!npm install -g npm@latest
!npm cache verify
!npm install -g --unsafe-perm ijavascript
!ijsinstall --install=global
!jupyter-kernelspec list

var { spawn } = require('child_process')
var sh = (cmd) => { 
    $$.async()
  var sp = spawn(cmd, { cwd: process.cwd(), stdio: 'pipe', shell: true, encoding: 'utf-8' })
  sp.stdout.on('data', data => console.log(data.toString()))
  sp.stderr.on('data', data => console.error(data.toString()))
  sp.on('close', () => $$.done())
}
var run_async = async (pf) => {
  $$.async()
  await pf()
  $$.done()
}
sh('npm init -y')

sh('npm install @tensorflow/tfjs-node')
sh('npm install plotly-notebook-js')

var tf = require('@tensorflow/tfjs-node')
console.log(tf.backend().isUsingGpuDevice)

var trainData = {
  sizeMB: [0.080, 9.000, 0.001, 0.100, 8.000, 5.000, 0.100, 6.000, 0.050, 0.500,
    0.002, 2.000, 0.005, 10.00, 0.010, 7.000, 6.000, 5.000, 1.000, 1.000],
  timeSec: [0.135, 0.739, 0.067, 0.126, 0.646, 0.435, 0.069, 0.497, 0.068, 0.116,
    0.070, 0.289, 0.076, 0.744, 0.083, 0.560, 0.480, 0.399, 0.153, 0.149]
}
var testData = {
  sizeMB: [5.000, 0.200, 0.001, 9.000, 0.002, 0.020, 0.008, 4.000, 0.001, 1.000,
    0.005, 0.080, 0.800, 0.200, 0.050, 7.000, 0.005, 0.002, 8.000, 0.008],
  timeSec: [0.425, 0.098, 0.052, 0.686, 0.066, 0.078, 0.070, 0.375, 0.058, 0.136,
    0.052, 0.063, 0.183, 0.087, 0.066, 0.558, 0.066, 0.068, 0.610, 0.057]
}

var trainTensors = {
  sizeMB: tf.tensor2d(trainData.sizeMB, [20, 1]),
  timeSec: tf.tensor2d(trainData.timeSec, [20, 1])
}
var testTensors = {
  sizeMB: tf.tensor2d(testData.sizeMB, [20, 1]),
  timeSec: tf.tensor2d(testData.timeSec, [20, 1])  
}

var dataTraceTrain = {
  x: trainData.sizeMB,
  y: trainData.timeSec,
  name: 'trainData',
  mode: 'markers',
  type: 'scatter',
  marker: {symbol: "circle", size: 8}
};
var dataTraceTest = {
  x: testData.sizeMB,
  y: testData.timeSec,
  name: 'testData',
  mode: 'markers',
  type: 'scatter',
  marker: {symbol: "triangle-up", size: 10}
};

var Plot = require('plotly-notebook-js')
var NotebookPlot = Plot.createPlot().constructor
NotebookPlot.prototype._toHtml = NotebookPlot.prototype.render

Plot.createPlot([dataTraceTrain, dataTraceTest], {
  width: 700,
  title: 'File download duration',
  xaxis: {
     title: 'size (MB)'
   },
  yaxis: {
    title: 'time (sec)'
  }
})

var model = tf.sequential();
model.add(tf.layers.dense({
  units: 1,
  inputShape: [1],
}))
model.compile({ optimizer: 'sgd', loss: 'meanAbsoluteError' })


var k = 0
var b = 0
model.setWeights([tf.tensor2d([k], [1, 1]), tf.tensor1d([b])])

model.fit(trainTensors.sizeMB, trainTensors.timeSec, {epochs: 10})

model.evaluate(testTensors.sizeMB, testTensors.timeSec).print()

model.predict(tf.tensor2d([[7.8]])).print()

k = model.getWeights()[0].dataSync()[0]
b = model.getWeights()[1].dataSync()[0];
console.log(k, b)

var traceLine = {
  x: [0, 10],
  y: [b, b + (k * 10)],
  name: 'trace line',
  mode: 'lines'
}

Plot.createPlot([dataTraceTrain, dataTraceTest, traceLine], {
  width: 700,
  title: 'File download duration',
  xaxis: {
     title: 'size (MB)'
   },
  yaxis: {
    title: 'time (sec)'
  }
})