Cluster#

Params#

#include <cuvs/cluster/kmeans.hpp>

namespace cuvs::cluster::kmeans

struct params : public cuvs::cluster::kmeans::base_params#

#include <kmeans.hpp>

Simple object to specify hyper-parameters to the kmeans algorithm.

Public Members

int n_clusters = 8#: The number of clusters to form as well as the number of centroids to generate (default:8).

InitMethod init = KMeansPlusPlus#

Method for initialization, defaults to k-means++:

InitMethod::KMeansPlusPlus (k-means++): Use scalable k-means++ algorithm to select the initial cluster centers.
InitMethod::Random (random): Choose ‘n_clusters’ observations (rows) at random from the input data for the initial centroids.
InitMethod::Array (ndarray): Use ‘centroids’ as initial cluster centers.

int max_iter = 300#: Maximum number of iterations of the k-means algorithm for a single run.

double tol = 1e-4#: Relative tolerance with regards to inertia to declare convergence.

int verbosity = RAFT_LEVEL_INFO#: verbosity level.

raft::random::RngState rng_state = {0}#: Seed to the random number generator.

int n_init = 1#: Number of instance k-means algorithm will be run with different seeds.

double oversampling_factor = 2.0#: Oversampling factor for use in the k-means|| algorithm

int batch_centroids = 0#: if 0 then batch_centroids = n_clusters

struct balanced_params : public cuvs::cluster::kmeans::base_params#

#include <kmeans.hpp>

Simple object to specify hyper-parameters to the balanced k-means algorithm.

The following metrics are currently supported in k-means balanced:

CosineExpanded
InnerProduct
L2Expanded
L2SqrtExpanded

Public Members

uint32_t n_iters = 20#: Number of training iterations

K-means#

include <cuvs/cluster/kmeans.hpp>

namespace cuvs::cluster::kmeans

void fit(raft::resources const &handle, const cuvs::cluster::kmeans::params &params, raft::device_matrix_view<const float, int> X, std::optional<raft::device_vector_view<const float, int>> sample_weight, raft::device_matrix_view<float, int> centroids, raft::host_scalar_view<float, int> inertia, raft::host_scalar_view<int, int> n_iter)#

Find clusters with k-means algorithm. Initial centroids are chosen with k-means++ algorithm. Empty clusters are reinitialized by choosing new centroids with k-means++ algorithm.

#include <raft/core/resources.hpp>
#include <cuvs/cluster/kmeans.hpp>
using namespace  cuvs::cluster;
...
raft::resources handle;
cuvs::cluster::kmeans::params params;
int n_features = 15, inertia, n_iter;
auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);

kmeans::fit(handle,
            params,
            X,
            std::nullopt,
            centroids,
            raft::make_scalar_view(&inertia),
            raft::make_scalar_view(&n_iter));

Parameters:

handle – [in] The raft handle.
params – [in] Parameters for KMeans model.
X – [in] Training instances to cluster. The data must be in row-major format. [dim = n_samples x n_features]
sample_weight – [in] Optional weights for each observation in X. [len = n_samples]
centroids – [inout] [in] When init is InitMethod::Array, use centroids as the initial cluster centers. [out] The generated centroids from the kmeans algorithm are stored at the address pointed by ‘centroids’. [dim = n_clusters x n_features]
inertia – [out] Sum of squared distances of samples to their closest cluster center.
n_iter – [out] Number of iterations run.

void fit(raft::resources const &handle, const cuvs::cluster::kmeans::params &params, raft::device_matrix_view<const float, int64_t> X, std::optional<raft::device_vector_view<const float, int64_t>> sample_weight, raft::device_matrix_view<float, int64_t> centroids, raft::host_scalar_view<float, int64_t> inertia, raft::host_scalar_view<int64_t, int64_t> n_iter)#

Find clusters with k-means algorithm. Initial centroids are chosen with k-means++ algorithm. Empty clusters are reinitialized by choosing new centroids with k-means++ algorithm.

