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48 lines
2.3 KiB
Python
Executable File
48 lines
2.3 KiB
Python
Executable File
#!/usr/bin/python
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# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
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#
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# This simple example shows how to call dlib's optimal linear assignment
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# problem solver. It is an implementation of the famous Hungarian algorithm
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# and is quite fast, operating in O(N^3) time.
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#
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# COMPILING THE DLIB PYTHON INTERFACE
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# Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
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# you are using another python version or operating system then you need to
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# compile the dlib python interface before you can use this file. To do this,
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# run compile_dlib_python_module.bat. This should work on any operating
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# system so long as you have CMake and boost-python installed.
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# On Ubuntu, this can be done easily by running the command:
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# sudo apt-get install libboost-python-dev cmake
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import dlib
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# Let's imagine you need to assign N people to N jobs. Additionally, each
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# person will make your company a certain amount of money at each job, but each
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# person has different skills so they are better at some jobs and worse at
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# others. You would like to find the best way to assign people to these jobs.
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# In particular, you would like to maximize the amount of money the group makes
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# as a whole. This is an example of an assignment problem and is what is solved
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# by the dlib.max_cost_assignment() routine.
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# So in this example, let's imagine we have 3 people and 3 jobs. We represent
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# the amount of money each person will produce at each job with a cost matrix.
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# Each row corresponds to a person and each column corresponds to a job. So for
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# example, below we are saying that person 0 will make $1 at job 0, $2 at job 1,
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# and $6 at job 2.
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cost = dlib.matrix([[1, 2, 6],
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[5, 3, 6],
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[4, 5, 0]])
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# To find out the best assignment of people to jobs we just need to call this
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# function.
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assignment = dlib.max_cost_assignment(cost)
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# This prints optimal assignments: [2, 0, 1]
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# which indicates that we should assign the person from the first row of the
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# cost matrix to job 2, the middle row person to job 0, and the bottom row
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# person to job 1.
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print("Optimal assignments: {}".format(assignment))
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# This prints optimal cost: 16.0
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# which is correct since our optimal assignment is 6+5+5.
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print("Optimal cost: {}".format(dlib.assignment_cost(cost, assignment)))
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