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April 10, 2019 14:47
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view.json for visualizeR dataExplorer 1.1
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| { | |
| "version": "2.95.0", | |
| "grid": { | |
| "layers": { | |
| "Default layer": { | |
| "name": "Default layer" | |
| }, | |
| "admin": { | |
| "name": "admin" | |
| } | |
| }, | |
| "xWidth": 10, | |
| "yHeight": 10 | |
| }, | |
| "modules": [ | |
| { | |
| "url": "modules/types/client_interaction/code_executor/", | |
| "configuration": { | |
| "sections": {}, | |
| "groups": { | |
| "group": [ | |
| { | |
| "display": [ | |
| [ | |
| "editor", | |
| "buttons" | |
| ] | |
| ], | |
| "execOnLoad": [ | |
| [ | |
| "yes" | |
| ] | |
| ], | |
| "asyncAwait": [ | |
| [ | |
| "top" | |
| ] | |
| ], | |
| "script": [ | |
| "let fh = API.cache('helperFunctions');\nlet data = API.getData('data').resurrect();\n\n\nlet d = data.map(x => x.dataMatrix.filter((v, i) => i % 2 == 1));\nlet dataT = fh.transpose(d);\n\nlet ceiling = dataT.map(x => Math.max.apply(null, x));\nlet floor = dataT.map(x => Math.min.apply(null, x));\n\nvar col = [];\nceiling.forEach( i => col.push({'_rgb':[240,240,240,1]}) );\n\nvar x = data[0].dataMatrix.filter(function(value, index, Arr) {\n return index % 2 == 0\n});\n\nlet ceilingSpectra = (\n {'type': 'color',\n 'y': ceiling,\n 'x': x,\n 'color': col\n }\n);\nAPI.createData('ceilingSpectra', {'data':ceilingSpectra});\n\nlet floorSpectra = (\n {'type': 'color',\n 'y': floor,\n 'x': x,\n 'color': col\n }\n);\nAPI.createData('floorSpectra', {'data':floorSpectra});\n\nlet action = null;\nlet value = null;\n\nif(this.action) {\n action = this.action.name;\n value = this.action.value;\n} else {\n\nswitch(this.button) {\n case 'createHeaders': \n action = 'createHeaders';\n break;\n case 'selectColor':\n action = 'selectColor';\n break;\n default:\n action = this.variable;\n break;\n }\n}\n\nconsole.log('action:' + action);\n\nlet selectedData = [];\nlet currentID = [];\nvar colors = [];\nlet selectedColors = [];\n\n\nswitch(action) {\n case 'selectColor':\n let currentHeader = API.getData('currentHeader');\n \n if(!currentHeader) {\n currentHeader = 'ID';\n }\n \n console.log(currentHeader);\n let m = API.getData('metadata');\n \n let factor = [];\n m.map(x => x[currentHeader])\n .forEach((i, j) => factor[i] = (factor[i] || 0) + 1);\n console.log(factor);\n let colorScale = chroma.scale(['#0FFFFF','#FF7F0F','#FF000F']).domain([0, (Object.keys(factor).length)]).mode('lch');\n let colorVect = []; \n let count = 0;\n for (let key in factor){\n colorVect[key] = colorScale(count);\n count++;\n }\n colors = m.map(x => x[currentHeader]).map((x) => colorVect[x]);\n console.log(colorScale(1))\n API.cache('colors', colors);\n API.doAction('selectedHeaders', colors);\n API.doAction('colorScores', colors);\n //API.doAction('removeSerie',[0]);\n \n break;\n \n case 'colorScores':\n let scores = API.getData(\"score12\");\n \n colors = API.cache('colors');\n let rgbaColors = colors.map(x => \n 'rgba('+x['_rgb'].toString()+')');\n \n scores.data[0].styles.unselected.map((x,i) => x.fill = rgbaColors[i]);\n scores.data[0].styles.selected.map((x,i) => x.fill = rgbaColors[i]);\n \n API.createData('scoresToDisplay', scores);\n \n case 'createHeaders':\n \n if (API.getData('headers') == null){\n let metadata = data.map(x => x.metadata);\n API.createData('metadata', metadata);\n\n let headersList = Object.keys(data[0].metadata);\n let headers = [];\n headersList.forEach(i => headers.push({'header': i}));\n API.createData('headers', headers);\n \n }\n break;\n \n case 'selectedHeaders':\n let selectedHeaders = API.getData('selectedHeaders');\n \n if(!value) {\n var col = API.getData('colors');\n } else {\n var col = value;\n }\n \n let metadata = API.getData('metadata');\n \n let newMetadata = [];\n let t = metadata.map((x, j) => {\n let newRow = {};\n newRow['ID'] = data[j].ID;\n newRow['_highlight'] = j;\n newRow['colors'] = col[j].toString();\n selectedHeaders.forEach(i => newRow[i.header] = x[i.header]);\n return newRow;\n })\n\n API.createData('selectedMetadata', t);\n API.createData('currentID', null);\n API.createData('selectedID', null);\n API.createData('selectedScores', null);\n API.createData('currentSpectraToDisplay', null);\n API.doAction('unsetActiveRow');\n break;\n \n case 'selectedID':\n let selectedID = API.getData('selectedID');\n \n if (selectedID != null) {\n let selectedData = data.filter(val => \n selectedID.includes(val.ID));\n \n let jcampUrl = selectedData[0].metadata.JcampUrl;\n let jcamp = (await superagent.get(jcampUrl).withCredentials()).text;\n API.createData('selectedJcamp', jcamp);\n\n API.createData('selectedID', null);\n console.log('selected jcamp loaded');\n }\n break;\n \n case 'currentID':\n currentID = API.getData('currentID').resurrect();\n\n selectedData = data.filter(val => \n currentID.includes(val.ID));\n \n let jcampUrl = selectedData[0].metadata.JcampUrl;\n let jcamp = (await superagent.get(jcampUrl).withCredentials()).text;\n API.createData('currentJcamp', jcamp);\n console.log('current jcamp loaded');\n currentID = ([currentID]);\n let currentData = data.filter(f =>\n currentID.includes(f.ID));\n \n\n \n let y = selectedData.map( x => \n x.dataMatrix.filter((value, index, Arr) => \n index % 2 == 1\n )\n );\n col = [];\n x.forEach( i => col.push({'_rgb':[0,0,255,1]}) );\n \n let currentSpectra = (\n {'type': 'color',\n 'y': currentData[0].dataMatrix.filter((value, index, Arr) => index % 2 == 1),\n 'x': x,\n 'color': col\n }\n );\n API.createData('currentSpectraToDisplay', {'data':currentSpectra});\n break;\n \n case 'selectedIDs':\n \n let s = API.getData('selectedIDs');\n console.log(s)\n \n if(s){\n let selectedIDs = s.map(x => x.ID);\n \n selectedData = data.filter(f =>\n selectedIDs.includes(f.ID));\n \n \n let y = selectedData.map( x => \n x.dataMatrix.filter((value, index, Arr) => \n index % 2 == 1\n )\n );\n \n selectedColors = API.getData('selectedIDs')\n .map(x => x.colors);\n \n let color = [];\n let spectra = selectedData.map((e, i) => {\n let groupColor = fh.hexToRgb(selectedColors[i]);\n color =[];\n x.forEach( () => color.push({'_rgb':groupColor}) )\n let s = (\n {'type': 'color',\n 'y': y[i],\n 'x': x,\n 'color': color\n });\n return s\n }\n );\n\n API.createData('spectraToDisplay', {'data':spectra});\n \n }\n \n default:\n break;\n}\n\n\n" | |
| ] | |
| } | |
| ], | |
| "libs": [ | |
| [ | |
| { | |
| "lib": "superagent", | |
| "alias": "superagent" | |
| }, | |
| { | |
| "lib": "chroma", | |
| "alias": "chroma" | |
| } | |
| ] | |
| ], | |
| "buttons": [ | |
| [ | |
| { | |
| "name": "createHeaders", | |
