org.apache.spark.ml.feature

Class StandardScalerModel

java.lang.Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

org.apache.spark.ml.Model<StandardScalerModel>

org.apache.spark.ml.feature.StandardScalerModel

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable, MLWritable

public class StandardScalerModel
extends Model<StandardScalerModel>
implements MLWritable

See Also:

Serialized Form

Method Summary

Methods

Modifier and Type	Method and Description
StandardScalerModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
boolean	getWithMean()
boolean	getWithStd()
static StandardScalerModel	load(java.lang.String path)
Vector	mean()
static MLReader<StandardScalerModel>	read()
StandardScalerModel	setInputCol(java.lang.String value)
StandardScalerModel	setOutputCol(java.lang.String value)
Vector	std()
DataFrame	transform(DataFrame dataset) Transforms the input dataset.
StructType	transformSchema(StructType schema) :: DeveloperApi ::
java.lang.String	uid() An immutable unique ID for the object and its derivatives.
BooleanParam	withMean() Whether to center the data with mean before scaling.
BooleanParam	withStd() Whether to scale the data to unit standard deviation.
MLWriter	write() Returns an MLWriter instance for this ML instance.

Methods inherited from class org.apache.spark.ml.Model

hasParent, parent, setParent

Methods inherited from class org.apache.spark.ml.Transformer

transform, transform, transform

Methods inherited from class org.apache.spark.ml.PipelineStage

transformSchema

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn, validateParams

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Method Detail

read

public static MLReader<StandardScalerModel> read()

load

public static StandardScalerModel load(java.lang.String path)

uid

public java.lang.String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

std

public Vector std()

mean

public Vector mean()

setInputCol

public StandardScalerModel setInputCol(java.lang.String value)

setOutputCol

public StandardScalerModel setOutputCol(java.lang.String value)

transform

public DataFrame transform(DataFrame dataset)

Description copied from class: Transformer

Transforms the input dataset.

Specified by:

transform in class Transformer

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

:: DeveloperApi ::

Derives the output schema from the input schema.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

copy

public StandardScalerModel copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly.

Specified by:

copy in interface Params

Specified by:

copy in class Model<StandardScalerModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

write

public MLWriter write()

Description copied from interface: MLWritable

Returns an MLWriter instance for this ML instance.

Specified by:

write in interface MLWritable

Returns:

(undocumented)

withMean

public BooleanParam withMean()

Whether to center the data with mean before scaling. It will build a dense output, so this does not work on sparse input and will raise an exception. Default: false

Returns:

(undocumented)

getWithMean

public boolean getWithMean()

withStd

public BooleanParam withStd()

Whether to scale the data to unit standard deviation. Default: true

Returns:

(undocumented)

getWithStd

public boolean getWithStd()