|
||||||||||
| PREV NEXT | FRAMES NO FRAMES | |||||||||
| Packages that use InstanceList | |
|---|---|
| cc.mallet.classify | Classes for training and classifying instances. |
| cc.mallet.classify.constraints.ge | |
| cc.mallet.classify.constraints.pr | |
| cc.mallet.classify.evaluate | Classes for computing and displaying the quaility of a classification trial, including accuracy, precision, and confusion matrix. |
| cc.mallet.cluster | Unsupervised clustering of Instance objects within an
InstanceList. |
| cc.mallet.cluster.iterator | |
| cc.mallet.cluster.util | |
| cc.mallet.extract | Unimplemented. |
| cc.mallet.fst | Transducers, including Conditional Random Fields (CRFs). |
| cc.mallet.fst.confidence | |
| cc.mallet.fst.semi_supervised | |
| cc.mallet.fst.semi_supervised.constraints | |
| cc.mallet.fst.semi_supervised.pr | |
| cc.mallet.fst.semi_supervised.pr.constraints | |
| cc.mallet.fst.semi_supervised.tui | |
| cc.mallet.grmm.learning | |
| cc.mallet.grmm.learning.extract | |
| cc.mallet.pipe | Classes for processing arbitrary data into instances. |
| cc.mallet.pipe.iterator | Classes that generate instances from different kinds of input or data structures. |
| cc.mallet.topics | |
| cc.mallet.types | Fundamental MALLET types, including FeatureVector, Instance, Label etc. |
| cc.mallet.util | Miscellaneous utilities including command line processing, math functions, lexing, logging. |
| Uses of InstanceList in cc.mallet.classify |
|---|
| Fields in cc.mallet.classify declared as InstanceList | |
|---|---|
protected InstanceList |
MaxEntOptimizableByGE.trainingList
|
protected InstanceList |
ClassifierTrainer.validationSet
|
| Methods in cc.mallet.classify that return InstanceList | |
|---|---|
InstanceList |
C45.Node.getInstances()
|
InstanceList |
ClassifierTrainer.getValidationInstances()
|
| Methods in cc.mallet.classify with parameters of type InstanceList | |
|---|---|
java.util.ArrayList<Classification> |
Classifier.classify(InstanceList instances)
|
double |
NaiveBayes.dataLogLikelihood(InstanceList ilist)
|
abstract void |
ClassifierEvaluator.evaluateInstanceList(ClassifierTrainer trainer,
InstanceList instances,
java.lang.String description)
|
void |
ClassifierAccuracyEvaluator.evaluateInstanceList(ClassifierTrainer trainer,
InstanceList instances,
java.lang.String description)
|
double |
Classifier.getAccuracy(InstanceList ilist)
|
double |
Classifier.getAverageRank(InstanceList ilist)
|
double |
Classifier.getF1(InstanceList ilist,
int index)
|
double |
Classifier.getF1(InstanceList ilist,
Labeling labeling)
|
double |
Classifier.getF1(InstanceList ilist,
java.lang.Object labelEntry)
|
static double[][] |
FeatureConstraintUtil.getFeatureLabelCounts(InstanceList list,
boolean useValues)
|
Optimizable.ByGradientValue |
RankMaxEntTrainer.getMaximizableTrainer(InstanceList ilist)
|
Optimizable.ByGradientValue |
MCMaxEntTrainer.getMaximizableTrainer(InstanceList ilist)
|
MaxEntOptimizableByLabelLikelihood |
MaxEntTrainer.getOptimizable(InstanceList trainingSet)
|
Optimizable.ByGradientValue |
MaxEntGETrainer.getOptimizable(InstanceList trainingList)
|
Optimizable.ByGradientValue |
MaxEntGERangeTrainer.getOptimizable(InstanceList trainingList)
|
MaxEntOptimizableByLabelLikelihood |
MaxEntTrainer.getOptimizable(InstanceList trainingSet,
MaxEnt initialClassifier)
|
Optimizer |
MaxEntTrainer.getOptimizer(InstanceList trainingSet)
This method is called by the train method. |
Optimizer |
MaxEntL1Trainer.getOptimizer(InstanceList trainingSet)
|
double |
Classifier.getPrecision(InstanceList ilist,
int index)
|
double |
Classifier.getPrecision(InstanceList ilist,
Labeling labeling)
|
double |
Classifier.getPrecision(InstanceList ilist,
java.lang.Object labelEntry)
|
double |
Classifier.getRecall(InstanceList ilist,
int index)
|
double |
Classifier.getRecall(InstanceList ilist,
Labeling labeling)
|
double |
Classifier.getRecall(InstanceList ilist,
java.lang.Object labelEntry)
|
void |
DecisionTree.induceFeatures(InstanceList ilist,
boolean withFeatureShrinkage,
boolean inducePerClassFeatures)
|
static java.util.HashMap<java.lang.Integer,java.util.ArrayList<java.lang.Integer>> |
FeatureConstraintUtil.labelFeatures(InstanceList list,
java.util.ArrayList<java.lang.Integer> features)
|
static java.util.HashMap<java.lang.Integer,java.util.ArrayList<java.lang.Integer>> |
FeatureConstraintUtil.labelFeatures(InstanceList list,
java.util.ArrayList<java.lang.Integer> features,
boolean reject)
Label features using heuristic described in "Learning from Labeled Features using Generalized Expectation Criteria" Gregory Druck, Gideon Mann, Andrew McCallum. |
double |
NaiveBayes.labelLogLikelihood(InstanceList ilist)
|
static java.util.HashMap<java.lang.Integer,double[]> |
FeatureConstraintUtil.readConstraintsFromFile(java.lang.String filename,
InstanceList data)
Reads feature constraints from a file, whether they are stored using Strings or indices. |
static java.util.HashMap<java.lang.Integer,double[]> |
FeatureConstraintUtil.readConstraintsFromFileIndex(java.lang.String filename,
InstanceList data)
Reads feature constraints stored using strings from a file. |
static java.util.HashMap<java.lang.Integer,double[]> |
FeatureConstraintUtil.readConstraintsFromFileString(java.lang.String filename,
InstanceList data)
Reads feature constraints stored using strings from a file. |
static java.util.HashMap<java.lang.Integer,double[][]> |
FeatureConstraintUtil.readRangeConstraintsFromFile(java.lang.String filename,
InstanceList data)
Reads range constraints stored using strings from a file. |
static java.util.ArrayList<java.lang.Integer> |
FeatureConstraintUtil.selectFeaturesByInfoGain(InstanceList list,
int numFeatures)
Select features with the highest information gain. |
static java.util.HashMap<java.lang.Integer,double[]> |
FeatureConstraintUtil.setTargetsUsingData(InstanceList list,
java.util.ArrayList<java.lang.Integer> features)
|
static java.util.HashMap<java.lang.Integer,double[]> |
FeatureConstraintUtil.setTargetsUsingData(InstanceList list,
java.util.ArrayList<java.lang.Integer> features,
boolean normalize)
|
static java.util.HashMap<java.lang.Integer,double[]> |
FeatureConstraintUtil.setTargetsUsingData(InstanceList list,
java.util.ArrayList<java.lang.Integer> features,
boolean useValues,
boolean normalize)
Set target distributions using estimates from data. |
static java.util.HashMap<java.lang.Integer,double[]> |
FeatureConstraintUtil.setTargetsUsingFeatureVoting(java.util.HashMap<java.lang.Integer,java.util.ArrayList<java.lang.Integer>> labeledFeatures,
InstanceList trainingData)
Set target distributions using feature voting heuristic described in "Learning from Labeled Features using Generalized Expectation Criteria" Gregory Druck, Gideon Mann, Andrew McCallum. |
void |
ClassifierTrainer.setValidationInstances(InstanceList validationSet)
|
Winnow |
WinnowTrainer.train(InstanceList trainingList)
Trains winnow on the instance list, updating weights according to errors |
MaxEnt |
RankMaxEntTrainer.train(InstanceList trainingSet)
|
NaiveBayes |
NaiveBayesTrainer.train(InstanceList trainingList)
Create a NaiveBayes classifier from a set of training data. |
NaiveBayes |
NaiveBayesEMTrainer.train(InstanceList trainingSet)
|
MCMaxEnt |
MCMaxEntTrainer.train(InstanceList trainingSet)
|
MaxEnt |
MaxEntTrainer.train(InstanceList trainingSet)
|
MaxEnt |
MaxEntPRTrainer.train(InstanceList trainingSet)
|
MaxEnt |
MaxEntGETrainer.train(InstanceList trainingList)
