21 #include <cuvs/cluster/kmeans.hpp>
62 const float* sample_weight,
73 const double* sample_weight,
83 const float* sample_weight,
94 const double* sample_weight,
124 const float* centroids,
128 const float* sample_weight,
129 bool normalize_weights,
135 const double* centroids,
139 const double* sample_weight,
140 bool normalize_weights,
145 const float* centroids,
149 const float* sample_weight,
150 bool normalize_weights,
156 const double* centroids,
160 const double* sample_weight,
161 bool normalize_weights,
183 const float* centroids,
191 const double* centroids,
198 const float* centroids,
206 const double* centroids,
Definition: params.hpp:34
void fit_predict(const raft::handle_t &handle, const KMeansParams ¶ms, const float *X, int n_samples, int n_features, const float *sample_weight, float *centroids, int *labels, float &inertia, int &n_iter)
Compute k-means clustering and predicts cluster index for each sample in the input.
cuvs::cluster::kmeans::params KMeansParams
Definition: kmeans.hpp:31
void transform(const raft::handle_t &handle, const KMeansParams ¶ms, const float *centroids, const float *X, int n_samples, int n_features, float *X_new)
Transform X to a cluster-distance space.
void predict(const raft::handle_t &handle, const KMeansParams ¶ms, const float *centroids, const float *X, int n_samples, int n_features, const float *sample_weight, bool normalize_weights, int *labels, float &inertia)
Predict the closest cluster each sample in X belongs to.
Definition: dbscan.hpp:30
Definition: dbscan.hpp:26