This little code snippet converts MATLAB Struct to xml, enjoy!!


How To Unit Test A LINQPad Code Snippet

Writing code is a programmers life; sometimes it becomes necessary to write pieces of code that you can conveniently run and evaluate  without spinning up a full fledged IDE. For those tasks there is a tool called LINQPad. LINQPad allows you to write/run snippets in C#, SQL and a few other languages. In oder to run the example in this tutorial you will need to download LINQPad and NUnit Lite.

After installing LINQPad and NUnit Lite open an instance of LINQPad and change the language to C# Program.


Next hit the F4 key on you keyboard to bring up the additional references dialog and browse to the location where you installed NUnit Lite. You will need to add NUnit Lite as a reference.


Copy preceding code into the LINQPad edit window then hit the run button, the results of the test will be displayed in the console window, enjoy 🙂

Normalizing Constraints For A Multi Objective Fitness Function

Most optimization problems involving genetic algorithms will contain multiple constraints; a useful way of handling these constraints, is by multiplying them by a factor which will scale the constraint to a predefined range. It is generally a good idea to have all constraints contributing equally to the final fitness value.


//calculate fitness
m_dFitness = distFit + 400*rotFit + 4*fitAirTime;

The factor been used in the above example is 400, distFit has a max of 400 therefore, rotFit which has a max of 1 is multiplied by 400 and fitAirTime which has a max of 100 is multiplied by 4.

If you wanted to normalize each constraint to a different range then you could use this formula to calculate the normalized value y = 1 + (x-A)*(r2-r1)/(B-A) where A,B is the original range and r1, r2 is the range that the value x should be normalized to.


//calculate fitness
var distFit = 1 + (x-0)*(10-1)/(50-0);
var rotFit  = 1 + (x-0)*(10-1)/(90-0);
var fitAirTime = 1 + (x-0)*(10-1)/(30-0);

m_dFitness = distFit + rotFit + fitAirTime;

This option is more feasible since it does not rely on any constraint value to derive a multiplication factor.

Mapping A Maze Using MATLAB Image Processing Toolbox

Recently I saw an article about mapping a maze and I thought to myself; how could this be achieved using my favorite tool MATLAB :-)? Well it turns out that its a pretty simple task. Some functions used in this post require that you have MATLAB 2012a or higher installed, so if u do not then go away and get it.

Wow you are back, ok so lets begin, first we will look at the code that was used and then further down we will look at individual functions and what they were used to do.

The maze:

The code:

%% Map Maze
im = imread('maze.png');
bw = im2bw(im(1:287, 1:400), 0.45);
cc = bwconncomp(bw, 8);
obj = false(size(bw));
obj(cc.PixelIdxList{13}) = true;
sln = bwmorph(bwmorph(obj,'thin',Inf),'spur', Inf);
figure, imshow(imfuse(im,sln,'blend','Scaling','joint'))

clear Clears all variables from the MATLAB workspace.
im = imread(‘maze.png’);
Reads the maze image and stores it.
bw = im2bw(im(1:287, 1:400), 0.45); Converts the image to binary, scales it and stores it.
cc = bwconncomp(bw, 8); Find connecting components within the image.
obj(cc.PixelIdxList{13}) = true; Find the largest component and select it.
sln = bwmorph(bwmorph(obj,’thin’,Inf),’spur’,Inf); Use Morphological functions to refine image.
figure, imshow(imfuse(im,sln,’blend’,’Scaling’,’joint’)); Overlay and display images


solved maze

The end 🙂