{"group":{"group":{"id":623,"name":"Computer Games III","lockable":false,"created_at":"2019-04-22T15:37:29.000Z","updated_at":"2025-12-14T01:33:56.000Z","description":"Test your MATLAB skills at playing or solving computer games.","is_default":false,"created_by":26769,"badge_id":62,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2025-12-14T00:00:00.000Z","image_id":549,"published":true,"community_created":true,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTest your MATLAB skills at playing or solving computer games.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}","description_html":"\u003cdiv style = \"text-align: start; line-height: normal; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"display: block; min-width: 0px; padding-top: 0px; perspective-origin: 289.5px 10.5px; transform-origin: 289.5px 10.5px; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; perspective-origin: 266.5px 10.5px; transform-origin: 266.5px 10.5px; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eTest your MATLAB skills at playing or solving computer games.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","published_at":"2019-10-15T21:11:15.000Z"},"current_player":null},"problems":[{"id":1978,"title":"Sokoban: Puzzle 10.45","description":"The \u003chttp://www.game-sokoban.com/index.php?mode=level\u0026lid=16138 Sokoban Site\u003e has many puzzles to solve.  This Challenge is to solve puzzle 10.45.  The link may place the Cody enthusiast at 10.55. \u003chttp://en.wikipedia.org/wiki/Sokoban wiki Sokoban reference\u003e. \r\n\r\nThe basic rules are to places the Blocks on the Pedestals. Blocks can only be pushed, never pulled. A connected Pair of blocks can not be moved along their long axis. A 2x2 square of blocks is immoveable. A Wall can not be moved.\r\n\r\nSokoban can not jump blocks or move diagonally.\r\n\r\nThe map will be double ringed by a Wall(1). Map definitions: Empty(0) Block(2) Pedestal(3) Sokoban(4) Block on Pedestal(5) Sokoban on Pedestal(7).  \r\n\r\nSokoban Movement is a numeric vector L(-nr) U(-1) R(nr) D(+1).\r\n\r\n*Input:* Map, [nr,nc] of  Sokoban characters [0,1,2,3,4,5,7]\r\n\r\n*Output:* Moves, Vector of [-1 +1 -nr +nr] values\r\n\r\n*Scoring:* Sum of Moves and Pushes\r\n\r\n*Examples:* \r\n\r\nMap\r\n\r\n  11111111\r\n  11111111 Moves=[5]  push right for a 5 row array\r\n  11042311\r\n  11111111\r\n  11111111\r\n\r\n*Test Suite Visualization:* A visualization option is provided.\r\n\r\n*Algorithms:* Recursive routines that check all possible pushes can solve small Sokoban puzzles. Routines that limit their depth can find minimal Push solutions at the cost of time. Identification of Locked conditions is important to avoid being stuck in recursion. Pairs of blocks on a wall or too many blocks on a wall are unsolveable conditions to avoid. \r\n","description_html":"\u003cp\u003eThe \u003ca href = \"http://www.game-sokoban.com/index.php?mode=level\u0026lid=16138\"\u003eSokoban Site\u003c/a\u003e has many puzzles to solve.  This Challenge is to solve puzzle 10.45.  The link may place the Cody enthusiast at 10.55. \u003ca href = \"http://en.wikipedia.org/wiki/Sokoban\"\u003ewiki Sokoban reference\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eThe basic rules are to places the Blocks on the Pedestals. Blocks can only be pushed, never pulled. A connected Pair of blocks can not be moved along their long axis. A 2x2 square of blocks is immoveable. A Wall can not be moved.\u003c/p\u003e\u003cp\u003eSokoban can not jump blocks or move diagonally.\u003c/p\u003e\u003cp\u003eThe map will be double ringed by a Wall(1). Map definitions: Empty(0) Block(2) Pedestal(3) Sokoban(4) Block on Pedestal(5) Sokoban on Pedestal(7).\u003c/p\u003e\u003cp\u003eSokoban Movement is a numeric vector L(-nr) U(-1) R(nr) D(+1).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e Map, [nr,nc] of  Sokoban characters [0,1,2,3,4,5,7]\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Moves, Vector of [-1 +1 -nr +nr] values\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Sum of Moves and Pushes\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMap\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e11111111\r\n11111111 Moves=[5]  push right for a 5 row array\r\n11042311\r\n11111111\r\n11111111\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eTest Suite Visualization:\u003c/b\u003e A visualization option is provided.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAlgorithms:\u003c/b\u003e Recursive routines that check all possible pushes can solve small Sokoban puzzles. Routines that limit their depth can find minimal Push solutions at the cost of time. Identification of Locked conditions is important to avoid being stuck in recursion. Pairs of blocks on a wall or too many blocks on a wall are unsolveable conditions to avoid.\u003c/p\u003e","function_template":"function moves=solve_Sokoban(m)\r\n% 0 Empty; 1 Wall; 2 Block; 3 Pedestal;\r\n% 4 Sokoban; 5 Block \u0026 Pedestal;6 Nothing; 7 Soko \u0026 Pedestal\r\n\r\n moves=[];\r\nend","test_suite":"assignin('caller','score',200);\r\n%%\r\nvisualize=0;\r\nif visualize\r\n figure(1); % Start\r\n map=[.5 .5 .5;0 0 0;.5 .5 .5;0 1 0;0 0 1;\r\n    1 0 0;1 1 0;0 0 0;1 0 1;.5 .5 .5];\r\n colormap(map);\r\n figure(2); % Move map\r\n% -1 0 1 2 3 4 5 6 7 8\r\n% -1 color limit, 8 color limit\r\n% 0 Empty; 1 Wall; 2 Block; 3 Pedestal;\r\n% 4 Sokoban; 5 Block \u0026 Pedestal;6 Nothing; 7 Soko \u0026 Pedestal\r\n colormap(map)\r\nend\r\n\r\n%Sokoban map http://www.game-sokoban.com/index.php?mode=level\u0026lid=16138 \r\n%Puzzle 45 \r\nsmap=[0 0 0 0 0 0;0 3 2 2 4 0;3 3 2 0 2 0;3 5 0 0 1 1];\r\n[nr,nc]=size(smap);\r\nm=ones(nr+4,nc+4);\r\nm(3:end-2,3:end-2)=smap;\r\n\r\nif visualize\r\n im=m;\r\n mend=size(map,1)-2;\r\n im(1)=-1;im(end)=mend;\r\n figure(1);imagesc(im)\r\n m\r\nend\r\n\r\ntic\r\nmoves=solve_Sokoban(m);\r\ntoc\r\n\r\n% Check Solution\r\n valid=1;\r\n ptr=find(m==4);\r\n pushes=0;\r\n if isempty(ptr),ptr=find(m==7);end\r\n for i=1:length(moves)\r\n  mv=moves(i);\r\n  mvptr=m(ptr+mv);\r\n  mvptr2=m(ptr+2*mv);\r\n  if mvptr==1 % Illegal run into wall\r\n   valid=0;\r\n   break;\r\n  end\r\n  if (mvptr2==5 || mvptr2==2 || mvptr2==1) \u0026\u0026 (mvptr==5 || mvptr==2) % Illegal double block push\r\n   valid=0;\r\n   break;\r\n  end\r\n  if mvptr==0 || mvptr==3\r\n   m(ptr)=m(ptr)-4;\r\n   m(ptr+mv)=m(ptr+mv)+4;\r\n   ptr=ptr+mv;\r\n  elseif mvptr==2 || mvptr==5\r\n   m(ptr)=m(ptr)-4;\r\n   m(ptr+2*mv)=m(ptr+2*mv)+2;\r\n   m(ptr+mv)=m(ptr+mv)-2+4;\r\n   ptr=ptr+mv;\r\n   pushes=pushes+1;\r\n  end\r\n end\r\n \r\n fprintf('Moves %i  Pushes %i\\n',length(moves),pushes)\r\n valid=valid \u0026\u0026  nnz(m==3)==0 \u0026\u0026 nnz(m==7)==0;\r\n assert(valid)\r\n\r\nif visualize \u0026\u0026 valid\r\n % display moves\r\n figure(2);imagesc(im)\r\n pause(0.2)\r\n ptr=find(im==4);\r\n if isempty(ptr),ptr=find(im==7);end\r\n for i=1:length(moves)\r\n  mv=moves(i);\r\n  mvptr=im(ptr+mv);\r\n  if mvptr==0 || mvptr==3\r\n   im(ptr)=im(ptr)-4;\r\n   im(ptr+mv)=im(ptr+mv)+4;\r\n   ptr=ptr+mv;\r\n  elseif mvptr==2 || mvptr==5\r\n   im(ptr)=im(ptr)-4;\r\n   im(ptr+2*mv)=im(ptr+2*mv)+2;\r\n   im(ptr+mv)=im(ptr+mv)-2+4;\r\n   ptr=ptr+mv;\r\n  end\r\n  \r\n  figure(2);imagesc(im)\r\n  pause(0.2)\r\n end\r\n \r\nend % vis and valid\r\n\r\n\r\nmovs=length(moves);\r\nassignin('caller','score',min(200,max(0,movs+pushes)));","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":"2013-11-11T01:51:09.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-10T23:21:50.000Z","updated_at":"2025-12-03T12:16:08.000Z","published_at":"2013-11-11T01:51:09.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.game-sokoban.com/index.php?mode=level\u0026amp;lid=16138\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSokoban Site\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e has many puzzles to solve. This Challenge is to solve puzzle 10.45. The link may place the Cody enthusiast at 10.55.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Sokoban\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ewiki Sokoban reference\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe basic rules are to places the Blocks on the Pedestals. Blocks can only be pushed, never pulled. A connected Pair of blocks can not be moved along their long axis. A 2x2 square of blocks is immoveable. A Wall can not be moved.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSokoban can not jump blocks or move diagonally.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe map will be double ringed by a Wall(1). Map definitions: Empty(0) Block(2) Pedestal(3) Sokoban(4) Block on Pedestal(5) Sokoban on Pedestal(7).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSokoban Movement is a numeric vector L(-nr) U(-1) R(nr) D(+1).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Map, [nr,nc] of Sokoban characters [0,1,2,3,4,5,7]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Moves, Vector of [-1 +1 -nr +nr] values\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Sum of Moves and Pushes\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMap\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[11111111\\n11111111 Moves=[5]  push right for a 5 row array\\n11042311\\n11111111\\n11111111]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTest Suite Visualization:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e A visualization option is provided.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithms:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Recursive routines that check all possible pushes can solve small Sokoban puzzles. Routines that limit their depth can find minimal Push solutions at the cost of time. Identification of Locked conditions is important to avoid being stuck in recursion. Pairs of blocks on a wall or too many blocks on a wall are unsolveable conditions to avoid.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44709,"title":"Toads and Frogs Puzzle","description":"On a one-dimensional board with n + m + 1 cells, there are n counters in the first n cells representing Toads and m counters in the last m cells representing Frogs. The empty cell is represented by X. For illustration, if n = 4 and m = 3, then the problem is as depicted below:\r\n\r\n      T T T T X F F F\r\n\r\nToads and Frogs take turns moving. Moves consist of sliding a Toad or Frog into the empty cell or jumping over one opposing creature into the empty cell. (Toads cannot jump over themselves and neither can Frogs.) Toads can only move rightward and Frogs can only move leftward.\r\n\r\nWhat is the total number of moves (i.e. jumps and slides) required for the toads to switch their positions with the frogs as depicted below:  \r\n\r\n     F F F X T T T T\r\n\r\n*ALGORITHM:* To solve the problem, whenever there is a choice between a slide and a jump, the jump must be made.\r\n\r\n*ILLUSTRATION:* Consider n = 2 toads and m = 1 frog, then the algorithm could proceed as follows:\r\n\r\n T T X F\r\n T X T F   Slide\r\n T F T X   Jump\r\n T F X T   Slide\r\n X F T T   Jump\r\n F X T T   Slide\r\n\r\nHence, a total of five moves is required for n = 2 toads and m = 1 frog.","description_html":"\u003cp\u003eOn a one-dimensional board with n + m + 1 cells, there are n counters in the first n cells representing Toads and m counters in the last m cells representing Frogs. The empty cell is represented by X. For illustration, if n = 4 and m = 3, then the problem is as depicted below:\u003c/p\u003e\u003cpre\u003e      T T T T X F F F\u003c/pre\u003e\u003cp\u003eToads and Frogs take turns moving. Moves consist of sliding a Toad or Frog into the empty cell or jumping over one opposing creature into the empty cell. (Toads cannot jump over themselves and neither can Frogs.) Toads can only move rightward and Frogs can only move leftward.\u003c/p\u003e\u003cp\u003eWhat is the total number of moves (i.e. jumps and slides) required for the toads to switch their positions with the frogs as depicted below:\u003c/p\u003e\u003cpre\u003e     F F F X T T T T\u003c/pre\u003e\u003cp\u003e\u003cb\u003eALGORITHM:\u003c/b\u003e To solve the problem, whenever there is a choice between a slide and a jump, the jump must be made.\u003c/p\u003e\u003cp\u003e\u003cb\u003eILLUSTRATION:\u003c/b\u003e Consider n = 2 toads and m = 1 frog, then the algorithm could proceed as follows:\u003c/p\u003e\u003cpre\u003e T T X F\r\n T X T F   Slide\r\n T F T X   Jump\r\n T F X T   Slide\r\n X F T T   Jump\r\n F X T T   Slide\u003c/pre\u003e\u003cp\u003eHence, a total of five moves is required for n = 2 toads and m = 1 frog.\u003c/p\u003e","function_template":"function moves = ToadsFrogs(n,m)\r\n  \r\nend","test_suite":"%%\r\nassert(isequal(ToadsFrogs(0,0),0))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(1,1),3))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(3,4),19))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(2,7),23))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(4,6),34))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(8,3),35))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(4,8),44))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(7,6),55))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(5,9),59))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(9,7),79))","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":178544,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":"2018-09-07T17:35:43.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-08-03T12:58:09.000Z","updated_at":"2026-01-20T13:33:38.000Z","published_at":"2018-08-03T13:42:48.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOn a one-dimensional board with n + m + 1 cells, there are n counters in the first n cells representing Toads and m counters in the last m cells representing Frogs. The empty cell is represented by X. For illustration, if n = 4 and m = 3, then the problem is as depicted below:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[      T T T T X F F F]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eToads and Frogs take turns moving. Moves consist of sliding a Toad or Frog into the empty cell or jumping over one opposing creature into the empty cell. (Toads cannot jump over themselves and neither can Frogs.) Toads can only move rightward and Frogs can only move leftward.