modelbag.html

weka是机器学习和数据挖掘领域最有影响力的开源项目之一

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<B>Constructor Detail</B></FONT></TH>
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<A NAME="ModelBag(double[][][], double, boolean)"><!-- --></A><H3>
ModelBag</H3>
<PRE>
public <B>ModelBag</B>(double[][][]&nbsp;models,
                double&nbsp;bag_percent,
                boolean&nbsp;debug)</PRE>
<DL>
<DD>Constructor for ModelBag.
<P>
<DL>
<DT><B>Parameters:</B><DD><CODE>models</CODE> - The complete set of models from which to draw our bag. First
            index is for the model, second is for the instance. The last
            is a prediction distribution for that instance. Models are
            represented by this array of predictions for validation data,
            since that's all ensemble selection needs to know.<DD><CODE>bag_percent</CODE> - The percentage of the set of given models that should be used
            in the Model Bag.<DD><CODE>debug</CODE> - Whether the ModelBag should print debug information.</DL>
</DL>

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<B>Method Detail</B></FONT></TH>
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<A NAME="shuffle(java.util.Random)"><!-- --></A><H3>
shuffle</H3>
<PRE>
public void <B>shuffle</B>(java.util.Random&nbsp;rand)</PRE>
<DL>
<DD>Shuffle the models. The order in m_models is preserved, but we change our
 virtual indexes around.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>rand</CODE> - the random number generator to use</DL>
</DD>
</DL>
<HR>

<A NAME="sortInitialize(int, boolean, weka.core.Instances, int)"><!-- --></A><H3>
sortInitialize</H3>
<PRE>
public int[] <B>sortInitialize</B>(int&nbsp;num,
                            boolean&nbsp;greedy,
                            <A HREF="../../../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;instances,
                            int&nbsp;metric)
                     throws java.lang.Exception</PRE>
<DL>
<DD>Sort initialize the bag.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>num</CODE> - the Maximum number of models to initialize with<DD><CODE>greedy</CODE> - True if we do greedy addition, up to num. Greedy sort
            initialization adds models (up to num) in order of best to
            worst performance until performance no longer improves.<DD><CODE>instances</CODE> - the data set (needed for performance evaluation)<DD><CODE>metric</CODE> - metric for which to optimize. See EnsembleMetricHelper
<DT><B>Returns:</B><DD>returns an array of indexes which were selected, in order
         starting from the model with best performance.
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL>
</DD>
</DL>
<HR>

<A NAME="weightAll(int)"><!-- --></A><H3>
weightAll</H3>
<PRE>
public void <B>weightAll</B>(int&nbsp;weight)</PRE>
<DL>
<DD>Add "weight" to the number of times each model in the bag was chosen.
 Typically for use with backward elimination.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>weight</CODE> - the weight to add</DL>
</DD>
</DL>
<HR>

<A NAME="forwardSelect(boolean, weka.core.Instances, int)"><!-- --></A><H3>
forwardSelect</H3>
<PRE>
public void <B>forwardSelect</B>(boolean&nbsp;withReplacement,
                          <A HREF="../../../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;instances,
                          int&nbsp;metric)
                   throws java.lang.Exception</PRE>
<DL>
<DD>Forward select one model. Will add the model which has the best effect on
 performance. If replacement is false, and all models are chosen, no
 action is taken. If a model can be added, one always is (even if it hurts
 performance).
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>withReplacement</CODE> - whether a model can be added more than once.<DD><CODE>instances</CODE> - The dataset, for calculating performance.<DD><CODE>metric</CODE> - The metric to which we will optimize. See EnsembleMetricHelper
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL>
</DD>
</DL>
<HR>

<A NAME="backwardEliminate(weka.core.Instances, int)"><!-- --></A><H3>
backwardEliminate</H3>
<PRE>
public void <B>backwardEliminate</B>(<A HREF="../../../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;instances,
                              int&nbsp;metric)
                       throws java.lang.Exception</PRE>
<DL>
<DD>Find the model whose removal will help the ensemble's performance the
 most, and remove it. If there is only one model left, we leave it in. If
 we can remove a model, we always do, even if it hurts performance.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>instances</CODE> - The data set, for calculating performance<DD><CODE>metric</CODE> - Metric to optimize for. See EnsembleMetricHelper.
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL>
</DD>
</DL>
<HR>

<A NAME="forwardSelectOrBackwardEliminate(boolean, weka.core.Instances, int)"><!-- --></A><H3>
forwardSelectOrBackwardEliminate</H3>
<PRE>
public void <B>forwardSelectOrBackwardEliminate</B>(boolean&nbsp;with_replacement,
                                             <A HREF="../../../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;instances,
                                             int&nbsp;metric)
                                      throws java.lang.Exception</PRE>
<DL>
<DD>Find the best action to perform, be it adding a model or removing a
 model, and perform it. Some action is always performed, even if it hurts
 performance.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>with_replacement</CODE> - whether we can add a model more than once<DD><CODE>instances</CODE> - The dataset, for determining performance.<DD><CODE>metric</CODE> - The metric for which to optimize. See EnsembleMetricHelper.
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL>
</DD>
</DL>
<HR>

<A NAME="getModelWeights()"><!-- --></A><H3>
getModelWeights</H3>
<PRE>
public int[] <B>getModelWeights</B>()</PRE>
<DL>
<DD>returns the model weights
<P>
<DD><DL>

<DT><B>Returns:</B><DD>the model weights</DL>
</DD>
</DL>
<HR>

<A NAME="getIndividualPerformance(weka.core.Instances, int)"><!-- --></A><H3>
getIndividualPerformance</H3>
<PRE>
public double[] <B>getIndividualPerformance</B>(<A HREF="../../../../weka/core/Instances.html" title="class in weka.core">Instances</A>&nbsp;instances,
                                         int&nbsp;metric)
                                  throws java.lang.Exception</PRE>
<DL>
<DD>Gets the individual performances of all the models in the bag.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>instances</CODE> - The validation data, for which we want performance.<DD><CODE>metric</CODE> - The desired metric (see EnsembleMetricHelper).
<DT><B>Returns:</B><DD>the performance
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL>
</DD>
</DL>
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