probabilityfunction.html

java bayes的bin文件

<A NAME="expected_value(BayesianNetworks.DiscreteFunction)"><!-- --></A><H3>
expected_value</H3>
<PRE>
public double <B>expected_value</B>(<A HREF="../BayesianNetworks/DiscreteFunction.html" title="class in BayesianNetworks">DiscreteFunction</A>&nbsp;df)</PRE>
<DL>
<DD>Obtain expected value of a DiscreteFunction The current implementation is very limited; it assumes that both the
 ProbabilityFunction object and the DiscreteFunctions object has a single variable, and the variable must be the
 same for both functions.
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<A NAME="posterior_expected_value(BayesianNetworks.DiscreteFunction)"><!-- --></A><H3>
posterior_expected_value</H3>
<PRE>
public double <B>posterior_expected_value</B>(<A HREF="../BayesianNetworks/DiscreteFunction.html" title="class in BayesianNetworks">DiscreteFunction</A>&nbsp;df)</PRE>
<DL>
<DD>Obtain posterior expected value of a DiscreteFunction This assumes that the probability values are unnormalized,
 equal to p(x, e) where e is the evidence. The current implementation is very limited; it assumes that both the
 ProbabilityFunction object and the DiscreteFunctions object has a single variable, and the variable must be the
 same for both functions.
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<A NAME="variance(BayesianNetworks.DiscreteFunction)"><!-- --></A><H3>
variance</H3>
<PRE>
public double <B>variance</B>(<A HREF="../BayesianNetworks/DiscreteFunction.html" title="class in BayesianNetworks">DiscreteFunction</A>&nbsp;df)</PRE>
<DL>
<DD>Calculate the variance of a DiscreteFunction. The current implementation is very limited; it assumes that both
 the ProbabilityFunction object and the DiscreteFunctions object has a single variable, and the variable must be
 the same for both functions.
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<A NAME="save_xml_0_3(java.io.PrintStream)"><!-- --></A><H3>
save_xml_0_3</H3>
<PRE>
public void <B>save_xml_0_3</B>(java.io.PrintStream&nbsp;out)</PRE>
<DL>
<DD>Save the contents of a ProbabilityFunction object into a PrintStream in the XMLBIF v0.3 format.
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<A NAME="save_xml(java.io.PrintStream)"><!-- --></A><H3>
save_xml</H3>
<PRE>
public void <B>save_xml</B>(java.io.PrintStream&nbsp;out)</PRE>
<DL>
<DD>Save the contents of a ProbabilityFunction object into a PrintStream.
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<A NAME="print(java.io.PrintStream)"><!-- --></A><H3>
print</H3>
<PRE>
public void <B>print</B>(java.io.PrintStream&nbsp;out)</PRE>
<DL>
<DD>Print method.
<P>
<DD><DL>
<DT><B>Overrides:</B><DD><CODE><A HREF="../BayesianNetworks/DiscreteFunction.html#print(java.io.PrintStream)">print</A></CODE> in class <CODE><A HREF="../BayesianNetworks/DiscreteFunction.html" title="class in BayesianNetworks">DiscreteFunction</A></CODE></DL>
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<A NAME="get_properties()"><!-- --></A><H3>
get_properties</H3>
<PRE>
public java.util.Vector <B>get_properties</B>()</PRE>
<DL>
<DD>Get the properties of the current ProbabilityFunction.
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</DD>
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<A NAME="set_properties(java.util.Vector)"><!-- --></A><H3>
set_properties</H3>
<PRE>
public void <B>set_properties</B>(java.util.Vector&nbsp;prop)</PRE>
<DL>
<DD>Set the properties.
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<A NAME="get_enumerated_properties()"><!-- --></A><H3>
get_enumerated_properties</H3>
<PRE>
public java.util.Enumeration <B>get_enumerated_properties</B>()</PRE>
<DL>
<DD>Get an Enumeration with the properties of the current ProbabilityFunction.
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<A NAME="add_property(java.lang.String)"><!-- --></A><H3>
add_property</H3>
<PRE>
public void <B>add_property</B>(java.lang.String&nbsp;prop)</PRE>
<DL>
<DD>Add a property to the current ProbabilityFunction.
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<A NAME="remove_property(java.lang.String)"><!-- --></A><H3>
remove_property</H3>
<PRE>
public void <B>remove_property</B>(java.lang.String&nbsp;prop)</PRE>
<DL>
<DD>Remove a property in the current ProbabilityFunction.
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</DD>
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<A NAME="remove_property(int)"><!-- --></A><H3>
remove_property</H3>
<PRE>
public void <B>remove_property</B>(int&nbsp;i)</PRE>
<DL>
<DD>Remove a property in a given position in the current ProbabilityFunction.
<P>
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</DD>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>i</CODE> - Position of the property.</DL>
</DD>
</DL>
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