trait Operators extends AnyRef
High level sequence annotation operators.
 Alphabetic
 By Inheritance
 Operators
 AnyRef
 Any
 by any2stringadd
 by StringFormat
 by Ensuring
 by ArrowAssoc
 Hide All
 Show All
 Public
 All
Value Members

final
def
!=(arg0: Any): Boolean
 Definition Classes
 AnyRef → Any

final
def
##(): Int
 Definition Classes
 AnyRef → Any
 def +(other: String): String
 def >[B](y: B): (Operators, B)

final
def
==(arg0: Any): Boolean
 Definition Classes
 AnyRef → Any

final
def
asInstanceOf[T0]: T0
 Definition Classes
 Any

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

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
Firstorder linear conditional random field.
Firstorder 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.
 def ensuring(cond: (Operators) ⇒ Boolean, msg: ⇒ Any): Operators
 def ensuring(cond: (Operators) ⇒ Boolean): Operators
 def ensuring(cond: Boolean, msg: ⇒ Any): Operators
 def ensuring(cond: Boolean): Operators

final
def
eq(arg0: AnyRef): Boolean
 Definition Classes
 AnyRef

def
equals(arg0: Any): Boolean
 Definition Classes
 AnyRef → Any

def
finalize(): Unit
 Attributes
 protected[java.lang]
 Definition Classes
 AnyRef
 Annotations
 @throws( classOf[java.lang.Throwable] )
 def formatted(fmtstr: String): String

final
def
getClass(): Class[_]
 Definition Classes
 AnyRef → Any

def
hashCode(): Int
 Definition Classes
 AnyRef → Any

def
hmm[T <: AnyRef](observations: Array[Array[T]], labels: Array[Array[Int]]): HMM[T]
Trains a firstorder Hidden Markov Model.
Trains a firstorder 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.

def
hmm(observations: Array[Array[Int]], labels: Array[Array[Int]]): HMM[Int]
Firstorder Hidden Markov Model.
Firstorder 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.

final
def
isInstanceOf[T0]: Boolean
 Definition Classes
 Any

final
def
ne(arg0: AnyRef): Boolean
 Definition Classes
 AnyRef

final
def
notify(): Unit
 Definition Classes
 AnyRef

final
def
notifyAll(): Unit
 Definition Classes
 AnyRef

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 →[B](y: B): (Operators, B)
High level Smile operators in Scala.