public class MutualInformationEquiwidthDependenceMeasure extends AbstractDependenceMeasure
mi/log(nbins)
.
This both cancels out the logarithm base, and normalizes for the number of
bins (a uniform distribution will yield a MI with itself of 1).
TODO: Offer normalized and non-normalized variants?
For a median-based discretization, see MCEDependenceMeasure
.Modifier and Type | Class and Description |
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static class |
MutualInformationEquiwidthDependenceMeasure.Parameterizer
Parameterization class.
|
Modifier and Type | Field and Description |
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static MutualInformationEquiwidthDependenceMeasure |
STATIC
Static instance.
|
Modifier | Constructor and Description |
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protected |
MutualInformationEquiwidthDependenceMeasure()
Constructor - use
STATIC instance. |
Modifier and Type | Method and Description |
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<A,B> double |
dependence(NumberArrayAdapter<?,A> adapter1,
A data1,
NumberArrayAdapter<?,B> adapter2,
B data2)
Measure the dependence of two variables.
|
clamp, computeNormalizedRanks, dependence, dependence, dependence, discretize, index, ranks, ranks, size, size, sortedIndex
public static final MutualInformationEquiwidthDependenceMeasure STATIC
protected MutualInformationEquiwidthDependenceMeasure()
STATIC
instance.public <A,B> double dependence(NumberArrayAdapter<?,A> adapter1, A data1, NumberArrayAdapter<?,B> adapter2, B data2)
DependenceMeasure
dependence
in interface DependenceMeasure
dependence
in class AbstractDependenceMeasure
A
- First array typeB
- Second array typeadapter1
- First data adapterdata1
- First data setadapter2
- Second data adapterdata2
- Second data setCopyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.