{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2025-12-14T01:33:56.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2025-12-14T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":58892,"title":"Neural Net: Calculate Perceptron","description":"This challenge is to calculate the Neural Net Perceptron value,P, given X, WH, and WP using ReLU on the hidden layer. This example is for two inputs, two hidden nodes, and a single output. No offset terms are applied.\r\n \r\nThe function template includes the code to create this figure using Matlab Latex","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; 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: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 626.85px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 313.425px; transform-origin: 407px 313.425px; 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; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\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: 375.5px 8px; transform-origin: 375.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to calculate the Neural Net Perceptron value,P, given X, WH, and WP using ReLU on the hidden layer. This example is for two inputs, two hidden nodes, and a single output. No offset terms are applied.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 545.85px; 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; perspective-origin: 384px 272.925px; text-align: left; transform-origin: 384px 272.925px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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\" data-image-state=\"image-loaded\"\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 8px; transform-origin: 0px 8px; 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; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\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: 250px 8px; transform-origin: 250px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes the code to create this figure using Matlab Latex\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function P=Calc_Perceptron(X,WH,WP)\r\n%The P value in this case is a single value\r\n%No ReLU,Softmax, or Sigmoid applied to the output P\r\n  P=0;\r\nend\r\n\r\n%The figure was generated using Matlab and Latex\r\n%{\r\nfunction perceptron\r\n% put figure code into cody\r\nfigure(1);clf;\r\nplot([0;140;140;0;0],[-5;-5;100;100;-5],'k','linewidth',2);hold on\r\nxticks([]);yticks([]); %Tciks/Labels off\r\n\r\naxis equal;\r\naxis tight\r\n\r\n\r\nr = 10;\r\nphi = 0:0.01:2*pi;\r\ncxA = r*cos(phi);\r\ncyA = r*sin(phi);\r\n\r\nrxA=[0 2*r 2*r   0 0]';\r\nryA=[0   0 2*r 2*r 0]';\r\n\r\npatch(cxA+10,cyA+80,'y');text(10-2,80,'X_{1}','Fontsize',20);\r\npatch(cxA+10,cyA+20,'y');text(10-2,20,'X_{2}','Fontsize',20);\r\n\r\npatch(cxA+80,cyA+80,'y');text(80-6,94,'H_{1}','Fontsize',20);text(80-8,80,'\\Sigma','Fontsize',30);\r\npatch(cxA+80,cyA+20,'y');text(80-6,34,'H_{2}','Fontsize',20);text(80-8,20,'\\Sigma','Fontsize',30);\r\n\r\npatch(cxA+130,cyA+50,'y');text(130-2,50,'P','Fontsize',20);\r\n\r\npatch(rxA+80,ryA+70,'y');text(85,80,'ReLU','Fontsize',20);text(102,88,'Y_{1}','Fontsize',20);\r\npatch(rxA+80,ryA+10,'y');text(85,20,'ReLU','Fontsize',20);text(102,32,'Y_{2}','Fontsize',20);\r\n\r\nquiver(20,20,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,20,58.5,58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,58.5,-58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\n\r\nquiver(100,20,25,25,'k','LineWidth',3.5,'MaxHeadSize',.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(100,80,25,-25,'k','LineWidth',3.5,'MaxHeadSize',.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\n\r\ntext(40,85,'WH_{11}','Fontsize',20);\r\ntext(20,65,'WH_{12}','Fontsize',20);\r\ntext(40,25,'WH_{22}','Fontsize',20);\r\ntext(20,38,'WH_{21}','Fontsize',20);\r\n\r\ntext(115,68,'WP_{11}','Fontsize',20);\r\ntext(115,28,'WP_{21}','Fontsize',20);\r\n\r\nstr='$$ X=\\left[\\matrix{ X_{1} \u0026 X_{2}} \\right]  $$'; %valid\r\ntext(1,50,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ WH=\\left[\\matrix{ WH_{11} \u0026 WH_{12}\\cr WH_{21} \u0026 WH_{22} } \\right]  $$'; %valid\r\ntext(1,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ H=\\left[\\matrix{ H_{1} \u0026 H_{2}} \\right]  $$'; %valid\r\ntext(75,65,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ ReLU(H)=Y  $$'; %valid\r\ntext(75,58,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ Y=\\left[\\matrix{ Y_{1} \u0026 Y_{2}} \\right]  $$'; %valid\r\ntext(75,51,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ X*WH=H  $$'; %valid\r\ntext(60,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ Y*WP=P  $$'; %valid\r\ntext(60,0,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ WP=\\left[\\matrix{ WP_{11}\\cr WP_{21} } \\right]  $$'; %valid\r\ntext(100,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\n\r\n\r\nend % Perceptron \r\n%}\r\n","test_suite":"%%\r\nX=[1 2]; WH=[1 2;3 4];WP=[.5; -1];\r\nP=Calc_Perceptron(X,WH,WP);\r\nPexp=-6.5;\r\nvalid=isequal(P,Pexp);\r\nassert(valid)\r\n%%\r\nX=[3 1.5]; WH=[1 -3;3 4];WP=[1; 2];\r\nP=Calc_Perceptron(X,WH,WP);\r\nPexp=7.5;\r\nvalid=isequal(P,Pexp);\r\nassert(valid)\r\n%%\r\nX=[-.5 .5]; WH=[4 2;1 4];WP=[.1; .4];\r\nP=Calc_Perceptron(X,WH,WP);\r\nPexp=0.4;\r\nvalid=isequal(P,Pexp);\r\nassert(valid)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-08-20T22:48:06.000Z","deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-20T22:16:25.000Z","updated_at":"2026-03-30T12:52:26.000Z","published_at":"2023-08-20T22:48:07.000Z","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\u003eThis challenge is to calculate the Neural Net Perceptron value,P, given X, WH, and WP using ReLU on the hidden layer. This example is for two inputs, two hidden nodes, and a single output. No offset terms are applied.\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:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"864\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"1228\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\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 function template includes the code to create this figure using Matlab 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"rela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Net: Calculate Bias Single Perceptron (AND/NAND/OR/NOR/XOR)","description":"This challenge is to calculate the Neural Net Bias Perceptron vector,P, given X, WH, and WP using ReLU on the hidden layer. Test Cases will be all two input conditions for functions  XOR, AND, NAND, OR, and NOR.\r\nSince X wll be a 4x3 matrix the output P will be a 4x1 vector with values 0 or 1.\r\n \r\nThe function template includes the code to create this figure using Matlab Latex","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; 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: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 664.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 332.325px; transform-origin: 407px 332.325px; 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; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\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: 374px 8px; transform-origin: 374px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to calculate the Neural Net Bias Perceptron vector,P, given X, WH, and WP using ReLU on the hidden layer. Test Cases will be all two input conditions for functions  XOR, AND, NAND, OR, and NOR.\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; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\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: 243.5px 8px; transform-origin: 243.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSince X wll be a 4x3 matrix the output P will be a 4x1 vector with values 0 or 1.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 553.65px; 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; perspective-origin: 384px 276.825px; text-align: left; transform-origin: 384px 276.825px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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\" data-image-state=\"image-loaded\"\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 8px; transform-origin: 0px 8px; 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; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\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: 250px 8px; transform-origin: 250px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes the code to create this figure using Matlab Latex\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function P=Calc_Single_Bias_Perceptron(X,WH,WP)\r\n%The P value in this case is a single value for each case\r\n%X will be four rows and three columns\r\n%No ReLU,Softmax, or