Packages

c

org.apache.spark.mllib.regression

IsotonicRegression

class IsotonicRegression extends Serializable

Isotonic regression. Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.

Sequential PAV implementation based on: Grotzinger, S. J., and C. Witzgall. "Projections onto order simplexes." Applied mathematics and Optimization 12.1 (1984): 247-270.

Sequential PAV parallelization based on: Kearsley, Anthony J., Richard A. Tapia, and Michael W. Trosset. "An approach to parallelizing isotonic regression." Applied Mathematics and Parallel Computing. Physica-Verlag HD, 1996. 141-147. Available from here

Annotations
@Since( "1.3.0" )
Source
IsotonicRegression.scala
See also

Isotonic regression (Wikipedia)

Linear Supertypes
Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. IsotonicRegression
  2. Serializable
  3. AnyRef
  4. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new IsotonicRegression()

    Constructs IsotonicRegression instance with default parameter isotonic = true.

    Constructs IsotonicRegression instance with default parameter isotonic = true.

    Annotations
    @Since( "1.3.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  11. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  12. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  14. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  15. def run(input: JavaRDD[(Double, Double, Double)]): IsotonicRegressionModel

    Run pool adjacent violators algorithm to obtain isotonic regression model.

    Run pool adjacent violators algorithm to obtain isotonic regression model.

    input

    JavaRDD of tuples (label, feature, weight) where label is dependent variable for which we calculate isotonic regression, feature is independent variable and weight represents number of measures with default 1. If multiple labels share the same feature value then they are aggregated using the weighted average before the algorithm is executed.

    returns

    Isotonic regression model.

    Annotations
    @Since( "1.3.0" )
  16. def run(input: RDD[(Double, Double, Double)]): IsotonicRegressionModel

    Run IsotonicRegression algorithm to obtain isotonic regression model.

    Run IsotonicRegression algorithm to obtain isotonic regression model.

    input

    RDD of tuples (label, feature, weight) where label is dependent variable for which we calculate isotonic regression, feature is independent variable and weight represents number of measures with default 1. If multiple labels share the same feature value then they are aggregated using the weighted average before the algorithm is executed.

    returns

    Isotonic regression model.

    Annotations
    @Since( "1.3.0" )
  17. def setIsotonic(isotonic: Boolean): IsotonicRegression.this.type

    Sets the isotonic parameter.

    Sets the isotonic parameter.

    isotonic

    Isotonic (increasing) or antitonic (decreasing) sequence.

    returns

    This instance of IsotonicRegression.

    Annotations
    @Since( "1.3.0" )
  18. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  19. def toString(): String
    Definition Classes
    AnyRef → Any
  20. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  21. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  22. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped