cc.factorie.app.chain

ChainModel

class ChainModel[Label <: MutableDiscreteVar, Features <: CategoricalVectorVar[String], Token <: Observation[Token]] extends model.Model with model.Parameters

Self Type
ChainModel[Label, Features, Token]
Linear Supertypes
model.Parameters, model.Model, AnyRef, Any
Known Subclasses
NER1Model, PosModel, StackedChainNereModel, model, model
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. ChainModel
  2. Parameters
  3. Model
  4. AnyRef
  5. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Instance Constructors

  1. new ChainModel(labelDomain: CategoricalDomain[String], featuresDomain: CategoricalVectorDomain[String], labelToFeatures: (Label) ⇒ Features, labelToToken: (Label) ⇒ Token, tokenToLabel: (Token) ⇒ Label)(implicit lm: ClassTag[Label], fm: ClassTag[Features], tm: ClassTag[Token])

Type Members

  1. class ChainLikelihoodExample extends optimize.Example

  2. class ChainStructuredSVMExample extends ChainViterbiExample

  3. class ChainViterbiExample extends optimize.Example

  4. case class InferenceResults(logZ: Double, alphas: Array[la.DenseTensor1], betas: Array[la.DenseTensor1], localScores: Array[la.DenseTensor1]) extends Product with Serializable

  5. case class ViterbiResults(mapScore: Double, mapValues: Array[Int], localScores: Array[la.DenseTensor1]) extends Product with Serializable

Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any

  6. def Weights(t4: ⇒ Tensor4): Weights4

    Definition Classes
    Parameters

  7. def Weights(t3: ⇒ Tensor3): Weights3

    Definition Classes
    Parameters

  8. def Weights(t2: ⇒ Tensor2): Weights2

    Definition Classes
    Parameters

  9. def Weights(t1: ⇒ Tensor1): Weights1

    Definition Classes
    Parameters

  10. def accumulateExtraObsGradients(gradient: WeightsMapAccumulator, obsMarginal: la.Tensor1, position: Int, labels: Seq[Label]): Unit

  11. def addFactors(dl: variable.DiffList, result: Set[model.Factor]): Unit

    Append to "result" all Factors in this Model that are affected by the given DiffList.

    Append to "result" all Factors in this Model that are affected by the given DiffList. This method must not append duplicates.

    Definition Classes
    Model

  12. def addFactors(d: variable.Diff, result: Set[model.Factor]): Unit

    Append to "result" all Factors in this Model that are affected by the given Diff.

    Append to "result" all Factors in this Model that are affected by the given Diff. This method must not append duplicates.

    Definition Classes
    Model

  13. def addFactors(variable: variable.Var, result: Set[model.Factor]): Unit

    Append to "result" all Factors in this Model that touch the given "variable".

    Append to "result" all Factors in this Model that touch the given "variable". This method must not append duplicates.

    Definition Classes
    Model

  14. def addFactors(variables: Iterable[variable.Var], result: Set[model.Factor]): Unit

    Append to "result" all Factors in this Model that touch any of the given "variables".

    Append to "result" all Factors in this Model that touch any of the given "variables". This method must not append duplicates.

    Definition Classes
    Model

  15. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  16. def assignmentScore(dl: variable.DiffList, assignment: variable.Assignment): Double

    Definition Classes
    Model

  17. def assignmentScore(d: variable.Diff, assignment: variable.Assignment): Double

    Definition Classes
    Model

  18. def assignmentScore(vars: Iterable[variable.Var], assignment: variable.Assignment): Double

    Definition Classes
    Model

  19. def assignmentScore(variable: variable.Var, assignment: variable.Assignment): Double

    Definition Classes
    Model

  20. val bias: DotFamilyWithStatistics1[Label] { val weights: cc.factorie.model.Weights1 }

  21. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  22. def currentScore(dl: variable.DiffList): Double

    Definition Classes
    Model

  23. def currentScore(d: variable.Diff): Double

    Definition Classes
    Model

  24. def currentScore(vars: Iterable[variable.Var]): Double

    Definition Classes
    Model

  25. def currentScore(variable: variable.Var): Double

    Definition Classes
    Model

  26. def deserialize(stream: InputStream): Unit

  27. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  28. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  29. def factors(v: variable.Var): Iterable[model.Factor]

    Return all Factors in this Model that touch the given "variable".