Sigmoid applied to the output P\r\n  P=[0 0 0 0]';\r\nend\r\n\r\n%The figure was generated using Matlab and Latex\r\n%{\r\nfunction perceptron_bias\r\nfigure(1);clf;\r\nplot([0;140;140;0;0],[-5;-5;100;100;-5],'k','linewidth',2);hold on\r\nxticks([]);yticks([]); %Ticks/Labels off\r\n\r\naxis equal;\r\naxis tight\r\n\r\nr = 10;\r\nphi = 0:0.01:2*pi;\r\ncxA = r*cos(phi);\r\ncyA = r*sin(phi);\r\n\r\nrxA=[0 2*r 2*r   0 0]';\r\nryA=[0   0 2*r 2*r 0]';\r\n\r\ntext(1,95,'Single Bias Perceptron','Fontsize',20);\r\n\r\npatch(cxA+10,cyA+80,'y');text(10-2,80,'X_{1}','Fontsize',20);\r\npatch(cxA+10,cyA+20,'y');text(10-2,20,'X_{2}','Fontsize',20);\r\n\r\npatch(cxA+80,cyA+80,'y');text(80-6,94,'H_{1}','Fontsize',20);text(80-8,80,'\\Sigma','Fontsize',30);\r\npatch(cxA+80,cyA+20,'y');text(80-6,34,'H_{2}','Fontsize',20);text(80-8,20,'\\Sigma','Fontsize',30);\r\n\r\npatch(cxA+130,cyA+50,'y');text(130-2,50,'P','Fontsize',20);\r\n\r\npatch(rxA+80,ryA+70,'y');text(85,80,'ReLU','Fontsize',20);text(100,92,'Y_{1}','Fontsize',20);\r\npatch(rxA+80,ryA+10,'y');text(85,20,'ReLU','Fontsize',20);text(100,32,'Y_{2}','Fontsize',20);\r\n\r\nquiver(20,20,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,20,58.5,58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,58.5,-58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\n\r\nquiver(100,20,25,25,'k','LineWidth',3.5,'MaxHeadSize',.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(100,80,25,-25,'k','LineWidth',3.5,'MaxHeadSize',.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\n%quiver(100,80,25,-25,'k','LineWidth',3.5,'Color','r'); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(53,93,20,-10,'k','LineWidth',3.5,'MaxHeadSize',.6); text(53-8,92,'bH_{31}','Fontsize',20);\r\nquiver(60,5,13,10,'k','LineWidth',3.5,'MaxHeadSize',.7); text(60-6,4,'bH_{32}','Fontsize',20);\r\nquiver(130,26,0,15,'k','LineWidth',3.5,'MaxHeadSize',.8); text(130-6,25,'bP_{31}','Fontsize',20);\r\n\r\n\r\ntext(30,85,'WH_{11}','Fontsize',20);\r\ntext(20,65,'WH_{12}','Fontsize',20);\r\ntext(40,25,'WH_{22}','Fontsize',20);\r\ntext(20,38,'WH_{21}','Fontsize',20);\r\n\r\ntext(110,73,'WP_{11}','Fontsize',20);\r\ntext(107,23,'WP_{21}','Fontsize',20);\r\n\r\nstr='$$ X=\\left[\\matrix{ X_{1} \u0026 X_{2} \u0026 1} \\right]  $$'; %valid\r\ntext(1,50,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ WH=\\left[\\matrix{ WH_{11} \u0026 WH_{12} \u0026 0 \\cr WH_{21} \u0026 WH_{22} \u0026 0 \\cr bH_{31} \u0026 bH_{32} \u0026 1 } \\right]  $$'; %valid\r\ntext(1,3,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ H=\\left[\\matrix{ H_{1} \u0026 H_{2} \u0026 1} \\right]  $$'; %valid\r\ntext(75,65,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ ReLU(H)=Y  $$'; %valid\r\ntext(75,58,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ Y=\\left[\\matrix{ Y_{1} \u0026 Y_{2} \u0026 1} \\right]  $$'; %valid\r\ntext(75,51,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ X*WH=H  $$'; %valid\r\ntext(80,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ Y*WP=P  $$'; %valid\r\ntext(80,0,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ WP=\\left[\\matrix{ WP_{11}\\cr WP_{21} \\cr bP_{31} } \\right]  $$'; %valid\r\ntext(110,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\nend %perceptron_bias\r\n\r\n%}","test_suite":"%%\r\n%XOR\r\nX=[0 0 1;0 1 1;1 0 1;1 1 1]; %All four cases  P will have results from all four input sets\r\nWH=[1 1 0;1 1 0;0 -1 1];\r\nWP=[1;-2;0];\r\n\r\nP=Calc_Single_Bias_Perceptron(X,WH,WP)\r\nvalid=isequal(P,[0 1 1 0]')\r\nassert(valid)\r\n%%\r\n%AND\r\nX=[0 0 1;0 1 1;1 0 1;1 1 1]; %All four cases  P will have results from all four input sets\r\nWH=[1 1 0;1 1 0;-1 -1 1];\r\nWP=[.5;.5;0];\r\n\r\nP=Calc_Single_Bias_Perceptron(X,WH,WP)\r\nvalid=isequal(P,[0 0 0 1]')\r\nassert(valid)\r\n%%\r\n%NAND\r\nX=[0 0 1;0 1 1;1 0 1;1 1 1]; %All four cases  P will have results from all four input sets\r\nWH=[1 1 0;1 1 0;-1 -1 1];\r\nWP=[-.5;-.5;1];\r\n\r\nP=Calc_Single_Bias_Perceptron(X,WH,WP)\r\nvalid=isequal(P,[1 1 1 0]')\r\nassert(valid)\r\n%%\r\n%OR\r\nX=[0 0 1;0 1 1;1 0 1;1 1 1]; %All four cases  P will have results from all four input sets\r\nWH=[1 -.5 0;-.5 1 0;0 0 1];\r\nWP=[1;1;0];\r\n\r\nP=Calc_Single_Bias_Perceptron(X,WH,WP)\r\nvalid=isequal(P,[0 1 1 1]')\r\nassert(valid)\r\n%%\r\n%NOR\r\nX=[0 0 1;0 1 1;1 0 1;1 1 1]; %All four cases  P will have results from all four input sets\r\nWH=[-1 -1 0;-1 -1 0;0.5 .5 1];\r\nWP=[1;1;0];\r\n\r\nP=Calc_Single_Bias_Perceptron(X,WH,WP)\r\nvalid=isequal(P,[1 0 0 0]')\r\nassert(valid)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-08-21T05:50:47.000Z","deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-21T04:23:55.000Z","updated_at":"2025-12-16T01:18:57.000Z","published_at":"2023-08-21T05:50:47.000Z","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\u003eThis challenge is to calculate the Neural Net Bias Perceptron vector,P, given X, WH, and WP using ReLU on the hidden layer. Test Cases will be all two input conditions for functions  XOR, AND, NAND, OR, and NOR.