bntools.html

bayes network classifier toolbox 贝叶斯网络分类器工具箱的bin文件

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<A NAME="BNTools()"><!-- --></A><H3>
BNTools</H3>
<PRE>
public <B>BNTools</B>()</PRE>
<DL>
</DL>

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<B>Method Detail</B></FONT></TH>
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<A NAME="getNetworkDimension(BayesianNetworks.BayesNet)"><!-- --></A><H3>
getNetworkDimension</H3>
<PRE>
public static int <B>getNetworkDimension</B>(BayesianNetworks.BayesNet&nbsp;net)
                               throws java.lang.Exception</PRE>
<DL>
<DD>Returns dimension of a Bayesian network. <p>
 <p/>
 <b>Dimension of a Bayesian network:</b> <i> Let X be a set of random
 variables and B be a Bayesian network defined over X. The dimension of
 this network, Dim(B), is the number of free parameters required to
 completely specify the joint probability distribution of X.</i> <br>
 <p/>
 <p/>
 E. Castillo, J. M. Gutierrez and A. S. Hadi, <i>Expert Systems and
 Probabilistic Network Models</i> , Springer, 1997. p.486.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>net</CODE> - Description of Parameter
<DT><B>Returns:</B><DD>The NetworkDimension value
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - .</DL>
</DD>
</DL>
<HR>

<A NAME="getASBMParamComponent(BayesianNetworks.BayesNet, jbnc.dataset.DatasetInt, boolean, double)"><!-- --></A><H3>
getASBMParamComponent</H3>
<PRE>
public static final double <B>getASBMParamComponent</B>(BayesianNetworks.BayesNet&nbsp;net,
                                                 <A HREF="../../jbnc/dataset/DatasetInt.html" title="class in jbnc.dataset">DatasetInt</A>&nbsp;dataset,
                                                 boolean&nbsp;usePriors,
                                                 double&nbsp;alphaK)
                                          throws java.lang.Exception</PRE>
<DL>
<DD>Return network parameters component of the asymptotic standard Bayesian
 measure (ASBM). <p>
 <p/>
 <i>q</i> = sum<sub><i>i</i> =1...<i>n</i> </sub> sum<sub><i>j</i>
 =1...<i> q<sub>i</sub> </i> </sub> sum<sub><i>k</i> =1...<i>r<sub>i</sub>
 </i> </sub> <i>N<sub>ijk</sub> </i> log <i>N<sub>ijk</sub> </i> /
 <i>N<sub>ij </sub></i> <p>
 <p/>
 where <i>N<sub>ijk</sub> </i> means that variable <i>X<sub>i</sub> </i>
 is in configuration <i>k</i> and parents of variable <i>X<sub>i</sub>
 </i> are in configuration <i>j</i> . <p>
 <p/>
 E. Castillo, J. M. Gutierrez and A. S. Hadi, <i>Expert Systems and
 Probabilistic Network Models</i> , Springer, 1997. p.494, eq.(11.28).
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>net</CODE> - Description of Parameter<DD><CODE>dataset</CODE> - Description of Parameter<DD><CODE>usePriors</CODE> - Description of Parameter<DD><CODE>alphaK</CODE> - Description of Parameter
<DT><B>Returns:</B><DD>The ASBMParamComponent value
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - .</DL>
</DD>
</DL>
<HR>

<A NAME="gammaLn(double)"><!-- --></A><H3>
gammaLn</H3>
<PRE>
public static double <B>gammaLn</B>(double&nbsp;xx)
                      throws java.lang.Exception</PRE>
<DL>
<DD>Returns the value ln[ gamma(xx)] for xx > 0 Implementation based on W.H.
 Press et al. <i>Numerical Recipes in C</i> , 2nd Ed., Cambridge
 University Press, 1992.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>xx</CODE> - 
<DT><B>Returns:</B><DD>the value of ln[ gamma(xx)] for xx > 0.
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - When xx <= 0.</DL>
</DD>
</DL>
<HR>