|
MaxEnt |
MaxEntGERangeTrainer.train(InstanceList trainingList)
|
Classifier |
FeatureSelectingClassifierTrainer.train(InstanceList trainingSet)
|
DecisionTree |
DecisionTreeTrainer.train(InstanceList trainingList)
|
ConfidencePredictingClassifier |
ConfidencePredictingClassifierTrainer.train(InstanceList trainList)
|
abstract C |
ClassifierTrainer.train(InstanceList trainingSet)
|
ClassifierEnsemble |
ClassifierEnsembleTrainer.train(InstanceList trainingSet)
|
C45 |
C45Trainer.train(InstanceList trainingList)
|
BalancedWinnow |
BalancedWinnowTrainer.train(InstanceList trainingList)
Trains the classifier on the instance list, updating class weight vectors as appropriate |
BaggingClassifier |
BaggingTrainer.train(InstanceList trainingList)
|
AdaBoost |
AdaBoostTrainer.train(InstanceList trainingList)
Boosting method that resamples instances using their weights |
AdaBoostM2 |
AdaBoostM2Trainer.train(InstanceList trainingList)
Boosting method that resamples instances using their weights |
MaxEnt |
MaxEntTrainer.train(InstanceList trainingSet,
int numIterations)
|
MaxEnt |
MaxEntPRTrainer.train(InstanceList trainingSet,
int maxIterations)
|
MaxEnt |
MaxEntGETrainer.train(InstanceList train,
int maxIterations)
|
MaxEnt |
MaxEntGERangeTrainer.train(InstanceList train,
int maxIterations)
|
C |
ClassifierTrainer.ByOptimization.train(InstanceList trainingSet,
int numIterations)
|
MaxEnt |
MaxEntPRTrainer.train(InstanceList data,
int minIterations,
int maxIterations)
|
C |
ClassifierTrainer.ByActiveLearning.train(InstanceList trainingAndUnlabeledSet,
Labeler labeler,
int numLabelRequests)
|
NaiveBayes |
NaiveBayesTrainer.trainIncremental(InstanceList trainingInstancesToAdd)
|
C |
ClassifierTrainer.ByIncrements.trainIncremental(InstanceList trainingInstancesToAdd)
|
| Constructors in cc.mallet.classify with parameters of type InstanceList | |
|---|---|
C45.Node(InstanceList ilist,
C45.Node parent,
int minNumInsts)
|
|
C45.Node(InstanceList ilist,
C45.Node parent,
int minNumInsts,
int[] instIndices)
|
|
ClassifierAccuracyEvaluator(InstanceList[] instances,
java.lang.String[] descriptions)
|
|
ClassifierAccuracyEvaluator(InstanceList instanceList1,
java.lang.String instanceListDescription1)
|
|
ClassifierAccuracyEvaluator(InstanceList instanceList1,
java.lang.String instanceListDescription1,
InstanceList instanceList2,
java.lang.String instanceListDescription2)
|
|
ClassifierAccuracyEvaluator(InstanceList instanceList1,
java.lang.String instanceListDescription1,
InstanceList instanceList2,
java.lang.String instanceListDescription2,
InstanceList instanceList3,
java.lang.String instanceListDescription3)
|
|
ClassifierEvaluator(InstanceList[] instanceLists,
java.lang.String[] instanceListDescriptions)
|
|
ClassifierEvaluator(InstanceList instanceList1,
java.lang.String instanceListDescription1)
|
|
ClassifierEvaluator(InstanceList instanceList1,
java.lang.String instanceListDescription1,
InstanceList instanceList2,
java.lang.String instanceListDescription2)
|
|
ClassifierEvaluator(InstanceList instanceList1,
java.lang.String instanceListDescription1,
InstanceList instanceList2,
java.lang.String instanceListDescription2,
InstanceList instanceList3,
java.lang.String instanceListDescription3)
|
|
ConfidencePredictingClassifierTrainer(ClassifierTrainer underlyingClassifierTrainer,
InstanceList validationSet)
|
|
ConfidencePredictingClassifierTrainer(ClassifierTrainer underlyingClassifierTrainer,
InstanceList validationSet,
Pipe confidencePredictingPipe)
|
|
DecisionTree.Node(InstanceList ilist,
DecisionTree.Node parent,
FeatureSelection fs)
|
|
MaxEntOptimizableByGE(InstanceList trainingList,
java.util.ArrayList<MaxEntGEConstraint> constraints,
MaxEnt initClassifier)
|
|
MaxEntOptimizableByLabelDistribution(InstanceList trainingSet,
MaxEnt initialClassifier)
|
|
MaxEntOptimizableByLabelLikelihood(InstanceList trainingSet,
MaxEnt initialClassifier)
|
|
PRAuxClassifierOptimizable(InstanceList trainingData,
double[][] baseDistribution,
PRAuxClassifier classifier)
|
|
Trial(Classifier c,
InstanceList ilist)
|
|
| Uses of InstanceList in cc.mallet.classify.constraints.ge |
|---|
| Methods in cc.mallet.classify.constraints.ge with parameters of type InstanceList | |
|---|---|
java.util.BitSet |
MaxEntRangeL2FLGEConstraints.preProcess(InstanceList data)
|
java.util.BitSet |
MaxEntGEConstraint.preProcess(InstanceList data)
|
java.util.BitSet |
MaxEntFLGEConstraints.preProcess(InstanceList data)
|
| Uses of InstanceList in cc.mallet.classify.constraints.pr |
|---|
| Methods in cc.mallet.classify.constraints.pr with parameters of type InstanceList | |
|---|---|
java.util.BitSet |
MaxEntPRConstraint.preProcess(InstanceList data)
|
java.util.BitSet |
MaxEntFLPRConstraints.preProcess(InstanceList data)
|
| Uses of InstanceList in cc.mallet.classify.evaluate |
|---|
| Constructors in cc.mallet.classify.evaluate with parameters of type InstanceList | |
|---|---|
AccuracyCoverage(Classifier C,
InstanceList ilist,
int numBuckets,
java.lang.String title)
|
|
AccuracyCoverage(Classifier C,
InstanceList ilist,
java.lang.String title)
|
|
| Uses of InstanceList in cc.mallet.cluster |
|---|
| Fields in cc.mallet.cluster declared as InstanceList | |
|---|---|
protected InstanceList |
Clustering.instances
|
| Methods in cc.mallet.cluster that return InstanceList | |
|---|---|
InstanceList |
Clustering.getCluster(int label)
Return an list of instances with a particular label. |
InstanceList[] |
Clustering.getClusters()
Returns an array of instance lists corresponding to clusters. |
InstanceList |
Clustering.getInstances()
|
| Methods in cc.mallet.cluster with parameters of type InstanceList | |
|---|---|
Clustering |
KMeans.cluster(InstanceList instances)
Cluster instances |
Clustering |
HillClimbingClusterer.cluster(InstanceList instances)
While not converged, calls improveClustering to modify the
current predicted Clustering. |
abstract Clustering |
Clusterer.cluster(InstanceList trainingSet)
Return a clustering of an InstanceList |
Clustering |
HillClimbingClusterer.cluster(InstanceList instances,
int iterations,
Clustering initialClustering)
While not converged, call improveClustering to
modify the current predicted Clustering. |
abstract Clustering[] |
KBestClusterer.clusterKBest(InstanceList trainingSet,
int k)
|
Clustering[] |
HillClimbingClusterer.clusterKBest(InstanceList instances,
int k)
|
Clustering[] |
HillClimbingClusterer.clusterKBest(InstanceList instances,
int iterations,
Clustering initialClustering,
int k)
Return the K most recent solutions. |
abstract Clustering |
HillClimbingClusterer.initializeClustering(InstanceList instances)
|
Clustering |
GreedyAgglomerative.initializeClustering(InstanceList instances)
|
| Constructors in cc.mallet.cluster with parameters of type InstanceList | |
|---|---|
Clustering(InstanceList instances,
int numLabels,
int[] labels)
Clustering constructor. |
|
| Uses of InstanceList in cc.mallet.cluster.iterator |
|---|
| Fields in cc.mallet.cluster.iterator declared as InstanceList | |
|---|---|
protected InstanceList |
PairSampleIterator.instances
|
| Uses of InstanceList in cc.mallet.cluster.util |
|---|
| Methods in cc.mallet.cluster.util that return InstanceList | |
|---|---|
static InstanceList |
ClusterUtils.combineLists(InstanceList li,
InstanceList lj)
|
static InstanceList |
ClusterUtils.makeList(Instance i,
Instance j)
|
| Methods in cc.mallet.cluster.util with parameters of type InstanceList | |
