SVMKernel

Abstract Value Members

abstract def buildCrossKernelMatrix[S <: Seq[DenseVector[Double]]](dataset1: S, dataset2: S): M

Definition Classes
CovarianceFunction
abstract def buildKernelMatrix[S <: Seq[DenseVector[Double]]](mappedData: S, length: Int): KernelMatrix[M]

Definition Classes
CovarianceFunction
abstract def evaluateAt(config: Map[String, Double])(x: DenseVector[Double], y: DenseVector[Double]): Double

Definition Classes
CovarianceFunction
abstract def gradientAt(config: Map[String, Double])(x: DenseVector[Double], y: DenseVector[Double]): Map[String, Double]

Definition Classes
CovarianceFunction
abstract val hyper_parameters: List[String]

Definition Classes
CovarianceFunction

Concrete 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
final def asInstanceOf[T0]: T0

Definition Classes
Any
def block(h: String*): Unit

Definition Classes
CovarianceFunction
def block_all_hyper_parameters: Unit

Definition Classes
CovarianceFunction
var blocked_hyper_parameters: List[String]

Definition Classes
CovarianceFunction
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def effective_hyper_parameters: List[String]

Definition Classes
CovarianceFunction
def effective_state: Map[String, Double]

Definition Classes
CovarianceFunction
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
def evaluate(x: DenseVector[Double], y: DenseVector[Double]): Double

Definition Classes
CovarianceFunction → Kernel
def featureMapping(decomposition: (DenseVector[Double], DenseMatrix[Double]))(prototypes: List[DenseVector[Double]])(data: DenseVector[Double]): DenseVector[Double]

Builds an approximate nonlinear feature map which corresponds to an SVM Kernel.
Builds an approximate nonlinear feature map which corresponds to an SVM Kernel. This is done using the Nystrom method i.e. approximating the eigenvalues and eigenvectors of the Kernel matrix of some data set.
For each data point, calculate m dimensions of the feature map where m is the number of eigenvalues/vectors obtained from the Eigen Decomposition.
phi_i(x) = (1/sqrt(eigenvalue(i)))*Sum(k, 1, m, K(k, x)*eigenvector(i)(k))
decomposition
The Eigenvalue decomposition calculated from the kernel matrix of the prototype subset.
prototypes
The prototype subset.
data
The dataset on which the feature map is to be applied.
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 gradient(x: DenseVector[Double], y: DenseVector[Double]): Map[String, Double]

Definition Classes
CovarianceFunction
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
def setHyperParameters(h: Map[String, Double]): SVMKernel.this.type

Definition Classes
CovarianceFunction
var state: Map[String, Double]

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

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
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: object SVMKernel | package kernels

trait SVMKernel[M] extends CovarianceFunction[DenseVector[Double], Double, M] with Serializable

Abstract Value Members

abstract def buildCrossKernelMatrix[S <: Seq[DenseVector[Double]]](dataset1: S, dataset2: S): M

abstract def buildKernelMatrix[S <: Seq[DenseVector[Double]]](mappedData: S, length: Int): KernelMatrix[M]

abstract def evaluateAt(config: Map[String, Double])(x: DenseVector[Double], y: DenseVector[Double]): Double

abstract def gradientAt(config: Map[String, Double])(x: DenseVector[Double], y: DenseVector[Double]): Map[String, Double]

abstract val hyper_parameters: List[String]

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def block(h: String*): Unit

def block_all_hyper_parameters: Unit

var blocked_hyper_parameters: List[String]

def clone(): AnyRef

def effective_hyper_parameters: List[String]

def effective_state: Map[String, Double]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def evaluate(x: DenseVector[Double], y: DenseVector[Double]): Double

def featureMapping(decomposition: (DenseVector[Double], DenseMatrix[Double]))(prototypes: List[DenseVector[Double]])(data: DenseVector[Double]): DenseVector[Double]

def finalize(): Unit

final def getClass(): Class[_]

def gradient(x: DenseVector[Double], y: DenseVector[Double]): Map[String, Double]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def setHyperParameters(h: Map[String, Double]): SVMKernel.this.type

var state: Map[String, Double]

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from CovarianceFunction[DenseVector[Double], Double, M]

Inherited from Kernel[DenseVector[Double], Double]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped