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