trait
Model[Datum] extends SafeLogging
Type Members
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abstract
type
Inference <: framework.Inference[Datum] { ... /* 2 definitions in type refinement */ }
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abstract
type
Scorer
Abstract Value Members
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abstract
def
accumulateCounts(inf: Inference, s: Scorer, d: Datum, m: Marginal, accum: ExpectedCounts, scale: Double): Unit
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abstract
def
expectedCountsToObjective(ecounts: ExpectedCounts): (Double, DenseVector[Double])
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abstract
def
featureIndex: Index[Feature]
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abstract
def
inferenceFromWeights(weights: DenseVector[Double]): Inference
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abstract
def
initialValueForFeature(f: Feature): Double
Concrete Value Members
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final
def
!=(arg0: AnyRef): Boolean
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: AnyRef): Boolean
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final
def
==(arg0: Any): Boolean
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final
def
accumulateCounts(inf: Inference, d: Datum, accum: ExpectedCounts, scale: Double): Unit
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final
def
asInstanceOf[T0]: T0
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def
cacheFeatureWeights(weights: DenseVector[Double], suffix: String = ""): Unit
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def
clone(): AnyRef
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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final
def
expectedCounts(inf: Inference, d: Datum, scale: Double = 1.0): ExpectedCounts
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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def
logger: Logger
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
numFeatures: Int
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def
readCachedFeatureWeights(suffix: String = ""): Option[DenseVector[Double]]
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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def
weightsCacheName: String
Inherited from AnyRef
Inherited from Any
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.
the kind of