Mean Shift#
A hierarchical clustering algorithm that uses peak (maxima) finding to locate the candidate centroids of a training set given a radius constraint. Near-duplicate centroids are merged together and the algorithm iterates on the remaining candidates in subsequent steps until the centroids stabilize.
Interfaces: Estimator, Learner, Probabilistic, Verbose, Persistable
Data Type Compatibility: Continuous
Parameters#
# | Name | Default | Type | Description |
---|---|---|---|---|
1 | radius | float | The bandwidth of the radial basis function. | |
2 | ratio | 0.1 | float | The ratio of samples from the training set to use as initial centroids. |
3 | epochs | 100 | int | The maximum number of training rounds to execute. |
4 | minShift | 1e-4 | float | The minimum shift in the position of the centroids necessary to continue training. |
5 | tree | BallTree | Spatial | The spatial tree used to run range searches. |
6 | seeder | Random | Seeder | The seeder used to initialize the cluster centroids. |
Example#
use Rubix\ML\Clusterers\MeanShift;
use Rubix\ML\Graph\Trees\BallTree;
use Rubix\ML\Clusterers\Seeders\KMC2;
$estimator = new MeanShift(2.5, 2000, 1e-6, 0.05, new BallTree(100), new KMC2());
Additional Methods#
Estimate the radius of a cluster that encompasses a certain percentage of the total training samples:
public static estimateRadius(Dataset $dataset, float $percentile = 30.0, ?Distance $kernel = null) : float
Note
Since radius estimation scales quadratically in the number of samples, for large datasets you can speed up the process by running it on a smaller subset of the training data.
Return the centroids computed from the training set:
public centroids() : array[]
Return an iterable progress table with the steps from the last training session:
public steps() : iterable
use Rubix\ML\Extractors\CSV;
$extractor = new CSV('progress.csv', true);
$extractor->export($estimator->steps());
Returns the amount of centroid shift during each epoch of training:
public losses() : float[]|null