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ZeroOneILPProblem.Av.
Accuracy testing metric that does not print a
table of results.
Accuracy testing metric that prints a table of
results if requested.
AdaBoost's configurable parameters.ZeroOneILPProblem.addConstraint(int[],double[],int,double) so
that it calls
ZeroOneILPProblem.addConstraint(int[],double[],double) thereby
ignoring the constraint's type.
true at any given time.
true at any given time.
true at any given time.
true at any given time.
true at any given time.
true at any given time.
true at any given time.
true at any given time.
true at any given time.
true at any given time.
Word as input and
generates features representing the prefixes and suffixes of the input
word.FirstOrderConstraint representations.WekaWrapper.defaultAttributeString.
ZeroOneILPProblem.Ac.
SparseWeightVector.weights, this vector provides
enough information to reconstruct the average of all weight vectors
arrived at during the course of learning.
LinearThresholdUnit.weightVector
casted to SparseAveragedPerceptron.AveragedWeightVector.
ILPSolver implements Egon Balas' zero-one ILP solving
algorithm.true.
true.
weka.classifiers.bayes.NaiveBayes.
WekaWrapper.defaultBaseClassifier.
null.
null.
SparseNetworkLearner.defaultBaseLTU.
SparseNetworkLearner.defaultBaseLTU.
Learner.BatchTrainer.train(int) so that additional
processing can be performed at the end of each round.BinaryMIRA.defaultBeta.
BinaryMIRA.defaultBeta.
1 /
LinearThresholdUnit.learningRate.
1
/ LinearThresholdUnit.learningRate.
SupportVectorMachine.bias >= 0, an instance vector x becomes [x; bias];
otherwise, if SupportVectorMachine.bias < 0, no bias term is added.
SupportVectorMachine.bias >= 0, an instance vector x
becomes [x; bias]; otherwise, if SupportVectorMachine.bias
< 0, no bias term is added.
RandomWeightVector class that it extends, except
that this vector also contains a bias term (also initialized randomly)
which is added to every dot product and affected by every vector addition
operation.SparseWeightVector class that it extends, except
that this vector also contains a bias term which is added to every dot
product and affected by every vector addition operation.BinaryMIRA's configurable
parameters.AtLeastQuantifier or an AtMostQuantifier is
not quantified.
ExceptionlessInputStream.readUTF(int).
ExceptionlessOutputStream.writeUTF(String).
String by directly storing an encoding of that
String in an array of bytes.SupportVectorMachine.defaultC
SupportVectorMachine.defaultC
Inference objects, indexed by
Keys.
x as the algorithm continues processing.
Word as input and
generates Boolean features representing the capitalizations of the words
in a [-2, +2] window around the input word.POS object representing the "coordinating conjunction" tag.
POS object representing the "cardinal number" tag.
ExceptionlessInputStream.readUTF(int).
ChildLexicon.
ChildLexicon.
ChildLexicon.
ChildLexicon.
ChildLexicon.
ChildLexicon.parents for the children of
f.
ChildLexicon.parents for the children of
f.
ChildLexicon.parents for the children of
f.
ChildLexicon.parents for the children of
f.
ChildLexicon.parents for the children of
f.
LinkedVectors, and this parser will return their
LinkedChildren.true iff
the class of this feature and f are different, but they
differ only because they encode their values differently.
true iff
the class of this feature and f are different, but they
differ only because they encode their values differently.
true iff
the class of this feature and f are different, but they
differ only because they encode their values differently.
true iff
the class of this feature and f are different, but they
differ only because they encode their values differently.
true iff
the class of this feature and f are different, but they
differ only because they encode their values differently.
true iff
the class of this feature and f are different, but they
differ only because they encode their values differently.
true iff
the class of this feature and f are different, but they
differ only because they encode their values differently.
true iff
the class of this feature and f are different, but they
differ only because they encode their values differently.
true iff
the class of this feature and f are different, but they
differ only because they encode their values differently.
true iff
the class of this feature and f are different, but they
differ only because they encode their values differently.
true iff
the class of this feature and f are different, but they
differ only because they encode their values differently.
true iff
the class of this feature and f are different, but they
differ only because they encode their values differently.
true iff
the class of this feature and f are different, but they
differ only because they encode their values differently.
Tokens.
Features in a vector.
Features in a vector.
Features in a vector.
ValueComparer.labeler applied to the
argument object contained the feature value referenced by
ValueComparer.value.
ValueComparer.labeler applied to the argument
object and ValueComparer.value.
Features in a vector.
Features in a vector.
Features in a vector.
Features in a vector.
DiscretePrimitiveStringFeature set to either the
second value from the label classifier's array of allowable values if
the score is greater than or equal to LinearThresholdUnit.threshold or the first
otherwise.
LinearThresholdUnit whose
score on the example object exceeds the threshold.
Learner.
liblinear's prediction
method.
FeatureVector.features and FeatureVector.labels.
FeatureVector.labels list.
Feature.
HashMap.
NaiveBayesVector.
AveragedWeightVector.
SparseWeightVector in which the
SparseWeightVector.weights variable has been cloned deeply.
POS object representing the "close double quote" tag.
POS object representing the "(semi-)colon" tag.
Strings representing the rows
of a file in column format.POS object representing the "comma" tag.
Scores
will be sorted first by value and then by score.
LinearThresholdUnit.learningRate variable if needed
and returns the value.
LinearThresholdUnit.learningRate (alpha) if it
is a positive example, and SparseWinnow.beta if it is a negative example.
SparseConfidenceWeighted.defaultConfidence.
SparseConfidenceWeighted.defaultConfidence.
SparseMIRA.scores(int[],double[],Collection) when the labeler is known to
produce conjunctive features.
SparseNetworkLearner.scores(int[],double[],Collection) when the labeler is known to
produce conjunctive features.
SparseMIRA.valueOf(int[],double[],Collection) when the labeler is known to
produce conjunctive features.
SparseNetworkLearner.valueOf(int[],double[],Collection) when the labeler is known to
produce conjunctive features.
SupportVectorMachine.valueOf(int[],double[],Collection) when the labeler is known to
produce conjunctive features.
null elements; all such
elements will be retained in the same relative order.
parameterized.
package containing the classifier that produced
this feature.
true if the given feature is already in the
lexicon (whether it's past the Lexicon.pruneCutoff or not) and
false otherwise.