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhat is the total number of moves (i.e. jumps and slides) required for the toads to switch their positions with the frogs as depicted below:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[     F F F X T T T T]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eALGORITHM:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e To solve the problem, whenever there is a choice between a slide and a jump, the jump must be made.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eILLUSTRATION:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Consider n = 2 toads and m = 1 frog, then the algorithm could proceed as follows:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ T T X F\\n T X T F   Slide\\n T F T X   Jump\\n T F X T   Slide\\n X F T T   Jump\\n F X T T   Slide]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHence, a total of five moves is required for n = 2 toads and m = 1 frog.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44711,"title":"Toads and Frogs Puzzle 2","description":"On a two-dimensional board with 2n + 1 rows and 2n + 1 columns, all (2n + 1)^2 positions of the board, except the central one, are occupied by toads (T) and frogs (F), as follows. \r\n\r\nIn the first n rows, the first n + 1 positions are occupied by toads followed by n frogs. In row n + 1, the first n positions are occupied by toads followed by one vacant position (X) followed by n toads. In the last n rows, the first n positions are occupied by n toads followed by n + 1 frogs. For n = 3, this is depicted by the board to left below.\r\n\r\n\r\n      T  T  T  T  F  F  F                  F  F  F  F  T  T  T\r\n      T  T  T  T  F  F  F                  F  F  F  F  T  T  T\r\n      T  T  T  T  F  F  F                  F  F  F  F  T  T  T\r\n      T  T  T  X  F  F  F        =\u003e        F  F  F  X  T  T  T\r\n      T  T  T  F  F  F  F                  F  F  F  T  T  T  T\r\n      T  T  T  F  F  F  F                  F  F  F  T  T  T  T\r\n      T  T  T  F  F  F  F                  F  F  F  T  T  T  T\r\n\r\nToads can move horizontally right or vertically down and frogs can move horizontally left or vertically up. A move can be either a slide to the empty neighboring position or a jump over an opposing creature to the empty position right beyond it. Toads cannot jump over themselves and neither can Frogs. \r\n\r\nHow many jumps and slides are required for the toads to switch their positions with the frogs as depicted on the right above.\r\n\r\n*HINT:* The puzzle is a two-dimensional version of the _Problem 44709: Toads and Frogs Puzzle_. It can be solved by applying the algorithm for that puzzle to the middle column. Whenever, a vacant cell is created in the board's row for the first time, switch to exchanging toads and frogs in that row by applying the same algorithm.\r\n\r\n  \r\n   T T F    T T F    T T F    T T F     T X F     X T F     F T X    F X T     F F T\r\n   T X F    X T F    F T X    F X T     F T T     F T T     F T T    F T T     F T T\r\n   T F F    T F F    T F F    T F F     T F F     T F F     T F F    T F F     T X F\r\n           Slide 1  Jump 1   Slide 2   Slide 3   Slide 4   Jump 2   Slide 5   Jump 3\r\n\r\n   F F T    F F T    F F T    F F T\r\n   F T T    F T T    F T T    F X T\r\n   X T F    F T X    F X T    F T T\r\n  Slide 6  Jump 4   Slide 7  Slide 8\r\n\r\nTherefore, to complete the puzzle for n = 1 requires four jumps and eight slides.\r\n\r\n\r\nGoodluck!!!","description_html":"\u003cp\u003eOn a two-dimensional board with 2n + 1 rows and 2n + 1 columns, all (2n + 1)^2 positions of the board, except the central one, are occupied by toads (T) and frogs (F), as follows.\u003c/p\u003e\u003cp\u003eIn the first n rows, the first n + 1 positions are occupied by toads followed by n frogs. In row n + 1, the first n positions are occupied by toads followed by one vacant position (X) followed by n toads. In the last n rows, the first n positions are occupied by n toads followed by n + 1 frogs. For n = 3, this is depicted by the board to left below.\u003c/p\u003e\u003cpre\u003e      T  T  T  T  F  F  F                  F  F  F  F  T  T  T\r\n      T  T  T  T  F  F  F                  F  F  F  F  T  T  T\r\n      T  T  T  T  F  F  F                  F  F  F  F  T  T  T\r\n      T  T  T  X  F  F  F        =\u0026gt;        F  F  F  X  T  T  T\r\n      T  T  T  F  F  F  F                  F  F  F  T  T  T  T\r\n      T  T  T  F  F  F  F                  F  F  F  T  T  T  T\r\n      T  T  T  F  F  F  F                  F  F  F  T  T  T  T\u003c/pre\u003e\u003cp\u003eToads can move horizontally right or vertically down and frogs can move horizontally left or vertically up. A move can be either a slide to the empty neighboring position or a jump over an opposing creature to the empty position right beyond it. Toads cannot jump over themselves and neither can Frogs.\u003c/p\u003e\u003cp\u003eHow many jumps and slides are required for the toads to switch their positions with the frogs as depicted on the right above.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHINT:\u003c/b\u003e The puzzle is a two-dimensional version of the \u003ci\u003eProblem 44709: Toads and Frogs Puzzle\u003c/i\u003e. It can be solved by applying the algorithm for that puzzle to the middle column. Whenever, a vacant cell is created in the board's row for the first time, switch to exchanging toads and frogs in that row by applying the same algorithm.\u003c/p\u003e\u003cpre\u003e   T T F    T T F    T T F    T T F     T X F     X T F     F T X    F X T     F F T\r\n   T X F    X T F    F T X    F X T     F T T     F T T     F T T    F T T     F T T\r\n   T F F    T F F    T F F    T F F     T F F     T F F     T F F    T F F     T X F\r\n           Slide 1  Jump 1   Slide 2   Slide 3   Slide 4   Jump 2   Slide 5   Jump 3\u003c/pre\u003e\u003cpre\u003e   F F T    F F T    F F T    F F T\r\n   F T T    F T T    F T T    F X T\r\n   X T F    F T X    F X T    F T T\r\n  Slide 6  Jump 4   Slide 7  Slide 8\u003c/pre\u003e\u003cp\u003eTherefore, to complete the puzzle for n = 1 requires four jumps and eight slides.\u003c/p\u003e\u003cp\u003eGoodluck!!!\u003c/p\u003e","function_template":"function [jump,slide] = ToadsFrogs2(n)\r\n  \r\nend","test_suite":"%%\r\n[jump,slide] = ToadsFrogs2(0);\r\nassert(isequal(jump,0)\u0026isequal(slide,0))\r\n\r\n%%\r\n[jump,slide] = ToadsFrogs2(1);\r\nassert(isequal(jump,4)\u0026isequal(slide,8))\r\n\r\n%%\r\n[jump,slide] = ToadsFrogs2(3);\r\nassert(isequal(jump,72)\u0026isequal(slide,48))\r\n\r\n%%\r\n[jump,slide] = ToadsFrogs2(7);\r\nassert(isequal(jump,784)\u0026isequal(slide,224))\r\n\r\n%%\r\n[jump,slide] = ToadsFrogs2(11);\r\nassert(isequal(jump,2904)\u0026isequal(slide,528))\r\n\r\n%%\r\n[jump,slide] = ToadsFrogs2(17);\r\nassert(isequal(jump,10404)\u0026isequal(slide,1224))\r\n\r\n%%\r\n[jump,slide] = ToadsFrogs2(23);\r\nassert(isequal(jump,25392)\u0026isequal(slide,2208))\r\n\r\n%%\r\n[jump,slide] = ToadsFrogs2(29);\r\nassert(isequal(jump,50460)\u0026isequal(slide,3480))\r\n\r\n%%\r\n[jump,slide] = ToadsFrogs2(41);\r\nassert(isequal(jump,141204)\u0026isequal(slide,6888))\r\n\r\n%%\r\n[jump,slide] = ToadsFrogs2(50);\r\nassert(isequal(jump,255000)\u0026isequal(slide,10200))\r\n\r\n%%\r\n[jump,slide] = ToadsFrogs2(75);\r\nassert(isequal(jump,855000)\u0026isequal(slide,22800))\r\n\r\n%%\r\n[jump,slide] = ToadsFrogs2(99);\r\nassert(isequal(jump,1960200)\u0026isequal(slide,39600))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":178544,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2018-09-07T17:33:17.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-08-03T16:49:46.000Z","updated_at":"2026-02-06T13:52:33.000Z","published_at":"2018-08-03T16:50:32.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOn a two-dimensional board with 2n + 1 rows and 2n + 1 columns, all (2n + 1)^2 positions of the board, except the central one, are occupied by toads (T) and frogs (F), as follows.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn the first n rows, the first n + 1 positions are occupied by toads followed by n frogs. In row n + 1, the first n positions are occupied by toads followed by one vacant position (X) followed by n toads. In the last n rows, the first n positions are occupied by n toads followed by n + 1 frogs. For n = 3, this is depicted by the board to left below.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[      T  T  T  T  F  F  F                  F  F  F  F  T  T  T\\n      T  T  T  T  F  F  F                  F  F  F  F  T  T  T\\n      T  T  T  T  F  F  F                  F  F  F  F  T  T  T\\n      T  T  T  X  F  F  F        =\u003e        F  F  F  X  T  T  T\\n      T  T  T  F  F  F  F                  F  F  F  T  T  T  T\\n      T  T  T  F  F  F  F                  F  F  F  T  T  T  T\\n      T  T  T  F  F  F  F                  F  F  F  T  T  T  T]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eToads can move horizontally right or vertically down and frogs can move horizontally left or vertically up. A move can be either a slide to the empty neighboring position or a jump over an opposing creature to the empty position right beyond it. Toads cannot jump over themselves and neither can Frogs.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHow many jumps and slides are required for the toads to switch their positions with the frogs as depicted on the right above.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHINT:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e The puzzle is a two-dimensional version of the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eProblem 44709: Toads and Frogs Puzzle\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. It can be solved by applying the algorithm for that puzzle to the middle column. Whenever, a vacant cell is created in the board's row for the first time, switch to exchanging toads and frogs in that row by applying the same algorithm.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[   T T F    T T F    T T F    T T F     T X F     X T F     F T X    F X T     F F T\\n   T X F    X T F    F T X    F X T     F T T     F T T     F T T    F T T     F T T\\n   T F F    T F F    T F F    T F F     T F F     T F F     T F F    T F F     T X F\\n           Slide 1  Jump 1   Slide 2   Slide 3   Slide 4   Jump 2   Slide 5   Jump 3\\n\\n   F F T    F F T    F F T    F F T\\n   F T T    F T T    F T T    F X T\\n   X T F    F T X    F X T    F T T\\n  Slide 6  Jump 4   Slide 7  Slide 8]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTherefore, to complete the puzzle for n = 1 requires four jumps and eight slides.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGoodluck!!!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44647,"title":"Win all the time! ","description":"This is a 2-players game. One of them is your algorithm. Your algorithm must win in all games. \r\n\r\nIn this game there are 3 rows and every row has some lines like this:\r\n\r\n  1) ||||||||||\r\n  2) ||||||||\r\n  3) |||||\r\n\r\nYou can choose a row and remove some lines from the row. \r\n\r\nRule 1: You should remove the lines only in one row. \r\n\r\nRule 2: In your turn, you should remove at least one line.  \r\n\r\nAs input you have row info like this:\r\n\r\n  [4,5,6]\r\n\r\nIt means there are 4 lines in row1, 5 lines in row2 and 6 lines in row3. \r\n\r\nYou should return 2 numbers as output that one of them is row number and other is number of lines you want to remove from the row. \r\n\r\nThis function repeated to remove all the lines. If you remove the last line, you lose. So you should force your competitor to take the last line. \r\n\r\nAt the start it's your turn and input is [10,10,10].\r\n\r\nGood luck!","description_html":"\u003cp\u003eThis is a 2-players game. One of them is your algorithm. Your algorithm must win in all games.\u003c/p\u003e\u003cp\u003eIn this game there are 3 rows and every row has some lines like this:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1) ||||||||||\r\n2) ||||||||\r\n3) |||||\r\n\u003c/pre\u003e\u003cp\u003eYou can choose a row and remove some lines from the row.\u003c/p\u003e\u003cp\u003eRule 1: You should remove the lines only in one row.\u003c/p\u003e\u003cp\u003eRule 2: In your turn, you should remove at least one line.\u003c/p\u003e\u003cp\u003eAs input you have row info like this:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e[4,5,6]\r\n\u003c/pre\u003e\u003cp\u003eIt means there are 4 lines in row1, 5 lines in row2 and 6 lines in row3.\u003c/p\u003e\u003cp\u003eYou should return 2 numbers as output that one of them is row number and other is number of lines you want to remove from the row.\u003c/p\u003e\u003cp\u003eThis function repeated to remove all the lines. If you remove the last line, you lose. So you should force your competitor to take the last line.\u003c/p\u003e\u003cp\u003eAt the start it's your turn and input is [10,10,10].\u003c/p\u003e\u003cp\u003eGood luck!