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, 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.
Models have features, and this defines the mapping from indices in the weight vector to features.
returns
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

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