The SED Toolbox (Sequential Experimental Design) is a powerful Matlab toolbox for the sequential design of experiments. In traditional design of experiments, the all the design points are selected at once, and all the experiments are performed at once without selecting any additional design points. This method is prone to over- or undersampling, because it is often very difficult to predict the required number of design points in advance.
The SED Toolbox solves this problem by providing the user with state-of-the-art algorithms that generate a design of experiments one point at a time, without having to provide the total number of design points in advance. This is called sequential experimental design. The SED Toolbox was designed to be extremely fast and easy to use, yet very powerful.
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Quick start guide
IMPORTANT: Before the toolbox can be used, you have to set it up for use, by browsing to the directory in which the toolbox was unpacked and running the startup command:
Now the toolbox is ready to be used. The SED Toolbox can be used in several ways, based on how much freedom you want in configuring and fine-tuning the parameters of the algorithms. We will now describe the three ways the toolbox can be used, in order of complexity, based on your requirements.
You want an ND design of X points
In order to quickly generate a good ND design in X points, you can use the following code:
startup % configure the toolbox config.inputs.nInputs = N; % set the number of inputs in the config struct generator = SequentialDesign(config); % set up the sequential design generator = generator.generateTotalPoints(X); % generate a total of X points points = generator.getAllPoints(); % return the entire design % optional: generator.plot(); % plot the design generator.getMetrics(); % get some metrics about the quality of the design
You want to use the more advanced features of the SED Toolbox
If you want to use some of the more advanced features of the SED Toolbox, such as input ranges and weights and constraints, you have two options. The first one is to use Matlab structs as in the previous example. The second one is to use simple XML files to configure the toolbox. Note that constraints will only work with XML configuration. You can open the 'problem.xml' file in the SED directory to get an idea of how a problem configuration looks like. You can edit this file to suit your needs and use it to configure the toolbox using the following command:
% generate a sequential design for the problem defined in problem.xml: generator = SequentialDesign('problem.xml'); % generate a sequential design using the specified method for the problem defined in problem.xml: generator = SequentialDesign('problem.xml', 'methods/mc-intersite-projected-threshold.xml');
If you instead prefer to use Matlab structs, you can use the following code to configure the toolbox:
config.inputs.nInputs = 2; % this is a 2D example config.inputs.minima = [-1 -1]; % define the minimum of each input config.inputs.maxima = [3 1]; % define the maximum of each input config.inputs.weights = [2 0]; % the first input is twice as important as the second one generator = SequentialDesign(config); % set up the sequential design
SED toolbox interface
The SED toolbox provides
--TO BE UPDATED--
two scripts dacefit.m and predictor.m that emulate the behavior of the DACE toolbox (). Note, that full compatibility between blindDACE and the DACE toolbox is not provided. The scripts merely aim to ease the transition from the DACE toolbox to the blindDACE toolbox.
krige = dacefit(samples, values, 'regpoly0', 'corrgauss', theta0, lb, ub ) y = predictor([1 2], krige)
Obviously, a lot less code is used to copy the setup described above. However, less code means less flexibility (e.g., blind kriging and regression kriging are not available using the wrapper scripts). Hence, it is suggested to learn the object oriented interface of SED and use it instead.
Suggestions on how to improve the SED toolbox are always welcome. For more information please see the feedback page.