public class KMeans extends Estimator<KMeansModel> implements KMeansParams, DefaultParamsWritable
| Modifier and Type | Method and Description |
|---|---|
KMeans |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
Param<String> |
distanceMeasure()
Param for The distance measure.
|
Param<String> |
featuresCol()
Param for features column name.
|
KMeansModel |
fit(Dataset<?> dataset)
Fits a model to the input data.
|
Param<String> |
initMode()
Param for the initialization algorithm.
|
IntParam |
initSteps()
Param for the number of steps for the k-means|| initialization mode.
|
IntParam |
k()
The number of clusters to create (k).
|
static KMeans |
load(String path) |
DoubleParam |
maxBlockSizeInMB()
Param for Maximum memory in MB for stacking input data into blocks.
|
IntParam |
maxIter()
Param for maximum number of iterations (>= 0).
|
Param<String> |
predictionCol()
Param for prediction column name.
|
static MLReader<T> |
read() |
LongParam |
seed()
Param for random seed.
|
KMeans |
setDistanceMeasure(String value) |
KMeans |
setFeaturesCol(String value) |
KMeans |
setInitMode(String value) |
KMeans |
setInitSteps(int value) |
KMeans |
setK(int value) |
KMeans |
setMaxBlockSizeInMB(double value)
Sets the value of param
maxBlockSizeInMB. |
KMeans |
setMaxIter(int value) |
KMeans |
setPredictionCol(String value) |
KMeans |
setSeed(long value) |
KMeans |
setSolver(String value)
Sets the value of param
solver. |
KMeans |
setTol(double value) |
KMeans |
setWeightCol(String value)
Sets the value of param
weightCol. |
Param<String> |
solver()
Param for the name of optimization method used in KMeans.
|
DoubleParam |
tol()
Param for the convergence tolerance for iterative algorithms (>= 0).
|
StructType |
transformSchema(StructType schema)
Check transform validity and derive the output schema from the input schema.
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
Param<String> |
weightCol()
Param for weight column name.
|
paramsequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitgetInitMode, getInitSteps, getK, validateAndTransformSchemagetMaxItergetFeaturesColgetPredictionColgetDistanceMeasuregetWeightColgetMaxBlockSizeInMBclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwntoStringwritesave$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitializepublic static KMeans load(String path)
public static MLReader<T> read()
public final IntParam k()
KMeansParamsk in interface KMeansParamspublic final Param<String> initMode()
KMeansParamsinitMode in interface KMeansParamspublic final IntParam initSteps()
KMeansParamsinitSteps in interface KMeansParamspublic final Param<String> solver()
KMeansParamssolver in interface KMeansParamssolver in interface HasSolverpublic final DoubleParam maxBlockSizeInMB()
HasMaxBlockSizeInMBmaxBlockSizeInMB in interface HasMaxBlockSizeInMBpublic final Param<String> weightCol()
HasWeightColweightCol in interface HasWeightColpublic final Param<String> distanceMeasure()
HasDistanceMeasuredistanceMeasure in interface HasDistanceMeasurepublic final DoubleParam tol()
HasTolpublic final Param<String> predictionCol()
HasPredictionColpredictionCol in interface HasPredictionColpublic final LongParam seed()
HasSeedpublic final Param<String> featuresCol()
HasFeaturesColfeaturesCol in interface HasFeaturesColpublic final IntParam maxIter()
HasMaxItermaxIter in interface HasMaxIterpublic String uid()
Identifiableuid in interface Identifiablepublic KMeans copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Estimator<KMeansModel>extra - (undocumented)public KMeans setFeaturesCol(String value)
public KMeans setPredictionCol(String value)
public KMeans setK(int value)
public KMeans setInitMode(String value)
public KMeans setDistanceMeasure(String value)
public KMeans setInitSteps(int value)
public KMeans setMaxIter(int value)
public KMeans setTol(double value)
public KMeans setSeed(long value)
public KMeans setWeightCol(String value)
weightCol.
If this is not set or empty, we treat all instance weights as 1.0.
Default is not set, so all instances have weight one.
value - (undocumented)public KMeans setSolver(String value)
solver.
Default is "auto".
value - (undocumented)public KMeans setMaxBlockSizeInMB(double value)
maxBlockSizeInMB.
Default is 0.0, then 1.0 MB will be chosen.
value - (undocumented)public KMeansModel fit(Dataset<?> dataset)
Estimatorfit in class Estimator<KMeansModel>dataset - (undocumented)public StructType transformSchema(StructType schema)
PipelineStage
We check validity for interactions between parameters during transformSchema and
raise an exception if any parameter value is invalid. Parameter value checks which
do not depend on other parameters are handled by Param.validate().
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
transformSchema in class PipelineStageschema - (undocumented)