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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 MarkovModelBuilder
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class MarkovModelBuilder</h1><p class="nomargin-top"><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder">source code</a></span></p>
<p>Interface to build up a Markov Model.</p>
<p>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.</p>
<p>So, this builder class should be used to create Markov models instead
of trying to initiate a Markov Model directly.</p>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.MarkovModelBuilder-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">state_alphabet</span>,
<span class="summary-sig-arg">emission_alphabet</span>)</span><br />
Initialize a builder to create Markov Models.</td>
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<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.__init__">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.MarkovModelBuilder-class.html#_all_blank" class="summary-sig-name" onclick="show_private();">_all_blank</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">first_alphabet</span>,
<span class="summary-sig-arg">second_alphabet</span>)</span><br />
Return a dictionary with all counts set to zero.</td>
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<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder._all_blank">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.MarkovModelBuilder-class.html#_all_pseudo" class="summary-sig-name" onclick="show_private();">_all_pseudo</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">first_alphabet</span>,
<span class="summary-sig-arg">second_alphabet</span>)</span><br />
Return a dictionary with all counts set to a default value.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder._all_pseudo">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.MarkovModelBuilder-class.html#get_markov_model" class="summary-sig-name">get_markov_model</a>(<span class="summary-sig-arg">self</span>)</span><br />
Return the markov model corresponding with the current parameters.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.get_markov_model">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.MarkovModelBuilder-class.html#set_equal_probabilities" class="summary-sig-name">set_equal_probabilities</a>(<span class="summary-sig-arg">self</span>)</span><br />
Reset all probabilities to be an average value.</td>
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<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.set_equal_probabilities">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.MarkovModelBuilder-class.html#set_random_probabilities" class="summary-sig-name">set_random_probabilities</a>(<span class="summary-sig-arg">self</span>)</span><br />
Set all probabilities to randomly generated numbers.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.set_random_probabilities">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.MarkovModelBuilder-class.html#allow_all_transitions" class="summary-sig-name">allow_all_transitions</a>(<span class="summary-sig-arg">self</span>)</span><br />
A convenience function to create transitions between all states.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.allow_all_transitions">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.MarkovModelBuilder-class.html#allow_transition" class="summary-sig-name">allow_transition</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">from_state</span>,
<span class="summary-sig-arg">to_state</span>,
<span class="summary-sig-arg">probability</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">pseudocount</span>=<span class="summary-sig-default">None</span>)</span><br />
Set a transition as being possible between the two states.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.allow_transition">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.MarkovModelBuilder-class.html#destroy_transition" class="summary-sig-name">destroy_transition</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">from_state</span>,
<span class="summary-sig-arg">to_state</span>)</span><br />
Restrict transitions between the two states.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.destroy_transition">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.MarkovModelBuilder-class.html#set_transition_score" class="summary-sig-name">set_transition_score</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">from_state</span>,
<span class="summary-sig-arg">to_state</span>,
<span class="summary-sig-arg">probability</span>)</span><br />
Set the probability of a transition between two states.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.set_transition_score">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.MarkovModelBuilder-class.html#set_transition_pseudocount" class="summary-sig-name">set_transition_pseudocount</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">from_state</span>,
<span class="summary-sig-arg">to_state</span>,
<span class="summary-sig-arg">count</span>)</span><br />
Set the default pseudocount for a transition.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.set_transition_pseudocount">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.MarkovModelBuilder-class.html#set_emission_score" class="summary-sig-name">set_emission_score</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">seq_state</span>,
<span class="summary-sig-arg">emission_state</span>,
<span class="summary-sig-arg">probability</span>)</span><br />
Set the probability of a emission from a particular state.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.set_emission_score">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a href="Bio.HMM.MarkovModel.MarkovModelBuilder-class.html#set_emission_pseudocount" class="summary-sig-name">set_emission_pseudocount</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">seq_state</span>,
<span class="summary-sig-arg">emission_state</span>,
<span class="summary-sig-arg">count</span>)</span><br />
Set the default pseudocount for an emission.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.set_emission_pseudocount">source code</a></span>
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<!-- ==================== CLASS VARIABLES ==================== -->
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<a name="DEFAULT_PSEUDO"></a><span class="summary-name">DEFAULT_PSEUDO</span> = <code title="1">1</code>
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<!-- ==================== METHOD DETAILS ==================== -->
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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">state_alphabet</span>,
<span class="sig-arg">emission_alphabet</span>)</span>
<br /><em class="fname">(Constructor)</em>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.__init__">source code</a></span>
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<p>Initialize a builder to create Markov Models.</p>
<p>Arguments:</p>
<p>o state_alphabet -- An alphabet containing all of the letters that can
appear in the states</p>
<p>o emission_alphabet -- An alphabet containing all of the letters for
states that can be emitted by the HMM.</p>
<dl class="fields">
</dl>
</td></tr></table>
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<a name="_all_blank"></a>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">_all_blank</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">first_alphabet</span>,
<span class="sig-arg">second_alphabet</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder._all_blank">source code</a></span>
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<p>Return a dictionary with all counts set to zero.</p>
<p>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.</p>
<dl class="fields">
</dl>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">_all_pseudo</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">first_alphabet</span>,
<span class="sig-arg">second_alphabet</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder._all_pseudo">source code</a></span>
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<p>Return a dictionary with all counts set to a default value.</p>
<p>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.</p>
<dl class="fields">
</dl>
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</div>
<a name="get_markov_model"></a>
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cellspacing="0" width="100%" bgcolor="white">
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<h3 class="epydoc"><span class="sig"><span class="sig-name">get_markov_model</span>(<span class="sig-arg">self</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.get_markov_model">source code</a></span>
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<p>Return the markov model corresponding with the current parameters.</p>
<p>Each markov model returned by a call to this function is unique (ie.
