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| <h2 title="Interface PriorFunction" class="title">Interface PriorFunction</h2> |
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| <pre>public interface <span class="typeNameLabel">PriorFunction</span> |
| extends org.apache.hadoop.io.Writable</pre> |
| <div class="block">A prior is used to regularize the learning algorithm. This allows a trade-off to |
| be made between complexity of the model being learned and the accuracy with which |
| the model fits the training data. There are different definitions of complexity |
| which can be approximated using different priors. For large sparse systems, such |
| as text classification, the L1 prior is often used which favors sparse models.</div> |
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| <td class="colFirst"><code>double</code></td> |
| <td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/mahout/classifier/sgd/PriorFunction.html#age-double-double-double-">age</a></span>(double oldValue, |
| double generations, |
| double learningRate)</code> |
| <div class="block">Applies the regularization to a coefficient.</div> |
| </td> |
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| <td class="colFirst"><code>double</code></td> |
| <td class="colLast"><code><span class="memberNameLink"><a href="../../../../../org/apache/mahout/classifier/sgd/PriorFunction.html#logP-double-">logP</a></span>(double betaIJ)</code> |
| <div class="block">Returns the log of the probability of a particular coefficient value according to the prior.</div> |
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| <h4>age</h4> |
| <pre>double age(double oldValue, |
| double generations, |
| double learningRate)</pre> |
| <div class="block">Applies the regularization to a coefficient.</div> |
| <dl> |
| <dt><span class="paramLabel">Parameters:</span></dt> |
| <dd><code>oldValue</code> - The previous value.</dd> |
| <dd><code>generations</code> - The number of generations.</dd> |
| <dd><code>learningRate</code> - The learning rate with lambda baked in.</dd> |
| <dt><span class="returnLabel">Returns:</span></dt> |
| <dd>The new coefficient value after regularization.</dd> |
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| <h4>logP</h4> |
| <pre>double logP(double betaIJ)</pre> |
| <div class="block">Returns the log of the probability of a particular coefficient value according to the prior.</div> |
| <dl> |
| <dt><span class="paramLabel">Parameters:</span></dt> |
| <dd><code>betaIJ</code> - The coefficient.</dd> |
| <dt><span class="returnLabel">Returns:</span></dt> |
| <dd>The log probability.</dd> |
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