# biopython v1.71.0 Bio.HMM.MarkovModel.MarkovModelBuilder

Interface to build up a Markov Model.

This class is designed to try to separate the task of specifying the Markov Model from the actual model itself. This is in hopes of making the actual Markov Model classes smaller.

So, this builder class should be used to create Markov models instead of trying to initiate a Markov Model directly.

# Link to this section Summary

## Functions

Initialize a builder to create Markov Models

Return a dictionary with all counts set to zero

Return a dictionary with all counts set to a default value

Create transitions between all states

Set a transition as being possible between the two states

Restrict transitions between the two states

Return the markov model corresponding with the current parameters

Set the default pseudocount for an emission

Set the probability of a emission from a particular state

Reset all probabilities to be an average value

Set initial state probabilities

Set all allowed emission probabilities to a randomly generated distribution

Set all initial state probabilities to a randomly generated distribution

Set all probabilities to randomly generated numbers

Set all allowed transition probabilities to a randomly generated distribution

Set the default pseudocount for a transition

Set the probability of a transition between two states

# Link to this section Functions

Initialize a builder to create Markov Models.

Arguments:

- state_alphabet — An alphabet containing all of the letters that can appear in the states
- emission_alphabet — An alphabet containing all of the letters for states that can be emitted by the HMM.

Return a dictionary with all counts set to zero.

This uses the letters in the first and second alphabet to create a dictionary with keys of two tuples organized as (letter of first alphabet, letter of second alphabet). The values are all set to 0.

Return a dictionary with all counts set to a default value.

This takes the letters in first alphabet and second alphabet and creates a dictionary with keys of two tuples organized as: (letter of first alphabet, letter of second alphabet). The values are all set to the value of the class attribute DEFAULT_PSEUDO.

Create transitions between all states.

By default all transitions within the alphabet are disallowed; this is a convenience function to change this to allow all possible transitions.

Set a transition as being possible between the two states.

probability and pseudocount are optional arguments specifying the probabilities and pseudo counts for the transition. If these are not supplied, then the values are set to the default values.

Raises: KeyError — if the two states already have an allowed transition.

Restrict transitions between the two states.

Raises: KeyError if the transition is not currently allowed.

Return the markov model corresponding with the current parameters.

Each markov model returned by a call to this function is unique (ie. they don’t influence each other).

Set the default pseudocount for an emission.

To avoid computational problems, it is helpful to be able to set a ‘default’ pseudocount to start with for estimating transition and emission probabilities (see p62 in Durbin et al for more discussion on this. By default, all emissions have a pseudocount of 1.

Raises: KeyError if the emission from the given state is not allowed.

Set the probability of a emission from a particular state.

Raises: KeyError if the emission from the given state is not allowed.

Reset all probabilities to be an average value.

Resets the values of all initial probabilities and all allowed transitions and all allowed emissions to be equal to 1 divided by the number of possible elements.

This is useful if you just want to initialize a Markov Model to starting values (ie. if you have no prior notions of what the probabilities should be — or if you are just feeling too lazy to calculate them :-).

Warning 1 — this will reset all currently set probabilities.

Warning 2 — This just sets all probabilities for transitions and emissions to total up to 1, so it doesn’t ensure that the sum of each set of transitions adds up to 1.

Set initial state probabilities.

initial_prob is a dictionary mapping states to probabilities. Suppose, for example, that the state alphabet is [‘A’, ‘B’]. Call set_initial_prob({‘A’: 1}) to guarantee that the initial state will be ‘A’. Call set_initial_prob({‘A’: 0.5, ‘B’: 0.5}) to make each initial state equally probable.

This method must now be called in order to use the Markov model because the calculation of initial probabilities has changed incompatibly; the previous calculation was incorrect.

If initial probabilities are set for all states, then they should add up to 1. Otherwise the sum should be <= 1. The residual probability is divided up evenly between all the states for which the initial probability has not been set. For example, calling set_initial_prob({}) results in P(‘A’) = 0.5 and P(‘B’) = 0.5, for the above example.

Set all allowed emission probabilities to a randomly generated distribution.

Returns the dictionary containing the emission probabilities.

Set all initial state probabilities to a randomly generated distribution.

Returns the dictionary containing the initial probabilities.

Set all probabilities to randomly generated numbers.

Resets probabilities of all initial states, transitions, and emissions to random values.

Set all allowed transition probabilities to a randomly generated distribution.

Returns the dictionary containing the transition probabilities.

Set the default pseudocount for a transition.

To avoid computational problems, it is helpful to be able to set a ‘default’ pseudocount to start with for estimating transition and emission probabilities (see p62 in Durbin et al for more discussion on this. By default, all transitions have a pseudocount of 1.

Raises: KeyError if the transition is not allowed.

Set the probability of a transition between two states.

Raises: KeyError if the transition is not allowed.