|---|---|
static InstanceList |
ClusterUtils.combineLists(InstanceList li,
InstanceList lj)
|
static Clustering |
ClusterUtils.createRandomClustering(InstanceList instances,
Randoms random)
|
static Clustering |
ClusterUtils.createSingletonClustering(InstanceList instances)
Initializes Clustering to one Instance per cluster. |
| Uses of InstanceList in cc.mallet.extract |
|---|
| Methods in cc.mallet.extract that return InstanceList | |
|---|---|
InstanceList |
CRFExtractor.pipeInstances(java.util.Iterator<Instance> source)
|
| Methods in cc.mallet.extract with parameters of type InstanceList | |
|---|---|
Extraction |
CRFExtractor.extract(InstanceList ilist)
Assumes Instance.source contains the Tokenization object. |
| Uses of InstanceList in cc.mallet.fst |
|---|
| Fields in cc.mallet.fst declared as InstanceList | |
|---|---|
protected InstanceList[] |
TransducerEvaluator.instanceLists
|
protected InstanceList |
ThreadedOptimizable.trainingSet
Data |
protected InstanceList |
CRFOptimizableByLabelLikelihood.trainingSet
|
protected InstanceList |
CRFOptimizableByBatchLabelLikelihood.trainingSet
|
| Methods in cc.mallet.fst with parameters of type InstanceList | |
|---|---|
void |
HMM.addFullyConnectedStatesForThreeQuarterLabels(InstanceList trainingSet)
|
void |
CRF.addFullyConnectedStatesForThreeQuarterLabels(InstanceList trainingSet)
|
java.lang.String |
HMM.addOrderNStates(InstanceList trainingSet,
int[] orders,
boolean[] defaults,
java.lang.String start,
java.util.regex.Pattern forbidden,
java.util.regex.Pattern allowed,
boolean fullyConnected)
Assumes that the HMM's output alphabet contains Strings. |
java.lang.String |
CRF.addOrderNStates(InstanceList trainingSet,
int[] orders,
boolean[] defaults,
java.lang.String start,
java.util.regex.Pattern forbidden,
java.util.regex.Pattern allowed,
boolean fullyConnected)
Assumes that the CRF's output alphabet contains Strings. |
void |
HMM.addStatesForBiLabelsConnectedAsIn(InstanceList trainingSet)
Add states to create a second-order Markov model on labels, adding only those transitions the occur in the given trainingSet. |
void |
CRF.addStatesForBiLabelsConnectedAsIn(InstanceList trainingSet)
Add states to create a second-order Markov model on labels, adding only those transitions the occur in the given trainingSet. |
void |
HMM.addStatesForHalfLabelsConnectedAsIn(InstanceList trainingSet)
Add as many states as there are labels, but don't create separate weights for each source-destination pair of states. |
void |
CRF.addStatesForHalfLabelsConnectedAsIn(InstanceList trainingSet)
Add as many states as there are labels, but don't create separate weights for each source-destination pair of states. |
void |
HMM.addStatesForLabelsConnectedAsIn(InstanceList trainingSet)
Add states to create a first-order Markov model on labels, adding only those transitions the occur in the given trainingSet. |
void |
CRF.addStatesForLabelsConnectedAsIn(InstanceList trainingSet)
Add states to create a first-order Markov model on labels, adding only those transitions the occur in the given trainingSet. |
void |
HMM.addStatesForThreeQuarterLabelsConnectedAsIn(InstanceList trainingSet)
Add as many states as there are labels, but don't create separate observational-test-weights for each source-destination pair of states---instead have all the incoming transitions to a state share the same observational-feature-test weights. |
void |
CRF.addStatesForThreeQuarterLabelsConnectedAsIn(InstanceList trainingSet)
Add as many states as there are labels, but don't create separate observational-test-weights for each source-destination pair of states---instead have all the incoming transitions to a state share the same observational-feature-test weights. |
double |
Transducer.averageTokenAccuracy(InstanceList ilist)
Runs inference across all the instances and returns the average token accuracy. |
void |
MultiSegmentationEvaluator.batchTest(InstanceList data,
java.util.List<Sequence> predictedSequences,
java.lang.String description,
java.io.PrintStream viterbiOutputStream)
Tests segmentation using an ArrayList of predicted Sequences instead of a Transducer. |
void |
CRF.evaluate(TransducerEvaluator eval,
InstanceList testing)
Deprecated. |
void |
ViterbiWriter.evaluateInstanceList(TransducerTrainer transducerTrainer,
InstanceList instances,
java.lang.String description)
|
abstract void |
TransducerEvaluator.evaluateInstanceList(TransducerTrainer transducer,
InstanceList instances,
java.lang.String description)
|
void |
TokenAccuracyEvaluator.evaluateInstanceList(TransducerTrainer trainer,
InstanceList instances,
java.lang.String description)
|
void |
SegmentationEvaluator.evaluateInstanceList(TransducerTrainer tt,
InstanceList data,
java.lang.String description)
|
void |
PerClassAccuracyEvaluator.evaluateInstanceList(TransducerTrainer tt,
InstanceList data,
java.lang.String description)
|
void |
MultiSegmentationEvaluator.evaluateInstanceList(TransducerTrainer tt,
InstanceList data,
java.lang.String description)
|
void |
LabelDistributionEvaluator.evaluateInstanceList(TransducerTrainer transducer,
InstanceList instances,
java.lang.String description)
|
void |
InstanceAccuracyEvaluator.evaluateInstanceList(TransducerTrainer tt,
InstanceList data,
java.lang.String description)
|
void |
CRFWriter.evaluateInstanceList(TransducerTrainer transducer,
InstanceList instances,
java.lang.String description)
|
protected void |
CRFOptimizableByLabelLikelihood.gatherConstraints(InstanceList ilist)
|
protected void |
CRFOptimizableByBatchLabelLikelihood.gatherConstraints(InstanceList ilist)
Set the constraints by running forward-backward with the output label sequence provided, thus restricting it to only those paths that agree with the label sequence. |
CRFTrainerByValueGradients.OptimizableCRF |
CRFTrainerByValueGradients.getOptimizableCRF(InstanceList trainingSet)
Returns an optimizable CRF that contains a collection of objective functions. |
CRFOptimizableByBatchLabelLikelihood |
CRFTrainerByThreadedLabelLikelihood.getOptimizableCRF(InstanceList trainingSet)
|
CRFOptimizableByLabelLikelihood |
CRFTrainerByLabelLikelihood.getOptimizableCRF(InstanceList trainingSet)
|
MEMMTrainer.MEMMOptimizableByLabelLikelihood |
MEMMTrainer.getOptimizableMEMM(InstanceList trainingSet)
|
Optimizer |
CRFTrainerByValueGradients.getOptimizer(InstanceList trainingSet)
Returns a L-BFGS optimizer, creating if one doesn't exist. |
Optimizer |
CRFTrainerByThreadedLabelLikelihood.getOptimizer(InstanceList trainingSet)
|
Optimizer |
CRFTrainerByLabelLikelihood.getOptimizer(InstanceList trainingSet)
|
Optimizer |
CRFTrainerByL1LabelLikelihood.getOptimizer(InstanceList trainingSet)
|
void |
CRF.induceFeaturesFor(InstanceList instances)
When the CRF has done feature induction, these new feature conjunctions must be created in the test or validation data in order for them to take effect. |
Optimizable.ByGradientValue |
CRFOptimizableByLabelLikelihood.Factory.newCRFOptimizable(CRF crf,
InstanceList trainingData)
|
Optimizable.ByCombiningBatchGradient |
CRFOptimizableByBatchLabelLikelihood.Factory.newCRFOptimizable(CRF crf,
InstanceList trainingData,
int numBatches)
|
Sequence[] |
CRF.predict(InstanceList testing)
Deprecated. |
void |
CRFTrainerByStochasticGradient.setLearningRateByLikelihood(InstanceList trainingSample)
Automatically sets the learning rate to one that would be good |
void |
CRF.setWeightsDimensionAsIn(InstanceList trainingData)
