public class BisectingKMeans extends Estimator<BisectingKMeansModel> implements BisectingKMeansParams, DefaultParamsWritable
k
leaf clusters in total or no leaf clusters are divisible.
The bisecting steps of clusters on the same level are grouped together to increase parallelism.
If bisecting all divisible clusters on the bottom level would result more than k
leaf clusters,
larger clusters get higher priority.
Constructor and Description |
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BisectingKMeans() |
BisectingKMeans(String uid) |
Modifier and Type | Method and Description |
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BisectingKMeans |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
BisectingKMeansModel |
fit(Dataset<?> dataset)
Fits a model to the input data.
|
static BisectingKMeans |
load(String path) |
static MLReader<T> |
read() |
BisectingKMeans |
setDistanceMeasure(String value) |
BisectingKMeans |
setFeaturesCol(String value) |
BisectingKMeans |
setK(int value) |
BisectingKMeans |
setMaxIter(int value) |
BisectingKMeans |
setMinDivisibleClusterSize(double value) |
BisectingKMeans |
setPredictionCol(String value) |
BisectingKMeans |
setSeed(long value) |
StructType |
transformSchema(StructType schema)
:: DeveloperApi ::
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
getK, getMinDivisibleClusterSize, k, minDivisibleClusterSize, validateAndTransformSchema
getMaxIter, maxIter
featuresCol, getFeaturesCol
getPredictionCol, predictionCol
distanceMeasure, getDistanceMeasure
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
toString
write
save
initializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning
public BisectingKMeans(String uid)
public BisectingKMeans()
public static BisectingKMeans load(String path)
public static MLReader<T> read()
public String uid()
Identifiable
uid
in interface Identifiable
public BisectingKMeans copy(ParamMap extra)
Params
defaultCopy()
.copy
in interface Params
copy
in class Estimator<BisectingKMeansModel>
extra
- (undocumented)public BisectingKMeans setFeaturesCol(String value)
public BisectingKMeans setPredictionCol(String value)
public BisectingKMeans setK(int value)
public BisectingKMeans setMaxIter(int value)
public BisectingKMeans setSeed(long value)
public BisectingKMeans setMinDivisibleClusterSize(double value)
public BisectingKMeans setDistanceMeasure(String value)
public BisectingKMeansModel fit(Dataset<?> dataset)
Estimator
fit
in class Estimator<BisectingKMeansModel>
dataset
- (undocumented)public StructType transformSchema(StructType schema)
PipelineStage
Check transform validity and derive the output schema from the input schema.
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 PipelineStage
schema
- (undocumented)