Spark 3.5.5 ScalaDoc - org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD (original) (raw)

class StreamingLinearRegressionWithSGD extends StreamingLinearAlgorithm[LinearRegressionModel, LinearRegressionWithSGD] with Serializable

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Inherited

  1. StreamingLinearRegressionWithSGD

  2. Serializable

  3. Serializable

  4. StreamingLinearAlgorithm

  5. Logging

  6. AnyRef

  7. Any

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Instance Constructors

  1. new StreamingLinearRegressionWithSGD()

Value Members

  1. final def !=(arg0: Any): Boolean
  2. final def ##(): Int
  3. final def ==(arg0: Any): Boolean
  4. val algorithm: LinearRegressionWithSGD
  5. final def asInstanceOf[T0]: T0
  6. def clone(): AnyRef
  7. final def eq(arg0: AnyRef): Boolean
  8. def equals(arg0: Any): Boolean
  9. def finalize(): Unit
  10. final def getClass(): Class[_]
  11. def hashCode(): Int
  12. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
  13. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
  14. final def isInstanceOf[T0]: Boolean
  15. def isTraceEnabled(): Boolean
  16. def latestModel(): LinearRegressionModel
  17. def log: Logger
  18. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
  19. def logDebug(msg: ⇒ String): Unit
  20. def logError(msg: ⇒ String, throwable: Throwable): Unit
  21. def logError(msg: ⇒ String): Unit
  22. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
  23. def logInfo(msg: ⇒ String): Unit
  24. def logName: String
  25. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
  26. def logTrace(msg: ⇒ String): Unit
  27. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
  28. def logWarning(msg: ⇒ String): Unit
  29. var model: Option[LinearRegressionModel]
  30. final def ne(arg0: AnyRef): Boolean
  31. final def notify(): Unit
  32. final def notifyAll(): Unit
  33. def predictOn(data: JavaDStream[Vector]): JavaDStream[Double]
  34. def predictOn(data: DStream[Vector]): DStream[Double]
  35. def predictOnValues[K](data: JavaPairDStream[K, Vector]): JavaPairDStream[K, Double]
  36. def predictOnValues[K](data: DStream[(K, Vector)])(implicit arg0: ClassTag[K]): DStream[(K, Double)]
  37. def setConvergenceTol(tolerance: Double): StreamingLinearRegressionWithSGD.this.type
  38. def setInitialWeights(initialWeights: Vector): StreamingLinearRegressionWithSGD.this.type
  39. def setMiniBatchFraction(miniBatchFraction: Double): StreamingLinearRegressionWithSGD.this.type
  40. def setNumIterations(numIterations: Int): StreamingLinearRegressionWithSGD.this.type
  41. def setRegParam(regParam: Double): StreamingLinearRegressionWithSGD.this.type
  42. def setStepSize(stepSize: Double): StreamingLinearRegressionWithSGD.this.type
  43. final def synchronized[T0](arg0: ⇒ T0): T0
  44. def toString(): String
  45. def trainOn(data: JavaDStream[LabeledPoint]): Unit
  46. def trainOn(data: DStream[LabeledPoint]): Unit
  47. final def wait(): Unit
  48. final def wait(arg0: Long, arg1: Int): Unit
  49. final def wait(arg0: Long): Unit

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