Package org.neuroph.util.data.norm

Provides data normalization techniques.

See: Description

Interface Summary

Interface	Description
Normalizer	Interface for data set normalization methods.

Class Summary

Class	Description
DecimalScaleNormalizer	Decimal scaling normalization method, which normalize data by moving decimal point in regard to max element in training set (by columns) Normalization is done according to formula: normalizedVector[i] = vector[i] / scaleFactor[i]
MaxMinNormalizer	MaxMin normalization method, which normalize data in regard to min and max elements in training set (by columns) Normalization is done according to formula: normalizedVector[i] = (vector[i] - min[i]) / (max[i] - min[i]) This class works fine if max and min are both positive and we want to normalize to [0,1]
MaxNormalizer	Max normalization method, which normalize data in regard to max element in training set (by columns) Normalization is done according to formula: normalizedVector[i] = vector[i] / abs(max[i])
RangeNormalizer	Performs normalization of a data set inputs and outputs to specified range.
ZeroMeanNormalizer	Normalizes data sets by shifting all values in such way that data set has mean of 0 and standard deviation 1.

Package org.neuroph.util.data.norm Description

Provides data normalization techniques.