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Description of demo files

Table of Contents

We have include a descriptions of the different demos than can be found in the library. Most of them are used to highlight the capabilities of the Bayesian optimization framework or the characteristics of the library.

Important: note that some demos requires some extra dependencies to work (OpenGL, SimpleCV, etc.)

C/C++ demos

These demos are automatically compiled and installed with the library. They can be found in the /bin subfolder. The source code of these demos is in /app

Interface test (continuous and discrete)

bo_cont and bo_disc provides examples of the C (callback) and C++ (inheritance) interfaces for a simple quadratic function. They are the best starting point to start playing with the library.

1D test

bo_oned deals with a more interesting, yet simple, multimodal 1D function. bo_display shows the same example, but includes a visualization tool (requires OpenGL and FreeGLUT).


Branin test

bo_branin shows the a 2D example with the Branin function, which is a standard function to evaluate nonlinear optimization algorithms.

\[ f(x,y) = \left(y-\frac{5.1}{4\pi^2}x^2 + \frac{5}{\pi}x-6\right)^2 + 10\left(1-\frac{1}{8\pi}\right) \cos(x) + 10 \]

with a search domain $-5 \leq x \leq 10$, $0 \leq y \leq 15$.


The function has three global minimum. The position of those points (after normalization to the [0,1] plane) are:

\begin{align*} x &= 0.1239, y = 0.8183\\ x &= 0.5428, y = 0.1517\\ x &= 0.9617, y = 0.1650 \end{align*}

Python demos

These demos use the Python interface of the library. They can be found in the /python subfolder.

Make sure that the interface has been generated and that it can be found in the corresponding path (i.e. PYTHONPATH).

Interface test

demo_quad provides an simple example (quadratic function). It shows the continuous and discrete cases and it also compares the callback and inheritance interfaces. It is the best starting point to start playing with the Python interface.

demo_dimscaling shows a 20D quadratic function with different smoothness in each dimension. It also show the speed of the library for high dimensional functions.

demo_distance is equivalent to the demo_quad example, but it includes a penalty term with respect to the distance between the current and previous sample. For example, it can be used to model sampling strategies which includes a mobile agent, like a robotic sensor as seen in[11].

Multiprocess demo

demo_multiprocess is a simple example that combines BayesOpt with the brilliant multiprocessing library. It shows how simple BayesOpt can be used in a parallelized setup, where one process is dedicated for the BayesOpt and the rests are dedicated to function evaluations.

Computer Vision demo

demo_cam is a demonstration of the potetial of BayesOpt for parameter tuning. The advantage of using BayesOpt versus traditional strategies is that it only requires knowledge of the desired behavior, while traditional methods for parameter tuning requires deep knowledge of the algorithm and the meaning of the parameters.

In this case, it takes a simple example (image binarization) and show how a simple behavior (balanced white/black result) matches the result of the adaptive thresholding from Otsu's method -default in SimpleCV-. Besides, it find the optimal with few samples (typically between 10 and 20)

demo_cam requires SimpleCV and a webcam.

MATLAB/Octave demos

These demos use the Matlab interface of the library. They can be found in the /matlab subfolder.

Make sure that the interface has been generated. If the library has been generated as a shared library, make sure that it can be found in the corresponding path (i.e. LD_LIBRARY_PATH in Linux/MacOS) before running MATLAB/Octave.

Interface test

runtest shows the discrete and continuous interface. The objective function can be selected among all the functions defined in the /matlab/testfunctions subfolder, which includes a selection of standard test functions for nonlinear optimization:

Demo in very high dimensions

demo_rembo evaluates the REMBO (Random EMbedding Bayesian Optimization) algorithm for optimization in very high dimensions. The idea is that Bayesian optimization can be used very high dimensions provided that the effective dimension is embedded in a lower space, by using random projections.

In this case, we test it against an artificially augmented Branin function with 1000 dimensions where only 2 dimensions are actually relevant (but unknown). The function is defined in the file: braninghighdim

For details about REMBO, see[24].