    Return all Factors in this Model that touch the given "variable". The result will not have any duplicate Factors.

    Definition Classes
    ChainModel → Model

  30. def factors(variables: Iterable[variable.Var]): Iterable[model.Factor]

    Return all Factors in this Model that touch any of the given "variables".

    Return all Factors in this Model that touch any of the given "variables". The result will not have any duplicate Factors.

    Definition Classes
    ChainModel → Model

  31. def factors(dl: variable.DiffList): Iterable[model.Factor]

    Return all Factors in this Model that are affected by the given DiffList.

    Return all Factors in this Model that are affected by the given DiffList. The result will not have any duplicate Factors. By default returns just the factors that neighbor the DiffList.variables, but this method may be overridden for special handling of the DiffList

    Definition Classes
    Model

  32. def factors(d: variable.Diff): Iterable[model.Factor]

    Return all Factors in this Model that are affected by the given Diff.

    Return all Factors in this Model that are affected by the given Diff. The result will not have any duplicate Factors. By default returns just the factors that neighbor Diff.variable, but this method may be overridden for special handling of the Diff

    Definition Classes
    Model

  33. def factorsOfClass[F <: model.Factor](d: variable.DiffList)(implicit fm: ClassTag[F]): Iterable[F]

    Definition Classes
    Model

  34. def factorsOfClass[F <: model.Factor](d: variable.DiffList, fclass: Class[F]): Iterable[F]

    Definition Classes
    Model

  35. def factorsOfClass[F <: model.Factor](variables: Iterable[variable.Var])(implicit fm: ClassTag[F]): Iterable[F]

    Definition Classes
    Model

  36. def factorsOfClass[F <: model.Factor](variable: variable.Var)(implicit fm: ClassTag[F]): Iterable[F]

    Definition Classes
    Model

  37. def factorsOfClass[F <: model.Factor](variables: Iterable[variable.Var], fclass: Class[F]): Iterable[F]

    Definition Classes
    Model

  38. def factorsOfClass[F <: model.Factor](variable: variable.Var, fclass: Class[F]): Iterable[F]

    Definition Classes
    Model

  39. def factorsOfFamilies[F <: Family](d: variable.DiffList, families: Seq[F]): Iterable[model.Model.factorsOfFamilies.F.Factor]

    Definition Classes
    Model

  40. def factorsOfFamilies[F <: Family](variables: Iterable[variable.Var], families: Seq[F]): Iterable[model.Model.factorsOfFamilies.F.Factor]

    Definition Classes
    Model

  41. def factorsOfFamilies[F <: Family](variable: variable.Var, families: Seq[F]): Iterable[model.Model.factorsOfFamilies.F.Factor]

    Definition Classes
    Model

  42. def factorsOfFamily[F <: Family](d: variable.DiffList, family: F): Iterable[model.Model.factorsOfFamily.F.Factor]

    Definition Classes
    Model

  43. def factorsOfFamily[F <: Family](variables: Iterable[variable.Var], family: F): Iterable[model.Model.factorsOfFamily.F.Factor]

    Definition Classes
    Model

  44. def factorsOfFamily[F <: Family](variable: variable.Var, family: F): Iterable[model.Model.factorsOfFamily.F.Factor]

    Definition Classes
    Model

  45. def factorsOfFamilyClass[F <: Family](d: variable.DiffList)(implicit fm: ClassTag[F]): Iterable[model.Model.factorsOfFamilyClass.F.Factor]

    Definition Classes
    Model

  46. def factorsOfFamilyClass[F <: Family](d: variable.DiffList, fclass: Class[F]): Iterable[model.Model.factorsOfFamilyClass.F.Factor]

    Definition Classes
    Model

  47. def factorsOfFamilyClass[F <: Family](variables: Iterable[variable.Var])(implicit fm: ClassTag[F]): Iterable[model.Model.factorsOfFamilyClass.F.Factor]