|
void |
CRF.setWeightsDimensionAsIn(InstanceList trainingData,
boolean useSomeUnsupportedTrick)
|
static void |
SimpleTagger.test(TransducerTrainer tt,
TransducerEvaluator eval,
InstanceList testing)
Test a transducer on the given test data, evaluating accuracy with the given evaluator |
boolean |
TransducerTrainer.train(InstanceList trainingSet)
|
boolean |
NoopTransducerTrainer.train(InstanceList trainingSet)
|
boolean |
MEMMTrainer.train(InstanceList training)
Trains a MEMM until convergence. |
boolean |
HMM.train(InstanceList ilist)
Trains a HMM without validation and evaluation. |
boolean |
HMM.train(InstanceList ilist,
InstanceList validation,
InstanceList testing)
Trains a HMM with evaluator set to null. |
boolean |
HMM.train(InstanceList ilist,
InstanceList validation,
InstanceList testing,
TransducerEvaluator eval)
|
boolean |
MEMMTrainer.train(InstanceList training,
InstanceList validation,
InstanceList testing,
TransducerEvaluator eval,
int numIterations,
int numIterationsPerProportion,
double[] trainingProportions)
Not implemented yet. |
boolean |
HMMTrainerByLikelihood.train(InstanceList trainingSet,
InstanceList unlabeledSet,
int numIterations)
|
static CRF |
SimpleTagger.train(InstanceList training,
InstanceList testing,
TransducerEvaluator eval,
int[] orders,
java.lang.String defaultLabel,
java.lang.String forbidden,
java.lang.String allowed,
boolean connected,
int iterations,
double var,
CRF crf)
Create and train a CRF model from the given training data, optionally testing it on the given test data. |
abstract boolean |
TransducerTrainer.train(InstanceList trainingSet,
int numIterations)
Train the transducer associated with this TransducerTrainer. |
boolean |
ShallowTransducerTrainer.train(InstanceList trainingSet,
int numIterations)
Deprecated. |
boolean |
NoopTransducerTrainer.train(InstanceList trainingSet,
int numIterations)
|
boolean |
MEMMTrainer.train(InstanceList training,
int numIterations)
Trains a MEMM for specified number of iterations or until convergence whichever occurs first; returns true if training converged within specified iterations. |
boolean |
HMMTrainerByLikelihood.train(InstanceList trainingSet,
int numIterations)
|
boolean |
CRFTrainerByValueGradients.train(InstanceList trainingSet,
int numIterations)
Trains a CRF until convergence or specified number of iterations, whichever is earlier. |
boolean |
CRFTrainerByThreadedLabelLikelihood.train(InstanceList trainingSet,
int numIterations)
|
boolean |
CRFTrainerByStochasticGradient.train(InstanceList trainingSet,
int numIterations)
|
boolean |
CRFTrainerByLabelLikelihood.train(InstanceList trainingSet,
int numIterations)
|
boolean |
CRFTrainerByValueGradients.train(InstanceList training,
int numIterationsPerProportion,
double[] trainingProportions)
Train a CRF on various-sized subsets of the data. |
boolean |
CRFTrainerByThreadedLabelLikelihood.train(InstanceList training,
int numIterationsPerProportion,
double[] trainingProportions)
Train a CRF on various-sized subsets of the data. |
boolean |
CRFTrainerByLabelLikelihood.train(InstanceList training,
int numIterationsPerProportion,
double[] trainingProportions)
Train a CRF on various-sized subsets of the data. |
boolean |
CRFTrainerByStochasticGradient.train(InstanceList trainingSet,
int numIterations,
int numIterationsBetweenEvaluation)
|
abstract boolean |
TransducerTrainer.ByIncrements.trainIncremental(InstanceList incrementalTrainingSet)
|
boolean |
CRFTrainerByValueGradients.trainIncremental(InstanceList training)
Trains a CRF until convergence. |
boolean |
CRFTrainerByThreadedLabelLikelihood.trainIncremental(InstanceList training)
|
boolean |
CRFTrainerByStochasticGradient.trainIncremental(InstanceList trainingSet)
|
boolean |
CRFTrainerByLabelLikelihood.trainIncremental(InstanceList training)
|
boolean |
CRFTrainerByLabelLikelihood.trainWithFeatureInduction(InstanceList trainingData,
InstanceList validationData,
InstanceList testingData,
TransducerEvaluator eval,
int numIterations,
int numIterationsBetweenFeatureInductions,
int numFeatureInductions,
int numFeaturesPerFeatureInduction,
double trueLabelProbThreshold,
boolean clusteredFeatureInduction,
double[] trainingProportions)
|
boolean |
MEMMTrainer.trainWithFeatureInduction(InstanceList trainingData,
InstanceList validationData,
InstanceList testingData,
TransducerEvaluator eval,
int numIterations,
int numIterationsBetweenFeatureInductions,
int numFeatureInductions,
int numFeaturesPerFeatureInduction,
double trueLabelProbThreshold,
boolean clusteredFeatureInduction,
double[] trainingProportions,
java.lang.String gainName)
Not implemented yet. |
boolean |
CRFTrainerByLabelLikelihood.trainWithFeatureInduction(InstanceList trainingData,
InstanceList validationData,
InstanceList testingData,
TransducerEvaluator eval,
int numIterations,
int numIterationsBetweenFeatureInductions,
int numFeatureInductions,
int numFeaturesPerFeatureInduction,
double trueLabelProbThreshold,
boolean clusteredFeatureInduction,
double[] trainingProportions,
java.lang.String gainName)
Train a CRF using feature induction to generate conjunctions of features. |
| Constructors in cc.mallet.fst with parameters of type InstanceList | |
|---|---|
CRFOptimizableByBatchLabelLikelihood(CRF crf,
InstanceList ilist,
int numBatches)
|
|
CRFOptimizableByLabelLikelihood(CRF crf,
InstanceList ilist)
|
|
CRFTrainerByStochasticGradient(CRF crf,
InstanceList trainingSample)
|
|
CRFTrainerByValueGradients.OptimizableCRF(CRF crf,
InstanceList ilist)
|
|
LabelDistributionEvaluator(InstanceList[] instanceLists,
java.lang.String[] descriptions)
|
|
MEMMTrainer.MEMMOptimizableByLabelLikelihood(MEMM memm,
InstanceList trainingData)
|
|
MultiSegmentationEvaluator(InstanceList[] instanceLists,
java.lang.String[] instanceListDescriptions,
java.lang.Object[] segmentStartTags,
java.lang.Object[] segmentContinueTags)
|
|
MultiSegmentationEvaluator(InstanceList instanceList1,
java.lang.String description1,
InstanceList instanceList2,
java.lang.String description2,
InstanceList instanceList3,
java.lang.String description3,
java.lang.Object[] segmentStartTags,
java.lang.Object[] segmentContinueTags)
|
|
MultiSegmentationEvaluator(InstanceList instanceList1,
java.lang.String description1,
InstanceList instanceList2,
java.lang.String description2,
java.lang.Object[] segmentStartTags,
java.lang.Object[] segmentContinueTags)
|
|
MultiSegmentationEvaluator(InstanceList instanceList1,
java.lang.String description1,
java.lang.Object[] segmentStartTags,
java.lang.Object[] segmentContinueTags)
|
|
PerClassAccuracyEvaluator(InstanceList[] instanceLists,
java.lang.String[] descriptions)
|
|
PerClassAccuracyEvaluator(InstanceList i1,
java.lang.String d1)
|
|
PerClassAccuracyEvaluator(InstanceList i1,
java.lang.String d1,
InstanceList i2,
java.lang.String d2)
|
|
SegmentationEvaluator(InstanceList[] instanceLists,
java.lang.String[] descriptions)
|
|
SegmentationEvaluator(InstanceList instanceList1,
java.lang.String description1)
|
|
SegmentationEvaluator(InstanceList instanceList1,
java.lang.String description1,
InstanceList instanceList2,
java.lang.String description2)
|
|
SegmentationEvaluator(InstanceList instanceList1,
java.lang.String description1,
InstanceList instanceList2,
java.lang.String description2,
InstanceList instanceList3,
java.lang.String description3)
|
|
ThreadedOptimizable(Optimizable.ByCombiningBatchGradient optimizable,
InstanceList trainingSet,
int numFactors,
