##

    #!/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

    # Let's 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, let's 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: {}".format(assignment))

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