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r(e,n,t){function o(i,f){if(!n[i]){if(!e[i]){var c=\"function\"==typeof require&&require;if(!f&&c)return c(i,!0);if(u)return u(i,!0);var a=new Error(\"Cannot find module '\"+i+\"'\");throw a.code=\"MODULE_NOT_FOUND\",a}var p=n[i]={exports:{}};e[i][0].call(p.exports,function(r){var n=e[i][1][r];return o(n||r)},p,p.exports,r,e,n,t)}return n[i].exports}for(var u=\"function\"==typeof require&&require,i=0;i<t.length;i++)o(t[i]);return o}return r})()","var currentcomputetime = 0;\n\nexport function updateDynamicVariables() {\n  currentcomputetime++;\n}\n\nexport function getCurrentComputeTime() {\n  return currentcomputetime;\n}\n\n\n//enables dynamic programming: values of variables are only recomputed if required.\nexport class DynamicVariable{\n  constructor(value = 0) {\n    this.value = value;\n    this.time = -1;\n  }\n\n  update(fun) {\n    if(this.time == currentcomputetime)\n      return this.value;\n    else {\n      this.value = fun();\n      this.time = currentcomputetime;\n      return this.value;\n    }\n  }\n}\n","import * as d3 from 'd3';\n\nimport {\n  unit,\n  parameterths,\n  noderadius\n} from './constants.js';\n\n//jshint: \"esversion\": 8\n\nimport {\n  DynamicVariable\n} from './DynamicVariable.js';\n\n//one-dimensional De Casteljau to clip Bezier curves\nconst casteljau1d = (p, alpha) => {\n  const q = [(1 - alpha) * p[0] + alpha * p[1], (1 - alpha) * p[1] + alpha * p[2], (1 - alpha) * p[2] + alpha * p[3]];\n  const r = [(1 - alpha) * q[0] + alpha * q[1], (1 - alpha) * q[1] + alpha * q[2]];\n  const s = [(1 - alpha) * r[0] + alpha * r[1]];\n  return [\n    [p[0], q[0], r[0], s[0]],\n    [s[0], r[1], q[2], p[3]]\n  ];\n};\n\n\nconst casteljau2d = (p, alpha) => {\n  const x = casteljau1d(p.map(c => c[0]), alpha);\n  const y = casteljau1d(p.map(c => c[1]), alpha);\n\n  return [\n    [\n      [x[0][0], y[0][0]],\n      [x[0][1], y[0][1]],\n      [x[0][2], y[0][2]],\n      [x[0][3], y[0][3]]\n    ],\n    [\n      [x[1][0], y[1][0]],\n      [x[1][1], y[1][1]],\n      [x[1][2], y[1][2]],\n      [x[1][3], y[1][3]]\n    ]\n  ];\n};\n\n\nexport class Edge {\n  constructor(from, to, weight) {\n    this.from = from;\n    this.to = to;\n    this.weight = weight;\n    this.dloss = 0;\n    this.dweight = new DynamicVariable();\n    this.adjustable = true;\n\n    this.multvis0 = 1 / 3;\n    this.multvis1 = 2 / 3;\n  }\n\n  bezier() {\n    const edge = this;\n    const x0 = edge.from.x + ((edge.from.constructor.name == \"InputNode\") ? noderadius : 0);\n    const x1 = edge.to.x - ((edge.to.constructor.name == \"OutputNode\") ? noderadius : 0);\n    return [\n      [\n        x0,\n        edge.from.y\n      ],\n      [\n        (x0 + x1) / 2,\n        edge.from.y\n      ],\n      [\n        (x0 + x1) / 2,\n        edge.to.y - unit * (edge.offset)\n      ],\n      [\n        x1,\n        edge.to.y - unit * (edge.offset)\n      ]\n    ];\n  }\n\n\n  generateActivatedCubicBezierSplines(sactivation) {\n    const b = this.bezier();\n    const c0 = casteljau2d(b, this.multvis0);\n    const c1 = casteljau2d(c0[1], (this.multvis1 - this.multvis0) / (1 - this.multvis0));\n    return [c0[0], c1[0], c1[1]];\n  }\n\n  generateActivatedPath(sactivation, a1, a2) {\n    const eactivation = sactivation * this.weight;\n    let p1, p2, p3;\n    [p1, p2, p3] = this.generateActivatedCubicBezierSplines(sactivation);\n    //p.lineTo(edge.to.x, edge.to.y);\n    const p = d3.path();\n    p.moveTo(p1[0][0], p1[0][1] - unit * sactivation);\n    p.bezierCurveTo(p1[1][0], p1[1][1] - unit * sactivation, p1[2][0], p1[2][1] - unit * sactivation, p1[3][0], p1[3][1] - unit * sactivation);\n    p.bezierCurveTo(p2[1][0], p2[1][1] - unit * sactivation, p2[2][0], p2[2][1] - unit * eactivation, p2[3][0], p2[3][1] - unit * eactivation);\n    p.bezierCurveTo(p3[1][0], p3[1][1] - unit * eactivation, p3[2][0], p3[2][1] - unit * eactivation, p3[3][0], p3[3][1] - unit * eactivation);\n\n    return p;\n  }\n\n  generateActivatedPathMiddle(sactivation) {\n    const eactivation = sactivation * this.weight;\n    const p2 = this.generateActivatedCubicBezierSplines(sactivation)[1];\n    //p.lineTo(edge.to.x, edge.to.y);\n    const p = d3.path();\n    p.moveTo(p2[0][0], p2[0][1] - unit * sactivation);\n    p.bezierCurveTo(p2[1][0], p2[1][1] - unit * sactivation, p2[2][0], p2[2][1] - unit * eactivation, p2[3][0], p2[3][1] - unit * eactivation);\n    return p;\n  }\n\n\n  normalizedParameterPosition() {\n    const edge = this;\n    return [(edge.from.x + edge.to.x) / 2, edge.firstHalfBezier()[3][1] - unit * edge.weight];\n  }\n\n  parameterPosition() {\n    let sactivation = this.from.getActivation();\n    if (Math.abs(sactivation) < parameterths) sactivation = (this.from.getActivation() < 0 ? -1 : 1) * parameterths;\n    const p2 = this.generateActivatedCubicBezierSplines(sactivation)[1];\n    const eactivation = sactivation * this.weight;\n    return [p2[3][0], p2[3][1] - unit * eactivation];\n  }\n\n  getdWeight() {\n    return this.dweight.update(() => {\n      return (this.to.active() ? this.from.getActivation() * this.to.getdActivation() : 0);\n    });\n  }\n\n  backup() {\n    this.backupWeight = this.weight;\n  }\n\n  restore() {\n    this.weight = this.backupWeight;\n  }\n}\n","import {\n  Node\n} from './Node.js';\n\nimport {\n  DynamicVariable,\n  updateDynamicVariables\n} from './DynamicVariable.js';\n\nexport class InputNode extends Node {\n  constructor(activationcb = (() => 0)) {\n    super();\n    this.getActivation = activationcb;\n    this.adjustable = false;\n    this.allownegative = true;\n  }\n\n  setUserParameter(val) {\n    if (!this.hasOwnProperty(\"userparamter\")) {\n      this.getActivation = (() => this.userparamter);\n    }\n    if (!this.allownegative) {\n      val = Math.max(0, val);\n    }\n    this.userparamter = val;\n\n    updateDynamicVariables();\n  }\n\n  temporarilyReplaceGetActivation(tempActivation) {\n    this.getActivationBackup = this.getActivation;\n    this.getActivation = tempActivation;\n    updateDynamicVariables();\n  }\n\n  restoreGetActivation() {\n    this.getActivation = this.getActivationBackup;\n    updateDynamicVariables();\n  }\n\n  pauseInput() {\n    this.setUserParameter(this.getActivation());\n  }\n}\n","//jshint: \"esversion\": 8\nimport * as d3 from 'd3';\n\nimport {\n  NetworkVisualization\n} from './NetworkVisualization.js';\n\n\nimport {\n  unit\n} from './constants.js';\n\n\nexport class Level {\n  constructor(title, network, xlabels, trainXs, ylabels, trainYs, description) {\n    this.title = title || \"\";\n\n    this.network = network;\n    this.trainXs = trainXs;\n    this.trainYs = trainYs;\n    this.xlabels = xlabels;\n    this.ylabels = ylabels;\n\n    this.description = description || \"\";\n    this.gradientdescentactive = false;\n\n    this.network.backup();\n  }\n\n  show() {\n    this.createUI();\n    this.t0 = Date.now();\n    const nv = this.nv = new NetworkVisualization(this.network, () => this.animatecallback());\n    nv.animate();\n    nv.addInteraction();\n    nv.resizeSVG(120);\n    