  #include <raft/core/resources.hpp>
  #include <cuvs/cluster/kmeans.hpp>
  using namespace  cuvs::cluster;
  ...
  raft::resources handle;
  cuvs::cluster::kmeans::params params;
  int64_t n_features = 15, inertia, n_iter;
  auto centroids = raft::make_device_matrix<float, int64_t>(handle, params.n_clusters,
n_features);

  kmeans::fit(handle,
              params,
              X,
              std::nullopt,
              centroids,
              raft::make_scalar_view(&inertia),
              raft::make_scalar_view(&n_iter));

Parameters:

handle – [in] The raft handle.
params – [in] Parameters for KMeans model.
X – [in] Training instances to cluster. The data must be in row-major format. [dim = n_samples x n_features]
sample_weight – [in] Optional weights for each observation in X. [len = n_samples]
centroids – [inout] [in] When init is InitMethod::Array, use centroids as the initial cluster centers. [out] The generated centroids from the kmeans algorithm are stored at the address pointed by ‘centroids’. [dim = n_clusters x n_features]
inertia – [out] Sum of squared distances of samples to their closest cluster center.
n_iter – [out] Number of iterations run.

void fit(raft::resources const &handle, const cuvs::cluster::kmeans::params &params, raft::device_matrix_view<const double, int> X, std::optional<raft::device_vector_view<const double, int>> sample_weight, raft::device_matrix_view<double, int> centroids, raft::host_scalar_view<double, int> inertia, raft::host_scalar_view<int, int> n_iter)#

Find clusters with k-means algorithm. Initial centroids are chosen with k-means++ algorithm. Empty clusters are reinitialized by choosing new centroids with k-means++ algorithm.

#include <raft/core/resources.hpp>
#include <cuvs/cluster/kmeans.hpp>
using namespace  cuvs::cluster;
...
raft::resources handle;
cuvs::cluster::kmeans::params params;
int n_features = 15, inertia, n_iter;
auto centroids = raft::make_device_matrix<double, int>(handle, params.n_clusters, n_features);

kmeans::fit(handle,
            params,
            X,
            std::nullopt,
            centroids,
            raft::make_scalar_view(&inertia),
            raft::make_scalar_view(&n_iter));

Parameters:

handle – [in] The raft handle.
params – [in] Parameters for KMeans model.
X – [in] Training instances to cluster. The data must be in row-major format. [dim = n_samples x n_features]
sample_weight – [in] Optional weights for each observation in X. [len = n_samples]
centroids – [inout] [in] When init is InitMethod::Array, use centroids as the initial cluster centers. [out] The generated centroids from the kmeans algorithm are stored at the address pointed by ‘centroids’. [dim = n_clusters x n_features]
inertia – [out] Sum of squared distances of samples to their closest cluster center.
n_iter – [out] Number of iterations run.

void fit(raft::resources const &handle, const cuvs::cluster::kmeans::params &params, raft::device_matrix_view<const double, int64_t> X, std::optional<raft::device_vector_view<const double, int64_t>> sample_weight, raft::device_matrix_view<double, int64_t> centroids, raft::host_scalar_view<double, int64_t> inertia, raft::host_scalar_view<int64_t, int64_t> n_iter)#

Find clusters with k-means algorithm. Initial centroids are chosen with k-means++ algorithm. Empty clusters are reinitialized by choosing new centroids with k-means++ algorithm.

  #include <raft/core/resources.hpp>
  #include <cuvs/cluster/kmeans.hpp>
  using namespace  cuvs::cluster;
  ...
  raft::resources handle;
  cuvs::cluster::kmeans::params params;
  int64_t n_features = 15, inertia, n_iter;
  auto centroids = raft::make_device_matrix<double, int64_t>(handle, params.n_clusters,
n_features);

  kmeans::fit(handle,
              params,
              X,
              std::nullopt,
              centroids,
              raft::make_scalar_view(&inertia),
              raft::make_scalar_view(&n_iter));

