Index (InformationTheory 4.3.1 API)

A B C D E F G H I J L M N O P R S T V W X Z
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form

A

a - Variable in class org.tribuo.util.infotheory.impl.CachedTriple: The first element.
add(int, Row<T>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
add(Row<T>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
addAll(int, Collection<? extends Row<T>>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
addAll(Collection<? extends Row<T>>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.

B

b - Variable in class org.tribuo.util.infotheory.impl.CachedTriple: The second element.

C

c - Variable in class org.tribuo.util.infotheory.impl.CachedTriple: The third element.
CachedPair<T1,T2> - Class in org.tribuo.util.infotheory.impl: A pair of things with a cached hashcode.
CachedPair(T1, T2) - Constructor for class org.tribuo.util.infotheory.impl.CachedPair: Constructs a CachedPair.
CachedTriple<T1,T2,T3> - Class in org.tribuo.util.infotheory.impl: A triple of things.
CachedTriple(T1, T2, T3) - Constructor for class org.tribuo.util.infotheory.impl.CachedTriple: Constructs a CachedTriple.
calculateCountDist(List<T>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Generate the counts for a single vector.
calculateEntropy(DoubleStream) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon entropy of a stream, assuming each element of the stream is an element of the same probability distribution.
calculateEntropy(Stream<Double>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon entropy of a stream, assuming each element of the stream is an element of the same probability distribution.
calculateHashCode() - Method in class org.tribuo.util.infotheory.impl.CachedTriple: Overridden hashcode.
calculateWeightedCountDist(ArrayList<T>, ArrayList<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Generate the counts for a single vector.
clear() - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
cmi(List<T1>, List<T2>, Set<List<T3>>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the conditional mutual information between first and second conditioned on the set.
conditionalEntropy(List<T1>, List<T2>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon conditional entropy of two arrays, using histogram probability estimators.
conditionalMI(List<T1>, List<T2>, List<T3>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon conditional mutual information, using histogram probability estimators.
conditionalMI(List<T1>, List<T2>, List<T3>, List<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the discrete weighted conditional mutual information, using histogram probability estimators.
conditionalMI(TripleDistribution<T1, T2, T3>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon conditional mutual information, using histogram probability estimators.
conditionalMI(TripleDistribution<T1, T2, T3>, Map<?, Double>, WeightedInformationTheory.VariableSelector) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the discrete weighted conditional mutual information, using histogram probability estimators.
conditionalMI(WeightedTripleDistribution<T1, T2, T3>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the discrete weighted conditional mutual information, using histogram probability estimators.
conditionalMIFlipped(TripleDistribution<T1, T2, T3>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon conditional mutual information, using histogram probability estimators.
constructFromLists(List<T1>, List<T2>) - Static method in class org.tribuo.util.infotheory.impl.PairDistribution: Generates the counts for two vectors.
constructFromLists(List<T1>, List<T2>, List<Double>) - Static method in class org.tribuo.util.infotheory.impl.WeightedPairDistribution: Generates the counts for two vectors.
constructFromLists(List<T1>, List<T2>, List<T3>) - Static method in class org.tribuo.util.infotheory.impl.TripleDistribution: Constructs a TripleDistribution from three lists of the same length.
constructFromLists(List<T1>, List<T2>, List<T3>, List<Double>) - Static method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution: Constructs a WeightedTripleDistribution from three lists of the same length and a list of weights of the same length.
constructFromMap(Map<CachedPair<T1, T2>, MutableLong>) - Static method in class org.tribuo.util.infotheory.impl.PairDistribution: Constructs a distribution from a joint count.
constructFromMap(Map<CachedPair<T1, T2>, MutableLong>, int, int) - Static method in class org.tribuo.util.infotheory.impl.PairDistribution: Constructs a distribution from a joint count.
constructFromMap(Map<CachedPair<T1, T2>, MutableLong>, Map<T1, MutableLong>, Map<T2, MutableLong>) - Static method in class org.tribuo.util.infotheory.impl.PairDistribution: Constructs a joint distribution from the counts.
constructFromMap(Map<CachedPair<T1, T2>, WeightCountTuple>) - Static method in class org.tribuo.util.infotheory.impl.WeightedPairDistribution: Generates a WeightedPairDistribution by generating the marginal distributions for the first and second elements.
constructFromMap(Map<CachedTriple<T1, T2, T3>, MutableLong>) - Static method in class org.tribuo.util.infotheory.impl.TripleDistribution: Constructs a TripleDistribution by marginalising the supplied joint distribution.
constructFromMap(Map<CachedTriple<T1, T2, T3>, MutableLong>, int, int, int, int, int, int) - Static method in class org.tribuo.util.infotheory.impl.TripleDistribution: Constructs a TripleDistribution by marginalising the supplied joint distribution.
constructFromMap(Map<CachedTriple<T1, T2, T3>, MutableLong>, Map<CachedPair<T1, T2>, MutableLong>, Map<CachedPair<T1, T3>, MutableLong>, Map<CachedPair<T2, T3>, MutableLong>, Map<T1, MutableLong>, Map<T2, MutableLong>, Map<T3, MutableLong>) - Static method in class org.tribuo.util.infotheory.impl.TripleDistribution: Constructs a TripleDistribution by marginalising the supplied joint distribution.
constructFromMap(Map<CachedTriple<T1, T2, T3>, WeightCountTuple>) - Static method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution: Constructs a WeightedTripleDistribution by marginalising the supplied joint distribution.
contains(Object) - Method in class org.tribuo.util.infotheory.impl.RowList
containsAll(Collection<?>) - Method in class org.tribuo.util.infotheory.impl.RowList
CORRELATED - Enum constant in enum org.tribuo.util.infotheory.example.InformationTheoryDemo.DistributionType: Correlated data.
count - Variable in class org.tribuo.util.infotheory.impl.PairDistribution: The number of samples this distribution has seen.
count - Variable in class org.tribuo.util.infotheory.impl.TripleDistribution: The number of samples in this distribution.
count - Variable in class org.tribuo.util.infotheory.impl.WeightCountTuple: The current count.
count - Variable in class org.tribuo.util.infotheory.impl.WeightedPairDistribution: The sample count.
count - Variable in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution: The sample count.

