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ManhattanDistance (Java Machine Learning Library 0.0.11)
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net.sf.javaml.distance</FONT>
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Class ManhattanDistance</H2>
<PRE>
java.lang.Object
<IMG SRC="../../../../resources/inherit.gif" ALT="extended by "><A HREF="../../../../net/sf/javaml/distance/AbstractDistance.html" title="class in net.sf.javaml.distance">net.sf.javaml.distance.AbstractDistance</A>
<IMG SRC="../../../../resources/inherit.gif" ALT="extended by "><B>net.sf.javaml.distance.ManhattanDistance</B>
</PRE>
<DL>
<DT><B>All Implemented Interfaces:</B> <DD><A HREF="../../../../net/sf/javaml/distance/DistanceMeasure.html" title="interface in net.sf.javaml.distance">DistanceMeasure</A></DD>
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<DT><PRE>public class <B>ManhattanDistance</B><DT>extends <A HREF="../../../../net/sf/javaml/distance/AbstractDistance.html" title="class in net.sf.javaml.distance">AbstractDistance</A></DL>
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<P>
The Manhattan distance is the sum of the (absolute) differences of their coordinates. The taxicab metric is also known as rectilinear distance, Minkowski's L1 distance, city block distance, or Manhattan distance.
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<DT><B>Author:</B></DT>
<DD>Thomas Abeel</DD>
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<TD><CODE><B><A HREF="../../../../net/sf/javaml/distance/ManhattanDistance.html#ManhattanDistance()">ManhattanDistance</A></B>()</CODE>
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<CODE> double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../../net/sf/javaml/distance/ManhattanDistance.html#calculateDistance(net.sf.javaml.core.Instance, net.sf.javaml.core.Instance)">calculateDistance</A></B>(<A HREF="../../../../net/sf/javaml/core/Instance.html" title="interface in net.sf.javaml.core">Instance</A> x,
<A HREF="../../../../net/sf/javaml/core/Instance.html" title="interface in net.sf.javaml.core">Instance</A> y)</CODE>
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Calculates the Manhattan distance as the sum of the absolute differences of their coordinates.</TD>
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<CODE> double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../../net/sf/javaml/distance/ManhattanDistance.html#getMaximumDistance(net.sf.javaml.core.Dataset)">getMaximumDistance</A></B>(<A HREF="../../../../net/sf/javaml/core/Dataset.html" title="interface in net.sf.javaml.core">Dataset</A> data)</CODE>
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Returns the theoretical maximum distance for the given dataset.</TD>
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<CODE> double</CODE></FONT></TD>
<TD><CODE><B><A HREF="../../../../net/sf/javaml/distance/ManhattanDistance.html#getMinimumDistance(net.sf.javaml.core.Dataset)">getMinimumDistance</A></B>(<A HREF="../../../../net/sf/javaml/core/Dataset.html" title="interface in net.sf.javaml.core">Dataset</A> data)</CODE>
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Return the minimal Manhattan distance between two instances in the given dataset.</TD>
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<TH ALIGN="left"><B>Methods inherited from class net.sf.javaml.distance.<A HREF="../../../../net/sf/javaml/distance/AbstractDistance.html" title="class in net.sf.javaml.distance">AbstractDistance</A></B></TH>
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<TD><CODE><A HREF="../../../../net/sf/javaml/distance/AbstractDistance.html#compare(double, double)">compare</A></CODE></TD>
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<A NAME="ManhattanDistance()"><!-- --></A><H3>
ManhattanDistance</H3>
<PRE>
public <B>ManhattanDistance</B>()</PRE>
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<A NAME="calculateDistance(net.sf.javaml.core.Instance, net.sf.javaml.core.Instance)"><!-- --></A><H3>
calculateDistance</H3>
<PRE>
public double <B>calculateDistance</B>(<A HREF="../../../../net/sf/javaml/core/Instance.html" title="interface in net.sf.javaml.core">Instance</A> x,
<A HREF="../../../../net/sf/javaml/core/Instance.html" title="interface in net.sf.javaml.core">Instance</A> y)</PRE>
<DL>
<DD>Calculates the Manhattan distance as the sum of the absolute differences of their coordinates.
<P>
<DD><DL>
<DT><B>Returns:</B><DD>the Manhattan distance between the two instances.</DL>
</DD>
</DL>
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<A NAME="getMaximumDistance(net.sf.javaml.core.Dataset)"><!-- --></A><H3>
getMaximumDistance</H3>
<PRE>
public double <B>getMaximumDistance</B>(<A HREF="../../../../net/sf/javaml/core/Dataset.html" title="interface in net.sf.javaml.core">Dataset</A> data)</PRE>
<DL>
<DD>Returns the theoretical maximum distance for the given dataset. This is based on the virtual Min and Max instances of the dataset.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>data</CODE> - the dataset for which the maximal possible distance should be calculated.
<DT><B>Returns:</B><DD>the maximum possible distance between instances in the dataset<DT><B>See Also:</B><DD><A HREF="../../../../net/sf/javaml/core/Dataset.html" title="interface in net.sf.javaml.core"><CODE>Dataset</CODE></A></DL>
</DD>
</DL>
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<A NAME="getMinimumDistance(net.sf.javaml.core.Dataset)"><!-- --></A><H3>
getMinimumDistance</H3>
<PRE>
public double <B>getMinimumDistance</B>(<A HREF="../../../../net/sf/javaml/core/Dataset.html" title="interface in net.sf.javaml.core">Dataset</A> data)</PRE>
<DL>
<DD>Return the minimal Manhattan distance between two instances in the given dataset. This is always zero as the Manhattan distances cannot be negative and the distance between two equal instances is zero.
<P>
<DD><DL>
<DT><B>Parameters:</B><DD><CODE>data</CODE> - the dataset for which the minimal possible distance should be calculated.
<DT><B>Returns:</B><DD>the minimum possible Manhattan distance for the dataset, i.e. zero</DL>
</DD>
</DL>
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