data:image/png;base64,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data:image/png;base64,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NeuralNetSpriteImageSprite120ControlNeuralNetSpriteControlSets the value of an existing property or inserts it anew.

minValuetypeOfData width height minValue maxValue columns rows backColorRed backColorGreen backColorBlueReads the value of an existing property.

minValuetypeOfData width height minValue maxValue columns rows backColorRed backColorGreen backColorBlueSets the default values of the properties of a DataSprite.

Creates an empty costume of the specified size and color.

Stores a string in a file. JS-code-snippets copied from Snap!-code.

Creates new random data in the specified ranges .swaying by a straight line

10-55102Calculates the parameters of a regression line.

myDatamatrixCalculates the ranges, the covariance or the correlation between two columns of a table

rangesranges covariance correlation12myDatatableReports a copy of a table.

myDatalisttableCreates a new empty table.

23resultDetermines a property of a list.

minmin max minpos maxpos number sum mean median variance standard-deviationmyDatavectormaxnumbersummeanminposmaxposvariancestandard-deviationstandard-deviationfrom "<feature> of": unknown optionERROR: unknown optionSorts a list, JS-code-snippets copied from Snap!-code.

myData1truetablelistDetermines the rows of a table with certain properties.

myData1less-thanless-than greater-than equal-to different-from123tablegreater-thanequal-todifferent-fromDetermines group properties.

meanmin max number sum mean2myData1tableStores a list in a CSV file, JS-code copied from Snap!-code

myDatalistLoads the contents of a file, JS-code copied from Snap!-code

Normalizes a table by dividing it by the specified value.

myDatameanmean max number sum median softmaxchoice1 vectore^0Deletes a row or column of a table.

rowrow column1myDatatablecolumnAdds a row, a column, or column headers to a table.

rowrow column column-headersmyDatatableReports a row or column of a table.

rowrow column1myDatatablecolumnGenerates a list of points "near" a given graph.

20-55Reports the element a position x|y of a table.

11myDatatableReplaces the value at x|y of a table.

11myData33tableDetermines the next k neighbours of a point in data. The class of the point must be in column 3.

5myDatatablePooling operation with matrices, images and vectors.

maxmax meanmyData2typeOfPooling1 matrixvectorFITSRGBmatrixvectordatawidthheighttypeOfPoolingstridetypeOfDatafunction getValue(x,y){return data.at((y-1)*width+x);} var result=[],y,x,max,sum,mean,n,value,row; width=Number(width); height=Number(height); stride=Number(stride); result.push(Math.ceil(1.0*width/stride)); result.push(Math.ceil(1.0*height/stride)); if(typeOfData=="FITS"){ y=1; while(y<=height){ x=1; while(x<=width){ max=0; mean=0;n=0;sum=0; for(var i=0;i<stride;i++){ for(var j=0;j<stride;j++){ if(((x+i)<=width)&&((y+j)<=height)){ n++; value=Number(getValue(x+i,y+j)); sum=sum+value; if(value>max) max=value; } } } if(n>0) mean=1.0*sum/n; if(typeOfPooling=="max") result.push(max); else result.push(mean); x=x+stride; } y=y+stride; } } if(typeOfData=="RGB"){ y=1; while(y<=height){ x=1; while(x<=width){ max=[0,0,0,0]; mean=[0,0,0,0];n=0;sum=[0,0,0,0]; for(var i=0;i<stride;i++){ for(var j=0;j<stride;j++){ if(((x+i)<=width)&&((y+j)<=height)){ n++; value=getValue(x+i,y+j); for(k=0;k<4;k++){ sum[k]=sum[k]+Number(value.at(k+1)); if(value.at(k+1)>max[k]) max[k]=value.at(k+1); } } } } if(n>0) for(k=0;k<4;k++) mean[k]=1.0*sum[k]/n; if(typeOfPooling=="max") result.push(new List(max)); else result.push(new List(mean)); x=x+stride; } y=y+stride; } } if(typeOfData=="matrix"){ y=1; while(y<=height){ x=1; row = new List(); while(x<=width){ max=data.at(y).at(x); mean=0;n=0;sum=0; for(var i=0;i<stride;i++){ for(var j=0;j<stride;j++){ if(((x+i)<=width)&&((y+j)<=height)){ n++; value=Number(data.at(y+j).at(x+i)); sum=sum+value; if(value>max) max=value; } } } if(n>0) mean=1.0*sum/n; if(typeOfPooling=="max") row.add(max); else row.add(mean); x=x+stride; } result.push(row); y=y+stride; } } if(typeOfData=="vector"){ x=1; while(x<=width){ max=data.at(x); mean=0;n=0;sum=0; for(var i=0;i<stride;i++){ if((x+i)<=width){ n++; value=Number(data.at(x+i)); sum=sum+value; if(value>max) max=value; } } if(n>0) mean=1.0*sum/n; if(typeOfPooling=="max") result.push(max); else result.push(mean); x=x+stride; } } return new List(result);Test whether data is a table.