CacheStaleIndicator cacheIndicator)
Initializes the optimizable and starts new threads. |
|
TokenAccuracyEvaluator(InstanceList[] instanceLists,
java.lang.String[] descriptions)
|
|
TokenAccuracyEvaluator(InstanceList instanceList1,
java.lang.String description1)
|
|
TokenAccuracyEvaluator(InstanceList instanceList1,
java.lang.String description1,
InstanceList instanceList2,
java.lang.String description2)
|
|
TokenAccuracyEvaluator(InstanceList instanceList1,
java.lang.String description1,
InstanceList instanceList2,
java.lang.String description2,
InstanceList instanceList3,
java.lang.String description3)
|
|
TransducerEvaluator(InstanceList[] instanceLists,
java.lang.String[] instanceListDescriptions)
|
|
ViterbiWriter(java.lang.String filenamePrefix,
InstanceList[] instanceLists,
java.lang.String[] descriptions)
|
|
ViterbiWriter(java.lang.String filenamePrefix,
InstanceList instanceList1,
java.lang.String description1)
|
|
ViterbiWriter(java.lang.String filenamePrefix,
InstanceList instanceList1,
java.lang.String description1,
InstanceList instanceList2,
java.lang.String description2)
|
|
ViterbiWriter(java.lang.String filenamePrefix,
InstanceList instanceList1,
java.lang.String description1,
InstanceList instanceList2,
java.lang.String description2,
InstanceList instanceList3,
java.lang.String description3)
|
|
| Uses of InstanceList in cc.mallet.fst.confidence |
|---|
| Methods in cc.mallet.fst.confidence with parameters of type InstanceList | |
|---|---|
java.util.ArrayList |
TransducerCorrector.correctLeastConfidentSegments(InstanceList ilist,
java.lang.Object[] startTags,
java.lang.Object[] continueTags)
|
java.util.ArrayList |
IsolatedSegmentTransducerCorrector.correctLeastConfidentSegments(InstanceList ilist,
java.lang.Object[] startTags,
java.lang.Object[] continueTags)
|
java.util.ArrayList |
ConstrainedViterbiTransducerCorrector.correctLeastConfidentSegments(InstanceList ilist,
java.lang.Object[] startTags,
java.lang.Object[] continueTags)
|
java.util.ArrayList |
ConstrainedViterbiTransducerCorrector.correctLeastConfidentSegments(InstanceList ilist,
java.lang.Object[] startTags,
java.lang.Object[] continueTags,
boolean findIncorrect)
Returns an ArrayList of corrected Sequences. |
void |
ConfidenceCorrectorEvaluator.evaluate(Transducer model,
java.util.ArrayList predictions,
InstanceList ilist,
java.util.ArrayList correctedSegments,
java.lang.String description,
java.io.PrintStream outputStream,
boolean errorsInUncorrected)
Only evaluates over sequences which contain errors. |
java.util.ArrayList |
ConstrainedViterbiTransducerCorrector.getLeastConfidentSegments(InstanceList ilist,
java.lang.Object[] startTags,
java.lang.Object[] continueTags)
Returns the least confident segments in ilist |
InstanceWithConfidence[] |
TransducerSequenceConfidenceEstimator.rankInstancesByConfidence(InstanceList ilist,
java.lang.Object[] startTags,
java.lang.Object[] continueTags)
Ranks all Sequencess in this InstanceList by
confidence estimate. |
PipedInstanceWithConfidence[] |
MaxEntSequenceConfidenceEstimator.rankPipedInstancesByConfidence(InstanceList ilist,
java.lang.Object[] startTags,
java.lang.Object[] continueTags)
|
Segment[] |
TransducerConfidenceEstimator.rankSegmentsByConfidence(InstanceList ilist,
java.lang.Object[] startTags,
java.lang.Object[] continueTags)
Ranks all Segments in this InstanceList by
confidence estimate. |
MaxEnt |
MaxEntSequenceConfidenceEstimator.trainClassifier(InstanceList ilist,
java.lang.String correct,
java.lang.String incorrect)
Train underlying classifier on ilist. |
MaxEnt |
MaxEntConfidenceEstimator.trainClassifier(InstanceList ilist,
java.lang.String correct,
java.lang.String incorrect)
|
| Uses of InstanceList in cc.mallet.fst.semi_supervised |
|---|
| Fields in cc.mallet.fst.semi_supervised declared as InstanceList | |
|---|---|
protected InstanceList |
CRFOptimizableByEntropyRegularization.data
|
| Methods in cc.mallet.fst.semi_supervised with parameters of type InstanceList | |
|---|---|
Optimizable.ByGradientValue |
CRFTrainerByGE.getOptimizable(InstanceList unlabeled)
|
static java.util.HashMap<java.lang.Integer,double[][]> |
FSTConstraintUtil.loadGEConstraints(java.io.Reader fileReader,
InstanceList data)
|
boolean |
CRFTrainerByLikelihoodAndGE.train(InstanceList trainingSet,
InstanceList unlabeledSet,
int numIterations)
|
boolean |
CRFTrainerByEntropyRegularization.train(InstanceList labeled,
InstanceList unlabeled,
int numIterations)
Performs CRF training with label likelihood and entropy regularization. |
boolean |
CRFTrainerByLikelihoodAndGE.train(InstanceList trainingSet,
int numIterations)
|
boolean |
CRFTrainerByGE.train(InstanceList unlabeledSet,
int numIterations)
|
boolean |
CRFTrainerByEntropyRegularization.train(InstanceList trainingSet,
int numIterations)
|
| Constructors in cc.mallet.fst.semi_supervised with parameters of type InstanceList | |
|---|---|
CRFOptimizableByEntropyRegularization(CRF crf,
InstanceList ilist)
Initializes the structures (sets the scaling factor to 1.0). |
|
CRFOptimizableByEntropyRegularization(CRF crf,
InstanceList ilist,
double scalingFactor)
Initializes the structures. |
|
CRFOptimizableByGE(CRF crf,
java.util.ArrayList<GEConstraint> constraints,
InstanceList data,
StateLabelMap map,
int numThreads)
|
|
CRFOptimizableByGE(CRF crf,
java.util.ArrayList<GEConstraint> constraints,
InstanceList data,
StateLabelMap map,
int numThreads,
double weight)
|
|
| Uses of InstanceList in cc.mallet.fst.semi_supervised.constraints |
|---|
| Methods in cc.mallet.fst.semi_supervised.constraints with parameters of type InstanceList | |
|---|---|
java.util.BitSet |
TwoLabelGEConstraints.preProcess(InstanceList data)
|
java.util.BitSet |
SelfTransitionGEConstraint.preProcess(InstanceList data)
|
java.util.BitSet |
OneLabelL2RangeGEConstraints.preProcess(InstanceList data)
|
java.util.BitSet |
OneLabelGEConstraints.preProcess(InstanceList data)
|
java.util.BitSet |
GEConstraint.preProcess(InstanceList data)
|
| Uses of InstanceList in cc.mallet.fst.semi_supervised.pr |
|---|
| Fields in cc.mallet.fst.semi_supervised.pr declared as InstanceList | |
|---|---|
protected InstanceList |
CRFOptimizableByKL.trainingSet
|
protected InstanceList |
ConstraintsOptimizableByPR.trainingSet
|
| Methods in cc.mallet.fst.semi_supervised.pr with parameters of type InstanceList | |
|---|---|
boolean |
CRFTrainerByPR.train(InstanceList train,
int numIterations)
|
boolean |
CRFTrainerByPR.train(InstanceList train,
int minIter,
int maxIter)
|
boolean |
CRFTrainerByPR.train(InstanceList train,
int minIter,
int maxIter,
int maxIterPerStep)
|
| Constructors in cc.mallet.fst.semi_supervised.pr with parameters of type InstanceList | |
|---|---|
ConstraintsOptimizableByPR(CRF crf,
InstanceList ilist,
PRAuxiliaryModel model)
|
|
ConstraintsOptimizableByPR(CRF crf,
InstanceList ilist,
PRAuxiliaryModel model,
int numThreads)
|
|
CRFOptimizableByKL(CRF crf,
InstanceList trainingSet,
PRAuxiliaryModel auxModel,
double[][][][] cachedDots,
int numThreads,
double weight)
|
|
| Uses of InstanceList in cc.mallet.fst.semi_supervised.pr.constraints |
|---|
| Methods in cc.mallet.fst.semi_supervised.pr.constraints with parameters of type InstanceList | |
|---|---|
java.util.BitSet |
PRConstraint.preProcess(InstanceList data)
|
java.util.BitSet |
OneLabelL2PRConstraints.preProcess(InstanceList data)
|
java.util.BitSet |
OneLabelL2IndPRConstraints.preProcess(InstanceList data)
|