if (this.onenter)\n      this.onenter();\n  }\n\n  hide() {\n    if (this.nv)\n      this.nv.stop();\n    if (this.table)\n      this.table.html('');\n    if (this.onleave)\n      this.onleave();\n\n    d3.select(\"#levelinfo\").html('');\n  }\n\n  createUI() {\n    document.querySelector(\".helper\").classList.remove(\"visible\");\n    document.querySelector(\".mission\").classList.add(\"visible\");\n    document.querySelector(\"#helpmebutton\").classList.remove(\"selected\");\n    document.querySelector(\"#missionbutton\").classList.add(\"selected\");\n\n    d3.select(\"#leveltitle\").text(this.title);\n    d3.select(\"#description\").text(this.description);\n\n    this.createTable();\n\n    document.querySelector('.reset').onclick = (() => {\n      this.network.restore();\n    });\n\n    document.querySelector('.single-step').onclick = (() => {\n      this.gradientdescentactive = false;\n      this.network.gradientstep(this.trainXs, this.trainYs, 0.01);\n    });\n\n    document.querySelector('.pause-resume').onclick = (() => {\n      this.gradientdescentactive = !this.gradientdescentactive;\n    });\n  }\n\n  createTable() {\n    this.columns = this.xlabels.concat(this.ylabels).concat(this.ylabels.map(k => k + \" (vorausgesagt)\"));\n    this.table = d3.select('#trainingtable');\n    this.thead = this.table.append('thead');\n    this.tbody = this.table.append('tbody');\n\n    // append the header row\n    this.thead.append('tr')\n      .selectAll('th')\n      .data(this.columns).enter()\n      .append('th')\n      .style(\"background-color\", (d, i) => (i >= this.xlabels.length) ? \"orange\" : \"yellow\")\n      .text(function(column) {\n        return column;\n      });\n  }\n\n  animatecallback() {\n    if (this.animatestep) this.animatestep(); //This function might be suitably overwritten by inherited levels\n    if (this.gradientdescentactive) {\n      for (let k = 0; k < 10; k++)\n        this.network.gradientstep(this.trainXs, this.trainYs, 0.001);\n    }\n    this.updateUI();\n  }\n\n  updateUI() {\n    if (this.gradientdescentactive && d3.select('#gradientanimating')) {\n      document.querySelector('.pause-resume').classList.add(\"pause\");\n      document.querySelector('.pause-resume').classList.remove(\"resume\");\n    } else {\n      document.querySelector('.pause-resume').classList.remove(\"pause\");\n      document.querySelector('.pause-resume').classList.add(\"resume\");\n    }\n\n    this.updatetable();\n\n    const closs = this.network.loss(this.trainXs, this.trainYs);\n    d3.select(\"#totalerror\")\n      .text(closs.toFixed(2));\n\n    d3.select(\"#nextbutton\")\n      .classed(\"disable\", closs > 0.02);\n\n    d3.select(\".tabbed\")\n      .classed(\"successful\", closs < 0.02);\n\n    d3.select(\"#totalerrorterm\")\n      .text(this.rows.map(r => `(${r[r.length-1]} - ${r[r.length-2]})²`).join(\" + \"));\n\n\n    if (document.querySelector(\"#showgradient\").checked) {\n      this.network.gradientLoss(this.trainXs, this.trainYs);\n    } else {\n      this.network.resetdloss();\n    }\n  }\n\n  updatetable() {\n    const tbody = this.tbody;\n\n    let rows = [];\n    this.rows = rows;\n    for (let k in this.trainXs) {\n      const row = this.trainXs[k].map((v, k) => this.network.inputnodes[k].format(v))\n        .concat(this.trainYs[k].map((v, k) => this.network.outputnodes[k].format(v)))\n        .concat(this.network.predict(this.trainXs[k]).map((v, k) => this.network.outputnodes[k].format(v)));\n      row.error = this.network.sqerror(this.trainXs[k], this.trainYs[k]);\n      rows.push(row);\n    }\n\n    var errorcolor = d3.scaleSequential().domain([1, 0])\n      .interpolator(d3.interpolateRdYlGn);\n\n    // create a row for each object in the data\n    var trs = tbody.selectAll('tr')\n      .data(rows)\n      .join('tr')\n      .on('click', (row, i) => {\n        for (let k in this.network.inputnodes) {\n          this.network.inputnodes[k].setUserParameter(this.trainXs[i][k]);\n        }\n        for (let k in this.network.outputnodes) {\n          this.network.outputnodes[k].target = (this.trainYs[i][k]);\n        }\n      });\n\n    // create a cell in each row for each column\n    var cells = trs\n      .selectAll('td')\n      .data(d => d.map(v => {\n        return {\n          value: v,\n          error: d.error\n        };\n      }))\n      .join('td')\n      .text(d => d.value)\n      .style(\"background-color\", (d, i) => (i >= this.trainXs[0].length + this.trainYs[0].length) ? errorcolor(d.error) : 'white');\n\n\n  }\n}\n","import * as d3 from 'd3';\n\nimport {\n  TutorialLevelA,\n  TutorialLevelB,\n  TutorialLevelC,\n  WeatherLevel,\n  FahrenheitLevel,\n  SumLevel,\n  MaxLevel,\n  XorLevel,\n  AndLevel,\n  AvgLevel\n} from './levels.js';\n\n\n\nexport class LevelController {\n  constructor() {\n    this.createEvents();\n    this.levels = [ //generators for levels\n      () => new TutorialLevelA(),\n      () => new SumLevel(),\n      () => new AvgLevel(),\n      () => new TutorialLevelB(),\n      () => new TutorialLevelC(),\n      () => new FahrenheitLevel(),\n      () => new AndLevel(),\n      () => new MaxLevel(),\n      () => new WeatherLevel(),\n      () => new XorLevel()\n    ];\n    this.NUMBER_OF_LEVELS = this.levels.length;\n\n    this.updateFooter(0);\n  }\n  updateFooter(lid) {\n    d3.select('#navcircles').selectAll(\"a\").data(this.levels)\n      .join(\"a\")\n      .attr(\"href\", (d, i) => `index.html#${i+1}`)\n      .classed(\"circ\", true)\n      .classed(\"selected\", (d, i) => i == lid);\n\n    d3.select('#backbutton').style(\"visibility\", lid == 0 ? \"hidden\" : \"visible\");\n    d3.select('#nextbutton').style(\"visibility\", lid == this.NUMBER_OF_LEVELS - 1 ? \"hidden\" : \"visible\");\n  }\n\n  createEvents() {\n    window.addEventListener('DOMContentLoaded', (event) => {\n      document.querySelector(\"#backbutton\").onclick = (() => this.goBack());\n      document.querySelector(\"#nextbutton\").onclick = (() => {\n        if(!document.querySelector(\"#nextbutton\").classList.contains(\"disable\"))\n          this.goNext();\n      });\n      this.showLevelByURL();\n    });\n\n\n    window.onhashchange = (event) => {\n      this.showLevelByURL();\n    };\n\n    window.addEventListener('keydown', (event) => {\n      const key = event.key;\n      switch (event.key) {\n        case \"ArrowLeft\":\n          this.goBack();\n          break;\n        case \"ArrowRight\":\n          this.goNext();\n          break;\n      }\n    });\n  }\n\n\n  goNext() {\n    this.setLevel((this.getCurrentLevel() + 1) % this.NUMBER_OF_LEVELS);\n  }\n\n  goBack() {\n    this.setLevel((this.getCurrentLevel() - 1 + this.NUMBER_OF_LEVELS) % this.NUMBER_OF_LEVELS);\n  }\n\n  getCurrentLevel() {\n    let hash = window.location.hash.substring(1);\n    if (hash === \"\") {\n      hash = 1;\n    } else {\n      hash = (hash | 0);\n    }\n    return hash - 1;\n  }\n\n  setLevel(id) {\n    window.location.hash = id + 1;\n  }\n\n  showLevelByURL() {\n    this.showLevel(this.getCurrentLevel());\n  }\n\n  showLevel(lid) {\n    