Package Bio :: Module MarkovModel
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Module Bio.MarkovModel

This is an implementation of a state-emitting MarkovModel. I am using terminology similar to Manning and Schutze.

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
Classes
MarkovModel  

Function Summary
  find_states(markov_model, output)
find_states(markov_model, output) -> list of (states, score)
  load(handle)
load(handle) -> MarkovModel()
  save(mm, handle)
save(mm, handle)
  train_bw(states, alphabet, training_data, pseudo_initial, pseudo_transition, pseudo_emission, update_fn)
train_bw(states, alphabet, training_data[, pseudo_initial] [, pseudo_transition][, pseudo_emission][, update_fn]) -> MarkovModel
  train_visible(states, alphabet, training_data, pseudo_initial, pseudo_transition, pseudo_emission)
train_visible(states, alphabet, training_data[, pseudo_initial] [, pseudo_transition][, pseudo_emission]) -> MarkovModel
  _argmaxes(vector, allowance)
  _backward(N, T, lp_transition, lp_emission, outputs)
  _baum_welch(N, M, training_outputs, p_initial, p_transition, p_emission, pseudo_initial, pseudo_transition, pseudo_emission, update_fn)
  _baum_welch_one(N, M, outputs, lp_initial, lp_transition, lp_emission, lpseudo_initial, lpseudo_transition, lpseudo_emission)
  _copy_and_check(matrix, desired_shape)
  _exp_logsum(numbers)
  _forward(N, T, lp_initial, lp_transition, lp_emission, outputs)
  _logadd(logx, logy)
  _logsum(matrix)
  _logvecadd(logvec1, logvec2)
  _mle(N, M, training_outputs, training_states, pseudo_initial, pseudo_transition, pseudo_emission)
  _normalize(matrix)
  _random_norm(shape)
  _readline_and_check_start(handle, start)
  _safe_asarray(a, typecode)
  _safe_copy_and_check(matrix, desired_shape)
  _safe_log(n)
  _uniform_norm(shape)
  _viterbi(N, lp_initial, lp_transition, lp_emission, output)

Function Details

find_states(markov_model, output)

find_states(markov_model, output) -> list of (states, score)

load(handle)

load(handle) -> MarkovModel()

save(mm, handle)

save(mm, handle)

train_bw(states, alphabet, training_data, pseudo_initial=None, pseudo_transition=None, pseudo_emission=None, update_fn=None)

train_bw(states, alphabet, training_data[, pseudo_initial] [, pseudo_transition][, pseudo_emission][, update_fn]) -> MarkovModel

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_visible(states, alphabet, training_data, pseudo_initial=None, pseudo_transition=None, pseudo_emission=None)

train_visible(states, alphabet, training_data[, pseudo_initial] [, pseudo_transition][, pseudo_emission]) -> MarkovModel

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

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