| Alphabet |
A mapping between integers and objects where the mapping in each
direction is efficient.
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| AlphabetFactory |
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| ArrayListSequence<E> |
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| ArraySequence<E> |
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| AugmentableFeatureVector |
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| BiNormalSeparation |
Bi-Normal Separation is a feature weighting algorithm introduced in:
An Extensive Empirical Study of Feature Selection Metrics for Text Classification,
George Forman, Journal of Machine Learning Research, 3:1289--1305, 2003.
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| BiNormalSeparation.Factory |
Factory class.
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| 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 |
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| DenseVector |
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| Dirichlet |
Various useful functions related to Dirichlet distributions.
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| Dirichlet.Estimator |
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| Dirichlet.MethodOfMomentsEstimator |
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| EuclideanDistance |
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| ExpGain |
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| ExpGain.Factory |
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| FeatureConjunction |
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| FeatureConjunction.List |
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| FeatureCounter |
Efficient, compact, incremental counting of features in an alphabet.
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| FeatureCounts |
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| FeatureCounts.Factory |
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| FeatureInducer |
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| FeatureSelection |
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| FeatureSelector |
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| 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
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| FeatureVector |
A subset of an Alphabet in which each element of the subset has an associated value. |
| FeatureVectorSequence |
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| 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.
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| GradientGain |
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| GradientGain.Factory |
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| HashedSparseVector |
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| IDSorter |
This class is contains a comparator for use in sorting
integers that have associated floating point values.
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| IndexedSparseVector |
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| InfiniteDistance |
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| InfoGain |
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| InfoGain.Factory |
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| Instance |
A machine learning "example" to be used in training, testing or
performance of various machine learning algorithms.
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| InstanceList |
A list of machine learning instances, typically used for training
or testing of a machine learning algorithm.
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| InstanceListTUI |
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| InvertedIndex |
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| KLGain |
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| Label |
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| LabelAlphabet |
A mapping from arbitrary objects (usually String's) to integers
(and corresponding Label objects) and back.
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| 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).
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| Labels |
Usually some distribution over possible labels for an instance.
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| LabelSequence |
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| LabelsSequence |
A simple Sequence implementation where all of the
elements must be Labels. |
| LabelVector |
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| LogNumber |
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| ManhattenDistance |
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| Matrixn |
Implementation of Matrix that allows arbitrary
number of dimensions.
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| MatrixOps |
A class of static utility functions for manipulating arrays of
double.
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| Minkowski |
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| MultiInstanceList |
An implementation of InstanceList that logically combines multiple instance
lists so that they appear as one list without copying the original lists.
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| 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.
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| 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.
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| PartiallyRankedFeatureVector |
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| PerLabelFeatureCounts |
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| PerLabelFeatureCounts.Factory |
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| PerLabelInfoGain |
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| PerLabelInfoGain.Factory |
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| RankedFeatureVector |
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| ROCData |
Tracks ROC data for instances in Trial results. |
| SequencePair<I,O> |
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| SequencePairAlignment<I,O> |
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| SingleInstanceIterator |
|
| SparseMatrixn |
Implementation of Matrix that allows arbitrary
number of dimensions.
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| SparseVector |
A vector that allocates memory only for non-zero values.
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| StringEditFeatureVectorSequence |
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| StringEditVector |
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| 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.
|