this.updateFooter(lid);\n    if (this.clevel) {\n      this.clevel.hide();\n    }\n    d3.select(\"#leveltitleprefix\").text(`Level ${lid + 1}: `);\n\n    //regenerate level\n    this.clevel = (this.levels[lid])();\n    this.clevel.show();\n  }\n}\n","import {\n  updateDynamicVariables\n} from './DynamicVariable.js';\n\nexport class Network {\n  constructor(nodes, inputnodes, outputnodes) {\n    this.nodes = nodes;\n    this.inputnodes = inputnodes;\n    this.outputnodes = outputnodes;\n    this.edges = [];\n\n    for (let i in nodes) {\n      for (let k in nodes[i].outedges) {\n        this.edges.push(nodes[i].outedges[k]);\n      }\n    }\n  }\n\n  predict(input) {\n    if (this.inputnodes.length != input.length) {\n      console.error(\"Input does not fit input size of Network\");\n    }\n\n    //overwrite activation callbacks for input\n    for (let i in this.inputnodes) {\n      this.inputnodes[i].temporarilyReplaceGetActivation(() => input[i]);\n    }\n\n    //get prediction in this 'modified network'\n    updateDynamicVariables();\n    const predicted = this.outputnodes.map(o => o.getActivation());\n\n    //restore input functions from backup\n    for (let i in this.inputnodes) {\n      this.inputnodes[i].restoreGetActivation();\n    }\n    return predicted;\n  }\n\n  sqerror(trainX, trainY) {\n    const predicted = this.predict(trainX);\n    let sqsum = 0;\n    for (let k in predicted)\n      sqsum += (predicted[k] - trainY[k]) * (predicted[k] - trainY[k]);\n    return sqsum;\n  }\n\n  loss(trainXs, trainYs) {\n    let sqsum = 0;\n    for (let i in trainXs) {\n      sqsum += this.sqerror(trainXs[i], trainYs[i]);\n    }\n    return sqsum;\n  }\n\n  resetdloss() {\n    for (let i in this.nodes) {\n      this.nodes[i].dloss = 0;\n    }\n\n    for (let i in this.edges) {\n      this.edges[i].dloss = 0;\n    }\n  }\n\n  //computes loss function and saves its gradient as parameters to objects in network\n  gradientLoss(trainXs, trainYs) {\n    let sqsum = 0;\n    this.resetdloss();\n    for (let k in trainXs) {\n      const input = trainXs[k];\n      const target = trainYs[k];\n      //overwrite activation callbacks for input\n      for (let i in this.inputnodes) {\n        this.inputnodes[i].temporarilyReplaceGetActivation(() => input[i]);\n      }\n\n      //get prediction in this 'modified network'\n      updateDynamicVariables();\n      const predicted = this.outputnodes.map(o => o.getActivation());\n      for (let i in predicted)\n        sqsum += (predicted[i] - target[i]) * (predicted[i] - target[i]);\n\n      for (let i in this.outputnodes) {\n        this.outputnodes[i].temporarilyReplaceGetdActivation(() => 2 * (predicted[i] - target[i]));\n      }\n      updateDynamicVariables();\n\n      for (let i in this.nodes) {\n        this.nodes[i].dloss += this.nodes[i].getdBias();\n      }\n\n      for (let i in this.edges) {\n        this.edges[i].dloss += this.edges[i].getdWeight();\n      }\n\n\n\n      //restore functions from backup\n      for (let i in this.inputnodes) {\n        this.inputnodes[i].restoreGetActivation();\n      }\n\n      for (let i in this.outputnodes) {\n        this.outputnodes[i].restoreGetdActivation();\n      }\n    }\n    return sqsum;\n  }\n\n  gradientstep(trainXs, trainYs, stepsize) {\n    this.gradientLoss(trainXs, trainYs);\n\n    for (let i in this.nodes) {\n      if (this.nodes[i].constructor.name == \"Node\" && this.nodes[i].adjustable) { //only internal nodes\n        this.nodes[i].bias -= stepsize * this.nodes[i].dloss;\n      }\n    }\n\n    for (let i in this.edges) {\n      if(this.edges[i].adjustable)\n        this.edges[i].weight -= stepsize * this.edges[i].dloss;\n    }\n    updateDynamicVariables();\n  }\n\n  pauseAnimatedInput() {\n    for (let i in this.inputnodes) {\n      const node = this.inputnodes[i];\n      node.pauseInput();\n    }\n  }\n\n  backup() {\n    for (let k in this.nodes) {\n      this.nodes[k].backup();\n    }\n\n    for (let k in this.edges) {\n      this.edges[k].backup();\n    }\n  }\n\n  restore() {\n    for (let k in this.nodes) {\n      this.nodes[k].restore();\n    }\n\n    for (let k in this.edges) {\n      this.edges[k].restore();\n    }\n  }\n}\n","import * as d3 from 'd3';\n\nimport {\n  unit,\n  parameterths,\n  noderadius,\n} from './constants.js';\n\nimport {\n  DynamicVariable,\n  updateDynamicVariables\n} from './DynamicVariable.js';\n\nconst clamp = (x, mi, ma) => Math.min(ma, Math.max(mi, x));\n\nexport class NetworkVisualization {\n  constructor(network, animatecallback) {\n    const nodes = this.nodes = network.nodes;\n    this.network = network;\n    this.inputnodes = network.inputnodes;\n    this.outputnodes = network.outputnodes;\n    this.animatecallback = animatecallback;\n\n    this.edges = [];\n\n    for (let i in nodes) {\n      for (let k in nodes[i].outedges) {\n        this.edges.push(nodes[i].outedges[k]);\n      }\n    }\n    //sort based on y coordinates of incoming edges\n    this.edges = this.edges.sort((a, b) => (b.from.y - a.from.y));\n\n    //arrow from http://jsfiddle.net/igbatov/v0ekdzw1/\n    d3.select(\"svg\").append(\"svg:defs\").append(\"svg:marker\")\n      .attr(\"id\", \"triangle\")\n      .attr(\"refX\", 6)\n      .attr(\"refY\", 6)\n      .attr(\"markerWidth\", 30)\n      .attr(\"markerHeight\", 30)\n      .attr(\"markerUnits\", \"userSpaceOnUse\")\n      .attr(\"orient\", \"auto\")\n      .append(\"path\")\n      .attr(\"d\", \"M 0 0 12 6 0 12 3 6\")\n      .style(\"fill\", \"orange\");\n  }\n\n  stop() {\n    this.animating = false;\n  }\n\n  animate() {\n    this.animating = true;\n    this.animateloop();\n  }\n\n  resizeSVG(margin = 0) {\n    var svg = document.querySelector(\"svg\");\n    var bbox = svg.getBBox();\n    svg.setAttribute(\"viewBox\", `${bbox.x - margin} ${bbox.y - margin} ${Math.min(1000, bbox.width) + 2* margin} ${Math.min(1000, bbox.height) + 2*margin}`);\n  }\n\n  animateloop() {\n    if (!this.animating) return;\n\n    if (this.animatecallback)\n      this.animatecallback();\n    const nodes = this.nodes;\n    const inputnodes = this.inputnodes;\n    const outputnodes = this.outputnodes;\n    const edges = this.edges;\n\n    updateDynamicVariables();\n    for (let i in nodes) {\n      nodes[i].offset = nodes[i].bias;\n    }\n    for (let i in edges) {\n      const edge = edges[i];\n      edge.offset = edge.to.offset;\n      edge.to.offset += edge.from.getActivation() * edge.weight;\n    }\n\n    d3.select(\"#nodes\").select(\".nodes\").selectAll(\"circle\").data(nodes)\n      .join(\"circle\")\n      .attr(\"cx\", (d) => d.x)\n      .attr(\"cy\", (d) => d.y)\n      .attr(\"r\", noderadius)\n      .attr(\"fill\", d => d.constructor.name == \"InputNode\" ? \"yellow\" : (d.constructor.name == \"OutputNode\" ? \"orange\" : \"black\"))\n      .attr(\"stroke\", \"black\")\n      .attr(\"stroke-width\", 3)\n      .attr(\"fill-opacity\", n => n.constructor.name == \"Node\" ? 