StudentT.regularisedBetaFunction(double,double,double).
LinkedVector containing Words, this method
creates a new LinkedVector containing Tokens.
Strings, this method creates a new
LinkedVector containing Words.
SparseAveragedPerceptron.AveragedWeightVector.examples variable.
lookup(feature, true) (counting still won't
happen on a call to lookup(feature, false)).
Lexicon key.
Learner.predictions associated with the label
feature at the given index of Learner.labelLexicon.
Learner.predictions associated with the label
feature at the given index of lex.
C world.
null if we need a new sentence.
f is being removed, so we decrement
f's parent counts and remove it if it's ready.
WekaWrapper.attributeString field.
WekaWrapper.baseClassifier field.
MuxLearner.baseLearner.
SparseNetworkLearner.baseLTU.
BinaryMIRA.beta.
SupportVectorMachine.bias.
SupportVectorMachine.C.
Lexicon.lexiconInv and Lexicon.featureCounts.
SparseWeightVector.weights if not specified otherwise.
SparseConfidenceWeighted.confidence.
MuxLearner.defaultPrediction.
SupportVectorMachine.epsilon.
MuxLearner.defaultPrediction.
BiasedWeightVector.initialBias.
SparseConfidenceWeighted.initialVariance.
LinearThresholdUnit.initialWeight.
LinearThresholdUnit.initialWeight.
LinearThresholdUnit.learningRate.
LinearThresholdUnit.learningRate.
LinearThresholdUnit.learningRate.
StochasticGradientDescent.learningRate.
Learner doesn't exist; default
MuxLearner.defaultDefaultPrediction.
Learner doesn't exist; default
MuxLearner.defaultDefaultPrediction.
AdaBoost.rounds.
defaultSmoothing.
SupportVectorMachine.solverType.
RandomWeightVector.stddev.
LinearThresholdUnit.positiveThickness.
LinearThresholdUnit.threshold.
LinearThresholdUnit.threshold.
AdaBoost.weakLearner.
LinearThresholdUnit.weightVector.
LinearThresholdUnit.weightVector.
StochasticGradientDescent.weightVector.
C world.
Learner.readLexiconOnDemand(URL).
LinearThresholdUnit is mistake driven, this method
should be overridden and used to update the internal representation when
a mistake is made on a negative example.
w_i *= betax_i.
Lexicon.prune(Lexicon.PruningPolicy) as well as all feature counts.
DiscretePrimitiveFeature, however its DiscretePrimitiveStringFeature.value is stored as
a String instead of a ByteString.DiscretePrimitiveStringFeature set to either the
second value from the label classifier's array of allowable values if
the score is greater than or equal to LinearThresholdUnit.threshold or the first
otherwise.
String array.
liblinear-related messages are output
liblinear-related output should be
displayed; default false
POS object representing the "dollar sign" tag.
AdaBoost.allExamples, if
they exist; otherwise do nothing.
Learner.learn(Object) and Learner.learn(Object[]).
doneLearning() on every LTU in the network.
liblinear training method.
BatchTrainer.train(int) as described above.
Learner.doneWithRound() on every
LTU in the network.
BiasedRandomWeightVector with
the argument vector, using the specified default weight when one is not
yet present in this vector.
BiasedWeightVector with the
argument vector, using the specified default weight when one is not yet
present in this vector.
AveragedWeightVector with
the argument vector, using the hard coded default weight.
AveragedWeightVector with
the argument vector, using the specified default weight when one is
not yet present in this vector.
SparseWeightVector with the
argument vector, using the hard coded default weight.
SparseWeightVector with the
argument vector, using the specified default weight when one is not yet
present in this vector.
POS object representing the "determiner" tag.
doubles that
should be faster than java's Vector.DVector.defaultCapacity.
Vector.DVector2D.defaultCapacity1 and DVector2D.defaultCapacity2.
"".
Lexicon.encoding has been assigned a value
yet or not.
SupportVectorMachine.defaultEpsilon.
SupportVectorMachine.defaultEpsilon.
true if equality, false if inequality.
FirstOrderConstraint representations.DiscreteArrayFeatures are equivalent when their
containing packages, identifiers, indices, and values are equivalent.
DiscreteArrayStringFeatures are equivalent when their
containing packages, identifiers, indices, and values are equivalent.
DiscretePrimitive(String)Features are equivalent when
their containing packages, identifiers, and values are equivalent.
DiscretePrimitiveStringFeatures are equivalent when
their containing packages, identifiers, and values are equivalent.
DiscreteReferringFeatures are equivalent when their
containing packages, identifiers, and referent features are equivalent.
DiscreteReferringStringFeatures are equivalent when
their containing packages, identifiers, and referent features are
equivalent.
Features are equal when their packages and generating
classifiers are equivalent.
FeatureVectors are equivalent if they contain the same
features and labels, as defined by Feature equivalence.
RealArrayFeatures are equivalent when their containing
packages, identifiers, indices, and values are equivalent.
RealArrayStringFeatures are equivalent when their
containing packages, identifiers, indices, and values are equivalent.
RealPrimitiveFeatures are equivalent when their
containing packages and identifiers are equivalent and their values are
equal.
RealPrimitiveStringFeatures are equivalent when their
containing packages and identifiers are equivalent and their values are
equal.
RealReferringFeatures are equivalent when their
containing packages, identifiers, and referent features are equivalent.
RealReferringStringFeatures are equivalent when their
containing packages, identifiers, and referent features are equivalent.
AtLeastQuantifiers are equivalent when their children
are equivalent.
AtMostQuantifiers are equivalent when their children
are equivalent.
ExistentialQuantifiers are equivalent when their
children are equivalent.
FirstOrderConjunctions are equivalent when they are
topologically equivalent, respecting the associativity and commutivity
of disjunction.
FirstOrderConstants are equivalent when their constants
are equal.
FirstOrderDisjunctions are equivalent when they are
topologically equivalent, respecting the associativity and commutivity
of disjunction.
FirstOrderDoubleImplications are equivalent when
they are topologically equivalent, respecting the commutativity of
double implication.