\u003c/p\u003e","function_template":"function [Row_number, Num_of_lines] = remove_lines(status)\r\n  Row_number=1;\r\n  Num_of_lines=3;\r\n  % Note that: Num_of_lines ≤ status(Row_number) and 1 ≤ Row_number ≤ 3\r\nend","test_suite":"%%\r\nfor i=1:1000\r\n    status=[10,10,10];\r\n    while sum(status)~=0\r\n        [Row_number, Num_of_lines] = remove_lines(status);\r\n        if Num_of_lines\u003estatus(Row_number)\r\n            disp('Error: Check number of lines');\r\n            result=false;\r\n            break;\r\n        end\r\n        if Num_of_lines\u003c1\r\n            disp('Error: You must remove at least one line');\r\n            result=false;\r\n            break;\r\n        end\r\n        if Row_number~=1 \u0026\u0026 Row_number~=2 \u0026\u0026 Row_number~=3\r\n            disp('Error: Row number must be 1, 2 or 3.');\r\n            result=false;\r\n            break;\r\n        end\r\n        if numel(Row_number)~=1 || numel(Num_of_lines)~=1\r\n           disp('Error: Row number must have one element');\r\n           result=false;\r\n            break;\r\n        end\r\n        status(Row_number)=status(Row_number)-Num_of_lines;\r\n        if sum(status)==0\r\n            disp('You lose');\r\n            result=false;\r\n            break;\r\n        end\r\n        if sum(status)==1\r\n            disp('You win');\r\n            result=true;\r\n            break;\r\n        end\r\n        [~,L]=max(status);\r\n        status(L)=status(L)-randi(status(L));\r\n        if sum(status)==0\r\n            disp('You win');\r\n            result=true;\r\n            break;\r\n        end \r\n    end\r\n    res(i)=result;\r\nend\r\nnumber_of_wins= sum(res)\r\nassert(isequal(sum(res),1000))\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":2,"created_by":218677,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":19,"test_suite_updated_at":"2018-05-24T22:35:14.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-05-20T21:17:10.000Z","updated_at":"2025-12-04T11:51:22.000Z","published_at":"2018-05-20T22:09:13.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is a 2-players game. One of them is your algorithm. Your algorithm must win in all games.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this game there are 3 rows and every row has some lines like this:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1) ||||||||||\\n2) ||||||||\\n3) |||||]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can choose a row and remove some lines from the row.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRule 1: You should remove the lines only in one row.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRule 2: In your turn, you should remove at least one line.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAs input you have row info like this:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[[4,5,6]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIt means there are 4 lines in row1, 5 lines in row2 and 6 lines in row3.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou should return 2 numbers as output that one of them is row number and other is number of lines you want to remove from the row.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis function repeated to remove all the lines. If you remove the last line, you lose. So you should force your competitor to take the last line.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAt the start it's your turn and input is [10,10,10].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGood luck!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1254,"title":"PACMAT 06 - Optimized Ghosts, Equal Speed, Inf Lives; Interactive Download","description":"The Classic PACMAN game brought to Cody.\r\n\r\nPACMAT requires clearing all the Yellow Dots. Inf lives are available. Adjacent Ghosts will capture PACMAT.  Ghosts do not use the tunnel. On Ghost capture everyone gets reset. These trained ghosts take the minimum path to PACMAT assuming the other Ghosts are walls. \r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_300.jpg\u003e\u003e\r\n\r\nTo aid in development of your routine, a PACMAT_Interactive.m file that creates a solver script and video has been posted at \u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_Interactive_006c.m PACMAT_Interactive.m\u003e. (Right click, 'save link as'). The routine creates a PACMAT_solver.m script from the interactive play. The script demonstrates Interactivity, figure/KeyPressFcn, listdlg, and VideoWriter.\r\n\r\n\r\n\u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_G004_video_14_764.mp4 14 Lives Interactive\u003e (MP4)  Best Score seen is 9 Lives.\r\n\r\n\r\n*Inputs:* Map   Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u003e2=Ghost\r\n\r\n*Output:* Direction  Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\r\n\r\n*Pass Criteria:* Clear all dots in less than 4000 moves\r\n\r\n*Scoring:* Moves + 500 * Lives\r\n\r\n\r\n*Near Future:* Randomized Awesome Ghosts to make them non-deterministic","description_html":"\u003cp\u003eThe Classic PACMAN game brought to Cody.\u003c/p\u003e\u003cp\u003ePACMAT requires clearing all the Yellow Dots. Inf lives are available. Adjacent Ghosts will capture PACMAT.  Ghosts do not use the tunnel. On Ghost capture everyone gets reset. These trained ghosts take the minimum path to PACMAT assuming the other Ghosts are walls.\u003c/p\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_300.jpg\"\u003e\u003cp\u003eTo aid in development of your routine, a PACMAT_Interactive.m file that creates a solver script and video has been posted at \u003ca href=\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_Interactive_006c.m\"\u003ePACMAT_Interactive.m\u003c/a\u003e. (Right click, 'save link as'). The routine creates a PACMAT_solver.m script from the interactive play. The script demonstrates Interactivity, figure/KeyPressFcn, listdlg, and VideoWriter.\u003c/p\u003e\u003cp\u003e\u003ca href=\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_G004_video_14_764.mp4\"\u003e14 Lives Interactive\u003c/a\u003e (MP4)  Best Score seen is 9 Lives.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInputs:\u003c/b\u003e Map   Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u003e2=Ghost\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Direction  Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\u003c/p\u003e\u003cp\u003e\u003cb\u003ePass Criteria:\u003c/b\u003e Clear all dots in less than 4000 moves\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Moves + 500 * Lives\u003c/p\u003e\u003cp\u003e\u003cb\u003eNear Future:\u003c/b\u003e Randomized Awesome Ghosts to make them non-deterministic\u003c/p\u003e","function_template":"function  [newdir]=PACMAT_solver(map)\r\n% 314 move solver if Ghosts do not move\r\n persistent ptr\r\n if isempty(ptr)\r\n  ptr=['bbbbbbbcccbbbbbcccdddddddddddddddddddddddddaaa'...\r\n      'bbbbbaaaaaaaaaaaaaaaaaaaaaaaaadddddcccccccbbbbddddaaabbbbbbbb'...\r\n      'cccbbbdddaaabbbaaaadddddbbbbbccccbbbbbbbbbbbbbbaaaaddddddddddd'...\r\n      'ccccbbbcccdddbbbaaabbbaaaccccccbbbbbaaccdddddccccccccccccccaabbbbbcccddccc'...\r\n      'dddaaaaaaddddddcccbbbcccdddcccdddaaadddaaaddbbbbbaaadddddddddddcccbbccc'];\r\n  ptr=(ptr-'a')+1;\r\n end\r\n  \r\n newdir=ptr(1);\r\n ptr(1)=[];\r\n\r\nend","test_suite":"%%\r\nfeval(@assignin,'caller','score',10000);\r\n%%\r\nmax_moves=4000; % Interactive approx 1000 moves\r\n\r\nmap=[...\r\n      repmat('a',1,28);\r\n      'accccccccccccaacccccccccccca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acccccccccccccccccccccccccca';\r\n      'acaaaacaacaaaaaaaacaacaaaaca';\r\n      'acaaaacaacaaaaaaaacaacaaaaca';\r\n      'accccccaaccccaaccccaacccccca';\r\n      'aaaaaacaaaaabaabaaaaacaaaaaa';\r\n      'aaaaaacaaaaabaabaaaaacaaaaaa';\r\n      'aaaaaacaabbbbbbbbbbaacaaaaaa';\r\n      'aaaaaacaabaaabbaaabaacaaaaaa';\r\n      'aaaaaacaabalbbbblabaacaaaaaa';\r\n      'bbbbbbcbbbabbbbbbabbbcbbbbbb';\r\n      'aaaaaacaabalbbbblabaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'aaaaaacaabbbbbbbbbbaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'accccccccccccaacccccccccccca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acccaacccccccbdcccccccaaccca';\r\n      'aaacaacaacaaaaaaaacaacaacaaa';\r\n      'aaacaacaacaaaaaaaacaacaacaaa';\r\n      'accccccaaccccaaccccaacccccca';\r\n      'acaaaaaaaaaacaacaaaaaaaaaaca';\r\n      'acaaaaaaaaaacaacaaaaaaaaaaca';\r\n      'acccccccccccccccccccccccccca';\r\n      repmat('a',1,28);];\r\n  \r\n  map=map-'b';\r\n  [nr, nc]=size(map);\r\n\r\n  gmap=map; % Map used by ghosts to simplify PAC Capture\r\n  gmap(15,6)=Inf; %No tunnel ghosts\r\n  gmap(15,26)=Inf;\r\n  gmap(map==-1)=Inf; % walls to Inf\r\n  gmap(map\u003e2)=Inf; % Elim start points as viable moves, quicker box exit\r\n\r\n\r\n  mapdelta=[-1 nr 1 -nr]; % Valid as long as not on an edge\r\n  gmovxy=[0 -1;1 0;0 1;-1 0];\r\n\r\n  tunnel=find(map(:,1)==0); % tunnelptr\r\n  tunnel=[tunnel tunnel+nr*(nc-1)]; % Entrance/Exit Tunnel\r\n\r\n  [pmr, pmc]=find(map==2); % pi 24 row  pj 15 column of map\r\n   ptrpac=find(map==2);\r\n\r\n  ptrpac=find(map==2);\r\n  ptrpac_start=ptrpac;\r\n  ptrg_start=find(map\u003e2);\r\n  map(ptrg_start)=[10 20 30 40];% use deal?\r\n  [gstartx, gstarty]=find(map\u003e2);\r\n  \r\n  lives=0; % Lives\r\n  movepac=0;\r\n\r\nwhile any(mod(map(:),10)==1) \u0026\u0026 movepac\u003cmax_moves\r\n movepac=movepac+1;\r\n\r\n [curdir]=PACMAT_solver(map);\r\n [pmr, pmc]=find(map==2);\r\nif curdir\u003e0\r\n if map(ptrpac+mapdelta(curdir))==-1\r\n  % Do nothing - Ran into a Wall\r\n elseif map(ptrpac+mapdelta(curdir))\u003e2 % ran into ghost\r\n  map(ptrpac)=0; % remove PAC from the board\r\n  lives=lives+1;\r\n  %if lives==0,break;end\r\n  % reset the board\r\n  [ptrgx, ptrgy]=find(map\u003e2);\r\n  ptrg=find(map\u003e2);\r\n  map(ptrg)=mod(map(ptrg),10);\r\n  map(ptrpac_start)=2;\r\n  map(ptrg_start)=[10 20 30 40];\r\n  ptrpac=find(map==2);\r\n  continue;\r\n else % legal move\r\n  map(ptrpac)=0; % Eat Dot and clear PAC\r\n  ptrpac=ptrpac+mapdelta(curdir);\r\n  if ptrpac==tunnel(1),ptrpac=tunnel(2)-nr;end\r\n  if ptrpac==tunnel(2),ptrpac=tunnel(1)+nr;end\r\n  map(ptrpac)=2;\r\n end\r\nend % curdir \u003e0\r\n\r\n% Ghosts\r\n for i=1:4\r\n\r\n  ghosts=find(map\u003e2);\r\n  ptrpac=find(map==2); % Target\r\n\r\n  dot=false;\r\n  [gptrx, gptry]=find(map==10*i);\r\n  gidx=find(map==10*i);\r\n  if isempty(gidx)\r\n   [gptrx, gptry]=find(map==10*i+1); % ghost must be on a dot\r\n   gidx=find(map==10*i+1);\r\n   dot=true;\r\n  end\r\n\r\n% Find valid ghost moves using gmap\r\n% mapdelta=[-1 nr 1 -nr]; \r\n  gmov=find(map(gidx+mapdelta)==2); % adjacent to PACMAT\r\n  if ~isempty(gmov) % PAC adjacent\r\n   lives=lives+1;\r\n   %if lives==0,break;end\r\n   % reset the board\r\n   [pmr, pmc]=find(map==2); % PACMAT erase coords\r\n   map(map==2)=0;\r\n      \r\n   [ptrgx, ptrgy]=find(map\u003e2);\r\n   ptrg=find(map\u003e2);\r\n   map(ptrg)=mod(map(ptrg),10);\r\n   map(ptrpac_start)=2;\r\n   map(ptrg_start)=[10 20 30 40];\r\n   ptrpac=find(map==2);     \r\n   break; % Ghost move loop\r\n      \r\n  else % gmap no tunnel usage, Walls\r\n \r\n   gmap=map; gmap(15,1)=-1;gmap(15,28)=-1;\r\n       \r\n   ptctr=0;\r\n   gmap(gmap\u003e=0)=Inf;\r\n   \r\n% Ghost algor change   \r\n    gmap(ghosts)=-1; % other ghosts are like walls Ghosts_004/5\r\n    gmap(gidx)=Inf; % Ultimate target\r\n    gmap(ptrpac)=1; % Start at PACMAT and expand to ghost\r\n    while gmap(gidx)\u003e101 \u0026\u0026 ptctr\u003c100 % potential boxed dot\r\n % find dots, add a counter to distance form location, keep min value\r\n % when ptrpac gets a value it will be from nearest dot\r\n % find side with dmap(ptrpac)-1\r\n     ptctr=ptctr+1;\r\n     dpts=find(gmap==ptctr);\r\n     newpt_idx=repmat(dpts,1,4)+repmat(mapdelta,length(dpts),1);\r\n     gmap(newpt_idx(:))=min(gmap(newpt_idx(:)),ptctr+1);\r\n    end\r\n\r\n% Simplified by ghosts are walls: No Ghost Jumping\r\n    if ~isinf(gmap(gidx)) % Path(s) to Ghost found\r\n     for gmov=1:4 % execute with a find?\r\n       if gmap(gidx+mapdelta(gmov))==gmap(gidx)-1,break;end\r\n      end\r\n     else\r\n      gmov=[];\r\n     end\r\n \r\n   if ~isempty(gmov) % valid g move : ghost may not stand on ghost\r\n    map(gptrx,gptry)=mod(map(gptrx,gptry),10);\r\n    map(gidx+mapdelta(gmov))=map(gidx+mapdelta(gmov))+10*i;     \r\n   end % ~isempty(gmov) standard move - no capture\r\n\r\n  end % ~isempty(gmov) PACMAT adjacent\r\n  \r\n end % i ghost moves\r\nend % while any dots and \u003c max_moves\r\n%\r\ndots=length(find(mod(map,10)==1));\r\n%\r\nfprintf('moves %i\\n',movepac)\r\nfprintf('dots %i\\n',dots)\r\nfprintf('Lives Spent %i\\n',lives)\r\n%\r\n% To Pass need to leave at most 0 dots\r\nassert(dots==0,sprintf('Max Dots 0, Dots Remaining %i\\n',dots))\r\n\r\nscore= movepac + 500*lives; % All dots must be removed\r\n\r\nfeval( @assignin,'caller','score',floor(min( 10000,score )) );\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-02-08T04:11:21.000Z","updated_at":"2025-12-03T08:52:46.000Z","published_at":"2013-02-08T04:48:28.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.JPEG\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Classic PACMAN game brought to Cody.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePACMAT requires clearing all the Yellow Dots. Inf lives are available. Adjacent Ghosts will capture PACMAT. Ghosts do not use the tunnel. On Ghost capture everyone gets reset. These trained ghosts take the minimum path to PACMAT assuming the other Ghosts are walls.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo aid in development of your routine, a PACMAT_Interactive.m file that creates a solver script and video has been posted at\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_Interactive_006c.m\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePACMAT_Interactive.m\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. (Right click, 'save link as'). The routine creates a PACMAT_solver.m script from the interactive play. The script demonstrates Interactivity, figure/KeyPressFcn, listdlg, and VideoWriter.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_G004_video_14_764.mp4\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e14 Lives Interactive\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (MP4) Best Score seen is 9 Lives.