    Definition Classes
    Model

  48. def factorsOfFamilyClass[F <: Family](variable: variable.Var)(implicit fm: ClassTag[F]): Iterable[model.Model.factorsOfFamilyClass.F.Factor]

    Definition Classes
    Model

  49. def factorsOfFamilyClass[F <: Family](variables: Iterable[variable.Var], fclass: Class[F]): Iterable[model.Model.factorsOfFamilyClass.F.Factor]

    Definition Classes
    Model

  50. def factorsOfFamilyClass[F <: Family](variable: variable.Var, fclass: Class[F]): Iterable[model.Model.factorsOfFamilyClass.F.Factor]

    Definition Classes
    Model

  51. val featureClass: Class[_]

  52. val featuresDomain: CategoricalVectorDomain[String]

  53. def filterByFactorClass[F <: model.Factor](factors: Iterable[model.Factor], fclass: Class[F]): Iterable[F]

    Definition Classes
    Model

  54. def filterByFamilies[F <: Family](factors: Iterable[model.Factor], families: Seq[F]): Iterable[model.Model.filterByFamilies.F.Factor]

    Definition Classes
    Model

  55. def filterByFamily[F <: Family](factors: Iterable[model.Factor], family: F): Iterable[model.Model.filterByFamily.F.Factor]

    Definition Classes
    Model

  56. def filterByFamilyClass[F <: Family](factors: Iterable[model.Factor], fclass: Class[F]): Iterable[model.Model.filterByFamilyClass.F.Factor]

    Definition Classes
    Model

  57. def filterByNotFamilyClass[F <: Family](factors: Iterable[model.Factor], fclass: Class[F]): Iterable[model.Factor]

    Definition Classes
    Model

  58. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  59. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any

  60. def getHammingLossScores(varying: Seq[Label with LabeledMutableDiscreteVar]): Array[la.Tensor1]

  61. def getLocalScores(varying: Seq[Label]): Array[la.DenseTensor1]

  62. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  63. def inferFast(varying: Seq[Label], addToLocalScoresOpt: Option[Array[la.Tensor1]] = None): InferenceResults

  64. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  65. def itemizedModel(dl: variable.DiffList): ItemizedModel

    Definition Classes
    Model

  66. def itemizedModel(d: variable.Diff): ItemizedModel

    Definition Classes
    Model

  67. def itemizedModel(variables: Iterable[variable.Var]): ItemizedModel

    Definition Classes
    Model

  68. def itemizedModel(variable: variable.Var): ItemizedModel

    Definition Classes
    Model

  69. val labelClass: Class[_]

  70. val labelDomain: CategoricalDomain[String]

  71. val labelToFeatures: (Label) ⇒ Features

  72. val labelToToken: (Label) ⇒ Token

  73. val markov: DotFamilyWithStatistics2[Label, Label] { val weights: cc.factorie.model.Weights2 }

  74. def maximize(vars: Seq[Label])(implicit d: variable.DiffList): Unit

  75. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  76. def newFactorsCollection: Set[model.Factor]

    The "factors" methods need a new collection to return; this method is used by them to construct this collection.

    The "factors" methods need a new collection to return; this method is used by them to construct this collection.

    Definition Classes
    Model

  77. final def notify(): Unit

    Definition Classes
    AnyRef

  78. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  79. val obs: DotFamilyWithStatistics2[Features, Label] { val weights: cc.factorie.model.Weights2 }

  80. val obsmarkov: DotFamilyWithStatistics3[Label, Label, Features] { val weights: cc.factorie.model.Weights3 }

  81. val parameters: WeightsSet

    Definition Classes
    Parameters

  82. def serialize(stream: OutputStream): Unit

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

    Definition Classes
    AnyRef

  84. def toString(): String

    Definition Classes
    AnyRef → Any

  85. val tokenClass: Class[_]

  86. val tokenToLabel: (Token) ⇒ Label

  87. var useObsMarkov: Boolean

  88. def viterbiFast(varying: Seq[Label], addToLocalScoresOpt: Option[Array[la.Tensor1]] = None): ViterbiResults

  89. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  91. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from model.Parameters

Inherited from model.Model

Inherited from AnyRef

Inherited from Any

Ungrouped