| "label": "create headers", | |
| "hide": [], | |
| "disable": [] | |
| }, | |
| { | |
| "name": "selectColor", | |
| "label": "set color", | |
| "hide": [], | |
| "disable": [] | |
| } | |
| ] | |
| ] | |
| } | |
| }, | |
| "layers": { | |
| "Default layer": { | |
| "position": { | |
| "left": 0, | |
| "top": 0 | |
| }, | |
| "size": { | |
| "width": 22, | |
| "height": 5 | |
| }, | |
| "zIndex": 0, | |
| "display": true, | |
| "title": "main executor", | |
| "bgColor": [ | |
| 255, | |
| 255, | |
| 255, | |
| 0 | |
| ], | |
| "wrapper": false, | |
| "created": true, | |
| "name": "Default layer" | |
| }, | |
| "admin": { | |
| "position": { | |
| "left": 0, | |
| "top": 37 | |
| }, | |
| "size": { | |
| "width": 59, | |
| "height": 70 | |
| }, | |
| "zIndex": 0, | |
| "display": true, | |
| "title": "main executor", | |
| "bgColor": [ | |
| 255, | |
| 255, | |
| 255, | |
| 0 | |
| ], | |
| "wrapper": true, | |
| "created": true, | |
| "name": "Default layer" | |
| } | |
| }, | |
| "id": 1, | |
| "vars_in": [ | |
| { | |
| "rel": "inputValue", | |
| "name": "selectedHeaders" | |
| }, | |
| { | |
| "rel": "inputValue", | |
| "name": "currentID" | |
| }, | |
| { | |
| "rel": "inputValue", | |
| "name": "selectedID" | |
| }, | |
| { | |
| "rel": "inputValue", | |
| "name": "currentHeader" | |
| }, | |
| { | |
| "rel": "inputValue", | |
| "name": "selectedIDs" | |
| } | |
| ], | |
| "actions_in": [ | |
| { | |
| "rel": "execute", | |
| "name": "selectColor" | |
| }, | |
| { | |
| "rel": "execute", | |
| "name": "selectedHeaders" | |
| }, | |
| { | |
| "rel": "execute", | |
| "name": "colorScores" | |
| } | |
| ], | |
| "vars_out": [ | |
| { | |
| "jpath": [] | |
| } | |
| ], | |
| "actions_out": [ | |
| { | |
| "jpath": [] | |
| } | |
| ], | |
| "toolbar": { | |
| "custom": [ | |
| [ | |
| { | |
| "title": "", | |
| "icon": "", | |
| "action": "", | |
| "position": "begin", | |
| "color": [ | |
| 100, | |
| 100, | |
| 100, | |
| 1 | |
| ] | |
| } | |
| ] | |
| ], | |
| "common": [ | |
| { | |
| "toolbar": [ | |
| [ | |
| "Open Preferences" | |
| ] | |
| ] | |
| } | |
| ] | |
| }, | |
| "css": [ | |
| { | |
| "fontSize": [ | |
| "" | |
| ], | |
| "fontFamily": [ | |
| "" | |
| ] | |
| } | |
| ], | |
| "title": "main executor", | |
| "zindex": 3 | |
| }, | |
| { | |
| "url": "modules/types/edition/slick_grid/", | |
| "configuration": { | |
| "sections": {}, | |
| "groups": { | |
| "group": [ | |
| { | |
| "slickCheck": [ | |
| [ | |
| "enableCellNavigation", | |
| "rowNumbering", | |
| "forceFitColumns", | |
| "highlightScroll", | |
| "filterColumns", | |
| "editable" | |
| ] | |
| ], | |
| "copyPaste": [ | |
| [] | |
| ], | |
| "copyPasteOptions": [ | |
| [ | |
| "newRows" | |
| ] | |
| ], | |
| "autoColumns": [ | |
| [] | |
| ], | |
| "toolbar": [ | |
| [] | |
| ], | |
| "colorjpath": [ | |
| [ | |
| "colors" | |
| ] | |
| ], | |
| "slick.defaultColumnWidth": [ | |
| null | |
| ], | |
| "slick.rowHeight": [ | |
| null | |
| ], | |
| "slick.headerRowHeight": [ | |
| 30 | |
| ], | |
| "slick.selectionModel": [ | |
| "row" | |
| ], | |
| "idProperty": [ | |
| "" | |
| ], | |
| "filterType": [ | |
| "pref" | |
| ], | |
| "filterRow": [ | |
| "// Documentation: https://github.com/NPellet/visualizer/blob/46b40ca86345f8fa313563bf9c6ecb80ba323101/src/modules/types/edition/slick_grid/view.js#L1695-L1735" | |
| ], | |
| "customJpaths": [ | |
| "" | |
| ] | |
| } | |
| ], | |
| "cols": [ | |
| [ | |
| { | |
| "jpath": [], | |
| "editor": "none", | |
| "forceType": "", | |
| "formatter": "typerenderer", | |
| "copyFormatter": "default", | |
| "visibility": "both", | |
| "rendererOptions": "", | |
| "editorOptions": "", | |
| "hideColumn": [] | |
| } | |
| ] | |
| ], | |
| "actionCols": [ | |
| [ | |
| { | |
| "backgroundColor": [ | |
| 255, | |
| 255, | |
| 255, | |
| 0 | |
| ], | |
| "color": [ | |
| 0, | |
| 0, | |
| 0, | |
| 1 | |
| ], | |
| "position": "end", | |
| "clickMode": "text" | |
| } | |
| ] | |
| ], | |
| "groupings": [ | |
| [ | |
| { | |
| "getter": [] | |
| } | |
| ] | |
| ], | |
| "actionOutButtons": [ | |
| [ | |
| {} | |
| ] | |
| ], | |
| "data": [ | |
| { | |
| "saveInView": [ | |
| [] | |
| ], | |
| "varname": [ | |
| "" | |
| ], | |
| "data": [ | |
| "[]" | |
| ] | |
| } | |
| ] | |
| } | |
| }, | |
| "layers": { | |
| "Default layer": { | |
| "position": { | |
| "left": 20, | |
| "top": 0 | |
| }, | |
| "size": { | |
| "width": 145, | |
| "height": 42 | |
| }, | |
| "zIndex": 0, | |
| "display": true, | |
| "title": "Metadata Table (click to load original data / double click to superimpose)", | |
| "bgColor": [ | |
| 255, | |
| 255, | |
| 255, | |
| 0 | |
| ], | |
| "wrapper": true, | |
| "created": true, | |
| "name": "Default layer" | |
| }, | |
| "admin": { | |
| "position": { | |
| "left": 0, | |
| "top": 4 | |
| }, | |
| "size": { | |
| "width": 168, | |
| "height": 32 | |
| }, | |
| "zIndex": 0, | |
| "display": true, | |
| "title": "", | |
| "bgColor": [ | |
| 255, | |
| 255, | |
| 255, | |
| 0 | |
| ], | |
| "wrapper": true, | |
| "created": true, | |
| "name": "Default layer" | |
| } | |
| }, | |
| "id": 2, | |
| "vars_in": [ | |
| { | |
| "rel": "list", | |
| "name": "selectedMetadata" | |
| } | |
| ], | |
| "actions_in": [ | |
| { | |
| "rel": "unsetActiveRow", | |
| "name": "unsetActiveRow" | |
| }, | |
| { | |
| "rel": "selectRows", | |
| "name": "setSelectedRows" | |
| } | |
| ], | |
| "actions_out": [ | |
| { | |
| "jpath": [] | |
| } | |
| ], | |
| "vars_out": [ | |
| { | |
| "event": "onHover", | |
| "rel": "row", | |
| "jpath": [ | |
| "ID" | |
| ], | |
| "name": "currentID" | |
| }, | |
| { | |
| "event": "onDoubleClick", | |
| "rel": "row", | |
| "jpath": [ | |
| "ID" | |
| ], | |
| "name": "selectedID" | |
| }, | |
| { | |
| "event": "onRowsSelect", | |
| "rel": "rows", | |
| "jpath": [], | |
| "name": "selectedIDs" | |
| } | |
| ], | |
| "toolbar": { | |
| "custom": [ | |
| [ | |
| { | |
| "title": "", | |
| "icon": "", | |
| "action": "", | |
| "position": "begin", | |
| "color": [ | |
| 100, | |
| 100, | |
| 100, | |
| 1 | |
| ] | |
| } | |
| ] | |
| ], | |
| "common": [ | |
| { | |
| "toolbar": [ | |
| [ | |
| "Show fullscreen", | |
| "Print", | |
| "Export Data" | |
| ] | |
| ] | |
| } | |
| ] | |
| }, | |
| "css": [ | |
| { | |
| "fontSize": [ | |
| "" | |
| ], | |
| "fontFamily": [ | |
| "" | |
| ] | |
| } | |
| ], | |
| "title": "", | |
| "zindex": 3 | |
| }, | |
| { | |
| "url": "modules/types/edition/slick_grid/", | |
| "configuration": { | |
| "sections": {}, | |
| "groups": { | |
| "group": [ | |
| { | |
| "slickCheck": [ | |
| [ | |
| "enableCellNavigation", | |
| "rowNumbering", | |
| "forceFitColumns", | |
| "highlightScroll", | |
| "forgetLastActive", | |
