📄 ensembleselectionlibrarymodel.html
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<BR> Saves the given model to the specified file.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../weka/classifiers/meta/ensembleSelection/EnsembleSelectionLibraryModel.html#setChecksum(java.lang.String)">setChecksum</A></B>(java.lang.String instancesChecksum)</CODE><BR> set the checksum</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../weka/classifiers/meta/ensembleSelection/EnsembleSelectionLibraryModel.html#setDebug(boolean)">setDebug</A></B>(boolean debug)</CODE><BR> This is used to propagate the m_Debug flag of the EnsembleSelection classifier to this class.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../weka/classifiers/meta/ensembleSelection/EnsembleSelectionLibraryModel.html#setFileName(java.lang.String)">setFileName</A></B>(java.lang.String fileName)</CODE><BR> Sets the .elm file name for this library model</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../weka/classifiers/meta/ensembleSelection/EnsembleSelectionLibraryModel.html#setFolds(int)">setFolds</A></B>(int folds)</CODE><BR> Set the number of folds for cross validation.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../weka/classifiers/meta/ensembleSelection/EnsembleSelectionLibraryModel.html#setSeed(int)">setSeed</A></B>(int seed)</CODE><BR> Set the seed</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../weka/classifiers/meta/ensembleSelection/EnsembleSelectionLibraryModel.html#setValidationPredictions(double[][])">setValidationPredictions</A></B>(double[][] predictions)</CODE><BR> setter for validation predictions</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../weka/classifiers/meta/ensembleSelection/EnsembleSelectionLibraryModel.html#setValidationRatio(double)">setValidationRatio</A></B>(double validationRatio)</CODE><BR> Sets the validation set ratio (only meaningful if folds == 1)</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> void</CODE></FONT></TD><TD><CODE><B><A HREF="../../../../weka/classifiers/meta/ensembleSelection/EnsembleSelectionLibraryModel.html#train(weka.core.Instances, int)">train</A></B>(<A HREF="../../../../weka/core/Instances.html" title="class in weka.core">Instances</A> trainData, int fold)</CODE><BR> Train the classifier for the specified fold on the given data</TD></TR></TABLE> <A NAME="methods_inherited_from_class_weka.classifiers.EnsembleLibraryModel"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#EEEEFF" CLASS="TableSubHeadingColor"><TH ALIGN="left"><B>Methods inherited from class weka.classifiers.<A HREF="../../../../weka/classifiers/EnsembleLibraryModel.html" title="class in weka.classifiers">EnsembleLibraryModel</A></B></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE><A HREF="../../../../weka/classifiers/EnsembleLibraryModel.html#getClassifier()">getClassifier</A>, <A HREF="../../../../weka/classifiers/EnsembleLibraryModel.html#getDescriptionText()">getDescriptionText</A>, <A HREF="../../../../weka/classifiers/EnsembleLibraryModel.html#getErrorText()">getErrorText</A>, <A HREF="../../../../weka/classifiers/EnsembleLibraryModel.html#getModelClass()">getModelClass</A>, <A HREF="../../../../weka/classifiers/EnsembleLibraryModel.html#getOptions()">getOptions</A>, <A HREF="../../../../weka/classifiers/EnsembleLibraryModel.html#getOptionsWereValid()">getOptionsWereValid</A>, <A HREF="../../../../weka/classifiers/EnsembleLibraryModel.html#getStringRepresentation()">getStringRepresentation</A>, <A HREF="../../../../weka/classifiers/EnsembleLibraryModel.html#setDescriptionText(java.lang.String)">setDescriptionText</A>, <A HREF="../../../../weka/classifiers/EnsembleLibraryModel.html#setErrorText(java.lang.String)">setErrorText</A>, <A HREF="../../../../weka/classifiers/EnsembleLibraryModel.html#setOptionsWereValid(boolean)">setOptionsWereValid</A>, <A HREF="../../../../weka/classifiers/EnsembleLibraryModel.html#testOptions()">testOptions</A>, <A HREF="../../../../weka/classifiers/EnsembleLibraryModel.html#toString()">toString</A>, <A HREF="../../../../weka/classifiers/EnsembleLibraryModel.html#updateDescriptionText()">updateDescriptionText</A></CODE></TD></TR></TABLE> <A NAME="methods_inherited_from_class_java.lang.Object"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#EEEEFF" CLASS="TableSubHeadingColor"><TH ALIGN="left"><B>Methods inherited from class java.lang.Object</B></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE>equals, getClass, hashCode, notify, notifyAll, wait, wait, wait</CODE></TD></TR></TABLE> <P><!-- ============ FIELD DETAIL =========== --><A NAME="field_detail"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="1"><FONT SIZE="+2"><B>Field Detail</B></FONT></TH></TR></TABLE><A NAME="FILE_EXTENSION"><!-- --></A><H3>FILE_EXTENSION</H3><PRE>public static final java.lang.String <B>FILE_EXTENSION</B></PRE><DL><DD>The default file extension for ensemble library models<P><DL><DT><B>See Also:</B><DD><A HREF="../../../../constant-values.html#weka.classifiers.meta.ensembleSelection.EnsembleSelectionLibraryModel.FILE_EXTENSION">Constant Field Values</A></DL></DL><HR><A NAME="m_Debug"><!-- --></A><H3>m_Debug</H3><PRE>public transient boolean <B>m_Debug</B></PRE><DL><DD>The debug flag as propagated from the main EnsembleSelection class.<P><DL></DL></DL><!-- ========= CONSTRUCTOR DETAIL ======== --><A NAME="constructor_detail"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="1"><FONT SIZE="+2"><B>Constructor Detail</B></FONT></TH></TR></TABLE><A NAME="EnsembleSelectionLibraryModel()"><!