This is an implementation of a single iteration of Lloyd's algorithm for k-means. More...
|NaiveKMeans (const MatType &dataset, MetricType &metric)|
|Construct the NaiveKMeans object with the given dataset and metric. More...|
|size_t||DistanceCalculations () const|
|double||Iterate (const arma::mat ¢roids, arma::mat &newCentroids, arma::Col< size_t > &counts)|
|Run a single iteration of the Lloyd algorithm, updating the given centroids into the newCentroids matrix. More...|
class mlpack::kmeans::NaiveKMeans< MetricType, MatType >
This is an implementation of a single iteration of Lloyd's algorithm for k-means.
If your intention is to run the full k-means algorithm, you are looking for the mlpack::kmeans::KMeans class instead of this one. This class is used by KMeans as the actual implementation of the Lloyd iteration.
MetricType Type of metric used with this implementation. MatType Matrix type (arma::mat or arma::sp_mat).
Constructor & Destructor Documentation
|NaiveKMeans||(||const MatType &||dataset,|
Construct the NaiveKMeans object with the given dataset and metric.
dataset Dataset. metric Instantiated metric.
Member Function Documentation
|double Iterate||(||const arma::mat &||centroids,|
|arma::Col< size_t > &||counts|
Run a single iteration of the Lloyd algorithm, updating the given centroids into the newCentroids matrix.
If any cluster is empty (that is, if any cluster has no points assigned to it), then the centroid associated with that cluster may be filled with invalid data (it will be corrected later).
centroids Current cluster centroids. newCentroids New cluster centroids. counts Number of points in each cluster at the end of the iteration.
The documentation for this class was generated from the following file:
Generated by 1.8.13