classify

Type Members

1. class BatchOptimizingLinearVectorClassifierTrainer extends OptimizingLinearVectorClassifierTrainer

   An OptimizingLinearVectorClassifierTrainer pre-tuned with default arguments well-suited to batch training, operating on all the gradients of the Examples together.

2. class BinaryFeatures extends BinaryFeatureVectorVariable[String] with Features

3. class Classification[V <: DiscreteVar] extends MulticlassClassification with DiscreteMarginal1[V]

   A record of the result of applying a Classifier to a variable.

4. trait Classifier[L <: DiscreteVar] extends AnyRef

   Performs iid prediction of a DiscreteVar.

5. class DecisionTreeClassifier[L <: DiscreteVar, F <: VectorVar] extends VectorClassifier[L, F]

6. trait Features extends VectorVar

7. class FeaturesCubbie extends util.Cubbie

8. class ID3DecisionTreeClassifier extends VectorClassifierTrainer

9. class InfoGain[L <: DiscreteVar, F <: VectorVar] extends DenseDoubleSeq

   Calculate the information gain between all features of Instances and the Instances' labels.

10. class Label extends LabeledCategoricalVariable[String]

11. class LabelListCubbie extends util.Cubbie

12. case class LabeledClassification[L](label: L, classification: MulticlassClassification) extends Product with Serializable

13. class LinearVectorClassifier[L <: DiscreteVar, F <: VectorVar] extends LinearMulticlassClassifier with VectorClassifier[L, F]

    A VectorClassifier in which the score for each class is a dot-product between the observed feature vector and a vector of parameters.

14. trait LinearVectorClassifierTrainer extends VectorClassifierTrainer

    An object that can create and train a LinearVectorClassifier (or train a pre-existing LinearVectorClassifier) given labeled training data.

15. class NaiveBayesClassifierTrainer extends LinearVectorClassifierTrainer

    Creates a trained naive Bayes classifier by counting feature occurrences, smoothed with pseudo-counts (m-Estimates).

16. class NonBinaryFeatures extends FeatureVectorVariable[String] with Features

17. class OnlineOptimizingLinearVectorClassifierTrainer extends OptimizingLinearVectorClassifierTrainer

    An OptimizingLinearVectorClassifierTrainer pre-tuned with default arguments well-suited to online training, operating on the gradient of one Example at a time.

18. class OptimizingLinearVectorClassifierTrainer extends LinearVectorClassifierTrainer

    A LinearVectorClassifierTrainer that uses the cc.

19. class PerLabelInfoGain[L <: DiscreteVar, F <: VectorVar] extends AnyRef

    Calculate the information gain between the binary variable "in class" / "not in class" and the binary variable "has feature" / "does not have feature" for every (label,feature) combination.

20. class PerLabelLogOdds[L <: DiscreteVar, F <: VectorVar] extends AnyRef

    Calculate the weighted log-odds ratio: p(w|c) * log(p(w|c)/p(w|!c)) for each word w and label c.

21. class SVMLinearVectorClassifierTrainer extends OptimizingLinearVectorClassifierTrainer

    An OptimizingLinearVectorClassifierTrainer pre-tuned with default arguments well-suited to training an L2-regularized linear SVM.

22. class Trial[L <: LabeledMutableDiscreteVar, F] extends LabeledDiscreteEvaluation[Nothing] with IndexedSeq[LabeledClassification[L]]

    A collection of Classification results, along with methods for calculating several evaluation measures.

23. trait VectorClassifier[V <: DiscreteVar, Features <: VectorVar] extends Classifier[V] with MulticlassClassifier[la.Tensor1]

    A Classifier in which the "input, observed" object to be classified is a VectorVar (with value Tensor1).

24. trait VectorClassifierTrainer extends AnyRef

    An object that can create and train a VectorClassifier given labeled training data.