StandardScaler

Scales observations, so that all features have a user-specified mean and standard deviation. By default for StandardScaler transformer mean=0.0 and std=1.0.

This transformer takes a subtype of Vector of values and maps it to a scaled subtype of Vector such that each feature has a user-specified mean and standard deviation.

This transformer can be prepended to all Transformer and org.apache.flink.ml.pipeline.Predictor implementations which expect as input a subtype of Vector.

Example:

val trainingDS: DataSet[Vector] = env.fromCollection(data)
val transformer = StandardScaler().setMean(10.0).setStd(2.0)
transformer.fit(trainingDS)
val transformedDS = transformer.transform(trainingDS)

Parameters

- Mean: The mean value of transformed data set; by default equal to 0 - Std: The standard deviation of the transformed data set; by default equal to 1

Linear Supertypes

Transformer[StandardScaler], Serializable, Serializable, Estimator[StandardScaler], WithParameters, AnyRef, Any

Instance Constructors

new StandardScaler()

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 chainPredictor[P <: Predictor[P]](predictor: P): ChainedPredictor[StandardScaler, P]

Chains a Transformer with a Predictor to form a ChainedPredictor.
Chains a Transformer with a Predictor to form a ChainedPredictor.
P
Type of the Predictor
predictor
Trailing Predictor of the resulting pipeline

Definition Classes
Transformer
def chainTransformer[T <: Transformer[T]](transformer: T): ChainedTransformer[StandardScaler, T]

Chains two Transformer to form a ChainedTransformer.
Chains two Transformer to form a ChainedTransformer.
T
Type of the Transformer
transformer
Right side transformer of the resulting pipeline

Definition Classes
Transformer
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
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] )
def fit[Training](training: DataSet[Training], fitParameters: ParameterMap = ParameterMap.Empty)(implicit fitOperation: FitOperation[StandardScaler, Training]): Unit

Fits the estimator to the given input data.
Fits the estimator to the given input data. The fitting logic is contained in the FitOperation. The computed state will be stored in the implementing class.
Training
Type of the training data
training
Training data
fitParameters
Additional parameters for the FitOperation
fitOperation
FitOperation which encapsulates the algorithm logic

Definition Classes
Estimator
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
val parameters: ParameterMap

Definition Classes
WithParameters
def setMean(mu: Double): StandardScaler

Sets the target mean of the transformed data
Sets the target mean of the transformed data
mu
the user-specified mean value.
returns
the StandardScaler instance with its mean value set to the user-specified value
def setStd(std: Double): StandardScaler

Sets the target standard deviation of the transformed data
Sets the target standard deviation of the transformed data
std
the user-specified std value. In case the user gives 0.0 value as input, the std is set to the default value: 1.0.
returns
the StandardScaler instance with its std value set to the user-specified value
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def transform[Input, Output](input: DataSet[Input], transformParameters: ParameterMap = ParameterMap.Empty)(implicit transformOperation: TransformDataSetOperation[StandardScaler, Input, Output]): DataSet[Output]

Transform operation which transforms an input DataSet of type I into an output DataSet of type O.
Transform operation which transforms an input DataSet of type I into an output DataSet of type O. The actual transform operation is implemented within the TransformDataSetOperation.
Input
Input data type
Output
Output data type
input
Input DataSet of type I
transformParameters
Additional parameters for the TransformDataSetOperation
transformOperation
TransformDataSetOperation which encapsulates the algorithm's logic

Definition Classes
Transformer
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 Docs: object StandardScaler | package preprocessing

class StandardScaler extends Transformer[StandardScaler]

Parameters

Instance Constructors

new StandardScaler()

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def chainPredictor[P <: Predictor[P]](predictor: P): ChainedPredictor[StandardScaler, P]

def chainTransformer[T <: Transformer[T]](transformer: T): ChainedTransformer[StandardScaler, T]

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def fit[Training](training: DataSet[Training], fitParameters: ParameterMap = ParameterMap.Empty)(implicit fitOperation: FitOperation[StandardScaler, Training]): 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

val parameters: ParameterMap

def setMean(mu: Double): StandardScaler

def setStd(std: Double): StandardScaler

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

def toString(): String

def transform[Input, Output](input: DataSet[Input], transformParameters: ParameterMap = ParameterMap.Empty)(implicit transformOperation: TransformDataSetOperation[StandardScaler, Input, Output]): DataSet[Output]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Transformer[StandardScaler]

Inherited from Serializable

Inherited from Serializable

Inherited from Estimator[StandardScaler]

Inherited from WithParameters

Inherited from AnyRef

Inherited from Any

Ungrouped