Loda#
Lightweight Online Detector of Anomalies uses a collection of sparse random projection vectors to provide scalar inputs to an ensemble of unique one-dimensional equi-width histograms. Each histogram then estimates the probability density of the unknown sample using a limited feature set. The final predictions are derived from the averaged densities over the entire ensemble.
Interfaces: Estimator, Learner, Online, Scoring, Persistable
Data Type Compatibility: Continuous
Parameters#
| # | Param | Default | Type | Description |
|---|---|---|---|---|
| 1 | estimators | 100 | int | The number of projection/histogram pairs in the ensemble. |
| 2 | bins | null | int | The number of equi-width bins for each histogram. If null then will estimate bin count. |
| 3 | contamination | 0.1 | float | The proportion of outliers that are assumed to be present in the training set. |
Example#
use Rubix\ML\AnomalyDetectors\Loda;
$estimator = new Loda(250, 8, 0.01);
Additional Methods#
To estimate the number of histogram bins from the number of samples in a dataset:
public static estimateBins(int $n) : int
References#
- T. Pevný. (2015). Loda: Lightweight on-line detector of anomalies.
- L. Birg´e et al. (2005). How Many Bins Should Be Put In A Regular Histogram.