19 #include <cuvs/cluster/kmeans.hpp>
60 const float* sample_weight,
71 const double* sample_weight,
81 const float* sample_weight,
92 const double* sample_weight,
122 const float* centroids,
126 const float* sample_weight,
127 bool normalize_weights,
133 const double* centroids,
137 const double* sample_weight,
138 bool normalize_weights,
143 const float* centroids,
147 const float* sample_weight,
148 bool normalize_weights,
154 const double* centroids,
158 const double* sample_weight,
159 bool normalize_weights,
181 const float* centroids,
189 const double* centroids,
196 const float* centroids,
204 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:29
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