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Strings.
Strings.
Neighbor created by merging two clusters of the original
Clustering.n dimensions
BalancedWinnowTrainer
Transducer.
Instance objects within an
InstanceList.e to priority.
NeighborEvaluator that is backed by a Classifier.improveClustering to modify the
current predicted Clustering.
improveClustering to
modify the current predicted Clustering.
Clusterer instance.
ClusteringEvaluators.Segment extracted by a Transducer by performing a "constrained lattice"
calculation.Segments produced by a Transducer.InstanceList into n-folds and iterates
over the folds for use in n-fold cross-validation.alpha and the
number of dimensions of the given alphabet.
size
dimensions
Segment.
Segment.
Segment.
Segment.
Instance.
Instance.
Instance.
Instance.
Segment.
Instance.
Instance.
Segment.
Sequence.
Instance.
Instance.
Sequence that ensures that every
Object in the sequence has the same class.
FeaturesInWindow((namePrefix, leftBoundaryOffset, rightBoundaryOffset, null, true);
Alphabet in which each element of the subset has an associated value.Instance at the specified index.
Alphabet mapping features of the data to
integers.
correctLeastConfidentSegments
ilist
Instance is passed,
which may be null.
Alphabet mapping target output labels to
integers.
NeighborEvaluator.Neighbor
that would result from merging the two clusters.
e into the queue.
FeatureVectors.
CrossValidationIterator allows iterating over pairs of
InstanceList, where each pair is split into training/testing
based on nfolds.Segments produced by a Transducer.n dimensions
Sequence implementation where all of the
elements must be Labels.InstanceList, deserialized from file.
InstanceList, deserialized from
file.
logGammaStirling
JunctionTreeInferencer.computeMarginals(cc.mallet.grmm.types.FactorGraph).
Segment extracted by a Transducer using a MaxEnt classifier to classify segments
as "correct" or "incorrect." xxx needs some interface workSequence extracted by a Transducer using a MaxEnt classifier to classify Sequences
as "correct" or "incorrect." xxx needs some interface work.SparseVector whose entries (taken from the union of
those in the instances) are the expected values of those in the
InstanceList.
SparseVector whose entries (dense with the given
number of indices) are the expected values of those in the
InstanceList.
SparseVector whose entries (the given indices) are
the expected values of those in the InstanceList.
Classifier over pairs of Instances to score
Neighbor.MEMM.capacity.
MultiInstanceList with an array of InstanceList
MultiInstanceList with a List of InstanceList
FeatureVector.Clustering to the
modified Clustering specified in a Neighbor object.Neighbors.predOutput
Classifier over pairs of Instances to score
Neighbor.Instance from an array to a
FeatureVector leaving other fields unchanged.
Instance from a CharSequence
of comma-separated-values to an array, where each index is the
feature name.
PipeExtendedIterator instance.
stateFile.
cutoff times in the corpus, as indicated by
the provided counts.
Segment.Neighbors.Classifier that scores an array of Neighbors.Sequencess in this InstanceList by
confidence estimate.
RankMaxEnt classifier.Segments in this InstanceList by
confidence estimate.
Instance
null in all instances.
null in all instances.
Trial results.InstanceList of the same size, where the instances come from the
random sampling (with replacement) of this list using the given weights.
InstanceList to file.
NeighborhoodEvaluator.
Sequence segmented by a
Transducer, usually corresponding to some object extracted
from an input Sequence.Segments extracted by a Transducer
for some InstanceList.segmentStartTags[i] corresponds
to segmentContinueTags[i].
Segments for only one Instance.
Transduce is specified.
SGML2TokenSequence, except that only the tags
listed in allowedTags are converted to Labels.Sequence and a PropertyList, used when extracting
features from a Sequence in a pipe for confidence predictionInstance at position
index with a new one.
Instance at position index
with a new one.
train or trainWithFeatureInduction.
train or trainWithFeatureInduction.
train or trainWithFeatureInduction.
train or trainWithFeatureInduction.
FeatureVectorSequence.SimpleTaggerSentence2FeatureVectorSequence instance.
SimpleTaggerSentence2TokenSequence to use
{Slink StringTokenizations} for use with the extract package.SimpleTaggerSentence2StringTokenization instance.
SimpleTaggerSentence2StringTokenization instance
which includes tokens as features iff the supplied argument is true.
TokenSequence.SimpleTaggerSentence2TokenSequence instance.
SimpleTaggerSentence2TokenSequence instance
which includes tokens as features iff the supplied argument is true.
mth element of this list, starting with the first.
LabelSequence out of a TokenSequence that
is the target of an Instance.Token.text to a list
of Strings (space delimited).trainWithFeatureInduction, but
allows some default options to be changed.
trainWithFeatureInduction, but
allows some default options to be changed.
weights according to errors
ilist.
classify.Classifier.Segment
extracted by a Transducer.Segments produced by a Transducer.Sequence
extracted by a Transducer.Note that this is different from
TransducerConfidenceEstimator, which estimates the
confidence for a single Segment.SparseVector whose entries (taken from the union of
those in the instances) are the variance of those in the
InstanceList.
SparseVector whose entries (taken from the mean
argument) are the variance of those in the InstanceList.
WinnowTrainer
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