D

DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.impl.TripleDistribution: The default map size to initialise the marginalised count maps with.
DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution: The default map size.
DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.InformationTheory: The initial size of the various maps.
DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.WeightedInformationTheory: The initial size of the various maps.
DemoOptions() - Constructor for class org.tribuo.util.infotheory.example.InformationTheoryDemo.DemoOptions

E

entropy(List<T>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon entropy, using histogram probability estimators.
equals(Object) - Method in class org.tribuo.util.infotheory.impl.CachedPair
equals(Object) - Method in class org.tribuo.util.infotheory.impl.CachedTriple
equals(Object) - Method in class org.tribuo.util.infotheory.impl.Row
equals(Object) - Method in class org.tribuo.util.infotheory.impl.WeightCountTuple
expectedMI(List<T>, List<T>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Compute the expected mutual information assuming randomized inputs.

F

FIRST - Enum constant in enum org.tribuo.util.infotheory.WeightedInformationTheory.VariableSelector: The first variable is weighted.
firstCount - Variable in class org.tribuo.util.infotheory.impl.PairDistribution: The first marginal distribution.

G

gamma(double) - Static method in class org.tribuo.util.infotheory.Gamma: Function to calculate the value of a Gamma function.
Gamma - Class in org.tribuo.util.infotheory: Static functions for computing the Gamma and log Gamma functions on real valued inputs.
generateCorrelated(int, int, double, double) - Static method in class org.tribuo.util.infotheory.example.InformationTheoryDemo: These correlations don't map to mutual information values, as if xyDraw is above xyCorrelation then the draw is completely random.
generateUniform(int, int) - Static method in class org.tribuo.util.infotheory.example.InformationTheoryDemo: Generates a sample from a uniform distribution over the integers.
generateXOR(int) - Static method in class org.tribuo.util.infotheory.example.InformationTheoryDemo: Generates a sample from a three variable XOR function.
get(int) - Method in class org.tribuo.util.infotheory.impl.RowList
getA() - Method in class org.tribuo.util.infotheory.impl.CachedTriple: Gets the first element.
getAB() - Method in class org.tribuo.util.infotheory.impl.CachedTriple: Gets the pair of the first and second elements.
getABCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution: The joint distribution over the first and second variables.
getABCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution: The joint distribution over the first and second variables.
getAC() - Method in class org.tribuo.util.infotheory.impl.CachedTriple: Gets the pair of the first and third elements.
getACCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution: The joint distribution over the first and third variables.
getACCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution: The joint distribution over the first and third variables.
getACount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution: The marginal distribution over the first variable.
getACount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution: The marginal distribution over the first variable.
getB() - Method in class org.tribuo.util.infotheory.impl.CachedTriple: Gets the second element.
getBC() - Method in class org.tribuo.util.infotheory.impl.CachedTriple: Gets the pair of the second and third elements.
getBCCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution: The joint distribution over the second and third variables.
getBCCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution: The joint distribution over the second and third variables.
getBCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution: The marginal distribution over the second variable.
getBCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution: The marginal distribution over the second variable.
getC() - Method in class org.tribuo.util.infotheory.impl.CachedTriple: Gets the third element.
getCCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution: The marginal distribution over the third variable.
getCCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution: The marginal distribution over the third variable.
getFirstCount() - Method in class org.tribuo.util.infotheory.impl.WeightedPairDistribution: Gets the first marginal distribution.
getJointCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution: The joint distribution over the three variables.
getJointCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution: The joint distribution over the three variables.
getJointCounts() - Method in class org.tribuo.util.infotheory.impl.WeightedPairDistribution: Gets the joint distribution.
getOptionsDescription() - Method in class org.tribuo.util.infotheory.example.InformationTheoryDemo.DemoOptions
getSecondCount() - Method in class org.tribuo.util.infotheory.impl.WeightedPairDistribution: Gets the second marginal distribution.
gStatistic - Variable in class org.tribuo.util.infotheory.InformationTheory.GTestStatistics: The G test statistic.
gTest(List<T1>, List<T2>, Set<List<T3>>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the GTest statistics for the input variables conditioned on the set.
GTestStatistics(double, int, double) - Constructor for class org.tribuo.util.infotheory.InformationTheory.GTestStatistics: Constructs a GTestStatistics tuple with the supplied values.

