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Diffstat (limited to 'ml/dlib/python_examples/global_optimization.py')
-rwxr-xr-x | ml/dlib/python_examples/global_optimization.py | 47 |
1 files changed, 47 insertions, 0 deletions
diff --git a/ml/dlib/python_examples/global_optimization.py b/ml/dlib/python_examples/global_optimization.py new file mode 100755 index 000000000..e3fb3f8cd --- /dev/null +++ b/ml/dlib/python_examples/global_optimization.py @@ -0,0 +1,47 @@ +#!/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)); + |