myDatatablevector matrix tablelistmatrixtablefalseReturns a random number between 0 and 1.

Imports FITS-data, table-CSV-data or costume-data to myData.

costume-(RGB)-datatable-(CSV)-data costume-(RGB)-data FITS-datacurrent-costumetable-(CSV)-dataminValuenot setmaxValuenot setcostume-(RGB)-datacostumehmaxcolumnminValuemaxValueApplies a convolution to a table with FITS, RGB, or table values

tableimage tablemyDatalistmatrixkerneldatawidthheighttypeOfDatamIndexkWidthfunction getValue(x,y){ if(typeOfData=='FITS') return (data.at(x+(y-1)*width)); if(typeOfData=='RGB') return (data.at(x+(y-1)*width)); if(typeOfData=='table') return (data.at(y).at(x)); } function getKernelValue(x,y){ return (kernel.at(y).at(x)); } var result=[],x,y,value,r,g,b,s,row; width=Number(width); height=Number(height); mIndex=Number(mIndex);kWidth=Number(kWidth); for(var y=1; y<=height;y++){ row=new List(); for(var x=1;x<=width;x++){ if(typeOfData=='FITS') value = 0; if(typeOfData=='RGB'){r=0;g=0;b=0;s=255}; if(typeOfData=='table') value=0; for(var ky=1;ky<=kWidth;ky++) for(var kx=1;kx<=kWidth;kx++) if((y-mIndex+ky>0) && (y-mIndex+ky<=height) && (x-mIndex+kx>0) && (x-mIndex+kx<=width)){ if(typeOfData=='FITS') value = value+getValue(x-mIndex+kx,y-mIndex+ky)*getKernelValue(kx,ky); if(typeOfData=='table') value = value+getValue(x-mIndex+kx,y-mIndex+ky)*getKernelValue(kx,ky); if(typeOfData=='RGB'){ r = r + getValue(x-mIndex+kx,y-mIndex+ky).at(1)*getKernelValue(kx,ky); g = g + getValue(x-mIndex+kx,y-mIndex+ky).at(2)*getKernelValue(kx,ky); b = b + getValue(x-mIndex+kx,y-mIndex+ky).at(3)*getKernelValue(kx,ky); } } if(typeOfData=='FITS') result.push(value); if(typeOfData=='table') row.add(value); if(typeOfData=='RGB') result.push(new List([r,g,b,s])); } if(typeOfData=='table') result.push(row); } return new List(result);

myData2list

0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,77.52792617775619,166.69742593491983,226.76299174356478,153.6935405536668,64.52404079650314,4.4584749878581835,0,0,0,0,0,0,0,0,0,46.81398737251092,113.19572608062164,195.182127246236,240.50995628946092,236.67071393880525,158.89509470616804,66.75327829043225,4.210781932977174,0,0,0,0,0,0,0,4.334628460417678,44.70859640602234,0,0,0,0,17.462360369111227,77.40407965031568,35.048567265662946,0,0,0,0,0,0,0,5.573093734822729,0,0,0,0,0,0,0,19.320058280718797,0,0,0,0,0,0,0,27.493929091792133,0,0,0,0,0,0,0,22.540067994171924,38.14473045167557,0,0,0,0,0,0,26.503156872268093,26.87469645458961,24.893152015541528,0.37153958232151796,2.6007770762506066,72.20252549781448,129.91500728508984,186.63671685284115,117.0349684312773,59.32248664400194,0,0,0,0,0,0,9.783875667799906,81.36716852841185,156.04662457503642,228.00145701796984,248.1884409907722,255,157.65662943176298,27.741622146673148,0,0,0,0,0,0,0,0,0,45.20398251578436,140.93734822729482,249.42690626517728,216.85526954832443,43.71782418649829,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0typeOfData,FITSwidth,16height,16minValue,not setmaxValue,not setcolumns,0rows,0"from ""normalize"": wrong data format","from ""normalize"": wrong data format","from ""normalize"": wrong data format","from ""normalize"": wrong data format","from ""normalize"": wrong data format","from ""normalize"": wrong data format","from ""normalize"": wrong data format","from ""<feature> of"": wrong data format","from ""<feature> of"": wrong data format"

-10020

005030

Sets the default values of the properties of a ImageSprite.