H

hashCode() - Method in class org.tribuo.util.infotheory.impl.CachedPair: Overridden hashcode.
hashCode() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
hashCode() - Method in class org.tribuo.util.infotheory.impl.Row
hashCode() - Method in class org.tribuo.util.infotheory.impl.WeightCountTuple

I

indexOf(Object) - Method in class org.tribuo.util.infotheory.impl.RowList
InformationTheory - Class in org.tribuo.util.infotheory: A class of (discrete) information theoretic functions.
InformationTheory.GTestStatistics - Class in org.tribuo.util.infotheory: An immutable named tuple containing the statistics from a G test.
InformationTheoryDemo - Class in org.tribuo.util.infotheory.example: Demo showing how to calculate various mutual informations and entropies.
InformationTheoryDemo() - Constructor for class org.tribuo.util.infotheory.example.InformationTheoryDemo
InformationTheoryDemo.DemoOptions - Class in org.tribuo.util.infotheory.example: Command line options.
InformationTheoryDemo.DistributionType - Enum in org.tribuo.util.infotheory.example: Type of data distribution.
isEmpty() - Method in class org.tribuo.util.infotheory.impl.RowList
iterator() - Method in class org.tribuo.util.infotheory.impl.RowList

J

jointCounts - Variable in class org.tribuo.util.infotheory.impl.PairDistribution: The joint distribution.
jointEntropy(ArrayList<T1>, ArrayList<T2>, ArrayList<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the Shannon/Guiasu weighted joint entropy of two arrays, using histogram probability estimators.
jointEntropy(List<T1>, List<T2>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the Shannon joint entropy of two arrays, using histogram probability estimators.
jointMI(List<T1>, List<T2>, List<T3>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon joint mutual information, using histogram probability estimators.
jointMI(List<T1>, List<T2>, List<T3>, List<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the discrete weighted joint mutual information, using histogram probability estimators.
jointMI(TripleDistribution<T1, T2, T3>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon joint mutual information, using histogram probability estimators.
jointMI(TripleDistribution<T1, T2, T3>, Map<?, Double>, WeightedInformationTheory.VariableSelector) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the discrete weighted joint mutual information, using histogram probability estimators.
jointMI(WeightedTripleDistribution<T1, T2, T3>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the discrete weighted joint mutual information, using histogram probability estimators.

L

lastIndexOf(Object) - Method in class org.tribuo.util.infotheory.impl.RowList
listIterator() - Method in class org.tribuo.util.infotheory.impl.RowList
listIterator(int) - Method in class org.tribuo.util.infotheory.impl.RowList
LOG_2 - Static variable in class org.tribuo.util.infotheory.InformationTheory: Log base 2.
LOG_2 - Static variable in class org.tribuo.util.infotheory.WeightedInformationTheory: Log base 2.
LOG_BASE - Static variable in class org.tribuo.util.infotheory.InformationTheory: Sets the base of the logarithm used in the information theoretic calculations.
LOG_BASE - Static variable in class org.tribuo.util.infotheory.WeightedInformationTheory: Sets the base of the logarithm used in the information theoretic calculations.
LOG_E - Static variable in class org.tribuo.util.infotheory.InformationTheory: Log base e.
LOG_E - Static variable in class org.tribuo.util.infotheory.WeightedInformationTheory: Log base e.
logGamma(double) - Static method in class org.tribuo.util.infotheory.Gamma: Function to calculate the log of a Gamma function.

