I am trying to built some rules about cellular automata. In every cell I don't have one element (predator/prey) I have a number of population. To achieve movement between my population can I compare every cell with one of its neighbours every time? or do I have to compare the cell with all of its neighbours and add some conditions.
I am using Moore neighbourhood with the following update function
update[site,_,_,_,_,_,_,_,_]
I tried to make them move according to all of their neighbours but it's very complicated and I am wondering if by simplify it and check it with all of its neighbours individually it will be wrong.
Thanks
As a general advice I would recommend against going down the pattern recognition technique in Mathematica for specifying the rule table in CA, they tend to get out of hand very quickly.
Doing a predator-prey kind of simulation with CA is a little tricky since in each step, (unlike in traditional CA) the value of the center cell changes ALONG WITH THE VALUE OF THE NEIGHBOR CELL!
This will lead to issues since when the transition function is applied on the neighbor cell it will again compute a new value for itself, but it also needs to "remember" the changes done to it previously when it was a neighbor.
In fluid dynamics simulations using CA, they encounter problems like this and they use a different neighborhood called Margolus neighborhood. In a Margolus neighborhood, the CA lattice is broken into distinct blocks and the update rule applied on each block. In the next step the block boundaries are changed and the transition rules applied on the new boundary, hence information transfer occurs across block boundaries.
Before I give my best attempted answer, you have to realize that your question is oddly phrased. The update rules of your cellular automaton are specified by you, so I wouldn't know whether you have additional conditions you need to implement.
I think you're asking what is the best way to select a neighborhood, and you can do this with Part:
(* We abbreviate 'nbhd' for neighborhood *)
getNbhd[A_, i_Integer?Positive, j_Integer?Positive] :=
A[[i - 1 ;; i + 1, j - 1 ;; j + 1]];
This will select the appropriate Moore neighborhood, including the additional central cell, which you can filter out when you call your update function.
Specifically, to perform the update step of a cellular automaton, one must update all cells simultaneously. In the real world, this means creating a separate array, and placing the updated values there, scrapping the original array afterward.
For more details, see my blog post on Cellular Automata, which includes an implementation of Conway's Game of Life in Mathematica.
Related
This question is not limited to Sudoku, but may include Kakuro, Hitori, Nurikabe, etc.
I understand the algorithm to solve Sudoku and other similar puzzles, but I'm having a hard time figuring out how to create them.
Say I want a Sudoku generator (to take the most popular). I guess it needs to work in two steps:
Create a valid solution
Remove parts of the solution until the desired amount of clues are left.
Creating a solution isn't trivial, it usually works well if you go randomly until you reach the last steps and end up in a deadlock.
Removing some parts of the solution required to be sure to remove only redundant ones, which isn't trivial either.
Is there a generic algorithm to work it out? How can I implement such a thing?
I understand my question is "broad" and that I don't show a lot of what I've got so far (splitting the problem in two), but I don't have any lead to start thinking about the algorithm. I'm not asking for a solution, but rather for hints on how to begin.
You could in general approach this as follows:
Define a set of rules which can assist a human in progressing in a game. For instance, in Sudoku, one of those rules could be:
Call the "field of influence" of a given cell, the cells that are either in the same row, the same column or in the same 3x3 block as that given cell. The rule is that this cell cannot have any of the values that are already used in its field of influence. If that means there is only one valid value left, then place that value in this cell.
Another rule could be:
If there is a value that cannot be used anywhere else in the same 3x3 block, then place that value in this cell. Similarly if a value cannot be used anywhere else in the cell's row; or cannot be used anywhere else in the cell's column.
There are obviously other rules. These rules can be more complicated. Rank the rules by how difficult it is for a human to verify and apply them. Try to be as complete as you can, by looking how you, as a human, reason when solving the game. Implement these rules as functions in the program. In the Sudoku example, such a rule function can be applied to a given cell, and return success (i.e. the cell gets a value) or failure (the rule cannot be used to deduct its value).
Let's say the program should generate a Sudoku of a given difficulty. We will interpret that to mean that solving the Sudoku will require the player to use at least once a rule that has at least that difficulty, or an exotic rule that was never foreseen.
Now start from a solved Sudoku. Remove randomly 50% of the values. Check if the Sudoku can be solved by only using known rules that are within the difficulty range. If not, restore 25% of the removed cells, and repeat. If it could be solved, remove 25% more cells randomly. Continue halving the number of involved cells (either restoring them or removing them), much like a binary search algorithm, until you arrive at the end of this search. For a Sudoku game, this process would take about 7 iterations. Then you will have a kind of "local minimum", where the rules can be applied to get to a solution.
This is far from perfect, as it could well be there is some other cell that could be cleared, while still allowing the rules to work towards a solution. So, if you want to refine this search, you could add some additional iterations to remove random cells as long as the resulting board can still be solved by applying the rules.