<A NAME="learnParameters(BayesianNetworks.BayesNet, jbnc.util.FrequencyCalc, boolean, double)"><!-- --></A><H3>
learnParameters</H3>
<PRE>
public static void <B>learnParameters</B>(BayesianNetworks.BayesNet&nbsp;net,
                                   <A HREF="../../jbnc/util/FrequencyCalc.html" title="class in jbnc.util">FrequencyCalc</A>&nbsp;fc,
                                   boolean&nbsp;useDirihlet,
                                   double&nbsp;alphaK)
                            throws java.lang.Exception</PRE>
<DL>
<DD>Learns parameters for the current network structure. Existing network
 parameters are replaced with the new ones. This method can use "uniform"
 Dirihlet priors.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>net</CODE> - Bayesian network.<DD><CODE>useDirihlet</CODE> - Indicates whether Dirihlet priors should be used for
                    network parameters.<DD><CODE>alphaK</CODE> - alpha<sub>k</sub> parameter for Dirihlet priors. All
                    alpha<sub>k</sub> are assumed to be the same and
                    greater than zero.<DD><CODE>fc</CODE> - Description of Parameter
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE></DL>
</DD>
</DL>
<HR>

<A NAME="learnParameters(BayesianNetworks.BayesNet, jbnc.dataset.DatasetInt, boolean, double)"><!-- --></A><H3>
learnParameters</H3>
<PRE>
public static void <B>learnParameters</B>(BayesianNetworks.BayesNet&nbsp;net,
                                   <A HREF="../../jbnc/dataset/DatasetInt.html" title="class in jbnc.dataset">DatasetInt</A>&nbsp;data,
                                   boolean&nbsp;useDirihlet,
                                   double&nbsp;alphaK)
                            throws java.lang.Exception</PRE>
<DL>
<DD>Learns parameters for the current network structure. Existing network
 parameters are replaced with the new ones. This method can use "uniform"
 Dirihlet priors.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>net</CODE> - Bayesian network.<DD><CODE>useDirihlet</CODE> - Indicates whether Dirihlet priors should be used for
                    network parameters.<DD><CODE>alphaK</CODE> - alpha<sub>k</sub> parameter for Dirihlet priors. All
                    alpha<sub>k</sub> are assumed to be the same and
                    greater than zero.<DD><CODE>data</CODE> - Description of Parameter
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE></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;args)</PRE>
<DL>
<DD>Description of the Method
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>args</CODE> - Description of Parameter</DL>
</DD>
</DL>
<HR>

<A NAME="getNodes(jbnc.dataset.Dataset, InferenceGraphs.InferenceGraph)"><!-- --></A><H3>
getNodes</H3>
<PRE>
protected static InferenceGraphs.InferenceGraphNode[] <B>getNodes</B>(<A HREF="../../jbnc/dataset/Dataset.html" title="class in jbnc.dataset">Dataset</A>&nbsp;dataset,
                                                               InferenceGraphs.InferenceGraph&nbsp;graph)
                                                        throws java.lang.Exception</PRE>
<DL>
<DD>Get node names from a graph in an order they appear in the dataset.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>dataset</CODE> - Description of Parameter<DD><CODE>graph</CODE> - Description of Parameter
<DT><B>Returns:</B><DD>The Nodes value
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE> - Description of Exception</DL>
</DD>
</DL>
<HR>

<A NAME="learnParameters_old(BayesianNetworks.BayesNet, jbnc.dataset.Dataset, boolean, double)"><!-- --></A><H3>
learnParameters_old</H3>
<PRE>
protected static void <B>learnParameters_old</B>(BayesianNetworks.BayesNet&nbsp;net,
                                          <A HREF="../../jbnc/dataset/Dataset.html" title="class in jbnc.dataset">Dataset</A>&nbsp;data,
                                          boolean&nbsp;useDirihlet,
                                          double&nbsp;alphaK)
                                   throws java.lang.Exception</PRE>
<DL>
<DD>Learns parameters for the current network structure. Existing network
 parameters are replaced with the new ones. This method can use "uniform"
 Dirihlet priors.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>net</CODE> - Bayesian network.<DD><CODE>useDirihlet</CODE> - Indicates whether Dirihlet priors should be used for
                    network parameters.<DD><CODE>alphaK</CODE> - alpha<sub>k</sub> parameter for Dirihlet priors. All
                    alpha<sub>k</sub> are assumed to be the same and
                    greater than zero.<DD><CODE>data</CODE> - Description of Parameter
<DT><B>Throws:</B>
<DD><CODE>java.lang.Exception</CODE></DL>
</DD>
</DL>
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