ibk.html

< 数据挖掘--实用机器学习技术及java实现> 一书结合数据挖掘和机器学习的知识,作者陈述了自动挖掘模式的基础理论,并且以java语言实现了具有代表性的各类数据挖掘方法.例如:class

<A NAME="getKNN()"><!-- --></A><H3>
getKNN</H3>
<PRE>
public int <B>getKNN</B>()</PRE>
<DL>
<DD>Gets the number of neighbours the learner will use.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>the number of neighbours.</DL>
</DD>
</DL>
<HR>

<A NAME="getWindowSize()"><!-- --></A><H3>
getWindowSize</H3>
<PRE>
public int <B>getWindowSize</B>()</PRE>
<DL>
<DD>Gets the maximum number of instances allowed in the training
 pool. The addition of new instances above this value will result
 in old instances being removed. A value of 0 signifies no limit
 to the number of training instances.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>Value of WindowSize.</DL>
</DD>
</DL>
<HR>

<A NAME="setWindowSize(int)"><!-- --></A><H3>
setWindowSize</H3>
<PRE>
public void <B>setWindowSize</B>(int&nbsp;newWindowSize)</PRE>
<DL>
<DD>Sets the maximum number of instances allowed in the training
 pool. The addition of new instances above this value will result
 in old instances being removed. A value of 0 signifies no limit
 to the number of training instances.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>newWindowSize</CODE> - Value to assign to WindowSize.</DL>
</DD>
</DL>
<HR>

<A NAME="getDistanceWeighting()"><!-- --></A><H3>
getDistanceWeighting</H3>
<PRE>
public <A HREF="../../weka/core/SelectedTag.html">SelectedTag</A> <B>getDistanceWeighting</B>()</PRE>
<DL>
<DD>Gets the distance weighting method used. Will be one of
 WEIGHT_NONE, WEIGHT_INVERSE, or WEIGHT_SIMILARITY<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>the distance weighting method used.</DL>
</DD>
</DL>
<HR>

<A NAME="setDistanceWeighting(weka.core.SelectedTag)"><!-- --></A><H3>
setDistanceWeighting</H3>
<PRE>
public void <B>setDistanceWeighting</B>(<A HREF="../../weka/core/SelectedTag.html">SelectedTag</A>&nbsp;newMethod)</PRE>
<DL>
<DD>Sets the distance weighting method used. Values other than
 WEIGHT_NONE, WEIGHT_INVERSE, or WEIGHT_SIMILARITY will be ignored.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>newDistanceWeighting</CODE> - the distance weighting method to use</DL>
</DD>
</DL>
<HR>

<A NAME="getMeanSquared()"><!-- --></A><H3>
getMeanSquared</H3>
<PRE>
public boolean <B>getMeanSquared</B>()</PRE>
<DL>
<DD>Gets whether the mean squared error is used rather than mean
 absolute error when doing cross-validation.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>true if so.</DL>
</DD>
</DL>
<HR>

<A NAME="setMeanSquared(boolean)"><!-- --></A><H3>
setMeanSquared</H3>
<PRE>
public void <B>setMeanSquared</B>(boolean&nbsp;newMeanSquared)</PRE>
<DL>
<DD>Sets whether the mean squared error is used rather than mean
 absolute error when doing cross-validation.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>newMeanSquared</CODE> - true if so.</DL>
</DD>
</DL>
<HR>

<A NAME="getCrossValidate()"><!-- --></A><H3>
getCrossValidate</H3>
<PRE>
public boolean <B>getCrossValidate</B>()</PRE>
<DL>
<DD>Gets whether hold-one-out cross-validation will be used
 to select the best k value<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>true if cross-validation will be used.</DL>
</DD>
</DL>
<HR>

<A NAME="setCrossValidate(boolean)"><!-- --></A><H3>
setCrossValidate</H3>
<PRE>
public void <B>setCrossValidate</B>(boolean&nbsp;newCrossValidate)</PRE>
<DL>
<DD>Sets whether hold-one-out cross-validation will be used
 to select the best k value<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>newCrossValidate</CODE> - true if cross-validation should be used.</DL>
</DD>
</DL>
<HR>