Sets the value of an existing property or inserts it anew.

imageWidthtypeOfData imageWidth imageHeight minValue maxValue backColorRed backColorGreen backColorBlue lineStyle lineWidth lineColorRed lineColorGreen lineColorBlue lineStyle lineWidth lineColorRed lineColorGreen lineColorBlue surfaceColorRed surfaceColorGreen surfaceColorBlueReads the value of an existing property.

imageWidthtypeOfData imageWidth imageHeight minValue maxValue backColorRed backColorGreen backColorBlue lineStyle lineWidth lineColorRed lineColorGreen lineColorBlue surfaceColorRed surfaceColorGreen surfaceColorBlueGenerates an image from the FITS or RGB data of the DataSprite in gray or false colors

graygray false-color RGBmyData032000falsetypeOfDataFITSpixelsdataminmaxgraylogvar result=[], i=1, n, value, interval = (max-min)/8; while(i<= data.length()) { value = data.at(i); n = value; if(value<=min) n=min+1; if(value>max) n =max; if(log) n = Math.round(Math.log(n-min)/Math.log(max-min)*255); else n = Math.round((n-min)/(max-min)*255); if(gray) result.push(new List([n,n,n,255])); else { // result.push(new List([n,255-n,Math.round(255-n/2),255])); if(value<=min) {result.push(new List([0,0,0,255]));} else if(value<min+interval) result.push(new List([0,0,n,255])); else if(value<min+2*interval) result.push(new List([0,n,n,255])); else if(value<min+3*interval) result.push(new List([n,0,n,255])); else if(value<min+4*interval) result.push(new List([0,n,0,255])); else if(value<min+5*interval) result.push(new List([n,0,0,n])); else if(value<min+6*interval) result.push(new List([n,n/2,0,255])); else if(value<min+7*interval) result.push(new List([n,n,0,255])); else if(value<min+8*interval) result.push(new List([n,n,n,255])); else result.push(new List([255,255,255,255])); } i = i+1; } return new List(result);imageWidthimageHeight255225205pixelsdataminmaxgraylogvar result=[], i=1, n, value, interval = (max-min)/8; while(i<= data.length()) { value = (data.at(i).at(1)+data.at(i).at(2)+data.at(i).at(3))/3; n = value; if(value<=min) n=min+1; if(value>max) n =max; if(log) n = Math.round(Math.log(n-min)/Math.log(max-min)*255); else n = Math.round((n-min)/(max-min)*255); if(gray) result.push(new List([n,n,n,255])); else { // result.push(new List([n,255-n,Math.round(255-n/2),255])); if(value<=min) {result.push(new List([0,0,0,255]));} else if(value<min+interval) result.push(new List([0,0,n,255])); else if(value<min+2*interval) result.push(new List([0,n,n,255])); else if(value<min+3*interval) result.push(new List([n,0,n,255])); else if(value<min+4*interval) result.push(new List([0,n,0,255])); else if(value<min+5*interval) result.push(new List([n,0,0,n])); else if(value<min+6*interval) result.push(new List([n,n/2,0,255])); else if(value<min+7*interval) result.push(new List([n,n,0,255])); else if(value<min+8*interval) result.push(new List([n,n,n,255])); else result.push(new List([255,255,255,255])); } i = i+1; } return new List(result);imageWidthimageHeight225225255Reads the data value at the position (x/y) of an image stored in myData.

11Reads the RGB value at the position (x/y) of the costume. JS-code-snippets copied from Snap!-code.

11Sets the RGB value al (x|y) on costume.

