PoissonMixtureModel

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final def !=(arg0: AnyRef): Boolean

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final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: AnyRef): Boolean

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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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
def clone(): AnyRef

Attributes
protected[java.lang]
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@throws( ... )
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
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
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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def hashCode(): Int

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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
final def isInstanceOf[T0]: Boolean

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def isTraceEnabled(): Boolean

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protected
Definition Classes
Logging
def log: Logger

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

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Logging
def logDebug(msg: ⇒ String): Unit

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

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protected
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Logging
def logError(msg: ⇒ String): Unit

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protected
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Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

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protected
Definition Classes
Logging
def logName: String

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protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

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protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

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protected
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Logging
final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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

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def toString(): String

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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
final def wait(): Unit

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

Definition Classes
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@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
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@throws( ... )

object PoissonMixtureModel extends DiscreteKMeansMixtureModel[Poisson]

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def classMembership(value: Int, weighting: Array[Double], distributions: Array[Poisson]): (Array[Double], Double)

def clone(): AnyRef

def eStep(rdd: RDD[Int], distributions: Array[Poisson], weighting: Array[Double])(implicit dTag: ClassTag[Poisson]): (RDD[Array[Double]], Double)

def em(rdd: RDD[Int], initialDistributions: Array[Poisson], maxIterations: Int, initialWeights: Array[Double])(implicit dTag: ClassTag[Poisson]): Array[Poisson]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

def initializeDistribution(mean: Double, sigma: Double): Poisson

final def isInstanceOf[T0]: Boolean

def isTraceEnabled(): Boolean

def log: Logger

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

def logDebug(msg: ⇒ String): Unit

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

def logError(msg: ⇒ String): Unit

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

def logInfo(msg: ⇒ String): Unit

def logName: String

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

def logTrace(msg: ⇒ String): Unit

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

def logWarning(msg: ⇒ String): Unit

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def toString(): String

def train(rdd: RDD[Int], k: Int, maxIterations: Int)(implicit dTag: ClassTag[Poisson]): Array[Poisson]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

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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