0.2 : 1);\n\n\n    d3.select(\"#parameters\").select(\".nodes\").selectAll(\"circle\").data(nodes.filter(node => node.adjustable))\n      .join(\"circle\")\n      .attr(\"cx\", n => n.x)\n      .attr(\"cy\", n => n.y - unit * n.bias)\n      .attr(\"r\", 15)\n      .attr(\"fill\", \"white\")\n      .attr(\"fill-opacity\", 0.5)\n      .attr(\"stroke\", \"black\")\n      .attr(\"stroke-width\", 2)\n      .attr(\"stroke-opacity\", 0.5);\n\n\n    const DLOSS_SCALE = 0.1;\n    const DLOSS_CLAMP = 3;\n\n    d3.select(\"#nodes\").select(\".gradient\").selectAll(\"path\").data(nodes.filter(node => node.dloss != 0 && node.adjustable))\n      .join(\"path\")\n      .attr(\"d\", (node) => {\n        const p = d3.path();\n        p.moveTo(node.x, node.y - unit * node.bias);\n        p.lineTo(node.x, node.y - unit * (node.bias - clamp(node.dloss * DLOSS_SCALE, -DLOSS_CLAMP, DLOSS_CLAMP)));\n        return p;\n      })\n      .attr(\"marker-end\", \"url(#triangle)\")\n      .attr(\"stroke\", \"orange\")\n      .attr(\"stroke-width\", 2)\n      .attr(\"fill\", \"none\");\n\n    d3.select(\"#edges\").select(\".gradient\").selectAll(\"path\").data(edges.filter(edge => edge.dloss != 0 && edge.adjustable))\n      .join(\"path\")\n      .attr(\"d\", (edge) => {\n        const p = d3.path();\n        const x = edge.parameterPosition()[0];\n        const y = edge.parameterPosition()[1];\n        p.moveTo(x, y);\n        p.lineTo(x, y + unit * clamp(edge.dloss / edge.from.getActivation() * DLOSS_SCALE, -DLOSS_CLAMP, DLOSS_CLAMP));\n        return p;\n      })\n      .attr(\"marker-end\", \"url(#triangle)\")\n      .attr(\"stroke\", \"orange\")\n      .attr(\"stroke-width\", 2)\n      .attr(\"fill\", \"none\")\n      .style(\"opacity\", edge => Math.min(0.5, Math.abs(edge.from.getActivation())));\n\n\n    d3.select(\"#parameters\").select(\".edges\").selectAll(\"circle\").data(edges.filter(edge => edge.adjustable))\n      .join(\"circle\")\n      .attr(\"cx\", edge => edge.parameterPosition()[0])\n      .attr(\"cy\", edge => edge.parameterPosition()[1])\n      .attr(\"r\", 15)\n      .attr(\"fill\", edge => (edge.weight > 0 ? \"blue\" : \"red\"))\n      .attr(\"stroke\", \"black\")\n      .attr(\"stroke-width\", 2)\n      .attr(\"fill-opacity\", 0.5)\n      .attr(\"stroke-opacity\", 0.5);\n\n\n    d3.select(\"#input\").select(\".activations\").selectAll(\"rect\").data(inputnodes).join(\"rect\")\n      .attr(\"x\", node => node.x)\n      .attr(\"y\", node => node.y - Math.max(0, node.getActivation() * unit))\n      .attr(\"width\", noderadius)\n      .attr(\"height\", node => Math.abs(node.getActivation() * unit))\n      .attr(\"fill\", \"blue\")\n      .attr(\"fill-opacity\", 0.5);\n\n    d3.select(\"#parameters\").select(\".input\").selectAll(\"circle\").data(inputnodes)\n      .join(\"circle\")\n      .attr(\"cx\", node => (node.x + noderadius))\n      .attr(\"cy\", node => node.y - unit * node.getActivation())\n      .attr(\"r\", 15)\n      .attr(\"fill\", \"yellow\")\n      .attr(\"fill-opacity\", 0.6)\n      .attr(\"stroke\", \"black\")\n      .attr(\"stroke-width\", 2)\n      .attr(\"stroke-opacity\", 0.6);\n\n\n    d3.select(\"#outputs\").select(\".flow\").selectAll(\"rect\").data(outputnodes).join(\"rect\")\n      .attr(\"x\", node => node.x - noderadius)\n      .attr(\"y\", node => node.y - Math.max(0, node.getActivation() * unit))\n      .attr(\"width\", noderadius)\n      .attr(\"height\", node => Math.abs(node.getActivation() * unit))\n      .attr(\"fill\", \"blue\")\n      .attr(\"fill-opacity\", 0.5);\n\n    d3.select(\"#nodes\").select(\".target\").selectAll(\"text\")\n      .data(outputnodes.filter(n => typeof n.target == 'number'))\n      .join(\"text\")\n      .attr(\"font-size\", 40)\n      .attr(\"text-anchor\", \"left\")\n      .attr(\"pointer-events\", \"none\")\n      .text(n => \"Zielwert: \" + n.format(n.target))\n      .attr(\"x\", n => n.x - noderadius)\n      .attr(\"y\", n => n.y - unit * n.target)\n      .attr(\"opacity\", 1)\n      .attr(\"fill\", n => n.errorcolor());\n    //.attr(\"fill\", \"orange\");\n\n\n    d3.select(\"#nodes\").select(\".target\").selectAll(\"path\")\n      .data(outputnodes.filter(n => typeof n.target == 'number'))\n      .join(\"path\")\n      .attr(\"d\", n => {\n        const p = d3.path();\n        p.moveTo(n.x - 10 - noderadius, n.y - unit * n.target);\n        p.lineTo(n.x + noderadius, n.y - unit * n.target);\n        return p;\n      })\n      //.attr(\"stroke\", \"orange\")\n      .attr(\"stroke\", n => n.errorcolor())\n      .attr(\"stroke-width\", 4)\n      .attr(\"fill\", \"none\");\n\n\n    d3.select(\"#nodes\").select(\".values\").selectAll(\"text\")\n      .data(this.network.nodes)\n      .join(\"text\")\n      .attr(\"font-size\", 40)\n      .attr(\"text-anchor\", \"middle\")\n      .attr(\"alignment-baseline\", \"hanging\")\n      .attr(\"pointer-events\", \"none\")\n      //.attr(\"opacity\", 0.8)\n      .text(n => n.format(n.getActivation()))\n      .attr(\"x\", n => n.x)\n      .attr(\"y\", n => n.y - clamp(unit * n.getActivation(), -70 + 40, 70));\n\n    d3.select(\"#edges\").select(\".edges\").selectAll(\"path\").data(edges).join(\"path\")\n      .attr(\"d\", edge => {\n        const p = d3.path();\n        const b = edge.bezier();\n        if (edge.from.bias <= 0 || edge.from.constructor.name == \"InputNode\") {\n          p.moveTo(b[0][0], b[0][1]);\n        } else {\n          //make \"waterproof\"\n          p.moveTo(b[0][0], b[0][1] - unit * edge.from.bias);\n          p.lineTo(b[0][0], b[0][1]);\n        }\n\n        //p.lineTo(edge.to.x, edge.to.y);\n\n        p.bezierCurveTo(b[1][0], b[1][1], b[2][0], b[2][1], b[3][0], b[3][1]);\n        if (edge.offset < 0) //make \"waterproof\"\n          p.lineTo(b[3][0], b[3][1] + unit * edge.offset);\n        return p;\n      })\n      .attr(\"stroke\", \"black\")\n      .attr(\"stroke-width\", 2)\n      .attr(\"fill\", \"none\");\n\n\n    d3.select(\"#edges\").select(\".activations\").selectAll(\"path\").data(edges).join(\"path\")\n      .attr(\"d\", edge => {\n        const p = edge.generateActivatedPath(edge.from.getActivation());\n        const b = edge.bezier();\n        p.lineTo(b[3][0], b[3][1]);\n        p.bezierCurveTo(b[2][0], b[2][1], b[1][0], b[1][1], b[0][0], b[0][1]);\n        p.closePath();\n        return p;\n      })\n      .attr(\"fill\", edge => edge.weight >= 0 ? \"blue\" : \"red\")\n      .attr(\"fill-opacity\", 0.5);\n\n\n    const N = 1;\n    d3.select(\"#edges\").select(\".factorlines\").selectAll(\"g\").data(edges.filter(e => e.adjustable)).join(\"g\")\n      .selectAll(\"path\")\n      .data(edge => Array(Math.abs(edge.from.getActivation()) > parameterths ? 