<A NAME="getNumTraining()"><!-- --></A><H3>
getNumTraining</H3>
<PRE>
public int <B>getNumTraining</B>()</PRE>
<DL>
<DD>Get the number of training instances the classifier is currently using<DD><DL>
</DL>
</DD>
</DL>
<HR>

<A NAME="getAttributeMin(int)"><!-- --></A><H3>
getAttributeMin</H3>
<PRE>
public double <B>getAttributeMin</B>(int&nbsp;index)
                       throws java.lang.Exception</PRE>
<DL>
<DD>Get an attributes minimum observed value<DD><DL>
</DL>
</DD>
</DL>
<HR>

<A NAME="getAttributeMax(int)"><!-- --></A><H3>
getAttributeMax</H3>
<PRE>
public double <B>getAttributeMax</B>(int&nbsp;index)
                       throws java.lang.Exception</PRE>
<DL>
<DD>Get an attributes maximum observed value<DD><DL>
</DL>
</DD>
</DL>
<HR>

<A NAME="getNoNormalization()"><!-- --></A><H3>
getNoNormalization</H3>
<PRE>
public boolean <B>getNoNormalization</B>()</PRE>
<DL>
<DD>Gets whether normalization is turned off.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>Value of DontNormalize.</DL>
</DD>
</DL>
<HR>

<A NAME="setNoNormalization(boolean)"><!-- --></A><H3>
setNoNormalization</H3>
<PRE>
public void <B>setNoNormalization</B>(boolean&nbsp;v)</PRE>
<DL>
<DD>Set whether normalization is turned off.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>v</CODE> - Value to assign to DontNormalize.</DL>
</DD>
</DL>
<HR>

<A NAME="buildClassifier(weka.core.Instances)"><!-- --></A><H3>
buildClassifier</H3>
<PRE>
public void <B>buildClassifier</B>(<A HREF="../../weka/core/Instances.html">Instances</A>&nbsp;instances)
                     throws java.lang.Exception</PRE>
<DL>
<DD>Generates the classifier.<DD><DL>
<DT><B>Overrides:</B><DD><CODE><A HREF="../../weka/classifiers/Classifier.html#buildClassifier(weka.core.Instances)">buildClassifier</A></CODE> in class <CODE><A HREF="../../weka/classifiers/Classifier.html">Classifier</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>instances</CODE> - set of instances serving as training data<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if the classifier has not been generated successfully</DL>
</DD>
</DL>
<HR>

<A NAME="updateClassifier(weka.core.Instance)"><!-- --></A><H3>
updateClassifier</H3>
<PRE>
public void <B>updateClassifier</B>(<A HREF="../../weka/core/Instance.html">Instance</A>&nbsp;instance)
                      throws java.lang.Exception</PRE>
<DL>
<DD>Adds the supplied instance to the training set<DD><DL>
<DT><B>Specified by: </B><DD><CODE><A HREF="../../weka/classifiers/UpdateableClassifier.html#updateClassifier(weka.core.Instance)">updateClassifier</A></CODE> in interface <CODE><A HREF="../../weka/classifiers/UpdateableClassifier.html">UpdateableClassifier</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>instance</CODE> - the instance to add<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if instance could not be incorporated
 successfully</DL>
</DD>
</DL>
<HR>

<A NAME="distributionForInstance(weka.core.Instance)"><!-- --></A><H3>
distributionForInstance</H3>
<PRE>
public double[] <B>distributionForInstance</B>(<A HREF="../../weka/core/Instance.html">Instance</A>&nbsp;instance)
                                 throws java.lang.Exception</PRE>
<DL>
<DD>Calculates the class membership probabilities for the given test instance.<DD><DL>
<DT><B>Overrides:</B><DD><CODE><A HREF="../../weka/classifiers/DistributionClassifier.html#distributionForInstance(weka.core.Instance)">distributionForInstance</A></CODE> in class <CODE><A HREF="../../weka/classifiers/DistributionClassifier.html">DistributionClassifier</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>instance</CODE> - the instance to be classified<DT><B>Returns:</B><DD>predicted class probability distribution<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if an error occurred during the prediction</DL>
</DD>
</DL>
<HR>

