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<a href="Bio-module.html">Package Bio</a> ::
Module MarkovModel
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<!-- ==================== MODULE DESCRIPTION ==================== -->
<h1 class="epydoc">Module MarkovModel</h1><p class="nomargin-top"><span class="codelink"><a href="Bio.MarkovModel-pysrc.html">source code</a></span></p>
<p>This is an implementation of a state-emitting MarkovModel. I am using
terminology similar to Manning and Schutze.</p>
<p>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.</p>
<p>load Load a MarkovModel. save Save a
MarkovModel.</p>
<p>Classes: MarkovModel Holds the description of a markov model</p>
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<span class="summary-type"> </span>
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<a href="Bio.MarkovModel.MarkovModel-class.html" class="summary-name">MarkovModel</a>
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<td><span class="summary-sig"><a name="itemindex"></a><span class="summary-sig-name">itemindex</span>(<span class="summary-sig-arg">values</span>)</span></td>
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<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#itemindex">source code</a></span>
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<td><span class="summary-sig"><a name="_readline_and_check_start"></a><span class="summary-sig-name">_readline_and_check_start</span>(<span class="summary-sig-arg">handle</span>,
<span class="summary-sig-arg">start</span>)</span></td>
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<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#_readline_and_check_start">source code</a></span>
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<span class="summary-type">MarkovModel()</span>
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<td><span class="summary-sig"><a name="load"></a><span class="summary-sig-name">load</span>(<span class="summary-sig-arg">handle</span>)</span></td>
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<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#load">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a name="save"></a><span class="summary-sig-name">save</span>(<span class="summary-sig-arg">mm</span>,
<span class="summary-sig-arg">handle</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#save">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.MarkovModel-module.html#train_bw" class="summary-sig-name">train_bw</a>(<span class="summary-sig-arg">states</span>,
<span class="summary-sig-arg">alphabet</span>,
<span class="summary-sig-arg">training_data</span>,
<span class="summary-sig-arg">pseudo_initial</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">pseudo_transition</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">pseudo_emission</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">update_fn</span>=<span class="summary-sig-default">None</span>)</span><br />
train_bw(states, alphabet, training_data[, pseudo_initial] [,
pseudo_transition][, pseudo_emission][, update_fn]) -> MarkovModel</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#train_bw">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a name="_baum_welch"></a><span class="summary-sig-name">_baum_welch</span>(<span class="summary-sig-arg">N</span>,
<span class="summary-sig-arg">M</span>,
<span class="summary-sig-arg">training_outputs</span>,
<span class="summary-sig-arg">p_initial</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">p_transition</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">p_emission</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">pseudo_initial</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">pseudo_transition</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">pseudo_emission</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">update_fn</span>=<span class="summary-sig-default">None</span>)</span></td>
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<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#_baum_welch">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a name="_baum_welch_one"></a><span class="summary-sig-name">_baum_welch_one</span>(<span class="summary-sig-arg">N</span>,
<span class="summary-sig-arg">M</span>,
<span class="summary-sig-arg">outputs</span>,
<span class="summary-sig-arg">lp_initial</span>,
<span class="summary-sig-arg">lp_transition</span>,
<span class="summary-sig-arg">lp_emission</span>,
<span class="summary-sig-arg">lpseudo_initial</span>,
<span class="summary-sig-arg">lpseudo_transition</span>,
<span class="summary-sig-arg">lpseudo_emission</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#_baum_welch_one">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a name="_forward"></a><span class="summary-sig-name">_forward</span>(<span class="summary-sig-arg">N</span>,
<span class="summary-sig-arg">T</span>,
<span class="summary-sig-arg">lp_initial</span>,
<span class="summary-sig-arg">lp_transition</span>,
<span class="summary-sig-arg">lp_emission</span>,
<span class="summary-sig-arg">outputs</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#_forward">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a name="_backward"></a><span class="summary-sig-name">_backward</span>(<span class="summary-sig-arg">N</span>,
<span class="summary-sig-arg">T</span>,
<span class="summary-sig-arg">lp_transition</span>,
<span class="summary-sig-arg">lp_emission</span>,
<span class="summary-sig-arg">outputs</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#_backward">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.MarkovModel-module.html#train_visible" class="summary-sig-name">train_visible</a>(<span class="summary-sig-arg">states</span>,
<span class="summary-sig-arg">alphabet</span>,
<span class="summary-sig-arg">training_data</span>,
<span class="summary-sig-arg">pseudo_initial</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">pseudo_transition</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">pseudo_emission</span>=<span class="summary-sig-default">None</span>)</span><br />
train_visible(states, alphabet, training_data[, pseudo_initial] [,
pseudo_transition][, pseudo_emission]) -> MarkovModel</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#train_visible">source code</a></span>
</td>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a name="_mle"></a><span class="summary-sig-name">_mle</span>(<span class="summary-sig-arg">N</span>,
<span class="summary-sig-arg">M</span>,
<span class="summary-sig-arg">training_outputs</span>,
<span class="summary-sig-arg">training_states</span>,
<span class="summary-sig-arg">pseudo_initial</span>,
<span class="summary-sig-arg">pseudo_transition</span>,
<span class="summary-sig-arg">pseudo_emission</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#_mle">source code</a></span>
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<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
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<td><span class="summary-sig"><a name="_argmaxes"></a><span class="summary-sig-name">_argmaxes</span>(<span class="summary-sig-arg">vector</span>,