FirstOrderEqualityTwoValuess are equivalent when their
children are equivalent in either order.
FirstOrderEqualityWithValues are equivalent when their
children are equivalent.
FirstOrderEqualityWithVariables are equivalent when
their children are equivalent in either order.
FirstOrderImplications are equivalent when they are
topologically equivalent.
FirstOrderNegations are equivalent when their
constraints are equivalent.
FirstOrderVariables are equivalent when their
classifiers are equivalent and they store the same example object.
Inference objects are equal when they have the same
run-time type and store the same head object.
Inference objects are equal when they have the same
run-time type and store the same head object.
Inference objects are equal when they have the same
run-time type and store the same head object.
PropositionalAtLeasts are equivalent when they are
topologically equivalent; this implementation currently does not respect
the associativity and commutativity of at-least.
PropositionalConjunctions are equivalent when they are
topologically equivalent, respecting the associativity and commutivity
of conjunction.
PropositionalConstants are equivalent when their
constants are equal.
PropositionalDisjunctions are equivalent when they are
topologically equivalent, respecting the associativity and commutivity
of disjunction.
PropositionalDoubleImplications are equivalent when
they are topologically equivalent, respecting the commutativity of
double implication.
PropositionalImplications are equivalent when they are
topologically equivalent.
PropositionalNegations are equivalent when their
constraints are equivalent.
PropositionalVariables are equivalent when the string
representations of their classifiers are equivalent, they store the
same example object, and their values are equivalent.
QuantifiedConstraintInvocations are equivalent when
their children are equivalent.
Quantifiers are equivalent when their children are
equivalent.
UniversalQuantifiers are equivalent when their children
are equivalent.
POS objects are equal iff their value
variables are equal.
DVectors are considered equal if they contain the same
elements and have the same size.
DVector2Ds are considered equal if they contain all the
same elements, sizes, and capacities.
FVectors are considered equal if they contain
equivalent elements and have the same size.
IVectors are considered equal if they contain the same
elements and have the same size.
IVector2Ds are considered equal if they contain all the
same elements, sizes, and capacities.
OVectors are considered equal if they contain
equivalent elements and have the same size.
setQuantificationVariables(Vector), false will
be returned.
POS object representing the "existential there" tag.
FoldParser.parser.
DataInputStream with
some additional convenience methods and built-in exception handling.DataOutputStream with
some additional convenience methods and built-in exception handling.false
Classifier's
decision.FeatureVector.features list.
FeatureVector.
FeatureVector.
FeatureVector.
FeatureVector.
FeatureVector.
FeatureVector.
FeatureVector.
FeatureVector.
FeatureVector.
FeatureVector.
FeatureVector.
FeatureVector.
FeatureVector.
FeatureVector.
FeatureVector.
ArrayFileParser.FeatureVectors as input, and it
simply returns them as output.BatchTrainer.learner's isTraining field.
WordsInDocumentByDirectory.files to be parsed.
BatchTrainer.examples and BatchTrainer.lexiconSize
variables by querying BatchTrainer.parser and BatchTrainer.learner respectively.
deleteProblem().
FeatureVector.features.
FeatureVector.labels.
FirstOrderConjunction, its contents are flattened into
this FirstOrderConjunction.
true or false.FirstOrderDisjunction, its contents are flattened into
this FirstOrderDisjunction.
String values.weakLearners and alpha, although this
is not necessary since learn(Object[]) will overwrite them
fresh each time it is called.
WekaWrapper.instances collection of examples.
Word as input and
generates features representing the forms of the words in a [-2, +2]
window around the input word.POS object that represents the same
part of speech.
String tokens to POS objects.
Vector.FVector.defaultCapacity.
POS object representing the "foreign word" tag.
Inference object whose fully qualified name
and head object are specified.
d if the vector isn't long enough.
null if the vector isn't long enough.
d if the vector isn't long enough.
d if the vector isn't long enough.
null if the vector isn't long enough.
d if the vector isn't long enough.
Sigmoid.alpha.
Softmax.alpha.
DiscreteConjunctiveFeature.getFeatureKey(Lexicon,boolean,int), this
method computes the feature keys corresponding to the arguments of the
conjunction.
RealConjunctiveFeature.getFeatureKey(Lexicon,boolean,int), this
method computes the feature keys corresponding to the arguments of the
conjunction.
BinaryMIRA.beta member variable.
SparseWinnow.beta variable.
i of value.
Lexicon.lexiconChildren if the feature
isn't present in Lexicon.lexicon and Lexicon.lexiconInv, and then
stores the given feature in Lexicon.lexiconChildren if it wasn't
present anywhere.
Class object with the given name.
Class object with the given name.
Classifier by name using the no-argument
constructor.
Classifier by name using the no-argument
constructor.
Classifier by name using a constructor with
arguments.
Classifier by name using a constructor with
arguments.
SparseConfidenceWeighted.confidence variable.
Lexicon.pruneCutoff, or Lexicon.size() if
Lexicon.pruneCutoff is -1.
SparseAveragedPerceptron.AveragedWeightVector.examples variable.
ArrayParser.examples.
null if one was not specified.
InferenceNotOptimalException.head.
SparseConfidenceWeighted.initialVariance variable.
LinearThresholdUnit.initialWeight variable.
isTraining flag inside
BatchTrainer.learner's runtime class.
FoldParser.K, which may have been computed in the
constructor if the splitting policy is manual.
Learner by name using the no-argument
constructor.
Learner by name using the no-argument
constructor.
Learner by name using a constructor with
arguments.
Learner by name using a constructor with
arguments.
LinearThresholdUnit.learningRate variable.
LinearThresholdUnit.learningRate variable.
LinearThresholdUnit.learningRate variable.
StochasticGradientDescent.learningRate variable.
DiscreteConjunctiveFeature.left.
RealConjunctiveFeature.left.
m.
Lexicon.lexicon.
true iff the objective function is to be maximized.
LinearThresholdUnit.negativeThickness variable.
"discrete%".
"mixed%".
BatchTrainer.parser.
FoldParser.parser.
Parser by name using the no-argument
constructor.
Parser by name using the no-argument
constructor.
Parser by name using a constructor with
arguments.
Parser by name using a constructor with
arguments.
