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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.Trainer-module.html">Module Trainer</a> ::
Class KnownStateTrainer
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class KnownStateTrainer</h1><p class="nomargin-top"><span class="codelink"><a href="Bio.HMM.Trainer-pysrc.html#KnownStateTrainer">source code</a></span></p>
<pre class="base-tree">
<a href="Bio.HMM.Trainer.AbstractTrainer-class.html">AbstractTrainer</a> --+
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<strong class="uidshort">KnownStateTrainer</strong>
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<p>Estimate probabilities with known state sequences.</p>
<p>This should be used for direct estimation of emission and transition
probabilities when both the state path and emission sequence are known
for the training examples.</p>
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<td><span class="summary-sig"><a href="Bio.HMM.Trainer.KnownStateTrainer-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">markov_model</span>)</span></td>
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<span class="codelink"><a href="Bio.HMM.Trainer-pysrc.html#KnownStateTrainer.__init__">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.HMM.Trainer.KnownStateTrainer-class.html#train" class="summary-sig-name">train</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">training_seqs</span>)</span><br />
Estimate the Markov Model parameters with known state paths.</td>
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<span class="codelink"><a href="Bio.HMM.Trainer-pysrc.html#KnownStateTrainer.train">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.HMM.Trainer.KnownStateTrainer-class.html#_count_emissions" class="summary-sig-name" onclick="show_private();">_count_emissions</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">training_seq</span>,
<span class="summary-sig-arg">emission_counts</span>)</span><br />
Add emissions from the training sequence to the current counts.</td>
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<span class="codelink"><a href="Bio.HMM.Trainer-pysrc.html#KnownStateTrainer._count_emissions">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.HMM.Trainer.KnownStateTrainer-class.html#_count_transitions" class="summary-sig-name" onclick="show_private();">_count_transitions</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">state_seq</span>,
<span class="summary-sig-arg">transition_counts</span>)</span><br />
Add transitions from the training sequence to the current counts.</td>
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<span class="codelink"><a href="Bio.HMM.Trainer-pysrc.html#KnownStateTrainer._count_transitions">source code</a></span>
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<p class="indent-wrapped-lines"><b>Inherited from <code><a href="Bio.HMM.Trainer.AbstractTrainer-class.html">AbstractTrainer</a></code></b>:
<code><a href="Bio.HMM.Trainer.AbstractTrainer-class.html#estimate_params">estimate_params</a></code>,
<code><a href="Bio.HMM.Trainer.AbstractTrainer-class.html#log_likelihood">log_likelihood</a></code>,
<code><a href="Bio.HMM.Trainer.AbstractTrainer-class.html#ml_estimator">ml_estimator</a></code>
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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">markov_model</span>)</span>
<br /><em class="fname">(Constructor)</em>
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</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.Trainer-pysrc.html#KnownStateTrainer.__init__">source code</a></span>
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<dl class="fields">
<dt>Overrides:
<a href="Bio.HMM.Trainer.AbstractTrainer-class.html#__init__">AbstractTrainer.__init__</a>
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<a name="train"></a>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">train</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">training_seqs</span>)</span>
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><span class="codelink"><a href="Bio.HMM.Trainer-pysrc.html#KnownStateTrainer.train">source code</a></span>
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<p>Estimate the Markov Model parameters with known state paths.</p>
<p>This trainer requires that both the state and the emissions are known
for all of the training sequences in the list of TrainingSequence
objects. This training will then count all of the transitions and
emissions, and use this to estimate the parameters of the model.</p>
<dl class="fields">
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<h3 class="epydoc"><span class="sig"><span class="sig-name">_count_emissions</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">training_seq</span>,
<span class="sig-arg">emission_counts</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.Trainer-pysrc.html#KnownStateTrainer._count_emissions">source code</a></span>
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<p>Add emissions from the training sequence to the current counts.</p>
<p>Arguments:</p>
<p>o training_seq -- A TrainingSequence with states and emissions to get
the counts from</p>
<p>o emission_counts -- The current emission counts to add to.</p>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">_count_transitions</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">state_seq</span>,
<span class="sig-arg">transition_counts</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.Trainer-pysrc.html#KnownStateTrainer._count_transitions">source code</a></span>
</td>
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<p>Add transitions from the training sequence to the current counts.</p>
<p>Arguments:</p>
<p>o state_seq -- A Seq object with the states of the current training
sequence.</p>
<p>o transition_counts -- The current transition counts to add to.</p>
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