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com.github.cloudml.zen.ml.recommendation

FM

Related Doc: package recommendation

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object FM extends Serializable

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  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 clone(): AnyRef

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

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  7. def equals(arg0: Any): Boolean

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  8. def finalize(): Unit

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

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  10. def hashCode(): Int

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  11. final def isInstanceOf[T0]: Boolean

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

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  13. final def notify(): Unit

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  14. final def notifyAll(): Unit

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

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  16. def toString(): String

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  17. def trainClassification(input: RDD[(Long, LabeledPoint)], numIterations: Int, stepSize: Double, l2: (Double, Double, Double), rank: Int, useAdaGrad: Boolean = true, miniBatchFraction: Double = 1.0, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK): FMModel

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    FM clustering

    FM clustering

    input

    train data

    stepSize

    recommend 1e-2- 1e-1

    l2

    (w_0, w_i, v_{i,f}) in L2 regularization

    rank

    recommend 10-20

    useAdaGrad

    use AdaGrad to train

  18. def trainRegression(input: RDD[(Long, LabeledPoint)], numIterations: Int, stepSize: Double, l2: (Double, Double, Double), rank: Int, useAdaGrad: Boolean = true, miniBatchFraction: Double = 1.0, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK): FMModel

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    FM regression

    FM regression

    input

    train data

    stepSize

    recommend 1e-2- 1e-1

    l2

    (w_0, w_i, v_{i,f}) in L2 regularization

    rank

    recommend 10-20

    useAdaGrad

    use AdaGrad to train

  19. final def wait(): Unit

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

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

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