Lexicon.PruningPolicy.percentage.
FoldParser.pivot.
LinearThresholdUnit.positiveThickness variable.
BatchTrainer.progressOutput.
DiscreteReferrer.referent.
RealReferrer.referent.
DiscreteConjunctiveFeature.right.
RealConjunctiveFeature.right.
Score object associated with the given
classification value.
InferenceNotOptimalException.solver.
liblinear.SolverType object to be used
by liblinear during training.
RealPrimitiveFeature.value.
RealPrimitiveStringFeature.value.
RealReferrer.referent.
RealReferrer.referent.
RealReferrer.referent.
ith threshold in
Lexicon.PruningPolicy.thresholds when in "Percentage" mode, but ignores the
parameter i and returns the first element of
Lexicon.PruningPolicy.thresholds when in "Absolute" mode.
LinearThresholdUnit.threshold variable.
FeatureVector.weight.
GLPKHook object to the
ILPInference constructor.createProblem().
createProblem().
createProblem().
createProblem().
createProblem().
createProblem().
DiscreteArrayFeature is the sum of the
hash codes of the containing package, the identifier, the value and the
array index.
DiscreteArrayStringFeature is the sum of
the hash codes of the containing package, the identifier, the value and
the array index.
DiscreteConjunctiveFeature.left and
DiscreteConjunctiveFeature.right.
DiscretePrimitiveFeature is the sum of
the hash codes of its containing package, identifier, and value.
DiscretePrimitiveStringFeature is the
sum of the hash codes of its containing package, identifier, and value.
DiscreteReferrer is the sum of
the hash codes of its containing package, identifier, and the referent
feature.
DiscreteReferringFeature is the sum of
the hash codes of its containing package, identifier, and the referent
feature.
DiscreteReferringStringFeature is the
sum of the hash codes of its containing package, identifier, and the
referent feature.
Feature is a function of the hash codes
of Feature.containingPackage and Feature.generatingClassifier.
FeatureVector is simply the sum of the
hash codes of the features and the labels.
RealArrayFeature is the sum of the hash
codes of the containing package, the identifier, the value, and the
array index.
RealArrayStringFeature is the sum of the
hash codes of the containing package, the identifier, the value, and the
array index.
RealConjunctiveFeature.left and
RealConjunctiveFeature.right.
RealPrimitiveFeature is the sum of the
hash codes of the containing package, the identifier, and the value.
RealPrimitiveStringFeature is the sum of the
hash codes of the containing package, the identifier, and the value.
RealReferrer is the sum of the hash
codes of the containing package, the identifier, and the referent
feature.
RealReferringFeature is the sum of the
hash codes of the containing package, the identifier, and the referent
feature.
RealReferringStringFeature is the sum of the
hash codes of the containing package, the identifier, and the referent
feature.
AtLeastQuantifier is the sum of the hash
codes of its children plus one.
AtMostQuantifier is the sum of the hash
codes of its children.
ExistentialQuantifier is the sum of the
hash codes of its children plus one.
FirstOrderConjunction is the sum of
the hash codes of its children plus one.
FirstOrderConstant is the hash code of
the Boolean object formed from the constant.
FirstOrderDisjunction is the sum of
the hash codes of its children.
FirstOrderDoubleImplication is the sum
of the hash codes of its children plus three.
FirstOrderEqualityTwoValues is the sum
of the hash codes of its children.
FirstOrderEqualityWithValue is the sum
of the hash codes of its children plus 1.
FirstOrderEqualityWithVariable is the
sum of the hash codes of its children plus 2.
FirstOrderImplication is the sum of the
hash codes of its children plus two.
FirstOrderNegation is the hash code of
its child constraint plus 1.
FirstOrderVariable is the hash code of
the string representation of the classifier plus the system's hash code
for the example object.
head's hash code.
PropositionalAtLeast is the sum of
the hash codes of its children plus two.
PropositionalConjunction is the sum of
the hash codes of its children plus one.
PropositionalConstant is the hash code
of the Boolean object formed from the constant.
PropositionalDisjunction is the sum of
the hash codes of its children.
PropositionalDoubleImplication is the
sum of the hash codes of its children plus three.
PropositionalImplication is the sum of
the hash codes of its children plus two.
PropositionalNegation is the hash code
of its child constraint plus 1.
PropositionalVariable is the hash code
of the string representation of the classifier plus the system's hash
code for the example object plus the hash code of the prediction.
QuantifiedConstraintInvocation is the
sum of the hash codes of its children.
Quantifier is the sum of the hash codes
of its children plus three.
UniversalQuantifier is the sum of the
hash codes of its children.
value variable.
String decoding of this byte string.
DVector.vector.
DVector2D.vector.
FVector.vector.
IVector.vector.
IVector2D.vector.
OVector.vector.
true iff there exist "null" labels.
identifier string distinguishes this
Feature from other Features.
identifier string distinguishes this
Feature from other Features.
identifier string distinguishes this
Feature from other Features.
identifier string distinguishes this
Feature from other Features.
identifier string distinguishes this
Feature from other Features.
identifier string distinguishes this
Feature from other Features.
identifier string distinguishes this
Feature from other Features.
identifier string distinguishes this
Feature from other Features.
Normalizer simply returns the same ScoreSet
it was passed as input without modifying anything.ILPSolver to solve a constrained inference
problem.POS object representing the "preposition" tag.
ChildLexicon.childLexiconLookup(DiscreteConjunctiveFeature,int) that
actually does the work of looking up the child feature and updating its
parent counts.
Lexicon.CountPolicy.names array.
Lexicon.PruningPolicy.names array.
ArrayParser.examples array.
FoldParser.SplitPolicy.names array.
XpressMPHook.XMPDVector2D.flatten() indicating where each subvector began
and ended before they were flattened.
XpressMPHook.XMPIVector2D.flatten() indicating where each subvector began
and ended before they were flattened.
lpx_intopt(LPX*) C routine from the GLPK library
to solve the ILP proglem if it hasn't already been solved.
lpx_intopt(LPX*) C routine from the GLPK library
to solve the ILP proglem if it hasn't already been solved.
