A Dot state.
The interface for objects that can generate a DP object for a MarkovModel.
A state in a markov process that has an emission spectrum.
interface implemented by objects that train HMMs.
A markov model.
A state that contains an entire sub-model.
Encapsulates the training of an entire model.
This class computes the score that is used to be used in a DP optimisation.
A state in a markov process.
Extends the Alignment interface so that it is explicitly used to represent a state path through an HMM, and the associated emitted sequence and likelihoods.
A callback that is invoked during the training of an HMM.
Flags an object as being able to register itself with a model trainer.
An object that can be used to train the transitions within a MarkovModel.
A log odds weight matrix.
An abstract implementation of TrainingAlgorithm that provides a framework for plugging in per-cycle code for parameter optimization.
Train a hidden markov model using a sampling algorithm.
Train a hidden markov model using maximum likelihood.
Objects that can perform dymamic programming operations upon sequences with HMMs.
Start/end state for HMMs.
In this class, calculateScore returns the probability of a Symbol being emitted by the null model.
In this class, calculateScore returns the odds ratio of a symbol being emitted.
In this class, calculateScore returns the probability of a Symbol being emitted.
A Dot state that you can make and use.
A no-frills implementation of StatePath.
This is a small and ugly class for storing a trainer and a transition.
This is a small and ugly class for storing a transition.
Annotates a sequence with hits to a weight-matrix.
Wraps a weight matrix up so that it appears to be a very simple HMM.
This exception indicates that there is no transition between two states.
This package deals with dynamic programming. It uses the same notions of sequences, alphabets and alignments as org.biojava.bio.seq, and extends them to incorporate HMMs, HMM states and state paths. As far as possible, the implementation detail is hidden from the casual observer, so that these objects can be used as black-boxes. Alternatively, there is scope for you to implement your own efficient representations of states and dynamic programming algorithms.
HMMs are defined by a finite set of states and a finite set of transitions. The states are encapsulated as subinterfaces of Symbols, so that we can re-use alphabets and SymbolList to store legal states and sequences of states. States that emit residues must implement EmissionState. They define a probability distribution over an alphabet. Other states may contain entire HMMs, or be non-emitting states which make the model easier to wire. An HMM contains an alphabet of states and a set of transitions with scores. They really resemble directed weighted graphs with the nodes being the states and the arcs being the transitions.
A simple HMM can be aligned to a single sequence at a time. This effectively finds the most likely way that the HMM could have emitted that sequence. More complex algorithms may align more than one sequence to a model simultaneously. For example, Smith-Waterman is a three-state model that aligns two sequences to each other and to the model. These more complex models can still be represented as producing a single sequence, but in this case the sequence is an alignment of the two input sequences against one-another (including gap characters where appropriate).
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