Package cc.mallet.fst

Class HMM

  • All Implemented Interfaces:
    java.io.Serializable
    public class HMM
extends Transducer
implements java.io.Serializable
    A Hidden Markov Model.
    See Also:
    Serialized Form

    • Constructor Detail

      • HMM

        public HMM​(Pipe inputPipe,
           Pipe outputPipe)

    • Method Detail

      • getInputAlphabet

        public Alphabet getInputAlphabet()

      • getOutputAlphabet

        public Alphabet getOutputAlphabet()

      • getTransitionMultinomial

        public Multinomial[] getTransitionMultinomial()

      • getEmissionMultinomial

        public Multinomial[] getEmissionMultinomial()

      • getInitialMultinomial

        public Multinomial getInitialMultinomial()

      • addState

        public void addState​(java.lang.String name,
                     double initialWeight,
                     double finalWeight,
                     java.lang.String[] destinationNames,
                     java.lang.String[] labelNames)

      • addState

        public void addState​(java.lang.String name,
                     java.lang.String[] destinationNames)
        Add a state with parameters equal zero, and labels on out-going arcs the same name as their destination state names.

      • addFullyConnectedStates

        public void addFullyConnectedStates​(java.lang.String[] stateNames)
        Add a group of states that are fully connected with each other, with parameters equal zero, and labels on their out-going arcs the same name as their destination state names.

      • addFullyConnectedStatesForLabels

        public void addFullyConnectedStatesForLabels()

      • addStatesForLabelsConnectedAsIn

        public void addStatesForLabelsConnectedAsIn​(InstanceList trainingSet)
        Add states to create a first-order Markov model on labels, adding only those transitions the occur in the given trainingSet.

      • addStatesForHalfLabelsConnectedAsIn

        public void addStatesForHalfLabelsConnectedAsIn​(InstanceList trainingSet)
        Add as many states as there are labels, but don't create separate weights for each source-destination pair of states. Instead have all the incoming transitions to a state share the same weights.

      • addStatesForThreeQuarterLabelsConnectedAsIn

        public void addStatesForThreeQuarterLabelsConnectedAsIn​(InstanceList trainingSet)
        Add as many states as there are labels, but don't create separate observational-test-weights for each source-destination pair of states---instead have all the incoming transitions to a state share the same observational-feature-test weights. However, do create separate default feature for each transition, (which acts as an HMM-style transition probability).

      • addFullyConnectedStatesForThreeQuarterLabels

        public void addFullyConnectedStatesForThreeQuarterLabels​(InstanceList trainingSet)

      • addFullyConnectedStatesForBiLabels

        public void addFullyConnectedStatesForBiLabels()

      • addStatesForBiLabelsConnectedAsIn

        public void addStatesForBiLabelsConnectedAsIn​(InstanceList trainingSet)
        Add states to create a second-order Markov model on labels, adding only those transitions the occur in the given trainingSet.

      • addFullyConnectedStatesForTriLabels

        public void addFullyConnectedStatesForTriLabels()

      • addSelfTransitioningStateForAllLabels

        public void addSelfTransitioningStateForAllLabels​(java.lang.String name)

      • addOrderNStates

        public java.lang.String addOrderNStates​(InstanceList trainingSet,
                                        int[] orders,
                                        boolean[] defaults,
                                        java.lang.String start,
                                        java.util.regex.Pattern forbidden,
                                        java.util.regex.Pattern allowed,
                                        boolean fullyConnected)
        Assumes that the HMM's output alphabet contains Strings. Creates an order-n HMM with input predicates and output labels given by trainingSet and order, connectivity, and weights given by the remaining arguments.
        Parameters:
        trainingSet - the training instances
        orders - an array of increasing non-negative numbers giving the orders of the features for this HMM. The largest number n is the Markov order of the HMM. States are n-tuples of output labels. Each of the other numbers k in orders represents a weight set shared by all destination states whose last (most recent) k labels agree. If orders is null, an order-0 HMM is built.
        defaults - If non-null, it must be the same length as orders , with true positions indicating that the weight set for the corresponding order contains only the weight for a default feature; otherwise, the weight set has weights for all features built from input predicates.
        start - The label that represents the context of the start of a sequence. It may be also used for sequence labels.
        forbidden - If non-null, specifies what pairs of successive labels are not allowed, both for constructing norder states or for transitions. A label pair (u,v) is not allowed if u + "," + v matches forbidden.
        allowed - If non-null, specifies what pairs of successive labels are allowed, both for constructing norder states or for transitions. A label pair (u,v) is allowed only if u + "," + v matches allowed.
        fullyConnected - Whether to include all allowed transitions, even those not occurring in trainingSet,

      • getState

        public HMM.State getState​(java.lang.String name)

      • isTrainable

        public boolean isTrainable()

      • reset

        @Deprecated
public void reset()
        Deprecated.

      • initTransitions

        public void initTransitions​(java.util.Random random,
                            double noise)
        Separate initialization of initial/transitions and emissions. All probabilities are proportional to (1+Uniform[0,1])^noise.
        Parameters:
        random - Random object (if null use uniform distribution)
        noise - Noise exponent to use. If zero, then uniform distribution.

      • initEmissions

        public void initEmissions​(java.util.Random random,
                          double noise)

      • estimate

        public void estimate()

      • train

        public boolean train​(InstanceList ilist)
        Trains a HMM without validation and evaluation.

      • write

        public void write​(java.io.File f)