Inference objects
accessed via their names and head objects.ILPInference class when
the selected ILPSolver did not successfully find the optimal
solution to the inference problem.BiasedWeightVector.bias.
enclosingQuantificationSettings vector
exists, then adds a place holder for this quantifier's quantification
variable setting.
LinearThresholdUnit.initialWeight.
LinearThresholdUnit.initialWeight.
attributeString and initializes this wrapper's
WekaWrapper.instances collection to take those attributes.
SparseConfidenceWeighted.defaultInitialVariance.
SparseConfidenceWeighted.defaultInitialVariance.
LinearThresholdUnit.defaultInitialWeight.
LinearThresholdUnit.defaultInitialWeight.
Sentence.partOfURL(int).
QuantifiedConstraintInvocation representations.true iff the policy is absolute thresholding.
true iff the policy is no pruning.
true iff the policy is percentage thresholding.
ILPSolver
instance has been solved already.
ILPSolver
instance has been solved already.
ILPSolver
instance has been solved already.
ILPSolver
instance has been solved already.
ILPSolver
instance has been solved already.
ints that should
be faster than java's Vector.IVector.defaultCapacity.
Vector.IVector2D.defaultCapacity1 and IVector2D.defaultCapacity2.
POS object representing the "adjective" tag.
POS object representing the "comparative adjective" tag.
POS object representing the "superlative adjective" tag.
Tokens.
Lexicon.
FeatureVector.labels list.
FeatureVector as input, and it returns
the contents of its labels list in a new
FeatureVector as output.Lexicon.lexicon is populated before performing operations on it.
Inference), but most of the classes in this package are
used internally by LBJ at runtime to represent constraints and to translate
between constraint representations.GLPKHook.Normalizer), testing metrics (used in cross validation; see
TestingMetric), and other utility classes can be found in
this package.Parser interface, which is
central to the LBJ learning classifier syntax.LinearThresholdUnit.score(Object) method and
LinearThresholdUnit.threshold, checking the result of evaluation against the label,
and, if they are different, promoting when the label is positive or
demoting when the label is negative.
Learner(s).
LinearThresholdUnit.learn(int[],double[],int[],double[]), except
it notifies its weight vector when it got an example correct in addition
to updating it when it makes a mistake.
Instance object
from this example and adds it to a set of examples from which the
classifier will be built once WekaWrapper.doneLearning() is called.
Classifier that learns to mimic
one an oracle classifier given a feature extracting classifier and example
objects.Parameters classes are used to hold values for learning
algorithm parameters, and all learning algorithm implementations must
provide a constructor that takes such an object as input.BatchTrainer.learner's class.
STDOUT.LinearThresholdUnit.defaultLearningRate.
LinearThresholdUnit.defaultLearningRate.
StochasticGradientDescent.defaultLearningRate.
StochasticGradientDescent.defaultLearningRate.
POS object representing the "left bracket" tag.
value.
Lexicon.
Lexicon contains a mapping from Features to
integers.ints in the
example file.
LinearThresholdUnit is a Learner for binary
classification in which a score is computed as a linear function a
weight vector and the input example, and the decision is made by
comparing the score to some threshold quantity.LinearThresholdUnit's configurable
parameters.Parser does not define the next()
method, but it does define a constructor that opens the specified file and
a readLine() method that fetches the next line of text from
that file, taking care of exception handling.LinkedChild is the child of a LinkedVector.LinkedVector is used to store a vector of
LinkedChildren which all maintain links between each other
and the parent LinkedVector.ScoreSet returned by the
specified Normalizer into log(s).NaiveBayes.Count.count is sometimes stored here.
lookup(f, false).
lookup(f, training,
-1).
Lexicon, this method simply
checks Lexicon.lexicon for the feature and will throw an exception if
it can't be found.
Lexicon.lexiconChildren if the
feature isn't present in Lexicon.lexicon and Lexicon.lexiconInv, and
will throw an exception if it still can't be found.
Feature associated
with the given integer key, and null if no such feature
exists.
POS object representing the "list item marker" tag.
BIOTester.test() method.
STDOUT.
STDOUT rearranged so that each line
contains exactly one sentence, and so that character sequences deemed to
be "words" are delimited by whitespace.
STDOUT.
FeatureVector.
RealArrayFeature whose
value field is set to the strength of
the current feature, and whose DiscretePrimitiveFeature.identifier field contains all
the information necessary to distinguish this feature from other
features.
RealArrayFeature whose
value field is set to the strength of
the current feature, and whose DiscretePrimitiveStringFeature.identifier field contains all
the information necessary to distinguish this feature from other
features.
RealConjunctiveFeature with exactly the same children
as this feature.
RealPrimitiveFeature whose
value field is set to the strength of
the current feature, and whose DiscretePrimitiveFeature.identifier field contains all
the information necessary to distinguish this feature from other
features.
RealPrimitiveFeature whose
value field is set to the strength of
the current feature, and whose DiscretePrimitiveStringFeature.identifier field contains all
the information necessary to distinguish this feature from other
features.
RealPrimitiveFeature whose
value field is set to the strength of
the current feature, and whose DiscreteReferringFeature.identifier field contains all
the information necessary to distinguish this feature from other
features.
RealPrimitiveFeature whose
value field is set to the strength of
the current feature, and whose DiscreteReferringStringFeature.identifier field contains all
the information necessary to distinguish this feature from other
features.
RealFeature whose value is the strength of the current
feature, and whose identifier field contains all the
information necessary to distinguish this feature from other features.
FeatureVector.features list to
RealFeatures with appropriate strengths.
ith vector.
ith vector.
POS object representing the "modal" tag.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
false.
SparsePerceptron.
SparsePerceptron.
MultiLabelLearner's configurable
parameters.RealPrimitiveFeature.value field has been multiplied by the specified
number.
RealPrimitiveStringFeature.value field has been multiplied by the specified
number.
RealPrimitiveFeature.value field has been multiplied by the specified
number.
RealPrimitiveStringFeature.value field has been multiplied by the specified
number.
RealReferrer.referent field has been multiplied by the specified
number.
RealReferrer.referent field has been multiplied by the specified
number.
MuxLearner uses one of many Learners indexed
by the first feature in an example to produce a classification.MuxLearner's configurable
parameters.Count object stores two doubles, one which
holds a accumulated count value and the other intended to hold the
natural logarithm of the count.NaiveBayes.NaiveBayesVector.counts.
