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## 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 |
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
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.

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