they don't influence each other).</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="set_equal_probabilities"></a>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">set_equal_probabilities</span>(<span class="sig-arg">self</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.set_equal_probabilities">source code</a></span>
</td>
</tr></table>
<p>Reset all probabilities to be an average value.</p>
<p>This resets the values of all allowed transitions and all allowed
emissions to be equal to 1 divided by the number of possible
elements.</p>
<p>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 :-).</p>
<p>Warning 1 -- this will reset all currently set probabilities.</p>
<p>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.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="set_random_probabilities"></a>
<div>
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cellspacing="0" width="100%" bgcolor="white">
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<h3 class="epydoc"><span class="sig"><span class="sig-name">set_random_probabilities</span>(<span class="sig-arg">self</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.set_random_probabilities">source code</a></span>
</td>
</tr></table>
<p>Set all probabilities to randomly generated numbers.</p>
<p>This will reset the value of all allowed transitions and emissions to
random values.</p>
<p>Warning 1 -- This will reset any currently set probabibilities.</p>
<p>Warning 2 -- This does not check to ensure that the sum of all of the
probabilities is less then 1. It just randomly assigns a probability to
each</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="allow_all_transitions"></a>
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cellspacing="0" width="100%" bgcolor="white">
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<h3 class="epydoc"><span class="sig"><span class="sig-name">allow_all_transitions</span>(<span class="sig-arg">self</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.allow_all_transitions">source code</a></span>
</td>
</tr></table>
<p>A convenience function to create transitions between all states.</p>
<p>By default all transitions within the alphabet are disallowed; this is
a way to change this to allow all possible transitions.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="allow_transition"></a>
<div>
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cellspacing="0" width="100%" bgcolor="white">
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<h3 class="epydoc"><span class="sig"><span class="sig-name">allow_transition</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">from_state</span>,
<span class="sig-arg">to_state</span>,
<span class="sig-arg">probability</span>=<span class="sig-default">None</span>,
<span class="sig-arg">pseudocount</span>=<span class="sig-default">None</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.allow_transition">source code</a></span>
</td>
</tr></table>
<p>Set a transition as being possible between the two states.</p>
<p>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.</p>
<p>Raises: KeyError -- if the two states already have an allowed
transition.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="destroy_transition"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">destroy_transition</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">from_state</span>,
<span class="sig-arg">to_state</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.destroy_transition">source code</a></span>
</td>
</tr></table>
<p>Restrict transitions between the two states.</p>
<p>Raises: KeyError if the transition is not currently allowed.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="set_transition_score"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">set_transition_score</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">from_state</span>,
<span class="sig-arg">to_state</span>,
<span class="sig-arg">probability</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.set_transition_score">source code</a></span>
</td>
</tr></table>
<p>Set the probability of a transition between two states.</p>
<p>Raises: KeyError if the transition is not allowed.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="set_transition_pseudocount"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">set_transition_pseudocount</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">from_state</span>,
<span class="sig-arg">to_state</span>,
<span class="sig-arg">count</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.set_transition_pseudocount">source code</a></span>
</td>
</tr></table>
<p>Set the default pseudocount for a transition.</p>
<p>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.</p>
<p>Raises: KeyError if the transition is not allowed.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="set_emission_score"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">set_emission_score</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">seq_state</span>,
<span class="sig-arg">emission_state</span>,
<span class="sig-arg">probability</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.set_emission_score">source code</a></span>
</td>
</tr></table>
<p>Set the probability of a emission from a particular state.</p>
<p>Raises: KeyError if the emission from the given state is not
allowed.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="set_emission_pseudocount"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">set_emission_pseudocount</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">seq_state</span>,
<span class="sig-arg">emission_state</span>,
<span class="sig-arg">count</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.MarkovModel-pysrc.html#MarkovModelBuilder.set_emission_pseudocount">source code</a></span>
</td>
</tr></table>
<p>Set the default pseudocount for an emission.</p>
<p>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.</p>
<p>Raises: KeyError if the emission from the given state is not
allowed.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
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