11255180100Obtaining data with the mouse.

image-valueimage-value costume-coordinates slice-data line-data circle-data brightnesstypeOfDataRGBtypeOfDataFITS1whitespaceimage-valueimage value1whitespaceline-datadraggable?okfalseokfalsedraggable?line data1whitespaceslice-datadraggable?pointsline-datadraggable?slice data1whitespacecircle-datacircle datadraggable?okfalseokfalsedraggable?1whitespacebrightnessbrightnessReturns the copy of the current sprite-costume.

Sets line style and color for DataSprite diagrams.

continuouscontinuous dashed dash-dot dot-dot1000Draws a circle on present costume using line properties. Attention: JS coordinates are used.

10010020Draws a line on present costume using line properties. Attention: JS coordinates are used.

1010100100Draws a rectangle on present costume using line properties. Attention: JS coordinates are used.

100100200200costumex1y1whwidthrgbvar ctx = costume.contents.getContext('2d'); ctx.beginPath(); ctx.lineWidth = width; ctx.strokeStyle = new Color(r,g,b).toString(); ctx.strokeRect(x1,y1,w,h); ctx.closePath(); ctx.stroke();costumelineWidthlineColorRedlineColorGreenlineColorBluecostumeFills a rectangle on present costume using surface properties. Attention: JS coordinates are used.

100100200150costumex1y1whwidthrgbvar ctx = costume.contents.getContext('2d'); ctx.beginPath(); ctx.lineWidth = width; ctx.fillStyle = new Color(r,g,b).toString(); ctx.fillRect(x1,y1,w,h); ctx.closePath(); ctx.stroke();costumelineWidthsurfaceColorRedsurfaceColorGreensurfaceColorBluecostumeFills a circle on present costume using surface properties. Attention: JS coordinates are used.

10010020Draws a text on present costume using line properties. Attention: JS coordinates are used.

my text1005012true

220200180Sets the value at (x|y) in myData

11Reports the brightness of a FITS or RGB image around x|y in a circle with radius r.

10010010

Applies an affine transformation to an image.

myDatalistP11P22P33Q11Q22Q33S1221221220reads a file from filesystem

Determines a property of a list.

minmin max1myDatalistresultdatantypeOfDatavar min,max,isNumber,c,i,k,value; if(data.length()==0) return new List([0,0]); i=1; if(typeOfData=="list") min=data.at(1); else min=data.at(1).at(n); max=min; while(i <= data.length()) { if(typeOfData=="list") value = data.at(i); else value=data.at(i).at(n); if(typeof(value)=="number") isNumber=true; else { if(typeof(value)=="string"){ isNumber=true; k=0; while((k<value.length) && isNumber){ c=value.charAt(k); if((c<'0') || (c>'9')) if((c!='E') && (c!='e') && (c!='+') && (c!='-')&& (c!='.') && (c!=',')) isNumber=false; k++; } } else isNumber=false; } if(isNumber){ value=Number(value); if(min>value) {min=value;} if(max<value) {max=value;} } i++; } return new List([min,max]); selection1 minmax

350300225225255

costume-(RGB)-datacostume-(RGB)-data FITS-datacurrent-costumecostume-(RGB)-datacostume

matrixTest on type of data.

myDatavectorvector matrix RGB-image FITS-imagelistmatrixRGB-imageRGB-imagefalse

1

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55,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255typeOfData,FITSimageWidth,64imageHeight,64minValue,not setmaxValue,not setbackColorRed,225backColorGreen,225backColorBlue,255lineStyle,continuouslineWidth,1lineColorRed,0lineColorGreen,0lineColorBlue,0surfaceColorRed,180surfaceColorGreen,180surfaceColorBlue,180Sets the default values of the properties of a NeuralNet.

Sets the value of an existing property or inserts it anew.

layerstypeOfData imageWidth imageHeight backColorRed backColorGreen backColorBlue layerWidth layersReads the value of an existing property.

layerstypeOfData imageWidth imageHeight backColorRed backColorGreen backColorBlue layerWidth layersReturns a by softmax normalized list of values.

