Class

io.github.mandar2812.dynaml.models.lm

SparkLogisticGLM

Related Doc: package lm

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class SparkLogisticGLM extends GenericGLM[RDD[(DenseVector[Double], Double)], RDD[LabeledPoint]]

Linear Supertypes
GenericGLM[RDD[(DenseVector[Double], Double)], RDD[LabeledPoint]], LinearModel[RDD[(DenseVector[Double], Double)], DenseVector[Double], DenseVector[Double], Double, RDD[LabeledPoint]], ParameterizedLearner[RDD[(DenseVector[Double], Double)], DenseVector[Double], DenseVector[Double], Double, RDD[LabeledPoint]], Model[RDD[(DenseVector[Double], Double)], DenseVector[Double], Double], AnyRef, Any
Known Subclasses
SparkProbitGLM
Ordering
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Inherited
  1. SparkLogisticGLM
  2. GenericGLM
  3. LinearModel
  4. ParameterizedLearner
  5. Model
  6. AnyRef
  7. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new SparkLogisticGLM(data: RDD[(DenseVector[Double], Double)], numPoints: Long, map: (DenseVector[Double]) ⇒ DenseVector[Double] = identity[DenseVector[Double]])

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Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

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

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    Definition Classes
    AnyRef → Any

  4. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  5. def clearParameters(): Unit

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    Definition Classes
    GenericGLM → LinearModel

  6. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  7. def data: RDD[(DenseVector[Double], Double)]

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    Definition Classes
    Model

  8. def dimensions: Int

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

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    Definition Classes
    AnyRef

  10. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  11. var featureMap: (DenseVector[Double]) ⇒ DenseVector[Double]

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    The non linear feature mapping implicitly defined by the kernel applied, this is initialized to an identity map.

    The non linear feature mapping implicitly defined by the kernel applied, this is initialized to an identity map.

    Definition Classes
    LinearModel

  12. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  13. val g: RDD[(DenseVector[Double], Double)]

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    The training data

    The training data

    Attributes
    protected
    Definition Classes
    GenericGLM → Model

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

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    Definition Classes
    AnyRef → Any

  15. val h: (Double) ⇒ Double

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    The link function; in this case simply the identity map

    The link function; in this case simply the identity map

    Definition Classes
    SparkLogisticGLM → GenericGLM

  16. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

  17. def initParams(): DenseVector[Double]

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    Definition Classes
    SparkLogisticGLM → ParameterizedLearner

  18. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  19. def learn(): Unit

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    Learn the parameters of the model.

    Learn the parameters of the model.

    Definition Classes
    GenericGLM → ParameterizedLearner

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

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    Definition Classes
    AnyRef

  21. final def notify(): Unit

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    Definition Classes
    AnyRef

  22. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  23. val optimizer: RegularizedOptimizer[DenseVector[Double], DenseVector[Double], Double, RDD[LabeledPoint]]

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    Attributes
    protected
    Definition Classes
    SparkLogisticGLM → ParameterizedLearner

  24. def parameters(): DenseVector[Double]

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    Get the value of the parameters of the model.

    Get the value of the parameters of the model.

    Definition Classes
    ParameterizedLearner

  25. var params: DenseVector[Double]

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    Attributes
    protected
    Definition Classes
    SparkLogisticGLM → ParameterizedLearner

  26. def predict(point: DenseVector[Double]): Double

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    Predict the value of the target variable given a point.

    Predict the value of the target variable given a point.

    Definition Classes
    GenericGLM → Model

  27. def prepareData(d: RDD[(DenseVector[Double], Double)]): RDD[LabeledPoint]

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    Definition Classes
    SparkLogisticGLM → GenericGLM

  28. def setBatchFraction(f: Double): SparkLogisticGLM.this.type

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    Definition Classes
    ParameterizedLearner

  29. def setLearningRate(alpha: Double): SparkLogisticGLM.this.type

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    Definition Classes
    ParameterizedLearner

  30. def setMaxIterations(i: Int): SparkLogisticGLM.this.type

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    Definition Classes
    ParameterizedLearner

  31. def setRegParam(r: Double): SparkLogisticGLM.this.type

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    Definition Classes
    ParameterizedLearner

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

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    Definition Classes
    AnyRef

  33. def toString(): String

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    Definition Classes
    AnyRef → Any

  34. def updateParameters(param: DenseVector[Double]): Unit

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    Definition Classes
    ParameterizedLearner

  35. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from GenericGLM[RDD[(DenseVector[Double], Double)], RDD[LabeledPoint]]

Inherited from LinearModel[RDD[(DenseVector[Double], Double)], DenseVector[Double], DenseVector[Double], Double, RDD[LabeledPoint]]

Inherited from ParameterizedLearner[RDD[(DenseVector[Double], Double)], DenseVector[Double], DenseVector[Double], Double, RDD[LabeledPoint]]

Inherited from Model[RDD[(DenseVector[Double], Double)], DenseVector[Double], Double]

Inherited from AnyRef

Inherited from Any

Ungrouped