GaussianMixtureModelEstimator

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

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2. final def !=(arg0: Any): Boolean

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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5. final def ==(arg0: Any): Boolean

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6. final def asInstanceOf[T0]: T0

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7. def clone(): AnyRef

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8. final def eq(arg0: AnyRef): Boolean

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9. def equals(arg0: Any): Boolean

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10. def execute(deps: Seq[Expression]): TransformerExpression

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    EstimatorOperator → Operator

11. def finalize(): Unit

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12. def fit(samples: Array[DenseVector[Double]]): GaussianMixtureModel

    Fit a Gaussian mixture model with k centers to a sample array.

    Fit a Gaussian mixture model with k centers to a sample array.

    samples

    Sample Array - all elements must be the same size.

    returns

    A Gaussian Mixture Model.

13. def fit(samples: RDD[DenseVector[Double]]): GaussianMixtureModel

    Currently this model works on items that fit in local memory.

    Currently this model works on items that fit in local memory.

    samples

    returns

    A PipelineNode (Transformer) which can be called on new data.

    Definition Classes

    GaussianMixtureModelEstimator → Estimator

14. final def getClass(): Class[_]

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15. def hashCode(): Int

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16. final def isInstanceOf[T0]: Boolean

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17. def label: String

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18. final def ne(arg0: AnyRef): Boolean

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19. final def notify(): Unit

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20. final def notifyAll(): Unit

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21. final def synchronized[T0](arg0: ⇒ T0): T0

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22. def toString(): String

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23. final def wait(): Unit

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

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25. final def wait(arg0: Long): Unit

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26. final def withData(data: PipelineDataset[DenseVector[Double]]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline that fits this estimator to training data, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this estimator to training data, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    returns

    A pipeline that fits this estimator and applies the result to inputs.

    Definition Classes

    Estimator

27. final def withData(data: RDD[DenseVector[Double]]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline that fits this estimator to training data, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this estimator to training data, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    returns

    A pipeline that fits this estimator and applies the result to inputs.

    Definition Classes

    Estimator