There's so little information content in your checkerboard world that it makes localization difficult. You can imagine the same problem in an all-red world. Or all-white, as in driving on snow.

That is the whole point of this exercise to find out where the robot is, by moving around and sensing the colors of location.

There is enough information to get a probability matrix of possible locations. [edit] Which is exactly what you get. In real world you would move again and sense again. Depending on the complexity of the map, the number of moves required to get decent localization probability can be different.
[edit]The question is - in this definition of the world, does it matter on WHICH red square robot is ? it finds out that its on certain color, but since the world is cyclic, it does not matter otherwise.

It looks like in the code you have one too many columns - since it is a cyclical world, it isn't quite a checkerboard pattern in the horizontal direction; when you wrap around the same color will repeat twice.

Other than that, in a checkerboard world it would be hard to tell exactly where you are... no matter which direction you move in from the square you started on, the new square you end up on (if successful) will always be the same color. But you should be able to get a pretty good idea if you are on one of the green squares or one of the red squares.

When I started doing this demo I did it with a cyclical checkerboard world, the poor robot was more confused than I.