Trains the Clusterer on a collection of features, using the specified numeric properties of each feature as training data. The geometry of the features is ignored.
Clusterer.train(features, inputProperties, subsampling, subsamplingSeed)Clusterer
this: clustererClustererAn input Clusterer.
featuresFeatureCollectionThe collection to train on.
inputPropertiesList, default: nullThe list of property names to include as training data. Each feature must have all these properties, and their values must be numeric. This argument is optional if the input collection contains a 'band_order' property, (as produced by Image.sample).
subsamplingFloat, default: 1An optional subsampling factor, within (0, 1].
subsamplingSeedInteger, default: 0A randomization seed to use for subsampling.