| "filterColumns", | |
| "keepSelected", | |
| "editable" | |
| ] | |
| ], | |
| "copyPaste": [ | |
| [] | |
| ], | |
| "copyPasteOptions": [ | |
| [ | |
| "newRows", | |
| "readOnly" | |
| ] | |
| ], | |
| "autoColumns": [ | |
| [] | |
| ], | |
| "toolbar": [ | |
| [] | |
| ], | |
| "colorjpath": [ | |
| [] | |
| ], | |
| "slick.defaultColumnWidth": [ | |
| null | |
| ], | |
| "slick.rowHeight": [ | |
| null | |
| ], | |
| "slick.headerRowHeight": [ | |
| 30 | |
| ], | |
| "slick.selectionModel": [ | |
| "row" | |
| ], | |
| "idProperty": [ | |
| "" | |
| ], | |
| "filterType": [ | |
| "pref" | |
| ], | |
| "filterRow": [ | |
| "// Documentation: https://github.com/NPellet/visualizer/blob/46b40ca86345f8fa313563bf9c6ecb80ba323101/src/modules/types/edition/slick_grid/view.js#L1695-L1735" | |
| ], | |
| "customJpaths": [ | |
| "" | |
| ] | |
| } | |
| ], | |
| "cols": [ | |
| [ | |
| { | |
| "jpath": [], | |
| "editor": "none", | |
| "forceType": "", | |
| "formatter": "typerenderer", | |
| "copyFormatter": "default", | |
| "visibility": "both", | |
| "rendererOptions": "", | |
| "editorOptions": "", | |
| "hideColumn": [] | |
| } | |
| ] | |
| ], | |
| "actionCols": [ | |
| [ | |
| { | |
| "backgroundColor": [ | |
| 255, | |
| 255, | |
| 255, | |
| 0 | |
| ], | |
| "color": [ | |
| 0, | |
| 0, | |
| 0, | |
| 1 | |
| ], | |
| "position": "end", | |
| "clickMode": "text" | |
| } | |
| ] | |
| ], | |
| "groupings": [ | |
| [ | |
| { | |
| "getter": [] | |
| } | |
| ] | |
| ], | |
| "actionOutButtons": [ | |
| [ | |
| {} | |
| ] | |
| ], | |
| "data": [ | |
| { | |
| "saveInView": [ | |
| [] | |
| ], | |
| "varname": [ | |
| "" | |
| ], | |
| "data": [ | |
| "[]" | |
| ] | |
| } | |
| ] | |
| } | |
| }, | |
| "layers": { | |
| "Default layer": { | |
| "position": { | |
| "left": 0, | |
| "top": 6 | |
| }, | |
| "size": { | |
| "width": 19, | |
| "height": 88 | |
| }, | |
| "zIndex": 0, | |
| "display": true, | |
| "title": "Metadata headers (select to fill the table)", | |
| "bgColor": [ | |
| 255, | |
| 255, | |
| 255, | |
| 0 | |
| ], | |
| "wrapper": true, | |
| "created": true, | |
| "name": "Default layer" | |
| }, | |
| "admin": { | |
| "position": { | |
| "left": 60, | |
| "top": 37 | |
| }, | |
| "size": { | |
| "width": 39, | |
| "height": 29 | |
| }, | |
| "zIndex": 0, | |
| "display": true, | |
| "title": "", | |
| "bgColor": [ | |
| 255, | |
| 255, | |
| 255, | |
| 0 | |
| ], | |
| "wrapper": true, | |
| "created": true, | |
| "name": "Default layer" | |
| } | |
| }, | |
| "id": 3, | |
| "vars_in": [ | |
| { | |
| "rel": "list", | |
| "name": "headers" | |
| } | |
| ], | |
| "actions_in": [ | |
| { | |
| "rel": "selectRows", | |
| "name": "setHeader" | |
| } | |
| ], | |
| "actions_out": [ | |
| { | |
| "jpath": [] | |
| } | |
| ], | |
| "vars_out": [ | |
| { | |
| "event": "onRowsSelect", | |
| "rel": "rows", | |
| "jpath": [], | |
| "name": "selectedHeaders" | |
| }, | |
| { | |
| "event": "onSelect", | |
| "rel": "row", | |
| "jpath": [ | |
| "header" | |
| ], | |
| "name": "currentHeader" | |
| } | |
| ], | |
| "toolbar": { | |
| "custom": [ | |
| [ | |
| { | |
| "title": "", | |
| "icon": "", | |
| "action": "", | |
| "position": "begin", | |
| "color": [ | |
| 100, | |
| 100, | |
| 100, | |
| 1 | |
| ] | |
| } | |
| ] | |
| ], | |
| "common": [ | |
| { | |
| "toolbar": [ | |
| [ | |
| "Open Preferences" | |
| ] | |
| ] | |
| } | |
| ] | |
| }, | |
| "css": [ | |
| { | |
| "fontSize": [ | |
| "" | |
| ], | |
| "fontFamily": [ | |
| "" | |
| ] | |
| } | |
| ], | |
| "title": "", | |
| "zindex": 3 | |
| }, | |
| { | |
| "url": "modules/types/client_interaction/code_executor/", | |
| "configuration": { | |
| "sections": {}, | |
| "groups": { | |
| "group": [ | |
| { | |
| "display": [ | |
| [ | |
| "editor", | |
| "buttons" | |
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| "'use strict';\n\nlet Utils = {};\nUtils.norm = function norm(X) {\n return Math.sqrt(X.clone().apply(pow2array).sum());\n};\n\n\nconst {\n Matrix,\n PCA,\n CrossValidation: CV,\n MatrixStat: Stat,\n ArrayStat\n} = ML;\n\nconst computeMean = ArrayStat.mean;\nconst computeVariance = ArrayStat.variance;\nconst EVD = ML.EVD;\nconst SVD = ML.SVD;\nconst mean = Stat.mean;\nconst stdev = Stat.standardDeviation;\n\n\n\n\n// ============================================\n// Factory functions\n// ============================================\n\n// factory function to create datasets\n// factory function are preferred to class because of flexibility\n// https://medium.com/javascript-scene/javascript-factory-functions-with-es6-4d224591a8b1\nconst Dataset = ({ dataMatrix, options } = {}) => {\n let nObs = dataMatrix.rows;\n let nVar = dataMatrix.columns;\n\n let aa = {};\n const {\n descriptio = 123\n // observations = Array(nObs).fill(null).map((x, i) => 'OBS' + (i + 1)),\n // variables = Array(nVar).fill(null).map((x, i) => 'VAR' + (i + 1)),\n // description = 'NA'\n // metadata = [],\n // outliers = []\n } = aa;\n\n let defaults = {\n observations: Array(nObs).fill(null).map((x, i) => `OBS${i + 1}`),\n variables: Array(nVar).fill(null).map((x, i) => `VAR${i + 1}`),\n description: 'NA',\n metadata: [],\n outliers: []\n };\n\n options = Object.assign({}, defaults, options);\n\n let observations = options.observations;\n let variables = options.variables;\n let description = options.description;\n let dataClass = options.dataClass;\n let metadata = options.metadata;\n let outliers = options.outliers;\n\n if (options.observations.length !== nObs ||\n options.variables.length !== nVar ||\n options.dataClass[0].value.length !== nObs) {\n throw new RangeError('observations and dataMatrix have different number of rows');\n }\n\n // private util functions\n\n function getRowIndexByID() {\n let sampleList = observations.map((x) => x);\n let outlierList = outliers.map((x) => x.id);\n let ind = outlierList.map((e) => sampleList.indexOf(e));\n return ind;\n }\n\n function getClassVector(dataClass) {\n let title = dataClass.title;\n let classVector = dataClass.value;\n let type = typeof (classVector[0]);\n let counts = {};\n switch (type) {\n case 'string':\n counts = {};\n classVector.forEach((x) => counts[x] = (counts[x] || 0) + 1);\n break;\n case 'number':\n classVector = classVector.map((x) => x.toString());\n counts = {};\n classVector.forEach((x) => counts[x] = (counts[x] || 0) + 1);\n break;\n default:\n }\n let groupIDs = Object.keys(counts);\n let nClass = groupIDs.length;\n let classFactor = classVector.map((x) => groupIDs.indexOf(x));\n let classMatrix = Matrix.from1DArray(nObs, 1, classFactor);\n return ({ title,\n groupIDs,\n nClass,\n classVector,\n classFactor,\n classMatrix\n });\n }\n\n return ({ description,\n\n // API exposed functions\n\n getClass() {\n let a = dataClass.map((x) => getClassVector(x));\n return a;\n },\n\n getOutliers() {\n return outliers;\n },\n\n addOutliers(outliersList) {\n let outliersIDs = outliers.map((x) => x.id);\n let newList = outliersList.filter((f) => !outliersIDs.includes(f.id));\n console.log(newList);\n if (newList.length > 0) newList.forEach((e) => outliers.push(e));\n console.log(outliers);\n this.summary();\n // return this;\n },\n\n rmOutliers(outliersList) {\n outliers = outliers.filter((f) => !outliersList.includes(f.id));\n console.log(outliers);\n this.summary();\n // return this;\n },\n\n clean() {\n if (outliers.length > 0) {\n let ind = getRowIndexByID();\n console.log(ind);\n let cleanObservations = observations.filter((e, i) => !ind.includes(i));\n let cleanDataMatrix = new Matrix(nObs - ind.length, nVar);\n\n let counter = 0;\n dataMatrix.forEach((e, i) => {\n if (!ind.includes(i)) {\n cleanDataMatrix.setRow(counter, e);\n counter += 1;\n }\n });\n\n let cleanDataClass = dataClass.map((x) => {\n return { title: x.title,\n value: x.value.filter((e, i) => !ind.includes(i)) };\n });\n // let cleanMetadata = metadata.filter((e, i) => !ind.includes(i));\n\n return Dataset({\n dataMatrix: cleanDataMatrix,\n options: {\n observations: cleanObservations,\n variables: variables,\n dataClass: cleanDataClass,\n outliers: [],\n // metadata: cleanMetadata, // lack of test for dimensions\n description: `clean ${description}` } });\n } else {\n return this;\n }\n },\n\n sample(list) {\n // console.log(list.length)\n if (list.length > 0) {\n let ind = list;\n // console.log(ind);\n // filter Observations vector\n\n let trainObservations = observations.filter((e, i) => !ind.includes(i));\n let testObservations = observations.filter((e, i) => ind.includes(i));\n\n // filter data matrix\n let trainDataMatrix = new Matrix(nObs - ind.length, nVar);\n let testDataMatrix = new Matrix(ind.length, nVar);\n\n let counter = 0;\n dataMatrix.forEach((e, i) => {\n if (!ind.includes(i)) {\n trainDataMatrix.setRow(counter, e);\n counter += 1;\n }\n });\n\n counter = 0;\n dataMatrix.forEach((e, i) => {\n if (ind.includes(i)) {\n testDataMatrix.setRow(counter, e);\n counter += 1;\n }\n });\n\n // filter class vector\n let trainDataClass = dataClass.map((x) => {\n return { title: x.title,\n value: x.value.filter((e, i) => !ind.includes(i)) };\n });\n\n let testDataClass = dataClass.map((x) => {\n return { title: x.title,\n value: x.value.filter((e, i) => ind.includes(i)) };\n });\n\n // let cleanMetadata = metadata.filter((e, i) => !ind.includes(i));\n\n let train = Dataset({\n dataMatrix: trainDataMatrix,\n options: {\n observations: trainObservations,\n variables: variables,\n dataClass: trainDataClass,\n outliers: [],\n // metadata: cleanMetadata, // lack of test for dimensions\n description: `train ${description}` } });\n\n let test = Dataset({\n dataMatrix: testDataMatrix,\n options: {\n observations: testObservations,\n variables: variables,\n dataClass: testDataClass,\n outliers: [],\n // metadata: cleanMetadata, // lack of test for dimensions\n description: `test ${description}` } });\n\n return { train, test };\n } else {\n return this;\n }\n },\n\n // return everything but cannot be changed\n summary(verbose = 0) {\n if (verbose === 1) {\n console.log(`Description: ${description\n }\\nNumber of variables: ${nVar\n }\\nNumber of observations: ${nObs\n }\\nNumber of outliers:${outliers.length\n }\\nHas class: ${dataClass.length\n }\\nHas metadata: ${metadata.length > 0}`);\n }\n return ({ dataMatrix,\n dataClass,\n nObs,\n nVar,\n observations,\n variables,\n metadata,\n description\n });\n }\n\n });\n};\n\nconst oplsWrapper = (data) => {\n let train; let test;\n\n if (!data.train && !data.test) {\n train = data;\n } else {\n train = data.train;\n test = data.test;\n }\n\n // get data\n let matrixYData = train.summary().dataMatrix;\n let dataClass = train.getClass()[0].classMatrix;\n\n if (test) {\n var testMatrixYData = test.summary().dataMatrix;\n var testDataClass = test.getClass()[0].classMatrix;\n\n }\n \n // scaling\n let options = { center: true, scale: true };\n matrixYData = scale(matrixYData, options);\n dataClass = scale(dataClass, options);\n\n if (test) {\n testMatrixYData = scale(testMatrixYData, options);\n testDataClass = scale(testDataClass, options);\n }\n\n let variables = train.summary().variables;\n let observations = train.summary().observations;\n\n if (test) {\n let testVariables = test.summary().variables;\n let testObservations = test.summary().observations;\n }\n\n let oplsResultV = [];\n let plsCompV = [];\n let testPlsCompV = [];\n\n let R2Y = []; let R2X = []; let Q2 = [];\n let xResidual, testxResidual, scores, testScores, E_h, testE_h, Yhat, testYhat, oplsResult, plsComp, testPlsComp, msg, testOpls;\n\n let scoresExport = [];\n let testScoresExport = [];\n\n for (let i = 1; i < 5; i++) {\n if (i == 1) {\n oplsResult = OPLS(matrixYData.clone(), dataClass.clone());\n (test) ? testOpls = OPLS(testMatrixYData.clone(), testDataClass.clone()) : [];\n } else {\n oplsResult = OPLS(xResidual.clone(), dataClass.clone());\n (test) ? testOpls = OPLS(testxResidual.clone(), testDataClass.clone()) : [];\n }\n \n oplsResultV.push(oplsResult);\n console.log(oplsResult);\n xResidual = oplsResult.err;\n (test) ? testxResidual = testOpls.err : [];\n\n plsComp = PLS(xResidual.clone(), dataClass.clone());\n plsCompV.push(plsComp);\n console.log(plsComp);\n \n if (test) {\n testPlsComp = PLS(testxResidual.clone(), testDataClass.clone());\n testPlsCompV.push(testPlsComp);\n }\n \n\n scores = xResidual.mmul(plsComp.weights.transpose()); // ok\n E_h = xResidual.clone().sub(scores.clone().mmul(plsComp.loadings)); // ok\n Yhat = scores.clone().mul(plsComp.betas); // ok\n\n if (test) {\n testScores = testxResidual.mmul(testPlsComp.weights.transpose()); // ok\n testE_h = testxResidual.clone().sub(testScores.clone().mmul(testPlsComp.loadings)); // ok\n testYhat = testScores.clone().mul(testPlsComp.betas); // ok\n }\n\n if (test) {\n let testTssy = tss(testDataClass.clone());\n console.log('tssy:'+testTssy)\n let testRss = testDataClass.clone().sub(testYhat);\n \n testRss = testRss.clone().mul(testRss).sum();\n \n let Q2y = 1 - (testRss / testTssy);\n console.log('Q2y:'+Q2y)\n Q2.push(Q2y);\n msg = `Q2y: ${Q2y.toFixed(4).toString()}`;\n }\n\n let tssy = tss(dataClass.clone());\n let rss = dataClass.clone().sub(Yhat);\n rss = rss.clone().mul(rss).sum();\n let R2y = 1 - (rss / tssy);\n R2Y.push(R2y);\n msg = `R2y: ${R2y.toFixed(4).toString()}`;\n\n let tssx = tss(matrixYData.clone());\n let X_ex = plsComp.scores.clone().mmul(plsComp.loadings);\n let rssx = tss(X_ex.clone());\n let