0 : N).fill(edge))\n      .join(\"path\")\n      .attr(\"d\", (edge, k) => edge.generateActivatedPathMiddle(parameterths * (edge.from.getActivation() < 0 ? -1 : 1) * (k + 1) / N))\n      .attr(\"stroke\", edge => (edge.weight >= 0 ? \"blue\" : \"red\"))\n      .attr(\"stroke-width\", 2)\n      .attr(\"stroke-opacity\", 0.5)\n      .attr(\"fill\", \"none\");\n    requestAnimationFrame(() => this.animateloop());\n  }\n\n\n  addInteraction() {\n    const nodes = this.nodes;\n\n    const that = this;\n    const tooltip = d3.local();\n    const addToolTip = function() {\n      tooltip.set(\n        this,\n        d3.select(\"#tooltip\").append(\"text\").style(\"display\", \"none\")\n      );\n    };\n    \n    const nodedrag = d3.drag()\n      .on(\"start\", function() {\n        var current = d3.select(this);\n        this.y0 = d3.event.y;\n        const node = d3.select(this).data()[0];\n        if (node.adjustable) {\n          this.v0 = node.bias;\n          that.network.pauseAnimatedInput();\n          if(d3.event.active === 0) {\n            const tooltipForThis = tooltip.get(this);\n            tooltipForThis.style(\"display\", \"block\");\n          }\n        }\n\n        if (node.constructor.name == \"InputNode\") {\n          this.v0 = node.getActivation();\n          node.setUserParameter(this.v0);\n        }\n\n      })\n      .on(\"drag\", function() {\n        const node = d3.select(this).data()[0];\n        if (node.adjustable) {\n          node.bias = clamp(this.v0 - (d3.event.y - this.y0) / unit, -4, 4);\n          //node.y = d3.event.y + this.deltaX;\n          const tooltipForThis = tooltip.get(this);\n          tooltipForThis\n            .attr(\"x\", node.x)\n            .attr(\"y\", node.y - unit * node.bias)\n            .text(`+ ${node.format(node.bias)}`);\n        }\n        if (node.constructor.name == \"InputNode\") {\n          node.setUserParameter(clamp(this.v0 - (d3.event.y - this.y0) / unit, -4, 4));\n          for (let k in that.network.outputnodes) {\n            delete that.network.outputnodes[k].target;\n          }\n        }\n      })\n      .on(\"end\", function() {\n        if (d3.event.active === 0) {\n          const tooltipForThis = tooltip.get(this);\n          tooltipForThis.style(\"display\", \"none\");\n        }\n      });\n\n    const nodeDragSelections = [\n      d3.select(\"#nodes\").selectAll(\"circle\"),\n      d3.select(\"#parameters\").select(\".input\").selectAll(\"circle\"),\n      d3.select(\"#parameters\").select(\".nodes\").selectAll(\"circle\"),\n      d3.select(\"#input\").selectAll(\"rect\"),\n    ]\n    nodeDragSelections.forEach(s=>{\n      s.each(addToolTip);\n      nodedrag(s);\n    });\n\n    const edgedrag = d3.drag()\n      .on(\"start\", function() {\n        const edge = d3.select(this).data()[0];\n        if (edge.adjustable) {\n          var current = d3.select(this);\n          this.y0 = d3.event.y;\n          this.weight0 = edge.weight;\n          that.network.pauseAnimatedInput();\n          if(d3.event.active === 0) {\n            const tooltipForThis = tooltip.get(this);\n            tooltipForThis.style(\"display\", \"block\");\n          }\n        }\n      })\n      .on(\"drag\", function() {\n        const edge = d3.select(this).data()[0];\n        if (edge.adjustable) {\n          let sactivation = edge.from.getActivation();\n          if (Math.abs(sactivation) < parameterths) sactivation = (edge.from.getActivation() < 0 ? -1 : 1) * parameterths;\n          //if (Math.abs(edge.from.getActivation()) > 0.001) {\n          edge.weight = clamp(this.weight0 - (d3.event.y - this.y0) / sactivation / unit, -4, 4);\n          //}\n          const tooltipForThis = tooltip.get(this);\n          tooltipForThis\n            .attr(\"x\", edge.parameterPosition()[0])\n            .attr(\"y\", edge.parameterPosition()[1])\n            .text(`× ${edge.weight.toFixed(2)}`);\n        }\n      })\n      .on(\"end\", function() {\n        if (d3.event.active === 0) {\n          const tooltipForThis = tooltip.get(this);\n          tooltipForThis.style(\"display\", \"none\");\n        }\n      });\n\n    const edgeDragSelections = [\n      d3.select(\"#edges\").selectAll(\"path, circle\"),\n      d3.select(\"#parameters\").select(\".edges\").selectAll(\"circle\"),\n    ];\n    edgeDragSelections.forEach(s=>{\n      s.each(addToolTip);\n      edgedrag(s);\n    });\n  }\n}\n","//jshint: \"esversion\": 8\n\nimport {\n  Edge\n} from './Edge.js';\n\nimport {\n  DynamicVariable,\n  updateDynamicVariables\n} from './DynamicVariable.js';\n\nexport class Node {\n  constructor() {\n    this.dactivation = new DynamicVariable(0);\n    this.sum = new DynamicVariable(0);\n    this.bias = 0;\n    this.dloss = 0;\n    this.outedges = [];\n    this.inedges = [];\n    this.adjustable = true;\n  }\n\n  addChild(other, weight, reverse = true) {\n    const edge = new Edge(this, other, weight);\n    this.outedges.push(edge);\n    if (reverse) {\n      other.inedges.push(edge);\n    }\n  }\n\n  computeSum() {\n    return this.sum.update(() => {\n      let activation = this.bias;\n      for (let eid in this.inedges) {\n        const edge = this.inedges[eid];\n        activation += edge.weight * edge.from.getActivation();\n      }\n      return activation;\n    });\n  }\n\n  getActivation() {\n    return Math.max(0, this.computeSum()); //ReLu\n  }\n\n\n  active() {\n    return this.computeSum() >= 0;\n  }\n\n  getdActivation() {\n    return this.dactivation.update(() => {\n      let dactivation = 0;\n      for (let eid in this.outedges) {\n        const edge = this.outedges[eid];\n        if (edge.to.active()) {\n          dactivation += edge.weight * edge.to.getdActivation();\n        }\n      }\n      return dactivation;\n    });\n  }\n\n  getdBias() {\n    return (this.active() ? this.getdActivation() : 0);\n  }\n\n  format(v) {\n    return v.toFixed(1);\n  }\n\n  backup() {\n    this.backupBias = this.bias;\n  }\n\n  restore() {\n    this.bias = this.backupBias;\n  }\n}\n","import * as d3 from 'd3';\n\nimport {\n  Node\n} from './Node.js';\n\nimport {\n  DynamicVariable,\n  updateDynamicVariables\n} from './DynamicVariable.js';\n\n// a node that just sums up inputs (no ReLu to enable negative outputs)\n\nexport class OutputNode extends Node {\n  constructor() {\n    super();\n    this.bias = 0;\n    this.adjustable = false;\n  }\n\n  getActivation() {\n    return this.computeSum(); // no bias, no ReLu\n  }\n\n  //this function might be overwriten, such that it is dependent on loss function\n  getdActivation() {\n    return 1; //likely will be overwritten\n  }\n\n  active() {\n    return true;\n  }\n\n  temporarilyReplaceGetdActivation(tempdActivation) {\n    this.getdActivationBackup = this.getdActivation;\n    this.getdActivation = tempdActivation;\n    updateDynamicVariables();\n  }\n\n  restoreGetdActivation() {\n    this.getdActivation = this.getdActivationBackup;\n    updateDynamicVariables();\n  }\n\n  errorcolor() {\n    const n = this;\n    return d3.scaleSequential().domain([1, 0]).interpolator(d3.interpolateRdYlGn)(Math.abs(n.target - n.getActivation()));\n  }\n}\n","export const unit = 35;\nexport const parameterths = 0.3;\nexport const noderadius = 80;\n","//jshint: \"esversion\": 8\nimport * as d3 from 'd3';\n\nimport {\n  Node,\n} from './Node.js';\n\nimport {\n  InputNode\n} from './InputNode.js';\n\nimport {\n  OutputNode\n} from './OutputNode.js';\n\nimport {\n  Network\n} from './Network.js';\n\nimport {\n  Level\n} from './Level.js';\n\nimport {\n  unit,\n  noderadius\n} from './constants.js';\n\n\nconst addnodeinfo = (node, text, offset = noderadius + 30) => {\n  d3.select(\"#levelinfo\").append(\"text\")\n    .text(text)\n    .attr(\"pointer-events\", \"none\")\n    .attr(\"text-anchor\", \"middle\")\n    .attr(\"font-size\", 20)\n    .attr(\"x\", node.x)\n    .attr(\"y\", node.y + offset);\n};\n\n\nexport class TutorialLevelA extends Level {\n  constructor() {\n    const omega1 = 1 + Math.random();\n\n    const nodes = [\n      new InputNode(() => 0.5 + 0.5 * Math.sin(omega1 * Date.now() / 1000) * Math.exp(-0.1 * (Date.now() - this.t0) / 1000)),\n      new OutputNode()\n    ];\n    nodes[0].x = 200;\n    nodes[0].y = 500;\n    nodes[0].allownegative = true;\n    nodes[1].x = 800;\n    nodes[1].y = 550;\n    nodes[0].addChild(nodes[1], 1);\n\n    const f = c => ((c * 2));\n    const trainXs = [1, 2, 3];\n    const trainYs = trainXs.map(f);\n\n    super(\"Verdopple den Input!