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Logistic (Java Machine Learning Library 0.0.11)
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net.sf.javaml.classification</FONT>
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Class Logistic</H2>
<PRE>
java.lang.Object
<IMG SRC="../../../../resources/inherit.gif" ALT="extended by "><B>net.sf.javaml.classification.Logistic</B>
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<DL>
<DT><B>All Implemented Interfaces:</B> <DD><A HREF="../../../../net/sf/javaml/classification/Classifier.html" title="interface in net.sf.javaml.classification">Classifier</A></DD>
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<DT><PRE>public class <B>Logistic</B><DT>extends java.lang.Object<DT>implements <A HREF="../../../../net/sf/javaml/classification/Classifier.html" title="interface in net.sf.javaml.classification">Classifier</A></DL>
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<!-- globalinfo-start --> Class for building and using a multinomial logistic regression model with a ridge estimator.<br/> <br/> There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992): <br/> <br/> If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix.<br/> <br/> The probability for class j with the exception of the last class is<br/> <br/> Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1) <br/> <br/> The last class has probability<br/> <br/> 1-(sum[j=1..(k-1)]Pj(Xi)) <br/> = 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1)<br/> <br/> The (negative) multinomial log-likelihood is thus: <br/> <br/> L = -sum[i=1..n]{<br/> sum[j=1..(k-1)](Yij * ln(Pj(Xi)))<br/> +(1 - (sum[j=1..(k-1)]Yij)) <br/> * ln(1 - sum[j=1..(k-1)]Pj(Xi))<br/> } + ridge * (B^2)<br/> <br/> In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables. Note that before we use the optimization procedure, we 'squeeze' the matrix B into a m*(k-1) vector. For details of the optimization procedure, please check weka.core.Optimization class.<br/> <br/> Although original Logistic Regression does not deal with instance weights, we modify the algorithm a little bit to handle the instance weights.<br/> <br/> For more information see:<br/> <br/> le Cessie, S., van Houwelingen, J.C. (1992). Ridge Estimators in Logistic Regression. Applied Statistics. 41(1):191-201.<br/> <br/> Note: Missing values are replaced using a ReplaceMissingValuesFilter, and nominal attributes are transformed into numeric attributes using a NominalToBinaryFilter. <p/> <!-- globalinfo-end --> <!-- technical-bibtex-start --> BibTeX: <pre> @article{leCessie1992, author = {le Cessie, S. and van Houwelingen, J.C.}, journal = {Applied Statistics}, number = {1}, pages = {191-201}, title = {Ridge Estimators in Logistic Regression}, volume = {41}, year = {1992} } </pre> <p/> <!-- technical-bibtex-end --> <!-- options-start --> Valid options are: <p/> <pre> -D Turn on debugging output.</pre> <pre> -R <ridge> Set the ridge in the log-likelihood.</pre> <pre> -M <number> Set the maximum number of iterations (default -1, until convergence).</pre> <!-- options-end -->
<P>
<P>
<DL>
<DT><B>Version:</B></DT>
<DD>$Revision: 1.37 $</DD>
<DT><B>Author:</B></DT>
<DD>Xin Xu (xx5@cs.waikato.ac.nz)</DD>
</DL>
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<!-- =========== FIELD SUMMARY =========== -->
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<CODE>protected double[][]</CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../../net/sf/javaml/classification/Logistic.html#m_Data">m_Data</A></B></CODE>
<BR>
The data saved as a matrix</TD>
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<CODE>protected boolean</CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../../net/sf/javaml/classification/Logistic.html#m_Debug">m_Debug</A></B></CODE>
<BR>
Debugging output</TD>
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<CODE>protected double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../../net/sf/javaml/classification/Logistic.html#m_LL">m_LL</A></B></CODE>
<BR>
Log-likelihood of the searched model</TD>
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<CODE>protected int</CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../../net/sf/javaml/classification/Logistic.html#m_NumClasses">m_NumClasses</A></B></CODE>
<BR>
The number of the class labels</TD>
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<CODE>protected int</CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../../net/sf/javaml/classification/Logistic.html#m_NumPredictors">m_NumPredictors</A></B></CODE>
<BR>
The number of attributes in the model</TD>
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<CODE>protected double[][]</CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../../net/sf/javaml/classification/Logistic.html#m_Par">m_Par</A></B></CODE>
<BR>
The coefficients (optimized parameters) of the model</TD>
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<CODE>protected double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../../net/sf/javaml/classification/Logistic.html#m_Ridge">m_Ridge</A></B></CODE>
<BR>
The ridge parameter.</TD>
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<TD><CODE><B><A HREF="../../../../net/sf/javaml/classification/Logistic.html#Logistic()">Logistic</A></B>()</CODE>
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<CODE> void</CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../../net/sf/javaml/classification/Logistic.html#buildClassifier(net.sf.javaml.core.Dataset)">buildClassifier</A></B>(<A HREF="../../../../net/sf/javaml/core/Dataset.html" title="interface in net.sf.javaml.core">Dataset</A> train)</CODE>
<BR>
Builds the classifier</TD>
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<CODE> int</CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../../net/sf/javaml/classification/Logistic.html#classifyInstance(net.sf.javaml.core.Instance)">classifyInstance</A></B>(<A HREF="../../../../net/sf/javaml/core/Instance.html" title="interface in net.sf.javaml.core">Instance</A> instance)</CODE>
<BR>
Classify the instance according to this classifier.</TD>
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<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1">
<CODE> double[]</CODE></FONT></TD>
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