R2x = rssx / tssx;\n R2X.push(R2x);\n msg = `${msg}\\nR2x: ${R2x.toFixed(4).toString()}`;\n\n // store scores for export\n scoresExport.push({\n scoresX: scores.map((x) => x[0]),\n scoresY: oplsResult.tOrtho.map((x) => x[0])\n });\n \n if (test) {\n testScoresExport.push({\n scoresX: testScores.map((x) => x[0]),\n scoresY: testOpls.tOrtho.map((x) => x[0])\n });\n }\n \n console.log(`OPLS iteration over #ofComponents: ${i}`);\n } // end of loop\n\n let eigenVectorMatrix = new Matrix(plsCompV.length, variables.length);\n plsCompV.forEach((e, i) => eigenVectorMatrix.setRow(i, e.loadings));\n\n let eigenVectors = new Array(eigenVectorMatrix.rows);\n\n let xAxis;\n if (typeof (variables[0]) === 'number') {\n xAxis = variables;\n } else {\n xAxis = seq_along(variables);\n }\n\n for (let i = 0, l = eigenVectors.length; i < l; i++) {\n eigenVectors[i] = { id: [i + 1],\n eigenVal: null,\n data: { x: xAxis, y: eigenVectorMatrix[i] } };\n }\n API.createData('loadings', eigenVectors);\n\n\n let R2YPlot = bar([], R2Y);\n API.createData('R2Y', R2YPlot);\n\n let R2XPlot = bar([], R2X);\n API.createData('R2X', R2XPlot);\n \n if (test) {\n let Q2Plot = bar([], Q2);\n API.createData('Q2', Q2Plot);\n }\n\n return ({\n R2X,\n R2Y,\n\n getScores(markers, radius) {\n\n let scoresPlot = [];\n scoresExport.forEach((e, i) => {\n let splot = plot(e.scoresX, e.scoresY, observations, train.getClass()[0], markers, radius);\n scoresPlot.push({index: i + 1, chart: splot});\n })\n \n // API.createData('pcaResult', scoresPlot[0].chart.chart);\n // API.createData('pcaResult' + 'ellipse', scoresPlot[0].chart.ellipse);\n //API.createData('scoresPlot', scoresPlot);\n \n let testScoresPlot = [];\n if (test) {\n \n testScoresExport.forEach((e, i) => {\n let splot = plot(e.scoresX, e.scoresY, observations, test.getClass()[0], markers, radius * 2);\n testScoresPlot.push({index: i + 1, chart: splot});\n })\n \n // API.createData('testPcaResult', testScoresPlot[0].chart.chart);\n // API.createData('testScoresPlot', testScoresPlot);\n \n }\n if (test) {\n return {scoresPlot, testScoresPlot};\n // return {scoresExport, testScoresExport};\n } else {\n return {scoresPlot};\n // return {scoresExport};\n }\n \n // console.log('scores');\n // let scorePlot = plot(scoresExport[nc - 1].scoresX,\n // scoresExport[nc - 1].scoresY,\n // observations,\n // train.getClass()[0]\n // );\n\n // API.createData('pcaResult', scorePlot.chart);\n // API.createData('pcaResult' + 'ellipse', scorePlot.ellipse);\n\n // return scoresExport;\n }\n });\n};\n\n\n// outlier factory\nconst Outlier = ([id, comment] = {}) => ({\n id,\n comment\n});\n// let llist = [];\n// let list = ['ID2', 'ID3'];\n// list.forEach((e,i) => llist.push(Outlier([e])));\n// console.log(llist);\n//\n// a = list.map(x => Outlier([x]));\n\n\n// is this function a closure? it works as is, but has not\n// the signature Object = (() => {})();\n// const count = (() => {\n// let counter = 0;\n// return () => counter += 1; return counter;\n// })();\nconst DataClass = (title, value) => {\n let dataClasses = [];\n dataClasses.push({ title, value });\n return ({\n getClass() {\n return dataClasses;\n },\n addClass(title, value) {\n dataClasses.push({ title, value });\n return this;\n }\n });\n};\n// let a = DataClass( 'testClass', ['id1', 'id2', 'id2', 'id1', 'id3'] );\n// a.addClass( 'testClass', ['id1', 'id2', 'id2', 'id1', 'id3'] );\n// console.log(a.getClass());\n\n// ============================================\n// Stat functions\n// ============================================\n\nfunction tss(x) {\n return x.mul(x).sum();\n}\n\nfunction featureNormalize(dataset) {\n let means = Stat.mean(dataset);\n let std = Stat.standardDeviation(dataset, means, true);\n let result = Matrix.checkMatrix(dataset).subRowVector(means);\n return { result: result.divRowVector(std), means: means, std: std };\n}\n\nfunction pow2array(i, j) {\n this[i][j] = this[i][j] * this[i][j];\n return this;\n}\n\n// https://bost.ocks.org/mike/shuffle/\nfunction shuffle(array) {\n let m = array.length; let t; let i;\n\n // While there remain elements to shuffle…\n while (m) {\n // Pick a remaining element…\n i = Math.floor(Math.random() * m--);\n\n // And swap it with the current element.\n t = array[m];\n array[m] = array[i];\n array[i] = t;\n }\n\n return array;\n}\n\n// check this function to importa class from csv\n// it should be possible to import dataset from csv\nfunction createMatrixClass(classFile, spectraDataSet, options = {}) {\n options = Object.assign({}, { delimiter: ',', header: true, debug: false }, options);\n let classVector = [];\n let dataName = [];\n let meta = Papa.parse(classFile, { delimiter: options.delimiter, header: options.header }).data;\n let metaIndex = {};\n meta.forEach((a) => metaIndex[a.filename] = a);\n let withoutClass = [];\n let dataClass = new Array(spectraDataSet.length);\n let i = dataClass.length;\n while (i--) {\n dataClass[i] = new Array(2).fill(-1);\n let vector = dataClass[i];\n let filename = spectraDataSet[i].sd.filename;\n let index = metaIndex[filename];\n if (typeof index === 'undefined') {\n withOutClass.push(filename);\n } else {\n vector[index.class] = 1;\n }\n }\n return { dataClass: dataClass, withOutClass: withoutClass };\n}\n\nfunction computeQ2(realY, predictedY) {\n realY = Matrix.checkMatrix(realY);\n predictedY = Matrix.checkMatrix(predictedY);\n let meansY = Stat.mean(realY);\n\n let press = predictedY.map((row, rowIndex) => {\n return row.map((element, colIndex) => {\n return Math.pow(realY[rowIndex][colIndex] - element, 2);\n });\n });\n\n\n let tss = Y.map((row) => {\n return row.map((element, colIndex) => {\n return Math.pow(element - meansY[colIndex], 2);\n });\n });\n\n press = Matrix.checkMatrix(press).sum();\n tss = Matrix.checkMatrix(tss).sum();\n\n return 1 - press / tss;\n}\n\nfunction getColSum(matrix, column) {\n let sum = 0;\n for (let i = 0; i < matrix.rows; i++) {\n sum += matrix[i][column];\n }\n return sum;\n}\n\nfunction maxSumColIndex(data) {\n let maxIndex = 0;\n let maxSum = -Infinity;\n for (let i = 0; i < data.columns; ++i) {\n let currentSum = getColSum(data, i);\n if (currentSum > maxSum) {\n maxSum = currentSum;\n maxIndex = i;\n }\n }\n return maxIndex;\n}\n\n\nfunction seq_along(x) {\n return x.map((x, i) => i + 1);\n}\n\nfunction bar(x = [], y) {\n x.length == 0 ? x = y.map((x, i) => i + 1) : [];\n return ({ x, y });\n}\n\nfunction transpose(matrix) {\n return matrix[0].map((col, i) => matrix.map((row) => row[i]));\n}\n\nfunction sampleClass(classVector, fraction) {\n // sort the vector\n let classVectorSorted = JSON.parse(JSON.stringify(classVector));\n let result = Array.from(Array(classVectorSorted.length).keys())\n .sort((a, b) => (classVectorSorted[a] < classVectorSorted[b] ? -1 :\n (classVectorSorted[b] < classVectorSorted[a]) | 0));\n classVectorSorted.sort((a, b) => (a < b ? -1 : (b < a) | 0));\n\n // counts the class elements\n let counts = {};\n classVectorSorted.forEach((x) => counts[x] = (counts[x] || 0) + 1);\n\n // pick a few per class\n let indexOfSelected = [];\n\n Object.keys(counts).forEach((e, i) => {\n let shift = [];\n Object.values(counts).reduce((a, c, i) => shift[i] = a + c, 0);\n\n let arr = [...Array(counts[e]).keys()];\n\n let r = [];\n for (let i = 0; i < Math.floor(counts[e] * fraction); i++) {\n let n = arr[Math.floor(Math.random() * arr.length)];\n r.push(n);\n let ind = arr.indexOf(n);\n arr.splice(ind, 1);\n }\n\n (i == 0) ? indexOfSelected = indexOfSelected.concat(r) : indexOfSelected = indexOfSelected.concat(r.map((x) => x + shift[i - 1]));\n });\n\n // sort back the index\n let indexBack = [];\n indexOfSelected.forEach((e) => indexBack.push(result[e]));\n\n return indexBack;\n}\n\nfunction scale(dataset, options) {\n let center = !!options.center;\n let scale = !!options.scale;\n\n dataset = new Matrix(dataset);\n\n if (center) {\n const means = mean(dataset);\n const stdevs = scale ? stdev(dataset, means, true) : null;\n // means = means;\n dataset.subRowVector(means);\n if (scale) {\n for (var i = 0; i < stdevs.length; i++) {\n if (stdevs[i] === 0) {\n throw new RangeError(`Cannot scale the dataset (standard deviation is zero at index ${i}`);\n }\n }\n // stdevs = stdevs;\n dataset.divRowVector(stdevs);\n }\n }\n\n return dataset;\n}\n\nfunction plot(x, y, observations, dataClass, markerStyle, size) {\n let nClass = dataClass.nClass;\n\n let selectedScale = chroma.scale(['green', 'black']).domain([0, nClass - 1]).mode('hsl');\n let unselectedScale = chroma.scale(['red', 'blue']).domain([0, nClass - 1]).mode('hsl');\n // console.log(unselectedScale(0).toString());\n\n let result = {\n title: 'main plot',\n data: [\n {\n x: x,\n y: y,\n type: 'scatter',\n info: Array.from({ length: x.length }, (v, k) => k + 1),\n _highlight: [],\n styles: {\n unselected: new Array(x.length),\n selected: new Array(x.length)\n }\n }\n ]\n };\n\n let unselected = dataClass.classFactor.map((x, i) => ({\n fill: unselectedScale(x).toString(),\n shape: markerStyle,\n cx: 0,\n cy: 0,\n r: size,\n height: '5px',\n width: '5px',\n stroke: 'transparent'\n }));\n // console.log(unselected);\n\n let selected = dataClass.classFactor.map((x, i) => ({\n fill: selectedScale(x).toString(),\n shape: markerStyle,\n cx: 0,\n cy: 0,\n r: size,\n height: '5px',\n width: '5px',\n stroke: 'transparent'\n }));\n\n let highlight = observations.map((x, i) => ({ _highlight: x }));\n\n result.data[0].styles.unselected = unselected;\n result.data[0].styles.selected = selected;\n result.data[0]._highlight = highlight;\n\n let ellipse = predictEllipse(x, y, dataClass, 5, 0.95, 1024);\n\n return { chart: result, ellipse };\n}\n\nfunction plotResult(prediction, observations, dataClass, pcaPlot, varName) {\n let x = prediction.getColumn(pcaPlot.pcx - 1);\n let y = prediction.getColumn(pcaPlot.pcy - 1);\n\n let nbClasses = dataClass.nClass;\n\n let selectedScale = chroma.scale(['green', 'black']).domain([0, nbClasses - 1]).mode('hsl');\n let unselectedScale = chroma.scale(['red', 'blue']).domain([0, nbClasses - 1]).mode('hsl');\n // console.log(unselectedScale(0).toString());\n\n let result = {\n title: 'main plot',\n data: [\n {\n x: x,\n y: y,\n type: 'scatter',\n info: Array.from({ length: x.length }, (v, k) => k + 1),\n _highlight: [],\n styles: {\n unselected: new Array(x.length),\n selected: new Array(x.length)\n }\n }\n ]\n };\n\n let unselected = dataClass.classFactor.map((x, i) => ({\n fill: unselectedScale(x).toString(),\n shape: 'circle',\n cx: 0,\n cy: 0,\n r: 3,\n height: '5px',\n width: '5px',\n stroke: 'transparent'\n }));\n console.log(unselected);\n\n let selected = dataClass.classFactor.map((x, i) => ({\n fill: selectedScale(x).toString(),\n shape: 'circle',\n cx: 0,\n cy: 0,\n r: 3,\n height: '5px',\n width: '5px',\n stroke: 'transparent'\n }));\n\n let highlight = observations.map((x, i) => ({ _highlight: x }));\n\n result.data[0].styles.unselected = unselected;\n result.data[0].styles.selected = selected;\n result.data[0]._highlight = highlight;\n\n let ellipse = predictEllipse(x, y, dataClass, 5, 0.95, 1024);\n\n return { chart: result, ellipse };\n}\n\nfunction PCA_fast(dataset, options = {}) {\n let {\n nComponents = 2,\n threshold = 1e-9,\n maxIterations = 100\n } = options;\n\n let eMatrix = _adjust(dataset, options);\n\n let r = eMatrix.rows;\n let c = eMatrix.columns;\n\n let T = Matrix.zeros(r, nComponents);\n let P = Matrix.zeros(c, nComponents);\n let eigenvalues = new Array(nComponents);\n\n for (let i = 0; i < nComponents; i++) {\n let tIndex = maxSumColIndex(eMatrix.clone().mulM(eMatrix));\n let t = eMatrix.getColumnVector(tIndex);\n\n let k = 0;\n let tNew = t.dot(t);\n for (let tOld = Number.MAX_SAFE_INTEGER; Math.abs(tOld - tNew) > threshold && k < maxIterations; k++) {\n let p = getLoading(eMatrix, t);\n t = eMatrix.mmul(p);\n tOld = tNew;\n tNew = t.dot(t);\n }\n eigenvalues[i] = tNew;\n T.setColumn(i, t);\n P.setColumn(i, p);\n eMatrix.sub(t.mmul(p.transpose()));\n }\n return { T, P };\n}\n\nfunction getLoading(e, t) {\n let m = e.columns;\n let n = e.rows;\n\n let result = new Matrix(m, 1);\n\n let Bcolj = new Array(n);\n for (let i = 0; i < m; i++) {\n let s = 0;\n for (let k = 0; k < n; k++) {\n s += e.get(k, i) * t[k][0];\n }\n result.set(i, 0, s);\n }\n return result.mul(1 / result.norm());\n}\n\nfunction PLS(dataset, predictions, options = {}) {\n const {\n numberOSC = 2,\n scale = false\n } = options;\n\n var X = Matrix.checkMatrix(dataset);\n var Y = Matrix.checkMatrix(predictions);\n\n if (scale) {\n X = featureNormalize(X).result;\n Y = featureNormalize(y).result;\n }\n // console.log(X, Y);\n var rows = X.rows;\n var columns = X.columns;\n\n var u = Y.getColumnVector(0);\n let diff = 1;\n let t, q, w, tOld;\n for (var i = 0; i < numberOSC && diff > 1e-10; i++) {\n w = X.transpose().mmul(u).div(u.transpose().mmul(u)[0][0]);\n // console.log('w without norm', JSON.stringify(w));\n w = w.div(Utils.norm(w));\n // console.log('w', JSON.stringify(w));\n // console.log(w.transpose().mmul(w));\n // calc X scores\n t = X.mmul(w).div(w.transpose().mmul(w)[0][0]);// t_h paso 3\n // calc loading\n // console.log('scores', t);\n if (i > 0) {\n diff = t.clone().sub(tOld).pow(2).sum();\n // console.log('diff', diff);\n }\n tOld = t.clone();\n // Y block, calc weights, normalise and calc Y scores\n // steps can be omitted for 2 class Y (simply by setting q_h=1)\n q = Y.transpose().mmul(t).div(t.transpose().mmul(t)[0][0]);\n