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" )
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- GaussianMixtureModel.scala
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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
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val
gaussians: Array[MultivariateGaussian]
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def
k: Int
Number of gaussians in mixture
Number of gaussians in mixture
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def
predict(points: JavaRDD[Vector]): JavaRDD[Integer]
Java-friendly version of
predict()Java-friendly version of
predict()- Annotations
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def
predict(point: Vector): Int
Maps given point to its cluster index.
Maps given point to its cluster index.
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def
predict(points: RDD[Vector]): RDD[Int]
Maps given points to their cluster indices.
Maps given points to their cluster indices.
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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.
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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.
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def
save(sc: SparkContext, path: String): Unit
Save this model to the given path.
Save this model to the given path.
This saves:
- human-readable (JSON) model metadata to path/metadata/
- 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.
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- GaussianMixtureModel → Saveable
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val
weights: Array[Double]
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