\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\u003eSince X wll be a 4x3 matrix the output P will be a 4x1 vector with values 0 or 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\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"930\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"1303\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\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 function template includes the code to create this figure using Matlab 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.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TeX from string","description":"Matlab’s TeX support in figure windows is great for displaying information in a visually appealing way. However, many of the TeX elements just get in the way when displaying a label string in the command window and lead to lots of additional gibberish if you want to create a valid variable name from the TeX string using genvarname.\r\n\r\nGiven the TeX string as an input, return a string that contains only the plain text of what would be displayed in a figure window by title, xlabel, etc.\r\n\r\nRetain special characters, e.g., ‘\\beta’ becomes ‘beta’.\r\n\r\nCell array inputs should return multi-line strings.","description_html":"\u003cp\u003eMatlab’s TeX support in figure windows is great for displaying information in a visually appealing way. However, many of the TeX elements just get in the way when displaying a label string in the command window and lead to lots of additional gibberish if you want to create a valid variable name from the TeX string using genvarname.\u003c/p\u003e\u003cp\u003eGiven the TeX string as an input, return a string that contains only the plain text of what would be displayed in a figure window by title, xlabel, etc.\u003c/p\u003e\u003cp\u003eRetain special characters, e.g., ‘\\beta’ becomes ‘beta’.\u003c/p\u003e\u003cp\u003eCell array inputs should return multi-line strings.\u003c/p\u003e","function_template":"function txt = tex2txt(texStr)\r\n  txt = texStr;\r\nend","test_suite":"%%\r\ntexStr = '\\beta';\r\ntxt    = 'beta';\r\nassert(isequal(tex2txt(texStr),txt))\r\n\r\n\r\n%%\r\ntexStr = ['\\fontsize{16}black {\\color{magenta}magenta '...\r\n    '\\color[rgb]{0 .5 .5}teal \\color{red}red} black again'];\r\ntxt    = 'black magenta teal red black again';\r\nassert(isequal(tex2txt(texStr),txt))\r\n\r\n\r\n%%\r\ntexStr = {'We need to work with cells, too'\r\n          '(for {\\bfmultiline} labels) and'\r\n          'return strings with newline characters.'};\r\ntxt    = sprintf(['We need to work with cells, too\\n' ...\r\n          '(for multiline labels) and\\n' ...\r\n          'return strings with newline characters.']);\r\nassert(isequal(tex2txt(texStr),txt))\r\n\r\n\r\n%%\r\ntexStr = '\\slRemove {\\bfall} \\itof \\rmthese.';\r\ntxt    = 'Remove all of these.';\r\nassert(isequal(tex2txt(texStr),txt))\r\n\r\n\r\n%%\r\ntxt    = 'variable_i^2/pi';\r\ntexStr = texlabel(txt);\r\nassert(isequal(tex2txt(texStr),txt))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":10139,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-09-06T08:41:30.000Z","updated_at":"2025-09-18T11:44:13.000Z","published_at":"2013-09-06T08:41:30.000Z","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\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"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\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\u003eMatlab’s TeX support in figure windows is great for displaying information in a visually appealing way. However, many of the TeX elements just get in the way when displaying a label string in the command window and lead to lots of additional gibberish if you want to create a valid variable name from the TeX string using genvarname.