StochasticOutlierSelection

Type Members

case class BreezeLabeledVector(idx: Int, data: Vector[Double]) extends Product with Serializable

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
object ErrorTolerance extends Parameter[Double] with Product with Serializable
object MaxIterations extends Parameter[Int] with Product with Serializable
object Perplexity extends Parameter[Double] with Product with Serializable
def apply(): StochasticOutlierSelection
final def asInstanceOf[T0]: T0

Definition Classes
Any
def binarySearch(dissimilarityVector: Vector[Double], logPerplexity: Double, maxIterations: Int, tolerance: Double, beta: Double = 1.0, betaMin: Double = Double.NegativeInfinity, betaMax: Double = Double.PositiveInfinity, iteration: Int = 0): Vector[Double]

Performs a binary search to get affinities in such a way that each conditional Gaussian has the same perplexity.
Performs a binary search to get affinities in such a way that each conditional Gaussian has the same perplexity.
dissimilarityVector
The input dissimilarity vector which represents the current vector distance to the other vectors in the data set
logPerplexity
The log of the perplexity, which represents the probability of having affinity with another vector.
maxIterations
The maximum iterations to limit the computational time.
tolerance
The allowed tolerance to sacrifice precision for decreased computational time.
betaMin
The lower bound of beta
betaMax
The upper bound of beta
iteration
The current iteration
returns
Returns the affinity vector of the input vector.
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def computeAffinity(dissimilarityVectors: DataSet[BreezeLabeledVector], resultingParameters: ParameterMap): DataSet[BreezeLabeledVector]

Approximate the affinity by fitting a Gaussian-like function
Approximate the affinity by fitting a Gaussian-like function
dissimilarityVectors
The dissimilarity vectors which represents the distance to the other vectors in the data set.
resultingParameters
The user defined parameters of the algorithm
returns
Returns new set of BreezeLabeledVector with dissimilarity vector
def computeBindingProbabilities(affinityVectors: DataSet[BreezeLabeledVector]): DataSet[BreezeLabeledVector]

Normalizes the input vectors so each row sums up to one.
Normalizes the input vectors so each row sums up to one.
affinityVectors
The affinity vectors which is the quantification of the relationship between the original vectors.
returns
Returns new set of BreezeLabeledVector with represents the binding probabilities, which is in fact the affinity where each row sums up to one.
def computeDissimilarityVectors(inputVectors: DataSet[BreezeLabeledVector]): DataSet[BreezeLabeledVector]

Compute pair-wise distance from each vector, to all other vectors.
Compute pair-wise distance from each vector, to all other vectors.
inputVectors
The input vectors, will compare the vector to all other vectors based on an distance method.
returns
Returns new set of BreezeLabeledVector with dissimilarity vector
def computeOutlierProbability(bindingProbabilityVectors: DataSet[BreezeLabeledVector]): DataSet[(Int, Double)]

Compute the final outlier probability by taking the product of the column.
Compute the final outlier probability by taking the product of the column.
bindingProbabilityVectors
The binding probability vectors where the binding probability is based on the affinity and represents the probability of a vector binding with another vector.
returns
Returns a single double which represents the final outlierness of the input vector.
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val parameters: ParameterMap

Definition Classes
WithParameters
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
implicit val transformLabeledVectors: TransformDataSetOperation[StochasticOutlierSelection, LabeledVector, (Int, Double)]
implicit def transformVectors[T <: Vector](implicit arg0: BreezeVectorConverter[T], arg1: TypeInformation[T], arg2: ClassTag[T]): TransformDataSetOperation[StochasticOutlierSelection, T, Double]

TransformDataSetOperation applies the stochastic outlier selection algorithm on a Vector which will transform the high-dimensional input to a single Double output.
TransformDataSetOperation applies the stochastic outlier selection algorithm on a Vector which will transform the high-dimensional input to a single Double output.
T
Type of the input and output data which has to be a subtype of Vector
returns
TransformDataSetOperation a single double which represents the oulierness of the input vectors, where the output is in [0, 1]
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

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

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Docs: class StochasticOutlierSelection | package outlier

object StochasticOutlierSelection extends WithParameters with Serializable

Type Members

case class BreezeLabeledVector(idx: Int, data: Vector[Double]) extends Product with Serializable

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

object ErrorTolerance extends Parameter[Double] with Product with Serializable

object MaxIterations extends Parameter[Int] with Product with Serializable

object Perplexity extends Parameter[Double] with Product with Serializable

def apply(): StochasticOutlierSelection

final def asInstanceOf[T0]: T0

def binarySearch(dissimilarityVector: Vector[Double], logPerplexity: Double, maxIterations: Int, tolerance: Double, beta: Double = 1.0, betaMin: Double = Double.NegativeInfinity, betaMax: Double = Double.PositiveInfinity, iteration: Int = 0): Vector[Double]

def clone(): AnyRef

def computeAffinity(dissimilarityVectors: DataSet[BreezeLabeledVector], resultingParameters: ParameterMap): DataSet[BreezeLabeledVector]

def computeBindingProbabilities(affinityVectors: DataSet[BreezeLabeledVector]): DataSet[BreezeLabeledVector]

def computeDissimilarityVectors(inputVectors: DataSet[BreezeLabeledVector]): DataSet[BreezeLabeledVector]

def computeOutlierProbability(bindingProbabilityVectors: DataSet[BreezeLabeledVector]): DataSet[(Int, Double)]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val parameters: ParameterMap

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

def toString(): String

implicit val transformLabeledVectors: TransformDataSetOperation[StochasticOutlierSelection, LabeledVector, (Int, Double)]

implicit def transformVectors[T <: Vector](implicit arg0: BreezeVectorConverter[T], arg1: TypeInformation[T], arg2: ClassTag[T]): TransformDataSetOperation[StochasticOutlierSelection, T, Double]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from WithParameters

Inherited from AnyRef

Inherited from Any

Ungrouped