org.apache.spark.ml.clustering

Class GaussianMixtureModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

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

org.apache.spark.ml.clustering.GaussianMixtureModel

All Implemented Interfaces:

java.io.Serializable, Params, Identifiable, MLWritable

public class GaussianMixtureModel
extends Model<GaussianMixtureModel>
implements MLWritable

Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points are drawn from each Gaussian i with probability weights(i).

param: weights Weight for each Gaussian distribution in the mixture. This is a multinomial probability distribution over the k Gaussians, where weights(i) is the weight for Gaussian i, and weights sum to 1. param: gaussians Array of MultivariateGaussian where gaussians(i) represents the Multivariate Gaussian (Normal) Distribution for Gaussian i

See Also:

Serialized Form

Method Summary

Methods

Modifier and Type	Method and Description
static Params	clear(Param<?> param)
GaussianMixtureModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
static String	explainParam(Param<?> param)
static String	explainParams()
static ParamMap	extractParamMap()
static ParamMap	extractParamMap(ParamMap extra)
static Param<String>	featuresCol()
MultivariateGaussian[]	gaussians()
Dataset<Row>	gaussiansDF() Retrieve Gaussian distributions as a DataFrame.
static <T> scala.Option<T>	get(Param<T> param)
static <T> scala.Option<T>	getDefault(Param<T> param)
static String	getFeaturesCol()
static int	getK()
int	getK()
static int	getMaxIter()
static <T> T	getOrDefault(Param<T> param)
static Param<Object>	getParam(String paramName)
static String	getPredictionCol()
static String	getProbabilityCol()
static long	getSeed()
static double	getTol()
static <T> boolean	hasDefault(Param<T> param)
static boolean	hasParam(String paramName)
static boolean	hasParent()
boolean	hasSummary() Return true if there exists summary of model.
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
static IntParam	k()
IntParam	k() Number of independent Gaussians in the mixture model.
static GaussianMixtureModel	load(String path)
static IntParam	maxIter()
static Param<?>[]	params()
static void	parent_$eq(Estimator<M> x$1)
static Estimator<M>	parent()
static Param<String>	predictionCol()
static Param<String>	probabilityCol()
static MLReader<GaussianMixtureModel>	read()
static void	save(String path)
static LongParam	seed()
static <T> Params	set(Param<T> param, T value)
GaussianMixtureModel	setFeaturesCol(String value)
static M	setParent(Estimator<M> parent)
GaussianMixtureModel	setPredictionCol(String value)
GaussianMixtureModel	setProbabilityCol(String value)
GaussianMixtureSummary	summary() Gets summary of model on training set.
static DoubleParam	tol()
static String	toString()
Dataset<Row>	transform(Dataset<?> dataset) Transforms the input dataset.
StructType	transformSchema(StructType schema) :: DeveloperApi ::
String	uid() An immutable unique ID for the object and its derivatives.
StructType	validateAndTransformSchema(StructType schema) Validates and transforms the input schema.
double[]	weights()
MLWriter	write() Returns a 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 Object

equals, 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

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

toString

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

save

Method Detail

read

public static MLReader<GaussianMixtureModel> read()

load

public static GaussianMixtureModel load(String path)

toString

public static String toString()

params

public static Param<?>[] params()

explainParam

public static String explainParam(Param<?> param)

explainParams

public static String explainParams()

isSet

public static final boolean isSet(Param<?> param)

isDefined

public static final boolean isDefined(Param<?> param)

hasParam

public static boolean hasParam(String paramName)

getParam

public static Param<Object> getParam(String paramName)

set

public static final <T> Params set(Param<T> param,
             T value)

get

public static final <T> scala.Option<T> get(Param<T> param)

clear

public static final Params clear(Param<?> param)

getOrDefault

public static final <T> T getOrDefault(Param<T> param)

getDefault

public static final <T> scala.Option<T> getDefault(Param<T> param)

hasDefault

public static final <T> boolean hasDefault(Param<T> param)

extractParamMap

public static final ParamMap extractParamMap(ParamMap extra)

extractParamMap

public static final ParamMap extractParamMap()

parent

public static Estimator<M> parent()

parent_$eq

public static void parent_$eq(Estimator<M> x$1)

setParent

public static M setParent(Estimator<M> parent)

hasParent

public static boolean hasParent()

maxIter

public static final IntParam maxIter()

getMaxIter

public static final int getMaxIter()

featuresCol

public static final Param<String> featuresCol()

getFeaturesCol

public static final String getFeaturesCol()

seed

public static final LongParam seed()

getSeed

public static final long getSeed()

predictionCol

public static final Param<String> predictionCol()

getPredictionCol

public static final String getPredictionCol()

probabilityCol

public static final Param<String> probabilityCol()

getProbabilityCol

public static final String getProbabilityCol()

tol

public static final DoubleParam tol()

getTol

public static final double getTol()

k

public static final IntParam k()

getK

public static int getK()

save

public static void save(String path)
                 throws java.io.IOException

Throws:

java.io.IOException

uid

public 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)

weights

public double[] weights()

gaussians

public MultivariateGaussian[] gaussians()

setFeaturesCol

public GaussianMixtureModel setFeaturesCol(String value)

setPredictionCol

public GaussianMixtureModel setPredictionCol(String value)

setProbabilityCol

public GaussianMixtureModel setProbabilityCol(String value)

copy

public GaussianMixtureModel 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. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class Model<GaussianMixtureModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

transform

public Dataset<Row> transform(Dataset<?> 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 ::

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.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

gaussiansDF

public Dataset<Row> gaussiansDF()

Retrieve Gaussian distributions as a DataFrame. Each row represents a Gaussian Distribution. Two columns are defined: mean and cov. Schema:

  root
   |-- mean: vector (nullable = true)
   |-- cov: matrix (nullable = true)
 

Returns:

(undocumented)

write

public MLWriter write()

Returns a MLWriter instance for this ML instance.

For GaussianMixtureModel, this does NOT currently save the training summary. An option to save summary may be added in the future.

Specified by:

write in interface MLWritable

Returns:

(undocumented)

hasSummary

public boolean hasSummary()

Return true if there exists summary of model.

Returns:

(undocumented)

summary

public GaussianMixtureSummary summary()

Gets summary of model on training set. An exception is thrown if trainingSummary == None.

Returns:

(undocumented)

k

public IntParam k()

Number of independent Gaussians in the mixture model. Must be greater than 1. Default: 2.

Returns:

(undocumented)

getK

public int getK()

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema)

Validates and transforms the input schema.

Parameters:

schema - input schema

Returns:

output schema