q = q.div(Utils.norm(q));\n\n u = Y.mmul(q).div(q.transpose().mmul(q)[0][0]);\n console.log(`PLS iteration: ${i}`);\n }\n // calculate the X loadings and rescale scores and weights accordingly\n let xP = X.transpose().mmul(t).div(t.transpose().mmul(t)[0][0]);\n xP = xP.div(Utils.norm(xP));\n // calc Y loadings\n let yP = Y.transpose().mmul(u).div(u.transpose().mmul(u)[0][0]);\n // calc b for residuals Y\n // calculate beta (regression coefficient) via inverse insted of subtracting q_h directly\n let residual = u.transpose().mmul(t).div(t.transpose().mmul(t)[0][0]);\n // console.log('residuals', residual);\n // calc residual matrice X and Y\n let xRes = X.sub(t.clone().mmul(xP.transpose()));\n let yRes = Y.sub(t.clone().mulS(residual[0][0]).mmul(q.transpose()));\n return { xRes, yRes, scores: t, loadings: xP.transpose(), weights: w.transpose(), betas: residual[0][0], qPC: q };\n}\n\nfunction OPLS(dataset, predictions, options = {}) {\n const {\n numberOSC = 100,\n scale = true,\n verbose = false\n } = options;\n\n let X = Matrix.checkMatrix(dataset);\n let Y = Matrix.checkMatrix(predictions);\n if (verbose) console.log('data checked');\n\n if (scale) {\n X = featureNormalize(X).result;\n Y = featureNormalize(Y).result;\n if (verbose) console.log('data scaled, if scaling not wanted, set options.scale to false');\n }\n\n let rows = X.rows;\n let columns = X.columns;\n if (verbose) console.log(`number of rows: ${rows} / number of columns: ${columns}`);\n\n let sumOfSquaresX = X.clone().mul(X).sum();\n\n let u = Y.getColumnVector(0);\n let diff = 1;\n let t, c, w, uNew;\n for (let i = 0; i < numberOSC && diff > 1e-10; i++) {\n // for (let i = 0; i < 1; i++) {\n w = u.transpose().mmul(X).div(u.transpose().mmul(u)[0][0]);\n\n w = w.transpose().div(Utils.norm(w));\n\n t = X.mmul(w).div(w.transpose().mmul(w)[0][0]);// t_h paso 3\n\n // calc loading\n\n c = t.transpose().mmul(Y).div(t.transpose().mmul(t)[0][0]);\n\n // 4. calc new u and compare with one in previus iteration (stop criterion)\n uNew = Y.mmul(c.transpose());\n uNew = uNew.div(c.transpose().mmul(c)[0][0]);\n\n if (i > 0) {\n diff = uNew.clone().sub(u).pow(2).sum() / uNew.clone().pow(2).sum();\n if (verbose) console.log('diff', diff);\n }\n\n u = uNew.clone();\n if (verbose) console.log(`number of iterations: ${i}`);\n console.log(`OPLS iteration: ${i}`);\n }\n if (verbose) console.log('unew', uNew);\n // calc loadings\n let p = t.transpose().mmul(X).div(t.transpose().mmul(t)[0][0]);\n\n let wOrtho = p.clone().sub(w.transpose().mmul(p.transpose()).div(w.transpose().mmul(w)[0][0]).mmul(w.transpose()));\n wOrtho.div(Utils.norm(wOrtho));\n if (verbose) console.log('wOrtho norm', wOrtho);\n\n // orthogonal scores\n let tOrtho = X.mmul(wOrtho.transpose()).div(wOrtho.transpose().mmul(wOrtho)[0][0]);\n if (verbose) console.log(`scores: ${tOrtho}`);\n\n // orthogonal loadings\n let pOrtho = tOrtho.transpose().mmul(X).div(tOrtho.transpose().mmul(tOrtho)[0][0]);\n if (verbose) console.log(`loadings: ${pOrtho}`);\n\n // filtered data\n let err = X.sub(tOrtho.mmul(pOrtho));\n if (verbose) console.log(`filtered data: ${err}`);\n return { err, pOrtho, tOrtho, wOrtho, w, p, t, c };\n}\n\n\nfunction predictEllipse(x, y, dataClass, n, alpha, nbPoints) {\n // separate data by class\n let toExport = {\n type: 'chart',\n value: {\n data: []\n }\n };\n let nClass = dataClass.nClass;\n let scaleColor = chroma.scale(['green', 'black']).domain([0, nClass - 1]).mode('hsl');\n let yMatrix = new Array(nClass).fill(0).map((e) => []);\n let xMatrix = JSON.parse(JSON.stringify(yMatrix));\n for (let k = 0; k < x.length; k++) {\n let index = dataClass.classFactor[k];\n yMatrix[index].push(y[k]);\n xMatrix[index].push(x[k]);\n }\n for (let k = 0; k < nClass; k++) {\n // console.log(scaleColor(k).toString());\n let xyMatrix = Matrix.from1DArray(xMatrix[k].length, 1, xMatrix[k]);\n xyMatrix.addColumn(1, yMatrix[k]);\n let means = [computeMean(xMatrix[k]), computeMean(yMatrix[k])];\n let pca = new PCA(xyMatrix, { useCovarianceMatrix: true });\n var evec = new Matrix(pca.U);\n let elp = new Matrix(ellipse([0, 0], pca.S, nbPoints));\n elp = evec.mmul(elp.transpose()).transpose();\n let c = 2 * (n - 1) / n * (n + 1) / (n - 2);\n let F = Math.sqrt(c * 4.7374);\n elp = elp.map((point) => {\n return point.map((p, i) => F * p + means[i]);\n });\n elp = new Matrix(elp);\n let xData = elp.getColumn(0);\n let yData = elp.getColumn(1);\n toExport.value.data.push({\n y: yData,\n x: xData\n });\n }\n return toExport;\n}\n\nfunction ellipse(center, std, nbPoints) {\n let vector = new Array(nbPoints);\n let jump = 2 * Math.PI / (nbPoints - 1);\n let t = 0;\n for (let i = 0; i < nbPoints; i++) {\n vector[i] = [center[0] + Math.sqrt(std[0]) * Math.cos(t), center[1] + Math.sqrt(std[1]) * Math.sin(t)];\n t += jump;\n }\n return vector;\n}\n\nfunction log(title, txt) {\n \n let log = API.getData('log').resurrect();\n log = log.concat('\\n', '===============', title, '===============', '\\n');\n txt.forEach((e, i) => {\n \n log = log.concat(Object.keys(e), ' ', Object.values(e), '\\n');\n \n return log;\n });\n API.createData('log', log);\n \n}\n\nfunction covariance(a,b) {\n\tvar bias = false,\n\t\targs,\n\t\topts,\n\t\tnArgs,\n\t\tlen,\n\t\tdeltas,\n\t\tdelta,\n\t\tmeans,\n\t\tC,\n\t\tcov,\n\t\tarr,\n\t\tN, r, A, B, sum, val,\n\t\ti, j, n;\n\n// \targs = Array.prototype.slice.call( arguments );\n// \tnArgs = args.length;\n// \tconsole.log(args)\n\n// \tif ( isObject( args[nArgs-1] ) ) {\n// \t\topts = args.pop();\n// \t\tnArgs = nArgs - 1;\n// \t\tif ( opts.hasOwnProperty( 'bias' ) ) {\n// \t\t\tif ( typeof opts.bias !== 'boolean' ) {\n// \t\t\t\tthrow new TypeError( 'covariance()::invalid input argument. Bias option must be a boolean.' );\n// \t\t\t}\n// \t\t\tbias = opts.bias;\n// \t\t}\n// \t}\n// \tif ( !nArgs ) {\n// \t\tthrow new Error( 'covariance()::insufficient input arguments. Must provide array arguments.' );\n// \t}\n// \tfor ( i = 0; i < nArgs; i++ ) {\n// \t\tif ( !Array.isArray( args[i] ) ) {\n// \t\t\tthrow new TypeError( 'covariance()::invalid input argument. Must provide array arguments.' );\n// \t\t}\n// \t}\n// \tif ( Array.isArray( args[0][0] ) ) {\n// \t\t// If the first argument is an array of arrays, calculate the covariance over the nested arrays, disregarding any other arguments...\n// \t\targs = args[ 0 ];\n// \t}\n// \tnArgs = args.length;\n// \tlen = args[ 0 ].length;\n// \tfor ( i = 1; i < nArgs; i++ ) {\n// \t\tif ( args[i].length !== len ) {\n// \t\t\tthrow new Error( 'covariance()::invalid input argument. All arrays must have equal length.' );\n// \t\t}\n// \t}\n\t// [0] Initialization...