\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\u003eGiven the TeX string as an input, return a string that contains only the plain text of what would be displayed in a figure window by title, xlabel, etc.\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\u003eRetain special characters, e.g., ‘\\\\beta’ becomes ‘beta’.\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\u003eCell array inputs should return multi-line strings.\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\"}]}"}],"problem_search":{"errors":[],"problems":[{"id":58892,"title":"Neural Net: Calculate Perceptron","description":"This challenge is to calculate the Neural Net Perceptron value,P, given X, WH, and WP using ReLU on the hidden layer. This example is for two inputs, two hidden nodes, and a single output. No offset terms are applied.\r\n \r\nThe function template includes the code to create this figure using Matlab Latex","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; 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: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 626.85px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 313.425px; transform-origin: 407px 313.425px; 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; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\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: 375.5px 8px; transform-origin: 375.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to calculate the Neural Net Perceptron value,P, given X, WH, and WP using ReLU on the hidden layer. This example is for two inputs, two hidden nodes, and a single output. 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\" data-image-state=\"image-loaded\"\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 8px; transform-origin: 0px 8px; 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; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\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: 250px 8px; transform-origin: 250px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes the code to create this figure using Matlab Latex\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function P=Calc_Perceptron(X,WH,WP)\r\n%The P value in this case is a single value\r\n%No ReLU,Softmax, or Sigmoid applied to the output P\r\n  P=0;\r\nend\r\n\r\n%The figure was generated using Matlab and Latex\r\n%{\r\nfunction perceptron\r\n% put figure code into cody\r\nfigure(1);clf;\r\nplot([0;140;140;0;0],[-5;-5;100;100;-5],'k','linewidth',2);hold on\r\nxticks([]);yticks([]); %Tciks/Labels off\r\n\r\naxis equal;\r\naxis tight\r\n\r\n\r\nr = 10;\r\nphi = 0:0.01:2*pi;\r\ncxA = r*cos(phi);\r\ncyA = r*sin(phi);\r\n\r\nrxA=[0 2*r 2*r   0 0]';\r\nryA=[0   0 2*r 2*r 0]';\r\n\r\npatch(cxA+10,cyA+80,'y');text(10-2,80,'X_{1}','Fontsize',20);\r\npatch(cxA+10,cyA+20,'y');text(10-2,20,'X_{2}','Fontsize',20);\r\n\r\npatch(cxA+80,cyA+80,'y');text(80-6,94,'H_{1}','Fontsize',20);text(80-8,80,'\\Sigma','Fontsize',30);\r\npatch(cxA+80,cyA+20,'y');text(80-6,34,'H_{2}','Fontsize',20);text(80-8,20,'\\Sigma','Fontsize',30);\r\n\r\npatch(cxA+130,cyA+50,'y');text(130-2,50,'P','Fontsize',20);\r\n\r\npatch(rxA+80,ryA+70,'y');text(85,80,'ReLU','Fontsize',20);text(102,88,'Y_{1}','Fontsize',20);\r\npatch(rxA+80,ryA+10,'y');text(85,20,'ReLU','Fontsize',20);text(102,32,'Y_{2}','Fontsize',20);\r\n\r\nquiver(20,20,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,20,58.5,58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,58.5,-58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\n\r\nquiver(100,20,25,25,'k','LineWidth',3.5,'MaxHeadSize',.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(100,80,25,-25,'k','LineWidth',3.5,'MaxHeadSize',.