#!/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 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
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));