Packages

object PoissonMixtureModel extends DiscreteKMeansMixtureModel[Poisson]

Linear Supertypes
DiscreteKMeansMixtureModel[Poisson], DiscreteMixtureModel[Int, Poisson], Logging, Serializable, Serializable, AnyRef, Any
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  1. PoissonMixtureModel
  2. DiscreteKMeansMixtureModel
  3. DiscreteMixtureModel
  4. Logging
  5. Serializable
  6. Serializable
  7. AnyRef
  8. Any
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Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def classMembership(value: Int, weighting: Array[Double], distributions: Array[Poisson]): (Array[Double], Double)

    Computes the assignment weights of a single point to the different distributions that we are fitting.

    Computes the assignment weights of a single point to the different distributions that we are fitting.

    value

    The value of this point.

    weighting

    An array containing the weights of all current distributions.

    distributions

    An array containing all distributions we have fit.

    returns

    Returns a tuple containing the per-point weights of all distributions, and the expected complete log likelihood contribution of this point.

    Attributes
    protected
    Definition Classes
    DiscreteKMeansMixtureModel
  6. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  7. def debug(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  8. def debug(msg: ⇒ Any, t: ⇒ Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  9. def debug(msg: ⇒ Any): Unit
    Attributes
    protected
    Definition Classes
    Logging
  10. def eStep(rdd: RDD[Int], distributions: Array[Poisson], weighting: Array[Double])(implicit dTag: ClassTag[Poisson]): (RDD[Array[Double]], Double)

    Implements the basic expectation stage for most EM algorithms.

    Implements the basic expectation stage for most EM algorithms. Algorithms that diverge from the traditional E step should override this method.

    rdd

    An RDD of data points.

    distributions

    An array containing the distributions fit in the last iteration. This array should contain k distributions, where k is the number of components in the mixture.

    weighting

    The weights of the different distributions.

    returns

    Returns an RDD of assignments to classes, and the total ECLL.

    Attributes
    protected
    Definition Classes
    DiscreteKMeansMixtureModel
  11. def em(rdd: RDD[Int], initialDistributions: Array[Poisson], maxIterations: Int, initialWeights: Array[Double])(implicit dTag: ClassTag[Poisson]): Array[Poisson]

    Runs an EM loop to fit a mixture model.

    Runs an EM loop to fit a mixture model.

    rdd

    An RDD of doubles to fit the mixture model to.

    initialDistributions

    The initial distributions to start running EM from.

    maxIterations

    The maximum number of iterations to run.

    returns

    Returns an array of fit distributions.

    Attributes
    protected
    Definition Classes
    DiscreteKMeansMixtureModel
  12. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  14. def error(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  15. def error(msg: ⇒ Any, t: ⇒ Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  16. def error(msg: ⇒ Any): Unit
    Attributes
    protected
    Definition Classes
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  17. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  18. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  19. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  20. def info(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  21. def info(msg: ⇒ Any, t: ⇒ Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  22. def info(msg: ⇒ Any): Unit
    Attributes
    protected
    Definition Classes
    Logging
  23. def initializeDistribution(mean: Double, sigma: Double): Poisson

    Initializes the distributions, given a mean and a sigma.

    Initializes the distributions, given a mean and a sigma.

    mean

    Mean for an initial distribution.

    sigma

    Standard deviation for an initial distribution.

    returns

    Returns a distribution.

    Attributes
    protected
    Definition Classes
    PoissonMixtureModel → DiscreteKMeansMixtureModel
  24. def isDebugEnabled: Boolean
    Attributes
    protected
    Definition Classes
    Logging
  25. def isErrorEnabled: Boolean
    Attributes
    protected
    Definition Classes
    Logging
  26. def isInfoEnabled: Boolean
    Attributes
    protected
    Definition Classes
    Logging
  27. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  28. def isTraceEnabled: Boolean
    Attributes
    protected
    Definition Classes
    Logging
  29. def isWarnEnabled: Boolean
    Attributes
    protected
    Definition Classes
    Logging
  30. def logger: Logger
    Attributes
    protected
    Definition Classes
    Logging
  31. def loggerName: String
    Attributes
    protected
    Definition Classes
    Logging
  32. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  33. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  34. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  35. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  36. def toString(): String
    Definition Classes
    AnyRef → Any
  37. def trace(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  38. def trace(msg: ⇒ Any, t: ⇒ Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  39. def trace(msg: ⇒ Any): Unit
    Attributes
    protected
    Definition Classes
    Logging
  40. def train(rdd: RDD[Int], k: Int, maxIterations: Int)(implicit dTag: ClassTag[Poisson]): Array[Poisson]

    Trains a mixture model on an integer dataset.

    Trains a mixture model on an integer dataset.

    rdd

    Dataset to fit model to.

    k

    Number of mixture components.

    returns

    Returns an array of distributions.

    Definition Classes
    DiscreteKMeansMixtureModel → DiscreteMixtureModel
  41. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  42. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
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    @throws( ... )
  43. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
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    @native() @throws( ... )
  44. def warn(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  45. def warn(msg: ⇒ Any, t: ⇒ Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  46. def warn(msg: ⇒ Any): Unit
    Attributes
    protected
    Definition Classes
    Logging

Inherited from DiscreteKMeansMixtureModel[Poisson]

Inherited from DiscreteMixtureModel[Int, Poisson]

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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