e^Applies a convolution to a table with FITS, RBG, or table values

imageimage tablemyDatalistmatrixkerneldatawidthheighttypeOfDatamIndexkWidthfunction getValue(x,y){ if(typeOfData=='FITS') return (data.at(x+(y-1)*width)); if(typeOfData=='RGB') return (data.at(x+(y-1)*width)); if(typeOfData=='table') return (data.at(y).at(x)); } function getKernelValue(x,y){ return (kernel.at(y).at(x)); } var result=[],x,y,value,r,g,b,s,row; width=Number(width); height=Number(height); mIndex=Number(mIndex);kWidth=Number(kWidth); for(var y=1; y<=height;y++){ row=new List(); for(var x=1;x<=width;x++){ if(typeOfData=='FITS') value = 0; if(typeOfData=='RGB'){r=0;g=0;b=0;s=255}; if(typeOfData=='table') value=0; for(var ky=1;ky<=kWidth;ky++) for(var kx=1;kx<=kWidth;kx++) if((y-mIndex+ky>0) && (y-mIndex+ky<=height) && (x-mIndex+kx>0) && (x-mIndex+kx<=width)){ if(typeOfData=='FITS') value = value+getValue(x-mIndex+kx,y-mIndex+ky)*getKernelValue(kx,ky); if(typeOfData=='table') value = value+getValue(x-mIndex+kx,y-mIndex+ky)*getKernelValue(kx,ky); if(typeOfData=='RGB'){ r = r + getValue(x-mIndex+kx,y-mIndex+ky).at(1)*getKernelValue(kx,ky); g = g + getValue(x-mIndex+kx,y-mIndex+ky).at(2)*getKernelValue(kx,ky); b = b + getValue(x-mIndex+kx,y-mIndex+ky).at(3)*getKernelValue(kx,ky); } } if(typeOfData=='FITS') result.push(value); if(typeOfData=='table') row.add(value); if(typeOfData=='RGB') result.push(new List([r,g,b,s])); } if(typeOfData=='table') result.push(row); } return new List(result);

vectorvector matrix tablematrixtablefrom "is <data> a": general errorfalse

Loads the contents of a file, JS-code copied from Snap!-code

Imports table-data to myData.

myDatavectormatrixReports the output of the nth layer of a neural network with given input.

lasttypeOfDataNNwidthlayerWidthweightslayerNr1layerNr1

*+ - * X-*from "linear operation": not allowed operatorERROR: illegal operationvectormatrixfrom "linear operation": illegal operationERROR: illegal operationvectorvector+-*Xmatrixfrom "linear operation": illegal operationERROR: illegal operationmatrixvectormatrixfrom "linear operation": illegal operationERROR: illegal operation

0.01previousDeltah1n1previousDeltalayerNr-1myDataCreates a fully connected neural network of perceptrons. Inputs should be connected to layer 1, outputs to last layer.

321typeOfDataNNlayerslayerWidthDraws the nodes and current status of connections of an NN.

