15-puzzle | 15 puzzle game built with ReactJS & Material-UI | User Interface library
kandi X-RAY | 15-puzzle Summary
kandi X-RAY | 15-puzzle Summary
This is an implementation of the classic 15 puzzle built with React and styled-components. To win the game you need to re-arrange the numbers from 1 to 15 and leave the last tile empty.
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QUESTION
I am trying to find a way to programmatically solve a 24-piece sliding puzzle in a reasonable amount of time and moves. Here is an example of the solved state in the puzzle I am describing:
I have already found that the IDA* algorithm works fairly well to accomplish this for a 15-puzzle (4x4 grid). The IDA* algorithm is able to find the lowest number of moves for any 4x4 sliding puzzle in a very reasonable amount of time. I ran an adaptation of this code to test 4x4 sliding puzzles and was able to significantly reduce runtime further by using PyPy. Unfortunately, when this code is adapted for 5x5 sliding puzzles it runs horribly slow. I ran it for over an hour and eventually just gave up on seeing it finish, whereas it ran for only a few seconds on 4x4 grids. I understand this is because the number of nodes that need to searched goes up exponentially as the grid increases. However, I am not looking to find the optimal solution to a 5x5 sliding puzzle, only a solution that is close to optimal. For example, if the optimal solution for a given puzzle was 120 moves, then I would be satisfied with any solution that is under 150 moves and can be found in a few minutes.
Are there any specific algorithms that might accomplish this?
...ANSWER
Answered 2020-Mar-22 at 14:38It as been proved that finding the fewest number of moves of n-Puzzle is NP-Complete, see Daniel Ratner and Manfred Warmuth, The (n2-1)-Puzzle and Related Relocation Problems, Journal of Symbolic Computation (1990) 10, 111-137.
Interesting facts reviewed in Graham Kendall, A Survey of NP-Complete Puzzles, 2008:
- The 8-puzzle can be solved with A* algorithm;
- The 15-puzzle cannot be solved with A* algorithm but the IDA* algorithm can;
- Optimal solutions to the 24-puzzle cannot be generated in reasonable times using IDA* algorithm.
Therefore stopping the computation to change the methodology was the correct things to do.
It seems there is an available algorithm in polynomial time that can find sub-optimal solutions, see Ian Parberry, Solving the (n^2−1)-Puzzle with 8/3n^3 Expected Moves, Algorithms 2015, 8(3), 459-465. It may be what you are looking for.
QUESTION
I am trying to implement a solution for outputting the sequence of moves for a 15-puzzle problem in Python. This is part of an optional assignment for a MOOC. The problem statement is given at this link.
I have a version of the program (given below) which performs valid transitions.
I am first identifying the neighbors of the empty cell (represented by 0) and putting them in a list. Then, I am randomly choosing one of the neighbors from the list to perform swaps with the empty cell. All the swaps are accumulated in a different list to record the sequence of moves to solve the puzzle. This is then outputted at the end of the program.
However, the random selection of numbers to make the swap with the empty cell is just going on forever. To avoid "infinite" (very long run) of loops, I have limited the number of swaps to 30 for now.
...ANSWER
Answered 2019-Apr-30 at 06:09The 15-tiles problem is harder as it may seem at a first sight.
Computing the best (shortest) solution is a difficult problem and it has been proved than finding the optimal solution as N increases is NP-hard.
Finding a (non-optimal) solution is much easier. A very simple algorithm that can be made to work for example is:
- Define a "distance" of the current position as the sum of the manhattan distances of every tile from the position you want it to be
- Start from the given position and make some random moves
- If the distance after the moves improves or stays the same then keep the changes, otherwise undo them and return to the starting point.
This kind of algorithm could be described as a multi-step stochastic hill-climbing approach and is able to solve the 15 puzzle (just make sure to allow enough random moves to be able to escape a local minimum).
Python is probably not the best language to attack this problem, but if you use PyPy implementation you can get solutions in reasonable time.
My implementation finds a solution for a puzzle that has been mixed up with 1000 random moves in seconds, for example:
QUESTION
I'm trying the sample apps that come with OpenCV-android-sdk, but the colours in the preview seem to be off substantially. Here's the original preview from the Camera app in the emulator (API 25):
But sample apps like tutorial-1-camerpreview, color-blob-detection and 15-puzzle give a strong blue tint:
Does anyone know why this is the case?
...ANSWER
Answered 2017-Dec-29 at 11:43OpenCV uses BGR format instead of RGB in its default. A small Matlab script prooves that simply RGB and BGR have been switched. You can see it at the cups, that are red in the original but blue in the "wrong" image.
Here is the matlab script for prooving my theory
QUESTION
I made a little 15 puzzle which I want to be shuffled in the beginning. I start from the completed state and do 1000 random "fakeClicks" on tiles of which some are valid according to my code and most are not. The valid "fakeClicks" cause the emptyTile and the selectedTile to swap their positions by swaping their top and left values.
Everything (except the checkCompleteness() method which does not exist yet) seems to work - until one actually tries to solve the puzzle. With a small n, I reached the unsolvable game situation more than 50 percent of the time.
How is this possible when during shuffling I do valid moves only? Obviously it must be something about my code checking if a move is valid or not:
...ANSWER
Answered 2017-Nov-09 at 08:50You are right, your test is wrong.
Imagine a small board with the selected tile in the center
QUESTION
I'm trying to follow the tutorial at http://code.opencv.org/projects/opencv/wiki/Trunk_OpenCV_for_Android to build OpenCV 3.2.0 for Android from source. This is because eventually I want to customise the build, but for now I'm content to just be able to build it myself.
In accordance with the tutorial I'm running the following commands, which appear to complete successfully:
...ANSWER
Answered 2017-Jun-16 at 17:34While building OpenCV using scripts/cmake_android_arm.sh, the default behaviour defined in CMakeLists.txt is to build static libraries of all the modules, That's why you get .a files after running the .sh script, However if you want to build Shared Library (.so) file, then you need to force the cmake using the flag -DBUILD_FAT_JAVA_LIB=ON.
You need to edit the scripts/cmake_android_arm.sh as:
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