smile.sequence.package.scala Maven / Gradle / Ivy
/*
* Copyright (c) 2010-2021 Haifeng Li. All rights reserved.
*
* Smile is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* Smile is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with Smile. If not, see 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.
*
* @param observations the observation sequences, of which symbols take
* values in [0, n), where n is the number of unique symbols.
* @param 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 = time("Hidden Markov Model") {
HMM.fit(observations, labels)
}
/** Trains a first-order Hidden Markov Model.
*
* @param observations the observation sequences, of which symbols take
* values in [0, n), where n is the number of unique symbols.
* @param labels the state labels of observations, of which states take
* values in [0, p), where p is the number of hidden states.
*/
def hmm[T <: AnyRef](observations: Array[Array[T]], labels: Array[Array[Int]], ordinal: ToIntFunction[T]): HMMLabeler[T] = time("Hidden Markov Model") {
HMMLabeler.fit(observations, labels, ordinal)
}
/** 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.
*
* @param sequences the observation attribute sequences.
* @param labels sequence labels.
* @param ntrees the number of trees/iterations.
* @param maxDepth the maximum depth of the tree.
* @param maxNodes the maximum number of leaf nodes in the tree.
* @param nodeSize the number of instances in a node below which the tree will
* not split, setting nodeSize = 5 generally gives good results.
* @param shrinkage the shrinkage parameter in (0, 1] controls the learning rate of procedure.
*/
def crf(sequences: Array[Array[Tuple]], labels: Array[Array[Int]], ntrees: Int = 100, maxDepth: Int = 20, maxNodes: Int = 100, nodeSize: Int = 5, shrinkage: Double = 1.0): CRF = time("CRF") {
CRF.fit(sequences, labels, ntrees, maxDepth, maxNodes, nodeSize, shrinkage)
}
/** 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.
*
* @param sequences the observation attribute sequences.
* @param labels sequence labels.
* @param ntrees the number of trees/iterations.
* @param maxDepth the maximum depth of the tree.
* @param maxNodes the maximum number of leaf nodes in the tree.
* @param nodeSize the number of instances in a node below which the tree will
* not split, setting nodeSize = 5 generally gives good results.
* @param shrinkage the shrinkage parameter in (0, 1] controls the learning rate of procedure.
*/
def gcrf[T <: AnyRef](sequences: Array[Array[T]], labels: Array[Array[Int]], features: Function[T, Tuple], ntrees: Int = 100, maxDepth: Int = 20, maxNodes: Int = 100, nodeSize: Int = 5, shrinkage: Double = 1.0): CRFLabeler[T] = time("CRF") {
CRFLabeler.fit(sequences, labels, features, ntrees, maxDepth, maxNodes, nodeSize, shrinkage)
}
/** Hacking scaladoc [[https://github.com/scala/bug/issues/8124 issue-8124]].
* The user should ignore this object. */
object $dummy
}