Package 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:

Serializable, org.apache.spark.internal.Logging, GaussianMixtureParams, Params, HasAggregationDepth, HasFeaturesCol, HasMaxIter, HasPredictionCol, HasProbabilityCol, HasSeed, HasTol, HasWeightCol, HasTrainingSummary<GaussianMixtureSummary>, Identifiable, MLWritable

public class GaussianMixtureModel
extends Model<GaussianMixtureModel>
implements GaussianMixtureParams, MLWritable, HasTrainingSummary<GaussianMixtureSummary>

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

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter

Method Summary

Modifier and Type	Method	Description
final IntParam	aggregationDepth()	Param for suggested depth for treeAggregate (>= 2).
GaussianMixtureModel	copy(ParamMap extra)	Creates a copy of this instance with the same UID and some extra params.
final Param<String>	featuresCol()	Param for features column name.
MultivariateGaussian[]	gaussians()
Dataset<Row>	gaussiansDF()	Retrieve Gaussian distributions as a DataFrame.
final IntParam	k()	Number of independent Gaussians in the mixture model.
static GaussianMixtureModel	load(String path)
final IntParam	maxIter()	Param for maximum number of iterations (&gt;= 0).
int	numFeatures()
int	predict(Vector features)
final Param<String>	predictionCol()	Param for prediction column name.
Vector	predictProbability(Vector features)
final Param<String>	probabilityCol()	Param for Column name for predicted class conditional probabilities.
static MLReader<GaussianMixtureModel>	read()
final LongParam	seed()	Param for random seed.
GaussianMixtureModel	setFeaturesCol(String value)
GaussianMixtureModel	setPredictionCol(String value)
GaussianMixtureModel	setProbabilityCol(String value)
GaussianMixtureSummary	summary()	Gets summary of model on training set.
final DoubleParam	tol()	Param for the convergence tolerance for iterative algorithms (&gt;= 0).
String	toString()
Dataset<Row>	transform(Dataset<?> dataset)	Transforms the input dataset.
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.
final Param<String>	weightCol()	Param for weight column name.
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