You could create a Sudoku puzzle by solving one that starts with nothing assigned to start with. If your solver progresses by filling in squares, you could "remove" them by stopping (or rolling back) that process at the appropriate point.
Foreword: I am aware there is another question like this, however mine has very specific restrictions. I have done my best to make this question applicable to many, as it is a generic grid issue, but if it still does not belong here, then I am sorry, and please be nice about it. I have found in the past stackoverflow to be a very picky and hostile environment to question askers, but I'm hoping that was just a bad couple people.
Goal(abstract): Check all connected grid squares in a 3D grid that are of the same type and touching on one face.
Goal(specific/implementation): Create a "fill bucket" tool in Minecraft with command blocks.
Knowledge of Minecraft not really necessary to answer, this is more of an algorithm question, and I will be staying away from Minecraft specifics.
Restrictions: I can do this in code with recursive functions, but in Minecraft there are some limitations I am wondering if are possible to get around. 1: no arrays(data structure) permitted. In Minecraft I can store an integer variable and do basic calculations with it (+,-,*,/,%(mod),=,==), but that's it. I cannot dynamically create variables or have the program create anything with a name that I did not set out ahead of time. I can do "IF" and "OR" statements, and everything that derives from them. I CANNOT have multiple program pointers - that is, I can't have things like recursive functions, which require a program to stop executing, execute itself from beginning to end, and then resume executing where it was - I have minimal control over the program flow. I can use loops and conditional exits (so FOR loops). I can have a marker on the grid in 3D space that can move regardless of the presence of blocks (I'm using an armour stand, for those who know), and I can test grid squares relative to that marker.
So say my grid is full of empty spaces only. There are separate clusters of filled squares in opposite corners, not touching each other. If I "use" my fillbucket tool on one block / filled grid square, I want it to use a single marker to check and identify all the connected grid squares - basically, I need to be sure that it traverses the entire shape, all the nooks and crannies, but not the squares that are not connected to that shape. So in the end, one of the two clusters, from me only selecting a single square of it, will be erased/replaced by another kind of block, without affecting the other blocks around it.
Again, apologies if this doesn't belong here. And only answer this if you WANT to tackle the challenge - it's not important or anything, I just want to do this. You don't have to answer it if you don't want to. Or if you can solve this problem for a 2D grid, that would be helpful as well, as I could possibly extend that to work for 3D.
Thank you, and if I get nobody degrading me for how I wrote this post or the fact that I did, then I will consider this a success :)
With help from this and other sources, I figured it out! It turns out that, since all recursive functions (or at least most of them) can be written as FOR loops, that I can make a recursive function in Minecraft. So I did, and the general idea of it is as follows:
For explaining the program, you may assuming the situation is a largely empty grid with a grouping of filled squares in one part of it, and the goal is to replace the kind of block that that grouping is made of with a different block. We'll say the grouping currently consists of red blocks, and we want to change them to blue blocks.
Initialization:
IDs - A objective (data structure) for holding each marker's ID (score)
numIDs - An integer variable for holding number of IDs/markers active
Create one marker at selected grid position with ID [1] (aka give it a score of 1 in the "IDs" objective). This grid position will be a filled square from which to start replacing blocks.
Increment numIDs
Main program:
FOR loop that goes from 1 to numIDs
{
at marker with ID [1], fill grid square with blue block
step 1. test block one to the +x for a red block
step 2. if found, create marker there with ID [numIDs]
step 3. increment numIDs
[//repeat steps 1 2 and 3 for the other five adjacent grid squares: +z, -x, -z, +y, and -y]
delete stand[1]
numIDs -= 1
subtract 1 from every marker's ID's, so that the next marker to evaluate, which was [2], now has ID [1].
} (end loop)
So that's what I came up with, and it works like a charm. Sorry if my explanation is hard to understand, I'm trying to explain in a way that might make sense to both coders and Minecraft players, and maybe achieving neither :P
It's a homework. I have to design and lights out game using backtracking description is below.
The game consists of a 5-by-5 grid of lights; when the game starts, a set of these lights (random, or one of a set of stored puzzle patterns) are switched on. Pressing one of the lights will toggle it, and the four lights adjacent to it, on and off. (Diagonal neighbours are not affected.) The game provides a puzzle: given some initial configuration where some lights are on and some are off, the goal is to switch all the lights off, preferably in
as few button presses as possible.
My approach is go from 1 to 25 and check if all the lights are off or not. If not then I will check for 1 to 24 and so on until I reach 1 or found solution. No if there is no solution then I will start from 2 to 24 and follow the above process till I reach 2 or found solution.
But through this I am not getting the result ? for example light at (0,0) (1,1) (2,2) (3,3) (4,4) are ON?
