58 const float* sample_weight,
69 const double* sample_weight,
79 const float* sample_weight,
90 const double* sample_weight,
120 const float* centroids,
124 const float* sample_weight,
125 bool normalize_weights,
131 const double* centroids,
135 const double* sample_weight,
136 bool normalize_weights,
141 const float* centroids,
145 const float* sample_weight,
146 bool normalize_weights,
152 const double* centroids,
156 const double* sample_weight,
157 bool normalize_weights,
179 const float* centroids,
187 const double* centroids,
194 const float* centroids,
202 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.
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:29
Definition: dbscan.hpp:25
Definition: kmeans_params.hpp:33