##

    #!/usr/bin/python
    # The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
    #
    #
    # This simple example shows how to call dlib's optimal linear assignment problem solver.
    # It is an implementation of the famous Hungarian algorithm and is quite fast, operating in
    # O(N^3) time.
    #
    # COMPILING THE DLIB PYTHON INTERFACE
    #   Dlib comes with a compiled python interface for python 2.7 on MS Windows.  If
    #   you are using another python version or operating system then you need to
    #   compile the dlib python interface before you can use this file.  To do this,
    #   run compile_dlib_python_module.bat.  This should work on any operating system
    #   so long as you have CMake and boost-python installed.  On Ubuntu, this can be
    #   done easily by running the command:  sudo apt-get install libboost-python-dev cmake

    import dlib

    # Lets imagine you need to assign N people to N jobs.  Additionally, each person will make
    # your company a certain amount of money at each job, but each person has different skills
    # so they are better at some jobs and worse at others.  You would like to find the best way
    # to assign people to these jobs.  In particular, you would like to maximize the amount of
    # money the group makes as a whole.  This is an example of an assignment problem and is
    # what is solved by the dlib.max_cost_assignment() routine.

    # So in this example, lets imagine we have 3 people and 3 jobs.  We represent the amount of
    # money each person will produce at each job with a cost matrix.  Each row corresponds to a
    # person and each column corresponds to a job.  So for example, below we are saying that
    # person 0 will make $1 at job 0, $2 at job 1, and $6 at job 2.
    cost = dlib.matrix([[1, 2, 6],
                        [5, 3, 6],
                        [4, 5, 0]])

    # To find out the best assignment of people to jobs we just need to call this function.
    assignment = dlib.max_cost_assignment(cost)

    # This prints optimal assignments:  [2, 0, 1]
    # which indicates that we should assign the person from the first row of the cost matrix to
    # job 2, the middle row person to job 0, and the bottom row person to job 1.
    print "optimal assignments: ", assignment

    # This prints optimal cost:  16.0
    # which is correct since our optimal assignment is 6+5+5.
    print "optimal cost: ", dlib.assignment_cost(cost, assignment)