sequence

Value Members

1. 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.

   Definition Classes

   Operators

2. 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.

   Definition Classes

   Operators

3. 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.

   Definition Classes

   Operators