NakContext

Value Members

1. final def !=(arg0: AnyRef): Boolean

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2. final def !=(arg0: Any): Boolean

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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5. final def ==(arg0: Any): Boolean

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6. final def asInstanceOf[T0]: T0

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7. def clone(): AnyRef

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8. def condense(features: Seq[FeatureObservation[Int]]): Seq[FeatureObservation[Int]]

   Given a sequence of feature observations (a feature and its magnitude), combine multiple instances of the same feature, and then sort the result.

   Given a sequence of feature observations (a feature and its magnitude), combine multiple instances of the same feature, and then sort the result.

   E.g. Seq[("foo",1.0),("bar",1.0),("foo",2.0)] becomes Seq[("bar",1.0),("foo",3.0)]

9. final def eq(arg0: AnyRef): Boolean

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10. def equals(arg0: Any): Boolean

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11. def finalize(): Unit

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12. def fromCsvFile(filename: String): Iterator[Example[String, Seq[FeatureObservation[String]]]]

    Convert examples stored in CSV format (one per line) into a sequence of Examples.

13. def fromLabeledDirs(topdir: File)(implicit codec: Codec): Iterator[Example[String, String]]

    Convert examples that are stored as files in directories, where each directory name acts as the label for all the files it contains.

    Convert examples that are stored as files in directories, where each directory name acts as the label for all the files it contains. (E.g. the 20 News Groups data.)

14. final def getClass(): Class[_]

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15. def hashCode(): Int

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16. final def isInstanceOf[T0]: Boolean

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17. def loadClassifier[C <: Classifier](filename: String): C

    Read a classifier from disk by using Java deserialization.

18. def loadClassifierFromResource[C <: Classifier](resource: String): C

    Read a classifier from the classpath using Java deserialization.

19. def maxLabel(labels: Seq[String])(scores: Seq[Double]): String

    Given the labels and scores that have been produced for each, return the label with the highest score.

20. final def ne(arg0: AnyRef): Boolean

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21. final def notify(): Unit

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22. final def notifyAll(): Unit

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23. def saveClassifier(classifier: Classifier, filename: String): Unit

    Save a classifier to disk by using Java serialization.

24. final def synchronized[T0](arg0: ⇒ T0): T0

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25. def toString(): String

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26. def trainBinomialClassifier[I](config: LiblinearConfig, featurizer: Featurizer[I, String], rawExamples: Seq[Example[(Int, Int), I]]): IndexedClassifier[String] with FeaturizedClassifier[String, I]

27. def trainBinomialModel(config: LiblinearConfig, examples: TraversableOnce[Example[(Int, Int), Seq[FeatureObservation[Int]]]], numFeatures: Int): Model

28. def trainClassifier(config: LiblinearConfig, examples: TraversableOnce[Example[Int, Seq[FeatureObservation[Int]]]], lmap: Map[String, Int], fmap: Map[String, Int]): IndexedClassifier[String]

    Trains a classifier given indexed examples and the label and feature maps produced by indexation.

29. def trainClassifier[I](config: LiblinearConfig, featurizer: Featurizer[I, String], rawExamples: Seq[Example[String, I]]): IndexedClassifier[String] with FeaturizedClassifier[String, I]

    Trains a classifier given examples and featurizer.

    Trains a classifier given examples and featurizer. Handles indexation of features, and creates a classifier that can be applied directly to new raw observations.

    This is the easiest way to build and use a classifier.

30. def trainClassifierHashed[I](config: LiblinearConfig, featurizer: Featurizer[I, String], rawExamples: Seq[Example[String, I]], maxNumberOfFeatures: Int = 10000): IndexedClassifier[String] with FeaturizedClassifier[String, I]

    Trains a classifier given examples and featurizer.

    Trains a classifier given examples and featurizer. Uses the hashing trick for indexing features, and creates a classifier that can be applied directly to new raw observations.

    This is the easiest way to build and use a classifier that uses the hashing trick. For more details on the hashing trick, see: http://hunch.net/~jl/projects/hash_reps/index.html

31. def trainModel(config: LiblinearConfig, examples: TraversableOnce[Example[Int, Seq[FeatureObservation[Int]]]], numFeatures: Int): Model

    Trains a liblinear model given indexed examples.

    Trains a liblinear model given indexed examples. Note: a model is basically just the parameters. The classifiers returned by trainClassifier methods wrap the parameters into a number of convenience methods that makes it easier to use the model.

32. final def wait(): Unit

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33. final def wait(arg0: Long, arg1: Int): Unit

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34. final def wait(arg0: Long): Unit

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