# GaussianMixtureModel

#### case class GaussianMixtureModel(means: DenseMatrix[Double], variances: DenseMatrix[Double], weights: DenseVector[Double], weightThreshold: Double = 1.0E-4) extends Transformer[DenseVector[Double], DenseVector[Double]] with Product with Serializable

A Mixture of Gaussians, usually computed via some clustering process.

means

Cluster centers. # of Dims by # of Cluster. Each column represents a separate cluster.

variances

Cluster variances (diagonal). # of Dims by # of Clusters. Each column represents a separate cluster.

weights

Cluster weights.

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Product, Equals, Transformer[DenseVector[Double], DenseVector[Double]], Chainable[DenseVector[Double], DenseVector[Double]], TransformerOperator, Serializable, Serializable, Operator, AnyRef, Any
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9. Operator
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### Instance Constructors

1. #### new GaussianMixtureModel(means: DenseMatrix[Double], variances: DenseMatrix[Double], weights: DenseVector[Double], weightThreshold: Double = 1.0E-4)

means

Cluster centers. # of Dims by # of Cluster. Each column represents a separate cluster.

variances

Cluster variances (diagonal). # of Dims by # of Clusters. Each column represents a separate cluster.

weights

Cluster weights.

### Value Members

1. #### final def !=(arg0: AnyRef): Boolean

Definition Classes
AnyRef
2. #### final def !=(arg0: Any): Boolean

Definition Classes
Any
3. #### final def ##(): Int

Definition Classes
AnyRef → Any
4. #### final def ==(arg0: AnyRef): Boolean

Definition Classes
AnyRef
5. #### final def ==(arg0: Any): Boolean

Definition Classes
Any
6. #### final def andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: PipelineDataset[DenseVector[Double]], labels: PipelineDataset[L]): Pipeline[DenseVector[Double], C]

Chains a label estimator onto the end of this pipeline, producing a new pipeline.

Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

est

The estimator to chain onto the end of this pipeline

data

The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

labels

The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

Definition Classes
Chainable
7. #### final def andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: RDD[DenseVector[Double]], labels: PipelineDataset[L]): Pipeline[DenseVector[Double], C]

Chains a label estimator onto the end of this pipeline, producing a new pipeline.

Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

est

The estimator to chain onto the end of this pipeline

data

The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

labels

The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

Definition Classes
Chainable
8. #### final def andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: PipelineDataset[DenseVector[Double]], labels: RDD[L]): Pipeline[DenseVector[Double], C]

Chains a label estimator onto the end of this pipeline, producing a new pipeline.

Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

est

The estimator to chain onto the end of this pipeline

data

The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

labels

The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

Definition Classes
Chainable
9. #### final def andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: RDD[DenseVector[Double]], labels: RDD[L]): Pipeline[DenseVector[Double], C]

Chains a label estimator onto the end of this pipeline, producing a new pipeline.

Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

est

The estimator to chain onto the end of this pipeline

data

The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

labels

The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

Definition Classes
Chainable
10. #### final def andThen[C](est: Estimator[DenseVector[Double], C], data: PipelineDataset[DenseVector[Double]]): Pipeline[DenseVector[Double], C]

Chains an estimator onto the end of this pipeline, producing a new pipeline.

Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

est

The estimator to chain onto the end of this pipeline

data

The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

Definition Classes
Chainable
11. #### final def andThen[C](est: Estimator[DenseVector[Double], C], data: RDD[DenseVector[Double]]): Pipeline[DenseVector[Double], C]

Chains an estimator onto the end of this pipeline, producing a new pipeline.

Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

est

The estimator to chain onto the end of this pipeline

data

The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

Definition Classes
Chainable
12. #### final def andThen[C](next: Chainable[DenseVector[Double], C]): Pipeline[DenseVector[Double], C]

Chains a pipeline onto the end of this one, producing a new pipeline.

Chains a pipeline onto the end of this one, producing a new pipeline. If either this pipeline or the following has already been executed, it will not need to be fit again.

next

the pipeline to chain

Definition Classes
Chainable
13. #### def apply(in: RDD[DenseVector[Double]]): RDD[DenseVector[Double]]

The application of this Transformer to an RDD of input items.

The application of this Transformer to an RDD of input items. This method may optionally be overridden by ML developers.

in

The bulk RDD input to pass into this transformer

returns

The bulk RDD output for the given input

Definition Classes
GaussianMixtureModelTransformer

15. #### def apply(in: DenseVector[Double]): DenseVector[Double]

Returns (thresholded) assignments to each cluster.

Returns (thresholded) assignments to each cluster.

in

A Vector

returns

The thresholded assignments of the vector according to the mixture model.

Definition Classes
GaussianMixtureModelTransformer
16. #### final def asInstanceOf[T0]: T0

Definition Classes
Any
17. #### def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )

19. #### final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
20. #### def execute(deps: Seq[Expression]): Expression

Definition Classes
TransformerOperator → Operator
21. #### def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
22. #### final def getClass(): Class[_]

Definition Classes
AnyRef → Any
23. #### final def isInstanceOf[T0]: Boolean

Definition Classes
Any

25. #### def label: String

Definition Classes
Operator
26. #### val means: DenseMatrix[Double]

Cluster centers.

Cluster centers. # of Dims by # of Cluster. Each column represents a separate cluster.

27. #### final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
28. #### final def notify(): Unit

Definition Classes
AnyRef
29. #### final def notifyAll(): Unit

Definition Classes
AnyRef
30. #### final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
31. #### def toPipeline: Pipeline[DenseVector[Double], DenseVector[Double]]

A method that converts this object into a Pipeline.

A method that converts this object into a Pipeline. Must be implemented by anything that extends Chainable.

Definition Classes
TransformerChainable
32. #### val variances: DenseMatrix[Double]

Cluster variances (diagonal).

Cluster variances (diagonal). # of Dims by # of Clusters. Each column represents a separate cluster.

33. #### final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
34. #### final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
35. #### final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

37. #### val weights: DenseVector[Double]

Cluster weights.