costumenetWidthdepthweightsoutputsrgbvar newCostume,ctx,imageWidth,imageHeight,dx,dy,x,y,x1,y1,colorcode; //new costume imageWidth = costume.width(); imageHeight = costume.height(); newCostume=new Costume(); newCostume.contents.width = imageWidth ; newCostume.contents.height = imageHeight; ctx = newCostume.contents.getContext('2d'); ctx.beginPath(); ctx.fillStyle = new Color(r,g,b).toString(); ctx.strokeStyle = new Color(0,0,0).toString(); ctx.fillRect(0,0,imageWidth,imageHeight); ctx.strokeRect(0,0,imageWidth,imageHeight); ctx.closePath(); ctx.fill(); ctx.stroke(); newCostume.rotationCenter=new Point(imageWidth/2,imageHeight/2); //draw frame ctx.beginPath(); ctx.lineWidth = 2; ctx.moveTo(imageWidth-15,imageHeight-20); ctx.lineTo(imageWidth-15,40); ctx.lineTo(imageWidth-20,40); ctx.lineTo(imageWidth-15,20); ctx.lineTo(imageWidth-10,40); ctx.lineTo(imageWidth-15,40); ctx.closePath(); ctx.stroke(); ctx.beginPath(); ctx.lineWidth = 1; ctx.moveTo(imageWidth-30,1); ctx.lineTo(imageWidth-30,imageHeight-1); ctx.closePath(); ctx.stroke(); netWidth=Number(netWidth); depth=Number(depth); //draw connections if(netWidth>1) dx = 1.0*(imageWidth-50)/(netWidth-1); else dx = 1.0*(imageWidth-15)/2; if(depth>1) dy = 1.0*imageHeight/depth; else dy = 1.0*imageHeight/2; ctx.lineWidth = 1; for(var layer=1;layer<=depth;layer++){ for(var i=1;i<=netWidth;i++){ x=10+(i-1)*dx; if(layer==depth) y = 3; else y = imageHeight-layer*dy; for(var n=1;n<=netWidth;n++){ ctx.beginPath(); x1=10+(n-1)*dx; if(layer==1) y1=imageHeight-3; else y1 = imageHeight-(layer-1)*dy; colorcode=Math.round(255*weights.at(layer).at(i).at(n)); if(colorcode<0) ctx.strokeStyle = new Color(-colorcode,0,0).toString(); else ctx.strokeStyle = new Color(0,colorcode,0).toString(); ctx.moveTo(x,y); ctx.lineTo(x1,y1); ctx.closePath(); ctx.stroke(); } } } //draw connectors if(netWidth>1) dx = 1.0*(imageWidth-50)/(netWidth-1); else dx = 1.0*(imageWidth-15)/2; if(depth>1) dy = 1.0*(imageHeight-30)/(depth-1); else dy = 1.0*(imageHeight-15)/2; ctx.lineWidth = 1; for(var i=0;i<netWidth;i++){ ctx.beginPath(); x=5+i*dx; y=1; colorcode=Math.round(255*outputs.at(depth+1).at(i+1)); if(colorcode<0) ctx.fillStyle = new Color(-colorcode,0,0).toString(); else ctx.fillStyle = new Color(0,colorcode,0).toString(); ctx.strokeRect(x,y,10,5); ctx.fillRect(x,y,10,5); ctx.closePath(); ctx.stroke(); ctx.fill(); y=imageHeight-6; colorcode=Math.round(255*outputs.at(1).at(i+1)); if(colorcode<0) ctx.fillStyle = new Color(-colorcode,0,0).toString(); else ctx.fillStyle = new Color(0,colorcode,0).toString(); ctx.strokeRect(x,y,10,5); ctx.fillRect(x,y,10,5); ctx.closePath(); ctx.stroke(); ctx.fill(); } //draw inner layers if(netWidth>1) dx = 1.0*(imageWidth-50)/(netWidth-1); else dx = 1.0*(imageWidth-15)/2; if(depth>1) dy = 1.0*imageHeight/depth; else dy = 1.0*imageHeight/2; for(var layer=2;layer<=depth;layer++){ for(var i=0;i<netWidth;i++){ ctx.beginPath(); x=10+i*dx; y=imageHeight-(layer-1)*dy; colorcode=Math.round(255*outputs.at(layer).at(i+1)); if(colorcode<0) ctx.fillStyle = new Color(-colorcode,0,0).toString(); else ctx.fillStyle = new Color(0,colorcode,0).toString(); ctx.arc(x,y,5,0,6.283185307179586476925286766559); ctx.closePath(); ctx.stroke(); ctx.fill(); } } return newCostume; costumebackColorRedbackColorGreenbackColorBlue

minmin max minpos maxposmyDatavectormaxminposmaxpos

200150255225205

mean11mean22mean33mean44

0.5

1

1ready for next processtrue

0.687789654128889,0.32366191955670365,0.8926785021605994,0.8443496741711141,10.922754265645104,0.7519600338395849,0.25699417919467016,0.3250175502584236,10.3372202239299795,0.5843544681411761,0.5226053325555347,0.06013418697292816,10.43472563005328024,0.7309868163703916,0.21036361236809586,0.13024282642147145,1-0.2928556477118528,-0.24176352907847468,0.21669219259265218,0.287080921216177,1-0.33515964391347275,-0.8334589510829209,-0.1770112685745437,-0.3285295307103739,1-0.5251434423450897,-0.34635999133778156,-0.4496622777268095,-0.32566228857686036,1-0.00007412454285734768,0.33628168198249764,-0.07323292368097459,-0.24335027244754848,1typeOfData,NNimageWidth,200imageHeight,100backColorRed,255backColorGreen,225backColorBlue,205layers,2layerWidth,443.3,86.8,62.3,80.843.386.862.380.84

pt:altera o valor da configuração _ para _ Project notes Project name Language Zoom blocks Stage size Stage scale Visible palette

11myDatafalsetrue-1,0,1-1,0,1-1,0,1-1,-1,-10,0,01,1,1-1,-2,-10,0,01,2,11,2,12,4,21,2,10,1,01,-4,00,1,01,2,12,-13,21,2,10,0,0,00,00,00,00,0,0,00,0,0,00,0,0,0