-- --></A><H3>EnsembleSelectionLibraryModel</H3><PRE>public <B>EnsembleSelectionLibraryModel</B>()</PRE><DL><DD>Default Constructor<P></DL><HR><A NAME="EnsembleSelectionLibraryModel(weka.classifiers.Classifier, int, java.lang.String, double, int)"><!-- --></A><H3>EnsembleSelectionLibraryModel</H3><PRE>public <B>EnsembleSelectionLibraryModel</B>(<A HREF="../../../../weka/classifiers/Classifier.html" title="class in weka.classifiers">Classifier</A> classifier, int seed, java.lang.String checksum, double validationRatio, int folds)</PRE><DL><DD>Constructor for LibaryModel<P><DL><DT><B>Parameters:</B><DD><CODE>classifier</CODE> - the classifier to use<DD><CODE>seed</CODE> - the random seed value<DD><CODE>checksum</CODE> - the checksum<DD><CODE>validationRatio</CODE> - the ration to use<DD><CODE>folds</CODE> - the number of folds to use</DL></DL><HR><A NAME="EnsembleSelectionLibraryModel(weka.classifiers.Classifier)"><!-- --></A><H3>EnsembleSelectionLibraryModel</H3><PRE>public <B>EnsembleSelectionLibraryModel</B>(<A HREF="../../../../weka/classifiers/Classifier.html" title="class in weka.classifiers">Classifier</A> classifier)</PRE><DL><DD>Basic Constructor<P><DL><DT><B>Parameters:</B><DD><CODE>classifier</CODE> - the classifier to use</DL></DL><!-- ============ METHOD DETAIL ========== --><A NAME="method_detail"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="1"><FONT SIZE="+2"><B>Method Detail</B></FONT></TH></TR></TABLE><A NAME="setDebug(boolean)"><!-- --></A><H3>setDebug</H3><PRE>public void <B>setDebug</B>(boolean debug)</PRE><DL><DD>This is used to propagate the m_Debug flag of the EnsembleSelection classifier to this class. There are things we would want to print out here also.<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>debug</CODE> - if true additional information is output</DL></DD></DL><HR><A NAME="getAveragePrediction(weka.core.Instance)"><!-- --></A><H3>getAveragePrediction</H3><PRE>public double[] <B>getAveragePrediction</B>(<A HREF="../../../../weka/core/Instance.html" title="class in weka.core">Instance</A> instance) throws java.lang.Exception</PRE><DL><DD>Returns the average of the prediction of the models across all folds.<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>instance</CODE> - the instance to get predictions for<DT><B>Returns:</B><DD>the average prediction<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if something goes wrong</DL></DD></DL><HR><A NAME="getFoldPrediction(weka.core.Instance, int)"><!-- --></A><H3>getFoldPrediction</H3><PRE>public double[] <B>getFoldPrediction</B>(<A HREF="../../../../weka/core/Instance.html" title="class in weka.core">Instance</A> instance, int fold) throws java.lang.Exception</PRE><DL><DD>Returns prediction of the classifier for the specified fold.<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>instance</CODE> - instance for which to make a prediction.<DD><CODE>fold</CODE> - fold number of the classifier to use.<DT><B>Returns:</B><DD>the prediction for the classes<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if prediction fails</DL></DD></DL><HR><A NAME="createModel(weka.core.Instances[], weka.core.Instances[], java.lang.String, int)"><!-- --></A><H3>createModel</H3><PRE>public void <B>createModel</B>(<A HREF="../../../../weka/core/Instances.html" title="class in weka.core">Instances</A>[] data, <A HREF="../../../../weka/core/Instances.html" title="class in weka.core">Instances</A>[] hillclimbData, java.lang.String dataDirectoryName, int algorithm) throws java.lang.Exception</PRE><DL><DD>Creates the model. If there are n folds, it constructs n classifiers using the current Classifier class and options. If the model has already been created or loaded, starts fresh.<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>data</CODE> - the data to work with<DD><CODE>hillclimbData</CODE> - the data for hillclimbing<DD><CODE>dataDirectoryName</CODE> - the directory to use<DD><CODE>algorithm</CODE> - the type of algorithm<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if something goeds wrong</DL></DD></DL><HR><A NAME="rehydrateModel(java.lang.String)"><!-- --></A><H3>rehydrateModel</H3><PRE>public void <B>rehydrateModel</B>(java.lang.String workingDirectory)</PRE><DL><DD>The purpose of this method is to "rehydrate" the classifier object fot this library model from the filesystem.<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>workingDirectory</CODE> - the working directory to use</DL></DD></DL><HR><A NAME="releaseModel()"><!-- --></A><H3>releaseModel</H3><PRE>public void <B>releaseModel</B>()</PRE><DL><DD>Releases the model from memory. TODO - need to be saving these so we can retrieve them later!!<P><DD><DL></DL></DD><DD><DL></DL></DD></DL><HR><A NAME="train(weka.core.Instances, int)"><!-- --></A><H3>train</H3><PRE>public void <B>train</B>(<A HREF="../../../../weka/core/Instances.html" title="class in weka.core">Instances</A> trainData, int fold) throws java.lang.Exception</PRE><DL><DD>Train the classifier for the specified fold on the given data<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>trainData</CODE> - the data to train with<DD><CODE>fold</CODE> - the fold number<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if something goes wrong, e.g., out of memory</DL></DD></DL><HR><A NAME="setSeed(int)"><!-- --></A><H3>setSeed</H3><PRE>public void <B>setSeed</B>(int seed)</PRE><DL><DD>Set the seed<P><DD><DL></DL>⌨️ 快捷键说明
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