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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 AbstractTrainer
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<h1 class="epydoc">Class AbstractTrainer</h1><p class="nomargin-top"><span class="codelink"><a href="Bio.HMM.Trainer-pysrc.html#AbstractTrainer">source code</a></span></p>
<dl><dt>Known Subclasses:</dt>
<dd>
<ul class="subclass-list">
<li><a href="Bio.HMM.Trainer.BaumWelchTrainer-class.html">BaumWelchTrainer</a></li><li>, <a href="Bio.HMM.Trainer.KnownStateTrainer-class.html">KnownStateTrainer</a></li> </ul>
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<p>Provide generic functionality needed in all trainers.</p>
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<td><span class="summary-sig"><a name="__init__"></a><span class="summary-sig-name">__init__</span>(<span class="summary-sig-arg">self</span>,
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<span class="codelink"><a href="Bio.HMM.Trainer-pysrc.html#AbstractTrainer.__init__">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.HMM.Trainer.AbstractTrainer-class.html#log_likelihood" class="summary-sig-name">log_likelihood</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">probabilities</span>)</span><br />
Calculate the log likelihood of the training seqs.</td>
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<td><span class="summary-sig"><a href="Bio.HMM.Trainer.AbstractTrainer-class.html#estimate_params" class="summary-sig-name">estimate_params</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">transition_counts</span>,
<span class="summary-sig-arg">emission_counts</span>)</span><br />
Get a maximum likelihood estimation of transition and emmission.</td>
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<td><span class="summary-sig"><a href="Bio.HMM.Trainer.AbstractTrainer-class.html#ml_estimator" class="summary-sig-name">ml_estimator</a>(<span class="summary-sig-arg">self</span>,
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Calculate the maximum likelihood estimator.</td>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">log_likelihood</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">probabilities</span>)</span>
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<p>Calculate the log likelihood of the training seqs.</p>
<p>Arguments:</p>
<p>o probabilities -- A list of the probabilities of each training
sequence under the current paramters, calculated using the forward
algorithm.</p>
<dl class="fields">
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<a name="estimate_params"></a>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">estimate_params</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">transition_counts</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#AbstractTrainer.estimate_params">source code</a></span>
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<p>Get a maximum likelihood estimation of transition and emmission.</p>
<p>Arguments:</p>
<p>o transition_counts -- A dictionary with the total number of counts of
transitions between two states.</p>
<p>o emissions_counts -- A dictionary with the total number of counts of
emmissions of a particular emission letter by a state letter.</p>
<p>This then returns the maximum likelihood estimators for the
transitions and emissions, estimated by formulas 3.18 in Durbin et
al:</p>
<p>a_{kl} = A_{kl} / sum(A_{kl'}) e_{k}(b) = E_{k}(b) /
sum(E_{k}(b'))</p>
<p>Returns: Transition and emission dictionaries containing the maximum
likelihood estimators.</p>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">ml_estimator</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">counts</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.HMM.Trainer-pysrc.html#AbstractTrainer.ml_estimator">source code</a></span>
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<p>Calculate the maximum likelihood estimator.</p>
<p>This can calculate maximum likelihoods for both transitions and
emissions.</p>
<p>Arguments:</p>
<p>o counts -- A dictionary of the counts for each item.</p>
<p>See estimate_params for a description of the formula used for
calculation.</p>
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