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\n\r\ntext(40,85,'WH_{11}','Fontsize',20);\r\ntext(20,65,'WH_{12}','Fontsize',20);\r\ntext(40,25,'WH_{22}','Fontsize',20);\r\ntext(20,38,'WH_{21}','Fontsize',20);\r\n\r\ntext(115,68,'WP_{11}','Fontsize',20);\r\ntext(115,28,'WP_{21}','Fontsize',20);\r\n\r\nstr='$$ X=\\left[\\matrix{ X_{1} \u0026 X_{2}} \\right]  $$'; %valid\r\ntext(1,50,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ WH=\\left[\\matrix{ WH_{11} \u0026 WH_{12}\\cr WH_{21} \u0026 WH_{22} } \\right]  $$'; %valid\r\ntext(1,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ H=\\left[\\matrix{ H_{1} \u0026 H_{2}} \\right]  $$'; %valid\r\ntext(75,65,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ ReLU(H)=Y  $$'; %valid\r\ntext(75,58,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ Y=\\left[\\matrix{ Y_{1} \u0026 Y_{2}} \\right]  $$'; %valid\r\ntext(75,51,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ X*WH=H  $$'; %valid\r\ntext(60,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ Y*WP=P  $$'; %valid\r\ntext(60,0,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ WP=\\left[\\matrix{ WP_{11}\\cr WP_{21} } \\right]  $$'; %valid\r\ntext(100,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\n\r\n\r\nend % Perceptron \r\n%}\r\n","test_suite":"%%\r\nX=[1 2]; WH=[1 2;3 4];WP=[.5; -1];\r\nP=Calc_Perceptron(X,WH,WP);\r\nPexp=-6.5;\r\nvalid=isequal(P,Pexp);\r\nassert(valid)\r\n%%\r\nX=[3 1.5]; WH=[1 -3;3 4];WP=[1; 2];\r\nP=Calc_Perceptron(X,WH,WP);\r\nPexp=7.5;\r\nvalid=isequal(P,Pexp);\r\nassert(valid)\r\n%%\r\nX=[-.5 .5]; WH=[4 2;1 4];WP=[.1; .4];\r\nP=Calc_Perceptron(X,WH,WP);\r\nPexp=0.4;\r\nvalid=isequal(P,Pexp);\r\nassert(valid)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-08-20T22:48:06.000Z","deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-20T22:16:25.000Z","updated_at":"2026-03-30T12:52:26.000Z","published_at":"2023-08-20T22:48:07.000Z","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\u003eThis challenge is to calculate the Neural Net Perceptron value,P, given X, WH, and WP using ReLU on the hidden layer. This example is for two inputs, two hidden nodes, and a single output. No offset terms are applied.\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:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"864\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"1228\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\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 function template includes the code to create this figure using Matlab 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"rela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Net: Calculate Bias Single Perceptron (AND/NAND/OR/NOR/XOR)","description":"This challenge is to calculate the Neural Net Bias Perceptron vector,P, given X, WH, and WP using ReLU on the hidden layer. Test Cases will be all two input conditions for functions  XOR, AND, NAND, OR, and NOR.\r\nSince X wll be a 4x3 matrix the output P will be a 4x1 vector with values 0 or 1.\r\n \r\nThe function template includes the code to create this figure using Matlab Latex","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; 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: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 664.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 332.325px; transform-origin: 407px 332.325px; 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; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\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: 374px 8px; transform-origin: 374px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to calculate the Neural Net Bias Perceptron vector,P, given X, WH, and WP using ReLU on the hidden layer. Test Cases will be all two input conditions for functions  XOR, AND, NAND, OR, and NOR.\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; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\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: 243.5px 8px; transform-origin: 243.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSince X wll be a 4x3 matrix the output P will be a 4x1 vector with values 0 or 1.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 553.65px; 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; perspective-origin: 384px 276.825px; text-align: left; transform-origin: 384px 276.825px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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\" data-image-state=\"image-loaded\"\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 8px; transform-origin: 0px 8px; 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; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\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: 250px 8px; transform-origin: 250px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes the code to create this figure using Matlab Latex\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function P=Calc_Single_Bias_Perceptron(X,WH,WP)\r\n%The