public class MinMaxScalerExample
extends java.lang.Object

Example that shows how to use MinMaxScaler preprocessor to scale the given data.

Machine learning preprocessors are built as a chain. Most often a first preprocessor is a feature extractor as shown in this example. The second preprocessor here is a MinMaxScaler preprocessor which is built on top of the feature extractor and represents a chain of itself and the underlying feature extractor.

Code in this example launches Ignite grid and fills the cache with simple test data.

After that it defines preprocessors that extract features from an upstream data and normalize their values.

Finally, it creates the dataset based on the processed data and uses Dataset API to find and output various statistical metrics of the data.

You can change the test data used in this example and re-run it to explore this functionality further.