cuVS Cluster Kmeans | NVIDIA cuVS

Rust module: cuvs::cluster::kmeans

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

K-means clustering.

[fit] computes cluster centroids for a dataset, [predict] assigns points to clusters, and [cluster_cost] reports the inertia. All inputs and outputs reside in device memory and are borrowed through the AsDlTensor / AsDlTensorMut traits; see the dlpack module for the tensor model.

params::Params

pub use params::Params;

Source: rust/cuvs/src/cluster/kmeans/mod.rs:16

KMeansError

#[derive(Debug, thiserror::Error)]
#[non_exhaustive]
pub enum KMeansError {
    /* variants omitted */
}

Error type for k-means operations.

Source: rust/cuvs/src/cluster/kmeans/mod.rs:27

fit

pub fn fit<X, W, C>(
res: &Resources,
params: &Params,
x: &X,
sample_weight: Option<&W>,
centroids: &mut C,
) -> Result<(f64, i32)>
where
X: AsDlTensor + ?Sized,
W: AsDlTensor + ?Sized,
C: AsDlTensorMut + ?Sized,

Fits k-means centroids to x, returning (inertia, n_iterations).

x (shape m × k) is the input matrix and centroids (shape n_clusters × k) receives the fitted centroids; sample_weight is an optional per-sample weight. All reside in device memory and implement [AsDlTensor] / [AsDlTensorMut].

Source: rust/cuvs/src/cluster/kmeans/mod.rs:45

predict

pub fn predict<X, W, C, L>(
res: &Resources,
params: &Params,
x: &X,
sample_weight: Option<&W>,
centroids: &C,
labels: &mut L,
normalize_weight: bool,
) -> Result<f64>
where
X: AsDlTensor + ?Sized,
W: AsDlTensor + ?Sized,
C: AsDlTensor + ?Sized,
L: AsDlTensorMut + ?Sized,

Assigns each row of x to its nearest centroid, writing cluster labels into labels and returning the inertia.

x (shape m × k), centroids (shape n_clusters × k), the optional sample_weight, and labels (shape m × 1) reside in device memory and implement [AsDlTensor] / [AsDlTensorMut]. normalize_weight selects whether the sample weights are normalized.

Source: rust/cuvs/src/cluster/kmeans/mod.rs:87

cluster_cost

pub fn cluster_cost<X, C>(res: &Resources, x: &X, centroids: &C) -> Result<f64>
where
X: AsDlTensor + ?Sized,
C: AsDlTensor + ?Sized,

Computes the k-means cost (inertia) of x against existing centroids.

x (shape m × k) and centroids (shape n_clusters × k) reside in device memory and implement [AsDlTensor].

Source: rust/cuvs/src/cluster/kmeans/mod.rs:130