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 (feature type 'REAL')
from different files and initializes the distance to 'MINKOWSKI' with
norm 'k'. Each column of the matrices corresponds to one data point.

The target 'TRAIN' for 'set_features' controls the processing of the given
data points, where a pairwise distance matrix is computed by
'get_distance_matrix'.

The resulting distance matrix can be reaccessed by 'get_distance_matrix' and
target 'TRAIN'.

The target 'TEST' for 'set_features' controls the processing of the given
data points 'TRAIN' and 'TEST', where a pairwise distance matrix between
these two data sets is computed by 'get_distance_matrix'.

The resulting distance matrix can be reaccessed by 'get_distance_matrix'
and target 'TEST'. The 'TRAIN' distance matrix ceased to exist.

For more details see doc/classshogun_1_1CMinkowskiMetric.html.

Obviously, using the Minkowski metric is not limited to this showcase
example.
