LibSvmRegression

Instance Constructors

new LibSvmRegression(svmType: SvmType = EpsilonSVR, kernelType: KernelType, degree: Int = 3, gamma: Double = 0, coef0: Double = 0, C: Double = 1, nu: Double = 0.5, p: Double = 0.1, eps: Double = 1e-3, shrinking: Boolean = true, probability: Boolean = true, cacheSize: Int = 100)
new LibSvmRegression(parameters: svm_parameter)

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 clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
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Annotations
@throws( ... )
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
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def hashCode(): Int

Definition Classes
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final def isInstanceOf[T0]: Boolean

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

Definition Classes
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final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val parameters: svm_parameter
def saveTo(fn: String): Unit

Saves the current model to a file
Saves the current model to a file

Definition Classes
LibSvmRegression → Regression
def saveTo(writer: Writer): Unit

Saves the current model to a file
Saves the current model to a file

Definition Classes
LibSvmRegression → Regression
def scoreOf(d: Datum[Double, F]): Double

Returns the scores of all possible labels for this datum Convention: if the classifier can return probabilities, these must be probabilities
Returns the scores of all possible labels for this datum Convention: if the classifier can return probabilities, these must be probabilities

Definition Classes
LibSvmRegression → Regression
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def train(dataset: RegDataset[F], indices: Array[Int]): Unit

Trains a classifier, using only the datums specified in indices indices is useful for bagging
Trains a classifier, using only the datums specified in indices indices is useful for bagging

Definition Classes
LibSvmRegression → Regression
def train(dataset: RegDataset[F], spans: Option[Iterable[(Int, Int)]]): Unit

Trains the classifier on the given dataset spans is useful during cross validation
Trains the classifier on the given dataset spans is useful during cross validation

Definition Classes
Regression
def train(dataset: RegDataset[F]): Unit

Definition Classes
Regression
final def wait(): Unit

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

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

Definition Classes
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Annotations
@throws( ... )

Related Docs: object LibSvmRegression | package learning

class LibSvmRegression[F] extends Regression[F] with Serializable

Instance Constructors

new LibSvmRegression(svmType: SvmType = EpsilonSVR, kernelType: KernelType, degree: Int = 3, gamma: Double = 0, coef0: Double = 0, C: Double = 1, nu: Double = 0.5, p: Double = 0.1, eps: Double = 1e-3, shrinking: Boolean = true, probability: Boolean = true, cacheSize: Int = 100)

new LibSvmRegression(parameters: svm_parameter)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

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: svm_parameter

def saveTo(fn: String): Unit

def saveTo(writer: Writer): Unit

def scoreOf(d: Datum[Double, F]): Double

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

def toString(): String

def train(dataset: RegDataset[F], indices: Array[Int]): Unit

def train(dataset: RegDataset[F], spans: Option[Iterable[(Int, Int)]]): Unit

def train(dataset: RegDataset[F]): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Regression[F]

Inherited from AnyRef

Inherited from Any

Ungrouped