M

main(String[]) - Static method in class org.tribuo.util.infotheory.example.InformationTheoryDemo: Runs a simple demo of the information theory functions.
mi(ArrayList<T1>, ArrayList<T2>, ArrayList<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the discrete weighted mutual information, using histogram probability estimators.
mi(List<T1>, List<T2>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon mutual information, using histogram probability estimators.
mi(Set<List<T1>>, Set<List<T2>>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the mutual information between the two sets of random variables.
mi(PairDistribution<T1, T2>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon mutual information, using histogram probability estimators.
mi(PairDistribution<T1, T2>, Map<?, Double>, WeightedInformationTheory.VariableSelector) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the discrete weighted mutual information, using histogram probability estimators.
mi(WeightedPairDistribution<T1, T2>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the discrete weighted mutual information, using histogram probability estimators.

N

normaliseWeights(Map<T, WeightCountTuple>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Normalizes the weights in the map, i.e., divides each weight by it's count.
numStates - Variable in class org.tribuo.util.infotheory.InformationTheory.GTestStatistics: The number of states.

O

org.tribuo.util.infotheory - package org.tribuo.util.infotheory: This package provides static classes of information theoretic functions.
org.tribuo.util.infotheory.example - package org.tribuo.util.infotheory.example: This package provides demos for the information theoretic function classes in org.tribuo.util.infotheory.
org.tribuo.util.infotheory.impl - package org.tribuo.util.infotheory.impl: This package provides the implementations and helper classes for the information theoretic functions in org.tribuo.util.infotheory.

P

PairDistribution<T1,T2> - Class in org.tribuo.util.infotheory.impl: A count distribution over CachedPair objects.
PairDistribution(long, LinkedHashMap<CachedPair<T1, T2>, MutableLong>, LinkedHashMap<T1, MutableLong>, LinkedHashMap<T2, MutableLong>) - Constructor for class org.tribuo.util.infotheory.impl.PairDistribution: Constructs a pair distribution.
PairDistribution(long, Map<CachedPair<T1, T2>, MutableLong>, Map<T1, MutableLong>, Map<T2, MutableLong>) - Constructor for class org.tribuo.util.infotheory.impl.PairDistribution: Constructs a pair distribution.
probability - Variable in class org.tribuo.util.infotheory.InformationTheory.GTestStatistics: The probability of that statistic.

R

RANDOM - Enum constant in enum org.tribuo.util.infotheory.example.InformationTheoryDemo.DistributionType: Uniformly randomly generated data.
regularizedGammaP(int, double, double, int) - Static method in class org.tribuo.util.infotheory.Gamma: Computes the regularised partial gamma function P.
remove(int) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
remove(Object) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
removeAll(Collection<?>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
retainAll(Collection<?>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
Row<T> - Class in org.tribuo.util.infotheory.impl: A row of values from a RowList.
RowList<T> - Class in org.tribuo.util.infotheory.impl: An implementation of a List which wraps a set of lists.
RowList(Set<List<T>>) - Constructor for class org.tribuo.util.infotheory.impl.RowList: Constructs a RowList from a set of lists.

S

SAMPLES_RATIO - Static variable in class org.tribuo.util.infotheory.InformationTheory: The ratio of samples to symbols before emitting a warning.
SAMPLES_RATIO - Static variable in class org.tribuo.util.infotheory.WeightedInformationTheory: The ratio of samples to symbols before emitting a warning.
SECOND - Enum constant in enum org.tribuo.util.infotheory.WeightedInformationTheory.VariableSelector: The second variable is weighted.
secondCount - Variable in class org.tribuo.util.infotheory.impl.PairDistribution: The second marginal distribution.
set(int, Row<T>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
size() - Method in class org.tribuo.util.infotheory.impl.RowList
subList(int, int) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.