NaiveBayes.NaiveBayesVector.counts.
NaiveBayes.NaiveBayesVector.counts.
NaiveBayes's configurable
parameters.GLPKHook object to the
ILPInference constructor.lpx_intopt GLPK library function to solve the
integer linear program.
a is nearly equal to b based on
the value of the TOLERANCE member variable.
a is nearly equal to b based on
the value of the SparseMIRA.TOLERANCE member variable.
GRBModel.update() method needs to be
called before adding more constraints.
true iff the corresponding inference
variable's value in BalasHook.solution has been negated (which happens
iff that variable initially had a negative objective function
coefficient).
LinearThresholdUnit.positiveThickness.
NaiveBayes.NaiveBayesVector for each observed prediction value.
SupportVectorMachine.doneLearning() in case the training examples
observed by SupportVectorMachine.learn(int[],double[],int[],double[]) are only a
subset of a larger, pre-extracted set.
Strings representing the information in
the columns of this row.
LinkedVector from the files being
parsed.
Tokens until the input is exhausted, at
which point it returns null.
LinkedVector of Tokens.
Words.
LinkedVectors of Word objects one at
a time.
Object[] or a FoldSeparator
deserialized out of the given file.
ArrayParser.index pointer.
LinkedChild parsed.
FeatureVector deserialized out of the given file.
POS object representing the "singular noun" tag.
POS object representing the "proper singular noun" tag.
POS object representing the "proper plural noun" tag.
POS object representing the "plural noun" tag.
ScoreSet; its scores are modified in
place before it is returned.
ScoreSet; its scores are modified in
place before it is returned.
ScoreSet; its scores are modified in
place before it is returned.
ScoreSet; its scores are modified in
place before it is returned.
ScoreSet producing normalized
scores.BalasHook.solution.
POS object representing the "open double quote" tag.
Vector.OVector.defaultCapacity.
Lexicon.lexiconInv) serve a dual purpose; first, to indicate by
absolute value the number of other features currently stored in this
object that have the corresponding feature as a child, and second, to
indicate by sign if the corresponding feature has been marked for
removal.
LinkedVector.
Word that the text represents.
BatchTrainer.learner is received.
Tokens.
Words.
LinkedVectors of Words.
Strings.
Sentence returning parser.
LinkedVectors.
Learner for batch training.SparseWeightVector, while the name of the classifier gets the
empty string.
SparseWeightVector.
PassiveAggressive's configurable
parameters.POS object representing the "predeterminer" tag.
POS object representing the "final punctuation" tag.
Words in the representation created
by another Parser and creates a new representation
consisting of Tokens.String names of POS tags into
discrete integer values.value variable.
POS object representing the "possesive ending" tag.
POSBracketToVector, and it returns Token objects
representing that labeled data.LinkedVector objects representing
sentences given file names of POS bracket form files to parse.ChildrenFromVectors
parser used in conjunction with POSBracketToVector.LinearThresholdUnit.defaultThickness.
POS object representing the "pound sign" tag.
true.
BatchTrainer.parser with one that reads from that
file (or memory).
BatchTrainer.parser with one that reads from that
file (or memory).
BatchTrainer.parser with one that reads from that
file (or memory).
BatchTrainer.parser with one that reads from that
file (or memory).
STDOUT when
Accuracy.test(Classifier,Classifier,Parser) is called.
STDOUT a table of feature counts including a
line indicating the position of Lexicon.pruneCutoff.
STDOUT a table of feature counts including a
line indicating the position of Lexicon.pruneCutoff.
scaledAdd
method has been called.
C pointer to the C problem structure.
sentences member variable.
STDOUT, or 0 to suppress these messages.
LinearThresholdUnit is mistake driven, this method
should be overridden and used to update the internal representation when
a mistake is made on a positive example.
w_i *= learningRatex_i.
m of the children
constraints must be true.PropositionalConjunction, its contents are flattened into
this PropositionalConjunction.
true or
false.PropositionalDisjunction, its contents are flattened into
this PropositionalDisjunction.
setQuantificationVariables(Vector), the constant
representing false will be returned.
value member variable is set
to false.
Sentence.partOfURL(int).
POS object representing the "personal pronoun" tag.
POS object representing the "possessive pronoun" tag.
Lexicon.featureCounts or
Lexicon.perClassFeatureCounts so that pruned features are at the end of
the feature space.
Lexicon.lexiconInv or higher have been
pruned.
BatchTrainer.parser according to the given
policy, under the assumption that feature counts have already been
compiled in the given learner's lexicon.
BatchTrainer.parser according to the given
policy, under the assumption that feature counts have already been
compiled in the given learner's lexicon.
Inference object to the cache, indexed its
fully qualified name.
Collection of objects.FirstOrderConstraint representations.ints
according to the given comparator.
POS object representing the "adverb" tag.
POS object representing the "comparative adverb" tag.
POS object representing the "superlative adverb" tag.
true if that byte is
nonzero, false if that byte is zero.
char and returns the char
value.
double value.
float value.
int value.
false.
false.
LineByLine; otherwise,
it returns the next element of the array.
long value.
Strings are intern()ed.
Strings are intern()ed.
NaiveBayes.Count.updateLog is set to true.
Learner.Parameters object out of the specified
locaiton.
Lexicon.pruneCutoff from the specified stream,
discarding everything else.
short value.
int, and
returns the result, which is therefore in the range 0
through 255.
int value in the range
0 through 65535.
FeatureVector.makeReal() method.
double.double.double.double array.
children array with a new array containing all
the same elements except the element with the given index.
ChildLexicon, in
particular updating parent counts and removing children of this feature
if necessary.
ChildLexicon, in
particular updating parent counts and removing children of this feature
if necessary.
ChildLexicon, in
particular updating parent counts and removing children of this feature
if necessary.
ChildLexicon, in
particular updating parent counts and removing children of this feature
if necessary.
ChildLexicon, in
particular updating parent counts and removing children of this feature
if necessary.
null, it means this FirstOrderEquality is not
nested in a quantification.
ILPSolver
instance back to the state it was in when first constructed.
ILPSolver
instance back to the state it was in when first constructed.
