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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.
def ensuring(cond: (Operators) ⇒ Boolean, msg: ⇒ Any): Operators

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This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method any2Ensuring in scala.Predef.
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Ensuring
def ensuring(cond: (Operators) ⇒ Boolean): Operators

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This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method any2Ensuring in scala.Predef.
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def ensuring(cond: Boolean, msg: ⇒ Any): Operators

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This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method any2Ensuring in scala.Predef.
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Ensuring
def ensuring(cond: Boolean): Operators

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This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method any2Ensuring in scala.Predef.
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final def eq(arg0: AnyRef): Boolean

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def formatted(fmtstr: String): String

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final def getClass(): Class[_]

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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.
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.
final def isInstanceOf[T0]: Boolean

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def →[B](y: B): (Operators, B)

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def x: Operators
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This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method any2Ensuring in scala.Predef.
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(Since version 2.10.0) Use resultOfEnsuring instead

trait Operators extends AnyRef

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final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

def +(other: String): String

def ->[B](y: B): (Operators, B)

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

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

References:

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

def equals(arg0: Any): Boolean

def finalize(): Unit

def formatted(fmtstr: String): String

final def getClass(): Class[_]

def hashCode(): Int

def hmm[T <: AnyRef](observations: Array[Array[T]], labels: Array[Array[Int]]): HMM[T]

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

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

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def →[B](y: B): (Operators, B)

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