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInputs:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Map Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u0026gt;2=Ghost\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Direction Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ePass Criteria:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Clear all dots in less than 4000 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Moves + 500 * Lives\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle 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\"}]}"},{"id":1255,"title":"PACMAT 07 - Optimized Ghosts, PAC 2X Ghost Speed, 4 Lives; Interactive Download","description":"The Classic PACMAN game brought to Cody.\r\n\r\nPACMAT requires clearing all the Yellow Dots. PACMAT moves at 2X Ghost Speed, Four lives are available. Adjacent Ghosts will capture PACMAT.  Ghosts do not use the tunnel. On Ghost capture everyone gets reset. These trained ghosts take the minimum path to PACMAT assuming the other Ghosts are walls. \r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_300.jpg\u003e\u003e\r\n\r\nTo aid in development of your routine, a PACMAT_Interactive2X.m file that creates a solver script and video has been posted at \u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_Interactive_007a.m PACMAT_Interactive2X.m\u003e. (Right click, 'save link as'). The routine creates a PACMAT_2Xsolver.m script from the interactive play. The script demonstrates Interactivity, figure/KeyPressFcn, listdlg, and VideoWriter.\r\n\r\n\r\n\u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_G004_2Xvideo_1_548.mp4 PAC2X 1 Life Interactive\u003e (MP4)\r\n\r\n\u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_G004_2Xvideo_0_398.mp4 Alfonso 398\u003e (MP4)\r\n\r\n\r\n*Inputs:* Map   Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u003e2=Ghost\r\n\r\n*Output:* Direction  Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\r\n\r\n*Pass Criteria:* Clear all dots in less than 4000 moves and 4 Lives\r\n\r\n*Scoring:* Moves + 1000 * Lives\r\n\r\n\r\n*Near Future:* Tunneling Ghosts and then Randomized Awesome Ghosts to make them non-deterministic","description_html":"\u003cp\u003eThe Classic PACMAN game brought to Cody.\u003c/p\u003e\u003cp\u003ePACMAT requires clearing all the Yellow Dots. PACMAT moves at 2X Ghost Speed, Four lives are available. Adjacent Ghosts will capture PACMAT.  Ghosts do not use the tunnel. On Ghost capture everyone gets reset. These trained ghosts take the minimum path to PACMAT assuming the other Ghosts are walls.\u003c/p\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_300.jpg\"\u003e\u003cp\u003eTo aid in development of your routine, a PACMAT_Interactive2X.m file that creates a solver script and video has been posted at \u003ca href=\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_Interactive_007a.m\"\u003ePACMAT_Interactive2X.m\u003c/a\u003e. (Right click, 'save link as'). The routine creates a PACMAT_2Xsolver.m script from the interactive play. The script demonstrates Interactivity, figure/KeyPressFcn, listdlg, and VideoWriter.\u003c/p\u003e\u003cp\u003e\u003ca href=\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_G004_2Xvideo_1_548.mp4\"\u003ePAC2X 1 Life Interactive\u003c/a\u003e (MP4)\u003c/p\u003e\u003cp\u003e\u003ca href=\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_G004_2Xvideo_0_398.mp4\"\u003eAlfonso 398\u003c/a\u003e (MP4)\u003c/p\u003e\u003cp\u003e\u003cb\u003eInputs:\u003c/b\u003e Map   Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u003e2=Ghost\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Direction  Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\u003c/p\u003e\u003cp\u003e\u003cb\u003ePass Criteria:\u003c/b\u003e Clear all dots in less than 4000 moves and 4 Lives\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Moves + 1000 * Lives\u003c/p\u003e\u003cp\u003e\u003cb\u003eNear Future:\u003c/b\u003e Tunneling Ghosts and then Randomized Awesome Ghosts to make them non-deterministic\u003c/p\u003e","function_template":"function ans = PACMAT_2Xsolver(map)\r\npersistent mv\r\nif isempty(mv)\r\nmv=[2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 4 4 4 4 4 4 4 4 1 1 1 1 ];\r\nend\r\nmv(1);\r\nmv(1)=[];\r\nend","test_suite":"%%\r\nfeval(@assignin,'caller','score',5000);\r\n%%\r\nmax_moves=4000; % Interactive approx 1000 moves\r\n\r\nmap=[...\r\n      repmat('a',1,28);\r\n      'accccccccccccaacccccccccccca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acccccccccccccccccccccccccca';\r\n      'acaaaacaacaaaaaaaacaacaaaaca';\r\n      'acaaaacaacaaaaaaaacaacaaaaca';\r\n      'accccccaaccccaaccccaacccccca';\r\n      'aaaaaacaaaaabaabaaaaacaaaaaa';\r\n      'aaaaaacaaaaabaabaaaaacaaaaaa';\r\n      'aaaaaacaabbbbbbbbbbaacaaaaaa';\r\n      'aaaaaacaabaaabbaaabaacaaaaaa';\r\n      'aaaaaacaabalbbbblabaacaaaaaa';\r\n      'bbbbbbcbbbabbbbbbabbbcbbbbbb';\r\n      'aaaaaacaabalbbbblabaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'aaaaaacaabbbbbbbbbbaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'accccccccccccaacccccccccccca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acccaacccccccbdcccccccaaccca';\r\n      'aaacaacaacaaaaaaaacaacaacaaa';\r\n      'aaacaacaacaaaaaaaacaacaacaaa';\r\n      'accccccaaccccaaccccaacccccca';\r\n      'acaaaaaaaaaacaacaaaaaaaaaaca';\r\n      'acaaaaaaaaaacaacaaaaaaaaaaca';\r\n      'acccccccccccccccccccccccccca';\r\n      repmat('a',1,28);];\r\n  \r\n  map=map-'b';\r\n  [nr, nc]=size(map);\r\n\r\n  gmap=map; % Map used by ghosts to simplify PAC Capture\r\n  gmap(15,6)=Inf; %No tunnel ghosts\r\n  gmap(15,26)=Inf;\r\n  gmap(map==-1)=Inf; % walls to Inf\r\n  gmap(map\u003e2)=Inf; % Elim start points as viable moves, quicker box exit\r\n\r\n\r\n  mapdelta=[-1 nr 1 -nr]; % Valid as long as not on an edge\r\n  gmovxy=[0 -1;1 0;0 1;-1 0];\r\n\r\n  tunnel=find(map(:,1)==0); % tunnelptr\r\n  tunnel=[tunnel tunnel+nr*(nc-1)]; % Entrance/Exit Tunnel\r\n\r\n  [pmr, pmc]=find(map==2); % pi 24 row  pj 15 column of map\r\n   ptrpac=find(map==2);\r\n\r\n  ptrpac=find(map==2);\r\n  ptrpac_start=ptrpac;\r\n  ptrg_start=find(map\u003e2);\r\n  map(ptrg_start)=[10 20 30 40];\r\n  [gstartx, gstarty]=find(map\u003e2);\r\n  \r\n  lives=0; % Lives\r\n  movepac=0;\r\n\r\nwhile lives\u003c4 \u0026\u0026 any(mod(map(:),10)==1) \u0026\u0026 movepac\u003cmax_moves\r\n for pac2x=1:2 % PAC Speed Multiplier\r\n  if lives\u003e3 || ~any(mod(map(:),10)==1),break;end % Died or Completed\r\n movepac=movepac+1;\r\n [curdir]=PACMAT_2Xsolver(map);\r\n [pmr, pmc]=find(map==2);\r\nif curdir\u003e0\r\n if map(ptrpac+mapdelta(curdir))==-1\r\n  % Do nothing - Ran into a Wall\r\n elseif map(ptrpac+mapdelta(curdir))\u003e2 % ran into ghost\r\n  map(ptrpac)=0; % remove PAC from the board\r\n  lives=lives+1;\r\n  % reset the board\r\n  [ptrgx, ptrgy]=find(map\u003e2);\r\n  ptrg=find(map\u003e2);\r\n  map(ptrg)=mod(map(ptrg),10);\r\n  map(ptrpac_start)=2;\r\n  map(ptrg_start)=[10 20 30 40];\r\n  ptrpac=find(map==2);\r\n  continue;\r\n else % legal move\r\n  map(ptrpac)=0; % Eat Dot and clear PAC\r\n  ptrpac=ptrpac+mapdelta(curdir);\r\n  if ptrpac==tunnel(1),ptrpac=tunnel(2)-nr;end\r\n  if ptrpac==tunnel(2),ptrpac=tunnel(1)+nr;end\r\n  map(ptrpac)=2;\r\n end\r\nend % curdir \u003e0\r\nend % pac2x\r\nif lives\u003e3 || ~any(mod(map(:),10)==1),break;end % Completed\r\n\r\n% Ghosts\r\n for i=1:4\r\n\r\n  ghosts=find(map\u003e2);\r\n  ptrpac=find(map==2); % Target\r\n\r\n  dot=false;\r\n  [gptrx, gptry]=find(map==10*i);\r\n  gidx=find(map==10*i);\r\n  if isempty(gidx)\r\n   [gptrx, gptry]=find(map==10*i+1); % ghost must be on a dot\r\n   gidx=find(map==10*i+1);\r\n   dot=true;\r\n  end\r\n\r\n% Find valid ghost moves using gmap\r\n% mapdelta=[-1 nr 1 -nr]; \r\n  gmov=find(map(gidx+mapdelta)==2); % adjacent to PACMAT\r\n  if ~isempty(gmov) % PAC adjacent\r\n   lives=lives+1;\r\n   %if lives==0,break;end\r\n   % reset the board\r\n   [pmr, pmc]=find(map==2); % PACMAT erase coords\r\n   map(map==2)=0;\r\n      \r\n   [ptrgx, ptrgy]=find(map\u003e2);\r\n   ptrg=find(map\u003e2);\r\n   map(ptrg)=mod(map(ptrg),10);\r\n   map(ptrpac_start)=2;\r\n   map(ptrg_start)=[10 20 30 40];\r\n   ptrpac=find(map==2);     \r\n   break; % Ghost move loop\r\n      \r\n  else % gmap no tunnel usage, Walls\r\n \r\n   gmap=map; gmap(15,1)=-1;gmap(15,28)=-1;\r\n       \r\n   ptctr=0;\r\n   gmap(gmap\u003e=0)=Inf;\r\n   \r\n% Ghost algor change   \r\n    gmap(ghosts)=-1; % other ghosts are like walls Ghosts_004/5\r\n    gmap(gidx)=Inf; % Ultimate target\r\n    gmap(ptrpac)=1; % Start at PACMAT and expand to ghost\r\n    while gmap(gidx)\u003e101 \u0026\u0026 ptctr\u003c100 % potential boxed dot\r\n % find dots, add a counter to distance form location, keep min value\r\n % when ptrpac gets a value it will be from nearest dot\r\n % find side with dmap(ptrpac)-1\r\n     ptctr=ptctr+1;\r\n     dpts=find(gmap==ptctr);\r\n     newpt_idx=repmat(dpts,1,4)+repmat(mapdelta,length(dpts),1);\r\n     gmap(newpt_idx(:))=min(gmap(newpt_idx(:)),ptctr+1);\r\n    end\r\n\r\n% Simplified by ghosts are walls: No Ghost Jumping\r\n    if ~isinf(gmap(gidx)) % Path(s) to Ghost found\r\n     for gmov=1:4 % execute with a find?\r\n       if gmap(gidx+mapdelta(gmov))==gmap(gidx)-1,break;end\r\n      end\r\n     else\r\n      gmov=[];\r\n     end\r\n \r\n   if ~isempty(gmov) % valid g move : ghost may not stand on ghost\r\n    map(gptrx,gptry)=mod(map(gptrx,gptry),10);\r\n    map(gidx+mapdelta(gmov))=map(gidx+mapdelta(gmov))+10*i;     \r\n   end % ~isempty(gmov) standard move - no capture\r\n\r\n  end % ~isempty(gmov) PACMAT adjacent\r\n  \r\n end % i ghost moves\r\nend % while any dots and \u003c max_moves and lives\u003c4\r\n%\r\ndots=length(find(mod(map,10)==1));\r\n%\r\nfprintf('moves %i\\n',movepac)\r\nfprintf('dots %i\\n',dots)\r\nfprintf('Lives Spent %i\\n',lives)\r\n%\r\n% To Pass need to leave at most 0 dots\r\nassert(dots==0,sprintf('Max Dots 0, Dots Remaining %i\\n',dots))\r\n\r\nscore= movepac + 1000*lives; % All dots must be removed\r\n\r\nfeval( @assignin,'caller','score',floor(min( 5000,score )) );\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-02-09T03:33:37.000Z","updated_at":"2025-12-03T15:28:58.000Z","published_at":"2013-02-09T04:11:24.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.JPEG\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Classic PACMAN game brought to Cody.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePACMAT requires clearing all the Yellow Dots. PACMAT moves at 2X Ghost Speed, Four lives are available. Adjacent Ghosts will capture PACMAT. Ghosts do not use the tunnel. On Ghost capture everyone gets reset. These trained ghosts take the minimum path to PACMAT assuming the other Ghosts are walls.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo aid in development of your routine, a PACMAT_Interactive2X.m file that creates a solver script and video has been posted at\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_Interactive_007a.m\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePACMAT_Interactive2X.m\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. (Right click, 'save link as'). The routine creates a PACMAT_2Xsolver.m script from the interactive play. The script demonstrates Interactivity, figure/KeyPressFcn, listdlg, and VideoWriter.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_G004_2Xvideo_1_548.mp4\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePAC2X 1 Life Interactive\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (MP4)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_G004_2Xvideo_0_398.mp4\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eAlfonso 398\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (MP4)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInputs:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Map Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u0026gt;2=Ghost\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Direction Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ePass Criteria:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Clear all dots in less than 4000 moves and 4 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\"}]}"},{"id":1314,"title":"PACMAT 08 - Awesome Tunneling Ghosts, 2X Speed, 6 Lives, Game Download","description":"The Classic PACMAN game brought to Cody.\r\n\r\nPACMAT requires clearing all the Yellow Dots. PACMAT moves at 2X Ghost speed. Six lives are available. Adjacent Ghosts will capture PACMAT.  Awesome Ghosts use the tunnel. On Ghost capture everyone gets reset. These trained ghosts take the minimum path, including tunnel paths, to PACMAT assuming the other Ghosts are walls. \r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_300.jpg\u003e\u003e\r\n\r\nTo aid in development of your routine, a PACMAT_Interactive.m file that creates a solver script and video has been posted at \u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_Interactive_008a.m PACMAT_Interactive_008a.m\u003e. (Right click, 'save link as'). The routine creates a PACMAT8_2Xsolver.m script from the interactive play. The script demonstrates Interactivity, figure/KeyPressFcn, listdlg, and VideoWriter.\r\n\r\n\r\n\u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT8_2Xvideo_492.mp4 492 Moves Interactive\u003e (MP4)  Best Score seen is Zero Lives lost, 492 moves.\r\n\r\n\r\n*Inputs:* Map   Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u003e2=Ghost\r\n\r\n*Output:* Direction  Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\r\n\r\n*Pass Criteria:* Clear all dots in less than 4000 moves\r\n\r\n*Scoring:* Moves + 1000 * Lives\r\n\r\n\r\n*Near Future:* Randomized Awesome Tunnel Ghosts to make them non-deterministic to require Adaptive Bot solutions","description_html":"\u003cp\u003eThe Classic PACMAN game brought to Cody.\u003c/p\u003e\u003cp\u003ePACMAT requires clearing all the Yellow Dots. PACMAT moves at 2X Ghost speed. Six lives are available. Adjacent Ghosts will capture PACMAT.  Awesome Ghosts use the tunnel. On Ghost capture everyone gets reset. These trained ghosts take the minimum path, including tunnel paths, to PACMAT assuming the other Ghosts are walls.