| Uses of InstanceList in cc.mallet.fst.semi_supervised.tui |
|---|
| Methods in cc.mallet.fst.semi_supervised.tui with parameters of type InstanceList | |
|---|---|
static CRF |
SimpleTaggerWithConstraints.getCRF(InstanceList training,
int[] orders,
java.lang.String defaultLabel,
java.lang.String forbidden,
java.lang.String allowed,
boolean connected)
|
static void |
SimpleTaggerWithConstraints.test(TransducerTrainer tt,
TransducerEvaluator eval,
InstanceList testing)
Test a transducer on the given test data, evaluating accuracy with the given evaluator |
static CRF |
SimpleTaggerWithConstraints.trainGE(InstanceList training,
InstanceList testing,
java.util.ArrayList<GEConstraint> constraints,
CRF crf,
TransducerEvaluator eval,
int iterations,
double var,
int resets)
Create and train a CRF model from the given training data, optionally testing it on the given test data. |
static CRF |
SimpleTaggerWithConstraints.trainPR(InstanceList training,
InstanceList testing,
java.util.ArrayList<PRConstraint> constraints,
CRF crf,
TransducerEvaluator eval,
int iterations,
double var)
Create and train a CRF model from the given training data, optionally testing it on the given test data. |
| Uses of InstanceList in cc.mallet.grmm.learning |
|---|
| Methods in cc.mallet.grmm.learning with parameters of type InstanceList | |
|---|---|
int |
ACRF.Template.addSomeUnsupportedWeights(InstanceList training)
|
java.util.List |
ACRF.bestAssignment(InstanceList lst)
|
protected boolean |
DefaultAcrfTrainer.callEvaluator(ACRF acrf,
InstanceList trainingList,
InstanceList validationList,
InstanceList testSet,
int iter,
ACRFEvaluator eval)
|
void |
PwplACRFTrainer.Maxable.collectConstraints(InstanceList ilist)
|
void |
PseudolikelihoodACRFTrainer.Maxable.collectConstraints(InstanceList ilist)
|
void |
PiecewiseACRFTrainer.Maxable.collectConstraints(InstanceList ilist)
|
void |
ACRF.MaximizableACRF.collectConstraints(InstanceList ilist)
|
static DefaultAcrfTrainer.TestResults |
DefaultAcrfTrainer.LogEvaluator.computeTestResults(InstanceList testList,
java.util.List returnedList)
|
Optimizable.ByGradientValue |
PwplACRFTrainer.createOptimizable(ACRF acrf,
InstanceList training)
|
Optimizable.ByGradientValue |
PseudolikelihoodACRFTrainer.createOptimizable(ACRF acrf,
InstanceList training)
|
Optimizable.ByGradientValue |
PiecewiseACRFTrainer.createOptimizable(ACRF acrf,
InstanceList training)
|
protected Optimizable.ByGradientValue |
DefaultAcrfTrainer.createOptimizable(ACRF acrf,
InstanceList trainingList)
|
void |
ACRF.dumpUnrolledGraphs(InstanceList lst)
|
boolean |
MultiSegmentationEvaluatorACRF.evaluate(ACRF acrf,
int iter,
InstanceList training,
InstanceList validation,
InstanceList testing)
|
boolean |
DefaultAcrfTrainer.LogEvaluator.evaluate(ACRF acrf,
int iter,
InstanceList training,
InstanceList validation,
InstanceList testing)
|
boolean |
DefaultAcrfTrainer.FileEvaluator.evaluate(ACRF acrf,
int iter,
InstanceList training,
InstanceList validation,
InstanceList testing)
|
boolean |
AcrfSerialEvaluator.evaluate(ACRF acrf,
int iter,
InstanceList training,
InstanceList validation,
InstanceList testing)
|
abstract boolean |
ACRFEvaluator.evaluate(ACRF acrf,
int iter,
InstanceList training,
InstanceList validation,
InstanceList testing)
Evalutes the model in the middle of training. |
java.util.List |
ACRF.getBestLabels(InstanceList lst)
|
Optimizable.ByGradientValue |
ACRF.getMaximizable(InstanceList ilst)
|
boolean |
DefaultAcrfTrainer.incrementalTrain(ACRF acrf,
InstanceList training,
InstanceList validation,
InstanceList testing,
ACRFEvaluator eval,
int numIter)
|
boolean |
DefaultAcrfTrainer.incrementalTrain(ACRF acrf,
InstanceList training,
InstanceList validation,
InstanceList testing,
int numIter)
|
int |
ACRF.Template.initWeights(InstanceList training)
Initializes the weight vectors to the appropriate size for a set of training data. |
int |
ACRF.FixedFactorTemplate.initWeights(InstanceList training)
|
static void |
PwplACRFTrainer.reportTrainingLikelihood(ACRF acrf,
InstanceList trainingList)
|
boolean |
DefaultAcrfTrainer.someUnsupportedTrain(ACRF acrf,
InstanceList trainingList,
InstanceList validationList,
InstanceList testSet,
ACRFEvaluator eval,
int numIter)
|
void |
DefaultAcrfTrainer.test(ACRF acrf,
InstanceList testing,
ACRFEvaluator eval)
|
void |
DefaultAcrfTrainer.test(ACRF acrf,
InstanceList testing,
ACRFEvaluator[] evals)
|
void |
ACRFEvaluator.test(ACRF acrf,
InstanceList data,
java.lang.String description)
|
void |
MultiSegmentationEvaluatorACRF.test(InstanceList gold,
java.util.List returned,
java.lang.String description)
|
void |
DefaultAcrfTrainer.LogEvaluator.test(InstanceList testList,
java.util.List returnedList,
java.lang.String description)
|
void |
DefaultAcrfTrainer.FileEvaluator.test(InstanceList testList,
java.util.List returnedList,
java.lang.String description)
|
void |
AcrfSerialEvaluator.test(InstanceList gold,
java.util.List returned,
java.lang.String description)
|
abstract void |
ACRFEvaluator.test(InstanceList gold,
java.util.List returned,
java.lang.String description)
|
boolean |
DefaultAcrfTrainer.train(ACRF acrf,
InstanceList training)
|
boolean |
ACRFTrainer.train(ACRF acrf,
InstanceList training)
|
boolean |
DefaultAcrfTrainer.train(ACRF acrf,
InstanceList training,
ACRFEvaluator eval,
int numIter)
|
boolean |
ACRFTrainer.train(ACRF acrf,
InstanceList training,
ACRFEvaluator eval,
int numIter)
|
void |
DefaultAcrfTrainer.train(ACRF acrf,
InstanceList training,
InstanceList validation,
InstanceList testing,
ACRFEvaluator eval,
double[] proportions,
int iterPerProportion)
|
boolean |
DefaultAcrfTrainer.train(ACRF acrf,
InstanceList trainingList,
InstanceList validationList,
InstanceList testSet,
ACRFEvaluator eval,
int numIter)
|
boolean |
ACRFTrainer.train(ACRF acrf,
InstanceList training,
InstanceList validation,
InstanceList testing,
ACRFEvaluator eval,
int numIter)
|
boolean |
PwplACRFTrainer.train(ACRF acrf,
InstanceList trainingList,
InstanceList validationList,
InstanceList testSet,
ACRFEvaluator eval,
int numIter,
Optimizable.ByGradientValue macrf)
|
boolean |
DefaultAcrfTrainer.train(ACRF acrf,
InstanceList trainingList,
InstanceList validationList,
InstanceList testSet,
ACRFEvaluator eval,
int numIter,
Optimizable.ByGradientValue macrf)
|
boolean |
ACRFTrainer.train(ACRF acrf,
InstanceList training,
InstanceList validation,
InstanceList testing,
ACRFEvaluator eval,
int numIter,
Optimizable.ByGradientValue macrf)
|
boolean |
DefaultAcrfTrainer.train(ACRF acrf,
InstanceList training,
InstanceList validation,
InstanceList testing,
int numIter)
|
boolean |
ACRFTrainer.train(ACRF acrf,
InstanceList training,
InstanceList validation,
InstanceList testing,
int numIter)
|
boolean |
DefaultAcrfTrainer.train(ACRF acrf,
InstanceList training,
int numIter)
|
boolean |
ACRFTrainer.train(ACRF acrf,
InstanceList training,
int numIter)
|
| Constructors in cc.mallet.grmm.learning with parameters of type InstanceList | |
|---|---|
ACRF.MaximizableACRF(InstanceList ilist)
|
|
PiecewiseACRFTrainer.Maxable(ACRF acrf,
InstanceList ilist)
|
|
PseudolikelihoodACRFTrainer.Maxable(ACRF acrf,
InstanceList ilist)
|
|
PwplACRFTrainer.Maxable(ACRF acrf,
InstanceList ilist)
|
|
| Uses of InstanceList in cc.mallet.grmm.learning.extract |
|---|
| Fields in cc.mallet.grmm.learning.extract declared as InstanceList | |
|---|---|
protected InstanceList |
ACRFExtractorTrainer.testing
|
protected InstanceList |
ACRFExtractorTrainer.training
|
