Class

com.tencent.angel.ml.regression.linear

LinearRegTrainTask

Related Doc: package linear

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class LinearRegTrainTask extends TrainTask[LongWritable, Text]

Binomial logistic regression is a linear classification algorithm, each sample is labeled as positive(+1) or negtive(-1). In this algorithm, the probability describing a single sample being drawn from the positive class is modeled using a logistic function: P(Y=+1|X) = 1 / [1+ exp(-dot(w,x))]. This task learns a binomial logistic regression model with mini-batch gradient descent.

Linear Supertypes
TrainTask[LongWritable, Text], BaseTask[LongWritable, Text, LabeledData], BaseTaskInterface[LongWritable, Text, LabeledData], AnyRef, Any
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  1. LinearRegTrainTask
  2. TrainTask
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Instance Constructors

  1. new LinearRegTrainTask(ctx: TaskContext)

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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 clone(): AnyRef

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    Attributes
    protected[java.lang]
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    @throws( ... )

  6. val ctx: TaskContext

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

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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

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

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  11. def hashCode(): Int

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  12. final def isInstanceOf[T0]: Boolean

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

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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  16. def parse(key: LongWritable, value: Text): LabeledData

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    parse the input text to trainning data

    parse the input text to trainning data

    key

    the key

    value

    the text

    Definition Classes
    LinearRegTrainTask → BaseTask → BaseTaskInterface

  17. def preProcess(taskContext: TaskContext): Unit

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    before trainning, preprocess input text to trainning data and put them into trainning data storage and validation data storage separately

    before trainning, preprocess input text to trainning data and put them into trainning data storage and validation data storage separately

    Definition Classes
    LinearRegTrainTask → BaseTask → BaseTaskInterface

  18. final def run(taskContext: TaskContext): Unit

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    Definition Classes
    TrainTask → BaseTask → BaseTaskInterface

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

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  20. def toString(): String

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  21. def train(ctx: TaskContext): Unit

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    Definition Classes
    LinearRegTrainTask → TrainTask
    Annotations
    @throws( ... )

  22. var validDataStorage: MemoryDataBlock[LabeledData]

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  23. final def wait(): Unit

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

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

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Inherited from TrainTask[LongWritable, Text]

Inherited from BaseTask[LongWritable, Text, LabeledData]

Inherited from BaseTaskInterface[LongWritable, Text, LabeledData]

Inherited from AnyRef

Inherited from Any

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