\u003c/p\u003e\u003cimg src = \"https://sites.google.com/site/razapor/matlab_cody/PACMAT_300.jpg\"\u003e\u003cp\u003eTo aid in development of your routine, a PACMAT_Interactive.m file that creates a solver script and video has been posted at \u003ca href = \"https://sites.google.com/site/razapor/matlab_cody/PACMAT_Interactive_008a.m\"\u003ePACMAT_Interactive_008a.m\u003c/a\u003e. (Right click, 'save link as'). The routine creates a PACMAT8_2Xsolver.m script from the interactive play. The script demonstrates Interactivity, figure/KeyPressFcn, listdlg, and VideoWriter.\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://sites.google.com/site/razapor/matlab_cody/PACMAT8_2Xvideo_492.mp4\"\u003e492 Moves Interactive\u003c/a\u003e (MP4)  Best Score seen is Zero Lives lost, 492 moves.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInputs:\u003c/b\u003e Map   Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u003e2=Ghost\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Direction  Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\u003c/p\u003e\u003cp\u003e\u003cb\u003ePass Criteria:\u003c/b\u003e Clear all dots in less than 4000 moves\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Moves + 1000 * Lives\u003c/p\u003e\u003cp\u003e\u003cb\u003eNear Future:\u003c/b\u003e Randomized Awesome Tunnel Ghosts to make them non-deterministic to require Adaptive Bot solutions\u003c/p\u003e","function_template":"function ans = PACMAT8_2Xsolver(map)\r\npersistent mv\r\nif isempty(mv)\r\n mv=[2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1]; \r\nend\r\nmv(1);\r\nmv(1)=[];","test_suite":"%%\r\nfeval(@assignin,'caller','score',6000);\r\n%%\r\nmax_moves=4000; % Interactive approx 1000 moves\r\nmaxLives=6;\r\n\r\nmap=[...\r\n      repmat('a',1,28);\r\n      'accccccccccccaacccccccccccca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acccccccccccccccccccccccccca';\r\n      'acaaaacaacaaaaaaaacaacaaaaca';\r\n      'acaaaacaacaaaaaaaacaacaaaaca';\r\n      'accccccaaccccaaccccaacccccca';\r\n      'aaaaaacaaaaabaabaaaaacaaaaaa';\r\n      'aaaaaacaaaaabaabaaaaacaaaaaa';\r\n      'aaaaaacaabbbbbbbbbbaacaaaaaa';\r\n      'aaaaaacaabaaabbaaabaacaaaaaa';\r\n      'aaaaaacaabalbbbblabaacaaaaaa';\r\n      'bbbbbbcbbbabbbbbbabbbcbbbbbb';\r\n      'aaaaaacaabalbbbblabaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'aaaaaacaabbbbbbbbbbaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'accccccccccccaacccccccccccca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acccaacccccccbdcccccccaaccca';\r\n      'aaacaacaacaaaaaaaacaacaacaaa';\r\n      'aaacaacaacaaaaaaaacaacaacaaa';\r\n      'accccccaaccccaaccccaacccccca';\r\n      'acaaaaaaaaaacaacaaaaaaaaaaca';\r\n      'acaaaaaaaaaacaacaaaaaaaaaaca';\r\n      'acccccccccccccccccccccccccca';\r\n      repmat('a',1,28);];\r\n  \r\n  map=map-'b';\r\n  [nr, nc]=size(map);\r\n\r\n  mapdelta=[-1 nr 1 -nr]; % Valid as long as not on an edge\r\n  gmovxy=[0 -1;1 0;0 1;-1 0];\r\n\r\n  tunnel=find(map(:,1)==0); % tunnelptr\r\n  tunnel=[tunnel tunnel+nr*(nc-1)]; % Entrance/Exit Tunnel\r\n\r\n  [pmr, pmc]=find(map==2); % pi 24 row  pj 15 column of map\r\n   ptrpac=find(map==2);\r\n\r\n  ptrpac=find(map==2);\r\n  ptrpac_start=ptrpac;\r\n  ptrg_start=find(map\u003e2);\r\n  map(ptrg_start)=[10 20 30 40];\r\n  [gstartx, gstarty]=find(map\u003e2);\r\n  \r\n  lives=0; % Lives\r\n  movepac=0;\r\n\r\nwhile lives\u003cmaxLives \u0026\u0026 any(mod(map(:),10)==1) \u0026\u0026 movepac\u003cmax_moves\r\n for pac2x=1:2\r\n  if lives\u003emaxLives-1 || ~any(mod(map(:),10)==1),break;end % Died or Completed\r\n  movepac=movepac+1;\r\n\r\n  [curdir]=PACMAT8_2Xsolver(map);\r\n\r\n  [pmr, pmc]=find(map==2);\r\n\r\n if curdir\u003e0\r\n  if map(ptrpac+mapdelta(curdir))==-1\r\n   % Do nothing - Ran into a Wall\r\n  elseif map(ptrpac+mapdelta(curdir))\u003e2 % ran into ghost\r\n   map(ptrpac)=0; % remove PAC from the board\r\n   lives=lives+1;\r\n   %if lives==0,break;end\r\n   % reset the board\r\n   [ptrgx, ptrgy]=find(map\u003e2);\r\n   ptrg=find(map\u003e2);\r\n   map(ptrg)=mod(map(ptrg),10);\r\n   map(ptrpac_start)=2;\r\n   map(ptrg_start)=[10 20 30 40];\r\n   ptrpac=find(map==2);\r\n   continue; % Lost a Life\r\n  else % legal move\r\n   map(ptrpac)=0; % Eat Dot and clear PAC\r\n   ptrpac=ptrpac+mapdelta(curdir);\r\n   if ptrpac==tunnel(1),ptrpac=tunnel(2)-nr;end\r\n   if ptrpac==tunnel(2),ptrpac=tunnel(1)+nr;end\r\n   map(ptrpac)=2;\r\n  end\r\n end % curdir \u003e0\r\n\r\nend % pac2x\r\n\r\nif lives\u003emaxLives-1 || ~any(mod(map(:),10)==1),break;end % Completed\r\n\r\n\r\n% Ghosts\r\n for i=1:4\r\n\r\n  ghosts=find(map\u003e2);\r\n  ptrpac=find(map==2); % Target\r\n\r\n  dot=false;\r\n  [gptrx, gptry]=find(map==10*i);\r\n  gidx=find(map==10*i);\r\n  if isempty(gidx)\r\n   [gptrx, gptry]=find(map==10*i+1); % ghost must be on a dot\r\n   gidx=find(map==10*i+1);\r\n   dot=true;\r\n  end\r\n\r\n% Find valid ghost moves using gmap\r\n% mapdelta=[-1 nr 1 -nr]; \r\n  gmov=find(map(gidx+mapdelta)==2); % adjacent to PACMAT\r\n  if gidx==15+nr \u0026\u0026 ptrpac==nr*(nc-2)+15 % Ghost Tunnel Adj\r\n    gmov=4;\r\n   end\r\n   if gidx==nr*(nc-2)+15 \u0026\u0026 ptrpac==15+nr % Ghost Tunnel Adj\r\n    gmov=2;\r\n   end\r\n\r\n  if ~isempty(gmov) % PAC adjacent\r\n   lives=lives+1;\r\n   % reset the board\r\n   [pmr, pmc]=find(map==2); % PACMAT erase coords\r\n   map(map==2)=0;\r\n      \r\n   [ptrgx, ptrgy]=find(map\u003e2);\r\n   ptrg=find(map\u003e2);\r\n   map(ptrg)=mod(map(ptrg),10);\r\n   map(ptrpac_start)=2;\r\n   map(ptrg_start)=[10 20 30 40];\r\n   ptrpac=find(map==2);     \r\n   break; % Ghost move loop\r\n      \r\n  else % gmap avoid walls and other ghosts Walls\r\n \r\n   gmap=map;\r\n       \r\n   ptctr=0;\r\n   gmap(gmap\u003e=0)=Inf;\r\n      \r\n    gmap(ghosts)=-1; % other ghosts are like walls Ghosts_004/5\r\n    gmap(gidx)=Inf; % Ultimate target\r\n    gmap(ptrpac)=1; % Start at PACMAT and expand to ghost\r\n    while gmap(gidx)\u003e101 \u0026\u0026 ptctr\u003c100 % potential boxed dot\r\n % find dots, add a counter to distance form location, keep min value\r\n % when ptrpac gets a value it will be from nearest dot\r\n % find side with dmap(ptrpac)-1\r\n     ptctr=ptctr+1;\r\n     dpts=find(gmap==ptctr);\r\n     newpt_idx=repmat(dpts,1,4)+repmat(mapdelta,length(dpts),1);\r\n% Ghost Tunnel Access\r\n     tunL=find(newpt_idx==15);\r\n     tunR=find(newpt_idx==nr*nc-16);\r\n     if ~isempty(tunL)\r\n      newpt_idx(tunL)=nr*(nc-2)+15;\r\n     end\r\n     if ~isempty(tunR)\r\n      newpt_idx(tunR)=15+nr;\r\n     end\r\n\r\n     gmap(newpt_idx(:))=min(gmap(newpt_idx(:)),ptctr+1);\r\n    end\r\n\r\n% Simplified by ghosts are walls: No Ghost Jumping\r\n    if ~isinf(gmap(gidx)) % Path(s) to Ghost found\r\n% Tunnel Check\r\n    gmov=[];\r\n    if gidx==15+nr % Tunnel\r\n     gmov=2;\r\n     if gmap(gidx+mapdelta(2))==gmap(gidx)-1\r\n      gmov=2;\r\n     else\r\n      gmov=4; % Possible error ghost onto ghost\r\n     end   \r\n    end\r\n\r\n    if gidx==nr*(nc-2)+15 % Tunnel\r\n      if gmap(gidx+mapdelta(4))==gmap(gidx)-1\r\n        gmov=4;\r\n      else\r\n        gmov=2; % Possible error ghost onto ghost\r\n      end\r\n     end\r\n    \r\n     if isempty(gmov) % Non-Tunnel Move\r\n      for gmov=1:4 % execute with a find?\r\n        if gmap(gidx+mapdelta(gmov))==gmap(gidx)-1,break;end\r\n       end\r\n      end\r\n     else\r\n      gmov=[]; % No path to PACMAT found\r\n     end\r\n \r\n   if ~isempty(gmov) % valid g move : ghost may not stand on ghost\r\n    map(gptrx,gptry)=mod(map(gptrx,gptry),10);\r\n\r\n     %tunLtf=false;\r\n     %tunRtf=false;\r\n       if gidx==nr+15\r\n        if gmov==2 % Left Tunnel but go Right\r\n         map(gidx+mapdelta(gmov))=map(gidx+mapdelta(gmov))+10*i;\r\n        else\r\n         % Tunnel\r\n          map(nr*(nc-2)+15)=10*i;\r\n          %tunLtf=true;\r\n        end\r\n       elseif gidx==nr*(nc-2)+15\r\n        if gmov==4 % Right Tunnel but go Left\r\n         map(gidx+mapdelta(gmov))=map(gidx+mapdelta(gmov))+10*i;\r\n        else\r\n         % Tunnel\r\n         map(nr+15)=10*i;\r\n         %tunRtf=true;\r\n        end\r\n       else % Standard move\r\n        map(gidx+mapdelta(gmov))=map(gidx+mapdelta(gmov))+10*i;\r\n       end\r\n    \r\n   end % ~isempty(gmov) standard move - no capture\r\n\r\n  end % ~isempty(gmov) PACMAT adjacent\r\n  \r\n end % i ghost moves\r\nend % while any dots and \u003c max_moves maxLives\r\n%\r\ndots=length(find(mod(map,10)==1));\r\n%\r\nfprintf('moves %i\\n',movepac)\r\nfprintf('dots %i\\n',dots)\r\nfprintf('Lives Spent %i\\n',lives)\r\n%\r\n% To Pass need to leave at most 0 dots\r\nassert(dots==0,sprintf('Max Dots 0, Dots Remaining %i\\n',dots))\r\n\r\nscore= movepac + 1000*lives; % All dots must be removed\r\n\r\nfeval( @assignin,'caller','score',floor(min( 6000,score )) );\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-03-03T04:08:07.000Z","updated_at":"2013-03-03T04:17:37.000Z","published_at":"2013-03-03T04:17:37.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.JPEG\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Classic PACMAN game brought to Cody.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePACMAT requires clearing all the Yellow Dots. PACMAT moves at 2X Ghost speed. Six lives are available. Adjacent Ghosts will capture PACMAT. Awesome Ghosts use the tunnel. On Ghost capture everyone gets reset. These trained ghosts take the minimum path, including tunnel paths, to PACMAT assuming the other Ghosts are walls.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo aid in development of your routine, a PACMAT_Interactive.m file that creates a solver script and video has been posted at\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_Interactive_008a.m\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePACMAT_Interactive_008a.m\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. (Right click, 'save link as'). The routine creates a PACMAT8_2Xsolver.m script from the interactive play. The script demonstrates Interactivity, figure/KeyPressFcn, listdlg, and VideoWriter.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://sites.google.com/site/razapor/matlab_cody/PACMAT8_2Xvideo_492.mp4\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e492 Moves Interactive\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (MP4) Best Score seen is Zero Lives lost, 492 moves.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInputs:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Map Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u0026gt;2=Ghost\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Direction Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ePass Criteria:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Clear all dots in less than 4000 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Moves + 1000 * Lives\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle 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\"}]}"},{"id":1313,"title":"PACMAT 09 - Awesome Tunnelling Ghosts, Equal Speed, 20 Lives, Game Download","description":"The Classic PACMAN game brought to Cody.\r\n\r\nPACMAT requires clearing all the Yellow Dots. Twenty lives are available. Adjacent Ghosts will capture PACMAT.  Awesome Ghosts use the tunnel. On Ghost capture everyone gets reset. These trained ghosts take the minimum path, including tunnel paths, to PACMAT assuming the other Ghosts are walls. \r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_300.jpg\u003e\u003e\r\n\r\nTo aid in development of your routine, a PACMAT_Interactive.m file that creates a solver script and video has been posted at \u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_Interactive_009a.m PACMAT_Interactive_009a.m\u003e. (Right click, 'save link as'). The routine creates a PACMAT9_1Xsolver.m script from the interactive play. The script demonstrates Interactivity, figure/KeyPressFcn, listdlg, and VideoWriter.\r\n\r\n\r\n\u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT9_1Xvideo_11_630.mp4 11 Lives Interactive\u003e (MP4)  Best Score seen is 11 Lives.\r\n\r\n\r\n*Inputs:* Map   Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u003e2=Ghost\r\n\r\n*Output:* Direction  Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\r\n\r\n*Pass Criteria:* Clear all dots in less than 4000 moves\r\n\r\n*Scoring:* Moves + 1000 * Lives\r\n\r\n\r\n*Near Future:* Awesome Tunnel Ghosts and 2X Speed PACMAT; Randomized Awesome Tunnel Ghosts to make them non-deterministic","description_html":"\u003cp\u003eThe Classic PACMAN game brought to Cody.\u003c/p\u003e\u003cp\u003ePACMAT requires clearing all the Yellow Dots. Twenty lives are available. Adjacent Ghosts will capture PACMAT.  Awesome Ghosts use the tunnel. On Ghost capture everyone gets reset. These trained ghosts take the minimum path, including tunnel paths, to PACMAT assuming the other Ghosts are walls.\u003c/p\u003e\u003cimg src = \"https://sites.google.com/site/razapor/matlab_cody/PACMAT_300.jpg\"\u003e\u003cp\u003eTo aid in development of your routine, a PACMAT_Interactive.m file that creates a solver script and video has been posted at \u003ca href = \"https://sites.google.com/site/razapor/matlab_cody/PACMAT_Interactive_009a.m\"\u003ePACMAT_Interactive_009a.m\u003c/a\u003e. (Right click, 'save link as'). The routine creates a PACMAT9_1Xsolver.m script from the interactive play. The script demonstrates Interactivity, figure/KeyPressFcn, listdlg, and VideoWriter.\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://sites.google.com/site/razapor/matlab_cody/PACMAT9_1Xvideo_11_630.mp4\"\u003e11 Lives Interactive\u003c/a\u003e (MP4)  Best Score seen is 11 Lives.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInputs:\u003c/b\u003e Map   Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u003e2=Ghost\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Direction  Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\u003c/p\u003e\u003cp\u003e\u003cb\u003ePass Criteria:\u003c/b\u003e Clear all dots in less than 4000 moves\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Moves + 1000 * Lives\u003c/p\u003e\u003cp\u003e\u003cb\u003eNear Future:\u003c/b\u003e Awesome Tunnel Ghosts and 2X Speed PACMAT; Randomized Awesome Tunnel Ghosts to make them non-deterministic\u003c/p\u003e","function_template":"function ans = PACMAT9_1Xsolver(map)\r\npersistent mv\r\nif isempty(mv)\r\n mv=[2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1]; \r\nend\r\nmv(1);\r\nmv(1)=[];\r\n","test_suite":"%%\r\nfeval(@assignin,'caller','score',20000);\r\n%%\r\nmax_moves=4000; % Interactive approx 1000 moves\r\nmaxLives=20;\r\n\r\nmap=[...