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ViterbiLearningTDistribution, TObservation Class

Viterbi learning algorithm.
Inheritance Hierarchy
    Accord.Statistics.Models.Markov.LearningViterbiLearningTDistribution, TObservation

Namespace:  Accord.Statistics.Models.Markov.Learning
Assembly:  Accord.Statistics (in Accord.Statistics.dll) Version: 3.8.0
public class ViterbiLearning<TDistribution, TObservation> : BaseViterbiLearning<TObservation[]>, 
	IUnsupervisedLearning<HiddenMarkovModel<TDistribution, TObservation>, TObservation[], int[]>
where TDistribution : Object, IFittableDistribution<TObservation>
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Type Parameters


The ViterbiLearningTDistribution, TObservation type exposes the following members.

Public propertyBatches
Gets or sets on how many batches the learning data should be divided during learning. Batches are used to estimate adequately the first models so they can better compute the Viterbi paths for subsequent passes of the algorithm. Default is 1.
(Inherited from BaseViterbiLearningT.)
Public propertyCurrentIteration
Gets the current iteration.
(Inherited from BaseViterbiLearningT.)
Public propertyFittingOptions
Gets or sets the distribution fitting options to use when estimating distribution densities during learning.
Public propertyHasConverged
Gets a value indicating whether this instance has converged.
(Inherited from BaseViterbiLearningT.)
Public propertyIterations Obsolete.
Please use MaxIterations instead.
(Inherited from BaseViterbiLearningT.)
Public propertyMaxIterations
Gets or sets the maximum number of iterations performed by the learning algorithm.
(Inherited from BaseViterbiLearningT.)
Public propertyModel
Gets the model being trained.
Public propertyToken
Gets or sets a cancellation token that can be used to cancel the algorithm while it is running.
(Overrides BaseViterbiLearningTToken.)
Public propertyTolerance
Gets or sets the maximum change in the average log-likelihood after an iteration of the algorithm used to detect convergence.
(Inherited from BaseViterbiLearningT.)
Public propertyUseLaplaceRule
Gets or sets whether to use Laplace's rule of succession to avoid zero probabilities.
Protected methodComputeLogLikelihood
Computes the log-likelihood for the current model for the given observations.
(Overrides BaseViterbiLearningTComputeLogLikelihood(T).)
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodLearn
Learns a model that can map the given inputs to the desired outputs.
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodRun
Runs the learning algorithm.
(Inherited from BaseViterbiLearningT.)
Protected methodRunEpoch
Runs one single epoch (iteration) of the learning algorithm.
(Overrides BaseViterbiLearningTRunEpoch(T, Int32).)
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
Extension Methods
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodIsEqual
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.)
Public Extension MethodTo(Type)Overloaded.
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)
Public Extension MethodToTOverloaded.
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)

The Viterbi learning algorithm is an alternate learning algorithms for hidden Markov models. It works by obtaining the Viterbi path for the set of training observation sequences and then computing the maximum likelihood estimates for the model parameters. Those operations are repeated iteratively until model convergence.

The Viterbi learning algorithm is also known as the Segmental K-Means algorithm.

Accord.Math.Random.Generator.Seed = 0;

// Create continuous sequences. In the sequences below, there
//  seems to be two states, one for values between 0 and 1 and
//  another for values between 5 and 7. The states seems to be
//  switched on every observation.
double[][] sequences = new double[][] 
    new double[] { 0.1, 5.2, 0.3, 6.7, 0.1, 6.0 },
    new double[] { 0.2, 6.2, 0.3, 6.3, 0.1, 5.0 },
    new double[] { 0.1, 7.0, 0.1, 7.0, 0.2, 5.6 },

// Specify a initial normal distribution
var density = new NormalDistribution();

// Create a continuous hidden Markov Model with two states organized in a forward
//  topology and an underlying univariate Normal distribution as probability density.
var model = new HiddenMarkovModel<NormalDistribution, double>(new Forward(2), density);

// Configure the learning algorithms to train the sequence classifier until the
// difference in the average log-likelihood changes only by as little as 0.0001
var teacher = new ViterbiLearning<NormalDistribution, double>(model)
    Tolerance = 0.0001,
    Iterations = 0,

// Fit the model

// See the probability of the sequences learned
double a1 = model.LogLikelihood(new[] { 0.1, 5.2, 0.3, 6.7, 0.1, 6.0 }); // log(0.40)
double a2 = model.LogLikelihood(new[] { 0.2, 6.2, 0.3, 6.3, 0.1, 5.0 }); // log(0.46)

// See the probability of an unrelated sequence
double a3 = model.LogLikelihood(new[] { 1.1, 2.2, 1.3, 3.2, 4.2, 1.0 }); // log(1.42)
See Also