\n\tdeltas = new Array( nArgs );\n\tmeans = new Array( nArgs );\n\tC = new Array( nArgs );\n\tcov = new Array( nArgs );\n\tfor ( i = 0; i < nArgs; i++ ) {\n\t\tmeans[ i ] = args[ i ][ 0 ];\n\t\tarr = new Array( nArgs );\n\t\tfor ( j = 0; j < nArgs; j++ ) {\n\t\t\tarr[ j ] = 0;\n\t\t}\n\t\tC[ i ] = arr;\n\t\tcov[ i ] = arr.slice(); // copy!\n\t}\n\tif ( len < 2 ) {\n\t\treturn cov;\n\t}\n\t// [1] Compute the covariance...\n\tfor ( n = 1; n < len; n++ ) {\n\n\t\tN = n + 1;\n\t\tr = n / N;\n\n\t\t// [a] Extract the values and compute the deltas...\n\t\tfor ( i = 0; i < nArgs; i++ ) {\n\t\t\tdeltas[ i ] = args[ i ][ n ] - means[ i ];\n\t\t}\n\n\t\t// [b] Update the covariance between one array and every other array...\n\t\tfor ( i = 0; i < nArgs; i++ ) {\n\t\t\tarr = C[ i ];\n\t\t\tdelta = deltas[ i ];\n\t\t\tfor ( j = i; j < nArgs; j++ ) {\n\t\t\t\tA = arr[ j ];\n\t\t\t\tB = r * delta * deltas[ j ];\n\t\t\t\tsum = A + B;\n\t\t\t\t// Exploit the fact that the covariance matrix is symmetric...\n\t\t\t\tif ( i !== j ) {\n\t\t\t\t\tC[ j ][ i ] = sum;\n\t\t\t\t}\n\t\t\t\tarr[ j ] = sum;\n\t\t\t} // end FOR j\n\t\t} // end FOR i\n\n\t\t// [c] Update the means...\n\t\tfor ( i = 0; i < nArgs; i++ ) {\n\t\t\tmeans[ i ] += deltas[ i ] / N;\n\t\t}\n\t} // end FOR n\n\n\t// [2] Normalize the co-moments...\n\tn = N - 1;\n\tif ( bias ) {\n\t\tn = N;\n\t}\n\tfor ( i = 0; i < nArgs; i++ ) {\n\t\tarr = C[ i ];\n\t\tfor ( j = i; j < nArgs; j++ ) {\n\t\t\tval = arr[ j ] / n;\n\t\t\tcov[ i ][ j ] = val;\n\t\t\tif ( i !== j ) {\n\t\t\t\tcov[ j ][ i ] = val;\n\t\t\t}\n\t\t}\n\t}\n\treturn cov;\n} // end FUNCTION covariance()\n\nfunction hexToRgb(hex) {\n var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})$/i.exec(hex);\n return result ? [\n parseInt(result[1], 16),\n parseInt(result[2], 16),\n parseInt(result[3], 16),\n 1\n ] : null;\n}\n\nAPI.cache('helperFunctions', {\n DataClass,\n Outlier,\n Dataset,\n oplsWrapper,\n sampleClass,\n transpose,\n bar,\n seq_along,\n scale,\n tss,\n plotResult,\n plot,\n predictEllipse,\n ellipse,\n pow2array,\n featureNormalize,\n shuffle,\n maxSumColIndex,\n getColSum,\n computeQ2,\n PCA_fast,\n PLS,\n OPLS,\n createMatrixClass,\n log,\n covariance,\n hexToRgb\n});\n\n// ============================================\n// Dataset section\n// ============================================\n\nconst iris = () => {\n const iris = [\n [5.1, 3.5, 1.4, 0.2, 'setosa'],\n [4.9, 3, 1.4, 0.2, 'setosa'],\n [4.7, 3.2, 1.3, 0.2, 'setosa'],\n [4.6, 3.1, 1.5, 0.2, 'setosa'],\n [5, 3.6, 1.4, 0.2, 'setosa'],\n [5.4, 3.9, 1.7, 0.4, 'setosa'],\n [4.6, 3.4, 1.4, 0.3, 'setosa'],\n [5, 3.4, 1.5, 0.2, 'setosa'],\n [4.4, 2.9, 1.4, 0.2, 'setosa'],\n [4.9, 3.1, 1.5, 0.1, 'setosa'],\n [5.4, 3.7, 1.5, 0.2, 'setosa'],\n [4.8, 3.4, 1.6, 0.2, 'setosa'],\n [4.8, 3, 1.4, 0.1, 'setosa'],\n [4.3, 3, 1.1, 0.1, 'setosa'],\n [5.8, 4, 1.2, 0.2, 'setosa'],\n [5.7, 4.4, 1.5, 0.4, 'setosa'],\n [5.4, 3.9, 1.3, 0.4, 'setosa'],\n [5.1, 3.5, 1.4, 0.3, 'setosa'],\n [5.7, 3.8, 1.7, 0.3, 'setosa'],\n [5.1, 3.8, 1.5, 0.3, 'setosa'],\n [5.4, 3.4, 1.7, 0.2, 'setosa'],\n [5.1, 3.7, 1.5, 0.4, 'setosa'],\n [4.6, 3.6, 1, 0.2, 'setosa'],\n [5.1, 3.3, 1.7, 0.5, 'setosa'],\n [4.8, 3.4, 1.9, 0.2, 'setosa'],\n [5, 3, 1.6, 0.2, 'setosa'],\n [5, 3.4, 1.6, 0.4, 'setosa'],\n [5.2, 3.5, 1.5, 0.2, 'setosa'],\n [5.2, 3.4, 1.4, 0.2, 'setosa'],\n [4.7, 3.2, 1.6, 0.2, 'setosa'],\n [4.8, 3.1, 1.6, 0.2, 'setosa'],\n [5.4, 3.4, 1.5, 0.4, 'setosa'],\n [5.2, 4.1, 1.5, 0.1, 'setosa'],\n [5.5, 4.2, 1.4, 0.2, 'setosa'],\n [4.9, 3.1, 1.5, 0.2, 'setosa'],\n [5, 3.2, 1.2, 0.2, 'setosa'],\n [5.5, 3.5, 1.3, 0.2, 'setosa'],\n [4.9, 3.6, 1.4, 0.1, 'setosa'],\n [4.4, 3, 1.3, 0.2, 'setosa'],\n [5.1, 3.4, 1.5, 0.2, 'setosa'],\n [5, 3.5, 1.3, 0.3, 'setosa'],\n [4.5, 2.3, 1.3, 0.3, 'setosa'],\n [4.4, 3.2, 1.3, 0.2, 'setosa'],\n [5, 3.5, 1.6, 0.6, 'setosa'],\n [5.1, 3.8, 1.9, 0.4, 'setosa'],\n [4.8, 3, 1.4, 0.3, 'setosa'],\n [5.1, 3.8, 1.6, 0.2, 'setosa'],\n [4.6, 3.2, 1.4, 0.2, 'setosa'],\n [5.3, 3.7, 1.5, 0.2, 'setosa'],\n [5, 3.3, 1.4, 0.2, 'setosa'],\n [7, 3.2, 4.7, 1.4, 'versicolor'],\n [6.4, 3.2, 4.5, 1.5, 'versicolor'],\n [6.9, 3.1, 4.9, 1.5, 'versicolor'],\n [5.5, 2.3, 4, 1.3, 'versicolor'],\n [6.5, 2.8, 4.6, 1.5, 'versicolor'],\n [5.7, 2.8, 4.5, 1.3, 'versicolor'],\n [6.3, 3.3, 4.7, 1.6, 'versicolor'],\n [4.9, 2.4, 3.3, 1, 'versicolor'],\n [6.6, 2.9, 4.6, 1.3, 'versicolor'],\n [5.2, 2.7, 3.9, 1.4, 'versicolor'],\n [5, 2, 3.5, 1, 'versicolor'],\n [5.9, 3, 4.2, 1.5, 'versicolor'],\n [6, 2.2, 4, 1, 'versicolor'],\n [6.1, 2.9, 4.7, 1.4, 'versicolor'],\n [5.6, 2.9, 3.6, 1.3, 'versicolor'],\n [6.7, 3.1, 4.4, 1.4, 'versicolor'],\n [5.6, 3, 4.5, 1.5, 'versicolor'],\n [5.8, 2.7, 4.1, 1, 'versicolor'],\n [6.2, 2.2, 4.5, 1.5, 'versicolor'],\n [5.6, 2.5, 3.9, 1.1, 'versicolor'],\n [5.9, 3.2, 4.8, 1.8, 'versicolor'],\n [6.1, 2.8, 4, 1.3, 'versicolor'],\n [6.3, 2.5, 4.9, 1.5, 'versicolor'],\n [6.1, 2.8, 4.7, 1.2, 'versicolor'],\n [6.4, 2.9, 4.3, 1.3, 'versicolor'],\n [6.6, 3, 4.4, 1.4, 'versicolor'],\n [6.8, 2.8, 4.8, 1.4, 'versicolor'],\n [6.7, 3, 5, 1.7, 'versicolor'],\n [6, 2.9, 4.5, 1.5, 'versicolor'],\n [5.7, 2.6, 3.5, 1, 'versicolor'],\n [5.5, 2.4, 3.8, 1.1, 'versicolor'],\n [5.5, 2.4, 3.7, 1, 'versicolor'],\n [5.8, 2.7, 3.9, 1.2, 'versicolor'],\n [6, 2.7, 5.1, 1.6, 'versicolor'],\n [5.4, 3, 4.5, 1.5, 'versicolor'],\n [6, 3.4, 4.5, 1.6, 'versicolor'],\n [6.7, 3.1, 4.7, 1.5, 'versicolor'],\n [6.3, 2.3, 4.4, 1.3, 'versicolor'],\n [5.6, 3, 4.1, 1.3, 'versicolor'],\n [5.5, 2.5, 4, 1.3, 'versicolor'],\n [5.5, 2.6, 4.4, 1.2, 'versicolor'],\n [6.1, 3, 4.6, 1.4, 'versicolor'],\n [5.8, 2.6, 4, 1.2, 'versicolor'],\n [5, 2.3, 3.3, 1, 'versicolor'],\n [5.6, 2.7, 4.2, 1.3, 'versicolor'],\n [5.7, 3, 4.2, 1.2, 'versicolor'],\n [5.7, 2.9, 4.2, 1.3, 'versicolor'],\n [6.2, 2.9, 4.3, 1.3, 'versicolor'],\n [5.1, 2.5, 3, 1.1, 'versicolor'],\n [5.7, 2.8, 4.1, 1.3, 'versicolor'],\n [6.3, 3.3, 6, 2.5, 'virginica'],\n [5.8, 2.7, 5.1, 1.9, 'virginica'],\n [7.1, 3, 5.9, 2.1, 'virginica'],\n [6.3, 2.9, 5.6, 1.8, 'virginica'],\n [6.5, 3, 5.8, 2.2, 'virginica'],\n [7.6, 3, 6.6, 2.1, 'virginica'],\n [4.9, 2.5, 4.5, 1.7, 'virginica'],\n [7.3, 2.9, 6.3, 1.8, 'virginica'],\n [6.7, 2.5, 5.8, 1.8, 'virginica'],\n [7.2, 3.6, 6.1, 2.5, 'virginica'],\n [6.5, 3.2, 5.1, 2, 'virginica'],\n [6.4, 2.7, 5.3, 1.9, 'virginica'],\n [6.8, 3, 5.5, 2.1, 'virginica'],\n [5.7, 2.5, 5, 2, 'virginica'],\n [5.8, 2.8, 5.1, 2.4, 'virginica'],\n [6.4, 3.2, 5.3, 2.3, 'virginica'],\n [6.5, 3, 5.5, 1.8, 'virginica'],\n [7.7, 3.8, 6.7, 2.2, 'virginica'],\n [7.7, 2.6, 6.9, 2.3, 'virginica'],\n [6, 2.2, 5, 1.5, 'virginica'],\n [6.9, 3.2, 5.7, 2.3, 'virginica'],\n [5.6, 2.8, 4.9, 2, 'virginica'],\n [7.7, 2.8, 6.7, 2, 'virginica'],\n [6.3, 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