Package cc.mallet.types

Fundamental MALLET types, including FeatureVector, Instance, Label etc.

See:
          Description

Interface Summary
AlphabetCarrying An interface for objects that contain one or more Alphabets.
CachedMetric Stores a hash for each object being compared for efficient computation.
ConstantMatrix  
Labeler  
Labeling A distribution over possible labels for an instance.
Matrix  
Metric  
PartiallyRankedFeatureVector.Factory  
PartiallyRankedFeatureVector.PerLabelFactory  
PropertyHolder Author: saunders Created Nov 15, 2005 Copyright (C) Univ.
RankedFeatureVector.Factory  
RankedFeatureVector.PerLabelFactory  
Sequence<E>  
Vector Deprecated.
 

Class Summary
Alphabet A mapping between integers and objects where the mapping in each direction is efficient.
AlphabetFactory  
ArrayListSequence<E>  
ArraySequence<E>  
AugmentableFeatureVector  
ChainedInstanceIterator Deprecated.
CrossValidationIterator An iterator which splits an InstanceList into n-folds and iterates over the folds for use in n-fold cross-validation.
DenseMatrix  
DenseVector  
Dirichlet Various useful functions related to Dirichlet distributions.
Dirichlet.Estimator  
Dirichlet.MethodOfMomentsEstimator  
ExpGain  
ExpGain.Factory  
FeatureConjunction  
FeatureConjunction.List  
FeatureCounter Efficient, compact, incremental counting of features in an alphabet.
FeatureCounts  
FeatureCounts.Factory  
FeatureInducer  
FeatureSelection  
FeatureSelector  
FeatureSequence An implementation of Sequence that ensures that every Object in the sequence has the same class.
FeatureSequenceWithBigrams A FeatureSequence with a parallel record of bigrams, kept in a separate dictionary
FeatureVector A subset of an Alphabet in which each element of the subset has an associated value.
FeatureVectorSequence  
GainRatio List of features along with their thresholds sorted in descending order of the ratio of (1) information gained by splitting instances on the feature at its associated threshold value, to (2) the split information.
GradientGain  
GradientGain.Factory  
HashedSparseVector  
IDSorter This class is contains a comparator for use in sorting integers that have associated floating point values.
IndexedSparseVector  
InfoGain  
InfoGain.Factory  
Instance A machine learning "example" to be used in training, testing or performance of various machine learning algorithms.
InstanceList A list of machine learning instances, typically used for training or testing of a machine learning algorithm.
InstanceListTUI  
InvertedIndex  
KLGain  
Label  
LabelAlphabet A mapping from arbitrary objects (usually String's) to integers (and corresponding Label objects) and back.
Labelings A collection of labelings, either for a multi-label problem (all labels are part of the same label dictionary), or a factorized labeling, (each label is part of a different dictionary).
Labels Usually some distribution over possible labels for an instance.
LabelSequence  
LabelsSequence A simple Sequence implementation where all of the elements must be Labels.
LabelVector  
LogNumber  
Matrix2 Deprecated.
Matrixn Implementation of Matrix that allows arbitrary number of dimensions.
MatrixOps A class of static utility functions for manipulating arrays of double.
Minkowski  
MultiInstanceList An implementation of InstanceList that logically combines multiple instance lists so that they appear as one list without copying the original lists.
Multinomial A probability distribution over a set of features represented as a FeatureVector.
Multinomial.Estimator A hierarchy of classes used to produce estimates of probabilities, in the form of a Multinomial, from counts associated with the elements of an Alphabet.
Multinomial.LaplaceEstimator An MEstimator with m set to 1.
Multinomial.Logged A Multinomial in which the values associated with each feature index fi is Math.log(probability[fi]) instead of probability[fi].
Multinomial.MAPEstimator Unimplemented, but the MEstimators are.
Multinomial.MEstimator An Estimator in which probability estimates in a Multinomial are generated by adding a constant m (specified at construction time) to each count before dividing by the total of the m-biased counts.
Multinomial.MLEstimator An MEstimator with m set to 0.
NormalizedDotProductMetric Computes 1 - [ / sqrt (*)] aka 1 - cosine similarity
NullLabel Object that carries a LabelAlphabet.
PagedInstanceList An InstanceList which avoids OutOfMemoryErrors by saving Instances to disk when there is not enough memory to create a new Instance.
PartiallyRankedFeatureVector  
PerLabelFeatureCounts  
PerLabelFeatureCounts.Factory  
PerLabelInfoGain  
PerLabelInfoGain.Factory  
RankedFeatureVector  
ROCData Tracks ROC data for instances in Trial results.
SequencePair<I,O>  
SequencePairAlignment<I,O>  
SingleInstanceIterator  
SparseMatrixn Implementation of Matrix that allows arbitrary number of dimensions.
SparseVector A vector that allocates memory only for non-zero values.
StringEditFeatureVectorSequence  
StringEditVector  
StringKernel Computes a similarity metric between two strings, based on counts of common subsequences of characters.
Token A representation of a piece of text, usually a single word, to which we can attach properties.
TokenSequence A representation of a piece of text, usually a single word, to which we can attach properties.
 

Package cc.mallet.types Description

Fundamental MALLET types, including FeatureVector, Instance, Label etc.