IsotonicRegression

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

Sequential PAV implementation based on: Tibshirani, Ryan J., Holger Hoefling, and Robert Tibshirani. "Nearly-isotonic regression." Technometrics 53.1 (2011): 54-61. Available from http://www.stat.cmu.edu/~ryantibs/papers/neariso.pdf

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 http://softlib.rice.edu/pub/CRPC-TRs/reports/CRPC-TR96640.pdf

Annotations: @Since( "1.3.0" )
Source: IsotonicRegression.scala
See also: Isotonic regression (Wikipedia)

Linear Supertypes

Serializable, AnyRef, Any

Instance Constructors

new IsotonicRegression()

Constructs IsotonicRegression instance with default parameter isotonic = true.
Constructs IsotonicRegression instance with default parameter isotonic = true.
returns
New instance of IsotonicRegression.

Annotations
@Since( "1.3.0" )

Value Members

final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

Attributes
protected[java.lang]
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@throws( ... )
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

Definition Classes
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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 ordered before the algorithm is executed.
returns
Isotonic regression model.

Annotations
@Since( "1.3.0" )
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 ordered before the algorithm is executed.
returns
Isotonic regression model.

Annotations
@Since( "1.3.0" )
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" )
final def synchronized[T0](arg0: ⇒ T0): T0

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def toString(): String

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final def wait(): Unit

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

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@throws( ... )
final def wait(arg0: Long): Unit

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@throws( ... )

Related Doc: package regression

class IsotonicRegression extends Serializable

Instance Constructors

new IsotonicRegression()

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def run(input: JavaRDD[(Double, Double, Double)]): IsotonicRegressionModel

def run(input: RDD[(Double, Double, Double)]): IsotonicRegressionModel

def setIsotonic(isotonic: Boolean): IsotonicRegression.this.type

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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