\r\n      repmat('a',1,28);\r\n      'accccccccccccaacccccccccccca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acccccccccccccccccccccccccca';\r\n      'acaaaacaacaaaaaaaacaacaaaaca';\r\n      'acaaaacaacaaaaaaaacaacaaaaca';\r\n      'accccccaaccccaaccccaacccccca';\r\n      'aaaaaacaaaaabaabaaaaacaaaaaa';\r\n      'aaaaaacaaaaabaabaaaaacaaaaaa';\r\n      'aaaaaacaabbbbbbbbbbaacaaaaaa';\r\n      'aaaaaacaabaaabbaaabaacaaaaaa';\r\n      'aaaaaacaabalbbbblabaacaaaaaa';\r\n      'bbbbbbcbbbabbbbbbabbbcbbbbbb';\r\n      'aaaaaacaabalbbbblabaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'aaaaaacaabbbbbbbbbbaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'accccccccccccaacccccccccccca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acccaacccccccbdcccccccaaccca';\r\n      'aaacaacaacaaaaaaaacaacaacaaa';\r\n      'aaacaacaacaaaaaaaacaacaacaaa';\r\n      'accccccaaccccaaccccaacccccca';\r\n      'acaaaaaaaaaacaacaaaaaaaaaaca';\r\n      'acaaaaaaaaaacaacaaaaaaaaaaca';\r\n      'acccccccccccccccccccccccccca';\r\n      repmat('a',1,28);];\r\n  \r\n  map=map-'b';\r\n  [nr, nc]=size(map);\r\n\r\n  mapdelta=[-1 nr 1 -nr]; % Valid as long as not on an edge\r\n  gmovxy=[0 -1;1 0;0 1;-1 0];\r\n\r\n  tunnel=find(map(:,1)==0); % tunnelptr\r\n  tunnel=[tunnel tunnel+nr*(nc-1)]; % Entrance/Exit Tunnel\r\n\r\n  [pmr, pmc]=find(map==2); % pi 24 row  pj 15 column of map\r\n   ptrpac=find(map==2);\r\n\r\n  ptrpac=find(map==2);\r\n  ptrpac_start=ptrpac;\r\n  ptrg_start=find(map\u003e2);\r\n  map(ptrg_start)=[10 20 30 40];\r\n  [gstartx, gstarty]=find(map\u003e2);\r\n  \r\n  lives=0; % Lives\r\n  movepac=0;\r\n\r\nwhile lives\u003cmaxLives \u0026\u0026 any(mod(map(:),10)==1) \u0026\u0026 movepac\u003cmax_moves\r\n for pac2x=1:1\r\n  if lives\u003emaxLives-1 || ~any(mod(map(:),10)==1),break;end % Died or Completed\r\n  movepac=movepac+1;\r\n\r\n  [curdir]=PACMAT9_1Xsolver(map);\r\n\r\n  [pmr, pmc]=find(map==2);\r\n\r\n if curdir\u003e0\r\n  if map(ptrpac+mapdelta(curdir))==-1\r\n   % Do nothing - Ran into a Wall\r\n  elseif map(ptrpac+mapdelta(curdir))\u003e2 % ran into ghost\r\n   map(ptrpac)=0; % remove PAC from the board\r\n   lives=lives+1;\r\n   %if lives==0,break;end\r\n   % reset the board\r\n   [ptrgx, ptrgy]=find(map\u003e2);\r\n   ptrg=find(map\u003e2);\r\n   map(ptrg)=mod(map(ptrg),10);\r\n   map(ptrpac_start)=2;\r\n   map(ptrg_start)=[10 20 30 40];\r\n   ptrpac=find(map==2);\r\n   continue; % Lost a Life\r\n  else % legal move\r\n   map(ptrpac)=0; % Eat Dot and clear PAC\r\n   ptrpac=ptrpac+mapdelta(curdir);\r\n   if ptrpac==tunnel(1),ptrpac=tunnel(2)-nr;end\r\n   if ptrpac==tunnel(2),ptrpac=tunnel(1)+nr;end\r\n   map(ptrpac)=2;\r\n  end\r\n end % curdir \u003e0\r\n\r\nend % pac2x\r\n\r\nif lives\u003emaxLives-1 || ~any(mod(map(:),10)==1),break;end % Completed\r\n\r\n\r\n% Ghosts\r\n for i=1:4\r\n\r\n  ghosts=find(map\u003e2);\r\n  ptrpac=find(map==2); % Target\r\n\r\n  dot=false;\r\n  [gptrx, gptry]=find(map==10*i);\r\n  gidx=find(map==10*i);\r\n  if isempty(gidx)\r\n   [gptrx, gptry]=find(map==10*i+1); % ghost must be on a dot\r\n   gidx=find(map==10*i+1);\r\n   dot=true;\r\n  end\r\n\r\n% Find valid ghost moves using gmap\r\n% mapdelta=[-1 nr 1 -nr]; \r\n  gmov=find(map(gidx+mapdelta)==2); % adjacent to PACMAT\r\n  if gidx==15+nr \u0026\u0026 ptrpac==nr*(nc-2)+15 % Ghost Tunnel Adj\r\n    gmov=4;\r\n   end\r\n   if gidx==nr*(nc-2)+15 \u0026\u0026 ptrpac==15+nr % Ghost Tunnel Adj\r\n    gmov=2;\r\n   end\r\n\r\n  if ~isempty(gmov) % PAC adjacent\r\n   lives=lives+1;\r\n   % reset the board\r\n   [pmr, pmc]=find(map==2); % PACMAT erase coords\r\n   map(map==2)=0;\r\n      \r\n   [ptrgx, ptrgy]=find(map\u003e2);\r\n   ptrg=find(map\u003e2);\r\n   map(ptrg)=mod(map(ptrg),10);\r\n   map(ptrpac_start)=2;\r\n   map(ptrg_start)=[10 20 30 40];\r\n   ptrpac=find(map==2);     \r\n   break; % Ghost move loop\r\n      \r\n  else % gmap avoid walls and other ghosts Walls\r\n \r\n   gmap=map;\r\n       \r\n   ptctr=0;\r\n   gmap(gmap\u003e=0)=Inf;\r\n      \r\n    gmap(ghosts)=-1; % other ghosts are like walls Ghosts_004/5\r\n    gmap(gidx)=Inf; % Ultimate target\r\n    gmap(ptrpac)=1; % Start at PACMAT and expand to ghost\r\n    while gmap(gidx)\u003e101 \u0026\u0026 ptctr\u003c100 % potential boxed dot\r\n % find dots, add a counter to distance form location, keep min value\r\n % when ptrpac gets a value it will be from nearest dot\r\n % find side with dmap(ptrpac)-1\r\n     ptctr=ptctr+1;\r\n     dpts=find(gmap==ptctr);\r\n     newpt_idx=repmat(dpts,1,4)+repmat(mapdelta,length(dpts),1);\r\n% Ghost Tunnel Access\r\n     tunL=find(newpt_idx==15);\r\n     tunR=find(newpt_idx==nr*nc-16);\r\n     if ~isempty(tunL)\r\n      newpt_idx(tunL)=nr*(nc-2)+15;\r\n     end\r\n     if ~isempty(tunR)\r\n      newpt_idx(tunR)=15+nr;\r\n     end\r\n\r\n     gmap(newpt_idx(:))=min(gmap(newpt_idx(:)),ptctr+1);\r\n    end\r\n\r\n% Simplified by ghosts are walls: No Ghost Jumping\r\n    if ~isinf(gmap(gidx)) % Path(s) to Ghost found\r\n% Tunnel Check\r\n    gmov=[];\r\n    if gidx==15+nr % Tunnel\r\n     gmov=2;\r\n     if gmap(gidx+mapdelta(2))==gmap(gidx)-1\r\n      gmov=2;\r\n     else\r\n      gmov=4; % Possible error ghost onto ghost\r\n     end   \r\n    end\r\n\r\n    if gidx==nr*(nc-2)+15 % Tunnel\r\n      if gmap(gidx+mapdelta(4))==gmap(gidx)-1\r\n        gmov=4;\r\n      else\r\n        gmov=2; % Possible error ghost onto ghost\r\n      end\r\n     end\r\n    \r\n     if isempty(gmov) % Non-Tunnel Move\r\n      for gmov=1:4 % execute with a find?\r\n        if gmap(gidx+mapdelta(gmov))==gmap(gidx)-1,break;end\r\n       end\r\n      end\r\n     else\r\n      gmov=[]; % No path to PACMAT found\r\n     end\r\n \r\n   if ~isempty(gmov) % valid g move : ghost may not stand on ghost\r\n    map(gptrx,gptry)=mod(map(gptrx,gptry),10);\r\n\r\n     %tunLtf=false;\r\n     %tunRtf=false;\r\n       if gidx==nr+15\r\n        if gmov==2 % Left Tunnel but go Right\r\n         map(gidx+mapdelta(gmov))=map(gidx+mapdelta(gmov))+10*i;\r\n        else\r\n         % Tunnel\r\n          map(nr*(nc-2)+15)=10*i;\r\n          %tunLtf=true;\r\n        end\r\n       elseif gidx==nr*(nc-2)+15\r\n        if gmov==4 % Right Tunnel but go Left\r\n         map(gidx+mapdelta(gmov))=map(gidx+mapdelta(gmov))+10*i;\r\n        else\r\n         % Tunnel\r\n         map(nr+15)=10*i;\r\n         %tunRtf=true;\r\n        end\r\n       else % Standard move\r\n        map(gidx+mapdelta(gmov))=map(gidx+mapdelta(gmov))+10*i;\r\n       end\r\n    \r\n   end % ~isempty(gmov) standard move - no capture\r\n\r\n  end % ~isempty(gmov) PACMAT adjacent\r\n  \r\n end % i ghost moves\r\nend % while any dots and \u003c max_moves maxLives\r\n%\r\ndots=length(find(mod(map,10)==1));\r\n%\r\nfprintf('moves %i\\n',movepac)\r\nfprintf('dots %i\\n',dots)\r\nfprintf('Lives Spent %i\\n',lives)\r\n%\r\n% To Pass need to leave at most 0 dots\r\nassert(dots==0,sprintf('Max Dots 0, Dots Remaining %i\\n',dots))\r\n\r\nscore= movepac + 1000*lives; % All dots must be removed\r\n\r\nfeval( @assignin,'caller','score',floor(min( 20000,score )) );\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":2,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-03-02T19:16:46.000Z","updated_at":"2025-12-03T15:31:49.000Z","published_at":"2013-03-03T03:24:45.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.JPEG\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Classic PACMAN game brought to Cody.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePACMAT requires clearing all the Yellow Dots. Twenty lives are available. Adjacent Ghosts will capture PACMAT. Awesome Ghosts use the tunnel. On Ghost capture everyone gets reset. These trained ghosts take the minimum path, including tunnel paths, to PACMAT assuming the other Ghosts are walls.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo aid in development of your routine, a PACMAT_Interactive.m file that creates a solver script and video has been posted at\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_Interactive_009a.m\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePACMAT_Interactive_009a.m\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. (Right click, 'save link as'). The routine creates a PACMAT9_1Xsolver.m script from the interactive play. The script demonstrates Interactivity, figure/KeyPressFcn, listdlg, and VideoWriter.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://sites.google.com/site/razapor/matlab_cody/PACMAT9_1Xvideo_11_630.mp4\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e11 Lives Interactive\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (MP4) Best Score seen is 11 Lives.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInputs:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Map Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u0026gt;2=Ghost\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Direction Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ePass Criteria:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Clear all dots in less than 4000 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Moves + 1000 * Lives\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle 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\"}]}"},{"id":1907,"title":"Capture the flag(s)","description":"Flags are distributed randomly on a large board. Starting from the corner position your goal is to capture as many flags as possible in at most N moves.\r\nDescription:\r\nThe board is described by a matrix B with 1's at the flag positions, and 0's otherwise.\r\nE.g.\r\n B = [0 0 1 1; \r\n      0 0 1 1;\r\n      0 0 1 0];\r\n\r\n N = 6;\r\nYou are starting at the top-left corner (row=1, col=1) and are allowed N steps (steps are up/down/left/right movements, no diagonal movements allowed).\r\nReturn a trajectory attempting to maximize the number of flags captured. The output of your function should be a Nx2 matrix of the form [row, col] (not including the initial [1,1] position) visiting as many flags as possible.\r\nE.g.\r\n path = [1 2;\r\n         1 3;\r\n         1 4;\r\n         2 4;\r\n         2 3;\r\n         3 3];\r\nThis solution captures all 5 flags on the board.\r\nScoring:\r\nYour function will receive a score equal to the number of non-visited flags across all 50 of the testsuite problems. You need to leave at most 10,000 flags univisited (among 50,000 total flags) to pass this problem.\r\nNote:\r\nThe boards and number of movements allowed will be large. Optimizing over all possible trajectories is very likely to time out.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 676px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 401px 338px; transform-origin: 401px 338px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 21px; text-align: left; transform-origin: 377px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFlags are distributed randomly on a large board. Starting from the corner position your goal is to capture as many flags as possible in at most N moves.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eDescription\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe board is described by a matrix\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eB\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e with 1's at the flag positions, and 0's otherwise.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE.g.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 90px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 397px 45px; transform-origin: 397px 45px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e B = [0 0 1 1; \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e      0 0 1 1;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e      0 0 1 0];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e N = 6;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 21px; text-align: left; transform-origin: 377px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou are starting at the top-left corner (row=1, col=1) and are allowed\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e steps (steps are up/down/left/right movements, no diagonal movements allowed).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 21px; text-align: left; transform-origin: 377px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eReturn a trajectory attempting to maximize the number of flags captured. The output of your function should be a\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eNx2\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e matrix of the form\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e[row, col]\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (not including the initial [1,1] position) visiting as many flags as possible.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE.g.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 108px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 397px 54px; transform-origin: 397px 54px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e path = [1 2;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e         1 3;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e         1 4;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e         2 4;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e         2 3;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 397px 9px; text-wrap-mode: nowrap; transform-origin: 397px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e         3 3];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis solution captures all 5 flags on the board.