KMeansLearner

Instance Constructors

new KMeansLearner(ctx: TaskContext)

ctx
: context of this task

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 C: Double
val K: Int
val LOG: Log
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def computeObjValue(dataStorage: DataBlock[LabeledData], epoch: Int): Double

Compute the objective values of all samples, which is measured by the distance from a sample to its closest center.
Compute the objective values of all samples, which is measured by the distance from a sample to its closest center.
dataStorage
: the trainning dataset
epoch
: the epoch number
val conf: Configuration

Definition Classes
MLLearner
val ctx: TaskContext

: context of this task
: context of this task

Definition Classes
KMeansLearner → MLLearner
val epochNum: Int
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
val feaNum: Int
def finalize(): Unit

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

Definition Classes
AnyRef → Any
val globalMetrics: GlobalMetrics

Definition Classes
MLLearner
def hashCode(): Int

Definition Classes
AnyRef → Any
def initKCentersRandomly(dataStorage: DataBlock[LabeledData]): Unit

Pick up K samples as initial centers randomly, and push them to PS.
Pick up K samples as initial centers randomly, and push them to PS.
dataStorage
: trainning data storage, the cluster center candidates
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val kmeansModel: KMeansModel
def miniBatchUpdation(samples: DataBlock[LabeledData], per_center_step_counts: Array[Int]): Unit

Upate the centers with a mini batch samples, first find the closest center for each sample.
Upate the centers with a mini batch samples, first find the closest center for each sample. Second each sample is used to update its closest center using the per-center learning rate.
samples
: the samples picked up for mini batch updation
per_center_step_counts
: the array sotres the number of samples of each center
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def picInstances(trainData: DataBlock[LabeledData]): DataBlock[LabeledData]

Pick up #batch_sample_num samples randomly from the trainning data.
val spRatio: Double
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def train(trainData: DataBlock[LabeledData], valiData: DataBlock[LabeledData]): MLModel

Train a KMeans Model
Train a KMeans Model
trainData
: trainning dataset storage
returns
: a learned model

Definition Classes
KMeansLearner → MLLearner
def trainOneEpoch(epoch: Int, trainData: DataBlock[LabeledData], per_center_step_counts: Array[Int]): Unit

Each epoch updation is performed in three steps.
Each epoch updation is performed in three steps. First, pull the centers updated by last epoch from PS. Second, a mini batch of size batch_sample_num is sampled. Third, update the centers in a mini-batch way.
trainData
: the trainning data storage
per_center_step_counts
: the array caches the number of samples per center
def updateCenters(oldCenters: ArrayList[TIntDoubleVector]): Unit
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 kmeans

class KMeansLearner extends MLLearner

Instance Constructors

new KMeansLearner(ctx: TaskContext)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

val C: Double

val K: Int

val LOG: Log

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def computeObjValue(dataStorage: DataBlock[LabeledData], epoch: Int): Double

val conf: Configuration

val ctx: TaskContext

val epochNum: Int

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

val feaNum: Int

def finalize(): Unit

final def getClass(): Class[_]

val globalMetrics: GlobalMetrics

def hashCode(): Int

def initKCentersRandomly(dataStorage: DataBlock[LabeledData]): Unit

final def isInstanceOf[T0]: Boolean

val kmeansModel: KMeansModel

def miniBatchUpdation(samples: DataBlock[LabeledData], per_center_step_counts: Array[Int]): Unit

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def picInstances(trainData: DataBlock[LabeledData]): DataBlock[LabeledData]

val spRatio: Double

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

def toString(): String

def train(trainData: DataBlock[LabeledData], valiData: DataBlock[LabeledData]): MLModel

def trainOneEpoch(epoch: Int, trainData: DataBlock[LabeledData], per_center_step_counts: Array[Int]): Unit

def updateCenters(oldCenters: ArrayList[TIntDoubleVector]): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from MLLearner

Inherited from AnyRef

Inherited from Any

Ungrouped