<A NAME="listOptions()"><!-- --></A><H3>
listOptions</H3>
<PRE>
public java.util.Enumeration <B>listOptions</B>()</PRE>
<DL>
<DD>Returns an enumeration describing the available options<DD><DL>
<DT><B>Specified by: </B><DD><CODE><A HREF="../../weka/core/OptionHandler.html#listOptions()">listOptions</A></CODE> in interface <CODE><A HREF="../../weka/core/OptionHandler.html">OptionHandler</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>an enumeration of all the available options</DL>
</DD>
</DL>
<HR>

<A NAME="setOptions(java.lang.String[])"><!-- --></A><H3>
setOptions</H3>
<PRE>
public void <B>setOptions</B>(java.lang.String[]&nbsp;options)
                throws java.lang.Exception</PRE>
<DL>
<DD>Parses a given list of options. Valid options are:<p>

 -K num <br>
 Set the number of nearest neighbors to use in prediction
 (default 1) <p>

 -W num <br>
 Set a fixed window size for incremental train/testing. As
 new training instances are added, oldest instances are removed
 to maintain the number of training instances at this size.
 (default no window) <p>

 -D <br>
 Neighbors will be weighted by the inverse of their distance
 when voting. (default equal weighting) <p>

 -F <br>
 Neighbors will be weighted by their similarity when voting.
 (default equal weighting) <p>

 -X <br>
 Select the number of neighbors to use by hold-one-out cross
 validation, with an upper limit given by the -K option. <p>

 -S <br>
 When k is selected by cross-validation for numeric class attributes,
 minimize mean-squared error. (default mean absolute error) <p><DD><DL>
<DT><B>Specified by: </B><DD><CODE><A HREF="../../weka/core/OptionHandler.html#setOptions(java.lang.String[])">setOptions</A></CODE> in interface <CODE><A HREF="../../weka/core/OptionHandler.html">OptionHandler</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>options</CODE> - the list of options as an array of strings<DT><B>Throws:</B><DD><CODE>java.lang.Exception</CODE> - if an option is not supported</DL>
</DD>
</DL>
<HR>

<A NAME="getOptions()"><!-- --></A><H3>
getOptions</H3>
<PRE>
public java.lang.String[] <B>getOptions</B>()</PRE>
<DL>
<DD>Gets the current settings of IBk.<DD><DL>
<DT><B>Specified by: </B><DD><CODE><A HREF="../../weka/core/OptionHandler.html#getOptions()">getOptions</A></CODE> in interface <CODE><A HREF="../../weka/core/OptionHandler.html">OptionHandler</A></CODE></DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>an array of strings suitable for passing to setOptions()</DL>
</DD>
</DL>
<HR>

<A NAME="toString()"><!-- --></A><H3>
toString</H3>
<PRE>
public java.lang.String <B>toString</B>()</PRE>
<DL>
<DD>Returns a description of this classifier.<DD><DL>
<DT><B>Overrides:</B><DD><CODE>toString</CODE> in class <CODE>java.lang.Object</CODE></DL>
</DD>
<DD><DL>
<DT><B>Returns:</B><DD>a description of this classifier as a string.</DL>
</DD>
</DL>
<HR>

<A NAME="main(java.lang.String[])"><!-- --></A><H3>
main</H3>
<PRE>
public static void <B>main</B>(java.lang.String[]&nbsp;argv)</PRE>
<DL>
<DD>Main method for testing this class.<DD><DL>
</DL>
</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>argv</CODE> - should contain command line options (see setOptions)</DL>
</DD>
</DL>
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