\",\n      new Network(\n        nodes,\n        [nodes[0]], //input nodes\n        [nodes[1]] //output nodes\n      ),\n      [\"input\"], trainXs.map(x => [x]), //temperatures are internally divided by 10.\n      [\"desired output\"], trainYs.map(x => [x]),\n      \"Stelle in diesem einfachen Netz das Gewicht so ein, dass sich der Input verdoppelt, d.h. der Output soll den doppelten Inputwert ergeben (siehe Zielwert). Wenn du es geschafft hast, schalte mit dem Pfeil unten rechts weiter zum nächsten Level! \"\n    );\n\n    this.animatestep = function() {\n      nodes[1].target = f(nodes[0].getActivation());\n    };\n\n    this.onenter = function() {\n      addnodeinfo(nodes[0], \"Verändere hier den Inputwert!\");\n      addnodeinfo(nodes[1], \"Der Output\");\n\n      d3.select(\"#levelinfo\").append(\"text\")\n        .text(\"Der Multiplikationsfaktor (das “Gewicht”)\")\n        .attr(\"pointer-events\", \"none\")\n        .attr(\"text-anchor\", \"middle\")\n        .attr(\"font-size\", 20)\n        .attr(\"x\", (nodes[0].x + nodes[1].x) / 2)\n        .attr(\"y\", (nodes[0].y + nodes[1].y) / 2 - 100);\n\n      document.querySelector(\".trainingdata\").classList.remove(\"visible\");\n    };\n\n    this.onleave = function() {\n      document.querySelector(\".trainingdata\").classList.add(\"visible\");\n    };\n  }\n}\n\nexport class TutorialLevelB extends Level {\n  constructor() {\n    const omega1 = 1 + Math.random();\n\n    const nodes = [\n      new InputNode(() => 0.5 + 2 * Math.sin(omega1 * Date.now() / 1000) * Math.exp(-0.1 * (Date.now() - this.t0) / 1000)),\n      new Node(),\n      new OutputNode()\n    ];\n    nodes[0].allownegative = true;\n    nodes[0].x = 200;\n    nodes[0].y = 500;\n    nodes[1].x = 500;\n    nodes[1].y = 550;\n    nodes[1].adjustable = false;\n\n    nodes[2].x = 800;\n    nodes[2].y = 500;\n    nodes[0].addChild(nodes[1], -2);\n    nodes[1].addChild(nodes[2], 1);\n    nodes[1].outedges[0].adjustable = false;\n\n    const f = c => Math.max(0, (c * 1));\n    const trainXs = [-2, -1, 0, 1, 2, 3];\n    const trainYs = trainXs.map(f);\n\n    super(\"Lass nur positive Werte durch das Netz!\",\n      new Network(\n        nodes,\n        [nodes[0]], //input nodes\n        [nodes[2]] //output nodes\n      ),\n      [\"Input\"], trainXs.map(x => [x]), //temperatures are internally divided by 10.\n      [\"Output\"], trainYs.map(x => [x]),\n      \"Stelle das Netz so ein, dass der Output nur die positive Inputs durchlässt, und für andere (negative) Inputs die 0 anzeigt. Das mittlere Neuron verwendet dafür eine Aktivierungsfunktion.\"\n    );\n\n\n    this.animatestep = function() {\n      nodes[2].target = f(nodes[0].getActivation());\n    };\n\n    this.onenter = function() {\n      addnodeinfo(nodes[1], `Die mittleren Neuronen ignoriert negative Inputs.`);\n    };\n\n  }\n}\n\nexport class TutorialLevelC extends Level {\n  constructor() {\n    const omega1 = 1 + Math.random();\n\n    const nodes = [\n      new InputNode(() => -0.2 + 1 * Math.sin(omega1 * Date.now() / 1000) * Math.exp(-0.1 * (Date.now() - this.t0) / 1000)),\n      new Node(),\n      new OutputNode()\n    ];\n    nodes[0].allownegative = true;\n    nodes[0].x = 200;\n    nodes[0].y = 500;\n    nodes[1].x = 500;\n    nodes[1].y = 550;\n    nodes[1].adjustable = true;\n    nodes[2].x = 800;\n    nodes[2].y = 500;\n    nodes[0].addChild(nodes[1], 1);\n    nodes[1].addChild(nodes[2], 1);\n\n    nodes[0].outedges[0].adjustable = false;\n    nodes[1].outedges[0].adjustable = false;\n\n    nodes[1].bias = 1;\n\n    const f = c => Math.max(0, (c - 1));\n    const trainXs = [-2, -1, 0, 1, 2, 3];\n    const trainYs = trainXs.map(f);\n\n    super(\"Verändere den Bias!\",\n      new Network(\n        nodes,\n        [nodes[0]], //input nodes\n        [nodes[2]] //output nodes\n      ),\n      [\"Input\"], trainXs.map(x => [x]), //temperatures are internally divided by 10.\n      [\"Output\"], trainYs.map(x => [x]),\n      \"Dieses Netz hat einen neuen Parameter: den Bias. Stelle ihn so ein, dass der Output um 1.0 kleiner ist ist als der Input. Sollte der Input allerdings kleiner als 1 sein, soll der Output 0 ergeben.\"\n    );\n\n\n    this.animatestep = function() {\n      nodes[2].target = f(nodes[0].getActivation());\n    };\n\n    this.onenter = function() {\n      addnodeinfo(nodes[1], `Am Input des mittleren Neurons befindet sich nun ein einstellbarer Bias.`);\n    };\n\n\n  }\n}\n\nexport class WeatherLevel extends Level {\n  constructor() {\n    const omega1 = 1 + Math.random();\n\n    const nodes = [\n      new InputNode(() => 0.5 + 0.5 * Math.sin(omega1 * Date.now() / 1000) * Math.exp(-0.3 * (Date.now() - this.t0) / 1000)),\n      new InputNode(() => 1),\n\n      new Node(),\n      new Node(),\n\n      new OutputNode()\n    ];\n\n    for (let i in [2, 3]) {\n      nodes[[2, 3][i]].bias = 2 * (Math.random() - 0.5);\n    }\n\n    //output from console\n    nodes[0].x = 200;\n    nodes[0].y = 400;\n    nodes[1].x = 200;\n    nodes[1].y = 600;\n    nodes[2].x = 509;\n    nodes[2].y = 300;\n    nodes[3].x = 500;\n    nodes[3].y = 700;\n    nodes[4].x = 800;\n    nodes[4].y = 500;\n\n    nodes[0].addChild(nodes[2], 1);\n    nodes[0].addChild(nodes[3], 1);\n    nodes[1].addChild(nodes[2], 1);\n    nodes[1].addChild(nodes[3], 1);\n    nodes[2].addChild(nodes[4], 1);\n    nodes[3].addChild(nodes[4], 1);\n\n    const nw = new Network(\n      nodes,\n      [nodes[0], nodes[1]], //input nodes\n      [nodes[4]] //output nodes\n    );\n\n    const trainingdata = [{\n        cloudiness: 0,\n        inside: 0,\n      },\n      {\n        cloudiness: 0.5,\n        inside: 0,\n      },\n      {\n        cloudiness: 1,\n        inside: 0,\n      },\n      {\n        cloudiness: 1,\n        inside: 1,\n      },\n      {\n        cloudiness: 0.5,\n        inside: 1,\n      },\n      {\n        cloudiness: 0,\n        inside: 1,\n      }\n    ];\n    const formula = (c, i) => (i == 1) ? (2.1 - 0.1 * c) : (2.5 - 1.2 * c);\n\n    //nodes[0].format = cls => `cloudiness: ${cls.toFixed(1)}`;\n    //nodes[1].format = v => Math.round(v) == 1 ? '1 (inside)' : '0 (outside)';\n    nodes[1].format = v => v.toFixed(0);\n    nodes[4].format = temp => `${(temp*10).toFixed(0)}°C`;\n    super(\"Erstelle eine einfache Wettervorhersage!