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eScoring\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 21px; text-align: left; transform-origin: 377px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYour function will receive a score equal to the number of non-visited flags across all 50 of the testsuite problems. You need to leave at most 10,000 flags univisited (among 50,000 total flags) to pass this problem.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 10.5px; text-align: left; transform-origin: 377px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNote\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 377px 21px; text-align: left; transform-origin: 377px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe boards and number of movements allowed will be large. Optimizing over all possible trajectories is very likely to time out.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function path = capture_the_flag(B,N)\r\npath=[1 2];\r\n","test_suite":"%%\r\n% test cases\r\n\r\nrandn('seed',0);\r\nrand('seed',0);\r\nN=randi([1000 4000],50,1);\r\nS=randi([1,50],50,1);\r\nBoards=arrayfun(@(s)convn(randn(100),ones(s)/s^2,'same'),S,'uni',0); \r\n\r\nFLAGSLEFT=0;\r\nDOPLOT=false;\r\ntic;\r\nfor board=1:50\r\n B=Boards{board};\r\n sB=sort(B(:));\r\n B=double(B\u003esB(round(numel(sB)*.9)));\r\n n=N(board);\r\n path=capture_the_flag(B,n);\r\n assert(size(path,1)\u003c=n,'too many steps');\r\n assert(all(sum(abs(diff([1,1;path])),2)\u003c=1),'no jumping allowed');\r\n if DOPLOT\r\n    imagesc(B);\r\n    hold on;\r\n    plot(path(:,2),path(:,1),'y-');\r\n    hold off;\r\n    axis equal;\r\n    axis off;\r\n    set(gcf,'color',0*[1 1 1]);\r\n    colormap(.5*gray);\r\n    drawnow;\r\n end\r\n B(1)=0;\r\n B((path-1)*[1;size(B,1)]+1)=0;\r\n fprintf('test %d; left %d flags\\n',board,nnz(B));\r\n FLAGSLEFT=FLAGSLEFT+nnz(B);\r\nend\r\ntoc;","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":43,"edited_by":1,"edited_at":"2026-02-11T16:03:17.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":"2026-02-11T16:03:17.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-10-01T05:38:22.000Z","updated_at":"2026-03-16T11:31:01.000Z","published_at":"2013-10-01T06:27:29.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFlags are distributed randomly on a large board. Starting from the corner position your goal is to capture as many flags as possible in at most N moves.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eDescription\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe board is described by a matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eB\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e with 1's at the flag positions, and 0's otherwise.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eE.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ B = [0 0 1 1; \\n      0 0 1 1;\\n      0 0 1 0];\\n\\n N = 6;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou are starting at the top-left corner (row=1, col=1) and are allowed\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e steps (steps are up/down/left/right movements, no diagonal movements allowed).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReturn a trajectory attempting to maximize the number of flags captured. The output of your function should be a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNx2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e matrix of the form\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[row, col]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (not including the initial [1,1] position) visiting as many flags as possible.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eE.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ path = [1 2;\\n         1 3;\\n         1 4;\\n         2 4;\\n         2 3;\\n         3 3];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis solution captures all 5 flags on the board.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour function will receive a score equal to the number of non-visited flags across all 50 of the testsuite problems. You need to leave at most 10,000 flags univisited (among 50,000 total flags) to pass this problem.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNote\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe boards and number of movements allowed will be large. Optimizing over all possible trajectories is very likely to time out.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2451,"title":"BLOCK x3 (Version 1)","description":"\r\n\u003chttps://play.google.com/store/apps/details?id=com.noodlecake.blockblock BLOCK x3\u003e is a simple, fun and relaxing puzzle game.\r\n\r\nThe basics are easy. Solve the puzzles by getting *3 blocks* in a row or column. \r\n\r\nThe puzzle is coded a 6x6 matrix and the 3 blocks are indicated by 1 (and 0 for free space).\r\n\r\nIn this example :\r\n\r\n\u003c\u003chttp://1.bp.blogspot.com/-GbZ-AMjylSs/Udk6kRyp4uI/AAAAAAAA3Pk/3jdWrVSqQpc/s400/pack+1+level+1-1.png\u003e\u003e\r\n \r\n \r\n\r\n* [0 0 0 0 0 0\r\n* \r\n* 0 0 1 0 0 0\r\n* \r\n* 0 0 1 0 0 0\r\n* \r\n* 0 0 0 0 0 0\r\n* \r\n* 0 0 1 0 0 0\r\n* \r\n* 0 0 0 0 0 0]\r\n\r\nYou line up 3 blocks in one move by dragging the last block to the top (you can move a block in the free space only horizontally or vertically.)\r\n\r\n\r\n\r\nThe goal in this first problem is to give the *smallest number* of moves to solve the puzzle.\r\n\r\n\r\n\r\n","description_html":"\u003cp\u003e\u003ca href = \"https://play.google.com/store/apps/details?id=com.noodlecake.blockblock\"\u003eBLOCK x3\u003c/a\u003e is a simple, fun and relaxing puzzle game.\u003c/p\u003e\u003cp\u003eThe basics are easy. Solve the puzzles by getting \u003cb\u003e3 blocks\u003c/b\u003e in a row or column.\u003c/p\u003e\u003cp\u003eThe puzzle is coded a 6x6 matrix and the 3 blocks are indicated by 1 (and 0 for free space).\u003c/p\u003e\u003cp\u003eIn this example :\u003c/p\u003e\u003cimg src = \"http://1.bp.blogspot.com/-GbZ-AMjylSs/Udk6kRyp4uI/AAAAAAAA3Pk/3jdWrVSqQpc/s400/pack+1+level+1-1.png\"\u003e\u003cul\u003e\u003cli\u003e[0 0 0 0 0 0\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 1 0 0 0\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 1 0 0 0\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 1 0 0 0\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0]\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eYou line up 3 blocks in one move by dragging the last block to the top (you can move a block in the free space only horizontally or vertically.)\u003c/p\u003e\u003cp\u003eThe goal in this first problem is to give the \u003cb\u003esmallest number\u003c/b\u003e of moves to solve the puzzle.\u003c/p\u003e","function_template":"function y = block3(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 1 0 0 0;0 0 1 0 0 0;0 0 0 1 0 0;0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 1 0 0 0;0 1 0 0 0 0;0 0 1 0 0 0;0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 1 0 0 0;0 0 0 1 0 0;0 0 0 0 1 0;0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 1 0 0 0;0 0 0 0 0 0;0 0 1 0 0 0;0 0 0 0 0 0;0 0 1 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 1 0 1 1 0;0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 1 1 0;0 0 0 0 0 0;0 0 1 0 0 0;0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 1 1 0;0 0 0 1 0 0;0 0 0 0 0 0;0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 1 0 0;0 0 0 1 1 0;0 0 0 0 0 0;0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 1 0 1 0;0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 1 0 0;0 0 0 0 0 0];\r\ny_correct = 3;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 1 0 1 0;0 0 1 0 0 0;0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))","published":true,"deleted":false,"likes_count":0,"comments_count":1,"created_by":5390,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":"2014-07-20T00:15:18.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-07-19T23:58:55.000Z","updated_at":"2026-04-02T11:57:14.000Z","published_at":"2014-07-20T00:15:18.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.png\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://play.google.com/store/apps/details?id=com.noodlecake.blockblock\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eBLOCK x3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a simple, fun and relaxing puzzle game.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe basics are easy. Solve the puzzles by getting\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e3 blocks\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in a row or column.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe puzzle is coded a 6x6 matrix and the 3 blocks are indicated by 1 (and 0 for free space).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this example :\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" 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\"}]}"},{"id":2478,"title":"BLOCK x3 (Version 2)","description":"An extension to problem 2451 ( \u003chttps://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1\u003e ).\r\n\r\nIt is based on an android game - BLOCK x3. The objective is to align the 1's in the matrix using minimum movements. The valid movements are  horizontal and vertical (by one step). A zero (0) indicates an empty space. Your task is to count minimum number of movements required to align the three 1's vertically or horizontally.\r\n\r\nNew challenge in this problem : There are some static elements which can not be moved and can not be included in the alignment. These elements are indicated by 2. This makes the problem a bit challenging.\r\n\r\nExample:\r\n\r\n\r\n  Input =[0 0 0 0 0 0 0 0;\r\n          0 0 1 0 1 0 0 0;\r\n          0 0 0 2 1 0 0 0;\r\n          0 0 0 0 0 0 0 0]\r\n  \r\n  Output = 2;\r\n","description_html":"\u003cp\u003eAn extension to problem 2451 ( \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1\u003c/a\u003e ).\u003c/p\u003e\u003cp\u003eIt is based on an android game - BLOCK x3. The objective is to align the 1's in the matrix using minimum movements. The valid movements are  horizontal and vertical (by one step). A zero (0) indicates an empty space. Your task is to count minimum number of movements required to align the three 1's vertically or horizontally.\u003c/p\u003e\u003cp\u003eNew challenge in this problem : There are some static elements which can not be moved and can not be included in the alignment. These elements are indicated by 2. This makes the problem a bit challenging.\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eInput =[0 0 0 0 0 0 0 0;\r\n        0 0 1 0 1 0 0 0;\r\n        0 0 0 2 1 0 0 0;\r\n        0 0 0 0 0 0 0 0]\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eOutput = 2;\r\n\u003c/pre\u003e","function_template":"function y = blockx3_2(x)\r\n\r\nend","test_suite":"%%\r\n\r\nx =  [0 0 0 0 0 0 0 0;\r\n      0 0 1 0 1 0 0 0;\r\n      0 0 0 2 1 0 0 0;\r\n      0 0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(blockx3_2(x),y_correct));\r\n\r\n\r\n%%\r\nx =  [0 0 0 0 0 0 0 0;\r\n      0 0 1 0 1 0 0 0;\r\n      0 0 0 2 0 0 0 0;\r\n      0 0 1 0 0 0 0 0];\r\ny_correct = 3;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n%%\r\nx =  [1 1 0 0 0 0 0 0;\r\n      1 2 0 0 0 0 0 0;\r\n      0 0 0 0 0 0 0 0;\r\n      0 0 0 0 0 0 0 0];\r\ny_correct = 3;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n%%\r\nx =  [0 0 0 1 0 0 0 0;\r\n      0 0 1 2 1 0 0 0;\r\n      0 0 0 0 0 0 0 0;\r\n      0 0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n\r\n%%\r\nx =  [0 0 0 1 0 0 0 0;\r\n      0 0 1 2 0 0 0 0;\r\n      0 0 1 0 0 0 0 0;\r\n      0 0 0 0 0 0 0 0]\r\ny_correct = 1;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n\r\n%%\r\nx =  [0 0 0 1 0 0 0 0;\r\n      0 0 1 2 0 0 0 0;\r\n      0 0 0 0 0 0 0 1;\r\n      0 0 0 0 0 0 0 0]\r\ny_correct = 6;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n\r\n%%\r\nx =  [0 0 0 1 0 0 0 0;\r\n      0 0 0 2 0 0 0 0;\r\n      0 0 0 1 0 0 0 0;\r\n      0 0 0 1 0 0 0 0]\r\ny_correct = 4;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":7,"test_suite_updated_at":"2014-08-03T08:43:51.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-08-03T08:31:00.000Z","updated_at":"2026-04-02T12:08:17.000Z","published_at":"2014-08-03T08:31:00.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAn extension to problem 2451 (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt; ).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIt is based on an android game - BLOCK x3. The objective is to align the 1's in the matrix using minimum movements. The valid movements are horizontal and vertical (by one step). A zero (0) indicates an empty space. Your task is to count minimum number of movements required to align the three 1's vertically or horizontally.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNew challenge in this problem : There are some static elements which can not be moved and can not be included in the alignment. These elements are indicated by 2. This makes the problem a bit challenging.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Input =[0 0 0 0 0 0 0 0;\\n        0 0 1 0 1 0 0 0;\\n        0 0 0 2 1 0 0 0;\\n        0 0 0 0 0 0 0 0]\\n\\nOutput = 2;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2484,"title":"BLOCK x3 (Version 3)","description":"An extension to problems \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1/ 2451\u003e (by me) and \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2478-block-x3-version-2 2478\u003e (by Rifat Ahmed).\r\n\r\nAlways based on the android game \u003chttps://play.google.com/store/apps/details?id=com.noodlecake.blockblock BLOCK x3\u003e. The objective is to align the 1's in the matrix (7x7) using minimum movements. The valid movements are horizontal and vertical (by one step). A zero (0) indicates an empty space. \r\n\r\nYour task is to count *minimum number of movements* required to align the *FOUR* 1's vertically or horizontally (it was only 3 blocks in problem 2451).