| Methods in cc.mallet.grmm.learning.extract that return InstanceList | |
|---|---|
InstanceList |
ACRFExtractorTrainer.getTestingData()
|
InstanceList |
ACRFExtractorTrainer.getTrainingData()
|
| Methods in cc.mallet.grmm.learning.extract with parameters of type InstanceList | |
|---|---|
Extraction |
ACRFExtractor.extract(InstanceList testing)
|
ACRFExtractorTrainer |
ACRFExtractorTrainer.setData(InstanceList training,
InstanceList testing)
|
| Uses of InstanceList in cc.mallet.pipe |
|---|
| Methods in cc.mallet.pipe that return InstanceList | |
|---|---|
static InstanceList |
AddClassifierTokenPredictions.convert(InstanceList ilist,
Noop alphabetsPipe)
Converts each instance containing a FeatureVectorSequence to multiple instances, each containing an AugmentableFeatureVector as data. |
static InstanceList |
AddClassifierTokenPredictions.convert(Instance inst,
Noop alphabetsPipe)
|
| Methods in cc.mallet.pipe with parameters of type InstanceList | |
|---|---|
static InstanceList |
AddClassifierTokenPredictions.convert(InstanceList ilist,
Noop alphabetsPipe)
Converts each instance containing a FeatureVectorSequence to multiple instances, each containing an AugmentableFeatureVector as data. |
| Constructors in cc.mallet.pipe with parameters of type InstanceList | |
|---|---|
AddClassifierTokenPredictions.TokenClassifiers(ClassifierTrainer trainer,
InstanceList trainList,
int randSeed,
int numCV)
|
|
AddClassifierTokenPredictions.TokenClassifiers(InstanceList trainList)
Train a token classifier using the given Instances with 5-fold cross validation |
|
AddClassifierTokenPredictions.TokenClassifiers(InstanceList trainList,
int randSeed,
int numCV)
|
|
AddClassifierTokenPredictions(AddClassifierTokenPredictions.TokenClassifiers tokenClassifiers,
int[] predRanks2add,
boolean binary,
InstanceList testList)
|
|
AddClassifierTokenPredictions(InstanceList trainList)
|
|
AddClassifierTokenPredictions(InstanceList trainList,
InstanceList testList)
|
|
| Uses of InstanceList in cc.mallet.pipe.iterator |
|---|
| Constructors in cc.mallet.pipe.iterator with parameters of type InstanceList | |
|---|---|
SegmentIterator(InstanceList ilist,
java.lang.Object[] startTags,
java.lang.Object[] inTags,
java.util.ArrayList predictions)
Useful when no Transduce is specified. |
|
SegmentIterator(Transducer model,
InstanceList ilist,
java.lang.Object[] segmentStartTags,
java.lang.Object[] segmentContinueTags)
NOTE!: Assumes that segmentStartTags[i] corresponds
to segmentContinueTags[i]. |
|
| Uses of InstanceList in cc.mallet.topics |
|---|
| Fields in cc.mallet.topics declared as InstanceList | |
|---|---|
protected InstanceList |
LDAHyper.testing
Deprecated. |
| Methods in cc.mallet.topics that return InstanceList | |
|---|---|
InstanceList |
LDA.getInstanceList()
Deprecated. |
| Methods in cc.mallet.topics with parameters of type InstanceList | |
|---|---|
void |
LDA.addDocuments(InstanceList additionalDocuments,
int numIterations,
int showTopicsInterval,
int outputModelInterval,
java.lang.String outputModelFilename,
Randoms r)
Deprecated. |
void |
SimpleLDA.addInstances(InstanceList training)
|
void |
ParallelTopicModel.addInstances(InstanceList training)
|
void |
LDAHyper.addInstances(InstanceList training)
Deprecated. |
void |
PolylingualTopicModel.addInstances(InstanceList[] training)
|
void |
NPTopicModel.addInstances(InstanceList training,
int initialTopics)
|
void |
LDAHyper.addInstances(InstanceList training,
java.util.List<LabelSequence> topics)
Deprecated. |
double |
LDAHyper.empiricalLikelihood(int numSamples,
InstanceList testing)
Deprecated. |
double |
HierarchicalLDA.empiricalLikelihood(int numSamples,
InstanceList testing)
For use with empirical likelihood evaluation: sample a path through the tree, then sample a multinomial over topics in that path, then return a weighted sum of words. |
void |
HierarchicalPAM.estimate(InstanceList documents,
InstanceList testing,
int numIterations,
int showTopicsInterval,
int outputModelInterval,
int optimizeInterval,
java.lang.String outputModelFilename,
Randoms r)
|
void |
PAM4L.estimate(InstanceList documents,
int numIterations,
int optimizeInterval,
int showTopicsInterval,
int outputModelInterval,
java.lang.String outputModelFilename,
Randoms r)
|
void |
TopicalNGrams.estimate(InstanceList documents,
int numIterations,
int showTopicsInterval,
int outputModelInterval,
java.lang.String outputModelFilename,
Randoms r)
|
void |
LDA.estimate(InstanceList documents,
int numIterations,
int showTopicsInterval,
int outputModelInterval,
java.lang.String outputModelFilename,
Randoms r)
Deprecated. |
double |
MarginalProbEstimator.evaluateLeftToRight(InstanceList testing,
int numParticles,
boolean usingResampling,
java.io.PrintStream docProbabilityStream)
|
void |
LDAStream.inferenceWithTheta(int maxIteration,
InstanceList theta)
|
void |
HierarchicalLDA.initialize(InstanceList instances,
InstanceList testing,
int numLevels,
Randoms random)
|
void |
LDAHyper.setTestingInstances(InstanceList testing)
Deprecated. Held-out instances for empirical likelihood calculation |
void |
TopicInferencer.writeInferredDistributions(InstanceList instances,
java.io.File distributionsFile,
int numIterations,
int thinning,
int burnIn,
double threshold,
int max)
Infer topics for the provided instances and write distributions to the provided file. |
| Constructors in cc.mallet.topics with parameters of type InstanceList | |
|---|---|
DMROptimizable(InstanceList instances,
MaxEnt initialClassifier)
|
|
| Uses of InstanceList in cc.mallet.types |
|---|
| Subclasses of InstanceList in cc.mallet.types | |
|---|---|
class |
MultiInstanceList
An implementation of InstanceList that logically combines multiple instance lists so that they appear as one list without copying the original lists. |
class |
PagedInstanceList
An InstanceList which avoids OutOfMemoryErrors by saving Instances to disk when there is not enough memory to create a new Instance. |
| Methods in cc.mallet.types that return InstanceList | |
|---|---|
InstanceList |
PagedInstanceList.cloneEmpty()
|
InstanceList |
MultiInstanceList.cloneEmpty()
|
InstanceList |
InstanceList.cloneEmpty()
|
protected InstanceList |
MultiInstanceList.cloneEmptyInto(InstanceList ret)
|
protected InstanceList |
InstanceList.cloneEmptyInto(InstanceList ret)
|
InstanceList |
InvertedIndex.getInstanceList()
|
static InstanceList |
PagedInstanceList.load(java.io.File file)
Constructs a new InstanceList, deserialized from
file. |
static InstanceList |
InstanceList.load(java.io.File file)
Constructs a new InstanceList, deserialized from file. |
InstanceList[] |
InstanceList.CrossValidationIterator.next()
|
InstanceList[] |
CrossValidationIterator.next()
Returns the next training/testing split. |
InstanceList[] |
InstanceList.CrossValidationIterator.nextSplit()
Returns the next training/testing split. |
InstanceList[] |
CrossValidationIterator.nextSplit()
Returns the next training/testing split. |
InstanceList[] |
InstanceList.CrossValidationIterator.nextSplit(int numTrainFolds)
Returns the next split, given the number of folds you want in the training data. |
InstanceList[] |
CrossValidationIterator.nextSplit(int numTrainFolds)
Returns the next training/testing split. |
InstanceList |
InstanceList.sampleWithInstanceWeights(java.util.Random r)
Deprecated. |
InstanceList |
InstanceList.sampleWithReplacement(java.util.Random r,
int numSamples)
|
InstanceList |
InstanceList.sampleWithWeights(java.util.Random r,
