org.apache.spark.ml.odkl

LinearRegressor

abstract class LinearRegressor[M <: LinearModel[M], O <: Optimizer, T <: LinearRegressor[M, O, T]] extends LinearEstimator[M, T] with DefaultParamsWritable with Logging with HasCacheTrainData

Linear Supertypes
HasCacheTrainData, DefaultParamsWritable, MLWritable, LinearEstimator[M, T], HasWeights, LinearModelParams, SummarizableEstimator[M], Predictor[Vector, T, M], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[M], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
LinearRegressionSGD, LogisticRegressionLBFSG
Ordering
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Inherited
  1. LinearRegressor
  2. HasCacheTrainData
  3. DefaultParamsWritable
  4. MLWritable
  5. LinearEstimator
  6. HasWeights
  7. LinearModelParams
  8. SummarizableEstimator
  9. Predictor
  10. PredictorParams
  11. HasPredictionCol
  12. HasFeaturesCol
  13. HasLabelCol
  14. Estimator
  15. PipelineStage
  16. Logging
  17. Params
  18. Serializable
  19. Serializable
  20. Identifiable
  21. AnyRef
  22. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new LinearRegressor(uid: String)

Abstract Value Members

  1. abstract def createModel(optimizer: O, coefficients: Vector, sqlContext: SQLContext, features: StructField): M

    Attributes
    protected

  2. abstract def createOptimizer(): O

    Attributes
    protected

Concrete Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def $[T](param: Param[T]): T

    Attributes
    protected
    Definition Classes
    Params

  5. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  8. final val cacheTrainData: BooleanParam

    Definition Classes
    HasCacheTrainData

  9. final def clear(param: Param[_]): LinearRegressor.this.type

    Definition Classes
    Params

  10. def clone(): AnyRef

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

  11. def copy(extra: ParamMap): T

    Definition Classes
    LinearRegressor → LinearEstimator → SummarizableEstimator → Predictor → Estimator → PipelineStage → Params

  12. def copyValues[T <: Params](to: T, extra: ParamMap): T

    Attributes
    protected
    Definition Classes
    Params

  13. def createWeightsSummary(coefficients: Vector, sqlContext: SQLContext, features: StructField): DataFrame

    Attributes
    protected

  14. final def defaultCopy[T <: Params](extra: ParamMap): T

    Attributes
    protected
    Definition Classes
    Params

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

    Definition Classes
    AnyRef

  16. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  17. def explainParam(param: Param[_]): String

    Definition Classes
    Params

  18. def explainParams(): String

    Definition Classes
    Params

  19. def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

    Attributes
    protected
    Definition Classes
    Predictor

  20. final def extractParamMap(): ParamMap

    Definition Classes
    Params

  21. final def extractParamMap(extra: ParamMap): ParamMap

    Definition Classes
    Params

  22. final val featuresCol: Param[String]

    Definition Classes
    HasFeaturesCol

  23. def finalize(): Unit

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

  24. def fit(dataset: Dataset[_]): M

    Definition Classes
    Predictor → Estimator

  25. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[M]

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )

  26. def fit(dataset: Dataset[_], paramMap: ParamMap): M

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )

  27. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): M

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()

  28. final def get[T](param: Param[T]): Option[T]

    Definition Classes
    Params

  29. final def getCacheTrainData: Boolean

    Definition Classes
    HasCacheTrainData

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

    Definition Classes
    AnyRef → Any

  31. final def getDefault[T](param: Param[T]): Option[T]

    Definition Classes
    Params

  32. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol

  33. final def getLabelCol: String

    Definition Classes
    HasLabelCol

  34. final def getOrDefault[T](param: Param[T]): T

    Definition Classes
    Params

  35. def getParam(paramName: String): Param[Any]

    Definition Classes
    Params

  36. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol

  37. final def hasDefault[T](param: Param[T]): Boolean

    Definition Classes
    Params

  38. def hasParam(paramName: String): Boolean

    Definition Classes
    Params

  39. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  40. val index: String

    Definition Classes
    HasWeights

  41. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    Attributes
    protected
    Definition Classes
    Logging

  42. final def isDefined(param: Param[_]): Boolean

    Definition Classes
    Params

  43. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  44. final def isSet(param: Param[_]): Boolean

    Definition Classes
    Params

  45. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging

  46. final val labelCol: Param[String]

    Definition Classes
    HasLabelCol

  47. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging

  48. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  49. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  50. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  51. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  52. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  53. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  54. def logName: String

    Attributes
    protected
    Definition Classes
    Logging

  55. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  56. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  57. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  58. def logWarning(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  59. val name: String

    Definition Classes
    HasWeights

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

    Definition Classes
    AnyRef

  61. final def notify(): Unit

    Definition Classes
    AnyRef

  62. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  63. lazy val params: Array[Param[_]]

    Definition Classes
    Params

  64. final val predictionCol: Param[String]

    Definition Classes
    HasPredictionCol

  65. def save(path: String): Unit

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )

  66. final def set(paramPair: ParamPair[_]): LinearRegressor.this.type

    Attributes
    protected
    Definition Classes
    Params

  67. final def set(param: String, value: Any): LinearRegressor.this.type

    Attributes
    protected
    Definition Classes
    Params

  68. final def set[T](param: Param[T], value: T): LinearRegressor.this.type

    Definition Classes
    Params

  69. def setCacheTrainData(value: Boolean): LinearRegressor.this.type

    Definition Classes
    HasCacheTrainData

  70. final def setDefault(paramPairs: ParamPair[_]*): LinearRegressor.this.type

    Attributes
    protected
    Definition Classes
    Params

  71. final def setDefault[T](param: Param[T], value: T): LinearRegressor.this.type

    Attributes
    protected
    Definition Classes
    Params

  72. def setFeaturesCol(value: String): T

    Definition Classes
    Predictor

  73. def setLabelCol(value: String): T

    Definition Classes
    Predictor

  74. def setPredictionCol(value: String): T

    Definition Classes
    Predictor

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

    Definition Classes
    AnyRef

  76. def toString(): String

    Definition Classes
    Identifiable → AnyRef → Any

  77. def train(dataset: Dataset[_]): M

    Attributes
    protected
    Definition Classes
    LinearRegressor → Predictor

  78. def transformSchema(schema: StructType): StructType

    Definition Classes
    Predictor → PipelineStage

  79. def transformSchema(schema: StructType, logging: Boolean): StructType

    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()

  80. val uid: String

    Definition Classes
    LinearRegressor → Identifiable

  81. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

    Attributes
    protected
    Definition Classes
    PredictorParams

  82. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  85. val weight: String

    Definition Classes
    HasWeights

  86. val weights: Block

    Definition Classes
    HasWeights

  87. def write: MLWriter

    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from HasCacheTrainData

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from LinearEstimator[M, T]

Inherited from HasWeights

Inherited from LinearModelParams

Inherited from SummarizableEstimator[M]

Inherited from Predictor[Vector, T, M]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[M]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

getParam

Ungrouped