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String
s.
String
s.
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.Segment
s 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.
FeatureVector
s.
CrossValidationIterator
allows iterating over pairs of
InstanceList
, where each pair is split into training/testing
based on nfolds.Segment
s 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.Neighbor
s.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
.Neighbor
s.Classifier
that scores an array of Neighbor
s.Sequences
s in this InstanceList
by
confidence estimate.
RankMaxEnt
classifier.Segment
s 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
.Segment
s extracted by a Transducer
for some InstanceList
.segmentStartTags[i]
corresponds
to segmentContinueTags[i]
.
Segment
s for only one Instance
.
Transduce
is specified.
SGML2TokenSequence
, except that only the tags
listed in allowedTags
are converted to Label
s.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.
m
th 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
.Segment
s 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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