An approach as applied below, which shows the processing of input data
from a file becomes a crucial factor for writing your own sample applications.
This approach is just one example of what can be done using the distance
functions provided by shogun.

First, you need to determine what type your data will be, because this
will determine the distance function you can use.

This example loads two stored matrices of real values from different
files and initializes the matrices to 'RealFeatures'.
Each column of the matrices corresponds to one data point.

The distance initialized by two data sets (the same data set as shown in the
first call) controls the processing of the given data points, where a pairwise
distance (divergence measure based on the Kullback-Leibler divergence) matrix
is computed by 'get_distance_matrix'.

The resulting distance matrix can be reaccessed by 'get_distance_matrix'.

The method call 'init'* binds the given data sets, where a pairwise distance
(divergence measure based on the Kullback-Leibler divergence) matrix between
these two data sets is computed by 'get_distance_matrix'.

The resulting distance matrix can be reaccessed by 'get_distance_matrix'.

*Note that the previous computed distance matrix can no longer be
reaccessed by 'get_distance_matrix'.

For more details see doc/classshogun_1_1CJensenMetric.html.

Obviously, using the Jensen-Shannon distance/divergence is not limited to
this showcase example.
