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package root

Definition Classes
root
package org

Definition Classes
root
package apache

Definition Classes
org
package spark
Core Spark functionality.
Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.
In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.
Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.
Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.
Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

Definition Classes
apache
package mllib
RDD-based machine learning APIs (in maintenance mode).
RDD-based machine learning APIs (in maintenance mode).
The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,
- no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
- bug fixes in the RDD-based APIs will still be accepted.
The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.
Definition Classes
spark
See also
SPARK-4591 to track the progress of feature parity
package regression

Definition Classes
mllib
GeneralizedLinearAlgorithm
GeneralizedLinearModel
IsotonicRegression
IsotonicRegressionModel
LabeledPoint
LassoModel
LassoWithSGD
LinearRegressionModel
LinearRegressionWithSGD
RegressionModel
RidgeRegressionModel
RidgeRegressionWithSGD
StreamingLinearAlgorithm
StreamingLinearRegressionWithSGD

org.apache.spark.mllib.regression

GeneralizedLinearModel

abstract class GeneralizedLinearModel extends Serializable

GeneralizedLinearModel (GLM) represents a model trained using GeneralizedLinearAlgorithm. GLMs consist of a weight vector and an intercept.

Annotations: @Since( "0.8.0" )
Source: GeneralizedLinearAlgorithm.scala

Linear Supertypes

Serializable, Serializable, AnyRef, Any

Known Subclasses

LogisticRegressionModel, SVMModel, LassoModel, LinearRegressionModel, RidgeRegressionModel

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Inherited

GeneralizedLinearModel
Serializable
Serializable
AnyRef
Any

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Instance Constructors

new GeneralizedLinearModel(weights: Vector, intercept: Double)
weights
Weights computed for every feature.
intercept
Intercept computed for this model.

Annotations
@Since( "1.0.0" )

Abstract Value Members

abstract def predictPoint(dataMatrix: Vector, weightMatrix: Vector, intercept: Double): Double
Predict the result given a data point and the weights learned.
Predict the result given a data point and the weights learned.
dataMatrix
Row vector containing the features for this data point
weightMatrix
Column vector containing the weights of the model
intercept
Intercept of the model.

Attributes
protected

Concrete Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native()
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native()
val intercept: Double

Annotations
@Since( "0.8.0" )
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
def predict(testData: Vector): Double
Predict values for a single data point using the model trained.
Predict values for a single data point using the model trained.
testData
array representing a single data point
returns
Double prediction from the trained model

Annotations
@Since( "1.0.0" )
def predict(testData: RDD[Vector]): RDD[Double]
Predict values for the given data set using the model trained.
Predict values for the given data set using the model trained.
testData
RDD representing data points to be predicted
returns
RDD[Double] where each entry contains the corresponding prediction

Annotations
@Since( "1.0.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String
Print a summary of the model.
Print a summary of the model.

Definition Classes
GeneralizedLinearModel → AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
val weights: Vector

Annotations
@Since( "1.0.0" )

Packages

GeneralizedLinearModel

abstract class GeneralizedLinearModel extends Serializable

Instance Constructors

Abstract Value Members

Concrete Value Members

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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Packages

GeneralizedLinearModel 

abstract class GeneralizedLinearModel extends Serializable

Instance Constructors

Abstract Value Members

Concrete Value Members

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped

GeneralizedLinearModel