If any one need code I can post it.
Can any one tell me correct approach using backtracking to solve this game ?
Thanks.
There is a standard algorithm for solving this problem that is based on Gaussian elimination over GF(2). The idea is to set up a matrix representing the button presses a column vector representing the lights and then to use standard matrix simplification techniques to determine which buttons to press. It runs in polynomial time and does not require any backtracking.
I have an implementation of this algorithm that includes a mathematical description of how it works available on my personal site. I hope you find it useful!
Edit: If you are forced to use backtracking, you can use the following facts to do so:
Any solution will never push the same button twice, since doing so would cancel out a previous move.
Any solution either pushes the first button or does not.
Given this approach, you could solve this using backtracking using a simple recursive algorithm that keeps track of the current state of the board and which buttons you've already made decisions about:
If you've decided about each button, then return whether the board is solved or not.
Otherwise:
Try pushing the next button and seeing if the board is recursively solvable from there.
If so, return success.
Otherwise, try not pushing the next button and seeing if the board is recursively solvable from there.
If so, return success. If not, return failure.
This will explore a search space of size 225, which is about 32 million. That's big, but not insurmountably big.
Hope this helps!
Backtracking means:
Incrementally build a solution, throwing away impossible solutions.
Here is one approach using the fact that there is locality of inputs and outputs (pressing a button affects square around it).
problem = GIVEN
solutions = [[0],[1]] // array of binary matrix answers (two entries, a zero and a one)
for (problemSize = 1; problemSize <= 5; problemSize++) {
newSolutions = [];
foreach (solutions as oldSolution) {
candidateSolutions = arrayOfNByNMatriciesWithMatrixAtTopLeft(min(5,problemSize+1), oldSolution);
// size of candidateSolutions is 2^((problemSize+1)^2 - problemSize^2)
// except last round candidateSolutions == solutions
foreach (candidateSolutions as candidateSolution) {
candidateProblem = boardFromPressingButtonsInSolution(candidateSolution);
if (compareMatrix(problem, candidateProblem, 0, 0, problemSize, problemSize)==0)
newSolutions[] = candidateSolution;
}
}
solutions = newSolutions;
}
return solutions;
As already suggested, you should first form a set of simultaneous equations.
First thing to note is that a particular light button shall be pressed at most once because it does not make sense to toggle the set of lights twice.
Let Aij = Light ij Toggled { Aij = 0 or 1 }
There shall be 25 such variables.
Now for each of the lights, you can form an equation looking like
summation (Amn) = 0. { Amn = 5 light buttons that toggle the light mn }
So you will have 25 variables and 25 unknowns. You can solve these equations simultaneously.
If you need to solve it using backtracking or recursion you can solve the equations that way. Just assume an initial value of variables, see if they satisfy all the equations. If not, then back track.
The Naive Solution
First, you're going to need a way to represent the state of the board and a stack to store all of the states. At each step, make a copy of the board, changed to the new state. Compare that state to all states of the board you've encountered so far. If you haven't seen it, push that state on top of the stack and move on to the next move. If you have seen it, try the next move. Each level will have to try all possible 64 moves before popping the state from the stack (backtracking). You will want to use recursion to manage the state of the next move to check.
There are at most 264 possible board configurations, meaning you could potentially go on a very long chain of unique states and still run out of memory. (For reference, 1 GB is 230 bytes and you need a minimum of 8 bytes to store the board configuration) This algorithm is not likely to terminate in the lifetime of the known universe.
You need to do something clever to reduce your search space...
Greedy-First Search
You can do better by searching the states that are closest to the solved configuration first. At each step, sort the possible moves in order from most lights off to least lights off. Iterate in that order. This should work reasonably well but is not guaranteed to get the optimal solution.
Not All Lights-Out Puzzles Are Solvable
No matter what algorithm you use, there may not be a solution, meaning you might search forever (or several trillion years, at least) without finding a solution.
You will want to check the board for solvability (which is a much faster algorithm as it turns out) before wasting any time trying to find a solution.
I'm working on character recognition (and later fingerprint recognition) using neural networks. I'm getting confused with the sequence of events. I'm training the net with 26 letters. Later I will increase this to include 26 clean letters and 26 noisy letters. If I want to recognize one letter say "A", what is the right way to do this? Here is what I'm doing now.
1) Train network with a 26x100 matrix; each row contains a letter from segmentation of the bmp (10x10).
2) However, for the test targets I use my input matrix for "A". I had 25 rows of zeros after the first row so that my input matrix is the same size as my target matrix.
3) I run perform(net, testTargets,outputs) where outputs are the outputs from the net trained with the 26x100 matrix. testTargets is the matrix for "A".
