# biopython v1.71.0 Bio.MarkovModel

A state-emitting MarkovModel.

Note terminology similar to Manning and Schutze is used.

Functions: train_bw Train a markov model using the Baum-Welch algorithm. train_visible Train a visible markov model using MLE. find_states Find the a state sequence that explains some observations.

load Load a MarkovModel. save Save a MarkovModel.

Classes: MarkovModel Holds the description of a markov model

# Link to this section Summary

## Functions

Return indeces of the maximum values aong the vector (PRIVATE)

Implement backward algorithm

Implement the Baum-Welch algorithm to evaluate unknown parameters in the MarkovModel object (PRIVATE)

Execute one step for Baum-Welch algorithm (PRIVATE)

Copy a matrix and check its dimension. Normalize at the end (PRIVATE)

Return the exponential of a logsum (PRIVATE)

Implement forward algorithm (PRIVATE)

Implement logsum for a matrix object (PRIVATE)

Implement a log sum for two vector objects (PRIVATE)

Implement Maximum likelihood estimation algorithm (PRIVATE)

Normalize matrix object (PRIVATE)

Normalize a random matrix (PRIVATE)

Read the first line and evaluate that begisn with the correct start (PRIVATE)

Normalize a uniform matrix (PRIVATE)

Implement Viterbi algorithm to find most likely states for a given input (PRIVATE)

Find states in the given Markov model output

Return a dictionary of values with their sequence offset as keys

Parse a file handle into a MarkovModel object

Implement logaddexp method if Numpy version is older than 1.3

Save MarkovModel object into handle

Train a MarkovModel using the Baum-Welch algorithm

Train a visible MarkovModel using maximum likelihoood estimates for each of the parameters

# Link to this section Functions

Return indeces of the maximum values aong the vector (PRIVATE).

Implement backward algorithm.

Implement the Baum-Welch algorithm to evaluate unknown parameters in the MarkovModel object (PRIVATE).

Execute one step for Baum-Welch algorithm (PRIVATE).

Do one iteration of Baum-Welch based on a sequence of output. Changes the value for lp_initial, lp_transition and lp_emission in place.

Copy a matrix and check its dimension. Normalize at the end (PRIVATE).

Return the exponential of a logsum (PRIVATE).

Implement forward algorithm (PRIVATE).

Calculate a Nx(T+1) matrix, where the last column is the total probability of the output.

Implement logsum for a matrix object (PRIVATE).

Implement a log sum for two vector objects (PRIVATE).

Implement Maximum likelihood estimation algorithm (PRIVATE).

Normalize matrix object (PRIVATE).

Normalize a random matrix (PRIVATE).

Read the first line and evaluate that begisn with the correct start (PRIVATE).

Normalize a uniform matrix (PRIVATE).

Implement Viterbi algorithm to find most likely states for a given input (PRIVATE).

Find states in the given Markov model output.

Returns a list of (states, score) tuples.

Return a dictionary of values with their sequence offset as keys.

Parse a file handle into a MarkovModel object.

Implement logaddexp method if Numpy version is older than 1.3.

Save MarkovModel object into handle.

Train a MarkovModel using the Baum-Welch algorithm.

Train a MarkovModel using the Baum-Welch algorithm. states is a list of strings that describe the names of each state. alphabet is a list of objects that indicate the allowed outputs. training_data is a list of observations. Each observation is a list of objects from the alphabet.

pseudo_initial, pseudo_transition, and pseudo_emission are optional parameters that you can use to assign pseudo-counts to different matrices. They should be matrices of the appropriate size that contain numbers to add to each parameter matrix, before normalization.

update_fn is an optional callback that takes parameters (iteration, log_likelihood). It is called once per iteration.

Train a visible MarkovModel using maximum likelihoood estimates for each of the parameters.

Train a visible MarkovModel using maximum likelihoood estimates for each of the parameters. states is a list of strings that describe the names of each state. alphabet is a list of objects that indicate the allowed outputs. training_data is a list of (outputs, observed states) where outputs is a list of the emission from the alphabet, and observed states is a list of states from states.

pseudo_initial, pseudo_transition, and pseudo_emission are optional parameters that you can use to assign pseudo-counts to different matrices. They should be matrices of the appropriate size that contain numbers to add to each parameter matrix.