39 void fit(
const raft::handle_t& handle,
44 const float* sample_weight,
49 void fit(
const raft::handle_t& handle,
54 const double* sample_weight,
59 void fit(
const raft::handle_t& handle,
64 const float* sample_weight,
69 void fit(
const raft::handle_t& handle,
74 const double* sample_weight,
103 const float* centroids,
107 const float* sample_weight,
108 bool normalize_weights,
114 const double* centroids,
118 const double* sample_weight,
119 bool normalize_weights,
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,
162 const float* centroids,
170 const double* centroids,
177 const float* centroids,
185 const double* centroids,
Definition: params.hpp:23
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 fit(const raft::handle_t &handle, const KMeansParams ¶ms, const float *X, int n_samples, int n_features, const float *sample_weight, float *centroids, float &inertia, int &n_iter)
Compute k-means clustering for each sample in the input.
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:18
Definition: dbscan.hpp:14
Definition: kmeans_params.hpp:22