: &ManagedTensor,
5
sample_weight: &Option<ManagedTensor>,
6
centroids: &ManagedTensor,
7
labels: &mut ManagedTensor,
8
normalize_weight: bool,
9
) -> Result<f64> { ... }

Predict clusters with the k-means algorithm

Arguments

• res - Resources to use
• params - Parameters to use to fit KMeans model
• x - Input matrix in device memory - shape (m, k)
• sample_weight - Optional device matrix shape (n_clusters, 1)
• centroids - Centroids calculated by fit in device memory, shape (n_clusters, k)
• labels - preallocated CUDA array interface matrix shape (m, 1) to hold the output labels
• normalize_weight - whether or not to normalize the weights

Source: rust/cuvs/src/cluster/kmeans/mod.rs:95

cluster_cost

1
pub fn cluster_cost(res: &Resources, x: &ManagedTensor, centroids: &ManagedTensor) -> Result<f64> { ... }

Compute cluster cost given an input matrix and existing centroids

Arguments

• res - Resources to use
• x - Input matrix in device memory - shape (m, k)
• centroids - Centroids calculated by fit in device memory, shape (n_clusters, k)

Source: rust/cuvs/src/cluster/kmeans/mod.rs:131