RFClassifier

Value Members

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

   

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2. final def ##(): Int

   

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

   

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4. final def asInstanceOf[T0]: T0

   

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5. def buildTree(job: RFJob[L, F]): RFTree

   

   Constructs a single decision tree from the given dataset sample

6. def buildTreeMain(job: RFJob[L, F]): RFTree

   

7. def classOf(d: Datum[L, F]): L

   

   Returns the argmax for this datum

   Returns the argmax for this datum

   Definition Classes

   RFClassifier → Classifier

8. def clone(): AnyRef

   

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   protected[java.lang]

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9. def computeContingencyTables(job: RFJob[L, F], features: Array[Int]): Array[Array[(Counter[Int], Counter[Int])]]

   

   Computes the contingency tables for all given features and dataset partition For each feature and possible threshold (hence the double array), we store a distribution of datum labels that are <= than the threshold (_1 in the tuple), or larger than the threshold (_2 in the tuple) This method does not consider 0 values! See updateContingencyTables for that.

10. def computeFeatureThresholds(dataset: CounterDataset[L, F]): Array[Array[Double]]

    

    Computes the value thresholds for all features in this dataset

    Computes the value thresholds for all features in this dataset

    dataset

    The dataset

    returns

    An array of thresholds (Double) for each feature in the dataset; feature indices are used for indexing

11. def debugUtility(utility: Utility, job: RFJob[L, F]): Unit

    

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

    

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13. def equals(arg0: Any): Boolean

    

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14. def featureUtility(feature: Int, thresholds: Array[Double], contingencyTables: Array[(Counter[Int], Counter[Int])], activeNodes: Set[(Int, Double)], currentUtility: Double): Option[Utility]

    

    Computes the utility of the given feature

15. def finalize(): Unit

    

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

    

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17. def hashCode(): Int

    

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18. def informationGain(feature: Int, thresholds: Array[Double], contingencyTables: Array[(Counter[Int], Counter[Int])], activeNodes: Set[(Int, Double)], currentEntropy: Double): Option[Utility]

    

    Computes the utility of the given feature using information gain

19. def informationGainForThreshold(feature: Int, threshold: Double, contingencyTable: (Counter[Int], Counter[Int]), currentEntropy: Double): Option[Utility]

    

    Computes IG for a given feature and threshold

20. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

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21. def mkBag(dataset: CounterDataset[L, F], indices: Array[Int], thresholds: Array[Array[Double]], trainIndicesLength: Int, entropy: Double, random: Random, offset: Int): RFJob[L, F]

    

22. def mkLeftJob(job: RFJob[L, F], feature: Int, threshold: Double, entropy: Double, activeNodes: Set[(Int, Double)]): RFJob[L, F]

    

    Constructs a job from the datums containing values of this feature smaller or equal than the threshold

23. def mkRightJob(job: RFJob[L, F], feature: Int, threshold: Double, entropy: Double, activeNodes: Set[(Int, Double)]): RFJob[L, F]

    

    Constructs a job from the datums containing values of this feature larger than the threshold

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

    

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25. final def notify(): Unit

    

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26. final def notifyAll(): Unit

    

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27. def printContingencyTables(tables: Array[Array[(Counter[Int], Counter[Int])]], thresholds: Array[Array[Double]]): Unit

    

28. def prune(tree: RFTree): RFTree

    

29. def quantiles(values: Counter[Double], binCount: Int): Array[Double]

    

    Computes binCount-1 quantile values, such that the sequence of values is split into binCount bins

30. def randomFeatureSelection(presentFeatures: Set[Int], numFeats: Int, random: Random): Array[Int]

    

    Randomly picks selectedFeats features between 0 ..

    Randomly picks selectedFeats features between 0 .. numFeats

31. def sameLabels(job: RFJob[L, F]): Boolean

    

32. def saveTo(writer: Writer): Unit

    

    Saves to writer.

    Saves to writer. Does NOT close the writer

    Definition Classes

    RFClassifier → Classifier

33. def saveTo(fileName: String): Unit

    

    Saves the current model to a file

    Saves the current model to a file

    Definition Classes

    Classifier

34. def scoresOf(d: Datum[L, F]): Counter[L]

    

    Returns the scores of all possible labels for this datum Convention: if the classifier can return probabilities, these must be probabilities

    Returns the scores of all possible labels for this datum Convention: if the classifier can return probabilities, these must be probabilities

    Definition Classes

    RFClassifier → Classifier

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

    

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36. def toString(): String

    

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37. def train(dataset: CounterDataset[L, F], indices: Array[Int]): Unit

    

    Trains a classifier using a CounterDataset (better to compute feature utility)

38. def train(dataset: Dataset[L, F], indices: Array[Int]): Unit

    

    Trains a classifier, using only the datums specified in indices indices is useful for bagging

    Trains a classifier, using only the datums specified in indices indices is useful for bagging

    Definition Classes

    RFClassifier → Classifier

39. def train(dataset: Dataset[L, F], spans: Option[Iterable[(Int, Int)]] = None): Unit

    

    Trains the classifier on the given dataset spans is useful during cross validation

    Trains the classifier on the given dataset spans is useful during cross validation

    Definition Classes

    Classifier

40. var treeCount: Int

    

41. var trees: Option[Array[RFTree]]

    

42. def updateContingencyTables(tables: Array[(Counter[Int], Counter[Int])], label: Int, fv: Double, thresholds: Array[Double]): Unit

    

43. def updateContingencyTables(features: Array[Int], contingencyTables: Array[Array[(Counter[Int], Counter[Int])]], overallLabels: Counter[Int]): Unit

    

44. var verbose: Boolean

    

45. final def wait(): Unit

    

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

    

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

    

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