com.datumbox.framework.core.machinelearning.preprocessing

Class MinMaxScaler

java.lang.Object

com.datumbox.framework.core.machinelearning.common.abstracts.AbstractTrainer<MP,TP>

com.datumbox.framework.core.machinelearning.common.abstracts.transformers.AbstractTransformer<MP,TP>

com.datumbox.framework.core.machinelearning.common.abstracts.transformers.AbstractScaler<MinMaxScaler.ModelParameters,MinMaxScaler.TrainingParameters>

com.datumbox.framework.core.machinelearning.preprocessing.MinMaxScaler

All Implemented Interfaces:

Savable, Parallelizable, Trainable<MinMaxScaler.ModelParameters,MinMaxScaler.TrainingParameters>, java.lang.AutoCloseable

public class MinMaxScaler
extends AbstractScaler<MinMaxScaler.ModelParameters,MinMaxScaler.TrainingParameters>

Rescales the numerical features of the dataset between 0 and 1.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	MinMaxScaler.ModelParameters Base class for every ModelParameter class in the framework.
static class	MinMaxScaler.TrainingParameters The AbstractTrainingParameters class stores the parameters that can be changed before training the algorithm.

Nested classes/interfaces inherited from class com.datumbox.framework.core.machinelearning.common.abstracts.transformers.AbstractScaler

AbstractScaler.AbstractModelParameters, AbstractScaler.AbstractTrainingParameters

Method Summary

Methods inherited from class com.datumbox.framework.core.machinelearning.common.abstracts.transformers.AbstractTransformer

fit_transform, isParallelized, setParallelized, transform

Methods inherited from class com.datumbox.framework.core.machinelearning.common.abstracts.AbstractTrainer

close, delete, fit, getModelParameters, getTrainingParameters, save

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait