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org.bdgenomics.utils.statistics.mixtures

DiscreteKMeansMixtureModel

trait DiscreteKMeansMixtureModel[D <: DiscreteDistr[Int]] extends DiscreteMixtureModel[Int, D]

Linear Supertypes
DiscreteMixtureModel[Int, D], Logging, Serializable, Serializable, AnyRef, Any
Known Subclasses
PoissonMixtureModel
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  1. DiscreteKMeansMixtureModel
  2. DiscreteMixtureModel
  3. Logging
  4. Serializable
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Abstract Value Members

  1. abstract def initializeDistribution(mean: Double, sigma: Double): D

    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

Concrete 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[D]): (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
  6. def clone(): AnyRef
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    protected[java.lang]
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    @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
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    Logging
  9. def debug(msg: ⇒ Any): Unit
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    Logging
  10. def eStep(rdd: RDD[Int], distributions: Array[D], weighting: Array[Double])(implicit dTag: ClassTag[D]): (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
  11. def em(rdd: RDD[Int], initialDistributions: Array[D], maxIterations: Int, initialWeights: Array[Double])(implicit dTag: ClassTag[D]): Array[D]

    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
  12. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean
    Definition Classes
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  14. def error(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
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  15. def error(msg: ⇒ Any, t: ⇒ Throwable): Unit
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  16. def error(msg: ⇒ Any): Unit
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  17. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] )
  18. final def getClass(): Class[_]
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    @native()
  19. def hashCode(): Int
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    @native()
  20. def info(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
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  21. def info(msg: ⇒ Any, t: ⇒ Throwable): Unit
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  22. def info(msg: ⇒ Any): Unit
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  23. def isDebugEnabled: Boolean
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  24. def isErrorEnabled: Boolean
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  25. def isInfoEnabled: Boolean
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  26. final def isInstanceOf[T0]: Boolean
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  27. def isTraceEnabled: Boolean
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  28. def isWarnEnabled: Boolean
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  29. def logger: Logger
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  30. def loggerName: String
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  31. final def ne(arg0: AnyRef): Boolean
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  32. final def notify(): Unit
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    @native()
  33. final def notifyAll(): Unit
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    @native()
  34. final def synchronized[T0](arg0: ⇒ T0): T0
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  35. def toString(): String
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  36. def trace(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
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    Logging
  37. def trace(msg: ⇒ Any, t: ⇒ Throwable): Unit
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  38. def trace(msg: ⇒ Any): Unit
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    Logging
  39. def train(rdd: RDD[Int], k: Int, maxIterations: Int)(implicit dTag: ClassTag[D]): Array[D]

    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
  40. final def wait(): Unit
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    @throws( ... )
  41. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  42. final def wait(arg0: Long): Unit
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  43. def warn(mkr: Marker, msg: ⇒ Any, t: ⇒ Throwable): Unit
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  44. def warn(msg: ⇒ Any, t: ⇒ Throwable): Unit
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  45. def warn(msg: ⇒ Any): Unit
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Inherited from DiscreteMixtureModel[Int, D]

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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