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<a href="Bio-module.html">Package Bio</a> ::
<a href="Bio.HMM-module.html">Package HMM</a> ::
<a href="Bio.HMM.MarkovModel-module.html">Module MarkovModel</a> ::
Class HiddenMarkovModel
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class HiddenMarkovModel</h1><p class="nomargin-top"><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#HiddenMarkovModel">source code</a></span></p>
<p>Represent a hidden markov model that can be used for state
estimation.</p>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.HiddenMarkovModel-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">transition_prob</span>,
<span class="summary-sig-arg">emission_prob</span>,
<span class="summary-sig-arg">transition_pseudo</span>,
<span class="summary-sig-arg">emission_pseudo</span>)</span><br />
Initialize a Markov Model.</td>
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<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#HiddenMarkovModel.__init__">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.HiddenMarkovModel-class.html#_calculate_from_transitions" class="summary-sig-name" onclick="show_private();">_calculate_from_transitions</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">trans_probs</span>)</span><br />
Calculate which 'from transitions' are allowed for each letter.</td>
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<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#HiddenMarkovModel._calculate_from_transitions">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.HiddenMarkovModel-class.html#get_blank_transitions" class="summary-sig-name">get_blank_transitions</a>(<span class="summary-sig-arg">self</span>)</span><br />
Get the default transitions for the model.</td>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.HiddenMarkovModel-class.html#get_blank_emissions" class="summary-sig-name">get_blank_emissions</a>(<span class="summary-sig-arg">self</span>)</span><br />
Get the starting default emmissions for each sequence.</td>
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<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#HiddenMarkovModel.get_blank_emissions">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.HiddenMarkovModel-class.html#transitions_from" class="summary-sig-name">transitions_from</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">state_letter</span>)</span><br />
Get all transitions which can happen from the given state.</td>
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<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#HiddenMarkovModel.transitions_from">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.HiddenMarkovModel-class.html#viterbi" class="summary-sig-name">viterbi</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">sequence</span>,
<span class="summary-sig-arg">state_alphabet</span>)</span><br />
Calculate the most probable state path using the Viterbi algorithm.</td>
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<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#HiddenMarkovModel.viterbi">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.HiddenMarkovModel-class.html#_log_transform" class="summary-sig-name" onclick="show_private();">_log_transform</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">probability</span>)</span><br />
Return log transform of the given probability dictionary.</td>
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<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#HiddenMarkovModel._log_transform">source code</a></span>
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<a name="__init__"></a>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">transition_prob</span>,
<span class="sig-arg">emission_prob</span>,
<span class="sig-arg">transition_pseudo</span>,
<span class="sig-arg">emission_pseudo</span>)</span>
<br /><em class="fname">(Constructor)</em>
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<p>Initialize a Markov Model.</p>
<p>Note: You should use the MarkovModelBuilder class instead of
initiating this class directly.</p>
<p>Arguments:</p>
<p>o transition_prob -- A dictionary of transition probabilities for all
possible transitions in the sequence.</p>
<p>o emission_prob -- A dictionary of emissions probabilities for all
possible emissions from the sequence states.</p>
<p>o transition_pseudo -- Pseudo-counts to be used for the transitions,
when counting for purposes of estimating transition probabilities.</p>
<p>o emission_pseduo -- Pseudo-counts fo tbe used for the emissions, when
counting for purposes of estimating emission probabilities.</p>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">_calculate_from_transitions</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">trans_probs</span>)</span>
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><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#HiddenMarkovModel._calculate_from_transitions">source code</a></span>
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<p>Calculate which 'from transitions' are allowed for each letter.</p>
<p>This looks through all of the trans_probs, and uses this dictionary to
determine allowed transitions. It converts this information into a
dictionary, whose keys are the transition letters and whose values are a
list of allowed letters to transition to.</p>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">get_blank_transitions</span>(<span class="sig-arg">self</span>)</span>
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><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#HiddenMarkovModel.get_blank_transitions">source code</a></span>
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<p>Get the default transitions for the model.</p>
<p>Returns a dictionary of all of the default transitions between any two
letters in the sequence alphabet. The dictionary is structured with keys
as (letter1, letter2) and values as the starting number of
transitions.</p>
<dl class="fields">
</dl>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">get_blank_emissions</span>(<span class="sig-arg">self</span>)</span>
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><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#HiddenMarkovModel.get_blank_emissions">source code</a></span>
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<p>Get the starting default emmissions for each sequence.</p>
<p>This returns a dictionary of the default emmissions for each letter.
The dictionary is structured with keys as (seq_letter, emmission_letter)
and values as the starting number of emmissions.</p>
<dl class="fields">
</dl>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">transitions_from</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">state_letter</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#HiddenMarkovModel.transitions_from">source code</a></span>
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<p>Get all transitions which can happen from the given state.</p>
<p>This returns all letters which the given state_letter is allowed to
transition to. An empty list is returned if no letters are possible.</p>
<dl class="fields">
</dl>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">viterbi</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">sequence</span>,
<span class="sig-arg">state_alphabet</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#HiddenMarkovModel.viterbi">source code</a></span>
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<p>Calculate the most probable state path using the Viterbi
algorithm.</p>
<p>This implements the Viterbi algorithm (see pgs 55-57 in Durbin et al
for a full explanation -- this is where I took my implementation ideas
from), to allow decoding of the state path, given a sequence of
emissions.</p>
<p>Arguments:</p>
<p>o sequence -- A Seq object with the emission sequence that we want to
decode.</p>
<p>o state_alphabet -- The alphabet of the possible state sequences that
can be generated.</p>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">_log_transform</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">probability</span>)</span>
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<p>Return log transform of the given probability dictionary.</p>
<p>When calculating the Viterbi equation, we need to deal with things as
sums of logs instead of products of probabilities, so that we don't get
underflow errors.. This copies the given probability dictionary and
returns the same dictionary with everything transformed with a log.</p>
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