\", nw,\n      [\"Bewölkung\", \"Innenraum\"], trainingdata.map(td => [td.cloudiness, td.inside]),\n      [\"Temperatur\"], trainingdata.map(td => [formula(td.cloudiness, td.inside)]),\n      \"Draußen (d.h. Innenraumwert = 0) hängt die Temperatur davon ab, wie bewölkt es ist: Je bewölkter der Himmel ist, desto niedriger die Temperatur. In Innenräumen (d.h. Innenraumwert = 1) ist die Temperatur fast immer 20°C.\"\n    );\n\n    this.animatestep = function() {\n      //TODO add some nicer visualization for inside, cloudiness, and temperature.\n\n      nodes[0].setUserParameter(Math.min(1, Math.max(0, (nodes[0].getActivation()))));\n      //round input\n      nodes[1].setUserParameter(Math.min(1, Math.max(0, Math.round(nodes[1].getActivation()))));\n\n      nodes[4].target = formula(nodes[0].getActivation(), nodes[1].getActivation());\n    };\n\n    this.onenter = function() {\n      addnodeinfo(nodes[0], `Bewölkung`, -noderadius - 20);\n      addnodeinfo(nodes[1], `drinnen/draußen`);\n      addnodeinfo(nodes[4], `vorhergesagte Temperatur`);\n    };\n  }\n\n}\n\n\nexport class FahrenheitLevel extends Level {\n  constructor() {\n    const omega1 = 1 + Math.random();\n\n    const nodes = [\n      new InputNode(() => 0.5 + 0.5 * Math.sin(omega1 * Date.now() / 1000) * Math.exp(-0.3 * (Date.now() - this.t0) / 1000)),\n      new Node(), //TODO: No ReLu Nodes here!\n      new OutputNode()\n    ];\n\n    for (let i in [1]) {\n      nodes[[1][i]].bias = 1 + 2 * (Math.random());\n    }\n\n    nodes[0].x = 200;\n    nodes[0].y = 500;\n    nodes[0].allownegative = false;\n    nodes[1].x = 500;\n    nodes[1].y = 550;\n    nodes[2].x = 800;\n    nodes[2].y = 500;\n\n    nodes[0].addChild(nodes[1], 1);\n    nodes[1].addChild(nodes[2], 1);\n    nodes[1].outedges[0].adjustable = false;\n\n    const c2f = c => ((c * 1.8) + 32);\n    const trainXs = [0, 10, 20, 30];\n    const trainYs = trainXs.map(c2f);\n\n    nodes[0].format = temp => `${(temp*10).toFixed(0)}°C`;\n    nodes[1].format = temp => `${(temp*10).toFixed(0)}`;\n    nodes[2].format = temp => `${(temp*10).toFixed(0)}°F`;\n    super(\"Wandle Celsius in Fahrenheit um!\",\n      new Network(\n        nodes,\n        [nodes[0]], //input nodes\n        [nodes[2]] //output nodes\n      ),\n      [\"Celsius\"], trainXs.map(v => [v / 10]), //temperatures are internally divided by 10.\n      [\"Fahrenheit\"], trainYs.map(v => [v / 10]),\n      \"Ein positiver Temperaturwert in Grad Celsius (links) soll in einen Wert in Grad Fahrenheit umgewandelt werden (rechts). Stell die Parameter (blaue und weiße Regler) des Netzes so ein, dass der Output dem Zielwert jeden Inputs entspricht. In der Tabelle unten kannst du die korrekten Werte ablesen.\"\n    );\n    this.animatestep = function() {\n      nodes[2].target = c2f(nodes[0].getActivation() * 10) / 10;\n    };\n\n  }\n}\n\n\nexport class SumLevel extends Level {\n  constructor() {\n    const omega1 = 1 + Math.random();\n    const omega2 = 1 + Math.random();\n\n    const nodes = [\n      new InputNode(() => 0.5 + 0.5 * Math.sin(omega1 * Date.now() / 1000) * Math.exp(-0.3 * (Date.now() - this.t0) / 1000)),\n      new InputNode(() => 0.5 + 0.5 * Math.sin(omega2 * Date.now() / 1000) * Math.exp(-0.3 * (Date.now() - this.t0) / 1000)),\n      new OutputNode()\n    ];\n\n    nodes[2].bias = 0;\n\n    nodes[0].x = 200;\n    nodes[0].y = 300;\n    nodes[0].allownegative = true;\n    nodes[1].x = 200;\n    nodes[1].y = 700;\n    nodes[1].allownegative = true;\n\n    nodes[2].x = 800;\n    nodes[2].y = 500;\n\n    nodes[0].addChild(nodes[2], 1);\n    nodes[1].addChild(nodes[2], -1);\n\n\n    const c2f = c => ((c * 1.8) + 32);\n    const trainXs = [\n      [0, 0],\n      [0, 1],\n      [2, 1],\n      [3, 1],\n      [4, 2],\n      [0, 2]\n    ];\n    const trainYs = trainXs.map(p => [p[0] + p[1]]);\n\n    super(\"Addiere die beiden Inputwerte!\",\n      new Network(\n        nodes,\n        [nodes[0], nodes[1]], //input nodes\n        [nodes[2]] //output nodes\n      ),\n      [\"Summand 1\", \"Summand 2\"], trainXs, //temperatures are internally divided by 10.\n      [\"Summe\"], trainYs,\n      \"Stelle die Gewichte des Netzes so ein, dass der Outputwert (rechts) die Summe beider Inputwerte ergibt. Die Voraussagen des Netzes sollen möglichst den Werten der Trainingsdaten entsprechen, die du in der Tabelle unten siehst.\"\n    );\n    this.animatestep = function() {\n      nodes[2].target = (nodes[0].getActivation()) + (nodes[1].getActivation());\n    };\n\n  }\n}\n\n\nexport class AndLevel extends Level {\n  constructor() {\n    const omega1 = 1 + Math.random();\n    const omega2 = 1 + Math.random();\n\n    const nodes = [\n      new InputNode(() => 1),\n      new InputNode(() => 1),\n\n      new Node(),\n\n      new OutputNode()\n    ];\n\n    for (let i in [2]) {\n      nodes[[2][i]].bias = 2 * (Math.random());\n    }\n\n\n    nodes[0].x = 200;\n    nodes[0].y = 350;\n    nodes[1].x = 200;\n    nodes[1].y = 650;\n    nodes[2].x = 500;\n    nodes[2].y = 500;\n\n    nodes[3].x = 800;\n    nodes[3].y = 500;\n\n    nodes[0].addChild(nodes[2], 1);\n    //nodes[0].addChild(nodes[3], 1);\n    nodes[1].addChild(nodes[2], -0.2);\n\n\n    nodes[2].addChild(nodes[3], 1);\n    //nodes[1].addChild(nodes[3], 1);\n\n    const nw = new Network(\n      nodes,\n      [nodes[0], nodes[1]], //input nodes\n      [nodes[3]] //output nodes\n    );\n    const trainXs = [\n      [0, 0],\n      [1, 0],\n      [0, 1],\n      [1, 1]\n    ];\n    const trainYs = trainXs.map(p => [(p[0] * p[1])]);\n\n    super(\"Finde heraus ob beide Inputs auf 1 gestellt sind!\",\n      nw,\n      [\"Bit 1\", \"Bit 2\"], trainXs, //temperatures are internally divided by 10.\n      [\"AND\"], trainYs,\n      \"Die Inputwerte des Netzes sind entweder 0 oder 1. Der Outputwert rechts soll nur dann 1 sein, wenn beide Inputwerte 1 sind. Anderfalls soll er 0 sein.\"\n    );\n    this.animatestep = function() {\n      nodes[0].format = v => Math.round(v);\n      nodes[1].format = v => Math.round(v);\n      nodes[0].setUserParameter(Math.min(1, Math.max(0, Math.round(nodes[0].getActivation()))));\n      nodes[1].setUserParameter(Math.min(1, Math.max(0, Math.round(nodes[1].getActivation()))));\n\n      nodes[3].target = (nodes[0].getActivation() * nodes[1].getActivation());\n    };\n\n  }\n}\n\nexport class MaxLevel extends Level {\n  constructor() {\n    const omega1 = 1 + Math.random();\n    const omega2 = 1 + Math.random();\n\n    const nodes = [\n      new InputNode(() => 1.5 + 1 * Math.sin(omega1 * Date.now() / 1000) * Math.exp(-0.3 * (Date.now() - this.t0) / 1000)),\n      new InputNode(() => 1 + 1 * Math.sin(omega2 * Date.now() / 1000) * Math.exp(-0.3 * (Date.now() - this.t0) / 1000)),\n\n      new Node(),\n\n      new OutputNode()\n    ];\n\n    for (let i in [2]) {\n      nodes[[2][i]].bias = 2 * (Math.random() - 0.5);\n    }\n\n\n    nodes[0].x = 200;\n    nodes[0].y = 350;\n    nodes[1].x = 200;\n    nodes[1].y = 650;\n    nodes[2].x = 500;\n    nodes[2].y = 400;\n\n    nodes[3].x = 800;\n    nodes[3].y = 500;\n\n    nodes[0].addChild(nodes[2], 1);\n    //nodes[0].addChild(nodes[3], 1);\n    nodes[1].addChild(nodes[2], -0.2);\n\n\n    nodes[2].addChild(nodes[3], 1);\n    nodes[1].addChild(nodes[3], 1);\n\n    const nw = new Network(\n      nodes,\n      [nodes[0], nodes[1]], //input nodes\n      [nodes[3]] //output nodes\n    );\n    const trainXs = [0, 0, 0, 0, 0, 0, 0].map(v => [Math.random(), Math.random()]);\n    const trainYs = trainXs.map(p => [Math.max(p[0], p[1])]);\n\n    super(\"Berechne das Maximum der Inputwerte!