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 data sets in 'STRING' representation
(feature type 'CHAR' with alphabet 'DNA') from different files and
initializes the distance to 'MANHATTAN' with feature type 'WORD'.

Data points in this example are defined by the transformation function
'convert' and the preprocessing step applied afterwards (defined by
'add_preproc' and preprocessor 'SORTWORDSTRING').

The target 'TRAIN' for 'set_features' controls the binding of the given
data points. In order to compute a pairwise distance matrix by
'get_distance_matrix', we have to perform two preprocessing steps for
input data 'TRAIN'. The method 'convert' transforms the input data to
a string representation suitable for the selected distance. The individual
strings are sorted in ascending order after the execution of 'attach_preproc'.
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 binding of the given
data points 'TRAIN' and 'TEST'. In order to compute a pairwise distance
matrix between these two data sets by 'get_distance_matrix', we have to
perform two preprocessing steps for input data 'TEST'. The method 'convert'
transforms the input data 'TEST' to a string representation suitable for
the selected distance. The individual strings are sorted in ascending order
after the execution of 'attach_preproc'. A pairwise distance matrix between
the data sets 'TRAIN' and 'TEST' 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_1CSortWordString.html,
doc/classshogun_1_1CPreprocessor.html,
doc/classshogun_1_1CStringFeatures.html (method obtain_from_char_features) and
doc/classshogun_1_1CManhattanWordDistance.html.

Obviously, using the Manhattan word distance is not limited to this showcase
example.
