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t

smile.sequence

Operators

trait Operators extends AnyRef

High level sequence annotation operators.

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  1. Operators
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  1. by any2stringadd
  2. by StringFormat
  3. by Ensuring
  4. by ArrowAssoc
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Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. def +(other: String): String
    Implicit
    This member is added by an implicit conversion from Operators to any2stringadd[Operators] performed by method any2stringadd in scala.Predef.
    Definition Classes
    any2stringadd
  4. def ->[B](y: B): (Operators, B)
    Implicit
    This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.
    Definition Classes
    ArrowAssoc
    Annotations
    @inline()
  5. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. def crf(sequences: Array[Array[Array[Double]]], labels: Array[Array[Int]], attributes: Array[Attribute], k: Int, eta: Double = 1.0, ntrees: Int = 100, maxNodes: Int = 100): CRF

    First-order linear conditional random field.

    First-order linear conditional random field. A conditional random field is a type of discriminative undirected probabilistic graphical model. It is most often used for labeling or parsing of sequential data.

    A CRF is a Markov random field that was trained discriminatively. Therefore it is not necessary to model the distribution over always observed variables, which makes it possible to include arbitrarily complicated features of the observed variables into the model.

    References:
    • J. Lafferty, A. McCallum and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. ICML, 2001.
    • Thomas G. Dietterich, Guohua Hao, and Adam Ashenfelter. Gradient Tree Boosting for Training Conditional Random Fields. JMLR, 2008.
    sequences

    the observation attribute sequences.

    labels

    sequence labels.

    attributes

    the feature attributes.

    k

    the number of classes.

    eta

    the learning rate of potential function.

    ntrees

    the number of trees/iterations.

    maxNodes

    the maximum number of leaf nodes in the tree.

  9. def ensuring(cond: (Operators) ⇒ Boolean, msg: ⇒ Any): Operators
    Implicit
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  10. def ensuring(cond: (Operators) ⇒ Boolean): Operators
    Implicit
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  11. def ensuring(cond: Boolean, msg: ⇒ Any): Operators
    Implicit
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  12. def ensuring(cond: Boolean): Operators
    Implicit
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  16. def formatted(fmtstr: String): String
    Implicit
    This member is added by an implicit conversion from Operators to StringFormat[Operators] performed by method StringFormat in scala.Predef.
    Definition Classes
    StringFormat
    Annotations
    @inline()
  17. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  18. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  19. def hmm[T <: AnyRef](observations: Array[Array[T]], labels: Array[Array[Int]]): HMM[T]

    Trains a first-order Hidden Markov Model.

    Trains a first-order Hidden Markov Model.

    observations

    the observation sequences, of which symbols take values in [0, n), where n is the number of unique symbols.

    labels

    the state labels of observations, of which states take values in [0, p), where p is the number of hidden states.

  20. def hmm(observations: Array[Array[Int]], labels: Array[Array[Int]]): HMM[Int]

    First-order Hidden Markov Model.

    First-order Hidden Markov Model. A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states. An HMM can be considered as the simplest dynamic Bayesian network.

    In a regular Markov model, the state is directly visible to the observer, and therefore the state transition probabilities are the only parameters. In a hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible. Each state has a probability distribution over the possible output tokens. Therefore the sequence of tokens generated by an HMM gives some information about the sequence of states.

    observations

    the observation sequences, of which symbols take values in [0, n), where n is the number of unique symbols.

    labels

    the state labels of observations, of which states take values in [0, p), where p is the number of hidden states.

  21. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  22. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  23. final def notify(): Unit
    Definition Classes
    AnyRef
  24. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  25. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  26. def toString(): String
    Definition Classes
    AnyRef → Any
  27. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  28. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  29. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  30. def [B](y: B): (Operators, B)
    Implicit
    This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.
    Definition Classes
    ArrowAssoc

Inherited from AnyRef

Inherited from Any

Inherited by implicit conversion any2stringadd from Operators to any2stringadd[Operators]

Inherited by implicit conversion StringFormat from Operators to StringFormat[Operators]

Inherited by implicit conversion Ensuring from Operators to Ensuring[Operators]

Inherited by implicit conversion ArrowAssoc from Operators to ArrowAssoc[Operators]

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