P value in this case is a single value for each case\r\n%X will be four rows and three columns\r\n%No ReLU,Softmax, or Sigmoid applied to the output P\r\n  P=[0 0 0 0]';\r\nend\r\n\r\n%The figure was generated using Matlab and Latex\r\n%{\r\nfunction perceptron_bias\r\nfigure(1);clf;\r\nplot([0;140;140;0;0],[-5;-5;100;100;-5],'k','linewidth',2);hold on\r\nxticks([]);yticks([]); %Ticks/Labels off\r\n\r\naxis equal;\r\naxis tight\r\n\r\nr = 10;\r\nphi = 0:0.01:2*pi;\r\ncxA = r*cos(phi);\r\ncyA = r*sin(phi);\r\n\r\nrxA=[0 2*r 2*r   0 0]';\r\nryA=[0   0 2*r 2*r 0]';\r\n\r\ntext(1,95,'Single Bias Perceptron','Fontsize',20);\r\n\r\npatch(cxA+10,cyA+80,'y');text(10-2,80,'X_{1}','Fontsize',20);\r\npatch(cxA+10,cyA+20,'y');text(10-2,20,'X_{2}','Fontsize',20);\r\n\r\npatch(cxA+80,cyA+80,'y');text(80-6,94,'H_{1}','Fontsize',20);text(80-8,80,'\\Sigma','Fontsize',30);\r\npatch(cxA+80,cyA+20,'y');text(80-6,34,'H_{2}','Fontsize',20);text(80-8,20,'\\Sigma','Fontsize',30);\r\n\r\npatch(cxA+130,cyA+50,'y');text(130-2,50,'P','Fontsize',20);\r\n\r\npatch(rxA+80,ryA+70,'y');text(85,80,'ReLU','Fontsize',20);text(100,92,'Y_{1}','Fontsize',20);\r\npatch(rxA+80,ryA+10,'y');text(85,20,'ReLU','Fontsize',20);text(100,32,'Y_{2}','Fontsize',20);\r\n\r\nquiver(20,20,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,20,58.5,58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,58.5,-58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\n\r\nquiver(100,20,25,25,'k','LineWidth',3.5,'MaxHeadSize',.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(100,80,25,-25,'k','LineWidth',3.5,'MaxHeadSize',.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\n%quiver(100,80,25,-25,'k','LineWidth',3.5,'Color','r'); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(53,93,20,-10,'k','LineWidth',3.5,'MaxHeadSize',.6); text(53-8,92,'bH_{31}','Fontsize',20);\r\nquiver(60,5,13,10,'k','LineWidth',3.5,'MaxHeadSize',.7); text(60-6,4,'bH_{32}','Fontsize',20);\r\nquiver(130,26,0,15,'k','LineWidth',3.5,'MaxHeadSize',.8); text(130-6,25,'bP_{31}','Fontsize',20);\r\n\r\n\r\ntext(30,85,'WH_{11}','Fontsize',20);\r\ntext(20,65,'WH_{12}','Fontsize',20);\r\ntext(40,25,'WH_{22}','Fontsize',20);\r\ntext(20,38,'WH_{21}','Fontsize',20);\r\n\r\ntext(110,73,'WP_{11}','Fontsize',20);\r\ntext(107,23,'WP_{21}','Fontsize',20);\r\n\r\nstr='$$ X=\\left[\\matrix{ X_{1} \u0026 X_{2} \u0026 1} \\right]  $$'; %valid\r\ntext(1,50,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ WH=\\left[\\matrix{ WH_{11} \u0026 WH_{12} \u0026 0 \\cr WH_{21} \u0026 WH_{22} \u0026 0 \\cr bH_{31} \u0026 bH_{32} \u0026 1 } \\right]  $$'; %valid\r\ntext(1,3,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ H=\\left[\\matrix{ H_{1} \u0026 H_{2} \u0026 1} \\right]  $$'; %valid\r\ntext(75,65,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ ReLU(H)=Y  $$'; %valid\r\ntext(75,58,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ Y=\\left[\\matrix{ Y_{1} \u0026 Y_{2} \u0026 1} \\right]  $$'; %valid\r\ntext(75,51,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ X*WH=H  $$'; %valid\r\ntext(80,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ Y*WP=P  $$'; %valid\r\ntext(80,0,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ WP=\\left[\\matrix{ WP_{11}\\cr WP_{21} \\cr bP_{31} } \\right]  $$'; %valid\r\ntext(110,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\nend %perceptron_bias\r\n\r\n%}","test_suite":"%%\r\n%XOR\r\nX=[0 0 1;0 1 1;1 0 1;1 1 1]; %All four cases  P will have results from all four input sets\r\nWH=[1 1 0;1 1 0;0 -1 1];\r\nWP=[1;-2;0];\r\n\r\nP=Calc_Single_Bias_Perceptron(X,WH,WP)\r\nvalid=isequal(P,[0 1 1 0]')\r\nassert(valid)\r\n%%\r\n%AND\r\nX=[0 0 1;0 1 1;1 0 1;1 1 1]; %All four cases  P will have results from all four input sets\r\nWH=[1 1 0;1 1 0;-1 -1 1];\r\nWP=[.5;.5;0];\r\n\r\nP=Calc_Single_Bias_Perceptron(X,WH,WP)\r\nvalid=isequal(P,[0 0 0 1]')\r\nassert(valid)\r\n%%\r\n%NAND\r\nX=[0 0 1;0 1 1;1 0 1;1 1 1]; %All four cases  P will have results from all four input sets\r\nWH=[1 1 0;1 1 0;-1 -1 1];\r\nWP=[-.5;-.5;1];\r\n\r\nP=Calc_Single_Bias_Perceptron(X,WH,WP)\r\nvalid=isequal(P,[1 1 1 0]')\r\nassert(valid)\r\n%%\r\n%OR\r\nX=[0 0 1;0 1 1;1 0 1;1 1 1]; %All four cases  P will have results from all four input sets\r\nWH=[1 -.5 0;-.5 1 0;0 0 1];\r\nWP=[1;1;0];\r\n\r\nP=Calc_Single_Bias_Perceptron(X,WH,WP)\r\nvalid=isequal(P,[0 1 1 