<span class="summary-sig-arg">allowance</span>=<span class="summary-sig-default">None</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#_argmaxes">source code</a></span>
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<span class="summary-type">list of (states, score)</span>
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<td><span class="summary-sig"><a name="find_states"></a><span class="summary-sig-name">find_states</span>(<span class="summary-sig-arg">markov_model</span>,
<span class="summary-sig-arg">output</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#find_states">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a name="_viterbi"></a><span class="summary-sig-name">_viterbi</span>(<span class="summary-sig-arg">N</span>,
<span class="summary-sig-arg">lp_initial</span>,
<span class="summary-sig-arg">lp_transition</span>,
<span class="summary-sig-arg">lp_emission</span>,
<span class="summary-sig-arg">output</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#_viterbi">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a name="_normalize"></a><span class="summary-sig-name">_normalize</span>(<span class="summary-sig-arg">matrix</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#_normalize">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a name="_uniform_norm"></a><span class="summary-sig-name">_uniform_norm</span>(<span class="summary-sig-arg">shape</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#_uniform_norm">source code</a></span>
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<td><span class="summary-sig"><a name="_random_norm"></a><span class="summary-sig-name">_random_norm</span>(<span class="summary-sig-arg">shape</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#_random_norm">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a name="_copy_and_check"></a><span class="summary-sig-name">_copy_and_check</span>(<span class="summary-sig-arg">matrix</span>,
<span class="summary-sig-arg">desired_shape</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#_copy_and_check">source code</a></span>
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<td><span class="summary-sig"><a name="_logsum"></a><span class="summary-sig-name">_logsum</span>(<span class="summary-sig-arg">matrix</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#_logsum">source code</a></span>
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<td><span class="summary-sig"><a name="_logvecadd"></a><span class="summary-sig-name">_logvecadd</span>(<span class="summary-sig-arg">logvec1</span>,
<span class="summary-sig-arg">logvec2</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#_logvecadd">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a name="_exp_logsum"></a><span class="summary-sig-name">_exp_logsum</span>(<span class="summary-sig-arg">numbers</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.MarkovModel-pysrc.html#_exp_logsum">source code</a></span>
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<!-- ==================== VARIABLES ==================== -->
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<td align="left"><span class="table-header">Variables</span></td>
<td align="right" valign="top"
><span class="options">[<a href="#section-Variables"
class="privatelink" onclick="toggle_private();"
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<span class="summary-type"> </span>
</td><td class="summary">
<a name="logaddexp"></a><span class="summary-name">logaddexp</span> = <code title="<ufunc 'logaddexp'>"><ufunc 'logaddexp'></code>
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<span class="summary-type"> </span>
</td><td class="summary">
<a name="VERY_SMALL_NUMBER"></a><span class="summary-name">VERY_SMALL_NUMBER</span> = <code title="1e-300">1e-300</code>
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<span class="summary-type"> </span>
</td><td class="summary">
<a name="LOG0"></a><span class="summary-name">LOG0</span> = <code title="-690.77552789821368">-690.77552789821368</code>
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<span class="summary-type"> </span>
</td><td class="summary">
<a name="MAX_ITERATIONS"></a><span class="summary-name">MAX_ITERATIONS</span> = <code title="1000">1000</code>
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<span class="summary-type"> </span>
</td><td class="summary">
<a name="__package__"></a><span class="summary-name">__package__</span> = <code title="'Bio'"><code class="variable-quote">'</code><code class="variable-string">Bio</code><code class="variable-quote">'</code></code>
</td>
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<!-- ==================== FUNCTION DETAILS ==================== -->
<a name="section-FunctionDetails"></a>
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<td align="left"><span class="table-header">Function Details</span></td>
<td align="right" valign="top"
><span class="options">[<a href="#section-FunctionDetails"
class="privatelink" onclick="toggle_private();"
>hide private</a>]</span></td>
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<a name="train_bw"></a>
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<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">train_bw</span>(<span class="sig-arg">states</span>,
<span class="sig-arg">alphabet</span>,
<span class="sig-arg">training_data</span>,
<span class="sig-arg">pseudo_initial</span>=<span class="sig-default">None</span>,
<span class="sig-arg">pseudo_transition</span>=<span class="sig-default">None</span>,
<span class="sig-arg">pseudo_emission</span>=<span class="sig-default">None</span>,
<span class="sig-arg">update_fn</span>=<span class="sig-default">None</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.MarkovModel-pysrc.html#train_bw">source code</a></span>
</td>
</tr></table>
<p>train_bw(states, alphabet, training_data[, pseudo_initial] [,
pseudo_transition][, pseudo_emission][, update_fn]) -> MarkovModel</p>
<p>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.</p>
<p>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.</p>
<p>update_fn is an optional callback that takes parameters (iteration,
log_likelihood). It is called once per iteration.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="train_visible"></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">train_visible</span>(<span class="sig-arg">states</span>,
<span class="sig-arg">alphabet</span>,
<span class="sig-arg">training_data</span>,
<span class="sig-arg">pseudo_initial</span>=<span class="sig-default">None</span>,
<span class="sig-arg">pseudo_transition</span>=<span class="sig-default">None</span>,
<span class="sig-arg">pseudo_emission</span>=<span class="sig-default">None</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.MarkovModel-pysrc.html#train_visible">source code</a></span>
</td>
</tr></table>
<p>train_visible(states, alphabet, training_data[, pseudo_initial] [,
pseudo_transition][, pseudo_emission]) -> MarkovModel</p>
<p>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.</p>
<p>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</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
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