ml

Type Members

1. class BinaryTrainingData extends TrainingData

   

   A subinstance of TrainingData whose labels are -1 or 1.

2. case class BrownClusters(clusters: Iterable[(Symbol, Seq[Int])]) extends Product with Serializable

   

3. case class CandidatePool(scoredCandidates: Map[FeatureVector, Double]) extends Product with Serializable

   

   A CandidatePool is an abstraction for a "pool" of comparable objects.

   A CandidatePool is an abstraction for a "pool" of comparable objects.

   Each object is represented as a feature vector, and is associated with a cost. Lower-cost feature vectors are "better." Usually we want to use a corpus of these "pools" to learn to distinguish "good" feature vectors from "bad" feature vectors.

   scoredCandidates

   a mapping of feature vectors to costs (lower is better)

4. case class CandidatePoolCorpus(pools: Iterable[CandidatePool]) extends Product with Serializable

   

   A CandidatePoolCorpus is a set of candidate pools (see CandidatePool, above).

   A CandidatePoolCorpus is a set of candidate pools (see CandidatePool, above).

   pools

   the pools in the corpus

5. case class FeatureEncoding(featureNames: IndexedSeq[FeatureName]) extends Product with Serializable

   

   Maps feature names to integers.

   Maps feature names to integers. Useful for serializing TrainingData instances for consumption by command-line machine learning tools.

   featureNames

   an indexed sequence of feature names

6. case class FeatureName(symbols: List[Symbol]) extends Product with Serializable

   

   The name of a feature, represented as a list of Symbols.

   The name of a feature, represented as a list of Symbols.

   symbols

   the list of symbols comprising the feature name

7. case class FeatureVector(values: Seq[(FeatureName, Double)]) extends Product with Serializable

   

   A mapping from feature names to values.

   A mapping from feature names to values.

   Unspecified feature names are assumed to correspond to a value of zero.

   values

   the map from feature names to values

8. case class LinearModel(coefficients: Seq[(FeatureName, Double)]) extends Product with Serializable

   

   A weighted linear combination of features.

   A weighted linear combination of features.

   coefficients

   map from feature names to weight coefficients

9. case class TrainingData(labeledVectors: Iterable[(FeatureVector, Double)]) extends Product with Serializable

   

   Abstraction for a set of labeled feature vectors.

   Abstraction for a set of labeled feature vectors.

   Provides various serialization options for different machine learning tools.

   labeledVectors

   a sequence of feature vectors labeled with doubles

10. case class TrainingVectorPool(trainingVectors: Iterable[(FeatureVector, Double)]) extends Product with Serializable