keystoneml.nodes.learning

GaussianMixtureModelEstimator

case class GaussianMixtureModelEstimator(k: Int, maxIterations: Int = 100, minClusterSize: Int = 40, stopTolerance: Double = 1.0E-4, weightThreshold: Double = 1.0E-4, smallVarianceThreshold: Double = 0.01, absoluteVarianceThreshold: Double = 1.0E-9, initializationMethod: GMMInitializationMethod = KMEANS_PLUS_PLUS_INITIALIZATION, seed: Int = 0) extends Estimator[DenseVector[Double], DenseVector[Double]] with Logging with Product with Serializable

Fit a Gaussian Mixture model to Data. We assume diagonal covariances.

Trains the GMM using the guidelines described in Appendix B of:

Jorge Sanchez, Florent Perronnin, Thomas Mensink, Jakob Verbeek. Image Classification with the Fisher Vector: Theory and Practice. International Journal of Computer Vision, Springer Verlag, 2013, 105 (3), pp.222-245. <10.1007/s11263-013-0636-x>.

k

Number of centers to estimate.

Linear Supertypes
Product, Equals, Logging, Estimator[DenseVector[Double], DenseVector[Double]], EstimatorOperator, Serializable, Serializable, Operator, AnyRef, Any
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  1. GaussianMixtureModelEstimator
  2. Product
  3. Equals
  4. Logging
  5. Estimator
  6. EstimatorOperator
  7. Serializable
  8. Serializable
  9. Operator
  10. AnyRef
  11. Any
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Instance Constructors

  1. new GaussianMixtureModelEstimator(k: Int, maxIterations: Int = 100, minClusterSize: Int = 40, stopTolerance: Double = 1.0E-4, weightThreshold: Double = 1.0E-4, smallVarianceThreshold: Double = 0.01, absoluteVarianceThreshold: Double = 1.0E-9, initializationMethod: GMMInitializationMethod = KMEANS_PLUS_PLUS_INITIALIZATION, seed: Int = 0)

    k

    Number of centers to estimate.

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. val absoluteVarianceThreshold: Double

  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  8. def clone(): AnyRef

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

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

    Definition Classes
    AnyRef

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

    Definition Classes
    EstimatorOperator → Operator

  11. def finalize(): Unit

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

  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[_]

    Definition Classes
    AnyRef → Any

  15. val initializationMethod: GMMInitializationMethod

  16. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  17. val k: Int

    Number of centers to estimate.

  18. def label: String

    Definition Classes
    Operator

  19. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging

  20. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  21. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  22. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  23. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  24. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  25. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  26. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  27. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  28. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  29. def logWarning(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  30. val maxIterations: Int

  31. val minClusterSize: Int

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

    Definition Classes
    AnyRef

  33. final def notify(): Unit

    Definition Classes
    AnyRef

  34. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  35. val seed: Int

  36. val smallVarianceThreshold: Double

  37. val stopTolerance: Double

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

    Definition Classes
    AnyRef

  39. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  42. val weightThreshold: Double

  43. 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

  44. 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

Inherited from Product

Inherited from Equals

Inherited from Logging

Inherited from Estimator[DenseVector[Double], DenseVector[Double]]

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

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