This doesn't seem right though. Is training supposed by separate from recognizing any character? What I want to happen is as follows.
1) Training the network for an image file that I select (after processing the image into logical arrays).
2) Use this trained network to recognize letter in a different image file.
So train the network to recognize A through Z. Then pick an image, run the network to see what letters are recognized from the picked image.
Okay, so it seems that the question here seems to be more along the lines of "How do I neural networks" I can outline the basic procedure here to try to solidify the idea in your mind, but as far as actually implementing it goes you're on your own. Personally I believe that proprietary languages (MATLAB) are an abomination, but I always appreciate intellectual zeal.
The basic concept of a neural net is that you have a series of nodes in layers with weights that connect them (depending on what you want to do you can either just connect each node to the layer above and beneath, or connect every node, or anywhere in betweeen.). Each node has a "work function" or a probabilistic function that represents the chance that the given node, or neuron will evaluate to "on" or 1.
The general workflow starts from whatever top layer neurons/nodes you've got, initializing them to the values of your data (in your case, you would probably start each of these off as the pixel values in your image, normalized to be binary would be simplest). Each of those nodes would then be multiplied by a weight and fed down towards your second layer, which would be considered a "hidden layer" depending on the sum (either geometric or arithmetic sum, depending on your implementation) which would be used with the work function to determine the state of your hidden layer.
That last point was a little theoretical and hard to follow, so here's an example. Imagine your first row has three nodes ([1,0,1]), and the weights connecting the three of those nodes to the first node in your second layer are something like ([0.5, 2.0, 0.6]). If you're doing an arithmetic sum that means that the weighting on the first node in your "hidden layer" would be
1*0.5 + 0*2.0 + 1*0.6 = 1.1
If you're using a logistic function as your work function (a very common choice, though tanh is also common) this would make the chance of that node evaluating to 1 approximately 75%.
You would probably want your final layer to have 26 nodes, one for each letter, but you could add in more hidden layers to improve your model. You would assume that the letter your model predicted would be the final node with the largest weighting heading in.
After you have that up and running you want to train it though, because you probably just randomly seeded your weights, which makes sense. There are a lot of different methods for this, but I'll generally outline back-propagation which is a very common method of training neural nets. The idea is essentially, since you know which character the image should have been recognized, you compare the result to the one that your model actually predicted. If your model accurately predicted the character you're fine, you can leave the model as is, since it worked. If you predicted an incorrect character you want to go back through your neural net and increment the weights that lead from the pixel nodes you fed in to the ending node that is the character that should have been predicted. You should also decrement the weights that led to the character it incorrectly returned.
Hope that helps, let me know if you have any more questions.
I currently try implementing the D* Lite Algorithm for path-planning (see also here) to get a grasp on it. I found two implementations on the web, both for C/C++, but somehow couldn't entirely follow the ideas since they seem to differ more than expected from the pseudo code in the whitepapers. I especially use the following two papers:
Koening,S.;Likhachev,M. - D* Lite, 2002
Koenig & Likkachev, Fast replanning for Navigation in Unknown Terrain, IEEE Transactions on Robotics, Vol. 21, No. 3, June 2005
I tried implementing the optimized version of D* Lite from the first whitepaper (p.5,Fig.4) and for "debugging" I use the example as shown and explained in the second whitepaper (p.6,Fig.6 and Fig.7). All work is done in MatLab (easier for exchanging code with others).
So far I got the code running to find the initial shortest path by running computeShortestPath() once. But now I am stuck at lines {36''} and {37''} of the pseudo-code:
{36”} Scan graph for changed edge costs;
{37”} if any edge costs changed
How do I detect those changes? I somehow don't seem to have a grasp on how this is being detected? In my implementation so far, I mainly used 3 matrices.
One matrix of same size as the grid map containing all rhs-values. One matrix of same size containing similarly all g-values. And one matrix with variable row count for the priority queue with the first two columns being the priority keys and the third and fourth row being the x- and y-coordinates.
Comparing my results, I get the same result for the first run of computeShortestPath() in Step5 as seen in the second whitepaper, p.6 Fig.6. Moving the robot one step also isn't that a problem. But I really have no clue how the next step of scanning for changed edge costs should be implemented.
Thanks for any hint, advice and/or help!!!
The following was pointed out to me by someone else:
In real-world code, you almost never have to "scan the graph for
changes." Your graph only changes when you change it in the code, so
you already know exactly when and where it can change!
One common way of implementing this is to have a OnGraphChanged
callback in your Graph class, which can be setup to call the
OnGraphChanged method in your PathFinder class. Then anywhere the
graph changes in your Graph class, make sure the OnGraphChanged
callback is called.
I personally implemented it by using a "true map" and a "known map" and after every move letting the robot check/scan all next possible successors and comparing them on the true map and the known map.