double[] weights)
Returns an InstanceList of the same size, where the instances come from the
random sampling (with replacement) of this list using the given weights. |
InstanceList |
PagedInstanceList.shallowClone()
|
InstanceList |
MultiInstanceList.shallowClone()
|
InstanceList |
InstanceList.shallowClone()
|
InstanceList[] |
MultiInstanceList.split(double[] proportions)
|
InstanceList[] |
InstanceList.split(double[] proportions)
|
InstanceList[] |
PagedInstanceList.split(java.util.Random r,
double[] proportions)
Shuffles the elements of this list among several smaller lists. |
InstanceList[] |
MultiInstanceList.split(java.util.Random r,
double[] proportions)
|
InstanceList[] |
InstanceList.split(java.util.Random r,
double[] proportions)
Shuffles the elements of this list among several smaller lists. |
InstanceList[] |
MultiInstanceList.splitInOrder(double[] proportions)
|
InstanceList[] |
InstanceList.splitInOrder(double[] proportions)
Chops this list into several sequential sublists. |
InstanceList[] |
MultiInstanceList.splitInOrder(int[] counts)
|
InstanceList[] |
InstanceList.splitInOrder(int[] counts)
|
InstanceList[] |
MultiInstanceList.splitInTwoByModulo(int m)
|
InstanceList[] |
InstanceList.splitInTwoByModulo(int m)
Returns a pair of new lists such that the first list in the pair contains every mth element of this list, starting with the first. |
InstanceList |
MultiInstanceList.subList(double proportion)
|
InstanceList |
InstanceList.subList(double proportion)
|
InstanceList |
MultiInstanceList.subList(int start,
int end)
|
InstanceList |
InstanceList.subList(int start,
int end)
|
| Methods in cc.mallet.types with parameters of type InstanceList | |
|---|---|
protected static java.lang.Object[] |
GainRatio.calcGainRatios(InstanceList ilist,
int[] instIndices,
int minNumInsts)
Calculates gain ratios for all (feature, split point) pairs snd returns array of: |
static double[][] |
PerLabelInfoGain.calcPerLabelInfoGains(InstanceList ilist)
|
protected InstanceList |
MultiInstanceList.cloneEmptyInto(InstanceList ret)
|
protected InstanceList |
InstanceList.cloneEmptyInto(InstanceList ret)
|
static GainRatio |
GainRatio.createGainRatio(InstanceList ilist)
Constructs a GainRatio object. |
static GainRatio |
GainRatio.createGainRatio(InstanceList ilist,
int[] instIndices,
int minNumInsts)
Constructs a GainRatio object |
void |
FeatureInducer.induceFeaturesFor(InstanceList ilist,
boolean withFeatureShrinkage,
boolean addPerClassFeatures)
|
PartiallyRankedFeatureVector |
PartiallyRankedFeatureVector.Factory.newPartiallyRankedFeatureVector(InstanceList ilist,
LabelVector[] posteriors)
|
PartiallyRankedFeatureVector[] |
PartiallyRankedFeatureVector.PerLabelFactory.newPartiallyRankedFeatureVectors(InstanceList ilist,
LabelVector[] posteriors)
|
RankedFeatureVector |
RankedFeatureVector.Factory.newRankedFeatureVector(InstanceList ilist)
|
RankedFeatureVector |
InfoGain.Factory.newRankedFeatureVector(InstanceList ilist)
|
RankedFeatureVector |
GradientGain.Factory.newRankedFeatureVector(InstanceList ilist)
|
RankedFeatureVector |
FeatureCounts.Factory.newRankedFeatureVector(InstanceList ilist)
|
RankedFeatureVector |
ExpGain.Factory.newRankedFeatureVector(InstanceList ilist)
|
RankedFeatureVector[] |
RankedFeatureVector.PerLabelFactory.newRankedFeatureVectors(InstanceList ilist)
|
RankedFeatureVector[] |
PerLabelInfoGain.Factory.newRankedFeatureVectors(InstanceList ilist)
|
RankedFeatureVector[] |
PerLabelFeatureCounts.Factory.newRankedFeatureVectors(InstanceList ilist)
|
void |
FeatureSelector.selectFeaturesFor(InstanceList ilist)
|
void |
FeatureSelector.selectFeaturesForAllLabels(InstanceList ilist)
|
void |
FeatureSelector.selectFeaturesForPerLabel(InstanceList ilist)
|
static int[] |
GainRatio.sortInstances(InstanceList ilist,
int[] instIndices,
int featureIndex)
|
| Constructors in cc.mallet.types with parameters of type InstanceList | |
|---|---|
CrossValidationIterator(InstanceList ilist,
int _nfolds)
Constructs a new n-fold cross-validation iterator |
|
CrossValidationIterator(InstanceList ilist,
int nfolds,
java.util.Random r)
Constructs a new n-fold cross-validation iterator |
|
ExpGain(InstanceList ilist,
Classification[] classifications,
double gaussianPriorVariance)
|
|
ExpGain(InstanceList ilist,
LabelVector[] classifications,
double gaussianPriorVariance)
|
|
FeatureCounts(InstanceList ilist)
|
|
FeatureInducer(RankedFeatureVector.Factory ranker,
InstanceList ilist,
int numNewFeatures)
|
|
FeatureInducer(RankedFeatureVector.Factory ranker,
InstanceList ilist,
int numNewFeatures,
int beam1,
int beam2)
|
|
GradientGain(InstanceList ilist,
Classification[] classifications)
|
|
GradientGain(InstanceList ilist,
LabelVector[] classifications)
|
|
InfoGain(InstanceList ilist)
|
|
InvertedIndex(InstanceList ilist)
|
|
KLGain(InstanceList ilist,
Classification[] classifications)
|
|
KLGain(InstanceList ilist,
LabelVector[] classifications)
|
|
MultiInstanceList(InstanceList[] lists)
Constructs a MultiInstanceList with an array of InstanceList |
|
PerLabelFeatureCounts(InstanceList ilist)
|
|
PerLabelInfoGain(InstanceList ilist)
|
|
| Constructor parameters in cc.mallet.types with type arguments of type InstanceList | |
|---|---|
MultiInstanceList(java.util.List<InstanceList> lists)
Constructs a MultiInstanceList with a List of InstanceList |
|
| Uses of InstanceList in cc.mallet.util |
|---|
| Methods in cc.mallet.util with parameters of type InstanceList | |
|---|---|
static SparseVector |
VectorStats.mean(InstanceList instances)
Returns a SparseVector whose entries (taken from the union of
those in the instances) are the expected values of those in the
InstanceList. |
static SparseVector |
VectorStats.mean(InstanceList instances,
int numIndices)
Returns a SparseVector whose entries (dense with the given
number of indices) are the expected values of those in the
InstanceList. |
static SparseVector |
VectorStats.mean(InstanceList instances,
int[] indices)
Returns a SparseVector whose entries (the given indices) are
the expected values of those in the InstanceList. |
static SparseVector |
VectorStats.stddev(InstanceList instances)
Square root of unbiased variance. |
static SparseVector |
VectorStats.stddev(InstanceList instances,
boolean unbiased)
Square root of variance. |
static SparseVector |
VectorStats.stddev(InstanceList instances,
SparseVector mean)
Square root of unbiased variance of instances having the given mean |
static SparseVector |
VectorStats.stddev(InstanceList instances,
SparseVector mean,
boolean unbiased)
Square root of variance. |
static SparseVector |
VectorStats.variance(InstanceList instances)
Returns unbiased variance |
static SparseVector |
VectorStats.variance(InstanceList instances,
boolean unbiased)
Returns a SparseVector whose entries (taken from the union of
those in the instances) are the variance of those in the
InstanceList. |
static SparseVector |
VectorStats.variance(InstanceList instances,
SparseVector mean)
Returns unbiased variance of instances having the given mean. |
static SparseVector |
VectorStats.variance(InstanceList instances,
SparseVector mean,
boolean unbiased)
Returns a SparseVector whose entries (taken from the mean
argument) are the variance of those in the InstanceList. |
| Constructors in cc.mallet.util with parameters of type InstanceList | |
|---|---|
FeatureCounter(InstanceList instances)
|
|
|
||||||||||
| PREV NEXT | FRAMES NO FRAMES | |||||||||