ILPSolver
instance back to the state it was in when first constructed.
ILPSolver
instance back to the state it was in when first constructed.
ILPSolver
instance back to the state it was in when first constructed.
WordsInDocumentByDirectory.filesIndex back to 0.
ArrayParser.index pointer to 0.
POS object representing the "right bracket" tag.
XpressMPHook.typeCodes array representing "equal".
XpressMPHook.typeCodes array representing "greater than".
XpressMPHook.typeCodes array representing "less than".
POS object representing the "particle" tag.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
visit(·) method of the given
Inference for this Constraint, as per the
visitor pattern.
Learner.lcFilePath, and writes the binary
representation of the feature lexicon if there is a location cached in
Learner.lexFilePath.
Learner.lexFilePath.
Learner.lcFilePath.
SparseWeightVector except that
NaiveBayes.NaiveBayesVector.incrementCount(int,double)
is called instead of
SparseWeightVector.setWeight(int,double).
SparseWeightVector except that the defaultW
argument is ignored and
NaiveBayes.NaiveBayesVector.incrementCount(int,double)
is called instead of
SparseWeightVector.setWeight(int,double).
w * x + bias
where * is dot product, w is the weight
vector, and x is the feature vector produced by the
extractor.
ScoreSet are the posterior
probabilities of each possible label given the example.
Scores.Word classifier.MuxLearner.baseLearner.
BinaryMIRA.beta member variable to the specified value.
SparseWinnow.beta member variable to the specified value.
SparseConfidenceWeighted.confidence member variable to the specified
value.
MuxLearner.defaultFeature according to the current value
of MuxLearner.defaultPrediction.
BalasHook.first.
FoldParser.fromPivot, which controls whether examples
will be taken from the pivot fold or from all other folds.
ArrayFileParser.includePruned.
SparseConfidenceWeighted.initialVariance member variable to the specified
value.
LinearThresholdUnit.initialWeight member variable to the specified value.
isTraining flag inside BatchTrainer.learner's
runtime class to the specified value.
ValueComparer.labeler.
LinearThresholdUnit.learningRate member variable to the specified
value.
LinearThresholdUnit.learningRate member variable to the specified value.
StochasticGradientDescent.learningRate member variable to the specified
value.
URL.
baseLTU variable.
URL.
LinearThresholdUnit.negativeThickness member variable to the specified
value.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
Learner.setParameters(Parameters)
method for this Parameters object.
ChildLexicon.parentLexicon and makes sure that any
features marked for removal in this lexicon are the identical objects
also present in the parent.
FoldParser.parser to be reset.
LinearThresholdUnit.positiveThickness member variable to the specified
value.
FirstOrderEquality children.
FirstOrderEquality children.
FirstOrderEquality children.
FirstOrderEquality children.
FirstOrderEquality children.
FirstOrderEquality children.
FirstOrderEquality children.
FirstOrderEquality children.
FirstOrderEquality children.
LinearThresholdUnit.positiveThickness and LinearThresholdUnit.negativeThickness
member variables to the specified value.
LinearThresholdUnit.threshold member variable to the specified value.
NaiveBayes.NaiveBayesVector.incrementCount(int,double) instead.
short that
acts as a pointer into DiscreteFeature.BooleanValues.
short that
acts as a pointer into DiscreteFeature.BooleanValues.
FoldParser.shuffled.
xi with
1 / (1 + exp(-alpha xi)), where alpha
is a user-specified constant.Sigmoid.alpha to 1.
simplify(), except this method gives the caller the
ability to optionally leave double implications that are immediate
children of this conjunction in tact.
FeatureVector.features plus
the size of FeatureVector.labels.
Lexicon.lexicon.
DVector.size.
FVector.size.
IVector.size.
OVector.size.
NaiveBayes.defaultSmoothing.
NaiveBayes.defaultSmoothing.
Softmax.alpha to 1.
GLPKHook.nativeSolve(), saving the result in
GLPKHook.solved.
SupportVectorMachine.defaultSolverType.
SupportVectorMachine.defaultSolverType unless there
are more than 2 labels observed in the training data, in which case
"MCSVM_CS" becomes the default.
ints according to the given
comparator.
fromIndex and
excluding toIndex) of the given array of ints
according to the given comparator.
doubles with each Feature.SparseAveragedPerceptron's
configurable parameters.SparseConfidenceWeighted.confidence parameter.
SparseConfidenceWeighted.confidence and SparseConfidenceWeighted.initialVariance parameters.
SparseConfidenceWeighted.confidence, SparseConfidenceWeighted.initialVariance, and
LinearThresholdUnit.weightVector parameters.
SparseConfidenceWeighted.confidence, SparseConfidenceWeighted.initialVariance,
LinearThresholdUnit.weightVector, and SparseConfidenceWeighted.variances
parameters.
SparseConfidenceWeighted.confidence parameter.
SparseConfidenceWeighted.confidence and SparseConfidenceWeighted.initialVariance parameters.
SparseConfidenceWeighted.confidence, SparseConfidenceWeighted.initialVariance, and
LinearThresholdUnit.weightVector parameters.
SparseConfidenceWeighted.confidence, SparseConfidenceWeighted.initialVariance,
LinearThresholdUnit.weightVector, and SparseConfidenceWeighted.variances
parameters.
SparseConfidenceWeighted's
configurable parameters.SparseMIRA's
configurable parameters.SparseNetworkLearner uses multiple
LinearThresholdUnits to make a multi-class classification.SparseNetworkLearner.defaultBaseLTU.
SparseNetworkLearner.defaultBaseLTU.
SparseNetworkLearner's
configurable parameters.SparseWeightVector, while the name of the classifier gets the
empty string.
SparseWeightVector.
SparsePerceptron's configurable
parameters.SparseWeightVector.weights.
SparseWeightVector.weights.
SparseWeightVector.weights.
LinearThresholdUnit.learningRate, SparseWinnow.beta, and
LinearThresholdUnit.threshold take default values, while the
name of the classifier gets the empty string.
LinearThresholdUnit.learningRate to the specified value, SparseWinnow.beta to 1 /
LinearThresholdUnit.learningRate, and the LinearThresholdUnit.threshold
takes the default, while the name of the classifier gets the empty
string.
