LRTrainTask

Binomial logistic regression is a linear classification algorithm, each sample is labeled as positive(+1) or negtive(-1). In this algorithm, the probability describing a single sample being drawn from the positive class is modeled using a logistic function: P(Y=+1|X) = 1 / [1+ exp(-dot(w,x))]. This task learns a binomial logistic regression model with mini-batch gradient descent.

Linear Supertypes

TrainTask[LongWritable, Text], BaseTask[LongWritable, Text, LabeledData], BaseTaskInterface[LongWritable, Text, LabeledData], AnyRef, Any

Instance Constructors

new LRTrainTask(ctx: TaskContext)

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
val LOG: Log
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

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

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

Definition Classes
AnyRef → Any
def finalize(): Unit

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

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def parse(key: LongWritable, value: Text): LabeledData

parse the input text to trainning data
parse the input text to trainning data
key
the key
value
the text

Definition Classes
LRTrainTask → BaseTask → BaseTaskInterface
def preProcess(taskContext: TaskContext): Unit

before trainning, preprocess input text to trainning data and put them into trainning data storage and validation data storage separately
before trainning, preprocess input text to trainning data and put them into trainning data storage and validation data storage separately

Definition Classes
LRTrainTask → BaseTask → BaseTaskInterface
final def run(taskContext: TaskContext): Unit

Definition Classes
TrainTask → BaseTask → BaseTaskInterface
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def train(ctx: TaskContext): Unit

Definition Classes
LRTrainTask → TrainTask
var validDataBlock: MemoryDataBlock[LabeledData]
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( ... )

Related Doc: package lr

class LRTrainTask extends TrainTask[LongWritable, Text]

Instance Constructors

new LRTrainTask(ctx: TaskContext)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

val LOG: Log

final def asInstanceOf[T0]: T0

def clone(): AnyRef

val ctx: TaskContext

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 parse(key: LongWritable, value: Text): LabeledData

def preProcess(taskContext: TaskContext): Unit

final def run(taskContext: TaskContext): Unit

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

def toString(): String

def train(ctx: TaskContext): Unit

var validDataBlock: MemoryDataBlock[LabeledData]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from TrainTask[LongWritable, Text]

Inherited from BaseTask[LongWritable, Text, LabeledData]

Inherited from BaseTaskInterface[LongWritable, Text, LabeledData]

Inherited from AnyRef

Inherited from Any

Ungrouped