📄 multilayerperceptron.html
字号:
<TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/functions/MultilayerPerceptron.html#randomSeedTipText()">randomSeedTipText</A></B>()</CODE><BR> </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/functions/MultilayerPerceptron.html#resetTipText()">resetTipText</A></B>()</CODE><BR> </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/functions/MultilayerPerceptron.html#setAutoBuild(boolean)">setAutoBuild</A></B>(boolean a)</CODE><BR> This will set whether the network is automatically built or if it is left up to the user.</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/functions/MultilayerPerceptron.html#setDecay(boolean)">setDecay</A></B>(boolean d)</CODE><BR> </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/functions/MultilayerPerceptron.html#setGUI(boolean)">setGUI</A></B>(boolean a)</CODE><BR> This will set whether A GUI is brought up to allow interaction by the user with the neural network during training.</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/functions/MultilayerPerceptron.html#setHiddenLayers(java.lang.String)">setHiddenLayers</A></B>(java.lang.String h)</CODE><BR> This will set what the hidden layers are made up of when auto build is enabled.</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/functions/MultilayerPerceptron.html#setLearningRate(double)">setLearningRate</A></B>(double l)</CODE><BR> The learning rate can be set using this command.</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/functions/MultilayerPerceptron.html#setMomentum(double)">setMomentum</A></B>(double m)</CODE><BR> The momentum can be set using this command.</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/functions/MultilayerPerceptron.html#setNominalToBinaryFilter(boolean)">setNominalToBinaryFilter</A></B>(boolean f)</CODE><BR> </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/functions/MultilayerPerceptron.html#setNormalizeAttributes(boolean)">setNormalizeAttributes</A></B>(boolean a)</CODE><BR> </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/functions/MultilayerPerceptron.html#setNormalizeNumericClass(boolean)">setNormalizeNumericClass</A></B>(boolean c)</CODE><BR> </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/functions/MultilayerPerceptron.html#setOptions(java.lang.String[])">setOptions</A></B>(java.lang.String[] options)</CODE><BR> Parses a given list of options.</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/functions/MultilayerPerceptron.html#setRandomSeed(long)">setRandomSeed</A></B>(long l)</CODE><BR> This seeds the random number generator, that is used when a random number is needed for the network.</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/functions/MultilayerPerceptron.html#setReset(boolean)">setReset</A></B>(boolean r)</CODE><BR> This sets the network up to be able to reset itself with the current settings and the learning rate at half of what it is currently.</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/functions/MultilayerPerceptron.html#setTrainingTime(int)">setTrainingTime</A></B>(int n)</CODE><BR> Set the number of training epochs to perform.</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/functions/MultilayerPerceptron.html#setValidationSetSize(int)">setValidationSetSize</A></B>(int a)</CODE><BR> This will set the size of the validation set.</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/functions/MultilayerPerceptron.html#setValidationThreshold(int)">setValidationThreshold</A></B>(int t)</CODE><BR> This sets the threshold to use for when validation testing is being done.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/functions/MultilayerPerceptron.html#toString()">toString</A></B>()</CODE><BR> </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/functions/MultilayerPerceptron.html#trainingTimeTipText()">trainingTimeTipText</A></B>()</CODE><BR> </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/functions/MultilayerPerceptron.html#validationSetSizeTipText()">validationSetSizeTipText</A></B>()</CODE><BR> </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD ALIGN="right" VALIGN="top" WIDTH="1%"><FONT SIZE="-1"><CODE> java.lang.String</CODE></FONT></TD><TD><CODE><B><A HREF="../../../weka/classifiers/functions/MultilayerPerceptron.html#validationThresholdTipText()">validationThresholdTipText</A></B>()</CODE><BR> </TD></TR></TABLE> <A NAME="methods_inherited_from_class_weka.classifiers.Classifier"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#EEEEFF" CLASS="TableSubHeadingColor"><TD><B>Methods inherited from class weka.classifiers.