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48 lines
2.0 KiB
48 lines
2.0 KiB
#!/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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#
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# This is an example illustrating the use of the global optimization routine,
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# find_min_global(), from the dlib C++ Library. This is a tool for finding the
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# inputs to a function that result in the function giving its minimal output.
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# This is a very useful tool for hyper parameter search when applying machine
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# learning methods. There are also many other applications for this kind of
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# general derivative free optimization. However, in this example program, we
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# simply show how to call the method. For that, we use a common global
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# optimization test function, as you can see below.
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#
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#
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# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
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# You can install dlib using the command:
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# pip install dlib
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#
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# Alternatively, if you want to compile dlib yourself then go into the dlib
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# root folder and run:
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# python setup.py install
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#
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# Compiling dlib should work on any operating system so long as you have
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# CMake installed. On Ubuntu, this can be done easily by running the
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# command:
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# sudo apt-get install cmake
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#
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import dlib
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from math import sin,cos,pi,exp,sqrt
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# This is a standard test function for these kinds of optimization problems.
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# It has a bunch of local minima, with the global minimum resulting in
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# holder_table()==-19.2085025679.
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def holder_table(x0,x1):
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return -abs(sin(x0)*cos(x1)*exp(abs(1-sqrt(x0*x0+x1*x1)/pi)))
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# Find the optimal inputs to holder_table(). The print statements that follow
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# show that find_min_global() finds the optimal settings to high precision.
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x,y = dlib.find_min_global(holder_table,
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[-10,-10], # Lower bound constraints on x0 and x1 respectively
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[10,10], # Upper bound constraints on x0 and x1 respectively
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80) # The number of times find_min_global() will call holder_table()
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print("optimal inputs: {}".format(x));
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print("optimal output: {}".format(y));
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