FMLearner

Value Members

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

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

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

   

   Definition Classes

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4. val LOG: Log

   

5. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

6. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

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   Annotations

   @throws( ... )

7. val conf: Configuration

   

   Definition Classes

   MLLearner

8. val ctx: TaskContext

   

   Definition Classes

   FMLearner → MLLearner

9. def derviationMultipler(y: Double, pre: Double): Double

   

   \frac{\partial loss}{\partial x} = dm * \frac{\partial y}{\partial x}

   \frac{\partial loss}{\partial x} = dm * \frac{\partial y}{\partial x}

   returns

   : dm value

10. val epochNum: Int

    

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

    

    Definition Classes

    AnyRef

12. def equals(arg0: Any): Boolean

    

    Definition Classes

    AnyRef → Any

13. def evaluate(dataBlock: DataBlock[LabeledData], w0: Double, w: DenseDoubleVector, v: HashMap[Int, DenseDoubleVector]): Double

    

    Evaluate the objective value For regression: loss(y,\hat y) = (y - \hat y)^{2 For classification: loss(y,\hat y) = -\ln (\delta(y, \hat y))), in which \delta(x) = \frac{1}{1+e}{-x}}

14. val feaNum: Int

    

15. val feaUsed: Array[Int]

    

16. val feaUsedN: Int

    

17. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

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    Annotations

    @throws( classOf[java.lang.Throwable] )

18. val fmmodel: FMModel

    

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

    

    Definition Classes

    AnyRef → Any

20. val globalMetrics: GlobalMetrics

    

    Definition Classes

    MLLearner

21. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

22. def initModels(): Unit

    

    Initialize with random values

23. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

24. val learnType: String

    

25. val lr: Double

    

26. val maxP: Double

    

27. val minP: Double

    

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

    

    Definition Classes

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29. final def notify(): Unit

    

    Definition Classes

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30. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

31. def oneIteration(dataBlock: DataBlock[LabeledData]): (DenseDoubleVector, DenseDoubleVector, HashMap[Int, DenseDoubleVector])

    

    One iteration to train Factorization Machines

32. def predict(x: SparseDoubleSortedVector, y: Double, w0: Double, w: DenseDoubleVector, v: HashMap[Int, DenseDoubleVector]): Double

    

    Predict an instance

33. val rank: Int

    

34. val reg0: Double

    

35. val reg1: Double

    

36. val reg2: Double

    

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

    

    Definition Classes

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38. def toString(): String

    

    Definition Classes

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39. def train(trainData: DataBlock[LabeledData], vali: DataBlock[LabeledData]): MLModel

    

    Train a Factorization machines Model

    Train a Factorization machines Model

    trainData

    : input train data storage

    vali

    : validate data storage

    returns

    : a learned model

    Definition Classes

    FMLearner → MLLearner

40. def updateV(x: SparseDoubleSortedVector, dm: Double, v: HashMap[Int, DenseDoubleVector]): Unit

    

    Update v mat

41. val vIndexs: RowIndex

    

42. val vStddev: Double

    

43. final def wait(): Unit

    

    Definition Classes

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    Annotations

    @throws( ... )

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

    

    Definition Classes

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    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

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    @throws( ... )