<A HREF="../../../weka/classifiers/Classifier.html" title="class in weka.classifiers">Classifier</A></B></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE><A HREF="../../../weka/classifiers/Classifier.html#classifyInstance(weka.core.Instance)">classifyInstance</A>, <A HREF="../../../weka/classifiers/Classifier.html#debugTipText()">debugTipText</A>, <A HREF="../../../weka/classifiers/Classifier.html#forName(java.lang.String, java.lang.String[])">forName</A>, <A HREF="../../../weka/classifiers/Classifier.html#getDebug()">getDebug</A>, <A HREF="../../../weka/classifiers/Classifier.html#makeCopies(weka.classifiers.Classifier, int)">makeCopies</A>, <A HREF="../../../weka/classifiers/Classifier.html#makeCopy(weka.classifiers.Classifier)">makeCopy</A>, <A HREF="../../../weka/classifiers/Classifier.html#setDebug(boolean)">setDebug</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"><TD><B>Methods inherited from class java.lang.Object</B></TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><CODE>equals, getClass, hashCode, notify, notifyAll, wait, wait, wait</CODE></TD></TR></TABLE> <P><!-- ============ FIELD DETAIL =========== --><!-- ========= CONSTRUCTOR DETAIL ======== --><A NAME="constructor_detail"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TD COLSPAN=1><FONT SIZE="+2"><B>Constructor Detail</B></FONT></TD></TR></TABLE><A NAME="MultilayerPerceptron()"><!-- --></A><H3>MultilayerPerceptron</H3><PRE>public <B>MultilayerPerceptron</B>()</PRE><DL><DD>The constructor.<P></DL><!-- ============ METHOD DETAIL ========== --><A NAME="method_detail"><!-- --></A><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TD COLSPAN=1><FONT SIZE="+2"><B>Method Detail</B></FONT></TD></TR></TABLE><A NAME="main(java.lang.String[])"><!-- --></A><H3>main</H3><PRE>public static void <B>main</B>(java.lang.String[] argv)</PRE><DL><DD>Main method for testing this class.<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>argv</CODE> - should contain command line options (see setOptions)</DL></DD></DL><HR><A NAME="setDecay(boolean)"><!-- --></A><H3>setDecay</H3><PRE>public void <B>setDecay</B>(boolean d)</PRE><DL><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>d</CODE> - True if the learning rate should decay.</DL></DD></DL><HR><A NAME="getDecay()"><!-- --></A><H3>getDecay</H3><PRE>public boolean <B>getDecay</B>()</PRE><DL><DD><DL></DL></DD><DD><DL><DT><B>Returns:</B><DD>the flag for having the learning rate decay.</DL></DD></DL><HR><A NAME="setReset(boolean)"><!-- --></A><H3>setReset</H3><PRE>public void <B>setReset</B>(boolean r)</PRE><DL><DD>This sets the network up to be able to reset itself with the current settings and the learning rate at half of what it is currently. This will only happen if the network creates NaN or infinite errors. Also this will continue to happen until the network is trained properly. The learning rate will also get set back to it's original value at the end of this. This can only be set to true if the GUI is not brought up.<P><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>r</CODE> - True if the network should restart with it's current options and set the learning rate to half what it currently is.</DL></DD></DL><HR><A NAME="getReset()"><!-- --></A><H3>getReset</H3><PRE>public boolean <B>getReset</B>()</PRE><DL><DD><DL></DL></DD><DD><DL><DT><B>Returns:</B><DD>The flag for reseting the network.</DL></DD></DL><HR><A NAME="setNormalizeNumericClass(boolean)"><!-- --></A><H3>setNormalizeNumericClass</H3><PRE>public void <B>setNormalizeNumericClass</B>(boolean c)</PRE><DL><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>c</CODE> - True if the class should be normalized (the class will only ever be normalized if it is numeric). (Normalization puts the range between -1 - 1).</DL></DD></DL><HR><A NAME="getNormalizeNumericClass()"><!-- --></A><H3>getNormalizeNumericClass</H3><PRE>public boolean <B>getNormalizeNumericClass</B>()</PRE><DL><DD><DL></DL></DD><DD><DL><DT><B>Returns:</B><DD>The flag for normalizing a numeric class.</DL></DD></DL><HR><A NAME="setNormalizeAttributes(boolean)"><!-- --></A><H3>setNormalizeAttributes</H3><PRE>public void <B>setNormalizeAttributes</B>(boolean a)</PRE><DL><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>a</CODE> - True if the attributes should be normalized (even nominal attributes will get normalized here) (range goes between -1 - 1).</DL></DD></DL><HR><A NAME="getNormalizeAttributes()"><!-- --></A><H3>getNormalizeAttributes</H3><PRE>public boolean <B>getNormalizeAttributes</B>()</PRE><DL><DD><DL></DL></DD><DD><DL><DT><B>Returns:</B><DD>The flag for normalizing attributes.</DL></DD></DL><HR><A NAME="setNominalToBinaryFilter(boolean)"><!-- --></A><H3>setNominalToBinaryFilter</H3><PRE>public void <B>setNominalToBinaryFilter</B>(boolean f)</PRE><DL><DD><DL></DL></DD><DD><DL><DT><B>Parameters:</B><DD><CODE>f</CODE> - True if a nominalToBinary filter should be used on the data.</DL></DD></DL><HR><A NAME="getNominalToBinaryFilter()"><!-- --></A><H3>getNominalToBinaryFilter</H3><PRE>public boolean <B>getNominalToBinaryFilter</B>()</PRE><DL><DD><DL></DL>⌨️ 快捷键说明
复制代码
Ctrl + C
搜索代码
Ctrl + F
全屏模式
F11
切换主题
Ctrl + Shift + D
显示快捷键
?
增大字号
Ctrl + =
减小字号
Ctrl + -