modelExplorer_1_1.view.json

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              ],
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          },
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            255,
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              ],
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              ],
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          "name": "modifiedScores"
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          "name": "changeLabel"
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          "rel": "selectedData",
          "jpath": [],
          "name": "selectedMarker"
        },
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          "event": "onMouseOverShape",
          "rel": "shapeInfos",
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          "name": "selectedAnnotation"
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      ],
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          "event": "onSelectMarker",
          "rel": "markerInfos",
          "jpath": [],
          "name": "test"
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              "icon": "",
              "action": "",
              "position": "begin",
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    },
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                  "buttons"
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            "height": 18
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          "zIndex": 0,
          "display": true,
          "title": "exec highlight score hoovered in the table",
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          "created": true,
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      "actions_out": [
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          "rel": "fromToX",
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          "name": "zoomSync"
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              "icon": "",
              "action": "",
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      },
      "css": [
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          "fontFamily": [
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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\n// const computeMean = ArrayStat.mean;\n// const computeVariance = ArrayStat.variance;\nconst EVD = ML.EVD;\nconst SVD = ML.SVD;\n// const mean = Stat.mean;\n// const stdev = Stat.standardDeviation;\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 = 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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 = 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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', 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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\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\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});\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   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