\r\n\r\nBeware to align the 4 blocks WITHOUT aligning 3 blocks before\r\n\r\nExample:\r\n\r\n\u003c\u003chttp://3.bp.blogspot.com/-hBjCtx2ycJM/Udk7jP5xyoI/AAAAAAAA3SY/fZVvhhg3hEs/s400/pack+1+level+1-23.png\u003e\u003e\r\n\r\n\r\n\r\n* [0 0 0 0 0 0 0;\r\n* \r\n* 0 0 0 1 0 0 0;\r\n* \r\n* 0 0 0 0 1 0 0;\r\n* \r\n* 0 0 0 1 0 0 0;\r\n* \r\n* 0 0 0 0 1 0 0;\r\n* \r\n* 0 0 0 0 0 0 0;\r\n* \r\n* 0 0 0 0 0 0 0]\r\n\r\n\r\nIf you move the second 1 to the left, you align 3 blocks and loose because the last 1 remains alone. But if you begin by the first (to the right) and after the third (to the right), you win. Note that there exist multiple solutions. (you can also move the blocks n°4 to the left and n°2 to the left).\r\n\r\nSo here Output = 2;\r\n\r\n","description_html":"\u003cp\u003eAn extension to problems \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1/\"\u003e2451\u003c/a\u003e (by me) and \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2478-block-x3-version-2\"\u003e2478\u003c/a\u003e (by Rifat Ahmed).\u003c/p\u003e\u003cp\u003eAlways based on the android game \u003ca href = \"https://play.google.com/store/apps/details?id=com.noodlecake.blockblock\"\u003eBLOCK x3\u003c/a\u003e. The objective is to align the 1's in the matrix (7x7) using minimum movements. The valid movements are horizontal and vertical (by one step). A zero (0) indicates an empty space.\u003c/p\u003e\u003cp\u003eYour task is to count \u003cb\u003eminimum number of movements\u003c/b\u003e required to align the \u003cb\u003eFOUR\u003c/b\u003e 1's vertically or horizontally (it was only 3 blocks in problem 2451).\u003c/p\u003e\u003cp\u003eBeware to align the 4 blocks WITHOUT aligning 3 blocks before\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cimg src = \"http://3.bp.blogspot.com/-hBjCtx2ycJM/Udk7jP5xyoI/AAAAAAAA3SY/fZVvhhg3hEs/s400/pack+1+level+1-23.png\"\u003e\u003cul\u003e\u003cli\u003e[0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 0 1 0 0 0;\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 0 0 1 0 0;\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 0 1 0 0 0;\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 0 0 1 0 0;\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0]\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eIf you move the second 1 to the left, you align 3 blocks and loose because the last 1 remains alone. But if you begin by the first (to the right) and after the third (to the right), you win. Note that there exist multiple solutions. (you can also move the blocks n°4 to the left and n°2 to the left).\u003c/p\u003e\u003cp\u003eSo here Output = 2;\u003c/p\u003e","function_template":"function y = block3_1(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = [0 0 0 0 0 0 0;0 0 1 0 1 1 0;0 0 0 1 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3_1(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 1 1 0 0;0 0 1 0 0 1 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_1(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 0 0 0;0 0 0 0 1 0 0;0 0 0 1 0 0 0;0 0 0 0 1 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_1(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 0 0 0;0 0 0 0 1 0 0;0 0 1 0 0 0 0;0 0 0 1 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_1(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 0 0 0;0 0 0 0 1 0 0;0 0 0 1 0 0 0;0 0 1 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_1(x),y_correct))\r\n\r\n%%\r\n% More tricky now...\r\nx = [0 0 1 0 0 0 0;0 0 0 1 0 0 0;0 0 0 1 0 0 0;0 0 0 0 1 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 4;\r\nassert(isequal(block3_1(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 1 0 1 0 0;0 0 0 1 0 0 0;0 0 0 1 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 3;\r\nassert(isequal(block3_1(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 1 1 0 0 0;0 0 1 1 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 4;\r\nassert(isequal(block3_1(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 0 0 0;0 0 1 0 1 0 0;0 0 0 1 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 4;\r\nassert(isequal(block3_1(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 0 0 0;0 0 0 1 0 0 0;0 0 0 0 0 0 0;0 0 0 1 0 0 0;0 0 0 1 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 4;\r\nassert(isequal(block3_1(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 0 1 0;0 0 0 1 0 1 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 4;\r\nassert(isequal(block3_1(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 1 0 0;0 0 0 0 0 1 0;0 0 0 0 0 1 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 4;\r\nassert(isequal(block3_1(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 1 0 1 0 0;0 0 0 0 0 0 0;0 0 1 0 1 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 6;\r\nassert(isequal(block3_1(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":5390,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":"2014-08-08T01:02:36.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-08-06T17:30:29.000Z","updated_at":"2026-04-02T12:20:51.000Z","published_at":"2014-08-06T18:17:47.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.png\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAn extension to problems\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e2451\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (by me) and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2478-block-x3-version-2\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e2478\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (by Rifat Ahmed).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAlways based on the android game\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://play.google.com/store/apps/details?id=com.noodlecake.blockblock\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eBLOCK x3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. The objective is to align the 1's in the matrix (7x7) using minimum movements. The valid movements are horizontal and vertical (by one step). A zero (0) indicates an empty space.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour task is to count\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eminimum number of movements\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e required to align the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFOUR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 1's vertically or horizontally (it was only 3 blocks in problem 2451).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBeware to align the 4 blocks WITHOUT aligning 3 blocks before\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 1 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 1 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 1 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 1 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf you move the second 1 to the left, you align 3 blocks and loose because the last 1 remains alone. But if you begin by the first (to the right) and after the third (to the right), you win. Note that there exist multiple solutions. (you can also move the blocks n°4 to the left and n°2 to the left).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo here Output = 2;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray 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\"}]}"},{"id":2511,"title":" BLOCK x3 (Version 4) ","description":"Always in this series ( \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1/ 2451\u003e, \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2484-block-x3-version-3/ 2484\u003e, and \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2478-block-x3-version-2 2478\u003e ).\r\n\r\nNow we are in color (1 for red  and 2 for white).\r\nYour task is always to count the  *minimum number* of movements required to align the 3 blocks in each colors (vertically or horizontally).\r\n\r\n\r\n\r\n\u003c\u003chttp://4.bp.blogspot.com/-iveYgRwOmLs/Udk9ZctxnUI/AAAAAAAA3Uw/Z9XNHw6f-f4/s400/pack+2+level+2-1.png\u003e\u003e\r\n\r\n* [0 0 0 0 0 0 0;\r\n*  0 0 1 2 0 0 0;\r\n*  0 0 0 0 0 0 0;\r\n*  0 0 1 2 0 0 0;\r\n*  0 0 1 2 0 0 0;\r\n*  0 0 0 0 0 0 0;\r\n*  0 0 0 0 0 0 0]\r\n\r\nIn this example you can move down the first two blocks ( *in two moves* ).\r\n\r\n\r\nNote that blocks can be *swapped* . \r\n\r\n\r\nIn this second example :\r\n\r\n\u003c\u003chttp://1.bp.blogspot.com/-XJP6Mn4X9Ws/Udk9bHEKx_I/AAAAAAAA3Vo/V_Ar3oF9gYw/s400/pack+2+level+2-7.png\u003e\u003e\r\n\r\n* [0 0 0 0 0 0 0;\r\n*  0 0 0 0 0 0 0;\r\n*  0 0 1 2 0 0 0;\r\n*  0 0 2 1 0 0 0;\r\n*  0 0 1 2 0 0 0;\r\n*  0 0 0 0 0 0 0;\r\n*  0 0 0 0 0 0 0]\r\n\r\nHere you win in only *one move* by swapping the two central blocks.\r\n\r\nGood luck !","description_html":"\u003cp\u003eAlways in this series ( \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1/\"\u003e2451\u003c/a\u003e, \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2484-block-x3-version-3/\"\u003e2484\u003c/a\u003e, and \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2478-block-x3-version-2\"\u003e2478\u003c/a\u003e ).\u003c/p\u003e\u003cp\u003eNow we are in color (1 for red  and 2 for white).\r\nYour task is always to count the  \u003cb\u003eminimum number\u003c/b\u003e of movements required to align the 3 blocks in each colors (vertically or horizontally).\u003c/p\u003e\u003cimg src = \"http://4.bp.blogspot.com/-iveYgRwOmLs/Udk9ZctxnUI/AAAAAAAA3Uw/Z9XNHw6f-f4/s400/pack+2+level+2-1.png\"\u003e\u003cul\u003e\u003cli\u003e[0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 1 2 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 1 2 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 1 2 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0]\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eIn this example you can move down the first two blocks ( \u003cb\u003ein two moves\u003c/b\u003e ).\u003c/p\u003e\u003cp\u003eNote that blocks can be \u003cb\u003eswapped\u003c/b\u003e .\u003c/p\u003e\u003cp\u003eIn this second example :\u003c/p\u003e\u003cimg src = \"http://1.bp.blogspot.com/-XJP6Mn4X9Ws/Udk9bHEKx_I/AAAAAAAA3Vo/V_Ar3oF9gYw/s400/pack+2+level+2-7.png\"\u003e\u003cul\u003e\u003cli\u003e[0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 1 2 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 2 1 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 1 2 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0]\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eHere you win in only \u003cb\u003eone move\u003c/b\u003e by swapping the two central blocks.\u003c/p\u003e\u003cp\u003eGood luck !\u003c/p\u003e","function_template":"function y = block3_4(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 1 2 0 0 0;0 0 0 0 0 0 0;0 0 1 2 0 0 0;0 0 1 2 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 2 1 0 0;0 0 0 1 2 0 0;0 0 0 2 1 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 2 1 2 0 0;0 0 1 2 1 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 1 0 1 1 0;0 0 2 2 0 2 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 1 1 2 0 0;0 0 2 2 1 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 1 1 0 0 0;0 0 2 2 0 0 0;0 0 2 1 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 0 0 0;0 0 2 0 1 0 0;0 0 1 0 2 0 0;0 0 0 2 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 3;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 0 0 0;0 0 1 2 1 0 0;0 0 2 0 2 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 0 0 0;0 0 0 1 0 0 0;0 0 1 2 0 0 0;0 0 2 0 0 0 0;0 0 2 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3_4(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":5390,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":7,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2014-08-15T07:15:35.000Z","updated_at":"2026-04-02T12:33:43.000Z","published_at":"2014-08-15T07:17:45.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.png\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/media/image2.png\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAlways in this series (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e2451\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2484-block-x3-version-3/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e2484\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2478-block-x3-version-2\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e2478\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e ).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow we are in color (1 for red and 2 for white). Your task is always to count the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eminimum number\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of movements required to align the 3 blocks in each colors (vertically or horizontally).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 1 2 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 1 2 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 1 2 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this example you can move down the first two blocks (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ein two moves\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote that blocks can be\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eswapped\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e .\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this second example :\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 1 2 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 2 1 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 1 2 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere you win in only\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eone move\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e by swapping the two central blocks.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGood luck !\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray 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\"},{\"partUri\":\"/media/image2.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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