Model

A Model represents a class for turning weight vectors into epic.framework.Inferences. It's main job is to hook up with a epic.framework.ModelObjective and mediate computation of ExpectedCounts and conversion to the objective that's needed for optimization.

Datum: the kind of

Self Type: Model[Datum]

Linear Supertypes

SafeLogging, AnyRef, Any

Known Subclasses

CRFModel, ClassificationModel, EPModel, EPParserModel, EvaluableModel, LatentParserModel, LexModel, Model, Model, OneBestModelAdaptor, ParserModel, PositionalNeuralModel, SemiCRFModel, SpanModel, StructModel, ThreePointModel, TransformModel

Type Members

abstract type ExpectedCounts >: Null <: framework.ExpectedCounts[ExpectedCounts]
abstract type Inference <: framework.Inference[Datum] { ... /* 2 definitions in type refinement */ }
abstract type Marginal <: framework.Marginal
abstract type Scorer

Abstract Value Members

abstract def accumulateCounts(inf: Inference, s: Scorer, d: Datum, m: Marginal, accum: ExpectedCounts, scale: Double): Unit
abstract def emptyCounts: ExpectedCounts
abstract def expectedCountsToObjective(ecounts: ExpectedCounts): (Double, DenseVector[Double])
abstract def featureIndex: Index[Feature]

Models have features, and this defines the mapping from indices in the weight vector to features.
abstract def inferenceFromWeights(weights: DenseVector[Double]): Inference
abstract def initialValueForFeature(f: Feature): Double

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 accumulateCounts(inf: Inference, d: Datum, accum: ExpectedCounts, scale: Double): Unit
final def asInstanceOf[T0]: T0

Definition Classes
Any
def cacheFeatureWeights(weights: DenseVector[Double], suffix: String = ""): Unit

Caches the weights using the cache broker.
def clone(): AnyRef

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

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

Definition Classes
AnyRef → Any
final def expectedCounts(inf: Inference, d: Datum, scale: Double = 1.0): ExpectedCounts
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
def logger: Logger

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def numFeatures: Int
def readCachedFeatureWeights(suffix: String = ""): Option[DenseVector[Double]]

just saves feature weights to disk as a serialized counter.
just saves feature weights to disk as a serialized counter. The file is prefix.ser.gz
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( ... )
def weightsCacheName: String

Attributes
protected

Related Doc: package framework

trait Model[Datum] extends SafeLogging

Type Members

abstract type ExpectedCounts >: Null <: framework.ExpectedCounts[ExpectedCounts]

abstract type Inference <: framework.Inference[Datum] { ... /* 2 definitions in type refinement */ }

abstract type Marginal <: framework.Marginal

abstract type Scorer

Abstract Value Members

abstract def accumulateCounts(inf: Inference, s: Scorer, d: Datum, m: Marginal, accum: ExpectedCounts, scale: Double): Unit

abstract def emptyCounts: ExpectedCounts

abstract def expectedCountsToObjective(ecounts: ExpectedCounts): (Double, DenseVector[Double])

abstract def featureIndex: Index[Feature]

abstract def inferenceFromWeights(weights: DenseVector[Double]): Inference

abstract def initialValueForFeature(f: Feature): Double

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def accumulateCounts(inf: Inference, d: Datum, accum: ExpectedCounts, scale: Double): Unit

final def asInstanceOf[T0]: T0

def cacheFeatureWeights(weights: DenseVector[Double], suffix: String = ""): Unit

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

final def expectedCounts(inf: Inference, d: Datum, scale: Double = 1.0): ExpectedCounts

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def logger: Logger

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def numFeatures: Int

def readCachedFeatureWeights(suffix: String = ""): Option[DenseVector[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

def weightsCacheName: String

Inherited from SafeLogging

Inherited from AnyRef

Inherited from Any

Ungrouped