T

THIRD - Enum constant in enum org.tribuo.util.infotheory.WeightedInformationTheory.VariableSelector: The third variable is weighted.
toArray() - Method in class org.tribuo.util.infotheory.impl.RowList
toArray(U[]) - Method in class org.tribuo.util.infotheory.impl.RowList
toString() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
toString() - Method in class org.tribuo.util.infotheory.impl.Row
toString() - Method in class org.tribuo.util.infotheory.InformationTheory.GTestStatistics
TripleDistribution<T1,T2,T3> - Class in org.tribuo.util.infotheory.impl: Generates the counts for a triplet of vectors.
TripleDistribution(long, Map<CachedTriple<T1, T2, T3>, MutableLong>, Map<CachedPair<T1, T2>, MutableLong>, Map<CachedPair<T1, T3>, MutableLong>, Map<CachedPair<T2, T3>, MutableLong>, Map<T1, MutableLong>, Map<T2, MutableLong>, Map<T3, MutableLong>) - Constructor for class org.tribuo.util.infotheory.impl.TripleDistribution: Constructs a triple distribution from the supplied distributions.
type - Variable in class org.tribuo.util.infotheory.example.InformationTheoryDemo.DemoOptions: The type of the input distribution.

V

valueOf(String) - Static method in enum org.tribuo.util.infotheory.example.InformationTheoryDemo.DistributionType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.util.infotheory.WeightedInformationTheory.VariableSelector: Returns the enum constant of this type with the specified name.
values() - Static method in enum org.tribuo.util.infotheory.example.InformationTheoryDemo.DistributionType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.util.infotheory.WeightedInformationTheory.VariableSelector: Returns an array containing the constants of this enum type, in the order they are declared.

W

weight - Variable in class org.tribuo.util.infotheory.impl.WeightCountTuple: The current weight.
WeightCountTuple - Class in org.tribuo.util.infotheory.impl: A mutable tuple of a double and a long.
WeightCountTuple() - Constructor for class org.tribuo.util.infotheory.impl.WeightCountTuple: Creates a zeroed WeightCountTuple.
WeightCountTuple(double, long) - Constructor for class org.tribuo.util.infotheory.impl.WeightCountTuple: Creates a WeightCountTuple with the specifed values.
weightedConditionalEntropy(ArrayList<T1>, ArrayList<T2>, ArrayList<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the discrete Shannon/Guiasu Weighted Conditional Entropy of two arrays, using histogram probability estimators.
weightedEntropy(ArrayList<T>, ArrayList<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the discrete Shannon/Guiasu Weighted Entropy, using histogram probability estimators.
WeightedInformationTheory - Class in org.tribuo.util.infotheory: A class of (discrete) weighted information theoretic functions.
WeightedInformationTheory.VariableSelector - Enum in org.tribuo.util.infotheory: Chooses which variable is the one with associated weights.
WeightedPairDistribution<T1,T2> - Class in org.tribuo.util.infotheory.impl: Generates the counts for a pair of vectors.
WeightedPairDistribution(long, LinkedHashMap<CachedPair<T1, T2>, WeightCountTuple>, LinkedHashMap<T1, WeightCountTuple>, LinkedHashMap<T2, WeightCountTuple>) - Constructor for class org.tribuo.util.infotheory.impl.WeightedPairDistribution: Constructs a weighted pair distribution from the supplied values.
WeightedPairDistribution(long, Map<CachedPair<T1, T2>, WeightCountTuple>, Map<T1, WeightCountTuple>, Map<T2, WeightCountTuple>) - Constructor for class org.tribuo.util.infotheory.impl.WeightedPairDistribution: Constructs a weighted pair distribution from the supplied values.
WeightedTripleDistribution<T1,T2,T3> - Class in org.tribuo.util.infotheory.impl: Generates the counts for a triplet of vectors.
WeightedTripleDistribution(long, Map<CachedTriple<T1, T2, T3>, WeightCountTuple>, Map<CachedPair<T1, T2>, WeightCountTuple>, Map<CachedPair<T1, T3>, WeightCountTuple>, Map<CachedPair<T2, T3>, WeightCountTuple>, Map<T1, WeightCountTuple>, Map<T2, WeightCountTuple>, Map<T3, WeightCountTuple>) - Constructor for class org.tribuo.util.infotheory.impl.WeightedTripleDistribution: Constructs a weighted triple distribution from the supplied values.

X

XOR - Enum constant in enum org.tribuo.util.infotheory.example.InformationTheoryDemo.DistributionType: Data generated from an XOR function.

Z

zipArraysCached(ArrayList<T1>, ArrayList<T2>) - Static method in class org.tribuo.util.infotheory.impl.CachedPair: Takes two arrays and zips them together into an array of CachedPairs.

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