1]')\r\nassert(valid)\r\n%%\r\n%NOR\r\nX=[0 0 1;0 1 1;1 0 1;1 1 1]; %All four cases  P will have results from all four input sets\r\nWH=[-1 -1 0;-1 -1 0;0.5 .5 1];\r\nWP=[1;1;0];\r\n\r\nP=Calc_Single_Bias_Perceptron(X,WH,WP)\r\nvalid=isequal(P,[1 0 0 0]')\r\nassert(valid)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-08-21T05:50:47.000Z","deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-21T04:23:55.000Z","updated_at":"2025-12-16T01:18:57.000Z","published_at":"2023-08-21T05:50:47.000Z","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\u003eThis challenge is to calculate the Neural Net Bias Perceptron vector,P, given X, WH, and WP using ReLU on the hidden layer. Test Cases will be all two input conditions for functions  XOR, AND, NAND, OR, and NOR.\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\u003eSince X wll be a 4x3 matrix the output P will be a 4x1 vector with values 0 or 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\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"930\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"1303\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\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 function template includes the code to create this figure using Matlab 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.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TeX from string","description":"Matlab’s TeX support in figure windows is great for displaying information in a visually appealing way. However, many of the TeX elements just get in the way when displaying a label string in the command window and lead to lots of additional gibberish if you want to create a valid variable name from the TeX string using genvarname.\r\n\r\nGiven the TeX string as an input, return a string that contains only the plain text of what would be displayed in a figure window by title, xlabel, etc.\r\n\r\nRetain special characters, e.g., ‘\\beta’ becomes ‘beta’.\r\n\r\nCell array inputs should return multi-line strings.","description_html":"\u003cp\u003eMatlab’s TeX support in figure windows is great for displaying information in a visually appealing way. However, many of the TeX elements just get in the way when displaying a label string in the command window and lead to lots of additional gibberish if you want to create a valid variable name from the TeX string using genvarname.\u003c/p\u003e\u003cp\u003eGiven the TeX string as an input, return a string that contains only the plain text of what would be displayed in a figure window by title, xlabel, etc.\u003c/p\u003e\u003cp\u003eRetain special characters, e.g., ‘\\beta’ becomes ‘beta’.\u003c/p\u003e\u003cp\u003eCell array inputs should return multi-line strings.\u003c/p\u003e","function_template":"function txt = tex2txt(texStr)\r\n  txt = texStr;\r\nend","test_suite":"%%\r\ntexStr = '\\beta';\r\ntxt    = 'beta';\r\nassert(isequal(tex2txt(texStr),txt))\r\n\r\n\r\n%%\r\ntexStr = ['\\fontsize{16}black {\\color{magenta}magenta '...\r\n    '\\color[rgb]{0 .5 .5}teal \\color{red}red} black again'];\r\ntxt    = 'black magenta teal red black again';\r\nassert(isequal(tex2txt(texStr),txt))\r\n\r\n\r\n%%\r\ntexStr = {'We need to work with cells, too'\r\n          '(for {\\bfmultiline} labels) and'\r\n          'return strings with newline characters.'};\r\ntxt    = sprintf(['We need to work with cells, too\\n' ...\r\n          '(for multiline labels) and\\n' ...\r\n          'return strings with newline characters.']);\r\nassert(isequal(tex2txt(texStr),txt))\r\n\r\n\r\n%%\r\ntexStr = '\\slRemove {\\bfall} \\itof \\rmthese.';\r\ntxt    = 'Remove all of these.';\r\nassert(isequal(tex2txt(texStr),txt))\r\n\r\n\r\n%%\r\ntxt    = 'variable_i^2/pi';\r\ntexStr = texlabel(txt);\r\nassert(isequal(tex2txt(texStr),txt))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":10139,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-09-06T08:41:30.000Z","updated_at":"2025-09-18T11:44:13.000Z","published_at":"2013-09-06T08:41:30.000Z","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\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"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\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\u003eMatlab’s TeX support in figure windows is great for displaying information in a visually appealing way. 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