LinearThresholdUnit.learningRate and SparseWinnow.beta to the specified values,
and the LinearThresholdUnit.threshold takes the default, while
the name of the classifier gets the empty string.
LinearThresholdUnit.learningRate, SparseWinnow.beta, and
LinearThresholdUnit.threshold to the specified values, while the
name of the classifier gets the empty string.
SparseWeightVector, while the name of the classifier gets the
empty string.
LinearThresholdUnit.learningRate, SparseWinnow.beta, and
LinearThresholdUnit.threshold take default values.
LinearThresholdUnit.learningRate to the specified value, SparseWinnow.beta to 1 /
LinearThresholdUnit.learningRate, and the LinearThresholdUnit.threshold
takes the default.
LinearThresholdUnit.learningRate and SparseWinnow.beta to the specified values,
and the LinearThresholdUnit.threshold takes the default.
LinearThresholdUnit.learningRate, SparseWinnow.beta, and
LinearThresholdUnit.threshold to the specified values.
SparseWeightVector.
SparseWinnow's configurable
parameters.SparseWeightVector.
StochasticGradientDescent's
configurable parameters.Strings, converts the Strings to Words,
and returns LinkedVectors of Words.liblinear library which supports support vector machine
classification.SupportVectorMachine's configurable
parameters.x[i1] with the element at
x[i2].
POS object representing the "symbol" tag.
TestDiscrete object.
Classifier
against an oracle Classifier on the objects returned from a
Parser.TestingMetric is an interface through which the user may
implement their own testing method for use by LBJ's internal cross
validation algorithm.BatchTrainer.learner on the specified data while making provisions
under the assumption that this test happens in between rounds of
training.
LinearThresholdUnit.Parameters.positiveThickness and
LinearThresholdUnit.Parameters.negativeThickness; default
LinearThresholdUnit.defaultThickness.
LinearThresholdUnit.defaultThreshold.
LinearThresholdUnit.defaultThreshold.
POS object representing the "to" tag.
Scores contained in this set.
doubles containing the same data as
this vector.
doubles containing the same data
as this vector.
ints containing the same data as
this vector.
ints containing the same data as
this vector.
objectss containing the same data as
this vector.
Word class from LBJ's NLP
library.Token can be constructed from a Word
object representing the same word, a Token representing
the previous word in the sentence, and the type label found in the data.
Sentence.partOfURL(int).
Feature.
FeatureVector as
created by FeatureVector.write(StringBuffer).
"FeatureVectorReturner".
"LabelVectorReturner".
String representation of a ValueComparer
has the form "ValueComparer(child),
where child is the String representation of the
classifier whose value is being compared.
Score is the value followed
by the score separated by a colon.
ScoreSet is the
concatenation of the string representations of each Score
in the set sorted by value, separated by commas, and surrounded by curly
braces.
String representation of a ValueComparer
has the form "ValueComparer(child),
where child is the String representation of the
classifier whose value is being compared.
ZeroOneILPProblem.write(StringBuffer).
Count object is simply the
integer count.
SparseWeightVector.
SparseWeightVector.
String representation of this word in
which the Token.label field appears followed by the word's part
of speech and finally the form (i.e., spelling) of the word all
surrounded by parentheses.
Sentence is just its text.
SparseWeightVector
to a stream just like SparseWeightVector.write(PrintStream), but without the
"Begin" and "End" annotations.
SparseWeightVector
to a stream just like SparseWeightVector.write(PrintStream), but without the
"Begin" and "End" annotations.
Feature omitting
the package.
POS object to the token that represents the same
part of speech.
BatchTrainer.learner for the specified number of rounds.
BatchTrainer.learner for the specified number of rounds.
BatchTrainer.learner for the specified number of rounds.
BatchTrainer.learner for the specified number of rounds.
WekaWrapper.doneLearning() method has been called
and the WekaWrapper.forget() method has not yet been called.
true
qrtype array parameter of the XPRSloadglobal
method.
POS object representing the "interjection" tag.
isClone field to false.
w_i *= basex_i,
initalizing weights in the weight vector as needed.
NaiveBayes.Count.logCount is not up to date.
FeatureVectors have equal value if they contain the
same number of Features and if the values of those
Features are pair-wise equivalent according to the
Feature.valueEquals(String) method.
null if variable consolidation has not been performed.
FirstOrderVariable objects found in this
inference's constraints.
LinearThresholdUnit.defaultWeightVector.
SparseConfidenceWeighted.variances vector.
POS object representing the "base form verb" tag.
POS object representing the "verb past tense" tag.
POS object representing the "verb gerund / present
participle" tag.
POS object representing the "verb past participle" tag.
POS object representing the "verb non 3rd ps sing present"
tag.
POS object representing the "verb 3rd ps sing present" tag.
ILPInference.verbosity.
ILPInference.verbosity.
ILPInference.verbosity.
POS object representing the "wh-determiner" tag.
Lexicon key.
liblinear.
StochasticGradientDescent.defaultWeightVector.
StochasticGradientDescent.defaultWeightVector.
WekaWrapper's configurable
parameters.s.
s.
s.
s.
s.
s.
s.
s.
s.
s.
s.
s.
s.
s.
s.
Strings,
each representing all the words in a document.LinkedVector representation of this
sentence in which every LinkedChild is a Word.
Sentences returned by
another parser (e.g., SentenceSplitter) and splits them into
Word objects.LinkedVectors of
Words, converts the Words to Tokens, and returns
LinkedVectors of Tokens.Word as input and
generates Boolean features representing interesting information about the
forms of the words in a [-2, +2] window around the input word.POS object representing the "wh-pronoun" tag.
POS object representing the "possesive wh-pronoun" tag.
double to
Double.
POS object representing the "wh-adverb" tag.
Feature to the
specified buffer.
Feature to the
specified buffer.
Feature to the
specified buffer.
Feature to the
specified buffer.
Feature to the
specified buffer.
Feature to the
specified buffer.
Feature to the
specified buffer.
FeatureVector.
Feature to the
specified buffer.
Feature to the
specified buffer.
Feature to the
specified buffer.
Feature to the
specified buffer.
Feature to the
specified buffer.
Feature to the
specified buffer.