 # GaussianMixtureModel

### Related Docs: object GaussianMixtureModel | package clustering

#### class GaussianMixtureModel extends Serializable with Saveable

Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points are drawn from each Gaussian i=1..k with probability w(i); mu(i) and sigma(i) are the respective mean and covariance for each Gaussian distribution i=1..k.

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@Since( "1.3.0" )
Source
GaussianMixtureModel.scala
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Saveable, Serializable, Serializable, AnyRef, Any
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1. GaussianMixtureModel
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### Instance Constructors

1. #### new GaussianMixtureModel(weights: Array[Double], gaussians: Array[MultivariateGaussian])

weights

Weights for each Gaussian distribution in the mixture, where weights(i) is the weight for Gaussian i, and weights.sum == 1

gaussians

Array of MultivariateGaussian where gaussians(i) represents the Multivariate Gaussian (Normal) Distribution for Gaussian i

Annotations
@Since( "1.3.0" )

### Value Members

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

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AnyRef → Any
2. #### final def ##(): Int

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3. #### final def ==(arg0: Any): Boolean

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4. #### final def asInstanceOf[T0]: T0

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5. #### def clone(): AnyRef

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@throws( ... )
6. #### final def eq(arg0: AnyRef): Boolean

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7. #### def equals(arg0: Any): Boolean

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8. #### def finalize(): Unit

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protected[java.lang]
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9. #### def formatVersion: String

Current version of model save/load format.

Current version of model save/load format.

Attributes
protected
Definition Classes
GaussianMixtureModelSaveable
10. #### val gaussians: Array[MultivariateGaussian]

Array of MultivariateGaussian where gaussians(i) represents the Multivariate Gaussian (Normal) Distribution for Gaussian i

Array of MultivariateGaussian where gaussians(i) represents the Multivariate Gaussian (Normal) Distribution for Gaussian i

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@Since( "1.3.0" )
11. #### final def getClass(): Class[_]

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12. #### def hashCode(): Int

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13. #### final def isInstanceOf[T0]: Boolean

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14. #### def k: Int

Number of gaussians in mixture

Number of gaussians in mixture

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@Since( "1.3.0" )
15. #### final def ne(arg0: AnyRef): Boolean

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16. #### final def notify(): Unit

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17. #### final def notifyAll(): Unit

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AnyRef
18. #### def predict(points: JavaRDD[Vector]): JavaRDD[Integer]

Java-friendly version of `predict()`

Java-friendly version of `predict()`

Annotations
@Since( "1.4.0" )
19. #### def predict(point: Vector): Int

Maps given point to its cluster index.

Maps given point to its cluster index.

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@Since( "1.5.0" )
20. #### def predict(points: RDD[Vector]): RDD[Int]

Maps given points to their cluster indices.

Maps given points to their cluster indices.

Annotations
@Since( "1.3.0" )
21. #### def predictSoft(point: Vector): Array[Double]

Given the input vector, return the membership values to all mixture components.

Given the input vector, return the membership values to all mixture components.

Annotations
@Since( "1.4.0" )
22. #### def predictSoft(points: RDD[Vector]): RDD[Array[Double]]

Given the input vectors, return the membership value of each vector to all mixture components.

Given the input vectors, return the membership value of each vector to all mixture components.

Annotations
@Since( "1.3.0" )
23. #### def save(sc: SparkContext, path: String): Unit

Save this model to the given path.

Save this model to the given path.

This saves:

• Parquet formatted data to path/data/

The model may be loaded using `Loader.load`.

sc

Spark context used to save model data.

path

Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

Definition Classes
GaussianMixtureModelSaveable
Annotations
@Since( "1.4.0" )
24. #### final def synchronized[T0](arg0: ⇒ T0): T0

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25. #### def toString(): String

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

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27. #### final def wait(arg0: Long, arg1: Int): Unit

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28. #### final def wait(arg0: Long): Unit

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29. #### val weights: Array[Double]

Weights for each Gaussian distribution in the mixture, where weights(i) is the weight for Gaussian i, and weights.sum == 1

Weights for each Gaussian distribution in the mixture, where weights(i) is the weight for Gaussian i, and weights.sum == 1

Annotations
@Since( "1.3.0" )