StandardScaler

Type Members

abstract class StandardScalerTransformOperation[T] extends TransformOperation[StandardScaler, (Vector[Double], Vector[Double]), T, T]

Base class for StandardScaler's TransformOperation.
Base class for StandardScaler's TransformOperation. This class has to be extended for all types which are supported by StandardScaler's transform operation.

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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object Mean extends Parameter[Double] with Product with Serializable
object Std extends Parameter[Double] with Product with Serializable
def apply(): StandardScaler
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] )
implicit def fitLabelVectorTupleStandardScaler[T <: Vector](implicit arg0: BreezeVectorConverter[T], arg1: TypeInformation[T], arg2: ClassTag[T]): FitOperation[StandardScaler, (T, Double)]

Trains the StandardScaler by learning the mean and standard deviation of the training data which is of type (Vector, Double).
Trains the StandardScaler by learning the mean and standard deviation of the training data which is of type (Vector, Double). The mean and standard deviation are used to transform the given input data.
implicit val fitLabeledVectorStandardScaler: FitOperation[StandardScaler, LabeledVector]

Trains the StandardScaler by learning the mean and standard deviation of the training data which is of type LabeledVector.
Trains the StandardScaler by learning the mean and standard deviation of the training data which is of type LabeledVector. The mean and standard deviation are used to transform the given input data.
implicit def fitVectorStandardScaler[T <: Vector]: FitOperation[StandardScaler, T]

Trains the org.apache.flink.ml.preprocessing.StandardScaler by learning the mean and standard deviation of the training data.
Trains the org.apache.flink.ml.preprocessing.StandardScaler by learning the mean and standard deviation of the training data. These values are used inthe transform step to transform the given input data.
T
Input data type which is a subtype of Vector
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

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

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def toString(): String

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implicit val transformLabeledVector: StandardScalerTransformOperation[LabeledVector]

TransformOperation to transform LabeledVector.
implicit def transformTupleVectorDouble[T <: Vector](implicit arg0: BreezeVectorConverter[T], arg1: TypeInformation[T], arg2: ClassTag[T]): StandardScalerTransformOperation[(T, Double)]

TransformOperation to transform tuples of type (Vector, Double).
implicit def transformVectors[T <: Vector](implicit arg0: BreezeVectorConverter[T], arg1: TypeInformation[T], arg2: ClassTag[T]): StandardScalerTransformOperation[T]

TransformOperation to transform Vector types
final def wait(): Unit

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

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final def wait(arg0: Long): Unit

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Related Docs: class StandardScaler | package preprocessing

object StandardScaler extends Serializable

Type Members

abstract class StandardScalerTransformOperation[T] extends TransformOperation[StandardScaler, (Vector[Double], Vector[Double]), T, T]

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

object Mean extends Parameter[Double] with Product with Serializable

object Std extends Parameter[Double] with Product with Serializable

def apply(): StandardScaler

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

implicit def fitLabelVectorTupleStandardScaler[T <: Vector](implicit arg0: BreezeVectorConverter[T], arg1: TypeInformation[T], arg2: ClassTag[T]): FitOperation[StandardScaler, (T, Double)]

implicit val fitLabeledVectorStandardScaler: FitOperation[StandardScaler, LabeledVector]

implicit def fitVectorStandardScaler[T <: Vector]: FitOperation[StandardScaler, T]

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

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

def toString(): String

implicit val transformLabeledVector: StandardScalerTransformOperation[LabeledVector]

implicit def transformTupleVectorDouble[T <: Vector](implicit arg0: BreezeVectorConverter[T], arg1: TypeInformation[T], arg2: ClassTag[T]): StandardScalerTransformOperation[(T, Double)]

implicit def transformVectors[T <: Vector](implicit arg0: BreezeVectorConverter[T], arg1: TypeInformation[T], arg2: ClassTag[T]): StandardScalerTransformOperation[T]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

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