Parameters:

handle – [in] The raft handle.
params – [in] Parameters for KMeans model.
X – [in] Training instances to cluster. The data must be in row-major format. [dim = n_samples x n_features]
sample_weight – [in] Optional weights for each observation in X. [len = n_samples]
centroids – [inout] [in] When init is InitMethod::Array, use centroids as the initial cluster centers. [out] The generated centroids from the kmeans algorithm are stored at the address pointed by ‘centroids’. [dim = n_clusters x n_features]
inertia – [out] Sum of squared distances of samples to their closest cluster center.
n_iter – [out] Number of iterations run.

void fit(raft::resources const &handle, const cuvs::cluster::kmeans::params &params, raft::device_matrix_view<const int8_t, int> X, std::optional<raft::device_vector_view<const int8_t, int>> sample_weight, raft::device_matrix_view<int8_t, int> centroids, raft::host_scalar_view<int8_t, int> inertia, raft::host_scalar_view<int, int> n_iter)#

Find clusters with k-means algorithm. Initial centroids are chosen with k-means++ algorithm. Empty clusters are reinitialized by choosing new centroids with k-means++ algorithm.

#include <raft/core/resources.hpp>
#include <cuvs/cluster/kmeans.hpp>
using namespace  cuvs::cluster;
...
raft::resources handle;
cuvs::cluster::kmeans::params params;
int n_features = 15, inertia, n_iter;
auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);

kmeans::fit(handle,
            params,
            X,
            std::nullopt,
            centroids,
            raft::make_scalar_view(&inertia),
            raft::make_scalar_view(&n_iter));

Parameters:

handle – [in] The raft handle.
params – [in] Parameters for KMeans model.
X – [in] Training instances to cluster. The data must be in row-major format. [dim = n_samples x n_features]
sample_weight – [in] Optional weights for each observation in X. [len = n_samples]
centroids – [inout] [in] When init is InitMethod::Array, use centroids as the initial cluster centers. [out] The generated centroids from the kmeans algorithm are stored at the address pointed by ‘centroids’. [dim = n_clusters x n_features]
inertia – [out] Sum of squared distances of samples to their closest cluster center.
n_iter – [out] Number of iterations run.

void fit(const raft::resources &handle, cuvs::cluster::kmeans::balanced_params const &params, raft::device_matrix_view<const float, int> X, raft::device_matrix_view<float, int> centroids)#

Find balanced clusters with k-means algorithm.

#include <raft/core/resources.hpp>
#include <cuvs/cluster/kmeans.hpp>
using namespace  cuvs::cluster;
...
raft::resources handle;
cuvs::cluster::kmeans::balanced_params params;
int n_features = 15;
auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);

kmeans::fit(handle,
            params,
            X,
            centroids);

Parameters:

handle – [in] The raft handle.
params – [in] Parameters for KMeans model.
X – [in] Training instances to cluster. The data must be in row-major format. [dim = n_samples x n_features]
centroids – [out] [out] The generated centroids from the kmeans algorithm are stored at the address pointed by ‘centroids’. [dim = n_clusters x n_features]

void fit(const raft::resources &handle, cuvs::cluster::kmeans::balanced_params const &params, raft::device_matrix_view<const int8_t, int> X, raft::device_matrix_view<int8_t, int> centroids)#

Find balanced clusters with k-means algorithm.

#include <raft/core/resources.hpp>
#include <cuvs/cluster/kmeans.hpp>
using namespace  cuvs::cluster;
...
raft::resources handle;
cuvs::cluster::kmeans::balanced_params params;
int n_features = 15;
auto centroids = raft::make_device_matrix<float, int>(handle, params.n_clusters, n_features);

kmeans::fit(handle,
            params,
            X,
            centroids);

Parameters:

handle – [in] The raft handle.
params – [in] Parameters for KMeans model.
X – [in] Training instances to cluster. The data must be in row-major format. [dim = n_samples x n_features]
centroids – [inout] [out] The generated centroids from the kmeans algorithm are stored at the address pointed by ‘centroids’. [dim = n_clusters x n_features]

void predict(raft::resources const &handle, const kmeans::params &params, raft::device_matrix_view<const float, int> X, std::optional<raft::device_vector_view<const float, int>> sample_weight, raft::device_matrix_view<const float, int> centroids, raft::device_vector_view<int, int> labels, bool normalize_weight, raft::host_scalar_view<float> inertia)#