\",\n      nw,\n      [\"Input 1\", \"Input 2\"], trainXs, //temperatures are internally divided by 10.\n      [\"Maximum\"], trainYs,\n      \"Der Output rechts soll dem Maximalwert der beiden Inputs entsprechen. Tipp: max(a, b) = max(0, a-b)+b. Denk daran, dass das mittlere Neuron negative Werte ignoriert.\"\n    );\n    this.animatestep = function() {\n      nodes[3].target = Math.max(nodes[0].getActivation(), nodes[1].getActivation());\n    };\n\n  }\n}\n\n\n\nexport class XorLevel extends Level {\n  constructor() {\n    const omega1 = 1 + Math.random();\n    const omega2 = 1 + Math.random();\n\n    const nodes = [\n      new InputNode(() => 0.5 + 0.5 * Math.sin(omega1 * Date.now() / 1000) * Math.exp(-0.3 * (Date.now() - this.t0) / 1000)),\n      new InputNode(() => 0.5 + 0.5 * Math.sin(omega2 * Date.now() / 1000) * Math.exp(-0.3 * (Date.now() - this.t0) / 1000)),\n\n      new Node(),\n      new Node(),\n      new Node(),\n\n      new OutputNode()\n    ];\n\n    for (let i in [2, 3, 4]) {\n      nodes[[2, 3, 4][i]].bias = 2 * (Math.random() - 0.5);\n    }\n\n\n    nodes[0].x = 200;\n    nodes[0].y = 400;\n    nodes[1].x = 200;\n    nodes[1].y = 600;\n    nodes[2].x = 500;\n    nodes[2].y = 300;\n    nodes[3].x = 500;\n    nodes[3].y = 500;\n    nodes[4].x = 500;\n    nodes[4].y = 700;\n    nodes[5].x = 800;\n    nodes[5].y = 500;\n\n    nodes[0].addChild(nodes[2], 1);\n    nodes[0].addChild(nodes[3], 1);\n    nodes[0].addChild(nodes[4], 1);\n    nodes[1].addChild(nodes[2], 1);\n    nodes[1].addChild(nodes[3], 1);\n    nodes[1].addChild(nodes[4], 1);\n\n    nodes[2].addChild(nodes[5], 1);\n    nodes[3].addChild(nodes[5], 1);\n    nodes[4].addChild(nodes[5], 1);\n\n    const nw = new Network(\n      nodes,\n      [nodes[0], nodes[1]], //input nodes\n      [nodes[5]] //output nodes\n    );\n    const trainXs = [\n      [0, 0],\n      [1, 0],\n      [0, 1],\n      [1, 1]\n    ];\n    const trainYs = [\n      [0],\n      [1],\n      [1],\n      [0]\n    ];\n\n    super(\"Berechne das exklusive Oder (XOR) der Inputwerte!\",\n      nw,\n      [\"Bit 1\", \"Bit 2\"], trainXs, //temperatures are internally divided by 10.\n      [\"XOR\"], trainYs,\n      \"Nehmen wir an, dass das Netz nur 0 oder 1 als Inputwerte akzeptiert. Der Output des Netzes soll 1 sein, wenn beide Inputs verschieden sind. Andernfalls (wenn also beide Inputs gleich sind) soll der Output 0 sein.\"\n    );\n    this.animatestep = function() {\n      //round input\n      nodes[0].format = v => Math.round(v);\n      nodes[1].format = v => Math.round(v);\n      nodes[0].setUserParameter(Math.min(1, Math.max(0, Math.round(nodes[0].getActivation()))));\n      nodes[1].setUserParameter(Math.min(1, Math.max(0, Math.round(nodes[1].getActivation()))));\n      nodes[5].target = ((nodes[0].getActivation() + nodes[1].getActivation()) | 0) % 2;\n    };\n\n  }\n}\n\n\nexport class AvgLevel extends Level {\n  constructor() {\n    const omega1 = 1 + Math.random();\n    const omega2 = 1 + Math.random();\n    const omega3 = 1 + Math.random();\n\n    const nodes = [\n      new InputNode(() => 0.5 + 0.5 * Math.sin(omega1 * Date.now() / 1000) * Math.exp(-0.3 * (Date.now() - this.t0) / 1000)),\n      new InputNode(() => 0.5 + 0.5 * Math.sin(omega2 * Date.now() / 1000) * Math.exp(-0.3 * (Date.now() - this.t0) / 1000)),\n      new InputNode(() => 0.5 + 0.5 * Math.sin(omega2 * Date.now() / 1000) * Math.exp(-0.3 * (Date.now() - this.t0) / 1000)),\n\n      //new Node(),\n\n      new OutputNode()\n    ];\n\n\n    //output from console\n    nodes[0].x = 200;\n    nodes[0].y = 300;\n    nodes[0].allownegative = true;\n    nodes[1].x = 200;\n    nodes[1].y = 500;\n    nodes[1].allownegative = true;\n    nodes[2].x = 200;\n    nodes[2].y = 700;\n    nodes[2].allownegative = true;\n\n    nodes[3].x = 800;\n    nodes[3].y = 500;\n    //nodes[4].x = 800;\n    //nodes[4].y = 500;\n\n    nodes[0].addChild(nodes[3], 1);\n    nodes[1].addChild(nodes[3], 1);\n    nodes[2].addChild(nodes[3], 1);\n    //nodes[3].addChild(nodes[4], 1);\n\n    const nw = new Network(\n      nodes,\n      [nodes[0], nodes[1], nodes[2]], //input nodes\n      [nodes[3]] //output nodes\n    );\n    const trainXs = [0, 0, 0, 0, 0, 0, 0].map(v => [Math.random(), Math.random(), Math.random()]);\n    const trainYs = trainXs.map(p => [(p[0] + p[1] + p[2]) / 3]);\n\n    super(\"Berechne den Durchschnitt der Inputwerte!\",\n      nw,\n      [\"Nummer 1\", \"Nummer 2\", \"Nummer 3\"], trainXs, //temperatures are internally divided by 10.\n      [\"Durchschnitt\"], trainYs,\n      \"Es gibt drei Inputs. Stelle die Gewichte so ein, dass der Output der Durchschnitt der drei Inputwerte ist. Insbesondere für die Inputwerte in der Tabelle unten soll das Netz korrekte Outputs produzieren.\"\n    );\n    this.animatestep = function() {\n      nodes[3].target = (nodes[0].getActivation() + nodes[1].getActivation() + nodes[2].getActivation()) / 3;\n    };\n\n  }\n}\n","import {\n  LevelController\n} from './LevelController.js';\n\n\nnew LevelController();\n\n\n\nwindow.addEventListener('DOMContentLoaded', (event) => {\n  document.querySelector(\"#helpmebutton\").onclick = () => {\n    document.querySelector(\".helper\").classList.add(\"visible\");\n    document.querySelector(\".mission\").classList.remove(\"visible\");\n    document.querySelector(\"#helpmebutton\").classList.add(\"selected\");\n    document.querySelector(\"#missionbutton\").classList.remove(\"selected\");\n  };\n  \n  document.querySelector(\"#missionbutton\").onclick = () => {\n    document.querySelector(\".helper\").classList.remove(\"visible\");\n    document.querySelector(\".mission\").classList.add(\"visible\");\n    document.querySelector(\"#helpmebutton\").classList.remove(\"selected\");\n    document.querySelector(\"#missionbutton\").classList.add(\"selected\");\n  };\n/* FIXME: There is no #creditsbutton id, so why is this code here if it breaks anyway?\n  document.querySelector(\"#creditsbutton\").onclick = () => {\n    document.querySelector(\".credits\").classList.toggle(\"visible\");\n    document.querySelector(\"#screenoverlay\").classList.toggle(\"visible\");\n    document.querySelector(\"#creditsbutton\").classList.toggle(\"selected\");\n  };\n*/\n  document.querySelector(\"#screenoverlay\").onclick = () => {\n    document.querySelector(\".credits\").classList.remove(\"visible\");\n    document.querySelector(\"#screenoverlay\").classList.remove(\"visible\");\n    document.querySelector(\"#creditsbutton\").classList.remove(\"selected\");\n  };\n});\n"]}