Model

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

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

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

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

final def asInstanceOf[T0]: T0

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

Caches the weights using the cache broker.

def clone(): AnyRef

def emptyCounts: StandardExpectedCounts[Feature]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

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

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

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

def numFeatures: Int

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

just saves feature weights to disk as a serialized counter.

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