LogisticRegressionClassifier

Instance Constructors

new LogisticRegressionClassifier(C: Double = 1.0, eps: Double = 0.01, bias: Boolean = false, classWeights: Option[Array[(L, Double)]] = None)

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
val C: Double

Definition Classes
LiblinearClassifier
final def asInstanceOf[T0]: T0

Definition Classes
Any
val bias: Boolean

Definition Classes
LiblinearClassifier
def classOf(d: Datum[L, F]): L

Returns the argmax for this datum
Returns the argmax for this datum

Definition Classes
LiblinearClassifier → Classifier
val classWeights: Option[Array[(L, Double)]]

Definition Classes
LiblinearClassifier
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val eps: Double

Definition Classes
LiblinearClassifier
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 getWeights(verbose: Boolean = false): Map[L, Counter[F]]

Definition Classes
LiblinearClassifier
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 saveTo(w: Writer): Unit

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

Definition Classes
LiblinearClassifier → Classifier
def saveTo(fileName: String): Unit

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

Definition Classes
Classifier
def scoresOf(d: Datum[L, F]): Counter[L]

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
LiblinearClassifier → Classifier
val solverType: SolverType

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

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def train(dataset: Dataset[L, 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
LiblinearClassifier → Classifier
def train(dataset: Dataset[L, F], spans: Option[Iterable[(Int, Int)]] = None): 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
Classifier
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 Doc: package learning

class LogisticRegressionClassifier[L, F] extends LiblinearClassifier[L, F]

Instance Constructors

new LogisticRegressionClassifier(C: Double = 1.0, eps: Double = 0.01, bias: Boolean = false, classWeights: Option[Array[(L, Double)]] = None)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

val C: Double

final def asInstanceOf[T0]: T0

val bias: Boolean

def classOf(d: Datum[L, F]): L

val classWeights: Option[Array[(L, Double)]]

def clone(): AnyRef

val eps: Double

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getWeights(verbose: Boolean = false): Map[L, Counter[F]]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def saveTo(w: Writer): Unit

def saveTo(fileName: String): Unit

def scoresOf(d: Datum[L, F]): Counter[L]

val solverType: SolverType

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

def toString(): String

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

def train(dataset: Dataset[L, F], spans: Option[Iterable[(Int, Int)]] = None): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from LiblinearClassifier[L, F]

Inherited from Serializable

Inherited from Classifier[L, F]

Inherited from AnyRef

Inherited from Any

Ungrouped