![]() ![]() The discovery strategies are basically self-contained, such as: You have to handle multiple branches in your search space but that's reasonably easy with recursion. In that final case, you just have to do the same thing again. You get the same situation (at least two possibilities in every empty cell). You finish the puzzle successfully in which case you made the right choice originally. ![]() You end up with a duplicate value in a group (in which case you go back and set the original cell to the other possibility). You'll eventually figure out that either: Just set one of the empty cells with two (or the smallest number of) possibilities to a specific value and then carry on. For example, let's say you followed all the basic discovery rules but you still have undiscovered cells. It fully solved simple Sudoku puzzles with relatively simple rules like those given above, but the more complicated ones had to use look-ahead to find impossible situations and discard them. I actually implemented such a beast that would actually do this (a). The second undiscovered cell in row one now has all but 8 eliminated because of the row (it's the one value remaining). That means the cell is now 3 and it is now discovered. The 3x3 eliminates 152486 of which only 8 has an effect. It then has 246 eliminated because of the column (irrelevant because the row already eliminated them). The first undiscovered cell in row one has all but 38 eliminated because all the other values in row one dictate that. You now have discovered cells (with one possible value) and undiscovered cells (with two or more possibilities).įor each undiscovered cell, apply discovery rules one by one. Start with all empty cells on the board containing the full range 123456789. You just get your code to do the same thing, along the lines of: Have a set of discovery rules that you apply to groups (rows, columns, or 3-by-3s) to eliminate possibilities, or select values where there is but one possibility.įor example, consider the first three rows in the first puzzle: ,Īs a human, I would realise that only 38 is allowed in the two empty cells in row one but 8 is disallowed in the first cell because it's already in the top left 3x3. You would solve it programmatically the same way you would solve it as an intelligent human. Print('Invalid Player Type!\nMake sure you use "Human" or "Computer"') If Player_Type = 'Human' or 'human' or 'HUMAN':Įlif Player_Type = 'Computer' or 'computer' or 'COMPUTER': Player_Type = input('"Human" or "Computer" solve?: ') While Player_Type != 'Human' or 'Computer': # Player Types | Computer or Player depending on the input below! # Don't change the layout of the following sudoku examples But when I would run the program it would be working but not printing the results each time it was solving it. I tried doing these using things such as for 'i in range'. I tried using rows and columns to solve and it just wouldn't print the results on the board every time it would work. python sudoku_solver_web.I was wondering how to solve this Sudoku board in Python using Human input and Computer solving. Testing python sudoku_solver.py -i images/dataset-original.pngĪnd why not with my webcam. savetxt ( 'generalresponses.data' ,responses )ģ. savetxt ( 'generalsamples.data' ,samples ) size, 1 ) ) print ( "training complete" ) waitKey ( 0 ) if key = 27 : # (escape to quit) adaptiveThreshold (blur, 255, 1, 1, 11, 2 ) # Now finding Contours # In train.py, we train our model with exists numbers and then we save the model for later use in Recognize. In the Recognize class we train our model with an image of numbers from 0-9 by running the code in train.py. findNearest (roismall, k = 1 ) return str ( int ( (results ) ) ) return 0 Retval, results, neigh_resp, dists = self. """Eliminate d from values propagate when values or places = minBoxW and w = minBoxH and h = 5 or len (blocks ) = 81 or len (sort_cnts ) 50 : = cv2. """Eliminate all the other values (except d) from values and propagate. Peers = dict((s, set(sum(units,))-set()) for s in squares) We are going to use the algorithm written by Peter Norvig in his ( ) where he wrote a very good detail about his algorithm. Use OpenCV to read blocks of Sudoku from image.And there are steps where we are going to use for this project: The Sudoku that we are going to solve is a 9X9 Sudoku and digital printed numbers. This project is about using OpenCV to solve one the popular Japanese puzzle game Sudoku called " Sudoku". Therefore, in this article, we are going to build a toy project to refresh our self. Most of my previous articles are about NLP where we are on the road of building a Khmer spelling checker and I am pretty sure that you are bored with it. ![]()
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