See: Description
Class | Description |
---|---|
HellingerHistogramNormalization<V extends NumberVector> |
Normalize histograms by scaling them to unit absolute sum, then taking the
square root of the absolute value in each attribute, times the normalization
constant \(1/\sqrt{2}\).
|
HellingerHistogramNormalization.Parameterizer |
Parameterization class.
|
InstanceLogRankNormalization<V extends NumberVector> |
Normalize vectors such that the smallest value of each instance is 0, the
largest is 1, but using \( \log_2(1+x) \).
|
InstanceLogRankNormalization.Parameterizer |
Parameterization class.
|
InstanceMeanVarianceNormalization<V extends NumberVector> |
Normalize vectors such that they have zero mean and unit variance.
|
InstanceMeanVarianceNormalization.Parameterizer<V extends NumberVector> |
Parameterization class.
|
InstanceMinMaxNormalization<V extends NumberVector> |
Normalize vectors with respect to a given minimum and maximum in each
dimension.
|
InstanceMinMaxNormalization.Parameterizer<V extends NumberVector> |
Parameterization class.
|
InstanceRankNormalization<V extends NumberVector> |
Normalize vectors such that the smallest value of each instance is 0, the
largest is 1.
|
InstanceRankNormalization.Parameterizer |
Parameterization class.
|
LengthNormalization<V extends NumberVector> |
Class to perform a normalization on vectors to norm 1.
|
LengthNormalization.Parameterizer<V extends NumberVector> |
Parameterization class.
|
Log1PlusNormalization<V extends NumberVector> |
Normalize the data set by applying \( \frac{\log(1+|x|b)}{\log 1+b} \) to any
value.
|
Log1PlusNormalization.Parameterizer<V extends NumberVector> |
Parameterization class.
|
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